1. 2. star spectral type and temperature interpolator
   3. needs predefined table of staller tspectral types, masses, temperatures ...
   4. python 3 code
   6. 13.2.2023 v 0000.0000

import numpy as np

1. 1. import scipy as sp

import matplotlib.pyplot as plt import math import pandas as pd

from sklearn import metrics from sklearn.linear_model import LinearRegression from sklearn.ensemble import RandomForestRegressor from sklearn.metrics import r2_score from pygam import GAM, s, te from pygam import LinearGAM from pygam import PoissonGAM

1. 1. from pygam import LogisticGAM

from scipy import interpolate

def linear_log_predict1(x,y): logx=np.log10(x).reshape(-1, 1) logy=np.log10(y).reshape(-1, 1) logmodel= LinearRegression().fit(logx, logy) logk=logmodel.coef_ logb=logmodel.intercept_ #print(logmodel) logpre=logmodel.predict(logx) pre=np.power(10, logpre) return(pre, logk, logb)

def linear_log_predict2(x,y, x2): logx=np.log10(x).reshape(-1, 1) logy=np.log10(y).reshape(-1, 1) logx2=np.log10(x2).reshape(-1, 1) logmodel= LinearRegression().fit(logx, logy) logk=logmodel.coef_ logb=logmodel.intercept_ #print(loglums_model) logpre=logmodel.predict(logx2) pre=np.power(10, logpre) return(pre)

def linear_log_predict_point(x,y, px): logx=np.log10(x).reshape(-1, 1) logy=np.log10(y).reshape(-1, 1) logmodel= LinearRegression().fit(logx, logy) logk=logmodel.coef_ logb=logmodel.intercept_ logpx=math.log10(px) logpy=logb+logpx*logk py=math.pow(10,logpy) return(py)

def gamma_log_predict2(x,y, x2): logx=np.log10(x).reshape(-1, 1) logy=np.log10(y).reshape(-1, 1) logx2=np.log10(x2).reshape(-1, 1) gam = LinearGAM(s(0, n_splines=10)).fit(logx,logy) #gam = PoissonGAM(s(0, n_splines=10)).fit(logx,logy) logpre = gam.predict(logx2) pre=np.power(10, logpre) return(pre)

def gamma_predict3(x,y, x2): ex=x.reshape(-1, 1) ey=y.reshape(-1, 1) ex2=x2.reshape(-1, 1) gam = LogisticGAM(s(0, n_splines=10)).fit(ex,ey) pre = gam.predict(ex2) return(pre)

def rf_log_predict2(x,y, x2): logx=np.log10(x).reshape(-1, 1) logy=np.log10(y).reshape(-1, 1) logx2=np.log10(x2).reshape(-1, 1) regressor = RandomForestRegressor(n_estimators=10000, random_state=0) regressor.fit(logx,logy) logpre = regressor.predict(logx2) pre=np.power(10, logpre) return(pre)

def interpolate1(x,y, x2): f = interpolate.interp1d(x, y) y2 = f(x2) return(y2)

def interpolate_point(x,y, px): f = interpolate.interp1d(x, y) y2 = f(px) return(y2)

def interpolate2(x,y, x2): logx=np.log10(x).reshape(-1, 1) logy=np.log10(y).reshape(-1, 1) logx2=np.log10(x2).reshape(-1, 1) f = interpolate.interp1d(logx, logy) y2 = f(logx2) pre=np.power(10,y2) return(y2)

def speknum(spt): spe=spt spe=spe.replace('O', '1') spe=spe.replace('B', '2') spe=spe.replace('A', '3') spe=spe.replace('F', '4') spe=spe.replace('G', '5') spe=spe.replace('K', '6') spe=spe.replace('M', '7') spe=spe.replace('L', '8') return(int(spe))

1. 1. load main sequence star data, edited from wiki 2023
   2. https://en.wikipedia.org/wiki/Main_sequence

arrname1="mainseq1.txt"

starr1 = np.loadtxt(arrname1,skiprows=1,dtype="float",delimiter=",",usecols =(1,2,3,4)) print(starr1)

masses1=starr1[:,1] lums1=starr1[:,2] temps1=starr1[:,3] radiuses1=starr1[:,0]

print (masses1) print (lums1) print (temps1)

df00 = pd.read_csv(arrname1, sep=",")

1. print(df00)

spek1=df00['spectral']

1. print(spek1)

classes="OBAFGKML"

spek2=spek1.tolist()

1. print(spek2)

spek3 = [sub.replace('O', '1') for sub in spek2] spek3 = [sub.replace('B', '2') for sub in spek3] spek3 = [sub.replace('A', '3') for sub in spek3] spek3 = [sub.replace('F', '4') for sub in spek3] spek3 = [sub.replace('G', '5') for sub in spek3] spek3 = [sub.replace('K', '6') for sub in spek3] spek3 = [sub.replace('M', '7') for sub in spek3] spek3 = [sub.replace('L', '8') for sub in spek3]

1. print(spek3)

spek4=np.array(spek3).astype(float)

1. print(spek4)

speg1=np.arange(12,82,1)

1. print(speg1)

speg2=speg1.astype(int)

1. print(speg2)

speg3=speg2.tolist()

1. print(speg3)

speg4 = [str(x) for x in speg3]

1. print(speg4)

speg5=[]

first1=0

for sx in speg4:

sy=sx print(sy) chari1=sy[0] if(sy[0]=='1'): sy=sy.replace(sy[0],"O",1) if(sy[0]=='2'): sy=sy.replace(sy[0],"B",1) if(sy[0]=='3'): sy=sy.replace(sy[0],"A",1) if(sy[0]=='4'): sy=sy.replace(sy[0],"F",1) if(sy[0]=='5'): sy=sy.replace(sy[0],"G",1) if(sy[0]=='6'): sy=sy.replace(sy[0],"K",1) if(sy[0]=='7'): sy=sy.replace(sy[0],"M",1) if(sy[0]=='8'): sy=sy.replace(sy[0],"L",1) speg5.append(sy)

1. print("speg5")

1. print(spek4)
2. print(speg1)
3. print(speg5)

spektr1=spek4 spektr2=speg1

sally1=spek2 sally2=speg5

print(sally1) print(sally2)

print(spektr1) print(spektr2)

masses2=interpolate1(spektr1, masses1, spektr2) radiuses2=interpolate1(spektr1, radiuses1, spektr2) lums2=interpolate1(spektr1, lums1, spektr2) temps2=interpolate1(spektr1, temps1, spektr2)

spt1="G2"

spnum1=speknum(spt1)

teff1=interpolate_point(spektr1,temps1, spnum1)

print(spt1, spnum1,teff1)

1. lum1=interpolate_point(masses1, lums1, m1)

1. plt.xlim(0.1, 1.5)
2. plt.ylim(0.0, 3)

font = {'family' : 'DejaVu Sans',

       'weight' : 'normal',
       'size'   : 18}

plt.rc('font', **font)

plt.title ("Main sequence spectral class - surface temperature", size=20)

1. plt.plot(spektr1, temps1, "o", lw=6)

plt.plot(spektr2, temps2, "-", color="r", lw=10)

plt.grid(color='b', linestyle=':', linewidth=1)

plt.xticks(spektr1, sally1)

1. plt.plot(spnum1, teff1, "x")

plt.show()