modify lib/plots.py for better overplotting tool
This commit is contained in:
@@ -12,9 +12,10 @@ import lib.reduction as proj_red #Functions used in reduction pipeline
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import lib.plots as proj_plots #Functions for plotting data
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from lib.deconvolve import from_file_psf
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from lib.query import retrieve_products, path_exists, system
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from matplotlib.colors import LogNorm
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def main(target=None, proposal_id=None, infiles=None, output_dir="./data"):
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def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=0, interactive=0):
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## Reduction parameters
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# Deconvolution
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deconvolve = False
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@@ -53,18 +54,18 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data"):
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smoothing_scale = 'arcsec' #pixel or arcsec
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# Rotation
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rotate_stokes = True
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rotate_data = False #rotation to North convention can give erroneous results
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rotate_stokes = True
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# Final crop
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crop = False #Crop to desired ROI
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final_display = True #Whether to display all polarization map outputs
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#crop = False #Crop to desired ROI
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#interactive = False #Whether to output to intercative analysis tool
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# Polarization map output
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SNRp_cut = 3. #P measurments with SNR>3
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SNRi_cut = 30. #I measurments with SNR>30, which implies an uncertainty in P of 4.7%.
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flux_lim = [5e-19,5e-14] #lowest and highest flux displayed on plot, defaults to bkg and maximum in cut if None
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vec_scale = 2.0
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flux_lim = None #lowest and highest flux displayed on plot, defaults to bkg and maximum in cut if None
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vec_scale = 5
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step_vec = 1 #plot all vectors in the array. if step_vec = 2, then every other vector will be plotted
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# if step_vec = 0 then all vectors are displayed at full length
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@@ -168,21 +169,21 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data"):
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## Step 5:
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# crop to desired region of interest (roi)
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if crop:
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figtype += "crop"
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stokescrop = proj_plots.crop_Stokes(deepcopy(Stokes_test))
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figtype += "_crop"
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stokescrop = proj_plots.crop_Stokes(deepcopy(Stokes_test),norm=LogNorm())
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stokescrop.crop()
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stokescrop.writeto("/".join([data_folder,"_".join([figname,figtype+".fits"])]))
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Stokes_test, data_mask = stokescrop.hdul_crop, stokescrop.data_mask
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Stokes_test, data_mask, headers = stokescrop.hdul_crop, stokescrop.data_mask, [dataset.header for dataset in stokescrop.hdul_crop]
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print("F_int({0:.0f} Angs) = ({1} ± {2})e{3} ergs.cm^-2.s^-1.Angs^-1".format(headers[0]['photplam'],*proj_plots.sci_not(Stokes_test[0].data[data_mask].sum()*headers[0]['photflam'],np.sqrt(Stokes_test[3].data[0,0][data_mask].sum())*headers[0]['photflam'],2,out=int)))
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print("P_int = {0:.1f} ± {1:.1f} %".format(headers[0]['p_int']*100.,np.ceil(headers[0]['p_int_err']*1000.)/10.))
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print("PA_int = {0:.1f} ± {1:.1f} °".format(headers[0]['pa_int'],np.ceil(headers[0]['pa_int_err']*10.)/10.))
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print("PA_int = {0:.1f} ±t {1:.1f} °".format(headers[0]['pa_int'],np.ceil(headers[0]['pa_int_err']*10.)/10.))
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# Background values
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print("F_bkg({0:.0f} Angs) = ({1} ± {2})e{3} ergs.cm^-2.s^-1.Angs^-1".format(headers[0]['photplam'],*proj_plots.sci_not(I_bkg[0,0]*headers[0]['photflam'],np.sqrt(S_cov_bkg[0,0][0,0])*headers[0]['photflam'],2,out=int)))
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print("P_bkg = {0:.1f} ± {1:.1f} %".format(debiased_P_bkg[0,0]*100.,np.ceil(s_P_bkg[0,0]*1000.)/10.))
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print("PA_bkg = {0:.1f} ± {1:.1f} °".format(PA_bkg[0,0],np.ceil(s_PA_bkg[0,0]*10.)/10.))
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# Plot polarization map (Background is either total Flux, Polarization degree or Polarization degree error).
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if px_scale.lower() not in ['full','integrate'] and final_display:
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if px_scale.lower() not in ['full','integrate'] and not interactive:
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proj_plots.polarization_map(deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec, vec_scale=vec_scale, savename="_".join([figname,figtype]), plots_folder=plots_folder)
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proj_plots.polarization_map(deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec, vec_scale=vec_scale, savename="_".join([figname,figtype,"I"]), plots_folder=plots_folder, display='Intensity')
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proj_plots.polarization_map(deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec, vec_scale=vec_scale, savename="_".join([figname,figtype,"P_flux"]), plots_folder=plots_folder, display='Pol_Flux')
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@@ -192,7 +193,7 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data"):
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proj_plots.polarization_map(deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec, vec_scale=vec_scale, savename="_".join([figname,figtype,"P_err"]), plots_folder=plots_folder, display='Pol_deg_err')
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proj_plots.polarization_map(deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec, vec_scale=vec_scale, savename="_".join([figname,figtype,"SNRi"]), plots_folder=plots_folder, display='SNRi')
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proj_plots.polarization_map(deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec, vec_scale=vec_scale, savename="_".join([figname,figtype,"SNRp"]), plots_folder=plots_folder, display='SNRp')
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elif final_display:
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elif not interactive:
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proj_plots.polarization_map(deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, savename="_".join([figname,figtype]), plots_folder=plots_folder, display='integrate')
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elif px_scale.lower() not in ['full', 'integrate']:
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pol_map = proj_plots.pol_map(Stokes_test, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim)
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@@ -212,6 +213,10 @@ if __name__ == "__main__":
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help='the full or relative path to the data products', default=None)
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parser.add_argument('-o','--output_dir', metavar='directory_path', required=False,
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help='output directory path for the data products', type=str, default="./data")
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parser.add_argument('-c','--crop', metavar='crop_boolean', required=False,
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help='whether to crop the analysis region', type=int, default=0)
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parser.add_argument('-i','--interactive', metavar='interactive_boolean', required=False,
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help='whether to output to the interactive analysis tool', type=int, default=0)
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args = parser.parse_args()
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exitcode = main(target=args.target, proposal_id=args.proposal_id, infiles=args.files, output_dir=args.output_dir)
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exitcode = main(target=args.target, proposal_id=args.proposal_id, infiles=args.files, output_dir=args.output_dir, crop=args.crop, interactive=args.interactive)
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print("Finished with ExitCode: ",exitcode)
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@@ -1068,7 +1068,7 @@ class crop_map(object):
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"""
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Class to interactively crop a map to desired Region of Interest
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"""
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def __init__(self, hdul, fig=None, ax=None):
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def __init__(self, hdul, fig=None, ax=None, **kwargs):
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#Get data
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self.cropped=False
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self.hdul = hdul
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@@ -1080,10 +1080,13 @@ class crop_map(object):
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self.convert_flux = self.header['photflam']
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except KeyError:
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self.convert_flux = 1.
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try:
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self.kwargs = kwargs
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except AttributeError:
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self.kwargs = {}
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#Plot the map
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plt.rcParams.update({'font.size': 12})
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plt.ioff()
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if fig is None:
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self.fig = plt.figure(figsize=(15,15))
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self.fig.suptitle("Click and drag to crop to desired Region of Interest.")
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@@ -1104,28 +1107,36 @@ class crop_map(object):
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self.rect_selector = RectangleSelector(self.ax, self.onselect_crop,
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button=[1])
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self.embedded = True
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self.display()
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plt.ion()
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self.display(self.data, self.wcs, self.convert_flux, **self.kwargs)
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self.extent = np.array([0.,self.data.shape[0],0., self.data.shape[1]])
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self.center = np.array(self.data.shape)/2
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self.RSextent = deepcopy(self.extent)
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self.RScenter = deepcopy(self.center)
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plt.show()
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def display(self, data=None, wcs=None, convert_flux=None):
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def display(self, data=None, wcs=None, convert_flux=None, **kwargs):
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if data is None:
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data = self.data
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if wcs is None:
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wcs = self.wcs
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if convert_flux is None:
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convert_flux = self.convert_flux
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if kwargs is None:
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kwargs = self.kwargs
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else:
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kwargs = {**self.kwargs, **kwargs}
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vmin, vmax = 0., np.max(data[data > 0.]*convert_flux)
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vmin, vmax = np.min(data[data > 0.]*convert_flux), np.max(data[data > 0.]*convert_flux)
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for key, value in [["cmap",[["cmap","inferno"]]], ["origin",[["origin","lower"]]], ["aspect",[["aspect","equal"]]], ["alpha",[["alpha",self.mask_alpha]]], ["norm",[["vmin",vmin],["vmax",vmax]]]]:
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try:
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test = kwargs[key]
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except KeyError:
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for key_i, val_i in value:
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kwargs[key_i] = val_i
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if hasattr(self, 'im'):
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self.im.remove()
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self.im = self.ax.imshow(data*convert_flux, vmin=vmin, vmax=vmax, aspect='equal', cmap='inferno', alpha=self.mask_alpha, origin='lower')
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self.im = self.ax.imshow(data*convert_flux, **kwargs)
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if hasattr(self, 'cr'):
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self.cr[0].set_data(*wcs.wcs.crpix)
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else:
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@@ -1199,14 +1210,9 @@ class crop_map(object):
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self.header_crop.update(self.wcs_crop.to_header())
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self.hdul_crop = fits.HDUList([fits.PrimaryHDU(self.data_crop,self.header_crop)])
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try:
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convert_flux = self.header_crop['photflam']
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except KeyError:
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convert_flux = 1.
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self.rect_selector.clear()
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self.ax.reset_wcs(self.wcs_crop)
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self.display(data=self.data_crop, wcs=self.wcs_crop, convert_flux=convert_flux)
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self.display(data=self.data_crop, wcs=self.wcs_crop)
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xlim, ylim = self.RSextent[1::2]-self.RSextent[0::2]
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self.ax.set_xlim(0,xlim)
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@@ -1215,6 +1221,7 @@ class crop_map(object):
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if self.fig.canvas.manager.toolbar.mode == '':
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self.rect_selector = RectangleSelector(self.ax, self.onselect_crop,
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button=[1])
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self.fig.canvas.draw_idle()
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def on_close(self, event) -> None:
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@@ -1283,16 +1290,11 @@ class crop_Stokes(crop_map):
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dataset.data = deepcopy(dataset.data[vertex[2]:vertex[3], vertex[0]:vertex[1]])
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dataset.header.update(self.wcs_crop.to_header())
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try:
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convert_flux = self.hdul_crop[0].header['photflam']
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except KeyError:
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convert_flux = 1.
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self.data_crop = self.hdul_crop[0].data
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self.rect_selector.clear()
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if not self.embedded:
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self.ax.reset_wcs(self.wcs_crop)
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self.display(data=self.data_crop, wcs=self.wcs_crop, convert_flux=convert_flux)
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self.display(data=self.data_crop, wcs=self.wcs_crop)
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xlim, ylim = self.RSextent[1::2]-self.RSextent[0::2]
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self.ax.set_xlim(0,xlim)
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@@ -1303,11 +1305,34 @@ class crop_Stokes(crop_map):
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if self.fig.canvas.manager.toolbar.mode == '':
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self.rect_selector = RectangleSelector(self.ax, self.onselect_crop,
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button=[1])
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# Update integrated values
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mask = np.logical_and(self.hdul_crop[-1].data.astype(bool), self.hdul_crop[0].data >0)
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I_diluted = self.hdul_crop[0].data[mask].sum()
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Q_diluted = self.hdul_crop[1].data[mask].sum()
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U_diluted = self.hdul_crop[2].data[mask].sum()
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I_diluted_err = np.sqrt(np.sum(self.hdul_crop[3].data[0,0][mask]))
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Q_diluted_err = np.sqrt(np.sum(self.hdul_crop[3].data[1,1][mask]))
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U_diluted_err = np.sqrt(np.sum(self.hdul_crop[3].data[2,2][mask]))
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IQ_diluted_err = np.sqrt(np.sum(self.hdul_crop[3].data[0,1][mask]**2))
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IU_diluted_err = np.sqrt(np.sum(self.hdul_crop[3].data[0,2][mask]**2))
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QU_diluted_err = np.sqrt(np.sum(self.hdul_crop[3].data[1,2][mask]**2))
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P_diluted = np.sqrt(Q_diluted**2+U_diluted**2)/I_diluted
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P_diluted_err = (1./I_diluted)*np.sqrt((Q_diluted**2*Q_diluted_err**2 + U_diluted**2*U_diluted_err**2 + 2.*Q_diluted*U_diluted*QU_diluted_err)/(Q_diluted**2 + U_diluted**2) + ((Q_diluted/I_diluted)**2 + (U_diluted/I_diluted)**2)*I_diluted_err**2 - 2.*(Q_diluted/I_diluted)*IQ_diluted_err - 2.*(U_diluted/I_diluted)*IU_diluted_err)
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PA_diluted = princ_angle((90./np.pi)*np.arctan2(U_diluted,Q_diluted))
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PA_diluted_err = (90./(np.pi*(Q_diluted**2 + U_diluted**2)))*np.sqrt(U_diluted**2*Q_diluted_err**2 + Q_diluted**2*U_diluted_err**2 - 2.*Q_diluted*U_diluted*QU_diluted_err)
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for dataset in self.hdul_crop:
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dataset.header['P_int'] = (P_diluted, 'Integrated polarization degree')
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dataset.header['P_int_err'] = (np.ceil(P_diluted_err*1000.)/1000., 'Integrated polarization degree error')
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dataset.header['PA_int'] = (PA_diluted, 'Integrated polarization angle')
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dataset.header['PA_int_err'] = (np.ceil(PA_diluted_err*10.)/10., 'Integrated polarization angle error')
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self.fig.canvas.draw_idle()
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@property
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def data_mask(self):
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return self.hdul_crop[-1].data
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return self.hdul_crop[-1].data.astype(int)
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class image_lasso_selector(object):
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25
src/overplot_MRK463E.py
Executable file
25
src/overplot_MRK463E.py
Executable file
@@ -0,0 +1,25 @@
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#!/usr/bin/python3
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from os import system as command
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from astropy.io import fits
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import numpy as np
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from copy import deepcopy
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from lib.plots import overplot_chandra, overplot_pol, align_pol
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from matplotlib.colors import LogNorm
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Stokes_UV = fits.open("./data/MRK463E/5960/MRK463E_FOC_b0.05arcsec_c0.10arcsec.fits")
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Stokes_IR = fits.open("./data/MRK463E/WFPC2/IR_rot_crop.fits")
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Stokes_Xr = fits.open("./data/MRK463E/Chandra/4913/primary/acisf04913N004_cntr_img2.fits")
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levels = np.geomspace(1.,99.,10)
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A = overplot_chandra(Stokes_UV, Stokes_Xr)
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A.plot(levels=levels, SNRp_cut=3.0, SNRi_cut=20.0, zoom=1, savename='./plots/MRK463E/Chandra_overplot.pdf')
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B = overplot_chandra(Stokes_UV, Stokes_Xr, norm=LogNorm())
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B.plot(levels=levels, SNRp_cut=3.0, SNRi_cut=20.0, zoom=1, savename='./plots/MRK463E/Chandra_overplot_forced.pdf')
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C = overplot_pol(Stokes_UV, Stokes_IR)
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C.plot(SNRp_cut=3.0, SNRi_cut=20.0, savename='./plots/MRK463E/IR_overplot.pdf')
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D = overplot_pol(Stokes_UV, Stokes_IR, norm=LogNorm())
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D.plot(SNRp_cut=3.0, SNRi_cut=30.0, vec_scale=2, norm=LogNorm(1e-18,1e-15), savename='./plots/MRK463E/IR_overplot_forced.pdf')
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