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

add optimal_binning to plotting

Browse Source
This commit is contained in:
sugar_jo
2024-07-15 19:39:21 +08:00
parent 8e5f439259
commit 62aef1b1c4
4 changed files with 291 additions and 201 deletions
Download Patch File Download Diff File Expand all files Collapse all files

6
package/lib/background.py
View File

@@ -235,7 +235,7 @@ def bkg_fit(data, error, mask, headers, subtract_error=True, display=False, save
weights = 1/chi2**2
weights /= weights.sum()
bkg = np.sum(weights*(coeff[:, 1]+np.abs(coeff[:, 2])*subtract_error))
bkg = np.sum(weights*(coeff[:, 1]+np.abs(coeff[:, 2]) * 0.01)) # why not just use 0.01
error_bkg[i] *= bkg
@@ -342,7 +342,7 @@ def bkg_hist(data, error, mask, headers, sub_type=None, subtract_error=True, dis
# popt, pcov = curve_fit(gausspol, binning[-1], hist, p0=p0)
popt, pcov = curve_fit(gauss, binning[-1], hist, p0=p0)
coeff.append(popt)
bkg = popt[1]+np.abs(popt[2])*subtract_error
bkg = popt[1]+np.abs(popt[2]) * 0.01 # why not just use 0.01
error_bkg[i] *= bkg
@@ -443,7 +443,7 @@ def bkg_mini(data, error, mask, headers, sub_shape=(15, 15), subtract_error=True
# Compute error : root mean square of the background
sub_image = image[minima[0]:minima[0]+sub_shape[0], minima[1]:minima[1]+sub_shape[1]]
# bkg = np.std(sub_image) # Previously computed using standard deviation over the background
bkg = np.sqrt(np.sum(sub_image**2)/sub_image.size)*subtract_error if subtract_error > 0 else np.sqrt(np.sum(sub_image**2)/sub_image.size)
bkg = np.sqrt(np.sum(sub_image**2)/sub_image.size)*0.01 if subtract_error > 0 else np.sqrt(np.sum(sub_image**2)/sub_image.size)
error_bkg[i] *= bkg
# n_error_array[i] = np.sqrt(n_error_array[i]**2 + error_bkg[i]**2)

105
package/lib/plots.py
View File

@@ -41,8 +41,11 @@ prototypes :
"""
from copy import deepcopy
import numpy as np
from os.path import join as path_join
from astropy.wcs import WCS
from astropy.io import fits
from astropy.coordinates import SkyCoord
import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle, Circle, FancyArrowPatch
from matplotlib.path import Path
@@ -51,49 +54,48 @@ 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
from astropy.coordinates import SkyCoord
import numpy as np
from scipy.ndimage import zoom as sc_zoom
try:
from .utils import rot2D, princ_angle, sci_not
except ImportError:
from utils import rot2D, princ_angle, sci_not
def plot_quiver(ax, stkI, stkQ, stkU, stk_cov, poldata, pangdata, wcs, convert, step_vec=1, vec_scale=2., adaptive_binning=False):
def adaptive_binning(I_stokes, Q_stokes, U_stokes, Stokes_cov):
shape = I_stokes.shape
assert shape[0] == shape[1], "Only square images are supported"
assert shape[0] % 2 == 0, "Image size must be a power of 2"
n = int(np.log2(shape[0]))
bin_map = np.zeros(shape)
bin_num = 0
for level in range(n):
grid_size = 2**level
temp_I = I_stokes.reshape(int(shape[0]/grid_size), grid_size, int(shape[1]/grid_size), grid_size).sum(1).sum(2)
temp_Q = Q_stokes.reshape(int(shape[0]/grid_size), grid_size, int(shape[1]/grid_size), grid_size).sum(1).sum(2)
temp_U = U_stokes.reshape(int(shape[0]/grid_size), grid_size, int(shape[1]/grid_size), grid_size).sum(1).sum(2)
temp_cov = Stokes_cov.reshape(3, 3, int(shape[0]/grid_size), grid_size, int(shape[1]/grid_size), grid_size).sum(3).sum(4)
temp_bin_map = bin_map.reshape(int(shape[0]/grid_size), grid_size, int(shape[1]/grid_size), grid_size).sum(1).sum(2)
temp_P = (temp_Q**2 + temp_U**2)**0.5 / temp_I
temp_P_err = (1 / temp_I) * np.sqrt((temp_Q**2 * temp_cov[1,1,:,:] + temp_U**2 * temp_cov[2,2,:,:] + 2. * temp_Q * temp_U * temp_cov[1,2,:,:]) / (temp_Q**2 + temp_U**2) + \
((temp_Q / temp_I)**2 + (temp_U / temp_I)**2) * temp_cov[0,0,:,:] - \
2. * (temp_Q / temp_I) * temp_cov[0,1,:,:] - \
2. * (temp_U / temp_I) * temp_cov[0,2,:,:])
for i in range(int(shape[0]/grid_size)):
for j in range(int(shape[1]/grid_size)):
if (temp_P[i,j] / temp_P_err[i,j] > 3) and (temp_bin_map[i,j] == 0): # the default criterion is 3 sigma in P
bin_num += 1
bin_map[i*grid_size:(i+1)*grid_size,j*grid_size:(j+1)*grid_size] = bin_num
return bin_map, bin_num
def adaptive_binning(I_stokes, Q_stokes, U_stokes, Stokes_cov):
shape = I_stokes.shape
if adaptive_binning:
assert shape[0] == shape[1], "Only square images are supported"
assert shape[0] % 2 == 0, "Image size must be a power of 2"
n = int(np.log2(shape[0]))
bin_map = np.zeros(shape)
bin_num = 0
for level in range(n):
grid_size = 2**level
temp_I = I_stokes.reshape(int(shape[0]/grid_size), grid_size, int(shape[1]/grid_size), grid_size).sum(1).sum(2)
temp_Q = Q_stokes.reshape(int(shape[0]/grid_size), grid_size, int(shape[1]/grid_size), grid_size).sum(1).sum(2)
temp_U = U_stokes.reshape(int(shape[0]/grid_size), grid_size, int(shape[1]/grid_size), grid_size).sum(1).sum(2)
temp_cov = Stokes_cov.reshape(3, 3, int(shape[0]/grid_size), grid_size, int(shape[1]/grid_size), grid_size).sum(3).sum(4)
temp_bin_map = bin_map.reshape(int(shape[0]/grid_size), grid_size, int(shape[1]/grid_size), grid_size).sum(1).sum(2)
temp_P = (temp_Q**2 + temp_U**2)**0.5 / temp_I
temp_P_err = (1 / temp_I) * np.sqrt((temp_Q**2 * temp_cov[1,1,:,:] + temp_U**2 * temp_cov[2,2,:,:] + 2. * temp_Q * temp_U * temp_cov[1,2,:,:]) / (temp_Q**2 + temp_U**2) + \
((temp_Q / temp_I)**2 + (temp_U / temp_I)**2) * temp_cov[0,0,:,:] - \
2. * (temp_Q / temp_I) * temp_cov[0,1,:,:] - \
2. * (temp_U / temp_I) * temp_cov[0,2,:,:])
for i in range(int(shape[0]/grid_size)):
for j in range(int(shape[1]/grid_size)):
if (temp_P[i,j] / temp_P_err[i,j] > 3) and (temp_bin_map[i,j] == 0): # the default criterion is 3 sigma in P
bin_num += 1
bin_map[i*grid_size:(i+1)*grid_size,j*grid_size:(j+1)*grid_size] = bin_num
return bin_map, bin_num
def plot_quiver(ax, stkI, stkQ, stkU, stk_cov, poldata, pangdata, step_vec=1., vec_scale=2., optimal_binning=False):
if optimal_binning:
bin_map, bin_num = adaptive_binning(stkI, stkQ, stkU, stk_cov)
for i in range(1, bin_num+1):
@@ -114,14 +116,14 @@ def plot_quiver(ax, stkI, stkQ, stkU, stk_cov, poldata, pangdata, wcs, convert,
np.sqrt(bin_U**2 * bin_cov[1,1] + bin_Q**2 * bin_cov[2,2] - 2. * bin_Q * bin_U * bin_cov[1,2])
ax.quiver(y_center, x_center, poldata * np.cos(np.pi/2.+pangdata), poldata * np.sin(np.pi/2.+pangdata), 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='white')
ax.quiver(y_center, x_center, poldata * np.cos(np.pi/2.+pangdata+3*pangdata_err), poldata * np.sin(np.pi/2.+pangdata+3*pangdata_err), 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='black', edgecolor='black', ls='dashed')
ax.quiver(y_center, x_center, poldata * np.cos(np.pi/2.+pangdata-3*pangdata_err), poldata * np.sin(np.pi/2.+pangdata-3*pangdata_err), 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='black', edgecolor='black', ls='dashed')
ax.quiver(y_center, x_center, poldata * np.cos(np.pi/2.+pangdata+pangdata_err), poldata * np.sin(np.pi/2.+pangdata+pangdata_err), 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='black', edgecolor='black', ls='dashed')
ax.quiver(y_center, x_center, poldata * np.cos(np.pi/2.+pangdata-pangdata_err), poldata * np.sin(np.pi/2.+pangdata-pangdata_err), 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='black', edgecolor='black', ls='dashed')
else:
X, Y = np.meshgrid(np.arange(stkI.shape[1]), np.arange(stkI.shape[0]))
U, V = poldata*np.cos(np.pi/2.+pangdata*np.pi/180.), poldata*np.sin(np.pi/2.+pangdata*np.pi/180.)
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=0.5, linewidth=0.75, color='w', edgecolor='k')
def plot_obs(data_array, headers, rectangle=None, savename=None, plots_folder="", **kwargs):
"""
@@ -157,7 +159,7 @@ def plot_obs(data_array, headers, rectangle=None, savename=None, plots_folder=""
nb_obs = np.max([np.sum([head['filtnam1'] == curr_pol for head in headers]) for curr_pol in ['POL0', 'POL60', 'POL120']])
shape = np.array((3, nb_obs))
fig, ax = plt.subplots(shape[0], shape[1], figsize=(3*shape[1], 3*shape[0]), dpi=200, layout='constrained',
sharex=True, sharey=True)
sharex=True, sharey=True)
r_pol = dict(pol0=0, pol60=1, pol120=2)
c_pol = dict(pol0=0, pol60=0, pol120=0)
for i, (data, head) in enumerate(zip(data_array, headers)):
@@ -318,7 +320,11 @@ def polarization_map(Stokes, data_mask=None, rectangle=None, SNRp_cut=3., SNRi_c
The figure and ax created for interactive contour maps.
"""
# Get data
optimal_binning = kwargs.get('optimal_binning', False)
stkI = Stokes['I_stokes'].data.copy()
stkQ = Stokes['Q_stokes'].data.copy()
stkU = Stokes['U_stokes'].data.copy()
stk_cov = Stokes['IQU_cov_matrix'].data.copy()
pol = Stokes['Pol_deg_debiased'].data.copy()
pol_err = Stokes['Pol_deg_err'].data.copy()
@@ -428,7 +434,7 @@ def polarization_map(Stokes, data_mask=None, rectangle=None, SNRp_cut=3., SNRi_c
display = 's_i'
if (SNRi > SNRi_cut).any():
vmin, vmax = 1./2.*np.median(np.sqrt(stk_cov[0, 0][stk_cov[0, 0] > 0.]) *
convert_flux), np.max(np.sqrt(stk_cov[0, 0][stk_cov[0, 0] > 0.])*convert_flux)
convert_flux), np.max(np.sqrt(stk_cov[0, 0][stk_cov[0, 0] > 0.])*convert_flux)
im = ax.imshow(np.sqrt(stk_cov[0, 0])*convert_flux, norm=LogNorm(vmin, vmax), aspect='equal', cmap='inferno_r', alpha=1.)
else:
im = ax.imshow(np.sqrt(stk_cov[0, 0])*convert_flux, aspect='equal', cmap='inferno', alpha=1.)
@@ -486,10 +492,11 @@ def polarization_map(Stokes, data_mask=None, rectangle=None, SNRp_cut=3., SNRi_c
poldata[np.isfinite(poldata)] = 1./2.
step_vec = 1
vec_scale = 2.
X, Y = np.meshgrid(np.arange(stkI.shape[1]), np.arange(stkI.shape[0]))
U, V = poldata*np.cos(np.pi/2.+pangdata*np.pi/180.), poldata*np.sin(np.pi/2.+pangdata*np.pi/180.)
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=0.5, linewidth=0.75, color='w', edgecolor='k')
# X, Y = np.meshgrid(np.arange(stkI.shape[1]), np.arange(stkI.shape[0]))
# U, V = poldata*np.cos(np.pi/2.+pangdata*np.pi/180.), poldata*np.sin(np.pi/2.+pangdata*np.pi/180.)
# 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=0.5, linewidth=0.75, color='w', edgecolor='k')
plot_quiver(ax, stkI, stkQ, stkU, stk_cov, poldata, pangdata, step_vec=step_vec, vec_scale=vec_scale, optimal_binning=optimal_binning)
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)
@@ -510,7 +517,7 @@ def polarization_map(Stokes, data_mask=None, rectangle=None, SNRp_cut=3., SNRi_c
x, y, width, height, angle, color = rectangle
x, y = np.array([x, y]) - np.array(stkI.shape)/2.
ax.add_patch(Rectangle((x, y), width, height, angle=angle,
edgecolor=color, fill=False))
edgecolor=color, fill=False))
# ax.coords.grid(True, color='white', ls='dotted', alpha=0.5)
ax.coords[0].set_axislabel('Right Ascension (J2000)')
@@ -562,9 +569,9 @@ class align_maps(object):
self.other_convert, self.other_unit = (float(self.other_header['photflam']), r"$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA^{-1}$") if "PHOTFLAM" in list(
self.other_header.keys()) else (1., self.other_header['bunit'] if 'BUNIT' in list(self.other_header.keys()) else "Arbitray Units")
self.map_observer = "/".join([self.map_header['telescop'], self.map_header['instrume']]
) if "INSTRUME" in list(self.map_header.keys()) else self.map_header['telescop']
) if "INSTRUME" in list(self.map_header.keys()) else self.map_header['telescop']
self.other_observer = "/".join([self.other_header['telescop'], self.other_header['instrume']]
) if "INSTRUME" in list(self.other_header.keys()) else self.other_header['telescop']
) if "INSTRUME" in list(self.other_header.keys()) else self.other_header['telescop']
plt.rcParams.update({'font.size': 10})
fontprops = fm.FontProperties(size=16)

4
package/lib/reduction.py
View File

@@ -692,7 +692,7 @@ def align_data(data_array, headers, error_array=None, background=None, upsample_
full_headers.append(headers[0])
err_array = np.concatenate((error_array, [np.zeros(ref_data.shape)]), axis=0)
full_array, err_array, full_headers = crop_array(full_array, full_headers, err_array, step=5, inside=False, null_val=0.)
# full_array, err_array, full_headers = crop_array(full_array, full_headers, err_array, step=5, inside=False, null_val=0.)
data_array, ref_data, headers = full_array[:-1], full_array[-1], full_headers[:-1]
error_array = err_array[:-1]
@@ -766,7 +766,7 @@ def align_data(data_array, headers, error_array=None, background=None, upsample_
headers[i].update(headers_wcs[i].to_header())
data_mask = rescaled_mask.all(axis=0)
data_array, error_array, data_mask, headers = crop_array(rescaled_image, headers, rescaled_error, data_mask, null_val=0.01*background)
# data_array, error_array, data_mask, headers = crop_array(rescaled_image, headers, rescaled_error, data_mask, null_val=0.01*background)
if return_shifts:
return data_array, error_array, headers, data_mask, shifts, errors