def plot_motion(delta=30000):
"""Rotate to a new position and plot the position, speed, and other parameters measured during motion.
This is used to debug motion control when moving the motor to hit a specific encoder value.
"""
global win
win = pg.GraphicsWindow()
p1 = win.addPlot(title='position')
p2 = win.addPlot(title='speed', row=1, col=0)
p3 = win.addPlot(title='target speed', row=2, col=0)
p4 = win.addPlot(
title=
'distance, <span style="color: green">distance-until-decel</span>',
row=3,
col=0)
p2.setXLink(p1)
p3.setXLink(p1)
p4.setXLink(p1)
t = []
x = []
v = []
vt = []
dx = []
dxt = []
start = time.time()
started = False
while True:
now = time.time()
pos = mfc.position()
if not started and now - start > 0.2:
move = pos + delta
print("move:", move)
mfc.move(move)
started = True
if now - start > 2:
break
t.append(now - start)
x.append(pos)
v.append(mfc.mcm['actual_speed'])
vt.append(mfc.mcm['target_speed'])
dx.append(mfc.mcm.get_global('gp1'))
dxt.append(mfc.mcm.get_global('gp2'))
p1.plot(t, x, clear=True)
p2.plot(t, v, clear=True)
p3.plot(t, vt, clear=True)
p4.plot(t, dx, clear=True)
p4.plot(t, dxt, pen='g')
print("Final:", mfc.position())
def showAll():
global xVals, data, psp, fits
w = pg.GraphicsWindow()
for i in range(psp.shape[0]):
for j in range(psp.shape[1]):
p = w.addPlot()
fit = pspFunc(fits[i, j], xVals)
err = (abs(data[i, j] - fit)**2).sum() / (data[i, j]**2).sum()
green = np.clip((np.log(err) + 3) * 60, 0, 255)
p.plot(xVals, data[i, j], pen=(80, 60, 60))
p.plot(xVals, fit, pen=(50, green, 255 - green, 200))
#p.plot(xVals, pspFunc(psp[i,j], xVals), pen='b')
p.hideAxis('left')
p.hideAxis('bottom')
p.hideButtons()
w.nextRow()
def plot_motion():
win = pg.GraphicsWindow()
p1 = win.addPlot(title='position')
p2 = win.addPlot(title='speed', row=1, col=0)
p3 = win.addPlot(title='target speed', row=2, col=0)
p4 = win.addPlot(title='distance', row=3, col=0)
p2.setXLink(p1)
p3.setXLink(p1)
p4.setXLink(p1)
t = []
x = []
v = []
vt = []
dx = []
dxt = []
start = time.time()
started = False
while True:
now = time.time()
pos = mfc.position()
if not started and now - start > 0.2:
move = pos + 20000
print "move:", move
mfc.move(move)
started = True
if now - start > 2:
break
t.append(now-start)
x.append(pos)
v.append(mfc.mcm['actual_speed'])
vt.append(mfc.mcm['target_speed'])
dx.append(mfc.mcm.get_global('gp1'))
dxt.append(mfc.mcm.get_global('gp2'))
p1.plot(t, x, clear=True)
p2.plot(t, v, clear=True)
p3.plot(t, vt, clear=True)
p4.plot(t, dx, clear=True)
p4.plot(t, dxt, pen='g')
print "Final:", mfc.position()
import acq4.Manager
import acq4.analysis.atlas.CochlearNucleus as cn
man = acq4.Manager.getManager()
initialized = False
def __reload__(old): ## re-use existing objects if module is reloaded
global initialized, w, v, atlas
initialized = True
w = old['w']
v = old['v']
atlas = old['atlas']
if not initialized:
atlas = cn.CochlearNucleus()
w = pg.GraphicsWindow()
w.setRenderHints(Qt.QPainter.Antialiasing | Qt.QPainter.TextAntialiasing | Qt.QPainter.SmoothPixmapTransform)
w.setBackground(pg.mkColor('w'))
v = w.addViewBox()
v.setAspectLocked()
v.invertY()
w.show()
initialized = True
def show(dh=None):
"""
Display a graphic of the currently selected slice / cell
"""
global v, g, atlas
if dh is None:
def showErrorAnalysis(self):
if not hasattr(self, 'errorMap'):
filename = self.dev.configFileName('error_map.np')
self.errorMap = np.load(open(filename, 'rb'))[np.newaxis][0]
err = self.errorMap
imx = pg.image(err['err'][..., 0].transpose(1, 0, 2), title='X error')
imy = pg.image(err['err'][..., 1], title='Y error')
imz = pg.image(err['err'][..., 2], title='Z error')
# get N,3 array of offset values used to randomize hysteresis
off = np.vstack(err['offsets'])
sh = err['err'].shape
# Get N,3 array of measured position errors
errf = err['err'].reshape(sh[0] * sh[1] * sh[2], 3)[err['order']]
# Display histogram of errors
win = pg.GraphicsWindow(title="%s error" % self.dev.name())
# subtract out slow drift
normErr = errf - scipy.ndimage.gaussian_filter(errf, (20, 0))
# calculate magnitude of error
absErr = (normErr**2).sum(axis=1)**0.5
# errPlot.plot(absErr)
title = "Error Histogram (mean=%s)" % pg.siFormat(absErr.mean(),
suffix='m')
errPlot = win.addPlot(row=0,
col=0,
title=title,
labels={'bottom': ('Position error', 'm')})
hist = np.histogram(absErr, bins=50)
errPlot.plot(hist[1], hist[0], stepMode=True)
# display drift and hysteresis plots
driftPlot = win.addPlot(row=0,
col=1,
rowspan=1,
colspan=2,
title="Pipette Drift",
labels={
'left': ('Position error', 'm'),
'bottom': ('Time', 's')
})
driftPlot.plot(np.linspace(0, err['time'], errf.shape[0]),
errf[:, 0],
pen='r')
driftPlot.plot(np.linspace(0, err['time'], errf.shape[0]),
errf[:, 1],
pen='g')
driftPlot.plot(np.linspace(0, err['time'], errf.shape[0]),
errf[:, 2],
pen='b')
xhplot = win.addPlot(row=1,
col=0,
title='X Hysteresis',
labels={
'left': ('Position error', 'm'),
'bottom': ('Last pipette movement', 'm')
})
xhplot.plot(-off[:, 0], errf[:, 0], pen=None, symbol='o')
yhplot = win.addPlot(row=1,
col=1,
title='Y Hysteresis',
labels={
'left': ('Position error', 'm'),
'bottom': ('Last pipette movement', 'm')
})
yhplot.plot(-off[:, 1], errf[:, 1], pen=None, symbol='o')
zhplot = win.addPlot(row=1,
col=2,
title='Z Hysteresis',
labels={
'left': ('Position error', 'm'),
'bottom': ('Last pipette movement', 'm')
})
zhplot.plot(-off[:, 2], errf[:, 2], pen=None, symbol='o')
# Print best fit for manipulator axes
expPos = err['inds'] * err['stepSize']
measPos = expPos + off
guess = np.array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0]],
dtype='float')
def errFn(v):
return ((measPos -
np.dot(expPos, v.reshape(3, 4))[:, :3])**2).sum()
fit = scipy.optimize.minimize(errFn, guess)
print("Pipette position transform:", fit)
self.errorMapAnalysis = (imx, imy, imz, win)
if len(a) != len(b):
print " WARNING: optimize changed size of list"
return
for i in range(len(a)):
if a[i] not in bLocs:
print " WARNING: optimize changed contents of list"
print a[i], "not in solution"
return
elif bLocs[i] not in a:
print " WARNING: optimize changed contents of list"
print bLocs[i], "not in original"
return
#print "List check OK"
view = pg.GraphicsWindow(border=(50, 50, 50))
greed = 1.0
for d in [0.2e-3, 0.5e-3, 2e-3]:
for n in [20]:
for compMethod in ['max', 'rms', 'sum']:
locs = []
for i in np.linspace(-d, d, n):
for j in np.linspace(-d, d, n):
locs.append((i,j))
key = "grid\td=%0.1gmm\tn=%d\tgreed=%0.2g" % (d*2000, n, greed)
locSets[key] = locs
start = time.time()
for step, last in opt2(locs, minTime, minDist, deadTime, greed=greed, compMethod=compMethod):
if last is None:
l2 = step