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]
p = eval(self.fp[i])
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_format(chi2), number_format(N))
for k, v in enumerate(p):
try:
t += '\np[{}] = {}$\\pm${}'.format(k, number_format(v), number_format(np.sqrt(c[k, k])))
except:
t += '\np[{}] = {}$\\pm${}'.format(k, v, c)
self.textboxes.append(t)
ll = ('Fit' + fit_status + ' y=' + ff)
for k, v in enumerate(p):
ll = ll.replace('p[{}]'.format(k), number_format(v, 3))
self.legend.append((l, ll))
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]
p = eval(self.fp[i])
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_format(chi2),
number_format(N))
for k, v in enumerate(p):
try:
t += '\np[{}] = {}$\\pm${}'.format(
k, number_format(v), number_format(np.sqrt(c[k, k])))
except:
t += '\np[{}] = {}$\\pm${}'.format(k, v, c)
self.textboxes.append(t)
ll = ('Fit' + fit_status + ' y=' + ff)
for k, v in enumerate(p):
ll = ll.replace('p[{}]'.format(k), number_format(v, 3))
self.legend.append((l, ll))
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_format(v)
except:
v = '({})'.format(','.join(map(number_format, 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_format(stats[k]))
self.textboxes.append(text)
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_format(v)
except:
v = '({})'.format(','.join(map(number_format, 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_format(stats[k]))
self.textboxes.append(text)
