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 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 extend(data):
m = filter_measurements(
data.measurements,
['pwm_value', 'rail'],
['Aout', 'Ain', 'Aamp'])
R = data.config.R
vcc = data.vcc
data.Vmax = data.vcc
data.Imax = data.vcc / R
col = ColsProxy(m)
col.Vin = an2v(col.Ain, vcc)
col.Vout = an2v(col.Aout, vcc)
col.Vamp = an2v(col.Aamp, vcc)
col.Vx = col.Vout - col.Vin
col.I = (col.Vin - col.Vamp) / R
m = m.sort_index(by='Vx')
col.I = pandas.rolling_median(col.I, window=5)
data.measurements = m
def calculate_beta(m, vcc):
col = ColsProxy(m)
col.Ab=uarray((col.Ab,1))
col.Ac=uarray((col.Ac,1))
col.Ub = an2v(col.Ab, vcc)
col.Uc = an2v(col.Ac, vcc)
Rout = 20
pnp = col.polarity == 'PNP'
npn = col.polarity == 'NPN'
col.Urb[pnp] = col.Ub[pnp]
col.Urb[npn] = vcc - col.Ub[npn]
col.Urc[pnp] = col.Uc[pnp]
col.Urc[npn] = vcc - col.Uc[npn]
col.Ib = col.Urb / (col.Rb + Rout)
col.Ic = col.Urc / (col.Rc + Rout)
col.Ie = col.Ib + col.Ic
col.beta = col.Ic / col.Ib[nominal_values(col.Ib) != 0]
col.Ue[pnp] = vcc - (Rout * col.Ie[pnp])
col.Ue[npn] = Rout * col.Ie[npn]
col.Ube[pnp] = -(col.Ub[pnp] - col.Ue[pnp])
col.Ube[npn] = (col.Ub[npn] - col.Ue[npn])
def calculate_beta(m, vcc):
col = ColsProxy(m)
col.Ab = uarray((col.Ab, 1))
col.Ac = uarray((col.Ac, 1))
col.Ub = an2v(col.Ab, vcc)
col.Uc = an2v(col.Ac, vcc)
Rout = 20
pnp = col.polarity == 'PNP'
npn = col.polarity == 'NPN'
col.Urb[pnp] = col.Ub[pnp]
col.Urb[npn] = vcc - col.Ub[npn]
col.Urc[pnp] = col.Uc[pnp]
col.Urc[npn] = vcc - col.Uc[npn]
col.Ib = col.Urb / (col.Rb + Rout)
col.Ic = col.Urc / (col.Rc + Rout)
col.Ie = col.Ib + col.Ic
col.beta = col.Ic / col.Ib[nominal_values(col.Ib) != 0]
col.Ue[pnp] = vcc - (Rout * col.Ie[pnp])
col.Ue[npn] = Rout * col.Ie[npn]
col.Ube[pnp] = -(col.Ub[pnp] - col.Ue[pnp])
col.Ube[npn] = (col.Ub[npn] - col.Ue[npn])
