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TempOther.py
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TempOther.py
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import pandas
import seaborn as sns
import matplotlib as mpl
import matplotlib.pyplot as plot
import math
import pickle, os
import numpy as np
import csv
import random
from sklearn import linear_model, datasets
from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import LogisticRegression
import warnings
from matplotlib import cm
from collections import OrderedDict
warnings.filterwarnings("ignore")
data = pandas.read_csv('ecc-mjax-teff-num.csv')
data2 = pandas.read_csv('predictions.csv')
print(data.shape)
import pandas as pd
eccentricity = list(data['pl_orbeccen'])
eccentricityerrorhigh = list(data['pl_orbeccenerr1'])
eccentricityerrorlow = list(data['pl_orbeccenerr2'])
name = list(data2['pl_name'])
del eccentricity[0]
del eccentricityerrorhigh[0]
del eccentricityerrorlow[0]
eccentricity = [float(c) for c in eccentricity]
eccentricityerrorhigh = [float(c) for c in eccentricityerrorhigh]
eccentricityerrorlow = [float(c) for c in eccentricityerrorlow]
eccentricityhigh = [eccentricity[i]+eccentricityerrorhigh[i] for i in range(len(eccentricity))]
eccentricitylow = [eccentricity[i]+eccentricityerrorlow[i] for i in range(len(eccentricity))]
PlanetRadius = list(data['pl_radj'])
StarMass = list(data['st_mass'])
Distance = list(data['pl_orbsmax'])
PlanetNumber = list(data['pl_pnum'])
EffTemp = list(data2['st_teff'])
StarDistance = list(data2['st_dist'])
Magnitude = list(data2['st_optmag'])
PlanetRadius2 = list(data2['pl_radj'])
PlanetNumber2 = list(data2['pl_pnum'])
Distance2 = list(data2['pl_orbsmax'])
del EffTemp[0]
del StarDistance[0]
del PlanetRadius[0]
del StarMass[0]
del Distance[0]
del PlanetNumber[0]
del Magnitude[0]
del Distance2[0]
del PlanetNumber2[0]
del PlanetRadius2[0]
PlanetRadius = [float(c) for c in PlanetRadius]
StarMass = [float(c) for c in StarMass]
Distance = [float(c) for c in Distance]
PlanetNumber = [float(c) for c in PlanetNumber]
EffTemp = [float(c) for c in EffTemp]
StarDistance = [float(c) for c in StarDistance]
Magnitude = [float(c) for c in Magnitude]
PlanetRadius2 = [float(c) for c in PlanetRadius2]
PlanetNumber2 = [float(c) for c in PlanetNumber2]
Distance2 = [float(c) for c in Distance2]
PlanetRadius = [7.1492*10**7*i for i in PlanetRadius]
StarMass = [1.989*10**30*i for i in StarMass]
Distance = [149597870691.*i for i in Distance]
PlanetRadius2 = [7.1492*10**7*i for i in PlanetRadius2]
Distance2 = [149597870691*i for i in Distance2]
PlanetRadiusErrorHigh = list(data['pl_radjerr1'])
PlanetRadiusErrorLow = list(data['pl_radjerr2'])
DistanceErrorHigh = list(data['pl_orbsmaxerr1'])
DistanceErrorLow = list(data['pl_orbsmaxerr2'])
StarMassErrorHigh = list(data['st_masserr1'])
StarMassErrorLow = list(data['st_masserr2'])
del PlanetRadiusErrorHigh[0]
del PlanetRadiusErrorLow[0]
del DistanceErrorHigh[0]
del DistanceErrorLow[0]
del StarMassErrorHigh[0]
del StarMassErrorLow[0]
PlanetRadiusErrorHigh2 = [float (c) for c in PlanetRadiusErrorHigh]
PlanetRadiusErrorLow2 = [float (c) for c in PlanetRadiusErrorLow]
DistanceErrorHigh2 = [float (c) for c in DistanceErrorHigh]
DistanceErrorLow2 = [float (c) for c in DistanceErrorLow]
StarMassErrorHigh2 = [float (c) for c in StarMassErrorHigh]
StarMassErrorLow2 = [float (c) for c in StarMassErrorLow]
PlanetRadiusErrorHigh =[]
PlanetRadiusErrorLow =[]
StarMassErrorHigh =[]
StarMassErrorLow =[]
DistanceErrorHigh =[]
DistanceErrorLow =[]
for i in range(len(PlanetRadiusErrorHigh2)):
PlanetRadiusErrorHigh.append(7.1492*10**7*PlanetRadiusErrorHigh2[i])
for i in range(len(PlanetRadiusErrorLow2)):
PlanetRadiusErrorLow.append(7.1492*10**7*PlanetRadiusErrorLow2[i])
for i in range(len(StarMassErrorHigh2)):
StarMassErrorHigh.append(1.989*10**30*StarMassErrorHigh2[i])
for i in range(len(StarMassErrorLow2)):
StarMassErrorLow.append(1.989*10**30*StarMassErrorLow2[i])
for i in range(len(DistanceErrorHigh2)):
DistanceErrorHigh.append(149597870691*DistanceErrorHigh2[i])
for i in range(len(DistanceErrorLow2)):
DistanceErrorLow.append(149597870691*DistanceErrorLow2[i])
PlanetRadiusHigh = []
PlanetRadiusLow= []
for i in range(len(PlanetRadius)):
PlanetRadiusHigh.append(PlanetRadius[i]+PlanetRadiusErrorHigh[i])
for i in range(len(PlanetRadius)):
PlanetRadiusLow.append(PlanetRadius[i]+PlanetRadiusErrorLow[i])
StarMassHigh= []
StarMassLow= []
for i in range(len(StarMass)):
StarMassHigh.append(StarMass[i]+StarMassErrorHigh[i])
for i in range(len(StarMass)):
StarMassLow.append(StarMass[i]+StarMassErrorLow[i])
DistanceHigh= []
DistanceLow= []
for i in range(len(Distance)):
DistanceHigh.append(Distance[i]+DistanceErrorHigh[i])
for i in range(len(Distance)):
DistanceLow.append(Distance[i]+DistanceErrorLow[i])
def Energy (M, m, d):
return (6.674*10**-11)*(-1.)*M*m*(1./(2.*d))
highenergy =[Energy(StarMassHigh[i], PlanetRadiusHigh[i], DistanceLow[i]) for i in range(len(PlanetRadius))]
lowenergy =[Energy(StarMassLow[i], PlanetRadiusLow[i], DistanceHigh[i]) for i in range(len(PlanetRadius))]
midenergy =[Energy(StarMass[i], PlanetRadius[i], Distance[i]) for i in range(len(PlanetRadius))]
def AngularMomentum (m,d,M):
return (m*d*(6.674*10**-11*M*(1/d))**0.5)
highmomentum = [AngularMomentum(PlanetRadiusHigh[i], DistanceHigh[i], StarMassHigh[i]) for i in range(len(PlanetRadius))]
lowmomentum = [AngularMomentum(PlanetRadiusLow[i], DistanceLow[i], StarMassLow[i]) for i in range(len(PlanetRadius))]
midmomentum = [AngularMomentum(PlanetRadius[i], Distance[i], StarMass[i]) for i in range(len(PlanetRadius))]
def SGP (M):
return -6.674*10**-11*M
StandardGPhigh = [SGP(StarMassHigh[i]) for i in range(len(StarMass))]
StandardGPlow = [SGP(StarMassLow[i]) for i in range(len(StarMass))]
StandardGPmid = [SGP(StarMass[i]) for i in range(len(StarMass))]
PlanetRadiusLow1 = []
for i in range(len(PlanetRadiusLow)):
if PlanetRadiusLow[i]!=0:
PlanetRadiusLow1.append(PlanetRadiusLow[i])
def eccentricitynoroot (Energy, l, m, u):
return (1+(2*Energy*(l**2))/((m**3)*(u**2)))
higheccentricity = [eccentricitynoroot(lowenergy[i], lowmomentum[i], PlanetRadiusHigh[i], StandardGPhigh[i]) for i in range(len(PlanetRadiusHigh))]
mideccentricity = [eccentricitynoroot(midenergy[i], midmomentum[i], PlanetRadius[i], StandardGPmid[i]) for i in range(len(PlanetRadiusHigh))]
loweccentricity = [eccentricitynoroot(highenergy[i], highmomentum[i], PlanetRadiusLow1[i], StandardGPlow[i]) for i in range(len(PlanetRadiusLow1))]
higheccentricitynozeroes = []
for i in range(len(PlanetRadius)):
if higheccentricity[i]>0:
higheccentricitynozeroes.append((higheccentricity[i])**0.5)
mideccentricitynozeroes = []
for i in range(len(PlanetRadius)):
if mideccentricity[i] > 0:
mideccentricitynozeroes.append((mideccentricity[i]) ** 0.5)
eccentricitysquared = []
for i in range(len(eccentricity)):
eccentricitysquared.append(eccentricity[i]**2)
energyerrors = []
for i in range(len(midenergy)):
energyerrors.append(math.fabs(highenergy[i]-midenergy[i]))
momentumerrors = []
for i in range(len(midmomentum)):
momentumerrors.append(math.fabs(highmomentum[i]-midmomentum[i]))
StandardGPerrors = []
for i in range(len(StandardGPmid)):
StandardGPerrors.append(math.fabs(StandardGPhigh[i]-StandardGPmid[i]))
PlanetRadiuserrors = []
for i in range(len(PlanetRadius)):
PlanetRadiuserrors.append(math.fabs(PlanetRadiusHigh[i]-PlanetRadius[i]))
testeccentricity = [eccentricitynoroot(midenergy[i]+0.0005*energyerrors[i], midmomentum[i]-0.00002*momentumerrors[i], PlanetRadius[i]+0.09*PlanetRadiuserrors[i], StandardGPmid[i]+0.10000*StandardGPerrors[i]) for i in range(len(PlanetRadiusHigh))]
eccentricitydifference = []
for i in range(len(eccentricitysquared)):
eccentricitydifference.append(testeccentricity[i]-eccentricitysquared[i])
clf = linear_model.LinearRegression()
yeet = zip(eccentricity, PlanetRadius)
x_1, y_1 = PlanetRadius, eccentricity
import pandas as pd
df = pd.DataFrame(list(zip(PlanetRadius, Distance, PlanetNumber)), columns =['rad', 'dist', 'pnum'])
x=df[:-80]
y=eccentricity[:-80]
poly = PolynomialFeatures(degree=3)
X_ = poly.fit_transform(x)
clf = linear_model.LinearRegression()
clf.fit(X_, y)
predict = [PlanetRadius[-27], Distance[-27], PlanetNumber[-27]]
predict_ = poly.fit_transform(predict)
def error (pred, act):
return (pred-act)
predict2 =[]
for i in range (80):
predict2.append([PlanetRadius[-i], Distance[-i], PlanetNumber[-i]])
predict2_ = poly.fit_transform(predict2)
preds2 = []
for i in range(80):
preds2.append(clf.predict(predict2_[i]))
del preds2[8]
acts2 = eccentricity[-80:]
del acts2[8]
errordiff = []
for i in range(79):
errordiff.append(error(preds2[i], acts2[i]))
df2 = pd.DataFrame(list(zip(preds2, acts2)),
columns =['pred', 'act'])
predictreal = [6.371*10**6, 149597870691, 8]
predictreal_ = poly.fit_transform(predictreal)
print 'the error of the eccentricity model is', (sum(errordiff))/80
BolCorr = []
for i in range(len(EffTemp)):
if EffTemp[i]>10000:
BolCorr.append(-2.0)
elif 10000>EffTemp[i]>7500:
BolCorr.append(-0.3)
elif 7500>EffTemp[i]>6000:
BolCorr.append(-0.15)
elif 6000>EffTemp[i]>5300:
BolCorr.append(-0.4)
elif 5300>EffTemp[i]>3500:
BolCorr.append(-0.8)
else:
BolCorr.append(-2.0)
def AbsoluteMagnitude(app, d):
return (app-5*math.log((d/10), 10))
def BolometricMagnitude(AbsMag, Correction):
return AbsMag+Correction
def AbsoluteLuminosity(BolMag):
return 10**((BolMag-4.72)/(-2.512))
def InnerRadius (AbsLum):
return ((AbsLum/1.1)**0.5)*149597870691.*0.8
def OuterRadius (AbsLum):
return ((AbsLum/0.53)**0.5)*149597870691.*1.2
StarAbMag = []
for i in range(len(Magnitude)):
StarAbMag.append(AbsoluteMagnitude(Magnitude[i], StarDistance[i]))
StarBolMag = []
for i in range(len(Magnitude)):
StarBolMag.append(BolometricMagnitude(StarAbMag[i], BolCorr[i]))
StarAbLum = []
for i in range(len(Magnitude)):
StarAbLum.append(AbsoluteLuminosity(StarBolMag[i]))
HabitableInner = []
for i in range(len(Magnitude)):
HabitableInner.append(InnerRadius(StarAbLum[i]))
HabitableOuter = []
for i in range(len(Magnitude)):
HabitableOuter.append(OuterRadius(StarAbLum[i]))
predict3 =[]
for i in range(len(PlanetRadius2)):
predict3.append([PlanetRadius2[i], Distance2[i], PlanetNumber2[i]])
predict3_ = poly.fit_transform(predict3)
preds3 = []
for i in range(len(PlanetRadius2)):
preds3.append(clf.predict(predict3_[i]))
maxdist =[]
for i in range(len(preds3)):
maxdist.append(Distance2[i]/(1-preds3[i]))
mindist =[]
for i in range(len(preds3)):
mindist.append(Distance2[i]/(1+preds3[i]))
habitables = []
for i in range(len(mindist)):
if mindist[i]>HabitableInner[i]:
if maxdist[i]<HabitableOuter[i]:
habitables.append(i+3)
names = []
for i in range(len(habitables)):
names.append(name[habitables[i]-2])
data2.to_csv(r'C:\Users\Krithi\Documents\export_dataframe.csv')
Distance = pickle.load(open(os.getcwd()+'/Distance', 'rb'))
firstcoefficient = pickle.load(open(os.getcwd()+'/firstcoefficient', 'rb'))
import pandas as pd
df3 = pd.DataFrame(list(Distance), columns=['d'])
clf2 = linear_model.LinearRegression()
x2 = df3[80:]
y2 = firstcoefficient[80:]
clf2.fit(x2, y2)
predict3 =[]
for i in range (80):
predict3.append(Distance[i])
acts2 = firstcoefficient[0:80]
preds4 = []
for i in range(80):
preds4.append(clf2.predict(Distance[i]))
def error (pred, act):
return (pred-act)/act
errordiff2 = []
for i in range(80):
errordiff2.append(error(preds4[i], acts2[i]))
print "average error of first coefficient model is", sum(errordiff2)/len(errordiff2)
preds5 = []
for i in range(len(Distance2)):
preds5.append(-1*clf2.predict(Distance2[i]))
plot.style.use('default')
fig = plot.figure()
fig.add_subplot(222, projection='polar')
# Set the title of the polar plot
plot.title('Graph of Exoplanet Orbit vs Habitable Zones')
# Radian values upto 2*pi
rads = np.arange(0, (2 * np.pi), 0.01)
a = 1
b = 1
k = 1
#
# fig = plot.figure()
#
# for radian in rads:
# radius = ((Distance2[69]/149597870691)/(1+preds3[69]*np.cos(k * radian)))
#
#
# plot.polar(radian, radius, 'o', label='o')
#
# for radian in rads:
# plot.polar(radian,HabitableInner[69]/149597870691,'o', label='o')
# plot.style.use('seaborn')
#
# for radian in rads:
# plot.polar(radian,HabitableOuter[69]/149597870691,'o', label='o')
# plot.style.use('seaborn')
#
# fig =plot.figure(figsize=(1, 1))
for i in range(len(habitables)):
print names[i]
print Distance2[habitables[i]-3]/149597870691
print preds5[habitables[i]-3]/149597870691
print preds3[habitables[i]]
print HabitableInner[habitables[i]-3]/149597870691
print HabitableOuter[habitables[i]-3]/149597870691
eckslow = []
for i in range(len(eccentricityerrorlow)):
if eccentricityerrorlow[i]<0:
eckslow.append(eccentricityerrorlow[i])
for i in range(280):
eckslow.append(0*i)
# plot.show()
habitables2 = []
for i in range(len(mindist)):
if mindist[i]>0.95*HabitableInner[i]:
if maxdist[i]<HabitableOuter[i]:
habitables2.append(i+3)
print clf.predict(predictreal_)