test params
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13
test.py
13
test.py
@ -1,5 +1,3 @@
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from zipfile import stringFileHeader
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if __name__ == "__main__":
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if __name__ == "__main__":
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import sys
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import sys
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if __package__ is None:
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if __package__ is None:
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@ -51,12 +49,18 @@ def _measure_both(monochromator: Bentham, lockin: SR830, shutter: ShutterProbe,
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sample_rate = 512
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sample_rate = 512
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timeout_s = 60
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timeout_s = 60
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timeout_interval = 0.5
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timeout_interval = 0.5
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lockin.run("SENS 17") # 1 mV/nA
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# lockin.run("SENS 17") # 1 mV/nA
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# lockin.run("OFLT 5") # 3 ms
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lockin.run("SENS 20") # 10 mV/nA
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# trigger on the falling edge, since the light comes through when the ref signal is low
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# could of course also trigger on rising and apply 180° shift
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lockin.run("RSLP 2")
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# since we dont expect changes in our signal, we can use large time constants and aggresive filter slope
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# since we dont expect changes in our signal, we can use large time constants and aggresive filter slope
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# for better signal to noise
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# for better signal to noise
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# lockin.run("OFLT 5") # 3 ms
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lockin.run("RMOD 2") # low noise (small reserve)
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lockin.run("OFLT 8") # 100 ms
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lockin.run("OFLT 8") # 100 ms
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lockin.run("OFSL 3") # 24dB/Oct ms
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lockin.run("OFSL 3") # 24dB/Oct ms
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lockin.run("SYNC 0") # test without sync filter
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if AC:
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if AC:
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input("Plug the detector into lock-in port 'A/I' (front panel) and press enter > ")
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input("Plug the detector into lock-in port 'A/I' (front panel) and press enter > ")
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input("Make sure the laser is turned on and press enter > ")
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input("Make sure the laser is turned on and press enter > ")
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@ -74,7 +78,6 @@ def _measure_both(monochromator: Bentham, lockin: SR830, shutter: ShutterProbe,
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if t < 0: raise RuntimeError("Timed out waiting for buffer measurement to finish")
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if t < 0: raise RuntimeError("Timed out waiting for buffer measurement to finish")
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arr = lockin.buffer_get_data(CH1=True, CH2=True)
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arr = lockin.buffer_get_data(CH1=True, CH2=True)
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data_raw.append([wl, arr])
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data_raw.append([wl, arr])
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arr[1] += 180
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# calculate means, for theta use circular mean
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# calculate means, for theta use circular mean
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dR = np.mean(arr[0,:])
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dR = np.mean(arr[0,:])
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sdR = np.std(arr[0,:])
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sdR = np.std(arr[0,:])
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