cana.spectools.slopetool¶

Tool for measuring spectral slope.

Functions

slope(spec[, wmin, wmax, norm, errormethod, …]) Calculate the spectral gradient.

Classes

`Slope`([wmin, wmax, norm])	Calculate the spectral gradient of a spectrum.
`SlopeValue`(model, spec, slp[, slp_unc, label])	Representation of a slope mesurement.

class cana.spectools.slopetool.Slope(wmin=0.4, wmax=0.9, norm=0.55)[source]¶

Calculate the spectral gradient of a spectrum.

Methods

measure(spec[, error, label]) Calculate the slope in a spectral region.

measure(spec, error=<cana.spectools.uncertainties.SpecError object>, label=None)[source]¶

Calculate the slope in a spectral region.

If the “error” is set as True, then uses a Monte-Carlo model to estimate the error in the slope.

Parameters:	spec: Spectrum The spectrum object error: SpecError The model to estimate the slope uncertainty. label: str (optional) The label of the spectrum. This is just to organize the output.
Returns:	slp: The slope value. If error==True, then also returns the uncertainty.

class cana.spectools.slopetool.SlopeValue(model, spec, slp, slp_unc=None, label=None)[source]¶

Representation of a slope mesurement.

Methods

plot([fax, show, savefig, axistitles, …]) Plot the Spectrum and the measured slope.

plot(fax=None, show=True, savefig=None, axistitles=True, speckwargs=None, slopekwargs=None, legendkwargs=None)[source]¶

Plot the Spectrum and the measured slope.

Parameters:	fax (Optional): matplotlib.axes If desired to subplot image in a figure. Default is ‘None’, which will open a new plt.figure() show (Optional): boolean True if want to plt.show(). savefig (Optional): str The path to save the figure. If set to None, wont save the figure. **kwargs: See matplotlib.pyplot.plot kwargs

cana.spectools.slopetool.slope(spec, wmin=0.4, wmax=0.9, norm=0.55, errormethod='rms', error_param=None, montecarlo=1000, speckwargs=None)[source]¶

Calculate the spectral gradient.

Parameters:

Parameters:	spec: Spectrum, spectrum file, spectrum file list The input can be a Spectrum object, a spectrum file or a list of spectrum files wmin: float wavelength lower limit for the adjust. Default: 0.4 wmax: float wavelength upper limit for the adjust. Default: 0.9 norm: float The wavelength for normalizing the slope. Default: 0.55 errormethod: ‘rms’, ‘removal’ or ‘bin’ The error methodology that will be applied for estimating the slope error. Default is ‘rms’ error_param: None or float The error methodoly parameter if needed. If errormethod=’rms’, then no value is necessary. If it is set for removal, the percentage of points to remove. For rebin, the param represents the binsize. montecarlo: integer Default is 1000
Returns:	slp: SlopeValue or Pandas.DataFrame For a single spectrum it will return a SlopeValue For a list of spectra, returns a pandas.DataFrame with the results.

spec: Spectrum, spectrum file, spectrum file list: The input can be a Spectrum object, a spectrum file or a list of spectrum files
wmin: float: wavelength lower limit for the adjust. Default: 0.4
wmax: float: wavelength upper limit for the adjust. Default: 0.9
norm: float: The wavelength for normalizing the slope. Default: 0.55
errormethod: ‘rms’, ‘removal’ or ‘bin’: The error methodology that will be applied for estimating the slope error. Default is ‘rms’
error_param: None or float: The error methodoly parameter if needed. If errormethod=’rms’, then no value is necessary. If it is set for removal, the percentage of points to remove. For rebin, the param represents the binsize.
montecarlo: integer: Default is 1000

Returns:

slp: SlopeValue or Pandas.DataFrame: For a single spectrum it will return a SlopeValue For a list of spectra, returns a pandas.DataFrame with the results.