def save(output_file_path, data_array, header_list, dtype_list):
print(output_file_path)
if output_file_path.lower().endswith('.csv'):
# See http://docs.astropy.org/en/stable/io/fits/usage/table.html
np.savetxt(
output_file_path,
data_array,
fmt=
"%s", # See http://stackoverflow.com/questions/16621351/how-to-use-python-numpy-savetxt-to-write-strings-and-float-number-to-an-ascii-fi
delimiter=",",
header=",".join(header_list),
comments=
"" # String that will be prepended to the ``header`` and ``footer`` strings, to mark them as comments. Default: '# '.
)
elif output_file_path.lower().endswith(('.fits', '.fit')):
table = astropy.table.Table(names=header_list, dtype=dtype_list)
for row in data_array:
table.add_row(row)
#print(table)
table.write(output_file_path, overwrite=True)
else:
raise Exception('Unknown output format.')
def save(output_file_path, data_array, header_list, dtype_list):
print(output_file_path)
if output_file_path.lower().endswith('.csv'):
# See http://docs.astropy.org/en/stable/io/fits/usage/table.html
np.savetxt(output_file_path,
data_array,
fmt="%s", # See http://stackoverflow.com/questions/16621351/how-to-use-python-numpy-savetxt-to-write-strings-and-float-number-to-an-ascii-fi
delimiter=",",
header=",".join(header_list),
comments="" # String that will be prepended to the ``header`` and ``footer`` strings, to mark them as comments. Default: '# '.
)
elif output_file_path.lower().endswith(('.fits', '.fit')):
table = astropy.table.Table(names=header_list, dtype=dtype_list)
for row in data_array:
table.add_row(row)
#print(table)
table.write(output_file_path, overwrite=True)
else:
raise Exception('Unknown output format.')
def add_source(table, time, mag, mag_error, ysovarid, fname, sname = None, ra = None, dec = None, useful = 1, kind = 0, note = ''):
'''Add info about a fake source to the table.
If ra or dec ad None, then look if a source with same ID exisits in the
table already. If so, copy their value, if not, generate randomnly.
'''
if len(time) != len(mag):
raise ValueError('Time and mag need to have same number of elements')
if len(mag) != len(mag_error):
raise ValueError('mag and mag_error need to have some number of elements')
if ra is None:
ind = np.nonzero(tab['ysovarid'] == ysovarid)[0]
if len(ind) > 0:
ra = table['ra'][ind][0]
else:
ra = np.random.random() * 360.
if dec is None:
ind = np.nonzero(tab['ysovarid'] == ysovarid)[0]
if len(ind) > 0:
dec = table['de'][ind][0]
else:
dec = (np.random.random() * 180) - 90.
sname = sname or 'test_'+str(abs(ysovarid))
for i in range(len(time)):
table.add_row({"ysovarid": ysovarid,"sname": sname,"ra":ra,"de":dec,"hmjd":time[i],"fname": fname,"mag1":mag[i],"emag1":mag_error[i],"useful":useful,"kind":kind,"note":note})
def add_parinfo_to_table(table, parinfo, chi2, dof, opt_chi2, opt_red_chi2, ra,
dec, radius, frontback="", frontbackbest="", name=""):
new_row = [name]
new_row.append(0)
new_row += [frontback, frontbackbest, chi2, dof, opt_chi2, opt_red_chi2,
ra, dec, radius]
for pp in parinfo[:8]:
new_row.append(pp.value)
new_row.append(np.nan if pp.fixed else pp.error)
table.add_row(new_row)
if len(parinfo)>8:
new_row = [name]
new_row.append(1)
new_row += [frontback, frontbackbest, chi2, dof, opt_chi2,
opt_red_chi2, ra, dec, radius]
for pp in parinfo[8:]:
new_row.append(pp.value)
new_row.append(np.nan if pp.fixed else pp.error)
table.add_row(new_row)
def build_table_from_output_dict(output_dict):
""" Traverses the output dict to build a plottable table. """
table = astropy.table.Table()
column_names = ("trial_number", "noise_added", "n_clouds", "total_mass",
'inner_larson_A', 'inner_larson_beta', 'outer_larson_A',
'outer_larson_beta', 'inner_M0', 'inner_N0', 'inner_gamma',
'outer_M0', 'outer_N0', 'outer_gamma')
for name in column_names:
col = astropy.table.Column(data=[], name=name)
table.add_column(col)
for key, value in output_dict.items():
# we are gonna try and add things row by row
preamble = value[0:4]
larson, mspec = value[-2:]
larson_names = [
'inner_larson_A', 'inner_larson_beta', 'outer_larson_A',
'outer_larson_beta'
]
larson_tuple = tuple(larson[name] for name in larson_names)
mspec_names = [
'inner_M0', 'inner_N0', 'inner_gamma', 'outer_M0', 'outer_N0',
'outer_gamma'
]
mspec_tuple = tuple(mspec[name] for name in mspec_names)
table.add_row((preamble + larson_tuple + mspec_tuple))
return table
# Documentation:
# - http://docs.astropy.org/en/stable/table/index.html#getting-started
# - http://www.astropy.org/astropy-tutorials/FITS-tables.html
# - http://www.astropy.org/astropy-tutorials/FITS-header.html
import argparse
from astropy.io import fits
import numpy as np
import astropy.table
# PARSE OPTIONS ###############################################################
parser = argparse.ArgumentParser(description="An astropy snippet")
parser.add_argument("filearg", nargs=1, metavar="FILE", help="the output FITS file")
args = parser.parse_args()
file_path = args.filearg[0]
# WRITE DATA ##################################################################
table = astropy.table.Table(names=("column1", "column2", "column3"))
table.add_row([1, 2, 3])
table.add_row([10, 20, 30])
table.add_row([100, 200, 300])
print(table)
table.write(file_path, overwrite=True)
"""Generate an astropy-readable .ecsv files for `butler ingest-files`, to ingest an existing gen2 refcat.
The `refcat_dir` variable needs to be modified for each refcat being converted.
"""
import os
import glob
import astropy.table
refcat_dir = "ref_cats/gaia_dr2_20200414"
out_dir = "."
out_file = f"{out_dir}/{os.path.basename(refcat_dir)}.ecsv"
table = astropy.table.Table(names=("filename", "htm7"), dtype=("str", "int"))
files = glob.glob(f"{refcat_dir}/[0-9]*.fits")
for i, file in enumerate(files):
# running status, overwriting each print statement as it proceeds
print(f"{i}/{len(files)} ({100*i/len(files):0.1f}%)", end="\r")
# extract file index; add row to table
file_index = int(os.path.basename(os.path.splitext(file)[0]))
table.add_row((file, file_index))
table.write(out_file)
print(f"Saving to: {out_file}")
column_names = [
'Inner edge (pc)', 'Outer edge (pc)', 'GMC index', 'R', 'p',
'Truncation mass (M$_\odot$)', 'Largest cloud (M$_\odot$)',
'5th largest cloud (M$_\odot$)'
]
column_types = ['f4', 'f4', 'f4', 'f4', 'f4', 'f4', 'f4', 'f4']
table = Table(names=column_names, dtype=column_types)
for inneredge, outeredge in zip(inneredge, outeredge):
idx = np.where((t['RADIUS_PC'] >= inneredge)
& (t['RADIUS_PC'] < outeredge))
mass = t['MASS_EXTRAP'][idx].data
#don't have to create an index for the xmin mass - defined in the fit_subset
fit = powerlaw.Fit(mass)
fit_subset = powerlaw.Fit(mass, xmin=3e5)
R, p = fit_subset.distribution_compare('power_law', 'truncated_power_law')
table.add_row()
table[-1]['Inner edge (pc)'] = inneredge
table[-1]['Outer edge (pc)'] = outeredge
table[-1]['GMC index'] = -fit_subset.alpha
table[-1]['R'] = R
table[-1]['p'] = p
table[-1][
'Truncation mass (M$_\odot$)'] = 1 / fit_subset.truncated_power_law.parameter2
table[-1]['Largest cloud (M$_\odot$)'] = mass.max()
table[-1]['5th largest cloud (M$_\odot$)'] = np.sort(
t['MASS_EXTRAP'][idx])[-5]
print(table)
#print(-fit.alpha, -fit_subset.alpha, R, p, 1/fit_subset.truncated_power_law.parameter2)
table.write('m83bininfo.fits', overwrite=True)
column_names = ['Inner edge (pc)', 'Outer edge (pc)',
'GMC index', 'R', 'p',
'Truncation mass (M$_\odot$)',
'Largest cloud (M$_\odot$)',
'5th largest cloud (M$_\odot$)']
column_types = ['f4', 'f4', 'f4', 'f4', 'f4', 'f4', 'f4', 'f4']
table = Table(names=column_names, dtype=column_types)
for inneredge, outeredge in zip(inneredge, outeredge):
idx = np.where((t['RADIUS_PC'] >= inneredge) &
(t['RADIUS_PC'] < outeredge))
mass = t['MASS_GCORR'][idx].data
optresult = plf.plfit_adstat(mass / minmass)
pvec = optresult.x
# don't have to create an index for the xmin mass - defined in the fit_subset
table.add_row()
table[-1]['Inner edge (pc)'] = inneredge
table[-1]['Outer edge (pc)'] = outeredge
table[-1]['GMC index'] = pvec[0]
table[-1]['R'] = 0
table[-1]['p'] = 0
table[-1]['Truncation mass (M$_\odot$)'] = pvec[1] * minmass
table[-1]['Largest cloud (M$_\odot$)'] = mass.max()
table[-1]['5th largest cloud (M$_\odot$)'] =\
np.sort(t['MASS_GCORR'][idx])[-5]
print(table)
table.write('m83bininfo_ad.fits', overwrite=True)
def data(galaxyname, data, n_bins=1, r_nuc=0):
# Import the libraries.
from galaxies import Galaxy
from astropy.table import Table
from astropy.table import Column
import astropy
import powerlaw
import numpy as np
import astropy.table
import astropy.units as u
import matplotlib.pyplot as plt
import matplotlib as mpl
# Load its FITS file.
t = Table.read(data)
# Load the information about the galaxy.
gxy = Galaxy(galaxyname)
# Calculate the galaxy's properties.
distance = np.asarray(gxy.distance)
inclination = np.asarray(gxy.inclination)
rgal = gxy.radius(ra=(t['XPOS']), dec=(t['YPOS']))
rgal = rgal.to(u.kpc)
rpgal = np.asarray(rgal)
# Append these to the FITS table.
col_rgal = Column(name='RADIUS_KPC', data=(rgal))
t.add_column(col_rgal)
# Sort the masses according to galactocentric radius.
mass = t['MASS_EXTRAP'].data
i_sorted = np.argsort(rgal)
rgal_sorted = np.asarray(rgal[i_sorted])
mass_sorted = np.asarray(mass[i_sorted])
# Initiate a loop to calculate the bin boundaries and the indeces of these boundaries in the sorted list.
totmass = np.sum(mass) / n_bins
edge_f = 1.1 * np.max(rgal_sorted)
edges = np.zeros(n_bins - 1)
start = 0
mass_equiv = [0] #indeces for the sorted mass bins of equal mass
mass_area = [0] #indeces for the sorted mass bins of equal area
rgal_equiv = [
0, 2, 8**0.5, 12**0.5, 4, 20**0.5, 24**0.5, 28**0.5, 32**0.5, 6
]
r = 1 #equal-area radial index
e = 0 #edge index
f = 0 #loop flag to skip the mass_area loop
c = 0 #loop counter
for i in range(len(mass_sorted)):
#Find the indeces for bins of equal mass (equivalent to totmass)
if np.sum(mass_sorted[start:i]) > totmass:
edges[e] = 0.5 * (rgal_sorted[i] + rgal_sorted[i - 1])
start = i
mass_equiv = np.append(mass_equiv, i)
e = e + 1
#Find the indeces for bins of equal area (4pi kpc^2)
if rgal_sorted[i] > rgal_equiv[r] and not f:
if rgal_sorted[i] < rgal_equiv[3] and not f:
mass_area = np.append(mass_area, i)
r = r + 1
c = 0
if rgal_sorted[i] > rgal_equiv[3]:
f = 1
mass_area = np.append(mass_area, i)
mass_equiv = np.append(mass_equiv, i)
inneredge = np.concatenate(([0.000000], edges))
outeredge = np.concatenate((edges, [edge_f]))
# Create a template for a new table.
column_names = [
'Inner edge (kpc)', 'Outer edge (kpc)', 'GMC index', 'R', 'p',
'Truncation mass ($M_\mathrm{\odot}$)',
'Largest cloud ($M_\mathrm{\odot}$)',
'5th largest cloud ($M_\mathrm{\odot}$)'
]
column_types = ['f4', 'f4', 'f4', 'f4', 'f4', 'f4', 'f4', 'f4']
table = Table(names=column_names, dtype=column_types)
# Fill the table.
for inneredge, outeredge in zip(inneredge, outeredge):
idx = np.where((t['RADIUS_KPC'] >= inneredge)
& (t['RADIUS_KPC'] < outeredge))
mass = t['MASS_EXTRAP'][idx].data
fit = powerlaw.Fit(mass)
fit_subset = powerlaw.Fit(mass, xmin=3e5)
R, p = fit.distribution_compare('power_law', 'truncated_power_law')
table.add_row()
table[-1]['R'] = R
table[-1]['p'] = p
table[-1]['GMC index'] = -fit.alpha
table[-1]['Inner edge (kpc)'] = inneredge
table[-1]['Outer edge (kpc)'] = outeredge
table[-1]['Largest cloud ($M_\mathrm{\odot}$)'] = np.nanmax(mass)
table[-1][
'Truncation mass ($M_\mathrm{\odot}$)'] = 1 / fit.truncated_power_law.parameter2
table[-1]['5th largest cloud ($M_\mathrm{\odot}$)'] = np.sort(
t['MASS_EXTRAP'][idx])[-5]
# Write the data to a FITS file.
table.write('../Data/' + galaxyname + '_data.fits', overwrite=True)
# Plot the mass distribution trends for equal-mass bins.
t = Table.read('../Data/' + galaxyname + '_data.fits')
inneredge = t['Inner edge (kpc)'].data
outeredge = t['Outer edge (kpc)'].data
subplot_label = ('(a)', '(b)', '(c)', '(d)', '(e)', '(f)')
for i in range(len(mass_equiv) - 1):
binmass = mass_sorted[mass_equiv[i]:mass_equiv[i + 1]]
myfit = powerlaw.Fit(binmass)
R, p = myfit.distribution_compare('power_law', 'truncated_power_law')
fig = myfit.truncated_power_law.plot_ccdf(label='Truncated\nPower Law')
myfit.power_law.plot_ccdf(label='Power Law', ax=fig)
myfit.plot_ccdf(drawstyle='steps', label='Data', ax=fig)
# Format the plot.
plt.legend(loc=0)
#plt.title(galaxyname+'Equal-mass Mass Distribution, bin '+repr(i+1))
plt.ylim(ymin=10**-3)
plt.xlabel(r'$M_\mathrm{\odot}$', fontsize=20)
plt.ylabel('CCDF', fontsize=20)
mpl.rc('xtick', labelsize=16)
mpl.rc('ytick', labelsize=16)
plt.text(0.01,
0.5,
subplot_label[i],
ha='left',
va='center',
transform=fig.transAxes,
fontsize=16)
plt.text(
0.35,
0.01,
r'$M_{bin}\ =\ %e M_\mathrm{\odot}$' % (totmass) + '\n' +
r'$R_{gal}\ =\ %5.4f\ \mathrm{kpc}\ \mathrm{to}\ %5.4f\ \mathrm{kpc}$'
% (inneredge[i], outeredge[i]) + '\n' +
r'$\mathrm{R}\ =\ %5.4f,\ \mathrm{p}\ =\ %5.4f$' % (R, p) + '\n' +
r'$\alpha\ =\ %5.4f,\ M_\mathrm{0}\ =\ %5.4eM_\mathrm{\odot}$' %
(-myfit.alpha, 1 / myfit.truncated_power_law.parameter2),
ha='left',
va='bottom',
transform=fig.transAxes,
fontsize=16)
plt.savefig('../Data/' + galaxyname + '_power_law_equal_mass_' +
repr(i + 1) + '.png')
plt.close()
# Plot the mass distribution trend for equal-area bins.
for i in range(len(mass_area) - 1):
f = 0 #loop flag
if mass_area[i + 1] - mass_area[i] < 3:
f = 1
if not f:
binmass = mass_sorted[mass_area[i]:mass_area[i + 1]]
myfit = powerlaw.Fit(binmass)
R, p = myfit.distribution_compare('power_law',
'truncated_power_law')
fig = myfit.truncated_power_law.plot_ccdf(
label='Truncated\nPower Law')
myfit.power_law.plot_ccdf(label='Power Law', ax=fig)
myfit.plot_ccdf(drawstyle='steps', label='Data', ax=fig)
# Format the plot.
plt.legend(loc=0)
#plt.title(galaxyname+'Equal-area Mass Distribution, bin '+repr(i+1))
plt.ylim(ymin=10**-3)
plt.xlabel(r'$M_\mathrm{\odot}$', fontsize=20)
plt.ylabel('CCDF', fontsize=20)
mpl.rc('xtick', labelsize=16)
mpl.rc('ytick', labelsize=16)
plt.text(0.01,
0.5,
subplot_label[i],
ha='left',
va='center',
transform=fig.transAxes,
fontsize=16)
plt.text(
0.35,
0.01,
r'$R_{gal}\ =\ %5.4f\ \mathrm{kpc}\ \mathrm{to}\ %5.4f\ \mathrm{kpc}$'
% (rgal_equiv[i], rgal_equiv[i + 1]) + '\n' +
r'$\mathrm{R}\ =\ %5.4f,\ \mathrm{p}\ =\ %5.4f$' % (R, p) +
'\n' +
r'$\alpha\ =\ %5.4f,\ M_\mathrm{0}\ =\ %5.4eM_\mathrm{\odot}$'
% (-myfit.alpha, 1 / myfit.truncated_power_law.parameter2),
ha='left',
va='bottom',
transform=fig.transAxes,
fontsize=16)
plt.savefig('../Data/' + galaxyname + '_power_law_equal_area_' +
repr(i + 1) + '.png')
plt.close()
return [distance, inclination]
import argparse
from astropy.io import fits
import numpy as np
import astropy.table
# PARSE OPTIONS ###############################################################
parser = argparse.ArgumentParser(description="An astropy snippet")
parser.add_argument("filearg", nargs=1, metavar="FILE", help="the output FITS file")
args = parser.parse_args()
file_path = args.filearg[0]
# WRITE DATA ##################################################################
name_list = ("column1", "column2", "column3")
dtype_list = ("S128", # column1 -> 128 bytes string
"i4", # column2 -> 4 bytes integer
"f8") # column3 -> 8 bytes float
table = astropy.table.Table(names=name_list,
dtype=dtype_list)
table.add_row(["A", 1, 1.1])
table.add_row(["B", 2, 2.2])
table.add_row(["C", 3, 3.3])
print(table)
table.write(file_path, overwrite=True)
import numpy as np
import astropy.table
# PARSE OPTIONS ###############################################################
parser = argparse.ArgumentParser(description="An astropy snippet")
parser.add_argument("filearg",
nargs=1,
metavar="FILE",
help="the output FITS file")
args = parser.parse_args()
file_path = args.filearg[0]
# WRITE DATA ##################################################################
name_list = ("column1", "column2", "column3")
dtype_list = (
"S128", # column1 -> 128 bytes string
"i4", # column2 -> 4 bytes integer
"f8") # column3 -> 8 bytes float
table = astropy.table.Table(names=name_list, dtype=dtype_list)
table.add_row(["A", 1, 1.1])
table.add_row(["B", 2, 2.2])
table.add_row(["C", 3, 3.3])
print(table)
table.write(file_path, overwrite=True)
