Tibeuleu/FOC_Reduction
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change some variable names

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sugar_jo
2024-07-16 21:59:22 +08:00
parent 3c8ca6ac1a
commit fa4dce398f
2 changed files with 72 additions and 61 deletions
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113
package/FOC_reduction.py
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@@ -23,6 +23,7 @@ from lib.utils import sci_not, princ_angle
def main(target=None, proposal_id=None, data_dir=None, infiles=None, output_dir="./data", crop=False, interactive=False):
# Reduction parameters
# Deconvolution
@@ -90,8 +91,8 @@ def main(target=None, proposal_id=None, data_dir=None, infiles=None, output_dir=
# Step 1:
# Get data from fits files and translate to flux in erg/cm²/s/Angstrom.
if data_dir is None:
outfiles = []
if infiles is not None:
prod = np.array([["/".join(filepath.split('/')[:-1]), filepath.split('/')[-1]] for filepath in infiles], dtype=str)
obs_dir = "/".join(infiles[0].split("/")[:-1])
@@ -104,7 +105,7 @@ def main(target=None, proposal_id=None, data_dir=None, infiles=None, output_dir=
target, products = retrieve_products(target, proposal_id, output_dir=output_dir)
prod = products.pop()
for prods in products:
main(target=target, infiles=["/".join(pr) for pr in prods], output_dir=output_dir, crop=crop, interactive=interactive)
outfiles.append(main(target=target, infiles=["/".join(pr) for pr in prods], output_dir=output_dir, crop=crop, interactive=interactive))
data_folder = prod[0][0]
infiles = [p[1] for p in prod]
@@ -139,6 +140,7 @@ def main(target=None, proposal_id=None, data_dir=None, infiles=None, output_dir=
if deconvolve:
figtype = "_".join([figtype, "deconv"] if figtype != "" else ["deconv"])
if align_center is None:
figtype = "_".join([figtype, "not_aligned"] if figtype != "" else ["not_aligned"])
@@ -201,29 +203,29 @@ def main(target=None, proposal_id=None, data_dir=None, infiles=None, output_dir=
print("P_bkg = {0:.1f} ± {1:.1f} %".format(_debiased_P_bkg[0, 0]*100., np.ceil(_s_P_bkg[0, 0]*1000.)/10.))
print("PA_bkg = {0:.1f} ± {1:.1f} °".format(princ_angle(_PA_bkg[0, 0]), princ_angle(np.ceil(_s_PA_bkg[0, 0]*10.)/10.)))
# Plot polarization map (Background is either total Flux, Polarization degree or Polarization degree error).
if px_scale.lower() not in ['full', 'integrate'] and not interactive:
if pxscale.lower() not in ['full', 'integrate'] and not interactive:
proj_plots.polarization_map(deepcopy(_Stokes_test), _data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim,
step_vec=step_vec, vec_scale=vec_scale, savename="_".join([figname]), plots_folder=plots_folder, **options)
step_vec=step_vec, vec_scale=scale_vec, savename="_".join([figname]), plots_folder=plots_folder, **options)
proj_plots.polarization_map(deepcopy(_Stokes_test), _data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec,
vec_scale=vec_scale, savename="_".join([figname, "I"]), plots_folder=plots_folder, display='Intensity', **options)
vec_scale=scale_vec, savename="_".join([figname, "I"]), plots_folder=plots_folder, display='Intensity', **options)
proj_plots.polarization_map(deepcopy(_Stokes_test), _data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec,
vec_scale=vec_scale, savename="_".join([figname, "P_flux"]), plots_folder=plots_folder, display='Pol_Flux', **options)
vec_scale=scale_vec, savename="_".join([figname, "P_flux"]), plots_folder=plots_folder, display='Pol_Flux', **options)
proj_plots.polarization_map(deepcopy(_Stokes_test), _data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec,
vec_scale=vec_scale, savename="_".join([figname, "P"]), plots_folder=plots_folder, display='Pol_deg', **options)
vec_scale=scale_vec, savename="_".join([figname, "P"]), plots_folder=plots_folder, display='Pol_deg', **options)
proj_plots.polarization_map(deepcopy(_Stokes_test), _data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec,
vec_scale=vec_scale, savename="_".join([figname, "PA"]), plots_folder=plots_folder, display='Pol_ang', **options)
vec_scale=scale_vec, savename="_".join([figname, "PA"]), plots_folder=plots_folder, display='Pol_ang', **options)
proj_plots.polarization_map(deepcopy(_Stokes_test), _data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec,
vec_scale=vec_scale, savename="_".join([figname, "I_err"]), plots_folder=plots_folder, display='I_err', **options)
vec_scale=scale_vec, savename="_".join([figname, "I_err"]), plots_folder=plots_folder, display='I_err', **options)
proj_plots.polarization_map(deepcopy(_Stokes_test), _data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec,
vec_scale=vec_scale, savename="_".join([figname, "P_err"]), plots_folder=plots_folder, display='Pol_deg_err', **options)
vec_scale=scale_vec, savename="_".join([figname, "P_err"]), plots_folder=plots_folder, display='Pol_deg_err', **options)
proj_plots.polarization_map(deepcopy(_Stokes_test), _data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec,
vec_scale=vec_scale, savename="_".join([figname, "SNRi"]), plots_folder=plots_folder, display='SNRi', **options)
vec_scale=scale_vec, savename="_".join([figname, "SNRi"]), plots_folder=plots_folder, display='SNRi', **options)
proj_plots.polarization_map(deepcopy(_Stokes_test), _data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec,
vec_scale=vec_scale, savename="_".join([figname, "SNRp"]), plots_folder=plots_folder, display='SNRp', **options)
vec_scale=scale_vec, savename="_".join([figname, "SNRp"]), plots_folder=plots_folder, display='SNRp', **options)
elif not interactive:
proj_plots.polarization_map(deepcopy(_Stokes_test), _data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut,
savename=figname, plots_folder=plots_folder, display='integrate', **options)
elif px_scale.lower() not in ['full', 'integrate']:
elif pxscale.lower() not in ['full', 'integrate']:
proj_plots.pol_map(_Stokes_test, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim)
else:
@@ -251,18 +253,30 @@ def main(target=None, proposal_id=None, data_dir=None, infiles=None, output_dir=
vmin=data_array[data_array > 0.].min()*headers[0]['photflam'], vmax=data_array[data_array > 0.].max()*headers[0]['photflam']))
# Rebin data to desired pixel size.
if rebin:
if (pxsize is not None) and not (pxsize == 1 and pxscale.lower() in ["px", "pixel", "pixels"]):
data_array, error_array, headers, Dxy, data_mask = proj_red.rebin_array(
data_array, error_array, headers, pxsize=pxsize, scale=px_scale, operation=rebin_operation, data_mask=data_mask)
data_array, error_array, headers, pxsize=pxsize, scale=pxscale, operation=rebin_operation, data_mask=data_mask)
# Rotate data to have North up
# Rotate data to have same orientation
rotate_data = np.unique([np.round(float(head["ORIENTAT"]), 3) for head in headers]).size != 1
if rotate_data:
data_mask = np.ones(data_array.shape[1:]).astype(bool)
alpha = headers[0]['orientat']
data_array, error_array, data_mask, headers = proj_red.rotate_data(data_array, error_array, data_mask, headers, -alpha)
ang = np.mean([head["ORIENTAT"] for head in headers])
for head in headers:
head["ORIENTAT"] -= ang
data_array, error_array, data_mask, headers = proj_red.rotate_data(data_array, error_array, data_mask, headers)
if display_data:
proj_plots.plot_obs(
data_array,
headers,
savename="_".join([figname, "rotate_data"]),
plots_folder=plots_folder,
norm=LogNorm(
vmin=data_array[data_array > 0.0].min() * headers[0]["photflam"], vmax=data_array[data_array > 0.0].max() * headers[0]["photflam"]
),
)
# Plot array for checking output
if display_data and px_scale.lower() not in ['full', 'integrate']:
if display_data and pxscale.lower() not in ['full', 'integrate']:
proj_plots.plot_obs(data_array, headers, savename="_".join([figname, "rebin"]), plots_folder=plots_folder, norm=LogNorm(
vmin=data_array[data_array > 0.].min()*headers[0]['photflam'], vmax=data_array[data_array > 0.].max()*headers[0]['photflam']))
@@ -283,7 +297,7 @@ def main(target=None, proposal_id=None, data_dir=None, infiles=None, output_dir=
# Step 3:
# Rotate images to have North up
if rotate_stokes:
if rotate_North:
I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, headers = proj_red.rotate_Stokes(
I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, headers, SNRi_cut=None)
I_bkg, Q_bkg, U_bkg, S_cov_bkg, _, _ = proj_red.rotate_Stokes(I_bkg, Q_bkg, U_bkg, S_cov_bkg, np.array(True).reshape(1, 1), headers, SNRi_cut=None)
@@ -295,21 +309,23 @@ def main(target=None, proposal_id=None, data_dir=None, infiles=None, output_dir=
# Step 4:
# Save image to FITS.
figname = "_".join([figname, figtype]) if figtype != "" else figname
Stokes_test = proj_fits.save_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, P, debiased_P, s_P, s_P_P, PA, s_PA, s_PA_P,
Stokes_hdul = proj_fits.save_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, P, debiased_P, s_P, s_P_P, PA, s_PA, s_PA_P,
headers, data_mask, figname, data_folder=data_folder, return_hdul=True)
outfiles.append("/".join([data_folder, Stokes_hdul[0].header["FILENAME"] + ".fits"]))
# Step 5:
# crop to desired region of interest (roi)
if crop:
figname += "_crop"
stokescrop = proj_plots.crop_Stokes(deepcopy(Stokes_test), norm=LogNorm())
stokescrop = proj_plots.crop_Stokes(deepcopy(Stokes_hdul), norm=LogNorm())
stokescrop.crop()
stokescrop.write_to("/".join([data_folder, figname+".fits"]))
Stokes_test, headers = stokescrop.hdul_crop, [dataset.header for dataset in stokescrop.hdul_crop]
Stokes_hdul, headers = stokescrop.hdul_crop, [dataset.header for dataset in stokescrop.hdul_crop]
outfiles.append("/".join([data_folder, Stokes_hdul[0].header["FILENAME"] + ".fits"]))
data_mask = Stokes_test['data_mask'].data.astype(bool)
data_mask = Stokes_hdul['data_mask'].data.astype(bool)
print("F_int({0:.0f} Angs) = ({1} ± {2})e{3} ergs.cm^-2.s^-1.Angs^-1".format(headers[0]['photplam'], *sci_not(
Stokes_test[0].data[data_mask].sum()*headers[0]['photflam'], np.sqrt(Stokes_test[3].data[0, 0][data_mask].sum())*headers[0]['photflam'], 2, out=int)))
Stokes_hdul[0].data[data_mask].sum()*headers[0]['photflam'], np.sqrt(Stokes_hdul[3].data[0, 0][data_mask].sum())*headers[0]['photflam'], 2, out=int)))
print("P_int = {0:.1f} ± {1:.1f} %".format(headers[0]['p_int']*100., np.ceil(headers[0]['p_int_err']*1000.)/10.))
print("PA_int = {0:.1f} ± {1:.1f} °".format(princ_angle(headers[0]['pa_int']), princ_angle(np.ceil(headers[0]['pa_int_err']*10.)/10.)))
# Background values
@@ -318,30 +334,30 @@ def main(target=None, proposal_id=None, data_dir=None, infiles=None, output_dir=
print("P_bkg = {0:.1f} ± {1:.1f} %".format(debiased_P_bkg[0, 0]*100., np.ceil(s_P_bkg[0, 0]*1000.)/10.))
print("PA_bkg = {0:.1f} ± {1:.1f} °".format(princ_angle(PA_bkg[0, 0]), princ_angle(np.ceil(s_PA_bkg[0, 0]*10.)/10.)))
# Plot polarization map (Background is either total Flux, Polarization degree or Polarization degree error).
if px_scale.lower() not in ['full', 'integrate'] and not interactive:
proj_plots.polarization_map(deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim,
step_vec=step_vec, vec_scale=vec_scale, savename="_".join([figname]), plots_folder=plots_folder, **options)
proj_plots.polarization_map(deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec,
vec_scale=vec_scale, savename="_".join([figname, "I"]), plots_folder=plots_folder, display='Intensity', **options)
proj_plots.polarization_map(deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec,
vec_scale=vec_scale, savename="_".join([figname, "P_flux"]), plots_folder=plots_folder, display='Pol_Flux', **options)
proj_plots.polarization_map(deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec,
vec_scale=vec_scale, savename="_".join([figname, "P"]), plots_folder=plots_folder, display='Pol_deg', **options)
proj_plots.polarization_map(deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec,
vec_scale=vec_scale, savename="_".join([figname, "PA"]), plots_folder=plots_folder, display='Pol_ang', **options)
proj_plots.polarization_map(deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec,
vec_scale=vec_scale, savename="_".join([figname, "I_err"]), plots_folder=plots_folder, display='I_err', **options)
proj_plots.polarization_map(deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec,
vec_scale=vec_scale, savename="_".join([figname, "P_err"]), plots_folder=plots_folder, display='Pol_deg_err', **options)
proj_plots.polarization_map(deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec,
vec_scale=vec_scale, savename="_".join([figname, "SNRi"]), plots_folder=plots_folder, display='SNRi', **options)
proj_plots.polarization_map(deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec,
vec_scale=vec_scale, savename="_".join([figname, "SNRp"]), plots_folder=plots_folder, display='SNRp', **options)
if pxscale.lower() not in ['full', 'integrate'] and not interactive:
proj_plots.polarization_map(deepcopy(Stokes_hdul), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim,
step_vec=step_vec, scale_vec=scale_vec, savename="_".join([figname]), plots_folder=plots_folder, **options)
proj_plots.polarization_map(deepcopy(Stokes_hdul), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec,
scale_vec=scale_vec, savename="_".join([figname, "I"]), plots_folder=plots_folder, display='Intensity', **options)
proj_plots.polarization_map(deepcopy(Stokes_hdul), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec,
scale_vece=scale_vec, savename="_".join([figname, "P_flux"]), plots_folder=plots_folder, display='Pol_Flux', **options)
proj_plots.polarization_map(deepcopy(Stokes_hdul), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec,
scale_vec=scale_vec, savename="_".join([figname, "P"]), plots_folder=plots_folder, display='Pol_deg', **options)
proj_plots.polarization_map(deepcopy(Stokes_hdul), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec,
scale_vec=scale_vec, savename="_".join([figname, "PA"]), plots_folder=plots_folder, display='Pol_ang', **options)
proj_plots.polarization_map(deepcopy(Stokes_hdul), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec,
scale_vec=scale_vec, savename="_".join([figname, "I_err"]), plots_folder=plots_folder, display='I_err', **options)
proj_plots.polarization_map(deepcopy(Stokes_hdul), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec,
scale_vec=scale_vec, savename="_".join([figname, "P_err"]), plots_folder=plots_folder, display='Pol_deg_err', **options)
proj_plots.polarization_map(deepcopy(Stokes_hdul), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec,
scale_vec=scale_vec, savename="_".join([figname, "SNRi"]), plots_folder=plots_folder, display='SNRi', **options)
proj_plots.polarization_map(deepcopy(Stokes_hdul), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec,
scale_vec=scale_vec, savename="_".join([figname, "SNRp"]), plots_folder=plots_folder, display='SNRp', **options)
elif not interactive:
proj_plots.polarization_map(deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut,
proj_plots.polarization_map(deepcopy(Stokes_hdul), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut,
savename=figname, plots_folder=plots_folder, display='integrate', **options)
elif px_scale.lower() not in ['full', 'integrate']:
proj_plots.pol_map(Stokes_test, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim)
elif pxscale.lower() not in ['full', 'integrate']:
proj_plots.pol_map(Stokes_hdul, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim)
return outfiles
@@ -350,7 +366,6 @@ def main(target=None, proposal_id=None, data_dir=None, infiles=None, output_dir=
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser(description='Query MAST for target products')
parser.add_argument('-t', '--target', metavar='targetname', required=False, help='the name of the target', type=str, default=None)
parser.add_argument('-p', '--proposal_id', metavar='proposal_id', required=False, help='the proposal id of the data products', type=int, default=None)

20
package/lib/plots.py
View File

@@ -99,7 +99,7 @@ def adaptive_binning(I_stokes, Q_stokes, U_stokes, Stokes_cov):
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):
def plot_quiver(ax, stkI, stkQ, stkU, stk_cov, poldata, pangdata, step_vec=1., scale_vec=2., optimal_binning=False):
if optimal_binning:
bin_map, bin_num = adaptive_binning(stkI, stkQ, stkU, stk_cov)
@@ -120,14 +120,14 @@ def plot_quiver(ax, stkI, stkQ, stkU, stk_cov, poldata, pangdata, step_vec=1., v
pangdata_err = (1 / (2. *(bin_Q**2 + bin_U**2))) * \
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+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')
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./scale_vec, 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+pangdata_err), poldata * np.sin(np.pi/2.+pangdata+pangdata_err), units='xy', angles='uv', scale=1./scale_vec, 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./scale_vec, 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')
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./scale_vec, 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):
@@ -545,14 +545,10 @@ def polarization_map(
if step_vec == 0:
poldata[np.isfinite(poldata)] = 1.0 / 2.0
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')
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')
scale_vec = 2.
plot_quiver(ax, stkI, stkQ, stkU, stk_cov, poldata, pangdata, step_vec=step_vec, scale_vec=scale_vec, optimal_binning=optimal_binning)
pol_sc = AnchoredSizeBar(ax.transData, scale_vec, 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)