def sgr_sim_density(stripes, params):
SGA = fi.read_data("../sgrnorth_paper/stream_gen_analysis.txt", ",")
low_lam, high_lam = 200.0, 314.8 # from Sgr stripes: (200.025901082, 314.707561185)
size = (high_lam - low_lam) / 15.0
N_stars = [19386, 18734, 11096, 17044, 18736, 15409, 12519, 12248, 8853, 7328,
5479, 4450, 3486, 2425, 971, 9511, 16119, 16603]
south = [79, 82, 86]
out = []
for i in range(15):
print "# - Lambda wedge {0}:".format(i)
num = str(i)
name = "./sim_streams/lamsim_out_"+num+".txt"
data = fi.read_data(name, echo=1)
total = len(data[:,0])
other = sc.sum(data[:,3])
length = 7.653333333 # bin size
out.append([(low_lam+(size*i)+(size*0.5)), total-other, total, 0.0, total/length])
for i in range(15,18):
print "# - Southern wedge {0}:".format(south[i-15])
data = fi.read_data("./sim_streams/sim_stripe_"+str(south[i-15])+".txt")
total = len(data[:,0])
other = sc.sum(data[:,3])
length = SGA[i,3]
mu, r, theta, phi, wedge = eval(params[i][4]), eval(params[i][5]), \
eval(params[i][6]), eval(params[i][7]), stripes[i]
x,y,z = coor.stream2xyz(0.0, 0.0, 0.0, mu, r, theta, phi, wedge)
Xs, Ys, Zs, lam, beta, r = sl.law_xyz2sgr_sun(x,y,z)
out.append([lam, total-other, total, 0.0, total/length])
fi.write_data(sc.array(out), "sgrNorth_density_sim.txt", ",", " lam(mid), count, SDSS Deff, SDSS/FTO Deff, per degree")
def sgr_sim_density(stripes, params):
SGA = fi.read_data("../sgrnorth_paper/stream_gen_analysis.txt", ",")
low_lam, high_lam = 200.0, 314.8 # from Sgr stripes: (200.025901082, 314.707561185)
size = (high_lam - low_lam) / 15.0
N_stars = [
19386, 18734, 11096, 17044, 18736, 15409, 12519, 12248, 8853, 7328,
5479, 4450, 3486, 2425, 971, 9511, 16119, 16603
]
south = [79, 82, 86]
out = []
for i in range(15):
print "# - Lambda wedge {0}:".format(i)
num = str(i)
name = "./sim_streams/lamsim_out_" + num + ".txt"
data = fi.read_data(name, echo=1)
total = len(data[:, 0])
other = sc.sum(data[:, 3])
length = 7.653333333 # bin size
out.append([(low_lam + (size * i) + (size * 0.5)), total - other,
total, 0.0, total / length])
for i in range(15, 18):
print "# - Southern wedge {0}:".format(south[i - 15])
data = fi.read_data("./sim_streams/sim_stripe_" + str(south[i - 15]) +
".txt")
total = len(data[:, 0])
other = sc.sum(data[:, 3])
length = SGA[i, 3]
mu, r, theta, phi, wedge = eval(params[i][4]), eval(params[i][5]), \
eval(params[i][6]), eval(params[i][7]), stripes[i]
x, y, z = coor.stream2xyz(0.0, 0.0, 0.0, mu, r, theta, phi, wedge)
Xs, Ys, Zs, lam, beta, r = sl.law_xyz2sgr_sun(x, y, z)
out.append([lam, total - other, total, 0.0, total / length])
fi.write_data(sc.array(out), "sgrNorth_density_sim.txt", ",",
" lam(mid), count, SDSS Deff, SDSS/FTO Deff, per degree")
def do_cuts(infile, delimiter, outfile, cuts):
# Get data
data = fi.read_data(infile, delimiter)
# cut data
for i in range(len(cuts)):
data = do_one_cut(data, cuts[i])
# write data
fi.write_data(data, outfile, delimiter)
print "# - Finished with cuts"
def do_cuts(infile, delimiter, outfile, cuts):
# Get data
data = fi.read_data(infile, delimiter)
# cut data
for i in range(len(cuts)):
data = do_one_cut(data, cuts[i])
# write data
fi.write_data(data, outfile, delimiter)
print "# - Finished with cuts"
def rejection(filename, min=None, max=None, bin=0.2, reject=2.0, thresh=1000.0):
data = fi.read_data(filename, ",")
if min == None:
min = np.ma.min(data[:,2])
if max == None:
max = np.ma.max(data[:,2])
data_out = []
steps = int((max-min)/bin)+1
for i in range(steps):
# build a data set for each bin
data_temp = []
min_temp, max_temp = (min + i*bin), (min + (i+1)*bin)
for j in range(len(data[:,0])):
if (data[j,2] >= min_temp) and (data[j,2] < max_temp):
data_temp.append(data[j,:])
print "number of stars in bin {0}: {1}".format(i, len(data_temp))
if len(data_temp)==0: continue #skip empty sets
# continue checking for rejections until no rejections occur
kills = [1]
while len(kills) > 0:
kills, new_temp = [], []
# pack the g-r values into a single list
g_minus_r = []
for j in range(len(data_temp)):
g_minus_r.append(data_temp[j][2]-data_temp[j][3])
g_minus_r = sc.array(g_minus_r)
avg = sc.mean(g_minus_r)
stdev = sc.std(g_minus_r)
#print avg, stdev
# Find the values that lie outside 'reject' sigma
for j in range(len(g_minus_r)):
diff = ma.fabs( g_minus_r[j] - avg )
if diff > (reject*stdev): kills.append(data_temp[j])
elif diff > thresh: kills.append(data_temp[j])
else: new_temp.append(data_temp[j])
data_temp = new_temp
data_out = data_out + data_temp
print "Stars kept in bin: {0}".format(len(data_temp))
# restructure data
data_out = sc.array(data_out)
print " {0} stars remaining out of {1} original stars".format(len(data_out[:,0]), len(data[:,0]))
g_minus_r_ori = data[:,2] - data[:,3]
g_minus_r_out = data_out[:,2] - data_out[:,3]
# show plot, then ask to save data set
fig = plt.figure()
#plt.scatter(g_minus_r_ori, data[:,2], 2, marker='o', facecolor="r", edgecolor='face')
plt.scatter(g_minus_r_out, data_out[:,2], 1, 'k', 'o')
plt.ylim(max, min)
plt.xlim(-1.0, 2.0)
plt.ylabel(r'$g_0$')
plt.xlabel(r"$(g-r)_0$")
plt.show()
fname = raw_input("save data? [filename to save, empty to skip]")
if fname != "":
fi.write_data(data_out, fname, header="#")
print "# --- Done"
def all_stripes(stripes, file_in, out_form="stripe_"):
t1 = time.time()
for stripe in stripes:
fileout = out_form + str(stripe) + ".txt"
out, map = get_stripe(stripe, file_in)
fi.write_data(out, fileout)
plot_sky(map, stripe)
t2 = time.time()
print "Time elapsed, stripe {0}: {1} minutes".format(
stripe, ((t2 - t1) / 60.0))
def sgr_lam_split(stripes, params):
# get lambda for stripes
holder = []
for i in range(len(stripes)):
mu, r, theta, phi, wedge = eval(params[i][4]), eval(params[i][5]), \
eval(params[i][6]), eval(params[i][7]), stripes[i]
x, y, z = coor.stream2xyz(0.0, 0.0, 0.0, mu, r, theta * deg, phi * deg,
wedge)
Xs, Ys, Zs, lam, beta, r = sl.law_xyz2sgr_sun(x, y, z)
#if lam > 180.0: lam = lam - 360.0
#lam = -lam
print lam, beta, r, stripes[i]
holder.append([lam, beta, r])
Xs, Ys, Zs, lam, beta, r = sl.law_xyz2sgr_sun(14.655646, 2.270431,
-5.887377)
print lam, beta, r
# get lambda range for northern Sgr 15 N stripes, 0-14 -> 9-23
low_lam, high_lam = 200.0, 314.8 # from Sgr stripes: (200.025901082, 314.707561185)
size = (high_lam - low_lam) / 15.0
print "# - LAMBDA BIN SIZE: {0}".format(size)
for h in range(15):
array = []
lam_min, lam_max = low_lam + (size * h), low_lam + (size * (h + 1))
for i in range(15):
if stripes[i] == 9: num = "09"
else: num = str(stripes[i])
name = "./sim_streams/sim_stripe_" + num + ".txt"
Sgr = open(name, "r")
for line in Sgr:
if line.strip() == "": continue
if line.strip()[0] == "#": continue
l, b, r, f = line.strip().split()
x, y, z = coor.lbr2xyz(float(l), float(b), float(r))
Xs, Ys, Zs, lam, beta, r = sl.law_xyz2sgr_sun(x, y, z)
#if lam > high_lam: high_lam = lam
#if lam < low_lam: low_lam = lam
if lam > lam_min:
if lam <= lam_max:
array.append([lam, beta, r, eval(f)])
Sgr.close()
print "# - Stripe " + num + " successfully read in"
fi.write_data(sc.array(array),
"lamsim_out_" + str(h) + ".txt",
header="Lambda {0}:{1}, lam, beta, r".format(
lam_min, lam_max))
#print low_lam, high_lam
# bin stripes in lambda, g
# correct stripes for detection, FTO efficiencies
# reflect stars about midpoint to correct for missing data on far end
print "### --- Done"
def sgr_lam_split(stripes, params):
# get lambda for stripes
holder = []
for i in range(len(stripes)):
mu, r, theta, phi, wedge = eval(params[i][4]), eval(params[i][5]), \
eval(params[i][6]), eval(params[i][7]), stripes[i]
x,y,z = coor.stream2xyz(0.0, 0.0, 0.0, mu, r, theta*deg, phi*deg, wedge)
Xs, Ys, Zs, lam, beta, r = sl.law_xyz2sgr_sun(x,y,z)
#if lam > 180.0: lam = lam - 360.0
#lam = -lam
print lam, beta, r, stripes[i]
holder.append([lam, beta, r])
Xs, Ys, Zs, lam, beta, r = sl.law_xyz2sgr_sun(14.655646, 2.270431, -5.887377)
print lam, beta, r
# get lambda range for northern Sgr 15 N stripes, 0-14 -> 9-23
low_lam, high_lam = 200.0, 314.8 # from Sgr stripes: (200.025901082, 314.707561185)
size = (high_lam - low_lam) / 15.0
print "# - LAMBDA BIN SIZE: {0}".format(size)
for h in range(15):
array = []
lam_min, lam_max = low_lam+(size*h), low_lam+(size*(h+1))
for i in range(15):
if stripes[i] == 9: num = "09"
else: num = str(stripes[i])
name = "./sim_streams/sim_stripe_"+num+".txt"
Sgr = open(name, "r")
for line in Sgr:
if line.strip() == "": continue
if line.strip()[0] == "#": continue
l,b,r,f = line.strip().split()
x,y,z = coor.lbr2xyz(float(l), float(b), float(r))
Xs, Ys, Zs, lam, beta, r = sl.law_xyz2sgr_sun(x,y,z)
#if lam > high_lam: high_lam = lam
#if lam < low_lam: low_lam = lam
if lam > lam_min:
if lam <= lam_max:
array.append([lam, beta, r, eval(f)])
Sgr.close()
print "# - Stripe "+num+" successfully read in"
fi.write_data(sc.array(array), "lamsim_out_"+str(h)+".txt",
header="Lambda {0}:{1}, lam, beta, r".format(lam_min, lam_max))
#print low_lam, high_lam
# bin stripes in lambda, g
# correct stripes for detection, FTO efficiencies
# reflect stars about midpoint to correct for missing data on far end
print "### --- Done"
def convert(filename, RaDec=0):
readfile = open(filename, "r")
holder, MW = [], []
for line in readfile:
if line[0] == "#": continue
if line.strip() == "": continue
holder = line.split(",")
for i in range(len(holder)): holder[i] = eval(holder[i])
if RaDec==1:
l,b = coor.EqTolb(holder[0], holder[1])
holder[0], holder[1] = round(l,6) , round(b,6)
r = coor.getr(holder[2], 4.2)
holder[2] = round(r, 6)
MW.append(holder[:3])
readfile.close()
nstars = len(MW)
out = sc.array(MW)
fi.write_data(out, filename[:-4]+"_new.txt", " ", str(nstars))
def convert(filename, RaDec=0):
readfile = open(filename, "r")
holder, MW = [], []
for line in readfile:
if line[0] == "#": continue
if line.strip() == "": continue
holder = line.split(",")
for i in range(len(holder)):
holder[i] = eval(holder[i])
if RaDec == 1:
l, b = coor.EqTolb(holder[0], holder[1])
holder[0], holder[1] = round(l, 6), round(b, 6)
r = coor.getr(holder[2], 4.2)
holder[2] = round(r, 6)
MW.append(holder[:3])
readfile.close()
nstars = len(MW)
out = sc.array(MW)
fi.write_data(out, filename[:-4] + "_new.txt", " ", str(nstars))
def separate_stars(sort,
files=["matched.txt", "nomatch1.txt", "nomatch2.txt"]):
# Matched stars
holder = []
for i in range(sort.l1):
if sort.flags1[i] == 1:
holder.append(sort.data1[i, :])
fi.write_data(sc.array(holder), files[0])
s1 = len(holder)
# Unmatched 1
holder = []
for i in range(sort.l1):
if sort.flags1[i] == 0:
holder.append(sort.data1[i, :])
fi.write_data(sc.array(holder), files[1])
s2 = len(holder)
# Unmatched 2
holder = []
for i in range(sort.l2):
if sort.flags2[i] == 0:
holder.append(sort.data2[i, :])
fi.write_data(sc.array(holder), files[2])
s3 = len(holder)
print "# - {0} matched stars, {1} unmatched in file 1, {2} unmatched in file 2".format(
s1, s2, s3)
def save_hists(l_hist=None, red_hist=None, dered_hist=None):
if l_hist != None:
print "# --- length of l_hist: {0}".format(len(l_hist))
l_out = sc.zeros((360, 2),float)
for i in range(len(l_out[:,0])):
l_out[i,0] = float(i)
l_out[i,1] = l_hist[i]
fi.write_data(l_out, "l_hist_data.csv", delimiter=",",
header="angle, number of stars")
if red_hist != None:
print "# --- shape of red_hist: {0}".format(red_hist.shape)
fi.write_data(red_hist, "red_hist_data.csv", delimiter=",")
if dered_hist != None:
print "# --- shape of dered_hist: {0}".format(dered_hist.shape)
fi.write_data(dered_hist, "dered_hist_data.csv", delimiter=",")
def save_hists(l_hist=None, red_hist=None, dered_hist=None):
if l_hist != None:
print "# --- length of l_hist: {0}".format(len(l_hist))
l_out = sc.zeros((360, 2), float)
for i in range(len(l_out[:, 0])):
l_out[i, 0] = float(i)
l_out[i, 1] = l_hist[i]
fi.write_data(l_out,
"l_hist_data.csv",
delimiter=",",
header="angle, number of stars")
if red_hist != None:
print "# --- shape of red_hist: {0}".format(red_hist.shape)
fi.write_data(red_hist, "red_hist_data.csv", delimiter=",")
if dered_hist != None:
print "# --- shape of dered_hist: {0}".format(dered_hist.shape)
fi.write_data(dered_hist, "dered_hist_data.csv", delimiter=",")
def separate_stars(sort, files=["matched.txt", "nomatch1.txt", "nomatch2.txt"]):
# Matched stars
holder = []
for i in range(sort.l1):
if sort.flags1[i] == 1:
holder.append(sort.data1[i,:])
fi.write_data(sc.array(holder), files[0])
s1 = len(holder)
# Unmatched 1
holder = []
for i in range(sort.l1):
if sort.flags1[i] == 0:
holder.append(sort.data1[i,:])
fi.write_data(sc.array(holder), files[1])
s2 = len(holder)
# Unmatched 2
holder = []
for i in range(sort.l2):
if sort.flags2[i] == 0:
holder.append(sort.data2[i,:])
fi.write_data(sc.array(holder), files[2])
s3 = len(holder)
print "# - {0} matched stars, {1} unmatched in file 1, {2} unmatched in file 2".format(s1,s2,s3)
def get_dist(point, center):
# both ra, dec tuples
x = (point[0] - center[0])*(point[0] - center[0])
y = (point[1] - center[1])*(point[1] - center[1])
return np.sqrt(x + y)
def make_annulus_series(data, step, limits, center, c_cut=None):
""" """
incs = int((limits[1] - limits[0]) / step) + 1
an_hist = []
for i in range(incs):
d_cut = ( (i*step)+limits[0], ((i+1)*step)+limits[0])
height = get_annulus(data, c_cut, center, d_cut)
pos = (i*step)+limits[0]
an_hist.append([pos, height])
return sc.array(an_hist)
if __name__ == "__main__":
data = fi.read_data('HR_NGC_5053.csv', ",")
step = 0.005
limits = (0.0, 0.14)
center = (199.109, 17.697) #Ra, dec
c_cut = (0.2, 0.5)
gc_prof = make_annulus_series(data, step, limits, center, c_cut)
fi.write_data(gc_prof, "annuli_out_med.txt")
fig = plt.figure(1)
plt.bar(gc_prof[:,0], gc_prof[:,1], step)
plt.xlabel("Radius, degrees")
plt.ylabel("counts")
plt.show()
print "# --- Done"
def compare_plot(out, size):
#Make plot
fig = plt.figure()
plt.bar(out[:, 0], out[:, 1], size)
#x = sc.arange(0.0, 47.0, 1.0)
#y = func.SDSS_error_function(x, [1.02, 0.119, -3.53])
#plt.plot(x, y, 'k-')
plt.xlabel(r'Sun-centered $r$ (kpc)')
plt.ylabel(r'$(\rho_{1} / \rho_{2})$ (stars/kpc)')
#plt.savefig(("diff.ps"), papertype='letter')
plt.show()
plt.close('all')
if __name__ == "__main__":
file1 = "stars-82.txt"
file2 = "stars-82-coadd2.txt"
file3 = "matched-82-1arcsec.txt"
mag = 0
size = 1.0
out1 = do_compare_wedges(file1, file3, stripe=82, mag=mag, size=size)
out2 = do_compare_wedges(file3, file2, stripe=82, mag=mag, size=size)
out = sc.zeros((len(out2[:, 0]), 2))
for i in range(len(out[:, 0])):
out[i, 0], out[i, 1] = out2[i, 0], (out1[i, 1] * out2[i, 1])
compare_plot(out, size)
for i in range(len(out[:, 0])):
out[i, 0] = out[i, 0] + (size / 2.0)
fi.write_data(out, "eps1_eps2.txt")
errors = hes.get_hessian_errors(gfe.get_2gauss_y, best_parameters,
hist_data[:, 0], hist_data[:, 1],
sig_in, step_size)
print '#- Distance, fwhm, mu, sigma_l, sigma_r, amplitude, errors for run', gc_name
print '$ ', con_dist, (gfe.full_width_half_max(best_parameters[1],best_parameters[2])), \
best_parameters[0], best_parameters[1], best_parameters[2], best_parameters[3], \
errors[0], errors[1], errors[2], errors[3]
if (gcp.plot_hist(hist_data, gc_name, 1, best_parameters, 0,
plot_file) == 1):
print '#-Histogram plotted'
if (gcp.plot_HR_diagram(con_clus, real_dist, gc_name, [1.0, 7.0],
[0.0, 0.6], plot_file) == 1):
#if (gcp.plot_HR_diagram(gce.select_stars_with_sigmoid(con_clus, con_dist, low_limit=0.0,high_limit=0.6),
# real_dist, gc_name,[1.0,7.0],[0.0,0.6], plot_file) == 1):
print '#-HR diagram plotted'
if run == 0:
if (gcp.plot_infiles(main_data, back_data, gc_name,
plot_file) == 1):
print '-Skyplot finished'
print '******', gc_name, '-process complete*****'
""" EXTRA"""
#print cluster, run
f.write_data(hist_data,
(NAME[cluster] + '_' +
str(round(CONVOLVE[cluster][run])) + '_' + '.txt'),
delimiter='\t',
header=str(best_parameters))
print '#---ALL RUNS COMPLETE---'
no_g = no_g + 1
if (sigma_r > 0.0):
r[i] = np.random.normal(y[i,3], sigma_r)
else:
r[i] = y[i,3]
no_r = no_r + 1
#print sigma_g, sigma_r, g[i], r[i]
###Two columns, absolute g and g-r
print '-uncon g:', no_g, 'uncon r:', no_r
if columns == 2:
m = sc.zeros((l,2))
for i in range(l):
m[i,0] = g[i] - 5.*(np.log10(distance*1000) - 1.)
m[i,1] = g[i] - r[i]
###Four columns: Ra, Dec, app_g, app_r; usually used for .csv output
elif columns == 4:
m = sc.zeros((l,4))
for i in range(l):
m[i,0] = y[i,0]
m[i,1] = y[i,1]
m[i,2] = g[i]
m[i,3] = r[i]
else:
m = [0, 0, 0, 0]
print "-Please use a valid column number"
if f.write_data(m, outfile, ' ') == 1:
print '-Convolved data written successfully'
for j in range(len(kill_list)):
star_list.pop(kill_list[j])
g_list.pop(kill_list[j])
for k in range(len(kill_list)): kill_list[k] = (kill_list[k]-1)
#Fit strips to Girardi isocrones using different fit functions
#now have 'iso_array' and 'fit_array' - save to file!
if (save_data==1):
outname = cluster_name + '_out.txt'
out_data = sc.zeros((iso_l, 4),float)
out_data[:,0], out_data[:,1], out_data[:,2], out_data[:,3] = \
iso_array[:,0], iso_array[:,1], fit_array[:,1], fit_array[:,2]
dist_str = (str(distance))
header_txt = 'g, iso g-r, fiducial g-r, uncertainty mean, from file ' + iso_file + ' with distance ' + dist_str
#print out_data
#print out_data.shape
if (f.write_data(out_data, outname, header=header_txt) == 1):
print 'iso g, iso g-r, fid g-r, fiducial sigma, data successfully saved'
#fit distances? Hard! Need to start from beginning..."""
plt.figure()
A = -0.01463
B = 0.08929
y = iso_array[:,0]
x = (fit_array[:,1] - iso_array[:,1])
plt.scatter(x,y, c='black', marker='+', label='Subtracted data')
plt.plot([0.0,0.0], [np.ma.min(iso_array[:,0]),np.ma.max(iso_array[:,0])], 'r-', label='_nolegend_')
plt.plot([(A*np.ma.min(y)+B),(A*np.ma.max(y)+B)], [np.ma.min(y), np.ma.max(y)], 'g:', label='Line Fit')
#plt.plot([(A*np.ma.min(y)+B),np.ma.min(y)], [(A*np.ma.max(y)+B), np.ma.max(y)], 'g:', label='Line Fit')
#plt.errorbar(x,y,xerr=fit_array[:,2])
plt.errorbar(iso_array[:,1],iso_array[:,0], fmt='o', mfc='c', mec='c', mew=1, label='Girardi isocrone')
plt.errorbar(fit_array[:,1],iso_array[:,0],xerr=fit_array[:,2], fmt=None, ecolor='k')
plt.scatter(fit_array[:,1],iso_array[:,0], marker='d', c='black', label='Fiducial sequence')
points = []
for i in range(len(margins)):
holder = []
for j in range(len(avg_in[:, 0])):
if ((avg_in[j, 0] > (margins[i] - 2))
and avg_in[j, 0] < margins[i]):
holder.append(avg_in[j, y])
data_point = [(margins[i] - 1.0), sc.mean(holder), sc.std(holder)]
points.append(data_point)
averages = sc.array(points)
plt.errorbar(averages[:, 0],
averages[:, 1],
yerr=averages[:, 2],
fmt='o',
color='b',
ecolor='b',
markersize=4)
f.write_data(averages,
averages_file,
header=('Distance, Average, Standard Deviation for ' +
axes_names[y]))
print '#-Printed and plotted averages and standard deviations'
#plt.title('Plot of convolved data')
ax1.set_xlabel(x_label)
ax2.set_xlabel(r'$\bar{g}_0$')
ax1.set_ylabel(y_label)
fig.savefig(plot_file, papertype='letter')
plt.close('all')
print '#-plot successfully created'
print '#---All Plots sucessfully created'
#newer_far = far_data[:,1] - sc.mean(y)
#for i in range(len(newer_far)):
# if newer_far[i] < 0.0: newer_far[i] = 0.0
out_array = sc.zeros((len(far_data), 3), float)
out_array[:,0] = avg_data[:,0]
out_array[:,1] = new_far[:]
#h.plot_histogram(out_array, 0.2, limits = [], name='far_clus_renormed', x_label=r'$M_g$')
h.plot_multiple_hist(out_array[:,0], [avg_data[:,1], new_far], 0.2,
limits = [], name=(clus_name+'_and_avg_two'), x_label=r'$M_g$')
ratio_data = []
for i in range(len(new_far)):
ratio_data.append( [ far_data[i,0], (new_far[i] / avg_data[i,1]), error_array[i] ])
f.write_data(sc.array(ratio_data), (clus_name+"_ratio_data_new.txt") )
"""
"""
### Get histogram files in, convert to absolute magnitude, then average
file_names = ['6205_normed_hist.txt', '5904_normed_hist.txt', '5272_normed_hist.txt']
dist = [7.7, 8.0, 10.4]
counts = [2164, 3698, 4364]
data = []
for cluster in file_names:
data.append(f.read_data(cluster))
# Get absolute magnitudes and check that they are equal
abs_mags = []
for i in range(len(data)):
modulus = (5.*(m.log10(dist[i]*1000) - 1.))
abs_mags.append(data[i][:,0] - modulus)
for i in range(len(abs_mags[0])):
if (round(abs_mags[0][i], 3) == round(abs_mags[1][i], 3) == round(abs_mags[2][i], 3)):
print "#--Absolute magnitudes check out"
import matplotlib.pyplot as plt
import files as f
'''selects out only a portion of a data set
Used for f-turnoff stars right now.
---Matthew Newby '''
starfile = "convolved_5904_cluster_lilcon.txt"
outfile = 'convolved_5904_cluster_lilcon_f.txt'
low_limit = 0.0
high_limit = 0.3
y = f.read_data(starfile)
l, w = y.shape
index = sc.zeros(l, int)
for i in range(l):
if (y[i,2] - y[i,3]) >= low_limit:
if (y[i,2] - y[i,3]) <= high_limit:
index[i] = 1
s = sc.sum(index)
print '-Stars in data set:', l, 'Stars in f-turnoff:', s
new_data = sc.zeros((s,w))
j = 0
for i in range(l):
if index[i] == 1:
new_data[j,:] = y[i,:]
j = j + 1
if (j != s): print '-!!! stars out not equal to f-turnoff:', j, s
if f.write_data(new_data, outfile, ' ') == 1:
print '-F-turnoff stars written successfully'
for i in range(len(x1)):
if np.random.uniform() < 0.2:
x2[i] = x1[i] + 0.05
else:
x2[i] = x1[i]
y1, z1 = np.histogram(x1, 40, range=(0.0, 0.4))
y2, z2 = np.histogram(x2, 40, range=(0.0, 0.4))
m1 = sc.zeros((len(y1), 2))
m1[:, 0] = (z1[:-1] + 0.005)
m1[:, 1] = y1
m2 = sc.zeros((len(y2), 2))
m2[:, 0] = (z2[:-1] + 0.005)
m2[:, 1] = y2
fi.write_data(m1, "ori_color.txt")
fi.write_data(m2, "new_color.txt")
plt.figure(1)
plt.bar(z2[:-1], y2, 0.01, color="blue")
plt.bar(z1[:-1], y1, 0.01, color="green", fill=None)
plt.show()
''' For magnitude study
""" For double-sided Gaussian """
N1 = 642 #N2*(0.36/0.76)
N2 = 1357 #2000.0 / ((0.36/0.76)+1)
a1 = np.random.normal(4.2, 0.36, N1)
a2 = np.random.normal(4.2, 0.76, N2)
holder = []
for i in range(len(a1)):
r = sc.zeros(l)
for i in range(l):
#print y[i,2], y[i,3]
sigma_g = a_g + np.exp(b_g*(y[i,2] + diff_mag) + c_g)
sigma_r = a_r + np.exp(b_r*(y[i,3] + diff_mag) + c_r)
g[i] = np.random.normal(y[i,2], sigma_g)
r[i] = np.random.normal(y[i,3], sigma_r)
#print sigma_g, sigma_r, g[i], r[i]
###Two columns, absolute g and g-r
if columns == 2:
m = sc.zeros((l,2))
for i in range(l):
m[i,0] = g[i] - 5.*(np.log10(distance*1000) - 1.)
m[i,1] = g[i] - r[i]
###Four columns: Ra, Dec, app_g, app_r
elif columns == 4:
m = sc.zeros((l,4))
for i in range(l):
m[i,0] = y[i,0]
m[i,1] = y[i,1]
m[i,2] = g[i]
m[i,3] = r[i]
else:
m = [0, 0, 0, 0]
print "-Please use a valid column number"
if f.write_data(m, 'convolved_5904_cluster.csv', ',') == 1:
print '-Convolved data written succesfully'
x = (point[0] - center[0]) * (point[0] - center[0])
y = (point[1] - center[1]) * (point[1] - center[1])
return np.sqrt(x + y)
def make_annulus_series(data, step, limits, center, c_cut=None):
""" """
incs = int((limits[1] - limits[0]) / step) + 1
an_hist = []
for i in range(incs):
d_cut = ((i * step) + limits[0], ((i + 1) * step) + limits[0])
height = get_annulus(data, c_cut, center, d_cut)
pos = (i * step) + limits[0]
an_hist.append([pos, height])
return sc.array(an_hist)
if __name__ == "__main__":
data = fi.read_data('HR_NGC_5053.csv', ",")
step = 0.005
limits = (0.0, 0.14)
center = (199.109, 17.697) #Ra, dec
c_cut = (0.2, 0.5)
gc_prof = make_annulus_series(data, step, limits, center, c_cut)
fi.write_data(gc_prof, "annuli_out_med.txt")
fig = plt.figure(1)
plt.bar(gc_prof[:, 0], gc_prof[:, 1], step)
plt.xlabel("Radius, degrees")
plt.ylabel("counts")
plt.show()
print "# --- Done"
print '#-GD app mu:', (best_parameters[0] + 5.*(m.log10(real_dist*1000) - 1.))
print '#-GD fwhm:', (gfe.full_width_half_max(best_parameters[1],best_parameters[2]))
lh,wh = hist_data.shape
sig_in = sc.ones(lh)
step_size = [0.01, 0.001, 0.001, 0.01]
errors = hes.get_hessian_errors(gfe.get_2gauss_y, best_parameters, hist_data[:,0],
hist_data[:,1], sig_in, step_size)
print '#- Distance, fwhm, mu, sigma_l, sigma_r, amplitude, errors for run', gc_name
print '$ ', con_dist, (gfe.full_width_half_max(best_parameters[1],best_parameters[2])), \
best_parameters[0], best_parameters[1], best_parameters[2], best_parameters[3], \
errors[0], errors[1], errors[2], errors[3]
if (gcp.plot_hist(hist_data, gc_name, 1, best_parameters, 0, plot_file) == 1):
print '#-Histogram plotted'
if (gcp.plot_HR_diagram(con_clus, real_dist, gc_name,[1.0,7.0],[0.0,0.6], plot_file) == 1):
#if (gcp.plot_HR_diagram(gce.select_stars_with_sigmoid(con_clus, con_dist, low_limit=0.0,high_limit=0.6),
# real_dist, gc_name,[1.0,7.0],[0.0,0.6], plot_file) == 1):
print '#-HR diagram plotted'
if run == 0:
if (gcp.plot_infiles(main_data, back_data, gc_name, plot_file) == 1):
print '-Skyplot finished'
print '******', gc_name, '-process complete*****'
""" EXTRA"""
#print cluster, run
f.write_data(hist_data, (NAME[cluster]+'_'+str(round(CONVOLVE[cluster][run]))+'_'+'.txt'),
delimiter='\t', header=str(best_parameters))
print '#---ALL RUNS COMPLETE---'
if high_cut != None:
if data[i, col] > high_cut:
continue
holder.append(data[i, :])
return sc.array(holder)
""" Main Cutting Function """
def cut_data(data, *args):
new_data = []
if len(args) == 0:
print "!!! NO ARGUMENTS DEFINING CUT !!!\n"
usage()
sys.exit(2)
for arg in args:
if new_data == []: new_data = cut_once(data, arg)
else: new_data = cut_once(new_data, arg)
return new_data
if __name__ == "__main__":
import files as fi
file_in = "noU_NGC_5466_background.csv"
data = fi.read_data(file_in, ",")
data_out = cut_data(data, (2, 20.0, 21.0), (3, None, 20.0))
fi.write_data(data_out, (file_in[:-4] + "_cut.txt"))
#print data_out
#print "length of data:", len(data[:,0]), "; length of cut data:", len(data_out[:,0])
if data[j,2] > (mu+spread): continue
if data[j,2] < (mu-spread): continue
holder.append(data[j,2]-data[j,3])
histogram = h.make_hist(holder, 0.01)
h.plot_histogram(histogram, 0.01, name=('hist_'+str(i)), x_label=r'$(g-r)_0$')
#h.plot_multiple_hist(histogram[:,0], [histogram[:,1]], 0.01, name=('hist_'+str(i)), x_label=r'$(g-r)_0$')
#x_col, y_col, sig_col = histogram[:,0], (histogram[:,1]/len(holder)), (func.poisson_errors(histogram[:,1])/len(holder) )
x_col, y_col, sig_col = histogram[:,0], (histogram[:,1]), (func.poisson_errors(histogram[:,1]))
#print x_col, y_col, sig_col
start_params = sc.array([0.2, 0.1, sc.ma.max(y_col)])
steps = sc.array([0.001, 0.001, 0.01])
function = func.gaussian_function
#Fit function
MCMC_fit, errors, best_fit = mc.do_MCMC(function, start_params, steps, x_col, y_col,
sig_col, name=('histfit_'+str(i)), number_steps=10000, save=0)
best_fit = gd.gradient_descent(function, best_fit, x_col, y_col, sig_col)
errors = he.get_hessian_errors(function, best_fit, x_col, y_col, sig_col, steps)
turnoff.append([best_fit[0], best_fit[1], errors[0], errors[1]])
# if (pdf.plot_function(x_col, y_col, function, best_fit, save_name=('histfitplot_'+str(i)),
# title=['', r'$(g-r)_0$', 'Normalized Counts'], save=1) == 1):
plt.figure()
plt.scatter(x_col, y_col)
func_x = sc.arange( (sc.ma.min(x_col)*0.9), (sc.ma.max(x_col)*1.1), 0.01)
func_y = function(func_x, best_fit)
plt.plot(func_x, func_y)
plt.savefig(('histfitplot_'+str(i)+'.ps'), papertype='letter')
print "#-Fit dataplot saved"
#print turnoff
print turnoff
if f.write_data(sc.array(turnoff), 'turnoff_out_fixed.txt') == 1:
print "#-All Results Successfully saved"
l, w = y.shape
g = sc.zeros(l)
r = sc.zeros(l)
for i in range(l):
#print y[i,2], y[i,3]
sigma_g = a_g + np.exp(b_g * (y[i, 2] + diff_mag) + c_g)
sigma_r = a_r + np.exp(b_r * (y[i, 3] + diff_mag) + c_r)
g[i] = np.random.normal(y[i, 2], sigma_g)
r[i] = np.random.normal(y[i, 3], sigma_r)
#print sigma_g, sigma_r, g[i], r[i]
###Two columns, absolute g and g-r
if columns == 2:
m = sc.zeros((l, 2))
for i in range(l):
m[i, 0] = g[i] - 5. * (np.log10(distance * 1000) - 1.)
m[i, 1] = g[i] - r[i]
###Four columns: Ra, Dec, app_g, app_r
elif columns == 4:
m = sc.zeros((l, 4))
for i in range(l):
m[i, 0] = y[i, 0]
m[i, 1] = y[i, 1]
m[i, 2] = g[i]
m[i, 3] = r[i]
else:
m = [0, 0, 0, 0]
print "-Please use a valid column number"
if f.write_data(m, 'convolved_5904_cluster.csv', ',') == 1:
print '-Convolved data written succesfully'
import math as m
import numpy as np
import scipy as sc
import files as f
import gradient_descent as gd
import monte_carlo as mc
import Hessian_errors as he
import plot_data_function as pdf
import time
import functions as func
'''python script for running gc scripts
Matthew Newby, Sept 6, 2010'''
'''Add Limits to parameters and data! '''
"""
# Generate Gaussian Data
sig = 10.0
x = sc.arange(0.0, 10.0, 0.1)
y = (func.gaussian_function(x, [10.0, 3.5, 0.8]) )#+ np.random.normal(0.0, sig, len(x)))
out = sc.zeros((len(x),3))
for i in range(len(x)):
out[i,0], out[i,1], out[i,2] = x[i], y[i], sig
f.write_data(out, "test_out_p.txt")
"""
#MAIN
#Setup variables
filename = 'Pal13_backsub_cluscut.txt'
function = func.get_2gauss_y
identifier = ['text', 'A', 'B', 'C', 'D', 'F', 'G', 'H']
#Load data
def rejection(filename,
min=None,
max=None,
bin=0.2,
reject=2.0,
thresh=1000.0):
data = fi.read_data(filename, ",")
if min == None:
min = np.ma.min(data[:, 2])
if max == None:
max = np.ma.max(data[:, 2])
data_out = []
steps = int((max - min) / bin) + 1
for i in range(steps):
# build a data set for each bin
data_temp = []
min_temp, max_temp = (min + i * bin), (min + (i + 1) * bin)
for j in range(len(data[:, 0])):
if (data[j, 2] >= min_temp) and (data[j, 2] < max_temp):
data_temp.append(data[j, :])
print "number of stars in bin {0}: {1}".format(i, len(data_temp))
if len(data_temp) == 0: continue #skip empty sets
# continue checking for rejections until no rejections occur
kills = [1]
while len(kills) > 0:
kills, new_temp = [], []
# pack the g-r values into a single list
g_minus_r = []
for j in range(len(data_temp)):
g_minus_r.append(data_temp[j][2] - data_temp[j][3])
g_minus_r = sc.array(g_minus_r)
avg = sc.mean(g_minus_r)
stdev = sc.std(g_minus_r)
#print avg, stdev
# Find the values that lie outside 'reject' sigma
for j in range(len(g_minus_r)):
diff = ma.fabs(g_minus_r[j] - avg)
if diff > (reject * stdev): kills.append(data_temp[j])
elif diff > thresh: kills.append(data_temp[j])
else: new_temp.append(data_temp[j])
data_temp = new_temp
data_out = data_out + data_temp
print "Stars kept in bin: {0}".format(len(data_temp))
# restructure data
data_out = sc.array(data_out)
print " {0} stars remaining out of {1} original stars".format(
len(data_out[:, 0]), len(data[:, 0]))
g_minus_r_ori = data[:, 2] - data[:, 3]
g_minus_r_out = data_out[:, 2] - data_out[:, 3]
# show plot, then ask to save data set
fig = plt.figure()
#plt.scatter(g_minus_r_ori, data[:,2], 2, marker='o', facecolor="r", edgecolor='face')
plt.scatter(g_minus_r_out, data_out[:, 2], 1, 'k', 'o')
plt.ylim(max, min)
plt.xlim(-1.0, 2.0)
plt.ylabel(r'$g_0$')
plt.xlabel(r"$(g-r)_0$")
plt.show()
fname = raw_input("save data? [filename to save, empty to skip]")
if fname != "":
fi.write_data(data_out, fname, header="#")
print "# --- Done"
import matplotlib as mpl
import matplotlib.pyplot as plt
import files as f
'''selects out only a portion of a data set
Used for f-turnoff stars right now.
---Matthew Newby '''
starfile = "convolved_5904_cluster_lilcon.txt"
outfile = 'convolved_5904_cluster_lilcon_f.txt'
low_limit = 0.0
high_limit = 0.3
y = f.read_data(starfile)
l, w = y.shape
index = sc.zeros(l, int)
for i in range(l):
if (y[i, 2] - y[i, 3]) >= low_limit:
if (y[i, 2] - y[i, 3]) <= high_limit:
index[i] = 1
s = sc.sum(index)
print '-Stars in data set:', l, 'Stars in f-turnoff:', s
new_data = sc.zeros((s, w))
j = 0
for i in range(l):
if index[i] == 1:
new_data[j, :] = y[i, :]
j = j + 1
if (j != s): print '-!!! stars out not equal to f-turnoff:', j, s
if f.write_data(new_data, outfile, ' ') == 1:
print '-F-turnoff stars written successfully'
holder.append(yellow_stars)
holder.append(red_stars)
'''stick it in the value holder'''
print '#---', holder
values.append(holder)
print "### DEAD", killed, "###"
'''Average all the values in 'values' '''
hold_array = sc.array(values)
holder = []
for i in range(10):
holder.append(sc.mean(hold_array[:, i]))
print holder
data_out.append(holder)
f.write_data(
sc.array(data_out),
'new_output.txt',
header=
"distance (kpc), blue-in-blue, b-y, b-r; y-b, y-y, y-r; r-b, r-y, r-r")
print '#---All Done!'
""" Probably crap!
def convolve(data_in, real_dist, con_dist):
a_u = 2.71e-03
b_u = 0.80
c_u = -19.2
a_g = 3.11e-04
b_g = 0.79
c_g = -20.0
a_r = -2.63e-05
b_r = 0.80
c_r = -19.8
diff_mag = 5.0*(np.log10(con_dist / real_dist))
# 'k--')
#now finds the average y of all points in dist. bins of size 2.0,
# and outputs the set of points
if (find_average == 1):
avg_in = f.read_data(file_to_average)
distances = avg_in[:,x]
values = avg_in[:,y]
margins = [9,11,13,15,17,19,21,23,25,27,29]
points = []
for i in range(len(margins)):
holder = []
for j in range(len(avg_in[:,0])):
if ( (avg_in[j,0] > (margins[i]-2) ) and avg_in[j,0] < margins[i]):
holder.append(avg_in[j,y])
data_point = [ (margins[i]-1.0), sc.mean(holder), sc.std(holder) ]
points.append(data_point)
averages = sc.array(points)
plt.errorbar(averages[:,0], averages[:,1], yerr=averages[:,2], fmt='o', color='b',
ecolor='b', markersize=4)
f.write_data(averages, averages_file,
header=('Distance, Average, Standard Deviation for '+axes_names[y]) )
print '#-Printed and plotted averages and standard deviations'
#plt.title('Plot of convolved data')
ax1.set_xlabel(x_label)
ax2.set_xlabel(r'$\bar{g}_0$')
ax1.set_ylabel(y_label)
fig.savefig(plot_file, papertype='letter')
plt.close('all')
print '#-plot successfully created'
print '#---All Plots sucessfully created'
reject = 0
else:
print 'kill length', len(kill_list), 'bin', (i)
for j in range(len(kill_list)):
star_list.pop(kill_list[j])
g_list.pop(kill_list[j])
for k in range(len(kill_list)):
kill_list[k] = (kill_list[k] - 1)
#now have 'iso_array' and 'fit_array' - save to file!
if (save_data == 1):
outname = cluster_name + '_fiducial_out.txt'
dist_str = (str(distance))
header_txt = 'g, g-r, uncertainty mean, from file ' + data_file + ' with distance ' + dist_str
#print out_data
#print out_data.shape
if (f.write_data(fit_array, outname, header=header_txt) == 1):
print 'iso g, g-r, sigma, data successfully saved'
#fit distances? Hard! Need to start from beginning..."""
plt.figure()
y = gc_array[:, 0]
x = gc_array[:, 1]
plt.scatter(x, y, c='black', marker='+', label='HR data')
#plt.errorbar(x,y,xerr=fit_array[:,2])
plt.errorbar(fit_array[:, 1],
fit_array[:, 0],
xerr=fit_array[:, 2],
fmt=None,
ecolor='g')
plt.scatter(fit_array[:, 1],
fit_array[:, 0],
c='green',
l, w = y.shape
g = sc.zeros(l)
r = sc.zeros(l)
for i in range(l):
#print y[i,2], y[i,3]
sigma_g = a_g + np.exp(b_g * (y[i, 2] + diff_mag) + c_g)
sigma_r = a_r + np.exp(b_r * (y[i, 3] + diff_mag) + c_r)
g[i] = np.random.normal(y[i, 2], sigma_g)
r[i] = np.random.normal(y[i, 3], sigma_r)
#print sigma_g, sigma_r, g[i], r[i]
###Two columns, absolute g and g-r
if columns == 2:
m = sc.zeros((l, 2))
for i in range(l):
m[i, 0] = g[i] - 5. * (np.log10(distance * 1000) - 1.)
m[i, 1] = g[i] - r[i]
###Four columns: Ra, Dec, app_g, app_r; usually used for .csv output
elif columns == 4:
m = sc.zeros((l, 4))
for i in range(l):
m[i, 0] = y[i, 0]
m[i, 1] = y[i, 1]
m[i, 2] = g[i]
m[i, 3] = r[i]
else:
m = [0, 0, 0, 0]
print "-Please use a valid column number"
if f.write_data(m, outfile, ',') == 1:
print '-Convolved data written successfully'
# Read arguments
filename = sys.argv[1]
column = int(sys.argv[2])
size = float(sys.argv[3])
if len(sys.argv) > 4:
spread = [float(sys.argv[4]), float(sys.argv[5])]
else:
spread = []
if len(sys.argv) > 6:
name = sys.argv[6]
else:
name = 'quick'
# Load Data
suffix = filename[-4:]
if suffix == '.txt':
data = f.read_data(filename)
elif suffix == '.csv':
data = f.read_csv(filename)
# Run scripts
to_bin = data[:, column]
reg_hist = make_hist(to_bin, size, spread)
plot_histogram(reg_hist, size, name=(name + '_normal'))
f.write_data(reg_hist,
fileout=(name + '_normal.txt'),
header='# Centers, Counts')
cum_hist = cumulative_hist(to_bin, size, spread)
plot_histogram(cum_hist, size, name=(name + '_cumulative'))
f.write_data(cum_hist,
fileout=(name + '_cumulative.txt'),
header='# Centers, Counts')
print '#done with quick histograms'
for i in range(len(data[:,0])):
if low_cut != None:
if data[i,col] < low_cut:
continue
if high_cut != None:
if data[i,col] > high_cut:
continue
holder.append(data[i,:])
return sc.array(holder)
""" Main Cutting Function """
def cut_data(data, *args):
new_data = []
if len(args) == 0:
print "!!! NO ARGUMENTS DEFINING CUT !!!\n"
usage()
sys.exit(2)
for arg in args:
if new_data == []: new_data = cut_once(data, arg)
else: new_data = cut_once(new_data, arg)
return new_data
if __name__ == "__main__":
import files as fi
file_in = "noU_NGC_5466_background.csv"
data = fi.read_data(file_in, ",")
data_out = cut_data(data, (2, 20.0, 21.0), (3, None, 20.0))
fi.write_data(data_out, (file_in[:-4]+"_cut.txt"))
#print data_out
#print "length of data:", len(data[:,0]), "; length of cut data:", len(data_out[:,0])
plt.close('all')
"""
""" for randomly selecting stars from the unmatched star file
import astro_coordinates as coor
data = fi.read_data("no_matches_82.txt", ",")
samples = np.random.uniform(0, len(data[:,0]), 20)
for i in range(len(samples)):
l, b = data[samples[i],0], data[samples[i],1]
ra, dec = coor.lbToEq(l,b)
print float(ra), float(dec)
"""
'''
""" For double-sided Gaussian """
import matplotlib.pyplot as plt
N1 = 642 #N2*(0.36/0.76)
N2 = 1357 #2000.0 / ((0.36/0.76)+1)
a1 = np.random.normal(4.2, 0.36, N1)
a2 = np.random.normal(4.2, 0.76, N2)
holder = []
for i in range(len(a1)):
if a1[i] < 4.2: holder.append(a1[i])
for i in range(len(a2)):
if a2[i] > 4.2: holder.append(a2[i])
x1 = sc.array(holder)
#END OF DOBLE_GAUSSIAN
import functions as func
import time
'''python script for quickly creating test data sets.
Matthew Newby, May 26, 2011'''
out_file = "test_poly_out.txt"
function = func.Nth_order_polynomial
parameters = [11.0, -1.0, 0.5]
x = sc.arange(-4.0, 4.0, 0.1)
y = function(x, parameters)
sig = 1.0 #1 sigma value for errors, takes: None or float
out = sc.ones((len(x), 3))
out[:, 0] = x
if (sig == None): out[:, 1] = y
else:
np.random.seed(int(time.time()))
for i in range(len(y)):
out[i, 1] = np.random.normal(y[i], sig)
out[i, 2] = float(sig)
if fi.write_data(out, out_file, header="#-x, y, sig"):
print "#- File output succeeded"
plt.figure(1)
plt.scatter(out[:, 0], out[:, 1], 2, 'k', 'o')
plt.show()
plt.close('all')
print "# - Done"
return out
def compare_plot(out, size):
#Make plot
fig = plt.figure()
plt.bar(out[:,0], out[:,1], size)
#x = sc.arange(0.0, 47.0, 1.0)
#y = func.SDSS_error_function(x, [1.02, 0.119, -3.53])
#plt.plot(x, y, 'k-')
plt.xlabel(r'Sun-centered $r$ (kpc)')
plt.ylabel(r'$(\rho_{1} / \rho_{2})$ (stars/kpc)')
#plt.savefig(("diff.ps"), papertype='letter')
plt.show()
plt.close('all')
if __name__ == "__main__":
file1="stars-82.txt"
file2="stars-82-coadd2.txt"
file3="matched-82-1arcsec.txt"
mag = 0
size = 1.0
out1 = do_compare_wedges(file1, file3, stripe=82, mag=mag, size=size)
out2 = do_compare_wedges(file3, file2, stripe=82, mag=mag, size=size)
out = sc.zeros((len(out2[:,0]),2))
for i in range(len(out[:,0])):
out[i,0], out[i,1] = out2[i,0], (out1[i,1]*out2[i,1])
compare_plot(out, size)
for i in range(len(out[:,0])):
out[i,0] = out[i,0] + (size/2.0)
fi.write_data(out, "eps1_eps2.txt")
red_stars = red_stars + 1
else:
yellow_stars = yellow_stars + 1
holder.append(blue_stars)
holder.append(yellow_stars)
holder.append(red_stars)
'''Count Data - red'''
blue_stars, yellow_stars, red_stars = 0, 0, 0
for i in range(len(red_con)):
g_minus_r = (red_con[i][2] - red_con[i][3])
if g_minus_r < BLUE_LIMIT:
blue_stars = blue_stars + 1
elif g_minus_r > RED_LIMIT:
red_stars = red_stars + 1
else:
yellow_stars = yellow_stars + 1
holder.append(blue_stars)
holder.append(yellow_stars)
holder.append(red_stars)
'''stick it in the value holder'''
print '#---', holder
values.append(holder)
'''Average all the values in 'values' '''
hold_array = sc.array(values)
holder = []
for i in range(10):
holder.append(sc.mean(hold_array[:,i]))
print holder
data_out.append(holder)
f.write_data(sc.array(data_out), 'new_output.txt')
print '#---All Done!'
fig = plt.figure(i+1)
plt.bar(histograms[i][1][:-1], histograms[i][0], 0.1)
plt.bar(ref[1][:-1], ref[0], 0.1, fill=False, edgecolor='r', ls="dashed")
plt.xlabel(r"[Fe/H]")
plt.ylabel("counts")
if ids != None:
out = "{0} kpc, {1} stars,\n{2} of original".format(ids[i],
t_stars, round( (float(t_stars)/float(o_stars)), 2 ) )
plt.text(0.0, 120.0, out, fontsize=12)
plt.savefig(tag+"_"+str(ids[i])[:5]+".ps", papertype='letter')
#plt.show()
if __name__ == "__main__":
d = sc.array([7.7, 10.0, 15.0, 20.0, 25.0, 30.0, 40.0])
#d = sc.arange(1.0, 82.0, 10.0)
tag = "real_noFx"
averages = 10
limits = (-4.0, 2.0)
bins = (limits[1] - limits[0]) / 0.1
print "limits = {0}, bins = {1}, averages = {2}".format(limits, bins, averages)
output = metal_con("NGC_6205_nofield.txt", d, 7.7, bins=bins, limits=limits,
avgs=averages, detection=1, tag=tag)
for i in range(len(output)):
out = sc.zeros( (len(output[i][0]),2), float)
out[:,0], out[:,1] = output[i][0], output[i][1][:-1]
#print out.shape
fi.write_data(out, "hist_"+tag+"_"+str(d[i])[:5]+".txt", header=tag+"_"+str(d[i])[:5])
#do background as well?
""" Address distance, color cuts? Distances only make sense when convolving a
cluster, need to modify for convolution of background."""
print "# --- Done"
start_params = sc.array([0.2, 0.1, sc.ma.max(y_col)])
steps = sc.array([0.001, 0.001, 0.01])
function = func.gaussian_function
#Fit function
MCMC_fit, errors, best_fit = mc.do_MCMC(function,
start_params,
steps,
x_col,
y_col,
sig_col,
name=('histfit_' + str(i)),
number_steps=10000,
save=0)
best_fit = gd.gradient_descent(function, best_fit, x_col, y_col, sig_col)
errors = he.get_hessian_errors(function, best_fit, x_col, y_col, sig_col,
steps)
turnoff.append([best_fit[0], best_fit[1], errors[0], errors[1]])
# if (pdf.plot_function(x_col, y_col, function, best_fit, save_name=('histfitplot_'+str(i)),
# title=['', r'$(g-r)_0$', 'Normalized Counts'], save=1) == 1):
plt.figure()
plt.scatter(x_col, y_col)
func_x = sc.arange((sc.ma.min(x_col) * 0.9), (sc.ma.max(x_col) * 1.1),
0.01)
func_y = function(func_x, best_fit)
plt.plot(func_x, func_y)
plt.savefig(('histfitplot_' + str(i) + '.ps'), papertype='letter')
print "#-Fit dataplot saved"
#print turnoff
print turnoff
if f.write_data(sc.array(turnoff), 'turnoff_out_fixed.txt') == 1:
print "#-All Results Successfully saved"
fit_array[(i-1),2] = (fit_array[(i-1),2] / float(len(star_list)))
reject = 0
else:
print 'kill length', len(kill_list), 'bin', (i)
for j in range(len(kill_list)):
star_list.pop(kill_list[j])
g_list.pop(kill_list[j])
for k in range(len(kill_list)): kill_list[k] = (kill_list[k]-1)
#now have 'iso_array' and 'fit_array' - save to file!
if (save_data==1):
outname = cluster_name + '_fiducial_out.txt'
dist_str = (str(distance))
header_txt = 'g, g-r, uncertainty mean, from file ' + data_file + ' with distance ' + dist_str
#print out_data
#print out_data.shape
if (f.write_data(fit_array, outname, header=header_txt) == 1):
print 'iso g, g-r, sigma, data successfully saved'
#fit distances? Hard! Need to start from beginning..."""
plt.figure()
y = gc_array[:,0]
x = gc_array[:,1]
plt.scatter(x,y, c='black', marker='+', label='HR data')
#plt.errorbar(x,y,xerr=fit_array[:,2])
plt.errorbar(fit_array[:,1],fit_array[:,0],xerr=fit_array[:,2], fmt=None, ecolor='g')
plt.scatter(fit_array[:,1],fit_array[:,0], c='green', label='fiducial sequence')
plt.title(plot_title, fontsize='small')
plt.xlabel('g-r')
plt.ylabel('Mg')
leg = plt.legend(loc='best')
for t in leg.get_texts():
t.set_fontsize('small')
import time
'''python script for quickly creating test data sets.
Matthew Newby, May 26, 2011'''
out_file = "test_poly_out.txt"
function = func.Nth_order_polynomial
parameters = [11.0, -1.0, 0.5]
x = sc.arange(-4.0, 4.0, 0.1)
y = function(x, parameters)
sig = 1.0 #1 sigma value for errors, takes: None or float
out = sc.ones((len(x), 3))
out[:,0] = x
if (sig == None): out[:,1] = y
else:
np.random.seed( int(time.time()) )
for i in range(len(y)):
out[i,1] = np.random.normal(y[i], sig)
out[i,2] = float(sig)
if fi.write_data(out, out_file, header="#-x, y, sig"):
print "#- File output succeeded"
plt.figure(1)
plt.scatter(out[:,0],out[:,1],2,'k','o')
plt.show()
plt.close('all')
print "# - Done"
plt.close('all')
return 1
if __name__ == '__main__':
print '#- number of arguments:', (len(sys.argv)-1)
# Read arguments
filename = sys.argv[1]
column = int(sys.argv[2])
size = float(sys.argv[3])
if len(sys.argv) > 4:
spread = [float(sys.argv[4]), float(sys.argv[5])]
else: spread = []
if len(sys.argv) > 6:
name = sys.argv[6]
else: name = 'quick'
# Load Data
suffix = filename[-4:]
if suffix == '.txt':
data = f.read_data(filename)
elif suffix == '.csv':
data = f.read_csv(filename)
# Run scripts
to_bin = data[:,column]
reg_hist = make_hist(to_bin, size, spread)
plot_histogram(reg_hist, size, name=(name+'_normal') )
f.write_data(reg_hist, fileout=(name+'_normal.txt'), header='# Centers, Counts')
cum_hist = cumulative_hist(to_bin, size, spread)
plot_histogram(cum_hist, size, name=(name+'_cumulative') )
f.write_data(cum_hist, fileout=(name+'_cumulative.txt'), header='# Centers, Counts')
print '#done with quick histograms'
def sgr_xz_plot(folder="./sep_lbr/",
bin_size=1.0,
outfile=None,
infile=None,
primary=1,
color=1,
scale=1):
x_min, x_max, x_size = -50.0, 50.0, bin_size
z_min, z_max, z_size = 0.0, 100.0, bin_size
x_bins, z_bins = int((x_max - x_min) / x_size) + 1, int(
(z_max - z_min) / z_size) + 1
if infile == None:
H = sc.zeros((z_bins, x_bins), float)
files = glob.glob(folder + "/*.txt")
for file in files:
data = fi.read_data(file)
wedge = int(file[-6:-4])
for i in range(len(data[:, 0])):
if primary == 1:
if test_primary(data[i, 0],
data[i, 1],
wedge,
low=9,
high=23) == 0:
continue
x, y, z = coor.lbr2xyz(data[i, 0], data[i, 1], data[i, 2])
if x < x_min or x > x_max: continue
if z < z_min or z > z_max: continue #FLAG
xi, zi = int((x - x_min) / x_size), int(
(z - z_min) / z_size) #FLAG
H[zi, xi] = H[zi, xi] + 1.0
if outfile != None: fi.write_data(H, outfile)
else: H = fi.read_data(infile)
if scale == 1: H = sc.sqrt(H)
#Make the plot
plt.figure(1)
sp = plt.subplot(111)
if color == 1: cmap = spectral_wb
else: cmap = binary
plt.imshow(H, interpolation='nearest', cmap=cmap) #vmin=0.0, vmax=15.0)
bar = plt.colorbar(orientation='vertical')
if scale == 1:
bar_ticks = sc.arange(0.0, 17.0, 4.0)
bar_labels = []
for i in range(len(bar_ticks)):
bar_labels.append(str(int(bar_ticks[i]**2)))
bar.set_ticks(bar_ticks)
bar.set_ticklabels(bar_labels, update_ticks=True)
Earth = [-8.5, 0.0, 0.0]
plt.scatter((Earth[0] - x_min) / x_size, (Earth[2] - z_min) / z_size,
c='yellow',
s=5)
# Clean up
y_lim = plt.ylim()
plt.ylim(y_lim[1], y_lim[0])
#plt.xlim(-25.0, 270.0)
#plt.ylim(-25.0, 270.0)
#plt.setp(sp.get_xticklabels(), visible=False)
#plt.setp(sp.get_yticklabels(), visible=False)
#plt.setp(sp.get_xticklines(), visible=False)
#plt.setp(sp.get_yticklines(), visible=False)
# Show plot
plt.show()
plt.close('all')
plt.text(-0.2, 16.0, "NGC 5024 - dirty", fontsize=8)
plt.show()
plt.close('all')
"""
""" for randomly selecting stars from the unmatched star file
import astro_coordinates as coor
data = fi.read_data("no_matches_82.txt", ",")
samples = np.random.uniform(0, len(data[:,0]), 20)
for i in range(len(samples)):
l, b = data[samples[i],0], data[samples[i],1]
ra, dec = coor.lbToEq(l,b)
print float(ra), float(dec)
"""
'''
""" For double-sided Gaussian """
import matplotlib.pyplot as plt
N1 = 642 #N2*(0.36/0.76)
N2 = 1357 #2000.0 / ((0.36/0.76)+1)
a1 = np.random.normal(4.2, 0.36, N1)
a2 = np.random.normal(4.2, 0.76, N2)
holder = []
for i in range(len(a1)):
if a1[i] < 4.2: holder.append(a1[i])
for i in range(len(a2)):
if a2[i] > 4.2: holder.append(a2[i])
x1 = sc.array(holder)
#END OF DOBLE_GAUSSIAN
def lbpolar_plot(folder,
hemi='N',
bin_size=1.0,
outfile=None,
infile=None,
color=1,
scale=1,
primary=1):
""" Makes a polar plot centered at the north or south Galactic caps,
using all files in a given folder. Files should be in l,b,r format.
hemi: 'N' for Northern Galactic cap, 'S' for Southern Galactic cap
bin_size: size of bins, in (roughly) degrees
outfile: if not None, outputs the derived histogram to the file
infile: if not None, reads in the histogram from the file
color: 1 for spectral color map, 0 for black and white
scale: 1 to sqrt bin counts, 0 to leave the bin counts
primary: 1 to test wedges for primary stars (SDSS), 0 to skip test
"""
x_size, y_size, b_min = bin_size, bin_size, 30.0
b_min = 90.0 - b_min # Flips it around so that 90.0 is at the origin
# Initialize custom histogram
x_bins, y_bins = int(2.0 * b_min / x_size) + 1, int(
2.0 * b_min / y_size) + 1
if infile == None:
H = sc.zeros((y_bins, x_bins), float)
# Iterate over files
files = glob.glob(folder + "/*.txt")
for file in files:
data = fi.read_data(file)
wedge = int(file.split("/")[-1][6:8])
if hemi == 'S': data[:, 1] = -1.0 * data[:, 1]
for i in range(len(data[:, 0])):
if primary == 1:
if test_primary(data[i, 0],
data[i, 1],
wedge,
low=9,
high=23) == 0:
continue
xi = int(
((90.0 - data[i, 1]) * sc.cos(data[i, 0] * rad) + b_min) /
x_size)
yi = int(
((90.0 - data[i, 1]) * sc.sin(data[i, 0] * rad) + b_min) /
y_size)
if xi < 0 or xi > x_bins - 1: continue
if yi < 0 or yi > y_bins - 1: continue
H[yi, xi] = H[yi, xi] + 1.0
if outfile != None: fi.write_data(H, outfile)
else: H = fi.read_data(infile, delimiter=None)
if scale == 1: H = sc.sqrt(H)
# plot data
plt.figure(1)
sp = plt.subplot(111)
if color == 1: cmap = spectral_wb
else: cmap = binary
plt.imshow(H, interpolation='nearest', cmap=cmap, vmin=0.0,
vmax=12.0) #extent=extent,
bar = plt.colorbar(orientation='vertical')
if scale == 1:
bar_ticks = sc.arange(0.0, 17.0, 4.0)
bar_labels = []
#for i in range(len(bar_ticks)):
# bar_labels.append(str(int(bar_ticks[i]**2)))
#bar.set_ticks(bar_ticks)
#bar.set_ticklabels(bar_labels, update_ticks=True)
# plot axes - l
for i in [30.0, 45.0, 60.0, 75.0]:
l = sc.arange(0.0, 361.0, 1.0)
x = ((90.0 - i) /
(bin_size)) * sc.cos(l * rad) + (b_min / x_size) # FLAG x_size
y = ((90.0 - i) /
(bin_size)) * sc.sin(l * rad) + (b_min / y_size) # FLAG y_size
if i % 2 == 0: plt.plot(x, y, 'k-')
else: plt.plot(x, y, 'k:', linewidth=0.5)
if hemi == 'S':
plt.text(x[89] + 1.0,
y[89] + 1.0,
"$b=" + str(-1.0 * int(i)) + "^{\circ}$",
horizontalalignment='left',
verticalalignment='bottom',
fontsize=12)
else:
plt.text(x[89] + 1.0,
y[89] + 1.0,
r"$b=" + str(int(i)) + r"^{\circ}$",
horizontalalignment='left',
verticalalignment='bottom',
fontsize=12)
# plot axes - b
for i in sc.arange(0.0, 180.0, 15.0):
b = sc.arange(-1.0 * (b_min + 5.0),
(b_min + 5.0) + 1.0, 10.0) / bin_size # FLAG
x = b * sc.cos(i * rad) + (b_min / x_size) # FLAG x_size
y = b * sc.sin(i * rad) + (b_min / y_size) # FLAG y_size
if i % 90 == 0: plt.plot(x, y, 'k-')
else: plt.plot(x, y, 'k:')
if i % 30 == 0:
if i == 0.0: pre = r"$l="
else: pre = r"$"
x0 = (b[0] - 4.0) * sc.cos(i * rad) + (b_min / x_size
) # FLAG x_size
y0 = (b[0] - 4.0) * sc.sin(i * rad) + (b_min / y_size
) # FLAG y_size
plt.text(x0,
y0,
r"$" + str(int(i + 180.0)) + r"^{\circ}$",
horizontalalignment='center',
verticalalignment='center',
fontsize=12,
rotation=(i + 90.0))
x1 = (b[-1] + 4.0) * sc.cos(i * rad) + (b_min / x_size
) # FLAG x_size
y1 = (b[-1] + 4.0) * sc.sin(i * rad) + (b_min / y_size
) # FLAG y_size
plt.text(x1,
y1,
pre + str(int(i)) + r"^{\circ}$",
horizontalalignment='center',
verticalalignment='center',
fontsize=12,
rotation=(i - 90.0))
# Clean up
#y_lim = plt.ylim()
#plt.ylim(y_lim[1], y_lim[0])
plt.xlim(-25.0, 270.0)
plt.ylim(-25.0, 270.0)
plt.setp(sp.get_xticklabels(), visible=False)
plt.setp(sp.get_yticklabels(), visible=False)
plt.setp(sp.get_xticklines(), visible=False)
plt.setp(sp.get_yticklines(), visible=False)
# Show plot
plt.show()
plt.close('all')
star_list.pop(kill_list[j])
g_list.pop(kill_list[j])
for k in range(len(kill_list)):
kill_list[k] = (kill_list[k] - 1)
#Fit strips to Girardi isocrones using different fit functions
#now have 'iso_array' and 'fit_array' - save to file!
if (save_data == 1):
outname = cluster_name + '_out.txt'
out_data = sc.zeros((iso_l, 4), float)
out_data[:,0], out_data[:,1], out_data[:,2], out_data[:,3] = \
iso_array[:,0], iso_array[:,1], fit_array[:,1], fit_array[:,2]
dist_str = (str(distance))
header_txt = 'g, iso g-r, fiducial g-r, uncertainty mean, from file ' + iso_file + ' with distance ' + dist_str
#print out_data
#print out_data.shape
if (f.write_data(out_data, outname, header=header_txt) == 1):
print 'iso g, iso g-r, fid g-r, fiducial sigma, data successfully saved'
#fit distances? Hard! Need to start from beginning..."""
plt.figure()
A = -0.01463
B = 0.08929
y = iso_array[:, 0]
x = (fit_array[:, 1] - iso_array[:, 1])
plt.scatter(x, y, c='black', marker='+', label='Subtracted data')
plt.plot([0.0, 0.0], [np.ma.min(iso_array[:, 0]),
np.ma.max(iso_array[:, 0])],
'r-',
label='_nolegend_')
plt.plot([(A * np.ma.min(y) + B), (A * np.ma.max(y) + B)],
[np.ma.min(y), np.ma.max(y)],
'g:',
#plt.show()
if __name__ == "__main__":
d = sc.array([7.7, 10.0, 15.0, 20.0, 25.0, 30.0, 40.0])
#d = sc.arange(1.0, 82.0, 10.0)
tag = "real_noFx"
averages = 10
limits = (-4.0, 2.0)
bins = (limits[1] - limits[0]) / 0.1
print "limits = {0}, bins = {1}, averages = {2}".format(
limits, bins, averages)
output = metal_con("NGC_6205_nofield.txt",
d,
7.7,
bins=bins,
limits=limits,
avgs=averages,
detection=1,
tag=tag)
for i in range(len(output)):
out = sc.zeros((len(output[i][0]), 2), float)
out[:, 0], out[:, 1] = output[i][0], output[i][1][:-1]
#print out.shape
fi.write_data(out,
"hist_" + tag + "_" + str(d[i])[:5] + ".txt",
header=tag + "_" + str(d[i])[:5])
#do background as well?
""" Address distance, color cuts? Distances only make sense when convolving a
cluster, need to modify for convolution of background."""
print "# --- Done"
