/
visualize.py
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/
visualize.py
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import matplotlib.pyplot as pl
import scipy.stats as sta
import stats as st
import numpy as np
def splineevalplot(spline,spec,lmbd):
pl.plot(lmbd,spec,'r')
lmbds = np.linspace(lmbd[0],lmbd[-1],1e4)
pl.plot(lmbds,spline(lmbds),'k')
pl.show()
def kde(measure,axis=None,norm=False):
rt = sta.gaussian_kde(measure)
x = np.linspace(measure.min(),measure.max(),121)
if norm:
norm = rt(x).sum()
else:
norm = 1
if axis is not None:
axis.plot(x,rt(x)/norm)
return axis
else:
pl.plot(x,rt(x)/norm)
pl.show()
def kde_multiplot(datlist,order=None):
cols = 3
rows = int(np.ceil(len(datlist)/cols))
fig,ax = pl.subplots(rows,cols)
if order is None:
order = range(0,len(datlist))
for el in list(order):
fig.axes[el] = kde(datlist[el],fig.axes[el])
return fig
def make_linemap_lims(measure):
props = [ "bot", "vel", "fwhm", "as12", "fw13", "as13", "fw23", "as23", "err", "ew", "con"]
proplims = {}
lims = {}
for i,name in enumerate(props):
proplims[name] = []
for line in measure:
# Argsort returns the index, pick third lowest and third highest value as limits
if name == "ew":
mew = line[:,i].mean()
proplims[name].append(line[ line[:,i].argsort()[[3,-2]],i ]/mew)
else:
proplims[name].append(line[ line[:,i].argsort()[[3,-2]],i ])
most = np.max(proplims[name]); least = np.min(proplims[name])
# print(least,most)
dx = (most-least)*5e-2
if name == "bot":
name = "rel"
lims["{}lim".format(name)] = (least-dx,most+dx)
return lims
def spline_linemap(measure,line,mesbin=None,lims=None,errs=None,regbins=73):
bot = 0; vel = 1; fwhm = 2; as12 = 3; fw13 = 4; as13 = 5; fw23 = 6; as23 = 7; err = 8; ew = 9; con = 10;
if lims is None:
ewlim = ( 0.3 , 1.8 )
vellim = (-5.8 , 6.1 )
rellim = ( 0.2 , 1.3 )
fw13lim = ( 0.0 , 1.0 )
fwhmlim = ( 0.0 , 1.0 )
fw23lim = ( 0.0 , 1.0 )
as13lim = (-0.2 , 0.2 )
as12lim = (-0.2 , 0.2 )
as23lim = (-0.1 , 0.1 )
else:
ewlim = lims["ewlim"]
vellim = lims["vellim"]
rellim = lims["rellim"]
fw13lim = lims["fw13lim"]
fwhmlim = lims["fwhmlim"]
fw23lim = lims["fw23lim"]
as13lim = lims["as13lim"]
as12lim = lims["as12lim"]
as23lim = lims["as23lim"]
fig, axs = pl.subplots(3,3,sharex=True)
mew = measure[:,ew].mean()
prop_plot(axs[0,0],measure[:,con],measure[:,ew]/mew,
{"label" : "Average EW = {:.3f}".format(mew),
"title" : "Relative equivalent width",
"ylabel": "Relative equivalent width",
"xlabel": "Continuum intensity",
"ylim" : ewlim},regbins)
prop_plot(axs[0,1],measure[:,con],measure[:,vel],
{"title" : "Line centre",
"ylabel": "Line centre [km/s]",
"xlabel": "Continuum intensity",
"ylim" : vellim},regbins)
prop_plot(axs[0,2],measure[:,con],measure[:,bot],
{"title" : "Relative line bottom",
"ylabel": "Relative Line min intesity",
"xlabel": "Continuum intensity",
"ylim" : rellim},regbins)
prop_plot(axs[1,0],measure[:,con],measure[:,fw13],
{"title" : "Full width 1/3 maximum",
"ylabel": "Relative line width",
"xlabel": "Continuum intensity",
"ylim" : fw13lim},regbins)
prop_plot(axs[1,1],measure[:,con],measure[:,fwhm],
{"title" : "Full width half maximum",
"ylabel": "Relative line width",
"xlabel": "Continuum intensity",
"ylim" : fwhmlim},regbins)
prop_plot(axs[1,2],measure[:,con],measure[:,fw23],
{"title" : "Full width 2/3 maximum",
"ylabel": "Relative line width",
"xlabel": "Continuum intensity",
"ylim" : fw23lim},regbins)
prop_plot(axs[2,0],measure[:,con],measure[:,as13],
{"title" : "Line asymmetry 1/3 maximum",
"ylabel": "Line asymmetry",
"xlabel": "Continuum intensity",
"ylim" : as13lim},regbins)
prop_plot(axs[2,1],measure[:,con],measure[:,as12],
{"title" : "Line asymmetry half maximum",
"ylabel": "Line width [Å]",
"xlabel": "Continuum intensity",
"ylim" : as12lim},regbins)
prop_plot(axs[2,2],measure[:,con],measure[:,as23],
{"title" : "Line asymmetry 2/3 maximum",
"ylabel": "Line asymmetry",
"xlabel": "Continuum intensity",
"ylim" : as23lim},regbins)
if mesbin is not None:
axs[0,0].plot(mesbin[:,con],mesbin[:,ew]/mew,'ko')
axs[0,1].plot(mesbin[:,con],mesbin[:,vel],'ko')
axs[0,2].plot(mesbin[:,con],mesbin[:,bot],'ko')
axs[1,0].plot(mesbin[:,con],mesbin[:,fw13]/line.width,'ko')
axs[1,1].plot(mesbin[:,con],mesbin[:,fwhm]/line.width,'ko')
axs[1,2].plot(mesbin[:,con],mesbin[:,fw23]/line.width,'ko')
axs[2,0].plot(mesbin[:,con],(mesbin[:,as13]-measure[:,as13].mean())/line.width,'ko')
axs[2,1].plot(mesbin[:,con],(mesbin[:,as12]-measure[:,as12].mean())/line.width,'ko')
axs[2,2].plot(mesbin[:,con],(mesbin[:,as23]-measure[:,as23].mean())/line.width,'ko')
return fig
def add_errs_linemap(fig,errs,mew,xs=None,ys=None):
porder = np.array([9,1,0,4,2,6,5,3,7])
if xs is None:
xs = np.ones(11)*1.17
if ys is None:
ys = [1.2,4.5,0.93,0.85,0.87,0.9,0.0015,0.0015,0.0015,1,0]
perr = np.abs(errs[porder,:])
perr[0,:] = perr[0,:]/mew
for i in range(0,9):
s2err = np.array([perr[i,3],perr[i,0]]).reshape(2,1)
s1err = np.array([perr[i,2],perr[i,1]]).reshape(2,1)
yl,yu = fig.axes[i].get_ylim();
dy = (yu-yl)*0.05; y = yu - s2err[1] - dy
if i == 0:
y = yl + s2err[0] + dy
fig.axes[i].errorbar( xs[i],y,yerr=s1err,fmt='b',linewidth=1.5 )
fig.axes[i].errorbar( xs[i],y,yerr=s2err,fmt='r',alpha=0.3 )
return fig
def prop_plot(ax,x,y,conf,bins=73):
regx,regy = st.kern_reg(x,y,bins=bins)
ax.plot(x,y,'b.',alpha=0.1,label=conf.get("label",""))
ax.plot(regx,regy,'w',linewidth=2.1)
ax.plot(regx,regy,'r',linewidth=1.2)
ax.set_title(conf["title"])
ax.set_ylabel(conf["ylabel"])
ax.set_xlabel(conf["xlabel"])
if "label" in conf:
ax.legend()
if "ylim" in conf:
ax.set_ylim(conf["ylim"])
def addreg(fig,measure,line,bins=73,colour="r",label=""):
bot = 0; vel = 1; fwhm = 2; as12 = 3; fw13 = 4; as13 = 5; fw23 = 6; as23 = 7; err = 8; ew = 9; con = 10;
x = measure[:,con]
ys =[
measure[:,ew]/measure[:,ew].mean(),
measure[:,vel],
measure[:,bot],
measure[:,fw13]/line.width,
measure[:,fwhm]/line.width,
measure[:,fw23]/line.width,
(measure[:,as13]-measure[:,as13].mean())/line.width,
(measure[:,as12]-measure[:,as13].mean())/line.width,
(measure[:,as23]-measure[:,as13].mean())/line.width,
]
for i,y in enumerate(ys):
regx,regy = st.kern_reg(x,y,bins=bins)
fig.axes[i].plot(regx,regy,color=colour)
return fig