def analyse(data):
config = data.config
vcc = data.vcc
m = data.measurements
grouped = segmented_measurements(m, ["charge", "R"], as_index=1)
def voltage(measurement):
return u_an2v(measurement.Amiddle, vcc)
def first(ls):
return ls.values[0]
def last(ls):
return ls.values[-1]
t = grouped["t"].agg(dict(t1=first, t2=last))
A = grouped["Amiddle"].agg(dict(A1=first, A2=last))
mreg = DataFrame([(charge, R, regc(g, R)) for (charge, R), g in grouped], columns=["charge", "R", "Creg"])
df = A.join(t)
df = df.reset_index()
df = df[df["charge"] == 0]
del df["charge"]
col = ColsProxy(df)
col.Creg = mreg["Creg"]
col.dt = col.t2 - col.t1
col.C = calculate_c(col.A1, col.A2, col.R, col.t1, col.t2)
movecol(df, "Creg", 0)
movecol(df, "C", 0)
return df
def analyse1(data, calibration=0.94):
vcc = data.vcc
grouped = segmented_measurements(
data.measurements[['frequency', 'dc', 'Ain']], ['frequency', 'dc'])
# print (0.0, 0) in dict(grouped).keys()
for (f, dc), group in grouped:
if f == 0:
# print dc
# print group
if dc == 0:
_, V0 = Ain, Vin = avgAV(group, vcc)
else:
_, V255 = Ain, Vin = avgAV(group, vcc)
Vnull = average([V0, V255])
# d = SortedDict(d)[2:]
# dRx = SortedDict()
# for (f, dc), ls in d.items():
# if not f:
# continue
# R, calibration_open = calculate_ohm(data, Vnull, ls)
# dRx[f] = dict(frequency=f,
# R=R,
# )
def maxi(ls):
# drop 1% on top
# ls = list(df)
# ls.sort()
# drop = int(len(ls) * 0.05)
# i = len(ls) - drop - 1
# a = ls[i]
a = ls.quantile(q=0.99)
# a = averaged_median()
Ain = ufloat((a, 1))
# Vin = an2v(Ain, vcc)
return Ain
filtered = grouped.agg(maxi)
del filtered['dc']
filtered = filtered.drop([0, 1]) # rows
# print 44,filtered
dRx, calibration_open = calculate_ohm(data, Vnull, filtered['Ain'])
# dRx=
# print 44,filtered
filtered['Rx'] = dRx
# print 44,filtered
# filtered['calibration_open']=calibration_open
# print 55,filtered
# print filtered
# print dict(x=filtered)
# print dict(row)
return filtered
def analyse1(data, calibration=0.94):
vcc = data.vcc
grouped = segmented_measurements(
data.measurements[['frequency', 'dc', 'Ain']], ['frequency', 'dc'])
# print (0.0, 0) in dict(grouped).keys()
for (f, dc), group in grouped:
if f == 0:
# print dc
# print group
if dc == 0:
_, V0 = Ain, Vin = avgAV(group, vcc)
else:
_, V255 = Ain, Vin = avgAV(group, vcc)
Vnull = average([V0, V255])
# d = SortedDict(d)[2:]
# dRx = SortedDict()
# for (f, dc), ls in d.items():
# if not f:
# continue
# R, calibration_open = calculate_ohm(data, Vnull, ls)
# dRx[f] = dict(frequency=f,
# R=R,
# )
def maxi(ls):
# drop 1% on top
# ls = list(df)
# ls.sort()
# drop = int(len(ls) * 0.05)
# i = len(ls) - drop - 1
# a = ls[i]
a = ls.quantile(q=0.99)
# a = averaged_median()
Ain = ufloat((a, 1))
# Vin = an2v(Ain, vcc)
return Ain
filtered = grouped.agg(maxi)
del filtered['dc']
filtered = filtered.drop([0, 1]) # rows
# print 44,filtered
dRx, calibration_open = calculate_ohm(data, Vnull, filtered['Ain'])
# dRx=
# print 44,filtered
filtered['Rx'] = dRx
# print 44,filtered
# filtered['calibration_open']=calibration_open
# print 55,filtered
# print filtered
# print dict(x=filtered)
# print dict(row)
return filtered
def analyse(data):
config = data.config
vcc = data.vcc
m = data.measurements
grouped = segmented_measurements(m, ['charge', 'R'], as_index=1)
def voltage(measurement):
return u_an2v(measurement.Amiddle, vcc)
def first(ls):
return ls.values[0]
def last(ls):
return ls.values[-1]
t = grouped['t'].agg(dict(t1=first, t2=last))
A = grouped['Amiddle'].agg(dict(A1=first, A2=last))
mreg = DataFrame([(charge, R, regc(g, R)) for (charge, R), g in grouped],
columns=['charge', 'R', 'Creg'])
df = A.join(t)
df = df.reset_index()
df = df[df['charge'] == 0]
del df['charge']
col = ColsProxy(df)
col.Creg = mreg['Creg']
col.dt = col.t2 - col.t1
col.C = calculate_c(col.A1, col.A2, col.R, col.t1, col.t2)
movecol(df, 'Creg', 0)
movecol(df, 'C', 0)
return df
