187 lines
5.4 KiB
Python
187 lines
5.4 KiB
Python
import serial
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import queue
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import threading
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import SoapySDR
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import numpy as np
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from scipy.signal import blackmanharris
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class BufferedSDR:
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def __init__(self, sdr, channels, bufsize, nbufs):
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self.sdr = sdr
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self.channels = channels
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self.bufsize = bufsize
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self.stream = None
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self.terminate = False
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self.thread = None
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self.queue = queue.Queue(nbufs)
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self.available_buffers = queue.Queue(nbufs)
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for _ in range(nbufs):
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self.available_buffers.put([np.array([0]*bufsize, np.complex64) for _ in self.channels])
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def start(self):
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self.stream = self.sdr.setupStream(SoapySDR.SOAPY_SDR_RX, SoapySDR.SOAPY_SDR_CF32, self.channels)
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try:
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self.thread = threading.Thread(target=self.thread_target)
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self.thread.start()
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except:
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self.sdr.closeStream(self.stream)
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raise
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def stop(self):
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self.terminate = True
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self.thread.join()
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self.sdr.closeStream(self.stream)
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def get(self):
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return self.queue.get()
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def dispose(self, buffers):
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self.available_buffers.put(buffers)
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def thread_target(self):
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self.sdr.activateStream(self.stream)
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try:
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while not self.terminate:
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buffers = self.available_buffers.get()
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sr = self.sdr.readStream(self.stream, buffers, self.bufsize)
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if sr.ret != self.bufsize:
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print("SDR sampling error")
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return
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self.queue.put(buffers)
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finally:
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self.sdr.deactivateStream(self.stream)
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class DummyInductionHeater:
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def __init__(self, port, induction_min, induction_max):
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pass
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def start(self):
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pass
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def set(self, amount):
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print("induction", amount)
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def stop(self):
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pass
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class InductionHeater:
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"""Interface to the MHS5200A function generator driving the LC tank"""
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def __init__(self, port, induction_min, induction_max):
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self.port = port
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self.induction_min = induction_min
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self.induction_max = induction_max
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self.queue = queue.Queue(1)
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def start(self):
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self.serial = serial.Serial(self.port, 57600)
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self.thread = threading.Thread(target=self.thread_target)
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self.thread.start()
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def thread_target(self):
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while True:
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amount = self.queue.get()
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if amount is None:
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break
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assert -0.5 <= amount <= 0.5
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freq = ((self.induction_min + self.induction_max)/2
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+ amount*(self.induction_max - self.induction_min))
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command = ":s1f{:010d}\n".format(int(freq*1e2))
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self.serial.write(command.encode())
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self.serial.readline()
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def set(self, amount):
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self.queue.put(amount, block=False)
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def stop(self):
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self.queue.put(0.0, block=True)
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self.queue.put(None, block=True)
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self.thread.join()
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self.serial.close()
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class Stabilizer:
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def __init__(self, freq_sample, block_size, freq_target, cb):
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self.freqs = np.fft.fftfreq(block_size, d=1/freq_sample)
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self.freq_target = freq_target
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self.cb = cb
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self.lock_counter = 0
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self.unlock_counter = 0
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self.wiggle = 0.0
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self.amp_threshold = 80.0
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self.k = 30.0e-6
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self.tolerance = 10e3
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self.lock_counter_threshold = 60
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self.unlock_counter_threshold = 500
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self.wiggle_amplitude = 0.15
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def input(self, samples):
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spectrum = np.abs(np.fft.fft(samples*blackmanharris(len(samples))))
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i = np.argmax(spectrum)
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amplitude = spectrum[i]
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success = False
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if amplitude > self.amp_threshold:
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freq = self.freqs[i]
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delta = freq - self.freq_target
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tuning = delta*self.k
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if abs(delta) < self.tolerance:
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success = True
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else:
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freq = None
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tuning = 0.0
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max_tuning_abs = 0.5 - self.wiggle_amplitude - 1e-9
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tuning = max(min(tuning, max_tuning_abs), -max_tuning_abs)
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if success:
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self.lock_counter += 1
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else:
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self.lock_counter = 0
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if self.locked():
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self.unlock_counter = 0
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else:
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self.unlock_counter += 1
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if not success and (self.unlock_counter > self.unlock_counter_threshold):
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print("wiggle")
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self.wiggle = self.wiggle_amplitude*np.random.uniform(-1.0, 1.0)
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self.unlock_counter = 0
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self.cb(spectrum, freq, self.locked(), tuning + self.wiggle)
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def locked(self):
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return self.lock_counter > self.lock_counter_threshold
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def continuous_unwrap(last_phase, last_phase_unwrapped, p):
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# note: np.unwrap always preserves first element of array
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p = np.unwrap(p)
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glue = np.array([last_phase_unwrapped, last_phase_unwrapped + (p[0] - last_phase)])
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new_p0 = np.unwrap(glue)[1]
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return new_p0 + p - p[0]
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class PositionTracker:
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def __init__(self):
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self.reset()
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def reset(self):
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self.last_phase = 0.0
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self.last_position = 0.0
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def input(self, ref, meas):
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demod = np.conjugate(ref)*meas
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phase = np.angle(demod)
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position = continuous_unwrap(self.last_phase, self.last_position, phase)/(2.0*np.pi)
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self.last_phase = phase[-1]
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self.last_position = position[-1]
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return position
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