rg_space.py 26.2 KB
Newer Older
1
2
# NIFTY (Numerical Information Field Theory) has been developed at the
# Max-Planck-Institute for Astrophysics.
Marco Selig's avatar
Marco Selig committed
3
##
4
# Copyright (C) 2015 Max-Planck-Society
Marco Selig's avatar
Marco Selig committed
5
##
6
7
# Author: Marco Selig
# Project homepage: <http://www.mpa-garching.mpg.de/ift/nifty/>
Marco Selig's avatar
Marco Selig committed
8
##
9
10
11
12
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
Marco Selig's avatar
Marco Selig committed
13
##
14
15
16
17
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
# See the GNU General Public License for more details.
Marco Selig's avatar
Marco Selig committed
18
##
19
20
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
Marco Selig's avatar
Marco Selig committed
21
22
23
24
25
26
27
28
29
30

"""
    ..                  __   ____   __
    ..                /__/ /   _/ /  /_
    ..      __ ___    __  /  /_  /   _/  __   __
    ..    /   _   | /  / /   _/ /  /   /  / /  /
    ..   /  / /  / /  / /  /   /  /_  /  /_/  /
    ..  /__/ /__/ /__/ /__/    \___/  \___   /  rg
    ..                               /______/

Marco Selig's avatar
Marco Selig committed
31
    NIFTY submodule for regular Cartesian grids.
Marco Selig's avatar
Marco Selig committed
32
33
34

"""
from __future__ import division
Ultimanet's avatar
Ultimanet committed
35

Marco Selig's avatar
Marco Selig committed
36
import numpy as np
37
import os
38

Marco Selig's avatar
Marco Selig committed
39
40
41
import pylab as pl
from matplotlib.colors import LogNorm as ln
from matplotlib.ticker import LogFormatter as lf
Ultimanet's avatar
Ultimanet committed
42

43
44
from d2o import distributed_data_object,\
                STRATEGIES as DISTRIBUTION_STRATEGIES
45

46
from nifty.spaces.space import Space
csongor's avatar
csongor committed
47

48
49
50
from nifty.config import about,\
                         nifty_configuration as gc,\
                         dependency_injector as gdi
theos's avatar
theos committed
51
from rg_space_paradict import RGSpaceParadict
Ultima's avatar
Ultima committed
52
import nifty.nifty_utilities as utilities
53

Ultima's avatar
Ultima committed
54
MPI = gdi[gc['mpi_module']]
55
RG_DISTRIBUTION_STRATEGIES = DISTRIBUTION_STRATEGIES['global']
Ultimanet's avatar
Ultimanet committed
56

Marco Selig's avatar
Marco Selig committed
57

58
class RGSpace(Space):
Marco Selig's avatar
Marco Selig committed
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
104
105
    """
        ..      _____   _______
        ..    /   __/ /   _   /
        ..   /  /    /  /_/  /
        ..  /__/     \____  /  space class
        ..          /______/

        NIFTY subclass for spaces of regular Cartesian grids.

        Parameters
        ----------
        num : {int, numpy.ndarray}
            Number of gridpoints or numbers of gridpoints along each axis.
        naxes : int, *optional*
            Number of axes (default: None).
        zerocenter : {bool, numpy.ndarray}, *optional*
            Whether the Fourier zero-mode is located in the center of the grid
            (or the center of each axis speparately) or not (default: True).
        hermitian : bool, *optional*
            Whether the fields living in the space follow hermitian symmetry or
            not (default: True).
        purelyreal : bool, *optional*
            Whether the field values are purely real (default: True).
        dist : {float, numpy.ndarray}, *optional*
            Distance between two grid points along each axis (default: None).
        fourier : bool, *optional*
            Whether the space represents a Fourier or a position grid
            (default: False).

        Notes
        -----
        Only even numbers of grid points per axis are supported.
        The basis transformations between position `x` and Fourier mode `k`
        rely on (inverse) fast Fourier transformations using the
        :math:`exp(2 \pi i k^\dagger x)`-formulation.

        Attributes
        ----------
        para : numpy.ndarray
            One-dimensional array containing information on the axes of the
            space in the following form: The first entries give the grid-points
            along each axis in reverse order; the next entry is 0 if the
            fields defined on the space are purely real-valued, 1 if they are
            hermitian and complex, and 2 if they are not hermitian, but
            complex-valued; the last entries hold the information on whether
            the axes are centered on zero or not, containing a one for each
            zero-centered axis and a zero for each other one, in reverse order.
106
        dtype : numpy.dtype
Marco Selig's avatar
Marco Selig committed
107
108
109
110
111
112
113
114
115
116
117
118
119
            Data type of the field values for a field defined on this space,
            either ``numpy.float64`` or ``numpy.complex128``.
        discrete : bool
            Whether or not the underlying space is discrete, always ``False``
            for regular grids.
        vol : numpy.ndarray
            One-dimensional array containing the distances between two grid
            points along each axis, in reverse order. By default, the total
            length of each axis is assumed to be one.
        fourier : bool
            Whether or not the grid represents a Fourier basis.
    """

120
121
    def __init__(self, shape=(1,), zerocenter=False, distances=None,
                 harmonic=False, dtype=np.dtype('float'), ):
Marco Selig's avatar
Marco Selig committed
122
123
124
125
126
127
128
129
130
131
132
133
        """
            Sets the attributes for an rg_space class instance.

            Parameters
            ----------
            num : {int, numpy.ndarray}
                Number of gridpoints or numbers of gridpoints along each axis.
            naxes : int, *optional*
                Number of axes (default: None).
            zerocenter : {bool, numpy.ndarray}, *optional*
                Whether the Fourier zero-mode is located in the center of the
                grid (or the center of each axis speparately) or not
Ultimanet's avatar
Ultimanet committed
134
                (default: False).
Marco Selig's avatar
Marco Selig committed
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
            hermitian : bool, *optional*
                Whether the fields living in the space follow hermitian
                symmetry or not (default: True).
            purelyreal : bool, *optional*
                Whether the field values are purely real (default: True).
            dist : {float, numpy.ndarray}, *optional*
                Distance between two grid points along each axis
                (default: None).
            fourier : bool, *optional*
                Whether the space represents a Fourier or a position grid
                (default: False).

            Returns
            -------
            None
        """
151

Jait Dixit's avatar
Jait Dixit committed
152
153
        super(RGSpace, self).__init__(dtype=np.dtype('float64'))

theos's avatar
theos committed
154
155
156
157
        self.paradict = RGSpaceParadict(shape=shape,
                                        zerocenter=zerocenter,
                                        distances=distances,
                                        harmonic=harmonic)
theos's avatar
theos committed
158
159
160
161

    @property
    def harmonic(self):
        return self.paradict['harmonic']
162

163
    def copy(self):
164
165
        return RGSpace(dtype=self.dtype, harmonic=self.harmonic,
                       **self.paradict.parameters)
166

167
168
    @property
    def shape(self):
169
        return self.paradict['shape']
Marco Selig's avatar
Marco Selig committed
170

171
172
173
    @property
    def dim(self):
        return reduce(lambda x, y: x*y, self.shape)
174

175
176
177
    @property
    def total_volume(self):
        return self.dim * reduce(lambda x, y: x*y, self.paradict['distances'])
Marco Selig's avatar
Marco Selig committed
178

179
    def weight(self, x, power=1, axes=None, inplace=False):
180
        weight = reduce(lambda x, y: x*y, self.paradict['distances'])**power
181
182
183
184
185
186
        if inplace:
            x *= weight
            result_x = x
        else:
            result_x = x*weight
        return result_x
Marco Selig's avatar
Marco Selig committed
187

188
    def compute_k_array(self, distribution_strategy):
Marco Selig's avatar
Marco Selig committed
189
        """
190
191
            Calculates an n-dimensional array with its entries being the
            lengths of the k-vectors from the zero point of the grid.
Marco Selig's avatar
Marco Selig committed
192
193
194

            Parameters
            ----------
195
            None : All information is taken from the parent object.
Marco Selig's avatar
Marco Selig committed
196
197
198

            Returns
            -------
199
            nkdict : distributed_data_object
Marco Selig's avatar
Marco Selig committed
200
        """
201
202
203
204
205
206
207
208
209
210
211
212
213
        shape = self.shape
        # prepare the distributed_data_object
        nkdict = distributed_data_object(
                        global_shape=shape,
                        dtype=np.float128,
                        distribution_strategy=distribution_strategy)

        if distribution_strategy in DISTRIBUTION_STRATEGIES['slicing']:
            # get the node's individual slice of the first dimension
            slice_of_first_dimension = slice(
                                    *nkdict.distributor.local_slice[0:2])
        elif distribution_strategy in DISTRIBUTION_STRATEGIES['not']:
            slice_of_first_dimension = slice(0, shape[0])
Ultimanet's avatar
Ultimanet committed
214
        else:
215
            raise ValueError(about._errors.cstring(
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
                "ERROR: Unsupported distribution strategy"))
        dists = self._compute_k_array_helper(slice_of_first_dimension)
        nkdict.set_local_data(dists)

        return nkdict

    def _compute_k_array_helper(self, slice_of_first_dimension):
        dk = self.paradict['distances']
        shape = self.shape

        inds = []
        for a in shape:
            inds += [slice(0, a)]

        cords = np.ogrid[inds]

        dists = ((np.float128(0) + cords[0] - shape[0] // 2) * dk[0])**2
        # apply zerocenterQ shift
        if self.paradict['zerocenter'][0] == False:
            dists = np.fft.fftshift(dists)
        # only save the individual slice
        dists = dists[slice_of_first_dimension]
        for ii in range(1, len(shape)):
            temp = ((cords[ii] - shape[ii] // 2) * dk[ii])**2
            if self.paradict['zerocenter'][ii] == False:
                temp = np.fft.fftshift(temp)
            dists = dists + temp
        dists = np.sqrt(dists)
        return dists

Ultima's avatar
Ultima committed
246
    def get_plot(self,x,title="",vmin=None,vmax=None,power=None,unit="",
Jait Dixit's avatar
Jait Dixit committed
247
248
                 norm=None,cmap=None,cbar=True,other=None,legend=False,
                 mono=True,**kwargs):
Marco Selig's avatar
Marco Selig committed
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
        """
            Creates a plot of field values according to the specifications
            given by the parameters.

            Parameters
            ----------
            x : numpy.ndarray
                Array containing the field values.

            Returns
            -------
            None

            Other parameters
            ----------------
            title : string, *optional*
                Title of the plot (default: "").
            vmin : float, *optional*
                Minimum value to be displayed (default: ``min(x)``).
            vmax : float, *optional*
                Maximum value to be displayed (default: ``max(x)``).
            power : bool, *optional*
                Whether to plot the power contained in the field or the field
                values themselves (default: False).
            unit : string, *optional*
                Unit of the field values (default: "").
            norm : string, *optional*
                Scaling of the field values before plotting (default: None).
            cmap : matplotlib.colors.LinearSegmentedColormap, *optional*
                Color map to be used for two-dimensional plots (default: None).
            cbar : bool, *optional*
                Whether to show the color bar or not (default: True).
            other : {single object, tuple of objects}, *optional*
                Object or tuple of objects to be added, where objects can be
                scalars, arrays, or fields (default: None).
            legend : bool, *optional*
                Whether to show the legend or not (default: False).
            mono : bool, *optional*
                Whether to plot the monopole or not (default: True).
            save : string, *optional*
                Valid file name where the figure is to be stored, by default
                the figure is not saved (default: False).
            error : {float, numpy.ndarray, nifty.field}, *optional*
                Object indicating some confidence interval to be plotted
                (default: None).
            kindex : numpy.ndarray, *optional*
                Scale corresponding to each band in the power spectrum
                (default: None).
            codomain : nifty.space, *optional*
                A compatible codomain for power indexing (default: None).
            log : bool, *optional*
                Flag specifying if the spectral binning is performed on logarithmic
                scale or not; if set, the number of used bins is set
                automatically (if not given otherwise); by default no binning
                is done (default: None).
            nbin : integer, *optional*
                Number of used spectral bins; if given `log` is set to ``False``;
                integers below the minimum of 3 induce an automatic setting;
                by default no binning is done (default: None).
            binbounds : {list, array}, *optional*
                User specific inner boundaries of the bins, which are preferred
                over the above parameters; by default no binning is done
Jait Dixit's avatar
Jait Dixit committed
311
312
                (default: None).
            vmin : {scalar, list, ndarray, field}, *optional*
Marco Selig's avatar
Marco Selig committed
313
314
315
316
                Lower limit of the uniform distribution if ``random == "uni"``
                (default: 0).

        """
317
        from nifty.field import Field
Ultima's avatar
Ultima committed
318

Marco Selig's avatar
Marco Selig committed
319
320
321
322
323
324
325
326
327
        if(not pl.isinteractive())and(not bool(kwargs.get("save",False))):
            about.warnings.cprint("WARNING: interactive mode off.")

        naxes = (np.size(self.para)-1)//2
        if(power is None):
            power = bool(self.para[naxes])

        if(power):
            x = self.calc_power(x,**kwargs)
Ultima's avatar
Ultima committed
328
329
330
331
            try:
                x = x.get_full_data()
            except AttributeError:
                pass
Marco Selig's avatar
Marco Selig committed
332

Ultima's avatar
Ultima committed
333
334
            fig = pl.figure(num=None,figsize=(6.4,4.8),dpi=None,
                            facecolor="none",edgecolor="none",frameon=False,FigureClass=pl.Figure)
Marco Selig's avatar
Marco Selig committed
335
336
337
338
339
340
341
            ax0 = fig.add_axes([0.12,0.12,0.82,0.76])

            ## explicit kindex
            xaxes = kwargs.get("kindex",None)
            ## implicit kindex
            if(xaxes is None):
                try:
Ultima's avatar
Ultima committed
342
343
                    self.power_indices
                    kindex_supply_space = self
Marco Selig's avatar
Marco Selig committed
344
                except:
Ultima's avatar
Ultima committed
345
346
347
                    kindex_supply_space = self.get_codomain()

                xaxes = kindex_supply_space.power_indices.get_index_dict(
Ultima's avatar
Ultima committed
348
                                                **kwargs)['kindex']
Ultima's avatar
Ultima committed
349
350
351
352
353
354
355
356
357
358


#                try:
#                    self.set_power_indices(**kwargs)
#                except:
#                    codomain = kwargs.get("codomain",self.get_codomain())
#                    codomain.set_power_indices(**kwargs)
#                    xaxes = codomain.power_indices.get("kindex")
#                else:
#                    xaxes = self.power_indices.get("kindex")
Marco Selig's avatar
Marco Selig committed
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373

            if(norm is None)or(not isinstance(norm,int)):
                norm = naxes
            if(vmin is None):
                vmin = np.min(x[:mono].tolist()+(xaxes**norm*x)[1:].tolist(),axis=None,out=None)
            if(vmax is None):
                vmax = np.max(x[:mono].tolist()+(xaxes**norm*x)[1:].tolist(),axis=None,out=None)
            ax0.loglog(xaxes[1:],(xaxes**norm*x)[1:],color=[0.0,0.5,0.0],label="graph 0",linestyle='-',linewidth=2.0,zorder=1)
            if(mono):
                ax0.scatter(0.5*(xaxes[1]+xaxes[2]),x[0],s=20,color=[0.0,0.5,0.0],marker='o',cmap=None,norm=None,vmin=None,vmax=None,alpha=None,linewidths=None,verts=None,zorder=1)

            if(other is not None):
                if(isinstance(other,tuple)):
                    other = list(other)
                    for ii in xrange(len(other)):
374
                        if(isinstance(other[ii],Field)):
Marco Selig's avatar
Marco Selig committed
375
376
377
                            other[ii] = other[ii].power(**kwargs)
                        else:
                            other[ii] = self.enforce_power(other[ii],size=np.size(xaxes),kindex=xaxes)
378
                elif(isinstance(other,Field)):
Marco Selig's avatar
Marco Selig committed
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
                    other = [other.power(**kwargs)]
                else:
                    other = [self.enforce_power(other,size=np.size(xaxes),kindex=xaxes)]
                imax = max(1,len(other)-1)
                for ii in xrange(len(other)):
                    ax0.loglog(xaxes[1:],(xaxes**norm*other[ii])[1:],color=[max(0.0,1.0-(2*ii/imax)**2),0.5*((2*ii-imax)/imax)**2,max(0.0,1.0-(2*(ii-imax)/imax)**2)],label="graph "+str(ii+1),linestyle='-',linewidth=1.0,zorder=-ii)
                    if(mono):
                        ax0.scatter(0.5*(xaxes[1]+xaxes[2]),other[ii][0],s=20,color=[max(0.0,1.0-(2*ii/imax)**2),0.5*((2*ii-imax)/imax)**2,max(0.0,1.0-(2*(ii-imax)/imax)**2)],marker='o',cmap=None,norm=None,vmin=None,vmax=None,alpha=None,linewidths=None,verts=None,zorder=-ii)
                if(legend):
                    ax0.legend()

            ax0.set_xlim(xaxes[1],xaxes[-1])
            ax0.set_xlabel(r"$|k|$")
            ax0.set_ylim(vmin,vmax)
            ax0.set_ylabel(r"$|k|^{%i} P_k$"%norm)
            ax0.set_title(title)

        else:
Ultima's avatar
Ultima committed
397
398
399
400
            try:
                x = x.get_full_data()
            except AttributeError:
                pass
Marco Selig's avatar
Marco Selig committed
401
402
403
404
            if(naxes==1):
                fig = pl.figure(num=None,figsize=(6.4,4.8),dpi=None,facecolor="none",edgecolor="none",frameon=False,FigureClass=pl.Figure)
                ax0 = fig.add_axes([0.12,0.12,0.82,0.76])

405
                xaxes = (np.arange(self.para[0],dtype=np.int)+self.para[2]*(self.para[0]//2))*self.distances
Marco Selig's avatar
Marco Selig committed
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
                if(vmin is None):
                    if(np.iscomplexobj(x)):
                        vmin = min(np.min(np.absolute(x),axis=None,out=None),np.min(np.real(x),axis=None,out=None),np.min(np.imag(x),axis=None,out=None))
                    else:
                        vmin = np.min(x,axis=None,out=None)
                if(vmax is None):
                    if(np.iscomplexobj(x)):
                        vmax = max(np.max(np.absolute(x),axis=None,out=None),np.max(np.real(x),axis=None,out=None),np.max(np.imag(x),axis=None,out=None))
                    else:
                        vmax = np.max(x,axis=None,out=None)
                if(norm=="log"):
                    ax0graph = ax0.semilogy
                    if(vmin<=0):
                        raise ValueError(about._errors.cstring("ERROR: nonpositive value(s)."))
                else:
                    ax0graph = ax0.plot

                if(np.iscomplexobj(x)):
                    ax0graph(xaxes,np.absolute(x),color=[0.0,0.5,0.0],label="graph (absolute)",linestyle='-',linewidth=2.0,zorder=1)
                    ax0graph(xaxes,np.real(x),color=[0.0,0.5,0.0],label="graph (real part)",linestyle="--",linewidth=1.0,zorder=0)
                    ax0graph(xaxes,np.imag(x),color=[0.0,0.5,0.0],label="graph (imaginary part)",linestyle=':',linewidth=1.0,zorder=0)
                    if(legend):
                        ax0.legend()
                elif(other is not None):
                    ax0graph(xaxes,x,color=[0.0,0.5,0.0],label="graph 0",linestyle='-',linewidth=2.0,zorder=1)
                    if(isinstance(other,tuple)):
432
                        other = [self._enforce_values(xx,extend=True) for xx in other]
Marco Selig's avatar
Marco Selig committed
433
                    else:
434
                        other = [self._enforce_values(other,extend=True)]
Marco Selig's avatar
Marco Selig committed
435
436
437
438
                    imax = max(1,len(other)-1)
                    for ii in xrange(len(other)):
                        ax0graph(xaxes,other[ii],color=[max(0.0,1.0-(2*ii/imax)**2),0.5*((2*ii-imax)/imax)**2,max(0.0,1.0-(2*(ii-imax)/imax)**2)],label="graph "+str(ii+1),linestyle='-',linewidth=1.0,zorder=-ii)
                    if("error" in kwargs):
439
                        error = self._enforce_values(np.absolute(kwargs.get("error")),extend=True)
Marco Selig's avatar
Marco Selig committed
440
441
442
443
444
445
                        ax0.fill_between(xaxes,x-error,x+error,color=[0.8,0.8,0.8],label="error 0",zorder=-len(other))
                    if(legend):
                        ax0.legend()
                else:
                    ax0graph(xaxes,x,color=[0.0,0.5,0.0],label="graph 0",linestyle='-',linewidth=2.0,zorder=1)
                    if("error" in kwargs):
446
                        error = self._enforce_values(np.absolute(kwargs.get("error")),extend=True)
Marco Selig's avatar
Marco Selig committed
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
                        ax0.fill_between(xaxes,x-error,x+error,color=[0.8,0.8,0.8],label="error 0",zorder=0)

                ax0.set_xlim(xaxes[0],xaxes[-1])
                ax0.set_xlabel("coordinate")
                ax0.set_ylim(vmin,vmax)
                if(unit):
                    unit = " ["+unit+"]"
                ax0.set_ylabel("values"+unit)
                ax0.set_title(title)

            elif(naxes==2):
                if(np.iscomplexobj(x)):
                    about.infos.cprint("INFO: absolute values and phases are plotted.")
                    if(title):
                        title += " "
                    if(bool(kwargs.get("save",False))):
                        save_ = os.path.splitext(os.path.basename(str(kwargs.get("save"))))
                        kwargs.update(save=save_[0]+"_absolute"+save_[1])
                    self.get_plot(np.absolute(x),title=title+"(absolute)",vmin=vmin,vmax=vmax,power=False,unit=unit,norm=norm,cmap=cmap,cbar=cbar,other=None,legend=False,**kwargs)
#                    self.get_plot(np.real(x),title=title+"(real part)",vmin=vmin,vmax=vmax,power=False,unit=unit,norm=norm,cmap=cmap,cbar=cbar,other=None,legend=False,**kwargs)
#                    self.get_plot(np.imag(x),title=title+"(imaginary part)",vmin=vmin,vmax=vmax,power=False,unit=unit,norm=norm,cmap=cmap,cbar=cbar,other=None,legend=False,**kwargs)
                    if(unit):
                        unit = "rad"
                    if(cmap is None):
                        cmap = pl.cm.hsv_r
                    if(bool(kwargs.get("save",False))):
                        kwargs.update(save=save_[0]+"_phase"+save_[1])
                    self.get_plot(np.angle(x,deg=False),title=title+"(phase)",vmin=-3.1416,vmax=3.1416,power=False,unit=unit,norm=None,cmap=cmap,cbar=cbar,other=None,legend=False,**kwargs) ## values in [-pi,pi]
                    return None ## leave method
                else:
                    if(vmin is None):
                        vmin = np.min(x,axis=None,out=None)
                    if(vmax is None):
                        vmax = np.max(x,axis=None,out=None)
                    if(norm=="log")and(vmin<=0):
                        raise ValueError(about._errors.cstring("ERROR: nonpositive value(s)."))

484
                    s_ = np.array([self.para[1]*self.distances[1]/np.max(self.para[:naxes]*self.distances,axis=None,out=None),self.para[0]*self.distances[0]/np.max(self.para[:naxes]*self.distances,axis=None,out=None)*(1.0+0.159*bool(cbar))])
Marco Selig's avatar
Marco Selig committed
485
486
487
                    fig = pl.figure(num=None,figsize=(6.4*s_[0],6.4*s_[1]),dpi=None,facecolor="none",edgecolor="none",frameon=False,FigureClass=pl.Figure)
                    ax0 = fig.add_axes([0.06/s_[0],0.06/s_[1],1.0-0.12/s_[0],1.0-0.12/s_[1]])

488
489
                    xaxes = (np.arange(self.para[1]+1,dtype=np.int)-0.5+self.para[4]*(self.para[1]//2))*self.distances[1]
                    yaxes = (np.arange(self.para[0]+1,dtype=np.int)-0.5+self.para[3]*(self.para[0]//2))*self.distances[0]
Marco Selig's avatar
Marco Selig committed
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
                    if(norm=="log"):
                        n_ = ln(vmin=vmin,vmax=vmax)
                    else:
                        n_ = None
                    sub = ax0.pcolormesh(xaxes,yaxes,x,cmap=cmap,norm=n_,vmin=vmin,vmax=vmax)
                    ax0.set_xlim(xaxes[0],xaxes[-1])
                    ax0.set_xticks([0],minor=False)
                    ax0.set_ylim(yaxes[0],yaxes[-1])
                    ax0.set_yticks([0],minor=False)
                    ax0.set_aspect("equal")
                    if(cbar):
                        if(norm=="log"):
                            f_ = lf(10,labelOnlyBase=False)
                            b_ = sub.norm.inverse(np.linspace(0,1,sub.cmap.N+1))
                            v_ = np.linspace(sub.norm.vmin,sub.norm.vmax,sub.cmap.N)
                        else:
                            f_ = None
                            b_ = None
                            v_ = None
                        cb0 = fig.colorbar(sub,ax=ax0,orientation="horizontal",fraction=0.1,pad=0.05,shrink=0.75,aspect=20,ticks=[vmin,vmax],format=f_,drawedges=False,boundaries=b_,values=v_)
                        cb0.ax.text(0.5,-1.0,unit,fontdict=None,withdash=False,transform=cb0.ax.transAxes,horizontalalignment="center",verticalalignment="center")
                    ax0.set_title(title)

            else:
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
                raise ValueError(about._errors.cstring("ERROR: unsupported number of axes ( "+str(naxes)+" > 2 )."))

        if(bool(kwargs.get("save",False))):
            fig.savefig(str(kwargs.get("save")),dpi=None,facecolor="none",edgecolor="none",orientation="portrait",papertype=None,format=None,transparent=False,bbox_inches=None,pad_inches=0.1)
            pl.close(fig)
        else:
            fig.canvas.draw()


    def _enforce_values(self, x, extend=True):
        """
            Computes valid field values from a given object, taking care of
            data types, shape, and symmetry.

            Parameters
            ----------
            x : {float, numpy.ndarray, nifty.field}
                Object to be transformed into an array of valid field values.

            Returns
            -------
            x : numpy.ndarray
                Array containing the valid field values.

            Other parameters
            ----------------
            extend : bool, *optional*
                Whether a scalar is extented to a constant array or not
                (default: True).
        """
544
545
        from nifty.field import Field

546
547
        about.warnings.cflush(
            "WARNING: _enforce_values is deprecated function. Please use self.cast")
548
        if(isinstance(x, Field)):
549
550
551
552
553
554
555
556
557
558
559
560
561
            if(self == x.domain):
                if(self.dtype is not x.domain.dtype):
                    raise TypeError(about._errors.cstring("ERROR: inequal data types ( '" + str(
                        np.result_type(self.dtype)) + "' <> '" + str(np.result_type(x.domain.dtype)) + "' )."))
                else:
                    x = np.copy(x.val)
            else:
                raise ValueError(about._errors.cstring(
                    "ERROR: inequal domains."))
        else:
            if(np.size(x) == 1):
                if(extend):
                    x = self.dtype(
562
                        x) * np.ones(self.dim_split, dtype=self.dtype, order='C')
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
                else:
                    if(np.isscalar(x)):
                        x = np.array([x], dtype=self.dtype)
                    else:
                        x = np.array(x, dtype=self.dtype)
            else:
                x = self.enforce_shape(np.array(x, dtype=self.dtype))

        # hermitianize if ...
        if(about.hermitianize.status)and(np.size(x) != 1)and(self.para[(np.size(self.para) - 1) // 2] == 1):
            #x = gp.nhermitianize_fast(x,self.para[-((np.size(self.para)-1)//2):].astype(np.bool),special=False)
            x = utilities.hermitianize(x)
        # check finiteness
        if(not np.all(np.isfinite(x))):
            about.warnings.cprint("WARNING: infinite value(s).")

        return x