update user privileges, correct and add axis error estimation, replot for NGC1068
This commit is contained in:
Thibault Barnouin
@@ -17,11 +17,11 @@ from lib.convex_hull import image_hull
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def main():
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##### User inputs
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## Input and output locations
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# globals()['data_folder'] = "../data/NGC1068_x274020/"
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# infiles = ['x274020at.c0f.fits','x274020bt.c0f.fits','x274020ct.c0f.fits',
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# 'x274020dt.c0f.fits','x274020et.c0f.fits','x274020ft.c0f.fits',
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# 'x274020gt.c0f.fits','x274020ht.c0f.fits','x274020it.c0f.fits']
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# globals()['plots_folder'] = "../plots/NGC1068_x274020/"
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globals()['data_folder'] = "../data/NGC1068_x274020/"
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infiles = ['x274020at.c0f.fits','x274020bt.c0f.fits','x274020ct.c0f.fits',
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'x274020dt.c0f.fits','x274020et.c0f.fits','x274020ft.c0f.fits',
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'x274020gt.c0f.fits','x274020ht.c0f.fits','x274020it.c0f.fits']
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globals()['plots_folder'] = "../plots/NGC1068_x274020/"
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# globals()['data_folder'] = "../data/NGC1068_x14w010/"
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# infiles = ['x14w0101t_c0f.fits','x14w0102t_c0f.fits','x14w0103t_c0f.fits',
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@@ -77,9 +77,9 @@ def main():
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# infiles = ['x3nl0201r_c0f.fits','x3nl0202r_c0f.fits','x3nl0203r_c0f.fits']
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# globals()['plots_folder'] = "../plots/MKN78_x3nl020/"
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globals()['data_folder'] = "../data/PictorA_x25d040/"
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infiles = ['x25d0401t_c0f.fits','x25d0402t_c0f.fits','x25d0403t_c0f.fits']
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globals()['plots_folder'] = "../plots/PictorA_x25d040/"
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# globals()['data_folder'] = "../data/PictorA_x25d040/"
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# infiles = ['x25d0401t_c0f.fits','x25d0402t_c0f.fits','x25d0403t_c0f.fits']
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# globals()['plots_folder'] = "../plots/PictorA_x25d040/"
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## Reduction parameters
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# Deconvolution
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@@ -112,10 +112,10 @@ def main():
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rotate_stokes = True #rotation to North convention can give erroneous results
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rotate_data = False #rotation to North convention can give erroneous results
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# Polarization map output
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figname = 'PictorA_FOC' #target/intrument name
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figname = 'NGC1068_FOC' #target/intrument name
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figtype = '_combine_FWHM020_rot' #additionnal informations
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SNRp_cut = 20. #P measurments with SNR>3
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SNRi_cut = 120. #I measurments with SNR>30, which implies an uncertainty in P of 4.7%.
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SNRp_cut = 15. #P measurments with SNR>3
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SNRi_cut = 80. #I measurments with SNR>30, which implies an uncertainty in P of 4.7%.
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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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##### Pipeline start
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@@ -144,7 +144,7 @@ def main():
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for data in data_array:
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if (data < 0.).any():
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print("ETAPE 4 : ", data)
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#Align and rescale images with oversampling.
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# Align and rescale images with oversampling.
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data_array, error_array, data_mask = proj_red.align_data(data_array, headers, error_array, upsample_factor=int(Dxy.min()), ref_center=align_center, return_shifts=False)
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for data in data_array:
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if (data < 0.).any():
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@@ -154,7 +154,7 @@ def main():
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shape = np.array([vertex[1]-vertex[0],vertex[3]-vertex[2]])
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rectangle = [vertex[2], vertex[0], shape[1], shape[0], 0., 'w']
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# Rotate data to have North up
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# Rotate data to have North up
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ref_header = copy.deepcopy(headers[0])
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if rotate_data:
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alpha = ref_header['orientat']
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@@ -196,6 +196,11 @@ def main():
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Stokes_test = proj_fits.save_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, P, debiased_P, s_P, s_P_P, PA, s_PA, s_PA_P, headers, figname+figtype, data_folder=data_folder, return_hdul=True)
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## Step 5:
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# Crop to desired Region of Interest (ROI)
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# StokesCrop = proj_plots.crop_map(copy.deepcopy(Stokes_test), copy.deepcopy(data_mask), SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut)
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# StokesCrop.run()
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# Stokes_crop, data_mask = StokesCrop.crop()
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# Plot polarization map (Background is either total Flux, Polarization degree or Polarization degree error).
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proj_plots.polarization_map(copy.deepcopy(Stokes_test), data_mask, rectangle=None, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype, plots_folder=plots_folder, display=None)
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proj_plots.polarization_map(copy.deepcopy(Stokes_test), data_mask, rectangle=None, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype+"_P_flux", plots_folder=plots_folder, display='Pol_Flux')
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0
src/lib/Derivative.cpp
Normal file → Executable file
0
src/lib/Derivative.cpp
Normal file → Executable file
100
src/lib/plots.py
100
src/lib/plots.py
@@ -9,9 +9,11 @@ prototypes :
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Plots polarization map of polarimetric parameters saved in an HDUList
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"""
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import copy
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import numpy as np
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import matplotlib.pyplot as plt
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from matplotlib.patches import Rectangle
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from matplotlib.widgets import RectangleSelector
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import matplotlib.font_manager as fm
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from mpl_toolkits.axes_grid1.anchored_artists import AnchoredSizeBar, AnchoredDirectionArrows
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from astropy.wcs import WCS
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@@ -167,8 +169,102 @@ def plot_Stokes(Stokes, savename=None, plots_folder=""):
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return 0
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def polarization_map(Stokes, data_mask, rectangle=None, SNRp_cut=3., SNRi_cut=30., step_vec=1,
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savename=None, plots_folder="", display=None):
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class crop_map(object):
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"""
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Class to interactively crop I_stokes map to desired Region of Interest
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"""
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def __init__(self, Stokes, data_mask, SNRp_cut=3., SNRi_cut=30.):
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#Get data
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stkI = Stokes[np.argmax([Stokes[i].header['datatype']=='I_stokes' for i in range(len(Stokes))])]
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stk_cov = Stokes[np.argmax([Stokes[i].header['datatype']=='IQU_cov_matrix' for i in range(len(Stokes))])]
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pol = Stokes[np.argmax([Stokes[i].header['datatype']=='Pol_deg_debiased' for i in range(len(Stokes))])]
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pol_err = Stokes[np.argmax([Stokes[i].header['datatype']=='Pol_deg_err' for i in range(len(Stokes))])]
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self.Stokes = Stokes
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self.data_mask = data_mask
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wcs = WCS(Stokes[0]).deepcopy()
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#Compute SNR and apply cuts
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pol.data[pol.data == 0.] = np.nan
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SNRp = pol.data/pol_err.data
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SNRp[np.isnan(SNRp)] = 0.
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pol.data[SNRp < SNRp_cut] = np.nan
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SNRi = stkI.data/np.sqrt(stk_cov.data[0,0])
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SNRi[np.isnan(SNRi)] = 0.
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pol.data[SNRi < SNRi_cut] = np.nan
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convert_flux = Stokes[0].header['photflam']
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#Plot the map
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plt.rcParams.update({'font.size': 16})
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self.fig = plt.figure(figsize=(15,15))
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self.ax = self.fig.add_subplot(111, projection=wcs)
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self.ax.set_facecolor('k')
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self.fig.subplots_adjust(hspace=0, wspace=0, right=0.9)
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cbar_ax = self.fig.add_axes([0.95, 0.12, 0.01, 0.75])
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self.extent = [-stkI.data.shape[1]/2.,stkI.data.shape[1]/2.,-stkI.data.shape[0]/2.,stkI.data.shape[0]/2.]
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self.center = [stkI.data.shape[1]/2.,stkI.data.shape[0]/2.]
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vmin, vmax = 0., np.max(stkI.data[stkI.data > 0.]*convert_flux)
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im = self.ax.imshow(stkI.data*convert_flux,extent=self.extent, vmin=vmin, vmax=vmax, aspect='auto', cmap='inferno', alpha=1.)
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cbar = plt.colorbar(im, cax=cbar_ax, label=r"$F_{\lambda}$ [$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA^{-1}$]")
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levelsI = np.linspace(SNRi_cut, np.max(SNRi[SNRi > 0.]), 10)
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cont = self.ax.contour(SNRi, extent=self.extent, levels=levelsI, colors='grey', linewidths=0.5)
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fontprops = fm.FontProperties(size=16)
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px_size = wcs.wcs.get_cdelt()[0]
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px_sc = AnchoredSizeBar(self.ax.transData, 1./px_size, '1 arcsec', 3, pad=0.5, sep=5, borderpad=0.5, frameon=False, size_vertical=0.005, color='w', fontproperties=fontprops)
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self.ax.add_artist(px_sc)
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self.ax.set_title(
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"Click and drag to crop to desired Region of Interest.\n"
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"Press 'c' to toggle the selector on and off")
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def onselect_crop(self, eclick, erelease) -> None:
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# Obtain (xmin, xmax, ymin, ymax) values
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self.RSextent = self.rect_selector.extents
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self.RScenter = [self.center[i]+self.rect_selector.center[i] for i in range(2)]
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# Zoom to selection
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print("CROP TO : ",self.RSextent)
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print("CENTER : ",self.RScenter)
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#self.ax.set_xlim(self.extent[0], self.extent[1])
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#self.ax.set_ylim(self.extent[2], self.extent[3])
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def run(self) -> None:
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self.rect_selector = RectangleSelector(self.ax, self.onselect_crop,
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drawtype='box', button=[1], interactive=True)
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#self.fig.canvas.mpl_connect('key_press_event', self.toggle_selector)
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plt.show()
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def crop(self):
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Stokes_crop = copy.deepcopy(self.Stokes)
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# Data sets to crop
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stkI = Stokes_crop[np.argmax([Stokes_crop[i].header['datatype']=='I_stokes' for i in range(len(Stokes_crop))])]
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stkQ = Stokes_crop[np.argmax([Stokes_crop[i].header['datatype']=='Q_stokes' for i in range(len(Stokes_crop))])]
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stkU = Stokes_crop[np.argmax([Stokes_crop[i].header['datatype']=='U_stokes' for i in range(len(Stokes_crop))])]
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stk_cov = Stokes_crop[np.argmax([Stokes_crop[i].header['datatype']=='IQU_cov_matrix' for i in range(len(Stokes_crop))])]
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pol = Stokes_crop[np.argmax([Stokes_crop[i].header['datatype']=='Pol_deg_debiased' for i in range(len(Stokes_crop))])]
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pol_err = Stokes_crop[np.argmax([Stokes_crop[i].header['datatype']=='Pol_deg_err' for i in range(len(Stokes_crop))])]
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pang = Stokes_crop[np.argmax([Stokes_crop[i].header['datatype']=='Pol_ang' for i in range(len(Stokes_crop))])]
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pang_err = Stokes_crop[np.argmax([Stokes_crop[i].header['datatype']=='Pol_ang_err' for i in range(len(Stokes_crop))])]
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# Crop datasets
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vertex = [int(self.RScenter[0]+self.RSextent[0]), int(self.RScenter[0]+self.RSextent[1]), int(self.RScenter[1]+self.RSextent[2]), int(self.RScenter[1]+self.RSextent[3])]
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for dataset in [stkI, stkQ, stkU, pol, pol_err, pang, pang_err]:
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dataset.data = dataset.data[vertex[2]:vertex[3], vertex[0]:vertex[1]]
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data_mask = self.data_mask[vertex[2]:vertex[3], vertex[0]:vertex[1]]
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new_stk_cov = np.zeros((3,3,stkI.data.shape[0],stkI.data.shape[1]))
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for i in range(3):
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for j in range(3):
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new_stk_cov[i,j] = stk_cov.data[i,j][vertex[2]:vertex[3], vertex[0]:vertex[1]]
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stk_cov.data = new_stk_cov
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return Stokes_crop, data_mask
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def polarization_map(Stokes, data_mask, rectangle=None, SNRp_cut=3., SNRi_cut=30.,
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step_vec=1, savename=None, plots_folder="", display=None):
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"""
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Plots polarization map from Stokes HDUList.
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----------
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@@ -1143,28 +1143,63 @@ def compute_Stokes(data_array, error_array, data_mask, headers,
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Stokes_cov[0,2] = Stokes_cov[2,0] = coeff_stokes[0,0]*coeff_stokes[2,0]*pol_cov[0,0]+coeff_stokes[0,1]*coeff_stokes[2,1]*pol_cov[1,1]+coeff_stokes[0,2]*coeff_stokes[2,2]*pol_cov[2,2]+(coeff_stokes[0,0]*coeff_stokes[2,1]+coeff_stokes[2,0]*coeff_stokes[0,1])*pol_cov[0,1]+(coeff_stokes[0,0]*coeff_stokes[2,2]+coeff_stokes[2,0]*coeff_stokes[0,2])*pol_cov[0,2]+(coeff_stokes[0,1]*coeff_stokes[2,2]+coeff_stokes[2,1]*coeff_stokes[0,2])*pol_cov[1,2]
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Stokes_cov[1,2] = Stokes_cov[2,1] = coeff_stokes[1,0]*coeff_stokes[2,0]*pol_cov[0,0]+coeff_stokes[1,1]*coeff_stokes[2,1]*pol_cov[1,1]+coeff_stokes[1,2]*coeff_stokes[2,2]*pol_cov[2,2]+(coeff_stokes[1,0]*coeff_stokes[2,1]+coeff_stokes[2,0]*coeff_stokes[1,1])*pol_cov[0,1]+(coeff_stokes[1,0]*coeff_stokes[2,2]+coeff_stokes[2,0]*coeff_stokes[1,2])*pol_cov[0,2]+(coeff_stokes[1,1]*coeff_stokes[2,2]+coeff_stokes[2,1]*coeff_stokes[1,2])*pol_cov[1,2]
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dI_dtheta1 = 2.*pol_eff[0]/A*(pol_eff[2]*np.cos(-2.*theta[2]+2.*theta[0])*(pol_flux[1]-I_stokes) - pol_eff[1]*np.cos(-2.*theta[0]+2.*theta[1])*(pol_flux[2]-I_stokes) + A*coeff_stokes[0,0]*(np.sin(2.*theta[0]*Q_stokes) - np.cos(2.*theta[0]*U_stokes)))
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dI_dtheta2 = 2.*pol_eff[1]/A*(pol_eff[0]*np.cos(-2.*theta[0]+2.*theta[1])*(pol_flux[2]-I_stokes) - pol_eff[2]*np.cos(-2.*theta[1]+2.*theta[2])*(pol_flux[0]-I_stokes) + A*coeff_stokes[0,1]*(np.sin(2.*theta[1]*Q_stokes) - np.cos(2.*theta[1]*U_stokes)))
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dI_dtheta3 = 2.*pol_eff[2]/A*(pol_eff[1]*np.cos(-2.*theta[1]+2.*theta[2])*(pol_flux[0]-I_stokes) - pol_eff[0]*np.cos(-2.*theta[2]+2.*theta[0])*(pol_flux[1]-I_stokes) + A*coeff_stokes[0,2]*(np.sin(2.*theta[2]*Q_stokes) - np.cos(2.*theta[2]*U_stokes)))
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dQ_dtheta1 = 2.*pol_eff[0]/A*(np.cos(2.*theta[0])*(pol_flux[1]-pol_flux[2]) - (pol_eff[2]*np.cos(-2.*theta[2]+2.*theta[0]) - pol_eff[1]*np.cos(-2.*theta[0]+2.*theta[1]))*Q_stokes + A*coeff_stokes[1,0]*(np.sin(2.*theta[0]*Q_stokes) - np.cos(2.*theta[0]*U_stokes)))
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dQ_dtheta2 = 2.*pol_eff[1]/A*(np.cos(2.*theta[1])*(pol_flux[2]-pol_flux[0]) - (pol_eff[0]*np.cos(-2.*theta[0]+2.*theta[1]) - pol_eff[2]*np.cos(-2.*theta[1]+2.*theta[2]))*Q_stokes + A*coeff_stokes[1,1]*(np.sin(2.*theta[1]*Q_stokes) - np.cos(2.*theta[1]*U_stokes)))
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dQ_dtheta3 = 2.*pol_eff[2]/A*(np.cos(2.*theta[2])*(pol_flux[0]-pol_flux[1]) - (pol_eff[1]*np.cos(-2.*theta[1]+2.*theta[2]) - pol_eff[0]*np.cos(-2.*theta[2]+2.*theta[0]))*Q_stokes + A*coeff_stokes[1,2]*(np.sin(2.*theta[2]*Q_stokes) - np.cos(2.*theta[2]*U_stokes)))
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dU_dtheta1 = 2.*pol_eff[0]/A*(np.sin(2.*theta[0])*(pol_flux[1]-pol_flux[2]) - (pol_eff[2]*np.cos(-2.*theta[2]+2.*theta[0]) - pol_eff[1]*np.cos(-2.*theta[0]+2.*theta[1]))*U_stokes + A*coeff_stokes[2,0]*(np.sin(2.*theta[0]*Q_stokes) - np.cos(2.*theta[0]*U_stokes)))
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dU_dtheta2 = 2.*pol_eff[1]/A*(np.sin(2.*theta[1])*(pol_flux[2]-pol_flux[0]) - (pol_eff[0]*np.cos(-2.*theta[0]+2.*theta[1]) - pol_eff[2]*np.cos(-2.*theta[1]+2.*theta[2]))*U_stokes + A*coeff_stokes[2,1]*(np.sin(2.*theta[1]*Q_stokes) - np.cos(2.*theta[1]*U_stokes)))
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dU_dtheta3 = 2.*pol_eff[2]/A*(np.sin(2.*theta[2])*(pol_flux[0]-pol_flux[1]) - (pol_eff[1]*np.cos(-2.*theta[1]+2.*theta[2]) - pol_eff[0]*np.cos(-2.*theta[2]+2.*theta[0]))*U_stokes + A*coeff_stokes[2,2]*(np.sin(2.*theta[2]*Q_stokes) - np.cos(2.*theta[2]*U_stokes)))
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dI_dtheta1 = 2.*pol_eff[0]/A*(pol_eff[2]*np.cos(-2.*theta[2]+2.*theta[0])*(pol_flux[1]-I_stokes) - pol_eff[1]*np.cos(-2.*theta[0]+2.*theta[1])*(pol_flux[2]-I_stokes))
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dI_dtheta2 = 2.*pol_eff[1]/A*(pol_eff[0]*np.cos(-2.*theta[0]+2.*theta[1])*(pol_flux[2]-I_stokes) - pol_eff[2]*np.cos(-2.*theta[1]+2.*theta[2])*(pol_flux[0]-I_stokes))
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dI_dtheta3 = 2.*pol_eff[2]/A*(pol_eff[1]*np.cos(-2.*theta[1]+2.*theta[2])*(pol_flux[0]-I_stokes) - pol_eff[0]*np.cos(-2.*theta[2]+2.*theta[0])*(pol_flux[1]-I_stokes))
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dQ_dtheta1 = 2.*pol_eff[0]/A*(np.cos(2.*theta[0])*(pol_flux[1]-pol_flux[2]) - (pol_eff[2]*np.cos(-2.*theta[2]+2.*theta[0]) - pol_eff[1]*np.cos(-2.*theta[0]+2.*theta[1]))*Q_stokes)
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dQ_dtheta2 = 2.*pol_eff[1]/A*(np.cos(2.*theta[1])*(pol_flux[2]-pol_flux[0]) - (pol_eff[0]*np.cos(-2.*theta[0]+2.*theta[1]) - pol_eff[2]*np.cos(-2.*theta[1]+2.*theta[2]))*Q_stokes)
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dQ_dtheta3 = 2.*pol_eff[2]/A*(np.cos(2.*theta[2])*(pol_flux[0]-pol_flux[1]) - (pol_eff[1]*np.cos(-2.*theta[1]+2.*theta[2]) - pol_eff[0]*np.cos(-2.*theta[2]+2.*theta[0]))*Q_stokes)
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dU_dtheta1 = 2.*pol_eff[0]/A*(np.sin(2.*theta[0])*(pol_flux[1]-pol_flux[2]) - (pol_eff[2]*np.cos(-2.*theta[2]+2.*theta[0]) - pol_eff[1]*np.cos(-2.*theta[0]+2.*theta[1]))*U_stokes)
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dU_dtheta2 = 2.*pol_eff[1]/A*(np.sin(2.*theta[1])*(pol_flux[2]-pol_flux[0]) - (pol_eff[0]*np.cos(-2.*theta[0]+2.*theta[1]) - pol_eff[2]*np.cos(-2.*theta[1]+2.*theta[2]))*U_stokes)
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dU_dtheta3 = 2.*pol_eff[2]/A*(np.sin(2.*theta[2])*(pol_flux[0]-pol_flux[1]) - (pol_eff[1]*np.cos(-2.*theta[1]+2.*theta[2]) - pol_eff[0]*np.cos(-2.*theta[2]+2.*theta[0]))*U_stokes)
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#Stokes_cov[0,0] += (dI_dtheta1**2*sigma_theta[0]**2 + dI_dtheta2**2*sigma_theta[1]**2 + dI_dtheta3**2*sigma_theta[2]**2)
|
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#Stokes_cov[1,1] += (dQ_dtheta1**2*sigma_theta[0]**2 + dQ_dtheta2**2*sigma_theta[1]**2 + dQ_dtheta3**2*sigma_theta[2]**2)
|
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#Stokes_cov[2,2] += (dU_dtheta1**2*sigma_theta[0]**2 + dU_dtheta2**2*sigma_theta[1]**2 + dU_dtheta3**2*sigma_theta[2]**2)
|
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plt.imshow(np.abs(Stokes_cov[0,0]/I_stokes)*100., origin='lower')
|
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plt.colorbar()
|
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plt.show()
|
||||
plt.imshow(np.abs(Stokes_cov[1,1]/Q_stokes)*100., origin='lower')
|
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plt.colorbar()
|
||||
plt.show()
|
||||
plt.imshow(np.abs(Stokes_cov[2,2]/U_stokes)*100., origin='lower')
|
||||
plt.colorbar()
|
||||
plt.show()
|
||||
s_I2_axis = (dI_dtheta1**2*sigma_theta[0]**2 + dI_dtheta2**2*sigma_theta[1]**2 + dI_dtheta3**2*sigma_theta[2]**2)
|
||||
s_Q2_axis = (dQ_dtheta1**2*sigma_theta[0]**2 + dQ_dtheta2**2*sigma_theta[1]**2 + dQ_dtheta3**2*sigma_theta[2]**2)
|
||||
s_U2_axis = (dU_dtheta1**2*sigma_theta[0]**2 + dU_dtheta2**2*sigma_theta[1]**2 + dU_dtheta3**2*sigma_theta[2]**2)
|
||||
|
||||
Stokes_cov[0,0] += s_I2_axis
|
||||
Stokes_cov[1,1] += s_Q2_axis
|
||||
Stokes_cov[2,2] += s_U2_axis
|
||||
|
||||
# s_I_I = np.sqrt(Stokes_cov[0,0])/I_stokes*100.
|
||||
# s_I_axis_I = np.sqrt(s_I2_axis)/I_stokes*100.
|
||||
# s_Q_Q = np.sqrt(Stokes_cov[1,1])/Q_stokes*100.
|
||||
# s_Q_axis_Q = np.sqrt(s_Q2_axis)/Q_stokes*100.
|
||||
# s_U_U = np.sqrt(Stokes_cov[2,2])/U_stokes*100.
|
||||
# s_U_axis_U = np.sqrt(s_U2_axis)/U_stokes*100.
|
||||
#
|
||||
# fig, ax = plt.subplots(3,3)
|
||||
# im = ax[0,0].imshow(s_I_I, origin='lower')
|
||||
# ax[0,0].set_title(r"$\frac{\sigma_{I}}{I}$")
|
||||
# fig.colorbar(im, ax=ax[0,0])
|
||||
# im = ax[0,1].imshow(s_I_axis_I, origin='lower')
|
||||
# ax[0,1].set_title(r"$\frac{\sigma_{I}^{axis}}{I}$")
|
||||
# fig.colorbar(im, ax=ax[0,1])
|
||||
# im = ax[0,2].imshow(s_I_axis_I/s_I_I, origin='lower')
|
||||
# ax[0,2].set_title(r"$\frac{\sigma_{I}^{axis}}{\sigma_{I}}$")
|
||||
# fig.colorbar(im, ax=ax[0,2])
|
||||
# im = ax[1,0].imshow(s_Q_Q, origin='lower')
|
||||
# ax[1,0].set_title(r"$\frac{\sigma_{Q}}{Q}$")
|
||||
# fig.colorbar(im, ax=ax[1,0])
|
||||
# im = ax[1,1].imshow(s_Q_axis_Q, origin='lower')
|
||||
# ax[1,1].set_title(r"$\frac{\sigma_{Q}^{axis}}{Q}$")
|
||||
# fig.colorbar(im, ax=ax[1,1])
|
||||
# im = ax[1,2].imshow(s_Q_axis_Q/s_Q_Q, origin='lower')
|
||||
# ax[1,2].set_title(r"$\frac{\sigma_{Q}^{axis}}{\sigma_{Q}}$")
|
||||
# fig.colorbar(im, ax=ax[1,2])
|
||||
# im = ax[2,0].imshow(s_U_U, origin='lower')
|
||||
# ax[2,0].set_title(r"$\frac{\sigma_{U}}{U}$")
|
||||
# fig.colorbar(im, ax=ax[2,0])
|
||||
# im = ax[2,1].imshow(s_U_axis_U, origin='lower')
|
||||
# ax[2,1].set_title(r"$\frac{\sigma_{U}^{axis}}{U}$")
|
||||
# fig.colorbar(im, ax=ax[2,1])
|
||||
# im = ax[2,2].imshow(s_U_axis_U/s_U_U, origin='lower')
|
||||
# ax[2,2].set_title(r"$\frac{\sigma_{U}^{axis}}{\sigma_{U}}$")
|
||||
# fig.colorbar(im, ax=ax[2,2])
|
||||
# plt.show()
|
||||
# print("s_I/I = {}% ; s_I_axis/I = {}%".format(np.mean(s_I_I[I_stokes > 0.]), np.mean(s_I_axis_I[I_stokes > 0.])))
|
||||
# print("s_Q/Q = {}% ; s_Q_axis/Q = {}%".format(np.mean(s_Q_Q[Q_stokes > 0.]), np.mean(s_Q_axis_Q[Q_stokes > 0.])))
|
||||
# print("s_U/U = {}% ; s_U_axis/U = {}%".format(np.mean(s_U_U[U_stokes > 0.]), np.mean(s_U_axis_U[U_stokes > 0.])))
|
||||
|
||||
if not(FWHM is None) and (smoothing.lower() in ['gaussian_after','gauss_after']):
|
||||
Stokes_array = np.array([I_stokes, Q_stokes, U_stokes])
|
||||
|
||||
Reference in New Issue
Block a user