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 extend(data):
# data.measurements.to_csv('y.csv')
config = data.config
vcc = data.vcc
# m=data.measurements
## data.measurements['Rload']=data.measurements['Rload'].fillna(NO_LOAD),
# m['Rload']=m['Rload'].astype(str).fillna(NO_LOAD)
# data.measurements=m
m = filter_measurements(
data.measurements.fillna(NO_LOAD),
# ['Rload','phase'],
['phase', 'Rload', 'pwm_index_plus', 'pwm_index_minus'],
['Aminus', 'Aplus', 'Aout']
)
# m.to_csv('x.csv')
# print set(data.measurements['phase'])
# print m.to_string()
# print set(m['phase'])
# assert 0
A = config.R2 / config.R1
m['Vminus'] = an2v(m.Aminus, vcc)
m['Vplus'] = an2v(m.Aplus, vcc)
m['Vout'] = an2v(m.Aout, vcc)
m['Vout_expected'] = Circuit(A, Vminus=m.Vminus, Vplus=m.Vplus).Vout
m['Vout_diff'] = (m.Vout - m.Vout_expected)
m['Vopamp_minus'] = Circuit(A, Vminus=m.Vminus, Vout=m.Vout).Vplus
m['Vin_diff'] = (m.Vopamp_minus - m.Vplus)
data.measurements = m
def analyse(data):
config = data.config
vcc = data.vcc
m = filter_measurements(data.measurements, ['B', 'C', 'E', 'polarity'],
['Ab', 'Ac'])
calculate_beta(m, vcc)
movecol(m, 'beta', 0)
m = m.sort_index(by='polarity')
candidates = m[np.logical_and(m.Ube > 0.1, m.Ube < 1)]
s = candidates.groupby(['polarity', 'B'], sort=False).size()
ok = s[s == 2]
if len(ok) == 1:
polarity, B = ok.index[0]
m = m[np.logical_and(m.polarity == polarity, m.B == B)]
del m['Ue']
del m['Urc']
del m['Urb']
del m['Ub']
del m['Uc']
del m['Ab']
del m['Ac']
del m['t']
# del m['Ib']
del m['Ic']
del m['Ie']
del m['Rb']
del m['Rc']
return m
def analyse(data):
config = data.config
vcc = data.vcc
m = filter_measurements(
data.measurements,
['B', 'C', 'E', 'polarity'],
['Ab', 'Ac'])
calculate_beta(m, vcc)
movecol(m, 'beta', 0)
m = m.sort_index(by='polarity')
candidates = m[np.logical_and(m.Ube > 0.1, m.Ube < 1)]
s = candidates.groupby(['polarity', 'B'], sort=False).size()
ok = s[s==2]
if len(ok)==1:
polarity,B=ok.index[0]
m=m[np.logical_and(m.polarity==polarity,m.B==B)]
del m['Ue']
del m['Urc']
del m['Urb']
del m['Ub']
del m['Uc']
del m['Ab']
del m['Ac']
del m['t']
# del m['Ib']
del m['Ic']
del m['Ie']
del m['Rb']
del m['Rc']
return m
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):
# data.measurements.to_csv('y.csv')
config = data.config
vcc = data.vcc
# m=data.measurements
## data.measurements['Rload']=data.measurements['Rload'].fillna(NO_LOAD),
# m['Rload']=m['Rload'].astype(str).fillna(NO_LOAD)
# data.measurements=m
m = filter_measurements(
data.measurements.fillna(NO_LOAD),
# ['Rload','phase'],
['phase', 'Rload', 'pwm_index_plus', 'pwm_index_minus'],
['Aminus', 'Aplus', 'Aout'])
# m.to_csv('x.csv')
# print set(data.measurements['phase'])
# print m.to_string()
# print set(m['phase'])
# assert 0
A = config.R2 / config.R1
m['Vminus'] = an2v(m.Aminus, vcc)
m['Vplus'] = an2v(m.Aplus, vcc)
m['Vout'] = an2v(m.Aout, vcc)
m['Vout_expected'] = Circuit(A, Vminus=m.Vminus, Vplus=m.Vplus).Vout
m['Vout_diff'] = (m.Vout - m.Vout_expected)
m['Vopamp_minus'] = Circuit(A, Vminus=m.Vminus, Vout=m.Vout).Vplus
m['Vin_diff'] = (m.Vopamp_minus - m.Vplus)
data.measurements = m
def analyse(data):
m = filter_measurements(
data.measurements,
['R'],
)
del m['t']
m.insert(0, 'Rx', calculate_ohm(m.R, m.Atop, m.Amiddle, m.Abottom))
m.insert(0, 'Rx_E12', map(format_ohmE12, nominal_values(m.Rx)))
return 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 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 test_filt():
df = DataFrame(
dict(
a=['aa', 'aa', 'aa', 'aa', 'b', 'b'],
b=[2, 2, 3, 3, 5, 5],
c=[1, 2, 3, 4, 5, 6],
d=[31, 32, 33, 44, 44, 66],
))
m = filter_measurements(
df,
['a', 'b'], # keys
['c', 'd'])
print m.to_string()
eq_(['aa', 'aa', 'b'], list(m.a))
eq_([2, 3, 5], list(m.b))
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 test_filt():
df = DataFrame(
dict(
a=['aa', 'aa', 'aa', 'aa', 'b', 'b'],
b=[2, 2, 3, 3, 5, 5],
c=[1, 2, 3, 4, 5, 6],
d=[31, 32, 33, 44, 44, 66],
)
)
m = filter_measurements(
df,
['a', 'b'], # keys
['c', 'd']
)
print m.to_string()
eq_(['aa', 'aa', 'b'], list(m.a))
eq_([2, 3, 5], list(m.b))
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 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
