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In [8]:

    
import os
import sys
import numpy as np
import matplotlib
matplotlib.use('nbagg')
#from matplotlib import style
#style.use('ggplot')
import matplotlib.pyplot as plt

from skimage import feature
from scipy import stats, ndimage
from scipy.misc import factorial as fac

import photutils

from astropy import units as u
from astropy import stats
from astropy import visualization
from astropy import coordinates
from astropy.table import Table, vstack
from astropy.io import ascii, fits
from astropy.modeling import models, fitting, custom_model

%load_ext autoreload
%autoreload 2









    



/Users/tim/conda/envs/astroconda/lib/python3.5/site-packages/matplotlib/__init__.py:1401: UserWarning:  This call to matplotlib.use() has no effect
because the backend has already been chosen;
matplotlib.use() must be called *before* pylab, matplotlib.pyplot,
or matplotlib.backends is imported for the first time.

  warnings.warn(_use_error_msg)



In [2]:

    
ACT = 104
NODE = 3222
BRAD = 3228.5



In [3]:

    
NODE*ACT









    Out[3]:





335088



In [4]:

    
# for each node, the influence from each actuator
surf2act = np.fromfile("../mmtwfs/data/Surf2ActTEL_32.bin", dtype=np.float32).reshape(NODE, ACT)
nodecoor = ascii.read("../mmtwfs/data/bcv_node_coordinates.dat", names=["bcv_id", "bcv_x", "bcv_y", "bcv_z"])
actcoor = ascii.read("../mmtwfs/data/actuator_coordinates.dat", names=["act_i", "act_x", "act_y", "act_type"])
for ax in ["bcv_x", "bcv_y"]:
    nodecoor[ax] /= BRAD
nodecoor['bcv_rho'] = np.sqrt(nodecoor['bcv_x']**2 + nodecoor['bcv_y']**2)
nodecoor['bcv_phi'] = np.arctan2(nodecoor['bcv_y'], nodecoor['bcv_x'])



In [5]:

    
actcoor['act_x'].unit = u.mm



In [6]:

    
actcoor









    Out[6]:




<Table length=104>

act_i act_x act_y act_type
mm 
int64 float64 float64 int64
1 297.9 -3207.0 0
2 1085.0 -3033.0 0
3 1441.0 -2880.0 0
4 444.0 -2883.0 0
5 1054.0 -2787.0 0
6 1928.0 -2573.0 0
7 166.4 -2306.0 0
8 776.7 -2306.0 1
9 1442.0 -2306.0 1
... ... ... ...
143 -166.4 2306.0 0
144 -776.7 2306.0 -1
145 -1442.0 2306.0 -1
146 -2053.0 2210.0 0
147 -444.0 2883.0 0
148 -1054.0 2787.0 0
149 -1928.0 2573.0 0
150 -297.9 3207.0 0
151 -1085.0 3033.0 0
152 -1441.0 2880.0 0



In [7]:

    
im = np.arange(90).reshape(9, 10)



In [8]:

    
np.indices(im.shape, dtype=float)









    Out[8]:





array([[[ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
        [ 1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.],
        [ 2.,  2.,  2.,  2.,  2.,  2.,  2.,  2.,  2.,  2.],
        [ 3.,  3.,  3.,  3.,  3.,  3.,  3.,  3.,  3.,  3.],
        [ 4.,  4.,  4.,  4.,  4.,  4.,  4.,  4.,  4.,  4.],
        [ 5.,  5.,  5.,  5.,  5.,  5.,  5.,  5.,  5.,  5.],
        [ 6.,  6.,  6.,  6.,  6.,  6.,  6.,  6.,  6.,  6.],
        [ 7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.],
        [ 8.,  8.,  8.,  8.,  8.,  8.,  8.,  8.,  8.,  8.]],

       [[ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9.],
        [ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9.],
        [ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9.],
        [ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9.],
        [ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9.],
        [ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9.],
        [ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9.],
        [ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9.],
        [ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9.]]])



In [9]:

    
slopes = (np.indices(im.shape, dtype=float)/(np.r_[im.shape].reshape(-1, 1, 1))).reshape(2, -1)
slopes = np.vstack([slopes, np.ones(slopes.shape[1])])
slopes









    Out[9]:





array([[ 0.        ,  0.        ,  0.        ,  0.        ,  0.        ,
         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,
         0.11111111,  0.11111111,  0.11111111,  0.11111111,  0.11111111,
         0.11111111,  0.11111111,  0.11111111,  0.11111111,  0.11111111,
         0.22222222,  0.22222222,  0.22222222,  0.22222222,  0.22222222,
         0.22222222,  0.22222222,  0.22222222,  0.22222222,  0.22222222,
         0.33333333,  0.33333333,  0.33333333,  0.33333333,  0.33333333,
         0.33333333,  0.33333333,  0.33333333,  0.33333333,  0.33333333,
         0.44444444,  0.44444444,  0.44444444,  0.44444444,  0.44444444,
         0.44444444,  0.44444444,  0.44444444,  0.44444444,  0.44444444,
         0.55555556,  0.55555556,  0.55555556,  0.55555556,  0.55555556,
         0.55555556,  0.55555556,  0.55555556,  0.55555556,  0.55555556,
         0.66666667,  0.66666667,  0.66666667,  0.66666667,  0.66666667,
         0.66666667,  0.66666667,  0.66666667,  0.66666667,  0.66666667,
         0.77777778,  0.77777778,  0.77777778,  0.77777778,  0.77777778,
         0.77777778,  0.77777778,  0.77777778,  0.77777778,  0.77777778,
         0.88888889,  0.88888889,  0.88888889,  0.88888889,  0.88888889,
         0.88888889,  0.88888889,  0.88888889,  0.88888889,  0.88888889],
       [ 0.        ,  0.1       ,  0.2       ,  0.3       ,  0.4       ,
         0.5       ,  0.6       ,  0.7       ,  0.8       ,  0.9       ,
         0.        ,  0.1       ,  0.2       ,  0.3       ,  0.4       ,
         0.5       ,  0.6       ,  0.7       ,  0.8       ,  0.9       ,
         0.        ,  0.1       ,  0.2       ,  0.3       ,  0.4       ,
         0.5       ,  0.6       ,  0.7       ,  0.8       ,  0.9       ,
         0.        ,  0.1       ,  0.2       ,  0.3       ,  0.4       ,
         0.5       ,  0.6       ,  0.7       ,  0.8       ,  0.9       ,
         0.        ,  0.1       ,  0.2       ,  0.3       ,  0.4       ,
         0.5       ,  0.6       ,  0.7       ,  0.8       ,  0.9       ,
         0.        ,  0.1       ,  0.2       ,  0.3       ,  0.4       ,
         0.5       ,  0.6       ,  0.7       ,  0.8       ,  0.9       ,
         0.        ,  0.1       ,  0.2       ,  0.3       ,  0.4       ,
         0.5       ,  0.6       ,  0.7       ,  0.8       ,  0.9       ,
         0.        ,  0.1       ,  0.2       ,  0.3       ,  0.4       ,
         0.5       ,  0.6       ,  0.7       ,  0.8       ,  0.9       ,
         0.        ,  0.1       ,  0.2       ,  0.3       ,  0.4       ,
         0.5       ,  0.6       ,  0.7       ,  0.8       ,  0.9       ],
       [ 1.        ,  1.        ,  1.        ,  1.        ,  1.        ,
         1.        ,  1.        ,  1.        ,  1.        ,  1.        ,
         1.        ,  1.        ,  1.        ,  1.        ,  1.        ,
         1.        ,  1.        ,  1.        ,  1.        ,  1.        ,
         1.        ,  1.        ,  1.        ,  1.        ,  1.        ,
         1.        ,  1.        ,  1.        ,  1.        ,  1.        ,
         1.        ,  1.        ,  1.        ,  1.        ,  1.        ,
         1.        ,  1.        ,  1.        ,  1.        ,  1.        ,
         1.        ,  1.        ,  1.        ,  1.        ,  1.        ,
         1.        ,  1.        ,  1.        ,  1.        ,  1.        ,
         1.        ,  1.        ,  1.        ,  1.        ,  1.        ,
         1.        ,  1.        ,  1.        ,  1.        ,  1.        ,
         1.        ,  1.        ,  1.        ,  1.        ,  1.        ,
         1.        ,  1.        ,  1.        ,  1.        ,  1.        ,
         1.        ,  1.        ,  1.        ,  1.        ,  1.        ,
         1.        ,  1.        ,  1.        ,  1.        ,  1.        ,
         1.        ,  1.        ,  1.        ,  1.        ,  1.        ,
         1.        ,  1.        ,  1.        ,  1.        ,  1.        ]])



In [10]:

    
pinv = np.linalg.pinv(slopes)



In [11]:

    
np.dot(im.reshape(1, -1), pinv).ravel()[:2]









    Out[11]:





array([ 90.,  10.])



In [12]:

    
im









    Out[12]:





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, 45, 46, 47, 48, 49],
       [50, 51, 52, 53, 54, 55, 56, 57, 58, 59],
       [60, 61, 62, 63, 64, 65, 66, 67, 68, 69],
       [70, 71, 72, 73, 74, 75, 76, 77, 78, 79],
       [80, 81, 82, 83, 84, 85, 86, 87, 88, 89]])



In [13]:

    
import astropy.units as u



In [25]:

    
u.radian









    Out[25]:





$\mathrm{rad}$



In [15]:

    
a.to(u.nm).value









    Out[15]:





1000000.0



In [16]:

    
from mmtwfs.telescope import MMT



In [26]:

    
mmt = MMT()



In [27]:

    
mmt.diameter









    Out[27]:





$6502.4 \; \mathrm{mm}$



In [31]:

    
mmt.obscuration









    Out[31]:





$0.25959646 \; \mathrm{}$



In [35]:

    
12e6/(128*128)









    Out[35]:





732.421875



In [36]:

    
12e6/(4008*2672)









    Out[36]:





1.1205134640897845



In [1]:

    
import poppy









    



/Users/tim/conda/envs/astroconda/lib/python3.5/site-packages/pysynphot/locations.py:46: UserWarning: PYSYN_CDBS is undefined; functionality will be SEVERELY crippled.
  warnings.warn("PYSYN_CDBS is undefined; functionality will be SEVERELY "
/Users/tim/conda/envs/astroconda/lib/python3.5/site-packages/pysynphot/locations.py:329: UserWarning: Extinction files not found in extinction
  warnings.warn('Extinction files not found in %s' % (extdir, ))



In [4]:

    
ap = poppy.HexagonAperture?



In [5]:

    
ap = poppy.HexagonAperture(side=1.0, rotation=30)



In [11]:

    
ap.display(colorbar=False)
plt.show()



In [17]:

    
arr = ap.to_fits(npix=27)[0].data.astype(float)



In [18]:

    
plt.imshow(arr)
plt.show()



In [21]:

    
from mmtwfs.telescope import MMT
from mmtwfs.zernike import ZernikeVector



In [20]:

    
t = MMT()



In [30]:

    
zv = ZernikeVector(Z05=-1000)



In [31]:

    
zv.plot_map()
plt.show()



In [27]:

    
print(zv)









    



Phase Amplitude Coefficients
 Z06:    -1e+03 nm 	 Primary Astig at 0˚ (2, 2)



In [ ]:

act_i	act_x	act_y	act_type
	mm
int64	float64	float64	int64
1	297.9	-3207.0	0
2	1085.0	-3033.0	0
3	1441.0	-2880.0	0
4	444.0	-2883.0	0
5	1054.0	-2787.0	0
6	1928.0	-2573.0	0
7	166.4	-2306.0	0
8	776.7	-2306.0	1
9	1442.0	-2306.0	1
...	...	...	...
143	-166.4	2306.0	0
144	-776.7	2306.0	-1
145	-1442.0	2306.0	-1
146	-2053.0	2210.0	0
147	-444.0	2883.0	0
148	-1054.0	2787.0	0
149	-1928.0	2573.0	0
150	-297.9	3207.0	0
151	-1085.0	3033.0	0
152	-1441.0	2880.0	0