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])
def extend(data):
# dw = DataWrapper(data)
data.measurements = filter_measurements(data.measurements,
['pwm_value', 'rail'],
['Aout', 'Ain', 'Aamp'])
# print 111,unbunchify(dw.data)
# print 222,(dw.data)
R = data.config.R
vcc = data.vcc
# def calc(x):
# x.Vin = an2v(x.Ain, vcc)
# x.Vout = an2v(x.Aout, vcc)
# x.Vamp = an2v(x.Aamp, vcc)
# x.Vx = x.Vout - x.Vin
# x.I = (x.Vin - x.Vamp) / R
data.Vmax = data.vcc
data.Imax = data.vcc / R
# ls=[]
# pwm_current=0
# for x in dw.data.measurements:
# # group by pwm_value
# x.pwm_value==pwm_current:
# ls.append(x)
# else:
# calc(x)
# ls=[]
# pwm_current=x.pwm_value
d = groupped_measurements(data.measurements, ['pwm_value', 'rail'])
for p in range(256):
for r in [0, 1]:
ls = d[p, r]
# ls = [x for x in dw.data.measurements if x.pwm_value == p and x.rail ==
# r]
if not len(ls):
continue
Ain = ls[0].Ain
Aout = ls[0].Aout
Aamp = ls[0].Aamp
Vin = an2v(Ain, vcc)
Vout = an2v(Aout, vcc)
Vamp = an2v(Aamp, vcc)
Vx = Vout - Vin
I = (Vin - Vamp) / R
for x in ls:
x.I = I
x.Vx = Vx
x.Vin = Vin
x.Vout = Vout
x.Vamp = Vamp
def extend(data):
m = filter_measurements(data.measurements, ['Dout', 'pwm_value'],
['Aout', 'Aamp'])
R = data.config.R
vcc = data.vcc
m['Vout'] = an2v(m.Aout, vcc)
m['Vamp'] = an2v(m.Aamp, vcc)
m['I'] = (m.Vout - m.Vamp) / R
m['Iabs'] = abs(m.I)
data.measurements = m
def extend(data):
# dw = DataWrapper(data)
data.measurements = filter_measurements(
data.measurements, ['pwm_value', 'rail'], ['Aout', 'Ain', 'Aamp'])
# print 111,unbunchify(dw.data)
# print 222,(dw.data)
R = data.config.R
vcc = data.vcc
# def calc(x):
# x.Vin = an2v(x.Ain, vcc)
# x.Vout = an2v(x.Aout, vcc)
# x.Vamp = an2v(x.Aamp, vcc)
# x.Vx = x.Vout - x.Vin
# x.I = (x.Vin - x.Vamp) / R
data.Vmax = data.vcc
data.Imax = data.vcc / R
# ls=[]
# pwm_current=0
# for x in dw.data.measurements:
# # group by pwm_value
# x.pwm_value==pwm_current:
# ls.append(x)
# else:
# calc(x)
# ls=[]
# pwm_current=x.pwm_value
d = groupped_measurements(data.measurements, ['pwm_value', 'rail'])
for p in range(256):
for r in [0, 1]:
ls = d[p, r]
# ls = [x for x in dw.data.measurements if x.pwm_value == p and x.rail ==
# r]
if not len(ls):
continue
Ain = ls[0].Ain
Aout = ls[0].Aout
Aamp = ls[0].Aamp
Vin = an2v(Ain, vcc)
Vout = an2v(Aout, vcc)
Vamp = an2v(Aamp, vcc)
Vx = Vout - Vin
I = (Vin - Vamp) / R
for x in ls:
x.I = I
x.Vx = Vx
x.Vin = Vin
x.Vout = Vout
x.Vamp = Vamp
def transfer_plot(data, fig, error_plot=False):
data.measurements = filter_measurements(data.measurements, ['pwm_value'],
['Ain'])
title = 'Transfer error plot (%s)' if error_plot else 'Transfer plot (%s)'
title = title % (data.model)
fw = FigureWrapper(
data,
fig,
x='voltage',
y='error (V)' if error_plot else 'voltage',
title=title,
)
vcc = data.vcc
fw.addcol(
legend=data.config.pin_in,
x=lambda e: pwm2v(e.pwm_value, vcc),
y=lambda e: an2v(e.Ain, vcc) - (pwm2v(e.pwm_value, vcc)
if error_plot else 0),
)
ax = fw.subplot
if error_plot:
ax.axis([0, 5.1, -0.050, 0.050])
return fig
def transfer_plot(data, fig, error_plot=False):
data.measurements = filter_measurements(data.measurements, ['value'],
['Ain'])
title = 'Transfer error plot' if error_plot else 'Transfer plot'
title = title
fw = FigureWrapper(
data,
fig,
x='voltage',
y='error (V)' if error_plot else 'voltage',
title=title,
)
vcc = data.vcc
if error_plot:
fw.subplot.plot([0, vcc], [vcc / 99, vcc / 99], 'r-')
fw.subplot.plot([0, vcc], [-vcc / 99, -vcc / 99], 'r-')
else:
fw.subplot.plot([0, 0], [vcc, vcc], 'r-')
fw.addcol(
legend=data.config.pin_in,
x=lambda e: pot2v(e.value, vcc),
y=lambda e: an2v(e.Ain, vcc) - (pot2v(e.value, vcc)
if error_plot else 0),
)
ax = fw.subplot
if error_plot:
ax.axis([0, 5.1, -0.1, 0.1])
return fig
def extend(data):
m = filter_measurements(
data.measurements,
['Dout', 'pwm_value'],
['Aout', 'Aamp']
)
R = data.config.R
vcc = data.vcc
m['Vout'] = an2v(m.Aout, vcc)
m['Vamp'] = an2v(m.Aamp, vcc)
m['I'] = (m.Vout - m.Vamp) / R
m['Iabs'] = abs(m.I)
data.measurements = m
def calculate_ohm(data, Vnull, Ain):
vcc = data.vcc
# print 'calibration', calibration
Vin = an2v(Ain, vcc)
# Ain, Vin = maxi(ls, vcc)
R1 = data.config.R1
R2 = data.config.R2
Ramp1 = data.config.Ramp1
Ramp2 = data.config.Ramp2
# def perc1(x):
# return ufloat((x, x * 0.01))
Rout = 24
# R1 = perc1(R1) + ufloat((Rout, Rout * 0.1))
# R2 = perc1(R2)
R1 = R1 + Rout
# Ramp1 = perc1(Ramp1)
# Ramp2 = perc1(Ramp2)
A = Ramp2 / Ramp1
Vgen = R2 / (R2 + R1) * vcc
Rgen = R1 * R2 / (R1 + R2)
# print R2, Vgen, Rgen, vcc, R1
Vampout = Vin
Vampout_pp = 2 * (Vampout - Vnull)
Vampin_pp = Vampout_pp / A
divider = (Vampin_pp / Vgen)
def calc_Rx(cal):
return Rgen / (1 - divider / cal) - Rgen
Rx_raw = calc_Rx(1)
calibration_open = Vampin_pp / Vgen
# calibration = 0.93
# Rx_cal = calc_Rx(calibration)
# def calibration_x(x):
# return divider / (1 - (Rgen / (x + Rgen)))
# print 555, calibration_x(1.8)
Rx = Rx_raw
# print 'Rx_raw,Rx_cal', Rx_raw, Rx_cal
# if Rx > 30:
# Rx = None
return Rx, calibration_open
def analyse(data):
m = data.measurements
vcc = data.vcc
stats = m.Ain.describe()
count = stats['count']
Amean = stats['mean']
Amin = stats['min']
Amax = stats['max']
Arange = Amax - Amin
Amiddle = (Amin + Amax) / 2
duty_cycle = len(m.Ain[m.Ain > Amiddle]) / len(m.Ain)
Astd = stats['std']
Arms = (Astd ** 2 + Amean ** 2) ** 0.5
return dict(
count=count,
Amin=Amin,
Amax=Amax,
Arange=Arange,
Amean=Amean,
duty_cycle=duty_cycle,
Astd=Astd,
Arms=Arms,
Vmean=an2v(Amean, vcc),
Vstd=an2v(Astd, vcc),
Vrms=an2v(Arms, vcc),
Vmin=an2v(Amin, vcc),
Vmax=an2v(Amax, vcc),
Vrange=an2v(Arange, vcc),
)
def calculate_ohm(data, Vnull, Ain):
vcc = data.vcc
# print 'calibration', calibration
Vin = an2v(Ain, vcc)
# Ain, Vin = maxi(ls, vcc)
R1 = data.config.R1
R2 = data.config.R2
Ramp1 = data.config.Ramp1
Ramp2 = data.config.Ramp2
# def perc1(x):
# return ufloat((x, x * 0.01))
Rout = 24
# R1 = perc1(R1) + ufloat((Rout, Rout * 0.1))
# R2 = perc1(R2)
R1 = R1 + Rout
# Ramp1 = perc1(Ramp1)
# Ramp2 = perc1(Ramp2)
A = Ramp2 / Ramp1
Vgen = R2 / (R2 + R1) * vcc
Rgen = R1 * R2 / (R1 + R2)
# print R2, Vgen, Rgen, vcc, R1
Vampout = Vin
Vampout_pp = 2 * (Vampout - Vnull)
Vampin_pp = Vampout_pp / A
divider = (Vampin_pp / Vgen)
def calc_Rx(cal):
return Rgen / (1 - divider / cal) - Rgen
Rx_raw = calc_Rx(1)
calibration_open = Vampin_pp / Vgen
# calibration = 0.93
# Rx_cal = calc_Rx(calibration)
# def calibration_x(x):
# return divider / (1 - (Rgen / (x + Rgen)))
# print 555, calibration_x(1.8)
Rx = Rx_raw
# print 'Rx_raw,Rx_cal', Rx_raw, Rx_cal
# if Rx > 30:
# Rx = None
return Rx, calibration_open
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 addcol(self,
x=None,
y=None,
legend=None,
lineformat=None,
filter_func=None,
sortbyx=None):
ax = self.subplot
if x and not callable(x):
ax.set_xlabel(x)
else:
if self.x == 'time':
x = 't'
if not x:
x = self.x
if not lineformat:
lineformat = '-o'
if not callable(y):
if not legend:
legend = y
if self.convert2voltage and self.y == 'analog_value':
f2 = lambda e: an2v(e[y], self.data['vcc'])
else:
f2 = y
xcol = self._col(x, filter_func=filter_func)
ycol = self._col(f2, filter_func=filter_func)
if sortbyx:
ls = sorted(zip(xcol, ycol), key=lambda e: e[0])
xcol = [e[0] for e in ls]
ycol = [e[1] for e in ls]
assert len(xcol) == len(ycol)
assert len(xcol)
assert len(ycol)
ax.plot(nominal_values(xcol),
nominal_values(ycol),
lineformat,
label=legend)
self.update_legend()
def addcol(self, x=None, y=None, legend=None, lineformat=None, filter_func=None, sortbyx=None):
ax = self.subplot
if x and not callable(x):
ax.set_xlabel(x)
else:
if self.x == 'time':
x = 't'
if not x:
x = self.x
if not lineformat:
lineformat = '-o'
if not callable(y):
if not legend:
legend = y
if self.convert2voltage and self.y == 'analog_value':
f2 = lambda e: an2v(e[y], self.data['vcc'])
else:
f2 = y
xcol = self._col(x, filter_func=filter_func)
ycol = self._col(f2, filter_func=filter_func)
if sortbyx:
ls = sorted(zip(xcol, ycol), key=lambda e: e[0])
xcol = [e[0] for e in ls]
ycol = [e[1] for e in ls]
assert len(xcol) == len(ycol)
assert len(xcol)
assert len(ycol)
ax.plot(
nominal_values(xcol),
nominal_values(ycol),
lineformat,
label=legend)
self.update_legend()
def transfer_plot(data, fig, error_plot=False):
data.measurements = filter_measurements(
data.measurements, ['pwm_value'], ['Ain'])
title = 'Transfer error plot (%s)' if error_plot else 'Transfer plot (%s)'
title = title % (data.model)
fw = FigureWrapper(
data,
fig,
x='voltage',
y='error (V)' if error_plot else 'voltage',
title=title,
)
vcc = data.vcc
fw.addcol(legend=data.config.pin_in,
x=lambda e: pwm2v(e.pwm_value, vcc),
y=lambda e: an2v(
e.Ain, vcc) - (pwm2v(e.pwm_value, vcc) if error_plot else 0),
)
ax = fw.subplot
if error_plot:
ax.axis([0, 5.1, -0.050, 0.050])
return fig
def transfer_plot(data, fig, error_plot=False):
data.measurements = filter_measurements(data.measurements, ["value"], ["Ain"])
title = "Transfer error plot" if error_plot else "Transfer plot"
title = title
fw = FigureWrapper(data, fig, x="voltage", y="error (V)" if error_plot else "voltage", title=title)
vcc = data.vcc
if error_plot:
fw.subplot.plot([0, vcc], [vcc / 99, vcc / 99], "r-")
fw.subplot.plot([0, vcc], [-vcc / 99, -vcc / 99], "r-")
else:
fw.subplot.plot([0, 0], [vcc, vcc], "r-")
fw.addcol(
legend=data.config.pin_in,
x=lambda e: pot2v(e.value, vcc),
y=lambda e: an2v(e.Ain, vcc) - (pot2v(e.value, vcc) if error_plot else 0),
)
ax = fw.subplot
if error_plot:
ax.axis([0, 5.1, -0.1, 0.1])
return fig
def avgAV(df, vcc):
a = averaged_median_arr(df.Ain)
Ain = ufloat((a, 1))
Vin = an2v(Ain, vcc)
return Ain, Vin
def avgAV(df, vcc):
a = averaged_median_arr(df.Ain)
Ain = ufloat((a, 1))
Vin = an2v(Ain, vcc)
return Ain, Vin
