SpecDisperser.py 20.5 KB
Newer Older
Fang Yuedong's avatar
Fang Yuedong committed
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
90
91
92
93
94
95
96
97
98
99
100
101
102
103
from astropy.table import Table

import astropy.constants as cons

import collections
from collections import OrderedDict
from scipy import interpolate

from astropy import units as u

# from numpy import *

import numpy as np

from pylab import *

import galsim

import sys
import os


def rotate90(array_orig=None, xc=0, yc=0, isClockwise=0):
    if array_orig is None:
        return

    l1 = array_orig.shape[0]
    l2 = array_orig.shape[1]

    if xc < 0 or xc > l2 - 1 or yc < 0 or yc > l1 - 1:
        return

    n_xc = xc
    n_yc = yc

    array_final = np.zeros_like(array_orig.T)

    if isClockwise == 1:
        for i in np.arange(l2):
            array_final[i] = array_orig[:, l2 - i - 1]
        n_xc = yc
        n_yc = l2 - 1 - xc
    else:
        for i in np.arange(l2):
            array_final[i] = array_orig[::-1, i]

        n_xc = l1 - 1 - yc
        n_yc = xc
    return array_final, n_xc, n_yc


class SpecDisperser(object):
    def __init__(self, orig_img=None, xcenter=0, ycenter=0, origin=[100, 100], tar_spec=None, band_start=6200,
                 band_end=10000, isAlongY=0, conf='../param/CONF/csst.conf', gid=0):
        """
        orig_img: normal image,galsim image
        xcenter, ycenter: the center of galaxy in orig_img
        origin : [int, int]
            `origin` defines the lower left pixel index (y,x) of the `direct`
            cutout from a larger detector-frame image
        tar_spec: galsim.SED

        """

        # self.img_x = orig_img.shape[1]
        # self.img_y = orig_img.shape[0]

        self.thumb_img = np.abs(orig_img.array)
        self.thumb_x = orig_img.center.x
        self.thumb_y = orig_img.center.y
        self.img_sh = orig_img.array.shape

        self.id = gid

        self.xcenter = xcenter
        self.ycenter = ycenter

        self.isAlongY = isAlongY
        if self.isAlongY == 1:
            self.thumb_img, self.thumb_x, self.thumb_y = rotate90(array_orig=self.thumb_img, xc=orig_img.center.x,
                                                                       yc=orig_img.center.y, isClockwise=1)

            self.img_sh = orig_img.array.T.shape
            self.xcenter = ycenter
            self.ycenter = xcenter

        self.origin = origin
        self.band_start = band_start
        self.band_end = band_end
        self.spec = tar_spec

        self.beam_flux = OrderedDict()

        self.grating_conf = aXeConf(conf)
        self.grating_conf.get_beams()
        self.grating_conf_file = conf

    def compute_spec_orders(self):

        all_orders = OrderedDict()
        beam_names = self.grating_conf.beams

        for beam in beam_names:
Zhang Xin's avatar
Zhang Xin committed
104
        #for beam in ['A','B']:
Fang Yuedong's avatar
Fang Yuedong committed
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
            all_orders[beam] = self.compute_spec(beam)

        return all_orders

    def compute_spec(self, beam):

        from .disperse_c import interp
        from .disperse_c import disperse
        # from MockObject.disperse_c import disperse

        dx = self.grating_conf.dxlam[beam]
        xoff = 0
        ytrace_beam, lam_beam = self.grating_conf.get_beam_trace(x=self.xcenter, y=self.ycenter, dx=(dx + xoff),
                                                                 beam=beam)

        ### Account for pixel centering of the trace
        yfrac_beam = ytrace_beam - floor(ytrace_beam)

        ysens = lam_beam * 0
        lam_index = argsort(lam_beam)
        conf_sens = self.grating_conf.sens[beam]

Zhang Xin's avatar
Zhang Xin committed
127
128
        lam_intep = np.linspace(self.band_start, self.band_end, int((self.band_end - self.band_start) / 0.1))
        
Fang Yuedong's avatar
Fang Yuedong committed
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
        thri = interpolate.interp1d(conf_sens['WAVELENGTH'], conf_sens['SENSITIVITY'])
        spci = interpolate.interp1d(self.spec['WAVELENGTH'], self.spec['FLUX'])

        beam_thr = thri(lam_intep)
        spec_sample = spci(lam_intep)

        bean_thr_spec = beam_thr * spec_sample

        ysensitivity = lam_beam * 0

        ysensitivity[lam_index] = interp.interp_conserve_c(lam_beam[lam_index], lam_intep,
                                                           beam_thr * math.pi * 100 * 100 * 1e-7 / (
                                                                       cons.h.value * cons.c.value / (
                                                                           lam_intep * 1e-10)), integrate=0, left=0,
                                                           right=0)
        
        self.writerSensitivityFile(conffile = self.grating_conf_file,  beam = beam, w = lam_beam[lam_index], sens = ysensitivity[lam_index])

        ysens[lam_index] = interp.interp_conserve_c(lam_beam[lam_index], lam_intep, bean_thr_spec, integrate=1, left=0,
                                                    right=0)

        sensitivity_beam = ysens

        len_spec_x = len(dx)
        len_spec_y = int(ceil(yfrac_beam[-1]) - floor(yfrac_beam[0]) + 1)

        beam_sh = (self.img_sh[0] + len_spec_y, self.img_sh[1] + len_spec_x)
        modelf = zeros(product(beam_sh), dtype=float)
        model = modelf.reshape(beam_sh)
        idx = np.arange(modelf.size, dtype=int64).reshape(beam_sh)
        x0 = array((self.thumb_y, self.thumb_x), dtype=int64)

        dxpix = dx - dx[0] + x0[1]

        dyc = cast[int](ytrace_beam)

        dypix = dyc - dyc[0] + x0[0]
        flat_index = idx[dypix, dxpix]

        nonz = sensitivity_beam != 0

        status = disperse.disperse_grism_object(self.thumb_img,
                                                flat_index[nonz], yfrac_beam[nonz],
                                                sensitivity_beam[nonz],
                                                modelf, x0,
                                                array(self.img_sh, dtype=int64),
                                                array(beam_sh, dtype=int64))

        model = modelf.reshape(beam_sh)
        self.beam_flux[beam] = sum(modelf)

        origin_in = zeros_like(self.origin)
        dx0_in = dx[0]
        dy0_in = dyc[0]
        if self.isAlongY == 1:
            model, tmx, tmy = rotate90(array_orig=model, isClockwise=0)
            origin_in[0] = self.origin[0]
            origin_in[1] = self.origin[1] - len_spec_y
            dx0_in = -dyc[0]
            dy0_in = dx[0]
        else:
            origin_in[0] = self.origin[0]
            origin_in[1] = self.origin[1]
            dx0_in = dx[0]
            dy0_in = dyc[0]
        originOut_x = origin_in[1] + dx0_in - 1
        originOut_y = origin_in[0] + dy0_in - 1

        return model, originOut_x, originOut_y

    def writerSensitivityFile(self, conffile = '', beam = '', w = None, sens = None):
Zhang Xin's avatar
Zhang Xin committed
200
        orders={'A':'1st','B':'0st','C':'2st','D':'-1st','E':'-2st'}        
Fang Yuedong's avatar
Fang Yuedong committed
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
        sens_file_name = conffile[0:-5]+'_sensitivity_'+ orders[beam] + '.fits'
        if not os.path.exists(sens_file_name) == True:
            senstivity_out = Table(array([w,sens]).T, names=('WAVELENGTH', 'SENSITIVITY'))
            senstivity_out.write(sens_file_name, format='fits')


"""
Demonstrate aXe trace polynomials.
"""

class aXeConf():
    def __init__(self, conf_file='WFC3.IR.G141.V2.5.conf'):
        """Read an aXe-compatible configuration file
        
        Parameters
        ----------
        conf_file: str
            Filename of the configuration file to read
        
        """
        if conf_file is not None:
            self.conf = self.read_conf_file(conf_file)
            self.conf_file = conf_file
            self.count_beam_orders()

            ## Global XOFF/YOFF offsets
            if 'XOFF' in self.conf.keys():
                self.xoff = np.float(self.conf['XOFF'])
            else:
                self.xoff = 0.

            if 'YOFF' in self.conf.keys():
                self.yoff = np.float(self.conf['YOFF'])
            else:
                self.yoff = 0.

    def read_conf_file(self, conf_file='WFC3.IR.G141.V2.5.conf'):
        """Read an aXe config file, convert floats and arrays
        
        Parameters
        ----------
        conf_file: str
            Filename of the configuration file to read.
        
        Parameters are stored in an OrderedDict in `self.conf`.
        """
        from collections import OrderedDict

        conf = OrderedDict()
        lines = open(conf_file).readlines()
        for line in lines:
            ## empty / commented lines
            if (line.startswith('#')) | (line.strip() == '') | ('"' in line):
                continue

            ## split the line, taking out ; and # comments
            spl = line.split(';')[0].split('#')[0].split()
            param = spl[0]
            if len(spl) > 2:
                value = np.cast[float](spl[1:])
            else:
                try:
                    value = float(spl[1])
                except:
                    value = spl[1]

            conf[param] = value

        return conf

    def count_beam_orders(self):
        """Get the maximum polynomial order in DYDX or DLDP for each beam
        """
        self.orders = {}
        for beam in ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J']:
            order = 0
            while 'DYDX_{0:s}_{1:d}'.format(beam, order) in self.conf.keys():
                order += 1

            while 'DLDP_{0:s}_{1:d}'.format(beam, order) in self.conf.keys():
                order += 1

            self.orders[beam] = order - 1

    def get_beams(self):
        """Get beam parameters and read sensitivity curves
        """
        import os
        from collections import OrderedDict
        from astropy.table import Table, Column

        self.dxlam = OrderedDict()
        self.nx = OrderedDict()
        self.sens = OrderedDict()
        self.beams = []

        for beam in self.orders:
            if self.orders[beam] > 0:
                self.beams.append(beam)
                self.dxlam[beam] = np.arange(self.conf['BEAM{0}'.format(beam)].min(),
                                             self.conf['BEAM{0}'.format(beam)].max(), dtype=int)
                self.nx[beam] = int(self.dxlam[beam].max() - self.dxlam[beam].min()) + 1
                self.sens[beam] = Table.read(
                    '{0}/{1}'.format(os.path.dirname(self.conf_file), self.conf['SENSITIVITY_{0}'.format(beam)]))
                # self.sens[beam].wave = np.cast[np.double](self.sens[beam]['WAVELENGTH'])
                # self.sens[beam].sens = np.cast[np.double](self.sens[beam]['SENSITIVITY'])

                ### Need doubles for interpolating functions
                for col in self.sens[beam].colnames:
                    data = np.cast[np.double](self.sens[beam][col])
                    self.sens[beam].remove_column(col)
                    self.sens[beam].add_column(Column(data=data, name=col))

        self.beams.sort()

    def field_dependent(self, xi, yi, coeffs):
        """aXe field-dependent coefficients
        
        See the `aXe manual <http://axe.stsci.edu/axe/manual/html/node7.html#SECTION00721200000000000000>`_ for a description of how the field-dependent coefficients are specified.
        
        Parameters
        ----------
        xi, yi : float or array-like
            Coordinate to evaluate the field dependent coefficients, where
            `xi = x-REFX` and `yi = y-REFY`.
        
        coeffs : array-like
            Field-dependency coefficients
        
        Returns
        -------
        a : float or array-like
            Evaluated field-dependent coefficients
            
        """
        ## number of coefficients for a given polynomial order
        ## 1:1, 2:3, 3:6, 4:10, order:order*(order+1)/2
        if isinstance(coeffs, float):
            order = 1
        else:
            order = int(-1 + np.sqrt(1 + 8 * len(coeffs))) // 2

        ## Build polynomial terms array
        ## $a = a_0+a_1x_i+a_2y_i+a_3x_i^2+a_4x_iy_i+a_5yi^2+$ ...
        xy = []
        for p in range(order):
            for px in range(p + 1):
                # print 'x**%d y**%d' %(p-px, px)
                xy.append(xi ** (p - px) * yi ** (px))

        ## Evaluate the polynomial, allowing for N-dimensional inputs
        a = np.sum((np.array(xy).T * coeffs).T, axis=0)

        return a

    def evaluate_dp(self, dx, dydx):
        """Evalate arc length along the trace given trace polynomial coefficients
        
        Parameters
        ----------
        dx : array-like
            x pixel to evaluate
        
        dydx : array-like
            Coefficients of the trace polynomial
        
        Returns
        -------
        dp : array-like
            Arc length along the trace at position `dx`.
            
        For `dydx` polynomial orders 0, 1 or 2, integrate analytically.  
        Higher orders must be integrated numerically.
        
        **Constant:** 
            .. math:: dp = dx

        **Linear:** 
            .. math:: dp = \sqrt{1+\mathrm{DYDX}[1]}\cdot dx
        
        **Quadratic:** 
            .. math:: u = \mathrm{DYDX}[1] + 2\ \mathrm{DYDX}[2]\cdot dx
            
            .. math:: dp = (u \sqrt{1+u^2} + \mathrm{arcsinh}\ u) / (4\cdot \mathrm{DYDX}[2])
        
        """
        ## dp is the arc length along the trace
        ## $\lambda = dldp_0 + dldp_1 dp + dldp_2 dp^2$ ...

        poly_order = len(dydx) - 1
        if (poly_order == 2):
            if np.abs(np.unique(dydx[2])).max() == 0:
                poly_order = 1

        if poly_order == 0:  ## dy=0
            dp = dx
        elif poly_order == 1:  ## constant dy/dx
            dp = np.sqrt(1 + dydx[1] ** 2) * (dx)
        elif poly_order == 2:  ## quadratic trace
            u0 = dydx[1] + 2 * dydx[2] * (0)
            dp0 = (u0 * np.sqrt(1 + u0 ** 2) + np.arcsinh(u0)) / (4 * dydx[2])
            u = dydx[1] + 2 * dydx[2] * (dx)
            dp = (u * np.sqrt(1 + u ** 2) + np.arcsinh(u)) / (4 * dydx[2]) - dp0
        else:
            ## high order shape, numerical integration along trace
            ## (this can be slow)
            xmin = np.minimum((dx).min(), 0)
            xmax = np.maximum((dx).max(), 0)
            xfull = np.arange(xmin, xmax)
            dyfull = 0
            for i in range(1, poly_order):
                dyfull += i * dydx[i] * (xfull - 0.5) ** (i - 1)

            ## Integrate from 0 to dx / -dx
            dpfull = xfull * 0.
            lt0 = xfull < 0
            if lt0.sum() > 1:
                dpfull[lt0] = np.cumsum(np.sqrt(1 + dyfull[lt0][::-1] ** 2))[::-1]
                dpfull[lt0] *= -1

            #
            gt0 = xfull > 0
            if gt0.sum() > 0:
                dpfull[gt0] = np.cumsum(np.sqrt(1 + dyfull[gt0] ** 2))

            dp = np.interp(dx, xfull, dpfull)
            if dp[-1] == dp[-2]:
                dp[-1] = dp[-2] + np.diff(dp)[-2]

        return dp

    def get_beam_trace(self, x=507, y=507, dx=0., beam='A'):
        """Get an aXe beam trace for an input reference pixel and list of output x pixels `dx`
        
        Parameters
        ----------
        x, y : float or array-like
            Evaluate trace definition at detector coordinates `x` and `y`.
            
        dx : float or array-like
            Offset in x pixels from `(x,y)` where to compute trace offset and 
            effective wavelength
            
        beam : str
            Beam name (i.e., spectral order) to compute.  By aXe convention, 
            `beam='A'` is the first order, 'B' is the zeroth order and 
            additional beams are the higher positive and negative orders.
        
        Returns
        -------
        dy : float or array-like
            Center of the trace in y pixels offset from `(x,y)` evaluated at
            `dx`.
            
        lam : float or array-like
            Effective wavelength along the trace evaluated at `dx`.
            
        """
        NORDER = self.orders[beam] + 1

        xi, yi = x - self.xoff, y - self.yoff
        xoff_beam = self.field_dependent(xi, yi, self.conf['XOFF_{0}'.format(beam)])
        yoff_beam = self.field_dependent(xi, yi, self.conf['YOFF_{0}'.format(beam)])

        ## y offset of trace (DYDX)
        dydx = np.zeros(NORDER)  # 0 #+1.e-80
        dydx = [0] * NORDER

        for i in range(NORDER):
            if 'DYDX_{0:s}_{1:d}'.format(beam, i) in self.conf.keys():
                coeffs = self.conf['DYDX_{0:s}_{1:d}'.format(beam, i)]
                dydx[i] = self.field_dependent(xi, yi, coeffs)

        # $dy = dydx_0+dydx_1 dx+dydx_2 dx^2+$ ...

        dy = yoff_beam
        for i in range(NORDER):
            dy += dydx[i] * (dx - xoff_beam) ** i

        ## wavelength solution    
        dldp = np.zeros(NORDER)
        dldp = [0] * NORDER

        for i in range(NORDER):
            if 'DLDP_{0:s}_{1:d}'.format(beam, i) in self.conf.keys():
                coeffs = self.conf['DLDP_{0:s}_{1:d}'.format(beam, i)]
                dldp[i] = self.field_dependent(xi, yi, coeffs)

        self.eval_input = {'x': x, 'y': y, 'beam': beam, 'dx': dx}
        self.eval_output = {'xi': xi, 'yi': yi, 'dldp': dldp, 'dydx': dydx,
                            'xoff_beam': xoff_beam, 'yoff_beam': yoff_beam,
                            'dy': dy}

        dp = self.evaluate_dp(dx - xoff_beam, dydx)
        # ## dp is the arc length along the trace
        # ## $\lambda = dldp_0 + dldp_1 dp + dldp_2 dp^2$ ...
        # if self.conf['DYDX_ORDER_%s' %(beam)] == 0:   ## dy=0
        #     dp = dx-xoff_beam                      
        # elif self.conf['DYDX_ORDER_%s' %(beam)] == 1: ## constant dy/dx
        #     dp = np.sqrt(1+dydx[1]**2)*(dx-xoff_beam)
        # elif self.conf['DYDX_ORDER_%s' %(beam)] == 2: ## quadratic trace
        #     u0 = dydx[1]+2*dydx[2]*(0)
        #     dp0 = (u0*np.sqrt(1+u0**2)+np.arcsinh(u0))/(4*dydx[2])
        #     u = dydx[1]+2*dydx[2]*(dx-xoff_beam)
        #     dp = (u*np.sqrt(1+u**2)+np.arcsinh(u))/(4*dydx[2])-dp0
        # else:
        #     ## high order shape, numerical integration along trace
        #     ## (this can be slow)
        #     xmin = np.minimum((dx-xoff_beam).min(), 0)
        #     xmax = np.maximum((dx-xoff_beam).max(), 0)
        #     xfull = np.arange(xmin, xmax)
        #     dyfull = 0
        #     for i in range(1, NORDER):
        #         dyfull += i*dydx[i]*(xfull-0.5)**(i-1)
        #     
        #     ## Integrate from 0 to dx / -dx
        #     dpfull = xfull*0.
        #     lt0 = xfull <= 0
        #     if lt0.sum() > 1:
        #         dpfull[lt0] = np.cumsum(np.sqrt(1+dyfull[lt0][::-1]**2))[::-1]
        #         dpfull[lt0] *= -1
        #     #
        #     gt0 = xfull >= 0
        #     if gt0.sum() > 0:
        #         dpfull[gt0] = np.cumsum(np.sqrt(1+dyfull[gt0]**2))
        #       
        #     dp = np.interp(dx-xoff_beam, xfull, dpfull)

        ## Evaluate dldp    
        lam = dp * 0.
        for i in range(NORDER):
            lam += dldp[i] * dp ** i

        return dy, lam

    def show_beams(self, beams=['E', 'D', 'C', 'B', 'A']):
        """
        Make a demo plot of the beams of a given configuration file
        """
        import matplotlib.pyplot as plt

        x0, x1 = 507, 507
        dx = np.arange(-800, 1200)

        if 'WFC3.UV' in self.conf_file:
            x0, x1 = 2073, 250
            dx = np.arange(-1200, 1200)
        if 'G800L' in self.conf_file:
            x0, x1 = 2124, 1024
            dx = np.arange(-1200, 1200)

        s = 200  # marker size
        fig = plt.figure(figsize=[10, 3])
        plt.scatter(0, 0, marker='s', s=s, color='black', edgecolor='0.8',
                    label='Direct')

        for beam in beams:
            if 'XOFF_{0}'.format(beam) not in self.conf.keys():
                continue

            xoff = self.field_dependent(x0, x1, self.conf['XOFF_{0}'.format(beam)])
            dy, lam = self.get_beam_trace(x0, x1, dx=dx, beam=beam)
            xlim = self.conf['BEAM{0}'.format(beam)]
            ok = (dx >= xlim[0]) & (dx <= xlim[1])
            plt.scatter(dx[ok] + xoff, dy[ok], c=lam[ok] / 1.e4, marker='s', s=s,
                        alpha=0.5, edgecolor='None')
            plt.text(np.median(dx[ok]), np.median(dy[ok]) + 1, beam,
                     ha='center', va='center', fontsize=14)
            print('Beam {0}, lambda=({1:.1f} - {2:.1f})'.format(beam, lam[ok].min(), lam[ok].max()))

        plt.grid()
        plt.xlabel(r'$\Delta x$')
        plt.ylabel(r'$\Delta y$')

        cb = plt.colorbar(pad=0.01, fraction=0.05)
        cb.set_label(r'$\lambda\,(\mu\mathrm{m})$')
        plt.title(self.conf_file)
        plt.tight_layout()
        plt.savefig('{0}.pdf'.format(self.conf_file))

    # def load_grism_config(conf_file):
#     """Load parameters from an aXe configuration file

#     Parameters
#     ----------
#     conf_file : str
#         Filename of the configuration file

#     Returns
#     -------
#     conf : `~grizli.grismconf.aXeConf`
#         Configuration file object.  Runs `conf.get_beams()` to read the 
#         sensitivity curves.
#     """
#     conf = aXeConf(conf_file)
#     conf.get_beams()
#     return conf