def _plot_fwhm_axis(self,
arr,
iqcalc,
skybg,
color1,
color2,
color3,
fwhm_method='gaussian'):
N = len(arr)
X = np.array(list(range(N)))
Y = arr
# subtract sky background
Y = Y - skybg
maxv = Y.max()
# clamp to 0..max
Y = Y.clip(0, maxv)
self.logger.debug("Y=%s" % (str(Y)))
self.ax.plot(X, Y, color=color1, marker='.')
res = iqcalc.calc_fwhm(arr, method_name=fwhm_method)
fwhm, mu = res.fwhm, res.mu
# Make a little smoother fitted curve by plotting intermediate
# points
XN = np.linspace(0.0, float(N), N * 10)
Z = np.array([res.fit_fn(x, res.fit_args) for x in XN])
self.ax.plot(XN, Z, color=color1, linestyle=':')
self.ax.axvspan(mu - fwhm / 2.0,
mu + fwhm / 2.0,
facecolor=color3,
alpha=0.25)
return fwhm
def _plot_fwhm_axis(self, arr, iqcalc, skybg, color1, color2, color3):
N = len(arr)
X = numpy.array(list(range(N)))
Y = arr
# subtract sky background
Y = Y - skybg
maxv = Y.max()
# clamp to 0..max
Y = Y.clip(0, maxv)
self.logger.debug("Y=%s" % (str(Y)))
self.ax.plot(X, Y, color=color1, marker='.')
fwhm, mu, sdev, maxv = iqcalc.calc_fwhm(arr)
# Make a little smoother gaussian curve by plotting intermediate
# points
XN = numpy.linspace(0.0, float(N), N*10)
Z = numpy.array([iqcalc.gaussian(x, (mu, sdev, maxv))
for x in XN])
self.ax.plot(XN, Z, color=color1, linestyle=':')
self.ax.axvspan(mu-fwhm/2.0, mu+fwhm/2.0,
facecolor=color3, alpha=0.25)
return (fwhm, mu, sdev, maxv)
def _plot_fwhm_axis(self, arr, iqcalc, skybg, color1, color2, color3):
N = len(arr)
X = numpy.array(list(range(N)))
Y = arr
# subtract sky background
Y = Y - skybg
maxv = Y.max()
# clamp to 0..max
Y = Y.clip(0, maxv)
self.logger.debug("Y=%s" % (str(Y)))
self.ax.plot(X, Y, color=color1, marker='.')
fwhm, mu, sdev, maxv = iqcalc.calc_fwhm(arr)
# Make a little smoother gaussian curve by plotting intermediate
# points
XN = numpy.linspace(0.0, float(N), N*10)
Z = numpy.array([iqcalc.gaussian(x, (mu, sdev, maxv))
for x in XN])
self.ax.plot(XN, Z, color=color1, linestyle=':')
self.ax.axvspan(mu-fwhm/2.0, mu+fwhm/2.0,
facecolor=color3, alpha=0.25)
return (fwhm, mu, sdev, maxv)
def _plot_fwhm_axis(self, arr, iqcalc, skybg, color1, color2, color3,
fwhm_method='gaussian'):
N = len(arr)
X = np.array(list(range(N)))
Y = arr
# subtract sky background
Y = Y - skybg
maxv = Y.max()
# clamp to 0..max
Y = Y.clip(0, maxv)
self.logger.debug("Y=%s" % (str(Y)))
self.ax.plot(X, Y, color=color1, marker='.')
res = iqcalc.calc_fwhm(arr, method_name=fwhm_method)
fwhm, mu = res.fwhm, res.mu
# Make a little smoother fitted curve by plotting intermediate
# points
XN = np.linspace(0.0, float(N), N * 10)
Z = np.array([res.fit_fn(x, res.fit_args) for x in XN])
self.ax.plot(XN, Z, color=color1, linestyle=':')
self.ax.axvspan(mu - fwhm / 2.0, mu + fwhm / 2.0,
facecolor=color3, alpha=0.25)
return fwhm