def histoline(self, x, y, *args, **kwds):
l = len(x)
doubledX = np.zeros(2 * l)
doubledY = np.zeros(2 * l)
for i in range(0, l):
doubledY[2 * i] = y.value[i]
doubledY[2 * i + 1] = y.value[i]
deltaX = x.value[1] - x.value[0]
doubledX[0] = x.value[0] - deltaX / 2
for i in range(0, l - 1):
doubledX[2 * i + 1] = (x.value[i] + x.value[i + 1]) / 2
doubledX[2 * i + 2] = (x.value[i] + x.value[i + 1]) / 2
deltaX = x.value[-1] - x.value[-2]
doubledX[-1] = x.value[-1] + deltaX / 2
doubledX = [doubledX[0]] + list(doubledX) + [doubledX[-1]]
doubledY = [0] + list(doubledY) + [0]
nx = Quantity(doubledX, 0, x.uvec)
nx.name(x.label, x.symbol, x.latex)
nx.preferredUnit = x.preferredUnit
ny = Quantity(doubledY, 0, y.uvec)
ny.name(y.label, y.symbol, y.latex)
ny.preferredUnit = y.preferredUnit
return self.line(nx, ny, *args, **kwds)
def fit(self, mf, box=True, bpos=None, xmin=None, xmax=None, chi2=True):
if not isinstance(mf, ModelFit):
raise TypeError(
"Argument of Plot.fit must be a ModelFit, but {} given.".
format(type(mf)))
self._fitcount += 1
text = 'Fit: ${}$\n\n'.format(mf.eq())
if mf.sr >= 3:
text += 'fit failed ({})!\n'.format(mf.sr)
for p in mf.parameters:
text += ' ${}$\n'.format(p.tex())
if chi2:
text += ' $\chi^2/\mathrm{ndf} = ' + ('{:.2f} / {}$'.format(
*mf.chi))
xmin = xmin or min(mf.xo.value)
xmax = xmax or max(mf.xo.value)
if box:
self.box((self._fitcount, text, mf.xo, mf.yo, bpos))
x = Quantity(np.linspace(xmin, xmax, 200), unit=mf.xo.uvec)
p0 = [Quantity(p.value, 0, p.uvec) for p in mf.parameters]
self.line(x,
mf.func(x, *p0),
fmt='-' + Plot.fitcolors[(self._fitcount - 1) % 6])
return self
def promt(self, x, y):
p = Plot()
p.error(x, y, ecolor='0.3')
p.make()
pylab.ion()
p.show()
pylab.ioff()
print(' *** RANGE PICKER for "{}":'.format(self.id))
if RPicker.storage is not None and self.id in RPicker.storage:
r = RPicker.storage[self.id]
print(' previously from {:.5g} to {:.5g}'.format(r[0], r[1]))
xunit = p._xaxis.sprefunit()
lower = input(' lower limit ({}) = '.format(xunit))
upper = input(' upper limit ({}) = '.format(xunit))
print('')
lower = float(lower)
upper = float(upper)
f = Quantity(xunit) / Quantity(unit=x.uvec)
f = float(f)
lower *= f
upper *= f
if RPicker.storage is not None:
RPicker.storage[self.id] = (lower, upper)
print(' stored...')
return lower, upper
def ylabel(self, s):
self._made = False
if self._yaxis is None:
self._yaxis = Quantity()
self._yaxis.label = s
if symbol is not None: self._xaxis.symbol = symbol
if latex is not None: self._yaxis.latex = latex
return self
def yaxis(self, q):
self._made = False
if not isinstance(q, Quantity):
raise TypeError("yaxis argument must be Quantity!")
self._yaxis = Quantity(q.sprefunit())
self._yaxis.label = q.label
self._yaxis.latex = q.latex
self._yaxis.symbol = q.symbol
return self
def estimate(self, x, y, xrange=None):
x, y = self.xtrim(x, y, xrange)
offset = (y.value[0] + y.value[-1]) / 2
self.parameters[3].value = offset
_y = Quantity()
_y.value = y.value - offset
mf = GaussFit(*self.parameters[:3])
mf.estimate(x, _y)
return self
def estimate(self, x, y, xrange=None):
x, y = self.xtrim(x, y, xrange)
self.xo = x
self.yo = y
offset = y.value.mean()
self.parameters[3].value = offset
_y = Quantity()
_y.value = y.value - offset
mf = ErfFit(*self.parameters[:3])
mf.estimate(x, _y)
return self
def estimate(self, x, y, xrange=None):
x, y = self.xtrim(x, y, xrange)
self.xo = x
self.yo = y
offset = (y.value[0] + y.value[-1]) / 2
self.parameters[4].value = offset
_y = Quantity()
_y.value = y.value - offset
mf = VoigtFit(*self.parameters[:4])
mf.estimate(x, _y)
return self
def estimate(self, x, y, xrange=None):
x, y = self.xtrim(x, y, xrange)
self.xo = x
self.yo = y
dy = y.value[-1] - y.value[0]
dx = x.value[-1] - x.value[0]
self.parameters[4].value = dy / dx
_y = Quantity()
_y.value = y.value - dy / dx * (x.value - x.value[0])
mf = GaussCFit(*self.parameters[:4])
mf.estimate(x, _y)
return self
def __init__(self, y, x, n=1):
self.p = []
self.x = x
self.y = y
xU = Quantity(x.siunit())
yU = Quantity(y.siunit())
for i in range(n + 1):
q = yU / xU**i
q.name(latex='a_{}'.format(i))
self.p.append(q)
self.f = PolynomFit(*self.p)
self.f.fit(x, y)
def scatter(it, m=None):
"""
Calculates the scattering of an iterable around m, which defaults to the
non-weighed mean of the iterable. The square of scattering is sometimes
called the empirical variance. The scattering s is calculated by
s = sqrt( 1/(n-1) sum (x - m)^2 ).
The scattering of values can be used to estimate the statistical error of
values, or be compare with the calculated statistical error.
Parameters:
it - Iterable which contains the values. This can also be Quantity
object. Its standard deviations are ignored.
m - This is the mean around which the scattering should be calculated,
see equation above. The default value is None, which causes the
method to calculate the non-weighted mean of the value.
Returns:
A single value with the scattering. When a Quantity object was given,
the initial unit will be preserved. A standard deviation of the
scattering is not calculated.
"""
if isinstance(it, np.ndarray):
if m is None: m = sum(it) / len(it)
return math.sqrt(((it - m)**2).sum() / (len(it) - 1))
elif isinstance(it, Quantity):
return Quantity(scatter(it.value, m), unit=it.uvec)
elif isinstance(it, collections.Iterable):
return scatter(np.array(it), m)
else:
raise TypeError('First argument must be iterable.')
def daq(f,
*columns,
skip=0,
sep=r'\s*[,]\s*',
default=0.,
convert=float,
c2d=False):
l = len(columns)
if l == 0: return
for c in columns:
if not isinstance(c, (Quantity, StdDev)):
raise TypeError("Column must be quantity")
val = []
if isinstance(f, Quantity):
raise TypeError("Fist argument must be file, not quantity!")
elif isinstance(f, str):
f = open(f, 'r')
for line in f.readlines():
if skip:
skip -= 1
continue
line = line.strip()
if not line: continue
row = re.split(sep, line, l)
if c2d:
row = [r.replace(',', '.') for r in row]
row = [(convert(r) if r else default) for r in row]
if len(row) < l: row += [default] * (l - len(row))
val.append(row)
f.close()
val = list(zip(*val))
result = []
for i in range(l):
f = float(
Quantity(unit=columns[i].sprefunit()) /
Quantity(unit=columns[i].uvec))
columns[i].value = np.array(val[i]) * f
def mean(it, stddev=None):
"""
Calculates the (weighted) mean, and if standard deviations are give, also the
standard deviation of the mean.
Parameters:
it - This must be an iterable containing the values to be averaged.
This can also be Quantity object, which also contains standard
deviations. In this case the stddev parameter must be None.
stddev - This can be an iterable or a single value or None. If this is a
single value, all values are weighted equally, if this is an
iterable the weighted mean is calculated instead. If this is
None, all values are weighted equally and no standard deviation
of th mean is caluculated.
If it is a Quantity object, this must be None.
Return:
The return value depends on the given parameters.
No stddev, None: mean
Single valued stddev: (mean, stddev of mean)
Iterable stddev: (mean, stddev of mean)
Quantity object: Quantity(mean, stddev of mean, Unit)
"""
if isinstance(it, np.ndarray):
if isinstance(stddev, np.ndarray):
if (stddev == 0).any():
return mean(it[stddev == 0])
else:
weights = 1 / stddev**2
m = sum(weights * it) / sum(weights)
stddev = np.sqrt(1 / sum(weights))
return (m, stddev)
elif isinstance(stddev, collections.Iterable):
return mean(it, np.array(stddev))
elif stddev is None:
return mean(it, 0)[0]
else:
m = sum(it) / len(it)
stddev = stddev / math.sqrt(len(it))
return (m, stddev)
elif isinstance(it, Quantity):
if stddev is not None:
raise ValueError('If Quantity given std must not be give')
(m, stddev) = mean(it.value, it.stddev())
return Quantity(m, stddev, it.uvec)
elif isinstance(it, collections.Iterable):
if stddev is None:
return mean(np.array(it), 0)[0]
else:
return mean(np.array(it), stddev)
else:
raise TypeError('First argument must be iterable.')
def __init__(self, *cols, units=None, align=None):
self.cols = cols
if align is None:
self.align = 'l' * len(cols)
else:
self.align = align
if units is not None:
if len(cols) != len(units):
raise ValueError(
'Number of cols {} differs from number of units {}.'.
format(len(cols), len(units)))
self.units = []
for u in units:
if isinstance(u, str):
self.units.append(Quantity(u))
else:
self.units.append(None)
else:
self.units = [None] * len(cols)
self.rows = []
def make(self, clf=True, fig=None, axes=None):
if clf: plt.clf()
if fig is None and axes is None: fig = plt.figure()
if axes is None: axes = fig.add_subplot(111)
xlim = None
ylim = None
for x, y, fmt, errorbar, d in self._data:
if isinstance(x, Quantity):
f = Quantity(unit=x.uvec) / self._xaxis
if not f.unitless():
warnings.warn(
'The ratio of x axis unit ({}) and real unit ({}) must be unit less.'
.format(self._xaxis.siunit(), y.siunit()))
f = float(f)
sx = x.stddev() * f
x = x.value * f
else:
#f = float(1 / Quantity(self._xunit))
#x = x * f
sx = 0
if isinstance(y, Quantity):
f = Quantity(unit=y.uvec) / self._yaxis
if not f.unitless():
warnings.warn(
'The ratio of preferred unit ({}) and real unit ({}) must be unit less.'
.format(self._yaxis.siunit(), y.siunit()))
f = float(f)
sy = y.stddev() * f
y = y.value * f
else:
#f = float(1 / Quantity(self.ypreferredUnit))
#y = y * f
sy = 0
xmin = np.nanmin(x - sx)
xmax = np.nanmax(x + sx)
ymin = np.nanmin(y - sy)
ymax = np.nanmax(y + sy)
if xlim is None: xlim = [xmin, xmax]
if ylim is None: ylim = [ymin, ymax]
xlim[0] = min(xlim[0], xmin)
xlim[1] = max(xlim[1], xmax)
ylim[0] = min(ylim[0], ymin)
ylim[1] = max(ylim[1], ymax)
xlimraw = xlim
ylimraw = ylim
xerr = sx
yerr = sy
if errorbar:
axes.errorbar(x, y, sy, sx, fmt=fmt, **d)
else:
axes.plot(x, y, fmt, **d)
xdif = xlim[1] - xlim[0]
ydif = ylim[1] - ylim[0]
xlim[0] -= xdif * self._enlarge[0]
xlim[1] += xdif * self._enlarge[1]
ylim[0] -= ydif * self._enlarge[2]
ylim[1] += ydif * self._enlarge[3]
i = 0
taken = [[0] * 2, [0] * 2, [0] * 2]
for b in self._boxes:
if isinstance(b, tuple):
fitnum, b, x, y, bpos = b
if isinstance(bpos, tuple):
x = bpos[0] / 3
y = bpos[1] / 2
else:
penalty = []
for x in range(3):
for y in range(2):
points = 0
total = 0
for dx, dy, fmt, errorbar, d in self._data:
fx = float(Quantity(unit=dx.uvec) / self._xaxis)
fy = float(Quantity(unit=dy.uvec) / self._yaxis)
idx1 = (dx.value * fx >
(xlim[0] + xdif * (x / 3 - 0.1)))
idx2 = (dx.value * fx <
(xlim[0] + xdif * (x + 1.3) / 3))
idx3 = (dy.value * fy >
(ylim[0] + ydif * (y / 2 - 0.1)))
idx4 = (dy.value * fy <
(ylim[0] + ydif * (y + 1.2) / 2))
idx = idx1 * idx2 * idx3 * idx4
points += len(dx.value[idx]) / len(dx.value)
total = len(dx.value)
points += total * taken[x][y]
penalty.append((x, y, points))
x, y, points = min(penalty, key=lambda y: y[2])
taken[x][y] += 1
if y == 0:
yalign = 'bottom'
y = ylimraw[0] + ydif * 0.05
elif y == 1:
yalign = 'top'
y = ylimraw[1] - ydif * 0.05
if x == 0:
xalign = 'left'
x = xlimraw[0] + xdif * 0.05
elif x == 1:
xalign = 'center'
x = xlimraw[0] + 0.5 * xdif
elif x == 2:
xalign = 'right'
x = xlimraw[1] - xdif * 0.05
if self._fitcount > 1 and False:
b = '({}) {}'.format(fitnum, b)
fx = Quantity(unit=x.uvec) / self._xaxis
fy = Quantity(unit=y.uvec) / self._yaxis
if not fx.unitless():
warnings.warn(
'The ratio of preferred unit ({}) and real unit ({}) must be unit less.'
.format(self._xaxis.siunit(), x.siunit()))
if not fy.unitless():
warnings.warn(
'The ratio of preferred unit ({}) and real unit ({}) must be unit less.'
.format(self._yaxis.siunit(), y.siunit()))
fx = float(fx)
fy = float(fy)
tx = x.value * fx
ty = y.value * fy
tx = (min(tx) + max(tx)) / 2
ty = max(ty)
ty += 0.05 * ydif
axes.text(tx,
ty,
'({})'.format(fitnum),
color=Plot.fitcolors[(fitnum - 1) % 6],
horizontalalignment='center',
verticalalignment='bottom')
#plt.figtext(*xy, s=b,bbox=dict(facecolor='w', edgecolor='black', pad=10), multialignment='left', **align)
axes.annotate(b,
xy=(x, y),
bbox=dict(facecolor='w', edgecolor='k', pad=10),
multialignment='left',
horizontalalignment=xalign,
verticalalignment=yalign)
if self._grid: pylab.grid()
axes.set_xscale(self._xscale)
axes.set_yscale(self._yscale)
if self._leg: pylab.legend()
if self.makex(): axes.set_xlabel(self.makex())
if self.makey(): axes.set_ylabel(self.makey())
self._made = True
axes.set_xlim(*xlimraw)
axes.set_ylim(*ylimraw)
return self # cascade
class Plot:
def __init__(self):
self._made = False
self._data = []
self._boxes = []
self._enlarge = (0.02, 0.02, 0.02, 0.02)
self._grid = True
self._xscale = 'linear'
self._yscale = 'linear'
self._leg = False
self._xaxis = None
self._yaxis = None
self._fitcount = 0
def xaxis(self, q):
self._made = False
if not isinstance(q, Quantity):
raise TypeError("xaxis argument must be Quantity!")
self._xaxis = Quantity(q.sprefunit())
self._xaxis.label = q.label
self._xaxis.latex = q.latex
self._xaxis.symbol = q.symbol
return self
def yaxis(self, q):
self._made = False
if not isinstance(q, Quantity):
raise TypeError("yaxis argument must be Quantity!")
self._yaxis = Quantity(q.sprefunit())
self._yaxis.label = q.label
self._yaxis.latex = q.latex
self._yaxis.symbol = q.symbol
return self
def makex(self):
if self._xaxis is None: return ''
lab = []
if self._xaxis.label:
lab.append(self._xaxis.label)
sl = self._xaxis.latex or self._xaxis.symbol
if sl:
lab.append("${}$".format(sl))
punit = self._xaxis.sprefunit(latex=True)
if punit:
lab.append('in ${}$'.format(punit))
return ' '.join(lab)
def makey(self):
if self._yaxis is None: return ''
lab = []
if self._yaxis.label:
lab.append(self._yaxis.label)
sl = self._yaxis.latex or self._yaxis.symbol
if sl:
lab.append("${}$".format(sl))
punit = self._yaxis.sprefunit(latex=True)
if punit:
lab.append('in ${}$'.format(punit))
return ' '.join(lab)
""" def xunit(self, s):
self._made = False
if self._xaxis is None:
self._xaxis = Quantity()
q = Quantity(s)
self._xaxis.preferredUnit = q.preferredUnit
self._xaxis.value = q.value
self._xaxis.uvec = q.uvec
return self
def yunit(self, s):
self._made = False
if self._yaxis is None:
self._yaxis = Quantity()
q = Quantity(s)
self._yaxis.preferredUnit = q.preferredUnit
self._yaxis.value = q.value
self._yaxis.uvec = q.uvec
return self"""
def xlabel(self, s, symbol=None, latex=None):
self._made = False
if self._xaxis is None:
self._xaxis = Quantity()
self._xaxis.label = s
if symbol is not None: self._xaxis.symbol = symbol
if latex is not None: self._yaxis.latex = latex
return self
def ylabel(self, s):
self._made = False
if self._yaxis is None:
self._yaxis = Quantity()
self._yaxis.label = s
if symbol is not None: self._xaxis.symbol = symbol
if latex is not None: self._yaxis.latex = latex
return self
def legend(self, on=True):
self._made = False
self._leg = on
return self
def data(self, x, y, fmt, errorbar, label=True, **kwds):
self._made = False
if label == True:
kwds['label'] = y.label
elif label is not None:
kwds['label'] = label
self._data.append((x, y, fmt, errorbar, kwds))
if isinstance(x, Quantity):
if self._xaxis is None:
self.xaxis(x)
if isinstance(y, Quantity):
if self._yaxis is None:
self.yaxis(y)
return self
def error(self,
x,
y,
fmt='.k',
markersize=5,
capsize=0,
label=True,
ecolor='0.3',
**kwds):
kwds['capsize'] = capsize
self._made = False
self.data(x,
y,
fmt=fmt,
markersize=markersize,
label=label,
ecolor=ecolor,
errorbar=True,
**kwds)
return self # cascade
def points(self, x, y, fmt='.k', markersize=3, label=True, **kwds):
self._made = False
self.data(x,
y,
fmt=fmt,
markersize=markersize,
label=label,
errorbar=False,
**kwds)
return self # cascade
def line(self, x, y, fmt='-b', linewidth=1, label=True, **kwds):
self._made = False
self.data(x,
y,
fmt=fmt,
linewidth=linewidth,
label=label,
errorbar=False,
**kwds)
return self # cascade
def histoline(self, x, y, *args, **kwds):
l = len(x)
doubledX = np.zeros(2 * l)
doubledY = np.zeros(2 * l)
for i in range(0, l):
doubledY[2 * i] = y.value[i]
doubledY[2 * i + 1] = y.value[i]
deltaX = x.value[1] - x.value[0]
doubledX[0] = x.value[0] - deltaX / 2
for i in range(0, l - 1):
doubledX[2 * i + 1] = (x.value[i] + x.value[i + 1]) / 2
doubledX[2 * i + 2] = (x.value[i] + x.value[i + 1]) / 2
deltaX = x.value[-1] - x.value[-2]
doubledX[-1] = x.value[-1] + deltaX / 2
doubledX = [doubledX[0]] + list(doubledX) + [doubledX[-1]]
doubledY = [0] + list(doubledY) + [0]
nx = Quantity(doubledX, 0, x.uvec)
nx.name(x.label, x.symbol, x.latex)
nx.preferredUnit = x.preferredUnit
ny = Quantity(doubledY, 0, y.uvec)
ny.name(y.label, y.symbol, y.latex)
ny.preferredUnit = y.preferredUnit
return self.line(nx, ny, *args, **kwds)
fitcolors = 'brgcmy'
def fit(self, mf, box=True, bpos=None, xmin=None, xmax=None, chi2=True):
if not isinstance(mf, ModelFit):
raise TypeError(
"Argument of Plot.fit must be a ModelFit, but {} given.".
format(type(mf)))
self._fitcount += 1
text = 'Fit: ${}$\n\n'.format(mf.eq())
if mf.sr >= 3:
text += 'fit failed ({})!\n'.format(mf.sr)
for p in mf.parameters:
text += ' ${}$\n'.format(p.tex())
if chi2:
text += ' $\chi^2/\mathrm{ndf} = ' + ('{:.2f} / {}$'.format(
*mf.chi))
xmin = xmin or min(mf.xo.value)
xmax = xmax or max(mf.xo.value)
if box:
self.box((self._fitcount, text, mf.xo, mf.yo, bpos))
x = Quantity(np.linspace(xmin, xmax, 200), unit=mf.xo.uvec)
p0 = [Quantity(p.value, 0, p.uvec) for p in mf.parameters]
self.line(x,
mf.func(x, *p0),
fmt='-' + Plot.fitcolors[(self._fitcount - 1) % 6])
return self
def box(self, text):
self._made = False
self._boxes.append(text)
return self
def xlog(self, log=True):
self._made = False
self._xscale = 'log' if log else 'linear'
return self
def ylog(self, log=True):
self._made = False
self._yscale = 'log' if log else 'linear'
return self
def save(self, filename, **kwds):
if not self._made: self.make()
# dpi not set here anymore
# do this in a matlibplotrc file, e.g.
# figure.dpi : 300
plt.tight_layout()
plt.savefig(filename, **kwds)
return self
def show(self):
if not self._made: self.make()
plt.show()
return self
def make(self, clf=True, fig=None, axes=None):
if clf: plt.clf()
if fig is None and axes is None: fig = plt.figure()
if axes is None: axes = fig.add_subplot(111)
xlim = None
ylim = None
for x, y, fmt, errorbar, d in self._data:
if isinstance(x, Quantity):
f = Quantity(unit=x.uvec) / self._xaxis
if not f.unitless():
warnings.warn(
'The ratio of x axis unit ({}) and real unit ({}) must be unit less.'
.format(self._xaxis.siunit(), y.siunit()))
f = float(f)
sx = x.stddev() * f
x = x.value * f
else:
#f = float(1 / Quantity(self._xunit))
#x = x * f
sx = 0
if isinstance(y, Quantity):
f = Quantity(unit=y.uvec) / self._yaxis
if not f.unitless():
warnings.warn(
'The ratio of preferred unit ({}) and real unit ({}) must be unit less.'
.format(self._yaxis.siunit(), y.siunit()))
f = float(f)
sy = y.stddev() * f
y = y.value * f
else:
#f = float(1 / Quantity(self.ypreferredUnit))
#y = y * f
sy = 0
xmin = np.nanmin(x - sx)
xmax = np.nanmax(x + sx)
ymin = np.nanmin(y - sy)
ymax = np.nanmax(y + sy)
if xlim is None: xlim = [xmin, xmax]
if ylim is None: ylim = [ymin, ymax]
xlim[0] = min(xlim[0], xmin)
xlim[1] = max(xlim[1], xmax)
ylim[0] = min(ylim[0], ymin)
ylim[1] = max(ylim[1], ymax)
xlimraw = xlim
ylimraw = ylim
xerr = sx
yerr = sy
if errorbar:
axes.errorbar(x, y, sy, sx, fmt=fmt, **d)
else:
axes.plot(x, y, fmt, **d)
xdif = xlim[1] - xlim[0]
ydif = ylim[1] - ylim[0]
xlim[0] -= xdif * self._enlarge[0]
xlim[1] += xdif * self._enlarge[1]
ylim[0] -= ydif * self._enlarge[2]
ylim[1] += ydif * self._enlarge[3]
i = 0
taken = [[0] * 2, [0] * 2, [0] * 2]
for b in self._boxes:
if isinstance(b, tuple):
fitnum, b, x, y, bpos = b
if isinstance(bpos, tuple):
x = bpos[0] / 3
y = bpos[1] / 2
else:
penalty = []
for x in range(3):
for y in range(2):
points = 0
total = 0
for dx, dy, fmt, errorbar, d in self._data:
fx = float(Quantity(unit=dx.uvec) / self._xaxis)
fy = float(Quantity(unit=dy.uvec) / self._yaxis)
idx1 = (dx.value * fx >
(xlim[0] + xdif * (x / 3 - 0.1)))
idx2 = (dx.value * fx <
(xlim[0] + xdif * (x + 1.3) / 3))
idx3 = (dy.value * fy >
(ylim[0] + ydif * (y / 2 - 0.1)))
idx4 = (dy.value * fy <
(ylim[0] + ydif * (y + 1.2) / 2))
idx = idx1 * idx2 * idx3 * idx4
points += len(dx.value[idx]) / len(dx.value)
total = len(dx.value)
points += total * taken[x][y]
penalty.append((x, y, points))
x, y, points = min(penalty, key=lambda y: y[2])
taken[x][y] += 1
if y == 0:
yalign = 'bottom'
y = ylimraw[0] + ydif * 0.05
elif y == 1:
yalign = 'top'
y = ylimraw[1] - ydif * 0.05
if x == 0:
xalign = 'left'
x = xlimraw[0] + xdif * 0.05
elif x == 1:
xalign = 'center'
x = xlimraw[0] + 0.5 * xdif
elif x == 2:
xalign = 'right'
x = xlimraw[1] - xdif * 0.05
if self._fitcount > 1 and False:
b = '({}) {}'.format(fitnum, b)
fx = Quantity(unit=x.uvec) / self._xaxis
fy = Quantity(unit=y.uvec) / self._yaxis
if not fx.unitless():
warnings.warn(
'The ratio of preferred unit ({}) and real unit ({}) must be unit less.'
.format(self._xaxis.siunit(), x.siunit()))
if not fy.unitless():
warnings.warn(
'The ratio of preferred unit ({}) and real unit ({}) must be unit less.'
.format(self._yaxis.siunit(), y.siunit()))
fx = float(fx)
fy = float(fy)
tx = x.value * fx
ty = y.value * fy
tx = (min(tx) + max(tx)) / 2
ty = max(ty)
ty += 0.05 * ydif
axes.text(tx,
ty,
'({})'.format(fitnum),
color=Plot.fitcolors[(fitnum - 1) % 6],
horizontalalignment='center',
verticalalignment='bottom')
#plt.figtext(*xy, s=b,bbox=dict(facecolor='w', edgecolor='black', pad=10), multialignment='left', **align)
axes.annotate(b,
xy=(x, y),
bbox=dict(facecolor='w', edgecolor='k', pad=10),
multialignment='left',
horizontalalignment=xalign,
verticalalignment=yalign)
if self._grid: pylab.grid()
axes.set_xscale(self._xscale)
axes.set_yscale(self._yscale)
if self._leg: pylab.legend()
if self.makex(): axes.set_xlabel(self.makex())
if self.makey(): axes.set_ylabel(self.makey())
self._made = True
axes.set_xlim(*xlimraw)
axes.set_ylim(*ylimraw)
return self # cascade
def readq(filename,
label='',
symbol='',
latex='',
unit='',
skip=0,
sep=r"\s*(,|\s)\s*",
default=0.,
convert=float,
c2d=False,
stddev=[],
stdlink={},
n=0):
"""
Reads a file of data. The file is supposed to contain columnes which are separated by some string. A row is defined
by a line. Each columns will be read into a numpy array or quantity object. The function returns a list with all the columns.
"""
f = open(filename)
if isinstance(label, str): label = [s.strip() for s in label.split('|')]
if isinstance(symbol, str): symbol = [s.strip() for s in symbol.split('|')]
if isinstance(latex, str): latex = [s.strip() for s in latex.split('|')]
if isinstance(unit, str): unit = [s.strip() for s in unit.split('|')]
l = max(len(label), len(symbol), len(latex), len(unit), len(stddev), n)
if len(label) < l: label += [''] * (l - len(label))
if len(symbol) < l: symbol += [''] * (l - len(symbol))
if len(latex) < l: latex += [''] * (l - len(latex))
if len(unit) < l: unit += [1] * (l - len(unit))
if len(stddev) < l: stddev += [0] * (l - len(stddev))
val = []
for line in f.readlines():
if skip:
skip -= 1
continue
line = line.strip()
row = re.split(sep, line, l)
if not l: l = len(row)
row = [(convert(r) if r else default) for r in row]
if len(row) < l: row += [default] * (l - len(row))
val.append(row)
columns = list(zip(*val))
result = []
for i in range(l):
value = columns[i]
u = unit[i]
ll = label[i]
lx = latex[i]
s = symbol[i]
std = stddev[i]
if i in stdlink:
q = Quantity(value, std, u, label=ll, symbol=s, latex=lx)
k = stdlink[i]
if unit[k]:
q = q | Quantity(val[k], unit=unit[k])
else:
q = q | Quantity(val[k], unit=unit[i])
result.append(q)
else:
result.append(Quantity(value, std, u, label=ll, symbol=s,
latex=lx))
return result
