from IPython.display import Image
Image('question.jpg')

%matplotlib inline

import numpy as np
import matplotlib.pyplot as plt

plt.rcParams['figure.figsize'] = (8, 6)

from astropy.io import fits

help(plt.plot)

dir(fits)

help(fits.open)

%ls data

ls data

hdus = fits.open('data/mystery-picture.fits')

hdus

ihdu = hdus[0]
ihdu

len(hdus)

len("Carlos é magnífico")

len(ihdu)

2 + 3
"David Hasselhoff en alemán significa impresionante"
(-3.4 + 2j) * (4.1 - 0.8j)

print(ihdu)

print ihdu

dir(ihdu)

carlos = ihdu.data()

carlos = ihdu.data

carlos == hdus[0].data

np.all(carlos == hdus[0].data)

carlos

print(carlos)

carlos.shape

carlos.dtype

plt.imshow(carlos)

plt.set_cmap('grey')

plt.imshow(carlos)
plt.set_cmap('gray')

mariano = carlos[400:,:]

mariano.shape

plt.imshow(mariano, cmap='gray')

from skimage.filter import canny

matteo = canny(mariano)

matteo.shape

matteo.dtype

plt.imshow(matteo)

hdus = fits.open("data/evt2.fits")
print(hdus)

hdus.info()

thdu = hdus[1]

print(thdu.columns)

coldata = thdu.data

type(coldata)

coldata.size

coldata.shape

coldata[0]

times = coldata.field('time')

times.shape

times[0]

dt = times - times[0]

dt[0:10]

print(thdu.header['TIMEDEL'], thdu.header['EXPTIME'])

print("timedel={} exptime={}".format(thdu.header['TIMEDEL'], thdu.header['EXPTIME']))

print "timedel=%f exptime=%f" % (thdu.header['TIMEDEL'], thdu.header['EXPTIME'])

dt[:20] / thdu.header['EXPTIME']

dt[0:20] / thdu.header['TIMEDEL']

(dt[:20] / thdu.header['TIMEDEL']).astype(np.int)

Image(filename='wat.jpg')

dt[-1]

vals = np.histogram(dt, bins=100)

vals

Image('question.jpg')

(y,x) = vals
print(y.shape)
print(x.shape)

plt.plot(x[:-1], y)

plt.hist(dt, bins=100)

cvals = np.fft.fft(y)
print("shape={}".format(cvals.shape))
print("data type={}".format(cvals.dtype))

avals = np.abs(cvals)**2
print("data type={}".format(avals.dtype))

plt.plot(avals)
plt.yscale('LOG')
plt.xlim(-5, 105)

Image('question.jpg')

tstep = thdu.header['TIMEDEL']
freqs = np.fft.fftfreq(y.size, tstep)
idx = np.argsort(freqs)

plt.plot(freqs[idx], avals[idx])
plt.yscale('LOG')

def diego(times, tstep):
    """What Would Diego Do?"""
    
    fft = np.fft.fft(times)
    real = np.abs(fft)**2
    freqs = np.fft.fftfreq(times.size, tstep)
    idx = np.argsort(freqs)
    return (freqs[idx], real[idx])

(a,b) = diego(y - y.mean(), tstep)
plt.plot(a, b)

stimes = fits.open("data/swift.evt")[1].data.field('time')

Image(filename='wat.jpg')

dts = stimes - stimes[0]

print(dts[0:10])
print(dts.size)
print(dts.dtype)

test = fits.open("data/lc.fits")
test.info()

test[1].columns

t = test[1].data.field('time')
r = test[1].data.field('count_rate')

plt.plot(t, r)

t - t[0]

(x,y) = diego(r, t[1] - t[0])

plt.plot(x, y)

plt.plot(x[x >= 0], y[x >= 0])

idx = x >= 0
plt.plot(x[idx], y[idx])
plt.yscale('log')

idx[0:10]

idx[-11:-1]

np.where((x > 0.003) & (x < 0.004))

i, = np.where((x > 0.003) & (x < 0.004))

for ii in i[0:7]:
    print("x={:e}  y={:8.1f}".format(x[ii], y[ii]))

rone = r.copy()
rone[r > 0] = 1.0
plt.plot(t, rone)

(xone,yone) = diego(rone, t[1] - t[0])
plt.plot(x[x>=0], y[x>=0])
plt.plot(xone[xone>=0], yone[xone>=0])
plt.yscale('log')
plt.text(0.001, 5e7, 'data', size=20, color='blue')
plt.text(0.003, 5e7, 'gti', size=20, color='green')