def main():
import grid
import numpy as np
from os import system,path
import myclumpfinder as clump_finder
reload(clump_finder)
import mygeometry as myg
reload(myg)
from mycoords import make_velocity_axis
import mymath
reload(mymath)
# define directory locations
output_dir = '/d/bip3/ezbc/perseus/data/python_output/co_dispersion/'
figure_dir = '/d/bip3/ezbc/perseus/figures/'
av_dir = '/d/bip3/ezbc/perseus/data/av/'
hi_dir = '/d/bip3/ezbc/perseus/data/galfa/'
cfa_dir = '/d/bip3/ezbc/perseus/data/cfa/'
core_dir = output_dir + 'core_arrays/'
region_dir = '/d/bip3/ezbc/taurus/data/python_output/ds9_regions/'
# load 2mass Av and GALFA HI images, on same grid
cfa_data, cfa_header = load_fits(cfa_dir + \
'perseus_cfa_cube_galfa_regrid.fits',
return_header=True)
#av_data += - 0.4 # subtracts background of 0.4 mags
hi_data, hi_header = load_fits(hi_dir + \
'perseus_galfa_cube_bin_3.7arcmin.fits',
return_header = True)
# make the velocity axis
hi_velocity_axis = make_velocity_axis(hi_header)
cfa_velocity_axis = make_velocity_axis(cfa_header)
cfa_mom2 = mymath.calc_moment(cfa_data, moment = 2, spectral_axis = 0)
# define core properties
cores = {'L1495':
{'center_wcs': [(4,14,0), (28, 11, 0)],
'map': None,
'threshold': 4.75,
'box_wcs': [(4,16,30), (27,44,30), (4,5,20), (28,28,33)]
},
'L1495A':
{'center_wcs': [(4,18,0), (28,23., 0)],
'map': None,
'threshold': 4.75,
'box_wcs': [(4,28,23),(28,12,50),(4,16,23),(29,46,5)],
},
'B213':
{'center_wcs': [(4, 19, 0), (27, 15,0)],
'map': None,
'threshold': 4.75,
'box_wcs': [(4,22,27), (26,45,47),(4,5,25),(27,18,48)],
},
'B220':
{'center_wcs': [(4, 41, 0.), (26,7,0)],
'map': None,
'threshold': 7,
'box_wcs': [(4,47,49),(25,31,13),(4,40,37),(27,31,17)],
},
'L1527':
{'center_wcs': [(4, 39, 0.), (25,47, 0)],
'map': None,
'threshold': 7,
'box_wcs': [(4,40,13), (24,46,38), (4,34,35), (25,56,7)],
},
'B215':
{'center_wcs': [(4, 23, 0), (25, 3, 0)],
'map': None,
'threshold': 3,
'box_wcs': [(4,24,51), (22,36,7), (4,20,54), (25,26,31)],
},
'L1524':
{'center_wcs': [(4,29,0.), (24,31.,0)],
'map': None,
'threshold': 3,
'box_wcs': [(4,31,0), (22,4,6), (4,25,33), (25,0,55)],
}
}
cores = convert_core_coordinates(cores, hi_header)
if True:
hsd_limits =[0.1,300]
hisd_limits = [2,20]
av_limits =[0.01,100]
nhi_limits = [2,20]
cores = load_ds9_region(cores,
filename_base = region_dir + 'taurus_av_boxes_',
header = hi_header)
# save cores for later
if 0:
mask = np.zeros((cfa_mom2.shape))
for core in cores:
print('Calculating for core %s' % core)
av_limits = [0.01,100]
# Grab the mask
xy = cores[core]['box_center_pix']
box_width = cores[core]['box_width']
box_height = cores[core]['box_height']
box_angle = cores[core]['box_angle']
mask += myg.get_rectangular_mask(cfa_mom2,
xy[0], xy[1],
width = box_width,
height = box_height,
angle = box_angle)
cores[core]['box_vertices'] = myg.get_rect(
xy[0], xy[1],
width = box_width,
height = box_height,
angle = box_angle,)
indices = np.where(mask == 1)
mask[mask > 1] = 1
cfa_mom2[mask == 0] = np.nan
# currently have variance, need dispersion
cfa_mom2 = cfa_mom2**0.5
# Plot
plot_mom2_image(mom2_image = cfa_mom2,
header = cfa_header,
boxes = False,
#limits=[128,37,308,206],
title = r'Perseus: $\sigma_{\rm CO}$ map with core ' + \
'boxed-regions.',
savedir = figure_dir,
filename='perseus_co_veldisp_map.png',
show=True)
def main():
''' Executes script.
'''
# import external modules
import pyfits as pf
import numpy as np
from mycoords import make_velocity_axis
import mygeometry as myg
reload(myg)
# define directory locations
output_dir = '/d/bip3/ezbc/taurus/data/python_output/nhi_av/'
figure_dir = '/d/bip3/ezbc/taurus/figures/maps/'
av_dir = '/d/bip3/ezbc/taurus/data/av/'
hi_dir = '/d/bip3/ezbc/taurus/data/galfa/'
core_dir = output_dir + 'core_arrays/'
region_dir = '/d/bip3/ezbc/taurus/data/python_output/ds9_regions/'
# Load hi fits file
hi_image, hi_header = pf.getdata(hi_dir + \
'taurus_galfa_cube_bin_3.7arcmin.fits', header=True)
h = hi_header
# Load av fits file
av_image, av_header = pf.getdata(av_dir + 'taurus_av_k09_regrid.fits',
header=True)
# make velocity axis for hi cube
velocity_axis = make_velocity_axis(hi_header)
# create nhi image
nhi_image = calculate_nhi(hi_cube=hi_image,
velocity_axis=velocity_axis,
velocity_range=[-100, 100])
if False:
# trim hi_image to av_image size
nhi_image_trim = np.ma.array(nhi_image, mask=av_image != av_image)
plot_nhi_image(nhi_image=nhi_image_trim,
header=hi_header,
contour_image=av_image,
contours=[5, 10, 15],
savedir=figure_dir,
filename='taurus_nhi_cores_map.png',
show=True)
cores = {
'L1495': {
'center_wcs': [(4, 14, 0), (28, 11, 0)],
'map': None,
'threshold': 4.75,
'box_wcs': [(4, 16, 30), (27, 44, 30), (4, 5, 20), (28, 28, 33)]
},
'L1495A': {
'center_wcs': [(4, 18, 0), (28, 23., 0)],
'map': None,
'threshold': 4.75,
'box_wcs': [(4, 28, 23), (28, 12, 50), (4, 16, 23), (29, 46, 5)],
},
'B213': {
'center_wcs': [(4, 19, 0), (27, 15, 0)],
'map': None,
'threshold': 4.75,
'box_wcs': [(4, 22, 27), (26, 45, 47), (4, 5, 25), (27, 18, 48)],
},
'B220': {
'center_wcs': [(4, 41, 0.), (26, 7, 0)],
'map': None,
'threshold': 7,
'box_wcs': [(4, 47, 49), (25, 31, 13), (4, 40, 37), (27, 31, 17)],
},
'L1527': {
'center_wcs': [(4, 39, 0.), (25, 47, 0)],
'map': None,
'threshold': 7,
'box_wcs': [(4, 40, 13), (24, 46, 38), (4, 34, 35), (25, 56, 7)],
},
'B215': {
'center_wcs': [(4, 23, 0), (25, 3, 0)],
'map': None,
'threshold': 3,
'box_wcs': [(4, 24, 51), (22, 36, 7), (4, 20, 54), (25, 26, 31)],
},
'L1524': {
'center_wcs': [(4, 29, 0.), (24, 31., 0)],
'map': None,
'threshold': 3,
'box_wcs': [(4, 31, 0), (22, 4, 6), (4, 25, 33), (25, 0, 55)],
}
}
cores = convert_core_coordinates(cores, h)
if False:
nhi_image = np.zeros(nhi_image.shape)
for core in cores:
core_image = np.load(core_dir + core + '.npy')
core_indices = np.where(core_image == core_image)
nhi_image[core_indices] += core_image[core_indices]
nhi_image_trim =np.ma.array(nhi_image, mask=((av_image != av_image) &\
(nhi_image == 0)))
nhi_image_trim[nhi_image_trim == 0] = np.NaN
read_ds9_region(av_dir + 'taurus_av_boxes.reg')
plot_nhi_image(nhi_image=nhi_image_trim,
header=hi_header,
savedir=figure_dir,
cores=cores,
filename='taurus_nhi_core_regions_map.png',
show=True)
if True:
cores = load_ds9_region(cores,
filename_base=region_dir + 'taurus_av_boxes_',
header=h)
# Grab the mask
mask = np.zeros((nhi_image.shape))
for core in cores:
xy = cores[core]['box_center_pix']
box_width = cores[core]['box_width']
box_height = cores[core]['box_height']
box_angle = cores[core]['box_angle']
mask += myg.get_rectangular_mask(nhi_image,
xy[0],
xy[1],
width=box_width,
height=box_height,
angle=box_angle)
cores[core]['box_vertices'] = myg.get_rect(
xy[0],
xy[1],
width=box_width,
height=box_height,
angle=box_angle,
)
#print(cores[core]['box_vertices'])
#print core, xy, box_width, box_height, box_angle
mask[mask > 1] = 1
#nhi_image[mask == 0] = np.nan
# trim hi_image to av_image size
nhi_image_trim = np.ma.array(nhi_image, mask=(av_image != av_image))
# Plot
figure_types = ['pdf', 'png']
for figure_type in figure_types:
plot_nhi_image(nhi_image=nhi_image_trim, header=hi_header,
contour_image=av_image, contours=[5,10,15],
boxes=True, cores = cores, limits=[128,37,308,206],
title='Taurus: N(HI) map with core boxed-regions.',
savedir=figure_dir, filename='taurus_nhi_cores_map.%s' % \
figure_type,
show=False)
def main():
''' Executes script.
'''
# import external modules
import pyfits as pf
import numpy as np
from mycoords import make_velocity_axis
import mygeometry as myg
reload(myg)
# define directory locations
output_dir = '/d/bip3/ezbc/california/data/python_output/nhi_av/'
figure_dir = '/d/bip3/ezbc/california/figures/'
av_dir = '/d/bip3/ezbc/california/data/av/'
hi_dir = '/d/bip3/ezbc/california/data/galfa/'
core_dir = output_dir + 'core_arrays/'
region_dir = '/d/bip3/ezbc/california/data/python_output/ds9_regions/'
# Load hi fits file
hi_image, hi_header = pf.getdata(hi_dir + \
'california_galfa_cube_bin_3.7arcmin.fits', header=True)
h = hi_header
# Load av fits file
av_image, av_header = pf.getdata(av_dir + \
'california_planck_av_regrid.fits',
header=True)
# make velocity axis for hi cube
velocity_axis = make_velocity_axis(hi_header)
# create nhi image
nhi_image = calculate_nhi(hi_cube=hi_image,
velocity_axis=velocity_axis, velocity_range=[-100,100])
if False:
# trim hi_image to av_image size
nhi_image_trim = np.ma.array(nhi_image, mask=av_image != av_image)
plot_nhi_image(nhi_image=nhi_image_trim, header=hi_header,
contour_image=av_image, contours=[5,10,15],
savedir=figure_dir, filename='california_nhi_cores_map.png',
show=True)
# define core properties
cores = {'L1482':
{'center_wcs': [(4, 29, 41), (35, 48, 41)],
'map': None,
'threshold': None,
},
'L1483':
{'center_wcs': [(4, 34, 57), (36, 18, 12)],
'map': None,
'threshold': None,
},
'L1478':
{'center_wcs': [(4, 25, 7), (37, 13, 0)],
'map': None,
'threshold': None,
},
'L1434':
{'center_wcs': [(3, 50, 51), (35, 15, 10)],
'map': None,
'threshold': None,
},
'L1503':
{'center_wcs': [(4, 40, 27), (29, 57, 12)],
'map': None,
'threshold': None,
},
'L1507':
{'center_wcs': [(4, 42, 51), (29, 44, 47)],
'map': None,
'threshold': None,
},
}
cores = convert_core_coordinates(cores, h)
if False:
nhi_image = np.zeros(nhi_image.shape)
for core in cores:
core_image = np.load(core_dir + core + '.npy')
core_indices = np.where(core_image == core_image)
nhi_image[core_indices] += core_image[core_indices]
nhi_image_trim =np.ma.array(nhi_image, mask=((av_image != av_image) &\
(nhi_image == 0)))
nhi_image_trim[nhi_image_trim == 0] = np.NaN
read_ds9_region(av_dir + 'california_av_boxes.reg')
plot_nhi_image(nhi_image=nhi_image_trim, header=hi_header,
savedir=figure_dir,
cores=cores,
filename='california_nhi_core_regions_map.png',
show=True)
if True:
cores = load_ds9_region(cores,
filename_base = region_dir + 'california_av_boxes_',
header = h)
# Grab the mask
mask = np.zeros((nhi_image.shape))
for core in cores:
xy = cores[core]['box_center_pix']
box_width = cores[core]['box_width']
box_height = cores[core]['box_height']
box_angle = cores[core]['box_angle']
mask += myg.get_rectangular_mask(nhi_image,
xy[0], xy[1],
width = box_width,
height = box_height,
angle = box_angle)
cores[core]['box_vertices'] = myg.get_rect(
xy[0], xy[1],
width = box_width,
height = box_height,
angle = box_angle,)
#print(cores[core]['box_vertices'])
#print core, xy, box_width, box_height, box_angle
mask[mask > 1] = 1
#nhi_image[mask == 0] = np.nan
# trim hi_image to av_image size
nhi_image_trim = np.ma.array(nhi_image,
mask = (av_image != av_image))
# Plot
plot_nhi_image(nhi_image=nhi_image_trim, header=hi_header,
contour_image=av_image, contours=[3,6,10],
boxes=True, cores = cores, #limits=[128,37,308,206],
title='California: N(HI) map with core boxed-regions.',
savedir=figure_dir, filename='california_nhi_cores_map.pdf',
show=True)
def main():
''' Executes script.
'''
# import external modules
import pyfits as pf
import numpy as np
from mycoords import make_velocity_axis
import mygeometry as myg
reload(myg)
# define directory locations
output_dir = '/d/bip3/ezbc/perseus/data/python_output/nhi_av/'
figure_dir = '/d/bip3/ezbc/perseus/figures/'
av_dir = '/d/bip3/ezbc/perseus/data/av/'
hi_dir = '/d/bip3/ezbc/perseus/data/galfa/'
core_dir = output_dir + 'core_arrays/'
region_dir = '/d/bip3/ezbc/perseus/data/python_output/ds9_regions/'
# Load hi fits file
hi_image, hi_header = pf.getdata(hi_dir + \
'perseus_galfa_cube_bin_3.7arcmin.fits', header=True)
h = hi_header
# Load av fits file
av_image, av_header = \
pf.getdata('/d/bip3/ezbc/perseus/data/2mass/perseus_av_2mass_galfa_regrid.fits',
header=True)
# make velocity axis for hi cube
velocity_axis = make_velocity_axis(hi_header)
# create nhi image
nhi_image = calculate_nhi(hi_cube=hi_image,
velocity_axis=velocity_axis, velocity_range=[-100,100])
if False:
# trim hi_image to av_image size
nhi_image_trim = np.ma.array(nhi_image, mask=av_image != av_image)
plot_nhi_image(nhi_image=nhi_image_trim, header=hi_header,
contour_image=av_image, contours=[5,10,15],
savedir=figure_dir, filename='perseus_nhi_cores_map.png',
show=True)
cores = {'IC348':
{'center_wcs': [(3, 44, 0), (32, 8, 0)],
'map': None,
'threshold': None,
'box_wcs': [(3,46,13), (26,3,24), (3,43,4), (32,25,41)],
},
'NGC1333':
{'center_wcs': [(3, 29, 11), (31, 16, 53)],
'map': None,
'threshold': None,
'box_wcs': None,
},
'B4':
{'center_wcs': [(3, 45, 50), (31, 42, 0)],
'map': None,
'threshold': None,
'box_wcs': None,
},
'B5':
{'center_wcs': [(3, 47, 34), (32, 48, 17)],
'map': None,
'threshold': None,
'box_wcs': None,
},
#'':
# {'center_wcs': [],
# 'map': None,
# 'threshold': None,
# 'box_wcs': None,
# },
}
cores = convert_core_coordinates(cores, h)
if False:
nhi_image = np.zeros(nhi_image.shape)
for core in cores:
core_image = np.load(core_dir + core + '.npy')
core_indices = np.where(core_image == core_image)
nhi_image[core_indices] += core_image[core_indices]
nhi_image_trim =np.ma.array(nhi_image, mask=((av_image != av_image) &\
(nhi_image == 0)))
nhi_image_trim[nhi_image_trim == 0] = np.NaN
read_ds9_region(av_dir + 'perseus_av_boxes.reg')
plot_nhi_image(nhi_image=nhi_image_trim, header=hi_header,
savedir=figure_dir,
cores=cores,
filename='perseus_nhi_core_regions_map.png',
show=True)
if True:
cores = load_ds9_region(cores,
filename_base = region_dir + 'perseus_av_boxes_',
header = h)
# Grab the mask
mask = np.zeros((nhi_image.shape))
for core in cores:
xy = cores[core]['box_center_pix']
box_width = cores[core]['box_width']
box_height = cores[core]['box_height']
box_angle = cores[core]['box_angle']
mask += myg.get_rectangular_mask(nhi_image,
xy[0], xy[1],
width = box_width,
height = box_height,
angle = box_angle)
cores[core]['box_vertices'] = myg.get_rect(
xy[0], xy[1],
width = box_width,
height = box_height,
angle = box_angle,)
#print(cores[core]['box_vertices'])
#print core, xy, box_width, box_height, box_angle
mask[mask > 1] = 1
#nhi_image[mask == 0] = np.nan
# trim hi_image to av_image size
nhi_image_trim = np.ma.array(nhi_image,
mask = (av_image != av_image))
# Plot
figure_types = ['pdf', 'png']
for figure_type in figure_types:
plot_nhi_image(nhi_image=nhi_image_trim,
header=hi_header,
contour_image=av_image,
contours=[2.5,5,8],
boxes=True,
cores = cores,
limits=[47,128,231,222,],
title='Perseus: N(HI) map with core boxed-regions.',
savedir=figure_dir,
filename='perseus_nhi_cores_map.%s' % figure_type,
show=False)
def main():
''' Executes script.
'''
# import external modules
import pyfits as pf
import numpy as np
from mycoords import make_velocity_axis
import mygeometry as myg
reload(myg)
# define directory locations
output_dir = '/d/bip3/ezbc/taurus/data/python_output/nhi_av/'
figure_dir = '/d/bip3/ezbc/taurus/figures/maps/'
av_dir = '/d/bip3/ezbc/taurus/data/av/'
hi_dir = '/d/bip3/ezbc/taurus/data/hi/'
core_dir = output_dir + 'core_arrays/'
region_dir = '/d/bip3/ezbc/taurus/data/python_output/ds9_regions/'
# Load hi fits file
hi_image, hi_header = pf.getdata(hi_dir + \
'taurus_hi_galfa_cube_regrid_planckres.fits', header=True)
h = hi_header
# Load av fits file
av_image, av_header = pf.getdata(av_dir + \
'taurus_av_planck_5arcmin.fits',
header=True)
# make velocity axis for hi cube
velocity_axis = make_velocity_axis(hi_header)
# create nhi image
nhi_image = calculate_nhi(hi_cube=hi_image,
velocity_axis=velocity_axis, velocity_range=[-16.53,28.83])
if False:
# trim hi_image to av_image size
nhi_image_trim = np.ma.array(nhi_image, mask=av_image != av_image)
plot_nhi_image(nhi_image=nhi_image_trim, header=hi_header,
contour_image=av_image, contours=[5,10,15],
savedir=figure_dir, filename='taurus_nhi_cores_map.png',
show=True)
cores = {'L1495':
{'center_wcs': [(4,14,0), (28, 11, 0)],
'map': None,
'threshold': 4.75,
'box_wcs': [(4,16,30), (27,44,30), (4,5,20), (28,28,33)]
},
'L1495A':
{'center_wcs': [(4,18,0), (28,23., 0)],
'map': None,
'threshold': 4.75,
'box_wcs': [(4,28,23),(28,12,50),(4,16,23),(29,46,5)],
},
'B213':
{'center_wcs': [(4, 19, 0), (27, 15,0)],
'map': None,
'threshold': 4.75,
'box_wcs': [(4,22,27), (26,45,47),(4,5,25),(27,18,48)],
},
'B220':
{'center_wcs': [(4, 41, 0.), (26,7,0)],
'map': None,
'threshold': 7,
'box_wcs': [(4,47,49),(25,31,13),(4,40,37),(27,31,17)],
},
'L1527':
{'center_wcs': [(4, 39, 0.), (25,47, 0)],
'map': None,
'threshold': 7,
'box_wcs': [(4,40,13), (24,46,38), (4,34,35), (25,56,7)],
},
'B215':
{'center_wcs': [(4, 23, 0), (25, 3, 0)],
'map': None,
'threshold': 3,
'box_wcs': [(4,24,51), (22,36,7), (4,20,54), (25,26,31)],
},
'L1524':
{'center_wcs': [(4,29,0.), (24,31.,0)],
'map': None,
'threshold': 3,
'box_wcs': [(4,31,0), (22,4,6), (4,25,33), (25,0,55)],
}
}
cores = convert_core_coordinates(cores, h)
if False:
nhi_image = np.zeros(nhi_image.shape)
for core in cores:
core_image = np.load(core_dir + core + '.npy')
core_indices = np.where(core_image == core_image)
nhi_image[core_indices] += core_image[core_indices]
nhi_image_trim =np.ma.array(nhi_image, mask=((av_image != av_image) &\
(nhi_image == 0)))
nhi_image_trim[nhi_image_trim == 0] = np.NaN
read_ds9_region(av_dir + 'taurus_av_boxes.reg')
plot_nhi_image(nhi_image=nhi_image_trim, header=hi_header,
savedir=figure_dir,
cores=cores,
filename='taurus_nhi_core_regions_map.png',
show=True)
if True:
cores = load_ds9_region(cores,
filename_base = region_dir + 'taurus_av_boxes_',
header = h)
# Grab the mask
mask = np.zeros((nhi_image.shape))
for core in cores:
xy = cores[core]['box_center_pix']
box_width = cores[core]['box_width']
box_height = cores[core]['box_height']
box_angle = cores[core]['box_angle']
mask += myg.get_rectangular_mask(nhi_image,
xy[0], xy[1],
width = box_width,
height = box_height,
angle = box_angle)
cores[core]['box_vertices'] = myg.get_rect(
xy[0], xy[1],
width = box_width,
height = box_height,
angle = box_angle,)
mask[mask > 1] = 1
# trim hi_image to av_image size
nhi_image_trim = np.ma.array(nhi_image,
mask = ((av_image != av_image) & \
(av_image > 1.0)))
av_image_trim = np.ma.array(av_image,
mask = ((nhi_image != nhi_image) & \
(av_image > 1.0)))
nhi_image_trim = np.copy(nhi_image)
nhi_image_trim[av_image > 1.] = np.nan
av_image_trim = np.copy(av_image)
av_image_trim[av_image > 1.] = np.nan
# Plot
figure_types = ['png',]
for figure_type in figure_types:
# N(HI) alone
plot_nhi_image(nhi_image=nhi_image_trim, header=hi_header,
contour_image=av_image, contours=[5,10,15],
boxes=True, cores = cores,
limits=[50,37,200,160],
savedir=figure_dir,
filename='taurus_nhi_cores_map.%s' % \
figure_type,
show=0)
# N(HI) + Av
plot_nhi_image(nhi_image=nhi_image_trim, header=hi_header,
av_image=av_image_trim,
#boxes=True, cores = cores,
limits=[50,37,200,160],
savedir=figure_dir,
filename='taurus_nhi_av_map.%s' % \
figure_type,
show=0)
