Tibeuleu/FOC_Reduction
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revamp plots for better background and units

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Tibeuleu
2023-03-29 13:23:02 +02:00
parent 9010dcf0ac
commit 5f43549125
43 changed files with 89 additions and 66 deletions
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12
src/lib/background.py
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@@ -66,14 +66,14 @@ def display_bkg(data, background, std_bkg, headers, histograms=None, binning=Non
fig_h, ax_h = plt.subplots(figsize=(10,6), constrained_layout=True)
for i, (hist, bins) in enumerate(zip(histograms, binning)):
filt_obs[headers[i]['filtnam1']] += 1
ax_h.plot(bins,hist,'+',color="C{0:d}".format(i),alpha=0.8,label=headers[i]['filtnam1']+' (Obs '+str(filt_obs[headers[i]['filtnam1']])+')')
ax_h.plot([background[i],background[i]],[hist.min(), hist.max()],'x--',color="C{0:d}".format(i),alpha=0.8)
ax_h.plot(bins*convert_flux[i],hist,'+',color="C{0:d}".format(i),alpha=0.8,label=headers[i]['filtnam1']+' (Obs '+str(filt_obs[headers[i]['filtnam1']])+')')
ax_h.plot([background[i]*convert_flux[i],background[i]*convert_flux[i]],[hist.min(), hist.max()],'x--',color="C{0:d}".format(i),alpha=0.8)
if not(coeff is None):
ax_h.plot(bins,gausspol(bins,*coeff[i]),'--',color="C{0:d}".format(i),alpha=0.8)
ax_h.plot(bins*convert_flux[i],gausspol(bins,*coeff[i]),'--',color="C{0:d}".format(i),alpha=0.8)
ax_h.set_xscale('log')
ax_h.set_ylim([0.,np.max([hist.max() for hist in histograms])])
ax_h.set_xlim([np.min(background)*1e-2,np.max(background)*1e2])
ax_h.set_xlabel(r"Count rate [$s^{-1}$]")
ax_h.set_xlim([np.min(background*convert_flux)*1e-2,np.max(background*convert_flux)*1e2])
ax_h.set_xlabel(r"Flux [$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA^{-1}$]")
ax_h.set_ylabel(r"Number of pixels in bin")
ax_h.set_title("Histogram for each observation")
plt.legend()
@@ -105,7 +105,7 @@ def display_bkg(data, background, std_bkg, headers, histograms=None, binning=Non
if not(savename is None):
fig2.savefig(plots_folder+savename+'_'+filt+'_background_location.png', bbox_inches='tight')
if not(rectangle is None):
plot_obs(data, headers, vmin=data[data > 0.].min(), vmax=data[data > 0.].max(), rectangle=rectangle,
plot_obs(data, headers, vmin=data[data > 0.].min()*convert_flux.mean(), vmax=data[data > 0.].max()*convert_flux.mean(), rectangle=rectangle,
savename=savename+"_background_location",plots_folder=plots_folder)
elif not(rectangle is None):
plot_obs(data, headers, vmin=data[data > 0.].min(), vmax=data[data > 0.].max(), rectangle=rectangle)

87
src/lib/plots.py
View File

@@ -44,6 +44,7 @@ from matplotlib.path import Path
from matplotlib.widgets import RectangleSelector, LassoSelector, Button, Slider, TextBox
from matplotlib.colors import LogNorm
import matplotlib.font_manager as fm
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
@@ -127,37 +128,36 @@ def plot_obs(data_array, headers, shape=None, vmin=None, vmax=None, rectangle=No
fig, ax = plt.subplots(shape[0], shape[1], figsize=(10,10), dpi=200,
sharex=True, sharey=True)
for i, enum in enumerate(list(zip(ax.flatten(),data_array))):
ax = enum[0]
data = enum[1]
instr = headers[i]['instrume']
rootname = headers[i]['rootname']
exptime = headers[i]['exptime']
filt = headers[i]['filtnam1']
for i, (axe,data,head) in enumerate(zip(ax.flatten(),data_array,headers)):
instr = head['instrume']
rootname = head['rootname']
exptime = head['exptime']
filt = head['filtnam1']
convert = head['photflam']
#plots
if vmin is None or vmax is None:
vmin, vmax = data[data>0.].min()/10., data[data>0.].max()
#im = ax.imshow(data, vmin=vmin, vmax=vmax, origin='lower', cmap='gray')
im = ax.imshow(data, norm=LogNorm(vmin,vmax), origin='lower', cmap='gray')
vmin, vmax = convert*data[data>0.].min()/10., convert*data[data>0.].max()
#im = axe.imshow(convert*data, vmin=vmin, vmax=vmax, origin='lower', cmap='gray')
im = axe.imshow(convert*data, norm=LogNorm(vmin,vmax), origin='lower', cmap='gray')
if not(rectangle is None):
x, y, width, height, angle, color = rectangle[i]
ax.add_patch(Rectangle((x, y), width, height, angle=angle,
axe.add_patch(Rectangle((x, y), width, height, angle=angle,
edgecolor=color, fill=False))
#position of centroid
ax.plot([data.shape[1]/2, data.shape[1]/2], [0,data.shape[0]-1], '--', lw=1,
axe.plot([data.shape[1]/2, data.shape[1]/2], [0,data.shape[0]-1], '--', lw=1,
color='grey', alpha=0.5)
ax.plot([0,data.shape[1]-1], [data.shape[1]/2, data.shape[1]/2], '--', lw=1,
axe.plot([0,data.shape[1]-1], [data.shape[1]/2, data.shape[1]/2], '--', lw=1,
color='grey', alpha=0.5)
ax.annotate(instr+":"+rootname,color='white',fontsize=5,xy=(0.02, 0.95),
axe.annotate(instr+":"+rootname,color='white',fontsize=5,xy=(0.02, 0.95),
xycoords='axes fraction')
ax.annotate(filt,color='white',fontsize=10,xy=(0.02, 0.02),
axe.annotate(filt,color='white',fontsize=10,xy=(0.02, 0.02),
xycoords='axes fraction')
ax.annotate(exptime,color='white',fontsize=5,xy=(0.80, 0.02),
axe.annotate(exptime,color='white',fontsize=5,xy=(0.80, 0.02),
xycoords='axes fraction')
fig.subplots_adjust(hspace=0.01, wspace=0.01, right=0.85)
cbar_ax = fig.add_axes([0.9, 0.12, 0.02, 0.75])
fig.colorbar(im, cax=cbar_ax, label=r'$Counts \cdot s^{-1}$')
fig.colorbar(im, cax=cbar_ax, label=r"Flux [$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA^{-1}$]")
if not (savename is None):
#fig.suptitle(savename)
@@ -217,7 +217,7 @@ def plot_Stokes(Stokes, savename=None, plots_folder=""):
def polarization_map(Stokes, data_mask=None, rectangle=None, SNRp_cut=3., SNRi_cut=30.,
step_vec=1, savename=None, plots_folder="", display="default"):
step_vec=1, vec_scale=2., savename=None, plots_folder="", display="default"):
"""
Plots polarization map from Stokes HDUList.
----------
@@ -241,6 +241,10 @@ def polarization_map(Stokes, data_mask=None, rectangle=None, SNRp_cut=3., SNRi_c
Number of steps between each displayed polarization vector.
If step_vec = 2, every other vector will be displayed.
Defaults to 1
vec_scale : float, optional
Pixel length of displayed 100% polarization vector.
If vec_scale = 2, a vector of 50% polarization will be 1 pixel wide.
Defaults to 2.
savename : str, optional
Name of the figure the map should be saved to. If None, the map won't
be saved (only displayed).
@@ -316,7 +320,7 @@ def polarization_map(Stokes, data_mask=None, rectangle=None, SNRp_cut=3., SNRi_c
if display.lower() in ['intensity']:
# If no display selected, show intensity map
display='i'
vmin, vmax = 1./3.*np.mean(np.sqrt(stk_cov.data[0,0][mask])*convert_flux), np.max(stkI.data[stkI.data > 0.]*convert_flux)
vmin, vmax = 1.*np.mean(np.sqrt(stk_cov.data[0,0][mask])*convert_flux), np.max(stkI.data[stkI.data > 0.]*convert_flux)
im = ax.imshow(stkI.data*convert_flux, norm=LogNorm(vmin,vmax), aspect='equal', cmap='inferno', alpha=1.)
cbar = plt.colorbar(im, cax=cbar_ax, label=r"$F_{\lambda}$ [$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA^{-1}$]")
levelsI = np.linspace(vmax*0.01, vmax*0.99, 10)
@@ -327,7 +331,7 @@ def polarization_map(Stokes, data_mask=None, rectangle=None, SNRp_cut=3., SNRi_c
# Display polarisation flux
display='pf'
pf_mask = (stkI.data > 0.) * (pol.data > 0.)
vmin, vmax = 1./3.*np.mean(np.sqrt(stk_cov.data[0,0][mask])*convert_flux), np.max(stkI.data[stkI.data > 0.]*convert_flux)
vmin, vmax = 1.*np.mean(np.sqrt(stk_cov.data[0,0][mask])*convert_flux), np.max(stkI.data[stkI.data > 0.]*convert_flux)
im = ax.imshow(stkI.data*convert_flux*pol.data, norm=LogNorm(vmin,vmax), aspect='equal', cmap='inferno', alpha=1.)
cbar = plt.colorbar(im, cax=cbar_ax, label=r"$F_{\lambda} \cdot P$ [$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA^{-1}$]")
levelsPf = np.linspace(vmax*0.01, vmax*0.99, 10)
@@ -382,7 +386,7 @@ def polarization_map(Stokes, data_mask=None, rectangle=None, SNRp_cut=3., SNRi_c
#ax.clabel(cont,inline=True,fontsize=6)
else:
# Defaults to intensity map
vmin, vmax = 3.*np.mean(np.sqrt(stk_cov.data[0,0][mask])*convert_flux), np.max(stkI.data[stkI.data > 0.]*convert_flux)
vmin, vmax = 1.*np.mean(np.sqrt(stk_cov.data[0,0][mask])*convert_flux), np.max(stkI.data[stkI.data > 0.]*convert_flux)
#im = ax.imshow(stkI.data*convert_flux, vmin=vmin, vmax=vmax, aspect='equal', cmap='inferno', alpha=1.)
#cbar = plt.colorbar(im, cax=cbar_ax, label=r"$F_{\lambda}$ [$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA$]")
im = ax.imshow(stkI.data*convert_flux, norm=LogNorm(vmin,vmax), aspect='equal', cmap='inferno', alpha=1.)
@@ -400,27 +404,28 @@ def polarization_map(Stokes, data_mask=None, rectangle=None, SNRp_cut=3., SNRi_c
px_size = wcs.wcs.get_cdelt()[0]*3600.
px_sc = AnchoredSizeBar(ax.transData, 1./px_size, '1 arcsec', 3, pad=0.5, sep=5, borderpad=0.5, frameon=False, size_vertical=0.005, color='w')
north_dir = AnchoredDirectionArrows(ax.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=-Stokes[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})
north_dir = AnchoredDirectionArrows(ax.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=-Stokes[0].header['orientat'], color='white', text_props={'ec': 'k', 'fc': 'w', 'alpha': 1, 'lw': 0.4}, arrow_props={'ec': 'k','fc':'w','alpha': 1,'lw': 1})
if display.lower() in ['i','s_i','snri','pf','p','pa','s_p','snrp']:
if step_vec == 0:
pol.data[np.isfinite(pol.data)] = 1./2.
step_vec = 1
vec_scale = 2.
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 = 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')
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=1./vec_scale,scale_units='xy',pivot='mid',headwidth=0.,headlength=0.,headaxislength=0.,width=0.1,linewidth=0.5,color='w',edgecolor='k')
pol_sc = AnchoredSizeBar(ax.transData, vec_scale, 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)
ax.add_artist(px_sc)
ax.add_artist(north_dir)
ax.annotate(r"$F_{{\lambda}}^{{int}}$({0:.0f} $\AA$) = {1} $ergs \cdot cm^{{-2}} \cdot s^{{-1}} \cdot \AA^{{-1}}$".format(pivot_wav,sci_not(I_diluted*convert_flux,I_diluted_err*convert_flux,2))+"\n"+r"$P^{{int}}$ = {0:.1f} $\pm$ {1:.1f} %".format(P_diluted*100.,P_diluted_err*100.)+"\n"+r"$\theta_{{P}}^{{int}}$ = {0:.1f} $\pm$ {1:.1f} °".format(PA_diluted,PA_diluted_err), color='white', xy=(0.01, 0.92), xycoords='axes fraction')
ax.annotate(r"$F_{{\lambda}}^{{int}}$({0:.0f} $\AA$) = {1} $ergs \cdot cm^{{-2}} \cdot s^{{-1}} \cdot \AA^{{-1}}$".format(pivot_wav,sci_not(I_diluted*convert_flux,I_diluted_err*convert_flux,2))+"\n"+r"$P^{{int}}$ = {0:.1f} $\pm$ {1:.1f} %".format(P_diluted*100.,P_diluted_err*100.)+"\n"+r"$\theta_{{P}}^{{int}}$ = {0:.1f} $\pm$ {1:.1f} °".format(PA_diluted,PA_diluted_err), color='white', xy=(0.01, 0.92), xycoords='axes fraction',path_effects=[pe.withStroke(linewidth=0.5,foreground='k')])
else:
if display.lower() == 'default':
ax.add_artist(px_sc)
ax.add_artist(north_dir)
ax.annotate(r"$F_{{\lambda}}^{{int}}$({0:.0f} $\AA$) = {1} $ergs \cdot cm^{{-2}} \cdot s^{{-1}} \cdot \AA^{{-1}}$".format(pivot_wav,sci_not(I_diluted*convert_flux,I_diluted_err*convert_flux,2)), color='white', xy=(0.01, 0.97), xycoords='axes fraction')
ax.annotate(r"$F_{{\lambda}}^{{int}}$({0:.0f} $\AA$) = {1} $ergs \cdot cm^{{-2}} \cdot s^{{-1}} \cdot \AA^{{-1}}$".format(pivot_wav,sci_not(I_diluted*convert_flux,I_diluted_err*convert_flux,2)), color='white', xy=(0.01, 0.97), xycoords='axes fraction',path_effects=[pe.withStroke(linewidth=0.5,foreground='k')])
# Display instrument FOV
if not(rectangle is None):
@@ -1339,6 +1344,7 @@ class pol_map(object):
self.region = None
self.data = None
self.display_selection = selection
self.vec_scale = 2.
#Get data
self.targ = self.Stokes[0].header['targname']
@@ -1363,11 +1369,13 @@ class pol_map(object):
#Set axes for sliders (SNRp_cut, SNRi_cut)
ax_I_cut = self.fig.add_axes([0.125, 0.080, 0.35, 0.01])
ax_P_cut = self.fig.add_axes([0.125, 0.055, 0.35, 0.01])
ax_vec_sc = self.fig.add_axes([0.300, 0.030, 0.175, 0.01])
ax_snr_reset = self.fig.add_axes([0.125, 0.020, 0.05, 0.02])
SNRi_max = np.max(self.I[self.IQU_cov[0,0]>0.]/np.sqrt(self.IQU_cov[0,0][self.IQU_cov[0,0]>0.]))
SNRp_max = np.max(self.P[self.s_P>0.]/self.s_P[self.s_P > 0.])
s_I_cut = Slider(ax_I_cut,r"$SNR^{I}_{cut}$",1.,int(SNRi_max*0.95),valstep=1,valinit=self.SNRi_cut)
s_P_cut = Slider(ax_P_cut,r"$SNR^{P}_{cut}$",1.,int(SNRp_max*0.95),valstep=1,valinit=self.SNRp_cut)
s_vec_sc = Slider(ax_vec_sc,r"Vectors scale",1.,10.,valstep=1,valinit=self.vec_scale)
b_snr_reset = Button(ax_snr_reset,"Reset")
b_snr_reset.label.set_fontsize(8)
@@ -1383,14 +1391,23 @@ class pol_map(object):
self.pol_int()
self.fig.canvas.draw_idle()
def update_vecsc(val):
self.vec_scale = val
self.pol_vector()
self.ax_cosmetics()
self.fig.canvas.draw_idle()
def reset_snr(event):
s_I_cut.reset()
s_P_cut.reset()
s_vec_sc.reset()
s_I_cut.on_changed(update_snri)
s_P_cut.on_changed(update_snrp)
s_vec_sc.on_changed(update_vecsc)
b_snr_reset.on_clicked(reset_snr)
#Set axe for Aperture selection
ax_aper = self.fig.add_axes([0.55, 0.040, 0.05, 0.02])
ax_aper_reset = self.fig.add_axes([0.605, 0.040, 0.05, 0.02])
@@ -1543,6 +1560,7 @@ class pol_map(object):
def saveplot(event):
ax_text_save.set(visible=True)
ax_snr_reset.set(visible=False)
ax_vec_sc.set(visible=False)
ax_save.set(visible=False)
ax_dump.set(visible=False)
self.fig.canvas.draw_idle()
@@ -1566,6 +1584,7 @@ class pol_map(object):
plt.close(save_fig)
text_save.set_val('')
ax_snr_reset.set(visible=True)
ax_vec_sc.set(visible=True)
ax_save.set(visible=True)
ax_dump.set(visible=True)
plt.rcParams.update({'font.size': 10})
@@ -1583,6 +1602,7 @@ class pol_map(object):
def dump(event):
ax_text_dump.set(visible=True)
ax_snr_reset.set(visible=False)
ax_vec_sc.set(visible=False)
ax_save.set(visible=False)
ax_dump.set(visible=False)
self.fig.canvas.draw_idle()
@@ -1612,6 +1632,7 @@ class pol_map(object):
np.savetxt(expression, self.data_dump)
text_dump.set_val('')
ax_snr_reset.set(visible=True)
ax_vec_sc.set(visible=True)
ax_save.set(visible=True)
ax_dump.set(visible=True)
self.fig.canvas.draw_idle()
@@ -1732,7 +1753,7 @@ class pol_map(object):
ax.add_artist(self.px_sc)
if hasattr(self,'pol_sc'):
self.pol_sc.remove()
self.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='white', fontproperties=fontprops)
self.pol_sc = AnchoredSizeBar(ax.transData, self.vec_scale, r"$P$= 100%", 4, pad=0.5, sep=5, borderpad=0.5, frameon=False, size_vertical=0.005, color='white', fontproperties=fontprops)
ax.add_artist(self.pol_sc)
if hasattr(self,'north_dir'):
self.north_dir.remove()
@@ -1810,11 +1831,11 @@ class pol_map(object):
ax = self.ax
if hasattr(self, 'quiver'):
self.quiver.remove()
self.quiver = ax.quiver(X, Y, XY_U, XY_V, units='xy', scale=0.5, scale_units='xy', pivot='mid', headwidth=0., headlength=0., headaxislength=0., width=0.1, color='white')
self.quiver = ax.quiver(X, Y, XY_U, XY_V, units='xy', 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='black')
fig.canvas.draw_idle()
return self.quiver
else:
ax.quiver(X, Y, XY_U, XY_V, units='xy', scale=0.5, scale_units='xy', pivot='mid', headwidth=0., headlength=0., headaxislength=0., width=0.1, color='white')
ax.quiver(X, Y, XY_U, XY_V, units='xy', 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='black')
fig.canvas.draw_idle()
def pol_int(self, fig=None, ax=None):
@@ -1906,15 +1927,15 @@ class pol_map(object):
ax = self.ax
if hasattr(self, 'an_int'):
self.an_int.remove()
self.an_int = ax.annotate(r"$F_{{\lambda}}^{{int}}$({0:.0f} $\AA$) = {1} $ergs \cdot cm^{{-2}} \cdot s^{{-1}} \cdot \AA^{{-1}}$".format(self.pivot_wav,sci_not(I_reg*self.convert_flux,I_reg_err*self.convert_flux,2))+"\n"+r"$P^{{int}}$ = {0:.1f} $\pm$ {1:.1f} %".format(P_reg*100.,np.ceil(P_reg_err*1000.)/10.)+"\n"+r"$\theta_{{P}}^{{int}}$ = {0:.1f} $\pm$ {1:.1f} °".format(PA_reg,np.ceil(PA_reg_err*10.)/10.), color='white', fontsize=12, xy=(0.01, 0.93), xycoords='axes fraction')
#self.an_int = ax.annotate(r"$F_{{\lambda}}^{{int}}$({0:.0f} $\AA$) = {1} $ergs \cdot cm^{{-2}} \cdot s^{{-1}} \cdot \AA^{{-1}}$".format(self.pivot_wav,sci_not(I_reg*self.convert_flux,I_reg_err*self.convert_flux,2))+"\n"+r"$P^{{int}}$ = {0:.1f} $\pm$ {1:.1f} %".format(P_reg*100.,np.ceil(P_reg_err*1000.)/10.)+"\n"+r"$\theta_{{P}}^{{int}}$ = {0:.1f} $\pm$ {1:.1f} °".format(PA_reg,np.ceil(PA_reg_err*10.)/10.)+"\n"+r"$P^{{cut}}$ = {0:.1f} $\pm$ {1:.1f} %".format(P_cut*100.,np.ceil(P_cut_err*1000.)/10.)+"\n"+r"$\theta_{{P}}^{{cut}}$ = {0:.1f} $\pm$ {1:.1f} °".format(PA_cut,np.ceil(PA_cut_err*10.)/10.), color='white', fontsize=12, xy=(0.01, 0.85), xycoords='axes fraction')
self.an_int = ax.annotate(r"$F_{{\lambda}}^{{int}}$({0:.0f} $\AA$) = {1} $ergs \cdot cm^{{-2}} \cdot s^{{-1}} \cdot \AA^{{-1}}$".format(self.pivot_wav,sci_not(I_reg*self.convert_flux,I_reg_err*self.convert_flux,2))+"\n"+r"$P^{{int}}$ = {0:.1f} $\pm$ {1:.1f} %".format(P_reg*100.,np.ceil(P_reg_err*1000.)/10.)+"\n"+r"$\theta_{{P}}^{{int}}$ = {0:.1f} $\pm$ {1:.1f} °".format(PA_reg,np.ceil(PA_reg_err*10.)/10.), color='white', fontsize=12, xy=(0.01, 0.93), xycoords='axes fraction',path_effects=[pe.withStroke(linewidth=0.5,foreground='k')])
#self.an_int = ax.annotate(r"$F_{{\lambda}}^{{int}}$({0:.0f} $\AA$) = {1} $ergs \cdot cm^{{-2}} \cdot s^{{-1}} \cdot \AA^{{-1}}$".format(self.pivot_wav,sci_not(I_reg*self.convert_flux,I_reg_err*self.convert_flux,2))+"\n"+r"$P^{{int}}$ = {0:.1f} $\pm$ {1:.1f} %".format(P_reg*100.,np.ceil(P_reg_err*1000.)/10.)+"\n"+r"$\theta_{{P}}^{{int}}$ = {0:.1f} $\pm$ {1:.1f} °".format(PA_reg,np.ceil(PA_reg_err*10.)/10.)+"\n"+r"$P^{{cut}}$ = {0:.1f} $\pm$ {1:.1f} %".format(P_cut*100.,np.ceil(P_cut_err*1000.)/10.)+"\n"+r"$\theta_{{P}}^{{cut}}$ = {0:.1f} $\pm$ {1:.1f} °".format(PA_cut,np.ceil(PA_cut_err*10.)/10.), color='white', fontsize=12, xy=(0.01, 0.85), xycoords='axes fraction',path_effects=[pe.withStroke(linewidth=0.5,foreground='k')])
if not self.region is None:
self.cont = ax.contour(self.region.astype(float),levels=[0.5], colors='white', linewidths=0.8)
fig.canvas.draw_idle()
return self.an_int
else:
ax.annotate(r"$F_{{\lambda}}^{{int}}$({0:.0f} $\AA$) = {1} $ergs \cdot cm^{{-2}} \cdot s^{{-1}} \cdot \AA^{{-1}}$".format(self.pivot_wav,sci_not(I_reg*self.convert_flux,I_reg_err*self.convert_flux,2))+"\n"+r"$P^{{int}}$ = {0:.1f} $\pm$ {1:.1f} %".format(P_reg*100.,np.ceil(P_reg_err*1000.)/10.)+"\n"+r"$\theta_{{P}}^{{int}}$ = {0:.1f} $\pm$ {1:.1f} °".format(PA_reg,np.ceil(PA_reg_err*10.)/10.), color='white', fontsize=12, xy=(0.01, 0.94), xycoords='axes fraction')
#ax.annotate(r"$F_{{\lambda}}^{{int}}$({0:.0f} $\AA$) = {1} $ergs \cdot cm^{{-2}} \cdot s^{{-1}} \cdot \AA^{{-1}}$".format(self.pivot_wav,sci_not(I_reg*self.convert_flux,I_reg_err*self.convert_flux,2))+"\n"+r"$P^{{int}}$ = {0:.1f} $\pm$ {1:.1f} %".format(P_reg*100.,np.ceil(P_reg_err*1000.)/10.)+"\n"+r"$\theta_{{P}}^{{int}}$ = {0:.1f} $\pm$ {1:.1f} °".format(PA_reg,np.ceil(PA_reg_err*10.)/10.)+"\n"+r"$P^{{cut}}$ = {0:.1f} $\pm$ {1:.1f} %".format(P_cut*100.,np.ceil(P_cut_err*1000.)/10.)+"\n"+r"$\theta_{{P}}^{{cut}}$ = {0:.1f} $\pm$ {1:.1f} °".format(PA_cut,np.ceil(PA_cut_err*10.)/10.), color='white', fontsize=12, xy=(0.01, 0.90), xycoords='axes fraction')
ax.annotate(r"$F_{{\lambda}}^{{int}}$({0:.0f} $\AA$) = {1} $ergs \cdot cm^{{-2}} \cdot s^{{-1}} \cdot \AA^{{-1}}$".format(self.pivot_wav,sci_not(I_reg*self.convert_flux,I_reg_err*self.convert_flux,2))+"\n"+r"$P^{{int}}$ = {0:.1f} $\pm$ {1:.1f} %".format(P_reg*100.,np.ceil(P_reg_err*1000.)/10.)+"\n"+r"$\theta_{{P}}^{{int}}$ = {0:.1f} $\pm$ {1:.1f} °".format(PA_reg,np.ceil(PA_reg_err*10.)/10.), color='white', fontsize=12, xy=(0.01, 0.94), xycoords='axes fraction',path_effects=[pe.withStroke(linewidth=0.5,foreground='k')])
#ax.annotate(r"$F_{{\lambda}}^{{int}}$({0:.0f} $\AA$) = {1} $ergs \cdot cm^{{-2}} \cdot s^{{-1}} \cdot \AA^{{-1}}$".format(self.pivot_wav,sci_not(I_reg*self.convert_flux,I_reg_err*self.convert_flux,2))+"\n"+r"$P^{{int}}$ = {0:.1f} $\pm$ {1:.1f} %".format(P_reg*100.,np.ceil(P_reg_err*1000.)/10.)+"\n"+r"$\theta_{{P}}^{{int}}$ = {0:.1f} $\pm$ {1:.1f} °".format(PA_reg,np.ceil(PA_reg_err*10.)/10.)+"\n"+r"$P^{{cut}}$ = {0:.1f} $\pm$ {1:.1f} %".format(P_cut*100.,np.ceil(P_cut_err*1000.)/10.)+"\n"+r"$\theta_{{P}}^{{cut}}$ = {0:.1f} $\pm$ {1:.1f} °".format(PA_cut,np.ceil(PA_cut_err*10.)/10.), color='white', fontsize=12, xy=(0.01, 0.90), xycoords='axes fraction',path_effects=[pe.withStroke(linewidth=0.5,foreground='k')])
if not self.region is None:
ax.contour(self.region.astype(float),levels=[0.5], colors='white', linewidths=0.8)
fig.canvas.draw_idle()

11
src/lib/reduction.py
View File

@@ -292,9 +292,10 @@ def crop_array(data_array, headers, error_array=None, data_mask=None, step=5,
if display:
plt.rcParams.update({'font.size': 20})
fig, ax = plt.subplots(figsize=(10,10))
data = deepcopy(data_array[0])
convert_flux = headers[0]['photflam']
data = deepcopy(data_array[0]*convert_flux)
data[data <= data[data>0.].min()] = data[data > 0.].min()
crop = crop_array[0]
crop = crop_array[0]*convert_flux
instr = headers[0]['instrume']
rootname = headers[0]['rootname']
exptime = headers[0]['exptime']
@@ -321,14 +322,14 @@ def crop_array(data_array, headers, error_array=None, data_mask=None, step=5,
fig.subplots_adjust(hspace=0, wspace=0, right=0.85)
cbar_ax = fig.add_axes([0.9, 0.12, 0.02, 0.75])
fig.colorbar(im, cax=cbar_ax, label=r'$Counts \cdot s^{-1}$')
fig.colorbar(im, cax=cbar_ax, label=r"Flux [$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA^{-1}$]")
if not(savename is None):
#fig.suptitle(savename+'_'+filt+'_crop_region')
fig.savefig(plots_folder+savename+'_'+filt+'_crop_region.png',
bbox_inches='tight')
plot_obs(data_array, headers, vmin=data_array[data_array>0.].min(),
vmax=data_array[data_array>0.].max(), rectangle=[rectangle,]*len(headers),
plot_obs(data_array, headers, vmin=convert_flux*data_array[data_array>0.].mean()/5.,
vmax=convert_flux*data_array[data_array>0.].max(), rectangle=[rectangle,]*len(headers),
savename=savename+'_crop_region',plots_folder=plots_folder)
plt.show()