def sgr_rv_fits():
data = np.loadtxt("/home/newbym2/Dropbox/Research/sgrLetter/sgr_spec.csv", delimiter=",")
#dered_g,dered_r,l,b,ELODIERVFINAL,ELODIERVFINALERR
g0, r0 = data[:,0], data[:,1]
l, b = data[:,2], data[:,3]
rv, rv_err = data[:,4], data[:,5]
d = ac.getr(g0)
# Transform to vgsr from Yanny+ 2009
vgsr = ac.rv_to_vgsr(rv,l,b)
X,Y,Z, lsgr, bsgr, r_sgr = ac.lb2sgr(l, b, d)
for w in [0.5, 1.0, 2.5, 5.0, 7.5, 10.0, 15.0, 20.0, 25.0, 30.0, 50.0]:
keep = []
for i in range(len(data[:,0])):
if abs(bsgr[i]) < w:
if g0[i] < 20.0: continue
if g0[i] > 23.0: continue
keep.append(vgsr[i])
hist, edges = np.histogram(np.array(keep), bins=60, range=(-300.0, 300.0))
y, x = hist, edges[:-1]
e = func.poisson_errors(y)
fitter = fit.ToFit(x,y,e)
fitter.function=func.double_gaussian_one_fixed
fitter.update_params([3.0, -120.0, 30.0, 2.0, 0.0, 120.0])
fitter.step = [1.0, 10.0, 1.0, 1.0, 0.0, 0.0]
fitter.param_names = ["amp", "mu", "sigma", "amp", "mu", "sigma"]
path1 = fit.gradient_descent(fitter, its=10000, line_search=0)
path2 = fit.MCMC(fitter)
new_params=fitter.params
xx = sc.arange(-300.0, 300.0, 1.0)
yy = func.double_gaussian_one_fixed(xx, new_params)
y1 = func.gaussian_function(xx, new_params[:3])
y2 = func.gaussian_function(xx, new_params[3:])
fig = plt.figure()
plt.bar(edges[:-1], hist, width=10.0, color="white")
plt.plot(xx,yy, "k-")
plt.plot(xx,y1, "k--")
plt.plot(xx,y2, "k--")
plt.title("cut width = "+str(w))
plt.xlabel(r"$v_{\rm gsr}$", fontsize=16)
plt.ylabel(r"Counts")
#plt.ylim(0.0, 60.0)
#plt.show()
plt.savefig("/home/newbym2/Dropbox/Research/sgrLetter/sgr_spec/r_cut_relative"+str(w)+".png")
plt.close()
i += 1
if eval(name) > 291: i = -20 #-20
elif eval(name) > 274: i = -40
elif eval(name) < 201: i = -40
else: i = -1
while len(x1) < 5:
if y[i] > minbin: x1.append(x[i]); y1.append(y[i])
i -= 1
xi, yi = np.mean(x0), np.mean(y0)
xf, yf = np.mean(x1), np.mean(y1)
#xi, yi = np.mean(x[2:6]), np.mean(y[2:6])
#xf, yf = np.mean(x[-7:-3]), np.mean(y[-7:-3])
aa = (yf-yi)/(xf-xi)
bb = yf - (aa*xf)
#fit it
fitter = fit.ToFit(x,y,e)
"""
fitter.function=func.double_gauss_line
fitter.update_params([6.0, 0.0, 5.0, 6.0, -6.0, 5.0, aa, bb])
fitter.step = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]
fitter.param_names = ["amp", "mu", "sigma", "amp", "mu", "sigma", "slope", "intercept"]
"""
fitter.function=func.quad_fat_gauss_line
fitter.update_params([6.0, 5.0, 1.0, 6.0, -10.0, 1.0, 5.0, 10.0, 5.0, 10.0, aa, bb])
fitter.step = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]
fitter.param_names = ["amp", "mu", "sigma", "amp", "mu", "sigma", "amp","sigma", "amp","sigma","slope","intercept"]
"""
fitter.function=func.double_gaussian
fitter.update_params([20.0, 0.0, 5.0, 20.0, 20.0, 15.0])
fitter.step = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
fitter.param_names = ["amp", "mu", "sigma", "amp", "mu", "sigma"]
for i in run:
print "# --- Fitting region {0}".format(i)
data = fi.read_data("/home/newbym2/Dropbox/Research/Cetus_Polar/bhb_dmod" +
str(i) + ".txt",
skip=1)
print "# --- l-coordinate mean, std: {0}, {1}".format(
np.mean(data[:, 0]), np.std(data[:, 0]))
b_hist, b_edges = np.histogram(data[:, 1], bins, range=[80.0, 180.0])
b_err = func.poisson_errors(b_hist)
b_wid = (np.ma.max(data[:, 1]) - np.ma.min(data[:, 1])) / bins
d_hist, d_edges = np.histogram(data[:, 2], bins, range=[16.0, 19.0])
d_err = func.poisson_errors(d_hist)
d_wid = (np.ma.max(data[:, 2]) - np.ma.min(data[:, 2])) / bins
# Initialize
fitter = fit.ToFit(d_edges[:-1] + (d_wid / 2.0), d_hist, d_err)
fitter.function = func.gauss_plus_floor
fitter.update_params(start_params)
fitter.step = [0.01, 0.01, 0.01, 0.01]
fitter.param_names = ["amp", "mu", "sigma", "floor"]
fitter.min = [None, None, 0.0, None]
fitter.max = [None, None, None, None]
print "# - Starting from:", fitter.params, fitter.RR
print "# - With steps:", fitter.step
# Fit
path1 = fit.gradient_descent(fitter, its=10000, line_search=0)
errors = fit.get_Hessian_errors(fitter)
gd_params = fitter.params, fitter.RR
#fitter.update_params(start_params)
print "# - Starting from:", fitter.params, fitter.RR
path2 = fit.MCMC(fitter, iters=10000, annealing=0, verbose=0)
import numpy as np
import scipy as sc
import functions as func
import files as fi
import fit as fit
import plane_fit as pf
import astro_coordinates as co
#data = fi.read_data("../sgrnorth_paper/Newberg2007_Sgr.txt", ",")
#data = fi.read_data("../sgrnorth_paper/Koposov2012_Sgr.txt", ",")
data = fi.read_data("../sgrnorth_paper/Koposov2012_2nd.txt", ",")
# In fit parameter format:
#data = fi.read_data("../sgrnorth_paper/sgr_params_south.txt", ",")
#data = fi.read_data("../sgrnorth_paper/sgr_params_north.txt", ",")
fitter=fit.ToFit(data[:,0], data[:,1], "1.0")
"""
# gots to be tricksy, yes... plane fitting requires dark magiks...
#wedges = [9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]
#wedges = [79, 82, 86]
# errors from sgr_north_utils.py
#errors = [9.31738638849, 9.1020519394, 19.7856077035, 20.8318952656, 9.39314333747,
# 26.9966491404, 40.6343350862, 15.3631542798, 11.270906999, 10.3242787823,
# 10.290985838, 8.75140991672, 14.6281676367, 16.0532641683, 6.69586402716]
#errors = [10.3012569286, 9.26151305927, 10.4476312495] # Errors are wrong!!
# These are right?
# 7.57640162772, 7.67810980299, 11.5363484879, 25.6937588232, 8.75918164562,
# 37.6255221984, 54.5484852169, 13.1237001504, 12.2803690866, 11.9300143607,
# 12.2150368604, 13.3084651939, 14.9866923184, 15.7846605126, 27.9365850481,
# 11.2583497941, 10.1111224829, 9.12819009006
to_x = sc.zeros((len(data[:,0]), 3) ) # RAISE THIS TO 4? WHEN ERRORS ARE USED
def fit_hists(x_raw,
y_raw,
name,
outfile=None,
outpath="",
fit_type='double',
cuts=None,
background="common",
binsize=0.5,
lam_offset=0.0):
run = eval(name.split("_")[-1])
# clean data; don't fit bins with certain criteria
if cuts != None:
mask = sc.ones(len(y_raw), dtype=bool)
for i in range(cuts.shape[0]):
if cuts[i, 0] + lam_offset == run: #for offset Lambda wedges
if binsize == 0.5:
for j in range(cuts[i, 1], cuts[i, 2]):
mask[(j - 1)] = cuts[i, 3]
if binsize == 1.0:
for j in range(cuts[i, 1] / 2, cuts[i, 2] / 2):
mask[(j - 1)] = cuts[i, 3]
else:
mask = sc.zeros(len(y_raw), dtype=bool)
for i in range(len(y_raw)):
if y_raw[i] <= 0.0: mask[i] = 1 #flag zeros
#if y_raw[i] > 1000.0: mask[i] = 1 #flag globular clusters
# make new array that contains only mask==0
x, y = [], []
for i in range(len(y_raw)):
if mask[i] == 0:
x.append(x_raw[i])
y.append(y_raw[i])
# Set up data
x, y = np.array(x), np.array(y)
e = func.poisson_errors(y)
if background == "common":
# Make a flat background line set to most common bin height
hline, hedges = np.histogram(y, bins=40, range=(0.0, 1000.0))
best = 25.0 * np.argmax(hline) - 12.5
aa, bb = 0.0, best
elif background == "least":
ids = np.argsort(y)[:10]
aa, bb = 0.0, np.mean(y[ids])
elif background == "fitline":
# make a line, using the average of 10 bins on each end as the anchor points
x0, y0, x1, y1 = [], [], [], []
minbin = 1.0
if run < 120: i = 10
else: i = 1
while len(x0) < 10:
if y[i] > minbin:
x0.append(x[i])
y0.append(y[i])
i += 1
if run < 120: i = (len(y) / 2.0) - 5
elif run > 254: i = 65
else: i = -1
while len(x1) < 10:
if y[i] > minbin:
x1.append(x[i])
y1.append(y[i])
i -= 1
xi, yi = np.mean(x0), np.mean(y0)
xf, yf = np.mean(x1), np.mean(y1)
#xi, yi = np.mean(x[2:6]), np.mean(y[2:6])
#xf, yf = np.mean(x[-7:-3]), np.mean(y[-7:-3])
aa = (yf - yi) / (xf - xi)
bb = yf - (aa * xf)
else:
# null line
aa, bb = 0.0, 0.0
# fit it
fitter = fit.ToFit(x, y, e)
if fit_type == "double":
fitter.function = func.double_gauss_line
fitter.update_params([6.0, 0.0, 5.0, 6.0, -6.0, 5.0, aa, bb])
fitter.step = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
fitter.param_names = [
"amp", "mu", "sigma", "amp", "mu", "sigma", "slope", "intercept"
]
elif fit_type == "quad":
fitter.function = func.quad_fat_gauss_line
fitter.update_params(
[6.0, 5.0, 1.0, 6.0, -10.0, 1.0, 5.0, 10.0, 5.0, 10.0, aa, bb])
fitter.step = [
1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0
]
fitter.param_names = [
"amp", "mu", "sigma", "amp", "mu", "sigma", "amp", "sigma", "amp",
"sigma", "slope", "intercept"
]
elif fit_type == "triple":
if run < 120: tf, tm = 200.0, -50.0
if run > 120: tf, tm = 150.0, 40.0
fitter.function = func.triple_gauss_floor
fitter.update_params(
[6.0, 5.0, 1.0, 6.0, -10.0, 1.0, 2.0, tm, 40.0, tf])
fitter.step = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 5.0, 10.0]
fitter.param_names = [
"amp", "mu", "sigma", "amp", "mu", "sigma", "amp", "mu", "sigma",
"floor"
]
print "\n {0}:".format(name)
print "# - Starting from:", fitter.params, fitter.RR
print "# - With steps:", fitter.step
path1 = fit.gradient_descent(fitter, its=10000, line_search=0)
path2 = fit.MCMC(fitter)
#errors = fit.get_Hessian_errors(fitter)
# cut from ToFit
new_params = fitter.params
xx = np.arange(-70.0, 40.0, 0.1)
plt.figure(1)
plt.bar(x_raw, y_raw, binsize, align='center', color='white', zorder=1)
plt.bar(fitter.x,
fitter.y,
binsize,
align='center',
color='grey',
zorder=2)
plt.plot(xx, fitter.function(xx, new_params), 'k-')
print new_params[:4]
plt.plot(xx, func.gaussian_function(xx, new_params[:3]), 'k--')
plt.plot(xx, func.gaussian_function(xx, new_params[3:6]), 'k--')
if fit_type == "triple":
plt.plot(
xx,
func.gaussian_function(
xx, [new_params[6], new_params[7], new_params[8]]), 'k--')
plt.plot(xx, new_params[9] * sc.ones(len(xx)), "k--")
if fit_type == "quad":
plt.plot(
xx,
func.gaussian_function(
xx, [new_params[6], new_params[1], new_params[7]]), 'k--')
plt.plot(
xx,
func.gaussian_function(
xx, [new_params[8], new_params[4], new_params[9]]), 'k--')
plt.xlabel("B")
plt.ylabel("Counts")
plt.xlim(-70.0, 40.0)
plt.ylim(0.0, 700.0)
#plt.text(-65.0, 1100.0, name, fontsize=8 )
out_name = outpath + name + "_plot" + ".png"
#plt.show()
plt.savefig(out_name)
plt.close('all')
return np.insert(np.concatenate((fitter.params, fitter.error)), -1,
fitter.RR)