source: sasview/sansdataloader/src/sans/dataloader/manipulations.py @ a36a22a

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Last change on this file since a36a22a was d555416, checked in by smk78, 12 years ago

Fix to Ticket #101; Modified Slicer Panel for Box Sum to display total sum and number of points summed; renamed captions for values accordingly

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[f60a8c2]1"""
2Data manipulations for 2D data sets.
3Using the meta data information, various types of averaging
4are performed in Q-space
5"""
[0997158f]6#####################################################################
7#This software was developed by the University of Tennessee as part of the
8#Distributed Data Analysis of Neutron Scattering Experiments (DANSE)
[f60a8c2]9#project funded by the US National Science Foundation.
[0997158f]10#See the license text in license.txt
11#copyright 2008, University of Tennessee
12######################################################################
13
[76e2369]14#TODO: copy the meta data from the 2D object to the resulting 1D object
15import math
16import numpy
17
[a7a5886]18#from data_info import plottable_2D
19from data_info import Data1D
20
21
[76e2369]22def get_q(dx, dy, det_dist, wavelength):
23    """
[0997158f]24    :param dx: x-distance from beam center [mm]
25    :param dy: y-distance from beam center [mm]
26   
27    :return: q-value at the given position
[76e2369]28    """
29    # Distance from beam center in the plane of detector
30    plane_dist = math.sqrt(dx*dx + dy*dy)
31    # Half of the scattering angle
[a7a5886]32    theta = 0.5 * math.atan(plane_dist/det_dist)
33    return (4.0 * math.pi/wavelength) * math.sin(theta)
[acb37d9]34
[f60a8c2]35
[a7a5886]36def get_q_compo(dx, dy, det_dist, wavelength, compo=None):
[0997158f]37    """
38    This reduces tiny error at very large q.
39    Implementation of this func is not started yet.<--ToDo
40    """
[a7a5886]41    if dy == 0:
42        if dx >= 0:
43            angle_xy = 0
[acb37d9]44        else:
[a7a5886]45            angle_xy = math.pi
[acb37d9]46    else:
[a7a5886]47        angle_xy = math.atan(dx/dy)
[acb37d9]48       
[a7a5886]49    if compo == "x":
50        out = get_q(dx, dy, det_dist, wavelength) * math.cos(angle_xy)
51    elif compo == "y":
52        out = get_q(dx, dy, det_dist, wavelength) * math.sin(angle_xy)
[acb37d9]53    else:
[a7a5886]54        out = get_q(dx, dy, det_dist, wavelength)
[acb37d9]55    return out
[095ab1b]56
[f60a8c2]57
[095ab1b]58def flip_phi(phi):
59    """
[0997158f]60    Correct phi to within the 0 <= to <= 2pi range
61   
62    :return: phi in >=0 and <=2Pi
[095ab1b]63    """
64    Pi = math.pi
65    if phi < 0:
[f60a8c2]66        phi_out = phi + (2 * Pi)
[a7a5886]67    elif phi > (2 * Pi):
[f60a8c2]68        phi_out = phi - (2 * Pi)
[095ab1b]69    else:
[f60a8c2]70        phi_out = phi
[095ab1b]71    return phi_out
72
[f60a8c2]73
[095ab1b]74def reader2D_converter(data2d=None):
75    """
[a7a5886]76    convert old 2d format opened by IhorReader or danse_reader
77    to new Data2D format
[0997158f]78   
79    :param data2d: 2d array of Data2D object
80   
81    :return: 1d arrays of Data2D object
82   
[095ab1b]83    """
[a7a5886]84    if data2d.data == None or data2d.x_bins == None or data2d.y_bins == None:
85        raise ValueError, "Can't convert this data: data=None..."
[76e2369]86   
[47045e6]87    from sans.dataloader.data_info import Data2D
[095ab1b]88
[a7a5886]89    new_x = numpy.tile(data2d.x_bins, (len(data2d.y_bins), 1))
90    new_y = numpy.tile(data2d.y_bins, (len(data2d.x_bins), 1))
91    new_y = new_y.swapaxes(0, 1)
[095ab1b]92
93    new_data = data2d.data.flatten()
94    qx_data = new_x.flatten()
95    qy_data = new_y.flatten()
[a7a5886]96    q_data = numpy.sqrt(qx_data*qx_data + qy_data*qy_data)
[f60a8c2]97    if data2d.err_data == None or numpy.any(data2d.err_data <= 0):
[f265927]98        new_err_data = numpy.sqrt(numpy.abs(new_data))
[dde2d44]99    else:
100        new_err_data = data2d.err_data.flatten()
[f60a8c2]101    mask = numpy.ones(len(new_data), dtype=bool)
[095ab1b]102
[f60a8c2]103    #TODO: make sense of the following two lines...
[095ab1b]104    output = Data2D()
105    output = data2d
106    output.data = new_data
107    output.err_data = new_err_data
108    output.qx_data = qx_data
109    output.qy_data = qy_data
110    output.q_data = q_data
111    output.mask = mask
112
113    return output
114
[f60a8c2]115
[70975f3]116class _Slab(object):
117    """
[0997158f]118    Compute average I(Q) for a region of interest
[70975f3]119    """
[a7a5886]120    def __init__(self, x_min=0.0, x_max=0.0, y_min=0.0,
121                 y_max=0.0, bin_width=0.001):
[70975f3]122        # Minimum Qx value [A-1]
123        self.x_min = x_min
124        # Maximum Qx value [A-1]
125        self.x_max = x_max
126        # Minimum Qy value [A-1]
127        self.y_min = y_min
128        # Maximum Qy value [A-1]
129        self.y_max = y_max
130        # Bin width (step size) [A-1]
131        self.bin_width = bin_width
[a7a5886]132        # If True, I(|Q|) will be return, otherwise,
133        # negative q-values are allowed
[70975f3]134        self.fold = False
135       
[a7a5886]136    def __call__(self, data2D):
137        return NotImplemented
[70975f3]138       
139    def _avg(self, data2D, maj):
140        """
[0997158f]141        Compute average I(Q_maj) for a region of interest.
142        The major axis is defined as the axis of Q_maj.
143        The minor axis is the axis that we average over.
144         
145        :param data2D: Data2D object
146        :param maj_min: min value on the major axis
147       
148        :return: Data1D object
[70975f3]149        """
150        if len(data2D.detector) != 1:
[a7a5886]151            msg = "_Slab._avg: invalid number of "
152            msg += " detectors: %g" % len(data2D.detector)
153            raise RuntimeError, msg
[70975f3]154       
[f60a8c2]155        # Get data
[c6f95bb]156        data = data2D.data[numpy.isfinite(data2D.data)]
157        q_data = data2D.q_data[numpy.isfinite(data2D.data)]
158        err_data = data2D.err_data[numpy.isfinite(data2D.data)]
[f60a8c2]159        qx_data = data2D.qx_data[numpy.isfinite(data2D.data)]
[c6f95bb]160        qy_data = data2D.qy_data[numpy.isfinite(data2D.data)]
[095ab1b]161             
[70975f3]162        # Build array of Q intervals
[a7a5886]163        if maj == 'x':
164            if self.fold:
[f60a8c2]165                x_min = 0
166            else:
167                x_min = self.x_min
168            nbins = int(math.ceil((self.x_max - x_min) / self.bin_width))
[a7a5886]169            qbins = self.bin_width * numpy.arange(nbins) + x_min
170        elif maj == 'y':
[f60a8c2]171            if self.fold:
172                y_min = 0
173            else:
174                y_min = self.y_min
[a7a5886]175            nbins = int(math.ceil((self.y_max - y_min)/self.bin_width))
[f60a8c2]176            qbins = self.bin_width * numpy.arange(nbins) + y_min
[70975f3]177        else:
178            raise RuntimeError, "_Slab._avg: unrecognized axis %s" % str(maj)
179                               
[f60a8c2]180        x = numpy.zeros(nbins)
181        y = numpy.zeros(nbins)
[70975f3]182        err_y = numpy.zeros(nbins)
183        y_counts = numpy.zeros(nbins)
184
[f60a8c2]185        # Average pixelsize in q space
186        for npts in range(len(data)):
187            # default frac
[095ab1b]188            frac_x = 0
189            frac_y = 0
190            # get ROI
191            if self.x_min <= qx_data[npts] and self.x_max > qx_data[npts]:
192                frac_x = 1
193            if self.y_min <= qy_data[npts] and self.y_max > qy_data[npts]:
194                frac_y = 1
195            frac = frac_x * frac_y
196           
[a7a5886]197            if frac == 0:
198                continue
[095ab1b]199            # binning: find axis of q
[f60a8c2]200            if maj == 'x':
[095ab1b]201                q_value = qx_data[npts]
[f60a8c2]202                min = x_min
203            if maj == 'y':
204                q_value = qy_data[npts]
[095ab1b]205                min = y_min
[a7a5886]206            if self.fold and q_value < 0:
[f60a8c2]207                q_value = -q_value
[095ab1b]208            # bin
[f60a8c2]209            i_q = int(math.ceil((q_value - min) / self.bin_width)) - 1
[095ab1b]210           
211            # skip outside of max bins
[a7a5886]212            if i_q < 0 or i_q >= nbins:
213                continue
[095ab1b]214           
215            #TODO: find better definition of x[i_q] based on q_data
[f60a8c2]216            x[i_q] += frac * q_value  # min + (i_q + 1) * self.bin_width / 2.0
[a7a5886]217            y[i_q] += frac * data[npts]
[095ab1b]218           
[a7a5886]219            if err_data == None or err_data[npts] == 0.0:
[f60a8c2]220                if data[npts] < 0:
221                    data[npts] = -data[npts]
[c6f95bb]222                err_y[i_q] += frac * frac * data[npts]
[095ab1b]223            else:
224                err_y[i_q] += frac * frac * err_data[npts] * err_data[npts]
[f60a8c2]225            y_counts[i_q] += frac
[8ba103f]226               
[f60a8c2]227        # Average the sums
[095ab1b]228        for n in range(nbins):
229            err_y[n] = math.sqrt(err_y[n])
230         
[a7a5886]231        err_y = err_y / y_counts
[f60a8c2]232        y = y / y_counts
233        x = x / y_counts
234        idx = (numpy.isfinite(y) & numpy.isfinite(x))
[095ab1b]235       
236        if not idx.any(): 
[f60a8c2]237            msg = "Average Error: No points inside ROI to average..."
[a7a5886]238            raise ValueError, msg
239        #elif len(y[idx])!= nbins:
240        #    msg = "empty bin(s) due to tight binning..."
241        #    print "resulted",nbins- len(y[idx]), msg
[095ab1b]242        return Data1D(x=x[idx], y=y[idx], dy=err_y[idx])
[70975f3]243       
[f60a8c2]244       
[70975f3]245class SlabY(_Slab):
246    """
[0997158f]247    Compute average I(Qy) for a region of interest
[70975f3]248    """
249    def __call__(self, data2D):
250        """
[0997158f]251        Compute average I(Qy) for a region of interest
252         
253        :param data2D: Data2D object
254       
255        :return: Data1D object
[70975f3]256        """
257        return self._avg(data2D, 'y')
258       
[f60a8c2]259       
[70975f3]260class SlabX(_Slab):
261    """
[0997158f]262    Compute average I(Qx) for a region of interest
[70975f3]263    """
264    def __call__(self, data2D):
265        """
[0997158f]266        Compute average I(Qx) for a region of interest
267         
268        :param data2D: Data2D object
269       
270        :return: Data1D object
271       
[70975f3]272        """
[f60a8c2]273        return self._avg(data2D, 'x')
274
275
[f8d0ee7]276class Boxsum(object):
277    """
[0997158f]278    Perform the sum of counts in a 2D region of interest.
[f8d0ee7]279    """
280    def __init__(self, x_min=0.0, x_max=0.0, y_min=0.0, y_max=0.0):
281        # Minimum Qx value [A-1]
282        self.x_min = x_min
283        # Maximum Qx value [A-1]
284        self.x_max = x_max
285        # Minimum Qy value [A-1]
286        self.y_min = y_min
287        # Maximum Qy value [A-1]
288        self.y_max = y_max
289
290    def __call__(self, data2D):
291        """
[f60a8c2]292        Perform the sum in the region of interest
[0997158f]293         
294        :param data2D: Data2D object
295       
[d555416]296        :return: number of counts, error on number of counts,
297            number of points summed
[0997158f]298       
[f8d0ee7]299        """
300        y, err_y, y_counts = self._sum(data2D)
301       
302        # Average the sums
[a7a5886]303        counts = 0 if y_counts == 0 else y
[f60a8c2]304        error = 0 if y_counts == 0 else math.sqrt(err_y)
[f8d0ee7]305       
[d555416]306        # Added y_counts to return, SMK & PDB, 04/03/2013
307        return counts, error, y_counts
[f8d0ee7]308       
309    def _sum(self, data2D):
310        """
[f60a8c2]311        Perform the sum in the region of interest
[0997158f]312       
313        :param data2D: Data2D object
314       
[f60a8c2]315        :return: number of counts,
[a7a5886]316            error on number of counts, number of entries summed
[0997158f]317       
[f8d0ee7]318        """
319        if len(data2D.detector) != 1:
[a7a5886]320            msg = "Circular averaging: invalid number "
321            msg += "of detectors: %g" % len(data2D.detector)
322            raise RuntimeError, msg
[f60a8c2]323        # Get data
[c6f95bb]324        data = data2D.data[numpy.isfinite(data2D.data)]
325        q_data = data2D.q_data[numpy.isfinite(data2D.data)]
326        err_data = data2D.err_data[numpy.isfinite(data2D.data)]
[f60a8c2]327        qx_data = data2D.qx_data[numpy.isfinite(data2D.data)]
[c6f95bb]328        qy_data = data2D.qy_data[numpy.isfinite(data2D.data)]
[095ab1b]329   
[f60a8c2]330        y = 0.0
[f8d0ee7]331        err_y = 0.0
332        y_counts = 0.0
333
[f60a8c2]334        # Average pixelsize in q space
335        for npts in range(len(data)):
336            # default frac
337            frac_x = 0
338            frac_y = 0
[095ab1b]339           
340            # get min and max at each points
341            qx = qx_data[npts]
342            qy = qy_data[npts]
343           
344            # get the ROI
345            if self.x_min <= qx and self.x_max > qx:
346                frac_x = 1
347            if self.y_min <= qy and self.y_max > qy:
348                frac_y = 1
[f60a8c2]349            #Find the fraction along each directions
[095ab1b]350            frac = frac_x * frac_y
[a7a5886]351            if frac == 0:
352                continue
[095ab1b]353            y += frac * data[npts]
[a7a5886]354            if err_data == None or err_data[npts] == 0.0:
355                if data[npts] < 0:
356                    data[npts] = -data[npts]
[c6f95bb]357                err_y += frac * frac * data[npts]
[095ab1b]358            else:
359                err_y += frac * frac * err_data[npts] * err_data[npts]
[f60a8c2]360            y_counts += frac
[f8d0ee7]361        return y, err_y, y_counts
[095ab1b]362
363
[f8d0ee7]364class Boxavg(Boxsum):
365    """
[0997158f]366    Perform the average of counts in a 2D region of interest.
[f8d0ee7]367    """
368    def __init__(self, x_min=0.0, x_max=0.0, y_min=0.0, y_max=0.0):
[a7a5886]369        super(Boxavg, self).__init__(x_min=x_min, x_max=x_max,
370                                      y_min=y_min, y_max=y_max)
[f8d0ee7]371
372    def __call__(self, data2D):
373        """
[f60a8c2]374        Perform the sum in the region of interest
[0997158f]375         
376        :param data2D: Data2D object
377       
378        :return: average counts, error on average counts
379       
[f8d0ee7]380        """
381        y, err_y, y_counts = self._sum(data2D)
382       
383        # Average the sums
[f60a8c2]384        counts = 0 if y_counts == 0 else y / y_counts
385        error = 0 if y_counts == 0 else math.sqrt(err_y) / y_counts
[f8d0ee7]386       
387        return counts, error
388       
[f60a8c2]389       
[f8d0ee7]390def get_pixel_fraction_square(x, xmin, xmax):
391    """
[f60a8c2]392    Return the fraction of the length
[0997158f]393    from xmin to x.::
394   
395     
396           A            B
397       +-----------+---------+
398       xmin        x         xmax
399     
400    :param x: x-value
401    :param xmin: minimum x for the length considered
402    :param xmax: minimum x for the length considered
403   
404    :return: (x-xmin)/(xmax-xmin) when xmin < x < xmax
405   
[f8d0ee7]406    """
[a7a5886]407    if x <= xmin:
[f8d0ee7]408        return 0.0
[a7a5886]409    if x > xmin and x < xmax:
410        return (x - xmin) / (xmax - xmin)
[f8d0ee7]411    else:
412        return 1.0
413
[76e2369]414
415class CircularAverage(object):
416    """
[0997158f]417    Perform circular averaging on 2D data
418   
419    The data returned is the distribution of counts
420    as a function of Q
[76e2369]421    """
[095ab1b]422    def __init__(self, r_min=0.0, r_max=0.0, bin_width=0.0005):
[76e2369]423        # Minimum radius included in the average [A-1]
424        self.r_min = r_min
425        # Maximum radius included in the average [A-1]
426        self.r_max = r_max
427        # Bin width (step size) [A-1]
428        self.bin_width = bin_width
429
[8f12385]430    def __call__(self, data2D, ismask=False):
[76e2369]431        """
[0997158f]432        Perform circular averaging on the data
433       
434        :param data2D: Data2D object
435       
436        :return: Data1D object
[76e2369]437        """
[729bcf6]438        # Get data W/ finite values
[c6f95bb]439        data = data2D.data[numpy.isfinite(data2D.data)]
440        q_data = data2D.q_data[numpy.isfinite(data2D.data)]
[729bcf6]441        qx_data = data2D.qx_data[numpy.isfinite(data2D.data)]
[c6f95bb]442        err_data = data2D.err_data[numpy.isfinite(data2D.data)]
[8f12385]443        mask_data = data2D.mask[numpy.isfinite(data2D.data)]
[729bcf6]444       
[342a506]445        dq_data = None
[729bcf6]446       
447        # Get the dq for resolution averaging
[342a506]448        if data2D.dqx_data != None and data2D.dqy_data != None:
[f60a8c2]449            # The pinholes and det. pix contribution present
[729bcf6]450            # in both direction of the 2D which must be subtracted when
451            # converting to 1D: dq_overlap should calculated ideally at
[f60a8c2]452            # q = 0. Note This method works on only pinhole geometry.
[729bcf6]453            # Extrapolate dqx(r) and dqy(phi) at q = 0, and take an average.
454            z_max = max(data2D.q_data)
455            z_min = min(data2D.q_data)
456            x_max = data2D.dqx_data[data2D.q_data[z_max]]
[f60a8c2]457            x_min = data2D.dqx_data[data2D.q_data[z_min]]
[729bcf6]458            y_max = data2D.dqy_data[data2D.q_data[z_max]]
[f60a8c2]459            y_min = data2D.dqy_data[data2D.q_data[z_min]]
[729bcf6]460            # Find qdx at q = 0
461            dq_overlap_x = (x_min * z_max - x_max * z_min) / (z_max - z_min)
462            # when extrapolation goes wrong
463            if dq_overlap_x > min(data2D.dqx_data):
464                dq_overlap_x = min(data2D.dqx_data)
[f60a8c2]465            dq_overlap_x *= dq_overlap_x
[729bcf6]466            # Find qdx at q = 0
467            dq_overlap_y = (y_min * z_max - y_max * z_min) / (z_max - z_min)
468            # when extrapolation goes wrong
469            if dq_overlap_y > min(data2D.dqy_data):
470                dq_overlap_y = min(data2D.dqy_data)
471            # get dq at q=0.
472            dq_overlap_y *= dq_overlap_y
473
[f60a8c2]474            dq_overlap = numpy.sqrt((dq_overlap_x + dq_overlap_y) / 2.0)
[729bcf6]475            # Final protection of dq
476            if dq_overlap < 0:
477                dq_overlap = y_min
478            dqx_data = data2D.dqx_data[numpy.isfinite(data2D.data)]
479            dqy_data = data2D.dqy_data[numpy.isfinite(data2D.data)] - dq_overlap
480            # def; dqx_data = dq_r dqy_data = dq_phi
481            # Convert dq 2D to 1D here
[f60a8c2]482            dqx = dqx_data * dqx_data
[729bcf6]483            dqy = dqy_data * dqy_data
484            dq_data = numpy.add(dqx, dqy)
[342a506]485            dq_data = numpy.sqrt(dq_data)
486           
[729bcf6]487        #q_data_max = numpy.max(q_data)
[095ab1b]488        if len(data2D.q_data) == None:
[a7a5886]489            msg = "Circular averaging: invalid q_data: %g" % data2D.q_data
490            raise RuntimeError, msg
[095ab1b]491
[76e2369]492        # Build array of Q intervals
[a7a5886]493        nbins = int(math.ceil((self.r_max - self.r_min) / self.bin_width))
494        qbins = self.bin_width * numpy.arange(nbins) + self.r_min
[095ab1b]495
[f60a8c2]496        x = numpy.zeros(nbins)
497        y = numpy.zeros(nbins)
[76e2369]498        err_y = numpy.zeros(nbins)
[342a506]499        err_x = numpy.zeros(nbins)
[76e2369]500        y_counts = numpy.zeros(nbins)
[095ab1b]501
[f60a8c2]502        for npt in range(len(data)):
[8f12385]503           
504            if ismask and not mask_data[npt]:
[f60a8c2]505                continue
[8f12385]506           
[095ab1b]507            frac = 0
[76e2369]508           
[095ab1b]509            # q-value at the pixel (j,i)
[f60a8c2]510            q_value = q_data[npt]
511            data_n = data[npt]
[3c67340]512           
[095ab1b]513            ## No need to calculate the frac when all data are within range
514            if self.r_min >= self.r_max:
[f60a8c2]515                raise ValueError, "Limit Error: min > max"
[76e2369]516           
[a7a5886]517            if self.r_min <= q_value and q_value <= self.r_max:
[f60a8c2]518                frac = 1
[a7a5886]519            if frac == 0:
520                continue 
[f60a8c2]521            i_q = int(math.floor((q_value - self.r_min) / self.bin_width))
[095ab1b]522
[f60a8c2]523            # Take care of the edge case at phi = 2pi.
524            if i_q == nbins:
525                i_q = nbins - 1
[095ab1b]526            y[i_q] += frac * data_n
[729bcf6]527            # Take dqs from data to get the q_average
528            x[i_q] += frac * q_value
[a7a5886]529            if err_data == None or err_data[npt] == 0.0:
530                if data_n < 0:
531                    data_n = -data_n
[c6f95bb]532                err_y[i_q] += frac * frac * data_n
[8ba103f]533            else:
[095ab1b]534                err_y[i_q] += frac * frac * err_data[npt] * err_data[npt]
[342a506]535            if dq_data != None:
[f60a8c2]536                # To be consistent with dq calculation in 1d reduction,
537                # we need just the averages (not quadratures) because
538                # it should not depend on the number of the q points
[729bcf6]539                # in the qr bins.
540                err_x[i_q] += frac * dq_data[npt]
[342a506]541            else:
542                err_x = None
[f60a8c2]543            y_counts[i_q] += frac
[095ab1b]544       
[f60a8c2]545        # Average the sums
[095ab1b]546        for n in range(nbins):
[f60a8c2]547            if err_y[n] < 0:
548                err_y[n] = -err_y[n]
[095ab1b]549            err_y[n] = math.sqrt(err_y[n])
[729bcf6]550            #if err_x != None:
551            #    err_x[n] = math.sqrt(err_x[n])
[342a506]552           
[a7a5886]553        err_y = err_y / y_counts
[f60a8c2]554        err_y[err_y == 0] = numpy.average(err_y)
555        y = y / y_counts
556        x = x / y_counts
557        idx = (numpy.isfinite(y)) & (numpy.isfinite(x))
[8f12385]558       
[342a506]559        if err_x != None:
560            d_x = err_x[idx] / y_counts[idx]
561        else:
562            d_x = None
563
[f60a8c2]564        if not idx.any():
565            msg = "Average Error: No points inside ROI to average..."
[a7a5886]566            raise ValueError, msg
[342a506]567       
568        return Data1D(x=x[idx], y=y[idx], dy=err_y[idx], dx=d_x)
[76e2369]569   
570
571class Ring(object):
572    """
[0997158f]573    Defines a ring on a 2D data set.
574    The ring is defined by r_min, r_max, and
575    the position of the center of the ring.
576   
577    The data returned is the distribution of counts
578    around the ring as a function of phi.
579   
[f60a8c2]580    Phi_min and phi_max should be defined between 0 and 2*pi
[0997158f]581    in anti-clockwise starting from the x- axis on the left-hand side
[76e2369]582    """
[095ab1b]583    #Todo: remove center.
[a7a5886]584    def __init__(self, r_min=0, r_max=0, center_x=0, center_y=0, nbins=20):
[76e2369]585        # Minimum radius
586        self.r_min = r_min
587        # Maximum radius
588        self.r_max = r_max
589        # Center of the ring in x
590        self.center_x = center_x
591        # Center of the ring in y
592        self.center_y = center_y
593        # Number of angular bins
[8ba103f]594        self.nbins_phi = nbins
[76e2369]595       
596    def __call__(self, data2D):
597        """
[0997158f]598        Apply the ring to the data set.
599        Returns the angular distribution for a given q range
600       
601        :param data2D: Data2D object
602       
603        :return: Data1D object
[76e2369]604        """
605        if data2D.__class__.__name__ not in ["Data2D", "plottable_2D"]:
606            raise RuntimeError, "Ring averaging only take plottable_2D objects"
607       
[095ab1b]608        Pi = math.pi
609 
610        # Get data
[c6f95bb]611        data = data2D.data[numpy.isfinite(data2D.data)]
612        q_data = data2D.q_data[numpy.isfinite(data2D.data)]
613        err_data = data2D.err_data[numpy.isfinite(data2D.data)]
[f60a8c2]614        qx_data = data2D.qx_data[numpy.isfinite(data2D.data)]
[c6f95bb]615        qy_data = data2D.qy_data[numpy.isfinite(data2D.data)]
616       
[095ab1b]617        q_data_max = numpy.max(q_data)
618       
619        # Set space for 1d outputs
[76e2369]620        phi_bins   = numpy.zeros(self.nbins_phi)
621        phi_counts = numpy.zeros(self.nbins_phi)
622        phi_values = numpy.zeros(self.nbins_phi)
623        phi_err    = numpy.zeros(self.nbins_phi)
624       
[f60a8c2]625        for npt in range(len(data)):
[095ab1b]626            frac = 0
627            # q-value at the point (npt)
628            q_value = q_data[npt]
[f60a8c2]629            data_n = data[npt]
[095ab1b]630                       
631            # phi-value at the point (npt)
[a7a5886]632            phi_value = math.atan2(qy_data[npt], qx_data[npt]) + Pi
[76e2369]633           
[a7a5886]634            if self.r_min <= q_value and q_value <= self.r_max:
[f60a8c2]635                frac = 1
[a7a5886]636            if frac == 0:
637                continue
[095ab1b]638            # binning           
[a7a5886]639            i_phi = int(math.floor((self.nbins_phi) * phi_value / (2 * Pi)))
[76e2369]640           
[f60a8c2]641            # Take care of the edge case at phi = 2pi.
642            if i_phi == self.nbins_phi:
643                i_phi =  self.nbins_phi - 1
[095ab1b]644            phi_bins[i_phi] += frac * data[npt]
[76e2369]645           
[a7a5886]646            if err_data == None or err_data[npt] == 0.0:
647                if data_n < 0:
648                    data_n = -data_n
[095ab1b]649                phi_err[i_phi] += frac * frac * math.fabs(data_n)
650            else:
[a7a5886]651                phi_err[i_phi] += frac * frac * err_data[npt] * err_data[npt]
[095ab1b]652            phi_counts[i_phi] += frac
653                         
[76e2369]654        for i in range(self.nbins_phi):
655            phi_bins[i] = phi_bins[i] / phi_counts[i]
656            phi_err[i] = math.sqrt(phi_err[i]) / phi_counts[i]
[a7a5886]657            phi_values[i] = 2.0 * math.pi / self.nbins_phi * (1.0 * i + 0.5)
[76e2369]658           
[f60a8c2]659        idx = (numpy.isfinite(phi_bins))
[095ab1b]660
[a7a5886]661        if not idx.any():
[f60a8c2]662            msg = "Average Error: No points inside ROI to average..."
[a7a5886]663            raise ValueError, msg
664        #elif len(phi_bins[idx])!= self.nbins_phi:
665        #    print "resulted",self.nbins_phi- len(phi_bins[idx])
666        #,"empty bin(s) due to tight binning..."
[095ab1b]667        return Data1D(x=phi_values[idx], y=phi_bins[idx], dy=phi_err[idx])
[76e2369]668   
[f60a8c2]669   
[76e2369]670def get_pixel_fraction(qmax, q_00, q_01, q_10, q_11):
671    """
[0997158f]672    Returns the fraction of the pixel defined by
[f60a8c2]673    the four corners (q_00, q_01, q_10, q_11) that
[0997158f]674    has q < qmax.::
675   
[76e2369]676                q_01                q_11
677        y=1         +--------------+
678                    |              |
679                    |              |
680                    |              |
681        y=0         +--------------+
[bb0b12c]682                q_00                q_10
[76e2369]683       
684                    x=0            x=1
[0997158f]685   
[76e2369]686    """
687    # y side for x = minx
688    x_0 = get_intercept(qmax, q_00, q_01)
689    # y side for x = maxx
690    x_1 = get_intercept(qmax, q_10, q_11)
691   
692    # x side for y = miny
693    y_0 = get_intercept(qmax, q_00, q_10)
694    # x side for y = maxy
695    y_1 = get_intercept(qmax, q_01, q_11)
696   
697    # surface fraction for a 1x1 pixel
698    frac_max = 0
699   
700    if x_0 and x_1:
[a7a5886]701        frac_max = (x_0 + x_1) / 2.0
[76e2369]702    elif y_0 and y_1:
[a7a5886]703        frac_max = (y_0 + y_1) / 2.0
[76e2369]704    elif x_0 and y_0:
705        if q_00 < q_10:
[a7a5886]706            frac_max = x_0 * y_0 / 2.0
[76e2369]707        else:
[a7a5886]708            frac_max = 1.0 - x_0 * y_0 / 2.0
[76e2369]709    elif x_0 and y_1:
710        if q_00 < q_10:
[a7a5886]711            frac_max = x_0 * y_1 / 2.0
[76e2369]712        else:
[a7a5886]713            frac_max = 1.0 - x_0 * y_1 / 2.0
[76e2369]714    elif x_1 and y_0:
715        if q_00 > q_10:
[a7a5886]716            frac_max = x_1 * y_0 / 2.0
[76e2369]717        else:
[a7a5886]718            frac_max = 1.0 - x_1 * y_0 / 2.0
[76e2369]719    elif x_1 and y_1:
720        if q_00 < q_10:
[a7a5886]721            frac_max = 1.0 - (1.0 - x_1) * (1.0 - y_1) / 2.0
[76e2369]722        else:
[a7a5886]723            frac_max = (1.0 - x_1) * (1.0 - y_1) / 2.0
[76e2369]724           
725    # If we make it here, there is no intercept between
726    # this pixel and the constant-q ring. We only need
727    # to know if we have to include it or exclude it.
[f60a8c2]728    elif (q_00 + q_01 + q_10 + q_11) / 4.0 <  qmax:
[76e2369]729        frac_max = 1.0
[095ab1b]730
[76e2369]731    return frac_max
732             
[f60a8c2]733             
[76e2369]734def get_intercept(q, q_0, q_1):
735    """
[0997158f]736    Returns the fraction of the side at which the
737    q-value intercept the pixel, None otherwise.
738    The values returned is the fraction ON THE SIDE
739    OF THE LOWEST Q. ::
740   
741   
[f60a8c2]742            A           B
[0997158f]743        +-----------+--------+    <--- pixel size
[f60a8c2]744        0                    1
[0997158f]745        Q_0 -------- Q ----- Q_1   <--- equivalent Q range
[76e2369]746        if Q_1 > Q_0, A is returned
747        if Q_1 < Q_0, B is returned
748        if Q is outside the range of [Q_0, Q_1], None is returned
749         
750    """
751    if q_1 > q_0:
752        if (q > q_0 and q <= q_1):
[f60a8c2]753            return (q - q_0) / (q_1 - q_0)
[76e2369]754    else:
755        if (q > q_1 and q <= q_0):
[f60a8c2]756            return (q - q_1) / (q_0 - q_1)
[76e2369]757    return None
[095ab1b]758     
[f60a8c2]759     
[fb198a9]760class _Sector:
761    """
[0997158f]762    Defines a sector region on a 2D data set.
763    The sector is defined by r_min, r_max, phi_min, phi_max,
[f60a8c2]764    and the position of the center of the ring
[a7a5886]765    where phi_min and phi_max are defined by the right
766    and left lines wrt central line
[f60a8c2]767    and phi_max could be less than phi_min.
[0997158f]768   
[f60a8c2]769    Phi is defined between 0 and 2*pi in anti-clockwise
[a7a5886]770    starting from the x- axis on the left-hand side
[fb198a9]771    """
[a7a5886]772    def __init__(self, r_min, r_max, phi_min=0, phi_max=2*math.pi, nbins=20):
[fb198a9]773        self.r_min = r_min
774        self.r_max = r_max
775        self.phi_min = phi_min
776        self.phi_max = phi_max
777        self.nbins = nbins
778       
779    def _agv(self, data2D, run='phi'):
780        """
[0997158f]781        Perform sector averaging.
782       
783        :param data2D: Data2D object
784        :param run:  define the varying parameter ('phi' , 'q' , or 'q2')
785       
786        :return: Data1D object
[fb198a9]787        """
788        if data2D.__class__.__name__ not in ["Data2D", "plottable_2D"]:
789            raise RuntimeError, "Ring averaging only take plottable_2D objects"
[095ab1b]790        Pi = math.pi
[c6f95bb]791
[095ab1b]792        # Get the all data & info
[c6f95bb]793        data = data2D.data[numpy.isfinite(data2D.data)]
794        q_data = data2D.q_data[numpy.isfinite(data2D.data)]
795        err_data = data2D.err_data[numpy.isfinite(data2D.data)]
[f60a8c2]796        qx_data = data2D.qx_data[numpy.isfinite(data2D.data)]
[c6f95bb]797        qy_data = data2D.qy_data[numpy.isfinite(data2D.data)]
[342a506]798        dq_data = None
[729bcf6]799           
800        # Get the dq for resolution averaging
[342a506]801        if data2D.dqx_data != None and data2D.dqy_data != None:
[f60a8c2]802            # The pinholes and det. pix contribution present
[729bcf6]803            # in both direction of the 2D which must be subtracted when
804            # converting to 1D: dq_overlap should calculated ideally at
[f60a8c2]805            # q = 0.
[729bcf6]806            # Extrapolate dqy(perp) at q = 0
807            z_max = max(data2D.q_data)
808            z_min = min(data2D.q_data)
809            x_max = data2D.dqx_data[data2D.q_data[z_max]]
[f60a8c2]810            x_min = data2D.dqx_data[data2D.q_data[z_min]]
[729bcf6]811            y_max = data2D.dqy_data[data2D.q_data[z_max]]
[f60a8c2]812            y_min = data2D.dqy_data[data2D.q_data[z_min]]
[729bcf6]813            # Find qdx at q = 0
814            dq_overlap_x = (x_min * z_max - x_max * z_min) / (z_max - z_min)
815            # when extrapolation goes wrong
816            if dq_overlap_x > min(data2D.dqx_data):
817                dq_overlap_x = min(data2D.dqx_data)
[f60a8c2]818            dq_overlap_x *= dq_overlap_x
[729bcf6]819            # Find qdx at q = 0
820            dq_overlap_y = (y_min * z_max - y_max * z_min) / (z_max - z_min)
821            # when extrapolation goes wrong
822            if dq_overlap_y > min(data2D.dqy_data):
823                dq_overlap_y = min(data2D.dqy_data)
824            # get dq at q=0.
825            dq_overlap_y *= dq_overlap_y
826
[f60a8c2]827            dq_overlap = numpy.sqrt((dq_overlap_x + dq_overlap_y) / 2.0)
[729bcf6]828            if dq_overlap < 0:
829                dq_overlap = y_min
830            dqx_data = data2D.dqx_data[numpy.isfinite(data2D.data)]
831            dqy_data = data2D.dqy_data[numpy.isfinite(data2D.data)] - dq_overlap
832            # def; dqx_data = dq_r dqy_data = dq_phi
833            # Convert dq 2D to 1D here
[f60a8c2]834            dqx = dqx_data * dqx_data
[729bcf6]835            dqy = dqy_data * dqy_data
836            dq_data = numpy.add(dqx, dqy)
[342a506]837            dq_data = numpy.sqrt(dq_data)
838           
[095ab1b]839        #set space for 1d outputs
840        x        = numpy.zeros(self.nbins)
[fb198a9]841        y        = numpy.zeros(self.nbins)
[f60a8c2]842        y_err    = numpy.zeros(self.nbins)
843        x_err    = numpy.zeros(self.nbins)
[fb198a9]844        y_counts = numpy.zeros(self.nbins)
[095ab1b]845                     
846        # Get the min and max into the region: 0 <= phi < 2Pi
847        phi_min = flip_phi(self.phi_min)
848        phi_max = flip_phi(self.phi_max)
[bb0b12c]849       
[095ab1b]850        q_data_max = numpy.max(q_data)
851                     
[f60a8c2]852        for n in range(len(data)):
[a7a5886]853            frac = 0
854           
855            # q-value at the pixel (j,i)
856            q_value = q_data[n]
857            data_n = data[n]
858           
859            # Is pixel within range?
860            is_in = False
861           
862            # phi-value of the pixel (j,i)
[f60a8c2]863            phi_value = math.atan2(qy_data[n], qx_data[n]) + Pi
[a7a5886]864           
865            ## No need to calculate the frac when all data are within range
866            if self.r_min <= q_value and q_value <= self.r_max:
[f60a8c2]867                frac = 1
[a7a5886]868            if frac == 0:
869                continue
870            #In case of two ROIs (symmetric major and minor regions)(for 'q2')
[f60a8c2]871            if run.lower() == 'q2':
872                ## For minor sector wing
[a7a5886]873                # Calculate the minor wing phis
874                phi_min_minor = flip_phi(phi_min - Pi)
875                phi_max_minor = flip_phi(phi_max - Pi)
876                # Check if phis of the minor ring is within 0 to 2pi
877                if phi_min_minor > phi_max_minor:
878                    is_in = (phi_value > phi_min_minor or \
879                              phi_value < phi_max_minor)
880                else:
881                    is_in = (phi_value > phi_min_minor and \
882                              phi_value < phi_max_minor)
[3c67340]883
[f60a8c2]884            #For all cases(i.e.,for 'q', 'q2', and 'phi')
885            #Find pixels within ROI
886            if phi_min > phi_max:
[a7a5886]887                is_in = is_in or (phi_value > phi_min or \
[f60a8c2]888                                   phi_value < phi_max)
[a7a5886]889            else:
890                is_in = is_in or (phi_value >= phi_min  and \
891                                    phi_value < phi_max)
892           
893            if not is_in:
[f60a8c2]894                frac = 0
[a7a5886]895            if frac == 0:
896                continue
897            # Check which type of averaging we need
[f60a8c2]898            if run.lower() == 'phi':
[a7a5886]899                temp_x = (self.nbins) * (phi_value - self.phi_min)
900                temp_y = (self.phi_max - self.phi_min)
901                i_bin = int(math.floor(temp_x / temp_y))
902            else:
903                temp_x = (self.nbins) * (q_value - self.r_min)
[ec3959ab]904                temp_y = (self.r_max - self.r_min)
[a7a5886]905                i_bin = int(math.floor(temp_x / temp_y))
[bb0b12c]906
[f60a8c2]907            # Take care of the edge case at phi = 2pi.
908            if i_bin == self.nbins:
909                i_bin = self.nbins - 1
[095ab1b]910               
[f60a8c2]911            ## Get the total y
[a7a5886]912            y[i_bin] += frac * data_n
[729bcf6]913            x[i_bin] += frac * q_value
[342a506]914            if err_data[n] == None or err_data[n] == 0.0:
[a7a5886]915                if data_n < 0:
916                    data_n = -data_n
917                y_err[i_bin] += frac * frac * data_n
918            else:
919                y_err[i_bin] += frac * frac * err_data[n] * err_data[n]
[342a506]920               
921            if dq_data != None:
[f60a8c2]922                # To be consistent with dq calculation in 1d reduction,
923                # we need just the averages (not quadratures) because
924                # it should not depend on the number of the q points
[729bcf6]925                # in the qr bins.
926                x_err[i_bin] += frac * dq_data[n]
[342a506]927            else:
928                x_err = None
[a7a5886]929            y_counts[i_bin] += frac
930   
[095ab1b]931        # Organize the results
[fb198a9]932        for i in range(self.nbins):
933            y[i] = y[i] / y_counts[i]
934            y_err[i] = math.sqrt(y_err[i]) / y_counts[i]
[729bcf6]935
[095ab1b]936            # The type of averaging: phi,q2, or q
937            # Calculate x[i]should be at the center of the bin
[f60a8c2]938            if run.lower() == 'phi':
[12c5b87]939                x[i] = (self.phi_max - self.phi_min) / self.nbins * \
940                    (1.0 * i + 0.5) + self.phi_min
[095ab1b]941            else:
[f60a8c2]942                # We take the center of ring area, not radius.
[342a506]943                # This is more accurate than taking the radial center of ring.
[729bcf6]944                #delta_r = (self.r_max - self.r_min) / self.nbins
945                #r_inner = self.r_min + delta_r * i
946                #r_outer = r_inner + delta_r
947                #x[i] = math.sqrt((r_inner * r_inner + r_outer * r_outer) / 2)
948                x[i] = x[i] / y_counts[i]
[f60a8c2]949        y_err[y_err == 0] = numpy.average(y_err)
[a7a5886]950        idx = (numpy.isfinite(y) & numpy.isfinite(y_err))
[342a506]951        if x_err != None:
[729bcf6]952            d_x = x_err[idx] / y_counts[idx]
[342a506]953        else:
954            d_x = None
[a7a5886]955        if not idx.any():
[f60a8c2]956            msg = "Average Error: No points inside sector of ROI to average..."
[a7a5886]957            raise ValueError, msg
958        #elif len(y[idx])!= self.nbins:
959        #    print "resulted",self.nbins- len(y[idx]),
960        #"empty bin(s) due to tight binning..."
[342a506]961        return Data1D(x=x[idx], y=y[idx], dy=y_err[idx], dx=d_x)
[fb198a9]962               
[f60a8c2]963               
[2e83ff3]964class SectorPhi(_Sector):
965    """
[0997158f]966    Sector average as a function of phi.
967    I(phi) is return and the data is averaged over Q.
968   
969    A sector is defined by r_min, r_max, phi_min, phi_max.
970    The number of bin in phi also has to be defined.
[2e83ff3]971    """
972    def __call__(self, data2D):
973        """
[0997158f]974        Perform sector average and return I(phi).
975       
976        :param data2D: Data2D object
977        :return: Data1D object
[2e83ff3]978        """
979        return self._agv(data2D, 'phi')
[fb198a9]980   
[f60a8c2]981   
[fb198a9]982class SectorQ(_Sector):
983    """
[0997158f]984    Sector average as a function of Q for both symatric wings.
985    I(Q) is return and the data is averaged over phi.
986   
987    A sector is defined by r_min, r_max, phi_min, phi_max.
[f60a8c2]988    r_min, r_max, phi_min, phi_max >0.
[0997158f]989    The number of bin in Q also has to be defined.
[fb198a9]990    """
991    def __call__(self, data2D):
992        """
[0997158f]993        Perform sector average and return I(Q).
994       
995        :param data2D: Data2D object
996       
997        :return: Data1D object
[fb198a9]998        """
999        return self._agv(data2D, 'q2')
[c6f95bb]1000
[f60a8c2]1001
[f265927]1002class Ringcut(object):
1003    """
[0997158f]1004    Defines a ring on a 2D data set.
1005    The ring is defined by r_min, r_max, and
1006    the position of the center of the ring.
1007   
1008    The data returned is the region inside the ring
1009   
[f60a8c2]1010    Phi_min and phi_max should be defined between 0 and 2*pi
[0997158f]1011    in anti-clockwise starting from the x- axis on the left-hand side
[f265927]1012    """
[f60a8c2]1013    def __init__(self, r_min=0, r_max=0, center_x=0, center_y=0):
[f265927]1014        # Minimum radius
1015        self.r_min = r_min
1016        # Maximum radius
1017        self.r_max = r_max
1018        # Center of the ring in x
1019        self.center_x = center_x
1020        # Center of the ring in y
1021        self.center_y = center_y
1022
1023    def __call__(self, data2D):
1024        """
[0997158f]1025        Apply the ring to the data set.
1026        Returns the angular distribution for a given q range
1027       
1028        :param data2D: Data2D object
1029       
1030        :return: index array in the range
[f265927]1031        """
1032        if data2D.__class__.__name__ not in ["Data2D", "plottable_2D"]:
1033            raise RuntimeError, "Ring cut only take plottable_2D objects"
1034
1035        # Get data
[f60a8c2]1036        qx_data = data2D.qx_data
[f265927]1037        qy_data = data2D.qy_data
1038        mask = data2D.mask
[a7a5886]1039        q_data = numpy.sqrt(qx_data * qx_data + qy_data * qy_data)
[f265927]1040
1041        # check whether or not the data point is inside ROI
1042        out = (self.r_min <= q_data) & (self.r_max >= q_data)
1043
1044        return (out)
1045       
1046
[c6f95bb]1047class Boxcut(object):
1048    """
[0997158f]1049    Find a rectangular 2D region of interest.
[c6f95bb]1050    """
1051    def __init__(self, x_min=0.0, x_max=0.0, y_min=0.0, y_max=0.0):
1052        # Minimum Qx value [A-1]
1053        self.x_min = x_min
1054        # Maximum Qx value [A-1]
1055        self.x_max = x_max
1056        # Minimum Qy value [A-1]
1057        self.y_min = y_min
1058        # Maximum Qy value [A-1]
1059        self.y_max = y_max
1060
1061    def __call__(self, data2D):
1062        """
[0997158f]1063       Find a rectangular 2D region of interest.
1064         
1065       :param data2D: Data2D object
[f60a8c2]1066       :return: mask, 1d array (len = len(data))
[0997158f]1067           with Trues where the data points are inside ROI, otherwise False
[c6f95bb]1068        """
1069        mask = self._find(data2D)
1070       
1071        return mask
1072       
1073    def _find(self, data2D):
1074        """
[f60a8c2]1075        Find a rectangular 2D region of interest.
[0997158f]1076       
1077        :param data2D: Data2D object
1078       
[f60a8c2]1079        :return: out, 1d array (length = len(data))
[0997158f]1080           with Trues where the data points are inside ROI, otherwise Falses
[c6f95bb]1081        """
1082        if data2D.__class__.__name__ not in ["Data2D", "plottable_2D"]:
1083            raise RuntimeError, "Boxcut take only plottable_2D objects"
[f60a8c2]1084        # Get qx_ and qy_data
[c6f95bb]1085        qx_data = data2D.qx_data
1086        qy_data = data2D.qy_data
[f265927]1087        mask = data2D.mask
[c6f95bb]1088       
1089        # check whether or not the data point is inside ROI
[f265927]1090        outx = (self.x_min <= qx_data) & (self.x_max > qx_data)
1091        outy = (self.y_min <= qy_data) & (self.y_max > qy_data)
[c6f95bb]1092
1093        return (outx & outy)
1094
[f60a8c2]1095
[c6f95bb]1096class Sectorcut(object):
1097    """
[0997158f]1098    Defines a sector (major + minor) region on a 2D data set.
1099    The sector is defined by phi_min, phi_max,
[f60a8c2]1100    where phi_min and phi_max are defined by the right
1101    and left lines wrt central line.
[0997158f]1102   
[f60a8c2]1103    Phi_min and phi_max are given in units of radian
[0997158f]1104    and (phi_max-phi_min) should not be larger than pi
[c6f95bb]1105    """
[a7a5886]1106    def __init__(self, phi_min=0, phi_max=math.pi):
[c6f95bb]1107        self.phi_min = phi_min
1108        self.phi_max = phi_max
1109             
1110    def __call__(self, data2D):
1111        """
[0997158f]1112        Find a rectangular 2D region of interest.
1113       
1114        :param data2D: Data2D object
1115       
[f60a8c2]1116        :return: mask, 1d array (len = len(data))
[0997158f]1117       
1118        with Trues where the data points are inside ROI, otherwise False
[c6f95bb]1119        """
1120        mask = self._find(data2D)
1121       
1122        return mask
1123       
1124    def _find(self, data2D):
1125        """
[f60a8c2]1126        Find a rectangular 2D region of interest.
[0997158f]1127       
1128        :param data2D: Data2D object
1129       
[f60a8c2]1130        :return: out, 1d array (length = len(data))
[0997158f]1131       
1132        with Trues where the data points are inside ROI, otherwise Falses
[c6f95bb]1133        """
1134        if data2D.__class__.__name__ not in ["Data2D", "plottable_2D"]:
[f60a8c2]1135            raise RuntimeError, "Sectorcut take only plottable_2D objects"
[c6f95bb]1136        Pi = math.pi
1137        # Get data
1138        qx_data = data2D.qx_data
[f60a8c2]1139        qy_data = data2D.qy_data
[c6f95bb]1140        phi_data = numpy.zeros(len(qx_data))
1141
1142        # get phi from data
[f265927]1143        phi_data = numpy.arctan2(qy_data, qx_data)
1144       
1145        # Get the min and max into the region: -pi <= phi < Pi
[a7a5886]1146        phi_min_major = flip_phi(self.phi_min + Pi) - Pi
[f60a8c2]1147        phi_max_major = flip_phi(self.phi_max + Pi) - Pi
[c6f95bb]1148        # check for major sector
[f265927]1149        if phi_min_major > phi_max_major:
1150            out_major = (phi_min_major <= phi_data) + (phi_max_major > phi_data)
[c6f95bb]1151        else:
[f265927]1152            out_major = (phi_min_major <= phi_data) & (phi_max_major > phi_data)
1153         
[c6f95bb]1154        # minor sector
1155        # Get the min and max into the region: -pi <= phi < Pi
[a7a5886]1156        phi_min_minor = flip_phi(self.phi_min) - Pi
1157        phi_max_minor = flip_phi(self.phi_max) - Pi
[c6f95bb]1158             
1159        # check for minor sector
1160        if phi_min_minor > phi_max_minor:
[a7a5886]1161            out_minor = (phi_min_minor <= phi_data) + \
[f60a8c2]1162                            (phi_max_minor >= phi_data)
[c6f95bb]1163        else:
[a7a5886]1164            out_minor = (phi_min_minor <= phi_data) & \
[f60a8c2]1165                            (phi_max_minor >= phi_data)
[c6f95bb]1166        out = out_major + out_minor
[f265927]1167       
[c6f95bb]1168        return out
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