def fit(self, i, x, y, yerr = None):
ff = self.ff[i]
if ff:
ff = ff.replace(' ', '')
log.info('fitting function {}'.format(ff))
fitfunc = eval('lambda x,*p:' + ff)
x, y = np.array(x), np.array(y)
m = np.logical_and(np.isfinite(x), np.isfinite(y))
if yerr is not None:
yerr = np.array(yerr)
m = np.logical_and(m, np.isfinite(yerr))
yerr = yerr[m]
x , y = x[m], y[m]
# gather fit parameters
p = tuple([float(fp) for fp in self.fp[i].split(',')])
try:
p, c = curve_fit(fitfunc, x, y, p, yerr)
log.info('parameters = {}'.format(p))
log.info('covariance = {}'.format(c))
fit_status = ''
except Exception as e:
fit_status = ' (failed: {})'.format(e)
c = None
log.exception('fit failed')
# plot fit result
xfit = np.linspace(np.nanmin(x), np.nanmax(x), 1000)
yfit = fitfunc(xfit, *p)
args = [xfit, yfit]
if self.fl[i]: args.append(self.fl[i])
l, = plt.plot(*args)
N = len(x)
chi2 = fitfunc(x, *p) - y
if yerr is not None:
chi2 = chi2 / yerr
chi2 = (chi2 ** 2).sum()
# add textbox
t = 'y=' + ff
t += '\n$\\chi^2$/N = {}/{}'.format(number_mathformat(chi2), number_mathformat(N))
for k, v in enumerate(p):
try:
t += '\np[{}] = {}$\\pm${}'.format(k, number_mathformat(v), number_mathformat(np.sqrt(c[k, k])))
except:
t += '\np[{}] = {}$\\pm${}'.format(k, v, c)
self.fitboxes.append(t)
ll = ('Fit' + fit_status + ' y=' + ff)
for k, v in enumerate(p):
ll = ll.replace('p[{}]'.format(k), number_mathformat(v, 3))
self.legend.append((l, ll))
def _map(self, i):
import maps
log.debug('map of {}'.format([getattr(self, v)[i] for v in 'sxyzc']))
kwargs = self.opts(i)
x, y, z = self.data(i)
o = get_args_from(kwargs, margin = 0.05, width = 10e6, height = None, boundarylat = 50, projection = 'cyl',
drawcoastline = 1, drawgrid = 1, drawspecgrid = 1, drawcountries = 0, bluemarble = 0, nightshade = None)
m = maps.drawmap(y, x, **o)
x, y = m(x, y)
if z is None:
l, = plt.plot(x, y, **kwargs)
else:
# linestyle must not be 'none' when plotting 3D
if 'linestyle' in kwargs and kwargs['linestyle'] == 'none':
kwargs['linestyle'] = ':'
o = get_args_from(kwargs, markersize = 6, cbfrac = 0.04, cblabel = self.alabel('z'))
p = set_defaults(kwargs, zorder = 100)
l = plt.scatter(x, y, c = z, s = o.markersize ** 2, edgecolor = 'none', **p)
m = 6.0
dmin, dmax = np.nanmin(z), np.nanmax(z)
cticks = ticks.get_ticks(dmin, dmax, m, only_inside = 1)
formatter = mpl.ticker.FuncFormatter(func = lambda x, i:number_mathformat(x))
cb = plt.colorbar(fraction = o.cbfrac, pad = 0.01, aspect = 40, ticks = cticks, format = formatter)
cb.set_label(o.cblabel)
self.legend.append((l, self.llabel(i)))
def _xy(self, i):
log.debug('xy plot of {}'.format([getattr(self, v)[i] for v in 'sxyzc']))
kwargs = self.opts(i)
x, y, z = self.data(i)
if x is not None:
args = (x, y)
else:
args = (y,)
if z is None:
l, = plt.plot(*args, **kwargs)
else:
# linestyle must not be 'none' when plotting 3D
if 'linestyle' in kwargs and kwargs['linestyle'] == 'none':
kwargs['linestyle'] = ':'
o = get_args_from(kwargs, markersize = 2, cbfrac = 0.04, cblabel = self.alabel('z'))
l = plt.scatter(x, y, c = z, s = o.markersize ** 2, edgecolor = 'none', **kwargs)
m = 6.0
dmin, dmax = np.nanmin(z), np.nanmax(z)
cticks = ticks.get_ticks(dmin, dmax, m, only_inside = 1)
formatter = mpl.ticker.FuncFormatter(func = lambda x, i:number_mathformat(x))
cb = plt.colorbar(fraction = o.cbfrac, pad = 0.01, aspect = 40, ticks = cticks, format = formatter)
cb.set_label(o.cblabel)
self.legend.append((l, self.llabel(i)))
# fit
self.fit(i, x, y)
def stats_fields2d(self, i, contents, xcenters, ycenters):
stats = {}
stats['N'] = N = contents.sum()
stats['mean'] = mean = np.array([ (contents.sum(axis = 0) * xcenters).sum(),
(contents.sum(axis = 1) * ycenters).sum()]) / N
stats['std'] = np.sqrt(np.array([(contents.sum(axis = 0) * (xcenters - mean[0]) ** 2).sum(),
(contents.sum(axis = 1) * (ycenters - mean[1]) ** 2).sum()]) / N)
cov = 0
for k, l in product(xrange(contents.shape[1]), xrange(contents.shape[0])):
cov += contents[l, k] * (xcenters[k] - mean[0]) * (ycenters[l] - mean[1])
stats['cov'] = cov / N
log.debug(stats)
text = '{:6} {}'.format('hist', self.llabel(i))
sb = self.sb[i]
if 'a' in sb: sb = 'nmscpewx'
if 'uflow' in stats and stats['uflow']: sb += 'u'
if 'oflow' in stats and stats['oflow']: sb += 'o'
for k in sb:
k = stats_abrv[k]
if k in stats:
v = stats[k]
try:
v = number_mathformat(v)
except:
v = '({})'.format(','.join(map(number_mathformat, v)))
text += '\n{:6} {}'.format(_(k), v)
self.textboxes.append(text)
def stats_fields1d(self, i, data, contents, errors, edges):
centers = (edges[1:] + edges[:-1]) / 2
widths = np.diff(edges)
stats = {}
stats['N'] = N = np.sum(contents)
stats['uflow'] = np.sum(data < edges[0])
stats['oflow'] = np.sum(edges[-1] < data)
stats['mean'] = mean = np.sum(centers * contents) / N
stats['std'] = std = np.sqrt(np.sum((centers - mean) ** 2 * contents) / N)
stats['mode'] = centers[np.argmax(contents)]
bc, be = get_density(contents, errors, widths)
bc, be = get_cumulative(bc, be, 1, widths)
median_i = np.minimum(len(centers)-1, np.searchsorted(bc, 0.5, side = 'right'))
stats['median'] = median = centers[median_i]
if len(centers) % 2 == 0: # even # of s
stats['median'] = median = (median + centers[median_i - 1]) / 2
stats['skew'] = np.sum(((centers - mean) / std) ** 3 * contents) / N
stats['kurtos'] = kurtosis = np.sum(((centers - mean) / std) ** 4 * contents) / N
stats['excess'] = kurtosis - 3
log.debug(stats)
text = '{:6} {}'.format('hist', self.llabel(i))
sb = self.sb[i]
if 'a' in sb: sb = 'nmscpewx'
if 'uflow' in stats and stats['uflow']: sb += 'u'
if 'oflow' in stats and stats['oflow']: sb += 'o'
for k in sb:
k = stats_abrv[k]
if k in stats:
text += '\n{:6} {}'.format(_(k), number_mathformat(stats[k]))
self.textboxes.append(text)
def _hist2d(self, i):
log.debug('2D histogram of {}'.format([getattr(self, v)[i] for v in 'sxyzc']))
kwargs = self.opts(i)
x, y, z = self.data(i)
o = get_args_from(kwargs, style = 'color', density = False, log = False, cbfrac = 0.04, cblabel = 'bincontent', levels = 10)
filled = 'color' in o.style or ('fill' in o.style)
o.update(get_args_from(kwargs, hidezero = o.log or filled, colorbar = filled, clabels = not filled))
# make binnings
bins = self.bins(i, 'x')
if bins == 0:
bins = int(1 + np.log2(len(x)))
xedges, xcenters, xwidths = get_binning(bins, x)
bins = self.bins(i, 'y')
if bins == 0:
bins = int(1 + np.log2(len(y)))
yedges, ycenters, ywidths = get_binning(bins, y)
bincontents, _d1, _d2 = np.histogram2d(x, y, [xedges, yedges])
bincontents = np.transpose(bincontents)
assert np.all(_d1 == xedges)
assert np.all(_d2 == yedges)
# statsbox
self.stats_fields2d(i, bincontents, xcenters, ycenters)
if o.density:
bincontents = get_density2d(bincontents, xwidths, ywidths)
if o.hidezero:
bincontents[bincontents == 0] = np.nan
if o.log:
bincontents = np.log10(bincontents)
formatter = mpl.ticker.FuncFormatter(func = lambda x, i:number_mathformat(np.power(10, x)))
else:
formatter = mpl.ticker.FuncFormatter(func = lambda x, i:number_mathformat(x))
if 'color' in o.style:
pargs = set_defaults(kwargs, cmap = 'jet', edgecolor = 'none')
plt.pcolor(xedges, yedges, ma.array(bincontents, mask = np.isnan(bincontents)), **kwargs)
elif 'box' in o.style:
pargs = set_defaults(kwargs, color = (1, 1, 1, 0), marker = 's', edgecolor = 'k')
n = bincontents.size
s = bincontents.reshape(n)
s = s / np.nanmax(s) * (72. / 2. * self.w / max(len(xcenters), len(ycenters))) ** 2
xcenters, ycenters = np.meshgrid(xcenters, ycenters)
plt.scatter(xcenters.reshape(n), ycenters.reshape(n), s = s, **pargs)
elif 'contour' in o.style:
pargs = set_defaults(kwargs, cmap = 'jet')
if not isinstance(pargs['cmap'], mpl.colors.Colormap):
pargs['cmap'] = mpl.cm.get_cmap(pargs['cmap'])
if filled:
cs = plt.contourf(xcenters, ycenters, bincontents, o.levels, **pargs)
else:
cs = plt.contour(xcenters, ycenters, bincontents, o.levels, **pargs)
if o.clabels:
plt.clabel(cs, inline = 1)
else:
raise ValueError('unknown style ' + o.style)
if o.colorbar:
m = 6.0
dmin, dmax = np.nanmin(bincontents), np.nanmax(bincontents)
if o.log:
dmin, dmax = np.ceil(dmin), np.floor(dmax) + 1
step = max(1, np.floor((dmax - dmin) / m))
cticks = np.arange(dmin, dmax, step)
else:
cticks = ticks.get_ticks(dmin, dmax, m, only_inside = 1)
cb = plt.colorbar(fraction = o.cbfrac, pad = 0.01, aspect = 40, ticks = cticks, format = formatter)
cb.set_label(o.cblabel)
