def load_wedges(
av_image,
core_sample,
wedge_angle=40,
wedge_radius=10,
av_image_error=None,
core_rel_pos=0.1,
angle_res=1.0,
wedge_width=3,
):
region_dict = {}
core_wedge_angles = {}
# angles are counter-clockwise from north
core_wedge_angles = {
# Taurus
'G174.40-13.45': 45,
'G174.70-15.47': 100,
'G174.05-15.82': 20,
'G172.93-16.73': 160,
'G172.12-16.94': 190,
'G171.14-17.57': 200,
'G171.49-14.91': 0,
'G171.00-15.80': 195,
'G169.32-16.17': 210,
'G168.10-16.38': 230,
# Perseus
'G160.49-16.81': 20,
'G160.46-17.99': 90,
'G159.80-18.49': 0,
'G160.14-19.08': 120,
'G160.53-19.73': 170,
'G159.19-20.11': 10,
'G158.39-20.72': 0,
'G159.17-21.09': 100,
'G158.89-21.60': 170,
'G158.26-21.81': 270,
# California
'G168.54-6.22': 100,
'G168.12-6.42': 0,
'G166.91-7.76': 60,
'G165.71-9.15': 180,
'G165.36-7.51': 90,
'G164.99-8.60': 250,
'G164.70-7.63': 40,
'G164.18-8.84': 290,
'G164.26-8.39': 0,
'G164.65-8.12': 140,
}
for cloud_name in core_sample:
#for cloud_name in ('perseus',):
cloud_df = core_sample[cloud_name]
gradient_sums_list = []
for core_name in cloud_df['Name']:
#for core_name in ('G158.26-21.81',):
core = cloud_df[cloud_df['Name'] == core_name]
#print('Calculating optimal angle for core {:s}'.format(core_name))
# Get center position in pixels
core_pos = [core['xpix'].values[0], core['ypix'].values[0]][::-1]
wedge_vertices = myg.create_wedge(
core_pos,
wedge_radius,
wedge_angle,
center_rel_pos=core_rel_pos,
width=wedge_width,
)
# angle of wedge
if core_name not in core_wedge_angles:
core_wedge_angles[core_name] = 0
angle_ideal = core_wedge_angles[core_name]
wedge_vertices_rotated = myg.rotate_wedge(wedge_vertices, core_pos,
angle_ideal)
region_dict[core_name] = {}
region_dict[core_name]['xpix'] = wedge_vertices_rotated[:, 1]
region_dict[core_name]['ypix'] = wedge_vertices_rotated[:, 0]
return region_dict
def load_wedges(av_image, core_sample, wedge_angle=40, wedge_radius=10,
av_image_error=None, core_rel_pos=0.1, angle_res=1.0, wedge_width=3,):
region_dict = {}
core_wedge_angles = {}
# angles are counter-clockwise from north
core_wedge_angles = {
# Taurus
'G174.40-13.45': 45,
'G174.70-15.47': 100,
'G174.05-15.82': 20,
'G172.93-16.73': 160,
'G172.12-16.94': 190,
'G171.14-17.57': 200,
'G171.49-14.91': 0,
'G171.00-15.80': 195,
'G169.32-16.17': 210,
'G168.10-16.38': 230,
# Perseus
'G160.49-16.81': 20,
'G160.46-17.99': 90,
'G159.80-18.49': 0,
'G160.14-19.08': 120,
'G160.53-19.73': 170,
'G159.19-20.11': 10,
'G158.39-20.72': 0,
'G159.17-21.09': 100,
'G158.89-21.60': 170,
'G158.26-21.81': 270,
# California
'G168.54-6.22': 100,
'G168.12-6.42': 0,
'G166.91-7.76': 60,
'G165.71-9.15': 180,
'G165.36-7.51': 90,
'G164.99-8.60': 250,
'G164.70-7.63': 40,
'G164.18-8.84': 290,
'G164.26-8.39': 0,
'G164.65-8.12': 140,
}
for cloud_name in core_sample:
#for cloud_name in ('perseus',):
cloud_df = core_sample[cloud_name]
gradient_sums_list = []
for core_name in cloud_df['Name']:
#for core_name in ('G158.26-21.81',):
core = cloud_df[cloud_df['Name'] == core_name]
#print('Calculating optimal angle for core {:s}'.format(core_name))
# Get center position in pixels
core_pos = [core['xpix'].values[0], core['ypix'].values[0]][::-1]
wedge_vertices = myg.create_wedge(core_pos,
wedge_radius,
wedge_angle,
center_rel_pos=core_rel_pos,
width=wedge_width,
)
# angle of wedge
if core_name not in core_wedge_angles:
core_wedge_angles[core_name] = 0
angle_ideal = core_wedge_angles[core_name]
wedge_vertices_rotated = myg.rotate_wedge(wedge_vertices,
core_pos,
angle_ideal)
region_dict[core_name] = {}
region_dict[core_name]['xpix'] = wedge_vertices_rotated[:,1]
region_dict[core_name]['ypix'] = wedge_vertices_rotated[:,0]
return region_dict
def derive_ideal_wedge(av_image,
core_sample,
wedge_angle=40,
wedge_radius=10,
av_image_error=None,
core_rel_pos=0.1,
angle_res=1.0,
width=3):
import mygeometry as myg
import myimage_analysis as myim
"""
Parameters
----------
angle_res : float
Resolution with which to rotate each new box in degrees. 1.0 degree
gives 360 different box orientations.
"""
angle_grid = np.arange(0, 360, angle_res)
region_dict = {}
for cloud_name in core_sample:
#for cloud_name in ('perseus',):
cloud_df = core_sample[cloud_name]
gradient_sums_list = []
for core_name in cloud_df['Name']:
#for core_name in ('G158.26-21.81',):
core = cloud_df[cloud_df['Name'] == core_name]
print('Calculating optimal angle for core {:s}'.format(core_name))
# Get center position in pixels
core_pos = [core['xpix'].values[0], core['ypix'].values[0]][::-1]
wedge_vertices = myg.create_wedge(
core_pos,
wedge_radius,
wedge_angle,
center_rel_pos=core_rel_pos,
width=wedge_width,
)
gradient_sums = np.zeros((len(angle_grid)))
for i, angle in enumerate(angle_grid):
wedge_vertices_rotated = myg.rotate_wedge(
wedge_vertices, core_pos, angle)
try:
mask = \
myg.get_polygon_mask(av_image,
wedge_vertices_rotated)
av_image_masked = np.copy(av_image)
# extract radial profile weighted by SNR
radii, profile = \
myim.get_radial_profile(av_image,
binsize=1,
center=core_pos, #[::-1],
#weights=av_image_error,
mask=mask
)
if angle == 90:
av_image_masked = np.copy(av_image)
mask = myg.get_polygon_mask(av_image_masked,
wedge_vertices)
av_image_masked[mask == 0] = np.NaN
indices = np.where((radii == radii) & \
(profile == profile))
profile, radii = profile[indices], radii[indices]
# steeper gradients will have smaller sums
gradient_sum = np.sum(np.gradient(profile, radii))
gradient_sums[i] = gradient_sum
except IndexError:
gradient_sums[i] = 0.
gradient_sums_list.append(gradient_sums)
#print wedge_vertices_rotated
# find steepest profile and recreate the box mask
angle_ideal = angle_grid[gradient_sums == np.min(gradient_sums)][0]
wedge_vertices_rotated = myg.rotate_wedge(wedge_vertices, core_pos,
angle_ideal)
region_dict[core_name] = {}
region_dict[core_name]['xpix'] = wedge_vertices_rotated[:, 1]
region_dict[core_name]['ypix'] = wedge_vertices_rotated[:, 0]
return region_dict
def derive_ideal_wedge(av_image, core_sample, wedge_angle=40, wedge_radius=10,
av_image_error=None, core_rel_pos=0.1, angle_res=1.0, width=3):
import mygeometry as myg
import myimage_analysis as myim
"""
Parameters
----------
angle_res : float
Resolution with which to rotate each new box in degrees. 1.0 degree
gives 360 different box orientations.
"""
angle_grid = np.arange(0, 360, angle_res)
region_dict = {}
for cloud_name in core_sample:
#for cloud_name in ('perseus',):
cloud_df = core_sample[cloud_name]
gradient_sums_list = []
for core_name in cloud_df['Name']:
#for core_name in ('G158.26-21.81',):
core = cloud_df[cloud_df['Name'] == core_name]
print('Calculating optimal angle for core {:s}'.format(core_name))
# Get center position in pixels
core_pos = [core['xpix'].values[0], core['ypix'].values[0]][::-1]
wedge_vertices = myg.create_wedge(core_pos,
wedge_radius,
wedge_angle,
center_rel_pos=core_rel_pos,
width=wedge_width,
)
gradient_sums = np.zeros((len(angle_grid)))
for i, angle in enumerate(angle_grid):
wedge_vertices_rotated = myg.rotate_wedge(wedge_vertices,
core_pos,
angle)
try:
mask = \
myg.get_polygon_mask(av_image,
wedge_vertices_rotated)
av_image_masked = np.copy(av_image)
# extract radial profile weighted by SNR
radii, profile = \
myim.get_radial_profile(av_image,
binsize=1,
center=core_pos, #[::-1],
#weights=av_image_error,
mask=mask
)
if angle == 90:
av_image_masked = np.copy(av_image)
mask = myg.get_polygon_mask(av_image_masked,
wedge_vertices)
av_image_masked[mask==0] = np.NaN
indices = np.where((radii == radii) & \
(profile == profile))
profile, radii = profile[indices], radii[indices]
# steeper gradients will have smaller sums
gradient_sum = np.sum(np.gradient(profile, radii))
gradient_sums[i] = gradient_sum
except IndexError:
gradient_sums[i] = 0.
gradient_sums_list.append(gradient_sums)
#print wedge_vertices_rotated
# find steepest profile and recreate the box mask
angle_ideal = angle_grid[gradient_sums == np.min(gradient_sums)][0]
wedge_vertices_rotated = myg.rotate_wedge(wedge_vertices,
core_pos,
angle_ideal)
region_dict[core_name] = {}
region_dict[core_name]['xpix'] = wedge_vertices_rotated[:,1]
region_dict[core_name]['ypix'] = wedge_vertices_rotated[:,0]
return region_dict
