#=============================================================================
# FILES
#=============================================================================
file_star = 'star_luminos.txt'
#=============================================================================
# LOAD DATA
#=============================================================================
sData = np.genfromtxt(file_star, skip_header=1, usecols=(0, 1)).T
s_temp, s_lumi = sData[0], sData[1]
print(s_temp, s_lumi)
#=============================================================================
# FIT DATA
#=============================================================================
sLS = utils.lin_LS(np.log10(abs(s_temp)), np.log10(s_lumi))
for tag, item in sLS.items():
if isinstance(item, (int, float)):
print(tag, item)
s_yhat = 10**sLS['a'] * (s_temp**sLS['b'])
#=============================================================================
# PLOTS
#=============================================================================
plt.figure(1)
ax1 = plt.subplot(111)
ax1.loglog(s_temp, s_lumi, 'ko', ms=1, mew=1, mfc='none', label='obs.')
ax1.plot(s_temp,
s_yhat,
'r--',
L = Luminosity
"""
a = 10 #alpha random number
b = 15 #beta random number
#=============================
# Data files
#=============================
file_in = 'star_luminos.txt'
T, L = np.genfromtxt(file_in).T
#=============================
a_x = np.linspace(10, 1000, 9981)
a_y = a * T**b
#===================================================================================
# data fits
#===================================================================================
dLS = utils.lin_LS(a_x, a_y)
for tag, item in dLS.items():
if isinstance(item, (int, float)):
print(tag, item)
print(dLS['R2'], dLS['r_p']**2)
## same result with scipy
slope, intercept, r_p, prob, stderr = scipy.stats.linregress(a_x, a_y)
print('scipy', slope, intercept, r_p)
#===================================================================================
# plots
#===================================================================================
plt.figure(1)
ax1 = plt.subplot(111)
ax1.plot(a_x, a_y, 'ko', ms=5, mew=1, mfc='none')
ax1.plot(a_x,
# -*- coding: utf-8 -*- import numpy as np import matplotlib.pyplot as plt import opt_utils as opt_utils #=============== Data/ Data Import ===============# file_eq = 'star_luminos.txt' mData = np.loadtxt(file_eq).T #=============== Power Law Fitting ===============# x = np.log10(mData[0]) y = np.log10(mData[1]) #=============== Power Law Fitting ===============# l_Ls = opt_utils.lin_LS(x, y) T = np.arange(10, 1000) ay_hat = (l_Ls['a']) * T * l_Ls['b'] plt.figure() plt.plot(mData[0], mData[1], 'bo') plt.loglog(mData[0], mData[1], 'g-') #plt.loglog(mData[0], ay_hat, 'ro', mfc = 'none')
@author: maduong
"""
import numpy as np
import matplotlib.pyplot as plt
import opt_utils as opt_utils
#==============================================================================
# files, params
#==============================================================================
file_in = 'star_luminos.txt'
mData = np.genfromtxt(file_in).T
Tmin, Tmax = 10, 1000
sel_T = np.logical_and(mData[0] >= Tmin, mData[0] <= Tmax)
dLS = opt_utils.lin_LS(mData[0][sel_T], mData[1][sel_T])
print dLS['b']
#==============================================================================
# plots
#==============================================================================
plt.figure(1)
plt.title('Temperature versus Luminosity Graph')
plt.plot(mData[0], mData[1], 'ko')
plt.xlabel('Temperature (Degree C)')
plt.ylabel('Luminosity (Solar Units)')
plt.figure(2)
plt.title('Logarithmic Temperature versus Luminosity Graph')
plt.loglog(mData[0][sel_T],
mData[1][sel_T],
'bo',
np.random.seed(123456)
tmin, tmax = 10, 1000
N = 1000
alpha = 0.5 # i dont know what alpha or whats beta are so im picking random numbers
beta = 2
sigma = 2.1
at = np.linspace(tmin, tmax, N)
ay = alpha * at**beta + np.random.randn(N) * sigma
file_in = 'star_luminos.txt'
mData = np.genfromtxt(file_in, usecols=(0, 1), skip_header=1).T
F_mdata = ou.lin_LS(at, ay)
slope, intercept, r_p, prob, stderr = scipy.stats.linregress(at, ay)
print('scipy', slope, intercept, r_p)
#print(mData)
plt.figure(1)
ax1 = plt.subplot(111)
ax1.plot(at, ay, 'ko', ms=1, mew=1, mfc='none')
plt.xlabel('Temperature')
plt.ylabel('Luminosity')
ax1.legend(loc='upper right')
plt.show()
# import #===================================== import numpy as np import matplotlib.pyplot as plt import opt_utils #===================================== # definitions #===================================== file_star = 'star_luminos.txt' mData = np.loadtxt(file_star).T dDic = opt_utils.lin_LS( mData[0], mData[1]) a_y = dDic['a']*mData[0]**dDic['b'] #=================================== # parameters #=================================== Tmin, Tmax = 10, 1000 #=================================== # plot #=================================== mData[0] = np.linspace( Tmin, Tmax, 9981) plt.figure( 1) plt.plot( mData[0], mData[1], 'ko')
# =============================================================================
file_in = 'star_luminos.txt'
Tmin, Tmax = 10, 1000
# =============================================================================
# Load and select data
# =============================================================================
Data = np.loadtxt(file_in).T
sel = np.logical_and(Data[0] > Tmin, Data[0] < Tmax)
# =============================================================================
# Linear fit
# =============================================================================
dLS = opt.lin_LS(np.log10(Data[0][sel]), np.log10(Data[0][sel]))
# =============================================================================
# Plotting
# =============================================================================
plt.rcParams.update({'font.size': 12})
plt.plot(np.log10(Data[0][sel]), np.log10(Data[0][sel]), 'r+')
plt.plot(np.log10(Data[0][sel]), dLS['Y_hat'], 'b-')
plt.xlabel("log(T)")
plt.ylabel("log(L)")
plt.legend(('Data', 'Fit:y = %s*x+ %s\nbeta = %s\nalpha = %s' %
(dLS['b'], dLS['a'], dLS['b'], 10**dLS['a'])))
plt.title("Star Temperature vs Luminocity")
plt.savefig('./Problem1fig')
plt.show()
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# data fit
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#select data within 10 < temp < 10
seltemp = np.logical_and(temp > 10, temp < 1000)
#updated data set
sel_data = star_data.T[seltemp].T
temp = sel_data[0]
lumos = sel_data[1]
#fitting data using least squares fit
fit_data = opt_utils.lin_LS(temp, lumos)
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# plots
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#plot of the data
plt.title('star temp and luminosity')
plt.loglog(temp, lumos, label='lumos vs temp')
#plot of the fitted data
plt.loglog(temp, fit_data['Y_hat'], label='fitted lumos vs temp')
plt.xlabel('temp (degree C)')
plt.ylabel('lumos (solar units)')
plt.legend()
# -*- coding: utf-8 -*-
"""
Created on Wed Apr 24 08:24:51 2019
@author: maduong
"""
import numpy as np
import matplotlib.pyplot as plt
import opt_utils
Plan_Dist = np.genfromtxt('planet_distance.txt', usecols=(1, 2))
x = Plan_Dist[:, 0]
y = Plan_Dist[:, 1]
#plt.loglog(x,y)
#plt.show()
dDic = opt_utils.lin_LS(np.log10(x), np.log10(y))
print dDic
a_yhat = 10**(dDic['a'] * x**dDic['b'])
b = dDic['b']
print b
#================== Data ======================================================
#import data
mData = np.genfromtxt(inFile, skip_header=1).T
Temp = mData[0]
luminosity = mData[1]
# Change data so it only includes temperatures between 10K and 1000K
sel = np.logical_and(Temp > 10, Temp < 1000)
T = Temp[sel]
L = luminosity[sel]
#============= best fit =======================================================
#Find the line of best fit
dLS = opt.lin_LS(T, L)
print(dLS)
#============================ plot ============================================
plt.figure(1)
plt.subplot(211)
plt.loglog(T, L, 'ro', markersize=0.5, label='Data')
plt.ylabel('Luminosity (Solar Units)')
plt.legend(loc='lower left')
plt.subplot(212)
plt.plot(T, dLS['Y_hat'], 'k-', label='Best fit line')
plt.xlabel('Temperature (K)')
plt.ylabel('Luminosity (Solar Units)')
Midterm #1
Star Luminosity
"""
import numpy as np
import matplotlib.pyplot as plt
import opt_utils as ou
#===================================
# Load Data
#===================================
data_dir = 'C:\Users\conma\OneDrive\ASTR 119\My programs\Midterm'
file_in = 'star_luminos.txt'
m_Data = np.genfromtxt(file_in, usecols=(0, 1),
skip_header=1).T #Load data file
#===================================
# Variables
#===================================
Temp = m_Data[0]
Lumin = m_Data[1]
Tmin, Tmax = 0, 1000
least_sqr = ou.lin_LS(Temp, Lumin)
#pl_fit =
plt.plot(Temp, Lumin, 'ko', label='Raw Data')
plt.plot(Temp, least_sqr(4), 'r-', label='Best Fit')
plt.legend()
plt.show()
print least_sqr
import numpy as np
import matplotlib.pyplot as plt
import os
import opt_utils as utils
# Importing data file
data_file = 'star_luminos.txt'
mData = np.loadtxt(data_file).T
aTemp, aLumi = mData[0], mData[1]
xmin, xmax = 10, 1000
dLS = utils.lin_LS(aTemp, aLumi)
a_yhat = dLS['a'] + (dLS['b'] * aTemp)
plt.figure(1)
ax1 = plt.subplot(111)
ax1.plot(aTemp, aLumi, 'ko', ms=2, mew=1, mfc='none', label='scatterplot')
ax1.plot(aTemp, a_yhat, 'r--', label='L = a * T^b ')
ax1.set_xlabel('Temperature [Degrees Celsius]')
ax1.set_ylabel('Luminosity [Solar Units]')
ax1.set_xlim(10, 1000)
ax1.set_ylim(0, 1000)
ax1.legend()
plt.show()
# -*- coding: utf-8 -*-
"""
Created on Wed May 8 08:23:48 2019
@author: kardalto
"""
import numpy as np
import matplotlib.pyplot as plt
import opt_utils
data_star_lum = np.genfromtxt('star_luminos.txt').T
afterFitting = opt_utils.lin_LS(data_star_lum[0], data_star_lum[1])
a = 515.9896349043293
b = -0.24207087442733877
fitLum = []
for i in range(10, 1001):
lumosity = a * (data_star_lum[0][i]**b)
fitLum.append(lumosity)
plt.figure(1)
plt.subplot(211)
plt.plot(data_star_lum[0], data_star_lum[1], 'ko', ms=2)
plt.title('Star Lumosity Data')
plt.xlabel('Temperature(degree C)')
plt.ylabel('Lumosity (solar units)')
plt.subplot(212)
plt.plot(data_star_lum[0][10:1001], fitLum, 'bo', ms=2)
plt.xlabel('Temperature(degree C)')
import numpy as np
import matplotlib.pyplot as plt
import opt_utils
#import data
file_in = 'data/star_luminos.txt'
mData = np.genfromtxt(file_in).T
T = mData[0]
L = mData[1]
#use opt_utils to find power law fit
dLS = opt_utils.lin_LS(T,np.log(L))
L_yhat = np.exp( dLS['a']) * (np.exp( dLS['b']*T))
#plot everything
plt.figure()
plt.title('Power law exponent:' + str(dLS['R2']))
ax1 = plt.subplot(111)
ax1.plot(T,L,'ko',ms=5,mew=1.5,mfc='none',label='Observed data')
ax1.plot(T,L_yhat, 'r-', label = 'Model fit')
ax1.legend(loc=1)
plt.xlabel('Temperature')
plt.ylabel('Luminosity')
plt.show()
plt.savefig('Midterm_1_plot.png')
ax.set_xlabel('Time [dec. year]')
ax.set_ylabel('events/day')
plt.show()
#--------------------------3---------------------------------------------
# power-law fitting
#------------------------------------------------------------------------
# TODO: subtract time of mainshock (at_days[MS_ID]) from aftershock times (at_days[MS_ID+1??]- replace ??) - new vector has only AS with time relative to MS in days
at_AS = at_days[MS_ID + 1::] - at_days[MS_ID]
# TODO: compute AS rates
at_bin_AS, aN_bin_AS = seis_utils.eqRate(at_AS, dPar['k'])
# select events within tmin and tmax for which the data can be described by a powerlaw
sel_t = np.logical_and(at_bin_AS >= dPar['tmin'], at_bin_AS <= dPar['tmax'])
# TODO: fot the powerlaw to the log-transformed data between tmin and tmax
dPL = opt_utils.lin_LS(np.log(at_bin_AS.T[sel_t].T),
np.log(aN_bin_AS.T[sel_t].T))
p_omori = dPL['b']
print 'Omori p-value: ', p_omori
print 'R2 value: ', dPL['R2']
#TODO: use dN/dt = alpha*t**(-p) to compute the modeled rates
aOmori_rate = 10**(np.log10(at_bin_AS) * p_omori + dPL['a'])
#--------------------------4---------------------------------------------
# plots
#------------------------------------------------------------------------
plt.figure()
ax = plt.subplot()
# note: careful with the different time vectors during plotting
# we have at_AS - aftershock times in days after mainshock
# at_bin_AS - binned time vector for rate computation, this vector is smaller than at_AS
import numpy as np import matplotlib.pyplot as plt import opt_utils as opt_utils np.random.seed(12345) #=================================================================================== # params, dirs, files #=================================================================================== xmin, xmax = 0, 5 N = 20 f_a = 5.0 f_b = -2.4 f_sigma = .5 #=================================================================================== # synthetic data #=================================================================================== a_x = np.linspace(xmin, xmax, N) a_y = f_b * a_x + f_a + np.random.randn(N) * f_sigma #=================================================================================== # lin. LS. and plot #=================================================================================== dLS = opt_utils.lin_LS(a_x, a_y) plt.figure() plt.title(str(dLS['R2'])) ax1 = plt.subplot(111) ax1.plot(a_x, a_y, 'ko', ms=5, mew=1.5, mfc='none', label='obs.') ax1.plot(a_x, dLS['Y_hat'], 'r-', label='Model') ax1.legend(loc='upper right') plt.show()
# -*- coding: utf-8 -*-
"""
Created on Wed May 8 07:58:54 2019
@author: Brady
"""
import numpy as np
import matplotlib.pyplot as plt
import opt_utils as opt
Star_lums = 'star_luminos.txt'
data = np.genfromtxt(Star_lums, dtype=float, delimiter=(' '), skip_header=(1), usecols=(0, 1)).T
#print data
aX= data[0]>10
aY= data[1]<592
#print aX
#print aY
pow_law_fit=opt.lin_LS(aX, aY)
print pow_law_fit
a = pow_law_fit['a']
b = pow_law_fit['b']
T = aX
L = a*T**b
print L
#plt.plot(aY, aX)
plt.plot(aX, L, set(label('star_luminosity')))
#('Star_Luminosity')
#plt.set_xlabel('Temp')
#plt.set_ylabel('Luminosity')
plt.show()
@author: williamdean
"""
import opt_utils as utils
import numpy as np
import matplotlib.pyplot as plt
# load data
star = np.genfromtxt('data/star_luminos.txt')
T, L = star.T[0], star.T[1]
#print ( T, L)
Tmin, Tmax = 10, 1000
# fit data
dLS = utils.lin_LS((T), (L))
for tag, item in dLS.items():
if isinstance(item, (int, float)):
print(tag, item)
#L = alpha*T**beta
#at_bin_T, aN_bin_L = seis_utils.eqRate( at_AS)
#sel_t = np.logical_and( at_bin_T >= dPar['Tmin'], at_bin_L <= dPar['Tmax'])
plt.figure(1)
ax1 = plt.subplot(111)
ax1.plot(T, L, 'ko', ms=5, mew=1, mfc='none')
#a_x = np.linspace( Tmin, Tmax, 1000)
#plt.plot( star)
plt.xlabel('Temperature(degree C)')
#==============================================================================
#computations
#==============================================================================
#we're only interested in temperatures between 10 and 1000 exclusive
sel = np.logical_and(temp > 10, temp < 1000)
temp = temp[sel]
lum = lum[sel]
#data can be described by l=alphaT^beta so it can be linearized by plotting logT vs logl
#take the log
lintemp = np.log10(temp)
linlum = np.log10(lum)
#fit the data
model = opt_utils.lin_LS(lintemp, linlum) #note model is a dictionary
#==============================================================================
#plotting
#==============================================================================
plt.figure()
#linearized data
plt.plot(lintemp, linlum, 'ko')
#predicted values using our least squares model
plt.plot(lintemp, model['Y_hat'], 'b-')
#labelling
plt.xlabel('Log of Temperature')
plt.ylabel('Log of Lumosity')
#===================================================================================
# files, parameters
#===================================================================================
star_file = 'star_luminos.txt'
#===================================================================================
# load data
#===================================================================================
mData = np.genfromtxt(star_file, skip_header=1, usecols=(0, 1)).T
temp, sol = mData[0], mData[1]
print(temp, sol)
#===================================================================================
# fit data
#===================================================================================
dLS = utils.lin_LS(np.log10(sol), np.log10(temp))
for tag, item in dLS.items():
if isinstance(item, (int, float)):
print(tag, item)
# compute model fit y_hat
a_yhat = 10**dLS['a'] * (sol**dLS['b'])
#===================================================================================
# plots
#===================================================================================
plt.figure(1)
ax1 = plt.subplot(111)
ax1.loglog(sol, temp, 'ko', ms=5, mew=1, mfc='none')
# ax1.hexbin( a_x, a_y, cmap = plt.cm.Blues)
ax1.set_xlabel('Luminosity(Solar Units)')
