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ckpts/universal/global_step80/zero/11.mlp.dense_h_to_4h_swiglu.weight/exp_avg.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:dcccbe15e31fde6fd37986a1e18172c87d943d4d52f10f471b05ceb2fe0f1bdf
+size 33555612

ckpts/universal/global_step80/zero/9.mlp.dense_h_to_4h_swiglu.weight/exp_avg.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e12f74df9fb92ff8bfa3f23d6bdc7be15ac673a0f8be266e60ad6fe54a78a242
+size 33555612

venv/lib/python3.10/site-packages/markupsafe/__init__.py

@@ -0,0 +1,332 @@
+import functools
+import string
+import sys
+import typing as t
+
+if t.TYPE_CHECKING:
+    import typing_extensions as te
+
+    class HasHTML(te.Protocol):
+        def __html__(self) -> str:
+            pass
+
+    _P = te.ParamSpec("_P")
+
+
+__version__ = "2.1.5"
+
+
+def _simple_escaping_wrapper(func: "t.Callable[_P, str]") -> "t.Callable[_P, Markup]":
+    @functools.wraps(func)
+    def wrapped(self: "Markup", *args: "_P.args", **kwargs: "_P.kwargs") -> "Markup":
+        arg_list = _escape_argspec(list(args), enumerate(args), self.escape)
+        _escape_argspec(kwargs, kwargs.items(), self.escape)
+        return self.__class__(func(self, *arg_list, **kwargs))  # type: ignore[arg-type]
+
+    return wrapped  # type: ignore[return-value]
+
+
+class Markup(str):
+    """A string that is ready to be safely inserted into an HTML or XML
+    document, either because it was escaped or because it was marked
+    safe.
+
+    Passing an object to the constructor converts it to text and wraps
+    it to mark it safe without escaping. To escape the text, use the
+    :meth:`escape` class method instead.
+
+    >>> Markup("Hello, <em>World</em>!")
+    Markup('Hello, <em>World</em>!')
+    >>> Markup(42)
+    Markup('42')
+    >>> Markup.escape("Hello, <em>World</em>!")
+    Markup('Hello &lt;em&gt;World&lt;/em&gt;!')
+
+    This implements the ``__html__()`` interface that some frameworks
+    use. Passing an object that implements ``__html__()`` will wrap the
+    output of that method, marking it safe.
+
+    >>> class Foo:
+    ...     def __html__(self):
+    ...         return '<a href="/foo">foo</a>'
+    ...
+    >>> Markup(Foo())
+    Markup('<a href="/foo">foo</a>')
+
+    This is a subclass of :class:`str`. It has the same methods, but
+    escapes their arguments and returns a ``Markup`` instance.
+
+    >>> Markup("<em>%s</em>") % ("foo & bar",)
+    Markup('<em>foo &amp; bar</em>')
+    >>> Markup("<em>Hello</em> ") + "<foo>"
+    Markup('<em>Hello</em> &lt;foo&gt;')
+    """
+
+    __slots__ = ()
+
+    def __new__(
+        cls, base: t.Any = "", encoding: t.Optional[str] = None, errors: str = "strict"
+    ) -> "te.Self":
+        if hasattr(base, "__html__"):
+            base = base.__html__()
+
+        if encoding is None:
+            return super().__new__(cls, base)
+
+        return super().__new__(cls, base, encoding, errors)
+
+    def __html__(self) -> "te.Self":
+        return self
+
+    def __add__(self, other: t.Union[str, "HasHTML"]) -> "te.Self":
+        if isinstance(other, str) or hasattr(other, "__html__"):
+            return self.__class__(super().__add__(self.escape(other)))
+
+        return NotImplemented
+
+    def __radd__(self, other: t.Union[str, "HasHTML"]) -> "te.Self":
+        if isinstance(other, str) or hasattr(other, "__html__"):
+            return self.escape(other).__add__(self)
+
+        return NotImplemented
+
+    def __mul__(self, num: "te.SupportsIndex") -> "te.Self":
+        if isinstance(num, int):
+            return self.__class__(super().__mul__(num))
+
+        return NotImplemented
+
+    __rmul__ = __mul__
+
+    def __mod__(self, arg: t.Any) -> "te.Self":
+        if isinstance(arg, tuple):
+            # a tuple of arguments, each wrapped
+            arg = tuple(_MarkupEscapeHelper(x, self.escape) for x in arg)
+        elif hasattr(type(arg), "__getitem__") and not isinstance(arg, str):
+            # a mapping of arguments, wrapped
+            arg = _MarkupEscapeHelper(arg, self.escape)
+        else:
+            # a single argument, wrapped with the helper and a tuple
+            arg = (_MarkupEscapeHelper(arg, self.escape),)
+
+        return self.__class__(super().__mod__(arg))
+
+    def __repr__(self) -> str:
+        return f"{self.__class__.__name__}({super().__repr__()})"
+
+    def join(self, seq: t.Iterable[t.Union[str, "HasHTML"]]) -> "te.Self":
+        return self.__class__(super().join(map(self.escape, seq)))
+
+    join.__doc__ = str.join.__doc__
+
+    def split(  # type: ignore[override]
+        self, sep: t.Optional[str] = None, maxsplit: int = -1
+    ) -> t.List["te.Self"]:
+        return [self.__class__(v) for v in super().split(sep, maxsplit)]
+
+    split.__doc__ = str.split.__doc__
+
+    def rsplit(  # type: ignore[override]
+        self, sep: t.Optional[str] = None, maxsplit: int = -1
+    ) -> t.List["te.Self"]:
+        return [self.__class__(v) for v in super().rsplit(sep, maxsplit)]
+
+    rsplit.__doc__ = str.rsplit.__doc__
+
+    def splitlines(  # type: ignore[override]
+        self, keepends: bool = False
+    ) -> t.List["te.Self"]:
+        return [self.__class__(v) for v in super().splitlines(keepends)]
+
+    splitlines.__doc__ = str.splitlines.__doc__
+
+    def unescape(self) -> str:
+        """Convert escaped markup back into a text string. This replaces
+        HTML entities with the characters they represent.
+
+        >>> Markup("Main &raquo; <em>About</em>").unescape()
+        'Main » <em>About</em>'
+        """
+        from html import unescape
+
+        return unescape(str(self))
+
+    def striptags(self) -> str:
+        """:meth:`unescape` the markup, remove tags, and normalize
+        whitespace to single spaces.
+
+        >>> Markup("Main &raquo;\t<em>About</em>").striptags()
+        'Main » About'
+        """
+        value = str(self)
+
+        # Look for comments then tags separately. Otherwise, a comment that
+        # contains a tag would end early, leaving some of the comment behind.
+
+        while True:
+            # keep finding comment start marks
+            start = value.find("<!--")
+
+            if start == -1:
+                break
+
+            # find a comment end mark beyond the start, otherwise stop
+            end = value.find("-->", start)
+
+            if end == -1:
+                break
+
+            value = f"{value[:start]}{value[end + 3:]}"
+
+        # remove tags using the same method
+        while True:
+            start = value.find("<")
+
+            if start == -1:
+                break
+
+            end = value.find(">", start)
+
+            if end == -1:
+                break
+
+            value = f"{value[:start]}{value[end + 1:]}"
+
+        # collapse spaces
+        value = " ".join(value.split())
+        return self.__class__(value).unescape()
+
+    @classmethod
+    def escape(cls, s: t.Any) -> "te.Self":
+        """Escape a string. Calls :func:`escape` and ensures that for
+        subclasses the correct type is returned.
+        """
+        rv = escape(s)
+
+        if rv.__class__ is not cls:
+            return cls(rv)
+
+        return rv  # type: ignore[return-value]
+
+    __getitem__ = _simple_escaping_wrapper(str.__getitem__)
+    capitalize = _simple_escaping_wrapper(str.capitalize)
+    title = _simple_escaping_wrapper(str.title)
+    lower = _simple_escaping_wrapper(str.lower)
+    upper = _simple_escaping_wrapper(str.upper)
+    replace = _simple_escaping_wrapper(str.replace)
+    ljust = _simple_escaping_wrapper(str.ljust)
+    rjust = _simple_escaping_wrapper(str.rjust)
+    lstrip = _simple_escaping_wrapper(str.lstrip)
+    rstrip = _simple_escaping_wrapper(str.rstrip)
+    center = _simple_escaping_wrapper(str.center)
+    strip = _simple_escaping_wrapper(str.strip)
+    translate = _simple_escaping_wrapper(str.translate)
+    expandtabs = _simple_escaping_wrapper(str.expandtabs)
+    swapcase = _simple_escaping_wrapper(str.swapcase)
+    zfill = _simple_escaping_wrapper(str.zfill)
+    casefold = _simple_escaping_wrapper(str.casefold)
+
+    if sys.version_info >= (3, 9):
+        removeprefix = _simple_escaping_wrapper(str.removeprefix)
+        removesuffix = _simple_escaping_wrapper(str.removesuffix)
+
+    def partition(self, sep: str) -> t.Tuple["te.Self", "te.Self", "te.Self"]:
+        l, s, r = super().partition(self.escape(sep))
+        cls = self.__class__
+        return cls(l), cls(s), cls(r)
+
+    def rpartition(self, sep: str) -> t.Tuple["te.Self", "te.Self", "te.Self"]:
+        l, s, r = super().rpartition(self.escape(sep))
+        cls = self.__class__
+        return cls(l), cls(s), cls(r)
+
+    def format(self, *args: t.Any, **kwargs: t.Any) -> "te.Self":
+        formatter = EscapeFormatter(self.escape)
+        return self.__class__(formatter.vformat(self, args, kwargs))
+
+    def format_map(  # type: ignore[override]
+        self, map: t.Mapping[str, t.Any]
+    ) -> "te.Self":
+        formatter = EscapeFormatter(self.escape)
+        return self.__class__(formatter.vformat(self, (), map))
+
+    def __html_format__(self, format_spec: str) -> "te.Self":
+        if format_spec:
+            raise ValueError("Unsupported format specification for Markup.")
+
+        return self
+
+
+class EscapeFormatter(string.Formatter):
+    __slots__ = ("escape",)
+
+    def __init__(self, escape: t.Callable[[t.Any], Markup]) -> None:
+        self.escape = escape
+        super().__init__()
+
+    def format_field(self, value: t.Any, format_spec: str) -> str:
+        if hasattr(value, "__html_format__"):
+            rv = value.__html_format__(format_spec)
+        elif hasattr(value, "__html__"):
+            if format_spec:
+                raise ValueError(
+                    f"Format specifier {format_spec} given, but {type(value)} does not"
+                    " define __html_format__. A class that defines __html__ must define"
+                    " __html_format__ to work with format specifiers."
+                )
+            rv = value.__html__()
+        else:
+            # We need to make sure the format spec is str here as
+            # otherwise the wrong callback methods are invoked.
+            rv = string.Formatter.format_field(self, value, str(format_spec))
+        return str(self.escape(rv))
+
+
+_ListOrDict = t.TypeVar("_ListOrDict", list, dict)
+
+
+def _escape_argspec(
+    obj: _ListOrDict, iterable: t.Iterable[t.Any], escape: t.Callable[[t.Any], Markup]
+) -> _ListOrDict:
+    """Helper for various string-wrapped functions."""
+    for key, value in iterable:
+        if isinstance(value, str) or hasattr(value, "__html__"):
+            obj[key] = escape(value)
+
+    return obj
+
+
+class _MarkupEscapeHelper:
+    """Helper for :meth:`Markup.__mod__`."""
+
+    __slots__ = ("obj", "escape")
+
+    def __init__(self, obj: t.Any, escape: t.Callable[[t.Any], Markup]) -> None:
+        self.obj = obj
+        self.escape = escape
+
+    def __getitem__(self, item: t.Any) -> "te.Self":
+        return self.__class__(self.obj[item], self.escape)
+
+    def __str__(self) -> str:
+        return str(self.escape(self.obj))
+
+    def __repr__(self) -> str:
+        return str(self.escape(repr(self.obj)))
+
+    def __int__(self) -> int:
+        return int(self.obj)
+
+    def __float__(self) -> float:
+        return float(self.obj)
+
+
+# circular import
+try:
+    from ._speedups import escape as escape
+    from ._speedups import escape_silent as escape_silent
+    from ._speedups import soft_str as soft_str
+except ImportError:
+    from ._native import escape as escape
+    from ._native import escape_silent as escape_silent  # noqa: F401
+    from ._native import soft_str as soft_str  # noqa: F401

venv/lib/python3.10/site-packages/markupsafe/_native.py

@@ -0,0 +1,63 @@
+import typing as t
+
+from . import Markup
+
+
+def escape(s: t.Any) -> Markup:
+    """Replace the characters ``&``, ``<``, ``>``, ``'``, and ``"`` in
+    the string with HTML-safe sequences. Use this if you need to display
+    text that might contain such characters in HTML.
+
+    If the object has an ``__html__`` method, it is called and the
+    return value is assumed to already be safe for HTML.
+
+    :param s: An object to be converted to a string and escaped.
+    :return: A :class:`Markup` string with the escaped text.
+    """
+    if hasattr(s, "__html__"):
+        return Markup(s.__html__())
+
+    return Markup(
+        str(s)
+        .replace("&", "&amp;")
+        .replace(">", "&gt;")
+        .replace("<", "&lt;")
+        .replace("'", "&#39;")
+        .replace('"', "&#34;")
+    )
+
+
+def escape_silent(s: t.Optional[t.Any]) -> Markup:
+    """Like :func:`escape` but treats ``None`` as the empty string.
+    Useful with optional values, as otherwise you get the string
+    ``'None'`` when the value is ``None``.
+
+    >>> escape(None)
+    Markup('None')
+    >>> escape_silent(None)
+    Markup('')
+    """
+    if s is None:
+        return Markup()
+
+    return escape(s)
+
+
+def soft_str(s: t.Any) -> str:
+    """Convert an object to a string if it isn't already. This preserves
+    a :class:`Markup` string rather than converting it back to a basic
+    string, so it will still be marked as safe and won't be escaped
+    again.
+
+    >>> value = escape("<User 1>")
+    >>> value
+    Markup('&lt;User 1&gt;')
+    >>> escape(str(value))
+    Markup('&amp;lt;User 1&amp;gt;')
+    >>> escape(soft_str(value))
+    Markup('&lt;User 1&gt;')
+    """
+    if not isinstance(s, str):
+        return str(s)
+
+    return s

venv/lib/python3.10/site-packages/markupsafe/_speedups.c

@@ -0,0 +1,320 @@
+#include <Python.h>
+
+static PyObject* markup;
+
+static int
+init_constants(void)
+{
+	PyObject *module;
+
+	/* import markup type so that we can mark the return value */
+	module = PyImport_ImportModule("markupsafe");
+	if (!module)
+		return 0;
+	markup = PyObject_GetAttrString(module, "Markup");
+	Py_DECREF(module);
+
+	return 1;
+}
+
+#define GET_DELTA(inp, inp_end, delta) \
+	while (inp < inp_end) { \
+		switch (*inp++) { \
+		case '"': \
+		case '\'': \
+		case '&': \
+			delta += 4; \
+			break; \
+		case '<': \
+		case '>': \
+			delta += 3; \
+			break; \
+		} \
+	}
+
+#define DO_ESCAPE(inp, inp_end, outp) \
+	{ \
+		Py_ssize_t ncopy = 0; \
+		while (inp < inp_end) { \
+			switch (*inp) { \
+			case '"': \
+				memcpy(outp, inp-ncopy, sizeof(*outp)*ncopy); \
+				outp += ncopy; ncopy = 0; \
+				*outp++ = '&'; \
+				*outp++ = '#'; \
+				*outp++ = '3'; \
+				*outp++ = '4'; \
+				*outp++ = ';'; \
+				break; \
+			case '\'': \
+				memcpy(outp, inp-ncopy, sizeof(*outp)*ncopy); \
+				outp += ncopy; ncopy = 0; \
+				*outp++ = '&'; \
+				*outp++ = '#'; \
+				*outp++ = '3'; \
+				*outp++ = '9'; \
+				*outp++ = ';'; \
+				break; \
+			case '&': \
+				memcpy(outp, inp-ncopy, sizeof(*outp)*ncopy); \
+				outp += ncopy; ncopy = 0; \
+				*outp++ = '&'; \
+				*outp++ = 'a'; \
+				*outp++ = 'm'; \
+				*outp++ = 'p'; \
+				*outp++ = ';'; \
+				break; \
+			case '<': \
+				memcpy(outp, inp-ncopy, sizeof(*outp)*ncopy); \
+				outp += ncopy; ncopy = 0; \
+				*outp++ = '&'; \
+				*outp++ = 'l'; \
+				*outp++ = 't'; \
+				*outp++ = ';'; \
+				break; \
+			case '>': \
+				memcpy(outp, inp-ncopy, sizeof(*outp)*ncopy); \
+				outp += ncopy; ncopy = 0; \
+				*outp++ = '&'; \
+				*outp++ = 'g'; \
+				*outp++ = 't'; \
+				*outp++ = ';'; \
+				break; \
+			default: \
+				ncopy++; \
+			} \
+			inp++; \
+		} \
+		memcpy(outp, inp-ncopy, sizeof(*outp)*ncopy); \
+	}
+
+static PyObject*
+escape_unicode_kind1(PyUnicodeObject *in)
+{
+	Py_UCS1 *inp = PyUnicode_1BYTE_DATA(in);
+	Py_UCS1 *inp_end = inp + PyUnicode_GET_LENGTH(in);
+	Py_UCS1 *outp;
+	PyObject *out;
+	Py_ssize_t delta = 0;
+
+	GET_DELTA(inp, inp_end, delta);
+	if (!delta) {
+		Py_INCREF(in);
+		return (PyObject*)in;
+	}
+
+	out = PyUnicode_New(PyUnicode_GET_LENGTH(in) + delta,
+						PyUnicode_IS_ASCII(in) ? 127 : 255);
+	if (!out)
+		return NULL;
+
+	inp = PyUnicode_1BYTE_DATA(in);
+	outp = PyUnicode_1BYTE_DATA(out);
+	DO_ESCAPE(inp, inp_end, outp);
+	return out;
+}
+
+static PyObject*
+escape_unicode_kind2(PyUnicodeObject *in)
+{
+	Py_UCS2 *inp = PyUnicode_2BYTE_DATA(in);
+	Py_UCS2 *inp_end = inp + PyUnicode_GET_LENGTH(in);
+	Py_UCS2 *outp;
+	PyObject *out;
+	Py_ssize_t delta = 0;
+
+	GET_DELTA(inp, inp_end, delta);
+	if (!delta) {
+		Py_INCREF(in);
+		return (PyObject*)in;
+	}
+
+	out = PyUnicode_New(PyUnicode_GET_LENGTH(in) + delta, 65535);
+	if (!out)
+		return NULL;
+
+	inp = PyUnicode_2BYTE_DATA(in);
+	outp = PyUnicode_2BYTE_DATA(out);
+	DO_ESCAPE(inp, inp_end, outp);
+	return out;
+}
+
+
+static PyObject*
+escape_unicode_kind4(PyUnicodeObject *in)
+{
+	Py_UCS4 *inp = PyUnicode_4BYTE_DATA(in);
+	Py_UCS4 *inp_end = inp + PyUnicode_GET_LENGTH(in);
+	Py_UCS4 *outp;
+	PyObject *out;
+	Py_ssize_t delta = 0;
+
+	GET_DELTA(inp, inp_end, delta);
+	if (!delta) {
+		Py_INCREF(in);
+		return (PyObject*)in;
+	}
+
+	out = PyUnicode_New(PyUnicode_GET_LENGTH(in) + delta, 1114111);
+	if (!out)
+		return NULL;
+
+	inp = PyUnicode_4BYTE_DATA(in);
+	outp = PyUnicode_4BYTE_DATA(out);
+	DO_ESCAPE(inp, inp_end, outp);
+	return out;
+}
+
+static PyObject*
+escape_unicode(PyUnicodeObject *in)
+{
+	if (PyUnicode_READY(in))
+		return NULL;
+
+	switch (PyUnicode_KIND(in)) {
+	case PyUnicode_1BYTE_KIND:
+		return escape_unicode_kind1(in);
+	case PyUnicode_2BYTE_KIND:
+		return escape_unicode_kind2(in);
+	case PyUnicode_4BYTE_KIND:
+		return escape_unicode_kind4(in);
+	}
+	assert(0);  /* shouldn't happen */
+	return NULL;
+}
+
+static PyObject*
+escape(PyObject *self, PyObject *text)
+{
+	static PyObject *id_html;
+	PyObject *s = NULL, *rv = NULL, *html;
+
+	if (id_html == NULL) {
+		id_html = PyUnicode_InternFromString("__html__");
+		if (id_html == NULL) {
+			return NULL;
+		}
+	}
+
+	/* we don't have to escape integers, bools or floats */
+	if (PyLong_CheckExact(text) ||
+		PyFloat_CheckExact(text) || PyBool_Check(text) ||
+		text == Py_None)
+		return PyObject_CallFunctionObjArgs(markup, text, NULL);
+
+	/* if the object has an __html__ method that performs the escaping */
+	html = PyObject_GetAttr(text ,id_html);
+	if (html) {
+		s = PyObject_CallObject(html, NULL);
+		Py_DECREF(html);
+		if (s == NULL) {
+			return NULL;
+		}
+		/* Convert to Markup object */
+		rv = PyObject_CallFunctionObjArgs(markup, (PyObject*)s, NULL);
+		Py_DECREF(s);
+		return rv;
+	}
+
+	/* otherwise make the object unicode if it isn't, then escape */
+	PyErr_Clear();
+	if (!PyUnicode_Check(text)) {
+		PyObject *unicode = PyObject_Str(text);
+		if (!unicode)
+			return NULL;
+		s = escape_unicode((PyUnicodeObject*)unicode);
+		Py_DECREF(unicode);
+	}
+	else
+		s = escape_unicode((PyUnicodeObject*)text);
+
+	/* convert the unicode string into a markup object. */
+	rv = PyObject_CallFunctionObjArgs(markup, (PyObject*)s, NULL);
+	Py_DECREF(s);
+	return rv;
+}
+
+
+static PyObject*
+escape_silent(PyObject *self, PyObject *text)
+{
+	if (text != Py_None)
+		return escape(self, text);
+	return PyObject_CallFunctionObjArgs(markup, NULL);
+}
+
+
+static PyObject*
+soft_str(PyObject *self, PyObject *s)
+{
+	if (!PyUnicode_Check(s))
+		return PyObject_Str(s);
+	Py_INCREF(s);
+	return s;
+}
+
+
+static PyMethodDef module_methods[] = {
+	{
+		"escape",
+		(PyCFunction)escape,
+		METH_O,
+		"Replace the characters ``&``, ``<``, ``>``, ``'``, and ``\"`` in"
+		" the string with HTML-safe sequences. Use this if you need to display"
+		" text that might contain such characters in HTML.\n\n"
+		"If the object has an ``__html__`` method, it is called and the"
+		" return value is assumed to already be safe for HTML.\n\n"
+		":param s: An object to be converted to a string and escaped.\n"
+		":return: A :class:`Markup` string with the escaped text.\n"
+	},
+	{
+		"escape_silent",
+		(PyCFunction)escape_silent,
+		METH_O,
+		"Like :func:`escape` but treats ``None`` as the empty string."
+		" Useful with optional values, as otherwise you get the string"
+		" ``'None'`` when the value is ``None``.\n\n"
+		">>> escape(None)\n"
+		"Markup('None')\n"
+		">>> escape_silent(None)\n"
+		"Markup('')\n"
+	},
+	{
+		"soft_str",
+		(PyCFunction)soft_str,
+		METH_O,
+		"Convert an object to a string if it isn't already. This preserves"
+		" a :class:`Markup` string rather than converting it back to a basic"
+		" string, so it will still be marked as safe and won't be escaped"
+		" again.\n\n"
+		">>> value = escape(\"<User 1>\")\n"
+		">>> value\n"
+		"Markup('&lt;User 1&gt;')\n"
+		">>> escape(str(value))\n"
+		"Markup('&amp;lt;User 1&amp;gt;')\n"
+		">>> escape(soft_str(value))\n"
+		"Markup('&lt;User 1&gt;')\n"
+	},
+	{NULL, NULL, 0, NULL}  /* Sentinel */
+};
+
+static struct PyModuleDef module_definition = {
+	PyModuleDef_HEAD_INIT,
+	"markupsafe._speedups",
+	NULL,
+	-1,
+	module_methods,
+	NULL,
+	NULL,
+	NULL,
+	NULL
+};
+
+PyMODINIT_FUNC
+PyInit__speedups(void)
+{
+	if (!init_constants())
+		return NULL;
+
+	return PyModule_Create(&module_definition);
+}

venv/lib/python3.10/site-packages/markupsafe/_speedups.pyi

@@ -0,0 +1,9 @@
+from typing import Any
+from typing import Optional
+
+from . import Markup
+
+def escape(s: Any) -> Markup: ...
+def escape_silent(s: Optional[Any]) -> Markup: ...
+def soft_str(s: Any) -> str: ...
+def soft_unicode(s: Any) -> str: ...

venv/lib/python3.10/site-packages/mpmath/calculus/__init__.py

@@ -0,0 +1,6 @@
+from . import calculus
+# XXX: hack to set methods
+from . import approximation
+from . import differentiation
+from . import extrapolation
+from . import polynomials

venv/lib/python3.10/site-packages/mpmath/calculus/approximation.py

@@ -0,0 +1,246 @@
+from ..libmp.backend import xrange
+from .calculus import defun
+
+#----------------------------------------------------------------------------#
+#                              Approximation methods                         #
+#----------------------------------------------------------------------------#
+
+# The Chebyshev approximation formula is given at:
+# http://mathworld.wolfram.com/ChebyshevApproximationFormula.html
+
+# The only major changes in the following code is that we return the
+# expanded polynomial coefficients instead of Chebyshev coefficients,
+# and that we automatically transform [a,b] -> [-1,1] and back
+# for convenience.
+
+# Coefficient in Chebyshev approximation
+def chebcoeff(ctx,f,a,b,j,N):
+    s = ctx.mpf(0)
+    h = ctx.mpf(0.5)
+    for k in range(1, N+1):
+        t = ctx.cospi((k-h)/N)
+        s += f(t*(b-a)*h + (b+a)*h) * ctx.cospi(j*(k-h)/N)
+    return 2*s/N
+
+# Generate Chebyshev polynomials T_n(ax+b) in expanded form
+def chebT(ctx, a=1, b=0):
+    Tb = [1]
+    yield Tb
+    Ta = [b, a]
+    while 1:
+        yield Ta
+        # Recurrence: T[n+1](ax+b) = 2*(ax+b)*T[n](ax+b) - T[n-1](ax+b)
+        Tmp = [0] + [2*a*t for t in Ta]
+        for i, c in enumerate(Ta): Tmp[i] += 2*b*c
+        for i, c in enumerate(Tb): Tmp[i] -= c
+        Ta, Tb = Tmp, Ta
+
+@defun
+def chebyfit(ctx, f, interval, N, error=False):
+    r"""
+    Computes a polynomial of degree `N-1` that approximates the
+    given function `f` on the interval `[a, b]`. With ``error=True``,
+    :func:`~mpmath.chebyfit` also returns an accurate estimate of the
+    maximum absolute error; that is, the maximum value of
+    `|f(x) - P(x)|` for `x \in [a, b]`.
+
+    :func:`~mpmath.chebyfit` uses the Chebyshev approximation formula,
+    which gives a nearly optimal solution: that is, the maximum
+    error of the approximating polynomial is very close to
+    the smallest possible for any polynomial of the same degree.
+
+    Chebyshev approximation is very useful if one needs repeated
+    evaluation of an expensive function, such as function defined
+    implicitly by an integral or a differential equation. (For
+    example, it could be used to turn a slow mpmath function
+    into a fast machine-precision version of the same.)
+
+    **Examples**
+
+    Here we use :func:`~mpmath.chebyfit` to generate a low-degree approximation
+    of `f(x) = \cos(x)`, valid on the interval `[1, 2]`::
+
+        >>> from mpmath import *
+        >>> mp.dps = 15; mp.pretty = True
+        >>> poly, err = chebyfit(cos, [1, 2], 5, error=True)
+        >>> nprint(poly)
+        [0.00291682, 0.146166, -0.732491, 0.174141, 0.949553]
+        >>> nprint(err, 12)
+        1.61351758081e-5
+
+    The polynomial can be evaluated using ``polyval``::
+
+        >>> nprint(polyval(poly, 1.6), 12)
+        -0.0291858904138
+        >>> nprint(cos(1.6), 12)
+        -0.0291995223013
+
+    Sampling the true error at 1000 points shows that the error
+    estimate generated by ``chebyfit`` is remarkably good::
+
+        >>> error = lambda x: abs(cos(x) - polyval(poly, x))
+        >>> nprint(max([error(1+n/1000.) for n in range(1000)]), 12)
+        1.61349954245e-5
+
+    **Choice of degree**
+
+    The degree `N` can be set arbitrarily high, to obtain an
+    arbitrarily good approximation. As a rule of thumb, an
+    `N`-term Chebyshev approximation is good to `N/(b-a)` decimal
+    places on a unit interval (although this depends on how
+    well-behaved `f` is). The cost grows accordingly: ``chebyfit``
+    evaluates the function `(N^2)/2` times to compute the
+    coefficients and an additional `N` times to estimate the error.
+
+    **Possible issues**
+
+    One should be careful to use a sufficiently high working
+    precision both when calling ``chebyfit`` and when evaluating
+    the resulting polynomial, as the polynomial is sometimes
+    ill-conditioned. It is for example difficult to reach
+    15-digit accuracy when evaluating the polynomial using
+    machine precision floats, no matter the theoretical
+    accuracy of the polynomial. (The option to return the
+    coefficients in Chebyshev form should be made available
+    in the future.)
+
+    It is important to note the Chebyshev approximation works
+    poorly if `f` is not smooth. A function containing singularities,
+    rapid oscillation, etc can be approximated more effectively by
+    multiplying it by a weight function that cancels out the
+    nonsmooth features, or by dividing the interval into several
+    segments.
+    """
+    a, b = ctx._as_points(interval)
+    orig = ctx.prec
+    try:
+        ctx.prec = orig + int(N**0.5) + 20
+        c = [chebcoeff(ctx,f,a,b,k,N) for k in range(N)]
+        d = [ctx.zero] * N
+        d[0] = -c[0]/2
+        h = ctx.mpf(0.5)
+        T = chebT(ctx, ctx.mpf(2)/(b-a), ctx.mpf(-1)*(b+a)/(b-a))
+        for (k, Tk) in zip(range(N), T):
+            for i in range(len(Tk)):
+                d[i] += c[k]*Tk[i]
+        d = d[::-1]
+        # Estimate maximum error
+        err = ctx.zero
+        for k in range(N):
+            x = ctx.cos(ctx.pi*k/N) * (b-a)*h + (b+a)*h
+            err = max(err, abs(f(x) - ctx.polyval(d, x)))
+    finally:
+        ctx.prec = orig
+    if error:
+        return d, +err
+    else:
+        return d
+
+@defun
+def fourier(ctx, f, interval, N):
+    r"""
+    Computes the Fourier series of degree `N` of the given function
+    on the interval `[a, b]`. More precisely, :func:`~mpmath.fourier` returns
+    two lists `(c, s)` of coefficients (the cosine series and sine
+    series, respectively), such that
+
+    .. math ::
+
+        f(x) \sim \sum_{k=0}^N
+            c_k \cos(k m x) + s_k \sin(k m x)
+
+    where `m = 2 \pi / (b-a)`.
+
+    Note that many texts define the first coefficient as `2 c_0` instead
+    of `c_0`. The easiest way to evaluate the computed series correctly
+    is to pass it to :func:`~mpmath.fourierval`.
+
+    **Examples**
+
+    The function `f(x) = x` has a simple Fourier series on the standard
+    interval `[-\pi, \pi]`. The cosine coefficients are all zero (because
+    the function has odd symmetry), and the sine coefficients are
+    rational numbers::
+
+        >>> from mpmath import *
+        >>> mp.dps = 15; mp.pretty = True
+        >>> c, s = fourier(lambda x: x, [-pi, pi], 5)
+        >>> nprint(c)
+        [0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
+        >>> nprint(s)
+        [0.0, 2.0, -1.0, 0.666667, -0.5, 0.4]
+
+    This computes a Fourier series of a nonsymmetric function on
+    a nonstandard interval::
+
+        >>> I = [-1, 1.5]
+        >>> f = lambda x: x**2 - 4*x + 1
+        >>> cs = fourier(f, I, 4)
+        >>> nprint(cs[0])
+        [0.583333, 1.12479, -1.27552, 0.904708, -0.441296]
+        >>> nprint(cs[1])
+        [0.0, -2.6255, 0.580905, 0.219974, -0.540057]
+
+    It is instructive to plot a function along with its truncated
+    Fourier series::
+
+        >>> plot([f, lambda x: fourierval(cs, I, x)], I) #doctest: +SKIP
+
+    Fourier series generally converge slowly (and may not converge
+    pointwise). For example, if `f(x) = \cosh(x)`, a 10-term Fourier
+    series gives an `L^2` error corresponding to 2-digit accuracy::
+
+        >>> I = [-1, 1]
+        >>> cs = fourier(cosh, I, 9)
+        >>> g = lambda x: (cosh(x) - fourierval(cs, I, x))**2
+        >>> nprint(sqrt(quad(g, I)))
+        0.00467963
+
+    :func:`~mpmath.fourier` uses numerical quadrature. For nonsmooth functions,
+    the accuracy (and speed) can be improved by including all singular
+    points in the interval specification::
+
+        >>> nprint(fourier(abs, [-1, 1], 0), 10)
+        ([0.5000441648], [0.0])
+        >>> nprint(fourier(abs, [-1, 0, 1], 0), 10)
+        ([0.5], [0.0])
+
+    """
+    interval = ctx._as_points(interval)
+    a = interval[0]
+    b = interval[-1]
+    L = b-a
+    cos_series = []
+    sin_series = []
+    cutoff = ctx.eps*10
+    for n in xrange(N+1):
+        m = 2*n*ctx.pi/L
+        an = 2*ctx.quadgl(lambda t: f(t)*ctx.cos(m*t), interval)/L
+        bn = 2*ctx.quadgl(lambda t: f(t)*ctx.sin(m*t), interval)/L
+        if n == 0:
+            an /= 2
+        if abs(an) < cutoff: an = ctx.zero
+        if abs(bn) < cutoff: bn = ctx.zero
+        cos_series.append(an)
+        sin_series.append(bn)
+    return cos_series, sin_series
+
+@defun
+def fourierval(ctx, series, interval, x):
+    """
+    Evaluates a Fourier series (in the format computed by
+    by :func:`~mpmath.fourier` for the given interval) at the point `x`.
+
+    The series should be a pair `(c, s)` where `c` is the
+    cosine series and `s` is the sine series. The two lists
+    need not have the same length.
+    """
+    cs, ss = series
+    ab = ctx._as_points(interval)
+    a = interval[0]
+    b = interval[-1]
+    m = 2*ctx.pi/(ab[-1]-ab[0])
+    s = ctx.zero
+    s += ctx.fsum(cs[n]*ctx.cos(m*n*x) for n in xrange(len(cs)) if cs[n])
+    s += ctx.fsum(ss[n]*ctx.sin(m*n*x) for n in xrange(len(ss)) if ss[n])
+    return s

venv/lib/python3.10/site-packages/mpmath/calculus/calculus.py

@@ -0,0 +1,6 @@
+class CalculusMethods(object):
+    pass
+
+def defun(f):
+    setattr(CalculusMethods, f.__name__, f)
+    return f

venv/lib/python3.10/site-packages/mpmath/calculus/differentiation.py

@@ -0,0 +1,647 @@
+from ..libmp.backend import xrange
+from .calculus import defun
+
+try:
+    iteritems = dict.iteritems
+except AttributeError:
+    iteritems = dict.items
+
+#----------------------------------------------------------------------------#
+#                                Differentiation                             #
+#----------------------------------------------------------------------------#
+
+@defun
+def difference(ctx, s, n):
+    r"""
+    Given a sequence `(s_k)` containing at least `n+1` items, returns the
+    `n`-th forward difference,
+
+    .. math ::
+
+        \Delta^n = \sum_{k=0}^{\infty} (-1)^{k+n} {n \choose k} s_k.
+    """
+    n = int(n)
+    d = ctx.zero
+    b = (-1) ** (n & 1)
+    for k in xrange(n+1):
+        d += b * s[k]
+        b = (b * (k-n)) // (k+1)
+    return d
+
+def hsteps(ctx, f, x, n, prec, **options):
+    singular = options.get('singular')
+    addprec = options.get('addprec', 10)
+    direction = options.get('direction', 0)
+    workprec = (prec+2*addprec) * (n+1)
+    orig = ctx.prec
+    try:
+        ctx.prec = workprec
+        h = options.get('h')
+        if h is None:
+            if options.get('relative'):
+                hextramag = int(ctx.mag(x))
+            else:
+                hextramag = 0
+            h = ctx.ldexp(1, -prec-addprec-hextramag)
+        else:
+            h = ctx.convert(h)
+        # Directed: steps x, x+h, ... x+n*h
+        direction = options.get('direction', 0)
+        if direction:
+            h *= ctx.sign(direction)
+            steps = xrange(n+1)
+            norm = h
+        # Central: steps x-n*h, x-(n-2)*h ..., x, ..., x+(n-2)*h, x+n*h
+        else:
+            steps = xrange(-n, n+1, 2)
+            norm = (2*h)
+        # Perturb
+        if singular:
+            x += 0.5*h
+        values = [f(x+k*h) for k in steps]
+        return values, norm, workprec
+    finally:
+        ctx.prec = orig
+
+
+@defun
+def diff(ctx, f, x, n=1, **options):
+    r"""
+    Numerically computes the derivative of `f`, `f'(x)`, or generally for
+    an integer `n \ge 0`, the `n`-th derivative `f^{(n)}(x)`.
+    A few basic examples are::
+
+        >>> from mpmath import *
+        >>> mp.dps = 15; mp.pretty = True
+        >>> diff(lambda x: x**2 + x, 1.0)
+        3.0
+        >>> diff(lambda x: x**2 + x, 1.0, 2)
+        2.0
+        >>> diff(lambda x: x**2 + x, 1.0, 3)
+        0.0
+        >>> nprint([diff(exp, 3, n) for n in range(5)])   # exp'(x) = exp(x)
+        [20.0855, 20.0855, 20.0855, 20.0855, 20.0855]
+
+    Even more generally, given a tuple of arguments `(x_1, \ldots, x_k)`
+    and order `(n_1, \ldots, n_k)`, the partial derivative
+    `f^{(n_1,\ldots,n_k)}(x_1,\ldots,x_k)` is evaluated. For example::
+
+        >>> diff(lambda x,y: 3*x*y + 2*y - x, (0.25, 0.5), (0,1))
+        2.75
+        >>> diff(lambda x,y: 3*x*y + 2*y - x, (0.25, 0.5), (1,1))
+        3.0
+
+    **Options**
+
+    The following optional keyword arguments are recognized:
+
+    ``method``
+        Supported methods are ``'step'`` or ``'quad'``: derivatives may be
+        computed using either a finite difference with a small step
+        size `h` (default), or numerical quadrature.
+    ``direction``
+        Direction of finite difference: can be -1 for a left
+        difference, 0 for a central difference (default), or +1
+        for a right difference; more generally can be any complex number.
+    ``addprec``
+        Extra precision for `h` used to account for the function's
+        sensitivity to perturbations (default = 10).
+    ``relative``
+        Choose `h` relative to the magnitude of `x`, rather than an
+        absolute value; useful for large or tiny `x` (default = False).
+    ``h``
+        As an alternative to ``addprec`` and ``relative``, manually
+        select the step size `h`.
+    ``singular``
+        If True, evaluation exactly at the point `x` is avoided; this is
+        useful for differentiating functions with removable singularities.
+        Default = False.
+    ``radius``
+        Radius of integration contour (with ``method = 'quad'``).
+        Default = 0.25. A larger radius typically is faster and more
+        accurate, but it must be chosen so that `f` has no
+        singularities within the radius from the evaluation point.
+
+    A finite difference requires `n+1` function evaluations and must be
+    performed at `(n+1)` times the target precision. Accordingly, `f` must
+    support fast evaluation at high precision.
+
+    With integration, a larger number of function evaluations is
+    required, but not much extra precision is required. For high order
+    derivatives, this method may thus be faster if f is very expensive to
+    evaluate at high precision.
+
+    **Further examples**
+
+    The direction option is useful for computing left- or right-sided
+    derivatives of nonsmooth functions::
+
+        >>> diff(abs, 0, direction=0)
+        0.0
+        >>> diff(abs, 0, direction=1)
+        1.0
+        >>> diff(abs, 0, direction=-1)
+        -1.0
+
+    More generally, if the direction is nonzero, a right difference
+    is computed where the step size is multiplied by sign(direction).
+    For example, with direction=+j, the derivative from the positive
+    imaginary direction will be computed::
+
+        >>> diff(abs, 0, direction=j)
+        (0.0 - 1.0j)
+
+    With integration, the result may have a small imaginary part
+    even even if the result is purely real::
+
+        >>> diff(sqrt, 1, method='quad')    # doctest:+ELLIPSIS
+        (0.5 - 4.59...e-26j)
+        >>> chop(_)
+        0.5
+
+    Adding precision to obtain an accurate value::
+
+        >>> diff(cos, 1e-30)
+        0.0
+        >>> diff(cos, 1e-30, h=0.0001)
+        -9.99999998328279e-31
+        >>> diff(cos, 1e-30, addprec=100)
+        -1.0e-30
+
+    """
+    partial = False
+    try:
+        orders = list(n)
+        x = list(x)
+        partial = True
+    except TypeError:
+        pass
+    if partial:
+        x = [ctx.convert(_) for _ in x]
+        return _partial_diff(ctx, f, x, orders, options)
+    method = options.get('method', 'step')
+    if n == 0 and method != 'quad' and not options.get('singular'):
+        return f(ctx.convert(x))
+    prec = ctx.prec
+    try:
+        if method == 'step':
+            values, norm, workprec = hsteps(ctx, f, x, n, prec, **options)
+            ctx.prec = workprec
+            v = ctx.difference(values, n) / norm**n
+        elif method == 'quad':
+            ctx.prec += 10
+            radius = ctx.convert(options.get('radius', 0.25))
+            def g(t):
+                rei = radius*ctx.expj(t)
+                z = x + rei
+                return f(z) / rei**n
+            d = ctx.quadts(g, [0, 2*ctx.pi])
+            v = d * ctx.factorial(n) / (2*ctx.pi)
+        else:
+            raise ValueError("unknown method: %r" % method)
+    finally:
+        ctx.prec = prec
+    return +v
+
+def _partial_diff(ctx, f, xs, orders, options):
+    if not orders:
+        return f()
+    if not sum(orders):
+        return f(*xs)
+    i = 0
+    for i in range(len(orders)):
+        if orders[i]:
+            break
+    order = orders[i]
+    def fdiff_inner(*f_args):
+        def inner(t):
+            return f(*(f_args[:i] + (t,) + f_args[i+1:]))
+        return ctx.diff(inner, f_args[i], order, **options)
+    orders[i] = 0
+    return _partial_diff(ctx, fdiff_inner, xs, orders, options)
+
+@defun
+def diffs(ctx, f, x, n=None, **options):
+    r"""
+    Returns a generator that yields the sequence of derivatives
+
+    .. math ::
+
+        f(x), f'(x), f''(x), \ldots, f^{(k)}(x), \ldots
+
+    With ``method='step'``, :func:`~mpmath.diffs` uses only `O(k)`
+    function evaluations to generate the first `k` derivatives,
+    rather than the roughly `O(k^2)` evaluations
+    required if one calls :func:`~mpmath.diff` `k` separate times.
+
+    With `n < \infty`, the generator stops as soon as the
+    `n`-th derivative has been generated. If the exact number of
+    needed derivatives is known in advance, this is further
+    slightly more efficient.
+
+    Options are the same as for :func:`~mpmath.diff`.
+
+    **Examples**
+
+        >>> from mpmath import *
+        >>> mp.dps = 15
+        >>> nprint(list(diffs(cos, 1, 5)))
+        [0.540302, -0.841471, -0.540302, 0.841471, 0.540302, -0.841471]
+        >>> for i, d in zip(range(6), diffs(cos, 1)):
+        ...     print("%s %s" % (i, d))
+        ...
+        0 0.54030230586814
+        1 -0.841470984807897
+        2 -0.54030230586814
+        3 0.841470984807897
+        4 0.54030230586814
+        5 -0.841470984807897
+
+    """
+    if n is None:
+        n = ctx.inf
+    else:
+        n = int(n)
+    if options.get('method', 'step') != 'step':
+        k = 0
+        while k < n + 1:
+            yield ctx.diff(f, x, k, **options)
+            k += 1
+        return
+    singular = options.get('singular')
+    if singular:
+        yield ctx.diff(f, x, 0, singular=True)
+    else:
+        yield f(ctx.convert(x))
+    if n < 1:
+        return
+    if n == ctx.inf:
+        A, B = 1, 2
+    else:
+        A, B = 1, n+1
+    while 1:
+        callprec = ctx.prec
+        y, norm, workprec = hsteps(ctx, f, x, B, callprec, **options)
+        for k in xrange(A, B):
+            try:
+                ctx.prec = workprec
+                d = ctx.difference(y, k) / norm**k
+            finally:
+                ctx.prec = callprec
+            yield +d
+            if k >= n:
+                return
+        A, B = B, int(A*1.4+1)
+        B = min(B, n)
+
+def iterable_to_function(gen):
+    gen = iter(gen)
+    data = []
+    def f(k):
+        for i in xrange(len(data), k+1):
+            data.append(next(gen))
+        return data[k]
+    return f
+
+@defun
+def diffs_prod(ctx, factors):
+    r"""
+    Given a list of `N` iterables or generators yielding
+    `f_k(x), f'_k(x), f''_k(x), \ldots` for `k = 1, \ldots, N`,
+    generate `g(x), g'(x), g''(x), \ldots` where
+    `g(x) = f_1(x) f_2(x) \cdots f_N(x)`.
+
+    At high precision and for large orders, this is typically more efficient
+    than numerical differentiation if the derivatives of each `f_k(x)`
+    admit direct computation.
+
+    Note: This function does not increase the working precision internally,
+    so guard digits may have to be added externally for full accuracy.
+
+    **Examples**
+
+        >>> from mpmath import *
+        >>> mp.dps = 15; mp.pretty = True
+        >>> f = lambda x: exp(x)*cos(x)*sin(x)
+        >>> u = diffs(f, 1)
+        >>> v = mp.diffs_prod([diffs(exp,1), diffs(cos,1), diffs(sin,1)])
+        >>> next(u); next(v)
+        1.23586333600241
+        1.23586333600241
+        >>> next(u); next(v)
+        0.104658952245596
+        0.104658952245596
+        >>> next(u); next(v)
+        -5.96999877552086
+        -5.96999877552086
+        >>> next(u); next(v)
+        -12.4632923122697
+        -12.4632923122697
+
+    """
+    N = len(factors)
+    if N == 1:
+        for c in factors[0]:
+            yield c
+    else:
+        u = iterable_to_function(ctx.diffs_prod(factors[:N//2]))
+        v = iterable_to_function(ctx.diffs_prod(factors[N//2:]))
+        n = 0
+        while 1:
+            #yield sum(binomial(n,k)*u(n-k)*v(k) for k in xrange(n+1))
+            s = u(n) * v(0)
+            a = 1
+            for k in xrange(1,n+1):
+                a = a * (n-k+1) // k
+                s += a * u(n-k) * v(k)
+            yield s
+            n += 1
+
+def dpoly(n, _cache={}):
+    """
+    nth differentiation polynomial for exp (Faa di Bruno's formula).
+
+    TODO: most exponents are zero, so maybe a sparse representation
+    would be better.
+    """
+    if n in _cache:
+        return _cache[n]
+    if not _cache:
+        _cache[0] = {(0,):1}
+    R = dpoly(n-1)
+    R = dict((c+(0,),v) for (c,v) in iteritems(R))
+    Ra = {}
+    for powers, count in iteritems(R):
+        powers1 = (powers[0]+1,) + powers[1:]
+        if powers1 in Ra:
+            Ra[powers1] += count
+        else:
+            Ra[powers1] = count
+    for powers, count in iteritems(R):
+        if not sum(powers):
+            continue
+        for k,p in enumerate(powers):
+            if p:
+                powers2 = powers[:k] + (p-1,powers[k+1]+1) + powers[k+2:]
+                if powers2 in Ra:
+                    Ra[powers2] += p*count
+                else:
+                    Ra[powers2] = p*count
+    _cache[n] = Ra
+    return _cache[n]
+
+@defun
+def diffs_exp(ctx, fdiffs):
+    r"""
+    Given an iterable or generator yielding `f(x), f'(x), f''(x), \ldots`
+    generate `g(x), g'(x), g''(x), \ldots` where `g(x) = \exp(f(x))`.
+
+    At high precision and for large orders, this is typically more efficient
+    than numerical differentiation if the derivatives of `f(x)`
+    admit direct computation.
+
+    Note: This function does not increase the working precision internally,
+    so guard digits may have to be added externally for full accuracy.
+
+    **Examples**
+
+    The derivatives of the gamma function can be computed using
+    logarithmic differentiation::
+
+        >>> from mpmath import *
+        >>> mp.dps = 15; mp.pretty = True
+        >>>
+        >>> def diffs_loggamma(x):
+        ...     yield loggamma(x)
+        ...     i = 0
+        ...     while 1:
+        ...         yield psi(i,x)
+        ...         i += 1
+        ...
+        >>> u = diffs_exp(diffs_loggamma(3))
+        >>> v = diffs(gamma, 3)
+        >>> next(u); next(v)
+        2.0
+        2.0
+        >>> next(u); next(v)
+        1.84556867019693
+        1.84556867019693
+        >>> next(u); next(v)
+        2.49292999190269
+        2.49292999190269
+        >>> next(u); next(v)
+        3.44996501352367
+        3.44996501352367
+
+    """
+    fn = iterable_to_function(fdiffs)
+    f0 = ctx.exp(fn(0))
+    yield f0
+    i = 1
+    while 1:
+        s = ctx.mpf(0)
+        for powers, c in iteritems(dpoly(i)):
+            s += c*ctx.fprod(fn(k+1)**p for (k,p) in enumerate(powers) if p)
+        yield s * f0
+        i += 1
+
+@defun
+def differint(ctx, f, x, n=1, x0=0):
+    r"""
+    Calculates the Riemann-Liouville differintegral, or fractional
+    derivative, defined by
+
+    .. math ::
+
+        \,_{x_0}{\mathbb{D}}^n_xf(x) = \frac{1}{\Gamma(m-n)} \frac{d^m}{dx^m}
+        \int_{x_0}^{x}(x-t)^{m-n-1}f(t)dt
+
+    where `f` is a given (presumably well-behaved) function,
+    `x` is the evaluation point, `n` is the order, and `x_0` is
+    the reference point of integration (`m` is an arbitrary
+    parameter selected automatically).
+
+    With `n = 1`, this is just the standard derivative `f'(x)`; with `n = 2`,
+    the second derivative `f''(x)`, etc. With `n = -1`, it gives
+    `\int_{x_0}^x f(t) dt`, with `n = -2`
+    it gives `\int_{x_0}^x \left( \int_{x_0}^t f(u) du \right) dt`, etc.
+
+    As `n` is permitted to be any number, this operator generalizes
+    iterated differentiation and iterated integration to a single
+    operator with a continuous order parameter.
+
+    **Examples**
+
+    There is an exact formula for the fractional derivative of a
+    monomial `x^p`, which may be used as a reference. For example,
+    the following gives a half-derivative (order 0.5)::
+
+        >>> from mpmath import *
+        >>> mp.dps = 15; mp.pretty = True
+        >>> x = mpf(3); p = 2; n = 0.5
+        >>> differint(lambda t: t**p, x, n)
+        7.81764019044672
+        >>> gamma(p+1)/gamma(p-n+1) * x**(p-n)
+        7.81764019044672
+
+    Another useful test function is the exponential function, whose
+    integration / differentiation formula easy generalizes
+    to arbitrary order. Here we first compute a third derivative,
+    and then a triply nested integral. (The reference point `x_0`
+    is set to `-\infty` to avoid nonzero endpoint terms.)::
+
+        >>> differint(lambda x: exp(pi*x), -1.5, 3)
+        0.278538406900792
+        >>> exp(pi*-1.5) * pi**3
+        0.278538406900792
+        >>> differint(lambda x: exp(pi*x), 3.5, -3, -inf)
+        1922.50563031149
+        >>> exp(pi*3.5) / pi**3
+        1922.50563031149
+
+    However, for noninteger `n`, the differentiation formula for the
+    exponential function must be modified to give the same result as the
+    Riemann-Liouville differintegral::
+
+        >>> x = mpf(3.5)
+        >>> c = pi
+        >>> n = 1+2*j
+        >>> differint(lambda x: exp(c*x), x, n)
+        (-123295.005390743 + 140955.117867654j)
+        >>> x**(-n) * exp(c)**x * (x*c)**n * gammainc(-n, 0, x*c) / gamma(-n)
+        (-123295.005390743 + 140955.117867654j)
+
+
+    """
+    m = max(int(ctx.ceil(ctx.re(n)))+1, 1)
+    r = m-n-1
+    g = lambda x: ctx.quad(lambda t: (x-t)**r * f(t), [x0, x])
+    return ctx.diff(g, x, m) / ctx.gamma(m-n)
+
+@defun
+def diffun(ctx, f, n=1, **options):
+    r"""
+    Given a function `f`, returns a function `g(x)` that evaluates the nth
+    derivative `f^{(n)}(x)`::
+
+        >>> from mpmath import *
+        >>> mp.dps = 15; mp.pretty = True
+        >>> cos2 = diffun(sin)
+        >>> sin2 = diffun(sin, 4)
+        >>> cos(1.3), cos2(1.3)
+        (0.267498828624587, 0.267498828624587)
+        >>> sin(1.3), sin2(1.3)
+        (0.963558185417193, 0.963558185417193)
+
+    The function `f` must support arbitrary precision evaluation.
+    See :func:`~mpmath.diff` for additional details and supported
+    keyword options.
+    """
+    if n == 0:
+        return f
+    def g(x):
+        return ctx.diff(f, x, n, **options)
+    return g
+
+@defun
+def taylor(ctx, f, x, n, **options):
+    r"""
+    Produces a degree-`n` Taylor polynomial around the point `x` of the
+    given function `f`. The coefficients are returned as a list.
+
+        >>> from mpmath import *
+        >>> mp.dps = 15; mp.pretty = True
+        >>> nprint(chop(taylor(sin, 0, 5)))
+        [0.0, 1.0, 0.0, -0.166667, 0.0, 0.00833333]
+
+    The coefficients are computed using high-order numerical
+    differentiation. The function must be possible to evaluate
+    to arbitrary precision. See :func:`~mpmath.diff` for additional details
+    and supported keyword options.
+
+    Note that to evaluate the Taylor polynomial as an approximation
+    of `f`, e.g. with :func:`~mpmath.polyval`, the coefficients must be reversed,
+    and the point of the Taylor expansion must be subtracted from
+    the argument:
+
+        >>> p = taylor(exp, 2.0, 10)
+        >>> polyval(p[::-1], 2.5 - 2.0)
+        12.1824939606092
+        >>> exp(2.5)
+        12.1824939607035
+
+    """
+    gen = enumerate(ctx.diffs(f, x, n, **options))
+    if options.get("chop", True):
+        return [ctx.chop(d)/ctx.factorial(i) for i, d in gen]
+    else:
+        return [d/ctx.factorial(i) for i, d in gen]
+
+@defun
+def pade(ctx, a, L, M):
+    r"""
+    Computes a Pade approximation of degree `(L, M)` to a function.
+    Given at least `L+M+1` Taylor coefficients `a` approximating
+    a function `A(x)`, :func:`~mpmath.pade` returns coefficients of
+    polynomials `P, Q` satisfying
+
+    .. math ::
+
+        P = \sum_{k=0}^L p_k x^k
+
+        Q = \sum_{k=0}^M q_k x^k
+
+        Q_0 = 1
+
+        A(x) Q(x) = P(x) + O(x^{L+M+1})
+
+    `P(x)/Q(x)` can provide a good approximation to an analytic function
+    beyond the radius of convergence of its Taylor series (example
+    from G.A. Baker 'Essentials of Pade Approximants' Academic Press,
+    Ch.1A)::
+
+        >>> from mpmath import *
+        >>> mp.dps = 15; mp.pretty = True
+        >>> one = mpf(1)
+        >>> def f(x):
+        ...     return sqrt((one + 2*x)/(one + x))
+        ...
+        >>> a = taylor(f, 0, 6)
+        >>> p, q = pade(a, 3, 3)
+        >>> x = 10
+        >>> polyval(p[::-1], x)/polyval(q[::-1], x)
+        1.38169105566806
+        >>> f(x)
+        1.38169855941551
+
+    """
+    # To determine L+1 coefficients of P and M coefficients of Q
+    # L+M+1 coefficients of A must be provided
+    if len(a) < L+M+1:
+        raise ValueError("L+M+1 Coefficients should be provided")
+
+    if M == 0:
+        if L == 0:
+            return [ctx.one], [ctx.one]
+        else:
+            return a[:L+1], [ctx.one]
+
+    # Solve first
+    # a[L]*q[1] + ... + a[L-M+1]*q[M] = -a[L+1]
+    # ...
+    # a[L+M-1]*q[1] + ... + a[L]*q[M] = -a[L+M]
+    A = ctx.matrix(M)
+    for j in range(M):
+        for i in range(min(M, L+j+1)):
+            A[j, i] = a[L+j-i]
+    v = -ctx.matrix(a[(L+1):(L+M+1)])
+    x = ctx.lu_solve(A, v)
+    q = [ctx.one] + list(x)
+    # compute p
+    p = [0]*(L+1)
+    for i in range(L+1):
+        s = a[i]
+        for j in range(1, min(M,i) + 1):
+            s += q[j]*a[i-j]
+        p[i] = s
+    return p, q

venv/lib/python3.10/site-packages/mpmath/calculus/extrapolation.py

@@ -0,0 +1,2115 @@
+try:
+    from itertools import izip
+except ImportError:
+    izip = zip
+
+from ..libmp.backend import xrange
+from .calculus import defun
+
+try:
+    next = next
+except NameError:
+    next = lambda _: _.next()
+
+@defun
+def richardson(ctx, seq):
+    r"""
+    Given a list ``seq`` of the first `N` elements of a slowly convergent
+    infinite sequence, :func:`~mpmath.richardson` computes the `N`-term
+    Richardson extrapolate for the limit.
+
+    :func:`~mpmath.richardson` returns `(v, c)` where `v` is the estimated
+    limit and `c` is the magnitude of the largest weight used during the
+    computation. The weight provides an estimate of the precision
+    lost to cancellation. Due to cancellation effects, the sequence must
+    be typically be computed at a much higher precision than the target
+    accuracy of the extrapolation.
+
+    **Applicability and issues**
+
+    The `N`-step Richardson extrapolation algorithm used by
+    :func:`~mpmath.richardson` is described in [1].
+
+    Richardson extrapolation only works for a specific type of sequence,
+    namely one converging like partial sums of
+    `P(1)/Q(1) + P(2)/Q(2) + \ldots` where `P` and `Q` are polynomials.
+    When the sequence does not convergence at such a rate
+    :func:`~mpmath.richardson` generally produces garbage.
+
+    Richardson extrapolation has the advantage of being fast: the `N`-term
+    extrapolate requires only `O(N)` arithmetic operations, and usually
+    produces an estimate that is accurate to `O(N)` digits. Contrast with
+    the Shanks transformation (see :func:`~mpmath.shanks`), which requires
+    `O(N^2)` operations.
+
+    :func:`~mpmath.richardson` is unable to produce an estimate for the
+    approximation error. One way to estimate the error is to perform
+    two extrapolations with slightly different `N` and comparing the
+    results.
+
+    Richardson extrapolation does not work for oscillating sequences.
+    As a simple workaround, :func:`~mpmath.richardson` detects if the last
+    three elements do not differ monotonically, and in that case
+    applies extrapolation only to the even-index elements.
+
+    **Example**
+
+    Applying Richardson extrapolation to the Leibniz series for `\pi`::
+
+        >>> from mpmath import *
+        >>> mp.dps = 30; mp.pretty = True
+        >>> S = [4*sum(mpf(-1)**n/(2*n+1) for n in range(m))
+        ...     for m in range(1,30)]
+        >>> v, c = richardson(S[:10])
+        >>> v
+        3.2126984126984126984126984127
+        >>> nprint([v-pi, c])
+        [0.0711058, 2.0]
+
+        >>> v, c = richardson(S[:30])
+        >>> v
+        3.14159265468624052829954206226
+        >>> nprint([v-pi, c])
+        [1.09645e-9, 20833.3]
+
+    **References**
+
+    1. [BenderOrszag]_ pp. 375-376
+
+    """
+    if len(seq) < 3:
+        raise ValueError("seq should be of minimum length 3")
+    if ctx.sign(seq[-1]-seq[-2]) != ctx.sign(seq[-2]-seq[-3]):
+        seq = seq[::2]
+    N = len(seq)//2-1
+    s = ctx.zero
+    # The general weight is c[k] = (N+k)**N * (-1)**(k+N) / k! / (N-k)!
+    # To avoid repeated factorials, we simplify the quotient
+    # of successive weights to obtain a recurrence relation
+    c = (-1)**N * N**N / ctx.mpf(ctx._ifac(N))
+    maxc = 1
+    for k in xrange(N+1):
+        s += c * seq[N+k]
+        maxc = max(abs(c), maxc)
+        c *= (k-N)*ctx.mpf(k+N+1)**N
+        c /= ((1+k)*ctx.mpf(k+N)**N)
+    return s, maxc
+
+@defun
+def shanks(ctx, seq, table=None, randomized=False):
+    r"""
+    Given a list ``seq`` of the first `N` elements of a slowly
+    convergent infinite sequence `(A_k)`, :func:`~mpmath.shanks` computes the iterated
+    Shanks transformation `S(A), S(S(A)), \ldots, S^{N/2}(A)`. The Shanks
+    transformation often provides strong convergence acceleration,
+    especially if the sequence is oscillating.
+
+    The iterated Shanks transformation is computed using the Wynn
+    epsilon algorithm (see [1]). :func:`~mpmath.shanks` returns the full
+    epsilon table generated by Wynn's algorithm, which can be read
+    off as follows:
+
+    * The table is a list of lists forming a lower triangular matrix,
+      where higher row and column indices correspond to more accurate
+      values.
+    * The columns with even index hold dummy entries (required for the
+      computation) and the columns with odd index hold the actual
+      extrapolates.
+    * The last element in the last row is typically the most
+      accurate estimate of the limit.
+    * The difference to the third last element in the last row
+      provides an estimate of the approximation error.
+    * The magnitude of the second last element provides an estimate
+      of the numerical accuracy lost to cancellation.
+
+    For convenience, so the extrapolation is stopped at an odd index
+    so that ``shanks(seq)[-1][-1]`` always gives an estimate of the
+    limit.
+
+    Optionally, an existing table can be passed to :func:`~mpmath.shanks`.
+    This can be used to efficiently extend a previous computation after
+    new elements have been appended to the sequence. The table will
+    then be updated in-place.
+
+    **The Shanks transformation**
+
+    The Shanks transformation is defined as follows (see [2]): given
+    the input sequence `(A_0, A_1, \ldots)`, the transformed sequence is
+    given by
+
+    .. math ::
+
+        S(A_k) = \frac{A_{k+1}A_{k-1}-A_k^2}{A_{k+1}+A_{k-1}-2 A_k}
+
+    The Shanks transformation gives the exact limit `A_{\infty}` in a
+    single step if `A_k = A + a q^k`. Note in particular that it
+    extrapolates the exact sum of a geometric series in a single step.
+
+    Applying the Shanks transformation once often improves convergence
+    substantially for an arbitrary sequence, but the optimal effect is
+    obtained by applying it iteratively:
+    `S(S(A_k)), S(S(S(A_k))), \ldots`.
+
+    Wynn's epsilon algorithm provides an efficient way to generate
+    the table of iterated Shanks transformations. It reduces the
+    computation of each element to essentially a single division, at
+    the cost of requiring dummy elements in the table. See [1] for
+    details.
+
+    **Precision issues**
+
+    Due to cancellation effects, the sequence must be typically be
+    computed at a much higher precision than the target accuracy
+    of the extrapolation.
+
+    If the Shanks transformation converges to the exact limit (such
+    as if the sequence is a geometric series), then a division by
+    zero occurs. By default, :func:`~mpmath.shanks` handles this case by
+    terminating the iteration and returning the table it has
+    generated so far. With *randomized=True*, it will instead
+    replace the zero by a pseudorandom number close to zero.
+    (TODO: find a better solution to this problem.)
+
+    **Examples**
+
+    We illustrate by applying Shanks transformation to the Leibniz
+    series for `\pi`::
+
+        >>> from mpmath import *
+        >>> mp.dps = 50
+        >>> S = [4*sum(mpf(-1)**n/(2*n+1) for n in range(m))
+        ...     for m in range(1,30)]
+        >>>
+        >>> T = shanks(S[:7])
+        >>> for row in T:
+        ...     nprint(row)
+        ...
+        [-0.75]
+        [1.25, 3.16667]
+        [-1.75, 3.13333, -28.75]
+        [2.25, 3.14524, 82.25, 3.14234]
+        [-2.75, 3.13968, -177.75, 3.14139, -969.937]
+        [3.25, 3.14271, 327.25, 3.14166, 3515.06, 3.14161]
+
+    The extrapolated accuracy is about 4 digits, and about 4 digits
+    may have been lost due to cancellation::
+
+        >>> L = T[-1]
+        >>> nprint([abs(L[-1] - pi), abs(L[-1] - L[-3]), abs(L[-2])])
+        [2.22532e-5, 4.78309e-5, 3515.06]
+
+    Now we extend the computation::
+
+        >>> T = shanks(S[:25], T)
+        >>> L = T[-1]
+        >>> nprint([abs(L[-1] - pi), abs(L[-1] - L[-3]), abs(L[-2])])
+        [3.75527e-19, 1.48478e-19, 2.96014e+17]
+
+    The value for pi is now accurate to 18 digits. About 18 digits may
+    also have been lost to cancellation.
+
+    Here is an example with a geometric series, where the convergence
+    is immediate (the sum is exactly 1)::
+
+        >>> mp.dps = 15
+        >>> for row in shanks([0.5, 0.75, 0.875, 0.9375, 0.96875]):
+        ...     nprint(row)
+        [4.0]
+        [8.0, 1.0]
+
+    **References**
+
+    1. [GravesMorris]_
+
+    2. [BenderOrszag]_ pp. 368-375
+
+    """
+    if len(seq) < 2:
+        raise ValueError("seq should be of minimum length 2")
+    if table:
+        START = len(table)
+    else:
+        START = 0
+        table = []
+    STOP = len(seq) - 1
+    if STOP & 1:
+        STOP -= 1
+    one = ctx.one
+    eps = +ctx.eps
+    if randomized:
+        from random import Random
+        rnd = Random()
+        rnd.seed(START)
+    for i in xrange(START, STOP):
+        row = []
+        for j in xrange(i+1):
+            if j == 0:
+                a, b = 0, seq[i+1]-seq[i]
+            else:
+                if j == 1:
+                    a = seq[i]
+                else:
+                    a = table[i-1][j-2]
+                b = row[j-1] - table[i-1][j-1]
+            if not b:
+                if randomized:
+                    b = (1 + rnd.getrandbits(10))*eps
+                elif i & 1:
+                    return table[:-1]
+                else:
+                    return table
+            row.append(a + one/b)
+        table.append(row)
+    return table
+
+
+class levin_class:
+    # levin: Copyright 2013 Timo Hartmann (thartmann15 at gmail.com)
+    r"""
+    This interface implements Levin's (nonlinear) sequence transformation for
+    convergence acceleration and summation of divergent series. It performs
+    better than the Shanks/Wynn-epsilon algorithm for logarithmic convergent
+    or alternating divergent series.
+
+    Let *A* be the series we want to sum:
+
+    .. math ::
+
+        A = \sum_{k=0}^{\infty} a_k
+
+    Attention: all `a_k` must be non-zero!
+
+    Let `s_n` be the partial sums of this series:
+
+    .. math ::
+
+        s_n = \sum_{k=0}^n a_k.
+
+    **Methods**
+
+    Calling ``levin`` returns an object with the following methods.
+
+    ``update(...)`` works with the list of individual terms `a_k` of *A*, and
+    ``update_step(...)`` works with the list of partial sums `s_k` of *A*:
+
+    .. code ::
+
+        v, e = ...update([a_0, a_1,..., a_k])
+        v, e = ...update_psum([s_0, s_1,..., s_k])
+
+    ``step(...)`` works with the individual terms `a_k` and ``step_psum(...)``
+    works with the partial sums `s_k`:
+
+    .. code ::
+
+        v, e = ...step(a_k)
+        v, e = ...step_psum(s_k)
+
+    *v* is the current estimate for *A*, and *e* is an error estimate which is
+    simply the difference between the current estimate and the last estimate.
+    One should not mix ``update``, ``update_psum``, ``step`` and ``step_psum``.
+
+    **A word of caution**
+
+    One can only hope for good results (i.e. convergence acceleration or
+    resummation) if the `s_n` have some well defind asymptotic behavior for
+    large `n` and are not erratic or random. Furthermore one usually needs very
+    high working precision because of the numerical cancellation. If the working
+    precision is insufficient, levin may produce silently numerical garbage.
+    Furthermore even if the Levin-transformation converges, in the general case
+    there is no proof that the result is mathematically sound. Only for very
+    special classes of problems one can prove that the Levin-transformation
+    converges to the expected result (for example Stieltjes-type integrals).
+    Furthermore the Levin-transform is quite expensive (i.e. slow) in comparison
+    to Shanks/Wynn-epsilon, Richardson & co.
+    In summary one can say that the Levin-transformation is powerful but
+    unreliable and that it may need a copious amount of working precision.
+
+    The Levin transform has several variants differing in the choice of weights.
+    Some variants are better suited for the possible flavours of convergence
+    behaviour of *A* than other variants:
+
+    .. code ::
+
+       convergence behaviour   levin-u   levin-t   levin-v   shanks/wynn-epsilon
+
+       logarithmic               +         -         +           -
+       linear                    +         +         +           +
+       alternating divergent     +         +         +           +
+
+         "+" means the variant is suitable,"-" means the variant is not suitable;
+         for comparison the Shanks/Wynn-epsilon transform is listed, too.
+
+    The variant is controlled though the variant keyword (i.e. ``variant="u"``,
+    ``variant="t"`` or ``variant="v"``). Overall "u" is probably the best choice.
+
+    Finally it is possible to use the Sidi-S transform instead of the Levin transform
+    by using the keyword ``method='sidi'``. The Sidi-S transform works better than the
+    Levin transformation for some divergent series (see the examples).
+
+    Parameters:
+
+    .. code ::
+
+       method      "levin" or "sidi" chooses either the Levin or the Sidi-S transformation
+       variant     "u","t" or "v" chooses the weight variant.
+
+    The Levin transform is also accessible through the nsum interface.
+    ``method="l"`` or ``method="levin"`` select the normal Levin transform while
+    ``method="sidi"``
+    selects the Sidi-S transform. The variant is in both cases selected through the
+    levin_variant keyword. The stepsize in :func:`~mpmath.nsum` must not be chosen too large, otherwise
+    it will miss the point where the Levin transform converges resulting in numerical
+    overflow/garbage. For highly divergent series a copious amount of working precision
+    must be chosen.
+
+    **Examples**
+
+    First we sum the zeta function::
+
+        >>> from mpmath import mp
+        >>> mp.prec = 53
+        >>> eps = mp.mpf(mp.eps)
+        >>> with mp.extraprec(2 * mp.prec): # levin needs a high working precision
+        ...     L = mp.levin(method = "levin", variant = "u")
+        ...     S, s, n = [], 0, 1
+        ...     while 1:
+        ...         s += mp.one / (n * n)
+        ...         n += 1
+        ...         S.append(s)
+        ...         v, e = L.update_psum(S)
+        ...         if e < eps:
+        ...             break
+        ...         if n > 1000: raise RuntimeError("iteration limit exceeded")
+        >>> print(mp.chop(v - mp.pi ** 2 / 6))
+        0.0
+        >>> w = mp.nsum(lambda n: 1 / (n*n), [1, mp.inf], method = "levin", levin_variant = "u")
+        >>> print(mp.chop(v - w))
+        0.0
+
+    Now we sum the zeta function outside its range of convergence
+    (attention: This does not work at the negative integers!)::
+
+        >>> eps = mp.mpf(mp.eps)
+        >>> with mp.extraprec(2 * mp.prec): # levin needs a high working precision
+        ...     L = mp.levin(method = "levin", variant = "v")
+        ...     A, n = [], 1
+        ...     while 1:
+        ...         s = mp.mpf(n) ** (2 + 3j)
+        ...         n += 1
+        ...         A.append(s)
+        ...         v, e = L.update(A)
+        ...         if e < eps:
+        ...             break
+        ...         if n > 1000: raise RuntimeError("iteration limit exceeded")
+        >>> print(mp.chop(v - mp.zeta(-2-3j)))
+        0.0
+        >>> w = mp.nsum(lambda n: n ** (2 + 3j), [1, mp.inf], method = "levin", levin_variant = "v")
+        >>> print(mp.chop(v - w))
+        0.0
+
+    Now we sum the divergent asymptotic expansion of an integral related to the
+    exponential integral (see also [2] p.373). The Sidi-S transform works best here::
+
+        >>> z = mp.mpf(10)
+        >>> exact = mp.quad(lambda x: mp.exp(-x)/(1+x/z),[0,mp.inf])
+        >>> # exact = z * mp.exp(z) * mp.expint(1,z) # this is the symbolic expression for the integral
+        >>> eps = mp.mpf(mp.eps)
+        >>> with mp.extraprec(2 * mp.prec): # high working precisions are mandatory for divergent resummation
+        ...     L = mp.levin(method = "sidi", variant = "t")
+        ...     n = 0
+        ...     while 1:
+        ...         s = (-1)**n * mp.fac(n) * z ** (-n)
+        ...         v, e = L.step(s)
+        ...         n += 1
+        ...         if e < eps:
+        ...             break
+        ...         if n > 1000: raise RuntimeError("iteration limit exceeded")
+        >>> print(mp.chop(v - exact))
+        0.0
+        >>> w = mp.nsum(lambda n: (-1) ** n * mp.fac(n) * z ** (-n), [0, mp.inf], method = "sidi", levin_variant = "t")
+        >>> print(mp.chop(v - w))
+        0.0
+
+    Another highly divergent integral is also summable::
+
+        >>> z = mp.mpf(2)
+        >>> eps = mp.mpf(mp.eps)
+        >>> exact = mp.quad(lambda x: mp.exp( -x * x / 2 - z * x ** 4), [0,mp.inf]) * 2 / mp.sqrt(2 * mp.pi)
+        >>> # exact = mp.exp(mp.one / (32 * z)) * mp.besselk(mp.one / 4, mp.one / (32 * z)) / (4 * mp.sqrt(z * mp.pi)) # this is the symbolic expression for the integral
+        >>> with mp.extraprec(7 * mp.prec):  # we need copious amount of precision to sum this highly divergent series
+        ...     L = mp.levin(method = "levin", variant = "t")
+        ...     n, s = 0, 0
+        ...     while 1:
+        ...         s += (-z)**n * mp.fac(4 * n) / (mp.fac(n) * mp.fac(2 * n) * (4 ** n))
+        ...         n += 1
+        ...         v, e = L.step_psum(s)
+        ...         if e < eps:
+        ...             break
+        ...         if n > 1000: raise RuntimeError("iteration limit exceeded")
+        >>> print(mp.chop(v - exact))
+        0.0
+        >>> w = mp.nsum(lambda n: (-z)**n * mp.fac(4 * n) / (mp.fac(n) * mp.fac(2 * n) * (4 ** n)),
+        ...   [0, mp.inf], method = "levin", levin_variant = "t", workprec = 8*mp.prec, steps = [2] + [1 for x in xrange(1000)])
+        >>> print(mp.chop(v - w))
+        0.0
+
+    These examples run with 15-20 decimal digits precision. For higher precision the
+    working precision must be raised.
+
+    **Examples for nsum**
+
+    Here we calculate Euler's constant as the constant term in the Laurent
+    expansion of `\zeta(s)` at `s=1`. This sum converges extremly slowly because of
+    the logarithmic convergence behaviour of the Dirichlet series for zeta::
+
+        >>> mp.dps = 30
+        >>> z = mp.mpf(10) ** (-10)
+        >>> a = mp.nsum(lambda n: n**(-(1+z)), [1, mp.inf], method = "l") - 1 / z
+        >>> print(mp.chop(a - mp.euler, tol = 1e-10))
+        0.0
+
+    The Sidi-S transform performs excellently for the alternating series of `\log(2)`::
+
+        >>> a = mp.nsum(lambda n: (-1)**(n-1) / n, [1, mp.inf], method = "sidi")
+        >>> print(mp.chop(a - mp.log(2)))
+        0.0
+
+    Hypergeometric series can also be summed outside their range of convergence.
+    The stepsize in :func:`~mpmath.nsum` must not be chosen too large, otherwise it will miss the
+    point where the Levin transform converges resulting in numerical overflow/garbage::
+
+        >>> z = 2 + 1j
+        >>> exact = mp.hyp2f1(2 / mp.mpf(3), 4 / mp.mpf(3), 1 / mp.mpf(3), z)
+        >>> f = lambda n: mp.rf(2 / mp.mpf(3), n) * mp.rf(4 / mp.mpf(3), n) * z**n / (mp.rf(1 / mp.mpf(3), n) * mp.fac(n))
+        >>> v = mp.nsum(f, [0, mp.inf], method = "levin", steps = [10 for x in xrange(1000)])
+        >>> print(mp.chop(exact-v))
+        0.0
+
+    References:
+
+      [1] E.J. Weniger - "Nonlinear Sequence Transformations for the Acceleration of
+          Convergence and the Summation of Divergent Series" arXiv:math/0306302
+
+      [2] A. Sidi - "Pratical Extrapolation Methods"
+
+      [3] H.H.H. Homeier - "Scalar Levin-Type Sequence Transformations" arXiv:math/0005209
+
+    """
+
+    def __init__(self, method = "levin", variant = "u"):
+        self.variant = variant
+        self.n = 0
+        self.a0 = 0
+        self.theta = 1
+        self.A = []
+        self.B = []
+        self.last = 0
+        self.last_s = False
+
+        if method == "levin":
+            self.factor = self.factor_levin
+        elif method == "sidi":
+            self.factor = self.factor_sidi
+        else:
+            raise ValueError("levin: unknown method \"%s\"" % method)
+
+    def factor_levin(self, i):
+        # original levin
+        # [1] p.50,e.7.5-7 (with n-j replaced by i)
+        return (self.theta + i) * (self.theta + self.n - 1) ** (self.n - i - 2) / self.ctx.mpf(self.theta + self.n) ** (self.n - i - 1)
+
+    def factor_sidi(self, i):
+        # sidi analogon to levin (factorial series)
+        # [1] p.59,e.8.3-16 (with n-j replaced by i)
+        return (self.theta + self.n - 1) * (self.theta + self.n - 2) / self.ctx.mpf((self.theta + 2 * self.n - i - 2) * (self.theta + 2 * self.n - i - 3))
+
+    def run(self, s, a0, a1 = 0):
+        if self.variant=="t":
+            # levin t
+            w=a0
+        elif self.variant=="u":
+            # levin u
+            w=a0*(self.theta+self.n)
+        elif self.variant=="v":
+            # levin v
+            w=a0*a1/(a0-a1)
+        else:
+            assert False, "unknown variant"
+
+        if w==0:
+            raise ValueError("levin: zero weight")
+
+        self.A.append(s/w)
+        self.B.append(1/w)
+
+        for i in range(self.n-1,-1,-1):
+            if i==self.n-1:
+                f=1
+            else:
+                f=self.factor(i)
+
+            self.A[i]=self.A[i+1]-f*self.A[i]
+            self.B[i]=self.B[i+1]-f*self.B[i]
+
+        self.n+=1
+
+    ###########################################################################
+
+    def update_psum(self,S):
+        """
+        This routine applies the convergence acceleration to the list of partial sums.
+
+        A   = sum(a_k, k = 0..infinity)
+        s_n = sum(a_k, k = 0..n)
+
+        v, e = ...update_psum([s_0, s_1,..., s_k])
+
+        output:
+          v      current estimate of the series A
+          e      an error estimate which is simply the difference between the current
+                 estimate and the last estimate.
+        """
+
+        if self.variant!="v":
+            if self.n==0:
+                self.run(S[0],S[0])
+            while self.n<len(S):
+                self.run(S[self.n],S[self.n]-S[self.n-1])
+        else:
+            if len(S)==1:
+                self.last=0
+                return S[0],abs(S[0])
+
+            if self.n==0:
+                self.a1=S[1]-S[0]
+                self.run(S[0],S[0],self.a1)
+
+            while self.n<len(S)-1:
+                na1=S[self.n+1]-S[self.n]
+                self.run(S[self.n],self.a1,na1)
+                self.a1=na1
+
+        value=self.A[0]/self.B[0]
+        err=abs(value-self.last)
+        self.last=value
+
+        return value,err
+
+    def update(self,X):
+        """
+        This routine applies the convergence acceleration to the list of individual terms.
+
+        A = sum(a_k, k = 0..infinity)
+
+        v, e = ...update([a_0, a_1,..., a_k])
+
+        output:
+          v      current estimate of the series A
+          e      an error estimate which is simply the difference between the current
+                 estimate and the last estimate.
+        """
+
+        if self.variant!="v":
+            if self.n==0:
+                self.s=X[0]
+                self.run(self.s,X[0])
+            while self.n<len(X):
+                self.s+=X[self.n]
+                self.run(self.s,X[self.n])
+        else:
+            if len(X)==1:
+                self.last=0
+                return X[0],abs(X[0])
+
+            if self.n==0:
+                self.s=X[0]
+                self.run(self.s,X[0],X[1])
+
+            while self.n<len(X)-1:
+                self.s+=X[self.n]
+                self.run(self.s,X[self.n],X[self.n+1])
+
+        value=self.A[0]/self.B[0]
+        err=abs(value-self.last)
+        self.last=value
+
+        return value,err
+
+    ###########################################################################
+
+    def step_psum(self,s):
+        """
+        This routine applies the convergence acceleration to the partial sums.
+
+        A   = sum(a_k, k = 0..infinity)
+        s_n = sum(a_k, k = 0..n)
+
+        v, e = ...step_psum(s_k)
+
+        output:
+          v      current estimate of the series A
+          e      an error estimate which is simply the difference between the current
+                 estimate and the last estimate.
+        """
+
+        if self.variant!="v":
+            if self.n==0:
+                self.last_s=s
+                self.run(s,s)
+            else:
+                self.run(s,s-self.last_s)
+                self.last_s=s
+        else:
+            if isinstance(self.last_s,bool):
+                self.last_s=s
+                self.last_w=s
+                self.last=0
+                return s,abs(s)
+
+            na1=s-self.last_s
+            self.run(self.last_s,self.last_w,na1)
+            self.last_w=na1
+            self.last_s=s
+
+        value=self.A[0]/self.B[0]
+        err=abs(value-self.last)
+        self.last=value
+
+        return value,err
+
+    def step(self,x):
+        """
+        This routine applies the convergence acceleration to the individual terms.
+
+        A = sum(a_k, k = 0..infinity)
+
+        v, e = ...step(a_k)
+
+        output:
+          v      current estimate of the series A
+          e      an error estimate which is simply the difference between the current
+                 estimate and the last estimate.
+        """
+
+        if self.variant!="v":
+            if self.n==0:
+                self.s=x
+                self.run(self.s,x)
+            else:
+                self.s+=x
+                self.run(self.s,x)
+        else:
+            if isinstance(self.last_s,bool):
+                self.last_s=x
+                self.s=0
+                self.last=0
+                return x,abs(x)
+
+            self.s+=self.last_s
+            self.run(self.s,self.last_s,x)
+            self.last_s=x
+
+        value=self.A[0]/self.B[0]
+        err=abs(value-self.last)
+        self.last=value
+
+        return value,err
+
+def levin(ctx, method = "levin", variant = "u"):
+    L = levin_class(method = method, variant = variant)
+    L.ctx = ctx
+    return L
+
+levin.__doc__ = levin_class.__doc__
+defun(levin)
+
+
+class cohen_alt_class:
+    # cohen_alt: Copyright 2013 Timo Hartmann (thartmann15 at gmail.com)
+    r"""
+    This interface implements the convergence acceleration of alternating series
+    as described in H. Cohen, F.R. Villegas, D. Zagier - "Convergence Acceleration
+    of Alternating Series". This series transformation works only well if the
+    individual terms of the series have an alternating sign. It belongs to the
+    class of linear series transformations (in contrast to the Shanks/Wynn-epsilon
+    or Levin transform). This series transformation is also able to sum some types
+    of divergent series. See the paper under which conditions this resummation is
+    mathematical sound.
+
+    Let *A* be the series we want to sum:
+
+    .. math ::
+
+        A = \sum_{k=0}^{\infty} a_k
+
+    Let `s_n` be the partial sums of this series:
+
+    .. math ::
+
+        s_n = \sum_{k=0}^n a_k.
+
+
+    **Interface**
+
+    Calling ``cohen_alt`` returns an object with the following methods.
+
+    Then ``update(...)`` works with the list of individual terms `a_k` and
+    ``update_psum(...)`` works with the list of partial sums `s_k`:
+
+    .. code ::
+
+        v, e = ...update([a_0, a_1,..., a_k])
+        v, e = ...update_psum([s_0, s_1,..., s_k])
+
+    *v* is the current estimate for *A*, and *e* is an error estimate which is
+    simply the difference between the current estimate and the last estimate.
+
+    **Examples**
+
+    Here we compute the alternating zeta function using ``update_psum``::
+
+        >>> from mpmath import mp
+        >>> AC = mp.cohen_alt()
+        >>> S, s, n = [], 0, 1
+        >>> while 1:
+        ...     s += -((-1) ** n) * mp.one / (n * n)
+        ...     n += 1
+        ...     S.append(s)
+        ...     v, e = AC.update_psum(S)
+        ...     if e < mp.eps:
+        ...         break
+        ...     if n > 1000: raise RuntimeError("iteration limit exceeded")
+        >>> print(mp.chop(v - mp.pi ** 2 / 12))
+        0.0
+
+    Here we compute the product `\prod_{n=1}^{\infty} \Gamma(1+1/(2n-1)) / \Gamma(1+1/(2n))`::
+
+        >>> A = []
+        >>> AC = mp.cohen_alt()
+        >>> n = 1
+        >>> while 1:
+        ...     A.append( mp.loggamma(1 + mp.one / (2 * n - 1)))
+        ...     A.append(-mp.loggamma(1 + mp.one / (2 * n)))
+        ...     n += 1
+        ...     v, e = AC.update(A)
+        ...     if e < mp.eps:
+        ...         break
+        ...     if n > 1000: raise RuntimeError("iteration limit exceeded")
+        >>> v = mp.exp(v)
+        >>> print(mp.chop(v - 1.06215090557106, tol = 1e-12))
+        0.0
+
+    ``cohen_alt`` is also accessible through the :func:`~mpmath.nsum` interface::
+
+        >>> v = mp.nsum(lambda n: (-1)**(n-1) / n, [1, mp.inf], method = "a")
+        >>> print(mp.chop(v - mp.log(2)))
+        0.0
+        >>> v = mp.nsum(lambda n: (-1)**n / (2 * n + 1), [0, mp.inf], method = "a")
+        >>> print(mp.chop(v - mp.pi / 4))
+        0.0
+        >>> v = mp.nsum(lambda n: (-1)**n * mp.log(n) * n, [1, mp.inf], method = "a")
+        >>> print(mp.chop(v - mp.diff(lambda s: mp.altzeta(s), -1)))
+        0.0
+
+    """
+
+    def __init__(self):
+        self.last=0
+
+    def update(self, A):
+        """
+        This routine applies the convergence acceleration to the list of individual terms.
+
+        A    = sum(a_k, k = 0..infinity)
+
+        v, e = ...update([a_0, a_1,..., a_k])
+
+        output:
+          v      current estimate of the series A
+          e      an error estimate which is simply the difference between the current
+                 estimate and the last estimate.
+        """
+
+        n = len(A)
+        d = (3 + self.ctx.sqrt(8)) ** n
+        d = (d + 1 / d) / 2
+        b = -self.ctx.one
+        c = -d
+        s = 0
+
+        for k in xrange(n):
+            c = b - c
+            if k % 2 == 0:
+                s = s + c * A[k]
+            else:
+                s = s - c * A[k]
+            b = 2 * (k + n) * (k - n) * b / ((2 * k + 1) * (k + self.ctx.one))
+
+        value = s / d
+
+        err = abs(value - self.last)
+        self.last = value
+
+        return value, err
+
+    def update_psum(self, S):
+        """
+        This routine applies the convergence acceleration to the list of partial sums.
+
+        A   = sum(a_k, k = 0..infinity)
+        s_n = sum(a_k ,k = 0..n)
+
+        v, e = ...update_psum([s_0, s_1,..., s_k])
+
+        output:
+          v      current estimate of the series A
+          e      an error estimate which is simply the difference between the current
+                 estimate and the last estimate.
+        """
+
+        n = len(S)
+        d = (3 + self.ctx.sqrt(8)) ** n
+        d = (d + 1 / d) / 2
+        b = self.ctx.one
+        s = 0
+
+        for k in xrange(n):
+            b = 2 * (n + k) * (n - k) * b / ((2 * k + 1) * (k + self.ctx.one))
+            s += b * S[k]
+
+        value = s / d
+
+        err = abs(value - self.last)
+        self.last = value
+
+        return value, err
+
+def cohen_alt(ctx):
+    L = cohen_alt_class()
+    L.ctx = ctx
+    return L
+
+cohen_alt.__doc__ = cohen_alt_class.__doc__
+defun(cohen_alt)
+
+
+@defun
+def sumap(ctx, f, interval, integral=None, error=False):
+    r"""
+    Evaluates an infinite series of an analytic summand *f* using the
+    Abel-Plana formula
+
+    .. math ::
+
+        \sum_{k=0}^{\infty} f(k) = \int_0^{\infty} f(t) dt + \frac{1}{2} f(0) +
+            i \int_0^{\infty} \frac{f(it)-f(-it)}{e^{2\pi t}-1} dt.
+
+    Unlike the Euler-Maclaurin formula (see :func:`~mpmath.sumem`),
+    the Abel-Plana formula does not require derivatives. However,
+    it only works when `|f(it)-f(-it)|` does not
+    increase too rapidly with `t`.
+
+    **Examples**
+
+    The Abel-Plana formula is particularly useful when the summand
+    decreases like a power of `k`; for example when the sum is a pure
+    zeta function::
+
+        >>> from mpmath import *
+        >>> mp.dps = 25; mp.pretty = True
+        >>> sumap(lambda k: 1/k**2.5, [1,inf])
+        1.34148725725091717975677
+        >>> zeta(2.5)
+        1.34148725725091717975677
+        >>> sumap(lambda k: 1/(k+1j)**(2.5+2.5j), [1,inf])
+        (-3.385361068546473342286084 - 0.7432082105196321803869551j)
+        >>> zeta(2.5+2.5j, 1+1j)
+        (-3.385361068546473342286084 - 0.7432082105196321803869551j)
+
+    If the series is alternating, numerical quadrature along the real
+    line is likely to give poor results, so it is better to evaluate
+    the first term symbolically whenever possible:
+
+        >>> n=3; z=-0.75
+        >>> I = expint(n,-log(z))
+        >>> chop(sumap(lambda k: z**k / k**n, [1,inf], integral=I))
+        -0.6917036036904594510141448
+        >>> polylog(n,z)
+        -0.6917036036904594510141448
+
+    """
+    prec = ctx.prec
+    try:
+        ctx.prec += 10
+        a, b = interval
+        if  b != ctx.inf:
+            raise ValueError("b should be equal to ctx.inf")
+        g = lambda x: f(x+a)
+        if integral is None:
+            i1, err1 = ctx.quad(g, [0,ctx.inf], error=True)
+        else:
+            i1, err1 = integral, 0
+        j = ctx.j
+        p = ctx.pi * 2
+        if ctx._is_real_type(i1):
+            h = lambda t: -2 * ctx.im(g(j*t)) / ctx.expm1(p*t)
+        else:
+            h = lambda t: j*(g(j*t)-g(-j*t)) / ctx.expm1(p*t)
+        i2, err2 = ctx.quad(h, [0,ctx.inf], error=True)
+        err = err1+err2
+        v = i1+i2+0.5*g(ctx.mpf(0))
+    finally:
+        ctx.prec = prec
+    if error:
+        return +v, err
+    return +v
+
+
+@defun
+def sumem(ctx, f, interval, tol=None, reject=10, integral=None,
+    adiffs=None, bdiffs=None, verbose=False, error=False,
+    _fast_abort=False):
+    r"""
+    Uses the Euler-Maclaurin formula to compute an approximation accurate
+    to within ``tol`` (which defaults to the present epsilon) of the sum
+
+    .. math ::
+
+        S = \sum_{k=a}^b f(k)
+
+    where `(a,b)` are given by ``interval`` and `a` or `b` may be
+    infinite. The approximation is
+
+    .. math ::
+
+        S \sim \int_a^b f(x) \,dx + \frac{f(a)+f(b)}{2} +
+        \sum_{k=1}^{\infty} \frac{B_{2k}}{(2k)!}
+        \left(f^{(2k-1)}(b)-f^{(2k-1)}(a)\right).
+
+    The last sum in the Euler-Maclaurin formula is not generally
+    convergent (a notable exception is if `f` is a polynomial, in
+    which case Euler-Maclaurin actually gives an exact result).
+
+    The summation is stopped as soon as the quotient between two
+    consecutive terms falls below *reject*. That is, by default
+    (*reject* = 10), the summation is continued as long as each
+    term adds at least one decimal.
+
+    Although not convergent, convergence to a given tolerance can
+    often be "forced" if `b = \infty` by summing up to `a+N` and then
+    applying the Euler-Maclaurin formula to the sum over the range
+    `(a+N+1, \ldots, \infty)`. This procedure is implemented by
+    :func:`~mpmath.nsum`.
+
+    By default numerical quadrature and differentiation is used.
+    If the symbolic values of the integral and endpoint derivatives
+    are known, it is more efficient to pass the value of the
+    integral explicitly as ``integral`` and the derivatives
+    explicitly as ``adiffs`` and ``bdiffs``. The derivatives
+    should be given as iterables that yield
+    `f(a), f'(a), f''(a), \ldots` (and the equivalent for `b`).
+
+    **Examples**
+
+    Summation of an infinite series, with automatic and symbolic
+    integral and derivative values (the second should be much faster)::
+
+        >>> from mpmath import *
+        >>> mp.dps = 50; mp.pretty = True
+        >>> sumem(lambda n: 1/n**2, [32, inf])
+        0.03174336652030209012658168043874142714132886413417
+        >>> I = mpf(1)/32
+        >>> D = adiffs=((-1)**n*fac(n+1)*32**(-2-n) for n in range(999))
+        >>> sumem(lambda n: 1/n**2, [32, inf], integral=I, adiffs=D)
+        0.03174336652030209012658168043874142714132886413417
+
+    An exact evaluation of a finite polynomial sum::
+
+        >>> sumem(lambda n: n**5-12*n**2+3*n, [-100000, 200000])
+        10500155000624963999742499550000.0
+        >>> print(sum(n**5-12*n**2+3*n for n in range(-100000, 200001)))
+        10500155000624963999742499550000
+
+    """
+    tol = tol or +ctx.eps
+    interval = ctx._as_points(interval)
+    a = ctx.convert(interval[0])
+    b = ctx.convert(interval[-1])
+    err = ctx.zero
+    prev = 0
+    M = 10000
+    if a == ctx.ninf: adiffs = (0 for n in xrange(M))
+    else:             adiffs = adiffs or ctx.diffs(f, a)
+    if b == ctx.inf:  bdiffs = (0 for n in xrange(M))
+    else:             bdiffs = bdiffs or ctx.diffs(f, b)
+    orig = ctx.prec
+    #verbose = 1
+    try:
+        ctx.prec += 10
+        s = ctx.zero
+        for k, (da, db) in enumerate(izip(adiffs, bdiffs)):
+            if k & 1:
+                term = (db-da) * ctx.bernoulli(k+1) / ctx.factorial(k+1)
+                mag = abs(term)
+                if verbose:
+                    print("term", k, "magnitude =", ctx.nstr(mag))
+                if k > 4 and mag < tol:
+                    s += term
+                    break
+                elif k > 4 and abs(prev) / mag < reject:
+                    err += mag
+                    if _fast_abort:
+                        return [s, (s, err)][error]
+                    if verbose:
+                        print("Failed to converge")
+                    break
+                else:
+                    s += term
+                prev = term
+        # Endpoint correction
+        if a != ctx.ninf: s += f(a)/2
+        if b != ctx.inf: s += f(b)/2
+        # Tail integral
+        if verbose:
+            print("Integrating f(x) from x = %s to %s" % (ctx.nstr(a), ctx.nstr(b)))
+        if integral:
+            s += integral
+        else:
+            integral, ierr = ctx.quad(f, interval, error=True)
+            if verbose:
+                print("Integration error:", ierr)
+            s += integral
+            err += ierr
+    finally:
+        ctx.prec = orig
+    if error:
+        return s, err
+    else:
+        return s
+
+@defun
+def adaptive_extrapolation(ctx, update, emfun, kwargs):
+    option = kwargs.get
+    if ctx._fixed_precision:
+        tol = option('tol', ctx.eps*2**10)
+    else:
+        tol = option('tol', ctx.eps/2**10)
+    verbose = option('verbose', False)
+    maxterms = option('maxterms', ctx.dps*10)
+    method = set(option('method', 'r+s').split('+'))
+    skip = option('skip', 0)
+    steps = iter(option('steps', xrange(10, 10**9, 10)))
+    strict = option('strict')
+    #steps = (10 for i in xrange(1000))
+    summer=[]
+    if 'd' in method or 'direct' in method:
+        TRY_RICHARDSON = TRY_SHANKS = TRY_EULER_MACLAURIN = False
+    else:
+        TRY_RICHARDSON = ('r' in method) or ('richardson' in method)
+        TRY_SHANKS = ('s' in method) or ('shanks' in method)
+        TRY_EULER_MACLAURIN = ('e' in method) or \
+            ('euler-maclaurin' in method)
+
+        def init_levin(m):
+            variant = kwargs.get("levin_variant", "u")
+            if isinstance(variant, str):
+                if variant == "all":
+                    variant = ["u", "v", "t"]
+                else:
+                    variant = [variant]
+            for s in variant:
+                L = levin_class(method = m, variant = s)
+                L.ctx = ctx
+                L.name = m + "(" + s + ")"
+                summer.append(L)
+
+        if ('l' in method) or ('levin' in method):
+            init_levin("levin")
+
+        if ('sidi' in method):
+            init_levin("sidi")
+
+        if ('a' in method) or ('alternating' in method):
+            L = cohen_alt_class()
+            L.ctx = ctx
+            L.name = "alternating"
+            summer.append(L)
+
+    last_richardson_value = 0
+    shanks_table = []
+    index = 0
+    step = 10
+    partial = []
+    best = ctx.zero
+    orig = ctx.prec
+    try:
+        if 'workprec' in kwargs:
+            ctx.prec = kwargs['workprec']
+        elif TRY_RICHARDSON or TRY_SHANKS or len(summer)!=0:
+            ctx.prec = (ctx.prec+10) * 4
+        else:
+            ctx.prec += 30
+        while 1:
+            if index >= maxterms:
+                break
+
+            # Get new batch of terms
+            try:
+                step = next(steps)
+            except StopIteration:
+                pass
+            if verbose:
+                print("-"*70)
+                print("Adding terms #%i-#%i" % (index, index+step))
+            update(partial, xrange(index, index+step))
+            index += step
+
+            # Check direct error
+            best = partial[-1]
+            error = abs(best - partial[-2])
+            if verbose:
+                print("Direct error: %s" % ctx.nstr(error))
+            if error <= tol:
+                return best
+
+            # Check each extrapolation method
+            if TRY_RICHARDSON:
+                value, maxc = ctx.richardson(partial)
+                # Convergence
+                richardson_error = abs(value - last_richardson_value)
+                if verbose:
+                    print("Richardson error: %s" % ctx.nstr(richardson_error))
+                # Convergence
+                if richardson_error <= tol:
+                    return value
+                last_richardson_value = value
+                # Unreliable due to cancellation
+                if ctx.eps*maxc > tol:
+                    if verbose:
+                        print("Ran out of precision for Richardson")
+                    TRY_RICHARDSON = False
+                if richardson_error < error:
+                    error = richardson_error
+                    best = value
+            if TRY_SHANKS:
+                shanks_table = ctx.shanks(partial, shanks_table, randomized=True)
+                row = shanks_table[-1]
+                if len(row) == 2:
+                    est1 = row[-1]
+                    shanks_error = 0
+                else:
+                    est1, maxc, est2 = row[-1], abs(row[-2]), row[-3]
+                    shanks_error = abs(est1-est2)
+                if verbose:
+                    print("Shanks error: %s" % ctx.nstr(shanks_error))
+                if shanks_error <= tol:
+                    return est1
+                if ctx.eps*maxc > tol:
+                    if verbose:
+                        print("Ran out of precision for Shanks")
+                    TRY_SHANKS = False
+                if shanks_error < error:
+                    error = shanks_error
+                    best = est1
+            for L in summer:
+                est, lerror = L.update_psum(partial)
+                if verbose:
+                    print("%s error: %s" % (L.name, ctx.nstr(lerror)))
+                if lerror <= tol:
+                    return est
+                if lerror < error:
+                    error = lerror
+                    best = est
+            if TRY_EULER_MACLAURIN:
+                if ctx.almosteq(ctx.mpc(ctx.sign(partial[-1]) / ctx.sign(partial[-2])), -1):
+                    if verbose:
+                        print ("NOT using Euler-Maclaurin: the series appears"
+                            " to be alternating, so numerical\n quadrature"
+                            " will most likely fail")
+                    TRY_EULER_MACLAURIN = False
+                else:
+                    value, em_error = emfun(index, tol)
+                    value += partial[-1]
+                    if verbose:
+                        print("Euler-Maclaurin error: %s" % ctx.nstr(em_error))
+                    if em_error <= tol:
+                        return value
+                    if em_error < error:
+                        best = value
+    finally:
+        ctx.prec = orig
+    if strict:
+        raise ctx.NoConvergence
+    if verbose:
+        print("Warning: failed to converge to target accuracy")
+    return best
+
+@defun
+def nsum(ctx, f, *intervals, **options):
+    r"""
+    Computes the sum
+
+    .. math :: S = \sum_{k=a}^b f(k)
+
+    where `(a, b)` = *interval*, and where `a = -\infty` and/or
+    `b = \infty` are allowed, or more generally
+
+    .. math :: S = \sum_{k_1=a_1}^{b_1} \cdots
+                   \sum_{k_n=a_n}^{b_n} f(k_1,\ldots,k_n)
+
+    if multiple intervals are given.
+
+    Two examples of infinite series that can be summed by :func:`~mpmath.nsum`,
+    where the first converges rapidly and the second converges slowly,
+    are::
+
+        >>> from mpmath import *
+        >>> mp.dps = 15; mp.pretty = True
+        >>> nsum(lambda n: 1/fac(n), [0, inf])
+        2.71828182845905
+        >>> nsum(lambda n: 1/n**2, [1, inf])
+        1.64493406684823
+
+    When appropriate, :func:`~mpmath.nsum` applies convergence acceleration to
+    accurately estimate the sums of slowly convergent series. If the series is
+    finite, :func:`~mpmath.nsum` currently does not attempt to perform any
+    extrapolation, and simply calls :func:`~mpmath.fsum`.
+
+    Multidimensional infinite series are reduced to a single-dimensional
+    series over expanding hypercubes; if both infinite and finite dimensions
+    are present, the finite ranges are moved innermost. For more advanced
+    control over the summation order, use nested calls to :func:`~mpmath.nsum`,
+    or manually rewrite the sum as a single-dimensional series.
+
+    **Options**
+
+    *tol*
+        Desired maximum final error. Defaults roughly to the
+        epsilon of the working precision.
+
+    *method*
+        Which summation algorithm to use (described below).
+        Default: ``'richardson+shanks'``.
+
+    *maxterms*
+        Cancel after at most this many terms. Default: 10*dps.
+
+    *steps*
+        An iterable giving the number of terms to add between
+        each extrapolation attempt. The default sequence is
+        [10, 20, 30, 40, ...]. For example, if you know that
+        approximately 100 terms will be required, efficiency might be
+        improved by setting this to [100, 10]. Then the first
+        extrapolation will be performed after 100 terms, the second
+        after 110, etc.
+
+    *verbose*
+        Print details about progress.
+
+    *ignore*
+        If enabled, any term that raises ``ArithmeticError``
+        or ``ValueError`` (e.g. through division by zero) is replaced
+        by a zero. This is convenient for lattice sums with
+        a singular term near the origin.
+
+    **Methods**
+
+    Unfortunately, an algorithm that can efficiently sum any infinite
+    series does not exist. :func:`~mpmath.nsum` implements several different
+    algorithms that each work well in different cases. The *method*
+    keyword argument selects a method.
+
+    The default method is ``'r+s'``, i.e. both Richardson extrapolation
+    and Shanks transformation is attempted. A slower method that
+    handles more cases is ``'r+s+e'``. For very high precision
+    summation, or if the summation needs to be fast (for example if
+    multiple sums need to be evaluated), it is a good idea to
+    investigate which one method works best and only use that.
+
+    ``'richardson'`` / ``'r'``:
+        Uses Richardson extrapolation. Provides useful extrapolation
+        when `f(k) \sim P(k)/Q(k)` or when `f(k) \sim (-1)^k P(k)/Q(k)`
+        for polynomials `P` and `Q`. See :func:`~mpmath.richardson` for
+        additional information.
+
+    ``'shanks'`` / ``'s'``:
+        Uses Shanks transformation. Typically provides useful
+        extrapolation when `f(k) \sim c^k` or when successive terms
+        alternate signs. Is able to sum some divergent series.
+        See :func:`~mpmath.shanks` for additional information.
+
+    ``'levin'`` / ``'l'``:
+        Uses the Levin transformation. It performs better than the Shanks
+        transformation for logarithmic convergent or alternating divergent
+        series. The ``'levin_variant'``-keyword selects the variant. Valid
+        choices are "u", "t", "v" and "all" whereby "all" uses all three
+        u,t and v simultanously (This is good for performance comparison in
+        conjunction with "verbose=True"). Instead of the Levin transform one can
+        also use the Sidi-S transform by selecting the method ``'sidi'``.
+        See :func:`~mpmath.levin` for additional details.
+
+    ``'alternating'`` / ``'a'``:
+        This is the convergence acceleration of alternating series developped
+        by Cohen, Villegras and Zagier.
+        See :func:`~mpmath.cohen_alt` for additional details.
+
+    ``'euler-maclaurin'`` / ``'e'``:
+        Uses the Euler-Maclaurin summation formula to approximate
+        the remainder sum by an integral. This requires high-order
+        numerical derivatives and numerical integration. The advantage
+        of this algorithm is that it works regardless of the
+        decay rate of `f`, as long as `f` is sufficiently smooth.
+        See :func:`~mpmath.sumem` for additional information.
+
+    ``'direct'`` / ``'d'``:
+        Does not perform any extrapolation. This can be used
+        (and should only be used for) rapidly convergent series.
+        The summation automatically stops when the terms
+        decrease below the target tolerance.
+
+    **Basic examples**
+
+    A finite sum::
+
+        >>> nsum(lambda k: 1/k, [1, 6])
+        2.45
+
+    Summation of a series going to negative infinity and a doubly
+    infinite series::
+
+        >>> nsum(lambda k: 1/k**2, [-inf, -1])
+        1.64493406684823
+        >>> nsum(lambda k: 1/(1+k**2), [-inf, inf])
+        3.15334809493716
+
+    :func:`~mpmath.nsum` handles sums of complex numbers::
+
+        >>> nsum(lambda k: (0.5+0.25j)**k, [0, inf])
+        (1.6 + 0.8j)
+
+    The following sum converges very rapidly, so it is most
+    efficient to sum it by disabling convergence acceleration::
+
+        >>> mp.dps = 1000
+        >>> a = nsum(lambda k: -(-1)**k * k**2 / fac(2*k), [1, inf],
+        ...     method='direct')
+        >>> b = (cos(1)+sin(1))/4
+        >>> abs(a-b) < mpf('1e-998')
+        True
+
+    **Examples with Richardson extrapolation**
+
+    Richardson extrapolation works well for sums over rational
+    functions, as well as their alternating counterparts::
+
+        >>> mp.dps = 50
+        >>> nsum(lambda k: 1 / k**3, [1, inf],
+        ...     method='richardson')
+        1.2020569031595942853997381615114499907649862923405
+        >>> zeta(3)
+        1.2020569031595942853997381615114499907649862923405
+
+        >>> nsum(lambda n: (n + 3)/(n**3 + n**2), [1, inf],
+        ...     method='richardson')
+        2.9348022005446793094172454999380755676568497036204
+        >>> pi**2/2-2
+        2.9348022005446793094172454999380755676568497036204
+
+        >>> nsum(lambda k: (-1)**k / k**3, [1, inf],
+        ...     method='richardson')
+        -0.90154267736969571404980362113358749307373971925537
+        >>> -3*zeta(3)/4
+        -0.90154267736969571404980362113358749307373971925538
+
+    **Examples with Shanks transformation**
+
+    The Shanks transformation works well for geometric series
+    and typically provides excellent acceleration for Taylor
+    series near the border of their disk of convergence.
+    Here we apply it to a series for `\log(2)`, which can be
+    seen as the Taylor series for `\log(1+x)` with `x = 1`::
+
+        >>> nsum(lambda k: -(-1)**k/k, [1, inf],
+        ...     method='shanks')
+        0.69314718055994530941723212145817656807550013436025
+        >>> log(2)
+        0.69314718055994530941723212145817656807550013436025
+
+    Here we apply it to a slowly convergent geometric series::
+
+        >>> nsum(lambda k: mpf('0.995')**k, [0, inf],
+        ...     method='shanks')
+        200.0
+
+    Finally, Shanks' method works very well for alternating series
+    where `f(k) = (-1)^k g(k)`, and often does so regardless of
+    the exact decay rate of `g(k)`::
+
+        >>> mp.dps = 15
+        >>> nsum(lambda k: (-1)**(k+1) / k**1.5, [1, inf],
+        ...     method='shanks')
+        0.765147024625408
+        >>> (2-sqrt(2))*zeta(1.5)/2
+        0.765147024625408
+
+    The following slowly convergent alternating series has no known
+    closed-form value. Evaluating the sum a second time at higher
+    precision indicates that the value is probably correct::
+
+        >>> nsum(lambda k: (-1)**k / log(k), [2, inf],
+        ...     method='shanks')
+        0.924299897222939
+        >>> mp.dps = 30
+        >>> nsum(lambda k: (-1)**k / log(k), [2, inf],
+        ...     method='shanks')
+        0.92429989722293885595957018136
+
+    **Examples with Levin transformation**
+
+    The following example calculates Euler's constant as the constant term in
+    the Laurent expansion of zeta(s) at s=1. This sum converges extremly slow
+    because of the logarithmic convergence behaviour of the Dirichlet series
+    for zeta.
+
+      >>> mp.dps = 30
+      >>> z = mp.mpf(10) ** (-10)
+      >>> a = mp.nsum(lambda n: n**(-(1+z)), [1, mp.inf], method = "levin") - 1 / z
+      >>> print(mp.chop(a - mp.euler, tol = 1e-10))
+      0.0
+
+    Now we sum the zeta function outside its range of convergence
+    (attention: This does not work at the negative integers!):
+
+      >>> mp.dps = 15
+      >>> w = mp.nsum(lambda n: n ** (2 + 3j), [1, mp.inf], method = "levin", levin_variant = "v")
+      >>> print(mp.chop(w - mp.zeta(-2-3j)))
+      0.0
+
+    The next example resummates an asymptotic series expansion of an integral
+    related to the exponential integral.
+
+      >>> mp.dps = 15
+      >>> z = mp.mpf(10)
+      >>> # exact = mp.quad(lambda x: mp.exp(-x)/(1+x/z),[0,mp.inf])
+      >>> exact = z * mp.exp(z) * mp.expint(1,z) # this is the symbolic expression for the integral
+      >>> w = mp.nsum(lambda n: (-1) ** n * mp.fac(n) * z ** (-n), [0, mp.inf], method = "sidi", levin_variant = "t")
+      >>> print(mp.chop(w - exact))
+      0.0
+
+    Following highly divergent asymptotic expansion needs some care. Firstly we
+    need copious amount of working precision. Secondly the stepsize must not be
+    chosen to large, otherwise nsum may miss the point where the Levin transform
+    converges and reach the point where only numerical garbage is produced due to
+    numerical cancellation.
+
+      >>> mp.dps = 15
+      >>> z = mp.mpf(2)
+      >>> # exact = mp.quad(lambda x: mp.exp( -x * x / 2 - z * x ** 4), [0,mp.inf]) * 2 / mp.sqrt(2 * mp.pi)
+      >>> exact = mp.exp(mp.one / (32 * z)) * mp.besselk(mp.one / 4, mp.one / (32 * z)) / (4 * mp.sqrt(z * mp.pi)) # this is the symbolic expression for the integral
+      >>> w = mp.nsum(lambda n: (-z)**n * mp.fac(4 * n) / (mp.fac(n) * mp.fac(2 * n) * (4 ** n)),
+      ...   [0, mp.inf], method = "levin", levin_variant = "t", workprec = 8*mp.prec, steps = [2] + [1 for x in xrange(1000)])
+      >>> print(mp.chop(w - exact))
+      0.0
+
+    The hypergeoemtric function can also be summed outside its range of convergence:
+
+      >>> mp.dps = 15
+      >>> z = 2 + 1j
+      >>> exact = mp.hyp2f1(2 / mp.mpf(3), 4 / mp.mpf(3), 1 / mp.mpf(3), z)
+      >>> f = lambda n: mp.rf(2 / mp.mpf(3), n) * mp.rf(4 / mp.mpf(3), n) * z**n / (mp.rf(1 / mp.mpf(3), n) * mp.fac(n))
+      >>> v = mp.nsum(f, [0, mp.inf], method = "levin", steps = [10 for x in xrange(1000)])
+      >>> print(mp.chop(exact-v))
+      0.0
+
+    **Examples with Cohen's alternating series resummation**
+
+      The next example sums the alternating zeta function:
+
+      >>> v = mp.nsum(lambda n: (-1)**(n-1) / n, [1, mp.inf], method = "a")
+      >>> print(mp.chop(v - mp.log(2)))
+      0.0
+
+      The derivate of the alternating zeta function outside its range of
+      convergence:
+
+      >>> v = mp.nsum(lambda n: (-1)**n * mp.log(n) * n, [1, mp.inf], method = "a")
+      >>> print(mp.chop(v - mp.diff(lambda s: mp.altzeta(s), -1)))
+      0.0
+
+    **Examples with Euler-Maclaurin summation**
+
+    The sum in the following example has the wrong rate of convergence
+    for either Richardson or Shanks to be effective.
+
+        >>> f = lambda k: log(k)/k**2.5
+        >>> mp.dps = 15
+        >>> nsum(f, [1, inf], method='euler-maclaurin')
+        0.38734195032621
+        >>> -diff(zeta, 2.5)
+        0.38734195032621
+
+    Increasing ``steps`` improves speed at higher precision::
+
+        >>> mp.dps = 50
+        >>> nsum(f, [1, inf], method='euler-maclaurin', steps=[250])
+        0.38734195032620997271199237593105101319948228874688
+        >>> -diff(zeta, 2.5)
+        0.38734195032620997271199237593105101319948228874688
+
+    **Divergent series**
+
+    The Shanks transformation is able to sum some *divergent*
+    series. In particular, it is often able to sum Taylor series
+    beyond their radius of convergence (this is due to a relation
+    between the Shanks transformation and Pade approximations;
+    see :func:`~mpmath.pade` for an alternative way to evaluate divergent
+    Taylor series). Furthermore the Levin-transform examples above
+    contain some divergent series resummation.
+
+    Here we apply it to `\log(1+x)` far outside the region of
+    convergence::
+
+        >>> mp.dps = 50
+        >>> nsum(lambda k: -(-9)**k/k, [1, inf],
+        ...     method='shanks')
+        2.3025850929940456840179914546843642076011014886288
+        >>> log(10)
+        2.3025850929940456840179914546843642076011014886288
+
+    A particular type of divergent series that can be summed
+    using the Shanks transformation is geometric series.
+    The result is the same as using the closed-form formula
+    for an infinite geometric series::
+
+        >>> mp.dps = 15
+        >>> for n in range(-8, 8):
+        ...     if n == 1:
+        ...         continue
+        ...     print("%s %s %s" % (mpf(n), mpf(1)/(1-n),
+        ...         nsum(lambda k: n**k, [0, inf], method='shanks')))
+        ...
+        -8.0 0.111111111111111 0.111111111111111
+        -7.0 0.125 0.125
+        -6.0 0.142857142857143 0.142857142857143
+        -5.0 0.166666666666667 0.166666666666667
+        -4.0 0.2 0.2
+        -3.0 0.25 0.25
+        -2.0 0.333333333333333 0.333333333333333
+        -1.0 0.5 0.5
+        0.0 1.0 1.0
+        2.0 -1.0 -1.0
+        3.0 -0.5 -0.5
+        4.0 -0.333333333333333 -0.333333333333333
+        5.0 -0.25 -0.25
+        6.0 -0.2 -0.2
+        7.0 -0.166666666666667 -0.166666666666667
+
+    **Multidimensional sums**
+
+    Any combination of finite and infinite ranges is allowed for the
+    summation indices::
+
+        >>> mp.dps = 15
+        >>> nsum(lambda x,y: x+y, [2,3], [4,5])
+        28.0
+        >>> nsum(lambda x,y: x/2**y, [1,3], [1,inf])
+        6.0
+        >>> nsum(lambda x,y: y/2**x, [1,inf], [1,3])
+        6.0
+        >>> nsum(lambda x,y,z: z/(2**x*2**y), [1,inf], [1,inf], [3,4])
+        7.0
+        >>> nsum(lambda x,y,z: y/(2**x*2**z), [1,inf], [3,4], [1,inf])
+        7.0
+        >>> nsum(lambda x,y,z: x/(2**z*2**y), [3,4], [1,inf], [1,inf])
+        7.0
+
+    Some nice examples of double series with analytic solutions or
+    reductions to single-dimensional series (see [1])::
+
+        >>> nsum(lambda m, n: 1/2**(m*n), [1,inf], [1,inf])
+        1.60669515241529
+        >>> nsum(lambda n: 1/(2**n-1), [1,inf])
+        1.60669515241529
+
+        >>> nsum(lambda i,j: (-1)**(i+j)/(i**2+j**2), [1,inf], [1,inf])
+        0.278070510848213
+        >>> pi*(pi-3*ln2)/12
+        0.278070510848213
+
+        >>> nsum(lambda i,j: (-1)**(i+j)/(i+j)**2, [1,inf], [1,inf])
+        0.129319852864168
+        >>> altzeta(2) - altzeta(1)
+        0.129319852864168
+
+        >>> nsum(lambda i,j: (-1)**(i+j)/(i+j)**3, [1,inf], [1,inf])
+        0.0790756439455825
+        >>> altzeta(3) - altzeta(2)
+        0.0790756439455825
+
+        >>> nsum(lambda m,n: m**2*n/(3**m*(n*3**m+m*3**n)),
+        ...     [1,inf], [1,inf])
+        0.28125
+        >>> mpf(9)/32
+        0.28125
+
+        >>> nsum(lambda i,j: fac(i-1)*fac(j-1)/fac(i+j),
+        ...     [1,inf], [1,inf], workprec=400)
+        1.64493406684823
+        >>> zeta(2)
+        1.64493406684823
+
+    A hard example of a multidimensional sum is the Madelung constant
+    in three dimensions (see [2]). The defining sum converges very
+    slowly and only conditionally, so :func:`~mpmath.nsum` is lucky to
+    obtain an accurate value through convergence acceleration. The
+    second evaluation below uses a much more efficient, rapidly
+    convergent 2D sum::
+
+        >>> nsum(lambda x,y,z: (-1)**(x+y+z)/(x*x+y*y+z*z)**0.5,
+        ...     [-inf,inf], [-inf,inf], [-inf,inf], ignore=True)
+        -1.74756459463318
+        >>> nsum(lambda x,y: -12*pi*sech(0.5*pi * \
+        ...     sqrt((2*x+1)**2+(2*y+1)**2))**2, [0,inf], [0,inf])
+        -1.74756459463318
+
+    Another example of a lattice sum in 2D::
+
+        >>> nsum(lambda x,y: (-1)**(x+y) / (x**2+y**2), [-inf,inf],
+        ...     [-inf,inf], ignore=True)
+        -2.1775860903036
+        >>> -pi*ln2
+        -2.1775860903036
+
+    An example of an Eisenstein series::
+
+        >>> nsum(lambda m,n: (m+n*1j)**(-4), [-inf,inf], [-inf,inf],
+        ...     ignore=True)
+        (3.1512120021539 + 0.0j)
+
+    **References**
+
+    1. [Weisstein]_ http://mathworld.wolfram.com/DoubleSeries.html,
+    2. [Weisstein]_ http://mathworld.wolfram.com/MadelungConstants.html
+
+    """
+    infinite, g = standardize(ctx, f, intervals, options)
+    if not infinite:
+        return +g()
+
+    def update(partial_sums, indices):
+        if partial_sums:
+            psum = partial_sums[-1]
+        else:
+            psum = ctx.zero
+        for k in indices:
+            psum = psum + g(ctx.mpf(k))
+            partial_sums.append(psum)
+
+    prec = ctx.prec
+
+    def emfun(point, tol):
+        workprec = ctx.prec
+        ctx.prec = prec + 10
+        v = ctx.sumem(g, [point, ctx.inf], tol, error=1)
+        ctx.prec = workprec
+        return v
+
+    return +ctx.adaptive_extrapolation(update, emfun, options)
+
+
+def wrapsafe(f):
+    def g(*args):
+        try:
+            return f(*args)
+        except (ArithmeticError, ValueError):
+            return 0
+    return g
+
+def standardize(ctx, f, intervals, options):
+    if options.get("ignore"):
+        f = wrapsafe(f)
+    finite = []
+    infinite = []
+    for k, points in enumerate(intervals):
+        a, b = ctx._as_points(points)
+        if b < a:
+            return False, (lambda: ctx.zero)
+        if a == ctx.ninf or b == ctx.inf:
+            infinite.append((k, (a,b)))
+        else:
+            finite.append((k, (int(a), int(b))))
+    if finite:
+        f = fold_finite(ctx, f, finite)
+        if not infinite:
+            return False, lambda: f(*([0]*len(intervals)))
+    if infinite:
+        f = standardize_infinite(ctx, f, infinite)
+        f = fold_infinite(ctx, f, infinite)
+        args = [0] * len(intervals)
+        d = infinite[0][0]
+        def g(k):
+            args[d] = k
+            return f(*args)
+        return True, g
+
+# backwards compatible itertools.product
+def cartesian_product(args):
+    pools = map(tuple, args)
+    result = [[]]
+    for pool in pools:
+        result = [x+[y] for x in result for y in pool]
+    for prod in result:
+        yield tuple(prod)
+
+def fold_finite(ctx, f, intervals):
+    if not intervals:
+        return f
+    indices = [v[0] for v in intervals]
+    points = [v[1] for v in intervals]
+    ranges = [xrange(a, b+1) for (a,b) in points]
+    def g(*args):
+        args = list(args)
+        s = ctx.zero
+        for xs in cartesian_product(ranges):
+            for dim, x in zip(indices, xs):
+                args[dim] = ctx.mpf(x)
+            s += f(*args)
+        return s
+    #print "Folded finite", indices
+    return g
+
+# Standardize each interval to [0,inf]
+def standardize_infinite(ctx, f, intervals):
+    if not intervals:
+        return f
+    dim, [a,b] = intervals[-1]
+    if a == ctx.ninf:
+        if b == ctx.inf:
+            def g(*args):
+                args = list(args)
+                k = args[dim]
+                if k:
+                    s = f(*args)
+                    args[dim] = -k
+                    s += f(*args)
+                    return s
+                else:
+                    return f(*args)
+        else:
+            def g(*args):
+                args = list(args)
+                args[dim] = b - args[dim]
+                return f(*args)
+    else:
+        def g(*args):
+            args = list(args)
+            args[dim] += a
+            return f(*args)
+    #print "Standardized infinity along dimension", dim, a, b
+    return standardize_infinite(ctx, g, intervals[:-1])
+
+def fold_infinite(ctx, f, intervals):
+    if len(intervals) < 2:
+        return f
+    dim1 = intervals[-2][0]
+    dim2 = intervals[-1][0]
+    # Assume intervals are [0,inf] x [0,inf] x ...
+    def g(*args):
+        args = list(args)
+        #args.insert(dim2, None)
+        n = int(args[dim1])
+        s = ctx.zero
+        #y = ctx.mpf(n)
+        args[dim2] = ctx.mpf(n) #y
+        for x in xrange(n+1):
+            args[dim1] = ctx.mpf(x)
+            s += f(*args)
+        args[dim1] = ctx.mpf(n) #ctx.mpf(n)
+        for y in xrange(n):
+            args[dim2] = ctx.mpf(y)
+            s += f(*args)
+        return s
+    #print "Folded infinite from", len(intervals), "to", (len(intervals)-1)
+    return fold_infinite(ctx, g, intervals[:-1])
+
+@defun
+def nprod(ctx, f, interval, nsum=False, **kwargs):
+    r"""
+    Computes the product
+
+    .. math ::
+
+        P = \prod_{k=a}^b f(k)
+
+    where `(a, b)` = *interval*, and where `a = -\infty` and/or
+    `b = \infty` are allowed.
+
+    By default, :func:`~mpmath.nprod` uses the same extrapolation methods as
+    :func:`~mpmath.nsum`, except applied to the partial products rather than
+    partial sums, and the same keyword options as for :func:`~mpmath.nsum` are
+    supported. If ``nsum=True``, the product is instead computed via
+    :func:`~mpmath.nsum` as
+
+    .. math ::
+
+        P = \exp\left( \sum_{k=a}^b \log(f(k)) \right).
+
+    This is slower, but can sometimes yield better results. It is
+    also required (and used automatically) when Euler-Maclaurin
+    summation is requested.
+
+    **Examples**
+
+    A simple finite product::
+
+        >>> from mpmath import *
+        >>> mp.dps = 25; mp.pretty = True
+        >>> nprod(lambda k: k, [1, 4])
+        24.0
+
+    A large number of infinite products have known exact values,
+    and can therefore be used as a reference. Most of the following
+    examples are taken from MathWorld [1].
+
+    A few infinite products with simple values are::
+
+        >>> 2*nprod(lambda k: (4*k**2)/(4*k**2-1), [1, inf])
+        3.141592653589793238462643
+        >>> nprod(lambda k: (1+1/k)**2/(1+2/k), [1, inf])
+        2.0
+        >>> nprod(lambda k: (k**3-1)/(k**3+1), [2, inf])
+        0.6666666666666666666666667
+        >>> nprod(lambda k: (1-1/k**2), [2, inf])
+        0.5
+
+    Next, several more infinite products with more complicated
+    values::
+
+        >>> nprod(lambda k: exp(1/k**2), [1, inf]); exp(pi**2/6)
+        5.180668317897115748416626
+        5.180668317897115748416626
+
+        >>> nprod(lambda k: (k**2-1)/(k**2+1), [2, inf]); pi*csch(pi)
+        0.2720290549821331629502366
+        0.2720290549821331629502366
+
+        >>> nprod(lambda k: (k**4-1)/(k**4+1), [2, inf])
+        0.8480540493529003921296502
+        >>> pi*sinh(pi)/(cosh(sqrt(2)*pi)-cos(sqrt(2)*pi))
+        0.8480540493529003921296502
+
+        >>> nprod(lambda k: (1+1/k+1/k**2)**2/(1+2/k+3/k**2), [1, inf])
+        1.848936182858244485224927
+        >>> 3*sqrt(2)*cosh(pi*sqrt(3)/2)**2*csch(pi*sqrt(2))/pi
+        1.848936182858244485224927
+
+        >>> nprod(lambda k: (1-1/k**4), [2, inf]); sinh(pi)/(4*pi)
+        0.9190194775937444301739244
+        0.9190194775937444301739244
+
+        >>> nprod(lambda k: (1-1/k**6), [2, inf])
+        0.9826842777421925183244759
+        >>> (1+cosh(pi*sqrt(3)))/(12*pi**2)
+        0.9826842777421925183244759
+
+        >>> nprod(lambda k: (1+1/k**2), [2, inf]); sinh(pi)/(2*pi)
+        1.838038955187488860347849
+        1.838038955187488860347849
+
+        >>> nprod(lambda n: (1+1/n)**n * exp(1/(2*n)-1), [1, inf])
+        1.447255926890365298959138
+        >>> exp(1+euler/2)/sqrt(2*pi)
+        1.447255926890365298959138
+
+    The following two products are equivalent and can be evaluated in
+    terms of a Jacobi theta function. Pi can be replaced by any value
+    (as long as convergence is preserved)::
+
+        >>> nprod(lambda k: (1-pi**-k)/(1+pi**-k), [1, inf])
+        0.3838451207481672404778686
+        >>> nprod(lambda k: tanh(k*log(pi)/2), [1, inf])
+        0.3838451207481672404778686
+        >>> jtheta(4,0,1/pi)
+        0.3838451207481672404778686
+
+    This product does not have a known closed form value::
+
+        >>> nprod(lambda k: (1-1/2**k), [1, inf])
+        0.2887880950866024212788997
+
+    A product taken from `-\infty`::
+
+        >>> nprod(lambda k: 1-k**(-3), [-inf,-2])
+        0.8093965973662901095786805
+        >>> cosh(pi*sqrt(3)/2)/(3*pi)
+        0.8093965973662901095786805
+
+    A doubly infinite product::
+
+        >>> nprod(lambda k: exp(1/(1+k**2)), [-inf, inf])
+        23.41432688231864337420035
+        >>> exp(pi/tanh(pi))
+        23.41432688231864337420035
+
+    A product requiring the use of Euler-Maclaurin summation to compute
+    an accurate value::
+
+        >>> nprod(lambda k: (1-1/k**2.5), [2, inf], method='e')
+        0.696155111336231052898125
+
+    **References**
+
+    1. [Weisstein]_ http://mathworld.wolfram.com/InfiniteProduct.html
+
+    """
+    if nsum or ('e' in kwargs.get('method', '')):
+        orig = ctx.prec
+        try:
+            # TODO: we are evaluating log(1+eps) -> eps, which is
+            # inaccurate. This currently works because nsum greatly
+            # increases the working precision. But we should be
+            # more intelligent and handle the precision here.
+            ctx.prec += 10
+            v = ctx.nsum(lambda n: ctx.ln(f(n)), interval, **kwargs)
+        finally:
+            ctx.prec = orig
+        return +ctx.exp(v)
+
+    a, b = ctx._as_points(interval)
+    if a == ctx.ninf:
+        if b == ctx.inf:
+            return f(0) * ctx.nprod(lambda k: f(-k) * f(k), [1, ctx.inf], **kwargs)
+        return ctx.nprod(f, [-b, ctx.inf], **kwargs)
+    elif b != ctx.inf:
+        return ctx.fprod(f(ctx.mpf(k)) for k in xrange(int(a), int(b)+1))
+
+    a = int(a)
+
+    def update(partial_products, indices):
+        if partial_products:
+            pprod = partial_products[-1]
+        else:
+            pprod = ctx.one
+        for k in indices:
+            pprod = pprod * f(a + ctx.mpf(k))
+            partial_products.append(pprod)
+
+    return +ctx.adaptive_extrapolation(update, None, kwargs)
+
+
+@defun
+def limit(ctx, f, x, direction=1, exp=False, **kwargs):
+    r"""
+    Computes an estimate of the limit
+
+    .. math ::
+
+        \lim_{t \to x} f(t)
+
+    where `x` may be finite or infinite.
+
+    For finite `x`, :func:`~mpmath.limit` evaluates `f(x + d/n)` for
+    consecutive integer values of `n`, where the approach direction
+    `d` may be specified using the *direction* keyword argument.
+    For infinite `x`, :func:`~mpmath.limit` evaluates values of
+    `f(\mathrm{sign}(x) \cdot n)`.
+
+    If the approach to the limit is not sufficiently fast to give
+    an accurate estimate directly, :func:`~mpmath.limit` attempts to find
+    the limit using Richardson extrapolation or the Shanks
+    transformation. You can select between these methods using
+    the *method* keyword (see documentation of :func:`~mpmath.nsum` for
+    more information).
+
+    **Options**
+
+    The following options are available with essentially the
+    same meaning as for :func:`~mpmath.nsum`: *tol*, *method*, *maxterms*,
+    *steps*, *verbose*.
+
+    If the option *exp=True* is set, `f` will be
+    sampled at exponentially spaced points `n = 2^1, 2^2, 2^3, \ldots`
+    instead of the linearly spaced points `n = 1, 2, 3, \ldots`.
+    This can sometimes improve the rate of convergence so that
+    :func:`~mpmath.limit` may return a more accurate answer (and faster).
+    However, do note that this can only be used if `f`
+    supports fast and accurate evaluation for arguments that
+    are extremely close to the limit point (or if infinite,
+    very large arguments).
+
+    **Examples**
+
+    A basic evaluation of a removable singularity::
+
+        >>> from mpmath import *
+        >>> mp.dps = 30; mp.pretty = True
+        >>> limit(lambda x: (x-sin(x))/x**3, 0)
+        0.166666666666666666666666666667
+
+    Computing the exponential function using its limit definition::
+
+        >>> limit(lambda n: (1+3/n)**n, inf)
+        20.0855369231876677409285296546
+        >>> exp(3)
+        20.0855369231876677409285296546
+
+    A limit for `\pi`::
+
+        >>> f = lambda n: 2**(4*n+1)*fac(n)**4/(2*n+1)/fac(2*n)**2
+        >>> limit(f, inf)
+        3.14159265358979323846264338328
+
+    Calculating the coefficient in Stirling's formula::
+
+        >>> limit(lambda n: fac(n) / (sqrt(n)*(n/e)**n), inf)
+        2.50662827463100050241576528481
+        >>> sqrt(2*pi)
+        2.50662827463100050241576528481
+
+    Evaluating Euler's constant `\gamma` using the limit representation
+
+    .. math ::
+
+        \gamma = \lim_{n \rightarrow \infty } \left[ \left(
+        \sum_{k=1}^n \frac{1}{k} \right) - \log(n) \right]
+
+    (which converges notoriously slowly)::
+
+        >>> f = lambda n: sum([mpf(1)/k for k in range(1,int(n)+1)]) - log(n)
+        >>> limit(f, inf)
+        0.577215664901532860606512090082
+        >>> +euler
+        0.577215664901532860606512090082
+
+    With default settings, the following limit converges too slowly
+    to be evaluated accurately. Changing to exponential sampling
+    however gives a perfect result::
+
+        >>> f = lambda x: sqrt(x**3+x**2)/(sqrt(x**3)+x)
+        >>> limit(f, inf)
+        0.992831158558330281129249686491
+        >>> limit(f, inf, exp=True)
+        1.0
+
+    """
+
+    if ctx.isinf(x):
+        direction = ctx.sign(x)
+        g = lambda k: f(ctx.mpf(k+1)*direction)
+    else:
+        direction *= ctx.one
+        g = lambda k: f(x + direction/(k+1))
+    if exp:
+        h = g
+        g = lambda k: h(2**k)
+
+    def update(values, indices):
+        for k in indices:
+            values.append(g(k+1))
+
+    # XXX: steps used by nsum don't work well
+    if not 'steps' in kwargs:
+        kwargs['steps'] = [10]
+
+    return +ctx.adaptive_extrapolation(update, None, kwargs)

venv/lib/python3.10/site-packages/mpmath/calculus/inverselaplace.py

@@ -0,0 +1,973 @@
+# contributed to mpmath by Kristopher L. Kuhlman, February 2017
+# contributed to mpmath by Guillermo Navas-Palencia, February 2022
+
+class InverseLaplaceTransform(object):
+    r"""
+    Inverse Laplace transform methods are implemented using this
+    class, in order to simplify the code and provide a common
+    infrastructure.
+
+    Implement a custom inverse Laplace transform algorithm by
+    subclassing :class:`InverseLaplaceTransform` and implementing the
+    appropriate methods. The subclass can then be used by
+    :func:`~mpmath.invertlaplace` by passing it as the *method*
+    argument.
+    """
+
+    def __init__(self, ctx):
+        self.ctx = ctx
+
+    def calc_laplace_parameter(self, t, **kwargs):
+        r"""
+        Determine the vector of Laplace parameter values needed for an
+        algorithm, this will depend on the choice of algorithm (de
+        Hoog is default), the algorithm-specific parameters passed (or
+        default ones), and desired time.
+        """
+        raise NotImplementedError
+
+    def calc_time_domain_solution(self, fp):
+        r"""
+        Compute the time domain solution, after computing the
+        Laplace-space function evaluations at the abscissa required
+        for the algorithm. Abscissa computed for one algorithm are
+        typically not useful for another algorithm.
+        """
+        raise NotImplementedError
+
+
+class FixedTalbot(InverseLaplaceTransform):
+
+    def calc_laplace_parameter(self, t, **kwargs):
+        r"""The "fixed" Talbot method deforms the Bromwich contour towards
+        `-\infty` in the shape of a parabola. Traditionally the Talbot
+        algorithm has adjustable parameters, but the "fixed" version
+        does not. The `r` parameter could be passed in as a parameter,
+        if you want to override the default given by (Abate & Valko,
+        2004).
+
+        The Laplace parameter is sampled along a parabola opening
+        along the negative imaginary axis, with the base of the
+        parabola along the real axis at
+        `p=\frac{r}{t_\mathrm{max}}`. As the number of terms used in
+        the approximation (degree) grows, the abscissa required for
+        function evaluation tend towards `-\infty`, requiring high
+        precision to prevent overflow.  If any poles, branch cuts or
+        other singularities exist such that the deformed Bromwich
+        contour lies to the left of the singularity, the method will
+        fail.
+
+        **Optional arguments**
+
+        :class:`~mpmath.calculus.inverselaplace.FixedTalbot.calc_laplace_parameter`
+        recognizes the following keywords
+
+        *tmax*
+            maximum time associated with vector of times
+            (typically just the time requested)
+        *degree*
+            integer order of approximation (M = number of terms)
+        *r*
+            abscissa for `p_0` (otherwise computed using rule
+            of thumb `2M/5`)
+
+        The working precision will be increased according to a rule of
+        thumb. If 'degree' is not specified, the working precision and
+        degree are chosen to hopefully achieve the dps of the calling
+        context. If 'degree' is specified, the working precision is
+        chosen to achieve maximum resulting precision for the
+        specified degree.
+
+        .. math ::
+
+            p_0=\frac{r}{t}
+
+        .. math ::
+
+            p_i=\frac{i r \pi}{Mt_\mathrm{max}}\left[\cot\left(
+            \frac{i\pi}{M}\right) + j \right] \qquad 1\le i <M
+
+        where `j=\sqrt{-1}`, `r=2M/5`, and `t_\mathrm{max}` is the
+        maximum specified time.
+
+        """
+
+        # required
+        # ------------------------------
+        # time of desired approximation
+        self.t = self.ctx.convert(t)
+
+        # optional
+        # ------------------------------
+        # maximum time desired (used for scaling) default is requested
+        # time.
+        self.tmax = self.ctx.convert(kwargs.get('tmax', self.t))
+
+        # empirical relationships used here based on a linear fit of
+        # requested and delivered dps for exponentially decaying time
+        # functions for requested dps up to 512.
+
+        if 'degree' in kwargs:
+            self.degree = kwargs['degree']
+            self.dps_goal = self.degree
+        else:
+            self.dps_goal = int(1.72*self.ctx.dps)
+            self.degree = max(12, int(1.38*self.dps_goal))
+
+        M = self.degree
+
+        # this is adjusting the dps of the calling context hopefully
+        # the caller doesn't monkey around with it between calling
+        # this routine and calc_time_domain_solution()
+        self.dps_orig = self.ctx.dps
+        self.ctx.dps = self.dps_goal
+
+        # Abate & Valko rule of thumb for r parameter
+        self.r = kwargs.get('r', self.ctx.fraction(2, 5)*M)
+
+        self.theta = self.ctx.linspace(0.0, self.ctx.pi, M+1)
+
+        self.cot_theta = self.ctx.matrix(M, 1)
+        self.cot_theta[0] = 0  # not used
+
+        # all but time-dependent part of p
+        self.delta = self.ctx.matrix(M, 1)
+        self.delta[0] = self.r
+
+        for i in range(1, M):
+            self.cot_theta[i] = self.ctx.cot(self.theta[i])
+            self.delta[i] = self.r*self.theta[i]*(self.cot_theta[i] + 1j)
+
+        self.p = self.ctx.matrix(M, 1)
+        self.p = self.delta/self.tmax
+
+        # NB: p is complex (mpc)
+
+    def calc_time_domain_solution(self, fp, t, manual_prec=False):
+        r"""The fixed Talbot time-domain solution is computed from the
+        Laplace-space function evaluations using
+
+        .. math ::
+
+            f(t,M)=\frac{2}{5t}\sum_{k=0}^{M-1}\Re \left[
+            \gamma_k \bar{f}(p_k)\right]
+
+        where
+
+        .. math ::
+
+            \gamma_0 = \frac{1}{2}e^{r}\bar{f}(p_0)
+
+        .. math ::
+
+            \gamma_k = e^{tp_k}\left\lbrace 1 + \frac{jk\pi}{M}\left[1 +
+            \cot \left( \frac{k \pi}{M} \right)^2 \right] - j\cot\left(
+            \frac{k \pi}{M}\right)\right \rbrace \qquad 1\le k<M.
+
+        Again, `j=\sqrt{-1}`.
+
+        Before calling this function, call
+        :class:`~mpmath.calculus.inverselaplace.FixedTalbot.calc_laplace_parameter`
+        to set the parameters and compute the required coefficients.
+
+        **References**
+
+        1. Abate, J., P. Valko (2004). Multi-precision Laplace
+           transform inversion. *International Journal for Numerical
+           Methods in Engineering* 60:979-993,
+           http://dx.doi.org/10.1002/nme.995
+        2. Talbot, A. (1979). The accurate numerical inversion of
+           Laplace transforms. *IMA Journal of Applied Mathematics*
+           23(1):97, http://dx.doi.org/10.1093/imamat/23.1.97
+        """
+
+        # required
+        # ------------------------------
+        self.t = self.ctx.convert(t)
+
+        # assume fp was computed from p matrix returned from
+        # calc_laplace_parameter(), so is already a list or matrix of
+        # mpmath 'mpc' types
+
+        # these were computed in previous call to
+        # calc_laplace_parameter()
+        theta = self.theta
+        delta = self.delta
+        M = self.degree
+        p = self.p
+        r = self.r
+
+        ans = self.ctx.matrix(M, 1)
+        ans[0] = self.ctx.exp(delta[0])*fp[0]/2
+
+        for i in range(1, M):
+            ans[i] = self.ctx.exp(delta[i])*fp[i]*(
+                1 + 1j*theta[i]*(1 + self.cot_theta[i]**2) -
+                1j*self.cot_theta[i])
+
+        result = self.ctx.fraction(2, 5)*self.ctx.fsum(ans)/self.t
+
+        # setting dps back to value when calc_laplace_parameter was
+        # called, unless flag is set.
+        if not manual_prec:
+            self.ctx.dps = self.dps_orig
+
+        return result.real
+
+
+# ****************************************
+
+class Stehfest(InverseLaplaceTransform):
+
+    def calc_laplace_parameter(self, t, **kwargs):
+        r"""
+        The Gaver-Stehfest method is a discrete approximation of the
+        Widder-Post inversion algorithm, rather than a direct
+        approximation of the Bromwich contour integral.
+
+        The method abscissa along the real axis, and therefore has
+        issues inverting oscillatory functions (which have poles in
+        pairs away from the real axis).
+
+        The working precision will be increased according to a rule of
+        thumb. If 'degree' is not specified, the working precision and
+        degree are chosen to hopefully achieve the dps of the calling
+        context. If 'degree' is specified, the working precision is
+        chosen to achieve maximum resulting precision for the
+        specified degree.
+
+        .. math ::
+
+            p_k = \frac{k \log 2}{t} \qquad 1 \le k \le M
+        """
+
+        # required
+        # ------------------------------
+        # time of desired approximation
+        self.t = self.ctx.convert(t)
+
+        # optional
+        # ------------------------------
+
+        # empirical relationships used here based on a linear fit of
+        # requested and delivered dps for exponentially decaying time
+        # functions for requested dps up to 512.
+
+        if 'degree' in kwargs:
+            self.degree = kwargs['degree']
+            self.dps_goal = int(1.38*self.degree)
+        else:
+            self.dps_goal = int(2.93*self.ctx.dps)
+            self.degree = max(16, self.dps_goal)
+
+        # _coeff routine requires even degree
+        if self.degree % 2 > 0:
+            self.degree += 1
+
+        M = self.degree
+
+        # this is adjusting the dps of the calling context
+        # hopefully the caller doesn't monkey around with it
+        # between calling this routine and calc_time_domain_solution()
+        self.dps_orig = self.ctx.dps
+        self.ctx.dps = self.dps_goal
+
+        self.V = self._coeff()
+        self.p = self.ctx.matrix(self.ctx.arange(1, M+1))*self.ctx.ln2/self.t
+
+        # NB: p is real (mpf)
+
+    def _coeff(self):
+        r"""Salzer summation weights (aka, "Stehfest coefficients")
+        only depend on the approximation order (M) and the precision"""
+
+        M = self.degree
+        M2 = int(M/2)  # checked earlier that M is even
+
+        V = self.ctx.matrix(M, 1)
+
+        # Salzer summation weights
+        # get very large in magnitude and oscillate in sign,
+        # if the precision is not high enough, there will be
+        # catastrophic cancellation
+        for k in range(1, M+1):
+            z = self.ctx.matrix(min(k, M2)+1, 1)
+            for j in range(int((k+1)/2), min(k, M2)+1):
+                z[j] = (self.ctx.power(j, M2)*self.ctx.fac(2*j)/
+                        (self.ctx.fac(M2-j)*self.ctx.fac(j)*
+                         self.ctx.fac(j-1)*self.ctx.fac(k-j)*
+                         self.ctx.fac(2*j-k)))
+            V[k-1] = self.ctx.power(-1, k+M2)*self.ctx.fsum(z)
+
+        return V
+
+    def calc_time_domain_solution(self, fp, t, manual_prec=False):
+        r"""Compute time-domain Stehfest algorithm solution.
+
+        .. math ::
+
+            f(t,M) = \frac{\log 2}{t} \sum_{k=1}^{M} V_k \bar{f}\left(
+            p_k \right)
+
+        where
+
+        .. math ::
+
+            V_k = (-1)^{k + N/2} \sum^{\min(k,N/2)}_{i=\lfloor(k+1)/2 \rfloor}
+            \frac{i^{\frac{N}{2}}(2i)!}{\left(\frac{N}{2}-i \right)! \, i! \,
+            \left(i-1 \right)! \, \left(k-i\right)! \, \left(2i-k \right)!}
+
+        As the degree increases, the abscissa (`p_k`) only increase
+        linearly towards `\infty`, but the Stehfest coefficients
+        (`V_k`) alternate in sign and increase rapidly in sign,
+        requiring high precision to prevent overflow or loss of
+        significance when evaluating the sum.
+
+        **References**
+
+        1. Widder, D. (1941). *The Laplace Transform*. Princeton.
+        2. Stehfest, H. (1970). Algorithm 368: numerical inversion of
+           Laplace transforms. *Communications of the ACM* 13(1):47-49,
+           http://dx.doi.org/10.1145/361953.361969
+
+        """
+
+        # required
+        self.t = self.ctx.convert(t)
+
+        # assume fp was computed from p matrix returned from
+        # calc_laplace_parameter(), so is already
+        # a list or matrix of mpmath 'mpf' types
+
+        result = self.ctx.fdot(self.V, fp)*self.ctx.ln2/self.t
+
+        # setting dps back to value when calc_laplace_parameter was called
+        if not manual_prec:
+            self.ctx.dps = self.dps_orig
+
+        # ignore any small imaginary part
+        return result.real
+
+
+# ****************************************
+
+class deHoog(InverseLaplaceTransform):
+
+    def calc_laplace_parameter(self, t, **kwargs):
+        r"""the de Hoog, Knight & Stokes algorithm is an
+        accelerated form of the Fourier series numerical
+        inverse Laplace transform algorithms.
+
+        .. math ::
+
+            p_k = \gamma + \frac{jk}{T} \qquad 0 \le k < 2M+1
+
+        where
+
+        .. math ::
+
+            \gamma = \alpha - \frac{\log \mathrm{tol}}{2T},
+
+        `j=\sqrt{-1}`, `T = 2t_\mathrm{max}` is a scaled time,
+        `\alpha=10^{-\mathrm{dps\_goal}}` is the real part of the
+        rightmost pole or singularity, which is chosen based on the
+        desired accuracy (assuming the rightmost singularity is 0),
+        and `\mathrm{tol}=10\alpha` is the desired tolerance, which is
+        chosen in relation to `\alpha`.`
+
+        When increasing the degree, the abscissa increase towards
+        `j\infty`, but more slowly than the fixed Talbot
+        algorithm. The de Hoog et al. algorithm typically does better
+        with oscillatory functions of time, and less well-behaved
+        functions. The method tends to be slower than the Talbot and
+        Stehfest algorithsm, especially so at very high precision
+        (e.g., `>500` digits precision).
+
+        """
+
+        # required
+        # ------------------------------
+        self.t = self.ctx.convert(t)
+
+        # optional
+        # ------------------------------
+        self.tmax = kwargs.get('tmax', self.t)
+
+        # empirical relationships used here based on a linear fit of
+        # requested and delivered dps for exponentially decaying time
+        # functions for requested dps up to 512.
+
+        if 'degree' in kwargs:
+            self.degree = kwargs['degree']
+            self.dps_goal = int(1.38*self.degree)
+        else:
+            self.dps_goal = int(self.ctx.dps*1.36)
+            self.degree = max(10, self.dps_goal)
+
+        # 2*M+1 terms in approximation
+        M = self.degree
+
+        # adjust alpha component of abscissa of convergence for higher
+        # precision
+        tmp = self.ctx.power(10.0, -self.dps_goal)
+        self.alpha = self.ctx.convert(kwargs.get('alpha', tmp))
+
+        # desired tolerance (here simply related to alpha)
+        self.tol = self.ctx.convert(kwargs.get('tol', self.alpha*10.0))
+        self.np = 2*self.degree+1  # number of terms in approximation
+
+        # this is adjusting the dps of the calling context
+        # hopefully the caller doesn't monkey around with it
+        # between calling this routine and calc_time_domain_solution()
+        self.dps_orig = self.ctx.dps
+        self.ctx.dps = self.dps_goal
+
+        # scaling factor (likely tun-able, but 2 is typical)
+        self.scale = kwargs.get('scale', 2)
+        self.T = self.ctx.convert(kwargs.get('T', self.scale*self.tmax))
+
+        self.p = self.ctx.matrix(2*M+1, 1)
+        self.gamma = self.alpha - self.ctx.log(self.tol)/(self.scale*self.T)
+        self.p = (self.gamma + self.ctx.pi*
+                  self.ctx.matrix(self.ctx.arange(self.np))/self.T*1j)
+
+        # NB: p is complex (mpc)
+
+    def calc_time_domain_solution(self, fp, t, manual_prec=False):
+        r"""Calculate time-domain solution for
+        de Hoog, Knight & Stokes algorithm.
+
+        The un-accelerated Fourier series approach is:
+
+        .. math ::
+
+            f(t,2M+1) = \frac{e^{\gamma t}}{T} \sum_{k=0}^{2M}{}^{'}
+            \Re\left[\bar{f}\left( p_k \right)
+            e^{i\pi t/T} \right],
+
+        where the prime on the summation indicates the first term is halved.
+
+        This simplistic approach requires so many function evaluations
+        that it is not practical. Non-linear acceleration is
+        accomplished via Pade-approximation and an analytic expression
+        for the remainder of the continued fraction. See the original
+        paper (reference 2 below) a detailed description of the
+        numerical approach.
+
+        **References**
+
+        1. Davies, B. (2005). *Integral Transforms and their
+           Applications*, Third Edition. Springer.
+        2. de Hoog, F., J. Knight, A. Stokes (1982). An improved
+           method for numerical inversion of Laplace transforms. *SIAM
+           Journal of Scientific and Statistical Computing* 3:357-366,
+           http://dx.doi.org/10.1137/0903022
+
+        """
+
+        M = self.degree
+        np = self.np
+        T = self.T
+
+        self.t = self.ctx.convert(t)
+
+        # would it be useful to try re-using
+        # space between e&q and A&B?
+        e = self.ctx.zeros(np, M+1)
+        q = self.ctx.matrix(2*M, M)
+        d = self.ctx.matrix(np, 1)
+        A = self.ctx.zeros(np+1, 1)
+        B = self.ctx.ones(np+1, 1)
+
+        # initialize Q-D table
+        e[:, 0] = 0.0 + 0j
+        q[0, 0] = fp[1]/(fp[0]/2)
+        for i in range(1, 2*M):
+            q[i, 0] = fp[i+1]/fp[i]
+
+        # rhombus rule for filling triangular Q-D table (e & q)
+        for r in range(1, M+1):
+            # start with e, column 1, 0:2*M-2
+            mr = 2*(M-r) + 1
+            e[0:mr, r] = q[1:mr+1, r-1] - q[0:mr, r-1] + e[1:mr+1, r-1]
+            if not r == M:
+                rq = r+1
+                mr = 2*(M-rq)+1 + 2
+                for i in range(mr):
+                    q[i, rq-1] = q[i+1, rq-2]*e[i+1, rq-1]/e[i, rq-1]
+
+        # build up continued fraction coefficients (d)
+        d[0] = fp[0]/2
+        for r in range(1, M+1):
+            d[2*r-1] = -q[0, r-1]  # even terms
+            d[2*r]   = -e[0, r]    # odd terms
+
+        # seed A and B for recurrence
+        A[0] = 0.0 + 0.0j
+        A[1] = d[0]
+        B[0:2] = 1.0 + 0.0j
+
+        # base of the power series
+        z = self.ctx.expjpi(self.t/T)  # i*pi is already in fcn
+
+        # coefficients of Pade approximation (A & B)
+        # using recurrence for all but last term
+        for i in range(1, 2*M):
+            A[i+1] = A[i] + d[i]*A[i-1]*z
+            B[i+1] = B[i] + d[i]*B[i-1]*z
+
+        # "improved remainder" to continued fraction
+        brem = (1 + (d[2*M-1] - d[2*M])*z)/2
+        # powm1(x,y) computes x^y - 1 more accurately near zero
+        rem = brem*self.ctx.powm1(1 + d[2*M]*z/brem,
+                                  self.ctx.fraction(1, 2))
+
+        # last term of recurrence using new remainder
+        A[np] = A[2*M] + rem*A[2*M-1]
+        B[np] = B[2*M] + rem*B[2*M-1]
+
+        # diagonal Pade approximation
+        # F=A/B represents accelerated trapezoid rule
+        result = self.ctx.exp(self.gamma*self.t)/T*(A[np]/B[np]).real
+
+        # setting dps back to value when calc_laplace_parameter was called
+        if not manual_prec:
+            self.ctx.dps = self.dps_orig
+
+        return result
+
+
+# ****************************************
+
+class Cohen(InverseLaplaceTransform):
+
+    def calc_laplace_parameter(self, t, **kwargs):
+        r"""The Cohen algorithm accelerates the convergence of the nearly
+        alternating series resulting from the application of the trapezoidal
+        rule to the Bromwich contour inversion integral.
+
+        .. math ::
+
+            p_k = \frac{\gamma}{2 t} + \frac{\pi i k}{t} \qquad 0 \le k < M
+
+        where
+
+        .. math ::
+
+            \gamma = \frac{2}{3} (d + \log(10) + \log(2 t)),
+
+        `d = \mathrm{dps\_goal}`, which is chosen based on the desired
+        accuracy using the method developed in [1] to improve numerical
+        stability. The Cohen algorithm shows robustness similar to the de Hoog
+        et al. algorithm, but it is faster than the fixed Talbot algorithm.
+
+        **Optional arguments**
+
+        *degree*
+            integer order of the approximation (M = number of terms)
+        *alpha*
+            abscissa for `p_0` (controls the discretization error)
+
+        The working precision will be increased according to a rule of
+        thumb. If 'degree' is not specified, the working precision and
+        degree are chosen to hopefully achieve the dps of the calling
+        context. If 'degree' is specified, the working precision is
+        chosen to achieve maximum resulting precision for the
+        specified degree.
+
+        **References**
+
+        1. P. Glasserman, J. Ruiz-Mata (2006). Computing the credit loss
+        distribution in the Gaussian copula model: a comparison of methods.
+        *Journal of Credit Risk* 2(4):33-66, 10.21314/JCR.2006.057
+
+        """
+        self.t = self.ctx.convert(t)
+
+        if 'degree' in kwargs:
+            self.degree = kwargs['degree']
+            self.dps_goal = int(1.5 * self.degree)
+        else:
+            self.dps_goal = int(self.ctx.dps * 1.74)
+            self.degree = max(22, int(1.31 * self.dps_goal))
+
+        M = self.degree + 1
+
+        # this is adjusting the dps of the calling context hopefully
+        # the caller doesn't monkey around with it between calling
+        # this routine and calc_time_domain_solution()
+        self.dps_orig = self.ctx.dps
+        self.ctx.dps = self.dps_goal
+
+        ttwo = 2 * self.t
+        tmp = self.ctx.dps * self.ctx.log(10) + self.ctx.log(ttwo)
+        tmp = self.ctx.fraction(2, 3) * tmp
+        self.alpha = self.ctx.convert(kwargs.get('alpha', tmp))
+
+        # all but time-dependent part of p
+        a_t = self.alpha / ttwo
+        p_t = self.ctx.pi * 1j / self.t
+
+        self.p = self.ctx.matrix(M, 1)
+        self.p[0] = a_t
+
+        for i in range(1, M):
+            self.p[i] = a_t + i * p_t
+
+    def calc_time_domain_solution(self, fp, t, manual_prec=False):
+        r"""Calculate time-domain solution for Cohen algorithm.
+
+        The accelerated nearly alternating series is:
+
+        .. math ::
+
+            f(t, M) = \frac{e^{\gamma / 2}}{t} \left[\frac{1}{2}
+            \Re\left(\bar{f}\left(\frac{\gamma}{2t}\right) \right) -
+            \sum_{k=0}^{M-1}\frac{c_{M,k}}{d_M}\Re\left(\bar{f}
+            \left(\frac{\gamma + 2(k+1) \pi i}{2t}\right)\right)\right],
+
+        where coefficients `\frac{c_{M, k}}{d_M}` are described in [1].
+
+        1. H. Cohen, F. Rodriguez Villegas, D. Zagier (2000). Convergence
+        acceleration of alternating series. *Experiment. Math* 9(1):3-12
+
+        """
+        self.t = self.ctx.convert(t)
+
+        n = self.degree
+        M = n + 1
+
+        A = self.ctx.matrix(M, 1)
+        for i in range(M):
+            A[i] = fp[i].real
+
+        d = (3 + self.ctx.sqrt(8)) ** n
+        d = (d + 1 / d) / 2
+        b = -self.ctx.one
+        c = -d
+        s = 0
+
+        for k in range(n):
+            c = b - c
+            s = s + c * A[k + 1]
+            b = 2 * (k + n) * (k - n) * b / ((2 * k + 1) * (k + self.ctx.one))
+
+        result = self.ctx.exp(self.alpha / 2) / self.t * (A[0] / 2 - s / d)
+
+        # setting dps back to value when calc_laplace_parameter was
+        # called, unless flag is set.
+        if not manual_prec:
+            self.ctx.dps = self.dps_orig
+
+        return result
+
+
+# ****************************************
+
+class LaplaceTransformInversionMethods(object):
+    def __init__(ctx, *args, **kwargs):
+        ctx._fixed_talbot = FixedTalbot(ctx)
+        ctx._stehfest = Stehfest(ctx)
+        ctx._de_hoog = deHoog(ctx)
+        ctx._cohen = Cohen(ctx)
+
+    def invertlaplace(ctx, f, t, **kwargs):
+        r"""Computes the numerical inverse Laplace transform for a
+        Laplace-space function at a given time.  The function being
+        evaluated is assumed to be a real-valued function of time.
+
+        The user must supply a Laplace-space function `\bar{f}(p)`,
+        and a desired time at which to estimate the time-domain
+        solution `f(t)`.
+
+        A few basic examples of Laplace-space functions with known
+        inverses (see references [1,2]) :
+
+        .. math ::
+
+            \mathcal{L}\left\lbrace f(t) \right\rbrace=\bar{f}(p)
+
+        .. math ::
+
+            \mathcal{L}^{-1}\left\lbrace \bar{f}(p) \right\rbrace = f(t)
+
+        .. math ::
+
+            \bar{f}(p) = \frac{1}{(p+1)^2}
+
+        .. math ::
+
+            f(t) = t e^{-t}
+
+        >>> from mpmath import *
+        >>> mp.dps = 15; mp.pretty = True
+        >>> tt = [0.001, 0.01, 0.1, 1, 10]
+        >>> fp = lambda p: 1/(p+1)**2
+        >>> ft = lambda t: t*exp(-t)
+        >>> ft(tt[0]),ft(tt[0])-invertlaplace(fp,tt[0],method='talbot')
+        (0.000999000499833375, 8.57923043561212e-20)
+        >>> ft(tt[1]),ft(tt[1])-invertlaplace(fp,tt[1],method='talbot')
+        (0.00990049833749168, 3.27007646698047e-19)
+        >>> ft(tt[2]),ft(tt[2])-invertlaplace(fp,tt[2],method='talbot')
+        (0.090483741803596, -1.75215800052168e-18)
+        >>> ft(tt[3]),ft(tt[3])-invertlaplace(fp,tt[3],method='talbot')
+        (0.367879441171442, 1.2428864009344e-17)
+        >>> ft(tt[4]),ft(tt[4])-invertlaplace(fp,tt[4],method='talbot')
+        (0.000453999297624849, 4.04513489306658e-20)
+
+        The methods also work for higher precision:
+
+        >>> mp.dps = 100; mp.pretty = True
+        >>> nstr(ft(tt[0]),15),nstr(ft(tt[0])-invertlaplace(fp,tt[0],method='talbot'),15)
+        ('0.000999000499833375', '-4.96868310693356e-105')
+        >>> nstr(ft(tt[1]),15),nstr(ft(tt[1])-invertlaplace(fp,tt[1],method='talbot'),15)
+        ('0.00990049833749168', '1.23032291513122e-104')
+
+        .. math ::
+
+            \bar{f}(p) = \frac{1}{p^2+1}
+
+        .. math ::
+
+            f(t) = \mathrm{J}_0(t)
+
+        >>> mp.dps = 15; mp.pretty = True
+        >>> fp = lambda p: 1/sqrt(p*p + 1)
+        >>> ft = lambda t: besselj(0,t)
+        >>> ft(tt[0]),ft(tt[0])-invertlaplace(fp,tt[0],method='dehoog')
+        (0.999999750000016, -6.09717765032273e-18)
+        >>> ft(tt[1]),ft(tt[1])-invertlaplace(fp,tt[1],method='dehoog')
+        (0.99997500015625, -5.61756281076169e-17)
+
+        .. math ::
+
+            \bar{f}(p) = \frac{\log p}{p}
+
+        .. math ::
+
+            f(t) = -\gamma -\log t
+
+        >>> mp.dps = 15; mp.pretty = True
+        >>> fp = lambda p: log(p)/p
+        >>> ft = lambda t: -euler-log(t)
+        >>> ft(tt[0]),ft(tt[0])-invertlaplace(fp,tt[0],method='stehfest')
+        (6.3305396140806, -1.92126634837863e-16)
+        >>> ft(tt[1]),ft(tt[1])-invertlaplace(fp,tt[1],method='stehfest')
+        (4.02795452108656, -4.81486093200704e-16)
+
+        **Options**
+
+        :func:`~mpmath.invertlaplace` recognizes the following optional
+        keywords valid for all methods:
+
+        *method*
+            Chooses numerical inverse Laplace transform algorithm
+            (described below).
+        *degree*
+            Number of terms used in the approximation
+
+        **Algorithms**
+
+        Mpmath implements four numerical inverse Laplace transform
+        algorithms, attributed to: Talbot, Stehfest, and de Hoog,
+        Knight and Stokes. These can be selected by using
+        *method='talbot'*, *method='stehfest'*, *method='dehoog'* or
+        *method='cohen'* or by passing the classes *method=FixedTalbot*,
+        *method=Stehfest*, *method=deHoog*, or *method=Cohen*. The functions
+        :func:`~mpmath.invlaptalbot`, :func:`~mpmath.invlapstehfest`,
+        :func:`~mpmath.invlapdehoog`, and :func:`~mpmath.invlapcohen`
+        are also available as shortcuts.
+
+        All four algorithms implement a heuristic balance between the
+        requested precision and the precision used internally for the
+        calculations. This has been tuned for a typical exponentially
+        decaying function and precision up to few hundred decimal
+        digits.
+
+        The Laplace transform converts the variable time (i.e., along
+        a line) into a parameter given by the right half of the
+        complex `p`-plane.  Singularities, poles, and branch cuts in
+        the complex `p`-plane contain all the information regarding
+        the time behavior of the corresponding function. Any numerical
+        method must therefore sample `p`-plane "close enough" to the
+        singularities to accurately characterize them, while not
+        getting too close to have catastrophic cancellation, overflow,
+        or underflow issues. Most significantly, if one or more of the
+        singularities in the `p`-plane is not on the left side of the
+        Bromwich contour, its effects will be left out of the computed
+        solution, and the answer will be completely wrong.
+
+        *Talbot*
+
+        The fixed Talbot method is high accuracy and fast, but the
+        method can catastrophically fail for certain classes of time-domain
+        behavior, including a Heaviside step function for positive
+        time (e.g., `H(t-2)`), or some oscillatory behaviors. The
+        Talbot method usually has adjustable parameters, but the
+        "fixed" variety implemented here does not. This method
+        deforms the Bromwich integral contour in the shape of a
+        parabola towards `-\infty`, which leads to problems
+        when the solution has a decaying exponential in it (e.g., a
+        Heaviside step function is equivalent to multiplying by a
+        decaying exponential in Laplace space).
+
+        *Stehfest*
+
+        The Stehfest algorithm only uses abscissa along the real axis
+        of the complex `p`-plane to estimate the time-domain
+        function. Oscillatory time-domain functions have poles away
+        from the real axis, so this method does not work well with
+        oscillatory functions, especially high-frequency ones. This
+        method also depends on summation of terms in a series that
+        grows very large, and will have catastrophic cancellation
+        during summation if the working precision is too low.
+
+        *de Hoog et al.*
+
+        The de Hoog, Knight, and Stokes method is essentially a
+        Fourier-series quadrature-type approximation to the Bromwich
+        contour integral, with non-linear series acceleration and an
+        analytical expression for the remainder term. This method is
+        typically one of the most robust. This method also involves the
+        greatest amount of overhead, so it is typically the slowest of the
+        four methods at high precision.
+
+        *Cohen*
+
+        The Cohen method is a trapezoidal rule approximation to the Bromwich
+        contour integral, with linear acceleration for alternating
+        series. This method is as robust as the de Hoog et al method and the
+        fastest of the four methods at high precision, and is therefore the
+        default method.
+
+        **Singularities**
+
+        All numerical inverse Laplace transform methods have problems
+        at large time when the Laplace-space function has poles,
+        singularities, or branch cuts to the right of the origin in
+        the complex plane. For simple poles in `\bar{f}(p)` at the
+        `p`-plane origin, the time function is constant in time (e.g.,
+        `\mathcal{L}\left\lbrace 1 \right\rbrace=1/p` has a pole at
+        `p=0`). A pole in `\bar{f}(p)` to the left of the origin is a
+        decreasing function of time (e.g., `\mathcal{L}\left\lbrace
+        e^{-t/2} \right\rbrace=1/(p+1/2)` has a pole at `p=-1/2`), and
+        a pole to the right of the origin leads to an increasing
+        function in time (e.g., `\mathcal{L}\left\lbrace t e^{t/4}
+        \right\rbrace = 1/(p-1/4)^2` has a pole at `p=1/4`).  When
+        singularities occur off the real `p` axis, the time-domain
+        function is oscillatory. For example `\mathcal{L}\left\lbrace
+        \mathrm{J}_0(t) \right\rbrace=1/\sqrt{p^2+1}` has a branch cut
+        starting at `p=j=\sqrt{-1}` and is a decaying oscillatory
+        function, This range of behaviors is illustrated in Duffy [3]
+        Figure 4.10.4, p. 228.
+
+        In general as `p \rightarrow \infty` `t \rightarrow 0` and
+        vice-versa. All numerical inverse Laplace transform methods
+        require their abscissa to shift closer to the origin for
+        larger times. If the abscissa shift left of the rightmost
+        singularity in the Laplace domain, the answer will be
+        completely wrong (the effect of singularities to the right of
+        the Bromwich contour are not included in the results).
+
+        For example, the following exponentially growing function has
+        a pole at `p=3`:
+
+        .. math ::
+
+            \bar{f}(p)=\frac{1}{p^2-9}
+
+        .. math ::
+
+            f(t)=\frac{1}{3}\sinh 3t
+
+        >>> mp.dps = 15; mp.pretty = True
+        >>> fp = lambda p: 1/(p*p-9)
+        >>> ft = lambda t: sinh(3*t)/3
+        >>> tt = [0.01,0.1,1.0,10.0]
+        >>> ft(tt[0]),invertlaplace(fp,tt[0],method='talbot')
+        (0.0100015000675014, 0.0100015000675014)
+        >>> ft(tt[1]),invertlaplace(fp,tt[1],method='talbot')
+        (0.101506764482381, 0.101506764482381)
+        >>> ft(tt[2]),invertlaplace(fp,tt[2],method='talbot')
+        (3.33929164246997, 3.33929164246997)
+        >>> ft(tt[3]),invertlaplace(fp,tt[3],method='talbot')
+        (1781079096920.74, -1.61331069624091e-14)
+
+        **References**
+
+        1. [DLMF]_ section 1.14 (http://dlmf.nist.gov/1.14T4)
+        2. Cohen, A.M. (2007). Numerical Methods for Laplace Transform
+           Inversion, Springer.
+        3. Duffy, D.G. (1998). Advanced Engineering Mathematics, CRC Press.
+
+        **Numerical Inverse Laplace Transform Reviews**
+
+        1. Bellman, R., R.E. Kalaba, J.A. Lockett (1966). *Numerical
+           inversion of the Laplace transform: Applications to Biology,
+           Economics, Engineering, and Physics*. Elsevier.
+        2. Davies, B., B. Martin (1979). Numerical inversion of the
+           Laplace transform: a survey and comparison of methods. *Journal
+           of Computational Physics* 33:1-32,
+           http://dx.doi.org/10.1016/0021-9991(79)90025-1
+        3. Duffy, D.G. (1993). On the numerical inversion of Laplace
+           transforms: Comparison of three new methods on characteristic
+           problems from applications. *ACM Transactions on Mathematical
+           Software* 19(3):333-359, http://dx.doi.org/10.1145/155743.155788
+        4. Kuhlman, K.L., (2013). Review of Inverse Laplace Transform
+           Algorithms for Laplace-Space Numerical Approaches, *Numerical
+           Algorithms*, 63(2):339-355.
+           http://dx.doi.org/10.1007/s11075-012-9625-3
+
+        """
+
+        rule = kwargs.get('method', 'cohen')
+        if type(rule) is str:
+            lrule = rule.lower()
+            if lrule == 'talbot':
+                rule = ctx._fixed_talbot
+            elif lrule == 'stehfest':
+                rule = ctx._stehfest
+            elif lrule == 'dehoog':
+                rule = ctx._de_hoog
+            elif rule == 'cohen':
+                rule = ctx._cohen
+            else:
+                raise ValueError("unknown invlap algorithm: %s" % rule)
+        else:
+            rule = rule(ctx)
+
+        # determine the vector of Laplace-space parameter
+        # needed for the requested method and desired time
+        rule.calc_laplace_parameter(t, **kwargs)
+
+        # compute the Laplace-space function evalutations
+        # at the required abscissa.
+        fp = [f(p) for p in rule.p]
+
+        # compute the time-domain solution from the
+        # Laplace-space function evaluations
+        return rule.calc_time_domain_solution(fp, t)
+
+    # shortcuts for the above function for specific methods
+    def invlaptalbot(ctx, *args, **kwargs):
+        kwargs['method'] = 'talbot'
+        return ctx.invertlaplace(*args, **kwargs)
+
+    def invlapstehfest(ctx, *args, **kwargs):
+        kwargs['method'] = 'stehfest'
+        return ctx.invertlaplace(*args, **kwargs)
+
+    def invlapdehoog(ctx, *args, **kwargs):
+        kwargs['method'] = 'dehoog'
+        return ctx.invertlaplace(*args, **kwargs)
+
+    def invlapcohen(ctx, *args, **kwargs):
+        kwargs['method'] = 'cohen'
+        return ctx.invertlaplace(*args, **kwargs)
+
+
+# ****************************************
+
+if __name__ == '__main__':
+    import doctest
+    doctest.testmod()

venv/lib/python3.10/site-packages/mpmath/calculus/odes.py

@@ -0,0 +1,288 @@
+from bisect import bisect
+from ..libmp.backend import xrange
+
+class ODEMethods(object):
+    pass
+
+def ode_taylor(ctx, derivs, x0, y0, tol_prec, n):
+    h = tol = ctx.ldexp(1, -tol_prec)
+    dim = len(y0)
+    xs = [x0]
+    ys = [y0]
+    x = x0
+    y = y0
+    orig = ctx.prec
+    try:
+        ctx.prec = orig*(1+n)
+        # Use n steps with Euler's method to get
+        # evaluation points for derivatives
+        for i in range(n):
+            fxy = derivs(x, y)
+            y = [y[i]+h*fxy[i] for i in xrange(len(y))]
+            x += h
+            xs.append(x)
+            ys.append(y)
+        # Compute derivatives
+        ser = [[] for d in range(dim)]
+        for j in range(n+1):
+            s = [0]*dim
+            b = (-1) ** (j & 1)
+            k = 1
+            for i in range(j+1):
+                for d in range(dim):
+                    s[d] += b * ys[i][d]
+                b = (b * (j-k+1)) // (-k)
+                k += 1
+            scale = h**(-j) / ctx.fac(j)
+            for d in range(dim):
+                s[d] = s[d] * scale
+                ser[d].append(s[d])
+    finally:
+        ctx.prec = orig
+    # Estimate radius for which we can get full accuracy.
+    # XXX: do this right for zeros
+    radius = ctx.one
+    for ts in ser:
+        if ts[-1]:
+            radius = min(radius, ctx.nthroot(tol/abs(ts[-1]), n))
+    radius /= 2  # XXX
+    return ser, x0+radius
+
+def odefun(ctx, F, x0, y0, tol=None, degree=None, method='taylor', verbose=False):
+    r"""
+    Returns a function `y(x) = [y_0(x), y_1(x), \ldots, y_n(x)]`
+    that is a numerical solution of the `n+1`-dimensional first-order
+    ordinary differential equation (ODE) system
+
+    .. math ::
+
+        y_0'(x) = F_0(x, [y_0(x), y_1(x), \ldots, y_n(x)])
+
+        y_1'(x) = F_1(x, [y_0(x), y_1(x), \ldots, y_n(x)])
+
+        \vdots
+
+        y_n'(x) = F_n(x, [y_0(x), y_1(x), \ldots, y_n(x)])
+
+    The derivatives are specified by the vector-valued function
+    *F* that evaluates
+    `[y_0', \ldots, y_n'] = F(x, [y_0, \ldots, y_n])`.
+    The initial point `x_0` is specified by the scalar argument *x0*,
+    and the initial value `y(x_0) =  [y_0(x_0), \ldots, y_n(x_0)]` is
+    specified by the vector argument *y0*.
+
+    For convenience, if the system is one-dimensional, you may optionally
+    provide just a scalar value for *y0*. In this case, *F* should accept
+    a scalar *y* argument and return a scalar. The solution function
+    *y* will return scalar values instead of length-1 vectors.
+
+    Evaluation of the solution function `y(x)` is permitted
+    for any `x \ge x_0`.
+
+    A high-order ODE can be solved by transforming it into first-order
+    vector form. This transformation is described in standard texts
+    on ODEs. Examples will also be given below.
+
+    **Options, speed and accuracy**
+
+    By default, :func:`~mpmath.odefun` uses a high-order Taylor series
+    method. For reasonably well-behaved problems, the solution will
+    be fully accurate to within the working precision. Note that
+    *F* must be possible to evaluate to very high precision
+    for the generation of Taylor series to work.
+
+    To get a faster but less accurate solution, you can set a large
+    value for *tol* (which defaults roughly to *eps*). If you just
+    want to plot the solution or perform a basic simulation,
+    *tol = 0.01* is likely sufficient.
+
+    The *degree* argument controls the degree of the solver (with
+    *method='taylor'*, this is the degree of the Taylor series
+    expansion). A higher degree means that a longer step can be taken
+    before a new local solution must be generated from *F*,
+    meaning that fewer steps are required to get from `x_0` to a given
+    `x_1`. On the other hand, a higher degree also means that each
+    local solution becomes more expensive (i.e., more evaluations of
+    *F* are required per step, and at higher precision).
+
+    The optimal setting therefore involves a tradeoff. Generally,
+    decreasing the *degree* for Taylor series is likely to give faster
+    solution at low precision, while increasing is likely to be better
+    at higher precision.
+
+    The function
+    object returned by :func:`~mpmath.odefun` caches the solutions at all step
+    points and uses polynomial interpolation between step points.
+    Therefore, once `y(x_1)` has been evaluated for some `x_1`,
+    `y(x)` can be evaluated very quickly for any `x_0 \le x \le x_1`.
+    and continuing the evaluation up to `x_2 > x_1` is also fast.
+
+    **Examples of first-order ODEs**
+
+    We will solve the standard test problem `y'(x) = y(x), y(0) = 1`
+    which has explicit solution `y(x) = \exp(x)`::
+
+        >>> from mpmath import *
+        >>> mp.dps = 15; mp.pretty = True
+        >>> f = odefun(lambda x, y: y, 0, 1)
+        >>> for x in [0, 1, 2.5]:
+        ...     print((f(x), exp(x)))
+        ...
+        (1.0, 1.0)
+        (2.71828182845905, 2.71828182845905)
+        (12.1824939607035, 12.1824939607035)
+
+    The solution with high precision::
+
+        >>> mp.dps = 50
+        >>> f = odefun(lambda x, y: y, 0, 1)
+        >>> f(1)
+        2.7182818284590452353602874713526624977572470937
+        >>> exp(1)
+        2.7182818284590452353602874713526624977572470937
+
+    Using the more general vectorized form, the test problem
+    can be input as (note that *f* returns a 1-element vector)::
+
+        >>> mp.dps = 15
+        >>> f = odefun(lambda x, y: [y[0]], 0, [1])
+        >>> f(1)
+        [2.71828182845905]
+
+    :func:`~mpmath.odefun` can solve nonlinear ODEs, which are generally
+    impossible (and at best difficult) to solve analytically. As
+    an example of a nonlinear ODE, we will solve `y'(x) = x \sin(y(x))`
+    for `y(0) = \pi/2`. An exact solution happens to be known
+    for this problem, and is given by
+    `y(x) = 2 \tan^{-1}\left(\exp\left(x^2/2\right)\right)`::
+
+        >>> f = odefun(lambda x, y: x*sin(y), 0, pi/2)
+        >>> for x in [2, 5, 10]:
+        ...     print((f(x), 2*atan(exp(mpf(x)**2/2))))
+        ...
+        (2.87255666284091, 2.87255666284091)
+        (3.14158520028345, 3.14158520028345)
+        (3.14159265358979, 3.14159265358979)
+
+    If `F` is independent of `y`, an ODE can be solved using direct
+    integration. We can therefore obtain a reference solution with
+    :func:`~mpmath.quad`::
+
+        >>> f = lambda x: (1+x**2)/(1+x**3)
+        >>> g = odefun(lambda x, y: f(x), pi, 0)
+        >>> g(2*pi)
+        0.72128263801696
+        >>> quad(f, [pi, 2*pi])
+        0.72128263801696
+
+    **Examples of second-order ODEs**
+
+    We will solve the harmonic oscillator equation `y''(x) + y(x) = 0`.
+    To do this, we introduce the helper functions `y_0 = y, y_1 = y_0'`
+    whereby the original equation can be written as `y_1' + y_0' = 0`. Put
+    together, we get the first-order, two-dimensional vector ODE
+
+    .. math ::
+
+        \begin{cases}
+        y_0' = y_1 \\
+        y_1' = -y_0
+        \end{cases}
+
+    To get a well-defined IVP, we need two initial values. With
+    `y(0) = y_0(0) = 1` and `-y'(0) = y_1(0) = 0`, the problem will of
+    course be solved by `y(x) = y_0(x) = \cos(x)` and
+    `-y'(x) = y_1(x) = \sin(x)`. We check this::
+
+        >>> f = odefun(lambda x, y: [-y[1], y[0]], 0, [1, 0])
+        >>> for x in [0, 1, 2.5, 10]:
+        ...     nprint(f(x), 15)
+        ...     nprint([cos(x), sin(x)], 15)
+        ...     print("---")
+        ...
+        [1.0, 0.0]
+        [1.0, 0.0]
+        ---
+        [0.54030230586814, 0.841470984807897]
+        [0.54030230586814, 0.841470984807897]
+        ---
+        [-0.801143615546934, 0.598472144103957]
+        [-0.801143615546934, 0.598472144103957]
+        ---
+        [-0.839071529076452, -0.54402111088937]
+        [-0.839071529076452, -0.54402111088937]
+        ---
+
+    Note that we get both the sine and the cosine solutions
+    simultaneously.
+
+    **TODO**
+
+    * Better automatic choice of degree and step size
+    * Make determination of Taylor series convergence radius
+      more robust
+    * Allow solution for `x < x_0`
+    * Allow solution for complex `x`
+    * Test for difficult (ill-conditioned) problems
+    * Implement Runge-Kutta and other algorithms
+
+    """
+    if tol:
+        tol_prec = int(-ctx.log(tol, 2))+10
+    else:
+        tol_prec = ctx.prec+10
+    degree = degree or (3 + int(3*ctx.dps/2.))
+    workprec = ctx.prec + 40
+    try:
+        len(y0)
+        return_vector = True
+    except TypeError:
+        F_ = F
+        F = lambda x, y: [F_(x, y[0])]
+        y0 = [y0]
+        return_vector = False
+    ser, xb = ode_taylor(ctx, F, x0, y0, tol_prec, degree)
+    series_boundaries = [x0, xb]
+    series_data = [(ser, x0, xb)]
+    # We will be working with vectors of Taylor series
+    def mpolyval(ser, a):
+        return [ctx.polyval(s[::-1], a) for s in ser]
+    # Find nearest expansion point; compute if necessary
+    def get_series(x):
+        if x < x0:
+            raise ValueError
+        n = bisect(series_boundaries, x)
+        if n < len(series_boundaries):
+            return series_data[n-1]
+        while 1:
+            ser, xa, xb = series_data[-1]
+            if verbose:
+                print("Computing Taylor series for [%f, %f]" % (xa, xb))
+            y = mpolyval(ser, xb-xa)
+            xa = xb
+            ser, xb = ode_taylor(ctx, F, xb, y, tol_prec, degree)
+            series_boundaries.append(xb)
+            series_data.append((ser, xa, xb))
+            if x <= xb:
+                return series_data[-1]
+    # Evaluation function
+    def interpolant(x):
+        x = ctx.convert(x)
+        orig = ctx.prec
+        try:
+            ctx.prec = workprec
+            ser, xa, xb = get_series(x)
+            y = mpolyval(ser, x-xa)
+        finally:
+            ctx.prec = orig
+        if return_vector:
+            return [+yk for yk in y]
+        else:
+            return +y[0]
+    return interpolant
+
+ODEMethods.odefun = odefun
+
+if __name__ == "__main__":
+    import doctest
+    doctest.testmod()

venv/lib/python3.10/site-packages/mpmath/calculus/optimization.py

@@ -0,0 +1,1102 @@
+from __future__ import print_function
+
+from copy import copy
+
+from ..libmp.backend import xrange
+
+class OptimizationMethods(object):
+    def __init__(ctx):
+        pass
+
+##############
+# 1D-SOLVERS #
+##############
+
+class Newton:
+    """
+    1d-solver generating pairs of approximative root and error.
+
+    Needs starting points x0 close to the root.
+
+    Pro:
+
+    * converges fast
+    * sometimes more robust than secant with bad second starting point
+
+    Contra:
+
+    * converges slowly for multiple roots
+    * needs first derivative
+    * 2 function evaluations per iteration
+    """
+    maxsteps = 20
+
+    def __init__(self, ctx, f, x0, **kwargs):
+        self.ctx = ctx
+        if len(x0) == 1:
+            self.x0 = x0[0]
+        else:
+            raise ValueError('expected 1 starting point, got %i' % len(x0))
+        self.f = f
+        if not 'df' in kwargs:
+            def df(x):
+                return self.ctx.diff(f, x)
+        else:
+            df = kwargs['df']
+        self.df = df
+
+    def __iter__(self):
+        f = self.f
+        df = self.df
+        x0 = self.x0
+        while True:
+            x1 = x0 - f(x0) / df(x0)
+            error = abs(x1 - x0)
+            x0 = x1
+            yield (x1, error)
+
+class Secant:
+    """
+    1d-solver generating pairs of approximative root and error.
+
+    Needs starting points x0 and x1 close to the root.
+    x1 defaults to x0 + 0.25.
+
+    Pro:
+
+    * converges fast
+
+    Contra:
+
+    * converges slowly for multiple roots
+    """
+    maxsteps = 30
+
+    def __init__(self, ctx, f, x0, **kwargs):
+        self.ctx = ctx
+        if len(x0) == 1:
+            self.x0 = x0[0]
+            self.x1 = self.x0 + 0.25
+        elif len(x0) == 2:
+            self.x0 = x0[0]
+            self.x1 = x0[1]
+        else:
+            raise ValueError('expected 1 or 2 starting points, got %i' % len(x0))
+        self.f = f
+
+    def __iter__(self):
+        f = self.f
+        x0 = self.x0
+        x1 = self.x1
+        f0 = f(x0)
+        while True:
+            f1 = f(x1)
+            l = x1 - x0
+            if not l:
+                break
+            s = (f1 - f0) / l
+            if not s:
+                break
+            x0, x1 = x1, x1 - f1/s
+            f0 = f1
+            yield x1, abs(l)
+
+class MNewton:
+    """
+    1d-solver generating pairs of approximative root and error.
+
+    Needs starting point x0 close to the root.
+    Uses modified Newton's method that converges fast regardless of the
+    multiplicity of the root.
+
+    Pro:
+
+    * converges fast for multiple roots
+
+    Contra:
+
+    * needs first and second derivative of f
+    * 3 function evaluations per iteration
+    """
+    maxsteps = 20
+
+    def __init__(self, ctx, f, x0, **kwargs):
+        self.ctx = ctx
+        if not len(x0) == 1:
+            raise ValueError('expected 1 starting point, got %i' % len(x0))
+        self.x0 = x0[0]
+        self.f = f
+        if not 'df' in kwargs:
+            def df(x):
+                return self.ctx.diff(f, x)
+        else:
+            df = kwargs['df']
+        self.df = df
+        if not 'd2f' in kwargs:
+            def d2f(x):
+                return self.ctx.diff(df, x)
+        else:
+            d2f = kwargs['df']
+        self.d2f = d2f
+
+    def __iter__(self):
+        x = self.x0
+        f = self.f
+        df = self.df
+        d2f = self.d2f
+        while True:
+            prevx = x
+            fx = f(x)
+            if fx == 0:
+                break
+            dfx = df(x)
+            d2fx = d2f(x)
+            # x = x - F(x)/F'(x) with F(x) = f(x)/f'(x)
+            x -= fx / (dfx - fx * d2fx / dfx)
+            error = abs(x - prevx)
+            yield x, error
+
+class Halley:
+    """
+    1d-solver generating pairs of approximative root and error.
+
+    Needs a starting point x0 close to the root.
+    Uses Halley's method with cubic convergence rate.
+
+    Pro:
+
+    * converges even faster the Newton's method
+    * useful when computing with *many* digits
+
+    Contra:
+
+    * needs first and second derivative of f
+    * 3 function evaluations per iteration
+    * converges slowly for multiple roots
+    """
+
+    maxsteps = 20
+
+    def __init__(self, ctx, f, x0, **kwargs):
+        self.ctx = ctx
+        if not len(x0) == 1:
+            raise ValueError('expected 1 starting point, got %i' % len(x0))
+        self.x0 = x0[0]
+        self.f = f
+        if not 'df' in kwargs:
+            def df(x):
+                return self.ctx.diff(f, x)
+        else:
+            df = kwargs['df']
+        self.df = df
+        if not 'd2f' in kwargs:
+            def d2f(x):
+                return self.ctx.diff(df, x)
+        else:
+            d2f = kwargs['df']
+        self.d2f = d2f
+
+    def __iter__(self):
+        x = self.x0
+        f = self.f
+        df = self.df
+        d2f = self.d2f
+        while True:
+            prevx = x
+            fx = f(x)
+            dfx = df(x)
+            d2fx = d2f(x)
+            x -=  2*fx*dfx / (2*dfx**2 - fx*d2fx)
+            error = abs(x - prevx)
+            yield x, error
+
+class Muller:
+    """
+    1d-solver generating pairs of approximative root and error.
+
+    Needs starting points x0, x1 and x2 close to the root.
+    x1 defaults to x0 + 0.25; x2 to x1 + 0.25.
+    Uses Muller's method that converges towards complex roots.
+
+    Pro:
+
+    * converges fast (somewhat faster than secant)
+    * can find complex roots
+
+    Contra:
+
+    * converges slowly for multiple roots
+    * may have complex values for real starting points and real roots
+
+    http://en.wikipedia.org/wiki/Muller's_method
+    """
+    maxsteps = 30
+
+    def __init__(self, ctx, f, x0, **kwargs):
+        self.ctx = ctx
+        if len(x0) == 1:
+            self.x0 = x0[0]
+            self.x1 = self.x0 + 0.25
+            self.x2 = self.x1 + 0.25
+        elif len(x0) == 2:
+            self.x0 = x0[0]
+            self.x1 = x0[1]
+            self.x2 = self.x1 + 0.25
+        elif len(x0) == 3:
+            self.x0 = x0[0]
+            self.x1 = x0[1]
+            self.x2 = x0[2]
+        else:
+            raise ValueError('expected 1, 2 or 3 starting points, got %i'
+                             % len(x0))
+        self.f = f
+        self.verbose = kwargs['verbose']
+
+    def __iter__(self):
+        f = self.f
+        x0 = self.x0
+        x1 = self.x1
+        x2 = self.x2
+        fx0 = f(x0)
+        fx1 = f(x1)
+        fx2 = f(x2)
+        while True:
+            # TODO: maybe refactoring with function for divided differences
+            # calculate divided differences
+            fx2x1 = (fx1 - fx2) / (x1 - x2)
+            fx2x0 = (fx0 - fx2) / (x0 - x2)
+            fx1x0 = (fx0 - fx1) / (x0 - x1)
+            w = fx2x1 + fx2x0 - fx1x0
+            fx2x1x0 = (fx1x0 - fx2x1) / (x0 - x2)
+            if w == 0 and fx2x1x0 == 0:
+                if self.verbose:
+                    print('canceled with')
+                    print('x0 =', x0, ', x1 =', x1, 'and x2 =', x2)
+                break
+            x0 = x1
+            fx0 = fx1
+            x1 = x2
+            fx1 = fx2
+            # denominator should be as large as possible => choose sign
+            r = self.ctx.sqrt(w**2 - 4*fx2*fx2x1x0)
+            if abs(w - r) > abs(w + r):
+                r = -r
+            x2 -= 2*fx2 / (w + r)
+            fx2 = f(x2)
+            error = abs(x2 - x1)
+            yield x2, error
+
+# TODO: consider raising a ValueError when there's no sign change in a and b
+class Bisection:
+    """
+    1d-solver generating pairs of approximative root and error.
+
+    Uses bisection method to find a root of f in [a, b].
+    Might fail for multiple roots (needs sign change).
+
+    Pro:
+
+    * robust and reliable
+
+    Contra:
+
+    * converges slowly
+    * needs sign change
+    """
+    maxsteps = 100
+
+    def __init__(self, ctx, f, x0, **kwargs):
+        self.ctx = ctx
+        if len(x0) != 2:
+            raise ValueError('expected interval of 2 points, got %i' % len(x0))
+        self.f = f
+        self.a = x0[0]
+        self.b = x0[1]
+
+    def __iter__(self):
+        f = self.f
+        a = self.a
+        b = self.b
+        l = b - a
+        fb = f(b)
+        while True:
+            m = self.ctx.ldexp(a + b, -1)
+            fm = f(m)
+            sign = fm * fb
+            if sign < 0:
+                a = m
+            elif sign > 0:
+                b = m
+                fb = fm
+            else:
+                yield m, self.ctx.zero
+            l /= 2
+            yield (a + b)/2, abs(l)
+
+def _getm(method):
+    """
+    Return a function to calculate m for Illinois-like methods.
+    """
+    if method == 'illinois':
+        def getm(fz, fb):
+            return 0.5
+    elif method == 'pegasus':
+        def getm(fz, fb):
+            return fb/(fb + fz)
+    elif method == 'anderson':
+        def getm(fz, fb):
+            m = 1 - fz/fb
+            if m > 0:
+                return m
+            else:
+                return 0.5
+    else:
+        raise ValueError("method '%s' not recognized" % method)
+    return getm
+
+class Illinois:
+    """
+    1d-solver generating pairs of approximative root and error.
+
+    Uses Illinois method or similar to find a root of f in [a, b].
+    Might fail for multiple roots (needs sign change).
+    Combines bisect with secant (improved regula falsi).
+
+    The only difference between the methods is the scaling factor m, which is
+    used to ensure convergence (you can choose one using the 'method' keyword):
+
+    Illinois method ('illinois'):
+        m = 0.5
+
+    Pegasus method ('pegasus'):
+        m = fb/(fb + fz)
+
+    Anderson-Bjoerk method ('anderson'):
+        m = 1 - fz/fb if positive else 0.5
+
+    Pro:
+
+    * converges very fast
+
+    Contra:
+
+    * has problems with multiple roots
+    * needs sign change
+    """
+    maxsteps = 30
+
+    def __init__(self, ctx, f, x0, **kwargs):
+        self.ctx = ctx
+        if len(x0) != 2:
+            raise ValueError('expected interval of 2 points, got %i' % len(x0))
+        self.a = x0[0]
+        self.b = x0[1]
+        self.f = f
+        self.tol = kwargs['tol']
+        self.verbose = kwargs['verbose']
+        self.method = kwargs.get('method', 'illinois')
+        self.getm = _getm(self.method)
+        if self.verbose:
+            print('using %s method' % self.method)
+
+    def __iter__(self):
+        method = self.method
+        f = self.f
+        a = self.a
+        b = self.b
+        fa = f(a)
+        fb = f(b)
+        m = None
+        while True:
+            l = b - a
+            if l == 0:
+                break
+            s = (fb - fa) / l
+            z = a - fa/s
+            fz = f(z)
+            if abs(fz) < self.tol:
+                # TODO: better condition (when f is very flat)
+                if self.verbose:
+                    print('canceled with z =', z)
+                yield z, l
+                break
+            if fz * fb < 0: # root in [z, b]
+                a = b
+                fa = fb
+                b = z
+                fb = fz
+            else: # root in [a, z]
+                m = self.getm(fz, fb)
+                b = z
+                fb = fz
+                fa = m*fa # scale down to ensure convergence
+            if self.verbose and m and not method == 'illinois':
+                print('m:', m)
+            yield (a + b)/2, abs(l)
+
+def Pegasus(*args, **kwargs):
+    """
+    1d-solver generating pairs of approximative root and error.
+
+    Uses Pegasus method to find a root of f in [a, b].
+    Wrapper for illinois to use method='pegasus'.
+    """
+    kwargs['method'] = 'pegasus'
+    return Illinois(*args, **kwargs)
+
+def Anderson(*args, **kwargs):
+    """
+    1d-solver generating pairs of approximative root and error.
+
+    Uses Anderson-Bjoerk method to find a root of f in [a, b].
+    Wrapper for illinois to use method='pegasus'.
+    """
+    kwargs['method'] = 'anderson'
+    return Illinois(*args, **kwargs)
+
+# TODO: check whether it's possible to combine it with Illinois stuff
+class Ridder:
+    """
+    1d-solver generating pairs of approximative root and error.
+
+    Ridders' method to find a root of f in [a, b].
+    Is told to perform as well as Brent's method while being simpler.
+
+    Pro:
+
+    * very fast
+    * simpler than Brent's method
+
+    Contra:
+
+    * two function evaluations per step
+    * has problems with multiple roots
+    * needs sign change
+
+    http://en.wikipedia.org/wiki/Ridders'_method
+    """
+    maxsteps = 30
+
+    def __init__(self, ctx, f, x0, **kwargs):
+        self.ctx = ctx
+        self.f = f
+        if len(x0) != 2:
+            raise ValueError('expected interval of 2 points, got %i' % len(x0))
+        self.x1 = x0[0]
+        self.x2 = x0[1]
+        self.verbose = kwargs['verbose']
+        self.tol = kwargs['tol']
+
+    def __iter__(self):
+        ctx = self.ctx
+        f = self.f
+        x1 = self.x1
+        fx1 = f(x1)
+        x2 = self.x2
+        fx2 = f(x2)
+        while True:
+            x3 = 0.5*(x1 + x2)
+            fx3 = f(x3)
+            x4 = x3 + (x3 - x1) * ctx.sign(fx1 - fx2) * fx3 / ctx.sqrt(fx3**2 - fx1*fx2)
+            fx4 = f(x4)
+            if abs(fx4) < self.tol:
+                # TODO: better condition (when f is very flat)
+                if self.verbose:
+                    print('canceled with f(x4) =', fx4)
+                yield x4, abs(x1 - x2)
+                break
+            if fx4 * fx2 < 0: # root in [x4, x2]
+                x1 = x4
+                fx1 = fx4
+            else: # root in [x1, x4]
+                x2 = x4
+                fx2 = fx4
+            error = abs(x1 - x2)
+            yield (x1 + x2)/2, error
+
+class ANewton:
+    """
+    EXPERIMENTAL 1d-solver generating pairs of approximative root and error.
+
+    Uses Newton's method modified to use Steffensens method when convergence is
+    slow. (I.e. for multiple roots.)
+    """
+    maxsteps = 20
+
+    def __init__(self, ctx, f, x0, **kwargs):
+        self.ctx = ctx
+        if not len(x0) == 1:
+            raise ValueError('expected 1 starting point, got %i' % len(x0))
+        self.x0 = x0[0]
+        self.f = f
+        if not 'df' in kwargs:
+            def df(x):
+                return self.ctx.diff(f, x)
+        else:
+            df = kwargs['df']
+        self.df = df
+        def phi(x):
+            return x - f(x) / df(x)
+        self.phi = phi
+        self.verbose = kwargs['verbose']
+
+    def __iter__(self):
+        x0 = self.x0
+        f = self.f
+        df = self.df
+        phi = self.phi
+        error = 0
+        counter = 0
+        while True:
+            prevx = x0
+            try:
+                x0 = phi(x0)
+            except ZeroDivisionError:
+                if self.verbose:
+                    print('ZeroDivisionError: canceled with x =', x0)
+                break
+            preverror = error
+            error = abs(prevx - x0)
+            # TODO: decide not to use convergence acceleration
+            if error and abs(error - preverror) / error < 1:
+                if self.verbose:
+                    print('converging slowly')
+                counter += 1
+            if counter >= 3:
+                # accelerate convergence
+                phi = steffensen(phi)
+                counter = 0
+                if self.verbose:
+                    print('accelerating convergence')
+            yield x0, error
+
+# TODO: add Brent
+
+############################
+# MULTIDIMENSIONAL SOLVERS #
+############################
+
+def jacobian(ctx, f, x):
+    """
+    Calculate the Jacobian matrix of a function at the point x0.
+
+    This is the first derivative of a vectorial function:
+
+        f : R^m -> R^n with m >= n
+    """
+    x = ctx.matrix(x)
+    h = ctx.sqrt(ctx.eps)
+    fx = ctx.matrix(f(*x))
+    m = len(fx)
+    n = len(x)
+    J = ctx.matrix(m, n)
+    for j in xrange(n):
+        xj = x.copy()
+        xj[j] += h
+        Jj = (ctx.matrix(f(*xj)) - fx) / h
+        for i in xrange(m):
+            J[i,j] = Jj[i]
+    return J
+
+# TODO: test with user-specified jacobian matrix
+class MDNewton:
+    """
+    Find the root of a vector function numerically using Newton's method.
+
+    f is a vector function representing a nonlinear equation system.
+
+    x0 is the starting point close to the root.
+
+    J is a function returning the Jacobian matrix for a point.
+
+    Supports overdetermined systems.
+
+    Use the 'norm' keyword to specify which norm to use. Defaults to max-norm.
+    The function to calculate the Jacobian matrix can be given using the
+    keyword 'J'. Otherwise it will be calculated numerically.
+
+    Please note that this method converges only locally. Especially for high-
+    dimensional systems it is not trivial to find a good starting point being
+    close enough to the root.
+
+    It is recommended to use a faster, low-precision solver from SciPy [1] or
+    OpenOpt [2] to get an initial guess. Afterwards you can use this method for
+    root-polishing to any precision.
+
+    [1] http://scipy.org
+
+    [2] http://openopt.org/Welcome
+    """
+    maxsteps = 10
+
+    def __init__(self, ctx, f, x0, **kwargs):
+        self.ctx = ctx
+        self.f = f
+        if isinstance(x0, (tuple, list)):
+            x0 = ctx.matrix(x0)
+        assert x0.cols == 1, 'need a vector'
+        self.x0 = x0
+        if 'J' in kwargs:
+            self.J = kwargs['J']
+        else:
+            def J(*x):
+                return ctx.jacobian(f, x)
+            self.J = J
+        self.norm = kwargs['norm']
+        self.verbose = kwargs['verbose']
+
+    def __iter__(self):
+        f = self.f
+        x0 = self.x0
+        norm = self.norm
+        J = self.J
+        fx = self.ctx.matrix(f(*x0))
+        fxnorm = norm(fx)
+        cancel = False
+        while not cancel:
+            # get direction of descent
+            fxn = -fx
+            Jx = J(*x0)
+            s = self.ctx.lu_solve(Jx, fxn)
+            if self.verbose:
+                print('Jx:')
+                print(Jx)
+                print('s:', s)
+            # damping step size TODO: better strategy (hard task)
+            l = self.ctx.one
+            x1 = x0 + s
+            while True:
+                if x1 == x0:
+                    if self.verbose:
+                        print("canceled, won't get more excact")
+                    cancel = True
+                    break
+                fx = self.ctx.matrix(f(*x1))
+                newnorm = norm(fx)
+                if newnorm < fxnorm:
+                    # new x accepted
+                    fxnorm = newnorm
+                    x0 = x1
+                    break
+                l /= 2
+                x1 = x0 + l*s
+            yield (x0, fxnorm)
+
+#############
+# UTILITIES #
+#############
+
+str2solver = {'newton':Newton, 'secant':Secant, 'mnewton':MNewton,
+              'halley':Halley, 'muller':Muller, 'bisect':Bisection,
+              'illinois':Illinois, 'pegasus':Pegasus, 'anderson':Anderson,
+              'ridder':Ridder, 'anewton':ANewton, 'mdnewton':MDNewton}
+
+def findroot(ctx, f, x0, solver='secant', tol=None, verbose=False, verify=True, **kwargs):
+    r"""
+    Find an approximate solution to `f(x) = 0`, using *x0* as starting point or
+    interval for *x*.
+
+    Multidimensional overdetermined systems are supported.
+    You can specify them using a function or a list of functions.
+
+    Mathematically speaking, this function returns `x` such that
+    `|f(x)|^2 \leq \mathrm{tol}` is true within the current working precision.
+    If the computed value does not meet this criterion, an exception is raised.
+    This exception can be disabled with *verify=False*.
+
+    For interval arithmetic (``iv.findroot()``), please note that
+    the returned interval ``x`` is not guaranteed to contain `f(x)=0`!
+    It is only some `x` for which `|f(x)|^2 \leq \mathrm{tol}` certainly holds
+    regardless of numerical error. This may be improved in the future.
+
+    **Arguments**
+
+    *f*
+        one dimensional function
+    *x0*
+        starting point, several starting points or interval (depends on solver)
+    *tol*
+        the returned solution has an error smaller than this
+    *verbose*
+        print additional information for each iteration if true
+    *verify*
+        verify the solution and raise a ValueError if `|f(x)|^2 > \mathrm{tol}`
+    *solver*
+        a generator for *f* and *x0* returning approximative solution and error
+    *maxsteps*
+        after how many steps the solver will cancel
+    *df*
+        first derivative of *f* (used by some solvers)
+    *d2f*
+        second derivative of *f* (used by some solvers)
+    *multidimensional*
+        force multidimensional solving
+    *J*
+        Jacobian matrix of *f* (used by multidimensional solvers)
+    *norm*
+        used vector norm (used by multidimensional solvers)
+
+    solver has to be callable with ``(f, x0, **kwargs)`` and return an generator
+    yielding pairs of approximative solution and estimated error (which is
+    expected to be positive).
+    You can use the following string aliases:
+    'secant', 'mnewton', 'halley', 'muller', 'illinois', 'pegasus', 'anderson',
+    'ridder', 'anewton', 'bisect'
+
+    See mpmath.calculus.optimization for their documentation.
+
+    **Examples**
+
+    The function :func:`~mpmath.findroot` locates a root of a given function using the
+    secant method by default. A simple example use of the secant method is to
+    compute `\pi` as the root of `\sin x` closest to `x_0 = 3`::
+
+        >>> from mpmath import *
+        >>> mp.dps = 30; mp.pretty = True
+        >>> findroot(sin, 3)
+        3.14159265358979323846264338328
+
+    The secant method can be used to find complex roots of analytic functions,
+    although it must in that case generally be given a nonreal starting value
+    (or else it will never leave the real line)::
+
+        >>> mp.dps = 15
+        >>> findroot(lambda x: x**3 + 2*x + 1, j)
+        (0.226698825758202 + 1.46771150871022j)
+
+    A nice application is to compute nontrivial roots of the Riemann zeta
+    function with many digits (good initial values are needed for convergence)::
+
+        >>> mp.dps = 30
+        >>> findroot(zeta, 0.5+14j)
+        (0.5 + 14.1347251417346937904572519836j)
+
+    The secant method can also be used as an optimization algorithm, by passing
+    it a derivative of a function. The following example locates the positive
+    minimum of the gamma function::
+
+        >>> mp.dps = 20
+        >>> findroot(lambda x: diff(gamma, x), 1)
+        1.4616321449683623413
+
+    Finally, a useful application is to compute inverse functions, such as the
+    Lambert W function which is the inverse of `w e^w`, given the first
+    term of the solution's asymptotic expansion as the initial value. In basic
+    cases, this gives identical results to mpmath's built-in ``lambertw``
+    function::
+
+        >>> def lambert(x):
+        ...     return findroot(lambda w: w*exp(w) - x, log(1+x))
+        ...
+        >>> mp.dps = 15
+        >>> lambert(1); lambertw(1)
+        0.567143290409784
+        0.567143290409784
+        >>> lambert(1000); lambert(1000)
+        5.2496028524016
+        5.2496028524016
+
+    Multidimensional functions are also supported::
+
+        >>> f = [lambda x1, x2: x1**2 + x2,
+        ...      lambda x1, x2: 5*x1**2 - 3*x1 + 2*x2 - 3]
+        >>> findroot(f, (0, 0))
+        [-0.618033988749895]
+        [-0.381966011250105]
+        >>> findroot(f, (10, 10))
+        [ 1.61803398874989]
+        [-2.61803398874989]
+
+    You can verify this by solving the system manually.
+
+    Please note that the following (more general) syntax also works::
+
+        >>> def f(x1, x2):
+        ...     return x1**2 + x2, 5*x1**2 - 3*x1 + 2*x2 - 3
+        ...
+        >>> findroot(f, (0, 0))
+        [-0.618033988749895]
+        [-0.381966011250105]
+
+
+    **Multiple roots**
+
+    For multiple roots all methods of the Newtonian family (including secant)
+    converge slowly. Consider this example::
+
+        >>> f = lambda x: (x - 1)**99
+        >>> findroot(f, 0.9, verify=False)
+        0.918073542444929
+
+    Even for a very close starting point the secant method converges very
+    slowly. Use ``verbose=True`` to illustrate this.
+
+    It is possible to modify Newton's method to make it converge regardless of
+    the root's multiplicity::
+
+        >>> findroot(f, -10, solver='mnewton')
+        1.0
+
+    This variant uses the first and second derivative of the function, which is
+    not very efficient.
+
+    Alternatively you can use an experimental Newtonian solver that keeps track
+    of the speed of convergence and accelerates it using Steffensen's method if
+    necessary::
+
+        >>> findroot(f, -10, solver='anewton', verbose=True)
+        x:     -9.88888888888888888889
+        error: 0.111111111111111111111
+        converging slowly
+        x:     -9.77890011223344556678
+        error: 0.10998877665544332211
+        converging slowly
+        x:     -9.67002233332199662166
+        error: 0.108877778911448945119
+        converging slowly
+        accelerating convergence
+        x:     -9.5622443299551077669
+        error: 0.107778003366888854764
+        converging slowly
+        x:     0.99999999999999999214
+        error: 10.562244329955107759
+        x:     1.0
+        error: 7.8598304758094664213e-18
+        ZeroDivisionError: canceled with x = 1.0
+        1.0
+
+    **Complex roots**
+
+    For complex roots it's recommended to use Muller's method as it converges
+    even for real starting points very fast::
+
+        >>> findroot(lambda x: x**4 + x + 1, (0, 1, 2), solver='muller')
+        (0.727136084491197 + 0.934099289460529j)
+
+
+    **Intersection methods**
+
+    When you need to find a root in a known interval, it's highly recommended to
+    use an intersection-based solver like ``'anderson'`` or ``'ridder'``.
+    Usually they converge faster and more reliable. They have however problems
+    with multiple roots and usually need a sign change to find a root::
+
+        >>> findroot(lambda x: x**3, (-1, 1), solver='anderson')
+        0.0
+
+    Be careful with symmetric functions::
+
+        >>> findroot(lambda x: x**2, (-1, 1), solver='anderson') #doctest:+ELLIPSIS
+        Traceback (most recent call last):
+          ...
+        ZeroDivisionError
+
+    It fails even for better starting points, because there is no sign change::
+
+        >>> findroot(lambda x: x**2, (-1, .5), solver='anderson')
+        Traceback (most recent call last):
+          ...
+        ValueError: Could not find root within given tolerance. (1.0 > 2.16840434497100886801e-19)
+        Try another starting point or tweak arguments.
+
+    """
+    prec = ctx.prec
+    try:
+        ctx.prec += 20
+
+        # initialize arguments
+        if tol is None:
+            tol = ctx.eps * 2**10
+
+        kwargs['verbose'] = kwargs.get('verbose', verbose)
+
+        if 'd1f' in kwargs:
+            kwargs['df'] = kwargs['d1f']
+
+        kwargs['tol'] = tol
+        if isinstance(x0, (list, tuple)):
+            x0 = [ctx.convert(x) for x in x0]
+        else:
+            x0 = [ctx.convert(x0)]
+
+        if isinstance(solver, str):
+            try:
+                solver = str2solver[solver]
+            except KeyError:
+                raise ValueError('could not recognize solver')
+
+        # accept list of functions
+        if isinstance(f, (list, tuple)):
+            f2 = copy(f)
+            def tmp(*args):
+                return [fn(*args) for fn in f2]
+            f = tmp
+
+        # detect multidimensional functions
+        try:
+            fx = f(*x0)
+            multidimensional = isinstance(fx, (list, tuple, ctx.matrix))
+        except TypeError:
+            fx = f(x0[0])
+            multidimensional = False
+        if 'multidimensional' in kwargs:
+            multidimensional = kwargs['multidimensional']
+        if multidimensional:
+            # only one multidimensional solver available at the moment
+            solver = MDNewton
+            if not 'norm' in kwargs:
+                norm = lambda x: ctx.norm(x, 'inf')
+                kwargs['norm'] = norm
+            else:
+                norm = kwargs['norm']
+        else:
+            norm = abs
+
+        # happily return starting point if it's a root
+        if norm(fx) == 0:
+            if multidimensional:
+                return ctx.matrix(x0)
+            else:
+                return x0[0]
+
+        # use solver
+        iterations = solver(ctx, f, x0, **kwargs)
+        if 'maxsteps' in kwargs:
+            maxsteps = kwargs['maxsteps']
+        else:
+            maxsteps = iterations.maxsteps
+        i = 0
+        for x, error in iterations:
+            if verbose:
+                print('x:    ', x)
+                print('error:', error)
+            i += 1
+            if error < tol * max(1, norm(x)) or i >= maxsteps:
+                break
+        else:
+            if not i:
+                raise ValueError('Could not find root using the given solver.\n'
+                                 'Try another starting point or tweak arguments.')
+        if not isinstance(x, (list, tuple, ctx.matrix)):
+            xl = [x]
+        else:
+            xl = x
+        if verify and norm(f(*xl))**2 > tol: # TODO: better condition?
+            raise ValueError('Could not find root within given tolerance. '
+                             '(%s > %s)\n'
+                             'Try another starting point or tweak arguments.'
+                             % (norm(f(*xl))**2, tol))
+        return x
+    finally:
+        ctx.prec = prec
+
+
+def multiplicity(ctx, f, root, tol=None, maxsteps=10, **kwargs):
+    """
+    Return the multiplicity of a given root of f.
+
+    Internally, numerical derivatives are used. This might be inefficient for
+    higher order derviatives. Due to this, ``multiplicity`` cancels after
+    evaluating 10 derivatives by default. You can be specify the n-th derivative
+    using the dnf keyword.
+
+    >>> from mpmath import *
+    >>> multiplicity(lambda x: sin(x) - 1, pi/2)
+    2
+
+    """
+    if tol is None:
+        tol = ctx.eps ** 0.8
+    kwargs['d0f'] = f
+    for i in xrange(maxsteps):
+        dfstr = 'd' + str(i) + 'f'
+        if dfstr in kwargs:
+            df = kwargs[dfstr]
+        else:
+            df = lambda x: ctx.diff(f, x, i)
+        if not abs(df(root)) < tol:
+            break
+    return i
+
+def steffensen(f):
+    """
+    linear convergent function -> quadratic convergent function
+
+    Steffensen's method for quadratic convergence of a linear converging
+    sequence.
+    Don not use it for higher rates of convergence.
+    It may even work for divergent sequences.
+
+    Definition:
+    F(x) = (x*f(f(x)) - f(x)**2) / (f(f(x)) - 2*f(x) + x)
+
+    Example
+    .......
+
+    You can use Steffensen's method to accelerate a fixpoint iteration of linear
+    (or less) convergence.
+
+    x* is a fixpoint of the iteration x_{k+1} = phi(x_k) if x* = phi(x*). For
+    phi(x) = x**2 there are two fixpoints: 0 and 1.
+
+    Let's try Steffensen's method:
+
+    >>> f = lambda x: x**2
+    >>> from mpmath.calculus.optimization import steffensen
+    >>> F = steffensen(f)
+    >>> for x in [0.5, 0.9, 2.0]:
+    ...     fx = Fx = x
+    ...     for i in xrange(9):
+    ...         try:
+    ...             fx = f(fx)
+    ...         except OverflowError:
+    ...             pass
+    ...         try:
+    ...             Fx = F(Fx)
+    ...         except ZeroDivisionError:
+    ...             pass
+    ...         print('%20g  %20g' % (fx, Fx))
+                    0.25                  -0.5
+                  0.0625                   0.1
+              0.00390625            -0.0011236
+             1.52588e-05           1.41691e-09
+             2.32831e-10          -2.84465e-27
+             5.42101e-20           2.30189e-80
+             2.93874e-39          -1.2197e-239
+             8.63617e-78                     0
+            7.45834e-155                     0
+                    0.81               1.02676
+                  0.6561               1.00134
+                0.430467                     1
+                0.185302                     1
+               0.0343368                     1
+              0.00117902                     1
+             1.39008e-06                     1
+             1.93233e-12                     1
+             3.73392e-24                     1
+                       4                   1.6
+                      16                1.2962
+                     256               1.10194
+                   65536               1.01659
+             4.29497e+09               1.00053
+             1.84467e+19                     1
+             3.40282e+38                     1
+             1.15792e+77                     1
+            1.34078e+154                     1
+
+    Unmodified, the iteration converges only towards 0. Modified it converges
+    not only much faster, it converges even to the repelling fixpoint 1.
+    """
+    def F(x):
+        fx = f(x)
+        ffx = f(fx)
+        return (x*ffx - fx**2) / (ffx - 2*fx + x)
+    return F
+
+OptimizationMethods.jacobian = jacobian
+OptimizationMethods.findroot = findroot
+OptimizationMethods.multiplicity = multiplicity
+
+if __name__ == '__main__':
+    import doctest
+    doctest.testmod()

venv/lib/python3.10/site-packages/mpmath/calculus/polynomials.py

@@ -0,0 +1,213 @@
+from ..libmp.backend import xrange
+from .calculus import defun
+
+#----------------------------------------------------------------------------#
+#                                Polynomials                                 #
+#----------------------------------------------------------------------------#
+
+# XXX: extra precision
+@defun
+def polyval(ctx, coeffs, x, derivative=False):
+    r"""
+    Given coefficients `[c_n, \ldots, c_2, c_1, c_0]` and a number `x`,
+    :func:`~mpmath.polyval` evaluates the polynomial
+
+    .. math ::
+
+        P(x) = c_n x^n + \ldots + c_2 x^2 + c_1 x + c_0.
+
+    If *derivative=True* is set, :func:`~mpmath.polyval` simultaneously
+    evaluates `P(x)` with the derivative, `P'(x)`, and returns the
+    tuple `(P(x), P'(x))`.
+
+        >>> from mpmath import *
+        >>> mp.pretty = True
+        >>> polyval([3, 0, 2], 0.5)
+        2.75
+        >>> polyval([3, 0, 2], 0.5, derivative=True)
+        (2.75, 3.0)
+
+    The coefficients and the evaluation point may be any combination
+    of real or complex numbers.
+    """
+    if not coeffs:
+        return ctx.zero
+    p = ctx.convert(coeffs[0])
+    q = ctx.zero
+    for c in coeffs[1:]:
+        if derivative:
+            q = p + x*q
+        p = c + x*p
+    if derivative:
+        return p, q
+    else:
+        return p
+
+@defun
+def polyroots(ctx, coeffs, maxsteps=50, cleanup=True, extraprec=10,
+        error=False, roots_init=None):
+    """
+    Computes all roots (real or complex) of a given polynomial.
+
+    The roots are returned as a sorted list, where real roots appear first
+    followed by complex conjugate roots as adjacent elements. The polynomial
+    should be given as a list of coefficients, in the format used by
+    :func:`~mpmath.polyval`. The leading coefficient must be nonzero.
+
+    With *error=True*, :func:`~mpmath.polyroots` returns a tuple *(roots, err)*
+    where *err* is an estimate of the maximum error among the computed roots.
+
+    **Examples**
+
+    Finding the three real roots of `x^3 - x^2 - 14x + 24`::
+
+        >>> from mpmath import *
+        >>> mp.dps = 15; mp.pretty = True
+        >>> nprint(polyroots([1,-1,-14,24]), 4)
+        [-4.0, 2.0, 3.0]
+
+    Finding the two complex conjugate roots of `4x^2 + 3x + 2`, with an
+    error estimate::
+
+        >>> roots, err = polyroots([4,3,2], error=True)
+        >>> for r in roots:
+        ...     print(r)
+        ...
+        (-0.375 + 0.59947894041409j)
+        (-0.375 - 0.59947894041409j)
+        >>>
+        >>> err
+        2.22044604925031e-16
+        >>>
+        >>> polyval([4,3,2], roots[0])
+        (2.22044604925031e-16 + 0.0j)
+        >>> polyval([4,3,2], roots[1])
+        (2.22044604925031e-16 + 0.0j)
+
+    The following example computes all the 5th roots of unity; that is,
+    the roots of `x^5 - 1`::
+
+        >>> mp.dps = 20
+        >>> for r in polyroots([1, 0, 0, 0, 0, -1]):
+        ...     print(r)
+        ...
+        1.0
+        (-0.8090169943749474241 + 0.58778525229247312917j)
+        (-0.8090169943749474241 - 0.58778525229247312917j)
+        (0.3090169943749474241 + 0.95105651629515357212j)
+        (0.3090169943749474241 - 0.95105651629515357212j)
+
+    **Precision and conditioning**
+
+    The roots are computed to the current working precision accuracy. If this
+    accuracy cannot be achieved in ``maxsteps`` steps, then a
+    ``NoConvergence`` exception is raised. The algorithm internally is using
+    the current working precision extended by ``extraprec``. If
+    ``NoConvergence`` was raised, that is caused either by not having enough
+    extra precision to achieve convergence (in which case increasing
+    ``extraprec`` should fix the problem) or too low ``maxsteps`` (in which
+    case increasing ``maxsteps`` should fix the problem), or a combination of
+    both.
+
+    The user should always do a convergence study with regards to
+    ``extraprec`` to ensure accurate results. It is possible to get
+    convergence to a wrong answer with too low ``extraprec``.
+
+    Provided there are no repeated roots, :func:`~mpmath.polyroots` can
+    typically compute all roots of an arbitrary polynomial to high precision::
+
+        >>> mp.dps = 60
+        >>> for r in polyroots([1, 0, -10, 0, 1]):
+        ...     print(r)
+        ...
+        -3.14626436994197234232913506571557044551247712918732870123249
+        -0.317837245195782244725757617296174288373133378433432554879127
+        0.317837245195782244725757617296174288373133378433432554879127
+        3.14626436994197234232913506571557044551247712918732870123249
+        >>>
+        >>> sqrt(3) + sqrt(2)
+        3.14626436994197234232913506571557044551247712918732870123249
+        >>> sqrt(3) - sqrt(2)
+        0.317837245195782244725757617296174288373133378433432554879127
+
+    **Algorithm**
+
+    :func:`~mpmath.polyroots` implements the Durand-Kerner method [1], which
+    uses complex arithmetic to locate all roots simultaneously.
+    The Durand-Kerner method can be viewed as approximately performing
+    simultaneous Newton iteration for all the roots. In particular,
+    the convergence to simple roots is quadratic, just like Newton's
+    method.
+
+    Although all roots are internally calculated using complex arithmetic, any
+    root found to have an imaginary part smaller than the estimated numerical
+    error is truncated to a real number (small real parts are also chopped).
+    Real roots are placed first in the returned list, sorted by value. The
+    remaining complex roots are sorted by their real parts so that conjugate
+    roots end up next to each other.
+
+    **References**
+
+    1. http://en.wikipedia.org/wiki/Durand-Kerner_method
+
+    """
+    if len(coeffs) <= 1:
+        if not coeffs or not coeffs[0]:
+            raise ValueError("Input to polyroots must not be the zero polynomial")
+        # Constant polynomial with no roots
+        return []
+
+    orig = ctx.prec
+    tol = +ctx.eps
+    with ctx.extraprec(extraprec):
+        deg = len(coeffs) - 1
+        # Must be monic
+        lead = ctx.convert(coeffs[0])
+        if lead == 1:
+            coeffs = [ctx.convert(c) for c in coeffs]
+        else:
+            coeffs = [c/lead for c in coeffs]
+        f = lambda x: ctx.polyval(coeffs, x)
+        if roots_init is None:
+            roots = [ctx.mpc((0.4+0.9j)**n) for n in xrange(deg)]
+        else:
+            roots = [None]*deg;
+            deg_init = min(deg, len(roots_init))
+            roots[:deg_init] = list(roots_init[:deg_init])
+            roots[deg_init:] = [ctx.mpc((0.4+0.9j)**n) for n
+                                in xrange(deg_init,deg)]
+        err = [ctx.one for n in xrange(deg)]
+        # Durand-Kerner iteration until convergence
+        for step in xrange(maxsteps):
+            if abs(max(err)) < tol:
+                break
+            for i in xrange(deg):
+                p = roots[i]
+                x = f(p)
+                for j in range(deg):
+                    if i != j:
+                        try:
+                            x /= (p-roots[j])
+                        except ZeroDivisionError:
+                            continue
+                roots[i] = p - x
+                err[i] = abs(x)
+        if abs(max(err)) >= tol:
+            raise ctx.NoConvergence("Didn't converge in maxsteps=%d steps." \
+                    % maxsteps)
+        # Remove small real or imaginary parts
+        if cleanup:
+            for i in xrange(deg):
+                if abs(roots[i]) < tol:
+                    roots[i] = ctx.zero
+                elif abs(ctx._im(roots[i])) < tol:
+                    roots[i] = roots[i].real
+                elif abs(ctx._re(roots[i])) < tol:
+                    roots[i] = roots[i].imag * 1j
+        roots.sort(key=lambda x: (abs(ctx._im(x)), ctx._re(x)))
+    if error:
+        err = max(err)
+        err = max(err, ctx.ldexp(1, -orig+1))
+        return [+r for r in roots], +err
+    else:
+        return [+r for r in roots]

venv/lib/python3.10/site-packages/mpmath/calculus/quadrature.py

@@ -0,0 +1,1115 @@
+import math
+
+from ..libmp.backend import xrange
+
+class QuadratureRule(object):
+    """
+    Quadrature rules are implemented using this class, in order to
+    simplify the code and provide a common infrastructure
+    for tasks such as error estimation and node caching.
+
+    You can implement a custom quadrature rule by subclassing
+    :class:`QuadratureRule` and implementing the appropriate
+    methods. The subclass can then be used by :func:`~mpmath.quad` by
+    passing it as the *method* argument.
+
+    :class:`QuadratureRule` instances are supposed to be singletons.
+    :class:`QuadratureRule` therefore implements instance caching
+    in :func:`~mpmath.__new__`.
+    """
+
+    def __init__(self, ctx):
+        self.ctx = ctx
+        self.standard_cache = {}
+        self.transformed_cache = {}
+        self.interval_count = {}
+
+    def clear(self):
+        """
+        Delete cached node data.
+        """
+        self.standard_cache = {}
+        self.transformed_cache = {}
+        self.interval_count = {}
+
+    def calc_nodes(self, degree, prec, verbose=False):
+        r"""
+        Compute nodes for the standard interval `[-1, 1]`. Subclasses
+        should probably implement only this method, and use
+        :func:`~mpmath.get_nodes` method to retrieve the nodes.
+        """
+        raise NotImplementedError
+
+    def get_nodes(self, a, b, degree, prec, verbose=False):
+        """
+        Return nodes for given interval, degree and precision. The
+        nodes are retrieved from a cache if already computed;
+        otherwise they are computed by calling :func:`~mpmath.calc_nodes`
+        and are then cached.
+
+        Subclasses should probably not implement this method,
+        but just implement :func:`~mpmath.calc_nodes` for the actual
+        node computation.
+        """
+        key = (a, b, degree, prec)
+        if key in self.transformed_cache:
+            return self.transformed_cache[key]
+        orig = self.ctx.prec
+        try:
+            self.ctx.prec = prec+20
+            # Get nodes on standard interval
+            if (degree, prec) in self.standard_cache:
+                nodes = self.standard_cache[degree, prec]
+            else:
+                nodes = self.calc_nodes(degree, prec, verbose)
+                self.standard_cache[degree, prec] = nodes
+            # Transform to general interval
+            nodes = self.transform_nodes(nodes, a, b, verbose)
+            if key in self.interval_count:
+                self.transformed_cache[key] = nodes
+            else:
+                self.interval_count[key] = True
+        finally:
+            self.ctx.prec = orig
+        return nodes
+
+    def transform_nodes(self, nodes, a, b, verbose=False):
+        r"""
+        Rescale standardized nodes (for `[-1, 1]`) to a general
+        interval `[a, b]`. For a finite interval, a simple linear
+        change of variables is used. Otherwise, the following
+        transformations are used:
+
+        .. math ::
+
+            \lbrack a, \infty \rbrack : t = \frac{1}{x} + (a-1)
+
+            \lbrack -\infty, b \rbrack : t = (b+1) - \frac{1}{x}
+
+            \lbrack -\infty, \infty \rbrack : t = \frac{x}{\sqrt{1-x^2}}
+
+        """
+        ctx = self.ctx
+        a = ctx.convert(a)
+        b = ctx.convert(b)
+        one = ctx.one
+        if (a, b) == (-one, one):
+            return nodes
+        half = ctx.mpf(0.5)
+        new_nodes = []
+        if ctx.isinf(a) or ctx.isinf(b):
+            if (a, b) == (ctx.ninf, ctx.inf):
+                p05 = -half
+                for x, w in nodes:
+                    x2 = x*x
+                    px1 = one-x2
+                    spx1 = px1**p05
+                    x = x*spx1
+                    w *= spx1/px1
+                    new_nodes.append((x, w))
+            elif a == ctx.ninf:
+                b1 = b+1
+                for x, w in nodes:
+                    u = 2/(x+one)
+                    x = b1-u
+                    w *= half*u**2
+                    new_nodes.append((x, w))
+            elif b == ctx.inf:
+                a1 = a-1
+                for x, w in nodes:
+                    u = 2/(x+one)
+                    x = a1+u
+                    w *= half*u**2
+                    new_nodes.append((x, w))
+            elif a == ctx.inf or b == ctx.ninf:
+                return [(x,-w) for (x,w) in self.transform_nodes(nodes, b, a, verbose)]
+            else:
+                raise NotImplementedError
+        else:
+            # Simple linear change of variables
+            C = (b-a)/2
+            D = (b+a)/2
+            for x, w in nodes:
+                new_nodes.append((D+C*x, C*w))
+        return new_nodes
+
+    def guess_degree(self, prec):
+        """
+        Given a desired precision `p` in bits, estimate the degree `m`
+        of the quadrature required to accomplish full accuracy for
+        typical integrals. By default, :func:`~mpmath.quad` will perform up
+        to `m` iterations. The value of `m` should be a slight
+        overestimate, so that "slightly bad" integrals can be dealt
+        with automatically using a few extra iterations. On the
+        other hand, it should not be too big, so :func:`~mpmath.quad` can
+        quit within a reasonable amount of time when it is given
+        an "unsolvable" integral.
+
+        The default formula used by :func:`~mpmath.guess_degree` is tuned
+        for both :class:`TanhSinh` and :class:`GaussLegendre`.
+        The output is roughly as follows:
+
+            +---------+---------+
+            | `p`     | `m`     |
+            +=========+=========+
+            | 50      | 6       |
+            +---------+---------+
+            | 100     | 7       |
+            +---------+---------+
+            | 500     | 10      |
+            +---------+---------+
+            | 3000    | 12      |
+            +---------+---------+
+
+        This formula is based purely on a limited amount of
+        experimentation and will sometimes be wrong.
+        """
+        # Expected degree
+        # XXX: use mag
+        g = int(4 + max(0, self.ctx.log(prec/30.0, 2)))
+        # Reasonable "worst case"
+        g += 2
+        return g
+
+    def estimate_error(self, results, prec, epsilon):
+        r"""
+        Given results from integrations `[I_1, I_2, \ldots, I_k]` done
+        with a quadrature of rule of degree `1, 2, \ldots, k`, estimate
+        the error of `I_k`.
+
+        For `k = 2`, we estimate  `|I_{\infty}-I_2|` as `|I_2-I_1|`.
+
+        For `k > 2`, we extrapolate `|I_{\infty}-I_k| \approx |I_{k+1}-I_k|`
+        from `|I_k-I_{k-1}|` and `|I_k-I_{k-2}|` under the assumption
+        that each degree increment roughly doubles the accuracy of
+        the quadrature rule (this is true for both :class:`TanhSinh`
+        and :class:`GaussLegendre`). The extrapolation formula is given
+        by Borwein, Bailey & Girgensohn. Although not very conservative,
+        this method seems to be very robust in practice.
+        """
+        if len(results) == 2:
+            return abs(results[0]-results[1])
+        try:
+            if results[-1] == results[-2] == results[-3]:
+                return self.ctx.zero
+            D1 = self.ctx.log(abs(results[-1]-results[-2]), 10)
+            D2 = self.ctx.log(abs(results[-1]-results[-3]), 10)
+        except ValueError:
+            return epsilon
+        D3 = -prec
+        D4 = min(0, max(D1**2/D2, 2*D1, D3))
+        return self.ctx.mpf(10) ** int(D4)
+
+    def summation(self, f, points, prec, epsilon, max_degree, verbose=False):
+        """
+        Main integration function. Computes the 1D integral over
+        the interval specified by *points*. For each subinterval,
+        performs quadrature of degree from 1 up to *max_degree*
+        until :func:`~mpmath.estimate_error` signals convergence.
+
+        :func:`~mpmath.summation` transforms each subintegration to
+        the standard interval and then calls :func:`~mpmath.sum_next`.
+        """
+        ctx = self.ctx
+        I = total_err = ctx.zero
+        for i in xrange(len(points)-1):
+            a, b = points[i], points[i+1]
+            if a == b:
+                continue
+            # XXX: we could use a single variable transformation,
+            # but this is not good in practice. We get better accuracy
+            # by having 0 as an endpoint.
+            if (a, b) == (ctx.ninf, ctx.inf):
+                _f = f
+                f = lambda x: _f(-x) + _f(x)
+                a, b = (ctx.zero, ctx.inf)
+            results = []
+            err = ctx.zero
+            for degree in xrange(1, max_degree+1):
+                nodes = self.get_nodes(a, b, degree, prec, verbose)
+                if verbose:
+                    print("Integrating from %s to %s (degree %s of %s)" % \
+                        (ctx.nstr(a), ctx.nstr(b), degree, max_degree))
+                result = self.sum_next(f, nodes, degree, prec, results, verbose)
+                results.append(result)
+                if degree > 1:
+                    err = self.estimate_error(results, prec, epsilon)
+                    if verbose:
+                        print("Estimated error:", ctx.nstr(err), " epsilon:", ctx.nstr(epsilon), " result: ", ctx.nstr(result))
+                    if err <= epsilon:
+                        break
+            I += results[-1]
+            total_err += err
+        if total_err > epsilon:
+            if verbose:
+                print("Failed to reach full accuracy. Estimated error:", ctx.nstr(total_err))
+        return I, total_err
+
+    def sum_next(self, f, nodes, degree, prec, previous, verbose=False):
+        r"""
+        Evaluates the step sum `\sum w_k f(x_k)` where the *nodes* list
+        contains the `(w_k, x_k)` pairs.
+
+        :func:`~mpmath.summation` will supply the list *results* of
+        values computed by :func:`~mpmath.sum_next` at previous degrees, in
+        case the quadrature rule is able to reuse them.
+        """
+        return self.ctx.fdot((w, f(x)) for (x,w) in nodes)
+
+
+class TanhSinh(QuadratureRule):
+    r"""
+    This class implements "tanh-sinh" or "doubly exponential"
+    quadrature. This quadrature rule is based on the Euler-Maclaurin
+    integral formula. By performing a change of variables involving
+    nested exponentials / hyperbolic functions (hence the name), the
+    derivatives at the endpoints vanish rapidly. Since the error term
+    in the Euler-Maclaurin formula depends on the derivatives at the
+    endpoints, a simple step sum becomes extremely accurate. In
+    practice, this means that doubling the number of evaluation
+    points roughly doubles the number of accurate digits.
+
+    Comparison to Gauss-Legendre:
+      * Initial computation of nodes is usually faster
+      * Handles endpoint singularities better
+      * Handles infinite integration intervals better
+      * Is slower for smooth integrands once nodes have been computed
+
+    The implementation of the tanh-sinh algorithm is based on the
+    description given in Borwein, Bailey & Girgensohn, "Experimentation
+    in Mathematics - Computational Paths to Discovery", A K Peters,
+    2003, pages 312-313. In the present implementation, a few
+    improvements have been made:
+
+      * A more efficient scheme is used to compute nodes (exploiting
+        recurrence for the exponential function)
+      * The nodes are computed successively instead of all at once
+
+    **References**
+
+    * [Bailey]_
+    * http://users.cs.dal.ca/~jborwein/tanh-sinh.pdf
+
+    """
+
+    def sum_next(self, f, nodes, degree, prec, previous, verbose=False):
+        """
+        Step sum for tanh-sinh quadrature of degree `m`. We exploit the
+        fact that half of the abscissas at degree `m` are precisely the
+        abscissas from degree `m-1`. Thus reusing the result from
+        the previous level allows a 2x speedup.
+        """
+        h = self.ctx.mpf(2)**(-degree)
+        # Abscissas overlap, so reusing saves half of the time
+        if previous:
+            S = previous[-1]/(h*2)
+        else:
+            S = self.ctx.zero
+        S += self.ctx.fdot((w,f(x)) for (x,w) in nodes)
+        return h*S
+
+    def calc_nodes(self, degree, prec, verbose=False):
+        r"""
+        The abscissas and weights for tanh-sinh quadrature of degree
+        `m` are given by
+
+        .. math::
+
+            x_k = \tanh(\pi/2 \sinh(t_k))
+
+            w_k = \pi/2 \cosh(t_k) / \cosh(\pi/2 \sinh(t_k))^2
+
+        where `t_k = t_0 + hk` for a step length `h \sim 2^{-m}`. The
+        list of nodes is actually infinite, but the weights die off so
+        rapidly that only a few are needed.
+        """
+        ctx = self.ctx
+        nodes = []
+
+        extra = 20
+        ctx.prec += extra
+        tol = ctx.ldexp(1, -prec-10)
+        pi4 = ctx.pi/4
+
+        # For simplicity, we work in steps h = 1/2^n, with the first point
+        # offset so that we can reuse the sum from the previous degree
+
+        # We define degree 1 to include the "degree 0" steps, including
+        # the point x = 0. (It doesn't work well otherwise; not sure why.)
+        t0 = ctx.ldexp(1, -degree)
+        if degree == 1:
+            #nodes.append((mpf(0), pi4))
+            #nodes.append((-mpf(0), pi4))
+            nodes.append((ctx.zero, ctx.pi/2))
+            h = t0
+        else:
+            h = t0*2
+
+        # Since h is fixed, we can compute the next exponential
+        # by simply multiplying by exp(h)
+        expt0 = ctx.exp(t0)
+        a = pi4 * expt0
+        b = pi4 / expt0
+        udelta = ctx.exp(h)
+        urdelta = 1/udelta
+
+        for k in xrange(0, 20*2**degree+1):
+            # Reference implementation:
+            # t = t0 + k*h
+            # x = tanh(pi/2 * sinh(t))
+            # w = pi/2 * cosh(t) / cosh(pi/2 * sinh(t))**2
+
+            # Fast implementation. Note that c = exp(pi/2 * sinh(t))
+            c = ctx.exp(a-b)
+            d = 1/c
+            co = (c+d)/2
+            si = (c-d)/2
+            x = si / co
+            w = (a+b) / co**2
+            diff = abs(x-1)
+            if diff <= tol:
+                break
+
+            nodes.append((x, w))
+            nodes.append((-x, w))
+
+            a *= udelta
+            b *= urdelta
+
+            if verbose and k % 300 == 150:
+                # Note: the number displayed is rather arbitrary. Should
+                # figure out how to print something that looks more like a
+                # percentage
+                print("Calculating nodes:", ctx.nstr(-ctx.log(diff, 10) / prec))
+
+        ctx.prec -= extra
+        return nodes
+
+
+class GaussLegendre(QuadratureRule):
+    r"""
+    This class implements Gauss-Legendre quadrature, which is
+    exceptionally efficient for polynomials and polynomial-like (i.e.
+    very smooth) integrands.
+
+    The abscissas and weights are given by roots and values of
+    Legendre polynomials, which are the orthogonal polynomials
+    on `[-1, 1]` with respect to the unit weight
+    (see :func:`~mpmath.legendre`).
+
+    In this implementation, we take the "degree" `m` of the quadrature
+    to denote a Gauss-Legendre rule of degree `3 \cdot 2^m` (following
+    Borwein, Bailey & Girgensohn). This way we get quadratic, rather
+    than linear, convergence as the degree is incremented.
+
+    Comparison to tanh-sinh quadrature:
+      * Is faster for smooth integrands once nodes have been computed
+      * Initial computation of nodes is usually slower
+      * Handles endpoint singularities worse
+      * Handles infinite integration intervals worse
+
+    """
+
+    def calc_nodes(self, degree, prec, verbose=False):
+        r"""
+        Calculates the abscissas and weights for Gauss-Legendre
+        quadrature of degree of given degree (actually `3 \cdot 2^m`).
+        """
+        ctx = self.ctx
+        # It is important that the epsilon is set lower than the
+        # "real" epsilon
+        epsilon = ctx.ldexp(1, -prec-8)
+        # Fairly high precision might be required for accurate
+        # evaluation of the roots
+        orig = ctx.prec
+        ctx.prec = int(prec*1.5)
+        if degree == 1:
+            x = ctx.sqrt(ctx.mpf(3)/5)
+            w = ctx.mpf(5)/9
+            nodes = [(-x,w),(ctx.zero,ctx.mpf(8)/9),(x,w)]
+            ctx.prec = orig
+            return nodes
+        nodes = []
+        n = 3*2**(degree-1)
+        upto = n//2 + 1
+        for j in xrange(1, upto):
+            # Asymptotic formula for the roots
+            r = ctx.mpf(math.cos(math.pi*(j-0.25)/(n+0.5)))
+            # Newton iteration
+            while 1:
+                t1, t2 = 1, 0
+                # Evaluates the Legendre polynomial using its defining
+                # recurrence relation
+                for j1 in xrange(1,n+1):
+                    t3, t2, t1 = t2, t1, ((2*j1-1)*r*t1 - (j1-1)*t2)/j1
+                t4 = n*(r*t1-t2)/(r**2-1)
+                a = t1/t4
+                r = r - a
+                if abs(a) < epsilon:
+                    break
+            x = r
+            w = 2/((1-r**2)*t4**2)
+            if verbose  and j % 30 == 15:
+                print("Computing nodes (%i of %i)" % (j, upto))
+            nodes.append((x, w))
+            nodes.append((-x, w))
+        ctx.prec = orig
+        return nodes
+
+class QuadratureMethods(object):
+
+    def __init__(ctx, *args, **kwargs):
+        ctx._gauss_legendre = GaussLegendre(ctx)
+        ctx._tanh_sinh = TanhSinh(ctx)
+
+    def quad(ctx, f, *points, **kwargs):
+        r"""
+        Computes a single, double or triple integral over a given
+        1D interval, 2D rectangle, or 3D cuboid. A basic example::
+
+            >>> from mpmath import *
+            >>> mp.dps = 15; mp.pretty = True
+            >>> quad(sin, [0, pi])
+            2.0
+
+        A basic 2D integral::
+
+            >>> f = lambda x, y: cos(x+y/2)
+            >>> quad(f, [-pi/2, pi/2], [0, pi])
+            4.0
+
+        **Interval format**
+
+        The integration range for each dimension may be specified
+        using a list or tuple. Arguments are interpreted as follows:
+
+        ``quad(f, [x1, x2])`` -- calculates
+        `\int_{x_1}^{x_2} f(x) \, dx`
+
+        ``quad(f, [x1, x2], [y1, y2])`` -- calculates
+        `\int_{x_1}^{x_2} \int_{y_1}^{y_2} f(x,y) \, dy \, dx`
+
+        ``quad(f, [x1, x2], [y1, y2], [z1, z2])`` -- calculates
+        `\int_{x_1}^{x_2} \int_{y_1}^{y_2} \int_{z_1}^{z_2} f(x,y,z)
+        \, dz \, dy \, dx`
+
+        Endpoints may be finite or infinite. An interval descriptor
+        may also contain more than two points. In this
+        case, the integration is split into subintervals, between
+        each pair of consecutive points. This is useful for
+        dealing with mid-interval discontinuities, or integrating
+        over large intervals where the function is irregular or
+        oscillates.
+
+        **Options**
+
+        :func:`~mpmath.quad` recognizes the following keyword arguments:
+
+        *method*
+            Chooses integration algorithm (described below).
+        *error*
+            If set to true, :func:`~mpmath.quad` returns `(v, e)` where `v` is the
+            integral and `e` is the estimated error.
+        *maxdegree*
+            Maximum degree of the quadrature rule to try before
+            quitting.
+        *verbose*
+            Print details about progress.
+
+        **Algorithms**
+
+        Mpmath presently implements two integration algorithms: tanh-sinh
+        quadrature and Gauss-Legendre quadrature. These can be selected
+        using *method='tanh-sinh'* or *method='gauss-legendre'* or by
+        passing the classes *method=TanhSinh*, *method=GaussLegendre*.
+        The functions :func:`~mpmath.quadts` and :func:`~mpmath.quadgl` are also available
+        as shortcuts.
+
+        Both algorithms have the property that doubling the number of
+        evaluation points roughly doubles the accuracy, so both are ideal
+        for high precision quadrature (hundreds or thousands of digits).
+
+        At high precision, computing the nodes and weights for the
+        integration can be expensive (more expensive than computing the
+        function values). To make repeated integrations fast, nodes
+        are automatically cached.
+
+        The advantages of the tanh-sinh algorithm are that it tends to
+        handle endpoint singularities well, and that the nodes are cheap
+        to compute on the first run. For these reasons, it is used by
+        :func:`~mpmath.quad` as the default algorithm.
+
+        Gauss-Legendre quadrature often requires fewer function
+        evaluations, and is therefore often faster for repeated use, but
+        the algorithm does not handle endpoint singularities as well and
+        the nodes are more expensive to compute. Gauss-Legendre quadrature
+        can be a better choice if the integrand is smooth and repeated
+        integrations are required (e.g. for multiple integrals).
+
+        See the documentation for :class:`TanhSinh` and
+        :class:`GaussLegendre` for additional details.
+
+        **Examples of 1D integrals**
+
+        Intervals may be infinite or half-infinite. The following two
+        examples evaluate the limits of the inverse tangent function
+        (`\int 1/(1+x^2) = \tan^{-1} x`), and the Gaussian integral
+        `\int_{\infty}^{\infty} \exp(-x^2)\,dx = \sqrt{\pi}`::
+
+            >>> mp.dps = 15
+            >>> quad(lambda x: 2/(x**2+1), [0, inf])
+            3.14159265358979
+            >>> quad(lambda x: exp(-x**2), [-inf, inf])**2
+            3.14159265358979
+
+        Integrals can typically be resolved to high precision.
+        The following computes 50 digits of `\pi` by integrating the
+        area of the half-circle defined by `x^2 + y^2 \le 1`,
+        `-1 \le x \le 1`, `y \ge 0`::
+
+            >>> mp.dps = 50
+            >>> 2*quad(lambda x: sqrt(1-x**2), [-1, 1])
+            3.1415926535897932384626433832795028841971693993751
+
+        One can just as well compute 1000 digits (output truncated)::
+
+            >>> mp.dps = 1000
+            >>> 2*quad(lambda x: sqrt(1-x**2), [-1, 1])  #doctest:+ELLIPSIS
+            3.141592653589793238462643383279502884...216420199
+
+        Complex integrals are supported. The following computes
+        a residue at `z = 0` by integrating counterclockwise along the
+        diamond-shaped path from `1` to `+i` to `-1` to `-i` to `1`::
+
+            >>> mp.dps = 15
+            >>> chop(quad(lambda z: 1/z, [1,j,-1,-j,1]))
+            (0.0 + 6.28318530717959j)
+
+        **Examples of 2D and 3D integrals**
+
+        Here are several nice examples of analytically solvable
+        2D integrals (taken from MathWorld [1]) that can be evaluated
+        to high precision fairly rapidly by :func:`~mpmath.quad`::
+
+            >>> mp.dps = 30
+            >>> f = lambda x, y: (x-1)/((1-x*y)*log(x*y))
+            >>> quad(f, [0, 1], [0, 1])
+            0.577215664901532860606512090082
+            >>> +euler
+            0.577215664901532860606512090082
+
+            >>> f = lambda x, y: 1/sqrt(1+x**2+y**2)
+            >>> quad(f, [-1, 1], [-1, 1])
+            3.17343648530607134219175646705
+            >>> 4*log(2+sqrt(3))-2*pi/3
+            3.17343648530607134219175646705
+
+            >>> f = lambda x, y: 1/(1-x**2 * y**2)
+            >>> quad(f, [0, 1], [0, 1])
+            1.23370055013616982735431137498
+            >>> pi**2 / 8
+            1.23370055013616982735431137498
+
+            >>> quad(lambda x, y: 1/(1-x*y), [0, 1], [0, 1])
+            1.64493406684822643647241516665
+            >>> pi**2 / 6
+            1.64493406684822643647241516665
+
+        Multiple integrals may be done over infinite ranges::
+
+            >>> mp.dps = 15
+            >>> print(quad(lambda x,y: exp(-x-y), [0, inf], [1, inf]))
+            0.367879441171442
+            >>> print(1/e)
+            0.367879441171442
+
+        For nonrectangular areas, one can call :func:`~mpmath.quad` recursively.
+        For example, we can replicate the earlier example of calculating
+        `\pi` by integrating over the unit-circle, and actually use double
+        quadrature to actually measure the area circle::
+
+            >>> f = lambda x: quad(lambda y: 1, [-sqrt(1-x**2), sqrt(1-x**2)])
+            >>> quad(f, [-1, 1])
+            3.14159265358979
+
+        Here is a simple triple integral::
+
+            >>> mp.dps = 15
+            >>> f = lambda x,y,z: x*y/(1+z)
+            >>> quad(f, [0,1], [0,1], [1,2], method='gauss-legendre')
+            0.101366277027041
+            >>> (log(3)-log(2))/4
+            0.101366277027041
+
+        **Singularities**
+
+        Both tanh-sinh and Gauss-Legendre quadrature are designed to
+        integrate smooth (infinitely differentiable) functions. Neither
+        algorithm copes well with mid-interval singularities (such as
+        mid-interval discontinuities in `f(x)` or `f'(x)`).
+        The best solution is to split the integral into parts::
+
+            >>> mp.dps = 15
+            >>> quad(lambda x: abs(sin(x)), [0, 2*pi])   # Bad
+            3.99900894176779
+            >>> quad(lambda x: abs(sin(x)), [0, pi, 2*pi])  # Good
+            4.0
+
+        The tanh-sinh rule often works well for integrands having a
+        singularity at one or both endpoints::
+
+            >>> mp.dps = 15
+            >>> quad(log, [0, 1], method='tanh-sinh')  # Good
+            -1.0
+            >>> quad(log, [0, 1], method='gauss-legendre')  # Bad
+            -0.999932197413801
+
+        However, the result may still be inaccurate for some functions::
+
+            >>> quad(lambda x: 1/sqrt(x), [0, 1], method='tanh-sinh')
+            1.99999999946942
+
+        This problem is not due to the quadrature rule per se, but to
+        numerical amplification of errors in the nodes. The problem can be
+        circumvented by temporarily increasing the precision::
+
+            >>> mp.dps = 30
+            >>> a = quad(lambda x: 1/sqrt(x), [0, 1], method='tanh-sinh')
+            >>> mp.dps = 15
+            >>> +a
+            2.0
+
+        **Highly variable functions**
+
+        For functions that are smooth (in the sense of being infinitely
+        differentiable) but contain sharp mid-interval peaks or many
+        "bumps", :func:`~mpmath.quad` may fail to provide full accuracy. For
+        example, with default settings, :func:`~mpmath.quad` is able to integrate
+        `\sin(x)` accurately over an interval of length 100 but not over
+        length 1000::
+
+            >>> quad(sin, [0, 100]); 1-cos(100)   # Good
+            0.137681127712316
+            0.137681127712316
+            >>> quad(sin, [0, 1000]); 1-cos(1000)   # Bad
+            -37.8587612408485
+            0.437620923709297
+
+        One solution is to break the integration into 10 intervals of
+        length 100::
+
+            >>> quad(sin, linspace(0, 1000, 10))   # Good
+            0.437620923709297
+
+        Another is to increase the degree of the quadrature::
+
+            >>> quad(sin, [0, 1000], maxdegree=10)   # Also good
+            0.437620923709297
+
+        Whether splitting the interval or increasing the degree is
+        more efficient differs from case to case. Another example is the
+        function `1/(1+x^2)`, which has a sharp peak centered around
+        `x = 0`::
+
+            >>> f = lambda x: 1/(1+x**2)
+            >>> quad(f, [-100, 100])   # Bad
+            3.64804647105268
+            >>> quad(f, [-100, 100], maxdegree=10)   # Good
+            3.12159332021646
+            >>> quad(f, [-100, 0, 100])   # Also good
+            3.12159332021646
+
+        **References**
+
+        1. http://mathworld.wolfram.com/DoubleIntegral.html
+
+        """
+        rule = kwargs.get('method', 'tanh-sinh')
+        if type(rule) is str:
+            if rule == 'tanh-sinh':
+                rule = ctx._tanh_sinh
+            elif rule == 'gauss-legendre':
+                rule = ctx._gauss_legendre
+            else:
+                raise ValueError("unknown quadrature rule: %s" % rule)
+        else:
+            rule = rule(ctx)
+        verbose = kwargs.get('verbose')
+        dim = len(points)
+        orig = prec = ctx.prec
+        epsilon = ctx.eps/8
+        m = kwargs.get('maxdegree') or rule.guess_degree(prec)
+        points = [ctx._as_points(p) for p in points]
+        try:
+            ctx.prec += 20
+            if dim == 1:
+                v, err = rule.summation(f, points[0], prec, epsilon, m, verbose)
+            elif dim == 2:
+                v, err = rule.summation(lambda x: \
+                        rule.summation(lambda y: f(x,y), \
+                        points[1], prec, epsilon, m)[0],
+                    points[0], prec, epsilon, m, verbose)
+            elif dim == 3:
+                v, err = rule.summation(lambda x: \
+                        rule.summation(lambda y: \
+                            rule.summation(lambda z: f(x,y,z), \
+                            points[2], prec, epsilon, m)[0],
+                        points[1], prec, epsilon, m)[0],
+                    points[0], prec, epsilon, m, verbose)
+            else:
+                raise NotImplementedError("quadrature must have dim 1, 2 or 3")
+        finally:
+            ctx.prec = orig
+        if kwargs.get("error"):
+            return +v, err
+        return +v
+
+    def quadts(ctx, *args, **kwargs):
+        """
+        Performs tanh-sinh quadrature. The call
+
+            quadts(func, *points, ...)
+
+        is simply a shortcut for:
+
+            quad(func, *points, ..., method=TanhSinh)
+
+        For example, a single integral and a double integral:
+
+            quadts(lambda x: exp(cos(x)), [0, 1])
+            quadts(lambda x, y: exp(cos(x+y)), [0, 1], [0, 1])
+
+        See the documentation for quad for information about how points
+        arguments and keyword arguments are parsed.
+
+        See documentation for TanhSinh for algorithmic information about
+        tanh-sinh quadrature.
+        """
+        kwargs['method'] = 'tanh-sinh'
+        return ctx.quad(*args, **kwargs)
+
+    def quadgl(ctx, *args, **kwargs):
+        """
+        Performs Gauss-Legendre quadrature. The call
+
+            quadgl(func, *points, ...)
+
+        is simply a shortcut for:
+
+            quad(func, *points, ..., method=GaussLegendre)
+
+        For example, a single integral and a double integral:
+
+            quadgl(lambda x: exp(cos(x)), [0, 1])
+            quadgl(lambda x, y: exp(cos(x+y)), [0, 1], [0, 1])
+
+        See the documentation for quad for information about how points
+        arguments and keyword arguments are parsed.
+
+        See documentation for TanhSinh for algorithmic information about
+        tanh-sinh quadrature.
+        """
+        kwargs['method'] = 'gauss-legendre'
+        return ctx.quad(*args, **kwargs)
+
+    def quadosc(ctx, f, interval, omega=None, period=None, zeros=None):
+        r"""
+        Calculates
+
+        .. math ::
+
+            I = \int_a^b f(x) dx
+
+        where at least one of `a` and `b` is infinite and where
+        `f(x) = g(x) \cos(\omega x  + \phi)` for some slowly
+        decreasing function `g(x)`. With proper input, :func:`~mpmath.quadosc`
+        can also handle oscillatory integrals where the oscillation
+        rate is different from a pure sine or cosine wave.
+
+        In the standard case when `|a| < \infty, b = \infty`,
+        :func:`~mpmath.quadosc` works by evaluating the infinite series
+
+        .. math ::
+
+            I = \int_a^{x_1} f(x) dx +
+            \sum_{k=1}^{\infty} \int_{x_k}^{x_{k+1}} f(x) dx
+
+        where `x_k` are consecutive zeros (alternatively
+        some other periodic reference point) of `f(x)`.
+        Accordingly, :func:`~mpmath.quadosc` requires information about the
+        zeros of `f(x)`. For a periodic function, you can specify
+        the zeros by either providing the angular frequency `\omega`
+        (*omega*) or the *period* `2 \pi/\omega`. In general, you can
+        specify the `n`-th zero by providing the *zeros* arguments.
+        Below is an example of each::
+
+            >>> from mpmath import *
+            >>> mp.dps = 15; mp.pretty = True
+            >>> f = lambda x: sin(3*x)/(x**2+1)
+            >>> quadosc(f, [0,inf], omega=3)
+            0.37833007080198
+            >>> quadosc(f, [0,inf], period=2*pi/3)
+            0.37833007080198
+            >>> quadosc(f, [0,inf], zeros=lambda n: pi*n/3)
+            0.37833007080198
+            >>> (ei(3)*exp(-3)-exp(3)*ei(-3))/2  # Computed by Mathematica
+            0.37833007080198
+
+        Note that *zeros* was specified to multiply `n` by the
+        *half-period*, not the full period. In theory, it does not matter
+        whether each partial integral is done over a half period or a full
+        period. However, if done over half-periods, the infinite series
+        passed to :func:`~mpmath.nsum` becomes an *alternating series* and this
+        typically makes the extrapolation much more efficient.
+
+        Here is an example of an integration over the entire real line,
+        and a half-infinite integration starting at `-\infty`::
+
+            >>> quadosc(lambda x: cos(x)/(1+x**2), [-inf, inf], omega=1)
+            1.15572734979092
+            >>> pi/e
+            1.15572734979092
+            >>> quadosc(lambda x: cos(x)/x**2, [-inf, -1], period=2*pi)
+            -0.0844109505595739
+            >>> cos(1)+si(1)-pi/2
+            -0.0844109505595738
+
+        Of course, the integrand may contain a complex exponential just as
+        well as a real sine or cosine::
+
+            >>> quadosc(lambda x: exp(3*j*x)/(1+x**2), [-inf,inf], omega=3)
+            (0.156410688228254 + 0.0j)
+            >>> pi/e**3
+            0.156410688228254
+            >>> quadosc(lambda x: exp(3*j*x)/(2+x+x**2), [-inf,inf], omega=3)
+            (0.00317486988463794 - 0.0447701735209082j)
+            >>> 2*pi/sqrt(7)/exp(3*(j+sqrt(7))/2)
+            (0.00317486988463794 - 0.0447701735209082j)
+
+        **Non-periodic functions**
+
+        If `f(x) = g(x) h(x)` for some function `h(x)` that is not
+        strictly periodic, *omega* or *period* might not work, and it might
+        be necessary to use *zeros*.
+
+        A notable exception can be made for Bessel functions which, though not
+        periodic, are "asymptotically periodic" in a sufficiently strong sense
+        that the sum extrapolation will work out::
+
+            >>> quadosc(j0, [0, inf], period=2*pi)
+            1.0
+            >>> quadosc(j1, [0, inf], period=2*pi)
+            1.0
+
+        More properly, one should provide the exact Bessel function zeros::
+
+            >>> j0zero = lambda n: findroot(j0, pi*(n-0.25))
+            >>> quadosc(j0, [0, inf], zeros=j0zero)
+            1.0
+
+        For an example where *zeros* becomes necessary, consider the
+        complete Fresnel integrals
+
+        .. math ::
+
+            \int_0^{\infty} \cos x^2\,dx = \int_0^{\infty} \sin x^2\,dx
+            = \sqrt{\frac{\pi}{8}}.
+
+        Although the integrands do not decrease in magnitude as
+        `x \to \infty`, the integrals are convergent since the oscillation
+        rate increases (causing consecutive periods to asymptotically
+        cancel out). These integrals are virtually impossible to calculate
+        to any kind of accuracy using standard quadrature rules. However,
+        if one provides the correct asymptotic distribution of zeros
+        (`x_n \sim \sqrt{n}`), :func:`~mpmath.quadosc` works::
+
+            >>> mp.dps = 30
+            >>> f = lambda x: cos(x**2)
+            >>> quadosc(f, [0,inf], zeros=lambda n:sqrt(pi*n))
+            0.626657068657750125603941321203
+            >>> f = lambda x: sin(x**2)
+            >>> quadosc(f, [0,inf], zeros=lambda n:sqrt(pi*n))
+            0.626657068657750125603941321203
+            >>> sqrt(pi/8)
+            0.626657068657750125603941321203
+
+        (Interestingly, these integrals can still be evaluated if one
+        places some other constant than `\pi` in the square root sign.)
+
+        In general, if `f(x) \sim g(x) \cos(h(x))`, the zeros follow
+        the inverse-function distribution `h^{-1}(x)`::
+
+            >>> mp.dps = 15
+            >>> f = lambda x: sin(exp(x))
+            >>> quadosc(f, [1,inf], zeros=lambda n: log(n))
+            -0.25024394235267
+            >>> pi/2-si(e)
+            -0.250243942352671
+
+        **Non-alternating functions**
+
+        If the integrand oscillates around a positive value, without
+        alternating signs, the extrapolation might fail. A simple trick
+        that sometimes works is to multiply or divide the frequency by 2::
+
+            >>> f = lambda x: 1/x**2+sin(x)/x**4
+            >>> quadosc(f, [1,inf], omega=1)  # Bad
+            1.28642190869861
+            >>> quadosc(f, [1,inf], omega=0.5)  # Perfect
+            1.28652953559617
+            >>> 1+(cos(1)+ci(1)+sin(1))/6
+            1.28652953559617
+
+        **Fast decay**
+
+        :func:`~mpmath.quadosc` is primarily useful for slowly decaying
+        integrands. If the integrand decreases exponentially or faster,
+        :func:`~mpmath.quad` will likely handle it without trouble (and generally be
+        much faster than :func:`~mpmath.quadosc`)::
+
+            >>> quadosc(lambda x: cos(x)/exp(x), [0, inf], omega=1)
+            0.5
+            >>> quad(lambda x: cos(x)/exp(x), [0, inf])
+            0.5
+
+        """
+        a, b = ctx._as_points(interval)
+        a = ctx.convert(a)
+        b = ctx.convert(b)
+        if [omega, period, zeros].count(None) != 2:
+            raise ValueError( \
+                "must specify exactly one of omega, period, zeros")
+        if a == ctx.ninf and b == ctx.inf:
+            s1 = ctx.quadosc(f, [a, 0], omega=omega, zeros=zeros, period=period)
+            s2 = ctx.quadosc(f, [0, b], omega=omega, zeros=zeros, period=period)
+            return s1 + s2
+        if a == ctx.ninf:
+            if zeros:
+                return ctx.quadosc(lambda x:f(-x), [-b,-a], lambda n: zeros(-n))
+            else:
+                return ctx.quadosc(lambda x:f(-x), [-b,-a], omega=omega, period=period)
+        if b != ctx.inf:
+            raise ValueError("quadosc requires an infinite integration interval")
+        if not zeros:
+            if omega:
+                period = 2*ctx.pi/omega
+            zeros = lambda n: n*period/2
+        #for n in range(1,10):
+        #    p = zeros(n)
+        #    if p > a:
+        #        break
+        #if n >= 9:
+        #    raise ValueError("zeros do not appear to be correctly indexed")
+        n = 1
+        s = ctx.quadgl(f, [a, zeros(n)])
+        def term(k):
+            return ctx.quadgl(f, [zeros(k), zeros(k+1)])
+        s += ctx.nsum(term, [n, ctx.inf])
+        return s
+
+    def quadsubdiv(ctx, f, interval, tol=None, maxintervals=None, **kwargs):
+        """
+        Computes the integral of *f* over the interval or path specified
+        by *interval*, using :func:`~mpmath.quad` together with adaptive
+        subdivision of the interval.
+
+        This function gives an accurate answer for some integrals where
+        :func:`~mpmath.quad` fails::
+
+            >>> from mpmath import *
+            >>> mp.dps = 15; mp.pretty = True
+            >>> quad(lambda x: abs(sin(x)), [0, 2*pi])
+            3.99900894176779
+            >>> quadsubdiv(lambda x: abs(sin(x)), [0, 2*pi])
+            4.0
+            >>> quadsubdiv(sin, [0, 1000])
+            0.437620923709297
+            >>> quadsubdiv(lambda x: 1/(1+x**2), [-100, 100])
+            3.12159332021646
+            >>> quadsubdiv(lambda x: ceil(x), [0, 100])
+            5050.0
+            >>> quadsubdiv(lambda x: sin(x+exp(x)), [0,8])
+            0.347400172657248
+
+        The argument *maxintervals* can be set to limit the permissible
+        subdivision::
+
+            >>> quadsubdiv(lambda x: sin(x**2), [0,100], maxintervals=5, error=True)
+            (-5.40487904307774, 5.011)
+            >>> quadsubdiv(lambda x: sin(x**2), [0,100], maxintervals=100, error=True)
+            (0.631417921866934, 1.10101120134116e-17)
+
+        Subdivision does not guarantee a correct answer since, the error
+        estimate on subintervals may be inaccurate::
+
+            >>> quadsubdiv(lambda x: sech(10*x-2)**2 + sech(100*x-40)**4 + sech(1000*x-600)**6, [0,1], error=True)
+            (0.210802735500549, 1.0001111101e-17)
+            >>> mp.dps = 20
+            >>> quadsubdiv(lambda x: sech(10*x-2)**2 + sech(100*x-40)**4 + sech(1000*x-600)**6, [0,1], error=True)
+            (0.21080273550054927738, 2.200000001e-24)
+
+        The second answer is correct. We can get an accurate result at lower
+        precision by forcing a finer initial subdivision::
+
+            >>> mp.dps = 15
+            >>> quadsubdiv(lambda x: sech(10*x-2)**2 + sech(100*x-40)**4 + sech(1000*x-600)**6, linspace(0,1,5))
+            0.210802735500549
+
+        The following integral is too oscillatory for convergence, but we can get a
+        reasonable estimate::
+
+            >>> v, err = fp.quadsubdiv(lambda x: fp.sin(1/x), [0,1], error=True)
+            >>> round(v, 6), round(err, 6)
+            (0.504067, 1e-06)
+            >>> sin(1) - ci(1)
+            0.504067061906928
+
+        """
+        queue = []
+        for i in range(len(interval)-1):
+            queue.append((interval[i], interval[i+1]))
+        total = ctx.zero
+        total_error = ctx.zero
+        if maxintervals is None:
+            maxintervals = 10 * ctx.prec
+        count = 0
+        quad_args = kwargs.copy()
+        quad_args["verbose"] = False
+        quad_args["error"] = True
+        if tol is None:
+            tol = +ctx.eps
+        orig = ctx.prec
+        try:
+            ctx.prec += 5
+            while queue:
+                a, b = queue.pop()
+                s, err = ctx.quad(f, [a, b], **quad_args)
+                if kwargs.get("verbose"):
+                    print("subinterval", count, a, b, err)
+                if err < tol or count > maxintervals:
+                    total += s
+                    total_error += err
+                else:
+                    count += 1
+                    if count == maxintervals and kwargs.get("verbose"):
+                        print("warning: number of intervals exceeded maxintervals")
+                    if a == -ctx.inf and b == ctx.inf:
+                        m = 0
+                    elif a == -ctx.inf:
+                        m = min(b-1, 2*b)
+                    elif b == ctx.inf:
+                        m = max(a+1, 2*a)
+                    else:
+                        m = a + (b - a) / 2
+                    queue.append((a, m))
+                    queue.append((m, b))
+        finally:
+            ctx.prec = orig
+        if kwargs.get("error"):
+            return +total, +total_error
+        else:
+            return +total
+
+if __name__ == '__main__':
+    import doctest
+    doctest.testmod()

venv/lib/python3.10/site-packages/mpmath/libmp/__init__.py

@@ -0,0 +1,77 @@
+from .libmpf import (prec_to_dps, dps_to_prec, repr_dps,
+  round_down, round_up, round_floor, round_ceiling, round_nearest,
+  to_pickable, from_pickable, ComplexResult,
+  fzero, fnzero, fone, fnone, ftwo, ften, fhalf, fnan, finf, fninf,
+  math_float_inf, round_int, normalize, normalize1,
+  from_man_exp, from_int, to_man_exp, to_int, mpf_ceil, mpf_floor,
+  mpf_nint, mpf_frac,
+  from_float, from_npfloat, from_Decimal, to_float, from_rational, to_rational, to_fixed,
+  mpf_rand, mpf_eq, mpf_hash, mpf_cmp, mpf_lt, mpf_le, mpf_gt, mpf_ge,
+  mpf_pos, mpf_neg, mpf_abs, mpf_sign, mpf_add, mpf_sub, mpf_sum,
+  mpf_mul, mpf_mul_int, mpf_shift, mpf_frexp,
+  mpf_div, mpf_rdiv_int, mpf_mod, mpf_pow_int,
+  mpf_perturb,
+  to_digits_exp, to_str, str_to_man_exp, from_str, from_bstr, to_bstr,
+  mpf_sqrt, mpf_hypot)
+
+from .libmpc import (mpc_one, mpc_zero, mpc_two, mpc_half,
+  mpc_is_inf, mpc_is_infnan, mpc_to_str, mpc_to_complex, mpc_hash,
+  mpc_conjugate, mpc_is_nonzero, mpc_add, mpc_add_mpf,
+  mpc_sub, mpc_sub_mpf, mpc_pos, mpc_neg, mpc_shift, mpc_abs,
+  mpc_arg, mpc_floor, mpc_ceil,  mpc_nint, mpc_frac, mpc_mul, mpc_square,
+  mpc_mul_mpf, mpc_mul_imag_mpf, mpc_mul_int,
+  mpc_div, mpc_div_mpf, mpc_reciprocal, mpc_mpf_div,
+  complex_int_pow, mpc_pow, mpc_pow_mpf, mpc_pow_int,
+  mpc_sqrt, mpc_nthroot, mpc_cbrt, mpc_exp, mpc_log, mpc_cos, mpc_sin,
+  mpc_tan, mpc_cos_pi, mpc_sin_pi, mpc_cosh, mpc_sinh, mpc_tanh,
+  mpc_atan, mpc_acos, mpc_asin, mpc_asinh, mpc_acosh, mpc_atanh,
+  mpc_fibonacci, mpf_expj, mpf_expjpi, mpc_expj, mpc_expjpi,
+  mpc_cos_sin, mpc_cos_sin_pi)
+
+from .libelefun import (ln2_fixed, mpf_ln2, ln10_fixed, mpf_ln10,
+  pi_fixed, mpf_pi, e_fixed, mpf_e, phi_fixed, mpf_phi,
+  degree_fixed, mpf_degree,
+  mpf_pow, mpf_nthroot, mpf_cbrt, log_int_fixed, agm_fixed,
+  mpf_log, mpf_log_hypot, mpf_exp, mpf_cos_sin, mpf_cos, mpf_sin, mpf_tan,
+  mpf_cos_sin_pi, mpf_cos_pi, mpf_sin_pi, mpf_cosh_sinh,
+  mpf_cosh, mpf_sinh, mpf_tanh, mpf_atan, mpf_atan2, mpf_asin,
+  mpf_acos, mpf_asinh, mpf_acosh, mpf_atanh, mpf_fibonacci)
+
+from .libhyper import (NoConvergence, make_hyp_summator,
+  mpf_erf, mpf_erfc, mpf_ei, mpc_ei, mpf_e1, mpc_e1, mpf_expint,
+  mpf_ci_si, mpf_ci, mpf_si, mpc_ci, mpc_si, mpf_besseljn,
+  mpc_besseljn, mpf_agm, mpf_agm1, mpc_agm, mpc_agm1,
+  mpf_ellipk, mpc_ellipk, mpf_ellipe, mpc_ellipe)
+
+from .gammazeta import (catalan_fixed, mpf_catalan,
+  khinchin_fixed, mpf_khinchin, glaisher_fixed, mpf_glaisher,
+  apery_fixed, mpf_apery, euler_fixed, mpf_euler, mertens_fixed,
+  mpf_mertens, twinprime_fixed, mpf_twinprime,
+  mpf_bernoulli, bernfrac, mpf_gamma_int,
+  mpf_factorial, mpc_factorial, mpf_gamma, mpc_gamma,
+  mpf_loggamma, mpc_loggamma, mpf_rgamma, mpc_rgamma,
+  mpf_harmonic, mpc_harmonic, mpf_psi0, mpc_psi0,
+  mpf_psi, mpc_psi, mpf_zeta_int, mpf_zeta, mpc_zeta,
+  mpf_altzeta, mpc_altzeta, mpf_zetasum, mpc_zetasum)
+
+from .libmpi import (mpi_str,
+  mpi_from_str, mpi_to_str,
+  mpi_eq, mpi_ne,
+  mpi_lt, mpi_le, mpi_gt, mpi_ge,
+  mpi_add, mpi_sub, mpi_delta, mpi_mid,
+  mpi_pos, mpi_neg, mpi_abs, mpi_mul, mpi_div, mpi_exp,
+  mpi_log, mpi_sqrt, mpi_pow_int, mpi_pow, mpi_cos_sin,
+  mpi_cos, mpi_sin, mpi_tan, mpi_cot,
+  mpi_atan, mpi_atan2,
+  mpci_pos, mpci_neg, mpci_add, mpci_sub, mpci_mul, mpci_div, mpci_pow,
+  mpci_abs, mpci_pow, mpci_exp, mpci_log, mpci_cos, mpci_sin,
+  mpi_gamma, mpci_gamma, mpi_loggamma, mpci_loggamma,
+  mpi_rgamma, mpci_rgamma, mpi_factorial, mpci_factorial)
+
+from .libintmath import (trailing, bitcount, numeral, bin_to_radix,
+  isqrt, isqrt_small, isqrt_fast, sqrt_fixed, sqrtrem, ifib, ifac,
+  list_primes, isprime, moebius, gcd, eulernum, stirling1, stirling2)
+
+from .backend import (gmpy, sage, BACKEND, STRICT, MPZ, MPZ_TYPE,
+  MPZ_ZERO, MPZ_ONE, MPZ_TWO, MPZ_THREE, MPZ_FIVE, int_types,
+  HASH_MODULUS, HASH_BITS)

venv/lib/python3.10/site-packages/mpmath/libmp/backend.py

@@ -0,0 +1,115 @@
+import os
+import sys
+
+#----------------------------------------------------------------------------#
+# Support GMPY for high-speed large integer arithmetic.                      #
+#                                                                            #
+# To allow an external module to handle arithmetic, we need to make sure     #
+# that all high-precision variables are declared of the correct type. MPZ    #
+# is the constructor for the high-precision type. It defaults to Python's    #
+# long type but can be assinged another type, typically gmpy.mpz.            #
+#                                                                            #
+# MPZ must be used for the mantissa component of an mpf and must be used     #
+# for internal fixed-point operations.                                       #
+#                                                                            #
+# Side-effects                                                               #
+# 1) "is" cannot be used to test for special values. Must use "==".          #
+# 2) There are bugs in GMPY prior to v1.02 so we must use v1.03 or later.    #
+#----------------------------------------------------------------------------#
+
+# So we can import it from this module
+gmpy = None
+sage = None
+sage_utils = None
+
+if sys.version_info[0] < 3:
+    python3 = False
+else:
+    python3 = True
+
+BACKEND = 'python'
+
+if not python3:
+    MPZ = long
+    xrange = xrange
+    basestring = basestring
+
+    def exec_(_code_, _globs_=None, _locs_=None):
+        """Execute code in a namespace."""
+        if _globs_ is None:
+            frame = sys._getframe(1)
+            _globs_ = frame.f_globals
+            if _locs_ is None:
+                _locs_ = frame.f_locals
+            del frame
+        elif _locs_ is None:
+            _locs_ = _globs_
+        exec("""exec _code_ in _globs_, _locs_""")
+else:
+    MPZ = int
+    xrange = range
+    basestring = str
+
+    import builtins
+    exec_ = getattr(builtins, "exec")
+
+# Define constants for calculating hash on Python 3.2.
+if sys.version_info >= (3, 2):
+    HASH_MODULUS = sys.hash_info.modulus
+    if sys.hash_info.width == 32:
+        HASH_BITS = 31
+    else:
+        HASH_BITS = 61
+else:
+    HASH_MODULUS = None
+    HASH_BITS = None
+
+if 'MPMATH_NOGMPY' not in os.environ:
+    try:
+        try:
+            import gmpy2 as gmpy
+        except ImportError:
+            try:
+                import gmpy
+            except ImportError:
+                raise ImportError
+        if gmpy.version() >= '1.03':
+            BACKEND = 'gmpy'
+            MPZ = gmpy.mpz
+    except:
+        pass
+
+if ('MPMATH_NOSAGE' not in os.environ and 'SAGE_ROOT' in os.environ or
+        'MPMATH_SAGE' in os.environ):
+    try:
+        import sage.all
+        import sage.libs.mpmath.utils as _sage_utils
+        sage = sage.all
+        sage_utils = _sage_utils
+        BACKEND = 'sage'
+        MPZ = sage.Integer
+    except:
+        pass
+
+if 'MPMATH_STRICT' in os.environ:
+    STRICT = True
+else:
+    STRICT = False
+
+MPZ_TYPE = type(MPZ(0))
+MPZ_ZERO = MPZ(0)
+MPZ_ONE = MPZ(1)
+MPZ_TWO = MPZ(2)
+MPZ_THREE = MPZ(3)
+MPZ_FIVE = MPZ(5)
+
+try:
+    if BACKEND == 'python':
+        int_types = (int, long)
+    else:
+        int_types = (int, long, MPZ_TYPE)
+except NameError:
+    if BACKEND == 'python':
+        int_types = (int,)
+    else:
+        int_types = (int, MPZ_TYPE)

venv/lib/python3.10/site-packages/mpmath/libmp/gammazeta.py

@@ -0,0 +1,2167 @@
+"""
+-----------------------------------------------------------------------
+This module implements gamma- and zeta-related functions:
+
+* Bernoulli numbers
+* Factorials
+* The gamma function
+* Polygamma functions
+* Harmonic numbers
+* The Riemann zeta function
+* Constants related to these functions
+
+-----------------------------------------------------------------------
+"""
+
+import math
+import sys
+
+from .backend import xrange
+from .backend import MPZ, MPZ_ZERO, MPZ_ONE, MPZ_THREE, gmpy
+
+from .libintmath import list_primes, ifac, ifac2, moebius
+
+from .libmpf import (\
+    round_floor, round_ceiling, round_down, round_up,
+    round_nearest, round_fast,
+    lshift, sqrt_fixed, isqrt_fast,
+    fzero, fone, fnone, fhalf, ftwo, finf, fninf, fnan,
+    from_int, to_int, to_fixed, from_man_exp, from_rational,
+    mpf_pos, mpf_neg, mpf_abs, mpf_add, mpf_sub,
+    mpf_mul, mpf_mul_int, mpf_div, mpf_sqrt, mpf_pow_int,
+    mpf_rdiv_int,
+    mpf_perturb, mpf_le, mpf_lt, mpf_gt, mpf_shift,
+    negative_rnd, reciprocal_rnd,
+    bitcount, to_float, mpf_floor, mpf_sign, ComplexResult
+)
+
+from .libelefun import (\
+    constant_memo,
+    def_mpf_constant,
+    mpf_pi, pi_fixed, ln2_fixed, log_int_fixed, mpf_ln2,
+    mpf_exp, mpf_log, mpf_pow, mpf_cosh,
+    mpf_cos_sin, mpf_cosh_sinh, mpf_cos_sin_pi, mpf_cos_pi, mpf_sin_pi,
+    ln_sqrt2pi_fixed, mpf_ln_sqrt2pi, sqrtpi_fixed, mpf_sqrtpi,
+    cos_sin_fixed, exp_fixed
+)
+
+from .libmpc import (\
+    mpc_zero, mpc_one, mpc_half, mpc_two,
+    mpc_abs, mpc_shift, mpc_pos, mpc_neg,
+    mpc_add, mpc_sub, mpc_mul, mpc_div,
+    mpc_add_mpf, mpc_mul_mpf, mpc_div_mpf, mpc_mpf_div,
+    mpc_mul_int, mpc_pow_int,
+    mpc_log, mpc_exp, mpc_pow,
+    mpc_cos_pi, mpc_sin_pi,
+    mpc_reciprocal, mpc_square,
+    mpc_sub_mpf
+)
+
+
+
+# Catalan's constant is computed using Lupas's rapidly convergent series
+# (listed on http://mathworld.wolfram.com/CatalansConstant.html)
+#            oo
+#            ___       n-1  8n     2                   3    2
+#        1  \      (-1)    2   (40n  - 24n + 3) [(2n)!] (n!)
+#  K =  ---  )     -----------------------------------------
+#       64  /___               3               2
+#                             n  (2n-1) [(4n)!]
+#           n = 1
+
+@constant_memo
+def catalan_fixed(prec):
+    prec = prec + 20
+    a = one = MPZ_ONE << prec
+    s, t, n = 0, 1, 1
+    while t:
+        a *= 32 * n**3 * (2*n-1)
+        a //= (3-16*n+16*n**2)**2
+        t = a * (-1)**(n-1) * (40*n**2-24*n+3) // (n**3 * (2*n-1))
+        s += t
+        n += 1
+    return s >> (20 + 6)
+
+# Khinchin's constant is relatively difficult to compute. Here
+# we use the rational zeta series
+
+#                    oo                2*n-1
+#                   ___                ___
+#                   \   ` zeta(2*n)-1  \   ` (-1)^(k+1)
+#  log(K)*log(2) =   )    ------------  )    ----------
+#                   /___.      n       /___.      k
+#                   n = 1              k = 1
+
+# which adds half a digit per term. The essential trick for achieving
+# reasonable efficiency is to recycle both the values of the zeta
+# function (essentially Bernoulli numbers) and the partial terms of
+# the inner sum.
+
+# An alternative might be to use K = 2*exp[1/log(2) X] where
+
+#      / 1     1       [ pi*x*(1-x^2) ]
+#  X = |    ------ log [ ------------ ].
+#      / 0  x(1+x)     [  sin(pi*x)   ]
+
+# and integrate numerically. In practice, this seems to be slightly
+# slower than the zeta series at high precision.
+
+@constant_memo
+def khinchin_fixed(prec):
+    wp = int(prec + prec**0.5 + 15)
+    s = MPZ_ZERO
+    fac = from_int(4)
+    t = ONE = MPZ_ONE << wp
+    pi = mpf_pi(wp)
+    pipow = twopi2 = mpf_shift(mpf_mul(pi, pi, wp), 2)
+    n = 1
+    while 1:
+        zeta2n = mpf_abs(mpf_bernoulli(2*n, wp))
+        zeta2n = mpf_mul(zeta2n, pipow, wp)
+        zeta2n = mpf_div(zeta2n, fac, wp)
+        zeta2n = to_fixed(zeta2n, wp)
+        term = (((zeta2n - ONE) * t) // n) >> wp
+        if term < 100:
+            break
+        #if not n % 10:
+        #    print n, math.log(int(abs(term)))
+        s += term
+        t += ONE//(2*n+1) - ONE//(2*n)
+        n += 1
+        fac = mpf_mul_int(fac, (2*n)*(2*n-1), wp)
+        pipow = mpf_mul(pipow, twopi2, wp)
+    s = (s << wp) // ln2_fixed(wp)
+    K = mpf_exp(from_man_exp(s, -wp), wp)
+    K = to_fixed(K, prec)
+    return K
+
+
+# Glaisher's constant is defined as A = exp(1/2 - zeta'(-1)).
+# One way to compute it would be to perform direct numerical
+# differentiation, but computing arbitrary Riemann zeta function
+# values at high precision is expensive. We instead use the formula
+
+#     A = exp((6 (-zeta'(2))/pi^2 + log 2 pi + gamma)/12)
+
+# and compute zeta'(2) from the series representation
+
+#              oo
+#              ___
+#             \     log k
+#  -zeta'(2) = )    -----
+#             /___     2
+#                    k
+#            k = 2
+
+# This series converges exceptionally slowly, but can be accelerated
+# using Euler-Maclaurin formula. The important insight is that the
+# E-M integral can be done in closed form and that the high order
+# are given by
+
+#    n  /       \
+#   d   | log x |   a + b log x
+#   --- | ----- | = -----------
+#     n |   2   |      2 + n
+#   dx  \  x    /     x
+
+# where a and b are integers given by a simple recurrence. Note
+# that just one logarithm is needed. However, lots of integer
+# logarithms are required for the initial summation.
+
+# This algorithm could possibly be turned into a faster algorithm
+# for general evaluation of zeta(s) or zeta'(s); this should be
+# looked into.
+
+@constant_memo
+def glaisher_fixed(prec):
+    wp = prec + 30
+    # Number of direct terms to sum before applying the Euler-Maclaurin
+    # formula to the tail. TODO: choose more intelligently
+    N = int(0.33*prec + 5)
+    ONE = MPZ_ONE << wp
+    # Euler-Maclaurin, step 1: sum log(k)/k**2 for k from 2 to N-1
+    s = MPZ_ZERO
+    for k in range(2, N):
+        #print k, N
+        s += log_int_fixed(k, wp) // k**2
+    logN = log_int_fixed(N, wp)
+    #logN = to_fixed(mpf_log(from_int(N), wp+20), wp)
+    # E-M step 2: integral of log(x)/x**2 from N to inf
+    s += (ONE + logN) // N
+    # E-M step 3: endpoint correction term f(N)/2
+    s += logN // (N**2 * 2)
+    # E-M step 4: the series of derivatives
+    pN = N**3
+    a = 1
+    b = -2
+    j = 3
+    fac = from_int(2)
+    k = 1
+    while 1:
+        # D(2*k-1) * B(2*k) / fac(2*k) [D(n) = nth derivative]
+        D = ((a << wp) + b*logN) // pN
+        D = from_man_exp(D, -wp)
+        B = mpf_bernoulli(2*k, wp)
+        term = mpf_mul(B, D, wp)
+        term = mpf_div(term, fac, wp)
+        term = to_fixed(term, wp)
+        if abs(term) < 100:
+            break
+        #if not k % 10:
+        #    print k, math.log(int(abs(term)), 10)
+        s -= term
+        # Advance derivative twice
+        a, b, pN, j = b-a*j, -j*b, pN*N, j+1
+        a, b, pN, j = b-a*j, -j*b, pN*N, j+1
+        k += 1
+        fac = mpf_mul_int(fac, (2*k)*(2*k-1), wp)
+    # A = exp((6*s/pi**2 + log(2*pi) + euler)/12)
+    pi = pi_fixed(wp)
+    s *= 6
+    s = (s << wp) // (pi**2 >> wp)
+    s += euler_fixed(wp)
+    s += to_fixed(mpf_log(from_man_exp(2*pi, -wp), wp), wp)
+    s //= 12
+    A = mpf_exp(from_man_exp(s, -wp), wp)
+    return to_fixed(A, prec)
+
+# Apery's constant can be computed using the very rapidly convergent
+# series
+#              oo
+#              ___              2                      10
+#             \         n  205 n  + 250 n + 77     (n!)
+#  zeta(3) =   )    (-1)   -------------------  ----------
+#             /___               64                      5
+#             n = 0                             ((2n+1)!)
+
+@constant_memo
+def apery_fixed(prec):
+    prec += 20
+    d = MPZ_ONE << prec
+    term = MPZ(77) << prec
+    n = 1
+    s = MPZ_ZERO
+    while term:
+        s += term
+        d *= (n**10)
+        d //= (((2*n+1)**5) * (2*n)**5)
+        term = (-1)**n * (205*(n**2) + 250*n + 77) * d
+        n += 1
+    return s >> (20 + 6)
+
+"""
+Euler's constant (gamma) is computed using the Brent-McMillan formula,
+gamma ~= I(n)/J(n) - log(n), where
+
+   I(n) = sum_{k=0,1,2,...} (n**k / k!)**2 * H(k)
+   J(n) = sum_{k=0,1,2,...} (n**k / k!)**2
+   H(k) = 1 + 1/2 + 1/3 + ... + 1/k
+
+The error is bounded by O(exp(-4n)). Choosing n to be a power
+of two, 2**p, the logarithm becomes particularly easy to calculate.[1]
+
+We use the formulation of Algorithm 3.9 in [2] to make the summation
+more efficient.
+
+Reference:
+[1] Xavier Gourdon & Pascal Sebah, The Euler constant: gamma
+http://numbers.computation.free.fr/Constants/Gamma/gamma.pdf
+
+[2] [BorweinBailey]_
+"""
+
+@constant_memo
+def euler_fixed(prec):
+    extra = 30
+    prec += extra
+    # choose p such that exp(-4*(2**p)) < 2**-n
+    p = int(math.log((prec/4) * math.log(2), 2)) + 1
+    n = 2**p
+    A = U = -p*ln2_fixed(prec)
+    B = V = MPZ_ONE << prec
+    k = 1
+    while 1:
+        B = B*n**2//k**2
+        A = (A*n**2//k + B)//k
+        U += A
+        V += B
+        if max(abs(A), abs(B)) < 100:
+            break
+        k += 1
+    return (U<<(prec-extra))//V
+
+# Use zeta accelerated formulas for the Mertens and twin
+# prime constants; see
+# http://mathworld.wolfram.com/MertensConstant.html
+# http://mathworld.wolfram.com/TwinPrimesConstant.html
+
+@constant_memo
+def mertens_fixed(prec):
+    wp = prec + 20
+    m = 2
+    s = mpf_euler(wp)
+    while 1:
+        t = mpf_zeta_int(m, wp)
+        if t == fone:
+            break
+        t = mpf_log(t, wp)
+        t = mpf_mul_int(t, moebius(m), wp)
+        t = mpf_div(t, from_int(m), wp)
+        s = mpf_add(s, t)
+        m += 1
+    return to_fixed(s, prec)
+
+@constant_memo
+def twinprime_fixed(prec):
+    def I(n):
+        return sum(moebius(d)<<(n//d) for d in xrange(1,n+1) if not n%d)//n
+    wp = 2*prec + 30
+    res = fone
+    primes = [from_rational(1,p,wp) for p in [2,3,5,7]]
+    ppowers = [mpf_mul(p,p,wp) for p in primes]
+    n = 2
+    while 1:
+        a = mpf_zeta_int(n, wp)
+        for i in range(4):
+            a = mpf_mul(a, mpf_sub(fone, ppowers[i]), wp)
+            ppowers[i] = mpf_mul(ppowers[i], primes[i], wp)
+        a = mpf_pow_int(a, -I(n), wp)
+        if mpf_pos(a, prec+10, 'n') == fone:
+            break
+        #from libmpf import to_str
+        #print n, to_str(mpf_sub(fone, a), 6)
+        res = mpf_mul(res, a, wp)
+        n += 1
+    res = mpf_mul(res, from_int(3*15*35), wp)
+    res = mpf_div(res, from_int(4*16*36), wp)
+    return to_fixed(res, prec)
+
+
+mpf_euler = def_mpf_constant(euler_fixed)
+mpf_apery = def_mpf_constant(apery_fixed)
+mpf_khinchin = def_mpf_constant(khinchin_fixed)
+mpf_glaisher = def_mpf_constant(glaisher_fixed)
+mpf_catalan = def_mpf_constant(catalan_fixed)
+mpf_mertens = def_mpf_constant(mertens_fixed)
+mpf_twinprime = def_mpf_constant(twinprime_fixed)
+
+
+#-----------------------------------------------------------------------#
+#                                                                       #
+#                          Bernoulli numbers                            #
+#                                                                       #
+#-----------------------------------------------------------------------#
+
+MAX_BERNOULLI_CACHE = 3000
+
+
+r"""
+Small Bernoulli numbers and factorials are used in numerous summations,
+so it is critical for speed that sequential computation is fast and that
+values are cached up to a fairly high threshold.
+
+On the other hand, we also want to support fast computation of isolated
+large numbers. Currently, no such acceleration is provided for integer
+factorials (though it is for large floating-point factorials, which are
+computed via gamma if the precision is low enough).
+
+For sequential computation of Bernoulli numbers, we use Ramanujan's formula
+
+                           / n + 3 \
+  B   =  (A(n) - S(n))  /  |       |
+   n                       \   n   /
+
+where A(n) = (n+3)/3 when n = 0 or 2 (mod 6), A(n) = -(n+3)/6
+when n = 4 (mod 6), and
+
+         [n/6]
+          ___
+         \      /  n + 3  \
+  S(n) =  )     |         | * B
+         /___   \ n - 6*k /    n-6*k
+         k = 1
+
+For isolated large Bernoulli numbers, we use the Riemann zeta function
+to calculate a numerical value for B_n. The von Staudt-Clausen theorem
+can then be used to optionally find the exact value of the
+numerator and denominator.
+"""
+
+bernoulli_cache = {}
+f3 = from_int(3)
+f6 = from_int(6)
+
+def bernoulli_size(n):
+    """Accurately estimate the size of B_n (even n > 2 only)"""
+    lgn = math.log(n,2)
+    return int(2.326 + 0.5*lgn + n*(lgn - 4.094))
+
+BERNOULLI_PREC_CUTOFF = bernoulli_size(MAX_BERNOULLI_CACHE)
+
+def mpf_bernoulli(n, prec, rnd=None):
+    """Computation of Bernoulli numbers (numerically)"""
+    if n < 2:
+        if n < 0:
+            raise ValueError("Bernoulli numbers only defined for n >= 0")
+        if n == 0:
+            return fone
+        if n == 1:
+            return mpf_neg(fhalf)
+    # For odd n > 1, the Bernoulli numbers are zero
+    if n & 1:
+        return fzero
+    # If precision is extremely high, we can save time by computing
+    # the Bernoulli number at a lower precision that is sufficient to
+    # obtain the exact fraction, round to the exact fraction, and
+    # convert the fraction back to an mpf value at the original precision
+    if prec > BERNOULLI_PREC_CUTOFF and prec > bernoulli_size(n)*1.1 + 1000:
+        p, q = bernfrac(n)
+        return from_rational(p, q, prec, rnd or round_floor)
+    if n > MAX_BERNOULLI_CACHE:
+        return mpf_bernoulli_huge(n, prec, rnd)
+    wp = prec + 30
+    # Reuse nearby precisions
+    wp += 32 - (prec & 31)
+    cached = bernoulli_cache.get(wp)
+    if cached:
+        numbers, state = cached
+        if n in numbers:
+            if not rnd:
+                return numbers[n]
+            return mpf_pos(numbers[n], prec, rnd)
+        m, bin, bin1 = state
+        if n - m > 10:
+            return mpf_bernoulli_huge(n, prec, rnd)
+    else:
+        if n > 10:
+            return mpf_bernoulli_huge(n, prec, rnd)
+        numbers = {0:fone}
+        m, bin, bin1 = state = [2, MPZ(10), MPZ_ONE]
+        bernoulli_cache[wp] = (numbers, state)
+    while m <= n:
+        #print m
+        case = m % 6
+        # Accurately estimate size of B_m so we can use
+        # fixed point math without using too much precision
+        szbm = bernoulli_size(m)
+        s = 0
+        sexp = max(0, szbm)  - wp
+        if m < 6:
+            a = MPZ_ZERO
+        else:
+            a = bin1
+        for j in xrange(1, m//6+1):
+            usign, uman, uexp, ubc = u = numbers[m-6*j]
+            if usign:
+                uman = -uman
+            s += lshift(a*uman, uexp-sexp)
+            # Update inner binomial coefficient
+            j6 = 6*j
+            a *= ((m-5-j6)*(m-4-j6)*(m-3-j6)*(m-2-j6)*(m-1-j6)*(m-j6))
+            a //= ((4+j6)*(5+j6)*(6+j6)*(7+j6)*(8+j6)*(9+j6))
+        if case == 0: b = mpf_rdiv_int(m+3, f3, wp)
+        if case == 2: b = mpf_rdiv_int(m+3, f3, wp)
+        if case == 4: b = mpf_rdiv_int(-m-3, f6, wp)
+        s = from_man_exp(s, sexp, wp)
+        b = mpf_div(mpf_sub(b, s, wp), from_int(bin), wp)
+        numbers[m] = b
+        m += 2
+        # Update outer binomial coefficient
+        bin = bin * ((m+2)*(m+3)) // (m*(m-1))
+        if m > 6:
+            bin1 = bin1 * ((2+m)*(3+m)) // ((m-7)*(m-6))
+        state[:] = [m, bin, bin1]
+    return numbers[n]
+
+def mpf_bernoulli_huge(n, prec, rnd=None):
+    wp = prec + 10
+    piprec = wp + int(math.log(n,2))
+    v = mpf_gamma_int(n+1, wp)
+    v = mpf_mul(v, mpf_zeta_int(n, wp), wp)
+    v = mpf_mul(v, mpf_pow_int(mpf_pi(piprec), -n, wp))
+    v = mpf_shift(v, 1-n)
+    if not n & 3:
+        v = mpf_neg(v)
+    return mpf_pos(v, prec, rnd or round_fast)
+
+def bernfrac(n):
+    r"""
+    Returns a tuple of integers `(p, q)` such that `p/q = B_n` exactly,
+    where `B_n` denotes the `n`-th Bernoulli number. The fraction is
+    always reduced to lowest terms. Note that for `n > 1` and `n` odd,
+    `B_n = 0`, and `(0, 1)` is returned.
+
+    **Examples**
+
+    The first few Bernoulli numbers are exactly::
+
+        >>> from mpmath import *
+        >>> for n in range(15):
+        ...     p, q = bernfrac(n)
+        ...     print("%s %s/%s" % (n, p, q))
+        ...
+        0 1/1
+        1 -1/2
+        2 1/6
+        3 0/1
+        4 -1/30
+        5 0/1
+        6 1/42
+        7 0/1
+        8 -1/30
+        9 0/1
+        10 5/66
+        11 0/1
+        12 -691/2730
+        13 0/1
+        14 7/6
+
+    This function works for arbitrarily large `n`::
+
+        >>> p, q = bernfrac(10**4)
+        >>> print(q)
+        2338224387510
+        >>> print(len(str(p)))
+        27692
+        >>> mp.dps = 15
+        >>> print(mpf(p) / q)
+        -9.04942396360948e+27677
+        >>> print(bernoulli(10**4))
+        -9.04942396360948e+27677
+
+    .. note ::
+
+        :func:`~mpmath.bernoulli` computes a floating-point approximation
+        directly, without computing the exact fraction first.
+        This is much faster for large `n`.
+
+    **Algorithm**
+
+    :func:`~mpmath.bernfrac` works by computing the value of `B_n` numerically
+    and then using the von Staudt-Clausen theorem [1] to reconstruct
+    the exact fraction. For large `n`, this is significantly faster than
+    computing `B_1, B_2, \ldots, B_2` recursively with exact arithmetic.
+    The implementation has been tested for `n = 10^m` up to `m = 6`.
+
+    In practice, :func:`~mpmath.bernfrac` appears to be about three times
+    slower than the specialized program calcbn.exe [2]
+
+    **References**
+
+    1. MathWorld, von Staudt-Clausen Theorem:
+       http://mathworld.wolfram.com/vonStaudt-ClausenTheorem.html
+
+    2. The Bernoulli Number Page:
+       http://www.bernoulli.org/
+
+    """
+    n = int(n)
+    if n < 3:
+        return [(1, 1), (-1, 2), (1, 6)][n]
+    if n & 1:
+        return (0, 1)
+    q = 1
+    for k in list_primes(n+1):
+        if not (n % (k-1)):
+            q *= k
+    prec = bernoulli_size(n) + int(math.log(q,2)) + 20
+    b = mpf_bernoulli(n, prec)
+    p = mpf_mul(b, from_int(q))
+    pint = to_int(p, round_nearest)
+    return (pint, q)
+
+
+#-----------------------------------------------------------------------#
+#                                                                       #
+#                         Polygamma functions                           #
+#                                                                       #
+#-----------------------------------------------------------------------#
+
+r"""
+For all polygamma (psi) functions, we use the Euler-Maclaurin summation
+formula. It looks slightly different in the m = 0 and m > 0 cases.
+
+For m = 0, we have
+                                 oo
+                                ___   B
+       (0)                1    \       2 k    -2 k
+    psi   (z)  ~ log z + --- -  )    ------  z
+                         2 z   /___  (2 k)!
+                               k = 1
+
+Experiment shows that the minimum term of the asymptotic series
+reaches 2^(-p) when Re(z) > 0.11*p. So we simply use the recurrence
+for psi (equivalent, in fact, to summing to the first few terms
+directly before applying E-M) to obtain z large enough.
+
+Since, very crudely, log z ~= 1 for Re(z) > 1, we can use
+fixed-point arithmetic  (if z is extremely large, log(z) itself
+is a sufficient approximation, so we can stop there already).
+
+For Re(z) << 0, we could use recurrence, but this is of course
+inefficient for large negative z, so there we use the
+reflection formula instead.
+
+For m > 0, we have
+
+                  N - 1
+                   ___
+  ~~~(m)       [  \          1    ]         1            1
+  psi   (z)  ~ [   )     -------- ] +  ---------- +  -------- +
+               [  /___        m+1 ]           m+1           m
+                  k = 1  (z+k)    ]    2 (z+N)       m (z+N)
+
+      oo
+     ___    B
+    \        2 k   (m+1) (m+2) ... (m+2k-1)
+  +  )     ------  ------------------------
+    /___   (2 k)!            m + 2 k
+    k = 1               (z+N)
+
+where ~~~ denotes the function rescaled by 1/((-1)^(m+1) m!).
+
+Here again N is chosen to make z+N large enough for the minimum
+term in the last series to become smaller than eps.
+
+TODO: the current estimation of N for m > 0 is *very suboptimal*.
+
+TODO: implement the reflection formula for m > 0, Re(z) << 0.
+It is generally a combination of multiple cotangents. Need to
+figure out a reasonably simple way to generate these formulas
+on the fly.
+
+TODO: maybe use exact algorithms to compute psi for integral
+and certain rational arguments, as this can be much more
+efficient. (On the other hand, the availability of these
+special values provides a convenient way to test the general
+algorithm.)
+"""
+
+# Harmonic numbers are just shifted digamma functions
+# We should calculate these exactly when x is an integer
+# and when doing so is faster.
+
+def mpf_harmonic(x, prec, rnd):
+    if x in (fzero, fnan, finf):
+        return x
+    a = mpf_psi0(mpf_add(fone, x, prec+5), prec)
+    return mpf_add(a, mpf_euler(prec+5, rnd), prec, rnd)
+
+def mpc_harmonic(z, prec, rnd):
+    if z[1] == fzero:
+        return (mpf_harmonic(z[0], prec, rnd), fzero)
+    a = mpc_psi0(mpc_add_mpf(z, fone, prec+5), prec)
+    return mpc_add_mpf(a, mpf_euler(prec+5, rnd), prec, rnd)
+
+def mpf_psi0(x, prec, rnd=round_fast):
+    """
+    Computation of the digamma function (psi function of order 0)
+    of a real argument.
+    """
+    sign, man, exp, bc = x
+    wp = prec + 10
+    if not man:
+        if x == finf: return x
+        if x == fninf or x == fnan: return fnan
+    if x == fzero or (exp >= 0 and sign):
+        raise ValueError("polygamma pole")
+    # Near 0 -- fixed-point arithmetic becomes bad
+    if exp+bc < -5:
+        v = mpf_psi0(mpf_add(x, fone, prec, rnd), prec, rnd)
+        return mpf_sub(v, mpf_div(fone, x, wp, rnd), prec, rnd)
+    # Reflection formula
+    if sign and exp+bc > 3:
+        c, s = mpf_cos_sin_pi(x, wp)
+        q = mpf_mul(mpf_div(c, s, wp), mpf_pi(wp), wp)
+        p = mpf_psi0(mpf_sub(fone, x, wp), wp)
+        return mpf_sub(p, q, prec, rnd)
+    # The logarithmic term is accurate enough
+    if (not sign) and bc + exp > wp:
+        return mpf_log(mpf_sub(x, fone, wp), prec, rnd)
+    # Initial recurrence to obtain a large enough x
+    m = to_int(x)
+    n = int(0.11*wp) + 2
+    s = MPZ_ZERO
+    x = to_fixed(x, wp)
+    one = MPZ_ONE << wp
+    if m < n:
+        for k in xrange(m, n):
+            s -= (one << wp) // x
+            x += one
+    x -= one
+    # Logarithmic term
+    s += to_fixed(mpf_log(from_man_exp(x, -wp, wp), wp), wp)
+    # Endpoint term in Euler-Maclaurin expansion
+    s += (one << wp) // (2*x)
+    # Euler-Maclaurin remainder sum
+    x2 = (x*x) >> wp
+    t = one
+    prev = 0
+    k = 1
+    while 1:
+        t = (t*x2) >> wp
+        bsign, bman, bexp, bbc = mpf_bernoulli(2*k, wp)
+        offset = (bexp + 2*wp)
+        if offset >= 0: term = (bman << offset) // (t*(2*k))
+        else:           term = (bman >> (-offset)) // (t*(2*k))
+        if k & 1: s -= term
+        else:     s += term
+        if k > 2 and term >= prev:
+            break
+        prev = term
+        k += 1
+    return from_man_exp(s, -wp, wp, rnd)
+
+def mpc_psi0(z, prec, rnd=round_fast):
+    """
+    Computation of the digamma function (psi function of order 0)
+    of a complex argument.
+    """
+    re, im = z
+    # Fall back to the real case
+    if im == fzero:
+        return (mpf_psi0(re, prec, rnd), fzero)
+    wp = prec + 20
+    sign, man, exp, bc = re
+    # Reflection formula
+    if sign and exp+bc > 3:
+        c = mpc_cos_pi(z, wp)
+        s = mpc_sin_pi(z, wp)
+        q = mpc_mul_mpf(mpc_div(c, s, wp), mpf_pi(wp), wp)
+        p = mpc_psi0(mpc_sub(mpc_one, z, wp), wp)
+        return mpc_sub(p, q, prec, rnd)
+    # Just the logarithmic term
+    if (not sign) and bc + exp > wp:
+        return mpc_log(mpc_sub(z, mpc_one, wp), prec, rnd)
+    # Initial recurrence to obtain a large enough z
+    w = to_int(re)
+    n = int(0.11*wp) + 2
+    s = mpc_zero
+    if w < n:
+        for k in xrange(w, n):
+            s = mpc_sub(s, mpc_reciprocal(z, wp), wp)
+            z = mpc_add_mpf(z, fone, wp)
+    z = mpc_sub(z, mpc_one, wp)
+    # Logarithmic and endpoint term
+    s = mpc_add(s, mpc_log(z, wp), wp)
+    s = mpc_add(s, mpc_div(mpc_half, z, wp), wp)
+    # Euler-Maclaurin remainder sum
+    z2 = mpc_square(z, wp)
+    t = mpc_one
+    prev = mpc_zero
+    szprev = fzero
+    k = 1
+    eps = mpf_shift(fone, -wp+2)
+    while 1:
+        t = mpc_mul(t, z2, wp)
+        bern = mpf_bernoulli(2*k, wp)
+        term = mpc_mpf_div(bern, mpc_mul_int(t, 2*k, wp), wp)
+        s = mpc_sub(s, term, wp)
+        szterm = mpc_abs(term, 10)
+        if k > 2 and (mpf_le(szterm, eps) or mpf_le(szprev, szterm)):
+            break
+        prev = term
+        szprev = szterm
+        k += 1
+    return s
+
+# Currently unoptimized
+def mpf_psi(m, x, prec, rnd=round_fast):
+    """
+    Computation of the polygamma function of arbitrary integer order
+    m >= 0, for a real argument x.
+    """
+    if m == 0:
+        return mpf_psi0(x, prec, rnd=round_fast)
+    return mpc_psi(m, (x, fzero), prec, rnd)[0]
+
+def mpc_psi(m, z, prec, rnd=round_fast):
+    """
+    Computation of the polygamma function of arbitrary integer order
+    m >= 0, for a complex argument z.
+    """
+    if m == 0:
+        return mpc_psi0(z, prec, rnd)
+    re, im = z
+    wp = prec + 20
+    sign, man, exp, bc = re
+    if not im[1]:
+        if im in (finf, fninf, fnan):
+            return (fnan, fnan)
+    if not man:
+        if re == finf and im == fzero:
+            return (fzero, fzero)
+        if re == fnan:
+            return (fnan, fnan)
+    # Recurrence
+    w = to_int(re)
+    n = int(0.4*wp + 4*m)
+    s = mpc_zero
+    if w < n:
+        for k in xrange(w, n):
+            t = mpc_pow_int(z, -m-1, wp)
+            s = mpc_add(s, t, wp)
+            z = mpc_add_mpf(z, fone, wp)
+    zm = mpc_pow_int(z, -m, wp)
+    z2 = mpc_pow_int(z, -2, wp)
+    # 1/m*(z+N)^m
+    integral_term = mpc_div_mpf(zm, from_int(m), wp)
+    s = mpc_add(s, integral_term, wp)
+    # 1/2*(z+N)^(-(m+1))
+    s = mpc_add(s, mpc_mul_mpf(mpc_div(zm, z, wp), fhalf, wp), wp)
+    a = m + 1
+    b = 2
+    k = 1
+    # Important: we want to sum up to the *relative* error,
+    # not the absolute error, because psi^(m)(z) might be tiny
+    magn = mpc_abs(s, 10)
+    magn = magn[2]+magn[3]
+    eps = mpf_shift(fone, magn-wp+2)
+    while 1:
+        zm = mpc_mul(zm, z2, wp)
+        bern = mpf_bernoulli(2*k, wp)
+        scal = mpf_mul_int(bern, a, wp)
+        scal = mpf_div(scal, from_int(b), wp)
+        term = mpc_mul_mpf(zm, scal, wp)
+        s = mpc_add(s, term, wp)
+        szterm = mpc_abs(term, 10)
+        if k > 2 and mpf_le(szterm, eps):
+            break
+        #print k, to_str(szterm, 10), to_str(eps, 10)
+        a *= (m+2*k)*(m+2*k+1)
+        b *= (2*k+1)*(2*k+2)
+        k += 1
+    # Scale and sign factor
+    v = mpc_mul_mpf(s, mpf_gamma(from_int(m+1), wp), prec, rnd)
+    if not (m & 1):
+        v = mpf_neg(v[0]), mpf_neg(v[1])
+    return v
+
+
+#-----------------------------------------------------------------------#
+#                                                                       #
+#                         Riemann zeta function                         #
+#                                                                       #
+#-----------------------------------------------------------------------#
+
+r"""
+We use zeta(s) = eta(s) / (1 - 2**(1-s)) and Borwein's approximation
+
+                  n-1
+                  ___       k
+             -1  \      (-1)  (d_k - d_n)
+  eta(s) ~= ----  )     ------------------
+             d_n /___              s
+                 k = 0      (k + 1)
+where
+             k
+             ___                i
+            \     (n + i - 1)! 4
+  d_k  =  n  )    ---------------.
+            /___   (n - i)! (2i)!
+            i = 0
+
+If s = a + b*I, the absolute error for eta(s) is bounded by
+
+    3 (1 + 2|b|)
+    ------------ * exp(|b| pi/2)
+               n
+    (3+sqrt(8))
+
+Disregarding the linear term, we have approximately,
+
+  log(err) ~= log(exp(1.58*|b|)) - log(5.8**n)
+  log(err) ~= 1.58*|b| - log(5.8)*n
+  log(err) ~= 1.58*|b| - 1.76*n
+  log2(err) ~= 2.28*|b| - 2.54*n
+
+So for p bits, we should choose n > (p + 2.28*|b|) / 2.54.
+
+References:
+-----------
+
+Peter Borwein, "An Efficient Algorithm for the Riemann Zeta Function"
+http://www.cecm.sfu.ca/personal/pborwein/PAPERS/P117.ps
+
+http://en.wikipedia.org/wiki/Dirichlet_eta_function
+"""
+
+borwein_cache = {}
+
+def borwein_coefficients(n):
+    if n in borwein_cache:
+        return borwein_cache[n]
+    ds = [MPZ_ZERO] * (n+1)
+    d = MPZ_ONE
+    s = ds[0] = MPZ_ONE
+    for i in range(1, n+1):
+        d = d * 4 * (n+i-1) * (n-i+1)
+        d //= ((2*i) * ((2*i)-1))
+        s += d
+        ds[i] = s
+    borwein_cache[n] = ds
+    return ds
+
+ZETA_INT_CACHE_MAX_PREC = 1000
+zeta_int_cache = {}
+
+def mpf_zeta_int(s, prec, rnd=round_fast):
+    """
+    Optimized computation of zeta(s) for an integer s.
+    """
+    wp = prec + 20
+    s = int(s)
+    if s in zeta_int_cache and zeta_int_cache[s][0] >= wp:
+        return mpf_pos(zeta_int_cache[s][1], prec, rnd)
+    if s < 2:
+        if s == 1:
+            raise ValueError("zeta(1) pole")
+        if not s:
+            return mpf_neg(fhalf)
+        return mpf_div(mpf_bernoulli(-s+1, wp), from_int(s-1), prec, rnd)
+    # 2^-s term vanishes?
+    if s >= wp:
+        return mpf_perturb(fone, 0, prec, rnd)
+    # 5^-s term vanishes?
+    elif s >= wp*0.431:
+        t = one = 1 << wp
+        t += 1 << (wp - s)
+        t += one // (MPZ_THREE ** s)
+        t += 1 << max(0, wp - s*2)
+        return from_man_exp(t, -wp, prec, rnd)
+    else:
+        # Fast enough to sum directly?
+        # Even better, we use the Euler product (idea stolen from pari)
+        m = (float(wp)/(s-1) + 1)
+        if m < 30:
+            needed_terms = int(2.0**m + 1)
+            if needed_terms < int(wp/2.54 + 5) / 10:
+                t = fone
+                for k in list_primes(needed_terms):
+                    #print k, needed_terms
+                    powprec = int(wp - s*math.log(k,2))
+                    if powprec < 2:
+                        break
+                    a = mpf_sub(fone, mpf_pow_int(from_int(k), -s, powprec), wp)
+                    t = mpf_mul(t, a, wp)
+                return mpf_div(fone, t, wp)
+    # Use Borwein's algorithm
+    n = int(wp/2.54 + 5)
+    d = borwein_coefficients(n)
+    t = MPZ_ZERO
+    s = MPZ(s)
+    for k in xrange(n):
+        t += (((-1)**k * (d[k] - d[n])) << wp) // (k+1)**s
+    t = (t << wp) // (-d[n])
+    t = (t << wp) // ((1 << wp) - (1 << (wp+1-s)))
+    if (s in zeta_int_cache and zeta_int_cache[s][0] < wp) or (s not in zeta_int_cache):
+        zeta_int_cache[s] = (wp, from_man_exp(t, -wp-wp))
+    return from_man_exp(t, -wp-wp, prec, rnd)
+
+def mpf_zeta(s, prec, rnd=round_fast, alt=0):
+    sign, man, exp, bc = s
+    if not man:
+        if s == fzero:
+            if alt:
+                return fhalf
+            else:
+                return mpf_neg(fhalf)
+        if s == finf:
+            return fone
+        return fnan
+    wp = prec + 20
+    # First term vanishes?
+    if (not sign) and (exp + bc > (math.log(wp,2) + 2)):
+        return mpf_perturb(fone, alt, prec, rnd)
+    # Optimize for integer arguments
+    elif exp >= 0:
+        if alt:
+            if s == fone:
+                return mpf_ln2(prec, rnd)
+            z = mpf_zeta_int(to_int(s), wp, negative_rnd[rnd])
+            q = mpf_sub(fone, mpf_pow(ftwo, mpf_sub(fone, s, wp), wp), wp)
+            return mpf_mul(z, q, prec, rnd)
+        else:
+            return mpf_zeta_int(to_int(s), prec, rnd)
+    # Negative: use the reflection formula
+    # Borwein only proves the accuracy bound for x >= 1/2. However, based on
+    # tests, the accuracy without reflection is quite good even some distance
+    # to the left of 1/2. XXX: verify this.
+    if sign:
+        # XXX: could use the separate refl. formula for Dirichlet eta
+        if alt:
+            q = mpf_sub(fone, mpf_pow(ftwo, mpf_sub(fone, s, wp), wp), wp)
+            return mpf_mul(mpf_zeta(s, wp), q, prec, rnd)
+        # XXX: -1 should be done exactly
+        y = mpf_sub(fone, s, 10*wp)
+        a = mpf_gamma(y, wp)
+        b = mpf_zeta(y, wp)
+        c = mpf_sin_pi(mpf_shift(s, -1), wp)
+        wp2 = wp + max(0,exp+bc)
+        pi = mpf_pi(wp+wp2)
+        d = mpf_div(mpf_pow(mpf_shift(pi, 1), s, wp2), pi, wp2)
+        return mpf_mul(a,mpf_mul(b,mpf_mul(c,d,wp),wp),prec,rnd)
+
+    # Near pole
+    r = mpf_sub(fone, s, wp)
+    asign, aman, aexp, abc = mpf_abs(r)
+    pole_dist = -2*(aexp+abc)
+    if pole_dist > wp:
+        if alt:
+            return mpf_ln2(prec, rnd)
+        else:
+            q = mpf_neg(mpf_div(fone, r, wp))
+            return mpf_add(q, mpf_euler(wp), prec, rnd)
+    else:
+        wp += max(0, pole_dist)
+
+    t = MPZ_ZERO
+    #wp += 16 - (prec & 15)
+    # Use Borwein's algorithm
+    n = int(wp/2.54 + 5)
+    d = borwein_coefficients(n)
+    t = MPZ_ZERO
+    sf = to_fixed(s, wp)
+    ln2 = ln2_fixed(wp)
+    for k in xrange(n):
+        u = (-sf*log_int_fixed(k+1, wp, ln2)) >> wp
+        #esign, eman, eexp, ebc = mpf_exp(u, wp)
+        #offset = eexp + wp
+        #if offset >= 0:
+        #    w = ((d[k] - d[n]) * eman) << offset
+        #else:
+        #    w = ((d[k] - d[n]) * eman) >> (-offset)
+        eman = exp_fixed(u, wp, ln2)
+        w = (d[k] - d[n]) * eman
+        if k & 1:
+            t -= w
+        else:
+            t += w
+    t = t // (-d[n])
+    t = from_man_exp(t, -wp, wp)
+    if alt:
+        return mpf_pos(t, prec, rnd)
+    else:
+        q = mpf_sub(fone, mpf_pow(ftwo, mpf_sub(fone, s, wp), wp), wp)
+        return mpf_div(t, q, prec, rnd)
+
+def mpc_zeta(s, prec, rnd=round_fast, alt=0, force=False):
+    re, im = s
+    if im == fzero:
+        return mpf_zeta(re, prec, rnd, alt), fzero
+
+    # slow for large s
+    if (not force) and mpf_gt(mpc_abs(s, 10), from_int(prec)):
+        raise NotImplementedError
+
+    wp = prec + 20
+
+    # Near pole
+    r = mpc_sub(mpc_one, s, wp)
+    asign, aman, aexp, abc = mpc_abs(r, 10)
+    pole_dist = -2*(aexp+abc)
+    if pole_dist > wp:
+        if alt:
+            q = mpf_ln2(wp)
+            y = mpf_mul(q, mpf_euler(wp), wp)
+            g = mpf_shift(mpf_mul(q, q, wp), -1)
+            g = mpf_sub(y, g)
+            z = mpc_mul_mpf(r, mpf_neg(g), wp)
+            z = mpc_add_mpf(z, q, wp)
+            return mpc_pos(z, prec, rnd)
+        else:
+            q = mpc_neg(mpc_div(mpc_one, r, wp))
+            q = mpc_add_mpf(q, mpf_euler(wp), wp)
+            return mpc_pos(q, prec, rnd)
+    else:
+        wp += max(0, pole_dist)
+
+    # Reflection formula. To be rigorous, we should reflect to the left of
+    # re = 1/2 (see comments for mpf_zeta), but this leads to unnecessary
+    # slowdown for interesting values of s
+    if mpf_lt(re, fzero):
+        # XXX: could use the separate refl. formula for Dirichlet eta
+        if alt:
+            q = mpc_sub(mpc_one, mpc_pow(mpc_two, mpc_sub(mpc_one, s, wp),
+                wp), wp)
+            return mpc_mul(mpc_zeta(s, wp), q, prec, rnd)
+        # XXX: -1 should be done exactly
+        y = mpc_sub(mpc_one, s, 10*wp)
+        a = mpc_gamma(y, wp)
+        b = mpc_zeta(y, wp)
+        c = mpc_sin_pi(mpc_shift(s, -1), wp)
+        rsign, rman, rexp, rbc = re
+        isign, iman, iexp, ibc = im
+        mag = max(rexp+rbc, iexp+ibc)
+        wp2 = wp + max(0, mag)
+        pi = mpf_pi(wp+wp2)
+        pi2 = (mpf_shift(pi, 1), fzero)
+        d = mpc_div_mpf(mpc_pow(pi2, s, wp2), pi, wp2)
+        return mpc_mul(a,mpc_mul(b,mpc_mul(c,d,wp),wp),prec,rnd)
+    n = int(wp/2.54 + 5)
+    n += int(0.9*abs(to_int(im)))
+    d = borwein_coefficients(n)
+    ref = to_fixed(re, wp)
+    imf = to_fixed(im, wp)
+    tre = MPZ_ZERO
+    tim = MPZ_ZERO
+    one = MPZ_ONE << wp
+    one_2wp = MPZ_ONE << (2*wp)
+    critical_line = re == fhalf
+    ln2 = ln2_fixed(wp)
+    pi2 = pi_fixed(wp-1)
+    wp2 = wp+wp
+    for k in xrange(n):
+        log = log_int_fixed(k+1, wp, ln2)
+        # A square root is much cheaper than an exp
+        if critical_line:
+            w = one_2wp // isqrt_fast((k+1) << wp2)
+        else:
+            w = exp_fixed((-ref*log) >> wp, wp)
+        if k & 1:
+            w *= (d[n] - d[k])
+        else:
+            w *= (d[k] - d[n])
+        wre, wim = cos_sin_fixed((-imf*log)>>wp, wp, pi2)
+        tre += (w * wre) >> wp
+        tim += (w * wim) >> wp
+    tre //= (-d[n])
+    tim //= (-d[n])
+    tre = from_man_exp(tre, -wp, wp)
+    tim = from_man_exp(tim, -wp, wp)
+    if alt:
+        return mpc_pos((tre, tim), prec, rnd)
+    else:
+        q = mpc_sub(mpc_one, mpc_pow(mpc_two, r, wp), wp)
+        return mpc_div((tre, tim), q, prec, rnd)
+
+def mpf_altzeta(s, prec, rnd=round_fast):
+    return mpf_zeta(s, prec, rnd, 1)
+
+def mpc_altzeta(s, prec, rnd=round_fast):
+    return mpc_zeta(s, prec, rnd, 1)
+
+# Not optimized currently
+mpf_zetasum = None
+
+
+def pow_fixed(x, n, wp):
+    if n == 1:
+        return x
+    y = MPZ_ONE << wp
+    while n:
+        if n & 1:
+            y = (y*x) >> wp
+            n -= 1
+        x = (x*x) >> wp
+        n //= 2
+    return y
+
+# TODO: optimize / cleanup interface / unify with list_primes
+sieve_cache = []
+primes_cache = []
+mult_cache = []
+
+def primesieve(n):
+    global sieve_cache, primes_cache, mult_cache
+    if n < len(sieve_cache):
+        sieve = sieve_cache#[:n+1]
+        primes = primes_cache[:primes_cache.index(max(sieve))+1]
+        mult = mult_cache#[:n+1]
+        return sieve, primes, mult
+    sieve = [0] * (n+1)
+    mult = [0] * (n+1)
+    primes = list_primes(n)
+    for p in primes:
+        #sieve[p::p] = p
+        for k in xrange(p,n+1,p):
+            sieve[k] = p
+    for i, p in enumerate(sieve):
+        if i >= 2:
+            m = 1
+            n = i // p
+            while not n % p:
+                n //= p
+                m += 1
+            mult[i] = m
+    sieve_cache = sieve
+    primes_cache = primes
+    mult_cache = mult
+    return sieve, primes, mult
+
+def zetasum_sieved(critical_line, sre, sim, a, n, wp):
+    if a < 1:
+        raise ValueError("a cannot be less than 1")
+    sieve, primes, mult = primesieve(a+n)
+    basic_powers = {}
+    one = MPZ_ONE << wp
+    one_2wp = MPZ_ONE << (2*wp)
+    wp2 = wp+wp
+    ln2 = ln2_fixed(wp)
+    pi2 = pi_fixed(wp-1)
+    for p in primes:
+        if p*2 > a+n:
+            break
+        log = log_int_fixed(p, wp, ln2)
+        cos, sin = cos_sin_fixed((-sim*log)>>wp, wp, pi2)
+        if critical_line:
+            u = one_2wp // isqrt_fast(p<<wp2)
+        else:
+            u = exp_fixed((-sre*log)>>wp, wp)
+        pre = (u*cos) >> wp
+        pim = (u*sin) >> wp
+        basic_powers[p] = [(pre, pim)]
+        tre, tim = pre, pim
+        for m in range(1,int(math.log(a+n,p)+0.01)+1):
+            tre, tim = ((pre*tre-pim*tim)>>wp), ((pim*tre+pre*tim)>>wp)
+            basic_powers[p].append((tre,tim))
+    xre = MPZ_ZERO
+    xim = MPZ_ZERO
+    if a == 1:
+        xre += one
+    aa = max(a,2)
+    for k in xrange(aa, a+n+1):
+        p = sieve[k]
+        if p in basic_powers:
+            m = mult[k]
+            tre, tim = basic_powers[p][m-1]
+            while 1:
+                k //= p**m
+                if k == 1:
+                    break
+                p = sieve[k]
+                m = mult[k]
+                pre, pim = basic_powers[p][m-1]
+                tre, tim = ((pre*tre-pim*tim)>>wp), ((pim*tre+pre*tim)>>wp)
+        else:
+            log = log_int_fixed(k, wp, ln2)
+            cos, sin = cos_sin_fixed((-sim*log)>>wp, wp, pi2)
+            if critical_line:
+                u = one_2wp // isqrt_fast(k<<wp2)
+            else:
+                u = exp_fixed((-sre*log)>>wp, wp)
+            tre = (u*cos) >> wp
+            tim = (u*sin) >> wp
+        xre += tre
+        xim += tim
+    return xre, xim
+
+# Set to something large to disable
+ZETASUM_SIEVE_CUTOFF = 10
+
+def mpc_zetasum(s, a, n, derivatives, reflect, prec):
+    """
+    Fast version of mp._zetasum, assuming s = complex, a = integer.
+    """
+
+    wp = prec + 10
+    derivatives = list(derivatives)
+    have_derivatives = derivatives != [0]
+    have_one_derivative = len(derivatives) == 1
+
+    # parse s
+    sre, sim = s
+    critical_line = (sre == fhalf)
+    sre = to_fixed(sre, wp)
+    sim = to_fixed(sim, wp)
+
+    if a > 0 and n > ZETASUM_SIEVE_CUTOFF and not have_derivatives \
+            and not reflect and (n < 4e7 or sys.maxsize > 2**32):
+        re, im = zetasum_sieved(critical_line, sre, sim, a, n, wp)
+        xs = [(from_man_exp(re, -wp, prec, 'n'), from_man_exp(im, -wp, prec, 'n'))]
+        return xs, []
+
+    maxd = max(derivatives)
+    if not have_one_derivative:
+        derivatives = range(maxd+1)
+
+    # x_d = 0, y_d = 0
+    xre = [MPZ_ZERO for d in derivatives]
+    xim = [MPZ_ZERO for d in derivatives]
+    if reflect:
+        yre = [MPZ_ZERO for d in derivatives]
+        yim = [MPZ_ZERO for d in derivatives]
+    else:
+        yre = yim = []
+
+    one = MPZ_ONE << wp
+    one_2wp = MPZ_ONE << (2*wp)
+
+    ln2 = ln2_fixed(wp)
+    pi2 = pi_fixed(wp-1)
+    wp2 = wp+wp
+
+    for w in xrange(a, a+n+1):
+        log = log_int_fixed(w, wp, ln2)
+        cos, sin = cos_sin_fixed((-sim*log)>>wp, wp, pi2)
+        if critical_line:
+            u = one_2wp // isqrt_fast(w<<wp2)
+        else:
+            u = exp_fixed((-sre*log)>>wp, wp)
+        xterm_re = (u * cos) >> wp
+        xterm_im = (u * sin) >> wp
+        if reflect:
+            reciprocal = (one_2wp // (u*w))
+            yterm_re = (reciprocal * cos) >> wp
+            yterm_im = (reciprocal * sin) >> wp
+
+        if have_derivatives:
+            if have_one_derivative:
+                log = pow_fixed(log, maxd, wp)
+                xre[0] += (xterm_re * log) >> wp
+                xim[0] += (xterm_im * log) >> wp
+                if reflect:
+                    yre[0] += (yterm_re * log) >> wp
+                    yim[0] += (yterm_im * log) >> wp
+            else:
+                t = MPZ_ONE << wp
+                for d in derivatives:
+                    xre[d] += (xterm_re * t) >> wp
+                    xim[d] += (xterm_im * t) >> wp
+                    if reflect:
+                        yre[d] += (yterm_re * t) >> wp
+                        yim[d] += (yterm_im * t) >> wp
+                    t = (t * log) >> wp
+        else:
+            xre[0] += xterm_re
+            xim[0] += xterm_im
+            if reflect:
+                yre[0] += yterm_re
+                yim[0] += yterm_im
+    if have_derivatives:
+        if have_one_derivative:
+            if maxd % 2:
+                xre[0] = -xre[0]
+                xim[0] = -xim[0]
+                if reflect:
+                    yre[0] = -yre[0]
+                    yim[0] = -yim[0]
+        else:
+            xre = [(-1)**d * xre[d] for d in derivatives]
+            xim = [(-1)**d * xim[d] for d in derivatives]
+            if reflect:
+                yre = [(-1)**d * yre[d] for d in derivatives]
+                yim = [(-1)**d * yim[d] for d in derivatives]
+    xs = [(from_man_exp(xa, -wp, prec, 'n'), from_man_exp(xb, -wp, prec, 'n'))
+        for (xa, xb) in zip(xre, xim)]
+    ys = [(from_man_exp(ya, -wp, prec, 'n'), from_man_exp(yb, -wp, prec, 'n'))
+        for (ya, yb) in zip(yre, yim)]
+    return xs, ys
+
+
+#-----------------------------------------------------------------------#
+#                                                                       #
+#              The gamma function  (NEW IMPLEMENTATION)                 #
+#                                                                       #
+#-----------------------------------------------------------------------#
+
+# Higher means faster, but more precomputation time
+MAX_GAMMA_TAYLOR_PREC = 5000
+# Need to derive higher bounds for Taylor series to go higher
+assert MAX_GAMMA_TAYLOR_PREC < 15000
+
+# Use Stirling's series if abs(x) > beta*prec
+# Important: must be large enough for convergence!
+GAMMA_STIRLING_BETA = 0.2
+
+SMALL_FACTORIAL_CACHE_SIZE = 150
+
+gamma_taylor_cache = {}
+gamma_stirling_cache = {}
+
+small_factorial_cache = [from_int(ifac(n)) for \
+    n in range(SMALL_FACTORIAL_CACHE_SIZE+1)]
+
+def zeta_array(N, prec):
+    """
+    zeta(n) = A * pi**n / n! + B
+
+    where A is a rational number (A = Bernoulli number
+    for n even) and B is an infinite sum over powers of exp(2*pi).
+    (B = 0 for n even).
+
+    TODO: this is currently only used for gamma, but could
+    be very useful elsewhere.
+    """
+    extra = 30
+    wp = prec+extra
+    zeta_values = [MPZ_ZERO] * (N+2)
+    pi = pi_fixed(wp)
+    # STEP 1:
+    one = MPZ_ONE << wp
+    zeta_values[0] = -one//2
+    f_2pi = mpf_shift(mpf_pi(wp),1)
+    exp_2pi_k = exp_2pi = mpf_exp(f_2pi, wp)
+    # Compute exponential series
+    # Store values of 1/(exp(2*pi*k)-1),
+    # exp(2*pi*k)/(exp(2*pi*k)-1)**2, 1/(exp(2*pi*k)-1)**2
+    # pi*k*exp(2*pi*k)/(exp(2*pi*k)-1)**2
+    exps3 = []
+    k = 1
+    while 1:
+        tp = wp - 9*k
+        if tp < 1:
+            break
+        # 1/(exp(2*pi*k-1)
+        q1 = mpf_div(fone, mpf_sub(exp_2pi_k, fone, tp), tp)
+        # pi*k*exp(2*pi*k)/(exp(2*pi*k)-1)**2
+        q2 = mpf_mul(exp_2pi_k, mpf_mul(q1,q1,tp), tp)
+        q1 = to_fixed(q1, wp)
+        q2 = to_fixed(q2, wp)
+        q2 = (k * q2 * pi) >> wp
+        exps3.append((q1, q2))
+        # Multiply for next round
+        exp_2pi_k = mpf_mul(exp_2pi_k, exp_2pi, wp)
+        k += 1
+    # Exponential sum
+    for n in xrange(3, N+1, 2):
+        s = MPZ_ZERO
+        k = 1
+        for e1, e2 in exps3:
+            if n%4 == 3:
+                t = e1 // k**n
+            else:
+                U = (n-1)//4
+                t = (e1 + e2//U) // k**n
+            if not t:
+                break
+            s += t
+            k += 1
+        zeta_values[n] = -2*s
+    # Even zeta values
+    B = [mpf_abs(mpf_bernoulli(k,wp)) for k in xrange(N+2)]
+    pi_pow = fpi = mpf_pow_int(mpf_shift(mpf_pi(wp), 1), 2, wp)
+    pi_pow = mpf_div(pi_pow, from_int(4), wp)
+    for n in xrange(2,N+2,2):
+        z = mpf_mul(B[n], pi_pow, wp)
+        zeta_values[n] = to_fixed(z, wp)
+        pi_pow = mpf_mul(pi_pow, fpi, wp)
+        pi_pow = mpf_div(pi_pow, from_int((n+1)*(n+2)), wp)
+    # Zeta sum
+    reciprocal_pi = (one << wp) // pi
+    for n in xrange(3, N+1, 4):
+        U = (n-3)//4
+        s = zeta_values[4*U+4]*(4*U+7)//4
+        for k in xrange(1, U+1):
+            s -= (zeta_values[4*k] * zeta_values[4*U+4-4*k]) >> wp
+        zeta_values[n] += (2*s*reciprocal_pi) >> wp
+    for n in xrange(5, N+1, 4):
+        U = (n-1)//4
+        s = zeta_values[4*U+2]*(2*U+1)
+        for k in xrange(1, 2*U+1):
+            s += ((-1)**k*2*k* zeta_values[2*k] * zeta_values[4*U+2-2*k])>>wp
+        zeta_values[n] += ((s*reciprocal_pi)>>wp)//(2*U)
+    return [x>>extra for x in zeta_values]
+
+def gamma_taylor_coefficients(inprec):
+    """
+    Gives the Taylor coefficients of 1/gamma(1+x) as
+    a list of fixed-point numbers. Enough coefficients are returned
+    to ensure that the series converges to the given precision
+    when x is in [0.5, 1.5].
+    """
+    # Reuse nearby cache values (small case)
+    if inprec < 400:
+        prec = inprec + (10-(inprec%10))
+    elif inprec < 1000:
+        prec = inprec + (30-(inprec%30))
+    else:
+        prec = inprec
+    if prec in gamma_taylor_cache:
+        return gamma_taylor_cache[prec], prec
+
+    # Experimentally determined bounds
+    if prec < 1000:
+        N = int(prec**0.76 + 2)
+    else:
+        # Valid to at least 15000 bits
+        N = int(prec**0.787 + 2)
+
+    # Reuse higher precision values
+    for cprec in gamma_taylor_cache:
+        if cprec > prec:
+            coeffs = [x>>(cprec-prec) for x in gamma_taylor_cache[cprec][-N:]]
+            if inprec < 1000:
+                gamma_taylor_cache[prec] = coeffs
+            return coeffs, prec
+
+    # Cache at a higher precision (large case)
+    if prec > 1000:
+        prec = int(prec * 1.2)
+
+    wp = prec + 20
+    A = [0] * N
+    A[0] = MPZ_ZERO
+    A[1] = MPZ_ONE << wp
+    A[2] = euler_fixed(wp)
+    # SLOW, reference implementation
+    #zeta_values = [0,0]+[to_fixed(mpf_zeta_int(k,wp),wp) for k in xrange(2,N)]
+    zeta_values = zeta_array(N, wp)
+    for k in xrange(3, N):
+        a = (-A[2]*A[k-1])>>wp
+        for j in xrange(2,k):
+            a += ((-1)**j * zeta_values[j] * A[k-j]) >> wp
+        a //= (1-k)
+        A[k] = a
+    A = [a>>20 for a in A]
+    A = A[::-1]
+    A = A[:-1]
+    gamma_taylor_cache[prec] = A
+    #return A, prec
+    return gamma_taylor_coefficients(inprec)
+
+def gamma_fixed_taylor(xmpf, x, wp, prec, rnd, type):
+    # Determine nearest multiple of N/2
+    #n = int(x >> (wp-1))
+    #steps = (n-1)>>1
+    nearest_int = ((x >> (wp-1)) + MPZ_ONE) >> 1
+    one = MPZ_ONE << wp
+    coeffs, cwp = gamma_taylor_coefficients(wp)
+    if nearest_int > 0:
+        r = one
+        for i in xrange(nearest_int-1):
+            x -= one
+            r = (r*x) >> wp
+        x -= one
+        p = MPZ_ZERO
+        for c in coeffs:
+            p = c + ((x*p)>>wp)
+        p >>= (cwp-wp)
+        if type == 0:
+            return from_man_exp((r<<wp)//p, -wp, prec, rnd)
+        if type == 2:
+            return mpf_shift(from_rational(p, (r<<wp), prec, rnd), wp)
+        if type == 3:
+            return mpf_log(mpf_abs(from_man_exp((r<<wp)//p, -wp)), prec, rnd)
+    else:
+        r = one
+        for i in xrange(-nearest_int):
+            r = (r*x) >> wp
+            x += one
+        p = MPZ_ZERO
+        for c in coeffs:
+            p = c + ((x*p)>>wp)
+        p >>= (cwp-wp)
+        if wp - bitcount(abs(x)) > 10:
+            # pass very close to 0, so do floating-point multiply
+            g = mpf_add(xmpf, from_int(-nearest_int))  # exact
+            r = from_man_exp(p*r,-wp-wp)
+            r = mpf_mul(r, g, wp)
+            if type == 0:
+                return mpf_div(fone, r, prec, rnd)
+            if type == 2:
+                return mpf_pos(r, prec, rnd)
+            if type == 3:
+                return mpf_log(mpf_abs(mpf_div(fone, r, wp)), prec, rnd)
+        else:
+            r = from_man_exp(x*p*r,-3*wp)
+            if type == 0: return mpf_div(fone, r, prec, rnd)
+            if type == 2: return mpf_pos(r, prec, rnd)
+            if type == 3: return mpf_neg(mpf_log(mpf_abs(r), prec, rnd))
+
+def stirling_coefficient(n):
+    if n in gamma_stirling_cache:
+        return gamma_stirling_cache[n]
+    p, q = bernfrac(n)
+    q *= MPZ(n*(n-1))
+    gamma_stirling_cache[n] = p, q, bitcount(abs(p)), bitcount(q)
+    return gamma_stirling_cache[n]
+
+def real_stirling_series(x, prec):
+    """
+    Sums the rational part of Stirling's expansion,
+
+    log(sqrt(2*pi)) - z + 1/(12*z) - 1/(360*z^3) + ...
+
+    """
+    t = (MPZ_ONE<<(prec+prec)) // x   # t = 1/x
+    u = (t*t)>>prec                  # u = 1/x**2
+    s = ln_sqrt2pi_fixed(prec) - x
+    # Add initial terms of Stirling's series
+    s += t//12;            t = (t*u)>>prec
+    s -= t//360;           t = (t*u)>>prec
+    s += t//1260;          t = (t*u)>>prec
+    s -= t//1680;          t = (t*u)>>prec
+    if not t: return s
+    s += t//1188;          t = (t*u)>>prec
+    s -= 691*t//360360;    t = (t*u)>>prec
+    s += t//156;           t = (t*u)>>prec
+    if not t: return s
+    s -= 3617*t//122400;   t = (t*u)>>prec
+    s += 43867*t//244188;  t = (t*u)>>prec
+    s -= 174611*t//125400;  t = (t*u)>>prec
+    if not t: return s
+    k = 22
+    # From here on, the coefficients are growing, so we
+    # have to keep t at a roughly constant size
+    usize = bitcount(abs(u))
+    tsize = bitcount(abs(t))
+    texp = 0
+    while 1:
+        p, q, pb, qb = stirling_coefficient(k)
+        term_mag = tsize + pb + texp
+        shift = -texp
+        m = pb - term_mag
+        if m > 0 and shift < m:
+            p >>= m
+            shift -= m
+        m = tsize - term_mag
+        if m > 0 and shift < m:
+            w = t >> m
+            shift -= m
+        else:
+            w = t
+        term = (t*p//q) >> shift
+        if not term:
+            break
+        s += term
+        t = (t*u) >> usize
+        texp -= (prec - usize)
+        k += 2
+    return s
+
+def complex_stirling_series(x, y, prec):
+    # t = 1/z
+    _m = (x*x + y*y) >> prec
+    tre = (x << prec) // _m
+    tim = (-y << prec) // _m
+    # u = 1/z**2
+    ure = (tre*tre - tim*tim) >> prec
+    uim = tim*tre >> (prec-1)
+    # s = log(sqrt(2*pi)) - z
+    sre = ln_sqrt2pi_fixed(prec) - x
+    sim = -y
+
+    # Add initial terms of Stirling's series
+    sre += tre//12; sim += tim//12;
+    tre, tim = ((tre*ure-tim*uim)>>prec), ((tre*uim+tim*ure)>>prec)
+    sre -= tre//360; sim -= tim//360;
+    tre, tim = ((tre*ure-tim*uim)>>prec), ((tre*uim+tim*ure)>>prec)
+    sre += tre//1260; sim += tim//1260;
+    tre, tim = ((tre*ure-tim*uim)>>prec), ((tre*uim+tim*ure)>>prec)
+    sre -= tre//1680; sim -= tim//1680;
+    tre, tim = ((tre*ure-tim*uim)>>prec), ((tre*uim+tim*ure)>>prec)
+    if abs(tre) + abs(tim) < 5: return sre, sim
+    sre += tre//1188; sim += tim//1188;
+    tre, tim = ((tre*ure-tim*uim)>>prec), ((tre*uim+tim*ure)>>prec)
+    sre -= 691*tre//360360; sim -= 691*tim//360360;
+    tre, tim = ((tre*ure-tim*uim)>>prec), ((tre*uim+tim*ure)>>prec)
+    sre += tre//156; sim += tim//156;
+    tre, tim = ((tre*ure-tim*uim)>>prec), ((tre*uim+tim*ure)>>prec)
+    if abs(tre) + abs(tim) < 5: return sre, sim
+    sre -= 3617*tre//122400; sim -= 3617*tim//122400;
+    tre, tim = ((tre*ure-tim*uim)>>prec), ((tre*uim+tim*ure)>>prec)
+    sre += 43867*tre//244188; sim += 43867*tim//244188;
+    tre, tim = ((tre*ure-tim*uim)>>prec), ((tre*uim+tim*ure)>>prec)
+    sre -= 174611*tre//125400; sim -= 174611*tim//125400;
+    tre, tim = ((tre*ure-tim*uim)>>prec), ((tre*uim+tim*ure)>>prec)
+    if abs(tre) + abs(tim) < 5: return sre, sim
+
+    k = 22
+    # From here on, the coefficients are growing, so we
+    # have to keep t at a roughly constant size
+    usize = bitcount(max(abs(ure), abs(uim)))
+    tsize = bitcount(max(abs(tre), abs(tim)))
+    texp = 0
+    while 1:
+        p, q, pb, qb = stirling_coefficient(k)
+        term_mag = tsize + pb + texp
+        shift = -texp
+        m = pb - term_mag
+        if m > 0 and shift < m:
+            p >>= m
+            shift -= m
+        m = tsize - term_mag
+        if m > 0 and shift < m:
+            wre = tre >> m
+            wim = tim >> m
+            shift -= m
+        else:
+            wre = tre
+            wim = tim
+        termre = (tre*p//q) >> shift
+        termim = (tim*p//q) >> shift
+        if abs(termre) + abs(termim) < 5:
+            break
+        sre += termre
+        sim += termim
+        tre, tim = ((tre*ure - tim*uim)>>usize), \
+            ((tre*uim + tim*ure)>>usize)
+        texp -= (prec - usize)
+        k += 2
+    return sre, sim
+
+
+def mpf_gamma(x, prec, rnd='d', type=0):
+    """
+    This function implements multipurpose evaluation of the gamma
+    function, G(x), as well as the following versions of the same:
+
+    type = 0 -- G(x)                    [standard gamma function]
+    type = 1 -- G(x+1) = x*G(x+1) = x!  [factorial]
+    type = 2 -- 1/G(x)                  [reciprocal gamma function]
+    type = 3 -- log(|G(x)|)             [log-gamma function, real part]
+    """
+
+    # Specal values
+    sign, man, exp, bc = x
+    if not man:
+        if x == fzero:
+            if type == 1: return fone
+            if type == 2: return fzero
+            raise ValueError("gamma function pole")
+        if x == finf:
+            if type == 2: return fzero
+            return finf
+        return fnan
+
+    # First of all, for log gamma, numbers can be well beyond the fixed-point
+    # range, so we must take care of huge numbers before e.g. trying
+    # to convert x to the nearest integer
+    if type == 3:
+        wp = prec+20
+        if exp+bc > wp and not sign:
+            return mpf_sub(mpf_mul(x, mpf_log(x, wp), wp), x, prec, rnd)
+
+    # We strongly want to special-case small integers
+    is_integer = exp >= 0
+    if is_integer:
+        # Poles
+        if sign:
+            if type == 2:
+                return fzero
+            raise ValueError("gamma function pole")
+        # n = x
+        n = man << exp
+        if n < SMALL_FACTORIAL_CACHE_SIZE:
+            if type == 0:
+                return mpf_pos(small_factorial_cache[n-1], prec, rnd)
+            if type == 1:
+                return mpf_pos(small_factorial_cache[n], prec, rnd)
+            if type == 2:
+                return mpf_div(fone, small_factorial_cache[n-1], prec, rnd)
+            if type == 3:
+                return mpf_log(small_factorial_cache[n-1], prec, rnd)
+    else:
+        # floor(abs(x))
+        n = int(man >> (-exp))
+
+    # Estimate size and precision
+    # Estimate log(gamma(|x|),2) as x*log(x,2)
+    mag = exp + bc
+    gamma_size = n*mag
+
+    if type == 3:
+        wp = prec + 20
+    else:
+        wp = prec + bitcount(gamma_size) + 20
+
+    # Very close to 0, pole
+    if mag < -wp:
+        if type == 0:
+            return mpf_sub(mpf_div(fone,x, wp),mpf_shift(fone,-wp),prec,rnd)
+        if type == 1: return mpf_sub(fone, x, prec, rnd)
+        if type == 2: return mpf_add(x, mpf_shift(fone,mag-wp), prec, rnd)
+        if type == 3: return mpf_neg(mpf_log(mpf_abs(x), prec, rnd))
+
+    # From now on, we assume having a gamma function
+    if type == 1:
+        return mpf_gamma(mpf_add(x, fone), prec, rnd, 0)
+
+    # Special case integers (those not small enough to be caught above,
+    # but still small enough for an exact factorial to be faster
+    # than an approximate algorithm), and half-integers
+    if exp >= -1:
+        if is_integer:
+            if gamma_size < 10*wp:
+                if type == 0:
+                    return from_int(ifac(n-1), prec, rnd)
+                if type == 2:
+                    return from_rational(MPZ_ONE, ifac(n-1), prec, rnd)
+                if type == 3:
+                    return mpf_log(from_int(ifac(n-1)), prec, rnd)
+        # half-integer
+        if n < 100 or gamma_size < 10*wp:
+            if sign:
+                w = sqrtpi_fixed(wp)
+                if n % 2: f = ifac2(2*n+1)
+                else:     f = -ifac2(2*n+1)
+                if type == 0:
+                    return mpf_shift(from_rational(w, f, prec, rnd), -wp+n+1)
+                if type == 2:
+                    return mpf_shift(from_rational(f, w, prec, rnd), wp-n-1)
+                if type == 3:
+                    return mpf_log(mpf_shift(from_rational(w, abs(f),
+                        prec, rnd), -wp+n+1), prec, rnd)
+            elif n == 0:
+                if type == 0: return mpf_sqrtpi(prec, rnd)
+                if type == 2: return mpf_div(fone, mpf_sqrtpi(wp), prec, rnd)
+                if type == 3: return mpf_log(mpf_sqrtpi(wp), prec, rnd)
+            else:
+                w = sqrtpi_fixed(wp)
+                w = from_man_exp(w * ifac2(2*n-1), -wp-n)
+                if type == 0: return mpf_pos(w, prec, rnd)
+                if type == 2: return mpf_div(fone, w, prec, rnd)
+                if type == 3: return mpf_log(mpf_abs(w), prec, rnd)
+
+    # Convert to fixed point
+    offset = exp + wp
+    if offset >= 0: absxman = man << offset
+    else:           absxman = man >> (-offset)
+
+    # For log gamma, provide accurate evaluation for x = 1+eps and 2+eps
+    if type == 3 and not sign:
+        one = MPZ_ONE << wp
+        one_dist = abs(absxman-one)
+        two_dist = abs(absxman-2*one)
+        cancellation = (wp - bitcount(min(one_dist, two_dist)))
+        if cancellation > 10:
+            xsub1 = mpf_sub(fone, x)
+            xsub2 = mpf_sub(ftwo, x)
+            xsub1mag = xsub1[2]+xsub1[3]
+            xsub2mag = xsub2[2]+xsub2[3]
+            if xsub1mag < -wp:
+                return mpf_mul(mpf_euler(wp), mpf_sub(fone, x), prec, rnd)
+            if xsub2mag < -wp:
+                return mpf_mul(mpf_sub(fone, mpf_euler(wp)),
+                    mpf_sub(x, ftwo), prec, rnd)
+            # Proceed but increase precision
+            wp += max(-xsub1mag, -xsub2mag)
+            offset = exp + wp
+            if offset >= 0: absxman = man << offset
+            else:           absxman = man >> (-offset)
+
+    # Use Taylor series if appropriate
+    n_for_stirling = int(GAMMA_STIRLING_BETA*wp)
+    if n < max(100, n_for_stirling) and wp < MAX_GAMMA_TAYLOR_PREC:
+        if sign:
+            absxman = -absxman
+        return gamma_fixed_taylor(x, absxman, wp, prec, rnd, type)
+
+    # Use Stirling's series
+    # First ensure that |x| is large enough for rapid convergence
+    xorig = x
+
+    # Argument reduction
+    r = 0
+    if n < n_for_stirling:
+        r = one = MPZ_ONE << wp
+        d = n_for_stirling - n
+        for k in xrange(d):
+            r = (r * absxman) >> wp
+            absxman += one
+        x = xabs = from_man_exp(absxman, -wp)
+        if sign:
+            x = mpf_neg(x)
+    else:
+        xabs = mpf_abs(x)
+
+    # Asymptotic series
+    y = real_stirling_series(absxman, wp)
+    u = to_fixed(mpf_log(xabs, wp), wp)
+    u = ((absxman - (MPZ_ONE<<(wp-1))) * u) >> wp
+    y += u
+    w = from_man_exp(y, -wp)
+
+    # Compute final value
+    if sign:
+        # Reflection formula
+        A = mpf_mul(mpf_sin_pi(xorig, wp), xorig, wp)
+        B = mpf_neg(mpf_pi(wp))
+        if type == 0 or type == 2:
+            A = mpf_mul(A, mpf_exp(w, wp))
+            if r:
+                B = mpf_mul(B, from_man_exp(r, -wp), wp)
+            if type == 0:
+                return mpf_div(B, A, prec, rnd)
+            if type == 2:
+                return mpf_div(A, B, prec, rnd)
+        if type == 3:
+            if r:
+                B = mpf_mul(B, from_man_exp(r, -wp), wp)
+            A = mpf_add(mpf_log(mpf_abs(A), wp), w, wp)
+            return mpf_sub(mpf_log(mpf_abs(B), wp), A, prec, rnd)
+    else:
+        if type == 0:
+            if r:
+                return mpf_div(mpf_exp(w, wp),
+                    from_man_exp(r, -wp), prec, rnd)
+            return mpf_exp(w, prec, rnd)
+        if type == 2:
+            if r:
+                return mpf_div(from_man_exp(r, -wp),
+                    mpf_exp(w, wp), prec, rnd)
+            return mpf_exp(mpf_neg(w), prec, rnd)
+        if type == 3:
+            if r:
+                return mpf_sub(w, mpf_log(from_man_exp(r,-wp), wp), prec, rnd)
+            return mpf_pos(w, prec, rnd)
+
+
+def mpc_gamma(z, prec, rnd='d', type=0):
+    a, b = z
+    asign, aman, aexp, abc = a
+    bsign, bman, bexp, bbc = b
+
+    if b == fzero:
+        # Imaginary part on negative half-axis for log-gamma function
+        if type == 3 and asign:
+            re = mpf_gamma(a, prec, rnd, 3)
+            n = (-aman) >> (-aexp)
+            im = mpf_mul_int(mpf_pi(prec+10), n, prec, rnd)
+            return re, im
+        return mpf_gamma(a, prec, rnd, type), fzero
+
+    # Some kind of complex inf/nan
+    if (not aman and aexp) or (not bman and bexp):
+        return (fnan, fnan)
+
+    # Initial working precision
+    wp = prec + 20
+
+    amag = aexp+abc
+    bmag = bexp+bbc
+    if aman:
+        mag = max(amag, bmag)
+    else:
+        mag = bmag
+
+    # Close to 0
+    if mag < -8:
+        if mag < -wp:
+            # 1/gamma(z) = z + euler*z^2 + O(z^3)
+            v = mpc_add(z, mpc_mul_mpf(mpc_mul(z,z,wp),mpf_euler(wp),wp), wp)
+            if type == 0: return mpc_reciprocal(v, prec, rnd)
+            if type == 1: return mpc_div(z, v, prec, rnd)
+            if type == 2: return mpc_pos(v, prec, rnd)
+            if type == 3: return mpc_log(mpc_reciprocal(v, prec), prec, rnd)
+        elif type != 1:
+            wp += (-mag)
+
+    # Handle huge log-gamma values; must do this before converting to
+    # a fixed-point value. TODO: determine a precise cutoff of validity
+    # depending on amag and bmag
+    if type == 3 and mag > wp and ((not asign) or (bmag >= amag)):
+        return mpc_sub(mpc_mul(z, mpc_log(z, wp), wp), z, prec, rnd)
+
+    # From now on, we assume having a gamma function
+    if type == 1:
+        return mpc_gamma((mpf_add(a, fone), b), prec, rnd, 0)
+
+    an = abs(to_int(a))
+    bn = abs(to_int(b))
+    absn = max(an, bn)
+    gamma_size = absn*mag
+    if type == 3:
+        pass
+    else:
+        wp += bitcount(gamma_size)
+
+    # Reflect to the right half-plane. Note that Stirling's expansion
+    # is valid in the left half-plane too, as long as we're not too close
+    # to the real axis, but in order to use this argument reduction
+    # in the negative direction must be implemented.
+    #need_reflection = asign and ((bmag < 0) or (amag-bmag > 4))
+    need_reflection = asign
+    zorig = z
+    if need_reflection:
+        z = mpc_neg(z)
+        asign, aman, aexp, abc = a = z[0]
+        bsign, bman, bexp, bbc = b = z[1]
+
+    # Imaginary part very small compared to real one?
+    yfinal = 0
+    balance_prec = 0
+    if bmag < -10:
+        # Check z ~= 1 and z ~= 2 for loggamma
+        if type == 3:
+            zsub1 = mpc_sub_mpf(z, fone)
+            if zsub1[0] == fzero:
+                cancel1 = -bmag
+            else:
+                cancel1 = -max(zsub1[0][2]+zsub1[0][3], bmag)
+            if cancel1 > wp:
+                pi = mpf_pi(wp)
+                x = mpc_mul_mpf(zsub1, pi, wp)
+                x = mpc_mul(x, x, wp)
+                x = mpc_div_mpf(x, from_int(12), wp)
+                y = mpc_mul_mpf(zsub1, mpf_neg(mpf_euler(wp)), wp)
+                yfinal = mpc_add(x, y, wp)
+                if not need_reflection:
+                    return mpc_pos(yfinal, prec, rnd)
+            elif cancel1 > 0:
+                wp += cancel1
+            zsub2 = mpc_sub_mpf(z, ftwo)
+            if zsub2[0] == fzero:
+                cancel2 = -bmag
+            else:
+                cancel2 = -max(zsub2[0][2]+zsub2[0][3], bmag)
+            if cancel2 > wp:
+                pi = mpf_pi(wp)
+                t = mpf_sub(mpf_mul(pi, pi), from_int(6))
+                x = mpc_mul_mpf(mpc_mul(zsub2, zsub2, wp), t, wp)
+                x = mpc_div_mpf(x, from_int(12), wp)
+                y = mpc_mul_mpf(zsub2, mpf_sub(fone, mpf_euler(wp)), wp)
+                yfinal = mpc_add(x, y, wp)
+                if not need_reflection:
+                    return mpc_pos(yfinal, prec, rnd)
+            elif cancel2 > 0:
+                wp += cancel2
+        if bmag < -wp:
+            # Compute directly from the real gamma function.
+            pp = 2*(wp+10)
+            aabs = mpf_abs(a)
+            eps = mpf_shift(fone, amag-wp)
+            x1 = mpf_gamma(aabs, pp, type=type)
+            x2 = mpf_gamma(mpf_add(aabs, eps), pp, type=type)
+            xprime = mpf_div(mpf_sub(x2, x1, pp), eps, pp)
+            y = mpf_mul(b, xprime, prec, rnd)
+            yfinal = (x1, y)
+            # Note: we still need to use the reflection formula for
+            # near-poles, and the correct branch of the log-gamma function
+            if not need_reflection:
+                return mpc_pos(yfinal, prec, rnd)
+        else:
+            balance_prec += (-bmag)
+
+    wp += balance_prec
+    n_for_stirling = int(GAMMA_STIRLING_BETA*wp)
+    need_reduction = absn < n_for_stirling
+
+    afix = to_fixed(a, wp)
+    bfix = to_fixed(b, wp)
+
+    r = 0
+    if not yfinal:
+        zprered = z
+        # Argument reduction
+        if absn < n_for_stirling:
+            absn = complex(an, bn)
+            d = int((1 + n_for_stirling**2 - bn**2)**0.5 - an)
+            rre = one = MPZ_ONE << wp
+            rim = MPZ_ZERO
+            for k in xrange(d):
+                rre, rim = ((afix*rre-bfix*rim)>>wp), ((afix*rim + bfix*rre)>>wp)
+                afix += one
+            r = from_man_exp(rre, -wp), from_man_exp(rim, -wp)
+            a = from_man_exp(afix, -wp)
+            z = a, b
+
+        yre, yim = complex_stirling_series(afix, bfix, wp)
+        # (z-1/2)*log(z) + S
+        lre, lim = mpc_log(z, wp)
+        lre = to_fixed(lre, wp)
+        lim = to_fixed(lim, wp)
+        yre = ((lre*afix - lim*bfix)>>wp) - (lre>>1) + yre
+        yim = ((lre*bfix + lim*afix)>>wp) - (lim>>1) + yim
+        y = from_man_exp(yre, -wp), from_man_exp(yim, -wp)
+
+        if r and type == 3:
+            # If re(z) > 0 and abs(z) <= 4, the branches of loggamma(z)
+            # and log(gamma(z)) coincide. Otherwise, use the zeroth order
+            # Stirling expansion to compute the correct imaginary part.
+            y = mpc_sub(y, mpc_log(r, wp), wp)
+            zfa = to_float(zprered[0])
+            zfb = to_float(zprered[1])
+            zfabs = math.hypot(zfa,zfb)
+            #if not (zfa > 0.0 and zfabs <= 4):
+            yfb = to_float(y[1])
+            u = math.atan2(zfb, zfa)
+            if zfabs <= 0.5:
+                gi = 0.577216*zfb - u
+            else:
+                gi = -zfb - 0.5*u + zfa*u + zfb*math.log(zfabs)
+            n = int(math.floor((gi-yfb)/(2*math.pi)+0.5))
+            y = (y[0], mpf_add(y[1], mpf_mul_int(mpf_pi(wp), 2*n, wp), wp))
+
+    if need_reflection:
+        if type == 0 or type == 2:
+            A = mpc_mul(mpc_sin_pi(zorig, wp), zorig, wp)
+            B = (mpf_neg(mpf_pi(wp)), fzero)
+            if yfinal:
+                if type == 2:
+                    A = mpc_div(A, yfinal, wp)
+                else:
+                    A = mpc_mul(A, yfinal, wp)
+            else:
+                A = mpc_mul(A, mpc_exp(y, wp), wp)
+            if r:
+                B = mpc_mul(B, r, wp)
+            if type == 0: return mpc_div(B, A, prec, rnd)
+            if type == 2: return mpc_div(A, B, prec, rnd)
+
+        # Reflection formula for the log-gamma function with correct branch
+        # http://functions.wolfram.com/GammaBetaErf/LogGamma/16/01/01/0006/
+        # LogGamma[z] == -LogGamma[-z] - Log[-z] +
+        # Sign[Im[z]] Floor[Re[z]] Pi I + Log[Pi] -
+        #      Log[Sin[Pi (z - Floor[Re[z]])]] -
+        # Pi I (1 - Abs[Sign[Im[z]]]) Abs[Floor[Re[z]]]
+        if type == 3:
+            if yfinal:
+                s1 = mpc_neg(yfinal)
+            else:
+                s1 = mpc_neg(y)
+            # s -= log(-z)
+            s1 = mpc_sub(s1, mpc_log(mpc_neg(zorig), wp), wp)
+            # floor(re(z))
+            rezfloor = mpf_floor(zorig[0])
+            imzsign = mpf_sign(zorig[1])
+            pi = mpf_pi(wp)
+            t = mpf_mul(pi, rezfloor)
+            t = mpf_mul_int(t, imzsign, wp)
+            s1 = (s1[0], mpf_add(s1[1], t, wp))
+            s1 = mpc_add_mpf(s1, mpf_log(pi, wp), wp)
+            t = mpc_sin_pi(mpc_sub_mpf(zorig, rezfloor), wp)
+            t = mpc_log(t, wp)
+            s1 = mpc_sub(s1, t, wp)
+            # Note: may actually be unused, because we fall back
+            # to the mpf_ function for real arguments
+            if not imzsign:
+                t = mpf_mul(pi, mpf_floor(rezfloor), wp)
+                s1 = (s1[0], mpf_sub(s1[1], t, wp))
+            return mpc_pos(s1, prec, rnd)
+    else:
+        if type == 0:
+            if r:
+                return mpc_div(mpc_exp(y, wp), r, prec, rnd)
+            return mpc_exp(y, prec, rnd)
+        if type == 2:
+            if r:
+                return mpc_div(r, mpc_exp(y, wp), prec, rnd)
+            return mpc_exp(mpc_neg(y), prec, rnd)
+        if type == 3:
+            return mpc_pos(y, prec, rnd)
+
+def mpf_factorial(x, prec, rnd='d'):
+    return mpf_gamma(x, prec, rnd, 1)
+
+def mpc_factorial(x, prec, rnd='d'):
+    return mpc_gamma(x, prec, rnd, 1)
+
+def mpf_rgamma(x, prec, rnd='d'):
+    return mpf_gamma(x, prec, rnd, 2)
+
+def mpc_rgamma(x, prec, rnd='d'):
+    return mpc_gamma(x, prec, rnd, 2)
+
+def mpf_loggamma(x, prec, rnd='d'):
+    sign, man, exp, bc = x
+    if sign:
+        raise ComplexResult
+    return mpf_gamma(x, prec, rnd, 3)
+
+def mpc_loggamma(z, prec, rnd='d'):
+    a, b = z
+    asign, aman, aexp, abc = a
+    bsign, bman, bexp, bbc = b
+    if b == fzero and asign:
+        re = mpf_gamma(a, prec, rnd, 3)
+        n = (-aman) >> (-aexp)
+        im = mpf_mul_int(mpf_pi(prec+10), n, prec, rnd)
+        return re, im
+    return mpc_gamma(z, prec, rnd, 3)
+
+def mpf_gamma_int(n, prec, rnd=round_fast):
+    if n < SMALL_FACTORIAL_CACHE_SIZE:
+        return mpf_pos(small_factorial_cache[n-1], prec, rnd)
+    return mpf_gamma(from_int(n), prec, rnd)

venv/lib/python3.10/site-packages/mpmath/libmp/libelefun.py

@@ -0,0 +1,1428 @@
+"""
+This module implements computation of elementary transcendental
+functions (powers, logarithms, trigonometric and hyperbolic
+functions, inverse trigonometric and hyperbolic) for real
+floating-point numbers.
+
+For complex and interval implementations of the same functions,
+see libmpc and libmpi.
+
+"""
+
+import math
+from bisect import bisect
+
+from .backend import xrange
+from .backend import MPZ, MPZ_ZERO, MPZ_ONE, MPZ_TWO, MPZ_FIVE, BACKEND
+
+from .libmpf import (
+    round_floor, round_ceiling, round_down, round_up,
+    round_nearest, round_fast,
+    ComplexResult,
+    bitcount, bctable, lshift, rshift, giant_steps, sqrt_fixed,
+    from_int, to_int, from_man_exp, to_fixed, to_float, from_float,
+    from_rational, normalize,
+    fzero, fone, fnone, fhalf, finf, fninf, fnan,
+    mpf_cmp, mpf_sign, mpf_abs,
+    mpf_pos, mpf_neg, mpf_add, mpf_sub, mpf_mul, mpf_div, mpf_shift,
+    mpf_rdiv_int, mpf_pow_int, mpf_sqrt,
+    reciprocal_rnd, negative_rnd, mpf_perturb,
+    isqrt_fast
+)
+
+from .libintmath import ifib
+
+
+#-------------------------------------------------------------------------------
+# Tuning parameters
+#-------------------------------------------------------------------------------
+
+# Cutoff for computing exp from cosh+sinh. This reduces the
+# number of terms by half, but also requires a square root which
+# is expensive with the pure-Python square root code.
+if BACKEND == 'python':
+    EXP_COSH_CUTOFF = 600
+else:
+    EXP_COSH_CUTOFF = 400
+# Cutoff for using more than 2 series
+EXP_SERIES_U_CUTOFF = 1500
+
+# Also basically determined by sqrt
+if BACKEND == 'python':
+    COS_SIN_CACHE_PREC = 400
+else:
+    COS_SIN_CACHE_PREC = 200
+COS_SIN_CACHE_STEP = 8
+cos_sin_cache = {}
+
+# Number of integer logarithms to cache (for zeta sums)
+MAX_LOG_INT_CACHE = 2000
+log_int_cache = {}
+
+LOG_TAYLOR_PREC = 2500  # Use Taylor series with caching up to this prec
+LOG_TAYLOR_SHIFT = 9    # Cache log values in steps of size 2^-N
+log_taylor_cache = {}
+# prec/size ratio of x for fastest convergence in AGM formula
+LOG_AGM_MAG_PREC_RATIO = 20
+
+ATAN_TAYLOR_PREC = 3000  # Same as for log
+ATAN_TAYLOR_SHIFT = 7   # steps of size 2^-N
+atan_taylor_cache = {}
+
+
+# ~= next power of two + 20
+cache_prec_steps = [22,22]
+for k in xrange(1, bitcount(LOG_TAYLOR_PREC)+1):
+    cache_prec_steps += [min(2**k,LOG_TAYLOR_PREC)+20] * 2**(k-1)
+
+
+#----------------------------------------------------------------------------#
+#                                                                            #
+#                   Elementary mathematical constants                        #
+#                                                                            #
+#----------------------------------------------------------------------------#
+
+def constant_memo(f):
+    """
+    Decorator for caching computed values of mathematical
+    constants. This decorator should be applied to a
+    function taking a single argument prec as input and
+    returning a fixed-point value with the given precision.
+    """
+    f.memo_prec = -1
+    f.memo_val = None
+    def g(prec, **kwargs):
+        memo_prec = f.memo_prec
+        if prec <= memo_prec:
+            return f.memo_val >> (memo_prec-prec)
+        newprec = int(prec*1.05+10)
+        f.memo_val = f(newprec, **kwargs)
+        f.memo_prec = newprec
+        return f.memo_val >> (newprec-prec)
+    g.__name__ = f.__name__
+    g.__doc__ = f.__doc__
+    return g
+
+def def_mpf_constant(fixed):
+    """
+    Create a function that computes the mpf value for a mathematical
+    constant, given a function that computes the fixed-point value.
+
+    Assumptions: the constant is positive and has magnitude ~= 1;
+    the fixed-point function rounds to floor.
+    """
+    def f(prec, rnd=round_fast):
+        wp = prec + 20
+        v = fixed(wp)
+        if rnd in (round_up, round_ceiling):
+            v += 1
+        return normalize(0, v, -wp, bitcount(v), prec, rnd)
+    f.__doc__ = fixed.__doc__
+    return f
+
+def bsp_acot(q, a, b, hyperbolic):
+    if b - a == 1:
+        a1 = MPZ(2*a + 3)
+        if hyperbolic or a&1:
+            return MPZ_ONE, a1 * q**2, a1
+        else:
+            return -MPZ_ONE, a1 * q**2, a1
+    m = (a+b)//2
+    p1, q1, r1 = bsp_acot(q, a, m, hyperbolic)
+    p2, q2, r2 = bsp_acot(q, m, b, hyperbolic)
+    return q2*p1 + r1*p2, q1*q2, r1*r2
+
+# the acoth(x) series converges like the geometric series for x^2
+# N = ceil(p*log(2)/(2*log(x)))
+def acot_fixed(a, prec, hyperbolic):
+    """
+    Compute acot(a) or acoth(a) for an integer a with binary splitting; see
+    http://numbers.computation.free.fr/Constants/Algorithms/splitting.html
+    """
+    N = int(0.35 * prec/math.log(a) + 20)
+    p, q, r = bsp_acot(a, 0,N, hyperbolic)
+    return ((p+q)<<prec)//(q*a)
+
+def machin(coefs, prec, hyperbolic=False):
+    """
+    Evaluate a Machin-like formula, i.e., a linear combination of
+    acot(n) or acoth(n) for specific integer values of n, using fixed-
+    point arithmetic. The input should be a list [(c, n), ...], giving
+    c*acot[h](n) + ...
+    """
+    extraprec = 10
+    s = MPZ_ZERO
+    for a, b in coefs:
+        s += MPZ(a) * acot_fixed(MPZ(b), prec+extraprec, hyperbolic)
+    return (s >> extraprec)
+
+# Logarithms of integers are needed for various computations involving
+# logarithms, powers, radix conversion, etc
+
+@constant_memo
+def ln2_fixed(prec):
+    """
+    Computes ln(2). This is done with a hyperbolic Machin-type formula,
+    with binary splitting at high precision.
+    """
+    return machin([(18, 26), (-2, 4801), (8, 8749)], prec, True)
+
+@constant_memo
+def ln10_fixed(prec):
+    """
+    Computes ln(10). This is done with a hyperbolic Machin-type formula.
+    """
+    return machin([(46, 31), (34, 49), (20, 161)], prec, True)
+
+
+r"""
+For computation of pi, we use the Chudnovsky series:
+
+             oo
+             ___        k
+      1     \       (-1)  (6 k)! (A + B k)
+    ----- =  )     -----------------------
+    12 pi   /___               3  3k+3/2
+                    (3 k)! (k!)  C
+            k = 0
+
+where A, B, and C are certain integer constants. This series adds roughly
+14 digits per term. Note that C^(3/2) can be extracted so that the
+series contains only rational terms. This makes binary splitting very
+efficient.
+
+The recurrence formulas for the binary splitting were taken from
+ftp://ftp.gmplib.org/pub/src/gmp-chudnovsky.c
+
+Previously, Machin's formula was used at low precision and the AGM iteration
+was used at high precision. However, the Chudnovsky series is essentially as
+fast as the Machin formula at low precision and in practice about 3x faster
+than the AGM at high precision (despite theoretically having a worse
+asymptotic complexity), so there is no reason not to use it in all cases.
+
+"""
+
+# Constants in Chudnovsky's series
+CHUD_A = MPZ(13591409)
+CHUD_B = MPZ(545140134)
+CHUD_C = MPZ(640320)
+CHUD_D = MPZ(12)
+
+def bs_chudnovsky(a, b, level, verbose):
+    """
+    Computes the sum from a to b of the series in the Chudnovsky
+    formula. Returns g, p, q where p/q is the sum as an exact
+    fraction and g is a temporary value used to save work
+    for recursive calls.
+    """
+    if b-a == 1:
+        g = MPZ((6*b-5)*(2*b-1)*(6*b-1))
+        p = b**3 * CHUD_C**3 // 24
+        q = (-1)**b * g * (CHUD_A+CHUD_B*b)
+    else:
+        if verbose and level < 4:
+            print("  binary splitting", a, b)
+        mid = (a+b)//2
+        g1, p1, q1 = bs_chudnovsky(a, mid, level+1, verbose)
+        g2, p2, q2 = bs_chudnovsky(mid, b, level+1, verbose)
+        p = p1*p2
+        g = g1*g2
+        q = q1*p2 + q2*g1
+    return g, p, q
+
+@constant_memo
+def pi_fixed(prec, verbose=False, verbose_base=None):
+    """
+    Compute floor(pi * 2**prec) as a big integer.
+
+    This is done using Chudnovsky's series (see comments in
+    libelefun.py for details).
+    """
+    # The Chudnovsky series gives 14.18 digits per term
+    N = int(prec/3.3219280948/14.181647462 + 2)
+    if verbose:
+        print("binary splitting with N =", N)
+    g, p, q = bs_chudnovsky(0, N, 0, verbose)
+    sqrtC = isqrt_fast(CHUD_C<<(2*prec))
+    v = p*CHUD_C*sqrtC//((q+CHUD_A*p)*CHUD_D)
+    return v
+
+def degree_fixed(prec):
+    return pi_fixed(prec)//180
+
+def bspe(a, b):
+    """
+    Sum series for exp(1)-1 between a, b, returning the result
+    as an exact fraction (p, q).
+    """
+    if b-a == 1:
+        return MPZ_ONE, MPZ(b)
+    m = (a+b)//2
+    p1, q1 = bspe(a, m)
+    p2, q2 = bspe(m, b)
+    return p1*q2+p2, q1*q2
+
+@constant_memo
+def e_fixed(prec):
+    """
+    Computes exp(1). This is done using the ordinary Taylor series for
+    exp, with binary splitting. For a description of the algorithm,
+    see:
+
+        http://numbers.computation.free.fr/Constants/
+            Algorithms/splitting.html
+    """
+    # Slight overestimate of N needed for 1/N! < 2**(-prec)
+    # This could be tightened for large N.
+    N = int(1.1*prec/math.log(prec) + 20)
+    p, q = bspe(0,N)
+    return ((p+q)<<prec)//q
+
+@constant_memo
+def phi_fixed(prec):
+    """
+    Computes the golden ratio, (1+sqrt(5))/2
+    """
+    prec += 10
+    a = isqrt_fast(MPZ_FIVE<<(2*prec)) + (MPZ_ONE << prec)
+    return a >> 11
+
+mpf_phi    = def_mpf_constant(phi_fixed)
+mpf_pi     = def_mpf_constant(pi_fixed)
+mpf_e      = def_mpf_constant(e_fixed)
+mpf_degree = def_mpf_constant(degree_fixed)
+mpf_ln2    = def_mpf_constant(ln2_fixed)
+mpf_ln10   = def_mpf_constant(ln10_fixed)
+
+
+@constant_memo
+def ln_sqrt2pi_fixed(prec):
+    wp = prec + 10
+    # ln(sqrt(2*pi)) = ln(2*pi)/2
+    return to_fixed(mpf_log(mpf_shift(mpf_pi(wp), 1), wp), prec-1)
+
+@constant_memo
+def sqrtpi_fixed(prec):
+    return sqrt_fixed(pi_fixed(prec), prec)
+
+mpf_sqrtpi   = def_mpf_constant(sqrtpi_fixed)
+mpf_ln_sqrt2pi   = def_mpf_constant(ln_sqrt2pi_fixed)
+
+
+#----------------------------------------------------------------------------#
+#                                                                            #
+#                                    Powers                                  #
+#                                                                            #
+#----------------------------------------------------------------------------#
+
+def mpf_pow(s, t, prec, rnd=round_fast):
+    """
+    Compute s**t. Raises ComplexResult if s is negative and t is
+    fractional.
+    """
+    ssign, sman, sexp, sbc = s
+    tsign, tman, texp, tbc = t
+    if ssign and texp < 0:
+        raise ComplexResult("negative number raised to a fractional power")
+    if texp >= 0:
+        return mpf_pow_int(s, (-1)**tsign * (tman<<texp), prec, rnd)
+    # s**(n/2) = sqrt(s)**n
+    if texp == -1:
+        if tman == 1:
+            if tsign:
+                return mpf_div(fone, mpf_sqrt(s, prec+10,
+                    reciprocal_rnd[rnd]), prec, rnd)
+            return mpf_sqrt(s, prec, rnd)
+        else:
+            if tsign:
+                return mpf_pow_int(mpf_sqrt(s, prec+10,
+                    reciprocal_rnd[rnd]), -tman, prec, rnd)
+            return mpf_pow_int(mpf_sqrt(s, prec+10, rnd), tman, prec, rnd)
+    # General formula: s**t = exp(t*log(s))
+    # TODO: handle rnd direction of the logarithm carefully
+    c = mpf_log(s, prec+10, rnd)
+    return mpf_exp(mpf_mul(t, c), prec, rnd)
+
+def int_pow_fixed(y, n, prec):
+    """n-th power of a fixed point number with precision prec
+
+       Returns the power in the form man, exp,
+       man * 2**exp ~= y**n
+    """
+    if n == 2:
+        return (y*y), 0
+    bc = bitcount(y)
+    exp = 0
+    workprec = 2 * (prec + 4*bitcount(n) + 4)
+    _, pm, pe, pbc = fone
+    while 1:
+        if n & 1:
+            pm = pm*y
+            pe = pe+exp
+            pbc += bc - 2
+            pbc = pbc + bctable[int(pm >> pbc)]
+            if pbc > workprec:
+                pm = pm >> (pbc-workprec)
+                pe += pbc - workprec
+                pbc = workprec
+            n -= 1
+            if not n:
+                break
+        y = y*y
+        exp = exp+exp
+        bc = bc + bc - 2
+        bc = bc + bctable[int(y >> bc)]
+        if bc > workprec:
+            y = y >> (bc-workprec)
+            exp += bc - workprec
+            bc = workprec
+        n = n // 2
+    return pm, pe
+
+# froot(s, n, prec, rnd) computes the real n-th root of a
+# positive mpf tuple s.
+# To compute the root we start from a 50-bit estimate for r
+# generated with ordinary floating-point arithmetic, and then refine
+# the value to full accuracy using the iteration
+
+#            1  /                     y       \
+#   r     = --- | (n-1)  * r   +  ----------  |
+#    n+1     n  \           n     r_n**(n-1)  /
+
+# which is simply Newton's method applied to the equation r**n = y.
+# With giant_steps(start, prec+extra) = [p0,...,pm, prec+extra]
+# and y = man * 2**-shift  one has
+# (man * 2**exp)**(1/n) =
+# y**(1/n) * 2**(start-prec/n) * 2**(p0-start) * ... * 2**(prec+extra-pm) *
+# 2**((exp+shift-(n-1)*prec)/n -extra))
+# The last factor is accounted for in the last line of froot.
+
+def nthroot_fixed(y, n, prec, exp1):
+    start = 50
+    try:
+        y1 = rshift(y, prec - n*start)
+        r = MPZ(int(y1**(1.0/n)))
+    except OverflowError:
+        y1 = from_int(y1, start)
+        fn = from_int(n)
+        fn = mpf_rdiv_int(1, fn, start)
+        r = mpf_pow(y1, fn, start)
+        r = to_int(r)
+    extra = 10
+    extra1 = n
+    prevp = start
+    for p in giant_steps(start, prec+extra):
+        pm, pe = int_pow_fixed(r, n-1, prevp)
+        r2 = rshift(pm, (n-1)*prevp - p - pe - extra1)
+        B = lshift(y, 2*p-prec+extra1)//r2
+        r = (B + (n-1) * lshift(r, p-prevp))//n
+        prevp = p
+    return r
+
+def mpf_nthroot(s, n, prec, rnd=round_fast):
+    """nth-root of a positive number
+
+    Use the Newton method when faster, otherwise use x**(1/n)
+    """
+    sign, man, exp, bc = s
+    if sign:
+        raise ComplexResult("nth root of a negative number")
+    if not man:
+        if s == fnan:
+            return fnan
+        if s == fzero:
+            if n > 0:
+                return fzero
+            if n == 0:
+                return fone
+            return finf
+        # Infinity
+        if not n:
+            return fnan
+        if n < 0:
+            return fzero
+        return finf
+    flag_inverse = False
+    if n < 2:
+        if n == 0:
+            return fone
+        if n == 1:
+            return mpf_pos(s, prec, rnd)
+        if n == -1:
+            return mpf_div(fone, s, prec, rnd)
+        # n < 0
+        rnd = reciprocal_rnd[rnd]
+        flag_inverse = True
+        extra_inverse = 5
+        prec += extra_inverse
+        n = -n
+    if n > 20 and (n >= 20000 or prec < int(233 + 28.3 * n**0.62)):
+        prec2 = prec + 10
+        fn = from_int(n)
+        nth = mpf_rdiv_int(1, fn, prec2)
+        r = mpf_pow(s, nth, prec2, rnd)
+        s = normalize(r[0], r[1], r[2], r[3], prec, rnd)
+        if flag_inverse:
+            return mpf_div(fone, s, prec-extra_inverse, rnd)
+        else:
+            return s
+    # Convert to a fixed-point number with prec2 bits.
+    prec2 = prec + 2*n - (prec%n)
+    # a few tests indicate that
+    # for 10 < n < 10**4 a bit more precision is needed
+    if n > 10:
+        prec2 += prec2//10
+        prec2 = prec2 - prec2%n
+    # Mantissa may have more bits than we need. Trim it down.
+    shift = bc - prec2
+    # Adjust exponents to make prec2 and exp+shift multiples of n.
+    sign1 = 0
+    es = exp+shift
+    if es < 0:
+        sign1 = 1
+        es = -es
+    if sign1:
+        shift += es%n
+    else:
+        shift -= es%n
+    man = rshift(man, shift)
+    extra = 10
+    exp1 = ((exp+shift-(n-1)*prec2)//n) - extra
+    rnd_shift = 0
+    if flag_inverse:
+        if rnd == 'u' or rnd == 'c':
+            rnd_shift = 1
+    else:
+        if rnd == 'd' or rnd == 'f':
+            rnd_shift = 1
+    man = nthroot_fixed(man+rnd_shift, n, prec2, exp1)
+    s = from_man_exp(man, exp1, prec, rnd)
+    if flag_inverse:
+        return mpf_div(fone, s, prec-extra_inverse, rnd)
+    else:
+        return s
+
+def mpf_cbrt(s, prec, rnd=round_fast):
+    """cubic root of a positive number"""
+    return mpf_nthroot(s, 3, prec, rnd)
+
+#----------------------------------------------------------------------------#
+#                                                                            #
+#                                Logarithms                                  #
+#                                                                            #
+#----------------------------------------------------------------------------#
+
+
+def log_int_fixed(n, prec, ln2=None):
+    """
+    Fast computation of log(n), caching the value for small n,
+    intended for zeta sums.
+    """
+    if n in log_int_cache:
+        value, vprec = log_int_cache[n]
+        if vprec >= prec:
+            return value >> (vprec - prec)
+    wp = prec + 10
+    if wp <= LOG_TAYLOR_SHIFT:
+        if ln2 is None:
+            ln2 = ln2_fixed(wp)
+        r = bitcount(n)
+        x = n << (wp-r)
+        v = log_taylor_cached(x, wp) + r*ln2
+    else:
+        v = to_fixed(mpf_log(from_int(n), wp+5), wp)
+    if n < MAX_LOG_INT_CACHE:
+        log_int_cache[n] = (v, wp)
+    return v >> (wp-prec)
+
+def agm_fixed(a, b, prec):
+    """
+    Fixed-point computation of agm(a,b), assuming
+    a, b both close to unit magnitude.
+    """
+    i = 0
+    while 1:
+        anew = (a+b)>>1
+        if i > 4 and abs(a-anew) < 8:
+            return a
+        b = isqrt_fast(a*b)
+        a = anew
+        i += 1
+    return a
+
+def log_agm(x, prec):
+    """
+    Fixed-point computation of -log(x) = log(1/x), suitable
+    for large precision. It is required that 0 < x < 1. The
+    algorithm used is the Sasaki-Kanada formula
+
+        -log(x) = pi/agm(theta2(x)^2,theta3(x)^2). [1]
+
+    For faster convergence in the theta functions, x should
+    be chosen closer to 0.
+
+    Guard bits must be added by the caller.
+
+    HYPOTHESIS: if x = 2^(-n), n bits need to be added to
+    account for the truncation to a fixed-point number,
+    and this is the only significant cancellation error.
+
+    The number of bits lost to roundoff is small and can be
+    considered constant.
+
+    [1] Richard P. Brent, "Fast Algorithms for High-Precision
+        Computation of Elementary Functions (extended abstract)",
+        http://wwwmaths.anu.edu.au/~brent/pd/RNC7-Brent.pdf
+
+    """
+    x2 = (x*x) >> prec
+    # Compute jtheta2(x)**2
+    s = a = b = x2
+    while a:
+        b = (b*x2) >> prec
+        a = (a*b) >> prec
+        s += a
+    s += (MPZ_ONE<<prec)
+    s = (s*s)>>(prec-2)
+    s = (s*isqrt_fast(x<<prec))>>prec
+    # Compute jtheta3(x)**2
+    t = a = b = x
+    while a:
+        b = (b*x2) >> prec
+        a = (a*b) >> prec
+        t += a
+    t = (MPZ_ONE<<prec) + (t<<1)
+    t = (t*t)>>prec
+    # Final formula
+    p = agm_fixed(s, t, prec)
+    return (pi_fixed(prec) << prec) // p
+
+def log_taylor(x, prec, r=0):
+    """
+    Fixed-point calculation of log(x). It is assumed that x is close
+    enough to 1 for the Taylor series to converge quickly. Convergence
+    can be improved by specifying r > 0 to compute
+    log(x^(1/2^r))*2^r, at the cost of performing r square roots.
+
+    The caller must provide sufficient guard bits.
+    """
+    for i in xrange(r):
+        x = isqrt_fast(x<<prec)
+    one = MPZ_ONE << prec
+    v = ((x-one)<<prec)//(x+one)
+    sign = v < 0
+    if sign:
+        v = -v
+    v2 = (v*v) >> prec
+    v4 = (v2*v2) >> prec
+    s0 = v
+    s1 = v//3
+    v = (v*v4) >> prec
+    k = 5
+    while v:
+        s0 += v // k
+        k += 2
+        s1 += v // k
+        v = (v*v4) >> prec
+        k += 2
+    s1 = (s1*v2) >> prec
+    s = (s0+s1) << (1+r)
+    if sign:
+        return -s
+    return s
+
+def log_taylor_cached(x, prec):
+    """
+    Fixed-point computation of log(x), assuming x in (0.5, 2)
+    and prec <= LOG_TAYLOR_PREC.
+    """
+    n = x >> (prec-LOG_TAYLOR_SHIFT)
+    cached_prec = cache_prec_steps[prec]
+    dprec = cached_prec - prec
+    if (n, cached_prec) in log_taylor_cache:
+        a, log_a = log_taylor_cache[n, cached_prec]
+    else:
+        a = n << (cached_prec - LOG_TAYLOR_SHIFT)
+        log_a = log_taylor(a, cached_prec, 8)
+        log_taylor_cache[n, cached_prec] = (a, log_a)
+    a >>= dprec
+    log_a >>= dprec
+    u = ((x - a) << prec) // a
+    v = (u << prec) // ((MPZ_TWO << prec) + u)
+    v2 = (v*v) >> prec
+    v4 = (v2*v2) >> prec
+    s0 = v
+    s1 = v//3
+    v = (v*v4) >> prec
+    k = 5
+    while v:
+        s0 += v//k
+        k += 2
+        s1 += v//k
+        v = (v*v4) >> prec
+        k += 2
+    s1 = (s1*v2) >> prec
+    s = (s0+s1) << 1
+    return log_a + s
+
+def mpf_log(x, prec, rnd=round_fast):
+    """
+    Compute the natural logarithm of the mpf value x. If x is negative,
+    ComplexResult is raised.
+    """
+    sign, man, exp, bc = x
+    #------------------------------------------------------------------
+    # Handle special values
+    if not man:
+        if x == fzero: return fninf
+        if x == finf: return finf
+        if x == fnan: return fnan
+    if sign:
+        raise ComplexResult("logarithm of a negative number")
+    wp = prec + 20
+    #------------------------------------------------------------------
+    # Handle log(2^n) = log(n)*2.
+    # Here we catch the only possible exact value, log(1) = 0
+    if man == 1:
+        if not exp:
+            return fzero
+        return from_man_exp(exp*ln2_fixed(wp), -wp, prec, rnd)
+    mag = exp+bc
+    abs_mag = abs(mag)
+    #------------------------------------------------------------------
+    # Handle x = 1+eps, where log(x) ~ x. We need to check for
+    # cancellation when moving to fixed-point math and compensate
+    # by increasing the precision. Note that abs_mag in (0, 1) <=>
+    # 0.5 < x < 2 and x != 1
+    if abs_mag <= 1:
+        # Calculate t = x-1 to measure distance from 1 in bits
+        tsign = 1-abs_mag
+        if tsign:
+            tman = (MPZ_ONE<<bc) - man
+        else:
+            tman = man - (MPZ_ONE<<(bc-1))
+        tbc = bitcount(tman)
+        cancellation = bc - tbc
+        if cancellation > wp:
+            t = normalize(tsign, tman, abs_mag-bc, tbc, tbc, 'n')
+            return mpf_perturb(t, tsign, prec, rnd)
+        else:
+            wp += cancellation
+        # TODO: if close enough to 1, we could use Taylor series
+        # even in the AGM precision range, since the Taylor series
+        # converges rapidly
+    #------------------------------------------------------------------
+    # Another special case:
+    # n*log(2) is a good enough approximation
+    if abs_mag > 10000:
+        if bitcount(abs_mag) > wp:
+            return from_man_exp(exp*ln2_fixed(wp), -wp, prec, rnd)
+    #------------------------------------------------------------------
+    # General case.
+    # Perform argument reduction using log(x) = log(x*2^n) - n*log(2):
+    # If we are in the Taylor precision range, choose magnitude 0 or 1.
+    # If we are in the AGM precision range, choose magnitude -m for
+    # some large m; benchmarking on one machine showed m = prec/20 to be
+    # optimal between 1000 and 100,000 digits.
+    if wp <= LOG_TAYLOR_PREC:
+        m = log_taylor_cached(lshift(man, wp-bc), wp)
+        if mag:
+            m += mag*ln2_fixed(wp)
+    else:
+        optimal_mag = -wp//LOG_AGM_MAG_PREC_RATIO
+        n = optimal_mag - mag
+        x = mpf_shift(x, n)
+        wp += (-optimal_mag)
+        m = -log_agm(to_fixed(x, wp), wp)
+        m -= n*ln2_fixed(wp)
+    return from_man_exp(m, -wp, prec, rnd)
+
+def mpf_log_hypot(a, b, prec, rnd):
+    """
+    Computes log(sqrt(a^2+b^2)) accurately.
+    """
+    # If either a or b is inf/nan/0, assume it to be a
+    if not b[1]:
+        a, b = b, a
+    # a is inf/nan/0
+    if not a[1]:
+        # both are inf/nan/0
+        if not b[1]:
+            if a == b == fzero:
+                return fninf
+            if fnan in (a, b):
+                return fnan
+            # at least one term is (+/- inf)^2
+            return finf
+        # only a is inf/nan/0
+        if a == fzero:
+            # log(sqrt(0+b^2)) = log(|b|)
+            return mpf_log(mpf_abs(b), prec, rnd)
+        if a == fnan:
+            return fnan
+        return finf
+    # Exact
+    a2 = mpf_mul(a,a)
+    b2 = mpf_mul(b,b)
+    extra = 20
+    # Not exact
+    h2 = mpf_add(a2, b2, prec+extra)
+    cancelled = mpf_add(h2, fnone, 10)
+    mag_cancelled = cancelled[2]+cancelled[3]
+    # Just redo the sum exactly if necessary (could be smarter
+    # and avoid memory allocation when a or b is precisely 1
+    # and the other is tiny...)
+    if cancelled == fzero or mag_cancelled < -extra//2:
+        h2 = mpf_add(a2, b2, prec+extra-min(a2[2],b2[2]))
+    return mpf_shift(mpf_log(h2, prec, rnd), -1)
+
+
+#----------------------------------------------------------------------
+# Inverse tangent
+#
+
+def atan_newton(x, prec):
+    if prec >= 100:
+        r = math.atan(int((x>>(prec-53)))/2.0**53)
+    else:
+        r = math.atan(int(x)/2.0**prec)
+    prevp = 50
+    r = MPZ(int(r * 2.0**53) >> (53-prevp))
+    extra_p = 50
+    for wp in giant_steps(prevp, prec):
+        wp += extra_p
+        r = r << (wp-prevp)
+        cos, sin = cos_sin_fixed(r, wp)
+        tan = (sin << wp) // cos
+        a = ((tan-rshift(x, prec-wp)) << wp) // ((MPZ_ONE<<wp) + ((tan**2)>>wp))
+        r = r - a
+        prevp = wp
+    return rshift(r, prevp-prec)
+
+def atan_taylor_get_cached(n, prec):
+    # Taylor series with caching wins up to huge precisions
+    # To avoid unnecessary precomputation at low precision, we
+    # do it in steps
+    # Round to next power of 2
+    prec2 = (1<<(bitcount(prec-1))) + 20
+    dprec = prec2 - prec
+    if (n, prec2) in atan_taylor_cache:
+        a, atan_a = atan_taylor_cache[n, prec2]
+    else:
+        a = n << (prec2 - ATAN_TAYLOR_SHIFT)
+        atan_a = atan_newton(a, prec2)
+        atan_taylor_cache[n, prec2] = (a, atan_a)
+    return (a >> dprec), (atan_a >> dprec)
+
+def atan_taylor(x, prec):
+    n = (x >> (prec-ATAN_TAYLOR_SHIFT))
+    a, atan_a = atan_taylor_get_cached(n, prec)
+    d = x - a
+    s0 = v = (d << prec) // ((a**2 >> prec) + (a*d >> prec) + (MPZ_ONE << prec))
+    v2 = (v**2 >> prec)
+    v4 = (v2 * v2) >> prec
+    s1 = v//3
+    v = (v * v4) >> prec
+    k = 5
+    while v:
+        s0 += v // k
+        k += 2
+        s1 += v // k
+        v = (v * v4) >> prec
+        k += 2
+    s1 = (s1 * v2) >> prec
+    s = s0 - s1
+    return atan_a + s
+
+def atan_inf(sign, prec, rnd):
+    if not sign:
+        return mpf_shift(mpf_pi(prec, rnd), -1)
+    return mpf_neg(mpf_shift(mpf_pi(prec, negative_rnd[rnd]), -1))
+
+def mpf_atan(x, prec, rnd=round_fast):
+    sign, man, exp, bc = x
+    if not man:
+        if x == fzero: return fzero
+        if x == finf: return atan_inf(0, prec, rnd)
+        if x == fninf: return atan_inf(1, prec, rnd)
+        return fnan
+    mag = exp + bc
+    # Essentially infinity
+    if mag > prec+20:
+        return atan_inf(sign, prec, rnd)
+    # Essentially ~ x
+    if -mag > prec+20:
+        return mpf_perturb(x, 1-sign, prec, rnd)
+    wp = prec + 30 + abs(mag)
+    # For large x, use atan(x) = pi/2 - atan(1/x)
+    if mag >= 2:
+        x = mpf_rdiv_int(1, x, wp)
+        reciprocal = True
+    else:
+        reciprocal = False
+    t = to_fixed(x, wp)
+    if sign:
+        t = -t
+    if wp < ATAN_TAYLOR_PREC:
+        a = atan_taylor(t, wp)
+    else:
+        a = atan_newton(t, wp)
+    if reciprocal:
+        a = ((pi_fixed(wp)>>1)+1) - a
+    if sign:
+        a = -a
+    return from_man_exp(a, -wp, prec, rnd)
+
+# TODO: cleanup the special cases
+def mpf_atan2(y, x, prec, rnd=round_fast):
+    xsign, xman, xexp, xbc = x
+    ysign, yman, yexp, ybc = y
+    if not yman:
+        if y == fzero and x != fnan:
+            if mpf_sign(x) >= 0:
+                return fzero
+            return mpf_pi(prec, rnd)
+        if y in (finf, fninf):
+            if x in (finf, fninf):
+                return fnan
+            # pi/2
+            if y == finf:
+                return mpf_shift(mpf_pi(prec, rnd), -1)
+            # -pi/2
+            return mpf_neg(mpf_shift(mpf_pi(prec, negative_rnd[rnd]), -1))
+        return fnan
+    if ysign:
+        return mpf_neg(mpf_atan2(mpf_neg(y), x, prec, negative_rnd[rnd]))
+    if not xman:
+        if x == fnan:
+            return fnan
+        if x == finf:
+            return fzero
+        if x == fninf:
+            return mpf_pi(prec, rnd)
+        if y == fzero:
+            return fzero
+        return mpf_shift(mpf_pi(prec, rnd), -1)
+    tquo = mpf_atan(mpf_div(y, x, prec+4), prec+4)
+    if xsign:
+        return mpf_add(mpf_pi(prec+4), tquo, prec, rnd)
+    else:
+        return mpf_pos(tquo, prec, rnd)
+
+def mpf_asin(x, prec, rnd=round_fast):
+    sign, man, exp, bc = x
+    if bc+exp > 0 and x not in (fone, fnone):
+        raise ComplexResult("asin(x) is real only for -1 <= x <= 1")
+    # asin(x) = 2*atan(x/(1+sqrt(1-x**2)))
+    wp = prec + 15
+    a = mpf_mul(x, x)
+    b = mpf_add(fone, mpf_sqrt(mpf_sub(fone, a, wp), wp), wp)
+    c = mpf_div(x, b, wp)
+    return mpf_shift(mpf_atan(c, prec, rnd), 1)
+
+def mpf_acos(x, prec, rnd=round_fast):
+    # acos(x) = 2*atan(sqrt(1-x**2)/(1+x))
+    sign, man, exp, bc = x
+    if bc + exp > 0:
+        if x not in (fone, fnone):
+            raise ComplexResult("acos(x) is real only for -1 <= x <= 1")
+        if x == fnone:
+            return mpf_pi(prec, rnd)
+    wp = prec + 15
+    a = mpf_mul(x, x)
+    b = mpf_sqrt(mpf_sub(fone, a, wp), wp)
+    c = mpf_div(b, mpf_add(fone, x, wp), wp)
+    return mpf_shift(mpf_atan(c, prec, rnd), 1)
+
+def mpf_asinh(x, prec, rnd=round_fast):
+    wp = prec + 20
+    sign, man, exp, bc = x
+    mag = exp+bc
+    if mag < -8:
+        if mag < -wp:
+            return mpf_perturb(x, 1-sign, prec, rnd)
+        wp += (-mag)
+    # asinh(x) = log(x+sqrt(x**2+1))
+    # use reflection symmetry to avoid cancellation
+    q = mpf_sqrt(mpf_add(mpf_mul(x, x), fone, wp), wp)
+    q = mpf_add(mpf_abs(x), q, wp)
+    if sign:
+        return mpf_neg(mpf_log(q, prec, negative_rnd[rnd]))
+    else:
+        return mpf_log(q, prec, rnd)
+
+def mpf_acosh(x, prec, rnd=round_fast):
+    # acosh(x) = log(x+sqrt(x**2-1))
+    wp = prec + 15
+    if mpf_cmp(x, fone) == -1:
+        raise ComplexResult("acosh(x) is real only for x >= 1")
+    q = mpf_sqrt(mpf_add(mpf_mul(x,x), fnone, wp), wp)
+    return mpf_log(mpf_add(x, q, wp), prec, rnd)
+
+def mpf_atanh(x, prec, rnd=round_fast):
+    # atanh(x) = log((1+x)/(1-x))/2
+    sign, man, exp, bc = x
+    if (not man) and exp:
+        if x in (fzero, fnan):
+            return x
+        raise ComplexResult("atanh(x) is real only for -1 <= x <= 1")
+    mag = bc + exp
+    if mag > 0:
+        if mag == 1 and man == 1:
+            return [finf, fninf][sign]
+        raise ComplexResult("atanh(x) is real only for -1 <= x <= 1")
+    wp = prec + 15
+    if mag < -8:
+        if mag < -wp:
+            return mpf_perturb(x, sign, prec, rnd)
+        wp += (-mag)
+    a = mpf_add(x, fone, wp)
+    b = mpf_sub(fone, x, wp)
+    return mpf_shift(mpf_log(mpf_div(a, b, wp), prec, rnd), -1)
+
+def mpf_fibonacci(x, prec, rnd=round_fast):
+    sign, man, exp, bc = x
+    if not man:
+        if x == fninf:
+            return fnan
+        return x
+    # F(2^n) ~= 2^(2^n)
+    size = abs(exp+bc)
+    if exp >= 0:
+        # Exact
+        if size < 10 or size <= bitcount(prec):
+            return from_int(ifib(to_int(x)), prec, rnd)
+    # Use the modified Binet formula
+    wp = prec + size + 20
+    a = mpf_phi(wp)
+    b = mpf_add(mpf_shift(a, 1), fnone, wp)
+    u = mpf_pow(a, x, wp)
+    v = mpf_cos_pi(x, wp)
+    v = mpf_div(v, u, wp)
+    u = mpf_sub(u, v, wp)
+    u = mpf_div(u, b, prec, rnd)
+    return u
+
+
+#-------------------------------------------------------------------------------
+# Exponential-type functions
+#-------------------------------------------------------------------------------
+
+def exponential_series(x, prec, type=0):
+    """
+    Taylor series for cosh/sinh or cos/sin.
+
+    type = 0 -- returns exp(x)  (slightly faster than cosh+sinh)
+    type = 1 -- returns (cosh(x), sinh(x))
+    type = 2 -- returns (cos(x), sin(x))
+    """
+    if x < 0:
+        x = -x
+        sign = 1
+    else:
+        sign = 0
+    r = int(0.5*prec**0.5)
+    xmag = bitcount(x) - prec
+    r = max(0, xmag + r)
+    extra = 10 + 2*max(r,-xmag)
+    wp = prec + extra
+    x <<= (extra - r)
+    one = MPZ_ONE << wp
+    alt = (type == 2)
+    if prec < EXP_SERIES_U_CUTOFF:
+        x2 = a = (x*x) >> wp
+        x4 = (x2*x2) >> wp
+        s0 = s1 = MPZ_ZERO
+        k = 2
+        while a:
+            a //= (k-1)*k; s0 += a; k += 2
+            a //= (k-1)*k; s1 += a; k += 2
+            a = (a*x4) >> wp
+        s1 = (x2*s1) >> wp
+        if alt:
+            c = s1 - s0 + one
+        else:
+            c = s1 + s0 + one
+    else:
+        u = int(0.3*prec**0.35)
+        x2 = a = (x*x) >> wp
+        xpowers = [one, x2]
+        for i in xrange(1, u):
+            xpowers.append((xpowers[-1]*x2)>>wp)
+        sums = [MPZ_ZERO] * u
+        k = 2
+        while a:
+            for i in xrange(u):
+                a //= (k-1)*k
+                if alt and k & 2: sums[i] -= a
+                else:             sums[i] += a
+                k += 2
+            a = (a*xpowers[-1]) >> wp
+        for i in xrange(1, u):
+            sums[i] = (sums[i]*xpowers[i]) >> wp
+        c = sum(sums) + one
+    if type == 0:
+        s = isqrt_fast(c*c - (one<<wp))
+        if sign:
+            v = c - s
+        else:
+            v = c + s
+        for i in xrange(r):
+            v = (v*v) >> wp
+        return v >> extra
+    else:
+        # Repeatedly apply the double-angle formula
+        # cosh(2*x) = 2*cosh(x)^2 - 1
+        # cos(2*x) = 2*cos(x)^2 - 1
+        pshift = wp-1
+        for i in xrange(r):
+            c = ((c*c) >> pshift) - one
+        # With the abs, this is the same for sinh and sin
+        s = isqrt_fast(abs((one<<wp) - c*c))
+        if sign:
+            s = -s
+        return (c>>extra), (s>>extra)
+
+def exp_basecase(x, prec):
+    """
+    Compute exp(x) as a fixed-point number. Works for any x,
+    but for speed should have |x| < 1. For an arbitrary number,
+    use exp(x) = exp(x-m*log(2)) * 2^m where m = floor(x/log(2)).
+    """
+    if prec > EXP_COSH_CUTOFF:
+        return exponential_series(x, prec, 0)
+    r = int(prec**0.5)
+    prec += r
+    s0 = s1 = (MPZ_ONE << prec)
+    k = 2
+    a = x2 = (x*x) >> prec
+    while a:
+        a //= k; s0 += a; k += 1
+        a //= k; s1 += a; k += 1
+        a = (a*x2) >> prec
+    s1 = (s1*x) >> prec
+    s = s0 + s1
+    u = r
+    while r:
+        s = (s*s) >> prec
+        r -= 1
+    return s >> u
+
+def exp_expneg_basecase(x, prec):
+    """
+    Computation of exp(x), exp(-x)
+    """
+    if prec > EXP_COSH_CUTOFF:
+        cosh, sinh = exponential_series(x, prec, 1)
+        return cosh+sinh, cosh-sinh
+    a = exp_basecase(x, prec)
+    b = (MPZ_ONE << (prec+prec)) // a
+    return a, b
+
+def cos_sin_basecase(x, prec):
+    """
+    Compute cos(x), sin(x) as fixed-point numbers, assuming x
+    in [0, pi/2). For an arbitrary number, use x' = x - m*(pi/2)
+    where m = floor(x/(pi/2)) along with quarter-period symmetries.
+    """
+    if prec > COS_SIN_CACHE_PREC:
+        return exponential_series(x, prec, 2)
+    precs = prec - COS_SIN_CACHE_STEP
+    t = x >> precs
+    n = int(t)
+    if n not in cos_sin_cache:
+        w = t<<(10+COS_SIN_CACHE_PREC-COS_SIN_CACHE_STEP)
+        cos_t, sin_t = exponential_series(w, 10+COS_SIN_CACHE_PREC, 2)
+        cos_sin_cache[n] = (cos_t>>10), (sin_t>>10)
+    cos_t, sin_t = cos_sin_cache[n]
+    offset = COS_SIN_CACHE_PREC - prec
+    cos_t >>= offset
+    sin_t >>= offset
+    x -= t << precs
+    cos = MPZ_ONE << prec
+    sin = x
+    k = 2
+    a = -((x*x) >> prec)
+    while a:
+        a //= k; cos += a; k += 1; a = (a*x) >> prec
+        a //= k; sin += a; k += 1; a = -((a*x) >> prec)
+    return ((cos*cos_t-sin*sin_t) >> prec), ((sin*cos_t+cos*sin_t) >> prec)
+
+def mpf_exp(x, prec, rnd=round_fast):
+    sign, man, exp, bc = x
+    if man:
+        mag = bc + exp
+        wp = prec + 14
+        if sign:
+            man = -man
+        # TODO: the best cutoff depends on both x and the precision.
+        if prec > 600 and exp >= 0:
+            # Need about log2(exp(n)) ~= 1.45*mag extra precision
+            e = mpf_e(wp+int(1.45*mag))
+            return mpf_pow_int(e, man<<exp, prec, rnd)
+        if mag < -wp:
+            return mpf_perturb(fone, sign, prec, rnd)
+        # |x| >= 2
+        if mag > 1:
+            # For large arguments: exp(2^mag*(1+eps)) =
+            # exp(2^mag)*exp(2^mag*eps) = exp(2^mag)*(1 + 2^mag*eps + ...)
+            # so about mag extra bits is required.
+            wpmod = wp + mag
+            offset = exp + wpmod
+            if offset >= 0:
+                t = man << offset
+            else:
+                t = man >> (-offset)
+            lg2 = ln2_fixed(wpmod)
+            n, t = divmod(t, lg2)
+            n = int(n)
+            t >>= mag
+        else:
+            offset = exp + wp
+            if offset >= 0:
+                t = man << offset
+            else:
+                t = man >> (-offset)
+            n = 0
+        man = exp_basecase(t, wp)
+        return from_man_exp(man, n-wp, prec, rnd)
+    if not exp:
+        return fone
+    if x == fninf:
+        return fzero
+    return x
+
+
+def mpf_cosh_sinh(x, prec, rnd=round_fast, tanh=0):
+    """Simultaneously compute (cosh(x), sinh(x)) for real x"""
+    sign, man, exp, bc = x
+    if (not man) and exp:
+        if tanh:
+            if x == finf: return fone
+            if x == fninf: return fnone
+            return fnan
+        if x == finf: return (finf, finf)
+        if x == fninf: return (finf, fninf)
+        return fnan, fnan
+    mag = exp+bc
+    wp = prec+14
+    if mag < -4:
+        # Extremely close to 0, sinh(x) ~= x and cosh(x) ~= 1
+        if mag < -wp:
+            if tanh:
+                return mpf_perturb(x, 1-sign, prec, rnd)
+            cosh = mpf_perturb(fone, 0, prec, rnd)
+            sinh = mpf_perturb(x, sign, prec, rnd)
+            return cosh, sinh
+        # Fix for cancellation when computing sinh
+        wp += (-mag)
+    # Does exp(-2*x) vanish?
+    if mag > 10:
+        if 3*(1<<(mag-1)) > wp:
+            # XXX: rounding
+            if tanh:
+                return mpf_perturb([fone,fnone][sign], 1-sign, prec, rnd)
+            c = s = mpf_shift(mpf_exp(mpf_abs(x), prec, rnd), -1)
+            if sign:
+                s = mpf_neg(s)
+            return c, s
+    # |x| > 1
+    if mag > 1:
+        wpmod = wp + mag
+        offset = exp + wpmod
+        if offset >= 0:
+            t = man << offset
+        else:
+            t = man >> (-offset)
+        lg2 = ln2_fixed(wpmod)
+        n, t = divmod(t, lg2)
+        n = int(n)
+        t >>= mag
+    else:
+        offset = exp + wp
+        if offset >= 0:
+            t = man << offset
+        else:
+            t = man >> (-offset)
+        n = 0
+    a, b = exp_expneg_basecase(t, wp)
+    # TODO: optimize division precision
+    cosh = a + (b>>(2*n))
+    sinh = a - (b>>(2*n))
+    if sign:
+        sinh = -sinh
+    if tanh:
+        man = (sinh << wp) // cosh
+        return from_man_exp(man, -wp, prec, rnd)
+    else:
+        cosh = from_man_exp(cosh, n-wp-1, prec, rnd)
+        sinh = from_man_exp(sinh, n-wp-1, prec, rnd)
+        return cosh, sinh
+
+
+def mod_pi2(man, exp, mag, wp):
+    # Reduce to standard interval
+    if mag > 0:
+        i = 0
+        while 1:
+            cancellation_prec = 20 << i
+            wpmod = wp + mag + cancellation_prec
+            pi2 = pi_fixed(wpmod-1)
+            pi4 = pi2 >> 1
+            offset = wpmod + exp
+            if offset >= 0:
+                t = man << offset
+            else:
+                t = man >> (-offset)
+            n, y = divmod(t, pi2)
+            if y > pi4:
+                small = pi2 - y
+            else:
+                small = y
+            if small >> (wp+mag-10):
+                n = int(n)
+                t = y >> mag
+                wp = wpmod - mag
+                break
+            i += 1
+    else:
+        wp += (-mag)
+        offset = exp + wp
+        if offset >= 0:
+            t = man << offset
+        else:
+            t = man >> (-offset)
+        n = 0
+    return t, n, wp
+
+
+def mpf_cos_sin(x, prec, rnd=round_fast, which=0, pi=False):
+    """
+    which:
+    0 -- return cos(x), sin(x)
+    1 -- return cos(x)
+    2 -- return sin(x)
+    3 -- return tan(x)
+
+    if pi=True, compute for pi*x
+    """
+    sign, man, exp, bc = x
+    if not man:
+        if exp:
+            c, s = fnan, fnan
+        else:
+            c, s = fone, fzero
+        if which == 0: return c, s
+        if which == 1: return c
+        if which == 2: return s
+        if which == 3: return s
+
+    mag = bc + exp
+    wp = prec + 10
+
+    # Extremely small?
+    if mag < 0:
+        if mag < -wp:
+            if pi:
+                x = mpf_mul(x, mpf_pi(wp))
+            c = mpf_perturb(fone, 1, prec, rnd)
+            s = mpf_perturb(x, 1-sign, prec, rnd)
+            if which == 0: return c, s
+            if which == 1: return c
+            if which == 2: return s
+            if which == 3: return mpf_perturb(x, sign, prec, rnd)
+    if pi:
+        if exp >= -1:
+            if exp == -1:
+                c = fzero
+                s = (fone, fnone)[bool(man & 2) ^ sign]
+            elif exp == 0:
+                c, s = (fnone, fzero)
+            else:
+                c, s = (fone, fzero)
+            if which == 0: return c, s
+            if which == 1: return c
+            if which == 2: return s
+            if which == 3: return mpf_div(s, c, prec, rnd)
+        # Subtract nearest half-integer (= mod by pi/2)
+        n = ((man >> (-exp-2)) + 1) >> 1
+        man = man - (n << (-exp-1))
+        mag2 = bitcount(man) + exp
+        wp = prec + 10 - mag2
+        offset = exp + wp
+        if offset >= 0:
+            t = man << offset
+        else:
+            t = man >> (-offset)
+        t = (t*pi_fixed(wp)) >> wp
+    else:
+        t, n, wp = mod_pi2(man, exp, mag, wp)
+    c, s = cos_sin_basecase(t, wp)
+    m = n & 3
+    if   m == 1: c, s = -s, c
+    elif m == 2: c, s = -c, -s
+    elif m == 3: c, s = s, -c
+    if sign:
+        s = -s
+    if which == 0:
+        c = from_man_exp(c, -wp, prec, rnd)
+        s = from_man_exp(s, -wp, prec, rnd)
+        return c, s
+    if which == 1:
+        return from_man_exp(c, -wp, prec, rnd)
+    if which == 2:
+        return from_man_exp(s, -wp, prec, rnd)
+    if which == 3:
+        return from_rational(s, c, prec, rnd)
+
+def mpf_cos(x, prec, rnd=round_fast): return mpf_cos_sin(x, prec, rnd, 1)
+def mpf_sin(x, prec, rnd=round_fast): return mpf_cos_sin(x, prec, rnd, 2)
+def mpf_tan(x, prec, rnd=round_fast): return mpf_cos_sin(x, prec, rnd, 3)
+def mpf_cos_sin_pi(x, prec, rnd=round_fast): return mpf_cos_sin(x, prec, rnd, 0, 1)
+def mpf_cos_pi(x, prec, rnd=round_fast): return mpf_cos_sin(x, prec, rnd, 1, 1)
+def mpf_sin_pi(x, prec, rnd=round_fast): return mpf_cos_sin(x, prec, rnd, 2, 1)
+def mpf_cosh(x, prec, rnd=round_fast): return mpf_cosh_sinh(x, prec, rnd)[0]
+def mpf_sinh(x, prec, rnd=round_fast): return mpf_cosh_sinh(x, prec, rnd)[1]
+def mpf_tanh(x, prec, rnd=round_fast): return mpf_cosh_sinh(x, prec, rnd, tanh=1)
+
+
+# Low-overhead fixed-point versions
+
+def cos_sin_fixed(x, prec, pi2=None):
+    if pi2 is None:
+        pi2 = pi_fixed(prec-1)
+    n, t = divmod(x, pi2)
+    n = int(n)
+    c, s = cos_sin_basecase(t, prec)
+    m = n & 3
+    if m == 0: return c, s
+    if m == 1: return -s, c
+    if m == 2: return -c, -s
+    if m == 3: return s, -c
+
+def exp_fixed(x, prec, ln2=None):
+    if ln2 is None:
+        ln2 = ln2_fixed(prec)
+    n, t = divmod(x, ln2)
+    n = int(n)
+    v = exp_basecase(t, prec)
+    if n >= 0:
+        return v << n
+    else:
+        return v >> (-n)
+
+
+if BACKEND == 'sage':
+    try:
+        import sage.libs.mpmath.ext_libmp as _lbmp
+        mpf_sqrt = _lbmp.mpf_sqrt
+        mpf_exp = _lbmp.mpf_exp
+        mpf_log = _lbmp.mpf_log
+        mpf_cos = _lbmp.mpf_cos
+        mpf_sin = _lbmp.mpf_sin
+        mpf_pow = _lbmp.mpf_pow
+        exp_fixed = _lbmp.exp_fixed
+        cos_sin_fixed = _lbmp.cos_sin_fixed
+        log_int_fixed = _lbmp.log_int_fixed
+    except (ImportError, AttributeError):
+        print("Warning: Sage imports in libelefun failed")

venv/lib/python3.10/site-packages/mpmath/libmp/libhyper.py

@@ -0,0 +1,1150 @@
+"""
+This module implements computation of hypergeometric and related
+functions. In particular, it provides code for generic summation
+of hypergeometric series. Optimized versions for various special
+cases are also provided.
+"""
+
+import operator
+import math
+
+from .backend import MPZ_ZERO, MPZ_ONE, BACKEND, xrange, exec_
+
+from .libintmath import gcd
+
+from .libmpf import (\
+    ComplexResult, round_fast, round_nearest,
+    negative_rnd, bitcount, to_fixed, from_man_exp, from_int, to_int,
+    from_rational,
+    fzero, fone, fnone, ftwo, finf, fninf, fnan,
+    mpf_sign, mpf_add, mpf_abs, mpf_pos,
+    mpf_cmp, mpf_lt, mpf_le, mpf_gt, mpf_min_max,
+    mpf_perturb, mpf_neg, mpf_shift, mpf_sub, mpf_mul, mpf_div,
+    sqrt_fixed, mpf_sqrt, mpf_rdiv_int, mpf_pow_int,
+    to_rational,
+)
+
+from .libelefun import (\
+    mpf_pi, mpf_exp, mpf_log, pi_fixed, mpf_cos_sin, mpf_cos, mpf_sin,
+    mpf_sqrt, agm_fixed,
+)
+
+from .libmpc import (\
+    mpc_one, mpc_sub, mpc_mul_mpf, mpc_mul, mpc_neg, complex_int_pow,
+    mpc_div, mpc_add_mpf, mpc_sub_mpf,
+    mpc_log, mpc_add, mpc_pos, mpc_shift,
+    mpc_is_infnan, mpc_zero, mpc_sqrt, mpc_abs,
+    mpc_mpf_div, mpc_square, mpc_exp
+)
+
+from .libintmath import ifac
+from .gammazeta import mpf_gamma_int, mpf_euler, euler_fixed
+
+class NoConvergence(Exception):
+    pass
+
+
+#-----------------------------------------------------------------------#
+#                                                                       #
+#                     Generic hypergeometric series                     #
+#                                                                       #
+#-----------------------------------------------------------------------#
+
+"""
+TODO:
+
+1. proper mpq parsing
+2. imaginary z special-cased (also: rational, integer?)
+3. more clever handling of series that don't converge because of stupid
+   upwards rounding
+4. checking for cancellation
+
+"""
+
+def make_hyp_summator(key):
+    """
+    Returns a function that sums a generalized hypergeometric series,
+    for given parameter types (integer, rational, real, complex).
+
+    """
+    p, q, param_types, ztype = key
+
+    pstring = "".join(param_types)
+    fname = "hypsum_%i_%i_%s_%s_%s" % (p, q, pstring[:p], pstring[p:], ztype)
+    #print "generating hypsum", fname
+
+    have_complex_param = 'C' in param_types
+    have_complex_arg = ztype == 'C'
+    have_complex = have_complex_param or have_complex_arg
+
+    source = []
+    add = source.append
+
+    aint = []
+    arat = []
+    bint = []
+    brat = []
+    areal = []
+    breal = []
+    acomplex = []
+    bcomplex = []
+
+    #add("wp = prec + 40")
+    add("MAX = kwargs.get('maxterms', wp*100)")
+    add("HIGH = MPZ_ONE<<epsshift")
+    add("LOW = -HIGH")
+
+    # Setup code
+    add("SRE = PRE = one = (MPZ_ONE << wp)")
+    if have_complex:
+        add("SIM = PIM = MPZ_ZERO")
+
+    if have_complex_arg:
+        add("xsign, xm, xe, xbc = z[0]")
+        add("if xsign: xm = -xm")
+        add("ysign, ym, ye, ybc = z[1]")
+        add("if ysign: ym = -ym")
+    else:
+        add("xsign, xm, xe, xbc = z")
+        add("if xsign: xm = -xm")
+
+    add("offset = xe + wp")
+    add("if offset >= 0:")
+    add("    ZRE = xm << offset")
+    add("else:")
+    add("    ZRE = xm >> (-offset)")
+    if have_complex_arg:
+        add("offset = ye + wp")
+        add("if offset >= 0:")
+        add("    ZIM = ym << offset")
+        add("else:")
+        add("    ZIM = ym >> (-offset)")
+
+    for i, flag in enumerate(param_types):
+        W = ["A", "B"][i >= p]
+        if flag == 'Z':
+            ([aint,bint][i >= p]).append(i)
+            add("%sINT_%i = coeffs[%i]" % (W, i, i))
+        elif flag == 'Q':
+            ([arat,brat][i >= p]).append(i)
+            add("%sP_%i, %sQ_%i = coeffs[%i]._mpq_" % (W, i, W, i, i))
+        elif flag == 'R':
+            ([areal,breal][i >= p]).append(i)
+            add("xsign, xm, xe, xbc = coeffs[%i]._mpf_" % i)
+            add("if xsign: xm = -xm")
+            add("offset = xe + wp")
+            add("if offset >= 0:")
+            add("    %sREAL_%i = xm << offset" % (W, i))
+            add("else:")
+            add("    %sREAL_%i = xm >> (-offset)" % (W, i))
+        elif flag == 'C':
+            ([acomplex,bcomplex][i >= p]).append(i)
+            add("__re, __im = coeffs[%i]._mpc_" % i)
+            add("xsign, xm, xe, xbc = __re")
+            add("if xsign: xm = -xm")
+            add("ysign, ym, ye, ybc = __im")
+            add("if ysign: ym = -ym")
+
+            add("offset = xe + wp")
+            add("if offset >= 0:")
+            add("    %sCRE_%i = xm << offset" % (W, i))
+            add("else:")
+            add("    %sCRE_%i = xm >> (-offset)" % (W, i))
+            add("offset = ye + wp")
+            add("if offset >= 0:")
+            add("    %sCIM_%i = ym << offset" % (W, i))
+            add("else:")
+            add("    %sCIM_%i = ym >> (-offset)" % (W, i))
+        else:
+            raise ValueError
+
+    l_areal = len(areal)
+    l_breal = len(breal)
+    cancellable_real = min(l_areal, l_breal)
+    noncancellable_real_num = areal[cancellable_real:]
+    noncancellable_real_den = breal[cancellable_real:]
+
+    # LOOP
+    add("for n in xrange(1,10**8):")
+
+    add("    if n in magnitude_check:")
+    add("        p_mag = bitcount(abs(PRE))")
+    if have_complex:
+        add("        p_mag = max(p_mag, bitcount(abs(PIM)))")
+    add("        magnitude_check[n] = wp-p_mag")
+
+    # Real factors
+    multiplier = " * ".join(["AINT_#".replace("#", str(i)) for i in aint] + \
+                            ["AP_#".replace("#", str(i)) for i in arat] + \
+                            ["BQ_#".replace("#", str(i)) for i in brat])
+
+    divisor    = " * ".join(["BINT_#".replace("#", str(i)) for i in bint] + \
+                            ["BP_#".replace("#", str(i)) for i in brat] + \
+                            ["AQ_#".replace("#", str(i)) for i in arat] + ["n"])
+
+    if multiplier:
+        add("    mul = " + multiplier)
+    add("    div = " + divisor)
+
+    # Check for singular terms
+    add("    if not div:")
+    if multiplier:
+        add("        if not mul:")
+        add("            break")
+    add("        raise ZeroDivisionError")
+
+    # Update product
+    if have_complex:
+
+        # TODO: when there are several real parameters and just a few complex
+        # (maybe just the complex argument), we only need to do about
+        # half as many ops if we accumulate the real factor in a single real variable
+        for k in range(cancellable_real): add("    PRE = PRE * AREAL_%i // BREAL_%i" % (areal[k], breal[k]))
+        for i in noncancellable_real_num: add("    PRE = (PRE * AREAL_#) >> wp".replace("#", str(i)))
+        for i in noncancellable_real_den: add("    PRE = (PRE << wp) // BREAL_#".replace("#", str(i)))
+        for k in range(cancellable_real): add("    PIM = PIM * AREAL_%i // BREAL_%i" % (areal[k], breal[k]))
+        for i in noncancellable_real_num: add("    PIM = (PIM * AREAL_#) >> wp".replace("#", str(i)))
+        for i in noncancellable_real_den: add("    PIM = (PIM << wp) // BREAL_#".replace("#", str(i)))
+
+        if multiplier:
+            if have_complex_arg:
+                add("    PRE, PIM = (mul*(PRE*ZRE-PIM*ZIM))//div, (mul*(PIM*ZRE+PRE*ZIM))//div")
+                add("    PRE >>= wp")
+                add("    PIM >>= wp")
+            else:
+                add("    PRE = ((mul * PRE * ZRE) >> wp) // div")
+                add("    PIM = ((mul * PIM * ZRE) >> wp) // div")
+        else:
+            if have_complex_arg:
+                add("    PRE, PIM = (PRE*ZRE-PIM*ZIM)//div, (PIM*ZRE+PRE*ZIM)//div")
+                add("    PRE >>= wp")
+                add("    PIM >>= wp")
+            else:
+                add("    PRE = ((PRE * ZRE) >> wp) // div")
+                add("    PIM = ((PIM * ZRE) >> wp) // div")
+
+        for i in acomplex:
+            add("    PRE, PIM = PRE*ACRE_#-PIM*ACIM_#, PIM*ACRE_#+PRE*ACIM_#".replace("#", str(i)))
+            add("    PRE >>= wp")
+            add("    PIM >>= wp")
+
+        for i in bcomplex:
+            add("    mag = BCRE_#*BCRE_#+BCIM_#*BCIM_#".replace("#", str(i)))
+            add("    re = PRE*BCRE_# + PIM*BCIM_#".replace("#", str(i)))
+            add("    im = PIM*BCRE_# - PRE*BCIM_#".replace("#", str(i)))
+            add("    PRE = (re << wp) // mag".replace("#", str(i)))
+            add("    PIM = (im << wp) // mag".replace("#", str(i)))
+
+    else:
+        for k in range(cancellable_real): add("    PRE = PRE * AREAL_%i // BREAL_%i" % (areal[k], breal[k]))
+        for i in noncancellable_real_num: add("    PRE = (PRE * AREAL_#) >> wp".replace("#", str(i)))
+        for i in noncancellable_real_den: add("    PRE = (PRE << wp) // BREAL_#".replace("#", str(i)))
+        if multiplier:
+            add("    PRE = ((PRE * mul * ZRE) >> wp) // div")
+        else:
+            add("    PRE = ((PRE * ZRE) >> wp) // div")
+
+    # Add product to sum
+    if have_complex:
+        add("    SRE += PRE")
+        add("    SIM += PIM")
+        add("    if (HIGH > PRE > LOW) and (HIGH > PIM > LOW):")
+        add("        break")
+    else:
+        add("    SRE += PRE")
+        add("    if HIGH > PRE > LOW:")
+        add("        break")
+
+    #add("    from mpmath import nprint, log, ldexp")
+    #add("    nprint([n, log(abs(PRE),2), ldexp(PRE,-wp)])")
+
+    add("    if n > MAX:")
+    add("        raise NoConvergence('Hypergeometric series converges too slowly. Try increasing maxterms.')")
+
+    # +1 all parameters for next loop
+    for i in aint:     add("    AINT_# += 1".replace("#", str(i)))
+    for i in bint:     add("    BINT_# += 1".replace("#", str(i)))
+    for i in arat:     add("    AP_# += AQ_#".replace("#", str(i)))
+    for i in brat:     add("    BP_# += BQ_#".replace("#", str(i)))
+    for i in areal:    add("    AREAL_# += one".replace("#", str(i)))
+    for i in breal:    add("    BREAL_# += one".replace("#", str(i)))
+    for i in acomplex: add("    ACRE_# += one".replace("#", str(i)))
+    for i in bcomplex: add("    BCRE_# += one".replace("#", str(i)))
+
+    if have_complex:
+        add("a = from_man_exp(SRE, -wp, prec, 'n')")
+        add("b = from_man_exp(SIM, -wp, prec, 'n')")
+
+        add("if SRE:")
+        add("    if SIM:")
+        add("        magn = max(a[2]+a[3], b[2]+b[3])")
+        add("    else:")
+        add("        magn = a[2]+a[3]")
+        add("elif SIM:")
+        add("    magn = b[2]+b[3]")
+        add("else:")
+        add("    magn = -wp+1")
+
+        add("return (a, b), True, magn")
+    else:
+        add("a = from_man_exp(SRE, -wp, prec, 'n')")
+
+        add("if SRE:")
+        add("    magn = a[2]+a[3]")
+        add("else:")
+        add("    magn = -wp+1")
+
+        add("return a, False, magn")
+
+    source = "\n".join(("    " + line) for line in source)
+    source = ("def %s(coeffs, z, prec, wp, epsshift, magnitude_check, **kwargs):\n" % fname) + source
+
+    namespace = {}
+
+    exec_(source, globals(), namespace)
+
+    #print source
+    return source, namespace[fname]
+
+
+if BACKEND == 'sage':
+
+    def make_hyp_summator(key):
+        """
+        Returns a function that sums a generalized hypergeometric series,
+        for given parameter types (integer, rational, real, complex).
+        """
+        from sage.libs.mpmath.ext_main import hypsum_internal
+        p, q, param_types, ztype = key
+        def _hypsum(coeffs, z, prec, wp, epsshift, magnitude_check, **kwargs):
+            return hypsum_internal(p, q, param_types, ztype, coeffs, z,
+                prec, wp, epsshift, magnitude_check, kwargs)
+
+        return "(none)", _hypsum
+
+
+#-----------------------------------------------------------------------#
+#                                                                       #
+#                              Error functions                          #
+#                                                                       #
+#-----------------------------------------------------------------------#
+
+# TODO: mpf_erf should call mpf_erfc when appropriate (currently
+#    only the converse delegation is implemented)
+
+def mpf_erf(x, prec, rnd=round_fast):
+    sign, man, exp, bc = x
+    if not man:
+        if x == fzero: return fzero
+        if x == finf: return fone
+        if x== fninf: return fnone
+        return fnan
+    size = exp + bc
+    lg = math.log
+    # The approximation erf(x) = 1 is accurate to > x^2 * log(e,2) bits
+    if size > 3 and 2*(size-1) + 0.528766 > lg(prec,2):
+        if sign:
+            return mpf_perturb(fnone, 0, prec, rnd)
+        else:
+            return mpf_perturb(fone, 1, prec, rnd)
+    # erf(x) ~ 2*x/sqrt(pi) close to 0
+    if size < -prec:
+        # 2*x
+        x = mpf_shift(x,1)
+        c = mpf_sqrt(mpf_pi(prec+20), prec+20)
+        # TODO: interval rounding
+        return mpf_div(x, c, prec, rnd)
+    wp = prec + abs(size) + 25
+    # Taylor series for erf, fixed-point summation
+    t = abs(to_fixed(x, wp))
+    t2 = (t*t) >> wp
+    s, term, k = t, 12345, 1
+    while term:
+        t = ((t * t2) >> wp) // k
+        term = t // (2*k+1)
+        if k & 1:
+            s -= term
+        else:
+            s += term
+        k += 1
+    s = (s << (wp+1)) // sqrt_fixed(pi_fixed(wp), wp)
+    if sign:
+        s = -s
+    return from_man_exp(s, -wp, prec, rnd)
+
+# If possible, we use the asymptotic series for erfc.
+# This is an alternating divergent asymptotic series, so
+# the error is at most equal to the first omitted term.
+# Here we check if the smallest term is small enough
+# for a given x and precision
+def erfc_check_series(x, prec):
+    n = to_int(x)
+    if n**2 * 1.44 > prec:
+        return True
+    return False
+
+def mpf_erfc(x, prec, rnd=round_fast):
+    sign, man, exp, bc = x
+    if not man:
+        if x == fzero: return fone
+        if x == finf: return fzero
+        if x == fninf: return ftwo
+        return fnan
+    wp = prec + 20
+    mag = bc+exp
+    # Preserve full accuracy when exponent grows huge
+    wp += max(0, 2*mag)
+    regular_erf = sign or mag < 2
+    if regular_erf or not erfc_check_series(x, wp):
+        if regular_erf:
+            return mpf_sub(fone, mpf_erf(x, prec+10, negative_rnd[rnd]), prec, rnd)
+        # 1-erf(x) ~ exp(-x^2), increase prec to deal with cancellation
+        n = to_int(x)+1
+        return mpf_sub(fone, mpf_erf(x, prec + int(n**2*1.44) + 10), prec, rnd)
+    s = term = MPZ_ONE << wp
+    term_prev = 0
+    t = (2 * to_fixed(x, wp) ** 2) >> wp
+    k = 1
+    while 1:
+        term = ((term * (2*k - 1)) << wp) // t
+        if k > 4 and term > term_prev or not term:
+            break
+        if k & 1:
+            s -= term
+        else:
+            s += term
+        term_prev = term
+        #print k, to_str(from_man_exp(term, -wp, 50), 10)
+        k += 1
+    s = (s << wp) // sqrt_fixed(pi_fixed(wp), wp)
+    s = from_man_exp(s, -wp, wp)
+    z = mpf_exp(mpf_neg(mpf_mul(x,x,wp),wp),wp)
+    y = mpf_div(mpf_mul(z, s, wp), x, prec, rnd)
+    return y
+
+
+#-----------------------------------------------------------------------#
+#                                                                       #
+#                         Exponential integrals                         #
+#                                                                       #
+#-----------------------------------------------------------------------#
+
+def ei_taylor(x, prec):
+    s = t = x
+    k = 2
+    while t:
+        t = ((t*x) >> prec) // k
+        s += t // k
+        k += 1
+    return s
+
+def complex_ei_taylor(zre, zim, prec):
+    _abs = abs
+    sre = tre = zre
+    sim = tim = zim
+    k = 2
+    while _abs(tre) + _abs(tim) > 5:
+        tre, tim = ((tre*zre-tim*zim)//k)>>prec, ((tre*zim+tim*zre)//k)>>prec
+        sre += tre // k
+        sim += tim // k
+        k += 1
+    return sre, sim
+
+def ei_asymptotic(x, prec):
+    one = MPZ_ONE << prec
+    x = t = ((one << prec) // x)
+    s = one + x
+    k = 2
+    while t:
+        t = (k*t*x) >> prec
+        s += t
+        k += 1
+    return s
+
+def complex_ei_asymptotic(zre, zim, prec):
+    _abs = abs
+    one = MPZ_ONE << prec
+    M = (zim*zim + zre*zre) >> prec
+    # 1 / z
+    xre = tre = (zre << prec) // M
+    xim = tim = ((-zim) << prec) // M
+    sre = one + xre
+    sim = xim
+    k = 2
+    while _abs(tre) + _abs(tim) > 1000:
+        #print tre, tim
+        tre, tim = ((tre*xre-tim*xim)*k)>>prec, ((tre*xim+tim*xre)*k)>>prec
+        sre += tre
+        sim += tim
+        k += 1
+        if k > prec:
+            raise NoConvergence
+    return sre, sim
+
+def mpf_ei(x, prec, rnd=round_fast, e1=False):
+    if e1:
+        x = mpf_neg(x)
+    sign, man, exp, bc = x
+    if e1 and not sign:
+        if x == fzero:
+            return finf
+        raise ComplexResult("E1(x) for x < 0")
+    if man:
+        xabs = 0, man, exp, bc
+        xmag = exp+bc
+        wp = prec + 20
+        can_use_asymp = xmag > wp
+        if not can_use_asymp:
+            if exp >= 0:
+                xabsint = man << exp
+            else:
+                xabsint = man >> (-exp)
+            can_use_asymp = xabsint > int(wp*0.693) + 10
+        if can_use_asymp:
+            if xmag > wp:
+                v = fone
+            else:
+                v = from_man_exp(ei_asymptotic(to_fixed(x, wp), wp), -wp)
+            v = mpf_mul(v, mpf_exp(x, wp), wp)
+            v = mpf_div(v, x, prec, rnd)
+        else:
+            wp += 2*int(to_int(xabs))
+            u = to_fixed(x, wp)
+            v = ei_taylor(u, wp) + euler_fixed(wp)
+            t1 = from_man_exp(v,-wp)
+            t2 = mpf_log(xabs,wp)
+            v = mpf_add(t1, t2, prec, rnd)
+    else:
+        if x == fzero: v = fninf
+        elif x == finf: v = finf
+        elif x == fninf: v = fzero
+        else: v = fnan
+    if e1:
+        v = mpf_neg(v)
+    return v
+
+def mpc_ei(z, prec, rnd=round_fast, e1=False):
+    if e1:
+        z = mpc_neg(z)
+    a, b = z
+    asign, aman, aexp, abc = a
+    bsign, bman, bexp, bbc = b
+    if b == fzero:
+        if e1:
+            x = mpf_neg(mpf_ei(a, prec, rnd))
+            if not asign:
+                y = mpf_neg(mpf_pi(prec, rnd))
+            else:
+                y = fzero
+            return x, y
+        else:
+            return mpf_ei(a, prec, rnd), fzero
+    if a != fzero:
+        if not aman or not bman:
+            return (fnan, fnan)
+    wp = prec + 40
+    amag = aexp+abc
+    bmag = bexp+bbc
+    zmag = max(amag, bmag)
+    can_use_asymp = zmag > wp
+    if not can_use_asymp:
+        zabsint = abs(to_int(a)) + abs(to_int(b))
+        can_use_asymp = zabsint > int(wp*0.693) + 20
+    try:
+        if can_use_asymp:
+            if zmag > wp:
+                v = fone, fzero
+            else:
+                zre = to_fixed(a, wp)
+                zim = to_fixed(b, wp)
+                vre, vim = complex_ei_asymptotic(zre, zim, wp)
+                v = from_man_exp(vre, -wp), from_man_exp(vim, -wp)
+            v = mpc_mul(v, mpc_exp(z, wp), wp)
+            v = mpc_div(v, z, wp)
+            if e1:
+                v = mpc_neg(v, prec, rnd)
+            else:
+                x, y = v
+                if bsign:
+                    v = mpf_pos(x, prec, rnd), mpf_sub(y, mpf_pi(wp), prec, rnd)
+                else:
+                    v = mpf_pos(x, prec, rnd), mpf_add(y, mpf_pi(wp), prec, rnd)
+            return v
+    except NoConvergence:
+        pass
+    #wp += 2*max(0,zmag)
+    wp += 2*int(to_int(mpc_abs(z, 5)))
+    zre = to_fixed(a, wp)
+    zim = to_fixed(b, wp)
+    vre, vim = complex_ei_taylor(zre, zim, wp)
+    vre += euler_fixed(wp)
+    v = from_man_exp(vre,-wp), from_man_exp(vim,-wp)
+    if e1:
+        u = mpc_log(mpc_neg(z),wp)
+    else:
+        u = mpc_log(z,wp)
+    v = mpc_add(v, u, prec, rnd)
+    if e1:
+        v = mpc_neg(v)
+    return v
+
+def mpf_e1(x, prec, rnd=round_fast):
+    return mpf_ei(x, prec, rnd, True)
+
+def mpc_e1(x, prec, rnd=round_fast):
+    return mpc_ei(x, prec, rnd, True)
+
+def mpf_expint(n, x, prec, rnd=round_fast, gamma=False):
+    """
+    E_n(x), n an integer, x real
+
+    With gamma=True, computes Gamma(n,x)   (upper incomplete gamma function)
+
+    Returns (real, None) if real, otherwise (real, imag)
+    The imaginary part is an optional branch cut term
+
+    """
+    sign, man, exp, bc = x
+    if not man:
+        if gamma:
+            if x == fzero:
+                # Actually gamma function pole
+                if n <= 0:
+                    return finf, None
+                return mpf_gamma_int(n, prec, rnd), None
+            if x == finf:
+                return fzero, None
+            # TODO: could return finite imaginary value at -inf
+            return fnan, fnan
+        else:
+            if x == fzero:
+                if n > 1:
+                    return from_rational(1, n-1, prec, rnd), None
+                else:
+                    return finf, None
+            if x == finf:
+                return fzero, None
+            return fnan, fnan
+    n_orig = n
+    if gamma:
+        n = 1-n
+    wp = prec + 20
+    xmag = exp + bc
+    # Beware of near-poles
+    if xmag < -10:
+        raise NotImplementedError
+    nmag = bitcount(abs(n))
+    have_imag = n > 0 and sign
+    negx = mpf_neg(x)
+    # Skip series if direct convergence
+    if n == 0 or 2*nmag - xmag < -wp:
+        if gamma:
+            v = mpf_exp(negx, wp)
+            re = mpf_mul(v, mpf_pow_int(x, n_orig-1, wp), prec, rnd)
+        else:
+            v = mpf_exp(negx, wp)
+            re = mpf_div(v, x, prec, rnd)
+    else:
+        # Finite number of terms, or...
+        can_use_asymptotic_series = -3*wp < n <= 0
+        # ...large enough?
+        if not can_use_asymptotic_series:
+            xi = abs(to_int(x))
+            m = min(max(1, xi-n), 2*wp)
+            siz = -n*nmag + (m+n)*bitcount(abs(m+n)) - m*xmag - (144*m//100)
+            tol = -wp-10
+            can_use_asymptotic_series = siz < tol
+        if can_use_asymptotic_series:
+            r = ((-MPZ_ONE) << (wp+wp)) // to_fixed(x, wp)
+            m = n
+            t = r*m
+            s = MPZ_ONE << wp
+            while m and t:
+                s += t
+                m += 1
+                t = (m*r*t) >> wp
+            v = mpf_exp(negx, wp)
+            if gamma:
+                # ~ exp(-x) * x^(n-1) * (1 + ...)
+                v = mpf_mul(v, mpf_pow_int(x, n_orig-1, wp), wp)
+            else:
+                # ~ exp(-x)/x * (1 + ...)
+                v = mpf_div(v, x, wp)
+            re = mpf_mul(v, from_man_exp(s, -wp), prec, rnd)
+        elif n == 1:
+            re = mpf_neg(mpf_ei(negx, prec, rnd))
+        elif n > 0 and n < 3*wp:
+            T1 = mpf_neg(mpf_ei(negx, wp))
+            if gamma:
+                if n_orig & 1:
+                    T1 = mpf_neg(T1)
+            else:
+                T1 = mpf_mul(T1, mpf_pow_int(negx, n-1, wp), wp)
+            r = t = to_fixed(x, wp)
+            facs = [1] * (n-1)
+            for k in range(1,n-1):
+                facs[k] = facs[k-1] * k
+            facs = facs[::-1]
+            s = facs[0] << wp
+            for k in range(1, n-1):
+                if k & 1:
+                    s -= facs[k] * t
+                else:
+                    s += facs[k] * t
+                t = (t*r) >> wp
+            T2 = from_man_exp(s, -wp, wp)
+            T2 = mpf_mul(T2, mpf_exp(negx, wp))
+            if gamma:
+                T2 = mpf_mul(T2, mpf_pow_int(x, n_orig, wp), wp)
+            R = mpf_add(T1, T2)
+            re = mpf_div(R, from_int(ifac(n-1)), prec, rnd)
+        else:
+            raise NotImplementedError
+    if have_imag:
+        M = from_int(-ifac(n-1))
+        if gamma:
+            im = mpf_div(mpf_pi(wp), M, prec, rnd)
+            if n_orig & 1:
+                im = mpf_neg(im)
+        else:
+            im = mpf_div(mpf_mul(mpf_pi(wp), mpf_pow_int(negx, n_orig-1, wp), wp), M, prec, rnd)
+        return re, im
+    else:
+        return re, None
+
+def mpf_ci_si_taylor(x, wp, which=0):
+    """
+    0 - Ci(x) - (euler+log(x))
+    1 - Si(x)
+    """
+    x = to_fixed(x, wp)
+    x2 = -(x*x) >> wp
+    if which == 0:
+        s, t, k = 0, (MPZ_ONE<<wp), 2
+    else:
+        s, t, k = x, x, 3
+    while t:
+        t = (t*x2//(k*(k-1)))>>wp
+        s += t//k
+        k += 2
+    return from_man_exp(s, -wp)
+
+def mpc_ci_si_taylor(re, im, wp, which=0):
+    # The following code is only designed for small arguments,
+    # and not too small arguments (for relative accuracy)
+    if re[1]:
+        mag = re[2]+re[3]
+    elif im[1]:
+        mag = im[2]+im[3]
+    if im[1]:
+        mag = max(mag, im[2]+im[3])
+    if mag > 2 or mag < -wp:
+        raise NotImplementedError
+    wp += (2-mag)
+    zre = to_fixed(re, wp)
+    zim = to_fixed(im, wp)
+    z2re = (zim*zim-zre*zre)>>wp
+    z2im = (-2*zre*zim)>>wp
+    tre = zre
+    tim = zim
+    one = MPZ_ONE<<wp
+    if which == 0:
+        sre, sim, tre, tim, k = 0, 0, (MPZ_ONE<<wp), 0, 2
+    else:
+        sre, sim, tre, tim, k = zre, zim, zre, zim, 3
+    while max(abs(tre), abs(tim)) > 2:
+        f = k*(k-1)
+        tre, tim = ((tre*z2re-tim*z2im)//f)>>wp, ((tre*z2im+tim*z2re)//f)>>wp
+        sre += tre//k
+        sim += tim//k
+        k += 2
+    return from_man_exp(sre, -wp), from_man_exp(sim, -wp)
+
+def mpf_ci_si(x, prec, rnd=round_fast, which=2):
+    """
+    Calculation of Ci(x), Si(x) for real x.
+
+    which = 0 -- returns (Ci(x), -)
+    which = 1 -- returns (Si(x), -)
+    which = 2 -- returns (Ci(x), Si(x))
+
+    Note: if x < 0, Ci(x) needs an additional imaginary term, pi*i.
+    """
+    wp = prec + 20
+    sign, man, exp, bc = x
+    ci, si = None, None
+    if not man:
+        if x == fzero:
+            return (fninf, fzero)
+        if x == fnan:
+            return (x, x)
+        ci = fzero
+        if which != 0:
+            if x == finf:
+                si = mpf_shift(mpf_pi(prec, rnd), -1)
+            if x == fninf:
+                si = mpf_neg(mpf_shift(mpf_pi(prec, negative_rnd[rnd]), -1))
+        return (ci, si)
+    # For small x: Ci(x) ~ euler + log(x), Si(x) ~ x
+    mag = exp+bc
+    if mag < -wp:
+        if which != 0:
+            si = mpf_perturb(x, 1-sign, prec, rnd)
+        if which != 1:
+            y = mpf_euler(wp)
+            xabs = mpf_abs(x)
+            ci = mpf_add(y, mpf_log(xabs, wp), prec, rnd)
+        return ci, si
+    # For huge x: Ci(x) ~ sin(x)/x, Si(x) ~ pi/2
+    elif mag > wp:
+        if which != 0:
+            if sign:
+                si = mpf_neg(mpf_pi(prec, negative_rnd[rnd]))
+            else:
+                si = mpf_pi(prec, rnd)
+            si = mpf_shift(si, -1)
+        if which != 1:
+            ci = mpf_div(mpf_sin(x, wp), x, prec, rnd)
+        return ci, si
+    else:
+        wp += abs(mag)
+    # Use an asymptotic series? The smallest value of n!/x^n
+    # occurs for n ~ x, where the magnitude is ~ exp(-x).
+    asymptotic = mag-1 > math.log(wp, 2)
+    # Case 1: convergent series near 0
+    if not asymptotic:
+        if which != 0:
+            si = mpf_pos(mpf_ci_si_taylor(x, wp, 1), prec, rnd)
+        if which != 1:
+            ci = mpf_ci_si_taylor(x, wp, 0)
+            ci = mpf_add(ci, mpf_euler(wp), wp)
+            ci = mpf_add(ci, mpf_log(mpf_abs(x), wp), prec, rnd)
+        return ci, si
+    x = mpf_abs(x)
+    # Case 2: asymptotic series for x >> 1
+    xf = to_fixed(x, wp)
+    xr = (MPZ_ONE<<(2*wp)) // xf   # 1/x
+    s1 = (MPZ_ONE << wp)
+    s2 = xr
+    t = xr
+    k = 2
+    while t:
+        t = -t
+        t = (t*xr*k)>>wp
+        k += 1
+        s1 += t
+        t = (t*xr*k)>>wp
+        k += 1
+        s2 += t
+    s1 = from_man_exp(s1, -wp)
+    s2 = from_man_exp(s2, -wp)
+    s1 = mpf_div(s1, x, wp)
+    s2 = mpf_div(s2, x, wp)
+    cos, sin = mpf_cos_sin(x, wp)
+    # Ci(x) = sin(x)*s1-cos(x)*s2
+    # Si(x) = pi/2-cos(x)*s1-sin(x)*s2
+    if which != 0:
+        si = mpf_add(mpf_mul(cos, s1), mpf_mul(sin, s2), wp)
+        si = mpf_sub(mpf_shift(mpf_pi(wp), -1), si, wp)
+        if sign:
+            si = mpf_neg(si)
+        si = mpf_pos(si, prec, rnd)
+    if which != 1:
+        ci = mpf_sub(mpf_mul(sin, s1), mpf_mul(cos, s2), prec, rnd)
+    return ci, si
+
+def mpf_ci(x, prec, rnd=round_fast):
+    if mpf_sign(x) < 0:
+        raise ComplexResult
+    return mpf_ci_si(x, prec, rnd, 0)[0]
+
+def mpf_si(x, prec, rnd=round_fast):
+    return mpf_ci_si(x, prec, rnd, 1)[1]
+
+def mpc_ci(z, prec, rnd=round_fast):
+    re, im = z
+    if im == fzero:
+        ci = mpf_ci_si(re, prec, rnd, 0)[0]
+        if mpf_sign(re) < 0:
+            return (ci, mpf_pi(prec, rnd))
+        return (ci, fzero)
+    wp = prec + 20
+    cre, cim = mpc_ci_si_taylor(re, im, wp, 0)
+    cre = mpf_add(cre, mpf_euler(wp), wp)
+    ci = mpc_add((cre, cim), mpc_log(z, wp), prec, rnd)
+    return ci
+
+def mpc_si(z, prec, rnd=round_fast):
+    re, im = z
+    if im == fzero:
+        return (mpf_ci_si(re, prec, rnd, 1)[1], fzero)
+    wp = prec + 20
+    z = mpc_ci_si_taylor(re, im, wp, 1)
+    return mpc_pos(z, prec, rnd)
+
+
+#-----------------------------------------------------------------------#
+#                                                                       #
+#                             Bessel functions                          #
+#                                                                       #
+#-----------------------------------------------------------------------#
+
+# A Bessel function of the first kind of integer order, J_n(x), is
+# given by the power series
+
+#             oo
+#             ___         k         2 k + n
+#            \        (-1)     / x \
+#    J_n(x) = )    ----------- | - |
+#            /___  k! (k + n)! \ 2 /
+#            k = 0
+
+# Simplifying the quotient between two successive terms gives the
+# ratio x^2 / (-4*k*(k+n)). Hence, we only need one full-precision
+# multiplication and one division by a small integer per term.
+# The complex version is very similar, the only difference being
+# that the multiplication is actually 4 multiplies.
+
+# In the general case, we have
+# J_v(x) = (x/2)**v / v! * 0F1(v+1, (-1/4)*z**2)
+
+# TODO: for extremely large x, we could use an asymptotic
+# trigonometric approximation.
+
+# TODO: recompute at higher precision if the fixed-point mantissa
+# is very small
+
+def mpf_besseljn(n, x, prec, rounding=round_fast):
+    prec += 50
+    negate = n < 0 and n & 1
+    mag = x[2]+x[3]
+    n = abs(n)
+    wp = prec + 20 + n*bitcount(n)
+    if mag < 0:
+        wp -= n * mag
+    x = to_fixed(x, wp)
+    x2 = (x**2) >> wp
+    if not n:
+        s = t = MPZ_ONE << wp
+    else:
+        s = t = (x**n // ifac(n)) >> ((n-1)*wp + n)
+    k = 1
+    while t:
+        t = ((t * x2) // (-4*k*(k+n))) >> wp
+        s += t
+        k += 1
+    if negate:
+        s = -s
+    return from_man_exp(s, -wp, prec, rounding)
+
+def mpc_besseljn(n, z, prec, rounding=round_fast):
+    negate = n < 0 and n & 1
+    n = abs(n)
+    origprec = prec
+    zre, zim = z
+    mag = max(zre[2]+zre[3], zim[2]+zim[3])
+    prec += 20 + n*bitcount(n) + abs(mag)
+    if mag < 0:
+        prec -= n * mag
+    zre = to_fixed(zre, prec)
+    zim = to_fixed(zim, prec)
+    z2re = (zre**2 - zim**2) >> prec
+    z2im = (zre*zim) >> (prec-1)
+    if not n:
+        sre = tre = MPZ_ONE << prec
+        sim = tim = MPZ_ZERO
+    else:
+        re, im = complex_int_pow(zre, zim, n)
+        sre = tre = (re // ifac(n)) >> ((n-1)*prec + n)
+        sim = tim = (im // ifac(n)) >> ((n-1)*prec + n)
+    k = 1
+    while abs(tre) + abs(tim) > 3:
+        p = -4*k*(k+n)
+        tre, tim = tre*z2re - tim*z2im, tim*z2re + tre*z2im
+        tre = (tre // p) >> prec
+        tim = (tim // p) >> prec
+        sre += tre
+        sim += tim
+        k += 1
+    if negate:
+        sre = -sre
+        sim = -sim
+    re = from_man_exp(sre, -prec, origprec, rounding)
+    im = from_man_exp(sim, -prec, origprec, rounding)
+    return (re, im)
+
+def mpf_agm(a, b, prec, rnd=round_fast):
+    """
+    Computes the arithmetic-geometric mean agm(a,b) for
+    nonnegative mpf values a, b.
+    """
+    asign, aman, aexp, abc = a
+    bsign, bman, bexp, bbc = b
+    if asign or bsign:
+        raise ComplexResult("agm of a negative number")
+    # Handle inf, nan or zero in either operand
+    if not (aman and bman):
+        if a == fnan or b == fnan:
+            return fnan
+        if a == finf:
+            if b == fzero:
+                return fnan
+            return finf
+        if b == finf:
+            if a == fzero:
+                return fnan
+            return finf
+        # agm(0,x) = agm(x,0) = 0
+        return fzero
+    wp = prec + 20
+    amag = aexp+abc
+    bmag = bexp+bbc
+    mag_delta = amag - bmag
+    # Reduce to roughly the same magnitude using floating-point AGM
+    abs_mag_delta = abs(mag_delta)
+    if abs_mag_delta > 10:
+        while abs_mag_delta > 10:
+            a, b = mpf_shift(mpf_add(a,b,wp),-1), \
+                mpf_sqrt(mpf_mul(a,b,wp),wp)
+            abs_mag_delta //= 2
+        asign, aman, aexp, abc = a
+        bsign, bman, bexp, bbc = b
+        amag = aexp+abc
+        bmag = bexp+bbc
+        mag_delta = amag - bmag
+    #print to_float(a), to_float(b)
+    # Use agm(a,b) = agm(x*a,x*b)/x to obtain a, b ~= 1
+    min_mag = min(amag,bmag)
+    max_mag = max(amag,bmag)
+    n = 0
+    # If too small, we lose precision when going to fixed-point
+    if min_mag < -8:
+        n = -min_mag
+    # If too large, we waste time using fixed-point with large numbers
+    elif max_mag > 20:
+        n = -max_mag
+    if n:
+        a = mpf_shift(a, n)
+        b = mpf_shift(b, n)
+    #print to_float(a), to_float(b)
+    af = to_fixed(a, wp)
+    bf = to_fixed(b, wp)
+    g = agm_fixed(af, bf, wp)
+    return from_man_exp(g, -wp-n, prec, rnd)
+
+def mpf_agm1(a, prec, rnd=round_fast):
+    """
+    Computes the arithmetic-geometric mean agm(1,a) for a nonnegative
+    mpf value a.
+    """
+    return mpf_agm(fone, a, prec, rnd)
+
+def mpc_agm(a, b, prec, rnd=round_fast):
+    """
+    Complex AGM.
+
+    TODO:
+    * check that convergence works as intended
+    * optimize
+    * select a nonarbitrary branch
+    """
+    if mpc_is_infnan(a) or mpc_is_infnan(b):
+        return fnan, fnan
+    if mpc_zero in (a, b):
+        return fzero, fzero
+    if mpc_neg(a) == b:
+        return fzero, fzero
+    wp = prec+20
+    eps = mpf_shift(fone, -wp+10)
+    while 1:
+        a1 = mpc_shift(mpc_add(a, b, wp), -1)
+        b1 = mpc_sqrt(mpc_mul(a, b, wp), wp)
+        a, b = a1, b1
+        size = mpf_min_max([mpc_abs(a,10), mpc_abs(b,10)])[1]
+        err = mpc_abs(mpc_sub(a, b, 10), 10)
+        if size == fzero or mpf_lt(err, mpf_mul(eps, size)):
+            return a
+
+def mpc_agm1(a, prec, rnd=round_fast):
+    return mpc_agm(mpc_one, a, prec, rnd)
+
+def mpf_ellipk(x, prec, rnd=round_fast):
+    if not x[1]:
+        if x == fzero:
+            return mpf_shift(mpf_pi(prec, rnd), -1)
+        if x == fninf:
+            return fzero
+        if x == fnan:
+            return x
+    if x == fone:
+        return finf
+    # TODO: for |x| << 1/2, one could use fall back to
+    # pi/2 * hyp2f1_rat((1,2),(1,2),(1,1), x)
+    wp = prec + 15
+    # Use K(x) = pi/2/agm(1,a) where a = sqrt(1-x)
+    # The sqrt raises ComplexResult if x > 0
+    a = mpf_sqrt(mpf_sub(fone, x, wp), wp)
+    v = mpf_agm1(a, wp)
+    r = mpf_div(mpf_pi(wp), v, prec, rnd)
+    return mpf_shift(r, -1)
+
+def mpc_ellipk(z, prec, rnd=round_fast):
+    re, im = z
+    if im == fzero:
+        if re == finf:
+            return mpc_zero
+        if mpf_le(re, fone):
+            return mpf_ellipk(re, prec, rnd), fzero
+    wp = prec + 15
+    a = mpc_sqrt(mpc_sub(mpc_one, z, wp), wp)
+    v = mpc_agm1(a, wp)
+    r = mpc_mpf_div(mpf_pi(wp), v, prec, rnd)
+    return mpc_shift(r, -1)
+
+def mpf_ellipe(x, prec, rnd=round_fast):
+    # http://functions.wolfram.com/EllipticIntegrals/
+    # EllipticK/20/01/0001/
+    # E = (1-m)*(K'(m)*2*m + K(m))
+    sign, man, exp, bc = x
+    if not man:
+        if x == fzero:
+            return mpf_shift(mpf_pi(prec, rnd), -1)
+        if x == fninf:
+            return finf
+        if x == fnan:
+            return x
+        if x == finf:
+            raise ComplexResult
+    if x == fone:
+        return fone
+    wp = prec+20
+    mag = exp+bc
+    if mag < -wp:
+        return mpf_shift(mpf_pi(prec, rnd), -1)
+    # Compute a finite difference for K'
+    p = max(mag, 0) - wp
+    h = mpf_shift(fone, p)
+    K = mpf_ellipk(x, 2*wp)
+    Kh = mpf_ellipk(mpf_sub(x, h), 2*wp)
+    Kdiff = mpf_shift(mpf_sub(K, Kh), -p)
+    t = mpf_sub(fone, x)
+    b = mpf_mul(Kdiff, mpf_shift(x,1), wp)
+    return mpf_mul(t, mpf_add(K, b), prec, rnd)
+
+def mpc_ellipe(z, prec, rnd=round_fast):
+    re, im = z
+    if im == fzero:
+        if re == finf:
+            return (fzero, finf)
+        if mpf_le(re, fone):
+            return mpf_ellipe(re, prec, rnd), fzero
+    wp = prec + 15
+    mag = mpc_abs(z, 1)
+    p = max(mag[2]+mag[3], 0) - wp
+    h = mpf_shift(fone, p)
+    K = mpc_ellipk(z, 2*wp)
+    Kh = mpc_ellipk(mpc_add_mpf(z, h, 2*wp), 2*wp)
+    Kdiff = mpc_shift(mpc_sub(Kh, K, wp), -p)
+    t = mpc_sub(mpc_one, z, wp)
+    b = mpc_mul(Kdiff, mpc_shift(z,1), wp)
+    return mpc_mul(t, mpc_add(K, b, wp), prec, rnd)

venv/lib/python3.10/site-packages/mpmath/libmp/libintmath.py

@@ -0,0 +1,584 @@
+"""
+Utility functions for integer math.
+
+TODO: rename, cleanup, perhaps move the gmpy wrapper code
+here from settings.py
+
+"""
+
+import math
+from bisect import bisect
+
+from .backend import xrange
+from .backend import BACKEND, gmpy, sage, sage_utils, MPZ, MPZ_ONE, MPZ_ZERO
+
+small_trailing = [0] * 256
+for j in range(1,8):
+    small_trailing[1<<j::1<<(j+1)] = [j] * (1<<(7-j))
+
+def giant_steps(start, target, n=2):
+    """
+    Return a list of integers ~=
+
+    [start, n*start, ..., target/n^2, target/n, target]
+
+    but conservatively rounded so that the quotient between two
+    successive elements is actually slightly less than n.
+
+    With n = 2, this describes suitable precision steps for a
+    quadratically convergent algorithm such as Newton's method;
+    with n = 3 steps for cubic convergence (Halley's method), etc.
+
+        >>> giant_steps(50,1000)
+        [66, 128, 253, 502, 1000]
+        >>> giant_steps(50,1000,4)
+        [65, 252, 1000]
+
+    """
+    L = [target]
+    while L[-1] > start*n:
+        L = L + [L[-1]//n + 2]
+    return L[::-1]
+
+def rshift(x, n):
+    """For an integer x, calculate x >> n with the fastest (floor)
+    rounding. Unlike the plain Python expression (x >> n), n is
+    allowed to be negative, in which case a left shift is performed."""
+    if n >= 0: return x >> n
+    else:      return x << (-n)
+
+def lshift(x, n):
+    """For an integer x, calculate x << n. Unlike the plain Python
+    expression (x << n), n is allowed to be negative, in which case a
+    right shift with default (floor) rounding is performed."""
+    if n >= 0: return x << n
+    else:      return x >> (-n)
+
+if BACKEND == 'sage':
+    import operator
+    rshift = operator.rshift
+    lshift = operator.lshift
+
+def python_trailing(n):
+    """Count the number of trailing zero bits in abs(n)."""
+    if not n:
+        return 0
+    low_byte = n & 0xff
+    if low_byte:
+        return small_trailing[low_byte]
+    t = 8
+    n >>= 8
+    while not n & 0xff:
+        n >>= 8
+        t += 8
+    return t + small_trailing[n & 0xff]
+
+if BACKEND == 'gmpy':
+    if gmpy.version() >= '2':
+        def gmpy_trailing(n):
+            """Count the number of trailing zero bits in abs(n) using gmpy."""
+            if n: return MPZ(n).bit_scan1()
+            else: return 0
+    else:
+        def gmpy_trailing(n):
+            """Count the number of trailing zero bits in abs(n) using gmpy."""
+            if n: return MPZ(n).scan1()
+            else: return 0
+
+# Small powers of 2
+powers = [1<<_ for _ in range(300)]
+
+def python_bitcount(n):
+    """Calculate bit size of the nonnegative integer n."""
+    bc = bisect(powers, n)
+    if bc != 300:
+        return bc
+    bc = int(math.log(n, 2)) - 4
+    return bc + bctable[n>>bc]
+
+def gmpy_bitcount(n):
+    """Calculate bit size of the nonnegative integer n."""
+    if n: return MPZ(n).numdigits(2)
+    else: return 0
+
+#def sage_bitcount(n):
+#    if n: return MPZ(n).nbits()
+#    else: return 0
+
+def sage_trailing(n):
+    return MPZ(n).trailing_zero_bits()
+
+if BACKEND == 'gmpy':
+    bitcount = gmpy_bitcount
+    trailing = gmpy_trailing
+elif BACKEND == 'sage':
+    sage_bitcount = sage_utils.bitcount
+    bitcount = sage_bitcount
+    trailing = sage_trailing
+else:
+    bitcount = python_bitcount
+    trailing = python_trailing
+
+if BACKEND == 'gmpy' and 'bit_length' in dir(gmpy):
+    bitcount = gmpy.bit_length
+
+# Used to avoid slow function calls as far as possible
+trailtable = [trailing(n) for n in range(256)]
+bctable = [bitcount(n) for n in range(1024)]
+
+# TODO: speed up for bases 2, 4, 8, 16, ...
+
+def bin_to_radix(x, xbits, base, bdigits):
+    """Changes radix of a fixed-point number; i.e., converts
+    x * 2**xbits to floor(x * 10**bdigits)."""
+    return x * (MPZ(base)**bdigits) >> xbits
+
+stddigits = '0123456789abcdefghijklmnopqrstuvwxyz'
+
+def small_numeral(n, base=10, digits=stddigits):
+    """Return the string numeral of a positive integer in an arbitrary
+    base. Most efficient for small input."""
+    if base == 10:
+        return str(n)
+    digs = []
+    while n:
+        n, digit = divmod(n, base)
+        digs.append(digits[digit])
+    return "".join(digs[::-1])
+
+def numeral_python(n, base=10, size=0, digits=stddigits):
+    """Represent the integer n as a string of digits in the given base.
+    Recursive division is used to make this function about 3x faster
+    than Python's str() for converting integers to decimal strings.
+
+    The 'size' parameters specifies the number of digits in n; this
+    number is only used to determine splitting points and need not be
+    exact."""
+    if n <= 0:
+        if not n:
+            return "0"
+        return "-" + numeral(-n, base, size, digits)
+    # Fast enough to do directly
+    if size < 250:
+        return small_numeral(n, base, digits)
+    # Divide in half
+    half = (size // 2) + (size & 1)
+    A, B = divmod(n, base**half)
+    ad = numeral(A, base, half, digits)
+    bd = numeral(B, base, half, digits).rjust(half, "0")
+    return ad + bd
+
+def numeral_gmpy(n, base=10, size=0, digits=stddigits):
+    """Represent the integer n as a string of digits in the given base.
+    Recursive division is used to make this function about 3x faster
+    than Python's str() for converting integers to decimal strings.
+
+    The 'size' parameters specifies the number of digits in n; this
+    number is only used to determine splitting points and need not be
+    exact."""
+    if n < 0:
+        return "-" + numeral(-n, base, size, digits)
+    # gmpy.digits() may cause a segmentation fault when trying to convert
+    # extremely large values to a string. The size limit may need to be
+    # adjusted on some platforms, but 1500000 works on Windows and Linux.
+    if size < 1500000:
+        return gmpy.digits(n, base)
+    # Divide in half
+    half = (size // 2) + (size & 1)
+    A, B = divmod(n, MPZ(base)**half)
+    ad = numeral(A, base, half, digits)
+    bd = numeral(B, base, half, digits).rjust(half, "0")
+    return ad + bd
+
+if BACKEND == "gmpy":
+    numeral = numeral_gmpy
+else:
+    numeral = numeral_python
+
+_1_800 = 1<<800
+_1_600 = 1<<600
+_1_400 = 1<<400
+_1_200 = 1<<200
+_1_100 = 1<<100
+_1_50 = 1<<50
+
+def isqrt_small_python(x):
+    """
+    Correctly (floor) rounded integer square root, using
+    division. Fast up to ~200 digits.
+    """
+    if not x:
+        return x
+    if x < _1_800:
+        # Exact with IEEE double precision arithmetic
+        if x < _1_50:
+            return int(x**0.5)
+        # Initial estimate can be any integer >= the true root; round up
+        r = int(x**0.5 * 1.00000000000001) + 1
+    else:
+        bc = bitcount(x)
+        n = bc//2
+        r = int((x>>(2*n-100))**0.5+2)<<(n-50)  # +2 is to round up
+    # The following iteration now precisely computes floor(sqrt(x))
+    # See e.g. Crandall & Pomerance, "Prime Numbers: A Computational
+    # Perspective"
+    while 1:
+        y = (r+x//r)>>1
+        if y >= r:
+            return r
+        r = y
+
+def isqrt_fast_python(x):
+    """
+    Fast approximate integer square root, computed using division-free
+    Newton iteration for large x. For random integers the result is almost
+    always correct (floor(sqrt(x))), but is 1 ulp too small with a roughly
+    0.1% probability. If x is very close to an exact square, the answer is
+    1 ulp wrong with high probability.
+
+    With 0 guard bits, the largest error over a set of 10^5 random
+    inputs of size 1-10^5 bits was 3 ulp. The use of 10 guard bits
+    almost certainly guarantees a max 1 ulp error.
+    """
+    # Use direct division-based iteration if sqrt(x) < 2^400
+    # Assume floating-point square root accurate to within 1 ulp, then:
+    # 0 Newton iterations good to 52 bits
+    # 1 Newton iterations good to 104 bits
+    # 2 Newton iterations good to 208 bits
+    # 3 Newton iterations good to 416 bits
+    if x < _1_800:
+        y = int(x**0.5)
+        if x >= _1_100:
+            y = (y + x//y) >> 1
+            if x >= _1_200:
+                y = (y + x//y) >> 1
+                if x >= _1_400:
+                    y = (y + x//y) >> 1
+        return y
+    bc = bitcount(x)
+    guard_bits = 10
+    x <<= 2*guard_bits
+    bc += 2*guard_bits
+    bc += (bc&1)
+    hbc = bc//2
+    startprec = min(50, hbc)
+    # Newton iteration for 1/sqrt(x), with floating-point starting value
+    r = int(2.0**(2*startprec) * (x >> (bc-2*startprec)) ** -0.5)
+    pp = startprec
+    for p in giant_steps(startprec, hbc):
+        # r**2, scaled from real size 2**(-bc) to 2**p
+        r2 = (r*r) >> (2*pp - p)
+        # x*r**2, scaled from real size ~1.0 to 2**p
+        xr2 = ((x >> (bc-p)) * r2) >> p
+        # New value of r, scaled from real size 2**(-bc/2) to 2**p
+        r = (r * ((3<<p) - xr2)) >> (pp+1)
+        pp = p
+    # (1/sqrt(x))*x = sqrt(x)
+    return (r*(x>>hbc)) >> (p+guard_bits)
+
+def sqrtrem_python(x):
+    """Correctly rounded integer (floor) square root with remainder."""
+    # to check cutoff:
+    # plot(lambda x: timing(isqrt, 2**int(x)), [0,2000])
+    if x < _1_600:
+        y = isqrt_small_python(x)
+        return y, x - y*y
+    y = isqrt_fast_python(x) + 1
+    rem = x - y*y
+    # Correct remainder
+    while rem < 0:
+        y -= 1
+        rem += (1+2*y)
+    else:
+        if rem:
+            while rem > 2*(1+y):
+                y += 1
+                rem -= (1+2*y)
+    return y, rem
+
+def isqrt_python(x):
+    """Integer square root with correct (floor) rounding."""
+    return sqrtrem_python(x)[0]
+
+def sqrt_fixed(x, prec):
+    return isqrt_fast(x<<prec)
+
+sqrt_fixed2 = sqrt_fixed
+
+if BACKEND == 'gmpy':
+    if gmpy.version() >= '2':
+        isqrt_small = isqrt_fast = isqrt = gmpy.isqrt
+        sqrtrem = gmpy.isqrt_rem
+    else:
+        isqrt_small = isqrt_fast = isqrt = gmpy.sqrt
+        sqrtrem = gmpy.sqrtrem
+elif BACKEND == 'sage':
+    isqrt_small = isqrt_fast = isqrt = \
+        getattr(sage_utils, "isqrt", lambda n: MPZ(n).isqrt())
+    sqrtrem = lambda n: MPZ(n).sqrtrem()
+else:
+    isqrt_small = isqrt_small_python
+    isqrt_fast = isqrt_fast_python
+    isqrt = isqrt_python
+    sqrtrem = sqrtrem_python
+
+
+def ifib(n, _cache={}):
+    """Computes the nth Fibonacci number as an integer, for
+    integer n."""
+    if n < 0:
+        return (-1)**(-n+1) * ifib(-n)
+    if n in _cache:
+        return _cache[n]
+    m = n
+    # Use Dijkstra's logarithmic algorithm
+    # The following implementation is basically equivalent to
+    # http://en.literateprograms.org/Fibonacci_numbers_(Scheme)
+    a, b, p, q = MPZ_ONE, MPZ_ZERO, MPZ_ZERO, MPZ_ONE
+    while n:
+        if n & 1:
+            aq = a*q
+            a, b = b*q+aq+a*p, b*p+aq
+            n -= 1
+        else:
+            qq = q*q
+            p, q = p*p+qq, qq+2*p*q
+            n >>= 1
+    if m < 250:
+        _cache[m] = b
+    return b
+
+MAX_FACTORIAL_CACHE = 1000
+
+def ifac(n, memo={0:1, 1:1}):
+    """Return n factorial (for integers n >= 0 only)."""
+    f = memo.get(n)
+    if f:
+        return f
+    k = len(memo)
+    p = memo[k-1]
+    MAX = MAX_FACTORIAL_CACHE
+    while k <= n:
+        p *= k
+        if k <= MAX:
+            memo[k] = p
+        k += 1
+    return p
+
+def ifac2(n, memo_pair=[{0:1}, {1:1}]):
+    """Return n!! (double factorial), integers n >= 0 only."""
+    memo = memo_pair[n&1]
+    f = memo.get(n)
+    if f:
+        return f
+    k = max(memo)
+    p = memo[k]
+    MAX = MAX_FACTORIAL_CACHE
+    while k < n:
+        k += 2
+        p *= k
+        if k <= MAX:
+            memo[k] = p
+    return p
+
+if BACKEND == 'gmpy':
+    ifac = gmpy.fac
+elif BACKEND == 'sage':
+    ifac = lambda n: int(sage.factorial(n))
+    ifib = sage.fibonacci
+
+def list_primes(n):
+    n = n + 1
+    sieve = list(xrange(n))
+    sieve[:2] = [0, 0]
+    for i in xrange(2, int(n**0.5)+1):
+        if sieve[i]:
+            for j in xrange(i**2, n, i):
+                sieve[j] = 0
+    return [p for p in sieve if p]
+
+if BACKEND == 'sage':
+    # Note: it is *VERY* important for performance that we convert
+    # the list to Python ints.
+    def list_primes(n):
+        return [int(_) for _ in sage.primes(n+1)]
+
+small_odd_primes = (3,5,7,11,13,17,19,23,29,31,37,41,43,47)
+small_odd_primes_set = set(small_odd_primes)
+
+def isprime(n):
+    """
+    Determines whether n is a prime number. A probabilistic test is
+    performed if n is very large. No special trick is used for detecting
+    perfect powers.
+
+        >>> sum(list_primes(100000))
+        454396537
+        >>> sum(n*isprime(n) for n in range(100000))
+        454396537
+
+    """
+    n = int(n)
+    if not n & 1:
+        return n == 2
+    if n < 50:
+        return n in small_odd_primes_set
+    for p in small_odd_primes:
+        if not n % p:
+            return False
+    m = n-1
+    s = trailing(m)
+    d = m >> s
+    def test(a):
+        x = pow(a,d,n)
+        if x == 1 or x == m:
+            return True
+        for r in xrange(1,s):
+            x = x**2 % n
+            if x == m:
+                return True
+        return False
+    # See http://primes.utm.edu/prove/prove2_3.html
+    if n < 1373653:
+        witnesses = [2,3]
+    elif n < 341550071728321:
+        witnesses = [2,3,5,7,11,13,17]
+    else:
+        witnesses = small_odd_primes
+    for a in witnesses:
+        if not test(a):
+            return False
+    return True
+
+def moebius(n):
+    """
+    Evaluates the Moebius function which is `mu(n) = (-1)^k` if `n`
+    is a product of `k` distinct primes and `mu(n) = 0` otherwise.
+
+    TODO: speed up using factorization
+    """
+    n = abs(int(n))
+    if n < 2:
+        return n
+    factors = []
+    for p in xrange(2, n+1):
+        if not (n % p):
+            if not (n % p**2):
+                return 0
+            if not sum(p % f for f in factors):
+                factors.append(p)
+    return (-1)**len(factors)
+
+def gcd(*args):
+    a = 0
+    for b in args:
+        if a:
+            while b:
+                a, b = b, a % b
+        else:
+            a = b
+    return a
+
+
+#  Comment by Juan Arias de Reyna:
+#
+#  I learn this method to compute EulerE[2n] from van de Lune.
+#
+#  We apply the formula   EulerE[2n] = (-1)^n 2**(-2n) sum_{j=0}^n a(2n,2j+1)
+#
+#  where the numbers a(n,j) vanish for  j > n+1 or j <= -1  and satisfies
+#
+#  a(0,-1) = a(0,0) = 0;  a(0,1)= 1; a(0,2) = a(0,3) = 0
+#
+#  a(n,j) = a(n-1,j)                              when n+j is even
+#  a(n,j) = (j-1) a(n-1,j-1) + (j+1) a(n-1,j+1)   when n+j is odd
+#
+#
+#  But we can use only one array unidimensional a(j) since to compute
+#  a(n,j) we only need to know a(n-1,k) where k and j are of different parity
+#  and we have not to conserve the used values.
+#
+#  We cached up the values of Euler numbers to sufficiently high order.
+#
+#  Important Observation: If we pretend to use the numbers
+#     EulerE[1], EulerE[2], ... , EulerE[n]
+#     it is convenient to compute first EulerE[n], since the algorithm
+#     computes first all
+#     the previous ones, and keeps them in the CACHE
+
+MAX_EULER_CACHE = 500
+
+def eulernum(m, _cache={0:MPZ_ONE}):
+    r"""
+    Computes the Euler numbers `E(n)`, which can be defined as
+    coefficients of the Taylor expansion of `1/cosh x`:
+
+    .. math ::
+
+        \frac{1}{\cosh x} = \sum_{n=0}^\infty \frac{E_n}{n!} x^n
+
+    Example::
+
+        >>> [int(eulernum(n)) for n in range(11)]
+        [1, 0, -1, 0, 5, 0, -61, 0, 1385, 0, -50521]
+        >>> [int(eulernum(n)) for n in range(11)]   # test cache
+        [1, 0, -1, 0, 5, 0, -61, 0, 1385, 0, -50521]
+
+    """
+    # for odd m > 1, the Euler numbers are zero
+    if m & 1:
+        return MPZ_ZERO
+    f = _cache.get(m)
+    if f:
+        return f
+    MAX = MAX_EULER_CACHE
+    n = m
+    a = [MPZ(_) for _ in [0,0,1,0,0,0]]
+    for  n in range(1, m+1):
+        for j in range(n+1, -1, -2):
+            a[j+1] = (j-1)*a[j] + (j+1)*a[j+2]
+        a.append(0)
+        suma = 0
+        for k in range(n+1, -1, -2):
+            suma += a[k+1]
+            if n <= MAX:
+                _cache[n] = ((-1)**(n//2))*(suma // 2**n)
+        if n == m:
+            return ((-1)**(n//2))*suma // 2**n
+
+def stirling1(n, k):
+    """
+    Stirling number of the first kind.
+    """
+    if n < 0 or k < 0:
+        raise ValueError
+    if k >= n:
+        return MPZ(n == k)
+    if k < 1:
+        return MPZ_ZERO
+    L = [MPZ_ZERO] * (k+1)
+    L[1] = MPZ_ONE
+    for m in xrange(2, n+1):
+        for j in xrange(min(k, m), 0, -1):
+            L[j] = (m-1) * L[j] + L[j-1]
+    return (-1)**(n+k) * L[k]
+
+def stirling2(n, k):
+    """
+    Stirling number of the second kind.
+    """
+    if n < 0 or k < 0:
+        raise ValueError
+    if k >= n:
+        return MPZ(n == k)
+    if k <= 1:
+        return MPZ(k == 1)
+    s = MPZ_ZERO
+    t = MPZ_ONE
+    for j in xrange(k+1):
+        if (k + j) & 1:
+            s -= t * MPZ(j)**n
+        else:
+            s += t * MPZ(j)**n
+        t = t * (k - j) // (j + 1)
+    return s // ifac(k)

venv/lib/python3.10/site-packages/mpmath/libmp/libmpc.py

@@ -0,0 +1,835 @@
+"""
+Low-level functions for complex arithmetic.
+"""
+
+import sys
+
+from .backend import MPZ, MPZ_ZERO, MPZ_ONE, MPZ_TWO, BACKEND
+
+from .libmpf import (\
+    round_floor, round_ceiling, round_down, round_up,
+    round_nearest, round_fast, bitcount,
+    bctable, normalize, normalize1, reciprocal_rnd, rshift, lshift, giant_steps,
+    negative_rnd,
+    to_str, to_fixed, from_man_exp, from_float, to_float, from_int, to_int,
+    fzero, fone, ftwo, fhalf, finf, fninf, fnan, fnone,
+    mpf_abs, mpf_pos, mpf_neg, mpf_add, mpf_sub, mpf_mul,
+    mpf_div, mpf_mul_int, mpf_shift, mpf_sqrt, mpf_hypot,
+    mpf_rdiv_int, mpf_floor, mpf_ceil, mpf_nint, mpf_frac,
+    mpf_sign, mpf_hash,
+    ComplexResult
+)
+
+from .libelefun import (\
+    mpf_pi, mpf_exp, mpf_log, mpf_cos_sin, mpf_cosh_sinh, mpf_tan, mpf_pow_int,
+    mpf_log_hypot,
+    mpf_cos_sin_pi, mpf_phi,
+    mpf_cos, mpf_sin, mpf_cos_pi, mpf_sin_pi,
+    mpf_atan, mpf_atan2, mpf_cosh, mpf_sinh, mpf_tanh,
+    mpf_asin, mpf_acos, mpf_acosh, mpf_nthroot, mpf_fibonacci
+)
+
+# An mpc value is a (real, imag) tuple
+mpc_one = fone, fzero
+mpc_zero = fzero, fzero
+mpc_two = ftwo, fzero
+mpc_half = (fhalf, fzero)
+
+_infs = (finf, fninf)
+_infs_nan = (finf, fninf, fnan)
+
+def mpc_is_inf(z):
+    """Check if either real or imaginary part is infinite"""
+    re, im = z
+    if re in _infs: return True
+    if im in _infs: return True
+    return False
+
+def mpc_is_infnan(z):
+    """Check if either real or imaginary part is infinite or nan"""
+    re, im = z
+    if re in _infs_nan: return True
+    if im in _infs_nan: return True
+    return False
+
+def mpc_to_str(z, dps, **kwargs):
+    re, im = z
+    rs = to_str(re, dps)
+    if im[0]:
+        return rs + " - " + to_str(mpf_neg(im), dps, **kwargs) + "j"
+    else:
+        return rs + " + " + to_str(im, dps, **kwargs) + "j"
+
+def mpc_to_complex(z, strict=False, rnd=round_fast):
+    re, im = z
+    return complex(to_float(re, strict, rnd), to_float(im, strict, rnd))
+
+def mpc_hash(z):
+    if sys.version_info >= (3, 2):
+        re, im = z
+        h = mpf_hash(re) + sys.hash_info.imag * mpf_hash(im)
+        # Need to reduce either module 2^32 or 2^64
+        h = h % (2**sys.hash_info.width)
+        return int(h)
+    else:
+        try:
+            return hash(mpc_to_complex(z, strict=True))
+        except OverflowError:
+            return hash(z)
+
+def mpc_conjugate(z, prec, rnd=round_fast):
+    re, im = z
+    return re, mpf_neg(im, prec, rnd)
+
+def mpc_is_nonzero(z):
+    return z != mpc_zero
+
+def mpc_add(z, w, prec, rnd=round_fast):
+    a, b = z
+    c, d = w
+    return mpf_add(a, c, prec, rnd), mpf_add(b, d, prec, rnd)
+
+def mpc_add_mpf(z, x, prec, rnd=round_fast):
+    a, b = z
+    return mpf_add(a, x, prec, rnd), b
+
+def mpc_sub(z, w, prec=0, rnd=round_fast):
+    a, b = z
+    c, d = w
+    return mpf_sub(a, c, prec, rnd), mpf_sub(b, d, prec, rnd)
+
+def mpc_sub_mpf(z, p, prec=0, rnd=round_fast):
+    a, b = z
+    return mpf_sub(a, p, prec, rnd), b
+
+def mpc_pos(z, prec, rnd=round_fast):
+    a, b = z
+    return mpf_pos(a, prec, rnd), mpf_pos(b, prec, rnd)
+
+def mpc_neg(z, prec=None, rnd=round_fast):
+    a, b = z
+    return mpf_neg(a, prec, rnd), mpf_neg(b, prec, rnd)
+
+def mpc_shift(z, n):
+    a, b = z
+    return mpf_shift(a, n), mpf_shift(b, n)
+
+def mpc_abs(z, prec, rnd=round_fast):
+    """Absolute value of a complex number, |a+bi|.
+    Returns an mpf value."""
+    a, b = z
+    return mpf_hypot(a, b, prec, rnd)
+
+def mpc_arg(z, prec, rnd=round_fast):
+    """Argument of a complex number. Returns an mpf value."""
+    a, b = z
+    return mpf_atan2(b, a, prec, rnd)
+
+def mpc_floor(z, prec, rnd=round_fast):
+    a, b = z
+    return mpf_floor(a, prec, rnd), mpf_floor(b, prec, rnd)
+
+def mpc_ceil(z, prec, rnd=round_fast):
+    a, b = z
+    return mpf_ceil(a, prec, rnd), mpf_ceil(b, prec, rnd)
+
+def mpc_nint(z, prec, rnd=round_fast):
+    a, b = z
+    return mpf_nint(a, prec, rnd), mpf_nint(b, prec, rnd)
+
+def mpc_frac(z, prec, rnd=round_fast):
+    a, b = z
+    return mpf_frac(a, prec, rnd), mpf_frac(b, prec, rnd)
+
+
+def mpc_mul(z, w, prec, rnd=round_fast):
+    """
+    Complex multiplication.
+
+    Returns the real and imaginary part of (a+bi)*(c+di), rounded to
+    the specified precision. The rounding mode applies to the real and
+    imaginary parts separately.
+    """
+    a, b = z
+    c, d = w
+    p = mpf_mul(a, c)
+    q = mpf_mul(b, d)
+    r = mpf_mul(a, d)
+    s = mpf_mul(b, c)
+    re = mpf_sub(p, q, prec, rnd)
+    im = mpf_add(r, s, prec, rnd)
+    return re, im
+
+def mpc_square(z, prec, rnd=round_fast):
+    # (a+b*I)**2 == a**2 - b**2 + 2*I*a*b
+    a, b = z
+    p = mpf_mul(a,a)
+    q = mpf_mul(b,b)
+    r = mpf_mul(a,b, prec, rnd)
+    re = mpf_sub(p, q, prec, rnd)
+    im = mpf_shift(r, 1)
+    return re, im
+
+def mpc_mul_mpf(z, p, prec, rnd=round_fast):
+    a, b = z
+    re = mpf_mul(a, p, prec, rnd)
+    im = mpf_mul(b, p, prec, rnd)
+    return re, im
+
+def mpc_mul_imag_mpf(z, x, prec, rnd=round_fast):
+    """
+    Multiply the mpc value z by I*x where x is an mpf value.
+    """
+    a, b = z
+    re = mpf_neg(mpf_mul(b, x, prec, rnd))
+    im = mpf_mul(a, x, prec, rnd)
+    return re, im
+
+def mpc_mul_int(z, n, prec, rnd=round_fast):
+    a, b = z
+    re = mpf_mul_int(a, n, prec, rnd)
+    im = mpf_mul_int(b, n, prec, rnd)
+    return re, im
+
+def mpc_div(z, w, prec, rnd=round_fast):
+    a, b = z
+    c, d = w
+    wp = prec + 10
+    # mag = c*c + d*d
+    mag = mpf_add(mpf_mul(c, c), mpf_mul(d, d), wp)
+    # (a*c+b*d)/mag, (b*c-a*d)/mag
+    t = mpf_add(mpf_mul(a,c), mpf_mul(b,d), wp)
+    u = mpf_sub(mpf_mul(b,c), mpf_mul(a,d), wp)
+    return mpf_div(t,mag,prec,rnd), mpf_div(u,mag,prec,rnd)
+
+def mpc_div_mpf(z, p, prec, rnd=round_fast):
+    """Calculate z/p where p is real"""
+    a, b = z
+    re = mpf_div(a, p, prec, rnd)
+    im = mpf_div(b, p, prec, rnd)
+    return re, im
+
+def mpc_reciprocal(z, prec, rnd=round_fast):
+    """Calculate 1/z efficiently"""
+    a, b = z
+    m = mpf_add(mpf_mul(a,a),mpf_mul(b,b),prec+10)
+    re = mpf_div(a, m, prec, rnd)
+    im = mpf_neg(mpf_div(b, m, prec, rnd))
+    return re, im
+
+def mpc_mpf_div(p, z, prec, rnd=round_fast):
+    """Calculate p/z where p is real efficiently"""
+    a, b = z
+    m = mpf_add(mpf_mul(a,a),mpf_mul(b,b), prec+10)
+    re = mpf_div(mpf_mul(a,p), m, prec, rnd)
+    im = mpf_div(mpf_neg(mpf_mul(b,p)), m, prec, rnd)
+    return re, im
+
+def complex_int_pow(a, b, n):
+    """Complex integer power: computes (a+b*I)**n exactly for
+    nonnegative n (a and b must be Python ints)."""
+    wre = 1
+    wim = 0
+    while n:
+        if n & 1:
+            wre, wim = wre*a - wim*b, wim*a + wre*b
+            n -= 1
+        a, b = a*a - b*b, 2*a*b
+        n //= 2
+    return wre, wim
+
+def mpc_pow(z, w, prec, rnd=round_fast):
+    if w[1] == fzero:
+        return mpc_pow_mpf(z, w[0], prec, rnd)
+    return mpc_exp(mpc_mul(mpc_log(z, prec+10), w, prec+10), prec, rnd)
+
+def mpc_pow_mpf(z, p, prec, rnd=round_fast):
+    psign, pman, pexp, pbc = p
+    if pexp >= 0:
+        return mpc_pow_int(z, (-1)**psign * (pman<<pexp), prec, rnd)
+    if pexp == -1:
+        sqrtz = mpc_sqrt(z, prec+10)
+        return mpc_pow_int(sqrtz, (-1)**psign * pman, prec, rnd)
+    return mpc_exp(mpc_mul_mpf(mpc_log(z, prec+10), p, prec+10), prec, rnd)
+
+def mpc_pow_int(z, n, prec, rnd=round_fast):
+    a, b = z
+    if b == fzero:
+        return mpf_pow_int(a, n, prec, rnd), fzero
+    if a == fzero:
+        v = mpf_pow_int(b, n, prec, rnd)
+        n %= 4
+        if n == 0:
+            return v, fzero
+        elif n == 1:
+            return fzero, v
+        elif n == 2:
+            return mpf_neg(v), fzero
+        elif n == 3:
+            return fzero, mpf_neg(v)
+    if n == 0: return mpc_one
+    if n == 1: return mpc_pos(z, prec, rnd)
+    if n == 2: return mpc_square(z, prec, rnd)
+    if n == -1: return mpc_reciprocal(z, prec, rnd)
+    if n < 0: return mpc_reciprocal(mpc_pow_int(z, -n, prec+4), prec, rnd)
+    asign, aman, aexp, abc = a
+    bsign, bman, bexp, bbc = b
+    if asign: aman = -aman
+    if bsign: bman = -bman
+    de = aexp - bexp
+    abs_de = abs(de)
+    exact_size = n*(abs_de + max(abc, bbc))
+    if exact_size < 10000:
+        if de > 0:
+            aman <<= de
+            aexp = bexp
+        else:
+            bman <<= (-de)
+            bexp = aexp
+        re, im = complex_int_pow(aman, bman, n)
+        re = from_man_exp(re, int(n*aexp), prec, rnd)
+        im = from_man_exp(im, int(n*bexp), prec, rnd)
+        return re, im
+    return mpc_exp(mpc_mul_int(mpc_log(z, prec+10), n, prec+10), prec, rnd)
+
+def mpc_sqrt(z, prec, rnd=round_fast):
+    """Complex square root (principal branch).
+
+    We have sqrt(a+bi) = sqrt((r+a)/2) + b/sqrt(2*(r+a))*i where
+    r = abs(a+bi), when a+bi is not a negative real number."""
+    a, b = z
+    if b == fzero:
+        if a == fzero:
+            return (a, b)
+        # When a+bi is a negative real number, we get a real sqrt times i
+        if a[0]:
+            im = mpf_sqrt(mpf_neg(a), prec, rnd)
+            return (fzero, im)
+        else:
+            re = mpf_sqrt(a, prec, rnd)
+            return (re, fzero)
+    wp = prec+20
+    if not a[0]:                               # case a positive
+        t  = mpf_add(mpc_abs((a, b), wp), a, wp)  # t = abs(a+bi) + a
+        u = mpf_shift(t, -1)                      # u = t/2
+        re = mpf_sqrt(u, prec, rnd)               # re = sqrt(u)
+        v = mpf_shift(t, 1)                       # v = 2*t
+        w  = mpf_sqrt(v, wp)                      # w = sqrt(v)
+        im = mpf_div(b, w, prec, rnd)             # im = b / w
+    else:                                      # case a negative
+        t = mpf_sub(mpc_abs((a, b), wp), a, wp)   # t = abs(a+bi) - a
+        u = mpf_shift(t, -1)                      # u = t/2
+        im = mpf_sqrt(u, prec, rnd)               # im = sqrt(u)
+        v = mpf_shift(t, 1)                       # v = 2*t
+        w  = mpf_sqrt(v, wp)                      # w = sqrt(v)
+        re = mpf_div(b, w, prec, rnd)             # re = b/w
+        if b[0]:
+            re = mpf_neg(re)
+            im = mpf_neg(im)
+    return re, im
+
+def mpc_nthroot_fixed(a, b, n, prec):
+    # a, b signed integers at fixed precision prec
+    start = 50
+    a1 = int(rshift(a, prec - n*start))
+    b1 = int(rshift(b, prec - n*start))
+    try:
+        r = (a1 + 1j * b1)**(1.0/n)
+        re = r.real
+        im = r.imag
+        re = MPZ(int(re))
+        im = MPZ(int(im))
+    except OverflowError:
+        a1 = from_int(a1, start)
+        b1 = from_int(b1, start)
+        fn = from_int(n)
+        nth = mpf_rdiv_int(1, fn, start)
+        re, im = mpc_pow((a1, b1), (nth, fzero), start)
+        re = to_int(re)
+        im = to_int(im)
+    extra = 10
+    prevp = start
+    extra1 = n
+    for p in giant_steps(start, prec+extra):
+        # this is slow for large n, unlike int_pow_fixed
+        re2, im2 = complex_int_pow(re, im, n-1)
+        re2 = rshift(re2, (n-1)*prevp - p - extra1)
+        im2 = rshift(im2, (n-1)*prevp - p - extra1)
+        r4 = (re2*re2 + im2*im2) >> (p + extra1)
+        ap = rshift(a, prec - p)
+        bp = rshift(b, prec - p)
+        rec = (ap * re2 + bp * im2) >> p
+        imc = (-ap * im2 + bp * re2) >> p
+        reb = (rec << p) // r4
+        imb = (imc << p) // r4
+        re = (reb + (n-1)*lshift(re, p-prevp))//n
+        im = (imb + (n-1)*lshift(im, p-prevp))//n
+        prevp = p
+    return re, im
+
+def mpc_nthroot(z, n, prec, rnd=round_fast):
+    """
+    Complex n-th root.
+
+    Use Newton method as in the real case when it is faster,
+    otherwise use z**(1/n)
+    """
+    a, b = z
+    if a[0] == 0 and b == fzero:
+        re = mpf_nthroot(a, n, prec, rnd)
+        return (re, fzero)
+    if n < 2:
+        if n == 0:
+            return mpc_one
+        if n == 1:
+            return mpc_pos((a, b), prec, rnd)
+        if n == -1:
+            return mpc_div(mpc_one, (a, b), prec, rnd)
+        inverse = mpc_nthroot((a, b), -n, prec+5, reciprocal_rnd[rnd])
+        return mpc_div(mpc_one, inverse, prec, rnd)
+    if n <= 20:
+        prec2 = int(1.2 * (prec + 10))
+        asign, aman, aexp, abc = a
+        bsign, bman, bexp, bbc = b
+        pf = mpc_abs((a,b), prec)
+        if pf[-2] + pf[-1] > -10  and pf[-2] + pf[-1] < prec:
+            af = to_fixed(a, prec2)
+            bf = to_fixed(b, prec2)
+            re, im = mpc_nthroot_fixed(af, bf, n, prec2)
+            extra = 10
+            re = from_man_exp(re, -prec2-extra, prec2, rnd)
+            im = from_man_exp(im, -prec2-extra, prec2, rnd)
+            return re, im
+    fn = from_int(n)
+    prec2 = prec+10 + 10
+    nth = mpf_rdiv_int(1, fn, prec2)
+    re, im = mpc_pow((a, b), (nth, fzero), prec2, rnd)
+    re = normalize(re[0], re[1], re[2], re[3], prec, rnd)
+    im = normalize(im[0], im[1], im[2], im[3], prec, rnd)
+    return re, im
+
+def mpc_cbrt(z, prec, rnd=round_fast):
+    """
+    Complex cubic root.
+    """
+    return mpc_nthroot(z, 3, prec, rnd)
+
+def mpc_exp(z, prec, rnd=round_fast):
+    """
+    Complex exponential function.
+
+    We use the direct formula exp(a+bi) = exp(a) * (cos(b) + sin(b)*i)
+    for the computation. This formula is very nice because it is
+    pefectly stable; since we just do real multiplications, the only
+    numerical errors that can creep in are single-ulp rounding errors.
+
+    The formula is efficient since mpmath's real exp is quite fast and
+    since we can compute cos and sin simultaneously.
+
+    It is no problem if a and b are large; if the implementations of
+    exp/cos/sin are accurate and efficient for all real numbers, then
+    so is this function for all complex numbers.
+    """
+    a, b = z
+    if a == fzero:
+        return mpf_cos_sin(b, prec, rnd)
+    if b == fzero:
+        return mpf_exp(a, prec, rnd), fzero
+    mag = mpf_exp(a, prec+4, rnd)
+    c, s = mpf_cos_sin(b, prec+4, rnd)
+    re = mpf_mul(mag, c, prec, rnd)
+    im = mpf_mul(mag, s, prec, rnd)
+    return re, im
+
+def mpc_log(z, prec, rnd=round_fast):
+    re = mpf_log_hypot(z[0], z[1], prec, rnd)
+    im = mpc_arg(z, prec, rnd)
+    return re, im
+
+def mpc_cos(z, prec, rnd=round_fast):
+    """Complex cosine. The formula used is cos(a+bi) = cos(a)*cosh(b) -
+    sin(a)*sinh(b)*i.
+
+    The same comments apply as for the complex exp: only real
+    multiplications are pewrormed, so no cancellation errors are
+    possible. The formula is also efficient since we can compute both
+    pairs (cos, sin) and (cosh, sinh) in single stwps."""
+    a, b = z
+    if b == fzero:
+        return mpf_cos(a, prec, rnd), fzero
+    if a == fzero:
+        return mpf_cosh(b, prec, rnd), fzero
+    wp = prec + 6
+    c, s = mpf_cos_sin(a, wp)
+    ch, sh = mpf_cosh_sinh(b, wp)
+    re = mpf_mul(c, ch, prec, rnd)
+    im = mpf_mul(s, sh, prec, rnd)
+    return re, mpf_neg(im)
+
+def mpc_sin(z, prec, rnd=round_fast):
+    """Complex sine. We have sin(a+bi) = sin(a)*cosh(b) +
+    cos(a)*sinh(b)*i. See the docstring for mpc_cos for additional
+    comments."""
+    a, b = z
+    if b == fzero:
+        return mpf_sin(a, prec, rnd), fzero
+    if a == fzero:
+        return fzero, mpf_sinh(b, prec, rnd)
+    wp = prec + 6
+    c, s = mpf_cos_sin(a, wp)
+    ch, sh = mpf_cosh_sinh(b, wp)
+    re = mpf_mul(s, ch, prec, rnd)
+    im = mpf_mul(c, sh, prec, rnd)
+    return re, im
+
+def mpc_tan(z, prec, rnd=round_fast):
+    """Complex tangent. Computed as tan(a+bi) = sin(2a)/M + sinh(2b)/M*i
+    where M = cos(2a) + cosh(2b)."""
+    a, b = z
+    asign, aman, aexp, abc = a
+    bsign, bman, bexp, bbc = b
+    if b == fzero: return mpf_tan(a, prec, rnd), fzero
+    if a == fzero: return fzero, mpf_tanh(b, prec, rnd)
+    wp = prec + 15
+    a = mpf_shift(a, 1)
+    b = mpf_shift(b, 1)
+    c, s = mpf_cos_sin(a, wp)
+    ch, sh = mpf_cosh_sinh(b, wp)
+    # TODO: handle cancellation when c ~=  -1 and ch ~= 1
+    mag = mpf_add(c, ch, wp)
+    re = mpf_div(s, mag, prec, rnd)
+    im = mpf_div(sh, mag, prec, rnd)
+    return re, im
+
+def mpc_cos_pi(z, prec, rnd=round_fast):
+    a, b = z
+    if b == fzero:
+        return mpf_cos_pi(a, prec, rnd), fzero
+    b = mpf_mul(b, mpf_pi(prec+5), prec+5)
+    if a == fzero:
+        return mpf_cosh(b, prec, rnd), fzero
+    wp = prec + 6
+    c, s = mpf_cos_sin_pi(a, wp)
+    ch, sh = mpf_cosh_sinh(b, wp)
+    re = mpf_mul(c, ch, prec, rnd)
+    im = mpf_mul(s, sh, prec, rnd)
+    return re, mpf_neg(im)
+
+def mpc_sin_pi(z, prec, rnd=round_fast):
+    a, b = z
+    if b == fzero:
+        return mpf_sin_pi(a, prec, rnd), fzero
+    b = mpf_mul(b, mpf_pi(prec+5), prec+5)
+    if a == fzero:
+        return fzero, mpf_sinh(b, prec, rnd)
+    wp = prec + 6
+    c, s = mpf_cos_sin_pi(a, wp)
+    ch, sh = mpf_cosh_sinh(b, wp)
+    re = mpf_mul(s, ch, prec, rnd)
+    im = mpf_mul(c, sh, prec, rnd)
+    return re, im
+
+def mpc_cos_sin(z, prec, rnd=round_fast):
+    a, b = z
+    if a == fzero:
+        ch, sh = mpf_cosh_sinh(b, prec, rnd)
+        return (ch, fzero), (fzero, sh)
+    if b == fzero:
+        c, s = mpf_cos_sin(a, prec, rnd)
+        return (c, fzero), (s, fzero)
+    wp = prec + 6
+    c, s = mpf_cos_sin(a, wp)
+    ch, sh = mpf_cosh_sinh(b, wp)
+    cre = mpf_mul(c, ch, prec, rnd)
+    cim = mpf_mul(s, sh, prec, rnd)
+    sre = mpf_mul(s, ch, prec, rnd)
+    sim = mpf_mul(c, sh, prec, rnd)
+    return (cre, mpf_neg(cim)), (sre, sim)
+
+def mpc_cos_sin_pi(z, prec, rnd=round_fast):
+    a, b = z
+    if b == fzero:
+        c, s = mpf_cos_sin_pi(a, prec, rnd)
+        return (c, fzero), (s, fzero)
+    b = mpf_mul(b, mpf_pi(prec+5), prec+5)
+    if a == fzero:
+        ch, sh = mpf_cosh_sinh(b, prec, rnd)
+        return (ch, fzero), (fzero, sh)
+    wp = prec + 6
+    c, s = mpf_cos_sin_pi(a, wp)
+    ch, sh = mpf_cosh_sinh(b, wp)
+    cre = mpf_mul(c, ch, prec, rnd)
+    cim = mpf_mul(s, sh, prec, rnd)
+    sre = mpf_mul(s, ch, prec, rnd)
+    sim = mpf_mul(c, sh, prec, rnd)
+    return (cre, mpf_neg(cim)), (sre, sim)
+
+def mpc_cosh(z, prec, rnd=round_fast):
+    """Complex hyperbolic cosine. Computed as cosh(z) = cos(z*i)."""
+    a, b = z
+    return mpc_cos((b, mpf_neg(a)), prec, rnd)
+
+def mpc_sinh(z, prec, rnd=round_fast):
+    """Complex hyperbolic sine. Computed as sinh(z) = -i*sin(z*i)."""
+    a, b = z
+    b, a = mpc_sin((b, a), prec, rnd)
+    return a, b
+
+def mpc_tanh(z, prec, rnd=round_fast):
+    """Complex hyperbolic tangent. Computed as tanh(z) = -i*tan(z*i)."""
+    a, b = z
+    b, a = mpc_tan((b, a), prec, rnd)
+    return a, b
+
+# TODO: avoid loss of accuracy
+def mpc_atan(z, prec, rnd=round_fast):
+    a, b = z
+    # atan(z) = (I/2)*(log(1-I*z) - log(1+I*z))
+    # x = 1-I*z = 1 + b - I*a
+    # y = 1+I*z = 1 - b + I*a
+    wp = prec + 15
+    x = mpf_add(fone, b, wp), mpf_neg(a)
+    y = mpf_sub(fone, b, wp), a
+    l1 = mpc_log(x, wp)
+    l2 = mpc_log(y, wp)
+    a, b = mpc_sub(l1, l2, prec, rnd)
+    # (I/2) * (a+b*I) = (-b/2 + a/2*I)
+    v = mpf_neg(mpf_shift(b,-1)), mpf_shift(a,-1)
+    # Subtraction at infinity gives correct real part but
+    # wrong imaginary part (should be zero)
+    if v[1] == fnan and mpc_is_inf(z):
+        v = (v[0], fzero)
+    return v
+
+beta_crossover = from_float(0.6417)
+alpha_crossover = from_float(1.5)
+
+def acos_asin(z, prec, rnd, n):
+    """ complex acos for n = 0, asin for n = 1
+    The algorithm is described in
+    T.E. Hull, T.F. Fairgrieve and P.T.P. Tang
+    'Implementing the Complex Arcsine and Arcosine Functions
+    using Exception Handling',
+    ACM Trans. on Math. Software Vol. 23 (1997), p299
+    The complex acos and asin can be defined as
+    acos(z) = acos(beta) - I*sign(a)* log(alpha + sqrt(alpha**2 -1))
+    asin(z) = asin(beta) + I*sign(a)* log(alpha + sqrt(alpha**2 -1))
+    where z = a + I*b
+    alpha = (1/2)*(r + s); beta = (1/2)*(r - s) = a/alpha
+    r = sqrt((a+1)**2 + y**2); s = sqrt((a-1)**2 + y**2)
+    These expressions are rewritten in different ways in different
+    regions, delimited by two crossovers alpha_crossover and beta_crossover,
+    and by abs(a) <= 1, in order to improve the numerical accuracy.
+    """
+    a, b = z
+    wp = prec + 10
+    # special cases with real argument
+    if b == fzero:
+        am = mpf_sub(fone, mpf_abs(a), wp)
+        # case abs(a) <= 1
+        if not am[0]:
+            if n == 0:
+                return mpf_acos(a, prec, rnd), fzero
+            else:
+                return mpf_asin(a, prec, rnd), fzero
+        # cases abs(a) > 1
+        else:
+            # case a < -1
+            if a[0]:
+                pi = mpf_pi(prec, rnd)
+                c = mpf_acosh(mpf_neg(a), prec, rnd)
+                if n == 0:
+                    return pi, mpf_neg(c)
+                else:
+                    return mpf_neg(mpf_shift(pi, -1)), c
+            # case a > 1
+            else:
+                c = mpf_acosh(a, prec, rnd)
+                if n == 0:
+                    return fzero, c
+                else:
+                    pi = mpf_pi(prec, rnd)
+                    return mpf_shift(pi, -1), mpf_neg(c)
+    asign = bsign = 0
+    if a[0]:
+        a = mpf_neg(a)
+        asign = 1
+    if b[0]:
+        b = mpf_neg(b)
+        bsign = 1
+    am = mpf_sub(fone, a, wp)
+    ap = mpf_add(fone, a, wp)
+    r = mpf_hypot(ap, b, wp)
+    s = mpf_hypot(am, b, wp)
+    alpha = mpf_shift(mpf_add(r, s, wp), -1)
+    beta = mpf_div(a, alpha, wp)
+    b2 = mpf_mul(b,b, wp)
+    # case beta <= beta_crossover
+    if not mpf_sub(beta_crossover, beta, wp)[0]:
+        if n == 0:
+            re = mpf_acos(beta, wp)
+        else:
+            re = mpf_asin(beta, wp)
+    else:
+        # to compute the real part in this region use the identity
+        # asin(beta) = atan(beta/sqrt(1-beta**2))
+        # beta/sqrt(1-beta**2) = (alpha + a) * (alpha - a)
+        # alpha + a is numerically accurate; alpha - a can have
+        # cancellations leading to numerical inaccuracies, so rewrite
+        # it in differente ways according to the region
+        Ax = mpf_add(alpha, a, wp)
+        # case a <= 1
+        if not am[0]:
+            # c = b*b/(r + (a+1)); d = (s + (1-a))
+            # alpha - a = (1/2)*(c + d)
+            # case n=0: re = atan(sqrt((1/2) * Ax * (c + d))/a)
+            # case n=1: re = atan(a/sqrt((1/2) * Ax * (c + d)))
+            c = mpf_div(b2, mpf_add(r, ap, wp), wp)
+            d = mpf_add(s, am, wp)
+            re = mpf_shift(mpf_mul(Ax, mpf_add(c, d, wp), wp), -1)
+            if n == 0:
+                re = mpf_atan(mpf_div(mpf_sqrt(re, wp), a, wp), wp)
+            else:
+                re = mpf_atan(mpf_div(a, mpf_sqrt(re, wp), wp), wp)
+        else:
+            # c = Ax/(r + (a+1)); d = Ax/(s - (1-a))
+            # alpha - a = (1/2)*(c + d)
+            # case n = 0: re = atan(b*sqrt(c + d)/2/a)
+            # case n = 1: re = atan(a/(b*sqrt(c + d)/2)
+            c = mpf_div(Ax, mpf_add(r, ap, wp), wp)
+            d = mpf_div(Ax, mpf_sub(s, am, wp), wp)
+            re = mpf_shift(mpf_add(c, d, wp), -1)
+            re = mpf_mul(b, mpf_sqrt(re, wp), wp)
+            if n == 0:
+                re = mpf_atan(mpf_div(re, a, wp), wp)
+            else:
+                re = mpf_atan(mpf_div(a, re, wp), wp)
+    # to compute alpha + sqrt(alpha**2 - 1), if alpha <= alpha_crossover
+    # replace it with 1 + Am1 + sqrt(Am1*(alpha+1)))
+    # where Am1 = alpha -1
+    # if alpha <= alpha_crossover:
+    if not mpf_sub(alpha_crossover, alpha, wp)[0]:
+        c1 = mpf_div(b2, mpf_add(r, ap, wp), wp)
+        # case a < 1
+        if mpf_neg(am)[0]:
+            # Am1 = (1/2) * (b*b/(r + (a+1)) + b*b/(s + (1-a))
+            c2 = mpf_add(s, am, wp)
+            c2 = mpf_div(b2, c2, wp)
+            Am1 = mpf_shift(mpf_add(c1, c2, wp), -1)
+        else:
+            # Am1 = (1/2) * (b*b/(r + (a+1)) + (s - (1-a)))
+            c2 = mpf_sub(s, am, wp)
+            Am1 = mpf_shift(mpf_add(c1, c2, wp), -1)
+        # im = log(1 + Am1 + sqrt(Am1*(alpha+1)))
+        im = mpf_mul(Am1, mpf_add(alpha, fone, wp), wp)
+        im = mpf_log(mpf_add(fone, mpf_add(Am1, mpf_sqrt(im, wp), wp), wp), wp)
+    else:
+        # im = log(alpha + sqrt(alpha*alpha - 1))
+        im = mpf_sqrt(mpf_sub(mpf_mul(alpha, alpha, wp), fone, wp), wp)
+        im = mpf_log(mpf_add(alpha, im, wp), wp)
+    if asign:
+        if n == 0:
+            re = mpf_sub(mpf_pi(wp), re, wp)
+        else:
+            re = mpf_neg(re)
+    if not bsign and n == 0:
+        im = mpf_neg(im)
+    if bsign and n == 1:
+        im = mpf_neg(im)
+    re = normalize(re[0], re[1], re[2], re[3], prec, rnd)
+    im = normalize(im[0], im[1], im[2], im[3], prec, rnd)
+    return re, im
+
+def mpc_acos(z, prec, rnd=round_fast):
+    return acos_asin(z, prec, rnd, 0)
+
+def mpc_asin(z, prec, rnd=round_fast):
+    return acos_asin(z, prec, rnd, 1)
+
+def mpc_asinh(z, prec, rnd=round_fast):
+    # asinh(z) = I * asin(-I z)
+    a, b = z
+    a, b =  mpc_asin((b, mpf_neg(a)), prec, rnd)
+    return mpf_neg(b), a
+
+def mpc_acosh(z, prec, rnd=round_fast):
+    # acosh(z) = -I * acos(z)   for Im(acos(z)) <= 0
+    #            +I * acos(z)   otherwise
+    a, b = mpc_acos(z, prec, rnd)
+    if b[0] or b == fzero:
+        return mpf_neg(b), a
+    else:
+        return b, mpf_neg(a)
+
+def mpc_atanh(z, prec, rnd=round_fast):
+    # atanh(z) = (log(1+z)-log(1-z))/2
+    wp = prec + 15
+    a = mpc_add(z, mpc_one, wp)
+    b = mpc_sub(mpc_one, z, wp)
+    a = mpc_log(a, wp)
+    b = mpc_log(b, wp)
+    v = mpc_shift(mpc_sub(a, b, wp), -1)
+    # Subtraction at infinity gives correct imaginary part but
+    # wrong real part (should be zero)
+    if v[0] == fnan and mpc_is_inf(z):
+        v = (fzero, v[1])
+    return v
+
+def mpc_fibonacci(z, prec, rnd=round_fast):
+    re, im = z
+    if im == fzero:
+        return (mpf_fibonacci(re, prec, rnd), fzero)
+    size = max(abs(re[2]+re[3]), abs(re[2]+re[3]))
+    wp = prec + size + 20
+    a = mpf_phi(wp)
+    b = mpf_add(mpf_shift(a, 1), fnone, wp)
+    u = mpc_pow((a, fzero), z, wp)
+    v = mpc_cos_pi(z, wp)
+    v = mpc_div(v, u, wp)
+    u = mpc_sub(u, v, wp)
+    u = mpc_div_mpf(u, b, prec, rnd)
+    return u
+
+def mpf_expj(x, prec, rnd='f'):
+    raise ComplexResult
+
+def mpc_expj(z, prec, rnd='f'):
+    re, im = z
+    if im == fzero:
+        return mpf_cos_sin(re, prec, rnd)
+    if re == fzero:
+        return mpf_exp(mpf_neg(im), prec, rnd), fzero
+    ey = mpf_exp(mpf_neg(im), prec+10)
+    c, s = mpf_cos_sin(re, prec+10)
+    re = mpf_mul(ey, c, prec, rnd)
+    im = mpf_mul(ey, s, prec, rnd)
+    return re, im
+
+def mpf_expjpi(x, prec, rnd='f'):
+    raise ComplexResult
+
+def mpc_expjpi(z, prec, rnd='f'):
+    re, im = z
+    if im == fzero:
+        return mpf_cos_sin_pi(re, prec, rnd)
+    sign, man, exp, bc = im
+    wp = prec+10
+    if man:
+        wp += max(0, exp+bc)
+    im = mpf_neg(mpf_mul(mpf_pi(wp), im, wp))
+    if re == fzero:
+        return mpf_exp(im, prec, rnd), fzero
+    ey = mpf_exp(im, prec+10)
+    c, s = mpf_cos_sin_pi(re, prec+10)
+    re = mpf_mul(ey, c, prec, rnd)
+    im = mpf_mul(ey, s, prec, rnd)
+    return re, im
+
+
+if BACKEND == 'sage':
+    try:
+        import sage.libs.mpmath.ext_libmp as _lbmp
+        mpc_exp = _lbmp.mpc_exp
+        mpc_sqrt = _lbmp.mpc_sqrt
+    except (ImportError, AttributeError):
+        print("Warning: Sage imports in libmpc failed")

venv/lib/python3.10/site-packages/mpmath/libmp/libmpf.py

@@ -0,0 +1,1414 @@
+"""
+Low-level functions for arbitrary-precision floating-point arithmetic.
+"""
+
+__docformat__ = 'plaintext'
+
+import math
+
+from bisect import bisect
+
+import sys
+
+# Importing random is slow
+#from random import getrandbits
+getrandbits = None
+
+from .backend import (MPZ, MPZ_TYPE, MPZ_ZERO, MPZ_ONE, MPZ_TWO, MPZ_FIVE,
+    BACKEND, STRICT, HASH_MODULUS, HASH_BITS, gmpy, sage, sage_utils)
+
+from .libintmath import (giant_steps,
+    trailtable, bctable, lshift, rshift, bitcount, trailing,
+    sqrt_fixed, numeral, isqrt, isqrt_fast, sqrtrem,
+    bin_to_radix)
+
+# We don't pickle tuples directly for the following reasons:
+#   1: pickle uses str() for ints, which is inefficient when they are large
+#   2: pickle doesn't work for gmpy mpzs
+# Both problems are solved by using hex()
+
+if BACKEND == 'sage':
+    def to_pickable(x):
+        sign, man, exp, bc = x
+        return sign, hex(man), exp, bc
+else:
+    def to_pickable(x):
+        sign, man, exp, bc = x
+        return sign, hex(man)[2:], exp, bc
+
+def from_pickable(x):
+    sign, man, exp, bc = x
+    return (sign, MPZ(man, 16), exp, bc)
+
+class ComplexResult(ValueError):
+    pass
+
+try:
+    intern
+except NameError:
+    intern = lambda x: x
+
+# All supported rounding modes
+round_nearest = intern('n')
+round_floor = intern('f')
+round_ceiling = intern('c')
+round_up = intern('u')
+round_down = intern('d')
+round_fast = round_down
+
+def prec_to_dps(n):
+    """Return number of accurate decimals that can be represented
+    with a precision of n bits."""
+    return max(1, int(round(int(n)/3.3219280948873626)-1))
+
+def dps_to_prec(n):
+    """Return the number of bits required to represent n decimals
+    accurately."""
+    return max(1, int(round((int(n)+1)*3.3219280948873626)))
+
+def repr_dps(n):
+    """Return the number of decimal digits required to represent
+    a number with n-bit precision so that it can be uniquely
+    reconstructed from the representation."""
+    dps = prec_to_dps(n)
+    if dps == 15:
+        return 17
+    return dps + 3
+
+#----------------------------------------------------------------------------#
+#                    Some commonly needed float values                       #
+#----------------------------------------------------------------------------#
+
+# Regular number format:
+# (-1)**sign * mantissa * 2**exponent, plus bitcount of mantissa
+fzero = (0, MPZ_ZERO, 0, 0)
+fnzero = (1, MPZ_ZERO, 0, 0)
+fone = (0, MPZ_ONE, 0, 1)
+fnone = (1, MPZ_ONE, 0, 1)
+ftwo = (0, MPZ_ONE, 1, 1)
+ften = (0, MPZ_FIVE, 1, 3)
+fhalf = (0, MPZ_ONE, -1, 1)
+
+# Arbitrary encoding for special numbers: zero mantissa, nonzero exponent
+fnan = (0, MPZ_ZERO, -123, -1)
+finf = (0, MPZ_ZERO, -456, -2)
+fninf = (1, MPZ_ZERO, -789, -3)
+
+# Was 1e1000; this is broken in Python 2.4
+math_float_inf = 1e300 * 1e300
+
+
+#----------------------------------------------------------------------------#
+#                                  Rounding                                  #
+#----------------------------------------------------------------------------#
+
+# This function can be used to round a mantissa generally. However,
+# we will try to do most rounding inline for efficiency.
+def round_int(x, n, rnd):
+    if rnd == round_nearest:
+        if x >= 0:
+            t = x >> (n-1)
+            if t & 1 and ((t & 2) or (x & h_mask[n<300][n])):
+                return (t>>1)+1
+            else:
+                return t>>1
+        else:
+            return -round_int(-x, n, rnd)
+    if rnd == round_floor:
+        return x >> n
+    if rnd == round_ceiling:
+        return -((-x) >> n)
+    if rnd == round_down:
+        if x >= 0:
+            return x >> n
+        return -((-x) >> n)
+    if rnd == round_up:
+        if x >= 0:
+            return -((-x) >> n)
+        return x >> n
+
+# These masks are used to pick out segments of numbers to determine
+# which direction to round when rounding to nearest.
+class h_mask_big:
+    def __getitem__(self, n):
+        return (MPZ_ONE<<(n-1))-1
+
+h_mask_small = [0]+[((MPZ_ONE<<(_-1))-1) for _ in range(1, 300)]
+h_mask = [h_mask_big(), h_mask_small]
+
+# The >> operator rounds to floor. shifts_down[rnd][sign]
+# tells whether this is the right direction to use, or if the
+# number should be negated before shifting
+shifts_down = {round_floor:(1,0), round_ceiling:(0,1),
+    round_down:(1,1), round_up:(0,0)}
+
+
+#----------------------------------------------------------------------------#
+#                          Normalization of raw mpfs                         #
+#----------------------------------------------------------------------------#
+
+# This function is called almost every time an mpf is created.
+# It has been optimized accordingly.
+
+def _normalize(sign, man, exp, bc, prec, rnd):
+    """
+    Create a raw mpf tuple with value (-1)**sign * man * 2**exp and
+    normalized mantissa. The mantissa is rounded in the specified
+    direction if its size exceeds the precision. Trailing zero bits
+    are also stripped from the mantissa to ensure that the
+    representation is canonical.
+
+    Conditions on the input:
+    * The input must represent a regular (finite) number
+    * The sign bit must be 0 or 1
+    * The mantissa must be positive
+    * The exponent must be an integer
+    * The bitcount must be exact
+
+    If these conditions are not met, use from_man_exp, mpf_pos, or any
+    of the conversion functions to create normalized raw mpf tuples.
+    """
+    if not man:
+        return fzero
+    # Cut mantissa down to size if larger than target precision
+    n = bc - prec
+    if n > 0:
+        if rnd == round_nearest:
+            t = man >> (n-1)
+            if t & 1 and ((t & 2) or (man & h_mask[n<300][n])):
+                man = (t>>1)+1
+            else:
+                man = t>>1
+        elif shifts_down[rnd][sign]:
+            man >>= n
+        else:
+            man = -((-man)>>n)
+        exp += n
+        bc = prec
+    # Strip trailing bits
+    if not man & 1:
+        t = trailtable[int(man & 255)]
+        if not t:
+            while not man & 255:
+                man >>= 8
+                exp += 8
+                bc -= 8
+            t = trailtable[int(man & 255)]
+        man >>= t
+        exp += t
+        bc -= t
+    # Bit count can be wrong if the input mantissa was 1 less than
+    # a power of 2 and got rounded up, thereby adding an extra bit.
+    # With trailing bits removed, all powers of two have mantissa 1,
+    # so this is easy to check for.
+    if man == 1:
+        bc = 1
+    return sign, man, exp, bc
+
+def _normalize1(sign, man, exp, bc, prec, rnd):
+    """same as normalize, but with the added condition that
+       man is odd or zero
+    """
+    if not man:
+        return fzero
+    if bc <= prec:
+        return sign, man, exp, bc
+    n = bc - prec
+    if rnd == round_nearest:
+        t = man >> (n-1)
+        if t & 1 and ((t & 2) or (man & h_mask[n<300][n])):
+            man = (t>>1)+1
+        else:
+            man = t>>1
+    elif shifts_down[rnd][sign]:
+        man >>= n
+    else:
+        man = -((-man)>>n)
+    exp += n
+    bc = prec
+    # Strip trailing bits
+    if not man & 1:
+        t = trailtable[int(man & 255)]
+        if not t:
+            while not man & 255:
+                man >>= 8
+                exp += 8
+                bc -= 8
+            t = trailtable[int(man & 255)]
+        man >>= t
+        exp += t
+        bc -= t
+    # Bit count can be wrong if the input mantissa was 1 less than
+    # a power of 2 and got rounded up, thereby adding an extra bit.
+    # With trailing bits removed, all powers of two have mantissa 1,
+    # so this is easy to check for.
+    if man == 1:
+        bc = 1
+    return sign, man, exp, bc
+
+try:
+    _exp_types = (int, long)
+except NameError:
+    _exp_types = (int,)
+
+def strict_normalize(sign, man, exp, bc, prec, rnd):
+    """Additional checks on the components of an mpf. Enable tests by setting
+       the environment variable MPMATH_STRICT to Y."""
+    assert type(man) == MPZ_TYPE
+    assert type(bc) in _exp_types
+    assert type(exp) in _exp_types
+    assert bc == bitcount(man)
+    return _normalize(sign, man, exp, bc, prec, rnd)
+
+def strict_normalize1(sign, man, exp, bc, prec, rnd):
+    """Additional checks on the components of an mpf. Enable tests by setting
+       the environment variable MPMATH_STRICT to Y."""
+    assert type(man) == MPZ_TYPE
+    assert type(bc) in _exp_types
+    assert type(exp) in _exp_types
+    assert bc == bitcount(man)
+    assert (not man) or (man & 1)
+    return _normalize1(sign, man, exp, bc, prec, rnd)
+
+if BACKEND == 'gmpy' and '_mpmath_normalize' in dir(gmpy):
+    _normalize = gmpy._mpmath_normalize
+    _normalize1 = gmpy._mpmath_normalize
+
+if BACKEND == 'sage':
+    _normalize = _normalize1 = sage_utils.normalize
+
+if STRICT:
+    normalize = strict_normalize
+    normalize1 = strict_normalize1
+else:
+    normalize = _normalize
+    normalize1 = _normalize1
+
+#----------------------------------------------------------------------------#
+#                            Conversion functions                            #
+#----------------------------------------------------------------------------#
+
+def from_man_exp(man, exp, prec=None, rnd=round_fast):
+    """Create raw mpf from (man, exp) pair. The mantissa may be signed.
+    If no precision is specified, the mantissa is stored exactly."""
+    man = MPZ(man)
+    sign = 0
+    if man < 0:
+        sign = 1
+        man = -man
+    if man < 1024:
+        bc = bctable[int(man)]
+    else:
+        bc = bitcount(man)
+    if not prec:
+        if not man:
+            return fzero
+        if not man & 1:
+            if man & 2:
+                return (sign, man >> 1, exp + 1, bc - 1)
+            t = trailtable[int(man & 255)]
+            if not t:
+                while not man & 255:
+                    man >>= 8
+                    exp += 8
+                    bc -= 8
+                t = trailtable[int(man & 255)]
+            man >>= t
+            exp += t
+            bc -= t
+        return (sign, man, exp, bc)
+    return normalize(sign, man, exp, bc, prec, rnd)
+
+int_cache = dict((n, from_man_exp(n, 0)) for n in range(-10, 257))
+
+if BACKEND == 'gmpy' and '_mpmath_create' in dir(gmpy):
+    from_man_exp = gmpy._mpmath_create
+
+if BACKEND == 'sage':
+    from_man_exp = sage_utils.from_man_exp
+
+def from_int(n, prec=0, rnd=round_fast):
+    """Create a raw mpf from an integer. If no precision is specified,
+    the mantissa is stored exactly."""
+    if not prec:
+        if n in int_cache:
+            return int_cache[n]
+    return from_man_exp(n, 0, prec, rnd)
+
+def to_man_exp(s):
+    """Return (man, exp) of a raw mpf. Raise an error if inf/nan."""
+    sign, man, exp, bc = s
+    if (not man) and exp:
+        raise ValueError("mantissa and exponent are undefined for %s" % man)
+    return man, exp
+
+def to_int(s, rnd=None):
+    """Convert a raw mpf to the nearest int. Rounding is done down by
+    default (same as int(float) in Python), but can be changed. If the
+    input is inf/nan, an exception is raised."""
+    sign, man, exp, bc = s
+    if (not man) and exp:
+        raise ValueError("cannot convert inf or nan to int")
+    if exp >= 0:
+        if sign:
+            return (-man) << exp
+        return man << exp
+    # Make default rounding fast
+    if not rnd:
+        if sign:
+            return -(man >> (-exp))
+        else:
+            return man >> (-exp)
+    if sign:
+        return round_int(-man, -exp, rnd)
+    else:
+        return round_int(man, -exp, rnd)
+
+def mpf_round_int(s, rnd):
+    sign, man, exp, bc = s
+    if (not man) and exp:
+        return s
+    if exp >= 0:
+        return s
+    mag = exp+bc
+    if mag < 1:
+        if rnd == round_ceiling:
+            if sign: return fzero
+            else:    return fone
+        elif rnd == round_floor:
+            if sign: return fnone
+            else:    return fzero
+        elif rnd == round_nearest:
+            if mag < 0 or man == MPZ_ONE: return fzero
+            elif sign: return fnone
+            else:      return fone
+        else:
+            raise NotImplementedError
+    return mpf_pos(s, min(bc, mag), rnd)
+
+def mpf_floor(s, prec=0, rnd=round_fast):
+    v = mpf_round_int(s, round_floor)
+    if prec:
+        v = mpf_pos(v, prec, rnd)
+    return v
+
+def mpf_ceil(s, prec=0, rnd=round_fast):
+    v = mpf_round_int(s, round_ceiling)
+    if prec:
+        v = mpf_pos(v, prec, rnd)
+    return v
+
+def mpf_nint(s, prec=0, rnd=round_fast):
+    v = mpf_round_int(s, round_nearest)
+    if prec:
+        v = mpf_pos(v, prec, rnd)
+    return v
+
+def mpf_frac(s, prec=0, rnd=round_fast):
+    return mpf_sub(s, mpf_floor(s), prec, rnd)
+
+def from_float(x, prec=53, rnd=round_fast):
+    """Create a raw mpf from a Python float, rounding if necessary.
+    If prec >= 53, the result is guaranteed to represent exactly the
+    same number as the input. If prec is not specified, use prec=53."""
+    # frexp only raises an exception for nan on some platforms
+    if x != x:
+        return fnan
+    # in Python2.5 math.frexp gives an exception for float infinity
+    # in Python2.6 it returns (float infinity, 0)
+    try:
+        m, e = math.frexp(x)
+    except:
+        if x == math_float_inf: return finf
+        if x == -math_float_inf: return fninf
+        return fnan
+    if x == math_float_inf: return finf
+    if x == -math_float_inf: return fninf
+    return from_man_exp(int(m*(1<<53)), e-53, prec, rnd)
+
+def from_npfloat(x, prec=113, rnd=round_fast):
+    """Create a raw mpf from a numpy float, rounding if necessary.
+    If prec >= 113, the result is guaranteed to represent exactly the
+    same number as the input. If prec is not specified, use prec=113."""
+    y = float(x)
+    if x == y: # ldexp overflows for float16
+        return from_float(y, prec, rnd)
+    import numpy as np
+    if np.isfinite(x):
+        m, e = np.frexp(x)
+        return from_man_exp(int(np.ldexp(m, 113)), int(e-113), prec, rnd)
+    if np.isposinf(x): return finf
+    if np.isneginf(x): return fninf
+    return fnan
+
+def from_Decimal(x, prec=None, rnd=round_fast):
+    """Create a raw mpf from a Decimal, rounding if necessary.
+    If prec is not specified, use the equivalent bit precision
+    of the number of significant digits in x."""
+    if x.is_nan(): return fnan
+    if x.is_infinite(): return fninf if x.is_signed() else finf
+    if prec is None:
+        prec = int(len(x.as_tuple()[1])*3.3219280948873626)
+    return from_str(str(x), prec, rnd)
+
+def to_float(s, strict=False, rnd=round_fast):
+    """
+    Convert a raw mpf to a Python float. The result is exact if the
+    bitcount of s is <= 53 and no underflow/overflow occurs.
+
+    If the number is too large or too small to represent as a regular
+    float, it will be converted to inf or 0.0. Setting strict=True
+    forces an OverflowError to be raised instead.
+
+    Warning: with a directed rounding mode, the correct nearest representable
+    floating-point number in the specified direction might not be computed
+    in case of overflow or (gradual) underflow.
+    """
+    sign, man, exp, bc = s
+    if not man:
+        if s == fzero: return 0.0
+        if s == finf: return math_float_inf
+        if s == fninf: return -math_float_inf
+        return math_float_inf/math_float_inf
+    if bc > 53:
+        sign, man, exp, bc = normalize1(sign, man, exp, bc, 53, rnd)
+    if sign:
+        man = -man
+    try:
+        return math.ldexp(man, exp)
+    except OverflowError:
+        if strict:
+            raise
+        # Overflow to infinity
+        if exp + bc > 0:
+            if sign:
+                return -math_float_inf
+            else:
+                return math_float_inf
+        # Underflow to zero
+        return 0.0
+
+def from_rational(p, q, prec, rnd=round_fast):
+    """Create a raw mpf from a rational number p/q, round if
+    necessary."""
+    return mpf_div(from_int(p), from_int(q), prec, rnd)
+
+def to_rational(s):
+    """Convert a raw mpf to a rational number. Return integers (p, q)
+    such that s = p/q exactly."""
+    sign, man, exp, bc = s
+    if sign:
+        man = -man
+    if bc == -1:
+        raise ValueError("cannot convert %s to a rational number" % man)
+    if exp >= 0:
+        return man * (1<<exp), 1
+    else:
+        return man, 1<<(-exp)
+
+def to_fixed(s, prec):
+    """Convert a raw mpf to a fixed-point big integer"""
+    sign, man, exp, bc = s
+    offset = exp + prec
+    if sign:
+        if offset >= 0: return (-man) << offset
+        else:           return (-man) >> (-offset)
+    else:
+        if offset >= 0: return man << offset
+        else:           return man >> (-offset)
+
+
+##############################################################################
+##############################################################################
+
+#----------------------------------------------------------------------------#
+#                       Arithmetic operations, etc.                          #
+#----------------------------------------------------------------------------#
+
+def mpf_rand(prec):
+    """Return a raw mpf chosen randomly from [0, 1), with prec bits
+    in the mantissa."""
+    global getrandbits
+    if not getrandbits:
+        import random
+        getrandbits = random.getrandbits
+    return from_man_exp(getrandbits(prec), -prec, prec, round_floor)
+
+def mpf_eq(s, t):
+    """Test equality of two raw mpfs. This is simply tuple comparison
+    unless either number is nan, in which case the result is False."""
+    if not s[1] or not t[1]:
+        if s == fnan or t == fnan:
+            return False
+    return s == t
+
+def mpf_hash(s):
+    # Duplicate the new hash algorithm introduces in Python 3.2.
+    if sys.version_info >= (3, 2):
+        ssign, sman, sexp, sbc = s
+
+        # Handle special numbers
+        if not sman:
+            if s == fnan: return sys.hash_info.nan
+            if s == finf: return sys.hash_info.inf
+            if s == fninf: return -sys.hash_info.inf
+        h = sman % HASH_MODULUS
+        if sexp >= 0:
+            sexp = sexp % HASH_BITS
+        else:
+            sexp = HASH_BITS - 1 - ((-1 - sexp) % HASH_BITS)
+        h = (h << sexp) % HASH_MODULUS
+        if ssign: h = -h
+        if h == -1: h = -2
+        return int(h)
+    else:
+        try:
+            # Try to be compatible with hash values for floats and ints
+            return hash(to_float(s, strict=1))
+        except OverflowError:
+            # We must unfortunately sacrifice compatibility with ints here.
+            # We could do hash(man << exp) when the exponent is positive, but
+            # this would cause unreasonable inefficiency for large numbers.
+            return hash(s)
+
+def mpf_cmp(s, t):
+    """Compare the raw mpfs s and t. Return -1 if s < t, 0 if s == t,
+    and 1 if s > t. (Same convention as Python's cmp() function.)"""
+
+    # In principle, a comparison amounts to determining the sign of s-t.
+    # A full subtraction is relatively slow, however, so we first try to
+    # look at the components.
+    ssign, sman, sexp, sbc = s
+    tsign, tman, texp, tbc = t
+
+    # Handle zeros and special numbers
+    if not sman or not tman:
+        if s == fzero: return -mpf_sign(t)
+        if t == fzero: return mpf_sign(s)
+        if s == t: return 0
+        # Follow same convention as Python's cmp for float nan
+        if t == fnan: return 1
+        if s == finf: return 1
+        if t == fninf: return 1
+        return -1
+    # Different sides of zero
+    if ssign != tsign:
+        if not ssign: return 1
+        return -1
+    # This reduces to direct integer comparison
+    if sexp == texp:
+        if sman == tman:
+            return 0
+        if sman > tman:
+            if ssign: return -1
+            else:     return 1
+        else:
+            if ssign: return 1
+            else:     return -1
+    # Check position of the highest set bit in each number. If
+    # different, there is certainly an inequality.
+    a = sbc + sexp
+    b = tbc + texp
+    if ssign:
+        if a < b: return 1
+        if a > b: return -1
+    else:
+        if a < b: return -1
+        if a > b: return 1
+
+    # Both numbers have the same highest bit. Subtract to find
+    # how the lower bits compare.
+    delta = mpf_sub(s, t, 5, round_floor)
+    if delta[0]:
+        return -1
+    return 1
+
+def mpf_lt(s, t):
+    if s == fnan or t == fnan:
+        return False
+    return mpf_cmp(s, t) < 0
+
+def mpf_le(s, t):
+    if s == fnan or t == fnan:
+        return False
+    return mpf_cmp(s, t) <= 0
+
+def mpf_gt(s, t):
+    if s == fnan or t == fnan:
+        return False
+    return mpf_cmp(s, t) > 0
+
+def mpf_ge(s, t):
+    if s == fnan or t == fnan:
+        return False
+    return mpf_cmp(s, t) >= 0
+
+def mpf_min_max(seq):
+    min = max = seq[0]
+    for x in seq[1:]:
+        if mpf_lt(x, min): min = x
+        if mpf_gt(x, max): max = x
+    return min, max
+
+def mpf_pos(s, prec=0, rnd=round_fast):
+    """Calculate 0+s for a raw mpf (i.e., just round s to the specified
+    precision)."""
+    if prec:
+        sign, man, exp, bc = s
+        if (not man) and exp:
+            return s
+        return normalize1(sign, man, exp, bc, prec, rnd)
+    return s
+
+def mpf_neg(s, prec=None, rnd=round_fast):
+    """Negate a raw mpf (return -s), rounding the result to the
+    specified precision. The prec argument can be omitted to do the
+    operation exactly."""
+    sign, man, exp, bc = s
+    if not man:
+        if exp:
+            if s == finf: return fninf
+            if s == fninf: return finf
+        return s
+    if not prec:
+        return (1-sign, man, exp, bc)
+    return normalize1(1-sign, man, exp, bc, prec, rnd)
+
+def mpf_abs(s, prec=None, rnd=round_fast):
+    """Return abs(s) of the raw mpf s, rounded to the specified
+    precision. The prec argument can be omitted to generate an
+    exact result."""
+    sign, man, exp, bc = s
+    if (not man) and exp:
+        if s == fninf:
+            return finf
+        return s
+    if not prec:
+        if sign:
+            return (0, man, exp, bc)
+        return s
+    return normalize1(0, man, exp, bc, prec, rnd)
+
+def mpf_sign(s):
+    """Return -1, 0, or 1 (as a Python int, not a raw mpf) depending on
+    whether s is negative, zero, or positive. (Nan is taken to give 0.)"""
+    sign, man, exp, bc = s
+    if not man:
+        if s == finf: return 1
+        if s == fninf: return -1
+        return 0
+    return (-1) ** sign
+
+def mpf_add(s, t, prec=0, rnd=round_fast, _sub=0):
+    """
+    Add the two raw mpf values s and t.
+
+    With prec=0, no rounding is performed. Note that this can
+    produce a very large mantissa (potentially too large to fit
+    in memory) if exponents are far apart.
+    """
+    ssign, sman, sexp, sbc = s
+    tsign, tman, texp, tbc = t
+    tsign ^= _sub
+    # Standard case: two nonzero, regular numbers
+    if sman and tman:
+        offset = sexp - texp
+        if offset:
+            if offset > 0:
+                # Outside precision range; only need to perturb
+                if offset > 100 and prec:
+                    delta = sbc + sexp - tbc - texp
+                    if delta > prec + 4:
+                        offset = prec + 4
+                        sman <<= offset
+                        if tsign == ssign: sman += 1
+                        else:              sman -= 1
+                        return normalize1(ssign, sman, sexp-offset,
+                            bitcount(sman), prec, rnd)
+                # Add
+                if ssign == tsign:
+                    man = tman + (sman << offset)
+                # Subtract
+                else:
+                    if ssign: man = tman - (sman << offset)
+                    else:     man = (sman << offset) - tman
+                    if man >= 0:
+                        ssign = 0
+                    else:
+                        man = -man
+                        ssign = 1
+                bc = bitcount(man)
+                return normalize1(ssign, man, texp, bc, prec or bc, rnd)
+            elif offset < 0:
+                # Outside precision range; only need to perturb
+                if offset < -100 and prec:
+                    delta = tbc + texp - sbc - sexp
+                    if delta > prec + 4:
+                        offset = prec + 4
+                        tman <<= offset
+                        if ssign == tsign: tman += 1
+                        else:              tman -= 1
+                        return normalize1(tsign, tman, texp-offset,
+                            bitcount(tman), prec, rnd)
+                # Add
+                if ssign == tsign:
+                    man = sman + (tman << -offset)
+                # Subtract
+                else:
+                    if tsign: man = sman - (tman << -offset)
+                    else:     man = (tman << -offset) - sman
+                    if man >= 0:
+                        ssign = 0
+                    else:
+                        man = -man
+                        ssign = 1
+                bc = bitcount(man)
+                return normalize1(ssign, man, sexp, bc, prec or bc, rnd)
+        # Equal exponents; no shifting necessary
+        if ssign == tsign:
+            man = tman + sman
+        else:
+            if ssign: man = tman - sman
+            else:     man = sman - tman
+            if man >= 0:
+                ssign = 0
+            else:
+                man = -man
+                ssign = 1
+        bc = bitcount(man)
+        return normalize(ssign, man, texp, bc, prec or bc, rnd)
+    # Handle zeros and special numbers
+    if _sub:
+        t = mpf_neg(t)
+    if not sman:
+        if sexp:
+            if s == t or tman or not texp:
+                return s
+            return fnan
+        if tman:
+            return normalize1(tsign, tman, texp, tbc, prec or tbc, rnd)
+        return t
+    if texp:
+        return t
+    if sman:
+        return normalize1(ssign, sman, sexp, sbc, prec or sbc, rnd)
+    return s
+
+def mpf_sub(s, t, prec=0, rnd=round_fast):
+    """Return the difference of two raw mpfs, s-t. This function is
+    simply a wrapper of mpf_add that changes the sign of t."""
+    return mpf_add(s, t, prec, rnd, 1)
+
+def mpf_sum(xs, prec=0, rnd=round_fast, absolute=False):
+    """
+    Sum a list of mpf values efficiently and accurately
+    (typically no temporary roundoff occurs). If prec=0,
+    the final result will not be rounded either.
+
+    There may be roundoff error or cancellation if extremely
+    large exponent differences occur.
+
+    With absolute=True, sums the absolute values.
+    """
+    man = 0
+    exp = 0
+    max_extra_prec = prec*2 or 1000000  # XXX
+    special = None
+    for x in xs:
+        xsign, xman, xexp, xbc = x
+        if xman:
+            if xsign and not absolute:
+                xman = -xman
+            delta = xexp - exp
+            if xexp >= exp:
+                # x much larger than existing sum?
+                # first: quick test
+                if (delta > max_extra_prec) and \
+                    ((not man) or delta-bitcount(abs(man)) > max_extra_prec):
+                    man = xman
+                    exp = xexp
+                else:
+                    man += (xman << delta)
+            else:
+                delta = -delta
+                # x much smaller than existing sum?
+                if delta-xbc > max_extra_prec:
+                    if not man:
+                        man, exp = xman, xexp
+                else:
+                    man = (man << delta) + xman
+                    exp = xexp
+        elif xexp:
+            if absolute:
+                x = mpf_abs(x)
+            special = mpf_add(special or fzero, x, 1)
+    # Will be inf or nan
+    if special:
+        return special
+    return from_man_exp(man, exp, prec, rnd)
+
+def gmpy_mpf_mul(s, t, prec=0, rnd=round_fast):
+    """Multiply two raw mpfs"""
+    ssign, sman, sexp, sbc = s
+    tsign, tman, texp, tbc = t
+    sign = ssign ^ tsign
+    man = sman*tman
+    if man:
+        bc = bitcount(man)
+        if prec:
+            return normalize1(sign, man, sexp+texp, bc, prec, rnd)
+        else:
+            return (sign, man, sexp+texp, bc)
+    s_special = (not sman) and sexp
+    t_special = (not tman) and texp
+    if not s_special and not t_special:
+        return fzero
+    if fnan in (s, t): return fnan
+    if (not tman) and texp: s, t = t, s
+    if t == fzero: return fnan
+    return {1:finf, -1:fninf}[mpf_sign(s) * mpf_sign(t)]
+
+def gmpy_mpf_mul_int(s, n, prec, rnd=round_fast):
+    """Multiply by a Python integer."""
+    sign, man, exp, bc = s
+    if not man:
+        return mpf_mul(s, from_int(n), prec, rnd)
+    if not n:
+        return fzero
+    if n < 0:
+        sign ^= 1
+        n = -n
+    man *= n
+    return normalize(sign, man, exp, bitcount(man), prec, rnd)
+
+def python_mpf_mul(s, t, prec=0, rnd=round_fast):
+    """Multiply two raw mpfs"""
+    ssign, sman, sexp, sbc = s
+    tsign, tman, texp, tbc = t
+    sign = ssign ^ tsign
+    man = sman*tman
+    if man:
+        bc = sbc + tbc - 1
+        bc += int(man>>bc)
+        if prec:
+            return normalize1(sign, man, sexp+texp, bc, prec, rnd)
+        else:
+            return (sign, man, sexp+texp, bc)
+    s_special = (not sman) and sexp
+    t_special = (not tman) and texp
+    if not s_special and not t_special:
+        return fzero
+    if fnan in (s, t): return fnan
+    if (not tman) and texp: s, t = t, s
+    if t == fzero: return fnan
+    return {1:finf, -1:fninf}[mpf_sign(s) * mpf_sign(t)]
+
+def python_mpf_mul_int(s, n, prec, rnd=round_fast):
+    """Multiply by a Python integer."""
+    sign, man, exp, bc = s
+    if not man:
+        return mpf_mul(s, from_int(n), prec, rnd)
+    if not n:
+        return fzero
+    if n < 0:
+        sign ^= 1
+        n = -n
+    man *= n
+    # Generally n will be small
+    if n < 1024:
+        bc += bctable[int(n)] - 1
+    else:
+        bc += bitcount(n) - 1
+    bc += int(man>>bc)
+    return normalize(sign, man, exp, bc, prec, rnd)
+
+
+if BACKEND == 'gmpy':
+    mpf_mul = gmpy_mpf_mul
+    mpf_mul_int = gmpy_mpf_mul_int
+else:
+    mpf_mul = python_mpf_mul
+    mpf_mul_int = python_mpf_mul_int
+
+def mpf_shift(s, n):
+    """Quickly multiply the raw mpf s by 2**n without rounding."""
+    sign, man, exp, bc = s
+    if not man:
+        return s
+    return sign, man, exp+n, bc
+
+def mpf_frexp(x):
+    """Convert x = y*2**n to (y, n) with abs(y) in [0.5, 1) if nonzero"""
+    sign, man, exp, bc = x
+    if not man:
+        if x == fzero:
+            return (fzero, 0)
+        else:
+            raise ValueError
+    return mpf_shift(x, -bc-exp), bc+exp
+
+def mpf_div(s, t, prec, rnd=round_fast):
+    """Floating-point division"""
+    ssign, sman, sexp, sbc = s
+    tsign, tman, texp, tbc = t
+    if not sman or not tman:
+        if s == fzero:
+            if t == fzero: raise ZeroDivisionError
+            if t == fnan: return fnan
+            return fzero
+        if t == fzero:
+            raise ZeroDivisionError
+        s_special = (not sman) and sexp
+        t_special = (not tman) and texp
+        if s_special and t_special:
+            return fnan
+        if s == fnan or t == fnan:
+            return fnan
+        if not t_special:
+            if t == fzero:
+                return fnan
+            return {1:finf, -1:fninf}[mpf_sign(s) * mpf_sign(t)]
+        return fzero
+    sign = ssign ^ tsign
+    if tman == 1:
+        return normalize1(sign, sman, sexp-texp, sbc, prec, rnd)
+    # Same strategy as for addition: if there is a remainder, perturb
+    # the result a few bits outside the precision range before rounding
+    extra = prec - sbc + tbc + 5
+    if extra < 5:
+        extra = 5
+    quot, rem = divmod(sman<<extra, tman)
+    if rem:
+        quot = (quot<<1) + 1
+        extra += 1
+        return normalize1(sign, quot, sexp-texp-extra, bitcount(quot), prec, rnd)
+    return normalize(sign, quot, sexp-texp-extra, bitcount(quot), prec, rnd)
+
+def mpf_rdiv_int(n, t, prec, rnd=round_fast):
+    """Floating-point division n/t with a Python integer as numerator"""
+    sign, man, exp, bc = t
+    if not n or not man:
+        return mpf_div(from_int(n), t, prec, rnd)
+    if n < 0:
+        sign ^= 1
+        n = -n
+    extra = prec + bc + 5
+    quot, rem = divmod(n<<extra, man)
+    if rem:
+        quot = (quot<<1) + 1
+        extra += 1
+        return normalize1(sign, quot, -exp-extra, bitcount(quot), prec, rnd)
+    return normalize(sign, quot, -exp-extra, bitcount(quot), prec, rnd)
+
+def mpf_mod(s, t, prec, rnd=round_fast):
+    ssign, sman, sexp, sbc = s
+    tsign, tman, texp, tbc = t
+    if ((not sman) and sexp) or ((not tman) and texp):
+        return fnan
+    # Important special case: do nothing if t is larger
+    if ssign == tsign and texp > sexp+sbc:
+        return s
+    # Another important special case: this allows us to do e.g. x % 1.0
+    # to find the fractional part of x, and it will work when x is huge.
+    if tman == 1 and sexp > texp+tbc:
+        return fzero
+    base = min(sexp, texp)
+    sman = (-1)**ssign * sman
+    tman = (-1)**tsign * tman
+    man = (sman << (sexp-base)) % (tman << (texp-base))
+    if man >= 0:
+        sign = 0
+    else:
+        man = -man
+        sign = 1
+    return normalize(sign, man, base, bitcount(man), prec, rnd)
+
+reciprocal_rnd = {
+  round_down : round_up,
+  round_up : round_down,
+  round_floor : round_ceiling,
+  round_ceiling : round_floor,
+  round_nearest : round_nearest
+}
+
+negative_rnd = {
+  round_down : round_down,
+  round_up : round_up,
+  round_floor : round_ceiling,
+  round_ceiling : round_floor,
+  round_nearest : round_nearest
+}
+
+def mpf_pow_int(s, n, prec, rnd=round_fast):
+    """Compute s**n, where s is a raw mpf and n is a Python integer."""
+    sign, man, exp, bc = s
+
+    if (not man) and exp:
+        if s == finf:
+            if n > 0: return s
+            if n == 0: return fnan
+            return fzero
+        if s == fninf:
+            if n > 0: return [finf, fninf][n & 1]
+            if n == 0: return fnan
+            return fzero
+        return fnan
+
+    n = int(n)
+    if n == 0: return fone
+    if n == 1: return mpf_pos(s, prec, rnd)
+    if n == 2:
+        _, man, exp, bc = s
+        if not man:
+            return fzero
+        man = man*man
+        if man == 1:
+            return (0, MPZ_ONE, exp+exp, 1)
+        bc = bc + bc - 2
+        bc += bctable[int(man>>bc)]
+        return normalize1(0, man, exp+exp, bc, prec, rnd)
+    if n == -1: return mpf_div(fone, s, prec, rnd)
+    if n < 0:
+        inverse = mpf_pow_int(s, -n, prec+5, reciprocal_rnd[rnd])
+        return mpf_div(fone, inverse, prec, rnd)
+
+    result_sign = sign & n
+
+    # Use exact integer power when the exact mantissa is small
+    if man == 1:
+        return (result_sign, MPZ_ONE, exp*n, 1)
+    if bc*n < 1000:
+        man **= n
+        return normalize1(result_sign, man, exp*n, bitcount(man), prec, rnd)
+
+    # Use directed rounding all the way through to maintain rigorous
+    # bounds for interval arithmetic
+    rounds_down = (rnd == round_nearest) or \
+        shifts_down[rnd][result_sign]
+
+    # Now we perform binary exponentiation. Need to estimate precision
+    # to avoid rounding errors from temporary operations. Roughly log_2(n)
+    # operations are performed.
+    workprec = prec + 4*bitcount(n) + 4
+    _, pm, pe, pbc = fone
+    while 1:
+        if n & 1:
+            pm = pm*man
+            pe = pe+exp
+            pbc += bc - 2
+            pbc = pbc + bctable[int(pm >> pbc)]
+            if pbc > workprec:
+                if rounds_down:
+                    pm = pm >> (pbc-workprec)
+                else:
+                    pm = -((-pm) >> (pbc-workprec))
+                pe += pbc - workprec
+                pbc = workprec
+            n -= 1
+            if not n:
+                break
+        man = man*man
+        exp = exp+exp
+        bc = bc + bc - 2
+        bc = bc + bctable[int(man >> bc)]
+        if bc > workprec:
+            if rounds_down:
+                man = man >> (bc-workprec)
+            else:
+                man = -((-man) >> (bc-workprec))
+            exp += bc - workprec
+            bc = workprec
+        n = n // 2
+
+    return normalize(result_sign, pm, pe, pbc, prec, rnd)
+
+
+def mpf_perturb(x, eps_sign, prec, rnd):
+    """
+    For nonzero x, calculate x + eps with directed rounding, where
+    eps < prec relatively and eps has the given sign (0 for
+    positive, 1 for negative).
+
+    With rounding to nearest, this is taken to simply normalize
+    x to the given precision.
+    """
+    if rnd == round_nearest:
+        return mpf_pos(x, prec, rnd)
+    sign, man, exp, bc = x
+    eps = (eps_sign, MPZ_ONE, exp+bc-prec-1, 1)
+    if sign:
+        away = (rnd in (round_down, round_ceiling)) ^ eps_sign
+    else:
+        away = (rnd in (round_up, round_ceiling)) ^ eps_sign
+    if away:
+        return mpf_add(x, eps, prec, rnd)
+    else:
+        return mpf_pos(x, prec, rnd)
+
+
+#----------------------------------------------------------------------------#
+#                              Radix conversion                              #
+#----------------------------------------------------------------------------#
+
+def to_digits_exp(s, dps):
+    """Helper function for representing the floating-point number s as
+    a decimal with dps digits. Returns (sign, string, exponent) where
+    sign is '' or '-', string is the digit string, and exponent is
+    the decimal exponent as an int.
+
+    If inexact, the decimal representation is rounded toward zero."""
+
+    # Extract sign first so it doesn't mess up the string digit count
+    if s[0]:
+        sign = '-'
+        s = mpf_neg(s)
+    else:
+        sign = ''
+    _sign, man, exp, bc = s
+
+    if not man:
+        return '', '0', 0
+
+    bitprec = int(dps * math.log(10,2)) + 10
+
+    # Cut down to size
+    # TODO: account for precision when doing this
+    exp_from_1 = exp + bc
+    if abs(exp_from_1) > 3500:
+        from .libelefun import mpf_ln2, mpf_ln10
+        # Set b = int(exp * log(2)/log(10))
+        # If exp is huge, we must use high-precision arithmetic to
+        # find the nearest power of ten
+        expprec = bitcount(abs(exp)) + 5
+        tmp = from_int(exp)
+        tmp = mpf_mul(tmp, mpf_ln2(expprec))
+        tmp = mpf_div(tmp, mpf_ln10(expprec), expprec)
+        b = to_int(tmp)
+        s = mpf_div(s, mpf_pow_int(ften, b, bitprec), bitprec)
+        _sign, man, exp, bc = s
+        exponent = b
+    else:
+        exponent = 0
+
+    # First, calculate mantissa digits by converting to a binary
+    # fixed-point number and then converting that number to
+    # a decimal fixed-point number.
+    fixprec = max(bitprec - exp - bc, 0)
+    fixdps = int(fixprec / math.log(10,2) + 0.5)
+    sf = to_fixed(s, fixprec)
+    sd = bin_to_radix(sf, fixprec, 10, fixdps)
+    digits = numeral(sd, base=10, size=dps)
+
+    exponent += len(digits) - fixdps - 1
+    return sign, digits, exponent
+
+def to_str(s, dps, strip_zeros=True, min_fixed=None, max_fixed=None,
+    show_zero_exponent=False):
+    """
+    Convert a raw mpf to a decimal floating-point literal with at
+    most `dps` decimal digits in the mantissa (not counting extra zeros
+    that may be inserted for visual purposes).
+
+    The number will be printed in fixed-point format if the position
+    of the leading digit is strictly between min_fixed
+    (default = min(-dps/3,-5)) and max_fixed (default = dps).
+
+    To force fixed-point format always, set min_fixed = -inf,
+    max_fixed = +inf. To force floating-point format, set
+    min_fixed >= max_fixed.
+
+    The literal is formatted so that it can be parsed back to a number
+    by to_str, float() or Decimal().
+    """
+
+    # Special numbers
+    if not s[1]:
+        if s == fzero:
+            if dps: t = '0.0'
+            else:   t = '.0'
+            if show_zero_exponent:
+                t += 'e+0'
+            return t
+        if s == finf: return '+inf'
+        if s == fninf: return '-inf'
+        if s == fnan: return 'nan'
+        raise ValueError
+
+    if min_fixed is None: min_fixed = min(-(dps//3), -5)
+    if max_fixed is None: max_fixed = dps
+
+    # to_digits_exp rounds to floor.
+    # This sometimes kills some instances of "...00001"
+    sign, digits, exponent = to_digits_exp(s, dps+3)
+
+    # No digits: show only .0; round exponent to nearest
+    if not dps:
+        if digits[0] in '56789':
+            exponent += 1
+        digits = ".0"
+
+    else:
+        # Rounding up kills some instances of "...99999"
+        if len(digits) > dps and digits[dps] in '56789':
+            digits = digits[:dps]
+            i = dps - 1
+            while i >= 0 and digits[i] == '9':
+                i -= 1
+            if i >= 0:
+                digits = digits[:i] + str(int(digits[i]) + 1) + '0' * (dps - i - 1)
+            else:
+                digits = '1' + '0' * (dps - 1)
+                exponent += 1
+        else:
+            digits = digits[:dps]
+
+        # Prettify numbers close to unit magnitude
+        if min_fixed < exponent < max_fixed:
+            if exponent < 0:
+                digits = ("0"*int(-exponent)) + digits
+                split = 1
+            else:
+                split = exponent + 1
+                if split > dps:
+                    digits += "0"*(split-dps)
+            exponent = 0
+        else:
+            split = 1
+
+        digits = (digits[:split] + "." + digits[split:])
+
+        if strip_zeros:
+            # Clean up trailing zeros
+            digits = digits.rstrip('0')
+            if digits[-1] == ".":
+                digits += "0"
+
+    if exponent == 0 and dps and not show_zero_exponent: return sign + digits
+    if exponent >= 0: return sign + digits + "e+" + str(exponent)
+    if exponent < 0: return sign + digits + "e" + str(exponent)
+
+def str_to_man_exp(x, base=10):
+    """Helper function for from_str."""
+    x = x.lower().rstrip('l')
+    # Verify that the input is a valid float literal
+    float(x)
+    # Split into mantissa, exponent
+    parts = x.split('e')
+    if len(parts) == 1:
+        exp = 0
+    else: # == 2
+        x = parts[0]
+        exp = int(parts[1])
+    # Look for radix point in mantissa
+    parts = x.split('.')
+    if len(parts) == 2:
+        a, b = parts[0], parts[1].rstrip('0')
+        exp -= len(b)
+        x = a + b
+    x = MPZ(int(x, base))
+    return x, exp
+
+special_str = {'inf':finf, '+inf':finf, '-inf':fninf, 'nan':fnan}
+
+def from_str(x, prec, rnd=round_fast):
+    """Create a raw mpf from a decimal literal, rounding in the
+    specified direction if the input number cannot be represented
+    exactly as a binary floating-point number with the given number of
+    bits. The literal syntax accepted is the same as for Python
+    floats.
+
+    TODO: the rounding does not work properly for large exponents.
+    """
+    x = x.lower().strip()
+    if x in special_str:
+        return special_str[x]
+
+    if '/' in x:
+        p, q = x.split('/')
+        p, q = p.rstrip('l'), q.rstrip('l')
+        return from_rational(int(p), int(q), prec, rnd)
+
+    man, exp = str_to_man_exp(x, base=10)
+
+    # XXX: appropriate cutoffs & track direction
+    # note no factors of 5
+    if abs(exp) > 400:
+        s = from_int(man, prec+10)
+        s = mpf_mul(s, mpf_pow_int(ften, exp, prec+10), prec, rnd)
+    else:
+        if exp >= 0:
+            s = from_int(man * 10**exp, prec, rnd)
+        else:
+            s = from_rational(man, 10**-exp, prec, rnd)
+    return s
+
+# Binary string conversion. These are currently mainly used for debugging
+# and could use some improvement in the future
+
+def from_bstr(x):
+    man, exp = str_to_man_exp(x, base=2)
+    man = MPZ(man)
+    sign = 0
+    if man < 0:
+        man = -man
+        sign = 1
+    bc = bitcount(man)
+    return normalize(sign, man, exp, bc, bc, round_floor)
+
+def to_bstr(x):
+    sign, man, exp, bc = x
+    return ['','-'][sign] + numeral(man, size=bitcount(man), base=2) + ("e%i" % exp)
+
+
+#----------------------------------------------------------------------------#
+#                                Square roots                                #
+#----------------------------------------------------------------------------#
+
+
+def mpf_sqrt(s, prec, rnd=round_fast):
+    """
+    Compute the square root of a nonnegative mpf value. The
+    result is correctly rounded.
+    """
+    sign, man, exp, bc = s
+    if sign:
+        raise ComplexResult("square root of a negative number")
+    if not man:
+        return s
+    if exp & 1:
+        exp -= 1
+        man <<= 1
+        bc += 1
+    elif man == 1:
+        return normalize1(sign, man, exp//2, bc, prec, rnd)
+    shift = max(4, 2*prec-bc+4)
+    shift += shift & 1
+    if rnd in 'fd':
+        man = isqrt(man<<shift)
+    else:
+        man, rem = sqrtrem(man<<shift)
+        # Perturb up
+        if rem:
+            man = (man<<1)+1
+            shift += 2
+    return from_man_exp(man, (exp-shift)//2, prec, rnd)
+
+def mpf_hypot(x, y, prec, rnd=round_fast):
+    """Compute the Euclidean norm sqrt(x**2 + y**2) of two raw mpfs
+    x and y."""
+    if y == fzero: return mpf_abs(x, prec, rnd)
+    if x == fzero: return mpf_abs(y, prec, rnd)
+    hypot2 = mpf_add(mpf_mul(x,x), mpf_mul(y,y), prec+4)
+    return mpf_sqrt(hypot2, prec, rnd)
+
+
+if BACKEND == 'sage':
+    try:
+        import sage.libs.mpmath.ext_libmp as ext_lib
+        mpf_add = ext_lib.mpf_add
+        mpf_sub = ext_lib.mpf_sub
+        mpf_mul = ext_lib.mpf_mul
+        mpf_div = ext_lib.mpf_div
+        mpf_sqrt = ext_lib.mpf_sqrt
+    except ImportError:
+        pass

venv/lib/python3.10/site-packages/mpmath/libmp/libmpi.py

@@ -0,0 +1,935 @@
+"""
+Computational functions for interval arithmetic.
+
+"""
+
+from .backend import xrange
+
+from .libmpf import (
+    ComplexResult,
+    round_down, round_up, round_floor, round_ceiling, round_nearest,
+    prec_to_dps, repr_dps, dps_to_prec,
+    bitcount,
+    from_float,
+    fnan, finf, fninf, fzero, fhalf, fone, fnone,
+    mpf_sign, mpf_lt, mpf_le, mpf_gt, mpf_ge, mpf_eq, mpf_cmp,
+    mpf_min_max,
+    mpf_floor, from_int, to_int, to_str, from_str,
+    mpf_abs, mpf_neg, mpf_pos, mpf_add, mpf_sub, mpf_mul, mpf_mul_int,
+    mpf_div, mpf_shift, mpf_pow_int,
+    from_man_exp, MPZ_ONE)
+
+from .libelefun import (
+    mpf_log, mpf_exp, mpf_sqrt, mpf_atan, mpf_atan2,
+    mpf_pi, mod_pi2, mpf_cos_sin
+)
+
+from .gammazeta import mpf_gamma, mpf_rgamma, mpf_loggamma, mpc_loggamma
+
+def mpi_str(s, prec):
+    sa, sb = s
+    dps = prec_to_dps(prec) + 5
+    return "[%s, %s]" % (to_str(sa, dps), to_str(sb, dps))
+    #dps = prec_to_dps(prec)
+    #m = mpi_mid(s, prec)
+    #d = mpf_shift(mpi_delta(s, 20), -1)
+    #return "%s +/- %s" % (to_str(m, dps), to_str(d, 3))
+
+mpi_zero = (fzero, fzero)
+mpi_one = (fone, fone)
+
+def mpi_eq(s, t):
+    return s == t
+
+def mpi_ne(s, t):
+    return s != t
+
+def mpi_lt(s, t):
+    sa, sb = s
+    ta, tb = t
+    if mpf_lt(sb, ta): return True
+    if mpf_ge(sa, tb): return False
+    return None
+
+def mpi_le(s, t):
+    sa, sb = s
+    ta, tb = t
+    if mpf_le(sb, ta): return True
+    if mpf_gt(sa, tb): return False
+    return None
+
+def mpi_gt(s, t): return mpi_lt(t, s)
+def mpi_ge(s, t): return mpi_le(t, s)
+
+def mpi_add(s, t, prec=0):
+    sa, sb = s
+    ta, tb = t
+    a = mpf_add(sa, ta, prec, round_floor)
+    b = mpf_add(sb, tb, prec, round_ceiling)
+    if a == fnan: a = fninf
+    if b == fnan: b = finf
+    return a, b
+
+def mpi_sub(s, t, prec=0):
+    sa, sb = s
+    ta, tb = t
+    a = mpf_sub(sa, tb, prec, round_floor)
+    b = mpf_sub(sb, ta, prec, round_ceiling)
+    if a == fnan: a = fninf
+    if b == fnan: b = finf
+    return a, b
+
+def mpi_delta(s, prec):
+    sa, sb = s
+    return mpf_sub(sb, sa, prec, round_up)
+
+def mpi_mid(s, prec):
+    sa, sb = s
+    return mpf_shift(mpf_add(sa, sb, prec, round_nearest), -1)
+
+def mpi_pos(s, prec):
+    sa, sb = s
+    a = mpf_pos(sa, prec, round_floor)
+    b = mpf_pos(sb, prec, round_ceiling)
+    return a, b
+
+def mpi_neg(s, prec=0):
+    sa, sb = s
+    a = mpf_neg(sb, prec, round_floor)
+    b = mpf_neg(sa, prec, round_ceiling)
+    return a, b
+
+def mpi_abs(s, prec=0):
+    sa, sb = s
+    sas = mpf_sign(sa)
+    sbs = mpf_sign(sb)
+    # Both points nonnegative?
+    if sas >= 0:
+        a = mpf_pos(sa, prec, round_floor)
+        b = mpf_pos(sb, prec, round_ceiling)
+    # Upper point nonnegative?
+    elif sbs >= 0:
+        a = fzero
+        negsa = mpf_neg(sa)
+        if mpf_lt(negsa, sb):
+            b = mpf_pos(sb, prec, round_ceiling)
+        else:
+            b = mpf_pos(negsa, prec, round_ceiling)
+    # Both negative?
+    else:
+        a = mpf_neg(sb, prec, round_floor)
+        b = mpf_neg(sa, prec, round_ceiling)
+    return a, b
+
+# TODO: optimize
+def mpi_mul_mpf(s, t, prec):
+    return mpi_mul(s, (t, t), prec)
+
+def mpi_div_mpf(s, t, prec):
+    return mpi_div(s, (t, t), prec)
+
+def mpi_mul(s, t, prec=0):
+    sa, sb = s
+    ta, tb = t
+    sas = mpf_sign(sa)
+    sbs = mpf_sign(sb)
+    tas = mpf_sign(ta)
+    tbs = mpf_sign(tb)
+    if sas == sbs == 0:
+        # Should maybe be undefined
+        if ta == fninf or tb == finf:
+            return fninf, finf
+        return fzero, fzero
+    if tas == tbs == 0:
+        # Should maybe be undefined
+        if sa == fninf or sb == finf:
+            return fninf, finf
+        return fzero, fzero
+    if sas >= 0:
+        # positive * positive
+        if tas >= 0:
+            a = mpf_mul(sa, ta, prec, round_floor)
+            b = mpf_mul(sb, tb, prec, round_ceiling)
+            if a == fnan: a = fzero
+            if b == fnan: b = finf
+        # positive * negative
+        elif tbs <= 0:
+            a = mpf_mul(sb, ta, prec, round_floor)
+            b = mpf_mul(sa, tb, prec, round_ceiling)
+            if a == fnan: a = fninf
+            if b == fnan: b = fzero
+        # positive * both signs
+        else:
+            a = mpf_mul(sb, ta, prec, round_floor)
+            b = mpf_mul(sb, tb, prec, round_ceiling)
+            if a == fnan: a = fninf
+            if b == fnan: b = finf
+    elif sbs <= 0:
+        # negative * positive
+        if tas >= 0:
+            a = mpf_mul(sa, tb, prec, round_floor)
+            b = mpf_mul(sb, ta, prec, round_ceiling)
+            if a == fnan: a = fninf
+            if b == fnan: b = fzero
+        # negative * negative
+        elif tbs <= 0:
+            a = mpf_mul(sb, tb, prec, round_floor)
+            b = mpf_mul(sa, ta, prec, round_ceiling)
+            if a == fnan: a = fzero
+            if b == fnan: b = finf
+        # negative * both signs
+        else:
+            a = mpf_mul(sa, tb, prec, round_floor)
+            b = mpf_mul(sa, ta, prec, round_ceiling)
+            if a == fnan: a = fninf
+            if b == fnan: b = finf
+    else:
+        # General case: perform all cross-multiplications and compare
+        # Since the multiplications can be done exactly, we need only
+        # do 4 (instead of 8: two for each rounding mode)
+        cases = [mpf_mul(sa, ta), mpf_mul(sa, tb), mpf_mul(sb, ta), mpf_mul(sb, tb)]
+        if fnan in cases:
+            a, b = (fninf, finf)
+        else:
+            a, b = mpf_min_max(cases)
+            a = mpf_pos(a, prec, round_floor)
+            b = mpf_pos(b, prec, round_ceiling)
+    return a, b
+
+def mpi_square(s, prec=0):
+    sa, sb = s
+    if mpf_ge(sa, fzero):
+        a = mpf_mul(sa, sa, prec, round_floor)
+        b = mpf_mul(sb, sb, prec, round_ceiling)
+    elif mpf_le(sb, fzero):
+        a = mpf_mul(sb, sb, prec, round_floor)
+        b = mpf_mul(sa, sa, prec, round_ceiling)
+    else:
+        sa = mpf_neg(sa)
+        sa, sb = mpf_min_max([sa, sb])
+        a = fzero
+        b = mpf_mul(sb, sb, prec, round_ceiling)
+    return a, b
+
+def mpi_div(s, t, prec):
+    sa, sb = s
+    ta, tb = t
+    sas = mpf_sign(sa)
+    sbs = mpf_sign(sb)
+    tas = mpf_sign(ta)
+    tbs = mpf_sign(tb)
+    # 0 / X
+    if sas == sbs == 0:
+        # 0 / <interval containing 0>
+        if (tas < 0 and tbs > 0) or (tas == 0 or tbs == 0):
+            return fninf, finf
+        return fzero, fzero
+    # Denominator contains both negative and positive numbers;
+    # this should properly be a multi-interval, but the closest
+    # match is the entire (extended) real line
+    if tas < 0 and tbs > 0:
+        return fninf, finf
+    # Assume denominator to be nonnegative
+    if tas < 0:
+        return mpi_div(mpi_neg(s), mpi_neg(t), prec)
+    # Division by zero
+    # XXX: make sure all results make sense
+    if tas == 0:
+        # Numerator contains both signs?
+        if sas < 0 and sbs > 0:
+            return fninf, finf
+        if tas == tbs:
+            return fninf, finf
+        # Numerator positive?
+        if sas >= 0:
+            a = mpf_div(sa, tb, prec, round_floor)
+            b = finf
+        if sbs <= 0:
+            a = fninf
+            b = mpf_div(sb, tb, prec, round_ceiling)
+    # Division with positive denominator
+    # We still have to handle nans resulting from inf/0 or inf/inf
+    else:
+        # Nonnegative numerator
+        if sas >= 0:
+            a = mpf_div(sa, tb, prec, round_floor)
+            b = mpf_div(sb, ta, prec, round_ceiling)
+            if a == fnan: a = fzero
+            if b == fnan: b = finf
+        # Nonpositive numerator
+        elif sbs <= 0:
+            a = mpf_div(sa, ta, prec, round_floor)
+            b = mpf_div(sb, tb, prec, round_ceiling)
+            if a == fnan: a = fninf
+            if b == fnan: b = fzero
+        # Numerator contains both signs?
+        else:
+            a = mpf_div(sa, ta, prec, round_floor)
+            b = mpf_div(sb, ta, prec, round_ceiling)
+            if a == fnan: a = fninf
+            if b == fnan: b = finf
+    return a, b
+
+def mpi_pi(prec):
+    a = mpf_pi(prec, round_floor)
+    b = mpf_pi(prec, round_ceiling)
+    return a, b
+
+def mpi_exp(s, prec):
+    sa, sb = s
+    # exp is monotonic
+    a = mpf_exp(sa, prec, round_floor)
+    b = mpf_exp(sb, prec, round_ceiling)
+    return a, b
+
+def mpi_log(s, prec):
+    sa, sb = s
+    # log is monotonic
+    a = mpf_log(sa, prec, round_floor)
+    b = mpf_log(sb, prec, round_ceiling)
+    return a, b
+
+def mpi_sqrt(s, prec):
+    sa, sb = s
+    # sqrt is monotonic
+    a = mpf_sqrt(sa, prec, round_floor)
+    b = mpf_sqrt(sb, prec, round_ceiling)
+    return a, b
+
+def mpi_atan(s, prec):
+    sa, sb = s
+    a = mpf_atan(sa, prec, round_floor)
+    b = mpf_atan(sb, prec, round_ceiling)
+    return a, b
+
+def mpi_pow_int(s, n, prec):
+    sa, sb = s
+    if n < 0:
+        return mpi_div((fone, fone), mpi_pow_int(s, -n, prec+20), prec)
+    if n == 0:
+        return (fone, fone)
+    if n == 1:
+        return s
+    if n == 2:
+        return mpi_square(s, prec)
+    # Odd -- signs are preserved
+    if n & 1:
+        a = mpf_pow_int(sa, n, prec, round_floor)
+        b = mpf_pow_int(sb, n, prec, round_ceiling)
+    # Even -- important to ensure positivity
+    else:
+        sas = mpf_sign(sa)
+        sbs = mpf_sign(sb)
+        # Nonnegative?
+        if sas >= 0:
+            a = mpf_pow_int(sa, n, prec, round_floor)
+            b = mpf_pow_int(sb, n, prec, round_ceiling)
+        # Nonpositive?
+        elif sbs <= 0:
+            a = mpf_pow_int(sb, n, prec, round_floor)
+            b = mpf_pow_int(sa, n, prec, round_ceiling)
+        # Mixed signs?
+        else:
+            a = fzero
+            # max(-a,b)**n
+            sa = mpf_neg(sa)
+            if mpf_ge(sa, sb):
+                b = mpf_pow_int(sa, n, prec, round_ceiling)
+            else:
+                b = mpf_pow_int(sb, n, prec, round_ceiling)
+    return a, b
+
+def mpi_pow(s, t, prec):
+    ta, tb = t
+    if ta == tb and ta not in (finf, fninf):
+        if ta == from_int(to_int(ta)):
+            return mpi_pow_int(s, to_int(ta), prec)
+        if ta == fhalf:
+            return mpi_sqrt(s, prec)
+    u = mpi_log(s, prec + 20)
+    v = mpi_mul(u, t, prec + 20)
+    return mpi_exp(v, prec)
+
+def MIN(x, y):
+    if mpf_le(x, y):
+        return x
+    return y
+
+def MAX(x, y):
+    if mpf_ge(x, y):
+        return x
+    return y
+
+def cos_sin_quadrant(x, wp):
+    sign, man, exp, bc = x
+    if x == fzero:
+        return fone, fzero, 0
+    # TODO: combine evaluation code to avoid duplicate modulo
+    c, s = mpf_cos_sin(x, wp)
+    t, n, wp_ = mod_pi2(man, exp, exp+bc, 15)
+    if sign:
+        n = -1-n
+    return c, s, n
+
+def mpi_cos_sin(x, prec):
+    a, b = x
+    if a == b == fzero:
+        return (fone, fone), (fzero, fzero)
+    # Guaranteed to contain both -1 and 1
+    if (finf in x) or (fninf in x):
+        return (fnone, fone), (fnone, fone)
+    wp = prec + 20
+    ca, sa, na = cos_sin_quadrant(a, wp)
+    cb, sb, nb = cos_sin_quadrant(b, wp)
+    ca, cb = mpf_min_max([ca, cb])
+    sa, sb = mpf_min_max([sa, sb])
+    # Both functions are monotonic within one quadrant
+    if na == nb:
+        pass
+    # Guaranteed to contain both -1 and 1
+    elif nb - na >= 4:
+        return (fnone, fone), (fnone, fone)
+    else:
+        # cos has maximum between a and b
+        if na//4 != nb//4:
+            cb = fone
+        # cos has minimum
+        if (na-2)//4 != (nb-2)//4:
+            ca = fnone
+        # sin has maximum
+        if (na-1)//4 != (nb-1)//4:
+            sb = fone
+        # sin has minimum
+        if (na-3)//4 != (nb-3)//4:
+            sa = fnone
+    # Perturb to force interval rounding
+    more = from_man_exp((MPZ_ONE<<wp) + (MPZ_ONE<<10), -wp)
+    less = from_man_exp((MPZ_ONE<<wp) - (MPZ_ONE<<10), -wp)
+    def finalize(v, rounding):
+        if bool(v[0]) == (rounding == round_floor):
+            p = more
+        else:
+            p = less
+        v = mpf_mul(v, p, prec, rounding)
+        sign, man, exp, bc = v
+        if exp+bc >= 1:
+            if sign:
+                return fnone
+            return fone
+        return v
+    ca = finalize(ca, round_floor)
+    cb = finalize(cb, round_ceiling)
+    sa = finalize(sa, round_floor)
+    sb = finalize(sb, round_ceiling)
+    return (ca,cb), (sa,sb)
+
+def mpi_cos(x, prec):
+    return mpi_cos_sin(x, prec)[0]
+
+def mpi_sin(x, prec):
+    return mpi_cos_sin(x, prec)[1]
+
+def mpi_tan(x, prec):
+    cos, sin = mpi_cos_sin(x, prec+20)
+    return mpi_div(sin, cos, prec)
+
+def mpi_cot(x, prec):
+    cos, sin = mpi_cos_sin(x, prec+20)
+    return mpi_div(cos, sin, prec)
+
+def mpi_from_str_a_b(x, y, percent, prec):
+    wp = prec + 20
+    xa = from_str(x, wp, round_floor)
+    xb = from_str(x, wp, round_ceiling)
+    #ya = from_str(y, wp, round_floor)
+    y = from_str(y, wp, round_ceiling)
+    assert mpf_ge(y, fzero)
+    if percent:
+        y = mpf_mul(MAX(mpf_abs(xa), mpf_abs(xb)), y, wp, round_ceiling)
+        y = mpf_div(y, from_int(100), wp, round_ceiling)
+    a = mpf_sub(xa, y, prec, round_floor)
+    b = mpf_add(xb, y, prec, round_ceiling)
+    return a, b
+
+def mpi_from_str(s, prec):
+    """
+    Parse an interval number given as a string.
+
+    Allowed forms are
+
+    "-1.23e-27"
+        Any single decimal floating-point literal.
+    "a +- b"  or  "a (b)"
+        a is the midpoint of the interval and b is the half-width
+    "a +- b%"  or  "a (b%)"
+        a is the midpoint of the interval and the half-width
+        is b percent of a (`a \times b / 100`).
+    "[a, b]"
+        The interval indicated directly.
+    "x[y,z]e"
+        x are shared digits, y and z are unequal digits, e is the exponent.
+
+    """
+    e = ValueError("Improperly formed interval number '%s'" % s)
+    s = s.replace(" ", "")
+    wp = prec + 20
+    if "+-" in s:
+        x, y = s.split("+-")
+        return mpi_from_str_a_b(x, y, False, prec)
+    # case 2
+    elif "(" in s:
+        # Don't confuse with a complex number (x,y)
+        if s[0] == "(" or ")" not in s:
+            raise e
+        s = s.replace(")", "")
+        percent = False
+        if "%" in s:
+            if s[-1] != "%":
+                raise e
+            percent = True
+            s = s.replace("%", "")
+        x, y = s.split("(")
+        return mpi_from_str_a_b(x, y, percent, prec)
+    elif "," in s:
+        if ('[' not in s) or (']' not in s):
+            raise e
+        if s[0] == '[':
+            # case 3
+            s = s.replace("[", "")
+            s = s.replace("]", "")
+            a, b = s.split(",")
+            a = from_str(a, prec, round_floor)
+            b = from_str(b, prec, round_ceiling)
+            return a, b
+        else:
+            # case 4
+            x, y = s.split('[')
+            y, z = y.split(',')
+            if 'e' in s:
+                z, e = z.split(']')
+            else:
+                z, e = z.rstrip(']'), ''
+            a = from_str(x+y+e, prec, round_floor)
+            b = from_str(x+z+e, prec, round_ceiling)
+            return a, b
+    else:
+        a = from_str(s, prec, round_floor)
+        b = from_str(s, prec, round_ceiling)
+        return a, b
+
+def mpi_to_str(x, dps, use_spaces=True, brackets='[]', mode='brackets', error_dps=4, **kwargs):
+    """
+    Convert a mpi interval to a string.
+
+    **Arguments**
+
+    *dps*
+        decimal places to use for printing
+    *use_spaces*
+        use spaces for more readable output, defaults to true
+    *brackets*
+        pair of strings (or two-character string) giving left and right brackets
+    *mode*
+        mode of display: 'plusminus', 'percent', 'brackets' (default) or 'diff'
+    *error_dps*
+        limit the error to *error_dps* digits (mode 'plusminus and 'percent')
+
+    Additional keyword arguments are forwarded to the mpf-to-string conversion
+    for the components of the output.
+
+    **Examples**
+
+        >>> from mpmath import mpi, mp
+        >>> mp.dps = 30
+        >>> x = mpi(1, 2)._mpi_
+        >>> mpi_to_str(x, 2, mode='plusminus')
+        '1.5 +- 0.5'
+        >>> mpi_to_str(x, 2, mode='percent')
+        '1.5 (33.33%)'
+        >>> mpi_to_str(x, 2, mode='brackets')
+        '[1.0, 2.0]'
+        >>> mpi_to_str(x, 2, mode='brackets' , brackets=('<', '>'))
+        '<1.0, 2.0>'
+        >>> x = mpi('5.2582327113062393041', '5.2582327113062749951')._mpi_
+        >>> mpi_to_str(x, 15, mode='diff')
+        '5.2582327113062[4, 7]'
+        >>> mpi_to_str(mpi(0)._mpi_, 2, mode='percent')
+        '0.0 (0.0%)'
+
+    """
+    prec = dps_to_prec(dps)
+    wp = prec + 20
+    a, b = x
+    mid = mpi_mid(x, prec)
+    delta = mpi_delta(x, prec)
+    a_str = to_str(a, dps, **kwargs)
+    b_str = to_str(b, dps, **kwargs)
+    mid_str = to_str(mid, dps, **kwargs)
+    sp = ""
+    if use_spaces:
+        sp = " "
+    br1, br2 = brackets
+    if mode == 'plusminus':
+        delta_str = to_str(mpf_shift(delta,-1), dps, **kwargs)
+        s = mid_str + sp + "+-" + sp + delta_str
+    elif mode == 'percent':
+        if mid == fzero:
+            p = fzero
+        else:
+            # p = 100 * delta(x) / (2*mid(x))
+            p = mpf_mul(delta, from_int(100))
+            p = mpf_div(p, mpf_mul(mid, from_int(2)), wp)
+        s = mid_str + sp + "(" + to_str(p, error_dps) + "%)"
+    elif mode == 'brackets':
+        s = br1 + a_str + "," + sp + b_str + br2
+    elif mode == 'diff':
+        # use more digits if str(x.a) and str(x.b) are equal
+        if a_str == b_str:
+            a_str = to_str(a, dps+3, **kwargs)
+            b_str = to_str(b, dps+3, **kwargs)
+        # separate mantissa and exponent
+        a = a_str.split('e')
+        if len(a) == 1:
+            a.append('')
+        b = b_str.split('e')
+        if len(b) == 1:
+            b.append('')
+        if a[1] == b[1]:
+            if a[0] != b[0]:
+                for i in xrange(len(a[0]) + 1):
+                    if a[0][i] != b[0][i]:
+                        break
+                s = (a[0][:i] + br1 + a[0][i:] + ',' + sp + b[0][i:] + br2
+                     + 'e'*min(len(a[1]), 1) + a[1])
+            else: # no difference
+                s = a[0] + br1 + br2 + 'e'*min(len(a[1]), 1) + a[1]
+        else:
+            s = br1 + 'e'.join(a) + ',' + sp + 'e'.join(b) + br2
+    else:
+        raise ValueError("'%s' is unknown mode for printing mpi" % mode)
+    return s
+
+def mpci_add(x, y, prec):
+    a, b = x
+    c, d = y
+    return mpi_add(a, c, prec), mpi_add(b, d, prec)
+
+def mpci_sub(x, y, prec):
+    a, b = x
+    c, d = y
+    return mpi_sub(a, c, prec), mpi_sub(b, d, prec)
+
+def mpci_neg(x, prec=0):
+    a, b = x
+    return mpi_neg(a, prec), mpi_neg(b, prec)
+
+def mpci_pos(x, prec):
+    a, b = x
+    return mpi_pos(a, prec), mpi_pos(b, prec)
+
+def mpci_mul(x, y, prec):
+    # TODO: optimize for real/imag cases
+    a, b = x
+    c, d = y
+    r1 = mpi_mul(a,c)
+    r2 = mpi_mul(b,d)
+    re = mpi_sub(r1,r2,prec)
+    i1 = mpi_mul(a,d)
+    i2 = mpi_mul(b,c)
+    im = mpi_add(i1,i2,prec)
+    return re, im
+
+def mpci_div(x, y, prec):
+    # TODO: optimize for real/imag cases
+    a, b = x
+    c, d = y
+    wp = prec+20
+    m1 = mpi_square(c)
+    m2 = mpi_square(d)
+    m = mpi_add(m1,m2,wp)
+    re = mpi_add(mpi_mul(a,c), mpi_mul(b,d), wp)
+    im = mpi_sub(mpi_mul(b,c), mpi_mul(a,d), wp)
+    re = mpi_div(re, m, prec)
+    im = mpi_div(im, m, prec)
+    return re, im
+
+def mpci_exp(x, prec):
+    a, b = x
+    wp = prec+20
+    r = mpi_exp(a, wp)
+    c, s = mpi_cos_sin(b, wp)
+    a = mpi_mul(r, c, prec)
+    b = mpi_mul(r, s, prec)
+    return a, b
+
+def mpi_shift(x, n):
+    a, b = x
+    return mpf_shift(a,n), mpf_shift(b,n)
+
+def mpi_cosh_sinh(x, prec):
+    # TODO: accuracy for small x
+    wp = prec+20
+    e1 = mpi_exp(x, wp)
+    e2 = mpi_div(mpi_one, e1, wp)
+    c = mpi_add(e1, e2, prec)
+    s = mpi_sub(e1, e2, prec)
+    c = mpi_shift(c, -1)
+    s = mpi_shift(s, -1)
+    return c, s
+
+def mpci_cos(x, prec):
+    a, b = x
+    wp = prec+10
+    c, s = mpi_cos_sin(a, wp)
+    ch, sh = mpi_cosh_sinh(b, wp)
+    re = mpi_mul(c, ch, prec)
+    im = mpi_mul(s, sh, prec)
+    return re, mpi_neg(im)
+
+def mpci_sin(x, prec):
+    a, b = x
+    wp = prec+10
+    c, s = mpi_cos_sin(a, wp)
+    ch, sh = mpi_cosh_sinh(b, wp)
+    re = mpi_mul(s, ch, prec)
+    im = mpi_mul(c, sh, prec)
+    return re, im
+
+def mpci_abs(x, prec):
+    a, b = x
+    if a == mpi_zero:
+        return mpi_abs(b)
+    if b == mpi_zero:
+        return mpi_abs(a)
+    # Important: nonnegative
+    a = mpi_square(a)
+    b = mpi_square(b)
+    t = mpi_add(a, b, prec+20)
+    return mpi_sqrt(t, prec)
+
+def mpi_atan2(y, x, prec):
+    ya, yb = y
+    xa, xb = x
+    # Constrained to the real line
+    if ya == yb == fzero:
+        if mpf_ge(xa, fzero):
+            return mpi_zero
+        return mpi_pi(prec)
+    # Right half-plane
+    if mpf_ge(xa, fzero):
+        if mpf_ge(ya, fzero):
+            a = mpf_atan2(ya, xb, prec, round_floor)
+        else:
+            a = mpf_atan2(ya, xa, prec, round_floor)
+        if mpf_ge(yb, fzero):
+            b = mpf_atan2(yb, xa, prec, round_ceiling)
+        else:
+            b = mpf_atan2(yb, xb, prec, round_ceiling)
+    # Upper half-plane
+    elif mpf_ge(ya, fzero):
+        b = mpf_atan2(ya, xa, prec, round_ceiling)
+        if mpf_le(xb, fzero):
+            a = mpf_atan2(yb, xb, prec, round_floor)
+        else:
+            a = mpf_atan2(ya, xb, prec, round_floor)
+    # Lower half-plane
+    elif mpf_le(yb, fzero):
+        a = mpf_atan2(yb, xa, prec, round_floor)
+        if mpf_le(xb, fzero):
+            b = mpf_atan2(ya, xb, prec, round_ceiling)
+        else:
+            b = mpf_atan2(yb, xb, prec, round_ceiling)
+    # Covering the origin
+    else:
+        b = mpf_pi(prec, round_ceiling)
+        a = mpf_neg(b)
+    return a, b
+
+def mpci_arg(z, prec):
+    x, y = z
+    return mpi_atan2(y, x, prec)
+
+def mpci_log(z, prec):
+    x, y = z
+    re = mpi_log(mpci_abs(z, prec+20), prec)
+    im = mpci_arg(z, prec)
+    return re, im
+
+def mpci_pow(x, y, prec):
+    # TODO: recognize/speed up real cases, integer y
+    yre, yim = y
+    if yim == mpi_zero:
+        ya, yb = yre
+        if ya == yb:
+            sign, man, exp, bc = yb
+            if man and exp >= 0:
+                return mpci_pow_int(x, (-1)**sign * int(man<<exp), prec)
+            # x^0
+            if yb == fzero:
+                return mpci_pow_int(x, 0, prec)
+    wp = prec+20
+    return mpci_exp(mpci_mul(y, mpci_log(x, wp), wp), prec)
+
+def mpci_square(x, prec):
+    a, b = x
+    # (a+bi)^2 = (a^2-b^2) + 2abi
+    re = mpi_sub(mpi_square(a), mpi_square(b), prec)
+    im = mpi_mul(a, b, prec)
+    im = mpi_shift(im, 1)
+    return re, im
+
+def mpci_pow_int(x, n, prec):
+    if n < 0:
+        return mpci_div((mpi_one,mpi_zero), mpci_pow_int(x, -n, prec+20), prec)
+    if n == 0:
+        return mpi_one, mpi_zero
+    if n == 1:
+        return mpci_pos(x, prec)
+    if n == 2:
+        return mpci_square(x, prec)
+    wp = prec + 20
+    result = (mpi_one, mpi_zero)
+    while n:
+        if n & 1:
+            result = mpci_mul(result, x, wp)
+            n -= 1
+        x = mpci_square(x, wp)
+        n >>= 1
+    return mpci_pos(result, prec)
+
+gamma_min_a = from_float(1.46163214496)
+gamma_min_b = from_float(1.46163214497)
+gamma_min = (gamma_min_a, gamma_min_b)
+gamma_mono_imag_a = from_float(-1.1)
+gamma_mono_imag_b = from_float(1.1)
+
+def mpi_overlap(x, y):
+    a, b = x
+    c, d = y
+    if mpf_lt(d, a): return False
+    if mpf_gt(c, b): return False
+    return True
+
+# type = 0 -- gamma
+# type = 1 -- factorial
+# type = 2 -- 1/gamma
+# type = 3 -- log-gamma
+
+def mpi_gamma(z, prec, type=0):
+    a, b = z
+    wp = prec+20
+
+    if type == 1:
+        return mpi_gamma(mpi_add(z, mpi_one, wp), prec, 0)
+
+    # increasing
+    if mpf_gt(a, gamma_min_b):
+        if type == 0:
+            c = mpf_gamma(a, prec, round_floor)
+            d = mpf_gamma(b, prec, round_ceiling)
+        elif type == 2:
+            c = mpf_rgamma(b, prec, round_floor)
+            d = mpf_rgamma(a, prec, round_ceiling)
+        elif type == 3:
+            c = mpf_loggamma(a, prec, round_floor)
+            d = mpf_loggamma(b, prec, round_ceiling)
+    # decreasing
+    elif mpf_gt(a, fzero) and mpf_lt(b, gamma_min_a):
+        if type == 0:
+            c = mpf_gamma(b, prec, round_floor)
+            d = mpf_gamma(a, prec, round_ceiling)
+        elif type == 2:
+            c = mpf_rgamma(a, prec, round_floor)
+            d = mpf_rgamma(b, prec, round_ceiling)
+        elif type == 3:
+            c = mpf_loggamma(b, prec, round_floor)
+            d = mpf_loggamma(a, prec, round_ceiling)
+    else:
+        # TODO: reflection formula
+        znew = mpi_add(z, mpi_one, wp)
+        if type == 0: return mpi_div(mpi_gamma(znew, prec+2, 0), z, prec)
+        if type == 2: return mpi_mul(mpi_gamma(znew, prec+2, 2), z, prec)
+        if type == 3: return mpi_sub(mpi_gamma(znew, prec+2, 3), mpi_log(z, prec+2), prec)
+    return c, d
+
+def mpci_gamma(z, prec, type=0):
+    (a1,a2), (b1,b2) = z
+
+    # Real case
+    if b1 == b2 == fzero and (type != 3 or mpf_gt(a1,fzero)):
+        return mpi_gamma(z, prec, type), mpi_zero
+
+    # Estimate precision
+    wp = prec+20
+    if type != 3:
+        amag = a2[2]+a2[3]
+        bmag = b2[2]+b2[3]
+        if a2 != fzero:
+            mag = max(amag, bmag)
+        else:
+            mag = bmag
+        an = abs(to_int(a2))
+        bn = abs(to_int(b2))
+        absn = max(an, bn)
+        gamma_size = max(0,absn*mag)
+        wp += bitcount(gamma_size)
+
+    # Assume type != 1
+    if type == 1:
+        (a1,a2) = mpi_add((a1,a2), mpi_one, wp); z = (a1,a2), (b1,b2)
+        type = 0
+
+    # Avoid non-monotonic region near the negative real axis
+    if mpf_lt(a1, gamma_min_b):
+        if mpi_overlap((b1,b2), (gamma_mono_imag_a, gamma_mono_imag_b)):
+            # TODO: reflection formula
+            #if mpf_lt(a2, mpf_shift(fone,-1)):
+            #    znew = mpci_sub((mpi_one,mpi_zero),z,wp)
+            #    ...
+            # Recurrence:
+            # gamma(z) = gamma(z+1)/z
+            znew = mpi_add((a1,a2), mpi_one, wp), (b1,b2)
+            if type == 0: return mpci_div(mpci_gamma(znew, prec+2, 0), z, prec)
+            if type == 2: return mpci_mul(mpci_gamma(znew, prec+2, 2), z, prec)
+            if type == 3: return mpci_sub(mpci_gamma(znew, prec+2, 3), mpci_log(z,prec+2), prec)
+
+    # Use monotonicity (except for a small region close to the
+    # origin and near poles)
+    # upper half-plane
+    if mpf_ge(b1, fzero):
+        minre = mpc_loggamma((a1,b2), wp, round_floor)
+        maxre = mpc_loggamma((a2,b1), wp, round_ceiling)
+        minim = mpc_loggamma((a1,b1), wp, round_floor)
+        maxim = mpc_loggamma((a2,b2), wp, round_ceiling)
+    # lower half-plane
+    elif mpf_le(b2, fzero):
+        minre = mpc_loggamma((a1,b1), wp, round_floor)
+        maxre = mpc_loggamma((a2,b2), wp, round_ceiling)
+        minim = mpc_loggamma((a2,b1), wp, round_floor)
+        maxim = mpc_loggamma((a1,b2), wp, round_ceiling)
+    # crosses real axis
+    else:
+        maxre = mpc_loggamma((a2,fzero), wp, round_ceiling)
+        # stretches more into the lower half-plane
+        if mpf_gt(mpf_neg(b1), b2):
+            minre = mpc_loggamma((a1,b1), wp, round_ceiling)
+        else:
+            minre = mpc_loggamma((a1,b2), wp, round_ceiling)
+        minim = mpc_loggamma((a2,b1), wp, round_floor)
+        maxim = mpc_loggamma((a2,b2), wp, round_floor)
+
+    w = (minre[0], maxre[0]), (minim[1], maxim[1])
+    if type == 3:
+        return mpi_pos(w[0], prec), mpi_pos(w[1], prec)
+    if type == 2:
+        w = mpci_neg(w)
+    return mpci_exp(w, prec)
+
+def mpi_loggamma(z, prec): return mpi_gamma(z, prec, type=3)
+def mpci_loggamma(z, prec): return mpci_gamma(z, prec, type=3)
+
+def mpi_rgamma(z, prec): return mpi_gamma(z, prec, type=2)
+def mpci_rgamma(z, prec): return mpci_gamma(z, prec, type=2)
+
+def mpi_factorial(z, prec): return mpi_gamma(z, prec, type=1)
+def mpci_factorial(z, prec): return mpci_gamma(z, prec, type=1)