def make_white_image(inputfile, outputfile, verbose=False):
t0 = time()
if outputfile is None:
outputfile = inputfile.replace('DATACUBE', 'IMAGE')
if outputfile == inputfile:
sys.exit('Input and output files are identical')
print('Creating white light image {}'.format(outputfile))
cube = Cube(inputfile, convert_float64=False)
if verbose:
cube.info()
im = cube.mean(axis=0)
im.write(outputfile, savemask='nan')
if verbose:
print('Execution time {:.3f} seconds.'.format(time() - t0))
class 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
----------
path : str
Path where the ORIGIN data will be stored.
name : str
Name of the session and basename for the sources.
param : dict
Parameters values.
cube_raw : array (Nz, Ny, Nx)
Raw data.
var : array (Nz, Ny, Nx)
Variance.
wcs : `mpdaf.obj.WCS`
RA-DEC coordinates.
wave : `mpdaf.obj.WaveCoord`
Spectral coordinates.
profiles : list of array
List of spectral profiles to test
FWHM_profiles : list
FWHM of the profiles in pixels.
wfields : None or list of arrays
List of weight maps (one per fields in the case of MUSE mosaic)
None: just one field
PSF : array (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_PSF : float or list of float
Mean of the fwhm of the PSF in pixel (one per field).
imawhite : `~mpdaf.obj.Image`
White image
segmap : `~mpdaf.obj.Image`
Segmentation map
cube_std : `~mpdaf.obj.Cube`
standardized data for PCA. Result of step01.
cont_dct : `~mpdaf.obj.Cube`
DCT continuum. Result of step01.
ima_std : `~mpdaf.obj.Image`
Mean of standardized data for PCA along the wavelength axis.
Result of step01.
ima_dct : `~mpdaf.obj.Image`
Mean of DCT continuum cube along the wavelength axis.
Result of step01.
nbAreas : int
Number of area (segmentation) for the PCA computation.
Result of step02.
areamap : `~mpdaf.obj.Image`
PCA area. Result of step02.
testO2 : list of arrays (one per PCA area)
Result of the O2 test (step03).
histO2 : list of arrays (one per PCA area)
PCA histogram (step03).
binO2 : list of arrays (one per PCA area)
Bins for the PCA histogram (step03).
thresO2 : list of float
For each area, threshold value (step03).
meaO2 : list of float
Location parameter of the Gaussian fit used to
estimate the threshold (step03).
stdO2 : list of float
Scale parameter of the Gaussian fit used to
estimate the threshold (step03).
cube_faint : `~mpdaf.obj.Cube`
Projection on the eigenvectors associated to the lower eigenvalues
of the data cube (representing the faint signal). Result of step04.
mapO2 : `~mpdaf.obj.Image`
The numbers of iterations used by testO2 for each spaxel.
Result of step04.
cube_correl : `~mpdaf.obj.Cube`
Cube of T_GLR values (step05).
cube_profile : `~mpdaf.obj.Cube` (type int)
PSF profile associated to the T_GLR (step05).
maxmap : `~mpdaf.obj.Image`
Map of maxima along the wavelength axis (step05).
cube_local_max : `~mpdaf.obj.Cube`
Local maxima from max correlation (step05).
cube_local_min : `~mpdaf.obj.Cube`
Local maxima from min correlation (step05).
threshold : float
Estimated threshold (step06).
Pval : `astropy.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)
Cat0 : `astropy.table.Table`
Catalog returned by step07
Pval_comp : `astropy.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)
Cat1 : `astropy.table.Table`
Catalog returned by step08
spectra : list of `~mpdaf.obj.Spectrum`
Estimated lines. Result of step09.
Cat2 : `astropy.table.Table`
Catalog returned by step09.
"""
def __init__(
self,
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,
):
self.path = path
self.name = name
self.outpath = os.path.join(path, name)
self.param = param or {}
self.file_handler = None
os.makedirs(self.outpath, exist_ok=True)
# stdout & file logger
setup_logging(
name="muse_origin",
level=loglevel,
color=logcolor,
fmt="%(levelname)-05s: %(message)s",
stream=sys.stdout,
)
self.logger = logging.getLogger("muse_origin")
self._setup_logfile(self.logger)
self.param["loglevel"] = loglevel
self.param["logcolor"] = logcolor
self._loginfo("Step 00 - Initialization (ORIGIN v%s)", __version__)
# dict of Step instances, indexed by step names
self.steps = OrderedDict()
# dict containing the data attributes of each step, to expose them on
# the ORIGIN object
self._dataobjs = {}
for i, cls in enumerate(steps.STEPS, start=1):
# Instantiate the step object, give it a step number
step = cls(self, i, self.param)
# force its signature to be the same as step.run (without the
# ORIGIN instance), which allows to see its arguments and their
# default value.
sig = inspect.signature(step.run)
step.__signature__ = sig.replace(parameters=[
p for p in sig.parameters.values() if p.name != "orig"
])
self.steps[step.name] = step
# Insert the __call__ method of the step in the ORIGIN object. This
# allows to run a step with a method like "step01_preprocessing".
self.__dict__[step.method_name] = step
for name, _ in step._dataobjs:
self._dataobjs[name] = step
# MUSE data cube
self._loginfo("Read the Data Cube %s", filename)
self.param["cubename"] = filename
self.cube = Cube(filename)
self.Nz, self.Ny, self.Nx = self.shape = self.cube.shape
# RA-DEC coordinates
self.wcs = self.cube.wcs
# spectral coordinates
self.wave = self.cube.wave
# List of spectral profile
if profiles is None:
profiles = os.path.join(CURDIR, "Dico_3FWHM.fits")
self.param["profiles"] = profiles
# FSF
self.param["fieldmap"] = fieldmap
self.param["PSF_size"] = PSF_size
self._read_fsf(
self.cube,
fieldmap=fieldmap,
wfields=wfields,
PSF=PSF,
LBDA_FWHM_PSF=LBDA_FWHM_PSF,
FWHM_PSF=FWHM_PSF,
PSF_size=PSF_size,
)
# additional images
self.ima_white = imawhite if imawhite else self.cube.mean(axis=0)
self.testO2, self.histO2, self.binO2 = None, None, None
self._loginfo("00 Done")
def __getattr__(self, name):
# Use __getattr__ to provide access to the steps data attributes
# via the ORIGIN object. This will also trigger the loading of
# the objects if needed.
if name in self._dataobjs:
return getattr(self._dataobjs[name], name)
else:
raise AttributeError(f"unknown attribute {name}")
def __dir__(self):
return (super().__dir__() + list(self._dataobjs.keys()) +
[o.method_name for o in self.steps.values()])
@lazyproperty
def cube_raw(self):
# Flux - set to 0 the Nan
return self.cube.data.filled(fill_value=0)
@lazyproperty
def mask(self):
return self.cube._mask
@lazyproperty
def var(self):
# variance - set to Inf the Nan
return self.cube.var.filled(np.inf)
@classmethod
def init(
cls,
cube,
fieldmap=None,
profiles=None,
PSF=None,
LBDA_FWHM_PSF=None,
FWHM_PSF=None,
PSF_size=25,
name="origin",
path=".",
loglevel="DEBUG",
logcolor=False,
):
"""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
----------
cube : str
Cube FITS file name
fieldmap : str
FITS file containing the field map (mosaic)
profiles : str
FITS of spectral profiles
If None, a default dictionary of 20 profiles is used.
PSF : str
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_PSF : list of float
FWHM of the PSFs in pixels, one per field.
PSF_size : int
Spatial size of the PSF (when reconstructed from the cube header).
name : str
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.
loglevel : str
Level for the logger (defaults to DEBUG).
logcolor : bool
Use color for the logger levels.
"""
return cls(
cube,
path=path,
name=name,
fieldmap=fieldmap,
profiles=profiles,
PSF=PSF,
LBDA_FWHM_PSF=LBDA_FWHM_PSF,
FWHM_PSF=FWHM_PSF,
PSF_size=PSF_size,
loglevel=loglevel,
logcolor=logcolor,
)
@classmethod
@timeit
def load(cls, folder, newname=None, loglevel=None, logcolor=None):
"""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
----------
folder : str
Folder name (with the relative path) where the ORIGIN data
have been stored.
newname : str
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.
loglevel : str
Level for the logger (by default reuse the saved level).
logcolor : bool
Use color for the logger levels.
"""
path = os.path.dirname(os.path.abspath(folder))
name = os.path.basename(folder)
with open(f"{folder}/{name}.yaml", "r") as stream:
param = load_yaml(stream)
if "FWHM PSF" in param:
FWHM_PSF = np.asarray(param["FWHM PSF"])
else:
FWHM_PSF = None
if "LBDA_FWHM PSF" in param:
LBDA_FWHM_PSF = np.asarray(param["LBDA FWHM PSF"])
else:
LBDA_FWHM_PSF = None
if os.path.isfile(param["PSF"]):
PSF = param["PSF"]
else:
if os.path.isfile("%s/cube_psf.fits" % folder):
PSF = "%s/cube_psf.fits" % folder
else:
PSF_files = glob.glob("%s/cube_psf_*.fits" % folder)
if len(PSF_files) == 0:
PSF = None
elif len(PSF_files) == 1:
PSF = PSF_files[0]
else:
PSF = sorted(PSF_files)
wfield_files = glob.glob("%s/wfield_*.fits" % folder)
if len(wfield_files) == 0:
wfields = None
else:
wfields = sorted(wfield_files)
# step0
if os.path.isfile("%s/ima_white.fits" % folder):
ima_white = Image("%s/ima_white.fits" % folder)
else:
ima_white = None
if newname is not None:
# copy outpath to the new path
shutil.copytree(os.path.join(path, name),
os.path.join(path, newname))
name = newname
loglevel = loglevel if loglevel is not None else param["loglevel"]
logcolor = logcolor if logcolor is not None else param["logcolor"]
obj = cls(
path=path,
name=name,
param=param,
imawhite=ima_white,
loglevel=loglevel,
logcolor=logcolor,
filename=param["cubename"],
fieldmap=param["fieldmap"],
wfields=wfields,
profiles=param["profiles"],
PSF=PSF,
FWHM_PSF=FWHM_PSF,
LBDA_FWHM_PSF=LBDA_FWHM_PSF,
)
for step in obj.steps.values():
step.load(obj.outpath)
# special case for step3
NbAreas = param.get("nbareas")
if NbAreas is not None:
if os.path.isfile("%s/testO2_1.txt" % folder):
obj.testO2 = [
np.loadtxt("%s/testO2_%d.txt" % (folder, area), ndmin=1)
for area in range(1, NbAreas + 1)
]
if os.path.isfile("%s/histO2_1.txt" % folder):
obj.histO2 = [
np.loadtxt("%s/histO2_%d.txt" % (folder, area), ndmin=1)
for area in range(1, NbAreas + 1)
]
if os.path.isfile("%s/binO2_1.txt" % folder):
obj.binO2 = [
np.loadtxt("%s/binO2_%d.txt" % (folder, area), ndmin=1)
for area in range(1, NbAreas + 1)
]
return obj
def info(self):
"""Prints the processing log."""
with open(self.logfile) as f:
for line in f:
if line.find("Done") == -1:
print(line, end="")
def status(self):
"""Prints the processing status."""
for name, step in self.steps.items():
print(f"- {step.idx:02d}, {name}: {step.status.name}")
def _setup_logfile(self, logger):
if self.file_handler is not None:
# Remove the handlers before adding a new one
self.file_handler.close()
logger.handlers.remove(self.file_handler)
self.logfile = os.path.join(self.outpath, self.name + ".log")
self.file_handler = RotatingFileHandler(self.logfile, "a", 1000000, 1)
self.file_handler.setLevel(logging.DEBUG)
formatter = logging.Formatter("%(asctime)s %(message)s")
self.file_handler.setFormatter(formatter)
logger.addHandler(self.file_handler)
def set_loglevel(self, level):
"""Set the logging level for the console logger."""
handler = next(h for h in self.logger.handlers
if isinstance(h, logging.StreamHandler))
handler.setLevel(level)
self.param["loglevel"] = level
@property
def nbAreas(self):
"""Number of area (segmentation) for the PCA."""
return self.param.get("nbareas")
@property
def threshold_correl(self):
"""Estimated threshold used to detect lines on local maxima of max
correl."""
return self.param.get("threshold")
@threshold_correl.setter
def threshold_correl(self, value):
self.param["threshold"] = value
@property
def threshold_std(self):
"""Estimated threshold used to detect complementary lines on local
maxima of std cube."""
return self.param.get("threshold_std")
@threshold_std.setter
def threshold_std(self, value):
self.param["threshold_std"] = value
@lazyproperty
def profiles(self):
"""Read the list of spectral profiles."""
profiles = self.param["profiles"]
self._loginfo("Load dictionary of spectral profile %s", profiles)
with fits.open(profiles) as hdul:
profiles = [hdu.data for hdu in hdul[1:]]
# check that the profiles have the same size
if len({p.shape[0] for p in profiles}) != 1:
raise ValueError("The profiles must have the same size")
return profiles
@lazyproperty
def FWHM_profiles(self):
"""Read the list of FWHM of the spectral profiles."""
with fits.open(self.param["profiles"]) as hdul:
return [hdu.header["FWHM"] for hdu in hdul[1:]]
def _read_fsf(
self,
cube,
fieldmap=None,
wfields=None,
PSF=None,
LBDA_FWHM_PSF=None,
FWHM_PSF=None,
PSF_size=25,
):
"""Read FSF cube(s), with fieldmap in the case of MUSE mosaic.
There are two ways to specify the PSF informations:
- with the ``PSF``, ``FWHM_PSF``, and ``LBDA_FWHM`` parameters.
- or read from the cube header and fieldmap.
If there are multiple fields, for a mosaic, we also need weight maps.
If the cube contains a FSF model and a fieldmap is given, these weight
maps are computed automatically.
Parameters
----------
cube : mpdaf.obj.Cube
The input datacube.
fieldmap : str
FITS file containing the field map (mosaic).
wfields : list of str
List of weight maps (one per fields in the case of MUSE mosaic).
PSF : str or list of str
Cube FITS filename containing a MUSE PSF per wavelength, or a list
of filenames for multiple fields (mosaic).
LBDA_FWHM_PSF: list of float
Value of the FWMH of the PSF in pixel for each wavelength step
(mean of the fields).
FWHM_PSF : list of float
FWHM of the PSFs in pixels, one per field.
PSF_size : int
Spatial size of the PSF (when reconstructed from the cube header).
"""
self.wfields = None
info = self.logger.info
if PSF is None or FWHM_PSF is None or LBDA_FWHM_PSF is None:
info("Compute FSFs from the datacube FITS header keywords")
if "FSFMODE" not in cube.primary_header:
raise ValueError(
"missing PSF keywords in the cube FITS header")
# FSF created from FSF*** keywords
try:
from mpdaf.MUSE import FSFModel
except ImportError:
sys.exit("you must upgrade MPDAF")
fsf = FSFModel.read(cube)
lbda = cube.wave.coord()
shape = (PSF_size, PSF_size)
if isinstance(fsf, FSFModel): # just one FSF
self.PSF = fsf.get_3darray(lbda, shape)
self.LBDA_FWHM_PSF = fsf.get_fwhm(lbda, unit="pix")
self.FWHM_PSF = np.mean(self.LBDA_FWHM_PSF)
# mean of the fwhm of the FSF in pixel
info("mean FWHM of the FSFs = %.2f pixels", self.FWHM_PSF)
else:
self.PSF = [f.get_3darray(lbda, shape) for f in fsf]
fwhm = np.array([f.get_fwhm(lbda, unit="pix") for f in fsf])
self.LBDA_FWHM_PSF = np.mean(fwhm, axis=0)
self.FWHM_PSF = np.mean(fwhm, axis=1)
for i, fwhm in enumerate(self.FWHM_PSF):
info("mean FWHM of the FSFs (field %d) = %.2f pixels", i,
fwhm)
info("Compute weight maps from field map %s", fieldmap)
fmap = FieldsMap(fieldmap, nfields=len(fsf))
# weighted field map
self.wfields = fmap.compute_weights()
self.param["PSF"] = cube.primary_header["FSFMODE"]
else:
self.LBDA_FWHM_PSF = LBDA_FWHM_PSF
if isinstance(PSF, str):
info("Load FSFs from %s", PSF)
self.param["PSF"] = PSF
self.PSF = fits.getdata(PSF)
if self.PSF.shape[1] != self.PSF.shape[2]:
raise ValueError("PSF must be a square image.")
if not self.PSF.shape[1] % 2:
raise ValueError(
"The spatial size of the PSF must be odd.")
if self.PSF.shape[0] != self.shape[0]:
raise ValueError("PSF and data cube have not the same"
"dimensions along the spectral axis.")
# mean of the fwhm of the FSF in pixel
self.FWHM_PSF = np.mean(FWHM_PSF)
self.param["FWHM PSF"] = FWHM_PSF.tolist()
info("mean FWHM of the FSFs = %.2f pixels", self.FWHM_PSF)
else:
nfields = len(PSF)
self.wfields = []
self.PSF = []
self.FWHM_PSF = list(FWHM_PSF)
for n in range(nfields):
info("Load FSF from %s", PSF[n])
self.PSF.append(fits.getdata(PSF[n]))
info("Load weight maps from %s", wfields[n])
self.wfields.append(fits.getdata(wfields[n]))
info("mean FWHM of the FSFs (field %d) = %.2f pixels", n,
FWHM_PSF[n])
self.param["FWHM PSF"] = self.FWHM_PSF.tolist()
self.param["LBDA FWHM PSF"] = self.LBDA_FWHM_PSF.tolist()
@timeit
def write(self, path=None, erase=False):
"""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
----------
path : str
Path where the folder (self.name) will be stored.
erase : bool
Remove the folder if it exists.
"""
self._loginfo("Writing...")
# adapt session if path changes
if path is not None and path != self.path:
if not os.path.exists(path):
raise ValueError(f"path does not exist: {path}")
self.path = path
outpath = os.path.join(path, self.name)
# copy outpath to the new path
shutil.copytree(self.outpath, outpath)
self.outpath = outpath
self._setup_logfile(self.logger)
if erase:
shutil.rmtree(self.outpath)
os.makedirs(self.outpath, exist_ok=True)
# PSF
if isinstance(self.PSF, list):
for i, psf in enumerate(self.PSF):
cube = Cube(data=psf, mask=np.ma.nomask, copy=False)
cube.write(os.path.join(self.outpath,
"cube_psf_%02d.fits" % i))
else:
cube = Cube(data=self.PSF, mask=np.ma.nomask, copy=False)
cube.write(os.path.join(self.outpath, "cube_psf.fits"))
if self.wfields is not None:
for i, wfield in enumerate(self.wfields):
im = Image(data=wfield, mask=np.ma.nomask)
im.write(os.path.join(self.outpath, "wfield_%02d.fits" % i))
if self.ima_white is not None:
self.ima_white.write("%s/ima_white.fits" % self.outpath)
for step in self.steps.values():
step.dump(self.outpath)
# parameters in .yaml
with open(f"{self.outpath}/{self.name}.yaml", "w") as stream:
dump_yaml(self.param, stream)
# step3 - saving this manually for now
if self.nbAreas is not None:
if self.testO2 is not None:
for area in range(1, self.nbAreas + 1):
np.savetxt("%s/testO2_%d.txt" % (self.outpath, area),
self.testO2[area - 1])
if self.histO2 is not None:
for area in range(1, self.nbAreas + 1):
np.savetxt("%s/histO2_%d.txt" % (self.outpath, area),
self.histO2[area - 1])
if self.binO2 is not None:
for area in range(1, self.nbAreas + 1):
np.savetxt("%s/binO2_%d.txt" % (self.outpath, area),
self.binO2[area - 1])
self._loginfo("Current session saved in %s", self.outpath)
def plot_areas(self, ax=None, **kwargs):
""" Plot the 2D segmentation for PCA from self.step02_areas()
on the test used to perform this segmentation.
Parameters
----------
ax : matplotlib.Axes
The Axes instance in which the image is drawn.
kwargs : matplotlib.artist.Artist
Optional extra keyword/value arguments to be passed to ``ax.imshow()``.
"""
if ax is None:
ax = plt.gca()
kwargs.setdefault("cmap", "jet")
kwargs.setdefault("alpha", 0.7)
kwargs.setdefault("interpolation", "nearest")
kwargs["origin"] = "lower"
cax = ax.imshow(self.areamap._data, **kwargs)
i0 = np.min(self.areamap._data)
i1 = np.max(self.areamap._data)
if i0 != i1:
from matplotlib.colors import BoundaryNorm
from mpl_toolkits.axes_grid1 import make_axes_locatable
n = i1 - i0 + 1
bounds = np.linspace(i0, i1 + 1, n + 1) - 0.5
norm = BoundaryNorm(bounds, n + 1)
divider = make_axes_locatable(ax)
cax2 = divider.append_axes("right", size="5%", pad=1)
plt.colorbar(
cax,
cax=cax2,
cmap=kwargs["cmap"],
norm=norm,
spacing="proportional",
ticks=bounds + 0.5,
boundaries=bounds,
format="%1i",
)
def plot_step03_PCA_threshold(self,
log10=False,
ncol=3,
legend=True,
xlim=None,
fig=None,
**fig_kw):
""" Plot the histogram and the threshold for the starting point of the PCA.
Parameters
----------
log10 : bool
Draw histogram in logarithmic scale or not
ncol : int
Number of colomns in the subplots
legend : bool
If true, write pfa and threshold values as legend
xlim : (float, float)
Set the data limits for the x-axes
fig : matplotlib.Figure
Figure instance in which the image is drawn
**fig_kw : matplotlib.artist.Artist
All additional keyword arguments are passed to the figure() call.
"""
if self.nbAreas is None:
raise ValueError("Run the step 02 to initialize self.nbAreas")
if fig is None:
fig = plt.figure()
if self.nbAreas <= ncol:
n = 1
m = self.nbAreas
else:
n = self.nbAreas // ncol
m = ncol
if (n * m) < self.nbAreas:
n = n + 1
for area in range(1, self.nbAreas + 1):
if area == 1:
ax = fig.add_subplot(n, m, area, **fig_kw)
else:
ax = fig.add_subplot(n, m, area, sharey=fig.axes[0], **fig_kw)
self.plot_PCA_threshold(area, "step03", log10, legend, xlim, ax)
# Fine-tune figure
for a in fig.axes[:-1]:
a.set_xlabel("")
for a in fig.axes[1:]:
a.set_ylabel("")
plt.setp([a.get_yticklabels() for a in fig.axes], visible=False)
plt.setp([a.get_yticklabels() for a in fig.axes[0::m]], visible=True)
plt.setp([a.get_yticklines() for a in fig.axes], visible=False)
plt.setp([a.get_yticklines() for a in fig.axes[0::m]], visible=True)
fig.subplots_adjust(wspace=0)
if xlim is not None:
plt.setp([a.get_xticklabels() for a in fig.axes[:-m]],
visible=False)
plt.setp([a.get_xticklines() for a in fig.axes[:-m]],
visible=False)
fig.subplots_adjust(hspace=0)
def plot_step03_PCA_stat(self, cutoff=5, ax=None):
"""Plot the threshold value according to the area.
Median Absolute Deviation is used to find outliers.
Parameters
----------
cutoff : float
Median Absolute Deviation cutoff
ax : matplotlib.Axes
The Axes instance in which the image is drawn
"""
if self.nbAreas is None:
raise ValueError("Run the step 02 to initialize self.nbAreas")
if self.thresO2 is None:
raise ValueError("Run the step 03 to compute the threshold values")
if ax is None:
ax = plt.gca()
ax.plot(np.arange(1, self.nbAreas + 1), self.thresO2, "+")
med = np.median(self.thresO2)
diff = np.absolute(self.thresO2 - med)
mad = np.median(diff)
if mad != 0:
ksel = (diff / mad) > cutoff
if ksel.any():
ax.plot(
np.arange(1, self.nbAreas + 1)[ksel],
np.asarray(self.thresO2)[ksel],
"ro",
)
ax.set_xlabel("area")
ax.set_ylabel("Threshold")
ax.set_title(f"PCA threshold (med={med:.2f}, mad= {mad:.2f})")
def plot_PCA_threshold(self,
area,
pfa_test="step03",
log10=False,
legend=True,
xlim=None,
ax=None):
""" Plot the histogram and the threshold for the starting point of the PCA.
Parameters
----------
area : int in [1, nbAreas]
Area ID
pfa_test : float or str
PFA of the test (if 'step03', the value set during step03 is used)
log10 : bool
Draw histogram in logarithmic scale or not
legend : bool
If true, write pfa and threshold values as legend
xlim : (float, float)
Set the data limits for the x-axis
ax : matplotlib.Axes
Axes instance in which the image is drawn
"""
if self.nbAreas is None:
raise ValueError("Run the step 02 to initialize self.nbAreas")
if pfa_test == "step03":
param = self.param["compute_PCA_threshold"]["params"]
if "pfa_test" in param:
pfa_test = param["pfa_test"]
hist = self.histO2[area - 1]
bins = self.binO2[area - 1]
thre = self.thresO2[area - 1]
mea = self.meaO2[area - 1]
std = self.stdO2[area - 1]
else:
raise ValueError(
"pfa_test param is None: set a value or run the Step03")
else:
if self.cube_std is None:
raise ValueError("Run the step 01 to initialize self.cube_std")
# limits of each spatial zone
ksel = self.areamap._data == area
# Data in this spatio-spectral zone
cube_temp = self.cube_std._data[:, ksel]
# Compute_PCA_threshold
from .lib_origin import Compute_PCA_threshold
testO2, hist, bins, thre, mea, std = Compute_PCA_threshold(
cube_temp, pfa_test)
if ax is None:
ax = plt.gca()
from scipy import stats
center = (bins[:-1] + bins[1:]) / 2
gauss = stats.norm.pdf(center, loc=mea, scale=std)
gauss *= hist.max() / gauss.max()
if log10:
with warnings.catch_warnings():
warnings.simplefilter("ignore")
gauss = np.log10(gauss)
hist = np.log10(hist)
ax.plot(center, hist, "-k")
ax.plot(center, hist, ".r")
ax.plot(center, gauss, "-b", alpha=0.5)
ax.axvline(thre, color="b", lw=2, alpha=0.5)
ax.grid()
if xlim is None:
ax.set_xlim((center.min(), center.max()))
else:
ax.set_xlim(xlim)
ax.set_xlabel("frequency")
ax.set_ylabel("value")
kwargs = dict(transform=ax.transAxes,
bbox=dict(facecolor="red", alpha=0.5))
if legend:
text = "zone %d\npfa %.2f\nthreshold %.2f" % (area, pfa_test, thre)
ax.text(0.1, 0.8, text, **kwargs)
else:
ax.text(0.9, 0.9, "%d" % area, **kwargs)
def plot_mapPCA(self, area=None, iteration=None, ax=None, **kwargs):
""" 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
iteration : int
Display the nuisance/bacground pixels at iteration k
ax : matplotlib.Axes
The Axes instance in which the image is drawn
kwargs : matplotlib.artist.Artist
Optional extra keyword/value arguments to be passed to ``ax.imshow()``.
"""
if self.mapO2 is None:
raise ValueError("Run the step 04 to initialize self.mapO2")
themap = self.mapO2.copy()
title = "Number of times the spaxel got cleaned by the PCA"
if iteration is not None:
title += "\n%d iterations" % iteration
if area is not None:
mask = np.ones_like(self.mapO2._data, dtype=np.bool)
mask[self.areamap._data == area] = False
themap._mask = mask
title += " (zone %d)" % area
if iteration is not None:
themap[themap._data < iteration] = np.ma.masked
if ax is None:
ax = plt.gca()
kwargs.setdefault("cmap", "jet")
themap.plot(title=title, colorbar="v", ax=ax, **kwargs)
def plot_purity(self, comp=False, ax=None, log10=False, legend=True):
"""Draw number of sources per threshold computed in step06/step08.
Parameters
----------
comp : bool
If True, plot purity curves for the complementary lines (step08).
ax : matplotlib.Axes
The Axes instance in which the image is drawn.
log10 : bool
To draw histogram in logarithmic scale or not.
legend : bool
To draw the legend.
"""
if ax is None:
ax = plt.gca()
if comp:
threshold = self.threshold_std
purity = self.param["purity_std"]
Pval = self.Pval_comp
else:
threshold = self.threshold_correl
purity = self.param["purity"]
Pval = self.Pval
if Pval is None:
raise ValueError("Run the step 06")
Tval_r = Pval["Tval_r"]
ax2 = ax.twinx()
ax2.plot(Tval_r, Pval["Pval_r"], "y.-", label="purity")
ax.plot(Tval_r, Pval["Det_M"], "b.-", label="n detections (+DATA)")
ax.plot(Tval_r, Pval["Det_m"], "g.-", label="n detections (-DATA)")
ax2.plot(threshold, purity, "xr")
if log10:
ax.set_yscale("log")
ax2.set_yscale("log")
ym, yM = ax.get_ylim()
ax.plot(
[threshold, threshold],
[ym, yM],
"r",
alpha=0.25,
lw=2,
label="automatic threshold",
)
ax.set_ylim((ym, yM))
ax.set_xlabel("Threshold")
ax2.set_ylabel("Purity")
ax.set_ylabel("Number of detections")
ax.set_title("threshold %f" % threshold)
h1, l1 = ax.get_legend_handles_labels()
h2, l2 = ax2.get_legend_handles_labels()
if legend:
ax.legend(h1 + h2, l1 + l2, loc=2)
def plot_NB(self, src_ind, ax1=None, ax2=None, ax3=None):
"""Plot the narrow band images.
Parameters
----------
src_ind : int
Index of the object in self.Cat0.
ax1 : matplotlib.Axes
The Axes instance in which the NB image around the source is drawn.
ax2 : matplotlib.Axes
The Axes instance in which a other NB image for check is drawn.
ax3 : matplotlib.Axes
The Axes instance in which the difference is drawn.
"""
if self.Cat0 is None:
raise ValueError("Run the step 05 to initialize self.Cat0")
if ax1 is None and ax2 is None and ax3 is None:
fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(12, 4))
# Coordinates of the source
x0 = self.Cat0[src_ind]["x0"]
y0 = self.Cat0[src_ind]["y0"]
z0 = self.Cat0[src_ind]["z0"]
# Larger spatial ranges for the plots
longxy0 = 20
y01 = max(0, y0 - longxy0)
y02 = min(self.shape[1], y0 + longxy0 + 1)
x01 = max(0, x0 - longxy0)
x02 = min(self.shape[2], x0 + longxy0 + 1)
# Coordinates in this window
y00 = y0 - y01
x00 = x0 - x01
# spectral profile
num_prof = self.Cat0[src_ind]["profile"]
profil0 = self.profiles[num_prof]
# length of the spectral profile
profil1 = profil0[profil0 > 1e-13]
long0 = profil1.shape[0]
# half-length of the spectral profile
longz = long0 // 2
# spectral range
intz1 = max(0, z0 - longz)
intz2 = min(self.shape[0], z0 + longz + 1)
# subcube for the plot
cube_test_plot = self.cube_raw[intz1:intz2, y01:y02, x01:x02]
wcs = self.wcs[y01:y02, x01:x02]
# controle cube
nb_ranges = 3
if (z0 + longz + nb_ranges * long0) < self.shape[0]:
intz1c = intz1 + nb_ranges * long0
intz2c = intz2 + nb_ranges * long0
else:
intz1c = intz1 - nb_ranges * long0
intz2c = intz2 - nb_ranges * long0
cube_controle_plot = self.cube_raw[intz1c:intz2c, y01:y02, x01:x02]
# (1/sqrt(2)) * difference of the 2 sububes
diff_cube_plot = (1 / np.sqrt(2)) * (cube_test_plot -
cube_controle_plot)
if ax1 is not None:
ax1.plot(x00, y00, "m+")
ima_test_plot = Image(data=cube_test_plot.sum(axis=0), wcs=wcs)
title = "cube test - (%d,%d)\n" % (x0, y0)
title += "lambda=%d int=[%d,%d[" % (z0, intz1, intz2)
ima_test_plot.plot(colorbar="v", title=title, ax=ax1)
ax1.get_xaxis().set_visible(False)
ax1.get_yaxis().set_visible(False)
if ax2 is not None:
ax2.plot(x00, y00, "m+")
ima_controle_plot = Image(data=cube_controle_plot.sum(axis=0),
wcs=wcs)
title = "check - (%d,%d)\n" % (x0, y0) + "int=[%d,%d[" % (intz1c,
intz2c)
ima_controle_plot.plot(colorbar="v", title=title, ax=ax2)
ax2.get_xaxis().set_visible(False)
ax2.get_yaxis().set_visible(False)
if ax3 is not None:
ax3.plot(x00, y00, "m+")
ima_diff_plot = Image(data=diff_cube_plot.sum(axis=0), wcs=wcs)
title = "Difference narrow band - (%d,%d)\n" % (
x0, y0) + "int=[%d,%d[" % (
intz1c,
intz2c,
)
ima_diff_plot.plot(colorbar="v", title=title, ax=ax3)
ax3.get_xaxis().set_visible(False)
ax3.get_yaxis().set_visible(False)
def plot_sources(self,
x,
y,
circle=False,
vmin=0,
vmax=30,
title=None,
ax=None,
**kwargs):
"""Plot detected emission lines on the 2D map of maximum of the T_GLR
values over the spectral channels.
Parameters
----------
x : array
Coordinates along the x-axis of the estimated lines in pixels.
y : array
Coordinates along the y-axis of the estimated lines in pixels.
circle : bool
If true, plot circles with a diameter equal to the
mean of the fwhm of the PSF.
vmin : float
Minimum pixel value to use for the scaling.
vmax : float
Maximum pixel value to use for the scaling.
title : str
An optional title for the figure (None by default).
ax : matplotlib.Axes
the Axes instance in which the image is drawn
kwargs : matplotlib.artist.Artist
Optional arguments passed to ``ax.imshow()``.
"""
if ax is None:
ax = plt.gca()
self.maxmap.plot(vmin=vmin, vmax=vmax, title=title, ax=ax, **kwargs)
if circle:
fwhm = (self.FWHM_PSF if self.wfields is None else np.max(
np.array(self.FWHM_PSF)))
radius = np.round(fwhm / 2)
for pos in zip(x, y):
ax.add_artist(plt.Circle(pos, radius, color="k", fill=False))
else:
ax.plot(x, y, "k+")
def plot_segmaps(self, axes=None, figsize=(6, 6)):
"""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.
"""
segmaps = {}
ncolors = 0
for name in ("segmap_cont", "segmap_merged", "segmap_purity",
"segmap_label"):
segm = getattr(self, name, None)
if segm:
segmaps[name] = segm
ncolors = max(ncolors, len(np.unique(segm._data)))
nseg = len(segmaps)
if nseg == 0:
self.logger.warning("nothing to plot")
return
try:
# TODO: this will be renamed to make_random_cmap in a future
# version of photutils
from photutils.utils.colormaps import random_cmap
except ImportError:
self.logger.error("photutils is needed for this")
cmap = "jet"
else:
cmap = random_cmap(ncolors=ncolors)
cmap.colors[0] = (0.0, 0.0, 0.0)
if axes is None:
figsize = (figsize[0] * nseg, figsize[1])
fig, axes = plt.subplots(1,
nseg,
sharex=True,
sharey=True,
figsize=figsize)
if nseg == 1:
axes = [axes]
for ax, (name, im) in zip(axes, segmaps.items()):
im.plot(ax=ax, cmap=cmap, title=name, colorbar="v")
def plot_min_max_hist(self, ax=None, comp=False):
"""Plot the histograms of local maxima and minima."""
if comp:
cube_local_max = self.cube_std_local_max._data
cube_local_min = self.cube_std_local_min._data
else:
cube_local_max = self.cube_local_max._data
cube_local_min = self.cube_local_min._data
if ax is None:
fig, ax = plt.subplots(1, 1, figsize=(12, 6))
ax.set_yscale("log")
ax.grid(which="major", linewidth=1)
ax.grid(which="minor", linewidth=1, linestyle=":")
maxloc = cube_local_max[cube_local_max > 0]
bins = np.arange((maxloc.max() + 1) * 2) / 2
ax.hist(maxloc,
bins=bins,
histtype="step",
label="max",
linewidth=2,
cumulative=-1)
minloc = cube_local_min[cube_local_min > 0]
bins = np.arange((minloc.max() + 1) * 2) / 2
ax.hist(minloc,
bins=bins,
histtype="step",
label="min",
linewidth=2,
cumulative=-1)
minloc2 = cube_local_min[:, self.segmap_purity._data == 0]
minloc2 = minloc2[minloc2 > 0]
ax.hist(
minloc2,
bins=bins,
histtype="step",
label="min filt",
linewidth=2,
cumulative=-1,
)
ax.legend()
ax.set_title("Cumulative histogram of min/max loc")
def timestat(self, table=False):
"""Print CPU usage by steps.
If ``table`` is True, an astropy.table.Table is returned.
"""
if table:
name = []
exdate = []
extime = []
tot = 0
for s in self.steps.items():
if "execution_date" in s[1].meta.keys():
name.append(s[1].method_name)
exdate.append(s[1].meta["execution_date"])
t = s[1].meta["runtime"]
tot += t
extime.append(datetime.timedelta(seconds=t))
name.append("Total")
exdate.append("")
extime.append(str(datetime.timedelta(seconds=tot)))
return Table(
data=[name, exdate, extime],
names=["Step", "Exec Date", "Exec Time"],
masked=True,
)
else:
tot = 0
for s in self.steps.items():
name = s[1].method_name
if "execution_date" in s[1].meta.keys():
exdate = s[1].meta["execution_date"]
t = s[1].meta["runtime"]
tot += t
extime = datetime.timedelta(seconds=t)
self.logger.info("%s executed: %s run time: %s", name,
exdate, str(extime))
self.logger.info("*** Total run time: %s",
str(datetime.timedelta(seconds=tot)))
def stat(self):
"""Print detection summary."""
d = self._get_stat()
self.logger.info(
"ORIGIN PCA pfa %.2f Back Purity: %.2f "
"Threshold: %.2f Bright Purity %.2f Threshold %.2f",
d["pca"],
d["back_purity"],
d["back_threshold"],
d["bright_purity"],
d["bright_threshold"],
)
self.logger.info("Nb of detected lines: %d", d["tot_nlines"])
self.logger.info(
"Nb of sources Total: %d Background: %d Cont: %d",
d["tot_nsources"],
d["back_nsources"],
d["cont_nsources"],
)
self.logger.info(
"Nb of sources detected in faint (after PCA): %d "
"in std (before PCA): %d",
d["faint_nsources"],
d["bright_nsources"],
)
def _get_stat(self):
p = self.param
cat = self.Cat3_sources
if cat:
back = cat[cat["seg_label"] == 0]
cont = cat[cat["seg_label"] > 0]
bright = cat[cat["comp"] == 1]
faint = cat[cat["comp"] == 0]
return dict(
pca=p["compute_PCA_threshold"]["params"]["pfa_test"],
back_purity=p["purity"],
back_threshold=p["threshold"],
bright_purity=p["purity_std"],
bright_threshold=p["threshold_std"],
tot_nlines=len(self.Cat3_lines),
tot_nsources=len(cat),
back_nsources=len(back),
cont_nsources=len(cont),
faint_nsources=len(faint),
bright_nsources=len(bright),
)
def test_add_image(tmpdir, source2, a478hst, a370II):
"""Source class: testing add_image method"""
minicube = Cube(get_data_file('sdetect', 'minicube.fits'), dtype=float)
source2.add_white_image(minicube)
ima = minicube.mean(axis=0)
# The position source2.dec, source2.ra corresponds
# to pixel index 18.817,32.432 in the cube. The default 5
# arcsecond requested size of the white-light image corresponds
# to 25 pixels. There will thus be 12 pixels on either side of
# a central pixel. The nearest pixel to the center is 19,32, so
# we expect add_white_image() to have selected the following
# range of pixels cube[19-12:19+12+1, 32-12:32+12+1], which
# is cube[7:32, 20:45]. However the cube only has 40 pixels
# along the X-axis, so the white-light image should correspond
# to:
#
# white[:,:] = cube[7:32, 20:40]
# white.shape=(25,20)
#
# So: cube[15,25] = white[15-7, 25-20] = white[8, 5]
assert ima[15, 25] == source2.images['MUSE_WHITE'][8, 5]
# Add a square patch of an HST image equal in width and height
# to the height of the white-light image, which has a height
# of 25 white-light pixels.
source2.add_image(a478hst, 'HST1')
# Add the same HST image, but this time set the width and height
# equal to the height of the above HST patch (ie. 50 pixels). This
# should have the same result as giving it the same size as the
# white-light image.
size = source2.images['HST1'].shape[0]
source2.add_image(a478hst, 'HST2', size=size, minsize=size, unit_size=None)
assert source2.images['HST1'][10, 10] == source2.images['HST2'][10, 10]
# Add the HST image again, but this time rotate it to the same
# orientation as the white-light image, then check that they end
# up with the same rotation angles.
source2.add_image(a478hst, 'HST3', rotate=True)
assert_almost_equal(source2.images['HST3'].get_rot(),
source2.images['MUSE_WHITE'].get_rot(), 3)
# Trying to add image not overlapping with Source
assert source2.add_image(a370II, 'ERROR') is None
white = source2.images['MUSE_WHITE']
mean, std = white.background()
mask = white.data > (mean + 2 * std)
mask = Image.new_from_obj(white, data=mask.astype(int))
mask.data = mask.data.astype(int)
source2.add_image(mask, 'MYMASK')
filename = str(tmpdir.join('source.fits'))
source2.write(filename)
src = Source.from_file(filename)
assert src.images['MYMASK'].data.dtype == '>i8'
assert_masked_allclose(mask.data, src.images['MYMASK'].data)
with fits.open(filename) as hdul:
'IMA_MYMASK_DQ' in hdul
assert (np.count_nonzero(hdul['IMA_MYMASK_DQ'].data) ==
np.count_nonzero(white.mask))
class MuseApp(object):
def __init__(self):
self.img = None
self.cube = None
self.spec = None
if os.path.exists(SKYREF):
self.sky = Spectrum(SKYREF)
self.sky.data /= self.sky.data.max()
else:
print('Sky file missing')
self.sky = None
pg.mkQApp()
self.win = win = QtGui.QWidget()
win.setWindowTitle('MUSE Cube Viewer')
# layout = QtGui.QGridLayout()
layout = QtGui.QVBoxLayout()
layout.setContentsMargins(0, 0, 0, 0)
win.setLayout(layout)
self.splitter = QtGui.QSplitter()
self.splitter.setOrientation(QtCore.Qt.Horizontal)
layout.addWidget(self.splitter)
self.tree = ParameterTree(showHeader=False)
self.splitter.addWidget(self.tree)
self.params = Parameter.create(name='params',
type='group',
children=PARAMS)
self.tree.setParameters(self.params, showTop=False)
self.tree.setWindowTitle('pyqtgraph example: Parameter Tree')
self.connect(('Median filter', ), self.update_spec_plot)
self.connect(('Sky', ), self.update_spec_plot)
self.connect(('Spectrum', 'Lambda Max'), self.show_image)
self.connect(('Spectrum', 'Lambda Min'), self.show_image)
self.connect(('Spectrum', 'Line color'), self.update_spec_plot)
self.connect(('Spectrum', 'Selection color'), self.update_spec_plot)
self.connect(('Spectrum', 'Show Zoom'), self.add_zoom_window)
self.win_inner = win = pg.GraphicsLayoutWidget()
self.splitter2 = QtGui.QSplitter()
self.splitter2.setOrientation(QtCore.Qt.Vertical)
self.splitter.addWidget(self.win_inner)
# self.img_label = win.addLabel('Hello !', justify='right')
# A plot area (ViewBox + axes) for displaying the white-light image
win.nextRow()
self.white_plot = win.addPlot(title='White-light Image')
self.white_plot.setAspectLocked(lock=True, ratio=1)
self.white_item = pg.ImageItem()
self.white_plot.addItem(self.white_item)
# A plot area (ViewBox + axes) for displaying the image
win.nextColumn()
self.img_plot = win.addPlot(title='Band Image')
self.img_plot.setAspectLocked(lock=True, ratio=1)
self.img_item = pg.ImageItem()
self.img_plot.addItem(self.img_item)
# pg.SignalProxy(self.img_plot.scene().sigMouseMoved, rateLimit=60,
# slot=self.on_mouse_moved)
# Contrast/color control
self.hist = hist = pg.HistogramLUTItem()
hist.setImageItem(self.img_item)
win.addItem(hist)
# self.specplot = win.addPlot(row=2, col=0, colspan=2)
win.nextRow()
self.specplot = win.addPlot(title='Spectrum', colspan=3)
self.zoomplot = None
self.zoomplot_visible = False
# self.lbdareg = region = pg.LinearRegionItem(movable=False)
# self.specplot.addItem(region)
# region.setZValue(10)
self.zoomreg = region = pg.LinearRegionItem()
region.setZValue(10)
self.specplot.addItem(self.zoomreg, ignoreBounds=True)
region.sigRegionChanged.connect(self.update_zoom_spec_from_region)
p = self.params
lmin = p['Spectrum', 'Lambda Min']
lmax = p['Spectrum', 'Lambda Max']
region.setRegion([lmin, lmax])
self.win.resize(1000, 800)
self.win.show()
def connect(self, names, func):
self.params.param(*names).sigTreeStateChanged.connect(func)
def add_zoom_window(self):
if self.zoomplot_visible:
return
else:
self.zoomplot_visible = True
self.win_inner.nextRow()
self.zoomplot = self.win_inner.addPlot(title='Zoomed Spectrum',
colspan=3)
self.zoomplot.setAutoVisible(y=True)
def update_region_from_zoom():
self.zoomreg.setRegion(self.zoomplot.getViewBox().viewRange()[0])
self.zoomplot.sigRangeChanged.connect(update_region_from_zoom)
def update_zoom_spec_from_region(self):
lmin, lmax = self.zoomreg.getRegion()
print('update_zoom_spec_from_region', lmin, lmax)
p = self.params
p['Spectrum', 'Lambda Min'] = int(lmin)
p['Spectrum', 'Lambda Max'] = int(lmax)
if self.zoomplot:
self.zoomplot.plot(self.spec.data.data,
clear=True,
pen=self.params['Spectrum', 'Line color'])
# Add the LinearRegionItem to the ViewBox, but tell the ViewBox to
# exclude this item when doing auto-range calculations.
self.specplot.addItem(self.zoomreg, ignoreBounds=True)
self.zoomreg.setZValue(10)
self.zoomplot.setXRange(lmin, lmax, padding=0)
def load_cube(self, filename):
print('Loading cube {} ... '.format(filename), end='')
self.cube = Cube(filename, dtype=None, copy=False)
print('OK')
with fits.open(filename) as hdul:
if 'WHITE' in hdul:
print('Loading white image ... ', end='')
img = hdul['WHITE'].data
print('OK')
else:
print('Creating white-light image ... ', end='')
img = self.cube.mean(axis=0).data.data
print('OK')
self.white_item.setImage(img.T)
# self.white_item.setLevels(zscale(img.data.filled(0)))
self.white_item.setLevels(zscale(img))
self.show_image()
self.add_roi(center=np.array(self.img.shape) / 2)
# zoom to fit image
self.white_plot.autoRange()
self.img_plot.autoRange()
self.update_spec_plot()
def show_image(self):
p = self.params
lmin = p['Spectrum', 'Lambda Min']
lmax = p['Spectrum', 'Lambda Max']
print(f'Creating image {lmin}-{lmax} ... ', end='')
self.img = self.cube[lmin:lmax, :, :].mean(axis=0)
print('OK')
self.img_item.setImage(self.img.data.data.T)
# self.hist.setLevels(self.img.data.min(), self.img.data.max())
self.hist.setLevels(*zscale(self.img.data.filled(0)))
# self.lbdareg.setBrush(self.params['Spectrum', 'Selection color'])
# self.lbdareg.setRegion([self.params['Spectrum', 'Lambda Min'],
# self.params['Spectrum', 'Lambda Max']])
def add_roi(self, center=(0, 0), size=(5, 5)):
# Custom ROI for selecting an image region
size = np.asarray(size) / self.cube.wcs.get_step(unit=u.arcsec)
position = np.asarray(center) - size / 2
self.roi = roi = pg.ROI(position, size, pen=dict(color='g', size=5))
roi.addScaleHandle([0.5, 1], [0.5, 0.5])
roi.addScaleHandle([0, 0.5], [0.5, 0.5])
# roi.addRotateHandle([1, 0], [0.5, 0.5])
self.img_plot.addItem(roi)
roi.setZValue(10) # make sure ROI is drawn above image
roi.sigRegionChangeFinished.connect(self.update_spec_plot)
# def on_mouse_moved(self, evt):
# # using signal proxy turns original arguments into a tuple
# pos = evt[0]
# if self.img_plot.sceneBoundingRect().contains(pos):
# mousePoint = self.img_plot.vb.mapSceneToView(pos)
# pos = list(mousePoint.pos())
# if pos[0] > 0 and pos[0] < self.img.shape[0] and \
# pos[1] > 0 and pos[1] < self.img.shape[1]:
# self.img_label.setText(
# "<span style='font-size: 12pt'>x=%0.1f, "
# "<span style='color: red'>y1=%0.1f</span>, "
# "<span style='color: green'>y2=%0.1f</span>" % (
# mousePoint.x(), mousePoint.y(), 0))
def update_spec_plot(self):
"""Callbacks for handling user interaction"""
if self.img is None:
return
p = self.params
pos = np.array(self.roi.pos(), dtype=int)
size = np.array(self.roi.size(), dtype=int)
imin = np.clip(pos, 0, self.cube.shape[1])
imax = np.clip(pos + size, 0, self.cube.shape[2])
print('Extract mean spectrum for {}'.format(list(zip(imin, imax))))
data = self.cube[:, imin[1]:imax[1], imin[0]:imax[0]]
self.spec = spec = data.mean(axis=(1, 2))
self.specplot.clearPlots()
if p['Sky', 'Show'] and self.sky is not None:
sp = self.sky.data.data * (2 * spec.data.max()) + spec.data.min()
self.specplot.plot(sp, pen=p['Sky', 'Line Color'])
self.specplot.plot(spec.data.data, pen=p['Spectrum', 'Line color'])
if p['Median filter', 'Show']:
sp = spec.median_filter(p['Median filter', 'Kernel Size'])
self.specplot.plot(sp.data.data,
pen={
'color': p['Median filter', 'Line Color'],
'width': p['Median filter', 'Line Size']
})
# self.specplot.autoRange()
if self.zoomplot is not None:
self.update_zoom_spec_from_region()
