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ad_phat_tools.py
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ad_phat_tools.py
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'''
ad_phat_tools.py:
alex deich PHAT analysis tools.
Author: Alex Deich
Date: June 12 2015
'''
from astropy.io import fits
import errorcalcs3filt as get_error
import numpy as np
import scipy.interpolate as interpolate
from scipy.optimize import fsolve
import os.path
import os
import time
import matplotlib.pyplot as plt
import numpy.lib.recfunctions as rfn
import pickle
import sys
class armpit(object):
def __init__(self,data_path,iso_age,extinct_col = None,
usefile = None,rv = 3.1,filenum = 0,comments = ''):
# get isochrone and chop out the part we're interested in (the nondegenerate part)
self.iso_age = iso_age
self.iso_path = 'isochrones/{}Myr_finez.fits'.format(self.iso_age)
self.isochrones = fits.open(self.iso_path)
self.isochrones = self.isochrones[1].data
self.isochrones = [self.isochrones[np.where(np.logical_and(self.isochrones["M_ini"]<10,
self.isochrones["M_ini"]>2))]][0]
self.isochrones = [self.isochrones[np.where((self.isochrones['W336MAG']-self.isochrones['F475MAG'])<0)]][0]
self.iso_colors = np.rec.array([(self.isochrones["W336MAG"]-self.isochrones["F475MAG"]),
(self.isochrones["F475MAG"]-self.isochrones["F814MAG"])],
names=("Iso(336-475)","Iso(475-814)"))
self.iso_color_x = sorted(self.iso_colors["Iso(336-475)"])
self.iso_color_y = sorted(self.iso_colors["Iso(475-814)"])
self.iso_mag = self.isochrones['F475MAG']
self.zvalues = self.isochrones['Z']
#make sure you *don't* use the sorted color for the metallicity interpolation
self.interp_points = zip(self.iso_colors['Iso(336-475)'],self.iso_mag)
self.metal_interp_function = interpolate.LinearNDInterpolator(self.interp_points,
self.zvalues)
self.data_path = data_path
self.filenum = filenum
self.extinct_col = extinct_col
# get the path of the fits file
### TO DO: make it accept csv files
if type(self.data_path) is StellarPopulationSimulator:
self.raw_file = self.data_path.output_file
elif type(self.data_path) is str:
if self.data_path.endswith('.fits'):
self.raw_file = self.data_path
else:
raise IOError('Data input must either be a fits file containing the relevant columns or a simulation object.')
self.raw_fits = fits.open(self.raw_file)
self.data_header = self.raw_fits[0].header
# check if it's a simulation output-- I *could* just do this when I do
# if type(...) above, but I can conceive of having simulation output
# that's not just an object.
if 'IS_SIM' in self.data_header:
self.is_sim = self.data_header['IS_SIM']
self.sim_params = self.data_header['FREEPRMS']
else:
self.is_sim = False
self.sim_params = np.nan
# trim the data
self.raw_data = self.raw_fits[1].data
self.raw_data = self.raw_data[np.where(self.raw_data['F814W_ERR']<0.25)]
self.raw_data = self.raw_data[np.where(self.raw_data['F475W_ERR']<0.25)]
# calculate the polynomial fit of the isochrone
self.fit_coeffs = np.polyfit(self.iso_color_x,self.iso_color_y,3)
self.month = time.strftime('%m')
self.day = time.strftime('%d')
self.usefile = usefile
self.comments = comments
self.Rv = rv
self.E336m475,self.E475m814 = self.redvec(self.Rv)
if self.usefile == None:
self.filename = 'multi_age_analysis/armpit_out_{}_{}_{}_{}.csv'.format(self.iso_age,
self.month,
self.day,
self.filenum)
else:
self.filename = self.usefile
'''
Arguments: which columns you want, list
Returns: data from inputted columns
If argument left blank, loads whole data file
'''
def load_data(self,*cols):
filename = self.data()
data_cols = np.genfromtxt(filename,names=True,delimiter=',',skip_header = 4).dtype.names
if cols == ():
req_cols = data_cols
else:
req_cols = cols
data_file_len = len(np.genfromtxt(filename,names=True,delimiter=',',skip_header = 4))
bad_cols = []
for col in cols:
if col not in data_cols:
bad_cols.append(col)
if bad_cols != []:
raise ValueError('{} not found for data column names.\n\nAvailable columns:\n{}'.format(bad_cols, data_cols))
return_data = np.nan
else:
return_data = np.genfromtxt(filename,
names=True,
delimiter=',',
skip_header = 4,
usecols=req_cols)
return return_data
'''
returns path of any data that might have been written that day
'''
def data(self):
if os.path.isfile(self.filename):
return self.filename
else:
raise IOError('There is no output data for {} yet. Do write_data() first.'.format(self.data_path))
'''
playing around with different values of Rv
'''
def redvec(self,Rv_value):
if Rv_value == 3.1:
return (0.446,0.610)
elif Rv_value == 5:
return (0.208,0.467)
else:
return ValueError("{} is not a valid Rv value".format(Rv_value))
def IsochroneFit(self,x):
return (self.fit_coeffs[0]*x**3 + self.fit_coeffs[1]*x**2 + self.fit_coeffs[2]*x + self.fit_coeffs[3])
def extinction_func(self,x,x1,x2):
return ((self.E475m814/self.E336m475)*(x-x1)+x2)
def findIntersection(self,isochrone_func,extinction_func,x1,x2):
return (lambda x: isochrone_func(x)-extinction_func(x,x1,x2))
#implementing the Deich Optical Photometry Extinction (DOPE) method
#r1, r2 are the two colors F336W-F475W and F475W-F814W respectively
def dope(self,r1,r2):
intersect_function = self.findIntersection(self.IsochroneFit,
self.extinction_func,
r1,r2)
intersectpoint = fsolve(intersect_function,-1.5)
newx = intersectpoint
newy = self.IsochroneFit(intersectpoint)
dx = r1 - newx
dy = r2 - newy
a475 = dx/self.E336m475
return (newx,newy,a475)
#Creating the Panchromatic Hubble Andromeda Reddening Treasury (PHART)
def phart(self,star):
np.seterr(all='ignore')
mag_dict = {'F336W_VEGA','F475W_VEGA','F814W_VEGA'}
err336,err475,err814 = get_error.get_in_out(star)
mag_dist_336 = np.random.normal(star['F336W_VEGA'],err336,100)
mag_dist_475 = np.random.normal(star['F475W_VEGA'],err475,100)
mag_dist_814 = np.random.normal(star['F814W_VEGA'],err814,100)
dist336475arr = np.zeros_like(mag_dist_336)
dist475814arr = np.zeros_like(mag_dist_336)
distavarr = np.zeros_like(mag_dist_336)
for i in xrange(len(mag_dist_336)):
new336475,new475814,av = self.dope(mag_dist_336[i]-mag_dist_475[i],
mag_dist_475[i]-mag_dist_814[i])
dist336475arr[i] = new336475
dist475814arr[i] = new475814
distavarr[i] = av
new336475 = np.nanmean(dist336475arr)
err336475 = np.nanstd(dist336475arr)
new475814 = np.nanmean(dist475814arr)
err475814 = np.nanstd(dist475814arr)
av = np.nanmean(distavarr)
av_err = np.nanstd(distavarr)
intrinsic_475 = np.nanmean(mag_dist_475)-24.4-av*1.219
intrinsic_475_err = np.std(mag_dist_475-24.4-distavarr*1.219)
return(new336475,
err336475,
new475814,
err475814,
intrinsic_475,
intrinsic_475_err,
av,
av_err)
#If you want to de-extinct the stars with some other av included in the input file
# doesn't do error
def subtract_av(self,star,avcol):
av = star[avcol]
intrinsic_475 = star['F475W_VEGA'] - 24.4 - av*1.219
dx = av*self.E336m475
dy = av*self.E475m814
new336475 = (star['F336W_VEGA']-star['F475W_VEGA'])-dx
new475814 = (star['F475W_VEGA']-star['F814W_VEGA'])-dy
return(new336475,0,new475814,0,intrinsic_475,0,av,0)
def metal_fit(self,data_color,color_err,data_mag,mag_err):
color_dist_arr = np.random.normal(data_color,color_err,100)
mag_dist_arr = np.random.normal(data_mag,mag_err,100)
zdist_arr = np.zeros_like(mag_dist_arr)
for i in xrange(len(mag_dist_arr)):
z = self.metal_interp_function(color_dist_arr[i],mag_dist_arr[i])
zdist_arr[i] = z
z = np.nanmean(zdist_arr)
z_err = np.nanstd(zdist_arr)
return (z,z_err)
def csv_header(self):
csvcomments = '# Simulation: {}\n'.format(self.is_sim)+'# Free parameters in simulation: {}\n'.format(self.sim_params)+'# Fitted Age: {}\n'.format(self.iso_age)+'# Comments: {}\n'.format(self.comments)
if self.is_sim == True:
csvhdr = csvcomments+'RA,DEC,MASS,F336W_NAKED-F475W_NAKED,F475W_NAKED-F814W_NAKED,F475W_NAKED,F336W-F475W_VEGA,F475W-F814W_VEGA,F475W_VEGA,F336W-F475W,F336W-F475W_ERR,F475W-F814W,F475W-F814W_ERR,F475W,F475W_ERR,AV_IN,AV,AV_ERR,AV_DIFF,Z,Z_ERR\n'
elif self.is_sim == False:
csvhdr = csvcomments+'RA,DEC,F336W-F475W_VEGA,F475W-F814W_VEGA,F475W_VEGA,F336W-F475W,F336W-F475W_ERR,F475W-F814W,F475W-F814W_ERR,F475W,F475W_ERR,AV,AV_ERR,Z,Z_ERR\n'
return csvhdr
def write_data(self,lim=None,draw_plots=False):
if lim is None:
limit = len(self.raw_data)
else:
limit = lim
n = 0
data_length = limit+0.0
with open(self.filename, 'wb') as outfile:
outfile.write(self.csv_header())
with open(self.filename, 'ab') as outfile:
for star in self.raw_data[:limit]:
if self.extinct_col == None:
new336475, err336475, new475814, err475814, new475, err475, av, av_err = self.phart(star)
else:
new336475, err336475, new475814, err475814, new475, err475, av, av_err = self.subtract_av(star,self.extinct_col)
new336 = new336475 + new475
new814 = (-1*new475814) - new475
z,z_err = self.metal_fit(new336475,err336475,new475,err475)
if self.is_sim == True:
data = (star['RA'],
star['DEC'],
star['MASS'],
star['F336W_NAKED']-star['F475W_NAKED'],
star['F475W_NAKED']-star['F814W_NAKED'],
star['F475W_NAKED'],
star['F336W_VEGA']-star['F475W_VEGA'],
star['F475W_VEGA']-star['F814W_VEGA'],
star['F475W_VEGA'],
new336475,
err336475,
new475814,
err475814,
new475,
err475,
star['AV_IN'],
av,
av_err,
star['AV_IN']-av,
z,
z_err)
elif self.is_sim == False:
data = (star['RA'],
star['DEC'],
star['F336W_VEGA']-star['F475W_VEGA'],
star['F475W_VEGA']-star['F814W_VEGA'],
star['F475W_VEGA'],
new336475,
err336475,
new475814,
err475814,
new475,
err475,
av,
av_err,
z,
z_err)
outfile.write('{}\n'.format(','.join(map(str,(data)))))
n += 1
sys.stdout.write('\rDoing line {}, {}% done '.format(n,(n/data_length)*100))
sys.stdout.flush()
class region_draw(object):
### To do: be able to write out and save a dictionary of regions
### and then be able to pass it this dictionary again.
### and figure out why the cmd is wonky
### and make all plots look final
def __init__(self,
ax,
map_fig,
zplot_fig,
cmdplot_fig,
ccdplot_fig,
avplot_fig,
cmd_ax,
ccd_ax,
avhist_ax,
zhist_ax,
data,
point_list = {}):
self.previous_point = []
self.start_point = []
self.end_point = []
self.line = None
if point_list == {}:
self.point_list = point_list
else:
self.point_list = pickle.load(open(point_list,'rb'))
self.map_fig = map_fig
self.map_fig.canvas.draw()
self.map_fig.canvas.set_window_title('Spatial Map')
self.data = data
self.zplot_fig = zplot_fig
self.zplot_fig.canvas.set_window_title('Metallicity Histogram')
self.avplot_fig = avplot_fig
self.avplot_fig.canvas.set_window_title('Av Histogram')
self.cmdplot_fig = cmdplot_fig
self.cmdplot_fig.canvas.set_window_title('Color-Magnitude Diagram')
self.ccdplot_fig = ccdplot_fig
self.ccdplot_fig.canvas.set_window_title('Color-Color Diagram')
self.cmd_ax = cmd_ax
self.ccd_ax = ccd_ax
self.avhist_ax = avhist_ax
self.zhist_ax = zhist_ax
self.ax = ax
self.star_arr = np.zeros((len(self.data),1),dtype=[('REGION_NUM',int)])
self.star_arr['REGION_NUM'] += 999
self.region_arr = rfn.merge_arrays([self.data,self.star_arr],flatten = True)
self.region_arr = np.ma.masked_array(self.region_arr,np.isnan(self.region_arr['Z']))
self.region_counter = 0
self.draw_spatial()
self.color_list = ['red','blue','green','purple','black']
self.color_num = 0
self.color_mod = 0
self.region_data = 0
self.region_check = False
def button_press_callback(self, event):
if event.inaxes:
#if there is no previous_point, create a new key in the point_list dictionary.
#in either case, put x,y in the most recent key.
if self.previous_point == []:
self.region_counter += 1
self.point_list[self.region_counter] = []
x, y = event.xdata, event.ydata
if event.button == 1: # If you press the right button
if self.line == None: # if there is no line, create a line
self.line = plt.Line2D([x, x],
[y, y],
marker = 'o',
color = self.color_list[self.color_num],
linewidth = 2)
self.start_point = [x,y]
self.previous_point = self.start_point
self.ax.add_line(self.line)
self.map_fig.canvas.draw()
# add a segment
else: # if there is a line, create a segment
self.line = plt.Line2D([self.previous_point[0], x],
[self.previous_point[1], y],
marker = 'o',
color = self.color_list[self.color_num],
linewidth = 2)
self.previous_point = [x,y]
event.inaxes.add_line(self.line)
self.map_fig.canvas.draw()
self.point_list[self.region_counter].append((x,y))
elif event.button == 3 and self.line != None: # close the loop
print self.point_list.keys()
self.line = plt.Line2D([self.previous_point[0],
self.start_point[0]],
[self.previous_point[1],
self.start_point[1]],
marker = 'o',
color = self.color_list[self.color_num],
linewidth = 2)
self.color_mod += 1
self.color_num = self.color_mod%len(self.color_list)
self.previous_point = []
self.ax.add_line(self.line)
self.map_fig.canvas.draw()
self.line = None
self.region_check = False
def key_press_callback(self,event):
# the keys I want to define are: clear point_list and plots: (c)
# make cmd,ccd,zhist,avhist: ('1','2','3','4')
# save cmd,ccd,zhist,zvhist: (ctrl + 1,2,3 or 4)
# do/save all: (a)/(ctrl+a)
if event.key == '1':
self.cmdplot_fig.canvas.draw()
if self.point_list == {}:
color_data = self.data['F336WF475W_VEGA']
mag_data = self.data['F475W_VEGA']-24.4
z_data = np.log10(self.data['Z']/0.019)
else:
self.set_data()
color_data = self.region_data['F336WF475W_VEGA']
mag_data = self.region_data['F475W_VEGA']-24.4
z_data = np.log10(self.region_data['Z']/0.019)
self.make_cmd(self.cmd_ax,
self.cmdplot_fig,
color_data,
mag_data,
z_data)
elif event.key == '2':
self.ccdplot_fig.canvas.draw()
if self.point_list == {}:
color1_data = self.data['F336WF475W_VEGA']
color2_data = self.data['F475WF814W_VEGA']
av_data = self.data['AF475W']
else:
self.set_data()
color1_data = self.region_data['F336WF475W_VEGA']
color2_data = self.region_data['F475WF814W_VEGA']
av_data = self.region_data['AF475W']
self.make_ccd(self.ccd_ax,
self.ccdplot_fig,
color1_data,
color2_data,
av_data)
elif event.key == '3':
self.zplot_fig.canvas.draw()
if self.point_list == {}:
histdata = self.data['Z']
self.make_hist(self.zhist_ax,self.zplot_fig,histdata,1,-1,.25)
else:
self.set_data()
for region in self.point_list:
histdata = self.region_data[self.region_data['REGION_NUM']==region]
histdata = np.log10(histdata['Z']/0.019)
self.make_hist(self.zhist_ax,self.zplot_fig,histdata,region,-1,.25)
self.zhist_ax.set_title('Histogram of log(Z/0.019) for selected regions')
elif event.key == '4':
self.avplot_fig.canvas.draw()
if self.point_list == {}:
histdata = self.data['AF475W']
self.make_hist(self.avhist_ax,self.avplot_fig,histdata,1,0,max(self.data['AF475W']))
else:
self.set_data()
for region in self.point_list:
histdata = self.region_data[self.region_data['REGION_NUM']==region]
histdata = histdata['AF475W']
self.make_hist(self.avhist_ax,self.avplot_fig,histdata,region,0,max(self.data['AF475W']))
self.avhist_ax.set_title('Histogram of A(F475W) for selected regions')
elif event.key == 'ctrl+1':
extent = self.cmd_ax.get_window_extent().transformed(self.cmdplot_fig.dpi_scale_trans.inverted())
self.plot_fig.savefig('awesomecmd.png', bbox_inches = extent)
print 'CMD saved '
elif event.key == 'ctrl+2':
extent = self.ccd_ax.get_window_extent().transformed(self.ccdplot_fig.dpi_scale_trans.inverted())
self.plot_fig.savefig('awesomeccd.png', bbox_inches = extent)
print 'CCD saved '
elif event.key == 'ctrl+3':
extent = self.zhist_ax.get_window_extent().transformed(self.zplot_fig.dpi_scale_trans.inverted())
self.plot_fig.savefig('awesomezhist.png', bbox_inches = extent)
print 'Metallicity histogram saved '
elif event.key == 'ctrl+4':
extent = self.avhist_ax.get_window_extent().transformed(self.avplot_fig.dpi_scale_trans.inverted())
self.plot_fig.savefig('awesomeavhist.png', bbox_inches = extent)
print 'Av Histogram saved '
elif event.key == 'w':
##write out point list
month = time.strftime('%m')
day = time.strftime('%d')
pickle.dump(self.point_list,open('skyselect_regions_{}_{}.p'.format(month,day),'wb'))
elif event.key == 'c':
self.region_counter = 0
self.point_list = {}
self.cmd_ax.clear()
self.ccd_ax.clear()
self.zhist_ax.clear()
self.avhist_ax.clear()
self.ax.clear()
self.draw_spatial()
self.map_fig.canvas.draw()
self.zplot_fig.canvas.draw()
self.avplot_fig.canvas.draw()
self.ccdplot_fig.canvas.draw()
self.cmdplot_fig.canvas.draw()
self.color_num = 0
self.color_mod = 0
self.line = None
self.region_check = False
self.region_arr['REGION_NUM'] = 999
def set_data(self):
if self.region_check == False:
print 'Getting stars in region(s)\n'
self.region_data = self.get_star_in_region()
self.region_check = True
for region in self.point_list:
print 'found {} stars in region {}'.format(len(self.region_data[self.region_data['REGION_NUM'] == region]),region)
else:
pass
def get_star_in_region(self):
for star in self.region_arr:
if star['REGION_NUM'] == 999:
for region in self.point_list:
if self.point_in_poly(star['RA'],star['DEC'],self.point_list[region]):
star['REGION_NUM'] = region
else:
pass
return self.region_arr[self.region_arr['REGION_NUM']!=999]
def draw_spatial(self):
self.ax.set_title('Spatial Map')
self.ax.scatter(self.data['RA'],
self.data['DEC'],
s = 2,
edgecolors = 'none',
c=self.data['Z'])
def make_cmd(self,ax,fig,magdata,colordata,z_data):
ax.scatter(self.data['F336WF475W_VEGA'],self.data['F475W_VEGA']-24.4,edgecolors='none',c='gray')
ax = ax.scatter(magdata,colordata,edgecolors='none',c=z_data)
ax.set_title('CMD of selected region(s)')
ax.set_xlim(-2,0)
ax.set_ylim(-6,0)
ax.invert_yaxis()
fig.canvas.draw()
def make_ccd(self,ax,fig,color1,color2,av_data):
ax.scatter(self.data['F336WF475W_VEGA'],self.data['F475WF814W_VEGA'],edgecolors='none',c='gray')
ax.scatter(color1,color2,edgecolors='none',c=av_data)
ax.set_title('CCD of selected region(s)')
ax.set_xlim(-2,0)
ax.set_ylim(-1,2)
cb = fig.colorbar()
cb.set_clim(0,6)
fig.canvas.draw()
def make_hist(self,ax,fig,value,region_color,xmin,xmax):
plot_data = value[~np.isnan(value)]
histcolor = self.color_list[region_color-1]
n, bins, patches = ax.hist(plot_data,
50,
normed=1,
cumulative = True,
histtype='step',
color = histcolor)
ax.set_xlim(xmin,xmax)
fig.canvas.draw()
def point_in_poly(self,x,y,poly):
n = len(poly)
inside = False
p1x,p1y = poly[0]
for i in xrange(n+1):
p2x,p2y = poly[i % n]
if y > min(p1y,p2y):
if y <= max(p1y,p2y):
if x <= max(p1x,p2x):
if p1y != p2y:
xints = (y-p1y)*(p2x-p1x)/(p2y-p1y)+p1x
if p1x == p2x or x <= xints:
inside = not inside
p1x,p1y = p2x,p2y
return inside
class armplot(object):
def __init__(self,data_object):
print '\n loading {}... \n'.format(data_object.filename)
self.data = data_object.load_data()
print '\n{} with {} data points loaded.'.format(data_object.filename,
len(self.data))
def skyselect(self):
map_fig = plt.figure()
ax = map_fig.add_subplot(111)
zplot_fig = plt.figure()
cmdplot_fig = plt.figure()
ccdplot_fig = plt.figure()
avplot_fig = plt.figure()
cmd_ax = cmdplot_fig.add_subplot(111)
ccd_ax = ccdplot_fig.add_subplot(111)
avhist_ax = avplot_fig.add_subplot(111)
zhist_ax = zplot_fig.add_subplot(111)
ax.set_title('')
cursor = region_draw(ax,map_fig,zplot_fig,cmdplot_fig,ccdplot_fig,avplot_fig,cmd_ax,
ccd_ax,
avhist_ax,
zhist_ax,self.data)
map_fig.canvas.mpl_connect('button_press_event', cursor.button_press_callback)
map_fig.canvas.mpl_connect('key_release_event',cursor.key_press_callback)
zplot_fig.canvas.mpl_connect('key_release_event',cursor.key_press_callback)
avplot_fig.canvas.mpl_connect('key_release_event',cursor.key_press_callback)
ccdplot_fig.canvas.mpl_connect('key_release_event',cursor.key_press_callback)
cmdplot_fig.canvas.mpl_connect('key_release_event',cursor.key_press_callback)
plt.show()
class simulation(object):
# Integrating dN/dM ~ M^(-2.35) to get masses of a population
def masslist(self,numstars):
population = np.random.random(numstars)
intconst = 1.35
normconst = 1.3527
masses = (intconst/normconst)*(population**(1/(-intconst)))
return masses
# to do: make it grab the two closest isochrones, not just rounding to
# the nearest integer, so that you can have any range of isochrones in
# the directory.
class ArtificialInterpolatedMagnitudes(simulation):
def __init__(self):
self.interp_dict = {}
self.iso_dir = 'shitload_of_isochrones/'
self.filters = ['F336W','F475W','F814W']
self.iso_dict = {}
print '\nCollecting isochrones...'
self.fill_iso_dict()
print 'Found {} isochrones with age range from {}Myr to {}Myr.'.format(len(self.iso_dict),min(self.iso_dict.keys()),max(self.iso_dict.keys()))
def __call__(self,age,mass,z_val,solar_norm = False):
self.mass = mass
self.age = float(age)
self.z_val = z_val
self.age_gap = self.get_age_gap()
self.solar_norm = solar_norm
self.iso_names = {'upper':'{}'.format(self.age_gap[0]),
'lower':'{}'.format(self.age_gap[1])}
mags = self.simulate()
return mags
#print 'Magnitude in F336W: {}\nMagnitude in F475W: {}\nMagnitude in F814W: {}'.format(mags[0],mags[1],mags[2])
# looks at available isochrones and fills iso_dict with isochrones and their
# corresponding ages
def fill_iso_dict(self):
iso_list = os.listdir(self.iso_dir)
self.fits_files = [self.iso_dir+file for file in iso_list if '.fits' in file]
if self.fits_files ==[]:
raise IOError('Isochrones must be in fits format')
else:
for file in self.fits_files:
isochrone = fits.open(file)[1].data
if 'log(age/yr)' in isochrone.dtype.names:
iso_age = (10**float(isochrone['log(age/yr)'][1]))/1e6
self.iso_dict[iso_age] = file
else:
raise ValueError('Isochrones must have \'log(age/yr)\' column as provided by Padova. Offending file: {}'.format(file))
# looks at iso_dict and finds the closest two isochrones
def get_age_gap(self):
available_ages = np.zeros(len(self.fits_files))
counter=0
for key in self.iso_dict:
available_ages[counter] = key
counter+=1
available_ages = np.sort([available_ages])[0]
nearest_isochrone_index = (np.abs(available_ages-self.age)).argmin()
nearest_isochrone_age = available_ages[nearest_isochrone_index]
if nearest_isochrone_age > self.age:
if nearest_isochrone_age == np.min(available_ages):
self.upper_age = self.lower_age = nearest_isochrone_age
raise RuntimeWarning('{} younger than maximum available isochrone age of {}. This may lead to an unreliable simulated value.'.format(self.age, nearest_isochrone_age))
return (self.iso_dict[nearest_isochrone_age],self.iso_dict[nearest_isochrone_age])
else:
next_isochrone_age = available_ages[nearest_isochrone_index-1]
self.upper_age = nearest_isochrone_age
self.lower_age = next_isochrone_age
return (self.iso_dict[nearest_isochrone_age],self.iso_dict[next_isochrone_age])
elif nearest_isochrone_age < self.age:
if nearest_isochrone_age == np.max(available_ages):
self.upper_age = self.lower_age = nearest_isochrone_age
raise RuntimeWarning('{} older than maximum available isochrone age of {}. This may lead to an unreliable simulated value.'.format(self.age, nearest_isochrone_age))
return (self.iso_dict[nearest_isochrone_age],self.iso_dict[nearest_isochrone_age])
else:
next_isochrone_age = available_ages[nearest_isochrone_index+1]
self.upper_age = next_isochrone_age
self.lower_age = nearest_isochrone_age
return (self.iso_dict[next_isochrone_age],self.iso_dict[nearest_isochrone_age])
def simulate(self):
if self.iso_names['upper'] not in self.interp_dict:
self.interpolate(self.iso_names['upper'])
elif self.iso_names['upper'] in self.interp_dict:
pass
if self.iso_names['lower'] not in self.interp_dict:
self.interpolate(self.iso_names['lower'])
elif self.iso_names['lower'] in self.interp_dict:
pass
uf336w_sim,uf475w_sim,uf814w_sim = [self.interp_dict[self.iso_names['upper']][filt](self.mass,self.z_val) for filt in self.filters]
lf336w_sim,lf475w_sim,lf814w_sim = [self.interp_dict[self.iso_names['lower']][filt](self.mass,self.z_val) for filt in self.filters]
lower_weight = (self.age-self.lower_age)/(self.upper_age-self.lower_age)
upper_weight = 1-lower_weight
f336w_sim = (upper_weight*uf336w_sim)+(lower_weight*lf336w_sim)
f475w_sim = (upper_weight*uf475w_sim)+(lower_weight*lf475w_sim)
f814w_sim = (upper_weight*uf814w_sim)+(lower_weight*lf814w_sim)
return f336w_sim,f475w_sim,f814w_sim
def interpolate(self,iso_file):
isochrone = fits.open(iso_file)[1].data
mass = isochrone['M_ini'].copy()
if self.solar_norm == False:
metals = isochrone['Z'].copy()
elif self.solar_norm == True:
metals = isochrone['Z'].copy()
metals = np.log10(metals/0.019)
else:
raise ValueError('solar_norm must be True or False, not {}.'.format(self.solar_norm))
points = zip(mass,metals)
filt_dict = {}
for filt in self.filters:
filt_dict[filt] = interpolate.LinearNDInterpolator(points,isochrone[filt].copy())
self.interp_dict[iso_file] = filt_dict
## To rewrite: make it do all stars in between two given isochrones at once, then
## clear the isochrone dictionary and go on to the next isochrone pair so that
## you don't have to keep the huge isochrone dictionary in memory.
## Use the keys from iso_dict to go iterate through all the stars of a given
## isochrone pair.
class StellarPopulationSimulator(simulation):
def __init__(self,numstars,ages='all',z_values='all',masses='all',av='none',rv=3.1,output_file='',fits_comments=''):
self.numstars = int(numstars)
self.z_values = z_values
self.masses = masses
self.av = av
self.rv = rv
self.ages = ages
self.month = time.strftime('%m')
self.day = time.strftime('%d')
self.fits_comments = fits_comments
self.output_file = output_file
if self.output_file == '':
self.output_file = 'sim_pops/sim_out_{}_{}_{}.fits'.format('%.2E'%self.numstars,
self.month,
self.day)
else:
if type(self.output_file) is not str:
raise TypeError('Any user-defined output filename must be a string')
self.simstar = ArtificialInterpolatedMagnitudes()
self.age_lim = (max(self.simstar.iso_dict.keys()),
min(self.simstar.iso_dict.keys()))
self.free_parameters = []
if numstars == '':
if len(ages) == len(z_values) == len(masses):
numstars = len(ages)
else:
raise ValueError('All input parameters must have the same length')
if self.ages == 'all':
self.free_parameters.append('ages')
#self.age_list = (100-4)*np.random.random(self.numstars)-4
else:
try:
if len(ages) == self.numstars:
self.age_list = ages
elif len(ages) == 1:
self.age_list = np.zeros(self.numstars)+age[0]
elif len(ages) != self.numstars or len(self.ages) != 1:
raise ValueError('Age array must equal number of stars or 1.')
elif max(ages) > self.age_lim[0] or min(ages) < self.age_lim[1]:
raise ValueError('Maximum allowed age: {}Myr, minimum allowed age: {}Myr'.format(age_lim[0],age_lim[1]))
except TypeError:
self.age_list = np.zeros(self.numstars)+ages
if self.z_values == 'all':
self.free_parameters.append('z')
#self.z_list = (0.03-0.0001)*np.random.random(self.numstars)-0.0001
else:
try:
if len(z_values) == self.numstars:
self.z_list = z_values
elif len(z_values) == 1:
self.z_list = np.zeros(self.numstars)+self.z_values[0]
elif len(z_values) != self.numstars or len(self.z_values) != 1:
raise ValueError('Z array must equal number of stars or 1.')
elif max(z_values) > 0.03 or min(self.z_values) < 0.0001:
raise ValueError('Maximum allowed Z: 0.03, minimum allowed Z: 0.0001')
except TypeError:
self.z_list = np.zeros(numstars)+z_values
if self.masses == 'all':
self.free_parameters.append('masses')
#self.mass_list = masslist(numstars)
else:
try:
if len(masses) == numstars:
self.mass_list = masses
elif len(masses) == 1:
self.mass_list = np.zeros(numstars)+mass[0]
elif len(masses) != numstars or len(masses) != 1:
raise ValueError('Mass array must equal number of stars or 1.')
except TypeError:
self.mass_list = np.zeros(numstars)+masses
if self.av == 'none':
self.av_list = np.zeros(self.numstars)
elif self.av == 'grid':
self.av_list = (3.5)*np.random.random(self.numstars)
else:
try:
if len(av) == numstars:
self.av_list = av
elif len(av) == 1:
self.av_list = np.zeros(numstars)+av[0]
elif len(av) != numstars or len(av) != 1:
raise ValueError('Av array must equal number of stars or 1.')
except TypeError:
self.av_list = np.zeros(numstars)+av
if self.av is not 'none' and self.rv == 3.1:
self.ext_vals = {'f336w':1.667,'f475w':1.221,'f814w':0.61}
elif self.av is not 'none' and self.rv == 5:
self.ext_vals = {'f336w':1.349,'f475w':1.14,'f814w':0.67}
elif self.av is not 'none':
raise ValueError('Rv must be either 3.1 or 5. Not {}.'.format(self.rv))
elif self.av is 'none':
self.ext_vals = {'f336w':1,'f475w':1,'f814w':1}
#initialize all the arrays
self.ra_arr = np.zeros(self.numstars)
self.dec_arr = np.zeros(self.numstars)
self.mass_arr = np.zeros(self.numstars)
self.age_arr = np.zeros(self.numstars)
self.z_arr = np.zeros(self.numstars)
self.f336w_naked_arr = np.zeros(self.numstars)
self.f475w_naked_arr = np.zeros(self.numstars)
self.f814w_naked_arr = np.zeros(self.numstars)
self.av_arr = np.zeros(self.numstars)
self.f336w_arr = np.zeros(self.numstars)
self.f475w_arr = np.zeros(self.numstars)
self.f814w_arr = np.zeros(self.numstars)
self.dummy_arr = np.zeros(self.numstars)
self.starlist()
print '\ndone with simulation, writing FITS...'
self.write_to_file()
print '\nFITS file {} written'.format(self.output_file)
def simulate(self,starnum):
perc_done = round(25*(starnum/float(self.numstars)))
progress_bar = '=' * int(perc_done+1)
sys.stdout.write('\rSimulating star {}... |{}{}| {}%'.format(starnum,
progress_bar,
' '*(25-int(perc_done+1)),
round(100*((starnum+1)/float(self.numstars)),2)))
sys.stdout.flush()
self.age,self.mass,self.z,self.av = self.get_params(starnum)
self.star = [0,0,0]
if self.free_parameters != []:
while (self.star[0]-self.star[1]>0.6) or np.isnan(self.star[0]) or np.isnan(self.star[1]) or np.isnan(self.star[2]) or self.star[0] == self.star[1] == self.star[2] == 0:
self.age,self.mass,self.z,self.av = self.get_params(starnum)
self.star = self.simstar(self.age,self.mass,self.z)
elif self.free_parameters == []:
self.star = self.simstar(self.age,self.mass,self.z)
self.mass_arr[starnum] = self.mass
self.age_arr[starnum] = self.age
self.ra_arr[starnum] = self.dec_arr[starnum] = starnum
self.f336w = self.star[0]
self.f475w = self.star[1]
self.f814w = self.star[2]
self.f336w_naked_arr[starnum] = self.f336w
self.f475w_naked_arr[starnum] = self.f475w
self.f814w_naked_arr[starnum] = self.f814w
self.a336 = self.av*self.ext_vals['f336w']
self.a475 = self.av*self.ext_vals['f475w']
self.a814 = self.av*self.ext_vals['f814w']
self.av_arr[starnum] = self.av
# add av
self.f336w += self.a336
self.f475w += self.a475
self.f814w += self.a814
self.f336w_arr[starnum] = self.f336w
self.f475w_arr[starnum] = self.f475w
self.f814w_arr[starnum] = self.f814w
self.z_arr[starnum] = self.z
def get_params(self,starnum):
if 'ages' in self.free_parameters:
self.star_age = (self.age_lim[0]-self.age_lim[1])*np.random.random(1)+self.age_lim[1]
elif 'ages' not in self.free_parameters:
self.star_age = self.ages[starnum]
if 'masses' in self.free_parameters:
self.star_mass = self.masslist(1)
elif 'masses' not in self.free_parameters:
self.star_mass = self.mass_list[starnum]
if 'z' in self.free_parameters:
self.z_val = (0.03-0.0001)*np.random.random(1)+0.0001
elif 'z' not in self.free_parameters:
self.z_val = self.z_list[starnum]
self.star_av = self.av_list[starnum]
return self.star_age,self.star_mass,self.z_val,self.star_av
def starlist(self):
stars = xrange(self.numstars)