Scientifc Python ver.2¶

Author: Juhyung Kang¶

e-mail: jhkang@astro.snu.ac.kr¶

Python is the useful high-level computational language for science, machine learing, IoT and web design. The language is quite simliar with the MATLAB but the license is free and no need to access LAN. There are many user communities.

From this second tutorial we learn the basic command line for the data handling and graphics.

This tutorial is uploaded on the 'Tutorials' menu at https://jhkang.netlify.com

The basic resource of python for scientist is available in Scipy Lecture Notes.

Required packages¶

1. Array handling¶

To call an array in python, we should import the numpy

In [1]:

import numpy as np

1.1. Manual construction¶

In [2]:

a = np.array([1,2,3,4,5])

In [3]:

Out[3]:

array([1, 2, 3, 4, 5])

In [4]:

a.shape

Out[4]:

(5,)

In [5]:

a.ndim

Out[5]:

In [6]:

a.dtype

Out[6]:

dtype('int32')

In [7]:

a = a.astype(float)

In [8]:

a.dtype

Out[8]:

dtype('float64')

In [9]:

a = np.array([1,2,3,4,5],dtype=float)

Slicing

In [10]:

a[3]

Out[10]:

4.0

In [11]:

a[-1]

Out[11]:

5.0

In [12]:

a[0:2]

Out[12]:

array([1., 2.])

In [13]:

a[2:5]

Out[13]:

array([3., 4., 5.])

In [14]:

a[:]

Out[14]:

array([1., 2., 3., 4., 5.])

In [15]:

a[0:5:2]

Out[15]:

array([1., 3., 5.])

In [16]:

a[-1:0:-1]

Out[16]:

array([5., 4., 3., 2.])

In [17]:

a[::-1]

Out[17]:

array([5., 4., 3., 2., 1.])

Convert this row array to the column array

In [18]:

col = a[:,None]

In [19]:

col

Out[19]:

array([[1.],
       [2.],
       [3.],
       [4.],
       [5.]])

In [20]:

col.shape

Out[20]:

(5, 1)

In [21]:

newarr = a[None,:,None]

In [22]:

newarr

Out[22]:

array([[[1.],
        [2.],
        [3.],
        [4.],
        [5.]]])

In [23]:

newarr.shape

Out[23]:

(1, 5, 1)

Array appending

In [24]:

a = np.append(a,[6,7])

In [25]:

Out[25]:

array([1., 2., 3., 4., 5., 6., 7.])

In [26]:

b = np.concatenate([a, a])

In [27]:

Out[27]:

array([1., 2., 3., 4., 5., 6., 7., 1., 2., 3., 4., 5., 6., 7.])

Note) np.array(1) and np.array([1]) are different

In [28]:

a1 = np.array(1)
a2 = np.array([1])

In [29]:

a1

Out[29]:

array(1)

In [30]:

a2

Out[30]:

array([1])

In [31]:

a1[0]

---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<ipython-input-31-24bd6a1fc18f> in <module>()
----> 1 a1[0]

IndexError: too many indices for array

In [32]:

a2[0]

Out[32]:

In [33]:

a1 = a1.flatten()

In [34]:

a1

Out[34]:

array([1])

In [35]:

a1[0]

Out[35]:

Make 2D array.

In [36]:

arr2d = np.array([[1,2,3],[4,5,6]], dtype=float)

In [37]:

arr2d

Out[37]:

array([[1., 2., 3.],
       [4., 5., 6.]])

In [38]:

arr2d[0]

Out[38]:

array([1., 2., 3.])

In [39]:

arr2d[0,0]

Out[39]:

1.0

In [40]:

arr2d[0][0]

Out[40]:

1.0

1.2. Array generator¶

From the np.arange or np.linspace, we can generate the evenly spaced array automatically.

np.arange([start,]stop,[step,]dtype=None)
Return evenly spaced values within a given interval.

np.linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis=0)
Return evenly spaced numbers over a specified interval.

arange

In [41]:

ara = np.arange(6)

In [42]:

ara

Out[42]:

array([0, 1, 2, 3, 4, 5])

In [43]:

ara2 = np.arange(2,6,0.5)

In [44]:

ara2

Out[44]:

array([2. , 2.5, 3. , 3.5, 4. , 4.5, 5. , 5.5])

linspace

In [45]:

alin = np.linspace(0,6,5)

In [46]:

alin

Out[46]:

array([0. , 1.5, 3. , 4.5, 6. ])

1.3. Broadcasting¶

The high adventage for array handling in python is broadcasting.

Basic operations on numpy arrays (addition, etc.) are elementwise This works on arrays of the same size. Nevertheless, It’s also possible to do operations on arrays of different sizes if NumPy can transform these arrays so that they all have the same size: this conversion is called broadcasting.

In [47]:

a = np.linspace(1,9,9)
b = np.arange(45).reshape(5,9)

In [48]:

Out[48]:

array([1., 2., 3., 4., 5., 6., 7., 8., 9.])

In [49]:

Out[49]:

array([[ 0,  1,  2,  3,  4,  5,  6,  7,  8],
       [ 9, 10, 11, 12, 13, 14, 15, 16, 17],
       [18, 19, 20, 21, 22, 23, 24, 25, 26],
       [27, 28, 29, 30, 31, 32, 33, 34, 35],
       [36, 37, 38, 39, 40, 41, 42, 43, 44]])

In [50]:

a+b

Out[50]:

array([[ 1.,  3.,  5.,  7.,  9., 11., 13., 15., 17.],
       [10., 12., 14., 16., 18., 20., 22., 24., 26.],
       [19., 21., 23., 25., 27., 29., 31., 33., 35.],
       [28., 30., 32., 34., 36., 38., 40., 42., 44.],
       [37., 39., 41., 43., 45., 47., 49., 51., 53.]])

In [51]:

acol = a[:,None]

In [52]:

acol.shape

Out[52]:

(9, 1)

In [53]:

a*acol

Out[53]:

array([[ 1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9.],
       [ 2.,  4.,  6.,  8., 10., 12., 14., 16., 18.],
       [ 3.,  6.,  9., 12., 15., 18., 21., 24., 27.],
       [ 4.,  8., 12., 16., 20., 24., 28., 32., 36.],
       [ 5., 10., 15., 20., 25., 30., 35., 40., 45.],
       [ 6., 12., 18., 24., 30., 36., 42., 48., 54.],
       [ 7., 14., 21., 28., 35., 42., 49., 56., 63.],
       [ 8., 16., 24., 32., 40., 48., 56., 64., 72.],
       [ 9., 18., 27., 36., 45., 54., 63., 72., 81.]])

In [54]:

one=np.ones([9,1])

In [55]:

one

Out[55]:

array([[1.],
       [1.],
       [1.],
       [1.],
       [1.],
       [1.],
       [1.],
       [1.],
       [1.]])

In [56]:

a*one

Out[56]:

array([[1., 2., 3., 4., 5., 6., 7., 8., 9.],
       [1., 2., 3., 4., 5., 6., 7., 8., 9.],
       [1., 2., 3., 4., 5., 6., 7., 8., 9.],
       [1., 2., 3., 4., 5., 6., 7., 8., 9.],
       [1., 2., 3., 4., 5., 6., 7., 8., 9.],
       [1., 2., 3., 4., 5., 6., 7., 8., 9.],
       [1., 2., 3., 4., 5., 6., 7., 8., 9.],
       [1., 2., 3., 4., 5., 6., 7., 8., 9.],
       [1., 2., 3., 4., 5., 6., 7., 8., 9.]])

1.4. Basic reduction¶

In [57]:

x = np.arange(5)

In [58]:

Out[58]:

array([0, 1, 2, 3, 4])

In [59]:

x.sum()

Out[59]:

In [60]:

x.mean()

Out[60]:

2.0

Sum by rows and by columns:

In [61]:

x2 = np.arange(30).reshape(5,6)

In [62]:

x2

Out[62]:

array([[ 0,  1,  2,  3,  4,  5],
       [ 6,  7,  8,  9, 10, 11],
       [12, 13, 14, 15, 16, 17],
       [18, 19, 20, 21, 22, 23],
       [24, 25, 26, 27, 28, 29]])

In [63]:

x2.shape

Out[63]:

(5, 6)

In [64]:

x2.sum(0)  # colums (first dimension)

Out[64]:

array([60, 65, 70, 75, 80, 85])

In [65]:

x2.sum(1)  # rows (second dimension)

Out[65]:

array([ 15,  51,  87, 123, 159])

2. Graphics¶

From this session, we learn how to visulize the arrays by using matplotlib package.

At first, enable interactive plots:

In [66]:

matplotlib

Using matplotlib backend: Qt5Agg

Let's import the 2D plotting package, matplotlib.

In [67]:

import matplotlib.pyplot as plt

2.1. 1D plotting¶

In [68]:

x = np.arange(10)

In [69]:

y = x**2

In [70]:

plt.plot(x,y)

Out[70]:

[<matplotlib.lines.Line2D at 0x2f2bf4c7b8>]

In [71]:

plt.plot(x,y,'o',color='black')

Out[71]:

[<matplotlib.lines.Line2D at 0x2f2c0f4748>]

2.2. 2D plotting¶

In [72]:

x2 = x * x[:,None]

In [73]:

x2

Out[73]:

array([[ 0,  0,  0,  0,  0,  0,  0,  0,  0,  0],
       [ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9],
       [ 0,  2,  4,  6,  8, 10, 12, 14, 16, 18],
       [ 0,  3,  6,  9, 12, 15, 18, 21, 24, 27],
       [ 0,  4,  8, 12, 16, 20, 24, 28, 32, 36],
       [ 0,  5, 10, 15, 20, 25, 30, 35, 40, 45],
       [ 0,  6, 12, 18, 24, 30, 36, 42, 48, 54],
       [ 0,  7, 14, 21, 28, 35, 42, 49, 56, 63],
       [ 0,  8, 16, 24, 32, 40, 48, 56, 64, 72],
       [ 0,  9, 18, 27, 36, 45, 54, 63, 72, 81]])

In [74]:

plt.imshow(x2)

Out[74]:

<matplotlib.image.AxesImage at 0x2f2c1269e8>

In [75]:

plt.colorbar()

Out[75]:

<matplotlib.colorbar.Colorbar at 0x2f2cf78e10>

In [76]:

im = plt.imshow(x2,origin='lower', cmap=plt.cm.jet)

In [77]:

plt.colorbar(im)

Out[77]:

<matplotlib.colorbar.Colorbar at 0x2f2d027c18>

In [78]:

im.set_cmap(plt.cm.gray)

In [79]:

im.set_interpolation('bilinear')

2.3. 3D plotting¶

Plot 2D data on 3D plot

At first we import 3D axes

In [80]:

from mpl_toolkits.mplot3d import Axes3D

In [81]:

x = np.linspace(-4,4,40)
xx, yy = np.meshgrid(x,x)
R = np.sqrt(xx**2 +yy**2)
zz = np.sin(R)
data = zz

In [82]:

fig = plt.figure()
ax = fig.gca(projection='3d')
ax.set_aspect('equal')

In [83]:

ax.plot_surface(xx,yy,data,cmap=plt.cm.plasma)

Out[83]:

<mpl_toolkits.mplot3d.art3d.Poly3DCollection at 0x2f2c4067f0>

In [84]:

plane = np.ones((data.shape))

In [85]:

ax.plot_surface(xx,yy,plane*-2,facecolors=plt.cm.plasma(data/data.max()),shade=False)

Out[85]:

<mpl_toolkits.mplot3d.art3d.Poly3DCollection at 0x2f2ccc17b8>

In [86]:

fig.tight_layout()

2.4. Subplots with GridSpec¶

Easy way to add subplots is use the matplotlib subplots function (see the Lecture. However, it is difficult to make details. There is another option to create subplots: gridspec

In this section we learn how to use the one of the gridspec class: ImgaeGrid (see example) :

In [87]:

from mpl_toolkits.axes_grid1 import ImageGrid

In [88]:

fig = plt.figure()
grid = ImageGrid(fig, 111, nrows_ncols=(2, 5),
                 axes_pad=0, share_all=True,
                 label_mode='1')

In [89]:

img = np.arange(10) * np.ones((10,10))

In [90]:

# grid[i].imshow(images)
for i in grid:
    i.imshow(img,origin='lower')

3. AstroPy¶

AstroPy is the useful python package for astronomers. From this we learn how to read the fits file and its header.

3.1. Read Fits file¶

Download the sample fits file.

In [91]:

from astropy.io import fits

Read fits

In [92]:

data = fits.getdata('C:/Users/User/Downloads/HorseHead.fits')

In [93]:

data.shape

Out[93]:

(893, 891)

In [94]:

plt.imshow(data,origin='lower')

Out[94]:

<matplotlib.image.AxesImage at 0x2f30a14d68>

In [95]:

plt.imshow(data.T,origin='lower')

Out[95]:

<matplotlib.image.AxesImage at 0x2f30c99240>

In [96]:

plt.imshow(data.T[:,::-1],origin='lower')

Out[96]:

<matplotlib.image.AxesImage at 0x2f30cf2c88>

Read header

In [97]:

h = fits.getheader('C:/Users/User/Downloads/HorseHead.fits')

In [98]:

Out[98]:

SIMPLE  =                    T /FITS: Compliance                                
BITPIX  =                   16 /FITS: I*2 Data                                  
NAXIS   =                    2 /FITS: 2-D Image Data                            
NAXIS1  =                  891 /FITS: X Dimension                               
NAXIS2  =                  893 /FITS: Y Dimension                               
EXTEND  =                    T /FITS: File can contain extensions               
DATE    = '2014-01-09        '  /FITS: Creation Date                            
ORIGIN  = 'STScI/MAST'         /GSSS: STScI Digitized Sky Survey                
SURVEY  = 'SERC-ER '           /GSSS: Sky Survey                                
REGION  = 'ER768   '           /GSSS: Region Name                               
PLATEID = 'A0JP    '           /GSSS: Plate ID                                  
SCANNUM = '01      '           /GSSS: Scan Number                               
DSCNDNUM= '00      '           /GSSS: Descendant Number                         
TELESCID=                    4 /GSSS: Telescope ID                              
BANDPASS=                   36 /GSSS: Bandpass Code                             
COPYRGHT= 'AAO/ROE '           /GSSS: Copyright Holder                          
SITELAT =              -31.277 /Observatory: Latitude                           
SITELONG=              210.934 /Observatory: Longitude                          
TELESCOP= 'UK Schmidt - Doubl' /Observatory: Telescope                          
INSTRUME= 'Photographic Plate' /Detector: Photographic Plate                    
EMULSION= 'IIIaF   '           /Detector: Emulsion                              
FILTER  = 'OG590   '           /Detector: Filter                                
PLTSCALE=                67.20 /Detector: Plate Scale arcsec per mm             
PLTSIZEX=              355.000 /Detector: Plate X Dimension mm                  
PLTSIZEY=              355.000 /Detector: Plate Y Dimension mm                  
PLATERA =        85.5994550000 /Observation: Field centre RA degrees            
PLATEDEC=       -4.94660910000 /Observation: Field centre Dec degrees           
PLTLABEL= 'OR14052 '           /Observation: Plate Label                        
DATE-OBS= '1990-12-22T13:49:00' /Observation: Date/Time                         
EXPOSURE=                 65.0 /Observation: Exposure Minutes                   
PLTGRADE= 'AD2     '           /Observation: Plate Grade                        
OBSHA   =             0.158333 /Observation: Hour Angle                         
OBSZD   =              26.3715 /Observation: Zenith Distance                    
AIRMASS =              1.11587 /Observation: Airmass                            
REFBETA =        66.3196420000 /Observation: Refraction Coeff                   
REFBETAP=     -0.0820000000000 /Observation: Refraction Coeff                   
REFK1   =        6423.52290000 /Observation: Refraction Coeff                   
REFK2   =       -102122.550000 /Observation: Refraction Coeff                   
CNPIX1  =                12237 /Scan: X Corner                                  
CNPIX2  =                19965 /Scan: Y Corner                                  
XPIXELS =                23040 /Scan: X Dimension                               
YPIXELS =                23040 /Scan: Y Dimension                               
XPIXELSZ=              15.0295 /Scan: Pixel Size microns                        
YPIXELSZ=              15.0000 /Scan: Pixel Size microns                        
PPO1    =       -3069417.00000 /Scan: Orientation Coeff                         
PPO2    =       0.000000000000 /Scan: Orientation Coeff                         
PPO3    =        177500.000000 /Scan: Orientation Coeff                         
PPO4    =       0.000000000000 /Scan: Orientation Coeff                         
PPO5    =        3069417.00000 /Scan: Orientation Coeff                         
PPO6    =        177500.000000 /Scan: Orientation Coeff                         
PLTRAH  =                    5 /Astrometry: Plate Centre H                      
PLTRAM  =                   42 /Astrometry: Plate Centre M                      
PLTRAS  =                23.86 /Astrometry: Plate Centre S                      
PLTDECSN= '-       '           /Astrometry: Plate Centre +/-                    
PLTDECD =                    4 /Astrometry: Plate Centre D                      
PLTDECM =                   56 /Astrometry: Plate Centre M                      
PLTDECS =                 47.9 /Astrometry: Plate Centre S                      
EQUINOX =               2000.0 /Astrometry: Equinox                             
AMDX1   =        67.1550859799 /Astrometry: GSC1 Coeff                          
AMDX2   =      0.0431478884485 /Astrometry: GSC1 Coeff                          
AMDX3   =       -292.435619180 /Astrometry: GSC1 Coeff                          
AMDX4   =  -2.68934864702E-005 /Astrometry: GSC1 Coeff                          
AMDX5   =   1.99133423290E-005 /Astrometry: GSC1 Coeff                          
AMDX6   =  -2.37011931379E-006 /Astrometry: GSC1 Coeff                          
AMDX7   =       0.000000000000 /Astrometry: GSC1 Coeff                          
AMDX8   =   2.21426387429E-006 /Astrometry: GSC1 Coeff                          
AMDX9   =  -8.12841581455E-008 /Astrometry: GSC1 Coeff                          
AMDX10  =   2.48169090021E-006 /Astrometry: GSC1 Coeff                          
AMDX11  =   2.77618933926E-008 /Astrometry: GSC1 Coeff                          
AMDX12  =       0.000000000000 /Astrometry: GSC1 Coeff                          
AMDX13  =       0.000000000000 /Astrometry: GSC1 Coeff                          
AMDX14  =       0.000000000000 /Astrometry: GSC1 Coeff                          
AMDX15  =       0.000000000000 /Astrometry: GSC1 Coeff                          
AMDX16  =       0.000000000000 /Astrometry: GSC1 Coeff                          
AMDX17  =       0.000000000000 /Astrometry: GSC1 Coeff                          
AMDX18  =       0.000000000000 /Astrometry: GSC1 Coeff                          
AMDX19  =       0.000000000000 /Astrometry: GSC1 Coeff                          
AMDX20  =       0.000000000000 /Astrometry: GSC1 Coeff                          
AMDY1   =        67.1593591466 /Astrometry: GSC1 Coeff                          
AMDY2   =     -0.0471363749174 /Astrometry: GSC1 Coeff                          
AMDY3   =        316.004963520 /Astrometry: GSC1 Coeff                          
AMDY4   =   2.86798151430E-005 /Astrometry: GSC1 Coeff                          
AMDY5   =  -2.00968236347E-005 /Astrometry: GSC1 Coeff                          
AMDY6   =   2.27840393227E-005 /Astrometry: GSC1 Coeff                          
AMDY7   =       0.000000000000 /Astrometry: GSC1 Coeff                          
AMDY8   =   2.23885090381E-006 /Astrometry: GSC1 Coeff                          
AMDY9   =  -2.28360163464E-008 /Astrometry: GSC1 Coeff                          
AMDY10  =   2.44828851495E-006 /Astrometry: GSC1 Coeff                          
AMDY11  =  -5.76717487998E-008 /Astrometry: GSC1 Coeff                          
AMDY12  =       0.000000000000 /Astrometry: GSC1 Coeff                          
AMDY13  =       0.000000000000 /Astrometry: GSC1 Coeff                          
AMDY14  =       0.000000000000 /Astrometry: GSC1 Coeff                          
AMDY15  =       0.000000000000 /Astrometry: GSC1 Coeff                          
AMDY16  =       0.000000000000 /Astrometry: GSC1 Coeff                          
AMDY17  =       0.000000000000 /Astrometry: GSC1 Coeff                          
AMDY18  =       0.000000000000 /Astrometry: GSC1 Coeff                          
AMDY19  =       0.000000000000 /Astrometry: GSC1 Coeff                          
AMDY20  =       0.000000000000 /Astrometry: GSC1 Coeff                          
AMDREX1 =        67.1532034737 /Astrometry: GSC2 Coeff                          
AMDREX2 =      0.0434354199559 /Astrometry: GSC2 Coeff                          
AMDREX3 =       -292.435438892 /Astrometry: GSC2 Coeff                          
AMDREX4 =   4.60919247070E-006 /Astrometry: GSC2 Coeff                          
AMDREX5 =  -3.21138058537E-006 /Astrometry: GSC2 Coeff                          
AMDREX6 =   7.23651736725E-006 /Astrometry: GSC2 Coeff                          
AMDREX7 =       0.000000000000 /Astrometry: GSC2 Coeff                          
AMDREX8 =       0.000000000000 /Astrometry: GSC2 Coeff                          
AMDREX9 =       0.000000000000 /Astrometry: GSC2 Coeff                          
AMDREX10=       0.000000000000 /Astrometry: GSC2 Coeff                          
AMDREX11=       0.000000000000 /Astrometry: GSC2 Coeff                          
AMDREX12=       0.000000000000 /Astrometry: GSC2 Coeff                          
AMDREX13=       0.000000000000 /Astrometry: GSC2 Coeff                          
AMDREX14=       0.000000000000 /Astrometry: GSC2 Coeff                          
AMDREX15=       0.000000000000 /Astrometry: GSC2 Coeff                          
AMDREX16=       0.000000000000 /Astrometry: GSC2 Coeff                          
AMDREX17=       0.000000000000 /Astrometry: GSC2 Coeff                          
AMDREX18=       0.000000000000 /Astrometry: GSC2 Coeff                          
AMDREX19=       0.000000000000 /Astrometry: GSC2 Coeff                          
AMDREX20=       0.000000000000 /Astrometry: GSC2 Coeff                          
AMDREY1 =        67.1522589487 /Astrometry: GSC2 Coeff                          
AMDREY2 =     -0.0481758265285 /Astrometry: GSC2 Coeff                          
AMDREY3 =        315.995683716 /Astrometry: GSC2 Coeff                          
AMDREY4 =  -7.47397531230E-006 /Astrometry: GSC2 Coeff                          
AMDREY5 =   9.55221105409E-007 /Astrometry: GSC2 Coeff                          
AMDREY6 =   7.60954485251E-006 /Astrometry: GSC2 Coeff                          
AMDREY7 =       0.000000000000 /Astrometry: GSC2 Coeff                          
AMDREY8 =       0.000000000000 /Astrometry: GSC2 Coeff                          
AMDREY9 =       0.000000000000 /Astrometry: GSC2 Coeff                          
AMDREY10=       0.000000000000 /Astrometry: GSC2 Coeff                          
AMDREY11=       0.000000000000 /Astrometry: GSC2 Coeff                          
AMDREY12=       0.000000000000 /Astrometry: GSC2 Coeff                          
AMDREY13=       0.000000000000 /Astrometry: GSC2 Coeff                          
AMDREY14=       0.000000000000 /Astrometry: GSC2 Coeff                          
AMDREY15=       0.000000000000 /Astrometry: GSC2 Coeff                          
AMDREY16=       0.000000000000 /Astrometry: GSC2 Coeff                          
AMDREY17=       0.000000000000 /Astrometry: GSC2 Coeff                          
AMDREY18=       0.000000000000 /Astrometry: GSC2 Coeff                          
AMDREY19=       0.000000000000 /Astrometry: GSC2 Coeff                          
AMDREY20=       0.000000000000 /Astrometry: GSC2 Coeff                          
ASTRMASK= 'er.mask '           /Astrometry: GSC2 Mask                           
WCSAXES =                    2 /GetImage: Number WCS axes                       
WCSNAME = 'DSS               ' /GetImage: Local WCS approximation from full plat
RADESYS = 'ICRS              ' /GetImage: GSC-II calibration using ICRS system  
CTYPE1  = 'RA---TAN          ' /GetImage: RA-Gnomic projection                  
CRPIX1  =           446.000000 /GetImage: X reference pixel                     
CRVAL1  =            85.274970 /GetImage: RA of reference pixel                 
CUNIT1  = 'deg               ' /GetImage: degrees                               
CTYPE2  = 'DEC--TAN          ' /GetImage: Dec-Gnomic projection                 
CRPIX2  =           447.000000 /GetImage: Y reference pixel                     
CRVAL2  =            -2.458265 /GetImage: Dec of reference pixel                
CUNIT2  = 'deg               ' /Getimage: degrees                               
CD1_1   =        -0.0002802651 /GetImage: rotation matrix coefficient           
CD1_2   =         0.0000003159 /GetImage: rotation matrix coefficient           
CD2_1   =         0.0000002767 /GetImage: rotation matrix coefficient           
CD2_2   =         0.0002798187 /GetImage: rotation matrix coefficient           
OBJECT  = 'data              ' /GetImage: Requested Object Name                 
DATAMIN =                 3759 /GetImage: Minimum returned pixel value          
DATAMAX =                22918 /GetImage: Maximum returned pixel value          
OBJCTRA = '05 41 06.000      ' /GetImage: Requested Right Ascension (J2000)     
OBJCTDEC= '-02 27 30.00      ' /GetImage: Requested Declination (J2000)         
OBJCTX  =             12682.48 /GetImage: Requested X on plate (pixels)         
OBJCTY  =             20411.37 /GetImage: Requested Y on plate (pixels)

In [99]:

h['exposure']

Out[99]:

65.0

In [100]:

h['date-obs']

Out[100]:

'1990-12-22T13:49:00'

3.1. Unit handling¶

As we use the astropy.units and constants module, we can easily calculate the physical parameters

In [101]:

import astropy.constants as con

In [102]:

con.R_sun

Out[102]:

$6.957 \times 10^{8} \; \mathrm{m}$

In [103]:

con.R_sun.cgs

Out[103]:

$6.957 \times 10^{10} \; \mathrm{cm}$

In [104]:

con.R_sun.to('nm')

Out[104]:

$6.957 \times 10^{17} \; \mathrm{nm}$

In [105]:

con.c

Out[105]:

$2.9979246 \times 10^{8} \; \mathrm{\frac{m}{s}}$

In [106]:

con.au / con.c

Out[106]:

$499.00478 \; \mathrm{s}$

In [107]:

(con.au / con.c).to('min')

Out[107]:

$8.3167464 \; \mathrm{min}$

unit module

In [108]:

import astropy.units as u

In [109]:

u.cm

Out[109]:

$\mathrm{cm}$

In [110]:

1*u.cm

Out[110]:

$1 \; \mathrm{cm}$

In [111]:

con.au + 1e13*u.cm

Out[111]:

$2.4959787 \times 10^{11} \; \mathrm{m}$

Time module

In [112]:

from astropy.time import Time

In [113]:

date = h['date-obs']

In [114]:

date

Out[114]:

'1990-12-22T13:49:00'

In [115]:

t = Time(date)

In [116]:

Out[116]:

<Time object: scale='utc' format='isot' value=1990-12-22T13:49:00.000>

In [117]:

t + 1*u.year

Out[117]:

<Time object: scale='utc' format='isot' value=1991-12-22T19:48:59.000>

In [118]:

t.jd  # julian date

Out[118]:

2448248.0756944446