def _calculate_optimal_operating_voltage(self, xs, ys):
'''
find the flattest region of the grad
1. smooth the signal
2. calculate the gradient
3. get x of min y
@todo: this algorithm may not work properly. need to collect a real scan to test on
'''
# 1. smooth
smoothed = smooth(ys, window_len=100)
# truncate the smooth to elimate edge artifacts
trunc = 20
xs = xs[trunc:-trunc]
smoothed = smoothed[trunc:-trunc]
self.graph.new_series(xs, smoothed)
# 2. gradient
grads = np.gradient(smoothed)
# 3. get min
cp = xs[np.argmin(grads)]
return cp
def _calculate_optimal_operating_voltage(self, xs, ys):
'''
find the flattest region of the grad
1. smooth the signal
2. calculate the gradient
3. get x of min y
@todo: this algorithm may not work properly. need to collect a real scan to test on
'''
# 1. smooth
smoothed = smooth(ys, window_len=100)
# truncate the smooth to elimate edge artifacts
trunc = 20
xs = xs[trunc:-trunc]
smoothed = smoothed[trunc:-trunc]
self.graph.new_series(xs, smoothed)
# 2. gradient
grads = np.gradient(smoothed)
# 3. get min
cp = xs[np.argmin(grads)]
return cp
def _gc(self, p, det, kind):
g = Graph(container_dict=dict(padding=5), window_width=1000, window_height=800, window_x=40, window_y=20)
with open(p, "r") as rfile:
# gather data
reader = csv.reader(rfile)
header = reader.next()
groups = self._parse_data(reader)
"""
groups= [data,]
data shape = nrow,ncols
"""
data = groups[0]
x = data[0]
y = data[header.index(det)]
sy = smooth(y, window_len=120) # , window='flat')
x = x[::50]
y = y[::50]
sy = sy[::50]
# smooth
# plot
g.new_plot(zoom=True, xtitle="Time (s)", ytitle="{} Baseline Intensity (fA)".format(det))
g.new_series(x, y, type=kind, marker="dot", marker_size=2)
g.new_series(x, sy, line_width=2)
# g.set_x_limits(500, 500 + 60 * 30)
# g.edit_traits()
return g
def run(self):
ages = [(1, 0.1), (1.5, 0.4), (1.7, 0.1), (1.8, 0.2), (2.1, 0.5)]
pool = Pool(processes=10)
n = 1e5
# st = time.time()
# aa = ((ai, ages) for ai in arange(n))
# print 'a"', time.time() - st
age_gen = age_generator(ages, n)
st = time.time()
results = pool.map(_monte_carlo_step, age_gen)
print 'a', time.time() - st
st = time.time()
results = vstack((ri for ri in results if ri is not None))
print 'b', time.time() - st
for xx, (ai, ei) in zip(results.T, ages):
# print 'dev ', abs(xx.mean() - ai) / ai * 100, abs(xx.std() - ei) / ei * 100
# print xx
f, v = histogram(xx, 40)
lp = plot(v[:-1], f)[0]
c = lp.get_color()
nf = smooth(f, window='flat')
plot(v[:-1], nf, c=c, ls='--', lw=2)
axvline(ai, c=c)
# print f, v
idx = argmax(nf)
# axvline(xx.mean(), c=c, ls='--')
axvline(v[idx], c=c, ls='--')
show()
def new_series(self, x=None, y=None, plotid=0, normalize=False,
time_series=True, timescale=False, downsample=None,
use_smooth=False, scale=None, ** kw):
'''
'''
if not time_series:
return super(TimeSeriesGraph, self).new_series(x=x, y=y, plotid=plotid, **kw)
xd = x
if x is not None:
if isinstance(x[0], str):
# convert the time stamp into seconds since the Epoch
# Epoch = 12:00 am 1/1/1970
fmt = "%Y-%m-%d %H:%M:%S"
args = x[0].split(' +')
timefunc = lambda xi, fmt: time.mktime(time.strptime(xi, fmt))
if len(args) > 1:
xd = [timefunc(xi.split(' +')[0], fmt) + float(xi.split(' +')[1]) / 1000.0 for xi in x]
else:
fmt = '%a %b %d %H:%M:%S %Y'
xd = [timefunc(xi, fmt) for xi in x]
if downsample:
xd = downsample_1d(x, downsample)
y = downsample_1d(y, downsample)
if use_smooth:
y = smooth(y)
if xd is not None:
xd = array(xd)
if normalize:
xd = xd - xd[0]
if scale:
xd = xd * scale
plot, names, rd = self._series_factory(xd, y, plotid=plotid, **kw)
if 'type' in rd:
if rd['type'] == 'line_scatter':
plot.plot(names, type='scatter', marker_size=2,
marker='circle')
rd['type'] = 'line'
plota = plot.plot(names, **rd)[0]
# plota.unified_draw = True
# plota.use_downsampling = True
# if the plot is not visible dont remove the underlays
if plota.visible:
self._set_bottom_axis(plota, plot, plotid, timescale=timescale)
return plota, plot
def new_series(self, x=None, y=None, plotid=0, normalize=False,
time_series=True, timescale=False, downsample=None,
use_smooth=False, scale=None, ** kw):
'''
'''
if not time_series:
return super(TimeSeriesGraph, self).new_series(x=x, y=y, plotid=plotid, **kw)
xd = x
if x is not None:
if isinstance(x[0], str):
# convert the time stamp into seconds since the Epoch
# Epoch = 12:00 am 1/1/1970
fmt = "%Y-%m-%d %H:%M:%S"
args = x[0].split(' +')
timefunc = lambda xi, fmt: time.mktime(time.strptime(xi, fmt))
if len(args) > 1:
xd = [timefunc(xi.split(' +')[0], fmt) + float(xi.split(' +')[1]) / 1000.0 for xi in x]
else:
fmt = '%a %b %d %H:%M:%S %Y'
xd = [timefunc(xi, fmt) for xi in x]
if downsample:
xd = downsample_1d(x, downsample)
y = downsample_1d(y, downsample)
if use_smooth:
y = smooth(y)
if xd is not None:
xd = array(xd)
if normalize:
xd = xd - xd[0]
if scale:
xd = xd * scale
plot, names, rd = self._series_factory(xd, y, plotid=plotid, **kw)
if 'type' in rd:
if rd['type'] == 'line_scatter':
plot.plot(names, type='scatter', marker_size=2,
marker='circle')
rd['type'] = 'line'
plota = plot.plot(names, **rd)[0]
# plota.unified_draw = True
# plota.use_downsampling = True
# if the plot is not visible dont remove the underlays
if plota.visible:
self._set_bottom_axis(plota, plot, plotid, timescale=timescale)
return plota, plot
def _calculate_nominal_focal_point(self, fms, focussteps):
if fms:
sfms = smooth(fms)
if sfms is not None:
self.graph.new_series(focussteps, sfms)
self.graph.redraw()
fmi = focussteps[argmin(sfms)]
fma = focussteps[argmax(sfms)]
mi = min(sfms)
ma = max(sfms)
return mi, fmi, ma, fma
def _calculate_nominal_focal_point(self, fms, focussteps):
if fms:
sfms = smooth(fms)
if sfms is not None:
self.graph.new_series(focussteps, sfms)
self.graph.redraw()
fmi = focussteps[argmin(sfms)]
fma = focussteps[argmax(sfms)]
mi = min(sfms)
ma = max(sfms)
return mi, fmi, ma, fma
def _gc(self, p, det, kind):
g = Graph(container_dict=dict(padding=5),
window_width=1000,
window_height=800,
window_x=40,
window_y=20)
with open(p, 'r') as rfile:
# gather data
reader = csv.reader(rfile)
header = reader.next()
groups = self._parse_data(reader)
'''
groups= [data,]
data shape = nrow,ncols
'''
data = groups[0]
x = data[0]
y = data[header.index(det)]
sy = smooth(y, window_len=120) # , window='flat')
x = x[::50]
y = y[::50]
sy = sy[::50]
# smooth
# plot
g.new_plot(zoom=True,
xtitle='Time (s)',
ytitle='{} Baseline Intensity (fA)'.format(det))
g.new_series(x, y, type=kind, marker='dot', marker_size=2)
g.new_series(x, sy, line_width=2)
# g.set_x_limits(500, 500 + 60 * 30)
# g.edit_traits()
return g
def run(self):
ages = [(1, 0.1), (1.5, 0.4), (1.7, 0.1), (1.8, 0.2), (2.1, 0.5)]
pool = Pool(processes=10)
n = 1e5
# st = time.time()
# aa = ((ai, ages) for ai in arange(n))
# print 'a"', time.time() - st
age_gen = age_generator(ages, n)
st = time.time()
results = pool.map(_monte_carlo_step, age_gen)
print 'a', time.time() - st
st = time.time()
results = vstack((ri for ri in results if ri is not None))
print 'b', time.time() - st
for xx, (ai, ei)in zip(results.T, ages):
# print 'dev ', abs(xx.mean() - ai) / ai * 100, abs(xx.std() - ei) / ei * 100
# print xx
f, v = histogram(xx, 40)
lp = plot(v[:-1], f)[0]
c = lp.get_color()
nf = smooth(f, window='flat')
plot(v[:-1], nf, c=c, ls='--', lw=2)
axvline(ai, c=c)
# print f, v
idx = argmax(nf)
# axvline(xx.mean(), c=c, ls='--')
axvline(v[idx], c=c, ls='--')
show()
def smooth(self, x, **kw):
return smooth(x, **kw)
def smooth(self, x, **kw):
return smooth(x, **kw)
