This file is indexed.

/usr/share/pyshared/mpmath/ctx_base.py is in python-mpmath 0.18-1.

This file is owned by root:root, with mode 0o644.

The actual contents of the file can be viewed below.

  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
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
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
200
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
from operator import gt, lt

from .libmp.backend import xrange

from .functions.functions import SpecialFunctions
from .functions.rszeta import RSCache
from .calculus.quadrature import QuadratureMethods
from .calculus.calculus import CalculusMethods
from .calculus.optimization import OptimizationMethods
from .calculus.odes import ODEMethods
from .matrices.matrices import MatrixMethods
from .matrices.calculus import MatrixCalculusMethods
from .matrices.linalg import LinearAlgebraMethods
from .matrices.eigen import Eigen
from .identification import IdentificationMethods
from .visualization import VisualizationMethods

from . import libmp

class Context(object):
    pass

class StandardBaseContext(Context,
    SpecialFunctions,
    RSCache,
    QuadratureMethods,
    CalculusMethods,
    MatrixMethods,
    MatrixCalculusMethods,
    LinearAlgebraMethods,
    Eigen,
    IdentificationMethods,
    OptimizationMethods,
    ODEMethods,
    VisualizationMethods):

    NoConvergence = libmp.NoConvergence
    ComplexResult = libmp.ComplexResult

    def __init__(ctx):
        ctx._aliases = {}
        # Call those that need preinitialization (e.g. for wrappers)
        SpecialFunctions.__init__(ctx)
        RSCache.__init__(ctx)
        QuadratureMethods.__init__(ctx)
        CalculusMethods.__init__(ctx)
        MatrixMethods.__init__(ctx)

    def _init_aliases(ctx):
        for alias, value in ctx._aliases.items():
            try:
                setattr(ctx, alias, getattr(ctx, value))
            except AttributeError:
                pass

    _fixed_precision = False

    # XXX
    verbose = False

    def warn(ctx, msg):
        print("Warning:", msg)

    def bad_domain(ctx, msg):
        raise ValueError(msg)

    def _re(ctx, x):
        if hasattr(x, "real"):
            return x.real
        return x

    def _im(ctx, x):
        if hasattr(x, "imag"):
            return x.imag
        return ctx.zero

    def _as_points(ctx, x):
        return x

    def fneg(ctx, x, **kwargs):
        return -ctx.convert(x)

    def fadd(ctx, x, y, **kwargs):
        return ctx.convert(x)+ctx.convert(y)

    def fsub(ctx, x, y, **kwargs):
        return ctx.convert(x)-ctx.convert(y)

    def fmul(ctx, x, y, **kwargs):
        return ctx.convert(x)*ctx.convert(y)

    def fdiv(ctx, x, y, **kwargs):
        return ctx.convert(x)/ctx.convert(y)

    def fsum(ctx, args, absolute=False, squared=False):
        if absolute:
            if squared:
                return sum((abs(x)**2 for x in args), ctx.zero)
            return sum((abs(x) for x in args), ctx.zero)
        if squared:
            return sum((x**2 for x in args), ctx.zero)
        return sum(args, ctx.zero)

    def fdot(ctx, xs, ys=None, conjugate=False):
        if ys is not None:
            xs = zip(xs, ys)
        if conjugate:
            cf = ctx.conj
            return sum((x*cf(y) for (x,y) in xs), ctx.zero)
        else:
            return sum((x*y for (x,y) in xs), ctx.zero)

    def fprod(ctx, args):
        prod = ctx.one
        for arg in args:
            prod *= arg
        return prod

    def nprint(ctx, x, n=6, **kwargs):
        """
        Equivalent to ``print(nstr(x, n))``.
        """
        print(ctx.nstr(x, n, **kwargs))

    def chop(ctx, x, tol=None):
        """
        Chops off small real or imaginary parts, or converts
        numbers close to zero to exact zeros. The input can be a
        single number or an iterable::

            >>> from mpmath import *
            >>> mp.dps = 15; mp.pretty = False
            >>> chop(5+1e-10j, tol=1e-9)
            mpf('5.0')
            >>> nprint(chop([1.0, 1e-20, 3+1e-18j, -4, 2]))
            [1.0, 0.0, 3.0, -4.0, 2.0]

        The tolerance defaults to ``100*eps``.
        """
        if tol is None:
            tol = 100*ctx.eps
        try:
            x = ctx.convert(x)
            absx = abs(x)
            if abs(x) < tol:
                return ctx.zero
            if ctx._is_complex_type(x):
                #part_tol = min(tol, absx*tol)
                part_tol = max(tol, absx*tol)
                if abs(x.imag) < part_tol:
                    return x.real
                if abs(x.real) < part_tol:
                    return ctx.mpc(0, x.imag)
        except TypeError:
            if isinstance(x, ctx.matrix):
                return x.apply(lambda a: ctx.chop(a, tol))
            if hasattr(x, "__iter__"):
                return [ctx.chop(a, tol) for a in x]
        return x

    def almosteq(ctx, s, t, rel_eps=None, abs_eps=None):
        r"""
        Determine whether the difference between `s` and `t` is smaller
        than a given epsilon, either relatively or absolutely.

        Both a maximum relative difference and a maximum difference
        ('epsilons') may be specified. The absolute difference is
        defined as `|s-t|` and the relative difference is defined
        as `|s-t|/\max(|s|, |t|)`.

        If only one epsilon is given, both are set to the same value.
        If none is given, both epsilons are set to `2^{-p+m}` where
        `p` is the current working precision and `m` is a small
        integer. The default setting typically allows :func:`~mpmath.almosteq`
        to be used to check for mathematical equality
        in the presence of small rounding errors.

        **Examples**

            >>> from mpmath import *
            >>> mp.dps = 15
            >>> almosteq(3.141592653589793, 3.141592653589790)
            True
            >>> almosteq(3.141592653589793, 3.141592653589700)
            False
            >>> almosteq(3.141592653589793, 3.141592653589700, 1e-10)
            True
            >>> almosteq(1e-20, 2e-20)
            True
            >>> almosteq(1e-20, 2e-20, rel_eps=0, abs_eps=0)
            False

        """
        t = ctx.convert(t)
        if abs_eps is None and rel_eps is None:
            rel_eps = abs_eps = ctx.ldexp(1, -ctx.prec+4)
        if abs_eps is None:
            abs_eps = rel_eps
        elif rel_eps is None:
            rel_eps = abs_eps
        diff = abs(s-t)
        if diff <= abs_eps:
            return True
        abss = abs(s)
        abst = abs(t)
        if abss < abst:
            err = diff/abst
        else:
            err = diff/abss
        return err <= rel_eps

    def arange(ctx, *args):
        r"""
        This is a generalized version of Python's :func:`~mpmath.range` function
        that accepts fractional endpoints and step sizes and
        returns a list of ``mpf`` instances. Like :func:`~mpmath.range`,
        :func:`~mpmath.arange` can be called with 1, 2 or 3 arguments:

        ``arange(b)``
            `[0, 1, 2, \ldots, x]`
        ``arange(a, b)``
            `[a, a+1, a+2, \ldots, x]`
        ``arange(a, b, h)``
            `[a, a+h, a+h, \ldots, x]`

        where `b-1 \le x < b` (in the third case, `b-h \le x < b`).

        Like Python's :func:`~mpmath.range`, the endpoint is not included. To
        produce ranges where the endpoint is included, :func:`~mpmath.linspace`
        is more convenient.

        **Examples**

            >>> from mpmath import *
            >>> mp.dps = 15; mp.pretty = False
            >>> arange(4)
            [mpf('0.0'), mpf('1.0'), mpf('2.0'), mpf('3.0')]
            >>> arange(1, 2, 0.25)
            [mpf('1.0'), mpf('1.25'), mpf('1.5'), mpf('1.75')]
            >>> arange(1, -1, -0.75)
            [mpf('1.0'), mpf('0.25'), mpf('-0.5')]

        """
        if not len(args) <= 3:
            raise TypeError('arange expected at most 3 arguments, got %i'
                            % len(args))
        if not len(args) >= 1:
            raise TypeError('arange expected at least 1 argument, got %i'
                            % len(args))
        # set default
        a = 0
        dt = 1
        # interpret arguments
        if len(args) == 1:
            b = args[0]
        elif len(args) >= 2:
            a = args[0]
            b = args[1]
        if len(args) == 3:
            dt = args[2]
        a, b, dt = ctx.mpf(a), ctx.mpf(b), ctx.mpf(dt)
        assert a + dt != a, 'dt is too small and would cause an infinite loop'
        # adapt code for sign of dt
        if a > b:
            if dt > 0:
                return []
            op = gt
        else:
            if dt < 0:
                return []
            op = lt
        # create list
        result = []
        i = 0
        t = a
        while 1:
            t = a + dt*i
            i += 1
            if op(t, b):
                result.append(t)
            else:
                break
        return result

    def linspace(ctx, *args, **kwargs):
        """
        ``linspace(a, b, n)`` returns a list of `n` evenly spaced
        samples from `a` to `b`. The syntax ``linspace(mpi(a,b), n)``
        is also valid.

        This function is often more convenient than :func:`~mpmath.arange`
        for partitioning an interval into subintervals, since
        the endpoint is included::

            >>> from mpmath import *
            >>> mp.dps = 15; mp.pretty = False
            >>> linspace(1, 4, 4)
            [mpf('1.0'), mpf('2.0'), mpf('3.0'), mpf('4.0')]

        You may also provide the keyword argument ``endpoint=False``::

            >>> linspace(1, 4, 4, endpoint=False)
            [mpf('1.0'), mpf('1.75'), mpf('2.5'), mpf('3.25')]

        """
        if len(args) == 3:
            a = ctx.mpf(args[0])
            b = ctx.mpf(args[1])
            n = int(args[2])
        elif len(args) == 2:
            assert hasattr(args[0], '_mpi_')
            a = args[0].a
            b = args[0].b
            n = int(args[1])
        else:
            raise TypeError('linspace expected 2 or 3 arguments, got %i' \
                            % len(args))
        if n < 1:
            raise ValueError('n must be greater than 0')
        if not 'endpoint' in kwargs or kwargs['endpoint']:
            if n == 1:
                return [ctx.mpf(a)]
            step = (b - a) / ctx.mpf(n - 1)
            y = [i*step + a for i in xrange(n)]
            y[-1] = b
        else:
            step = (b - a) / ctx.mpf(n)
            y = [i*step + a for i in xrange(n)]
        return y

    def cos_sin(ctx, z, **kwargs):
        return ctx.cos(z, **kwargs), ctx.sin(z, **kwargs)

    def cospi_sinpi(ctx, z, **kwargs):
        return ctx.cospi(z, **kwargs), ctx.sinpi(z, **kwargs)

    def _default_hyper_maxprec(ctx, p):
        return int(1000 * p**0.25 + 4*p)

    _gcd = staticmethod(libmp.gcd)
    list_primes = staticmethod(libmp.list_primes)
    isprime = staticmethod(libmp.isprime)
    bernfrac = staticmethod(libmp.bernfrac)
    moebius = staticmethod(libmp.moebius)
    _ifac = staticmethod(libmp.ifac)
    _eulernum = staticmethod(libmp.eulernum)
    _stirling1 = staticmethod(libmp.stirling1)
    _stirling2 = staticmethod(libmp.stirling2)

    def sum_accurately(ctx, terms, check_step=1):
        prec = ctx.prec
        try:
            extraprec = 10
            while 1:
                ctx.prec = prec + extraprec + 5
                max_mag = ctx.ninf
                s = ctx.zero
                k = 0
                for term in terms():
                    s += term
                    if (not k % check_step) and term:
                        term_mag = ctx.mag(term)
                        max_mag = max(max_mag, term_mag)
                        sum_mag = ctx.mag(s)
                        if sum_mag - term_mag > ctx.prec:
                            break
                    k += 1
                cancellation = max_mag - sum_mag
                if cancellation != cancellation:
                    break
                if cancellation < extraprec or ctx._fixed_precision:
                    break
                extraprec += min(ctx.prec, cancellation)
            return s
        finally:
            ctx.prec = prec

    def mul_accurately(ctx, factors, check_step=1):
        prec = ctx.prec
        try:
            extraprec = 10
            while 1:
                ctx.prec = prec + extraprec + 5
                max_mag = ctx.ninf
                one = ctx.one
                s = one
                k = 0
                for factor in factors():
                    s *= factor
                    term = factor - one
                    if (not k % check_step):
                        term_mag = ctx.mag(term)
                        max_mag = max(max_mag, term_mag)
                        sum_mag = ctx.mag(s-one)
                        #if sum_mag - term_mag > ctx.prec:
                        #    break
                        if -term_mag > ctx.prec:
                            break
                    k += 1
                cancellation = max_mag - sum_mag
                if cancellation != cancellation:
                    break
                if cancellation < extraprec or ctx._fixed_precision:
                    break
                extraprec += min(ctx.prec, cancellation)
            return s
        finally:
            ctx.prec = prec

    def power(ctx, x, y):
        r"""Converts `x` and `y` to mpmath numbers and evaluates
        `x^y = \exp(y \log(x))`::

            >>> from mpmath import *
            >>> mp.dps = 30; mp.pretty = True
            >>> power(2, 0.5)
            1.41421356237309504880168872421

        This shows the leading few digits of a large Mersenne prime
        (performing the exact calculation ``2**43112609-1`` and
        displaying the result in Python would be very slow)::

            >>> power(2, 43112609)-1
            3.16470269330255923143453723949e+12978188
        """
        return ctx.convert(x) ** ctx.convert(y)

    def _zeta_int(ctx, n):
        return ctx.zeta(n)

    def maxcalls(ctx, f, N):
        """
        Return a wrapped copy of *f* that raises ``NoConvergence`` when *f*
        has been called more than *N* times::

            >>> from mpmath import *
            >>> mp.dps = 15
            >>> f = maxcalls(sin, 10)
            >>> print(sum(f(n) for n in range(10)))
            1.95520948210738
            >>> f(10)
            Traceback (most recent call last):
              ...
            NoConvergence: maxcalls: function evaluated 10 times

        """
        counter = [0]
        def f_maxcalls_wrapped(*args, **kwargs):
            counter[0] += 1
            if counter[0] > N:
                raise ctx.NoConvergence("maxcalls: function evaluated %i times" % N)
            return f(*args, **kwargs)
        return f_maxcalls_wrapped

    def memoize(ctx, f):
        """
        Return a wrapped copy of *f* that caches computed values, i.e.
        a memoized copy of *f*. Values are only reused if the cached precision
        is equal to or higher than the working precision::

            >>> from mpmath import *
            >>> mp.dps = 15; mp.pretty = True
            >>> f = memoize(maxcalls(sin, 1))
            >>> f(2)
            0.909297426825682
            >>> f(2)
            0.909297426825682
            >>> mp.dps = 25
            >>> f(2)
            Traceback (most recent call last):
              ...
            NoConvergence: maxcalls: function evaluated 1 times

        """
        f_cache = {}
        def f_cached(*args, **kwargs):
            if kwargs:
                key = args, tuple(kwargs.items())
            else:
                key = args
            prec = ctx.prec
            if key in f_cache:
                cprec, cvalue = f_cache[key]
                if cprec >= prec:
                    return +cvalue
            value = f(*args, **kwargs)
            f_cache[key] = (prec, value)
            return value
        f_cached.__name__ = f.__name__
        f_cached.__doc__ = f.__doc__
        return f_cached