def errorArea(xaxis,
bin_up,
bin_down,
fillcolor='blue',
hatch=None,
hatchcolor='green',
**kwargs):
if hatch == None:
hatchcolor = 'none'
if xaxis[-1] == inf:
xbin = xaxis[-2] - xaxis[-3]
xaxis[-1] = xaxis[-2] + xbin
xlim([xaxis[0], xaxis[-1]])
fill_xaxis = repeat(xaxis, 2)[1:-1]
fill_bins_up = repeat(bin_up, 2)
fill_bins_down = repeat(bin_down, 2)
return fill_between(fill_xaxis,
fill_bins_up,
fill_bins_down,
facecolor=fillcolor,
edgecolor=hatchcolor,
hatch=hatch,
**kwargs)
def stackedBarPlot(xaxis, hist_list,
colors = ['C0','C1','C2','C3','C4','C5'],
linewidth = 1,
fill = True,
outer_line = True,
hatch = [None]*6,
zorder = -9,
data_axis = 0,
relative = False,
**kwargs):
bin_array = array(hist_list)
cumsum_axis = 0
# Do the cumulative sum of the data
bins_cumsum = insert(cumsum(bin_array, axis = cumsum_axis), 0, zeros(len(xaxis)-1), cumsum_axis)
if relative:
bins_cumsum /= bins_cumsum[-1, :]
# Fix the xaxis
original_xaxis = xaxis
if xaxis[-1] == inf:
xbin = xaxis[-2] - xaxis[-3]
xaxis[-1] = xaxis[-2] + xbin
xlim([xaxis[0], xaxis[-1]])
fill_xaxis = repeat(xaxis,2)[1:-1]
areas = []
lines = []
rectangles = []
print('Cumsum results ', sum(bins_cumsum, axis = 1))
nhistograms = bins_cumsum.shape[cumsum_axis]
for index in range(1, bins_cumsum.shape[cumsum_axis]):
if outer_line:
lines.append(unfilledBar(original_xaxis, bins_cumsum[index, :], color = colors[index-1], linewidth = linewidth))
lwbox = 1
else:
lwbox = 0
#lines.append(unfilledBar(original_xaxis, bins_cumsum[:, index], color = colors[index-1], label = labels[index-1]))
if hatch[index-1]:
facecolor = 'white'
else:
facecolor = colors[index-1]
if fill:
areas.append( fill_between(repeat(xaxis,2)[1:-1], repeat(bins_cumsum[index,:],2), repeat(bins_cumsum[index-1,:],2),
zorder = zorder-nhistograms+index, linewidth = 0,
facecolor=facecolor, edgecolor = colors[index-1], hatch = hatch[index-1], **kwargs ))
rectangles.append(matplotlib.patches.Rectangle((0,0), 1, 1, fc = facecolor, linewidth=2, zorder = zorder-1,
edgecolor = colors[index-1], hatch = hatch[index-1]))
return lines, areas, rectangles
def errorHatch(xaxis, bin_up, bin_down,
hatch = '/',
fillcolor = 'blue',
**kwargs):
if xaxis[-1] == inf:
xbin = xaxis[-2] - xaxis[-3]
xaxis[-1] = xaxis[-2] + xbin
xlim([xaxis[0], xaxis[-1]])
fill_xaxis = repeat(xaxis,2)[1:-1]
fill_bins_up = repeat(bin_up,2)
fill_bins_down = repeat(bin_down, 2)
return fill_between(fill_xaxis , fill_bins_up, fill_bins_down, edgecolor = hatchcolor, hatch = hatch, rasterized = True, **kwargs)
def plot_upd_impz(self, fig, taps, a=1):
if not signal:
return
self.plot_init_impz(fig)
ax1, ax2 = fig.get_axes()
l = len(taps)
impulse = pylab.repeat(0.,l); impulse[0] =1.
x = arange(0,l)
response = signal.lfilter(taps,a,impulse)
ax1.stem(x, response)
step = pylab.cumsum(response)
ax2.stem(x, step)
def plot_upd_impz(self, fig, taps, a=1):
if not signal:
return
self.plot_init_impz(fig)
ax1, ax2 = fig.get_axes()
l = len(taps)
impulse = pylab.repeat(0., l)
impulse[0] = 1.
x = arange(0, l)
response = signal.lfilter(taps, a, impulse)
ax1.stem(x, response)
step = pylab.cumsum(response)
ax2.stem(x, step)
def snakeScan(centre=(0, 0), range=(10e-6, 10e-6), spacing=(1.0e-6, 1.0e-6)):
from pylab import arange, concatenate, repeat
if len(spacing) == 1:
spacing = (spacing, spacing)
# define some grids
xgrid = arange(20, 31)
ygrid = arange(10, 16)
xscan = concatenate([xgrid[:: (-1) ** i] for i in range(len(ygrid))])
yscan = repeat(ygrid, len(xgrid))
return list(zip(a, b))
def _ichroma(self, V, **kwargs):
"""
::
Inverse chromagram transform. Make a signal from a folded constant-Q transform.
"""
if not (self._have_hcqft or self._have_cqft):
return None
a,b = self.HCQFT.shape if self._have_hcqft else self.CQFT.shape
complete_octaves = a/self.nbpo # integer division, number of complete octaves
if P.remainder(a,self.nbpo):
complete_octaves += 1
X = P.repeat(V, complete_octaves, 0)[:a,:] # truncate if necessary
X /= X.max()
X *= P.atleast_2d(P.linspace(1,0,X.shape[0])).T # weight the spectrum
self.x_hat = self._icqft(X, **kwargs)
return self.x_hat
def _ichroma(self, V, **kwargs):
"""
::
Inverse chromagram transform. Make a signal from a folded constant-Q transform.
"""
if not (self._have_hcqft or self._have_cqft):
return None
a,b = self.HCQFT.shape if self._have_hcqft else self.CQFT.shape
complete_octaves = a/self.nbpo # integer division, number of complete octaves
if P.remainder(a,self.nbpo):
complete_octaves += 1
X = P.repeat(V, complete_octaves, 0)[:a,:] # truncate if necessary
X /= X.max()
X *= P.atleast_2d(P.linspace(1,0,X.shape[0])).T # weight the spectrum
self.x_hat = self._icqft(X, **kwargs)
return self.x_hat
def impz(b,a=1):
'''
#Plot step and impulse response
from http://mpastell.com/2010/01/18/fir-with-scipy/
'''
l = len(b)
impulse = pylab.repeat(0.,l); impulse[0] =1.
x = numpy.arange(0,l)
response = signal.lfilter(b,a,impulse)
pylab.subplot(211)
pylab.stem(x, response)
pylab.ylabel('Amplitude')
pylab.xlabel(r'n (samples)')
pylab.title(r'Impulse response')
pylab.subplot(212)
step = numpy.cumsum(response)
pylab.stem(x, step)
pylab.ylabel('Amplitude')
pylab.xlabel(r'n (samples)')
pylab.title(r'Step response')
pylab.subplots_adjust(hspace=0.5)
def impz(b, a=1):
'''
#Plot step and impulse response
from http://mpastell.com/2010/01/18/fir-with-scipy/
'''
l = len(b)
impulse = pylab.repeat(0., l)
impulse[0] = 1.
x = numpy.arange(0, l)
response = signal.lfilter(b, a, impulse)
pylab.subplot(211)
pylab.stem(x, response)
pylab.ylabel('Amplitude')
pylab.xlabel(r'n (samples)')
pylab.title(r'Impulse response')
pylab.subplot(212)
step = numpy.cumsum(response)
pylab.stem(x, step)
pylab.ylabel('Amplitude')
pylab.xlabel(r'n (samples)')
pylab.title(r'Step response')
pylab.subplots_adjust(hspace=0.5)
def print_QE_tables(Qs,Es,qs,delim="\t"):
"""
Returns string of 'delim'-delimited printout. Assumes that
Qs,Es are of the form returned by the Stats.QEPlot method, and that
qs is the list of quantile percentages that were supplied to the same
method.
"""
ret = ""
rows = array(Qs.keys())
rows.sort()
# we want 3 values for each Q and also the E
qs = map(str,repeat(qs,3))
for i in range(len(qs)/3):
qs[i*3+0] += " lower CI"
qs[i*3+1] += " upper CI"
cols = qs+["E","E-moe","E+moe"]
ret += delim + delim.join(cols) + "\n"
for row in rows:
Q = Qs[row]
E,moe = Es[row]
ret += str(row) + delim
for qlh in Q:
#excel wants low,high,middle
ret += delim.join(map(str,(qlh[1],qlh[2],qlh[0]))) + delim
ret += str(E) + delim + str(E-moe) + delim + str(E+moe)
ret += "\n"
return ret
def print_QE_tables(Qs, Es, qs, delim="\t"):
"""
Returns string of 'delim'-delimited printout. Assumes that
Qs,Es are of the form returned by the Stats.QEPlot method, and that
qs is the list of quantile percentages that were supplied to the same
method.
"""
ret = ""
rows = array(Qs.keys())
rows.sort()
# we want 3 values for each Q and also the E
qs = map(str, repeat(qs, 3))
for i in range(len(qs) / 3):
qs[i * 3 + 0] += " lower CI"
qs[i * 3 + 1] += " upper CI"
cols = qs + ["E", "E-moe", "E+moe"]
ret += delim + delim.join(cols) + "\n"
for row in rows:
Q = Qs[row]
E, moe = Es[row]
ret += str(row) + delim
for qlh in Q:
#excel wants low,high,middle
ret += delim.join(map(str, (qlh[1], qlh[2], qlh[0]))) + delim
ret += str(E) + delim + str(E - moe) + delim + str(E + moe)
ret += "\n"
return ret
# keep only lowest level of each selected branch
nz = len(zelong)
keep = pl.ones(nz, dtype=bool)
for i in range(nz):
if keep[i]:
t = d[zelong[i]]
for k in t.descendants:
if k.idx in zelong:
keep[i] = False
zelong = zelong[keep]
n_elong_dendro = len(zelong)
# this will be for the list of fil,branch pairs that overlap this dendro, and by how many pixels:
overlapping_fil_branches = pl.repeat({}, n_elong_dendro)
for i in range(n_elong_dendro):
overlapping_fil_branches[i] = {
"dendro_id": 0,
"filbranches": [],
"npixoverlap": []
}.copy()
#================================================================
mom8 = fits.getdata(mom8file)
pl.clf()
pl.imshow((pl.nanmax(mom8) - mom8), origin="bottom", cmap="gray")
pl.subplots_adjust(left=0.05, right=0.98, bottom=0.05, top=0.98)
pl.xlim(xra)
pl.ylim(yra)