"""
Support functions for the RTL-SDR using pyrtlsdr
Copyright (c) July 2017, Mark Wickert
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
The views and conclusions contained in the software and documentation are those
of the authors and should not be interpreted as representing official policies,
either expressed or implied, of the FreeBSD Project.
"""
import warnings
try:
from rtlsdr import RtlSdr
except ImportError:
warnings.warn("Please install the helpers extras for full functionality", ImportWarning)
from . import sigsys as ss
from . import digitalcom as dc
import numpy as np
import scipy.signal as signal
import asyncio
try:
import colorama
except ImportError:
warnings.warn("Please install colorama for full functionality", ImportWarning)
from . import pyaudio_helper as pah
import matplotlib.pyplot as plt
try:
from IPython.display import display, Math
except ImportError:
warnings.warn("Please install IPython for full functionality", ImportWarning)
try:
from ipywidgets import interactive
from ipywidgets import ToggleButtons
from ipywidgets import FloatSlider
from ipywidgets import Layout
from ipywidgets import widgets
except ImportError:
warnings.warn("Please install ipywidgets for full functionality", ImportWarning)
from matplotlib.mlab import psd
# Bokeh plotting
# from bokeh.io import push_notebook, show, output_notebook
# from bokeh.models import HoverTool
# import bokeh.plotting.figure as bfigure
# from bokeh.models.annotations import Title as bTitle
[docs]class RTLSDR_stream(object):
"""
Class used to set up an RTLSDR stream object
"""
def __init__(self, rtl_in=0, rtl_fs=2.4e6, fc=103.9e6, gain=40, rtl_buffer_size=2**15, audio_out=1, audio_buffsize=4096, audio_fs=48000):
'''
RTLSDR async streaming class
Parameters
----------
rtl_in: The index of the RTLSDR (either 0 or 1)
rtl_fs: Sets the sample rate of the RTLSDR (Generally want to keep 2.4e6)
fc: Sets the tuning frequency of the RTLSDR
gain: Sets the gain of the RTLSDR
rtl_buffer_size: Sets the size of the circular buffer (see below)
audio_out: Select the audio output device index (check devices with )
The following shows the basic flow of an RTLSDR_stream object
__________________________________________
| Audio Sink |
____________ __________ |____________ ___________ _________|
|Stage1 Dec| -> |callback| -> ||Stage2 Dec| -> |Circ Buff| -> |PyAudio||
|__________| |________| | ||__________| |_________| |_______||
or |________________________________________|
|
| ______________
-->| Data Sink |
|____________|
It consists of a Stage 1 Decimator that can be used to decimate the RF
signal coming in from the RTLSDR to a lower rate to make processing
easier. The stage 1 decimator can be defined by setting the filter
taps, initial conditions, and decimation factor M. The numerator
coefficients, initial conditions, and denominator coefficients of the
filter are available as parameters in the run_user_stream() method.
Custom Stage 1 FIR decimation filter example:
>>> sdr_stream = RTLSDR_stream()
>>> M1 = 10
>>> M2 = 5
>>> b = signal.firwin(32,2*200e3/2.4e6)
>>> stage1_ic = signal.lfilter_zi(b,1)
>>> sdr_stream.run_user_stream(callback,M1,M2,b,stage1_ic)
The Callback block can be used to process the decimated samples. This is
where the "meat" of the processing is done. This processing callback receives
the following parameters:
samples: incoming decimated frame of samples
fs: RTLSDR sample rate
user_var: user-defined variable that gets passed through the class
The callback can return data in two forms. It can either return an array of
process samples of the same length as the input frame which can then be sent
along to the stage 2 decimation filter, circular buffer, and pyAudio output, or
the data can be stored in an output buffer that can be accessed through an
async method (see below)
The following is an example of a callback function that implements an FM
discriminator:
>>> def callback(samples,fs,user_var):
>>> # discriminator
>>> x = samples
>>> X=np.real(x)
>>> Y=np.imag(x)
>>> b=np.array([1,-1])
>>> a=np.array([1,0])
>>> derY=signal.lfilter(b,a,Y)
>>> derX=signal.lfilter(b,a,Y)
>>> z_bb=(X*derY-Y*derX)/(X**2+Y**2)
>>> return z_bb,user_var
The Stage 2 Decimator callback can be used to decimate the processed frame
of samples down to an audio rate. The interface is basically the same as the
stage 1 decimator callback. The numerator and denominator coefficients can
be given for the stage 2 filter
Custom Stage 2 IIR Filter:
>>> import sk_dsp_comm.iir_design_helper as iir_d
>>> sdr_stream = RTLSDR_Stream()
>>> M1 = 10
>>> M2 = 5
>>> fc2 = 15e3
>>> bb,aa,sos2 = iir_d.IIR_lpf(fc2,fc2+5000,1,10,2.4e6/M1)
>>> stage2_ic = signal.lfilter_zi(bb,aa)
>>> sdr_stream.run_user_stream(callback,M1,M2,bb=bb,stage2_ic=stage2_ic,aa=aa)
When the audio sink parameter is set to True, the processed audio rate
samples are stored in a circular buffer using the _write_circular_buffer()
private method. PyAudio runs in a separate thread that reads from the
circular buffer using the _read_circular_buffer() private method. It then
sends the audio samples to the selected audio output.
When the audio sink parameter is set to False, then the data is stored in a
buffer that can be accessed by an async method get_data_out_async(). This
method waits on a queue to return the filled output data buffer. The following
example shows a very simple application of this idea. The example shows a
frame counter which simply
Async Data Out Example:
>>> import asyncio
>>> sdr_stream.set_rtl_buffer_size(16)
>>> def no_audio_callback(samples,fs,user_var):
>>> frame_count = user_var
>>> user_var = user_var+1
>>> return np.array([frame_count]),user_var
>>> global keep_collecting
>>> async def handle_data_out():
>>> global keep_collecting
>>> keep_collecting = True
>>> while keep_collecting:
>>> data_out = await sdr_stream.get_data_out_async()
>>> print(data_out)
>>> sdr_stream.reset_data_out_queue()
>>> print('Done')
>>> sdr_stream.run_user_stream(no_audio_callback,1,1,audio_sink=False,user_var=1)
>>> task = asyncio.create_task(handle_data_out())
>>> keep_collecting = False
>>> sdr_stream.stop()
Andrew Smit April 2019
.. ._.. .._
'''
self.rtl_in = rtl_in
self.fs = rtl_fs
self.fc = fc
self.gain = gain
self.rtl_buffer_size = rtl_buffer_size
self.audio_buffsize = audio_buffsize
self.audio_fs = audio_fs
self.keep_streaming = False
self.audio_in = 0
self.audio_out = audio_out
self.output = widgets.Output()
self.z_out = np.zeros(rtl_buffer_size)
self.rx_idx = 0
self.audio_idx = int(rtl_buffer_size/2)
self.audio_gain = 1.0
self.audio_sink = True
self.user_var = False
self.buffer_exceeded = False
# Stage 1 Decimation Filter
self.M1 = 10.0
self.b = signal.firwin(32,2.0*200e3/float(self.fs))
self.a = np.array([1])
self.stage1_ic = signal.lfilter_zi(self.b,self.a)
# Stage 2 Decimation Filter
self.M2 = 5.0
self.bb = signal.firwin(32,2.0*16.0e3/float(self.fs)*10.0)
self.aa = np.array([1])
self.stage2_ic = signal.lfilter_zi(self.bb,self.aa)
# Discriminator Filter Initial conditions
self.Y_ic=signal.lfilter_zi(np.array([1,-1]),np.array([1,0]))
self.X_ic = self.Y_ic
# Connect to the SDR
self.sdr = RtlSdr(rtl_in)
self.output.append_stdout('LOGS:\n')
self.sdr.set_sample_rate(rtl_fs)
self.sdr.set_center_freq(fc)
self.sdr.set_gain(gain)
# Audio
self.DSP_IO = pah.DSP_io_stream(self._audio_callback, self.audio_in, self.audio_out, self.audio_buffsize, self.audio_fs,0,0)
# Async Queues/Plotting
self.rx_data = asyncio.Queue()
self.rf_queue = asyncio.Queue()
self.plot_NFFT = 1024
self.update_rf = False
self.refresh_rate = 1
self.stage1_queue = asyncio.Queue()
self.update_stage1 = False
self.processed_stage1_queue = asyncio.Queue()
self.update_processed_stage1 = False
self.stage2_queue = asyncio.Queue()
self.update_stage2 = False
self.data_out_queue = asyncio.Queue()
self.store_rf = False
self.rf_frame = np.array([])
self.store_stage1 = False
self.stage1_frame = np.array([])
self.store_processed_stage1 = False
self.processed_stage1_frame = np.array([])
self.store_stage2 = False
self.stage2_frame = np.array([])
self.invert = False
#output_notebook()
def _interaction(self,Stream):
'''
Enables Jupyter Widgets for mono FM example
'''
if(Stream == 'Start Streaming'):
self.clear_buffer()
task = asyncio.create_task(self._start_streaming())
print('Status: Streaming')
else:
self.stop()
print('Status: Stopped')
[docs] def interactive_FM_Rx(self,fc=103.9e6,gain=40,audio_out=1,audio_buffsize=4096,audio_fs=48000):
'''
Sets up interactive mono FM example
'''
self.set_fs(2.4e6)
self.set_fc(fc)
self.set_gain(gain)
self.set_audio_in(0)
self.set_audio_out(audio_out)
self.set_audio_buffsize(audio_buffsize)
self.set_audio_fs(audio_fs)
self.togglebutts = ToggleButtons(
options=['Start Streaming', 'Stop Streaming'],
description = ' ',
value = 'Stop Streaming',
)
self.togglebutts.style.button_width = "400px"
self.togglebutts.style.description_width = "1px"
self.play = interactive(self._interaction, Stream = self.togglebutts)
title = widgets.Output()
title.append_stdout("Interactive FM Receiver")
display(title)
display(self.play)
self._interact_audio_gain()
self._interact_frequency(self.fc/1e6)
def _interact_frequency(self,freq_val,min_freq=87.5,max_freq=108,freq_step=0.2):
'''
Sets up tuning frequency slider widget for Mono FM Example
'''
self.slider = FloatSlider(
value=freq_val,
min=min_freq,
max=max_freq,
step=freq_step,
description=r'$f_c\;$',
continuous_update=False,
orientation='horizontal',
readout_format='0.1f',
layout=Layout(
width='90%',
)
)
self.slider.style.handle_color = 'lightblue'
self.center_freq_widget = interactive(self.set_fc_mhz, fc = self.slider)
display(self.center_freq_widget)
def _interact_audio_gain(self,gain_val=0,min_gain=-60,max_gain=6,gain_step=0.1):
'''
Sets up audio gain slider widget for Mono FM Example
'''
self.gain_slider = FloatSlider(
value=gain_val,
min=min_gain,
max=max_gain,
step=gain_step,
description='Gain (dB)',
continuous_update=True,
orientation='horizontal',
readout_format='0.1f',
layout=Layout(
width='90%',
)
)
self.gain_slider.style.handle_color = 'lightgreen'
self.audio_gain_widget = interactive(self.set_audio_gain_db,gain=self.gain_slider)
display(self.audio_gain_widget)
async def _get_rx_data(self):
'''
Gets samples from RTLSDR and decimates by 10 for mono FM example
'''
extra = np.array([])
async for samples in self.sdr.stream():
# Do something with the incoming samples
samples = np.concatenate((extra,samples))
mod = len(samples) % 10
if(mod):
extra = samples[len(samples)-mod:]
samples = samples[:len(samples)-mod]
else:
extra = np.array([])
if(self.store_rf):
self.store_rf = False
await self.rf_queue.put(samples)
z = self._decimate(samples,10,self.fs)
if(self.store_stage1):
self.store_stage1 = False
await self.stage1_queue.put(z)
await self.rx_data.put(z)
async def _process_rx_data(self):
'''
Processes decimated samples, and decimates further to audio rate.
Implements an FM discriminator for the mono FM example.
'''
while self.keep_streaming:
samples = await self.rx_data.get()
samples = np.array(samples)
##############################################
# Process Downconverted Data
z_bb = self._discrim(samples)
if(self.store_processed_stage1):
self.store_processed_stage1 = False
await self.processed_stage1_queue.put(z_bb)
z = self._decimate(z_bb,5,self.fs,2)
if(self.store_stage2):
self.store_stage2 = False
await self.stage2_queue.put(z)
# Wrap circular buffer
self._write_circ_buff(z)
###############################################
with self.output:
self.output.append_stdout(colorama.Fore.BLUE + 'Stopping SDR\n')
self.sdr.stop()
if(self.DSP_IO):
with self.output:
self.output.append_stdout(colorama.Fore.BLUE + "Stopping Audio\n")
self.DSP_IO.stop()
self.play.children[0].value = "Stop Streaming"
async def _get_rx_data_user(self):
'''
Used by run_user_stream() method. Asynchronously reads in samples from
RTLSDR and implements the stage 1 decimator. The stage 1 decimator
can be defined by the user in run_user_stream() or a default decimator
can be used. This is a private method that is only used internally.
Decimated samples are stored in the rx_data queue which is consumed by
the _process_rx_data_user private method.
'''
extra = np.array([])
async for samples in self.sdr.stream():
# Do something with the incoming samples
samples = np.concatenate((extra,samples))
mod = len(samples) % self.M1
if(mod):
extra = samples[len(samples)-mod:]
samples = samples[:len(samples)-mod]
else:
extra = np.array([])
if(self.store_rf):
self.store_rf = False
await self.rf_queue.put(samples)
y,self.stage1_ic = signal.lfilter(self.b,self.a,samples,zi=self.stage1_ic)
z = ss.downsample(y,self.M1)
if(self.store_stage1):
self.store_stage1 = False
await self.stage1_queue.put(z)
await self.rx_data.put(z)
async def _process_rx_data_user(self,callback):
'''
Used by run_user_stream() method. Consumed decimated samples from
stage 1 decimator stored in the rx_data queue. Passes the data along
to a user defined callback. The processed samples are passed along
to either the audio sink or the data sink. The audio sink contains
a stage 2 decimator and outputs to an audio device via PyAudio. The
data sink stores processed samples in a buffer that can be read out
asynchronously. This is a private method that is used internally.
parameters:
-----------
callback: user-defined callback passed in from run_user_stream()
'''
while self.keep_streaming:
samples = await self.rx_data.get()
samples = np.array(samples)
##############################################
# Process Downconverted Data in user callback
z_bb,self.user_var = callback(samples,self.fs/self.M1,self.user_var)
##############################################
if(self.audio_sink):
if(self.store_processed_stage1):
self.store_processed_stage1 = False
await self.processed_stage1_queue.put(z_bb)
y,self.stage2_ic = signal.lfilter(self.bb,self.aa,z_bb,zi=self.stage2_ic)
z = ss.downsample(y,self.M2)
if(self.store_stage2):
self.store_stage2 = False
await self.stage2_queue.put(z)
# Wrap circular buffer
self._write_circ_buff(z)
else:
await self._write_circ_buff_async(z_bb)
print(colorama.Fore.BLUE + 'Stopping SDR')
self.sdr.stop()
if(self.DSP_IO and self.audio_sink):
print(colorama.Fore.BLUE + "Stopping Audio")
self.DSP_IO.stop()
print(colorama.Fore.BLUE + 'Completed')
def _write_circ_buff(self,samples):
'''
Private method used to write samples to a circular buffer. This circular
buffer takes in audio-rate samples from the _process_rx_user_data method
and _read_circ_buff reads the samples back out asynchronously in a PyAudio
thread. This method increments a write pointer by the length of the samples
being written to the buffer and wraps the pointer when the buffer is filled.
Parameters:
-----------
samples: audio-rate samples to be written to the circular buffer
'''
# Wrap circular buffer
if(self.rx_idx + len(samples) >= self.rtl_buffer_size):
self.z_out[self.rx_idx:] = samples[:(self.rtl_buffer_size-self.rx_idx)]
if(not self.audio_sink):
print(colorama.Fore.RED + 'Exceeded allocated output buffer space. Returning, then overwriting buffer')
self.buffer_exceeded = True
# await self.data_out_queue.put(self.z_out)
self.z_out[:abs(self.rtl_buffer_size-self.rx_idx-len(samples))] = samples[(abs(self.rtl_buffer_size-self.rx_idx)):]
self.rx_idx = abs(self.rtl_buffer_size-self.rx_idx-len(samples))
else:
self.z_out[self.rx_idx:self.rx_idx+len(samples)] = samples
self.rx_idx = self.rx_idx + len(samples)
async def _write_circ_buff_async(self,samples):
'''
Private method used to asynchronously store processed data from the user
callback to a buffer when the data sink is being used. This method wraps the
buffer and increments the buffer pointer.
parameters:
-----------
samples: decimated processed samples to be stored in data sink buffer
'''
# Wrap circular buffer
if(self.rx_idx + len(samples) >= self.rtl_buffer_size):
if(not self.audio_sink):
#print(colorama.Fore.RED + 'Exceeded allocated output buffer space. Returning, then overwriting buffer')
self.buffer_exceeded = True
await self.data_out_queue.put(self.z_out)
self.z_out[self.rx_idx:] = samples[:(self.rtl_buffer_size-self.rx_idx)]
self.z_out[:abs(self.rtl_buffer_size-self.rx_idx-len(samples))] = samples[(abs(self.rtl_buffer_size-self.rx_idx)):]
self.rx_idx = abs(self.rtl_buffer_size-self.rx_idx-len(samples))
else:
self.z_out[self.rx_idx:self.rx_idx+len(samples)] = samples
self.rx_idx = self.rx_idx + len(samples)
def _read_circ_buff(self,frame_count):
'''
Private method used to read samples from the circular buffer. This is used
by the audio sink to consume audio-rate samples. This method handles incrementing
a read pointer and wrapping the circular buffer.
'''
y = np.zeros(frame_count)
if(self.audio_idx + frame_count >= self.rtl_buffer_size):
y[:(self.rtl_buffer_size-self.audio_idx)] = self.z_out[self.audio_idx:]
y[(self.rtl_buffer_size-self.audio_idx):] = self.z_out[:(self.rtl_buffer_size-frame_count-self.audio_idx)]
self.audio_idx = abs(self.rtl_buffer_size-self.audio_idx-frame_count)
else:
y = self.z_out[self.audio_idx:self.audio_idx+frame_count]
self.audio_idx = self.audio_idx+frame_count
return y
async def _audio(self):
'''
private method that starts a PyAudio Thread
'''
self.DSP_IO.thread_stream(0,1)
def _audio_callback(self, in_data, frame_count, time_info, status):
'''
private audio callback method that is used by the PyAudio thread in
the audio sink. Reads samples out of the circular buffer and sends the
samples out an audio device.
'''
# convert byte data to ndarray
#in_data_nda = np.frombuffer(in_data, dtype=np.int16)
#***********************************************
# DSP operations here
# Read samples in from circular buffer
y = self._read_circ_buff(frame_count)
y = y*self.audio_gain*2**14
#***********************************************
# Convert from float back to int16
y = y.astype(np.int16)
# Convert ndarray back to bytes
return y.tobytes(), pah.pyaudio.paContinue
async def _start_streaming(self):
'''
Async method used to start coroutine for the Mono FM example
'''
self.rx_data = asyncio.Queue()
self.clear_buffer()
self.DSP_IO = pah.DSP_io_stream(self._audio_callback,self.audio_in,self.audio_out,self.audio_buffsize,self.audio_fs,0,0)
self.keep_streaming = True
loop = asyncio.get_event_loop()
with self.output:
self.output.append_stdout(colorama.Fore.LIGHTBLUE_EX + 'Starting SDR and Audio Event Loop\n')
await asyncio.gather(
self._get_rx_data(),
self._process_rx_data(),
self._audio()
)
async def _start_user_stream(self,callback,M1,M2,b,a,stage1_ic,bb,aa,stage2_ic,audio_sink,user_var):
'''
Async method used by run_user_stream method to start a coroutine running all of the
different async stages in the chain.
parameters:
-----------
callback: user-defined callback passed in from run_user_stream()
M1: Stage 1 decimation factor passed in from run_user_stream()
M2: Stage 2 decimation factor passed in from run_user_stream()
b: Stage 1 filter numerator coefficients passed in from run_user_stream()
stage1_ic: Stage 1 filter initial conditions passed in from run_user_stream()
a: Stage 1 filter denominator coefficients passed in from run_user_stream()
bb: Stage 2 filter numerator coefficients passed in from run_user_stream()
stage2_ic: Stage 2 filter initial conditions passed in from run_user_stream()
aa: Stage 2 filter denominator coefficients passed in from run_user_stream()
'''
if(type(b) == np.ndarray):
self.b = b
else:
print(colorama.Fore.LIGHTBLUE_EX + 'Using default stage 1 decimation filter')
if(type(bb) == np.ndarray):
self.bb = bb
else:
if(audio_sink):
print(colorama.Fore.LIGHTBLUE_EX + 'Using default stage 2 decimation filter')
if(type(stage1_ic) == np.ndarray):
if(len(stage1_ic) == len(self.b)-1):
self.stage1_ic = stage1_ic
else:
raise ValueError('Stage 1 Filter initial conditions length does not match filter taps')
else:
if(len(self.stage1_ic) == len(self.b)-1):
print(colorama.Fore.LIGHTBLUE_EX + 'Using default stage 1 initial conditions')
else:
self.stage1_ic = np.zeros(len(self.b)-1)
#raise ValueError('Stage 1 Filter initial conditions length does not match filter taps')
if(audio_sink):
if(type(stage2_ic) == np.ndarray):
if(len(stage2_ic) == len(self.bb)-1):
self.stage2_ic = stage2_ic
else:
raise ValueError('Stage 2 Filter initial conditions length does not match filter taps')
else:
if(len(self.stage2_ic) == len(self.bb)-1):
print(colorama.Fore.LIGHTBLUE_EX + 'Using default stage 2 initial conditions')
else:
self.stage2_ic = np.zeros(len(self.bb)-1)
#raise ValueError('Stage 2 Filter initial conditions length does not match filter taps')
if(type(a) == np.ndarray):
self.a = a
if(type(aa) == np.ndarray):
self.aa = aa
self.audio_sink = audio_sink
self.rx_data = asyncio.Queue()
self.clear_buffer()
self.DSP_IO = pah.DSP_io_stream(self._audio_callback,self.audio_in,self.audio_out,self.audio_buffsize,self.audio_fs,0,0)
self.keep_streaming = True
self.M1 = M1
self.M2 = M2
if(user_var is not None):
self.user_var = user_var
if(int(self.fs/self.M1/self.M2) != int(self.audio_fs) and audio_sink):
print(colorama.Fore.RED + 'Stage 2 Decimated rate does not match audio sample rate')
print('\t Decimated Rate: %.2f' %(self.fs/self.M1/self.M2))
print('\t Audio Rate: %.2f' %(self.audio_fs))
self.buffer_exceeded = False
loop = asyncio.get_event_loop()
print(colorama.Fore.LIGHTBLUE_EX + 'Starting SDR and Audio Event Loop')
print(colorama.Fore.BLACK + '')
if(audio_sink):
await asyncio.gather(
self._get_rx_data_user(),
self._process_rx_data_user(callback),
self._audio()
)
else:
self.reset_data_out_queue()
await asyncio.gather(
self._get_rx_data_user(),
self._process_rx_data_user(callback),
)
[docs] def run_user_stream(self,callback,M1,M2,b=False,stage1_ic=False,a=False,bb=False,stage2_ic=False,aa=False,audio_sink=True,user_var=None):
'''
Starts a user stream. A user stream follows the flow diagram in the
class description. When audio_sink is True, the audio_sink blocks will
be used and when audio_sink is False, the data sink block will be used.
For any optional parameters set to false, default values will be used
for stage 1 or stage 2 filters. The stop() method may be used to stop
the stream.
Parameters:
-----------
callback: User-defined processing callback (see example in class
description)
M1: Stage 1 decimation factor - must be >= 1
M2: Stage 2 decimation factor - must be >= 1
b: ndarray of stage 1 decimation filter numerator coefficients
stage1_ic: ndarray of stage 1 decimation filter initial conditions. Must
be of length len(b)-1
a: ndarray of stage 1 decimation filter denominator coefficients
bb: ndarray of stage 2 decimation filter numerator coefficients
stage2_ic: ndarray of stage 2 decimation filter initial conditions. Must
be of length len(b)-1
a: ndarray of stage 2 decimation filter numerator coefficients
audio_sink: When True, the audio sink path is used. When false, the
data_sink path is used. (see class definition)
user_var: Initialization of a user-defined variable that can be used
within the user-defined callback. The state of the user-defined
variable is maintained within the class
callback example:
>>> def callback(samples,fs,user_var):
>>> # discriminator
>>> x = samples
>>> X=np.real(x)
>>> Y=np.imag(x)
>>> b=np.array([1,-1])
>>> a=np.array([1,0])
>>> derY=signal.lfilter(b,a,Y)
>>> derX=signal.lfilter(b,a,Y)
>>> z_bb=(X*derY-Y*derX)/(X**2+Y**2)
>>> return z_bb,user_var
method call:
>>> sdr_stream = RTLSDR_stream()
>>> run_user_stream(callback,10,5)
stop streaming:
>>> sdr_stream.stop()
'''
task = asyncio.create_task(self._start_user_stream(callback,M1,M2,b,a,stage1_ic,bb,aa,stage2_ic,audio_sink,user_var))
[docs] async def get_data_out_async(self):
'''
This method asynchronously returns data from the data_sink buffer when
it is full. This is used in the data_sink mode (audio_sink=False).
The following example shows how to continuously stream data and handle
the buffer when it is full. The buffer will automatically get rewritten
whenever it runs out of space, so the returned buffer must be handled
whenever it is filled.
Async Data Out Example:
-----------------------
import asyncio in order to create coroutines and set the data_sink buffer
size
>>> import asyncio
>>> sdr_stream.set_rtl_buffer_size(16)
define an data_sink callback that will count the number of frames coming
into the radio and store the count in the data_sink buffer
>>> def no_audio_callback(samples,fs,user_var):
>>> frame_count = user_var
>>> user_var = user_var+1
>>> return np.array([frame_count]),user_var
create a global variable in order to stop the data_sink buffer processing
loop
>>> global keep_collecting
create an async function to handle the returned data_sink buffer. Simply
print out the buffer for this scenario
>>> async def handle_data_out():
>>> global keep_collecting
>>> keep_collecting = True
>>> while keep_collecting:
>>> data_out = await sdr_stream.get_data_out_async()
>>> print(data_out)
>>> sdr_stream.reset_data_out_queue()
>>> print('Done')
start a user stream as well as our async data handler coroutine. Should
see the data_sink buffer values being printed whenever the buffer is full.
>>> sdr_stream.run_user_stream(no_audio_callback,1,1,audio_sink=False,user_var=1)
>>> task = asyncio.create_task(handle_data_out())
Stop handling data and stop streaming
>>> keep_collecting = False
>>> sdr_stream.stop()
'''
data_out = await self.data_out_queue.get()
return data_out
[docs] async def plot_rf(self,NFFT=2**10,w=6,h=5):
'''
Async method that can be used to plot the PSD of a frame of incoming
samples from the SDR. This essentially acts as a power spectrum "probe"
right before the Stage 1 decimator. Make sure a stream is running
before calling this method. This method must be awaited when called.
parameters:
NFFT: Number of points used in the spectrum plot. Should be 2^N value
w: width of figure
h: height of figure
Example:
>>> sdr_stream = RTLSDR_stream()
>>> sdr_stream.run_user_stream(callback,10,5)
>>> await sdr_stream.plot_rf(1024,6,5)
This will return a spectrum plot
'''
if(not self.keep_streaming):
raise RuntimeError('No running stream. Plot cannot be awaited.')
self.store_rf = True
samples = await self.rf_queue.get()
plt.figure(figsize=(w,h))
plt.psd(samples,NFFT,self.sdr.get_sample_rate(),self.sdr.get_center_freq())
plt.title('PSD of RF Input')
plt.show()
[docs] async def plot_stage1(self,NFFT=2**10,w=6,h=5):
'''
Async method that can be used to plot the PSD of a frame of decimated
samples from the SDR. This essentially acts as a power spectrum "probe"
after the stage 1 decimator and before the user-defined callback.
Make sure a stream is running before calling this method. This method
must be awaited when called.
parameters:
NFFT: Number of points used in the spectrum plot. Should be 2^N value
w: width of figure
h: height of figure
Example:
>>> sdr_stream = RTLSDR_stream()
>>> sdr_stream.run_user_stream(callback,10,5)
>>> await sdr_stream.plot_stage1(1024,6,5)
This will return a spectrum plot
'''
if(not self.keep_streaming):
raise RuntimeError('No running stream. Plot cannot be awaited.')
self.store_stage1 = True
samples = await self.stage1_queue.get()
plt.figure(figsize=(w,h))
plt.psd(samples,NFFT,self.sdr.get_sample_rate()/self.M1,self.sdr.get_center_freq())
plt.title('PSD after Stage 1 Decimation')
plt.show()
[docs] async def plot_processed_stage1(self,NFFT=2**10,FC=0,w=6,h=5):
'''
Async method that can be used to plot the PSD of a frame of
decimated and processed samples from the SDR. This essentially
acts as a power spectrum "probe" after the user-defined callback
and before the audio_sink or data_sink blocks.1 decimator and
before the user-defined callback. Make sure a stream is running
before calling this method. This method must be awaited when
called.
parameters:
NFFT: Number of points used in the spectrum plot. Should be 2^N value
FC: Frequency offset for plotting
w: width of figure
h: height of figure
Example:
>>> sdr_stream = RTLSDR_stream()
>>> sdr_stream.run_user_stream(callback,10,5)
>>> await sdr_stream.plot_processed_stage1(1024,0,6,5)
This will return a spectrum plot
'''
if(not self.keep_streaming):
raise RuntimeError('No running stream. Plot cannot be awaited.')
self.store_processed_stage1 = True
samples = await self.processed_stage1_queue.get()
plt.figure(figsize=(w,h))
plt.psd(samples,NFFT,self.fs/self.M1,FC)
plt.title('PSD after Processing')
plt.show()
[docs] async def plot_stage2(self,NFFT=2**10,FC=0,w=6,h=5):
'''
Async method that can be used to plot the PSD of a frame of data
after the stage 2 decimator. This essentially acts as a power
spectrum "probe" after the stage2 decimator Make sure a stream is
running before calling this method. This method must be awaited
when called.
parameters:
NFFT: Number of points used in the spectrum plot. Should be 2^N value
FC: Frequency offset for plotting
w: width of figure
h: height of figure
Example:
>>> sdr_stream = RTLSDR_stream()
>>> sdr_stream.run_user_stream(callback,10,5)
>>> await sdr_stream.plot_processed_stage1(1024,0,6,5)
This will return a spectrum plot
'''
if(not self.keep_streaming):
raise RuntimeError('No running stream. Plot cannot be awaited.')
self.store_stage2 = True
samples = await self.stage2_queue.get()
plt.figure(figsize=(w,h))
plt.psd(samples,NFFT,self.fs/self.M1/self.M2,FC)
plt.title('PSD after Stage 2 Decimation')
plt.show()
async def _plot_rf_stream(self,w,h):
'''
Private method used to create and update a spectrum analyzer plot of the
RF input using matplotlib.
'''
fig = plt.figure(figsize=(w,h))
ax = fig.add_subplot(111)
plt.ion()
fig.show()
fig.canvas.draw()
while(self.update_rf):
samples = await self.rf_queue.get()
Px,f = psd(samples,self.plot_NFFT,self.fs)
f = f+self.fc
ax.clear()
ax.grid()
plt.title('PSD at RF Input')
plt.ylabel('Power Spectral Density (dB/Hz)')
plt.xlabel('Frequency (MHz)')
power = 10*np.log10(Px*self.fs/self.plot_NFFT)
f = f/1e6
if(self.invert):
ax.set_facecolor('xkcd:black')
ax.plot(f,power,'g')
ax.plot([self.fc/1e6,self.fc/1e6],[-100,20],'--',color='orange')
else:
ax.set_facecolor('xkcd:white')
ax.plot(f,power)
ax.plot([self.fc/1e6,self.fc/1e6],[-100,20],'--')
plt.ylim([np.min(power)-3,np.max(power)+3])
fig.canvas.draw()
self.update_rf = False
async def _plot_rf_stream_bokeh(self,w,h):
'''
Private method used the create a spectrum analyzer of the RF input using
a bokeh plot.
'''
fig = bfigure(width=w,height=h,title='PSD at RF Input')
fig.xaxis.axis_label = "Frequency (MHz)"
fig.yaxis.axis_label = "Power Spectral Density (dB/Hz)"
samples = await self.rf_queue.get()
Px,f = psd(samples,self.plot_NFFT,self.fs)
Px = 10*np.log10(Px*self.fs/self.plot_NFFT)
f = (f+self.fc)/1e6
r = fig.line(f,Px)
if(self.invert):
fig.background_fill_color = "Black"
fig.background_fill_alpha = 0.8
r.glyph.line_color="Green"
else:
fig.background_fill_color = "White"
fig.background_fill_alpha = 1.0
r.glyph.line_color="Blue"
fc_line = fig.line(np.array([self.fc/1e6,self.fc/1e6]),np.array([np.min(Px)-2,np.max(Px)+2]))
fc_line.glyph.line_color="Orange"
fc_line.glyph.line_alpha=0.5
fc_line.glyph.line_width=3
fc_line.glyph.line_dash=[10,5]
target = show(fig, notebook_handle=True)
while(self.update_rf):
samples = await self.rf_queue.get()
Px,f = psd(samples,self.plot_NFFT,self.fs)
Px = 10*np.log10(Px*self.fs/self.plot_NFFT)
f = (f+self.fc)/1e6
r.data_source.data['x'] = f
r.data_source.data['y'] = Px
fc_line.data_source.data['y'] = np.array([np.min(Px)-2,np.max(Px)+2])
fc_line.data_source.data['x'] = np.array([self.fc/1e6,self.fc/1e6])
if(self.invert):
fig.background_fill_color = "Black"
fig.background_fill_alpha = 0.8
r.glyph.line_color = "Green"
else:
fig.background_fill_color = "White"
fig.background_fill_alpha = 1.0
r.glyph.line_color="Blue"
push_notebook(handle=target)
async def _update_rf_plot(self):
'''
Private method used to control the refresh rate of the rf spectrum
analyzer plot.
'''
while(self.update_rf):
#for i in range(0,10):
await asyncio.sleep(1.0/self.refresh_rate)
self.store_rf = True
print(colorama.Fore.LIGHTBLUE_EX + 'Stopped RF PSD Stream')
async def _start_plot_rf_stream(self,NFFT,refresh_rate,invert,w,h):
'''
Private method used to initialize and start the RF spectrum analyzer.
'''
if(not self.keep_streaming):
raise RuntimeError('No running stream. Plot cannot be awaited')
# Stop any other running plots
self.stop_all_plots()
self.update_rf = True
self.refresh_rate = refresh_rate
self.plot_NFFT = NFFT
self.invert = invert
loop = asyncio.get_event_loop()
await asyncio.gather(
#self._plot_rf_stream_bokeh(w,h),
self._plot_rf_stream(w,h),
self._update_rf_plot()
)
[docs] def run_plot_rf_stream(self,NFFT=2**10,refresh_rate=2,invert=True,w=8,h=5):
'''
This method can be used to instantiate a spectrum analyzer of the RF input
during a stream. Call the stop_plot_rf_plot method in order to stop the
plot from updating. Only one spectrum analyzer instance my be running at
once. This only works when using %pylab widget or %pylab notebook
parameters:
----------
NFFT: fftsize used in plotting
refresh_rate: defines how often the spectrum analyzer updates (in Hz)
invert: Inverts the background to black when true or leaves it white when false
w: width of figure
h: height of figure
Example:
>>> %pylab widget
>>> sdr_stream = RTLSDR_stream()
>>> sdr_stream.run_user_stream(callback,10,5)
>>> sdr_stream.run_plot_rf_stream(1024,2,True,8,5)
>>> sdr_stream.stop_rf_plot()
>>> sdr_stream.stop()
'''
task = asyncio.create_task(self._start_plot_rf_stream(NFFT,refresh_rate,invert,w,h))
async def _plot_stage1_stream(self,w,h):
'''
Private method used to create and update a spectrum analyzer plot after the
stage 1 decimator using matplotlib.
'''
fig = plt.figure(figsize=(w,h))
ax = fig.add_subplot(111)
plt.ion()
fig.show()
fig.canvas.draw()
while(self.update_stage1):
samples = await self.stage1_queue.get()
Px,f = psd(samples,self.plot_NFFT,self.fs/self.M1)
f = f+self.fc
f = f/1e3
ax.clear()
ax.grid()
plt.title('PSD after Stage 1')
plt.ylabel('Power Spectral Density (dB/Hz)')
plt.xlabel('Frequency (kHz)')
if(self.invert):
ax.set_facecolor('xkcd:black')
ax.plot(f,10*np.log10(Px*self.fs/self.plot_NFFT),'g')
else:
ax.set_facecolor('xkcd:white')
ax.plot(f,10*np.log10(Px*self.fs/self.plot_NFFT))
fig.canvas.draw()
self.update_stage1 = False
async def _plot_stage1_stream_bokeh(self,w,h):
'''
Private method used the create a spectrum analyzer after the
stage 1 decimator using a bokeh plot.
'''
fig = bfigure(width=w,height=h,title='PSD at RF Input')
fig.xaxis.axis_label = "Frequency (MHz)"
fig.yaxis.axis_label = "Power Spectral Density (dB/Hz)"
samples = await self.rf_queue.get()
Px,f = psd(samples,self.plot_NFFT,self.fs)
Px = 10*np.log10(Px*self.fs/self.plot_NFFT)
f = (f+self.fc)/1e6
r = fig.line(f,Px)
if(self.invert):
fig.background_fill_color = "Black"
fig.background_fill_alpha = 0.8
r.glyph.line_color="Green"
else:
fig.background_fill_color = "White"
fig.background_fill_alpha = 1.0
r.glyph.line_color="Blue"
self.target1 = show(fig, notebook_handle=True)
while(self.update_rf):
samples = await self.rf_queue.get()
Px,f = psd(samples,self.plot_NFFT,self.fs)
Px = 10*np.log10(Px*self.fs/self.plot_NFFT)
f = (f+self.fc)/1e6
r.data_source.data['x'] = f
r.data_source.data['y'] = Px
if(self.invert):
fig.background_fill_color = "Black"
fig.background_fill_alpha = 0.8
r.glyph.line_color = "Green"
else:
fig.background_fill_color = "White"
fig.background_fill_alpha = 1.0
r.glyph.line_color="Blue"
push_notebook(handle=self.target1)
async def _update_stage1_plot(self):
'''
Private method used to control the refresh rate of the stage 1 spectrum
analyzer plot.
'''
while(self.update_stage1):
#for i in range(0,10):
await asyncio.sleep(1.0/self.refresh_rate)
self.store_stage1 = True
print(colorama.Fore.LIGHTBLUE_EX + 'Stopped Stage 1 PSD Stream')
async def _start_plot_stage1_stream(self,NFFT,refresh_rate,invert,w,h):
'''
Private method used to initialize and start the stage 1 spectrum analyzer.
'''
if(not self.keep_streaming):
raise RuntimeError('No running stream. Plot cannot be awaited')
# Stop any other running plots
self.stop_all_plots()
self.update_stage1 = True
self.refresh_rate = refresh_rate
self.plot_NFFT = NFFT
self.invert = invert
loop = asyncio.get_event_loop()
await asyncio.gather(
#self.plot_stage1_stream_bokeh(w,h),
self._plot_stage1_stream(w,h),
self._update_stage1_plot()
)
[docs] def run_plot_stage1_stream(self,NFFT=2**10,refresh_rate=2,invert=True,w=8,h=5):
'''
This method can be used to instantiate a spectrum analyzer after stage 1
during a stream. Call the stop_plot_rf_plot method in order to stop the
plot from updating. Only one spectrum analyzer instance my be running at
once. This only works when using %pylab widget or %pylab notebook
parameters:
----------
NFFT: fftsize used in plotting
refresh_rate: defines how often the spectrum analyzer updates (in Hz)
invert: Inverts the background to black when true or leaves it white when false
w: width of figure
h: height of figure
Example:
>>> %pylab widget
>>> sdr_stream = RTLSDR_stream()
>>> sdr_stream.run_user_stream(callback,10,5)
>>> sdr_stream.run_plot_stage1_stream(1024,2,True,8,5)
>>> sdr_stream.stop_stage1_plot()
>>> sdr_stream.stop()
'''
task = asyncio.create_task(self._start_plot_stage1_stream(NFFT,refresh_rate,invert,w,h))
async def _plot_processed_stage1_stream(self,w,h):
'''
Private method used to create and update a spectrum analyzer plot after the
callback using matplotlib.
'''
fig = plt.figure(figsize=(w,h))
ax = fig.add_subplot(111)
plt.ion()
fig.show()
fig.canvas.draw()
while(self.update_processed_stage1):
samples = await self.processed_stage1_queue.get()
Px,f = psd(samples,self.plot_NFFT,self.fs/self.M1)
ax.clear()
ax.grid()
plt.title('PSD after Processing')
plt.ylabel('Power Spectral Density (dB/Hz)')
plt.xlabel('Frequency')
if(self.invert):
ax.set_facecolor('xkcd:black')
ax.plot(f,10*np.log10(Px*self.fs/self.plot_NFFT),'g')
else:
ax.set_facecolor('xkcd:white')
ax.plot(f,10*np.log10(Px*self.fs/self.plot_NFFT))
fig.canvas.draw()
self.update_processed_stage1 = False
async def _plot_processed_stage1_stream_bokeh(self,w,h):
'''
Private method used the create a spectrum analyzer after the
callback using a bokeh plot.
'''
fig = bfigure(width=w,height=h,title='PSD after User Callback')
fig.xaxis.axis_label = "Frequency (Hz)"
fig.yaxis.axis_label = "Power Spectral Density (dB/Hz)"
samples = await self.rf_queue.get()
Px,f = psd(samples,self.plot_NFFT,self.fs)
Px = 10*np.log10(Px*self.fs/self.plot_NFFT)
f = (f+self.fc)
r = fig.line(f,Px)
if(self.invert):
fig.background_fill_color = "Black"
fig.background_fill_alpha = 0.8
r.glyph.line_color="Green"
else:
fig.background_fill_color = "White"
fig.background_fill_alpha = 1.0
r.glyph.line_color="Blue"
self.target2 = show(fig, notebook_handle=True)
while(self.update_rf):
samples = await self.rf_queue.get()
Px,f = psd(samples,self.plot_NFFT,self.fs)
Px = 10*np.log10(Px*self.fs/self.plot_NFFT)
f = (f+self.fc)
r.data_source.data['x'] = f
r.data_source.data['y'] = Px
if(self.invert):
fig.background_fill_color = "Black"
fig.background_fill_alpha = 0.8
r.glyph.line_color = "Green"
else:
fig.background_fill_color = "White"
fig.background_fill_alpha = 1.0
r.glyph.line_color="Blue"
push_notebook(handle=self.target2)
async def _update_processed_stage1_plot(self):
'''
Private method used to control the refresh rate of the callback spectrum
analyzer plot.
'''
while(self.update_processed_stage1):
#for i in range(0,10):
await asyncio.sleep(1.0/self.refresh_rate)
self.store_processed_stage1 = True
print(colorama.Fore.LIGHTBLUE_EX + 'Stopped Processed Stage 1 PSD Stream')
async def _start_plot_processed_stage1_stream(self,NFFT,refresh_rate,invert,w,h):
'''
Private method used to initialize and start the callback spectrum analyzer.
'''
if(not self.keep_streaming):
raise RuntimeError('No running stream. Plot cannot be awaited')
# Stop any other running plots
self.stop_all_plots()
self.update_processed_stage1 = True
self.refresh_rate = refresh_rate
self.plot_NFFT = NFFT
self.invert = invert
loop = asyncio.get_event_loop()
await asyncio.gather(
#self._plot_processed_stage1_stream_bokeh(w,h),
self._plot_processed_stage1_stream(w,h),
self._update_processed_stage1_plot()
)
[docs] def run_plot_processed_stage1_stream(self,NFFT=2**10,refresh_rate=2,invert=True,w=8,h=5):
'''
This method can be used to instantiate a spectrum analyzer after the callback
during a stream. Call the stop_plot_rf_plot method in order to stop the
plot from updating. Only one spectrum analyzer instance my be running at
once. This only works when using %pylab widget or %pylab notebook
parameters:
----------
NFFT: fftsize used in plotting
refresh_rate: defines how often the spectrum analyzer updates (in Hz)
invert: Inverts the background to black when true or leaves it white when false
w: width of figure
h: height of figure
Example:
>>> %pylab widget
>>> sdr_stream = RTLSDR_stream()
>>> sdr_stream.run_user_stream(callback,10,5)
>>> sdr_stream.run_plot_processed_stage1_stream(1024,2,True,8,5)
>>> sdr_stream.stop_processed_stage1_plot()
>>> sdr_stream.stop()
'''
task = asyncio.create_task(self._start_plot_processed_stage1_stream(NFFT,refresh_rate,invert,w,h))
async def _plot_stage2_stream(self,w,h):
'''
Private method used to create and update a spectrum analyzer plot after the
stage 2 decimator using matplotlib.
'''
fig = plt.figure(figsize=(w,h))
ax = fig.add_subplot(111)
plt.ion()
fig.show()
fig.canvas.draw()
ax.grid()
while(self.update_stage2):
samples = await self.stage2_queue.get()
Px,f = psd(samples,self.plot_NFFT,self.fs/self.M1/self.M2)
ax.clear()
ax.grid()
plt.title('PSD after Stage 2')
plt.ylabel('Power Spectral Density (dB/Hz)')
plt.xlabel('Frequency')
if(self.invert):
ax.set_facecolor('xkcd:black')
ax.plot(f,10*np.log10(Px*self.fs/self.plot_NFFT),'g')
else:
ax.set_facecolor('xkcd:white')
ax.plot(f,10*np.log10(Px*self.fs/self.plot_NFFT))
fig.canvas.draw()
self.update_stage2 = False
async def _plot_stage2_stream_bokeh(self,w,h):
'''
Private method used the create a spectrum analyzer after the
stage 2 decimator using a bokeh plot.
'''
fig = bfigure(width=w,height=h,title='PSD after Stage 2 Decimation')
fig.xaxis.axis_label = "Frequency (Hz)"
fig.yaxis.axis_label = "Power Spectral Density (dB/Hz)"
samples = await self.rf_queue.get()
Px,f = psd(samples,self.plot_NFFT,self.fs)
Px = 10*np.log10(Px*self.fs/self.plot_NFFT)
f = (f+self.fc)
r = fig.line(f,Px)
if(self.invert):
fig.background_fill_color = "Black"
fig.background_fill_alpha = 0.8
r.glyph.line_color="Green"
else:
fig.background_fill_color = "White"
fig.background_fill_alpha = 1.0
r.glyph.line_color="Blue"
target = show(fig, notebook_handle=True)
while(self.update_rf):
samples = await self.rf_queue.get()
Px,f = psd(samples,self.plot_NFFT,self.fs)
Px = 10*np.log10(Px*self.fs/self.plot_NFFT)
f = (f+self.fc)
r.data_source.data['x'] = f
r.data_source.data['y'] = Px
if(self.invert):
fig.background_fill_color = "Black"
fig.background_fill_alpha = 0.8
r.glyph.line_color = "Green"
else:
fig.background_fill_color = "White"
fig.background_fill_alpha = 1.0
r.glyph.line_color="Blue"
push_notebook(handle=target)
async def _update_stage2_plot(self):
'''
Private method used to control the refresh rate of the stage 2 spectrum
analyzer plot.
'''
while(self.update_stage2):
#for i in range(0,10):
await asyncio.sleep(1.0/self.refresh_rate)
self.store_stage2 = True
print(colorama.Fore.LIGHTBLUE_EX + 'Stopped Stage 2 PSD Stream')
async def _start_plot_stage2_stream(self,NFFT,refresh_rate,invert,w,h):
'''
Private method used to initialize and start the stage 2 spectrum analyzer.
'''
if(not self.keep_streaming):
raise RuntimeError('No running stream. Plot cannot be awaited')
# Stop any other running plots
self.stop_all_plots()
self.update_stage2 = True
self.refresh_rate = refresh_rate
self.plot_NFFT = NFFT
self.invert = invert
loop = asyncio.get_event_loop()
await asyncio.gather(
#self._plot_stage2_stream_bokeh(w,h),
self._plot_stage2_stream(w,h),
self._update_stage2_plot()
)
[docs] def run_plot_stage2_stream(self,NFFT=2**10,refresh_rate=2,invert=True,w=8,h=5):
'''
This method can be used to instantiate a spectrum analyzer after the callback
during a stream. Call the stop_plot_rf_plot method in order to stop the
plot from updating. Only one spectrum analyzer instance my be running at
once. This only works when using %pylab widget or %pylab notebook
parameters:
----------
NFFT: fftsize used in plotting
refresh_rate: defines how often the spectrum analyzer updates (in Hz)
invert: Inverts the background to black when true or leaves it white when false
w: width of figure
h: height of figure
Example:
>>> %pylab widget
>>> sdr_stream = RTLSDR_stream()
>>> sdr_stream.run_user_stream(callback,10,5)
>>> sdr_stream.run_plot_stage2_stream(1024,2,True,8,5)
>>> sdr_stream.stop_stage2_plot()
>>> sdr_stream.stop()
'''
task = asyncio.create_task(self._start_plot_stage2_stream(NFFT,refresh_rate,invert,w,h))
[docs] def stop_rf_plot(self):
'''
Stops updating an RF spectrum analyzer instance
'''
self.update_rf = False
[docs] def stop_stage1_plot(self):
'''
Stops updating a stage 1 spectrum analyzer instance
'''
self.update_stage1 = False
[docs] def stop_processed_stage1_plot(self):
'''
Stops updating a callback spectrum analyzer instance
'''
self.update_processed_stage1 = False
[docs] def stop_stage2_plot(self):
'''
Stops updating a stage 2 spectrum analyzer instance
'''
self.update_stage2 = False
[docs] def stop_all(self):
'''
Stops any running spectrum analyzer and stops streaming
'''
self.update_rf = False
self.update_stage1 = False
self.update_processed_stage1 = False
self.update_stage2 = False
self.keep_streaming =False
[docs] def stop_all_plots(self):
'''
Stops any running spectrum analyzer
'''
self.update_rf = False
self.update_stage1 = False
self.update_processed_stage1 = False
self.update_stage2 = False
[docs] def show_logs(self):
'''
Used in Mono FM Receiver example to show logs inside of a widget
'''
display(self.output)
[docs] def clear_logs(self):
'''
Used in Mono FM Receiver example to clear widget logs
'''
self.output.clear_output()
def _decimate(self,x,M,fs=2.4e6,stage=1):
'''
Private method used to decimate a signal for the Mono FM Receiver Example
'''
# Filter and decimate (should be polyphase)
if(stage == 1):
y,self.stage1_ic = signal.lfilter(self.b,self.a,x,zi=self.stage1_ic)
else:
y,self.stage2_ic = signal.lfilter(self.bb,self.a,x,zi=self.stage2_ic)
z = ss.downsample(y,M)
return z
def _discrim(self,x):
"""
Private method used by Mono FM Receiver example discriminate FM signal
Mark Wickert
"""
X=np.real(x) # X is the real part of the received signal
Y=np.imag(x) # Y is the imaginary part of the received signal
b=np.array([1, -1]) # filter coefficients for discrete derivative
a=np.array([1, 0]) # filter coefficients for discrete derivative
derY,self.Y_ic=signal.lfilter(b,a,Y,zi=self.Y_ic) # derivative of Y,
derX,self.X_ic=signal.lfilter(b,a,X,zi=self.X_ic) # " X,
disdata=(X*derY-Y*derX)/(X**2+Y**2)
return disdata
[docs] def reset_data_out_queue(self):
'''
Clears data_sink queue
'''
del self.data_out_queue
self.data_out_queue = asyncio.Queue()
[docs] def set_fc_mhz(self,fc):
'''
Sets tuning center frequency value (in MHz) on the SDR
'''
display(Math(r'f_c:\;%.1f\;\mathrm{MHz}' %fc))
self.fc = fc*1e6
self.sdr.set_center_freq(self.fc)
with self.output:
self.output.append_stdout(colorama.Fore.GREEN + 'Changing Center Frequency to: {} MHz\n'.format(fc))
[docs] def set_fc(self,fc):
'''
Sets tuning center frequency value (in Hz) on the SDR
'''
self.fc = fc
self.sdr.set_center_freq(fc)
print(colorama.Fore.YELLOW + "Center Frequency: {}".format(self.sdr.get_center_freq()))
[docs] def set_gain(self,gain):
'''
Sets receiver gain value (in dB) on the SDR
'''
self.gain = gain
self.sdr.set_gain(gain)
print(colorama.Fore.YELLOW + "Gain: {}".format(self.sdr.get_gain()))
[docs] def set_audio_gain_db(self,gain):
'''
Sets the audio gain value (in dB) used to scale the audio_sink output volume
'''
self.audio_gain = 10**(gain/20)
[docs] def set_fs(self,fs):
'''
Sets the sample rate (in samples/second) to the SDR
This should generally be left at 2.4 Msps. The radio can
only operate at specific rates.
'''
self.fs = fs
self.sdr.set_sample_rate(fs)
print(colorama.Fore.YELLOW + "Sample Rate: {}".format(self.sdr.get_sample_rate()))
[docs] def clear_buffer(self):
'''
Clears the circular buffer used by the audio sink
'''
self.z_out = np.zeros(self.rtl_buffer_size)
self.rx_idx = 0
self.audio_idx = int(self.rtl_buffer_size/2)
[docs] def set_rtl_buffer_size(self,rtl_buffer_size):
'''
Sets the circular buffer size used by the audio_sink and the data_sink.
When the audio_sink is used, this should be set to a fairly high number
(around 2^15). When the data_sink is used, the buffer size can be changed
to accommodate the scenario.
'''
self.rtl_buffer_size = rtl_buffer_size
[docs] def set_audio_buffsize(self,audio_buffsize):
'''
Sets the buffer size used by PyAudio to consume frames processed audio frames
from the circular buffer.
'''
self.audio_buffsize = audio_buffsize
[docs] def set_audio_fs(self,audio_fs):
'''
Sets the audio sample rate. When the audio sink is used this should be equal
to the radio sample rate (fs) / stage 1 decimation factor / stage 2 decimation
factor
'''
self.audio_fs = audio_fs
[docs] def set_audio_in(self,audio_in):
'''
Selects the audio input device. This is not used in the class, but should be
set to a valid audio input.
'''
self.audio_in = audio_in
[docs] def set_audio_out(self,audio_out):
'''
Selects the audio output device. Use sk_dsp_comm.rtlsdr_helper.pah.available_devices()
to get device indices.
'''
self.audio_out = audio_out
[docs] def set_audio_gain(self,gain):
'''
Sets the audio gain value used to scale the PyAudio volume.
'''
self.audio_gain = gain
[docs] def stop(self):
'''
Stops a running stream.
'''
self.keep_streaming = False
[docs] def set_refresh_rate(self,refresh_rate):
'''
Sets the refresh_rate (in Hz) of any running spectrum analyzer
'''
self.refresh_rate = refresh_rate
[docs] def set_stage1_coeffs(self,b,a=[1],zi=False):
'''
Can be used to set the stage 1 decimation filter coefficients. This can
be used during an active stream.
parameters:
-----------
b: stage 1 filter numerator coefficients
a: stage 1 filter denominator coefficients
zi: stage 1 filter initial conditions
'''
if(type(b) == list or type(b) == np.ndarray):
self.b = b
else:
raise ValueError('Numerator coefficient parameter must be list or ndarray type')
if(type(a) == list or type(a) == np.ndarray):
self.a = a
else:
raise ValueError('Denominator coefficient parameter must be list or ndarray type')
if(type(zi) == np.ndarray or type(zi) == list):
if(len(zi) == len(b)-1):
self.stage1_ic = zi
else:
print(colorama.Fore.RED + 'Initial conditions are not correct length')
print('Initializing with zero vector')
self.stage1_ic = np.zeros(len(b)-1)
else:
raise ValueError('Filter initial conditions must be list or ndarray type')
[docs] def set_stage2_coeffs(self,bb,aa=[1],zi=False):
'''
Can be used to set the stage 2 decimation filter coefficients. This can
be used during an active stream.
parameters:
-----------
b: stage 2 filter numerator coefficients
a: stage 2 filter denominator coefficients
zi: stage 2 filter initial conditions
'''
if(type(bb) == list or type(bb) == np.ndarray):
self.bb = bb
else:
raise ValueError('Numerator coefficient parameter must be list or ndarray type')
if(type(aa) == list or type(aa) == np.ndarray):
self.aa = aa
else:
raise ValueError('Denominator coefficient parameter must be list or ndarray type')
if(type(zi) == np.ndarray or type(zi) == list):
if(len(zi) == len(bb)-1):
self.stage2_ic = zi
else:
print(colorama.Fore.RED + 'Initial conditions are not correct length')
print('Initializing with zero vector')
self.stage2_ic = np.zeros(len(bb)-1)
else:
raise ValueError('Filter initial conditions must be list or ndarray type')
[docs] def set_NFFT(self,NFFT):
'''
Sets the FFT size for any running spectrum analyzer
'''
self.plot_NFFT = NFFT
[docs] def toggle_invert(self):
'''
Toggles between black and white background of a running spectrum analyzer
'''
self.invert = not self.invert
[docs] async def get_stage1_frame(self):
'''
Async method that can be used to get a frame of decimated data after
the stage 1 decimation filter.
Example:
>>> sdr_stream = RTLSDR_stream()
>>> sdr_stream.run_user_stream(callback,10,5)
>>> stage1_data_frame = await sdr_stream.get_stage1_frame()
'''
self.store_stage1 = True
samples = await self.stage1_queue.get()
return samples
[docs] async def get_rf_frame(self):
'''
Async method that can be used to get a frame of incoming RF samples.
Example:
>>> sdr_stream = RTLSDR_stream()
>>> sdr_stream.run_user_stream(callback,10,5)
>>> rf_data_frame = await sdr_stream.get_stage1_frame()
'''
self.store_rf = True
samples = await self.rf_queue.get()
return samples
[docs] async def get_processed_stage1_frame(self):
'''
Async method that can be used to get a frame of decimated data after
the callback.
Example:
>>> sdr_stream = RTLSDR_stream()
>>> sdr_stream.run_user_stream(callback,10,5)
>>> callback_data_frame = await sdr_stream.get_stage1_frame()
'''
self.store_processed_stage1 = True
samples = await self.processed_stage1_queue.get()
return samples
[docs] async def get_stage2_frame(self):
'''
Async method that can be used to get a frame of decimated data after
the stage 2 decimation filter.
Example:
>>> sdr_stream = RTLSDR_stream()
>>> sdr_stream.run_user_stream(callback,10,5)
>>> stage2_data_frame = await sdr_stream.get_stage2_frame()
'''
self.store_stage2 = True
samples = await self.stage2_queue.get()
return samples
[docs] def get_center_freq(self):
'''
Returns the center frequency of the SDR
'''
return self.sdr.get_center_freq()
[docs] def get_gain(self):
'''
Returns the receiver gain of the SDR
'''
return self.sdr.get_gain()
[docs] def get_sample_rate(self):
'''
Returns the sample rate of the SDR
'''
return self.sdr.get_sample_rate()
[docs] def get_bandwidth(self):
'''
returns the bandwidth of the SDR
'''
return self.sdr.get_bandwidth()
[docs] def get_buffer(self):
'''
Returns the data_sink buffer at the current index
'''
if(self.buffer_exceeded):
return np.concatenate((self.z_out[self.rx_idx:],self.z_out[:self.rx_idx]))
else:
return self.z_out[:self.rx_idx]
def decimate(x,M,fs=2.4e6):
b = signal.firwin(64,2*200e3/float(fs))
# Filter and decimate (should be polyphase)
y = signal.lfilter(b,1,x)
z = ss.downsample(y,M)
return z
def capture(Tc,fo=88.7e6,fs=2.4e6,gain=40,device_index=0):
# Setup SDR
sdr = RtlSdr(device_index) #create a RtlSdr object
#sdr.get_tuner_type()
sdr.sample_rate = fs
sdr.center_freq = fo
#sdr.gain = 'auto'
sdr.gain = gain
# Capture samples
Nc = np.ceil(Tc*fs)
x = sdr.read_samples(Nc)
sdr.close()
return x
[docs]def discrim(x):
"""
function disdata = discrim(x)
where x is an angle modulated signal in complex baseband form.
Mark Wickert
"""
X=np.real(x) # X is the real part of the received signal
Y=np.imag(x) # Y is the imaginary part of the received signal
b=np.array([1, -1]) # filter coefficients for discrete derivative
a=np.array([1, 0]) # filter coefficients for discrete derivative
derY=signal.lfilter(b,a,Y) # derivative of Y,
derX=signal.lfilter(b,a,X) # " X,
disdata=(X*derY-Y*derX)/(X**2+Y**2)
return disdata
[docs]def mono_FM(x,fs=2.4e6,file_name='test.wav'):
"""
Decimate complex baseband input by 10
Design 1st decimation lowpass filter (f_c = 200 KHz)
"""
b = signal.firwin(64,2*200e3/float(fs))
# Filter and decimate (should be polyphase)
y = signal.lfilter(b,1,x)
z = ss.downsample(y,10)
# Apply complex baseband discriminator
z_bb = discrim(z)
# Design 2nd decimation lowpass filter (fc = 12 KHz)
bb = signal.firwin(64,2*12e3/(float(fs)/10))
# Filter and decimate
zz_bb = signal.lfilter(bb,1,z_bb)
# Decimate by 5
z_out = ss.downsample(zz_bb,5)
# Save to wave file
ss.to_wav(file_name, 48000, z_out/2)
print('Done!')
return z_bb, z_out
[docs]def stereo_FM(x,fs=2.4e6,file_name='test.wav'):
"""
Stereo demod from complex baseband at sampling rate fs.
Assume fs is 2400 ksps
Mark Wickert July 2017
"""
N1 = 10
b = signal.firwin(64,2*200e3/float(fs))
# Filter and decimate (should be polyphase)
y = signal.lfilter(b,1,x)
z = ss.downsample(y,N1)
# Apply complex baseband discriminator
z_bb = discrim(z)
# Work with the (3) stereo multiplex signals:
# Begin by designing a lowpass filter for L+R and DSP demoded (L-R)
# (fc = 12 KHz)
b12 = signal.firwin(128,2*12e3/(float(fs)/N1))
# The L + R term is at baseband, we just lowpass filter to remove
# other terms above 12 kHz.
y_lpr = signal.lfilter(b12,1,z_bb)
b19 = signal.firwin(128,2*1e3*np.array([19-5,19+5])/(float(fs)/N1),
pass_zero=False);
z_bb19 = signal.lfilter(b19,1,z_bb)
# Lock PLL to 19 kHz pilot
# A type 2 loop with bandwidth Bn = 10 Hz and damping zeta = 0.707
# The VCO quiescent frequency is set to 19000 Hz.
theta, phi_error = pilot_PLL(z_bb19,19000,fs/N1,2,10,0.707)
# Coherently demodulate the L - R subcarrier at 38 kHz.
# theta is the PLL output phase at 19 kHz, so to double multiply
# by 2 and wrap with cos() or sin().
# First bandpass filter
b38 = signal.firwin(128,2*1e3*np.array([38-5,38+5])/(float(fs)/N1),
pass_zero=False);
x_lmr = signal.lfilter(b38,1,z_bb)
# Coherently demodulate using the PLL output phase
x_lmr = 2*np.sqrt(2)*np.cos(2*theta)*x_lmr
# Lowpass at 12 kHz to recover the desired DSB demod term
y_lmr = signal.lfilter(b12,1,x_lmr)
# Matrix the y_lmr and y_lpr for form right and left channels:
y_left = y_lpr + y_lmr
y_right = y_lpr - y_lmr
# Decimate by N2 (nominally 5)
N2 = 5
fs2 = float(fs)/(N1*N2) # (nominally 48 ksps)
y_left_DN2 = ss.downsample(y_left,N2)
y_right_DN2 = ss.downsample(y_right,N2)
# Deemphasize with 75 us time constant to 'undo' the preemphasis
# applied at the transmitter in broadcast FM.
# A 1-pole digital lowpass works well here.
a_de = np.exp(-2.1*1e3*2*np.pi/fs2)
z_left = signal.lfilter([1-a_de],[1, -a_de],y_left_DN2)
z_right = signal.lfilter([1-a_de],[1, -a_de],y_right_DN2)
# Place left and righ channels as side-by-side columns in a 2D array
z_out = np.hstack((np.array([z_left]).T,(np.array([z_right]).T)))
ss.to_wav(file_name, 48000, z_out/2)
print('Done!')
#return z_bb, z_out
return z_bb, theta, y_lpr, y_lmr, z_out
def wide_PSD(Tc,f_start,f_stop,fs = 2.4e6,rho = 0.6):
K = np.ceil((f_stop - f_start)/(rho*fs))
K = int(K)
print(K)
fo = np.zeros(K)
for i in range(K):
if i == 0:
fo[i] = f_start + rho*fs/2.
else:
fo[i] = fo[i-1] + rho*fs
return fo
[docs]def pilot_PLL(xr,fq,fs,loop_type,Bn,zeta):
"""
theta, phi_error = pilot_PLL(xr,fq,fs,loop_type,Bn,zeta)
Mark Wickert, April 2014
"""
T = 1/float(fs)
# Set the VCO gain in Hz/V
Kv = 1.0
# Design a lowpass filter to remove the double freq term
Norder = 5
b_lp,a_lp = signal.butter(Norder,2*(fq/2.)/float(fs))
fstate = np.zeros(Norder) # LPF state vector
Kv = 2*np.pi*Kv # convert Kv in Hz/v to rad/s/v
if loop_type == 1:
# First-order loop parameters
fn = Bn
Kt = 2*np.pi*fn # loop natural frequency in rad/s
elif loop_type == 2:
# Second-order loop parameters
fn = 1/(2*np.pi)*2*Bn/(zeta + 1/(4*zeta)) # given Bn in Hz
Kt = 4*np.pi*zeta*fn # loop natural frequency in rad/s
a = np.pi*fn/zeta
else:
print('Loop type must be 1 or 2')
# Initialize integration approximation filters
filt_in_last = 0
filt_out_last = 0
vco_in_last = 0
vco_out = 0
vco_out_last = 0
# Initialize working and final output vectors
n = np.arange(0,len(xr))
theta = np.zeros(len(xr))
ev = np.zeros(len(xr))
phi_error = np.zeros(len(xr))
# Normalize total power in an attemp to make the 19kHz sinusoid
# component have amplitude ~1.
#xr = xr/(2/3*std(xr));
# Begin the simulation loop
for kk in range(len(n)):
# Sinusoidal phase detector (simple multiplier)
phi_error[kk] = 2*xr[kk]*np.sin(vco_out)
# LPF to remove double frequency term
phi_error[kk],fstate = signal.lfilter(b_lp,a_lp,np.array([phi_error[kk]]),zi=fstate)
pd_out = phi_error[kk]
#pd_out = 0
# Loop gain
gain_out = Kt/Kv*pd_out # apply VCO gain at VCO
# Loop filter
if loop_type == 2:
filt_in = a*gain_out
filt_out = filt_out_last + T/2.*(filt_in + filt_in_last)
filt_in_last = filt_in
filt_out_last = filt_out
filt_out = filt_out + gain_out
else:
filt_out = gain_out
# VCO
vco_in = filt_out + fq/(Kv/(2*np.pi)) # bias to quiescent freq.
vco_out = vco_out_last + T/2.*(vco_in + vco_in_last)
vco_in_last = vco_in
vco_out_last = vco_out
vco_out = Kv*vco_out # apply Kv
# Measured loop signals
ev[kk] = filt_out
theta[kk] = np.mod(vco_out,2*np.pi); # The vco phase mod 2pi
return theta,phi_error
[docs]def sccs_bit_sync(y,Ns):
"""
rx_symb_d,clk,track = sccs_bit_sync(y,Ns)
//////////////////////////////////////////////////////
Symbol synchronization algorithm using SCCS
//////////////////////////////////////////////////////
y = baseband NRZ data waveform
Ns = nominal number of samples per symbol
Reworked from ECE 5675 Project
Translated from m-code version
Mark Wickert April 2014
"""
# decimated symbol sequence for SEP
rx_symb_d = np.zeros(int(np.fix(len(y)/Ns)))
track = np.zeros(int(np.fix(len(y)/Ns)))
bit_count = -1
y_abs = np.zeros(len(y))
clk = np.zeros(len(y))
k = Ns+1 #initial 1-of-Ns symbol synch clock phase
# Sample-by-sample processing required
for i in range(len(y)):
#y_abs(i) = abs(round(real(y(i))))
if i >= Ns: # do not process first Ns samples
# Collect timing decision unit (TDU) samples
y_abs[i] = np.abs(np.sum(y[i-Ns+1:i+1]))
# Update sampling instant and take a sample
# For causality reason the early sample is 'i',
# the on-time or prompt sample is 'i-1', and
# the late sample is 'i-2'.
if (k == 0):
# Load the samples into the 3x1 TDU register w_hat.
# w_hat[1] = late, w_hat[2] = on-time; w_hat[3] = early.
w_hat = y_abs[i-2:i+1]
bit_count += 1
if w_hat[1] != 0:
if w_hat[0] < w_hat[2]:
k = Ns-1
clk[i-2] = 1
rx_symb_d[bit_count] = y[i-2-int(np.round(Ns/2))-1]
elif w_hat[0] > w_hat[2]:
k = Ns+1
clk[i] = 1
rx_symb_d[bit_count] = y[i-int(np.round(Ns/2))-1]
else:
k = Ns
clk[i-1] = 1
rx_symb_d[bit_count] = y[i-1-int(np.round(Ns/2))-1]
else:
k = Ns
clk[i-1] = 1
rx_symb_d[bit_count] = y[i-1-int(np.round(Ns/2))]
track[bit_count] = np.mod(i,Ns)
k -= 1
# Trim the final output to bit_count
rx_symb_d = rx_symb_d[:bit_count]
return rx_symb_d, clk, track
[docs]def fsk_BEP(rx_data,m,flip):
"""
fsk_BEP(rx_data,m,flip)
Estimate the BEP of the data bits recovered
by the RTL-SDR Based FSK Receiver.
The reference m-sequence generated in Python
was found to produce sequences running in the opposite
direction relative to the m-sequences generated by the
mbed. To allow error detection the reference m-sequence
is flipped.
Mark Wickert April 2014
"""
Nbits = len(rx_data)
c = dc.m_seq(m)
if flip == 1:
# Flip the sequence to compenstate for mbed code difference
# First make it a 1xN array
c.shape = (1,len(c))
c = np.fliplr(c).flatten()
L = int(np.ceil(Nbits/float(len(c))))
tx_data = np.dot(c.reshape(len(c),1),np.ones((1,L)))
tx_data = tx_data.T.reshape((1,len(c)*L)).flatten()
tx_data = tx_data[:Nbits]
# Convert to +1/-1 bits
tx_data = 2*tx_data - 1
Bit_count,Bit_errors = dc.BPSK_BEP(rx_data,tx_data)
print('len rx_data = %d, len tx_data = %d' % (len(rx_data),len(tx_data)))
Pe = Bit_errors/float(Bit_count)
print('/////////////////////////////////////')
print('Bit Errors: %d' % Bit_errors)
print('Bits Total: %d' % Bit_count)
print(' BEP: %2.2e' % Pe)
print('/////////////////////////////////////')
def to_wav_stereo(filename,rate,x_l,x_r=None):
if x_r == None:
ss.to_wav(filename,rate,x_l)
else:
xlr = np.hstack((np.array([x_l]).T,np.array([x_r]).T))
ss.to_wav(filename,rate,xlr)
[docs]def complex2wav(filename,rate,x):
"""
Save a complex signal vector to a wav file for compact binary
storage of 16-bit signal samples. The wav left and right channels
are used to save real (I) and imaginary (Q) values. The rate is
just a convent way of documenting the original signal sample rate.
complex2wav(filename,rate,x)
Mark Wickert April 2014
"""
x_wav = np.hstack((np.array([x.real]).T,np.array([x.imag]).T))
ss.to_wav(filename, rate, x_wav)
print('Saved as binary wav file with (I,Q)<=>(L,R)')
[docs]def wav2complex(filename):
"""
Return a complex signal vector from a wav file that was used to store
the real (I) and imaginary (Q) values of a complex signal ndarray.
The rate is included as means of recalling the original signal sample
rate.
fs,x = wav2complex(filename)
Mark Wickert April 2014
"""
fs, x_LR_cols = ss.from_wav(filename)
x = x_LR_cols[:,0] + 1j*x_LR_cols[:,1]
return fs,x
if __name__ == '__main__':
# Test bench area
pass