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NumPy is optimized for vectorized operations, which makes it #56

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NumPy is optimized for vectorized operations, which makes it #56

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14 changes: 10 additions & 4 deletions picosdk/functions.py
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Original file line number Diff line number Diff line change
Expand Up @@ -19,8 +19,10 @@ def adc2mV(bufferADC, range, maxADC):
"""

channelInputRanges = [10, 20, 50, 100, 200, 500, 1000, 2000, 5000, 10000, 20000, 50000, 100000, 200000]
vRange = channelInputRanges[range]
bufferV = [(np.int64(x) * vRange) / maxADC.value for x in bufferADC]
# vRange = channelInputRanges[range]
# bufferV = [(np.int64(x) * vRange) / maxADC.value for x in bufferADC]
normRange = channelInputRanges[range]/maxADC.value
bufferV = np.ctypeslib.as_array(bufferADC) * normRange

return bufferV

Expand All @@ -35,7 +37,9 @@ def adc2mVpl1000(bufferADC, range, maxADC):
Takes a buffer of raw adc count values and converts it into millvolts
"""

bufferV = [(x * range) / maxADC.value for x in bufferADC]
#bufferV = [(x * range) / maxADC.value for x in bufferADC]
normRange = range/maxADC.value
bufferV = np.ctypeslib.as_array(bufferADC) * normRange

return bufferV

Expand Down Expand Up @@ -196,6 +200,8 @@ def adc2mVV2(bufferADC, rangeMax, maxADC):

Takes a buffer of raw adc count values and converts it into millivolts for psospa driver scopes
"""
buffermV = [(x * (rangeMax/1000000)) / maxADC.value for x in bufferADC]
#buffermV = [(x * (rangeMax/1000000)) / maxADC.value for x in bufferADC]
normRange = (rangeMax/1000000)/maxADC.value
buffermV = np.ctypeslib.as_array(bufferADC) * normRange

return buffermV