

In [12]:

    
%matplotlib widget



In [1]:

    
%load_ext lab_black



In [2]:

    
import matplotlib.pyplot as plt



In [3]:

    
from pyuvis import QUBE, FUV_PDS



In [17]:

    
lbl = "/Users/klay6683/Dropbox/data/uvis/EUV2009_173_15_16.LBL"



In [18]:

    
qube = QUBE(lbl)



In [19]:

    
import xarray as xr



In [20]:

    
xarr = xr.DataArray(
    qube.data,
    dims=["spectral", "spatial", "samples"],
    coords={"spectral": qube.waves},
)









    



---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-20-4f5ab2dff843> in <module>
      2     qube.data,
      3     dims=["spectral", "spatial", "samples"],
----> 4     coords={"spectral": qube.waves},
      5 )

~/Dropbox/src/pyuvis/pyuvis/io.py in waves(self)
     58     @property
     59     def waves(self):
---> 60         return np.linspace(self.wave_min, self.wave_max, self.shape[0])
     61 
     62     @property

AttributeError: 'QUBE' object has no attribute 'wave_min'



In [21]:

    
plt.figure()
plt.imshow(qube.data[..., 0], aspect="auto")









    





 
 










    Out[21]:





<matplotlib.image.AxesImage at 0x1269929e8>



In [9]:

    
xarr









    Out[9]:





<xarray.DataArray (spectral: 1024, spatial: 5, samples: 1)>
array([[[    0],
        [    0],
        ...,
        [    0],
        [    0]],

       [[    0],
        [    0],
        ...,
        [    0],
        [    0]],

       ...,

       [[65535],
        [65535],
        ...,
        [65535],
        [65535]],

       [[65535],
        [65535],
        ...,
        [65535],
        [65535]]], dtype=uint16)
Coordinates:
  * spectral  (spectral) float64 111.5 111.6 111.7 111.7 ... 189.8 189.9 190.0
Dimensions without coordinates: spatial, samples



In [10]:

    
import hvplot.xarray



In [11]:

    
xarr.hvplot()









    Out[11]:



In [19]:

    
from astropy import units as u



In [30]:

    
np.linspace(qube.wave_min.value, qube.wave_max.value, qube.shape[0])









    Out[30]:





array([111.5       , 111.57673509, 111.65347019, ..., 189.84652981,
       189.92326491, 190.        ])



In [29]:

    
qube.shape[0]









    Out[29]:





1024



In [25]:

    
from astropy.units import QuantityIterator









    



---------------------------------------------------------------------------
ImportError                               Traceback (most recent call last)
<ipython-input-25-c92b32cc2b4d> in <module>
----> 1 from astropy.units import QuantityIterator

ImportError: cannot import name 'QuantityIterator' from 'astropy.units' (/Users/klay6683/miniconda3/envs/py37/lib/python3.7/site-packages/astropy/units/__init__.py)



In [18]:

    
xarr









    Out[18]:





<xarray.DataArray (spectral: 1024, spatial: 5, samples: 1)>
array([[[    0],
        [    0],
        ...,
        [    0],
        [    0]],

       [[    0],
        [    0],
        ...,
        [    0],
        [    0]],

       ...,

       [[65535],
        [65535],
        ...,
        [65535],
        [65535]],

       [[65535],
        [65535],
        ...,
        [65535],
        [65535]]], dtype=uint16)
Dimensions without coordinates: spectral, spatial, samples



In [14]:

    
def show_slice(i):
    im = plt.imshow(qube.data[i], aspect="auto", interpolation="nearest")
    plt.colorbar(im, ax=plt.gca())


interactive(show_slice, i=(0, qube.shape[0]))

The preview data from the UVIS data catalog seems to be often the mean value over the bands, at least I could match the preview images quite often. Just for your data file, it does not make sense yet, but maybe because of the missing binning implementation.



In [33]:

    
plt.imshow(qube.data.mean(axis=0), aspect='auto', interpolation='nearest',
           cmap=plt.cm.rainbow)









    Out[33]:





<matplotlib.image.AxesImage at 0x10d892eb8>



In [34]:

    
qube.line_range









    Out[34]:





(4, 57)



In [35]:

    
qube.band_range









    Out[35]:





(0, 1023)



In [36]:

    
qube.data1D









    Out[36]:





array([65535, 65535, 65535, ..., 65535, 65535, 65535], dtype=uint16)



In [37]:

    
qube.data.shape









    Out[37]:





(1024, 64, 9748)



In [ ]:

    
qube

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