In [1]:

    

%load_ext autoreload
%autoreload 2


    

In [2]:

    

%matplotlib inline
import matplotlib.pyplot as plt`
plt.rcParams['figure.figsize']=(14,10)


    

In [3]:

    

plt.rcParams['image.aspect'] = 'auto'
plt.rcParams['image.interpolation'] = 'none'


    

In [4]:

    

import numpy as np


    

In [6]:

    

from iuvs import io
fname = io.l1b_filenames("cruisecal2-mode080-muv", stage=False)
fname


    

    
Out[6]:





['/Users/klay6683/data/iuvs/level1b/mvn_iuv_l1b_cruisecal2-mode080-muv_20140521T120029_v01_r01.fits.gz']

In [7]:

    

l1b = io.L1BReader(fname[0])


    

In [3]:

    

l1b.detector_raw.shape


    

    
Out[3]:





(46, 62, 341)

In [79]:

    

for j,raw in enumerate(l1b.detector_raw[:10]):
    raw0 = raw
    means_ul = []
    means_ll = []
    means_ur = []
    means_lr = []
    for i in range(1,50):
        means_ul.append(raw0[:i, :i].mean())
        means_ll.append(raw0[-i:, :i].mean())
        means_ur.append(raw0[:i, -i:].mean())
        means_lr.append(raw0[-i:, -i:].mean())
    df = pd.DataFrame({'ul':means_ul,
              'll':means_ll,
              'ur':means_ur,
              'lr':means_lr})
    df.plot(title='raw{}'.format(j))


    

In [95]:

    

plt.imshow(raw0)
plt.colorbar()
plt.title('raw0')


    

    
Out[95]:





<matplotlib.text.Text at 0x7f469167d748>






    

In [53]:

    

dark1 = l1b.detector_dark[1]


    

In [91]:

    

plt.imshow(dark1)
plt.colorbar()
plt.title('dark1')


    

    
Out[91]:





<matplotlib.text.Text at 0x7f4690fe56d8>






    

In [92]:

    

plt.imshow(raw0-dark1)
plt.colorbar()
plt.title('raw0-dark1')


    

    
Out[92]:





<matplotlib.text.Text at 0x7f4691ae2ba8>






    

In [63]:

    

def get_ul(data, i):
    return data[:i, :i]
def get_ll(data, i):
    return data[-i:, :i]
def get_ur(data, i):
    return data[:i, -i:]
def get_lr(data, i):
    return data[-i:, -i:]


    

In [116]:

    

i = 10


    

In [125]:

    

sublight = get_ul(raw0, i)
subdark = get_ul(dark1, i)

def compare_corners(dark, light, figtitle):
    fig, axes = plt.subplots(nrows=3)
    j = 0
    for ax, img,t in zip(axes, 
                         [dark, light, light-dark],
                         ['dark','light', 'light-dark']):
        if j==2:
            vmin = -150
            vmax = 150
        else:
            vmin = img.min()
            vmax = img.max()
        im = ax.imshow(img, vmin=vmin, vmax=vmax)
        ax.set_title("{}, mean: {:.0f}".format(t, img.mean()),
                    fontsize=15)
        plt.colorbar(im, ax=ax)
        j += 1
    fig.suptitle(figtitle, fontsize=20)
compare_corners(subdark, sublight, 'Upper left')


    

In [126]:

    

compare_corners(get_ur(dark1, i), get_ur(raw0, i), "Upper right")


    

In [127]:

    

compare_corners(get_ll(dark1, i ), get_ll(raw0, i), 'Lower Left')


    

In [128]:

    

compare_corners(get_lr(dark1, i), get_lr(raw0, i), 'Lower right')


    

In [129]:

    

dark2 = l1b.detector_dark[2]
rawlast = l1b.detector_raw[-1]


    

In [133]:

    

fig, axes = plt.subplots(nrows=2,ncols=2, figsize=(14,12))
axes = axes.flatten()
for ax, img, title in zip(axes, 
                         [raw0, rawlast, dark1, dark2], 
                         ['raw0','rawlast','dark1', 'dark2']):
    im = ax.imshow(img)
    plt.colorbar(im, ax=ax)
    ax.set_title("{}, mean: {}".format(title, img.mean()))


    

In [135]:

    

fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(14,12))
axes = axes.ravel()
for ax, img, title in zip(axes,
                           [raw0-dark1, rawlast-dark1, raw0-dark2, rawlast-dark2],
                           ['raw0-dark1','rawlast-dark1','raw0-dark2','rawlast-dark2']):
    im =ax.imshow(img, vmin=-800, vmax=800)
    plt.colorbar(im, ax=ax)
    ax.set_title("{}, mean: {}".format(title, img.mean()))


    

In [142]:

    

funcs = [get_ul, get_ur, get_ll, get_lr]
for func,name in zip(funcs,['UL', 'UR', 'LL', 'LR']):
    print("Corner: {}".format(name))
    for img,imgname in zip([dark1, dark2, raw0, rawlast, 
                            raw0-dark1, rawlast-dark1, raw0-dark2, rawlast-dark2],
                           ['dark1','dark2','raw0','rawlast',
                           'raw0-dark1','rawlast-dark1','raw0-dark2','rawlast-dark2']):
        print("{imgname} mean: {avg:.0f}".format(imgname=imgname, avg=func(img,10).mean()))
    print('\n')


    

    




Corner: UL
dark1 mean: 487
dark2 mean: 762
raw0 mean: 621
rawlast mean: 909
raw0-dark1 mean: 134
rawlast-dark1 mean: 421
raw0-dark2 mean: -140
rawlast-dark2 mean: 147


Corner: UR
dark1 mean: 474
dark2 mean: 746
raw0 mean: 727
rawlast mean: 871
raw0-dark1 mean: 252
rawlast-dark1 mean: 397
raw0-dark2 mean: -19
rawlast-dark2 mean: 126


Corner: LL
dark1 mean: 468
dark2 mean: 739
raw0 mean: 607
rawlast mean: 837
raw0-dark1 mean: 139
rawlast-dark1 mean: 368
raw0-dark2 mean: -132
rawlast-dark2 mean: 98


Corner: LR
dark1 mean: 511
dark2 mean: 783
raw0 mean: 716
rawlast mean: 888
raw0-dark1 mean: 205
rawlast-dark1 mean: 378
raw0-dark2 mean: -67
rawlast-dark2 mean: 105

In [143]:

    

print((rawlast/raw0).mean())
print((dark2/dark1).mean())


    

    




1.30339501861
1.62750260181

In [158]:

    

cube = l1b.detector_raw


    

In [149]:

    

df = pd.DataFrame(l1b.detector_raw.mean(axis=2).mean(axis=1), columns=['light_total_means'])


    

In [155]:

    

df['ul_mean'] = l1b.detector_raw[:,:10,:10].mean(axis=2).mean(axis=1)
df['ll_mean'] = l1b.detector_raw[:,-10:, :10].mean(axis=2).mean(axis=1)
df['ur_mean'] = l1b.detector_raw[:,10:, -10:].mean(axis=2).mean(axis=1)
df['lr_mean'] = l1b.detector_raw[:,-10:,-10:].mean(axis=2).mean(axis=1)


    

In [157]:

    

df.plot()


    

    
Out[157]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f4690d55eb8>






    

In [160]:

    

(cube - dark1).shape


    

    
Out[160]:





(46, 62, 341)

In [161]:

    

dark1.shape


    

    
Out[161]:





(62, 341)

In [163]:

    

for img in cube[:10]:
    print((img - dark1).mean())


    

    




321.758111815
323.315864157
305.138870495
324.997824236
320.688014379
334.563049853
317.995837669
338.567401381
338.483492574
341.672689433

In [165]:

    

for img in (cube - dark1)[:10]:
    print(img.mean())


    

    




321.758111815
323.315864157
305.138870495
324.997824236
320.688014379
334.563049853
317.995837669
338.567401381
338.483492574
341.672689433

In [166]:

    

cube.shape


    

    
Out[166]:





(46, 62, 341)

In [167]:

    

dark1.shape


    

    
Out[167]:





(62, 341)

In [168]:

    

(cube - dark1).shape


    

    
Out[168]:





(46, 62, 341)

In [169]:

    

import plotly.plotly as py
from plotly.graph_objs import *

trace0 = Scatter(
    x=[1, 2, 3, 4],
    y=[10, 15, 13, 17]
)
trace1 = Scatter(
    x=[1, 2, 3, 4],
    y=[16, 5, 11, 9]
)
data = Data([trace0, trace1])

py.iplot(data, filename = 'basic-line')


    

    
Out[169]:

In [ ]: