def calcNoiseAndResolution(d, resolutionIndex, voltageRange):
"""
Takes 128 readings and calculates noise and resolution
"""
# Make a list to hold our readings
readings = []
# The feedback command to send to the device
# Analog input reading from channel 15 (GND) with settling factor 4 (200 microseconds)
cmd = u6.AIN24AR(15,
ResolutionIndex=resolutionIndex,
GainIndex=voltageRange,
SettlingFactor=4)
start = datetime.now()
# Collect 128 samples
for i in range(128):
readings.append(d.getFeedback(cmd)[0]['AIN'])
finish = datetime.now()
print("%s per sample" % ((finish - start) / 128))
# The Peak-To-Peak Noise is difference between the max and the min.
p2pn = max(readings) - min(readings)
# Noise-Free resolution in bits follows the formula:
nfrbits = 24 - math.log(p2pn, 2)
# Noise-Free Resolution (uV) =
# <range> / 2 ^ < Noise-Free resolution (bits) >
nfrres = (ranges[voltageRange] / (2**nfrbits)) * (10**6)
# Get the RMS Noise by calculating the standard deviation.
mean = sum(readings) / len(readings)
rms = 0
for r in readings:
rms += (r - mean)**2
rms = float(rms) / len(readings)
rms = math.sqrt(rms)
# Effective Resolution is uses a similar formulas as Noise-Free.
erbits = 24 - math.log(rms, 2)
erres = (ranges[voltageRange] / (2**erbits)) * (10**6)
return [p2pn, nfrbits, nfrres, rms, erbits, erres]
def get_ai_value(self, channel, verbose=False, **kwargs):
""" """
channel = self._check_ai_channel_input(channel)
# retrieve ai channel object from channel number
ai_name = list(AI_DCT.keys())[list(AI_DCT.values()).index(channel)]
ai_obj = getattr(self, ai_name.lower())
# measure voltage
ai_obj.settings.update(kwargs)
cmd = u6.AIN24AR(channel, **ai_obj.settings)
if verbose:
print('cmd: ', cmd)
reading = self.d.getFeedback(cmd)
volt = self.d.binaryToCalibratedAnalogVoltage(
ai_obj.settings['GainIndex'],
reading[0]['AIN']
)
return volt
def __init__(self, d, channel, **kwargs):
""" """
self.settings = U6Reader.AIN24AR_SETTINGS
self.settings.update(kwargs)
self.cmd = u6.AIN24AR(channel, **kwargs)
self.d = d