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prepare overplot_pol for NGC1068 radio analysis

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Thibault Barnouin
2024-02-08 17:39:15 +01:00
parent 4df270d56d
commit 0aca67c39c
6 changed files with 116 additions and 99 deletions
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6
src/FOC_reduction.py
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@@ -38,7 +38,7 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
# Data binning
rebin = True
pxsize = 0.10
pxsize = 0.05
px_scale = 'arcsec' #pixel, arcsec or full
rebin_operation = 'sum' #sum or average
@@ -50,7 +50,7 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
# Smoothing
smoothing_function = 'combine' #gaussian_after, weighted_gaussian_after, gaussian, weighted_gaussian or combine
smoothing_FWHM = 0.20 #If None, no smoothing is done
smoothing_FWHM = 0.10 #If None, no smoothing is done
smoothing_scale = 'arcsec' #pixel or arcsec
# Rotation
@@ -65,7 +65,7 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
SNRp_cut = 3. #P measurments with SNR>3
SNRi_cut = 30. #I measurments with SNR>30, which implies an uncertainty in P of 4.7%.
flux_lim = None #lowest and highest flux displayed on plot, defaults to bkg and maximum in cut if None
vec_scale = 5
vec_scale = 3
step_vec = 1 #plot all vectors in the array. if step_vec = 2, then every other vector will be plotted
# if step_vec = 0 then all vectors are displayed at full length

195
src/lib/plots.py
View File

@@ -52,6 +52,7 @@ import matplotlib.patheffects as pe
from mpl_toolkits.axes_grid1.anchored_artists import AnchoredSizeBar, AnchoredDirectionArrows
from astropy.wcs import WCS
from astropy.io import fits
from astropy.coordinates import SkyCoord
from scipy.ndimage import zoom as sc_zoom
@@ -564,7 +565,7 @@ class align_maps(object):
north_dir1 = AnchoredDirectionArrows(self.ax1.transAxes, "E", "N", length=-0.08, fontsize=0.025, loc=1, aspect_ratio=-1, sep_y=0.01, sep_x=0.01, back_length=0., head_length=10., head_width=10., angle=-self.map[0].header['orientat'], color='white', text_props={'ec': None, 'fc': 'w', 'alpha': 1, 'lw': 0.4}, arrow_props={'ec': None,'fc':'w','alpha': 1,'lw': 1})
self.ax1.add_artist(north_dir1)
except KeyError:
pass
passCTYPE
self.cr_map, = self.ax1.plot(*self.wcs_map.wcs.crpix, 'r+')
@@ -683,16 +684,16 @@ class overplot_radio(align_maps):
Class to overplot maps from different observations.
Inherit from class align_maps in order to get the same WCS on both maps.
"""
def overplot(self, other_levels, SNRp_cut=3., SNRi_cut=30., vec_scale=2, savename=None):
def overplot(self, other_levels, SNRp_cut=3., SNRi_cut=30., vec_scale=2, savename=None, **kwargs):
self.Stokes_UV = self.map
self.wcs_UV = self.wcs_map
#Get Data
obj = self.Stokes_UV[0].header['targname']
stkI = self.Stokes_UV[np.argmax([self.Stokes_UV[i].header['datatype']=='I_stokes' for i in range(len(self.Stokes_UV))])]
stk_cov = self.Stokes_UV[np.argmax([self.Stokes_UV[i].header['datatype']=='IQU_cov_matrix' for i in range(len(self.Stokes_UV))])]
pol = self.Stokes_UV[np.argmax([self.Stokes_UV[i].header['datatype']=='Pol_deg_debiased' for i in range(len(self.Stokes_UV))])]
pol_err = self.Stokes_UV[np.argmax([self.Stokes_UV[i].header['datatype']=='Pol_deg_err' for i in range(len(self.Stokes_UV))])]
pang = self.Stokes_UV[np.argmax([self.Stokes_UV[i].header['datatype']=='Pol_ang' for i in range(len(self.Stokes_UV))])]
stkI = deepcopy(self.Stokes_UV['I_STOKES'].data)
stk_cov = deepcopy(self.Stokes_UV['IQU_COV_MATRIX'].data)
pol = deepcopy(self.Stokes_UV['POL_DEG_DEBIASED'].data)
pol_err = deepcopy(self.Stokes_UV['POL_DEG_ERR'].data)
pang = deepcopy(self.Stokes_UV['POL_ANG'].data)
other_data = self.other_map[0].data
self.other_convert = 1.
@@ -700,40 +701,46 @@ class overplot_radio(align_maps):
if other_unit.lower() == 'jy/beam':
other_unit = r"mJy/Beam"
self.other_convert = 1e3
other_freq = self.other_map[0].header['crval3']
other_freq = self.other_map[0].header['crval3'] if hasattr(self.other_map[0].header,'srval3') else 1.
self.convert_flux = self.Stokes_UV[0].header['photflam']
#Compute SNR and apply cuts
pol.data[pol.data == 0.] = np.nan
SNRp = pol.data/pol_err.data
pol[pol == 0.] = np.nan
SNRp = pol/pol_err
SNRp[np.isnan(SNRp)] = 0.
pol.data[SNRp < SNRp_cut] = np.nan
SNRi = stkI.data/np.sqrt(stk_cov.data[0,0])
pol[SNRp < SNRp_cut] = np.nan
SNRi = stkI/np.sqrt(stk_cov[0,0])
SNRi[np.isnan(SNRi)] = 0.
pol.data[SNRi < SNRi_cut] = np.nan
pol[SNRi < SNRi_cut] = np.nan
plt.rcParams.update({'font.size': 16})
self.fig2 = plt.figure(figsize=(15,15))
self.ax = self.fig2.add_subplot(111, projection=self.wcs_UV)
self.ax = self.fig2.add_subplot(111, projection=self.wcs_UV.celestial)
self.ax.set_facecolor('k')
self.fig2.subplots_adjust(hspace=0, wspace=0, right=0.9)
#Display UV intensity map with polarization vectors
vmin, vmax = 0., np.max(stkI.data[stkI.data > 0.]*self.convert_flux)
im = self.ax.imshow(stkI.data*self.convert_flux, vmin=vmin, vmax=vmax, aspect='equal', cmap='inferno', alpha=1.)
vmin, vmax = 0., np.max(stkI[stkI > 0.]*self.convert_flux)
for key, value in [["cmap",[["cmap","inferno"]]], ["norm",[["vmin",vmin],["vmax",vmax]]]]:
try:
test = kwargs[key]
except KeyError:
for key_i, val_i in value:
kwargs[key_i] = val_i
im = self.ax.imshow(stkI*self.convert_flux, aspect='equal', **kwargs)
cbar_ax = self.fig2.add_axes([0.95, 0.12, 0.01, 0.75])
cbar = plt.colorbar(im, cax=cbar_ax, label=r"$F_{\lambda}$ [$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA^{-1}$]")
pol.data[np.isfinite(pol.data)] = 1./2.
pol[np.isfinite(pol)] = 1./2.
step_vec = 1
X, Y = np.meshgrid(np.arange(stkI.data.shape[1]), np.arange(stkI.data.shape[0]))
U, V = pol.data*np.cos(np.pi/2.+pang.data*np.pi/180.), pol.data*np.sin(np.pi/2.+pang.data*np.pi/180.)
X, Y = np.meshgrid(np.arange(stkI.shape[1]), np.arange(stkI.shape[0]))
U, V = pol*np.cos(np.pi/2.+pang*np.pi/180.), pol*np.sin(np.pi/2.+pang*np.pi/180.)
Q = self.ax.quiver(X[::step_vec,::step_vec],Y[::step_vec,::step_vec],U[::step_vec,::step_vec],V[::step_vec,::step_vec],units='xy',angles='uv',scale=1./vec_scale,scale_units='xy',pivot='mid',headwidth=0.,headlength=0.,headaxislength=0.,width=0.1,color='w')
self.ax.autoscale(False)
#Display other map as contours
other_cont = self.ax.contour(other_data*self.other_convert, transform=self.ax.get_transform(self.wcs_other), levels=other_levels*self.other_convert, colors='grey')
other_cont = self.ax.contour(other_data*self.other_convert, transform=self.ax.get_transform(self.wcs_other.celestial), levels=other_levels*self.other_convert, colors='grey')
self.ax.clabel(other_cont, inline=True, fontsize=8)
self.ax.set(xlabel="Right Ascension (J2000)", ylabel="Declination (J2000)", title="HST/FOC UV polarization map of {0:s} overplotted with {1:.2f}GHz map in {2:s}.".format(obj, other_freq*1e-9, other_unit))
@@ -757,10 +764,10 @@ class overplot_radio(align_maps):
self.fig2.canvas.draw()
def plot(self, levels, SNRp_cut=3., SNRi_cut=30., savename=None) -> None:
def plot(self, levels, SNRp_cut=3., SNRi_cut=30., savename=None, **kwargs) -> None:
while not self.aligned:
self.align()
self.overplot(other_levels=levels, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, savename=savename)
self.overplot(other_levels=levels, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, savename=savename, **kwargs)
plt.show(block=True)
class overplot_chandra(align_maps):
@@ -773,11 +780,11 @@ class overplot_chandra(align_maps):
self.wcs_UV = self.wcs_map
#Get Data
obj = self.Stokes_UV[0].header['targname']
stkI = self.Stokes_UV[np.argmax([self.Stokes_UV[i].header['datatype']=='I_stokes' for i in range(len(self.Stokes_UV))])]
stk_cov = self.Stokes_UV[np.argmax([self.Stokes_UV[i].header['datatype']=='IQU_cov_matrix' for i in range(len(self.Stokes_UV))])]
pol = self.Stokes_UV[np.argmax([self.Stokes_UV[i].header['datatype']=='Pol_deg_debiased' for i in range(len(self.Stokes_UV))])]
pol_err = self.Stokes_UV[np.argmax([self.Stokes_UV[i].header['datatype']=='Pol_deg_err' for i in range(len(self.Stokes_UV))])]
pang = self.Stokes_UV[np.argmax([self.Stokes_UV[i].header['datatype']=='Pol_ang' for i in range(len(self.Stokes_UV))])]
stkI = deepcopy(self.Stokes_UV['I_STOKES'].data)
stk_cov = deepcopy(self.Stokes_UV['IQU_COV_MATRIX'].data)
pol = deepcopy(self.Stokes_UV['POL_DEG_DEBIASED'].data)
pol_err = deepcopy(self.Stokes_UV['POL_DEG_ERR'].data)
pang = deepcopy(self.Stokes_UV['POL_ANG'].data)
other_data = sc_zoom(self.other_map[0].data,zoom)
self.wcs_other.wcs.crpix *= zoom
@@ -789,13 +796,13 @@ class overplot_chandra(align_maps):
self.convert_flux = self.Stokes_UV[0].header['photflam']
#Compute SNR and apply cuts
pol.data[pol.data == 0.] = np.nan
SNRp = pol.data/pol_err.data
pol[pol == 0.] = np.nan
SNRp = pol/pol_err
SNRp[np.isnan(SNRp)] = 0.
pol.data[SNRp < SNRp_cut] = np.nan
SNRi = stkI.data/np.sqrt(stk_cov.data[0,0])
pol[SNRp < SNRp_cut] = np.nan
SNRi = stkI/np.sqrt(stk_cov[0,0])
SNRi[np.isnan(SNRi)] = 0.
pol.data[SNRi < SNRi_cut] = np.nan
pol[SNRi < SNRi_cut] = np.nan
plt.rcParams.update({'font.size': 16})
self.fig2 = plt.figure(figsize=(15,15))
@@ -804,15 +811,15 @@ class overplot_chandra(align_maps):
self.fig2.subplots_adjust(hspace=0, wspace=0, right=0.9)
#Display UV intensity map with polarization vectors
vmin, vmax = 0., np.max(stkI.data[stkI.data > 0.]*self.convert_flux)
im = self.ax.imshow(stkI.data*self.convert_flux, vmin=vmin, vmax=vmax, aspect='equal', cmap='inferno', alpha=1.)
vmin, vmax = 0., np.max(stkI[stkI > 0.]*self.convert_flux)
im = self.ax.imshow(stkI*self.convert_flux, vmin=vmin, vmax=vmax, aspect='equal', cmap='inferno', alpha=1.)
cbar_ax = self.fig2.add_axes([0.95, 0.12, 0.01, 0.75])
cbar = plt.colorbar(im, cax=cbar_ax, label=r"$F_{\lambda}$ [$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA^{-1}$]")
pol.data[np.isfinite(pol.data)] = 1./2.
pol[np.isfinite(pol)] = 1./2.
step_vec = 1
X, Y = np.meshgrid(np.arange(stkI.data.shape[1]), np.arange(stkI.data.shape[0]))
U, V = pol.data*np.cos(np.pi/2.+pang.data*np.pi/180.), pol.data*np.sin(np.pi/2.+pang.data*np.pi/180.)
X, Y = np.meshgrid(np.arange(stkI.shape[1]), np.arange(stkI.shape[0]))
U, V = pol*np.cos(np.pi/2.+pang*np.pi/180.), pol*np.sin(np.pi/2.+pang*np.pi/180.)
Q = self.ax.quiver(X[::step_vec,::step_vec],Y[::step_vec,::step_vec],U[::step_vec,::step_vec],V[::step_vec,::step_vec],units='xy',angles='uv',scale=1./vec_scale,scale_units='xy',pivot='mid',headwidth=0.,headlength=0.,headaxislength=0.,width=0.1,color='w')
self.ax.autoscale(False)
@@ -858,44 +865,47 @@ class overplot_pol(align_maps):
self.wcs_UV = self.wcs_map
#Get Data
obj = self.Stokes_UV[0].header['targname']
stkI = self.Stokes_UV[np.argmax([self.Stokes_UV[i].header['datatype']=='I_stokes' for i in range(len(self.Stokes_UV))])]
stk_cov = self.Stokes_UV[np.argmax([self.Stokes_UV[i].header['datatype']=='IQU_cov_matrix' for i in range(len(self.Stokes_UV))])]
pol = self.Stokes_UV[np.argmax([self.Stokes_UV[i].header['datatype']=='Pol_deg_debiased' for i in range(len(self.Stokes_UV))])]
pol_err = self.Stokes_UV[np.argmax([self.Stokes_UV[i].header['datatype']=='Pol_deg_err' for i in range(len(self.Stokes_UV))])]
pang = self.Stokes_UV[np.argmax([self.Stokes_UV[i].header['datatype']=='Pol_ang' for i in range(len(self.Stokes_UV))])]
stkI = deepcopy(self.Stokes_UV['I_STOKES'].data)
stk_cov = deepcopy(self.Stokes_UV['IQU_COV_MATRIX'].data)
pol = deepcopy(self.Stokes_UV['POL_DEG_DEBIASED'].data)
pol_err = deepcopy(self.Stokes_UV['POL_DEG_ERR'].data)
pang = deepcopy(self.Stokes_UV['POL_ANG'].data)
self.convert_flux = self.Stokes_UV[0].header['photflam']
other_data = self.other_map[0].data
other_data = deepcopy(self.other_map[0].data)
#Compute SNR and apply cuts
pol.data[pol.data == 0.] = np.nan
SNRp = pol.data/pol_err.data
pol[pol == 0.] = np.nan
SNRp = pol/pol_err
SNRp[np.isnan(SNRp)] = 0.
pol.data[SNRp < SNRp_cut] = np.nan
SNRi = stkI.data/np.sqrt(stk_cov.data[0,0])
pol[SNRp < SNRp_cut] = np.nan
SNRi = stkI/np.sqrt(stk_cov[0,0])
SNRi[np.isnan(SNRi)] = 0.
pol.data[SNRi < SNRi_cut] = np.nan
pol[SNRi < SNRi_cut] = np.nan
plt.rcParams.update({'font.size': 16})
self.fig2 = plt.figure(figsize=(15,15))
self.ax = self.fig2.add_subplot(111, projection=self.wcs_UV)
self.fig2, self.ax = plt.subplots(figsize=(15,15), subplot_kw=dict(projection=self.wcs_UV))
self.ax.set_facecolor('k')
self.fig2.subplots_adjust(hspace=0, wspace=0, right=0.9)
self.fig2.subplots_adjust(hspace=0, wspace=0, right=0.85)
#Display Stokes I as contours
levels_stkI = np.rint(np.linspace(10,99,10))/100.*np.max(stkI.data[stkI.data > 0.]*self.convert_flux)
cont_stkI = self.ax.contour(stkI.data*self.convert_flux, transform=self.ax.get_transform(self.wcs_UV), levels=levels_stkI, colors='grey', alpha=0.5)
levels_stkI = np.logspace(np.log(3)/np.log(10),2.,5)/100.*np.max(stkI[stkI > 0.])*self.convert_flux
cont_stkI = self.ax.contour(stkI*self.convert_flux, levels=levels_stkI, colors='grey', alpha=0.5)
#self.ax.clabel(cont_stkI, inline=True, fontsize=8)
self.ax.autoscale(False)
#Display full size polarization vectors
pol.data[np.isfinite(pol.data)] = 1./2.
if vec_scale is None:
self.vec_scale = 2.
pol[np.isfinite(pol)] = 1./2.
else:
self.vec_scale = vec_scale
step_vec = 1
X, Y = np.meshgrid(np.arange(stkI.data.shape[1]), np.arange(stkI.data.shape[0]))
U, V = pol.data*np.cos(np.pi/2.+pang.data*np.pi/180.), pol.data*np.sin(np.pi/2.+pang.data*np.pi/180.)
Q = self.ax.quiver(X[::step_vec,::step_vec],Y[::step_vec,::step_vec],U[::step_vec,::step_vec],V[::step_vec,::step_vec],units='xy',angles='uv',scale=1./vec_scale,scale_units='xy',pivot='mid',headwidth=0.,headlength=0.,headaxislength=0.,width=0.1,linewidth=0.5,color='white',edgecolor='black')
self.X, self.Y = np.meshgrid(np.arange(stkI.shape[1]), np.arange(stkI.shape[0]))
self.U, self.V = pol*np.cos(np.pi/2.+pang*np.pi/180.), pol*np.sin(np.pi/2.+pang*np.pi/180.)
self.Q = self.ax.quiver(self.X[::step_vec,::step_vec],self.Y[::step_vec,::step_vec],self.U[::step_vec,::step_vec],self.V[::step_vec,::step_vec],units='xy',angles='uv',scale=1./self.vec_scale,scale_units='xy',pivot='mid',headwidth=0.,headlength=0.,headaxislength=0.,width=0.1,linewidth=0.5,color='white',edgecolor='gray')
#Display "other" intensity map
vmin, vmax = np.min(other_data[other_data > 0.]*self.other_convert), np.max(other_data[other_data > 0.]*self.other_convert)
@@ -905,9 +915,9 @@ class overplot_pol(align_maps):
except KeyError:
for key_i, val_i in value:
kwargs[key_i] = val_i
im = self.ax.imshow(other_data*self.other_convert, transform=self.ax.get_transform(self.wcs_other), alpha=1., **kwargs)
cbar_ax = self.fig2.add_axes([0.95, 0.12, 0.01, 0.75])
cbar = plt.colorbar(im, cax=cbar_ax, label=r"$F_{\lambda}$ [$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA^{-1}$]")
self.im = self.ax.imshow(other_data*self.other_convert, transform=self.ax.get_transform(self.wcs_other), alpha=1., **kwargs)
self.cbar_ax = self.fig2.add_axes([0.855, 0.15, 0.01, 0.7])
self.cbar = plt.colorbar(self.im, cax=self.cbar_ax, label=r"$F_{\lambda}$ [$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA^{-1}$]")
self.ax.set(xlabel="Right Ascension (J2000)", ylabel="Declination (J2000)", title="{0:s} overplotted with polarization vectors and Stokes I contours from HST/FOC".format(obj))
@@ -941,6 +951,15 @@ class overplot_pol(align_maps):
self.overplot(SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, vec_scale=vec_scale, savename=savename, **kwargs)
plt.show(block=True)
def add_vector(self,position='center',pol_deg=1.,pol_ang=0.,**kwargs):
if position == 'center':
position = np.array(self.X.shape)/2.
if type(position) == SkyCoord:
position = self.wcs_map.world_to_pixel(position)
u, v = pol_deg*np.cos(np.radians(pol_ang)+np.pi/2.), pol_deg*np.sin(np.radians(pol_ang)+np.pi/2.)
self.new_vec = self.ax.quiver(*position,u,v,units='xy',angles='uv',scale=1./self.vec_scale,scale_units='xy',pivot='mid',headwidth=0.,headlength=0.,headaxislength=0.,width=0.1,**kwargs)
self.fig2.canvas.draw()
class align_pol(object):
def __init__(self, maps, **kwargs):
@@ -957,15 +976,15 @@ class align_pol(object):
def single_plot(self, curr_map, wcs, v_lim=None, ax_lim=None, SNRp_cut=3., SNRi_cut=30., savename=None, **kwargs):
#Get data
stkI = curr_map[np.argmax([curr_map[i].header['datatype']=='I_stokes' for i in range(len(curr_map))])]
stkQ = curr_map[np.argmax([curr_map[i].header['datatype']=='Q_stokes' for i in range(len(curr_map))])]
stkU = curr_map[np.argmax([curr_map[i].header['datatype']=='U_stokes' for i in range(len(curr_map))])]
stk_cov = curr_map[np.argmax([curr_map[i].header['datatype']=='IQU_cov_matrix' for i in range(len(curr_map))])]
pol = curr_map[np.argmax([curr_map[i].header['datatype']=='Pol_deg_debiased' for i in range(len(curr_map))])]
pol_err = curr_map[np.argmax([curr_map[i].header['datatype']=='Pol_deg_err' for i in range(len(curr_map))])]
pang = curr_map[np.argmax([curr_map[i].header['datatype']=='Pol_ang' for i in range(len(curr_map))])]
stkI = deepcopy(curr_map['I_STOKES'].data)
stkQ = deepcopy(curr_map['Q_STOKES'].data)
stkU = deepcopy(curr_map['U_STOKES'].data)
stk_cov = deepcopy(curr_map['IQU_COV_MATRIX'].data)
pol = deepcopy(curr_map['POL_DEG_DEBIASED'].data)
pol_err = deepcopy(curr_map['POL_DEG_ERR'].data)
pang = deepcopy(curr_map['POL_ANG'].data)
try:
data_mask = curr_map[np.argmax([curr_map[i].header['datatype']=='Data_mask' for i in range(len(curr_map))])].data.astype(bool)
data_mask = curr_map['DATA_MASK'].data.astype(bool)
except KeyError:
data_mask = np.ones(stkI.shape).astype(bool)
@@ -973,16 +992,16 @@ class align_pol(object):
convert_flux = curr_map[0].header['photflam']
#Compute SNR and apply cuts
pol.data[pol.data == 0.] = np.nan
pol_err.data[pol_err.data == 0.] = np.nan
SNRp = pol.data/pol_err.data
pol[pol == 0.] = np.nan
pol_err[pol_err == 0.] = np.nan
SNRp = pol/pol_err
SNRp[np.isnan(SNRp)] = 0.
pol.data[SNRp < SNRp_cut] = np.nan
pol[SNRp < SNRp_cut] = np.nan
maskI = stk_cov.data[0,0] > 0
SNRi = np.zeros(stkI.data.shape)
SNRi[maskI] = stkI.data[maskI]/np.sqrt(stk_cov.data[0,0][maskI])
pol.data[SNRi < SNRi_cut] = np.nan
maskI = stk_cov[0,0] > 0
SNRi = np.zeros(stkI.shape)
SNRi[maskI] = stkI[maskI]/np.sqrt(stk_cov[0,0][maskI])
pol[SNRi < SNRi_cut] = np.nan
mask = (SNRp > SNRp_cut) * (SNRi > SNRi_cut)
@@ -1002,7 +1021,7 @@ class align_pol(object):
ax.set(xlim=x_lim,ylim=y_lim)
if v_lim is None:
vmin, vmax = 0., np.max(stkI.data[stkI.data > 0.]*convert_flux)
vmin, vmax = 0., np.max(stkI[stkI > 0.]*convert_flux)
else:
vmin, vmax = v_lim*convert_flux
@@ -1015,7 +1034,7 @@ class align_pol(object):
for key_i, val_i in value:
kwargs[key_i] = val_i
im = ax.imshow(stkI.data*convert_flux, aspect='equal', **kwargs)
im = ax.imshow(stkI*convert_flux, aspect='equal', **kwargs)
cbar = plt.colorbar(im, cax=cbar_ax, label=r"$F_{\lambda}$ [$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA^{-1}$]")
px_size = wcs.wcs.get_cdelt()[0]*3600.
@@ -1026,8 +1045,8 @@ class align_pol(object):
ax.add_artist(north_dir)
step_vec = 1
X, Y = np.meshgrid(np.arange(stkI.data.shape[1]), np.arange(stkI.data.shape[0]))
U, V = pol.data*np.cos(np.pi/2.+pang.data*np.pi/180.), pol.data*np.sin(np.pi/2.+pang.data*np.pi/180.)
X, Y = np.meshgrid(np.arange(stkI.shape[1]), np.arange(stkI.shape[0]))
U, V = pol*np.cos(np.pi/2.+pang*np.pi/180.), pol*np.sin(np.pi/2.+pang*np.pi/180.)
Q = ax.quiver(X[::step_vec,::step_vec],Y[::step_vec,::step_vec],U[::step_vec,::step_vec],V[::step_vec,::step_vec],units='xy',angles='uv',scale=0.5,scale_units='xy',pivot='mid',headwidth=0.,headlength=0.,headaxislength=0.,width=0.1,color='w')
pol_sc = AnchoredSizeBar(ax.transData, 2., r"$P$= 100 %", 4, pad=0.5, sep=5, borderpad=0.5, frameon=False, size_vertical=0.005, color='w')
ax.add_artist(pol_sc)
@@ -1874,28 +1893,28 @@ class pol_map(object):
return deepcopy(WCS(self.Stokes[0].header))
@property
def I(self):
return self.Stokes[np.argmax([self.Stokes[i].header['datatype']=='I_stokes' for i in range(len(self.Stokes))])].data
return self.Stokes['I_STOKES'].data
@property
def Q(self):
return self.Stokes[np.argmax([self.Stokes[i].header['datatype']=='Q_stokes' for i in range(len(self.Stokes))])].data
return self.Stokes['Q_STOKES'].data
@property
def U(self):
return self.Stokes[np.argmax([self.Stokes[i].header['datatype']=='U_stokes' for i in range(len(self.Stokes))])].data
return self.Stokes['U_STOKES'].data
@property
def IQU_cov(self):
return self.Stokes[np.argmax([self.Stokes[i].header['datatype']=='IQU_cov_matrix' for i in range(len(self.Stokes))])].data
return self.Stokes['IQU_COV_MATRIX'].data
@property
def P(self):
return self.Stokes[np.argmax([self.Stokes[i].header['datatype']=='Pol_deg_debiased' for i in range(len(self.Stokes))])].data
return self.Stokes['POL_DEG_DEBIASED'].data
@property
def s_P(self):
return self.Stokes[np.argmax([self.Stokes[i].header['datatype']=='Pol_deg_err' for i in range(len(self.Stokes))])].data
return self.Stokes['POL_DEG_ERR'].data
@property
def PA(self):
return self.Stokes[np.argmax([self.Stokes[i].header['datatype']=='Pol_ang' for i in range(len(self.Stokes))])].data
return self.Stokes['POL_ANG'].data
@property
def data_mask(self):
return self.Stokes[np.argmax([self.Stokes[i].header['datatype']=='Data_mask' for i in range(len(self.Stokes))])].data
return self.Stokes['DATA_MASK'].data
def set_data_mask(self, mask):
self.Stokes[np.argmax([self.Stokes[i].header['datatype']=='Data_mask' for i in range(len(self.Stokes))])].data = mask.astype(float)

14
src/overplot_MRK463E.py
View File

@@ -1,8 +1,6 @@
#!/usr/bin/python3
from os import system as command
from astropy.io import fits
import numpy as np
from copy import deepcopy
from lib.plots import overplot_chandra, overplot_pol, align_pol
from matplotlib.colors import LogNorm
@@ -12,14 +10,14 @@ Stokes_Xr = fits.open("./data/MRK463E/Chandra/4913/primary/acisf04913N004_cntr_i
levels = np.geomspace(1.,99.,10)
A = overplot_chandra(Stokes_UV, Stokes_Xr)
A.plot(levels=levels, SNRp_cut=3.0, SNRi_cut=20.0, zoom=1, savename='./plots/MRK463E/Chandra_overplot.pdf')
#A = overplot_chandra(Stokes_UV, Stokes_Xr)
#A.plot(levels=levels, SNRp_cut=3.0, SNRi_cut=20.0, zoom=1, savename='./plots/MRK463E/Chandra_overplot.pdf')
B = overplot_chandra(Stokes_UV, Stokes_Xr, norm=LogNorm())
B.plot(levels=levels, SNRp_cut=3.0, SNRi_cut=20.0, zoom=1, savename='./plots/MRK463E/Chandra_overplot_forced.pdf')
#B = overplot_chandra(Stokes_UV, Stokes_Xr, norm=LogNorm())
#B.plot(levels=levels, SNRp_cut=3.0, SNRi_cut=20.0, zoom=1, savename='./plots/MRK463E/Chandra_overplot_forced.pdf')
C = overplot_pol(Stokes_UV, Stokes_IR)
C.plot(SNRp_cut=3.0, SNRi_cut=20.0, savename='./plots/MRK463E/IR_overplot.pdf')
#C = overplot_pol(Stokes_UV, Stokes_IR)
#C.plot(SNRp_cut=3.0, SNRi_cut=20.0, savename='./plots/MRK463E/IR_overplot.pdf')
D = overplot_pol(Stokes_UV, Stokes_IR, norm=LogNorm())
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')