Tibeuleu/FOC_Reduction
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

implement axis error in compute_pol, redo some plots

Browse Source
This commit is contained in:
Thibault Barnouin
2022-03-18 17:44:25 +01:00
parent 44c96dc8a2
commit afc8c743f6
87 changed files with 186 additions and 148 deletions
Download Patch File Download Diff File Expand all files Collapse all files

217
src/lib/plots.py
View File

@@ -93,16 +93,16 @@ def plot_obs(data_array, headers, shape=None, vmin=0., vmax=6., rectangle=None,
exptime = headers[i]['exptime']
filt = headers[i]['filtnam1']
#plots
im = ax.imshow(data, vmin=vmin, vmax=vmax, origin='lower')
im = ax.imshow(data, vmin=vmin, vmax=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,
edgecolor=color, fill=False))
#position of centroid
ax.plot([data.shape[1]/2, data.shape[1]/2], [0,data.shape[0]-1], lw=1,
color='black')
ax.plot([0,data.shape[1]-1], [data.shape[1]/2, data.shape[1]/2], lw=1,
color='black')
ax.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,
color='grey', alpha=0.5)
ax.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),
@@ -110,12 +110,12 @@ def plot_obs(data_array, headers, shape=None, vmin=0., vmax=6., rectangle=None,
ax.annotate(exptime,color='white',fontsize=5,xy=(0.80, 0.02),
xycoords='axes fraction')
fig.subplots_adjust(hspace=0, wspace=0, right=0.85)
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)
fig.colorbar(im, cax=cbar_ax, label=r'$Counts \cdot s^{-1}$')
if not (savename is None):
fig.suptitle(savename)
#fig.suptitle(savename)
fig.savefig(plots_folder+savename+".png",bbox_inches='tight')
plt.show()
return 0
@@ -149,121 +149,27 @@ def plot_Stokes(Stokes, savename=None, plots_folder=""):
fig = plt.figure(figsize=(30,10))
ax = fig.add_subplot(131, projection=wcs)
im = ax.imshow(stkI, origin='lower')
im = ax.imshow(stkI, origin='lower', cmap='inferno')
plt.colorbar(im)
ax.set(xlabel="RA", ylabel="DEC", title=r"$I_{stokes}$")
ax = fig.add_subplot(132, projection=wcs)
im = ax.imshow(stkQ, origin='lower')
im = ax.imshow(stkQ, origin='lower', cmap='inferno')
plt.colorbar(im)
ax.set(xlabel="RA", ylabel="DEC", title=r"$Q_{stokes}$")
ax = fig.add_subplot(133, projection=wcs)
im = ax.imshow(stkU, origin='lower')
im = ax.imshow(stkU, origin='lower', cmap='inferno')
plt.colorbar(im)
ax.set(xlabel="RA", ylabel="DEC", title=r"$U_{stokes}$")
if not (savename is None):
fig.suptitle(savename+"_IQU")
#fig.suptitle(savename+"_IQU")
fig.savefig(plots_folder+savename+"_IQU.png",bbox_inches='tight')
plt.show()
return 0
class crop_map(object):
"""
Class to interactively crop I_stokes map to desired Region of Interest
"""
def __init__(self, Stokes, data_mask, SNRp_cut=3., SNRi_cut=30.):
#Get data
stkI = Stokes[np.argmax([Stokes[i].header['datatype']=='I_stokes' for i in range(len(Stokes))])]
stk_cov = Stokes[np.argmax([Stokes[i].header['datatype']=='IQU_cov_matrix' for i in range(len(Stokes))])]
pol = Stokes[np.argmax([Stokes[i].header['datatype']=='Pol_deg_debiased' for i in range(len(Stokes))])]
pol_err = Stokes[np.argmax([Stokes[i].header['datatype']=='Pol_deg_err' for i in range(len(Stokes))])]
self.Stokes = Stokes
self.data_mask = data_mask
wcs = WCS(Stokes[0]).deepcopy()
#Compute SNR and apply cuts
pol.data[pol.data == 0.] = np.nan
SNRp = pol.data/pol_err.data
SNRp[np.isnan(SNRp)] = 0.
pol.data[SNRp < SNRp_cut] = np.nan
SNRi = stkI.data/np.sqrt(stk_cov.data[0,0])
SNRi[np.isnan(SNRi)] = 0.
pol.data[SNRi < SNRi_cut] = np.nan
convert_flux = Stokes[0].header['photflam']
#Plot the map
plt.rcParams.update({'font.size': 16})
self.fig = plt.figure(figsize=(15,15))
self.ax = self.fig.add_subplot(111, projection=wcs)
self.ax.set_facecolor('k')
self.fig.subplots_adjust(hspace=0, wspace=0, right=0.9)
cbar_ax = self.fig.add_axes([0.95, 0.12, 0.01, 0.75])
self.extent = [-stkI.data.shape[1]/2.,stkI.data.shape[1]/2.,-stkI.data.shape[0]/2.,stkI.data.shape[0]/2.]
self.center = [stkI.data.shape[1]/2.,stkI.data.shape[0]/2.]
vmin, vmax = 0., np.max(stkI.data[stkI.data > 0.]*convert_flux)
im = self.ax.imshow(stkI.data*convert_flux,extent=self.extent, vmin=vmin, vmax=vmax, aspect='auto', 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(SNRi_cut, np.max(SNRi[SNRi > 0.]), 10)
cont = self.ax.contour(SNRi, extent=self.extent, levels=levelsI, colors='grey', linewidths=0.5)
fontprops = fm.FontProperties(size=16)
px_size = wcs.wcs.get_cdelt()[0]
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)
self.ax.add_artist(px_sc)
self.ax.set_title(
"Click and drag to crop to desired Region of Interest.\n"
"Press 'c' to toggle the selector on and off")
def onselect_crop(self, eclick, erelease) -> None:
# Obtain (xmin, xmax, ymin, ymax) values
self.RSextent = self.rect_selector.extents
self.RScenter = [self.center[i]+self.rect_selector.center[i] for i in range(2)]
# Zoom to selection
print("CROP TO : ",self.RSextent)
print("CENTER : ",self.RScenter)
#self.ax.set_xlim(self.extent[0], self.extent[1])
#self.ax.set_ylim(self.extent[2], self.extent[3])
def run(self) -> None:
self.rect_selector = RectangleSelector(self.ax, self.onselect_crop,
drawtype='box', button=[1], interactive=True)
#self.fig.canvas.mpl_connect('key_press_event', self.toggle_selector)
plt.show()
def crop(self):
Stokes_crop = copy.deepcopy(self.Stokes)
# Data sets to crop
stkI = Stokes_crop[np.argmax([Stokes_crop[i].header['datatype']=='I_stokes' for i in range(len(Stokes_crop))])]
stkQ = Stokes_crop[np.argmax([Stokes_crop[i].header['datatype']=='Q_stokes' for i in range(len(Stokes_crop))])]
stkU = Stokes_crop[np.argmax([Stokes_crop[i].header['datatype']=='U_stokes' for i in range(len(Stokes_crop))])]
stk_cov = Stokes_crop[np.argmax([Stokes_crop[i].header['datatype']=='IQU_cov_matrix' for i in range(len(Stokes_crop))])]
pol = Stokes_crop[np.argmax([Stokes_crop[i].header['datatype']=='Pol_deg_debiased' for i in range(len(Stokes_crop))])]
pol_err = Stokes_crop[np.argmax([Stokes_crop[i].header['datatype']=='Pol_deg_err' for i in range(len(Stokes_crop))])]
pang = Stokes_crop[np.argmax([Stokes_crop[i].header['datatype']=='Pol_ang' for i in range(len(Stokes_crop))])]
pang_err = Stokes_crop[np.argmax([Stokes_crop[i].header['datatype']=='Pol_ang_err' for i in range(len(Stokes_crop))])]
# Crop datasets
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])]
for dataset in [stkI, stkQ, stkU, pol, pol_err, pang, pang_err]:
dataset.data = dataset.data[vertex[2]:vertex[3], vertex[0]:vertex[1]]
data_mask = self.data_mask[vertex[2]:vertex[3], vertex[0]:vertex[1]]
new_stk_cov = np.zeros((3,3,stkI.data.shape[0],stkI.data.shape[1]))
for i in range(3):
for j in range(3):
new_stk_cov[i,j] = stk_cov.data[i,j][vertex[2]:vertex[3], vertex[0]:vertex[1]]
stk_cov.data = new_stk_cov
return Stokes_crop, data_mask
def polarization_map(Stokes, data_mask=None, rectangle=None, SNRp_cut=3., SNRi_cut=30.,
step_vec=1, savename=None, plots_folder="", display=None):
"""
@@ -366,7 +272,9 @@ def polarization_map(Stokes, data_mask=None, rectangle=None, SNRp_cut=3., SNRi_c
im = ax.imshow(stkI.data*convert_flux, vmin=vmin, vmax=vmax, aspect='auto', 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(SNRi_cut, np.max(SNRi[SNRi > 0.]), 10)
print("SNRi contour levels : ", levelsI)
cont = ax.contour(SNRi, levels=levelsI, colors='grey', linewidths=0.5)
#ax.clabel(cont,inline=True,fontsize=6)
elif display.lower() in ['pol_flux']:
# Display polarisation flux
pf_mask = (stkI.data > 0.) * (pol.data > 0.)
@@ -673,4 +581,99 @@ class overplot_maps(align_maps):
self.align()
if self.aligned:
self.overplot(other_levels=levels, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, savename=savename)
plt.show(block=True)
plt.show(block=True)
class crop_map(object):
"""
Class to interactively crop I_stokes map to desired Region of Interest
"""
def __init__(self, Stokes, data_mask, SNRp_cut=3., SNRi_cut=30.):
#Get data
stkI = Stokes[np.argmax([Stokes[i].header['datatype']=='I_stokes' for i in range(len(Stokes))])]
stk_cov = Stokes[np.argmax([Stokes[i].header['datatype']=='IQU_cov_matrix' for i in range(len(Stokes))])]
pol = Stokes[np.argmax([Stokes[i].header['datatype']=='Pol_deg_debiased' for i in range(len(Stokes))])]
pol_err = Stokes[np.argmax([Stokes[i].header['datatype']=='Pol_deg_err' for i in range(len(Stokes))])]
self.Stokes = Stokes
self.data_mask = data_mask
wcs = WCS(Stokes[0]).deepcopy()
#Compute SNR and apply cuts
pol.data[pol.data == 0.] = np.nan
SNRp = pol.data/pol_err.data
SNRp[np.isnan(SNRp)] = 0.
pol.data[SNRp < SNRp_cut] = np.nan
SNRi = stkI.data/np.sqrt(stk_cov.data[0,0])
SNRi[np.isnan(SNRi)] = 0.
pol.data[SNRi < SNRi_cut] = np.nan
convert_flux = Stokes[0].header['photflam']
#Plot the map
plt.rcParams.update({'font.size': 16})
self.fig = plt.figure(figsize=(15,15))
self.ax = self.fig.add_subplot(111, projection=wcs)
self.ax.set_facecolor('k')
self.fig.subplots_adjust(hspace=0, wspace=0, right=0.9)
cbar_ax = self.fig.add_axes([0.95, 0.12, 0.01, 0.75])
self.extent = [-stkI.data.shape[1]/2.,stkI.data.shape[1]/2.,-stkI.data.shape[0]/2.,stkI.data.shape[0]/2.]
self.center = [stkI.data.shape[1]/2.,stkI.data.shape[0]/2.]
vmin, vmax = 0., np.max(stkI.data[stkI.data > 0.]*convert_flux)
im = self.ax.imshow(stkI.data*convert_flux,extent=self.extent, vmin=vmin, vmax=vmax, aspect='auto', 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(SNRi_cut, np.max(SNRi[SNRi > 0.]), 10)
cont = self.ax.contour(SNRi, extent=self.extent, levels=levelsI, colors='grey', linewidths=0.5)
fontprops = fm.FontProperties(size=16)
px_size = wcs.wcs.get_cdelt()[0]
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)
self.ax.add_artist(px_sc)
self.ax.set_title(
"Click and drag to crop to desired Region of Interest.\n"
"Press 'c' to toggle the selector on and off")
def onselect_crop(self, eclick, erelease) -> None:
# Obtain (xmin, xmax, ymin, ymax) values
self.RSextent = self.rect_selector.extents
self.RScenter = [self.center[i]+self.rect_selector.center[i] for i in range(2)]
# Zoom to selection
print("CROP TO : ",self.RSextent)
print("CENTER : ",self.RScenter)
#self.ax.set_xlim(self.extent[0], self.extent[1])
#self.ax.set_ylim(self.extent[2], self.extent[3])
def run(self) -> None:
self.rect_selector = RectangleSelector(self.ax, self.onselect_crop,
drawtype='box', button=[1], interactive=True)
#self.fig.canvas.mpl_connect('key_press_event', self.toggle_selector)
plt.show()
def crop(self):
Stokes_crop = copy.deepcopy(self.Stokes)
# Data sets to crop
stkI = Stokes_crop[np.argmax([Stokes_crop[i].header['datatype']=='I_stokes' for i in range(len(Stokes_crop))])]
stkQ = Stokes_crop[np.argmax([Stokes_crop[i].header['datatype']=='Q_stokes' for i in range(len(Stokes_crop))])]
stkU = Stokes_crop[np.argmax([Stokes_crop[i].header['datatype']=='U_stokes' for i in range(len(Stokes_crop))])]
stk_cov = Stokes_crop[np.argmax([Stokes_crop[i].header['datatype']=='IQU_cov_matrix' for i in range(len(Stokes_crop))])]
pol = Stokes_crop[np.argmax([Stokes_crop[i].header['datatype']=='Pol_deg_debiased' for i in range(len(Stokes_crop))])]
pol_err = Stokes_crop[np.argmax([Stokes_crop[i].header['datatype']=='Pol_deg_err' for i in range(len(Stokes_crop))])]
pang = Stokes_crop[np.argmax([Stokes_crop[i].header['datatype']=='Pol_ang' for i in range(len(Stokes_crop))])]
pang_err = Stokes_crop[np.argmax([Stokes_crop[i].header['datatype']=='Pol_ang_err' for i in range(len(Stokes_crop))])]
# Crop datasets
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])]
for dataset in [stkI, stkQ, stkU, pol, pol_err, pang, pang_err]:
dataset.data = dataset.data[vertex[2]:vertex[3], vertex[0]:vertex[1]]
data_mask = self.data_mask[vertex[2]:vertex[3], vertex[0]:vertex[1]]
new_stk_cov = np.zeros((3,3,stkI.data.shape[0],stkI.data.shape[1]))
for i in range(3):
for j in range(3):
new_stk_cov[i,j] = stk_cov.data[i,j][vertex[2]:vertex[3], vertex[0]:vertex[1]]
stk_cov.data = new_stk_cov
return Stokes_crop, data_mask