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Use center values for interpolation

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Noe Brucy
2019-05-14 16:57:16 +02:00
parent 18cfbc6eb3
commit a587704ed5
1 changed files with 19 additions and 22 deletions
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disk_postprocess.py
+19 -22
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@@ -675,7 +675,7 @@ def disk_prop(
"time": time,
"mass_disk": total_mass_disk,
"mass_box": total_mass,
"rad": rad[0 : nb_bin - 1],
"rad": rad[:-1],
"center": pos_star,
"alpha_rey": alpha_rey_rad,
"alpha_rey_mean": alpha_rey_mean,
@@ -873,8 +873,7 @@ def disk_pdf(
nb_bin_hist=50,
tag="",
rad_min=0.08,
rad_max=0.35,
interpol=True,
rad_max=0.4,
):
# Load property file
@@ -905,8 +904,18 @@ def disk_pdf(
coldens_map = maps_disk["coldens_z"]
im_extent = maps_disk["im_extent"]
# radius of the corner of the box plus a margin
rad_box = (
np.sqrt((im_extent[1] - pos_star[0]) ** 2 + (im_extent[3] - pos_star[1]) ** 2)
+ 0.1
)
# radial bins
rad_bins = prop_disk["rad"]
rad_bins = 0.5 * (rad_bins[0:-1] + rad_bins[1:])
rad_bins = np.concatenate(([0.0], rad_bins, [rad_box]))
coldens_rad = prop_disk["coldens"]
# Add value for borders
coldens_rad = np.concatenate(([coldens_rad[0]], coldens_rad))
x = np.linspace(im_extent[0], im_extent[1], coldens_map.shape[0])
y = np.linspace(im_extent[2], im_extent[3], coldens_map.shape[1])
@@ -920,25 +929,13 @@ def disk_pdf(
bins_flat = bins.flatten()
rr_flat = rr.flatten()
# Retrieve the mean of the bi
if interpol: # use linear interpolation to improve accuracy
rad_bins_ext = np.append(
rad_bins,
np.sqrt(
(im_extent[1] - pos_star[0]) ** 2 + (im_extent[3] - pos_star[1]) ** 2
),
)
coldens_rad_ext = np.append(coldens_rad, coldens_rad[-1])
coldens_mean = (rad_bins_ext[bins_flat + 1] - rr_flat) * coldens_rad[bins_flat]
coldens_mean = (
coldens_mean
+ (rr_flat - rad_bins[bins_flat]) * coldens_rad_ext[bins_flat + 1]
)
coldens_mean = coldens_mean / (
rad_bins_ext[bins_flat + 1] - rad_bins[bins_flat]
)
else:
coldens_mean = coldens_rad[bins_flat]
# Retrieve the mean of the bins
# use linear interpolation to improve accuracy
coldens_mean = (rad_bins[bins_flat + 1] - rr_flat) * coldens_rad[bins_flat]
coldens_mean = (
coldens_mean + (rr_flat - rad_bins[bins_flat]) * coldens_rad[bins_flat + 1]
)
coldens_mean = coldens_mean / (rad_bins[bins_flat + 1] - rad_bins[bins_flat])
coldens_mean_map = np.reshape(coldens_mean, coldens_map.shape)