In [1]:

    

import batman
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline


    

In [2]:

    

params = batman.TransitParams()
params.t0 = 0.03
params.per = 10.0
params.rp = 0.1
params.a = 15.0
params.inc = 88.0
params.ecc = 0.05
params.w = 90.0
params.u = [0.1, 0.3]
params.limb_dark = "quadratic"


    

In [3]:

    

t = np.linspace(-0.5, 0.5, 100)
m = batman.TransitModel(params, t)
flux = m.light_curve(params)


    

In [4]:

    

plt.plot(t, flux)
plt.xlabel("time from central transit")
plt.ylabel("relative flux")
plt.ylim(0.985, 1.005)
plt.show()


    

In [5]:

    

from simulate import simulate
times, noise = simulate()
m = batman.TransitModel(params, times)

# generate simulated data!
params.rp = 1.0
flux = m.light_curve(params)
simulateddata = flux*noise


    

In [6]:

    

plt.scatter(times, simulateddata)

data = np.array([times, simulateddata])

# generate some model
params.t0 = 0.0
model = m.light_curve(params)
plt.plot(times, model, color='orange', linewidth=3)

theory = np.array([times, model])


    

In [7]:

    

print np.shape(data)
print np.shape(theory)


    

    




(2, 360)
(2, 360)

In [7]:

    

np.append?


    

In [8]:

    

def chisqa (data, model):
    chi = 0
    
    for i in range(len(data)):
        num = (data[i]-model[i])**2
        denom = model[i]
        summ = num/denom
        chi = chi + summ
    chisum = np.sum(chi)
    return chisum


    

In [9]:

    

print chisqa (data, theory)


    

    




6.24201803818

In [10]:

    

import scipy.optimize as spicy


    

In [12]:

    

spicy.leastsq?


    

In [11]:

    

t00 = np.arange(0, 100, 0.1)
print t00


    

    




[  0.    0.1   0.2   0.3   0.4   0.5   0.6   0.7   0.8   0.9   1.    1.1
   1.2   1.3   1.4   1.5   1.6   1.7   1.8   1.9   2.    2.1   2.2   2.3
   2.4   2.5   2.6   2.7   2.8   2.9   3.    3.1   3.2   3.3   3.4   3.5
   3.6   3.7   3.8   3.9   4.    4.1   4.2   4.3   4.4   4.5   4.6   4.7
   4.8   4.9   5.    5.1   5.2   5.3   5.4   5.5   5.6   5.7   5.8   5.9
   6.    6.1   6.2   6.3   6.4   6.5   6.6   6.7   6.8   6.9   7.    7.1
   7.2   7.3   7.4   7.5   7.6   7.7   7.8   7.9   8.    8.1   8.2   8.3
   8.4   8.5   8.6   8.7   8.8   8.9   9.    9.1   9.2   9.3   9.4   9.5
   9.6   9.7   9.8   9.9  10.   10.1  10.2  10.3  10.4  10.5  10.6  10.7
  10.8  10.9  11.   11.1  11.2  11.3  11.4  11.5  11.6  11.7  11.8  11.9
  12.   12.1  12.2  12.3  12.4  12.5  12.6  12.7  12.8  12.9  13.   13.1
  13.2  13.3  13.4  13.5  13.6  13.7  13.8  13.9  14.   14.1  14.2  14.3
  14.4  14.5  14.6  14.7  14.8  14.9  15.   15.1  15.2  15.3  15.4  15.5
  15.6  15.7  15.8  15.9  16.   16.1  16.2  16.3  16.4  16.5  16.6  16.7
  16.8  16.9  17.   17.1  17.2  17.3  17.4  17.5  17.6  17.7  17.8  17.9
  18.   18.1  18.2  18.3  18.4  18.5  18.6  18.7  18.8  18.9  19.   19.1
  19.2  19.3  19.4  19.5  19.6  19.7  19.8  19.9  20.   20.1  20.2  20.3
  20.4  20.5  20.6  20.7  20.8  20.9  21.   21.1  21.2  21.3  21.4  21.5
  21.6  21.7  21.8  21.9  22.   22.1  22.2  22.3  22.4  22.5  22.6  22.7
  22.8  22.9  23.   23.1  23.2  23.3  23.4  23.5  23.6  23.7  23.8  23.9
  24.   24.1  24.2  24.3  24.4  24.5  24.6  24.7  24.8  24.9  25.   25.1
  25.2  25.3  25.4  25.5  25.6  25.7  25.8  25.9  26.   26.1  26.2  26.3
  26.4  26.5  26.6  26.7  26.8  26.9  27.   27.1  27.2  27.3  27.4  27.5
  27.6  27.7  27.8  27.9  28.   28.1  28.2  28.3  28.4  28.5  28.6  28.7
  28.8  28.9  29.   29.1  29.2  29.3  29.4  29.5  29.6  29.7  29.8  29.9
  30.   30.1  30.2  30.3  30.4  30.5  30.6  30.7  30.8  30.9  31.   31.1
  31.2  31.3  31.4  31.5  31.6  31.7  31.8  31.9  32.   32.1  32.2  32.3
  32.4  32.5  32.6  32.7  32.8  32.9  33.   33.1  33.2  33.3  33.4  33.5
  33.6  33.7  33.8  33.9  34.   34.1  34.2  34.3  34.4  34.5  34.6  34.7
  34.8  34.9  35.   35.1  35.2  35.3  35.4  35.5  35.6  35.7  35.8  35.9
  36.   36.1  36.2  36.3  36.4  36.5  36.6  36.7  36.8  36.9  37.   37.1
  37.2  37.3  37.4  37.5  37.6  37.7  37.8  37.9  38.   38.1  38.2  38.3
  38.4  38.5  38.6  38.7  38.8  38.9  39.   39.1  39.2  39.3  39.4  39.5
  39.6  39.7  39.8  39.9  40.   40.1  40.2  40.3  40.4  40.5  40.6  40.7
  40.8  40.9  41.   41.1  41.2  41.3  41.4  41.5  41.6  41.7  41.8  41.9
  42.   42.1  42.2  42.3  42.4  42.5  42.6  42.7  42.8  42.9  43.   43.1
  43.2  43.3  43.4  43.5  43.6  43.7  43.8  43.9  44.   44.1  44.2  44.3
  44.4  44.5  44.6  44.7  44.8  44.9  45.   45.1  45.2  45.3  45.4  45.5
  45.6  45.7  45.8  45.9  46.   46.1  46.2  46.3  46.4  46.5  46.6  46.7
  46.8  46.9  47.   47.1  47.2  47.3  47.4  47.5  47.6  47.7  47.8  47.9
  48.   48.1  48.2  48.3  48.4  48.5  48.6  48.7  48.8  48.9  49.   49.1
  49.2  49.3  49.4  49.5  49.6  49.7  49.8  49.9  50.   50.1  50.2  50.3
  50.4  50.5  50.6  50.7  50.8  50.9  51.   51.1  51.2  51.3  51.4  51.5
  51.6  51.7  51.8  51.9  52.   52.1  52.2  52.3  52.4  52.5  52.6  52.7
  52.8  52.9  53.   53.1  53.2  53.3  53.4  53.5  53.6  53.7  53.8  53.9
  54.   54.1  54.2  54.3  54.4  54.5  54.6  54.7  54.8  54.9  55.   55.1
  55.2  55.3  55.4  55.5  55.6  55.7  55.8  55.9  56.   56.1  56.2  56.3
  56.4  56.5  56.6  56.7  56.8  56.9  57.   57.1  57.2  57.3  57.4  57.5
  57.6  57.7  57.8  57.9  58.   58.1  58.2  58.3  58.4  58.5  58.6  58.7
  58.8  58.9  59.   59.1  59.2  59.3  59.4  59.5  59.6  59.7  59.8  59.9
  60.   60.1  60.2  60.3  60.4  60.5  60.6  60.7  60.8  60.9  61.   61.1
  61.2  61.3  61.4  61.5  61.6  61.7  61.8  61.9  62.   62.1  62.2  62.3
  62.4  62.5  62.6  62.7  62.8  62.9  63.   63.1  63.2  63.3  63.4  63.5
  63.6  63.7  63.8  63.9  64.   64.1  64.2  64.3  64.4  64.5  64.6  64.7
  64.8  64.9  65.   65.1  65.2  65.3  65.4  65.5  65.6  65.7  65.8  65.9
  66.   66.1  66.2  66.3  66.4  66.5  66.6  66.7  66.8  66.9  67.   67.1
  67.2  67.3  67.4  67.5  67.6  67.7  67.8  67.9  68.   68.1  68.2  68.3
  68.4  68.5  68.6  68.7  68.8  68.9  69.   69.1  69.2  69.3  69.4  69.5
  69.6  69.7  69.8  69.9  70.   70.1  70.2  70.3  70.4  70.5  70.6  70.7
  70.8  70.9  71.   71.1  71.2  71.3  71.4  71.5  71.6  71.7  71.8  71.9
  72.   72.1  72.2  72.3  72.4  72.5  72.6  72.7  72.8  72.9  73.   73.1
  73.2  73.3  73.4  73.5  73.6  73.7  73.8  73.9  74.   74.1  74.2  74.3
  74.4  74.5  74.6  74.7  74.8  74.9  75.   75.1  75.2  75.3  75.4  75.5
  75.6  75.7  75.8  75.9  76.   76.1  76.2  76.3  76.4  76.5  76.6  76.7
  76.8  76.9  77.   77.1  77.2  77.3  77.4  77.5  77.6  77.7  77.8  77.9
  78.   78.1  78.2  78.3  78.4  78.5  78.6  78.7  78.8  78.9  79.   79.1
  79.2  79.3  79.4  79.5  79.6  79.7  79.8  79.9  80.   80.1  80.2  80.3
  80.4  80.5  80.6  80.7  80.8  80.9  81.   81.1  81.2  81.3  81.4  81.5
  81.6  81.7  81.8  81.9  82.   82.1  82.2  82.3  82.4  82.5  82.6  82.7
  82.8  82.9  83.   83.1  83.2  83.3  83.4  83.5  83.6  83.7  83.8  83.9
  84.   84.1  84.2  84.3  84.4  84.5  84.6  84.7  84.8  84.9  85.   85.1
  85.2  85.3  85.4  85.5  85.6  85.7  85.8  85.9  86.   86.1  86.2  86.3
  86.4  86.5  86.6  86.7  86.8  86.9  87.   87.1  87.2  87.3  87.4  87.5
  87.6  87.7  87.8  87.9  88.   88.1  88.2  88.3  88.4  88.5  88.6  88.7
  88.8  88.9  89.   89.1  89.2  89.3  89.4  89.5  89.6  89.7  89.8  89.9
  90.   90.1  90.2  90.3  90.4  90.5  90.6  90.7  90.8  90.9  91.   91.1
  91.2  91.3  91.4  91.5  91.6  91.7  91.8  91.9  92.   92.1  92.2  92.3
  92.4  92.5  92.6  92.7  92.8  92.9  93.   93.1  93.2  93.3  93.4  93.5
  93.6  93.7  93.8  93.9  94.   94.1  94.2  94.3  94.4  94.5  94.6  94.7
  94.8  94.9  95.   95.1  95.2  95.3  95.4  95.5  95.6  95.7  95.8  95.9
  96.   96.1  96.2  96.3  96.4  96.5  96.6  96.7  96.8  96.9  97.   97.1
  97.2  97.3  97.4  97.5  97.6  97.7  97.8  97.9  98.   98.1  98.2  98.3
  98.4  98.5  98.6  98.7  98.8  98.9  99.   99.1  99.2  99.3  99.4  99.5
  99.6  99.7  99.8  99.9]

In [12]:

    

def calcchisqa (data):
    model = np.array([range(len(data[0])), range(len(data[1]))])
    for i in range(len(model[1])):
        firstchi = chisqa(data, model[i])


    

In [23]:

    

'''params = batman.TransitParams()
params.t0 = 0.0
params.per = 10.0
params.rp = 0.1
params.a = 15.0
params.inc = 88.0
params.ecc = 0.05
params.w = 90.0
params.u = [0.1, 0.3]
params.limb_dark = "quadratic"

t = np.linspace(-0.5, 0.5, 100)
m = batman.TransitModel(params, t)
flux = m.light_curve(params)'''


plotting = False


t00 = np.arange(-0.3, 0.3, 0.001)
chiSq = np.array([])
for i in t00:

    # set the model t0 to one value of t00
    params.t0 = i
    modelflux = m.light_curve(params)
    chiSq = np.append(chiSq, chisqa(simulateddata, modelflux))

    if plotting:
        plt.plot(times, modelflux, color='orange', linewidth=3)
        plt.scatter(times, simulateddata)
        plt.title("t0 = {}, chi^2 = {}".format(i, chiSq[-1]))
        plt.show()
plt.scatter(t00, chiSq)
mint00 = t00[np.argmin(chiSq)]
#print chiSq
plt.title("Mininum t00 = {}".format(mint00))


    

    
Out[23]:





<matplotlib.text.Text at 0x1114a1b90>






    

In [29]:

    

plotting = False


t00 = np.arange(0.0, 0.5, 0.001)
chiSq = np.array([])
for i in t00:

    # set the model t0 to one value of t00
    params.rp = i
    modelflux = m.light_curve(params)
    chiSq = np.append(chiSq, chisqa(simulateddata, modelflux))

    if plotting:
        plt.plot(times, modelflux, color='orange', linewidth=3)
        plt.scatter(times, simulateddata)
        plt.title("t0 = {}, chi^2 = {}".format(i, chiSq[-1]))
        plt.show()
plt.scatter(t00, chiSq)
mint00 = t00[np.argmin(chiSq)]
#print chiSq
plt.title("Mininum t00 = {}".format(mint00))


    

    
Out[29]:





<matplotlib.text.Text at 0x1142e5bd0>






    

In [ ]: