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quadrant_analysis.py
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"""
Parallel to near_galaxy_distance_experiment
Uses kdist
"""
from __future__ import division
import numpy as np
import astropy
from astropy import units as u
from astropy import constants as c
import demo
from reid_distance_assigner import make_reid_distance_column
from kinematic_distance import make_blitz_distance_column
from assign_physical_values import assign_galactocentric_coordinates, assign_size_mass_alpha_pressure
from dendrogal.integrated_viewer import IntegratedViewer
from astropy.wcs import wcs
from astrodendro.scatter import Scatter
def secondquad_distance_demo(downsample=1, distance='reid', nearfar='near', min_npix=20, min_value=0.05, min_delta=0.3, **kwargs):
d, catalog, header, metadata = demo.secondquad_demo_mominterp(downsample_factor=downsample,
min_npix=min_npix, min_value=min_value, min_delta=min_delta, recenter=False, **kwargs)
if distance != 'reid':
blitz = make_blitz_distance_column(catalog)
catalog['Distance'] = blitz
else:
reid_distance = make_reid_distance_column(catalog, nearfar=nearfar)
catalog['Distance'] = reid_distance['D_k']
x, y, z = assign_galactocentric_coordinates(catalog, galactic_center_distance=0)
catalog['x_galactocentric'] = x
catalog['y_galactocentric'] = y
catalog['z_galactocentric'] = z
s, m, v, p = assign_size_mass_alpha_pressure(catalog)
catalog['size'] = s
catalog['mass'] = m
catalog['virial'] = v
catalog['pressure'] = p
# disqualify!
disqualified = ((catalog['mass'] > 1e8) |
(catalog['mass'] < 5e3) |
(catalog['major_sigma'] > 10) |
(catalog['v_rms'] > 30) |
(catalog['size'] > 1000) |
(np.abs(catalog['v_cen']) < 13) |
(np.abs(catalog['x_cen'] - 180) < 10) |
(catalog['area_exact'] > 50) )
catalog['Distance'][disqualified] = np.nan
catalog['v_rms'][disqualified] = np.nan
catalog['size'][disqualified] = np.nan
catalog['virial'][disqualified] = np.nan
catalog['mass'][disqualified] = np.nan
print("\n# Run these commands:\n"
"# (these assume you have called this function like following: )\n"
"# d, catalog, x, y = near_galaxy_distance_demo(resample=2) \n"
"dv = d.viewer()\n"
"iv = IntegratedViewer(d, dv.hub, wcs=y['wcs'].sub([wcs.WCSSUB_CELESTIAL]), cmap='gray_r')\n"
"dsd = Scatter(d, dv.hub, catalog, 'y_galactocentric', 'x_galactocentric')")
return d, catalog, header, metadata
def thirdquad_distance_demo(downsample=1, distance='reid', nearfar='near', min_npix=20, min_value=0.05, min_delta=0.3, **kwargs):
d, catalog, header, metadata = demo.thirdquad_demo_mominterp(downsample_factor=downsample,
min_npix=min_npix, min_value=min_value, min_delta=min_delta, recenter=False, **kwargs)
if distance != 'reid':
blitz = make_blitz_distance_column(catalog)
catalog['Distance'] = blitz
else:
reid_distance = make_reid_distance_column(catalog, nearfar=nearfar)
catalog['Distance'] = reid_distance['D_k']
x, y, z = assign_galactocentric_coordinates(catalog, galactic_center_distance=0)
catalog['x_galactocentric'] = x
catalog['y_galactocentric'] = y
catalog['z_galactocentric'] = z
s, m, v, p = assign_size_mass_alpha_pressure(catalog)
catalog['size'] = s
catalog['mass'] = m
catalog['virial'] = v
catalog['pressure'] = p
# disqualify!
disqualified = ((catalog['mass'] > 1e8) |
(catalog['mass'] < 5e3) |
# (catalog['major_sigma'] > 10) |
# (catalog['v_rms'] > 30) |
# (catalog['size'] > 1000) |
# (np.abs(catalog['v_cen']) < 13) |
# (np.abs(catalog['x_cen'] - 180) < 10) |
(catalog['area_exact'] > 50) )
catalog['Distance'][disqualified] = np.nan
catalog['v_rms'][disqualified] = np.nan
catalog['size'][disqualified] = np.nan
catalog['virial'][disqualified] = np.nan
catalog['mass'][disqualified] = np.nan
print("\n# Run these commands:\n"
"# (these assume you have called this function like following: )\n"
"# d, catalog, x, y = near_galaxy_distance_demo(resample=2) \n"
"dv = d.viewer()\n"
"iv = IntegratedViewer(d, dv.hub, wcs=y['wcs'].sub([wcs.WCSSUB_CELESTIAL]), cmap='gray_r')\n"
"dsd = Scatter(d, dv.hub, catalog, 'y_galactocentric', 'x_galactocentric')")
return d, catalog, header, metadata
def firstquad_distance_demo(downsample=1, distance='reid', min_npix=20, min_value=0.05, min_delta=0.3, **kwargs):
d, catalog, header, metadata = demo.firstquad_demo_mominterp(downsample_factor=downsample,
min_npix=min_npix, min_value=min_value, min_delta=min_delta, recenter=False, **kwargs)
near_distance_table = make_reid_distance_column(catalog, nearfar='near')
far_distance_table = make_reid_distance_column(catalog, nearfar='far')
near_distance_column = near_distance_table['D_k']
far_distance_column = far_distance_table['D_k']
best_distance = np.zeros_like(near_distance_table['D_k'])
sky_radius = u.Quantity(catalog['radius'].data * catalog['radius'].unit)
near_distance = u.Quantity(near_distance_column)
near_size = sky_radius.to(u.rad).value * near_distance
far_distance = u.Quantity(far_distance_column)
far_size = sky_radius.to(u.rad).value * far_distance
quad2_fit_constant = 0.48293812090592952
quad2_fit_power = 0.56796770148326814
expected_size = (1/quad2_fit_constant * catalog['v_rms'].data)**(1/quad2_fit_power) * u.pc
use_near_distance = (np.abs(near_size - expected_size) <= np.abs(far_size - expected_size))
use_far_distance = (np.abs(near_size - expected_size) > np.abs(far_size - expected_size))
best_distance[use_near_distance] = near_distance[use_near_distance]
best_distance[use_far_distance] = far_distance[use_far_distance]
catalog['Distance'] = best_distance
x, y, z = assign_galactocentric_coordinates(catalog, galactic_center_distance=0)
catalog['x_galactocentric'] = x
catalog['y_galactocentric'] = y
catalog['z_galactocentric'] = z
s, m, v, p = assign_size_mass_alpha_pressure(catalog)
catalog['size'] = s
catalog['mass'] = m
catalog['virial'] = v
catalog['pressure'] = p
print("\n# Run these commands:\n"
"# (these assume you have called this function like following: )\n"
"# d, catalog, x, y = near_galaxy_distance_demo(resample=2) \n"
"dv = d.viewer()\n"
"iv = IntegratedViewer(d, dv.hub, wcs=y['wcs'].sub([wcs.WCSSUB_CELESTIAL]), cmap='gray_r')\n"
"dsd = Scatter(d, dv.hub, catalog, 'y_galactocentric', 'x_galactocentric')")
return d, catalog, header, metadata, near_distance_table, far_distance_table