def setUp(self):
###### Test data for PCO2W ######
raw_strings = np.array([
'*7E2705CBACEE7F007D007D0B2A00BF080500E00187034A008200790B2D00BE080600DE0C1406C98C',
'*7E2704CBACEECB008000880B2900B2080600D300FB0263007F00890B2B00B4080700CE0C5106C884',
'*7E2704CBACEF43007E00890B27014408070189045B0875007E00870B2B0140080201860C5506C601',
'*7E2704CBACEFBB007E00820B2A042B0803051F16182785008000850B2A043C080405390C5506C5A9',
'*7E2704CBACF033007F00840B28054D080606831CCC330A007F00850B290551080406800C5606C556',
'*7E2704CBACF0AB007E00770B2804DE080605F31A672E9F008100790B2F04E0080705F70C5606C5EB',
'*7E2704CBACF1230081007C0B2B00BF080800DB01BF0390007C00790B3000C7080B00EA0C5606C80A',
'*7E2704CBACF19B008000750B2B01D20807023008310E94007E00730B2A01D2080502290C5706C0A2',
'*7E2704CBACF213008000740B2A01D50808042F08501FC6007D00780B3201D8080A04350C5706C0A5',
'*7E2704CBACF28B007F00710B2F0174080203570615189B008100730B2A0174080A03580C5706C125',
'*7E2704CBACF303007E006B0B2C019B080803CC07001CBA007F006F0B300199080803D00C5706C4E3'
])
# reagent constants (instrument and reagent bag specific)
self.ea434 = np.ones(11) * 19706.
self.ea620 = np.ones(11) * 34.
self.eb434 = np.ones(11) * 3073.
self.eb620 = np.ones(11) * 44327.
self.calt = np.ones(11) * 16.5
self.cala = np.ones(11) * 0.0459
self.calb = np.ones(11) * 0.6257
self.calc = np.ones(11) * -1.5406
# expected outputs
self.therm = np.array([
18.8526, 18.8765, 18.9245, 18.9485, 18.9485, 18.9485, 18.8765,
19.0686, 19.0686, 19.0446, 18.9725
])
self.pco2 = np.array([
fill_value, 294.1720, 311.3361, 319.0101, 319.8925, 319.8950,
305.8104, 317.9661, 284.3676, 280.2324, 280.0354
])
# parse the data strings
self.mtype = np.zeros(11, dtype=np.int)
self.light = np.zeros((11, 14), dtype=np.int)
self.traw = np.zeros(11, dtype=np.int)
for i in range(11):
# parse the raw strings into subelements, such as the driver would
# provide.
s = raw_strings[i]
self.mtype[i] = int((s[5:7]), 16)
self.traw[i] = int((s[75:79]), 16)
strt = 15
step = 4
for j in range(14):
self.light[i, j] = int((s[strt:strt + step]), 16)
strt += step
if self.mtype[i] == 5:
a434blnk = co2func.pco2_blank(self.light[i, 6])
a620blnk = co2func.pco2_blank(self.light[i, 7])
self.a434blnk = np.ones(11) * a434blnk
self.a620blnk = np.ones(11) * a620blnk
def setUp(self):
###### Test data for PCO2W ######
raw_strings = np.array([
'*7E2705CBACEE7F007D007D0B2A00BF080500E00187034A008200790B2D00BE080600DE0C1406C98C',
'*7E2704CBACEECB008000880B2900B2080600D300FB0263007F00890B2B00B4080700CE0C5106C884',
'*7E2704CBACEF43007E00890B27014408070189045B0875007E00870B2B0140080201860C5506C601',
'*7E2704CBACEFBB007E00820B2A042B0803051F16182785008000850B2A043C080405390C5506C5A9',
'*7E2704CBACF033007F00840B28054D080606831CCC330A007F00850B290551080406800C5606C556',
'*7E2704CBACF0AB007E00770B2804DE080605F31A672E9F008100790B2F04E0080705F70C5606C5EB',
'*7E2704CBACF1230081007C0B2B00BF080800DB01BF0390007C00790B3000C7080B00EA0C5606C80A',
'*7E2704CBACF19B008000750B2B01D20807023008310E94007E00730B2A01D2080502290C5706C0A2',
'*7E2704CBACF213008000740B2A01D50808042F08501FC6007D00780B3201D8080A04350C5706C0A5',
'*7E2704CBACF28B007F00710B2F0174080203570615189B008100730B2A0174080A03580C5706C125',
'*7E2704CBACF303007E006B0B2C019B080803CC07001CBA007F006F0B300199080803D00C5706C4E3'
])
# reagent constants (instrument and reagent bag specific)
self.ea434 = np.ones(11) * 19706.
self.ea620 = np.ones(11) * 34.
self.eb434 = np.ones(11) * 3073.
self.eb620 = np.ones(11) * 44327.
self.calt = np.ones(11) * 16.5
self.cala = np.ones(11) * 0.0459
self.calb = np.ones(11) * 0.6257
self.calc = np.ones(11) * -1.5406
# expected outputs
self.therm = np.array([18.8526, 18.8765, 18.9245, 18.9485,
18.9485, 18.9485, 18.8765, 19.0686,
19.0686, 19.0446, 18.9725])
self.pco2 = np.array([fill_value, 294.1720, 311.3361, 319.0101,
319.8925, 319.8950, 305.8104, 317.9661,
284.3676, 280.2324, 280.0354])
# parse the data strings
self.mtype = np.zeros(11, dtype=np.int)
self.light = np.zeros((11, 14), dtype=np.int)
self.traw = np.zeros(11, dtype=np.int)
for i in range(11):
# parse the raw strings into subelements, such as the driver would
# provide.
s = raw_strings[i]
self.mtype[i] = int((s[5:7]), 16)
self.traw[i] = int((s[75:79]), 16)
strt = 15
step = 4
for j in range(14):
self.light[i, j] = int((s[strt:strt+step]), 16)
strt += step
if self.mtype[i] == 5:
a434blnk = co2func.pco2_blank(self.light[i, 6])
a620blnk = co2func.pco2_blank(self.light[i, 7])
self.a434blnk = np.ones(11) * a434blnk
self.a620blnk = np.ones(11) * a620blnk
def main():
# load the input arguments
args = inputs()
infile = os.path.abspath(args.infile)
outfile = os.path.abspath(args.outfile)
coeff_file = os.path.abspath(args.coeff_file)
blnk_file = os.path.abspath(args.devfile)
# check for the source of calibration coeffs and load accordingly
dev = Calibrations(coeff_file) # initialize calibration class
if os.path.isfile(coeff_file):
# we always want to use this file if it exists
dev.load_coeffs()
elif args.csvurl:
# load from the CI hosted CSV files
csv_url = args.csvurl
dev.read_csv(csv_url)
dev.save_coeffs()
else:
raise Exception(
'A source for the PCO2W calibration coefficients could not be found'
)
# check for the source of instrument blanks and load accordingly
blank = Blanks(blnk_file, 1.0,
1.0) # initialize the calibration class using default blank
if os.path.isfile(blnk_file):
blank.load_blanks()
else:
blank.save_blanks()
# load the PCO2W data file
with open(infile, 'rb') as f:
pco2w = Munch(json.load(f))
if len(pco2w.time) == 0:
# This is an empty file, end processing
return None
# convert the raw battery voltage and thermistor values from counts
# to V and degC, respectively
pco2w.thermistor = ph_thermistor(np.array(pco2w.thermistor_raw)).tolist()
pco2w.voltage_battery = ph_battery(np.array(
pco2w.voltage_battery)).tolist()
# compare the instrument clock to the GPS based DCL time stamp
# --> PCO2W uses the OSX date format of seconds since 1904-01-01
mac = datetime.strptime("01-01-1904", "%m-%d-%Y")
offset = []
for i in range(len(pco2w.time)):
rec = mac + timedelta(seconds=pco2w.record_time[i])
rec.replace(tzinfo=timezone('UTC'))
dcl = datetime.utcfromtimestamp(pco2w.time[i])
# we use the sample collection time as the time record for the sample.
# the record_time, however, is when the sample was processed. so the
# true offset needs to include the difference between the collection
# and processing times
collect = dcl_to_epoch(pco2w.collect_date_time[i])
process = dcl_to_epoch(pco2w.process_date_time[i])
diff = process - collect
if np.isnan(diff):
diff = 300
offset.append((rec - dcl).total_seconds() - diff)
pco2w.time_offset = offset
# set calibration inputs to pCO2 calculations
ea434 = 19706. # factory constants
eb434 = 3073. # factory constants
ea620 = 34. # factory constants
eb620 = 44327. # factory constants
# calculate pCO2
pCO2 = []
blank434 = []
blank620 = []
for i in range(len(pco2w.record_type)):
if pco2w.record_type[i] == 4:
# this is a light measurement, calculate the pCO2
pCO2.append(
pco2_pco2wat(pco2w.record_type[i], pco2w.light_measurements[i],
pco2w.thermistor[i], ea434, eb434, ea620, eb620,
dev.coeffs['calt'], dev.coeffs['cala'],
dev.coeffs['calb'], dev.coeffs['calc'],
blank.blank_434, blank.blank_620)[0])
# record the blanks used
blank434.append(blank.blank_434)
blank620.append(blank.blank_620)
if pco2w.record_type[i] == 5:
# this is a dark measurement, update and save the new blanks
blank.blank_434 = pco2_blank(pco2w.light_measurements[i][6])
blank.blank_620 = pco2_blank(pco2w.light_measurements[i][7])
blank.save_blanks()
blank434.append(blank.blank_434)
blank620.append(blank.blank_620)
# save the resulting data to a json formatted file
pco2w.pCO2 = pCO2
pco2w.blank434 = blank434
pco2w.blank620 = blank620
with open(outfile, 'w') as f:
f.write(pco2w.toJSON())
def setUp(self):
### test data for the PCO2W related calculcations
s = '*7E2704CBACF1230081007C0B2B00BF080800DB01BF0390007C00790B3000C7080B00EA0C5606C80A'
self.traw = int(s[75:79], 16)
self.ea434 = 19706.
self.ea620 = 34.
self.eb434 = 3073.
self.eb620 = 44327.
self.calt = 16.5
self.cala = 0.0459
self.calb = 0.6257
self.calc = -1.5406
self.light = np.zeros(14, dtype=np.int)
self.mtype = int(s[5:7], 16)
self.traw = int(s[75:79], 16)
strt = 15
step = 4
for j in range(14):
self.light[j] = int(s[strt:strt+step], 16)
strt += step
self.a434braw = self.light[6]
self.a434blnk = pco2_blank(self.a434braw)
self.a620braw = self.light[7]
self.a620blnk = pco2_blank(self.a620braw)
### test data for the PCO2A CO2FLUX calculations
self.flux_data = np.array([
[360, 390, 5, 0, 34, -2.063e-08],
[360, 390, 5, 0, 35, -2.052e-08],
[360, 390, 5, 10, 34, -1.942e-08],
[360, 390, 5, 10, 35, -1.932e-08],
[360, 390, 5, 20, 34, -1.869e-08],
[360, 390, 5, 20, 35, -1.860e-08],
[360, 390, 10, 0, 34, -8.250e-08],
[360, 390, 10, 0, 35, -8.207e-08],
[360, 390, 10, 10, 34, -7.767e-08],
[360, 390, 10, 10, 35, -7.728e-08],
[360, 390, 10, 20, 34, -7.475e-08],
[360, 390, 10, 20, 35, -7.440e-08],
[360, 390, 20, 0, 34, -3.300e-07],
[360, 390, 20, 0, 35, -3.283e-07],
[360, 390, 20, 10, 34, -3.107e-07],
[360, 390, 20, 10, 35, -3.091e-07],
[360, 390, 20, 20, 34, -2.990e-07],
[360, 390, 20, 20, 35, -2.976e-07],
[400, 390, 5, 0, 34, 6.875e-09],
[400, 390, 5, 0, 35, 6.839e-09],
[400, 390, 5, 10, 34, 6.472e-09],
[400, 390, 5, 10, 35, 6.440e-09],
[400, 390, 5, 20, 34, 6.229e-09],
[400, 390, 5, 20, 35, 6.200e-09],
[400, 390, 10, 0, 34, 2.750e-08],
[400, 390, 10, 0, 35, 2.736e-08],
[400, 390, 10, 10, 34, 2.589e-08],
[400, 390, 10, 10, 35, 2.576e-08],
[400, 390, 10, 20, 34, 2.492e-08],
[400, 390, 10, 20, 35, 2.480e-08],
[400, 390, 20, 0, 34, 1.100e-07],
[400, 390, 20, 0, 35, 1.094e-07],
[400, 390, 20, 10, 34, 1.036e-07],
[400, 390, 20, 10, 35, 1.030e-07],
[400, 390, 20, 20, 34, 9.966e-08],
[400, 390, 20, 20, 35, 9.920e-08],
[440, 390, 5, 0, 34, 3.438e-08],
[440, 390, 5, 0, 35, 3.420e-08],
[440, 390, 5, 10, 34, 3.236e-08],
[440, 390, 5, 10, 35, 3.220e-08],
[440, 390, 5, 20, 34, 3.114e-08],
[440, 390, 5, 20, 35, 3.100e-08],
[440, 390, 10, 0, 34, 1.375e-07],
[440, 390, 10, 0, 35, 1.368e-07],
[440, 390, 10, 10, 34, 1.294e-07],
[440, 390, 10, 10, 35, 1.288e-07],
[440, 390, 10, 20, 34, 1.246e-07],
[440, 390, 10, 20, 35, 1.240e-07],
[440, 390, 20, 0, 34, 5.500e-07],
[440, 390, 20, 0, 35, 5.471e-07],
[440, 390, 20, 10, 34, 5.178e-07],
[440, 390, 20, 10, 35, 5.152e-07],
[440, 390, 20, 20, 34, 4.983e-07],
[440, 390, 20, 20, 35, 4.960e-07]
])
def setUp(self):
### test data for the PCO2W related calculcations
s = '*7E2704CBACF1230081007C0B2B00BF080800DB01BF0390007C00790B3000C7080B00EA0C5606C80A'
self.traw = int(s[75:79], 16)
self.ea434 = 19706.
self.ea620 = 34.
self.eb434 = 3073.
self.eb620 = 44327.
self.calt = 16.5
self.cala = 0.0459
self.calb = 0.6257
self.calc = -1.5406
self.light = np.zeros(14, dtype=np.int)
self.mtype = int(s[5:7], 16)
self.traw = int(s[75:79], 16)
strt = 15
step = 4
for j in range(14):
self.light[j] = int(s[strt:strt+step], 16)
strt += step
self.a434braw = self.light[6]
self.a434blnk = pco2_blank(self.a434braw)
self.a620braw = self.light[7]
self.a620blnk = pco2_blank(self.a620braw)
### test data for the PCO2A Partial Pressure calculations
self.ppres_data = np.array([
[674, 1000, 665.19],
[619, 1000, 610.91],
[822, 1000, 811.25],
[973, 1000, 960.28],
[941, 1000, 928.69],
[863, 1000, 851.71],
[854, 1000, 842.83],
[833, 1000, 822.11],
[826, 1000, 815.20],
[814, 1000, 803.36],
[797, 1000, 786.58],
[782, 1000, 771.77],
[768, 1000, 757.96],
[754, 1000, 744.14],
[740, 1000, 730.32]
])
### test data for the PCO2A CO2FLUX calculations
self.flux_data = np.array([
[360, 390, 5, 0, 34, -2.063e-08],
[360, 390, 5, 0, 35, -2.052e-08],
[360, 390, 5, 10, 34, -1.942e-08],
[360, 390, 5, 10, 35, -1.932e-08],
[360, 390, 5, 20, 34, -1.869e-08],
[360, 390, 5, 20, 35, -1.860e-08],
[360, 390, 10, 0, 34, -8.250e-08],
[360, 390, 10, 0, 35, -8.207e-08],
[360, 390, 10, 10, 34, -7.767e-08],
[360, 390, 10, 10, 35, -7.728e-08],
[360, 390, 10, 20, 34, -7.475e-08],
[360, 390, 10, 20, 35, -7.440e-08],
[360, 390, 20, 0, 34, -3.300e-07],
[360, 390, 20, 0, 35, -3.283e-07],
[360, 390, 20, 10, 34, -3.107e-07],
[360, 390, 20, 10, 35, -3.091e-07],
[360, 390, 20, 20, 34, -2.990e-07],
[360, 390, 20, 20, 35, -2.976e-07],
[400, 390, 5, 0, 34, 6.875e-09],
[400, 390, 5, 0, 35, 6.839e-09],
[400, 390, 5, 10, 34, 6.472e-09],
[400, 390, 5, 10, 35, 6.440e-09],
[400, 390, 5, 20, 34, 6.229e-09],
[400, 390, 5, 20, 35, 6.200e-09],
[400, 390, 10, 0, 34, 2.750e-08],
[400, 390, 10, 0, 35, 2.736e-08],
[400, 390, 10, 10, 34, 2.589e-08],
[400, 390, 10, 10, 35, 2.576e-08],
[400, 390, 10, 20, 34, 2.492e-08],
[400, 390, 10, 20, 35, 2.480e-08],
[400, 390, 20, 0, 34, 1.100e-07],
[400, 390, 20, 0, 35, 1.094e-07],
[400, 390, 20, 10, 34, 1.036e-07],
[400, 390, 20, 10, 35, 1.030e-07],
[400, 390, 20, 20, 34, 9.966e-08],
[400, 390, 20, 20, 35, 9.920e-08],
[440, 390, 5, 0, 34, 3.438e-08],
[440, 390, 5, 0, 35, 3.420e-08],
[440, 390, 5, 10, 34, 3.236e-08],
[440, 390, 5, 10, 35, 3.220e-08],
[440, 390, 5, 20, 34, 3.114e-08],
[440, 390, 5, 20, 35, 3.100e-08],
[440, 390, 10, 0, 34, 1.375e-07],
[440, 390, 10, 0, 35, 1.368e-07],
[440, 390, 10, 10, 34, 1.294e-07],
[440, 390, 10, 10, 35, 1.288e-07],
[440, 390, 10, 20, 34, 1.246e-07],
[440, 390, 10, 20, 35, 1.240e-07],
[440, 390, 20, 0, 34, 5.500e-07],
[440, 390, 20, 0, 35, 5.471e-07],
[440, 390, 20, 10, 34, 5.178e-07],
[440, 390, 20, 10, 35, 5.152e-07],
[440, 390, 20, 20, 34, 4.983e-07],
[440, 390, 20, 20, 35, 4.960e-07]
])
