def test1():
vector_full = NP.array([1.0, 2.5, 2.8, 4.1, 5.1, 5.9, 6.9, 8.1])
vector = vector_full[:-2]
t = NP.arange(vector.shape[0])
showArray('t', t)
showArray('vector', vector)
mask = [True] * vector.shape[0]
mask[2] = False
print 'mask', len(mask), mask
masked_vector = applyMask1D(vector, mask)
masked_t = applyMask1D(vector, mask)
trend = getTrend(t, vector)
print trend
for i in range(masked_t.shape[0]):
v_pred = trend[0] + masked_t[i] * trend[1]
print i, masked_vector[i], v_pred, v_pred - masked_vector[i]
predicted = NP.array([trend[0] + i * trend[1] for i in range(masked_vector.shape[0])])
corrected = NP.array([masked_vector[i] - predicted[i] for i in range(masked_vector.shape[0])])
masked_s = NP.transpose(NP.vstack([masked_vector, predicted, corrected]))
showArray('masked_t', masked_t)
showArray('masked_s', masked_s)
# the main axes is subplot(111) by default
PL.plot(masked_t, masked_s)
s_range = PL.amax(masked_s) - PL.amin(masked_s)
axis([PL.amin(masked_t), PL.amax(masked_t), PL.amin(masked_s) - s_range*0.1, PL.amax(masked_s) + s_range*0.1 ])
xlabel('time (days)')
ylabel('downloads')
title('Dowloads over time')
show()
def addDataVectorAccessor(self, data_vector_accessor):
self.__data_vectors_accessors__.append(data_vector_accessor)
_sum = pl.sum(data_vector_accessor.signal)
_min = pl.amin(data_vector_accessor.signal)
_max = pl.amax(data_vector_accessor.signal)
if self.__minimal_signal__ == None:
self.__minimal_signal__ = _sum
self.__minimal_data_vector_accessor__ = data_vector_accessor
self.__min_signal__ = _min
self.__max_signal__ = _max
if _sum < self.__minimal_signal__:
self.__minimal_data_vector_accessor__ = data_vector_accessor
self.__minimal_signal__ = _sum
if _min < self.__min_signal__:
self.__min_signal__ = _min
if _max > self.__max_signal__:
self.__max_signal__ = _max
#collects unique annotations (>0) as a set
if not data_vector_accessor.annotation == None:
unique_annotations = pl.unique(data_vector_accessor.annotation[
pl.where(data_vector_accessor.annotation > 0)])
if len(unique_annotations) > 0:
#union of sets
self.__unique_annotations__ |= set(unique_annotations)
def csnormalize(image,f=0.75):
"""Center and size-normalize an image."""
bimage = 1*(image>mean([amax(image),amin(image)]))
w,h = bimage.shape
[xs,ys] = mgrid[0:w,0:h]
s = sum(bimage)
if s<1e-4: return image
s = 1.0/s
cx = sum(xs*bimage)*s
cy = sum(ys*bimage)*s
sxx = sum((xs-cx)**2*bimage)*s
sxy = sum((xs-cx)*(ys-cy)*bimage)*s
syy = sum((ys-cy)**2*bimage)*s
w,v = eigh(array([[sxx,sxy],[sxy,syy]]))
l = sqrt(amax(w))
if l>0.01:
scale = f*max(image.shape)/(4.0*l)
else:
scale = 1.0
m = array([[1.0/scale,0],[0.0,1.0/scale]])
w,h = image.shape
c = array([cx,cy])
d = c-dot(m,array([w/2,h/2]))
image = interpolation.affine_transform(image,m,offset=d,order=1)
return image
def findFWHM(vector, maxPos=None, amplitude=None):
"""
Find FWHM of vector peak (width at value at maxPos - amplitude /2).
If maxPos is None, will find maximum in vector.
If amplitude is None, will calculate amplitude from maximum to minimum of vector.
"""
if maxPos == None:
maxPos = vector.argmax()
if amplitude == None:
maxVal = pl.amax(vector)
minVal = pl.amin(vector)
amplitude = float(maxVal - minVal)
maxSign = pl.sign(vector[maxPos])
for pos, val in enumerate(vector[maxPos:]):
if pl.sign(val) != maxSign:
# we passed 0
break
halfAbove = pos - abs(vector[maxPos + pos]) / abs(vector[maxPos + pos] -
vector[maxPos + pos - 1])
for pos, val in enumerate(vector[maxPos:0:-1]):
if pl.sign(val) != maxSign:
# we passed 0
break
halfBelow = pos - abs(vector[maxPos - pos]) / abs(vector[maxPos - pos] -
vector[maxPos - pos + 1])
FWHM = halfBelow + halfAbove
return FWHM, maxPos, amplitude
def csnormalize(image, f=0.75):
"""Center and size-normalize an image."""
bimage = 1 * (image > mean([amax(image), amin(image)]))
w, h = bimage.shape
[xs, ys] = mgrid[0:w, 0:h]
s = sum(bimage)
if s < 1e-4: return image
s = 1.0 / s
cx = sum(xs * bimage) * s
cy = sum(ys * bimage) * s
sxx = sum((xs - cx)**2 * bimage) * s
sxy = sum((xs - cx) * (ys - cy) * bimage) * s
syy = sum((ys - cy)**2 * bimage) * s
w, v = eigh(array([[sxx, sxy], [sxy, syy]]))
l = sqrt(amax(w))
if l > 0.01:
scale = f * max(image.shape) / (4.0 * l)
else:
scale = 1.0
m = array([[1.0 / scale, 0], [0.0, 1.0 / scale]])
w, h = image.shape
c = array([cx, cy])
d = c - dot(m, array([w / 2, h / 2]))
image = interpolation.affine_transform(image, m, offset=d, order=1)
return image
def __init__(self, data, sample, rep, well, growth_version=0):
# data format
# Pandas GroupBy group object
# Indices: sample rep well time
# Value: OD reading
self.rawcurve = data.values # OD values
self.time = py.array(data.index.get_level_values("time"))
# Lowest y0 is chosen
# Instances of condensation issue at beginning of experiment can cause
# high OD values
self.y0 = py.amin(self.rawcurve[0:3])
self.asymptote = self.__calcAsymptote()
self.maxGrowthRate, self.mgrTime = self.__calcMGR()
self.y0, self.asymptote, self.maxGrowthRate, self.lag = (
self.__calcParameters(
(self.y0, self.asymptote, self.maxGrowthRate, 0.01),
self.time, self.rawcurve, sample, rep, well)
)
self.dataLogistic = logistic(self.time,
self.y0,
self.asymptote,
self.maxGrowthRate,
self.lag)
if growth_version == 0:
self.growthLevel = default_growth(self.dataLogistic,
self.asymptote,
self.y0)
elif growth_version == 1:
self.growthLevel = calcNewGrowth(self.dataLogistic,
self.asymptote,
self.y0)
elif growth_version == 2:
self.growthLevel = calcGrowth(self.dataLogistic, self.asymptote)
elif growth_version == 3:
self.growthLevel = calcGrowthScore(self.asymptote,
self.maxGrowthRate)
else:
util.printStatus("Unexpected growth version:"
+ str(growth_version))
util.printStatus("Using default growth calculation instead")
self.growthLevel = default_growth(self.dataLogistic,
self.asymptote,
self.y0)
self.glScaled = calcGrowth2(self.dataLogistic, self.asymptote)
self.expGrowth = calcExpGrowth(self.maxGrowthRate, self.asymptote)
self.auc_raw = calcAUCData(self.rawcurve, self.time)
self.auc_rshift = calcShiftAUC(self.auc_raw, self.y0, self.time[-1])
self.auc_log = calcAUC(self.rawcurve, self.y0, self.lag,
self.maxGrowthRate, self.asymptote, self.time)
self.auc_lshift = calcShiftAUC(self.auc_log, self.y0, self.time[-1])
self.growthClass = growthClass(self.growthLevel)
self.sse = sum((self.dataLogistic - self.rawcurve) ** 2)
self.mse = self.sse / len(self.time)
def QuickHull(points):
"""Randomized divide and conquer convex hull.
Args:
points: NxD matrix of points in dimension D.
"""
N, D = points.shape
dim = random.randint(0, D-1)
min_dim = p.amin(points.T, dim)
max_dim = p.amax(points.T, dim)
def QuickHull(points):
"""Randomized divide and conquer convex hull.
Args:
points: NxD matrix of points in dimension D.
"""
N, D = points.shape
dim = random.randint(0, D - 1)
min_dim = p.amin(points.T, dim)
max_dim = p.amax(points.T, dim)
def plot_risetimes(a, b, **kwargs):
# plt.ion()
# if kwargs is not None:
# for key, value in kwargs.iteritems():
# if key == 'file_list':
# file_list = value
# if key == 'scan_line':
# scan_line = value
# varray = plt.array(get_value_from_cfg(file_list, scan_line))
n_files = a.shape[-1]
cmap = plt.get_cmap('jet')
c = [cmap(i) for i in plt.linspace(0, 1, n_files)]
fig1, (ax1, ax2) = plt.subplots(1, 2, figsize=(20, 10))
[ax.set_color_cycle(c) for ax in (ax1, ax2)]
r = []
for i in xrange(n_files):
x, y = a[:,i], b[:,i]
# xo, yo = x, y #, get_envelope(x, y)
xo, yo = get_envelope(x, y)
p = plt.polyfit(xo, np.log(yo), 1)
# Right way to fit... a la Nicolas - the fit expert!
l = ax1.plot(x, plt.log(plt.absolute(y)))
lcolor = l[-1].get_color()
ax1.plot(xo, plt.log(yo), color=lcolor, marker='o', mec=None)
ax1.plot(x, p[1] + x * p[0], color=lcolor, ls='--', lw=3)
l = ax2.plot(x, y)
lcolor = l[-1].get_color()
ax2.plot(xo, yo, 'o', color=lcolor)
xi = plt.linspace(plt.amin(x), plt.amax(x))
yi = plt.exp(p[1] + p[0] * xi)
ax2.plot(xi, yi, color=lcolor, ls='--', lw=3)
print p[1], p[0], 1 / p[0]
# plt.draw()
# ax1.cla()
# ax2.cla()
r.append(1/p[0])
ax2.set_ylim(0, 1000)
plt.figure(2)
plt.plot(r, lw=3, c='purple')
# plt.gca().set_ylim(0, 10000)
# ax3 = plt.subplot(111)
# ax3.semilogy(x, y)
# ax3.semilogy(xo, yo)
return r
def test2():
number_samples = 300
days_to_keep = [2,3,4,5,6]
vector_full = NP.array([2.0 + i * 10.0/number_samples + random.uniform(-.5, .5) for i in range(number_samples)])
mask_full = getDaysOfWeekMask(days_to_keep, vector_full.shape[0])
vector = vector_full[:int(vector_full.shape[0]*0.8)]
t = NP.arange(vector.shape[0])
showArray('t', t)
showArray('vector', vector)
mask = getDaysOfWeekMask(days_to_keep, vector.shape[0])
print 'mask', len(mask), mask
masked_t = applyMask1D(t, mask)
masked_vector = applyMask1D(vector, mask)
showArray('masked_t', masked_t)
showArray('masked_vector', masked_vector)
trend = getTrend(t, vector)
print trend
for i in range(masked_t.shape[0]):
v_pred = trend[0] + masked_t[i] * trend[1]
print masked_t[i], masked_vector[i], v_pred, v_pred - masked_vector[i]
predicted = NP.array([trend[0] + masked_t[i] * trend[1] for i in range(masked_vector.shape[0])])
corrected = NP.array([masked_vector[i] - predicted[i] for i in range(masked_vector.shape[0])])
masked_s = NP.transpose(NP.vstack([masked_vector, predicted, corrected]))
showArray('masked_t', masked_t)
showArray('masked_s', masked_s)
# the main axes is subplot(111) by default
PL.plot(masked_t, masked_s)
s_range = PL.amax(masked_s) - PL.amin(masked_s)
PL.axis([PL.amin(masked_t), PL.amax(masked_t), PL.amin(masked_s) - s_range*0.1, PL.amax(masked_s) + s_range*0.1 ])
PL.xlabel('Time (days)')
PL.ylabel('Downloads')
PL.title('Dowlnoads over time')
PL.show()
def isCorrectSignalRange(self, _signal):
_min = pl.amin(_signal)
if _min >= self.__filter__.min_value and \
_min <= self.__filter__.max_value:
return True
_max = pl.amax(_signal)
if _max >= self.__filter__.min_value and \
_max <= self.__filter__.max_value:
return True
if _min <= self.__filter__.min_value and \
_max >= self.__filter__.max_value:
return True
InformationWindow(message="Signal data out of range !")
return False
def isCorrectSignalRange(self, _signal):
_min = pl.amin(_signal)
if _min >= self.__filter__.min_value and \
_min <= self.__filter__.max_value:
return True
_max = pl.amax(_signal)
if _max >= self.__filter__.min_value and \
_max <= self.__filter__.max_value:
return True
if _min <= self.__filter__.min_value and \
_max >= self.__filter__.max_value:
return True
InformationWindow(message="Signal data out of range !")
return False
def readDatDirectory(key, directory):
global stats
#Don't read data in if it's already read
if not key in DATA["mean"]:
data = defaultdict(array)
#Process the dat files
for datfile in glob.glob(directory + "/*.dat"):
fileHandle = open(datfile, 'rb')
keys, dataDict = csvExtractAllCols(fileHandle)
stats = union(stats, keys)
for aKey in keys:
if not aKey in data:
data[aKey] = reshape(array(dataDict[aKey]),
(1, len(dataDict[aKey])))
else:
data[aKey] = append(data[aKey],
reshape(array(dataDict[aKey]),
(1, len(dataDict[aKey]))),
axis=0)
#Process the div files'
for datfile in glob.glob(directory + "/*.div"):
fileHandle = open(datfile, 'rb')
keys, dataDict = csvExtractAllCols(fileHandle)
stats = union(stats, keys)
for aKey in keys:
if not aKey in data:
data[aKey] = reshape(array(dataDict[aKey]),
(1, len(dataDict[aKey])))
else:
data[aKey] = append(data[aKey],
reshape(array(dataDict[aKey]),
(1, len(dataDict[aKey]))),
axis=0)
#Iterate through the stats and calculate mean/standard deviation
for aKey in stats:
if aKey in data:
DATA["mean"][key][aKey] = mean(data[aKey], axis=0)
DATA["median"][key][aKey] = median(data[aKey], axis=0)
DATA["std"][key][aKey] = std(data[aKey], axis=0)
DATA["ste"][key][aKey] = std(data[aKey], axis=0) / sqrt(
len(data[aKey]))
DATA["min"][key][aKey] = mean(data[aKey], axis=0) - amin(
data[aKey], axis=0)
DATA["max"][key][aKey] = amax(data[aKey], axis=0) - mean(
data[aKey], axis=0)
DATA["actual"][key][aKey] = data[aKey]
def plot(df, *args, **kw):
N = df["count"].sum()
deltas_ = df["count"].values[:-1] - df["count"].values[1:]
deltas = pl.array([deltas_[0]] + list(pl.amin([deltas_[1:], deltas_[:-1]], axis=0)) + [deltas_[-1]])
dfrac = (deltas + 1e-20) / N
def frac_leaked(q):
attacked = dfrac * q > (2 * q * df["count"] / N) ** 0.5
N_a = df["count"][attacked].sum()
return float(N_a) / N
Ns = pl.logspace(0, 15, 100)
fracs = pl.array(map(frac_leaked, Ns))
pl.semilogx(Ns, fracs * 100, *args, **kw)
def plot(df, *args, **kw):
N = df["count"].sum()
deltas_ = df['count'].values[:-1] - df['count'].values[1:]
deltas = pl.array([deltas_[0]] + list(pl.amin([deltas_[1:], deltas_[:-1]], axis=0)) + [deltas_[-1]])
dfrac = (deltas + 1e-20) / N
def frac_leaked(q):
attacked = dfrac * q > (2 * q * df['count'] / N) ** 0.5
N_a = df['count'][attacked].sum()
return float(N_a) / N
Ns = pl.logspace(0, 15, 100)
fracs = pl.array(map(frac_leaked, Ns))
pl.semilogx(Ns, fracs * 100, *args, **kw)
def fwhm_2gauss(x, y, dx=0.001): ''' Finds the FWHM for the profile y(x), with accuracy dx=0.001 Uses a 2-Gauss 1D fit. ''' popt, pcov = curve_fit(gauss2, x, y); xx = pl.arange(pl.amin(x), pl.amax(x)+dx, dx); ym = gauss2(xx, popt[0], popt[1], popt[2], popt[3], popt[4], popt[5]) hm = pl.amax(ym/2.0); y_diff = pl.absolute(ym-hm); y_diff_sorted = pl.sort(y_diff); i1 = pl.where(y_diff==y_diff_sorted[0]); i2 = pl.where(y_diff==y_diff_sorted[1]); fwhm = pl.absolute(xx[i1]-xx[i2]); return hm, fwhm, xx, ym
def leftover_phc_single(ds, attr="p_filt_value_phc", feature="CuKAlpha", ax=None):
cal = ds.calibration[attr]
pulse_timing.choose_laser_dataset(ds, "not_laser")
if ax is None:
plt.figure()
ax = plt.gca()
ax.plot(ds.p_promptness[ds.cuts.good()], getattr(ds, attr)[ds.cuts.good()],'.')
# ax.set_xlabel("promptness")
ax.set_ylabel(attr)
ax.set_title("chan %d %s"%(ds.channum, feature))
ax.set_ylim(np.array([.995, 1.005])*cal.name2ph(feature))
index = np.logical_and(getattr(ds, attr)[ds.cuts.good()]>ax.get_ylim()[0], getattr(ds, attr)[ds.cuts.good()]<ax.get_ylim()[1])
xmin = plt.amin(ds.p_promptness[ds.cuts.good()][index])
xmax = plt.amax(ds.p_promptness[ds.cuts.good()][index])
ax.set_xlim(xmin, xmax)
def __initiate_movie__(self):
FFMpegWriter = manimation.writers["ffmpeg"]
metadata = dict(title="Poincare plot movie", artist="HRV", comment="Movie support!")
self.writer = FFMpegWriter(fps=self.movie_parameters.fps, metadata=metadata)
self.fig = plt.figure()
# l, = plt.plot([], [], 'k-o')
self.movie_plot, = plt.plot([], [], "bo")
margin = 50
signal = self.data_vector_accessor_list[0].signal
_max = pl.amax(signal)
_min = pl.amin(signal)
plt.xlim(_min - margin, _max + margin)
plt.ylim(_min - margin, _max + margin)
movie_filename = "/tmp/movie.mp4"
return self.writer.saving(self.fig, movie_filename, 150)
def readDatDirectory(key, directory):
global stats
#Don't read data in if it's already read
if not key in DATA["mean"]:
data = defaultdict(array)
#Process the dat files
for datfile in glob.glob(directory + "/*.dat"):
fileHandle = open(datfile, 'rb')
keys, dataDict = csvExtractAllCols(fileHandle)
stats = union(stats, keys)
for aKey in keys:
if not aKey in data:
data[aKey] = reshape(array(dataDict[aKey]),
(1, len(dataDict[aKey])))
else:
data[aKey] = append(data[aKey],
reshape(array(dataDict[aKey]),
(1, len(dataDict[aKey]))),
axis=0)
#Process the div files'
for datfile in glob.glob(directory + "/*.div"):
fileHandle = open(datfile, 'rb')
keys, dataDict = csvExtractAllCols(fileHandle)
stats = union(stats, keys)
for aKey in keys:
if not aKey in data:
data[aKey] = reshape(array(dataDict[aKey]),
(1, len(dataDict[aKey])))
else:
data[aKey] = append(data[aKey],
reshape(array(dataDict[aKey]),
(1, len(dataDict[aKey]))),
axis=0)
#Iterate through the stats and calculate mean/standard deviation
for aKey in stats:
if aKey in data:
DATA["mean"][key][aKey] = mean(data[aKey], axis=0)
DATA["median"][key][aKey] = median(data[aKey], axis=0)
DATA["std"][key][aKey] = std(data[aKey], axis=0)
DATA["ste"][key][aKey] = std(data[aKey], axis=0)/ sqrt(len(data[aKey]))
DATA["min"][key][aKey] = mean(data[aKey], axis=0)-amin(data[aKey], axis=0)
DATA["max"][key][aKey] = amax(data[aKey], axis=0)-mean(data[aKey], axis=0)
DATA["actual"][key][aKey] = data[aKey]
def spectrum(wav_file,mi,mx,har,start,end,posX,posY,layer,origin,gap=0,arrange="",radius=30,sinheight=6.1):
spect = []
frame_rate, snd = wavfile.read(wav_file)
sound_info = snd[:,0]
spectrum, freqs, t, im = plt.specgram(sound_info,NFFT=1024,Fs=frame_rate,noverlap=5,mode='magnitude')
n = 0
rotation = 6.2831
sinpos = {}
cirpos = {}
if arrange is "sinus":
sinpos = sinus(har,radius,sinheight)
for i in range(har):
cirpos[i] = 0
elif arrange is "circle":
gap = 0
sinpos, cirpos = circle(har,radius)
rotation /= har
else:
for i in range(har):
sinpos[i] = 0
for i in range(har):
cirpos[i] = 0
maximum = plt.amax(spectrum)
minimum = plt.amin(spectrum)
position = 0
while n < har:
lastval = ((spectrum[n][0]-minimum)/(maximum - minimum))*(mx-mi)+mi
lastval = math.ceil(lastval*1000)/1000
lasttime = int(round(t[0]*1000))
spect.append(osbject("bar.png",layer,origin,posX+position*gap+int(round(float(cirpos[n]))),posY+int(round(float(sinpos[n])))))
position += 1
if arrange is "circle":
spect[n].rotate(0,start,start,math.ceil((1.5707+n*rotation)*1000)/1000,math.ceil((1.5707+n*rotation)*1000)/1000)
for index,power in enumerate(spectrum[n]):
power = ((power-minimum)/(maximum - minimum))*(mx-mi)+mi
power = math.ceil(power*1000)/1000
if power == lastval or int(round(t[index]*1000)) < start or int(round(t[index]*1000)) > end or index % 2 is not 0:
lasttime = int(round(t[index]*1000))
continue
else:
spect[n].vecscale(0,lasttime,int(round(t[index]*1000)),1,lastval,1,power)
lastval = power
lasttime = int(round(t[index]*1000))
n += 1
return spect
def spectrum(wav_file,mi,mx,har,start,end,posX,posY,layer,origin,gap=0,arrange="",radius=30,sinheight=6.1):
spect = []
frame_rate, snd = wavfile.read(wav_file)
sound_info = snd[:,0]
spectrum, freqs, t, im = plt.specgram(sound_info,NFFT=1024,Fs=frame_rate,noverlap=5,mode='magnitude')
n = 0
rotation = 6.2831
sinpos = {}
cirpos = {}
if arrange is "sinus":
sinpos = sinus(har,radius,sinheight)
for i in range(har):
cirpos[i] = 0
elif arrange is "circle":
gap = 0
sinpos, cirpos = circle(har,radius)
rotation /= har
else:
for i in range(har):
sinpos[i] = 0
for i in range(har):
cirpos[i] = 0
maximum = plt.amax(spectrum)
minimum = plt.amin(spectrum)
position = 0
while n < har:
lastval = ((spectrum[n][0]-minimum)/(maximum - minimum))*(mx-mi)+mi
lastval = math.ceil(lastval*1000)/1000
lasttime = int(round(t[0]*1000))
spect.append(osbject("bar.png",layer,origin,posX+position*gap+int(round(float(cirpos[n]))),posY+int(round(float(sinpos[n])))))
position += 1
if arrange is "circle":
spect[n].rotate(0,start,start,math.ceil((1.5707+n*rotation)*1000)/1000,math.ceil((1.5707+n*rotation)*1000)/1000)
for index,power in enumerate(spectrum[n]):
power = ((power-minimum)/(maximum - minimum))*(mx-mi)+mi
power = math.ceil(power*1000)/1000
if power == lastval or int(round(t[index]*1000)) < start or int(round(t[index]*1000)) > end or index % 2 is not 0:
lasttime = int(round(t[index]*1000))
continue
else:
spect[n].vecscale(0,lasttime,int(round(t[index]*1000)),1,lastval,1,power)
lastval = power
lasttime = int(round(t[index]*1000))
n += 1
return spect
def center_maxsize(image,r):
"""Center the image and fit it into an r x r output image.
If the input is larger in any dimension than r, it is
scaled down."""
from pylab import amin,amax,array,zeros
assert amin(image)>=0 and amax(image)<=1
image = array(image,'f')
w,h = image.shape
s = max(w,h)
# zoom down, but don't zoom up
if s>r:
image = interpolation.zoom(image,(r+0.5)/float(s))
image[image<0] = 0
image[image>1] = 1
w,h = image.shape
output = zeros((r,r),image.dtype)
dx = (r-w)/2
dy = (r-h)/2
output[dx:dx+w,dy:dy+h] = image
return output
def __initiate_movie__(self):
FFMpegWriter = manimation.writers['ffmpeg']
metadata = dict(title='Poincare plot movie',
artist='HRV',
comment='Movie support!')
self.writer = FFMpegWriter(fps=self.movie_parameters.fps,
metadata=metadata)
self.fig = plt.figure()
#l, = plt.plot([], [], 'k-o')
self.movie_plot, = plt.plot([], [], 'bo')
margin = 50
signal = self.data_vector_accessor_list[0].signal
_max = pl.amax(signal)
_min = pl.amin(signal)
plt.xlim(_min - margin, _max + margin)
plt.ylim(_min - margin, _max + margin)
movie_filename = '/tmp/movie.mp4'
return self.writer.saving(self.fig, movie_filename, 150)
def center_maxsize(image, r):
"""Center the image and fit it into an r x r output image.
If the input is larger in any dimension than r, it is
scaled down."""
from pylab import amin, amax, array, zeros
assert amin(image) >= 0 and amax(image) <= 1
image = array(image, 'f')
w, h = image.shape
s = max(w, h)
# zoom down, but don't zoom up
if s > r:
image = interpolation.zoom(image, (r + 0.5) / float(s))
image[image < 0] = 0
image[image > 1] = 1
w, h = image.shape
output = zeros((r, r), image.dtype)
dx = (r - w) / 2
dy = (r - h) / 2
output[dx:dx + w, dy:dy + h] = image
return output
def displayStats(self):
if self.meas is not None:
maxPos = pl.unravel_index(self.meas.argmax(), self.meas.shape)
maxVal = self.meas[maxPos]
minVal = pl.amin(self.meas)
amplitude = float(maxVal - minVal)
self.ui.lcdAmplitude.display('{:.0f}'.format(amplitude))
xVector = self.meas[maxPos[0], :].astype(float) - amplitude / 2
yVector = self.meas[:, maxPos[1]].astype(float) - amplitude / 2
# with interlaced camera in binning mode, pixels are size 2 in height
if self.ccd.ccdParams['isInterlaced'] and not self.ilAcq:
yVector = pl.array([yVector, yVector]).flatten('F')
maxPos = (2 * maxPos[0], maxPos[1])
xFWHM, _, _ = findFWHM(xVector,
maxPos=maxPos[1],
amplitude=amplitude)
yFWHM, _, _ = findFWHM(yVector,
maxPos=maxPos[0],
amplitude=amplitude)
FWHM = pl.mean([xFWHM, yFWHM])
self.ui.lcdFwhm.display('{:.2f}'.format(FWHM))
if maxVal == 2**16 - 1:
self.toggleSatIndicator(True)
else:
self.toggleSatIndicator(False)
thresholdArray = copy.deepcopy(self.meas)
low_values_indices = thresholdArray < 2 * minVal
thresholdArray[low_values_indices] = 0
comPos = center_of_mass(thresholdArray) #[::-1]
if self.ccd.ccdParams['isInterlaced'] and not self.ilAcq:
comPos = (comPos[0], 2 * comPos[1])
self.verticalLineMax.setValue(comPos)
self.horizontalLineMax.setValue(comPos)
#!/usr/bin/env python
import pandas as pd
import pylab as pl
df = pd.DataFrame.from_csv("surnames.csv", header=1)
N = df["count"].sum()
deltas_ = df["count"].values[:-1] - df["count"].values[1:]
deltas = pl.array([deltas_[0]] + list(pl.amin([deltas_[1:], deltas_[:-1]], axis=0)) + [deltas_[-1]])
dfrac = (deltas + 1e-20) / N
def frac_leaked(q):
attacked = dfrac * q > (2 * q * df["count"] / N) ** 0.5
N_a = df["count"][attacked].sum()
return float(N_a) / N
Ns = pl.logspace(0, 15, 100)
fracs = pl.array(map(frac_leaked, Ns))
pl.semilogx(Ns, fracs * 100)
pl.xlabel("Number of queries")
pl.ylabel("% of surnames revealed")
pl.yticks(range(0, 110, 10))
pl.grid(True)
pl.show()
'''connect neurons with multimeter'''
nest.Connect(mult,neuron,{'rule':'all_to_all'})
''' simulation '''
nest.Simulate(simtime)
endsimulate = time.time()
sim_time = endsimulate - startbuild
print 'simulaiton time: %f' %sim_time
pl.clf
pl.figure(1)
#nest.raster_plot.from_device(sp, hist=True)
y = nest.GetStatus(sp,'events')[0]
z = nest.GetStatus(neuron,'local_id')[0]
pl.scatter(y['times'],y['senders']-pl.amin(z))
pl.xlim([0.0,1000.])
pl.ylim([0,pop])
pl.xlabel('ms')
pl.ylabel('neuron id ')
pl.show()
#%%
''' 3D surface plot of membrane potential'''
m_data = nest.GetStatus(mult,'events')[0] # data of multimeter
z = nest.GetStatus(neuron,'local_id')[0]
#X, Y = np.meshgrid(y['times'],y['senders']-pl.amin(z))
#ax.plot_surface(X,Y,y['V_m'])
#uniq= pl.diff(pl.find((m_data['senders'])==m_data['senders'][0]))# find the distance of the same sender
def reset(self):
self.setSquareFilterParams(SquareFilterParams(
int(pl.amin(self.data_accessor.signal)),
int(pl.amax(self.data_accessor.signal)),
True))
def plot_slices(data):
[n_cols, n_slices, n_turns] = data.shape
A = data[2,:,:] * data[13,:,:]
# Color cycle
n_colors = n_turns
colmap = plt.get_cmap('jet')
c = [colmap(i) for i in plt.linspace(0., 1., n_colors)]
fig1 = plt.figure(figsize=(12, 8))
# ax1 = plt.gca()
# [ax1.plot(A[:,i], c=c[i]) for i in range(1, n_turns, 1)]
# plt.show()
# Smoothing
X = plt.arange(0, n_slices, 1)
Y = plt.arange(0, n_turns, 1)
A = A[X,:][:,Y]
Xi = plt.linspace(X[0], X[-1], 1000)
Yi = plt.linspace(Y[0], Y[-1], 1000)
sp = interpolate.RectBivariateSpline(X, Y, A)
Ai = sp(Xi, Yi)
X, Y = plt.meshgrid(X, Y)
X, Y = X.T, Y.T
Xi, Yi = plt.meshgrid(Xi, Yi)
Xi, Yi = Xi.T, Yi.T
#fig = figure(1)
#ax3d = fig.gca(projection='3d')
#pl = ax3d.plot_wireframe(Xi, Yi, Ai, \
#rstride=ns, cstride=ns, cmap=cm.jet, linewidth=0.1, alpha=0.3)
#cset = ax3d.contourf(Xi, Yi, Ai, zdir='z')#, offset=-100)
#cset = ax3d.contourf(Xi, Yi, Ai, zdir='x')#, offset=-40)
#cset = ax3d.contourf(Xi, Yi, Ai, zdir='y')#, offset=40)
#ax3d.zaxis.set_major_locator(LinearLocator(10))
#ax3d.zaxis.set_major_formatter(FormatStrFormatter('%.02f'))
#ax3d.view_init(elev=25, azim=-150)
#ax3d.set_xlabel('Slices')
#ax3d.set_ylabel('Turns')
#fig = figure(2)
#ax3d = fig.gca(projection='3d')
#pl = ax3d.plot_wireframe(X, Y, A, \
#rstride=ns, cstride=ns, cmap=cm.jet, linewidth=0.1, alpha=0.3)
#cset = ax3d.contourf(X, Y, A, zdir='z')#, offset=-100)
#cset = ax3d.contourf(X, Y, A, zdir='x')#, offset=-40)
#cset = ax3d.contourf(X, Y, A, zdir='y')#, offset=40)
#ax3d.zaxis.set_major_locator(LinearLocator(10))
#ax3d.zaxis.set_major_formatter(FormatStrFormatter('%.02f'))
#ax3d.view_init(elev=25, azim=-150)
#show()
from mayavi.modules.grid_plane import GridPlane
from mayavi.modules.outline import Outline
from mayavi.modules.volume import Volume
from mayavi.scripts import mayavi2
from mayavi import mlab
mlab.options.backend = 'envisage'
# graphics card driver problem
# workaround by casting int in line 246 of enthought/mayavi/tools/figure.py
#mlab.options.offscreen = True
#enthought.mayavi.engine.current_scene.scene.off_screen_rendering = True
mlab.figure(bgcolor=(1,1,1), fgcolor=(0.2,0.2,0.2))
aspect = (0, 10, 0, 20, -6, 6)
ranges = (plt.amin(X), plt.amax(X), plt.amin(Y), plt.amax(Y), plt.amin(A), plt.amax(A))
# s = mlab.surf(Xi, Yi, Ai, colormap='jet', representation='surface',
# warp_scale=1e-3)
s = mlab.surf(Xi, Yi, Ai, colormap='jet', representation='surface', extent=aspect,
warp_scale='auto')
mlab.outline(line_width=1)
mlab.axes(x_axis_visibility=True,
xlabel='Slice No.', ylabel='Turn No.', zlabel='BPM signal', ranges=ranges)
#mlab.title(('Electron cloud dynamics - slice passage: %03d/%03d' % (i, n_blocks)), size=0.25)
mlab.view(azimuth=230, elevation=60)
mlab.show()
#!/usr/bin/env python
import pandas as pd
import pylab as pl
df = pd.read_csv("surnames.csv", header=1)
N = df["count"].sum()
deltas_ = df['count'].values[:-1] - df['count'].values[1:]
deltas = pl.array([deltas_[0]] +
list(pl.amin([deltas_[1:], deltas_[:-1]], axis=0)) +
[deltas_[-1]])
dfrac = (deltas + 1e-20) / N
def frac_leaked(q):
attacked = dfrac * q > (2 * q * df['count'] / N)**0.5
N_a = df['count'][attacked].sum()
return float(N_a) / N
Ns = pl.logspace(0, 15, 100)
fracs = pl.array(map(frac_leaked, Ns))
pl.semilogx(Ns, fracs * 100)
pl.xlabel("Number of queries")
pl.ylabel("% of surnames revealed")
pl.yticks(range(0, 110, 10))
pl.grid(True)
pl.show()
sys.exit(1)
F = [];
F.append(watch_procs(proc, totaltime=options.totaltime));
for child in get_children(proc):
F.append(watch_procs(child, totaltime=options.totaltime));
for f in F:
f.start()
for f in F:
f.join()
time.sleep(1)
if options.saveas.upper()=='TXT':
header = "time cputimes cpu ram"
print f.name
for f in F:
x, cpu, ram, z = f.get_result()
pl.savetxt(proc.name+'.'+f.proc.name+'.'+str(f.proc.pid)+'.txt', zip(x, z, cpu, ram), header=header, fmt='%10.10f');
else:
for f in F:
x, cpu, ram, z = f.get_result()
pl.step(x-pl.amin(x), cpu, label=f.proc.name)
pl.step(x-pl.amin(x), ram);
pl.legend(loc=2)
pl.xlabel('Wall Clock (s)');
pl.ylabel('% Usage')
pl.savefig(options.proc+'.eps');
def __init__(self, data_vector, movie_parameters, **params):
self.p = movie_parameters
if not self.p.movie_name == None:
self.params = Params(**params)
mpl.rcParams['patch.edgecolor'] = 'none'
mpl.rcParams['savefig.edgecolor'] = 'none'
self.source = get_filename(self.params.reference_filename)
self.__prefixed_movie_name__ = self.__get_prefixed_movie_name__()
self.__prefixed_movie_dir__ = self.__get_prefixed_movie_dir__()
if not self.params.filter_manager == None:
data_vector = self.params.filter_manager.run_filters(
data_vector)
_max = pl.amax(data_vector.signal)
_min = pl.amin(data_vector.signal)
self.time = data_vector.time
margin = 50
self.signal_size = len(data_vector.signal)
self.range = [
_min - margin, _max + margin, _min - margin, _max + margin
]
self.x_data = pl.zeros(self.signal_size)
self.y_data = pl.zeros(self.signal_size)
self.old_signal_plus = None
self.idx = 0
self.active_color = get_extended_color_array(
movie_parameters.movie_active_color)
self.inactive_color = get_extended_color_array(
movie_parameters.movie_inactive_color)
self.centroid_color = get_extended_color_array(
movie_parameters.movie_centroid_color)
self.message = None
self.core_nums = multiprocessing.cpu_count(
) * self.p.movie_multiprocessing_factor # @IgnorePep8
self.pp_spec_manager = MiniPoincarePlotSpecManager()
self.pp_spec_manager.movie_dir = self.__prefixed_movie_dir__
self.pp_spec_manager.movie_name = self.__prefixed_movie_name__
self.pp_spec_manager.movie_dpi = self.p.movie_dpi
self.pp_spec_manager.movie_fps = self.p.movie_fps
self.pp_spec_manager.movie_height = self.p.movie_height
self.pp_spec_manager.movie_width = self.p.movie_width
self.pp_spec_manager.active_color = self.active_color
self.pp_spec_manager.inactive_color = self.inactive_color
self.pp_spec_manager.centroid_color = self.centroid_color
self.pp_spec_manager.active_point_size = self.p.movie_active_size
self.pp_spec_manager.inactive_point_size = \
self.p.movie_inactive_size
self.pp_spec_manager.centroid_point_size = \
self.p.movie_centroid_size
self.pp_spec_manager.show_plot_legends = \
self.p.movie_show_plot_legends
self.pp_spec_manager.x_label = self.p.x_label
self.pp_spec_manager.y_label = self.p.y_label
self.pp_spec_manager.clean_frames = self.p.movie_clean_frames
self.pp_spec_manager.movie_title = self.p.movie_title
self.pp_spec_manager.movie_frame_step = self.p.movie_frame_step
self.pp_spec_manager.movie_identity_line = self.p.movie_identity_line
self.pp_spec_manager.movie_hour_label = self.p.movie_hour_label
self.pp_spec_manager.movie_minute_label = self.p.movie_minute_label
self.pp_spec_manager.movie_second_label = self.p.movie_second_label
self.pp_spec_manager.movie_time_label_in_line = self.p.movie_time_label_in_line
self.pp_spec_manager.movie_time_label_font_size = self.p.movie_time_label_font_size
self.pp_spec_manager.movie_time_label_prefix = self.p.movie_time_label_prefix
self.pp_spec_manager.movie_title_font_size = self.p.movie_title_font_size
self.pp_spec_manager.movie_axis_font_size = self.p.movie_axis_font_size
self.pp_spec_manager.movie_axis_font = self.p.movie_axis_font
self.pp_spec_manager.movie_title_font = self.p.movie_title_font
self.pp_spec_manager.movie_tick_font = self.p.movie_tick_font
self.pp_spec_manager.movie_frame_pad = self.p.movie_frame_pad
self.pp_spec_manager.movie_create_time_label = self.p.movie_create_time_label
self.pp_spec_manager.movie_frame_filename_with_time = self.p.movie_frame_filename_with_time
self.pp_specs_managers = []
self.pp_specs_managers.append(self.pp_spec_manager)
self.sub_dir_counter = 0
self.scatter = None
self.legend_text = None
self.pp_specs = []
self.cum_inactive = 0
self._pp_spec_old = None
self.s_size = 0 # current calculated signal size
def main():
args = parseCMD()
tLoc = args.textLocation
Temp = args.Temperature
ChemPot = args.ChemPot
excVol = args.excVol
if tLoc == 'R':
boxSubtract = 1
else:
boxSubtract = 4
alphas = glob.glob('*alpha*')
if alphas == []:
sys.exit('Must be in CWD containing alphaN direcs')
minMus, maxMus = 5000, -5000
colors = [
'Salmon', 'Blue', 'DarkViolet', 'MediumSpringGreen', 'Fuchsia',
'Yellow', 'Maroon'
]
if excVol:
fig, ax = pl.subplots(1, figsize=(10, 8))
pl.tick_params(axis='both', which='major', labelsize=16)
pl.tick_params(axis='both', which='minor', labelsize=16)
yticks = ax.yaxis.get_major_ticks()
yticks[0].set_visible(False)
n = 0
for alpha in sorted(alphas):
os.chdir(alpha)
a = alpha[-1]
mus, fDens, fErr, bDens, bErr = pl.loadtxt(
'JackKnifeData_bipart.dat', unpack=True)
pl.errorbar(mus,
fDens,
fErr,
fmt='^',
label=('Film: ' + r'$S = $' + '%s' % a),
color=colors[n],
markeredgecolor='DarkSlateGray',
markersize=8)
pl.errorbar(mus,
bDens,
bErr,
fmt='o',
label=('Bulk: ' + r'$S = $' + '%s' % a),
color=colors[n],
markeredgecolor='DarkSlateGray',
markersize=8)
# determine max and min values of mu
if pl.amax(mus) > maxMus:
maxMus = pl.amax(mus)
if pl.amin(mus) < minMus:
minMus = pl.amin(mus)
os.chdir('..')
n += 1
# set up bulk SVP densities for plot
if Temp != 'T':
bulkVert = -30
else:
bulkVert = 30
pl.plot([minMus, maxMus], [0.02198, 0.02198], 'k-', lw=3)
pl.annotate(
'3D SVP',
xy=(maxMus - boxSubtract, 0.02195), #xycoords='data',
xytext=(-50, bulkVert),
textcoords='offset points',
bbox=dict(boxstyle="round", fc="0.8"),
arrowprops=dict(arrowstyle="->",
connectionstyle="angle,angleA=0,angleB=90,rad=10"),
)
pl.plot([minMus, maxMus], [0.0432, 0.0432], 'k-', lw=3)
pl.annotate(
'2D SVP',
xy=(maxMus - boxSubtract, 0.0432), #xycoords='data',
xytext=(-50, 30),
textcoords='offset points',
bbox=dict(boxstyle="round", fc="0.8"),
arrowprops=dict(arrowstyle="->",
connectionstyle="angle,angleA=0,angleB=90,rad=10"),
)
if Temp == 'T':
pl.xlabel('Temperature [K]', fontsize=16)
else:
pl.xlabel('Chemical Potential [K]', fontsize=16)
pl.ylabel('Spatial Density ' + r'$[\AA^{-d}]$', fontsize=16)
if Temp != 'T':
pl.title('T = %s K' % Temp)
else:
pl.title(r'$\mu\ =\ $' + ChemPot + ' K')
pl.legend(loc=2)
pl.savefig('density_vs_mu_allAlphas.pdf',
format='pdf',
bbox_inches='tight')
pl.savefig('density_vs_mu_allAlphas_trans.pdf',
format='pdf',
bbox_inches='tight',
transparent=True)
# SUPERFLUID STIFFNESS
fig2, ax2 = pl.subplots(1, figsize=(8, 6.5))
n = 0
for alpha in sorted(alphas):
os.chdir(alpha)
a = alpha[-1]
if alpha[-1] == '0':
lab = 'Bulk'
else:
lab = r'$\alpha = $' + '%s: ' % a
mus, stiff, stiffErr = pl.loadtxt('JackKnifeData_super.dat',
unpack=True,
usecols=(0, 1, 2))
pl.errorbar(mus,
stiff,
stiffErr,
fmt='o',
label=lab,
color=colors[n],
markeredgecolor='DarkSlateGray',
markersize=8)
# determine max and min values of mu
if pl.amax(mus) > maxMus:
maxMus = pl.amax(mus)
if pl.amin(mus) < minMus:
minMus = pl.amin(mus)
os.chdir('..')
n += 1
if Temp == 'T':
pl.xlabel('Temperature [K]', fontsize=16)
else:
pl.xlabel('Chemical Potential [K]', fontsize=16)
pl.ylabel('Superfluid Fraction', fontsize=16)
if Temp != 'T':
pl.title('T = %s K' % Temp)
else:
pl.title(r'$\mu\ =\ $' + ChemPot + ' K')
pl.legend()
pl.savefig('superFrac_vs_mu_allAlphas.pdf',
format='pdf',
bbox_inches='tight')
pl.savefig('superFrac_vs_mu_allAlphas_trans.pdf',
format='pdf',
bbox_inches='tight',
transparent=True)
'''
# WINDING NUMBER COMPONENTS
fig3,ax3 = pl.subplots(1, figsize=(8,6.5))
n = 0
for alpha in sorted(alphas):
os.chdir(alpha)
a = alpha[-1]
if alpha[-1] == '0':
lab = 'Bulk'
else:
lab = r'$\alpha = $'+'%s: ' % a
mus, Wx2, Wx2Err, Wy2, Wy2Err, Wz2, Wz2Err = pl.loadtxt(
'JackKnifeData_super.dat', unpack=True,
usecols=(0,3,4,5,6,7,8))
pl.errorbar(mus, Wx2, Wx2Err, fmt='o',
label=(lab+': '+r'$\langle W_x^2 \rangle$'),
color = colors[n], markeredgecolor='DarkSlateGray',
markersize=8)
pl.errorbar(mus, Wy2, Wy2Err, fmt='v',
label=(lab+': ' +r'$\langle W_y^2 \rangle$'),
color = colors[n], markeredgecolor='DarkSlateGray',
markersize=8)
pl.errorbar(mus, Wz2, Wz2Err, fmt='s',
label=(lab+': '+r'$\langle W_z^2 \rangle$' ),
color = colors[n], markeredgecolor='DarkSlateGray',
markersize=8)
# determine max and min values of mu
if pl.amax(mus) > maxMus:
maxMus = pl.amax(mus)
if pl.amin(mus) < minMus:
minMus = pl.amin(mus)
os.chdir('..')
n += 1
if Temp == 'T':
pl.xlabel('Temperature [K]', fontsize=16)
else:
pl.xlabel('Chemical Potential [K]', fontsize=16)
pl.ylabel(r'$\langle W_i^2 \rangle$', fontsize=16)
if Temp != 'T':
pl.title('T = %s K' % Temp)
else:
pl.title(r'$\mu\ =\ $'+ChemPot+' K')
pl.legend()
pl.savefig('windingNumbers_vs_mu_allAlphas.pdf', format='pdf',
bbox_inches='tight')
pl.savefig('windingNumbers_vs_mu_allAlphas_trans.pdf', format='pdf',
bbox_inches='tight', transparent=True)
# W_z^2
fig4,ax4 = pl.subplots(1, figsize=(8,6.5))
n = 0
for alpha in sorted(alphas):
os.chdir(alpha)
a = alpha[-1]
if alpha[-1] == '0':
lab = 'Bulk'
else:
lab = r'$\alpha = $'+'%s: ' % a
mus, Wz2, Wz2Err = pl.loadtxt(
'JackKnifeData_super.dat', unpack=True,
usecols=(0,7,8))
pl.errorbar(mus, Wz2, Wz2Err, fmt='s',
#label=(r'$\alpha = $'+'%s: ' % a),
label=lab,
color = colors[n], markeredgecolor='DarkSlateGray',
markersize=8)
# determine max and min values of mu
if pl.amax(mus) > maxMus:
maxMus = pl.amax(mus)
if pl.amin(mus) < minMus:
minMus = pl.amin(mus)
os.chdir('..')
n += 1
if Temp == 'T':
pl.xlabel('Temperature [K]', fontsize=16)
else:
pl.xlabel('Chemical Potential [K]', fontsize=16)
pl.ylabel(r'$\langle W_z^2 \rangle$', fontsize=16)
if Temp != 'T':
pl.title('T = %s K' % Temp)
else:
pl.title(r'$\mu\ =\ $'+ChemPot+' K')
pl.legend()
pl.savefig('windingZ_vs_mu_allAlphas.pdf', format='pdf',
bbox_inches='tight')
pl.savefig('windingZ_vs_mu_allAlphas_trans.pdf', format='pdf',
bbox_inches='tight', transparent=True)
'''
pl.show()
if lattice[a, b, c] > 0:
neighbors = get_neighbors(a, b, c)
border = False
for neigh in neighbors:
if lattice[neigh] == 0:
borderPositions1.append([a, b, c])
lattice[a, b, c] = -1
break
borderPositions1 = p.array(borderPositions1)
# ii) find closest distance to border site for each 1 and 2 (type one and spanning cluster or not)
distances = []
for a in range(N):
for b in range(N):
for c in range(N):
if lattice[a, b, c] > 0:
diffs = borderPositions1 - p.array([a, b, c])
minBorderDist = p.amin(p.sum((diffs * diffs).T, axis=0))
distances.append(p.sqrt(minBorderDist))
# p.figure()
# p.imshow(init_lattice, interpolation='none')
# p.suptitle("Initial configuration")
p.figure()
p.imshow(p.sum(lattice[N // 2:N // 2 + 1], axis=0),
interpolation='none',
cmap=p.get_cmap('afmhot'))
p.suptitle("Final configuration")
p.figure()
p.hist(distances)
p.suptitle("Distance distribution")
p.show()
def run(self, fpath, job):
param = self.param
base, _ = ocrolib.allsplitext(fpath)
basefile = ocrolib.allsplitext(os.path.basename(fpath))[0]
if param['parallel'] < 2:
print_info("=== %s %-3d" % (fpath, job))
raw = ocrolib.read_image_gray(fpath)
flat = raw
# estimate skew angle and rotate
if param['maxskew'] > 0:
if param['parallel'] < 2:
print_info("estimating skew angle")
d0, d1 = flat.shape
o0, o1 = int(param['bignore']*d0), int(param['bignore']*d1)
flat = amax(flat)-flat
flat -= amin(flat)
est = flat[o0:d0-o0, o1:d1-o1]
ma = param['maxskew']
ms = int(2*param['maxskew']*param['skewsteps'])
angle = self.estimate_skew_angle(est, linspace(-ma, ma, ms+1))
flat = interpolation.rotate(flat, angle, mode='constant', reshape=0)
flat = amax(flat)-flat
else:
angle = 0
# estimate low and high thresholds
if param['parallel'] < 2:
print_info("estimating thresholds")
d0, d1 = flat.shape
o0, o1 = int(param['bignore']*d0), int(param['bignore']*d1)
est = flat[o0:d0-o0, o1:d1-o1]
if param['escale'] > 0:
# by default, we use only regions that contain
# significant variance; this makes the percentile
# based low and high estimates more reliable
e = param['escale']
v = est-filters.gaussian_filter(est, e*20.0)
v = filters.gaussian_filter(v**2, e*20.0)**0.5
v = (v > 0.3*amax(v))
v = morphology.binary_dilation(v, structure=ones((int(e*50), 1)))
v = morphology.binary_dilation(v, structure=ones((1, int(e*50))))
if param['debug'] > 0:
imshow(v)
ginput(1, param['debug'])
est = est[v]
lo = stats.scoreatpercentile(est.ravel(), param['lo'])
hi = stats.scoreatpercentile(est.ravel(), param['hi'])
# rescale the image to get the gray scale image
if param['parallel'] < 2:
print_info("rescaling")
flat -= lo
flat /= (hi-lo)
flat = clip(flat, 0, 1)
if param['debug'] > 0:
imshow(flat, vmin=0, vmax=1)
ginput(1, param['debug'])
deskewed = 1*(flat > param['threshold'])
# output the normalized grayscale and the thresholded images
print_info("%s lo-hi (%.2f %.2f) angle %4.1f" % (basefile, lo, hi, angle))
if param['parallel'] < 2:
print_info("writing")
ocrolib.write_image_binary(base+".ds.png", deskewed)
return base+".ds.png"
def add_stations(station_selection,
phases0,
phases1,
flags,
mask,
station_names,
station_positions,
source_names,
source_selection,
times,
freqs,
r,
nband_min=2,
soln_type='phase',
nstations_max=None,
excluded_stations=None,
t_step=5,
tec_step1=5,
tec_step2=21,
search_full_tec_range=False):
"""
Adds stations to TEC fitting using an iterative initial-guess search to
ensure the global min is found
Keyword arguments:
station_selection -- indices of stations to use in fitting
phases0 -- XX phase solutions
phases1 -- YY phase solutions
flags -- phase solution flags (0 = use, 1 = flagged)
mask -- mask for sources and frequencies (0 = ignore, 1 = use)
station_names -- array of station names
source_names -- array of source names
source_selection -- indices of sources to use in fitting
times -- array of times
freqs -- array of frequencies
r -- array of TEC solutions returned by fit_tec_per_source_pair()
nband_min -- min number of bands for a source to be used
soln_type -- type of phase solution: 'phase' or 'scalarphase'
nstations_max -- max number of stations to use
excluded_stations -- stations to exclude
t_step -- try full TEC range every t_step number of solution times
tec_step1 -- number of steps in TEC subrange (+/- last TEC fit value)
tec_step2 -- number of steps in full TEC range (-0.1 -- 0.1)
search_full_tec_range -- always search the full TEC range (-0.1 -- 0.1)
"""
from pylab import pinv, newaxis, find, amin
import numpy as np
from lofar.expion import baselinefitting
import progressbar
N_sources_selected = len(source_selection)
N_stations_selected = len(station_selection)
N_piercepoints = N_sources_selected * N_stations_selected
N_times = len(times)
N_stations = len(station_names)
N_sources = len(source_names)
N_pairs = 0
for ii, i in enumerate(source_selection):
for jj, j in enumerate(source_selection):
if j == i:
break
subband_selection = find(mask[i, :] * mask[j, :])
if len(subband_selection) < nband_min:
continue
N_pairs += 1
D = np.resize(station_positions, (N_stations, N_stations, 3))
D = np.transpose(D, (1, 0, 2)) - D
D = np.sqrt(np.sum(D**2, axis=2))
station_selection1 = station_selection
stations_to_add = np.array([
i for i in range(len(station_names)) if i not in station_selection1
and station_names[i] not in excluded_stations
])
if len(stations_to_add) == 0:
return station_selection1, r
# Check if desired number of stations is already reached
if nstations_max is not None:
if len(station_selection1) >= nstations_max:
return station_selection1, r
logging.info("Using fitting with iterative search for remaining stations "
"(up to {0} stations in total)".format(nstations_max))
q = r
while len(stations_to_add) > 0:
D1 = D[stations_to_add[:, newaxis], station_selection1[newaxis, :]]
minimum_distance = amin(D1, axis=1)
station_to_add = stations_to_add[np.argmin(minimum_distance)]
station_selection1 = np.append(station_selection1, station_to_add)
N_stations_selected1 = len(station_selection1)
# Remove station from list
stations_to_add = stations_to_add[stations_to_add != station_to_add]
sols_list = []
eq_list = []
min_e_list = []
ipbar = 0
logging.info('Fitting TEC values with {0} included...'.format(
station_names[station_to_add]))
pbar = progressbar.ProgressBar(maxval=N_pairs * N_times).start()
for ii, i in enumerate(source_selection):
for jj, j in enumerate(source_selection):
if j == i:
break
subband_selection = find(mask[i, :] * mask[j, :])
if len(subband_selection) < nband_min:
continue
logging.debug('Adding {0} for source pair: {1}-{2}'.format(
station_names[station_to_add], i, j))
p0 = phases0[i, station_selection1[:, newaxis],
subband_selection[newaxis, :], :] - phases0[
j, station_selection1[:, newaxis],
subband_selection[newaxis, :], :]
p0 = p0 - np.mean(p0, axis=0)[newaxis, :, :]
if soln_type != 'scalarphase':
p1 = phases1[i, station_selection1[:, newaxis],
subband_selection[newaxis, :], :] - phases1[
j, station_selection1[:, newaxis],
subband_selection[newaxis, :], :]
p1 = p1 - np.mean(p1, axis=0)[newaxis, :, :]
A = np.zeros((len(subband_selection), 1))
A[:, 0] = 8.44797245e9 / freqs[subband_selection]
flags_source_pair = flags[
i, station_selection1[:, newaxis],
subband_selection[newaxis, :], :] * flags[
j, station_selection1[:, newaxis],
subband_selection[newaxis, :], :]
constant_parms = np.zeros((1, N_stations_selected1),
dtype=np.bool)
sols = np.zeros((N_times, N_stations_selected1),
dtype=np.float)
p_0_best = None
for t_idx in range(N_times):
if np.mod(t_idx, t_step) == 0:
min_e = np.Inf
if p_0_best is not None and not search_full_tec_range:
min_tec = p_0_best[0, -1] - 0.02
max_tec = p_0_best[0, -1] + 0.02
nsteps = tec_step1
else:
min_tec = -0.1
max_tec = 0.1
nsteps = tec_step2
logging.debug(
' Trying initial guesses between {0} and '
'{1} TECU'.format(min_tec, max_tec))
for offset in np.linspace(min_tec, max_tec, nsteps):
p_0 = np.zeros((1, N_stations_selected1),
np.double)
p_0[0, :N_stations_selected1 -
1] = (q[ii, t_idx, :] -
q[jj, t_idx, :])[newaxis, :]
p_0[0, -1] = offset
x = p0[:, :, t_idx].copy()
f = flags_source_pair[:, :, t_idx].copy()
sol0 = baselinefitting.fit(x.T, A, p_0, f,
constant_parms)
sol0 -= np.mean(sol0)
residual = np.mod(
np.dot(A, sol0) - x.T + np.pi,
2 * np.pi) - np.pi
residual = residual[f.T == 0]
e = np.var(residual)
if soln_type != 'scalarphase':
x = p1[:, :, t_idx].copy()
f = flags_source_pair[:, :, t_idx].copy()
sol1 = baselinefitting.fit(
x.T, A, p_0, f, constant_parms)
sol1 -= np.mean(sol1)
residual = np.mod(
np.dot(A, sol1) - x.T + np.pi,
2 * np.pi) - np.pi
residual = residual[f.T == 0]
e += np.var(residual)
else:
sol1 = sol0
if e < min_e:
logging.debug(
' Found new min variance of {0} '
'with initial guess of {1} TECU'.format(
e, p_0[0, -1]))
min_e = e
p_0_best = p_0
sols[t_idx, :] = (sol0[0, :] + sol1[0, :]) / 2
else:
# Use previous init
x = p0[:, :, t_idx].copy()
f = flags_source_pair[:, :, t_idx].copy()
sol0 = baselinefitting.fit(x.T, A, p_0_best, f,
constant_parms)
sol0 -= np.mean(sol0)
if soln_type != 'scalarphase':
x = p1[:, :, t_idx].copy()
f = flags_source_pair[:, :, t_idx].copy()
sol1 = baselinefitting.fit(x.T, A, p_0_best, f,
constant_parms)
sol1 -= np.mean(sol1)
else:
sol1 = sol0
sols[t_idx, :] = (sol0[0, :] + sol1[0, :]) / 2
ipbar += 1
pbar.update(ipbar)
### Remove outliers
logging.debug(' Searching for outliers...')
for kk in range(10):
s = sols[:, -1].copy()
selection = np.zeros(len(s), np.bool)
for t_idx in range(len(s)):
start_idx = np.max([t_idx - 10, 0])
end_idx = np.min([t_idx + 10, len(s)])
selection[t_idx] = np.sum(
abs(s[start_idx:end_idx] - s[t_idx]) < 0.02) > (
end_idx - start_idx - 8)
outliers = find(np.logical_not(selection))
if len(outliers) == 0:
break
for t_idx in outliers:
try:
idx0 = find(selection[:t_idx])[-1]
except IndexError:
idx0 = -1
try:
idx1 = find(selection[t_idx + 1:])[0] + t_idx + 1
except IndexError:
idx1 = -1
if idx0 == -1:
s[t_idx] = s[idx1]
elif idx1 == -1:
s[t_idx] = s[idx0]
else:
s[t_idx] = (s[idx0] * (idx1 - t_idx) + s[idx1] *
(t_idx - idx0)) / (idx1 - idx0)
p_0 = np.zeros((1, N_stations_selected1), np.double)
p_0[0, :] = sols[t_idx, :]
p_0[0, -1] = s[t_idx]
x = p0[:, :, t_idx].copy()
f = flags_source_pair[:, :, t_idx].copy()
sol0 = baselinefitting.fit(x.T, A, p_0, f,
constant_parms)
sol0 -= np.mean(sol0)
if soln_type != 'scalarphase':
x = p1[:, :, t_idx].copy()
sol1 = baselinefitting.fit(x.T, A, p_0, f,
constant_parms)
sol1 -= np.mean(sol1)
else:
sol1 = sol0
sols[t_idx, :] = (sol0[0, :] + sol1[0, :]) / 2
weight = 1.0
sols_list.append(weight * sols)
min_e_list.append(min_e)
eq = np.zeros(N_sources)
eq[ii] = weight
eq[jj] = -weight
eq_list.append(eq)
sols = np.array(sols_list)
B = np.array(eq_list)
pinvB = pinv(B)
q = np.dot(pinvB, sols.transpose([1, 0, 2]))
pbar.finish()
if nstations_max is not None:
if N_stations_selected1 == nstations_max:
break
return station_selection1, q
def __init__(self, data_vector, movie_parameters, **params):
self.p = movie_parameters
if not self.p.movie_name == None:
self.params = Params(**params)
mpl.rcParams['patch.edgecolor'] = 'none'
mpl.rcParams['savefig.edgecolor'] = 'none'
self.source = get_filename(self.params.reference_filename)
self.__prefixed_movie_name__ = self.__get_prefixed_movie_name__()
self.__prefixed_movie_dir__ = self.__get_prefixed_movie_dir__()
if not self.params.filter_manager == None:
data_vector = self.params.filter_manager.run_filters(
data_vector)
_max = pl.amax(data_vector.signal)
_min = pl.amin(data_vector.signal)
self.time = data_vector.time
margin = 50
self.signal_size = len(data_vector.signal)
self.range = [_min - margin, _max + margin,
_min - margin, _max + margin]
self.x_data = pl.zeros(self.signal_size)
self.y_data = pl.zeros(self.signal_size)
self.old_signal_plus = None
self.idx = 0
self.active_color = get_extended_color_array(
movie_parameters.movie_active_color)
self.inactive_color = get_extended_color_array(
movie_parameters.movie_inactive_color)
self.centroid_color = get_extended_color_array(
movie_parameters.movie_centroid_color)
self.message = None
self.core_nums = multiprocessing.cpu_count() * self.p.movie_multiprocessing_factor # @IgnorePep8
self.pp_spec_manager = MiniPoincarePlotSpecManager()
self.pp_spec_manager.movie_dir = self.__prefixed_movie_dir__
self.pp_spec_manager.movie_name = self.__prefixed_movie_name__
self.pp_spec_manager.movie_dpi = self.p.movie_dpi
self.pp_spec_manager.movie_fps = self.p.movie_fps
self.pp_spec_manager.movie_height = self.p.movie_height
self.pp_spec_manager.movie_width = self.p.movie_width
self.pp_spec_manager.active_color = self.active_color
self.pp_spec_manager.inactive_color = self.inactive_color
self.pp_spec_manager.centroid_color = self.centroid_color
self.pp_spec_manager.active_point_size = self.p.movie_active_size
self.pp_spec_manager.inactive_point_size = \
self.p.movie_inactive_size
self.pp_spec_manager.centroid_point_size = \
self.p.movie_centroid_size
self.pp_spec_manager.show_plot_legends = \
self.p.movie_show_plot_legends
self.pp_spec_manager.x_label = self.p.x_label
self.pp_spec_manager.y_label = self.p.y_label
self.pp_spec_manager.clean_frames = self.p.movie_clean_frames
self.pp_spec_manager.movie_title = self.p.movie_title
self.pp_spec_manager.movie_frame_step = self.p.movie_frame_step
self.pp_spec_manager.movie_identity_line = self.p.movie_identity_line
self.pp_spec_manager.movie_hour_label = self.p.movie_hour_label
self.pp_spec_manager.movie_minute_label = self.p.movie_minute_label
self.pp_spec_manager.movie_second_label = self.p.movie_second_label
self.pp_spec_manager.movie_time_label_in_line = self.p.movie_time_label_in_line
self.pp_spec_manager.movie_time_label_font_size = self.p.movie_time_label_font_size
self.pp_spec_manager.movie_time_label_prefix = self.p.movie_time_label_prefix
self.pp_spec_manager.movie_title_font_size = self.p.movie_title_font_size
self.pp_spec_manager.movie_axis_font_size = self.p.movie_axis_font_size
self.pp_spec_manager.movie_axis_font = self.p.movie_axis_font
self.pp_spec_manager.movie_title_font = self.p.movie_title_font
self.pp_spec_manager.movie_tick_font = self.p.movie_tick_font
self.pp_spec_manager.movie_frame_pad = self.p.movie_frame_pad
self.pp_spec_manager.movie_create_time_label = self.p.movie_create_time_label
self.pp_spec_manager.movie_frame_filename_with_time = self.p.movie_frame_filename_with_time
self.pp_specs_managers = []
self.pp_specs_managers.append(self.pp_spec_manager)
self.sub_dir_counter = 0
self.scatter = None
self.legend_text = None
self.pp_specs = []
self.cum_inactive = 0
self._pp_spec_old = None
self.s_size = 0 # current calculated signal size
def run(self):
'''
watch_process(params): module based on psutil to watch a specific
program/process.
Parameters required are:
params.cmd = command in a Popen supported list, i.e. no spaces,
each argument in its own cell.
params.outtag = tag handle for the text file and the plot file.
.txt and .png will be appended to this.
params.dt = incremental time-step to watch the process. Default is
10 milliseconds.
params.total = total time to watch the process. Default is 5
minutes.
'''
fout = open(params.outtag+'.txt', 'w');
d0 = psutil.disk_usage('/');
running=True;
cpu_usage = [];
mem_usage = [];
duration = [];
d = 0.0;
p = psutil.Popen(params.cmd, stdout=fout);
try:
while d<params.total:
d = d+params.dt;
if p.status=='running':
# If the program is running, this captures
# the vital-statistics.
cpu_usage.append(p.get_cpu_percent());
mem_usage.append(p.get_memory_percent());
#duration.append(d);
duration.append(p.get_cpu_times().user)
else:
# This watches and ensures that the
# process is indeed dead. This is the first
# level of exception handling and
# loop-breakage.
procs = psutil.get_process_list();
gone, alive = psutil.wait_procs(procs, 3, callback=on_terminate)
break
time.sleep(params.dt)
except psutil._error.AccessDenied:
# This exception watches for the natural death of the
# process. This is the second level of redundancy handling
# the death of the task, the first one is in the else
# statement above.
p.kill()
print "It has died and has become a... "+p.status;
procs = psutil.get_process_list();
gone, alive = psutil.wait_procs(procs, 3, callback=on_terminate)
except KeyboardInterrupt:
# This exception allows the user to Ctrl-C if you thing
# that the program is hanging.
p.kill()
print "I have killed it, and it has become a..."+p.status
procs = psutil.get_process_list();
gone, alive = psutil.wait_procs(procs, 3, callback=on_terminate)
cpu_usage = pl.array(cpu_usage);
duration = pl.array(duration);
mem_usage = pl.array(mem_usage);
pl.step(duration, cpu_usage, label='%CPU');
pl.step(duration, mem_usage, label='%MEM');
pl.xlabel('User Time (seconds)');
pl.ylabel('%');
d1 = psutil.disk_usage('/')
D = d1.used-d0.used;
MB = 1024.*1024. # 1MB in Bytes
data_written = D/MB;
total_time = pl.amax(duration)-pl.amin(duration);
dw = str(round(data_written, 2))+"MB"
tt = str(round(total_time, 5))+"s";
string_ann = "Writes: "+dw+' in '+tt;
title='Process: '+str(params.cmd)+'\n / '+string_ann;
pl.title('\n'.join(wrap(title,50)));
pl.legend();
pl.tight_layout();
pl.savefig(params.outtag+'.png', dpi=300);
pl.close();
fout.write(string_ann+'\n')
fout.close()
print "Completed."
def initiate(self):
if len(self.pp_specs) == 0:
return False
# only positive values are accepted
x = self.p0.x_data[pl.where(self.p0.x_data > 0)]
y = self.p0.y_data[pl.where(self.p0.y_data > 0)]
x_min = pl.amin(x)
x_max = pl.amax(x)
y_min = pl.amin(y)
y_max = pl.amax(y)
value_min = x_min if x_min < y_min else y_min
self.value_max = x_max if x_max > y_max else y_max
self.pd = ArrayPlotData()
self.pd.set_data("index", x)
self.pd.set_data("value", y)
index_ds = ArrayDataSource(x)
value_ds = ArrayDataSource(y)
# Create the plot
self._plot = Plot(self.pd)
axis_defaults = {
#'axis_line_weight': 2,
#'tick_weight': 2,
#'tick_label_color': 'green',
'title_font': self.axis_font,
}
if self.tick_font:
axis_defaults['tick_label_font'] = self.tick_font
#a very important and weird trick; used to remove default ticks labels
self._plot.x_axis = None
self._plot.y_axis = None
#end trick
#add new x label and x's ticks labels
x_axis = PlotAxis(orientation='bottom',
title=nvl(self.manager.x_label, 'RR(n) [ms]'),
mapper=self._plot.x_mapper,
**axis_defaults)
self._plot.overlays.append(x_axis)
#add new y label and y's ticks labels
y_axis = PlotAxis(orientation='left',
title=nvl(self.manager.y_label, 'RR(n+1) [ms]'),
mapper=self._plot.y_mapper,
**axis_defaults)
self._plot.overlays.append(y_axis)
self._plot.index_range.add(index_ds)
self._plot.value_range.add(value_ds)
self._plot.index_mapper.stretch_data = False
self._plot.value_mapper.stretch_data = False
self._plot.value_range.set_bounds(value_min, self.value_max)
self._plot.index_range.set_bounds(value_min, self.value_max)
# Create the index and value mappers using the plot data ranges
imapper = LinearMapper(range=self._plot.index_range)
vmapper = LinearMapper(range=self._plot.value_range)
color = "white"
self.scatter = __PoincarePlotScatterPlot__(
self.p0,
self.manager,
index=index_ds,
value=value_ds,
#color_data=color_ds,
#color_mapper=color_mapper,
#fill_alpha=0.4,
color=color,
index_mapper=imapper,
value_mapper=vmapper,
marker='circle',
marker_size=self.manager.active_point_size,
line_width=0
#outline_color='white'
)
self._plot.add(self.scatter)
#self._plot.plots['var_size_scatter'] = [self.scatter]
# Tweak some of the plot properties
_title = nvl(self.manager.movie_title, "Poincare plot")
if len(_title) > 0:
self._plot.title = _title
self._plot.title_font = self.title_font
self._plot.line_width = 0.5
self._plot.padding = self.frame_pad
self._plot.do_layout(force=True)
self._plot.outer_bounds = [self.manager.movie_width,
self.manager.movie_height]
self.gc = PlotGraphicsContext(self._plot.outer_bounds,
dpi=self.manager.movie_dpi)
self.gc.render_component(self._plot)
self.gc.set_line_width(0)
self.gc.save(self._get_filename(self.p0))
self.x_mean_old = None
self.y_mean_old = None
self._time_label_font = None
return True
def process(self):
for (n, input_file) in enumerate(self.input_files):
pcgts = page_from_file(self.workspace.download_file(input_file))
page_id = pcgts.pcGtsId or input_file.pageId or input_file.ID
page = pcgts.get_Page()
# why does it save the image ??
page_image, page_xywh, _ = self.workspace.image_from_page(
page, page_id)
if self.parameter['parallel'] < 2:
LOG.info("INPUT FILE %s ", input_file.pageId or input_file.ID)
raw = ocrolib.read_image_gray(page_image.filename)
flat = raw
#flat = np.array(binImg)
# estimate skew angle and rotate
if self.parameter['maxskew'] > 0:
if self.parameter['parallel'] < 2:
LOG.info("Estimating Skew Angle")
d0, d1 = flat.shape
o0, o1 = int(self.parameter['bignore'] * d0), int(
self.parameter['bignore'] * d1)
flat = amax(flat) - flat
flat -= amin(flat)
est = flat[o0:d0 - o0, o1:d1 - o1]
ma = self.parameter['maxskew']
ms = int(2 * self.parameter['maxskew'] *
self.parameter['skewsteps'])
angle = self.estimate_skew_angle(est,
linspace(-ma, ma, ms + 1))
flat = interpolation.rotate(flat,
angle,
mode='constant',
reshape=0)
flat = amax(flat) - flat
else:
angle = 0
# self.write_angles_to_pageXML(base,angle)
# estimate low and high thresholds
if self.parameter['parallel'] < 2:
LOG.info("Estimating Thresholds")
d0, d1 = flat.shape
o0, o1 = int(self.parameter['bignore'] * d0), int(
self.parameter['bignore'] * d1)
est = flat[o0:d0 - o0, o1:d1 - o1]
if self.parameter['escale'] > 0:
# by default, we use only regions that contain
# significant variance; this makes the percentile
# based low and high estimates more reliable
e = self.parameter['escale']
v = est - filters.gaussian_filter(est, e * 20.0)
v = filters.gaussian_filter(v**2, e * 20.0)**0.5
v = (v > 0.3 * amax(v))
v = morphology.binary_dilation(v,
structure=ones(
(int(e * 50), 1)))
v = morphology.binary_dilation(v,
structure=ones(
(1, int(e * 50))))
if self.parameter['debug'] > 0:
imshow(v)
ginput(1, self.parameter['debug'])
est = est[v]
lo = stats.scoreatpercentile(est.ravel(), self.parameter['lo'])
hi = stats.scoreatpercentile(est.ravel(), self.parameter['hi'])
# rescale the image to get the gray scale image
if self.parameter['parallel'] < 2:
LOG.info("Rescaling")
flat -= lo
flat /= (hi - lo)
flat = clip(flat, 0, 1)
if self.parameter['debug'] > 0:
imshow(flat, vmin=0, vmax=1)
ginput(1, self.parameter['debug'])
deskewed = 1 * (flat > self.parameter['threshold'])
# output the normalized grayscale and the thresholded images
LOG.info("%s lo-hi (%.2f %.2f) angle %4.1f" %
(pcgts.get_Page().imageFilename, lo, hi, angle))
if self.parameter['parallel'] < 2:
LOG.info("Writing")
#ocrolib.write_image_binary(base+".ds.png", deskewed)
#TODO: Need some clarification as the results effect the following pre-processing steps.
#orientation = -angle
#orientation = 180 - ((180 - orientation) % 360)
pcgts.get_Page().set_orientation(angle)
#print(orientation, angle)
file_id = input_file.ID.replace(self.input_file_grp,
self.output_file_grp)
if file_id == input_file.ID:
file_id = concat_padded(self.output_file_grp, n)
self.workspace.add_file(ID=file_id,
file_grp=self.output_file_grp,
pageId=input_file.pageId,
mimetype=MIMETYPE_PAGE,
local_filename=os.path.join(
self.output_file_grp,
file_id + '.xml'),
content=to_xml(pcgts).encode('utf-8'))
def binarize_image(job):
image_object, i = job
raw = read_image_gray(image_object)
image = raw - amin(raw)
if amax(image) == amin(image):
return # Image is empty
image /= amax(image)
check = check_page(amax(image) - image)
if check is not None:
return
if args.gray:
extreme = 0
else:
extreme = (sum(image < 0.05) + sum(image > 0.95)) * 1.0 / prod(
image.shape)
if extreme > 0.95:
comment = "no-normalization"
flat = image
else:
comment = ""
m = interpolation.zoom(image, args.zoom)
m = filters.percentile_filter(m, args.perc, size=(args.range, 2))
m = filters.percentile_filter(m, args.perc, size=(2, args.range))
m = interpolation.zoom(m, 1.0 / args.zoom)
w, h = minimum(array(image.shape), array(m.shape))
flat = clip(image[:w, :h] - m[:w, :h] + 1, 0, 1)
if args.maxskew > 0:
d0, d1 = flat.shape
o0, o1 = int(args.bignore * d0), int(args.bignore * d1)
flat = amax(flat) - flat
flat -= amin(flat)
est = flat[o0:d0 - o0, o1:d1 - o1]
ma = args.maxskew
ms = int(2 * args.maxskew * args.skewsteps)
angle = estimate_skew_angle(est, linspace(-ma, ma, ms + 1))
flat = interpolation.rotate(flat, angle, mode='constant', reshape=0)
flat = amax(flat) - flat
else:
angle = 0
d0, d1 = flat.shape
o0, o1 = int(args.bignore * d0), int(args.bignore * d1)
est = flat[o0:d0 - o0, o1:d1 - o1]
if args.escale > 0:
e = args.escale
v = est - filters.gaussian_filter(est, e * 20.0)
v = filters.gaussian_filter(v**2, e * 20.0)**0.5
v = (v > 0.3 * amax(v))
v = morphology.binary_dilation(v, structure=ones((int(e * 50), 1)))
v = morphology.binary_dilation(v, structure=ones((1, int(e * 50))))
est = est[v]
lo = stats.scoreatpercentile(est.ravel(), args.lo)
hi = stats.scoreatpercentile(est.ravel(), args.hi)
flat -= lo
flat /= (hi - lo)
flat = clip(flat, 0, 1)
binary = 1 * (flat > args.threshold)
return (binary, flat)
def process(self):
for (n, input_file) in enumerate(self.input_files):
pcgts = page_from_file(self.workspace.download_file(input_file))
fname = pcgts.get_Page().imageFilename
img = self.workspace.resolve_image_as_pil(fname)
param = self.parameter
base, _ = ocrolib.allsplitext(fname)
#basefile = ocrolib.allsplitext(os.path.basename(fpath))[0]
if param['parallel'] < 2:
print_info("=== %s " % (fname))
raw = ocrolib.read_image_gray(img.filename)
flat = raw
#flat = np.array(binImg)
# estimate skew angle and rotate
if param['maxskew'] > 0:
if param['parallel'] < 2:
print_info("estimating skew angle")
d0, d1 = flat.shape
o0, o1 = int(param['bignore'] * d0), int(param['bignore'] * d1)
flat = amax(flat) - flat
flat -= amin(flat)
est = flat[o0:d0 - o0, o1:d1 - o1]
ma = param['maxskew']
ms = int(2 * param['maxskew'] * param['skewsteps'])
angle = self.estimate_skew_angle(est,
linspace(-ma, ma, ms + 1))
flat = interpolation.rotate(flat,
angle,
mode='constant',
reshape=0)
flat = amax(flat) - flat
else:
angle = 0
# self.write_angles_to_pageXML(base,angle)
# estimate low and high thresholds
if param['parallel'] < 2:
print_info("estimating thresholds")
d0, d1 = flat.shape
o0, o1 = int(param['bignore'] * d0), int(param['bignore'] * d1)
est = flat[o0:d0 - o0, o1:d1 - o1]
if param['escale'] > 0:
# by default, we use only regions that contain
# significant variance; this makes the percentile
# based low and high estimates more reliable
e = param['escale']
v = est - filters.gaussian_filter(est, e * 20.0)
v = filters.gaussian_filter(v**2, e * 20.0)**0.5
v = (v > 0.3 * amax(v))
v = morphology.binary_dilation(v,
structure=ones(
(int(e * 50), 1)))
v = morphology.binary_dilation(v,
structure=ones(
(1, int(e * 50))))
if param['debug'] > 0:
imshow(v)
ginput(1, param['debug'])
est = est[v]
lo = stats.scoreatpercentile(est.ravel(), param['lo'])
hi = stats.scoreatpercentile(est.ravel(), param['hi'])
# rescale the image to get the gray scale image
if param['parallel'] < 2:
print_info("rescaling")
flat -= lo
flat /= (hi - lo)
flat = clip(flat, 0, 1)
if param['debug'] > 0:
imshow(flat, vmin=0, vmax=1)
ginput(1, param['debug'])
deskewed = 1 * (flat > param['threshold'])
# output the normalized grayscale and the thresholded images
print_info("%s lo-hi (%.2f %.2f) angle %4.1f" %
(pcgts.get_Page().imageFilename, lo, hi, angle))
if param['parallel'] < 2:
print_info("writing")
ocrolib.write_image_binary(base + ".ds.png", deskewed)
orientation = -angle
orientation = 180 - (180 - orientation) % 360
pcgts.get_Page().set_orientation(orientation)
ID = concat_padded(self.output_file_grp, n)
self.workspace.add_file(ID=ID,
file_grp=self.output_file_grp,
pageId=input_file.pageId,
mimetype="image/png",
url=base + ".ds.png",
local_filename='%s/%s' %
(self.output_file_grp, ID),
content=to_xml(pcgts).encode('utf-8'))
print "Proccessing logfile ",inputlogfile
lines=open(inputlogfile,"r").readlines()
metadata=[x for x in lines if x[0]=="#"] #select meta data
fields=[x.split()[2:] for x in metadata if x.split()[1]=="Fields:"][0]
print fields
R_index=fields.index("R")
Clock_index=fields.index("CLOCKTIME")
q=[x for x in lines if x[0]!="#"] #remove meta data
q=q[:-1] #last line might be malformed
print "first line: ",q[0]
R=P.array([float(x.split()[R_index]) for x in q])
Realtime=P.array([float(x.split()[Clock_index]) for x in q])
N=len(R)
ns=range(1,N+1)
avgR=P.cumsum(R)/ns
n50=range(50,len(R))
l50=[P.mean(R[i-50:i]) for i in n50]
P.plot(ns,avgR,"r")
P.plot(n50,l50,"b")
P.text(N/2, avgR[N/4]-1, "Cumulative Average reward",color="r")
P.text(N/2, P.amax(l50) +1, "Average of last 50 rewards",color="b")
ytime=P.amin(avgR[:20]) +1
for i in P.arange(60,P.amax(Realtime),60):
ind=bisect.bisect(Realtime,i)
P.text(ind,ytime,".\n%d minute"%(i/60))
P.xlabel("Timestep")
P.savefig(inputlogfile+".pdf")
def _process_segment(self, page_image, page, page_xywh, page_id,
input_file, n):
raw = ocrolib.pil2array(page_image)
flat = raw.astype("float64")
# estimate skew angle and rotate
if self.parameter['maxskew'] > 0:
if self.parameter['parallel'] < 2:
LOG.info("Estimating Skew Angle")
d0, d1 = flat.shape
o0, o1 = int(self.parameter['bignore'] * d0), int(
self.parameter['bignore'] * d1)
flat = amax(flat) - flat
flat -= amin(flat)
est = flat[o0:d0 - o0, o1:d1 - o1]
ma = self.parameter['maxskew']
ms = int(2 * self.parameter['maxskew'] *
self.parameter['skewsteps'])
angle = self.estimate_skew_angle(est, linspace(-ma, ma, ms + 1))
flat = interpolation.rotate(flat,
angle,
mode='constant',
reshape=0)
flat = amax(flat) - flat
else:
angle = 0
# self.write_angles_to_pageXML(base,angle)
# estimate low and high thresholds
if self.parameter['parallel'] < 2:
LOG.info("Estimating Thresholds")
d0, d1 = flat.shape
o0, o1 = int(self.parameter['bignore'] * d0), int(
self.parameter['bignore'] * d1)
est = flat[o0:d0 - o0, o1:d1 - o1]
if self.parameter['escale'] > 0:
# by default, we use only regions that contain
# significant variance; this makes the percentile
# based low and high estimates more reliable
e = self.parameter['escale']
v = est - filters.gaussian_filter(est, e * 20.0)
v = filters.gaussian_filter(v**2, e * 20.0)**0.5
v = (v > 0.3 * amax(v))
v = morphology.binary_dilation(v, structure=ones((int(e * 50), 1)))
v = morphology.binary_dilation(v, structure=ones((1, int(e * 50))))
if self.parameter['debug'] > 0:
imshow(v)
ginput(1, self.parameter['debug'])
est = est[v]
lo = stats.scoreatpercentile(est.ravel(), self.parameter['lo'])
hi = stats.scoreatpercentile(est.ravel(), self.parameter['hi'])
# rescale the image to get the gray scale image
if self.parameter['parallel'] < 2:
LOG.info("Rescaling")
flat -= lo
flat /= (hi - lo)
flat = clip(flat, 0, 1)
if self.parameter['debug'] > 0:
imshow(flat, vmin=0, vmax=1)
ginput(1, self.parameter['debug'])
deskewed = 1 * (flat > self.parameter['threshold'])
# output the normalized grayscale and the thresholded images
#LOG.info("%s lo-hi (%.2f %.2f) angle %4.1f" %(lo, hi, angle))
#TODO: Need some clarification as the results effect the following pre-processing steps.
#orientation = -angle
#orientation = 180 - ((180 - orientation) % 360)
if angle is None: # FIXME: quick fix to prevent angle of "none"
angle = 0
page.set_orientation(angle)
page_xywh['features'] += ',deskewed'
bin_array = array(255 * (deskewed > ocrolib.midrange(deskewed)), 'B')
page_image = ocrolib.array2pil(bin_array)
file_id = input_file.ID.replace(self.input_file_grp, self.image_grp)
if file_id == input_file.ID:
file_id = concat_padded(self.image_grp, n)
file_path = self.workspace.save_image_file(page_image,
file_id,
page_id=page_id,
file_grp=self.image_grp)
page.add_AlternativeImage(
AlternativeImageType(filename=file_path,
comments=page_xywh['features']))
def _process_segment(self, page, filename, page_id, file_id):
raw = ocrolib.read_image_gray(filename)
self.dshow(raw, "input")
# perform image normalization
image = raw - amin(raw)
if amax(image) == amin(image):
LOG.info("# image is empty: %s" % (page_id))
return
image /= amax(image)
if not self.parameter['nocheck']:
check = self.check_page(amax(image) - image)
if check is not None:
LOG.error(input_file.pageId or input_file.ID + " SKIPPED. " +
check + " (use -n to disable this check)")
return
# check whether the image is already effectively binarized
if self.parameter['gray']:
extreme = 0
else:
extreme = (np.sum(image < 0.05) +
np.sum(image > 0.95)) * 1.0 / np.prod(image.shape)
if extreme > 0.95:
comment = "no-normalization"
flat = image
else:
comment = ""
# if not, we need to flatten it by estimating the local whitelevel
LOG.info("Flattening")
m = interpolation.zoom(image, self.parameter['zoom'])
m = filters.percentile_filter(m,
self.parameter['perc'],
size=(self.parameter['range'], 2))
m = filters.percentile_filter(m,
self.parameter['perc'],
size=(2, self.parameter['range']))
m = interpolation.zoom(m, 1.0 / self.parameter['zoom'])
if self.parameter['debug'] > 0:
clf()
imshow(m, vmin=0, vmax=1)
ginput(1, self.parameter['debug'])
w, h = minimum(array(image.shape), array(m.shape))
flat = clip(image[:w, :h] - m[:w, :h] + 1, 0, 1)
if self.parameter['debug'] > 0:
clf()
imshow(flat, vmin=0, vmax=1)
ginput(1, self.parameter['debug'])
# estimate low and high thresholds
LOG.info("Estimating Thresholds")
d0, d1 = flat.shape
o0, o1 = int(self.parameter['bignore'] * d0), int(
self.parameter['bignore'] * d1)
est = flat[o0:d0 - o0, o1:d1 - o1]
if self.parameter['escale'] > 0:
# by default, we use only regions that contain
# significant variance; this makes the percentile
# based low and high estimates more reliable
e = self.parameter['escale']
v = est - filters.gaussian_filter(est, e * 20.0)
v = filters.gaussian_filter(v**2, e * 20.0)**0.5
v = (v > 0.3 * amax(v))
v = morphology.binary_dilation(v, structure=ones((int(e * 50), 1)))
v = morphology.binary_dilation(v, structure=ones((1, int(e * 50))))
if self.parameter['debug'] > 0:
imshow(v)
ginput(1, self.parameter['debug'])
est = est[v]
lo = stats.scoreatpercentile(est.ravel(), self.parameter['lo'])
hi = stats.scoreatpercentile(est.ravel(), self.parameter['hi'])
# rescale the image to get the gray scale image
LOG.info("Rescaling")
flat -= lo
flat /= (hi - lo)
flat = clip(flat, 0, 1)
if self.parameter['debug'] > 0:
imshow(flat, vmin=0, vmax=1)
ginput(1, self.parameter['debug'])
binarized = 1 * (flat > self.parameter['threshold'])
# output the normalized grayscale and the thresholded images
# print_info("%s lo-hi (%.2f %.2f) angle %4.1f %s" % (fname, lo, hi, angle, comment))
LOG.info("%s lo-hi (%.2f %.2f) %s" % (page_id, lo, hi, comment))
LOG.info("writing")
if self.parameter['debug'] > 0 or self.parameter['show']:
clf()
gray()
imshow(binarized)
ginput(1, max(0.1, self.parameter['debug']))
#base, _ = ocrolib.allsplitext(filename)
#ocrolib.write_image_binary(base + ".bin.png", binarized)
# ocrolib.write_image_gray(base +".nrm.png", flat)
# print("########### File path : ", base+".nrm.png")
# write_to_xml(base+".bin.png")
# return base+".bin.png"
bin_array = array(255 * (binarized > ocrolib.midrange(binarized)), 'B')
bin_image = ocrolib.array2pil(bin_array)
file_path = self.workspace.save_image_file(bin_image,
file_id,
page_id=page_id,
file_grp=self.image_grp)
page.add_AlternativeImage(
AlternativeImageType(filename=file_path, comment="binarized"))
def add_stations(station_selection, phases0, phases1, flags, mask,
station_names, station_positions, source_names, source_selection,
times, freqs, r, nband_min=2, soln_type='phase', nstations_max=None,
excluded_stations=None, t_step=5, tec_step1=5, tec_step2=21,
search_full_tec_range=True):
"""
Adds stations to TEC fitting using an iterative initial-guess search to
ensure the global min is found
Keyword arguments:
station_selection -- indices of stations to use in fitting
phases0 -- XX phase solutions
phases1 -- YY phase solutions
flags -- phase solution flags (0 = use, 1 = flagged)
mask -- mask for sources and frequencies (0 = ignore, 1 = use)
station_names -- array of station names
source_names -- array of source names
source_selection -- indices of sources to use in fitting
times -- array of times
freqs -- array of frequencies
r -- array of TEC solutions returned by fit_tec_per_source_pair()
nband_min -- min number of bands for a source to be used
soln_type -- type of phase solution: 'phase' or 'scalarphase'
nstations_max -- max number of stations to use
excluded_stations -- stations to exclude
t_step -- try full TEC range every t_step number of solution times
tec_step1 -- number of steps in TEC subrange (+/- last TEC fit value)
tec_step2 -- number of steps in full TEC range (-0.1 -- 0.1)
search_full_tec_range -- always search the full TEC range (-0.1 -- 0.1)
"""
from pylab import pinv, newaxis, find, amin
import numpy as np
from lofar.expion import baselinefitting
try:
import progressbar
except ImportError:
import losoto.progressbar as progressbar
N_sources_selected = len(source_selection)
N_stations_selected = len(station_selection)
N_piercepoints = N_sources_selected * N_stations_selected
N_times = len(times)
N_stations = len(station_names)
N_sources = len(source_names)
N_pairs = 0
for ii, i in enumerate(source_selection):
for jj, j in enumerate(source_selection):
if j == i:
break
subband_selection = find(mask[i,:] * mask[j,:])
if len(subband_selection) < nband_min:
continue
N_pairs += 1
D = np.resize(station_positions, (N_stations, N_stations, 3))
D = np.transpose(D, (1, 0, 2)) - D
D = np.sqrt(np.sum(D**2, axis=2))
station_selection1 = station_selection
stations_to_add = np.array([i for i in xrange(len(station_names))
if i not in station_selection1 and station_names[i] not in
excluded_stations])
if len(stations_to_add) == 0:
return station_selection1, r
# Check if desired number of stations is already reached
if nstations_max is not None:
if len(station_selection1) >= nstations_max:
return station_selection1, r
logging.info("Using fitting with iterative search for remaining stations "
"(up to {0} stations in total)".format(nstations_max))
q = r
while len(stations_to_add)>0:
D1 = D[stations_to_add[:,newaxis], station_selection1[newaxis,:]]
minimum_distance = amin(D1, axis=1)
station_to_add = stations_to_add[np.argmin(minimum_distance)]
station_selection1 = np.append(station_selection1, station_to_add)
N_stations_selected1 = len(station_selection1)
# Remove station from list
stations_to_add = stations_to_add[stations_to_add != station_to_add]
sols_list = []
eq_list = []
min_e_list = []
ipbar = 0
logging.info('Fitting TEC values with {0} included...'.format(
station_names[station_to_add]))
pbar = progressbar.ProgressBar(maxval=N_pairs*N_times).start()
for ii, i in enumerate(source_selection):
for jj, j in enumerate(source_selection):
if j == i:
break
subband_selection = find(mask[i,:] * mask[j,:])
if len(subband_selection) < nband_min:
continue
logging.debug('Adding {0} for source pair: {1}-{2}'.format(
station_names[station_to_add], i, j))
p0 = phases0[i, station_selection1[:,newaxis],
subband_selection[newaxis,:], :] - phases0[j,
station_selection1[:,newaxis], subband_selection[newaxis,:], :]
p0 = p0 - np.mean(p0, axis=0)[newaxis,:,:]
if soln_type != 'scalarphase':
p1 = phases1[i, station_selection1[:,newaxis],
subband_selection[newaxis,:], :] - phases1[j,
station_selection1[:,newaxis], subband_selection[newaxis,:], :]
p1 = p1 - np.mean(p1, axis=0)[newaxis, :, :]
A = np.zeros((len(subband_selection), 1))
A[:, 0] = 8.44797245e9 / freqs[subband_selection]
sd = (0.1 * 30e6) / freqs[subband_selection] # standard deviation of phase solutions as function of frequency (rad)
flags_source_pair = flags[i, station_selection1[:,newaxis],
subband_selection[newaxis,:], :] * flags[j,
station_selection1[:,newaxis], subband_selection[newaxis,:], :]
constant_parms = np.zeros((1, N_stations_selected1), dtype = np.bool)
sols = np.zeros((N_times, N_stations_selected1), dtype = np.float)
p_0_best = None
for t_idx in xrange(N_times):
if np.mod(t_idx, t_step) == 0:
min_e = np.Inf
if p_0_best is not None and not search_full_tec_range:
min_tec = p_0_best[0, -1] - 0.02
max_tec = p_0_best[0, -1] + 0.02
nsteps = tec_step1
else:
min_tec = -0.1
max_tec = 0.1
nsteps = tec_step2
logging.debug(' Trying initial guesses between {0} and '
'{1} TECU'.format(min_tec, max_tec))
for offset in np.linspace(min_tec, max_tec, nsteps):
p_0 = np.zeros((1, N_stations_selected1), np.double)
p_0[0, :N_stations_selected1-1] = (q[ii, t_idx, :] -
q[jj, t_idx, :])[newaxis,:]
p_0[0, -1] = offset
x = p0[:,:,t_idx].copy()
f = flags_source_pair[:, :, t_idx].copy()
sol0 = baselinefitting.fit(x.T, A, p_0, f, constant_parms)
sol0 -= np.mean(sol0)
residual = np.mod(np.dot(A, sol0) - x.T + np.pi,
2 * np.pi) - np.pi
e = np.var(residual[f.T==0])
if soln_type != 'scalarphase':
x = p1[:,:,t_idx].copy()
f = flags_source_pair[:, :, t_idx].copy()
sol1 = baselinefitting.fit(x.T, A, p_0, f,
constant_parms)
sol1 -= np.mean(sol1)
residual = np.mod(np.dot(A, sol1) - x.T + np.pi,
2 * np.pi) - np.pi
residual = residual[f.T==0]
e += np.var(residual)
else:
sol1 = sol0
if e < min_e:
logging.debug(' Found new min variance of {0} '
'with initial guess of {1} TECU'.format(e,
p_0[0, -1]))
min_e = e
p_0_best = p_0
sols[t_idx, :] = (sol0[0, :] + sol1[0, :])/2
else:
# Use previous init
x = p0[:, :, t_idx].copy()
f = flags_source_pair[:, :, t_idx].copy()
sol0 = baselinefitting.fit(x.T, A, p_0_best, f,
constant_parms)
sol0 -= np.mean(sol0)
if soln_type != 'scalarphase':
x = p1[:, :, t_idx].copy()
f = flags_source_pair[:, :, t_idx].copy()
sol1 = baselinefitting.fit(x.T, A, p_0_best, f,
constant_parms)
sol1 -= np.mean(sol1)
else:
sol1 = sol0
sols[t_idx, :] = (sol0[0, :] + sol1[0, :])/2
ipbar += 1
pbar.update(ipbar)
### Remove outliers
logging.debug(' Searching for outliers...')
for kk in xrange(10):
s = sols[:, -1].copy()
selection = np.zeros(len(s), np.bool)
for t_idx in xrange(len(s)):
start_idx = np.max([t_idx-10, 0])
end_idx = np.min([t_idx+10, len(s)])
selection[t_idx] = np.sum(abs(s[start_idx:end_idx] -
s[t_idx]) < 0.02) > (end_idx - start_idx - 8)
outliers = find(np.logical_not(selection))
if len(outliers) == 0:
break
for t_idx in outliers:
try:
idx0 = find(selection[:t_idx])[-1]
except IndexError:
idx0 = -1
try:
idx1 = find(selection[t_idx+1:])[0] + t_idx + 1
except IndexError:
idx1 = -1
if idx0 == -1:
s[t_idx] = s[idx1]
elif idx1 == -1:
s[t_idx] = s[idx0]
else:
s[t_idx] = (s[idx0] * (idx1-t_idx) + s[idx1] *
(t_idx-idx0)) / (idx1-idx0)
p_0 = np.zeros((1, N_stations_selected1), np.double)
p_0[0,:] = sols[t_idx,:]
p_0[0,-1] = s[t_idx]
x = p0[:,:,t_idx].copy()
f = flags_source_pair[:,:,t_idx].copy()
sol0 = baselinefitting.fit(x.T, A, p_0, f, constant_parms)
sol0 -= np.mean(sol0)
if soln_type != 'scalarphase':
x = p1[:,:,t_idx].copy()
sol1 = baselinefitting.fit(x.T, A, p_0, f,
constant_parms)
sol1 -= np.mean(sol1)
else:
sol1 = sol0
sols[t_idx, :] = (sol0[0,:] + sol1[0,:])/2
weight = 1.0
sols_list.append(weight*sols)
min_e_list.append(min_e)
eq = np.zeros(N_sources)
eq[ii] = weight
eq[jj] = -weight
eq_list.append(eq)
sols = np.array(sols_list)
B = np.array(eq_list)
pinvB = pinv(B)
q = np.dot(pinvB, sols.transpose([1,0,2]))
pbar.finish()
if nstations_max is not None:
if N_stations_selected1 == nstations_max:
break
return station_selection1, q
pl.figure(figsize=(10,5));
# Minor axis
pl.subplot(121);
pl.title('Minor Axis');
x_minor, y_minor = blib.imslice(myimage=myimage, v='minor', theta=theta, x0=x0, y0=y0, D=D);
pl.plot(x_minor,y_minor, color='black', ls='-', lw=1, label='Major-Axis');
if options.fit.upper()=='TRUE':
hm_minor, f_minor, xf_minor, yf_minor = blib.fwhm_2gauss(x_minor, y_minor);
pl.plot(xf_minor, yf_minor, 'k--', label='Fit')
pl.hlines(hm_minor, -f_minor/2, f_minor/2, color='gray', label='FWHM-Minor');
else:
hm_minor, f_minor = blib.fwhm(x_minor, y_minor);
pl.hlines(hm_minor, -f_minor/2, f_minor/2, color='gray', label='FWHM-Minor');
pl.xlim(pl.amin(x_minor), pl.amax(x_minor));
pl.ylim(pl.amin(y_minor)-0.1, pl.amax(y_minor)+0.1);
# Major Axis
pl.subplot(122);
pl.title('Major Axis')
x_major, y_major = blib.imslice(myimage=myimage, v='major', theta=theta, x0=x0, y0=y0, D=D);
pl.plot(x_major, y_major, color='black', ls='-', lw=1, label='Major-Axis');
hm_major, f_major, xf_major, yf_major = blib.fwhm_2gauss(x_major, y_major);
if options.fit.upper()=='TRUE':
pl.plot(xf_major, yf_major, 'k--', label='Fit')
pl.hlines(hm_major, -f_major/2, f_major/2, color='gray', label='FWHM-Major');
pl.xlim(pl.amin(x_major), pl.amax(x_major));
else:
hm_major, f_major = blib.fwhm(x_major, y_major);
pl.hlines(hm_major, -f_major/2, f_major/2, color='gray', label='FWHM-Minor');
H = listener.H.copy()
A = listener.A.copy()
B = listener.B.copy()
# get vertices
vert = []
for i in xrange(7):
for j in xrange(i + 1, 8): #xrange(8):
#if i != j:
for u in [A[i], B[i]]:
for v in [A[j], B[j]]:
# intersection of Ai = Hi.x and Bj = Hj.x
x = pl.dot(pl.inv(H[[i, j], :]), pl.array([u, v]))
# check constraints: A <= H.x <= B
if pl.amin(pl.dot(H, x) - A) >= -1e-6 and pl.amax(
pl.dot(H, x) - B) <= 1e-6:
vert.append(x.reshape(2).copy())
print('the number of vertices', len(vert))
# continue only if enough vertices
if len(vert) > 2:
ax.clear()
inter = inter + 1
vert_uns = pl.array(vert + [vert[0]])
xm, xM, ym, yM = pl.amin(vert_uns[:, 0]), pl.amax(
vert_uns[:, 0]), pl.amin(vert_uns[:,
1]), pl.amax(vert_uns[:,
1])
ax.set_xlim(xm - 0.05 * (xM - xm), xM + 0.05 * (xM - xm))
print verifdates
levs = levels[:]
lats = latitudes[:]
lons = longitudes[:]
lons, lats = meshgrid(lons,lats)
# unpack 2-meter temp forecast data.
t2mvar = data['tmp2m']
missval = t2mvar.missing_value
t2m = t2mvar[:,:,:]
if missval < 0:
t2m = ma.masked_values(where(t2m>-1.e20,t2m,1.e20), 1.e20)
else:
t2m = ma.masked_values(where(t2m<1.e20,t2m,1.e20), 1.e20)
t2min = amin(t2m.compressed()); t2max= amax(t2m.compressed())
print t2min,t2max
clevs = frange(around(t2min/10.)*10.-5.,around(t2max/10.)*10.+5.,4)
print clevs[0],clevs[-1]
llcrnrlat = 22.0
urcrnrlat = 48.0
latminout = 22.0
llcrnrlon = -125.0
urcrnrlon = -60.0
standardpar = 50.0
centerlon=-105.
# create Basemap instance for Lambert Conformal Conic projection.
m = Basemap(llcrnrlon=llcrnrlon,llcrnrlat=llcrnrlat,
urcrnrlon=urcrnrlon,urcrnrlat=urcrnrlat,
rsphere=6371200.,
resolution='l',area_thresh=5000.,projection='lcc',
