ORIGIN¶

class muse_origin.ORIGIN(filename, name='origin', path='.', loglevel='DEBUG', logcolor=False, fieldmap=None, profiles=None, PSF=None, LBDA_FWHM_PSF=None, FWHM_PSF=None, PSF_size=25, param=None, imawhite=None, wfields=None)[source]¶

Bases: muse_origin.steps.LogMixin

ORIGIN: detectiOn and extRactIon of Galaxy emIssion liNes

This is the main class to interact with all the steps. An Origin object is mainly composed by: - cube data (raw data and covariance) - 1D dictionary of spectral profiles - MUSE PSF

Attributes

pathstr: Path where the ORIGIN data will be stored.
namestr: Name of the session and basename for the sources.
paramdict: Parameters values.
cube_rawarray (Nz, Ny, Nx): Raw data.
vararray (Nz, Ny, Nx): Variance.
wcsmpdaf.obj.WCS: RA-DEC coordinates.
wavempdaf.obj.WaveCoord: Spectral coordinates.
profileslist of array: Read the list of spectral profiles.
FWHM_profileslist: Read the list of FWHM of the spectral profiles.
wfieldsNone or list of arrays: List of weight maps (one per fields in the case of MUSE mosaic) None: just one field
PSFarray (Nz, PSF_size, PSF_size) or list of arrays: MUSE PSF (one per field)
LBDA_FWHM_PSF: list of floats: Value of the FWMH of the PSF in pixel for each wavelength step (mean of the fields).
FWHM_PSFfloat or list of float: Mean of the fwhm of the PSF in pixel (one per field).
imawhiteImage: White image
segmapImage: Segmentation map
cube_stdCube: standardized data for PCA. Result of step01.
cont_dctCube: DCT continuum. Result of step01.
ima_stdImage: Mean of standardized data for PCA along the wavelength axis. Result of step01.
ima_dctImage: Mean of DCT continuum cube along the wavelength axis. Result of step01.
nbAreasint: Number of area (segmentation) for the PCA.
areamapImage: PCA area. Result of step02.
testO2list of arrays (one per PCA area): Result of the O2 test (step03).
histO2list of arrays (one per PCA area): PCA histogram (step03).
binO2list of arrays (one per PCA area): Bins for the PCA histogram (step03).
thresO2list of float: For each area, threshold value (step03).
meaO2list of float: Location parameter of the Gaussian fit used to estimate the threshold (step03).
stdO2list of float: Scale parameter of the Gaussian fit used to estimate the threshold (step03).
cube_faintCube: Projection on the eigenvectors associated to the lower eigenvalues of the data cube (representing the faint signal). Result of step04.
mapO2Image: The numbers of iterations used by testO2 for each spaxel. Result of step04.
cube_correlCube: Cube of T_GLR values (step05).
cube_profileCube (type int): PSF profile associated to the T_GLR (step05).
maxmapImage: Map of maxima along the wavelength axis (step05).
cube_local_maxCube: Local maxima from max correlation (step05).
cube_local_minCube: Local maxima from min correlation (step05).
thresholdfloat: Estimated threshold (step06).
Pvalastropy.table.Table: Table with the purity results for each threshold (step06): - PVal_r : The purity function - index_pval : index value to plot - Det_m : Number of detections (-DATA) - Det_M : Number of detections (+DATA)
Cat0astropy.table.Table: Catalog returned by step07
Pval_compastropy.table.Table: Table with the purity results for each threshold in compl (step08): - PVal_r : The purity function - index_pval : index value to plot - Det_m : Number of detections (-DATA) - Det_M : Number of detections (+DATA)
Cat1astropy.table.Table: Catalog returned by step08
spectralist of Spectrum: Estimated lines. Result of step09.
Cat2astropy.table.Table: Catalog returned by step09.

Attributes Summary

`FWHM_profiles`	Read the list of FWHM of the spectral profiles.
`cube_raw`
`mask`
`nbAreas`	Number of area (segmentation) for the PCA.
`profiles`	Read the list of spectral profiles.
`threshold_correl`	Estimated threshold used to detect lines on local maxima of max correl.
`threshold_std`	Estimated threshold used to detect complementary lines on local maxima of std cube.
`var`

Methods Summary

`info`(self)	Prints the processing log.
`init`(cube[, fieldmap, profiles, PSF, …])	Create a ORIGIN object.
`load`(folder[, newname, loglevel, logcolor])	Load a previous session of ORIGIN.
`plot_NB`(self, src_ind[, ax1, ax2, ax3])	Plot the narrow band images.
`plot_PCA_threshold`(self, area[, pfa_test, …])	Plot the histogram and the threshold for the starting point of the PCA.
`plot_areas`(self[, ax])	Plot the 2D segmentation for PCA from self.step02_areas() on the test used to perform this segmentation.
`plot_mapPCA`(self[, area, iteration, ax])	Plot at a given iteration (or at the end) the number of times a spaxel got cleaned by the PCA.
`plot_min_max_hist`(self[, ax, comp])	Plot the histograms of local maxima and minima.
`plot_purity`(self[, comp, ax, log10, legend])	Draw number of sources per threshold computed in step06/step08.
`plot_segmaps`(self[, axes, figsize])	Plot the segmentation maps:
`plot_sources`(self, x, y[, circle, vmin, …])	Plot detected emission lines on the 2D map of maximum of the T_GLR values over the spectral channels.
`plot_step03_PCA_stat`(self[, cutoff, ax])	Plot the threshold value according to the area.
`plot_step03_PCA_threshold`(self[, log10, …])	Plot the histogram and the threshold for the starting point of the PCA.
`set_loglevel`(self, level)	Set the logging level for the console logger.
`stat`(self)	Print detection summary.
`status`(self)	Prints the processing status.
`timestat`(self[, table])	Print CPU usage by steps.
`write`(self[, path, erase])	Save the current session in a folder that will have the name of the ORIGIN object (self.name).

Attributes Documentation

FWHM_profiles¶: Read the list of FWHM of the spectral profiles.

cube_raw¶

mask¶

nbAreas¶: Number of area (segmentation) for the PCA.

profiles¶: Read the list of spectral profiles.

threshold_correl¶: Estimated threshold used to detect lines on local maxima of max correl.

threshold_std¶: Estimated threshold used to detect complementary lines on local maxima of std cube.

var¶

Methods Documentation

info(self)[source]¶: Prints the processing log.

classmethod init(cube, fieldmap=None, profiles=None, PSF=None, LBDA_FWHM_PSF=None, FWHM_PSF=None, PSF_size=25, name='origin', path='.', loglevel='DEBUG', logcolor=False)[source]¶

Create a ORIGIN object.

An Origin object is composed by: - cube data (raw data and covariance) - 1D dictionary of spectral profiles - MUSE PSF - parameters used to segment the cube in different zones.

Parameters

cubestr: Cube FITS file name
fieldmapstr: FITS file containing the field map (mosaic)
profilesstr: FITS of spectral profiles If None, a default dictionary of 20 profiles is used.
PSFstr: Cube FITS filename containing a MUSE PSF per wavelength. If None, PSF are computed with a Moffat function (13x13 pixels, beta=2.6, fwhm1=0.76, fwhm2=0.66, lambda1=4750, lambda2=7000)
LBDA_FWHM_PSF: list of float: Value of the FWMH of the PSF in pixel for each wavelength step (mean of the fields).
FWHM_PSFlist of float: FWHM of the PSFs in pixels, one per field.
PSF_sizeint: Spatial size of the PSF (when reconstructed from the cube header).
namestr: Name of this session and basename for the sources. ORIGIN.write() method saves the session in a folder that has this name. The ORIGIN.load() method will be used to load a session, continue it or create a new from it.
loglevelstr: Level for the logger (defaults to DEBUG).
logcolorbool: Use color for the logger levels.

classmethod load(folder, newname=None, loglevel=None, logcolor=None)[source]¶

Load a previous session of ORIGIN.

ORIGIN.write() method saves a session in a folder that has the name of the ORIGIN object (self.name).

Parameters

folderstr: Folder name (with the relative path) where the ORIGIN data have been stored.
newnamestr: New name for this session. This parameter lets the user to load a previous session but continue in a new one. If None, the user will continue the loaded session.
loglevelstr: Level for the logger (by default reuse the saved level).
logcolorbool: Use color for the logger levels.

plot_NB(self, src_ind, ax1=None, ax2=None, ax3=None)[source]¶

Plot the narrow band images.

Parameters

src_indint: Index of the object in self.Cat0.
ax1matplotlib.Axes: The Axes instance in which the NB image around the source is drawn.
ax2matplotlib.Axes: The Axes instance in which a other NB image for check is drawn.
ax3matplotlib.Axes: The Axes instance in which the difference is drawn.

plot_PCA_threshold(self, area, pfa_test='step03', log10=False, legend=True, xlim=None, ax=None)[source]¶

Plot the histogram and the threshold for the starting point of the PCA.

Parameters

areaint in [1, nbAreas]: Area ID
pfa_testfloat or str: PFA of the test (if ‘step03’, the value set during step03 is used)
log10bool: Draw histogram in logarithmic scale or not
legendbool: If true, write pfa and threshold values as legend
xlim(float, float): Set the data limits for the x-axis
axmatplotlib.Axes: Axes instance in which the image is drawn

plot_areas(self, ax=None, **kwargs)[source]¶

Plot the 2D segmentation for PCA from self.step02_areas() on the test used to perform this segmentation.

Parameters

axmatplotlib.Axes: The Axes instance in which the image is drawn.
kwargsmatplotlib.artist.Artist: Optional extra keyword/value arguments to be passed to ax.imshow().

plot_mapPCA(self, area=None, iteration=None, ax=None, **kwargs)[source]¶

Plot at a given iteration (or at the end) the number of times a spaxel got cleaned by the PCA.

Parameters

area: int in [1, nbAreas]: if None draw the full map for all areas
iterationint: Display the nuisance/bacground pixels at iteration k
axmatplotlib.Axes: The Axes instance in which the image is drawn
kwargsmatplotlib.artist.Artist: Optional extra keyword/value arguments to be passed to ax.imshow().

plot_min_max_hist(self, ax=None, comp=False)[source]¶: Plot the histograms of local maxima and minima.

plot_purity(self, comp=False, ax=None, log10=False, legend=True)[source]¶

Draw number of sources per threshold computed in step06/step08.

Parameters

compbool: If True, plot purity curves for the complementary lines (step08).
axmatplotlib.Axes: The Axes instance in which the image is drawn.
log10bool: To draw histogram in logarithmic scale or not.
legendbool: To draw the legend.

plot_segmaps(self, axes=None, figsize=(6, 6))[source]¶

Plot the segmentation maps:

segmap_cont: segmentation map computed on the white-light image.
segmap_merged: segmentation map merged with the cont one and another one computed on the residual.
segmap_purity: combines self.segmap and a segmentation on the maxmap.
segmap_label: segmentation map used for the catalog, either the one given as input, otherwise self.segmap_cont.

plot_sources(self, x, y, circle=False, vmin=0, vmax=30, title=None, ax=None, **kwargs)[source]¶

Plot detected emission lines on the 2D map of maximum of the T_GLR values over the spectral channels.

Parameters

xarray: Coordinates along the x-axis of the estimated lines in pixels.
yarray: Coordinates along the y-axis of the estimated lines in pixels.
circlebool: If true, plot circles with a diameter equal to the mean of the fwhm of the PSF.
vminfloat: Minimum pixel value to use for the scaling.
vmaxfloat: Maximum pixel value to use for the scaling.
titlestr: An optional title for the figure (None by default).
axmatplotlib.Axes: the Axes instance in which the image is drawn
kwargsmatplotlib.artist.Artist: Optional arguments passed to ax.imshow().

plot_step03_PCA_stat(self, cutoff=5, ax=None)[source]¶

Plot the threshold value according to the area.

Median Absolute Deviation is used to find outliers.

Parameters

cutofffloat: Median Absolute Deviation cutoff
axmatplotlib.Axes: The Axes instance in which the image is drawn

plot_step03_PCA_threshold(self, log10=False, ncol=3, legend=True, xlim=None, fig=None, **fig_kw)[source]¶

Plot the histogram and the threshold for the starting point of the PCA.

Parameters

log10bool: Draw histogram in logarithmic scale or not
ncolint: Number of colomns in the subplots
legendbool: If true, write pfa and threshold values as legend
xlim(float, float): Set the data limits for the x-axes
figmatplotlib.Figure: Figure instance in which the image is drawn
**fig_kwmatplotlib.artist.Artist: All additional keyword arguments are passed to the figure() call.

set_loglevel(self, level)[source]¶: Set the logging level for the console logger.

stat(self)[source]¶: Print detection summary.

status(self)[source]¶: Prints the processing status.

timestat(self, table=False)[source]¶

Print CPU usage by steps.

If table is True, an astropy.table.Table is returned.

write(self, path=None, erase=False)[source]¶

Save the current session in a folder that will have the name of the ORIGIN object (self.name).

The ORIGIN.load(folder, newname=None) method will be used to load a session. The parameter newname will let the user to load a session but continue in a new one.

Parameters

pathstr: Path where the folder (self.name) will be stored.
erasebool: Remove the folder if it exists.