Tibeuleu/FOC_Reduction
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modify lib/plots.py for better overplotting tool

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This commit is contained in:
Thibault Barnouin
2023-12-08 17:28:39 +01:00
parent 824c63d0bc
commit 4df270d56d
4 changed files with 89 additions and 34 deletions
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67
src/lib/plots.py
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@@ -1068,7 +1068,7 @@ class crop_map(object):
"""
Class to interactively crop a map to desired Region of Interest
"""
def __init__(self, hdul, fig=None, ax=None):
def __init__(self, hdul, fig=None, ax=None, **kwargs):
#Get data
self.cropped=False
self.hdul = hdul
@@ -1080,10 +1080,13 @@ class crop_map(object):
self.convert_flux = self.header['photflam']
except KeyError:
self.convert_flux = 1.
try:
self.kwargs = kwargs
except AttributeError:
self.kwargs = {}
#Plot the map
plt.rcParams.update({'font.size': 12})
plt.ioff()
if fig is None:
self.fig = plt.figure(figsize=(15,15))
self.fig.suptitle("Click and drag to crop to desired Region of Interest.")
@@ -1104,28 +1107,36 @@ class crop_map(object):
self.rect_selector = RectangleSelector(self.ax, self.onselect_crop,
button=[1])
self.embedded = True
self.display()
plt.ion()
self.display(self.data, self.wcs, self.convert_flux, **self.kwargs)
self.extent = np.array([0.,self.data.shape[0],0., self.data.shape[1]])
self.center = np.array(self.data.shape)/2
self.RSextent = deepcopy(self.extent)
self.RScenter = deepcopy(self.center)
plt.show()
def display(self, data=None, wcs=None, convert_flux=None):
def display(self, data=None, wcs=None, convert_flux=None, **kwargs):
if data is None:
data = self.data
if wcs is None:
wcs = self.wcs
if convert_flux is None:
convert_flux = self.convert_flux
if kwargs is None:
kwargs = self.kwargs
else:
kwargs = {**self.kwargs, **kwargs}
vmin, vmax = 0., np.max(data[data > 0.]*convert_flux)
vmin, vmax = np.min(data[data > 0.]*convert_flux), np.max(data[data > 0.]*convert_flux)
for key, value in [["cmap",[["cmap","inferno"]]], ["origin",[["origin","lower"]]], ["aspect",[["aspect","equal"]]], ["alpha",[["alpha",self.mask_alpha]]], ["norm",[["vmin",vmin],["vmax",vmax]]]]:
try:
test = kwargs[key]
except KeyError:
for key_i, val_i in value:
kwargs[key_i] = val_i
if hasattr(self, 'im'):
self.im.remove()
self.im = self.ax.imshow(data*convert_flux, vmin=vmin, vmax=vmax, aspect='equal', cmap='inferno', alpha=self.mask_alpha, origin='lower')
self.im = self.ax.imshow(data*convert_flux, **kwargs)
if hasattr(self, 'cr'):
self.cr[0].set_data(*wcs.wcs.crpix)
else:
@@ -1199,14 +1210,9 @@ class crop_map(object):
self.header_crop.update(self.wcs_crop.to_header())
self.hdul_crop = fits.HDUList([fits.PrimaryHDU(self.data_crop,self.header_crop)])
try:
convert_flux = self.header_crop['photflam']
except KeyError:
convert_flux = 1.
self.rect_selector.clear()
self.ax.reset_wcs(self.wcs_crop)
self.display(data=self.data_crop, wcs=self.wcs_crop, convert_flux=convert_flux)
self.display(data=self.data_crop, wcs=self.wcs_crop)
xlim, ylim = self.RSextent[1::2]-self.RSextent[0::2]
self.ax.set_xlim(0,xlim)
@@ -1215,6 +1221,7 @@ class crop_map(object):
if self.fig.canvas.manager.toolbar.mode == '':
self.rect_selector = RectangleSelector(self.ax, self.onselect_crop,
button=[1])
self.fig.canvas.draw_idle()
def on_close(self, event) -> None:
@@ -1283,16 +1290,11 @@ class crop_Stokes(crop_map):
dataset.data = deepcopy(dataset.data[vertex[2]:vertex[3], vertex[0]:vertex[1]])
dataset.header.update(self.wcs_crop.to_header())
try:
convert_flux = self.hdul_crop[0].header['photflam']
except KeyError:
convert_flux = 1.
self.data_crop = self.hdul_crop[0].data
self.rect_selector.clear()
if not self.embedded:
self.ax.reset_wcs(self.wcs_crop)
self.display(data=self.data_crop, wcs=self.wcs_crop, convert_flux=convert_flux)
self.display(data=self.data_crop, wcs=self.wcs_crop)
xlim, ylim = self.RSextent[1::2]-self.RSextent[0::2]
self.ax.set_xlim(0,xlim)
@@ -1303,11 +1305,34 @@ class crop_Stokes(crop_map):
if self.fig.canvas.manager.toolbar.mode == '':
self.rect_selector = RectangleSelector(self.ax, self.onselect_crop,
button=[1])
# Update integrated values
mask = np.logical_and(self.hdul_crop[-1].data.astype(bool), self.hdul_crop[0].data >0)
I_diluted = self.hdul_crop[0].data[mask].sum()
Q_diluted = self.hdul_crop[1].data[mask].sum()
U_diluted = self.hdul_crop[2].data[mask].sum()
I_diluted_err = np.sqrt(np.sum(self.hdul_crop[3].data[0,0][mask]))
Q_diluted_err = np.sqrt(np.sum(self.hdul_crop[3].data[1,1][mask]))
U_diluted_err = np.sqrt(np.sum(self.hdul_crop[3].data[2,2][mask]))
IQ_diluted_err = np.sqrt(np.sum(self.hdul_crop[3].data[0,1][mask]**2))
IU_diluted_err = np.sqrt(np.sum(self.hdul_crop[3].data[0,2][mask]**2))
QU_diluted_err = np.sqrt(np.sum(self.hdul_crop[3].data[1,2][mask]**2))
P_diluted = np.sqrt(Q_diluted**2+U_diluted**2)/I_diluted
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)
PA_diluted = princ_angle((90./np.pi)*np.arctan2(U_diluted,Q_diluted))
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)
for dataset in self.hdul_crop:
dataset.header['P_int'] = (P_diluted, 'Integrated polarization degree')
dataset.header['P_int_err'] = (np.ceil(P_diluted_err*1000.)/1000., 'Integrated polarization degree error')
dataset.header['PA_int'] = (PA_diluted, 'Integrated polarization angle')
dataset.header['PA_int_err'] = (np.ceil(PA_diluted_err*10.)/10., 'Integrated polarization angle error')
self.fig.canvas.draw_idle()
@property
def data_mask(self):
return self.hdul_crop[-1].data
return self.hdul_crop[-1].data.astype(int)
class image_lasso_selector(object):