def main():
# Initialize the 'GaussPyDecompose' class and read in the parameter settings from 'gausspy+.ini'.
decompose = GaussPyDecompose(config_file='gausspy+.ini')
# The following lines will override the corresponding parameter settings defined in 'gausspy+.ini'.
# Filepath to pickled dictionary of the prepared data.
decompose.path_to_pickle_file = os.path.join('decomposition_grs',
'gpy_prepared',
'grs-test_field.pickle')
# First smoothing parameter
decompose.alpha1 = 2.58
# Second smoothing parameter
decompose.alpha2 = 5.14
# Suffix for the filename of the pickled dictionary with the decomposition results.
decompose.suffix = '_g+'
# Start the decomposition.
decompose.decompose()
# (Optional) Produce a FITS image showing the number of fitted components
decompose.produce_component_map()
# (Optional) Produce a FITS image showing the reduced chi-square values
decompose.produce_rchi2_map()
# (Optional) Plot some of the spectra and the decomposition results
# Filepath to pickled dictionary of the prepared data.
path_to_pickled_file = decompose.path_to_pickle_file
# Filepath to pickled dictionary with the decomposition results
path_to_decomp_pickle = os.path.join('decomposition_grs', 'gpy_decomposed',
'grs-test_field_g+_fit_fin.pickle')
# Directory in which the plots are saved.
path_to_plots = os.path.join('decomposition_grs', 'gpy_plots')
# Here we select a subregion of the data cube, whose spectra we want to plot.
pixel_range = {'x': [30, 34], 'y': [25, 29]}
plot_spectra(path_to_pickled_file,
path_to_plots=path_to_plots,
path_to_decomp_pickle=path_to_decomp_pickle,
signal_ranges=True,
pixel_range=pixel_range)
# Filepath to pickled dictionary of the prepared data.
decompose.path_to_pickle_file = os.path.join(
'decomposition', 'gpy_prepared',
"smc_HI_cube_askap_sub_" + filestr + '.pickle')
#
# # # Filepath to pickled dictionary of the prepared data.
path_to_pickled_file = decompose.path_to_pickle_file
# Filepath to pickled dictionary with the decomposition results
path_to_decomp_pickle = os.path.join(
"decomposition",
"gpy_decomposed",
"smc_HI_cube_askap_sub_" + filestr + "_g+_fit_fin.pickle",
)
print(path_to_decomp_pickle)
decompose.load_final_results(path_to_decomp_pickle)
# (Optional) Produce a FITS image showing the CNM_fraction
decompose.produce_component_map(vel_range=[80, 220], name_str='SMC')
decompose.produce_component_map(vel_range=[-100, 80], name_str='MW')
decompose.produce_cnm_map(lw=7., vel_range=[-100, 80], name_str='MW')
decompose.produce_cnm_map(lw=7., vel_range=[80, 220], name_str='SMC')
decompose.produce_cnm_map(lw=12., vel_range=[-100, 80], name_str='MW')
decompose.produce_cnm_map(lw=12., vel_range=[80, 220], name_str='SMC')
decompose.produce_nhi_map(vel_range=[-100, 80], name_str='MW')
decompose.produce_nhi_map(vel_range=[80, 220], name_str='SMC')
decompose.produce_nhi_map(name_str='all')
sp.spatial_fitting()
# (Optional) Plot maps of the reduced chi-square values and the number of fitted components
# Initialize the 'GaussPyDecompose' class and read in the parameter settings from 'gausspy+.ini'.
decompose = GaussPyDecompose(config_file='gausspy+.ini')
# Filepath to pickled dictionary of the prepared data.
decompose.path_to_pickle_file = sp.path_to_pickle_file
# Filepath to the pickled dictionary with the decomposition results
path_to_decomp_pickle = os.path.join(
'decomposition_grs', 'gpy_decomposed',
'grs-test_field_g+_fit_fin_sf-p1.pickle')
# Load the decomposition results
decompose.load_final_results(path_to_decomp_pickle)
# Produce a FITS image showing the number of fitted components
decompose.produce_component_map()
# Produce a FITS image showing the reduced chi-square values
decompose.produce_rchi2_map()
# (Optional) Plot some of the spectra and the decomposition results
# Filepath to pickled dictionary of the prepared data.
path_to_pickled_file = sp.path_to_pickle_file
# Directory in which the plots are saved.
path_to_plots = os.path.join(
'decomposition_grs', 'gpy_plots')
# Here we select a subregion of the data cube, whose spectra we want to plot.
pixel_range = {'x': [30, 34], 'y': [25, 29]}
plot_spectra(path_to_pickled_file, path_to_plots=path_to_plots,
path_to_decomp_pickle=path_to_decomp_pickle,
signal_ranges=True, pixel_range=pixel_range)
def main():
# Initialize the 'SpatialFitting' class and read in the parameter settings from 'gausspy+.ini'.
sp = SpatialFitting(config_file='gausspy+.ini')
# The following lines will override the corresponding parameter settings defined in 'gausspy+.ini'.
# filepath to the pickled dictionary of the prepared data
sp.path_to_pickle_file = os.path.join('decomposition_grs', 'gpy_prepared',
'grs-test_field.pickle')
# Filepath to the pickled dictionary of the decomposition results
sp.path_to_decomp_file = os.path.join('decomposition_grs',
'gpy_decomposed',
'grs-test_field_g+_fit_fin.pickle')
# Try to refit blended fit components
sp.refit_blended = True
# Try to refit spectra with negative residual features
sp.refit_neg_res_peak = True
# Try to refit broad fit components
sp.refit_broad = True
# Flag spectra with non-Gaussian distributed residuals
sp.flag_residual = True
# Do not try to refit spectra with non-Gaussian distributed residuals
sp.refit_residual = False
# Try to refit spectra for which the number of fit components is incompatible with its direct neighbors
sp.refit_ncomps = True
# We set the maximum allowed difference in the number of fitted components compared to the weighted median of all immediate neighbors to 1
sp.max_diff_comps = 1
# We set the maximum allowed difference in the number of fitted components between individual neighboring spectra to 2
sp.max_jump_comps = 2
# We will flag and try to refit all spectra which show jumps in the number of components of more than 2 to at least two direct neighbors
sp.n_max_jump_comps = 1
# Maximum difference in offset positions of fit components for grouping.
sp.mean_separation = 2.
# Maximum difference in FWHM values of fit components for grouping.
sp.fwhm_separation = 4.
# Start phase 1 of the spatially coherent refitting
sp.spatial_fitting()
# (Optional) Plot maps of the reduced chi-square values and the number of fitted components
# Initialize the 'GaussPyDecompose' class and read in the parameter settings from 'gausspy+.ini'.
decompose = GaussPyDecompose(config_file='gausspy+.ini')
# Filepath to pickled dictionary of the prepared data.
decompose.path_to_pickle_file = sp.path_to_pickle_file
# Filepath to the pickled dictionary with the decomposition results
path_to_decomp_pickle = os.path.join(
'decomposition_grs', 'gpy_decomposed',
'grs-test_field_g+_fit_fin_sf-p1.pickle')
# Load the decomposition results
decompose.load_final_results(path_to_decomp_pickle)
# Produce a FITS image showing the number of fitted components
decompose.produce_component_map()
# Produce a FITS image showing the reduced chi-square values
decompose.produce_rchi2_map()
# (Optional) Plot some of the spectra and the decomposition results
# Filepath to pickled dictionary of the prepared data.
path_to_pickled_file = sp.path_to_pickle_file
# Directory in which the plots are saved.
path_to_plots = os.path.join('decomposition_grs', 'gpy_plots')
# Here we select a subregion of the data cube, whose spectra we want to plot.
pixel_range = {'x': [30, 34], 'y': [25, 29]}
plot_spectra(path_to_pickled_file,
path_to_plots=path_to_plots,
path_to_decomp_pickle=path_to_decomp_pickle,
signal_ranges=True,
pixel_range=pixel_range)