diff --git a/.gitattributes b/.gitattributes index dba10c3c51eba248bbf57d00901ebbe88a4c3a97..2e3cec4dc675eac013d2e4de4cd57aa65bd9795f 100644 --- a/.gitattributes +++ b/.gitattributes @@ -64,3 +64,6 @@ venv/lib/python3.12/site-packages/huggingface_hub/inference/__pycache__/_client. venv/lib/python3.12/site-packages/huggingface_hub/inference/_generated/__pycache__/_async_client.cpython-312.pyc filter=lfs diff=lfs merge=lfs -text venv/lib/python3.12/site-packages/jinja2/__pycache__/compiler.cpython-312.pyc filter=lfs diff=lfs merge=lfs -text venv/lib/python3.12/site-packages/joblib/test/__pycache__/test_parallel.cpython-312.pyc filter=lfs diff=lfs merge=lfs -text +venv/lib/python3.12/site-packages/mpmath/__pycache__/function_docs.cpython-312.pyc filter=lfs diff=lfs merge=lfs -text +venv/lib/python3.12/site-packages/mpmath/tests/__pycache__/test_fp.cpython-312.pyc filter=lfs diff=lfs merge=lfs -text +venv/lib/python3.12/site-packages/mpmath/tests/__pycache__/test_functions2.cpython-312.pyc filter=lfs diff=lfs merge=lfs -text diff --git a/venv/lib/python3.12/site-packages/markdown_it/common/html_blocks.py b/venv/lib/python3.12/site-packages/markdown_it/common/html_blocks.py new file mode 100644 index 0000000000000000000000000000000000000000..8a3b0b7d5ab7bc174a3c96cbc8976816278a6c21 --- /dev/null +++ b/venv/lib/python3.12/site-packages/markdown_it/common/html_blocks.py @@ -0,0 +1,69 @@ +"""List of valid html blocks names, according to commonmark spec +http://jgm.github.io/CommonMark/spec.html#html-blocks +""" + +# see https://spec.commonmark.org/0.31.2/#html-blocks +block_names = [ + "address", + "article", + "aside", + "base", + "basefont", + "blockquote", + "body", + "caption", + "center", + "col", + "colgroup", + "dd", + "details", + "dialog", + "dir", + "div", + "dl", + "dt", + "fieldset", + "figcaption", + "figure", + "footer", + "form", + "frame", + "frameset", + "h1", + "h2", + "h3", + "h4", + "h5", + "h6", + "head", + "header", + "hr", + "html", + "iframe", + "legend", + "li", + "link", + "main", + "menu", + "menuitem", + "nav", + "noframes", + "ol", + "optgroup", + "option", + "p", + "param", + "search", + "section", + "summary", + "table", + "tbody", + "td", + "tfoot", + "th", + "thead", + "title", + "tr", + "track", + "ul", +] diff --git a/venv/lib/python3.12/site-packages/markdown_it/common/html_re.py b/venv/lib/python3.12/site-packages/markdown_it/common/html_re.py new file mode 100644 index 0000000000000000000000000000000000000000..ab822c5fc487c5a494966604a1e89b57a06e0564 --- /dev/null +++ b/venv/lib/python3.12/site-packages/markdown_it/common/html_re.py @@ -0,0 +1,39 @@ +"""Regexps to match html elements""" + +import re + +attr_name = "[a-zA-Z_:][a-zA-Z0-9:._-]*" + +unquoted = "[^\"'=<>`\\x00-\\x20]+" +single_quoted = "'[^']*'" +double_quoted = '"[^"]*"' + +attr_value = "(?:" + unquoted + "|" + single_quoted + "|" + double_quoted + ")" + +attribute = "(?:\\s+" + attr_name + "(?:\\s*=\\s*" + attr_value + ")?)" + +open_tag = "<[A-Za-z][A-Za-z0-9\\-]*" + attribute + "*\\s*\\/?>" + +close_tag = "<\\/[A-Za-z][A-Za-z0-9\\-]*\\s*>" +comment = "" +processing = "<[?][\\s\\S]*?[?]>" +declaration = "]*>" +cdata = "" + +HTML_TAG_RE = re.compile( + "^(?:" + + open_tag + + "|" + + close_tag + + "|" + + comment + + "|" + + processing + + "|" + + declaration + + "|" + + cdata + + ")" +) +HTML_OPEN_CLOSE_TAG_STR = "^(?:" + open_tag + "|" + close_tag + ")" +HTML_OPEN_CLOSE_TAG_RE = re.compile(HTML_OPEN_CLOSE_TAG_STR) diff --git a/venv/lib/python3.12/site-packages/markdown_it/common/normalize_url.py b/venv/lib/python3.12/site-packages/markdown_it/common/normalize_url.py new file mode 100644 index 0000000000000000000000000000000000000000..92720b31621b0f6b4ac853179d886cb58e4e2f36 --- /dev/null +++ b/venv/lib/python3.12/site-packages/markdown_it/common/normalize_url.py @@ -0,0 +1,81 @@ +from __future__ import annotations + +from collections.abc import Callable +from contextlib import suppress +import re +from urllib.parse import quote, unquote, urlparse, urlunparse # noqa: F401 + +import mdurl + +from .. import _punycode + +RECODE_HOSTNAME_FOR = ("http:", "https:", "mailto:") + + +def normalizeLink(url: str) -> str: + """Normalize destination URLs in links + + :: + + [label]: destination 'title' + ^^^^^^^^^^^ + """ + parsed = mdurl.parse(url, slashes_denote_host=True) + + # Encode hostnames in urls like: + # `http://host/`, `https://host/`, `mailto:user@host`, `//host/` + # + # We don't encode unknown schemas, because it's likely that we encode + # something we shouldn't (e.g. `skype:name` treated as `skype:host`) + # + if parsed.hostname and ( + not parsed.protocol or parsed.protocol in RECODE_HOSTNAME_FOR + ): + with suppress(Exception): + parsed = parsed._replace(hostname=_punycode.to_ascii(parsed.hostname)) + + return mdurl.encode(mdurl.format(parsed)) + + +def normalizeLinkText(url: str) -> str: + """Normalize autolink content + + :: + + + ~~~~~~~~~~~ + """ + parsed = mdurl.parse(url, slashes_denote_host=True) + + # Encode hostnames in urls like: + # `http://host/`, `https://host/`, `mailto:user@host`, `//host/` + # + # We don't encode unknown schemas, because it's likely that we encode + # something we shouldn't (e.g. `skype:name` treated as `skype:host`) + # + if parsed.hostname and ( + not parsed.protocol or parsed.protocol in RECODE_HOSTNAME_FOR + ): + with suppress(Exception): + parsed = parsed._replace(hostname=_punycode.to_unicode(parsed.hostname)) + + # add '%' to exclude list because of https://github.com/markdown-it/markdown-it/issues/720 + return mdurl.decode(mdurl.format(parsed), mdurl.DECODE_DEFAULT_CHARS + "%") + + +BAD_PROTO_RE = re.compile(r"^(vbscript|javascript|file|data):") +GOOD_DATA_RE = re.compile(r"^data:image\/(gif|png|jpeg|webp);") + + +def validateLink(url: str, validator: Callable[[str], bool] | None = None) -> bool: + """Validate URL link is allowed in output. + + This validator can prohibit more than really needed to prevent XSS. + It's a tradeoff to keep code simple and to be secure by default. + + Note: url should be normalized at this point, and existing entities decoded. + """ + if validator is not None: + return validator(url) + url = url.strip().lower() + return bool(GOOD_DATA_RE.search(url)) if BAD_PROTO_RE.search(url) else True diff --git a/venv/lib/python3.12/site-packages/markdown_it/common/utils.py b/venv/lib/python3.12/site-packages/markdown_it/common/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..11bda644c260b714cf010ed44d2ed345de80314e --- /dev/null +++ b/venv/lib/python3.12/site-packages/markdown_it/common/utils.py @@ -0,0 +1,313 @@ +"""Utilities for parsing source text""" + +from __future__ import annotations + +import re +from re import Match +from typing import TypeVar +import unicodedata + +from .entities import entities + + +def charCodeAt(src: str, pos: int) -> int | None: + """ + Returns the Unicode value of the character at the specified location. + + @param - index The zero-based index of the desired character. + If there is no character at the specified index, NaN is returned. + + This was added for compatibility with python + """ + try: + return ord(src[pos]) + except IndexError: + return None + + +def charStrAt(src: str, pos: int) -> str | None: + """ + Returns the Unicode value of the character at the specified location. + + @param - index The zero-based index of the desired character. + If there is no character at the specified index, NaN is returned. + + This was added for compatibility with python + """ + try: + return src[pos] + except IndexError: + return None + + +_ItemTV = TypeVar("_ItemTV") + + +def arrayReplaceAt( + src: list[_ItemTV], pos: int, newElements: list[_ItemTV] +) -> list[_ItemTV]: + """ + Remove element from array and put another array at those position. + Useful for some operations with tokens + """ + return src[:pos] + newElements + src[pos + 1 :] + + +def isValidEntityCode(c: int) -> bool: + # broken sequence + if c >= 0xD800 and c <= 0xDFFF: + return False + # never used + if c >= 0xFDD0 and c <= 0xFDEF: + return False + if ((c & 0xFFFF) == 0xFFFF) or ((c & 0xFFFF) == 0xFFFE): + return False + # control codes + if c >= 0x00 and c <= 0x08: + return False + if c == 0x0B: + return False + if c >= 0x0E and c <= 0x1F: + return False + if c >= 0x7F and c <= 0x9F: + return False + # out of range + return not (c > 0x10FFFF) + + +def fromCodePoint(c: int) -> str: + """Convert ordinal to unicode. + + Note, in the original Javascript two string characters were required, + for codepoints larger than `0xFFFF`. + But Python 3 can represent any unicode codepoint in one character. + """ + return chr(c) + + +# UNESCAPE_MD_RE = re.compile(r'\\([!"#$%&\'()*+,\-.\/:;<=>?@[\\\]^_`{|}~])') +# ENTITY_RE_g = re.compile(r'&([a-z#][a-z0-9]{1,31})', re.IGNORECASE) +UNESCAPE_ALL_RE = re.compile( + r'\\([!"#$%&\'()*+,\-.\/:;<=>?@[\\\]^_`{|}~])' + "|" + r"&([a-z#][a-z0-9]{1,31});", + re.IGNORECASE, +) +DIGITAL_ENTITY_BASE10_RE = re.compile(r"#([0-9]{1,8})") +DIGITAL_ENTITY_BASE16_RE = re.compile(r"#x([a-f0-9]{1,8})", re.IGNORECASE) + + +def replaceEntityPattern(match: str, name: str) -> str: + """Convert HTML entity patterns, + see https://spec.commonmark.org/0.30/#entity-references + """ + if name in entities: + return entities[name] + + code: None | int = None + if pat := DIGITAL_ENTITY_BASE10_RE.fullmatch(name): + code = int(pat.group(1), 10) + elif pat := DIGITAL_ENTITY_BASE16_RE.fullmatch(name): + code = int(pat.group(1), 16) + + if code is not None and isValidEntityCode(code): + return fromCodePoint(code) + + return match + + +def unescapeAll(string: str) -> str: + def replacer_func(match: Match[str]) -> str: + escaped = match.group(1) + if escaped: + return escaped + entity = match.group(2) + return replaceEntityPattern(match.group(), entity) + + if "\\" not in string and "&" not in string: + return string + return UNESCAPE_ALL_RE.sub(replacer_func, string) + + +ESCAPABLE = r"""\\!"#$%&'()*+,./:;<=>?@\[\]^`{}|_~-""" +ESCAPE_CHAR = re.compile(r"\\([" + ESCAPABLE + r"])") + + +def stripEscape(string: str) -> str: + """Strip escape \\ characters""" + return ESCAPE_CHAR.sub(r"\1", string) + + +def escapeHtml(raw: str) -> str: + """Replace special characters "&", "<", ">" and '"' to HTML-safe sequences.""" + # like html.escape, but without escaping single quotes + raw = raw.replace("&", "&") # Must be done first! + raw = raw.replace("<", "<") + raw = raw.replace(">", ">") + raw = raw.replace('"', """) + return raw + + +# ////////////////////////////////////////////////////////////////////////////// + +REGEXP_ESCAPE_RE = re.compile(r"[.?*+^$[\]\\(){}|-]") + + +def escapeRE(string: str) -> str: + string = REGEXP_ESCAPE_RE.sub("\\$&", string) + return string + + +# ////////////////////////////////////////////////////////////////////////////// + + +def isSpace(code: int | None) -> bool: + """Check if character code is a whitespace.""" + return code in (0x09, 0x20) + + +def isStrSpace(ch: str | None) -> bool: + """Check if character is a whitespace.""" + return ch in ("\t", " ") + + +MD_WHITESPACE = { + 0x09, # \t + 0x0A, # \n + 0x0B, # \v + 0x0C, # \f + 0x0D, # \r + 0x20, # space + 0xA0, + 0x1680, + 0x202F, + 0x205F, + 0x3000, +} + + +def isWhiteSpace(code: int) -> bool: + r"""Zs (unicode class) || [\t\f\v\r\n]""" + if code >= 0x2000 and code <= 0x200A: + return True + return code in MD_WHITESPACE + + +# ////////////////////////////////////////////////////////////////////////////// + + +def isPunctChar(ch: str) -> bool: + """Check if character is a punctuation character.""" + return unicodedata.category(ch).startswith(("P", "S")) + + +MD_ASCII_PUNCT = { + 0x21, # /* ! */ + 0x22, # /* " */ + 0x23, # /* # */ + 0x24, # /* $ */ + 0x25, # /* % */ + 0x26, # /* & */ + 0x27, # /* ' */ + 0x28, # /* ( */ + 0x29, # /* ) */ + 0x2A, # /* * */ + 0x2B, # /* + */ + 0x2C, # /* , */ + 0x2D, # /* - */ + 0x2E, # /* . */ + 0x2F, # /* / */ + 0x3A, # /* : */ + 0x3B, # /* ; */ + 0x3C, # /* < */ + 0x3D, # /* = */ + 0x3E, # /* > */ + 0x3F, # /* ? */ + 0x40, # /* @ */ + 0x5B, # /* [ */ + 0x5C, # /* \ */ + 0x5D, # /* ] */ + 0x5E, # /* ^ */ + 0x5F, # /* _ */ + 0x60, # /* ` */ + 0x7B, # /* { */ + 0x7C, # /* | */ + 0x7D, # /* } */ + 0x7E, # /* ~ */ +} + + +def isMdAsciiPunct(ch: int) -> bool: + """Markdown ASCII punctuation characters. + + :: + + !, ", #, $, %, &, ', (, ), *, +, ,, -, ., /, :, ;, <, =, >, ?, @, [, \\, ], ^, _, `, {, |, }, or ~ + + See http://spec.commonmark.org/0.15/#ascii-punctuation-character + + Don't confuse with unicode punctuation !!! It lacks some chars in ascii range. + + """ + return ch in MD_ASCII_PUNCT + + +def normalizeReference(string: str) -> str: + """Helper to unify [reference labels].""" + # Trim and collapse whitespace + # + string = re.sub(r"\s+", " ", string.strip()) + + # In node v10 'ẞ'.toLowerCase() === 'Ṿ', which is presumed to be a bug + # fixed in v12 (couldn't find any details). + # + # So treat this one as a special case + # (remove this when node v10 is no longer supported). + # + # if ('ẞ'.toLowerCase() === 'Ṿ') { + # str = str.replace(/ẞ/g, 'ß') + # } + + # .toLowerCase().toUpperCase() should get rid of all differences + # between letter variants. + # + # Simple .toLowerCase() doesn't normalize 125 code points correctly, + # and .toUpperCase doesn't normalize 6 of them (list of exceptions: + # İ, ϴ, ẞ, Ω, K, Å - those are already uppercased, but have differently + # uppercased versions). + # + # Here's an example showing how it happens. Lets take greek letter omega: + # uppercase U+0398 (Θ), U+03f4 (ϴ) and lowercase U+03b8 (θ), U+03d1 (ϑ) + # + # Unicode entries: + # 0398;GREEK CAPITAL LETTER THETA;Lu;0;L;;;;;N;;;;03B8 + # 03B8;GREEK SMALL LETTER THETA;Ll;0;L;;;;;N;;;0398;;0398 + # 03D1;GREEK THETA SYMBOL;Ll;0;L; 03B8;;;;N;GREEK SMALL LETTER SCRIPT THETA;;0398;;0398 + # 03F4;GREEK CAPITAL THETA SYMBOL;Lu;0;L; 0398;;;;N;;;;03B8 + # + # Case-insensitive comparison should treat all of them as equivalent. + # + # But .toLowerCase() doesn't change ϑ (it's already lowercase), + # and .toUpperCase() doesn't change ϴ (already uppercase). + # + # Applying first lower then upper case normalizes any character: + # '\u0398\u03f4\u03b8\u03d1'.toLowerCase().toUpperCase() === '\u0398\u0398\u0398\u0398' + # + # Note: this is equivalent to unicode case folding; unicode normalization + # is a different step that is not required here. + # + # Final result should be uppercased, because it's later stored in an object + # (this avoid a conflict with Object.prototype members, + # most notably, `__proto__`) + # + return string.lower().upper() + + +LINK_OPEN_RE = re.compile(r"^\s]", flags=re.IGNORECASE) +LINK_CLOSE_RE = re.compile(r"^", flags=re.IGNORECASE) + + +def isLinkOpen(string: str) -> bool: + return bool(LINK_OPEN_RE.search(string)) + + +def isLinkClose(string: str) -> bool: + return bool(LINK_CLOSE_RE.search(string)) diff --git a/venv/lib/python3.12/site-packages/markdown_it/helpers/__init__.py b/venv/lib/python3.12/site-packages/markdown_it/helpers/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..f4e2cd21b94b9ee9ecb0cac21da619233a5258b9 --- /dev/null +++ b/venv/lib/python3.12/site-packages/markdown_it/helpers/__init__.py @@ -0,0 +1,6 @@ +"""Functions for parsing Links""" + +__all__ = ("parseLinkDestination", "parseLinkLabel", "parseLinkTitle") +from .parse_link_destination import parseLinkDestination +from .parse_link_label import parseLinkLabel +from .parse_link_title import parseLinkTitle diff --git a/venv/lib/python3.12/site-packages/markdown_it/helpers/__pycache__/__init__.cpython-312.pyc b/venv/lib/python3.12/site-packages/markdown_it/helpers/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..15657dfb0f89be5cc85052ea829ab051fef6fc86 Binary files /dev/null and b/venv/lib/python3.12/site-packages/markdown_it/helpers/__pycache__/__init__.cpython-312.pyc differ diff --git a/venv/lib/python3.12/site-packages/markdown_it/helpers/__pycache__/parse_link_destination.cpython-312.pyc b/venv/lib/python3.12/site-packages/markdown_it/helpers/__pycache__/parse_link_destination.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..7fb7a3ccd011ca56856dbb7d0fb58740d8ec08f2 Binary files /dev/null and b/venv/lib/python3.12/site-packages/markdown_it/helpers/__pycache__/parse_link_destination.cpython-312.pyc differ diff --git a/venv/lib/python3.12/site-packages/markdown_it/helpers/__pycache__/parse_link_label.cpython-312.pyc b/venv/lib/python3.12/site-packages/markdown_it/helpers/__pycache__/parse_link_label.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..669b47009b8850b0c1a736203d81ed83b1f2baec Binary files /dev/null and b/venv/lib/python3.12/site-packages/markdown_it/helpers/__pycache__/parse_link_label.cpython-312.pyc differ diff --git a/venv/lib/python3.12/site-packages/markdown_it/helpers/__pycache__/parse_link_title.cpython-312.pyc b/venv/lib/python3.12/site-packages/markdown_it/helpers/__pycache__/parse_link_title.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a1b2f13b9ae6f13875bd5a6a0be6c214f6f9fda4 Binary files /dev/null and b/venv/lib/python3.12/site-packages/markdown_it/helpers/__pycache__/parse_link_title.cpython-312.pyc differ diff --git a/venv/lib/python3.12/site-packages/markdown_it/helpers/parse_link_destination.py b/venv/lib/python3.12/site-packages/markdown_it/helpers/parse_link_destination.py new file mode 100644 index 0000000000000000000000000000000000000000..c98323c056e0103a12849671fbbac58dc76de4b4 --- /dev/null +++ b/venv/lib/python3.12/site-packages/markdown_it/helpers/parse_link_destination.py @@ -0,0 +1,83 @@ +""" +Parse link destination +""" + +from ..common.utils import charCodeAt, unescapeAll + + +class _Result: + __slots__ = ("ok", "pos", "str") + + def __init__(self) -> None: + self.ok = False + self.pos = 0 + self.str = "" + + +def parseLinkDestination(string: str, pos: int, maximum: int) -> _Result: + start = pos + result = _Result() + + if charCodeAt(string, pos) == 0x3C: # /* < */ + pos += 1 + while pos < maximum: + code = charCodeAt(string, pos) + if code == 0x0A: # /* \n */) + return result + if code == 0x3C: # / * < * / + return result + if code == 0x3E: # /* > */) { + result.pos = pos + 1 + result.str = unescapeAll(string[start + 1 : pos]) + result.ok = True + return result + + if code == 0x5C and pos + 1 < maximum: # \ + pos += 2 + continue + + pos += 1 + + # no closing '>' + return result + + # this should be ... } else { ... branch + + level = 0 + while pos < maximum: + code = charCodeAt(string, pos) + + if code is None or code == 0x20: + break + + # ascii control characters + if code < 0x20 or code == 0x7F: + break + + if code == 0x5C and pos + 1 < maximum: + if charCodeAt(string, pos + 1) == 0x20: + break + pos += 2 + continue + + if code == 0x28: # /* ( */) + level += 1 + if level > 32: + return result + + if code == 0x29: # /* ) */) + if level == 0: + break + level -= 1 + + pos += 1 + + if start == pos: + return result + if level != 0: + return result + + result.str = unescapeAll(string[start:pos]) + result.pos = pos + result.ok = True + return result diff --git a/venv/lib/python3.12/site-packages/markdown_it/helpers/parse_link_label.py b/venv/lib/python3.12/site-packages/markdown_it/helpers/parse_link_label.py new file mode 100644 index 0000000000000000000000000000000000000000..c80da5a7ec54521281dac45b66977404f5384491 --- /dev/null +++ b/venv/lib/python3.12/site-packages/markdown_it/helpers/parse_link_label.py @@ -0,0 +1,44 @@ +""" +Parse link label + +this function assumes that first character ("[") already matches +returns the end of the label + +""" + +from markdown_it.rules_inline import StateInline + + +def parseLinkLabel(state: StateInline, start: int, disableNested: bool = False) -> int: + labelEnd = -1 + oldPos = state.pos + found = False + + state.pos = start + 1 + level = 1 + + while state.pos < state.posMax: + marker = state.src[state.pos] + if marker == "]": + level -= 1 + if level == 0: + found = True + break + + prevPos = state.pos + state.md.inline.skipToken(state) + if marker == "[": + if prevPos == state.pos - 1: + # increase level if we find text `[`, + # which is not a part of any token + level += 1 + elif disableNested: + state.pos = oldPos + return -1 + if found: + labelEnd = state.pos + + # restore old state + state.pos = oldPos + + return labelEnd diff --git a/venv/lib/python3.12/site-packages/markdown_it/helpers/parse_link_title.py b/venv/lib/python3.12/site-packages/markdown_it/helpers/parse_link_title.py new file mode 100644 index 0000000000000000000000000000000000000000..a38ff0d98ac7feaba80fd67f3c8f0d0454dde47f --- /dev/null +++ b/venv/lib/python3.12/site-packages/markdown_it/helpers/parse_link_title.py @@ -0,0 +1,75 @@ +"""Parse link title""" + +from ..common.utils import charCodeAt, unescapeAll + + +class _State: + __slots__ = ("can_continue", "marker", "ok", "pos", "str") + + def __init__(self) -> None: + self.ok = False + """if `true`, this is a valid link title""" + self.can_continue = False + """if `true`, this link can be continued on the next line""" + self.pos = 0 + """if `ok`, it's the position of the first character after the closing marker""" + self.str = "" + """if `ok`, it's the unescaped title""" + self.marker = 0 + """expected closing marker character code""" + + def __str__(self) -> str: + return self.str + + +def parseLinkTitle( + string: str, start: int, maximum: int, prev_state: _State | None = None +) -> _State: + """Parse link title within `str` in [start, max] range, + or continue previous parsing if `prev_state` is defined (equal to result of last execution). + """ + pos = start + state = _State() + + if prev_state is not None: + # this is a continuation of a previous parseLinkTitle call on the next line, + # used in reference links only + state.str = prev_state.str + state.marker = prev_state.marker + else: + if pos >= maximum: + return state + + marker = charCodeAt(string, pos) + + # /* " */ /* ' */ /* ( */ + if marker != 0x22 and marker != 0x27 and marker != 0x28: + return state + + start += 1 + pos += 1 + + # if opening marker is "(", switch it to closing marker ")" + if marker == 0x28: + marker = 0x29 + + state.marker = marker + + while pos < maximum: + code = charCodeAt(string, pos) + if code == state.marker: + state.pos = pos + 1 + state.str += unescapeAll(string[start:pos]) + state.ok = True + return state + elif code == 0x28 and state.marker == 0x29: # /* ( */ /* ) */ + return state + elif code == 0x5C and pos + 1 < maximum: # /* \ */ + pos += 1 + + pos += 1 + + # no closing marker found, but this link title may continue on the next line (for references) + state.can_continue = True + state.str += unescapeAll(string[start:pos]) + return state diff --git a/venv/lib/python3.12/site-packages/markdown_it/main.py b/venv/lib/python3.12/site-packages/markdown_it/main.py new file mode 100644 index 0000000000000000000000000000000000000000..87835e541f1d8500ef874cbf3450aac19e5e451b --- /dev/null +++ b/venv/lib/python3.12/site-packages/markdown_it/main.py @@ -0,0 +1,351 @@ +from __future__ import annotations + +from collections.abc import Callable, Generator, Iterable, Mapping, MutableMapping +from contextlib import contextmanager +from typing import Any, Literal, overload + +from . import helpers, presets +from .common import normalize_url, utils +from .parser_block import ParserBlock +from .parser_core import ParserCore +from .parser_inline import ParserInline +from .renderer import RendererHTML, RendererProtocol +from .rules_core.state_core import StateCore +from .token import Token +from .utils import EnvType, OptionsDict, OptionsType, PresetType + +try: + import linkify_it +except ModuleNotFoundError: + linkify_it = None + + +_PRESETS: dict[str, PresetType] = { + "default": presets.default.make(), + "js-default": presets.js_default.make(), + "zero": presets.zero.make(), + "commonmark": presets.commonmark.make(), + "gfm-like": presets.gfm_like.make(), + "gfm-like2": presets.gfm_like2.make(), +} + + +class MarkdownIt: + def __init__( + self, + config: str | PresetType = "commonmark", + options_update: Mapping[str, Any] | None = None, + *, + renderer_cls: Callable[[MarkdownIt], RendererProtocol] = RendererHTML, + ): + """Main parser class + + :param config: name of configuration to load or a pre-defined dictionary + :param options_update: dictionary that will be merged into ``config["options"]`` + :param renderer_cls: the class to load as the renderer: + ``self.renderer = renderer_cls(self) + """ + # add modules + self.utils = utils + self.helpers = helpers + + # initialise classes + self.inline = ParserInline() + self.block = ParserBlock() + self.core = ParserCore() + self.renderer = renderer_cls(self) + self.linkify = linkify_it.LinkifyIt() if linkify_it else None + + # set the configuration + if options_update and not isinstance(options_update, Mapping): + # catch signature change where renderer_cls was not used as a key-word + raise TypeError( + f"options_update should be a mapping: {options_update}" + "\n(Perhaps you intended this to be the renderer_cls?)" + ) + self.configure(config, options_update=options_update) + + def __repr__(self) -> str: + return f"{self.__class__.__module__}.{self.__class__.__name__}()" + + @overload + def __getitem__(self, name: Literal["inline"]) -> ParserInline: ... + + @overload + def __getitem__(self, name: Literal["block"]) -> ParserBlock: ... + + @overload + def __getitem__(self, name: Literal["core"]) -> ParserCore: ... + + @overload + def __getitem__(self, name: Literal["renderer"]) -> RendererProtocol: ... + + @overload + def __getitem__(self, name: str) -> Any: ... + + def __getitem__(self, name: str) -> Any: + return { + "inline": self.inline, + "block": self.block, + "core": self.core, + "renderer": self.renderer, + }[name] + + def set(self, options: OptionsType) -> None: + """Set parser options (in the same format as in constructor). + Probably, you will never need it, but you can change options after constructor call. + + __Note:__ To achieve the best possible performance, don't modify a + `markdown-it` instance options on the fly. If you need multiple configurations + it's best to create multiple instances and initialize each with separate config. + """ + self.options = OptionsDict(options) + + def configure( + self, presets: str | PresetType, options_update: Mapping[str, Any] | None = None + ) -> MarkdownIt: + """Batch load of all options and component settings. + This is an internal method, and you probably will not need it. + But if you will - see available presets and data structure + [here](https://github.com/markdown-it/markdown-it/tree/master/lib/presets) + + We strongly recommend to use presets instead of direct config loads. + That will give better compatibility with next versions. + """ + if isinstance(presets, str): + if presets not in _PRESETS: + raise KeyError(f"Wrong `markdown-it` preset '{presets}', check name") + config = _PRESETS[presets] + else: + config = presets + + if not config: + raise ValueError("Wrong `markdown-it` config, can't be empty") + + options = config.get("options", {}) or {} + if options_update: + options = {**options, **options_update} # type: ignore + + self.set(options) + + if "components" in config: + for name, component in config["components"].items(): + rules = component.get("rules", None) + if rules: + self[name].ruler.enableOnly(rules) + rules2 = component.get("rules2", None) + if rules2: + self[name].ruler2.enableOnly(rules2) + + return self + + def get_all_rules(self) -> dict[str, list[str]]: + """Return the names of all active rules.""" + rules = { + chain: self[chain].ruler.get_all_rules() + for chain in ["core", "block", "inline"] + } + rules["inline2"] = self.inline.ruler2.get_all_rules() + return rules + + def get_active_rules(self) -> dict[str, list[str]]: + """Return the names of all active rules.""" + rules = { + chain: self[chain].ruler.get_active_rules() + for chain in ["core", "block", "inline"] + } + rules["inline2"] = self.inline.ruler2.get_active_rules() + return rules + + def enable( + self, names: str | Iterable[str], ignoreInvalid: bool = False + ) -> MarkdownIt: + """Enable list or rules. (chainable) + + :param names: rule name or list of rule names to enable. + :param ignoreInvalid: set `true` to ignore errors when rule not found. + + It will automatically find appropriate components, + containing rules with given names. If rule not found, and `ignoreInvalid` + not set - throws exception. + + Example:: + + md = MarkdownIt().enable(['sub', 'sup']).disable('smartquotes') + + """ + result = [] + + if isinstance(names, str): + names = [names] + + for chain in ["core", "block", "inline"]: + result.extend(self[chain].ruler.enable(names, True)) + result.extend(self.inline.ruler2.enable(names, True)) + + missed = [name for name in names if name not in result] + if missed and not ignoreInvalid: + raise ValueError(f"MarkdownIt. Failed to enable unknown rule(s): {missed}") + + return self + + def disable( + self, names: str | Iterable[str], ignoreInvalid: bool = False + ) -> MarkdownIt: + """The same as [[MarkdownIt.enable]], but turn specified rules off. (chainable) + + :param names: rule name or list of rule names to disable. + :param ignoreInvalid: set `true` to ignore errors when rule not found. + + """ + result = [] + + if isinstance(names, str): + names = [names] + + for chain in ["core", "block", "inline"]: + result.extend(self[chain].ruler.disable(names, True)) + result.extend(self.inline.ruler2.disable(names, True)) + + missed = [name for name in names if name not in result] + if missed and not ignoreInvalid: + raise ValueError(f"MarkdownIt. Failed to disable unknown rule(s): {missed}") + return self + + @contextmanager + def reset_rules(self) -> Generator[None, None, None]: + """A context manager, that will reset the current enabled rules on exit.""" + chain_rules = self.get_active_rules() + yield + for chain, rules in chain_rules.items(): + if chain != "inline2": + self[chain].ruler.enableOnly(rules) + self.inline.ruler2.enableOnly(chain_rules["inline2"]) + + def add_render_rule( + self, name: str, function: Callable[..., Any], fmt: str = "html" + ) -> None: + """Add a rule for rendering a particular Token type. + + Only applied when ``renderer.__output__ == fmt`` + """ + if self.renderer.__output__ == fmt: + self.renderer.rules[name] = function.__get__(self.renderer) # type: ignore + + def use( + self, plugin: Callable[..., None], *params: Any, **options: Any + ) -> MarkdownIt: + """Load specified plugin with given params into current parser instance. (chainable) + + It's just a sugar to call `plugin(md, params)` with curring. + + Example:: + + def func(tokens, idx): + tokens[idx].content = tokens[idx].content.replace('foo', 'bar') + md = MarkdownIt().use(plugin, 'foo_replace', 'text', func) + + """ + plugin(self, *params, **options) + return self + + def parse(self, src: str, env: EnvType | None = None) -> list[Token]: + """Parse the source string to a token stream + + :param src: source string + :param env: environment sandbox + + Parse input string and return list of block tokens (special token type + "inline" will contain list of inline tokens). + + `env` is used to pass data between "distributed" rules and return additional + metadata like reference info, needed for the renderer. It also can be used to + inject data in specific cases. Usually, you will be ok to pass `{}`, + and then pass updated object to renderer. + """ + env = {} if env is None else env + if not isinstance(env, MutableMapping): + raise TypeError(f"Input data should be a MutableMapping, not {type(env)}") + if not isinstance(src, str): + raise TypeError(f"Input data should be a string, not {type(src)}") + state = StateCore(src, self, env) + self.core.process(state) + return state.tokens + + def render(self, src: str, env: EnvType | None = None) -> Any: + """Render markdown string into html. It does all magic for you :). + + :param src: source string + :param env: environment sandbox + :returns: The output of the loaded renderer + + `env` can be used to inject additional metadata (`{}` by default). + But you will not need it with high probability. See also comment + in [[MarkdownIt.parse]]. + """ + env = {} if env is None else env + return self.renderer.render(self.parse(src, env), self.options, env) + + def parseInline(self, src: str, env: EnvType | None = None) -> list[Token]: + """The same as [[MarkdownIt.parse]] but skip all block rules. + + :param src: source string + :param env: environment sandbox + + It returns the + block tokens list with the single `inline` element, containing parsed inline + tokens in `children` property. Also updates `env` object. + """ + env = {} if env is None else env + if not isinstance(env, MutableMapping): + raise TypeError(f"Input data should be an MutableMapping, not {type(env)}") + if not isinstance(src, str): + raise TypeError(f"Input data should be a string, not {type(src)}") + state = StateCore(src, self, env) + state.inlineMode = True + self.core.process(state) + return state.tokens + + def renderInline(self, src: str, env: EnvType | None = None) -> Any: + """Similar to [[MarkdownIt.render]] but for single paragraph content. + + :param src: source string + :param env: environment sandbox + + Similar to [[MarkdownIt.render]] but for single paragraph content. Result + will NOT be wrapped into `

` tags. + """ + env = {} if env is None else env + return self.renderer.render(self.parseInline(src, env), self.options, env) + + # link methods + + def validateLink(self, url: str) -> bool: + """Validate if the URL link is allowed in output. + + This validator can prohibit more than really needed to prevent XSS. + It's a tradeoff to keep code simple and to be secure by default. + + Note: the url should be normalized at this point, and existing entities decoded. + """ + return normalize_url.validateLink(url) + + def normalizeLink(self, url: str) -> str: + """Normalize destination URLs in links + + :: + + [label]: destination 'title' + ^^^^^^^^^^^ + """ + return normalize_url.normalizeLink(url) + + def normalizeLinkText(self, link: str) -> str: + """Normalize autolink content + + :: + + + ~~~~~~~~~~~ + """ + return normalize_url.normalizeLinkText(link) diff --git a/venv/lib/python3.12/site-packages/markdown_it/parser_block.py b/venv/lib/python3.12/site-packages/markdown_it/parser_block.py new file mode 100644 index 0000000000000000000000000000000000000000..50a7184cf4746fc86a4e8032efd0604bf819021c --- /dev/null +++ b/venv/lib/python3.12/site-packages/markdown_it/parser_block.py @@ -0,0 +1,113 @@ +"""Block-level tokenizer.""" + +from __future__ import annotations + +from collections.abc import Callable +import logging +from typing import TYPE_CHECKING + +from . import rules_block +from .ruler import Ruler +from .rules_block.state_block import StateBlock +from .token import Token +from .utils import EnvType + +if TYPE_CHECKING: + from markdown_it import MarkdownIt + +LOGGER = logging.getLogger(__name__) + + +RuleFuncBlockType = Callable[[StateBlock, int, int, bool], bool] +"""(state: StateBlock, startLine: int, endLine: int, silent: bool) -> matched: bool) + +`silent` disables token generation, useful for lookahead. +""" + +_rules: list[tuple[str, RuleFuncBlockType, list[str]]] = [ + # First 2 params - rule name & source. Secondary array - list of rules, + # which can be terminated by this one. + ("table", rules_block.table, ["paragraph", "reference"]), + ("code", rules_block.code, []), + ("fence", rules_block.fence, ["paragraph", "reference", "blockquote", "list"]), + ( + "blockquote", + rules_block.blockquote, + ["paragraph", "reference", "blockquote", "list"], + ), + ("hr", rules_block.hr, ["paragraph", "reference", "blockquote", "list"]), + ("list", rules_block.list_block, ["paragraph", "reference", "blockquote"]), + ("reference", rules_block.reference, []), + ("html_block", rules_block.html_block, ["paragraph", "reference", "blockquote"]), + ("heading", rules_block.heading, ["paragraph", "reference", "blockquote"]), + ("lheading", rules_block.lheading, []), + ("paragraph", rules_block.paragraph, []), +] + + +class ParserBlock: + """ + ParserBlock#ruler -> Ruler + + [[Ruler]] instance. Keep configuration of block rules. + """ + + def __init__(self) -> None: + self.ruler = Ruler[RuleFuncBlockType]() + for name, rule, alt in _rules: + self.ruler.push(name, rule, {"alt": alt}) + + def tokenize(self, state: StateBlock, startLine: int, endLine: int) -> None: + """Generate tokens for input range.""" + rules = self.ruler.getRules("") + line = startLine + maxNesting = state.md.options.maxNesting + hasEmptyLines = False + + while line < endLine: + state.line = line = state.skipEmptyLines(line) + if line >= endLine: + break + if state.sCount[line] < state.blkIndent: + # Termination condition for nested calls. + # Nested calls currently used for blockquotes & lists + break + if state.level >= maxNesting: + # If nesting level exceeded - skip tail to the end. + # That's not ordinary situation and we should not care about content. + state.line = endLine + break + + # Try all possible rules. + # On success, rule should: + # - update `state.line` + # - update `state.tokens` + # - return True + for rule in rules: + if rule(state, line, endLine, False): + break + + # set state.tight if we had an empty line before current tag + # i.e. latest empty line should not count + state.tight = not hasEmptyLines + + line = state.line + + # paragraph might "eat" one newline after it in nested lists + if (line - 1) < endLine and state.isEmpty(line - 1): + hasEmptyLines = True + + if line < endLine and state.isEmpty(line): + hasEmptyLines = True + line += 1 + state.line = line + + def parse( + self, src: str, md: MarkdownIt, env: EnvType, outTokens: list[Token] + ) -> list[Token] | None: + """Process input string and push block tokens into `outTokens`.""" + if not src: + return None + state = StateBlock(src, md, env, outTokens) + self.tokenize(state, state.line, state.lineMax) + return state.tokens diff --git a/venv/lib/python3.12/site-packages/markdown_it/parser_core.py b/venv/lib/python3.12/site-packages/markdown_it/parser_core.py new file mode 100644 index 0000000000000000000000000000000000000000..8f5b921cbd3bed2c85734b44ccd989a0fd0e6788 --- /dev/null +++ b/venv/lib/python3.12/site-packages/markdown_it/parser_core.py @@ -0,0 +1,46 @@ +""" +* class Core +* +* Top-level rules executor. Glues block/inline parsers and does intermediate +* transformations. +""" + +from __future__ import annotations + +from collections.abc import Callable + +from .ruler import Ruler +from .rules_core import ( + block, + inline, + linkify, + normalize, + replace, + smartquotes, + text_join, +) +from .rules_core.state_core import StateCore + +RuleFuncCoreType = Callable[[StateCore], None] + +_rules: list[tuple[str, RuleFuncCoreType]] = [ + ("normalize", normalize), + ("block", block), + ("inline", inline), + ("linkify", linkify), + ("replacements", replace), + ("smartquotes", smartquotes), + ("text_join", text_join), +] + + +class ParserCore: + def __init__(self) -> None: + self.ruler = Ruler[RuleFuncCoreType]() + for name, rule in _rules: + self.ruler.push(name, rule) + + def process(self, state: StateCore) -> None: + """Executes core chain rules.""" + for rule in self.ruler.getRules(""): + rule(state) diff --git a/venv/lib/python3.12/site-packages/markdown_it/parser_inline.py b/venv/lib/python3.12/site-packages/markdown_it/parser_inline.py new file mode 100644 index 0000000000000000000000000000000000000000..8fabb9884bb94973ba3f59458f7960be678a38da --- /dev/null +++ b/venv/lib/python3.12/site-packages/markdown_it/parser_inline.py @@ -0,0 +1,215 @@ +"""Tokenizes paragraph content.""" + +from __future__ import annotations + +from collections.abc import Callable +import functools +import re +from typing import TYPE_CHECKING + +from . import rules_inline +from .ruler import Ruler +from .rules_inline.state_inline import StateInline +from .token import Token +from .utils import EnvType + +if TYPE_CHECKING: + from markdown_it import MarkdownIt + + +# Default set of characters that terminate a text token and allow inline rules to fire. +# '{}$%@~+=:' reserved for extensions. +# Note: Don't confuse with "Markdown ASCII Punctuation" chars. +# http://spec.commonmark.org/0.15/#ascii-punctuation-character +_DEFAULT_TERMINATORS: frozenset[str] = frozenset( + { + "\n", + "!", + "#", + "$", + "%", + "&", + "*", + "+", + "-", + ":", + "<", + "=", + ">", + "@", + "[", + "\\", + "]", + "^", + "_", + "`", + "{", + "}", + "~", + } +) + + +# Lazily compiled regex for the default terminator set. The @cache ensures it is +# compiled at most once (on first ParserInline instantiation) and shared across all +# instances that have not added extra chars, keeping __init__ cost near zero. +@functools.cache +def _default_terminator_re() -> re.Pattern[str]: + return re.compile("[" + re.escape("".join(_DEFAULT_TERMINATORS)) + "]") + + +# Parser rules +RuleFuncInlineType = Callable[[StateInline, bool], bool] +"""(state: StateInline, silent: bool) -> matched: bool) + +`silent` disables token generation, useful for lookahead. +""" +_rules: list[tuple[str, RuleFuncInlineType]] = [ + ("text", rules_inline.text), + ("linkify", rules_inline.linkify), + ("newline", rules_inline.newline), + ("escape", rules_inline.escape), + ("backticks", rules_inline.backtick), + ("strikethrough", rules_inline.strikethrough.tokenize), + ("emphasis", rules_inline.emphasis.tokenize), + ("link", rules_inline.link), + ("image", rules_inline.image), + ("autolink", rules_inline.autolink), + ("html_inline", rules_inline.html_inline), + ("entity", rules_inline.entity), +] + +# Note `rule2` ruleset was created specifically for emphasis/strikethrough +# post-processing and may be changed in the future. +# +# Don't use this for anything except pairs (plugins working with `balance_pairs`). +# +RuleFuncInline2Type = Callable[[StateInline], None] +_rules2: list[tuple[str, RuleFuncInline2Type]] = [ + ("balance_pairs", rules_inline.link_pairs), + ("strikethrough", rules_inline.strikethrough.postProcess), + ("emphasis", rules_inline.emphasis.postProcess), + # rules for pairs separate '**' into its own text tokens, which may be left unused, + # rule below merges unused segments back with the rest of the text + ("fragments_join", rules_inline.fragments_join), +] + + +class ParserInline: + def __init__(self) -> None: + self.ruler = Ruler[RuleFuncInlineType]() + for name, rule in _rules: + self.ruler.push(name, rule) + # Second ruler used for post-processing (e.g. in emphasis-like rules) + self.ruler2 = Ruler[RuleFuncInline2Type]() + for name, rule2 in _rules2: + self.ruler2.push(name, rule2) + # Characters that stop the text rule, allowing other inline rules to fire. + # _extra_terminator_chars is only allocated when add_terminator_char() is called + # with a char outside the defaults, keeping __init__ allocation-free. + self._extra_terminator_chars: set[str] = set() + # Pre-compiled regex shared with all default instances (no copy in the common path). + self.terminator_re: re.Pattern[str] = _default_terminator_re() + + def add_terminator_char(self, ch: str) -> None: + """Register a character that stops the ``text`` rule, allowing inline rules to fire. + + This lets plugins declare which characters their inline rules react to, + mirroring the ``MARKER`` mechanism in the Rust markdown-it implementation. + + :param ch: A single character to add to the terminator set. + """ + if ch not in _DEFAULT_TERMINATORS and ch not in self._extra_terminator_chars: + self._extra_terminator_chars.add(ch) + self.terminator_re = re.compile( + "[" + + re.escape( + "".join(_DEFAULT_TERMINATORS | self._extra_terminator_chars) + ) + + "]" + ) + + def skipToken(self, state: StateInline) -> None: + """Skip single token by running all rules in validation mode; + returns `True` if any rule reported success + """ + ok = False + pos = state.pos + rules = self.ruler.getRules("") + maxNesting = state.md.options["maxNesting"] + cache = state.cache + + if pos in cache: + state.pos = cache[pos] + return + + if state.level < maxNesting: + for rule in rules: + # Increment state.level and decrement it later to limit recursion. + # It's harmless to do here, because no tokens are created. + # But ideally, we'd need a separate private state variable for this purpose. + state.level += 1 + ok = rule(state, True) + state.level -= 1 + if ok: + break + else: + # Too much nesting, just skip until the end of the paragraph. + # + # NOTE: this will cause links to behave incorrectly in the following case, + # when an amount of `[` is exactly equal to `maxNesting + 1`: + # + # [[[[[[[[[[[[[[[[[[[[[foo]() + # + # TODO: remove this workaround when CM standard will allow nested links + # (we can replace it by preventing links from being parsed in + # validation mode) + # + state.pos = state.posMax + + if not ok: + state.pos += 1 + cache[pos] = state.pos + + def tokenize(self, state: StateInline) -> None: + """Generate tokens for input range.""" + ok = False + rules = self.ruler.getRules("") + end = state.posMax + maxNesting = state.md.options["maxNesting"] + + while state.pos < end: + # Try all possible rules. + # On success, rule should: + # + # - update `state.pos` + # - update `state.tokens` + # - return true + + if state.level < maxNesting: + for rule in rules: + ok = rule(state, False) + if ok: + break + + if ok: + if state.pos >= end: + break + continue + + state.pending += state.src[state.pos] + state.pos += 1 + + if state.pending: + state.pushPending() + + def parse( + self, src: str, md: MarkdownIt, env: EnvType, tokens: list[Token] + ) -> list[Token]: + """Process input string and push inline tokens into `tokens`""" + state = StateInline(src, md, env, tokens) + self.tokenize(state) + rules2 = self.ruler2.getRules("") + for rule in rules2: + rule(state) + return state.tokens diff --git a/venv/lib/python3.12/site-packages/markdown_it/port.yaml b/venv/lib/python3.12/site-packages/markdown_it/port.yaml new file mode 100644 index 0000000000000000000000000000000000000000..ce2dde95faa6224dfe63ae1673b6c7363a5b3efb --- /dev/null +++ b/venv/lib/python3.12/site-packages/markdown_it/port.yaml @@ -0,0 +1,48 @@ +- package: markdown-it/markdown-it + version: 14.1.0 + commit: 0fe7ccb4b7f30236fb05f623be6924961d296d3d + date: Mar 19, 2024 + notes: + - Rename variables that use python built-in names, e.g. + - `max` -> `maximum` + - `len` -> `length` + - `str` -> `string` + - | + Convert JS `for` loops to `while` loops + this is generally the main difference between the codes, + because in python you can't do e.g. `for {i=1;i PresetType: + config = commonmark.make() + config["components"]["core"]["rules"].append("linkify") + config["components"]["block"]["rules"].append("table") + config["components"]["inline"]["rules"].extend(["strikethrough", "linkify"]) + config["components"]["inline"]["rules2"].append("strikethrough") + config["options"]["linkify"] = True + config["options"]["html"] = True + return config + + +class gfm_like2: # noqa: N801 + """GitHub Flavoured Markdown (GFM) like, extended. + + Builds on ``gfm-like`` and additionally enables: + + - Task lists (``- [x] done``) + - Alerts (``> [!NOTE]``) + - Single-tilde strikethrough (``~text~`` in addition to ``~~text~~``) + """ + + @staticmethod + def make() -> PresetType: + config = gfm_like.make() + config["options"]["tasklists"] = True + config["options"]["tasklists_editable"] = False + config["options"]["alerts"] = True + config["options"]["strikethrough_single_tilde"] = True + return config diff --git a/venv/lib/python3.12/site-packages/markdown_it/presets/__pycache__/__init__.cpython-312.pyc b/venv/lib/python3.12/site-packages/markdown_it/presets/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..995f10988b32316a8f73660df7c7de2eb6a6270b Binary files /dev/null and b/venv/lib/python3.12/site-packages/markdown_it/presets/__pycache__/__init__.cpython-312.pyc differ diff --git a/venv/lib/python3.12/site-packages/markdown_it/presets/__pycache__/commonmark.cpython-312.pyc b/venv/lib/python3.12/site-packages/markdown_it/presets/__pycache__/commonmark.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..c2fce44287fdc59d0594e61c10d8a42125d56459 Binary files /dev/null and b/venv/lib/python3.12/site-packages/markdown_it/presets/__pycache__/commonmark.cpython-312.pyc differ diff --git a/venv/lib/python3.12/site-packages/markdown_it/presets/__pycache__/default.cpython-312.pyc b/venv/lib/python3.12/site-packages/markdown_it/presets/__pycache__/default.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..c2cbb09e647ab0f5359b837efc8e86636670e3b8 Binary files /dev/null and b/venv/lib/python3.12/site-packages/markdown_it/presets/__pycache__/default.cpython-312.pyc differ diff --git a/venv/lib/python3.12/site-packages/markdown_it/presets/__pycache__/zero.cpython-312.pyc b/venv/lib/python3.12/site-packages/markdown_it/presets/__pycache__/zero.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..0bf10e00798bcfc105045166defaa76c35b43e9a Binary files /dev/null and b/venv/lib/python3.12/site-packages/markdown_it/presets/__pycache__/zero.cpython-312.pyc differ diff --git a/venv/lib/python3.12/site-packages/markdown_it/presets/commonmark.py b/venv/lib/python3.12/site-packages/markdown_it/presets/commonmark.py new file mode 100644 index 0000000000000000000000000000000000000000..ed0de0fe4dfbad9e3ab82477433805ea0b6650c6 --- /dev/null +++ b/venv/lib/python3.12/site-packages/markdown_it/presets/commonmark.py @@ -0,0 +1,75 @@ +"""Commonmark default options. + +This differs to presets.default, +primarily in that it allows HTML and does not enable components: + +- block: table +- inline: strikethrough +""" + +from ..utils import PresetType + + +def make() -> PresetType: + return { + "options": { + "maxNesting": 20, # Internal protection, recursion limit + "html": True, # Enable HTML tags in source, + # this is just a shorthand for .enable(["html_inline", "html_block"]) + # used by the linkify rule: + "linkify": False, # autoconvert URL-like texts to links + # used by the replacements and smartquotes rules + # Enable some language-neutral replacements + quotes beautification + "typographer": False, + # used by the smartquotes rule: + # Double + single quotes replacement pairs, when typographer enabled, + # and smartquotes on. Could be either a String or an Array. + # + # For example, you can use '«»„“' for Russian, '„“‚‘' for German, + # and ['«\xA0', '\xA0»', '‹\xA0', '\xA0›'] for French (including nbsp). + "quotes": "\u201c\u201d\u2018\u2019", # /* “”‘’ */ + # Renderer specific; these options are used directly in the HTML renderer + "xhtmlOut": True, # Use '/' to close single tags (
) + "breaks": False, # Convert '\n' in paragraphs into
+ "langPrefix": "language-", # CSS language prefix for fenced blocks + # Highlighter function. Should return escaped HTML, + # or '' if the source string is not changed and should be escaped externally. + # If result starts with PresetType: + return { + "options": { + "maxNesting": 100, # Internal protection, recursion limit + "html": False, # Enable HTML tags in source + # this is just a shorthand for .disable(["html_inline", "html_block"]) + # used by the linkify rule: + "linkify": False, # autoconvert URL-like texts to links + # used by the replacements and smartquotes rules: + # Enable some language-neutral replacements + quotes beautification + "typographer": False, + # used by the smartquotes rule: + # Double + single quotes replacement pairs, when typographer enabled, + # and smartquotes on. Could be either a String or an Array. + # For example, you can use '«»„“' for Russian, '„“‚‘' for German, + # and ['«\xA0', '\xA0»', '‹\xA0', '\xA0›'] for French (including nbsp). + "quotes": "\u201c\u201d\u2018\u2019", # /* “”‘’ */ + # Renderer specific; these options are used directly in the HTML renderer + "xhtmlOut": False, # Use '/' to close single tags (
) + "breaks": False, # Convert '\n' in paragraphs into
+ "langPrefix": "language-", # CSS language prefix for fenced blocks + # Highlighter function. Should return escaped HTML, + # or '' if the source string is not changed and should be escaped externally. + # If result starts with PresetType: + return { + "options": { + "maxNesting": 20, # Internal protection, recursion limit + "html": False, # Enable HTML tags in source + # this is just a shorthand for .disable(["html_inline", "html_block"]) + # used by the linkify rule: + "linkify": False, # autoconvert URL-like texts to links + # used by the replacements and smartquotes rules: + # Enable some language-neutral replacements + quotes beautification + "typographer": False, + # used by the smartquotes rule: + # Double + single quotes replacement pairs, when typographer enabled, + # and smartquotes on. Could be either a String or an Array. + # For example, you can use '«»„“' for Russian, '„“‚‘' for German, + # and ['«\xA0', '\xA0»', '‹\xA0', '\xA0›'] for French (including nbsp). + "quotes": "\u201c\u201d\u2018\u2019", # /* “”‘’ */ + # Renderer specific; these options are used directly in the HTML renderer + "xhtmlOut": False, # Use '/' to close single tags (
) + "breaks": False, # Convert '\n' in paragraphs into
+ "langPrefix": "language-", # CSS language prefix for fenced blocks + # Highlighter function. Should return escaped HTML, + # or '' if the source string is not changed and should be escaped externally. + # If result starts with Any: ... + + +class RendererHTML(RendererProtocol): + """Contains render rules for tokens. Can be updated and extended. + + Example: + + Each rule is called as independent static function with fixed signature: + + :: + + class Renderer: + def token_type_name(self, tokens, idx, options, env) { + # ... + return renderedHTML + + :: + + class CustomRenderer(RendererHTML): + def strong_open(self, tokens, idx, options, env): + return '' + def strong_close(self, tokens, idx, options, env): + return '' + + md = MarkdownIt(renderer_cls=CustomRenderer) + + result = md.render(...) + + See https://github.com/markdown-it/markdown-it/blob/master/lib/renderer.js + for more details and examples. + """ + + __output__ = "html" + + def __init__(self, parser: Any = None): + self.rules = { + k: v + for k, v in inspect.getmembers(self, predicate=inspect.ismethod) + if not (k.startswith("render") or k.startswith("_")) + } + + def render( + self, tokens: Sequence[Token], options: OptionsDict, env: EnvType + ) -> str: + """Takes token stream and generates HTML. + + :param tokens: list on block tokens to render + :param options: params of parser instance + :param env: additional data from parsed input + + """ + result = "" + + for i, token in enumerate(tokens): + if token.type == "inline": + if token.children: + result += self.renderInline(token.children, options, env) + elif token.type in self.rules: + result += self.rules[token.type](tokens, i, options, env) + else: + result += self.renderToken(tokens, i, options, env) + + return result + + def renderInline( + self, tokens: Sequence[Token], options: OptionsDict, env: EnvType + ) -> str: + """The same as ``render``, but for single token of `inline` type. + + :param tokens: list on block tokens to render + :param options: params of parser instance + :param env: additional data from parsed input (references, for example) + """ + result = "" + + for i, token in enumerate(tokens): + if token.type in self.rules: + result += self.rules[token.type](tokens, i, options, env) + else: + result += self.renderToken(tokens, i, options, env) + + return result + + def renderToken( + self, + tokens: Sequence[Token], + idx: int, + options: OptionsDict, + env: EnvType, + ) -> str: + """Default token renderer. + + Can be overridden by custom function + + :param idx: token index to render + :param options: params of parser instance + """ + result = "" + needLf = False + token = tokens[idx] + + # Tight list paragraphs + if token.hidden: + return "" + + # Insert a newline between hidden paragraph and subsequent opening + # block-level tag. + # + # For example, here we should insert a newline before blockquote: + # - a + # > + # + if token.block and token.nesting != -1 and idx and tokens[idx - 1].hidden: + result += "\n" + + # Add token name, e.g. ``. + # + needLf = False + + result += ">\n" if needLf else ">" + + return result + + @staticmethod + def renderAttrs(token: Token) -> str: + """Render token attributes to string.""" + result = "" + + for key, value in token.attrItems(): + result += " " + escapeHtml(key) + '="' + escapeHtml(str(value)) + '"' + + return result + + def renderInlineAsText( + self, + tokens: Sequence[Token] | None, + options: OptionsDict, + env: EnvType, + ) -> str: + """Special kludge for image `alt` attributes to conform CommonMark spec. + + Don't try to use it! Spec requires to show `alt` content with stripped markup, + instead of simple escaping. + + :param tokens: list on block tokens to render + :param options: params of parser instance + :param env: additional data from parsed input + """ + result = "" + + for token in tokens or []: + if token.type == "text": + result += token.content + elif token.type == "image": + if token.children: + result += self.renderInlineAsText(token.children, options, env) + elif token.type == "softbreak": + result += "\n" + + return result + + ################################################### + + def list_item_open( + self, + tokens: Sequence[Token], + idx: int, + options: OptionsDict, + env: EnvType, + ) -> str: + token = tokens[idx] + result = self.renderToken(tokens, idx, options, env) + if token.meta and "checked" in token.meta: + checked_attr = ' checked=""' if token.meta["checked"] else "" + disabled_attr = ( + "" if options.get("tasklists_editable", False) else ' disabled=""' + ) + result += ( + ' ' + ) + return result + + def code_inline( + self, tokens: Sequence[Token], idx: int, options: OptionsDict, env: EnvType + ) -> str: + token = tokens[idx] + return ( + "" + + escapeHtml(tokens[idx].content) + + "" + ) + + def code_block( + self, + tokens: Sequence[Token], + idx: int, + options: OptionsDict, + env: EnvType, + ) -> str: + token = tokens[idx] + + return ( + "" + + escapeHtml(tokens[idx].content) + + "\n" + ) + + def fence( + self, + tokens: Sequence[Token], + idx: int, + options: OptionsDict, + env: EnvType, + ) -> str: + token = tokens[idx] + info = unescapeAll(token.info).strip() if token.info else "" + langName = "" + langAttrs = "" + + if info: + arr = info.split(maxsplit=1) + langName = arr[0] + if len(arr) == 2: + langAttrs = arr[1] + + if options.highlight: + highlighted = options.highlight( + token.content, langName, langAttrs + ) or escapeHtml(token.content) + else: + highlighted = escapeHtml(token.content) + + if highlighted.startswith("" + + highlighted + + "\n" + ) + + return ( + "

"
+            + highlighted
+            + "
\n" + ) + + def image( + self, + tokens: Sequence[Token], + idx: int, + options: OptionsDict, + env: EnvType, + ) -> str: + token = tokens[idx] + + # "alt" attr MUST be set, even if empty. Because it's mandatory and + # should be placed on proper position for tests. + if token.children: + token.attrSet("alt", self.renderInlineAsText(token.children, options, env)) + else: + token.attrSet("alt", "") + + return self.renderToken(tokens, idx, options, env) + + def hardbreak( + self, tokens: Sequence[Token], idx: int, options: OptionsDict, env: EnvType + ) -> str: + return "
\n" if options.xhtmlOut else "
\n" + + def softbreak( + self, tokens: Sequence[Token], idx: int, options: OptionsDict, env: EnvType + ) -> str: + return ( + ("
\n" if options.xhtmlOut else "
\n") if options.breaks else "\n" + ) + + def text( + self, tokens: Sequence[Token], idx: int, options: OptionsDict, env: EnvType + ) -> str: + return escapeHtml(tokens[idx].content) + + def html_block( + self, tokens: Sequence[Token], idx: int, options: OptionsDict, env: EnvType + ) -> str: + return tokens[idx].content + + def html_inline( + self, tokens: Sequence[Token], idx: int, options: OptionsDict, env: EnvType + ) -> str: + return tokens[idx].content diff --git a/venv/lib/python3.12/site-packages/markdown_it/ruler.py b/venv/lib/python3.12/site-packages/markdown_it/ruler.py new file mode 100644 index 0000000000000000000000000000000000000000..91ab58044cc9f93c540fc5d7e49a6c704e805d58 --- /dev/null +++ b/venv/lib/python3.12/site-packages/markdown_it/ruler.py @@ -0,0 +1,275 @@ +""" +class Ruler + +Helper class, used by [[MarkdownIt#core]], [[MarkdownIt#block]] and +[[MarkdownIt#inline]] to manage sequences of functions (rules): + +- keep rules in defined order +- assign the name to each rule +- enable/disable rules +- add/replace rules +- allow assign rules to additional named chains (in the same) +- caching lists of active rules + +You will not need use this class directly until write plugins. For simple +rules control use [[MarkdownIt.disable]], [[MarkdownIt.enable]] and +[[MarkdownIt.use]]. +""" + +from __future__ import annotations + +from collections.abc import Iterable +from dataclasses import dataclass, field +from typing import TYPE_CHECKING, Generic, TypedDict, TypeVar +import warnings + +from .utils import EnvType + +if TYPE_CHECKING: + from markdown_it import MarkdownIt + + +class StateBase: + def __init__(self, src: str, md: MarkdownIt, env: EnvType): + self.src = src + self.env = env + self.md = md + + @property + def src(self) -> str: + return self._src + + @src.setter + def src(self, value: str) -> None: + self._src = value + self._srcCharCode: tuple[int, ...] | None = None + + @property + def srcCharCode(self) -> tuple[int, ...]: + warnings.warn( + "StateBase.srcCharCode is deprecated. Use StateBase.src instead.", + DeprecationWarning, + stacklevel=2, + ) + if self._srcCharCode is None: + self._srcCharCode = tuple(ord(c) for c in self._src) + return self._srcCharCode + + +class RuleOptionsType(TypedDict, total=False): + alt: list[str] + + +RuleFuncTv = TypeVar("RuleFuncTv") +"""A rule function, whose signature is dependent on the state type.""" + + +@dataclass(slots=True) +class Rule(Generic[RuleFuncTv]): + name: str + enabled: bool + fn: RuleFuncTv = field(repr=False) + alt: list[str] + + +class Ruler(Generic[RuleFuncTv]): + def __init__(self) -> None: + # List of added rules. + self.__rules__: list[Rule[RuleFuncTv]] = [] + # Cached rule chains. + # First level - chain name, '' for default. + # Second level - diginal anchor for fast filtering by charcodes. + self.__cache__: dict[str, list[RuleFuncTv]] | None = None + + def __find__(self, name: str) -> int: + """Find rule index by name""" + for i, rule in enumerate(self.__rules__): + if rule.name == name: + return i + return -1 + + def __compile__(self) -> None: + """Build rules lookup cache""" + chains = {""} + # collect unique names + for rule in self.__rules__: + if not rule.enabled: + continue + for name in rule.alt: + chains.add(name) + self.__cache__ = {} + for chain in chains: + self.__cache__[chain] = [] + for rule in self.__rules__: + if not rule.enabled: + continue + if chain and (chain not in rule.alt): + continue + self.__cache__[chain].append(rule.fn) + + def at( + self, ruleName: str, fn: RuleFuncTv, options: RuleOptionsType | None = None + ) -> None: + """Replace rule by name with new function & options. + + :param ruleName: rule name to replace. + :param fn: new rule function. + :param options: new rule options (not mandatory). + :raises: KeyError if name not found + """ + index = self.__find__(ruleName) + options = options or {} + if index == -1: + raise KeyError(f"Parser rule not found: {ruleName}") + self.__rules__[index].fn = fn + self.__rules__[index].alt = options.get("alt", []) + self.__cache__ = None + + def before( + self, + beforeName: str, + ruleName: str, + fn: RuleFuncTv, + options: RuleOptionsType | None = None, + ) -> None: + """Add new rule to chain before one with given name. + + :param beforeName: new rule will be added before this one. + :param ruleName: new rule will be added before this one. + :param fn: new rule function. + :param options: new rule options (not mandatory). + :raises: KeyError if name not found + """ + index = self.__find__(beforeName) + options = options or {} + if index == -1: + raise KeyError(f"Parser rule not found: {beforeName}") + self.__rules__.insert( + index, Rule[RuleFuncTv](ruleName, True, fn, options.get("alt", [])) + ) + self.__cache__ = None + + def after( + self, + afterName: str, + ruleName: str, + fn: RuleFuncTv, + options: RuleOptionsType | None = None, + ) -> None: + """Add new rule to chain after one with given name. + + :param afterName: new rule will be added after this one. + :param ruleName: new rule will be added after this one. + :param fn: new rule function. + :param options: new rule options (not mandatory). + :raises: KeyError if name not found + """ + index = self.__find__(afterName) + options = options or {} + if index == -1: + raise KeyError(f"Parser rule not found: {afterName}") + self.__rules__.insert( + index + 1, Rule[RuleFuncTv](ruleName, True, fn, options.get("alt", [])) + ) + self.__cache__ = None + + def push( + self, ruleName: str, fn: RuleFuncTv, options: RuleOptionsType | None = None + ) -> None: + """Push new rule to the end of chain. + + :param ruleName: new rule will be added to the end of chain. + :param fn: new rule function. + :param options: new rule options (not mandatory). + + """ + self.__rules__.append( + Rule[RuleFuncTv](ruleName, True, fn, (options or {}).get("alt", [])) + ) + self.__cache__ = None + + def enable( + self, names: str | Iterable[str], ignoreInvalid: bool = False + ) -> list[str]: + """Enable rules with given names. + + :param names: name or list of rule names to enable. + :param ignoreInvalid: ignore errors when rule not found + :raises: KeyError if name not found and not ignoreInvalid + :return: list of found rule names + """ + if isinstance(names, str): + names = [names] + result: list[str] = [] + for name in names: + idx = self.__find__(name) + if (idx < 0) and ignoreInvalid: + continue + if (idx < 0) and not ignoreInvalid: + raise KeyError(f"Rules manager: invalid rule name {name}") + self.__rules__[idx].enabled = True + result.append(name) + self.__cache__ = None + return result + + def enableOnly( + self, names: str | Iterable[str], ignoreInvalid: bool = False + ) -> list[str]: + """Enable rules with given names, and disable everything else. + + :param names: name or list of rule names to enable. + :param ignoreInvalid: ignore errors when rule not found + :raises: KeyError if name not found and not ignoreInvalid + :return: list of found rule names + """ + if isinstance(names, str): + names = [names] + for rule in self.__rules__: + rule.enabled = False + return self.enable(names, ignoreInvalid) + + def disable( + self, names: str | Iterable[str], ignoreInvalid: bool = False + ) -> list[str]: + """Disable rules with given names. + + :param names: name or list of rule names to enable. + :param ignoreInvalid: ignore errors when rule not found + :raises: KeyError if name not found and not ignoreInvalid + :return: list of found rule names + """ + if isinstance(names, str): + names = [names] + result = [] + for name in names: + idx = self.__find__(name) + if (idx < 0) and ignoreInvalid: + continue + if (idx < 0) and not ignoreInvalid: + raise KeyError(f"Rules manager: invalid rule name {name}") + self.__rules__[idx].enabled = False + result.append(name) + self.__cache__ = None + return result + + def getRules(self, chainName: str = "") -> list[RuleFuncTv]: + """Return array of active functions (rules) for given chain name. + It analyzes rules configuration, compiles caches if not exists and returns result. + + Default chain name is `''` (empty string). It can't be skipped. + That's done intentionally, to keep signature monomorphic for high speed. + + """ + if self.__cache__ is None: + self.__compile__() + assert self.__cache__ is not None + # Chain can be empty, if rules disabled. But we still have to return Array. + return self.__cache__.get(chainName, []) or [] + + def get_all_rules(self) -> list[str]: + """Return all available rule names.""" + return [r.name for r in self.__rules__] + + def get_active_rules(self) -> list[str]: + """Return the active rule names.""" + return [r.name for r in self.__rules__ if r.enabled] diff --git a/venv/lib/python3.12/site-packages/markdown_it/rules_block/__init__.py b/venv/lib/python3.12/site-packages/markdown_it/rules_block/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..2c5efa85ccf01dc6aaede7dc951e9db504c32a4a --- /dev/null +++ b/venv/lib/python3.12/site-packages/markdown_it/rules_block/__init__.py @@ -0,0 +1,28 @@ +__all__ = ( + "StateBlock", + "blockquote", + "code", + "fence", + "heading", + "hr", + "html_block", + "lheading", + "list_block", + "make_fence_rule", + "paragraph", + "reference", + "table", +) + +from .blockquote import blockquote +from .code import code +from .fence import fence, make_fence_rule +from .heading import heading +from .hr import hr +from .html_block import html_block +from .lheading import lheading +from .list import list_block +from .paragraph import paragraph +from .reference import reference +from .state_block import StateBlock +from .table import table diff --git a/venv/lib/python3.12/site-packages/markdown_it/rules_block/__pycache__/__init__.cpython-312.pyc b/venv/lib/python3.12/site-packages/markdown_it/rules_block/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..f59f7e6aac3076ba2adaa63bfa235437b6b847f4 Binary files /dev/null and b/venv/lib/python3.12/site-packages/markdown_it/rules_block/__pycache__/__init__.cpython-312.pyc differ diff --git a/venv/lib/python3.12/site-packages/markdown_it/rules_block/__pycache__/blockquote.cpython-312.pyc b/venv/lib/python3.12/site-packages/markdown_it/rules_block/__pycache__/blockquote.cpython-312.pyc new file mode 100644 index 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files /dev/null and b/venv/lib/python3.12/site-packages/markdown_it/rules_block/__pycache__/table.cpython-312.pyc differ diff --git a/venv/lib/python3.12/site-packages/markdown_it/rules_block/blockquote.py b/venv/lib/python3.12/site-packages/markdown_it/rules_block/blockquote.py new file mode 100644 index 0000000000000000000000000000000000000000..de2d4f2d2f1362de23a7a9c02fa113acf8b26be0 --- /dev/null +++ b/venv/lib/python3.12/site-packages/markdown_it/rules_block/blockquote.py @@ -0,0 +1,368 @@ +# Block quotes +from __future__ import annotations + +import logging + +from ..common.utils import isStrSpace +from .state_block import StateBlock + +LOGGER = logging.getLogger(__name__) + + +def blockquote(state: StateBlock, startLine: int, endLine: int, silent: bool) -> bool: + LOGGER.debug( + "entering blockquote: %s, %s, %s, %s", state, startLine, endLine, silent + ) + + oldLineMax = state.lineMax + pos = state.bMarks[startLine] + state.tShift[startLine] + max = state.eMarks[startLine] + + if state.is_code_block(startLine): + return False + + # check the block quote marker + try: + if state.src[pos] != ">": + return False + except IndexError: + return False + pos += 1 + + # we know that it's going to be a valid blockquote, + # so no point trying to find the end of it in silent mode + if silent: + return True + + # set offset past spaces and ">" + initial = offset = state.sCount[startLine] + 1 + + try: + second_char: str | None = state.src[pos] + except IndexError: + second_char = None + + # skip one optional space after '>' + if second_char == " ": + # ' > test ' + # ^ -- position start of line here: + pos += 1 + initial += 1 + offset += 1 + adjustTab = False + spaceAfterMarker = True + elif second_char == "\t": + spaceAfterMarker = True + + if (state.bsCount[startLine] + offset) % 4 == 3: + # ' >\t test ' + # ^ -- position start of line here (tab has width==1) + pos += 1 + initial += 1 + offset += 1 + adjustTab = False + else: + # ' >\t test ' + # ^ -- position start of line here + shift bsCount slightly + # to make extra space appear + adjustTab = True + + else: + spaceAfterMarker = False + + oldBMarks = [state.bMarks[startLine]] + state.bMarks[startLine] = pos + + while pos < max: + ch = state.src[pos] + + if isStrSpace(ch): + if ch == "\t": + offset += ( + 4 + - (offset + state.bsCount[startLine] + (1 if adjustTab else 0)) % 4 + ) + else: + offset += 1 + + else: + break + + pos += 1 + + oldBSCount = [state.bsCount[startLine]] + state.bsCount[startLine] = ( + state.sCount[startLine] + 1 + (1 if spaceAfterMarker else 0) + ) + + lastLineEmpty = pos >= max + + oldSCount = [state.sCount[startLine]] + state.sCount[startLine] = offset - initial + + oldTShift = [state.tShift[startLine]] + state.tShift[startLine] = pos - state.bMarks[startLine] + + terminatorRules = state.md.block.ruler.getRules("blockquote") + + oldParentType = state.parentType + state.parentType = "blockquote" + + # Search the end of the block + # + # Block ends with either: + # 1. an empty line outside: + # ``` + # > test + # + # ``` + # 2. an empty line inside: + # ``` + # > + # test + # ``` + # 3. another tag: + # ``` + # > test + # - - - + # ``` + + # for (nextLine = startLine + 1; nextLine < endLine; nextLine++) { + nextLine = startLine + 1 + while nextLine < endLine: + # check if it's outdented, i.e. it's inside list item and indented + # less than said list item: + # + # ``` + # 1. anything + # > current blockquote + # 2. checking this line + # ``` + isOutdented = state.sCount[nextLine] < state.blkIndent + + pos = state.bMarks[nextLine] + state.tShift[nextLine] + max = state.eMarks[nextLine] + + if pos >= max: + # Case 1: line is not inside the blockquote, and this line is empty. + break + + evaluatesTrue = state.src[pos] == ">" and not isOutdented + pos += 1 + if evaluatesTrue: + # This line is inside the blockquote. + + # set offset past spaces and ">" + initial = offset = state.sCount[nextLine] + 1 + + try: + next_char: str | None = state.src[pos] + except IndexError: + next_char = None + + # skip one optional space after '>' + if next_char == " ": + # ' > test ' + # ^ -- position start of line here: + pos += 1 + initial += 1 + offset += 1 + adjustTab = False + spaceAfterMarker = True + elif next_char == "\t": + spaceAfterMarker = True + + if (state.bsCount[nextLine] + offset) % 4 == 3: + # ' >\t test ' + # ^ -- position start of line here (tab has width==1) + pos += 1 + initial += 1 + offset += 1 + adjustTab = False + else: + # ' >\t test ' + # ^ -- position start of line here + shift bsCount slightly + # to make extra space appear + adjustTab = True + + else: + spaceAfterMarker = False + + oldBMarks.append(state.bMarks[nextLine]) + state.bMarks[nextLine] = pos + + while pos < max: + ch = state.src[pos] + + if isStrSpace(ch): + if ch == "\t": + offset += ( + 4 + - ( + offset + + state.bsCount[nextLine] + + (1 if adjustTab else 0) + ) + % 4 + ) + else: + offset += 1 + else: + break + + pos += 1 + + lastLineEmpty = pos >= max + + oldBSCount.append(state.bsCount[nextLine]) + state.bsCount[nextLine] = ( + state.sCount[nextLine] + 1 + (1 if spaceAfterMarker else 0) + ) + + oldSCount.append(state.sCount[nextLine]) + state.sCount[nextLine] = offset - initial + + oldTShift.append(state.tShift[nextLine]) + state.tShift[nextLine] = pos - state.bMarks[nextLine] + + nextLine += 1 + continue + + # Case 2: line is not inside the blockquote, and the last line was empty. + if lastLineEmpty: + break + + # Case 3: another tag found. + terminate = False + + for terminatorRule in terminatorRules: + if terminatorRule(state, nextLine, endLine, True): + terminate = True + break + + if terminate: + # Quirk to enforce "hard termination mode" for paragraphs; + # normally if you call `tokenize(state, startLine, nextLine)`, + # paragraphs will look below nextLine for paragraph continuation, + # but if blockquote is terminated by another tag, they shouldn't + state.lineMax = nextLine + + if state.blkIndent != 0: + # state.blkIndent was non-zero, we now set it to zero, + # so we need to re-calculate all offsets to appear as + # if indent wasn't changed + oldBMarks.append(state.bMarks[nextLine]) + oldBSCount.append(state.bsCount[nextLine]) + oldTShift.append(state.tShift[nextLine]) + oldSCount.append(state.sCount[nextLine]) + state.sCount[nextLine] -= state.blkIndent + + break + + oldBMarks.append(state.bMarks[nextLine]) + oldBSCount.append(state.bsCount[nextLine]) + oldTShift.append(state.tShift[nextLine]) + oldSCount.append(state.sCount[nextLine]) + + # A negative indentation means that this is a paragraph continuation + # + state.sCount[nextLine] = -1 + + nextLine += 1 + + oldIndent = state.blkIndent + state.blkIndent = 0 + + # Detect GitHub-style alert marker on the first content line. + # Note: `startLine` here refers to the first content line of the + # blockquote, after the `>` prefix has already been stripped by the + # blockquote parser above (bMarks/tShift adjusted to skip `> `). + alert_kind = None + if state.md.options.get("alerts", False) and nextLine > startLine: + alert_kind = _detect_alert(state, startLine) + + lines = [startLine, 0] + + if alert_kind is not None: + # Emit alert tokens instead of blockquote tokens + alert_lower = alert_kind.lower() + token = state.push("alert_open", "div", 1) + token.markup = ">" + token.attrSet("class", f"markdown-alert markdown-alert-{alert_lower}") + token.map = lines + token.info = alert_kind + token.meta = {"kind": alert_kind} + + # Emit a title paragraph:

Kind

+ token = state.push("alert_title_open", "p", 1) + token.attrSet("class", "markdown-alert-title") + title_token = state.push("inline", "", 0) + title_token.content = alert_kind.capitalize() + title_token.children = [] + token = state.push("alert_title_close", "p", -1) + + # Skip the marker line (startLine) and tokenize from startLine + 1. + contentStart = startLine + 1 + if contentStart < nextLine: + # tokenize() updates state.line to nextLine as part of its + # contract, consistent with the blockquote code path below. + state.md.block.tokenize(state, contentStart, nextLine) + else: + state.line = nextLine + + token = state.push("alert_close", "div", -1) + token.markup = ">" + else: + token = state.push("blockquote_open", "blockquote", 1) + token.markup = ">" + token.map = lines + + state.md.block.tokenize(state, startLine, nextLine) + + token = state.push("blockquote_close", "blockquote", -1) + token.markup = ">" + + state.lineMax = oldLineMax + state.parentType = oldParentType + # Update the opening token map for both alert and blockquote containers. + lines[1] = state.line + + # Restore original tShift; this might not be necessary since the parser + # has already been here, but just to make sure we can do that. + for i, item in enumerate(oldTShift): + state.bMarks[i + startLine] = oldBMarks[i] + state.tShift[i + startLine] = item + state.sCount[i + startLine] = oldSCount[i] + state.bsCount[i + startLine] = oldBSCount[i] + + state.blkIndent = oldIndent + + return True + + +_ALERT_TYPES = {"NOTE", "TIP", "IMPORTANT", "WARNING", "CAUTION"} + + +def _detect_alert(state: StateBlock, startLine: int) -> str | None: + """Detect ``[!TYPE]`` on *startLine* (after ``>`` prefix has been stripped). + + Returns the alert type string (e.g. ``"NOTE"``) or ``None``. + """ + pos = state.bMarks[startLine] + state.tShift[startLine] + maximum = state.eMarks[startLine] + src = state.src + + # Trim trailing whitespace + while maximum > pos and src[maximum - 1] in (" ", "\t"): + maximum -= 1 + + if maximum - pos < 4: + return None + if src[pos] != "[" or src[pos + 1] != "!": + return None + if src[maximum - 1] != "]": + return None + type_str = src[pos + 2 : maximum - 1].upper() + if type_str not in _ALERT_TYPES: + return None + return type_str diff --git a/venv/lib/python3.12/site-packages/markdown_it/rules_block/code.py b/venv/lib/python3.12/site-packages/markdown_it/rules_block/code.py new file mode 100644 index 0000000000000000000000000000000000000000..af8a41c8058b1a4887252469bdc6f20f085ad7d5 --- /dev/null +++ b/venv/lib/python3.12/site-packages/markdown_it/rules_block/code.py @@ -0,0 +1,36 @@ +"""Code block (4 spaces padded).""" + +import logging + +from .state_block import StateBlock + +LOGGER = logging.getLogger(__name__) + + +def code(state: StateBlock, startLine: int, endLine: int, silent: bool) -> bool: + LOGGER.debug("entering code: %s, %s, %s, %s", state, startLine, endLine, silent) + + if not state.is_code_block(startLine): + return False + + last = nextLine = startLine + 1 + + while nextLine < endLine: + if state.isEmpty(nextLine): + nextLine += 1 + continue + + if state.is_code_block(nextLine): + nextLine += 1 + last = nextLine + continue + + break + + state.line = last + + token = state.push("code_block", "code", 0) + token.content = state.getLines(startLine, last, 4 + state.blkIndent, False) + "\n" + token.map = [startLine, state.line] + + return True diff --git a/venv/lib/python3.12/site-packages/markdown_it/rules_block/fence.py b/venv/lib/python3.12/site-packages/markdown_it/rules_block/fence.py new file mode 100644 index 0000000000000000000000000000000000000000..0d7e651eb06b73386ef447f2d19e2aa0042f97e5 --- /dev/null +++ b/venv/lib/python3.12/site-packages/markdown_it/rules_block/fence.py @@ -0,0 +1,146 @@ +# fences (``` lang, ~~~ lang) +from __future__ import annotations + +from collections.abc import Callable +import logging + +from .state_block import StateBlock + +LOGGER = logging.getLogger(__name__) + + +def make_fence_rule( + *, + markers: tuple[str, ...] = ("~", "`"), + token_type: str = "fence", + exact_match: bool = False, + disallow_marker_in_info: tuple[str, ...] = ("`",), + min_markers: int = 3, +) -> Callable[[StateBlock, int, int, bool], bool]: + """Create a fence parsing rule with configurable options. + + :param markers: Tuple of single characters that can be used as fence markers. + :param token_type: The token type name to emit (e.g. "fence", "colon_fence"). + :param exact_match: If True, the closing fence must have exactly the same + number of marker characters as the opening fence (not "at least as many"). + This enables nesting of fences with different marker counts. + :param disallow_marker_in_info: Tuple of marker characters that are not allowed + to appear in the info string. The check only applies when the actual opening + marker is in this tuple (e.g. a tilde fence is unaffected by ``"`"`` being + listed). Per CommonMark, backtick fences cannot have backticks in the info + string. Use ``()`` to disable this restriction. + :param min_markers: Minimum number of marker characters to form a fence. + :return: A block rule function with signature + ``(state, startLine, endLine, silent) -> bool``. + """ + + closing_matcher: Callable[[int, int], bool] + if exact_match: + # closing code fence must have exactly the same number of markers as the opening one + closing_matcher = lambda opening_len, closing_len: closing_len == opening_len # noqa: E731 + else: + # closing code fence must be at least as long as the opening one + closing_matcher = lambda opening_len, closing_len: closing_len >= opening_len # noqa: E731 + + def _fence_rule( + state: StateBlock, startLine: int, endLine: int, silent: bool + ) -> bool: + LOGGER.debug( + "entering fence: %s, %s, %s, %s", state, startLine, endLine, silent + ) + + haveEndMarker = False + pos = state.bMarks[startLine] + state.tShift[startLine] + maximum = state.eMarks[startLine] + + if state.is_code_block(startLine): + return False + + if pos + min_markers > maximum: + return False + + marker = state.src[pos] + + if marker not in markers: + return False + + # scan marker length + mem = pos + pos = state.skipCharsStr(pos, marker) + + length = pos - mem + + if length < min_markers: + return False + + markup = state.src[mem:pos] + params = state.src[pos:maximum] + + if marker in disallow_marker_in_info and marker in params: + return False + + # Since start is found, we can report success here in validation mode + if silent: + return True + + # search end of block + nextLine = startLine + + while True: + nextLine += 1 + if nextLine >= endLine: + # unclosed block should be autoclosed by end of document. + # also block seems to be autoclosed by end of parent + break + + pos = mem = state.bMarks[nextLine] + state.tShift[nextLine] + maximum = state.eMarks[nextLine] + + if pos < maximum and state.sCount[nextLine] < state.blkIndent: + # non-empty line with negative indent should stop the list: + # - ``` + # test + break + + try: + if state.src[pos] != marker: + continue + except IndexError: + break + + if state.is_code_block(nextLine): + continue + + pos = state.skipCharsStr(pos, marker) + + if not closing_matcher(length, pos - mem): + continue + + # make sure tail has spaces only + pos = state.skipSpaces(pos) + + if pos < maximum: + continue + + haveEndMarker = True + # found! + break + + # If a fence has heading spaces, they should be removed from its inner block + length = state.sCount[startLine] + + state.line = nextLine + (1 if haveEndMarker else 0) + + token = state.push(token_type, "code", 0) + token.info = params + token.content = state.getLines(startLine + 1, nextLine, length, True) + token.markup = markup + token.map = [startLine, state.line] + + return True + + return _fence_rule + + +#: The default fence rule (backtick and tilde markers, CommonMark compliant). +fence = make_fence_rule() diff --git a/venv/lib/python3.12/site-packages/markdown_it/rules_block/heading.py b/venv/lib/python3.12/site-packages/markdown_it/rules_block/heading.py new file mode 100644 index 0000000000000000000000000000000000000000..afcf9ed458124e52b9b365a6c17440872ffa0975 --- /dev/null +++ b/venv/lib/python3.12/site-packages/markdown_it/rules_block/heading.py @@ -0,0 +1,69 @@ +"""Atex heading (#, ##, ...)""" + +from __future__ import annotations + +import logging + +from ..common.utils import isStrSpace +from .state_block import StateBlock + +LOGGER = logging.getLogger(__name__) + + +def heading(state: StateBlock, startLine: int, endLine: int, silent: bool) -> bool: + LOGGER.debug("entering heading: %s, %s, %s, %s", state, startLine, endLine, silent) + + pos = state.bMarks[startLine] + state.tShift[startLine] + maximum = state.eMarks[startLine] + + if state.is_code_block(startLine): + return False + + ch: str | None = state.src[pos] + + if ch != "#" or pos >= maximum: + return False + + # count heading level + level = 1 + pos += 1 + try: + ch = state.src[pos] + except IndexError: + ch = None + while ch == "#" and pos < maximum and level <= 6: + level += 1 + pos += 1 + try: + ch = state.src[pos] + except IndexError: + ch = None + + if level > 6 or (pos < maximum and not isStrSpace(ch)): + return False + + if silent: + return True + + # Let's cut tails like ' ### ' from the end of string + + maximum = state.skipSpacesBack(maximum, pos) + tmp = state.skipCharsStrBack(maximum, "#", pos) + if tmp > pos and isStrSpace(state.src[tmp - 1]): + maximum = tmp + + state.line = startLine + 1 + + token = state.push("heading_open", "h" + str(level), 1) + token.markup = "########"[:level] + token.map = [startLine, state.line] + + token = state.push("inline", "", 0) + token.content = state.src[pos:maximum].strip() + token.map = [startLine, state.line] + token.children = [] + + token = state.push("heading_close", "h" + str(level), -1) + token.markup = "########"[:level] + + return True diff --git a/venv/lib/python3.12/site-packages/markdown_it/rules_block/hr.py b/venv/lib/python3.12/site-packages/markdown_it/rules_block/hr.py new file mode 100644 index 0000000000000000000000000000000000000000..fca7d79d2780a5e656e689ac4843b8464b4ab5bd --- /dev/null +++ b/venv/lib/python3.12/site-packages/markdown_it/rules_block/hr.py @@ -0,0 +1,56 @@ +"""Horizontal rule + +At least 3 of these characters on a line * - _ +""" + +import logging + +from ..common.utils import isStrSpace +from .state_block import StateBlock + +LOGGER = logging.getLogger(__name__) + + +def hr(state: StateBlock, startLine: int, endLine: int, silent: bool) -> bool: + LOGGER.debug("entering hr: %s, %s, %s, %s", state, startLine, endLine, silent) + + pos = state.bMarks[startLine] + state.tShift[startLine] + maximum = state.eMarks[startLine] + + if state.is_code_block(startLine): + return False + + try: + marker = state.src[pos] + except IndexError: + return False + pos += 1 + + # Check hr marker + if marker not in ("*", "-", "_"): + return False + + # markers can be mixed with spaces, but there should be at least 3 of them + + cnt = 1 + while pos < maximum: + ch = state.src[pos] + pos += 1 + if ch != marker and not isStrSpace(ch): + return False + if ch == marker: + cnt += 1 + + if cnt < 3: + return False + + if silent: + return True + + state.line = startLine + 1 + + token = state.push("hr", "hr", 0) + token.map = [startLine, state.line] + token.markup = marker * (cnt + 1) + + return True diff --git a/venv/lib/python3.12/site-packages/markdown_it/rules_block/html_block.py b/venv/lib/python3.12/site-packages/markdown_it/rules_block/html_block.py new file mode 100644 index 0000000000000000000000000000000000000000..3d43f6ee1deb527a42f4d99da40bd052d9b02886 --- /dev/null +++ b/venv/lib/python3.12/site-packages/markdown_it/rules_block/html_block.py @@ -0,0 +1,90 @@ +# HTML block +from __future__ import annotations + +import logging +import re + +from ..common.html_blocks import block_names +from ..common.html_re import HTML_OPEN_CLOSE_TAG_STR +from .state_block import StateBlock + +LOGGER = logging.getLogger(__name__) + +# An array of opening and corresponding closing sequences for html tags, +# last argument defines whether it can terminate a paragraph or not +HTML_SEQUENCES: list[tuple[re.Pattern[str], re.Pattern[str], bool]] = [ + ( + re.compile(r"^<(script|pre|style|textarea)(?=(\s|>|$))", re.IGNORECASE), + re.compile(r"<\/(script|pre|style|textarea)>", re.IGNORECASE), + True, + ), + (re.compile(r"^"), True), + (re.compile(r"^<\?"), re.compile(r"\?>"), True), + (re.compile(r"^"), True), + (re.compile(r"^"), True), + ( + re.compile("^|$))", re.IGNORECASE), + re.compile(r"^$"), + True, + ), + (re.compile(HTML_OPEN_CLOSE_TAG_STR + "\\s*$"), re.compile(r"^$"), False), +] + + +def html_block(state: StateBlock, startLine: int, endLine: int, silent: bool) -> bool: + LOGGER.debug( + "entering html_block: %s, %s, %s, %s", state, startLine, endLine, silent + ) + pos = state.bMarks[startLine] + state.tShift[startLine] + maximum = state.eMarks[startLine] + + if state.is_code_block(startLine): + return False + + if not state.md.options.get("html", None): + return False + + if state.src[pos] != "<": + return False + + lineText = state.src[pos:maximum] + + html_seq = None + for HTML_SEQUENCE in HTML_SEQUENCES: + if HTML_SEQUENCE[0].search(lineText): + html_seq = HTML_SEQUENCE + break + + if not html_seq: + return False + + if silent: + # true if this sequence can be a terminator, false otherwise + return html_seq[2] + + nextLine = startLine + 1 + + # If we are here - we detected HTML block. + # Let's roll down till block end. + if not html_seq[1].search(lineText): + while nextLine < endLine: + if state.sCount[nextLine] < state.blkIndent: + break + + pos = state.bMarks[nextLine] + state.tShift[nextLine] + maximum = state.eMarks[nextLine] + lineText = state.src[pos:maximum] + + if html_seq[1].search(lineText): + if len(lineText) != 0: + nextLine += 1 + break + nextLine += 1 + + state.line = nextLine + + token = state.push("html_block", "", 0) + token.map = [startLine, nextLine] + token.content = state.getLines(startLine, nextLine, state.blkIndent, True) + + return True diff --git a/venv/lib/python3.12/site-packages/markdown_it/rules_block/lheading.py b/venv/lib/python3.12/site-packages/markdown_it/rules_block/lheading.py new file mode 100644 index 0000000000000000000000000000000000000000..3522207abb680510decdd6c54d0be81401128ad7 --- /dev/null +++ b/venv/lib/python3.12/site-packages/markdown_it/rules_block/lheading.py @@ -0,0 +1,86 @@ +# lheading (---, ==) +import logging + +from .state_block import StateBlock + +LOGGER = logging.getLogger(__name__) + + +def lheading(state: StateBlock, startLine: int, endLine: int, silent: bool) -> bool: + LOGGER.debug("entering lheading: %s, %s, %s, %s", state, startLine, endLine, silent) + + level = None + nextLine = startLine + 1 + ruler = state.md.block.ruler + terminatorRules = ruler.getRules("paragraph") + + if state.is_code_block(startLine): + return False + + oldParentType = state.parentType + state.parentType = "paragraph" # use paragraph to match terminatorRules + + # jump line-by-line until empty one or EOF + while nextLine < endLine and not state.isEmpty(nextLine): + # this would be a code block normally, but after paragraph + # it's considered a lazy continuation regardless of what's there + if state.sCount[nextLine] - state.blkIndent > 3: + nextLine += 1 + continue + + # Check for underline in setext header + if state.sCount[nextLine] >= state.blkIndent: + pos = state.bMarks[nextLine] + state.tShift[nextLine] + maximum = state.eMarks[nextLine] + + if pos < maximum: + marker = state.src[pos] + + if marker in ("-", "="): + pos = state.skipCharsStr(pos, marker) + pos = state.skipSpaces(pos) + + # /* = */ + if pos >= maximum: + level = 1 if marker == "=" else 2 + break + + # quirk for blockquotes, this line should already be checked by that rule + if state.sCount[nextLine] < 0: + nextLine += 1 + continue + + # Some tags can terminate paragraph without empty line. + terminate = False + for terminatorRule in terminatorRules: + if terminatorRule(state, nextLine, endLine, True): + terminate = True + break + if terminate: + break + + nextLine += 1 + + if not level: + # Didn't find valid underline + return False + + content = state.getLines(startLine, nextLine, state.blkIndent, False).strip() + + state.line = nextLine + 1 + + token = state.push("heading_open", "h" + str(level), 1) + token.markup = marker + token.map = [startLine, state.line] + + token = state.push("inline", "", 0) + token.content = content + token.map = [startLine, state.line - 1] + token.children = [] + + token = state.push("heading_close", "h" + str(level), -1) + token.markup = marker + + state.parentType = oldParentType + + return True diff --git a/venv/lib/python3.12/site-packages/markdown_it/rules_block/list.py b/venv/lib/python3.12/site-packages/markdown_it/rules_block/list.py new file mode 100644 index 0000000000000000000000000000000000000000..c8fe7af5d5d0a2a1aedb59c09c01a2f8d49b1eea --- /dev/null +++ b/venv/lib/python3.12/site-packages/markdown_it/rules_block/list.py @@ -0,0 +1,408 @@ +# Lists +import logging + +from ..common.utils import isStrSpace +from .state_block import StateBlock + +LOGGER = logging.getLogger(__name__) + + +# Search `[-+*][\n ]`, returns next pos after marker on success +# or -1 on fail. +def skipBulletListMarker(state: StateBlock, startLine: int) -> int: + pos = state.bMarks[startLine] + state.tShift[startLine] + maximum = state.eMarks[startLine] + + try: + marker = state.src[pos] + except IndexError: + return -1 + pos += 1 + + if marker not in ("*", "-", "+"): + return -1 + + if pos < maximum: + ch = state.src[pos] + + if not isStrSpace(ch): + # " -test " - is not a list item + return -1 + + return pos + + +# Search `\d+[.)][\n ]`, returns next pos after marker on success +# or -1 on fail. +def skipOrderedListMarker(state: StateBlock, startLine: int) -> int: + start = state.bMarks[startLine] + state.tShift[startLine] + pos = start + maximum = state.eMarks[startLine] + + # List marker should have at least 2 chars (digit + dot) + if pos + 1 >= maximum: + return -1 + + ch = state.src[pos] + pos += 1 + + ch_ord = ord(ch) + # /* 0 */ /* 9 */ + if ch_ord < 0x30 or ch_ord > 0x39: + return -1 + + while True: + # EOL -> fail + if pos >= maximum: + return -1 + + ch = state.src[pos] + pos += 1 + + # /* 0 */ /* 9 */ + ch_ord = ord(ch) + if ch_ord >= 0x30 and ch_ord <= 0x39: + # List marker should have no more than 9 digits + # (prevents integer overflow in browsers) + if pos - start >= 10: + return -1 + + continue + + # found valid marker + if ch in (")", "."): + break + + return -1 + + if pos < maximum: + ch = state.src[pos] + + if not isStrSpace(ch): + # " 1.test " - is not a list item + return -1 + + return pos + + +def markTightParagraphs(state: StateBlock, idx: int) -> None: + level = state.level + 2 + + i = idx + 2 + length = len(state.tokens) - 2 + while i < length: + if state.tokens[i].level == level and state.tokens[i].type == "paragraph_open": + state.tokens[i + 2].hidden = True + state.tokens[i].hidden = True + i += 2 + i += 1 + + +def list_block(state: StateBlock, startLine: int, endLine: int, silent: bool) -> bool: + LOGGER.debug("entering list: %s, %s, %s, %s", state, startLine, endLine, silent) + + isTerminatingParagraph = False + tight = True + + if state.is_code_block(startLine): + return False + + # Special case: + # - item 1 + # - item 2 + # - item 3 + # - item 4 + # - this one is a paragraph continuation + if ( + state.listIndent >= 0 + and state.sCount[startLine] - state.listIndent >= 4 + and state.sCount[startLine] < state.blkIndent + ): + return False + + # limit conditions when list can interrupt + # a paragraph (validation mode only) + # Next list item should still terminate previous list item + # + # This code can fail if plugins use blkIndent as well as lists, + # but I hope the spec gets fixed long before that happens. + # + if ( + silent + and state.parentType == "paragraph" + and state.sCount[startLine] >= state.blkIndent + ): + isTerminatingParagraph = True + + # Detect list type and position after marker + posAfterMarker = skipOrderedListMarker(state, startLine) + if posAfterMarker >= 0: + isOrdered = True + start = state.bMarks[startLine] + state.tShift[startLine] + markerValue = int(state.src[start : posAfterMarker - 1]) + + # If we're starting a new ordered list right after + # a paragraph, it should start with 1. + if isTerminatingParagraph and markerValue != 1: + return False + else: + posAfterMarker = skipBulletListMarker(state, startLine) + if posAfterMarker >= 0: + isOrdered = False + else: + return False + + # If we're starting a new unordered list right after + # a paragraph, first line should not be empty. + if ( + isTerminatingParagraph + and state.skipSpaces(posAfterMarker) >= state.eMarks[startLine] + ): + return False + + # We should terminate list on style change. Remember first one to compare. + markerChar = state.src[posAfterMarker - 1] + + # For validation mode we can terminate immediately + if silent: + return True + + # Start list + listTokIdx = len(state.tokens) + + if isOrdered: + token = state.push("ordered_list_open", "ol", 1) + if markerValue != 1: + token.attrs = {"start": markerValue} + + else: + token = state.push("bullet_list_open", "ul", 1) + + token.map = listLines = [startLine, 0] + token.markup = markerChar + + # + # Iterate list items + # + + nextLine = startLine + prevEmptyEnd = False + terminatorRules = state.md.block.ruler.getRules("list") + + oldParentType = state.parentType + state.parentType = "list" + + while nextLine < endLine: + pos = posAfterMarker + maximum = state.eMarks[nextLine] + + initial = offset = ( + state.sCount[nextLine] + + posAfterMarker + - (state.bMarks[startLine] + state.tShift[startLine]) + ) + + while pos < maximum: + ch = state.src[pos] + + if ch == "\t": + offset += 4 - (offset + state.bsCount[nextLine]) % 4 + elif ch == " ": + offset += 1 + else: + break + + pos += 1 + + contentStart = pos + + # trimming space in "- \n 3" case, indent is 1 here + indentAfterMarker = 1 if contentStart >= maximum else offset - initial + + # If we have more than 4 spaces, the indent is 1 + # (the rest is just indented code block) + if indentAfterMarker > 4: + indentAfterMarker = 1 + + # " - test" + # ^^^^^ - calculating total length of this thing + indent = initial + indentAfterMarker + + # Run subparser & write tokens + token = state.push("list_item_open", "li", 1) + token.markup = markerChar + token.map = itemLines = [startLine, 0] + if isOrdered: + token.info = state.src[start : posAfterMarker - 1] + + # Detect GFM task checkbox: `[ ] ` or `[x] `/`[X] ` at content start + checkboxLen = 0 + if state.md.options.get("tasklists", False) and contentStart < maximum: + checked = _detect_task_checkbox(state.src, contentStart, maximum) + if checked is not None: + token.meta = {"checked": checked} + # Advance content past the checkbox: `[x]` (3 chars) + whitespace. + # `_detect_task_checkbox` already guarantees a whitespace char at + # pos+3, so we always consume 4 characters. + checkboxLen = 4 + + # change current state, then restore it after parser subcall + oldTight = state.tight + oldBMark = state.bMarks[startLine] + oldTShift = state.tShift[startLine] + oldSCount = state.sCount[startLine] + + # - example list + # ^ listIndent position will be here + # ^ blkIndent position will be here + # + oldListIndent = state.listIndent + state.listIndent = state.blkIndent + state.blkIndent = indent + + state.tight = True + state.tShift[startLine] = contentStart - state.bMarks[startLine] + state.sCount[startLine] = offset + + # If we detected a checkbox, advance bMarks past it so that + # getLines() doesn't include the checkbox text in the content. + if checkboxLen: + state.bMarks[startLine] = contentStart + checkboxLen + state.tShift[startLine] = 0 + + if contentStart >= maximum and state.isEmpty(startLine + 1): + # workaround for this case + # (list item is empty, list terminates before "foo"): + # ~~~~~~~~ + # - + # + # foo + # ~~~~~~~~ + state.line = min(state.line + 2, endLine) + else: + # NOTE in list.js this was: + # state.md.block.tokenize(state, startLine, endLine, True) + # but tokeniz does not take the final parameter + state.md.block.tokenize(state, startLine, endLine) + + # If any of list item is tight, mark list as tight + if (not state.tight) or prevEmptyEnd: + tight = False + + # Item become loose if finish with empty line, + # but we should filter last element, because it means list finish + prevEmptyEnd = (state.line - startLine) > 1 and state.isEmpty(state.line - 1) + + state.blkIndent = state.listIndent + state.listIndent = oldListIndent + if checkboxLen: + state.bMarks[startLine] = oldBMark + state.tShift[startLine] = oldTShift + state.sCount[startLine] = oldSCount + state.tight = oldTight + + token = state.push("list_item_close", "li", -1) + token.markup = markerChar + + nextLine = startLine = state.line + itemLines[1] = nextLine + + if nextLine >= endLine: + break + + contentStart = state.bMarks[startLine] + + # + # Try to check if list is terminated or continued. + # + if state.sCount[nextLine] < state.blkIndent: + break + + if state.is_code_block(startLine): + break + + # fail if terminating block found + terminate = False + for terminatorRule in terminatorRules: + if terminatorRule(state, nextLine, endLine, True): + terminate = True + break + + if terminate: + break + + # fail if list has another type + if isOrdered: + posAfterMarker = skipOrderedListMarker(state, nextLine) + if posAfterMarker < 0: + break + start = state.bMarks[nextLine] + state.tShift[nextLine] + else: + posAfterMarker = skipBulletListMarker(state, nextLine) + if posAfterMarker < 0: + break + + if markerChar != state.src[posAfterMarker - 1]: + break + + # Finalize list + + # If any direct list item has a task checkbox, add class to the list + if state.md.options.get("tasklists", False): + containsTask = False + level = state.tokens[listTokIdx].level + for j in range(listTokIdx + 1, len(state.tokens)): + tok = state.tokens[j] + if ( + tok.level == level + 1 + and tok.type == "list_item_open" + and tok.meta + and "checked" in tok.meta + ): + tok.attrJoin("class", "task-list-item") + containsTask = True + if containsTask: + state.tokens[listTokIdx].attrJoin("class", "contains-task-list") + + if isOrdered: + token = state.push("ordered_list_close", "ol", -1) + else: + token = state.push("bullet_list_close", "ul", -1) + + token.markup = markerChar + + listLines[1] = nextLine + state.line = nextLine + + state.parentType = oldParentType + + # mark paragraphs tight if needed + if tight: + markTightParagraphs(state, listTokIdx) + + return True + + +def _detect_task_checkbox(src: str, pos: int, maximum: int) -> bool | None: + """Detect ``[ ]``, ``[x]``, or ``[X]`` at *pos*, followed by whitespace. + + Returns ``True`` (checked), ``False`` (unchecked), or ``None`` (no match). + """ + # Need at least 4 chars: `[`, char, `]`, whitespace + if pos + 4 > maximum: + return None + if src[pos] != "[": + return None + inner = src[pos + 1] + if src[pos + 2] != "]": + return None + if inner == " ": + checked = False + elif inner in ("x", "X"): + checked = True + else: + return None + # After `]`, must have whitespace + if src[pos + 3] not in (" ", "\t"): + return None + return checked diff --git a/venv/lib/python3.12/site-packages/markdown_it/rules_block/paragraph.py b/venv/lib/python3.12/site-packages/markdown_it/rules_block/paragraph.py new file mode 100644 index 0000000000000000000000000000000000000000..30ba877799beb764dc0a603caa21fe6e6b641375 --- /dev/null +++ b/venv/lib/python3.12/site-packages/markdown_it/rules_block/paragraph.py @@ -0,0 +1,66 @@ +"""Paragraph.""" + +import logging + +from .state_block import StateBlock + +LOGGER = logging.getLogger(__name__) + + +def paragraph(state: StateBlock, startLine: int, endLine: int, silent: bool) -> bool: + LOGGER.debug( + "entering paragraph: %s, %s, %s, %s", state, startLine, endLine, silent + ) + + nextLine = startLine + 1 + ruler = state.md.block.ruler + terminatorRules = ruler.getRules("paragraph") + endLine = state.lineMax + + oldParentType = state.parentType + state.parentType = "paragraph" + + # jump line-by-line until empty one or EOF + while nextLine < endLine: + if state.isEmpty(nextLine): + break + # this would be a code block normally, but after paragraph + # it's considered a lazy continuation regardless of what's there + if state.sCount[nextLine] - state.blkIndent > 3: + nextLine += 1 + continue + + # quirk for blockquotes, this line should already be checked by that rule + if state.sCount[nextLine] < 0: + nextLine += 1 + continue + + # Some tags can terminate paragraph without empty line. + terminate = False + for terminatorRule in terminatorRules: + if terminatorRule(state, nextLine, endLine, True): + terminate = True + break + + if terminate: + break + + nextLine += 1 + + content = state.getLines(startLine, nextLine, state.blkIndent, False).strip() + + state.line = nextLine + + token = state.push("paragraph_open", "p", 1) + token.map = [startLine, state.line] + + token = state.push("inline", "", 0) + token.content = content + token.map = [startLine, state.line] + token.children = [] + + token = state.push("paragraph_close", "p", -1) + + state.parentType = oldParentType + + return True diff --git a/venv/lib/python3.12/site-packages/markdown_it/rules_block/reference.py b/venv/lib/python3.12/site-packages/markdown_it/rules_block/reference.py new file mode 100644 index 0000000000000000000000000000000000000000..ad94d40941ee7cd43c7d3873ac64276a47c15d8b --- /dev/null +++ b/venv/lib/python3.12/site-packages/markdown_it/rules_block/reference.py @@ -0,0 +1,235 @@ +import logging + +from ..common.utils import charCodeAt, isSpace, normalizeReference +from .state_block import StateBlock + +LOGGER = logging.getLogger(__name__) + + +def reference(state: StateBlock, startLine: int, _endLine: int, silent: bool) -> bool: + LOGGER.debug( + "entering reference: %s, %s, %s, %s", state, startLine, _endLine, silent + ) + + pos = state.bMarks[startLine] + state.tShift[startLine] + maximum = state.eMarks[startLine] + nextLine = startLine + 1 + + if state.is_code_block(startLine): + return False + + if state.src[pos] != "[": + return False + + string = state.src[pos : maximum + 1] + + # string = state.getLines(startLine, nextLine, state.blkIndent, False).strip() + maximum = len(string) + + labelEnd = None + pos = 1 + while pos < maximum: + ch = charCodeAt(string, pos) + if ch == 0x5B: # /* [ */ + return False + elif ch == 0x5D: # /* ] */ + labelEnd = pos + break + elif ch == 0x0A: # /* \n */ + if (lineContent := getNextLine(state, nextLine)) is not None: + string += lineContent + maximum = len(string) + nextLine += 1 + elif ch == 0x5C: # /* \ */ + pos += 1 + if ( + pos < maximum + and charCodeAt(string, pos) == 0x0A + and (lineContent := getNextLine(state, nextLine)) is not None + ): + string += lineContent + maximum = len(string) + nextLine += 1 + pos += 1 + + if ( + labelEnd is None or labelEnd < 0 or charCodeAt(string, labelEnd + 1) != 0x3A + ): # /* : */ + return False + + # [label]: destination 'title' + # ^^^ skip optional whitespace here + pos = labelEnd + 2 + while pos < maximum: + ch = charCodeAt(string, pos) + if ch == 0x0A: + if (lineContent := getNextLine(state, nextLine)) is not None: + string += lineContent + maximum = len(string) + nextLine += 1 + elif isSpace(ch): + pass + else: + break + pos += 1 + + # [label]: destination 'title' + # ^^^^^^^^^^^ parse this + destRes = state.md.helpers.parseLinkDestination(string, pos, maximum) + if not destRes.ok: + return False + + href = state.md.normalizeLink(destRes.str) + if not state.md.validateLink(href): + return False + + pos = destRes.pos + + # save cursor state, we could require to rollback later + destEndPos = pos + destEndLineNo = nextLine + + # [label]: destination 'title' + # ^^^ skipping those spaces + start = pos + while pos < maximum: + ch = charCodeAt(string, pos) + if ch == 0x0A: + if (lineContent := getNextLine(state, nextLine)) is not None: + string += lineContent + maximum = len(string) + nextLine += 1 + elif isSpace(ch): + pass + else: + break + pos += 1 + + # [label]: destination 'title' + # ^^^^^^^ parse this + titleRes = state.md.helpers.parseLinkTitle(string, pos, maximum, None) + while titleRes.can_continue: + if (lineContent := getNextLine(state, nextLine)) is None: + break + string += lineContent + pos = maximum + maximum = len(string) + nextLine += 1 + titleRes = state.md.helpers.parseLinkTitle(string, pos, maximum, titleRes) + + if pos < maximum and start != pos and titleRes.ok: + title = titleRes.str + pos = titleRes.pos + else: + title = "" + pos = destEndPos + nextLine = destEndLineNo + + # skip trailing spaces until the rest of the line + while pos < maximum: + ch = charCodeAt(string, pos) + if not isSpace(ch): + break + pos += 1 + + if pos < maximum and charCodeAt(string, pos) != 0x0A and title: + # garbage at the end of the line after title, + # but it could still be a valid reference if we roll back + title = "" + pos = destEndPos + nextLine = destEndLineNo + while pos < maximum: + ch = charCodeAt(string, pos) + if not isSpace(ch): + break + pos += 1 + + if pos < maximum and charCodeAt(string, pos) != 0x0A: + # garbage at the end of the line + return False + + label = normalizeReference(string[1:labelEnd]) + if not label: + # CommonMark 0.20 disallows empty labels + return False + + # Reference can not terminate anything. This check is for safety only. + if silent: + return True + + if "references" not in state.env: + state.env["references"] = {} + + state.line = nextLine + + # note, this is not part of markdown-it JS, but is useful for renderers + if state.md.options.get("inline_definitions", False): + token = state.push("definition", "", 0) + token.meta = { + "id": label, + "title": title, + "url": href, + "label": string[1:labelEnd], + } + token.map = [startLine, state.line] + + if label not in state.env["references"]: + state.env["references"][label] = { + "title": title, + "href": href, + "map": [startLine, state.line], + } + else: + state.env.setdefault("duplicate_refs", []).append( + { + "title": title, + "href": href, + "label": label, + "map": [startLine, state.line], + } + ) + + return True + + +def getNextLine(state: StateBlock, nextLine: int) -> None | str: + endLine = state.lineMax + + if nextLine >= endLine or state.isEmpty(nextLine): + # empty line or end of input + return None + + isContinuation = False + + # this would be a code block normally, but after paragraph + # it's considered a lazy continuation regardless of what's there + if state.is_code_block(nextLine): + isContinuation = True + + # quirk for blockquotes, this line should already be checked by that rule + if state.sCount[nextLine] < 0: + isContinuation = True + + if not isContinuation: + terminatorRules = state.md.block.ruler.getRules("reference") + oldParentType = state.parentType + state.parentType = "reference" + + # Some tags can terminate paragraph without empty line. + terminate = False + for terminatorRule in terminatorRules: + if terminatorRule(state, nextLine, endLine, True): + terminate = True + break + + state.parentType = oldParentType + + if terminate: + # terminated by another block + return None + + pos = state.bMarks[nextLine] + state.tShift[nextLine] + maximum = state.eMarks[nextLine] + + # max + 1 explicitly includes the newline + return state.src[pos : maximum + 1] diff --git a/venv/lib/python3.12/site-packages/markdown_it/rules_block/state_block.py b/venv/lib/python3.12/site-packages/markdown_it/rules_block/state_block.py new file mode 100644 index 0000000000000000000000000000000000000000..445ad265a01e3f1dededf9f72848686a2b5ee901 --- /dev/null +++ b/venv/lib/python3.12/site-packages/markdown_it/rules_block/state_block.py @@ -0,0 +1,261 @@ +from __future__ import annotations + +from typing import TYPE_CHECKING, Literal + +from ..common.utils import isStrSpace +from ..ruler import StateBase +from ..token import Token +from ..utils import EnvType + +if TYPE_CHECKING: + from markdown_it.main import MarkdownIt + + +class StateBlock(StateBase): + def __init__( + self, src: str, md: MarkdownIt, env: EnvType, tokens: list[Token] + ) -> None: + self.src = src + + # link to parser instance + self.md = md + + self.env = env + + # + # Internal state variables + # + + self.tokens = tokens + + self.bMarks: list[int] = [] # line begin offsets for fast jumps + self.eMarks: list[int] = [] # line end offsets for fast jumps + # offsets of the first non-space characters (tabs not expanded) + self.tShift: list[int] = [] + self.sCount: list[int] = [] # indents for each line (tabs expanded) + + # An amount of virtual spaces (tabs expanded) between beginning + # of each line (bMarks) and real beginning of that line. + # + # It exists only as a hack because blockquotes override bMarks + # losing information in the process. + # + # It's used only when expanding tabs, you can think about it as + # an initial tab length, e.g. bsCount=21 applied to string `\t123` + # means first tab should be expanded to 4-21%4 === 3 spaces. + # + self.bsCount: list[int] = [] + + # block parser variables + self.blkIndent = 0 # required block content indent (for example, if we are + # inside a list, it would be positioned after list marker) + self.line = 0 # line index in src + self.lineMax = 0 # lines count + self.tight = False # loose/tight mode for lists + self.ddIndent = -1 # indent of the current dd block (-1 if there isn't any) + self.listIndent = -1 # indent of the current list block (-1 if there isn't any) + + # can be 'blockquote', 'list', 'root', 'paragraph' or 'reference' + # used in lists to determine if they interrupt a paragraph + self.parentType = "root" + + self.level = 0 + + # renderer + self.result = "" + + # Create caches + # Generate markers. + indent_found = False + + start = pos = indent = offset = 0 + length = len(self.src) + + for pos, character in enumerate(self.src): + if not indent_found: + if isStrSpace(character): + indent += 1 + + if character == "\t": + offset += 4 - offset % 4 + else: + offset += 1 + continue + else: + indent_found = True + + if character == "\n" or pos == length - 1: + if character != "\n": + pos += 1 + self.bMarks.append(start) + self.eMarks.append(pos) + self.tShift.append(indent) + self.sCount.append(offset) + self.bsCount.append(0) + + indent_found = False + indent = 0 + offset = 0 + start = pos + 1 + + # Push fake entry to simplify cache bounds checks + self.bMarks.append(length) + self.eMarks.append(length) + self.tShift.append(0) + self.sCount.append(0) + self.bsCount.append(0) + + self.lineMax = len(self.bMarks) - 1 # don't count last fake line + + # pre-check if code blocks are enabled, to speed up is_code_block method + self._code_enabled = "code" in self.md["block"].ruler.get_active_rules() + + def __repr__(self) -> str: + return ( + f"{self.__class__.__name__}" + f"(line={self.line},level={self.level},tokens={len(self.tokens)})" + ) + + def push(self, ttype: str, tag: str, nesting: Literal[-1, 0, 1]) -> Token: + """Push new token to "stream".""" + token = Token(ttype, tag, nesting) + token.block = True + if nesting < 0: + self.level -= 1 # closing tag + token.level = self.level + if nesting > 0: + self.level += 1 # opening tag + self.tokens.append(token) + return token + + def isEmpty(self, line: int) -> bool: + """.""" + return (self.bMarks[line] + self.tShift[line]) >= self.eMarks[line] + + def skipEmptyLines(self, from_pos: int) -> int: + """.""" + while from_pos < self.lineMax: + try: + if (self.bMarks[from_pos] + self.tShift[from_pos]) < self.eMarks[ + from_pos + ]: + break + except IndexError: + pass + from_pos += 1 + return from_pos + + def skipSpaces(self, pos: int) -> int: + """Skip spaces from given position.""" + while True: + try: + current = self.src[pos] + except IndexError: + break + if not isStrSpace(current): + break + pos += 1 + return pos + + def skipSpacesBack(self, pos: int, minimum: int) -> int: + """Skip spaces from given position in reverse.""" + if pos <= minimum: + return pos + while pos > minimum: + pos -= 1 + if not isStrSpace(self.src[pos]): + return pos + 1 + return pos + + def skipChars(self, pos: int, code: int) -> int: + """Skip character code from given position.""" + while True: + try: + current = self.srcCharCode[pos] + except IndexError: + break + if current != code: + break + pos += 1 + return pos + + def skipCharsStr(self, pos: int, ch: str) -> int: + """Skip character string from given position.""" + while True: + try: + current = self.src[pos] + except IndexError: + break + if current != ch: + break + pos += 1 + return pos + + def skipCharsBack(self, pos: int, code: int, minimum: int) -> int: + """Skip character code reverse from given position - 1.""" + if pos <= minimum: + return pos + while pos > minimum: + pos -= 1 + if code != self.srcCharCode[pos]: + return pos + 1 + return pos + + def skipCharsStrBack(self, pos: int, ch: str, minimum: int) -> int: + """Skip character string reverse from given position - 1.""" + if pos <= minimum: + return pos + while pos > minimum: + pos -= 1 + if ch != self.src[pos]: + return pos + 1 + return pos + + def getLines(self, begin: int, end: int, indent: int, keepLastLF: bool) -> str: + """Cut lines range from source.""" + line = begin + if begin >= end: + return "" + + queue = [""] * (end - begin) + + i = 1 + while line < end: + lineIndent = 0 + lineStart = first = self.bMarks[line] + last = ( + self.eMarks[line] + 1 + if line + 1 < end or keepLastLF + else self.eMarks[line] + ) + + while (first < last) and (lineIndent < indent): + ch = self.src[first] + if isStrSpace(ch): + if ch == "\t": + lineIndent += 4 - (lineIndent + self.bsCount[line]) % 4 + else: + lineIndent += 1 + elif first - lineStart < self.tShift[line]: + lineIndent += 1 + else: + break + first += 1 + + if lineIndent > indent: + # partially expanding tabs in code blocks, e.g '\t\tfoobar' + # with indent=2 becomes ' \tfoobar' + queue[i - 1] = (" " * (lineIndent - indent)) + self.src[first:last] + else: + queue[i - 1] = self.src[first:last] + + line += 1 + i += 1 + + return "".join(queue) + + def is_code_block(self, line: int) -> bool: + """Check if line is a code block, + i.e. the code block rule is enabled and text is indented by more than 3 spaces. + """ + return self._code_enabled and (self.sCount[line] - self.blkIndent) >= 4 diff --git a/venv/lib/python3.12/site-packages/markdown_it/rules_block/table.py b/venv/lib/python3.12/site-packages/markdown_it/rules_block/table.py new file mode 100644 index 0000000000000000000000000000000000000000..c52553d8c21265df65e23be253686ef00b3297eb --- /dev/null +++ b/venv/lib/python3.12/site-packages/markdown_it/rules_block/table.py @@ -0,0 +1,250 @@ +# GFM table, https://github.github.com/gfm/#tables-extension- +from __future__ import annotations + +import re + +from ..common.utils import charStrAt, isStrSpace +from .state_block import StateBlock + +headerLineRe = re.compile(r"^:?-+:?$") +enclosingPipesRe = re.compile(r"^\||\|$") + +# Limit the amount of empty autocompleted cells in a table, +# see https://github.com/markdown-it/markdown-it/issues/1000, +# Both pulldown-cmark and commonmark-hs limit the number of cells this way to ~200k. +# We set it to 65k, which can expand user input by a factor of x370 +# (256x256 square is 1.8kB expanded into 650kB). +MAX_AUTOCOMPLETED_CELLS = 0x10000 + + +def getLine(state: StateBlock, line: int) -> str: + pos = state.bMarks[line] + state.tShift[line] + maximum = state.eMarks[line] + + # return state.src.substr(pos, max - pos) + return state.src[pos:maximum] + + +def escapedSplit(string: str) -> list[str]: + result: list[str] = [] + pos = 0 + max = len(string) + isEscaped = False + lastPos = 0 + current = "" + ch = charStrAt(string, pos) + + while pos < max: + if ch == "|": + if not isEscaped: + # pipe separating cells, '|' + result.append(current + string[lastPos:pos]) + current = "" + lastPos = pos + 1 + else: + # escaped pipe, '\|' + current += string[lastPos : pos - 1] + lastPos = pos + + isEscaped = ch == "\\" + pos += 1 + + ch = charStrAt(string, pos) + + result.append(current + string[lastPos:]) + + return result + + +def table(state: StateBlock, startLine: int, endLine: int, silent: bool) -> bool: + tbodyLines = None + + # should have at least two lines + if startLine + 2 > endLine: + return False + + nextLine = startLine + 1 + + if state.sCount[nextLine] < state.blkIndent: + return False + + if state.is_code_block(nextLine): + return False + + # first character of the second line should be '|', '-', ':', + # and no other characters are allowed but spaces; + # basically, this is the equivalent of /^[-:|][-:|\s]*$/ regexp + + pos = state.bMarks[nextLine] + state.tShift[nextLine] + if pos >= state.eMarks[nextLine]: + return False + first_ch = state.src[pos] + pos += 1 + if first_ch not in ("|", "-", ":"): + return False + + if pos >= state.eMarks[nextLine]: + return False + second_ch = state.src[pos] + pos += 1 + if second_ch not in ("|", "-", ":") and not isStrSpace(second_ch): + return False + + # if first character is '-', then second character must not be a space + # (due to parsing ambiguity with list) + if first_ch == "-" and isStrSpace(second_ch): + return False + + while pos < state.eMarks[nextLine]: + ch = state.src[pos] + + if ch not in ("|", "-", ":") and not isStrSpace(ch): + return False + + pos += 1 + + lineText = getLine(state, startLine + 1) + + columns = lineText.split("|") + aligns = [] + for i in range(len(columns)): + t = columns[i].strip() + if not t: + # allow empty columns before and after table, but not in between columns; + # e.g. allow ` |---| `, disallow ` ---||--- ` + if i == 0 or i == len(columns) - 1: + continue + else: + return False + + if not headerLineRe.search(t): + return False + if charStrAt(t, len(t) - 1) == ":": + aligns.append("center" if charStrAt(t, 0) == ":" else "right") + elif charStrAt(t, 0) == ":": + aligns.append("left") + else: + aligns.append("") + + lineText = getLine(state, startLine).strip() + if "|" not in lineText: + return False + if state.is_code_block(startLine): + return False + columns = escapedSplit(lineText) + if columns and columns[0] == "": + columns.pop(0) + if columns and columns[-1] == "": + columns.pop() + + # header row will define an amount of columns in the entire table, + # and align row should be exactly the same (the rest of the rows can differ) + columnCount = len(columns) + if columnCount == 0 or columnCount != len(aligns): + return False + + if silent: + return True + + oldParentType = state.parentType + state.parentType = "table" + + # use 'blockquote' lists for termination because it's + # the most similar to tables + terminatorRules = state.md.block.ruler.getRules("blockquote") + + token = state.push("table_open", "table", 1) + token.map = tableLines = [startLine, 0] + + token = state.push("thead_open", "thead", 1) + token.map = [startLine, startLine + 1] + + token = state.push("tr_open", "tr", 1) + token.map = [startLine, startLine + 1] + + for i in range(len(columns)): + token = state.push("th_open", "th", 1) + if aligns[i]: + token.attrs = {"style": "text-align:" + aligns[i]} + + token = state.push("inline", "", 0) + # note in markdown-it this map was removed in v12.0.0 however, we keep it, + # since it is helpful to propagate to children tokens + token.map = [startLine, startLine + 1] + token.content = columns[i].strip() + token.children = [] + + token = state.push("th_close", "th", -1) + + token = state.push("tr_close", "tr", -1) + token = state.push("thead_close", "thead", -1) + + autocompleted_cells = 0 + nextLine = startLine + 2 + while nextLine < endLine: + if state.sCount[nextLine] < state.blkIndent: + break + + terminate = False + for i in range(len(terminatorRules)): + if terminatorRules[i](state, nextLine, endLine, True): + terminate = True + break + + if terminate: + break + lineText = getLine(state, nextLine).strip() + if not lineText: + break + if state.is_code_block(nextLine): + break + columns = escapedSplit(lineText) + if columns and columns[0] == "": + columns.pop(0) + if columns and columns[-1] == "": + columns.pop() + + # note: autocomplete count can be negative if user specifies more columns than header, + # but that does not affect intended use (which is limiting expansion) + autocompleted_cells += columnCount - len(columns) + if autocompleted_cells > MAX_AUTOCOMPLETED_CELLS: + break + + if nextLine == startLine + 2: + token = state.push("tbody_open", "tbody", 1) + token.map = tbodyLines = [startLine + 2, 0] + + token = state.push("tr_open", "tr", 1) + token.map = [nextLine, nextLine + 1] + + for i in range(columnCount): + token = state.push("td_open", "td", 1) + if aligns[i]: + token.attrs = {"style": "text-align:" + aligns[i]} + + token = state.push("inline", "", 0) + # note in markdown-it this map was removed in v12.0.0 however, we keep it, + # since it is helpful to propagate to children tokens + token.map = [nextLine, nextLine + 1] + try: + token.content = columns[i].strip() if columns[i] else "" + except IndexError: + token.content = "" + token.children = [] + + token = state.push("td_close", "td", -1) + + token = state.push("tr_close", "tr", -1) + + nextLine += 1 + + if tbodyLines: + token = state.push("tbody_close", "tbody", -1) + tbodyLines[1] = nextLine + + token = state.push("table_close", "table", -1) + + tableLines[1] = nextLine + state.parentType = oldParentType + state.line = nextLine + return True diff --git a/venv/lib/python3.12/site-packages/markdown_it/rules_core/__init__.py b/venv/lib/python3.12/site-packages/markdown_it/rules_core/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e7d775363c6e1d454e73a7e3fff9af3115be4339 --- /dev/null +++ b/venv/lib/python3.12/site-packages/markdown_it/rules_core/__init__.py @@ -0,0 +1,19 @@ +__all__ = ( + "StateCore", + "block", + "inline", + "linkify", + "normalize", + "replace", + "smartquotes", + "text_join", +) + +from .block import block +from .inline import inline +from .linkify import linkify +from .normalize import normalize +from .replacements import replace +from .smartquotes import smartquotes +from .state_core import StateCore +from .text_join import text_join diff --git a/venv/lib/python3.12/site-packages/markdown_it/rules_core/__pycache__/__init__.cpython-312.pyc 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diff --git a/venv/lib/python3.12/site-packages/markdown_it/rules_core/block.py b/venv/lib/python3.12/site-packages/markdown_it/rules_core/block.py new file mode 100644 index 0000000000000000000000000000000000000000..a6c3bb8d7ae18880fd638690fb5b09beb78b103c --- /dev/null +++ b/venv/lib/python3.12/site-packages/markdown_it/rules_core/block.py @@ -0,0 +1,13 @@ +from ..token import Token +from .state_core import StateCore + + +def block(state: StateCore) -> None: + if state.inlineMode: + token = Token("inline", "", 0) + token.content = state.src + token.map = [0, 1] + token.children = [] + state.tokens.append(token) + else: + state.md.block.parse(state.src, state.md, state.env, state.tokens) diff --git a/venv/lib/python3.12/site-packages/markdown_it/rules_core/inline.py b/venv/lib/python3.12/site-packages/markdown_it/rules_core/inline.py new file mode 100644 index 0000000000000000000000000000000000000000..c3fd0b5e25dda5d8a5a644cc9e460d0f92ae2d1d --- /dev/null +++ b/venv/lib/python3.12/site-packages/markdown_it/rules_core/inline.py @@ -0,0 +1,10 @@ +from .state_core import StateCore + + +def inline(state: StateCore) -> None: + """Parse inlines""" + for token in state.tokens: + if token.type == "inline": + if token.children is None: + token.children = [] + state.md.inline.parse(token.content, state.md, state.env, token.children) diff --git a/venv/lib/python3.12/site-packages/markdown_it/rules_core/linkify.py b/venv/lib/python3.12/site-packages/markdown_it/rules_core/linkify.py new file mode 100644 index 0000000000000000000000000000000000000000..efbc9d4c9b1cbada1c936401b3421d73fbff5b64 --- /dev/null +++ b/venv/lib/python3.12/site-packages/markdown_it/rules_core/linkify.py @@ -0,0 +1,149 @@ +from __future__ import annotations + +import re +from typing import Protocol + +from ..common.utils import arrayReplaceAt, isLinkClose, isLinkOpen +from ..token import Token +from .state_core import StateCore + +HTTP_RE = re.compile(r"^http://") +MAILTO_RE = re.compile(r"^mailto:") +TEST_MAILTO_RE = re.compile(r"^mailto:", flags=re.IGNORECASE) + + +def linkify(state: StateCore) -> None: + """Rule for identifying plain-text links.""" + if not state.md.options.linkify: + return + + if not state.md.linkify: + raise ModuleNotFoundError("Linkify enabled but not installed.") + + for inline_token in state.tokens: + if inline_token.type != "inline" or not state.md.linkify.pretest( + inline_token.content + ): + continue + + tokens = inline_token.children + + htmlLinkLevel = 0 + + # We scan from the end, to keep position when new tags added. + # Use reversed logic in links start/end match + assert tokens is not None + i = len(tokens) + while i >= 1: + i -= 1 + assert isinstance(tokens, list) + currentToken = tokens[i] + + # Skip content of markdown links + if currentToken.type == "link_close": + i -= 1 + while ( + tokens[i].level != currentToken.level + and tokens[i].type != "link_open" + ): + i -= 1 + continue + + # Skip content of html tag links + if currentToken.type == "html_inline": + if isLinkOpen(currentToken.content) and htmlLinkLevel > 0: + htmlLinkLevel -= 1 + if isLinkClose(currentToken.content): + htmlLinkLevel += 1 + if htmlLinkLevel > 0: + continue + + if currentToken.type == "text" and state.md.linkify.test( + currentToken.content + ): + text = currentToken.content + links: list[_LinkType] = state.md.linkify.match(text) or [] + + # Now split string to nodes + nodes = [] + level = currentToken.level + lastPos = 0 + + # forbid escape sequence at the start of the string, + # this avoids http\://example.com/ from being linkified as + # http://example.com/ + if ( + links + and links[0].index == 0 + and i > 0 + and tokens[i - 1].type == "text_special" + ): + links = links[1:] + + for link in links: + url = link.url + fullUrl = state.md.normalizeLink(url) + if not state.md.validateLink(fullUrl): + continue + + urlText = link.text + + # Linkifier might send raw hostnames like "example.com", where url + # starts with domain name. So we prepend http:// in those cases, + # and remove it afterwards. + if not link.schema: + urlText = HTTP_RE.sub( + "", state.md.normalizeLinkText("http://" + urlText) + ) + elif link.schema == "mailto:" and TEST_MAILTO_RE.search(urlText): + urlText = MAILTO_RE.sub( + "", state.md.normalizeLinkText("mailto:" + urlText) + ) + else: + urlText = state.md.normalizeLinkText(urlText) + + pos = link.index + + if pos > lastPos: + token = Token("text", "", 0) + token.content = text[lastPos:pos] + token.level = level + nodes.append(token) + + token = Token("link_open", "a", 1) + token.attrs = {"href": fullUrl} + token.level = level + level += 1 + token.markup = "linkify" + token.info = "auto" + nodes.append(token) + + token = Token("text", "", 0) + token.content = urlText + token.level = level + nodes.append(token) + + token = Token("link_close", "a", -1) + level -= 1 + token.level = level + token.markup = "linkify" + token.info = "auto" + nodes.append(token) + + lastPos = link.last_index + + if lastPos < len(text): + token = Token("text", "", 0) + token.content = text[lastPos:] + token.level = level + nodes.append(token) + + inline_token.children = tokens = arrayReplaceAt(tokens, i, nodes) + + +class _LinkType(Protocol): + url: str + text: str + index: int + last_index: int + schema: str | None diff --git a/venv/lib/python3.12/site-packages/markdown_it/rules_core/normalize.py b/venv/lib/python3.12/site-packages/markdown_it/rules_core/normalize.py new file mode 100644 index 0000000000000000000000000000000000000000..32439243ef6ebfa424d202ed63635983d4c9ea82 --- /dev/null +++ b/venv/lib/python3.12/site-packages/markdown_it/rules_core/normalize.py @@ -0,0 +1,19 @@ +"""Normalize input string.""" + +import re + +from .state_core import StateCore + +# https://spec.commonmark.org/0.29/#line-ending +NEWLINES_RE = re.compile(r"\r\n?|\n") +NULL_RE = re.compile(r"\0") + + +def normalize(state: StateCore) -> None: + # Normalize newlines + string = NEWLINES_RE.sub("\n", state.src) + + # Replace NULL characters + string = NULL_RE.sub("\ufffd", string) + + state.src = string diff --git a/venv/lib/python3.12/site-packages/markdown_it/rules_core/replacements.py b/venv/lib/python3.12/site-packages/markdown_it/rules_core/replacements.py new file mode 100644 index 0000000000000000000000000000000000000000..bcc9980046bf76723245b1ca2543af132efe5541 --- /dev/null +++ b/venv/lib/python3.12/site-packages/markdown_it/rules_core/replacements.py @@ -0,0 +1,127 @@ +"""Simple typographic replacements + +* ``(c)``, ``(C)`` → © +* ``(tm)``, ``(TM)`` → ™ +* ``(r)``, ``(R)`` → ® +* ``+-`` → ± +* ``...`` → … +* ``?....`` → ?.. +* ``!....`` → !.. +* ``????????`` → ??? +* ``!!!!!`` → !!! +* ``,,,`` → , +* ``--`` → &ndash +* ``---`` → &mdash +""" + +from __future__ import annotations + +import logging +import re + +from ..token import Token +from .state_core import StateCore + +LOGGER = logging.getLogger(__name__) + +# TODO: +# - fractionals 1/2, 1/4, 3/4 -> ½, ¼, ¾ +# - multiplication 2 x 4 -> 2 × 4 + +RARE_RE = re.compile(r"\+-|\.\.|\?\?\?\?|!!!!|,,|--") + +# Workaround for phantomjs - need regex without /g flag, +# or root check will fail every second time +# SCOPED_ABBR_TEST_RE = r"\((c|tm|r)\)" + +SCOPED_ABBR_RE = re.compile(r"\((c|tm|r)\)", flags=re.IGNORECASE) + +PLUS_MINUS_RE = re.compile(r"\+-") + +ELLIPSIS_RE = re.compile(r"\.{2,}") + +ELLIPSIS_QUESTION_EXCLAMATION_RE = re.compile(r"([?!])…") + +QUESTION_EXCLAMATION_RE = re.compile(r"([?!]){4,}") + +COMMA_RE = re.compile(r",{2,}") + +EM_DASH_RE = re.compile(r"(^|[^-])---(?=[^-]|$)", flags=re.MULTILINE) + +EN_DASH_RE = re.compile(r"(^|\s)--(?=\s|$)", flags=re.MULTILINE) + +EN_DASH_INDENT_RE = re.compile(r"(^|[^-\s])--(?=[^-\s]|$)", flags=re.MULTILINE) + + +SCOPED_ABBR = {"c": "©", "r": "®", "tm": "™"} + + +def replaceFn(match: re.Match[str]) -> str: + return SCOPED_ABBR[match.group(1).lower()] + + +def replace_scoped(inlineTokens: list[Token]) -> None: + inside_autolink = 0 + + for token in inlineTokens: + if token.type == "text" and not inside_autolink: + token.content = SCOPED_ABBR_RE.sub(replaceFn, token.content) + + if token.type == "link_open" and token.info == "auto": + inside_autolink -= 1 + + if token.type == "link_close" and token.info == "auto": + inside_autolink += 1 + + +def replace_rare(inlineTokens: list[Token]) -> None: + inside_autolink = 0 + + for token in inlineTokens: + if ( + token.type == "text" + and (not inside_autolink) + and RARE_RE.search(token.content) + ): + # +- -> ± + token.content = PLUS_MINUS_RE.sub("±", token.content) + + # .., ..., ....... -> … + token.content = ELLIPSIS_RE.sub("…", token.content) + + # but ?..... & !..... -> ?.. & !.. + token.content = ELLIPSIS_QUESTION_EXCLAMATION_RE.sub("\\1..", token.content) + token.content = QUESTION_EXCLAMATION_RE.sub("\\1\\1\\1", token.content) + + # ,, ,,, ,,,, -> , + token.content = COMMA_RE.sub(",", token.content) + + # em-dash + token.content = EM_DASH_RE.sub("\\1\u2014", token.content) + + # en-dash + token.content = EN_DASH_RE.sub("\\1\u2013", token.content) + token.content = EN_DASH_INDENT_RE.sub("\\1\u2013", token.content) + + if token.type == "link_open" and token.info == "auto": + inside_autolink -= 1 + + if token.type == "link_close" and token.info == "auto": + inside_autolink += 1 + + +def replace(state: StateCore) -> None: + if not state.md.options.typographer: + return + + for token in state.tokens: + if token.type != "inline": + continue + if token.children is None: + continue + + if SCOPED_ABBR_RE.search(token.content): + replace_scoped(token.children) + + if RARE_RE.search(token.content): + replace_rare(token.children) diff --git a/venv/lib/python3.12/site-packages/markdown_it/rules_core/smartquotes.py b/venv/lib/python3.12/site-packages/markdown_it/rules_core/smartquotes.py new file mode 100644 index 0000000000000000000000000000000000000000..f9b8b457b6c134f5736fabd3b14dc746e75ab86b --- /dev/null +++ b/venv/lib/python3.12/site-packages/markdown_it/rules_core/smartquotes.py @@ -0,0 +1,202 @@ +"""Convert straight quotation marks to typographic ones""" + +from __future__ import annotations + +import re +from typing import Any + +from ..common.utils import charCodeAt, isMdAsciiPunct, isPunctChar, isWhiteSpace +from ..token import Token +from .state_core import StateCore + +QUOTE_TEST_RE = re.compile(r"['\"]") +QUOTE_RE = re.compile(r"['\"]") +APOSTROPHE = "\u2019" # ’ + + +def replaceAt(string: str, index: int, ch: str) -> str: + # When the index is negative, the behavior is different from the js version. + # But basically, the index will not be negative. + assert index >= 0 + return string[:index] + ch + string[index + 1 :] + + +def process_inlines(tokens: list[Token], state: StateCore) -> None: + stack: list[dict[str, Any]] = [] + + for i, token in enumerate(tokens): + thisLevel = token.level + + j = 0 + for j in range(len(stack))[::-1]: + if stack[j]["level"] <= thisLevel: + break + else: + # When the loop is terminated without a "break". + # Subtract 1 to get the same index as the js version. + j -= 1 + + stack = stack[: j + 1] + + if token.type != "text": + continue + + text = token.content + pos = 0 + maximum = len(text) + + while pos < maximum: + goto_outer = False + lastIndex = pos + t = QUOTE_RE.search(text[lastIndex:]) + if not t: + break + + canOpen = canClose = True + pos = t.start(0) + lastIndex + 1 + isSingle = t.group(0) == "'" + + # Find previous character, + # default to space if it's the beginning of the line + lastChar: None | int = 0x20 + + if t.start(0) + lastIndex - 1 >= 0: + lastChar = charCodeAt(text, t.start(0) + lastIndex - 1) + else: + for j in range(i)[::-1]: + if tokens[j].type == "softbreak" or tokens[j].type == "hardbreak": + break + # should skip all tokens except 'text', 'html_inline' or 'code_inline' + if not tokens[j].content: + continue + + lastChar = charCodeAt(tokens[j].content, len(tokens[j].content) - 1) + break + + # Find next character, + # default to space if it's the end of the line + nextChar: None | int = 0x20 + + if pos < maximum: + nextChar = charCodeAt(text, pos) + else: + for j in range(i + 1, len(tokens)): + # nextChar defaults to 0x20 + if tokens[j].type == "softbreak" or tokens[j].type == "hardbreak": + break + # should skip all tokens except 'text', 'html_inline' or 'code_inline' + if not tokens[j].content: + continue + + nextChar = charCodeAt(tokens[j].content, 0) + break + + isLastPunctChar = lastChar is not None and ( + isMdAsciiPunct(lastChar) or isPunctChar(chr(lastChar)) + ) + isNextPunctChar = nextChar is not None and ( + isMdAsciiPunct(nextChar) or isPunctChar(chr(nextChar)) + ) + + isLastWhiteSpace = lastChar is not None and isWhiteSpace(lastChar) + isNextWhiteSpace = nextChar is not None and isWhiteSpace(nextChar) + + if isNextWhiteSpace: # noqa: SIM114 + canOpen = False + elif isNextPunctChar and not (isLastWhiteSpace or isLastPunctChar): + canOpen = False + + if isLastWhiteSpace: # noqa: SIM114 + canClose = False + elif isLastPunctChar and not (isNextWhiteSpace or isNextPunctChar): + canClose = False + + if nextChar == 0x22 and t.group(0) == '"': # 0x22: " # noqa: SIM102 + if ( + lastChar is not None and lastChar >= 0x30 and lastChar <= 0x39 + ): # 0x30: 0, 0x39: 9 + # special case: 1"" - count first quote as an inch + canClose = canOpen = False + + if canOpen and canClose: + # Replace quotes in the middle of punctuation sequence, but not + # in the middle of the words, i.e.: + # + # 1. foo " bar " baz - not replaced + # 2. foo-"-bar-"-baz - replaced + # 3. foo"bar"baz - not replaced + canOpen = isLastPunctChar + canClose = isNextPunctChar + + if not canOpen and not canClose: + # middle of word + if isSingle: + token.content = replaceAt( + token.content, t.start(0) + lastIndex, APOSTROPHE + ) + continue + + if canClose: + # this could be a closing quote, rewind the stack to get a match + for j in range(len(stack))[::-1]: + item = stack[j] + if stack[j]["level"] < thisLevel: + break + if item["single"] == isSingle and stack[j]["level"] == thisLevel: + item = stack[j] + + if isSingle: + openQuote = state.md.options.quotes[2] + closeQuote = state.md.options.quotes[3] + else: + openQuote = state.md.options.quotes[0] + closeQuote = state.md.options.quotes[1] + + # replace token.content *before* tokens[item.token].content, + # because, if they are pointing at the same token, replaceAt + # could mess up indices when quote length != 1 + token.content = replaceAt( + token.content, t.start(0) + lastIndex, closeQuote + ) + tokens[item["token"]].content = replaceAt( + tokens[item["token"]].content, item["pos"], openQuote + ) + + pos += len(closeQuote) - 1 + if item["token"] == i: + pos += len(openQuote) - 1 + + text = token.content + maximum = len(text) + + stack = stack[:j] + goto_outer = True + break + if goto_outer: + goto_outer = False + continue + + if canOpen: + stack.append( + { + "token": i, + "pos": t.start(0) + lastIndex, + "single": isSingle, + "level": thisLevel, + } + ) + elif canClose and isSingle: + token.content = replaceAt( + token.content, t.start(0) + lastIndex, APOSTROPHE + ) + + +def smartquotes(state: StateCore) -> None: + if not state.md.options.typographer: + return + + for token in state.tokens: + if token.type != "inline" or not QUOTE_RE.search(token.content): + continue + if token.children is not None: + process_inlines(token.children, state) diff --git a/venv/lib/python3.12/site-packages/markdown_it/rules_core/state_core.py b/venv/lib/python3.12/site-packages/markdown_it/rules_core/state_core.py new file mode 100644 index 0000000000000000000000000000000000000000..a938041d992fdf7ae3f2843a2e0f9ef298c45790 --- /dev/null +++ b/venv/lib/python3.12/site-packages/markdown_it/rules_core/state_core.py @@ -0,0 +1,25 @@ +from __future__ import annotations + +from typing import TYPE_CHECKING + +from ..ruler import StateBase +from ..token import Token +from ..utils import EnvType + +if TYPE_CHECKING: + from markdown_it import MarkdownIt + + +class StateCore(StateBase): + def __init__( + self, + src: str, + md: MarkdownIt, + env: EnvType, + tokens: list[Token] | None = None, + ) -> None: + self.src = src + self.md = md # link to parser instance + self.env = env + self.tokens: list[Token] = tokens or [] + self.inlineMode = False diff --git a/venv/lib/python3.12/site-packages/markdown_it/rules_core/text_join.py b/venv/lib/python3.12/site-packages/markdown_it/rules_core/text_join.py new file mode 100644 index 0000000000000000000000000000000000000000..939b83b29f75fa697a9b0f57ca86302744d639d1 --- /dev/null +++ b/venv/lib/python3.12/site-packages/markdown_it/rules_core/text_join.py @@ -0,0 +1,53 @@ +"""Join raw text tokens with the rest of the text + +This is set as a separate rule to provide an opportunity for plugins +to run text replacements after text join, but before escape join. + +For example, `\\:)` shouldn't be replaced with an emoji. +""" + +from __future__ import annotations + +from ..token import Token +from .state_core import StateCore + + +def text_join(state: StateCore) -> None: + """Join raw text for escape sequences (`text_special`) tokens with the rest of the text""" + + for inline_token in state.tokens[:]: + if inline_token.type != "inline": + continue + + # convert text_special to text and join all adjacent text nodes + new_tokens: list[Token] = [] + children = inline_token.children or [] + i = 0 + while i < len(children): + child_token = children[i] + if child_token.type == "text_special": + child_token.type = "text" + if ( + child_token.type == "text" + and new_tokens + and new_tokens[-1].type == "text" + ): + # Collapse a run of adjacent text nodes in a single join, instead + # of pairwise `a + b` concatenation. The pairwise form is O(L*k) + # in the size of the run because each step rebuilds the growing + # prefix; "".join is O(L). + parts = [new_tokens[-1].content, child_token.content] + i += 1 + while i < len(children): + next_token = children[i] + if next_token.type == "text_special": + next_token.type = "text" + if next_token.type != "text": + break + parts.append(next_token.content) + i += 1 + new_tokens[-1].content = "".join(parts) + else: + new_tokens.append(child_token) + i += 1 + inline_token.children = new_tokens diff --git a/venv/lib/python3.12/site-packages/markdown_it/rules_inline/__init__.py b/venv/lib/python3.12/site-packages/markdown_it/rules_inline/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..d82ef8fbcca54eab4bbb40e8410104eef7a27f57 --- /dev/null +++ b/venv/lib/python3.12/site-packages/markdown_it/rules_inline/__init__.py @@ -0,0 +1,31 @@ +__all__ = ( + "StateInline", + "autolink", + "backtick", + "emphasis", + "entity", + "escape", + "fragments_join", + "html_inline", + "image", + "link", + "link_pairs", + "linkify", + "newline", + "strikethrough", + "text", +) +from . import emphasis, strikethrough +from .autolink import autolink +from .backticks import backtick +from .balance_pairs import link_pairs +from .entity import entity +from .escape import escape +from .fragments_join import fragments_join +from .html_inline import html_inline +from .image import image +from .link import link +from .linkify import linkify +from .newline import newline +from .state_inline import StateInline +from .text import text diff --git a/venv/lib/python3.12/site-packages/markdown_it/rules_inline/__pycache__/__init__.cpython-312.pyc b/venv/lib/python3.12/site-packages/markdown_it/rules_inline/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..e64d7055d02ed0feb3f0815677d68b4663f0c0d2 Binary files /dev/null and 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1 + if pos >= maximum: + return False + + ch = state.src[pos] + + if ch == "<": + return False + if ch == ">": + break + + url = state.src[start + 1 : pos] + + if AUTOLINK_RE.search(url) is not None: + fullUrl = state.md.normalizeLink(url) + if not state.md.validateLink(fullUrl): + return False + + if not silent: + token = state.push("link_open", "a", 1) + token.attrs = {"href": fullUrl} + token.markup = "autolink" + token.info = "auto" + + token = state.push("text", "", 0) + token.content = state.md.normalizeLinkText(url) + + token = state.push("link_close", "a", -1) + token.markup = "autolink" + token.info = "auto" + + state.pos += len(url) + 2 + return True + + if EMAIL_RE.search(url) is not None: + fullUrl = state.md.normalizeLink("mailto:" + url) + if not state.md.validateLink(fullUrl): + return False + + if not silent: + token = state.push("link_open", "a", 1) + token.attrs = {"href": fullUrl} + token.markup = "autolink" + token.info = "auto" + + token = state.push("text", "", 0) + token.content = state.md.normalizeLinkText(url) + + token = state.push("link_close", "a", -1) + token.markup = "autolink" + token.info = "auto" + + state.pos += len(url) + 2 + return True + + return False diff --git a/venv/lib/python3.12/site-packages/markdown_it/rules_inline/backticks.py b/venv/lib/python3.12/site-packages/markdown_it/rules_inline/backticks.py new file mode 100644 index 0000000000000000000000000000000000000000..fc60d6b15cdfa7012a05bcf1ccbb06f44d870dfd --- /dev/null +++ b/venv/lib/python3.12/site-packages/markdown_it/rules_inline/backticks.py @@ -0,0 +1,72 @@ +# Parse backticks +import re + +from .state_inline import StateInline + +regex = re.compile("^ (.+) $") + + +def backtick(state: StateInline, silent: bool) -> bool: + pos = state.pos + + if state.src[pos] != "`": + return False + + start = pos + pos += 1 + maximum = state.posMax + + # scan marker length + while pos < maximum and (state.src[pos] == "`"): + pos += 1 + + marker = state.src[start:pos] + openerLength = len(marker) + + if state.backticksScanned and state.backticks.get(openerLength, 0) <= start: + if not silent: + state.pending += marker + state.pos += openerLength + return True + + matchStart = matchEnd = pos + + # Nothing found in the cache, scan until the end of the line (or until marker is found) + while True: + try: + matchStart = state.src.index("`", matchEnd) + except ValueError: + break + matchEnd = matchStart + 1 + + # scan marker length + while matchEnd < maximum and (state.src[matchEnd] == "`"): + matchEnd += 1 + + closerLength = matchEnd - matchStart + + if closerLength == openerLength: + # Found matching closer length. + if not silent: + token = state.push("code_inline", "code", 0) + token.markup = marker + token.content = state.src[pos:matchStart].replace("\n", " ") + if ( + token.content.startswith(" ") + and token.content.endswith(" ") + and len(token.content.strip()) > 0 + ): + token.content = token.content[1:-1] + state.pos = matchEnd + return True + + # Some different length found, put it in cache as upper limit of where closer can be found + state.backticks[closerLength] = matchStart + + # Scanned through the end, didn't find anything + state.backticksScanned = True + + if not silent: + state.pending += marker + state.pos += openerLength + return True diff --git a/venv/lib/python3.12/site-packages/markdown_it/rules_inline/balance_pairs.py b/venv/lib/python3.12/site-packages/markdown_it/rules_inline/balance_pairs.py new file mode 100644 index 0000000000000000000000000000000000000000..9c63b27f7186eb99c61938d27309fda7e900b88d --- /dev/null +++ b/venv/lib/python3.12/site-packages/markdown_it/rules_inline/balance_pairs.py @@ -0,0 +1,138 @@ +"""Balance paired characters (*, _, etc) in inline tokens.""" + +from __future__ import annotations + +from .state_inline import Delimiter, StateInline + + +def processDelimiters(state: StateInline, delimiters: list[Delimiter]) -> None: + """For each opening emphasis-like marker find a matching closing one.""" + if not delimiters: + return + + openersBottom = {} + maximum = len(delimiters) + + # headerIdx is the first delimiter of the current (where closer is) delimiter run + headerIdx = 0 + lastTokenIdx = -2 # needs any value lower than -1 + jumps: list[int] = [] + closerIdx = 0 + while closerIdx < maximum: + closer = delimiters[closerIdx] + + jumps.append(0) + + # markers belong to same delimiter run if: + # - they have adjacent tokens + # - AND markers are the same + # + if ( + delimiters[headerIdx].marker != closer.marker + or lastTokenIdx != closer.token - 1 + ): + headerIdx = closerIdx + lastTokenIdx = closer.token + + # Length is only used for emphasis-specific "rule of 3", + # if it's not defined (in strikethrough or 3rd party plugins), + # we can default it to 0 to disable those checks. + # + closer.length = closer.length or 0 + + if not closer.close: + closerIdx += 1 + continue + + # Previously calculated lower bounds (previous fails) + # for each marker, each delimiter length modulo 3, + # and for whether this closer can be an opener; + # https://github.com/commonmark/cmark/commit/34250e12ccebdc6372b8b49c44fab57c72443460 + if closer.marker not in openersBottom: + openersBottom[closer.marker] = [-1, -1, -1, -1, -1, -1] + + minOpenerIdx = openersBottom[closer.marker][ + (3 if closer.open else 0) + (closer.length % 3) + ] + + openerIdx = headerIdx - jumps[headerIdx] - 1 + + newMinOpenerIdx = openerIdx + + while openerIdx > minOpenerIdx: + opener = delimiters[openerIdx] + + if opener.marker != closer.marker: + openerIdx -= jumps[openerIdx] + 1 + continue + + if opener.open and opener.end < 0: + isOddMatch = False + + # from spec: + # + # If one of the delimiters can both open and close emphasis, then the + # sum of the lengths of the delimiter runs containing the opening and + # closing delimiters must not be a multiple of 3 unless both lengths + # are multiples of 3. + # + if ( + (opener.close or closer.open) + and ((opener.length + closer.length) % 3 == 0) + and (opener.length % 3 != 0 or closer.length % 3 != 0) + ): + isOddMatch = True + + if not isOddMatch: + # If previous delimiter cannot be an opener, we can safely skip + # the entire sequence in future checks. This is required to make + # sure algorithm has linear complexity (see *_*_*_*_*_... case). + # + if openerIdx > 0 and not delimiters[openerIdx - 1].open: + lastJump = jumps[openerIdx - 1] + 1 + else: + lastJump = 0 + + jumps[closerIdx] = closerIdx - openerIdx + lastJump + jumps[openerIdx] = lastJump + + closer.open = False + opener.end = closerIdx + opener.close = False + newMinOpenerIdx = -1 + + # treat next token as start of run, + # it optimizes skips in **<...>**a**<...>** pathological case + lastTokenIdx = -2 + + break + + openerIdx -= jumps[openerIdx] + 1 + + if newMinOpenerIdx != -1: + # If match for this delimiter run failed, we want to set lower bound for + # future lookups. This is required to make sure algorithm has linear + # complexity. + # + # See details here: + # https:#github.com/commonmark/cmark/issues/178#issuecomment-270417442 + # + openersBottom[closer.marker][ + (3 if closer.open else 0) + ((closer.length or 0) % 3) + ] = newMinOpenerIdx + + closerIdx += 1 + + +def link_pairs(state: StateInline) -> None: + tokens_meta = state.tokens_meta + maximum = len(state.tokens_meta) + + processDelimiters(state, state.delimiters) + + curr = 0 + while curr < maximum: + curr_meta = tokens_meta[curr] + if curr_meta and "delimiters" in curr_meta: + processDelimiters(state, curr_meta["delimiters"]) + curr += 1 diff --git a/venv/lib/python3.12/site-packages/markdown_it/rules_inline/emphasis.py b/venv/lib/python3.12/site-packages/markdown_it/rules_inline/emphasis.py new file mode 100644 index 0000000000000000000000000000000000000000..9a98f9e216c94db0217e986270aaaa72fcc99f7f --- /dev/null +++ b/venv/lib/python3.12/site-packages/markdown_it/rules_inline/emphasis.py @@ -0,0 +1,102 @@ +# Process *this* and _that_ +# +from __future__ import annotations + +from .state_inline import Delimiter, StateInline + + +def tokenize(state: StateInline, silent: bool) -> bool: + """Insert each marker as a separate text token, and add it to delimiter list""" + start = state.pos + marker = state.src[start] + + if silent: + return False + + if marker not in ("_", "*"): + return False + + scanned = state.scanDelims(state.pos, marker == "*") + + for _ in range(scanned.length): + token = state.push("text", "", 0) + token.content = marker + state.delimiters.append( + Delimiter( + marker=ord(marker), + length=scanned.length, + token=len(state.tokens) - 1, + end=-1, + open=scanned.can_open, + close=scanned.can_close, + ) + ) + + state.pos += scanned.length + + return True + + +def _postProcess(state: StateInline, delimiters: list[Delimiter]) -> None: + i = len(delimiters) - 1 + while i >= 0: + startDelim = delimiters[i] + + # /* _ */ /* * */ + if startDelim.marker != 0x5F and startDelim.marker != 0x2A: + i -= 1 + continue + + # Process only opening markers + if startDelim.end == -1: + i -= 1 + continue + + endDelim = delimiters[startDelim.end] + + # If the previous delimiter has the same marker and is adjacent to this one, + # merge those into one strong delimiter. + # + # `whatever` -> `whatever` + # + isStrong = ( + i > 0 + and delimiters[i - 1].end == startDelim.end + 1 + # check that first two markers match and adjacent + and delimiters[i - 1].marker == startDelim.marker + and delimiters[i - 1].token == startDelim.token - 1 + # check that last two markers are adjacent (we can safely assume they match) + and delimiters[startDelim.end + 1].token == endDelim.token + 1 + ) + + ch = chr(startDelim.marker) + + token = state.tokens[startDelim.token] + token.type = "strong_open" if isStrong else "em_open" + token.tag = "strong" if isStrong else "em" + token.nesting = 1 + token.markup = ch + ch if isStrong else ch + token.content = "" + + token = state.tokens[endDelim.token] + token.type = "strong_close" if isStrong else "em_close" + token.tag = "strong" if isStrong else "em" + token.nesting = -1 + token.markup = ch + ch if isStrong else ch + token.content = "" + + if isStrong: + state.tokens[delimiters[i - 1].token].content = "" + state.tokens[delimiters[startDelim.end + 1].token].content = "" + i -= 1 + + i -= 1 + + +def postProcess(state: StateInline) -> None: + """Walk through delimiter list and replace text tokens with tags.""" + _postProcess(state, state.delimiters) + + for token in state.tokens_meta: + if token and "delimiters" in token: + _postProcess(state, token["delimiters"]) diff --git a/venv/lib/python3.12/site-packages/markdown_it/rules_inline/entity.py b/venv/lib/python3.12/site-packages/markdown_it/rules_inline/entity.py new file mode 100644 index 0000000000000000000000000000000000000000..ec9d39650e5bc533e694d3d6699677068d22c69f --- /dev/null +++ b/venv/lib/python3.12/site-packages/markdown_it/rules_inline/entity.py @@ -0,0 +1,53 @@ +# Process html entity - {, ¯, ", ... +import re + +from ..common.entities import entities +from ..common.utils import fromCodePoint, isValidEntityCode +from .state_inline import StateInline + +DIGITAL_RE = re.compile(r"^&#((?:x[a-f0-9]{1,6}|[0-9]{1,7}));", re.IGNORECASE) +NAMED_RE = re.compile(r"^&([a-z][a-z0-9]{1,31});", re.IGNORECASE) + + +def entity(state: StateInline, silent: bool) -> bool: + pos = state.pos + maximum = state.posMax + + if state.src[pos] != "&": + return False + + if pos + 1 >= maximum: + return False + + if state.src[pos + 1] == "#": + if match := DIGITAL_RE.search(state.src[pos:]): + if not silent: + match1 = match.group(1) + code = ( + int(match1[1:], 16) if match1[0].lower() == "x" else int(match1, 10) + ) + + token = state.push("text_special", "", 0) + token.content = ( + fromCodePoint(code) + if isValidEntityCode(code) + else fromCodePoint(0xFFFD) + ) + token.markup = match.group(0) + token.info = "entity" + + state.pos += len(match.group(0)) + return True + + else: + if (match := NAMED_RE.search(state.src[pos:])) and match.group(1) in entities: + if not silent: + token = state.push("text_special", "", 0) + token.content = entities[match.group(1)] + token.markup = match.group(0) + token.info = "entity" + + state.pos += len(match.group(0)) + return True + + return False diff --git a/venv/lib/python3.12/site-packages/markdown_it/rules_inline/escape.py b/venv/lib/python3.12/site-packages/markdown_it/rules_inline/escape.py new file mode 100644 index 0000000000000000000000000000000000000000..0fca6c84e035b83b21d5224be92fd4973f25b80b --- /dev/null +++ b/venv/lib/python3.12/site-packages/markdown_it/rules_inline/escape.py @@ -0,0 +1,93 @@ +""" +Process escaped chars and hardbreaks +""" + +from ..common.utils import isStrSpace +from .state_inline import StateInline + + +def escape(state: StateInline, silent: bool) -> bool: + """Process escaped chars and hardbreaks.""" + pos = state.pos + maximum = state.posMax + + if state.src[pos] != "\\": + return False + + pos += 1 + + # '\' at the end of the inline block + if pos >= maximum: + return False + + ch1 = state.src[pos] + ch1_ord = ord(ch1) + if ch1 == "\n": + if not silent: + state.push("hardbreak", "br", 0) + pos += 1 + # skip leading whitespaces from next line + while pos < maximum: + ch = state.src[pos] + if not isStrSpace(ch): + break + pos += 1 + + state.pos = pos + return True + + escapedStr = state.src[pos] + + if ch1_ord >= 0xD800 and ch1_ord <= 0xDBFF and pos + 1 < maximum: + ch2 = state.src[pos + 1] + ch2_ord = ord(ch2) + if ch2_ord >= 0xDC00 and ch2_ord <= 0xDFFF: + escapedStr += ch2 + pos += 1 + + origStr = "\\" + escapedStr + + if not silent: + token = state.push("text_special", "", 0) + token.content = escapedStr if ch1 in _ESCAPED else origStr + token.markup = origStr + token.info = "escape" + + state.pos = pos + 1 + return True + + +_ESCAPED = { + "!", + '"', + "#", + "$", + "%", + "&", + "'", + "(", + ")", + "*", + "+", + ",", + "-", + ".", + "/", + ":", + ";", + "<", + "=", + ">", + "?", + "@", + "[", + "\\", + "]", + "^", + "_", + "`", + "{", + "|", + "}", + "~", +} diff --git a/venv/lib/python3.12/site-packages/markdown_it/rules_inline/fragments_join.py b/venv/lib/python3.12/site-packages/markdown_it/rules_inline/fragments_join.py new file mode 100644 index 0000000000000000000000000000000000000000..5eb88a14025793d02188392244c1099042cf3727 --- /dev/null +++ b/venv/lib/python3.12/site-packages/markdown_it/rules_inline/fragments_join.py @@ -0,0 +1,54 @@ +from .state_inline import StateInline + + +def fragments_join(state: StateInline) -> None: + """ + Clean up tokens after emphasis and strikethrough postprocessing: + merge adjacent text nodes into one and re-calculate all token levels + + This is necessary because initially emphasis delimiter markers (``*, _, ~``) + are treated as their own separate text tokens. Then emphasis rule either + leaves them as text (needed to merge with adjacent text) or turns them + into opening/closing tags (which messes up levels inside). + """ + level = 0 + maximum = len(state.tokens) + + curr = last = 0 + while curr < maximum: + # re-calculate levels after emphasis/strikethrough turns some text nodes + # into opening/closing tags + if state.tokens[curr].nesting < 0: + level -= 1 # closing tag + state.tokens[curr].level = level + if state.tokens[curr].nesting > 0: + level += 1 # opening tag + + if ( + state.tokens[curr].type == "text" + and curr + 1 < maximum + and state.tokens[curr + 1].type == "text" + ): + # Collapse a run of adjacent text nodes in a single join, instead + # of pairwise `a + b` concatenation. The pairwise form is O(L*k) + # in the size of the run because each step rebuilds the growing + # prefix; "".join is O(L). + parts = [state.tokens[curr].content] + curr += 1 + while curr < maximum and state.tokens[curr].type == "text": + parts.append(state.tokens[curr].content) + curr += 1 + merged = state.tokens[curr - 1] + merged.content = "".join(parts) + merged.level = level + state.tokens[last] = merged + last += 1 + continue + + if curr != last: + state.tokens[last] = state.tokens[curr] + last += 1 + curr += 1 + + if curr != last: + del state.tokens[last:] diff --git a/venv/lib/python3.12/site-packages/markdown_it/rules_inline/html_inline.py b/venv/lib/python3.12/site-packages/markdown_it/rules_inline/html_inline.py new file mode 100644 index 0000000000000000000000000000000000000000..9065e1d034da76270f7d3f1ba528132c8d57d341 --- /dev/null +++ b/venv/lib/python3.12/site-packages/markdown_it/rules_inline/html_inline.py @@ -0,0 +1,43 @@ +# Process html tags +from ..common.html_re import HTML_TAG_RE +from ..common.utils import isLinkClose, isLinkOpen +from .state_inline import StateInline + + +def isLetter(ch: int) -> bool: + lc = ch | 0x20 # to lower case + # /* a */ and /* z */ + return (lc >= 0x61) and (lc <= 0x7A) + + +def html_inline(state: StateInline, silent: bool) -> bool: + pos = state.pos + + if not state.md.options.get("html", None): + return False + + # Check start + maximum = state.posMax + if state.src[pos] != "<" or pos + 2 >= maximum: + return False + + # Quick fail on second char + ch = state.src[pos + 1] + if ch not in ("!", "?", "/") and not isLetter(ord(ch)): # /* / */ + return False + + match = HTML_TAG_RE.search(state.src[pos:]) + if not match: + return False + + if not silent: + token = state.push("html_inline", "", 0) + token.content = state.src[pos : pos + len(match.group(0))] + + if isLinkOpen(token.content): + state.linkLevel += 1 + if isLinkClose(token.content): + state.linkLevel -= 1 + + state.pos += len(match.group(0)) + return True diff --git a/venv/lib/python3.12/site-packages/markdown_it/rules_inline/image.py b/venv/lib/python3.12/site-packages/markdown_it/rules_inline/image.py new file mode 100644 index 0000000000000000000000000000000000000000..005105b1c7ed1a93772c62af8bd5ef54d97dfebe --- /dev/null +++ b/venv/lib/python3.12/site-packages/markdown_it/rules_inline/image.py @@ -0,0 +1,148 @@ +# Process ![image]( "title") +from __future__ import annotations + +from ..common.utils import isStrSpace, normalizeReference +from ..token import Token +from .state_inline import StateInline + + +def image(state: StateInline, silent: bool) -> bool: + label = None + href = "" + oldPos = state.pos + max = state.posMax + + if state.src[state.pos] != "!": + return False + + if state.pos + 1 < state.posMax and state.src[state.pos + 1] != "[": + return False + + labelStart = state.pos + 2 + labelEnd = state.md.helpers.parseLinkLabel(state, state.pos + 1, False) + + # parser failed to find ']', so it's not a valid link + if labelEnd < 0: + return False + + pos = labelEnd + 1 + + if pos < max and state.src[pos] == "(": + # + # Inline link + # + + # [link]( "title" ) + # ^^ skipping these spaces + pos += 1 + while pos < max: + ch = state.src[pos] + if not isStrSpace(ch) and ch != "\n": + break + pos += 1 + + if pos >= max: + return False + + # [link]( "title" ) + # ^^^^^^ parsing link destination + start = pos + res = state.md.helpers.parseLinkDestination(state.src, pos, state.posMax) + if res.ok: + href = state.md.normalizeLink(res.str) + if state.md.validateLink(href): + pos = res.pos + else: + href = "" + + # [link]( "title" ) + # ^^ skipping these spaces + start = pos + while pos < max: + ch = state.src[pos] + if not isStrSpace(ch) and ch != "\n": + break + pos += 1 + + # [link]( "title" ) + # ^^^^^^^ parsing link title + res = state.md.helpers.parseLinkTitle(state.src, pos, state.posMax, None) + if pos < max and start != pos and res.ok: + title = res.str + pos = res.pos + + # [link]( "title" ) + # ^^ skipping these spaces + while pos < max: + ch = state.src[pos] + if not isStrSpace(ch) and ch != "\n": + break + pos += 1 + else: + title = "" + + if pos >= max or state.src[pos] != ")": + state.pos = oldPos + return False + + pos += 1 + + else: + # + # Link reference + # + if "references" not in state.env: + return False + + # /* [ */ + if pos < max and state.src[pos] == "[": + start = pos + 1 + pos = state.md.helpers.parseLinkLabel(state, pos) + if pos >= 0: + label = state.src[start:pos] + pos += 1 + else: + pos = labelEnd + 1 + else: + pos = labelEnd + 1 + + # covers label == '' and label == undefined + # (collapsed reference link and shortcut reference link respectively) + if not label: + label = state.src[labelStart:labelEnd] + + label = normalizeReference(label) + + ref = state.env["references"].get(label, None) + if not ref: + state.pos = oldPos + return False + + href = ref["href"] + title = ref["title"] + + # + # We found the end of the link, and know for a fact it's a valid link + # so all that's left to do is to call tokenizer. + # + if not silent: + content = state.src[labelStart:labelEnd] + + tokens: list[Token] = [] + state.md.inline.parse(content, state.md, state.env, tokens) + + token = state.push("image", "img", 0) + token.attrs = {"src": href, "alt": ""} + token.children = tokens or None + token.content = content + + if title: + token.attrSet("title", title) + + # note, this is not part of markdown-it JS, but is useful for renderers + if label and state.md.options.get("store_labels", False): + token.meta["label"] = label + + state.pos = pos + state.posMax = max + return True diff --git a/venv/lib/python3.12/site-packages/markdown_it/rules_inline/link.py b/venv/lib/python3.12/site-packages/markdown_it/rules_inline/link.py new file mode 100644 index 0000000000000000000000000000000000000000..2e92c7d83629f00283b5ed885637c8c4a851ffc7 --- /dev/null +++ b/venv/lib/python3.12/site-packages/markdown_it/rules_inline/link.py @@ -0,0 +1,149 @@ +# Process [link]( "stuff") + +from ..common.utils import isStrSpace, normalizeReference +from .state_inline import StateInline + + +def link(state: StateInline, silent: bool) -> bool: + href = "" + title = "" + label = None + oldPos = state.pos + maximum = state.posMax + start = state.pos + parseReference = True + + if state.src[state.pos] != "[": + return False + + labelStart = state.pos + 1 + labelEnd = state.md.helpers.parseLinkLabel(state, state.pos, True) + + # parser failed to find ']', so it's not a valid link + if labelEnd < 0: + return False + + pos = labelEnd + 1 + + if pos < maximum and state.src[pos] == "(": + # + # Inline link + # + + # might have found a valid shortcut link, disable reference parsing + parseReference = False + + # [link]( "title" ) + # ^^ skipping these spaces + pos += 1 + while pos < maximum: + ch = state.src[pos] + if not isStrSpace(ch) and ch != "\n": + break + pos += 1 + + if pos >= maximum: + return False + + # [link]( "title" ) + # ^^^^^^ parsing link destination + start = pos + res = state.md.helpers.parseLinkDestination(state.src, pos, state.posMax) + if res.ok: + href = state.md.normalizeLink(res.str) + if state.md.validateLink(href): + pos = res.pos + else: + href = "" + + # [link]( "title" ) + # ^^ skipping these spaces + start = pos + while pos < maximum: + ch = state.src[pos] + if not isStrSpace(ch) and ch != "\n": + break + pos += 1 + + # [link]( "title" ) + # ^^^^^^^ parsing link title + res = state.md.helpers.parseLinkTitle(state.src, pos, state.posMax) + if pos < maximum and start != pos and res.ok: + title = res.str + pos = res.pos + + # [link]( "title" ) + # ^^ skipping these spaces + while pos < maximum: + ch = state.src[pos] + if not isStrSpace(ch) and ch != "\n": + break + pos += 1 + + if pos >= maximum or state.src[pos] != ")": + # parsing a valid shortcut link failed, fallback to reference + parseReference = True + + pos += 1 + + if parseReference: + # + # Link reference + # + if "references" not in state.env: + return False + + if pos < maximum and state.src[pos] == "[": + start = pos + 1 + pos = state.md.helpers.parseLinkLabel(state, pos) + if pos >= 0: + label = state.src[start:pos] + pos += 1 + else: + pos = labelEnd + 1 + + else: + pos = labelEnd + 1 + + # covers label == '' and label == undefined + # (collapsed reference link and shortcut reference link respectively) + if not label: + label = state.src[labelStart:labelEnd] + + label = normalizeReference(label) + + ref = state.env["references"].get(label, None) + if not ref: + state.pos = oldPos + return False + + href = ref["href"] + title = ref["title"] + + # + # We found the end of the link, and know for a fact it's a valid link + # so all that's left to do is to call tokenizer. + # + if not silent: + state.pos = labelStart + state.posMax = labelEnd + + token = state.push("link_open", "a", 1) + token.attrs = {"href": href} + + if title: + token.attrSet("title", title) + + # note, this is not part of markdown-it JS, but is useful for renderers + if label and state.md.options.get("store_labels", False): + token.meta["label"] = label + + state.linkLevel += 1 + state.md.inline.tokenize(state) + state.linkLevel -= 1 + + token = state.push("link_close", "a", -1) + + state.pos = pos + state.posMax = maximum + return True diff --git a/venv/lib/python3.12/site-packages/markdown_it/rules_inline/linkify.py b/venv/lib/python3.12/site-packages/markdown_it/rules_inline/linkify.py new file mode 100644 index 0000000000000000000000000000000000000000..3669396e3e7d51c125678f45a862139fe3b3fd9d --- /dev/null +++ b/venv/lib/python3.12/site-packages/markdown_it/rules_inline/linkify.py @@ -0,0 +1,62 @@ +"""Process links like https://example.org/""" + +import re + +from .state_inline import StateInline + +# RFC3986: scheme = ALPHA *( ALPHA / DIGIT / "+" / "-" / "." ) +SCHEME_RE = re.compile(r"(?:^|[^a-z0-9.+-])([a-z][a-z0-9.+-]*)$", re.IGNORECASE) + + +def linkify(state: StateInline, silent: bool) -> bool: + """Rule for identifying plain-text links.""" + if not state.md.options.linkify: + return False + if state.linkLevel > 0: + return False + if not state.md.linkify: + raise ModuleNotFoundError("Linkify enabled but not installed.") + + pos = state.pos + maximum = state.posMax + + if ( + (pos + 3) > maximum + or state.src[pos] != ":" + or state.src[pos + 1] != "/" + or state.src[pos + 2] != "/" + ): + return False + + if not (match := SCHEME_RE.search(state.pending)): + return False + + proto = match.group(1) + if not (link := state.md.linkify.match_at_start(state.src[pos - len(proto) :])): + return False + url: str = link.url + + # disallow '*' at the end of the link (conflicts with emphasis) + url = url.rstrip("*") + + full_url = state.md.normalizeLink(url) + if not state.md.validateLink(full_url): + return False + + if not silent: + state.pending = state.pending[: -len(proto)] + + token = state.push("link_open", "a", 1) + token.attrs = {"href": full_url} + token.markup = "linkify" + token.info = "auto" + + token = state.push("text", "", 0) + token.content = state.md.normalizeLinkText(url) + + token = state.push("link_close", "a", -1) + token.markup = "linkify" + token.info = "auto" + + state.pos += len(url) - len(proto) + return True diff --git a/venv/lib/python3.12/site-packages/markdown_it/rules_inline/newline.py b/venv/lib/python3.12/site-packages/markdown_it/rules_inline/newline.py new file mode 100644 index 0000000000000000000000000000000000000000..d05ee6dac712a2211ab24f90dbea64a99e4d80b0 --- /dev/null +++ b/venv/lib/python3.12/site-packages/markdown_it/rules_inline/newline.py @@ -0,0 +1,44 @@ +"""Proceess '\n'.""" + +from ..common.utils import charStrAt, isStrSpace +from .state_inline import StateInline + + +def newline(state: StateInline, silent: bool) -> bool: + pos = state.pos + + if state.src[pos] != "\n": + return False + + pmax = len(state.pending) - 1 + maximum = state.posMax + + # ' \n' -> hardbreak + # Lookup in pending chars is bad practice! Don't copy to other rules! + # Pending string is stored in concat mode, indexed lookups will cause + # conversion to flat mode. + if not silent: + if pmax >= 0 and charStrAt(state.pending, pmax) == " ": + if pmax >= 1 and charStrAt(state.pending, pmax - 1) == " ": + # Find whitespaces tail of pending chars. + ws = pmax - 1 + while ws >= 1 and charStrAt(state.pending, ws - 1) == " ": + ws -= 1 + state.pending = state.pending[:ws] + + state.push("hardbreak", "br", 0) + else: + state.pending = state.pending[:-1] + state.push("softbreak", "br", 0) + + else: + state.push("softbreak", "br", 0) + + pos += 1 + + # skip heading spaces for next line + while pos < maximum and isStrSpace(state.src[pos]): + pos += 1 + + state.pos = pos + return True diff --git a/venv/lib/python3.12/site-packages/markdown_it/rules_inline/state_inline.py b/venv/lib/python3.12/site-packages/markdown_it/rules_inline/state_inline.py new file mode 100644 index 0000000000000000000000000000000000000000..de35287d427969eb9354dd13a74e5372c85cbfab --- /dev/null +++ b/venv/lib/python3.12/site-packages/markdown_it/rules_inline/state_inline.py @@ -0,0 +1,167 @@ +from __future__ import annotations + +from dataclasses import dataclass +from typing import TYPE_CHECKING, Any, Literal, NamedTuple + +from ..common.utils import isMdAsciiPunct, isPunctChar, isWhiteSpace +from ..ruler import StateBase +from ..token import Token +from ..utils import EnvType + +if TYPE_CHECKING: + from markdown_it import MarkdownIt + + +@dataclass(slots=True) +class Delimiter: + # Char code of the starting marker (number). + marker: int + + # Total length of these series of delimiters. + length: int + + # A position of the token this delimiter corresponds to. + token: int + + # If this delimiter is matched as a valid opener, `end` will be + # equal to its position, otherwise it's `-1`. + end: int + + # Boolean flags that determine if this delimiter could open or close + # an emphasis. + open: bool + close: bool + + level: bool | None = None + + +class Scanned(NamedTuple): + can_open: bool + can_close: bool + length: int + + +class StateInline(StateBase): + def __init__( + self, src: str, md: MarkdownIt, env: EnvType, outTokens: list[Token] + ) -> None: + self.src = src + self.env = env + self.md = md + self.tokens = outTokens + self.tokens_meta: list[dict[str, Any] | None] = [None] * len(outTokens) + + self.pos = 0 + self.posMax = len(self.src) + self.level = 0 + self.pending = "" + self.pendingLevel = 0 + + # Stores { start: end } pairs. Useful for backtrack + # optimization of pairs parse (emphasis, strikes). + self.cache: dict[int, int] = {} + + # List of emphasis-like delimiters for current tag + self.delimiters: list[Delimiter] = [] + + # Stack of delimiter lists for upper level tags + self._prev_delimiters: list[list[Delimiter]] = [] + + # backticklength => last seen position + self.backticks: dict[int, int] = {} + self.backticksScanned = False + + # Counter used to disable inline linkify-it execution + # inside and markdown links + self.linkLevel = 0 + + def __repr__(self) -> str: + return ( + f"{self.__class__.__name__}" + f"(pos=[{self.pos} of {self.posMax}], token={len(self.tokens)})" + ) + + def pushPending(self) -> Token: + token = Token("text", "", 0) + token.content = self.pending + token.level = self.pendingLevel + self.tokens.append(token) + self.pending = "" + return token + + def push(self, ttype: str, tag: str, nesting: Literal[-1, 0, 1]) -> Token: + """Push new token to "stream". + If pending text exists - flush it as text token + """ + if self.pending: + self.pushPending() + + token = Token(ttype, tag, nesting) + token_meta = None + + if nesting < 0: + # closing tag + self.level -= 1 + self.delimiters = self._prev_delimiters.pop() + + token.level = self.level + + if nesting > 0: + # opening tag + self.level += 1 + self._prev_delimiters.append(self.delimiters) + self.delimiters = [] + token_meta = {"delimiters": self.delimiters} + + self.pendingLevel = self.level + self.tokens.append(token) + self.tokens_meta.append(token_meta) + return token + + def scanDelims(self, start: int, canSplitWord: bool) -> Scanned: + """ + Scan a sequence of emphasis-like markers, and determine whether + it can start an emphasis sequence or end an emphasis sequence. + + - start - position to scan from (it should point at a valid marker); + - canSplitWord - determine if these markers can be found inside a word + + """ + pos = start + maximum = self.posMax + marker = self.src[start] + + # treat beginning of the line as a whitespace + lastChar = self.src[start - 1] if start > 0 else " " + + while pos < maximum and self.src[pos] == marker: + pos += 1 + + count = pos - start + + # treat end of the line as a whitespace + nextChar = self.src[pos] if pos < maximum else " " + + isLastPunctChar = isMdAsciiPunct(ord(lastChar)) or isPunctChar(lastChar) + isNextPunctChar = isMdAsciiPunct(ord(nextChar)) or isPunctChar(nextChar) + + isLastWhiteSpace = isWhiteSpace(ord(lastChar)) + isNextWhiteSpace = isWhiteSpace(ord(nextChar)) + + left_flanking = not ( + isNextWhiteSpace + or (isNextPunctChar and not (isLastWhiteSpace or isLastPunctChar)) + ) + right_flanking = not ( + isLastWhiteSpace + or (isLastPunctChar and not (isNextWhiteSpace or isNextPunctChar)) + ) + + can_open = left_flanking and ( + canSplitWord or (not right_flanking) or isLastPunctChar + ) + can_close = right_flanking and ( + canSplitWord or (not left_flanking) or isNextPunctChar + ) + + return Scanned(can_open, can_close, count) diff --git a/venv/lib/python3.12/site-packages/markdown_it/rules_inline/strikethrough.py b/venv/lib/python3.12/site-packages/markdown_it/rules_inline/strikethrough.py new file mode 100644 index 0000000000000000000000000000000000000000..c9875e043abbacd6ce627964be7d7c70028a8199 --- /dev/null +++ b/venv/lib/python3.12/site-packages/markdown_it/rules_inline/strikethrough.py @@ -0,0 +1,173 @@ +# ~~strike through~~ (and optionally ~single tilde~) +from __future__ import annotations + +from .state_inline import Delimiter, StateInline + + +def tokenize(state: StateInline, silent: bool) -> bool: + """Insert each marker as a separate text token, and add it to delimiter list. + + When the ``strikethrough_single_tilde`` option is enabled on the + ``MarkdownIt`` instance, single ``~`` delimiters are also accepted and + runs of three or more tildes are rejected (matching GitHub's rendering behaviour). + """ + start = state.pos + ch = state.src[start] + + if silent: + return False + + if ch != "~": + return False + + scanned = state.scanDelims(state.pos, True) + length = scanned.length + + single_tilde = state.md.options.get("strikethrough_single_tilde", False) + + if single_tilde: + # GitHub mode: only accept exactly 1 or 2 tildes. + if length < 1: + return False + if length > 2: + # Consume 3+ tildes as plain text so the parser doesn't + # re-enter and match a subset of them. This intentionally + # matches GitHub's rendering, where ≥3 tildes are literal text. + token = state.push("text", "", 0) + token.content = ch * length + state.pos += scanned.length + return True + + token = state.push("text", "", 0) + token.content = ch * length + state.delimiters.append( + Delimiter( + marker=ord(ch), + length=0, # disable "rule of 3" length checks + token=len(state.tokens) - 1, + end=-1, + open=scanned.can_open, + close=scanned.can_close, + ) + ) + else: + # Original markdown-it behaviour: minimum 2, split odd runs. + if length < 2: + return False + + if length % 2: + token = state.push("text", "", 0) + token.content = ch + length -= 1 + + i = 0 + while i < length: + token = state.push("text", "", 0) + token.content = ch + ch + state.delimiters.append( + Delimiter( + marker=ord(ch), + length=0, # disable "rule of 3" length checks + token=len(state.tokens) - 1, + end=-1, + open=scanned.can_open, + close=scanned.can_close, + ) + ) + + i += 2 + + state.pos += scanned.length + + return True + + +def _postProcess(state: StateInline, delimiters: list[Delimiter]) -> None: + loneMarkers = [] + maximum = len(delimiters) + single_tilde = state.md.options.get("strikethrough_single_tilde", False) + + i = 0 + while i < maximum: + startDelim = delimiters[i] + + if startDelim.marker != 0x7E: # /* ~ */ + i += 1 + continue + + if startDelim.end == -1: + i += 1 + continue + + endDelim = delimiters[startDelim.end] + + # In single-tilde mode, opener and closer must have the same width + # (both `~` or both `~~`). The width is stored in the text token. + if single_tilde: + opener_content = state.tokens[startDelim.token].content + closer_content = state.tokens[endDelim.token].content + if opener_content != closer_content: + i += 1 + continue + + markup = state.tokens[startDelim.token].content + + token = state.tokens[startDelim.token] + token.type = "s_open" + token.tag = "s" + token.nesting = 1 + token.markup = markup + token.content = "" + + token = state.tokens[endDelim.token] + token.type = "s_close" + token.tag = "s" + token.nesting = -1 + token.markup = markup + token.content = "" + + if ( + state.tokens[endDelim.token - 1].type == "text" + and state.tokens[endDelim.token - 1].content == "~" + ): + loneMarkers.append(endDelim.token - 1) + + i += 1 + + # If a marker sequence has an odd number of characters, it's split + # like this: `~~~~~` -> `~` + `~~` + `~~`, leaving one marker at the + # start of the sequence. + # + # So, we have to move all those markers after subsequent s_close tags. + # + while loneMarkers: + i = loneMarkers.pop() + j = i + 1 + + while (j < len(state.tokens)) and (state.tokens[j].type == "s_close"): + j += 1 + + j -= 1 + + if i != j: + token = state.tokens[j] + state.tokens[j] = state.tokens[i] + state.tokens[i] = token + + +def postProcess(state: StateInline) -> None: + """Walk through delimiter list and replace text tokens with tags.""" + tokens_meta = state.tokens_meta + maximum = len(state.tokens_meta) + _postProcess(state, state.delimiters) + + curr = 0 + while curr < maximum: + try: + curr_meta = tokens_meta[curr] + except IndexError: + pass + else: + if curr_meta and "delimiters" in curr_meta: + _postProcess(state, curr_meta["delimiters"]) + curr += 1 diff --git a/venv/lib/python3.12/site-packages/markdown_it/rules_inline/text.py b/venv/lib/python3.12/site-packages/markdown_it/rules_inline/text.py new file mode 100644 index 0000000000000000000000000000000000000000..ef0cc9cec55e253a8eb17b37d593b1c320374cf5 --- /dev/null +++ b/venv/lib/python3.12/site-packages/markdown_it/rules_inline/text.py @@ -0,0 +1,23 @@ +# Skip text characters for text token, place those to pending buffer +# and increment current pos +from .state_inline import StateInline + +# Rule to skip pure text + + +def text(state: StateInline, silent: bool) -> bool: + pos = state.pos + posMax = state.posMax + + terminator_char = state.md.inline.terminator_re.search(state.src, pos) + pos = terminator_char.start() if terminator_char else posMax + + if pos == state.pos: + return False + + if not silent: + state.pending += state.src[state.pos : pos] + + state.pos = pos + + return True diff --git a/venv/lib/python3.12/site-packages/markdown_it/token.py b/venv/lib/python3.12/site-packages/markdown_it/token.py new file mode 100644 index 0000000000000000000000000000000000000000..d6d0b4530d283f4fb925ca4e95c75eef0818117d --- /dev/null +++ b/venv/lib/python3.12/site-packages/markdown_it/token.py @@ -0,0 +1,178 @@ +from __future__ import annotations + +from collections.abc import Callable, MutableMapping +import dataclasses as dc +from typing import Any, Literal +import warnings + + +def convert_attrs(value: Any) -> Any: + """Convert Token.attrs set as ``None`` or ``[[key, value], ...]`` to a dict. + + This improves compatibility with upstream markdown-it. + """ + if not value: + return {} + if isinstance(value, list): + return dict(value) + return value + + +@dc.dataclass(slots=True) +class Token: + type: str + """Type of the token (string, e.g. "paragraph_open")""" + + tag: str + """HTML tag name, e.g. 'p'""" + + nesting: Literal[-1, 0, 1] + """Level change (number in {-1, 0, 1} set), where: + - `1` means the tag is opening + - `0` means the tag is self-closing + - `-1` means the tag is closing + """ + + attrs: dict[str, str | int | float] = dc.field(default_factory=dict) + """HTML attributes. + Note this differs from the upstream "list of lists" format, + although than an instance can still be initialised with this format. + """ + + map: list[int] | None = None + """Source map info. Format: `[ line_begin, line_end ]`""" + + level: int = 0 + """Nesting level, the same as `state.level`""" + + children: list[Token] | None = None + """Array of child nodes (inline and img tokens).""" + + content: str = "" + """Inner content, in the case of a self-closing tag (code, html, fence, etc.),""" + + markup: str = "" + """'*' or '_' for emphasis, fence string for fence, etc.""" + + info: str = "" + """Additional information: + - Info string for "fence" tokens + - The value "auto" for autolink "link_open" and "link_close" tokens + - The string value of the item marker for ordered-list "list_item_open" tokens + """ + + meta: dict[Any, Any] = dc.field(default_factory=dict) + """A place for plugins to store any arbitrary data""" + + block: bool = False + """True for block-level tokens, false for inline tokens. + Used in renderer to calculate line breaks + """ + + hidden: bool = False + """If true, ignore this element when rendering. + Used for tight lists to hide paragraphs. + """ + + def __post_init__(self) -> None: + self.attrs = convert_attrs(self.attrs) + + def attrIndex(self, name: str) -> int: + warnings.warn( # noqa: B028 + "Token.attrIndex should not be used, since Token.attrs is a dictionary", + UserWarning, + ) + if name not in self.attrs: + return -1 + return list(self.attrs.keys()).index(name) + + def attrItems(self) -> list[tuple[str, str | int | float]]: + """Get (key, value) list of attrs.""" + return list(self.attrs.items()) + + def attrPush(self, attrData: tuple[str, str | int | float]) -> None: + """Add `[ name, value ]` attribute to list. Init attrs if necessary.""" + name, value = attrData + self.attrSet(name, value) + + def attrSet(self, name: str, value: str | int | float) -> None: + """Set `name` attribute to `value`. Override old value if exists.""" + self.attrs[name] = value + + def attrGet(self, name: str) -> None | str | int | float: + """Get the value of attribute `name`, or null if it does not exist.""" + return self.attrs.get(name, None) + + def attrJoin(self, name: str, value: str) -> None: + """Join value to existing attribute via space. + Or create new attribute if not exists. + Useful to operate with token classes. + """ + if name in self.attrs: + current = self.attrs[name] + if not isinstance(current, str): + raise TypeError( + f"existing attr 'name' is not a str: {self.attrs[name]}" + ) + self.attrs[name] = f"{current} {value}" + else: + self.attrs[name] = value + + def copy(self, **changes: Any) -> Token: + """Return a shallow copy of the instance.""" + return dc.replace(self, **changes) + + def as_dict( + self, + *, + children: bool = True, + as_upstream: bool = True, + meta_serializer: Callable[[dict[Any, Any]], Any] | None = None, + filter: Callable[[str, Any], bool] | None = None, + dict_factory: Callable[..., MutableMapping[str, Any]] = dict, + ) -> MutableMapping[str, Any]: + """Return the token as a dictionary. + + :param children: Also convert children to dicts + :param as_upstream: Ensure the output dictionary is equal to that created by markdown-it + For example, attrs are converted to null or lists + :param meta_serializer: hook for serializing ``Token.meta`` + :param filter: A callable whose return code determines whether an + attribute or element is included (``True``) or dropped (``False``). + Is called with the (key, value) pair. + :param dict_factory: A callable to produce dictionaries from. + For example, to produce ordered dictionaries instead of normal Python + dictionaries, pass in ``collections.OrderedDict``. + + """ + mapping = dict_factory((f.name, getattr(self, f.name)) for f in dc.fields(self)) + if filter: + mapping = dict_factory((k, v) for k, v in mapping.items() if filter(k, v)) + if as_upstream and "attrs" in mapping: + mapping["attrs"] = ( + None + if not mapping["attrs"] + else [[k, v] for k, v in mapping["attrs"].items()] + ) + if meta_serializer and "meta" in mapping: + mapping["meta"] = meta_serializer(mapping["meta"]) + if children and mapping.get("children", None): + mapping["children"] = [ + child.as_dict( + children=children, + filter=filter, + dict_factory=dict_factory, + as_upstream=as_upstream, + meta_serializer=meta_serializer, + ) + for child in mapping["children"] + ] + return mapping + + @classmethod + def from_dict(cls, dct: MutableMapping[str, Any]) -> Token: + """Convert a dict to a Token.""" + token = cls(**dct) + if token.children: + token.children = [cls.from_dict(c) for c in token.children] # type: ignore[arg-type] + return token diff --git a/venv/lib/python3.12/site-packages/markdown_it/tree.py b/venv/lib/python3.12/site-packages/markdown_it/tree.py new file mode 100644 index 0000000000000000000000000000000000000000..24bc24663f915ca81226a70237ae12b7b39321ea --- /dev/null +++ b/venv/lib/python3.12/site-packages/markdown_it/tree.py @@ -0,0 +1,333 @@ +"""A tree representation of a linear markdown-it token stream. + +This module is not part of upstream JavaScript markdown-it. +""" + +from __future__ import annotations + +from collections.abc import Generator, Sequence +import textwrap +from typing import Any, NamedTuple, TypeVar, overload + +from .token import Token + + +class _NesterTokens(NamedTuple): + opening: Token + closing: Token + + +_NodeType = TypeVar("_NodeType", bound="SyntaxTreeNode") + + +class SyntaxTreeNode: + """A Markdown syntax tree node. + + A class that can be used to construct a tree representation of a linear + `markdown-it-py` token stream. + + Each node in the tree represents either: + - root of the Markdown document + - a single unnested `Token` + - a `Token` "_open" and "_close" token pair, and the tokens nested in + between + """ + + def __init__( + self, tokens: Sequence[Token] = (), *, create_root: bool = True + ) -> None: + """Initialize a `SyntaxTreeNode` from a token stream. + + If `create_root` is True, create a root node for the document. + """ + # Only nodes representing an unnested token have self.token + self.token: Token | None = None + + # Only containers have nester tokens + self.nester_tokens: _NesterTokens | None = None + + # Root node does not have self.parent + self._parent: Any = None + + # Empty list unless a non-empty container, or unnested token that has + # children (i.e. inline or img) + self._children: list[Any] = [] + + if create_root: + self._set_children_from_tokens(tokens) + return + + if not tokens: + raise ValueError( + "Can only create root from empty token sequence." + " Set `create_root=True`." + ) + elif len(tokens) == 1: + inline_token = tokens[0] + if inline_token.nesting: + raise ValueError( + "Unequal nesting level at the start and end of token stream." + ) + self.token = inline_token + if inline_token.children: + self._set_children_from_tokens(inline_token.children) + else: + self.nester_tokens = _NesterTokens(tokens[0], tokens[-1]) + self._set_children_from_tokens(tokens[1:-1]) + + def __repr__(self) -> str: + return f"{type(self).__name__}({self.type})" + + @overload + def __getitem__(self: _NodeType, item: int) -> _NodeType: ... + + @overload + def __getitem__(self: _NodeType, item: slice) -> list[_NodeType]: ... + + def __getitem__(self: _NodeType, item: int | slice) -> _NodeType | list[_NodeType]: + return self.children[item] + + def to_tokens(self: _NodeType) -> list[Token]: + """Recover the linear token stream.""" + + def recursive_collect_tokens(node: _NodeType, token_list: list[Token]) -> None: + if node.type == "root": + for child in node.children: + recursive_collect_tokens(child, token_list) + elif node.token: + token_list.append(node.token) + else: + assert node.nester_tokens + token_list.append(node.nester_tokens.opening) + for child in node.children: + recursive_collect_tokens(child, token_list) + token_list.append(node.nester_tokens.closing) + + tokens: list[Token] = [] + recursive_collect_tokens(self, tokens) + return tokens + + @property + def children(self: _NodeType) -> list[_NodeType]: + return self._children + + @children.setter + def children(self: _NodeType, value: list[_NodeType]) -> None: + self._children = value + + @property + def parent(self: _NodeType) -> _NodeType | None: + return self._parent # type: ignore + + @parent.setter + def parent(self: _NodeType, value: _NodeType | None) -> None: + self._parent = value + + @property + def is_root(self) -> bool: + """Is the node a special root node?""" + return not (self.token or self.nester_tokens) + + @property + def is_nested(self) -> bool: + """Is this node nested?. + + Returns `True` if the node represents a `Token` pair and tokens in the + sequence between them, where `Token.nesting` of the first `Token` in + the pair is 1 and nesting of the other `Token` is -1. + """ + return bool(self.nester_tokens) + + @property + def siblings(self: _NodeType) -> Sequence[_NodeType]: + """Get siblings of the node. + + Gets the whole group of siblings, including self. + """ + if not self.parent: + return [self] + return self.parent.children + + @property + def type(self) -> str: + """Get a string type of the represented syntax. + + - "root" for root nodes + - `Token.type` if the node represents an unnested token + - `Token.type` of the opening token, with "_open" suffix stripped, if + the node represents a nester token pair + """ + if self.is_root: + return "root" + if self.token: + return self.token.type + assert self.nester_tokens + return self.nester_tokens.opening.type.removesuffix("_open") + + @property + def next_sibling(self: _NodeType) -> _NodeType | None: + """Get the next node in the sequence of siblings. + + Returns `None` if this is the last sibling. + """ + self_index = self.siblings.index(self) + if self_index + 1 < len(self.siblings): + return self.siblings[self_index + 1] + return None + + @property + def previous_sibling(self: _NodeType) -> _NodeType | None: + """Get the previous node in the sequence of siblings. + + Returns `None` if this is the first sibling. + """ + self_index = self.siblings.index(self) + if self_index - 1 >= 0: + return self.siblings[self_index - 1] + return None + + def _add_child( + self, + tokens: Sequence[Token], + ) -> None: + """Make a child node for `self`.""" + child = type(self)(tokens, create_root=False) + child.parent = self + self.children.append(child) + + def _set_children_from_tokens(self, tokens: Sequence[Token]) -> None: + """Convert the token stream to a tree structure and set the resulting + nodes as children of `self`.""" + reversed_tokens = list(reversed(tokens)) + while reversed_tokens: + token = reversed_tokens.pop() + + if not token.nesting: + self._add_child([token]) + continue + if token.nesting != 1: + raise ValueError("Invalid token nesting") + + nested_tokens = [token] + nesting = 1 + while reversed_tokens and nesting: + token = reversed_tokens.pop() + nested_tokens.append(token) + nesting += token.nesting + if nesting: + raise ValueError(f"unclosed tokens starting {nested_tokens[0]}") + + self._add_child(nested_tokens) + + def pretty( + self, *, indent: int = 2, show_text: bool = False, _current: int = 0 + ) -> str: + """Create an XML style string of the tree.""" + prefix = " " * _current + text = prefix + f"<{self.type}" + if not self.is_root and self.attrs: + text += " " + " ".join(f"{k}={v!r}" for k, v in self.attrs.items()) + text += ">" + if ( + show_text + and not self.is_root + and self.type in ("text", "text_special") + and self.content + ): + text += "\n" + textwrap.indent(self.content, prefix + " " * indent) + for child in self.children: + text += "\n" + child.pretty( + indent=indent, show_text=show_text, _current=_current + indent + ) + return text + + def walk( + self: _NodeType, *, include_self: bool = True + ) -> Generator[_NodeType, None, None]: + """Recursively yield all descendant nodes in the tree starting at self. + + The order mimics the order of the underlying linear token + stream (i.e. depth first). + """ + if include_self: + yield self + for child in self.children: + yield from child.walk(include_self=True) + + # NOTE: + # The values of the properties defined below directly map to properties + # of the underlying `Token`s. A root node does not translate to a `Token` + # object, so calling these property getters on a root node will raise an + # `AttributeError`. + # + # There is no mapping for `Token.nesting` because the `is_nested` property + # provides that data, and can be called on any node type, including root. + + def _attribute_token(self) -> Token: + """Return the `Token` that is used as the data source for the + properties defined below.""" + if self.token: + return self.token + if self.nester_tokens: + return self.nester_tokens.opening + raise AttributeError("Root node does not have the accessed attribute") + + @property + def tag(self) -> str: + """html tag name, e.g. \"p\"""" + return self._attribute_token().tag + + @property + def attrs(self) -> dict[str, str | int | float]: + """Html attributes.""" + return self._attribute_token().attrs + + def attrGet(self, name: str) -> None | str | int | float: + """Get the value of attribute `name`, or null if it does not exist.""" + return self._attribute_token().attrGet(name) + + @property + def map(self) -> tuple[int, int] | None: + """Source map info. Format: `tuple[ line_begin, line_end ]`""" + map_ = self._attribute_token().map + if map_: + # Type ignore because `Token`s attribute types are not perfect + return tuple(map_) # type: ignore + return None + + @property + def level(self) -> int: + """nesting level, the same as `state.level`""" + return self._attribute_token().level + + @property + def content(self) -> str: + """In a case of self-closing tag (code, html, fence, etc.), it + has contents of this tag.""" + return self._attribute_token().content + + @property + def markup(self) -> str: + """'*' or '_' for emphasis, fence string for fence, etc.""" + return self._attribute_token().markup + + @property + def info(self) -> str: + """fence infostring""" + return self._attribute_token().info + + @property + def meta(self) -> dict[Any, Any]: + """A place for plugins to store an arbitrary data.""" + return self._attribute_token().meta + + @property + def block(self) -> bool: + """True for block-level tokens, false for inline tokens.""" + return self._attribute_token().block + + @property + def hidden(self) -> bool: + """If it's true, ignore this element when rendering. + Used for tight lists to hide paragraphs.""" + return self._attribute_token().hidden diff --git a/venv/lib/python3.12/site-packages/markdown_it/utils.py b/venv/lib/python3.12/site-packages/markdown_it/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..09e60163160c6ab5d98af36b209d7f4ff9c6b1f8 --- /dev/null +++ b/venv/lib/python3.12/site-packages/markdown_it/utils.py @@ -0,0 +1,194 @@ +from __future__ import annotations + +from collections.abc import Callable, Iterable, MutableMapping +from collections.abc import MutableMapping as MutableMappingABC +from pathlib import Path +from typing import TYPE_CHECKING, Any, TypedDict, cast + +if TYPE_CHECKING: + from typing_extensions import NotRequired + + +EnvType = MutableMapping[str, Any] # note: could use TypeAlias in python 3.10 +"""Type for the environment sandbox used in parsing and rendering, +which stores mutable variables for use by plugins and rules. +""" + + +class OptionsType(TypedDict): + """Options for parsing.""" + + maxNesting: int + """Internal protection, recursion limit.""" + html: bool + """Enable HTML tags in source.""" + linkify: bool + """Enable autoconversion of URL-like texts to links.""" + typographer: bool + """Enable smartquotes and replacements.""" + quotes: str + """Quote characters.""" + xhtmlOut: bool + """Use '/' to close single tags (
).""" + breaks: bool + """Convert newlines in paragraphs into
.""" + langPrefix: str + """CSS language prefix for fenced blocks.""" + highlight: Callable[[str, str, str], str] | None + """Highlighter function: (content, lang, attrs) -> str.""" + store_labels: NotRequired[bool] + """Store link label in link/image token's metadata (under Token.meta['label']). + + This is a Python only option, and is intended for the use of round-trip parsing. + """ + tasklists: NotRequired[bool] + """Enable GFM task list checkbox detection in list items.""" + alerts: NotRequired[bool] + """Enable GitHub-style alert detection in blockquotes.""" + tasklists_editable: NotRequired[bool] + """When True, rendered task list checkboxes are interactive (no disabled attribute).""" + strikethrough_single_tilde: NotRequired[bool] + """Allow single tilde ``~text~`` for strikethrough in addition to double.""" + + +class PresetType(TypedDict): + """Preset configuration for markdown-it.""" + + options: OptionsType + """Options for parsing.""" + components: MutableMapping[str, MutableMapping[str, list[str]]] + """Components for parsing and rendering.""" + + +class OptionsDict(MutableMappingABC): # type: ignore + """A dictionary, with attribute access to core markdownit configuration options.""" + + # Note: ideally we would probably just remove attribute access entirely, + # but we keep it for backwards compatibility. + + def __init__(self, options: OptionsType) -> None: + self._options = cast(OptionsType, dict(options)) + + def __getitem__(self, key: str) -> Any: + return self._options[key] # type: ignore[literal-required] + + def __setitem__(self, key: str, value: Any) -> None: + self._options[key] = value # type: ignore[literal-required] + + def __delitem__(self, key: str) -> None: + del self._options[key] # type: ignore + + def __iter__(self) -> Iterable[str]: # type: ignore + return iter(self._options) + + def __len__(self) -> int: + return len(self._options) + + def __repr__(self) -> str: + return repr(self._options) + + def __str__(self) -> str: + return str(self._options) + + @property + def maxNesting(self) -> int: + """Internal protection, recursion limit.""" + return self._options["maxNesting"] + + @maxNesting.setter + def maxNesting(self, value: int) -> None: + self._options["maxNesting"] = value + + @property + def html(self) -> bool: + """Enable HTML tags in source.""" + return self._options["html"] + + @html.setter + def html(self, value: bool) -> None: + self._options["html"] = value + + @property + def linkify(self) -> bool: + """Enable autoconversion of URL-like texts to links.""" + return self._options["linkify"] + + @linkify.setter + def linkify(self, value: bool) -> None: + self._options["linkify"] = value + + @property + def typographer(self) -> bool: + """Enable smartquotes and replacements.""" + return self._options["typographer"] + + @typographer.setter + def typographer(self, value: bool) -> None: + self._options["typographer"] = value + + @property + def quotes(self) -> str: + """Quote characters.""" + return self._options["quotes"] + + @quotes.setter + def quotes(self, value: str) -> None: + self._options["quotes"] = value + + @property + def xhtmlOut(self) -> bool: + """Use '/' to close single tags (
).""" + return self._options["xhtmlOut"] + + @xhtmlOut.setter + def xhtmlOut(self, value: bool) -> None: + self._options["xhtmlOut"] = value + + @property + def breaks(self) -> bool: + """Convert newlines in paragraphs into
.""" + return self._options["breaks"] + + @breaks.setter + def breaks(self, value: bool) -> None: + self._options["breaks"] = value + + @property + def langPrefix(self) -> str: + """CSS language prefix for fenced blocks.""" + return self._options["langPrefix"] + + @langPrefix.setter + def langPrefix(self, value: str) -> None: + self._options["langPrefix"] = value + + @property + def highlight(self) -> Callable[[str, str, str], str] | None: + """Highlighter function: (content, langName, langAttrs) -> escaped HTML.""" + return self._options["highlight"] + + @highlight.setter + def highlight(self, value: Callable[[str, str, str], str] | None) -> None: + self._options["highlight"] = value + + +def read_fixture_file(path: str | Path) -> list[list[Any]]: + text = Path(path).read_text(encoding="utf-8") + tests = [] + section = 0 + last_pos = 0 + lines = text.splitlines(keepends=True) + for i in range(len(lines)): + if lines[i].rstrip() == ".": + if section == 0: + tests.append([i, lines[i - 1].strip()]) + section = 1 + elif section == 1: + tests[-1].append("".join(lines[last_pos + 1 : i])) + section = 2 + elif section == 2: + tests[-1].append("".join(lines[last_pos + 1 : i])) + section = 0 + + last_pos = i + return tests diff --git a/venv/lib/python3.12/site-packages/markdown_it_py-4.2.0.dist-info/INSTALLER b/venv/lib/python3.12/site-packages/markdown_it_py-4.2.0.dist-info/INSTALLER new file mode 100644 index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68 --- /dev/null +++ b/venv/lib/python3.12/site-packages/markdown_it_py-4.2.0.dist-info/INSTALLER @@ -0,0 +1 @@ +pip diff --git a/venv/lib/python3.12/site-packages/markdown_it_py-4.2.0.dist-info/METADATA b/venv/lib/python3.12/site-packages/markdown_it_py-4.2.0.dist-info/METADATA new file mode 100644 index 0000000000000000000000000000000000000000..c44604a66772697fba39a4e24d69d0f3d7bb3c1b --- /dev/null +++ b/venv/lib/python3.12/site-packages/markdown_it_py-4.2.0.dist-info/METADATA @@ -0,0 +1,221 @@ +Metadata-Version: 2.4 +Name: markdown-it-py +Version: 4.2.0 +Summary: Python port of markdown-it. Markdown parsing, done right! +Keywords: markdown,lexer,parser,commonmark,markdown-it +Author-email: Chris Sewell +Requires-Python: >=3.10 +Description-Content-Type: text/markdown +Classifier: Development Status :: 5 - Production/Stable +Classifier: Intended Audience :: Developers +Classifier: License :: OSI Approved :: MIT License +Classifier: Programming Language :: Python :: 3 +Classifier: Programming Language :: Python :: 3.10 +Classifier: Programming Language :: Python :: 3.11 +Classifier: Programming Language :: Python :: 3.12 +Classifier: Programming Language :: Python :: 3.13 +Classifier: Programming Language :: Python :: Implementation :: CPython +Classifier: Programming Language :: Python :: Implementation :: PyPy +Classifier: Topic :: Software Development :: Libraries :: Python Modules +Classifier: Topic :: Text Processing :: Markup +License-File: LICENSE +License-File: LICENSE.markdown-it +Requires-Dist: mdurl~=0.1 +Requires-Dist: psutil ; extra == "benchmarking" +Requires-Dist: pytest ; extra == "benchmarking" +Requires-Dist: pytest-benchmark ; extra == "benchmarking" +Requires-Dist: commonmark~=0.9 ; extra == "compare" +Requires-Dist: markdown~=3.4 ; extra == "compare" +Requires-Dist: mistletoe~=1.0 ; extra == "compare" +Requires-Dist: mistune~=3.0 ; extra == "compare" +Requires-Dist: panflute~=2.3 ; extra == "compare" +Requires-Dist: markdown-it-pyrs ; extra == "compare" +Requires-Dist: linkify-it-py>=1,<3 ; extra == "linkify" +Requires-Dist: mdit-py-plugins>=0.5.0 ; extra == "plugins" +Requires-Dist: gprof2dot ; extra == "profiling" +Requires-Dist: mdit-py-plugins>=0.5.0 ; extra == "rtd" +Requires-Dist: myst-parser ; extra == "rtd" +Requires-Dist: pyyaml ; extra == "rtd" +Requires-Dist: sphinx ; extra == "rtd" +Requires-Dist: sphinx-copybutton ; extra == "rtd" +Requires-Dist: sphinx-design ; extra == "rtd" +Requires-Dist: sphinx-book-theme~=1.0 ; extra == "rtd" +Requires-Dist: jupyter_sphinx ; extra == "rtd" +Requires-Dist: ipykernel ; extra == "rtd" +Requires-Dist: coverage ; extra == "testing" +Requires-Dist: pytest ; extra == "testing" +Requires-Dist: pytest-cov ; extra == "testing" +Requires-Dist: pytest-regressions ; extra == "testing" +Requires-Dist: pytest-timeout ; extra == "testing" +Requires-Dist: requests ; extra == "testing" +Project-URL: Documentation, https://markdown-it-py.readthedocs.io +Project-URL: Homepage, https://github.com/executablebooks/markdown-it-py +Provides-Extra: benchmarking +Provides-Extra: compare +Provides-Extra: linkify +Provides-Extra: plugins +Provides-Extra: profiling +Provides-Extra: rtd +Provides-Extra: testing + +# markdown-it-py + +[![Github-CI][github-ci]][github-link] +[![Coverage Status][codecov-badge]][codecov-link] +[![PyPI][pypi-badge]][pypi-link] +[![Conda][conda-badge]][conda-link] +[![PyPI - Downloads][install-badge]][install-link] + +

+ markdown-it-py icon +

+ +> Markdown parser done right. + +- Follows the __[CommonMark spec](http://spec.commonmark.org/)__ for baseline parsing +- Configurable syntax: you can add new rules and even replace existing ones. +- Pluggable: Adds syntax extensions to extend the parser (see the [plugin list][md-plugins]). +- High speed (see our [benchmarking tests][md-performance]) +- Easy to configure for [security][md-security] +- Member of [Google's Assured Open Source Software](https://cloud.google.com/assured-open-source-software/docs/supported-packages) + +This is a Python port of [markdown-it], and some of its associated plugins. +For more details see: . + +For details on [markdown-it] itself, see: + +- The __[Live demo](https://markdown-it.github.io)__ +- [The markdown-it README][markdown-it-readme] + +**See also:** [markdown-it-pyrs](https://github.com/chrisjsewell/markdown-it-pyrs) for an experimental Rust binding, +for even more speed! + +## Installation + +### PIP + +```bash +pip install markdown-it-py[plugins] +``` + +or with extras + +```bash +pip install markdown-it-py[linkify,plugins] +``` + +### Conda + +```bash +conda install -c conda-forge markdown-it-py +``` + +or with extras + +```bash +conda install -c conda-forge markdown-it-py linkify-it-py mdit-py-plugins +``` + +## Usage + +### Python API Usage + +Render markdown to HTML with markdown-it-py and a custom configuration +with and without plugins and features: + +```python +from markdown_it import MarkdownIt +from mdit_py_plugins.front_matter import front_matter_plugin +from mdit_py_plugins.footnote import footnote_plugin + +md = ( + MarkdownIt('commonmark', {'breaks':True,'html':True}) + .use(front_matter_plugin) + .use(footnote_plugin) + .enable('table') +) +text = (""" +--- +a: 1 +--- + +a | b +- | - +1 | 2 + +A footnote [^1] + +[^1]: some details +""") +tokens = md.parse(text) +html_text = md.render(text) + +## To export the html to a file, uncomment the lines below: +# from pathlib import Path +# Path("output.html").write_text(html_text) +``` + +### Command-line Usage + +Render markdown to HTML with markdown-it-py from the +command-line: + +```console +usage: markdown-it [-h] [-v] [--stdin|filenames [filenames ...]] + +Parse one or more markdown files, convert each to HTML, and print to stdout + +positional arguments: + --stdin read source Markdown file from standard input + filenames specify an optional list of files to convert + +optional arguments: + -h, --help show this help message and exit + -v, --version show program's version number and exit + +Interactive: + + $ markdown-it + markdown-it-py [version 0.0.0] (interactive) + Type Ctrl-D to complete input, or Ctrl-C to exit. + >>> # Example + ... > markdown *input* + ... +

Example

+
+

markdown input

+
+ +Batch: + + $ markdown-it README.md README.footer.md > index.html + +``` + +## References / Thanks + +Big thanks to the authors of [markdown-it]: + +- Alex Kocharin [github/rlidwka](https://github.com/rlidwka) +- Vitaly Puzrin [github/puzrin](https://github.com/puzrin) + +Also [John MacFarlane](https://github.com/jgm) for his work on the CommonMark spec and reference implementations. + +[github-ci]: https://github.com/executablebooks/markdown-it-py/actions/workflows/tests.yml/badge.svg?branch=master +[github-link]: https://github.com/executablebooks/markdown-it-py +[pypi-badge]: https://img.shields.io/pypi/v/markdown-it-py.svg +[pypi-link]: https://pypi.org/project/markdown-it-py +[conda-badge]: https://anaconda.org/conda-forge/markdown-it-py/badges/version.svg +[conda-link]: https://anaconda.org/conda-forge/markdown-it-py +[codecov-badge]: https://codecov.io/gh/executablebooks/markdown-it-py/branch/master/graph/badge.svg +[codecov-link]: https://codecov.io/gh/executablebooks/markdown-it-py +[install-badge]: https://img.shields.io/pypi/dw/markdown-it-py?label=pypi%20installs +[install-link]: https://pypistats.org/packages/markdown-it-py + +[CommonMark spec]: http://spec.commonmark.org/ +[markdown-it]: https://github.com/markdown-it/markdown-it +[markdown-it-readme]: https://github.com/markdown-it/markdown-it/blob/master/README.md +[md-security]: https://markdown-it-py.readthedocs.io/en/latest/security.html +[md-performance]: https://markdown-it-py.readthedocs.io/en/latest/performance.html +[md-plugins]: https://markdown-it-py.readthedocs.io/en/latest/plugins.html + diff --git a/venv/lib/python3.12/site-packages/markdown_it_py-4.2.0.dist-info/RECORD b/venv/lib/python3.12/site-packages/markdown_it_py-4.2.0.dist-info/RECORD new file mode 100644 index 0000000000000000000000000000000000000000..198c4ac594962dd9219c0751f3e3b87f8cdc111b --- /dev/null +++ b/venv/lib/python3.12/site-packages/markdown_it_py-4.2.0.dist-info/RECORD 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0000000000000000000000000000000000000000..7d829cd792a5d754844f433c6a8dd499564fdcbf --- /dev/null +++ b/venv/lib/python3.12/site-packages/markdown_it_py-4.2.0.dist-info/entry_points.txt @@ -0,0 +1,3 @@ +[console_scripts] +markdown-it=markdown_it.cli.parse:main + diff --git a/venv/lib/python3.12/site-packages/markdown_it_py-4.2.0.dist-info/licenses/LICENSE b/venv/lib/python3.12/site-packages/markdown_it_py-4.2.0.dist-info/licenses/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..582ddf59e08277fe6e78cee924d2c84805fe36fe --- /dev/null +++ b/venv/lib/python3.12/site-packages/markdown_it_py-4.2.0.dist-info/licenses/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2020 ExecutableBookProject + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/venv/lib/python3.12/site-packages/markdown_it_py-4.2.0.dist-info/licenses/LICENSE.markdown-it b/venv/lib/python3.12/site-packages/markdown_it_py-4.2.0.dist-info/licenses/LICENSE.markdown-it new file mode 100644 index 0000000000000000000000000000000000000000..7ffa058cb78f8fb9beb974d9fd429004d2d2e585 --- /dev/null +++ b/venv/lib/python3.12/site-packages/markdown_it_py-4.2.0.dist-info/licenses/LICENSE.markdown-it @@ -0,0 +1,22 @@ +Copyright (c) 2014 Vitaly Puzrin, Alex Kocharin. + +Permission is hereby granted, free of charge, to any person +obtaining a copy of this software and associated documentation +files (the "Software"), to deal in the Software without +restriction, including without limitation the rights to use, +copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the +Software is furnished to do so, subject to the following +conditions: + +The above copyright notice and this permission notice shall be +included in all copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, +EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES +OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND +NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT +HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, +WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING +FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR +OTHER DEALINGS IN THE SOFTWARE. diff --git a/venv/lib/python3.12/site-packages/markupsafe-3.0.3.dist-info/INSTALLER b/venv/lib/python3.12/site-packages/markupsafe-3.0.3.dist-info/INSTALLER new file mode 100644 index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68 --- /dev/null +++ b/venv/lib/python3.12/site-packages/markupsafe-3.0.3.dist-info/INSTALLER @@ -0,0 +1 @@ +pip diff --git a/venv/lib/python3.12/site-packages/markupsafe-3.0.3.dist-info/METADATA b/venv/lib/python3.12/site-packages/markupsafe-3.0.3.dist-info/METADATA new file mode 100644 index 0000000000000000000000000000000000000000..0282933652fda5b154bd36f91b14ca75fe9e0901 --- /dev/null +++ b/venv/lib/python3.12/site-packages/markupsafe-3.0.3.dist-info/METADATA @@ -0,0 +1,74 @@ +Metadata-Version: 2.4 +Name: MarkupSafe +Version: 3.0.3 +Summary: Safely add untrusted strings to HTML/XML markup. +Maintainer-email: Pallets +License-Expression: BSD-3-Clause +Project-URL: Donate, https://palletsprojects.com/donate +Project-URL: Documentation, https://markupsafe.palletsprojects.com/ +Project-URL: Changes, https://markupsafe.palletsprojects.com/page/changes/ +Project-URL: Source, https://github.com/pallets/markupsafe/ +Project-URL: Chat, https://discord.gg/pallets +Classifier: Development Status :: 5 - Production/Stable +Classifier: Environment :: Web Environment +Classifier: Intended Audience :: Developers +Classifier: Operating System :: OS Independent +Classifier: Programming Language :: Python +Classifier: Topic :: Internet :: WWW/HTTP :: Dynamic Content +Classifier: Topic :: Text Processing :: Markup :: HTML +Classifier: Typing :: Typed +Requires-Python: >=3.9 +Description-Content-Type: text/markdown +License-File: LICENSE.txt +Dynamic: license-file + +
+ +# MarkupSafe + +MarkupSafe implements a text object that escapes characters so it is +safe to use in HTML and XML. Characters that have special meanings are +replaced so that they display as the actual characters. This mitigates +injection attacks, meaning untrusted user input can safely be displayed +on a page. + + +## Examples + +```pycon +>>> from markupsafe import Markup, escape + +>>> # escape replaces special characters and wraps in Markup +>>> escape("") +Markup('<script>alert(document.cookie);</script>') + +>>> # wrap in Markup to mark text "safe" and prevent escaping +>>> Markup("Hello") +Markup('hello') + +>>> escape(Markup("Hello")) +Markup('hello') + +>>> # Markup is a str subclass +>>> # methods and operators escape their arguments +>>> template = Markup("Hello {name}") +>>> template.format(name='"World"') +Markup('Hello "World"') +``` + +## Donate + +The Pallets organization develops and supports MarkupSafe and other +popular packages. In order to grow the community of contributors and +users, and allow the maintainers to devote more time to the projects, +[please donate today][]. + +[please donate today]: https://palletsprojects.com/donate + +## Contributing + +See our [detailed contributing documentation][contrib] for many ways to +contribute, including reporting issues, requesting features, asking or answering +questions, and making PRs. + +[contrib]: https://palletsprojects.com/contributing/ diff --git a/venv/lib/python3.12/site-packages/markupsafe-3.0.3.dist-info/RECORD b/venv/lib/python3.12/site-packages/markupsafe-3.0.3.dist-info/RECORD new file mode 100644 index 0000000000000000000000000000000000000000..8df476b897fab0d70a53919754ad0d117ffbc29f --- /dev/null +++ b/venv/lib/python3.12/site-packages/markupsafe-3.0.3.dist-info/RECORD @@ -0,0 +1,14 @@ +markupsafe-3.0.3.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4 +markupsafe-3.0.3.dist-info/METADATA,sha256=ErTMYaf6KIz3Zn7j8hN4bZYZ21f7M_9vk0r7y1wS7IE,2690 +markupsafe-3.0.3.dist-info/RECORD,, +markupsafe-3.0.3.dist-info/WHEEL,sha256=DxRnWQz-Kp9-4a4hdDHsSv0KUC3H7sN9Nbef3-8RjXU,190 +markupsafe-3.0.3.dist-info/licenses/LICENSE.txt,sha256=SJqOEQhQntmKN7uYPhHg9-HTHwvY-Zp5yESOf_N9B-o,1475 +markupsafe-3.0.3.dist-info/top_level.txt,sha256=qy0Plje5IJuvsCBjejJyhDCjEAdcDLK_2agVcex8Z6U,11 +markupsafe/__init__.py,sha256=u1fLcNCx0P8E7LT6z3OXf6ipnSmgm6aefne5y-d7o40,13248 +markupsafe/__pycache__/__init__.cpython-312.pyc,, +markupsafe/__pycache__/_native.cpython-312.pyc,, +markupsafe/_native.py,sha256=hSLs8Jmz5aqayuengJJ3kdT5PwNpBWpKrmQSdipndC8,210 +markupsafe/_speedups.c,sha256=t3tC6oVV7-bmKUqvCO5pVSky-G8ACIXpWMaJwkNtJjg,4327 +markupsafe/_speedups.cpython-312-x86_64-linux-gnu.so,sha256=9KMd4HHFZZWQAtspvzbG-NNa55KfZHAK3uukb6T5Da8,44072 +markupsafe/_speedups.pyi,sha256=ENd1bYe7gbBUf2ywyYWOGUpnXOHNJ-cgTNqetlW8h5k,41 +markupsafe/py.typed,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0 diff --git a/venv/lib/python3.12/site-packages/markupsafe-3.0.3.dist-info/WHEEL b/venv/lib/python3.12/site-packages/markupsafe-3.0.3.dist-info/WHEEL new file mode 100644 index 0000000000000000000000000000000000000000..f3e8a970f16adf4526f1722547053522d94bf860 --- /dev/null +++ b/venv/lib/python3.12/site-packages/markupsafe-3.0.3.dist-info/WHEEL @@ -0,0 +1,7 @@ +Wheel-Version: 1.0 +Generator: setuptools (80.9.0) +Root-Is-Purelib: false +Tag: cp312-cp312-manylinux_2_17_x86_64 +Tag: cp312-cp312-manylinux2014_x86_64 +Tag: cp312-cp312-manylinux_2_28_x86_64 + diff --git a/venv/lib/python3.12/site-packages/markupsafe-3.0.3.dist-info/licenses/LICENSE.txt b/venv/lib/python3.12/site-packages/markupsafe-3.0.3.dist-info/licenses/LICENSE.txt new file mode 100644 index 0000000000000000000000000000000000000000..9d227a0cc43c3268d15722b763bd94ad298645a1 --- /dev/null +++ b/venv/lib/python3.12/site-packages/markupsafe-3.0.3.dist-info/licenses/LICENSE.txt @@ -0,0 +1,28 @@ +Copyright 2010 Pallets + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are +met: + +1. Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + +2. Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + +3. Neither the name of the copyright holder nor the names of its + contributors may be used to endorse or promote products derived from + this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS +"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT +LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A +PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT +HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, +SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED +TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR +PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF +LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING +NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS +SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/venv/lib/python3.12/site-packages/markupsafe-3.0.3.dist-info/top_level.txt b/venv/lib/python3.12/site-packages/markupsafe-3.0.3.dist-info/top_level.txt new file mode 100644 index 0000000000000000000000000000000000000000..75bf729258f9daef77370b6df1a57940f90fc23f --- /dev/null +++ b/venv/lib/python3.12/site-packages/markupsafe-3.0.3.dist-info/top_level.txt @@ -0,0 +1 @@ +markupsafe diff --git a/venv/lib/python3.12/site-packages/markupsafe/__init__.py b/venv/lib/python3.12/site-packages/markupsafe/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..4c395d7ba6005500b71f8d9bea5fb4c3abe371d2 --- /dev/null +++ b/venv/lib/python3.12/site-packages/markupsafe/__init__.py @@ -0,0 +1,396 @@ +from __future__ import annotations + +import collections.abc as cabc +import string +import typing as t + +try: + from ._speedups import _escape_inner +except ImportError: + from ._native import _escape_inner + +if t.TYPE_CHECKING: + import typing_extensions as te + + +class _HasHTML(t.Protocol): + def __html__(self, /) -> str: ... + + +class _TPEscape(t.Protocol): + def __call__(self, s: t.Any, /) -> 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 the object is already a plain string, skip __html__ check and string + # conversion. This is the most common use case. + # Use type(s) instead of s.__class__ because a proxy object may be reporting + # the __class__ of the proxied value. + if type(s) is str: + return Markup(_escape_inner(s)) + + if hasattr(s, "__html__"): + return Markup(s.__html__()) + + return Markup(_escape_inner(str(s))) + + +def escape_silent(s: t.Any | None, /) -> 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("") + >>> value + Markup('<User 1>') + >>> escape(str(value)) + Markup('&lt;User 1&gt;') + >>> escape(soft_str(value)) + Markup('<User 1>') + """ + if not isinstance(s, str): + return str(s) + + return s + + +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, World!") + Markup('Hello, World!') + >>> Markup(42) + Markup('42') + >>> Markup.escape("Hello, World!") + Markup('Hello <em>World</em>!') + + 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 'foo' + ... + >>> Markup(Foo()) + Markup('foo') + + This is a subclass of :class:`str`. It has the same methods, but + escapes their arguments and returns a ``Markup`` instance. + + >>> Markup("%s") % ("foo & bar",) + Markup('foo & bar') + >>> Markup("Hello ") + "" + Markup('Hello <foo>') + """ + + __slots__ = () + + def __new__( + cls, object: t.Any = "", encoding: str | None = None, errors: str = "strict" + ) -> te.Self: + if hasattr(object, "__html__"): + object = object.__html__() + + if encoding is None: + return super().__new__(cls, object) + + return super().__new__(cls, object, encoding, errors) + + def __html__(self, /) -> te.Self: + return self + + def __add__(self, value: str | _HasHTML, /) -> te.Self: + if isinstance(value, str) or hasattr(value, "__html__"): + return self.__class__(super().__add__(self.escape(value))) + + return NotImplemented + + def __radd__(self, value: str | _HasHTML, /) -> te.Self: + if isinstance(value, str) or hasattr(value, "__html__"): + return self.escape(value).__add__(self) + + return NotImplemented + + def __mul__(self, value: t.SupportsIndex, /) -> te.Self: + return self.__class__(super().__mul__(value)) + + def __rmul__(self, value: t.SupportsIndex, /) -> te.Self: + return self.__class__(super().__mul__(value)) + + def __mod__(self, value: t.Any, /) -> te.Self: + if isinstance(value, tuple): + # a tuple of arguments, each wrapped + value = tuple(_MarkupEscapeHelper(x, self.escape) for x in value) + elif hasattr(type(value), "__getitem__") and not isinstance(value, str): + # a mapping of arguments, wrapped + value = _MarkupEscapeHelper(value, self.escape) + else: + # a single argument, wrapped with the helper and a tuple + value = (_MarkupEscapeHelper(value, self.escape),) + + return self.__class__(super().__mod__(value)) + + def __repr__(self, /) -> str: + return f"{self.__class__.__name__}({super().__repr__()})" + + def join(self, iterable: cabc.Iterable[str | _HasHTML], /) -> te.Self: + return self.__class__(super().join(map(self.escape, iterable))) + + def split( # type: ignore[override] + self, /, sep: str | None = None, maxsplit: t.SupportsIndex = -1 + ) -> list[te.Self]: + return [self.__class__(v) for v in super().split(sep, maxsplit)] + + def rsplit( # type: ignore[override] + self, /, sep: str | None = None, maxsplit: t.SupportsIndex = -1 + ) -> list[te.Self]: + return [self.__class__(v) for v in super().rsplit(sep, maxsplit)] + + def splitlines( # type: ignore[override] + self, /, keepends: bool = False + ) -> list[te.Self]: + return [self.__class__(v) for v in super().splitlines(keepends)] + + def unescape(self, /) -> str: + """Convert escaped markup back into a text string. This replaces + HTML entities with the characters they represent. + + >>> Markup("Main » About").unescape() + 'Main » About' + """ + 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 »\tAbout").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. + + # keep finding comment start marks + while (start := value.find("", start)) == -1: + break + + value = f"{value[:start]}{value[end + 3 :]}" + + # remove tags using the same method + while (start := value.find("<")) != -1: + if (end := value.find(">", start)) == -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] + + def __getitem__(self, key: t.SupportsIndex | slice, /) -> te.Self: + return self.__class__(super().__getitem__(key)) + + def capitalize(self, /) -> te.Self: + return self.__class__(super().capitalize()) + + def title(self, /) -> te.Self: + return self.__class__(super().title()) + + def lower(self, /) -> te.Self: + return self.__class__(super().lower()) + + def upper(self, /) -> te.Self: + return self.__class__(super().upper()) + + def replace(self, old: str, new: str, count: t.SupportsIndex = -1, /) -> te.Self: + return self.__class__(super().replace(old, self.escape(new), count)) + + def ljust(self, width: t.SupportsIndex, fillchar: str = " ", /) -> te.Self: + return self.__class__(super().ljust(width, self.escape(fillchar))) + + def rjust(self, width: t.SupportsIndex, fillchar: str = " ", /) -> te.Self: + return self.__class__(super().rjust(width, self.escape(fillchar))) + + def lstrip(self, chars: str | None = None, /) -> te.Self: + return self.__class__(super().lstrip(chars)) + + def rstrip(self, chars: str | None = None, /) -> te.Self: + return self.__class__(super().rstrip(chars)) + + def center(self, width: t.SupportsIndex, fillchar: str = " ", /) -> te.Self: + return self.__class__(super().center(width, self.escape(fillchar))) + + def strip(self, chars: str | None = None, /) -> te.Self: + return self.__class__(super().strip(chars)) + + def translate( + self, + table: cabc.Mapping[int, str | int | None], # type: ignore[override] + /, + ) -> str: + return self.__class__(super().translate(table)) + + def expandtabs(self, /, tabsize: t.SupportsIndex = 8) -> te.Self: + return self.__class__(super().expandtabs(tabsize)) + + def swapcase(self, /) -> te.Self: + return self.__class__(super().swapcase()) + + def zfill(self, width: t.SupportsIndex, /) -> te.Self: + return self.__class__(super().zfill(width)) + + def casefold(self, /) -> te.Self: + return self.__class__(super().casefold()) + + def removeprefix(self, prefix: str, /) -> te.Self: + return self.__class__(super().removeprefix(prefix)) + + def removesuffix(self, suffix: str) -> te.Self: + return self.__class__(super().removesuffix(suffix)) + + def partition(self, sep: str, /) -> tuple[te.Self, te.Self, te.Self]: + left, sep, right = super().partition(sep) + cls = self.__class__ + return cls(left), cls(sep), cls(right) + + def rpartition(self, sep: str, /) -> tuple[te.Self, te.Self, te.Self]: + left, sep, right = super().rpartition(sep) + cls = self.__class__ + return cls(left), cls(sep), cls(right) + + 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( + self, + mapping: cabc.Mapping[str, t.Any], # type: ignore[override] + /, + ) -> te.Self: + formatter = EscapeFormatter(self.escape) + return self.__class__(formatter.vformat(self, (), mapping)) + + 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: _TPEscape) -> None: + self.escape: _TPEscape = 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 = super().format_field(value, str(format_spec)) + return str(self.escape(rv)) + + +class _MarkupEscapeHelper: + """Helper for :meth:`Markup.__mod__`.""" + + __slots__ = ("obj", "escape") + + def __init__(self, obj: t.Any, escape: _TPEscape) -> None: + self.obj: t.Any = obj + self.escape: _TPEscape = escape + + def __getitem__(self, key: t.Any, /) -> te.Self: + return self.__class__(self.obj[key], 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) + + +def __getattr__(name: str) -> t.Any: + if name == "__version__": + import importlib.metadata + import warnings + + warnings.warn( + "The '__version__' attribute is deprecated and will be removed in" + " MarkupSafe 3.1. Use feature detection, or" + ' `importlib.metadata.version("markupsafe")`, instead.', + DeprecationWarning, + stacklevel=2, + ) + return importlib.metadata.version("markupsafe") + + raise AttributeError(name) diff --git a/venv/lib/python3.12/site-packages/markupsafe/__pycache__/__init__.cpython-312.pyc b/venv/lib/python3.12/site-packages/markupsafe/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..8d3f5a691e056fd4708594275bef6c4842c78b03 Binary files /dev/null and b/venv/lib/python3.12/site-packages/markupsafe/__pycache__/__init__.cpython-312.pyc differ diff --git a/venv/lib/python3.12/site-packages/markupsafe/__pycache__/_native.cpython-312.pyc b/venv/lib/python3.12/site-packages/markupsafe/__pycache__/_native.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..c45c3b619dc7623dd30dcb5afb3100eba6ecfc54 Binary files /dev/null and b/venv/lib/python3.12/site-packages/markupsafe/__pycache__/_native.cpython-312.pyc differ diff --git a/venv/lib/python3.12/site-packages/markupsafe/_native.py b/venv/lib/python3.12/site-packages/markupsafe/_native.py new file mode 100644 index 0000000000000000000000000000000000000000..088b3bca9839ee489eefa546a0773a465b8cd0ca --- /dev/null +++ b/venv/lib/python3.12/site-packages/markupsafe/_native.py @@ -0,0 +1,8 @@ +def _escape_inner(s: str, /) -> str: + return ( + s.replace("&", "&") + .replace(">", ">") + .replace("<", "<") + .replace("'", "'") + .replace('"', """) + ) diff --git a/venv/lib/python3.12/site-packages/markupsafe/_speedups.c b/venv/lib/python3.12/site-packages/markupsafe/_speedups.c new file mode 100644 index 0000000000000000000000000000000000000000..8a315f23c52851b23525d205e392ee69e0a27f39 --- /dev/null +++ b/venv/lib/python3.12/site-packages/markupsafe/_speedups.c @@ -0,0 +1,200 @@ +#include + +#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(PyObject *self, PyObject *s) +{ + if (!PyUnicode_Check(s)) + return NULL; + + // This check is no longer needed in Python 3.12. + if (PyUnicode_READY(s)) + return NULL; + + switch (PyUnicode_KIND(s)) { + case PyUnicode_1BYTE_KIND: + return escape_unicode_kind1((PyUnicodeObject*) s); + case PyUnicode_2BYTE_KIND: + return escape_unicode_kind2((PyUnicodeObject*) s); + case PyUnicode_4BYTE_KIND: + return escape_unicode_kind4((PyUnicodeObject*) s); + } + assert(0); /* shouldn't happen */ + return NULL; +} + +static PyMethodDef module_methods[] = { + {"_escape_inner", (PyCFunction)escape_unicode, METH_O, NULL}, + {NULL, NULL, 0, NULL} /* Sentinel */ +}; + +static PyModuleDef_Slot module_slots[] = { +#ifdef Py_mod_multiple_interpreters // Python 3.12+ + {Py_mod_multiple_interpreters, Py_MOD_PER_INTERPRETER_GIL_SUPPORTED}, +#endif +#ifdef Py_mod_gil // Python 3.13+ + {Py_mod_gil, Py_MOD_GIL_NOT_USED}, +#endif + {0, NULL} /* Sentinel */ +}; + +static struct PyModuleDef module_definition = { + .m_base = PyModuleDef_HEAD_INIT, + .m_name = "markupsafe._speedups", + .m_size = 0, + .m_methods = module_methods, + .m_slots = module_slots, +}; + +PyMODINIT_FUNC +PyInit__speedups(void) +{ + return PyModuleDef_Init(&module_definition); +} diff --git a/venv/lib/python3.12/site-packages/markupsafe/_speedups.cpython-312-x86_64-linux-gnu.so b/venv/lib/python3.12/site-packages/markupsafe/_speedups.cpython-312-x86_64-linux-gnu.so new file mode 100644 index 0000000000000000000000000000000000000000..6d4b8f955db81fddbfe4f90b3bad053bb2edbe83 Binary files /dev/null and b/venv/lib/python3.12/site-packages/markupsafe/_speedups.cpython-312-x86_64-linux-gnu.so differ diff --git a/venv/lib/python3.12/site-packages/markupsafe/_speedups.pyi b/venv/lib/python3.12/site-packages/markupsafe/_speedups.pyi new file mode 100644 index 0000000000000000000000000000000000000000..8c8885852a26eba90d3ca1783beca535d4d43bb0 --- /dev/null +++ b/venv/lib/python3.12/site-packages/markupsafe/_speedups.pyi @@ -0,0 +1 @@ +def _escape_inner(s: str, /) -> str: ... diff --git a/venv/lib/python3.12/site-packages/markupsafe/py.typed b/venv/lib/python3.12/site-packages/markupsafe/py.typed new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/venv/lib/python3.12/site-packages/mdurl-0.1.2.dist-info/INSTALLER b/venv/lib/python3.12/site-packages/mdurl-0.1.2.dist-info/INSTALLER new file mode 100644 index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68 --- /dev/null +++ b/venv/lib/python3.12/site-packages/mdurl-0.1.2.dist-info/INSTALLER @@ -0,0 +1 @@ +pip diff --git a/venv/lib/python3.12/site-packages/mdurl-0.1.2.dist-info/LICENSE b/venv/lib/python3.12/site-packages/mdurl-0.1.2.dist-info/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..2a920c59d8abdd485a774087915986448495fd7c --- /dev/null +++ b/venv/lib/python3.12/site-packages/mdurl-0.1.2.dist-info/LICENSE @@ -0,0 +1,46 @@ +Copyright (c) 2015 Vitaly Puzrin, Alex Kocharin. +Copyright (c) 2021 Taneli Hukkinen + +Permission is hereby granted, free of charge, to any person +obtaining a copy of this software and associated documentation +files (the "Software"), to deal in the Software without +restriction, including without limitation the rights to use, +copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the +Software is furnished to do so, subject to the following +conditions: + +The above copyright notice and this permission notice shall be +included in all copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, +EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES +OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND +NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT +HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, +WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING +FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR +OTHER DEALINGS IN THE SOFTWARE. + +-------------------------------------------------------------------------------- + +.parse() is based on Joyent's node.js `url` code: + +Copyright Joyent, Inc. and other Node contributors. All rights reserved. +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to +deal in the Software without restriction, including without limitation the +rights to use, copy, modify, merge, publish, distribute, sublicense, and/or +sell copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in +all copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING +FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS +IN THE SOFTWARE. diff --git a/venv/lib/python3.12/site-packages/mdurl-0.1.2.dist-info/METADATA b/venv/lib/python3.12/site-packages/mdurl-0.1.2.dist-info/METADATA new file mode 100644 index 0000000000000000000000000000000000000000..b4670e86b6dc207c944c55c5d3b84911fb41157a --- /dev/null +++ b/venv/lib/python3.12/site-packages/mdurl-0.1.2.dist-info/METADATA @@ -0,0 +1,32 @@ +Metadata-Version: 2.1 +Name: mdurl +Version: 0.1.2 +Summary: Markdown URL utilities +Keywords: markdown,commonmark +Author-email: Taneli Hukkinen +Requires-Python: >=3.7 +Description-Content-Type: text/markdown +Classifier: License :: OSI Approved :: MIT License +Classifier: Operating System :: MacOS +Classifier: Operating System :: Microsoft :: Windows +Classifier: Operating System :: POSIX :: Linux +Classifier: Programming Language :: Python :: 3 :: Only +Classifier: Programming Language :: Python :: 3.7 +Classifier: Programming Language :: Python :: 3.8 +Classifier: Programming Language :: Python :: 3.9 +Classifier: Programming Language :: Python :: 3.10 +Classifier: Programming Language :: Python :: Implementation :: CPython +Classifier: Programming Language :: Python :: Implementation :: PyPy +Classifier: Topic :: Software Development :: Libraries :: Python Modules +Classifier: Typing :: Typed +Project-URL: Homepage, https://github.com/executablebooks/mdurl + +# mdurl + +[![Build Status](https://github.com/executablebooks/mdurl/workflows/Tests/badge.svg?branch=master)](https://github.com/executablebooks/mdurl/actions?query=workflow%3ATests+branch%3Amaster+event%3Apush) +[![codecov.io](https://codecov.io/gh/executablebooks/mdurl/branch/master/graph/badge.svg)](https://codecov.io/gh/executablebooks/mdurl) +[![PyPI version](https://img.shields.io/pypi/v/mdurl)](https://pypi.org/project/mdurl) + +This is a Python port of the JavaScript [mdurl](https://www.npmjs.com/package/mdurl) package. +See the [upstream README.md file](https://github.com/markdown-it/mdurl/blob/master/README.md) for API documentation. + diff --git a/venv/lib/python3.12/site-packages/mdurl-0.1.2.dist-info/RECORD b/venv/lib/python3.12/site-packages/mdurl-0.1.2.dist-info/RECORD new file mode 100644 index 0000000000000000000000000000000000000000..33e97b0cdbb0e4bc696a0738f6d01f8003a38522 --- /dev/null +++ b/venv/lib/python3.12/site-packages/mdurl-0.1.2.dist-info/RECORD @@ -0,0 +1,18 @@ +mdurl-0.1.2.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4 +mdurl-0.1.2.dist-info/LICENSE,sha256=fGBd9uKGZ6lgMRjpgnT2SknOPu0NJvzM6VNKNF4O-VU,2338 +mdurl-0.1.2.dist-info/METADATA,sha256=tTsp1I9Jk2cFP9o8gefOJ9JVg4Drv4PmYCOwLrfd0l0,1638 +mdurl-0.1.2.dist-info/RECORD,, +mdurl-0.1.2.dist-info/WHEEL,sha256=4TfKIB_xu-04bc2iKz6_zFt-gEFEEDU_31HGhqzOCE8,81 +mdurl/__init__.py,sha256=1vpE89NyXniIRZNC_4f6BPm3Ub4bPntjfyyhLRR7opU,547 +mdurl/__pycache__/__init__.cpython-312.pyc,, +mdurl/__pycache__/_decode.cpython-312.pyc,, +mdurl/__pycache__/_encode.cpython-312.pyc,, +mdurl/__pycache__/_format.cpython-312.pyc,, +mdurl/__pycache__/_parse.cpython-312.pyc,, +mdurl/__pycache__/_url.cpython-312.pyc,, +mdurl/_decode.py,sha256=3Q_gDQqU__TvDbu7x-b9LjbVl4QWy5g_qFwljcuvN_Y,3004 +mdurl/_encode.py,sha256=goJLUFt1h4rVZNqqm9t15Nw2W-bFXYQEy3aR01ImWvs,2602 +mdurl/_format.py,sha256=xZct0mdePXA0H3kAqxjGtlB5O86G35DAYMGkA44CmB4,626 +mdurl/_parse.py,sha256=ezZSkM2_4NQ2Zx047sEdcJG7NYQRFHiZK7Y8INHFzwY,11374 +mdurl/_url.py,sha256=5kQnRQN2A_G4svLnRzZcG0bfoD9AbBrYDXousDHZ3z0,284 +mdurl/py.typed,sha256=8PjyZ1aVoQpRVvt71muvuq5qE-jTFZkK-GLHkhdebmc,26 diff --git a/venv/lib/python3.12/site-packages/mdurl-0.1.2.dist-info/WHEEL b/venv/lib/python3.12/site-packages/mdurl-0.1.2.dist-info/WHEEL new file mode 100644 index 0000000000000000000000000000000000000000..668ba4d0151c5c76ed6e758061daa8c1b0bf5d21 --- /dev/null +++ b/venv/lib/python3.12/site-packages/mdurl-0.1.2.dist-info/WHEEL @@ -0,0 +1,4 @@ +Wheel-Version: 1.0 +Generator: flit 3.7.1 +Root-Is-Purelib: true +Tag: py3-none-any diff --git a/venv/lib/python3.12/site-packages/mdurl/__init__.py b/venv/lib/python3.12/site-packages/mdurl/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..cdbb640e004cef0e950a656a53d92d89d82c7472 --- /dev/null +++ b/venv/lib/python3.12/site-packages/mdurl/__init__.py @@ -0,0 +1,18 @@ +__all__ = ( + "decode", + "DECODE_DEFAULT_CHARS", + "DECODE_COMPONENT_CHARS", + "encode", + "ENCODE_DEFAULT_CHARS", + "ENCODE_COMPONENT_CHARS", + "format", + "parse", + "URL", +) +__version__ = "0.1.2" # DO NOT EDIT THIS LINE MANUALLY. LET bump2version UTILITY DO IT + +from mdurl._decode import DECODE_COMPONENT_CHARS, DECODE_DEFAULT_CHARS, decode +from mdurl._encode import ENCODE_COMPONENT_CHARS, ENCODE_DEFAULT_CHARS, encode +from mdurl._format import format +from mdurl._parse import url_parse as parse +from mdurl._url import URL diff --git a/venv/lib/python3.12/site-packages/mdurl/__pycache__/__init__.cpython-312.pyc b/venv/lib/python3.12/site-packages/mdurl/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..e6fcd84f86ad3792b8ec1386319772ec2e0c047b Binary files /dev/null and b/venv/lib/python3.12/site-packages/mdurl/__pycache__/__init__.cpython-312.pyc differ diff --git a/venv/lib/python3.12/site-packages/mdurl/__pycache__/_decode.cpython-312.pyc b/venv/lib/python3.12/site-packages/mdurl/__pycache__/_decode.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..e651952e72decc92c840aab84f47471587a862e1 Binary files /dev/null and b/venv/lib/python3.12/site-packages/mdurl/__pycache__/_decode.cpython-312.pyc differ diff --git a/venv/lib/python3.12/site-packages/mdurl/__pycache__/_encode.cpython-312.pyc b/venv/lib/python3.12/site-packages/mdurl/__pycache__/_encode.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..61120d3ba57c5e0475b11950f08bed64d1385af9 Binary files /dev/null and b/venv/lib/python3.12/site-packages/mdurl/__pycache__/_encode.cpython-312.pyc differ diff --git a/venv/lib/python3.12/site-packages/mdurl/__pycache__/_format.cpython-312.pyc b/venv/lib/python3.12/site-packages/mdurl/__pycache__/_format.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..61d5feb828db7ed16a1a100ecd0da9b1271e3420 Binary files /dev/null and b/venv/lib/python3.12/site-packages/mdurl/__pycache__/_format.cpython-312.pyc differ diff --git a/venv/lib/python3.12/site-packages/mdurl/__pycache__/_parse.cpython-312.pyc b/venv/lib/python3.12/site-packages/mdurl/__pycache__/_parse.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..9443af1288ea5fdec8cd3ef5c719447996309403 Binary files /dev/null and b/venv/lib/python3.12/site-packages/mdurl/__pycache__/_parse.cpython-312.pyc differ diff --git a/venv/lib/python3.12/site-packages/mdurl/__pycache__/_url.cpython-312.pyc b/venv/lib/python3.12/site-packages/mdurl/__pycache__/_url.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..840bc7d6f6c0b951141805ae9c0a767d65a8402c Binary files /dev/null and b/venv/lib/python3.12/site-packages/mdurl/__pycache__/_url.cpython-312.pyc differ diff --git a/venv/lib/python3.12/site-packages/mdurl/_decode.py b/venv/lib/python3.12/site-packages/mdurl/_decode.py new file mode 100644 index 0000000000000000000000000000000000000000..9b50a2dde976a6d43491ec6f20d12e60f6f6597f --- /dev/null +++ b/venv/lib/python3.12/site-packages/mdurl/_decode.py @@ -0,0 +1,104 @@ +from __future__ import annotations + +from collections.abc import Sequence +import functools +import re + +DECODE_DEFAULT_CHARS = ";/?:@&=+$,#" +DECODE_COMPONENT_CHARS = "" + +decode_cache: dict[str, list[str]] = {} + + +def get_decode_cache(exclude: str) -> Sequence[str]: + if exclude in decode_cache: + return decode_cache[exclude] + + cache: list[str] = [] + decode_cache[exclude] = cache + + for i in range(128): + ch = chr(i) + cache.append(ch) + + for i in range(len(exclude)): + ch_code = ord(exclude[i]) + cache[ch_code] = "%" + ("0" + hex(ch_code)[2:].upper())[-2:] + + return cache + + +# Decode percent-encoded string. +# +def decode(string: str, exclude: str = DECODE_DEFAULT_CHARS) -> str: + cache = get_decode_cache(exclude) + repl_func = functools.partial(repl_func_with_cache, cache=cache) + return re.sub(r"(%[a-f0-9]{2})+", repl_func, string, flags=re.IGNORECASE) + + +def repl_func_with_cache(match: re.Match, cache: Sequence[str]) -> str: + seq = match.group() + result = "" + + i = 0 + l = len(seq) # noqa: E741 + while i < l: + b1 = int(seq[i + 1 : i + 3], 16) + + if b1 < 0x80: + result += cache[b1] + i += 3 # emulate JS for loop statement3 + continue + + if (b1 & 0xE0) == 0xC0 and (i + 3 < l): + # 110xxxxx 10xxxxxx + b2 = int(seq[i + 4 : i + 6], 16) + + if (b2 & 0xC0) == 0x80: + all_bytes = bytes((b1, b2)) + try: + result += all_bytes.decode() + except UnicodeDecodeError: + result += "\ufffd" * 2 + + i += 3 + i += 3 # emulate JS for loop statement3 + continue + + if (b1 & 0xF0) == 0xE0 and (i + 6 < l): + # 1110xxxx 10xxxxxx 10xxxxxx + b2 = int(seq[i + 4 : i + 6], 16) + b3 = int(seq[i + 7 : i + 9], 16) + + if (b2 & 0xC0) == 0x80 and (b3 & 0xC0) == 0x80: + all_bytes = bytes((b1, b2, b3)) + try: + result += all_bytes.decode() + except UnicodeDecodeError: + result += "\ufffd" * 3 + + i += 6 + i += 3 # emulate JS for loop statement3 + continue + + if (b1 & 0xF8) == 0xF0 and (i + 9 < l): + # 111110xx 10xxxxxx 10xxxxxx 10xxxxxx + b2 = int(seq[i + 4 : i + 6], 16) + b3 = int(seq[i + 7 : i + 9], 16) + b4 = int(seq[i + 10 : i + 12], 16) + + if (b2 & 0xC0) == 0x80 and (b3 & 0xC0) == 0x80 and (b4 & 0xC0) == 0x80: + all_bytes = bytes((b1, b2, b3, b4)) + try: + result += all_bytes.decode() + except UnicodeDecodeError: + result += "\ufffd" * 4 + + i += 9 + i += 3 # emulate JS for loop statement3 + continue + + result += "\ufffd" + i += 3 # emulate JS for loop statement3 + + return result diff --git a/venv/lib/python3.12/site-packages/mdurl/_encode.py b/venv/lib/python3.12/site-packages/mdurl/_encode.py new file mode 100644 index 0000000000000000000000000000000000000000..bc2e5b917afe9e9ecaa6f11af7a9ac82704d3914 --- /dev/null +++ b/venv/lib/python3.12/site-packages/mdurl/_encode.py @@ -0,0 +1,85 @@ +from __future__ import annotations + +from collections.abc import Sequence +from string import ascii_letters, digits, hexdigits +from urllib.parse import quote as encode_uri_component + +ASCII_LETTERS_AND_DIGITS = ascii_letters + digits + +ENCODE_DEFAULT_CHARS = ";/?:@&=+$,-_.!~*'()#" +ENCODE_COMPONENT_CHARS = "-_.!~*'()" + +encode_cache: dict[str, list[str]] = {} + + +# Create a lookup array where anything but characters in `chars` string +# and alphanumeric chars is percent-encoded. +def get_encode_cache(exclude: str) -> Sequence[str]: + if exclude in encode_cache: + return encode_cache[exclude] + + cache: list[str] = [] + encode_cache[exclude] = cache + + for i in range(128): + ch = chr(i) + + if ch in ASCII_LETTERS_AND_DIGITS: + # always allow unencoded alphanumeric characters + cache.append(ch) + else: + cache.append("%" + ("0" + hex(i)[2:].upper())[-2:]) + + for i in range(len(exclude)): + cache[ord(exclude[i])] = exclude[i] + + return cache + + +# Encode unsafe characters with percent-encoding, skipping already +# encoded sequences. +# +# - string - string to encode +# - exclude - list of characters to ignore (in addition to a-zA-Z0-9) +# - keepEscaped - don't encode '%' in a correct escape sequence (default: true) +def encode( + string: str, exclude: str = ENCODE_DEFAULT_CHARS, *, keep_escaped: bool = True +) -> str: + result = "" + + cache = get_encode_cache(exclude) + + l = len(string) # noqa: E741 + i = 0 + while i < l: + code = ord(string[i]) + + # % + if keep_escaped and code == 0x25 and i + 2 < l: + if all(c in hexdigits for c in string[i + 1 : i + 3]): + result += string[i : i + 3] + i += 2 + i += 1 # JS for loop statement3 + continue + + if code < 128: + result += cache[code] + i += 1 # JS for loop statement3 + continue + + if code >= 0xD800 and code <= 0xDFFF: + if code >= 0xD800 and code <= 0xDBFF and i + 1 < l: + next_code = ord(string[i + 1]) + if next_code >= 0xDC00 and next_code <= 0xDFFF: + result += encode_uri_component(string[i] + string[i + 1]) + i += 1 + i += 1 # JS for loop statement3 + continue + result += "%EF%BF%BD" + i += 1 # JS for loop statement3 + continue + + result += encode_uri_component(string[i]) + i += 1 # JS for loop statement3 + + return result diff --git a/venv/lib/python3.12/site-packages/mdurl/_format.py b/venv/lib/python3.12/site-packages/mdurl/_format.py new file mode 100644 index 0000000000000000000000000000000000000000..12524ca626065183ec9974f3d7d08dadd4a7d3e8 --- /dev/null +++ b/venv/lib/python3.12/site-packages/mdurl/_format.py @@ -0,0 +1,27 @@ +from __future__ import annotations + +from typing import TYPE_CHECKING + +if TYPE_CHECKING: + from mdurl._url import URL + + +def format(url: URL) -> str: # noqa: A001 + result = "" + + result += url.protocol or "" + result += "//" if url.slashes else "" + result += url.auth + "@" if url.auth else "" + + if url.hostname and ":" in url.hostname: + # ipv6 address + result += "[" + url.hostname + "]" + else: + result += url.hostname or "" + + result += ":" + url.port if url.port else "" + result += url.pathname or "" + result += url.search or "" + result += url.hash or "" + + return result diff --git a/venv/lib/python3.12/site-packages/mdurl/_parse.py b/venv/lib/python3.12/site-packages/mdurl/_parse.py new file mode 100644 index 0000000000000000000000000000000000000000..ffeeac768dca3bff60c55c9b1f0bc0fbb4cec7b1 --- /dev/null +++ b/venv/lib/python3.12/site-packages/mdurl/_parse.py @@ -0,0 +1,304 @@ +# Copyright Joyent, Inc. and other Node contributors. +# +# Permission is hereby granted, free of charge, to any person obtaining a +# copy of this software and associated documentation files (the +# "Software"), to deal in the Software without restriction, including +# without limitation the rights to use, copy, modify, merge, publish, +# distribute, sublicense, and/or sell copies of the Software, and to permit +# persons to whom the Software is furnished to do so, subject to the +# following conditions: +# +# The above copyright notice and this permission notice shall be included +# in all copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS +# OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF +# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN +# NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, +# DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR +# OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE +# USE OR OTHER DEALINGS IN THE SOFTWARE. + + +# Changes from joyent/node: +# +# 1. No leading slash in paths, +# e.g. in `url.parse('http://foo?bar')` pathname is ``, not `/` +# +# 2. Backslashes are not replaced with slashes, +# so `http:\\example.org\` is treated like a relative path +# +# 3. Trailing colon is treated like a part of the path, +# i.e. in `http://example.org:foo` pathname is `:foo` +# +# 4. Nothing is URL-encoded in the resulting object, +# (in joyent/node some chars in auth and paths are encoded) +# +# 5. `url.parse()` does not have `parseQueryString` argument +# +# 6. Removed extraneous result properties: `host`, `path`, `query`, etc., +# which can be constructed using other parts of the url. + +from __future__ import annotations + +from collections import defaultdict +import re + +from mdurl._url import URL + +# Reference: RFC 3986, RFC 1808, RFC 2396 + +# define these here so at least they only have to be +# compiled once on the first module load. +PROTOCOL_PATTERN = re.compile(r"^([a-z0-9.+-]+:)", flags=re.IGNORECASE) +PORT_PATTERN = re.compile(r":[0-9]*$") + +# Special case for a simple path URL +SIMPLE_PATH_PATTERN = re.compile(r"^(//?(?!/)[^?\s]*)(\?[^\s]*)?$") + +# RFC 2396: characters reserved for delimiting URLs. +# We actually just auto-escape these. +DELIMS = ("<", ">", '"', "`", " ", "\r", "\n", "\t") + +# RFC 2396: characters not allowed for various reasons. +UNWISE = ("{", "}", "|", "\\", "^", "`") + DELIMS + +# Allowed by RFCs, but cause of XSS attacks. Always escape these. +AUTO_ESCAPE = ("'",) + UNWISE +# Characters that are never ever allowed in a hostname. +# Note that any invalid chars are also handled, but these +# are the ones that are *expected* to be seen, so we fast-path +# them. +NON_HOST_CHARS = ("%", "/", "?", ";", "#") + AUTO_ESCAPE +HOST_ENDING_CHARS = ("/", "?", "#") +HOSTNAME_MAX_LEN = 255 +HOSTNAME_PART_PATTERN = re.compile(r"^[+a-z0-9A-Z_-]{0,63}$") +HOSTNAME_PART_START = re.compile(r"^([+a-z0-9A-Z_-]{0,63})(.*)$") +# protocols that can allow "unsafe" and "unwise" chars. + +# protocols that never have a hostname. +HOSTLESS_PROTOCOL = defaultdict( + bool, + { + "javascript": True, + "javascript:": True, + }, +) +# protocols that always contain a // bit. +SLASHED_PROTOCOL = defaultdict( + bool, + { + "http": True, + "https": True, + "ftp": True, + "gopher": True, + "file": True, + "http:": True, + "https:": True, + "ftp:": True, + "gopher:": True, + "file:": True, + }, +) + + +class MutableURL: + def __init__(self) -> None: + self.protocol: str | None = None + self.slashes: bool = False + self.auth: str | None = None + self.port: str | None = None + self.hostname: str | None = None + self.hash: str | None = None + self.search: str | None = None + self.pathname: str | None = None + + def parse(self, url: str, slashes_denote_host: bool) -> "MutableURL": + lower_proto = "" + slashes = False + rest = url + + # trim before proceeding. + # This is to support parse stuff like " http://foo.com \n" + rest = rest.strip() + + if not slashes_denote_host and len(url.split("#")) == 1: + # Try fast path regexp + simple_path = SIMPLE_PATH_PATTERN.match(rest) + if simple_path: + self.pathname = simple_path.group(1) + if simple_path.group(2): + self.search = simple_path.group(2) + return self + + proto = "" + proto_match = PROTOCOL_PATTERN.match(rest) + if proto_match: + proto = proto_match.group() + lower_proto = proto.lower() + self.protocol = proto + rest = rest[len(proto) :] + + # figure out if it's got a host + # user@server is *always* interpreted as a hostname, and url + # resolution will treat //foo/bar as host=foo,path=bar because that's + # how the browser resolves relative URLs. + if slashes_denote_host or proto or re.search(r"^//[^@/]+@[^@/]+", rest): + slashes = rest.startswith("//") + if slashes and not (proto and HOSTLESS_PROTOCOL[proto]): + rest = rest[2:] + self.slashes = True + + if not HOSTLESS_PROTOCOL[proto] and ( + slashes or (proto and not SLASHED_PROTOCOL[proto]) + ): + + # there's a hostname. + # the first instance of /, ?, ;, or # ends the host. + # + # If there is an @ in the hostname, then non-host chars *are* allowed + # to the left of the last @ sign, unless some host-ending character + # comes *before* the @-sign. + # URLs are obnoxious. + # + # ex: + # http://a@b@c/ => user:a@b host:c + # http://a@b?@c => user:a host:c path:/?@c + + # v0.12 TODO(isaacs): This is not quite how Chrome does things. + # Review our test case against browsers more comprehensively. + + # find the first instance of any hostEndingChars + host_end = -1 + for i in range(len(HOST_ENDING_CHARS)): + hec = rest.find(HOST_ENDING_CHARS[i]) + if hec != -1 and (host_end == -1 or hec < host_end): + host_end = hec + + # at this point, either we have an explicit point where the + # auth portion cannot go past, or the last @ char is the decider. + if host_end == -1: + # atSign can be anywhere. + at_sign = rest.rfind("@") + else: + # atSign must be in auth portion. + # http://a@b/c@d => host:b auth:a path:/c@d + at_sign = rest.rfind("@", 0, host_end + 1) + + # Now we have a portion which is definitely the auth. + # Pull that off. + if at_sign != -1: + auth = rest[:at_sign] + rest = rest[at_sign + 1 :] + self.auth = auth + + # the host is the remaining to the left of the first non-host char + host_end = -1 + for i in range(len(NON_HOST_CHARS)): + hec = rest.find(NON_HOST_CHARS[i]) + if hec != -1 and (host_end == -1 or hec < host_end): + host_end = hec + # if we still have not hit it, then the entire thing is a host. + if host_end == -1: + host_end = len(rest) + + if host_end > 0 and rest[host_end - 1] == ":": + host_end -= 1 + host = rest[:host_end] + rest = rest[host_end:] + + # pull out port. + self.parse_host(host) + + # we've indicated that there is a hostname, + # so even if it's empty, it has to be present. + self.hostname = self.hostname or "" + + # if hostname begins with [ and ends with ] + # assume that it's an IPv6 address. + ipv6_hostname = self.hostname.startswith("[") and self.hostname.endswith( + "]" + ) + + # validate a little. + if not ipv6_hostname: + hostparts = self.hostname.split(".") + l = len(hostparts) # noqa: E741 + i = 0 + while i < l: + part = hostparts[i] + if not part: + i += 1 # emulate statement3 in JS for loop + continue + if not HOSTNAME_PART_PATTERN.search(part): + newpart = "" + k = len(part) + j = 0 + while j < k: + if ord(part[j]) > 127: + # we replace non-ASCII char with a temporary placeholder + # we need this to make sure size of hostname is not + # broken by replacing non-ASCII by nothing + newpart += "x" + else: + newpart += part[j] + j += 1 # emulate statement3 in JS for loop + + # we test again with ASCII char only + if not HOSTNAME_PART_PATTERN.search(newpart): + valid_parts = hostparts[:i] + not_host = hostparts[i + 1 :] + bit = HOSTNAME_PART_START.search(part) + if bit: + valid_parts.append(bit.group(1)) + not_host.insert(0, bit.group(2)) + if not_host: + rest = ".".join(not_host) + rest + self.hostname = ".".join(valid_parts) + break + i += 1 # emulate statement3 in JS for loop + + if len(self.hostname) > HOSTNAME_MAX_LEN: + self.hostname = "" + + # strip [ and ] from the hostname + # the host field still retains them, though + if ipv6_hostname: + self.hostname = self.hostname[1:-1] + + # chop off from the tail first. + hash = rest.find("#") # noqa: A001 + if hash != -1: + # got a fragment string. + self.hash = rest[hash:] + rest = rest[:hash] + qm = rest.find("?") + if qm != -1: + self.search = rest[qm:] + rest = rest[:qm] + if rest: + self.pathname = rest + if SLASHED_PROTOCOL[lower_proto] and self.hostname and not self.pathname: + self.pathname = "" + + return self + + def parse_host(self, host: str) -> None: + port_match = PORT_PATTERN.search(host) + if port_match: + port = port_match.group() + if port != ":": + self.port = port[1:] + host = host[: -len(port)] + if host: + self.hostname = host + + +def url_parse(url: URL | str, *, slashes_denote_host: bool = False) -> URL: + if isinstance(url, URL): + return url + u = MutableURL() + u.parse(url, slashes_denote_host) + return URL( + u.protocol, u.slashes, u.auth, u.port, u.hostname, u.hash, u.search, u.pathname + ) diff --git a/venv/lib/python3.12/site-packages/mdurl/_url.py b/venv/lib/python3.12/site-packages/mdurl/_url.py new file mode 100644 index 0000000000000000000000000000000000000000..f866e7a179c8854e37c9bba6294f48681e5d99d7 --- /dev/null +++ b/venv/lib/python3.12/site-packages/mdurl/_url.py @@ -0,0 +1,14 @@ +from __future__ import annotations + +from typing import NamedTuple + + +class URL(NamedTuple): + protocol: str | None + slashes: bool + auth: str | None + port: str | None + hostname: str | None + hash: str | None # noqa: A003 + search: str | None + pathname: str | None diff --git a/venv/lib/python3.12/site-packages/mdurl/py.typed b/venv/lib/python3.12/site-packages/mdurl/py.typed new file mode 100644 index 0000000000000000000000000000000000000000..7632ecf77545c5e5501cb3fc5719df0761104ca2 --- /dev/null +++ b/venv/lib/python3.12/site-packages/mdurl/py.typed @@ -0,0 +1 @@ +# Marker file for PEP 561 diff --git a/venv/lib/python3.12/site-packages/mpmath-1.3.0.dist-info/INSTALLER b/venv/lib/python3.12/site-packages/mpmath-1.3.0.dist-info/INSTALLER new file mode 100644 index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68 --- /dev/null +++ b/venv/lib/python3.12/site-packages/mpmath-1.3.0.dist-info/INSTALLER @@ -0,0 +1 @@ +pip diff --git a/venv/lib/python3.12/site-packages/mpmath-1.3.0.dist-info/LICENSE b/venv/lib/python3.12/site-packages/mpmath-1.3.0.dist-info/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..9ecdc7586d08805bc984539f6672476e86e538b6 --- /dev/null +++ b/venv/lib/python3.12/site-packages/mpmath-1.3.0.dist-info/LICENSE @@ -0,0 +1,27 @@ +Copyright (c) 2005-2021 Fredrik Johansson and mpmath contributors + +All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + + a. Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimer. + b. Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + c. Neither the name of the copyright holder nor the names of its + contributors may be used to endorse or promote products derived + from this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE +ARE DISCLAIMED. IN NO EVENT SHALL THE REGENTS OR CONTRIBUTORS BE LIABLE FOR +ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT +LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY +OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH +DAMAGE. diff --git a/venv/lib/python3.12/site-packages/mpmath-1.3.0.dist-info/METADATA b/venv/lib/python3.12/site-packages/mpmath-1.3.0.dist-info/METADATA new file mode 100644 index 0000000000000000000000000000000000000000..994b48acdba5cd0fdfb28cd1fbb0a84ebf81cba5 --- /dev/null +++ b/venv/lib/python3.12/site-packages/mpmath-1.3.0.dist-info/METADATA @@ -0,0 +1,233 @@ +Metadata-Version: 2.1 +Name: mpmath +Version: 1.3.0 +Summary: Python library for arbitrary-precision floating-point arithmetic +Home-page: http://mpmath.org/ +Author: Fredrik Johansson +Author-email: fredrik.johansson@gmail.com +License: BSD +Project-URL: Source, https://github.com/fredrik-johansson/mpmath +Project-URL: Tracker, https://github.com/fredrik-johansson/mpmath/issues +Project-URL: Documentation, http://mpmath.org/doc/current/ +Classifier: License :: OSI Approved :: BSD License +Classifier: Topic :: Scientific/Engineering :: Mathematics +Classifier: Topic :: Software Development :: Libraries :: Python Modules +Classifier: Programming Language :: Python +Classifier: Programming Language :: Python :: 2 +Classifier: Programming Language :: Python :: 2.7 +Classifier: Programming Language :: Python :: 3 +Classifier: Programming Language :: Python :: 3.5 +Classifier: Programming Language :: Python :: 3.6 +Classifier: Programming Language :: Python :: 3.7 +Classifier: Programming Language :: Python :: 3.8 +Classifier: Programming Language :: Python :: 3.9 +Classifier: Programming Language :: Python :: Implementation :: CPython +Classifier: Programming Language :: Python :: Implementation :: PyPy +License-File: LICENSE +Provides-Extra: develop +Requires-Dist: pytest (>=4.6) ; extra == 'develop' +Requires-Dist: pycodestyle ; extra == 'develop' +Requires-Dist: pytest-cov ; extra == 'develop' +Requires-Dist: codecov ; extra == 'develop' +Requires-Dist: wheel ; extra == 'develop' +Provides-Extra: docs +Requires-Dist: sphinx ; extra == 'docs' +Provides-Extra: gmpy +Requires-Dist: gmpy2 (>=2.1.0a4) ; (platform_python_implementation != "PyPy") and extra == 'gmpy' +Provides-Extra: tests +Requires-Dist: pytest (>=4.6) ; extra == 'tests' + +mpmath +====== + +|pypi version| |Build status| |Code coverage status| |Zenodo Badge| + +.. |pypi version| image:: https://img.shields.io/pypi/v/mpmath.svg + :target: https://pypi.python.org/pypi/mpmath +.. |Build status| image:: https://github.com/fredrik-johansson/mpmath/workflows/test/badge.svg + :target: https://github.com/fredrik-johansson/mpmath/actions?workflow=test +.. |Code coverage status| image:: https://codecov.io/gh/fredrik-johansson/mpmath/branch/master/graph/badge.svg + :target: https://codecov.io/gh/fredrik-johansson/mpmath +.. |Zenodo Badge| image:: https://zenodo.org/badge/2934512.svg + :target: https://zenodo.org/badge/latestdoi/2934512 + +A Python library for arbitrary-precision floating-point arithmetic. + +Website: http://mpmath.org/ +Main author: Fredrik Johansson + +Mpmath is free software released under the New BSD License (see the +LICENSE file for details) + +0. History and credits +---------------------- + +The following people (among others) have contributed major patches +or new features to mpmath: + +* Pearu Peterson +* Mario Pernici +* Ondrej Certik +* Vinzent Steinberg +* Nimish Telang +* Mike Taschuk +* Case Van Horsen +* Jorn Baayen +* Chris Smith +* Juan Arias de Reyna +* Ioannis Tziakos +* Aaron Meurer +* Stefan Krastanov +* Ken Allen +* Timo Hartmann +* Sergey B Kirpichev +* Kris Kuhlman +* Paul Masson +* Michael Kagalenko +* Jonathan Warner +* Max Gaukler +* Guillermo Navas-Palencia +* Nike Dattani + +Numerous other people have contributed by reporting bugs, +requesting new features, or suggesting improvements to the +documentation. + +For a detailed changelog, including individual contributions, +see the CHANGES file. + +Fredrik's work on mpmath during summer 2008 was sponsored by Google +as part of the Google Summer of Code program. + +Fredrik's work on mpmath during summer 2009 was sponsored by the +American Institute of Mathematics under the support of the National Science +Foundation Grant No. 0757627 (FRG: L-functions and Modular Forms). + +Any opinions, findings, and conclusions or recommendations expressed in this +material are those of the author(s) and do not necessarily reflect the +views of the sponsors. + +Credit also goes to: + +* The authors of the GMP library and the Python wrapper + gmpy, enabling mpmath to become much faster at + high precision +* The authors of MPFR, pari/gp, MPFUN, and other arbitrary- + precision libraries, whose documentation has been helpful + for implementing many of the algorithms in mpmath +* Wikipedia contributors; Abramowitz & Stegun; Gradshteyn & Ryzhik; + Wolfram Research for MathWorld and the Wolfram Functions site. + These are the main references used for special functions + implementations. +* George Brandl for developing the Sphinx documentation tool + used to build mpmath's documentation + +Release history: + +* Version 1.3.0 released on March 7, 2023 +* Version 1.2.0 released on February 1, 2021 +* Version 1.1.0 released on December 11, 2018 +* Version 1.0.0 released on September 27, 2017 +* Version 0.19 released on June 10, 2014 +* Version 0.18 released on December 31, 2013 +* Version 0.17 released on February 1, 2011 +* Version 0.16 released on September 24, 2010 +* Version 0.15 released on June 6, 2010 +* Version 0.14 released on February 5, 2010 +* Version 0.13 released on August 13, 2009 +* Version 0.12 released on June 9, 2009 +* Version 0.11 released on January 26, 2009 +* Version 0.10 released on October 15, 2008 +* Version 0.9 released on August 23, 2008 +* Version 0.8 released on April 20, 2008 +* Version 0.7 released on March 12, 2008 +* Version 0.6 released on January 13, 2008 +* Version 0.5 released on November 24, 2007 +* Version 0.4 released on November 3, 2007 +* Version 0.3 released on October 5, 2007 +* Version 0.2 released on October 2, 2007 +* Version 0.1 released on September 27, 2007 + +1. Download & installation +-------------------------- + +Mpmath requires Python 2.7 or 3.5 (or later versions). It has been tested +with CPython 2.7, 3.5 through 3.7 and for PyPy. + +The latest release of mpmath can be downloaded from the mpmath +website and from https://github.com/fredrik-johansson/mpmath/releases + +It should also be available in the Python Package Index at +https://pypi.python.org/pypi/mpmath + +To install latest release of Mpmath with pip, simply run + +``pip install mpmath`` + +Or unpack the mpmath archive and run + +``python setup.py install`` + +Mpmath can also be installed using + +``python -m easy_install mpmath`` + +The latest development code is available from +https://github.com/fredrik-johansson/mpmath + +See the main documentation for more detailed instructions. + +2. Running tests +---------------- + +The unit tests in mpmath/tests/ can be run via the script +runtests.py, but it is recommended to run them with py.test +(https://pytest.org/), especially +to generate more useful reports in case there are failures. + +You may also want to check out the demo scripts in the demo +directory. + +The master branch is automatically tested by Travis CI. + +3. Documentation +---------------- + +Documentation in reStructuredText format is available in the +doc directory included with the source package. These files +are human-readable, but can be compiled to prettier HTML using +the build.py script (requires Sphinx, http://sphinx.pocoo.org/). + +See setup.txt in the documentation for more information. + +The most recent documentation is also available in HTML format: + +http://mpmath.org/doc/current/ + +4. Known problems +----------------- + +Mpmath is a work in progress. Major issues include: + +* Some functions may return incorrect values when given extremely + large arguments or arguments very close to singularities. + +* Directed rounding works for arithmetic operations. It is implemented + heuristically for other operations, and their results may be off by one + or two units in the last place (even if otherwise accurate). + +* Some IEEE 754 features are not available. Inifinities and NaN are + partially supported; denormal rounding is currently not available + at all. + +* The interface for switching precision and rounding is not finalized. + The current method is not threadsafe. + +5. Help and bug reports +----------------------- + +General questions and comments can be sent to the mpmath mailinglist, +mpmath@googlegroups.com + +You can also report bugs and send patches to the mpmath issue tracker, +https://github.com/fredrik-johansson/mpmath/issues diff --git a/venv/lib/python3.12/site-packages/mpmath-1.3.0.dist-info/RECORD b/venv/lib/python3.12/site-packages/mpmath-1.3.0.dist-info/RECORD new file mode 100644 index 0000000000000000000000000000000000000000..79513be77d1067ef3596f8414cf6648423a7fc3d --- /dev/null +++ b/venv/lib/python3.12/site-packages/mpmath-1.3.0.dist-info/RECORD @@ -0,0 +1,180 @@ +mpmath-1.3.0.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4 +mpmath-1.3.0.dist-info/LICENSE,sha256=wmyugdpFCOXiSZhXd6M4IfGDIj67dNf4z7-Q_n7vL7c,1537 +mpmath-1.3.0.dist-info/METADATA,sha256=RLZupES5wNGa6UgV01a_BHrmtoDBkmi1wmVofNaoFAY,8630 +mpmath-1.3.0.dist-info/RECORD,, 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100644 index 0000000000000000000000000000000000000000..57e3d840d59a650ac5bccbad5baeec47d155f0ad --- /dev/null +++ b/venv/lib/python3.12/site-packages/mpmath-1.3.0.dist-info/WHEEL @@ -0,0 +1,5 @@ +Wheel-Version: 1.0 +Generator: bdist_wheel (0.38.4) +Root-Is-Purelib: true +Tag: py3-none-any + diff --git a/venv/lib/python3.12/site-packages/mpmath-1.3.0.dist-info/top_level.txt b/venv/lib/python3.12/site-packages/mpmath-1.3.0.dist-info/top_level.txt new file mode 100644 index 0000000000000000000000000000000000000000..dda7c273a8dd1c6adffa9d2d9901e0ce6876f4ac --- /dev/null +++ b/venv/lib/python3.12/site-packages/mpmath-1.3.0.dist-info/top_level.txt @@ -0,0 +1 @@ +mpmath diff --git a/venv/lib/python3.12/site-packages/mpmath/__init__.py b/venv/lib/python3.12/site-packages/mpmath/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..46a7c6f7c0875548f264612b604a9e1574b00a84 --- /dev/null +++ b/venv/lib/python3.12/site-packages/mpmath/__init__.py @@ -0,0 +1,468 @@ +__version__ = '1.3.0' + +from .usertools import monitor, timing + +from .ctx_fp import FPContext +from .ctx_mp import MPContext +from .ctx_iv import MPIntervalContext + +fp = FPContext() +mp = MPContext() +iv = MPIntervalContext() + +fp._mp = mp +mp._mp = mp +iv._mp = mp +mp._fp = fp +fp._fp = fp +mp._iv = iv +fp._iv = iv +iv._iv = iv + +# XXX: extremely bad pickle hack +from . import ctx_mp as _ctx_mp +_ctx_mp._mpf_module.mpf = mp.mpf +_ctx_mp._mpf_module.mpc = mp.mpc + +make_mpf = mp.make_mpf +make_mpc = mp.make_mpc + +extraprec = mp.extraprec +extradps = mp.extradps +workprec = mp.workprec +workdps = mp.workdps +autoprec = mp.autoprec +maxcalls = mp.maxcalls +memoize = mp.memoize + +mag = mp.mag + +bernfrac = mp.bernfrac + +qfrom = mp.qfrom +mfrom = mp.mfrom +kfrom = mp.kfrom +taufrom = mp.taufrom +qbarfrom = mp.qbarfrom +ellipfun = mp.ellipfun +jtheta = mp.jtheta +kleinj = mp.kleinj +eta = mp.eta + +qp = mp.qp +qhyper = mp.qhyper +qgamma = mp.qgamma +qfac = mp.qfac + +nint_distance = mp.nint_distance + +plot = mp.plot +cplot = mp.cplot +splot = mp.splot + +odefun = mp.odefun + +jacobian = mp.jacobian +findroot = mp.findroot +multiplicity = mp.multiplicity + +isinf = mp.isinf +isnan = mp.isnan +isnormal = mp.isnormal +isint = mp.isint +isfinite = mp.isfinite +almosteq = mp.almosteq +nan = mp.nan +rand = mp.rand + +absmin = mp.absmin +absmax = mp.absmax + +fraction = mp.fraction + +linspace = mp.linspace +arange = mp.arange + +mpmathify = convert = mp.convert +mpc = mp.mpc + +mpi = iv._mpi + +nstr = mp.nstr +nprint = mp.nprint +chop = mp.chop + +fneg = mp.fneg +fadd = mp.fadd +fsub = mp.fsub +fmul = mp.fmul +fdiv = mp.fdiv +fprod = mp.fprod + +quad = mp.quad +quadgl = mp.quadgl +quadts = mp.quadts +quadosc = mp.quadosc +quadsubdiv = mp.quadsubdiv + +invertlaplace = mp.invertlaplace +invlaptalbot = mp.invlaptalbot +invlapstehfest = mp.invlapstehfest +invlapdehoog = mp.invlapdehoog + +pslq = mp.pslq +identify = mp.identify +findpoly = mp.findpoly + +richardson = mp.richardson +shanks = mp.shanks +levin = mp.levin +cohen_alt = mp.cohen_alt +nsum = mp.nsum +nprod = mp.nprod +difference = mp.difference +diff = mp.diff +diffs = mp.diffs +diffs_prod = mp.diffs_prod +diffs_exp = mp.diffs_exp +diffun = mp.diffun +differint = mp.differint +taylor = mp.taylor +pade = mp.pade +polyval = mp.polyval +polyroots = mp.polyroots +fourier = mp.fourier +fourierval = mp.fourierval +sumem = mp.sumem +sumap = mp.sumap +chebyfit = mp.chebyfit +limit = mp.limit + +matrix = mp.matrix +eye = mp.eye +diag = mp.diag +zeros = mp.zeros +ones = mp.ones +hilbert = mp.hilbert +randmatrix = mp.randmatrix +swap_row = mp.swap_row +extend = mp.extend +norm = mp.norm +mnorm = mp.mnorm + +lu_solve = mp.lu_solve +lu = mp.lu +qr = mp.qr +unitvector = mp.unitvector +inverse = mp.inverse +residual = mp.residual +qr_solve = mp.qr_solve +cholesky = mp.cholesky +cholesky_solve = mp.cholesky_solve +det = mp.det +cond = mp.cond +hessenberg = mp.hessenberg +schur = mp.schur +eig = mp.eig +eig_sort = mp.eig_sort +eigsy = mp.eigsy +eighe = mp.eighe +eigh = mp.eigh +svd_r = mp.svd_r +svd_c = mp.svd_c +svd = mp.svd +gauss_quadrature = mp.gauss_quadrature + +expm = mp.expm +sqrtm = mp.sqrtm +powm = mp.powm +logm = mp.logm +sinm = mp.sinm +cosm = mp.cosm + +mpf = mp.mpf +j = mp.j +exp = mp.exp +expj = mp.expj +expjpi = mp.expjpi +ln = mp.ln +im = mp.im +re = mp.re +inf = mp.inf +ninf = mp.ninf +sign = mp.sign + +eps = mp.eps +pi = mp.pi +ln2 = mp.ln2 +ln10 = mp.ln10 +phi = mp.phi +e = mp.e +euler = mp.euler +catalan = mp.catalan +khinchin = mp.khinchin +glaisher = mp.glaisher +apery = mp.apery +degree = mp.degree +twinprime = mp.twinprime +mertens = mp.mertens + +ldexp = mp.ldexp +frexp = mp.frexp + +fsum = mp.fsum +fdot = mp.fdot + +sqrt = mp.sqrt +cbrt = mp.cbrt +exp = mp.exp +ln = mp.ln +log = mp.log +log10 = mp.log10 +power = mp.power +cos = mp.cos +sin = mp.sin +tan = mp.tan +cosh = mp.cosh +sinh = mp.sinh +tanh = mp.tanh +acos = mp.acos +asin = mp.asin +atan = mp.atan +asinh = mp.asinh +acosh = mp.acosh +atanh = mp.atanh +sec = mp.sec +csc = mp.csc +cot = mp.cot +sech = mp.sech +csch = mp.csch +coth = mp.coth +asec = mp.asec +acsc = mp.acsc +acot = mp.acot +asech = mp.asech +acsch = mp.acsch +acoth = mp.acoth +cospi = mp.cospi +sinpi = mp.sinpi +sinc = mp.sinc +sincpi = mp.sincpi +cos_sin = mp.cos_sin +cospi_sinpi = mp.cospi_sinpi +fabs = mp.fabs +re = mp.re +im = mp.im +conj = mp.conj +floor = mp.floor +ceil = mp.ceil +nint = mp.nint +frac = mp.frac +root = mp.root +nthroot = mp.nthroot +hypot = mp.hypot +fmod = mp.fmod +ldexp = mp.ldexp +frexp = mp.frexp +sign = mp.sign +arg = mp.arg +phase = mp.phase +polar = mp.polar +rect = mp.rect +degrees = mp.degrees +radians = mp.radians +atan2 = mp.atan2 +fib = mp.fib +fibonacci = mp.fibonacci +lambertw = mp.lambertw +zeta = mp.zeta +altzeta = mp.altzeta +gamma = mp.gamma +rgamma = mp.rgamma +factorial = mp.factorial +fac = mp.fac +fac2 = mp.fac2 +beta = mp.beta +betainc = mp.betainc +psi = mp.psi +#psi0 = mp.psi0 +#psi1 = mp.psi1 +#psi2 = mp.psi2 +#psi3 = mp.psi3 +polygamma = mp.polygamma +digamma = mp.digamma +#trigamma = mp.trigamma +#tetragamma = mp.tetragamma +#pentagamma = mp.pentagamma +harmonic = mp.harmonic +bernoulli = mp.bernoulli +bernfrac = mp.bernfrac +stieltjes = mp.stieltjes +hurwitz = mp.hurwitz +dirichlet = mp.dirichlet +bernpoly = mp.bernpoly +eulerpoly = mp.eulerpoly +eulernum = mp.eulernum +polylog = mp.polylog +clsin = mp.clsin +clcos = mp.clcos +gammainc = mp.gammainc +gammaprod = mp.gammaprod +binomial = mp.binomial +rf = mp.rf +ff = mp.ff +hyper = mp.hyper +hyp0f1 = mp.hyp0f1 +hyp1f1 = mp.hyp1f1 +hyp1f2 = mp.hyp1f2 +hyp2f1 = mp.hyp2f1 +hyp2f2 = mp.hyp2f2 +hyp2f0 = mp.hyp2f0 +hyp2f3 = mp.hyp2f3 +hyp3f2 = mp.hyp3f2 +hyperu = mp.hyperu +hypercomb = mp.hypercomb +meijerg = mp.meijerg +appellf1 = mp.appellf1 +appellf2 = mp.appellf2 +appellf3 = mp.appellf3 +appellf4 = mp.appellf4 +hyper2d = mp.hyper2d +bihyper = mp.bihyper +erf = mp.erf +erfc = mp.erfc +erfi = mp.erfi +erfinv = mp.erfinv +npdf = mp.npdf +ncdf = mp.ncdf +expint = mp.expint +e1 = mp.e1 +ei = mp.ei +li = mp.li +ci = mp.ci +si = mp.si +chi = mp.chi +shi = mp.shi +fresnels = mp.fresnels +fresnelc = mp.fresnelc +airyai = mp.airyai +airybi = mp.airybi +airyaizero = mp.airyaizero +airybizero = mp.airybizero +scorergi = mp.scorergi +scorerhi = mp.scorerhi +ellipk = mp.ellipk +ellipe = mp.ellipe +ellipf = mp.ellipf +ellippi = mp.ellippi +elliprc = mp.elliprc +elliprj = mp.elliprj +elliprf = mp.elliprf +elliprd = mp.elliprd +elliprg = mp.elliprg +agm = mp.agm +jacobi = mp.jacobi +chebyt = mp.chebyt +chebyu = mp.chebyu +legendre = mp.legendre +legenp = mp.legenp +legenq = mp.legenq +hermite = mp.hermite +pcfd = mp.pcfd +pcfu = mp.pcfu +pcfv = mp.pcfv +pcfw = mp.pcfw +gegenbauer = mp.gegenbauer +laguerre = mp.laguerre +spherharm = mp.spherharm +besselj = mp.besselj +j0 = mp.j0 +j1 = mp.j1 +besseli = mp.besseli +bessely = mp.bessely +besselk = mp.besselk +besseljzero = mp.besseljzero +besselyzero = mp.besselyzero +hankel1 = mp.hankel1 +hankel2 = mp.hankel2 +struveh = mp.struveh +struvel = mp.struvel +angerj = mp.angerj +webere = mp.webere +lommels1 = mp.lommels1 +lommels2 = mp.lommels2 +whitm = mp.whitm +whitw = mp.whitw +ber = mp.ber +bei = mp.bei +ker = mp.ker +kei = mp.kei +coulombc = mp.coulombc +coulombf = mp.coulombf +coulombg = mp.coulombg +barnesg = mp.barnesg +superfac = mp.superfac +hyperfac = mp.hyperfac +loggamma = mp.loggamma +siegeltheta = mp.siegeltheta +siegelz = mp.siegelz +grampoint = mp.grampoint +zetazero = mp.zetazero +riemannr = mp.riemannr +primepi = mp.primepi +primepi2 = mp.primepi2 +primezeta = mp.primezeta +bell = mp.bell +polyexp = mp.polyexp +expm1 = mp.expm1 +log1p = mp.log1p +powm1 = mp.powm1 +unitroots = mp.unitroots +cyclotomic = mp.cyclotomic +mangoldt = mp.mangoldt +secondzeta = mp.secondzeta +nzeros = mp.nzeros +backlunds = mp.backlunds +lerchphi = mp.lerchphi +stirling1 = mp.stirling1 +stirling2 = mp.stirling2 +squarew = mp.squarew +trianglew = mp.trianglew +sawtoothw = mp.sawtoothw +unit_triangle = mp.unit_triangle +sigmoid = mp.sigmoid + +# be careful when changing this name, don't use test*! +def runtests(): + """ + Run all mpmath tests and print output. + """ + import os.path + from inspect import getsourcefile + from .tests import runtests as tests + testdir = os.path.dirname(os.path.abspath(getsourcefile(tests))) + importdir = os.path.abspath(testdir + '/../..') + tests.testit(importdir, testdir) + +def doctests(filter=[]): + import sys + from timeit import default_timer as clock + for i, arg in enumerate(sys.argv): + if '__init__.py' in arg: + filter = [sn for sn in sys.argv[i+1:] if not sn.startswith("-")] + break + import doctest + globs = globals().copy() + for obj in globs: #sorted(globs.keys()): + if filter: + if not sum([pat in obj for pat in filter]): + continue + sys.stdout.write(str(obj) + " ") + sys.stdout.flush() + t1 = clock() + doctest.run_docstring_examples(globs[obj], {}, verbose=("-v" in sys.argv)) + t2 = clock() + print(round(t2-t1, 3)) + +if __name__ == '__main__': + doctests() diff --git a/venv/lib/python3.12/site-packages/mpmath/__pycache__/__init__.cpython-312.pyc b/venv/lib/python3.12/site-packages/mpmath/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..ee01081ee1f5e87563373f619dfd30aa98342e18 Binary files /dev/null and b/venv/lib/python3.12/site-packages/mpmath/__pycache__/__init__.cpython-312.pyc differ diff --git a/venv/lib/python3.12/site-packages/mpmath/__pycache__/ctx_base.cpython-312.pyc b/venv/lib/python3.12/site-packages/mpmath/__pycache__/ctx_base.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..6fd2ff57c71e9b3d00c12551d3c1f12ddca61fd8 Binary files /dev/null and b/venv/lib/python3.12/site-packages/mpmath/__pycache__/ctx_base.cpython-312.pyc differ diff --git a/venv/lib/python3.12/site-packages/mpmath/__pycache__/ctx_fp.cpython-312.pyc 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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 diff --git a/venv/lib/python3.12/site-packages/mpmath/calculus/calculus.py b/venv/lib/python3.12/site-packages/mpmath/calculus/calculus.py new file mode 100644 index 0000000000000000000000000000000000000000..24256f121d6c07e5ce954f2a5f5024f156f64016 --- /dev/null +++ b/venv/lib/python3.12/site-packages/mpmath/calculus/calculus.py @@ -0,0 +1,6 @@ +class CalculusMethods(object): + pass + +def defun(f): + setattr(CalculusMethods, f.__name__, f) + return f diff --git a/venv/lib/python3.12/site-packages/mpmath/calculus/differentiation.py b/venv/lib/python3.12/site-packages/mpmath/calculus/differentiation.py new file mode 100644 index 0000000000000000000000000000000000000000..f8186bebd4476476eded9e4b2f8dc3eb23ef5ff9 --- /dev/null +++ b/venv/lib/python3.12/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 diff --git a/venv/lib/python3.12/site-packages/mpmath/calculus/extrapolation.py b/venv/lib/python3.12/site-packages/mpmath/calculus/extrapolation.py new file mode 100644 index 0000000000000000000000000000000000000000..7df0fea3c62c9b71ee24d3f39fd9b7fd3318ed23 --- /dev/null +++ b/venv/lib/python3.12/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>> 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) diff --git a/venv/lib/python3.12/site-packages/mpmath/calculus/inverselaplace.py b/venv/lib/python3.12/site-packages/mpmath/calculus/inverselaplace.py new file mode 100644 index 0000000000000000000000000000000000000000..d2206b05c1601ee781b09dcbedf3c0fcd89cfa59 --- /dev/null +++ b/venv/lib/python3.12/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 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() diff --git a/venv/lib/python3.12/site-packages/mpmath/calculus/odes.py b/venv/lib/python3.12/site-packages/mpmath/calculus/odes.py new file mode 100644 index 0000000000000000000000000000000000000000..ac6bd3cb056c8841e6cf77ba4d08a0b1abb2f2c9 --- /dev/null +++ b/venv/lib/python3.12/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() diff --git a/venv/lib/python3.12/site-packages/mpmath/calculus/optimization.py b/venv/lib/python3.12/site-packages/mpmath/calculus/optimization.py new file mode 100644 index 0000000000000000000000000000000000000000..69724f78336ee24856366db48917a47108b1b416 --- /dev/null +++ b/venv/lib/python3.12/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() diff --git a/venv/lib/python3.12/site-packages/mpmath/calculus/polynomials.py b/venv/lib/python3.12/site-packages/mpmath/calculus/polynomials.py new file mode 100644 index 0000000000000000000000000000000000000000..ba75c1e88cbc5d40aa590a786c0af5229f193103 --- /dev/null +++ b/venv/lib/python3.12/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] diff --git a/venv/lib/python3.12/site-packages/mpmath/calculus/quadrature.py b/venv/lib/python3.12/site-packages/mpmath/calculus/quadrature.py new file mode 100644 index 0000000000000000000000000000000000000000..0545b3ad3e6f70a44f4ec31a0c3bcfb4ffde422b --- /dev/null +++ b/venv/lib/python3.12/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() diff --git a/venv/lib/python3.12/site-packages/mpmath/ctx_base.py b/venv/lib/python3.12/site-packages/mpmath/ctx_base.py new file mode 100644 index 0000000000000000000000000000000000000000..1946f8daf4dbe165b3943be09af361812828aab1 --- /dev/null +++ b/venv/lib/python3.12/site-packages/mpmath/ctx_base.py @@ -0,0 +1,494 @@ +from operator import gt, lt + +from .libmp.backend import xrange + +from .functions.functions import SpecialFunctions +from .functions.rszeta import RSCache +from .calculus.quadrature import QuadratureMethods +from .calculus.inverselaplace import LaplaceTransformInversionMethods +from .calculus.calculus import CalculusMethods +from .calculus.optimization import OptimizationMethods +from .calculus.odes import ODEMethods +from .matrices.matrices import MatrixMethods +from .matrices.calculus import MatrixCalculusMethods +from .matrices.linalg import LinearAlgebraMethods +from .matrices.eigen import Eigen +from .identification import IdentificationMethods +from .visualization import VisualizationMethods + +from . import libmp + +class Context(object): + pass + +class StandardBaseContext(Context, + SpecialFunctions, + RSCache, + QuadratureMethods, + LaplaceTransformInversionMethods, + CalculusMethods, + MatrixMethods, + MatrixCalculusMethods, + LinearAlgebraMethods, + Eigen, + IdentificationMethods, + OptimizationMethods, + ODEMethods, + VisualizationMethods): + + NoConvergence = libmp.NoConvergence + ComplexResult = libmp.ComplexResult + + def __init__(ctx): + ctx._aliases = {} + # Call those that need preinitialization (e.g. for wrappers) + SpecialFunctions.__init__(ctx) + RSCache.__init__(ctx) + QuadratureMethods.__init__(ctx) + LaplaceTransformInversionMethods.__init__(ctx) + CalculusMethods.__init__(ctx) + MatrixMethods.__init__(ctx) + + def _init_aliases(ctx): + for alias, value in ctx._aliases.items(): + try: + setattr(ctx, alias, getattr(ctx, value)) + except AttributeError: + pass + + _fixed_precision = False + + # XXX + verbose = False + + def warn(ctx, msg): + print("Warning:", msg) + + def bad_domain(ctx, msg): + raise ValueError(msg) + + def _re(ctx, x): + if hasattr(x, "real"): + return x.real + return x + + def _im(ctx, x): + if hasattr(x, "imag"): + return x.imag + return ctx.zero + + def _as_points(ctx, x): + return x + + def fneg(ctx, x, **kwargs): + return -ctx.convert(x) + + def fadd(ctx, x, y, **kwargs): + return ctx.convert(x)+ctx.convert(y) + + def fsub(ctx, x, y, **kwargs): + return ctx.convert(x)-ctx.convert(y) + + def fmul(ctx, x, y, **kwargs): + return ctx.convert(x)*ctx.convert(y) + + def fdiv(ctx, x, y, **kwargs): + return ctx.convert(x)/ctx.convert(y) + + def fsum(ctx, args, absolute=False, squared=False): + if absolute: + if squared: + return sum((abs(x)**2 for x in args), ctx.zero) + return sum((abs(x) for x in args), ctx.zero) + if squared: + return sum((x**2 for x in args), ctx.zero) + return sum(args, ctx.zero) + + def fdot(ctx, xs, ys=None, conjugate=False): + if ys is not None: + xs = zip(xs, ys) + if conjugate: + cf = ctx.conj + return sum((x*cf(y) for (x,y) in xs), ctx.zero) + else: + return sum((x*y for (x,y) in xs), ctx.zero) + + def fprod(ctx, args): + prod = ctx.one + for arg in args: + prod *= arg + return prod + + def nprint(ctx, x, n=6, **kwargs): + """ + Equivalent to ``print(nstr(x, n))``. + """ + print(ctx.nstr(x, n, **kwargs)) + + def chop(ctx, x, tol=None): + """ + Chops off small real or imaginary parts, or converts + numbers close to zero to exact zeros. The input can be a + single number or an iterable:: + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = False + >>> chop(5+1e-10j, tol=1e-9) + mpf('5.0') + >>> nprint(chop([1.0, 1e-20, 3+1e-18j, -4, 2])) + [1.0, 0.0, 3.0, -4.0, 2.0] + + The tolerance defaults to ``100*eps``. + """ + if tol is None: + tol = 100*ctx.eps + try: + x = ctx.convert(x) + absx = abs(x) + if abs(x) < tol: + return ctx.zero + if ctx._is_complex_type(x): + #part_tol = min(tol, absx*tol) + part_tol = max(tol, absx*tol) + if abs(x.imag) < part_tol: + return x.real + if abs(x.real) < part_tol: + return ctx.mpc(0, x.imag) + except TypeError: + if isinstance(x, ctx.matrix): + return x.apply(lambda a: ctx.chop(a, tol)) + if hasattr(x, "__iter__"): + return [ctx.chop(a, tol) for a in x] + return x + + def almosteq(ctx, s, t, rel_eps=None, abs_eps=None): + r""" + Determine whether the difference between `s` and `t` is smaller + than a given epsilon, either relatively or absolutely. + + Both a maximum relative difference and a maximum difference + ('epsilons') may be specified. The absolute difference is + defined as `|s-t|` and the relative difference is defined + as `|s-t|/\max(|s|, |t|)`. + + If only one epsilon is given, both are set to the same value. + If none is given, both epsilons are set to `2^{-p+m}` where + `p` is the current working precision and `m` is a small + integer. The default setting typically allows :func:`~mpmath.almosteq` + to be used to check for mathematical equality + in the presence of small rounding errors. + + **Examples** + + >>> from mpmath import * + >>> mp.dps = 15 + >>> almosteq(3.141592653589793, 3.141592653589790) + True + >>> almosteq(3.141592653589793, 3.141592653589700) + False + >>> almosteq(3.141592653589793, 3.141592653589700, 1e-10) + True + >>> almosteq(1e-20, 2e-20) + True + >>> almosteq(1e-20, 2e-20, rel_eps=0, abs_eps=0) + False + + """ + t = ctx.convert(t) + if abs_eps is None and rel_eps is None: + rel_eps = abs_eps = ctx.ldexp(1, -ctx.prec+4) + if abs_eps is None: + abs_eps = rel_eps + elif rel_eps is None: + rel_eps = abs_eps + diff = abs(s-t) + if diff <= abs_eps: + return True + abss = abs(s) + abst = abs(t) + if abss < abst: + err = diff/abst + else: + err = diff/abss + return err <= rel_eps + + def arange(ctx, *args): + r""" + This is a generalized version of Python's :func:`~mpmath.range` function + that accepts fractional endpoints and step sizes and + returns a list of ``mpf`` instances. Like :func:`~mpmath.range`, + :func:`~mpmath.arange` can be called with 1, 2 or 3 arguments: + + ``arange(b)`` + `[0, 1, 2, \ldots, x]` + ``arange(a, b)`` + `[a, a+1, a+2, \ldots, x]` + ``arange(a, b, h)`` + `[a, a+h, a+h, \ldots, x]` + + where `b-1 \le x < b` (in the third case, `b-h \le x < b`). + + Like Python's :func:`~mpmath.range`, the endpoint is not included. To + produce ranges where the endpoint is included, :func:`~mpmath.linspace` + is more convenient. + + **Examples** + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = False + >>> arange(4) + [mpf('0.0'), mpf('1.0'), mpf('2.0'), mpf('3.0')] + >>> arange(1, 2, 0.25) + [mpf('1.0'), mpf('1.25'), mpf('1.5'), mpf('1.75')] + >>> arange(1, -1, -0.75) + [mpf('1.0'), mpf('0.25'), mpf('-0.5')] + + """ + if not len(args) <= 3: + raise TypeError('arange expected at most 3 arguments, got %i' + % len(args)) + if not len(args) >= 1: + raise TypeError('arange expected at least 1 argument, got %i' + % len(args)) + # set default + a = 0 + dt = 1 + # interpret arguments + if len(args) == 1: + b = args[0] + elif len(args) >= 2: + a = args[0] + b = args[1] + if len(args) == 3: + dt = args[2] + a, b, dt = ctx.mpf(a), ctx.mpf(b), ctx.mpf(dt) + assert a + dt != a, 'dt is too small and would cause an infinite loop' + # adapt code for sign of dt + if a > b: + if dt > 0: + return [] + op = gt + else: + if dt < 0: + return [] + op = lt + # create list + result = [] + i = 0 + t = a + while 1: + t = a + dt*i + i += 1 + if op(t, b): + result.append(t) + else: + break + return result + + def linspace(ctx, *args, **kwargs): + """ + ``linspace(a, b, n)`` returns a list of `n` evenly spaced + samples from `a` to `b`. The syntax ``linspace(mpi(a,b), n)`` + is also valid. + + This function is often more convenient than :func:`~mpmath.arange` + for partitioning an interval into subintervals, since + the endpoint is included:: + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = False + >>> linspace(1, 4, 4) + [mpf('1.0'), mpf('2.0'), mpf('3.0'), mpf('4.0')] + + You may also provide the keyword argument ``endpoint=False``:: + + >>> linspace(1, 4, 4, endpoint=False) + [mpf('1.0'), mpf('1.75'), mpf('2.5'), mpf('3.25')] + + """ + if len(args) == 3: + a = ctx.mpf(args[0]) + b = ctx.mpf(args[1]) + n = int(args[2]) + elif len(args) == 2: + assert hasattr(args[0], '_mpi_') + a = args[0].a + b = args[0].b + n = int(args[1]) + else: + raise TypeError('linspace expected 2 or 3 arguments, got %i' \ + % len(args)) + if n < 1: + raise ValueError('n must be greater than 0') + if not 'endpoint' in kwargs or kwargs['endpoint']: + if n == 1: + return [ctx.mpf(a)] + step = (b - a) / ctx.mpf(n - 1) + y = [i*step + a for i in xrange(n)] + y[-1] = b + else: + step = (b - a) / ctx.mpf(n) + y = [i*step + a for i in xrange(n)] + return y + + def cos_sin(ctx, z, **kwargs): + return ctx.cos(z, **kwargs), ctx.sin(z, **kwargs) + + def cospi_sinpi(ctx, z, **kwargs): + return ctx.cospi(z, **kwargs), ctx.sinpi(z, **kwargs) + + def _default_hyper_maxprec(ctx, p): + return int(1000 * p**0.25 + 4*p) + + _gcd = staticmethod(libmp.gcd) + list_primes = staticmethod(libmp.list_primes) + isprime = staticmethod(libmp.isprime) + bernfrac = staticmethod(libmp.bernfrac) + moebius = staticmethod(libmp.moebius) + _ifac = staticmethod(libmp.ifac) + _eulernum = staticmethod(libmp.eulernum) + _stirling1 = staticmethod(libmp.stirling1) + _stirling2 = staticmethod(libmp.stirling2) + + def sum_accurately(ctx, terms, check_step=1): + prec = ctx.prec + try: + extraprec = 10 + while 1: + ctx.prec = prec + extraprec + 5 + max_mag = ctx.ninf + s = ctx.zero + k = 0 + for term in terms(): + s += term + if (not k % check_step) and term: + term_mag = ctx.mag(term) + max_mag = max(max_mag, term_mag) + sum_mag = ctx.mag(s) + if sum_mag - term_mag > ctx.prec: + break + k += 1 + cancellation = max_mag - sum_mag + if cancellation != cancellation: + break + if cancellation < extraprec or ctx._fixed_precision: + break + extraprec += min(ctx.prec, cancellation) + return s + finally: + ctx.prec = prec + + def mul_accurately(ctx, factors, check_step=1): + prec = ctx.prec + try: + extraprec = 10 + while 1: + ctx.prec = prec + extraprec + 5 + max_mag = ctx.ninf + one = ctx.one + s = one + k = 0 + for factor in factors(): + s *= factor + term = factor - one + if (not k % check_step): + term_mag = ctx.mag(term) + max_mag = max(max_mag, term_mag) + sum_mag = ctx.mag(s-one) + #if sum_mag - term_mag > ctx.prec: + # break + if -term_mag > ctx.prec: + break + k += 1 + cancellation = max_mag - sum_mag + if cancellation != cancellation: + break + if cancellation < extraprec or ctx._fixed_precision: + break + extraprec += min(ctx.prec, cancellation) + return s + finally: + ctx.prec = prec + + def power(ctx, x, y): + r"""Converts `x` and `y` to mpmath numbers and evaluates + `x^y = \exp(y \log(x))`:: + + >>> from mpmath import * + >>> mp.dps = 30; mp.pretty = True + >>> power(2, 0.5) + 1.41421356237309504880168872421 + + This shows the leading few digits of a large Mersenne prime + (performing the exact calculation ``2**43112609-1`` and + displaying the result in Python would be very slow):: + + >>> power(2, 43112609)-1 + 3.16470269330255923143453723949e+12978188 + """ + return ctx.convert(x) ** ctx.convert(y) + + def _zeta_int(ctx, n): + return ctx.zeta(n) + + def maxcalls(ctx, f, N): + """ + Return a wrapped copy of *f* that raises ``NoConvergence`` when *f* + has been called more than *N* times:: + + >>> from mpmath import * + >>> mp.dps = 15 + >>> f = maxcalls(sin, 10) + >>> print(sum(f(n) for n in range(10))) + 1.95520948210738 + >>> f(10) # doctest: +IGNORE_EXCEPTION_DETAIL + Traceback (most recent call last): + ... + NoConvergence: maxcalls: function evaluated 10 times + + """ + counter = [0] + def f_maxcalls_wrapped(*args, **kwargs): + counter[0] += 1 + if counter[0] > N: + raise ctx.NoConvergence("maxcalls: function evaluated %i times" % N) + return f(*args, **kwargs) + return f_maxcalls_wrapped + + def memoize(ctx, f): + """ + Return a wrapped copy of *f* that caches computed values, i.e. + a memoized copy of *f*. Values are only reused if the cached precision + is equal to or higher than the working precision:: + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = True + >>> f = memoize(maxcalls(sin, 1)) + >>> f(2) + 0.909297426825682 + >>> f(2) + 0.909297426825682 + >>> mp.dps = 25 + >>> f(2) # doctest: +IGNORE_EXCEPTION_DETAIL + Traceback (most recent call last): + ... + NoConvergence: maxcalls: function evaluated 1 times + + """ + f_cache = {} + def f_cached(*args, **kwargs): + if kwargs: + key = args, tuple(kwargs.items()) + else: + key = args + prec = ctx.prec + if key in f_cache: + cprec, cvalue = f_cache[key] + if cprec >= prec: + return +cvalue + value = f(*args, **kwargs) + f_cache[key] = (prec, value) + return value + f_cached.__name__ = f.__name__ + f_cached.__doc__ = f.__doc__ + return f_cached diff --git a/venv/lib/python3.12/site-packages/mpmath/ctx_fp.py b/venv/lib/python3.12/site-packages/mpmath/ctx_fp.py new file mode 100644 index 0000000000000000000000000000000000000000..aa72ea5b03fde4da66b0d8fbf8ffa4012e3f6178 --- /dev/null +++ b/venv/lib/python3.12/site-packages/mpmath/ctx_fp.py @@ -0,0 +1,253 @@ +from .ctx_base import StandardBaseContext + +import math +import cmath +from . import math2 + +from . import function_docs + +from .libmp import mpf_bernoulli, to_float, int_types +from . import libmp + +class FPContext(StandardBaseContext): + """ + Context for fast low-precision arithmetic (53-bit precision, giving at most + about 15-digit accuracy), using Python's builtin float and complex. + """ + + def __init__(ctx): + StandardBaseContext.__init__(ctx) + + # Override SpecialFunctions implementation + ctx.loggamma = math2.loggamma + ctx._bernoulli_cache = {} + ctx.pretty = False + + ctx._init_aliases() + + _mpq = lambda cls, x: float(x[0])/x[1] + + NoConvergence = libmp.NoConvergence + + def _get_prec(ctx): return 53 + def _set_prec(ctx, p): return + def _get_dps(ctx): return 15 + def _set_dps(ctx, p): return + + _fixed_precision = True + + prec = property(_get_prec, _set_prec) + dps = property(_get_dps, _set_dps) + + zero = 0.0 + one = 1.0 + eps = math2.EPS + inf = math2.INF + ninf = math2.NINF + nan = math2.NAN + j = 1j + + # Called by SpecialFunctions.__init__() + @classmethod + def _wrap_specfun(cls, name, f, wrap): + if wrap: + def f_wrapped(ctx, *args, **kwargs): + convert = ctx.convert + args = [convert(a) for a in args] + return f(ctx, *args, **kwargs) + else: + f_wrapped = f + f_wrapped.__doc__ = function_docs.__dict__.get(name, f.__doc__) + setattr(cls, name, f_wrapped) + + def bernoulli(ctx, n): + cache = ctx._bernoulli_cache + if n in cache: + return cache[n] + cache[n] = to_float(mpf_bernoulli(n, 53, 'n'), strict=True) + return cache[n] + + pi = math2.pi + e = math2.e + euler = math2.euler + sqrt2 = 1.4142135623730950488 + sqrt5 = 2.2360679774997896964 + phi = 1.6180339887498948482 + ln2 = 0.69314718055994530942 + ln10 = 2.302585092994045684 + euler = 0.57721566490153286061 + catalan = 0.91596559417721901505 + khinchin = 2.6854520010653064453 + apery = 1.2020569031595942854 + glaisher = 1.2824271291006226369 + + absmin = absmax = abs + + def is_special(ctx, x): + return x - x != 0.0 + + def isnan(ctx, x): + return x != x + + def isinf(ctx, x): + return abs(x) == math2.INF + + def isnormal(ctx, x): + if x: + return x - x == 0.0 + return False + + def isnpint(ctx, x): + if type(x) is complex: + if x.imag: + return False + x = x.real + return x <= 0.0 and round(x) == x + + mpf = float + mpc = complex + + def convert(ctx, x): + try: + return float(x) + except: + return complex(x) + + power = staticmethod(math2.pow) + sqrt = staticmethod(math2.sqrt) + exp = staticmethod(math2.exp) + ln = log = staticmethod(math2.log) + cos = staticmethod(math2.cos) + sin = staticmethod(math2.sin) + tan = staticmethod(math2.tan) + cos_sin = staticmethod(math2.cos_sin) + acos = staticmethod(math2.acos) + asin = staticmethod(math2.asin) + atan = staticmethod(math2.atan) + cosh = staticmethod(math2.cosh) + sinh = staticmethod(math2.sinh) + tanh = staticmethod(math2.tanh) + gamma = staticmethod(math2.gamma) + rgamma = staticmethod(math2.rgamma) + fac = factorial = staticmethod(math2.factorial) + floor = staticmethod(math2.floor) + ceil = staticmethod(math2.ceil) + cospi = staticmethod(math2.cospi) + sinpi = staticmethod(math2.sinpi) + cbrt = staticmethod(math2.cbrt) + _nthroot = staticmethod(math2.nthroot) + _ei = staticmethod(math2.ei) + _e1 = staticmethod(math2.e1) + _zeta = _zeta_int = staticmethod(math2.zeta) + + # XXX: math2 + def arg(ctx, z): + z = complex(z) + return math.atan2(z.imag, z.real) + + def expj(ctx, x): + return ctx.exp(ctx.j*x) + + def expjpi(ctx, x): + return ctx.exp(ctx.j*ctx.pi*x) + + ldexp = math.ldexp + frexp = math.frexp + + def mag(ctx, z): + if z: + return ctx.frexp(abs(z))[1] + return ctx.ninf + + def isint(ctx, z): + if hasattr(z, "imag"): # float/int don't have .real/.imag in py2.5 + if z.imag: + return False + z = z.real + try: + return z == int(z) + except: + return False + + def nint_distance(ctx, z): + if hasattr(z, "imag"): # float/int don't have .real/.imag in py2.5 + n = round(z.real) + else: + n = round(z) + if n == z: + return n, ctx.ninf + return n, ctx.mag(abs(z-n)) + + def _convert_param(ctx, z): + if type(z) is tuple: + p, q = z + return ctx.mpf(p) / q, 'R' + if hasattr(z, "imag"): # float/int don't have .real/.imag in py2.5 + intz = int(z.real) + else: + intz = int(z) + if z == intz: + return intz, 'Z' + return z, 'R' + + def _is_real_type(ctx, z): + return isinstance(z, float) or isinstance(z, int_types) + + def _is_complex_type(ctx, z): + return isinstance(z, complex) + + def hypsum(ctx, p, q, types, coeffs, z, maxterms=6000, **kwargs): + coeffs = list(coeffs) + num = range(p) + den = range(p,p+q) + tol = ctx.eps + s = t = 1.0 + k = 0 + while 1: + for i in num: t *= (coeffs[i]+k) + for i in den: t /= (coeffs[i]+k) + k += 1; t /= k; t *= z; s += t + if abs(t) < tol: + return s + if k > maxterms: + raise ctx.NoConvergence + + def atan2(ctx, x, y): + return math.atan2(x, y) + + def psi(ctx, m, z): + m = int(m) + if m == 0: + return ctx.digamma(z) + return (-1)**(m+1) * ctx.fac(m) * ctx.zeta(m+1, z) + + digamma = staticmethod(math2.digamma) + + def harmonic(ctx, x): + x = ctx.convert(x) + if x == 0 or x == 1: + return x + return ctx.digamma(x+1) + ctx.euler + + nstr = str + + def to_fixed(ctx, x, prec): + return int(math.ldexp(x, prec)) + + def rand(ctx): + import random + return random.random() + + _erf = staticmethod(math2.erf) + _erfc = staticmethod(math2.erfc) + + def sum_accurately(ctx, terms, check_step=1): + s = ctx.zero + k = 0 + for term in terms(): + s += term + if (not k % check_step) and term: + if abs(term) <= 1e-18*abs(s): + break + k += 1 + return s diff --git a/venv/lib/python3.12/site-packages/mpmath/ctx_iv.py b/venv/lib/python3.12/site-packages/mpmath/ctx_iv.py new file mode 100644 index 0000000000000000000000000000000000000000..c038e00a5677e318d222b63c22d225e3045e1c2b --- /dev/null +++ b/venv/lib/python3.12/site-packages/mpmath/ctx_iv.py @@ -0,0 +1,551 @@ +import operator + +from . import libmp + +from .libmp.backend import basestring + +from .libmp import ( + int_types, MPZ_ONE, + prec_to_dps, dps_to_prec, repr_dps, + round_floor, round_ceiling, + fzero, finf, fninf, fnan, + mpf_le, mpf_neg, + from_int, from_float, from_str, from_rational, + mpi_mid, mpi_delta, mpi_str, + mpi_abs, mpi_pos, mpi_neg, mpi_add, mpi_sub, + mpi_mul, mpi_div, mpi_pow_int, mpi_pow, + mpi_from_str, + mpci_pos, mpci_neg, mpci_add, mpci_sub, mpci_mul, mpci_div, mpci_pow, + mpci_abs, mpci_pow, mpci_exp, mpci_log, + ComplexResult, + mpf_hash, mpc_hash) +from .matrices.matrices import _matrix + +mpi_zero = (fzero, fzero) + +from .ctx_base import StandardBaseContext + +new = object.__new__ + +def convert_mpf_(x, prec, rounding): + if hasattr(x, "_mpf_"): return x._mpf_ + if isinstance(x, int_types): return from_int(x, prec, rounding) + if isinstance(x, float): return from_float(x, prec, rounding) + if isinstance(x, basestring): return from_str(x, prec, rounding) + raise NotImplementedError + + +class ivmpf(object): + """ + Interval arithmetic class. Precision is controlled by iv.prec. + """ + + def __new__(cls, x=0): + return cls.ctx.convert(x) + + def cast(self, cls, f_convert): + a, b = self._mpi_ + if a == b: + return cls(f_convert(a)) + raise ValueError + + def __int__(self): + return self.cast(int, libmp.to_int) + + def __float__(self): + return self.cast(float, libmp.to_float) + + def __complex__(self): + return self.cast(complex, libmp.to_float) + + def __hash__(self): + a, b = self._mpi_ + if a == b: + return mpf_hash(a) + else: + return hash(self._mpi_) + + @property + def real(self): return self + + @property + def imag(self): return self.ctx.zero + + def conjugate(self): return self + + @property + def a(self): + a, b = self._mpi_ + return self.ctx.make_mpf((a, a)) + + @property + def b(self): + a, b = self._mpi_ + return self.ctx.make_mpf((b, b)) + + @property + def mid(self): + ctx = self.ctx + v = mpi_mid(self._mpi_, ctx.prec) + return ctx.make_mpf((v, v)) + + @property + def delta(self): + ctx = self.ctx + v = mpi_delta(self._mpi_, ctx.prec) + return ctx.make_mpf((v,v)) + + @property + def _mpci_(self): + return self._mpi_, mpi_zero + + def _compare(*args): + raise TypeError("no ordering relation is defined for intervals") + + __gt__ = _compare + __le__ = _compare + __gt__ = _compare + __ge__ = _compare + + def __contains__(self, t): + t = self.ctx.mpf(t) + return (self.a <= t.a) and (t.b <= self.b) + + def __str__(self): + return mpi_str(self._mpi_, self.ctx.prec) + + def __repr__(self): + if self.ctx.pretty: + return str(self) + a, b = self._mpi_ + n = repr_dps(self.ctx.prec) + a = libmp.to_str(a, n) + b = libmp.to_str(b, n) + return "mpi(%r, %r)" % (a, b) + + def _compare(s, t, cmpfun): + if not hasattr(t, "_mpi_"): + try: + t = s.ctx.convert(t) + except: + return NotImplemented + return cmpfun(s._mpi_, t._mpi_) + + def __eq__(s, t): return s._compare(t, libmp.mpi_eq) + def __ne__(s, t): return s._compare(t, libmp.mpi_ne) + def __lt__(s, t): return s._compare(t, libmp.mpi_lt) + def __le__(s, t): return s._compare(t, libmp.mpi_le) + def __gt__(s, t): return s._compare(t, libmp.mpi_gt) + def __ge__(s, t): return s._compare(t, libmp.mpi_ge) + + def __abs__(self): + return self.ctx.make_mpf(mpi_abs(self._mpi_, self.ctx.prec)) + def __pos__(self): + return self.ctx.make_mpf(mpi_pos(self._mpi_, self.ctx.prec)) + def __neg__(self): + return self.ctx.make_mpf(mpi_neg(self._mpi_, self.ctx.prec)) + + def ae(s, t, rel_eps=None, abs_eps=None): + return s.ctx.almosteq(s, t, rel_eps, abs_eps) + +class ivmpc(object): + + def __new__(cls, re=0, im=0): + re = cls.ctx.convert(re) + im = cls.ctx.convert(im) + y = new(cls) + y._mpci_ = re._mpi_, im._mpi_ + return y + + def __hash__(self): + (a, b), (c,d) = self._mpci_ + if a == b and c == d: + return mpc_hash((a, c)) + else: + return hash(self._mpci_) + + def __repr__(s): + if s.ctx.pretty: + return str(s) + return "iv.mpc(%s, %s)" % (repr(s.real), repr(s.imag)) + + def __str__(s): + return "(%s + %s*j)" % (str(s.real), str(s.imag)) + + @property + def a(self): + (a, b), (c,d) = self._mpci_ + return self.ctx.make_mpf((a, a)) + + @property + def b(self): + (a, b), (c,d) = self._mpci_ + return self.ctx.make_mpf((b, b)) + + @property + def c(self): + (a, b), (c,d) = self._mpci_ + return self.ctx.make_mpf((c, c)) + + @property + def d(self): + (a, b), (c,d) = self._mpci_ + return self.ctx.make_mpf((d, d)) + + @property + def real(s): + return s.ctx.make_mpf(s._mpci_[0]) + + @property + def imag(s): + return s.ctx.make_mpf(s._mpci_[1]) + + def conjugate(s): + a, b = s._mpci_ + return s.ctx.make_mpc((a, mpf_neg(b))) + + def overlap(s, t): + t = s.ctx.convert(t) + real_overlap = (s.a <= t.a <= s.b) or (s.a <= t.b <= s.b) or (t.a <= s.a <= t.b) or (t.a <= s.b <= t.b) + imag_overlap = (s.c <= t.c <= s.d) or (s.c <= t.d <= s.d) or (t.c <= s.c <= t.d) or (t.c <= s.d <= t.d) + return real_overlap and imag_overlap + + def __contains__(s, t): + t = s.ctx.convert(t) + return t.real in s.real and t.imag in s.imag + + def _compare(s, t, ne=False): + if not isinstance(t, s.ctx._types): + try: + t = s.ctx.convert(t) + except: + return NotImplemented + if hasattr(t, '_mpi_'): + tval = t._mpi_, mpi_zero + elif hasattr(t, '_mpci_'): + tval = t._mpci_ + if ne: + return s._mpci_ != tval + return s._mpci_ == tval + + def __eq__(s, t): return s._compare(t) + def __ne__(s, t): return s._compare(t, True) + + def __lt__(s, t): raise TypeError("complex intervals cannot be ordered") + __le__ = __gt__ = __ge__ = __lt__ + + def __neg__(s): return s.ctx.make_mpc(mpci_neg(s._mpci_, s.ctx.prec)) + def __pos__(s): return s.ctx.make_mpc(mpci_pos(s._mpci_, s.ctx.prec)) + def __abs__(s): return s.ctx.make_mpf(mpci_abs(s._mpci_, s.ctx.prec)) + + def ae(s, t, rel_eps=None, abs_eps=None): + return s.ctx.almosteq(s, t, rel_eps, abs_eps) + +def _binary_op(f_real, f_complex): + def g_complex(ctx, sval, tval): + return ctx.make_mpc(f_complex(sval, tval, ctx.prec)) + def g_real(ctx, sval, tval): + try: + return ctx.make_mpf(f_real(sval, tval, ctx.prec)) + except ComplexResult: + sval = (sval, mpi_zero) + tval = (tval, mpi_zero) + return g_complex(ctx, sval, tval) + def lop_real(s, t): + if isinstance(t, _matrix): return NotImplemented + ctx = s.ctx + if not isinstance(t, ctx._types): t = ctx.convert(t) + if hasattr(t, "_mpi_"): return g_real(ctx, s._mpi_, t._mpi_) + if hasattr(t, "_mpci_"): return g_complex(ctx, (s._mpi_, mpi_zero), t._mpci_) + return NotImplemented + def rop_real(s, t): + ctx = s.ctx + if not isinstance(t, ctx._types): t = ctx.convert(t) + if hasattr(t, "_mpi_"): return g_real(ctx, t._mpi_, s._mpi_) + if hasattr(t, "_mpci_"): return g_complex(ctx, t._mpci_, (s._mpi_, mpi_zero)) + return NotImplemented + def lop_complex(s, t): + if isinstance(t, _matrix): return NotImplemented + ctx = s.ctx + if not isinstance(t, s.ctx._types): + try: + t = s.ctx.convert(t) + except (ValueError, TypeError): + return NotImplemented + return g_complex(ctx, s._mpci_, t._mpci_) + def rop_complex(s, t): + ctx = s.ctx + if not isinstance(t, s.ctx._types): + t = s.ctx.convert(t) + return g_complex(ctx, t._mpci_, s._mpci_) + return lop_real, rop_real, lop_complex, rop_complex + +ivmpf.__add__, ivmpf.__radd__, ivmpc.__add__, ivmpc.__radd__ = _binary_op(mpi_add, mpci_add) +ivmpf.__sub__, ivmpf.__rsub__, ivmpc.__sub__, ivmpc.__rsub__ = _binary_op(mpi_sub, mpci_sub) +ivmpf.__mul__, ivmpf.__rmul__, ivmpc.__mul__, ivmpc.__rmul__ = _binary_op(mpi_mul, mpci_mul) +ivmpf.__div__, ivmpf.__rdiv__, ivmpc.__div__, ivmpc.__rdiv__ = _binary_op(mpi_div, mpci_div) +ivmpf.__pow__, ivmpf.__rpow__, ivmpc.__pow__, ivmpc.__rpow__ = _binary_op(mpi_pow, mpci_pow) + +ivmpf.__truediv__ = ivmpf.__div__; ivmpf.__rtruediv__ = ivmpf.__rdiv__ +ivmpc.__truediv__ = ivmpc.__div__; ivmpc.__rtruediv__ = ivmpc.__rdiv__ + +class ivmpf_constant(ivmpf): + def __new__(cls, f): + self = new(cls) + self._f = f + return self + def _get_mpi_(self): + prec = self.ctx._prec[0] + a = self._f(prec, round_floor) + b = self._f(prec, round_ceiling) + return a, b + _mpi_ = property(_get_mpi_) + +class MPIntervalContext(StandardBaseContext): + + def __init__(ctx): + ctx.mpf = type('ivmpf', (ivmpf,), {}) + ctx.mpc = type('ivmpc', (ivmpc,), {}) + ctx._types = (ctx.mpf, ctx.mpc) + ctx._constant = type('ivmpf_constant', (ivmpf_constant,), {}) + ctx._prec = [53] + ctx._set_prec(53) + ctx._constant._ctxdata = ctx.mpf._ctxdata = ctx.mpc._ctxdata = [ctx.mpf, new, ctx._prec] + ctx._constant.ctx = ctx.mpf.ctx = ctx.mpc.ctx = ctx + ctx.pretty = False + StandardBaseContext.__init__(ctx) + ctx._init_builtins() + + def _mpi(ctx, a, b=None): + if b is None: + return ctx.mpf(a) + return ctx.mpf((a,b)) + + def _init_builtins(ctx): + ctx.one = ctx.mpf(1) + ctx.zero = ctx.mpf(0) + ctx.inf = ctx.mpf('inf') + ctx.ninf = -ctx.inf + ctx.nan = ctx.mpf('nan') + ctx.j = ctx.mpc(0,1) + ctx.exp = ctx._wrap_mpi_function(libmp.mpi_exp, libmp.mpci_exp) + ctx.sqrt = ctx._wrap_mpi_function(libmp.mpi_sqrt) + ctx.ln = ctx._wrap_mpi_function(libmp.mpi_log, libmp.mpci_log) + ctx.cos = ctx._wrap_mpi_function(libmp.mpi_cos, libmp.mpci_cos) + ctx.sin = ctx._wrap_mpi_function(libmp.mpi_sin, libmp.mpci_sin) + ctx.tan = ctx._wrap_mpi_function(libmp.mpi_tan) + ctx.gamma = ctx._wrap_mpi_function(libmp.mpi_gamma, libmp.mpci_gamma) + ctx.loggamma = ctx._wrap_mpi_function(libmp.mpi_loggamma, libmp.mpci_loggamma) + ctx.rgamma = ctx._wrap_mpi_function(libmp.mpi_rgamma, libmp.mpci_rgamma) + ctx.factorial = ctx._wrap_mpi_function(libmp.mpi_factorial, libmp.mpci_factorial) + ctx.fac = ctx.factorial + + ctx.eps = ctx._constant(lambda prec, rnd: (0, MPZ_ONE, 1-prec, 1)) + ctx.pi = ctx._constant(libmp.mpf_pi) + ctx.e = ctx._constant(libmp.mpf_e) + ctx.ln2 = ctx._constant(libmp.mpf_ln2) + ctx.ln10 = ctx._constant(libmp.mpf_ln10) + ctx.phi = ctx._constant(libmp.mpf_phi) + ctx.euler = ctx._constant(libmp.mpf_euler) + ctx.catalan = ctx._constant(libmp.mpf_catalan) + ctx.glaisher = ctx._constant(libmp.mpf_glaisher) + ctx.khinchin = ctx._constant(libmp.mpf_khinchin) + ctx.twinprime = ctx._constant(libmp.mpf_twinprime) + + def _wrap_mpi_function(ctx, f_real, f_complex=None): + def g(x, **kwargs): + if kwargs: + prec = kwargs.get('prec', ctx._prec[0]) + else: + prec = ctx._prec[0] + x = ctx.convert(x) + if hasattr(x, "_mpi_"): + return ctx.make_mpf(f_real(x._mpi_, prec)) + if hasattr(x, "_mpci_"): + return ctx.make_mpc(f_complex(x._mpci_, prec)) + raise ValueError + return g + + @classmethod + def _wrap_specfun(cls, name, f, wrap): + if wrap: + def f_wrapped(ctx, *args, **kwargs): + convert = ctx.convert + args = [convert(a) for a in args] + prec = ctx.prec + try: + ctx.prec += 10 + retval = f(ctx, *args, **kwargs) + finally: + ctx.prec = prec + return +retval + else: + f_wrapped = f + setattr(cls, name, f_wrapped) + + def _set_prec(ctx, n): + ctx._prec[0] = max(1, int(n)) + ctx._dps = prec_to_dps(n) + + def _set_dps(ctx, n): + ctx._prec[0] = dps_to_prec(n) + ctx._dps = max(1, int(n)) + + prec = property(lambda ctx: ctx._prec[0], _set_prec) + dps = property(lambda ctx: ctx._dps, _set_dps) + + def make_mpf(ctx, v): + a = new(ctx.mpf) + a._mpi_ = v + return a + + def make_mpc(ctx, v): + a = new(ctx.mpc) + a._mpci_ = v + return a + + def _mpq(ctx, pq): + p, q = pq + a = libmp.from_rational(p, q, ctx.prec, round_floor) + b = libmp.from_rational(p, q, ctx.prec, round_ceiling) + return ctx.make_mpf((a, b)) + + def convert(ctx, x): + if isinstance(x, (ctx.mpf, ctx.mpc)): + return x + if isinstance(x, ctx._constant): + return +x + if isinstance(x, complex) or hasattr(x, "_mpc_"): + re = ctx.convert(x.real) + im = ctx.convert(x.imag) + return ctx.mpc(re,im) + if isinstance(x, basestring): + v = mpi_from_str(x, ctx.prec) + return ctx.make_mpf(v) + if hasattr(x, "_mpi_"): + a, b = x._mpi_ + else: + try: + a, b = x + except (TypeError, ValueError): + a = b = x + if hasattr(a, "_mpi_"): + a = a._mpi_[0] + else: + a = convert_mpf_(a, ctx.prec, round_floor) + if hasattr(b, "_mpi_"): + b = b._mpi_[1] + else: + b = convert_mpf_(b, ctx.prec, round_ceiling) + if a == fnan or b == fnan: + a = fninf + b = finf + assert mpf_le(a, b), "endpoints must be properly ordered" + return ctx.make_mpf((a, b)) + + def nstr(ctx, x, n=5, **kwargs): + x = ctx.convert(x) + if hasattr(x, "_mpi_"): + return libmp.mpi_to_str(x._mpi_, n, **kwargs) + if hasattr(x, "_mpci_"): + re = libmp.mpi_to_str(x._mpci_[0], n, **kwargs) + im = libmp.mpi_to_str(x._mpci_[1], n, **kwargs) + return "(%s + %s*j)" % (re, im) + + def mag(ctx, x): + x = ctx.convert(x) + if isinstance(x, ctx.mpc): + return max(ctx.mag(x.real), ctx.mag(x.imag)) + 1 + a, b = libmp.mpi_abs(x._mpi_) + sign, man, exp, bc = b + if man: + return exp+bc + if b == fzero: + return ctx.ninf + if b == fnan: + return ctx.nan + return ctx.inf + + def isnan(ctx, x): + return False + + def isinf(ctx, x): + return x == ctx.inf + + def isint(ctx, x): + x = ctx.convert(x) + a, b = x._mpi_ + if a == b: + sign, man, exp, bc = a + if man: + return exp >= 0 + return a == fzero + return None + + def ldexp(ctx, x, n): + a, b = ctx.convert(x)._mpi_ + a = libmp.mpf_shift(a, n) + b = libmp.mpf_shift(b, n) + return ctx.make_mpf((a,b)) + + def absmin(ctx, x): + return abs(ctx.convert(x)).a + + def absmax(ctx, x): + return abs(ctx.convert(x)).b + + def atan2(ctx, y, x): + y = ctx.convert(y)._mpi_ + x = ctx.convert(x)._mpi_ + return ctx.make_mpf(libmp.mpi_atan2(y,x,ctx.prec)) + + def _convert_param(ctx, x): + if isinstance(x, libmp.int_types): + return x, 'Z' + if isinstance(x, tuple): + p, q = x + return (ctx.mpf(p) / ctx.mpf(q), 'R') + x = ctx.convert(x) + if isinstance(x, ctx.mpf): + return x, 'R' + if isinstance(x, ctx.mpc): + return x, 'C' + raise ValueError + + def _is_real_type(ctx, z): + return isinstance(z, ctx.mpf) or isinstance(z, int_types) + + def _is_complex_type(ctx, z): + return isinstance(z, ctx.mpc) + + def hypsum(ctx, p, q, types, coeffs, z, maxterms=6000, **kwargs): + coeffs = list(coeffs) + num = range(p) + den = range(p,p+q) + #tol = ctx.eps + s = t = ctx.one + k = 0 + while 1: + for i in num: t *= (coeffs[i]+k) + for i in den: t /= (coeffs[i]+k) + k += 1; t /= k; t *= z; s += t + if t == 0: + return s + #if abs(t) < tol: + # return s + if k > maxterms: + raise ctx.NoConvergence + + +# Register with "numbers" ABC +# We do not subclass, hence we do not use the @abstractmethod checks. While +# this is less invasive it may turn out that we do not actually support +# parts of the expected interfaces. See +# http://docs.python.org/2/library/numbers.html for list of abstract +# methods. +try: + import numbers + numbers.Complex.register(ivmpc) + numbers.Real.register(ivmpf) +except ImportError: + pass diff --git a/venv/lib/python3.12/site-packages/mpmath/ctx_mp.py b/venv/lib/python3.12/site-packages/mpmath/ctx_mp.py new file mode 100644 index 0000000000000000000000000000000000000000..93594dd44474a415c74e4b0beb83bd7012666c9d --- /dev/null +++ b/venv/lib/python3.12/site-packages/mpmath/ctx_mp.py @@ -0,0 +1,1339 @@ +""" +This module defines the mpf, mpc classes, and standard functions for +operating with them. +""" +__docformat__ = 'plaintext' + +import functools + +import re + +from .ctx_base import StandardBaseContext + +from .libmp.backend import basestring, BACKEND + +from . import libmp + +from .libmp import (MPZ, MPZ_ZERO, MPZ_ONE, int_types, repr_dps, + round_floor, round_ceiling, dps_to_prec, round_nearest, prec_to_dps, + ComplexResult, to_pickable, from_pickable, normalize, + from_int, from_float, from_str, to_int, to_float, to_str, + from_rational, from_man_exp, + fone, fzero, finf, fninf, fnan, + mpf_abs, mpf_pos, mpf_neg, mpf_add, mpf_sub, mpf_mul, mpf_mul_int, + mpf_div, mpf_rdiv_int, mpf_pow_int, mpf_mod, + mpf_eq, mpf_cmp, mpf_lt, mpf_gt, mpf_le, mpf_ge, + mpf_hash, mpf_rand, + mpf_sum, + bitcount, to_fixed, + mpc_to_str, + mpc_to_complex, mpc_hash, mpc_pos, mpc_is_nonzero, mpc_neg, mpc_conjugate, + mpc_abs, mpc_add, mpc_add_mpf, mpc_sub, mpc_sub_mpf, mpc_mul, mpc_mul_mpf, + mpc_mul_int, mpc_div, mpc_div_mpf, mpc_pow, mpc_pow_mpf, mpc_pow_int, + mpc_mpf_div, + mpf_pow, + mpf_pi, mpf_degree, mpf_e, mpf_phi, mpf_ln2, mpf_ln10, + mpf_euler, mpf_catalan, mpf_apery, mpf_khinchin, + mpf_glaisher, mpf_twinprime, mpf_mertens, + int_types) + +from . import function_docs +from . import rational + +new = object.__new__ + +get_complex = re.compile(r'^\(?(?P[\+\-]?\d*(\.\d*)?(e[\+\-]?\d+)?)??' + r'(?P[\+\-]?\d*(\.\d*)?(e[\+\-]?\d+)?j)?\)?$') + +if BACKEND == 'sage': + from sage.libs.mpmath.ext_main import Context as BaseMPContext + # pickle hack + import sage.libs.mpmath.ext_main as _mpf_module +else: + from .ctx_mp_python import PythonMPContext as BaseMPContext + from . import ctx_mp_python as _mpf_module + +from .ctx_mp_python import _mpf, _mpc, mpnumeric + +class MPContext(BaseMPContext, StandardBaseContext): + """ + Context for multiprecision arithmetic with a global precision. + """ + + def __init__(ctx): + BaseMPContext.__init__(ctx) + ctx.trap_complex = False + ctx.pretty = False + ctx.types = [ctx.mpf, ctx.mpc, ctx.constant] + ctx._mpq = rational.mpq + ctx.default() + StandardBaseContext.__init__(ctx) + + ctx.mpq = rational.mpq + ctx.init_builtins() + + ctx.hyp_summators = {} + + ctx._init_aliases() + + # XXX: automate + try: + ctx.bernoulli.im_func.func_doc = function_docs.bernoulli + ctx.primepi.im_func.func_doc = function_docs.primepi + ctx.psi.im_func.func_doc = function_docs.psi + ctx.atan2.im_func.func_doc = function_docs.atan2 + except AttributeError: + # python 3 + ctx.bernoulli.__func__.func_doc = function_docs.bernoulli + ctx.primepi.__func__.func_doc = function_docs.primepi + ctx.psi.__func__.func_doc = function_docs.psi + ctx.atan2.__func__.func_doc = function_docs.atan2 + + ctx.digamma.func_doc = function_docs.digamma + ctx.cospi.func_doc = function_docs.cospi + ctx.sinpi.func_doc = function_docs.sinpi + + def init_builtins(ctx): + + mpf = ctx.mpf + mpc = ctx.mpc + + # Exact constants + ctx.one = ctx.make_mpf(fone) + ctx.zero = ctx.make_mpf(fzero) + ctx.j = ctx.make_mpc((fzero,fone)) + ctx.inf = ctx.make_mpf(finf) + ctx.ninf = ctx.make_mpf(fninf) + ctx.nan = ctx.make_mpf(fnan) + + eps = ctx.constant(lambda prec, rnd: (0, MPZ_ONE, 1-prec, 1), + "epsilon of working precision", "eps") + ctx.eps = eps + + # Approximate constants + ctx.pi = ctx.constant(mpf_pi, "pi", "pi") + ctx.ln2 = ctx.constant(mpf_ln2, "ln(2)", "ln2") + ctx.ln10 = ctx.constant(mpf_ln10, "ln(10)", "ln10") + ctx.phi = ctx.constant(mpf_phi, "Golden ratio phi", "phi") + ctx.e = ctx.constant(mpf_e, "e = exp(1)", "e") + ctx.euler = ctx.constant(mpf_euler, "Euler's constant", "euler") + ctx.catalan = ctx.constant(mpf_catalan, "Catalan's constant", "catalan") + ctx.khinchin = ctx.constant(mpf_khinchin, "Khinchin's constant", "khinchin") + ctx.glaisher = ctx.constant(mpf_glaisher, "Glaisher's constant", "glaisher") + ctx.apery = ctx.constant(mpf_apery, "Apery's constant", "apery") + ctx.degree = ctx.constant(mpf_degree, "1 deg = pi / 180", "degree") + ctx.twinprime = ctx.constant(mpf_twinprime, "Twin prime constant", "twinprime") + ctx.mertens = ctx.constant(mpf_mertens, "Mertens' constant", "mertens") + + # Standard functions + ctx.sqrt = ctx._wrap_libmp_function(libmp.mpf_sqrt, libmp.mpc_sqrt) + ctx.cbrt = ctx._wrap_libmp_function(libmp.mpf_cbrt, libmp.mpc_cbrt) + ctx.ln = ctx._wrap_libmp_function(libmp.mpf_log, libmp.mpc_log) + ctx.atan = ctx._wrap_libmp_function(libmp.mpf_atan, libmp.mpc_atan) + ctx.exp = ctx._wrap_libmp_function(libmp.mpf_exp, libmp.mpc_exp) + ctx.expj = ctx._wrap_libmp_function(libmp.mpf_expj, libmp.mpc_expj) + ctx.expjpi = ctx._wrap_libmp_function(libmp.mpf_expjpi, libmp.mpc_expjpi) + ctx.sin = ctx._wrap_libmp_function(libmp.mpf_sin, libmp.mpc_sin) + ctx.cos = ctx._wrap_libmp_function(libmp.mpf_cos, libmp.mpc_cos) + ctx.tan = ctx._wrap_libmp_function(libmp.mpf_tan, libmp.mpc_tan) + ctx.sinh = ctx._wrap_libmp_function(libmp.mpf_sinh, libmp.mpc_sinh) + ctx.cosh = ctx._wrap_libmp_function(libmp.mpf_cosh, libmp.mpc_cosh) + ctx.tanh = ctx._wrap_libmp_function(libmp.mpf_tanh, libmp.mpc_tanh) + ctx.asin = ctx._wrap_libmp_function(libmp.mpf_asin, libmp.mpc_asin) + ctx.acos = ctx._wrap_libmp_function(libmp.mpf_acos, libmp.mpc_acos) + ctx.atan = ctx._wrap_libmp_function(libmp.mpf_atan, libmp.mpc_atan) + ctx.asinh = ctx._wrap_libmp_function(libmp.mpf_asinh, libmp.mpc_asinh) + ctx.acosh = ctx._wrap_libmp_function(libmp.mpf_acosh, libmp.mpc_acosh) + ctx.atanh = ctx._wrap_libmp_function(libmp.mpf_atanh, libmp.mpc_atanh) + ctx.sinpi = ctx._wrap_libmp_function(libmp.mpf_sin_pi, libmp.mpc_sin_pi) + ctx.cospi = ctx._wrap_libmp_function(libmp.mpf_cos_pi, libmp.mpc_cos_pi) + ctx.floor = ctx._wrap_libmp_function(libmp.mpf_floor, libmp.mpc_floor) + ctx.ceil = ctx._wrap_libmp_function(libmp.mpf_ceil, libmp.mpc_ceil) + ctx.nint = ctx._wrap_libmp_function(libmp.mpf_nint, libmp.mpc_nint) + ctx.frac = ctx._wrap_libmp_function(libmp.mpf_frac, libmp.mpc_frac) + ctx.fib = ctx.fibonacci = ctx._wrap_libmp_function(libmp.mpf_fibonacci, libmp.mpc_fibonacci) + + ctx.gamma = ctx._wrap_libmp_function(libmp.mpf_gamma, libmp.mpc_gamma) + ctx.rgamma = ctx._wrap_libmp_function(libmp.mpf_rgamma, libmp.mpc_rgamma) + ctx.loggamma = ctx._wrap_libmp_function(libmp.mpf_loggamma, libmp.mpc_loggamma) + ctx.fac = ctx.factorial = ctx._wrap_libmp_function(libmp.mpf_factorial, libmp.mpc_factorial) + + ctx.digamma = ctx._wrap_libmp_function(libmp.mpf_psi0, libmp.mpc_psi0) + ctx.harmonic = ctx._wrap_libmp_function(libmp.mpf_harmonic, libmp.mpc_harmonic) + ctx.ei = ctx._wrap_libmp_function(libmp.mpf_ei, libmp.mpc_ei) + ctx.e1 = ctx._wrap_libmp_function(libmp.mpf_e1, libmp.mpc_e1) + ctx._ci = ctx._wrap_libmp_function(libmp.mpf_ci, libmp.mpc_ci) + ctx._si = ctx._wrap_libmp_function(libmp.mpf_si, libmp.mpc_si) + ctx.ellipk = ctx._wrap_libmp_function(libmp.mpf_ellipk, libmp.mpc_ellipk) + ctx._ellipe = ctx._wrap_libmp_function(libmp.mpf_ellipe, libmp.mpc_ellipe) + ctx.agm1 = ctx._wrap_libmp_function(libmp.mpf_agm1, libmp.mpc_agm1) + ctx._erf = ctx._wrap_libmp_function(libmp.mpf_erf, None) + ctx._erfc = ctx._wrap_libmp_function(libmp.mpf_erfc, None) + ctx._zeta = ctx._wrap_libmp_function(libmp.mpf_zeta, libmp.mpc_zeta) + ctx._altzeta = ctx._wrap_libmp_function(libmp.mpf_altzeta, libmp.mpc_altzeta) + + # Faster versions + ctx.sqrt = getattr(ctx, "_sage_sqrt", ctx.sqrt) + ctx.exp = getattr(ctx, "_sage_exp", ctx.exp) + ctx.ln = getattr(ctx, "_sage_ln", ctx.ln) + ctx.cos = getattr(ctx, "_sage_cos", ctx.cos) + ctx.sin = getattr(ctx, "_sage_sin", ctx.sin) + + def to_fixed(ctx, x, prec): + return x.to_fixed(prec) + + def hypot(ctx, x, y): + r""" + Computes the Euclidean norm of the vector `(x, y)`, equal + to `\sqrt{x^2 + y^2}`. Both `x` and `y` must be real.""" + x = ctx.convert(x) + y = ctx.convert(y) + return ctx.make_mpf(libmp.mpf_hypot(x._mpf_, y._mpf_, *ctx._prec_rounding)) + + def _gamma_upper_int(ctx, n, z): + n = int(ctx._re(n)) + if n == 0: + return ctx.e1(z) + if not hasattr(z, '_mpf_'): + raise NotImplementedError + prec, rounding = ctx._prec_rounding + real, imag = libmp.mpf_expint(n, z._mpf_, prec, rounding, gamma=True) + if imag is None: + return ctx.make_mpf(real) + else: + return ctx.make_mpc((real, imag)) + + def _expint_int(ctx, n, z): + n = int(n) + if n == 1: + return ctx.e1(z) + if not hasattr(z, '_mpf_'): + raise NotImplementedError + prec, rounding = ctx._prec_rounding + real, imag = libmp.mpf_expint(n, z._mpf_, prec, rounding) + if imag is None: + return ctx.make_mpf(real) + else: + return ctx.make_mpc((real, imag)) + + def _nthroot(ctx, x, n): + if hasattr(x, '_mpf_'): + try: + return ctx.make_mpf(libmp.mpf_nthroot(x._mpf_, n, *ctx._prec_rounding)) + except ComplexResult: + if ctx.trap_complex: + raise + x = (x._mpf_, libmp.fzero) + else: + x = x._mpc_ + return ctx.make_mpc(libmp.mpc_nthroot(x, n, *ctx._prec_rounding)) + + def _besselj(ctx, n, z): + prec, rounding = ctx._prec_rounding + if hasattr(z, '_mpf_'): + return ctx.make_mpf(libmp.mpf_besseljn(n, z._mpf_, prec, rounding)) + elif hasattr(z, '_mpc_'): + return ctx.make_mpc(libmp.mpc_besseljn(n, z._mpc_, prec, rounding)) + + def _agm(ctx, a, b=1): + prec, rounding = ctx._prec_rounding + if hasattr(a, '_mpf_') and hasattr(b, '_mpf_'): + try: + v = libmp.mpf_agm(a._mpf_, b._mpf_, prec, rounding) + return ctx.make_mpf(v) + except ComplexResult: + pass + if hasattr(a, '_mpf_'): a = (a._mpf_, libmp.fzero) + else: a = a._mpc_ + if hasattr(b, '_mpf_'): b = (b._mpf_, libmp.fzero) + else: b = b._mpc_ + return ctx.make_mpc(libmp.mpc_agm(a, b, prec, rounding)) + + def bernoulli(ctx, n): + return ctx.make_mpf(libmp.mpf_bernoulli(int(n), *ctx._prec_rounding)) + + def _zeta_int(ctx, n): + return ctx.make_mpf(libmp.mpf_zeta_int(int(n), *ctx._prec_rounding)) + + def atan2(ctx, y, x): + x = ctx.convert(x) + y = ctx.convert(y) + return ctx.make_mpf(libmp.mpf_atan2(y._mpf_, x._mpf_, *ctx._prec_rounding)) + + def psi(ctx, m, z): + z = ctx.convert(z) + m = int(m) + if ctx._is_real_type(z): + return ctx.make_mpf(libmp.mpf_psi(m, z._mpf_, *ctx._prec_rounding)) + else: + return ctx.make_mpc(libmp.mpc_psi(m, z._mpc_, *ctx._prec_rounding)) + + def cos_sin(ctx, x, **kwargs): + if type(x) not in ctx.types: + x = ctx.convert(x) + prec, rounding = ctx._parse_prec(kwargs) + if hasattr(x, '_mpf_'): + c, s = libmp.mpf_cos_sin(x._mpf_, prec, rounding) + return ctx.make_mpf(c), ctx.make_mpf(s) + elif hasattr(x, '_mpc_'): + c, s = libmp.mpc_cos_sin(x._mpc_, prec, rounding) + return ctx.make_mpc(c), ctx.make_mpc(s) + else: + return ctx.cos(x, **kwargs), ctx.sin(x, **kwargs) + + def cospi_sinpi(ctx, x, **kwargs): + if type(x) not in ctx.types: + x = ctx.convert(x) + prec, rounding = ctx._parse_prec(kwargs) + if hasattr(x, '_mpf_'): + c, s = libmp.mpf_cos_sin_pi(x._mpf_, prec, rounding) + return ctx.make_mpf(c), ctx.make_mpf(s) + elif hasattr(x, '_mpc_'): + c, s = libmp.mpc_cos_sin_pi(x._mpc_, prec, rounding) + return ctx.make_mpc(c), ctx.make_mpc(s) + else: + return ctx.cos(x, **kwargs), ctx.sin(x, **kwargs) + + def clone(ctx): + """ + Create a copy of the context, with the same working precision. + """ + a = ctx.__class__() + a.prec = ctx.prec + return a + + # Several helper methods + # TODO: add more of these, make consistent, write docstrings, ... + + def _is_real_type(ctx, x): + if hasattr(x, '_mpc_') or type(x) is complex: + return False + return True + + def _is_complex_type(ctx, x): + if hasattr(x, '_mpc_') or type(x) is complex: + return True + return False + + def isnan(ctx, x): + """ + Return *True* if *x* is a NaN (not-a-number), or for a complex + number, whether either the real or complex part is NaN; + otherwise return *False*:: + + >>> from mpmath import * + >>> isnan(3.14) + False + >>> isnan(nan) + True + >>> isnan(mpc(3.14,2.72)) + False + >>> isnan(mpc(3.14,nan)) + True + + """ + if hasattr(x, "_mpf_"): + return x._mpf_ == fnan + if hasattr(x, "_mpc_"): + return fnan in x._mpc_ + if isinstance(x, int_types) or isinstance(x, rational.mpq): + return False + x = ctx.convert(x) + if hasattr(x, '_mpf_') or hasattr(x, '_mpc_'): + return ctx.isnan(x) + raise TypeError("isnan() needs a number as input") + + def isfinite(ctx, x): + """ + Return *True* if *x* is a finite number, i.e. neither + an infinity or a NaN. + + >>> from mpmath import * + >>> isfinite(inf) + False + >>> isfinite(-inf) + False + >>> isfinite(3) + True + >>> isfinite(nan) + False + >>> isfinite(3+4j) + True + >>> isfinite(mpc(3,inf)) + False + >>> isfinite(mpc(nan,3)) + False + + """ + if ctx.isinf(x) or ctx.isnan(x): + return False + return True + + def isnpint(ctx, x): + """ + Determine if *x* is a nonpositive integer. + """ + if not x: + return True + if hasattr(x, '_mpf_'): + sign, man, exp, bc = x._mpf_ + return sign and exp >= 0 + if hasattr(x, '_mpc_'): + return not x.imag and ctx.isnpint(x.real) + if type(x) in int_types: + return x <= 0 + if isinstance(x, ctx.mpq): + p, q = x._mpq_ + if not p: + return True + return q == 1 and p <= 0 + return ctx.isnpint(ctx.convert(x)) + + def __str__(ctx): + lines = ["Mpmath settings:", + (" mp.prec = %s" % ctx.prec).ljust(30) + "[default: 53]", + (" mp.dps = %s" % ctx.dps).ljust(30) + "[default: 15]", + (" mp.trap_complex = %s" % ctx.trap_complex).ljust(30) + "[default: False]", + ] + return "\n".join(lines) + + @property + def _repr_digits(ctx): + return repr_dps(ctx._prec) + + @property + def _str_digits(ctx): + return ctx._dps + + def extraprec(ctx, n, normalize_output=False): + """ + The block + + with extraprec(n): + + + increases the precision n bits, executes , and then + restores the precision. + + extraprec(n)(f) returns a decorated version of the function f + that increases the working precision by n bits before execution, + and restores the parent precision afterwards. With + normalize_output=True, it rounds the return value to the parent + precision. + """ + return PrecisionManager(ctx, lambda p: p + n, None, normalize_output) + + def extradps(ctx, n, normalize_output=False): + """ + This function is analogous to extraprec (see documentation) + but changes the decimal precision instead of the number of bits. + """ + return PrecisionManager(ctx, None, lambda d: d + n, normalize_output) + + def workprec(ctx, n, normalize_output=False): + """ + The block + + with workprec(n): + + + sets the precision to n bits, executes , and then restores + the precision. + + workprec(n)(f) returns a decorated version of the function f + that sets the precision to n bits before execution, + and restores the precision afterwards. With normalize_output=True, + it rounds the return value to the parent precision. + """ + return PrecisionManager(ctx, lambda p: n, None, normalize_output) + + def workdps(ctx, n, normalize_output=False): + """ + This function is analogous to workprec (see documentation) + but changes the decimal precision instead of the number of bits. + """ + return PrecisionManager(ctx, None, lambda d: n, normalize_output) + + def autoprec(ctx, f, maxprec=None, catch=(), verbose=False): + r""" + Return a wrapped copy of *f* that repeatedly evaluates *f* + with increasing precision until the result converges to the + full precision used at the point of the call. + + This heuristically protects against rounding errors, at the cost of + roughly a 2x slowdown compared to manually setting the optimal + precision. This method can, however, easily be fooled if the results + from *f* depend "discontinuously" on the precision, for instance + if catastrophic cancellation can occur. Therefore, :func:`~mpmath.autoprec` + should be used judiciously. + + **Examples** + + Many functions are sensitive to perturbations of the input arguments. + If the arguments are decimal numbers, they may have to be converted + to binary at a much higher precision. If the amount of required + extra precision is unknown, :func:`~mpmath.autoprec` is convenient:: + + >>> from mpmath import * + >>> mp.dps = 15 + >>> mp.pretty = True + >>> besselj(5, 125 * 10**28) # Exact input + -8.03284785591801e-17 + >>> besselj(5, '1.25e30') # Bad + 7.12954868316652e-16 + >>> autoprec(besselj)(5, '1.25e30') # Good + -8.03284785591801e-17 + + The following fails to converge because `\sin(\pi) = 0` whereas all + finite-precision approximations of `\pi` give nonzero values:: + + >>> autoprec(sin)(pi) # doctest: +IGNORE_EXCEPTION_DETAIL + Traceback (most recent call last): + ... + NoConvergence: autoprec: prec increased to 2910 without convergence + + As the following example shows, :func:`~mpmath.autoprec` can protect against + cancellation, but is fooled by too severe cancellation:: + + >>> x = 1e-10 + >>> exp(x)-1; expm1(x); autoprec(lambda t: exp(t)-1)(x) + 1.00000008274037e-10 + 1.00000000005e-10 + 1.00000000005e-10 + >>> x = 1e-50 + >>> exp(x)-1; expm1(x); autoprec(lambda t: exp(t)-1)(x) + 0.0 + 1.0e-50 + 0.0 + + With *catch*, an exception or list of exceptions to intercept + may be specified. The raised exception is interpreted + as signaling insufficient precision. This permits, for example, + evaluating a function where a too low precision results in a + division by zero:: + + >>> f = lambda x: 1/(exp(x)-1) + >>> f(1e-30) + Traceback (most recent call last): + ... + ZeroDivisionError + >>> autoprec(f, catch=ZeroDivisionError)(1e-30) + 1.0e+30 + + + """ + def f_autoprec_wrapped(*args, **kwargs): + prec = ctx.prec + if maxprec is None: + maxprec2 = ctx._default_hyper_maxprec(prec) + else: + maxprec2 = maxprec + try: + ctx.prec = prec + 10 + try: + v1 = f(*args, **kwargs) + except catch: + v1 = ctx.nan + prec2 = prec + 20 + while 1: + ctx.prec = prec2 + try: + v2 = f(*args, **kwargs) + except catch: + v2 = ctx.nan + if v1 == v2: + break + err = ctx.mag(v2-v1) - ctx.mag(v2) + if err < (-prec): + break + if verbose: + print("autoprec: target=%s, prec=%s, accuracy=%s" \ + % (prec, prec2, -err)) + v1 = v2 + if prec2 >= maxprec2: + raise ctx.NoConvergence(\ + "autoprec: prec increased to %i without convergence"\ + % prec2) + prec2 += int(prec2*2) + prec2 = min(prec2, maxprec2) + finally: + ctx.prec = prec + return +v2 + return f_autoprec_wrapped + + def nstr(ctx, x, n=6, **kwargs): + """ + Convert an ``mpf`` or ``mpc`` to a decimal string literal with *n* + significant digits. The small default value for *n* is chosen to + make this function useful for printing collections of numbers + (lists, matrices, etc). + + If *x* is a list or tuple, :func:`~mpmath.nstr` is applied recursively + to each element. For unrecognized classes, :func:`~mpmath.nstr` + simply returns ``str(x)``. + + The companion function :func:`~mpmath.nprint` prints the result + instead of returning it. + + The keyword arguments *strip_zeros*, *min_fixed*, *max_fixed* + and *show_zero_exponent* are forwarded to :func:`~mpmath.libmp.to_str`. + + 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. + + >>> from mpmath import * + >>> nstr([+pi, ldexp(1,-500)]) + '[3.14159, 3.05494e-151]' + >>> nprint([+pi, ldexp(1,-500)]) + [3.14159, 3.05494e-151] + >>> nstr(mpf("5e-10"), 5) + '5.0e-10' + >>> nstr(mpf("5e-10"), 5, strip_zeros=False) + '5.0000e-10' + >>> nstr(mpf("5e-10"), 5, strip_zeros=False, min_fixed=-11) + '0.00000000050000' + >>> nstr(mpf(0), 5, show_zero_exponent=True) + '0.0e+0' + + """ + if isinstance(x, list): + return "[%s]" % (", ".join(ctx.nstr(c, n, **kwargs) for c in x)) + if isinstance(x, tuple): + return "(%s)" % (", ".join(ctx.nstr(c, n, **kwargs) for c in x)) + if hasattr(x, '_mpf_'): + return to_str(x._mpf_, n, **kwargs) + if hasattr(x, '_mpc_'): + return "(" + mpc_to_str(x._mpc_, n, **kwargs) + ")" + if isinstance(x, basestring): + return repr(x) + if isinstance(x, ctx.matrix): + return x.__nstr__(n, **kwargs) + return str(x) + + def _convert_fallback(ctx, x, strings): + if strings and isinstance(x, basestring): + if 'j' in x.lower(): + x = x.lower().replace(' ', '') + match = get_complex.match(x) + re = match.group('re') + if not re: + re = 0 + im = match.group('im').rstrip('j') + return ctx.mpc(ctx.convert(re), ctx.convert(im)) + if hasattr(x, "_mpi_"): + a, b = x._mpi_ + if a == b: + return ctx.make_mpf(a) + else: + raise ValueError("can only create mpf from zero-width interval") + raise TypeError("cannot create mpf from " + repr(x)) + + def mpmathify(ctx, *args, **kwargs): + return ctx.convert(*args, **kwargs) + + def _parse_prec(ctx, kwargs): + if kwargs: + if kwargs.get('exact'): + return 0, 'f' + prec, rounding = ctx._prec_rounding + if 'rounding' in kwargs: + rounding = kwargs['rounding'] + if 'prec' in kwargs: + prec = kwargs['prec'] + if prec == ctx.inf: + return 0, 'f' + else: + prec = int(prec) + elif 'dps' in kwargs: + dps = kwargs['dps'] + if dps == ctx.inf: + return 0, 'f' + prec = dps_to_prec(dps) + return prec, rounding + return ctx._prec_rounding + + _exact_overflow_msg = "the exact result does not fit in memory" + + _hypsum_msg = """hypsum() failed to converge to the requested %i bits of accuracy +using a working precision of %i bits. Try with a higher maxprec, +maxterms, or set zeroprec.""" + + def hypsum(ctx, p, q, flags, coeffs, z, accurate_small=True, **kwargs): + if hasattr(z, "_mpf_"): + key = p, q, flags, 'R' + v = z._mpf_ + elif hasattr(z, "_mpc_"): + key = p, q, flags, 'C' + v = z._mpc_ + if key not in ctx.hyp_summators: + ctx.hyp_summators[key] = libmp.make_hyp_summator(key)[1] + summator = ctx.hyp_summators[key] + prec = ctx.prec + maxprec = kwargs.get('maxprec', ctx._default_hyper_maxprec(prec)) + extraprec = 50 + epsshift = 25 + # Jumps in magnitude occur when parameters are close to negative + # integers. We must ensure that these terms are included in + # the sum and added accurately + magnitude_check = {} + max_total_jump = 0 + for i, c in enumerate(coeffs): + if flags[i] == 'Z': + if i >= p and c <= 0: + ok = False + for ii, cc in enumerate(coeffs[:p]): + # Note: c <= cc or c < cc, depending on convention + if flags[ii] == 'Z' and cc <= 0 and c <= cc: + ok = True + if not ok: + raise ZeroDivisionError("pole in hypergeometric series") + continue + n, d = ctx.nint_distance(c) + n = -int(n) + d = -d + if i >= p and n >= 0 and d > 4: + if n in magnitude_check: + magnitude_check[n] += d + else: + magnitude_check[n] = d + extraprec = max(extraprec, d - prec + 60) + max_total_jump += abs(d) + while 1: + if extraprec > maxprec: + raise ValueError(ctx._hypsum_msg % (prec, prec+extraprec)) + wp = prec + extraprec + if magnitude_check: + mag_dict = dict((n,None) for n in magnitude_check) + else: + mag_dict = {} + zv, have_complex, magnitude = summator(coeffs, v, prec, wp, \ + epsshift, mag_dict, **kwargs) + cancel = -magnitude + jumps_resolved = True + if extraprec < max_total_jump: + for n in mag_dict.values(): + if (n is None) or (n < prec): + jumps_resolved = False + break + accurate = (cancel < extraprec-25-5 or not accurate_small) + if jumps_resolved: + if accurate: + break + # zero? + zeroprec = kwargs.get('zeroprec') + if zeroprec is not None: + if cancel > zeroprec: + if have_complex: + return ctx.mpc(0) + else: + return ctx.zero + + # Some near-singularities were not included, so increase + # precision and repeat until they are + extraprec *= 2 + # Possible workaround for bad roundoff in fixed-point arithmetic + epsshift += 5 + extraprec += 5 + + if type(zv) is tuple: + if have_complex: + return ctx.make_mpc(zv) + else: + return ctx.make_mpf(zv) + else: + return zv + + def ldexp(ctx, x, n): + r""" + Computes `x 2^n` efficiently. No rounding is performed. + The argument `x` must be a real floating-point number (or + possible to convert into one) and `n` must be a Python ``int``. + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = False + >>> ldexp(1, 10) + mpf('1024.0') + >>> ldexp(1, -3) + mpf('0.125') + + """ + x = ctx.convert(x) + return ctx.make_mpf(libmp.mpf_shift(x._mpf_, n)) + + def frexp(ctx, x): + r""" + Given a real number `x`, returns `(y, n)` with `y \in [0.5, 1)`, + `n` a Python integer, and such that `x = y 2^n`. No rounding is + performed. + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = False + >>> frexp(7.5) + (mpf('0.9375'), 3) + + """ + x = ctx.convert(x) + y, n = libmp.mpf_frexp(x._mpf_) + return ctx.make_mpf(y), n + + def fneg(ctx, x, **kwargs): + """ + Negates the number *x*, giving a floating-point result, optionally + using a custom precision and rounding mode. + + See the documentation of :func:`~mpmath.fadd` for a detailed description + of how to specify precision and rounding. + + **Examples** + + An mpmath number is returned:: + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = False + >>> fneg(2.5) + mpf('-2.5') + >>> fneg(-5+2j) + mpc(real='5.0', imag='-2.0') + + Precise control over rounding is possible:: + + >>> x = fadd(2, 1e-100, exact=True) + >>> fneg(x) + mpf('-2.0') + >>> fneg(x, rounding='f') + mpf('-2.0000000000000004') + + Negating with and without roundoff:: + + >>> n = 200000000000000000000001 + >>> print(int(-mpf(n))) + -200000000000000016777216 + >>> print(int(fneg(n))) + -200000000000000016777216 + >>> print(int(fneg(n, prec=log(n,2)+1))) + -200000000000000000000001 + >>> print(int(fneg(n, dps=log(n,10)+1))) + -200000000000000000000001 + >>> print(int(fneg(n, prec=inf))) + -200000000000000000000001 + >>> print(int(fneg(n, dps=inf))) + -200000000000000000000001 + >>> print(int(fneg(n, exact=True))) + -200000000000000000000001 + + """ + prec, rounding = ctx._parse_prec(kwargs) + x = ctx.convert(x) + if hasattr(x, '_mpf_'): + return ctx.make_mpf(mpf_neg(x._mpf_, prec, rounding)) + if hasattr(x, '_mpc_'): + return ctx.make_mpc(mpc_neg(x._mpc_, prec, rounding)) + raise ValueError("Arguments need to be mpf or mpc compatible numbers") + + def fadd(ctx, x, y, **kwargs): + """ + Adds the numbers *x* and *y*, giving a floating-point result, + optionally using a custom precision and rounding mode. + + The default precision is the working precision of the context. + You can specify a custom precision in bits by passing the *prec* keyword + argument, or by providing an equivalent decimal precision with the *dps* + keyword argument. If the precision is set to ``+inf``, or if the flag + *exact=True* is passed, an exact addition with no rounding is performed. + + When the precision is finite, the optional *rounding* keyword argument + specifies the direction of rounding. Valid options are ``'n'`` for + nearest (default), ``'f'`` for floor, ``'c'`` for ceiling, ``'d'`` + for down, ``'u'`` for up. + + **Examples** + + Using :func:`~mpmath.fadd` with precision and rounding control:: + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = False + >>> fadd(2, 1e-20) + mpf('2.0') + >>> fadd(2, 1e-20, rounding='u') + mpf('2.0000000000000004') + >>> nprint(fadd(2, 1e-20, prec=100), 25) + 2.00000000000000000001 + >>> nprint(fadd(2, 1e-20, dps=15), 25) + 2.0 + >>> nprint(fadd(2, 1e-20, dps=25), 25) + 2.00000000000000000001 + >>> nprint(fadd(2, 1e-20, exact=True), 25) + 2.00000000000000000001 + + Exact addition avoids cancellation errors, enforcing familiar laws + of numbers such as `x+y-x = y`, which don't hold in floating-point + arithmetic with finite precision:: + + >>> x, y = mpf(2), mpf('1e-1000') + >>> print(x + y - x) + 0.0 + >>> print(fadd(x, y, prec=inf) - x) + 1.0e-1000 + >>> print(fadd(x, y, exact=True) - x) + 1.0e-1000 + + Exact addition can be inefficient and may be impossible to perform + with large magnitude differences:: + + >>> fadd(1, '1e-100000000000000000000', prec=inf) + Traceback (most recent call last): + ... + OverflowError: the exact result does not fit in memory + + """ + prec, rounding = ctx._parse_prec(kwargs) + x = ctx.convert(x) + y = ctx.convert(y) + try: + if hasattr(x, '_mpf_'): + if hasattr(y, '_mpf_'): + return ctx.make_mpf(mpf_add(x._mpf_, y._mpf_, prec, rounding)) + if hasattr(y, '_mpc_'): + return ctx.make_mpc(mpc_add_mpf(y._mpc_, x._mpf_, prec, rounding)) + if hasattr(x, '_mpc_'): + if hasattr(y, '_mpf_'): + return ctx.make_mpc(mpc_add_mpf(x._mpc_, y._mpf_, prec, rounding)) + if hasattr(y, '_mpc_'): + return ctx.make_mpc(mpc_add(x._mpc_, y._mpc_, prec, rounding)) + except (ValueError, OverflowError): + raise OverflowError(ctx._exact_overflow_msg) + raise ValueError("Arguments need to be mpf or mpc compatible numbers") + + def fsub(ctx, x, y, **kwargs): + """ + Subtracts the numbers *x* and *y*, giving a floating-point result, + optionally using a custom precision and rounding mode. + + See the documentation of :func:`~mpmath.fadd` for a detailed description + of how to specify precision and rounding. + + **Examples** + + Using :func:`~mpmath.fsub` with precision and rounding control:: + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = False + >>> fsub(2, 1e-20) + mpf('2.0') + >>> fsub(2, 1e-20, rounding='d') + mpf('1.9999999999999998') + >>> nprint(fsub(2, 1e-20, prec=100), 25) + 1.99999999999999999999 + >>> nprint(fsub(2, 1e-20, dps=15), 25) + 2.0 + >>> nprint(fsub(2, 1e-20, dps=25), 25) + 1.99999999999999999999 + >>> nprint(fsub(2, 1e-20, exact=True), 25) + 1.99999999999999999999 + + Exact subtraction avoids cancellation errors, enforcing familiar laws + of numbers such as `x-y+y = x`, which don't hold in floating-point + arithmetic with finite precision:: + + >>> x, y = mpf(2), mpf('1e1000') + >>> print(x - y + y) + 0.0 + >>> print(fsub(x, y, prec=inf) + y) + 2.0 + >>> print(fsub(x, y, exact=True) + y) + 2.0 + + Exact addition can be inefficient and may be impossible to perform + with large magnitude differences:: + + >>> fsub(1, '1e-100000000000000000000', prec=inf) + Traceback (most recent call last): + ... + OverflowError: the exact result does not fit in memory + + """ + prec, rounding = ctx._parse_prec(kwargs) + x = ctx.convert(x) + y = ctx.convert(y) + try: + if hasattr(x, '_mpf_'): + if hasattr(y, '_mpf_'): + return ctx.make_mpf(mpf_sub(x._mpf_, y._mpf_, prec, rounding)) + if hasattr(y, '_mpc_'): + return ctx.make_mpc(mpc_sub((x._mpf_, fzero), y._mpc_, prec, rounding)) + if hasattr(x, '_mpc_'): + if hasattr(y, '_mpf_'): + return ctx.make_mpc(mpc_sub_mpf(x._mpc_, y._mpf_, prec, rounding)) + if hasattr(y, '_mpc_'): + return ctx.make_mpc(mpc_sub(x._mpc_, y._mpc_, prec, rounding)) + except (ValueError, OverflowError): + raise OverflowError(ctx._exact_overflow_msg) + raise ValueError("Arguments need to be mpf or mpc compatible numbers") + + def fmul(ctx, x, y, **kwargs): + """ + Multiplies the numbers *x* and *y*, giving a floating-point result, + optionally using a custom precision and rounding mode. + + See the documentation of :func:`~mpmath.fadd` for a detailed description + of how to specify precision and rounding. + + **Examples** + + The result is an mpmath number:: + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = False + >>> fmul(2, 5.0) + mpf('10.0') + >>> fmul(0.5j, 0.5) + mpc(real='0.0', imag='0.25') + + Avoiding roundoff:: + + >>> x, y = 10**10+1, 10**15+1 + >>> print(x*y) + 10000000001000010000000001 + >>> print(mpf(x) * mpf(y)) + 1.0000000001e+25 + >>> print(int(mpf(x) * mpf(y))) + 10000000001000011026399232 + >>> print(int(fmul(x, y))) + 10000000001000011026399232 + >>> print(int(fmul(x, y, dps=25))) + 10000000001000010000000001 + >>> print(int(fmul(x, y, exact=True))) + 10000000001000010000000001 + + Exact multiplication with complex numbers can be inefficient and may + be impossible to perform with large magnitude differences between + real and imaginary parts:: + + >>> x = 1+2j + >>> y = mpc(2, '1e-100000000000000000000') + >>> fmul(x, y) + mpc(real='2.0', imag='4.0') + >>> fmul(x, y, rounding='u') + mpc(real='2.0', imag='4.0000000000000009') + >>> fmul(x, y, exact=True) + Traceback (most recent call last): + ... + OverflowError: the exact result does not fit in memory + + """ + prec, rounding = ctx._parse_prec(kwargs) + x = ctx.convert(x) + y = ctx.convert(y) + try: + if hasattr(x, '_mpf_'): + if hasattr(y, '_mpf_'): + return ctx.make_mpf(mpf_mul(x._mpf_, y._mpf_, prec, rounding)) + if hasattr(y, '_mpc_'): + return ctx.make_mpc(mpc_mul_mpf(y._mpc_, x._mpf_, prec, rounding)) + if hasattr(x, '_mpc_'): + if hasattr(y, '_mpf_'): + return ctx.make_mpc(mpc_mul_mpf(x._mpc_, y._mpf_, prec, rounding)) + if hasattr(y, '_mpc_'): + return ctx.make_mpc(mpc_mul(x._mpc_, y._mpc_, prec, rounding)) + except (ValueError, OverflowError): + raise OverflowError(ctx._exact_overflow_msg) + raise ValueError("Arguments need to be mpf or mpc compatible numbers") + + def fdiv(ctx, x, y, **kwargs): + """ + Divides the numbers *x* and *y*, giving a floating-point result, + optionally using a custom precision and rounding mode. + + See the documentation of :func:`~mpmath.fadd` for a detailed description + of how to specify precision and rounding. + + **Examples** + + The result is an mpmath number:: + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = False + >>> fdiv(3, 2) + mpf('1.5') + >>> fdiv(2, 3) + mpf('0.66666666666666663') + >>> fdiv(2+4j, 0.5) + mpc(real='4.0', imag='8.0') + + The rounding direction and precision can be controlled:: + + >>> fdiv(2, 3, dps=3) # Should be accurate to at least 3 digits + mpf('0.6666259765625') + >>> fdiv(2, 3, rounding='d') + mpf('0.66666666666666663') + >>> fdiv(2, 3, prec=60) + mpf('0.66666666666666667') + >>> fdiv(2, 3, rounding='u') + mpf('0.66666666666666674') + + Checking the error of a division by performing it at higher precision:: + + >>> fdiv(2, 3) - fdiv(2, 3, prec=100) + mpf('-3.7007434154172148e-17') + + Unlike :func:`~mpmath.fadd`, :func:`~mpmath.fmul`, etc., exact division is not + allowed since the quotient of two floating-point numbers generally + does not have an exact floating-point representation. (In the + future this might be changed to allow the case where the division + is actually exact.) + + >>> fdiv(2, 3, exact=True) + Traceback (most recent call last): + ... + ValueError: division is not an exact operation + + """ + prec, rounding = ctx._parse_prec(kwargs) + if not prec: + raise ValueError("division is not an exact operation") + x = ctx.convert(x) + y = ctx.convert(y) + if hasattr(x, '_mpf_'): + if hasattr(y, '_mpf_'): + return ctx.make_mpf(mpf_div(x._mpf_, y._mpf_, prec, rounding)) + if hasattr(y, '_mpc_'): + return ctx.make_mpc(mpc_div((x._mpf_, fzero), y._mpc_, prec, rounding)) + if hasattr(x, '_mpc_'): + if hasattr(y, '_mpf_'): + return ctx.make_mpc(mpc_div_mpf(x._mpc_, y._mpf_, prec, rounding)) + if hasattr(y, '_mpc_'): + return ctx.make_mpc(mpc_div(x._mpc_, y._mpc_, prec, rounding)) + raise ValueError("Arguments need to be mpf or mpc compatible numbers") + + def nint_distance(ctx, x): + r""" + Return `(n,d)` where `n` is the nearest integer to `x` and `d` is + an estimate of `\log_2(|x-n|)`. If `d < 0`, `-d` gives the precision + (measured in bits) lost to cancellation when computing `x-n`. + + >>> from mpmath import * + >>> n, d = nint_distance(5) + >>> print(n); print(d) + 5 + -inf + >>> n, d = nint_distance(mpf(5)) + >>> print(n); print(d) + 5 + -inf + >>> n, d = nint_distance(mpf(5.00000001)) + >>> print(n); print(d) + 5 + -26 + >>> n, d = nint_distance(mpf(4.99999999)) + >>> print(n); print(d) + 5 + -26 + >>> n, d = nint_distance(mpc(5,10)) + >>> print(n); print(d) + 5 + 4 + >>> n, d = nint_distance(mpc(5,0.000001)) + >>> print(n); print(d) + 5 + -19 + + """ + typx = type(x) + if typx in int_types: + return int(x), ctx.ninf + elif typx is rational.mpq: + p, q = x._mpq_ + n, r = divmod(p, q) + if 2*r >= q: + n += 1 + elif not r: + return n, ctx.ninf + # log(p/q-n) = log((p-nq)/q) = log(p-nq) - log(q) + d = bitcount(abs(p-n*q)) - bitcount(q) + return n, d + if hasattr(x, "_mpf_"): + re = x._mpf_ + im_dist = ctx.ninf + elif hasattr(x, "_mpc_"): + re, im = x._mpc_ + isign, iman, iexp, ibc = im + if iman: + im_dist = iexp + ibc + elif im == fzero: + im_dist = ctx.ninf + else: + raise ValueError("requires a finite number") + else: + x = ctx.convert(x) + if hasattr(x, "_mpf_") or hasattr(x, "_mpc_"): + return ctx.nint_distance(x) + else: + raise TypeError("requires an mpf/mpc") + sign, man, exp, bc = re + mag = exp+bc + # |x| < 0.5 + if mag < 0: + n = 0 + re_dist = mag + elif man: + # exact integer + if exp >= 0: + n = man << exp + re_dist = ctx.ninf + # exact half-integer + elif exp == -1: + n = (man>>1)+1 + re_dist = 0 + else: + d = (-exp-1) + t = man >> d + if t & 1: + t += 1 + man = (t<>1 # int(t)>>1 + re_dist = exp+bitcount(man) + if sign: + n = -n + elif re == fzero: + re_dist = ctx.ninf + n = 0 + else: + raise ValueError("requires a finite number") + return n, max(re_dist, im_dist) + + def fprod(ctx, factors): + r""" + Calculates a product containing a finite number of factors (for + infinite products, see :func:`~mpmath.nprod`). The factors will be + converted to mpmath numbers. + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = False + >>> fprod([1, 2, 0.5, 7]) + mpf('7.0') + + """ + orig = ctx.prec + try: + v = ctx.one + for p in factors: + v *= p + finally: + ctx.prec = orig + return +v + + def rand(ctx): + """ + Returns an ``mpf`` with value chosen randomly from `[0, 1)`. + The number of randomly generated bits in the mantissa is equal + to the working precision. + """ + return ctx.make_mpf(mpf_rand(ctx._prec)) + + def fraction(ctx, p, q): + """ + Given Python integers `(p, q)`, returns a lazy ``mpf`` representing + the fraction `p/q`. The value is updated with the precision. + + >>> from mpmath import * + >>> mp.dps = 15 + >>> a = fraction(1,100) + >>> b = mpf(1)/100 + >>> print(a); print(b) + 0.01 + 0.01 + >>> mp.dps = 30 + >>> print(a); print(b) # a will be accurate + 0.01 + 0.0100000000000000002081668171172 + >>> mp.dps = 15 + """ + return ctx.constant(lambda prec, rnd: from_rational(p, q, prec, rnd), + '%s/%s' % (p, q)) + + def absmin(ctx, x): + return abs(ctx.convert(x)) + + def absmax(ctx, x): + return abs(ctx.convert(x)) + + def _as_points(ctx, x): + # XXX: remove this? + if hasattr(x, '_mpi_'): + a, b = x._mpi_ + return [ctx.make_mpf(a), ctx.make_mpf(b)] + return x + + ''' + def _zetasum(ctx, s, a, b): + """ + Computes sum of k^(-s) for k = a, a+1, ..., b with a, b both small + integers. + """ + a = int(a) + b = int(b) + s = ctx.convert(s) + prec, rounding = ctx._prec_rounding + if hasattr(s, '_mpf_'): + v = ctx.make_mpf(libmp.mpf_zetasum(s._mpf_, a, b, prec)) + elif hasattr(s, '_mpc_'): + v = ctx.make_mpc(libmp.mpc_zetasum(s._mpc_, a, b, prec)) + return v + ''' + + def _zetasum_fast(ctx, s, a, n, derivatives=[0], reflect=False): + if not (ctx.isint(a) and hasattr(s, "_mpc_")): + raise NotImplementedError + a = int(a) + prec = ctx._prec + xs, ys = libmp.mpc_zetasum(s._mpc_, a, n, derivatives, reflect, prec) + xs = [ctx.make_mpc(x) for x in xs] + ys = [ctx.make_mpc(y) for y in ys] + return xs, ys + +class PrecisionManager: + def __init__(self, ctx, precfun, dpsfun, normalize_output=False): + self.ctx = ctx + self.precfun = precfun + self.dpsfun = dpsfun + self.normalize_output = normalize_output + def __call__(self, f): + @functools.wraps(f) + def g(*args, **kwargs): + orig = self.ctx.prec + try: + if self.precfun: + self.ctx.prec = self.precfun(self.ctx.prec) + else: + self.ctx.dps = self.dpsfun(self.ctx.dps) + if self.normalize_output: + v = f(*args, **kwargs) + if type(v) is tuple: + return tuple([+a for a in v]) + return +v + else: + return f(*args, **kwargs) + finally: + self.ctx.prec = orig + return g + def __enter__(self): + self.origp = self.ctx.prec + if self.precfun: + self.ctx.prec = self.precfun(self.ctx.prec) + else: + self.ctx.dps = self.dpsfun(self.ctx.dps) + def __exit__(self, exc_type, exc_val, exc_tb): + self.ctx.prec = self.origp + return False + + +if __name__ == '__main__': + import doctest + doctest.testmod() diff --git a/venv/lib/python3.12/site-packages/mpmath/ctx_mp_python.py b/venv/lib/python3.12/site-packages/mpmath/ctx_mp_python.py new file mode 100644 index 0000000000000000000000000000000000000000..cfbd72fb8300bf840069c38529b7b41418d26eeb --- /dev/null +++ b/venv/lib/python3.12/site-packages/mpmath/ctx_mp_python.py @@ -0,0 +1,1149 @@ +#from ctx_base import StandardBaseContext + +from .libmp.backend import basestring, exec_ + +from .libmp import (MPZ, MPZ_ZERO, MPZ_ONE, int_types, repr_dps, + round_floor, round_ceiling, dps_to_prec, round_nearest, prec_to_dps, + ComplexResult, to_pickable, from_pickable, normalize, + from_int, from_float, from_npfloat, from_Decimal, from_str, to_int, to_float, to_str, + from_rational, from_man_exp, + fone, fzero, finf, fninf, fnan, + mpf_abs, mpf_pos, mpf_neg, mpf_add, mpf_sub, mpf_mul, mpf_mul_int, + mpf_div, mpf_rdiv_int, mpf_pow_int, mpf_mod, + mpf_eq, mpf_cmp, mpf_lt, mpf_gt, mpf_le, mpf_ge, + mpf_hash, mpf_rand, + mpf_sum, + bitcount, to_fixed, + mpc_to_str, + mpc_to_complex, mpc_hash, mpc_pos, mpc_is_nonzero, mpc_neg, mpc_conjugate, + mpc_abs, mpc_add, mpc_add_mpf, mpc_sub, mpc_sub_mpf, mpc_mul, mpc_mul_mpf, + mpc_mul_int, mpc_div, mpc_div_mpf, mpc_pow, mpc_pow_mpf, mpc_pow_int, + mpc_mpf_div, + mpf_pow, + mpf_pi, mpf_degree, mpf_e, mpf_phi, mpf_ln2, mpf_ln10, + mpf_euler, mpf_catalan, mpf_apery, mpf_khinchin, + mpf_glaisher, mpf_twinprime, mpf_mertens, + int_types) + +from . import rational +from . import function_docs + +new = object.__new__ + +class mpnumeric(object): + """Base class for mpf and mpc.""" + __slots__ = [] + def __new__(cls, val): + raise NotImplementedError + +class _mpf(mpnumeric): + """ + An mpf instance holds a real-valued floating-point number. mpf:s + work analogously to Python floats, but support arbitrary-precision + arithmetic. + """ + __slots__ = ['_mpf_'] + + def __new__(cls, val=fzero, **kwargs): + """A new mpf can be created from a Python float, an int, a + or a decimal string representing a number in floating-point + format.""" + prec, rounding = cls.context._prec_rounding + if kwargs: + prec = kwargs.get('prec', prec) + if 'dps' in kwargs: + prec = dps_to_prec(kwargs['dps']) + rounding = kwargs.get('rounding', rounding) + if type(val) is cls: + sign, man, exp, bc = val._mpf_ + if (not man) and exp: + return val + v = new(cls) + v._mpf_ = normalize(sign, man, exp, bc, prec, rounding) + return v + elif type(val) is tuple: + if len(val) == 2: + v = new(cls) + v._mpf_ = from_man_exp(val[0], val[1], prec, rounding) + return v + if len(val) == 4: + if val not in (finf, fninf, fnan): + sign, man, exp, bc = val + val = normalize(sign, MPZ(man), exp, bc, prec, rounding) + v = new(cls) + v._mpf_ = val + return v + raise ValueError + else: + v = new(cls) + v._mpf_ = mpf_pos(cls.mpf_convert_arg(val, prec, rounding), prec, rounding) + return v + + @classmethod + def mpf_convert_arg(cls, x, prec, rounding): + if isinstance(x, int_types): return from_int(x) + if isinstance(x, float): return from_float(x) + if isinstance(x, basestring): return from_str(x, prec, rounding) + if isinstance(x, cls.context.constant): return x.func(prec, rounding) + if hasattr(x, '_mpf_'): return x._mpf_ + if hasattr(x, '_mpmath_'): + t = cls.context.convert(x._mpmath_(prec, rounding)) + if hasattr(t, '_mpf_'): + return t._mpf_ + if hasattr(x, '_mpi_'): + a, b = x._mpi_ + if a == b: + return a + raise ValueError("can only create mpf from zero-width interval") + raise TypeError("cannot create mpf from " + repr(x)) + + @classmethod + def mpf_convert_rhs(cls, x): + if isinstance(x, int_types): return from_int(x) + if isinstance(x, float): return from_float(x) + if isinstance(x, complex_types): return cls.context.mpc(x) + if isinstance(x, rational.mpq): + p, q = x._mpq_ + return from_rational(p, q, cls.context.prec) + if hasattr(x, '_mpf_'): return x._mpf_ + if hasattr(x, '_mpmath_'): + t = cls.context.convert(x._mpmath_(*cls.context._prec_rounding)) + if hasattr(t, '_mpf_'): + return t._mpf_ + return t + return NotImplemented + + @classmethod + def mpf_convert_lhs(cls, x): + x = cls.mpf_convert_rhs(x) + if type(x) is tuple: + return cls.context.make_mpf(x) + return x + + man_exp = property(lambda self: self._mpf_[1:3]) + man = property(lambda self: self._mpf_[1]) + exp = property(lambda self: self._mpf_[2]) + bc = property(lambda self: self._mpf_[3]) + + real = property(lambda self: self) + imag = property(lambda self: self.context.zero) + + conjugate = lambda self: self + + def __getstate__(self): return to_pickable(self._mpf_) + def __setstate__(self, val): self._mpf_ = from_pickable(val) + + def __repr__(s): + if s.context.pretty: + return str(s) + return "mpf('%s')" % to_str(s._mpf_, s.context._repr_digits) + + def __str__(s): return to_str(s._mpf_, s.context._str_digits) + def __hash__(s): return mpf_hash(s._mpf_) + def __int__(s): return int(to_int(s._mpf_)) + def __long__(s): return long(to_int(s._mpf_)) + def __float__(s): return to_float(s._mpf_, rnd=s.context._prec_rounding[1]) + def __complex__(s): return complex(float(s)) + def __nonzero__(s): return s._mpf_ != fzero + + __bool__ = __nonzero__ + + def __abs__(s): + cls, new, (prec, rounding) = s._ctxdata + v = new(cls) + v._mpf_ = mpf_abs(s._mpf_, prec, rounding) + return v + + def __pos__(s): + cls, new, (prec, rounding) = s._ctxdata + v = new(cls) + v._mpf_ = mpf_pos(s._mpf_, prec, rounding) + return v + + def __neg__(s): + cls, new, (prec, rounding) = s._ctxdata + v = new(cls) + v._mpf_ = mpf_neg(s._mpf_, prec, rounding) + return v + + def _cmp(s, t, func): + if hasattr(t, '_mpf_'): + t = t._mpf_ + else: + t = s.mpf_convert_rhs(t) + if t is NotImplemented: + return t + return func(s._mpf_, t) + + def __cmp__(s, t): return s._cmp(t, mpf_cmp) + def __lt__(s, t): return s._cmp(t, mpf_lt) + def __gt__(s, t): return s._cmp(t, mpf_gt) + def __le__(s, t): return s._cmp(t, mpf_le) + def __ge__(s, t): return s._cmp(t, mpf_ge) + + def __ne__(s, t): + v = s.__eq__(t) + if v is NotImplemented: + return v + return not v + + def __rsub__(s, t): + cls, new, (prec, rounding) = s._ctxdata + if type(t) in int_types: + v = new(cls) + v._mpf_ = mpf_sub(from_int(t), s._mpf_, prec, rounding) + return v + t = s.mpf_convert_lhs(t) + if t is NotImplemented: + return t + return t - s + + def __rdiv__(s, t): + cls, new, (prec, rounding) = s._ctxdata + if isinstance(t, int_types): + v = new(cls) + v._mpf_ = mpf_rdiv_int(t, s._mpf_, prec, rounding) + return v + t = s.mpf_convert_lhs(t) + if t is NotImplemented: + return t + return t / s + + def __rpow__(s, t): + t = s.mpf_convert_lhs(t) + if t is NotImplemented: + return t + return t ** s + + def __rmod__(s, t): + t = s.mpf_convert_lhs(t) + if t is NotImplemented: + return t + return t % s + + def sqrt(s): + return s.context.sqrt(s) + + def ae(s, t, rel_eps=None, abs_eps=None): + return s.context.almosteq(s, t, rel_eps, abs_eps) + + def to_fixed(self, prec): + return to_fixed(self._mpf_, prec) + + def __round__(self, *args): + return round(float(self), *args) + +mpf_binary_op = """ +def %NAME%(self, other): + mpf, new, (prec, rounding) = self._ctxdata + sval = self._mpf_ + if hasattr(other, '_mpf_'): + tval = other._mpf_ + %WITH_MPF% + ttype = type(other) + if ttype in int_types: + %WITH_INT% + elif ttype is float: + tval = from_float(other) + %WITH_MPF% + elif hasattr(other, '_mpc_'): + tval = other._mpc_ + mpc = type(other) + %WITH_MPC% + elif ttype is complex: + tval = from_float(other.real), from_float(other.imag) + mpc = self.context.mpc + %WITH_MPC% + if isinstance(other, mpnumeric): + return NotImplemented + try: + other = mpf.context.convert(other, strings=False) + except TypeError: + return NotImplemented + return self.%NAME%(other) +""" + +return_mpf = "; obj = new(mpf); obj._mpf_ = val; return obj" +return_mpc = "; obj = new(mpc); obj._mpc_ = val; return obj" + +mpf_pow_same = """ + try: + val = mpf_pow(sval, tval, prec, rounding) %s + except ComplexResult: + if mpf.context.trap_complex: + raise + mpc = mpf.context.mpc + val = mpc_pow((sval, fzero), (tval, fzero), prec, rounding) %s +""" % (return_mpf, return_mpc) + +def binary_op(name, with_mpf='', with_int='', with_mpc=''): + code = mpf_binary_op + code = code.replace("%WITH_INT%", with_int) + code = code.replace("%WITH_MPC%", with_mpc) + code = code.replace("%WITH_MPF%", with_mpf) + code = code.replace("%NAME%", name) + np = {} + exec_(code, globals(), np) + return np[name] + +_mpf.__eq__ = binary_op('__eq__', + 'return mpf_eq(sval, tval)', + 'return mpf_eq(sval, from_int(other))', + 'return (tval[1] == fzero) and mpf_eq(tval[0], sval)') + +_mpf.__add__ = binary_op('__add__', + 'val = mpf_add(sval, tval, prec, rounding)' + return_mpf, + 'val = mpf_add(sval, from_int(other), prec, rounding)' + return_mpf, + 'val = mpc_add_mpf(tval, sval, prec, rounding)' + return_mpc) + +_mpf.__sub__ = binary_op('__sub__', + 'val = mpf_sub(sval, tval, prec, rounding)' + return_mpf, + 'val = mpf_sub(sval, from_int(other), prec, rounding)' + return_mpf, + 'val = mpc_sub((sval, fzero), tval, prec, rounding)' + return_mpc) + +_mpf.__mul__ = binary_op('__mul__', + 'val = mpf_mul(sval, tval, prec, rounding)' + return_mpf, + 'val = mpf_mul_int(sval, other, prec, rounding)' + return_mpf, + 'val = mpc_mul_mpf(tval, sval, prec, rounding)' + return_mpc) + +_mpf.__div__ = binary_op('__div__', + 'val = mpf_div(sval, tval, prec, rounding)' + return_mpf, + 'val = mpf_div(sval, from_int(other), prec, rounding)' + return_mpf, + 'val = mpc_mpf_div(sval, tval, prec, rounding)' + return_mpc) + +_mpf.__mod__ = binary_op('__mod__', + 'val = mpf_mod(sval, tval, prec, rounding)' + return_mpf, + 'val = mpf_mod(sval, from_int(other), prec, rounding)' + return_mpf, + 'raise NotImplementedError("complex modulo")') + +_mpf.__pow__ = binary_op('__pow__', + mpf_pow_same, + 'val = mpf_pow_int(sval, other, prec, rounding)' + return_mpf, + 'val = mpc_pow((sval, fzero), tval, prec, rounding)' + return_mpc) + +_mpf.__radd__ = _mpf.__add__ +_mpf.__rmul__ = _mpf.__mul__ +_mpf.__truediv__ = _mpf.__div__ +_mpf.__rtruediv__ = _mpf.__rdiv__ + + +class _constant(_mpf): + """Represents a mathematical constant with dynamic precision. + When printed or used in an arithmetic operation, a constant + is converted to a regular mpf at the working precision. A + regular mpf can also be obtained using the operation +x.""" + + def __new__(cls, func, name, docname=''): + a = object.__new__(cls) + a.name = name + a.func = func + a.__doc__ = getattr(function_docs, docname, '') + return a + + def __call__(self, prec=None, dps=None, rounding=None): + prec2, rounding2 = self.context._prec_rounding + if not prec: prec = prec2 + if not rounding: rounding = rounding2 + if dps: prec = dps_to_prec(dps) + return self.context.make_mpf(self.func(prec, rounding)) + + @property + def _mpf_(self): + prec, rounding = self.context._prec_rounding + return self.func(prec, rounding) + + def __repr__(self): + return "<%s: %s~>" % (self.name, self.context.nstr(self(dps=15))) + + +class _mpc(mpnumeric): + """ + An mpc represents a complex number using a pair of mpf:s (one + for the real part and another for the imaginary part.) The mpc + class behaves fairly similarly to Python's complex type. + """ + + __slots__ = ['_mpc_'] + + def __new__(cls, real=0, imag=0): + s = object.__new__(cls) + if isinstance(real, complex_types): + real, imag = real.real, real.imag + elif hasattr(real, '_mpc_'): + s._mpc_ = real._mpc_ + return s + real = cls.context.mpf(real) + imag = cls.context.mpf(imag) + s._mpc_ = (real._mpf_, imag._mpf_) + return s + + real = property(lambda self: self.context.make_mpf(self._mpc_[0])) + imag = property(lambda self: self.context.make_mpf(self._mpc_[1])) + + def __getstate__(self): + return to_pickable(self._mpc_[0]), to_pickable(self._mpc_[1]) + + def __setstate__(self, val): + self._mpc_ = from_pickable(val[0]), from_pickable(val[1]) + + def __repr__(s): + if s.context.pretty: + return str(s) + r = repr(s.real)[4:-1] + i = repr(s.imag)[4:-1] + return "%s(real=%s, imag=%s)" % (type(s).__name__, r, i) + + def __str__(s): + return "(%s)" % mpc_to_str(s._mpc_, s.context._str_digits) + + def __complex__(s): + return mpc_to_complex(s._mpc_, rnd=s.context._prec_rounding[1]) + + def __pos__(s): + cls, new, (prec, rounding) = s._ctxdata + v = new(cls) + v._mpc_ = mpc_pos(s._mpc_, prec, rounding) + return v + + def __abs__(s): + prec, rounding = s.context._prec_rounding + v = new(s.context.mpf) + v._mpf_ = mpc_abs(s._mpc_, prec, rounding) + return v + + def __neg__(s): + cls, new, (prec, rounding) = s._ctxdata + v = new(cls) + v._mpc_ = mpc_neg(s._mpc_, prec, rounding) + return v + + def conjugate(s): + cls, new, (prec, rounding) = s._ctxdata + v = new(cls) + v._mpc_ = mpc_conjugate(s._mpc_, prec, rounding) + return v + + def __nonzero__(s): + return mpc_is_nonzero(s._mpc_) + + __bool__ = __nonzero__ + + def __hash__(s): + return mpc_hash(s._mpc_) + + @classmethod + def mpc_convert_lhs(cls, x): + try: + y = cls.context.convert(x) + return y + except TypeError: + return NotImplemented + + def __eq__(s, t): + if not hasattr(t, '_mpc_'): + if isinstance(t, str): + return False + t = s.mpc_convert_lhs(t) + if t is NotImplemented: + return t + return s.real == t.real and s.imag == t.imag + + def __ne__(s, t): + b = s.__eq__(t) + if b is NotImplemented: + return b + return not b + + def _compare(*args): + raise TypeError("no ordering relation is defined for complex numbers") + + __gt__ = _compare + __le__ = _compare + __gt__ = _compare + __ge__ = _compare + + def __add__(s, t): + cls, new, (prec, rounding) = s._ctxdata + if not hasattr(t, '_mpc_'): + t = s.mpc_convert_lhs(t) + if t is NotImplemented: + return t + if hasattr(t, '_mpf_'): + v = new(cls) + v._mpc_ = mpc_add_mpf(s._mpc_, t._mpf_, prec, rounding) + return v + v = new(cls) + v._mpc_ = mpc_add(s._mpc_, t._mpc_, prec, rounding) + return v + + def __sub__(s, t): + cls, new, (prec, rounding) = s._ctxdata + if not hasattr(t, '_mpc_'): + t = s.mpc_convert_lhs(t) + if t is NotImplemented: + return t + if hasattr(t, '_mpf_'): + v = new(cls) + v._mpc_ = mpc_sub_mpf(s._mpc_, t._mpf_, prec, rounding) + return v + v = new(cls) + v._mpc_ = mpc_sub(s._mpc_, t._mpc_, prec, rounding) + return v + + def __mul__(s, t): + cls, new, (prec, rounding) = s._ctxdata + if not hasattr(t, '_mpc_'): + if isinstance(t, int_types): + v = new(cls) + v._mpc_ = mpc_mul_int(s._mpc_, t, prec, rounding) + return v + t = s.mpc_convert_lhs(t) + if t is NotImplemented: + return t + if hasattr(t, '_mpf_'): + v = new(cls) + v._mpc_ = mpc_mul_mpf(s._mpc_, t._mpf_, prec, rounding) + return v + t = s.mpc_convert_lhs(t) + v = new(cls) + v._mpc_ = mpc_mul(s._mpc_, t._mpc_, prec, rounding) + return v + + def __div__(s, t): + cls, new, (prec, rounding) = s._ctxdata + if not hasattr(t, '_mpc_'): + t = s.mpc_convert_lhs(t) + if t is NotImplemented: + return t + if hasattr(t, '_mpf_'): + v = new(cls) + v._mpc_ = mpc_div_mpf(s._mpc_, t._mpf_, prec, rounding) + return v + v = new(cls) + v._mpc_ = mpc_div(s._mpc_, t._mpc_, prec, rounding) + return v + + def __pow__(s, t): + cls, new, (prec, rounding) = s._ctxdata + if isinstance(t, int_types): + v = new(cls) + v._mpc_ = mpc_pow_int(s._mpc_, t, prec, rounding) + return v + t = s.mpc_convert_lhs(t) + if t is NotImplemented: + return t + v = new(cls) + if hasattr(t, '_mpf_'): + v._mpc_ = mpc_pow_mpf(s._mpc_, t._mpf_, prec, rounding) + else: + v._mpc_ = mpc_pow(s._mpc_, t._mpc_, prec, rounding) + return v + + __radd__ = __add__ + + def __rsub__(s, t): + t = s.mpc_convert_lhs(t) + if t is NotImplemented: + return t + return t - s + + def __rmul__(s, t): + cls, new, (prec, rounding) = s._ctxdata + if isinstance(t, int_types): + v = new(cls) + v._mpc_ = mpc_mul_int(s._mpc_, t, prec, rounding) + return v + t = s.mpc_convert_lhs(t) + if t is NotImplemented: + return t + return t * s + + def __rdiv__(s, t): + t = s.mpc_convert_lhs(t) + if t is NotImplemented: + return t + return t / s + + def __rpow__(s, t): + t = s.mpc_convert_lhs(t) + if t is NotImplemented: + return t + return t ** s + + __truediv__ = __div__ + __rtruediv__ = __rdiv__ + + def ae(s, t, rel_eps=None, abs_eps=None): + return s.context.almosteq(s, t, rel_eps, abs_eps) + + +complex_types = (complex, _mpc) + + +class PythonMPContext(object): + + def __init__(ctx): + ctx._prec_rounding = [53, round_nearest] + ctx.mpf = type('mpf', (_mpf,), {}) + ctx.mpc = type('mpc', (_mpc,), {}) + ctx.mpf._ctxdata = [ctx.mpf, new, ctx._prec_rounding] + ctx.mpc._ctxdata = [ctx.mpc, new, ctx._prec_rounding] + ctx.mpf.context = ctx + ctx.mpc.context = ctx + ctx.constant = type('constant', (_constant,), {}) + ctx.constant._ctxdata = [ctx.mpf, new, ctx._prec_rounding] + ctx.constant.context = ctx + + def make_mpf(ctx, v): + a = new(ctx.mpf) + a._mpf_ = v + return a + + def make_mpc(ctx, v): + a = new(ctx.mpc) + a._mpc_ = v + return a + + def default(ctx): + ctx._prec = ctx._prec_rounding[0] = 53 + ctx._dps = 15 + ctx.trap_complex = False + + def _set_prec(ctx, n): + ctx._prec = ctx._prec_rounding[0] = max(1, int(n)) + ctx._dps = prec_to_dps(n) + + def _set_dps(ctx, n): + ctx._prec = ctx._prec_rounding[0] = dps_to_prec(n) + ctx._dps = max(1, int(n)) + + prec = property(lambda ctx: ctx._prec, _set_prec) + dps = property(lambda ctx: ctx._dps, _set_dps) + + def convert(ctx, x, strings=True): + """ + Converts *x* to an ``mpf`` or ``mpc``. If *x* is of type ``mpf``, + ``mpc``, ``int``, ``float``, ``complex``, the conversion + will be performed losslessly. + + If *x* is a string, the result will be rounded to the present + working precision. Strings representing fractions or complex + numbers are permitted. + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = False + >>> mpmathify(3.5) + mpf('3.5') + >>> mpmathify('2.1') + mpf('2.1000000000000001') + >>> mpmathify('3/4') + mpf('0.75') + >>> mpmathify('2+3j') + mpc(real='2.0', imag='3.0') + + """ + if type(x) in ctx.types: return x + if isinstance(x, int_types): return ctx.make_mpf(from_int(x)) + if isinstance(x, float): return ctx.make_mpf(from_float(x)) + if isinstance(x, complex): + return ctx.make_mpc((from_float(x.real), from_float(x.imag))) + if type(x).__module__ == 'numpy': return ctx.npconvert(x) + if isinstance(x, numbers.Rational): # e.g. Fraction + try: x = rational.mpq(int(x.numerator), int(x.denominator)) + except: pass + prec, rounding = ctx._prec_rounding + if isinstance(x, rational.mpq): + p, q = x._mpq_ + return ctx.make_mpf(from_rational(p, q, prec)) + if strings and isinstance(x, basestring): + try: + _mpf_ = from_str(x, prec, rounding) + return ctx.make_mpf(_mpf_) + except ValueError: + pass + if hasattr(x, '_mpf_'): return ctx.make_mpf(x._mpf_) + if hasattr(x, '_mpc_'): return ctx.make_mpc(x._mpc_) + if hasattr(x, '_mpmath_'): + return ctx.convert(x._mpmath_(prec, rounding)) + if type(x).__module__ == 'decimal': + try: return ctx.make_mpf(from_Decimal(x, prec, rounding)) + except: pass + return ctx._convert_fallback(x, strings) + + def npconvert(ctx, x): + """ + Converts *x* to an ``mpf`` or ``mpc``. *x* should be a numpy + scalar. + """ + import numpy as np + if isinstance(x, np.integer): return ctx.make_mpf(from_int(int(x))) + if isinstance(x, np.floating): return ctx.make_mpf(from_npfloat(x)) + if isinstance(x, np.complexfloating): + return ctx.make_mpc((from_npfloat(x.real), from_npfloat(x.imag))) + raise TypeError("cannot create mpf from " + repr(x)) + + def isnan(ctx, x): + """ + Return *True* if *x* is a NaN (not-a-number), or for a complex + number, whether either the real or complex part is NaN; + otherwise return *False*:: + + >>> from mpmath import * + >>> isnan(3.14) + False + >>> isnan(nan) + True + >>> isnan(mpc(3.14,2.72)) + False + >>> isnan(mpc(3.14,nan)) + True + + """ + if hasattr(x, "_mpf_"): + return x._mpf_ == fnan + if hasattr(x, "_mpc_"): + return fnan in x._mpc_ + if isinstance(x, int_types) or isinstance(x, rational.mpq): + return False + x = ctx.convert(x) + if hasattr(x, '_mpf_') or hasattr(x, '_mpc_'): + return ctx.isnan(x) + raise TypeError("isnan() needs a number as input") + + def isinf(ctx, x): + """ + Return *True* if the absolute value of *x* is infinite; + otherwise return *False*:: + + >>> from mpmath import * + >>> isinf(inf) + True + >>> isinf(-inf) + True + >>> isinf(3) + False + >>> isinf(3+4j) + False + >>> isinf(mpc(3,inf)) + True + >>> isinf(mpc(inf,3)) + True + + """ + if hasattr(x, "_mpf_"): + return x._mpf_ in (finf, fninf) + if hasattr(x, "_mpc_"): + re, im = x._mpc_ + return re in (finf, fninf) or im in (finf, fninf) + if isinstance(x, int_types) or isinstance(x, rational.mpq): + return False + x = ctx.convert(x) + if hasattr(x, '_mpf_') or hasattr(x, '_mpc_'): + return ctx.isinf(x) + raise TypeError("isinf() needs a number as input") + + def isnormal(ctx, x): + """ + Determine whether *x* is "normal" in the sense of floating-point + representation; that is, return *False* if *x* is zero, an + infinity or NaN; otherwise return *True*. By extension, a + complex number *x* is considered "normal" if its magnitude is + normal:: + + >>> from mpmath import * + >>> isnormal(3) + True + >>> isnormal(0) + False + >>> isnormal(inf); isnormal(-inf); isnormal(nan) + False + False + False + >>> isnormal(0+0j) + False + >>> isnormal(0+3j) + True + >>> isnormal(mpc(2,nan)) + False + """ + if hasattr(x, "_mpf_"): + return bool(x._mpf_[1]) + if hasattr(x, "_mpc_"): + re, im = x._mpc_ + re_normal = bool(re[1]) + im_normal = bool(im[1]) + if re == fzero: return im_normal + if im == fzero: return re_normal + return re_normal and im_normal + if isinstance(x, int_types) or isinstance(x, rational.mpq): + return bool(x) + x = ctx.convert(x) + if hasattr(x, '_mpf_') or hasattr(x, '_mpc_'): + return ctx.isnormal(x) + raise TypeError("isnormal() needs a number as input") + + def isint(ctx, x, gaussian=False): + """ + Return *True* if *x* is integer-valued; otherwise return + *False*:: + + >>> from mpmath import * + >>> isint(3) + True + >>> isint(mpf(3)) + True + >>> isint(3.2) + False + >>> isint(inf) + False + + Optionally, Gaussian integers can be checked for:: + + >>> isint(3+0j) + True + >>> isint(3+2j) + False + >>> isint(3+2j, gaussian=True) + True + + """ + if isinstance(x, int_types): + return True + if hasattr(x, "_mpf_"): + sign, man, exp, bc = xval = x._mpf_ + return bool((man and exp >= 0) or xval == fzero) + if hasattr(x, "_mpc_"): + re, im = x._mpc_ + rsign, rman, rexp, rbc = re + isign, iman, iexp, ibc = im + re_isint = (rman and rexp >= 0) or re == fzero + if gaussian: + im_isint = (iman and iexp >= 0) or im == fzero + return re_isint and im_isint + return re_isint and im == fzero + if isinstance(x, rational.mpq): + p, q = x._mpq_ + return p % q == 0 + x = ctx.convert(x) + if hasattr(x, '_mpf_') or hasattr(x, '_mpc_'): + return ctx.isint(x, gaussian) + raise TypeError("isint() needs a number as input") + + def fsum(ctx, terms, absolute=False, squared=False): + """ + Calculates a sum containing a finite number of terms (for infinite + series, see :func:`~mpmath.nsum`). The terms will be converted to + mpmath numbers. For len(terms) > 2, this function is generally + faster and produces more accurate results than the builtin + Python function :func:`sum`. + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = False + >>> fsum([1, 2, 0.5, 7]) + mpf('10.5') + + With squared=True each term is squared, and with absolute=True + the absolute value of each term is used. + """ + prec, rnd = ctx._prec_rounding + real = [] + imag = [] + for term in terms: + reval = imval = 0 + if hasattr(term, "_mpf_"): + reval = term._mpf_ + elif hasattr(term, "_mpc_"): + reval, imval = term._mpc_ + else: + term = ctx.convert(term) + if hasattr(term, "_mpf_"): + reval = term._mpf_ + elif hasattr(term, "_mpc_"): + reval, imval = term._mpc_ + else: + raise NotImplementedError + if imval: + if squared: + if absolute: + real.append(mpf_mul(reval,reval)) + real.append(mpf_mul(imval,imval)) + else: + reval, imval = mpc_pow_int((reval,imval),2,prec+10) + real.append(reval) + imag.append(imval) + elif absolute: + real.append(mpc_abs((reval,imval), prec)) + else: + real.append(reval) + imag.append(imval) + else: + if squared: + reval = mpf_mul(reval, reval) + elif absolute: + reval = mpf_abs(reval) + real.append(reval) + s = mpf_sum(real, prec, rnd, absolute) + if imag: + s = ctx.make_mpc((s, mpf_sum(imag, prec, rnd))) + else: + s = ctx.make_mpf(s) + return s + + def fdot(ctx, A, B=None, conjugate=False): + r""" + Computes the dot product of the iterables `A` and `B`, + + .. math :: + + \sum_{k=0} A_k B_k. + + Alternatively, :func:`~mpmath.fdot` accepts a single iterable of pairs. + In other words, ``fdot(A,B)`` and ``fdot(zip(A,B))`` are equivalent. + The elements are automatically converted to mpmath numbers. + + With ``conjugate=True``, the elements in the second vector + will be conjugated: + + .. math :: + + \sum_{k=0} A_k \overline{B_k} + + **Examples** + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = False + >>> A = [2, 1.5, 3] + >>> B = [1, -1, 2] + >>> fdot(A, B) + mpf('6.5') + >>> list(zip(A, B)) + [(2, 1), (1.5, -1), (3, 2)] + >>> fdot(_) + mpf('6.5') + >>> A = [2, 1.5, 3j] + >>> B = [1+j, 3, -1-j] + >>> fdot(A, B) + mpc(real='9.5', imag='-1.0') + >>> fdot(A, B, conjugate=True) + mpc(real='3.5', imag='-5.0') + + """ + if B is not None: + A = zip(A, B) + prec, rnd = ctx._prec_rounding + real = [] + imag = [] + hasattr_ = hasattr + types = (ctx.mpf, ctx.mpc) + for a, b in A: + if type(a) not in types: a = ctx.convert(a) + if type(b) not in types: b = ctx.convert(b) + a_real = hasattr_(a, "_mpf_") + b_real = hasattr_(b, "_mpf_") + if a_real and b_real: + real.append(mpf_mul(a._mpf_, b._mpf_)) + continue + a_complex = hasattr_(a, "_mpc_") + b_complex = hasattr_(b, "_mpc_") + if a_real and b_complex: + aval = a._mpf_ + bre, bim = b._mpc_ + if conjugate: + bim = mpf_neg(bim) + real.append(mpf_mul(aval, bre)) + imag.append(mpf_mul(aval, bim)) + elif b_real and a_complex: + are, aim = a._mpc_ + bval = b._mpf_ + real.append(mpf_mul(are, bval)) + imag.append(mpf_mul(aim, bval)) + elif a_complex and b_complex: + #re, im = mpc_mul(a._mpc_, b._mpc_, prec+20) + are, aim = a._mpc_ + bre, bim = b._mpc_ + if conjugate: + bim = mpf_neg(bim) + real.append(mpf_mul(are, bre)) + real.append(mpf_neg(mpf_mul(aim, bim))) + imag.append(mpf_mul(are, bim)) + imag.append(mpf_mul(aim, bre)) + else: + raise NotImplementedError + s = mpf_sum(real, prec, rnd) + if imag: + s = ctx.make_mpc((s, mpf_sum(imag, prec, rnd))) + else: + s = ctx.make_mpf(s) + return s + + def _wrap_libmp_function(ctx, mpf_f, mpc_f=None, mpi_f=None, doc=""): + """ + Given a low-level mpf_ function, and optionally similar functions + for mpc_ and mpi_, defines the function as a context method. + + It is assumed that the return type is the same as that of + the input; the exception is that propagation from mpf to mpc is possible + by raising ComplexResult. + + """ + def f(x, **kwargs): + if type(x) not in ctx.types: + x = ctx.convert(x) + prec, rounding = ctx._prec_rounding + if kwargs: + prec = kwargs.get('prec', prec) + if 'dps' in kwargs: + prec = dps_to_prec(kwargs['dps']) + rounding = kwargs.get('rounding', rounding) + if hasattr(x, '_mpf_'): + try: + return ctx.make_mpf(mpf_f(x._mpf_, prec, rounding)) + except ComplexResult: + # Handle propagation to complex + if ctx.trap_complex: + raise + return ctx.make_mpc(mpc_f((x._mpf_, fzero), prec, rounding)) + elif hasattr(x, '_mpc_'): + return ctx.make_mpc(mpc_f(x._mpc_, prec, rounding)) + raise NotImplementedError("%s of a %s" % (name, type(x))) + name = mpf_f.__name__[4:] + f.__doc__ = function_docs.__dict__.get(name, "Computes the %s of x" % doc) + return f + + # Called by SpecialFunctions.__init__() + @classmethod + def _wrap_specfun(cls, name, f, wrap): + if wrap: + def f_wrapped(ctx, *args, **kwargs): + convert = ctx.convert + args = [convert(a) for a in args] + prec = ctx.prec + try: + ctx.prec += 10 + retval = f(ctx, *args, **kwargs) + finally: + ctx.prec = prec + return +retval + else: + f_wrapped = f + f_wrapped.__doc__ = function_docs.__dict__.get(name, f.__doc__) + setattr(cls, name, f_wrapped) + + def _convert_param(ctx, x): + if hasattr(x, "_mpc_"): + v, im = x._mpc_ + if im != fzero: + return x, 'C' + elif hasattr(x, "_mpf_"): + v = x._mpf_ + else: + if type(x) in int_types: + return int(x), 'Z' + p = None + if isinstance(x, tuple): + p, q = x + elif hasattr(x, '_mpq_'): + p, q = x._mpq_ + elif isinstance(x, basestring) and '/' in x: + p, q = x.split('/') + p = int(p) + q = int(q) + if p is not None: + if not p % q: + return p // q, 'Z' + return ctx.mpq(p,q), 'Q' + x = ctx.convert(x) + if hasattr(x, "_mpc_"): + v, im = x._mpc_ + if im != fzero: + return x, 'C' + elif hasattr(x, "_mpf_"): + v = x._mpf_ + else: + return x, 'U' + sign, man, exp, bc = v + if man: + if exp >= -4: + if sign: + man = -man + if exp >= 0: + return int(man) << exp, 'Z' + if exp >= -4: + p, q = int(man), (1<<(-exp)) + return ctx.mpq(p,q), 'Q' + x = ctx.make_mpf(v) + return x, 'R' + elif not exp: + return 0, 'Z' + else: + return x, 'U' + + def _mpf_mag(ctx, x): + sign, man, exp, bc = x + if man: + return exp+bc + if x == fzero: + return ctx.ninf + if x == finf or x == fninf: + return ctx.inf + return ctx.nan + + def mag(ctx, x): + """ + Quick logarithmic magnitude estimate of a number. Returns an + integer or infinity `m` such that `|x| <= 2^m`. It is not + guaranteed that `m` is an optimal bound, but it will never + be too large by more than 2 (and probably not more than 1). + + **Examples** + + >>> from mpmath import * + >>> mp.pretty = True + >>> mag(10), mag(10.0), mag(mpf(10)), int(ceil(log(10,2))) + (4, 4, 4, 4) + >>> mag(10j), mag(10+10j) + (4, 5) + >>> mag(0.01), int(ceil(log(0.01,2))) + (-6, -6) + >>> mag(0), mag(inf), mag(-inf), mag(nan) + (-inf, +inf, +inf, nan) + + """ + if hasattr(x, "_mpf_"): + return ctx._mpf_mag(x._mpf_) + elif hasattr(x, "_mpc_"): + r, i = x._mpc_ + if r == fzero: + return ctx._mpf_mag(i) + if i == fzero: + return ctx._mpf_mag(r) + return 1+max(ctx._mpf_mag(r), ctx._mpf_mag(i)) + elif isinstance(x, int_types): + if x: + return bitcount(abs(x)) + return ctx.ninf + elif isinstance(x, rational.mpq): + p, q = x._mpq_ + if p: + return 1 + bitcount(abs(p)) - bitcount(q) + return ctx.ninf + else: + x = ctx.convert(x) + if hasattr(x, "_mpf_") or hasattr(x, "_mpc_"): + return ctx.mag(x) + else: + raise TypeError("requires an mpf/mpc") + + +# Register with "numbers" ABC +# We do not subclass, hence we do not use the @abstractmethod checks. While +# this is less invasive it may turn out that we do not actually support +# parts of the expected interfaces. See +# http://docs.python.org/2/library/numbers.html for list of abstract +# methods. +try: + import numbers + numbers.Complex.register(_mpc) + numbers.Real.register(_mpf) +except ImportError: + pass diff --git a/venv/lib/python3.12/site-packages/mpmath/function_docs.py b/venv/lib/python3.12/site-packages/mpmath/function_docs.py new file mode 100644 index 0000000000000000000000000000000000000000..73c071dc30a25c0ea1366e06a407a20206bd18a2 --- /dev/null +++ b/venv/lib/python3.12/site-packages/mpmath/function_docs.py @@ -0,0 +1,10201 @@ +""" +Extended docstrings for functions.py +""" + + +pi = r""" +`\pi`, roughly equal to 3.141592654, represents the area of the unit +circle, the half-period of trigonometric functions, and many other +things in mathematics. + +Mpmath can evaluate `\pi` to arbitrary precision:: + + >>> from mpmath import * + >>> mp.dps = 50; mp.pretty = True + >>> +pi + 3.1415926535897932384626433832795028841971693993751 + +This shows digits 99991-100000 of `\pi` (the last digit is actually +a 4 when the decimal expansion is truncated, but here the nearest +rounding is used):: + + >>> mp.dps = 100000 + >>> str(pi)[-10:] + '5549362465' + +**Possible issues** + +:data:`pi` always rounds to the nearest floating-point +number when used. This means that exact mathematical identities +involving `\pi` will generally not be preserved in floating-point +arithmetic. In particular, multiples of :data:`pi` (except for +the trivial case ``0*pi``) are *not* the exact roots of +:func:`~mpmath.sin`, but differ roughly by the current epsilon:: + + >>> mp.dps = 15 + >>> sin(pi) + 1.22464679914735e-16 + +One solution is to use the :func:`~mpmath.sinpi` function instead:: + + >>> sinpi(1) + 0.0 + +See the documentation of trigonometric functions for additional +details. + +**References** + +* [BorweinBorwein]_ + +""" + +degree = r""" +Represents one degree of angle, `1^{\circ} = \pi/180`, or +about 0.01745329. This constant may be evaluated to arbitrary +precision:: + + >>> from mpmath import * + >>> mp.dps = 50; mp.pretty = True + >>> +degree + 0.017453292519943295769236907684886127134428718885417 + +The :data:`degree` object is convenient for conversion +to radians:: + + >>> sin(30 * degree) + 0.5 + >>> asin(0.5) / degree + 30.0 +""" + +e = r""" +The transcendental number `e` = 2.718281828... is the base of the +natural logarithm (:func:`~mpmath.ln`) and of the exponential function +(:func:`~mpmath.exp`). + +Mpmath can be evaluate `e` to arbitrary precision:: + + >>> from mpmath import * + >>> mp.dps = 50; mp.pretty = True + >>> +e + 2.7182818284590452353602874713526624977572470937 + +This shows digits 99991-100000 of `e` (the last digit is actually +a 5 when the decimal expansion is truncated, but here the nearest +rounding is used):: + + >>> mp.dps = 100000 + >>> str(e)[-10:] + '2100427166' + +**Possible issues** + +:data:`e` always rounds to the nearest floating-point number +when used, and mathematical identities involving `e` may not +hold in floating-point arithmetic. For example, ``ln(e)`` +might not evaluate exactly to 1. + +In particular, don't use ``e**x`` to compute the exponential +function. Use ``exp(x)`` instead; this is both faster and more +accurate. +""" + +phi = r""" +Represents the golden ratio `\phi = (1+\sqrt 5)/2`, +approximately equal to 1.6180339887. To high precision, +its value is:: + + >>> from mpmath import * + >>> mp.dps = 50; mp.pretty = True + >>> +phi + 1.6180339887498948482045868343656381177203091798058 + +Formulas for the golden ratio include the following:: + + >>> (1+sqrt(5))/2 + 1.6180339887498948482045868343656381177203091798058 + >>> findroot(lambda x: x**2-x-1, 1) + 1.6180339887498948482045868343656381177203091798058 + >>> limit(lambda n: fib(n+1)/fib(n), inf) + 1.6180339887498948482045868343656381177203091798058 +""" + +euler = r""" +Euler's constant or the Euler-Mascheroni constant `\gamma` += 0.57721566... is a number of central importance to +number theory and special functions. It is defined as the limit + +.. math :: + + \gamma = \lim_{n\to\infty} H_n - \log n + +where `H_n = 1 + \frac{1}{2} + \ldots + \frac{1}{n}` is a harmonic +number (see :func:`~mpmath.harmonic`). + +Evaluation of `\gamma` is supported at arbitrary precision:: + + >>> from mpmath import * + >>> mp.dps = 50; mp.pretty = True + >>> +euler + 0.57721566490153286060651209008240243104215933593992 + +We can also compute `\gamma` directly from the definition, +although this is less efficient:: + + >>> limit(lambda n: harmonic(n)-log(n), inf) + 0.57721566490153286060651209008240243104215933593992 + +This shows digits 9991-10000 of `\gamma` (the last digit is actually +a 5 when the decimal expansion is truncated, but here the nearest +rounding is used):: + + >>> mp.dps = 10000 + >>> str(euler)[-10:] + '4679858166' + +Integrals, series, and representations for `\gamma` in terms of +special functions include the following (there are many others):: + + >>> mp.dps = 25 + >>> -quad(lambda x: exp(-x)*log(x), [0,inf]) + 0.5772156649015328606065121 + >>> quad(lambda x,y: (x-1)/(1-x*y)/log(x*y), [0,1], [0,1]) + 0.5772156649015328606065121 + >>> nsum(lambda k: 1/k-log(1+1/k), [1,inf]) + 0.5772156649015328606065121 + >>> nsum(lambda k: (-1)**k*zeta(k)/k, [2,inf]) + 0.5772156649015328606065121 + >>> -diff(gamma, 1) + 0.5772156649015328606065121 + >>> limit(lambda x: 1/x-gamma(x), 0) + 0.5772156649015328606065121 + >>> limit(lambda x: zeta(x)-1/(x-1), 1) + 0.5772156649015328606065121 + >>> (log(2*pi*nprod(lambda n: + ... exp(-2+2/n)*(1+2/n)**n, [1,inf]))-3)/2 + 0.5772156649015328606065121 + +For generalizations of the identities `\gamma = -\Gamma'(1)` +and `\gamma = \lim_{x\to1} \zeta(x)-1/(x-1)`, see +:func:`~mpmath.psi` and :func:`~mpmath.stieltjes` respectively. + +**References** + +* [BorweinBailey]_ + +""" + +catalan = r""" +Catalan's constant `K` = 0.91596559... is given by the infinite +series + +.. math :: + + K = \sum_{k=0}^{\infty} \frac{(-1)^k}{(2k+1)^2}. + +Mpmath can evaluate it to arbitrary precision:: + + >>> from mpmath import * + >>> mp.dps = 50; mp.pretty = True + >>> +catalan + 0.91596559417721901505460351493238411077414937428167 + +One can also compute `K` directly from the definition, although +this is significantly less efficient:: + + >>> nsum(lambda k: (-1)**k/(2*k+1)**2, [0, inf]) + 0.91596559417721901505460351493238411077414937428167 + +This shows digits 9991-10000 of `K` (the last digit is actually +a 3 when the decimal expansion is truncated, but here the nearest +rounding is used):: + + >>> mp.dps = 10000 + >>> str(catalan)[-10:] + '9537871504' + +Catalan's constant has numerous integral representations:: + + >>> mp.dps = 50 + >>> quad(lambda x: -log(x)/(1+x**2), [0, 1]) + 0.91596559417721901505460351493238411077414937428167 + >>> quad(lambda x: atan(x)/x, [0, 1]) + 0.91596559417721901505460351493238411077414937428167 + >>> quad(lambda x: ellipk(x**2)/2, [0, 1]) + 0.91596559417721901505460351493238411077414937428167 + >>> quad(lambda x,y: 1/(1+(x*y)**2), [0, 1], [0, 1]) + 0.91596559417721901505460351493238411077414937428167 + +As well as series representations:: + + >>> pi*log(sqrt(3)+2)/8 + 3*nsum(lambda n: + ... (fac(n)/(2*n+1))**2/fac(2*n), [0, inf])/8 + 0.91596559417721901505460351493238411077414937428167 + >>> 1-nsum(lambda n: n*zeta(2*n+1)/16**n, [1,inf]) + 0.91596559417721901505460351493238411077414937428167 +""" + +khinchin = r""" +Khinchin's constant `K` = 2.68542... is a number that +appears in the theory of continued fractions. Mpmath can evaluate +it to arbitrary precision:: + + >>> from mpmath import * + >>> mp.dps = 50; mp.pretty = True + >>> +khinchin + 2.6854520010653064453097148354817956938203822939945 + +An integral representation is:: + + >>> I = quad(lambda x: log((1-x**2)/sincpi(x))/x/(1+x), [0, 1]) + >>> 2*exp(1/log(2)*I) + 2.6854520010653064453097148354817956938203822939945 + +The computation of ``khinchin`` is based on an efficient +implementation of the following series:: + + >>> f = lambda n: (zeta(2*n)-1)/n*sum((-1)**(k+1)/mpf(k) + ... for k in range(1,2*int(n))) + >>> exp(nsum(f, [1,inf])/log(2)) + 2.6854520010653064453097148354817956938203822939945 +""" + +glaisher = r""" +Glaisher's constant `A`, also known as the Glaisher-Kinkelin +constant, is a number approximately equal to 1.282427129 that +sometimes appears in formulas related to gamma and zeta functions. +It is also related to the Barnes G-function (see :func:`~mpmath.barnesg`). + +The constant is defined as `A = \exp(1/12-\zeta'(-1))` where +`\zeta'(s)` denotes the derivative of the Riemann zeta function +(see :func:`~mpmath.zeta`). + +Mpmath can evaluate Glaisher's constant to arbitrary precision: + + >>> from mpmath import * + >>> mp.dps = 50; mp.pretty = True + >>> +glaisher + 1.282427129100622636875342568869791727767688927325 + +We can verify that the value computed by :data:`glaisher` is +correct using mpmath's facilities for numerical +differentiation and arbitrary evaluation of the zeta function: + + >>> exp(mpf(1)/12 - diff(zeta, -1)) + 1.282427129100622636875342568869791727767688927325 + +Here is an example of an integral that can be evaluated in +terms of Glaisher's constant: + + >>> mp.dps = 15 + >>> quad(lambda x: log(gamma(x)), [1, 1.5]) + -0.0428537406502909 + >>> -0.5 - 7*log(2)/24 + log(pi)/4 + 3*log(glaisher)/2 + -0.042853740650291 + +Mpmath computes Glaisher's constant by applying Euler-Maclaurin +summation to a slowly convergent series. The implementation is +reasonably efficient up to about 10,000 digits. See the source +code for additional details. + +References: +http://mathworld.wolfram.com/Glaisher-KinkelinConstant.html +""" + +apery = r""" +Represents Apery's constant, which is the irrational number +approximately equal to 1.2020569 given by + +.. math :: + + \zeta(3) = \sum_{k=1}^\infty\frac{1}{k^3}. + +The calculation is based on an efficient hypergeometric +series. To 50 decimal places, the value is given by:: + + >>> from mpmath import * + >>> mp.dps = 50; mp.pretty = True + >>> +apery + 1.2020569031595942853997381615114499907649862923405 + +Other ways to evaluate Apery's constant using mpmath +include:: + + >>> zeta(3) + 1.2020569031595942853997381615114499907649862923405 + >>> -psi(2,1)/2 + 1.2020569031595942853997381615114499907649862923405 + >>> 8*nsum(lambda k: 1/(2*k+1)**3, [0,inf])/7 + 1.2020569031595942853997381615114499907649862923405 + >>> f = lambda k: 2/k**3/(exp(2*pi*k)-1) + >>> 7*pi**3/180 - nsum(f, [1,inf]) + 1.2020569031595942853997381615114499907649862923405 + +This shows digits 9991-10000 of Apery's constant:: + + >>> mp.dps = 10000 + >>> str(apery)[-10:] + '3189504235' +""" + +mertens = r""" +Represents the Mertens or Meissel-Mertens constant, which is the +prime number analog of Euler's constant: + +.. math :: + + B_1 = \lim_{N\to\infty} + \left(\sum_{p_k \le N} \frac{1}{p_k} - \log \log N \right) + +Here `p_k` denotes the `k`-th prime number. Other names for this +constant include the Hadamard-de la Vallee-Poussin constant or +the prime reciprocal constant. + +The following gives the Mertens constant to 50 digits:: + + >>> from mpmath import * + >>> mp.dps = 50; mp.pretty = True + >>> +mertens + 0.2614972128476427837554268386086958590515666482612 + +References: +http://mathworld.wolfram.com/MertensConstant.html +""" + +twinprime = r""" +Represents the twin prime constant, which is the factor `C_2` +featuring in the Hardy-Littlewood conjecture for the growth of the +twin prime counting function, + +.. math :: + + \pi_2(n) \sim 2 C_2 \frac{n}{\log^2 n}. + +It is given by the product over primes + +.. math :: + + C_2 = \prod_{p\ge3} \frac{p(p-2)}{(p-1)^2} \approx 0.66016 + +Computing `C_2` to 50 digits:: + + >>> from mpmath import * + >>> mp.dps = 50; mp.pretty = True + >>> +twinprime + 0.66016181584686957392781211001455577843262336028473 + +References: +http://mathworld.wolfram.com/TwinPrimesConstant.html +""" + +ln = r""" +Computes the natural logarithm of `x`, `\ln x`. +See :func:`~mpmath.log` for additional documentation.""" + +sqrt = r""" +``sqrt(x)`` gives the principal square root of `x`, `\sqrt x`. +For positive real numbers, the principal root is simply the +positive square root. For arbitrary complex numbers, the principal +square root is defined to satisfy `\sqrt x = \exp(\log(x)/2)`. +The function thus has a branch cut along the negative half real axis. + +For all mpmath numbers ``x``, calling ``sqrt(x)`` is equivalent to +performing ``x**0.5``. + +**Examples** + +Basic examples and limits:: + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = True + >>> sqrt(10) + 3.16227766016838 + >>> sqrt(100) + 10.0 + >>> sqrt(-4) + (0.0 + 2.0j) + >>> sqrt(1+1j) + (1.09868411346781 + 0.455089860562227j) + >>> sqrt(inf) + +inf + +Square root evaluation is fast at huge precision:: + + >>> mp.dps = 50000 + >>> a = sqrt(3) + >>> str(a)[-10:] + '9329332815' + +:func:`mpmath.iv.sqrt` supports interval arguments:: + + >>> iv.dps = 15; iv.pretty = True + >>> iv.sqrt([16,100]) + [4.0, 10.0] + >>> iv.sqrt(2) + [1.4142135623730949234, 1.4142135623730951455] + >>> iv.sqrt(2) ** 2 + [1.9999999999999995559, 2.0000000000000004441] + +""" + +cbrt = r""" +``cbrt(x)`` computes the cube root of `x`, `x^{1/3}`. This +function is faster and more accurate than raising to a floating-point +fraction:: + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = False + >>> 125**(mpf(1)/3) + mpf('4.9999999999999991') + >>> cbrt(125) + mpf('5.0') + +Every nonzero complex number has three cube roots. This function +returns the cube root defined by `\exp(\log(x)/3)` where the +principal branch of the natural logarithm is used. Note that this +does not give a real cube root for negative real numbers:: + + >>> mp.pretty = True + >>> cbrt(-1) + (0.5 + 0.866025403784439j) +""" + +exp = r""" +Computes the exponential function, + +.. math :: + + \exp(x) = e^x = \sum_{k=0}^{\infty} \frac{x^k}{k!}. + +For complex numbers, the exponential function also satisfies + +.. math :: + + \exp(x+yi) = e^x (\cos y + i \sin y). + +**Basic examples** + +Some values of the exponential function:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> exp(0) + 1.0 + >>> exp(1) + 2.718281828459045235360287 + >>> exp(-1) + 0.3678794411714423215955238 + >>> exp(inf) + +inf + >>> exp(-inf) + 0.0 + +Arguments can be arbitrarily large:: + + >>> exp(10000) + 8.806818225662921587261496e+4342 + >>> exp(-10000) + 1.135483865314736098540939e-4343 + +Evaluation is supported for interval arguments via +:func:`mpmath.iv.exp`:: + + >>> iv.dps = 25; iv.pretty = True + >>> iv.exp([-inf,0]) + [0.0, 1.0] + >>> iv.exp([0,1]) + [1.0, 2.71828182845904523536028749558] + +The exponential function can be evaluated efficiently to arbitrary +precision:: + + >>> mp.dps = 10000 + >>> exp(pi) #doctest: +ELLIPSIS + 23.140692632779269005729...8984304016040616 + +**Functional properties** + +Numerical verification of Euler's identity for the complex +exponential function:: + + >>> mp.dps = 15 + >>> exp(j*pi)+1 + (0.0 + 1.22464679914735e-16j) + >>> chop(exp(j*pi)+1) + 0.0 + +This recovers the coefficients (reciprocal factorials) in the +Maclaurin series expansion of exp:: + + >>> nprint(taylor(exp, 0, 5)) + [1.0, 1.0, 0.5, 0.166667, 0.0416667, 0.00833333] + +The exponential function is its own derivative and antiderivative:: + + >>> exp(pi) + 23.1406926327793 + >>> diff(exp, pi) + 23.1406926327793 + >>> quad(exp, [-inf, pi]) + 23.1406926327793 + +The exponential function can be evaluated using various methods, +including direct summation of the series, limits, and solving +the defining differential equation:: + + >>> nsum(lambda k: pi**k/fac(k), [0,inf]) + 23.1406926327793 + >>> limit(lambda k: (1+pi/k)**k, inf) + 23.1406926327793 + >>> odefun(lambda t, x: x, 0, 1)(pi) + 23.1406926327793 +""" + +cosh = r""" +Computes the hyperbolic cosine of `x`, +`\cosh(x) = (e^x + e^{-x})/2`. Values and limits include:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> cosh(0) + 1.0 + >>> cosh(1) + 1.543080634815243778477906 + >>> cosh(-inf), cosh(+inf) + (+inf, +inf) + +The hyperbolic cosine is an even, convex function with +a global minimum at `x = 0`, having a Maclaurin series +that starts:: + + >>> nprint(chop(taylor(cosh, 0, 5))) + [1.0, 0.0, 0.5, 0.0, 0.0416667, 0.0] + +Generalized to complex numbers, the hyperbolic cosine is +equivalent to a cosine with the argument rotated +in the imaginary direction, or `\cosh x = \cos ix`:: + + >>> cosh(2+3j) + (-3.724545504915322565473971 + 0.5118225699873846088344638j) + >>> cos(3-2j) + (-3.724545504915322565473971 + 0.5118225699873846088344638j) +""" + +sinh = r""" +Computes the hyperbolic sine of `x`, +`\sinh(x) = (e^x - e^{-x})/2`. Values and limits include:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> sinh(0) + 0.0 + >>> sinh(1) + 1.175201193643801456882382 + >>> sinh(-inf), sinh(+inf) + (-inf, +inf) + +The hyperbolic sine is an odd function, with a Maclaurin +series that starts:: + + >>> nprint(chop(taylor(sinh, 0, 5))) + [0.0, 1.0, 0.0, 0.166667, 0.0, 0.00833333] + +Generalized to complex numbers, the hyperbolic sine is +essentially a sine with a rotation `i` applied to +the argument; more precisely, `\sinh x = -i \sin ix`:: + + >>> sinh(2+3j) + (-3.590564589985779952012565 + 0.5309210862485198052670401j) + >>> j*sin(3-2j) + (-3.590564589985779952012565 + 0.5309210862485198052670401j) +""" + +tanh = r""" +Computes the hyperbolic tangent of `x`, +`\tanh(x) = \sinh(x)/\cosh(x)`. Values and limits include:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> tanh(0) + 0.0 + >>> tanh(1) + 0.7615941559557648881194583 + >>> tanh(-inf), tanh(inf) + (-1.0, 1.0) + +The hyperbolic tangent is an odd, sigmoidal function, similar +to the inverse tangent and error function. Its Maclaurin +series is:: + + >>> nprint(chop(taylor(tanh, 0, 5))) + [0.0, 1.0, 0.0, -0.333333, 0.0, 0.133333] + +Generalized to complex numbers, the hyperbolic tangent is +essentially a tangent with a rotation `i` applied to +the argument; more precisely, `\tanh x = -i \tan ix`:: + + >>> tanh(2+3j) + (0.9653858790221331242784803 - 0.009884375038322493720314034j) + >>> j*tan(3-2j) + (0.9653858790221331242784803 - 0.009884375038322493720314034j) +""" + +cos = r""" +Computes the cosine of `x`, `\cos(x)`. + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> cos(pi/3) + 0.5 + >>> cos(100000001) + -0.9802850113244713353133243 + >>> cos(2+3j) + (-4.189625690968807230132555 - 9.109227893755336597979197j) + >>> cos(inf) + nan + >>> nprint(chop(taylor(cos, 0, 6))) + [1.0, 0.0, -0.5, 0.0, 0.0416667, 0.0, -0.00138889] + +Intervals are supported via :func:`mpmath.iv.cos`:: + + >>> iv.dps = 25; iv.pretty = True + >>> iv.cos([0,1]) + [0.540302305868139717400936602301, 1.0] + >>> iv.cos([0,2]) + [-0.41614683654714238699756823214, 1.0] +""" + +sin = r""" +Computes the sine of `x`, `\sin(x)`. + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> sin(pi/3) + 0.8660254037844386467637232 + >>> sin(100000001) + 0.1975887055794968911438743 + >>> sin(2+3j) + (9.1544991469114295734673 - 4.168906959966564350754813j) + >>> sin(inf) + nan + >>> nprint(chop(taylor(sin, 0, 6))) + [0.0, 1.0, 0.0, -0.166667, 0.0, 0.00833333, 0.0] + +Intervals are supported via :func:`mpmath.iv.sin`:: + + >>> iv.dps = 25; iv.pretty = True + >>> iv.sin([0,1]) + [0.0, 0.841470984807896506652502331201] + >>> iv.sin([0,2]) + [0.0, 1.0] +""" + +tan = r""" +Computes the tangent of `x`, `\tan(x) = \frac{\sin(x)}{\cos(x)}`. +The tangent function is singular at `x = (n+1/2)\pi`, but +``tan(x)`` always returns a finite result since `(n+1/2)\pi` +cannot be represented exactly using floating-point arithmetic. + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> tan(pi/3) + 1.732050807568877293527446 + >>> tan(100000001) + -0.2015625081449864533091058 + >>> tan(2+3j) + (-0.003764025641504248292751221 + 1.003238627353609801446359j) + >>> tan(inf) + nan + >>> nprint(chop(taylor(tan, 0, 6))) + [0.0, 1.0, 0.0, 0.333333, 0.0, 0.133333, 0.0] + +Intervals are supported via :func:`mpmath.iv.tan`:: + + >>> iv.dps = 25; iv.pretty = True + >>> iv.tan([0,1]) + [0.0, 1.55740772465490223050697482944] + >>> iv.tan([0,2]) # Interval includes a singularity + [-inf, +inf] +""" + +sec = r""" +Computes the secant of `x`, `\mathrm{sec}(x) = \frac{1}{\cos(x)}`. +The secant function is singular at `x = (n+1/2)\pi`, but +``sec(x)`` always returns a finite result since `(n+1/2)\pi` +cannot be represented exactly using floating-point arithmetic. + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> sec(pi/3) + 2.0 + >>> sec(10000001) + -1.184723164360392819100265 + >>> sec(2+3j) + (-0.04167496441114427004834991 + 0.0906111371962375965296612j) + >>> sec(inf) + nan + >>> nprint(chop(taylor(sec, 0, 6))) + [1.0, 0.0, 0.5, 0.0, 0.208333, 0.0, 0.0847222] + +Intervals are supported via :func:`mpmath.iv.sec`:: + + >>> iv.dps = 25; iv.pretty = True + >>> iv.sec([0,1]) + [1.0, 1.85081571768092561791175326276] + >>> iv.sec([0,2]) # Interval includes a singularity + [-inf, +inf] +""" + +csc = r""" +Computes the cosecant of `x`, `\mathrm{csc}(x) = \frac{1}{\sin(x)}`. +This cosecant function is singular at `x = n \pi`, but with the +exception of the point `x = 0`, ``csc(x)`` returns a finite result +since `n \pi` cannot be represented exactly using floating-point +arithmetic. + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> csc(pi/3) + 1.154700538379251529018298 + >>> csc(10000001) + -1.864910497503629858938891 + >>> csc(2+3j) + (0.09047320975320743980579048 + 0.04120098628857412646300981j) + >>> csc(inf) + nan + +Intervals are supported via :func:`mpmath.iv.csc`:: + + >>> iv.dps = 25; iv.pretty = True + >>> iv.csc([0,1]) # Interval includes a singularity + [1.18839510577812121626159943988, +inf] + >>> iv.csc([0,2]) + [1.0, +inf] +""" + +cot = r""" +Computes the cotangent of `x`, +`\mathrm{cot}(x) = \frac{1}{\tan(x)} = \frac{\cos(x)}{\sin(x)}`. +This cotangent function is singular at `x = n \pi`, but with the +exception of the point `x = 0`, ``cot(x)`` returns a finite result +since `n \pi` cannot be represented exactly using floating-point +arithmetic. + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> cot(pi/3) + 0.5773502691896257645091488 + >>> cot(10000001) + 1.574131876209625656003562 + >>> cot(2+3j) + (-0.003739710376336956660117409 - 0.9967577965693583104609688j) + >>> cot(inf) + nan + +Intervals are supported via :func:`mpmath.iv.cot`:: + + >>> iv.dps = 25; iv.pretty = True + >>> iv.cot([0,1]) # Interval includes a singularity + [0.642092615934330703006419974862, +inf] + >>> iv.cot([1,2]) + [-inf, +inf] +""" + +acos = r""" +Computes the inverse cosine or arccosine of `x`, `\cos^{-1}(x)`. +Since `-1 \le \cos(x) \le 1` for real `x`, the inverse +cosine is real-valued only for `-1 \le x \le 1`. On this interval, +:func:`~mpmath.acos` is defined to be a monotonically decreasing +function assuming values between `+\pi` and `0`. + +Basic values are:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> acos(-1) + 3.141592653589793238462643 + >>> acos(0) + 1.570796326794896619231322 + >>> acos(1) + 0.0 + >>> nprint(chop(taylor(acos, 0, 6))) + [1.5708, -1.0, 0.0, -0.166667, 0.0, -0.075, 0.0] + +:func:`~mpmath.acos` is defined so as to be a proper inverse function of +`\cos(\theta)` for `0 \le \theta < \pi`. +We have `\cos(\cos^{-1}(x)) = x` for all `x`, but +`\cos^{-1}(\cos(x)) = x` only for `0 \le \Re[x] < \pi`:: + + >>> for x in [1, 10, -1, 2+3j, 10+3j]: + ... print("%s %s" % (cos(acos(x)), acos(cos(x)))) + ... + 1.0 1.0 + (10.0 + 0.0j) 2.566370614359172953850574 + -1.0 1.0 + (2.0 + 3.0j) (2.0 + 3.0j) + (10.0 + 3.0j) (2.566370614359172953850574 - 3.0j) + +The inverse cosine has two branch points: `x = \pm 1`. :func:`~mpmath.acos` +places the branch cuts along the line segments `(-\infty, -1)` and +`(+1, +\infty)`. In general, + +.. math :: + + \cos^{-1}(x) = \frac{\pi}{2} + i \log\left(ix + \sqrt{1-x^2} \right) + +where the principal-branch log and square root are implied. +""" + +asin = r""" +Computes the inverse sine or arcsine of `x`, `\sin^{-1}(x)`. +Since `-1 \le \sin(x) \le 1` for real `x`, the inverse +sine is real-valued only for `-1 \le x \le 1`. +On this interval, it is defined to be a monotonically increasing +function assuming values between `-\pi/2` and `\pi/2`. + +Basic values are:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> asin(-1) + -1.570796326794896619231322 + >>> asin(0) + 0.0 + >>> asin(1) + 1.570796326794896619231322 + >>> nprint(chop(taylor(asin, 0, 6))) + [0.0, 1.0, 0.0, 0.166667, 0.0, 0.075, 0.0] + +:func:`~mpmath.asin` is defined so as to be a proper inverse function of +`\sin(\theta)` for `-\pi/2 < \theta < \pi/2`. +We have `\sin(\sin^{-1}(x)) = x` for all `x`, but +`\sin^{-1}(\sin(x)) = x` only for `-\pi/2 < \Re[x] < \pi/2`:: + + >>> for x in [1, 10, -1, 1+3j, -2+3j]: + ... print("%s %s" % (chop(sin(asin(x))), asin(sin(x)))) + ... + 1.0 1.0 + 10.0 -0.5752220392306202846120698 + -1.0 -1.0 + (1.0 + 3.0j) (1.0 + 3.0j) + (-2.0 + 3.0j) (-1.141592653589793238462643 - 3.0j) + +The inverse sine has two branch points: `x = \pm 1`. :func:`~mpmath.asin` +places the branch cuts along the line segments `(-\infty, -1)` and +`(+1, +\infty)`. In general, + +.. math :: + + \sin^{-1}(x) = -i \log\left(ix + \sqrt{1-x^2} \right) + +where the principal-branch log and square root are implied. +""" + +atan = r""" +Computes the inverse tangent or arctangent of `x`, `\tan^{-1}(x)`. +This is a real-valued function for all real `x`, with range +`(-\pi/2, \pi/2)`. + +Basic values are:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> atan(-inf) + -1.570796326794896619231322 + >>> atan(-1) + -0.7853981633974483096156609 + >>> atan(0) + 0.0 + >>> atan(1) + 0.7853981633974483096156609 + >>> atan(inf) + 1.570796326794896619231322 + >>> nprint(chop(taylor(atan, 0, 6))) + [0.0, 1.0, 0.0, -0.333333, 0.0, 0.2, 0.0] + +The inverse tangent is often used to compute angles. However, +the atan2 function is often better for this as it preserves sign +(see :func:`~mpmath.atan2`). + +:func:`~mpmath.atan` is defined so as to be a proper inverse function of +`\tan(\theta)` for `-\pi/2 < \theta < \pi/2`. +We have `\tan(\tan^{-1}(x)) = x` for all `x`, but +`\tan^{-1}(\tan(x)) = x` only for `-\pi/2 < \Re[x] < \pi/2`:: + + >>> mp.dps = 25 + >>> for x in [1, 10, -1, 1+3j, -2+3j]: + ... print("%s %s" % (tan(atan(x)), atan(tan(x)))) + ... + 1.0 1.0 + 10.0 0.5752220392306202846120698 + -1.0 -1.0 + (1.0 + 3.0j) (1.000000000000000000000001 + 3.0j) + (-2.0 + 3.0j) (1.141592653589793238462644 + 3.0j) + +The inverse tangent has two branch points: `x = \pm i`. :func:`~mpmath.atan` +places the branch cuts along the line segments `(-i \infty, -i)` and +`(+i, +i \infty)`. In general, + +.. math :: + + \tan^{-1}(x) = \frac{i}{2}\left(\log(1-ix)-\log(1+ix)\right) + +where the principal-branch log is implied. +""" + +acot = r"""Computes the inverse cotangent of `x`, +`\mathrm{cot}^{-1}(x) = \tan^{-1}(1/x)`.""" + +asec = r"""Computes the inverse secant of `x`, +`\mathrm{sec}^{-1}(x) = \cos^{-1}(1/x)`.""" + +acsc = r"""Computes the inverse cosecant of `x`, +`\mathrm{csc}^{-1}(x) = \sin^{-1}(1/x)`.""" + +coth = r"""Computes the hyperbolic cotangent of `x`, +`\mathrm{coth}(x) = \frac{\cosh(x)}{\sinh(x)}`. +""" + +sech = r"""Computes the hyperbolic secant of `x`, +`\mathrm{sech}(x) = \frac{1}{\cosh(x)}`. +""" + +csch = r"""Computes the hyperbolic cosecant of `x`, +`\mathrm{csch}(x) = \frac{1}{\sinh(x)}`. +""" + +acosh = r"""Computes the inverse hyperbolic cosine of `x`, +`\mathrm{cosh}^{-1}(x) = \log(x+\sqrt{x+1}\sqrt{x-1})`. +""" + +asinh = r"""Computes the inverse hyperbolic sine of `x`, +`\mathrm{sinh}^{-1}(x) = \log(x+\sqrt{1+x^2})`. +""" + +atanh = r"""Computes the inverse hyperbolic tangent of `x`, +`\mathrm{tanh}^{-1}(x) = \frac{1}{2}\left(\log(1+x)-\log(1-x)\right)`. +""" + +acoth = r"""Computes the inverse hyperbolic cotangent of `x`, +`\mathrm{coth}^{-1}(x) = \tanh^{-1}(1/x)`.""" + +asech = r"""Computes the inverse hyperbolic secant of `x`, +`\mathrm{sech}^{-1}(x) = \cosh^{-1}(1/x)`.""" + +acsch = r"""Computes the inverse hyperbolic cosecant of `x`, +`\mathrm{csch}^{-1}(x) = \sinh^{-1}(1/x)`.""" + + + +sinpi = r""" +Computes `\sin(\pi x)`, more accurately than the expression +``sin(pi*x)``:: + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = True + >>> sinpi(10**10), sin(pi*(10**10)) + (0.0, -2.23936276195592e-6) + >>> sinpi(10**10+0.5), sin(pi*(10**10+0.5)) + (1.0, 0.999999999998721) +""" + +cospi = r""" +Computes `\cos(\pi x)`, more accurately than the expression +``cos(pi*x)``:: + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = True + >>> cospi(10**10), cos(pi*(10**10)) + (1.0, 0.999999999997493) + >>> cospi(10**10+0.5), cos(pi*(10**10+0.5)) + (0.0, 1.59960492420134e-6) +""" + +sinc = r""" +``sinc(x)`` computes the unnormalized sinc function, defined as + +.. math :: + + \mathrm{sinc}(x) = \begin{cases} + \sin(x)/x, & \mbox{if } x \ne 0 \\ + 1, & \mbox{if } x = 0. + \end{cases} + +See :func:`~mpmath.sincpi` for the normalized sinc function. + +Simple values and limits include:: + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = True + >>> sinc(0) + 1.0 + >>> sinc(1) + 0.841470984807897 + >>> sinc(inf) + 0.0 + +The integral of the sinc function is the sine integral Si:: + + >>> quad(sinc, [0, 1]) + 0.946083070367183 + >>> si(1) + 0.946083070367183 +""" + +sincpi = r""" +``sincpi(x)`` computes the normalized sinc function, defined as + +.. math :: + + \mathrm{sinc}_{\pi}(x) = \begin{cases} + \sin(\pi x)/(\pi x), & \mbox{if } x \ne 0 \\ + 1, & \mbox{if } x = 0. + \end{cases} + +Equivalently, we have +`\mathrm{sinc}_{\pi}(x) = \mathrm{sinc}(\pi x)`. + +The normalization entails that the function integrates +to unity over the entire real line:: + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = True + >>> quadosc(sincpi, [-inf, inf], period=2.0) + 1.0 + +Like, :func:`~mpmath.sinpi`, :func:`~mpmath.sincpi` is evaluated accurately +at its roots:: + + >>> sincpi(10) + 0.0 +""" + +expj = r""" +Convenience function for computing `e^{ix}`:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> expj(0) + (1.0 + 0.0j) + >>> expj(-1) + (0.5403023058681397174009366 - 0.8414709848078965066525023j) + >>> expj(j) + (0.3678794411714423215955238 + 0.0j) + >>> expj(1+j) + (0.1987661103464129406288032 + 0.3095598756531121984439128j) +""" + +expjpi = r""" +Convenience function for computing `e^{i \pi x}`. +Evaluation is accurate near zeros (see also :func:`~mpmath.cospi`, +:func:`~mpmath.sinpi`):: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> expjpi(0) + (1.0 + 0.0j) + >>> expjpi(1) + (-1.0 + 0.0j) + >>> expjpi(0.5) + (0.0 + 1.0j) + >>> expjpi(-1) + (-1.0 + 0.0j) + >>> expjpi(j) + (0.04321391826377224977441774 + 0.0j) + >>> expjpi(1+j) + (-0.04321391826377224977441774 + 0.0j) +""" + +floor = r""" +Computes the floor of `x`, `\lfloor x \rfloor`, defined as +the largest integer less than or equal to `x`:: + + >>> from mpmath import * + >>> mp.pretty = False + >>> floor(3.5) + mpf('3.0') + +.. note :: + + :func:`~mpmath.floor`, :func:`~mpmath.ceil` and :func:`~mpmath.nint` return a + floating-point number, not a Python ``int``. If `\lfloor x \rfloor` is + too large to be represented exactly at the present working precision, + the result will be rounded, not necessarily in the direction + implied by the mathematical definition of the function. + +To avoid rounding, use *prec=0*:: + + >>> mp.dps = 15 + >>> print(int(floor(10**30+1))) + 1000000000000000019884624838656 + >>> print(int(floor(10**30+1, prec=0))) + 1000000000000000000000000000001 + +The floor function is defined for complex numbers and +acts on the real and imaginary parts separately:: + + >>> floor(3.25+4.75j) + mpc(real='3.0', imag='4.0') +""" + +ceil = r""" +Computes the ceiling of `x`, `\lceil x \rceil`, defined as +the smallest integer greater than or equal to `x`:: + + >>> from mpmath import * + >>> mp.pretty = False + >>> ceil(3.5) + mpf('4.0') + +The ceiling function is defined for complex numbers and +acts on the real and imaginary parts separately:: + + >>> ceil(3.25+4.75j) + mpc(real='4.0', imag='5.0') + +See notes about rounding for :func:`~mpmath.floor`. +""" + +nint = r""" +Evaluates the nearest integer function, `\mathrm{nint}(x)`. +This gives the nearest integer to `x`; on a tie, it +gives the nearest even integer:: + + >>> from mpmath import * + >>> mp.pretty = False + >>> nint(3.2) + mpf('3.0') + >>> nint(3.8) + mpf('4.0') + >>> nint(3.5) + mpf('4.0') + >>> nint(4.5) + mpf('4.0') + +The nearest integer function is defined for complex numbers and +acts on the real and imaginary parts separately:: + + >>> nint(3.25+4.75j) + mpc(real='3.0', imag='5.0') + +See notes about rounding for :func:`~mpmath.floor`. +""" + +frac = r""" +Gives the fractional part of `x`, defined as +`\mathrm{frac}(x) = x - \lfloor x \rfloor` (see :func:`~mpmath.floor`). +In effect, this computes `x` modulo 1, or `x+n` where +`n \in \mathbb{Z}` is such that `x+n \in [0,1)`:: + + >>> from mpmath import * + >>> mp.pretty = False + >>> frac(1.25) + mpf('0.25') + >>> frac(3) + mpf('0.0') + >>> frac(-1.25) + mpf('0.75') + +For a complex number, the fractional part function applies to +the real and imaginary parts separately:: + + >>> frac(2.25+3.75j) + mpc(real='0.25', imag='0.75') + +Plotted, the fractional part function gives a sawtooth +wave. The Fourier series coefficients have a simple +form:: + + >>> mp.dps = 15 + >>> nprint(fourier(lambda x: frac(x)-0.5, [0,1], 4)) + ([0.0, 0.0, 0.0, 0.0, 0.0], [0.0, -0.31831, -0.159155, -0.106103, -0.0795775]) + >>> nprint([-1/(pi*k) for k in range(1,5)]) + [-0.31831, -0.159155, -0.106103, -0.0795775] + +.. note:: + + The fractional part is sometimes defined as a symmetric + function, i.e. returning `-\mathrm{frac}(-x)` if `x < 0`. + This convention is used, for instance, by Mathematica's + ``FractionalPart``. + +""" + +sign = r""" +Returns the sign of `x`, defined as `\mathrm{sign}(x) = x / |x|` +(with the special case `\mathrm{sign}(0) = 0`):: + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = False + >>> sign(10) + mpf('1.0') + >>> sign(-10) + mpf('-1.0') + >>> sign(0) + mpf('0.0') + +Note that the sign function is also defined for complex numbers, +for which it gives the projection onto the unit circle:: + + >>> mp.dps = 15; mp.pretty = True + >>> sign(1+j) + (0.707106781186547 + 0.707106781186547j) + +""" + +arg = r""" +Computes the complex argument (phase) of `x`, defined as the +signed angle between the positive real axis and `x` in the +complex plane:: + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = True + >>> arg(3) + 0.0 + >>> arg(3+3j) + 0.785398163397448 + >>> arg(3j) + 1.5707963267949 + >>> arg(-3) + 3.14159265358979 + >>> arg(-3j) + -1.5707963267949 + +The angle is defined to satisfy `-\pi < \arg(x) \le \pi` and +with the sign convention that a nonnegative imaginary part +results in a nonnegative argument. + +The value returned by :func:`~mpmath.arg` is an ``mpf`` instance. +""" + +fabs = r""" +Returns the absolute value of `x`, `|x|`. Unlike :func:`abs`, +:func:`~mpmath.fabs` converts non-mpmath numbers (such as ``int``) +into mpmath numbers:: + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = False + >>> fabs(3) + mpf('3.0') + >>> fabs(-3) + mpf('3.0') + >>> fabs(3+4j) + mpf('5.0') +""" + +re = r""" +Returns the real part of `x`, `\Re(x)`. :func:`~mpmath.re` +converts a non-mpmath number to an mpmath number:: + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = False + >>> re(3) + mpf('3.0') + >>> re(-1+4j) + mpf('-1.0') +""" + +im = r""" +Returns the imaginary part of `x`, `\Im(x)`. :func:`~mpmath.im` +converts a non-mpmath number to an mpmath number:: + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = False + >>> im(3) + mpf('0.0') + >>> im(-1+4j) + mpf('4.0') +""" + +conj = r""" +Returns the complex conjugate of `x`, `\overline{x}`. Unlike +``x.conjugate()``, :func:`~mpmath.im` converts `x` to a mpmath number:: + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = False + >>> conj(3) + mpf('3.0') + >>> conj(-1+4j) + mpc(real='-1.0', imag='-4.0') +""" + +polar = r""" +Returns the polar representation of the complex number `z` +as a pair `(r, \phi)` such that `z = r e^{i \phi}`:: + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = True + >>> polar(-2) + (2.0, 3.14159265358979) + >>> polar(3-4j) + (5.0, -0.927295218001612) +""" + +rect = r""" +Returns the complex number represented by polar +coordinates `(r, \phi)`:: + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = True + >>> chop(rect(2, pi)) + -2.0 + >>> rect(sqrt(2), -pi/4) + (1.0 - 1.0j) +""" + +expm1 = r""" +Computes `e^x - 1`, accurately for small `x`. + +Unlike the expression ``exp(x) - 1``, ``expm1(x)`` does not suffer from +potentially catastrophic cancellation:: + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = True + >>> exp(1e-10)-1; print(expm1(1e-10)) + 1.00000008274037e-10 + 1.00000000005e-10 + >>> exp(1e-20)-1; print(expm1(1e-20)) + 0.0 + 1.0e-20 + >>> 1/(exp(1e-20)-1) + Traceback (most recent call last): + ... + ZeroDivisionError + >>> 1/expm1(1e-20) + 1.0e+20 + +Evaluation works for extremely tiny values:: + + >>> expm1(0) + 0.0 + >>> expm1('1e-10000000') + 1.0e-10000000 + +""" + +log1p = r""" +Computes `\log(1+x)`, accurately for small `x`. + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = True + >>> log(1+1e-10); print(mp.log1p(1e-10)) + 1.00000008269037e-10 + 9.9999999995e-11 + >>> mp.log1p(1e-100j) + (5.0e-201 + 1.0e-100j) + >>> mp.log1p(0) + 0.0 + +""" + + +powm1 = r""" +Computes `x^y - 1`, accurately when `x^y` is very close to 1. + +This avoids potentially catastrophic cancellation:: + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = True + >>> power(0.99999995, 1e-10) - 1 + 0.0 + >>> powm1(0.99999995, 1e-10) + -5.00000012791934e-18 + +Powers exactly equal to 1, and only those powers, yield 0 exactly:: + + >>> powm1(-j, 4) + (0.0 + 0.0j) + >>> powm1(3, 0) + 0.0 + >>> powm1(fadd(-1, 1e-100, exact=True), 4) + -4.0e-100 + +Evaluation works for extremely tiny `y`:: + + >>> powm1(2, '1e-100000') + 6.93147180559945e-100001 + >>> powm1(j, '1e-1000') + (-1.23370055013617e-2000 + 1.5707963267949e-1000j) + +""" + +root = r""" +``root(z, n, k=0)`` computes an `n`-th root of `z`, i.e. returns a number +`r` that (up to possible approximation error) satisfies `r^n = z`. +(``nthroot`` is available as an alias for ``root``.) + +Every complex number `z \ne 0` has `n` distinct `n`-th roots, which are +equidistant points on a circle with radius `|z|^{1/n}`, centered around the +origin. A specific root may be selected using the optional index +`k`. The roots are indexed counterclockwise, starting with `k = 0` for the root +closest to the positive real half-axis. + +The `k = 0` root is the so-called principal `n`-th root, often denoted by +`\sqrt[n]{z}` or `z^{1/n}`, and also given by `\exp(\log(z) / n)`. If `z` is +a positive real number, the principal root is just the unique positive +`n`-th root of `z`. Under some circumstances, non-principal real roots exist: +for positive real `z`, `n` even, there is a negative root given by `k = n/2`; +for negative real `z`, `n` odd, there is a negative root given by `k = (n-1)/2`. + +To obtain all roots with a simple expression, use +``[root(z,n,k) for k in range(n)]``. + +An important special case, ``root(1, n, k)`` returns the `k`-th `n`-th root of +unity, `\zeta_k = e^{2 \pi i k / n}`. Alternatively, :func:`~mpmath.unitroots` +provides a slightly more convenient way to obtain the roots of unity, +including the option to compute only the primitive roots of unity. + +Both `k` and `n` should be integers; `k` outside of ``range(n)`` will be +reduced modulo `n`. If `n` is negative, `x^{-1/n} = 1/x^{1/n}` (or +the equivalent reciprocal for a non-principal root with `k \ne 0`) is computed. + +:func:`~mpmath.root` is implemented to use Newton's method for small +`n`. At high precision, this makes `x^{1/n}` not much more +expensive than the regular exponentiation, `x^n`. For very large +`n`, :func:`~mpmath.nthroot` falls back to use the exponential function. + +**Examples** + +:func:`~mpmath.nthroot`/:func:`~mpmath.root` is faster and more accurate than raising to a +floating-point fraction:: + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = False + >>> 16807 ** (mpf(1)/5) + mpf('7.0000000000000009') + >>> root(16807, 5) + mpf('7.0') + >>> nthroot(16807, 5) # Alias + mpf('7.0') + +A high-precision root:: + + >>> mp.dps = 50; mp.pretty = True + >>> nthroot(10, 5) + 1.584893192461113485202101373391507013269442133825 + >>> nthroot(10, 5) ** 5 + 10.0 + +Computing principal and non-principal square and cube roots:: + + >>> mp.dps = 15 + >>> root(10, 2) + 3.16227766016838 + >>> root(10, 2, 1) + -3.16227766016838 + >>> root(-10, 3) + (1.07721734501594 + 1.86579517236206j) + >>> root(-10, 3, 1) + -2.15443469003188 + >>> root(-10, 3, 2) + (1.07721734501594 - 1.86579517236206j) + +All the 7th roots of a complex number:: + + >>> for r in [root(3+4j, 7, k) for k in range(7)]: + ... print("%s %s" % (r, r**7)) + ... + (1.24747270589553 + 0.166227124177353j) (3.0 + 4.0j) + (0.647824911301003 + 1.07895435170559j) (3.0 + 4.0j) + (-0.439648254723098 + 1.17920694574172j) (3.0 + 4.0j) + (-1.19605731775069 + 0.391492658196305j) (3.0 + 4.0j) + (-1.05181082538903 - 0.691023585965793j) (3.0 + 4.0j) + (-0.115529328478668 - 1.25318497558335j) (3.0 + 4.0j) + (0.907748109144957 - 0.871672518271819j) (3.0 + 4.0j) + +Cube roots of unity:: + + >>> for k in range(3): print(root(1, 3, k)) + ... + 1.0 + (-0.5 + 0.866025403784439j) + (-0.5 - 0.866025403784439j) + +Some exact high order roots:: + + >>> root(75**210, 105) + 5625.0 + >>> root(1, 128, 96) + (0.0 - 1.0j) + >>> root(4**128, 128, 96) + (0.0 - 4.0j) + +""" + +unitroots = r""" +``unitroots(n)`` returns `\zeta_0, \zeta_1, \ldots, \zeta_{n-1}`, +all the distinct `n`-th roots of unity, as a list. If the option +*primitive=True* is passed, only the primitive roots are returned. + +Every `n`-th root of unity satisfies `(\zeta_k)^n = 1`. There are `n` distinct +roots for each `n` (`\zeta_k` and `\zeta_j` are the same when +`k = j \pmod n`), which form a regular polygon with vertices on the unit +circle. They are ordered counterclockwise with increasing `k`, starting +with `\zeta_0 = 1`. + +**Examples** + +The roots of unity up to `n = 4`:: + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = True + >>> nprint(unitroots(1)) + [1.0] + >>> nprint(unitroots(2)) + [1.0, -1.0] + >>> nprint(unitroots(3)) + [1.0, (-0.5 + 0.866025j), (-0.5 - 0.866025j)] + >>> nprint(unitroots(4)) + [1.0, (0.0 + 1.0j), -1.0, (0.0 - 1.0j)] + +Roots of unity form a geometric series that sums to 0:: + + >>> mp.dps = 50 + >>> chop(fsum(unitroots(25))) + 0.0 + +Primitive roots up to `n = 4`:: + + >>> mp.dps = 15 + >>> nprint(unitroots(1, primitive=True)) + [1.0] + >>> nprint(unitroots(2, primitive=True)) + [-1.0] + >>> nprint(unitroots(3, primitive=True)) + [(-0.5 + 0.866025j), (-0.5 - 0.866025j)] + >>> nprint(unitroots(4, primitive=True)) + [(0.0 + 1.0j), (0.0 - 1.0j)] + +There are only four primitive 12th roots:: + + >>> nprint(unitroots(12, primitive=True)) + [(0.866025 + 0.5j), (-0.866025 + 0.5j), (-0.866025 - 0.5j), (0.866025 - 0.5j)] + +The `n`-th roots of unity form a group, the cyclic group of order `n`. +Any primitive root `r` is a generator for this group, meaning that +`r^0, r^1, \ldots, r^{n-1}` gives the whole set of unit roots (in +some permuted order):: + + >>> for r in unitroots(6): print(r) + ... + 1.0 + (0.5 + 0.866025403784439j) + (-0.5 + 0.866025403784439j) + -1.0 + (-0.5 - 0.866025403784439j) + (0.5 - 0.866025403784439j) + >>> r = unitroots(6, primitive=True)[1] + >>> for k in range(6): print(chop(r**k)) + ... + 1.0 + (0.5 - 0.866025403784439j) + (-0.5 - 0.866025403784439j) + -1.0 + (-0.5 + 0.866025403784438j) + (0.5 + 0.866025403784438j) + +The number of primitive roots equals the Euler totient function `\phi(n)`:: + + >>> [len(unitroots(n, primitive=True)) for n in range(1,20)] + [1, 1, 2, 2, 4, 2, 6, 4, 6, 4, 10, 4, 12, 6, 8, 8, 16, 6, 18] + +""" + + +log = r""" +Computes the base-`b` logarithm of `x`, `\log_b(x)`. If `b` is +unspecified, :func:`~mpmath.log` computes the natural (base `e`) logarithm +and is equivalent to :func:`~mpmath.ln`. In general, the base `b` logarithm +is defined in terms of the natural logarithm as +`\log_b(x) = \ln(x)/\ln(b)`. + +By convention, we take `\log(0) = -\infty`. + +The natural logarithm is real if `x > 0` and complex if `x < 0` or if +`x` is complex. The principal branch of the complex logarithm is +used, meaning that `\Im(\ln(x)) = -\pi < \arg(x) \le \pi`. + +**Examples** + +Some basic values and limits:: + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = True + >>> log(1) + 0.0 + >>> log(2) + 0.693147180559945 + >>> log(1000,10) + 3.0 + >>> log(4, 16) + 0.5 + >>> log(j) + (0.0 + 1.5707963267949j) + >>> log(-1) + (0.0 + 3.14159265358979j) + >>> log(0) + -inf + >>> log(inf) + +inf + +The natural logarithm is the antiderivative of `1/x`:: + + >>> quad(lambda x: 1/x, [1, 5]) + 1.6094379124341 + >>> log(5) + 1.6094379124341 + >>> diff(log, 10) + 0.1 + +The Taylor series expansion of the natural logarithm around +`x = 1` has coefficients `(-1)^{n+1}/n`:: + + >>> nprint(taylor(log, 1, 7)) + [0.0, 1.0, -0.5, 0.333333, -0.25, 0.2, -0.166667, 0.142857] + +:func:`~mpmath.log` supports arbitrary precision evaluation:: + + >>> mp.dps = 50 + >>> log(pi) + 1.1447298858494001741434273513530587116472948129153 + >>> log(pi, pi**3) + 0.33333333333333333333333333333333333333333333333333 + >>> mp.dps = 25 + >>> log(3+4j) + (1.609437912434100374600759 + 0.9272952180016122324285125j) +""" + +log10 = r""" +Computes the base-10 logarithm of `x`, `\log_{10}(x)`. ``log10(x)`` +is equivalent to ``log(x, 10)``. +""" + +fmod = r""" +Converts `x` and `y` to mpmath numbers and returns `x \mod y`. +For mpmath numbers, this is equivalent to ``x % y``. + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = True + >>> fmod(100, pi) + 2.61062773871641 + +You can use :func:`~mpmath.fmod` to compute fractional parts of numbers:: + + >>> fmod(10.25, 1) + 0.25 + +""" + +radians = r""" +Converts the degree angle `x` to radians:: + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = True + >>> radians(60) + 1.0471975511966 +""" + +degrees = r""" +Converts the radian angle `x` to a degree angle:: + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = True + >>> degrees(pi/3) + 60.0 +""" + +atan2 = r""" +Computes the two-argument arctangent, `\mathrm{atan2}(y, x)`, +giving the signed angle between the positive `x`-axis and the +point `(x, y)` in the 2D plane. This function is defined for +real `x` and `y` only. + +The two-argument arctangent essentially computes +`\mathrm{atan}(y/x)`, but accounts for the signs of both +`x` and `y` to give the angle for the correct quadrant. The +following examples illustrate the difference:: + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = True + >>> atan2(1,1), atan(1/1.) + (0.785398163397448, 0.785398163397448) + >>> atan2(1,-1), atan(1/-1.) + (2.35619449019234, -0.785398163397448) + >>> atan2(-1,1), atan(-1/1.) + (-0.785398163397448, -0.785398163397448) + >>> atan2(-1,-1), atan(-1/-1.) + (-2.35619449019234, 0.785398163397448) + +The angle convention is the same as that used for the complex +argument; see :func:`~mpmath.arg`. +""" + +fibonacci = r""" +``fibonacci(n)`` computes the `n`-th Fibonacci number, `F(n)`. The +Fibonacci numbers are defined by the recurrence `F(n) = F(n-1) + F(n-2)` +with the initial values `F(0) = 0`, `F(1) = 1`. :func:`~mpmath.fibonacci` +extends this definition to arbitrary real and complex arguments +using the formula + +.. math :: + + F(z) = \frac{\phi^z - \cos(\pi z) \phi^{-z}}{\sqrt 5} + +where `\phi` is the golden ratio. :func:`~mpmath.fibonacci` also uses this +continuous formula to compute `F(n)` for extremely large `n`, where +calculating the exact integer would be wasteful. + +For convenience, :func:`~mpmath.fib` is available as an alias for +:func:`~mpmath.fibonacci`. + +**Basic examples** + +Some small Fibonacci numbers are:: + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = True + >>> for i in range(10): + ... print(fibonacci(i)) + ... + 0.0 + 1.0 + 1.0 + 2.0 + 3.0 + 5.0 + 8.0 + 13.0 + 21.0 + 34.0 + >>> fibonacci(50) + 12586269025.0 + +The recurrence for `F(n)` extends backwards to negative `n`:: + + >>> for i in range(10): + ... print(fibonacci(-i)) + ... + 0.0 + 1.0 + -1.0 + 2.0 + -3.0 + 5.0 + -8.0 + 13.0 + -21.0 + 34.0 + +Large Fibonacci numbers will be computed approximately unless +the precision is set high enough:: + + >>> fib(200) + 2.8057117299251e+41 + >>> mp.dps = 45 + >>> fib(200) + 280571172992510140037611932413038677189525.0 + +:func:`~mpmath.fibonacci` can compute approximate Fibonacci numbers +of stupendous size:: + + >>> mp.dps = 15 + >>> fibonacci(10**25) + 3.49052338550226e+2089876402499787337692720 + +**Real and complex arguments** + +The extended Fibonacci function is an analytic function. The +property `F(z) = F(z-1) + F(z-2)` holds for arbitrary `z`:: + + >>> mp.dps = 15 + >>> fib(pi) + 2.1170270579161 + >>> fib(pi-1) + fib(pi-2) + 2.1170270579161 + >>> fib(3+4j) + (-5248.51130728372 - 14195.962288353j) + >>> fib(2+4j) + fib(1+4j) + (-5248.51130728372 - 14195.962288353j) + +The Fibonacci function has infinitely many roots on the +negative half-real axis. The first root is at 0, the second is +close to -0.18, and then there are infinitely many roots that +asymptotically approach `-n+1/2`:: + + >>> findroot(fib, -0.2) + -0.183802359692956 + >>> findroot(fib, -2) + -1.57077646820395 + >>> findroot(fib, -17) + -16.4999999596115 + >>> findroot(fib, -24) + -23.5000000000479 + +**Mathematical relationships** + +For large `n`, `F(n+1)/F(n)` approaches the golden ratio:: + + >>> mp.dps = 50 + >>> fibonacci(101)/fibonacci(100) + 1.6180339887498948482045868343656381177203127439638 + >>> +phi + 1.6180339887498948482045868343656381177203091798058 + +The sum of reciprocal Fibonacci numbers converges to an irrational +number for which no closed form expression is known:: + + >>> mp.dps = 15 + >>> nsum(lambda n: 1/fib(n), [1, inf]) + 3.35988566624318 + +Amazingly, however, the sum of odd-index reciprocal Fibonacci +numbers can be expressed in terms of a Jacobi theta function:: + + >>> nsum(lambda n: 1/fib(2*n+1), [0, inf]) + 1.82451515740692 + >>> sqrt(5)*jtheta(2,0,(3-sqrt(5))/2)**2/4 + 1.82451515740692 + +Some related sums can be done in closed form:: + + >>> nsum(lambda k: 1/(1+fib(2*k+1)), [0, inf]) + 1.11803398874989 + >>> phi - 0.5 + 1.11803398874989 + >>> f = lambda k:(-1)**(k+1) / sum(fib(n)**2 for n in range(1,int(k+1))) + >>> nsum(f, [1, inf]) + 0.618033988749895 + >>> phi-1 + 0.618033988749895 + +**References** + +1. http://mathworld.wolfram.com/FibonacciNumber.html +""" + +altzeta = r""" +Gives the Dirichlet eta function, `\eta(s)`, also known as the +alternating zeta function. This function is defined in analogy +with the Riemann zeta function as providing the sum of the +alternating series + +.. math :: + + \eta(s) = \sum_{k=0}^{\infty} \frac{(-1)^k}{k^s} + = 1-\frac{1}{2^s}+\frac{1}{3^s}-\frac{1}{4^s}+\ldots + +The eta function, unlike the Riemann zeta function, is an entire +function, having a finite value for all complex `s`. The special case +`\eta(1) = \log(2)` gives the value of the alternating harmonic series. + +The alternating zeta function may expressed using the Riemann zeta function +as `\eta(s) = (1 - 2^{1-s}) \zeta(s)`. It can also be expressed +in terms of the Hurwitz zeta function, for example using +:func:`~mpmath.dirichlet` (see documentation for that function). + +**Examples** + +Some special values are:: + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = True + >>> altzeta(1) + 0.693147180559945 + >>> altzeta(0) + 0.5 + >>> altzeta(-1) + 0.25 + >>> altzeta(-2) + 0.0 + +An example of a sum that can be computed more accurately and +efficiently via :func:`~mpmath.altzeta` than via numerical summation:: + + >>> sum(-(-1)**n / mpf(n)**2.5 for n in range(1, 100)) + 0.867204951503984 + >>> altzeta(2.5) + 0.867199889012184 + +At positive even integers, the Dirichlet eta function +evaluates to a rational multiple of a power of `\pi`:: + + >>> altzeta(2) + 0.822467033424113 + >>> pi**2/12 + 0.822467033424113 + +Like the Riemann zeta function, `\eta(s)`, approaches 1 +as `s` approaches positive infinity, although it does +so from below rather than from above:: + + >>> altzeta(30) + 0.999999999068682 + >>> altzeta(inf) + 1.0 + >>> mp.pretty = False + >>> altzeta(1000, rounding='d') + mpf('0.99999999999999989') + >>> altzeta(1000, rounding='u') + mpf('1.0') + +**References** + +1. http://mathworld.wolfram.com/DirichletEtaFunction.html + +2. http://en.wikipedia.org/wiki/Dirichlet_eta_function +""" + +factorial = r""" +Computes the factorial, `x!`. For integers `n \ge 0`, we have +`n! = 1 \cdot 2 \cdots (n-1) \cdot n` and more generally the factorial +is defined for real or complex `x` by `x! = \Gamma(x+1)`. + +**Examples** + +Basic values and limits:: + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = True + >>> for k in range(6): + ... print("%s %s" % (k, fac(k))) + ... + 0 1.0 + 1 1.0 + 2 2.0 + 3 6.0 + 4 24.0 + 5 120.0 + >>> fac(inf) + +inf + >>> fac(0.5), sqrt(pi)/2 + (0.886226925452758, 0.886226925452758) + +For large positive `x`, `x!` can be approximated by +Stirling's formula:: + + >>> x = 10**10 + >>> fac(x) + 2.32579620567308e+95657055186 + >>> sqrt(2*pi*x)*(x/e)**x + 2.32579597597705e+95657055186 + +:func:`~mpmath.fac` supports evaluation for astronomically large values:: + + >>> fac(10**30) + 6.22311232304258e+29565705518096748172348871081098 + +Reciprocal factorials appear in the Taylor series of the +exponential function (among many other contexts):: + + >>> nsum(lambda k: 1/fac(k), [0, inf]), exp(1) + (2.71828182845905, 2.71828182845905) + >>> nsum(lambda k: pi**k/fac(k), [0, inf]), exp(pi) + (23.1406926327793, 23.1406926327793) + +""" + +gamma = r""" +Computes the gamma function, `\Gamma(x)`. The gamma function is a +shifted version of the ordinary factorial, satisfying +`\Gamma(n) = (n-1)!` for integers `n > 0`. More generally, it +is defined by + +.. math :: + + \Gamma(x) = \int_0^{\infty} t^{x-1} e^{-t}\, dt + +for any real or complex `x` with `\Re(x) > 0` and for `\Re(x) < 0` +by analytic continuation. + +**Examples** + +Basic values and limits:: + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = True + >>> for k in range(1, 6): + ... print("%s %s" % (k, gamma(k))) + ... + 1 1.0 + 2 1.0 + 3 2.0 + 4 6.0 + 5 24.0 + >>> gamma(inf) + +inf + >>> gamma(0) + Traceback (most recent call last): + ... + ValueError: gamma function pole + +The gamma function of a half-integer is a rational multiple of +`\sqrt{\pi}`:: + + >>> gamma(0.5), sqrt(pi) + (1.77245385090552, 1.77245385090552) + >>> gamma(1.5), sqrt(pi)/2 + (0.886226925452758, 0.886226925452758) + +We can check the integral definition:: + + >>> gamma(3.5) + 3.32335097044784 + >>> quad(lambda t: t**2.5*exp(-t), [0,inf]) + 3.32335097044784 + +:func:`~mpmath.gamma` supports arbitrary-precision evaluation and +complex arguments:: + + >>> mp.dps = 50 + >>> gamma(sqrt(3)) + 0.91510229697308632046045539308226554038315280564184 + >>> mp.dps = 25 + >>> gamma(2j) + (0.009902440080927490985955066 - 0.07595200133501806872408048j) + +Arguments can also be large. Note that the gamma function grows +very quickly:: + + >>> mp.dps = 15 + >>> gamma(10**20) + 1.9328495143101e+1956570551809674817225 + +**References** + +* [Spouge]_ + +""" + +psi = r""" +Gives the polygamma function of order `m` of `z`, `\psi^{(m)}(z)`. +Special cases are known as the *digamma function* (`\psi^{(0)}(z)`), +the *trigamma function* (`\psi^{(1)}(z)`), etc. The polygamma +functions are defined as the logarithmic derivatives of the gamma +function: + +.. math :: + + \psi^{(m)}(z) = \left(\frac{d}{dz}\right)^{m+1} \log \Gamma(z) + +In particular, `\psi^{(0)}(z) = \Gamma'(z)/\Gamma(z)`. In the +present implementation of :func:`~mpmath.psi`, the order `m` must be a +nonnegative integer, while the argument `z` may be an arbitrary +complex number (with exception for the polygamma function's poles +at `z = 0, -1, -2, \ldots`). + +**Examples** + +For various rational arguments, the polygamma function reduces to +a combination of standard mathematical constants:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> psi(0, 1), -euler + (-0.5772156649015328606065121, -0.5772156649015328606065121) + >>> psi(1, '1/4'), pi**2+8*catalan + (17.19732915450711073927132, 17.19732915450711073927132) + >>> psi(2, '1/2'), -14*apery + (-16.82879664423431999559633, -16.82879664423431999559633) + +The polygamma functions are derivatives of each other:: + + >>> diff(lambda x: psi(3, x), pi), psi(4, pi) + (-0.1105749312578862734526952, -0.1105749312578862734526952) + >>> quad(lambda x: psi(4, x), [2, 3]), psi(3,3)-psi(3,2) + (-0.375, -0.375) + +The digamma function diverges logarithmically as `z \to \infty`, +while higher orders tend to zero:: + + >>> psi(0,inf), psi(1,inf), psi(2,inf) + (+inf, 0.0, 0.0) + +Evaluation for a complex argument:: + + >>> psi(2, -1-2j) + (0.03902435405364952654838445 + 0.1574325240413029954685366j) + +Evaluation is supported for large orders `m` and/or large +arguments `z`:: + + >>> psi(3, 10**100) + 2.0e-300 + >>> psi(250, 10**30+10**20*j) + (-1.293142504363642687204865e-7010 + 3.232856260909107391513108e-7018j) + +**Application to infinite series** + +Any infinite series where the summand is a rational function of +the index `k` can be evaluated in closed form in terms of polygamma +functions of the roots and poles of the summand:: + + >>> a = sqrt(2) + >>> b = sqrt(3) + >>> nsum(lambda k: 1/((k+a)**2*(k+b)), [0, inf]) + 0.4049668927517857061917531 + >>> (psi(0,a)-psi(0,b)-a*psi(1,a)+b*psi(1,a))/(a-b)**2 + 0.4049668927517857061917531 + +This follows from the series representation (`m > 0`) + +.. math :: + + \psi^{(m)}(z) = (-1)^{m+1} m! \sum_{k=0}^{\infty} + \frac{1}{(z+k)^{m+1}}. + +Since the roots of a polynomial may be complex, it is sometimes +necessary to use the complex polygamma function to evaluate +an entirely real-valued sum:: + + >>> nsum(lambda k: 1/(k**2-2*k+3), [0, inf]) + 1.694361433907061256154665 + >>> nprint(polyroots([1,-2,3])) + [(1.0 - 1.41421j), (1.0 + 1.41421j)] + >>> r1 = 1-sqrt(2)*j + >>> r2 = r1.conjugate() + >>> (psi(0,-r2)-psi(0,-r1))/(r1-r2) + (1.694361433907061256154665 + 0.0j) + +""" + +digamma = r""" +Shortcut for ``psi(0,z)``. +""" + +harmonic = r""" +If `n` is an integer, ``harmonic(n)`` gives a floating-point +approximation of the `n`-th harmonic number `H(n)`, defined as + +.. math :: + + H(n) = 1 + \frac{1}{2} + \frac{1}{3} + \ldots + \frac{1}{n} + +The first few harmonic numbers are:: + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = True + >>> for n in range(8): + ... print("%s %s" % (n, harmonic(n))) + ... + 0 0.0 + 1 1.0 + 2 1.5 + 3 1.83333333333333 + 4 2.08333333333333 + 5 2.28333333333333 + 6 2.45 + 7 2.59285714285714 + +The infinite harmonic series `1 + 1/2 + 1/3 + \ldots` diverges:: + + >>> harmonic(inf) + +inf + +:func:`~mpmath.harmonic` is evaluated using the digamma function rather +than by summing the harmonic series term by term. It can therefore +be computed quickly for arbitrarily large `n`, and even for +nonintegral arguments:: + + >>> harmonic(10**100) + 230.835724964306 + >>> harmonic(0.5) + 0.613705638880109 + >>> harmonic(3+4j) + (2.24757548223494 + 0.850502209186044j) + +:func:`~mpmath.harmonic` supports arbitrary precision evaluation:: + + >>> mp.dps = 50 + >>> harmonic(11) + 3.0198773448773448773448773448773448773448773448773 + >>> harmonic(pi) + 1.8727388590273302654363491032336134987519132374152 + +The harmonic series diverges, but at a glacial pace. It is possible +to calculate the exact number of terms required before the sum +exceeds a given amount, say 100:: + + >>> mp.dps = 50 + >>> v = 10**findroot(lambda x: harmonic(10**x) - 100, 10) + >>> v + 15092688622113788323693563264538101449859496.864101 + >>> v = int(ceil(v)) + >>> print(v) + 15092688622113788323693563264538101449859497 + >>> harmonic(v-1) + 99.999999999999999999999999999999999999999999942747 + >>> harmonic(v) + 100.000000000000000000000000000000000000000000009 + +""" + +bernoulli = r""" +Computes the nth Bernoulli number, `B_n`, for any integer `n \ge 0`. + +The Bernoulli numbers are rational numbers, but this function +returns a floating-point approximation. To obtain an exact +fraction, use :func:`~mpmath.bernfrac` instead. + +**Examples** + +Numerical values of the first few Bernoulli numbers:: + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = True + >>> for n in range(15): + ... print("%s %s" % (n, bernoulli(n))) + ... + 0 1.0 + 1 -0.5 + 2 0.166666666666667 + 3 0.0 + 4 -0.0333333333333333 + 5 0.0 + 6 0.0238095238095238 + 7 0.0 + 8 -0.0333333333333333 + 9 0.0 + 10 0.0757575757575758 + 11 0.0 + 12 -0.253113553113553 + 13 0.0 + 14 1.16666666666667 + +Bernoulli numbers can be approximated with arbitrary precision:: + + >>> mp.dps = 50 + >>> bernoulli(100) + -2.8382249570693706959264156336481764738284680928013e+78 + +Arbitrarily large `n` are supported:: + + >>> mp.dps = 15 + >>> bernoulli(10**20 + 2) + 3.09136296657021e+1876752564973863312327 + +The Bernoulli numbers are related to the Riemann zeta function +at integer arguments:: + + >>> -bernoulli(8) * (2*pi)**8 / (2*fac(8)) + 1.00407735619794 + >>> zeta(8) + 1.00407735619794 + +**Algorithm** + +For small `n` (`n < 3000`) :func:`~mpmath.bernoulli` uses a recurrence +formula due to Ramanujan. All results in this range are cached, +so sequential computation of small Bernoulli numbers is +guaranteed to be fast. + +For larger `n`, `B_n` is evaluated in terms of the Riemann zeta +function. +""" + +stieltjes = r""" +For a nonnegative integer `n`, ``stieltjes(n)`` computes the +`n`-th Stieltjes constant `\gamma_n`, defined as the +`n`-th coefficient in the Laurent series expansion of the +Riemann zeta function around the pole at `s = 1`. That is, +we have: + +.. math :: + + \zeta(s) = \frac{1}{s-1} \sum_{n=0}^{\infty} + \frac{(-1)^n}{n!} \gamma_n (s-1)^n + +More generally, ``stieltjes(n, a)`` gives the corresponding +coefficient `\gamma_n(a)` for the Hurwitz zeta function +`\zeta(s,a)` (with `\gamma_n = \gamma_n(1)`). + +**Examples** + +The zeroth Stieltjes constant is just Euler's constant `\gamma`:: + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = True + >>> stieltjes(0) + 0.577215664901533 + +Some more values are:: + + >>> stieltjes(1) + -0.0728158454836767 + >>> stieltjes(10) + 0.000205332814909065 + >>> stieltjes(30) + 0.00355772885557316 + >>> stieltjes(1000) + -1.57095384420474e+486 + >>> stieltjes(2000) + 2.680424678918e+1109 + >>> stieltjes(1, 2.5) + -0.23747539175716 + +An alternative way to compute `\gamma_1`:: + + >>> diff(extradps(15)(lambda x: 1/(x-1) - zeta(x)), 1) + -0.0728158454836767 + +:func:`~mpmath.stieltjes` supports arbitrary precision evaluation:: + + >>> mp.dps = 50 + >>> stieltjes(2) + -0.0096903631928723184845303860352125293590658061013408 + +**Algorithm** + +:func:`~mpmath.stieltjes` numerically evaluates the integral in +the following representation due to Ainsworth, Howell and +Coffey [1], [2]: + +.. math :: + + \gamma_n(a) = \frac{\log^n a}{2a} - \frac{\log^{n+1}(a)}{n+1} + + \frac{2}{a} \Re \int_0^{\infty} + \frac{(x/a-i)\log^n(a-ix)}{(1+x^2/a^2)(e^{2\pi x}-1)} dx. + +For some reference values with `a = 1`, see e.g. [4]. + +**References** + +1. O. R. Ainsworth & L. W. Howell, "An integral representation of + the generalized Euler-Mascheroni constants", NASA Technical + Paper 2456 (1985), + http://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/19850014994_1985014994.pdf + +2. M. W. Coffey, "The Stieltjes constants, their relation to the + `\eta_j` coefficients, and representation of the Hurwitz + zeta function", arXiv:0706.0343v1 http://arxiv.org/abs/0706.0343 + +3. http://mathworld.wolfram.com/StieltjesConstants.html + +4. http://pi.lacim.uqam.ca/piDATA/stieltjesgamma.txt + +""" + +gammaprod = r""" +Given iterables `a` and `b`, ``gammaprod(a, b)`` computes the +product / quotient of gamma functions: + +.. math :: + + \frac{\Gamma(a_0) \Gamma(a_1) \cdots \Gamma(a_p)} + {\Gamma(b_0) \Gamma(b_1) \cdots \Gamma(b_q)} + +Unlike direct calls to :func:`~mpmath.gamma`, :func:`~mpmath.gammaprod` considers +the entire product as a limit and evaluates this limit properly if +any of the numerator or denominator arguments are nonpositive +integers such that poles of the gamma function are encountered. +That is, :func:`~mpmath.gammaprod` evaluates + +.. math :: + + \lim_{\epsilon \to 0} + \frac{\Gamma(a_0+\epsilon) \Gamma(a_1+\epsilon) \cdots + \Gamma(a_p+\epsilon)} + {\Gamma(b_0+\epsilon) \Gamma(b_1+\epsilon) \cdots + \Gamma(b_q+\epsilon)} + +In particular: + +* If there are equally many poles in the numerator and the + denominator, the limit is a rational number times the remaining, + regular part of the product. + +* If there are more poles in the numerator, :func:`~mpmath.gammaprod` + returns ``+inf``. + +* If there are more poles in the denominator, :func:`~mpmath.gammaprod` + returns 0. + +**Examples** + +The reciprocal gamma function `1/\Gamma(x)` evaluated at `x = 0`:: + + >>> from mpmath import * + >>> mp.dps = 15 + >>> gammaprod([], [0]) + 0.0 + +A limit:: + + >>> gammaprod([-4], [-3]) + -0.25 + >>> limit(lambda x: gamma(x-1)/gamma(x), -3, direction=1) + -0.25 + >>> limit(lambda x: gamma(x-1)/gamma(x), -3, direction=-1) + -0.25 + +""" + +beta = r""" +Computes the beta function, +`B(x,y) = \Gamma(x) \Gamma(y) / \Gamma(x+y)`. +The beta function is also commonly defined by the integral +representation + +.. math :: + + B(x,y) = \int_0^1 t^{x-1} (1-t)^{y-1} \, dt + +**Examples** + +For integer and half-integer arguments where all three gamma +functions are finite, the beta function becomes either rational +number or a rational multiple of `\pi`:: + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = True + >>> beta(5, 2) + 0.0333333333333333 + >>> beta(1.5, 2) + 0.266666666666667 + >>> 16*beta(2.5, 1.5) + 3.14159265358979 + +Where appropriate, :func:`~mpmath.beta` evaluates limits. A pole +of the beta function is taken to result in ``+inf``:: + + >>> beta(-0.5, 0.5) + 0.0 + >>> beta(-3, 3) + -0.333333333333333 + >>> beta(-2, 3) + +inf + >>> beta(inf, 1) + 0.0 + >>> beta(inf, 0) + nan + +:func:`~mpmath.beta` supports complex numbers and arbitrary precision +evaluation:: + + >>> beta(1, 2+j) + (0.4 - 0.2j) + >>> mp.dps = 25 + >>> beta(j,0.5) + (1.079424249270925780135675 - 1.410032405664160838288752j) + >>> mp.dps = 50 + >>> beta(pi, e) + 0.037890298781212201348153837138927165984170287886464 + +Various integrals can be computed by means of the +beta function:: + + >>> mp.dps = 15 + >>> quad(lambda t: t**2.5*(1-t)**2, [0, 1]) + 0.0230880230880231 + >>> beta(3.5, 3) + 0.0230880230880231 + >>> quad(lambda t: sin(t)**4 * sqrt(cos(t)), [0, pi/2]) + 0.319504062596158 + >>> beta(2.5, 0.75)/2 + 0.319504062596158 + +""" + +betainc = r""" +``betainc(a, b, x1=0, x2=1, regularized=False)`` gives the generalized +incomplete beta function, + +.. math :: + + I_{x_1}^{x_2}(a,b) = \int_{x_1}^{x_2} t^{a-1} (1-t)^{b-1} dt. + +When `x_1 = 0, x_2 = 1`, this reduces to the ordinary (complete) +beta function `B(a,b)`; see :func:`~mpmath.beta`. + +With the keyword argument ``regularized=True``, :func:`~mpmath.betainc` +computes the regularized incomplete beta function +`I_{x_1}^{x_2}(a,b) / B(a,b)`. This is the cumulative distribution of the +beta distribution with parameters `a`, `b`. + +.. note : + + Implementations of the incomplete beta function in some other + software uses a different argument order. For example, Mathematica uses the + reversed argument order ``Beta[x1,x2,a,b]``. For the equivalent of SciPy's + three-argument incomplete beta integral (implicitly with `x1 = 0`), use + ``betainc(a,b,0,x2,regularized=True)``. + +**Examples** + +Verifying that :func:`~mpmath.betainc` computes the integral in the +definition:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> x,y,a,b = 3, 4, 0, 6 + >>> betainc(x, y, a, b) + -4010.4 + >>> quad(lambda t: t**(x-1) * (1-t)**(y-1), [a, b]) + -4010.4 + +The arguments may be arbitrary complex numbers:: + + >>> betainc(0.75, 1-4j, 0, 2+3j) + (0.2241657956955709603655887 + 0.3619619242700451992411724j) + +With regularization:: + + >>> betainc(1, 2, 0, 0.25, regularized=True) + 0.4375 + >>> betainc(pi, e, 0, 1, regularized=True) # Complete + 1.0 + +The beta integral satisfies some simple argument transformation +symmetries:: + + >>> mp.dps = 15 + >>> betainc(2,3,4,5), -betainc(2,3,5,4), betainc(3,2,1-5,1-4) + (56.0833333333333, 56.0833333333333, 56.0833333333333) + +The beta integral can often be evaluated analytically. For integer and +rational arguments, the incomplete beta function typically reduces to a +simple algebraic-logarithmic expression:: + + >>> mp.dps = 25 + >>> identify(chop(betainc(0, 0, 3, 4))) + '-(log((9/8)))' + >>> identify(betainc(2, 3, 4, 5)) + '(673/12)' + >>> identify(betainc(1.5, 1, 1, 2)) + '((-12+sqrt(1152))/18)' + +""" + +binomial = r""" +Computes the binomial coefficient + +.. math :: + + {n \choose k} = \frac{n!}{k!(n-k)!}. + +The binomial coefficient gives the number of ways that `k` items +can be chosen from a set of `n` items. More generally, the binomial +coefficient is a well-defined function of arbitrary real or +complex `n` and `k`, via the gamma function. + +**Examples** + +Generate Pascal's triangle:: + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = True + >>> for n in range(5): + ... nprint([binomial(n,k) for k in range(n+1)]) + ... + [1.0] + [1.0, 1.0] + [1.0, 2.0, 1.0] + [1.0, 3.0, 3.0, 1.0] + [1.0, 4.0, 6.0, 4.0, 1.0] + +There is 1 way to select 0 items from the empty set, and 0 ways to +select 1 item from the empty set:: + + >>> binomial(0, 0) + 1.0 + >>> binomial(0, 1) + 0.0 + +:func:`~mpmath.binomial` supports large arguments:: + + >>> binomial(10**20, 10**20-5) + 8.33333333333333e+97 + >>> binomial(10**20, 10**10) + 2.60784095465201e+104342944813 + +Nonintegral binomial coefficients find use in series +expansions:: + + >>> nprint(taylor(lambda x: (1+x)**0.25, 0, 4)) + [1.0, 0.25, -0.09375, 0.0546875, -0.0375977] + >>> nprint([binomial(0.25, k) for k in range(5)]) + [1.0, 0.25, -0.09375, 0.0546875, -0.0375977] + +An integral representation:: + + >>> n, k = 5, 3 + >>> f = lambda t: exp(-j*k*t)*(1+exp(j*t))**n + >>> chop(quad(f, [-pi,pi])/(2*pi)) + 10.0 + >>> binomial(n,k) + 10.0 + +""" + +rf = r""" +Computes the rising factorial or Pochhammer symbol, + +.. math :: + + x^{(n)} = x (x+1) \cdots (x+n-1) = \frac{\Gamma(x+n)}{\Gamma(x)} + +where the rightmost expression is valid for nonintegral `n`. + +**Examples** + +For integral `n`, the rising factorial is a polynomial:: + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = True + >>> for n in range(5): + ... nprint(taylor(lambda x: rf(x,n), 0, n)) + ... + [1.0] + [0.0, 1.0] + [0.0, 1.0, 1.0] + [0.0, 2.0, 3.0, 1.0] + [0.0, 6.0, 11.0, 6.0, 1.0] + +Evaluation is supported for arbitrary arguments:: + + >>> rf(2+3j, 5.5) + (-7202.03920483347 - 3777.58810701527j) +""" + +ff = r""" +Computes the falling factorial, + +.. math :: + + (x)_n = x (x-1) \cdots (x-n+1) = \frac{\Gamma(x+1)}{\Gamma(x-n+1)} + +where the rightmost expression is valid for nonintegral `n`. + +**Examples** + +For integral `n`, the falling factorial is a polynomial:: + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = True + >>> for n in range(5): + ... nprint(taylor(lambda x: ff(x,n), 0, n)) + ... + [1.0] + [0.0, 1.0] + [0.0, -1.0, 1.0] + [0.0, 2.0, -3.0, 1.0] + [0.0, -6.0, 11.0, -6.0, 1.0] + +Evaluation is supported for arbitrary arguments:: + + >>> ff(2+3j, 5.5) + (-720.41085888203 + 316.101124983878j) +""" + +fac2 = r""" +Computes the double factorial `x!!`, defined for integers +`x > 0` by + +.. math :: + + x!! = \begin{cases} + 1 \cdot 3 \cdots (x-2) \cdot x & x \;\mathrm{odd} \\ + 2 \cdot 4 \cdots (x-2) \cdot x & x \;\mathrm{even} + \end{cases} + +and more generally by [1] + +.. math :: + + x!! = 2^{x/2} \left(\frac{\pi}{2}\right)^{(\cos(\pi x)-1)/4} + \Gamma\left(\frac{x}{2}+1\right). + +**Examples** + +The integer sequence of double factorials begins:: + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = True + >>> nprint([fac2(n) for n in range(10)]) + [1.0, 1.0, 2.0, 3.0, 8.0, 15.0, 48.0, 105.0, 384.0, 945.0] + +For large `x`, double factorials follow a Stirling-like asymptotic +approximation:: + + >>> x = mpf(10000) + >>> fac2(x) + 5.97272691416282e+17830 + >>> sqrt(pi)*x**((x+1)/2)*exp(-x/2) + 5.97262736954392e+17830 + +The recurrence formula `x!! = x (x-2)!!` can be reversed to +define the double factorial of negative odd integers (but +not negative even integers):: + + >>> fac2(-1), fac2(-3), fac2(-5), fac2(-7) + (1.0, -1.0, 0.333333333333333, -0.0666666666666667) + >>> fac2(-2) + Traceback (most recent call last): + ... + ValueError: gamma function pole + +With the exception of the poles at negative even integers, +:func:`~mpmath.fac2` supports evaluation for arbitrary complex arguments. +The recurrence formula is valid generally:: + + >>> fac2(pi+2j) + (-1.3697207890154e-12 + 3.93665300979176e-12j) + >>> (pi+2j)*fac2(pi-2+2j) + (-1.3697207890154e-12 + 3.93665300979176e-12j) + +Double factorials should not be confused with nested factorials, +which are immensely larger:: + + >>> fac(fac(20)) + 5.13805976125208e+43675043585825292774 + >>> fac2(20) + 3715891200.0 + +Double factorials appear, among other things, in series expansions +of Gaussian functions and the error function. Infinite series +include:: + + >>> nsum(lambda k: 1/fac2(k), [0, inf]) + 3.05940740534258 + >>> sqrt(e)*(1+sqrt(pi/2)*erf(sqrt(2)/2)) + 3.05940740534258 + >>> nsum(lambda k: 2**k/fac2(2*k-1), [1, inf]) + 4.06015693855741 + >>> e * erf(1) * sqrt(pi) + 4.06015693855741 + +A beautiful Ramanujan sum:: + + >>> nsum(lambda k: (-1)**k*(fac2(2*k-1)/fac2(2*k))**3, [0,inf]) + 0.90917279454693 + >>> (gamma('9/8')/gamma('5/4')/gamma('7/8'))**2 + 0.90917279454693 + +**References** + +1. http://functions.wolfram.com/GammaBetaErf/Factorial2/27/01/0002/ + +2. http://mathworld.wolfram.com/DoubleFactorial.html + +""" + +hyper = r""" +Evaluates the generalized hypergeometric function + +.. math :: + + \,_pF_q(a_1,\ldots,a_p; b_1,\ldots,b_q; z) = + \sum_{n=0}^\infty \frac{(a_1)_n (a_2)_n \ldots (a_p)_n} + {(b_1)_n(b_2)_n\ldots(b_q)_n} \frac{z^n}{n!} + +where `(x)_n` denotes the rising factorial (see :func:`~mpmath.rf`). + +The parameters lists ``a_s`` and ``b_s`` may contain integers, +real numbers, complex numbers, as well as exact fractions given in +the form of tuples `(p, q)`. :func:`~mpmath.hyper` is optimized to handle +integers and fractions more efficiently than arbitrary +floating-point parameters (since rational parameters are by +far the most common). + +**Examples** + +Verifying that :func:`~mpmath.hyper` gives the sum in the definition, by +comparison with :func:`~mpmath.nsum`:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> a,b,c,d = 2,3,4,5 + >>> x = 0.25 + >>> hyper([a,b],[c,d],x) + 1.078903941164934876086237 + >>> fn = lambda n: rf(a,n)*rf(b,n)/rf(c,n)/rf(d,n)*x**n/fac(n) + >>> nsum(fn, [0, inf]) + 1.078903941164934876086237 + +The parameters can be any combination of integers, fractions, +floats and complex numbers:: + + >>> a, b, c, d, e = 1, (-1,2), pi, 3+4j, (2,3) + >>> x = 0.2j + >>> hyper([a,b],[c,d,e],x) + (0.9923571616434024810831887 - 0.005753848733883879742993122j) + >>> b, e = -0.5, mpf(2)/3 + >>> fn = lambda n: rf(a,n)*rf(b,n)/rf(c,n)/rf(d,n)/rf(e,n)*x**n/fac(n) + >>> nsum(fn, [0, inf]) + (0.9923571616434024810831887 - 0.005753848733883879742993122j) + +The `\,_0F_0` and `\,_1F_0` series are just elementary functions:: + + >>> a, z = sqrt(2), +pi + >>> hyper([],[],z) + 23.14069263277926900572909 + >>> exp(z) + 23.14069263277926900572909 + >>> hyper([a],[],z) + (-0.09069132879922920160334114 + 0.3283224323946162083579656j) + >>> (1-z)**(-a) + (-0.09069132879922920160334114 + 0.3283224323946162083579656j) + +If any `a_k` coefficient is a nonpositive integer, the series terminates +into a finite polynomial:: + + >>> hyper([1,1,1,-3],[2,5],1) + 0.7904761904761904761904762 + >>> identify(_) + '(83/105)' + +If any `b_k` is a nonpositive integer, the function is undefined (unless the +series terminates before the division by zero occurs):: + + >>> hyper([1,1,1,-3],[-2,5],1) + Traceback (most recent call last): + ... + ZeroDivisionError: pole in hypergeometric series + >>> hyper([1,1,1,-1],[-2,5],1) + 1.1 + +Except for polynomial cases, the radius of convergence `R` of the hypergeometric +series is either `R = \infty` (if `p \le q`), `R = 1` (if `p = q+1`), or +`R = 0` (if `p > q+1`). + +The analytic continuations of the functions with `p = q+1`, i.e. `\,_2F_1`, +`\,_3F_2`, `\,_4F_3`, etc, are all implemented and therefore these functions +can be evaluated for `|z| \ge 1`. The shortcuts :func:`~mpmath.hyp2f1`, :func:`~mpmath.hyp3f2` +are available to handle the most common cases (see their documentation), +but functions of higher degree are also supported via :func:`~mpmath.hyper`:: + + >>> hyper([1,2,3,4], [5,6,7], 1) # 4F3 at finite-valued branch point + 1.141783505526870731311423 + >>> hyper([4,5,6,7], [1,2,3], 1) # 4F3 at pole + +inf + >>> hyper([1,2,3,4,5], [6,7,8,9], 10) # 5F4 + (1.543998916527972259717257 - 0.5876309929580408028816365j) + >>> hyper([1,2,3,4,5,6], [7,8,9,10,11], 1j) # 6F5 + (0.9996565821853579063502466 + 0.0129721075905630604445669j) + +Near `z = 1` with noninteger parameters:: + + >>> hyper(['1/3',1,'3/2',2], ['1/5','11/6','41/8'], 1) + 2.219433352235586121250027 + >>> hyper(['1/3',1,'3/2',2], ['1/5','11/6','5/4'], 1) + +inf + >>> eps1 = extradps(6)(lambda: 1 - mpf('1e-6'))() + >>> hyper(['1/3',1,'3/2',2], ['1/5','11/6','5/4'], eps1) + 2923978034.412973409330956 + +Please note that, as currently implemented, evaluation of `\,_pF_{p-1}` +with `p \ge 3` may be slow or inaccurate when `|z-1|` is small, +for some parameter values. + +Evaluation may be aborted if convergence appears to be too slow. +The optional ``maxterms`` (limiting the number of series terms) and ``maxprec`` +(limiting the internal precision) keyword arguments can be used +to control evaluation:: + + >>> hyper([1,2,3], [4,5,6], 10000) # doctest: +IGNORE_EXCEPTION_DETAIL + Traceback (most recent call last): + ... + NoConvergence: Hypergeometric series converges too slowly. Try increasing maxterms. + >>> hyper([1,2,3], [4,5,6], 10000, maxterms=10**6) + 7.622806053177969474396918e+4310 + +Additional options include ``force_series`` (which forces direct use of +a hypergeometric series even if another evaluation method might work better) +and ``asymp_tol`` which controls the target tolerance for using +asymptotic series. + +When `p > q+1`, ``hyper`` computes the (iterated) Borel sum of the divergent +series. For `\,_2F_0` the Borel sum has an analytic solution and can be +computed efficiently (see :func:`~mpmath.hyp2f0`). For higher degrees, the functions +is evaluated first by attempting to sum it directly as an asymptotic +series (this only works for tiny `|z|`), and then by evaluating the Borel +regularized sum using numerical integration. Except for +special parameter combinations, this can be extremely slow. + + >>> hyper([1,1], [], 0.5) # regularization of 2F0 + (1.340965419580146562086448 + 0.8503366631752726568782447j) + >>> hyper([1,1,1,1], [1], 0.5) # regularization of 4F1 + (1.108287213689475145830699 + 0.5327107430640678181200491j) + +With the following magnitude of argument, the asymptotic series for `\,_3F_1` +gives only a few digits. Using Borel summation, ``hyper`` can produce +a value with full accuracy:: + + >>> mp.dps = 15 + >>> hyper([2,0.5,4], [5.25], '0.08', force_series=True) # doctest: +IGNORE_EXCEPTION_DETAIL + Traceback (most recent call last): + ... + NoConvergence: Hypergeometric series converges too slowly. Try increasing maxterms. + >>> hyper([2,0.5,4], [5.25], '0.08', asymp_tol=1e-4) + 1.0725535790737 + >>> hyper([2,0.5,4], [5.25], '0.08') + (1.07269542893559 + 5.54668863216891e-5j) + >>> hyper([2,0.5,4], [5.25], '-0.08', asymp_tol=1e-4) + 0.946344925484879 + >>> hyper([2,0.5,4], [5.25], '-0.08') + 0.946312503737771 + >>> mp.dps = 25 + >>> hyper([2,0.5,4], [5.25], '-0.08') + 0.9463125037377662296700858 + +Note that with the positive `z` value, there is a complex part in the +correct result, which falls below the tolerance of the asymptotic series. + +By default, a parameter that appears in both ``a_s`` and ``b_s`` will be removed +unless it is a nonpositive integer. This generally speeds up evaluation +by producing a hypergeometric function of lower order. +This optimization can be disabled by passing ``eliminate=False``. + + >>> hyper([1,2,3], [4,5,3], 10000) + 1.268943190440206905892212e+4321 + >>> hyper([1,2,3], [4,5,3], 10000, eliminate=False) # doctest: +IGNORE_EXCEPTION_DETAIL + Traceback (most recent call last): + ... + NoConvergence: Hypergeometric series converges too slowly. Try increasing maxterms. + >>> hyper([1,2,3], [4,5,3], 10000, eliminate=False, maxterms=10**6) + 1.268943190440206905892212e+4321 + +If a nonpositive integer `-n` appears in both ``a_s`` and ``b_s``, this parameter +cannot be unambiguously removed since it creates a term 0 / 0. +In this case the hypergeometric series is understood to terminate before +the division by zero occurs. This convention is consistent with Mathematica. +An alternative convention of eliminating the parameters can be toggled +with ``eliminate_all=True``: + + >>> hyper([2,-1], [-1], 3) + 7.0 + >>> hyper([2,-1], [-1], 3, eliminate_all=True) + 0.25 + >>> hyper([2], [], 3) + 0.25 + +""" + +hypercomb = r""" +Computes a weighted combination of hypergeometric functions + +.. math :: + + \sum_{r=1}^N \left[ \prod_{k=1}^{l_r} {w_{r,k}}^{c_{r,k}} + \frac{\prod_{k=1}^{m_r} \Gamma(\alpha_{r,k})}{\prod_{k=1}^{n_r} + \Gamma(\beta_{r,k})} + \,_{p_r}F_{q_r}(a_{r,1},\ldots,a_{r,p}; b_{r,1}, + \ldots, b_{r,q}; z_r)\right]. + +Typically the parameters are linear combinations of a small set of base +parameters; :func:`~mpmath.hypercomb` permits computing a correct value in +the case that some of the `\alpha`, `\beta`, `b` turn out to be +nonpositive integers, or if division by zero occurs for some `w^c`, +assuming that there are opposing singularities that cancel out. +The limit is computed by evaluating the function with the base +parameters perturbed, at a higher working precision. + +The first argument should be a function that takes the perturbable +base parameters ``params`` as input and returns `N` tuples +``(w, c, alpha, beta, a, b, z)``, where the coefficients ``w``, ``c``, +gamma factors ``alpha``, ``beta``, and hypergeometric coefficients +``a``, ``b`` each should be lists of numbers, and ``z`` should be a single +number. + +**Examples** + +The following evaluates + +.. math :: + + (a-1) \frac{\Gamma(a-3)}{\Gamma(a-4)} \,_1F_1(a,a-1,z) = e^z(a-4)(a+z-1) + +with `a=1, z=3`. There is a zero factor, two gamma function poles, and +the 1F1 function is singular; all singularities cancel out to give a finite +value:: + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = True + >>> hypercomb(lambda a: [([a-1],[1],[a-3],[a-4],[a],[a-1],3)], [1]) + -180.769832308689 + >>> -9*exp(3) + -180.769832308689 + +""" + +hyp0f1 = r""" +Gives the hypergeometric function `\,_0F_1`, sometimes known as the +confluent limit function, defined as + +.. math :: + + \,_0F_1(a,z) = \sum_{k=0}^{\infty} \frac{1}{(a)_k} \frac{z^k}{k!}. + +This function satisfies the differential equation `z f''(z) + a f'(z) = f(z)`, +and is related to the Bessel function of the first kind (see :func:`~mpmath.besselj`). + +``hyp0f1(a,z)`` is equivalent to ``hyper([],[a],z)``; see documentation for +:func:`~mpmath.hyper` for more information. + +**Examples** + +Evaluation for arbitrary arguments:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> hyp0f1(2, 0.25) + 1.130318207984970054415392 + >>> hyp0f1((1,2), 1234567) + 6.27287187546220705604627e+964 + >>> hyp0f1(3+4j, 1000000j) + (3.905169561300910030267132e+606 + 3.807708544441684513934213e+606j) + +Evaluation is supported for arbitrarily large values of `z`, +using asymptotic expansions:: + + >>> hyp0f1(1, 10**50) + 2.131705322874965310390701e+8685889638065036553022565 + >>> hyp0f1(1, -10**50) + 1.115945364792025420300208e-13 + +Verifying the differential equation:: + + >>> a = 2.5 + >>> f = lambda z: hyp0f1(a,z) + >>> for z in [0, 10, 3+4j]: + ... chop(z*diff(f,z,2) + a*diff(f,z) - f(z)) + ... + 0.0 + 0.0 + 0.0 + +""" + +hyp1f1 = r""" +Gives the confluent hypergeometric function of the first kind, + +.. math :: + + \,_1F_1(a,b,z) = \sum_{k=0}^{\infty} \frac{(a)_k}{(b)_k} \frac{z^k}{k!}, + +also known as Kummer's function and sometimes denoted by `M(a,b,z)`. This +function gives one solution to the confluent (Kummer's) differential equation + +.. math :: + + z f''(z) + (b-z) f'(z) - af(z) = 0. + +A second solution is given by the `U` function; see :func:`~mpmath.hyperu`. +Solutions are also given in an alternate form by the Whittaker +functions (:func:`~mpmath.whitm`, :func:`~mpmath.whitw`). + +``hyp1f1(a,b,z)`` is equivalent +to ``hyper([a],[b],z)``; see documentation for :func:`~mpmath.hyper` for more +information. + +**Examples** + +Evaluation for real and complex values of the argument `z`, with +fixed parameters `a = 2, b = -1/3`:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> hyp1f1(2, (-1,3), 3.25) + -2815.956856924817275640248 + >>> hyp1f1(2, (-1,3), -3.25) + -1.145036502407444445553107 + >>> hyp1f1(2, (-1,3), 1000) + -8.021799872770764149793693e+441 + >>> hyp1f1(2, (-1,3), -1000) + 0.000003131987633006813594535331 + >>> hyp1f1(2, (-1,3), 100+100j) + (-3.189190365227034385898282e+48 - 1.106169926814270418999315e+49j) + +Parameters may be complex:: + + >>> hyp1f1(2+3j, -1+j, 10j) + (261.8977905181045142673351 + 160.8930312845682213562172j) + +Arbitrarily large values of `z` are supported:: + + >>> hyp1f1(3, 4, 10**20) + 3.890569218254486878220752e+43429448190325182745 + >>> hyp1f1(3, 4, -10**20) + 6.0e-60 + >>> hyp1f1(3, 4, 10**20*j) + (-1.935753855797342532571597e-20 - 2.291911213325184901239155e-20j) + +Verifying the differential equation:: + + >>> a, b = 1.5, 2 + >>> f = lambda z: hyp1f1(a,b,z) + >>> for z in [0, -10, 3, 3+4j]: + ... chop(z*diff(f,z,2) + (b-z)*diff(f,z) - a*f(z)) + ... + 0.0 + 0.0 + 0.0 + 0.0 + +An integral representation:: + + >>> a, b = 1.5, 3 + >>> z = 1.5 + >>> hyp1f1(a,b,z) + 2.269381460919952778587441 + >>> g = lambda t: exp(z*t)*t**(a-1)*(1-t)**(b-a-1) + >>> gammaprod([b],[a,b-a])*quad(g, [0,1]) + 2.269381460919952778587441 + + +""" + +hyp1f2 = r""" +Gives the hypergeometric function `\,_1F_2(a_1,a_2;b_1,b_2; z)`. +The call ``hyp1f2(a1,b1,b2,z)`` is equivalent to +``hyper([a1],[b1,b2],z)``. + +Evaluation works for complex and arbitrarily large arguments:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> a, b, c = 1.5, (-1,3), 2.25 + >>> hyp1f2(a, b, c, 10**20) + -1.159388148811981535941434e+8685889639 + >>> hyp1f2(a, b, c, -10**20) + -12.60262607892655945795907 + >>> hyp1f2(a, b, c, 10**20*j) + (4.237220401382240876065501e+6141851464 - 2.950930337531768015892987e+6141851464j) + >>> hyp1f2(2+3j, -2j, 0.5j, 10-20j) + (135881.9905586966432662004 - 86681.95885418079535738828j) + +""" + +hyp2f2 = r""" +Gives the hypergeometric function `\,_2F_2(a_1,a_2;b_1,b_2; z)`. +The call ``hyp2f2(a1,a2,b1,b2,z)`` is equivalent to +``hyper([a1,a2],[b1,b2],z)``. + +Evaluation works for complex and arbitrarily large arguments:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> a, b, c, d = 1.5, (-1,3), 2.25, 4 + >>> hyp2f2(a, b, c, d, 10**20) + -5.275758229007902299823821e+43429448190325182663 + >>> hyp2f2(a, b, c, d, -10**20) + 2561445.079983207701073448 + >>> hyp2f2(a, b, c, d, 10**20*j) + (2218276.509664121194836667 - 1280722.539991603850462856j) + >>> hyp2f2(2+3j, -2j, 0.5j, 4j, 10-20j) + (80500.68321405666957342788 - 20346.82752982813540993502j) + +""" + +hyp2f3 = r""" +Gives the hypergeometric function `\,_2F_3(a_1,a_2;b_1,b_2,b_3; z)`. +The call ``hyp2f3(a1,a2,b1,b2,b3,z)`` is equivalent to +``hyper([a1,a2],[b1,b2,b3],z)``. + +Evaluation works for arbitrarily large arguments:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> a1,a2,b1,b2,b3 = 1.5, (-1,3), 2.25, 4, (1,5) + >>> hyp2f3(a1,a2,b1,b2,b3,10**20) + -4.169178177065714963568963e+8685889590 + >>> hyp2f3(a1,a2,b1,b2,b3,-10**20) + 7064472.587757755088178629 + >>> hyp2f3(a1,a2,b1,b2,b3,10**20*j) + (-5.163368465314934589818543e+6141851415 + 1.783578125755972803440364e+6141851416j) + >>> hyp2f3(2+3j, -2j, 0.5j, 4j, -1-j, 10-20j) + (-2280.938956687033150740228 + 13620.97336609573659199632j) + >>> hyp2f3(2+3j, -2j, 0.5j, 4j, -1-j, 10000000-20000000j) + (4.849835186175096516193e+3504 - 3.365981529122220091353633e+3504j) + +""" + +hyp2f1 = r""" +Gives the Gauss hypergeometric function `\,_2F_1` (often simply referred to as +*the* hypergeometric function), defined for `|z| < 1` as + +.. math :: + + \,_2F_1(a,b,c,z) = \sum_{k=0}^{\infty} + \frac{(a)_k (b)_k}{(c)_k} \frac{z^k}{k!}. + +and for `|z| \ge 1` by analytic continuation, with a branch cut on `(1, \infty)` +when necessary. + +Special cases of this function include many of the orthogonal polynomials as +well as the incomplete beta function and other functions. Properties of the +Gauss hypergeometric function are documented comprehensively in many references, +for example Abramowitz & Stegun, section 15. + +The implementation supports the analytic continuation as well as evaluation +close to the unit circle where `|z| \approx 1`. The syntax ``hyp2f1(a,b,c,z)`` +is equivalent to ``hyper([a,b],[c],z)``. + +**Examples** + +Evaluation with `z` inside, outside and on the unit circle, for +fixed parameters:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> hyp2f1(2, (1,2), 4, 0.75) + 1.303703703703703703703704 + >>> hyp2f1(2, (1,2), 4, -1.75) + 0.7431290566046919177853916 + >>> hyp2f1(2, (1,2), 4, 1.75) + (1.418075801749271137026239 - 1.114976146679907015775102j) + >>> hyp2f1(2, (1,2), 4, 1) + 1.6 + >>> hyp2f1(2, (1,2), 4, -1) + 0.8235498012182875315037882 + >>> hyp2f1(2, (1,2), 4, j) + (0.9144026291433065674259078 + 0.2050415770437884900574923j) + >>> hyp2f1(2, (1,2), 4, 2+j) + (0.9274013540258103029011549 + 0.7455257875808100868984496j) + >>> hyp2f1(2, (1,2), 4, 0.25j) + (0.9931169055799728251931672 + 0.06154836525312066938147793j) + +Evaluation with complex parameter values:: + + >>> hyp2f1(1+j, 0.75, 10j, 1+5j) + (0.8834833319713479923389638 + 0.7053886880648105068343509j) + +Evaluation with `z = 1`:: + + >>> hyp2f1(-2.5, 3.5, 1.5, 1) + 0.0 + >>> hyp2f1(-2.5, 3, 4, 1) + 0.06926406926406926406926407 + >>> hyp2f1(2, 3, 4, 1) + +inf + +Evaluation for huge arguments:: + + >>> hyp2f1((-1,3), 1.75, 4, '1e100') + (7.883714220959876246415651e+32 + 1.365499358305579597618785e+33j) + >>> hyp2f1((-1,3), 1.75, 4, '1e1000000') + (7.883714220959876246415651e+333332 + 1.365499358305579597618785e+333333j) + >>> hyp2f1((-1,3), 1.75, 4, '1e1000000j') + (1.365499358305579597618785e+333333 - 7.883714220959876246415651e+333332j) + +An integral representation:: + + >>> a,b,c,z = -0.5, 1, 2.5, 0.25 + >>> g = lambda t: t**(b-1) * (1-t)**(c-b-1) * (1-t*z)**(-a) + >>> gammaprod([c],[b,c-b]) * quad(g, [0,1]) + 0.9480458814362824478852618 + >>> hyp2f1(a,b,c,z) + 0.9480458814362824478852618 + +Verifying the hypergeometric differential equation:: + + >>> f = lambda z: hyp2f1(a,b,c,z) + >>> chop(z*(1-z)*diff(f,z,2) + (c-(a+b+1)*z)*diff(f,z) - a*b*f(z)) + 0.0 + +""" + +hyp3f2 = r""" +Gives the generalized hypergeometric function `\,_3F_2`, defined for `|z| < 1` +as + +.. math :: + + \,_3F_2(a_1,a_2,a_3,b_1,b_2,z) = \sum_{k=0}^{\infty} + \frac{(a_1)_k (a_2)_k (a_3)_k}{(b_1)_k (b_2)_k} \frac{z^k}{k!}. + +and for `|z| \ge 1` by analytic continuation. The analytic structure of this +function is similar to that of `\,_2F_1`, generally with a singularity at +`z = 1` and a branch cut on `(1, \infty)`. + +Evaluation is supported inside, on, and outside +the circle of convergence `|z| = 1`:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> hyp3f2(1,2,3,4,5,0.25) + 1.083533123380934241548707 + >>> hyp3f2(1,2+2j,3,4,5,-10+10j) + (0.1574651066006004632914361 - 0.03194209021885226400892963j) + >>> hyp3f2(1,2,3,4,5,-10) + 0.3071141169208772603266489 + >>> hyp3f2(1,2,3,4,5,10) + (-0.4857045320523947050581423 - 0.5988311440454888436888028j) + >>> hyp3f2(0.25,1,1,2,1.5,1) + 1.157370995096772047567631 + >>> (8-pi-2*ln2)/3 + 1.157370995096772047567631 + >>> hyp3f2(1+j,0.5j,2,1,-2j,-1) + (1.74518490615029486475959 + 0.1454701525056682297614029j) + >>> hyp3f2(1+j,0.5j,2,1,-2j,sqrt(j)) + (0.9829816481834277511138055 - 0.4059040020276937085081127j) + >>> hyp3f2(-3,2,1,-5,4,1) + 1.41 + >>> hyp3f2(-3,2,1,-5,4,2) + 2.12 + +Evaluation very close to the unit circle:: + + >>> hyp3f2(1,2,3,4,5,'1.0001') + (1.564877796743282766872279 - 3.76821518787438186031973e-11j) + >>> hyp3f2(1,2,3,4,5,'1+0.0001j') + (1.564747153061671573212831 + 0.0001305757570366084557648482j) + >>> hyp3f2(1,2,3,4,5,'0.9999') + 1.564616644881686134983664 + >>> hyp3f2(1,2,3,4,5,'-0.9999') + 0.7823896253461678060196207 + +.. note :: + + Evaluation for `|z-1|` small can currently be inaccurate or slow + for some parameter combinations. + +For various parameter combinations, `\,_3F_2` admits representation in terms +of hypergeometric functions of lower degree, or in terms of +simpler functions:: + + >>> for a, b, z in [(1,2,-1), (2,0.5,1)]: + ... hyp2f1(a,b,a+b+0.5,z)**2 + ... hyp3f2(2*a,a+b,2*b,a+b+0.5,2*a+2*b,z) + ... + 0.4246104461966439006086308 + 0.4246104461966439006086308 + 7.111111111111111111111111 + 7.111111111111111111111111 + + >>> z = 2+3j + >>> hyp3f2(0.5,1,1.5,2,2,z) + (0.7621440939243342419729144 + 0.4249117735058037649915723j) + >>> 4*(pi-2*ellipe(z))/(pi*z) + (0.7621440939243342419729144 + 0.4249117735058037649915723j) + +""" + +hyperu = r""" +Gives the Tricomi confluent hypergeometric function `U`, also known as +the Kummer or confluent hypergeometric function of the second kind. This +function gives a second linearly independent solution to the confluent +hypergeometric differential equation (the first is provided by `\,_1F_1` -- +see :func:`~mpmath.hyp1f1`). + +**Examples** + +Evaluation for arbitrary complex arguments:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> hyperu(2,3,4) + 0.0625 + >>> hyperu(0.25, 5, 1000) + 0.1779949416140579573763523 + >>> hyperu(0.25, 5, -1000) + (0.1256256609322773150118907 - 0.1256256609322773150118907j) + +The `U` function may be singular at `z = 0`:: + + >>> hyperu(1.5, 2, 0) + +inf + >>> hyperu(1.5, -2, 0) + 0.1719434921288400112603671 + +Verifying the differential equation:: + + >>> a, b = 1.5, 2 + >>> f = lambda z: hyperu(a,b,z) + >>> for z in [-10, 3, 3+4j]: + ... chop(z*diff(f,z,2) + (b-z)*diff(f,z) - a*f(z)) + ... + 0.0 + 0.0 + 0.0 + +An integral representation:: + + >>> a,b,z = 2, 3.5, 4.25 + >>> hyperu(a,b,z) + 0.06674960718150520648014567 + >>> quad(lambda t: exp(-z*t)*t**(a-1)*(1+t)**(b-a-1),[0,inf]) / gamma(a) + 0.06674960718150520648014567 + + +[1] http://people.math.sfu.ca/~cbm/aands/page_504.htm +""" + +hyp2f0 = r""" +Gives the hypergeometric function `\,_2F_0`, defined formally by the +series + +.. math :: + + \,_2F_0(a,b;;z) = \sum_{n=0}^{\infty} (a)_n (b)_n \frac{z^n}{n!}. + +This series usually does not converge. For small enough `z`, it can be viewed +as an asymptotic series that may be summed directly with an appropriate +truncation. When this is not the case, :func:`~mpmath.hyp2f0` gives a regularized sum, +or equivalently, it uses a representation in terms of the +hypergeometric U function [1]. The series also converges when either `a` or `b` +is a nonpositive integer, as it then terminates into a polynomial +after `-a` or `-b` terms. + +**Examples** + +Evaluation is supported for arbitrary complex arguments:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> hyp2f0((2,3), 1.25, -100) + 0.07095851870980052763312791 + >>> hyp2f0((2,3), 1.25, 100) + (-0.03254379032170590665041131 + 0.07269254613282301012735797j) + >>> hyp2f0(-0.75, 1-j, 4j) + (-0.3579987031082732264862155 - 3.052951783922142735255881j) + +Even with real arguments, the regularized value of 2F0 is often complex-valued, +but the imaginary part decreases exponentially as `z \to 0`. In the following +example, the first call uses complex evaluation while the second has a small +enough `z` to evaluate using the direct series and thus the returned value +is strictly real (this should be taken to indicate that the imaginary +part is less than ``eps``):: + + >>> mp.dps = 15 + >>> hyp2f0(1.5, 0.5, 0.05) + (1.04166637647907 + 8.34584913683906e-8j) + >>> hyp2f0(1.5, 0.5, 0.0005) + 1.00037535207621 + +The imaginary part can be retrieved by increasing the working precision:: + + >>> mp.dps = 80 + >>> nprint(hyp2f0(1.5, 0.5, 0.009).imag) + 1.23828e-46 + +In the polynomial case (the series terminating), 2F0 can evaluate exactly:: + + >>> mp.dps = 15 + >>> hyp2f0(-6,-6,2) + 291793.0 + >>> identify(hyp2f0(-2,1,0.25)) + '(5/8)' + +The coefficients of the polynomials can be recovered using Taylor expansion:: + + >>> nprint(taylor(lambda x: hyp2f0(-3,0.5,x), 0, 10)) + [1.0, -1.5, 2.25, -1.875, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] + >>> nprint(taylor(lambda x: hyp2f0(-4,0.5,x), 0, 10)) + [1.0, -2.0, 4.5, -7.5, 6.5625, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] + + +[1] http://people.math.sfu.ca/~cbm/aands/page_504.htm +""" + + +gammainc = r""" +``gammainc(z, a=0, b=inf)`` computes the (generalized) incomplete +gamma function with integration limits `[a, b]`: + +.. math :: + + \Gamma(z,a,b) = \int_a^b t^{z-1} e^{-t} \, dt + +The generalized incomplete gamma function reduces to the +following special cases when one or both endpoints are fixed: + +* `\Gamma(z,0,\infty)` is the standard ("complete") + gamma function, `\Gamma(z)` (available directly + as the mpmath function :func:`~mpmath.gamma`) +* `\Gamma(z,a,\infty)` is the "upper" incomplete gamma + function, `\Gamma(z,a)` +* `\Gamma(z,0,b)` is the "lower" incomplete gamma + function, `\gamma(z,b)`. + +Of course, we have +`\Gamma(z,0,x) + \Gamma(z,x,\infty) = \Gamma(z)` +for all `z` and `x`. + +Note however that some authors reverse the order of the +arguments when defining the lower and upper incomplete +gamma function, so one should be careful to get the correct +definition. + +If also given the keyword argument ``regularized=True``, +:func:`~mpmath.gammainc` computes the "regularized" incomplete gamma +function + +.. math :: + + P(z,a,b) = \frac{\Gamma(z,a,b)}{\Gamma(z)}. + +**Examples** + +We can compare with numerical quadrature to verify that +:func:`~mpmath.gammainc` computes the integral in the definition:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> gammainc(2+3j, 4, 10) + (0.00977212668627705160602312 - 0.0770637306312989892451977j) + >>> quad(lambda t: t**(2+3j-1) * exp(-t), [4, 10]) + (0.00977212668627705160602312 - 0.0770637306312989892451977j) + +Argument symmetries follow directly from the integral definition:: + + >>> gammainc(3, 4, 5) + gammainc(3, 5, 4) + 0.0 + >>> gammainc(3,0,2) + gammainc(3,2,4); gammainc(3,0,4) + 1.523793388892911312363331 + 1.523793388892911312363331 + >>> findroot(lambda z: gammainc(2,z,3), 1) + 3.0 + +Evaluation for arbitrarily large arguments:: + + >>> gammainc(10, 100) + 4.083660630910611272288592e-26 + >>> gammainc(10, 10000000000000000) + 5.290402449901174752972486e-4342944819032375 + >>> gammainc(3+4j, 1000000+1000000j) + (-1.257913707524362408877881e-434284 + 2.556691003883483531962095e-434284j) + +Evaluation of a generalized incomplete gamma function automatically chooses +the representation that gives a more accurate result, depending on which +parameter is larger:: + + >>> gammainc(10000000, 3) - gammainc(10000000, 2) # Bad + 0.0 + >>> gammainc(10000000, 2, 3) # Good + 1.755146243738946045873491e+4771204 + >>> gammainc(2, 0, 100000001) - gammainc(2, 0, 100000000) # Bad + 0.0 + >>> gammainc(2, 100000000, 100000001) # Good + 4.078258353474186729184421e-43429441 + +The incomplete gamma functions satisfy simple recurrence +relations:: + + >>> mp.dps = 25 + >>> z, a = mpf(3.5), mpf(2) + >>> gammainc(z+1, a); z*gammainc(z,a) + a**z*exp(-a) + 10.60130296933533459267329 + 10.60130296933533459267329 + >>> gammainc(z+1,0,a); z*gammainc(z,0,a) - a**z*exp(-a) + 1.030425427232114336470932 + 1.030425427232114336470932 + +Evaluation at integers and poles:: + + >>> gammainc(-3, -4, -5) + (-0.2214577048967798566234192 + 0.0j) + >>> gammainc(-3, 0, 5) + +inf + +If `z` is an integer, the recurrence reduces the incomplete gamma +function to `P(a) \exp(-a) + Q(b) \exp(-b)` where `P` and +`Q` are polynomials:: + + >>> gammainc(1, 2); exp(-2) + 0.1353352832366126918939995 + 0.1353352832366126918939995 + >>> mp.dps = 50 + >>> identify(gammainc(6, 1, 2), ['exp(-1)', 'exp(-2)']) + '(326*exp(-1) + (-872)*exp(-2))' + +The incomplete gamma functions reduce to functions such as +the exponential integral Ei and the error function for special +arguments:: + + >>> mp.dps = 25 + >>> gammainc(0, 4); -ei(-4) + 0.00377935240984890647887486 + 0.00377935240984890647887486 + >>> gammainc(0.5, 0, 2); sqrt(pi)*erf(sqrt(2)) + 1.691806732945198336509541 + 1.691806732945198336509541 + +""" + +erf = r""" +Computes the error function, `\mathrm{erf}(x)`. The error +function is the normalized antiderivative of the Gaussian function +`\exp(-t^2)`. More precisely, + +.. math:: + + \mathrm{erf}(x) = \frac{2}{\sqrt \pi} \int_0^x \exp(-t^2) \,dt + +**Basic examples** + +Simple values and limits include:: + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = True + >>> erf(0) + 0.0 + >>> erf(1) + 0.842700792949715 + >>> erf(-1) + -0.842700792949715 + >>> erf(inf) + 1.0 + >>> erf(-inf) + -1.0 + +For large real `x`, `\mathrm{erf}(x)` approaches 1 very +rapidly:: + + >>> erf(3) + 0.999977909503001 + >>> erf(5) + 0.999999999998463 + +The error function is an odd function:: + + >>> nprint(chop(taylor(erf, 0, 5))) + [0.0, 1.12838, 0.0, -0.376126, 0.0, 0.112838] + +:func:`~mpmath.erf` implements arbitrary-precision evaluation and +supports complex numbers:: + + >>> mp.dps = 50 + >>> erf(0.5) + 0.52049987781304653768274665389196452873645157575796 + >>> mp.dps = 25 + >>> erf(1+j) + (1.316151281697947644880271 + 0.1904534692378346862841089j) + +Evaluation is supported for large arguments:: + + >>> mp.dps = 25 + >>> erf('1e1000') + 1.0 + >>> erf('-1e1000') + -1.0 + >>> erf('1e-1000') + 1.128379167095512573896159e-1000 + >>> erf('1e7j') + (0.0 + 8.593897639029319267398803e+43429448190317j) + >>> erf('1e7+1e7j') + (0.9999999858172446172631323 + 3.728805278735270407053139e-8j) + +**Related functions** + +See also :func:`~mpmath.erfc`, which is more accurate for large `x`, +and :func:`~mpmath.erfi` which gives the antiderivative of +`\exp(t^2)`. + +The Fresnel integrals :func:`~mpmath.fresnels` and :func:`~mpmath.fresnelc` +are also related to the error function. +""" + +erfc = r""" +Computes the complementary error function, +`\mathrm{erfc}(x) = 1-\mathrm{erf}(x)`. +This function avoids cancellation that occurs when naively +computing the complementary error function as ``1-erf(x)``:: + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = True + >>> 1 - erf(10) + 0.0 + >>> erfc(10) + 2.08848758376254e-45 + +:func:`~mpmath.erfc` works accurately even for ludicrously large +arguments:: + + >>> erfc(10**10) + 4.3504398860243e-43429448190325182776 + +Complex arguments are supported:: + + >>> erfc(500+50j) + (1.19739830969552e-107492 + 1.46072418957528e-107491j) + +""" + + +erfi = r""" +Computes the imaginary error function, `\mathrm{erfi}(x)`. +The imaginary error function is defined in analogy with the +error function, but with a positive sign in the integrand: + +.. math :: + + \mathrm{erfi}(x) = \frac{2}{\sqrt \pi} \int_0^x \exp(t^2) \,dt + +Whereas the error function rapidly converges to 1 as `x` grows, +the imaginary error function rapidly diverges to infinity. +The functions are related as +`\mathrm{erfi}(x) = -i\,\mathrm{erf}(ix)` for all complex +numbers `x`. + +**Examples** + +Basic values and limits:: + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = True + >>> erfi(0) + 0.0 + >>> erfi(1) + 1.65042575879754 + >>> erfi(-1) + -1.65042575879754 + >>> erfi(inf) + +inf + >>> erfi(-inf) + -inf + +Note the symmetry between erf and erfi:: + + >>> erfi(3j) + (0.0 + 0.999977909503001j) + >>> erf(3) + 0.999977909503001 + >>> erf(1+2j) + (-0.536643565778565 - 5.04914370344703j) + >>> erfi(2+1j) + (-5.04914370344703 - 0.536643565778565j) + +Large arguments are supported:: + + >>> erfi(1000) + 1.71130938718796e+434291 + >>> erfi(10**10) + 7.3167287567024e+43429448190325182754 + >>> erfi(-10**10) + -7.3167287567024e+43429448190325182754 + >>> erfi(1000-500j) + (2.49895233563961e+325717 + 2.6846779342253e+325717j) + >>> erfi(100000j) + (0.0 + 1.0j) + >>> erfi(-100000j) + (0.0 - 1.0j) + + +""" + +erfinv = r""" +Computes the inverse error function, satisfying + +.. math :: + + \mathrm{erf}(\mathrm{erfinv}(x)) = + \mathrm{erfinv}(\mathrm{erf}(x)) = x. + +This function is defined only for `-1 \le x \le 1`. + +**Examples** + +Special values include:: + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = True + >>> erfinv(0) + 0.0 + >>> erfinv(1) + +inf + >>> erfinv(-1) + -inf + +The domain is limited to the standard interval:: + + >>> erfinv(2) + Traceback (most recent call last): + ... + ValueError: erfinv(x) is defined only for -1 <= x <= 1 + +It is simple to check that :func:`~mpmath.erfinv` computes inverse values of +:func:`~mpmath.erf` as promised:: + + >>> erf(erfinv(0.75)) + 0.75 + >>> erf(erfinv(-0.995)) + -0.995 + +:func:`~mpmath.erfinv` supports arbitrary-precision evaluation:: + + >>> mp.dps = 50 + >>> x = erf(2) + >>> x + 0.99532226501895273416206925636725292861089179704006 + >>> erfinv(x) + 2.0 + +A definite integral involving the inverse error function:: + + >>> mp.dps = 15 + >>> quad(erfinv, [0, 1]) + 0.564189583547756 + >>> 1/sqrt(pi) + 0.564189583547756 + +The inverse error function can be used to generate random numbers +with a Gaussian distribution (although this is a relatively +inefficient algorithm):: + + >>> nprint([erfinv(2*rand()-1) for n in range(6)]) # doctest: +SKIP + [-0.586747, 1.10233, -0.376796, 0.926037, -0.708142, -0.732012] + +""" + +npdf = r""" +``npdf(x, mu=0, sigma=1)`` evaluates the probability density +function of a normal distribution with mean value `\mu` +and variance `\sigma^2`. + +Elementary properties of the probability distribution can +be verified using numerical integration:: + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = True + >>> quad(npdf, [-inf, inf]) + 1.0 + >>> quad(lambda x: npdf(x, 3), [3, inf]) + 0.5 + >>> quad(lambda x: npdf(x, 3, 2), [3, inf]) + 0.5 + +See also :func:`~mpmath.ncdf`, which gives the cumulative +distribution. +""" + +ncdf = r""" +``ncdf(x, mu=0, sigma=1)`` evaluates the cumulative distribution +function of a normal distribution with mean value `\mu` +and variance `\sigma^2`. + +See also :func:`~mpmath.npdf`, which gives the probability density. + +Elementary properties include:: + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = True + >>> ncdf(pi, mu=pi) + 0.5 + >>> ncdf(-inf) + 0.0 + >>> ncdf(+inf) + 1.0 + +The cumulative distribution is the integral of the density +function having identical mu and sigma:: + + >>> mp.dps = 15 + >>> diff(ncdf, 2) + 0.053990966513188 + >>> npdf(2) + 0.053990966513188 + >>> diff(lambda x: ncdf(x, 1, 0.5), 0) + 0.107981933026376 + >>> npdf(0, 1, 0.5) + 0.107981933026376 +""" + +expint = r""" +:func:`~mpmath.expint(n,z)` gives the generalized exponential integral +or En-function, + +.. math :: + + \mathrm{E}_n(z) = \int_1^{\infty} \frac{e^{-zt}}{t^n} dt, + +where `n` and `z` may both be complex numbers. The case with `n = 1` is +also given by :func:`~mpmath.e1`. + +**Examples** + +Evaluation at real and complex arguments:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> expint(1, 6.25) + 0.0002704758872637179088496194 + >>> expint(-3, 2+3j) + (0.00299658467335472929656159 + 0.06100816202125885450319632j) + >>> expint(2+3j, 4-5j) + (0.001803529474663565056945248 - 0.002235061547756185403349091j) + +At negative integer values of `n`, `E_n(z)` reduces to a +rational-exponential function:: + + >>> f = lambda n, z: fac(n)*sum(z**k/fac(k-1) for k in range(1,n+2))/\ + ... exp(z)/z**(n+2) + >>> n = 3 + >>> z = 1/pi + >>> expint(-n,z) + 584.2604820613019908668219 + >>> f(n,z) + 584.2604820613019908668219 + >>> n = 5 + >>> expint(-n,z) + 115366.5762594725451811138 + >>> f(n,z) + 115366.5762594725451811138 +""" + +e1 = r""" +Computes the exponential integral `\mathrm{E}_1(z)`, given by + +.. math :: + + \mathrm{E}_1(z) = \int_z^{\infty} \frac{e^{-t}}{t} dt. + +This is equivalent to :func:`~mpmath.expint` with `n = 1`. + +**Examples** + +Two ways to evaluate this function:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> e1(6.25) + 0.0002704758872637179088496194 + >>> expint(1,6.25) + 0.0002704758872637179088496194 + +The E1-function is essentially the same as the Ei-function (:func:`~mpmath.ei`) +with negated argument, except for an imaginary branch cut term:: + + >>> e1(2.5) + 0.02491491787026973549562801 + >>> -ei(-2.5) + 0.02491491787026973549562801 + >>> e1(-2.5) + (-7.073765894578600711923552 - 3.141592653589793238462643j) + >>> -ei(2.5) + -7.073765894578600711923552 + +""" + +ei = r""" +Computes the exponential integral or Ei-function, `\mathrm{Ei}(x)`. +The exponential integral is defined as + +.. math :: + + \mathrm{Ei}(x) = \int_{-\infty\,}^x \frac{e^t}{t} \, dt. + +When the integration range includes `t = 0`, the exponential +integral is interpreted as providing the Cauchy principal value. + +For real `x`, the Ei-function behaves roughly like +`\mathrm{Ei}(x) \approx \exp(x) + \log(|x|)`. + +The Ei-function is related to the more general family of exponential +integral functions denoted by `E_n`, which are available as :func:`~mpmath.expint`. + +**Basic examples** + +Some basic values and limits are:: + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = True + >>> ei(0) + -inf + >>> ei(1) + 1.89511781635594 + >>> ei(inf) + +inf + >>> ei(-inf) + 0.0 + +For `x < 0`, the defining integral can be evaluated +numerically as a reference:: + + >>> ei(-4) + -0.00377935240984891 + >>> quad(lambda t: exp(t)/t, [-inf, -4]) + -0.00377935240984891 + +:func:`~mpmath.ei` supports complex arguments and arbitrary +precision evaluation:: + + >>> mp.dps = 50 + >>> ei(pi) + 10.928374389331410348638445906907535171566338835056 + >>> mp.dps = 25 + >>> ei(3+4j) + (-4.154091651642689822535359 + 4.294418620024357476985535j) + +**Related functions** + +The exponential integral is closely related to the logarithmic +integral. See :func:`~mpmath.li` for additional information. + +The exponential integral is related to the hyperbolic +and trigonometric integrals (see :func:`~mpmath.chi`, :func:`~mpmath.shi`, +:func:`~mpmath.ci`, :func:`~mpmath.si`) similarly to how the ordinary +exponential function is related to the hyperbolic and +trigonometric functions:: + + >>> mp.dps = 15 + >>> ei(3) + 9.93383257062542 + >>> chi(3) + shi(3) + 9.93383257062542 + >>> chop(ci(3j) - j*si(3j) - pi*j/2) + 9.93383257062542 + +Beware that logarithmic corrections, as in the last example +above, are required to obtain the correct branch in general. +For details, see [1]. + +The exponential integral is also a special case of the +hypergeometric function `\,_2F_2`:: + + >>> z = 0.6 + >>> z*hyper([1,1],[2,2],z) + (ln(z)-ln(1/z))/2 + euler + 0.769881289937359 + >>> ei(z) + 0.769881289937359 + +**References** + +1. Relations between Ei and other functions: + http://functions.wolfram.com/GammaBetaErf/ExpIntegralEi/27/01/ + +2. Abramowitz & Stegun, section 5: + http://people.math.sfu.ca/~cbm/aands/page_228.htm + +3. Asymptotic expansion for Ei: + http://mathworld.wolfram.com/En-Function.html +""" + +li = r""" +Computes the logarithmic integral or li-function +`\mathrm{li}(x)`, defined by + +.. math :: + + \mathrm{li}(x) = \int_0^x \frac{1}{\log t} \, dt + +The logarithmic integral has a singularity at `x = 1`. + +Alternatively, ``li(x, offset=True)`` computes the offset +logarithmic integral (used in number theory) + +.. math :: + + \mathrm{Li}(x) = \int_2^x \frac{1}{\log t} \, dt. + +These two functions are related via the simple identity +`\mathrm{Li}(x) = \mathrm{li}(x) - \mathrm{li}(2)`. + +The logarithmic integral should also not be confused with +the polylogarithm (also denoted by Li), which is implemented +as :func:`~mpmath.polylog`. + +**Examples** + +Some basic values and limits:: + + >>> from mpmath import * + >>> mp.dps = 30; mp.pretty = True + >>> li(0) + 0.0 + >>> li(1) + -inf + >>> li(1) + -inf + >>> li(2) + 1.04516378011749278484458888919 + >>> findroot(li, 2) + 1.45136923488338105028396848589 + >>> li(inf) + +inf + >>> li(2, offset=True) + 0.0 + >>> li(1, offset=True) + -inf + >>> li(0, offset=True) + -1.04516378011749278484458888919 + >>> li(10, offset=True) + 5.12043572466980515267839286347 + +The logarithmic integral can be evaluated for arbitrary +complex arguments:: + + >>> mp.dps = 20 + >>> li(3+4j) + (3.1343755504645775265 + 2.6769247817778742392j) + +The logarithmic integral is related to the exponential integral:: + + >>> ei(log(3)) + 2.1635885946671919729 + >>> li(3) + 2.1635885946671919729 + +The logarithmic integral grows like `O(x/\log(x))`:: + + >>> mp.dps = 15 + >>> x = 10**100 + >>> x/log(x) + 4.34294481903252e+97 + >>> li(x) + 4.3619719871407e+97 + +The prime number theorem states that the number of primes less +than `x` is asymptotic to `\mathrm{Li}(x)` (equivalently +`\mathrm{li}(x)`). For example, it is known that there are +exactly 1,925,320,391,606,803,968,923 prime numbers less than +`10^{23}` [1]. The logarithmic integral provides a very +accurate estimate:: + + >>> li(10**23, offset=True) + 1.92532039161405e+21 + +A definite integral is:: + + >>> quad(li, [0, 1]) + -0.693147180559945 + >>> -ln(2) + -0.693147180559945 + +**References** + +1. http://mathworld.wolfram.com/PrimeCountingFunction.html + +2. http://mathworld.wolfram.com/LogarithmicIntegral.html + +""" + +ci = r""" +Computes the cosine integral, + +.. math :: + + \mathrm{Ci}(x) = -\int_x^{\infty} \frac{\cos t}{t}\,dt + = \gamma + \log x + \int_0^x \frac{\cos t - 1}{t}\,dt + +**Examples** + +Some values and limits:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> ci(0) + -inf + >>> ci(1) + 0.3374039229009681346626462 + >>> ci(pi) + 0.07366791204642548599010096 + >>> ci(inf) + 0.0 + >>> ci(-inf) + (0.0 + 3.141592653589793238462643j) + >>> ci(2+3j) + (1.408292501520849518759125 - 2.983617742029605093121118j) + +The cosine integral behaves roughly like the sinc function +(see :func:`~mpmath.sinc`) for large real `x`:: + + >>> ci(10**10) + -4.875060251748226537857298e-11 + >>> sinc(10**10) + -4.875060250875106915277943e-11 + >>> chop(limit(ci, inf)) + 0.0 + +It has infinitely many roots on the positive real axis:: + + >>> findroot(ci, 1) + 0.6165054856207162337971104 + >>> findroot(ci, 2) + 3.384180422551186426397851 + +Evaluation is supported for `z` anywhere in the complex plane:: + + >>> ci(10**6*(1+j)) + (4.449410587611035724984376e+434287 + 9.75744874290013526417059e+434287j) + +We can evaluate the defining integral as a reference:: + + >>> mp.dps = 15 + >>> -quadosc(lambda t: cos(t)/t, [5, inf], omega=1) + -0.190029749656644 + >>> ci(5) + -0.190029749656644 + +Some infinite series can be evaluated using the +cosine integral:: + + >>> nsum(lambda k: (-1)**k/(fac(2*k)*(2*k)), [1,inf]) + -0.239811742000565 + >>> ci(1) - euler + -0.239811742000565 + +""" + +si = r""" +Computes the sine integral, + +.. math :: + + \mathrm{Si}(x) = \int_0^x \frac{\sin t}{t}\,dt. + +The sine integral is thus the antiderivative of the sinc +function (see :func:`~mpmath.sinc`). + +**Examples** + +Some values and limits:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> si(0) + 0.0 + >>> si(1) + 0.9460830703671830149413533 + >>> si(-1) + -0.9460830703671830149413533 + >>> si(pi) + 1.851937051982466170361053 + >>> si(inf) + 1.570796326794896619231322 + >>> si(-inf) + -1.570796326794896619231322 + >>> si(2+3j) + (4.547513889562289219853204 + 1.399196580646054789459839j) + +The sine integral approaches `\pi/2` for large real `x`:: + + >>> si(10**10) + 1.570796326707584656968511 + >>> pi/2 + 1.570796326794896619231322 + +Evaluation is supported for `z` anywhere in the complex plane:: + + >>> si(10**6*(1+j)) + (-9.75744874290013526417059e+434287 + 4.449410587611035724984376e+434287j) + +We can evaluate the defining integral as a reference:: + + >>> mp.dps = 15 + >>> quad(sinc, [0, 5]) + 1.54993124494467 + >>> si(5) + 1.54993124494467 + +Some infinite series can be evaluated using the +sine integral:: + + >>> nsum(lambda k: (-1)**k/(fac(2*k+1)*(2*k+1)), [0,inf]) + 0.946083070367183 + >>> si(1) + 0.946083070367183 + +""" + +chi = r""" +Computes the hyperbolic cosine integral, defined +in analogy with the cosine integral (see :func:`~mpmath.ci`) as + +.. math :: + + \mathrm{Chi}(x) = -\int_x^{\infty} \frac{\cosh t}{t}\,dt + = \gamma + \log x + \int_0^x \frac{\cosh t - 1}{t}\,dt + +Some values and limits:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> chi(0) + -inf + >>> chi(1) + 0.8378669409802082408946786 + >>> chi(inf) + +inf + >>> findroot(chi, 0.5) + 0.5238225713898644064509583 + >>> chi(2+3j) + (-0.1683628683277204662429321 + 2.625115880451325002151688j) + +Evaluation is supported for `z` anywhere in the complex plane:: + + >>> chi(10**6*(1+j)) + (4.449410587611035724984376e+434287 - 9.75744874290013526417059e+434287j) + +""" + +shi = r""" +Computes the hyperbolic sine integral, defined +in analogy with the sine integral (see :func:`~mpmath.si`) as + +.. math :: + + \mathrm{Shi}(x) = \int_0^x \frac{\sinh t}{t}\,dt. + +Some values and limits:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> shi(0) + 0.0 + >>> shi(1) + 1.057250875375728514571842 + >>> shi(-1) + -1.057250875375728514571842 + >>> shi(inf) + +inf + >>> shi(2+3j) + (-0.1931890762719198291678095 + 2.645432555362369624818525j) + +Evaluation is supported for `z` anywhere in the complex plane:: + + >>> shi(10**6*(1+j)) + (4.449410587611035724984376e+434287 - 9.75744874290013526417059e+434287j) + +""" + +fresnels = r""" +Computes the Fresnel sine integral + +.. math :: + + S(x) = \int_0^x \sin\left(\frac{\pi t^2}{2}\right) \,dt + +Note that some sources define this function +without the normalization factor `\pi/2`. + +**Examples** + +Some basic values and limits:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> fresnels(0) + 0.0 + >>> fresnels(inf) + 0.5 + >>> fresnels(-inf) + -0.5 + >>> fresnels(1) + 0.4382591473903547660767567 + >>> fresnels(1+2j) + (36.72546488399143842838788 + 15.58775110440458732748279j) + +Comparing with the definition:: + + >>> fresnels(3) + 0.4963129989673750360976123 + >>> quad(lambda t: sin(pi*t**2/2), [0,3]) + 0.4963129989673750360976123 +""" + +fresnelc = r""" +Computes the Fresnel cosine integral + +.. math :: + + C(x) = \int_0^x \cos\left(\frac{\pi t^2}{2}\right) \,dt + +Note that some sources define this function +without the normalization factor `\pi/2`. + +**Examples** + +Some basic values and limits:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> fresnelc(0) + 0.0 + >>> fresnelc(inf) + 0.5 + >>> fresnelc(-inf) + -0.5 + >>> fresnelc(1) + 0.7798934003768228294742064 + >>> fresnelc(1+2j) + (16.08787137412548041729489 - 36.22568799288165021578758j) + +Comparing with the definition:: + + >>> fresnelc(3) + 0.6057207892976856295561611 + >>> quad(lambda t: cos(pi*t**2/2), [0,3]) + 0.6057207892976856295561611 +""" + +airyai = r""" +Computes the Airy function `\operatorname{Ai}(z)`, which is +the solution of the Airy differential equation `f''(z) - z f(z) = 0` +with initial conditions + +.. math :: + + \operatorname{Ai}(0) = + \frac{1}{3^{2/3}\Gamma\left(\frac{2}{3}\right)} + + \operatorname{Ai}'(0) = + -\frac{1}{3^{1/3}\Gamma\left(\frac{1}{3}\right)}. + +Other common ways of defining the Ai-function include +integrals such as + +.. math :: + + \operatorname{Ai}(x) = \frac{1}{\pi} + \int_0^{\infty} \cos\left(\frac{1}{3}t^3+xt\right) dt + \qquad x \in \mathbb{R} + + \operatorname{Ai}(z) = \frac{\sqrt{3}}{2\pi} + \int_0^{\infty} + \exp\left(-\frac{t^3}{3}-\frac{z^3}{3t^3}\right) dt. + +The Ai-function is an entire function with a turning point, +behaving roughly like a slowly decaying sine wave for `z < 0` and +like a rapidly decreasing exponential for `z > 0`. +A second solution of the Airy differential equation +is given by `\operatorname{Bi}(z)` (see :func:`~mpmath.airybi`). + +Optionally, with *derivative=alpha*, :func:`airyai` can compute the +`\alpha`-th order fractional derivative with respect to `z`. +For `\alpha = n = 1,2,3,\ldots` this gives the derivative +`\operatorname{Ai}^{(n)}(z)`, and for `\alpha = -n = -1,-2,-3,\ldots` +this gives the `n`-fold iterated integral + +.. math :: + + f_0(z) = \operatorname{Ai}(z) + + f_n(z) = \int_0^z f_{n-1}(t) dt. + +The Ai-function has infinitely many zeros, all located along the +negative half of the real axis. They can be computed with +:func:`~mpmath.airyaizero`. + +**Plots** + +.. literalinclude :: /plots/ai.py +.. image :: /plots/ai.png +.. literalinclude :: /plots/ai_c.py +.. image :: /plots/ai_c.png + +**Basic examples** + +Limits and values include:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> airyai(0); 1/(power(3,'2/3')*gamma('2/3')) + 0.3550280538878172392600632 + 0.3550280538878172392600632 + >>> airyai(1) + 0.1352924163128814155241474 + >>> airyai(-1) + 0.5355608832923521187995166 + >>> airyai(inf); airyai(-inf) + 0.0 + 0.0 + +Evaluation is supported for large magnitudes of the argument:: + + >>> airyai(-100) + 0.1767533932395528780908311 + >>> airyai(100) + 2.634482152088184489550553e-291 + >>> airyai(50+50j) + (-5.31790195707456404099817e-68 - 1.163588003770709748720107e-67j) + >>> airyai(-50+50j) + (1.041242537363167632587245e+158 + 3.347525544923600321838281e+157j) + +Huge arguments are also fine:: + + >>> airyai(10**10) + 1.162235978298741779953693e-289529654602171 + >>> airyai(-10**10) + 0.0001736206448152818510510181 + >>> w = airyai(10**10*(1+j)) + >>> w.real + 5.711508683721355528322567e-186339621747698 + >>> w.imag + 1.867245506962312577848166e-186339621747697 + +The first root of the Ai-function is:: + + >>> findroot(airyai, -2) + -2.338107410459767038489197 + >>> airyaizero(1) + -2.338107410459767038489197 + +**Properties and relations** + +Verifying the Airy differential equation:: + + >>> for z in [-3.4, 0, 2.5, 1+2j]: + ... chop(airyai(z,2) - z*airyai(z)) + ... + 0.0 + 0.0 + 0.0 + 0.0 + +The first few terms of the Taylor series expansion around `z = 0` +(every third term is zero):: + + >>> nprint(taylor(airyai, 0, 5)) + [0.355028, -0.258819, 0.0, 0.0591713, -0.0215683, 0.0] + +The Airy functions satisfy the Wronskian relation +`\operatorname{Ai}(z) \operatorname{Bi}'(z) - +\operatorname{Ai}'(z) \operatorname{Bi}(z) = 1/\pi`:: + + >>> z = -0.5 + >>> airyai(z)*airybi(z,1) - airyai(z,1)*airybi(z) + 0.3183098861837906715377675 + >>> 1/pi + 0.3183098861837906715377675 + +The Airy functions can be expressed in terms of Bessel +functions of order `\pm 1/3`. For `\Re[z] \le 0`, we have:: + + >>> z = -3 + >>> airyai(z) + -0.3788142936776580743472439 + >>> y = 2*power(-z,'3/2')/3 + >>> (sqrt(-z) * (besselj('1/3',y) + besselj('-1/3',y)))/3 + -0.3788142936776580743472439 + +**Derivatives and integrals** + +Derivatives of the Ai-function (directly and using :func:`~mpmath.diff`):: + + >>> airyai(-3,1); diff(airyai,-3) + 0.3145837692165988136507873 + 0.3145837692165988136507873 + >>> airyai(-3,2); diff(airyai,-3,2) + 1.136442881032974223041732 + 1.136442881032974223041732 + >>> airyai(1000,1); diff(airyai,1000) + -2.943133917910336090459748e-9156 + -2.943133917910336090459748e-9156 + +Several derivatives at `z = 0`:: + + >>> airyai(0,0); airyai(0,1); airyai(0,2) + 0.3550280538878172392600632 + -0.2588194037928067984051836 + 0.0 + >>> airyai(0,3); airyai(0,4); airyai(0,5) + 0.3550280538878172392600632 + -0.5176388075856135968103671 + 0.0 + >>> airyai(0,15); airyai(0,16); airyai(0,17) + 1292.30211615165475090663 + -3188.655054727379756351861 + 0.0 + +The integral of the Ai-function:: + + >>> airyai(3,-1); quad(airyai, [0,3]) + 0.3299203760070217725002701 + 0.3299203760070217725002701 + >>> airyai(-10,-1); quad(airyai, [0,-10]) + -0.765698403134212917425148 + -0.765698403134212917425148 + +Integrals of high or fractional order:: + + >>> airyai(-2,0.5); differint(airyai,-2,0.5,0) + (0.0 + 0.2453596101351438273844725j) + (0.0 + 0.2453596101351438273844725j) + >>> airyai(-2,-4); differint(airyai,-2,-4,0) + 0.2939176441636809580339365 + 0.2939176441636809580339365 + >>> airyai(0,-1); airyai(0,-2); airyai(0,-3) + 0.0 + 0.0 + 0.0 + +Integrals of the Ai-function can be evaluated at limit points:: + + >>> airyai(-1000000,-1); airyai(-inf,-1) + -0.6666843728311539978751512 + -0.6666666666666666666666667 + >>> airyai(10,-1); airyai(+inf,-1) + 0.3333333332991690159427932 + 0.3333333333333333333333333 + >>> airyai(+inf,-2); airyai(+inf,-3) + +inf + +inf + >>> airyai(-1000000,-2); airyai(-inf,-2) + 666666.4078472650651209742 + +inf + >>> airyai(-1000000,-3); airyai(-inf,-3) + -333333074513.7520264995733 + -inf + +**References** + +1. [DLMF]_ Chapter 9: Airy and Related Functions +2. [WolframFunctions]_ section: Bessel-Type Functions + +""" + +airybi = r""" +Computes the Airy function `\operatorname{Bi}(z)`, which is +the solution of the Airy differential equation `f''(z) - z f(z) = 0` +with initial conditions + +.. math :: + + \operatorname{Bi}(0) = + \frac{1}{3^{1/6}\Gamma\left(\frac{2}{3}\right)} + + \operatorname{Bi}'(0) = + \frac{3^{1/6}}{\Gamma\left(\frac{1}{3}\right)}. + +Like the Ai-function (see :func:`~mpmath.airyai`), the Bi-function +is oscillatory for `z < 0`, but it grows rather than decreases +for `z > 0`. + +Optionally, as for :func:`~mpmath.airyai`, derivatives, integrals +and fractional derivatives can be computed with the *derivative* +parameter. + +The Bi-function has infinitely many zeros along the negative +half-axis, as well as complex zeros, which can all be computed +with :func:`~mpmath.airybizero`. + +**Plots** + +.. literalinclude :: /plots/bi.py +.. image :: /plots/bi.png +.. literalinclude :: /plots/bi_c.py +.. image :: /plots/bi_c.png + +**Basic examples** + +Limits and values include:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> airybi(0); 1/(power(3,'1/6')*gamma('2/3')) + 0.6149266274460007351509224 + 0.6149266274460007351509224 + >>> airybi(1) + 1.207423594952871259436379 + >>> airybi(-1) + 0.10399738949694461188869 + >>> airybi(inf); airybi(-inf) + +inf + 0.0 + +Evaluation is supported for large magnitudes of the argument:: + + >>> airybi(-100) + 0.02427388768016013160566747 + >>> airybi(100) + 6.041223996670201399005265e+288 + >>> airybi(50+50j) + (-5.322076267321435669290334e+63 + 1.478450291165243789749427e+65j) + >>> airybi(-50+50j) + (-3.347525544923600321838281e+157 + 1.041242537363167632587245e+158j) + +Huge arguments:: + + >>> airybi(10**10) + 1.369385787943539818688433e+289529654602165 + >>> airybi(-10**10) + 0.001775656141692932747610973 + >>> w = airybi(10**10*(1+j)) + >>> w.real + -6.559955931096196875845858e+186339621747689 + >>> w.imag + -6.822462726981357180929024e+186339621747690 + +The first real root of the Bi-function is:: + + >>> findroot(airybi, -1); airybizero(1) + -1.17371322270912792491998 + -1.17371322270912792491998 + +**Properties and relations** + +Verifying the Airy differential equation:: + + >>> for z in [-3.4, 0, 2.5, 1+2j]: + ... chop(airybi(z,2) - z*airybi(z)) + ... + 0.0 + 0.0 + 0.0 + 0.0 + +The first few terms of the Taylor series expansion around `z = 0` +(every third term is zero):: + + >>> nprint(taylor(airybi, 0, 5)) + [0.614927, 0.448288, 0.0, 0.102488, 0.0373574, 0.0] + +The Airy functions can be expressed in terms of Bessel +functions of order `\pm 1/3`. For `\Re[z] \le 0`, we have:: + + >>> z = -3 + >>> airybi(z) + -0.1982896263749265432206449 + >>> p = 2*power(-z,'3/2')/3 + >>> sqrt(-mpf(z)/3)*(besselj('-1/3',p) - besselj('1/3',p)) + -0.1982896263749265432206449 + +**Derivatives and integrals** + +Derivatives of the Bi-function (directly and using :func:`~mpmath.diff`):: + + >>> airybi(-3,1); diff(airybi,-3) + -0.675611222685258537668032 + -0.675611222685258537668032 + >>> airybi(-3,2); diff(airybi,-3,2) + 0.5948688791247796296619346 + 0.5948688791247796296619346 + >>> airybi(1000,1); diff(airybi,1000) + 1.710055114624614989262335e+9156 + 1.710055114624614989262335e+9156 + +Several derivatives at `z = 0`:: + + >>> airybi(0,0); airybi(0,1); airybi(0,2) + 0.6149266274460007351509224 + 0.4482883573538263579148237 + 0.0 + >>> airybi(0,3); airybi(0,4); airybi(0,5) + 0.6149266274460007351509224 + 0.8965767147076527158296474 + 0.0 + >>> airybi(0,15); airybi(0,16); airybi(0,17) + 2238.332923903442675949357 + 5522.912562599140729510628 + 0.0 + +The integral of the Bi-function:: + + >>> airybi(3,-1); quad(airybi, [0,3]) + 10.06200303130620056316655 + 10.06200303130620056316655 + >>> airybi(-10,-1); quad(airybi, [0,-10]) + -0.01504042480614002045135483 + -0.01504042480614002045135483 + +Integrals of high or fractional order:: + + >>> airybi(-2,0.5); differint(airybi, -2, 0.5, 0) + (0.0 + 0.5019859055341699223453257j) + (0.0 + 0.5019859055341699223453257j) + >>> airybi(-2,-4); differint(airybi,-2,-4,0) + 0.2809314599922447252139092 + 0.2809314599922447252139092 + >>> airybi(0,-1); airybi(0,-2); airybi(0,-3) + 0.0 + 0.0 + 0.0 + +Integrals of the Bi-function can be evaluated at limit points:: + + >>> airybi(-1000000,-1); airybi(-inf,-1) + 0.000002191261128063434047966873 + 0.0 + >>> airybi(10,-1); airybi(+inf,-1) + 147809803.1074067161675853 + +inf + >>> airybi(+inf,-2); airybi(+inf,-3) + +inf + +inf + >>> airybi(-1000000,-2); airybi(-inf,-2) + 0.4482883750599908479851085 + 0.4482883573538263579148237 + >>> gamma('2/3')*power(3,'2/3')/(2*pi) + 0.4482883573538263579148237 + >>> airybi(-100000,-3); airybi(-inf,-3) + -44828.52827206932872493133 + -inf + >>> airybi(-100000,-4); airybi(-inf,-4) + 2241411040.437759489540248 + +inf + +""" + +airyaizero = r""" +Gives the `k`-th zero of the Airy Ai-function, +i.e. the `k`-th number `a_k` ordered by magnitude for which +`\operatorname{Ai}(a_k) = 0`. + +Optionally, with *derivative=1*, the corresponding +zero `a'_k` of the derivative function, i.e. +`\operatorname{Ai}'(a'_k) = 0`, is computed. + +**Examples** + +Some values of `a_k`:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> airyaizero(1) + -2.338107410459767038489197 + >>> airyaizero(2) + -4.087949444130970616636989 + >>> airyaizero(3) + -5.520559828095551059129856 + >>> airyaizero(1000) + -281.0315196125215528353364 + +Some values of `a'_k`:: + + >>> airyaizero(1,1) + -1.018792971647471089017325 + >>> airyaizero(2,1) + -3.248197582179836537875424 + >>> airyaizero(3,1) + -4.820099211178735639400616 + >>> airyaizero(1000,1) + -280.9378080358935070607097 + +Verification:: + + >>> chop(airyai(airyaizero(1))) + 0.0 + >>> chop(airyai(airyaizero(1,1),1)) + 0.0 + +""" + +airybizero = r""" +With *complex=False*, gives the `k`-th real zero of the Airy Bi-function, +i.e. the `k`-th number `b_k` ordered by magnitude for which +`\operatorname{Bi}(b_k) = 0`. + +With *complex=True*, gives the `k`-th complex zero in the upper +half plane `\beta_k`. Also the conjugate `\overline{\beta_k}` +is a zero. + +Optionally, with *derivative=1*, the corresponding +zero `b'_k` or `\beta'_k` of the derivative function, i.e. +`\operatorname{Bi}'(b'_k) = 0` or `\operatorname{Bi}'(\beta'_k) = 0`, +is computed. + +**Examples** + +Some values of `b_k`:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> airybizero(1) + -1.17371322270912792491998 + >>> airybizero(2) + -3.271093302836352715680228 + >>> airybizero(3) + -4.830737841662015932667709 + >>> airybizero(1000) + -280.9378112034152401578834 + +Some values of `b_k`:: + + >>> airybizero(1,1) + -2.294439682614123246622459 + >>> airybizero(2,1) + -4.073155089071828215552369 + >>> airybizero(3,1) + -5.512395729663599496259593 + >>> airybizero(1000,1) + -281.0315164471118527161362 + +Some values of `\beta_k`:: + + >>> airybizero(1,complex=True) + (0.9775448867316206859469927 + 2.141290706038744575749139j) + >>> airybizero(2,complex=True) + (1.896775013895336346627217 + 3.627291764358919410440499j) + >>> airybizero(3,complex=True) + (2.633157739354946595708019 + 4.855468179979844983174628j) + >>> airybizero(1000,complex=True) + (140.4978560578493018899793 + 243.3907724215792121244867j) + +Some values of `\beta'_k`:: + + >>> airybizero(1,1,complex=True) + (0.2149470745374305676088329 + 1.100600143302797880647194j) + >>> airybizero(2,1,complex=True) + (1.458168309223507392028211 + 2.912249367458445419235083j) + >>> airybizero(3,1,complex=True) + (2.273760763013482299792362 + 4.254528549217097862167015j) + >>> airybizero(1000,1,complex=True) + (140.4509972835270559730423 + 243.3096175398562811896208j) + +Verification:: + + >>> chop(airybi(airybizero(1))) + 0.0 + >>> chop(airybi(airybizero(1,1),1)) + 0.0 + >>> u = airybizero(1,complex=True) + >>> chop(airybi(u)) + 0.0 + >>> chop(airybi(conj(u))) + 0.0 + +The complex zeros (in the upper and lower half-planes respectively) +asymptotically approach the rays `z = R \exp(\pm i \pi /3)`:: + + >>> arg(airybizero(1,complex=True)) + 1.142532510286334022305364 + >>> arg(airybizero(1000,complex=True)) + 1.047271114786212061583917 + >>> arg(airybizero(1000000,complex=True)) + 1.047197624741816183341355 + >>> pi/3 + 1.047197551196597746154214 + +""" + + +ellipk = r""" +Evaluates the complete elliptic integral of the first kind, +`K(m)`, defined by + +.. math :: + + K(m) = \int_0^{\pi/2} \frac{dt}{\sqrt{1-m \sin^2 t}} \, = \, + \frac{\pi}{2} \,_2F_1\left(\frac{1}{2}, \frac{1}{2}, 1, m\right). + +Note that the argument is the parameter `m = k^2`, +not the modulus `k` which is sometimes used. + +**Plots** + +.. literalinclude :: /plots/ellipk.py +.. image :: /plots/ellipk.png + +**Examples** + +Values and limits include:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> ellipk(0) + 1.570796326794896619231322 + >>> ellipk(inf) + (0.0 + 0.0j) + >>> ellipk(-inf) + 0.0 + >>> ellipk(1) + +inf + >>> ellipk(-1) + 1.31102877714605990523242 + >>> ellipk(2) + (1.31102877714605990523242 - 1.31102877714605990523242j) + +Verifying the defining integral and hypergeometric +representation:: + + >>> ellipk(0.5) + 1.85407467730137191843385 + >>> quad(lambda t: (1-0.5*sin(t)**2)**-0.5, [0, pi/2]) + 1.85407467730137191843385 + >>> pi/2*hyp2f1(0.5,0.5,1,0.5) + 1.85407467730137191843385 + +Evaluation is supported for arbitrary complex `m`:: + + >>> ellipk(3+4j) + (0.9111955638049650086562171 + 0.6313342832413452438845091j) + +A definite integral:: + + >>> quad(ellipk, [0, 1]) + 2.0 +""" + +agm = r""" +``agm(a, b)`` computes the arithmetic-geometric mean of `a` and +`b`, defined as the limit of the following iteration: + +.. math :: + + a_0 = a + + b_0 = b + + a_{n+1} = \frac{a_n+b_n}{2} + + b_{n+1} = \sqrt{a_n b_n} + +This function can be called with a single argument, computing +`\mathrm{agm}(a,1) = \mathrm{agm}(1,a)`. + +**Examples** + +It is a well-known theorem that the geometric mean of +two distinct positive numbers is less than the arithmetic +mean. It follows that the arithmetic-geometric mean lies +between the two means:: + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = True + >>> a = mpf(3) + >>> b = mpf(4) + >>> sqrt(a*b) + 3.46410161513775 + >>> agm(a,b) + 3.48202767635957 + >>> (a+b)/2 + 3.5 + +The arithmetic-geometric mean is scale-invariant:: + + >>> agm(10*e, 10*pi) + 29.261085515723 + >>> 10*agm(e, pi) + 29.261085515723 + +As an order-of-magnitude estimate, `\mathrm{agm}(1,x) \approx x` +for large `x`:: + + >>> agm(10**10) + 643448704.760133 + >>> agm(10**50) + 1.34814309345871e+48 + +For tiny `x`, `\mathrm{agm}(1,x) \approx -\pi/(2 \log(x/4))`:: + + >>> agm('0.01') + 0.262166887202249 + >>> -pi/2/log('0.0025') + 0.262172347753122 + +The arithmetic-geometric mean can also be computed for complex +numbers:: + + >>> agm(3, 2+j) + (2.51055133276184 + 0.547394054060638j) + +The AGM iteration converges very quickly (each step doubles +the number of correct digits), so :func:`~mpmath.agm` supports efficient +high-precision evaluation:: + + >>> mp.dps = 10000 + >>> a = agm(1,2) + >>> str(a)[-10:] + '1679581912' + +**Mathematical relations** + +The arithmetic-geometric mean may be used to evaluate the +following two parametric definite integrals: + +.. math :: + + I_1 = \int_0^{\infty} + \frac{1}{\sqrt{(x^2+a^2)(x^2+b^2)}} \,dx + + I_2 = \int_0^{\pi/2} + \frac{1}{\sqrt{a^2 \cos^2(x) + b^2 \sin^2(x)}} \,dx + +We have:: + + >>> mp.dps = 15 + >>> a = 3 + >>> b = 4 + >>> f1 = lambda x: ((x**2+a**2)*(x**2+b**2))**-0.5 + >>> f2 = lambda x: ((a*cos(x))**2 + (b*sin(x))**2)**-0.5 + >>> quad(f1, [0, inf]) + 0.451115405388492 + >>> quad(f2, [0, pi/2]) + 0.451115405388492 + >>> pi/(2*agm(a,b)) + 0.451115405388492 + +A formula for `\Gamma(1/4)`:: + + >>> gamma(0.25) + 3.62560990822191 + >>> sqrt(2*sqrt(2*pi**3)/agm(1,sqrt(2))) + 3.62560990822191 + +**Possible issues** + +The branch cut chosen for complex `a` and `b` is somewhat +arbitrary. + +""" + +gegenbauer = r""" +Evaluates the Gegenbauer polynomial, or ultraspherical polynomial, + +.. math :: + + C_n^{(a)}(z) = {n+2a-1 \choose n} \,_2F_1\left(-n, n+2a; + a+\frac{1}{2}; \frac{1}{2}(1-z)\right). + +When `n` is a nonnegative integer, this formula gives a polynomial +in `z` of degree `n`, but all parameters are permitted to be +complex numbers. With `a = 1/2`, the Gegenbauer polynomial +reduces to a Legendre polynomial. + +**Examples** + +Evaluation for arbitrary arguments:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> gegenbauer(3, 0.5, -10) + -2485.0 + >>> gegenbauer(1000, 10, 100) + 3.012757178975667428359374e+2322 + >>> gegenbauer(2+3j, -0.75, -1000j) + (-5038991.358609026523401901 + 9414549.285447104177860806j) + +Evaluation at negative integer orders:: + + >>> gegenbauer(-4, 2, 1.75) + -1.0 + >>> gegenbauer(-4, 3, 1.75) + 0.0 + >>> gegenbauer(-4, 2j, 1.75) + 0.0 + >>> gegenbauer(-7, 0.5, 3) + 8989.0 + +The Gegenbauer polynomials solve the differential equation:: + + >>> n, a = 4.5, 1+2j + >>> f = lambda z: gegenbauer(n, a, z) + >>> for z in [0, 0.75, -0.5j]: + ... chop((1-z**2)*diff(f,z,2) - (2*a+1)*z*diff(f,z) + n*(n+2*a)*f(z)) + ... + 0.0 + 0.0 + 0.0 + +The Gegenbauer polynomials have generating function +`(1-2zt+t^2)^{-a}`:: + + >>> a, z = 2.5, 1 + >>> taylor(lambda t: (1-2*z*t+t**2)**(-a), 0, 3) + [1.0, 5.0, 15.0, 35.0] + >>> [gegenbauer(n,a,z) for n in range(4)] + [1.0, 5.0, 15.0, 35.0] + +The Gegenbauer polynomials are orthogonal on `[-1, 1]` with respect +to the weight `(1-z^2)^{a-\frac{1}{2}}`:: + + >>> a, n, m = 2.5, 4, 5 + >>> Cn = lambda z: gegenbauer(n, a, z, zeroprec=1000) + >>> Cm = lambda z: gegenbauer(m, a, z, zeroprec=1000) + >>> chop(quad(lambda z: Cn(z)*Cm(z)*(1-z**2)*(a-0.5), [-1, 1])) + 0.0 +""" + +laguerre = r""" +Gives the generalized (associated) Laguerre polynomial, defined by + +.. math :: + + L_n^a(z) = \frac{\Gamma(n+b+1)}{\Gamma(b+1) \Gamma(n+1)} + \,_1F_1(-n, a+1, z). + +With `a = 0` and `n` a nonnegative integer, this reduces to an ordinary +Laguerre polynomial, the sequence of which begins +`L_0(z) = 1, L_1(z) = 1-z, L_2(z) = z^2-2z+1, \ldots`. + +The Laguerre polynomials are orthogonal with respect to the weight +`z^a e^{-z}` on `[0, \infty)`. + +**Plots** + +.. literalinclude :: /plots/laguerre.py +.. image :: /plots/laguerre.png + +**Examples** + +Evaluation for arbitrary arguments:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> laguerre(5, 0, 0.25) + 0.03726399739583333333333333 + >>> laguerre(1+j, 0.5, 2+3j) + (4.474921610704496808379097 - 11.02058050372068958069241j) + >>> laguerre(2, 0, 10000) + 49980001.0 + >>> laguerre(2.5, 0, 10000) + -9.327764910194842158583189e+4328 + +The first few Laguerre polynomials, normalized to have integer +coefficients:: + + >>> for n in range(7): + ... chop(taylor(lambda z: fac(n)*laguerre(n, 0, z), 0, n)) + ... + [1.0] + [1.0, -1.0] + [2.0, -4.0, 1.0] + [6.0, -18.0, 9.0, -1.0] + [24.0, -96.0, 72.0, -16.0, 1.0] + [120.0, -600.0, 600.0, -200.0, 25.0, -1.0] + [720.0, -4320.0, 5400.0, -2400.0, 450.0, -36.0, 1.0] + +Verifying orthogonality:: + + >>> Lm = lambda t: laguerre(m,a,t) + >>> Ln = lambda t: laguerre(n,a,t) + >>> a, n, m = 2.5, 2, 3 + >>> chop(quad(lambda t: exp(-t)*t**a*Lm(t)*Ln(t), [0,inf])) + 0.0 + + +""" + +hermite = r""" +Evaluates the Hermite polynomial `H_n(z)`, which may be defined using +the recurrence + +.. math :: + + H_0(z) = 1 + + H_1(z) = 2z + + H_{n+1} = 2z H_n(z) - 2n H_{n-1}(z). + +The Hermite polynomials are orthogonal on `(-\infty, \infty)` with +respect to the weight `e^{-z^2}`. More generally, allowing arbitrary complex +values of `n`, the Hermite function `H_n(z)` is defined as + +.. math :: + + H_n(z) = (2z)^n \,_2F_0\left(-\frac{n}{2}, \frac{1-n}{2}, + -\frac{1}{z^2}\right) + +for `\Re{z} > 0`, or generally + +.. math :: + + H_n(z) = 2^n \sqrt{\pi} \left( + \frac{1}{\Gamma\left(\frac{1-n}{2}\right)} + \,_1F_1\left(-\frac{n}{2}, \frac{1}{2}, z^2\right) - + \frac{2z}{\Gamma\left(-\frac{n}{2}\right)} + \,_1F_1\left(\frac{1-n}{2}, \frac{3}{2}, z^2\right) + \right). + +**Plots** + +.. literalinclude :: /plots/hermite.py +.. image :: /plots/hermite.png + +**Examples** + +Evaluation for arbitrary arguments:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> hermite(0, 10) + 1.0 + >>> hermite(1, 10); hermite(2, 10) + 20.0 + 398.0 + >>> hermite(10000, 2) + 4.950440066552087387515653e+19334 + >>> hermite(3, -10**8) + -7999999999999998800000000.0 + >>> hermite(-3, -10**8) + 1.675159751729877682920301e+4342944819032534 + >>> hermite(2+3j, -1+2j) + (-0.07652130602993513389421901 - 0.1084662449961914580276007j) + +Coefficients of the first few Hermite polynomials are:: + + >>> for n in range(7): + ... chop(taylor(lambda z: hermite(n, z), 0, n)) + ... + [1.0] + [0.0, 2.0] + [-2.0, 0.0, 4.0] + [0.0, -12.0, 0.0, 8.0] + [12.0, 0.0, -48.0, 0.0, 16.0] + [0.0, 120.0, 0.0, -160.0, 0.0, 32.0] + [-120.0, 0.0, 720.0, 0.0, -480.0, 0.0, 64.0] + +Values at `z = 0`:: + + >>> for n in range(-5, 9): + ... hermite(n, 0) + ... + 0.02769459142039868792653387 + 0.08333333333333333333333333 + 0.2215567313631895034122709 + 0.5 + 0.8862269254527580136490837 + 1.0 + 0.0 + -2.0 + 0.0 + 12.0 + 0.0 + -120.0 + 0.0 + 1680.0 + +Hermite functions satisfy the differential equation:: + + >>> n = 4 + >>> f = lambda z: hermite(n, z) + >>> z = 1.5 + >>> chop(diff(f,z,2) - 2*z*diff(f,z) + 2*n*f(z)) + 0.0 + +Verifying orthogonality:: + + >>> chop(quad(lambda t: hermite(2,t)*hermite(4,t)*exp(-t**2), [-inf,inf])) + 0.0 + +""" + +jacobi = r""" +``jacobi(n, a, b, x)`` evaluates the Jacobi polynomial +`P_n^{(a,b)}(x)`. The Jacobi polynomials are a special +case of the hypergeometric function `\,_2F_1` given by: + +.. math :: + + P_n^{(a,b)}(x) = {n+a \choose n} + \,_2F_1\left(-n,1+a+b+n,a+1,\frac{1-x}{2}\right). + +Note that this definition generalizes to nonintegral values +of `n`. When `n` is an integer, the hypergeometric series +terminates after a finite number of terms, giving +a polynomial in `x`. + +**Evaluation of Jacobi polynomials** + +A special evaluation is `P_n^{(a,b)}(1) = {n+a \choose n}`:: + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = True + >>> jacobi(4, 0.5, 0.25, 1) + 2.4609375 + >>> binomial(4+0.5, 4) + 2.4609375 + +A Jacobi polynomial of degree `n` is equal to its +Taylor polynomial of degree `n`. The explicit +coefficients of Jacobi polynomials can therefore +be recovered easily using :func:`~mpmath.taylor`:: + + >>> for n in range(5): + ... nprint(taylor(lambda x: jacobi(n,1,2,x), 0, n)) + ... + [1.0] + [-0.5, 2.5] + [-0.75, -1.5, 5.25] + [0.5, -3.5, -3.5, 10.5] + [0.625, 2.5, -11.25, -7.5, 20.625] + +For nonintegral `n`, the Jacobi "polynomial" is no longer +a polynomial:: + + >>> nprint(taylor(lambda x: jacobi(0.5,1,2,x), 0, 4)) + [0.309983, 1.84119, -1.26933, 1.26699, -1.34808] + +**Orthogonality** + +The Jacobi polynomials are orthogonal on the interval +`[-1, 1]` with respect to the weight function +`w(x) = (1-x)^a (1+x)^b`. That is, +`w(x) P_n^{(a,b)}(x) P_m^{(a,b)}(x)` integrates to +zero if `m \ne n` and to a nonzero number if `m = n`. + +The orthogonality is easy to verify using numerical +quadrature:: + + >>> P = jacobi + >>> f = lambda x: (1-x)**a * (1+x)**b * P(m,a,b,x) * P(n,a,b,x) + >>> a = 2 + >>> b = 3 + >>> m, n = 3, 4 + >>> chop(quad(f, [-1, 1]), 1) + 0.0 + >>> m, n = 4, 4 + >>> quad(f, [-1, 1]) + 1.9047619047619 + +**Differential equation** + +The Jacobi polynomials are solutions of the differential +equation + +.. math :: + + (1-x^2) y'' + (b-a-(a+b+2)x) y' + n (n+a+b+1) y = 0. + +We can verify that :func:`~mpmath.jacobi` approximately satisfies +this equation:: + + >>> from mpmath import * + >>> mp.dps = 15 + >>> a = 2.5 + >>> b = 4 + >>> n = 3 + >>> y = lambda x: jacobi(n,a,b,x) + >>> x = pi + >>> A0 = n*(n+a+b+1)*y(x) + >>> A1 = (b-a-(a+b+2)*x)*diff(y,x) + >>> A2 = (1-x**2)*diff(y,x,2) + >>> nprint(A2 + A1 + A0, 1) + 4.0e-12 + +The difference of order `10^{-12}` is as close to zero as +it could be at 15-digit working precision, since the terms +are large:: + + >>> A0, A1, A2 + (26560.2328981879, -21503.7641037294, -5056.46879445852) + +""" + +legendre = r""" +``legendre(n, x)`` evaluates the Legendre polynomial `P_n(x)`. +The Legendre polynomials are given by the formula + +.. math :: + + P_n(x) = \frac{1}{2^n n!} \frac{d^n}{dx^n} (x^2 -1)^n. + +Alternatively, they can be computed recursively using + +.. math :: + + P_0(x) = 1 + + P_1(x) = x + + (n+1) P_{n+1}(x) = (2n+1) x P_n(x) - n P_{n-1}(x). + +A third definition is in terms of the hypergeometric function +`\,_2F_1`, whereby they can be generalized to arbitrary `n`: + +.. math :: + + P_n(x) = \,_2F_1\left(-n, n+1, 1, \frac{1-x}{2}\right) + +**Plots** + +.. literalinclude :: /plots/legendre.py +.. image :: /plots/legendre.png + +**Basic evaluation** + +The Legendre polynomials assume fixed values at the points +`x = -1` and `x = 1`:: + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = True + >>> nprint([legendre(n, 1) for n in range(6)]) + [1.0, 1.0, 1.0, 1.0, 1.0, 1.0] + >>> nprint([legendre(n, -1) for n in range(6)]) + [1.0, -1.0, 1.0, -1.0, 1.0, -1.0] + +The coefficients of Legendre polynomials can be recovered +using degree-`n` Taylor expansion:: + + >>> for n in range(5): + ... nprint(chop(taylor(lambda x: legendre(n, x), 0, n))) + ... + [1.0] + [0.0, 1.0] + [-0.5, 0.0, 1.5] + [0.0, -1.5, 0.0, 2.5] + [0.375, 0.0, -3.75, 0.0, 4.375] + +The roots of Legendre polynomials are located symmetrically +on the interval `[-1, 1]`:: + + >>> for n in range(5): + ... nprint(polyroots(taylor(lambda x: legendre(n, x), 0, n)[::-1])) + ... + [] + [0.0] + [-0.57735, 0.57735] + [-0.774597, 0.0, 0.774597] + [-0.861136, -0.339981, 0.339981, 0.861136] + +An example of an evaluation for arbitrary `n`:: + + >>> legendre(0.75, 2+4j) + (1.94952805264875 + 2.1071073099422j) + +**Orthogonality** + +The Legendre polynomials are orthogonal on `[-1, 1]` with respect +to the trivial weight `w(x) = 1`. That is, `P_m(x) P_n(x)` +integrates to zero if `m \ne n` and to `2/(2n+1)` if `m = n`:: + + >>> m, n = 3, 4 + >>> quad(lambda x: legendre(m,x)*legendre(n,x), [-1, 1]) + 0.0 + >>> m, n = 4, 4 + >>> quad(lambda x: legendre(m,x)*legendre(n,x), [-1, 1]) + 0.222222222222222 + +**Differential equation** + +The Legendre polynomials satisfy the differential equation + +.. math :: + + ((1-x^2) y')' + n(n+1) y' = 0. + +We can verify this numerically:: + + >>> n = 3.6 + >>> x = 0.73 + >>> P = legendre + >>> A = diff(lambda t: (1-t**2)*diff(lambda u: P(n,u), t), x) + >>> B = n*(n+1)*P(n,x) + >>> nprint(A+B,1) + 9.0e-16 + +""" + + +legenp = r""" +Calculates the (associated) Legendre function of the first kind of +degree *n* and order *m*, `P_n^m(z)`. Taking `m = 0` gives the ordinary +Legendre function of the first kind, `P_n(z)`. The parameters may be +complex numbers. + +In terms of the Gauss hypergeometric function, the (associated) Legendre +function is defined as + +.. math :: + + P_n^m(z) = \frac{1}{\Gamma(1-m)} \frac{(1+z)^{m/2}}{(1-z)^{m/2}} + \,_2F_1\left(-n, n+1, 1-m, \frac{1-z}{2}\right). + +With *type=3* instead of *type=2*, the alternative +definition + +.. math :: + + \hat{P}_n^m(z) = \frac{1}{\Gamma(1-m)} \frac{(z+1)^{m/2}}{(z-1)^{m/2}} + \,_2F_1\left(-n, n+1, 1-m, \frac{1-z}{2}\right). + +is used. These functions correspond respectively to ``LegendreP[n,m,2,z]`` +and ``LegendreP[n,m,3,z]`` in Mathematica. + +The general solution of the (associated) Legendre differential equation + +.. math :: + + (1-z^2) f''(z) - 2zf'(z) + \left(n(n+1)-\frac{m^2}{1-z^2}\right)f(z) = 0 + +is given by `C_1 P_n^m(z) + C_2 Q_n^m(z)` for arbitrary constants +`C_1`, `C_2`, where `Q_n^m(z)` is a Legendre function of the +second kind as implemented by :func:`~mpmath.legenq`. + +**Examples** + +Evaluation for arbitrary parameters and arguments:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> legenp(2, 0, 10); legendre(2, 10) + 149.5 + 149.5 + >>> legenp(-2, 0.5, 2.5) + (1.972260393822275434196053 - 1.972260393822275434196053j) + >>> legenp(2+3j, 1-j, -0.5+4j) + (-3.335677248386698208736542 - 5.663270217461022307645625j) + >>> chop(legenp(3, 2, -1.5, type=2)) + 28.125 + >>> chop(legenp(3, 2, -1.5, type=3)) + -28.125 + +Verifying the associated Legendre differential equation:: + + >>> n, m = 2, -0.5 + >>> C1, C2 = 1, -3 + >>> f = lambda z: C1*legenp(n,m,z) + C2*legenq(n,m,z) + >>> deq = lambda z: (1-z**2)*diff(f,z,2) - 2*z*diff(f,z) + \ + ... (n*(n+1)-m**2/(1-z**2))*f(z) + >>> for z in [0, 2, -1.5, 0.5+2j]: + ... chop(deq(mpmathify(z))) + ... + 0.0 + 0.0 + 0.0 + 0.0 +""" + +legenq = r""" +Calculates the (associated) Legendre function of the second kind of +degree *n* and order *m*, `Q_n^m(z)`. Taking `m = 0` gives the ordinary +Legendre function of the second kind, `Q_n(z)`. The parameters may be +complex numbers. + +The Legendre functions of the second kind give a second set of +solutions to the (associated) Legendre differential equation. +(See :func:`~mpmath.legenp`.) +Unlike the Legendre functions of the first kind, they are not +polynomials of `z` for integer `n`, `m` but rational or logarithmic +functions with poles at `z = \pm 1`. + +There are various ways to define Legendre functions of +the second kind, giving rise to different complex structure. +A version can be selected using the *type* keyword argument. +The *type=2* and *type=3* functions are given respectively by + +.. math :: + + Q_n^m(z) = \frac{\pi}{2 \sin(\pi m)} + \left( \cos(\pi m) P_n^m(z) - + \frac{\Gamma(1+m+n)}{\Gamma(1-m+n)} P_n^{-m}(z)\right) + + \hat{Q}_n^m(z) = \frac{\pi}{2 \sin(\pi m)} e^{\pi i m} + \left( \hat{P}_n^m(z) - + \frac{\Gamma(1+m+n)}{\Gamma(1-m+n)} \hat{P}_n^{-m}(z)\right) + +where `P` and `\hat{P}` are the *type=2* and *type=3* Legendre functions +of the first kind. The formulas above should be understood as limits +when `m` is an integer. + +These functions correspond to ``LegendreQ[n,m,2,z]`` (or ``LegendreQ[n,m,z]``) +and ``LegendreQ[n,m,3,z]`` in Mathematica. The *type=3* function +is essentially the same as the function defined in +Abramowitz & Stegun (eq. 8.1.3) but with `(z+1)^{m/2}(z-1)^{m/2}` instead +of `(z^2-1)^{m/2}`, giving slightly different branches. + +**Examples** + +Evaluation for arbitrary parameters and arguments:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> legenq(2, 0, 0.5) + -0.8186632680417568557122028 + >>> legenq(-1.5, -2, 2.5) + (0.6655964618250228714288277 + 0.3937692045497259717762649j) + >>> legenq(2-j, 3+4j, -6+5j) + (-10001.95256487468541686564 - 6011.691337610097577791134j) + +Different versions of the function:: + + >>> legenq(2, 1, 0.5) + 0.7298060598018049369381857 + >>> legenq(2, 1, 1.5) + (-7.902916572420817192300921 + 0.1998650072605976600724502j) + >>> legenq(2, 1, 0.5, type=3) + (2.040524284763495081918338 - 0.7298060598018049369381857j) + >>> chop(legenq(2, 1, 1.5, type=3)) + -0.1998650072605976600724502 + +""" + +chebyt = r""" +``chebyt(n, x)`` evaluates the Chebyshev polynomial of the first +kind `T_n(x)`, defined by the identity + +.. math :: + + T_n(\cos x) = \cos(n x). + +The Chebyshev polynomials of the first kind are a special +case of the Jacobi polynomials, and by extension of the +hypergeometric function `\,_2F_1`. They can thus also be +evaluated for nonintegral `n`. + +**Plots** + +.. literalinclude :: /plots/chebyt.py +.. image :: /plots/chebyt.png + +**Basic evaluation** + +The coefficients of the `n`-th polynomial can be recovered +using using degree-`n` Taylor expansion:: + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = True + >>> for n in range(5): + ... nprint(chop(taylor(lambda x: chebyt(n, x), 0, n))) + ... + [1.0] + [0.0, 1.0] + [-1.0, 0.0, 2.0] + [0.0, -3.0, 0.0, 4.0] + [1.0, 0.0, -8.0, 0.0, 8.0] + +**Orthogonality** + +The Chebyshev polynomials of the first kind are orthogonal +on the interval `[-1, 1]` with respect to the weight +function `w(x) = 1/\sqrt{1-x^2}`:: + + >>> f = lambda x: chebyt(m,x)*chebyt(n,x)/sqrt(1-x**2) + >>> m, n = 3, 4 + >>> nprint(quad(f, [-1, 1]),1) + 0.0 + >>> m, n = 4, 4 + >>> quad(f, [-1, 1]) + 1.57079632596448 + +""" + +chebyu = r""" +``chebyu(n, x)`` evaluates the Chebyshev polynomial of the second +kind `U_n(x)`, defined by the identity + +.. math :: + + U_n(\cos x) = \frac{\sin((n+1)x)}{\sin(x)}. + +The Chebyshev polynomials of the second kind are a special +case of the Jacobi polynomials, and by extension of the +hypergeometric function `\,_2F_1`. They can thus also be +evaluated for nonintegral `n`. + +**Plots** + +.. literalinclude :: /plots/chebyu.py +.. image :: /plots/chebyu.png + +**Basic evaluation** + +The coefficients of the `n`-th polynomial can be recovered +using using degree-`n` Taylor expansion:: + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = True + >>> for n in range(5): + ... nprint(chop(taylor(lambda x: chebyu(n, x), 0, n))) + ... + [1.0] + [0.0, 2.0] + [-1.0, 0.0, 4.0] + [0.0, -4.0, 0.0, 8.0] + [1.0, 0.0, -12.0, 0.0, 16.0] + +**Orthogonality** + +The Chebyshev polynomials of the second kind are orthogonal +on the interval `[-1, 1]` with respect to the weight +function `w(x) = \sqrt{1-x^2}`:: + + >>> f = lambda x: chebyu(m,x)*chebyu(n,x)*sqrt(1-x**2) + >>> m, n = 3, 4 + >>> quad(f, [-1, 1]) + 0.0 + >>> m, n = 4, 4 + >>> quad(f, [-1, 1]) + 1.5707963267949 +""" + +besselj = r""" +``besselj(n, x, derivative=0)`` gives the Bessel function of the first kind +`J_n(x)`. Bessel functions of the first kind are defined as +solutions of the differential equation + +.. math :: + + x^2 y'' + x y' + (x^2 - n^2) y = 0 + +which appears, among other things, when solving the radial +part of Laplace's equation in cylindrical coordinates. This +equation has two solutions for given `n`, where the +`J_n`-function is the solution that is nonsingular at `x = 0`. +For positive integer `n`, `J_n(x)` behaves roughly like a sine +(odd `n`) or cosine (even `n`) multiplied by a magnitude factor +that decays slowly as `x \to \pm\infty`. + +Generally, `J_n` is a special case of the hypergeometric +function `\,_0F_1`: + +.. math :: + + J_n(x) = \frac{x^n}{2^n \Gamma(n+1)} + \,_0F_1\left(n+1,-\frac{x^2}{4}\right) + +With *derivative* = `m \ne 0`, the `m`-th derivative + +.. math :: + + \frac{d^m}{dx^m} J_n(x) + +is computed. + +**Plots** + +.. literalinclude :: /plots/besselj.py +.. image :: /plots/besselj.png +.. literalinclude :: /plots/besselj_c.py +.. image :: /plots/besselj_c.png + +**Examples** + +Evaluation is supported for arbitrary arguments, and at +arbitrary precision:: + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = True + >>> besselj(2, 1000) + -0.024777229528606 + >>> besselj(4, 0.75) + 0.000801070086542314 + >>> besselj(2, 1000j) + (-2.48071721019185e+432 + 6.41567059811949e-437j) + >>> mp.dps = 25 + >>> besselj(0.75j, 3+4j) + (-2.778118364828153309919653 - 1.5863603889018621585533j) + >>> mp.dps = 50 + >>> besselj(1, pi) + 0.28461534317975275734531059968613140570981118184947 + +Arguments may be large:: + + >>> mp.dps = 25 + >>> besselj(0, 10000) + -0.007096160353388801477265164 + >>> besselj(0, 10**10) + 0.000002175591750246891726859055 + >>> besselj(2, 10**100) + 7.337048736538615712436929e-51 + >>> besselj(2, 10**5*j) + (-3.540725411970948860173735e+43426 + 4.4949812409615803110051e-43433j) + +The Bessel functions of the first kind satisfy simple +symmetries around `x = 0`:: + + >>> mp.dps = 15 + >>> nprint([besselj(n,0) for n in range(5)]) + [1.0, 0.0, 0.0, 0.0, 0.0] + >>> nprint([besselj(n,pi) for n in range(5)]) + [-0.304242, 0.284615, 0.485434, 0.333458, 0.151425] + >>> nprint([besselj(n,-pi) for n in range(5)]) + [-0.304242, -0.284615, 0.485434, -0.333458, 0.151425] + +Roots of Bessel functions are often used:: + + >>> nprint([findroot(j0, k) for k in [2, 5, 8, 11, 14]]) + [2.40483, 5.52008, 8.65373, 11.7915, 14.9309] + >>> nprint([findroot(j1, k) for k in [3, 7, 10, 13, 16]]) + [3.83171, 7.01559, 10.1735, 13.3237, 16.4706] + +The roots are not periodic, but the distance between successive +roots asymptotically approaches `2 \pi`. Bessel functions of +the first kind have the following normalization:: + + >>> quadosc(j0, [0, inf], period=2*pi) + 1.0 + >>> quadosc(j1, [0, inf], period=2*pi) + 1.0 + +For `n = 1/2` or `n = -1/2`, the Bessel function reduces to a +trigonometric function:: + + >>> x = 10 + >>> besselj(0.5, x), sqrt(2/(pi*x))*sin(x) + (-0.13726373575505, -0.13726373575505) + >>> besselj(-0.5, x), sqrt(2/(pi*x))*cos(x) + (-0.211708866331398, -0.211708866331398) + +Derivatives of any order can be computed (negative orders +correspond to integration):: + + >>> mp.dps = 25 + >>> besselj(0, 7.5, 1) + -0.1352484275797055051822405 + >>> diff(lambda x: besselj(0,x), 7.5) + -0.1352484275797055051822405 + >>> besselj(0, 7.5, 10) + -0.1377811164763244890135677 + >>> diff(lambda x: besselj(0,x), 7.5, 10) + -0.1377811164763244890135677 + >>> besselj(0,7.5,-1) - besselj(0,3.5,-1) + -0.1241343240399987693521378 + >>> quad(j0, [3.5, 7.5]) + -0.1241343240399987693521378 + +Differentiation with a noninteger order gives the fractional derivative +in the sense of the Riemann-Liouville differintegral, as computed by +:func:`~mpmath.differint`:: + + >>> mp.dps = 15 + >>> besselj(1, 3.5, 0.75) + -0.385977722939384 + >>> differint(lambda x: besselj(1, x), 3.5, 0.75) + -0.385977722939384 + +""" + +besseli = r""" +``besseli(n, x, derivative=0)`` gives the modified Bessel function of the +first kind, + +.. math :: + + I_n(x) = i^{-n} J_n(ix). + +With *derivative* = `m \ne 0`, the `m`-th derivative + +.. math :: + + \frac{d^m}{dx^m} I_n(x) + +is computed. + +**Plots** + +.. literalinclude :: /plots/besseli.py +.. image :: /plots/besseli.png +.. literalinclude :: /plots/besseli_c.py +.. image :: /plots/besseli_c.png + +**Examples** + +Some values of `I_n(x)`:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> besseli(0,0) + 1.0 + >>> besseli(1,0) + 0.0 + >>> besseli(0,1) + 1.266065877752008335598245 + >>> besseli(3.5, 2+3j) + (-0.2904369752642538144289025 - 0.4469098397654815837307006j) + +Arguments may be large:: + + >>> besseli(2, 1000) + 2.480717210191852440616782e+432 + >>> besseli(2, 10**10) + 4.299602851624027900335391e+4342944813 + >>> besseli(2, 6000+10000j) + (-2.114650753239580827144204e+2603 + 4.385040221241629041351886e+2602j) + +For integers `n`, the following integral representation holds:: + + >>> mp.dps = 15 + >>> n = 3 + >>> x = 2.3 + >>> quad(lambda t: exp(x*cos(t))*cos(n*t), [0,pi])/pi + 0.349223221159309 + >>> besseli(n,x) + 0.349223221159309 + +Derivatives and antiderivatives of any order can be computed:: + + >>> mp.dps = 25 + >>> besseli(2, 7.5, 1) + 195.8229038931399062565883 + >>> diff(lambda x: besseli(2,x), 7.5) + 195.8229038931399062565883 + >>> besseli(2, 7.5, 10) + 153.3296508971734525525176 + >>> diff(lambda x: besseli(2,x), 7.5, 10) + 153.3296508971734525525176 + >>> besseli(2,7.5,-1) - besseli(2,3.5,-1) + 202.5043900051930141956876 + >>> quad(lambda x: besseli(2,x), [3.5, 7.5]) + 202.5043900051930141956876 + +""" + +bessely = r""" +``bessely(n, x, derivative=0)`` gives the Bessel function of the second kind, + +.. math :: + + Y_n(x) = \frac{J_n(x) \cos(\pi n) - J_{-n}(x)}{\sin(\pi n)}. + +For `n` an integer, this formula should be understood as a +limit. With *derivative* = `m \ne 0`, the `m`-th derivative + +.. math :: + + \frac{d^m}{dx^m} Y_n(x) + +is computed. + +**Plots** + +.. literalinclude :: /plots/bessely.py +.. image :: /plots/bessely.png +.. literalinclude :: /plots/bessely_c.py +.. image :: /plots/bessely_c.png + +**Examples** + +Some values of `Y_n(x)`:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> bessely(0,0), bessely(1,0), bessely(2,0) + (-inf, -inf, -inf) + >>> bessely(1, pi) + 0.3588729167767189594679827 + >>> bessely(0.5, 3+4j) + (9.242861436961450520325216 - 3.085042824915332562522402j) + +Arguments may be large:: + + >>> bessely(0, 10000) + 0.00364780555898660588668872 + >>> bessely(2.5, 10**50) + -4.8952500412050989295774e-26 + >>> bessely(2.5, -10**50) + (0.0 + 4.8952500412050989295774e-26j) + +Derivatives and antiderivatives of any order can be computed:: + + >>> bessely(2, 3.5, 1) + 0.3842618820422660066089231 + >>> diff(lambda x: bessely(2, x), 3.5) + 0.3842618820422660066089231 + >>> bessely(0.5, 3.5, 1) + -0.2066598304156764337900417 + >>> diff(lambda x: bessely(0.5, x), 3.5) + -0.2066598304156764337900417 + >>> diff(lambda x: bessely(2, x), 0.5, 10) + -208173867409.5547350101511 + >>> bessely(2, 0.5, 10) + -208173867409.5547350101511 + >>> bessely(2, 100.5, 100) + 0.02668487547301372334849043 + >>> quad(lambda x: bessely(2,x), [1,3]) + -1.377046859093181969213262 + >>> bessely(2,3,-1) - bessely(2,1,-1) + -1.377046859093181969213262 + +""" + +besselk = r""" +``besselk(n, x)`` gives the modified Bessel function of the +second kind, + +.. math :: + + K_n(x) = \frac{\pi}{2} \frac{I_{-n}(x)-I_{n}(x)}{\sin(\pi n)} + +For `n` an integer, this formula should be understood as a +limit. + +**Plots** + +.. literalinclude :: /plots/besselk.py +.. image :: /plots/besselk.png +.. literalinclude :: /plots/besselk_c.py +.. image :: /plots/besselk_c.png + +**Examples** + +Evaluation is supported for arbitrary complex arguments:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> besselk(0,1) + 0.4210244382407083333356274 + >>> besselk(0, -1) + (0.4210244382407083333356274 - 3.97746326050642263725661j) + >>> besselk(3.5, 2+3j) + (-0.02090732889633760668464128 + 0.2464022641351420167819697j) + >>> besselk(2+3j, 0.5) + (0.9615816021726349402626083 + 0.1918250181801757416908224j) + +Arguments may be large:: + + >>> besselk(0, 100) + 4.656628229175902018939005e-45 + >>> besselk(1, 10**6) + 4.131967049321725588398296e-434298 + >>> besselk(1, 10**6*j) + (0.001140348428252385844876706 - 0.0005200017201681152909000961j) + >>> besselk(4.5, fmul(10**50, j, exact=True)) + (1.561034538142413947789221e-26 + 1.243554598118700063281496e-25j) + +The point `x = 0` is a singularity (logarithmic if `n = 0`):: + + >>> besselk(0,0) + +inf + >>> besselk(1,0) + +inf + >>> for n in range(-4, 5): + ... print(besselk(n, '1e-1000')) + ... + 4.8e+4001 + 8.0e+3000 + 2.0e+2000 + 1.0e+1000 + 2302.701024509704096466802 + 1.0e+1000 + 2.0e+2000 + 8.0e+3000 + 4.8e+4001 + +""" + +hankel1 = r""" +``hankel1(n,x)`` computes the Hankel function of the first kind, +which is the complex combination of Bessel functions given by + +.. math :: + + H_n^{(1)}(x) = J_n(x) + i Y_n(x). + +**Plots** + +.. literalinclude :: /plots/hankel1.py +.. image :: /plots/hankel1.png +.. literalinclude :: /plots/hankel1_c.py +.. image :: /plots/hankel1_c.png + +**Examples** + +The Hankel function is generally complex-valued:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> hankel1(2, pi) + (0.4854339326315091097054957 - 0.0999007139290278787734903j) + >>> hankel1(3.5, pi) + (0.2340002029630507922628888 - 0.6419643823412927142424049j) +""" + +hankel2 = r""" +``hankel2(n,x)`` computes the Hankel function of the second kind, +which is the complex combination of Bessel functions given by + +.. math :: + + H_n^{(2)}(x) = J_n(x) - i Y_n(x). + +**Plots** + +.. literalinclude :: /plots/hankel2.py +.. image :: /plots/hankel2.png +.. literalinclude :: /plots/hankel2_c.py +.. image :: /plots/hankel2_c.png + +**Examples** + +The Hankel function is generally complex-valued:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> hankel2(2, pi) + (0.4854339326315091097054957 + 0.0999007139290278787734903j) + >>> hankel2(3.5, pi) + (0.2340002029630507922628888 + 0.6419643823412927142424049j) +""" + +lambertw = r""" +The Lambert W function `W(z)` is defined as the inverse function +of `w \exp(w)`. In other words, the value of `W(z)` is such that +`z = W(z) \exp(W(z))` for any complex number `z`. + +The Lambert W function is a multivalued function with infinitely +many branches `W_k(z)`, indexed by `k \in \mathbb{Z}`. Each branch +gives a different solution `w` of the equation `z = w \exp(w)`. +All branches are supported by :func:`~mpmath.lambertw`: + +* ``lambertw(z)`` gives the principal solution (branch 0) + +* ``lambertw(z, k)`` gives the solution on branch `k` + +The Lambert W function has two partially real branches: the +principal branch (`k = 0`) is real for real `z > -1/e`, and the +`k = -1` branch is real for `-1/e < z < 0`. All branches except +`k = 0` have a logarithmic singularity at `z = 0`. + +The definition, implementation and choice of branches +is based on [Corless]_. + +**Plots** + +.. literalinclude :: /plots/lambertw.py +.. image :: /plots/lambertw.png +.. literalinclude :: /plots/lambertw_c.py +.. image :: /plots/lambertw_c.png + +**Basic examples** + +The Lambert W function is the inverse of `w \exp(w)`:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> w = lambertw(1) + >>> w + 0.5671432904097838729999687 + >>> w*exp(w) + 1.0 + +Any branch gives a valid inverse:: + + >>> w = lambertw(1, k=3) + >>> w + (-2.853581755409037807206819 + 17.11353553941214591260783j) + >>> w = lambertw(1, k=25) + >>> w + (-5.047020464221569709378686 + 155.4763860949415867162066j) + >>> chop(w*exp(w)) + 1.0 + +**Applications to equation-solving** + +The Lambert W function may be used to solve various kinds of +equations, such as finding the value of the infinite power +tower `z^{z^{z^{\ldots}}}`:: + + >>> def tower(z, n): + ... if n == 0: + ... return z + ... return z ** tower(z, n-1) + ... + >>> tower(mpf(0.5), 100) + 0.6411857445049859844862005 + >>> -lambertw(-log(0.5))/log(0.5) + 0.6411857445049859844862005 + +**Properties** + +The Lambert W function grows roughly like the natural logarithm +for large arguments:: + + >>> lambertw(1000); log(1000) + 5.249602852401596227126056 + 6.907755278982137052053974 + >>> lambertw(10**100); log(10**100) + 224.8431064451185015393731 + 230.2585092994045684017991 + +The principal branch of the Lambert W function has a rational +Taylor series expansion around `z = 0`:: + + >>> nprint(taylor(lambertw, 0, 6), 10) + [0.0, 1.0, -1.0, 1.5, -2.666666667, 5.208333333, -10.8] + +Some special values and limits are:: + + >>> lambertw(0) + 0.0 + >>> lambertw(1) + 0.5671432904097838729999687 + >>> lambertw(e) + 1.0 + >>> lambertw(inf) + +inf + >>> lambertw(0, k=-1) + -inf + >>> lambertw(0, k=3) + -inf + >>> lambertw(inf, k=2) + (+inf + 12.56637061435917295385057j) + >>> lambertw(inf, k=3) + (+inf + 18.84955592153875943077586j) + >>> lambertw(-inf, k=3) + (+inf + 21.9911485751285526692385j) + +The `k = 0` and `k = -1` branches join at `z = -1/e` where +`W(z) = -1` for both branches. Since `-1/e` can only be represented +approximately with binary floating-point numbers, evaluating the +Lambert W function at this point only gives `-1` approximately:: + + >>> lambertw(-1/e, 0) + -0.9999999999998371330228251 + >>> lambertw(-1/e, -1) + -1.000000000000162866977175 + +If `-1/e` happens to round in the negative direction, there might be +a small imaginary part:: + + >>> mp.dps = 15 + >>> lambertw(-1/e) + (-1.0 + 8.22007971483662e-9j) + >>> lambertw(-1/e+eps) + -0.999999966242188 + +**References** + +1. [Corless]_ +""" + +barnesg = r""" +Evaluates the Barnes G-function, which generalizes the +superfactorial (:func:`~mpmath.superfac`) and by extension also the +hyperfactorial (:func:`~mpmath.hyperfac`) to the complex numbers +in an analogous way to how the gamma function generalizes +the ordinary factorial. + +The Barnes G-function may be defined in terms of a Weierstrass +product: + +.. math :: + + G(z+1) = (2\pi)^{z/2} e^{-[z(z+1)+\gamma z^2]/2} + \prod_{n=1}^\infty + \left[\left(1+\frac{z}{n}\right)^ne^{-z+z^2/(2n)}\right] + +For positive integers `n`, we have have relation to superfactorials +`G(n) = \mathrm{sf}(n-2) = 0! \cdot 1! \cdots (n-2)!`. + +**Examples** + +Some elementary values and limits of the Barnes G-function:: + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = True + >>> barnesg(1), barnesg(2), barnesg(3) + (1.0, 1.0, 1.0) + >>> barnesg(4) + 2.0 + >>> barnesg(5) + 12.0 + >>> barnesg(6) + 288.0 + >>> barnesg(7) + 34560.0 + >>> barnesg(8) + 24883200.0 + >>> barnesg(inf) + +inf + >>> barnesg(0), barnesg(-1), barnesg(-2) + (0.0, 0.0, 0.0) + +Closed-form values are known for some rational arguments:: + + >>> barnesg('1/2') + 0.603244281209446 + >>> sqrt(exp(0.25+log(2)/12)/sqrt(pi)/glaisher**3) + 0.603244281209446 + >>> barnesg('1/4') + 0.29375596533861 + >>> nthroot(exp('3/8')/exp(catalan/pi)/ + ... gamma(0.25)**3/sqrt(glaisher)**9, 4) + 0.29375596533861 + +The Barnes G-function satisfies the functional equation +`G(z+1) = \Gamma(z) G(z)`:: + + >>> z = pi + >>> barnesg(z+1) + 2.39292119327948 + >>> gamma(z)*barnesg(z) + 2.39292119327948 + +The asymptotic growth rate of the Barnes G-function is related to +the Glaisher-Kinkelin constant:: + + >>> limit(lambda n: barnesg(n+1)/(n**(n**2/2-mpf(1)/12)* + ... (2*pi)**(n/2)*exp(-3*n**2/4)), inf) + 0.847536694177301 + >>> exp('1/12')/glaisher + 0.847536694177301 + +The Barnes G-function can be differentiated in closed form:: + + >>> z = 3 + >>> diff(barnesg, z) + 0.264507203401607 + >>> barnesg(z)*((z-1)*psi(0,z)-z+(log(2*pi)+1)/2) + 0.264507203401607 + +Evaluation is supported for arbitrary arguments and at arbitrary +precision:: + + >>> barnesg(6.5) + 2548.7457695685 + >>> barnesg(-pi) + 0.00535976768353037 + >>> barnesg(3+4j) + (-0.000676375932234244 - 4.42236140124728e-5j) + >>> mp.dps = 50 + >>> barnesg(1/sqrt(2)) + 0.81305501090451340843586085064413533788206204124732 + >>> q = barnesg(10j) + >>> q.real + 0.000000000021852360840356557241543036724799812371995850552234 + >>> q.imag + -0.00000000000070035335320062304849020654215545839053210041457588 + >>> mp.dps = 15 + >>> barnesg(100) + 3.10361006263698e+6626 + >>> barnesg(-101) + 0.0 + >>> barnesg(-10.5) + 5.94463017605008e+25 + >>> barnesg(-10000.5) + -6.14322868174828e+167480422 + >>> barnesg(1000j) + (5.21133054865546e-1173597 + 4.27461836811016e-1173597j) + >>> barnesg(-1000+1000j) + (2.43114569750291e+1026623 + 2.24851410674842e+1026623j) + + +**References** + +1. Whittaker & Watson, *A Course of Modern Analysis*, + Cambridge University Press, 4th edition (1927), p.264 +2. http://en.wikipedia.org/wiki/Barnes_G-function +3. http://mathworld.wolfram.com/BarnesG-Function.html + +""" + +superfac = r""" +Computes the superfactorial, defined as the product of +consecutive factorials + +.. math :: + + \mathrm{sf}(n) = \prod_{k=1}^n k! + +For general complex `z`, `\mathrm{sf}(z)` is defined +in terms of the Barnes G-function (see :func:`~mpmath.barnesg`). + +**Examples** + +The first few superfactorials are (OEIS A000178):: + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = True + >>> for n in range(10): + ... print("%s %s" % (n, superfac(n))) + ... + 0 1.0 + 1 1.0 + 2 2.0 + 3 12.0 + 4 288.0 + 5 34560.0 + 6 24883200.0 + 7 125411328000.0 + 8 5.05658474496e+15 + 9 1.83493347225108e+21 + +Superfactorials grow very rapidly:: + + >>> superfac(1000) + 3.24570818422368e+1177245 + >>> superfac(10**10) + 2.61398543581249e+467427913956904067453 + +Evaluation is supported for arbitrary arguments:: + + >>> mp.dps = 25 + >>> superfac(pi) + 17.20051550121297985285333 + >>> superfac(2+3j) + (-0.005915485633199789627466468 + 0.008156449464604044948738263j) + >>> diff(superfac, 1) + 0.2645072034016070205673056 + +**References** + +1. http://oeis.org/A000178 + +""" + + +hyperfac = r""" +Computes the hyperfactorial, defined for integers as the product + +.. math :: + + H(n) = \prod_{k=1}^n k^k. + + +The hyperfactorial satisfies the recurrence formula `H(z) = z^z H(z-1)`. +It can be defined more generally in terms of the Barnes G-function (see +:func:`~mpmath.barnesg`) and the gamma function by the formula + +.. math :: + + H(z) = \frac{\Gamma(z+1)^z}{G(z)}. + +The extension to complex numbers can also be done via +the integral representation + +.. math :: + + H(z) = (2\pi)^{-z/2} \exp \left[ + {z+1 \choose 2} + \int_0^z \log(t!)\,dt + \right]. + +**Examples** + +The rapidly-growing sequence of hyperfactorials begins +(OEIS A002109):: + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = True + >>> for n in range(10): + ... print("%s %s" % (n, hyperfac(n))) + ... + 0 1.0 + 1 1.0 + 2 4.0 + 3 108.0 + 4 27648.0 + 5 86400000.0 + 6 4031078400000.0 + 7 3.3197663987712e+18 + 8 5.56964379417266e+25 + 9 2.15779412229419e+34 + +Some even larger hyperfactorials are:: + + >>> hyperfac(1000) + 5.46458120882585e+1392926 + >>> hyperfac(10**10) + 4.60408207642219e+489142638002418704309 + +The hyperfactorial can be evaluated for arbitrary arguments:: + + >>> hyperfac(0.5) + 0.880449235173423 + >>> diff(hyperfac, 1) + 0.581061466795327 + >>> hyperfac(pi) + 205.211134637462 + >>> hyperfac(-10+1j) + (3.01144471378225e+46 - 2.45285242480185e+46j) + +The recurrence property of the hyperfactorial holds +generally:: + + >>> z = 3-4*j + >>> hyperfac(z) + (-4.49795891462086e-7 - 6.33262283196162e-7j) + >>> z**z * hyperfac(z-1) + (-4.49795891462086e-7 - 6.33262283196162e-7j) + >>> z = mpf(-0.6) + >>> chop(z**z * hyperfac(z-1)) + 1.28170142849352 + >>> hyperfac(z) + 1.28170142849352 + +The hyperfactorial may also be computed using the integral +definition:: + + >>> z = 2.5 + >>> hyperfac(z) + 15.9842119922237 + >>> (2*pi)**(-z/2)*exp(binomial(z+1,2) + + ... quad(lambda t: loggamma(t+1), [0, z])) + 15.9842119922237 + +:func:`~mpmath.hyperfac` supports arbitrary-precision evaluation:: + + >>> mp.dps = 50 + >>> hyperfac(10) + 215779412229418562091680268288000000000000000.0 + >>> hyperfac(1/sqrt(2)) + 0.89404818005227001975423476035729076375705084390942 + +**References** + +1. http://oeis.org/A002109 +2. http://mathworld.wolfram.com/Hyperfactorial.html + +""" + +rgamma = r""" +Computes the reciprocal of the gamma function, `1/\Gamma(z)`. This +function evaluates to zero at the poles +of the gamma function, `z = 0, -1, -2, \ldots`. + +**Examples** + +Basic examples:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> rgamma(1) + 1.0 + >>> rgamma(4) + 0.1666666666666666666666667 + >>> rgamma(0); rgamma(-1) + 0.0 + 0.0 + >>> rgamma(1000) + 2.485168143266784862783596e-2565 + >>> rgamma(inf) + 0.0 + +A definite integral that can be evaluated in terms of elementary +integrals:: + + >>> quad(rgamma, [0,inf]) + 2.807770242028519365221501 + >>> e + quad(lambda t: exp(-t)/(pi**2+log(t)**2), [0,inf]) + 2.807770242028519365221501 +""" + +loggamma = r""" +Computes the principal branch of the log-gamma function, +`\ln \Gamma(z)`. Unlike `\ln(\Gamma(z))`, which has infinitely many +complex branch cuts, the principal log-gamma function only has a single +branch cut along the negative half-axis. The principal branch +continuously matches the asymptotic Stirling expansion + +.. math :: + + \ln \Gamma(z) \sim \frac{\ln(2 \pi)}{2} + + \left(z-\frac{1}{2}\right) \ln(z) - z + O(z^{-1}). + +The real parts of both functions agree, but their imaginary +parts generally differ by `2 n \pi` for some `n \in \mathbb{Z}`. +They coincide for `z \in \mathbb{R}, z > 0`. + +Computationally, it is advantageous to use :func:`~mpmath.loggamma` +instead of :func:`~mpmath.gamma` for extremely large arguments. + +**Examples** + +Comparing with `\ln(\Gamma(z))`:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> loggamma('13.2'); log(gamma('13.2')) + 20.49400419456603678498394 + 20.49400419456603678498394 + >>> loggamma(3+4j) + (-1.756626784603784110530604 + 4.742664438034657928194889j) + >>> log(gamma(3+4j)) + (-1.756626784603784110530604 - 1.540520869144928548730397j) + >>> log(gamma(3+4j)) + 2*pi*j + (-1.756626784603784110530604 + 4.742664438034657928194889j) + +Note the imaginary parts for negative arguments:: + + >>> loggamma(-0.5); loggamma(-1.5); loggamma(-2.5) + (1.265512123484645396488946 - 3.141592653589793238462643j) + (0.8600470153764810145109327 - 6.283185307179586476925287j) + (-0.05624371649767405067259453 - 9.42477796076937971538793j) + +Some special values:: + + >>> loggamma(1); loggamma(2) + 0.0 + 0.0 + >>> loggamma(3); +ln2 + 0.6931471805599453094172321 + 0.6931471805599453094172321 + >>> loggamma(3.5); log(15*sqrt(pi)/8) + 1.200973602347074224816022 + 1.200973602347074224816022 + >>> loggamma(inf) + +inf + +Huge arguments are permitted:: + + >>> loggamma('1e30') + 6.807755278982137052053974e+31 + >>> loggamma('1e300') + 6.897755278982137052053974e+302 + >>> loggamma('1e3000') + 6.906755278982137052053974e+3003 + >>> loggamma('1e100000000000000000000') + 2.302585092994045684007991e+100000000000000000020 + >>> loggamma('1e30j') + (-1.570796326794896619231322e+30 + 6.807755278982137052053974e+31j) + >>> loggamma('1e300j') + (-1.570796326794896619231322e+300 + 6.897755278982137052053974e+302j) + >>> loggamma('1e3000j') + (-1.570796326794896619231322e+3000 + 6.906755278982137052053974e+3003j) + +The log-gamma function can be integrated analytically +on any interval of unit length:: + + >>> z = 0 + >>> quad(loggamma, [z,z+1]); log(2*pi)/2 + 0.9189385332046727417803297 + 0.9189385332046727417803297 + >>> z = 3+4j + >>> quad(loggamma, [z,z+1]); (log(z)-1)*z + log(2*pi)/2 + (-0.9619286014994750641314421 + 5.219637303741238195688575j) + (-0.9619286014994750641314421 + 5.219637303741238195688575j) + +The derivatives of the log-gamma function are given by the +polygamma function (:func:`~mpmath.psi`):: + + >>> diff(loggamma, -4+3j); psi(0, -4+3j) + (1.688493531222971393607153 + 2.554898911356806978892748j) + (1.688493531222971393607153 + 2.554898911356806978892748j) + >>> diff(loggamma, -4+3j, 2); psi(1, -4+3j) + (-0.1539414829219882371561038 - 0.1020485197430267719746479j) + (-0.1539414829219882371561038 - 0.1020485197430267719746479j) + +The log-gamma function satisfies an additive form of the +recurrence relation for the ordinary gamma function:: + + >>> z = 2+3j + >>> loggamma(z); loggamma(z+1) - log(z) + (-2.092851753092733349564189 + 2.302396543466867626153708j) + (-2.092851753092733349564189 + 2.302396543466867626153708j) + +""" + +siegeltheta = r""" +Computes the Riemann-Siegel theta function, + +.. math :: + + \theta(t) = \frac{ + \log\Gamma\left(\frac{1+2it}{4}\right) - + \log\Gamma\left(\frac{1-2it}{4}\right) + }{2i} - \frac{\log \pi}{2} t. + +The Riemann-Siegel theta function is important in +providing the phase factor for the Z-function +(see :func:`~mpmath.siegelz`). Evaluation is supported for real and +complex arguments:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> siegeltheta(0) + 0.0 + >>> siegeltheta(inf) + +inf + >>> siegeltheta(-inf) + -inf + >>> siegeltheta(1) + -1.767547952812290388302216 + >>> siegeltheta(10+0.25j) + (-3.068638039426838572528867 + 0.05804937947429712998395177j) + +Arbitrary derivatives may be computed with derivative = k + + >>> siegeltheta(1234, derivative=2) + 0.0004051864079114053109473741 + >>> diff(siegeltheta, 1234, n=2) + 0.0004051864079114053109473741 + + +The Riemann-Siegel theta function has odd symmetry around `t = 0`, +two local extreme points and three real roots including 0 (located +symmetrically):: + + >>> nprint(chop(taylor(siegeltheta, 0, 5))) + [0.0, -2.68609, 0.0, 2.69433, 0.0, -6.40218] + >>> findroot(diffun(siegeltheta), 7) + 6.28983598883690277966509 + >>> findroot(siegeltheta, 20) + 17.84559954041086081682634 + +For large `t`, there is a famous asymptotic formula +for `\theta(t)`, to first order given by:: + + >>> t = mpf(10**6) + >>> siegeltheta(t) + 5488816.353078403444882823 + >>> -t*log(2*pi/t)/2-t/2 + 5488816.745777464310273645 +""" + +grampoint = r""" +Gives the `n`-th Gram point `g_n`, defined as the solution +to the equation `\theta(g_n) = \pi n` where `\theta(t)` +is the Riemann-Siegel theta function (:func:`~mpmath.siegeltheta`). + +The first few Gram points are:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> grampoint(0) + 17.84559954041086081682634 + >>> grampoint(1) + 23.17028270124630927899664 + >>> grampoint(2) + 27.67018221781633796093849 + >>> grampoint(3) + 31.71797995476405317955149 + +Checking the definition:: + + >>> siegeltheta(grampoint(3)) + 9.42477796076937971538793 + >>> 3*pi + 9.42477796076937971538793 + +A large Gram point:: + + >>> grampoint(10**10) + 3293531632.728335454561153 + +Gram points are useful when studying the Z-function +(:func:`~mpmath.siegelz`). See the documentation of that function +for additional examples. + +:func:`~mpmath.grampoint` can solve the defining equation for +nonintegral `n`. There is a fixed point where `g(x) = x`:: + + >>> findroot(lambda x: grampoint(x) - x, 10000) + 9146.698193171459265866198 + +**References** + +1. http://mathworld.wolfram.com/GramPoint.html + +""" + +siegelz = r""" +Computes the Z-function, also known as the Riemann-Siegel Z function, + +.. math :: + + Z(t) = e^{i \theta(t)} \zeta(1/2+it) + +where `\zeta(s)` is the Riemann zeta function (:func:`~mpmath.zeta`) +and where `\theta(t)` denotes the Riemann-Siegel theta function +(see :func:`~mpmath.siegeltheta`). + +Evaluation is supported for real and complex arguments:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> siegelz(1) + -0.7363054628673177346778998 + >>> siegelz(3+4j) + (-0.1852895764366314976003936 - 0.2773099198055652246992479j) + +The first four derivatives are supported, using the +optional *derivative* keyword argument:: + + >>> siegelz(1234567, derivative=3) + 56.89689348495089294249178 + >>> diff(siegelz, 1234567, n=3) + 56.89689348495089294249178 + + +The Z-function has a Maclaurin expansion:: + + >>> nprint(chop(taylor(siegelz, 0, 4))) + [-1.46035, 0.0, 2.73588, 0.0, -8.39357] + +The Z-function `Z(t)` is equal to `\pm |\zeta(s)|` on the +critical line `s = 1/2+it` (i.e. for real arguments `t` +to `Z`). Its zeros coincide with those of the Riemann zeta +function:: + + >>> findroot(siegelz, 14) + 14.13472514173469379045725 + >>> findroot(siegelz, 20) + 21.02203963877155499262848 + >>> findroot(zeta, 0.5+14j) + (0.5 + 14.13472514173469379045725j) + >>> findroot(zeta, 0.5+20j) + (0.5 + 21.02203963877155499262848j) + +Since the Z-function is real-valued on the critical line +(and unlike `|\zeta(s)|` analytic), it is useful for +investigating the zeros of the Riemann zeta function. +For example, one can use a root-finding algorithm based +on sign changes:: + + >>> findroot(siegelz, [100, 200], solver='bisect') + 176.4414342977104188888926 + +To locate roots, Gram points `g_n` which can be computed +by :func:`~mpmath.grampoint` are useful. If `(-1)^n Z(g_n)` is +positive for two consecutive `n`, then `Z(t)` must have +a zero between those points:: + + >>> g10 = grampoint(10) + >>> g11 = grampoint(11) + >>> (-1)**10 * siegelz(g10) > 0 + True + >>> (-1)**11 * siegelz(g11) > 0 + True + >>> findroot(siegelz, [g10, g11], solver='bisect') + 56.44624769706339480436776 + >>> g10, g11 + (54.67523744685325626632663, 57.54516517954725443703014) + +""" + +riemannr = r""" +Evaluates the Riemann R function, a smooth approximation of the +prime counting function `\pi(x)` (see :func:`~mpmath.primepi`). The Riemann +R function gives a fast numerical approximation useful e.g. to +roughly estimate the number of primes in a given interval. + +The Riemann R function is computed using the rapidly convergent Gram +series, + +.. math :: + + R(x) = 1 + \sum_{k=1}^{\infty} + \frac{\log^k x}{k k! \zeta(k+1)}. + +From the Gram series, one sees that the Riemann R function is a +well-defined analytic function (except for a branch cut along +the negative real half-axis); it can be evaluated for arbitrary +real or complex arguments. + +The Riemann R function gives a very accurate approximation +of the prime counting function. For example, it is wrong by at +most 2 for `x < 1000`, and for `x = 10^9` differs from the exact +value of `\pi(x)` by 79, or less than two parts in a million. +It is about 10 times more accurate than the logarithmic integral +estimate (see :func:`~mpmath.li`), which however is even faster to evaluate. +It is orders of magnitude more accurate than the extremely +fast `x/\log x` estimate. + +**Examples** + +For small arguments, the Riemann R function almost exactly +gives the prime counting function if rounded to the nearest +integer:: + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = True + >>> primepi(50), riemannr(50) + (15, 14.9757023241462) + >>> max(abs(primepi(n)-int(round(riemannr(n)))) for n in range(100)) + 1 + >>> max(abs(primepi(n)-int(round(riemannr(n)))) for n in range(300)) + 2 + +The Riemann R function can be evaluated for arguments far too large +for exact determination of `\pi(x)` to be computationally +feasible with any presently known algorithm:: + + >>> riemannr(10**30) + 1.46923988977204e+28 + >>> riemannr(10**100) + 4.3619719871407e+97 + >>> riemannr(10**1000) + 4.3448325764012e+996 + +A comparison of the Riemann R function and logarithmic integral estimates +for `\pi(x)` using exact values of `\pi(10^n)` up to `n = 9`. +The fractional error is shown in parentheses:: + + >>> exact = [4,25,168,1229,9592,78498,664579,5761455,50847534] + >>> for n, p in enumerate(exact): + ... n += 1 + ... r, l = riemannr(10**n), li(10**n) + ... rerr, lerr = nstr((r-p)/p,3), nstr((l-p)/p,3) + ... print("%i %i %s(%s) %s(%s)" % (n, p, r, rerr, l, lerr)) + ... + 1 4 4.56458314100509(0.141) 6.1655995047873(0.541) + 2 25 25.6616332669242(0.0265) 30.1261415840796(0.205) + 3 168 168.359446281167(0.00214) 177.609657990152(0.0572) + 4 1229 1226.93121834343(-0.00168) 1246.13721589939(0.0139) + 5 9592 9587.43173884197(-0.000476) 9629.8090010508(0.00394) + 6 78498 78527.3994291277(0.000375) 78627.5491594622(0.00165) + 7 664579 664667.447564748(0.000133) 664918.405048569(0.000511) + 8 5761455 5761551.86732017(1.68e-5) 5762209.37544803(0.000131) + 9 50847534 50847455.4277214(-1.55e-6) 50849234.9570018(3.35e-5) + +The derivative of the Riemann R function gives the approximate +probability for a number of magnitude `x` to be prime:: + + >>> diff(riemannr, 1000) + 0.141903028110784 + >>> mpf(primepi(1050) - primepi(950)) / 100 + 0.15 + +Evaluation is supported for arbitrary arguments and at arbitrary +precision:: + + >>> mp.dps = 30 + >>> riemannr(7.5) + 3.72934743264966261918857135136 + >>> riemannr(-4+2j) + (-0.551002208155486427591793957644 + 2.16966398138119450043195899746j) + +""" + +primepi = r""" +Evaluates the prime counting function, `\pi(x)`, which gives +the number of primes less than or equal to `x`. The argument +`x` may be fractional. + +The prime counting function is very expensive to evaluate +precisely for large `x`, and the present implementation is +not optimized in any way. For numerical approximation of the +prime counting function, it is better to use :func:`~mpmath.primepi2` +or :func:`~mpmath.riemannr`. + +Some values of the prime counting function:: + + >>> from mpmath import * + >>> [primepi(k) for k in range(20)] + [0, 0, 1, 2, 2, 3, 3, 4, 4, 4, 4, 5, 5, 6, 6, 6, 6, 7, 7, 8] + >>> primepi(3.5) + 2 + >>> primepi(100000) + 9592 + +""" + +primepi2 = r""" +Returns an interval (as an ``mpi`` instance) providing bounds +for the value of the prime counting function `\pi(x)`. For small +`x`, :func:`~mpmath.primepi2` returns an exact interval based on +the output of :func:`~mpmath.primepi`. For `x > 2656`, a loose interval +based on Schoenfeld's inequality + +.. math :: + + |\pi(x) - \mathrm{li}(x)| < \frac{\sqrt x \log x}{8 \pi} + +is returned. This estimate is rigorous assuming the truth of +the Riemann hypothesis, and can be computed very quickly. + +**Examples** + +Exact values of the prime counting function for small `x`:: + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = True + >>> iv.dps = 15; iv.pretty = True + >>> primepi2(10) + [4.0, 4.0] + >>> primepi2(100) + [25.0, 25.0] + >>> primepi2(1000) + [168.0, 168.0] + +Loose intervals are generated for moderately large `x`: + + >>> primepi2(10000), primepi(10000) + ([1209.0, 1283.0], 1229) + >>> primepi2(50000), primepi(50000) + ([5070.0, 5263.0], 5133) + +As `x` increases, the absolute error gets worse while the relative +error improves. The exact value of `\pi(10^{23})` is +1925320391606803968923, and :func:`~mpmath.primepi2` gives 9 significant +digits:: + + >>> p = primepi2(10**23) + >>> p + [1.9253203909477020467e+21, 1.925320392280406229e+21] + >>> mpf(p.delta) / mpf(p.a) + 6.9219865355293e-10 + +A more precise, nonrigorous estimate for `\pi(x)` can be +obtained using the Riemann R function (:func:`~mpmath.riemannr`). +For large enough `x`, the value returned by :func:`~mpmath.primepi2` +essentially amounts to a small perturbation of the value returned by +:func:`~mpmath.riemannr`:: + + >>> primepi2(10**100) + [4.3619719871407024816e+97, 4.3619719871407032404e+97] + >>> riemannr(10**100) + 4.3619719871407e+97 +""" + +primezeta = r""" +Computes the prime zeta function, which is defined +in analogy with the Riemann zeta function (:func:`~mpmath.zeta`) +as + +.. math :: + + P(s) = \sum_p \frac{1}{p^s} + +where the sum is taken over all prime numbers `p`. Although +this sum only converges for `\mathrm{Re}(s) > 1`, the +function is defined by analytic continuation in the +half-plane `\mathrm{Re}(s) > 0`. + +**Examples** + +Arbitrary-precision evaluation for real and complex arguments is +supported:: + + >>> from mpmath import * + >>> mp.dps = 30; mp.pretty = True + >>> primezeta(2) + 0.452247420041065498506543364832 + >>> primezeta(pi) + 0.15483752698840284272036497397 + >>> mp.dps = 50 + >>> primezeta(3) + 0.17476263929944353642311331466570670097541212192615 + >>> mp.dps = 20 + >>> primezeta(3+4j) + (-0.12085382601645763295 - 0.013370403397787023602j) + +The prime zeta function has a logarithmic pole at `s = 1`, +with residue equal to the difference of the Mertens and +Euler constants:: + + >>> primezeta(1) + +inf + >>> extradps(25)(lambda x: primezeta(1+x)+log(x))(+eps) + -0.31571845205389007685 + >>> mertens-euler + -0.31571845205389007685 + +The analytic continuation to `0 < \mathrm{Re}(s) \le 1` +is implemented. In this strip the function exhibits +very complex behavior; on the unit interval, it has poles at +`1/n` for every squarefree integer `n`:: + + >>> primezeta(0.5) # Pole at s = 1/2 + (-inf + 3.1415926535897932385j) + >>> primezeta(0.25) + (-1.0416106801757269036 + 0.52359877559829887308j) + >>> primezeta(0.5+10j) + (0.54892423556409790529 + 0.45626803423487934264j) + +Although evaluation works in principle for any `\mathrm{Re}(s) > 0`, +it should be noted that the evaluation time increases exponentially +as `s` approaches the imaginary axis. + +For large `\mathrm{Re}(s)`, `P(s)` is asymptotic to `2^{-s}`:: + + >>> primezeta(inf) + 0.0 + >>> primezeta(10), mpf(2)**-10 + (0.00099360357443698021786, 0.0009765625) + >>> primezeta(1000) + 9.3326361850321887899e-302 + >>> primezeta(1000+1000j) + (-3.8565440833654995949e-302 - 8.4985390447553234305e-302j) + +**References** + +Carl-Erik Froberg, "On the prime zeta function", +BIT 8 (1968), pp. 187-202. + +""" + +bernpoly = r""" +Evaluates the Bernoulli polynomial `B_n(z)`. + +The first few Bernoulli polynomials are:: + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = True + >>> for n in range(6): + ... nprint(chop(taylor(lambda x: bernpoly(n,x), 0, n))) + ... + [1.0] + [-0.5, 1.0] + [0.166667, -1.0, 1.0] + [0.0, 0.5, -1.5, 1.0] + [-0.0333333, 0.0, 1.0, -2.0, 1.0] + [0.0, -0.166667, 0.0, 1.66667, -2.5, 1.0] + +At `z = 0`, the Bernoulli polynomial evaluates to a +Bernoulli number (see :func:`~mpmath.bernoulli`):: + + >>> bernpoly(12, 0), bernoulli(12) + (-0.253113553113553, -0.253113553113553) + >>> bernpoly(13, 0), bernoulli(13) + (0.0, 0.0) + +Evaluation is accurate for large `n` and small `z`:: + + >>> mp.dps = 25 + >>> bernpoly(100, 0.5) + 2.838224957069370695926416e+78 + >>> bernpoly(1000, 10.5) + 5.318704469415522036482914e+1769 + +""" + +polylog = r""" +Computes the polylogarithm, defined by the sum + +.. math :: + + \mathrm{Li}_s(z) = \sum_{k=1}^{\infty} \frac{z^k}{k^s}. + +This series is convergent only for `|z| < 1`, so elsewhere +the analytic continuation is implied. + +The polylogarithm should not be confused with the logarithmic +integral (also denoted by Li or li), which is implemented +as :func:`~mpmath.li`. + +**Examples** + +The polylogarithm satisfies a huge number of functional identities. +A sample of polylogarithm evaluations is shown below:: + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = True + >>> polylog(1,0.5), log(2) + (0.693147180559945, 0.693147180559945) + >>> polylog(2,0.5), (pi**2-6*log(2)**2)/12 + (0.582240526465012, 0.582240526465012) + >>> polylog(2,-phi), -log(phi)**2-pi**2/10 + (-1.21852526068613, -1.21852526068613) + >>> polylog(3,0.5), 7*zeta(3)/8-pi**2*log(2)/12+log(2)**3/6 + (0.53721319360804, 0.53721319360804) + +:func:`~mpmath.polylog` can evaluate the analytic continuation of the +polylogarithm when `s` is an integer:: + + >>> polylog(2, 10) + (0.536301287357863 - 7.23378441241546j) + >>> polylog(2, -10) + -4.1982778868581 + >>> polylog(2, 10j) + (-3.05968879432873 + 3.71678149306807j) + >>> polylog(-2, 10) + -0.150891632373114 + >>> polylog(-2, -10) + 0.067618332081142 + >>> polylog(-2, 10j) + (0.0384353698579347 + 0.0912451798066779j) + +Some more examples, with arguments on the unit circle (note that +the series definition cannot be used for computation here):: + + >>> polylog(2,j) + (-0.205616758356028 + 0.915965594177219j) + >>> j*catalan-pi**2/48 + (-0.205616758356028 + 0.915965594177219j) + >>> polylog(3,exp(2*pi*j/3)) + (-0.534247512515375 + 0.765587078525922j) + >>> -4*zeta(3)/9 + 2*j*pi**3/81 + (-0.534247512515375 + 0.765587078525921j) + +Polylogarithms of different order are related by integration +and differentiation:: + + >>> s, z = 3, 0.5 + >>> polylog(s+1, z) + 0.517479061673899 + >>> quad(lambda t: polylog(s,t)/t, [0, z]) + 0.517479061673899 + >>> z*diff(lambda t: polylog(s+2,t), z) + 0.517479061673899 + +Taylor series expansions around `z = 0` are:: + + >>> for n in range(-3, 4): + ... nprint(taylor(lambda x: polylog(n,x), 0, 5)) + ... + [0.0, 1.0, 8.0, 27.0, 64.0, 125.0] + [0.0, 1.0, 4.0, 9.0, 16.0, 25.0] + [0.0, 1.0, 2.0, 3.0, 4.0, 5.0] + [0.0, 1.0, 1.0, 1.0, 1.0, 1.0] + [0.0, 1.0, 0.5, 0.333333, 0.25, 0.2] + [0.0, 1.0, 0.25, 0.111111, 0.0625, 0.04] + [0.0, 1.0, 0.125, 0.037037, 0.015625, 0.008] + +The series defining the polylogarithm is simultaneously +a Taylor series and an L-series. For certain values of `z`, the +polylogarithm reduces to a pure zeta function:: + + >>> polylog(pi, 1), zeta(pi) + (1.17624173838258, 1.17624173838258) + >>> polylog(pi, -1), -altzeta(pi) + (-0.909670702980385, -0.909670702980385) + +Evaluation for arbitrary, nonintegral `s` is supported +for `z` within the unit circle: + + >>> polylog(3+4j, 0.25) + (0.24258605789446 - 0.00222938275488344j) + >>> nsum(lambda k: 0.25**k / k**(3+4j), [1,inf]) + (0.24258605789446 - 0.00222938275488344j) + +It is also supported outside of the unit circle:: + + >>> polylog(1+j, 20+40j) + (-7.1421172179728 - 3.92726697721369j) + >>> polylog(1+j, 200+400j) + (-5.41934747194626 - 9.94037752563927j) + +**References** + +1. Richard Crandall, "Note on fast polylogarithm computation" + http://www.reed.edu/physics/faculty/crandall/papers/Polylog.pdf +2. http://en.wikipedia.org/wiki/Polylogarithm +3. http://mathworld.wolfram.com/Polylogarithm.html + +""" + +bell = r""" +For `n` a nonnegative integer, ``bell(n,x)`` evaluates the Bell +polynomial `B_n(x)`, the first few of which are + +.. math :: + + B_0(x) = 1 + + B_1(x) = x + + B_2(x) = x^2+x + + B_3(x) = x^3+3x^2+x + +If `x = 1` or :func:`~mpmath.bell` is called with only one argument, it +gives the `n`-th Bell number `B_n`, which is the number of +partitions of a set with `n` elements. By setting the precision to +at least `\log_{10} B_n` digits, :func:`~mpmath.bell` provides fast +calculation of exact Bell numbers. + +In general, :func:`~mpmath.bell` computes + +.. math :: + + B_n(x) = e^{-x} \left(\mathrm{sinc}(\pi n) + E_n(x)\right) + +where `E_n(x)` is the generalized exponential function implemented +by :func:`~mpmath.polyexp`. This is an extension of Dobinski's formula [1], +where the modification is the sinc term ensuring that `B_n(x)` is +continuous in `n`; :func:`~mpmath.bell` can thus be evaluated, +differentiated, etc for arbitrary complex arguments. + +**Examples** + +Simple evaluations:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> bell(0, 2.5) + 1.0 + >>> bell(1, 2.5) + 2.5 + >>> bell(2, 2.5) + 8.75 + +Evaluation for arbitrary complex arguments:: + + >>> bell(5.75+1j, 2-3j) + (-10767.71345136587098445143 - 15449.55065599872579097221j) + +The first few Bell polynomials:: + + >>> for k in range(7): + ... nprint(taylor(lambda x: bell(k,x), 0, k)) + ... + [1.0] + [0.0, 1.0] + [0.0, 1.0, 1.0] + [0.0, 1.0, 3.0, 1.0] + [0.0, 1.0, 7.0, 6.0, 1.0] + [0.0, 1.0, 15.0, 25.0, 10.0, 1.0] + [0.0, 1.0, 31.0, 90.0, 65.0, 15.0, 1.0] + +The first few Bell numbers and complementary Bell numbers:: + + >>> [int(bell(k)) for k in range(10)] + [1, 1, 2, 5, 15, 52, 203, 877, 4140, 21147] + >>> [int(bell(k,-1)) for k in range(10)] + [1, -1, 0, 1, 1, -2, -9, -9, 50, 267] + +Large Bell numbers:: + + >>> mp.dps = 50 + >>> bell(50) + 185724268771078270438257767181908917499221852770.0 + >>> bell(50,-1) + -29113173035759403920216141265491160286912.0 + +Some even larger values:: + + >>> mp.dps = 25 + >>> bell(1000,-1) + -1.237132026969293954162816e+1869 + >>> bell(1000) + 2.989901335682408421480422e+1927 + >>> bell(1000,2) + 6.591553486811969380442171e+1987 + >>> bell(1000,100.5) + 9.101014101401543575679639e+2529 + +A determinant identity satisfied by Bell numbers:: + + >>> mp.dps = 15 + >>> N = 8 + >>> det([[bell(k+j) for j in range(N)] for k in range(N)]) + 125411328000.0 + >>> superfac(N-1) + 125411328000.0 + +**References** + +1. http://mathworld.wolfram.com/DobinskisFormula.html + +""" + +polyexp = r""" +Evaluates the polyexponential function, defined for arbitrary +complex `s`, `z` by the series + +.. math :: + + E_s(z) = \sum_{k=1}^{\infty} \frac{k^s}{k!} z^k. + +`E_s(z)` is constructed from the exponential function analogously +to how the polylogarithm is constructed from the ordinary +logarithm; as a function of `s` (with `z` fixed), `E_s` is an L-series +It is an entire function of both `s` and `z`. + +The polyexponential function provides a generalization of the +Bell polynomials `B_n(x)` (see :func:`~mpmath.bell`) to noninteger orders `n`. +In terms of the Bell polynomials, + +.. math :: + + E_s(z) = e^z B_s(z) - \mathrm{sinc}(\pi s). + +Note that `B_n(x)` and `e^{-x} E_n(x)` are identical if `n` +is a nonzero integer, but not otherwise. In particular, they differ +at `n = 0`. + +**Examples** + +Evaluating a series:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> nsum(lambda k: sqrt(k)/fac(k), [1,inf]) + 2.101755547733791780315904 + >>> polyexp(0.5,1) + 2.101755547733791780315904 + +Evaluation for arbitrary arguments:: + + >>> polyexp(-3-4j, 2.5+2j) + (2.351660261190434618268706 + 1.202966666673054671364215j) + +Evaluation is accurate for tiny function values:: + + >>> polyexp(4, -100) + 3.499471750566824369520223e-36 + +If `n` is a nonpositive integer, `E_n` reduces to a special +instance of the hypergeometric function `\,_pF_q`:: + + >>> n = 3 + >>> x = pi + >>> polyexp(-n,x) + 4.042192318847986561771779 + >>> x*hyper([1]*(n+1), [2]*(n+1), x) + 4.042192318847986561771779 + +""" + +cyclotomic = r""" +Evaluates the cyclotomic polynomial `\Phi_n(x)`, defined by + +.. math :: + + \Phi_n(x) = \prod_{\zeta} (x - \zeta) + +where `\zeta` ranges over all primitive `n`-th roots of unity +(see :func:`~mpmath.unitroots`). An equivalent representation, used +for computation, is + +.. math :: + + \Phi_n(x) = \prod_{d\mid n}(x^d-1)^{\mu(n/d)} = \Phi_n(x) + +where `\mu(m)` denotes the Moebius function. The cyclotomic +polynomials are integer polynomials, the first of which can be +written explicitly as + +.. math :: + + \Phi_0(x) = 1 + + \Phi_1(x) = x - 1 + + \Phi_2(x) = x + 1 + + \Phi_3(x) = x^3 + x^2 + 1 + + \Phi_4(x) = x^2 + 1 + + \Phi_5(x) = x^4 + x^3 + x^2 + x + 1 + + \Phi_6(x) = x^2 - x + 1 + +**Examples** + +The coefficients of low-order cyclotomic polynomials can be recovered +using Taylor expansion:: + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = True + >>> for n in range(9): + ... p = chop(taylor(lambda x: cyclotomic(n,x), 0, 10)) + ... print("%s %s" % (n, nstr(p[:10+1-p[::-1].index(1)]))) + ... + 0 [1.0] + 1 [-1.0, 1.0] + 2 [1.0, 1.0] + 3 [1.0, 1.0, 1.0] + 4 [1.0, 0.0, 1.0] + 5 [1.0, 1.0, 1.0, 1.0, 1.0] + 6 [1.0, -1.0, 1.0] + 7 [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0] + 8 [1.0, 0.0, 0.0, 0.0, 1.0] + +The definition as a product over primitive roots may be checked +by computing the product explicitly (for a real argument, this +method will generally introduce numerical noise in the imaginary +part):: + + >>> mp.dps = 25 + >>> z = 3+4j + >>> cyclotomic(10, z) + (-419.0 - 360.0j) + >>> fprod(z-r for r in unitroots(10, primitive=True)) + (-419.0 - 360.0j) + >>> z = 3 + >>> cyclotomic(10, z) + 61.0 + >>> fprod(z-r for r in unitroots(10, primitive=True)) + (61.0 - 3.146045605088568607055454e-25j) + +Up to permutation, the roots of a given cyclotomic polynomial +can be checked to agree with the list of primitive roots:: + + >>> p = taylor(lambda x: cyclotomic(6,x), 0, 6)[:3] + >>> for r in polyroots(p[::-1]): + ... print(r) + ... + (0.5 - 0.8660254037844386467637232j) + (0.5 + 0.8660254037844386467637232j) + >>> + >>> for r in unitroots(6, primitive=True): + ... print(r) + ... + (0.5 + 0.8660254037844386467637232j) + (0.5 - 0.8660254037844386467637232j) + +""" + +meijerg = r""" +Evaluates the Meijer G-function, defined as + +.. math :: + + G^{m,n}_{p,q} \left( \left. \begin{matrix} + a_1, \dots, a_n ; a_{n+1} \dots a_p \\ + b_1, \dots, b_m ; b_{m+1} \dots b_q + \end{matrix}\; \right| \; z ; r \right) = + \frac{1}{2 \pi i} \int_L + \frac{\prod_{j=1}^m \Gamma(b_j+s) \prod_{j=1}^n\Gamma(1-a_j-s)} + {\prod_{j=n+1}^{p}\Gamma(a_j+s) \prod_{j=m+1}^q \Gamma(1-b_j-s)} + z^{-s/r} ds + +for an appropriate choice of the contour `L` (see references). + +There are `p` elements `a_j`. +The argument *a_s* should be a pair of lists, the first containing the +`n` elements `a_1, \ldots, a_n` and the second containing +the `p-n` elements `a_{n+1}, \ldots a_p`. + +There are `q` elements `b_j`. +The argument *b_s* should be a pair of lists, the first containing the +`m` elements `b_1, \ldots, b_m` and the second containing +the `q-m` elements `b_{m+1}, \ldots b_q`. + +The implicit tuple `(m, n, p, q)` constitutes the order or degree of the +Meijer G-function, and is determined by the lengths of the coefficient +vectors. Confusingly, the indices in this tuple appear in a different order +from the coefficients, but this notation is standard. The many examples +given below should hopefully clear up any potential confusion. + +**Algorithm** + +The Meijer G-function is evaluated as a combination of hypergeometric series. +There are two versions of the function, which can be selected with +the optional *series* argument. + +*series=1* uses a sum of `m` `\,_pF_{q-1}` functions of `z` + +*series=2* uses a sum of `n` `\,_qF_{p-1}` functions of `1/z` + +The default series is chosen based on the degree and `|z|` in order +to be consistent with Mathematica's. This definition of the Meijer G-function +has a discontinuity at `|z| = 1` for some orders, which can +be avoided by explicitly specifying a series. + +Keyword arguments are forwarded to :func:`~mpmath.hypercomb`. + +**Examples** + +Many standard functions are special cases of the Meijer G-function +(possibly rescaled and/or with branch cut corrections). We define +some test parameters:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> a = mpf(0.75) + >>> b = mpf(1.5) + >>> z = mpf(2.25) + +The exponential function: +`e^z = G^{1,0}_{0,1} \left( \left. \begin{matrix} - \\ 0 \end{matrix} \; +\right| \; -z \right)` + + >>> meijerg([[],[]], [[0],[]], -z) + 9.487735836358525720550369 + >>> exp(z) + 9.487735836358525720550369 + +The natural logarithm: +`\log(1+z) = G^{1,2}_{2,2} \left( \left. \begin{matrix} 1, 1 \\ 1, 0 +\end{matrix} \; \right| \; -z \right)` + + >>> meijerg([[1,1],[]], [[1],[0]], z) + 1.178654996341646117219023 + >>> log(1+z) + 1.178654996341646117219023 + +A rational function: +`\frac{z}{z+1} = G^{1,2}_{2,2} \left( \left. \begin{matrix} 1, 1 \\ 1, 1 +\end{matrix} \; \right| \; z \right)` + + >>> meijerg([[1,1],[]], [[1],[1]], z) + 0.6923076923076923076923077 + >>> z/(z+1) + 0.6923076923076923076923077 + +The sine and cosine functions: + +`\frac{1}{\sqrt \pi} \sin(2 \sqrt z) = G^{1,0}_{0,2} \left( \left. \begin{matrix} +- \\ \frac{1}{2}, 0 \end{matrix} \; \right| \; z \right)` + +`\frac{1}{\sqrt \pi} \cos(2 \sqrt z) = G^{1,0}_{0,2} \left( \left. \begin{matrix} +- \\ 0, \frac{1}{2} \end{matrix} \; \right| \; z \right)` + + >>> meijerg([[],[]], [[0.5],[0]], (z/2)**2) + 0.4389807929218676682296453 + >>> sin(z)/sqrt(pi) + 0.4389807929218676682296453 + >>> meijerg([[],[]], [[0],[0.5]], (z/2)**2) + -0.3544090145996275423331762 + >>> cos(z)/sqrt(pi) + -0.3544090145996275423331762 + +Bessel functions: + +`J_a(2 \sqrt z) = G^{1,0}_{0,2} \left( \left. +\begin{matrix} - \\ \frac{a}{2}, -\frac{a}{2} +\end{matrix} \; \right| \; z \right)` + +`Y_a(2 \sqrt z) = G^{2,0}_{1,3} \left( \left. +\begin{matrix} \frac{-a-1}{2} \\ \frac{a}{2}, -\frac{a}{2}, \frac{-a-1}{2} +\end{matrix} \; \right| \; z \right)` + +`(-z)^{a/2} z^{-a/2} I_a(2 \sqrt z) = G^{1,0}_{0,2} \left( \left. +\begin{matrix} - \\ \frac{a}{2}, -\frac{a}{2} +\end{matrix} \; \right| \; -z \right)` + +`2 K_a(2 \sqrt z) = G^{2,0}_{0,2} \left( \left. +\begin{matrix} - \\ \frac{a}{2}, -\frac{a}{2} +\end{matrix} \; \right| \; z \right)` + +As the example with the Bessel *I* function shows, a branch +factor is required for some arguments when inverting the square root. + + >>> meijerg([[],[]], [[a/2],[-a/2]], (z/2)**2) + 0.5059425789597154858527264 + >>> besselj(a,z) + 0.5059425789597154858527264 + >>> meijerg([[],[(-a-1)/2]], [[a/2,-a/2],[(-a-1)/2]], (z/2)**2) + 0.1853868950066556941442559 + >>> bessely(a, z) + 0.1853868950066556941442559 + >>> meijerg([[],[]], [[a/2],[-a/2]], -(z/2)**2) + (0.8685913322427653875717476 + 2.096964974460199200551738j) + >>> (-z)**(a/2) / z**(a/2) * besseli(a, z) + (0.8685913322427653875717476 + 2.096964974460199200551738j) + >>> 0.5*meijerg([[],[]], [[a/2,-a/2],[]], (z/2)**2) + 0.09334163695597828403796071 + >>> besselk(a,z) + 0.09334163695597828403796071 + +Error functions: + +`\sqrt{\pi} z^{2(a-1)} \mathrm{erfc}(z) = G^{2,0}_{1,2} \left( \left. +\begin{matrix} a \\ a-1, a-\frac{1}{2} +\end{matrix} \; \right| \; z, \frac{1}{2} \right)` + + >>> meijerg([[],[a]], [[a-1,a-0.5],[]], z, 0.5) + 0.00172839843123091957468712 + >>> sqrt(pi) * z**(2*a-2) * erfc(z) + 0.00172839843123091957468712 + +A Meijer G-function of higher degree, (1,1,2,3): + + >>> meijerg([[a],[b]], [[a],[b,a-1]], z) + 1.55984467443050210115617 + >>> sin((b-a)*pi)/pi*(exp(z)-1)*z**(a-1) + 1.55984467443050210115617 + +A Meijer G-function of still higher degree, (4,1,2,4), that can +be expanded as a messy combination of exponential integrals: + + >>> meijerg([[a],[2*b-a]], [[b,a,b-0.5,-1-a+2*b],[]], z) + 0.3323667133658557271898061 + >>> chop(4**(a-b+1)*sqrt(pi)*gamma(2*b-2*a)*z**a*\ + ... expint(2*b-2*a, -2*sqrt(-z))*expint(2*b-2*a, 2*sqrt(-z))) + 0.3323667133658557271898061 + +In the following case, different series give different values:: + + >>> chop(meijerg([[1],[0.25]],[[3],[0.5]],-2)) + -0.06417628097442437076207337 + >>> meijerg([[1],[0.25]],[[3],[0.5]],-2,series=1) + 0.1428699426155117511873047 + >>> chop(meijerg([[1],[0.25]],[[3],[0.5]],-2,series=2)) + -0.06417628097442437076207337 + +**References** + +1. http://en.wikipedia.org/wiki/Meijer_G-function + +2. http://mathworld.wolfram.com/MeijerG-Function.html + +3. http://functions.wolfram.com/HypergeometricFunctions/MeijerG/ + +4. http://functions.wolfram.com/HypergeometricFunctions/MeijerG1/ + +""" + +clsin = r""" +Computes the Clausen sine function, defined formally by the series + +.. math :: + + \mathrm{Cl}_s(z) = \sum_{k=1}^{\infty} \frac{\sin(kz)}{k^s}. + +The special case `\mathrm{Cl}_2(z)` (i.e. ``clsin(2,z)``) is the classical +"Clausen function". More generally, the Clausen function is defined for +complex `s` and `z`, even when the series does not converge. The +Clausen function is related to the polylogarithm (:func:`~mpmath.polylog`) as + +.. math :: + + \mathrm{Cl}_s(z) = \frac{1}{2i}\left(\mathrm{Li}_s\left(e^{iz}\right) - + \mathrm{Li}_s\left(e^{-iz}\right)\right) + + = \mathrm{Im}\left[\mathrm{Li}_s(e^{iz})\right] \quad (s, z \in \mathbb{R}), + +and this representation can be taken to provide the analytic continuation of the +series. The complementary function :func:`~mpmath.clcos` gives the corresponding +cosine sum. + +**Examples** + +Evaluation for arbitrarily chosen `s` and `z`:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> s, z = 3, 4 + >>> clsin(s, z); nsum(lambda k: sin(z*k)/k**s, [1,inf]) + -0.6533010136329338746275795 + -0.6533010136329338746275795 + +Using `z + \pi` instead of `z` gives an alternating series:: + + >>> clsin(s, z+pi) + 0.8860032351260589402871624 + >>> nsum(lambda k: (-1)**k*sin(z*k)/k**s, [1,inf]) + 0.8860032351260589402871624 + +With `s = 1`, the sum can be expressed in closed form +using elementary functions:: + + >>> z = 1 + sqrt(3) + >>> clsin(1, z) + 0.2047709230104579724675985 + >>> chop((log(1-exp(-j*z)) - log(1-exp(j*z)))/(2*j)) + 0.2047709230104579724675985 + >>> nsum(lambda k: sin(k*z)/k, [1,inf]) + 0.2047709230104579724675985 + +The classical Clausen function `\mathrm{Cl}_2(\theta)` gives the +value of the integral `\int_0^{\theta} -\ln(2\sin(x/2)) dx` for +`0 < \theta < 2 \pi`:: + + >>> cl2 = lambda t: clsin(2, t) + >>> cl2(3.5) + -0.2465045302347694216534255 + >>> -quad(lambda x: ln(2*sin(0.5*x)), [0, 3.5]) + -0.2465045302347694216534255 + +This function is symmetric about `\theta = \pi` with zeros and extreme +points:: + + >>> cl2(0); cl2(pi/3); chop(cl2(pi)); cl2(5*pi/3); chop(cl2(2*pi)) + 0.0 + 1.014941606409653625021203 + 0.0 + -1.014941606409653625021203 + 0.0 + +Catalan's constant is a special value:: + + >>> cl2(pi/2) + 0.9159655941772190150546035 + >>> +catalan + 0.9159655941772190150546035 + +The Clausen sine function can be expressed in closed form when +`s` is an odd integer (becoming zero when `s` < 0):: + + >>> z = 1 + sqrt(2) + >>> clsin(1, z); (pi-z)/2 + 0.3636895456083490948304773 + 0.3636895456083490948304773 + >>> clsin(3, z); pi**2/6*z - pi*z**2/4 + z**3/12 + 0.5661751584451144991707161 + 0.5661751584451144991707161 + >>> clsin(-1, z) + 0.0 + >>> clsin(-3, z) + 0.0 + +It can also be expressed in closed form for even integer `s \le 0`, +providing a finite sum for series such as +`\sin(z) + \sin(2z) + \sin(3z) + \ldots`:: + + >>> z = 1 + sqrt(2) + >>> clsin(0, z) + 0.1903105029507513881275865 + >>> cot(z/2)/2 + 0.1903105029507513881275865 + >>> clsin(-2, z) + -0.1089406163841548817581392 + >>> -cot(z/2)*csc(z/2)**2/4 + -0.1089406163841548817581392 + +Call with ``pi=True`` to multiply `z` by `\pi` exactly:: + + >>> clsin(3, 3*pi) + -8.892316224968072424732898e-26 + >>> clsin(3, 3, pi=True) + 0.0 + +Evaluation for complex `s`, `z` in a nonconvergent case:: + + >>> s, z = -1-j, 1+2j + >>> clsin(s, z) + (-0.593079480117379002516034 + 0.9038644233367868273362446j) + >>> extraprec(20)(nsum)(lambda k: sin(k*z)/k**s, [1,inf]) + (-0.593079480117379002516034 + 0.9038644233367868273362446j) + +""" + +clcos = r""" +Computes the Clausen cosine function, defined formally by the series + +.. math :: + + \mathrm{\widetilde{Cl}}_s(z) = \sum_{k=1}^{\infty} \frac{\cos(kz)}{k^s}. + +This function is complementary to the Clausen sine function +:func:`~mpmath.clsin`. In terms of the polylogarithm, + +.. math :: + + \mathrm{\widetilde{Cl}}_s(z) = + \frac{1}{2}\left(\mathrm{Li}_s\left(e^{iz}\right) + + \mathrm{Li}_s\left(e^{-iz}\right)\right) + + = \mathrm{Re}\left[\mathrm{Li}_s(e^{iz})\right] \quad (s, z \in \mathbb{R}). + +**Examples** + +Evaluation for arbitrarily chosen `s` and `z`:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> s, z = 3, 4 + >>> clcos(s, z); nsum(lambda k: cos(z*k)/k**s, [1,inf]) + -0.6518926267198991308332759 + -0.6518926267198991308332759 + +Using `z + \pi` instead of `z` gives an alternating series:: + + >>> s, z = 3, 0.5 + >>> clcos(s, z+pi) + -0.8155530586502260817855618 + >>> nsum(lambda k: (-1)**k*cos(z*k)/k**s, [1,inf]) + -0.8155530586502260817855618 + +With `s = 1`, the sum can be expressed in closed form +using elementary functions:: + + >>> z = 1 + sqrt(3) + >>> clcos(1, z) + -0.6720334373369714849797918 + >>> chop(-0.5*(log(1-exp(j*z))+log(1-exp(-j*z)))) + -0.6720334373369714849797918 + >>> -log(abs(2*sin(0.5*z))) # Equivalent to above when z is real + -0.6720334373369714849797918 + >>> nsum(lambda k: cos(k*z)/k, [1,inf]) + -0.6720334373369714849797918 + +It can also be expressed in closed form when `s` is an even integer. +For example, + + >>> clcos(2,z) + -0.7805359025135583118863007 + >>> pi**2/6 - pi*z/2 + z**2/4 + -0.7805359025135583118863007 + +The case `s = 0` gives the renormalized sum of +`\cos(z) + \cos(2z) + \cos(3z) + \ldots` (which happens to be the same for +any value of `z`):: + + >>> clcos(0, z) + -0.5 + >>> nsum(lambda k: cos(k*z), [1,inf]) + -0.5 + +Also the sums + +.. math :: + + \cos(z) + 2\cos(2z) + 3\cos(3z) + \ldots + +and + +.. math :: + + \cos(z) + 2^n \cos(2z) + 3^n \cos(3z) + \ldots + +for higher integer powers `n = -s` can be done in closed form. They are zero +when `n` is positive and even (`s` negative and even):: + + >>> clcos(-1, z); 1/(2*cos(z)-2) + -0.2607829375240542480694126 + -0.2607829375240542480694126 + >>> clcos(-3, z); (2+cos(z))*csc(z/2)**4/8 + 0.1472635054979944390848006 + 0.1472635054979944390848006 + >>> clcos(-2, z); clcos(-4, z); clcos(-6, z) + 0.0 + 0.0 + 0.0 + +With `z = \pi`, the series reduces to that of the Riemann zeta function +(more generally, if `z = p \pi/q`, it is a finite sum over Hurwitz zeta +function values):: + + >>> clcos(2.5, 0); zeta(2.5) + 1.34148725725091717975677 + 1.34148725725091717975677 + >>> clcos(2.5, pi); -altzeta(2.5) + -0.8671998890121841381913472 + -0.8671998890121841381913472 + +Call with ``pi=True`` to multiply `z` by `\pi` exactly:: + + >>> clcos(-3, 2*pi) + 2.997921055881167659267063e+102 + >>> clcos(-3, 2, pi=True) + 0.008333333333333333333333333 + +Evaluation for complex `s`, `z` in a nonconvergent case:: + + >>> s, z = -1-j, 1+2j + >>> clcos(s, z) + (0.9407430121562251476136807 + 0.715826296033590204557054j) + >>> extraprec(20)(nsum)(lambda k: cos(k*z)/k**s, [1,inf]) + (0.9407430121562251476136807 + 0.715826296033590204557054j) + +""" + +whitm = r""" +Evaluates the Whittaker function `M(k,m,z)`, which gives a solution +to the Whittaker differential equation + +.. math :: + + \frac{d^2f}{dz^2} + \left(-\frac{1}{4}+\frac{k}{z}+ + \frac{(\frac{1}{4}-m^2)}{z^2}\right) f = 0. + +A second solution is given by :func:`~mpmath.whitw`. + +The Whittaker functions are defined in Abramowitz & Stegun, section 13.1. +They are alternate forms of the confluent hypergeometric functions +`\,_1F_1` and `U`: + +.. math :: + + M(k,m,z) = e^{-\frac{1}{2}z} z^{\frac{1}{2}+m} + \,_1F_1(\tfrac{1}{2}+m-k, 1+2m, z) + + W(k,m,z) = e^{-\frac{1}{2}z} z^{\frac{1}{2}+m} + U(\tfrac{1}{2}+m-k, 1+2m, z). + +**Examples** + +Evaluation for arbitrary real and complex arguments is supported:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> whitm(1, 1, 1) + 0.7302596799460411820509668 + >>> whitm(1, 1, -1) + (0.0 - 1.417977827655098025684246j) + >>> whitm(j, j/2, 2+3j) + (3.245477713363581112736478 - 0.822879187542699127327782j) + >>> whitm(2, 3, 100000) + 4.303985255686378497193063e+21707 + +Evaluation at zero:: + + >>> whitm(1,-1,0); whitm(1,-0.5,0); whitm(1,0,0) + +inf + nan + 0.0 + +We can verify that :func:`~mpmath.whitm` numerically satisfies the +differential equation for arbitrarily chosen values:: + + >>> k = mpf(0.25) + >>> m = mpf(1.5) + >>> f = lambda z: whitm(k,m,z) + >>> for z in [-1, 2.5, 3, 1+2j]: + ... chop(diff(f,z,2) + (-0.25 + k/z + (0.25-m**2)/z**2)*f(z)) + ... + 0.0 + 0.0 + 0.0 + 0.0 + +An integral involving both :func:`~mpmath.whitm` and :func:`~mpmath.whitw`, +verifying evaluation along the real axis:: + + >>> quad(lambda x: exp(-x)*whitm(3,2,x)*whitw(1,-2,x), [0,inf]) + 3.438869842576800225207341 + >>> 128/(21*sqrt(pi)) + 3.438869842576800225207341 + +""" + +whitw = r""" +Evaluates the Whittaker function `W(k,m,z)`, which gives a second +solution to the Whittaker differential equation. (See :func:`~mpmath.whitm`.) + +**Examples** + +Evaluation for arbitrary real and complex arguments is supported:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> whitw(1, 1, 1) + 1.19532063107581155661012 + >>> whitw(1, 1, -1) + (-0.9424875979222187313924639 - 0.2607738054097702293308689j) + >>> whitw(j, j/2, 2+3j) + (0.1782899315111033879430369 - 0.01609578360403649340169406j) + >>> whitw(2, 3, 100000) + 1.887705114889527446891274e-21705 + >>> whitw(-1, -1, 100) + 1.905250692824046162462058e-24 + +Evaluation at zero:: + + >>> for m in [-1, -0.5, 0, 0.5, 1]: + ... whitw(1, m, 0) + ... + +inf + nan + 0.0 + nan + +inf + +We can verify that :func:`~mpmath.whitw` numerically satisfies the +differential equation for arbitrarily chosen values:: + + >>> k = mpf(0.25) + >>> m = mpf(1.5) + >>> f = lambda z: whitw(k,m,z) + >>> for z in [-1, 2.5, 3, 1+2j]: + ... chop(diff(f,z,2) + (-0.25 + k/z + (0.25-m**2)/z**2)*f(z)) + ... + 0.0 + 0.0 + 0.0 + 0.0 + +""" + +ber = r""" +Computes the Kelvin function ber, which for real arguments gives the real part +of the Bessel J function of a rotated argument + +.. math :: + + J_n\left(x e^{3\pi i/4}\right) = \mathrm{ber}_n(x) + i \mathrm{bei}_n(x). + +The imaginary part is given by :func:`~mpmath.bei`. + +**Plots** + +.. literalinclude :: /plots/ber.py +.. image :: /plots/ber.png + +**Examples** + +Verifying the defining relation:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> n, x = 2, 3.5 + >>> ber(n,x) + 1.442338852571888752631129 + >>> bei(n,x) + -0.948359035324558320217678 + >>> besselj(n, x*root(1,8,3)) + (1.442338852571888752631129 - 0.948359035324558320217678j) + +The ber and bei functions are also defined by analytic continuation +for complex arguments:: + + >>> ber(1+j, 2+3j) + (4.675445984756614424069563 - 15.84901771719130765656316j) + >>> bei(1+j, 2+3j) + (15.83886679193707699364398 + 4.684053288183046528703611j) + +""" + +bei = r""" +Computes the Kelvin function bei, which for real arguments gives the +imaginary part of the Bessel J function of a rotated argument. +See :func:`~mpmath.ber`. +""" + +ker = r""" +Computes the Kelvin function ker, which for real arguments gives the real part +of the (rescaled) Bessel K function of a rotated argument + +.. math :: + + e^{-\pi i/2} K_n\left(x e^{3\pi i/4}\right) = \mathrm{ker}_n(x) + i \mathrm{kei}_n(x). + +The imaginary part is given by :func:`~mpmath.kei`. + +**Plots** + +.. literalinclude :: /plots/ker.py +.. image :: /plots/ker.png + +**Examples** + +Verifying the defining relation:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> n, x = 2, 4.5 + >>> ker(n,x) + 0.02542895201906369640249801 + >>> kei(n,x) + -0.02074960467222823237055351 + >>> exp(-n*pi*j/2) * besselk(n, x*root(1,8,1)) + (0.02542895201906369640249801 - 0.02074960467222823237055351j) + +The ker and kei functions are also defined by analytic continuation +for complex arguments:: + + >>> ker(1+j, 3+4j) + (1.586084268115490421090533 - 2.939717517906339193598719j) + >>> kei(1+j, 3+4j) + (-2.940403256319453402690132 - 1.585621643835618941044855j) + +""" + +kei = r""" +Computes the Kelvin function kei, which for real arguments gives the +imaginary part of the (rescaled) Bessel K function of a rotated argument. +See :func:`~mpmath.ker`. +""" + +struveh = r""" +Gives the Struve function + +.. math :: + + \,\mathbf{H}_n(z) = + \sum_{k=0}^\infty \frac{(-1)^k}{\Gamma(k+\frac{3}{2}) + \Gamma(k+n+\frac{3}{2})} {\left({\frac{z}{2}}\right)}^{2k+n+1} + +which is a solution to the Struve differential equation + +.. math :: + + z^2 f''(z) + z f'(z) + (z^2-n^2) f(z) = \frac{2 z^{n+1}}{\pi (2n-1)!!}. + +**Examples** + +Evaluation for arbitrary real and complex arguments:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> struveh(0, 3.5) + 0.3608207733778295024977797 + >>> struveh(-1, 10) + -0.255212719726956768034732 + >>> struveh(1, -100.5) + 0.5819566816797362287502246 + >>> struveh(2.5, 10000000000000) + 3153915652525200060.308937 + >>> struveh(2.5, -10000000000000) + (0.0 - 3153915652525200060.308937j) + >>> struveh(1+j, 1000000+4000000j) + (-3.066421087689197632388731e+1737173 - 1.596619701076529803290973e+1737173j) + +A Struve function of half-integer order is elementary; for example: + + >>> z = 3 + >>> struveh(0.5, 3) + 0.9167076867564138178671595 + >>> sqrt(2/(pi*z))*(1-cos(z)) + 0.9167076867564138178671595 + +Numerically verifying the differential equation:: + + >>> z = mpf(4.5) + >>> n = 3 + >>> f = lambda z: struveh(n,z) + >>> lhs = z**2*diff(f,z,2) + z*diff(f,z) + (z**2-n**2)*f(z) + >>> rhs = 2*z**(n+1)/fac2(2*n-1)/pi + >>> lhs + 17.40359302709875496632744 + >>> rhs + 17.40359302709875496632744 + +""" + +struvel = r""" +Gives the modified Struve function + +.. math :: + + \,\mathbf{L}_n(z) = -i e^{-n\pi i/2} \mathbf{H}_n(i z) + +which solves to the modified Struve differential equation + +.. math :: + + z^2 f''(z) + z f'(z) - (z^2+n^2) f(z) = \frac{2 z^{n+1}}{\pi (2n-1)!!}. + +**Examples** + +Evaluation for arbitrary real and complex arguments:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> struvel(0, 3.5) + 7.180846515103737996249972 + >>> struvel(-1, 10) + 2670.994904980850550721511 + >>> struvel(1, -100.5) + 1.757089288053346261497686e+42 + >>> struvel(2.5, 10000000000000) + 4.160893281017115450519948e+4342944819025 + >>> struvel(2.5, -10000000000000) + (0.0 - 4.160893281017115450519948e+4342944819025j) + >>> struvel(1+j, 700j) + (-0.1721150049480079451246076 + 0.1240770953126831093464055j) + >>> struvel(1+j, 1000000+4000000j) + (-2.973341637511505389128708e+434290 - 5.164633059729968297147448e+434290j) + +Numerically verifying the differential equation:: + + >>> z = mpf(3.5) + >>> n = 3 + >>> f = lambda z: struvel(n,z) + >>> lhs = z**2*diff(f,z,2) + z*diff(f,z) - (z**2+n**2)*f(z) + >>> rhs = 2*z**(n+1)/fac2(2*n-1)/pi + >>> lhs + 6.368850306060678353018165 + >>> rhs + 6.368850306060678353018165 +""" + +appellf1 = r""" +Gives the Appell F1 hypergeometric function of two variables, + +.. math :: + + F_1(a,b_1,b_2,c,x,y) = \sum_{m=0}^{\infty} \sum_{n=0}^{\infty} + \frac{(a)_{m+n} (b_1)_m (b_2)_n}{(c)_{m+n}} + \frac{x^m y^n}{m! n!}. + +This series is only generally convergent when `|x| < 1` and `|y| < 1`, +although :func:`~mpmath.appellf1` can evaluate an analytic continuation +with respecto to either variable, and sometimes both. + +**Examples** + +Evaluation is supported for real and complex parameters:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> appellf1(1,0,0.5,1,0.5,0.25) + 1.154700538379251529018298 + >>> appellf1(1,1+j,0.5,1,0.5,0.5j) + (1.138403860350148085179415 + 1.510544741058517621110615j) + +For some integer parameters, the F1 series reduces to a polynomial:: + + >>> appellf1(2,-4,-3,1,2,5) + -816.0 + >>> appellf1(-5,1,2,1,4,5) + -20528.0 + +The analytic continuation with respect to either `x` or `y`, +and sometimes with respect to both, can be evaluated:: + + >>> appellf1(2,3,4,5,100,0.5) + (0.0006231042714165329279738662 + 0.0000005769149277148425774499857j) + >>> appellf1('1.1', '0.3', '0.2+2j', '0.4', '0.2', 1.5+3j) + (-0.1782604566893954897128702 + 0.002472407104546216117161499j) + >>> appellf1(1,2,3,4,10,12) + -0.07122993830066776374929313 + +For certain arguments, F1 reduces to an ordinary hypergeometric function:: + + >>> appellf1(1,2,3,5,0.5,0.25) + 1.547902270302684019335555 + >>> 4*hyp2f1(1,2,5,'1/3')/3 + 1.547902270302684019335555 + >>> appellf1(1,2,3,4,0,1.5) + (-1.717202506168937502740238 - 2.792526803190927323077905j) + >>> hyp2f1(1,3,4,1.5) + (-1.717202506168937502740238 - 2.792526803190927323077905j) + +The F1 function satisfies a system of partial differential equations:: + + >>> a,b1,b2,c,x,y = map(mpf, [1,0.5,0.25,1.125,0.25,-0.25]) + >>> F = lambda x,y: appellf1(a,b1,b2,c,x,y) + >>> chop(x*(1-x)*diff(F,(x,y),(2,0)) + + ... y*(1-x)*diff(F,(x,y),(1,1)) + + ... (c-(a+b1+1)*x)*diff(F,(x,y),(1,0)) - + ... b1*y*diff(F,(x,y),(0,1)) - + ... a*b1*F(x,y)) + 0.0 + >>> + >>> chop(y*(1-y)*diff(F,(x,y),(0,2)) + + ... x*(1-y)*diff(F,(x,y),(1,1)) + + ... (c-(a+b2+1)*y)*diff(F,(x,y),(0,1)) - + ... b2*x*diff(F,(x,y),(1,0)) - + ... a*b2*F(x,y)) + 0.0 + +The Appell F1 function allows for closed-form evaluation of various +integrals, such as any integral of the form +`\int x^r (x+a)^p (x+b)^q dx`:: + + >>> def integral(a,b,p,q,r,x1,x2): + ... a,b,p,q,r,x1,x2 = map(mpmathify, [a,b,p,q,r,x1,x2]) + ... f = lambda x: x**r * (x+a)**p * (x+b)**q + ... def F(x): + ... v = x**(r+1)/(r+1) * (a+x)**p * (b+x)**q + ... v *= (1+x/a)**(-p) + ... v *= (1+x/b)**(-q) + ... v *= appellf1(r+1,-p,-q,2+r,-x/a,-x/b) + ... return v + ... print("Num. quad: %s" % quad(f, [x1,x2])) + ... print("Appell F1: %s" % (F(x2)-F(x1))) + ... + >>> integral('1/5','4/3','-2','3','1/2',0,1) + Num. quad: 9.073335358785776206576981 + Appell F1: 9.073335358785776206576981 + >>> integral('3/2','4/3','-2','3','1/2',0,1) + Num. quad: 1.092829171999626454344678 + Appell F1: 1.092829171999626454344678 + >>> integral('3/2','4/3','-2','3','1/2',12,25) + Num. quad: 1106.323225040235116498927 + Appell F1: 1106.323225040235116498927 + +Also incomplete elliptic integrals fall into this category [1]:: + + >>> def E(z, m): + ... if (pi/2).ae(z): + ... return ellipe(m) + ... return 2*round(re(z)/pi)*ellipe(m) + mpf(-1)**round(re(z)/pi)*\ + ... sin(z)*appellf1(0.5,0.5,-0.5,1.5,sin(z)**2,m*sin(z)**2) + ... + >>> z, m = 1, 0.5 + >>> E(z,m); quad(lambda t: sqrt(1-m*sin(t)**2), [0,pi/4,3*pi/4,z]) + 0.9273298836244400669659042 + 0.9273298836244400669659042 + >>> z, m = 3, 2 + >>> E(z,m); quad(lambda t: sqrt(1-m*sin(t)**2), [0,pi/4,3*pi/4,z]) + (1.057495752337234229715836 + 1.198140234735592207439922j) + (1.057495752337234229715836 + 1.198140234735592207439922j) + +**References** + +1. [WolframFunctions]_ http://functions.wolfram.com/EllipticIntegrals/EllipticE2/26/01/ +2. [SrivastavaKarlsson]_ +3. [CabralRosetti]_ +4. [Vidunas]_ +5. [Slater]_ + +""" + +angerj = r""" +Gives the Anger function + +.. math :: + + \mathbf{J}_{\nu}(z) = \frac{1}{\pi} + \int_0^{\pi} \cos(\nu t - z \sin t) dt + +which is an entire function of both the parameter `\nu` and +the argument `z`. It solves the inhomogeneous Bessel differential +equation + +.. math :: + + f''(z) + \frac{1}{z}f'(z) + \left(1-\frac{\nu^2}{z^2}\right) f(z) + = \frac{(z-\nu)}{\pi z^2} \sin(\pi \nu). + +**Examples** + +Evaluation for real and complex parameter and argument:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> angerj(2,3) + 0.4860912605858910769078311 + >>> angerj(-3+4j, 2+5j) + (-5033.358320403384472395612 + 585.8011892476145118551756j) + >>> angerj(3.25, 1e6j) + (4.630743639715893346570743e+434290 - 1.117960409887505906848456e+434291j) + >>> angerj(-1.5, 1e6) + 0.0002795719747073879393087011 + +The Anger function coincides with the Bessel J-function when `\nu` +is an integer:: + + >>> angerj(1,3); besselj(1,3) + 0.3390589585259364589255146 + 0.3390589585259364589255146 + >>> angerj(1.5,3); besselj(1.5,3) + 0.4088969848691080859328847 + 0.4777182150870917715515015 + +Verifying the differential equation:: + + >>> v,z = mpf(2.25), 0.75 + >>> f = lambda z: angerj(v,z) + >>> diff(f,z,2) + diff(f,z)/z + (1-(v/z)**2)*f(z) + -0.6002108774380707130367995 + >>> (z-v)/(pi*z**2) * sinpi(v) + -0.6002108774380707130367995 + +Verifying the integral representation:: + + >>> angerj(v,z) + 0.1145380759919333180900501 + >>> quad(lambda t: cos(v*t-z*sin(t))/pi, [0,pi]) + 0.1145380759919333180900501 + +**References** + +1. [DLMF]_ section 11.10: Anger-Weber Functions +""" + +webere = r""" +Gives the Weber function + +.. math :: + + \mathbf{E}_{\nu}(z) = \frac{1}{\pi} + \int_0^{\pi} \sin(\nu t - z \sin t) dt + +which is an entire function of both the parameter `\nu` and +the argument `z`. It solves the inhomogeneous Bessel differential +equation + +.. math :: + + f''(z) + \frac{1}{z}f'(z) + \left(1-\frac{\nu^2}{z^2}\right) f(z) + = -\frac{1}{\pi z^2} (z+\nu+(z-\nu)\cos(\pi \nu)). + +**Examples** + +Evaluation for real and complex parameter and argument:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> webere(2,3) + -0.1057668973099018425662646 + >>> webere(-3+4j, 2+5j) + (-585.8081418209852019290498 - 5033.314488899926921597203j) + >>> webere(3.25, 1e6j) + (-1.117960409887505906848456e+434291 - 4.630743639715893346570743e+434290j) + >>> webere(3.25, 1e6) + -0.00002812518265894315604914453 + +Up to addition of a rational function of `z`, the Weber function coincides +with the Struve H-function when `\nu` is an integer:: + + >>> webere(1,3); 2/pi-struveh(1,3) + -0.3834897968188690177372881 + -0.3834897968188690177372881 + >>> webere(5,3); 26/(35*pi)-struveh(5,3) + 0.2009680659308154011878075 + 0.2009680659308154011878075 + +Verifying the differential equation:: + + >>> v,z = mpf(2.25), 0.75 + >>> f = lambda z: webere(v,z) + >>> diff(f,z,2) + diff(f,z)/z + (1-(v/z)**2)*f(z) + -1.097441848875479535164627 + >>> -(z+v+(z-v)*cospi(v))/(pi*z**2) + -1.097441848875479535164627 + +Verifying the integral representation:: + + >>> webere(v,z) + 0.1486507351534283744485421 + >>> quad(lambda t: sin(v*t-z*sin(t))/pi, [0,pi]) + 0.1486507351534283744485421 + +**References** + +1. [DLMF]_ section 11.10: Anger-Weber Functions +""" + +lommels1 = r""" +Gives the Lommel function `s_{\mu,\nu}` or `s^{(1)}_{\mu,\nu}` + +.. math :: + + s_{\mu,\nu}(z) = \frac{z^{\mu+1}}{(\mu-\nu+1)(\mu+\nu+1)} + \,_1F_2\left(1; \frac{\mu-\nu+3}{2}, \frac{\mu+\nu+3}{2}; + -\frac{z^2}{4} \right) + +which solves the inhomogeneous Bessel equation + +.. math :: + + z^2 f''(z) + z f'(z) + (z^2-\nu^2) f(z) = z^{\mu+1}. + +A second solution is given by :func:`~mpmath.lommels2`. + +**Plots** + +.. literalinclude :: /plots/lommels1.py +.. image :: /plots/lommels1.png + +**Examples** + +An integral representation:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> u,v,z = 0.25, 0.125, mpf(0.75) + >>> lommels1(u,v,z) + 0.4276243877565150372999126 + >>> (bessely(v,z)*quad(lambda t: t**u*besselj(v,t), [0,z]) - \ + ... besselj(v,z)*quad(lambda t: t**u*bessely(v,t), [0,z]))*(pi/2) + 0.4276243877565150372999126 + +A special value:: + + >>> lommels1(v,v,z) + 0.5461221367746048054932553 + >>> gamma(v+0.5)*sqrt(pi)*power(2,v-1)*struveh(v,z) + 0.5461221367746048054932553 + +Verifying the differential equation:: + + >>> f = lambda z: lommels1(u,v,z) + >>> z**2*diff(f,z,2) + z*diff(f,z) + (z**2-v**2)*f(z) + 0.6979536443265746992059141 + >>> z**(u+1) + 0.6979536443265746992059141 + +**References** + +1. [GradshteynRyzhik]_ +2. [Weisstein]_ http://mathworld.wolfram.com/LommelFunction.html +""" + +lommels2 = r""" +Gives the second Lommel function `S_{\mu,\nu}` or `s^{(2)}_{\mu,\nu}` + +.. math :: + + S_{\mu,\nu}(z) = s_{\mu,\nu}(z) + 2^{\mu-1} + \Gamma\left(\tfrac{1}{2}(\mu-\nu+1)\right) + \Gamma\left(\tfrac{1}{2}(\mu+\nu+1)\right) \times + + \left[\sin(\tfrac{1}{2}(\mu-\nu)\pi) J_{\nu}(z) - + \cos(\tfrac{1}{2}(\mu-\nu)\pi) Y_{\nu}(z) + \right] + +which solves the same differential equation as +:func:`~mpmath.lommels1`. + +**Plots** + +.. literalinclude :: /plots/lommels2.py +.. image :: /plots/lommels2.png + +**Examples** + +For large `|z|`, `S_{\mu,\nu} \sim z^{\mu-1}`:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> lommels2(10,2,30000) + 1.968299831601008419949804e+40 + >>> power(30000,9) + 1.9683e+40 + +A special value:: + + >>> u,v,z = 0.5, 0.125, mpf(0.75) + >>> lommels2(v,v,z) + 0.9589683199624672099969765 + >>> (struveh(v,z)-bessely(v,z))*power(2,v-1)*sqrt(pi)*gamma(v+0.5) + 0.9589683199624672099969765 + +Verifying the differential equation:: + + >>> f = lambda z: lommels2(u,v,z) + >>> z**2*diff(f,z,2) + z*diff(f,z) + (z**2-v**2)*f(z) + 0.6495190528383289850727924 + >>> z**(u+1) + 0.6495190528383289850727924 + +**References** + +1. [GradshteynRyzhik]_ +2. [Weisstein]_ http://mathworld.wolfram.com/LommelFunction.html +""" + +appellf2 = r""" +Gives the Appell F2 hypergeometric function of two variables + +.. math :: + + F_2(a,b_1,b_2,c_1,c_2,x,y) = \sum_{m=0}^{\infty} \sum_{n=0}^{\infty} + \frac{(a)_{m+n} (b_1)_m (b_2)_n}{(c_1)_m (c_2)_n} + \frac{x^m y^n}{m! n!}. + +The series is generally absolutely convergent for `|x| + |y| < 1`. + +**Examples** + +Evaluation for real and complex arguments:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> appellf2(1,2,3,4,5,0.25,0.125) + 1.257417193533135344785602 + >>> appellf2(1,-3,-4,2,3,2,3) + -42.8 + >>> appellf2(0.5,0.25,-0.25,2,3,0.25j,0.25) + (0.9880539519421899867041719 + 0.01497616165031102661476978j) + >>> chop(appellf2(1,1+j,1-j,3j,-3j,0.25,0.25)) + 1.201311219287411337955192 + >>> appellf2(1,1,1,4,6,0.125,16) + (-0.09455532250274744282125152 - 0.7647282253046207836769297j) + +A transformation formula:: + + >>> a,b1,b2,c1,c2,x,y = map(mpf, [1,2,0.5,0.25,1.625,-0.125,0.125]) + >>> appellf2(a,b1,b2,c1,c2,x,y) + 0.2299211717841180783309688 + >>> (1-x)**(-a)*appellf2(a,c1-b1,b2,c1,c2,x/(x-1),y/(1-x)) + 0.2299211717841180783309688 + +A system of partial differential equations satisfied by F2:: + + >>> a,b1,b2,c1,c2,x,y = map(mpf, [1,0.5,0.25,1.125,1.5,0.0625,-0.0625]) + >>> F = lambda x,y: appellf2(a,b1,b2,c1,c2,x,y) + >>> chop(x*(1-x)*diff(F,(x,y),(2,0)) - + ... x*y*diff(F,(x,y),(1,1)) + + ... (c1-(a+b1+1)*x)*diff(F,(x,y),(1,0)) - + ... b1*y*diff(F,(x,y),(0,1)) - + ... a*b1*F(x,y)) + 0.0 + >>> chop(y*(1-y)*diff(F,(x,y),(0,2)) - + ... x*y*diff(F,(x,y),(1,1)) + + ... (c2-(a+b2+1)*y)*diff(F,(x,y),(0,1)) - + ... b2*x*diff(F,(x,y),(1,0)) - + ... a*b2*F(x,y)) + 0.0 + +**References** + +See references for :func:`~mpmath.appellf1`. +""" + +appellf3 = r""" +Gives the Appell F3 hypergeometric function of two variables + +.. math :: + + F_3(a_1,a_2,b_1,b_2,c,x,y) = \sum_{m=0}^{\infty} \sum_{n=0}^{\infty} + \frac{(a_1)_m (a_2)_n (b_1)_m (b_2)_n}{(c)_{m+n}} + \frac{x^m y^n}{m! n!}. + +The series is generally absolutely convergent for `|x| < 1, |y| < 1`. + +**Examples** + +Evaluation for various parameters and variables:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> appellf3(1,2,3,4,5,0.5,0.25) + 2.221557778107438938158705 + >>> appellf3(1,2,3,4,5,6,0); hyp2f1(1,3,5,6) + (-0.5189554589089861284537389 - 0.1454441043328607980769742j) + (-0.5189554589089861284537389 - 0.1454441043328607980769742j) + >>> appellf3(1,-2,-3,1,1,4,6) + -17.4 + >>> appellf3(1,2,-3,1,1,4,6) + (17.7876136773677356641825 + 19.54768762233649126154534j) + >>> appellf3(1,2,-3,1,1,6,4) + (85.02054175067929402953645 + 148.4402528821177305173599j) + >>> chop(appellf3(1+j,2,1-j,2,3,0.25,0.25)) + 1.719992169545200286696007 + +Many transformations and evaluations for special combinations +of the parameters are possible, e.g.: + + >>> a,b,c,x,y = map(mpf, [0.5,0.25,0.125,0.125,-0.125]) + >>> appellf3(a,c-a,b,c-b,c,x,y) + 1.093432340896087107444363 + >>> (1-y)**(a+b-c)*hyp2f1(a,b,c,x+y-x*y) + 1.093432340896087107444363 + >>> x**2*appellf3(1,1,1,1,3,x,-x) + 0.01568646277445385390945083 + >>> polylog(2,x**2) + 0.01568646277445385390945083 + >>> a1,a2,b1,b2,c,x = map(mpf, [0.5,0.25,0.125,0.5,4.25,0.125]) + >>> appellf3(a1,a2,b1,b2,c,x,1) + 1.03947361709111140096947 + >>> gammaprod([c,c-a2-b2],[c-a2,c-b2])*hyp3f2(a1,b1,c-a2-b2,c-a2,c-b2,x) + 1.03947361709111140096947 + +The Appell F3 function satisfies a pair of partial +differential equations:: + + >>> a1,a2,b1,b2,c,x,y = map(mpf, [0.5,0.25,0.125,0.5,0.625,0.0625,-0.0625]) + >>> F = lambda x,y: appellf3(a1,a2,b1,b2,c,x,y) + >>> chop(x*(1-x)*diff(F,(x,y),(2,0)) + + ... y*diff(F,(x,y),(1,1)) + + ... (c-(a1+b1+1)*x)*diff(F,(x,y),(1,0)) - + ... a1*b1*F(x,y)) + 0.0 + >>> chop(y*(1-y)*diff(F,(x,y),(0,2)) + + ... x*diff(F,(x,y),(1,1)) + + ... (c-(a2+b2+1)*y)*diff(F,(x,y),(0,1)) - + ... a2*b2*F(x,y)) + 0.0 + +**References** + +See references for :func:`~mpmath.appellf1`. +""" + +appellf4 = r""" +Gives the Appell F4 hypergeometric function of two variables + +.. math :: + + F_4(a,b,c_1,c_2,x,y) = \sum_{m=0}^{\infty} \sum_{n=0}^{\infty} + \frac{(a)_{m+n} (b)_{m+n}}{(c_1)_m (c_2)_n} + \frac{x^m y^n}{m! n!}. + +The series is generally absolutely convergent for +`\sqrt{|x|} + \sqrt{|y|} < 1`. + +**Examples** + +Evaluation for various parameters and arguments:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> appellf4(1,1,2,2,0.25,0.125) + 1.286182069079718313546608 + >>> appellf4(-2,-3,4,5,4,5) + 34.8 + >>> appellf4(5,4,2,3,0.25j,-0.125j) + (-0.2585967215437846642163352 + 2.436102233553582711818743j) + +Reduction to `\,_2F_1` in a special case:: + + >>> a,b,c,x,y = map(mpf, [0.5,0.25,0.125,0.125,-0.125]) + >>> appellf4(a,b,c,a+b-c+1,x*(1-y),y*(1-x)) + 1.129143488466850868248364 + >>> hyp2f1(a,b,c,x)*hyp2f1(a,b,a+b-c+1,y) + 1.129143488466850868248364 + +A system of partial differential equations satisfied by F4:: + + >>> a,b,c1,c2,x,y = map(mpf, [1,0.5,0.25,1.125,0.0625,-0.0625]) + >>> F = lambda x,y: appellf4(a,b,c1,c2,x,y) + >>> chop(x*(1-x)*diff(F,(x,y),(2,0)) - + ... y**2*diff(F,(x,y),(0,2)) - + ... 2*x*y*diff(F,(x,y),(1,1)) + + ... (c1-(a+b+1)*x)*diff(F,(x,y),(1,0)) - + ... ((a+b+1)*y)*diff(F,(x,y),(0,1)) - + ... a*b*F(x,y)) + 0.0 + >>> chop(y*(1-y)*diff(F,(x,y),(0,2)) - + ... x**2*diff(F,(x,y),(2,0)) - + ... 2*x*y*diff(F,(x,y),(1,1)) + + ... (c2-(a+b+1)*y)*diff(F,(x,y),(0,1)) - + ... ((a+b+1)*x)*diff(F,(x,y),(1,0)) - + ... a*b*F(x,y)) + 0.0 + +**References** + +See references for :func:`~mpmath.appellf1`. +""" + +zeta = r""" +Computes the Riemann zeta function + +.. math :: + + \zeta(s) = 1+\frac{1}{2^s}+\frac{1}{3^s}+\frac{1}{4^s}+\ldots + +or, with `a \ne 1`, the more general Hurwitz zeta function + +.. math :: + + \zeta(s,a) = \sum_{k=0}^\infty \frac{1}{(a+k)^s}. + +Optionally, ``zeta(s, a, n)`` computes the `n`-th derivative with +respect to `s`, + +.. math :: + + \zeta^{(n)}(s,a) = (-1)^n \sum_{k=0}^\infty \frac{\log^n(a+k)}{(a+k)^s}. + +Although these series only converge for `\Re(s) > 1`, the Riemann and Hurwitz +zeta functions are defined through analytic continuation for arbitrary +complex `s \ne 1` (`s = 1` is a pole). + +The implementation uses three algorithms: the Borwein algorithm for +the Riemann zeta function when `s` is close to the real line; +the Riemann-Siegel formula for the Riemann zeta function when `s` is +large imaginary, and Euler-Maclaurin summation in all other cases. +The reflection formula for `\Re(s) < 0` is implemented in some cases. +The algorithm can be chosen with ``method = 'borwein'``, +``method='riemann-siegel'`` or ``method = 'euler-maclaurin'``. + +The parameter `a` is usually a rational number `a = p/q`, and may be specified +as such by passing an integer tuple `(p, q)`. Evaluation is supported for +arbitrary complex `a`, but may be slow and/or inaccurate when `\Re(s) < 0` for +nonrational `a` or when computing derivatives. + +**Examples** + +Some values of the Riemann zeta function:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> zeta(2); pi**2 / 6 + 1.644934066848226436472415 + 1.644934066848226436472415 + >>> zeta(0) + -0.5 + >>> zeta(-1) + -0.08333333333333333333333333 + >>> zeta(-2) + 0.0 + +For large positive `s`, `\zeta(s)` rapidly approaches 1:: + + >>> zeta(50) + 1.000000000000000888178421 + >>> zeta(100) + 1.0 + >>> zeta(inf) + 1.0 + >>> 1-sum((zeta(k)-1)/k for k in range(2,85)); +euler + 0.5772156649015328606065121 + 0.5772156649015328606065121 + >>> nsum(lambda k: zeta(k)-1, [2, inf]) + 1.0 + +Evaluation is supported for complex `s` and `a`: + + >>> zeta(-3+4j) + (-0.03373057338827757067584698 + 0.2774499251557093745297677j) + >>> zeta(2+3j, -1+j) + (389.6841230140842816370741 + 295.2674610150305334025962j) + +The Riemann zeta function has so-called nontrivial zeros on +the critical line `s = 1/2 + it`:: + + >>> findroot(zeta, 0.5+14j); zetazero(1) + (0.5 + 14.13472514173469379045725j) + (0.5 + 14.13472514173469379045725j) + >>> findroot(zeta, 0.5+21j); zetazero(2) + (0.5 + 21.02203963877155499262848j) + (0.5 + 21.02203963877155499262848j) + >>> findroot(zeta, 0.5+25j); zetazero(3) + (0.5 + 25.01085758014568876321379j) + (0.5 + 25.01085758014568876321379j) + >>> chop(zeta(zetazero(10))) + 0.0 + +Evaluation on and near the critical line is supported for large +heights `t` by means of the Riemann-Siegel formula (currently +for `a = 1`, `n \le 4`):: + + >>> zeta(0.5+100000j) + (1.073032014857753132114076 + 5.780848544363503984261041j) + >>> zeta(0.75+1000000j) + (0.9535316058375145020351559 + 0.9525945894834273060175651j) + >>> zeta(0.5+10000000j) + (11.45804061057709254500227 - 8.643437226836021723818215j) + >>> zeta(0.5+100000000j, derivative=1) + (51.12433106710194942681869 + 43.87221167872304520599418j) + >>> zeta(0.5+100000000j, derivative=2) + (-444.2760822795430400549229 - 896.3789978119185981665403j) + >>> zeta(0.5+100000000j, derivative=3) + (3230.72682687670422215339 + 14374.36950073615897616781j) + >>> zeta(0.5+100000000j, derivative=4) + (-11967.35573095046402130602 - 218945.7817789262839266148j) + >>> zeta(1+10000000j) # off the line + (2.859846483332530337008882 + 0.491808047480981808903986j) + >>> zeta(1+10000000j, derivative=1) + (-4.333835494679647915673205 - 0.08405337962602933636096103j) + >>> zeta(1+10000000j, derivative=4) + (453.2764822702057701894278 - 581.963625832768189140995j) + +For investigation of the zeta function zeros, the Riemann-Siegel +Z-function is often more convenient than working with the Riemann +zeta function directly (see :func:`~mpmath.siegelz`). + +Some values of the Hurwitz zeta function:: + + >>> zeta(2, 3); -5./4 + pi**2/6 + 0.3949340668482264364724152 + 0.3949340668482264364724152 + >>> zeta(2, (3,4)); pi**2 - 8*catalan + 2.541879647671606498397663 + 2.541879647671606498397663 + +For positive integer values of `s`, the Hurwitz zeta function is +equivalent to a polygamma function (except for a normalizing factor):: + + >>> zeta(4, (1,5)); psi(3, '1/5')/6 + 625.5408324774542966919938 + 625.5408324774542966919938 + +Evaluation of derivatives:: + + >>> zeta(0, 3+4j, 1); loggamma(3+4j) - ln(2*pi)/2 + (-2.675565317808456852310934 + 4.742664438034657928194889j) + (-2.675565317808456852310934 + 4.742664438034657928194889j) + >>> zeta(2, 1, 20) + 2432902008176640000.000242 + >>> zeta(3+4j, 5.5+2j, 4) + (-0.140075548947797130681075 - 0.3109263360275413251313634j) + >>> zeta(0.5+100000j, 1, 4) + (-10407.16081931495861539236 + 13777.78669862804508537384j) + >>> zeta(-100+0.5j, (1,3), derivative=4) + (4.007180821099823942702249e+79 + 4.916117957092593868321778e+78j) + +Generating a Taylor series at `s = 2` using derivatives:: + + >>> for k in range(11): print("%s * (s-2)^%i" % (zeta(2,1,k)/fac(k), k)) + ... + 1.644934066848226436472415 * (s-2)^0 + -0.9375482543158437537025741 * (s-2)^1 + 0.9946401171494505117104293 * (s-2)^2 + -1.000024300473840810940657 * (s-2)^3 + 1.000061933072352565457512 * (s-2)^4 + -1.000006869443931806408941 * (s-2)^5 + 1.000000173233769531820592 * (s-2)^6 + -0.9999999569989868493432399 * (s-2)^7 + 0.9999999937218844508684206 * (s-2)^8 + -0.9999999996355013916608284 * (s-2)^9 + 1.000000000004610645020747 * (s-2)^10 + +Evaluation at zero and for negative integer `s`:: + + >>> zeta(0, 10) + -9.5 + >>> zeta(-2, (2,3)); mpf(1)/81 + 0.01234567901234567901234568 + 0.01234567901234567901234568 + >>> zeta(-3+4j, (5,4)) + (0.2899236037682695182085988 + 0.06561206166091757973112783j) + >>> zeta(-3.25, 1/pi) + -0.0005117269627574430494396877 + >>> zeta(-3.5, pi, 1) + 11.156360390440003294709 + >>> zeta(-100.5, (8,3)) + -4.68162300487989766727122e+77 + >>> zeta(-10.5, (-8,3)) + (-0.01521913704446246609237979 + 29907.72510874248161608216j) + >>> zeta(-1000.5, (-8,3)) + (1.031911949062334538202567e+1770 + 1.519555750556794218804724e+426j) + >>> zeta(-1+j, 3+4j) + (-16.32988355630802510888631 - 22.17706465801374033261383j) + >>> zeta(-1+j, 3+4j, 2) + (32.48985276392056641594055 - 51.11604466157397267043655j) + >>> diff(lambda s: zeta(s, 3+4j), -1+j, 2) + (32.48985276392056641594055 - 51.11604466157397267043655j) + +**References** + +1. http://mathworld.wolfram.com/RiemannZetaFunction.html + +2. http://mathworld.wolfram.com/HurwitzZetaFunction.html + +3. [BorweinZeta]_ + +""" + +dirichlet = r""" +Evaluates the Dirichlet L-function + +.. math :: + + L(s,\chi) = \sum_{k=1}^\infty \frac{\chi(k)}{k^s}. + +where `\chi` is a periodic sequence of length `q` which should be supplied +in the form of a list `[\chi(0), \chi(1), \ldots, \chi(q-1)]`. +Strictly, `\chi` should be a Dirichlet character, but any periodic +sequence will work. + +For example, ``dirichlet(s, [1])`` gives the ordinary +Riemann zeta function and ``dirichlet(s, [-1,1])`` gives +the alternating zeta function (Dirichlet eta function). + +Also the derivative with respect to `s` (currently only a first +derivative) can be evaluated. + +**Examples** + +The ordinary Riemann zeta function:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> dirichlet(3, [1]); zeta(3) + 1.202056903159594285399738 + 1.202056903159594285399738 + >>> dirichlet(1, [1]) + +inf + +The alternating zeta function:: + + >>> dirichlet(1, [-1,1]); ln(2) + 0.6931471805599453094172321 + 0.6931471805599453094172321 + +The following defines the Dirichlet beta function +`\beta(s) = \sum_{k=0}^\infty \frac{(-1)^k}{(2k+1)^s}` and verifies +several values of this function:: + + >>> B = lambda s, d=0: dirichlet(s, [0, 1, 0, -1], d) + >>> B(0); 1./2 + 0.5 + 0.5 + >>> B(1); pi/4 + 0.7853981633974483096156609 + 0.7853981633974483096156609 + >>> B(2); +catalan + 0.9159655941772190150546035 + 0.9159655941772190150546035 + >>> B(2,1); diff(B, 2) + 0.08158073611659279510291217 + 0.08158073611659279510291217 + >>> B(-1,1); 2*catalan/pi + 0.5831218080616375602767689 + 0.5831218080616375602767689 + >>> B(0,1); log(gamma(0.25)**2/(2*pi*sqrt(2))) + 0.3915943927068367764719453 + 0.3915943927068367764719454 + >>> B(1,1); 0.25*pi*(euler+2*ln2+3*ln(pi)-4*ln(gamma(0.25))) + 0.1929013167969124293631898 + 0.1929013167969124293631898 + +A custom L-series of period 3:: + + >>> dirichlet(2, [2,0,1]) + 0.7059715047839078092146831 + >>> 2*nsum(lambda k: (3*k)**-2, [1,inf]) + \ + ... nsum(lambda k: (3*k+2)**-2, [0,inf]) + 0.7059715047839078092146831 + +""" + +coulombf = r""" +Calculates the regular Coulomb wave function + +.. math :: + + F_l(\eta,z) = C_l(\eta) z^{l+1} e^{-iz} \,_1F_1(l+1-i\eta, 2l+2, 2iz) + +where the normalization constant `C_l(\eta)` is as calculated by +:func:`~mpmath.coulombc`. This function solves the differential equation + +.. math :: + + f''(z) + \left(1-\frac{2\eta}{z}-\frac{l(l+1)}{z^2}\right) f(z) = 0. + +A second linearly independent solution is given by the irregular +Coulomb wave function `G_l(\eta,z)` (see :func:`~mpmath.coulombg`) +and thus the general solution is +`f(z) = C_1 F_l(\eta,z) + C_2 G_l(\eta,z)` for arbitrary +constants `C_1`, `C_2`. +Physically, the Coulomb wave functions give the radial solution +to the Schrodinger equation for a point particle in a `1/z` potential; `z` is +then the radius and `l`, `\eta` are quantum numbers. + +The Coulomb wave functions with real parameters are defined +in Abramowitz & Stegun, section 14. However, all parameters are permitted +to be complex in this implementation (see references). + +**Plots** + +.. literalinclude :: /plots/coulombf.py +.. image :: /plots/coulombf.png +.. literalinclude :: /plots/coulombf_c.py +.. image :: /plots/coulombf_c.png + +**Examples** + +Evaluation is supported for arbitrary magnitudes of `z`:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> coulombf(2, 1.5, 3.5) + 0.4080998961088761187426445 + >>> coulombf(-2, 1.5, 3.5) + 0.7103040849492536747533465 + >>> coulombf(2, 1.5, '1e-10') + 4.143324917492256448770769e-33 + >>> coulombf(2, 1.5, 1000) + 0.4482623140325567050716179 + >>> coulombf(2, 1.5, 10**10) + -0.066804196437694360046619 + +Verifying the differential equation:: + + >>> l, eta, z = 2, 3, mpf(2.75) + >>> A, B = 1, 2 + >>> f = lambda z: A*coulombf(l,eta,z) + B*coulombg(l,eta,z) + >>> chop(diff(f,z,2) + (1-2*eta/z - l*(l+1)/z**2)*f(z)) + 0.0 + +A Wronskian relation satisfied by the Coulomb wave functions:: + + >>> l = 2 + >>> eta = 1.5 + >>> F = lambda z: coulombf(l,eta,z) + >>> G = lambda z: coulombg(l,eta,z) + >>> for z in [3.5, -1, 2+3j]: + ... chop(diff(F,z)*G(z) - F(z)*diff(G,z)) + ... + 1.0 + 1.0 + 1.0 + +Another Wronskian relation:: + + >>> F = coulombf + >>> G = coulombg + >>> for z in [3.5, -1, 2+3j]: + ... chop(F(l-1,eta,z)*G(l,eta,z)-F(l,eta,z)*G(l-1,eta,z) - l/sqrt(l**2+eta**2)) + ... + 0.0 + 0.0 + 0.0 + +An integral identity connecting the regular and irregular wave functions:: + + >>> l, eta, z = 4+j, 2-j, 5+2j + >>> coulombf(l,eta,z) + j*coulombg(l,eta,z) + (0.7997977752284033239714479 + 0.9294486669502295512503127j) + >>> g = lambda t: exp(-t)*t**(l-j*eta)*(t+2*j*z)**(l+j*eta) + >>> j*exp(-j*z)*z**(-l)/fac(2*l+1)/coulombc(l,eta)*quad(g, [0,inf]) + (0.7997977752284033239714479 + 0.9294486669502295512503127j) + +Some test case with complex parameters, taken from Michel [2]:: + + >>> mp.dps = 15 + >>> coulombf(1+0.1j, 50+50j, 100.156) + (-1.02107292320897e+15 - 2.83675545731519e+15j) + >>> coulombg(1+0.1j, 50+50j, 100.156) + (2.83675545731519e+15 - 1.02107292320897e+15j) + >>> coulombf(1e-5j, 10+1e-5j, 0.1+1e-6j) + (4.30566371247811e-14 - 9.03347835361657e-19j) + >>> coulombg(1e-5j, 10+1e-5j, 0.1+1e-6j) + (778709182061.134 + 18418936.2660553j) + +The following reproduces a table in Abramowitz & Stegun, at twice +the precision:: + + >>> mp.dps = 10 + >>> eta = 2; z = 5 + >>> for l in [5, 4, 3, 2, 1, 0]: + ... print("%s %s %s" % (l, coulombf(l,eta,z), + ... diff(lambda z: coulombf(l,eta,z), z))) + ... + 5 0.09079533488 0.1042553261 + 4 0.2148205331 0.2029591779 + 3 0.4313159311 0.320534053 + 2 0.7212774133 0.3952408216 + 1 0.9935056752 0.3708676452 + 0 1.143337392 0.2937960375 + +**References** + +1. I.J. Thompson & A.R. Barnett, "Coulomb and Bessel Functions of Complex + Arguments and Order", J. Comp. Phys., vol 64, no. 2, June 1986. + +2. N. Michel, "Precise Coulomb wave functions for a wide range of + complex `l`, `\eta` and `z`", http://arxiv.org/abs/physics/0702051v1 + +""" + +coulombg = r""" +Calculates the irregular Coulomb wave function + +.. math :: + + G_l(\eta,z) = \frac{F_l(\eta,z) \cos(\chi) - F_{-l-1}(\eta,z)}{\sin(\chi)} + +where `\chi = \sigma_l - \sigma_{-l-1} - (l+1/2) \pi` +and `\sigma_l(\eta) = (\ln \Gamma(1+l+i\eta)-\ln \Gamma(1+l-i\eta))/(2i)`. + +See :func:`~mpmath.coulombf` for additional information. + +**Plots** + +.. literalinclude :: /plots/coulombg.py +.. image :: /plots/coulombg.png +.. literalinclude :: /plots/coulombg_c.py +.. image :: /plots/coulombg_c.png + +**Examples** + +Evaluation is supported for arbitrary magnitudes of `z`:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> coulombg(-2, 1.5, 3.5) + 1.380011900612186346255524 + >>> coulombg(2, 1.5, 3.5) + 1.919153700722748795245926 + >>> coulombg(-2, 1.5, '1e-10') + 201126715824.7329115106793 + >>> coulombg(-2, 1.5, 1000) + 0.1802071520691149410425512 + >>> coulombg(-2, 1.5, 10**10) + 0.652103020061678070929794 + +The following reproduces a table in Abramowitz & Stegun, +at twice the precision:: + + >>> mp.dps = 10 + >>> eta = 2; z = 5 + >>> for l in [1, 2, 3, 4, 5]: + ... print("%s %s %s" % (l, coulombg(l,eta,z), + ... -diff(lambda z: coulombg(l,eta,z), z))) + ... + 1 1.08148276 0.6028279961 + 2 1.496877075 0.5661803178 + 3 2.048694714 0.7959909551 + 4 3.09408669 1.731802374 + 5 5.629840456 4.549343289 + +Evaluation close to the singularity at `z = 0`:: + + >>> mp.dps = 15 + >>> coulombg(0,10,1) + 3088184933.67358 + >>> coulombg(0,10,'1e-10') + 5554866000719.8 + >>> coulombg(0,10,'1e-100') + 5554866221524.1 + +Evaluation with a half-integer value for `l`:: + + >>> coulombg(1.5, 1, 10) + 0.852320038297334 +""" + +coulombc = r""" +Gives the normalizing Gamow constant for Coulomb wave functions, + +.. math :: + + C_l(\eta) = 2^l \exp\left(-\pi \eta/2 + [\ln \Gamma(1+l+i\eta) + + \ln \Gamma(1+l-i\eta)]/2 - \ln \Gamma(2l+2)\right), + +where the log gamma function with continuous imaginary part +away from the negative half axis (see :func:`~mpmath.loggamma`) is implied. + +This function is used internally for the calculation of +Coulomb wave functions, and automatically cached to make multiple +evaluations with fixed `l`, `\eta` fast. +""" + +ellipfun = r""" +Computes any of the Jacobi elliptic functions, defined +in terms of Jacobi theta functions as + +.. math :: + + \mathrm{sn}(u,m) = \frac{\vartheta_3(0,q)}{\vartheta_2(0,q)} + \frac{\vartheta_1(t,q)}{\vartheta_4(t,q)} + + \mathrm{cn}(u,m) = \frac{\vartheta_4(0,q)}{\vartheta_2(0,q)} + \frac{\vartheta_2(t,q)}{\vartheta_4(t,q)} + + \mathrm{dn}(u,m) = \frac{\vartheta_4(0,q)}{\vartheta_3(0,q)} + \frac{\vartheta_3(t,q)}{\vartheta_4(t,q)}, + +or more generally computes a ratio of two such functions. Here +`t = u/\vartheta_3(0,q)^2`, and `q = q(m)` denotes the nome (see +:func:`~mpmath.nome`). Optionally, you can specify the nome directly +instead of `m` by passing ``q=``, or you can directly +specify the elliptic parameter `k` with ``k=``. + +The first argument should be a two-character string specifying the +function using any combination of ``'s'``, ``'c'``, ``'d'``, ``'n'``. These +letters respectively denote the basic functions +`\mathrm{sn}(u,m)`, `\mathrm{cn}(u,m)`, `\mathrm{dn}(u,m)`, and `1`. +The identifier specifies the ratio of two such functions. +For example, ``'ns'`` identifies the function + +.. math :: + + \mathrm{ns}(u,m) = \frac{1}{\mathrm{sn}(u,m)} + +and ``'cd'`` identifies the function + +.. math :: + + \mathrm{cd}(u,m) = \frac{\mathrm{cn}(u,m)}{\mathrm{dn}(u,m)}. + +If called with only the first argument, a function object +evaluating the chosen function for given arguments is returned. + +**Examples** + +Basic evaluation:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> ellipfun('cd', 3.5, 0.5) + -0.9891101840595543931308394 + >>> ellipfun('cd', 3.5, q=0.25) + 0.07111979240214668158441418 + +The sn-function is doubly periodic in the complex plane with periods +`4 K(m)` and `2 i K(1-m)` (see :func:`~mpmath.ellipk`):: + + >>> sn = ellipfun('sn') + >>> sn(2, 0.25) + 0.9628981775982774425751399 + >>> sn(2+4*ellipk(0.25), 0.25) + 0.9628981775982774425751399 + >>> chop(sn(2+2*j*ellipk(1-0.25), 0.25)) + 0.9628981775982774425751399 + +The cn-function is doubly periodic with periods `4 K(m)` and `2 K(m) + 2 i K(1-m)`:: + + >>> cn = ellipfun('cn') + >>> cn(2, 0.25) + -0.2698649654510865792581416 + >>> cn(2+4*ellipk(0.25), 0.25) + -0.2698649654510865792581416 + >>> chop(cn(2+2*ellipk(0.25)+2*j*ellipk(1-0.25), 0.25)) + -0.2698649654510865792581416 + +The dn-function is doubly periodic with periods `2 K(m)` and `4 i K(1-m)`:: + + >>> dn = ellipfun('dn') + >>> dn(2, 0.25) + 0.8764740583123262286931578 + >>> dn(2+2*ellipk(0.25), 0.25) + 0.8764740583123262286931578 + >>> chop(dn(2+4*j*ellipk(1-0.25), 0.25)) + 0.8764740583123262286931578 + +""" + + +jtheta = r""" +Computes the Jacobi theta function `\vartheta_n(z, q)`, where +`n = 1, 2, 3, 4`, defined by the infinite series: + +.. math :: + + \vartheta_1(z,q) = 2 q^{1/4} \sum_{n=0}^{\infty} + (-1)^n q^{n^2+n\,} \sin((2n+1)z) + + \vartheta_2(z,q) = 2 q^{1/4} \sum_{n=0}^{\infty} + q^{n^{2\,} + n} \cos((2n+1)z) + + \vartheta_3(z,q) = 1 + 2 \sum_{n=1}^{\infty} + q^{n^2\,} \cos(2 n z) + + \vartheta_4(z,q) = 1 + 2 \sum_{n=1}^{\infty} + (-q)^{n^2\,} \cos(2 n z) + +The theta functions are functions of two variables: + +* `z` is the *argument*, an arbitrary real or complex number + +* `q` is the *nome*, which must be a real or complex number + in the unit disk (i.e. `|q| < 1`). For `|q| \ll 1`, the + series converge very quickly, so the Jacobi theta functions + can efficiently be evaluated to high precision. + +The compact notations `\vartheta_n(q) = \vartheta_n(0,q)` +and `\vartheta_n = \vartheta_n(0,q)` are also frequently +encountered. Finally, Jacobi theta functions are frequently +considered as functions of the half-period ratio `\tau` +and then usually denoted by `\vartheta_n(z|\tau)`. + +Optionally, ``jtheta(n, z, q, derivative=d)`` with `d > 0` computes +a `d`-th derivative with respect to `z`. + +**Examples and basic properties** + +Considered as functions of `z`, the Jacobi theta functions may be +viewed as generalizations of the ordinary trigonometric functions +cos and sin. They are periodic functions:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> jtheta(1, 0.25, '0.2') + 0.2945120798627300045053104 + >>> jtheta(1, 0.25 + 2*pi, '0.2') + 0.2945120798627300045053104 + +Indeed, the series defining the theta functions are essentially +trigonometric Fourier series. The coefficients can be retrieved +using :func:`~mpmath.fourier`:: + + >>> mp.dps = 10 + >>> nprint(fourier(lambda x: jtheta(2, x, 0.5), [-pi, pi], 4)) + ([0.0, 1.68179, 0.0, 0.420448, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0]) + +The Jacobi theta functions are also so-called quasiperiodic +functions of `z` and `\tau`, meaning that for fixed `\tau`, +`\vartheta_n(z, q)` and `\vartheta_n(z+\pi \tau, q)` are the same +except for an exponential factor:: + + >>> mp.dps = 25 + >>> tau = 3*j/10 + >>> q = exp(pi*j*tau) + >>> z = 10 + >>> jtheta(4, z+tau*pi, q) + (-0.682420280786034687520568 + 1.526683999721399103332021j) + >>> -exp(-2*j*z)/q * jtheta(4, z, q) + (-0.682420280786034687520568 + 1.526683999721399103332021j) + +The Jacobi theta functions satisfy a huge number of other +functional equations, such as the following identity (valid for +any `q`):: + + >>> q = mpf(3)/10 + >>> jtheta(3,0,q)**4 + 6.823744089352763305137427 + >>> jtheta(2,0,q)**4 + jtheta(4,0,q)**4 + 6.823744089352763305137427 + +Extensive listings of identities satisfied by the Jacobi theta +functions can be found in standard reference works. + +The Jacobi theta functions are related to the gamma function +for special arguments:: + + >>> jtheta(3, 0, exp(-pi)) + 1.086434811213308014575316 + >>> pi**(1/4.) / gamma(3/4.) + 1.086434811213308014575316 + +:func:`~mpmath.jtheta` supports arbitrary precision evaluation and complex +arguments:: + + >>> mp.dps = 50 + >>> jtheta(4, sqrt(2), 0.5) + 2.0549510717571539127004115835148878097035750653737 + >>> mp.dps = 25 + >>> jtheta(4, 1+2j, (1+j)/5) + (7.180331760146805926356634 - 1.634292858119162417301683j) + +Evaluation of derivatives:: + + >>> mp.dps = 25 + >>> jtheta(1, 7, 0.25, 1); diff(lambda z: jtheta(1, z, 0.25), 7) + 1.209857192844475388637236 + 1.209857192844475388637236 + >>> jtheta(1, 7, 0.25, 2); diff(lambda z: jtheta(1, z, 0.25), 7, 2) + -0.2598718791650217206533052 + -0.2598718791650217206533052 + >>> jtheta(2, 7, 0.25, 1); diff(lambda z: jtheta(2, z, 0.25), 7) + -1.150231437070259644461474 + -1.150231437070259644461474 + >>> jtheta(2, 7, 0.25, 2); diff(lambda z: jtheta(2, z, 0.25), 7, 2) + -0.6226636990043777445898114 + -0.6226636990043777445898114 + >>> jtheta(3, 7, 0.25, 1); diff(lambda z: jtheta(3, z, 0.25), 7) + -0.9990312046096634316587882 + -0.9990312046096634316587882 + >>> jtheta(3, 7, 0.25, 2); diff(lambda z: jtheta(3, z, 0.25), 7, 2) + -0.1530388693066334936151174 + -0.1530388693066334936151174 + >>> jtheta(4, 7, 0.25, 1); diff(lambda z: jtheta(4, z, 0.25), 7) + 0.9820995967262793943571139 + 0.9820995967262793943571139 + >>> jtheta(4, 7, 0.25, 2); diff(lambda z: jtheta(4, z, 0.25), 7, 2) + 0.3936902850291437081667755 + 0.3936902850291437081667755 + +**Possible issues** + +For `|q| \ge 1` or `\Im(\tau) \le 0`, :func:`~mpmath.jtheta` raises +``ValueError``. This exception is also raised for `|q|` extremely +close to 1 (or equivalently `\tau` very close to 0), since the +series would converge too slowly:: + + >>> jtheta(1, 10, 0.99999999 * exp(0.5*j)) + Traceback (most recent call last): + ... + ValueError: abs(q) > THETA_Q_LIM = 1.000000 + +""" + +eulernum = r""" +Gives the `n`-th Euler number, defined as the `n`-th derivative of +`\mathrm{sech}(t) = 1/\cosh(t)` evaluated at `t = 0`. Equivalently, the +Euler numbers give the coefficients of the Taylor series + +.. math :: + + \mathrm{sech}(t) = \sum_{n=0}^{\infty} \frac{E_n}{n!} t^n. + +The Euler numbers are closely related to Bernoulli numbers +and Bernoulli polynomials. They can also be evaluated in terms of +Euler polynomials (see :func:`~mpmath.eulerpoly`) as `E_n = 2^n E_n(1/2)`. + +**Examples** + +Computing the first few Euler numbers and verifying that they +agree with the Taylor series:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> [eulernum(n) for n in range(11)] + [1.0, 0.0, -1.0, 0.0, 5.0, 0.0, -61.0, 0.0, 1385.0, 0.0, -50521.0] + >>> chop(diffs(sech, 0, 10)) + [1.0, 0.0, -1.0, 0.0, 5.0, 0.0, -61.0, 0.0, 1385.0, 0.0, -50521.0] + +Euler numbers grow very rapidly. :func:`~mpmath.eulernum` efficiently +computes numerical approximations for large indices:: + + >>> eulernum(50) + -6.053285248188621896314384e+54 + >>> eulernum(1000) + 3.887561841253070615257336e+2371 + >>> eulernum(10**20) + 4.346791453661149089338186e+1936958564106659551331 + +Comparing with an asymptotic formula for the Euler numbers:: + + >>> n = 10**5 + >>> (-1)**(n//2) * 8 * sqrt(n/(2*pi)) * (2*n/(pi*e))**n + 3.69919063017432362805663e+436961 + >>> eulernum(n) + 3.699193712834466537941283e+436961 + +Pass ``exact=True`` to obtain exact values of Euler numbers as integers:: + + >>> print(eulernum(50, exact=True)) + -6053285248188621896314383785111649088103498225146815121 + >>> print(eulernum(200, exact=True) % 10**10) + 1925859625 + >>> eulernum(1001, exact=True) + 0 +""" + +eulerpoly = r""" +Evaluates the Euler polynomial `E_n(z)`, defined by the generating function +representation + +.. math :: + + \frac{2e^{zt}}{e^t+1} = \sum_{n=0}^\infty E_n(z) \frac{t^n}{n!}. + +The Euler polynomials may also be represented in terms of +Bernoulli polynomials (see :func:`~mpmath.bernpoly`) using various formulas, for +example + +.. math :: + + E_n(z) = \frac{2}{n+1} \left( + B_n(z)-2^{n+1}B_n\left(\frac{z}{2}\right) + \right). + +Special values include the Euler numbers `E_n = 2^n E_n(1/2)` (see +:func:`~mpmath.eulernum`). + +**Examples** + +Computing the coefficients of the first few Euler polynomials:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> for n in range(6): + ... chop(taylor(lambda z: eulerpoly(n,z), 0, n)) + ... + [1.0] + [-0.5, 1.0] + [0.0, -1.0, 1.0] + [0.25, 0.0, -1.5, 1.0] + [0.0, 1.0, 0.0, -2.0, 1.0] + [-0.5, 0.0, 2.5, 0.0, -2.5, 1.0] + +Evaluation for arbitrary `z`:: + + >>> eulerpoly(2,3) + 6.0 + >>> eulerpoly(5,4) + 423.5 + >>> eulerpoly(35, 11111111112) + 3.994957561486776072734601e+351 + >>> eulerpoly(4, 10+20j) + (-47990.0 - 235980.0j) + >>> eulerpoly(2, '-3.5e-5') + 0.000035001225 + >>> eulerpoly(3, 0.5) + 0.0 + >>> eulerpoly(55, -10**80) + -1.0e+4400 + >>> eulerpoly(5, -inf) + -inf + >>> eulerpoly(6, -inf) + +inf + +Computing Euler numbers:: + + >>> 2**26 * eulerpoly(26,0.5) + -4087072509293123892361.0 + >>> eulernum(26) + -4087072509293123892361.0 + +Evaluation is accurate for large `n` and small `z`:: + + >>> eulerpoly(100, 0.5) + 2.29047999988194114177943e+108 + >>> eulerpoly(1000, 10.5) + 3.628120031122876847764566e+2070 + >>> eulerpoly(10000, 10.5) + 1.149364285543783412210773e+30688 +""" + +spherharm = r""" +Evaluates the spherical harmonic `Y_l^m(\theta,\phi)`, + +.. math :: + + Y_l^m(\theta,\phi) = \sqrt{\frac{2l+1}{4\pi}\frac{(l-m)!}{(l+m)!}} + P_l^m(\cos \theta) e^{i m \phi} + +where `P_l^m` is an associated Legendre function (see :func:`~mpmath.legenp`). + +Here `\theta \in [0, \pi]` denotes the polar coordinate (ranging +from the north pole to the south pole) and `\phi \in [0, 2 \pi]` denotes the +azimuthal coordinate on a sphere. Care should be used since many different +conventions for spherical coordinate variables are used. + +Usually spherical harmonics are considered for `l \in \mathbb{N}`, +`m \in \mathbb{Z}`, `|m| \le l`. More generally, `l,m,\theta,\phi` +are permitted to be complex numbers. + +.. note :: + + :func:`~mpmath.spherharm` returns a complex number, even if the value is + purely real. + +**Plots** + +.. literalinclude :: /plots/spherharm40.py + +`Y_{4,0}`: + +.. image :: /plots/spherharm40.png + +`Y_{4,1}`: + +.. image :: /plots/spherharm41.png + +`Y_{4,2}`: + +.. image :: /plots/spherharm42.png + +`Y_{4,3}`: + +.. image :: /plots/spherharm43.png + +`Y_{4,4}`: + +.. image :: /plots/spherharm44.png + +**Examples** + +Some low-order spherical harmonics with reference values:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> theta = pi/4 + >>> phi = pi/3 + >>> spherharm(0,0,theta,phi); 0.5*sqrt(1/pi)*expj(0) + (0.2820947917738781434740397 + 0.0j) + (0.2820947917738781434740397 + 0.0j) + >>> spherharm(1,-1,theta,phi); 0.5*sqrt(3/(2*pi))*expj(-phi)*sin(theta) + (0.1221506279757299803965962 - 0.2115710938304086076055298j) + (0.1221506279757299803965962 - 0.2115710938304086076055298j) + >>> spherharm(1,0,theta,phi); 0.5*sqrt(3/pi)*cos(theta)*expj(0) + (0.3454941494713354792652446 + 0.0j) + (0.3454941494713354792652446 + 0.0j) + >>> spherharm(1,1,theta,phi); -0.5*sqrt(3/(2*pi))*expj(phi)*sin(theta) + (-0.1221506279757299803965962 - 0.2115710938304086076055298j) + (-0.1221506279757299803965962 - 0.2115710938304086076055298j) + +With the normalization convention used, the spherical harmonics are orthonormal +on the unit sphere:: + + >>> sphere = [0,pi], [0,2*pi] + >>> dS = lambda t,p: fp.sin(t) # differential element + >>> Y1 = lambda t,p: fp.spherharm(l1,m1,t,p) + >>> Y2 = lambda t,p: fp.conj(fp.spherharm(l2,m2,t,p)) + >>> l1 = l2 = 3; m1 = m2 = 2 + >>> fp.chop(fp.quad(lambda t,p: Y1(t,p)*Y2(t,p)*dS(t,p), *sphere)) + 1.0000000000000007 + >>> m2 = 1 # m1 != m2 + >>> print(fp.chop(fp.quad(lambda t,p: Y1(t,p)*Y2(t,p)*dS(t,p), *sphere))) + 0.0 + +Evaluation is accurate for large orders:: + + >>> spherharm(1000,750,0.5,0.25) + (3.776445785304252879026585e-102 - 5.82441278771834794493484e-102j) + +Evaluation works with complex parameter values:: + + >>> spherharm(1+j, 2j, 2+3j, -0.5j) + (64.44922331113759992154992 + 1981.693919841408089681743j) +""" + +scorergi = r""" +Evaluates the Scorer function + +.. math :: + + \operatorname{Gi}(z) = + \operatorname{Ai}(z) \int_0^z \operatorname{Bi}(t) dt + + \operatorname{Bi}(z) \int_z^{\infty} \operatorname{Ai}(t) dt + +which gives a particular solution to the inhomogeneous Airy +differential equation `f''(z) - z f(z) = 1/\pi`. Another +particular solution is given by the Scorer Hi-function +(:func:`~mpmath.scorerhi`). The two functions are related as +`\operatorname{Gi}(z) + \operatorname{Hi}(z) = \operatorname{Bi}(z)`. + +**Plots** + +.. literalinclude :: /plots/gi.py +.. image :: /plots/gi.png +.. literalinclude :: /plots/gi_c.py +.. image :: /plots/gi_c.png + +**Examples** + +Some values and limits:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> scorergi(0); 1/(power(3,'7/6')*gamma('2/3')) + 0.2049755424820002450503075 + 0.2049755424820002450503075 + >>> diff(scorergi, 0); 1/(power(3,'5/6')*gamma('1/3')) + 0.1494294524512754526382746 + 0.1494294524512754526382746 + >>> scorergi(+inf); scorergi(-inf) + 0.0 + 0.0 + >>> scorergi(1) + 0.2352184398104379375986902 + >>> scorergi(-1) + -0.1166722172960152826494198 + +Evaluation for large arguments:: + + >>> scorergi(10) + 0.03189600510067958798062034 + >>> scorergi(100) + 0.003183105228162961476590531 + >>> scorergi(1000000) + 0.0000003183098861837906721743873 + >>> 1/(pi*1000000) + 0.0000003183098861837906715377675 + >>> scorergi(-1000) + -0.08358288400262780392338014 + >>> scorergi(-100000) + 0.02886866118619660226809581 + >>> scorergi(50+10j) + (0.0061214102799778578790984 - 0.001224335676457532180747917j) + >>> scorergi(-50-10j) + (5.236047850352252236372551e+29 - 3.08254224233701381482228e+29j) + >>> scorergi(100000j) + (-8.806659285336231052679025e+6474077 + 8.684731303500835514850962e+6474077j) + +Verifying the connection between Gi and Hi:: + + >>> z = 0.25 + >>> scorergi(z) + scorerhi(z) + 0.7287469039362150078694543 + >>> airybi(z) + 0.7287469039362150078694543 + +Verifying the differential equation:: + + >>> for z in [-3.4, 0, 2.5, 1+2j]: + ... chop(diff(scorergi,z,2) - z*scorergi(z)) + ... + -0.3183098861837906715377675 + -0.3183098861837906715377675 + -0.3183098861837906715377675 + -0.3183098861837906715377675 + +Verifying the integral representation:: + + >>> z = 0.5 + >>> scorergi(z) + 0.2447210432765581976910539 + >>> Ai,Bi = airyai,airybi + >>> Bi(z)*(Ai(inf,-1)-Ai(z,-1)) + Ai(z)*(Bi(z,-1)-Bi(0,-1)) + 0.2447210432765581976910539 + +**References** + +1. [DLMF]_ section 9.12: Scorer Functions + +""" + +scorerhi = r""" +Evaluates the second Scorer function + +.. math :: + + \operatorname{Hi}(z) = + \operatorname{Bi}(z) \int_{-\infty}^z \operatorname{Ai}(t) dt - + \operatorname{Ai}(z) \int_{-\infty}^z \operatorname{Bi}(t) dt + +which gives a particular solution to the inhomogeneous Airy +differential equation `f''(z) - z f(z) = 1/\pi`. See also +:func:`~mpmath.scorergi`. + +**Plots** + +.. literalinclude :: /plots/hi.py +.. image :: /plots/hi.png +.. literalinclude :: /plots/hi_c.py +.. image :: /plots/hi_c.png + +**Examples** + +Some values and limits:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> scorerhi(0); 2/(power(3,'7/6')*gamma('2/3')) + 0.4099510849640004901006149 + 0.4099510849640004901006149 + >>> diff(scorerhi,0); 2/(power(3,'5/6')*gamma('1/3')) + 0.2988589049025509052765491 + 0.2988589049025509052765491 + >>> scorerhi(+inf); scorerhi(-inf) + +inf + 0.0 + >>> scorerhi(1) + 0.9722051551424333218376886 + >>> scorerhi(-1) + 0.2206696067929598945381098 + +Evaluation for large arguments:: + + >>> scorerhi(10) + 455641153.5163291358991077 + >>> scorerhi(100) + 6.041223996670201399005265e+288 + >>> scorerhi(1000000) + 7.138269638197858094311122e+289529652 + >>> scorerhi(-10) + 0.0317685352825022727415011 + >>> scorerhi(-100) + 0.003183092495767499864680483 + >>> scorerhi(100j) + (-6.366197716545672122983857e-9 + 0.003183098861710582761688475j) + >>> scorerhi(50+50j) + (-5.322076267321435669290334e+63 + 1.478450291165243789749427e+65j) + >>> scorerhi(-1000-1000j) + (0.0001591549432510502796565538 - 0.000159154943091895334973109j) + +Verifying the differential equation:: + + >>> for z in [-3.4, 0, 2, 1+2j]: + ... chop(diff(scorerhi,z,2) - z*scorerhi(z)) + ... + 0.3183098861837906715377675 + 0.3183098861837906715377675 + 0.3183098861837906715377675 + 0.3183098861837906715377675 + +Verifying the integral representation:: + + >>> z = 0.5 + >>> scorerhi(z) + 0.6095559998265972956089949 + >>> Ai,Bi = airyai,airybi + >>> Bi(z)*(Ai(z,-1)-Ai(-inf,-1)) - Ai(z)*(Bi(z,-1)-Bi(-inf,-1)) + 0.6095559998265972956089949 + +""" + + +stirling1 = r""" +Gives the Stirling number of the first kind `s(n,k)`, defined by + +.. math :: + + x(x-1)(x-2)\cdots(x-n+1) = \sum_{k=0}^n s(n,k) x^k. + +The value is computed using an integer recurrence. The implementation +is not optimized for approximating large values quickly. + +**Examples** + +Comparing with the generating function:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> taylor(lambda x: ff(x, 5), 0, 5) + [0.0, 24.0, -50.0, 35.0, -10.0, 1.0] + >>> [stirling1(5, k) for k in range(6)] + [0.0, 24.0, -50.0, 35.0, -10.0, 1.0] + +Recurrence relation:: + + >>> n, k = 5, 3 + >>> stirling1(n+1,k) + n*stirling1(n,k) - stirling1(n,k-1) + 0.0 + +The matrices of Stirling numbers of first and second kind are inverses +of each other:: + + >>> A = matrix(5, 5); B = matrix(5, 5) + >>> for n in range(5): + ... for k in range(5): + ... A[n,k] = stirling1(n,k) + ... B[n,k] = stirling2(n,k) + ... + >>> A * B + [1.0 0.0 0.0 0.0 0.0] + [0.0 1.0 0.0 0.0 0.0] + [0.0 0.0 1.0 0.0 0.0] + [0.0 0.0 0.0 1.0 0.0] + [0.0 0.0 0.0 0.0 1.0] + +Pass ``exact=True`` to obtain exact values of Stirling numbers as integers:: + + >>> stirling1(42, 5) + -2.864498971768501633736628e+50 + >>> print(stirling1(42, 5, exact=True)) + -286449897176850163373662803014001546235808317440000 + +""" + +stirling2 = r""" +Gives the Stirling number of the second kind `S(n,k)`, defined by + +.. math :: + + x^n = \sum_{k=0}^n S(n,k) x(x-1)(x-2)\cdots(x-k+1) + +The value is computed using integer arithmetic to evaluate a power sum. +The implementation is not optimized for approximating large values quickly. + +**Examples** + +Comparing with the generating function:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> taylor(lambda x: sum(stirling2(5,k) * ff(x,k) for k in range(6)), 0, 5) + [0.0, 0.0, 0.0, 0.0, 0.0, 1.0] + +Recurrence relation:: + + >>> n, k = 5, 3 + >>> stirling2(n+1,k) - k*stirling2(n,k) - stirling2(n,k-1) + 0.0 + +Pass ``exact=True`` to obtain exact values of Stirling numbers as integers:: + + >>> stirling2(52, 10) + 2.641822121003543906807485e+45 + >>> print(stirling2(52, 10, exact=True)) + 2641822121003543906807485307053638921722527655 + + +""" + +squarew = r""" +Computes the square wave function using the definition: + +.. math:: + x(t) = A(-1)^{\left\lfloor{2t / P}\right\rfloor} + +where `P` is the period of the wave and `A` is the amplitude. + +**Examples** + +Square wave with period = 2, amplitude = 1 :: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> squarew(0,1,2) + 1.0 + >>> squarew(0.5,1,2) + 1.0 + >>> squarew(1,1,2) + -1.0 + >>> squarew(1.5,1,2) + -1.0 + >>> squarew(2,1,2) + 1.0 +""" + +trianglew = r""" +Computes the triangle wave function using the definition: + +.. math:: + x(t) = 2A\left(\frac{1}{2}-\left|1-2 \operatorname{frac}\left(\frac{x}{P}+\frac{1}{4}\right)\right|\right) + +where :math:`\operatorname{frac}\left(\frac{t}{T}\right) = \frac{t}{T}-\left\lfloor{\frac{t}{T}}\right\rfloor` +, `P` is the period of the wave, and `A` is the amplitude. + +**Examples** + +Triangle wave with period = 2, amplitude = 1 :: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> trianglew(0,1,2) + 0.0 + >>> trianglew(0.25,1,2) + 0.5 + >>> trianglew(0.5,1,2) + 1.0 + >>> trianglew(1,1,2) + 0.0 + >>> trianglew(1.5,1,2) + -1.0 + >>> trianglew(2,1,2) + 0.0 +""" + +sawtoothw = r""" +Computes the sawtooth wave function using the definition: + +.. math:: + x(t) = A\operatorname{frac}\left(\frac{t}{T}\right) + +where :math:`\operatorname{frac}\left(\frac{t}{T}\right) = \frac{t}{T}-\left\lfloor{\frac{t}{T}}\right\rfloor`, +`P` is the period of the wave, and `A` is the amplitude. + +**Examples** + +Sawtooth wave with period = 2, amplitude = 1 :: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> sawtoothw(0,1,2) + 0.0 + >>> sawtoothw(0.5,1,2) + 0.25 + >>> sawtoothw(1,1,2) + 0.5 + >>> sawtoothw(1.5,1,2) + 0.75 + >>> sawtoothw(2,1,2) + 0.0 +""" + +unit_triangle = r""" +Computes the unit triangle using the definition: + +.. math:: + x(t) = A(-\left| t \right| + 1) + +where `A` is the amplitude. + +**Examples** + +Unit triangle with amplitude = 1 :: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> unit_triangle(-1,1) + 0.0 + >>> unit_triangle(-0.5,1) + 0.5 + >>> unit_triangle(0,1) + 1.0 + >>> unit_triangle(0.5,1) + 0.5 + >>> unit_triangle(1,1) + 0.0 +""" + +sigmoid = r""" +Computes the sigmoid function using the definition: + +.. math:: + x(t) = \frac{A}{1 + e^{-t}} + +where `A` is the amplitude. + +**Examples** + +Sigmoid function with amplitude = 1 :: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> sigmoid(-1,1) + 0.2689414213699951207488408 + >>> sigmoid(-0.5,1) + 0.3775406687981454353610994 + >>> sigmoid(0,1) + 0.5 + >>> sigmoid(0.5,1) + 0.6224593312018545646389006 + >>> sigmoid(1,1) + 0.7310585786300048792511592 + +""" diff --git a/venv/lib/python3.12/site-packages/mpmath/functions/__init__.py b/venv/lib/python3.12/site-packages/mpmath/functions/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..5896ed0579eceab086dc5c67eaa649b6061a53dc --- /dev/null +++ b/venv/lib/python3.12/site-packages/mpmath/functions/__init__.py @@ -0,0 +1,14 @@ +from . import functions +# Hack to update methods +from . import factorials +from . import hypergeometric +from . import expintegrals +from . import bessel +from . import orthogonal +from . import theta +from . import elliptic +from . import signals +from . import zeta +from . import rszeta +from . import zetazeros +from . import qfunctions diff --git a/venv/lib/python3.12/site-packages/mpmath/functions/__pycache__/__init__.cpython-312.pyc b/venv/lib/python3.12/site-packages/mpmath/functions/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 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a/venv/lib/python3.12/site-packages/mpmath/functions/bessel.py b/venv/lib/python3.12/site-packages/mpmath/functions/bessel.py new file mode 100644 index 0000000000000000000000000000000000000000..8b41d87bb0118de61d5561433dabcb181f872f84 --- /dev/null +++ b/venv/lib/python3.12/site-packages/mpmath/functions/bessel.py @@ -0,0 +1,1108 @@ +from .functions import defun, defun_wrapped + +@defun +def j0(ctx, x): + """Computes the Bessel function `J_0(x)`. See :func:`~mpmath.besselj`.""" + return ctx.besselj(0, x) + +@defun +def j1(ctx, x): + """Computes the Bessel function `J_1(x)`. See :func:`~mpmath.besselj`.""" + return ctx.besselj(1, x) + +@defun +def besselj(ctx, n, z, derivative=0, **kwargs): + if type(n) is int: + n_isint = True + else: + n = ctx.convert(n) + n_isint = ctx.isint(n) + if n_isint: + n = int(ctx._re(n)) + if n_isint and n < 0: + return (-1)**n * ctx.besselj(-n, z, derivative, **kwargs) + z = ctx.convert(z) + M = ctx.mag(z) + if derivative: + d = ctx.convert(derivative) + # TODO: the integer special-casing shouldn't be necessary. + # However, the hypergeometric series gets inaccurate for large d + # because of inaccurate pole cancellation at a pole far from + # zero (needs to be fixed in hypercomb or hypsum) + if ctx.isint(d) and d >= 0: + d = int(d) + orig = ctx.prec + try: + ctx.prec += 15 + v = ctx.fsum((-1)**k * ctx.binomial(d,k) * ctx.besselj(2*k+n-d,z) + for k in range(d+1)) + finally: + ctx.prec = orig + v *= ctx.mpf(2)**(-d) + else: + def h(n,d): + r = ctx.fmul(ctx.fmul(z, z, prec=ctx.prec+M), -0.25, exact=True) + B = [0.5*(n-d+1), 0.5*(n-d+2)] + T = [([2,ctx.pi,z],[d-2*n,0.5,n-d],[],B,[(n+1)*0.5,(n+2)*0.5],B+[n+1],r)] + return T + v = ctx.hypercomb(h, [n,d], **kwargs) + else: + # Fast case: J_n(x), n int, appropriate magnitude for fixed-point calculation + if (not derivative) and n_isint and abs(M) < 10 and abs(n) < 20: + try: + return ctx._besselj(n, z) + except NotImplementedError: + pass + if not z: + if not n: + v = ctx.one + n+z + elif ctx.re(n) > 0: + v = n*z + else: + v = ctx.inf + z + n + else: + #v = 0 + orig = ctx.prec + try: + # XXX: workaround for accuracy in low level hypergeometric series + # when alternating, large arguments + ctx.prec += min(3*abs(M), ctx.prec) + w = ctx.fmul(z, 0.5, exact=True) + def h(n): + r = ctx.fneg(ctx.fmul(w, w, prec=max(0,ctx.prec+M)), exact=True) + return [([w], [n], [], [n+1], [], [n+1], r)] + v = ctx.hypercomb(h, [n], **kwargs) + finally: + ctx.prec = orig + v = +v + return v + +@defun +def besseli(ctx, n, z, derivative=0, **kwargs): + n = ctx.convert(n) + z = ctx.convert(z) + if not z: + if derivative: + raise ValueError + if not n: + # I(0,0) = 1 + return 1+n+z + if ctx.isint(n): + return 0*(n+z) + r = ctx.re(n) + if r == 0: + return ctx.nan*(n+z) + elif r > 0: + return 0*(n+z) + else: + return ctx.inf+(n+z) + M = ctx.mag(z) + if derivative: + d = ctx.convert(derivative) + def h(n,d): + r = ctx.fmul(ctx.fmul(z, z, prec=ctx.prec+M), 0.25, exact=True) + B = [0.5*(n-d+1), 0.5*(n-d+2), n+1] + T = [([2,ctx.pi,z],[d-2*n,0.5,n-d],[n+1],B,[(n+1)*0.5,(n+2)*0.5],B,r)] + return T + v = ctx.hypercomb(h, [n,d], **kwargs) + else: + def h(n): + w = ctx.fmul(z, 0.5, exact=True) + r = ctx.fmul(w, w, prec=max(0,ctx.prec+M)) + return [([w], [n], [], [n+1], [], [n+1], r)] + v = ctx.hypercomb(h, [n], **kwargs) + return v + +@defun_wrapped +def bessely(ctx, n, z, derivative=0, **kwargs): + if not z: + if derivative: + # Not implemented + raise ValueError + if not n: + # ~ log(z/2) + return -ctx.inf + (n+z) + if ctx.im(n): + return ctx.nan * (n+z) + r = ctx.re(n) + q = n+0.5 + if ctx.isint(q): + if n > 0: + return -ctx.inf + (n+z) + else: + return 0 * (n+z) + if r < 0 and int(ctx.floor(q)) % 2: + return ctx.inf + (n+z) + else: + return ctx.ninf + (n+z) + # XXX: use hypercomb + ctx.prec += 10 + m, d = ctx.nint_distance(n) + if d < -ctx.prec: + h = +ctx.eps + ctx.prec *= 2 + n += h + elif d < 0: + ctx.prec -= d + # TODO: avoid cancellation for imaginary arguments + cos, sin = ctx.cospi_sinpi(n) + return (ctx.besselj(n,z,derivative,**kwargs)*cos - \ + ctx.besselj(-n,z,derivative,**kwargs))/sin + +@defun_wrapped +def besselk(ctx, n, z, **kwargs): + if not z: + return ctx.inf + M = ctx.mag(z) + if M < 1: + # Represent as limit definition + def h(n): + r = (z/2)**2 + T1 = [z, 2], [-n, n-1], [n], [], [], [1-n], r + T2 = [z, 2], [n, -n-1], [-n], [], [], [1+n], r + return T1, T2 + # We could use the limit definition always, but it leads + # to very bad cancellation (of exponentially large terms) + # for large real z + # Instead represent in terms of 2F0 + else: + ctx.prec += M + def h(n): + return [([ctx.pi/2, z, ctx.exp(-z)], [0.5,-0.5,1], [], [], \ + [n+0.5, 0.5-n], [], -1/(2*z))] + return ctx.hypercomb(h, [n], **kwargs) + +@defun_wrapped +def hankel1(ctx,n,x,**kwargs): + return ctx.besselj(n,x,**kwargs) + ctx.j*ctx.bessely(n,x,**kwargs) + +@defun_wrapped +def hankel2(ctx,n,x,**kwargs): + return ctx.besselj(n,x,**kwargs) - ctx.j*ctx.bessely(n,x,**kwargs) + +@defun_wrapped +def whitm(ctx,k,m,z,**kwargs): + if z == 0: + # M(k,m,z) = 0^(1/2+m) + if ctx.re(m) > -0.5: + return z + elif ctx.re(m) < -0.5: + return ctx.inf + z + else: + return ctx.nan * z + x = ctx.fmul(-0.5, z, exact=True) + y = 0.5+m + return ctx.exp(x) * z**y * ctx.hyp1f1(y-k, 1+2*m, z, **kwargs) + +@defun_wrapped +def whitw(ctx,k,m,z,**kwargs): + if z == 0: + g = abs(ctx.re(m)) + if g < 0.5: + return z + elif g > 0.5: + return ctx.inf + z + else: + return ctx.nan * z + x = ctx.fmul(-0.5, z, exact=True) + y = 0.5+m + return ctx.exp(x) * z**y * ctx.hyperu(y-k, 1+2*m, z, **kwargs) + +@defun +def hyperu(ctx, a, b, z, **kwargs): + a, atype = ctx._convert_param(a) + b, btype = ctx._convert_param(b) + z = ctx.convert(z) + if not z: + if ctx.re(b) <= 1: + return ctx.gammaprod([1-b],[a-b+1]) + else: + return ctx.inf + z + bb = 1+a-b + bb, bbtype = ctx._convert_param(bb) + try: + orig = ctx.prec + try: + ctx.prec += 10 + v = ctx.hypsum(2, 0, (atype, bbtype), [a, bb], -1/z, maxterms=ctx.prec) + return v / z**a + finally: + ctx.prec = orig + except ctx.NoConvergence: + pass + def h(a,b): + w = ctx.sinpi(b) + T1 = ([ctx.pi,w],[1,-1],[],[a-b+1,b],[a],[b],z) + T2 = ([-ctx.pi,w,z],[1,-1,1-b],[],[a,2-b],[a-b+1],[2-b],z) + return T1, T2 + return ctx.hypercomb(h, [a,b], **kwargs) + +@defun +def struveh(ctx,n,z, **kwargs): + n = ctx.convert(n) + z = ctx.convert(z) + # http://functions.wolfram.com/Bessel-TypeFunctions/StruveH/26/01/02/ + def h(n): + return [([z/2, 0.5*ctx.sqrt(ctx.pi)], [n+1, -1], [], [n+1.5], [1], [1.5, n+1.5], -(z/2)**2)] + return ctx.hypercomb(h, [n], **kwargs) + +@defun +def struvel(ctx,n,z, **kwargs): + n = ctx.convert(n) + z = ctx.convert(z) + # http://functions.wolfram.com/Bessel-TypeFunctions/StruveL/26/01/02/ + def h(n): + return [([z/2, 0.5*ctx.sqrt(ctx.pi)], [n+1, -1], [], [n+1.5], [1], [1.5, n+1.5], (z/2)**2)] + return ctx.hypercomb(h, [n], **kwargs) + +def _anger(ctx,which,v,z,**kwargs): + v = ctx._convert_param(v)[0] + z = ctx.convert(z) + def h(v): + b = ctx.mpq_1_2 + u = v*b + m = b*3 + a1,a2,b1,b2 = m-u, m+u, 1-u, 1+u + c, s = ctx.cospi_sinpi(u) + if which == 0: + A, B = [b*z, s], [c] + if which == 1: + A, B = [b*z, -c], [s] + w = ctx.square_exp_arg(z, mult=-0.25) + T1 = A, [1, 1], [], [a1,a2], [1], [a1,a2], w + T2 = B, [1], [], [b1,b2], [1], [b1,b2], w + return T1, T2 + return ctx.hypercomb(h, [v], **kwargs) + +@defun +def angerj(ctx, v, z, **kwargs): + return _anger(ctx, 0, v, z, **kwargs) + +@defun +def webere(ctx, v, z, **kwargs): + return _anger(ctx, 1, v, z, **kwargs) + +@defun +def lommels1(ctx, u, v, z, **kwargs): + u = ctx._convert_param(u)[0] + v = ctx._convert_param(v)[0] + z = ctx.convert(z) + def h(u,v): + b = ctx.mpq_1_2 + w = ctx.square_exp_arg(z, mult=-0.25) + return ([u-v+1, u+v+1, z], [-1, -1, u+1], [], [], [1], \ + [b*(u-v+3),b*(u+v+3)], w), + return ctx.hypercomb(h, [u,v], **kwargs) + +@defun +def lommels2(ctx, u, v, z, **kwargs): + u = ctx._convert_param(u)[0] + v = ctx._convert_param(v)[0] + z = ctx.convert(z) + # Asymptotic expansion (GR p. 947) -- need to be careful + # not to use for small arguments + # def h(u,v): + # b = ctx.mpq_1_2 + # w = -(z/2)**(-2) + # return ([z], [u-1], [], [], [b*(1-u+v)], [b*(1-u-v)], w), + def h(u,v): + b = ctx.mpq_1_2 + w = ctx.square_exp_arg(z, mult=-0.25) + T1 = [u-v+1, u+v+1, z], [-1, -1, u+1], [], [], [1], [b*(u-v+3),b*(u+v+3)], w + T2 = [2, z], [u+v-1, -v], [v, b*(u+v+1)], [b*(v-u+1)], [], [1-v], w + T3 = [2, z], [u-v-1, v], [-v, b*(u-v+1)], [b*(1-u-v)], [], [1+v], w + #c1 = ctx.cospi((u-v)*b) + #c2 = ctx.cospi((u+v)*b) + #s = ctx.sinpi(v) + #r1 = (u-v+1)*b + #r2 = (u+v+1)*b + #T2 = [c1, s, z, 2], [1, -1, -v, v], [], [-v+1], [], [-v+1], w + #T3 = [-c2, s, z, 2], [1, -1, v, -v], [], [v+1], [], [v+1], w + #T2 = [c1, s, z, 2], [1, -1, -v, v+u-1], [r1, r2], [-v+1], [], [-v+1], w + #T3 = [-c2, s, z, 2], [1, -1, v, -v+u-1], [r1, r2], [v+1], [], [v+1], w + return T1, T2, T3 + return ctx.hypercomb(h, [u,v], **kwargs) + +@defun +def ber(ctx, n, z, **kwargs): + n = ctx.convert(n) + z = ctx.convert(z) + # http://functions.wolfram.com/Bessel-TypeFunctions/KelvinBer2/26/01/02/0001/ + def h(n): + r = -(z/4)**4 + cos, sin = ctx.cospi_sinpi(-0.75*n) + T1 = [cos, z/2], [1, n], [], [n+1], [], [0.5, 0.5*(n+1), 0.5*n+1], r + T2 = [sin, z/2], [1, n+2], [], [n+2], [], [1.5, 0.5*(n+3), 0.5*n+1], r + return T1, T2 + return ctx.hypercomb(h, [n], **kwargs) + +@defun +def bei(ctx, n, z, **kwargs): + n = ctx.convert(n) + z = ctx.convert(z) + # http://functions.wolfram.com/Bessel-TypeFunctions/KelvinBei2/26/01/02/0001/ + def h(n): + r = -(z/4)**4 + cos, sin = ctx.cospi_sinpi(0.75*n) + T1 = [cos, z/2], [1, n+2], [], [n+2], [], [1.5, 0.5*(n+3), 0.5*n+1], r + T2 = [sin, z/2], [1, n], [], [n+1], [], [0.5, 0.5*(n+1), 0.5*n+1], r + return T1, T2 + return ctx.hypercomb(h, [n], **kwargs) + +@defun +def ker(ctx, n, z, **kwargs): + n = ctx.convert(n) + z = ctx.convert(z) + # http://functions.wolfram.com/Bessel-TypeFunctions/KelvinKer2/26/01/02/0001/ + def h(n): + r = -(z/4)**4 + cos1, sin1 = ctx.cospi_sinpi(0.25*n) + cos2, sin2 = ctx.cospi_sinpi(0.75*n) + T1 = [2, z, 4*cos1], [-n-3, n, 1], [-n], [], [], [0.5, 0.5*(1+n), 0.5*(n+2)], r + T2 = [2, z, -sin1], [-n-3, 2+n, 1], [-n-1], [], [], [1.5, 0.5*(3+n), 0.5*(n+2)], r + T3 = [2, z, 4*cos2], [n-3, -n, 1], [n], [], [], [0.5, 0.5*(1-n), 1-0.5*n], r + T4 = [2, z, -sin2], [n-3, 2-n, 1], [n-1], [], [], [1.5, 0.5*(3-n), 1-0.5*n], r + return T1, T2, T3, T4 + return ctx.hypercomb(h, [n], **kwargs) + +@defun +def kei(ctx, n, z, **kwargs): + n = ctx.convert(n) + z = ctx.convert(z) + # http://functions.wolfram.com/Bessel-TypeFunctions/KelvinKei2/26/01/02/0001/ + def h(n): + r = -(z/4)**4 + cos1, sin1 = ctx.cospi_sinpi(0.75*n) + cos2, sin2 = ctx.cospi_sinpi(0.25*n) + T1 = [-cos1, 2, z], [1, n-3, 2-n], [n-1], [], [], [1.5, 0.5*(3-n), 1-0.5*n], r + T2 = [-sin1, 2, z], [1, n-1, -n], [n], [], [], [0.5, 0.5*(1-n), 1-0.5*n], r + T3 = [-sin2, 2, z], [1, -n-1, n], [-n], [], [], [0.5, 0.5*(n+1), 0.5*(n+2)], r + T4 = [-cos2, 2, z], [1, -n-3, n+2], [-n-1], [], [], [1.5, 0.5*(n+3), 0.5*(n+2)], r + return T1, T2, T3, T4 + return ctx.hypercomb(h, [n], **kwargs) + +# TODO: do this more generically? +def c_memo(f): + name = f.__name__ + def f_wrapped(ctx): + cache = ctx._misc_const_cache + prec = ctx.prec + p,v = cache.get(name, (-1,0)) + if p >= prec: + return +v + else: + cache[name] = (prec, f(ctx)) + return cache[name][1] + return f_wrapped + +@c_memo +def _airyai_C1(ctx): + return 1 / (ctx.cbrt(9) * ctx.gamma(ctx.mpf(2)/3)) + +@c_memo +def _airyai_C2(ctx): + return -1 / (ctx.cbrt(3) * ctx.gamma(ctx.mpf(1)/3)) + +@c_memo +def _airybi_C1(ctx): + return 1 / (ctx.nthroot(3,6) * ctx.gamma(ctx.mpf(2)/3)) + +@c_memo +def _airybi_C2(ctx): + return ctx.nthroot(3,6) / ctx.gamma(ctx.mpf(1)/3) + +def _airybi_n2_inf(ctx): + prec = ctx.prec + try: + v = ctx.power(3,'2/3')*ctx.gamma('2/3')/(2*ctx.pi) + finally: + ctx.prec = prec + return +v + +# Derivatives at z = 0 +# TODO: could be expressed more elegantly using triple factorials +def _airyderiv_0(ctx, z, n, ntype, which): + if ntype == 'Z': + if n < 0: + return z + r = ctx.mpq_1_3 + prec = ctx.prec + try: + ctx.prec += 10 + v = ctx.gamma((n+1)*r) * ctx.power(3,n*r) / ctx.pi + if which == 0: + v *= ctx.sinpi(2*(n+1)*r) + v /= ctx.power(3,'2/3') + else: + v *= abs(ctx.sinpi(2*(n+1)*r)) + v /= ctx.power(3,'1/6') + finally: + ctx.prec = prec + return +v + z + else: + # singular (does the limit exist?) + raise NotImplementedError + +@defun +def airyai(ctx, z, derivative=0, **kwargs): + z = ctx.convert(z) + if derivative: + n, ntype = ctx._convert_param(derivative) + else: + n = 0 + # Values at infinities + if not ctx.isnormal(z) and z: + if n and ntype == 'Z': + if n == -1: + if z == ctx.inf: + return ctx.mpf(1)/3 + 1/z + if z == ctx.ninf: + return ctx.mpf(-2)/3 + 1/z + if n < -1: + if z == ctx.inf: + return z + if z == ctx.ninf: + return (-1)**n * (-z) + if (not n) and z == ctx.inf or z == ctx.ninf: + return 1/z + # TODO: limits + raise ValueError("essential singularity of Ai(z)") + # Account for exponential scaling + if z: + extraprec = max(0, int(1.5*ctx.mag(z))) + else: + extraprec = 0 + if n: + if n == 1: + def h(): + # http://functions.wolfram.com/03.07.06.0005.01 + if ctx._re(z) > 4: + ctx.prec += extraprec + w = z**1.5; r = -0.75/w; u = -2*w/3 + ctx.prec -= extraprec + C = -ctx.exp(u)/(2*ctx.sqrt(ctx.pi))*ctx.nthroot(z,4) + return ([C],[1],[],[],[(-1,6),(7,6)],[],r), + # http://functions.wolfram.com/03.07.26.0001.01 + else: + ctx.prec += extraprec + w = z**3 / 9 + ctx.prec -= extraprec + C1 = _airyai_C1(ctx) * 0.5 + C2 = _airyai_C2(ctx) + T1 = [C1,z],[1,2],[],[],[],[ctx.mpq_5_3],w + T2 = [C2],[1],[],[],[],[ctx.mpq_1_3],w + return T1, T2 + return ctx.hypercomb(h, [], **kwargs) + else: + if z == 0: + return _airyderiv_0(ctx, z, n, ntype, 0) + # http://functions.wolfram.com/03.05.20.0004.01 + def h(n): + ctx.prec += extraprec + w = z**3/9 + ctx.prec -= extraprec + q13,q23,q43 = ctx.mpq_1_3, ctx.mpq_2_3, ctx.mpq_4_3 + a1=q13; a2=1; b1=(1-n)*q13; b2=(2-n)*q13; b3=1-n*q13 + T1 = [3, z], [n-q23, -n], [a1], [b1,b2,b3], \ + [a1,a2], [b1,b2,b3], w + a1=q23; b1=(2-n)*q13; b2=1-n*q13; b3=(4-n)*q13 + T2 = [3, z, -z], [n-q43, -n, 1], [a1], [b1,b2,b3], \ + [a1,a2], [b1,b2,b3], w + return T1, T2 + v = ctx.hypercomb(h, [n], **kwargs) + if ctx._is_real_type(z) and ctx.isint(n): + v = ctx._re(v) + return v + else: + def h(): + if ctx._re(z) > 4: + # We could use 1F1, but it results in huge cancellation; + # the following expansion is better. + # TODO: asymptotic series for derivatives + ctx.prec += extraprec + w = z**1.5; r = -0.75/w; u = -2*w/3 + ctx.prec -= extraprec + C = ctx.exp(u)/(2*ctx.sqrt(ctx.pi)*ctx.nthroot(z,4)) + return ([C],[1],[],[],[(1,6),(5,6)],[],r), + else: + ctx.prec += extraprec + w = z**3 / 9 + ctx.prec -= extraprec + C1 = _airyai_C1(ctx) + C2 = _airyai_C2(ctx) + T1 = [C1],[1],[],[],[],[ctx.mpq_2_3],w + T2 = [z*C2],[1],[],[],[],[ctx.mpq_4_3],w + return T1, T2 + return ctx.hypercomb(h, [], **kwargs) + +@defun +def airybi(ctx, z, derivative=0, **kwargs): + z = ctx.convert(z) + if derivative: + n, ntype = ctx._convert_param(derivative) + else: + n = 0 + # Values at infinities + if not ctx.isnormal(z) and z: + if n and ntype == 'Z': + if z == ctx.inf: + return z + if z == ctx.ninf: + if n == -1: + return 1/z + if n == -2: + return _airybi_n2_inf(ctx) + if n < -2: + return (-1)**n * (-z) + if not n: + if z == ctx.inf: + return z + if z == ctx.ninf: + return 1/z + # TODO: limits + raise ValueError("essential singularity of Bi(z)") + if z: + extraprec = max(0, int(1.5*ctx.mag(z))) + else: + extraprec = 0 + if n: + if n == 1: + # http://functions.wolfram.com/03.08.26.0001.01 + def h(): + ctx.prec += extraprec + w = z**3 / 9 + ctx.prec -= extraprec + C1 = _airybi_C1(ctx)*0.5 + C2 = _airybi_C2(ctx) + T1 = [C1,z],[1,2],[],[],[],[ctx.mpq_5_3],w + T2 = [C2],[1],[],[],[],[ctx.mpq_1_3],w + return T1, T2 + return ctx.hypercomb(h, [], **kwargs) + else: + if z == 0: + return _airyderiv_0(ctx, z, n, ntype, 1) + def h(n): + ctx.prec += extraprec + w = z**3/9 + ctx.prec -= extraprec + q13,q23,q43 = ctx.mpq_1_3, ctx.mpq_2_3, ctx.mpq_4_3 + q16 = ctx.mpq_1_6 + q56 = ctx.mpq_5_6 + a1=q13; a2=1; b1=(1-n)*q13; b2=(2-n)*q13; b3=1-n*q13 + T1 = [3, z], [n-q16, -n], [a1], [b1,b2,b3], \ + [a1,a2], [b1,b2,b3], w + a1=q23; b1=(2-n)*q13; b2=1-n*q13; b3=(4-n)*q13 + T2 = [3, z], [n-q56, 1-n], [a1], [b1,b2,b3], \ + [a1,a2], [b1,b2,b3], w + return T1, T2 + v = ctx.hypercomb(h, [n], **kwargs) + if ctx._is_real_type(z) and ctx.isint(n): + v = ctx._re(v) + return v + else: + def h(): + ctx.prec += extraprec + w = z**3 / 9 + ctx.prec -= extraprec + C1 = _airybi_C1(ctx) + C2 = _airybi_C2(ctx) + T1 = [C1],[1],[],[],[],[ctx.mpq_2_3],w + T2 = [z*C2],[1],[],[],[],[ctx.mpq_4_3],w + return T1, T2 + return ctx.hypercomb(h, [], **kwargs) + +def _airy_zero(ctx, which, k, derivative, complex=False): + # Asymptotic formulas are given in DLMF section 9.9 + def U(t): return t**(2/3.)*(1-7/(t**2*48)) + def T(t): return t**(2/3.)*(1+5/(t**2*48)) + k = int(k) + if k < 1: + raise ValueError("k cannot be less than 1") + if not derivative in (0,1): + raise ValueError("Derivative should lie between 0 and 1") + if which == 0: + if derivative: + return ctx.findroot(lambda z: ctx.airyai(z,1), + -U(3*ctx.pi*(4*k-3)/8)) + return ctx.findroot(ctx.airyai, -T(3*ctx.pi*(4*k-1)/8)) + if which == 1 and complex == False: + if derivative: + return ctx.findroot(lambda z: ctx.airybi(z,1), + -U(3*ctx.pi*(4*k-1)/8)) + return ctx.findroot(ctx.airybi, -T(3*ctx.pi*(4*k-3)/8)) + if which == 1 and complex == True: + if derivative: + t = 3*ctx.pi*(4*k-3)/8 + 0.75j*ctx.ln2 + s = ctx.expjpi(ctx.mpf(1)/3) * T(t) + return ctx.findroot(lambda z: ctx.airybi(z,1), s) + t = 3*ctx.pi*(4*k-1)/8 + 0.75j*ctx.ln2 + s = ctx.expjpi(ctx.mpf(1)/3) * U(t) + return ctx.findroot(ctx.airybi, s) + +@defun +def airyaizero(ctx, k, derivative=0): + return _airy_zero(ctx, 0, k, derivative, False) + +@defun +def airybizero(ctx, k, derivative=0, complex=False): + return _airy_zero(ctx, 1, k, derivative, complex) + +def _scorer(ctx, z, which, kwargs): + z = ctx.convert(z) + if ctx.isinf(z): + if z == ctx.inf: + if which == 0: return 1/z + if which == 1: return z + if z == ctx.ninf: + return 1/z + raise ValueError("essential singularity") + if z: + extraprec = max(0, int(1.5*ctx.mag(z))) + else: + extraprec = 0 + if kwargs.get('derivative'): + raise NotImplementedError + # Direct asymptotic expansions, to avoid + # exponentially large cancellation + try: + if ctx.mag(z) > 3: + if which == 0 and abs(ctx.arg(z)) < ctx.pi/3 * 0.999: + def h(): + return (([ctx.pi,z],[-1,-1],[],[],[(1,3),(2,3),1],[],9/z**3),) + return ctx.hypercomb(h, [], maxterms=ctx.prec, force_series=True) + if which == 1 and abs(ctx.arg(-z)) < 2*ctx.pi/3 * 0.999: + def h(): + return (([-ctx.pi,z],[-1,-1],[],[],[(1,3),(2,3),1],[],9/z**3),) + return ctx.hypercomb(h, [], maxterms=ctx.prec, force_series=True) + except ctx.NoConvergence: + pass + def h(): + A = ctx.airybi(z, **kwargs)/3 + B = -2*ctx.pi + if which == 1: + A *= 2 + B *= -1 + ctx.prec += extraprec + w = z**3/9 + ctx.prec -= extraprec + T1 = [A], [1], [], [], [], [], 0 + T2 = [B,z], [-1,2], [], [], [1], [ctx.mpq_4_3,ctx.mpq_5_3], w + return T1, T2 + return ctx.hypercomb(h, [], **kwargs) + +@defun +def scorergi(ctx, z, **kwargs): + return _scorer(ctx, z, 0, kwargs) + +@defun +def scorerhi(ctx, z, **kwargs): + return _scorer(ctx, z, 1, kwargs) + +@defun_wrapped +def coulombc(ctx, l, eta, _cache={}): + if (l, eta) in _cache and _cache[l,eta][0] >= ctx.prec: + return +_cache[l,eta][1] + G3 = ctx.loggamma(2*l+2) + G1 = ctx.loggamma(1+l+ctx.j*eta) + G2 = ctx.loggamma(1+l-ctx.j*eta) + v = 2**l * ctx.exp((-ctx.pi*eta+G1+G2)/2 - G3) + if not (ctx.im(l) or ctx.im(eta)): + v = ctx.re(v) + _cache[l,eta] = (ctx.prec, v) + return v + +@defun_wrapped +def coulombf(ctx, l, eta, z, w=1, chop=True, **kwargs): + # Regular Coulomb wave function + # Note: w can be either 1 or -1; the other may be better in some cases + # TODO: check that chop=True chops when and only when it should + #ctx.prec += 10 + def h(l, eta): + try: + jw = ctx.j*w + jwz = ctx.fmul(jw, z, exact=True) + jwz2 = ctx.fmul(jwz, -2, exact=True) + C = ctx.coulombc(l, eta) + T1 = [C, z, ctx.exp(jwz)], [1, l+1, 1], [], [], [1+l+jw*eta], \ + [2*l+2], jwz2 + except ValueError: + T1 = [0], [-1], [], [], [], [], 0 + return (T1,) + v = ctx.hypercomb(h, [l,eta], **kwargs) + if chop and (not ctx.im(l)) and (not ctx.im(eta)) and (not ctx.im(z)) and \ + (ctx.re(z) >= 0): + v = ctx.re(v) + return v + +@defun_wrapped +def _coulomb_chi(ctx, l, eta, _cache={}): + if (l, eta) in _cache and _cache[l,eta][0] >= ctx.prec: + return _cache[l,eta][1] + def terms(): + l2 = -l-1 + jeta = ctx.j*eta + return [ctx.loggamma(1+l+jeta) * (-0.5j), + ctx.loggamma(1+l-jeta) * (0.5j), + ctx.loggamma(1+l2+jeta) * (0.5j), + ctx.loggamma(1+l2-jeta) * (-0.5j), + -(l+0.5)*ctx.pi] + v = ctx.sum_accurately(terms, 1) + _cache[l,eta] = (ctx.prec, v) + return v + +@defun_wrapped +def coulombg(ctx, l, eta, z, w=1, chop=True, **kwargs): + # Irregular Coulomb wave function + # Note: w can be either 1 or -1; the other may be better in some cases + # TODO: check that chop=True chops when and only when it should + if not ctx._im(l): + l = ctx._re(l) # XXX: for isint + def h(l, eta): + # Force perturbation for integers and half-integers + if ctx.isint(l*2): + T1 = [0], [-1], [], [], [], [], 0 + return (T1,) + l2 = -l-1 + try: + chi = ctx._coulomb_chi(l, eta) + jw = ctx.j*w + s = ctx.sin(chi); c = ctx.cos(chi) + C1 = ctx.coulombc(l,eta) + C2 = ctx.coulombc(l2,eta) + u = ctx.exp(jw*z) + x = -2*jw*z + T1 = [s, C1, z, u, c], [-1, 1, l+1, 1, 1], [], [], \ + [1+l+jw*eta], [2*l+2], x + T2 = [-s, C2, z, u], [-1, 1, l2+1, 1], [], [], \ + [1+l2+jw*eta], [2*l2+2], x + return T1, T2 + except ValueError: + T1 = [0], [-1], [], [], [], [], 0 + return (T1,) + v = ctx.hypercomb(h, [l,eta], **kwargs) + if chop and (not ctx._im(l)) and (not ctx._im(eta)) and (not ctx._im(z)) and \ + (ctx._re(z) >= 0): + v = ctx._re(v) + return v + +def mcmahon(ctx,kind,prime,v,m): + """ + Computes an estimate for the location of the Bessel function zero + j_{v,m}, y_{v,m}, j'_{v,m} or y'_{v,m} using McMahon's asymptotic + expansion (Abramowitz & Stegun 9.5.12-13, DLMF 20.21(vi)). + + Returns (r,err) where r is the estimated location of the root + and err is a positive number estimating the error of the + asymptotic expansion. + """ + u = 4*v**2 + if kind == 1 and not prime: b = (4*m+2*v-1)*ctx.pi/4 + if kind == 2 and not prime: b = (4*m+2*v-3)*ctx.pi/4 + if kind == 1 and prime: b = (4*m+2*v-3)*ctx.pi/4 + if kind == 2 and prime: b = (4*m+2*v-1)*ctx.pi/4 + if not prime: + s1 = b + s2 = -(u-1)/(8*b) + s3 = -4*(u-1)*(7*u-31)/(3*(8*b)**3) + s4 = -32*(u-1)*(83*u**2-982*u+3779)/(15*(8*b)**5) + s5 = -64*(u-1)*(6949*u**3-153855*u**2+1585743*u-6277237)/(105*(8*b)**7) + if prime: + s1 = b + s2 = -(u+3)/(8*b) + s3 = -4*(7*u**2+82*u-9)/(3*(8*b)**3) + s4 = -32*(83*u**3+2075*u**2-3039*u+3537)/(15*(8*b)**5) + s5 = -64*(6949*u**4+296492*u**3-1248002*u**2+7414380*u-5853627)/(105*(8*b)**7) + terms = [s1,s2,s3,s4,s5] + s = s1 + err = 0.0 + for i in range(1,len(terms)): + if abs(terms[i]) < abs(terms[i-1]): + s += terms[i] + else: + err = abs(terms[i]) + if i == len(terms)-1: + err = abs(terms[-1]) + return s, err + +def generalized_bisection(ctx,f,a,b,n): + """ + Given f known to have exactly n simple roots within [a,b], + return a list of n intervals isolating the roots + and having opposite signs at the endpoints. + + TODO: this can be optimized, e.g. by reusing evaluation points. + """ + if n < 1: + raise ValueError("n cannot be less than 1") + N = n+1 + points = [] + signs = [] + while 1: + points = ctx.linspace(a,b,N) + signs = [ctx.sign(f(x)) for x in points] + ok_intervals = [(points[i],points[i+1]) for i in range(N-1) \ + if signs[i]*signs[i+1] == -1] + if len(ok_intervals) == n: + return ok_intervals + N = N*2 + +def find_in_interval(ctx, f, ab): + return ctx.findroot(f, ab, solver='illinois', verify=False) + +def bessel_zero(ctx, kind, prime, v, m, isoltol=0.01, _interval_cache={}): + prec = ctx.prec + workprec = max(prec, ctx.mag(v), ctx.mag(m))+10 + try: + ctx.prec = workprec + v = ctx.mpf(v) + m = int(m) + prime = int(prime) + if v < 0: + raise ValueError("v cannot be negative") + if m < 1: + raise ValueError("m cannot be less than 1") + if not prime in (0,1): + raise ValueError("prime should lie between 0 and 1") + if kind == 1: + if prime: f = lambda x: ctx.besselj(v,x,derivative=1) + else: f = lambda x: ctx.besselj(v,x) + if kind == 2: + if prime: f = lambda x: ctx.bessely(v,x,derivative=1) + else: f = lambda x: ctx.bessely(v,x) + # The first root of J' is very close to 0 for small + # orders, and this needs to be special-cased + if kind == 1 and prime and m == 1: + if v == 0: + return ctx.zero + if v <= 1: + # TODO: use v <= j'_{v,1} < y_{v,1}? + r = 2*ctx.sqrt(v*(1+v)/(v+2)) + return find_in_interval(ctx, f, (r/10, 2*r)) + if (kind,prime,v,m) in _interval_cache: + return find_in_interval(ctx, f, _interval_cache[kind,prime,v,m]) + r, err = mcmahon(ctx, kind, prime, v, m) + if err < isoltol: + return find_in_interval(ctx, f, (r-isoltol, r+isoltol)) + # An x such that 0 < x < r_{v,1} + if kind == 1 and not prime: low = 2.4 + if kind == 1 and prime: low = 1.8 + if kind == 2 and not prime: low = 0.8 + if kind == 2 and prime: low = 2.0 + n = m+1 + while 1: + r1, err = mcmahon(ctx, kind, prime, v, n) + if err < isoltol: + r2, err2 = mcmahon(ctx, kind, prime, v, n+1) + intervals = generalized_bisection(ctx, f, low, 0.5*(r1+r2), n) + for k, ab in enumerate(intervals): + _interval_cache[kind,prime,v,k+1] = ab + return find_in_interval(ctx, f, intervals[m-1]) + else: + n = n*2 + finally: + ctx.prec = prec + +@defun +def besseljzero(ctx, v, m, derivative=0): + r""" + For a real order `\nu \ge 0` and a positive integer `m`, returns + `j_{\nu,m}`, the `m`-th positive zero of the Bessel function of the + first kind `J_{\nu}(z)` (see :func:`~mpmath.besselj`). Alternatively, + with *derivative=1*, gives the first nonnegative simple zero + `j'_{\nu,m}` of `J'_{\nu}(z)`. + + The indexing convention is that used by Abramowitz & Stegun + and the DLMF. Note the special case `j'_{0,1} = 0`, while all other + zeros are positive. In effect, only simple zeros are counted + (all zeros of Bessel functions are simple except possibly `z = 0`) + and `j_{\nu,m}` becomes a monotonic function of both `\nu` + and `m`. + + The zeros are interlaced according to the inequalities + + .. math :: + + j'_{\nu,k} < j_{\nu,k} < j'_{\nu,k+1} + + j_{\nu,1} < j_{\nu+1,2} < j_{\nu,2} < j_{\nu+1,2} < j_{\nu,3} < \cdots + + **Examples** + + Initial zeros of the Bessel functions `J_0(z), J_1(z), J_2(z)`:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> besseljzero(0,1); besseljzero(0,2); besseljzero(0,3) + 2.404825557695772768621632 + 5.520078110286310649596604 + 8.653727912911012216954199 + >>> besseljzero(1,1); besseljzero(1,2); besseljzero(1,3) + 3.831705970207512315614436 + 7.01558666981561875353705 + 10.17346813506272207718571 + >>> besseljzero(2,1); besseljzero(2,2); besseljzero(2,3) + 5.135622301840682556301402 + 8.417244140399864857783614 + 11.61984117214905942709415 + + Initial zeros of `J'_0(z), J'_1(z), J'_2(z)`:: + + 0.0 + 3.831705970207512315614436 + 7.01558666981561875353705 + >>> besseljzero(1,1,1); besseljzero(1,2,1); besseljzero(1,3,1) + 1.84118378134065930264363 + 5.331442773525032636884016 + 8.536316366346285834358961 + >>> besseljzero(2,1,1); besseljzero(2,2,1); besseljzero(2,3,1) + 3.054236928227140322755932 + 6.706133194158459146634394 + 9.969467823087595793179143 + + Zeros with large index:: + + >>> besseljzero(0,100); besseljzero(0,1000); besseljzero(0,10000) + 313.3742660775278447196902 + 3140.807295225078628895545 + 31415.14114171350798533666 + >>> besseljzero(5,100); besseljzero(5,1000); besseljzero(5,10000) + 321.1893195676003157339222 + 3148.657306813047523500494 + 31422.9947255486291798943 + >>> besseljzero(0,100,1); besseljzero(0,1000,1); besseljzero(0,10000,1) + 311.8018681873704508125112 + 3139.236339643802482833973 + 31413.57032947022399485808 + + Zeros of functions with large order:: + + >>> besseljzero(50,1) + 57.11689916011917411936228 + >>> besseljzero(50,2) + 62.80769876483536093435393 + >>> besseljzero(50,100) + 388.6936600656058834640981 + >>> besseljzero(50,1,1) + 52.99764038731665010944037 + >>> besseljzero(50,2,1) + 60.02631933279942589882363 + >>> besseljzero(50,100,1) + 387.1083151608726181086283 + + Zeros of functions with fractional order:: + + >>> besseljzero(0.5,1); besseljzero(1.5,1); besseljzero(2.25,4) + 3.141592653589793238462643 + 4.493409457909064175307881 + 15.15657692957458622921634 + + Both `J_{\nu}(z)` and `J'_{\nu}(z)` can be expressed as infinite + products over their zeros:: + + >>> v,z = 2, mpf(1) + >>> (z/2)**v/gamma(v+1) * \ + ... nprod(lambda k: 1-(z/besseljzero(v,k))**2, [1,inf]) + ... + 0.1149034849319004804696469 + >>> besselj(v,z) + 0.1149034849319004804696469 + >>> (z/2)**(v-1)/2/gamma(v) * \ + ... nprod(lambda k: 1-(z/besseljzero(v,k,1))**2, [1,inf]) + ... + 0.2102436158811325550203884 + >>> besselj(v,z,1) + 0.2102436158811325550203884 + + """ + return +bessel_zero(ctx, 1, derivative, v, m) + +@defun +def besselyzero(ctx, v, m, derivative=0): + r""" + For a real order `\nu \ge 0` and a positive integer `m`, returns + `y_{\nu,m}`, the `m`-th positive zero of the Bessel function of the + second kind `Y_{\nu}(z)` (see :func:`~mpmath.bessely`). Alternatively, + with *derivative=1*, gives the first positive zero `y'_{\nu,m}` of + `Y'_{\nu}(z)`. + + The zeros are interlaced according to the inequalities + + .. math :: + + y_{\nu,k} < y'_{\nu,k} < y_{\nu,k+1} + + y_{\nu,1} < y_{\nu+1,2} < y_{\nu,2} < y_{\nu+1,2} < y_{\nu,3} < \cdots + + **Examples** + + Initial zeros of the Bessel functions `Y_0(z), Y_1(z), Y_2(z)`:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> besselyzero(0,1); besselyzero(0,2); besselyzero(0,3) + 0.8935769662791675215848871 + 3.957678419314857868375677 + 7.086051060301772697623625 + >>> besselyzero(1,1); besselyzero(1,2); besselyzero(1,3) + 2.197141326031017035149034 + 5.429681040794135132772005 + 8.596005868331168926429606 + >>> besselyzero(2,1); besselyzero(2,2); besselyzero(2,3) + 3.384241767149593472701426 + 6.793807513268267538291167 + 10.02347797936003797850539 + + Initial zeros of `Y'_0(z), Y'_1(z), Y'_2(z)`:: + + >>> besselyzero(0,1,1); besselyzero(0,2,1); besselyzero(0,3,1) + 2.197141326031017035149034 + 5.429681040794135132772005 + 8.596005868331168926429606 + >>> besselyzero(1,1,1); besselyzero(1,2,1); besselyzero(1,3,1) + 3.683022856585177699898967 + 6.941499953654175655751944 + 10.12340465543661307978775 + >>> besselyzero(2,1,1); besselyzero(2,2,1); besselyzero(2,3,1) + 5.002582931446063945200176 + 8.350724701413079526349714 + 11.57419546521764654624265 + + Zeros with large index:: + + >>> besselyzero(0,100); besselyzero(0,1000); besselyzero(0,10000) + 311.8034717601871549333419 + 3139.236498918198006794026 + 31413.57034538691205229188 + >>> besselyzero(5,100); besselyzero(5,1000); besselyzero(5,10000) + 319.6183338562782156235062 + 3147.086508524556404473186 + 31421.42392920214673402828 + >>> besselyzero(0,100,1); besselyzero(0,1000,1); besselyzero(0,10000,1) + 313.3726705426359345050449 + 3140.807136030340213610065 + 31415.14112579761578220175 + + Zeros of functions with large order:: + + >>> besselyzero(50,1) + 53.50285882040036394680237 + >>> besselyzero(50,2) + 60.11244442774058114686022 + >>> besselyzero(50,100) + 387.1096509824943957706835 + >>> besselyzero(50,1,1) + 56.96290427516751320063605 + >>> besselyzero(50,2,1) + 62.74888166945933944036623 + >>> besselyzero(50,100,1) + 388.6923300548309258355475 + + Zeros of functions with fractional order:: + + >>> besselyzero(0.5,1); besselyzero(1.5,1); besselyzero(2.25,4) + 1.570796326794896619231322 + 2.798386045783887136720249 + 13.56721208770735123376018 + + """ + return +bessel_zero(ctx, 2, derivative, v, m) diff --git a/venv/lib/python3.12/site-packages/mpmath/functions/elliptic.py b/venv/lib/python3.12/site-packages/mpmath/functions/elliptic.py new file mode 100644 index 0000000000000000000000000000000000000000..1e198697fa042b7cc8bcba9e9e770f5c8106dad6 --- /dev/null +++ b/venv/lib/python3.12/site-packages/mpmath/functions/elliptic.py @@ -0,0 +1,1431 @@ +r""" +Elliptic functions historically comprise the elliptic integrals +and their inverses, and originate from the problem of computing the +arc length of an ellipse. From a more modern point of view, +an elliptic function is defined as a doubly periodic function, i.e. +a function which satisfies + +.. math :: + + f(z + 2 \omega_1) = f(z + 2 \omega_2) = f(z) + +for some half-periods `\omega_1, \omega_2` with +`\mathrm{Im}[\omega_1 / \omega_2] > 0`. The canonical elliptic +functions are the Jacobi elliptic functions. More broadly, this section +includes quasi-doubly periodic functions (such as the Jacobi theta +functions) and other functions useful in the study of elliptic functions. + +Many different conventions for the arguments of +elliptic functions are in use. It is even standard to use +different parameterizations for different functions in the same +text or software (and mpmath is no exception). +The usual parameters are the elliptic nome `q`, which usually +must satisfy `|q| < 1`; the elliptic parameter `m` (an arbitrary +complex number); the elliptic modulus `k` (an arbitrary complex +number); and the half-period ratio `\tau`, which usually must +satisfy `\mathrm{Im}[\tau] > 0`. +These quantities can be expressed in terms of each other +using the following relations: + +.. math :: + + m = k^2 + +.. math :: + + \tau = i \frac{K(1-m)}{K(m)} + +.. math :: + + q = e^{i \pi \tau} + +.. math :: + + k = \frac{\vartheta_2^2(q)}{\vartheta_3^2(q)} + +In addition, an alternative definition is used for the nome in +number theory, which we here denote by q-bar: + +.. math :: + + \bar{q} = q^2 = e^{2 i \pi \tau} + +For convenience, mpmath provides functions to convert +between the various parameters (:func:`~mpmath.qfrom`, :func:`~mpmath.mfrom`, +:func:`~mpmath.kfrom`, :func:`~mpmath.taufrom`, :func:`~mpmath.qbarfrom`). + +**References** + +1. [AbramowitzStegun]_ + +2. [WhittakerWatson]_ + +""" + +from .functions import defun, defun_wrapped + +@defun_wrapped +def eta(ctx, tau): + r""" + Returns the Dedekind eta function of tau in the upper half-plane. + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> eta(1j); gamma(0.25) / (2*pi**0.75) + (0.7682254223260566590025942 + 0.0j) + 0.7682254223260566590025942 + >>> tau = sqrt(2) + sqrt(5)*1j + >>> eta(-1/tau); sqrt(-1j*tau) * eta(tau) + (0.9022859908439376463573294 + 0.07985093673948098408048575j) + (0.9022859908439376463573295 + 0.07985093673948098408048575j) + >>> eta(tau+1); exp(pi*1j/12) * eta(tau) + (0.4493066139717553786223114 + 0.3290014793877986663915939j) + (0.4493066139717553786223114 + 0.3290014793877986663915939j) + >>> f = lambda z: diff(eta, z) / eta(z) + >>> chop(36*diff(f,tau)**2 - 24*diff(f,tau,2)*f(tau) + diff(f,tau,3)) + 0.0 + + """ + if ctx.im(tau) <= 0.0: + raise ValueError("eta is only defined in the upper half-plane") + q = ctx.expjpi(tau/12) + return q * ctx.qp(q**24) + +def nome(ctx, m): + m = ctx.convert(m) + if not m: + return m + if m == ctx.one: + return m + if ctx.isnan(m): + return m + if ctx.isinf(m): + if m == ctx.ninf: + return type(m)(-1) + else: + return ctx.mpc(-1) + a = ctx.ellipk(ctx.one-m) + b = ctx.ellipk(m) + v = ctx.exp(-ctx.pi*a/b) + if not ctx._im(m) and ctx._re(m) < 1: + if ctx._is_real_type(m): + return v.real + else: + return v.real + 0j + elif m == 2: + v = ctx.mpc(0, v.imag) + return v + +@defun_wrapped +def qfrom(ctx, q=None, m=None, k=None, tau=None, qbar=None): + r""" + Returns the elliptic nome `q`, given any of `q, m, k, \tau, \bar{q}`:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> qfrom(q=0.25) + 0.25 + >>> qfrom(m=mfrom(q=0.25)) + 0.25 + >>> qfrom(k=kfrom(q=0.25)) + 0.25 + >>> qfrom(tau=taufrom(q=0.25)) + (0.25 + 0.0j) + >>> qfrom(qbar=qbarfrom(q=0.25)) + 0.25 + + """ + if q is not None: + return ctx.convert(q) + if m is not None: + return nome(ctx, m) + if k is not None: + return nome(ctx, ctx.convert(k)**2) + if tau is not None: + return ctx.expjpi(tau) + if qbar is not None: + return ctx.sqrt(qbar) + +@defun_wrapped +def qbarfrom(ctx, q=None, m=None, k=None, tau=None, qbar=None): + r""" + Returns the number-theoretic nome `\bar q`, given any of + `q, m, k, \tau, \bar{q}`:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> qbarfrom(qbar=0.25) + 0.25 + >>> qbarfrom(q=qfrom(qbar=0.25)) + 0.25 + >>> qbarfrom(m=extraprec(20)(mfrom)(qbar=0.25)) # ill-conditioned + 0.25 + >>> qbarfrom(k=extraprec(20)(kfrom)(qbar=0.25)) # ill-conditioned + 0.25 + >>> qbarfrom(tau=taufrom(qbar=0.25)) + (0.25 + 0.0j) + + """ + if qbar is not None: + return ctx.convert(qbar) + if q is not None: + return ctx.convert(q) ** 2 + if m is not None: + return nome(ctx, m) ** 2 + if k is not None: + return nome(ctx, ctx.convert(k)**2) ** 2 + if tau is not None: + return ctx.expjpi(2*tau) + +@defun_wrapped +def taufrom(ctx, q=None, m=None, k=None, tau=None, qbar=None): + r""" + Returns the elliptic half-period ratio `\tau`, given any of + `q, m, k, \tau, \bar{q}`:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> taufrom(tau=0.5j) + (0.0 + 0.5j) + >>> taufrom(q=qfrom(tau=0.5j)) + (0.0 + 0.5j) + >>> taufrom(m=mfrom(tau=0.5j)) + (0.0 + 0.5j) + >>> taufrom(k=kfrom(tau=0.5j)) + (0.0 + 0.5j) + >>> taufrom(qbar=qbarfrom(tau=0.5j)) + (0.0 + 0.5j) + + """ + if tau is not None: + return ctx.convert(tau) + if m is not None: + m = ctx.convert(m) + return ctx.j*ctx.ellipk(1-m)/ctx.ellipk(m) + if k is not None: + k = ctx.convert(k) + return ctx.j*ctx.ellipk(1-k**2)/ctx.ellipk(k**2) + if q is not None: + return ctx.log(q) / (ctx.pi*ctx.j) + if qbar is not None: + qbar = ctx.convert(qbar) + return ctx.log(qbar) / (2*ctx.pi*ctx.j) + +@defun_wrapped +def kfrom(ctx, q=None, m=None, k=None, tau=None, qbar=None): + r""" + Returns the elliptic modulus `k`, given any of + `q, m, k, \tau, \bar{q}`:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> kfrom(k=0.25) + 0.25 + >>> kfrom(m=mfrom(k=0.25)) + 0.25 + >>> kfrom(q=qfrom(k=0.25)) + 0.25 + >>> kfrom(tau=taufrom(k=0.25)) + (0.25 + 0.0j) + >>> kfrom(qbar=qbarfrom(k=0.25)) + 0.25 + + As `q \to 1` and `q \to -1`, `k` rapidly approaches + `1` and `i \infty` respectively:: + + >>> kfrom(q=0.75) + 0.9999999999999899166471767 + >>> kfrom(q=-0.75) + (0.0 + 7041781.096692038332790615j) + >>> kfrom(q=1) + 1 + >>> kfrom(q=-1) + (0.0 + +infj) + """ + if k is not None: + return ctx.convert(k) + if m is not None: + return ctx.sqrt(m) + if tau is not None: + q = ctx.expjpi(tau) + if qbar is not None: + q = ctx.sqrt(qbar) + if q == 1: + return q + if q == -1: + return ctx.mpc(0,'inf') + return (ctx.jtheta(2,0,q)/ctx.jtheta(3,0,q))**2 + +@defun_wrapped +def mfrom(ctx, q=None, m=None, k=None, tau=None, qbar=None): + r""" + Returns the elliptic parameter `m`, given any of + `q, m, k, \tau, \bar{q}`:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> mfrom(m=0.25) + 0.25 + >>> mfrom(q=qfrom(m=0.25)) + 0.25 + >>> mfrom(k=kfrom(m=0.25)) + 0.25 + >>> mfrom(tau=taufrom(m=0.25)) + (0.25 + 0.0j) + >>> mfrom(qbar=qbarfrom(m=0.25)) + 0.25 + + As `q \to 1` and `q \to -1`, `m` rapidly approaches + `1` and `-\infty` respectively:: + + >>> mfrom(q=0.75) + 0.9999999999999798332943533 + >>> mfrom(q=-0.75) + -49586681013729.32611558353 + >>> mfrom(q=1) + 1.0 + >>> mfrom(q=-1) + -inf + + The inverse nome as a function of `q` has an integer + Taylor series expansion:: + + >>> taylor(lambda q: mfrom(q), 0, 7) + [0.0, 16.0, -128.0, 704.0, -3072.0, 11488.0, -38400.0, 117632.0] + + """ + if m is not None: + return m + if k is not None: + return k**2 + if tau is not None: + q = ctx.expjpi(tau) + if qbar is not None: + q = ctx.sqrt(qbar) + if q == 1: + return ctx.convert(q) + if q == -1: + return q*ctx.inf + v = (ctx.jtheta(2,0,q)/ctx.jtheta(3,0,q))**4 + if ctx._is_real_type(q) and q < 0: + v = v.real + return v + +jacobi_spec = { + 'sn' : ([3],[2],[1],[4], 'sin', 'tanh'), + 'cn' : ([4],[2],[2],[4], 'cos', 'sech'), + 'dn' : ([4],[3],[3],[4], '1', 'sech'), + 'ns' : ([2],[3],[4],[1], 'csc', 'coth'), + 'nc' : ([2],[4],[4],[2], 'sec', 'cosh'), + 'nd' : ([3],[4],[4],[3], '1', 'cosh'), + 'sc' : ([3],[4],[1],[2], 'tan', 'sinh'), + 'sd' : ([3,3],[2,4],[1],[3], 'sin', 'sinh'), + 'cd' : ([3],[2],[2],[3], 'cos', '1'), + 'cs' : ([4],[3],[2],[1], 'cot', 'csch'), + 'dc' : ([2],[3],[3],[2], 'sec', '1'), + 'ds' : ([2,4],[3,3],[3],[1], 'csc', 'csch'), + 'cc' : None, + 'ss' : None, + 'nn' : None, + 'dd' : None +} + +@defun +def ellipfun(ctx, kind, u=None, m=None, q=None, k=None, tau=None): + try: + S = jacobi_spec[kind] + except KeyError: + raise ValueError("First argument must be a two-character string " + "containing 's', 'c', 'd' or 'n', e.g.: 'sn'") + if u is None: + def f(*args, **kwargs): + return ctx.ellipfun(kind, *args, **kwargs) + f.__name__ = kind + return f + prec = ctx.prec + try: + ctx.prec += 10 + u = ctx.convert(u) + q = ctx.qfrom(m=m, q=q, k=k, tau=tau) + if S is None: + v = ctx.one + 0*q*u + elif q == ctx.zero: + if S[4] == '1': v = ctx.one + else: v = getattr(ctx, S[4])(u) + v += 0*q*u + elif q == ctx.one: + if S[5] == '1': v = ctx.one + else: v = getattr(ctx, S[5])(u) + v += 0*q*u + else: + t = u / ctx.jtheta(3, 0, q)**2 + v = ctx.one + for a in S[0]: v *= ctx.jtheta(a, 0, q) + for b in S[1]: v /= ctx.jtheta(b, 0, q) + for c in S[2]: v *= ctx.jtheta(c, t, q) + for d in S[3]: v /= ctx.jtheta(d, t, q) + finally: + ctx.prec = prec + return +v + +@defun_wrapped +def kleinj(ctx, tau=None, **kwargs): + r""" + Evaluates the Klein j-invariant, which is a modular function defined for + `\tau` in the upper half-plane as + + .. math :: + + J(\tau) = \frac{g_2^3(\tau)}{g_2^3(\tau) - 27 g_3^2(\tau)} + + where `g_2` and `g_3` are the modular invariants of the Weierstrass + elliptic function, + + .. math :: + + g_2(\tau) = 60 \sum_{(m,n) \in \mathbb{Z}^2 \setminus (0,0)} (m \tau+n)^{-4} + + g_3(\tau) = 140 \sum_{(m,n) \in \mathbb{Z}^2 \setminus (0,0)} (m \tau+n)^{-6}. + + An alternative, common notation is that of the j-function + `j(\tau) = 1728 J(\tau)`. + + **Plots** + + .. literalinclude :: /plots/kleinj.py + .. image :: /plots/kleinj.png + .. literalinclude :: /plots/kleinj2.py + .. image :: /plots/kleinj2.png + + **Examples** + + Verifying the functional equation `J(\tau) = J(\tau+1) = J(-\tau^{-1})`:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> tau = 0.625+0.75*j + >>> tau = 0.625+0.75*j + >>> kleinj(tau) + (-0.1507492166511182267125242 + 0.07595948379084571927228948j) + >>> kleinj(tau+1) + (-0.1507492166511182267125242 + 0.07595948379084571927228948j) + >>> kleinj(-1/tau) + (-0.1507492166511182267125242 + 0.07595948379084571927228946j) + + The j-function has a famous Laurent series expansion in terms of the nome + `\bar{q}`, `j(\tau) = \bar{q}^{-1} + 744 + 196884\bar{q} + \ldots`:: + + >>> mp.dps = 15 + >>> taylor(lambda q: 1728*q*kleinj(qbar=q), 0, 5, singular=True) + [1.0, 744.0, 196884.0, 21493760.0, 864299970.0, 20245856256.0] + + The j-function admits exact evaluation at special algebraic points + related to the Heegner numbers 1, 2, 3, 7, 11, 19, 43, 67, 163:: + + >>> @extraprec(10) + ... def h(n): + ... v = (1+sqrt(n)*j) + ... if n > 2: + ... v *= 0.5 + ... return v + ... + >>> mp.dps = 25 + >>> for n in [1,2,3,7,11,19,43,67,163]: + ... n, chop(1728*kleinj(h(n))) + ... + (1, 1728.0) + (2, 8000.0) + (3, 0.0) + (7, -3375.0) + (11, -32768.0) + (19, -884736.0) + (43, -884736000.0) + (67, -147197952000.0) + (163, -262537412640768000.0) + + Also at other special points, the j-function assumes explicit + algebraic values, e.g.:: + + >>> chop(1728*kleinj(j*sqrt(5))) + 1264538.909475140509320227 + >>> identify(cbrt(_)) # note: not simplified + '((100+sqrt(13520))/2)' + >>> (50+26*sqrt(5))**3 + 1264538.909475140509320227 + + """ + q = ctx.qfrom(tau=tau, **kwargs) + t2 = ctx.jtheta(2,0,q) + t3 = ctx.jtheta(3,0,q) + t4 = ctx.jtheta(4,0,q) + P = (t2**8 + t3**8 + t4**8)**3 + Q = 54*(t2*t3*t4)**8 + return P/Q + + +def RF_calc(ctx, x, y, z, r): + if y == z: return RC_calc(ctx, x, y, r) + if x == z: return RC_calc(ctx, y, x, r) + if x == y: return RC_calc(ctx, z, x, r) + if not (ctx.isnormal(x) and ctx.isnormal(y) and ctx.isnormal(z)): + if ctx.isnan(x) or ctx.isnan(y) or ctx.isnan(z): + return x*y*z + if ctx.isinf(x) or ctx.isinf(y) or ctx.isinf(z): + return ctx.zero + xm,ym,zm = x,y,z + A0 = Am = (x+y+z)/3 + Q = ctx.root(3*r, -6) * max(abs(A0-x),abs(A0-y),abs(A0-z)) + g = ctx.mpf(0.25) + pow4 = ctx.one + while 1: + xs = ctx.sqrt(xm) + ys = ctx.sqrt(ym) + zs = ctx.sqrt(zm) + lm = xs*ys + xs*zs + ys*zs + Am1 = (Am+lm)*g + xm, ym, zm = (xm+lm)*g, (ym+lm)*g, (zm+lm)*g + if pow4 * Q < abs(Am): + break + Am = Am1 + pow4 *= g + t = pow4/Am + X = (A0-x)*t + Y = (A0-y)*t + Z = -X-Y + E2 = X*Y-Z**2 + E3 = X*Y*Z + return ctx.power(Am,-0.5) * (9240-924*E2+385*E2**2+660*E3-630*E2*E3)/9240 + +def RC_calc(ctx, x, y, r, pv=True): + if not (ctx.isnormal(x) and ctx.isnormal(y)): + if ctx.isinf(x) or ctx.isinf(y): + return 1/(x*y) + if y == 0: + return ctx.inf + if x == 0: + return ctx.pi / ctx.sqrt(y) / 2 + raise ValueError + # Cauchy principal value + if pv and ctx._im(y) == 0 and ctx._re(y) < 0: + return ctx.sqrt(x/(x-y)) * RC_calc(ctx, x-y, -y, r) + if x == y: + return 1/ctx.sqrt(x) + extraprec = 2*max(0,-ctx.mag(x-y)+ctx.mag(x)) + ctx.prec += extraprec + if ctx._is_real_type(x) and ctx._is_real_type(y): + x = ctx._re(x) + y = ctx._re(y) + a = ctx.sqrt(x/y) + if x < y: + b = ctx.sqrt(y-x) + v = ctx.acos(a)/b + else: + b = ctx.sqrt(x-y) + v = ctx.acosh(a)/b + else: + sx = ctx.sqrt(x) + sy = ctx.sqrt(y) + v = ctx.acos(sx/sy)/(ctx.sqrt((1-x/y))*sy) + ctx.prec -= extraprec + return v + +def RJ_calc(ctx, x, y, z, p, r, integration): + """ + With integration == 0, computes RJ only using Carlson's algorithm + (may be wrong for some values). + With integration == 1, uses an initial integration to make sure + Carlson's algorithm is correct. + With integration == 2, uses only integration. + """ + if not (ctx.isnormal(x) and ctx.isnormal(y) and \ + ctx.isnormal(z) and ctx.isnormal(p)): + if ctx.isnan(x) or ctx.isnan(y) or ctx.isnan(z) or ctx.isnan(p): + return x*y*z + if ctx.isinf(x) or ctx.isinf(y) or ctx.isinf(z) or ctx.isinf(p): + return ctx.zero + if not p: + return ctx.inf + if (not x) + (not y) + (not z) > 1: + return ctx.inf + # Check conditions and fall back on integration for argument + # reduction if needed. The following conditions might be needlessly + # restrictive. + initial_integral = ctx.zero + if integration >= 1: + ok = (x.real >= 0 and y.real >= 0 and z.real >= 0 and p.real > 0) + if not ok: + if x == p or y == p or z == p: + ok = True + if not ok: + if p.imag != 0 or p.real >= 0: + if (x.imag == 0 and x.real >= 0 and ctx.conj(y) == z): + ok = True + if (y.imag == 0 and y.real >= 0 and ctx.conj(x) == z): + ok = True + if (z.imag == 0 and z.real >= 0 and ctx.conj(x) == y): + ok = True + if not ok or (integration == 2): + N = ctx.ceil(-min(x.real, y.real, z.real, p.real)) + 1 + # Integrate around any singularities + if all((t.imag >= 0 or t.real > 0) for t in [x, y, z, p]): + margin = ctx.j + elif all((t.imag < 0 or t.real > 0) for t in [x, y, z, p]): + margin = -ctx.j + else: + margin = 1 + # Go through the upper half-plane, but low enough that any + # parameter starting in the lower plane doesn't cross the + # branch cut + for t in [x, y, z, p]: + if t.imag >= 0 or t.real > 0: + continue + margin = min(margin, abs(t.imag) * 0.5) + margin *= ctx.j + N += margin + F = lambda t: 1/(ctx.sqrt(t+x)*ctx.sqrt(t+y)*ctx.sqrt(t+z)*(t+p)) + if integration == 2: + return 1.5 * ctx.quadsubdiv(F, [0, N, ctx.inf]) + initial_integral = 1.5 * ctx.quadsubdiv(F, [0, N]) + x += N; y += N; z += N; p += N + xm,ym,zm,pm = x,y,z,p + A0 = Am = (x + y + z + 2*p)/5 + delta = (p-x)*(p-y)*(p-z) + Q = ctx.root(0.25*r, -6) * max(abs(A0-x),abs(A0-y),abs(A0-z),abs(A0-p)) + g = ctx.mpf(0.25) + pow4 = ctx.one + S = 0 + while 1: + sx = ctx.sqrt(xm) + sy = ctx.sqrt(ym) + sz = ctx.sqrt(zm) + sp = ctx.sqrt(pm) + lm = sx*sy + sx*sz + sy*sz + Am1 = (Am+lm)*g + xm = (xm+lm)*g; ym = (ym+lm)*g; zm = (zm+lm)*g; pm = (pm+lm)*g + dm = (sp+sx) * (sp+sy) * (sp+sz) + em = delta * pow4**3 / dm**2 + if pow4 * Q < abs(Am): + break + T = RC_calc(ctx, ctx.one, ctx.one+em, r) * pow4 / dm + S += T + pow4 *= g + Am = Am1 + t = pow4 / Am + X = (A0-x)*t + Y = (A0-y)*t + Z = (A0-z)*t + P = (-X-Y-Z)/2 + E2 = X*Y + X*Z + Y*Z - 3*P**2 + E3 = X*Y*Z + 2*E2*P + 4*P**3 + E4 = (2*X*Y*Z + E2*P + 3*P**3)*P + E5 = X*Y*Z*P**2 + P = 24024 - 5148*E2 + 2457*E2**2 + 4004*E3 - 4158*E2*E3 - 3276*E4 + 2772*E5 + Q = 24024 + v1 = pow4 * ctx.power(Am, -1.5) * P/Q + v2 = 6*S + return initial_integral + v1 + v2 + +@defun +def elliprf(ctx, x, y, z): + r""" + Evaluates the Carlson symmetric elliptic integral of the first kind + + .. math :: + + R_F(x,y,z) = \frac{1}{2} + \int_0^{\infty} \frac{dt}{\sqrt{(t+x)(t+y)(t+z)}} + + which is defined for `x,y,z \notin (-\infty,0)`, and with + at most one of `x,y,z` being zero. + + For real `x,y,z \ge 0`, the principal square root is taken in the integrand. + For complex `x,y,z`, the principal square root is taken as `t \to \infty` + and as `t \to 0` non-principal branches are chosen as necessary so as to + make the integrand continuous. + + **Examples** + + Some basic values and limits:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> elliprf(0,1,1); pi/2 + 1.570796326794896619231322 + 1.570796326794896619231322 + >>> elliprf(0,1,inf) + 0.0 + >>> elliprf(1,1,1) + 1.0 + >>> elliprf(2,2,2)**2 + 0.5 + >>> elliprf(1,0,0); elliprf(0,0,1); elliprf(0,1,0); elliprf(0,0,0) + +inf + +inf + +inf + +inf + + Representing complete elliptic integrals in terms of `R_F`:: + + >>> m = mpf(0.75) + >>> ellipk(m); elliprf(0,1-m,1) + 2.156515647499643235438675 + 2.156515647499643235438675 + >>> ellipe(m); elliprf(0,1-m,1)-m*elliprd(0,1-m,1)/3 + 1.211056027568459524803563 + 1.211056027568459524803563 + + Some symmetries and argument transformations:: + + >>> x,y,z = 2,3,4 + >>> elliprf(x,y,z); elliprf(y,x,z); elliprf(z,y,x) + 0.5840828416771517066928492 + 0.5840828416771517066928492 + 0.5840828416771517066928492 + >>> k = mpf(100000) + >>> elliprf(k*x,k*y,k*z); k**(-0.5) * elliprf(x,y,z) + 0.001847032121923321253219284 + 0.001847032121923321253219284 + >>> l = sqrt(x*y) + sqrt(y*z) + sqrt(z*x) + >>> elliprf(x,y,z); 2*elliprf(x+l,y+l,z+l) + 0.5840828416771517066928492 + 0.5840828416771517066928492 + >>> elliprf((x+l)/4,(y+l)/4,(z+l)/4) + 0.5840828416771517066928492 + + Comparing with numerical integration:: + + >>> x,y,z = 2,3,4 + >>> elliprf(x,y,z) + 0.5840828416771517066928492 + >>> f = lambda t: 0.5*((t+x)*(t+y)*(t+z))**(-0.5) + >>> q = extradps(25)(quad) + >>> q(f, [0,inf]) + 0.5840828416771517066928492 + + With the following arguments, the square root in the integrand becomes + discontinuous at `t = 1/2` if the principal branch is used. To obtain + the right value, `-\sqrt{r}` must be taken instead of `\sqrt{r}` + on `t \in (0, 1/2)`:: + + >>> x,y,z = j-1,j,0 + >>> elliprf(x,y,z) + (0.7961258658423391329305694 - 1.213856669836495986430094j) + >>> -q(f, [0,0.5]) + q(f, [0.5,inf]) + (0.7961258658423391329305694 - 1.213856669836495986430094j) + + The so-called *first lemniscate constant*, a transcendental number:: + + >>> elliprf(0,1,2) + 1.31102877714605990523242 + >>> extradps(25)(quad)(lambda t: 1/sqrt(1-t**4), [0,1]) + 1.31102877714605990523242 + >>> gamma('1/4')**2/(4*sqrt(2*pi)) + 1.31102877714605990523242 + + **References** + + 1. [Carlson]_ + 2. [DLMF]_ Chapter 19. Elliptic Integrals + + """ + x = ctx.convert(x) + y = ctx.convert(y) + z = ctx.convert(z) + prec = ctx.prec + try: + ctx.prec += 20 + tol = ctx.eps * 2**10 + v = RF_calc(ctx, x, y, z, tol) + finally: + ctx.prec = prec + return +v + +@defun +def elliprc(ctx, x, y, pv=True): + r""" + Evaluates the degenerate Carlson symmetric elliptic integral + of the first kind + + .. math :: + + R_C(x,y) = R_F(x,y,y) = + \frac{1}{2} \int_0^{\infty} \frac{dt}{(t+y) \sqrt{(t+x)}}. + + If `y \in (-\infty,0)`, either a value defined by continuity, + or with *pv=True* the Cauchy principal value, can be computed. + + If `x \ge 0, y > 0`, the value can be expressed in terms of + elementary functions as + + .. math :: + + R_C(x,y) = + \begin{cases} + \dfrac{1}{\sqrt{y-x}} + \cos^{-1}\left(\sqrt{\dfrac{x}{y}}\right), & x < y \\ + \dfrac{1}{\sqrt{y}}, & x = y \\ + \dfrac{1}{\sqrt{x-y}} + \cosh^{-1}\left(\sqrt{\dfrac{x}{y}}\right), & x > y \\ + \end{cases}. + + **Examples** + + Some special values and limits:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> elliprc(1,2)*4; elliprc(0,1)*2; +pi + 3.141592653589793238462643 + 3.141592653589793238462643 + 3.141592653589793238462643 + >>> elliprc(1,0) + +inf + >>> elliprc(5,5)**2 + 0.2 + >>> elliprc(1,inf); elliprc(inf,1); elliprc(inf,inf) + 0.0 + 0.0 + 0.0 + + Comparing with the elementary closed-form solution:: + + >>> elliprc('1/3', '1/5'); sqrt(7.5)*acosh(sqrt('5/3')) + 2.041630778983498390751238 + 2.041630778983498390751238 + >>> elliprc('1/5', '1/3'); sqrt(7.5)*acos(sqrt('3/5')) + 1.875180765206547065111085 + 1.875180765206547065111085 + + Comparing with numerical integration:: + + >>> q = extradps(25)(quad) + >>> elliprc(2, -3, pv=True) + 0.3333969101113672670749334 + >>> elliprc(2, -3, pv=False) + (0.3333969101113672670749334 + 0.7024814731040726393156375j) + >>> 0.5*q(lambda t: 1/(sqrt(t+2)*(t-3)), [0,3-j,6,inf]) + (0.3333969101113672670749334 + 0.7024814731040726393156375j) + + """ + x = ctx.convert(x) + y = ctx.convert(y) + prec = ctx.prec + try: + ctx.prec += 20 + tol = ctx.eps * 2**10 + v = RC_calc(ctx, x, y, tol, pv) + finally: + ctx.prec = prec + return +v + +@defun +def elliprj(ctx, x, y, z, p, integration=1): + r""" + Evaluates the Carlson symmetric elliptic integral of the third kind + + .. math :: + + R_J(x,y,z,p) = \frac{3}{2} + \int_0^{\infty} \frac{dt}{(t+p)\sqrt{(t+x)(t+y)(t+z)}}. + + Like :func:`~mpmath.elliprf`, the branch of the square root in the integrand + is defined so as to be continuous along the path of integration for + complex values of the arguments. + + **Examples** + + Some values and limits:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> elliprj(1,1,1,1) + 1.0 + >>> elliprj(2,2,2,2); 1/(2*sqrt(2)) + 0.3535533905932737622004222 + 0.3535533905932737622004222 + >>> elliprj(0,1,2,2) + 1.067937989667395702268688 + >>> 3*(2*gamma('5/4')**2-pi**2/gamma('1/4')**2)/(sqrt(2*pi)) + 1.067937989667395702268688 + >>> elliprj(0,1,1,2); 3*pi*(2-sqrt(2))/4 + 1.380226776765915172432054 + 1.380226776765915172432054 + >>> elliprj(1,3,2,0); elliprj(0,1,1,0); elliprj(0,0,0,0) + +inf + +inf + +inf + >>> elliprj(1,inf,1,0); elliprj(1,1,1,inf) + 0.0 + 0.0 + >>> chop(elliprj(1+j, 1-j, 1, 1)) + 0.8505007163686739432927844 + + Scale transformation:: + + >>> x,y,z,p = 2,3,4,5 + >>> k = mpf(100000) + >>> elliprj(k*x,k*y,k*z,k*p); k**(-1.5)*elliprj(x,y,z,p) + 4.521291677592745527851168e-9 + 4.521291677592745527851168e-9 + + Comparing with numerical integration:: + + >>> elliprj(1,2,3,4) + 0.2398480997495677621758617 + >>> f = lambda t: 1/((t+4)*sqrt((t+1)*(t+2)*(t+3))) + >>> 1.5*quad(f, [0,inf]) + 0.2398480997495677621758617 + >>> elliprj(1,2+1j,3,4-2j) + (0.216888906014633498739952 + 0.04081912627366673332369512j) + >>> f = lambda t: 1/((t+4-2j)*sqrt((t+1)*(t+2+1j)*(t+3))) + >>> 1.5*quad(f, [0,inf]) + (0.216888906014633498739952 + 0.04081912627366673332369511j) + + """ + x = ctx.convert(x) + y = ctx.convert(y) + z = ctx.convert(z) + p = ctx.convert(p) + prec = ctx.prec + try: + ctx.prec += 20 + tol = ctx.eps * 2**10 + v = RJ_calc(ctx, x, y, z, p, tol, integration) + finally: + ctx.prec = prec + return +v + +@defun +def elliprd(ctx, x, y, z): + r""" + Evaluates the degenerate Carlson symmetric elliptic integral + of the third kind or Carlson elliptic integral of the + second kind `R_D(x,y,z) = R_J(x,y,z,z)`. + + See :func:`~mpmath.elliprj` for additional information. + + **Examples** + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> elliprd(1,2,3) + 0.2904602810289906442326534 + >>> elliprj(1,2,3,3) + 0.2904602810289906442326534 + + The so-called *second lemniscate constant*, a transcendental number:: + + >>> elliprd(0,2,1)/3 + 0.5990701173677961037199612 + >>> extradps(25)(quad)(lambda t: t**2/sqrt(1-t**4), [0,1]) + 0.5990701173677961037199612 + >>> gamma('3/4')**2/sqrt(2*pi) + 0.5990701173677961037199612 + + """ + return ctx.elliprj(x,y,z,z) + +@defun +def elliprg(ctx, x, y, z): + r""" + Evaluates the Carlson completely symmetric elliptic integral + of the second kind + + .. math :: + + R_G(x,y,z) = \frac{1}{4} \int_0^{\infty} + \frac{t}{\sqrt{(t+x)(t+y)(t+z)}} + \left( \frac{x}{t+x} + \frac{y}{t+y} + \frac{z}{t+z}\right) dt. + + **Examples** + + Evaluation for real and complex arguments:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> elliprg(0,1,1)*4; +pi + 3.141592653589793238462643 + 3.141592653589793238462643 + >>> elliprg(0,0.5,1) + 0.6753219405238377512600874 + >>> chop(elliprg(1+j, 1-j, 2)) + 1.172431327676416604532822 + + A double integral that can be evaluated in terms of `R_G`:: + + >>> x,y,z = 2,3,4 + >>> def f(t,u): + ... st = fp.sin(t); ct = fp.cos(t) + ... su = fp.sin(u); cu = fp.cos(u) + ... return (x*(st*cu)**2 + y*(st*su)**2 + z*ct**2)**0.5 * st + ... + >>> nprint(mpf(fp.quad(f, [0,fp.pi], [0,2*fp.pi])/(4*fp.pi)), 13) + 1.725503028069 + >>> nprint(elliprg(x,y,z), 13) + 1.725503028069 + + """ + x = ctx.convert(x) + y = ctx.convert(y) + z = ctx.convert(z) + zeros = (not x) + (not y) + (not z) + if zeros == 3: + return (x+y+z)*0 + if zeros == 2: + if x: return 0.5*ctx.sqrt(x) + if y: return 0.5*ctx.sqrt(y) + return 0.5*ctx.sqrt(z) + if zeros == 1: + if not z: + x, z = z, x + def terms(): + T1 = 0.5*z*ctx.elliprf(x,y,z) + T2 = -0.5*(x-z)*(y-z)*ctx.elliprd(x,y,z)/3 + T3 = 0.5*ctx.sqrt(x)*ctx.sqrt(y)/ctx.sqrt(z) + return T1,T2,T3 + return ctx.sum_accurately(terms) + + +@defun_wrapped +def ellipf(ctx, phi, m): + r""" + Evaluates the Legendre incomplete elliptic integral of the first kind + + .. math :: + + F(\phi,m) = \int_0^{\phi} \frac{dt}{\sqrt{1-m \sin^2 t}} + + or equivalently + + .. math :: + + F(\phi,m) = \int_0^{\sin \phi} + \frac{dt}{\left(\sqrt{1-t^2}\right)\left(\sqrt{1-mt^2}\right)}. + + The function reduces to a complete elliptic integral of the first kind + (see :func:`~mpmath.ellipk`) when `\phi = \frac{\pi}{2}`; that is, + + .. math :: + + F\left(\frac{\pi}{2}, m\right) = K(m). + + In the defining integral, it is assumed that the principal branch + of the square root is taken and that the path of integration avoids + crossing any branch cuts. Outside `-\pi/2 \le \Re(\phi) \le \pi/2`, + the function extends quasi-periodically as + + .. math :: + + F(\phi + n \pi, m) = 2 n K(m) + F(\phi,m), n \in \mathbb{Z}. + + **Plots** + + .. literalinclude :: /plots/ellipf.py + .. image :: /plots/ellipf.png + + **Examples** + + Basic values and limits:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> ellipf(0,1) + 0.0 + >>> ellipf(0,0) + 0.0 + >>> ellipf(1,0); ellipf(2+3j,0) + 1.0 + (2.0 + 3.0j) + >>> ellipf(1,1); log(sec(1)+tan(1)) + 1.226191170883517070813061 + 1.226191170883517070813061 + >>> ellipf(pi/2, -0.5); ellipk(-0.5) + 1.415737208425956198892166 + 1.415737208425956198892166 + >>> ellipf(pi/2+eps, 1); ellipf(-pi/2-eps, 1) + +inf + +inf + >>> ellipf(1.5, 1) + 3.340677542798311003320813 + + Comparing with numerical integration:: + + >>> z,m = 0.5, 1.25 + >>> ellipf(z,m) + 0.5287219202206327872978255 + >>> quad(lambda t: (1-m*sin(t)**2)**(-0.5), [0,z]) + 0.5287219202206327872978255 + + The arguments may be complex numbers:: + + >>> ellipf(3j, 0.5) + (0.0 + 1.713602407841590234804143j) + >>> ellipf(3+4j, 5-6j) + (1.269131241950351323305741 - 0.3561052815014558335412538j) + >>> z,m = 2+3j, 1.25 + >>> k = 1011 + >>> ellipf(z+pi*k,m); ellipf(z,m) + 2*k*ellipk(m) + (4086.184383622179764082821 - 3003.003538923749396546871j) + (4086.184383622179764082821 - 3003.003538923749396546871j) + + For `|\Re(z)| < \pi/2`, the function can be expressed as a + hypergeometric series of two variables + (see :func:`~mpmath.appellf1`):: + + >>> z,m = 0.5, 0.25 + >>> ellipf(z,m) + 0.5050887275786480788831083 + >>> sin(z)*appellf1(0.5,0.5,0.5,1.5,sin(z)**2,m*sin(z)**2) + 0.5050887275786480788831083 + + """ + z = phi + if not (ctx.isnormal(z) and ctx.isnormal(m)): + if m == 0: + return z + m + if z == 0: + return z * m + if m == ctx.inf or m == ctx.ninf: return z/m + raise ValueError + x = z.real + ctx.prec += max(0, ctx.mag(x)) + pi = +ctx.pi + away = abs(x) > pi/2 + if m == 1: + if away: + return ctx.inf + if away: + d = ctx.nint(x/pi) + z = z-pi*d + P = 2*d*ctx.ellipk(m) + else: + P = 0 + c, s = ctx.cos_sin(z) + return s * ctx.elliprf(c**2, 1-m*s**2, 1) + P + +@defun_wrapped +def ellipe(ctx, *args): + r""" + Called with a single argument `m`, evaluates the Legendre complete + elliptic integral of the second kind, `E(m)`, defined by + + .. math :: E(m) = \int_0^{\pi/2} \sqrt{1-m \sin^2 t} \, dt \,=\, + \frac{\pi}{2} + \,_2F_1\left(\frac{1}{2}, -\frac{1}{2}, 1, m\right). + + Called with two arguments `\phi, m`, evaluates the incomplete elliptic + integral of the second kind + + .. math :: + + E(\phi,m) = \int_0^{\phi} \sqrt{1-m \sin^2 t} \, dt = + \int_0^{\sin z} + \frac{\sqrt{1-mt^2}}{\sqrt{1-t^2}} \, dt. + + The incomplete integral reduces to a complete integral when + `\phi = \frac{\pi}{2}`; that is, + + .. math :: + + E\left(\frac{\pi}{2}, m\right) = E(m). + + In the defining integral, it is assumed that the principal branch + of the square root is taken and that the path of integration avoids + crossing any branch cuts. Outside `-\pi/2 \le \Re(z) \le \pi/2`, + the function extends quasi-periodically as + + .. math :: + + E(\phi + n \pi, m) = 2 n E(m) + E(\phi,m), n \in \mathbb{Z}. + + **Plots** + + .. literalinclude :: /plots/ellipe.py + .. image :: /plots/ellipe.png + + **Examples for the complete integral** + + Basic values and limits:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> ellipe(0) + 1.570796326794896619231322 + >>> ellipe(1) + 1.0 + >>> ellipe(-1) + 1.910098894513856008952381 + >>> ellipe(2) + (0.5990701173677961037199612 + 0.5990701173677961037199612j) + >>> ellipe(inf) + (0.0 + +infj) + >>> ellipe(-inf) + +inf + + Verifying the defining integral and hypergeometric + representation:: + + >>> ellipe(0.5) + 1.350643881047675502520175 + >>> quad(lambda t: sqrt(1-0.5*sin(t)**2), [0, pi/2]) + 1.350643881047675502520175 + >>> pi/2*hyp2f1(0.5,-0.5,1,0.5) + 1.350643881047675502520175 + + Evaluation is supported for arbitrary complex `m`:: + + >>> ellipe(0.5+0.25j) + (1.360868682163129682716687 - 0.1238733442561786843557315j) + >>> ellipe(3+4j) + (1.499553520933346954333612 - 1.577879007912758274533309j) + + A definite integral:: + + >>> quad(ellipe, [0,1]) + 1.333333333333333333333333 + + **Examples for the incomplete integral** + + Basic values and limits:: + + >>> ellipe(0,1) + 0.0 + >>> ellipe(0,0) + 0.0 + >>> ellipe(1,0) + 1.0 + >>> ellipe(2+3j,0) + (2.0 + 3.0j) + >>> ellipe(1,1); sin(1) + 0.8414709848078965066525023 + 0.8414709848078965066525023 + >>> ellipe(pi/2, -0.5); ellipe(-0.5) + 1.751771275694817862026502 + 1.751771275694817862026502 + >>> ellipe(pi/2, 1); ellipe(-pi/2, 1) + 1.0 + -1.0 + >>> ellipe(1.5, 1) + 0.9974949866040544309417234 + + Comparing with numerical integration:: + + >>> z,m = 0.5, 1.25 + >>> ellipe(z,m) + 0.4740152182652628394264449 + >>> quad(lambda t: sqrt(1-m*sin(t)**2), [0,z]) + 0.4740152182652628394264449 + + The arguments may be complex numbers:: + + >>> ellipe(3j, 0.5) + (0.0 + 7.551991234890371873502105j) + >>> ellipe(3+4j, 5-6j) + (24.15299022574220502424466 + 75.2503670480325997418156j) + >>> k = 35 + >>> z,m = 2+3j, 1.25 + >>> ellipe(z+pi*k,m); ellipe(z,m) + 2*k*ellipe(m) + (48.30138799412005235090766 + 17.47255216721987688224357j) + (48.30138799412005235090766 + 17.47255216721987688224357j) + + For `|\Re(z)| < \pi/2`, the function can be expressed as a + hypergeometric series of two variables + (see :func:`~mpmath.appellf1`):: + + >>> z,m = 0.5, 0.25 + >>> ellipe(z,m) + 0.4950017030164151928870375 + >>> sin(z)*appellf1(0.5,0.5,-0.5,1.5,sin(z)**2,m*sin(z)**2) + 0.4950017030164151928870376 + + """ + if len(args) == 1: + return ctx._ellipe(args[0]) + else: + phi, m = args + z = phi + if not (ctx.isnormal(z) and ctx.isnormal(m)): + if m == 0: + return z + m + if z == 0: + return z * m + if m == ctx.inf or m == ctx.ninf: + return ctx.inf + raise ValueError + x = z.real + ctx.prec += max(0, ctx.mag(x)) + pi = +ctx.pi + away = abs(x) > pi/2 + if away: + d = ctx.nint(x/pi) + z = z-pi*d + P = 2*d*ctx.ellipe(m) + else: + P = 0 + def terms(): + c, s = ctx.cos_sin(z) + x = c**2 + y = 1-m*s**2 + RF = ctx.elliprf(x, y, 1) + RD = ctx.elliprd(x, y, 1) + return s*RF, -m*s**3*RD/3 + return ctx.sum_accurately(terms) + P + +@defun_wrapped +def ellippi(ctx, *args): + r""" + Called with three arguments `n, \phi, m`, evaluates the Legendre + incomplete elliptic integral of the third kind + + .. math :: + + \Pi(n; \phi, m) = \int_0^{\phi} + \frac{dt}{(1-n \sin^2 t) \sqrt{1-m \sin^2 t}} = + \int_0^{\sin \phi} + \frac{dt}{(1-nt^2) \sqrt{1-t^2} \sqrt{1-mt^2}}. + + Called with two arguments `n, m`, evaluates the complete + elliptic integral of the third kind + `\Pi(n,m) = \Pi(n; \frac{\pi}{2},m)`. + + In the defining integral, it is assumed that the principal branch + of the square root is taken and that the path of integration avoids + crossing any branch cuts. Outside `-\pi/2 \le \Re(\phi) \le \pi/2`, + the function extends quasi-periodically as + + .. math :: + + \Pi(n,\phi+k\pi,m) = 2k\Pi(n,m) + \Pi(n,\phi,m), k \in \mathbb{Z}. + + **Plots** + + .. literalinclude :: /plots/ellippi.py + .. image :: /plots/ellippi.png + + **Examples for the complete integral** + + Some basic values and limits:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> ellippi(0,-5); ellipk(-5) + 0.9555039270640439337379334 + 0.9555039270640439337379334 + >>> ellippi(inf,2) + 0.0 + >>> ellippi(2,inf) + 0.0 + >>> abs(ellippi(1,5)) + +inf + >>> abs(ellippi(0.25,1)) + +inf + + Evaluation in terms of simpler functions:: + + >>> ellippi(0.25,0.25); ellipe(0.25)/(1-0.25) + 1.956616279119236207279727 + 1.956616279119236207279727 + >>> ellippi(3,0); pi/(2*sqrt(-2)) + (0.0 - 1.11072073453959156175397j) + (0.0 - 1.11072073453959156175397j) + >>> ellippi(-3,0); pi/(2*sqrt(4)) + 0.7853981633974483096156609 + 0.7853981633974483096156609 + + **Examples for the incomplete integral** + + Basic values and limits:: + + >>> ellippi(0.25,-0.5); ellippi(0.25,pi/2,-0.5) + 1.622944760954741603710555 + 1.622944760954741603710555 + >>> ellippi(1,0,1) + 0.0 + >>> ellippi(inf,0,1) + 0.0 + >>> ellippi(0,0.25,0.5); ellipf(0.25,0.5) + 0.2513040086544925794134591 + 0.2513040086544925794134591 + >>> ellippi(1,1,1); (log(sec(1)+tan(1))+sec(1)*tan(1))/2 + 2.054332933256248668692452 + 2.054332933256248668692452 + >>> ellippi(0.25, 53*pi/2, 0.75); 53*ellippi(0.25,0.75) + 135.240868757890840755058 + 135.240868757890840755058 + >>> ellippi(0.5,pi/4,0.5); 2*ellipe(pi/4,0.5)-1/sqrt(3) + 0.9190227391656969903987269 + 0.9190227391656969903987269 + + Complex arguments are supported:: + + >>> ellippi(0.5, 5+6j-2*pi, -7-8j) + (-0.3612856620076747660410167 + 0.5217735339984807829755815j) + + Some degenerate cases:: + + >>> ellippi(1,1) + +inf + >>> ellippi(1,0) + +inf + >>> ellippi(1,2,0) + +inf + >>> ellippi(1,2,1) + +inf + >>> ellippi(1,0,1) + 0.0 + + """ + if len(args) == 2: + n, m = args + complete = True + z = phi = ctx.pi/2 + else: + n, phi, m = args + complete = False + z = phi + if not (ctx.isnormal(n) and ctx.isnormal(z) and ctx.isnormal(m)): + if ctx.isnan(n) or ctx.isnan(z) or ctx.isnan(m): + raise ValueError + if complete: + if m == 0: + if n == 1: + return ctx.inf + return ctx.pi/(2*ctx.sqrt(1-n)) + if n == 0: return ctx.ellipk(m) + if ctx.isinf(n) or ctx.isinf(m): return ctx.zero + else: + if z == 0: return z + if ctx.isinf(n): return ctx.zero + if ctx.isinf(m): return ctx.zero + if ctx.isinf(n) or ctx.isinf(z) or ctx.isinf(m): + raise ValueError + if complete: + if m == 1: + if n == 1: + return ctx.inf + return -ctx.inf/ctx.sign(n-1) + away = False + else: + x = z.real + ctx.prec += max(0, ctx.mag(x)) + pi = +ctx.pi + away = abs(x) > pi/2 + if away: + d = ctx.nint(x/pi) + z = z-pi*d + P = 2*d*ctx.ellippi(n,m) + if ctx.isinf(P): + return ctx.inf + else: + P = 0 + def terms(): + if complete: + c, s = ctx.zero, ctx.one + else: + c, s = ctx.cos_sin(z) + x = c**2 + y = 1-m*s**2 + RF = ctx.elliprf(x, y, 1) + RJ = ctx.elliprj(x, y, 1, 1-n*s**2) + return s*RF, n*s**3*RJ/3 + return ctx.sum_accurately(terms) + P diff --git a/venv/lib/python3.12/site-packages/mpmath/functions/expintegrals.py b/venv/lib/python3.12/site-packages/mpmath/functions/expintegrals.py new file mode 100644 index 0000000000000000000000000000000000000000..0dee8356c0386819d8f0421fded476ee77229359 --- /dev/null +++ b/venv/lib/python3.12/site-packages/mpmath/functions/expintegrals.py @@ -0,0 +1,425 @@ +from .functions import defun, defun_wrapped + +@defun_wrapped +def _erf_complex(ctx, z): + z2 = ctx.square_exp_arg(z, -1) + #z2 = -z**2 + v = (2/ctx.sqrt(ctx.pi))*z * ctx.hyp1f1((1,2),(3,2), z2) + if not ctx._re(z): + v = ctx._im(v)*ctx.j + return v + +@defun_wrapped +def _erfc_complex(ctx, z): + if ctx.re(z) > 2: + z2 = ctx.square_exp_arg(z) + nz2 = ctx.fneg(z2, exact=True) + v = ctx.exp(nz2)/ctx.sqrt(ctx.pi) * ctx.hyperu((1,2),(1,2), z2) + else: + v = 1 - ctx._erf_complex(z) + if not ctx._re(z): + v = 1+ctx._im(v)*ctx.j + return v + +@defun +def erf(ctx, z): + z = ctx.convert(z) + if ctx._is_real_type(z): + try: + return ctx._erf(z) + except NotImplementedError: + pass + if ctx._is_complex_type(z) and not z.imag: + try: + return type(z)(ctx._erf(z.real)) + except NotImplementedError: + pass + return ctx._erf_complex(z) + +@defun +def erfc(ctx, z): + z = ctx.convert(z) + if ctx._is_real_type(z): + try: + return ctx._erfc(z) + except NotImplementedError: + pass + if ctx._is_complex_type(z) and not z.imag: + try: + return type(z)(ctx._erfc(z.real)) + except NotImplementedError: + pass + return ctx._erfc_complex(z) + +@defun +def square_exp_arg(ctx, z, mult=1, reciprocal=False): + prec = ctx.prec*4+20 + if reciprocal: + z2 = ctx.fmul(z, z, prec=prec) + z2 = ctx.fdiv(ctx.one, z2, prec=prec) + else: + z2 = ctx.fmul(z, z, prec=prec) + if mult != 1: + z2 = ctx.fmul(z2, mult, exact=True) + return z2 + +@defun_wrapped +def erfi(ctx, z): + if not z: + return z + z2 = ctx.square_exp_arg(z) + v = (2/ctx.sqrt(ctx.pi)*z) * ctx.hyp1f1((1,2), (3,2), z2) + if not ctx._re(z): + v = ctx._im(v)*ctx.j + return v + +@defun_wrapped +def erfinv(ctx, x): + xre = ctx._re(x) + if (xre != x) or (xre < -1) or (xre > 1): + return ctx.bad_domain("erfinv(x) is defined only for -1 <= x <= 1") + x = xre + #if ctx.isnan(x): return x + if not x: return x + if x == 1: return ctx.inf + if x == -1: return ctx.ninf + if abs(x) < 0.9: + a = 0.53728*x**3 + 0.813198*x + else: + # An asymptotic formula + u = ctx.ln(2/ctx.pi/(abs(x)-1)**2) + a = ctx.sign(x) * ctx.sqrt(u - ctx.ln(u))/ctx.sqrt(2) + ctx.prec += 10 + return ctx.findroot(lambda t: ctx.erf(t)-x, a) + +@defun_wrapped +def npdf(ctx, x, mu=0, sigma=1): + sigma = ctx.convert(sigma) + return ctx.exp(-(x-mu)**2/(2*sigma**2)) / (sigma*ctx.sqrt(2*ctx.pi)) + +@defun_wrapped +def ncdf(ctx, x, mu=0, sigma=1): + a = (x-mu)/(sigma*ctx.sqrt(2)) + if a < 0: + return ctx.erfc(-a)/2 + else: + return (1+ctx.erf(a))/2 + +@defun_wrapped +def betainc(ctx, a, b, x1=0, x2=1, regularized=False): + if x1 == x2: + v = 0 + elif not x1: + if x1 == 0 and x2 == 1: + v = ctx.beta(a, b) + else: + v = x2**a * ctx.hyp2f1(a, 1-b, a+1, x2) / a + else: + m, d = ctx.nint_distance(a) + if m <= 0: + if d < -ctx.prec: + h = +ctx.eps + ctx.prec *= 2 + a += h + elif d < -4: + ctx.prec -= d + s1 = x2**a * ctx.hyp2f1(a,1-b,a+1,x2) + s2 = x1**a * ctx.hyp2f1(a,1-b,a+1,x1) + v = (s1 - s2) / a + if regularized: + v /= ctx.beta(a,b) + return v + +@defun +def gammainc(ctx, z, a=0, b=None, regularized=False): + regularized = bool(regularized) + z = ctx.convert(z) + if a is None: + a = ctx.zero + lower_modified = False + else: + a = ctx.convert(a) + lower_modified = a != ctx.zero + if b is None: + b = ctx.inf + upper_modified = False + else: + b = ctx.convert(b) + upper_modified = b != ctx.inf + # Complete gamma function + if not (upper_modified or lower_modified): + if regularized: + if ctx.re(z) < 0: + return ctx.inf + elif ctx.re(z) > 0: + return ctx.one + else: + return ctx.nan + return ctx.gamma(z) + if a == b: + return ctx.zero + # Standardize + if ctx.re(a) > ctx.re(b): + return -ctx.gammainc(z, b, a, regularized) + # Generalized gamma + if upper_modified and lower_modified: + return +ctx._gamma3(z, a, b, regularized) + # Upper gamma + elif lower_modified: + return ctx._upper_gamma(z, a, regularized) + # Lower gamma + elif upper_modified: + return ctx._lower_gamma(z, b, regularized) + +@defun +def _lower_gamma(ctx, z, b, regularized=False): + # Pole + if ctx.isnpint(z): + return type(z)(ctx.inf) + G = [z] * regularized + negb = ctx.fneg(b, exact=True) + def h(z): + T1 = [ctx.exp(negb), b, z], [1, z, -1], [], G, [1], [1+z], b + return (T1,) + return ctx.hypercomb(h, [z]) + +@defun +def _upper_gamma(ctx, z, a, regularized=False): + # Fast integer case, when available + if ctx.isint(z): + try: + if regularized: + # Gamma pole + if ctx.isnpint(z): + return type(z)(ctx.zero) + orig = ctx.prec + try: + ctx.prec += 10 + return ctx._gamma_upper_int(z, a) / ctx.gamma(z) + finally: + ctx.prec = orig + else: + return ctx._gamma_upper_int(z, a) + except NotImplementedError: + pass + # hypercomb is unable to detect the exact zeros, so handle them here + if z == 2 and a == -1: + return (z+a)*0 + if z == 3 and (a == -1-1j or a == -1+1j): + return (z+a)*0 + nega = ctx.fneg(a, exact=True) + G = [z] * regularized + # Use 2F0 series when possible; fall back to lower gamma representation + try: + def h(z): + r = z-1 + return [([ctx.exp(nega), a], [1, r], [], G, [1, -r], [], 1/nega)] + return ctx.hypercomb(h, [z], force_series=True) + except ctx.NoConvergence: + def h(z): + T1 = [], [1, z-1], [z], G, [], [], 0 + T2 = [-ctx.exp(nega), a, z], [1, z, -1], [], G, [1], [1+z], a + return T1, T2 + return ctx.hypercomb(h, [z]) + +@defun +def _gamma3(ctx, z, a, b, regularized=False): + pole = ctx.isnpint(z) + if regularized and pole: + return ctx.zero + try: + ctx.prec += 15 + # We don't know in advance whether it's better to write as a difference + # of lower or upper gamma functions, so try both + T1 = ctx.gammainc(z, a, regularized=regularized) + T2 = ctx.gammainc(z, b, regularized=regularized) + R = T1 - T2 + if ctx.mag(R) - max(ctx.mag(T1), ctx.mag(T2)) > -10: + return R + if not pole: + T1 = ctx.gammainc(z, 0, b, regularized=regularized) + T2 = ctx.gammainc(z, 0, a, regularized=regularized) + R = T1 - T2 + # May be ok, but should probably at least print a warning + # about possible cancellation + if 1: #ctx.mag(R) - max(ctx.mag(T1), ctx.mag(T2)) > -10: + return R + finally: + ctx.prec -= 15 + raise NotImplementedError + +@defun_wrapped +def expint(ctx, n, z): + if ctx.isint(n) and ctx._is_real_type(z): + try: + return ctx._expint_int(n, z) + except NotImplementedError: + pass + if ctx.isnan(n) or ctx.isnan(z): + return z*n + if z == ctx.inf: + return 1/z + if z == 0: + # integral from 1 to infinity of t^n + if ctx.re(n) <= 1: + # TODO: reasonable sign of infinity + return type(z)(ctx.inf) + else: + return ctx.one/(n-1) + if n == 0: + return ctx.exp(-z)/z + if n == -1: + return ctx.exp(-z)*(z+1)/z**2 + return z**(n-1) * ctx.gammainc(1-n, z) + +@defun_wrapped +def li(ctx, z, offset=False): + if offset: + if z == 2: + return ctx.zero + return ctx.ei(ctx.ln(z)) - ctx.ei(ctx.ln2) + if not z: + return z + if z == 1: + return ctx.ninf + return ctx.ei(ctx.ln(z)) + +@defun +def ei(ctx, z): + try: + return ctx._ei(z) + except NotImplementedError: + return ctx._ei_generic(z) + +@defun_wrapped +def _ei_generic(ctx, z): + # Note: the following is currently untested because mp and fp + # both use special-case ei code + if z == ctx.inf: + return z + if z == ctx.ninf: + return ctx.zero + if ctx.mag(z) > 1: + try: + r = ctx.one/z + v = ctx.exp(z)*ctx.hyper([1,1],[],r, + maxterms=ctx.prec, force_series=True)/z + im = ctx._im(z) + if im > 0: + v += ctx.pi*ctx.j + if im < 0: + v -= ctx.pi*ctx.j + return v + except ctx.NoConvergence: + pass + v = z*ctx.hyp2f2(1,1,2,2,z) + ctx.euler + if ctx._im(z): + v += 0.5*(ctx.log(z) - ctx.log(ctx.one/z)) + else: + v += ctx.log(abs(z)) + return v + +@defun +def e1(ctx, z): + try: + return ctx._e1(z) + except NotImplementedError: + return ctx.expint(1, z) + +@defun +def ci(ctx, z): + try: + return ctx._ci(z) + except NotImplementedError: + return ctx._ci_generic(z) + +@defun_wrapped +def _ci_generic(ctx, z): + if ctx.isinf(z): + if z == ctx.inf: return ctx.zero + if z == ctx.ninf: return ctx.pi*1j + jz = ctx.fmul(ctx.j,z,exact=True) + njz = ctx.fneg(jz,exact=True) + v = 0.5*(ctx.ei(jz) + ctx.ei(njz)) + zreal = ctx._re(z) + zimag = ctx._im(z) + if zreal == 0: + if zimag > 0: v += ctx.pi*0.5j + if zimag < 0: v -= ctx.pi*0.5j + if zreal < 0: + if zimag >= 0: v += ctx.pi*1j + if zimag < 0: v -= ctx.pi*1j + if ctx._is_real_type(z) and zreal > 0: + v = ctx._re(v) + return v + +@defun +def si(ctx, z): + try: + return ctx._si(z) + except NotImplementedError: + return ctx._si_generic(z) + +@defun_wrapped +def _si_generic(ctx, z): + if ctx.isinf(z): + if z == ctx.inf: return 0.5*ctx.pi + if z == ctx.ninf: return -0.5*ctx.pi + # Suffers from cancellation near 0 + if ctx.mag(z) >= -1: + jz = ctx.fmul(ctx.j,z,exact=True) + njz = ctx.fneg(jz,exact=True) + v = (-0.5j)*(ctx.ei(jz) - ctx.ei(njz)) + zreal = ctx._re(z) + if zreal > 0: + v -= 0.5*ctx.pi + if zreal < 0: + v += 0.5*ctx.pi + if ctx._is_real_type(z): + v = ctx._re(v) + return v + else: + return z*ctx.hyp1f2((1,2),(3,2),(3,2),-0.25*z*z) + +@defun_wrapped +def chi(ctx, z): + nz = ctx.fneg(z, exact=True) + v = 0.5*(ctx.ei(z) + ctx.ei(nz)) + zreal = ctx._re(z) + zimag = ctx._im(z) + if zimag > 0: + v += ctx.pi*0.5j + elif zimag < 0: + v -= ctx.pi*0.5j + elif zreal < 0: + v += ctx.pi*1j + return v + +@defun_wrapped +def shi(ctx, z): + # Suffers from cancellation near 0 + if ctx.mag(z) >= -1: + nz = ctx.fneg(z, exact=True) + v = 0.5*(ctx.ei(z) - ctx.ei(nz)) + zimag = ctx._im(z) + if zimag > 0: v -= 0.5j*ctx.pi + if zimag < 0: v += 0.5j*ctx.pi + return v + else: + return z * ctx.hyp1f2((1,2),(3,2),(3,2),0.25*z*z) + +@defun_wrapped +def fresnels(ctx, z): + if z == ctx.inf: + return ctx.mpf(0.5) + if z == ctx.ninf: + return ctx.mpf(-0.5) + return ctx.pi*z**3/6*ctx.hyp1f2((3,4),(3,2),(7,4),-ctx.pi**2*z**4/16) + +@defun_wrapped +def fresnelc(ctx, z): + if z == ctx.inf: + return ctx.mpf(0.5) + if z == ctx.ninf: + return ctx.mpf(-0.5) + return z*ctx.hyp1f2((1,4),(1,2),(5,4),-ctx.pi**2*z**4/16) diff --git a/venv/lib/python3.12/site-packages/mpmath/functions/factorials.py b/venv/lib/python3.12/site-packages/mpmath/functions/factorials.py new file mode 100644 index 0000000000000000000000000000000000000000..9259e40b95bf1c908a7ad98b59bbb33528606b07 --- /dev/null +++ b/venv/lib/python3.12/site-packages/mpmath/functions/factorials.py @@ -0,0 +1,187 @@ +from ..libmp.backend import xrange +from .functions import defun, defun_wrapped + +@defun +def gammaprod(ctx, a, b, _infsign=False): + a = [ctx.convert(x) for x in a] + b = [ctx.convert(x) for x in b] + poles_num = [] + poles_den = [] + regular_num = [] + regular_den = [] + for x in a: [regular_num, poles_num][ctx.isnpint(x)].append(x) + for x in b: [regular_den, poles_den][ctx.isnpint(x)].append(x) + # One more pole in numerator or denominator gives 0 or inf + if len(poles_num) < len(poles_den): return ctx.zero + if len(poles_num) > len(poles_den): + # Get correct sign of infinity for x+h, h -> 0 from above + # XXX: hack, this should be done properly + if _infsign: + a = [x and x*(1+ctx.eps) or x+ctx.eps for x in poles_num] + b = [x and x*(1+ctx.eps) or x+ctx.eps for x in poles_den] + return ctx.sign(ctx.gammaprod(a+regular_num,b+regular_den)) * ctx.inf + else: + return ctx.inf + # All poles cancel + # lim G(i)/G(j) = (-1)**(i+j) * gamma(1-j) / gamma(1-i) + p = ctx.one + orig = ctx.prec + try: + ctx.prec = orig + 15 + while poles_num: + i = poles_num.pop() + j = poles_den.pop() + p *= (-1)**(i+j) * ctx.gamma(1-j) / ctx.gamma(1-i) + for x in regular_num: p *= ctx.gamma(x) + for x in regular_den: p /= ctx.gamma(x) + finally: + ctx.prec = orig + return +p + +@defun +def beta(ctx, x, y): + x = ctx.convert(x) + y = ctx.convert(y) + if ctx.isinf(y): + x, y = y, x + if ctx.isinf(x): + if x == ctx.inf and not ctx._im(y): + if y == ctx.ninf: + return ctx.nan + if y > 0: + return ctx.zero + if ctx.isint(y): + return ctx.nan + if y < 0: + return ctx.sign(ctx.gamma(y)) * ctx.inf + return ctx.nan + xy = ctx.fadd(x, y, prec=2*ctx.prec) + return ctx.gammaprod([x, y], [xy]) + +@defun +def binomial(ctx, n, k): + n1 = ctx.fadd(n, 1, prec=2*ctx.prec) + k1 = ctx.fadd(k, 1, prec=2*ctx.prec) + nk1 = ctx.fsub(n1, k, prec=2*ctx.prec) + return ctx.gammaprod([n1], [k1, nk1]) + +@defun +def rf(ctx, x, n): + xn = ctx.fadd(x, n, prec=2*ctx.prec) + return ctx.gammaprod([xn], [x]) + +@defun +def ff(ctx, x, n): + x1 = ctx.fadd(x, 1, prec=2*ctx.prec) + xn1 = ctx.fadd(ctx.fsub(x, n, prec=2*ctx.prec), 1, prec=2*ctx.prec) + return ctx.gammaprod([x1], [xn1]) + +@defun_wrapped +def fac2(ctx, x): + if ctx.isinf(x): + if x == ctx.inf: + return x + return ctx.nan + return 2**(x/2)*(ctx.pi/2)**((ctx.cospi(x)-1)/4)*ctx.gamma(x/2+1) + +@defun_wrapped +def barnesg(ctx, z): + if ctx.isinf(z): + if z == ctx.inf: + return z + return ctx.nan + if ctx.isnan(z): + return z + if (not ctx._im(z)) and ctx._re(z) <= 0 and ctx.isint(ctx._re(z)): + return z*0 + # Account for size (would not be needed if computing log(G)) + if abs(z) > 5: + ctx.dps += 2*ctx.log(abs(z),2) + # Reflection formula + if ctx.re(z) < -ctx.dps: + w = 1-z + pi2 = 2*ctx.pi + u = ctx.expjpi(2*w) + v = ctx.j*ctx.pi/12 - ctx.j*ctx.pi*w**2/2 + w*ctx.ln(1-u) - \ + ctx.j*ctx.polylog(2, u)/pi2 + v = ctx.barnesg(2-z)*ctx.exp(v)/pi2**w + if ctx._is_real_type(z): + v = ctx._re(v) + return v + # Estimate terms for asymptotic expansion + # TODO: fixme, obviously + N = ctx.dps // 2 + 5 + G = 1 + while abs(z) < N or ctx.re(z) < 1: + G /= ctx.gamma(z) + z += 1 + z -= 1 + s = ctx.mpf(1)/12 + s -= ctx.log(ctx.glaisher) + s += z*ctx.log(2*ctx.pi)/2 + s += (z**2/2-ctx.mpf(1)/12)*ctx.log(z) + s -= 3*z**2/4 + z2k = z2 = z**2 + for k in xrange(1, N+1): + t = ctx.bernoulli(2*k+2) / (4*k*(k+1)*z2k) + if abs(t) < ctx.eps: + #print k, N # check how many terms were needed + break + z2k *= z2 + s += t + #if k == N: + # print "warning: series for barnesg failed to converge", ctx.dps + return G*ctx.exp(s) + +@defun +def superfac(ctx, z): + return ctx.barnesg(z+2) + +@defun_wrapped +def hyperfac(ctx, z): + # XXX: estimate needed extra bits accurately + if z == ctx.inf: + return z + if abs(z) > 5: + extra = 4*int(ctx.log(abs(z),2)) + else: + extra = 0 + ctx.prec += extra + if not ctx._im(z) and ctx._re(z) < 0 and ctx.isint(ctx._re(z)): + n = int(ctx.re(z)) + h = ctx.hyperfac(-n-1) + if ((n+1)//2) & 1: + h = -h + if ctx._is_complex_type(z): + return h + 0j + return h + zp1 = z+1 + # Wrong branch cut + #v = ctx.gamma(zp1)**z + #ctx.prec -= extra + #return v / ctx.barnesg(zp1) + v = ctx.exp(z*ctx.loggamma(zp1)) + ctx.prec -= extra + return v / ctx.barnesg(zp1) + +''' +@defun +def psi0(ctx, z): + """Shortcut for psi(0,z) (the digamma function)""" + return ctx.psi(0, z) + +@defun +def psi1(ctx, z): + """Shortcut for psi(1,z) (the trigamma function)""" + return ctx.psi(1, z) + +@defun +def psi2(ctx, z): + """Shortcut for psi(2,z) (the tetragamma function)""" + return ctx.psi(2, z) + +@defun +def psi3(ctx, z): + """Shortcut for psi(3,z) (the pentagamma function)""" + return ctx.psi(3, z) +''' diff --git a/venv/lib/python3.12/site-packages/mpmath/functions/functions.py b/venv/lib/python3.12/site-packages/mpmath/functions/functions.py new file mode 100644 index 0000000000000000000000000000000000000000..4cdf5dd921418db10847ea75b32f8e6dfacdba64 --- /dev/null +++ b/venv/lib/python3.12/site-packages/mpmath/functions/functions.py @@ -0,0 +1,645 @@ +from ..libmp.backend import xrange + +class SpecialFunctions(object): + """ + This class implements special functions using high-level code. + + Elementary and some other functions (e.g. gamma function, basecase + hypergeometric series) are assumed to be predefined by the context as + "builtins" or "low-level" functions. + """ + defined_functions = {} + + # The series for the Jacobi theta functions converge for |q| < 1; + # in the current implementation they throw a ValueError for + # abs(q) > THETA_Q_LIM + THETA_Q_LIM = 1 - 10**-7 + + def __init__(self): + cls = self.__class__ + for name in cls.defined_functions: + f, wrap = cls.defined_functions[name] + cls._wrap_specfun(name, f, wrap) + + self.mpq_1 = self._mpq((1,1)) + self.mpq_0 = self._mpq((0,1)) + self.mpq_1_2 = self._mpq((1,2)) + self.mpq_3_2 = self._mpq((3,2)) + self.mpq_1_4 = self._mpq((1,4)) + self.mpq_1_16 = self._mpq((1,16)) + self.mpq_3_16 = self._mpq((3,16)) + self.mpq_5_2 = self._mpq((5,2)) + self.mpq_3_4 = self._mpq((3,4)) + self.mpq_7_4 = self._mpq((7,4)) + self.mpq_5_4 = self._mpq((5,4)) + self.mpq_1_3 = self._mpq((1,3)) + self.mpq_2_3 = self._mpq((2,3)) + self.mpq_4_3 = self._mpq((4,3)) + self.mpq_1_6 = self._mpq((1,6)) + self.mpq_5_6 = self._mpq((5,6)) + self.mpq_5_3 = self._mpq((5,3)) + + self._misc_const_cache = {} + + self._aliases.update({ + 'phase' : 'arg', + 'conjugate' : 'conj', + 'nthroot' : 'root', + 'polygamma' : 'psi', + 'hurwitz' : 'zeta', + #'digamma' : 'psi0', + #'trigamma' : 'psi1', + #'tetragamma' : 'psi2', + #'pentagamma' : 'psi3', + 'fibonacci' : 'fib', + 'factorial' : 'fac', + }) + + self.zetazero_memoized = self.memoize(self.zetazero) + + # Default -- do nothing + @classmethod + def _wrap_specfun(cls, name, f, wrap): + setattr(cls, name, f) + + # Optional fast versions of common functions in common cases. + # If not overridden, default (generic hypergeometric series) + # implementations will be used + def _besselj(ctx, n, z): raise NotImplementedError + def _erf(ctx, z): raise NotImplementedError + def _erfc(ctx, z): raise NotImplementedError + def _gamma_upper_int(ctx, z, a): raise NotImplementedError + def _expint_int(ctx, n, z): raise NotImplementedError + def _zeta(ctx, s): raise NotImplementedError + def _zetasum_fast(ctx, s, a, n, derivatives, reflect): raise NotImplementedError + def _ei(ctx, z): raise NotImplementedError + def _e1(ctx, z): raise NotImplementedError + def _ci(ctx, z): raise NotImplementedError + def _si(ctx, z): raise NotImplementedError + def _altzeta(ctx, s): raise NotImplementedError + +def defun_wrapped(f): + SpecialFunctions.defined_functions[f.__name__] = f, True + return f + +def defun(f): + SpecialFunctions.defined_functions[f.__name__] = f, False + return f + +def defun_static(f): + setattr(SpecialFunctions, f.__name__, f) + return f + +@defun_wrapped +def cot(ctx, z): return ctx.one / ctx.tan(z) + +@defun_wrapped +def sec(ctx, z): return ctx.one / ctx.cos(z) + +@defun_wrapped +def csc(ctx, z): return ctx.one / ctx.sin(z) + +@defun_wrapped +def coth(ctx, z): return ctx.one / ctx.tanh(z) + +@defun_wrapped +def sech(ctx, z): return ctx.one / ctx.cosh(z) + +@defun_wrapped +def csch(ctx, z): return ctx.one / ctx.sinh(z) + +@defun_wrapped +def acot(ctx, z): + if not z: + return ctx.pi * 0.5 + else: + return ctx.atan(ctx.one / z) + +@defun_wrapped +def asec(ctx, z): return ctx.acos(ctx.one / z) + +@defun_wrapped +def acsc(ctx, z): return ctx.asin(ctx.one / z) + +@defun_wrapped +def acoth(ctx, z): + if not z: + return ctx.pi * 0.5j + else: + return ctx.atanh(ctx.one / z) + + +@defun_wrapped +def asech(ctx, z): return ctx.acosh(ctx.one / z) + +@defun_wrapped +def acsch(ctx, z): return ctx.asinh(ctx.one / z) + +@defun +def sign(ctx, x): + x = ctx.convert(x) + if not x or ctx.isnan(x): + return x + if ctx._is_real_type(x): + if x > 0: + return ctx.one + else: + return -ctx.one + return x / abs(x) + +@defun +def agm(ctx, a, b=1): + if b == 1: + return ctx.agm1(a) + a = ctx.convert(a) + b = ctx.convert(b) + return ctx._agm(a, b) + +@defun_wrapped +def sinc(ctx, x): + if ctx.isinf(x): + return 1/x + if not x: + return x+1 + return ctx.sin(x)/x + +@defun_wrapped +def sincpi(ctx, x): + if ctx.isinf(x): + return 1/x + if not x: + return x+1 + return ctx.sinpi(x)/(ctx.pi*x) + +# TODO: tests; improve implementation +@defun_wrapped +def expm1(ctx, x): + if not x: + return ctx.zero + # exp(x) - 1 ~ x + if ctx.mag(x) < -ctx.prec: + return x + 0.5*x**2 + # TODO: accurately eval the smaller of the real/imag parts + return ctx.sum_accurately(lambda: iter([ctx.exp(x),-1]),1) + +@defun_wrapped +def log1p(ctx, x): + if not x: + return ctx.zero + if ctx.mag(x) < -ctx.prec: + return x - 0.5*x**2 + return ctx.log(ctx.fadd(1, x, prec=2*ctx.prec)) + +@defun_wrapped +def powm1(ctx, x, y): + mag = ctx.mag + one = ctx.one + w = x**y - one + M = mag(w) + # Only moderate cancellation + if M > -8: + return w + # Check for the only possible exact cases + if not w: + if (not y) or (x in (1, -1, 1j, -1j) and ctx.isint(y)): + return w + x1 = x - one + magy = mag(y) + lnx = ctx.ln(x) + # Small y: x^y - 1 ~ log(x)*y + O(log(x)^2 * y^2) + if magy + mag(lnx) < -ctx.prec: + return lnx*y + (lnx*y)**2/2 + # TODO: accurately eval the smaller of the real/imag part + return ctx.sum_accurately(lambda: iter([x**y, -1]), 1) + +@defun +def _rootof1(ctx, k, n): + k = int(k) + n = int(n) + k %= n + if not k: + return ctx.one + elif 2*k == n: + return -ctx.one + elif 4*k == n: + return ctx.j + elif 4*k == 3*n: + return -ctx.j + return ctx.expjpi(2*ctx.mpf(k)/n) + +@defun +def root(ctx, x, n, k=0): + n = int(n) + x = ctx.convert(x) + if k: + # Special case: there is an exact real root + if (n & 1 and 2*k == n-1) and (not ctx.im(x)) and (ctx.re(x) < 0): + return -ctx.root(-x, n) + # Multiply by root of unity + prec = ctx.prec + try: + ctx.prec += 10 + v = ctx.root(x, n, 0) * ctx._rootof1(k, n) + finally: + ctx.prec = prec + return +v + return ctx._nthroot(x, n) + +@defun +def unitroots(ctx, n, primitive=False): + gcd = ctx._gcd + prec = ctx.prec + try: + ctx.prec += 10 + if primitive: + v = [ctx._rootof1(k,n) for k in range(n) if gcd(k,n) == 1] + else: + # TODO: this can be done *much* faster + v = [ctx._rootof1(k,n) for k in range(n)] + finally: + ctx.prec = prec + return [+x for x in v] + +@defun +def arg(ctx, x): + x = ctx.convert(x) + re = ctx._re(x) + im = ctx._im(x) + return ctx.atan2(im, re) + +@defun +def fabs(ctx, x): + return abs(ctx.convert(x)) + +@defun +def re(ctx, x): + x = ctx.convert(x) + if hasattr(x, "real"): # py2.5 doesn't have .real/.imag for all numbers + return x.real + return x + +@defun +def im(ctx, x): + x = ctx.convert(x) + if hasattr(x, "imag"): # py2.5 doesn't have .real/.imag for all numbers + return x.imag + return ctx.zero + +@defun +def conj(ctx, x): + x = ctx.convert(x) + try: + return x.conjugate() + except AttributeError: + return x + +@defun +def polar(ctx, z): + return (ctx.fabs(z), ctx.arg(z)) + +@defun_wrapped +def rect(ctx, r, phi): + return r * ctx.mpc(*ctx.cos_sin(phi)) + +@defun +def log(ctx, x, b=None): + if b is None: + return ctx.ln(x) + wp = ctx.prec + 20 + return ctx.ln(x, prec=wp) / ctx.ln(b, prec=wp) + +@defun +def log10(ctx, x): + return ctx.log(x, 10) + +@defun +def fmod(ctx, x, y): + return ctx.convert(x) % ctx.convert(y) + +@defun +def degrees(ctx, x): + return x / ctx.degree + +@defun +def radians(ctx, x): + return x * ctx.degree + +def _lambertw_special(ctx, z, k): + # W(0,0) = 0; all other branches are singular + if not z: + if not k: + return z + return ctx.ninf + z + if z == ctx.inf: + if k == 0: + return z + else: + return z + 2*k*ctx.pi*ctx.j + if z == ctx.ninf: + return (-z) + (2*k+1)*ctx.pi*ctx.j + # Some kind of nan or complex inf/nan? + return ctx.ln(z) + +import math +import cmath + +def _lambertw_approx_hybrid(z, k): + imag_sign = 0 + if hasattr(z, "imag"): + x = float(z.real) + y = z.imag + if y: + imag_sign = (-1) ** (y < 0) + y = float(y) + else: + x = float(z) + y = 0.0 + imag_sign = 0 + # hack to work regardless of whether Python supports -0.0 + if not y: + y = 0.0 + z = complex(x,y) + if k == 0: + if -4.0 < y < 4.0 and -1.0 < x < 2.5: + if imag_sign: + # Taylor series in upper/lower half-plane + if y > 1.00: return (0.876+0.645j) + (0.118-0.174j)*(z-(0.75+2.5j)) + if y > 0.25: return (0.505+0.204j) + (0.375-0.132j)*(z-(0.75+0.5j)) + if y < -1.00: return (0.876-0.645j) + (0.118+0.174j)*(z-(0.75-2.5j)) + if y < -0.25: return (0.505-0.204j) + (0.375+0.132j)*(z-(0.75-0.5j)) + # Taylor series near -1 + if x < -0.5: + if imag_sign >= 0: + return (-0.318+1.34j) + (-0.697-0.593j)*(z+1) + else: + return (-0.318-1.34j) + (-0.697+0.593j)*(z+1) + # return real type + r = -0.367879441171442 + if (not imag_sign) and x > r: + z = x + # Singularity near -1/e + if x < -0.2: + return -1 + 2.33164398159712*(z-r)**0.5 - 1.81218788563936*(z-r) + # Taylor series near 0 + if x < 0.5: return z + # Simple linear approximation + return 0.2 + 0.3*z + if (not imag_sign) and x > 0.0: + L1 = math.log(x); L2 = math.log(L1) + else: + L1 = cmath.log(z); L2 = cmath.log(L1) + elif k == -1: + # return real type + r = -0.367879441171442 + if (not imag_sign) and r < x < 0.0: + z = x + if (imag_sign >= 0) and y < 0.1 and -0.6 < x < -0.2: + return -1 - 2.33164398159712*(z-r)**0.5 - 1.81218788563936*(z-r) + if (not imag_sign) and -0.2 <= x < 0.0: + L1 = math.log(-x) + return L1 - math.log(-L1) + else: + if imag_sign == -1 and (not y) and x < 0.0: + L1 = cmath.log(z) - 3.1415926535897932j + else: + L1 = cmath.log(z) - 6.2831853071795865j + L2 = cmath.log(L1) + return L1 - L2 + L2/L1 + L2*(L2-2)/(2*L1**2) + +def _lambertw_series(ctx, z, k, tol): + """ + Return rough approximation for W_k(z) from an asymptotic series, + sufficiently accurate for the Halley iteration to converge to + the correct value. + """ + magz = ctx.mag(z) + if (-10 < magz < 900) and (-1000 < k < 1000): + # Near the branch point at -1/e + if magz < 1 and abs(z+0.36787944117144) < 0.05: + if k == 0 or (k == -1 and ctx._im(z) >= 0) or \ + (k == 1 and ctx._im(z) < 0): + delta = ctx.sum_accurately(lambda: [z, ctx.exp(-1)]) + cancellation = -ctx.mag(delta) + ctx.prec += cancellation + # Use series given in Corless et al. + p = ctx.sqrt(2*(ctx.e*z+1)) + ctx.prec -= cancellation + u = {0:ctx.mpf(-1), 1:ctx.mpf(1)} + a = {0:ctx.mpf(2), 1:ctx.mpf(-1)} + if k != 0: + p = -p + s = ctx.zero + # The series converges, so we could use it directly, but unless + # *extremely* close, it is better to just use the first few + # terms to get a good approximation for the iteration + for l in xrange(max(2,cancellation)): + if l not in u: + a[l] = ctx.fsum(u[j]*u[l+1-j] for j in xrange(2,l)) + u[l] = (l-1)*(u[l-2]/2+a[l-2]/4)/(l+1)-a[l]/2-u[l-1]/(l+1) + term = u[l] * p**l + s += term + if ctx.mag(term) < -tol: + return s, True + l += 1 + ctx.prec += cancellation//2 + return s, False + if k == 0 or k == -1: + return _lambertw_approx_hybrid(z, k), False + if k == 0: + if magz < -1: + return z*(1-z), False + L1 = ctx.ln(z) + L2 = ctx.ln(L1) + elif k == -1 and (not ctx._im(z)) and (-0.36787944117144 < ctx._re(z) < 0): + L1 = ctx.ln(-z) + return L1 - ctx.ln(-L1), False + else: + # This holds both as z -> 0 and z -> inf. + # Relative error is O(1/log(z)). + L1 = ctx.ln(z) + 2j*ctx.pi*k + L2 = ctx.ln(L1) + return L1 - L2 + L2/L1 + L2*(L2-2)/(2*L1**2), False + +@defun +def lambertw(ctx, z, k=0): + z = ctx.convert(z) + k = int(k) + if not ctx.isnormal(z): + return _lambertw_special(ctx, z, k) + prec = ctx.prec + ctx.prec += 20 + ctx.mag(k or 1) + wp = ctx.prec + tol = wp - 5 + w, done = _lambertw_series(ctx, z, k, tol) + if not done: + # Use Halley iteration to solve w*exp(w) = z + two = ctx.mpf(2) + for i in xrange(100): + ew = ctx.exp(w) + wew = w*ew + wewz = wew-z + wn = w - wewz/(wew+ew-(w+two)*wewz/(two*w+two)) + if ctx.mag(wn-w) <= ctx.mag(wn) - tol: + w = wn + break + else: + w = wn + if i == 100: + ctx.warn("Lambert W iteration failed to converge for z = %s" % z) + ctx.prec = prec + return +w + +@defun_wrapped +def bell(ctx, n, x=1): + x = ctx.convert(x) + if not n: + if ctx.isnan(x): + return x + return type(x)(1) + if ctx.isinf(x) or ctx.isinf(n) or ctx.isnan(x) or ctx.isnan(n): + return x**n + if n == 1: return x + if n == 2: return x*(x+1) + if x == 0: return ctx.sincpi(n) + return _polyexp(ctx, n, x, True) / ctx.exp(x) + +def _polyexp(ctx, n, x, extra=False): + def _terms(): + if extra: + yield ctx.sincpi(n) + t = x + k = 1 + while 1: + yield k**n * t + k += 1 + t = t*x/k + return ctx.sum_accurately(_terms, check_step=4) + +@defun_wrapped +def polyexp(ctx, s, z): + if ctx.isinf(z) or ctx.isinf(s) or ctx.isnan(z) or ctx.isnan(s): + return z**s + if z == 0: return z*s + if s == 0: return ctx.expm1(z) + if s == 1: return ctx.exp(z)*z + if s == 2: return ctx.exp(z)*z*(z+1) + return _polyexp(ctx, s, z) + +@defun_wrapped +def cyclotomic(ctx, n, z): + n = int(n) + if n < 0: + raise ValueError("n cannot be negative") + p = ctx.one + if n == 0: + return p + if n == 1: + return z - p + if n == 2: + return z + p + # Use divisor product representation. Unfortunately, this sometimes + # includes singularities for roots of unity, which we have to cancel out. + # Matching zeros/poles pairwise, we have (1-z^a)/(1-z^b) ~ a/b + O(z-1). + a_prod = 1 + b_prod = 1 + num_zeros = 0 + num_poles = 0 + for d in range(1,n+1): + if not n % d: + w = ctx.moebius(n//d) + # Use powm1 because it is important that we get 0 only + # if it really is exactly 0 + b = -ctx.powm1(z, d) + if b: + p *= b**w + else: + if w == 1: + a_prod *= d + num_zeros += 1 + elif w == -1: + b_prod *= d + num_poles += 1 + #print n, num_zeros, num_poles + if num_zeros: + if num_zeros > num_poles: + p *= 0 + else: + p *= a_prod + p /= b_prod + return p + +@defun +def mangoldt(ctx, n): + r""" + Evaluates the von Mangoldt function `\Lambda(n) = \log p` + if `n = p^k` a power of a prime, and `\Lambda(n) = 0` otherwise. + + **Examples** + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> [mangoldt(n) for n in range(-2,3)] + [0.0, 0.0, 0.0, 0.0, 0.6931471805599453094172321] + >>> mangoldt(6) + 0.0 + >>> mangoldt(7) + 1.945910149055313305105353 + >>> mangoldt(8) + 0.6931471805599453094172321 + >>> fsum(mangoldt(n) for n in range(101)) + 94.04531122935739224600493 + >>> fsum(mangoldt(n) for n in range(10001)) + 10013.39669326311478372032 + + """ + n = int(n) + if n < 2: + return ctx.zero + if n % 2 == 0: + # Must be a power of two + if n & (n-1) == 0: + return +ctx.ln2 + else: + return ctx.zero + # TODO: the following could be generalized into a perfect + # power testing function + # --- + # Look for a small factor + for p in (3,5,7,11,13,17,19,23,29,31): + if not n % p: + q, r = n // p, 0 + while q > 1: + q, r = divmod(q, p) + if r: + return ctx.zero + return ctx.ln(p) + if ctx.isprime(n): + return ctx.ln(n) + # Obviously, we could use arbitrary-precision arithmetic for this... + if n > 10**30: + raise NotImplementedError + k = 2 + while 1: + p = int(n**(1./k) + 0.5) + if p < 2: + return ctx.zero + if p ** k == n: + if ctx.isprime(p): + return ctx.ln(p) + k += 1 + +@defun +def stirling1(ctx, n, k, exact=False): + v = ctx._stirling1(int(n), int(k)) + if exact: + return int(v) + else: + return ctx.mpf(v) + +@defun +def stirling2(ctx, n, k, exact=False): + v = ctx._stirling2(int(n), int(k)) + if exact: + return int(v) + else: + return ctx.mpf(v) diff --git a/venv/lib/python3.12/site-packages/mpmath/functions/hypergeometric.py b/venv/lib/python3.12/site-packages/mpmath/functions/hypergeometric.py new file mode 100644 index 0000000000000000000000000000000000000000..ddb50cbf3ea6daa5982678d3c26157a67a7d7945 --- /dev/null +++ b/venv/lib/python3.12/site-packages/mpmath/functions/hypergeometric.py @@ -0,0 +1,1413 @@ +from ..libmp.backend import xrange +from .functions import defun, defun_wrapped + +def _check_need_perturb(ctx, terms, prec, discard_known_zeros): + perturb = recompute = False + extraprec = 0 + discard = [] + for term_index, term in enumerate(terms): + w_s, c_s, alpha_s, beta_s, a_s, b_s, z = term + have_singular_nongamma_weight = False + # Avoid division by zero in leading factors (TODO: + # also check for near division by zero?) + for k, w in enumerate(w_s): + if not w: + if ctx.re(c_s[k]) <= 0 and c_s[k]: + perturb = recompute = True + have_singular_nongamma_weight = True + pole_count = [0, 0, 0] + # Check for gamma and series poles and near-poles + for data_index, data in enumerate([alpha_s, beta_s, b_s]): + for i, x in enumerate(data): + n, d = ctx.nint_distance(x) + # Poles + if n > 0: + continue + if d == ctx.ninf: + # OK if we have a polynomial + # ------------------------------ + ok = False + if data_index == 2: + for u in a_s: + if ctx.isnpint(u) and u >= int(n): + ok = True + break + if ok: + continue + pole_count[data_index] += 1 + # ------------------------------ + #perturb = recompute = True + #return perturb, recompute, extraprec + elif d < -4: + extraprec += -d + recompute = True + if discard_known_zeros and pole_count[1] > pole_count[0] + pole_count[2] \ + and not have_singular_nongamma_weight: + discard.append(term_index) + elif sum(pole_count): + perturb = recompute = True + return perturb, recompute, extraprec, discard + +_hypercomb_msg = """ +hypercomb() failed to converge to the requested %i bits of accuracy +using a working precision of %i bits. The function value may be zero or +infinite; try passing zeroprec=N or infprec=M to bound finite values between +2^(-N) and 2^M. Otherwise try a higher maxprec or maxterms. +""" + +@defun +def hypercomb(ctx, function, params=[], discard_known_zeros=True, **kwargs): + orig = ctx.prec + sumvalue = ctx.zero + dist = ctx.nint_distance + ninf = ctx.ninf + orig_params = params[:] + verbose = kwargs.get('verbose', False) + maxprec = kwargs.get('maxprec', ctx._default_hyper_maxprec(orig)) + kwargs['maxprec'] = maxprec # For calls to hypsum + zeroprec = kwargs.get('zeroprec') + infprec = kwargs.get('infprec') + perturbed_reference_value = None + hextra = 0 + try: + while 1: + ctx.prec += 10 + if ctx.prec > maxprec: + raise ValueError(_hypercomb_msg % (orig, ctx.prec)) + orig2 = ctx.prec + params = orig_params[:] + terms = function(*params) + if verbose: + print() + print("ENTERING hypercomb main loop") + print("prec =", ctx.prec) + print("hextra", hextra) + perturb, recompute, extraprec, discard = \ + _check_need_perturb(ctx, terms, orig, discard_known_zeros) + ctx.prec += extraprec + if perturb: + if "hmag" in kwargs: + hmag = kwargs["hmag"] + elif ctx._fixed_precision: + hmag = int(ctx.prec*0.3) + else: + hmag = orig + 10 + hextra + h = ctx.ldexp(ctx.one, -hmag) + ctx.prec = orig2 + 10 + hmag + 10 + for k in range(len(params)): + params[k] += h + # Heuristically ensure that the perturbations + # are "independent" so that two perturbations + # don't accidentally cancel each other out + # in a subtraction. + h += h/(k+1) + if recompute: + terms = function(*params) + if discard_known_zeros: + terms = [term for (i, term) in enumerate(terms) if i not in discard] + if not terms: + return ctx.zero + evaluated_terms = [] + for term_index, term_data in enumerate(terms): + w_s, c_s, alpha_s, beta_s, a_s, b_s, z = term_data + if verbose: + print() + print(" Evaluating term %i/%i : %iF%i" % \ + (term_index+1, len(terms), len(a_s), len(b_s))) + print(" powers", ctx.nstr(w_s), ctx.nstr(c_s)) + print(" gamma", ctx.nstr(alpha_s), ctx.nstr(beta_s)) + print(" hyper", ctx.nstr(a_s), ctx.nstr(b_s)) + print(" z", ctx.nstr(z)) + #v = ctx.hyper(a_s, b_s, z, **kwargs) + #for a in alpha_s: v *= ctx.gamma(a) + #for b in beta_s: v *= ctx.rgamma(b) + #for w, c in zip(w_s, c_s): v *= ctx.power(w, c) + v = ctx.fprod([ctx.hyper(a_s, b_s, z, **kwargs)] + \ + [ctx.gamma(a) for a in alpha_s] + \ + [ctx.rgamma(b) for b in beta_s] + \ + [ctx.power(w,c) for (w,c) in zip(w_s,c_s)]) + if verbose: + print(" Value:", v) + evaluated_terms.append(v) + + if len(terms) == 1 and (not perturb): + sumvalue = evaluated_terms[0] + break + + if ctx._fixed_precision: + sumvalue = ctx.fsum(evaluated_terms) + break + + sumvalue = ctx.fsum(evaluated_terms) + term_magnitudes = [ctx.mag(x) for x in evaluated_terms] + max_magnitude = max(term_magnitudes) + sum_magnitude = ctx.mag(sumvalue) + cancellation = max_magnitude - sum_magnitude + if verbose: + print() + print(" Cancellation:", cancellation, "bits") + print(" Increased precision:", ctx.prec - orig, "bits") + + precision_ok = cancellation < ctx.prec - orig + + if zeroprec is None: + zero_ok = False + else: + zero_ok = max_magnitude - ctx.prec < -zeroprec + if infprec is None: + inf_ok = False + else: + inf_ok = max_magnitude > infprec + + if precision_ok and (not perturb) or ctx.isnan(cancellation): + break + elif precision_ok: + if perturbed_reference_value is None: + hextra += 20 + perturbed_reference_value = sumvalue + continue + elif ctx.mag(sumvalue - perturbed_reference_value) <= \ + ctx.mag(sumvalue) - orig: + break + elif zero_ok: + sumvalue = ctx.zero + break + elif inf_ok: + sumvalue = ctx.inf + break + elif 'hmag' in kwargs: + break + else: + hextra *= 2 + perturbed_reference_value = sumvalue + # Increase precision + else: + increment = min(max(cancellation, orig//2), max(extraprec,orig)) + ctx.prec += increment + if verbose: + print(" Must start over with increased precision") + continue + finally: + ctx.prec = orig + return +sumvalue + +@defun +def hyper(ctx, a_s, b_s, z, **kwargs): + """ + Hypergeometric function, general case. + """ + z = ctx.convert(z) + p = len(a_s) + q = len(b_s) + a_s = [ctx._convert_param(a) for a in a_s] + b_s = [ctx._convert_param(b) for b in b_s] + # Reduce degree by eliminating common parameters + if kwargs.get('eliminate', True): + elim_nonpositive = kwargs.get('eliminate_all', False) + i = 0 + while i < q and a_s: + b = b_s[i] + if b in a_s and (elim_nonpositive or not ctx.isnpint(b[0])): + a_s.remove(b) + b_s.remove(b) + p -= 1 + q -= 1 + else: + i += 1 + # Handle special cases + if p == 0: + if q == 1: return ctx._hyp0f1(b_s, z, **kwargs) + elif q == 0: return ctx.exp(z) + elif p == 1: + if q == 1: return ctx._hyp1f1(a_s, b_s, z, **kwargs) + elif q == 2: return ctx._hyp1f2(a_s, b_s, z, **kwargs) + elif q == 0: return ctx._hyp1f0(a_s[0][0], z) + elif p == 2: + if q == 1: return ctx._hyp2f1(a_s, b_s, z, **kwargs) + elif q == 2: return ctx._hyp2f2(a_s, b_s, z, **kwargs) + elif q == 3: return ctx._hyp2f3(a_s, b_s, z, **kwargs) + elif q == 0: return ctx._hyp2f0(a_s, b_s, z, **kwargs) + elif p == q+1: + return ctx._hypq1fq(p, q, a_s, b_s, z, **kwargs) + elif p > q+1 and not kwargs.get('force_series'): + return ctx._hyp_borel(p, q, a_s, b_s, z, **kwargs) + coeffs, types = zip(*(a_s+b_s)) + return ctx.hypsum(p, q, types, coeffs, z, **kwargs) + +@defun +def hyp0f1(ctx,b,z,**kwargs): + return ctx.hyper([],[b],z,**kwargs) + +@defun +def hyp1f1(ctx,a,b,z,**kwargs): + return ctx.hyper([a],[b],z,**kwargs) + +@defun +def hyp1f2(ctx,a1,b1,b2,z,**kwargs): + return ctx.hyper([a1],[b1,b2],z,**kwargs) + +@defun +def hyp2f1(ctx,a,b,c,z,**kwargs): + return ctx.hyper([a,b],[c],z,**kwargs) + +@defun +def hyp2f2(ctx,a1,a2,b1,b2,z,**kwargs): + return ctx.hyper([a1,a2],[b1,b2],z,**kwargs) + +@defun +def hyp2f3(ctx,a1,a2,b1,b2,b3,z,**kwargs): + return ctx.hyper([a1,a2],[b1,b2,b3],z,**kwargs) + +@defun +def hyp2f0(ctx,a,b,z,**kwargs): + return ctx.hyper([a,b],[],z,**kwargs) + +@defun +def hyp3f2(ctx,a1,a2,a3,b1,b2,z,**kwargs): + return ctx.hyper([a1,a2,a3],[b1,b2],z,**kwargs) + +@defun_wrapped +def _hyp1f0(ctx, a, z): + return (1-z) ** (-a) + +@defun +def _hyp0f1(ctx, b_s, z, **kwargs): + (b, btype), = b_s + if z: + magz = ctx.mag(z) + else: + magz = 0 + if magz >= 8 and not kwargs.get('force_series'): + try: + # http://functions.wolfram.com/HypergeometricFunctions/ + # Hypergeometric0F1/06/02/03/0004/ + # TODO: handle the all-real case more efficiently! + # TODO: figure out how much precision is needed (exponential growth) + orig = ctx.prec + try: + ctx.prec += 12 + magz//2 + def h(): + w = ctx.sqrt(-z) + jw = ctx.j*w + u = 1/(4*jw) + c = ctx.mpq_1_2 - b + E = ctx.exp(2*jw) + T1 = ([-jw,E], [c,-1], [], [], [b-ctx.mpq_1_2, ctx.mpq_3_2-b], [], -u) + T2 = ([jw,E], [c,1], [], [], [b-ctx.mpq_1_2, ctx.mpq_3_2-b], [], u) + return T1, T2 + v = ctx.hypercomb(h, [], force_series=True) + v = ctx.gamma(b)/(2*ctx.sqrt(ctx.pi))*v + finally: + ctx.prec = orig + if ctx._is_real_type(b) and ctx._is_real_type(z): + v = ctx._re(v) + return +v + except ctx.NoConvergence: + pass + return ctx.hypsum(0, 1, (btype,), [b], z, **kwargs) + +@defun +def _hyp1f1(ctx, a_s, b_s, z, **kwargs): + (a, atype), = a_s + (b, btype), = b_s + if not z: + return ctx.one+z + magz = ctx.mag(z) + if magz >= 7 and not (ctx.isint(a) and ctx.re(a) <= 0): + if ctx.isinf(z): + if ctx.sign(a) == ctx.sign(b) == ctx.sign(z) == 1: + return ctx.inf + return ctx.nan * z + try: + try: + ctx.prec += magz + sector = ctx._im(z) < 0 + def h(a,b): + if sector: + E = ctx.expjpi(ctx.fneg(a, exact=True)) + else: + E = ctx.expjpi(a) + rz = 1/z + T1 = ([E,z], [1,-a], [b], [b-a], [a, 1+a-b], [], -rz) + T2 = ([ctx.exp(z),z], [1,a-b], [b], [a], [b-a, 1-a], [], rz) + return T1, T2 + v = ctx.hypercomb(h, [a,b], force_series=True) + if ctx._is_real_type(a) and ctx._is_real_type(b) and ctx._is_real_type(z): + v = ctx._re(v) + return +v + except ctx.NoConvergence: + pass + finally: + ctx.prec -= magz + v = ctx.hypsum(1, 1, (atype, btype), [a, b], z, **kwargs) + return v + +def _hyp2f1_gosper(ctx,a,b,c,z,**kwargs): + # Use Gosper's recurrence + # See http://www.math.utexas.edu/pipermail/maxima/2006/000126.html + _a,_b,_c,_z = a, b, c, z + orig = ctx.prec + maxprec = kwargs.get('maxprec', 100*orig) + extra = 10 + while 1: + ctx.prec = orig + extra + #a = ctx.convert(_a) + #b = ctx.convert(_b) + #c = ctx.convert(_c) + z = ctx.convert(_z) + d = ctx.mpf(0) + e = ctx.mpf(1) + f = ctx.mpf(0) + k = 0 + # Common subexpression elimination, unfortunately making + # things a bit unreadable. The formula is quite messy to begin + # with, though... + abz = a*b*z + ch = c * ctx.mpq_1_2 + c1h = (c+1) * ctx.mpq_1_2 + nz = 1-z + g = z/nz + abg = a*b*g + cba = c-b-a + z2 = z-2 + tol = -ctx.prec - 10 + nstr = ctx.nstr + nprint = ctx.nprint + mag = ctx.mag + maxmag = ctx.ninf + while 1: + kch = k+ch + kakbz = (k+a)*(k+b)*z / (4*(k+1)*kch*(k+c1h)) + d1 = kakbz*(e-(k+cba)*d*g) + e1 = kakbz*(d*abg+(k+c)*e) + ft = d*(k*(cba*z+k*z2-c)-abz)/(2*kch*nz) + f1 = f + e - ft + maxmag = max(maxmag, mag(f1)) + if mag(f1-f) < tol: + break + d, e, f = d1, e1, f1 + k += 1 + cancellation = maxmag - mag(f1) + if cancellation < extra: + break + else: + extra += cancellation + if extra > maxprec: + raise ctx.NoConvergence + return f1 + +@defun +def _hyp2f1(ctx, a_s, b_s, z, **kwargs): + (a, atype), (b, btype) = a_s + (c, ctype), = b_s + if z == 1: + # TODO: the following logic can be simplified + convergent = ctx.re(c-a-b) > 0 + finite = (ctx.isint(a) and a <= 0) or (ctx.isint(b) and b <= 0) + zerodiv = ctx.isint(c) and c <= 0 and not \ + ((ctx.isint(a) and c <= a <= 0) or (ctx.isint(b) and c <= b <= 0)) + #print "bz", a, b, c, z, convergent, finite, zerodiv + # Gauss's theorem gives the value if convergent + if (convergent or finite) and not zerodiv: + return ctx.gammaprod([c, c-a-b], [c-a, c-b], _infsign=True) + # Otherwise, there is a pole and we take the + # sign to be that when approaching from below + # XXX: this evaluation is not necessarily correct in all cases + return ctx.hyp2f1(a,b,c,1-ctx.eps*2) * ctx.inf + + # Equal to 1 (first term), unless there is a subsequent + # division by zero + if not z: + # Division by zero but power of z is higher than + # first order so cancels + if c or a == 0 or b == 0: + return 1+z + # Indeterminate + return ctx.nan + + # Hit zero denominator unless numerator goes to 0 first + if ctx.isint(c) and c <= 0: + if (ctx.isint(a) and c <= a <= 0) or \ + (ctx.isint(b) and c <= b <= 0): + pass + else: + # Pole in series + return ctx.inf + + absz = abs(z) + + # Fast case: standard series converges rapidly, + # possibly in finitely many terms + if absz <= 0.8 or (ctx.isint(a) and a <= 0 and a >= -1000) or \ + (ctx.isint(b) and b <= 0 and b >= -1000): + return ctx.hypsum(2, 1, (atype, btype, ctype), [a, b, c], z, **kwargs) + + orig = ctx.prec + try: + ctx.prec += 10 + + # Use 1/z transformation + if absz >= 1.3: + def h(a,b): + t = ctx.mpq_1-c; ab = a-b; rz = 1/z + T1 = ([-z],[-a], [c,-ab],[b,c-a], [a,t+a],[ctx.mpq_1+ab], rz) + T2 = ([-z],[-b], [c,ab],[a,c-b], [b,t+b],[ctx.mpq_1-ab], rz) + return T1, T2 + v = ctx.hypercomb(h, [a,b], **kwargs) + + # Use 1-z transformation + elif abs(1-z) <= 0.75: + def h(a,b): + t = c-a-b; ca = c-a; cb = c-b; rz = 1-z + T1 = [], [], [c,t], [ca,cb], [a,b], [1-t], rz + T2 = [rz], [t], [c,a+b-c], [a,b], [ca,cb], [1+t], rz + return T1, T2 + v = ctx.hypercomb(h, [a,b], **kwargs) + + # Use z/(z-1) transformation + elif abs(z/(z-1)) <= 0.75: + v = ctx.hyp2f1(a, c-b, c, z/(z-1)) / (1-z)**a + + # Remaining part of unit circle + else: + v = _hyp2f1_gosper(ctx,a,b,c,z,**kwargs) + + finally: + ctx.prec = orig + return +v + +@defun +def _hypq1fq(ctx, p, q, a_s, b_s, z, **kwargs): + r""" + Evaluates 3F2, 4F3, 5F4, ... + """ + a_s, a_types = zip(*a_s) + b_s, b_types = zip(*b_s) + a_s = list(a_s) + b_s = list(b_s) + absz = abs(z) + ispoly = False + for a in a_s: + if ctx.isint(a) and a <= 0: + ispoly = True + break + # Direct summation + if absz < 1 or ispoly: + try: + return ctx.hypsum(p, q, a_types+b_types, a_s+b_s, z, **kwargs) + except ctx.NoConvergence: + if absz > 1.1 or ispoly: + raise + # Use expansion at |z-1| -> 0. + # Reference: Wolfgang Buhring, "Generalized Hypergeometric Functions at + # Unit Argument", Proc. Amer. Math. Soc., Vol. 114, No. 1 (Jan. 1992), + # pp.145-153 + # The current implementation has several problems: + # 1. We only implement it for 3F2. The expansion coefficients are + # given by extremely messy nested sums in the higher degree cases + # (see reference). Is efficient sequential generation of the coefficients + # possible in the > 3F2 case? + # 2. Although the series converges, it may do so slowly, so we need + # convergence acceleration. The acceleration implemented by + # nsum does not always help, so results returned are sometimes + # inaccurate! Can we do better? + # 3. We should check conditions for convergence, and possibly + # do a better job of cancelling out gamma poles if possible. + if z == 1: + # XXX: should also check for division by zero in the + # denominator of the series (cf. hyp2f1) + S = ctx.re(sum(b_s)-sum(a_s)) + if S <= 0: + #return ctx.hyper(a_s, b_s, 1-ctx.eps*2, **kwargs) * ctx.inf + return ctx.hyper(a_s, b_s, 0.9, **kwargs) * ctx.inf + if (p,q) == (3,2) and abs(z-1) < 0.05: # and kwargs.get('sum1') + #print "Using alternate summation (experimental)" + a1,a2,a3 = a_s + b1,b2 = b_s + u = b1+b2-a3 + initial = ctx.gammaprod([b2-a3,b1-a3,a1,a2],[b2-a3,b1-a3,1,u]) + def term(k, _cache={0:initial}): + u = b1+b2-a3+k + if k in _cache: + t = _cache[k] + else: + t = _cache[k-1] + t *= (b1+k-a3-1)*(b2+k-a3-1) + t /= k*(u-1) + _cache[k] = t + return t * ctx.hyp2f1(a1,a2,u,z) + try: + S = ctx.nsum(term, [0,ctx.inf], verbose=kwargs.get('verbose'), + strict=kwargs.get('strict', True)) + return S * ctx.gammaprod([b1,b2],[a1,a2,a3]) + except ctx.NoConvergence: + pass + # Try to use convergence acceleration on and close to the unit circle. + # Problem: the convergence acceleration degenerates as |z-1| -> 0, + # except for special cases. Everywhere else, the Shanks transformation + # is very efficient. + if absz < 1.1 and ctx._re(z) <= 1: + + def term(kk, _cache={0:ctx.one}): + k = int(kk) + if k != kk: + t = z ** ctx.mpf(kk) / ctx.fac(kk) + for a in a_s: t *= ctx.rf(a,kk) + for b in b_s: t /= ctx.rf(b,kk) + return t + if k in _cache: + return _cache[k] + t = term(k-1) + m = k-1 + for j in xrange(p): t *= (a_s[j]+m) + for j in xrange(q): t /= (b_s[j]+m) + t *= z + t /= k + _cache[k] = t + return t + + sum_method = kwargs.get('sum_method', 'r+s+e') + + try: + return ctx.nsum(term, [0,ctx.inf], verbose=kwargs.get('verbose'), + strict=kwargs.get('strict', True), + method=sum_method.replace('e','')) + except ctx.NoConvergence: + if 'e' not in sum_method: + raise + pass + + if kwargs.get('verbose'): + print("Attempting Euler-Maclaurin summation") + + + """ + Somewhat slower version (one diffs_exp for each factor). + However, this would be faster with fast direct derivatives + of the gamma function. + + def power_diffs(k0): + r = 0 + l = ctx.log(z) + while 1: + yield z**ctx.mpf(k0) * l**r + r += 1 + + def loggamma_diffs(x, reciprocal=False): + sign = (-1) ** reciprocal + yield sign * ctx.loggamma(x) + i = 0 + while 1: + yield sign * ctx.psi(i,x) + i += 1 + + def hyper_diffs(k0): + b2 = b_s + [1] + A = [ctx.diffs_exp(loggamma_diffs(a+k0)) for a in a_s] + B = [ctx.diffs_exp(loggamma_diffs(b+k0,True)) for b in b2] + Z = [power_diffs(k0)] + C = ctx.gammaprod([b for b in b2], [a for a in a_s]) + for d in ctx.diffs_prod(A + B + Z): + v = C * d + yield v + """ + + def log_diffs(k0): + b2 = b_s + [1] + yield sum(ctx.loggamma(a+k0) for a in a_s) - \ + sum(ctx.loggamma(b+k0) for b in b2) + k0*ctx.log(z) + i = 0 + while 1: + v = sum(ctx.psi(i,a+k0) for a in a_s) - \ + sum(ctx.psi(i,b+k0) for b in b2) + if i == 0: + v += ctx.log(z) + yield v + i += 1 + + def hyper_diffs(k0): + C = ctx.gammaprod([b for b in b_s], [a for a in a_s]) + for d in ctx.diffs_exp(log_diffs(k0)): + v = C * d + yield v + + tol = ctx.eps / 1024 + prec = ctx.prec + try: + trunc = 50 * ctx.dps + ctx.prec += 20 + for i in xrange(5): + head = ctx.fsum(term(k) for k in xrange(trunc)) + tail, err = ctx.sumem(term, [trunc, ctx.inf], tol=tol, + adiffs=hyper_diffs(trunc), + verbose=kwargs.get('verbose'), + error=True, + _fast_abort=True) + if err < tol: + v = head + tail + break + trunc *= 2 + # Need to increase precision because calculation of + # derivatives may be inaccurate + ctx.prec += ctx.prec//2 + if i == 4: + raise ctx.NoConvergence(\ + "Euler-Maclaurin summation did not converge") + finally: + ctx.prec = prec + return +v + + # Use 1/z transformation + # http://functions.wolfram.com/HypergeometricFunctions/ + # HypergeometricPFQ/06/01/05/02/0004/ + def h(*args): + a_s = list(args[:p]) + b_s = list(args[p:]) + Ts = [] + recz = ctx.one/z + negz = ctx.fneg(z, exact=True) + for k in range(q+1): + ak = a_s[k] + C = [negz] + Cp = [-ak] + Gn = b_s + [ak] + [a_s[j]-ak for j in range(q+1) if j != k] + Gd = a_s + [b_s[j]-ak for j in range(q)] + Fn = [ak] + [ak-b_s[j]+1 for j in range(q)] + Fd = [1-a_s[j]+ak for j in range(q+1) if j != k] + Ts.append((C, Cp, Gn, Gd, Fn, Fd, recz)) + return Ts + return ctx.hypercomb(h, a_s+b_s, **kwargs) + +@defun +def _hyp_borel(ctx, p, q, a_s, b_s, z, **kwargs): + if a_s: + a_s, a_types = zip(*a_s) + a_s = list(a_s) + else: + a_s, a_types = [], () + if b_s: + b_s, b_types = zip(*b_s) + b_s = list(b_s) + else: + b_s, b_types = [], () + kwargs['maxterms'] = kwargs.get('maxterms', ctx.prec) + try: + return ctx.hypsum(p, q, a_types+b_types, a_s+b_s, z, **kwargs) + except ctx.NoConvergence: + pass + prec = ctx.prec + try: + tol = kwargs.get('asymp_tol', ctx.eps/4) + ctx.prec += 10 + # hypsum is has a conservative tolerance. So we try again: + def term(k, cache={0:ctx.one}): + if k in cache: + return cache[k] + t = term(k-1) + for a in a_s: t *= (a+(k-1)) + for b in b_s: t /= (b+(k-1)) + t *= z + t /= k + cache[k] = t + return t + s = ctx.one + for k in xrange(1, ctx.prec): + t = term(k) + s += t + if abs(t) <= tol: + return s + finally: + ctx.prec = prec + if p <= q+3: + contour = kwargs.get('contour') + if not contour: + if ctx.arg(z) < 0.25: + u = z / max(1, abs(z)) + if ctx.arg(z) >= 0: + contour = [0, 2j, (2j+2)/u, 2/u, ctx.inf] + else: + contour = [0, -2j, (-2j+2)/u, 2/u, ctx.inf] + #contour = [0, 2j/z, 2/z, ctx.inf] + #contour = [0, 2j, 2/z, ctx.inf] + #contour = [0, 2j, ctx.inf] + else: + contour = [0, ctx.inf] + quad_kwargs = kwargs.get('quad_kwargs', {}) + def g(t): + return ctx.exp(-t)*ctx.hyper(a_s, b_s+[1], t*z) + I, err = ctx.quad(g, contour, error=True, **quad_kwargs) + if err <= abs(I)*ctx.eps*8: + return I + raise ctx.NoConvergence + + +@defun +def _hyp2f2(ctx, a_s, b_s, z, **kwargs): + (a1, a1type), (a2, a2type) = a_s + (b1, b1type), (b2, b2type) = b_s + + absz = abs(z) + magz = ctx.mag(z) + orig = ctx.prec + + # Asymptotic expansion is ~ exp(z) + asymp_extraprec = magz + + # Asymptotic series is in terms of 3F1 + can_use_asymptotic = (not kwargs.get('force_series')) and \ + (ctx.mag(absz) > 3) + + # TODO: much of the following could be shared with 2F3 instead of + # copypasted + if can_use_asymptotic: + #print "using asymp" + try: + try: + ctx.prec += asymp_extraprec + # http://functions.wolfram.com/HypergeometricFunctions/ + # Hypergeometric2F2/06/02/02/0002/ + def h(a1,a2,b1,b2): + X = a1+a2-b1-b2 + A2 = a1+a2 + B2 = b1+b2 + c = {} + c[0] = ctx.one + c[1] = (A2-1)*X+b1*b2-a1*a2 + s1 = 0 + k = 0 + tprev = 0 + while 1: + if k not in c: + uu1 = 1-B2+2*a1+a1**2+2*a2+a2**2-A2*B2+a1*a2+b1*b2+(2*B2-3*(A2+1))*k+2*k**2 + uu2 = (k-A2+b1-1)*(k-A2+b2-1)*(k-X-2) + c[k] = ctx.one/k * (uu1*c[k-1]-uu2*c[k-2]) + t1 = c[k] * z**(-k) + if abs(t1) < 0.1*ctx.eps: + #print "Convergence :)" + break + # Quit if the series doesn't converge quickly enough + if k > 5 and abs(tprev) / abs(t1) < 1.5: + #print "No convergence :(" + raise ctx.NoConvergence + s1 += t1 + tprev = t1 + k += 1 + S = ctx.exp(z)*s1 + T1 = [z,S], [X,1], [b1,b2],[a1,a2],[],[],0 + T2 = [-z],[-a1],[b1,b2,a2-a1],[a2,b1-a1,b2-a1],[a1,a1-b1+1,a1-b2+1],[a1-a2+1],-1/z + T3 = [-z],[-a2],[b1,b2,a1-a2],[a1,b1-a2,b2-a2],[a2,a2-b1+1,a2-b2+1],[-a1+a2+1],-1/z + return T1, T2, T3 + v = ctx.hypercomb(h, [a1,a2,b1,b2], force_series=True, maxterms=4*ctx.prec) + if sum(ctx._is_real_type(u) for u in [a1,a2,b1,b2,z]) == 5: + v = ctx.re(v) + return v + except ctx.NoConvergence: + pass + finally: + ctx.prec = orig + + return ctx.hypsum(2, 2, (a1type, a2type, b1type, b2type), [a1, a2, b1, b2], z, **kwargs) + + + +@defun +def _hyp1f2(ctx, a_s, b_s, z, **kwargs): + (a1, a1type), = a_s + (b1, b1type), (b2, b2type) = b_s + + absz = abs(z) + magz = ctx.mag(z) + orig = ctx.prec + + # Asymptotic expansion is ~ exp(sqrt(z)) + asymp_extraprec = z and magz//2 + + # Asymptotic series is in terms of 3F0 + can_use_asymptotic = (not kwargs.get('force_series')) and \ + (ctx.mag(absz) > 19) and \ + (ctx.sqrt(absz) > 1.5*orig) # and \ + # ctx._hyp_check_convergence([a1, a1-b1+1, a1-b2+1], [], + # 1/absz, orig+40+asymp_extraprec) + + # TODO: much of the following could be shared with 2F3 instead of + # copypasted + if can_use_asymptotic: + #print "using asymp" + try: + try: + ctx.prec += asymp_extraprec + # http://functions.wolfram.com/HypergeometricFunctions/ + # Hypergeometric1F2/06/02/03/ + def h(a1,b1,b2): + X = ctx.mpq_1_2*(a1-b1-b2+ctx.mpq_1_2) + c = {} + c[0] = ctx.one + c[1] = 2*(ctx.mpq_1_4*(3*a1+b1+b2-2)*(a1-b1-b2)+b1*b2-ctx.mpq_3_16) + c[2] = 2*(b1*b2+ctx.mpq_1_4*(a1-b1-b2)*(3*a1+b1+b2-2)-ctx.mpq_3_16)**2+\ + ctx.mpq_1_16*(-16*(2*a1-3)*b1*b2 + \ + 4*(a1-b1-b2)*(-8*a1**2+11*a1+b1+b2-2)-3) + s1 = 0 + s2 = 0 + k = 0 + tprev = 0 + while 1: + if k not in c: + uu1 = (3*k**2+(-6*a1+2*b1+2*b2-4)*k + 3*a1**2 - \ + (b1-b2)**2 - 2*a1*(b1+b2-2) + ctx.mpq_1_4) + uu2 = (k-a1+b1-b2-ctx.mpq_1_2)*(k-a1-b1+b2-ctx.mpq_1_2)*\ + (k-a1+b1+b2-ctx.mpq_5_2) + c[k] = ctx.one/(2*k)*(uu1*c[k-1]-uu2*c[k-2]) + w = c[k] * (-z)**(-0.5*k) + t1 = (-ctx.j)**k * ctx.mpf(2)**(-k) * w + t2 = ctx.j**k * ctx.mpf(2)**(-k) * w + if abs(t1) < 0.1*ctx.eps: + #print "Convergence :)" + break + # Quit if the series doesn't converge quickly enough + if k > 5 and abs(tprev) / abs(t1) < 1.5: + #print "No convergence :(" + raise ctx.NoConvergence + s1 += t1 + s2 += t2 + tprev = t1 + k += 1 + S = ctx.expj(ctx.pi*X+2*ctx.sqrt(-z))*s1 + \ + ctx.expj(-(ctx.pi*X+2*ctx.sqrt(-z)))*s2 + T1 = [0.5*S, ctx.pi, -z], [1, -0.5, X], [b1, b2], [a1],\ + [], [], 0 + T2 = [-z], [-a1], [b1,b2],[b1-a1,b2-a1], \ + [a1,a1-b1+1,a1-b2+1], [], 1/z + return T1, T2 + v = ctx.hypercomb(h, [a1,b1,b2], force_series=True, maxterms=4*ctx.prec) + if sum(ctx._is_real_type(u) for u in [a1,b1,b2,z]) == 4: + v = ctx.re(v) + return v + except ctx.NoConvergence: + pass + finally: + ctx.prec = orig + + #print "not using asymp" + return ctx.hypsum(1, 2, (a1type, b1type, b2type), [a1, b1, b2], z, **kwargs) + + + +@defun +def _hyp2f3(ctx, a_s, b_s, z, **kwargs): + (a1, a1type), (a2, a2type) = a_s + (b1, b1type), (b2, b2type), (b3, b3type) = b_s + + absz = abs(z) + magz = ctx.mag(z) + + # Asymptotic expansion is ~ exp(sqrt(z)) + asymp_extraprec = z and magz//2 + orig = ctx.prec + + # Asymptotic series is in terms of 4F1 + # The square root below empirically provides a plausible criterion + # for the leading series to converge + can_use_asymptotic = (not kwargs.get('force_series')) and \ + (ctx.mag(absz) > 19) and (ctx.sqrt(absz) > 1.5*orig) + + if can_use_asymptotic: + #print "using asymp" + try: + try: + ctx.prec += asymp_extraprec + # http://functions.wolfram.com/HypergeometricFunctions/ + # Hypergeometric2F3/06/02/03/01/0002/ + def h(a1,a2,b1,b2,b3): + X = ctx.mpq_1_2*(a1+a2-b1-b2-b3+ctx.mpq_1_2) + A2 = a1+a2 + B3 = b1+b2+b3 + A = a1*a2 + B = b1*b2+b3*b2+b1*b3 + R = b1*b2*b3 + c = {} + c[0] = ctx.one + c[1] = 2*(B - A + ctx.mpq_1_4*(3*A2+B3-2)*(A2-B3) - ctx.mpq_3_16) + c[2] = ctx.mpq_1_2*c[1]**2 + ctx.mpq_1_16*(-16*(2*A2-3)*(B-A) + 32*R +\ + 4*(-8*A2**2 + 11*A2 + 8*A + B3 - 2)*(A2-B3)-3) + s1 = 0 + s2 = 0 + k = 0 + tprev = 0 + while 1: + if k not in c: + uu1 = (k-2*X-3)*(k-2*X-2*b1-1)*(k-2*X-2*b2-1)*\ + (k-2*X-2*b3-1) + uu2 = (4*(k-1)**3 - 6*(4*X+B3)*(k-1)**2 + \ + 2*(24*X**2+12*B3*X+4*B+B3-1)*(k-1) - 32*X**3 - \ + 24*B3*X**2 - 4*B - 8*R - 4*(4*B+B3-1)*X + 2*B3-1) + uu3 = (5*(k-1)**2+2*(-10*X+A2-3*B3+3)*(k-1)+2*c[1]) + c[k] = ctx.one/(2*k)*(uu1*c[k-3]-uu2*c[k-2]+uu3*c[k-1]) + w = c[k] * ctx.power(-z, -0.5*k) + t1 = (-ctx.j)**k * ctx.mpf(2)**(-k) * w + t2 = ctx.j**k * ctx.mpf(2)**(-k) * w + if abs(t1) < 0.1*ctx.eps: + break + # Quit if the series doesn't converge quickly enough + if k > 5 and abs(tprev) / abs(t1) < 1.5: + raise ctx.NoConvergence + s1 += t1 + s2 += t2 + tprev = t1 + k += 1 + S = ctx.expj(ctx.pi*X+2*ctx.sqrt(-z))*s1 + \ + ctx.expj(-(ctx.pi*X+2*ctx.sqrt(-z)))*s2 + T1 = [0.5*S, ctx.pi, -z], [1, -0.5, X], [b1, b2, b3], [a1, a2],\ + [], [], 0 + T2 = [-z], [-a1], [b1,b2,b3,a2-a1],[a2,b1-a1,b2-a1,b3-a1], \ + [a1,a1-b1+1,a1-b2+1,a1-b3+1], [a1-a2+1], 1/z + T3 = [-z], [-a2], [b1,b2,b3,a1-a2],[a1,b1-a2,b2-a2,b3-a2], \ + [a2,a2-b1+1,a2-b2+1,a2-b3+1],[-a1+a2+1], 1/z + return T1, T2, T3 + v = ctx.hypercomb(h, [a1,a2,b1,b2,b3], force_series=True, maxterms=4*ctx.prec) + if sum(ctx._is_real_type(u) for u in [a1,a2,b1,b2,b3,z]) == 6: + v = ctx.re(v) + return v + except ctx.NoConvergence: + pass + finally: + ctx.prec = orig + + return ctx.hypsum(2, 3, (a1type, a2type, b1type, b2type, b3type), [a1, a2, b1, b2, b3], z, **kwargs) + +@defun +def _hyp2f0(ctx, a_s, b_s, z, **kwargs): + (a, atype), (b, btype) = a_s + # We want to try aggressively to use the asymptotic expansion, + # and fall back only when absolutely necessary + try: + kwargsb = kwargs.copy() + kwargsb['maxterms'] = kwargsb.get('maxterms', ctx.prec) + return ctx.hypsum(2, 0, (atype,btype), [a,b], z, **kwargsb) + except ctx.NoConvergence: + if kwargs.get('force_series'): + raise + pass + def h(a, b): + w = ctx.sinpi(b) + rz = -1/z + T1 = ([ctx.pi,w,rz],[1,-1,a],[],[a-b+1,b],[a],[b],rz) + T2 = ([-ctx.pi,w,rz],[1,-1,1+a-b],[],[a,2-b],[a-b+1],[2-b],rz) + return T1, T2 + return ctx.hypercomb(h, [a, 1+a-b], **kwargs) + +@defun +def meijerg(ctx, a_s, b_s, z, r=1, series=None, **kwargs): + an, ap = a_s + bm, bq = b_s + n = len(an) + p = n + len(ap) + m = len(bm) + q = m + len(bq) + a = an+ap + b = bm+bq + a = [ctx.convert(_) for _ in a] + b = [ctx.convert(_) for _ in b] + z = ctx.convert(z) + if series is None: + if p < q: series = 1 + if p > q: series = 2 + if p == q: + if m+n == p and abs(z) > 1: + series = 2 + else: + series = 1 + if kwargs.get('verbose'): + print("Meijer G m,n,p,q,series =", m,n,p,q,series) + if series == 1: + def h(*args): + a = args[:p] + b = args[p:] + terms = [] + for k in range(m): + bases = [z] + expts = [b[k]/r] + gn = [b[j]-b[k] for j in range(m) if j != k] + gn += [1-a[j]+b[k] for j in range(n)] + gd = [a[j]-b[k] for j in range(n,p)] + gd += [1-b[j]+b[k] for j in range(m,q)] + hn = [1-a[j]+b[k] for j in range(p)] + hd = [1-b[j]+b[k] for j in range(q) if j != k] + hz = (-ctx.one)**(p-m-n) * z**(ctx.one/r) + terms.append((bases, expts, gn, gd, hn, hd, hz)) + return terms + else: + def h(*args): + a = args[:p] + b = args[p:] + terms = [] + for k in range(n): + bases = [z] + if r == 1: + expts = [a[k]-1] + else: + expts = [(a[k]-1)/ctx.convert(r)] + gn = [a[k]-a[j] for j in range(n) if j != k] + gn += [1-a[k]+b[j] for j in range(m)] + gd = [a[k]-b[j] for j in range(m,q)] + gd += [1-a[k]+a[j] for j in range(n,p)] + hn = [1-a[k]+b[j] for j in range(q)] + hd = [1+a[j]-a[k] for j in range(p) if j != k] + hz = (-ctx.one)**(q-m-n) / z**(ctx.one/r) + terms.append((bases, expts, gn, gd, hn, hd, hz)) + return terms + return ctx.hypercomb(h, a+b, **kwargs) + +@defun_wrapped +def appellf1(ctx,a,b1,b2,c,x,y,**kwargs): + # Assume x smaller + # We will use x for the outer loop + if abs(x) > abs(y): + x, y = y, x + b1, b2 = b2, b1 + def ok(x): + return abs(x) < 0.99 + # Finite cases + if ctx.isnpint(a): + pass + elif ctx.isnpint(b1): + pass + elif ctx.isnpint(b2): + x, y, b1, b2 = y, x, b2, b1 + else: + #print x, y + # Note: ok if |y| > 1, because + # 2F1 implements analytic continuation + if not ok(x): + u1 = (x-y)/(x-1) + if not ok(u1): + raise ValueError("Analytic continuation not implemented") + #print "Using analytic continuation" + return (1-x)**(-b1)*(1-y)**(c-a-b2)*\ + ctx.appellf1(c-a,b1,c-b1-b2,c,u1,y,**kwargs) + return ctx.hyper2d({'m+n':[a],'m':[b1],'n':[b2]}, {'m+n':[c]}, x,y, **kwargs) + +@defun +def appellf2(ctx,a,b1,b2,c1,c2,x,y,**kwargs): + # TODO: continuation + return ctx.hyper2d({'m+n':[a],'m':[b1],'n':[b2]}, + {'m':[c1],'n':[c2]}, x,y, **kwargs) + +@defun +def appellf3(ctx,a1,a2,b1,b2,c,x,y,**kwargs): + outer_polynomial = ctx.isnpint(a1) or ctx.isnpint(b1) + inner_polynomial = ctx.isnpint(a2) or ctx.isnpint(b2) + if not outer_polynomial: + if inner_polynomial or abs(x) > abs(y): + x, y = y, x + a1,a2,b1,b2 = a2,a1,b2,b1 + return ctx.hyper2d({'m':[a1,b1],'n':[a2,b2]}, {'m+n':[c]},x,y,**kwargs) + +@defun +def appellf4(ctx,a,b,c1,c2,x,y,**kwargs): + # TODO: continuation + return ctx.hyper2d({'m+n':[a,b]}, {'m':[c1],'n':[c2]},x,y,**kwargs) + +@defun +def hyper2d(ctx, a, b, x, y, **kwargs): + r""" + Sums the generalized 2D hypergeometric series + + .. math :: + + \sum_{m=0}^{\infty} \sum_{n=0}^{\infty} + \frac{P((a),m,n)}{Q((b),m,n)} + \frac{x^m y^n} {m! n!} + + where `(a) = (a_1,\ldots,a_r)`, `(b) = (b_1,\ldots,b_s)` and where + `P` and `Q` are products of rising factorials such as `(a_j)_n` or + `(a_j)_{m+n}`. `P` and `Q` are specified in the form of dicts, with + the `m` and `n` dependence as keys and parameter lists as values. + The supported rising factorials are given in the following table + (note that only a few are supported in `Q`): + + +------------+-------------------+--------+ + | Key | Rising factorial | `Q` | + +============+===================+========+ + | ``'m'`` | `(a_j)_m` | Yes | + +------------+-------------------+--------+ + | ``'n'`` | `(a_j)_n` | Yes | + +------------+-------------------+--------+ + | ``'m+n'`` | `(a_j)_{m+n}` | Yes | + +------------+-------------------+--------+ + | ``'m-n'`` | `(a_j)_{m-n}` | No | + +------------+-------------------+--------+ + | ``'n-m'`` | `(a_j)_{n-m}` | No | + +------------+-------------------+--------+ + | ``'2m+n'`` | `(a_j)_{2m+n}` | No | + +------------+-------------------+--------+ + | ``'2m-n'`` | `(a_j)_{2m-n}` | No | + +------------+-------------------+--------+ + | ``'2n-m'`` | `(a_j)_{2n-m}` | No | + +------------+-------------------+--------+ + + For example, the Appell F1 and F4 functions + + .. math :: + + F_1 = \sum_{m=0}^{\infty} \sum_{n=0}^{\infty} + \frac{(a)_{m+n} (b)_m (c)_n}{(d)_{m+n}} + \frac{x^m y^n}{m! n!} + + F_4 = \sum_{m=0}^{\infty} \sum_{n=0}^{\infty} + \frac{(a)_{m+n} (b)_{m+n}}{(c)_m (d)_{n}} + \frac{x^m y^n}{m! n!} + + can be represented respectively as + + ``hyper2d({'m+n':[a], 'm':[b], 'n':[c]}, {'m+n':[d]}, x, y)`` + + ``hyper2d({'m+n':[a,b]}, {'m':[c], 'n':[d]}, x, y)`` + + More generally, :func:`~mpmath.hyper2d` can evaluate any of the 34 distinct + convergent second-order (generalized Gaussian) hypergeometric + series enumerated by Horn, as well as the Kampe de Feriet + function. + + The series is computed by rewriting it so that the inner + series (i.e. the series containing `n` and `y`) has the form of an + ordinary generalized hypergeometric series and thereby can be + evaluated efficiently using :func:`~mpmath.hyper`. If possible, + manually swapping `x` and `y` and the corresponding parameters + can sometimes give better results. + + **Examples** + + Two separable cases: a product of two geometric series, and a + product of two Gaussian hypergeometric functions:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> x, y = mpf(0.25), mpf(0.5) + >>> hyper2d({'m':1,'n':1}, {}, x,y) + 2.666666666666666666666667 + >>> 1/(1-x)/(1-y) + 2.666666666666666666666667 + >>> hyper2d({'m':[1,2],'n':[3,4]}, {'m':[5],'n':[6]}, x,y) + 4.164358531238938319669856 + >>> hyp2f1(1,2,5,x)*hyp2f1(3,4,6,y) + 4.164358531238938319669856 + + Some more series that can be done in closed form:: + + >>> hyper2d({'m':1,'n':1},{'m+n':1},x,y) + 2.013417124712514809623881 + >>> (exp(x)*x-exp(y)*y)/(x-y) + 2.013417124712514809623881 + + Six of the 34 Horn functions, G1-G3 and H1-H3:: + + >>> from mpmath import * + >>> mp.dps = 10; mp.pretty = True + >>> x, y = 0.0625, 0.125 + >>> a1,a2,b1,b2,c1,c2,d = 1.1,-1.2,-1.3,-1.4,1.5,-1.6,1.7 + >>> hyper2d({'m+n':a1,'n-m':b1,'m-n':b2},{},x,y) # G1 + 1.139090746 + >>> nsum(lambda m,n: rf(a1,m+n)*rf(b1,n-m)*rf(b2,m-n)*\ + ... x**m*y**n/fac(m)/fac(n), [0,inf], [0,inf]) + 1.139090746 + >>> hyper2d({'m':a1,'n':a2,'n-m':b1,'m-n':b2},{},x,y) # G2 + 0.9503682696 + >>> nsum(lambda m,n: rf(a1,m)*rf(a2,n)*rf(b1,n-m)*rf(b2,m-n)*\ + ... x**m*y**n/fac(m)/fac(n), [0,inf], [0,inf]) + 0.9503682696 + >>> hyper2d({'2n-m':a1,'2m-n':a2},{},x,y) # G3 + 1.029372029 + >>> nsum(lambda m,n: rf(a1,2*n-m)*rf(a2,2*m-n)*\ + ... x**m*y**n/fac(m)/fac(n), [0,inf], [0,inf]) + 1.029372029 + >>> hyper2d({'m-n':a1,'m+n':b1,'n':c1},{'m':d},x,y) # H1 + -1.605331256 + >>> nsum(lambda m,n: rf(a1,m-n)*rf(b1,m+n)*rf(c1,n)/rf(d,m)*\ + ... x**m*y**n/fac(m)/fac(n), [0,inf], [0,inf]) + -1.605331256 + >>> hyper2d({'m-n':a1,'m':b1,'n':[c1,c2]},{'m':d},x,y) # H2 + -2.35405404 + >>> nsum(lambda m,n: rf(a1,m-n)*rf(b1,m)*rf(c1,n)*rf(c2,n)/rf(d,m)*\ + ... x**m*y**n/fac(m)/fac(n), [0,inf], [0,inf]) + -2.35405404 + >>> hyper2d({'2m+n':a1,'n':b1},{'m+n':c1},x,y) # H3 + 0.974479074 + >>> nsum(lambda m,n: rf(a1,2*m+n)*rf(b1,n)/rf(c1,m+n)*\ + ... x**m*y**n/fac(m)/fac(n), [0,inf], [0,inf]) + 0.974479074 + + **References** + + 1. [SrivastavaKarlsson]_ + 2. [Weisstein]_ http://mathworld.wolfram.com/HornFunction.html + 3. [Weisstein]_ http://mathworld.wolfram.com/AppellHypergeometricFunction.html + + """ + x = ctx.convert(x) + y = ctx.convert(y) + def parse(dct, key): + args = dct.pop(key, []) + try: + args = list(args) + except TypeError: + args = [args] + return [ctx.convert(arg) for arg in args] + a_s = dict(a) + b_s = dict(b) + a_m = parse(a, 'm') + a_n = parse(a, 'n') + a_m_add_n = parse(a, 'm+n') + a_m_sub_n = parse(a, 'm-n') + a_n_sub_m = parse(a, 'n-m') + a_2m_add_n = parse(a, '2m+n') + a_2m_sub_n = parse(a, '2m-n') + a_2n_sub_m = parse(a, '2n-m') + b_m = parse(b, 'm') + b_n = parse(b, 'n') + b_m_add_n = parse(b, 'm+n') + if a: raise ValueError("unsupported key: %r" % a.keys()[0]) + if b: raise ValueError("unsupported key: %r" % b.keys()[0]) + s = 0 + outer = ctx.one + m = ctx.mpf(0) + ok_count = 0 + prec = ctx.prec + maxterms = kwargs.get('maxterms', 20*prec) + try: + ctx.prec += 10 + tol = +ctx.eps + while 1: + inner_sign = 1 + outer_sign = 1 + inner_a = list(a_n) + inner_b = list(b_n) + outer_a = [a+m for a in a_m] + outer_b = [b+m for b in b_m] + # (a)_{m+n} = (a)_m (a+m)_n + for a in a_m_add_n: + a = a+m + inner_a.append(a) + outer_a.append(a) + # (b)_{m+n} = (b)_m (b+m)_n + for b in b_m_add_n: + b = b+m + inner_b.append(b) + outer_b.append(b) + # (a)_{n-m} = (a-m)_n / (a-m)_m + for a in a_n_sub_m: + inner_a.append(a-m) + outer_b.append(a-m-1) + # (a)_{m-n} = (-1)^(m+n) (1-a-m)_m / (1-a-m)_n + for a in a_m_sub_n: + inner_sign *= (-1) + outer_sign *= (-1)**(m) + inner_b.append(1-a-m) + outer_a.append(-a-m) + # (a)_{2m+n} = (a)_{2m} (a+2m)_n + for a in a_2m_add_n: + inner_a.append(a+2*m) + outer_a.append((a+2*m)*(1+a+2*m)) + # (a)_{2m-n} = (-1)^(2m+n) (1-a-2m)_{2m} / (1-a-2m)_n + for a in a_2m_sub_n: + inner_sign *= (-1) + inner_b.append(1-a-2*m) + outer_a.append((a+2*m)*(1+a+2*m)) + # (a)_{2n-m} = 4^n ((a-m)/2)_n ((a-m+1)/2)_n / (a-m)_m + for a in a_2n_sub_m: + inner_sign *= 4 + inner_a.append(0.5*(a-m)) + inner_a.append(0.5*(a-m+1)) + outer_b.append(a-m-1) + inner = ctx.hyper(inner_a, inner_b, inner_sign*y, + zeroprec=ctx.prec, **kwargs) + term = outer * inner * outer_sign + if abs(term) < tol: + ok_count += 1 + else: + ok_count = 0 + if ok_count >= 3 or not outer: + break + s += term + for a in outer_a: outer *= a + for b in outer_b: outer /= b + m += 1 + outer = outer * x / m + if m > maxterms: + raise ctx.NoConvergence("maxterms exceeded in hyper2d") + finally: + ctx.prec = prec + return +s + +""" +@defun +def kampe_de_feriet(ctx,a,b,c,d,e,f,x,y,**kwargs): + return ctx.hyper2d({'m+n':a,'m':b,'n':c}, + {'m+n':d,'m':e,'n':f}, x,y, **kwargs) +""" + +@defun +def bihyper(ctx, a_s, b_s, z, **kwargs): + r""" + Evaluates the bilateral hypergeometric series + + .. math :: + + \,_AH_B(a_1, \ldots, a_k; b_1, \ldots, b_B; z) = + \sum_{n=-\infty}^{\infty} + \frac{(a_1)_n \ldots (a_A)_n} + {(b_1)_n \ldots (b_B)_n} \, z^n + + where, for direct convergence, `A = B` and `|z| = 1`, although a + regularized sum exists more generally by considering the + bilateral series as a sum of two ordinary hypergeometric + functions. In order for the series to make sense, none of the + parameters may be integers. + + **Examples** + + The value of `\,_2H_2` at `z = 1` is given by Dougall's formula:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> a,b,c,d = 0.5, 1.5, 2.25, 3.25 + >>> bihyper([a,b],[c,d],1) + -14.49118026212345786148847 + >>> gammaprod([c,d,1-a,1-b,c+d-a-b-1],[c-a,d-a,c-b,d-b]) + -14.49118026212345786148847 + + The regularized function `\,_1H_0` can be expressed as the + sum of one `\,_2F_0` function and one `\,_1F_1` function:: + + >>> a = mpf(0.25) + >>> z = mpf(0.75) + >>> bihyper([a], [], z) + (0.2454393389657273841385582 + 0.2454393389657273841385582j) + >>> hyper([a,1],[],z) + (hyper([1],[1-a],-1/z)-1) + (0.2454393389657273841385582 + 0.2454393389657273841385582j) + >>> hyper([a,1],[],z) + hyper([1],[2-a],-1/z)/z/(a-1) + (0.2454393389657273841385582 + 0.2454393389657273841385582j) + + **References** + + 1. [Slater]_ (chapter 6: "Bilateral Series", pp. 180-189) + 2. [Wikipedia]_ http://en.wikipedia.org/wiki/Bilateral_hypergeometric_series + + """ + z = ctx.convert(z) + c_s = a_s + b_s + p = len(a_s) + q = len(b_s) + if (p, q) == (0,0) or (p, q) == (1,1): + return ctx.zero * z + neg = (p-q) % 2 + def h(*c_s): + a_s = list(c_s[:p]) + b_s = list(c_s[p:]) + aa_s = [2-b for b in b_s] + bb_s = [2-a for a in a_s] + rp = [(-1)**neg * z] + [1-b for b in b_s] + [1-a for a in a_s] + rc = [-1] + [1]*len(b_s) + [-1]*len(a_s) + T1 = [], [], [], [], a_s + [1], b_s, z + T2 = rp, rc, [], [], aa_s + [1], bb_s, (-1)**neg / z + return T1, T2 + return ctx.hypercomb(h, c_s, **kwargs) diff --git a/venv/lib/python3.12/site-packages/mpmath/functions/orthogonal.py b/venv/lib/python3.12/site-packages/mpmath/functions/orthogonal.py new file mode 100644 index 0000000000000000000000000000000000000000..aa33d8bd78290f55a970e78dab7a317d5f652dee --- /dev/null +++ b/venv/lib/python3.12/site-packages/mpmath/functions/orthogonal.py @@ -0,0 +1,493 @@ +from .functions import defun, defun_wrapped + +def _hermite_param(ctx, n, z, parabolic_cylinder): + """ + Combined calculation of the Hermite polynomial H_n(z) (and its + generalization to complex n) and the parabolic cylinder + function D. + """ + n, ntyp = ctx._convert_param(n) + z = ctx.convert(z) + q = -ctx.mpq_1_2 + # For re(z) > 0, 2F0 -- http://functions.wolfram.com/ + # HypergeometricFunctions/HermiteHGeneral/06/02/0009/ + # Otherwise, there is a reflection formula + # 2F0 + http://functions.wolfram.com/HypergeometricFunctions/ + # HermiteHGeneral/16/01/01/0006/ + # + # TODO: + # An alternative would be to use + # http://functions.wolfram.com/HypergeometricFunctions/ + # HermiteHGeneral/06/02/0006/ + # + # Also, the 1F1 expansion + # http://functions.wolfram.com/HypergeometricFunctions/ + # HermiteHGeneral/26/01/02/0001/ + # should probably be used for tiny z + if not z: + T1 = [2, ctx.pi], [n, 0.5], [], [q*(n-1)], [], [], 0 + if parabolic_cylinder: + T1[1][0] += q*n + return T1, + can_use_2f0 = ctx.isnpint(-n) or ctx.re(z) > 0 or \ + (ctx.re(z) == 0 and ctx.im(z) > 0) + expprec = ctx.prec*4 + 20 + if parabolic_cylinder: + u = ctx.fmul(ctx.fmul(z,z,prec=expprec), -0.25, exact=True) + w = ctx.fmul(z, ctx.sqrt(0.5,prec=expprec), prec=expprec) + else: + w = z + w2 = ctx.fmul(w, w, prec=expprec) + rw2 = ctx.fdiv(1, w2, prec=expprec) + nrw2 = ctx.fneg(rw2, exact=True) + nw = ctx.fneg(w, exact=True) + if can_use_2f0: + T1 = [2, w], [n, n], [], [], [q*n, q*(n-1)], [], nrw2 + terms = [T1] + else: + T1 = [2, nw], [n, n], [], [], [q*n, q*(n-1)], [], nrw2 + T2 = [2, ctx.pi, nw], [n+2, 0.5, 1], [], [q*n], [q*(n-1)], [1-q], w2 + terms = [T1,T2] + # Multiply by prefactor for D_n + if parabolic_cylinder: + expu = ctx.exp(u) + for i in range(len(terms)): + terms[i][1][0] += q*n + terms[i][0].append(expu) + terms[i][1].append(1) + return tuple(terms) + +@defun +def hermite(ctx, n, z, **kwargs): + return ctx.hypercomb(lambda: _hermite_param(ctx, n, z, 0), [], **kwargs) + +@defun +def pcfd(ctx, n, z, **kwargs): + r""" + Gives the parabolic cylinder function in Whittaker's notation + `D_n(z) = U(-n-1/2, z)` (see :func:`~mpmath.pcfu`). + It solves the differential equation + + .. math :: + + y'' + \left(n + \frac{1}{2} - \frac{1}{4} z^2\right) y = 0. + + and can be represented in terms of Hermite polynomials + (see :func:`~mpmath.hermite`) as + + .. math :: + + D_n(z) = 2^{-n/2} e^{-z^2/4} H_n\left(\frac{z}{\sqrt{2}}\right). + + **Plots** + + .. literalinclude :: /plots/pcfd.py + .. image :: /plots/pcfd.png + + **Examples** + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> pcfd(0,0); pcfd(1,0); pcfd(2,0); pcfd(3,0) + 1.0 + 0.0 + -1.0 + 0.0 + >>> pcfd(4,0); pcfd(-3,0) + 3.0 + 0.6266570686577501256039413 + >>> pcfd('1/2', 2+3j) + (-5.363331161232920734849056 - 3.858877821790010714163487j) + >>> pcfd(2, -10) + 1.374906442631438038871515e-9 + + Verifying the differential equation:: + + >>> n = mpf(2.5) + >>> y = lambda z: pcfd(n,z) + >>> z = 1.75 + >>> chop(diff(y,z,2) + (n+0.5-0.25*z**2)*y(z)) + 0.0 + + Rational Taylor series expansion when `n` is an integer:: + + >>> taylor(lambda z: pcfd(5,z), 0, 7) + [0.0, 15.0, 0.0, -13.75, 0.0, 3.96875, 0.0, -0.6015625] + + """ + return ctx.hypercomb(lambda: _hermite_param(ctx, n, z, 1), [], **kwargs) + +@defun +def pcfu(ctx, a, z, **kwargs): + r""" + Gives the parabolic cylinder function `U(a,z)`, which may be + defined for `\Re(z) > 0` in terms of the confluent + U-function (see :func:`~mpmath.hyperu`) by + + .. math :: + + U(a,z) = 2^{-\frac{1}{4}-\frac{a}{2}} e^{-\frac{1}{4} z^2} + U\left(\frac{a}{2}+\frac{1}{4}, + \frac{1}{2}, \frac{1}{2}z^2\right) + + or, for arbitrary `z`, + + .. math :: + + e^{-\frac{1}{4}z^2} U(a,z) = + U(a,0) \,_1F_1\left(-\tfrac{a}{2}+\tfrac{1}{4}; + \tfrac{1}{2}; -\tfrac{1}{2}z^2\right) + + U'(a,0) z \,_1F_1\left(-\tfrac{a}{2}+\tfrac{3}{4}; + \tfrac{3}{2}; -\tfrac{1}{2}z^2\right). + + **Examples** + + Connection to other functions:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> z = mpf(3) + >>> pcfu(0.5,z) + 0.03210358129311151450551963 + >>> sqrt(pi/2)*exp(z**2/4)*erfc(z/sqrt(2)) + 0.03210358129311151450551963 + >>> pcfu(0.5,-z) + 23.75012332835297233711255 + >>> sqrt(pi/2)*exp(z**2/4)*erfc(-z/sqrt(2)) + 23.75012332835297233711255 + >>> pcfu(0.5,-z) + 23.75012332835297233711255 + >>> sqrt(pi/2)*exp(z**2/4)*erfc(-z/sqrt(2)) + 23.75012332835297233711255 + + """ + n, _ = ctx._convert_param(a) + return ctx.pcfd(-n-ctx.mpq_1_2, z) + +@defun +def pcfv(ctx, a, z, **kwargs): + r""" + Gives the parabolic cylinder function `V(a,z)`, which can be + represented in terms of :func:`~mpmath.pcfu` as + + .. math :: + + V(a,z) = \frac{\Gamma(a+\tfrac{1}{2}) (U(a,-z)-\sin(\pi a) U(a,z)}{\pi}. + + **Examples** + + Wronskian relation between `U` and `V`:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> a, z = 2, 3 + >>> pcfu(a,z)*diff(pcfv,(a,z),(0,1))-diff(pcfu,(a,z),(0,1))*pcfv(a,z) + 0.7978845608028653558798921 + >>> sqrt(2/pi) + 0.7978845608028653558798921 + >>> a, z = 2.5, 3 + >>> pcfu(a,z)*diff(pcfv,(a,z),(0,1))-diff(pcfu,(a,z),(0,1))*pcfv(a,z) + 0.7978845608028653558798921 + >>> a, z = 0.25, -1 + >>> pcfu(a,z)*diff(pcfv,(a,z),(0,1))-diff(pcfu,(a,z),(0,1))*pcfv(a,z) + 0.7978845608028653558798921 + >>> a, z = 2+1j, 2+3j + >>> chop(pcfu(a,z)*diff(pcfv,(a,z),(0,1))-diff(pcfu,(a,z),(0,1))*pcfv(a,z)) + 0.7978845608028653558798921 + + """ + n, ntype = ctx._convert_param(a) + z = ctx.convert(z) + q = ctx.mpq_1_2 + r = ctx.mpq_1_4 + if ntype == 'Q' and ctx.isint(n*2): + # Faster for half-integers + def h(): + jz = ctx.fmul(z, -1j, exact=True) + T1terms = _hermite_param(ctx, -n-q, z, 1) + T2terms = _hermite_param(ctx, n-q, jz, 1) + for T in T1terms: + T[0].append(1j) + T[1].append(1) + T[3].append(q-n) + u = ctx.expjpi((q*n-r)) * ctx.sqrt(2/ctx.pi) + for T in T2terms: + T[0].append(u) + T[1].append(1) + return T1terms + T2terms + v = ctx.hypercomb(h, [], **kwargs) + if ctx._is_real_type(n) and ctx._is_real_type(z): + v = ctx._re(v) + return v + else: + def h(n): + w = ctx.square_exp_arg(z, -0.25) + u = ctx.square_exp_arg(z, 0.5) + e = ctx.exp(w) + l = [ctx.pi, q, ctx.exp(w)] + Y1 = l, [-q, n*q+r, 1], [r-q*n], [], [q*n+r], [q], u + Y2 = l + [z], [-q, n*q-r, 1, 1], [1-r-q*n], [], [q*n+1-r], [1+q], u + c, s = ctx.cospi_sinpi(r+q*n) + Y1[0].append(s) + Y2[0].append(c) + for Y in (Y1, Y2): + Y[1].append(1) + Y[3].append(q-n) + return Y1, Y2 + return ctx.hypercomb(h, [n], **kwargs) + + +@defun +def pcfw(ctx, a, z, **kwargs): + r""" + Gives the parabolic cylinder function `W(a,z)` defined in (DLMF 12.14). + + **Examples** + + Value at the origin:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> a = mpf(0.25) + >>> pcfw(a,0) + 0.9722833245718180765617104 + >>> power(2,-0.75)*sqrt(abs(gamma(0.25+0.5j*a)/gamma(0.75+0.5j*a))) + 0.9722833245718180765617104 + >>> diff(pcfw,(a,0),(0,1)) + -0.5142533944210078966003624 + >>> -power(2,-0.25)*sqrt(abs(gamma(0.75+0.5j*a)/gamma(0.25+0.5j*a))) + -0.5142533944210078966003624 + + """ + n, _ = ctx._convert_param(a) + z = ctx.convert(z) + def terms(): + phi2 = ctx.arg(ctx.gamma(0.5 + ctx.j*n)) + phi2 = (ctx.loggamma(0.5+ctx.j*n) - ctx.loggamma(0.5-ctx.j*n))/2j + rho = ctx.pi/8 + 0.5*phi2 + # XXX: cancellation computing k + k = ctx.sqrt(1 + ctx.exp(2*ctx.pi*n)) - ctx.exp(ctx.pi*n) + C = ctx.sqrt(k/2) * ctx.exp(0.25*ctx.pi*n) + yield C * ctx.expj(rho) * ctx.pcfu(ctx.j*n, z*ctx.expjpi(-0.25)) + yield C * ctx.expj(-rho) * ctx.pcfu(-ctx.j*n, z*ctx.expjpi(0.25)) + v = ctx.sum_accurately(terms) + if ctx._is_real_type(n) and ctx._is_real_type(z): + v = ctx._re(v) + return v + +""" +Even/odd PCFs. Useful? + +@defun +def pcfy1(ctx, a, z, **kwargs): + a, _ = ctx._convert_param(n) + z = ctx.convert(z) + def h(): + w = ctx.square_exp_arg(z) + w1 = ctx.fmul(w, -0.25, exact=True) + w2 = ctx.fmul(w, 0.5, exact=True) + e = ctx.exp(w1) + return [e], [1], [], [], [ctx.mpq_1_2*a+ctx.mpq_1_4], [ctx.mpq_1_2], w2 + return ctx.hypercomb(h, [], **kwargs) + +@defun +def pcfy2(ctx, a, z, **kwargs): + a, _ = ctx._convert_param(n) + z = ctx.convert(z) + def h(): + w = ctx.square_exp_arg(z) + w1 = ctx.fmul(w, -0.25, exact=True) + w2 = ctx.fmul(w, 0.5, exact=True) + e = ctx.exp(w1) + return [e, z], [1, 1], [], [], [ctx.mpq_1_2*a+ctx.mpq_3_4], \ + [ctx.mpq_3_2], w2 + return ctx.hypercomb(h, [], **kwargs) +""" + +@defun_wrapped +def gegenbauer(ctx, n, a, z, **kwargs): + # Special cases: a+0.5, a*2 poles + if ctx.isnpint(a): + return 0*(z+n) + if ctx.isnpint(a+0.5): + # TODO: something else is required here + # E.g.: gegenbauer(-2, -0.5, 3) == -12 + if ctx.isnpint(n+1): + raise NotImplementedError("Gegenbauer function with two limits") + def h(a): + a2 = 2*a + T = [], [], [n+a2], [n+1, a2], [-n, n+a2], [a+0.5], 0.5*(1-z) + return [T] + return ctx.hypercomb(h, [a], **kwargs) + def h(n): + a2 = 2*a + T = [], [], [n+a2], [n+1, a2], [-n, n+a2], [a+0.5], 0.5*(1-z) + return [T] + return ctx.hypercomb(h, [n], **kwargs) + +@defun_wrapped +def jacobi(ctx, n, a, b, x, **kwargs): + if not ctx.isnpint(a): + def h(n): + return (([], [], [a+n+1], [n+1, a+1], [-n, a+b+n+1], [a+1], (1-x)*0.5),) + return ctx.hypercomb(h, [n], **kwargs) + if not ctx.isint(b): + def h(n, a): + return (([], [], [-b], [n+1, -b-n], [-n, a+b+n+1], [b+1], (x+1)*0.5),) + return ctx.hypercomb(h, [n, a], **kwargs) + # XXX: determine appropriate limit + return ctx.binomial(n+a,n) * ctx.hyp2f1(-n,1+n+a+b,a+1,(1-x)/2, **kwargs) + +@defun_wrapped +def laguerre(ctx, n, a, z, **kwargs): + # XXX: limits, poles + #if ctx.isnpint(n): + # return 0*(a+z) + def h(a): + return (([], [], [a+n+1], [a+1, n+1], [-n], [a+1], z),) + return ctx.hypercomb(h, [a], **kwargs) + +@defun_wrapped +def legendre(ctx, n, x, **kwargs): + if ctx.isint(n): + n = int(n) + # Accuracy near zeros + if (n + (n < 0)) & 1: + if not x: + return x + mag = ctx.mag(x) + if mag < -2*ctx.prec-10: + return x + if mag < -5: + ctx.prec += -mag + return ctx.hyp2f1(-n,n+1,1,(1-x)/2, **kwargs) + +@defun +def legenp(ctx, n, m, z, type=2, **kwargs): + # Legendre function, 1st kind + n = ctx.convert(n) + m = ctx.convert(m) + # Faster + if not m: + return ctx.legendre(n, z, **kwargs) + # TODO: correct evaluation at singularities + if type == 2: + def h(n,m): + g = m*0.5 + T = [1+z, 1-z], [g, -g], [], [1-m], [-n, n+1], [1-m], 0.5*(1-z) + return (T,) + return ctx.hypercomb(h, [n,m], **kwargs) + if type == 3: + def h(n,m): + g = m*0.5 + T = [z+1, z-1], [g, -g], [], [1-m], [-n, n+1], [1-m], 0.5*(1-z) + return (T,) + return ctx.hypercomb(h, [n,m], **kwargs) + raise ValueError("requires type=2 or type=3") + +@defun +def legenq(ctx, n, m, z, type=2, **kwargs): + # Legendre function, 2nd kind + n = ctx.convert(n) + m = ctx.convert(m) + z = ctx.convert(z) + if z in (1, -1): + #if ctx.isint(m): + # return ctx.nan + #return ctx.inf # unsigned + return ctx.nan + if type == 2: + def h(n, m): + cos, sin = ctx.cospi_sinpi(m) + s = 2 * sin / ctx.pi + c = cos + a = 1+z + b = 1-z + u = m/2 + w = (1-z)/2 + T1 = [s, c, a, b], [-1, 1, u, -u], [], [1-m], \ + [-n, n+1], [1-m], w + T2 = [-s, a, b], [-1, -u, u], [n+m+1], [n-m+1, m+1], \ + [-n, n+1], [m+1], w + return T1, T2 + return ctx.hypercomb(h, [n, m], **kwargs) + if type == 3: + # The following is faster when there only is a single series + # Note: not valid for -1 < z < 0 (?) + if abs(z) > 1: + def h(n, m): + T1 = [ctx.expjpi(m), 2, ctx.pi, z, z-1, z+1], \ + [1, -n-1, 0.5, -n-m-1, 0.5*m, 0.5*m], \ + [n+m+1], [n+1.5], \ + [0.5*(2+n+m), 0.5*(1+n+m)], [n+1.5], z**(-2) + return [T1] + return ctx.hypercomb(h, [n, m], **kwargs) + else: + # not valid for 1 < z < inf ? + def h(n, m): + s = 2 * ctx.sinpi(m) / ctx.pi + c = ctx.expjpi(m) + a = 1+z + b = z-1 + u = m/2 + w = (1-z)/2 + T1 = [s, c, a, b], [-1, 1, u, -u], [], [1-m], \ + [-n, n+1], [1-m], w + T2 = [-s, c, a, b], [-1, 1, -u, u], [n+m+1], [n-m+1, m+1], \ + [-n, n+1], [m+1], w + return T1, T2 + return ctx.hypercomb(h, [n, m], **kwargs) + raise ValueError("requires type=2 or type=3") + +@defun_wrapped +def chebyt(ctx, n, x, **kwargs): + if (not x) and ctx.isint(n) and int(ctx._re(n)) % 2 == 1: + return x * 0 + return ctx.hyp2f1(-n,n,(1,2),(1-x)/2, **kwargs) + +@defun_wrapped +def chebyu(ctx, n, x, **kwargs): + if (not x) and ctx.isint(n) and int(ctx._re(n)) % 2 == 1: + return x * 0 + return (n+1) * ctx.hyp2f1(-n, n+2, (3,2), (1-x)/2, **kwargs) + +@defun +def spherharm(ctx, l, m, theta, phi, **kwargs): + l = ctx.convert(l) + m = ctx.convert(m) + theta = ctx.convert(theta) + phi = ctx.convert(phi) + l_isint = ctx.isint(l) + l_natural = l_isint and l >= 0 + m_isint = ctx.isint(m) + if l_isint and l < 0 and m_isint: + return ctx.spherharm(-(l+1), m, theta, phi, **kwargs) + if theta == 0 and m_isint and m < 0: + return ctx.zero * 1j + if l_natural and m_isint: + if abs(m) > l: + return ctx.zero * 1j + # http://functions.wolfram.com/Polynomials/ + # SphericalHarmonicY/26/01/02/0004/ + def h(l,m): + absm = abs(m) + C = [-1, ctx.expj(m*phi), + (2*l+1)*ctx.fac(l+absm)/ctx.pi/ctx.fac(l-absm), + ctx.sin(theta)**2, + ctx.fac(absm), 2] + P = [0.5*m*(ctx.sign(m)+1), 1, 0.5, 0.5*absm, -1, -absm-1] + return ((C, P, [], [], [absm-l, l+absm+1], [absm+1], + ctx.sin(0.5*theta)**2),) + else: + # http://functions.wolfram.com/HypergeometricFunctions/ + # SphericalHarmonicYGeneral/26/01/02/0001/ + def h(l,m): + if ctx.isnpint(l-m+1) or ctx.isnpint(l+m+1) or ctx.isnpint(1-m): + return (([0], [-1], [], [], [], [], 0),) + cos, sin = ctx.cos_sin(0.5*theta) + C = [0.5*ctx.expj(m*phi), (2*l+1)/ctx.pi, + ctx.gamma(l-m+1), ctx.gamma(l+m+1), + cos**2, sin**2] + P = [1, 0.5, 0.5, -0.5, 0.5*m, -0.5*m] + return ((C, P, [], [1-m], [-l,l+1], [1-m], sin**2),) + return ctx.hypercomb(h, [l,m], **kwargs) diff --git a/venv/lib/python3.12/site-packages/mpmath/functions/qfunctions.py b/venv/lib/python3.12/site-packages/mpmath/functions/qfunctions.py new file mode 100644 index 0000000000000000000000000000000000000000..5a20e53a8b6fa0d8fbc9ad098614d2694998f49a --- /dev/null +++ b/venv/lib/python3.12/site-packages/mpmath/functions/qfunctions.py @@ -0,0 +1,280 @@ +from .functions import defun, defun_wrapped + +@defun +def qp(ctx, a, q=None, n=None, **kwargs): + r""" + Evaluates the q-Pochhammer symbol (or q-rising factorial) + + .. math :: + + (a; q)_n = \prod_{k=0}^{n-1} (1-a q^k) + + where `n = \infty` is permitted if `|q| < 1`. Called with two arguments, + ``qp(a,q)`` computes `(a;q)_{\infty}`; with a single argument, ``qp(q)`` + computes `(q;q)_{\infty}`. The special case + + .. math :: + + \phi(q) = (q; q)_{\infty} = \prod_{k=1}^{\infty} (1-q^k) = + \sum_{k=-\infty}^{\infty} (-1)^k q^{(3k^2-k)/2} + + is also known as the Euler function, or (up to a factor `q^{-1/24}`) + the Dedekind eta function. + + **Examples** + + If `n` is a positive integer, the function amounts to a finite product:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> qp(2,3,5) + -725305.0 + >>> fprod(1-2*3**k for k in range(5)) + -725305.0 + >>> qp(2,3,0) + 1.0 + + Complex arguments are allowed:: + + >>> qp(2-1j, 0.75j) + (0.4628842231660149089976379 + 4.481821753552703090628793j) + + The regular Pochhammer symbol `(a)_n` is obtained in the + following limit as `q \to 1`:: + + >>> a, n = 4, 7 + >>> limit(lambda q: qp(q**a,q,n) / (1-q)**n, 1) + 604800.0 + >>> rf(a,n) + 604800.0 + + The Taylor series of the reciprocal Euler function gives + the partition function `P(n)`, i.e. the number of ways of writing + `n` as a sum of positive integers:: + + >>> taylor(lambda q: 1/qp(q), 0, 10) + [1.0, 1.0, 2.0, 3.0, 5.0, 7.0, 11.0, 15.0, 22.0, 30.0, 42.0] + + Special values include:: + + >>> qp(0) + 1.0 + >>> findroot(diffun(qp), -0.4) # location of maximum + -0.4112484791779547734440257 + >>> qp(_) + 1.228348867038575112586878 + + The q-Pochhammer symbol is related to the Jacobi theta functions. + For example, the following identity holds:: + + >>> q = mpf(0.5) # arbitrary + >>> qp(q) + 0.2887880950866024212788997 + >>> root(3,-2)*root(q,-24)*jtheta(2,pi/6,root(q,6)) + 0.2887880950866024212788997 + + """ + a = ctx.convert(a) + if n is None: + n = ctx.inf + else: + n = ctx.convert(n) + if n < 0: + raise ValueError("n cannot be negative") + if q is None: + q = a + else: + q = ctx.convert(q) + if n == 0: + return ctx.one + 0*(a+q) + infinite = (n == ctx.inf) + same = (a == q) + if infinite: + if abs(q) >= 1: + if same and (q == -1 or q == 1): + return ctx.zero * q + raise ValueError("q-function only defined for |q| < 1") + elif q == 0: + return ctx.one - a + maxterms = kwargs.get('maxterms', 50*ctx.prec) + if infinite and same: + # Euler's pentagonal theorem + def terms(): + t = 1 + yield t + k = 1 + x1 = q + x2 = q**2 + while 1: + yield (-1)**k * x1 + yield (-1)**k * x2 + x1 *= q**(3*k+1) + x2 *= q**(3*k+2) + k += 1 + if k > maxterms: + raise ctx.NoConvergence + return ctx.sum_accurately(terms) + # return ctx.nprod(lambda k: 1-a*q**k, [0,n-1]) + def factors(): + k = 0 + r = ctx.one + while 1: + yield 1 - a*r + r *= q + k += 1 + if k >= n: + return + if k > maxterms: + raise ctx.NoConvergence + return ctx.mul_accurately(factors) + +@defun_wrapped +def qgamma(ctx, z, q, **kwargs): + r""" + Evaluates the q-gamma function + + .. math :: + + \Gamma_q(z) = \frac{(q; q)_{\infty}}{(q^z; q)_{\infty}} (1-q)^{1-z}. + + + **Examples** + + Evaluation for real and complex arguments:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> qgamma(4,0.75) + 4.046875 + >>> qgamma(6,6) + 121226245.0 + >>> qgamma(3+4j, 0.5j) + (0.1663082382255199834630088 + 0.01952474576025952984418217j) + + The q-gamma function satisfies a functional equation similar + to that of the ordinary gamma function:: + + >>> q = mpf(0.25) + >>> z = mpf(2.5) + >>> qgamma(z+1,q) + 1.428277424823760954685912 + >>> (1-q**z)/(1-q)*qgamma(z,q) + 1.428277424823760954685912 + + """ + if abs(q) > 1: + return ctx.qgamma(z,1/q)*q**((z-2)*(z-1)*0.5) + return ctx.qp(q, q, None, **kwargs) / \ + ctx.qp(q**z, q, None, **kwargs) * (1-q)**(1-z) + +@defun_wrapped +def qfac(ctx, z, q, **kwargs): + r""" + Evaluates the q-factorial, + + .. math :: + + [n]_q! = (1+q)(1+q+q^2)\cdots(1+q+\cdots+q^{n-1}) + + or more generally + + .. math :: + + [z]_q! = \frac{(q;q)_z}{(1-q)^z}. + + **Examples** + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> qfac(0,0) + 1.0 + >>> qfac(4,3) + 2080.0 + >>> qfac(5,6) + 121226245.0 + >>> qfac(1+1j, 2+1j) + (0.4370556551322672478613695 + 0.2609739839216039203708921j) + + """ + if ctx.isint(z) and ctx._re(z) > 0: + n = int(ctx._re(z)) + return ctx.qp(q, q, n, **kwargs) / (1-q)**n + return ctx.qgamma(z+1, q, **kwargs) + +@defun +def qhyper(ctx, a_s, b_s, q, z, **kwargs): + r""" + Evaluates the basic hypergeometric series or hypergeometric q-series + + .. math :: + + \,_r\phi_s \left[\begin{matrix} + a_1 & a_2 & \ldots & a_r \\ + b_1 & b_2 & \ldots & b_s + \end{matrix} ; q,z \right] = + \sum_{n=0}^\infty + \frac{(a_1;q)_n, \ldots, (a_r;q)_n} + {(b_1;q)_n, \ldots, (b_s;q)_n} + \left((-1)^n q^{n\choose 2}\right)^{1+s-r} + \frac{z^n}{(q;q)_n} + + where `(a;q)_n` denotes the q-Pochhammer symbol (see :func:`~mpmath.qp`). + + **Examples** + + Evaluation works for real and complex arguments:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> qhyper([0.5], [2.25], 0.25, 4) + -0.1975849091263356009534385 + >>> qhyper([0.5], [2.25], 0.25-0.25j, 4) + (2.806330244925716649839237 + 3.568997623337943121769938j) + >>> qhyper([1+j], [2,3+0.5j], 0.25, 3+4j) + (9.112885171773400017270226 - 1.272756997166375050700388j) + + Comparing with a summation of the defining series, using + :func:`~mpmath.nsum`:: + + >>> b, q, z = 3, 0.25, 0.5 + >>> qhyper([], [b], q, z) + 0.6221136748254495583228324 + >>> nsum(lambda n: z**n / qp(q,q,n)/qp(b,q,n) * q**(n*(n-1)), [0,inf]) + 0.6221136748254495583228324 + + """ + #a_s = [ctx._convert_param(a)[0] for a in a_s] + #b_s = [ctx._convert_param(b)[0] for b in b_s] + #q = ctx._convert_param(q)[0] + a_s = [ctx.convert(a) for a in a_s] + b_s = [ctx.convert(b) for b in b_s] + q = ctx.convert(q) + z = ctx.convert(z) + r = len(a_s) + s = len(b_s) + d = 1+s-r + maxterms = kwargs.get('maxterms', 50*ctx.prec) + def terms(): + t = ctx.one + yield t + qk = 1 + k = 0 + x = 1 + while 1: + for a in a_s: + p = 1 - a*qk + t *= p + for b in b_s: + p = 1 - b*qk + if not p: + raise ValueError + t /= p + t *= z + x *= (-1)**d * qk ** d + qk *= q + t /= (1 - qk) + k += 1 + yield t * x + if k > maxterms: + raise ctx.NoConvergence + return ctx.sum_accurately(terms) diff --git a/venv/lib/python3.12/site-packages/mpmath/functions/rszeta.py b/venv/lib/python3.12/site-packages/mpmath/functions/rszeta.py new file mode 100644 index 0000000000000000000000000000000000000000..19e2c9a251b81bafe8cf77a2b0180636b1078ee4 --- /dev/null +++ b/venv/lib/python3.12/site-packages/mpmath/functions/rszeta.py @@ -0,0 +1,1403 @@ +""" +--------------------------------------------------------------------- +.. sectionauthor:: Juan Arias de Reyna + +This module implements zeta-related functions using the Riemann-Siegel +expansion: zeta_offline(s,k=0) + +* coef(J, eps): Need in the computation of Rzeta(s,k) + +* Rzeta_simul(s, der=0) computes Rzeta^(k)(s) and Rzeta^(k)(1-s) simultaneously + for 0 <= k <= der. Used by zeta_offline and z_offline + +* Rzeta_set(s, derivatives) computes Rzeta^(k)(s) for given derivatives, used by + z_half(t,k) and zeta_half + +* z_offline(w,k): Z(w) and its derivatives of order k <= 4 +* z_half(t,k): Z(t) (Riemann Siegel function) and its derivatives of order k <= 4 +* zeta_offline(s): zeta(s) and its derivatives of order k<= 4 +* zeta_half(1/2+it,k): zeta(s) and its derivatives of order k<= 4 + +* rs_zeta(s,k=0) Computes zeta^(k)(s) Unifies zeta_half and zeta_offline +* rs_z(w,k=0) Computes Z^(k)(w) Unifies z_offline and z_half +---------------------------------------------------------------------- + +This program uses Riemann-Siegel expansion even to compute +zeta(s) on points s = sigma + i t with sigma arbitrary not +necessarily equal to 1/2. + +It is founded on a new deduction of the formula, with rigorous +and sharp bounds for the terms and rest of this expansion. + +More information on the papers: + + J. Arias de Reyna, High Precision Computation of Riemann's + Zeta Function by the Riemann-Siegel Formula I, II + + We refer to them as I, II. + + In them we shall find detailed explanation of all the + procedure. + +The program uses Riemann-Siegel expansion. +This is useful when t is big, ( say t > 10000 ). +The precision is limited, roughly it can compute zeta(sigma+it) +with an error less than exp(-c t) for some constant c depending +on sigma. The program gives an error when the Riemann-Siegel +formula can not compute to the wanted precision. + +""" + +import math + +class RSCache(object): + def __init__(ctx): + ctx._rs_cache = [0, 10, {}, {}] + +from .functions import defun + +#-------------------------------------------------------------------------------# +# # +# coef(ctx, J, eps, _cache=[0, 10, {} ] ) # +# # +#-------------------------------------------------------------------------------# + +# This function computes the coefficients c[n] defined on (I, equation (47)) +# but see also (II, section 3.14). +# +# Since these coefficients are very difficult to compute we save the values +# in a cache. So if we compute several values of the functions Rzeta(s) for +# near values of s, we do not recompute these coefficients. +# +# c[n] are the Taylor coefficients of the function: +# +# F(z):= (exp(pi*j*(z*z/2+3/8))-j* sqrt(2) cos(pi*z/2))/(2*cos(pi *z)) +# +# + +def _coef(ctx, J, eps): + r""" + Computes the coefficients `c_n` for `0\le n\le 2J` with error less than eps + + **Definition** + + The coefficients c_n are defined by + + .. math :: + + \begin{equation} + F(z)=\frac{e^{\pi i + \bigl(\frac{z^2}{2}+\frac38\bigr)}-i\sqrt{2}\cos\frac{\pi}{2}z}{2\cos\pi + z}=\sum_{n=0}^\infty c_{2n} z^{2n} + \end{equation} + + they are computed applying the relation + + .. math :: + + \begin{multline} + c_{2n}=-\frac{i}{\sqrt{2}}\Bigl(\frac{\pi}{2}\Bigr)^{2n} + \sum_{k=0}^n\frac{(-1)^k}{(2k)!} + 2^{2n-2k}\frac{(-1)^{n-k}E_{2n-2k}}{(2n-2k)!}+\\ + +e^{3\pi i/8}\sum_{j=0}^n(-1)^j\frac{ + E_{2j}}{(2j)!}\frac{i^{n-j}\pi^{n+j}}{(n-j)!2^{n-j+1}}. + \end{multline} + """ + + newJ = J+2 # compute more coefficients that are needed + neweps6 = eps/2. # compute with a slight more precision that are needed + + # PREPARATION FOR THE COMPUTATION OF V(N) AND W(N) + # See II Section 3.16 + # + # Computing the exponent wpvw of the error II equation (81) + wpvw = max(ctx.mag(10*(newJ+3)), 4*newJ+5-ctx.mag(neweps6)) + + # Preparation of Euler numbers (we need until the 2*RS_NEWJ) + E = ctx._eulernum(2*newJ) + + # Now we have in the cache all the needed Euler numbers. + # + # Computing the powers of pi + # + # We need to compute the powers pi**n for 1<= n <= 2*J + # with relative error less than 2**(-wpvw) + # it is easy to show that this is obtained + # taking wppi as the least d with + # 2**d>40*J and 2**d> 4.24 *newJ + 2**wpvw + # In II Section 3.9 we need also that + # wppi > wptcoef[0], and that the powers + # here computed 0<= k <= 2*newJ are more + # than those needed there that are 2*L-2. + # so we need J >= L this will be checked + # before computing tcoef[] + wppi = max(ctx.mag(40*newJ), ctx.mag(newJ)+3 +wpvw) + ctx.prec = wppi + pipower = {} + pipower[0] = ctx.one + pipower[1] = ctx.pi + for n in range(2,2*newJ+1): + pipower[n] = pipower[n-1]*ctx.pi + + # COMPUTING THE COEFFICIENTS v(n) AND w(n) + # see II equation (61) and equations (81) and (82) + ctx.prec = wpvw+2 + v={} + w={} + for n in range(0,newJ+1): + va = (-1)**n * ctx._eulernum(2*n) + va = ctx.mpf(va)/ctx.fac(2*n) + v[n]=va*pipower[2*n] + for n in range(0,2*newJ+1): + wa = ctx.one/ctx.fac(n) + wa=wa/(2**n) + w[n]=wa*pipower[n] + + # COMPUTATION OF THE CONVOLUTIONS RS_P1 AND RS_P2 + # See II Section 3.16 + ctx.prec = 15 + wpp1a = 9 - ctx.mag(neweps6) + P1 = {} + for n in range(0,newJ+1): + ctx.prec = 15 + wpp1 = max(ctx.mag(10*(n+4)),4*n+wpp1a) + ctx.prec = wpp1 + sump = 0 + for k in range(0,n+1): + sump += ((-1)**k) * v[k]*w[2*n-2*k] + P1[n]=((-1)**(n+1))*ctx.j*sump + P2={} + for n in range(0,newJ+1): + ctx.prec = 15 + wpp2 = max(ctx.mag(10*(n+4)),4*n+wpp1a) + ctx.prec = wpp2 + sump = 0 + for k in range(0,n+1): + sump += (ctx.j**(n-k)) * v[k]*w[n-k] + P2[n]=sump + # COMPUTING THE COEFFICIENTS c[2n] + # See II Section 3.14 + ctx.prec = 15 + wpc0 = 5 - ctx.mag(neweps6) + wpc = max(6,4*newJ+wpc0) + ctx.prec = wpc + mu = ctx.sqrt(ctx.mpf('2'))/2 + nu = ctx.expjpi(3./8)/2 + c={} + for n in range(0,newJ): + ctx.prec = 15 + wpc = max(6,4*n+wpc0) + ctx.prec = wpc + c[2*n] = mu*P1[n]+nu*P2[n] + for n in range(1,2*newJ,2): + c[n] = 0 + return [newJ, neweps6, c, pipower] + +def coef(ctx, J, eps): + _cache = ctx._rs_cache + if J <= _cache[0] and eps >= _cache[1]: + return _cache[2], _cache[3] + orig = ctx._mp.prec + try: + data = _coef(ctx._mp, J, eps) + finally: + ctx._mp.prec = orig + if ctx is not ctx._mp: + data[2] = dict((k,ctx.convert(v)) for (k,v) in data[2].items()) + data[3] = dict((k,ctx.convert(v)) for (k,v) in data[3].items()) + ctx._rs_cache[:] = data + return ctx._rs_cache[2], ctx._rs_cache[3] + +#-------------------------------------------------------------------------------# +# # +# Rzeta_simul(s,k=0) # +# # +#-------------------------------------------------------------------------------# +# This function return a list with the values: +# Rzeta(sigma+it), conj(Rzeta(1-sigma+it)),Rzeta'(sigma+it), conj(Rzeta'(1-sigma+it)), +# .... , Rzeta^{(k)}(sigma+it), conj(Rzeta^{(k)}(1-sigma+it)) +# +# Useful to compute the function zeta(s) and Z(w) or its derivatives. +# + +def aux_M_Fp(ctx, xA, xeps4, a, xB1, xL): + # COMPUTING M NUMBER OF DERIVATIVES Fp[m] TO COMPUTE + # See II Section 3.11 equations (47) and (48) + aux1 = 126.0657606*xA/xeps4 # 126.06.. = 316/sqrt(2*pi) + aux1 = ctx.ln(aux1) + aux2 = (2*ctx.ln(ctx.pi)+ctx.ln(xB1)+ctx.ln(a))/3 -ctx.ln(2*ctx.pi)/2 + m = 3*xL-3 + aux3= (ctx.loggamma(m+1)-ctx.loggamma(m/3.0+2))/2 -ctx.loggamma((m+1)/2.) + while((aux1 < m*aux2+ aux3)and (m>1)): + m = m - 1 + aux3 = (ctx.loggamma(m+1)-ctx.loggamma(m/3.0+2))/2 -ctx.loggamma((m+1)/2.) + xM = m + return xM + +def aux_J_needed(ctx, xA, xeps4, a, xB1, xM): + # DETERMINATION OF J THE NUMBER OF TERMS NEEDED + # IN THE TAYLOR SERIES OF F. + # See II Section 3.11 equation (49)) + # Only determine one + h1 = xeps4/(632*xA) + h2 = xB1*a * 126.31337419529260248 # = pi^2*e^2*sqrt(3) + h2 = h1 * ctx.power((h2/xM**2),(xM-1)/3) / xM + h3 = min(h1,h2) + return h3 + +def Rzeta_simul(ctx, s, der=0): + # First we take the value of ctx.prec + wpinitial = ctx.prec + + # INITIALIZATION + # Take the real and imaginary part of s + t = ctx._im(s) + xsigma = ctx._re(s) + ysigma = 1 - xsigma + + # Now compute several parameter that appear on the program + ctx.prec = 15 + a = ctx.sqrt(t/(2*ctx.pi)) + xasigma = a ** xsigma + yasigma = a ** ysigma + + # We need a simple bound A1 < asigma (see II Section 3.1 and 3.3) + xA1=ctx.power(2, ctx.mag(xasigma)-1) + yA1=ctx.power(2, ctx.mag(yasigma)-1) + + # We compute various epsilon's (see II end of Section 3.1) + eps = ctx.power(2, -wpinitial) + eps1 = eps/6. + xeps2 = eps * xA1/3. + yeps2 = eps * yA1/3. + + # COMPUTING SOME COEFFICIENTS THAT DEPENDS + # ON sigma + # constant b and c (see I Theorem 2 formula (26) ) + # coefficients A and B1 (see I Section 6.1 equation (50)) + # + # here we not need high precision + ctx.prec = 15 + if xsigma > 0: + xb = 2. + xc = math.pow(9,xsigma)/4.44288 + # 4.44288 =(math.sqrt(2)*math.pi) + xA = math.pow(9,xsigma) + xB1 = 1 + else: + xb = 2.25158 # math.sqrt( (3-2* math.log(2))*math.pi ) + xc = math.pow(2,-xsigma)/4.44288 + xA = math.pow(2,-xsigma) + xB1 = 1.10789 # = 2*sqrt(1-log(2)) + + if(ysigma > 0): + yb = 2. + yc = math.pow(9,ysigma)/4.44288 + # 4.44288 =(math.sqrt(2)*math.pi) + yA = math.pow(9,ysigma) + yB1 = 1 + else: + yb = 2.25158 # math.sqrt( (3-2* math.log(2))*math.pi ) + yc = math.pow(2,-ysigma)/4.44288 + yA = math.pow(2,-ysigma) + yB1 = 1.10789 # = 2*sqrt(1-log(2)) + + # COMPUTING L THE NUMBER OF TERMS NEEDED IN THE RIEMANN-SIEGEL + # CORRECTION + # See II Section 3.2 + ctx.prec = 15 + xL = 1 + while 3*xc*ctx.gamma(xL*0.5) * ctx.power(xb*a,-xL) >= xeps2: + xL = xL+1 + xL = max(2,xL) + yL = 1 + while 3*yc*ctx.gamma(yL*0.5) * ctx.power(yb*a,-yL) >= yeps2: + yL = yL+1 + yL = max(2,yL) + + # The number L has to satify some conditions. + # If not RS can not compute Rzeta(s) with the prescribed precision + # (see II, Section 3.2 condition (20) ) and + # (II, Section 3.3 condition (22) ). Also we have added + # an additional technical condition in Section 3.17 Proposition 17 + if ((3*xL >= 2*a*a/25.) or (3*xL+2+xsigma<0) or (abs(xsigma) > a/2.) or \ + (3*yL >= 2*a*a/25.) or (3*yL+2+ysigma<0) or (abs(ysigma) > a/2.)): + ctx.prec = wpinitial + raise NotImplementedError("Riemann-Siegel can not compute with such precision") + + # We take the maximum of the two values + L = max(xL, yL) + + # INITIALIZATION (CONTINUATION) + # + # eps3 is the constant defined on (II, Section 3.5 equation (27) ) + # each term of the RS correction must be computed with error <= eps3 + xeps3 = xeps2/(4*xL) + yeps3 = yeps2/(4*yL) + + # eps4 is defined on (II Section 3.6 equation (30) ) + # each component of the formula (II Section 3.6 equation (29) ) + # must be computed with error <= eps4 + xeps4 = xeps3/(3*xL) + yeps4 = yeps3/(3*yL) + + # COMPUTING M NUMBER OF DERIVATIVES Fp[m] TO COMPUTE + xM = aux_M_Fp(ctx, xA, xeps4, a, xB1, xL) + yM = aux_M_Fp(ctx, yA, yeps4, a, yB1, yL) + M = max(xM, yM) + + # COMPUTING NUMBER OF TERMS J NEEDED + h3 = aux_J_needed(ctx, xA, xeps4, a, xB1, xM) + h4 = aux_J_needed(ctx, yA, yeps4, a, yB1, yM) + h3 = min(h3,h4) + J = 12 + jvalue = (2*ctx.pi)**J / ctx.gamma(J+1) + while jvalue > h3: + J = J+1 + jvalue = (2*ctx.pi)*jvalue/J + + # COMPUTING eps5[m] for 1 <= m <= 21 + # See II Section 10 equation (43) + # We choose the minimum of the two possibilities + eps5={} + xforeps5 = math.pi*math.pi*xB1*a + yforeps5 = math.pi*math.pi*yB1*a + for m in range(0,22): + xaux1 = math.pow(xforeps5, m/3)/(316.*xA) + yaux1 = math.pow(yforeps5, m/3)/(316.*yA) + aux1 = min(xaux1, yaux1) + aux2 = ctx.gamma(m+1)/ctx.gamma(m/3.0+0.5) + aux2 = math.sqrt(aux2) + eps5[m] = (aux1*aux2*min(xeps4,yeps4)) + + # COMPUTING wpfp + # See II Section 3.13 equation (59) + twenty = min(3*L-3, 21)+1 + aux = 6812*J + wpfp = ctx.mag(44*J) + for m in range(0,twenty): + wpfp = max(wpfp, ctx.mag(aux*ctx.gamma(m+1)/eps5[m])) + + # COMPUTING N AND p + # See II Section + ctx.prec = wpfp + ctx.mag(t)+20 + a = ctx.sqrt(t/(2*ctx.pi)) + N = ctx.floor(a) + p = 1-2*(a-N) + + # now we get a rounded version of p + # to the precision wpfp + # this possibly is not necessary + num=ctx.floor(p*(ctx.mpf('2')**wpfp)) + difference = p * (ctx.mpf('2')**wpfp)-num + if (difference < 0.5): + num = num + else: + num = num+1 + p = ctx.convert(num * (ctx.mpf('2')**(-wpfp))) + + # COMPUTING THE COEFFICIENTS c[n] = cc[n] + # We shall use the notation cc[n], since there is + # a constant that is called c + # See II Section 3.14 + # We compute the coefficients and also save then in a + # cache. The bulk of the computation is passed to + # the function coef() + # + # eps6 is defined in II Section 3.13 equation (58) + eps6 = ctx.power(ctx.convert(2*ctx.pi), J)/(ctx.gamma(J+1)*3*J) + + # Now we compute the coefficients + cc = {} + cont = {} + cont, pipowers = coef(ctx, J, eps6) + cc=cont.copy() # we need a copy since we have to change his values. + Fp={} # this is the adequate locus of this + for n in range(M, 3*L-2): + Fp[n] = 0 + Fp={} + ctx.prec = wpfp + for m in range(0,M+1): + sumP = 0 + for k in range(2*J-m-1,-1,-1): + sumP = (sumP * p)+ cc[k] + Fp[m] = sumP + # preparation of the new coefficients + for k in range(0,2*J-m-1): + cc[k] = (k+1)* cc[k+1] + + # COMPUTING THE NUMBERS xd[u,n,k], yd[u,n,k] + # See II Section 3.17 + # + # First we compute the working precisions xwpd[k] + # Se II equation (92) + xwpd={} + d1 = max(6,ctx.mag(40*L*L)) + xd2 = 13+ctx.mag((1+abs(xsigma))*xA)-ctx.mag(xeps4)-1 + xconst = ctx.ln(8/(ctx.pi*ctx.pi*a*a*xB1*xB1)) /2 + for n in range(0,L): + xd3 = ctx.mag(ctx.sqrt(ctx.gamma(n-0.5)))-ctx.floor(n*xconst)+xd2 + xwpd[n]=max(xd3,d1) + + # procedure of II Section 3.17 + ctx.prec = xwpd[1]+10 + xpsigma = 1-(2*xsigma) + xd = {} + xd[0,0,-2]=0; xd[0,0,-1]=0; xd[0,0,0]=1; xd[0,0,1]=0 + xd[0,-1,-2]=0; xd[0,-1,-1]=0; xd[0,-1,0]=1; xd[0,-1,1]=0 + for n in range(1,L): + ctx.prec = xwpd[n]+10 + for k in range(0,3*n//2+1): + m = 3*n-2*k + if(m!=0): + m1 = ctx.one/m + c1= m1/4 + c2=(xpsigma*m1)/2 + c3=-(m+1) + xd[0,n,k]=c3*xd[0,n-1,k-2]+c1*xd[0,n-1,k]+c2*xd[0,n-1,k-1] + else: + xd[0,n,k]=0 + for r in range(0,k): + add=xd[0,n,r]*(ctx.mpf('1.0')*ctx.fac(2*k-2*r)/ctx.fac(k-r)) + xd[0,n,k] -= ((-1)**(k-r))*add + xd[0,n,-2]=0; xd[0,n,-1]=0; xd[0,n,3*n//2+1]=0 + for mu in range(-2,der+1): + for n in range(-2,L): + for k in range(-3,max(1,3*n//2+2)): + if( (mu<0)or (n<0) or(k<0)or (k>3*n//2)): + xd[mu,n,k] = 0 + for mu in range(1,der+1): + for n in range(0,L): + ctx.prec = xwpd[n]+10 + for k in range(0,3*n//2+1): + aux=(2*mu-2)*xd[mu-2,n-2,k-3]+2*(xsigma+n-2)*xd[mu-1,n-2,k-3] + xd[mu,n,k] = aux - xd[mu-1,n-1,k-1] + + # Now we compute the working precisions ywpd[k] + # Se II equation (92) + ywpd={} + d1 = max(6,ctx.mag(40*L*L)) + yd2 = 13+ctx.mag((1+abs(ysigma))*yA)-ctx.mag(yeps4)-1 + yconst = ctx.ln(8/(ctx.pi*ctx.pi*a*a*yB1*yB1)) /2 + for n in range(0,L): + yd3 = ctx.mag(ctx.sqrt(ctx.gamma(n-0.5)))-ctx.floor(n*yconst)+yd2 + ywpd[n]=max(yd3,d1) + + # procedure of II Section 3.17 + ctx.prec = ywpd[1]+10 + ypsigma = 1-(2*ysigma) + yd = {} + yd[0,0,-2]=0; yd[0,0,-1]=0; yd[0,0,0]=1; yd[0,0,1]=0 + yd[0,-1,-2]=0; yd[0,-1,-1]=0; yd[0,-1,0]=1; yd[0,-1,1]=0 + for n in range(1,L): + ctx.prec = ywpd[n]+10 + for k in range(0,3*n//2+1): + m = 3*n-2*k + if(m!=0): + m1 = ctx.one/m + c1= m1/4 + c2=(ypsigma*m1)/2 + c3=-(m+1) + yd[0,n,k]=c3*yd[0,n-1,k-2]+c1*yd[0,n-1,k]+c2*yd[0,n-1,k-1] + else: + yd[0,n,k]=0 + for r in range(0,k): + add=yd[0,n,r]*(ctx.mpf('1.0')*ctx.fac(2*k-2*r)/ctx.fac(k-r)) + yd[0,n,k] -= ((-1)**(k-r))*add + yd[0,n,-2]=0; yd[0,n,-1]=0; yd[0,n,3*n//2+1]=0 + + for mu in range(-2,der+1): + for n in range(-2,L): + for k in range(-3,max(1,3*n//2+2)): + if( (mu<0)or (n<0) or(k<0)or (k>3*n//2)): + yd[mu,n,k] = 0 + for mu in range(1,der+1): + for n in range(0,L): + ctx.prec = ywpd[n]+10 + for k in range(0,3*n//2+1): + aux=(2*mu-2)*yd[mu-2,n-2,k-3]+2*(ysigma+n-2)*yd[mu-1,n-2,k-3] + yd[mu,n,k] = aux - yd[mu-1,n-1,k-1] + + # COMPUTING THE COEFFICIENTS xtcoef[k,l] + # See II Section 3.9 + # + # computing the needed wp + xwptcoef={} + xwpterm={} + ctx.prec = 15 + c1 = ctx.mag(40*(L+2)) + xc2 = ctx.mag(68*(L+2)*xA) + xc4 = ctx.mag(xB1*a*math.sqrt(ctx.pi))-1 + for k in range(0,L): + xc3 = xc2 - k*xc4+ctx.mag(ctx.fac(k+0.5))/2. + xwptcoef[k] = (max(c1,xc3-ctx.mag(xeps4)+1)+1 +20)*1.5 + xwpterm[k] = (max(c1,ctx.mag(L+2)+xc3-ctx.mag(xeps3)+1)+1 +20) + ywptcoef={} + ywpterm={} + ctx.prec = 15 + c1 = ctx.mag(40*(L+2)) + yc2 = ctx.mag(68*(L+2)*yA) + yc4 = ctx.mag(yB1*a*math.sqrt(ctx.pi))-1 + for k in range(0,L): + yc3 = yc2 - k*yc4+ctx.mag(ctx.fac(k+0.5))/2. + ywptcoef[k] = ((max(c1,yc3-ctx.mag(yeps4)+1))+10)*1.5 + ywpterm[k] = (max(c1,ctx.mag(L+2)+yc3-ctx.mag(yeps3)+1)+1)+10 + + # check of power of pi + # computing the fortcoef[mu,k,ell] + xfortcoef={} + for mu in range(0,der+1): + for k in range(0,L): + for ell in range(-2,3*k//2+1): + xfortcoef[mu,k,ell]=0 + for mu in range(0,der+1): + for k in range(0,L): + ctx.prec = xwptcoef[k] + for ell in range(0,3*k//2+1): + xfortcoef[mu,k,ell]=xd[mu,k,ell]*Fp[3*k-2*ell]/pipowers[2*k-ell] + xfortcoef[mu,k,ell]=xfortcoef[mu,k,ell]/((2*ctx.j)**ell) + + def trunc_a(t): + wp = ctx.prec + ctx.prec = wp + 2 + aa = ctx.sqrt(t/(2*ctx.pi)) + ctx.prec = wp + return aa + + # computing the tcoef[k,ell] + xtcoef={} + for mu in range(0,der+1): + for k in range(0,L): + for ell in range(-2,3*k//2+1): + xtcoef[mu,k,ell]=0 + ctx.prec = max(xwptcoef[0],ywptcoef[0])+3 + aa= trunc_a(t) + la = -ctx.ln(aa) + + for chi in range(0,der+1): + for k in range(0,L): + ctx.prec = xwptcoef[k] + for ell in range(0,3*k//2+1): + xtcoef[chi,k,ell] =0 + for mu in range(0, chi+1): + tcoefter=ctx.binomial(chi,mu)*ctx.power(la,mu)*xfortcoef[chi-mu,k,ell] + xtcoef[chi,k,ell] += tcoefter + + # COMPUTING THE COEFFICIENTS ytcoef[k,l] + # See II Section 3.9 + # + # computing the needed wp + # check of power of pi + # computing the fortcoef[mu,k,ell] + yfortcoef={} + for mu in range(0,der+1): + for k in range(0,L): + for ell in range(-2,3*k//2+1): + yfortcoef[mu,k,ell]=0 + for mu in range(0,der+1): + for k in range(0,L): + ctx.prec = ywptcoef[k] + for ell in range(0,3*k//2+1): + yfortcoef[mu,k,ell]=yd[mu,k,ell]*Fp[3*k-2*ell]/pipowers[2*k-ell] + yfortcoef[mu,k,ell]=yfortcoef[mu,k,ell]/((2*ctx.j)**ell) + # computing the tcoef[k,ell] + ytcoef={} + for chi in range(0,der+1): + for k in range(0,L): + for ell in range(-2,3*k//2+1): + ytcoef[chi,k,ell]=0 + for chi in range(0,der+1): + for k in range(0,L): + ctx.prec = ywptcoef[k] + for ell in range(0,3*k//2+1): + ytcoef[chi,k,ell] =0 + for mu in range(0, chi+1): + tcoefter=ctx.binomial(chi,mu)*ctx.power(la,mu)*yfortcoef[chi-mu,k,ell] + ytcoef[chi,k,ell] += tcoefter + + # COMPUTING tv[k,ell] + # See II Section 3.8 + # + # a has a good value + ctx.prec = max(xwptcoef[0], ywptcoef[0])+2 + av = {} + av[0] = 1 + av[1] = av[0]/a + + ctx.prec = max(xwptcoef[0],ywptcoef[0]) + for k in range(2,L): + av[k] = av[k-1] * av[1] + + # Computing the quotients + xtv = {} + for chi in range(0,der+1): + for k in range(0,L): + ctx.prec = xwptcoef[k] + for ell in range(0,3*k//2+1): + xtv[chi,k,ell] = xtcoef[chi,k,ell]* av[k] + # Computing the quotients + ytv = {} + for chi in range(0,der+1): + for k in range(0,L): + ctx.prec = ywptcoef[k] + for ell in range(0,3*k//2+1): + ytv[chi,k,ell] = ytcoef[chi,k,ell]* av[k] + + # COMPUTING THE TERMS xterm[k] + # See II Section 3.6 + xterm = {} + for chi in range(0,der+1): + for n in range(0,L): + ctx.prec = xwpterm[n] + te = 0 + for k in range(0, 3*n//2+1): + te += xtv[chi,n,k] + xterm[chi,n] = te + + # COMPUTING THE TERMS yterm[k] + # See II Section 3.6 + yterm = {} + for chi in range(0,der+1): + for n in range(0,L): + ctx.prec = ywpterm[n] + te = 0 + for k in range(0, 3*n//2+1): + te += ytv[chi,n,k] + yterm[chi,n] = te + + # COMPUTING rssum + # See II Section 3.5 + xrssum={} + ctx.prec=15 + xrsbound = math.sqrt(ctx.pi) * xc /(xb*a) + ctx.prec=15 + xwprssum = ctx.mag(4.4*((L+3)**2)*xrsbound / xeps2) + xwprssum = max(xwprssum, ctx.mag(10*(L+1))) + ctx.prec = xwprssum + for chi in range(0,der+1): + xrssum[chi] = 0 + for k in range(1,L+1): + xrssum[chi] += xterm[chi,L-k] + yrssum={} + ctx.prec=15 + yrsbound = math.sqrt(ctx.pi) * yc /(yb*a) + ctx.prec=15 + ywprssum = ctx.mag(4.4*((L+3)**2)*yrsbound / yeps2) + ywprssum = max(ywprssum, ctx.mag(10*(L+1))) + ctx.prec = ywprssum + for chi in range(0,der+1): + yrssum[chi] = 0 + for k in range(1,L+1): + yrssum[chi] += yterm[chi,L-k] + + # COMPUTING S3 + # See II Section 3.19 + ctx.prec = 15 + A2 = 2**(max(ctx.mag(abs(xrssum[0])), ctx.mag(abs(yrssum[0])))) + eps8 = eps/(3*A2) + T = t *ctx.ln(t/(2*ctx.pi)) + xwps3 = 5 + ctx.mag((1+(2/eps8)*ctx.power(a,-xsigma))*T) + ywps3 = 5 + ctx.mag((1+(2/eps8)*ctx.power(a,-ysigma))*T) + + ctx.prec = max(xwps3, ywps3) + + tpi = t/(2*ctx.pi) + arg = (t/2)*ctx.ln(tpi)-(t/2)-ctx.pi/8 + U = ctx.expj(-arg) + a = trunc_a(t) + xasigma = ctx.power(a, -xsigma) + yasigma = ctx.power(a, -ysigma) + xS3 = ((-1)**(N-1)) * xasigma * U + yS3 = ((-1)**(N-1)) * yasigma * U + + # COMPUTING S1 the zetasum + # See II Section 3.18 + ctx.prec = 15 + xwpsum = 4+ ctx.mag((N+ctx.power(N,1-xsigma))*ctx.ln(N) /eps1) + ywpsum = 4+ ctx.mag((N+ctx.power(N,1-ysigma))*ctx.ln(N) /eps1) + wpsum = max(xwpsum, ywpsum) + + ctx.prec = wpsum +10 + ''' + # This can be improved + xS1={} + yS1={} + for chi in range(0,der+1): + xS1[chi] = 0 + yS1[chi] = 0 + for n in range(1,int(N)+1): + ln = ctx.ln(n) + xexpn = ctx.exp(-ln*(xsigma+ctx.j*t)) + yexpn = ctx.conj(1/(n*xexpn)) + for chi in range(0,der+1): + pown = ctx.power(-ln, chi) + xterm = pown*xexpn + yterm = pown*yexpn + xS1[chi] += xterm + yS1[chi] += yterm + ''' + xS1, yS1 = ctx._zetasum(s, 1, int(N)-1, range(0,der+1), True) + + # END OF COMPUTATION of xrz, yrz + # See II Section 3.1 + ctx.prec = 15 + xabsS1 = abs(xS1[der]) + xabsS2 = abs(xrssum[der] * xS3) + xwpend = max(6, wpinitial+ctx.mag(6*(3*xabsS1+7*xabsS2) ) ) + + ctx.prec = xwpend + xrz={} + for chi in range(0,der+1): + xrz[chi] = xS1[chi]+xrssum[chi]*xS3 + + ctx.prec = 15 + yabsS1 = abs(yS1[der]) + yabsS2 = abs(yrssum[der] * yS3) + ywpend = max(6, wpinitial+ctx.mag(6*(3*yabsS1+7*yabsS2) ) ) + + ctx.prec = ywpend + yrz={} + for chi in range(0,der+1): + yrz[chi] = yS1[chi]+yrssum[chi]*yS3 + yrz[chi] = ctx.conj(yrz[chi]) + ctx.prec = wpinitial + return xrz, yrz + +def Rzeta_set(ctx, s, derivatives=[0]): + r""" + Computes several derivatives of the auxiliary function of Riemann `R(s)`. + + **Definition** + + The function is defined by + + .. math :: + + \begin{equation} + {\mathop{\mathcal R }\nolimits}(s)= + \int_{0\swarrow1}\frac{x^{-s} e^{\pi i x^2}}{e^{\pi i x}- + e^{-\pi i x}}\,dx + \end{equation} + + To this function we apply the Riemann-Siegel expansion. + """ + der = max(derivatives) + # First we take the value of ctx.prec + # During the computation we will change ctx.prec, and finally we will + # restaurate the initial value + wpinitial = ctx.prec + # Take the real and imaginary part of s + t = ctx._im(s) + sigma = ctx._re(s) + # Now compute several parameter that appear on the program + ctx.prec = 15 + a = ctx.sqrt(t/(2*ctx.pi)) # Careful + asigma = ctx.power(a, sigma) # Careful + # We need a simple bound A1 < asigma (see II Section 3.1 and 3.3) + A1 = ctx.power(2, ctx.mag(asigma)-1) + # We compute various epsilon's (see II end of Section 3.1) + eps = ctx.power(2, -wpinitial) + eps1 = eps/6. + eps2 = eps * A1/3. + # COMPUTING SOME COEFFICIENTS THAT DEPENDS + # ON sigma + # constant b and c (see I Theorem 2 formula (26) ) + # coefficients A and B1 (see I Section 6.1 equation (50)) + # here we not need high precision + ctx.prec = 15 + if sigma > 0: + b = 2. + c = math.pow(9,sigma)/4.44288 + # 4.44288 =(math.sqrt(2)*math.pi) + A = math.pow(9,sigma) + B1 = 1 + else: + b = 2.25158 # math.sqrt( (3-2* math.log(2))*math.pi ) + c = math.pow(2,-sigma)/4.44288 + A = math.pow(2,-sigma) + B1 = 1.10789 # = 2*sqrt(1-log(2)) + # COMPUTING L THE NUMBER OF TERMS NEEDED IN THE RIEMANN-SIEGEL + # CORRECTION + # See II Section 3.2 + ctx.prec = 15 + L = 1 + while 3*c*ctx.gamma(L*0.5) * ctx.power(b*a,-L) >= eps2: + L = L+1 + L = max(2,L) + # The number L has to satify some conditions. + # If not RS can not compute Rzeta(s) with the prescribed precision + # (see II, Section 3.2 condition (20) ) and + # (II, Section 3.3 condition (22) ). Also we have added + # an additional technical condition in Section 3.17 Proposition 17 + if ((3*L >= 2*a*a/25.) or (3*L+2+sigma<0) or (abs(sigma)> a/2.)): + #print 'Error Riemann-Siegel can not compute with such precision' + ctx.prec = wpinitial + raise NotImplementedError("Riemann-Siegel can not compute with such precision") + + # INITIALIZATION (CONTINUATION) + # + # eps3 is the constant defined on (II, Section 3.5 equation (27) ) + # each term of the RS correction must be computed with error <= eps3 + eps3 = eps2/(4*L) + + # eps4 is defined on (II Section 3.6 equation (30) ) + # each component of the formula (II Section 3.6 equation (29) ) + # must be computed with error <= eps4 + eps4 = eps3/(3*L) + + # COMPUTING M. NUMBER OF DERIVATIVES Fp[m] TO COMPUTE + M = aux_M_Fp(ctx, A, eps4, a, B1, L) + Fp = {} + for n in range(M, 3*L-2): + Fp[n] = 0 + + # But I have not seen an instance of M != 3*L-3 + # + # DETERMINATION OF J THE NUMBER OF TERMS NEEDED + # IN THE TAYLOR SERIES OF F. + # See II Section 3.11 equation (49)) + h1 = eps4/(632*A) + h2 = ctx.pi*ctx.pi*B1*a *ctx.sqrt(3)*math.e*math.e + h2 = h1 * ctx.power((h2/M**2),(M-1)/3) / M + h3 = min(h1,h2) + J=12 + jvalue = (2*ctx.pi)**J / ctx.gamma(J+1) + while jvalue > h3: + J = J+1 + jvalue = (2*ctx.pi)*jvalue/J + + # COMPUTING eps5[m] for 1 <= m <= 21 + # See II Section 10 equation (43) + eps5={} + foreps5 = math.pi*math.pi*B1*a + for m in range(0,22): + aux1 = math.pow(foreps5, m/3)/(316.*A) + aux2 = ctx.gamma(m+1)/ctx.gamma(m/3.0+0.5) + aux2 = math.sqrt(aux2) + eps5[m] = aux1*aux2*eps4 + + # COMPUTING wpfp + # See II Section 3.13 equation (59) + twenty = min(3*L-3, 21)+1 + aux = 6812*J + wpfp = ctx.mag(44*J) + for m in range(0, twenty): + wpfp = max(wpfp, ctx.mag(aux*ctx.gamma(m+1)/eps5[m])) + # COMPUTING N AND p + # See II Section + ctx.prec = wpfp + ctx.mag(t) + 20 + a = ctx.sqrt(t/(2*ctx.pi)) + N = ctx.floor(a) + p = 1-2*(a-N) + + # now we get a rounded version of p to the precision wpfp + # this possibly is not necessary + num = ctx.floor(p*(ctx.mpf(2)**wpfp)) + difference = p * (ctx.mpf(2)**wpfp)-num + if difference < 0.5: + num = num + else: + num = num+1 + p = ctx.convert(num * (ctx.mpf(2)**(-wpfp))) + + # COMPUTING THE COEFFICIENTS c[n] = cc[n] + # We shall use the notation cc[n], since there is + # a constant that is called c + # See II Section 3.14 + # We compute the coefficients and also save then in a + # cache. The bulk of the computation is passed to + # the function coef() + # + # eps6 is defined in II Section 3.13 equation (58) + eps6 = ctx.power(2*ctx.pi, J)/(ctx.gamma(J+1)*3*J) + + # Now we compute the coefficients + cc={} + cont={} + cont, pipowers = coef(ctx, J, eps6) + cc = cont.copy() # we need a copy since we have + Fp={} + for n in range(M, 3*L-2): + Fp[n] = 0 + ctx.prec = wpfp + for m in range(0,M+1): + sumP = 0 + for k in range(2*J-m-1,-1,-1): + sumP = (sumP * p) + cc[k] + Fp[m] = sumP + # preparation of the new coefficients + for k in range(0, 2*J-m-1): + cc[k] = (k+1) * cc[k+1] + + # COMPUTING THE NUMBERS d[n,k] + # See II Section 3.17 + + # First we compute the working precisions wpd[k] + # Se II equation (92) + wpd = {} + d1 = max(6, ctx.mag(40*L*L)) + d2 = 13+ctx.mag((1+abs(sigma))*A)-ctx.mag(eps4)-1 + const = ctx.ln(8/(ctx.pi*ctx.pi*a*a*B1*B1)) /2 + for n in range(0,L): + d3 = ctx.mag(ctx.sqrt(ctx.gamma(n-0.5)))-ctx.floor(n*const)+d2 + wpd[n] = max(d3,d1) + + # procedure of II Section 3.17 + ctx.prec = wpd[1]+10 + psigma = 1-(2*sigma) + d = {} + d[0,0,-2]=0; d[0,0,-1]=0; d[0,0,0]=1; d[0,0,1]=0 + d[0,-1,-2]=0; d[0,-1,-1]=0; d[0,-1,0]=1; d[0,-1,1]=0 + for n in range(1,L): + ctx.prec = wpd[n]+10 + for k in range(0,3*n//2+1): + m = 3*n-2*k + if (m!=0): + m1 = ctx.one/m + c1 = m1/4 + c2 = (psigma*m1)/2 + c3 = -(m+1) + d[0,n,k] = c3*d[0,n-1,k-2]+c1*d[0,n-1,k]+c2*d[0,n-1,k-1] + else: + d[0,n,k]=0 + for r in range(0,k): + add = d[0,n,r]*(ctx.one*ctx.fac(2*k-2*r)/ctx.fac(k-r)) + d[0,n,k] -= ((-1)**(k-r))*add + d[0,n,-2]=0; d[0,n,-1]=0; d[0,n,3*n//2+1]=0 + + for mu in range(-2,der+1): + for n in range(-2,L): + for k in range(-3,max(1,3*n//2+2)): + if ((mu<0)or (n<0) or(k<0)or (k>3*n//2)): + d[mu,n,k] = 0 + + for mu in range(1,der+1): + for n in range(0,L): + ctx.prec = wpd[n]+10 + for k in range(0,3*n//2+1): + aux=(2*mu-2)*d[mu-2,n-2,k-3]+2*(sigma+n-2)*d[mu-1,n-2,k-3] + d[mu,n,k] = aux - d[mu-1,n-1,k-1] + + # COMPUTING THE COEFFICIENTS t[k,l] + # See II Section 3.9 + # + # computing the needed wp + wptcoef = {} + wpterm = {} + ctx.prec = 15 + c1 = ctx.mag(40*(L+2)) + c2 = ctx.mag(68*(L+2)*A) + c4 = ctx.mag(B1*a*math.sqrt(ctx.pi))-1 + for k in range(0,L): + c3 = c2 - k*c4+ctx.mag(ctx.fac(k+0.5))/2. + wptcoef[k] = max(c1,c3-ctx.mag(eps4)+1)+1 +10 + wpterm[k] = max(c1,ctx.mag(L+2)+c3-ctx.mag(eps3)+1)+1 +10 + + # check of power of pi + + # computing the fortcoef[mu,k,ell] + fortcoef={} + for mu in derivatives: + for k in range(0,L): + for ell in range(-2,3*k//2+1): + fortcoef[mu,k,ell]=0 + + for mu in derivatives: + for k in range(0,L): + ctx.prec = wptcoef[k] + for ell in range(0,3*k//2+1): + fortcoef[mu,k,ell]=d[mu,k,ell]*Fp[3*k-2*ell]/pipowers[2*k-ell] + fortcoef[mu,k,ell]=fortcoef[mu,k,ell]/((2*ctx.j)**ell) + + def trunc_a(t): + wp = ctx.prec + ctx.prec = wp + 2 + aa = ctx.sqrt(t/(2*ctx.pi)) + ctx.prec = wp + return aa + + # computing the tcoef[chi,k,ell] + tcoef={} + for chi in derivatives: + for k in range(0,L): + for ell in range(-2,3*k//2+1): + tcoef[chi,k,ell]=0 + ctx.prec = wptcoef[0]+3 + aa = trunc_a(t) + la = -ctx.ln(aa) + + for chi in derivatives: + for k in range(0,L): + ctx.prec = wptcoef[k] + for ell in range(0,3*k//2+1): + tcoef[chi,k,ell] = 0 + for mu in range(0, chi+1): + tcoefter = ctx.binomial(chi,mu) * la**mu * \ + fortcoef[chi-mu,k,ell] + tcoef[chi,k,ell] += tcoefter + + # COMPUTING tv[k,ell] + # See II Section 3.8 + + # Computing the powers av[k] = a**(-k) + ctx.prec = wptcoef[0] + 2 + + # a has a good value of a. + # See II Section 3.6 + av = {} + av[0] = 1 + av[1] = av[0]/a + + ctx.prec = wptcoef[0] + for k in range(2,L): + av[k] = av[k-1] * av[1] + + # Computing the quotients + tv = {} + for chi in derivatives: + for k in range(0,L): + ctx.prec = wptcoef[k] + for ell in range(0,3*k//2+1): + tv[chi,k,ell] = tcoef[chi,k,ell]* av[k] + + # COMPUTING THE TERMS term[k] + # See II Section 3.6 + term = {} + for chi in derivatives: + for n in range(0,L): + ctx.prec = wpterm[n] + te = 0 + for k in range(0, 3*n//2+1): + te += tv[chi,n,k] + term[chi,n] = te + + # COMPUTING rssum + # See II Section 3.5 + rssum={} + ctx.prec=15 + rsbound = math.sqrt(ctx.pi) * c /(b*a) + ctx.prec=15 + wprssum = ctx.mag(4.4*((L+3)**2)*rsbound / eps2) + wprssum = max(wprssum, ctx.mag(10*(L+1))) + ctx.prec = wprssum + for chi in derivatives: + rssum[chi] = 0 + for k in range(1,L+1): + rssum[chi] += term[chi,L-k] + + # COMPUTING S3 + # See II Section 3.19 + ctx.prec = 15 + A2 = 2**(ctx.mag(rssum[0])) + eps8 = eps/(3* A2) + T = t * ctx.ln(t/(2*ctx.pi)) + wps3 = 5 + ctx.mag((1+(2/eps8)*ctx.power(a,-sigma))*T) + + ctx.prec = wps3 + tpi = t/(2*ctx.pi) + arg = (t/2)*ctx.ln(tpi)-(t/2)-ctx.pi/8 + U = ctx.expj(-arg) + a = trunc_a(t) + asigma = ctx.power(a, -sigma) + S3 = ((-1)**(N-1)) * asigma * U + + # COMPUTING S1 the zetasum + # See II Section 3.18 + ctx.prec = 15 + wpsum = 4 + ctx.mag((N+ctx.power(N,1-sigma))*ctx.ln(N)/eps1) + + ctx.prec = wpsum + 10 + ''' + # This can be improved + S1 = {} + for chi in derivatives: + S1[chi] = 0 + for n in range(1,int(N)+1): + ln = ctx.ln(n) + expn = ctx.exp(-ln*(sigma+ctx.j*t)) + for chi in derivatives: + term = ctx.power(-ln, chi)*expn + S1[chi] += term + ''' + S1 = ctx._zetasum(s, 1, int(N)-1, derivatives)[0] + + # END OF COMPUTATION + # See II Section 3.1 + ctx.prec = 15 + absS1 = abs(S1[der]) + absS2 = abs(rssum[der] * S3) + wpend = max(6, wpinitial + ctx.mag(6*(3*absS1+7*absS2))) + ctx.prec = wpend + rz = {} + for chi in derivatives: + rz[chi] = S1[chi]+rssum[chi]*S3 + ctx.prec = wpinitial + return rz + + +def z_half(ctx,t,der=0): + r""" + z_half(t,der=0) Computes Z^(der)(t) + """ + s=ctx.mpf('0.5')+ctx.j*t + wpinitial = ctx.prec + ctx.prec = 15 + tt = t/(2*ctx.pi) + wptheta = wpinitial +1 + ctx.mag(3*(tt**1.5)*ctx.ln(tt)) + wpz = wpinitial + 1 + ctx.mag(12*tt*ctx.ln(tt)) + ctx.prec = wptheta + theta = ctx.siegeltheta(t) + ctx.prec = wpz + rz = Rzeta_set(ctx,s, range(der+1)) + if der > 0: ps1 = ctx._re(ctx.psi(0,s/2)/2 - ctx.ln(ctx.pi)/2) + if der > 1: ps2 = ctx._re(ctx.j*ctx.psi(1,s/2)/4) + if der > 2: ps3 = ctx._re(-ctx.psi(2,s/2)/8) + if der > 3: ps4 = ctx._re(-ctx.j*ctx.psi(3,s/2)/16) + exptheta = ctx.expj(theta) + if der == 0: + z = 2*exptheta*rz[0] + if der == 1: + zf = 2j*exptheta + z = zf*(ps1*rz[0]+rz[1]) + if der == 2: + zf = 2 * exptheta + z = -zf*(2*rz[1]*ps1+rz[0]*ps1**2+rz[2]-ctx.j*rz[0]*ps2) + if der == 3: + zf = -2j*exptheta + z = 3*rz[1]*ps1**2+rz[0]*ps1**3+3*ps1*rz[2] + z = zf*(z-3j*rz[1]*ps2-3j*rz[0]*ps1*ps2+rz[3]-rz[0]*ps3) + if der == 4: + zf = 2*exptheta + z = 4*rz[1]*ps1**3+rz[0]*ps1**4+6*ps1**2*rz[2] + z = z-12j*rz[1]*ps1*ps2-6j*rz[0]*ps1**2*ps2-6j*rz[2]*ps2-3*rz[0]*ps2*ps2 + z = z + 4*ps1*rz[3]-4*rz[1]*ps3-4*rz[0]*ps1*ps3+rz[4]+ctx.j*rz[0]*ps4 + z = zf*z + ctx.prec = wpinitial + return ctx._re(z) + +def zeta_half(ctx, s, k=0): + """ + zeta_half(s,k=0) Computes zeta^(k)(s) when Re s = 0.5 + """ + wpinitial = ctx.prec + sigma = ctx._re(s) + t = ctx._im(s) + #--- compute wptheta, wpR, wpbasic --- + ctx.prec = 53 + # X see II Section 3.21 (109) and (110) + if sigma > 0: + X = ctx.sqrt(abs(s)) + else: + X = (2*ctx.pi)**(sigma-1) * abs(1-s)**(0.5-sigma) + # M1 see II Section 3.21 (111) and (112) + if sigma > 0: + M1 = 2*ctx.sqrt(t/(2*ctx.pi)) + else: + M1 = 4 * t * X + # T see II Section 3.21 (113) + abst = abs(0.5-s) + T = 2* abst*math.log(abst) + # computing wpbasic, wptheta, wpR see II Section 3.21 + wpbasic = max(6,3+ctx.mag(t)) + wpbasic2 = 2+ctx.mag(2.12*M1+21.2*M1*X+1.3*M1*X*T)+wpinitial+1 + wpbasic = max(wpbasic, wpbasic2) + wptheta = max(4, 3+ctx.mag(2.7*M1*X)+wpinitial+1) + wpR = 3+ctx.mag(1.1+2*X)+wpinitial+1 + ctx.prec = wptheta + theta = ctx.siegeltheta(t-ctx.j*(sigma-ctx.mpf('0.5'))) + if k > 0: ps1 = (ctx._re(ctx.psi(0,s/2)))/2 - ctx.ln(ctx.pi)/2 + if k > 1: ps2 = -(ctx._im(ctx.psi(1,s/2)))/4 + if k > 2: ps3 = -(ctx._re(ctx.psi(2,s/2)))/8 + if k > 3: ps4 = (ctx._im(ctx.psi(3,s/2)))/16 + ctx.prec = wpR + xrz = Rzeta_set(ctx,s,range(k+1)) + yrz={} + for chi in range(0,k+1): + yrz[chi] = ctx.conj(xrz[chi]) + ctx.prec = wpbasic + exptheta = ctx.expj(-2*theta) + if k==0: + zv = xrz[0]+exptheta*yrz[0] + if k==1: + zv1 = -yrz[1] - 2*yrz[0]*ps1 + zv = xrz[1] + exptheta*zv1 + if k==2: + zv1 = 4*yrz[1]*ps1+4*yrz[0]*(ps1**2)+yrz[2]+2j*yrz[0]*ps2 + zv = xrz[2]+exptheta*zv1 + if k==3: + zv1 = -12*yrz[1]*ps1**2-8*yrz[0]*ps1**3-6*yrz[2]*ps1-6j*yrz[1]*ps2 + zv1 = zv1 - 12j*yrz[0]*ps1*ps2-yrz[3]+2*yrz[0]*ps3 + zv = xrz[3]+exptheta*zv1 + if k == 4: + zv1 = 32*yrz[1]*ps1**3 +16*yrz[0]*ps1**4+24*yrz[2]*ps1**2 + zv1 = zv1 +48j*yrz[1]*ps1*ps2+48j*yrz[0]*(ps1**2)*ps2 + zv1 = zv1+12j*yrz[2]*ps2-12*yrz[0]*ps2**2+8*yrz[3]*ps1-8*yrz[1]*ps3 + zv1 = zv1-16*yrz[0]*ps1*ps3+yrz[4]-2j*yrz[0]*ps4 + zv = xrz[4]+exptheta*zv1 + ctx.prec = wpinitial + return zv + +def zeta_offline(ctx, s, k=0): + """ + Computes zeta^(k)(s) off the line + """ + wpinitial = ctx.prec + sigma = ctx._re(s) + t = ctx._im(s) + #--- compute wptheta, wpR, wpbasic --- + ctx.prec = 53 + # X see II Section 3.21 (109) and (110) + if sigma > 0: + X = ctx.power(abs(s), 0.5) + else: + X = ctx.power(2*ctx.pi, sigma-1)*ctx.power(abs(1-s),0.5-sigma) + # M1 see II Section 3.21 (111) and (112) + if (sigma > 0): + M1 = 2*ctx.sqrt(t/(2*ctx.pi)) + else: + M1 = 4 * t * X + # M2 see II Section 3.21 (111) and (112) + if (1-sigma > 0): + M2 = 2*ctx.sqrt(t/(2*ctx.pi)) + else: + M2 = 4*t*ctx.power(2*ctx.pi, -sigma)*ctx.power(abs(s),sigma-0.5) + # T see II Section 3.21 (113) + abst = abs(0.5-s) + T = 2* abst*math.log(abst) + # computing wpbasic, wptheta, wpR see II Section 3.21 + wpbasic = max(6,3+ctx.mag(t)) + wpbasic2 = 2+ctx.mag(2.12*M1+21.2*M2*X+1.3*M2*X*T)+wpinitial+1 + wpbasic = max(wpbasic, wpbasic2) + wptheta = max(4, 3+ctx.mag(2.7*M2*X)+wpinitial+1) + wpR = 3+ctx.mag(1.1+2*X)+wpinitial+1 + ctx.prec = wptheta + theta = ctx.siegeltheta(t-ctx.j*(sigma-ctx.mpf('0.5'))) + s1 = s + s2 = ctx.conj(1-s1) + ctx.prec = wpR + xrz, yrz = Rzeta_simul(ctx, s, k) + if k > 0: ps1 = (ctx.psi(0,s1/2)+ctx.psi(0,(1-s1)/2))/4 - ctx.ln(ctx.pi)/2 + if k > 1: ps2 = ctx.j*(ctx.psi(1,s1/2)-ctx.psi(1,(1-s1)/2))/8 + if k > 2: ps3 = -(ctx.psi(2,s1/2)+ctx.psi(2,(1-s1)/2))/16 + if k > 3: ps4 = -ctx.j*(ctx.psi(3,s1/2)-ctx.psi(3,(1-s1)/2))/32 + ctx.prec = wpbasic + exptheta = ctx.expj(-2*theta) + if k == 0: + zv = xrz[0]+exptheta*yrz[0] + if k == 1: + zv1 = -yrz[1]-2*yrz[0]*ps1 + zv = xrz[1]+exptheta*zv1 + if k == 2: + zv1 = 4*yrz[1]*ps1+4*yrz[0]*(ps1**2) +yrz[2]+2j*yrz[0]*ps2 + zv = xrz[2]+exptheta*zv1 + if k == 3: + zv1 = -12*yrz[1]*ps1**2 -8*yrz[0]*ps1**3-6*yrz[2]*ps1-6j*yrz[1]*ps2 + zv1 = zv1 - 12j*yrz[0]*ps1*ps2-yrz[3]+2*yrz[0]*ps3 + zv = xrz[3]+exptheta*zv1 + if k == 4: + zv1 = 32*yrz[1]*ps1**3 +16*yrz[0]*ps1**4+24*yrz[2]*ps1**2 + zv1 = zv1 +48j*yrz[1]*ps1*ps2+48j*yrz[0]*(ps1**2)*ps2 + zv1 = zv1+12j*yrz[2]*ps2-12*yrz[0]*ps2**2+8*yrz[3]*ps1-8*yrz[1]*ps3 + zv1 = zv1-16*yrz[0]*ps1*ps3+yrz[4]-2j*yrz[0]*ps4 + zv = xrz[4]+exptheta*zv1 + ctx.prec = wpinitial + return zv + +def z_offline(ctx, w, k=0): + r""" + Computes Z(w) and its derivatives off the line + """ + s = ctx.mpf('0.5')+ctx.j*w + s1 = s + s2 = ctx.conj(1-s1) + wpinitial = ctx.prec + ctx.prec = 35 + # X see II Section 3.21 (109) and (110) + # M1 see II Section 3.21 (111) and (112) + if (ctx._re(s1) >= 0): + M1 = 2*ctx.sqrt(ctx._im(s1)/(2 * ctx.pi)) + X = ctx.sqrt(abs(s1)) + else: + X = (2*ctx.pi)**(ctx._re(s1)-1) * abs(1-s1)**(0.5-ctx._re(s1)) + M1 = 4 * ctx._im(s1)*X + # M2 see II Section 3.21 (111) and (112) + if (ctx._re(s2) >= 0): + M2 = 2*ctx.sqrt(ctx._im(s2)/(2 * ctx.pi)) + else: + M2 = 4 * ctx._im(s2)*(2*ctx.pi)**(ctx._re(s2)-1)*abs(1-s2)**(0.5-ctx._re(s2)) + # T see II Section 3.21 Prop. 27 + T = 2*abs(ctx.siegeltheta(w)) + # defining some precisions + # see II Section 3.22 (115), (116), (117) + aux1 = ctx.sqrt(X) + aux2 = aux1*(M1+M2) + aux3 = 3 +wpinitial + wpbasic = max(6, 3+ctx.mag(T), ctx.mag(aux2*(26+2*T))+aux3) + wptheta = max(4,ctx.mag(2.04*aux2)+aux3) + wpR = ctx.mag(4*aux1)+aux3 + # now the computations + ctx.prec = wptheta + theta = ctx.siegeltheta(w) + ctx.prec = wpR + xrz, yrz = Rzeta_simul(ctx,s,k) + pta = 0.25 + 0.5j*w + ptb = 0.25 - 0.5j*w + if k > 0: ps1 = 0.25*(ctx.psi(0,pta)+ctx.psi(0,ptb)) - ctx.ln(ctx.pi)/2 + if k > 1: ps2 = (1j/8)*(ctx.psi(1,pta)-ctx.psi(1,ptb)) + if k > 2: ps3 = (-1./16)*(ctx.psi(2,pta)+ctx.psi(2,ptb)) + if k > 3: ps4 = (-1j/32)*(ctx.psi(3,pta)-ctx.psi(3,ptb)) + ctx.prec = wpbasic + exptheta = ctx.expj(theta) + if k == 0: + zv = exptheta*xrz[0]+yrz[0]/exptheta + j = ctx.j + if k == 1: + zv = j*exptheta*(xrz[1]+xrz[0]*ps1)-j*(yrz[1]+yrz[0]*ps1)/exptheta + if k == 2: + zv = exptheta*(-2*xrz[1]*ps1-xrz[0]*ps1**2-xrz[2]+j*xrz[0]*ps2) + zv =zv + (-2*yrz[1]*ps1-yrz[0]*ps1**2-yrz[2]-j*yrz[0]*ps2)/exptheta + if k == 3: + zv1 = -3*xrz[1]*ps1**2-xrz[0]*ps1**3-3*xrz[2]*ps1+j*3*xrz[1]*ps2 + zv1 = (zv1+ 3j*xrz[0]*ps1*ps2-xrz[3]+xrz[0]*ps3)*j*exptheta + zv2 = 3*yrz[1]*ps1**2+yrz[0]*ps1**3+3*yrz[2]*ps1+j*3*yrz[1]*ps2 + zv2 = j*(zv2 + 3j*yrz[0]*ps1*ps2+ yrz[3]-yrz[0]*ps3)/exptheta + zv = zv1+zv2 + if k == 4: + zv1 = 4*xrz[1]*ps1**3+xrz[0]*ps1**4 + 6*xrz[2]*ps1**2 + zv1 = zv1-12j*xrz[1]*ps1*ps2-6j*xrz[0]*ps1**2*ps2-6j*xrz[2]*ps2 + zv1 = zv1-3*xrz[0]*ps2*ps2+4*xrz[3]*ps1-4*xrz[1]*ps3-4*xrz[0]*ps1*ps3 + zv1 = zv1+xrz[4]+j*xrz[0]*ps4 + zv2 = 4*yrz[1]*ps1**3+yrz[0]*ps1**4 + 6*yrz[2]*ps1**2 + zv2 = zv2+12j*yrz[1]*ps1*ps2+6j*yrz[0]*ps1**2*ps2+6j*yrz[2]*ps2 + zv2 = zv2-3*yrz[0]*ps2*ps2+4*yrz[3]*ps1-4*yrz[1]*ps3-4*yrz[0]*ps1*ps3 + zv2 = zv2+yrz[4]-j*yrz[0]*ps4 + zv = exptheta*zv1+zv2/exptheta + ctx.prec = wpinitial + return zv + +@defun +def rs_zeta(ctx, s, derivative=0, **kwargs): + if derivative > 4: + raise NotImplementedError + s = ctx.convert(s) + re = ctx._re(s); im = ctx._im(s) + if im < 0: + z = ctx.conj(ctx.rs_zeta(ctx.conj(s), derivative)) + return z + critical_line = (re == 0.5) + if critical_line: + return zeta_half(ctx, s, derivative) + else: + return zeta_offline(ctx, s, derivative) + +@defun +def rs_z(ctx, w, derivative=0): + w = ctx.convert(w) + re = ctx._re(w); im = ctx._im(w) + if re < 0: + return rs_z(ctx, -w, derivative) + critical_line = (im == 0) + if critical_line : + return z_half(ctx, w, derivative) + else: + return z_offline(ctx, w, derivative) diff --git a/venv/lib/python3.12/site-packages/mpmath/functions/signals.py b/venv/lib/python3.12/site-packages/mpmath/functions/signals.py new file mode 100644 index 0000000000000000000000000000000000000000..6fadafb2dbb44fe19a2defa8d807d81d7c8e2789 --- /dev/null +++ b/venv/lib/python3.12/site-packages/mpmath/functions/signals.py @@ -0,0 +1,32 @@ +from .functions import defun_wrapped + +@defun_wrapped +def squarew(ctx, t, amplitude=1, period=1): + P = period + A = amplitude + return A*((-1)**ctx.floor(2*t/P)) + +@defun_wrapped +def trianglew(ctx, t, amplitude=1, period=1): + A = amplitude + P = period + + return 2*A*(0.5 - ctx.fabs(1 - 2*ctx.frac(t/P + 0.25))) + +@defun_wrapped +def sawtoothw(ctx, t, amplitude=1, period=1): + A = amplitude + P = period + return A*ctx.frac(t/P) + +@defun_wrapped +def unit_triangle(ctx, t, amplitude=1): + A = amplitude + if t <= -1 or t >= 1: + return ctx.zero + return A*(-ctx.fabs(t) + 1) + +@defun_wrapped +def sigmoid(ctx, t, amplitude=1): + A = amplitude + return A / (1 + ctx.exp(-t)) diff --git a/venv/lib/python3.12/site-packages/mpmath/functions/theta.py b/venv/lib/python3.12/site-packages/mpmath/functions/theta.py new file mode 100644 index 0000000000000000000000000000000000000000..2b3d8323a163a43186b85417a1b40f3b656c30d0 --- /dev/null +++ b/venv/lib/python3.12/site-packages/mpmath/functions/theta.py @@ -0,0 +1,1049 @@ +from .functions import defun, defun_wrapped + +@defun +def _jacobi_theta2(ctx, z, q): + extra1 = 10 + extra2 = 20 + # the loops below break when the fixed precision quantities + # a and b go to zero; + # right shifting small negative numbers by wp one obtains -1, not zero, + # so the condition a**2 + b**2 > MIN is used to break the loops. + MIN = 2 + if z == ctx.zero: + if (not ctx._im(q)): + wp = ctx.prec + extra1 + x = ctx.to_fixed(ctx._re(q), wp) + x2 = (x*x) >> wp + a = b = x2 + s = x2 + while abs(a) > MIN: + b = (b*x2) >> wp + a = (a*b) >> wp + s += a + s = (1 << (wp+1)) + (s << 1) + s = ctx.ldexp(s, -wp) + else: + wp = ctx.prec + extra1 + xre = ctx.to_fixed(ctx._re(q), wp) + xim = ctx.to_fixed(ctx._im(q), wp) + x2re = (xre*xre - xim*xim) >> wp + x2im = (xre*xim) >> (wp-1) + are = bre = x2re + aim = bim = x2im + sre = (1< MIN: + bre, bim = (bre * x2re - bim * x2im) >> wp, \ + (bre * x2im + bim * x2re) >> wp + are, aim = (are * bre - aim * bim) >> wp, \ + (are * bim + aim * bre) >> wp + sre += are + sim += aim + sre = (sre << 1) + sim = (sim << 1) + sre = ctx.ldexp(sre, -wp) + sim = ctx.ldexp(sim, -wp) + s = ctx.mpc(sre, sim) + else: + if (not ctx._im(q)) and (not ctx._im(z)): + wp = ctx.prec + extra1 + x = ctx.to_fixed(ctx._re(q), wp) + x2 = (x*x) >> wp + a = b = x2 + c1, s1 = ctx.cos_sin(ctx._re(z), prec=wp) + cn = c1 = ctx.to_fixed(c1, wp) + sn = s1 = ctx.to_fixed(s1, wp) + c2 = (c1*c1 - s1*s1) >> wp + s2 = (c1 * s1) >> (wp - 1) + cn, sn = (cn*c2 - sn*s2) >> wp, (sn*c2 + cn*s2) >> wp + s = c1 + ((a * cn) >> wp) + while abs(a) > MIN: + b = (b*x2) >> wp + a = (a*b) >> wp + cn, sn = (cn*c2 - sn*s2) >> wp, (sn*c2 + cn*s2) >> wp + s += (a * cn) >> wp + s = (s << 1) + s = ctx.ldexp(s, -wp) + s *= ctx.nthroot(q, 4) + return s + # case z real, q complex + elif not ctx._im(z): + wp = ctx.prec + extra2 + xre = ctx.to_fixed(ctx._re(q), wp) + xim = ctx.to_fixed(ctx._im(q), wp) + x2re = (xre*xre - xim*xim) >> wp + x2im = (xre*xim) >> (wp - 1) + are = bre = x2re + aim = bim = x2im + c1, s1 = ctx.cos_sin(ctx._re(z), prec=wp) + cn = c1 = ctx.to_fixed(c1, wp) + sn = s1 = ctx.to_fixed(s1, wp) + c2 = (c1*c1 - s1*s1) >> wp + s2 = (c1 * s1) >> (wp - 1) + cn, sn = (cn*c2 - sn*s2) >> wp, (sn*c2 + cn*s2) >> wp + sre = c1 + ((are * cn) >> wp) + sim = ((aim * cn) >> wp) + while are**2 + aim**2 > MIN: + bre, bim = (bre * x2re - bim * x2im) >> wp, \ + (bre * x2im + bim * x2re) >> wp + are, aim = (are * bre - aim * bim) >> wp, \ + (are * bim + aim * bre) >> wp + cn, sn = (cn*c2 - sn*s2) >> wp, (sn*c2 + cn*s2) >> wp + sre += ((are * cn) >> wp) + sim += ((aim * cn) >> wp) + sre = (sre << 1) + sim = (sim << 1) + sre = ctx.ldexp(sre, -wp) + sim = ctx.ldexp(sim, -wp) + s = ctx.mpc(sre, sim) + #case z complex, q real + elif not ctx._im(q): + wp = ctx.prec + extra2 + x = ctx.to_fixed(ctx._re(q), wp) + x2 = (x*x) >> wp + a = b = x2 + prec0 = ctx.prec + ctx.prec = wp + c1, s1 = ctx.cos_sin(z) + ctx.prec = prec0 + cnre = c1re = ctx.to_fixed(ctx._re(c1), wp) + cnim = c1im = ctx.to_fixed(ctx._im(c1), wp) + snre = s1re = ctx.to_fixed(ctx._re(s1), wp) + snim = s1im = ctx.to_fixed(ctx._im(s1), wp) + #c2 = (c1*c1 - s1*s1) >> wp + c2re = (c1re*c1re - c1im*c1im - s1re*s1re + s1im*s1im) >> wp + c2im = (c1re*c1im - s1re*s1im) >> (wp - 1) + #s2 = (c1 * s1) >> (wp - 1) + s2re = (c1re*s1re - c1im*s1im) >> (wp - 1) + s2im = (c1re*s1im + c1im*s1re) >> (wp - 1) + #cn, sn = (cn*c2 - sn*s2) >> wp, (sn*c2 + cn*s2) >> wp + t1 = (cnre*c2re - cnim*c2im - snre*s2re + snim*s2im) >> wp + t2 = (cnre*c2im + cnim*c2re - snre*s2im - snim*s2re) >> wp + t3 = (snre*c2re - snim*c2im + cnre*s2re - cnim*s2im) >> wp + t4 = (snre*c2im + snim*c2re + cnre*s2im + cnim*s2re) >> wp + cnre = t1 + cnim = t2 + snre = t3 + snim = t4 + sre = c1re + ((a * cnre) >> wp) + sim = c1im + ((a * cnim) >> wp) + while abs(a) > MIN: + b = (b*x2) >> wp + a = (a*b) >> wp + t1 = (cnre*c2re - cnim*c2im - snre*s2re + snim*s2im) >> wp + t2 = (cnre*c2im + cnim*c2re - snre*s2im - snim*s2re) >> wp + t3 = (snre*c2re - snim*c2im + cnre*s2re - cnim*s2im) >> wp + t4 = (snre*c2im + snim*c2re + cnre*s2im + cnim*s2re) >> wp + cnre = t1 + cnim = t2 + snre = t3 + snim = t4 + sre += ((a * cnre) >> wp) + sim += ((a * cnim) >> wp) + sre = (sre << 1) + sim = (sim << 1) + sre = ctx.ldexp(sre, -wp) + sim = ctx.ldexp(sim, -wp) + s = ctx.mpc(sre, sim) + # case z and q complex + else: + wp = ctx.prec + extra2 + xre = ctx.to_fixed(ctx._re(q), wp) + xim = ctx.to_fixed(ctx._im(q), wp) + x2re = (xre*xre - xim*xim) >> wp + x2im = (xre*xim) >> (wp - 1) + are = bre = x2re + aim = bim = x2im + prec0 = ctx.prec + ctx.prec = wp + # cos(z), sin(z) with z complex + c1, s1 = ctx.cos_sin(z) + ctx.prec = prec0 + cnre = c1re = ctx.to_fixed(ctx._re(c1), wp) + cnim = c1im = ctx.to_fixed(ctx._im(c1), wp) + snre = s1re = ctx.to_fixed(ctx._re(s1), wp) + snim = s1im = ctx.to_fixed(ctx._im(s1), wp) + c2re = (c1re*c1re - c1im*c1im - s1re*s1re + s1im*s1im) >> wp + c2im = (c1re*c1im - s1re*s1im) >> (wp - 1) + s2re = (c1re*s1re - c1im*s1im) >> (wp - 1) + s2im = (c1re*s1im + c1im*s1re) >> (wp - 1) + t1 = (cnre*c2re - cnim*c2im - snre*s2re + snim*s2im) >> wp + t2 = (cnre*c2im + cnim*c2re - snre*s2im - snim*s2re) >> wp + t3 = (snre*c2re - snim*c2im + cnre*s2re - cnim*s2im) >> wp + t4 = (snre*c2im + snim*c2re + cnre*s2im + cnim*s2re) >> wp + cnre = t1 + cnim = t2 + snre = t3 + snim = t4 + n = 1 + termre = c1re + termim = c1im + sre = c1re + ((are * cnre - aim * cnim) >> wp) + sim = c1im + ((are * cnim + aim * cnre) >> wp) + n = 3 + termre = ((are * cnre - aim * cnim) >> wp) + termim = ((are * cnim + aim * cnre) >> wp) + sre = c1re + ((are * cnre - aim * cnim) >> wp) + sim = c1im + ((are * cnim + aim * cnre) >> wp) + n = 5 + while are**2 + aim**2 > MIN: + bre, bim = (bre * x2re - bim * x2im) >> wp, \ + (bre * x2im + bim * x2re) >> wp + are, aim = (are * bre - aim * bim) >> wp, \ + (are * bim + aim * bre) >> wp + #cn, sn = (cn*c1 - sn*s1) >> wp, (sn*c1 + cn*s1) >> wp + t1 = (cnre*c2re - cnim*c2im - snre*s2re + snim*s2im) >> wp + t2 = (cnre*c2im + cnim*c2re - snre*s2im - snim*s2re) >> wp + t3 = (snre*c2re - snim*c2im + cnre*s2re - cnim*s2im) >> wp + t4 = (snre*c2im + snim*c2re + cnre*s2im + cnim*s2re) >> wp + cnre = t1 + cnim = t2 + snre = t3 + snim = t4 + termre = ((are * cnre - aim * cnim) >> wp) + termim = ((aim * cnre + are * cnim) >> wp) + sre += ((are * cnre - aim * cnim) >> wp) + sim += ((aim * cnre + are * cnim) >> wp) + n += 2 + sre = (sre << 1) + sim = (sim << 1) + sre = ctx.ldexp(sre, -wp) + sim = ctx.ldexp(sim, -wp) + s = ctx.mpc(sre, sim) + s *= ctx.nthroot(q, 4) + return s + +@defun +def _djacobi_theta2(ctx, z, q, nd): + MIN = 2 + extra1 = 10 + extra2 = 20 + if (not ctx._im(q)) and (not ctx._im(z)): + wp = ctx.prec + extra1 + x = ctx.to_fixed(ctx._re(q), wp) + x2 = (x*x) >> wp + a = b = x2 + c1, s1 = ctx.cos_sin(ctx._re(z), prec=wp) + cn = c1 = ctx.to_fixed(c1, wp) + sn = s1 = ctx.to_fixed(s1, wp) + c2 = (c1*c1 - s1*s1) >> wp + s2 = (c1 * s1) >> (wp - 1) + cn, sn = (cn*c2 - sn*s2) >> wp, (sn*c2 + cn*s2) >> wp + if (nd&1): + s = s1 + ((a * sn * 3**nd) >> wp) + else: + s = c1 + ((a * cn * 3**nd) >> wp) + n = 2 + while abs(a) > MIN: + b = (b*x2) >> wp + a = (a*b) >> wp + cn, sn = (cn*c2 - sn*s2) >> wp, (sn*c2 + cn*s2) >> wp + if nd&1: + s += (a * sn * (2*n+1)**nd) >> wp + else: + s += (a * cn * (2*n+1)**nd) >> wp + n += 1 + s = -(s << 1) + s = ctx.ldexp(s, -wp) + # case z real, q complex + elif not ctx._im(z): + wp = ctx.prec + extra2 + xre = ctx.to_fixed(ctx._re(q), wp) + xim = ctx.to_fixed(ctx._im(q), wp) + x2re = (xre*xre - xim*xim) >> wp + x2im = (xre*xim) >> (wp - 1) + are = bre = x2re + aim = bim = x2im + c1, s1 = ctx.cos_sin(ctx._re(z), prec=wp) + cn = c1 = ctx.to_fixed(c1, wp) + sn = s1 = ctx.to_fixed(s1, wp) + c2 = (c1*c1 - s1*s1) >> wp + s2 = (c1 * s1) >> (wp - 1) + cn, sn = (cn*c2 - sn*s2) >> wp, (sn*c2 + cn*s2) >> wp + if (nd&1): + sre = s1 + ((are * sn * 3**nd) >> wp) + sim = ((aim * sn * 3**nd) >> wp) + else: + sre = c1 + ((are * cn * 3**nd) >> wp) + sim = ((aim * cn * 3**nd) >> wp) + n = 5 + while are**2 + aim**2 > MIN: + bre, bim = (bre * x2re - bim * x2im) >> wp, \ + (bre * x2im + bim * x2re) >> wp + are, aim = (are * bre - aim * bim) >> wp, \ + (are * bim + aim * bre) >> wp + cn, sn = (cn*c2 - sn*s2) >> wp, (sn*c2 + cn*s2) >> wp + + if (nd&1): + sre += ((are * sn * n**nd) >> wp) + sim += ((aim * sn * n**nd) >> wp) + else: + sre += ((are * cn * n**nd) >> wp) + sim += ((aim * cn * n**nd) >> wp) + n += 2 + sre = -(sre << 1) + sim = -(sim << 1) + sre = ctx.ldexp(sre, -wp) + sim = ctx.ldexp(sim, -wp) + s = ctx.mpc(sre, sim) + #case z complex, q real + elif not ctx._im(q): + wp = ctx.prec + extra2 + x = ctx.to_fixed(ctx._re(q), wp) + x2 = (x*x) >> wp + a = b = x2 + prec0 = ctx.prec + ctx.prec = wp + c1, s1 = ctx.cos_sin(z) + ctx.prec = prec0 + cnre = c1re = ctx.to_fixed(ctx._re(c1), wp) + cnim = c1im = ctx.to_fixed(ctx._im(c1), wp) + snre = s1re = ctx.to_fixed(ctx._re(s1), wp) + snim = s1im = ctx.to_fixed(ctx._im(s1), wp) + #c2 = (c1*c1 - s1*s1) >> wp + c2re = (c1re*c1re - c1im*c1im - s1re*s1re + s1im*s1im) >> wp + c2im = (c1re*c1im - s1re*s1im) >> (wp - 1) + #s2 = (c1 * s1) >> (wp - 1) + s2re = (c1re*s1re - c1im*s1im) >> (wp - 1) + s2im = (c1re*s1im + c1im*s1re) >> (wp - 1) + #cn, sn = (cn*c2 - sn*s2) >> wp, (sn*c2 + cn*s2) >> wp + t1 = (cnre*c2re - cnim*c2im - snre*s2re + snim*s2im) >> wp + t2 = (cnre*c2im + cnim*c2re - snre*s2im - snim*s2re) >> wp + t3 = (snre*c2re - snim*c2im + cnre*s2re - cnim*s2im) >> wp + t4 = (snre*c2im + snim*c2re + cnre*s2im + cnim*s2re) >> wp + cnre = t1 + cnim = t2 + snre = t3 + snim = t4 + if (nd&1): + sre = s1re + ((a * snre * 3**nd) >> wp) + sim = s1im + ((a * snim * 3**nd) >> wp) + else: + sre = c1re + ((a * cnre * 3**nd) >> wp) + sim = c1im + ((a * cnim * 3**nd) >> wp) + n = 5 + while abs(a) > MIN: + b = (b*x2) >> wp + a = (a*b) >> wp + t1 = (cnre*c2re - cnim*c2im - snre*s2re + snim*s2im) >> wp + t2 = (cnre*c2im + cnim*c2re - snre*s2im - snim*s2re) >> wp + t3 = (snre*c2re - snim*c2im + cnre*s2re - cnim*s2im) >> wp + t4 = (snre*c2im + snim*c2re + cnre*s2im + cnim*s2re) >> wp + cnre = t1 + cnim = t2 + snre = t3 + snim = t4 + if (nd&1): + sre += ((a * snre * n**nd) >> wp) + sim += ((a * snim * n**nd) >> wp) + else: + sre += ((a * cnre * n**nd) >> wp) + sim += ((a * cnim * n**nd) >> wp) + n += 2 + sre = -(sre << 1) + sim = -(sim << 1) + sre = ctx.ldexp(sre, -wp) + sim = ctx.ldexp(sim, -wp) + s = ctx.mpc(sre, sim) + # case z and q complex + else: + wp = ctx.prec + extra2 + xre = ctx.to_fixed(ctx._re(q), wp) + xim = ctx.to_fixed(ctx._im(q), wp) + x2re = (xre*xre - xim*xim) >> wp + x2im = (xre*xim) >> (wp - 1) + are = bre = x2re + aim = bim = x2im + prec0 = ctx.prec + ctx.prec = wp + # cos(2*z), sin(2*z) with z complex + c1, s1 = ctx.cos_sin(z) + ctx.prec = prec0 + cnre = c1re = ctx.to_fixed(ctx._re(c1), wp) + cnim = c1im = ctx.to_fixed(ctx._im(c1), wp) + snre = s1re = ctx.to_fixed(ctx._re(s1), wp) + snim = s1im = ctx.to_fixed(ctx._im(s1), wp) + c2re = (c1re*c1re - c1im*c1im - s1re*s1re + s1im*s1im) >> wp + c2im = (c1re*c1im - s1re*s1im) >> (wp - 1) + s2re = (c1re*s1re - c1im*s1im) >> (wp - 1) + s2im = (c1re*s1im + c1im*s1re) >> (wp - 1) + t1 = (cnre*c2re - cnim*c2im - snre*s2re + snim*s2im) >> wp + t2 = (cnre*c2im + cnim*c2re - snre*s2im - snim*s2re) >> wp + t3 = (snre*c2re - snim*c2im + cnre*s2re - cnim*s2im) >> wp + t4 = (snre*c2im + snim*c2re + cnre*s2im + cnim*s2re) >> wp + cnre = t1 + cnim = t2 + snre = t3 + snim = t4 + if (nd&1): + sre = s1re + (((are * snre - aim * snim) * 3**nd) >> wp) + sim = s1im + (((are * snim + aim * snre)* 3**nd) >> wp) + else: + sre = c1re + (((are * cnre - aim * cnim) * 3**nd) >> wp) + sim = c1im + (((are * cnim + aim * cnre)* 3**nd) >> wp) + n = 5 + while are**2 + aim**2 > MIN: + bre, bim = (bre * x2re - bim * x2im) >> wp, \ + (bre * x2im + bim * x2re) >> wp + are, aim = (are * bre - aim * bim) >> wp, \ + (are * bim + aim * bre) >> wp + #cn, sn = (cn*c1 - sn*s1) >> wp, (sn*c1 + cn*s1) >> wp + t1 = (cnre*c2re - cnim*c2im - snre*s2re + snim*s2im) >> wp + t2 = (cnre*c2im + cnim*c2re - snre*s2im - snim*s2re) >> wp + t3 = (snre*c2re - snim*c2im + cnre*s2re - cnim*s2im) >> wp + t4 = (snre*c2im + snim*c2re + cnre*s2im + cnim*s2re) >> wp + cnre = t1 + cnim = t2 + snre = t3 + snim = t4 + if (nd&1): + sre += (((are * snre - aim * snim) * n**nd) >> wp) + sim += (((aim * snre + are * snim) * n**nd) >> wp) + else: + sre += (((are * cnre - aim * cnim) * n**nd) >> wp) + sim += (((aim * cnre + are * cnim) * n**nd) >> wp) + n += 2 + sre = -(sre << 1) + sim = -(sim << 1) + sre = ctx.ldexp(sre, -wp) + sim = ctx.ldexp(sim, -wp) + s = ctx.mpc(sre, sim) + s *= ctx.nthroot(q, 4) + if (nd&1): + return (-1)**(nd//2) * s + else: + return (-1)**(1 + nd//2) * s + +@defun +def _jacobi_theta3(ctx, z, q): + extra1 = 10 + extra2 = 20 + MIN = 2 + if z == ctx.zero: + if not ctx._im(q): + wp = ctx.prec + extra1 + x = ctx.to_fixed(ctx._re(q), wp) + s = x + a = b = x + x2 = (x*x) >> wp + while abs(a) > MIN: + b = (b*x2) >> wp + a = (a*b) >> wp + s += a + s = (1 << wp) + (s << 1) + s = ctx.ldexp(s, -wp) + return s + else: + wp = ctx.prec + extra1 + xre = ctx.to_fixed(ctx._re(q), wp) + xim = ctx.to_fixed(ctx._im(q), wp) + x2re = (xre*xre - xim*xim) >> wp + x2im = (xre*xim) >> (wp - 1) + sre = are = bre = xre + sim = aim = bim = xim + while are**2 + aim**2 > MIN: + bre, bim = (bre * x2re - bim * x2im) >> wp, \ + (bre * x2im + bim * x2re) >> wp + are, aim = (are * bre - aim * bim) >> wp, \ + (are * bim + aim * bre) >> wp + sre += are + sim += aim + sre = (1 << wp) + (sre << 1) + sim = (sim << 1) + sre = ctx.ldexp(sre, -wp) + sim = ctx.ldexp(sim, -wp) + s = ctx.mpc(sre, sim) + return s + else: + if (not ctx._im(q)) and (not ctx._im(z)): + s = 0 + wp = ctx.prec + extra1 + x = ctx.to_fixed(ctx._re(q), wp) + a = b = x + x2 = (x*x) >> wp + c1, s1 = ctx.cos_sin(ctx._re(z)*2, prec=wp) + c1 = ctx.to_fixed(c1, wp) + s1 = ctx.to_fixed(s1, wp) + cn = c1 + sn = s1 + s += (a * cn) >> wp + while abs(a) > MIN: + b = (b*x2) >> wp + a = (a*b) >> wp + cn, sn = (cn*c1 - sn*s1) >> wp, (sn*c1 + cn*s1) >> wp + s += (a * cn) >> wp + s = (1 << wp) + (s << 1) + s = ctx.ldexp(s, -wp) + return s + # case z real, q complex + elif not ctx._im(z): + wp = ctx.prec + extra2 + xre = ctx.to_fixed(ctx._re(q), wp) + xim = ctx.to_fixed(ctx._im(q), wp) + x2re = (xre*xre - xim*xim) >> wp + x2im = (xre*xim) >> (wp - 1) + are = bre = xre + aim = bim = xim + c1, s1 = ctx.cos_sin(ctx._re(z)*2, prec=wp) + c1 = ctx.to_fixed(c1, wp) + s1 = ctx.to_fixed(s1, wp) + cn = c1 + sn = s1 + sre = (are * cn) >> wp + sim = (aim * cn) >> wp + while are**2 + aim**2 > MIN: + bre, bim = (bre * x2re - bim * x2im) >> wp, \ + (bre * x2im + bim * x2re) >> wp + are, aim = (are * bre - aim * bim) >> wp, \ + (are * bim + aim * bre) >> wp + cn, sn = (cn*c1 - sn*s1) >> wp, (sn*c1 + cn*s1) >> wp + sre += (are * cn) >> wp + sim += (aim * cn) >> wp + sre = (1 << wp) + (sre << 1) + sim = (sim << 1) + sre = ctx.ldexp(sre, -wp) + sim = ctx.ldexp(sim, -wp) + s = ctx.mpc(sre, sim) + return s + #case z complex, q real + elif not ctx._im(q): + wp = ctx.prec + extra2 + x = ctx.to_fixed(ctx._re(q), wp) + a = b = x + x2 = (x*x) >> wp + prec0 = ctx.prec + ctx.prec = wp + c1, s1 = ctx.cos_sin(2*z) + ctx.prec = prec0 + cnre = c1re = ctx.to_fixed(ctx._re(c1), wp) + cnim = c1im = ctx.to_fixed(ctx._im(c1), wp) + snre = s1re = ctx.to_fixed(ctx._re(s1), wp) + snim = s1im = ctx.to_fixed(ctx._im(s1), wp) + sre = (a * cnre) >> wp + sim = (a * cnim) >> wp + while abs(a) > MIN: + b = (b*x2) >> wp + a = (a*b) >> wp + t1 = (cnre*c1re - cnim*c1im - snre*s1re + snim*s1im) >> wp + t2 = (cnre*c1im + cnim*c1re - snre*s1im - snim*s1re) >> wp + t3 = (snre*c1re - snim*c1im + cnre*s1re - cnim*s1im) >> wp + t4 = (snre*c1im + snim*c1re + cnre*s1im + cnim*s1re) >> wp + cnre = t1 + cnim = t2 + snre = t3 + snim = t4 + sre += (a * cnre) >> wp + sim += (a * cnim) >> wp + sre = (1 << wp) + (sre << 1) + sim = (sim << 1) + sre = ctx.ldexp(sre, -wp) + sim = ctx.ldexp(sim, -wp) + s = ctx.mpc(sre, sim) + return s + # case z and q complex + else: + wp = ctx.prec + extra2 + xre = ctx.to_fixed(ctx._re(q), wp) + xim = ctx.to_fixed(ctx._im(q), wp) + x2re = (xre*xre - xim*xim) >> wp + x2im = (xre*xim) >> (wp - 1) + are = bre = xre + aim = bim = xim + prec0 = ctx.prec + ctx.prec = wp + # cos(2*z), sin(2*z) with z complex + c1, s1 = ctx.cos_sin(2*z) + ctx.prec = prec0 + cnre = c1re = ctx.to_fixed(ctx._re(c1), wp) + cnim = c1im = ctx.to_fixed(ctx._im(c1), wp) + snre = s1re = ctx.to_fixed(ctx._re(s1), wp) + snim = s1im = ctx.to_fixed(ctx._im(s1), wp) + sre = (are * cnre - aim * cnim) >> wp + sim = (aim * cnre + are * cnim) >> wp + while are**2 + aim**2 > MIN: + bre, bim = (bre * x2re - bim * x2im) >> wp, \ + (bre * x2im + bim * x2re) >> wp + are, aim = (are * bre - aim * bim) >> wp, \ + (are * bim + aim * bre) >> wp + t1 = (cnre*c1re - cnim*c1im - snre*s1re + snim*s1im) >> wp + t2 = (cnre*c1im + cnim*c1re - snre*s1im - snim*s1re) >> wp + t3 = (snre*c1re - snim*c1im + cnre*s1re - cnim*s1im) >> wp + t4 = (snre*c1im + snim*c1re + cnre*s1im + cnim*s1re) >> wp + cnre = t1 + cnim = t2 + snre = t3 + snim = t4 + sre += (are * cnre - aim * cnim) >> wp + sim += (aim * cnre + are * cnim) >> wp + sre = (1 << wp) + (sre << 1) + sim = (sim << 1) + sre = ctx.ldexp(sre, -wp) + sim = ctx.ldexp(sim, -wp) + s = ctx.mpc(sre, sim) + return s + +@defun +def _djacobi_theta3(ctx, z, q, nd): + """nd=1,2,3 order of the derivative with respect to z""" + MIN = 2 + extra1 = 10 + extra2 = 20 + if (not ctx._im(q)) and (not ctx._im(z)): + s = 0 + wp = ctx.prec + extra1 + x = ctx.to_fixed(ctx._re(q), wp) + a = b = x + x2 = (x*x) >> wp + c1, s1 = ctx.cos_sin(ctx._re(z)*2, prec=wp) + c1 = ctx.to_fixed(c1, wp) + s1 = ctx.to_fixed(s1, wp) + cn = c1 + sn = s1 + if (nd&1): + s += (a * sn) >> wp + else: + s += (a * cn) >> wp + n = 2 + while abs(a) > MIN: + b = (b*x2) >> wp + a = (a*b) >> wp + cn, sn = (cn*c1 - sn*s1) >> wp, (sn*c1 + cn*s1) >> wp + if nd&1: + s += (a * sn * n**nd) >> wp + else: + s += (a * cn * n**nd) >> wp + n += 1 + s = -(s << (nd+1)) + s = ctx.ldexp(s, -wp) + # case z real, q complex + elif not ctx._im(z): + wp = ctx.prec + extra2 + xre = ctx.to_fixed(ctx._re(q), wp) + xim = ctx.to_fixed(ctx._im(q), wp) + x2re = (xre*xre - xim*xim) >> wp + x2im = (xre*xim) >> (wp - 1) + are = bre = xre + aim = bim = xim + c1, s1 = ctx.cos_sin(ctx._re(z)*2, prec=wp) + c1 = ctx.to_fixed(c1, wp) + s1 = ctx.to_fixed(s1, wp) + cn = c1 + sn = s1 + if (nd&1): + sre = (are * sn) >> wp + sim = (aim * sn) >> wp + else: + sre = (are * cn) >> wp + sim = (aim * cn) >> wp + n = 2 + while are**2 + aim**2 > MIN: + bre, bim = (bre * x2re - bim * x2im) >> wp, \ + (bre * x2im + bim * x2re) >> wp + are, aim = (are * bre - aim * bim) >> wp, \ + (are * bim + aim * bre) >> wp + cn, sn = (cn*c1 - sn*s1) >> wp, (sn*c1 + cn*s1) >> wp + if nd&1: + sre += (are * sn * n**nd) >> wp + sim += (aim * sn * n**nd) >> wp + else: + sre += (are * cn * n**nd) >> wp + sim += (aim * cn * n**nd) >> wp + n += 1 + sre = -(sre << (nd+1)) + sim = -(sim << (nd+1)) + sre = ctx.ldexp(sre, -wp) + sim = ctx.ldexp(sim, -wp) + s = ctx.mpc(sre, sim) + #case z complex, q real + elif not ctx._im(q): + wp = ctx.prec + extra2 + x = ctx.to_fixed(ctx._re(q), wp) + a = b = x + x2 = (x*x) >> wp + prec0 = ctx.prec + ctx.prec = wp + c1, s1 = ctx.cos_sin(2*z) + ctx.prec = prec0 + cnre = c1re = ctx.to_fixed(ctx._re(c1), wp) + cnim = c1im = ctx.to_fixed(ctx._im(c1), wp) + snre = s1re = ctx.to_fixed(ctx._re(s1), wp) + snim = s1im = ctx.to_fixed(ctx._im(s1), wp) + if (nd&1): + sre = (a * snre) >> wp + sim = (a * snim) >> wp + else: + sre = (a * cnre) >> wp + sim = (a * cnim) >> wp + n = 2 + while abs(a) > MIN: + b = (b*x2) >> wp + a = (a*b) >> wp + t1 = (cnre*c1re - cnim*c1im - snre*s1re + snim*s1im) >> wp + t2 = (cnre*c1im + cnim*c1re - snre*s1im - snim*s1re) >> wp + t3 = (snre*c1re - snim*c1im + cnre*s1re - cnim*s1im) >> wp + t4 = (snre*c1im + snim*c1re + cnre*s1im + cnim*s1re) >> wp + cnre = t1 + cnim = t2 + snre = t3 + snim = t4 + if (nd&1): + sre += (a * snre * n**nd) >> wp + sim += (a * snim * n**nd) >> wp + else: + sre += (a * cnre * n**nd) >> wp + sim += (a * cnim * n**nd) >> wp + n += 1 + sre = -(sre << (nd+1)) + sim = -(sim << (nd+1)) + sre = ctx.ldexp(sre, -wp) + sim = ctx.ldexp(sim, -wp) + s = ctx.mpc(sre, sim) + # case z and q complex + else: + wp = ctx.prec + extra2 + xre = ctx.to_fixed(ctx._re(q), wp) + xim = ctx.to_fixed(ctx._im(q), wp) + x2re = (xre*xre - xim*xim) >> wp + x2im = (xre*xim) >> (wp - 1) + are = bre = xre + aim = bim = xim + prec0 = ctx.prec + ctx.prec = wp + # cos(2*z), sin(2*z) with z complex + c1, s1 = ctx.cos_sin(2*z) + ctx.prec = prec0 + cnre = c1re = ctx.to_fixed(ctx._re(c1), wp) + cnim = c1im = ctx.to_fixed(ctx._im(c1), wp) + snre = s1re = ctx.to_fixed(ctx._re(s1), wp) + snim = s1im = ctx.to_fixed(ctx._im(s1), wp) + if (nd&1): + sre = (are * snre - aim * snim) >> wp + sim = (aim * snre + are * snim) >> wp + else: + sre = (are * cnre - aim * cnim) >> wp + sim = (aim * cnre + are * cnim) >> wp + n = 2 + while are**2 + aim**2 > MIN: + bre, bim = (bre * x2re - bim * x2im) >> wp, \ + (bre * x2im + bim * x2re) >> wp + are, aim = (are * bre - aim * bim) >> wp, \ + (are * bim + aim * bre) >> wp + t1 = (cnre*c1re - cnim*c1im - snre*s1re + snim*s1im) >> wp + t2 = (cnre*c1im + cnim*c1re - snre*s1im - snim*s1re) >> wp + t3 = (snre*c1re - snim*c1im + cnre*s1re - cnim*s1im) >> wp + t4 = (snre*c1im + snim*c1re + cnre*s1im + cnim*s1re) >> wp + cnre = t1 + cnim = t2 + snre = t3 + snim = t4 + if(nd&1): + sre += ((are * snre - aim * snim) * n**nd) >> wp + sim += ((aim * snre + are * snim) * n**nd) >> wp + else: + sre += ((are * cnre - aim * cnim) * n**nd) >> wp + sim += ((aim * cnre + are * cnim) * n**nd) >> wp + n += 1 + sre = -(sre << (nd+1)) + sim = -(sim << (nd+1)) + sre = ctx.ldexp(sre, -wp) + sim = ctx.ldexp(sim, -wp) + s = ctx.mpc(sre, sim) + if (nd&1): + return (-1)**(nd//2) * s + else: + return (-1)**(1 + nd//2) * s + +@defun +def _jacobi_theta2a(ctx, z, q): + """ + case ctx._im(z) != 0 + theta(2, z, q) = + q**1/4 * Sum(q**(n*n + n) * exp(j*(2*n + 1)*z), n=-inf, inf) + max term for minimum (2*n+1)*log(q).real - 2* ctx._im(z) + n0 = int(ctx._im(z)/log(q).real - 1/2) + theta(2, z, q) = + q**1/4 * Sum(q**(n*n + n) * exp(j*(2*n + 1)*z), n=n0, inf) + + q**1/4 * Sum(q**(n*n + n) * exp(j*(2*n + 1)*z), n, n0-1, -inf) + """ + n = n0 = int(ctx._im(z)/ctx._re(ctx.log(q)) - 1/2) + e2 = ctx.expj(2*z) + e = e0 = ctx.expj((2*n+1)*z) + a = q**(n*n + n) + # leading term + term = a * e + s = term + eps1 = ctx.eps*abs(term) + while 1: + n += 1 + e = e * e2 + term = q**(n*n + n) * e + if abs(term) < eps1: + break + s += term + e = e0 + e2 = ctx.expj(-2*z) + n = n0 + while 1: + n -= 1 + e = e * e2 + term = q**(n*n + n) * e + if abs(term) < eps1: + break + s += term + s = s * ctx.nthroot(q, 4) + return s + +@defun +def _jacobi_theta3a(ctx, z, q): + """ + case ctx._im(z) != 0 + theta3(z, q) = Sum(q**(n*n) * exp(j*2*n*z), n, -inf, inf) + max term for n*abs(log(q).real) + ctx._im(z) ~= 0 + n0 = int(- ctx._im(z)/abs(log(q).real)) + """ + n = n0 = int(-ctx._im(z)/abs(ctx._re(ctx.log(q)))) + e2 = ctx.expj(2*z) + e = e0 = ctx.expj(2*n*z) + s = term = q**(n*n) * e + eps1 = ctx.eps*abs(term) + while 1: + n += 1 + e = e * e2 + term = q**(n*n) * e + if abs(term) < eps1: + break + s += term + e = e0 + e2 = ctx.expj(-2*z) + n = n0 + while 1: + n -= 1 + e = e * e2 + term = q**(n*n) * e + if abs(term) < eps1: + break + s += term + return s + +@defun +def _djacobi_theta2a(ctx, z, q, nd): + """ + case ctx._im(z) != 0 + dtheta(2, z, q, nd) = + j* q**1/4 * Sum(q**(n*n + n) * (2*n+1)*exp(j*(2*n + 1)*z), n=-inf, inf) + max term for (2*n0+1)*log(q).real - 2* ctx._im(z) ~= 0 + n0 = int(ctx._im(z)/log(q).real - 1/2) + """ + n = n0 = int(ctx._im(z)/ctx._re(ctx.log(q)) - 1/2) + e2 = ctx.expj(2*z) + e = e0 = ctx.expj((2*n + 1)*z) + a = q**(n*n + n) + # leading term + term = (2*n+1)**nd * a * e + s = term + eps1 = ctx.eps*abs(term) + while 1: + n += 1 + e = e * e2 + term = (2*n+1)**nd * q**(n*n + n) * e + if abs(term) < eps1: + break + s += term + e = e0 + e2 = ctx.expj(-2*z) + n = n0 + while 1: + n -= 1 + e = e * e2 + term = (2*n+1)**nd * q**(n*n + n) * e + if abs(term) < eps1: + break + s += term + return ctx.j**nd * s * ctx.nthroot(q, 4) + +@defun +def _djacobi_theta3a(ctx, z, q, nd): + """ + case ctx._im(z) != 0 + djtheta3(z, q, nd) = (2*j)**nd * + Sum(q**(n*n) * n**nd * exp(j*2*n*z), n, -inf, inf) + max term for minimum n*abs(log(q).real) + ctx._im(z) + """ + n = n0 = int(-ctx._im(z)/abs(ctx._re(ctx.log(q)))) + e2 = ctx.expj(2*z) + e = e0 = ctx.expj(2*n*z) + a = q**(n*n) * e + s = term = n**nd * a + if n != 0: + eps1 = ctx.eps*abs(term) + else: + eps1 = ctx.eps*abs(a) + while 1: + n += 1 + e = e * e2 + a = q**(n*n) * e + term = n**nd * a + if n != 0: + aterm = abs(term) + else: + aterm = abs(a) + if aterm < eps1: + break + s += term + e = e0 + e2 = ctx.expj(-2*z) + n = n0 + while 1: + n -= 1 + e = e * e2 + a = q**(n*n) * e + term = n**nd * a + if n != 0: + aterm = abs(term) + else: + aterm = abs(a) + if aterm < eps1: + break + s += term + return (2*ctx.j)**nd * s + +@defun +def jtheta(ctx, n, z, q, derivative=0): + if derivative: + return ctx._djtheta(n, z, q, derivative) + + z = ctx.convert(z) + q = ctx.convert(q) + + # Implementation note + # If ctx._im(z) is close to zero, _jacobi_theta2 and _jacobi_theta3 + # are used, + # which compute the series starting from n=0 using fixed precision + # numbers; + # otherwise _jacobi_theta2a and _jacobi_theta3a are used, which compute + # the series starting from n=n0, which is the largest term. + + # TODO: write _jacobi_theta2a and _jacobi_theta3a using fixed-point + + if abs(q) > ctx.THETA_Q_LIM: + raise ValueError('abs(q) > THETA_Q_LIM = %f' % ctx.THETA_Q_LIM) + + extra = 10 + if z: + M = ctx.mag(z) + if M > 5 or (n == 1 and M < -5): + extra += 2*abs(M) + cz = 0.5 + extra2 = 50 + prec0 = ctx.prec + try: + ctx.prec += extra + if n == 1: + if ctx._im(z): + if abs(ctx._im(z)) < cz * abs(ctx._re(ctx.log(q))): + ctx.dps += extra2 + res = ctx._jacobi_theta2(z - ctx.pi/2, q) + else: + ctx.dps += 10 + res = ctx._jacobi_theta2a(z - ctx.pi/2, q) + else: + res = ctx._jacobi_theta2(z - ctx.pi/2, q) + elif n == 2: + if ctx._im(z): + if abs(ctx._im(z)) < cz * abs(ctx._re(ctx.log(q))): + ctx.dps += extra2 + res = ctx._jacobi_theta2(z, q) + else: + ctx.dps += 10 + res = ctx._jacobi_theta2a(z, q) + else: + res = ctx._jacobi_theta2(z, q) + elif n == 3: + if ctx._im(z): + if abs(ctx._im(z)) < cz * abs(ctx._re(ctx.log(q))): + ctx.dps += extra2 + res = ctx._jacobi_theta3(z, q) + else: + ctx.dps += 10 + res = ctx._jacobi_theta3a(z, q) + else: + res = ctx._jacobi_theta3(z, q) + elif n == 4: + if ctx._im(z): + if abs(ctx._im(z)) < cz * abs(ctx._re(ctx.log(q))): + ctx.dps += extra2 + res = ctx._jacobi_theta3(z, -q) + else: + ctx.dps += 10 + res = ctx._jacobi_theta3a(z, -q) + else: + res = ctx._jacobi_theta3(z, -q) + else: + raise ValueError + finally: + ctx.prec = prec0 + return res + +@defun +def _djtheta(ctx, n, z, q, derivative=1): + z = ctx.convert(z) + q = ctx.convert(q) + nd = int(derivative) + + if abs(q) > ctx.THETA_Q_LIM: + raise ValueError('abs(q) > THETA_Q_LIM = %f' % ctx.THETA_Q_LIM) + extra = 10 + ctx.prec * nd // 10 + if z: + M = ctx.mag(z) + if M > 5 or (n != 1 and M < -5): + extra += 2*abs(M) + cz = 0.5 + extra2 = 50 + prec0 = ctx.prec + try: + ctx.prec += extra + if n == 1: + if ctx._im(z): + if abs(ctx._im(z)) < cz * abs(ctx._re(ctx.log(q))): + ctx.dps += extra2 + res = ctx._djacobi_theta2(z - ctx.pi/2, q, nd) + else: + ctx.dps += 10 + res = ctx._djacobi_theta2a(z - ctx.pi/2, q, nd) + else: + res = ctx._djacobi_theta2(z - ctx.pi/2, q, nd) + elif n == 2: + if ctx._im(z): + if abs(ctx._im(z)) < cz * abs(ctx._re(ctx.log(q))): + ctx.dps += extra2 + res = ctx._djacobi_theta2(z, q, nd) + else: + ctx.dps += 10 + res = ctx._djacobi_theta2a(z, q, nd) + else: + res = ctx._djacobi_theta2(z, q, nd) + elif n == 3: + if ctx._im(z): + if abs(ctx._im(z)) < cz * abs(ctx._re(ctx.log(q))): + ctx.dps += extra2 + res = ctx._djacobi_theta3(z, q, nd) + else: + ctx.dps += 10 + res = ctx._djacobi_theta3a(z, q, nd) + else: + res = ctx._djacobi_theta3(z, q, nd) + elif n == 4: + if ctx._im(z): + if abs(ctx._im(z)) < cz * abs(ctx._re(ctx.log(q))): + ctx.dps += extra2 + res = ctx._djacobi_theta3(z, -q, nd) + else: + ctx.dps += 10 + res = ctx._djacobi_theta3a(z, -q, nd) + else: + res = ctx._djacobi_theta3(z, -q, nd) + else: + raise ValueError + finally: + ctx.prec = prec0 + return +res diff --git a/venv/lib/python3.12/site-packages/mpmath/functions/zeta.py b/venv/lib/python3.12/site-packages/mpmath/functions/zeta.py new file mode 100644 index 0000000000000000000000000000000000000000..d7ede50d95e5b6eff511619620c934529942cbdd --- /dev/null +++ b/venv/lib/python3.12/site-packages/mpmath/functions/zeta.py @@ -0,0 +1,1154 @@ +from __future__ import print_function + +from ..libmp.backend import xrange +from .functions import defun, defun_wrapped, defun_static + +@defun +def stieltjes(ctx, n, a=1): + n = ctx.convert(n) + a = ctx.convert(a) + if n < 0: + return ctx.bad_domain("Stieltjes constants defined for n >= 0") + if hasattr(ctx, "stieltjes_cache"): + stieltjes_cache = ctx.stieltjes_cache + else: + stieltjes_cache = ctx.stieltjes_cache = {} + if a == 1: + if n == 0: + return +ctx.euler + if n in stieltjes_cache: + prec, s = stieltjes_cache[n] + if prec >= ctx.prec: + return +s + mag = 1 + def f(x): + xa = x/a + v = (xa-ctx.j)*ctx.ln(a-ctx.j*x)**n/(1+xa**2)/(ctx.exp(2*ctx.pi*x)-1) + return ctx._re(v) / mag + orig = ctx.prec + try: + # Normalize integrand by approx. magnitude to + # speed up quadrature (which uses absolute error) + if n > 50: + ctx.prec = 20 + mag = ctx.quad(f, [0,ctx.inf], maxdegree=3) + ctx.prec = orig + 10 + int(n**0.5) + s = ctx.quad(f, [0,ctx.inf], maxdegree=20) + v = ctx.ln(a)**n/(2*a) - ctx.ln(a)**(n+1)/(n+1) + 2*s/a*mag + finally: + ctx.prec = orig + if a == 1 and ctx.isint(n): + stieltjes_cache[n] = (ctx.prec, v) + return +v + +@defun_wrapped +def siegeltheta(ctx, t, derivative=0): + d = int(derivative) + if (t == ctx.inf or t == ctx.ninf): + if d < 2: + if t == ctx.ninf and d == 0: + return ctx.ninf + return ctx.inf + else: + return ctx.zero + if d == 0: + if ctx._im(t): + # XXX: cancellation occurs + a = ctx.loggamma(0.25+0.5j*t) + b = ctx.loggamma(0.25-0.5j*t) + return -ctx.ln(ctx.pi)/2*t - 0.5j*(a-b) + else: + if ctx.isinf(t): + return t + return ctx._im(ctx.loggamma(0.25+0.5j*t)) - ctx.ln(ctx.pi)/2*t + if d > 0: + a = (-0.5j)**(d-1)*ctx.polygamma(d-1, 0.25-0.5j*t) + b = (0.5j)**(d-1)*ctx.polygamma(d-1, 0.25+0.5j*t) + if ctx._im(t): + if d == 1: + return -0.5*ctx.log(ctx.pi)+0.25*(a+b) + else: + return 0.25*(a+b) + else: + if d == 1: + return ctx._re(-0.5*ctx.log(ctx.pi)+0.25*(a+b)) + else: + return ctx._re(0.25*(a+b)) + +@defun_wrapped +def grampoint(ctx, n): + # asymptotic expansion, from + # http://mathworld.wolfram.com/GramPoint.html + g = 2*ctx.pi*ctx.exp(1+ctx.lambertw((8*n+1)/(8*ctx.e))) + return ctx.findroot(lambda t: ctx.siegeltheta(t)-ctx.pi*n, g) + + +@defun_wrapped +def siegelz(ctx, t, **kwargs): + d = int(kwargs.get("derivative", 0)) + t = ctx.convert(t) + t1 = ctx._re(t) + t2 = ctx._im(t) + prec = ctx.prec + try: + if abs(t1) > 500*prec and t2**2 < t1: + v = ctx.rs_z(t, d) + if ctx._is_real_type(t): + return ctx._re(v) + return v + except NotImplementedError: + pass + ctx.prec += 21 + e1 = ctx.expj(ctx.siegeltheta(t)) + z = ctx.zeta(0.5+ctx.j*t) + if d == 0: + v = e1*z + ctx.prec=prec + if ctx._is_real_type(t): + return ctx._re(v) + return +v + z1 = ctx.zeta(0.5+ctx.j*t, derivative=1) + theta1 = ctx.siegeltheta(t, derivative=1) + if d == 1: + v = ctx.j*e1*(z1+z*theta1) + ctx.prec=prec + if ctx._is_real_type(t): + return ctx._re(v) + return +v + z2 = ctx.zeta(0.5+ctx.j*t, derivative=2) + theta2 = ctx.siegeltheta(t, derivative=2) + comb1 = theta1**2-ctx.j*theta2 + if d == 2: + def terms(): + return [2*z1*theta1, z2, z*comb1] + v = ctx.sum_accurately(terms, 1) + v = -e1*v + ctx.prec = prec + if ctx._is_real_type(t): + return ctx._re(v) + return +v + ctx.prec += 10 + z3 = ctx.zeta(0.5+ctx.j*t, derivative=3) + theta3 = ctx.siegeltheta(t, derivative=3) + comb2 = theta1**3-3*ctx.j*theta1*theta2-theta3 + if d == 3: + def terms(): + return [3*theta1*z2, 3*z1*comb1, z3+z*comb2] + v = ctx.sum_accurately(terms, 1) + v = -ctx.j*e1*v + ctx.prec = prec + if ctx._is_real_type(t): + return ctx._re(v) + return +v + z4 = ctx.zeta(0.5+ctx.j*t, derivative=4) + theta4 = ctx.siegeltheta(t, derivative=4) + def terms(): + return [theta1**4, -6*ctx.j*theta1**2*theta2, -3*theta2**2, + -4*theta1*theta3, ctx.j*theta4] + comb3 = ctx.sum_accurately(terms, 1) + if d == 4: + def terms(): + return [6*theta1**2*z2, -6*ctx.j*z2*theta2, 4*theta1*z3, + 4*z1*comb2, z4, z*comb3] + v = ctx.sum_accurately(terms, 1) + v = e1*v + ctx.prec = prec + if ctx._is_real_type(t): + return ctx._re(v) + return +v + if d > 4: + h = lambda x: ctx.siegelz(x, derivative=4) + return ctx.diff(h, t, n=d-4) + + +_zeta_zeros = [ +14.134725142,21.022039639,25.010857580,30.424876126,32.935061588, +37.586178159,40.918719012,43.327073281,48.005150881,49.773832478, +52.970321478,56.446247697,59.347044003,60.831778525,65.112544048, +67.079810529,69.546401711,72.067157674,75.704690699,77.144840069, +79.337375020,82.910380854,84.735492981,87.425274613,88.809111208, +92.491899271,94.651344041,95.870634228,98.831194218,101.317851006, +103.725538040,105.446623052,107.168611184,111.029535543,111.874659177, +114.320220915,116.226680321,118.790782866,121.370125002,122.946829294, +124.256818554,127.516683880,129.578704200,131.087688531,133.497737203, +134.756509753,138.116042055,139.736208952,141.123707404,143.111845808, +146.000982487,147.422765343,150.053520421,150.925257612,153.024693811, +156.112909294,157.597591818,158.849988171,161.188964138,163.030709687, +165.537069188,167.184439978,169.094515416,169.911976479,173.411536520, +174.754191523,176.441434298,178.377407776,179.916484020,182.207078484, +184.874467848,185.598783678,187.228922584,189.416158656,192.026656361, +193.079726604,195.265396680,196.876481841,198.015309676,201.264751944, +202.493594514,204.189671803,205.394697202,207.906258888,209.576509717, +211.690862595,213.347919360,214.547044783,216.169538508,219.067596349, +220.714918839,221.430705555,224.007000255,224.983324670,227.421444280, +229.337413306,231.250188700,231.987235253,233.693404179,236.524229666, +] + +def _load_zeta_zeros(url): + import urllib + d = urllib.urlopen(url) + L = [float(x) for x in d.readlines()] + # Sanity check + assert round(L[0]) == 14 + _zeta_zeros[:] = L + +@defun +def oldzetazero(ctx, n, url='http://www.dtc.umn.edu/~odlyzko/zeta_tables/zeros1'): + n = int(n) + if n < 0: + return ctx.zetazero(-n).conjugate() + if n == 0: + raise ValueError("n must be nonzero") + if n > len(_zeta_zeros) and n <= 100000: + _load_zeta_zeros(url) + if n > len(_zeta_zeros): + raise NotImplementedError("n too large for zetazeros") + return ctx.mpc(0.5, ctx.findroot(ctx.siegelz, _zeta_zeros[n-1])) + +@defun_wrapped +def riemannr(ctx, x): + if x == 0: + return ctx.zero + # Check if a simple asymptotic estimate is accurate enough + if abs(x) > 1000: + a = ctx.li(x) + b = 0.5*ctx.li(ctx.sqrt(x)) + if abs(b) < abs(a)*ctx.eps: + return a + if abs(x) < 0.01: + # XXX + ctx.prec += int(-ctx.log(abs(x),2)) + # Sum Gram's series + s = t = ctx.one + u = ctx.ln(x) + k = 1 + while abs(t) > abs(s)*ctx.eps: + t = t * u / k + s += t / (k * ctx._zeta_int(k+1)) + k += 1 + return s + +@defun_static +def primepi(ctx, x): + x = int(x) + if x < 2: + return 0 + return len(ctx.list_primes(x)) + +# TODO: fix the interface wrt contexts +@defun_wrapped +def primepi2(ctx, x): + x = int(x) + if x < 2: + return ctx._iv.zero + if x < 2657: + return ctx._iv.mpf(ctx.primepi(x)) + mid = ctx.li(x) + # Schoenfeld's estimate for x >= 2657, assuming RH + err = ctx.sqrt(x,rounding='u')*ctx.ln(x,rounding='u')/8/ctx.pi(rounding='d') + a = ctx.floor((ctx._iv.mpf(mid)-err).a, rounding='d') + b = ctx.ceil((ctx._iv.mpf(mid)+err).b, rounding='u') + return ctx._iv.mpf([a,b]) + +@defun_wrapped +def primezeta(ctx, s): + if ctx.isnan(s): + return s + if ctx.re(s) <= 0: + raise ValueError("prime zeta function defined only for re(s) > 0") + if s == 1: + return ctx.inf + if s == 0.5: + return ctx.mpc(ctx.ninf, ctx.pi) + r = ctx.re(s) + if r > ctx.prec: + return 0.5**s + else: + wp = ctx.prec + int(r) + def terms(): + orig = ctx.prec + # zeta ~ 1+eps; need to set precision + # to get logarithm accurately + k = 0 + while 1: + k += 1 + u = ctx.moebius(k) + if not u: + continue + ctx.prec = wp + t = u*ctx.ln(ctx.zeta(k*s))/k + if not t: + return + #print ctx.prec, ctx.nstr(t) + ctx.prec = orig + yield t + return ctx.sum_accurately(terms) + +# TODO: for bernpoly and eulerpoly, ensure that all exact zeros are covered + +@defun_wrapped +def bernpoly(ctx, n, z): + # Slow implementation: + #return sum(ctx.binomial(n,k)*ctx.bernoulli(k)*z**(n-k) for k in xrange(0,n+1)) + n = int(n) + if n < 0: + raise ValueError("Bernoulli polynomials only defined for n >= 0") + if z == 0 or (z == 1 and n > 1): + return ctx.bernoulli(n) + if z == 0.5: + return (ctx.ldexp(1,1-n)-1)*ctx.bernoulli(n) + if n <= 3: + if n == 0: return z ** 0 + if n == 1: return z - 0.5 + if n == 2: return (6*z*(z-1)+1)/6 + if n == 3: return z*(z*(z-1.5)+0.5) + if ctx.isinf(z): + return z ** n + if ctx.isnan(z): + return z + if abs(z) > 2: + def terms(): + t = ctx.one + yield t + r = ctx.one/z + k = 1 + while k <= n: + t = t*(n+1-k)/k*r + if not (k > 2 and k & 1): + yield t*ctx.bernoulli(k) + k += 1 + return ctx.sum_accurately(terms) * z**n + else: + def terms(): + yield ctx.bernoulli(n) + t = ctx.one + k = 1 + while k <= n: + t = t*(n+1-k)/k * z + m = n-k + if not (m > 2 and m & 1): + yield t*ctx.bernoulli(m) + k += 1 + return ctx.sum_accurately(terms) + +@defun_wrapped +def eulerpoly(ctx, n, z): + n = int(n) + if n < 0: + raise ValueError("Euler polynomials only defined for n >= 0") + if n <= 2: + if n == 0: return z ** 0 + if n == 1: return z - 0.5 + if n == 2: return z*(z-1) + if ctx.isinf(z): + return z**n + if ctx.isnan(z): + return z + m = n+1 + if z == 0: + return -2*(ctx.ldexp(1,m)-1)*ctx.bernoulli(m)/m * z**0 + if z == 1: + return 2*(ctx.ldexp(1,m)-1)*ctx.bernoulli(m)/m * z**0 + if z == 0.5: + if n % 2: + return ctx.zero + # Use exact code for Euler numbers + if n < 100 or n*ctx.mag(0.46839865*n) < ctx.prec*0.25: + return ctx.ldexp(ctx._eulernum(n), -n) + # http://functions.wolfram.com/Polynomials/EulerE2/06/01/02/01/0002/ + def terms(): + t = ctx.one + k = 0 + w = ctx.ldexp(1,n+2) + while 1: + v = n-k+1 + if not (v > 2 and v & 1): + yield (2-w)*ctx.bernoulli(v)*t + k += 1 + if k > n: + break + t = t*z*(n-k+2)/k + w *= 0.5 + return ctx.sum_accurately(terms) / m + +@defun +def eulernum(ctx, n, exact=False): + n = int(n) + if exact: + return int(ctx._eulernum(n)) + if n < 100: + return ctx.mpf(ctx._eulernum(n)) + if n % 2: + return ctx.zero + return ctx.ldexp(ctx.eulerpoly(n,0.5), n) + +# TODO: this should be implemented low-level +def polylog_series(ctx, s, z): + tol = +ctx.eps + l = ctx.zero + k = 1 + zk = z + while 1: + term = zk / k**s + l += term + if abs(term) < tol: + break + zk *= z + k += 1 + return l + +def polylog_continuation(ctx, n, z): + if n < 0: + return z*0 + twopij = 2j * ctx.pi + a = -twopij**n/ctx.fac(n) * ctx.bernpoly(n, ctx.ln(z)/twopij) + if ctx._is_real_type(z) and z < 0: + a = ctx._re(a) + if ctx._im(z) < 0 or (ctx._im(z) == 0 and ctx._re(z) >= 1): + a -= twopij*ctx.ln(z)**(n-1)/ctx.fac(n-1) + return a + +def polylog_unitcircle(ctx, n, z): + tol = +ctx.eps + if n > 1: + l = ctx.zero + logz = ctx.ln(z) + logmz = ctx.one + m = 0 + while 1: + if (n-m) != 1: + term = ctx.zeta(n-m) * logmz / ctx.fac(m) + if term and abs(term) < tol: + break + l += term + logmz *= logz + m += 1 + l += ctx.ln(z)**(n-1)/ctx.fac(n-1)*(ctx.harmonic(n-1)-ctx.ln(-ctx.ln(z))) + elif n < 1: # else + l = ctx.fac(-n)*(-ctx.ln(z))**(n-1) + logz = ctx.ln(z) + logkz = ctx.one + k = 0 + while 1: + b = ctx.bernoulli(k-n+1) + if b: + term = b*logkz/(ctx.fac(k)*(k-n+1)) + if abs(term) < tol: + break + l -= term + logkz *= logz + k += 1 + else: + raise ValueError + if ctx._is_real_type(z) and z < 0: + l = ctx._re(l) + return l + +def polylog_general(ctx, s, z): + v = ctx.zero + u = ctx.ln(z) + if not abs(u) < 5: # theoretically |u| < 2*pi + j = ctx.j + v = 1-s + y = ctx.ln(-z)/(2*ctx.pi*j) + return ctx.gamma(v)*(j**v*ctx.zeta(v,0.5+y) + j**-v*ctx.zeta(v,0.5-y))/(2*ctx.pi)**v + t = 1 + k = 0 + while 1: + term = ctx.zeta(s-k) * t + if abs(term) < ctx.eps: + break + v += term + k += 1 + t *= u + t /= k + return ctx.gamma(1-s)*(-u)**(s-1) + v + +@defun_wrapped +def polylog(ctx, s, z): + s = ctx.convert(s) + z = ctx.convert(z) + if z == 1: + return ctx.zeta(s) + if z == -1: + return -ctx.altzeta(s) + if s == 0: + return z/(1-z) + if s == 1: + return -ctx.ln(1-z) + if s == -1: + return z/(1-z)**2 + if abs(z) <= 0.75 or (not ctx.isint(s) and abs(z) < 0.9): + return polylog_series(ctx, s, z) + if abs(z) >= 1.4 and ctx.isint(s): + return (-1)**(s+1)*polylog_series(ctx, s, 1/z) + polylog_continuation(ctx, int(ctx.re(s)), z) + if ctx.isint(s): + return polylog_unitcircle(ctx, int(ctx.re(s)), z) + return polylog_general(ctx, s, z) + +@defun_wrapped +def clsin(ctx, s, z, pi=False): + if ctx.isint(s) and s < 0 and int(s) % 2 == 1: + return z*0 + if pi: + a = ctx.expjpi(z) + else: + a = ctx.expj(z) + if ctx._is_real_type(z) and ctx._is_real_type(s): + return ctx.im(ctx.polylog(s,a)) + b = 1/a + return (-0.5j)*(ctx.polylog(s,a) - ctx.polylog(s,b)) + +@defun_wrapped +def clcos(ctx, s, z, pi=False): + if ctx.isint(s) and s < 0 and int(s) % 2 == 0: + return z*0 + if pi: + a = ctx.expjpi(z) + else: + a = ctx.expj(z) + if ctx._is_real_type(z) and ctx._is_real_type(s): + return ctx.re(ctx.polylog(s,a)) + b = 1/a + return 0.5*(ctx.polylog(s,a) + ctx.polylog(s,b)) + +@defun +def altzeta(ctx, s, **kwargs): + try: + return ctx._altzeta(s, **kwargs) + except NotImplementedError: + return ctx._altzeta_generic(s) + +@defun_wrapped +def _altzeta_generic(ctx, s): + if s == 1: + return ctx.ln2 + 0*s + return -ctx.powm1(2, 1-s) * ctx.zeta(s) + +@defun +def zeta(ctx, s, a=1, derivative=0, method=None, **kwargs): + d = int(derivative) + if a == 1 and not (d or method): + try: + return ctx._zeta(s, **kwargs) + except NotImplementedError: + pass + s = ctx.convert(s) + prec = ctx.prec + method = kwargs.get('method') + verbose = kwargs.get('verbose') + if (not s) and (not derivative): + return ctx.mpf(0.5) - ctx._convert_param(a)[0] + if a == 1 and method != 'euler-maclaurin': + im = abs(ctx._im(s)) + re = abs(ctx._re(s)) + #if (im < prec or method == 'borwein') and not derivative: + # try: + # if verbose: + # print "zeta: Attempting to use the Borwein algorithm" + # return ctx._zeta(s, **kwargs) + # except NotImplementedError: + # if verbose: + # print "zeta: Could not use the Borwein algorithm" + # pass + if abs(im) > 500*prec and 10*re < prec and derivative <= 4 or \ + method == 'riemann-siegel': + try: # py2.4 compatible try block + try: + if verbose: + print("zeta: Attempting to use the Riemann-Siegel algorithm") + return ctx.rs_zeta(s, derivative, **kwargs) + except NotImplementedError: + if verbose: + print("zeta: Could not use the Riemann-Siegel algorithm") + pass + finally: + ctx.prec = prec + if s == 1: + return ctx.inf + abss = abs(s) + if abss == ctx.inf: + if ctx.re(s) == ctx.inf: + if d == 0: + return ctx.one + return ctx.zero + return s*0 + elif ctx.isnan(abss): + return 1/s + if ctx.re(s) > 2*ctx.prec and a == 1 and not derivative: + return ctx.one + ctx.power(2, -s) + return +ctx._hurwitz(s, a, d, **kwargs) + +@defun +def _hurwitz(ctx, s, a=1, d=0, **kwargs): + prec = ctx.prec + verbose = kwargs.get('verbose') + try: + extraprec = 10 + ctx.prec += extraprec + # We strongly want to special-case rational a + a, atype = ctx._convert_param(a) + if ctx.re(s) < 0: + if verbose: + print("zeta: Attempting reflection formula") + try: + return _hurwitz_reflection(ctx, s, a, d, atype) + except NotImplementedError: + pass + if verbose: + print("zeta: Reflection formula failed") + if verbose: + print("zeta: Using the Euler-Maclaurin algorithm") + while 1: + ctx.prec = prec + extraprec + T1, T2 = _hurwitz_em(ctx, s, a, d, prec+10, verbose) + cancellation = ctx.mag(T1) - ctx.mag(T1+T2) + if verbose: + print("Term 1:", T1) + print("Term 2:", T2) + print("Cancellation:", cancellation, "bits") + if cancellation < extraprec: + return T1 + T2 + else: + extraprec = max(2*extraprec, min(cancellation + 5, 100*prec)) + if extraprec > kwargs.get('maxprec', 100*prec): + raise ctx.NoConvergence("zeta: too much cancellation") + finally: + ctx.prec = prec + +def _hurwitz_reflection(ctx, s, a, d, atype): + # TODO: implement for derivatives + if d != 0: + raise NotImplementedError + res = ctx.re(s) + negs = -s + # Integer reflection formula + if ctx.isnpint(s): + n = int(res) + if n <= 0: + return ctx.bernpoly(1-n, a) / (n-1) + if not (atype == 'Q' or atype == 'Z'): + raise NotImplementedError + t = 1-s + # We now require a to be standardized + v = 0 + shift = 0 + b = a + while ctx.re(b) > 1: + b -= 1 + v -= b**negs + shift -= 1 + while ctx.re(b) <= 0: + v += b**negs + b += 1 + shift += 1 + # Rational reflection formula + try: + p, q = a._mpq_ + except: + assert a == int(a) + p = int(a) + q = 1 + p += shift*q + assert 1 <= p <= q + g = ctx.fsum(ctx.cospi(t/2-2*k*b)*ctx._hurwitz(t,(k,q)) \ + for k in range(1,q+1)) + g *= 2*ctx.gamma(t)/(2*ctx.pi*q)**t + v += g + return v + +def _hurwitz_em(ctx, s, a, d, prec, verbose): + # May not be converted at this point + a = ctx.convert(a) + tol = -prec + # Estimate number of terms for Euler-Maclaurin summation; could be improved + M1 = 0 + M2 = prec // 3 + N = M2 + lsum = 0 + # This speeds up the recurrence for derivatives + if ctx.isint(s): + s = int(ctx._re(s)) + s1 = s-1 + while 1: + # Truncated L-series + l = ctx._zetasum(s, M1+a, M2-M1-1, [d])[0][0] + #if d: + # l = ctx.fsum((-ctx.ln(n+a))**d * (n+a)**negs for n in range(M1,M2)) + #else: + # l = ctx.fsum((n+a)**negs for n in range(M1,M2)) + lsum += l + M2a = M2+a + logM2a = ctx.ln(M2a) + logM2ad = logM2a**d + logs = [logM2ad] + logr = 1/logM2a + rM2a = 1/M2a + M2as = M2a**(-s) + if d: + tailsum = ctx.gammainc(d+1, s1*logM2a) / s1**(d+1) + else: + tailsum = 1/((s1)*(M2a)**s1) + tailsum += 0.5 * logM2ad * M2as + U = [1] + r = M2as + fact = 2 + for j in range(1, N+1): + # TODO: the following could perhaps be tidied a bit + j2 = 2*j + if j == 1: + upds = [1] + else: + upds = [j2-2, j2-1] + for m in upds: + D = min(m,d+1) + if m <= d: + logs.append(logs[-1] * logr) + Un = [0]*(D+1) + for i in xrange(D): Un[i] = (1-m-s)*U[i] + for i in xrange(1,D+1): Un[i] += (d-(i-1))*U[i-1] + U = Un + r *= rM2a + t = ctx.fdot(U, logs) * r * ctx.bernoulli(j2)/(-fact) + tailsum += t + if ctx.mag(t) < tol: + return lsum, (-1)**d * tailsum + fact *= (j2+1)*(j2+2) + if verbose: + print("Sum range:", M1, M2, "term magnitude", ctx.mag(t), "tolerance", tol) + M1, M2 = M2, M2*2 + if ctx.re(s) < 0: + N += N//2 + + + +@defun +def _zetasum(ctx, s, a, n, derivatives=[0], reflect=False): + """ + Returns [xd0,xd1,...,xdr], [yd0,yd1,...ydr] where + + xdk = D^k ( 1/a^s + 1/(a+1)^s + ... + 1/(a+n)^s ) + ydk = D^k conj( 1/a^(1-s) + 1/(a+1)^(1-s) + ... + 1/(a+n)^(1-s) ) + + D^k = kth derivative with respect to s, k ranges over the given list of + derivatives (which should consist of either a single element + or a range 0,1,...r). If reflect=False, the ydks are not computed. + """ + #print "zetasum", s, a, n + # don't use the fixed-point code if there are large exponentials + if abs(ctx.re(s)) < 0.5 * ctx.prec: + try: + return ctx._zetasum_fast(s, a, n, derivatives, reflect) + except NotImplementedError: + pass + negs = ctx.fneg(s, exact=True) + have_derivatives = derivatives != [0] + have_one_derivative = len(derivatives) == 1 + if not reflect: + if not have_derivatives: + return [ctx.fsum((a+k)**negs for k in xrange(n+1))], [] + if have_one_derivative: + d = derivatives[0] + x = ctx.fsum(ctx.ln(a+k)**d * (a+k)**negs for k in xrange(n+1)) + return [(-1)**d * x], [] + maxd = max(derivatives) + if not have_one_derivative: + derivatives = range(maxd+1) + xs = [ctx.zero for d in derivatives] + if reflect: + ys = [ctx.zero for d in derivatives] + else: + ys = [] + for k in xrange(n+1): + w = a + k + xterm = w ** negs + if reflect: + yterm = ctx.conj(ctx.one / (w * xterm)) + if have_derivatives: + logw = -ctx.ln(w) + if have_one_derivative: + logw = logw ** maxd + xs[0] += xterm * logw + if reflect: + ys[0] += yterm * logw + else: + t = ctx.one + for d in derivatives: + xs[d] += xterm * t + if reflect: + ys[d] += yterm * t + t *= logw + else: + xs[0] += xterm + if reflect: + ys[0] += yterm + return xs, ys + +@defun +def dirichlet(ctx, s, chi=[1], derivative=0): + s = ctx.convert(s) + q = len(chi) + d = int(derivative) + if d > 2: + raise NotImplementedError("arbitrary order derivatives") + prec = ctx.prec + try: + ctx.prec += 10 + if s == 1: + have_pole = True + for x in chi: + if x and x != 1: + have_pole = False + h = +ctx.eps + ctx.prec *= 2*(d+1) + s += h + if have_pole: + return +ctx.inf + z = ctx.zero + for p in range(1,q+1): + if chi[p%q]: + if d == 1: + z += chi[p%q] * (ctx.zeta(s, (p,q), 1) - \ + ctx.zeta(s, (p,q))*ctx.log(q)) + else: + z += chi[p%q] * ctx.zeta(s, (p,q)) + z /= q**s + finally: + ctx.prec = prec + return +z + + +def secondzeta_main_term(ctx, s, a, **kwargs): + tol = ctx.eps + f = lambda n: ctx.gammainc(0.5*s, a*gamm**2, regularized=True)*gamm**(-s) + totsum = term = ctx.zero + mg = ctx.inf + n = 0 + while mg > tol: + totsum += term + n += 1 + gamm = ctx.im(ctx.zetazero_memoized(n)) + term = f(n) + mg = abs(term) + err = 0 + if kwargs.get("error"): + sg = ctx.re(s) + err = 0.5*ctx.pi**(-1)*max(1,sg)*a**(sg-0.5)*ctx.log(gamm/(2*ctx.pi))*\ + ctx.gammainc(-0.5, a*gamm**2)/abs(ctx.gamma(s/2)) + err = abs(err) + return +totsum, err, n + +def secondzeta_prime_term(ctx, s, a, **kwargs): + tol = ctx.eps + f = lambda n: ctx.gammainc(0.5*(1-s),0.25*ctx.log(n)**2 * a**(-1))*\ + ((0.5*ctx.log(n))**(s-1))*ctx.mangoldt(n)/ctx.sqrt(n)/\ + (2*ctx.gamma(0.5*s)*ctx.sqrt(ctx.pi)) + totsum = term = ctx.zero + mg = ctx.inf + n = 1 + while mg > tol or n < 9: + totsum += term + n += 1 + term = f(n) + if term == 0: + mg = ctx.inf + else: + mg = abs(term) + if kwargs.get("error"): + err = mg + return +totsum, err, n + +def secondzeta_exp_term(ctx, s, a): + if ctx.isint(s) and ctx.re(s) <= 0: + m = int(round(ctx.re(s))) + if not m & 1: + return ctx.mpf('-0.25')**(-m//2) + tol = ctx.eps + f = lambda n: (0.25*a)**n/((n+0.5*s)*ctx.fac(n)) + totsum = ctx.zero + term = f(0) + mg = ctx.inf + n = 0 + while mg > tol: + totsum += term + n += 1 + term = f(n) + mg = abs(term) + v = a**(0.5*s)*totsum/ctx.gamma(0.5*s) + return v + +def secondzeta_singular_term(ctx, s, a, **kwargs): + factor = a**(0.5*(s-1))/(4*ctx.sqrt(ctx.pi)*ctx.gamma(0.5*s)) + extraprec = ctx.mag(factor) + ctx.prec += extraprec + factor = a**(0.5*(s-1))/(4*ctx.sqrt(ctx.pi)*ctx.gamma(0.5*s)) + tol = ctx.eps + f = lambda n: ctx.bernpoly(n,0.75)*(4*ctx.sqrt(a))**n*\ + ctx.gamma(0.5*n)/((s+n-1)*ctx.fac(n)) + totsum = ctx.zero + mg1 = ctx.inf + n = 1 + term = f(n) + mg2 = abs(term) + while mg2 > tol and mg2 <= mg1: + totsum += term + n += 1 + term = f(n) + totsum += term + n +=1 + term = f(n) + mg1 = mg2 + mg2 = abs(term) + totsum += term + pole = -2*(s-1)**(-2)+(ctx.euler+ctx.log(16*ctx.pi**2*a))*(s-1)**(-1) + st = factor*(pole+totsum) + err = 0 + if kwargs.get("error"): + if not ((mg2 > tol) and (mg2 <= mg1)): + if mg2 <= tol: + err = ctx.mpf(10)**int(ctx.log(abs(factor*tol),10)) + if mg2 > mg1: + err = ctx.mpf(10)**int(ctx.log(abs(factor*mg1),10)) + err = max(err, ctx.eps*1.) + ctx.prec -= extraprec + return +st, err + +@defun +def secondzeta(ctx, s, a = 0.015, **kwargs): + r""" + Evaluates the secondary zeta function `Z(s)`, defined for + `\mathrm{Re}(s)>1` by + + .. math :: + + Z(s) = \sum_{n=1}^{\infty} \frac{1}{\tau_n^s} + + where `\frac12+i\tau_n` runs through the zeros of `\zeta(s)` with + imaginary part positive. + + `Z(s)` extends to a meromorphic function on `\mathbb{C}` with a + double pole at `s=1` and simple poles at the points `-2n` for + `n=0`, 1, 2, ... + + **Examples** + + >>> from mpmath import * + >>> mp.pretty = True; mp.dps = 15 + >>> secondzeta(2) + 0.023104993115419 + >>> xi = lambda s: 0.5*s*(s-1)*pi**(-0.5*s)*gamma(0.5*s)*zeta(s) + >>> Xi = lambda t: xi(0.5+t*j) + >>> chop(-0.5*diff(Xi,0,n=2)/Xi(0)) + 0.023104993115419 + + We may ask for an approximate error value:: + + >>> secondzeta(0.5+100j, error=True) + ((-0.216272011276718 - 0.844952708937228j), 2.22044604925031e-16) + + The function has poles at the negative odd integers, + and dyadic rational values at the negative even integers:: + + >>> mp.dps = 30 + >>> secondzeta(-8) + -0.67236328125 + >>> secondzeta(-7) + +inf + + **Implementation notes** + + The function is computed as sum of four terms `Z(s)=A(s)-P(s)+E(s)-S(s)` + respectively main, prime, exponential and singular terms. + The main term `A(s)` is computed from the zeros of zeta. + The prime term depends on the von Mangoldt function. + The singular term is responsible for the poles of the function. + + The four terms depends on a small parameter `a`. We may change the + value of `a`. Theoretically this has no effect on the sum of the four + terms, but in practice may be important. + + A smaller value of the parameter `a` makes `A(s)` depend on + a smaller number of zeros of zeta, but `P(s)` uses more values of + von Mangoldt function. + + We may also add a verbose option to obtain data about the + values of the four terms. + + >>> mp.dps = 10 + >>> secondzeta(0.5 + 40j, error=True, verbose=True) + main term = (-30190318549.138656312556 - 13964804384.624622876523j) + computed using 19 zeros of zeta + prime term = (132717176.89212754625045 + 188980555.17563978290601j) + computed using 9 values of the von Mangoldt function + exponential term = (542447428666.07179812536 + 362434922978.80192435203j) + singular term = (512124392939.98154322355 + 348281138038.65531023921j) + ((0.059471043 + 0.3463514534j), 1.455191523e-11) + + >>> secondzeta(0.5 + 40j, a=0.04, error=True, verbose=True) + main term = (-151962888.19606243907725 - 217930683.90210294051982j) + computed using 9 zeros of zeta + prime term = (2476659342.3038722372461 + 28711581821.921627163136j) + computed using 37 values of the von Mangoldt function + exponential term = (178506047114.7838188264 + 819674143244.45677330576j) + singular term = (175877424884.22441310708 + 790744630738.28669174871j) + ((0.059471043 + 0.3463514534j), 1.455191523e-11) + + Notice the great cancellation between the four terms. Changing `a`, the + four terms are very different numbers but the cancellation gives + the good value of Z(s). + + **References** + + A. Voros, Zeta functions for the Riemann zeros, Ann. Institute Fourier, + 53, (2003) 665--699. + + A. Voros, Zeta functions over Zeros of Zeta Functions, Lecture Notes + of the Unione Matematica Italiana, Springer, 2009. + """ + s = ctx.convert(s) + a = ctx.convert(a) + tol = ctx.eps + if ctx.isint(s) and ctx.re(s) <= 1: + if abs(s-1) < tol*1000: + return ctx.inf + m = int(round(ctx.re(s))) + if m & 1: + return ctx.inf + else: + return ((-1)**(-m//2)*\ + ctx.fraction(8-ctx.eulernum(-m,exact=True),2**(-m+3))) + prec = ctx.prec + try: + t3 = secondzeta_exp_term(ctx, s, a) + extraprec = max(ctx.mag(t3),0) + ctx.prec += extraprec + 3 + t1, r1, gt = secondzeta_main_term(ctx,s,a,error='True', verbose='True') + t2, r2, pt = secondzeta_prime_term(ctx,s,a,error='True', verbose='True') + t4, r4 = secondzeta_singular_term(ctx,s,a,error='True') + t3 = secondzeta_exp_term(ctx, s, a) + err = r1+r2+r4 + t = t1-t2+t3-t4 + if kwargs.get("verbose"): + print('main term =', t1) + print(' computed using', gt, 'zeros of zeta') + print('prime term =', t2) + print(' computed using', pt, 'values of the von Mangoldt function') + print('exponential term =', t3) + print('singular term =', t4) + finally: + ctx.prec = prec + if kwargs.get("error"): + w = max(ctx.mag(abs(t)),0) + err = max(err*2**w, ctx.eps*1.*2**w) + return +t, err + return +t + + +@defun_wrapped +def lerchphi(ctx, z, s, a): + r""" + Gives the Lerch transcendent, defined for `|z| < 1` and + `\Re{a} > 0` by + + .. math :: + + \Phi(z,s,a) = \sum_{k=0}^{\infty} \frac{z^k}{(a+k)^s} + + and generally by the recurrence `\Phi(z,s,a) = z \Phi(z,s,a+1) + a^{-s}` + along with the integral representation valid for `\Re{a} > 0` + + .. math :: + + \Phi(z,s,a) = \frac{1}{2 a^s} + + \int_0^{\infty} \frac{z^t}{(a+t)^s} dt - + 2 \int_0^{\infty} \frac{\sin(t \log z - s + \operatorname{arctan}(t/a)}{(a^2 + t^2)^{s/2} + (e^{2 \pi t}-1)} dt. + + The Lerch transcendent generalizes the Hurwitz zeta function :func:`zeta` + (`z = 1`) and the polylogarithm :func:`polylog` (`a = 1`). + + **Examples** + + Several evaluations in terms of simpler functions:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> lerchphi(-1,2,0.5); 4*catalan + 3.663862376708876060218414 + 3.663862376708876060218414 + >>> diff(lerchphi, (-1,-2,1), (0,1,0)); 7*zeta(3)/(4*pi**2) + 0.2131391994087528954617607 + 0.2131391994087528954617607 + >>> lerchphi(-4,1,1); log(5)/4 + 0.4023594781085250936501898 + 0.4023594781085250936501898 + >>> lerchphi(-3+2j,1,0.5); 2*atanh(sqrt(-3+2j))/sqrt(-3+2j) + (1.142423447120257137774002 + 0.2118232380980201350495795j) + (1.142423447120257137774002 + 0.2118232380980201350495795j) + + Evaluation works for complex arguments and `|z| \ge 1`:: + + >>> lerchphi(1+2j, 3-j, 4+2j) + (0.002025009957009908600539469 + 0.003327897536813558807438089j) + >>> lerchphi(-2,2,-2.5) + -12.28676272353094275265944 + >>> lerchphi(10,10,10) + (-4.462130727102185701817349e-11 - 1.575172198981096218823481e-12j) + >>> lerchphi(10,10,-10.5) + (112658784011940.5605789002 - 498113185.5756221777743631j) + + Some degenerate cases:: + + >>> lerchphi(0,1,2) + 0.5 + >>> lerchphi(0,1,-2) + -0.5 + + Reduction to simpler functions:: + + >>> lerchphi(1, 4.25+1j, 1) + (1.044674457556746668033975 - 0.04674508654012658932271226j) + >>> zeta(4.25+1j) + (1.044674457556746668033975 - 0.04674508654012658932271226j) + >>> lerchphi(1 - 0.5**10, 4.25+1j, 1) + (1.044629338021507546737197 - 0.04667768813963388181708101j) + >>> lerchphi(3, 4, 1) + (1.249503297023366545192592 - 0.2314252413375664776474462j) + >>> polylog(4, 3) / 3 + (1.249503297023366545192592 - 0.2314252413375664776474462j) + >>> lerchphi(3, 4, 1 - 0.5**10) + (1.253978063946663945672674 - 0.2316736622836535468765376j) + + **References** + + 1. [DLMF]_ section 25.14 + + """ + if z == 0: + return a ** (-s) + # Faster, but these cases are useful for testing right now + if z == 1: + return ctx.zeta(s, a) + if a == 1: + return ctx.polylog(s, z) / z + if ctx.re(a) < 1: + if ctx.isnpint(a): + raise ValueError("Lerch transcendent complex infinity") + m = int(ctx.ceil(1-ctx.re(a))) + v = ctx.zero + zpow = ctx.one + for n in xrange(m): + v += zpow / (a+n)**s + zpow *= z + return zpow * ctx.lerchphi(z,s, a+m) + v + g = ctx.ln(z) + v = 1/(2*a**s) + ctx.gammainc(1-s, -a*g) * (-g)**(s-1) / z**a + h = s / 2 + r = 2*ctx.pi + f = lambda t: ctx.sin(s*ctx.atan(t/a)-t*g) / \ + ((a**2+t**2)**h * ctx.expm1(r*t)) + v += 2*ctx.quad(f, [0, ctx.inf]) + if not ctx.im(z) and not ctx.im(s) and not ctx.im(a) and ctx.re(z) < 1: + v = ctx.chop(v) + return v diff --git a/venv/lib/python3.12/site-packages/mpmath/functions/zetazeros.py b/venv/lib/python3.12/site-packages/mpmath/functions/zetazeros.py new file mode 100644 index 0000000000000000000000000000000000000000..37c11a29426b0114053ae61664541f7ae7de95d8 --- /dev/null +++ b/venv/lib/python3.12/site-packages/mpmath/functions/zetazeros.py @@ -0,0 +1,1018 @@ +""" +The function zetazero(n) computes the n-th nontrivial zero of zeta(s). + +The general strategy is to locate a block of Gram intervals B where we +know exactly the number of zeros contained and which of those zeros +is that which we search. + +If n <= 400 000 000 we know exactly the Rosser exceptions, contained +in a list in this file. Hence for n<=400 000 000 we simply +look at these list of exceptions. If our zero is implicated in one of +these exceptions we have our block B. In other case we simply locate +the good Rosser block containing our zero. + +For n > 400 000 000 we apply the method of Turing, as complemented by +Lehman, Brent and Trudgian to find a suitable B. +""" + +from .functions import defun, defun_wrapped + +def find_rosser_block_zero(ctx, n): + """for n<400 000 000 determines a block were one find our zero""" + for k in range(len(_ROSSER_EXCEPTIONS)//2): + a=_ROSSER_EXCEPTIONS[2*k][0] + b=_ROSSER_EXCEPTIONS[2*k][1] + if ((a<= n-2) and (n-1 <= b)): + t0 = ctx.grampoint(a) + t1 = ctx.grampoint(b) + v0 = ctx._fp.siegelz(t0) + v1 = ctx._fp.siegelz(t1) + my_zero_number = n-a-1 + zero_number_block = b-a + pattern = _ROSSER_EXCEPTIONS[2*k+1] + return (my_zero_number, [a,b], [t0,t1], [v0,v1]) + k = n-2 + t,v,b = compute_triple_tvb(ctx, k) + T = [t] + V = [v] + while b < 0: + k -= 1 + t,v,b = compute_triple_tvb(ctx, k) + T.insert(0,t) + V.insert(0,v) + my_zero_number = n-k-1 + m = n-1 + t,v,b = compute_triple_tvb(ctx, m) + T.append(t) + V.append(v) + while b < 0: + m += 1 + t,v,b = compute_triple_tvb(ctx, m) + T.append(t) + V.append(v) + return (my_zero_number, [k,m], T, V) + +def wpzeros(t): + """Precision needed to compute higher zeros""" + wp = 53 + if t > 3*10**8: + wp = 63 + if t > 10**11: + wp = 70 + if t > 10**14: + wp = 83 + return wp + +def separate_zeros_in_block(ctx, zero_number_block, T, V, limitloop=None, + fp_tolerance=None): + """Separate the zeros contained in the block T, limitloop + determines how long one must search""" + if limitloop is None: + limitloop = ctx.inf + loopnumber = 0 + variations = count_variations(V) + while ((variations < zero_number_block) and (loopnumber 0): + alpha = ctx.sqrt(u/v) + b= (alpha*a+b2)/(alpha+1) + else: + b = (a+b2)/2 + if fp_tolerance < 10: + w = ctx._fp.siegelz(b) + if abs(w)ITERATION_LIMIT)and(loopnumber>2)and(variations+2==zero_number_block): + dtMax=0 + dtSec=0 + kMax = 0 + for k1 in range(1,len(T)): + dt = T[k1]-T[k1-1] + if dt > dtMax: + kMax=k1 + dtSec = dtMax + dtMax = dt + elif (dtdtSec): + dtSec = dt + if dtMax>3*dtSec: + f = lambda x: ctx.rs_z(x,derivative=1) + t0=T[kMax-1] + t1 = T[kMax] + t=ctx.findroot(f, (t0,t1), solver ='illinois',verify=False, verbose=False) + v = ctx.siegelz(t) + if (t0 2*wpz: + index +=1 + precs = [precs[0] // 2 +3+2*index] + precs + ctx.prec = precs[0] + guard + r = ctx.findroot(lambda x:ctx.siegelz(x), (t0,t1), solver ='illinois', verbose=False) + #print "first step at", ctx.dps, "digits" + z=ctx.mpc(0.5,r) + for prec in precs[1:]: + ctx.prec = prec + guard + #print "refining to", ctx.dps, "digits" + znew = z - ctx.zeta(z) / ctx.zeta(z, derivative=1) + #print "difference", ctx.nstr(abs(z-znew)) + z=ctx.mpc(0.5,ctx.im(znew)) + return ctx.im(z) + +def sure_number_block(ctx, n): + """The number of good Rosser blocks needed to apply + Turing method + References: + R. P. Brent, On the Zeros of the Riemann Zeta Function + in the Critical Strip, Math. Comp. 33 (1979) 1361--1372 + T. Trudgian, Improvements to Turing Method, Math. Comp.""" + if n < 9*10**5: + return(2) + g = ctx.grampoint(n-100) + lg = ctx._fp.ln(g) + brent = 0.0061 * lg**2 +0.08*lg + trudgian = 0.0031 * lg**2 +0.11*lg + N = ctx.ceil(min(brent,trudgian)) + N = int(N) + return N + +def compute_triple_tvb(ctx, n): + t = ctx.grampoint(n) + v = ctx._fp.siegelz(t) + if ctx.mag(abs(v))400 000 000""" + sb = sure_number_block(ctx, n) + number_goodblocks = 0 + m2 = n-1 + t, v, b = compute_triple_tvb(ctx, m2) + Tf = [t] + Vf = [v] + while b < 0: + m2 += 1 + t,v,b = compute_triple_tvb(ctx, m2) + Tf.append(t) + Vf.append(v) + goodpoints = [m2] + T = [t] + V = [v] + while number_goodblocks < 2*sb: + m2 += 1 + t, v, b = compute_triple_tvb(ctx, m2) + T.append(t) + V.append(v) + while b < 0: + m2 += 1 + t,v,b = compute_triple_tvb(ctx, m2) + T.append(t) + V.append(v) + goodpoints.append(m2) + zn = len(T)-1 + A, B, separated =\ + separate_zeros_in_block(ctx, zn, T, V, limitloop=ITERATION_LIMIT, + fp_tolerance=fp_tolerance) + Tf.pop() + Tf.extend(A) + Vf.pop() + Vf.extend(B) + if separated: + number_goodblocks += 1 + else: + number_goodblocks = 0 + T = [t] + V = [v] + # Now the same procedure to the left + number_goodblocks = 0 + m2 = n-2 + t, v, b = compute_triple_tvb(ctx, m2) + Tf.insert(0,t) + Vf.insert(0,v) + while b < 0: + m2 -= 1 + t,v,b = compute_triple_tvb(ctx, m2) + Tf.insert(0,t) + Vf.insert(0,v) + goodpoints.insert(0,m2) + T = [t] + V = [v] + while number_goodblocks < 2*sb: + m2 -= 1 + t, v, b = compute_triple_tvb(ctx, m2) + T.insert(0,t) + V.insert(0,v) + while b < 0: + m2 -= 1 + t,v,b = compute_triple_tvb(ctx, m2) + T.insert(0,t) + V.insert(0,v) + goodpoints.insert(0,m2) + zn = len(T)-1 + A, B, separated =\ + separate_zeros_in_block(ctx, zn, T, V, limitloop=ITERATION_LIMIT, fp_tolerance=fp_tolerance) + A.pop() + Tf = A+Tf + B.pop() + Vf = B+Vf + if separated: + number_goodblocks += 1 + else: + number_goodblocks = 0 + T = [t] + V = [v] + r = goodpoints[2*sb] + lg = len(goodpoints) + s = goodpoints[lg-2*sb-1] + tr, vr, br = compute_triple_tvb(ctx, r) + ar = Tf.index(tr) + ts, vs, bs = compute_triple_tvb(ctx, s) + as1 = Tf.index(ts) + T = Tf[ar:as1+1] + V = Vf[ar:as1+1] + zn = s-r + A, B, separated =\ + separate_zeros_in_block(ctx, zn,T,V,limitloop=ITERATION_LIMIT, fp_tolerance=fp_tolerance) + if separated: + return (n-r-1,[r,s],A,B) + q = goodpoints[sb] + lg = len(goodpoints) + t = goodpoints[lg-sb-1] + tq, vq, bq = compute_triple_tvb(ctx, q) + aq = Tf.index(tq) + tt, vt, bt = compute_triple_tvb(ctx, t) + at = Tf.index(tt) + T = Tf[aq:at+1] + V = Vf[aq:at+1] + return (n-q-1,[q,t],T,V) + +def count_variations(V): + count = 0 + vold = V[0] + for n in range(1, len(V)): + vnew = V[n] + if vold*vnew < 0: + count +=1 + vold = vnew + return count + +def pattern_construct(ctx, block, T, V): + pattern = '(' + a = block[0] + b = block[1] + t0,v0,b0 = compute_triple_tvb(ctx, a) + k = 0 + k0 = 0 + for n in range(a+1,b+1): + t1,v1,b1 = compute_triple_tvb(ctx, n) + lgT =len(T) + while (k < lgT) and (T[k] <= t1): + k += 1 + L = V[k0:k] + L.append(v1) + L.insert(0,v0) + count = count_variations(L) + pattern = pattern + ("%s" % count) + if b1 > 0: + pattern = pattern + ')(' + k0 = k + t0,v0,b0 = t1,v1,b1 + pattern = pattern[:-1] + return pattern + +@defun +def zetazero(ctx, n, info=False, round=True): + r""" + Computes the `n`-th nontrivial zero of `\zeta(s)` on the critical line, + i.e. returns an approximation of the `n`-th largest complex number + `s = \frac{1}{2} + ti` for which `\zeta(s) = 0`. Equivalently, the + imaginary part `t` is a zero of the Z-function (:func:`~mpmath.siegelz`). + + **Examples** + + The first few zeros:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> zetazero(1) + (0.5 + 14.13472514173469379045725j) + >>> zetazero(2) + (0.5 + 21.02203963877155499262848j) + >>> zetazero(20) + (0.5 + 77.14484006887480537268266j) + + Verifying that the values are zeros:: + + >>> for n in range(1,5): + ... s = zetazero(n) + ... chop(zeta(s)), chop(siegelz(s.imag)) + ... + (0.0, 0.0) + (0.0, 0.0) + (0.0, 0.0) + (0.0, 0.0) + + Negative indices give the conjugate zeros (`n = 0` is undefined):: + + >>> zetazero(-1) + (0.5 - 14.13472514173469379045725j) + + :func:`~mpmath.zetazero` supports arbitrarily large `n` and arbitrary precision:: + + >>> mp.dps = 15 + >>> zetazero(1234567) + (0.5 + 727690.906948208j) + >>> mp.dps = 50 + >>> zetazero(1234567) + (0.5 + 727690.9069482075392389420041147142092708393819935j) + >>> chop(zeta(_)/_) + 0.0 + + with *info=True*, :func:`~mpmath.zetazero` gives additional information:: + + >>> mp.dps = 15 + >>> zetazero(542964976,info=True) + ((0.5 + 209039046.578535j), [542964969, 542964978], 6, '(013111110)') + + This means that the zero is between Gram points 542964969 and 542964978; + it is the 6-th zero between them. Finally (01311110) is the pattern + of zeros in this interval. The numbers indicate the number of zeros + in each Gram interval (Rosser blocks between parenthesis). In this case + there is only one Rosser block of length nine. + """ + n = int(n) + if n < 0: + return ctx.zetazero(-n).conjugate() + if n == 0: + raise ValueError("n must be nonzero") + wpinitial = ctx.prec + try: + wpz, fp_tolerance = comp_fp_tolerance(ctx, n) + ctx.prec = wpz + if n < 400000000: + my_zero_number, block, T, V =\ + find_rosser_block_zero(ctx, n) + else: + my_zero_number, block, T, V =\ + search_supergood_block(ctx, n, fp_tolerance) + zero_number_block = block[1]-block[0] + T, V, separated = separate_zeros_in_block(ctx, zero_number_block, T, V, + limitloop=ctx.inf, fp_tolerance=fp_tolerance) + if info: + pattern = pattern_construct(ctx,block,T,V) + prec = max(wpinitial, wpz) + t = separate_my_zero(ctx, my_zero_number, zero_number_block,T,V,prec) + v = ctx.mpc(0.5,t) + finally: + ctx.prec = wpinitial + if round: + v =+v + if info: + return (v,block,my_zero_number,pattern) + else: + return v + +def gram_index(ctx, t): + if t > 10**13: + wp = 3*ctx.log(t, 10) + else: + wp = 0 + prec = ctx.prec + try: + ctx.prec += wp + h = int(ctx.siegeltheta(t)/ctx.pi) + finally: + ctx.prec = prec + return(h) + +def count_to(ctx, t, T, V): + count = 0 + vold = V[0] + told = T[0] + tnew = T[1] + k = 1 + while tnew < t: + vnew = V[k] + if vold*vnew < 0: + count += 1 + vold = vnew + k += 1 + tnew = T[k] + a = ctx.siegelz(t) + if a*vold < 0: + count += 1 + return count + +def comp_fp_tolerance(ctx, n): + wpz = wpzeros(n*ctx.log(n)) + if n < 15*10**8: + fp_tolerance = 0.0005 + elif n <= 10**14: + fp_tolerance = 0.1 + else: + fp_tolerance = 100 + return wpz, fp_tolerance + +@defun +def nzeros(ctx, t): + r""" + Computes the number of zeros of the Riemann zeta function in + `(0,1) \times (0,t]`, usually denoted by `N(t)`. + + **Examples** + + The first zero has imaginary part between 14 and 15:: + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = True + >>> nzeros(14) + 0 + >>> nzeros(15) + 1 + >>> zetazero(1) + (0.5 + 14.1347251417347j) + + Some closely spaced zeros:: + + >>> nzeros(10**7) + 21136125 + >>> zetazero(21136125) + (0.5 + 9999999.32718175j) + >>> zetazero(21136126) + (0.5 + 10000000.2400236j) + >>> nzeros(545439823.215) + 1500000001 + >>> zetazero(1500000001) + (0.5 + 545439823.201985j) + >>> zetazero(1500000002) + (0.5 + 545439823.325697j) + + This confirms the data given by J. van de Lune, + H. J. J. te Riele and D. T. Winter in 1986. + """ + if t < 14.1347251417347: + return 0 + x = gram_index(ctx, t) + k = int(ctx.floor(x)) + wpinitial = ctx.prec + wpz, fp_tolerance = comp_fp_tolerance(ctx, k) + ctx.prec = wpz + a = ctx.siegelz(t) + if k == -1 and a < 0: + return 0 + elif k == -1 and a > 0: + return 1 + if k+2 < 400000000: + Rblock = find_rosser_block_zero(ctx, k+2) + else: + Rblock = search_supergood_block(ctx, k+2, fp_tolerance) + n1, n2 = Rblock[1] + if n2-n1 == 1: + b = Rblock[3][0] + if a*b > 0: + ctx.prec = wpinitial + return k+1 + else: + ctx.prec = wpinitial + return k+2 + my_zero_number,block, T, V = Rblock + zero_number_block = n2-n1 + T, V, separated = separate_zeros_in_block(ctx,\ + zero_number_block, T, V,\ + limitloop=ctx.inf,\ + fp_tolerance=fp_tolerance) + n = count_to(ctx, t, T, V) + ctx.prec = wpinitial + return n+n1+1 + +@defun_wrapped +def backlunds(ctx, t): + r""" + Computes the function + `S(t) = \operatorname{arg} \zeta(\frac{1}{2} + it) / \pi`. + + See Titchmarsh Section 9.3 for details of the definition. + + **Examples** + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = True + >>> backlunds(217.3) + 0.16302205431184 + + Generally, the value is a small number. At Gram points it is an integer, + frequently equal to 0:: + + >>> chop(backlunds(grampoint(200))) + 0.0 + >>> backlunds(extraprec(10)(grampoint)(211)) + 1.0 + >>> backlunds(extraprec(10)(grampoint)(232)) + -1.0 + + The number of zeros of the Riemann zeta function up to height `t` + satisfies `N(t) = \theta(t)/\pi + 1 + S(t)` (see :func:nzeros` and + :func:`siegeltheta`):: + + >>> t = 1234.55 + >>> nzeros(t) + 842 + >>> siegeltheta(t)/pi+1+backlunds(t) + 842.0 + + """ + return ctx.nzeros(t)-1-ctx.siegeltheta(t)/ctx.pi + + +""" +_ROSSER_EXCEPTIONS is a list of all exceptions to +Rosser's rule for n <= 400 000 000. + +Alternately the entry is of type [n,m], or a string. +The string is the zero pattern of the Block and the relevant +adjacent. For example (010)3 corresponds to a block +composed of three Gram intervals, the first ant third without +a zero and the intermediate with a zero. The next Gram interval +contain three zeros. So that in total we have 4 zeros in 4 Gram +blocks. n and m are the indices of the Gram points of this +interval of four Gram intervals. The Rosser exception is therefore +formed by the three Gram intervals that are signaled between +parenthesis. + +We have included also some Rosser's exceptions beyond n=400 000 000 +that are noted in the literature by some reason. + +The list is composed from the data published in the references: + +R. P. Brent, J. van de Lune, H. J. J. te Riele, D. T. Winter, +'On the Zeros of the Riemann Zeta Function in the Critical Strip. II', +Math. Comp. 39 (1982) 681--688. +See also Corrigenda in Math. Comp. 46 (1986) 771. + +J. van de Lune, H. J. J. te Riele, +'On the Zeros of the Riemann Zeta Function in the Critical Strip. III', +Math. Comp. 41 (1983) 759--767. +See also Corrigenda in Math. Comp. 46 (1986) 771. + +J. van de Lune, +'Sums of Equal Powers of Positive Integers', +Dissertation, +Vrije Universiteit te Amsterdam, Centrum voor Wiskunde en Informatica, +Amsterdam, 1984. + +Thanks to the authors all this papers and those others that have +contributed to make this possible. +""" + + + + + + + +_ROSSER_EXCEPTIONS = \ +[[13999525, 13999528], '(00)3', +[30783329, 30783332], '(00)3', +[30930926, 30930929], '3(00)', +[37592215, 37592218], '(00)3', +[40870156, 40870159], '(00)3', +[43628107, 43628110], '(00)3', +[46082042, 46082045], '(00)3', +[46875667, 46875670], '(00)3', +[49624540, 49624543], '3(00)', +[50799238, 50799241], '(00)3', +[55221453, 55221456], '3(00)', +[56948779, 56948782], '3(00)', +[60515663, 60515666], '(00)3', +[61331766, 61331770], '(00)40', +[69784843, 69784846], '3(00)', +[75052114, 75052117], '(00)3', +[79545240, 79545243], '3(00)', +[79652247, 79652250], '3(00)', +[83088043, 83088046], '(00)3', +[83689522, 83689525], '3(00)', +[85348958, 85348961], '(00)3', +[86513820, 86513823], '(00)3', +[87947596, 87947599], '3(00)', +[88600095, 88600098], '(00)3', +[93681183, 93681186], '(00)3', +[100316551, 100316554], '3(00)', +[100788444, 100788447], '(00)3', +[106236172, 106236175], '(00)3', +[106941327, 106941330], '3(00)', +[107287955, 107287958], '(00)3', +[107532016, 107532019], '3(00)', +[110571044, 110571047], '(00)3', +[111885253, 111885256], '3(00)', +[113239783, 113239786], '(00)3', +[120159903, 120159906], '(00)3', +[121424391, 121424394], '3(00)', +[121692931, 121692934], '3(00)', +[121934170, 121934173], '3(00)', +[122612848, 122612851], '3(00)', +[126116567, 126116570], '(00)3', +[127936513, 127936516], '(00)3', +[128710277, 128710280], '3(00)', +[129398902, 129398905], '3(00)', +[130461096, 130461099], '3(00)', +[131331947, 131331950], '3(00)', +[137334071, 137334074], '3(00)', +[137832603, 137832606], '(00)3', +[138799471, 138799474], '3(00)', +[139027791, 139027794], '(00)3', +[141617806, 141617809], '(00)3', +[144454931, 144454934], '(00)3', +[145402379, 145402382], '3(00)', +[146130245, 146130248], '3(00)', +[147059770, 147059773], '(00)3', +[147896099, 147896102], '3(00)', +[151097113, 151097116], '(00)3', +[152539438, 152539441], '(00)3', +[152863168, 152863171], '3(00)', +[153522726, 153522729], '3(00)', +[155171524, 155171527], '3(00)', +[155366607, 155366610], '(00)3', +[157260686, 157260689], '3(00)', +[157269224, 157269227], '(00)3', +[157755123, 157755126], '(00)3', +[158298484, 158298487], '3(00)', +[160369050, 160369053], '3(00)', +[162962787, 162962790], '(00)3', +[163724709, 163724712], '(00)3', +[164198113, 164198116], '3(00)', +[164689301, 164689305], '(00)40', +[164880228, 164880231], '3(00)', +[166201932, 166201935], '(00)3', +[168573836, 168573839], '(00)3', +[169750763, 169750766], '(00)3', +[170375507, 170375510], '(00)3', +[170704879, 170704882], '3(00)', +[172000992, 172000995], '3(00)', +[173289941, 173289944], '(00)3', +[173737613, 173737616], '3(00)', +[174102513, 174102516], '(00)3', +[174284990, 174284993], '(00)3', +[174500513, 174500516], '(00)3', +[175710609, 175710612], '(00)3', +[176870843, 176870846], '3(00)', +[177332732, 177332735], '3(00)', +[177902861, 177902864], '3(00)', +[179979095, 179979098], '(00)3', +[181233726, 181233729], '3(00)', +[181625435, 181625438], '(00)3', +[182105255, 182105259], '22(00)', +[182223559, 182223562], '3(00)', +[191116404, 191116407], '3(00)', +[191165599, 191165602], '3(00)', +[191297535, 191297539], '(00)22', +[192485616, 192485619], '(00)3', +[193264634, 193264638], '22(00)', +[194696968, 194696971], '(00)3', +[195876805, 195876808], '(00)3', +[195916548, 195916551], '3(00)', +[196395160, 196395163], '3(00)', +[196676303, 196676306], '(00)3', +[197889882, 197889885], '3(00)', +[198014122, 198014125], '(00)3', +[199235289, 199235292], '(00)3', +[201007375, 201007378], '(00)3', +[201030605, 201030608], '3(00)', +[201184290, 201184293], '3(00)', +[201685414, 201685418], '(00)22', +[202762875, 202762878], '3(00)', +[202860957, 202860960], '3(00)', +[203832577, 203832580], '3(00)', +[205880544, 205880547], '(00)3', +[206357111, 206357114], '(00)3', +[207159767, 207159770], '3(00)', +[207167343, 207167346], '3(00)', +[207482539, 207482543], '3(010)', +[207669540, 207669543], '3(00)', +[208053426, 208053429], '(00)3', +[208110027, 208110030], '3(00)', +[209513826, 209513829], '3(00)', +[212623522, 212623525], '(00)3', +[213841715, 213841718], '(00)3', +[214012333, 214012336], '(00)3', +[214073567, 214073570], '(00)3', +[215170600, 215170603], '3(00)', +[215881039, 215881042], '3(00)', +[216274604, 216274607], '3(00)', +[216957120, 216957123], '3(00)', +[217323208, 217323211], '(00)3', +[218799264, 218799267], '(00)3', +[218803557, 218803560], '3(00)', +[219735146, 219735149], '(00)3', +[219830062, 219830065], '3(00)', +[219897904, 219897907], '(00)3', +[221205545, 221205548], '(00)3', +[223601929, 223601932], '(00)3', +[223907076, 223907079], '3(00)', +[223970397, 223970400], '(00)3', +[224874044, 224874048], '22(00)', +[225291157, 225291160], '(00)3', +[227481734, 227481737], '(00)3', +[228006442, 228006445], '3(00)', +[228357900, 228357903], '(00)3', +[228386399, 228386402], '(00)3', +[228907446, 228907449], '(00)3', +[228984552, 228984555], '3(00)', +[229140285, 229140288], '3(00)', +[231810024, 231810027], '(00)3', +[232838062, 232838065], '3(00)', +[234389088, 234389091], '3(00)', +[235588194, 235588197], '(00)3', +[236645695, 236645698], '(00)3', +[236962876, 236962879], '3(00)', +[237516723, 237516727], '04(00)', +[240004911, 240004914], '(00)3', +[240221306, 240221309], '3(00)', +[241389213, 241389217], '(010)3', +[241549003, 241549006], '(00)3', +[241729717, 241729720], '(00)3', +[241743684, 241743687], '3(00)', +[243780200, 243780203], '3(00)', +[243801317, 243801320], '(00)3', +[244122072, 244122075], '(00)3', +[244691224, 244691227], '3(00)', +[244841577, 244841580], '(00)3', +[245813461, 245813464], '(00)3', +[246299475, 246299478], '(00)3', +[246450176, 246450179], '3(00)', +[249069349, 249069352], '(00)3', +[250076378, 250076381], '(00)3', +[252442157, 252442160], '3(00)', +[252904231, 252904234], '3(00)', +[255145220, 255145223], '(00)3', +[255285971, 255285974], '3(00)', +[256713230, 256713233], '(00)3', +[257992082, 257992085], '(00)3', +[258447955, 258447959], '22(00)', +[259298045, 259298048], '3(00)', +[262141503, 262141506], '(00)3', +[263681743, 263681746], '3(00)', +[266527881, 266527885], '(010)3', +[266617122, 266617125], '(00)3', +[266628044, 266628047], '3(00)', +[267305763, 267305766], '(00)3', +[267388404, 267388407], '3(00)', +[267441672, 267441675], '3(00)', +[267464886, 267464889], '(00)3', +[267554907, 267554910], '3(00)', +[269787480, 269787483], '(00)3', +[270881434, 270881437], '(00)3', +[270997583, 270997586], '3(00)', +[272096378, 272096381], '3(00)', +[272583009, 272583012], '(00)3', +[274190881, 274190884], '3(00)', +[274268747, 274268750], '(00)3', +[275297429, 275297432], '3(00)', +[275545476, 275545479], '3(00)', +[275898479, 275898482], '3(00)', +[275953000, 275953003], '(00)3', +[277117197, 277117201], '(00)22', +[277447310, 277447313], '3(00)', +[279059657, 279059660], '3(00)', +[279259144, 279259147], '3(00)', +[279513636, 279513639], '3(00)', +[279849069, 279849072], '3(00)', +[280291419, 280291422], '(00)3', +[281449425, 281449428], '3(00)', +[281507953, 281507956], '3(00)', +[281825600, 281825603], '(00)3', +[282547093, 282547096], '3(00)', +[283120963, 283120966], '3(00)', +[283323493, 283323496], '(00)3', +[284764535, 284764538], '3(00)', +[286172639, 286172642], '3(00)', +[286688824, 286688827], '(00)3', +[287222172, 287222175], '3(00)', +[287235534, 287235537], '3(00)', +[287304861, 287304864], '3(00)', +[287433571, 287433574], '(00)3', +[287823551, 287823554], '(00)3', +[287872422, 287872425], '3(00)', +[288766615, 288766618], '3(00)', +[290122963, 290122966], '3(00)', +[290450849, 290450853], '(00)22', +[291426141, 291426144], '3(00)', +[292810353, 292810356], '3(00)', +[293109861, 293109864], '3(00)', +[293398054, 293398057], '3(00)', +[294134426, 294134429], '3(00)', +[294216438, 294216441], '(00)3', +[295367141, 295367144], '3(00)', +[297834111, 297834114], '3(00)', +[299099969, 299099972], '3(00)', +[300746958, 300746961], '3(00)', +[301097423, 301097426], '(00)3', +[301834209, 301834212], '(00)3', +[302554791, 302554794], '(00)3', +[303497445, 303497448], '3(00)', +[304165344, 304165347], '3(00)', +[304790218, 304790222], '3(010)', +[305302352, 305302355], '(00)3', +[306785996, 306785999], '3(00)', +[307051443, 307051446], '3(00)', +[307481539, 307481542], '3(00)', +[308605569, 308605572], '3(00)', +[309237610, 309237613], '3(00)', +[310509287, 310509290], '(00)3', +[310554057, 310554060], '3(00)', +[310646345, 310646348], '3(00)', +[311274896, 311274899], '(00)3', +[311894272, 311894275], '3(00)', +[312269470, 312269473], '(00)3', +[312306601, 312306605], '(00)40', +[312683193, 312683196], '3(00)', +[314499804, 314499807], '3(00)', +[314636802, 314636805], '(00)3', +[314689897, 314689900], '3(00)', +[314721319, 314721322], '3(00)', +[316132890, 316132893], '3(00)', +[316217470, 316217474], '(010)3', +[316465705, 316465708], '3(00)', +[316542790, 316542793], '(00)3', +[320822347, 320822350], '3(00)', +[321733242, 321733245], '3(00)', +[324413970, 324413973], '(00)3', +[325950140, 325950143], '(00)3', +[326675884, 326675887], '(00)3', +[326704208, 326704211], '3(00)', +[327596247, 327596250], '3(00)', +[328123172, 328123175], '3(00)', +[328182212, 328182215], '(00)3', +[328257498, 328257501], '3(00)', +[328315836, 328315839], '(00)3', +[328800974, 328800977], '(00)3', +[328998509, 328998512], '3(00)', +[329725370, 329725373], '(00)3', +[332080601, 332080604], '(00)3', +[332221246, 332221249], '(00)3', +[332299899, 332299902], '(00)3', +[332532822, 332532825], '(00)3', +[333334544, 333334548], '(00)22', +[333881266, 333881269], '3(00)', +[334703267, 334703270], '3(00)', +[334875138, 334875141], '3(00)', +[336531451, 336531454], '3(00)', +[336825907, 336825910], '(00)3', +[336993167, 336993170], '(00)3', +[337493998, 337494001], '3(00)', +[337861034, 337861037], '3(00)', +[337899191, 337899194], '(00)3', +[337958123, 337958126], '(00)3', +[342331982, 342331985], '3(00)', +[342676068, 342676071], '3(00)', +[347063781, 347063784], '3(00)', +[347697348, 347697351], '3(00)', +[347954319, 347954322], '3(00)', +[348162775, 348162778], '3(00)', +[349210702, 349210705], '(00)3', +[349212913, 349212916], '3(00)', +[349248650, 349248653], '(00)3', +[349913500, 349913503], '3(00)', +[350891529, 350891532], '3(00)', +[351089323, 351089326], '3(00)', +[351826158, 351826161], '3(00)', +[352228580, 352228583], '(00)3', +[352376244, 352376247], '3(00)', +[352853758, 352853761], '(00)3', +[355110439, 355110442], '(00)3', +[355808090, 355808094], '(00)40', +[355941556, 355941559], '3(00)', +[356360231, 356360234], '(00)3', +[356586657, 356586660], '3(00)', +[356892926, 356892929], '(00)3', +[356908232, 356908235], '3(00)', +[357912730, 357912733], '3(00)', +[358120344, 358120347], '3(00)', +[359044096, 359044099], '(00)3', +[360819357, 360819360], '3(00)', +[361399662, 361399666], '(010)3', +[362361315, 362361318], '(00)3', +[363610112, 363610115], '(00)3', +[363964804, 363964807], '3(00)', +[364527375, 364527378], '(00)3', +[365090327, 365090330], '(00)3', +[365414539, 365414542], '3(00)', +[366738474, 366738477], '3(00)', +[368714778, 368714783], '04(010)', +[368831545, 368831548], '(00)3', +[368902387, 368902390], '(00)3', +[370109769, 370109772], '3(00)', +[370963333, 370963336], '3(00)', +[372541136, 372541140], '3(010)', +[372681562, 372681565], '(00)3', +[373009410, 373009413], '(00)3', +[373458970, 373458973], '3(00)', +[375648658, 375648661], '3(00)', +[376834728, 376834731], '3(00)', +[377119945, 377119948], '(00)3', +[377335703, 377335706], '(00)3', +[378091745, 378091748], '3(00)', +[379139522, 379139525], '3(00)', +[380279160, 380279163], '(00)3', +[380619442, 380619445], '3(00)', +[381244231, 381244234], '3(00)', +[382327446, 382327450], '(010)3', +[382357073, 382357076], '3(00)', +[383545479, 383545482], '3(00)', +[384363766, 384363769], '(00)3', +[384401786, 384401790], '22(00)', +[385198212, 385198215], '3(00)', +[385824476, 385824479], '(00)3', +[385908194, 385908197], '3(00)', +[386946806, 386946809], '3(00)', +[387592175, 387592179], '22(00)', +[388329293, 388329296], '(00)3', +[388679566, 388679569], '3(00)', +[388832142, 388832145], '3(00)', +[390087103, 390087106], '(00)3', +[390190926, 390190930], '(00)22', +[390331207, 390331210], '3(00)', +[391674495, 391674498], '3(00)', +[391937831, 391937834], '3(00)', +[391951632, 391951636], '(00)22', +[392963986, 392963989], '(00)3', +[393007921, 393007924], '3(00)', +[393373210, 393373213], '3(00)', +[393759572, 393759575], '(00)3', +[394036662, 394036665], '(00)3', +[395813866, 395813869], '(00)3', +[395956690, 395956693], '3(00)', +[396031670, 396031673], '3(00)', +[397076433, 397076436], '3(00)', +[397470601, 397470604], '3(00)', +[398289458, 398289461], '3(00)', +# +[368714778, 368714783], '04(010)', +[437953499, 437953504], '04(010)', +[526196233, 526196238], '032(00)', +[744719566, 744719571], '(010)40', +[750375857, 750375862], '032(00)', +[958241932, 958241937], '04(010)', +[983377342, 983377347], '(00)410', +[1003780080, 1003780085], '04(010)', +[1070232754, 1070232759], '(00)230', +[1209834865, 1209834870], '032(00)', +[1257209100, 1257209105], '(00)410', +[1368002233, 1368002238], '(00)230' +] diff --git a/venv/lib/python3.12/site-packages/mpmath/identification.py b/venv/lib/python3.12/site-packages/mpmath/identification.py new file mode 100644 index 0000000000000000000000000000000000000000..226f62d3fe9cacedbd9ba2b1e66ff0ad017fa604 --- /dev/null +++ b/venv/lib/python3.12/site-packages/mpmath/identification.py @@ -0,0 +1,844 @@ +""" +Implements the PSLQ algorithm for integer relation detection, +and derivative algorithms for constant recognition. +""" + +from .libmp.backend import xrange +from .libmp import int_types, sqrt_fixed + +# round to nearest integer (can be done more elegantly...) +def round_fixed(x, prec): + return ((x + (1<<(prec-1))) >> prec) << prec + +class IdentificationMethods(object): + pass + + +def pslq(ctx, x, tol=None, maxcoeff=1000, maxsteps=100, verbose=False): + r""" + Given a vector of real numbers `x = [x_0, x_1, ..., x_n]`, ``pslq(x)`` + uses the PSLQ algorithm to find a list of integers + `[c_0, c_1, ..., c_n]` such that + + .. math :: + + |c_1 x_1 + c_2 x_2 + ... + c_n x_n| < \mathrm{tol} + + and such that `\max |c_k| < \mathrm{maxcoeff}`. If no such vector + exists, :func:`~mpmath.pslq` returns ``None``. The tolerance defaults to + 3/4 of the working precision. + + **Examples** + + Find rational approximations for `\pi`:: + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = True + >>> pslq([-1, pi], tol=0.01) + [22, 7] + >>> pslq([-1, pi], tol=0.001) + [355, 113] + >>> mpf(22)/7; mpf(355)/113; +pi + 3.14285714285714 + 3.14159292035398 + 3.14159265358979 + + Pi is not a rational number with denominator less than 1000:: + + >>> pslq([-1, pi]) + >>> + + To within the standard precision, it can however be approximated + by at least one rational number with denominator less than `10^{12}`:: + + >>> p, q = pslq([-1, pi], maxcoeff=10**12) + >>> print(p); print(q) + 238410049439 + 75888275702 + >>> mpf(p)/q + 3.14159265358979 + + The PSLQ algorithm can be applied to long vectors. For example, + we can investigate the rational (in)dependence of integer square + roots:: + + >>> mp.dps = 30 + >>> pslq([sqrt(n) for n in range(2, 5+1)]) + >>> + >>> pslq([sqrt(n) for n in range(2, 6+1)]) + >>> + >>> pslq([sqrt(n) for n in range(2, 8+1)]) + [2, 0, 0, 0, 0, 0, -1] + + **Machin formulas** + + A famous formula for `\pi` is Machin's, + + .. math :: + + \frac{\pi}{4} = 4 \operatorname{acot} 5 - \operatorname{acot} 239 + + There are actually infinitely many formulas of this type. Two + others are + + .. math :: + + \frac{\pi}{4} = \operatorname{acot} 1 + + \frac{\pi}{4} = 12 \operatorname{acot} 49 + 32 \operatorname{acot} 57 + + 5 \operatorname{acot} 239 + 12 \operatorname{acot} 110443 + + We can easily verify the formulas using the PSLQ algorithm:: + + >>> mp.dps = 30 + >>> pslq([pi/4, acot(1)]) + [1, -1] + >>> pslq([pi/4, acot(5), acot(239)]) + [1, -4, 1] + >>> pslq([pi/4, acot(49), acot(57), acot(239), acot(110443)]) + [1, -12, -32, 5, -12] + + We could try to generate a custom Machin-like formula by running + the PSLQ algorithm with a few inverse cotangent values, for example + acot(2), acot(3) ... acot(10). Unfortunately, there is a linear + dependence among these values, resulting in only that dependence + being detected, with a zero coefficient for `\pi`:: + + >>> pslq([pi] + [acot(n) for n in range(2,11)]) + [0, 1, -1, 0, 0, 0, -1, 0, 0, 0] + + We get better luck by removing linearly dependent terms:: + + >>> pslq([pi] + [acot(n) for n in range(2,11) if n not in (3, 5)]) + [1, -8, 0, 0, 4, 0, 0, 0] + + In other words, we found the following formula:: + + >>> 8*acot(2) - 4*acot(7) + 3.14159265358979323846264338328 + >>> +pi + 3.14159265358979323846264338328 + + **Algorithm** + + This is a fairly direct translation to Python of the pseudocode given by + David Bailey, "The PSLQ Integer Relation Algorithm": + http://www.cecm.sfu.ca/organics/papers/bailey/paper/html/node3.html + + The present implementation uses fixed-point instead of floating-point + arithmetic, since this is significantly (about 7x) faster. + """ + + n = len(x) + if n < 2: + raise ValueError("n cannot be less than 2") + + # At too low precision, the algorithm becomes meaningless + prec = ctx.prec + if prec < 53: + raise ValueError("prec cannot be less than 53") + + if verbose and prec // max(2,n) < 5: + print("Warning: precision for PSLQ may be too low") + + target = int(prec * 0.75) + + if tol is None: + tol = ctx.mpf(2)**(-target) + else: + tol = ctx.convert(tol) + + extra = 60 + prec += extra + + if verbose: + print("PSLQ using prec %i and tol %s" % (prec, ctx.nstr(tol))) + + tol = ctx.to_fixed(tol, prec) + assert tol + + # Convert to fixed-point numbers. The dummy None is added so we can + # use 1-based indexing. (This just allows us to be consistent with + # Bailey's indexing. The algorithm is 100 lines long, so debugging + # a single wrong index can be painful.) + x = [None] + [ctx.to_fixed(ctx.mpf(xk), prec) for xk in x] + + # Sanity check on magnitudes + minx = min(abs(xx) for xx in x[1:]) + if not minx: + raise ValueError("PSLQ requires a vector of nonzero numbers") + if minx < tol//100: + if verbose: + print("STOPPING: (one number is too small)") + return None + + g = sqrt_fixed((4<> prec) + s[k] = sqrt_fixed(t, prec) + t = s[1] + y = x[:] + for k in xrange(1, n+1): + y[k] = (x[k] << prec) // t + s[k] = (s[k] << prec) // t + # step 3 + for i in xrange(1, n+1): + for j in xrange(i+1, n): + H[i,j] = 0 + if i <= n-1: + if s[i]: + H[i,i] = (s[i+1] << prec) // s[i] + else: + H[i,i] = 0 + for j in range(1, i): + sjj1 = s[j]*s[j+1] + if sjj1: + H[i,j] = ((-y[i]*y[j])<> prec) + for k in xrange(1, j+1): + H[i,k] = H[i,k] - (t*H[j,k] >> prec) + for k in xrange(1, n+1): + A[i,k] = A[i,k] - (t*A[j,k] >> prec) + B[k,j] = B[k,j] + (t*B[k,i] >> prec) + # Main algorithm + for REP in range(maxsteps): + # Step 1 + m = -1 + szmax = -1 + for i in range(1, n): + h = H[i,i] + sz = (g**i * abs(h)) >> (prec*(i-1)) + if sz > szmax: + m = i + szmax = sz + # Step 2 + y[m], y[m+1] = y[m+1], y[m] + for i in xrange(1,n+1): H[m,i], H[m+1,i] = H[m+1,i], H[m,i] + for i in xrange(1,n+1): A[m,i], A[m+1,i] = A[m+1,i], A[m,i] + for i in xrange(1,n+1): B[i,m], B[i,m+1] = B[i,m+1], B[i,m] + # Step 3 + if m <= n - 2: + t0 = sqrt_fixed((H[m,m]**2 + H[m,m+1]**2)>>prec, prec) + # A zero element probably indicates that the precision has + # been exhausted. XXX: this could be spurious, due to + # using fixed-point arithmetic + if not t0: + break + t1 = (H[m,m] << prec) // t0 + t2 = (H[m,m+1] << prec) // t0 + for i in xrange(m, n+1): + t3 = H[i,m] + t4 = H[i,m+1] + H[i,m] = (t1*t3+t2*t4) >> prec + H[i,m+1] = (-t2*t3+t1*t4) >> prec + # Step 4 + for i in xrange(m+1, n+1): + for j in xrange(min(i-1, m+1), 0, -1): + try: + t = round_fixed((H[i,j] << prec)//H[j,j], prec) + # Precision probably exhausted + except ZeroDivisionError: + break + y[j] = y[j] + ((t*y[i]) >> prec) + for k in xrange(1, j+1): + H[i,k] = H[i,k] - (t*H[j,k] >> prec) + for k in xrange(1, n+1): + A[i,k] = A[i,k] - (t*A[j,k] >> prec) + B[k,j] = B[k,j] + (t*B[k,i] >> prec) + # Until a relation is found, the error typically decreases + # slowly (e.g. a factor 1-10) with each step TODO: we could + # compare err from two successive iterations. If there is a + # large drop (several orders of magnitude), that indicates a + # "high quality" relation was detected. Reporting this to + # the user somehow might be useful. + best_err = maxcoeff<> prec) for j in \ + range(1,n+1)] + if max(abs(v) for v in vec) < maxcoeff: + if verbose: + print("FOUND relation at iter %i/%i, error: %s" % \ + (REP, maxsteps, ctx.nstr(err / ctx.mpf(2)**prec, 1))) + return vec + best_err = min(err, best_err) + # Calculate a lower bound for the norm. We could do this + # more exactly (using the Euclidean norm) but there is probably + # no practical benefit. + recnorm = max(abs(h) for h in H.values()) + if recnorm: + norm = ((1 << (2*prec)) // recnorm) >> prec + norm //= 100 + else: + norm = ctx.inf + if verbose: + print("%i/%i: Error: %8s Norm: %s" % \ + (REP, maxsteps, ctx.nstr(best_err / ctx.mpf(2)**prec, 1), norm)) + if norm >= maxcoeff: + break + if verbose: + print("CANCELLING after step %i/%i." % (REP, maxsteps)) + print("Could not find an integer relation. Norm bound: %s" % norm) + return None + +def findpoly(ctx, x, n=1, **kwargs): + r""" + ``findpoly(x, n)`` returns the coefficients of an integer + polynomial `P` of degree at most `n` such that `P(x) \approx 0`. + If no polynomial having `x` as a root can be found, + :func:`~mpmath.findpoly` returns ``None``. + + :func:`~mpmath.findpoly` works by successively calling :func:`~mpmath.pslq` with + the vectors `[1, x]`, `[1, x, x^2]`, `[1, x, x^2, x^3]`, ..., + `[1, x, x^2, .., x^n]` as input. Keyword arguments given to + :func:`~mpmath.findpoly` are forwarded verbatim to :func:`~mpmath.pslq`. In + particular, you can specify a tolerance for `P(x)` with ``tol`` + and a maximum permitted coefficient size with ``maxcoeff``. + + For large values of `n`, it is recommended to run :func:`~mpmath.findpoly` + at high precision; preferably 50 digits or more. + + **Examples** + + By default (degree `n = 1`), :func:`~mpmath.findpoly` simply finds a linear + polynomial with a rational root:: + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = True + >>> findpoly(0.7) + [-10, 7] + + The generated coefficient list is valid input to ``polyval`` and + ``polyroots``:: + + >>> nprint(polyval(findpoly(phi, 2), phi), 1) + -2.0e-16 + >>> for r in polyroots(findpoly(phi, 2)): + ... print(r) + ... + -0.618033988749895 + 1.61803398874989 + + Numbers of the form `m + n \sqrt p` for integers `(m, n, p)` are + solutions to quadratic equations. As we find here, `1+\sqrt 2` + is a root of the polynomial `x^2 - 2x - 1`:: + + >>> findpoly(1+sqrt(2), 2) + [1, -2, -1] + >>> findroot(lambda x: x**2 - 2*x - 1, 1) + 2.4142135623731 + + Despite only containing square roots, the following number results + in a polynomial of degree 4:: + + >>> findpoly(sqrt(2)+sqrt(3), 4) + [1, 0, -10, 0, 1] + + In fact, `x^4 - 10x^2 + 1` is the *minimal polynomial* of + `r = \sqrt 2 + \sqrt 3`, meaning that a rational polynomial of + lower degree having `r` as a root does not exist. Given sufficient + precision, :func:`~mpmath.findpoly` will usually find the correct + minimal polynomial of a given algebraic number. + + **Non-algebraic numbers** + + If :func:`~mpmath.findpoly` fails to find a polynomial with given + coefficient size and tolerance constraints, that means no such + polynomial exists. + + We can verify that `\pi` is not an algebraic number of degree 3 with + coefficients less than 1000:: + + >>> mp.dps = 15 + >>> findpoly(pi, 3) + >>> + + It is always possible to find an algebraic approximation of a number + using one (or several) of the following methods: + + 1. Increasing the permitted degree + 2. Allowing larger coefficients + 3. Reducing the tolerance + + One example of each method is shown below:: + + >>> mp.dps = 15 + >>> findpoly(pi, 4) + [95, -545, 863, -183, -298] + >>> findpoly(pi, 3, maxcoeff=10000) + [836, -1734, -2658, -457] + >>> findpoly(pi, 3, tol=1e-7) + [-4, 22, -29, -2] + + It is unknown whether Euler's constant is transcendental (or even + irrational). We can use :func:`~mpmath.findpoly` to check that if is + an algebraic number, its minimal polynomial must have degree + at least 7 and a coefficient of magnitude at least 1000000:: + + >>> mp.dps = 200 + >>> findpoly(euler, 6, maxcoeff=10**6, tol=1e-100, maxsteps=1000) + >>> + + Note that the high precision and strict tolerance is necessary + for such high-degree runs, since otherwise unwanted low-accuracy + approximations will be detected. It may also be necessary to set + maxsteps high to prevent a premature exit (before the coefficient + bound has been reached). Running with ``verbose=True`` to get an + idea what is happening can be useful. + """ + x = ctx.mpf(x) + if n < 1: + raise ValueError("n cannot be less than 1") + if x == 0: + return [1, 0] + xs = [ctx.mpf(1)] + for i in range(1,n+1): + xs.append(x**i) + a = ctx.pslq(xs, **kwargs) + if a is not None: + return a[::-1] + +def fracgcd(p, q): + x, y = p, q + while y: + x, y = y, x % y + if x != 1: + p //= x + q //= x + if q == 1: + return p + return p, q + +def pslqstring(r, constants): + q = r[0] + r = r[1:] + s = [] + for i in range(len(r)): + p = r[i] + if p: + z = fracgcd(-p,q) + cs = constants[i][1] + if cs == '1': + cs = '' + else: + cs = '*' + cs + if isinstance(z, int_types): + if z > 0: term = str(z) + cs + else: term = ("(%s)" % z) + cs + else: + term = ("(%s/%s)" % z) + cs + s.append(term) + s = ' + '.join(s) + if '+' in s or '*' in s: + s = '(' + s + ')' + return s or '0' + +def prodstring(r, constants): + q = r[0] + r = r[1:] + num = [] + den = [] + for i in range(len(r)): + p = r[i] + if p: + z = fracgcd(-p,q) + cs = constants[i][1] + if isinstance(z, int_types): + if abs(z) == 1: t = cs + else: t = '%s**%s' % (cs, abs(z)) + ([num,den][z<0]).append(t) + else: + t = '%s**(%s/%s)' % (cs, abs(z[0]), z[1]) + ([num,den][z[0]<0]).append(t) + num = '*'.join(num) + den = '*'.join(den) + if num and den: return "(%s)/(%s)" % (num, den) + if num: return num + if den: return "1/(%s)" % den + +def quadraticstring(ctx,t,a,b,c): + if c < 0: + a,b,c = -a,-b,-c + u1 = (-b+ctx.sqrt(b**2-4*a*c))/(2*c) + u2 = (-b-ctx.sqrt(b**2-4*a*c))/(2*c) + if abs(u1-t) < abs(u2-t): + if b: s = '((%s+sqrt(%s))/%s)' % (-b,b**2-4*a*c,2*c) + else: s = '(sqrt(%s)/%s)' % (-4*a*c,2*c) + else: + if b: s = '((%s-sqrt(%s))/%s)' % (-b,b**2-4*a*c,2*c) + else: s = '(-sqrt(%s)/%s)' % (-4*a*c,2*c) + return s + +# Transformation y = f(x,c), with inverse function x = f(y,c) +# The third entry indicates whether the transformation is +# redundant when c = 1 +transforms = [ + (lambda ctx,x,c: x*c, '$y/$c', 0), + (lambda ctx,x,c: x/c, '$c*$y', 1), + (lambda ctx,x,c: c/x, '$c/$y', 0), + (lambda ctx,x,c: (x*c)**2, 'sqrt($y)/$c', 0), + (lambda ctx,x,c: (x/c)**2, '$c*sqrt($y)', 1), + (lambda ctx,x,c: (c/x)**2, '$c/sqrt($y)', 0), + (lambda ctx,x,c: c*x**2, 'sqrt($y)/sqrt($c)', 1), + (lambda ctx,x,c: x**2/c, 'sqrt($c)*sqrt($y)', 1), + (lambda ctx,x,c: c/x**2, 'sqrt($c)/sqrt($y)', 1), + (lambda ctx,x,c: ctx.sqrt(x*c), '$y**2/$c', 0), + (lambda ctx,x,c: ctx.sqrt(x/c), '$c*$y**2', 1), + (lambda ctx,x,c: ctx.sqrt(c/x), '$c/$y**2', 0), + (lambda ctx,x,c: c*ctx.sqrt(x), '$y**2/$c**2', 1), + (lambda ctx,x,c: ctx.sqrt(x)/c, '$c**2*$y**2', 1), + (lambda ctx,x,c: c/ctx.sqrt(x), '$c**2/$y**2', 1), + (lambda ctx,x,c: ctx.exp(x*c), 'log($y)/$c', 0), + (lambda ctx,x,c: ctx.exp(x/c), '$c*log($y)', 1), + (lambda ctx,x,c: ctx.exp(c/x), '$c/log($y)', 0), + (lambda ctx,x,c: c*ctx.exp(x), 'log($y/$c)', 1), + (lambda ctx,x,c: ctx.exp(x)/c, 'log($c*$y)', 1), + (lambda ctx,x,c: c/ctx.exp(x), 'log($c/$y)', 0), + (lambda ctx,x,c: ctx.ln(x*c), 'exp($y)/$c', 0), + (lambda ctx,x,c: ctx.ln(x/c), '$c*exp($y)', 1), + (lambda ctx,x,c: ctx.ln(c/x), '$c/exp($y)', 0), + (lambda ctx,x,c: c*ctx.ln(x), 'exp($y/$c)', 1), + (lambda ctx,x,c: ctx.ln(x)/c, 'exp($c*$y)', 1), + (lambda ctx,x,c: c/ctx.ln(x), 'exp($c/$y)', 0), +] + +def identify(ctx, x, constants=[], tol=None, maxcoeff=1000, full=False, + verbose=False): + r""" + Given a real number `x`, ``identify(x)`` attempts to find an exact + formula for `x`. This formula is returned as a string. If no match + is found, ``None`` is returned. With ``full=True``, a list of + matching formulas is returned. + + As a simple example, :func:`~mpmath.identify` will find an algebraic + formula for the golden ratio:: + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = True + >>> identify(phi) + '((1+sqrt(5))/2)' + + :func:`~mpmath.identify` can identify simple algebraic numbers and simple + combinations of given base constants, as well as certain basic + transformations thereof. More specifically, :func:`~mpmath.identify` + looks for the following: + + 1. Fractions + 2. Quadratic algebraic numbers + 3. Rational linear combinations of the base constants + 4. Any of the above after first transforming `x` into `f(x)` where + `f(x)` is `1/x`, `\sqrt x`, `x^2`, `\log x` or `\exp x`, either + directly or with `x` or `f(x)` multiplied or divided by one of + the base constants + 5. Products of fractional powers of the base constants and + small integers + + Base constants can be given as a list of strings representing mpmath + expressions (:func:`~mpmath.identify` will ``eval`` the strings to numerical + values and use the original strings for the output), or as a dict of + formula:value pairs. + + In order not to produce spurious results, :func:`~mpmath.identify` should + be used with high precision; preferably 50 digits or more. + + **Examples** + + Simple identifications can be performed safely at standard + precision. Here the default recognition of rational, algebraic, + and exp/log of algebraic numbers is demonstrated:: + + >>> mp.dps = 15 + >>> identify(0.22222222222222222) + '(2/9)' + >>> identify(1.9662210973805663) + 'sqrt(((24+sqrt(48))/8))' + >>> identify(4.1132503787829275) + 'exp((sqrt(8)/2))' + >>> identify(0.881373587019543) + 'log(((2+sqrt(8))/2))' + + By default, :func:`~mpmath.identify` does not recognize `\pi`. At standard + precision it finds a not too useful approximation. At slightly + increased precision, this approximation is no longer accurate + enough and :func:`~mpmath.identify` more correctly returns ``None``:: + + >>> identify(pi) + '(2**(176/117)*3**(20/117)*5**(35/39))/(7**(92/117))' + >>> mp.dps = 30 + >>> identify(pi) + >>> + + Numbers such as `\pi`, and simple combinations of user-defined + constants, can be identified if they are provided explicitly:: + + >>> identify(3*pi-2*e, ['pi', 'e']) + '(3*pi + (-2)*e)' + + Here is an example using a dict of constants. Note that the + constants need not be "atomic"; :func:`~mpmath.identify` can just + as well express the given number in terms of expressions + given by formulas:: + + >>> identify(pi+e, {'a':pi+2, 'b':2*e}) + '((-2) + 1*a + (1/2)*b)' + + Next, we attempt some identifications with a set of base constants. + It is necessary to increase the precision a bit. + + >>> mp.dps = 50 + >>> base = ['sqrt(2)','pi','log(2)'] + >>> identify(0.25, base) + '(1/4)' + >>> identify(3*pi + 2*sqrt(2) + 5*log(2)/7, base) + '(2*sqrt(2) + 3*pi + (5/7)*log(2))' + >>> identify(exp(pi+2), base) + 'exp((2 + 1*pi))' + >>> identify(1/(3+sqrt(2)), base) + '((3/7) + (-1/7)*sqrt(2))' + >>> identify(sqrt(2)/(3*pi+4), base) + 'sqrt(2)/(4 + 3*pi)' + >>> identify(5**(mpf(1)/3)*pi*log(2)**2, base) + '5**(1/3)*pi*log(2)**2' + + An example of an erroneous solution being found when too low + precision is used:: + + >>> mp.dps = 15 + >>> identify(1/(3*pi-4*e+sqrt(8)), ['pi', 'e', 'sqrt(2)']) + '((11/25) + (-158/75)*pi + (76/75)*e + (44/15)*sqrt(2))' + >>> mp.dps = 50 + >>> identify(1/(3*pi-4*e+sqrt(8)), ['pi', 'e', 'sqrt(2)']) + '1/(3*pi + (-4)*e + 2*sqrt(2))' + + **Finding approximate solutions** + + The tolerance ``tol`` defaults to 3/4 of the working precision. + Lowering the tolerance is useful for finding approximate matches. + We can for example try to generate approximations for pi:: + + >>> mp.dps = 15 + >>> identify(pi, tol=1e-2) + '(22/7)' + >>> identify(pi, tol=1e-3) + '(355/113)' + >>> identify(pi, tol=1e-10) + '(5**(339/269))/(2**(64/269)*3**(13/269)*7**(92/269))' + + With ``full=True``, and by supplying a few base constants, + ``identify`` can generate almost endless lists of approximations + for any number (the output below has been truncated to show only + the first few):: + + >>> for p in identify(pi, ['e', 'catalan'], tol=1e-5, full=True): + ... print(p) + ... # doctest: +ELLIPSIS + e/log((6 + (-4/3)*e)) + (3**3*5*e*catalan**2)/(2*7**2) + sqrt(((-13) + 1*e + 22*catalan)) + log(((-6) + 24*e + 4*catalan)/e) + exp(catalan*((-1/5) + (8/15)*e)) + catalan*(6 + (-6)*e + 15*catalan) + sqrt((5 + 26*e + (-3)*catalan))/e + e*sqrt(((-27) + 2*e + 25*catalan)) + log(((-1) + (-11)*e + 59*catalan)) + ((3/20) + (21/20)*e + (3/20)*catalan) + ... + + The numerical values are roughly as close to `\pi` as permitted by the + specified tolerance: + + >>> e/log(6-4*e/3) + 3.14157719846001 + >>> 135*e*catalan**2/98 + 3.14166950419369 + >>> sqrt(e-13+22*catalan) + 3.14158000062992 + >>> log(24*e-6+4*catalan)-1 + 3.14158791577159 + + **Symbolic processing** + + The output formula can be evaluated as a Python expression. + Note however that if fractions (like '2/3') are present in + the formula, Python's :func:`~mpmath.eval()` may erroneously perform + integer division. Note also that the output is not necessarily + in the algebraically simplest form:: + + >>> identify(sqrt(2)) + '(sqrt(8)/2)' + + As a solution to both problems, consider using SymPy's + :func:`~mpmath.sympify` to convert the formula into a symbolic expression. + SymPy can be used to pretty-print or further simplify the formula + symbolically:: + + >>> from sympy import sympify # doctest: +SKIP + >>> sympify(identify(sqrt(2))) # doctest: +SKIP + 2**(1/2) + + Sometimes :func:`~mpmath.identify` can simplify an expression further than + a symbolic algorithm:: + + >>> from sympy import simplify # doctest: +SKIP + >>> x = sympify('-1/(-3/2+(1/2)*5**(1/2))*(3/2-1/2*5**(1/2))**(1/2)') # doctest: +SKIP + >>> x # doctest: +SKIP + (3/2 - 5**(1/2)/2)**(-1/2) + >>> x = simplify(x) # doctest: +SKIP + >>> x # doctest: +SKIP + 2/(6 - 2*5**(1/2))**(1/2) + >>> mp.dps = 30 # doctest: +SKIP + >>> x = sympify(identify(x.evalf(30))) # doctest: +SKIP + >>> x # doctest: +SKIP + 1/2 + 5**(1/2)/2 + + (In fact, this functionality is available directly in SymPy as the + function :func:`~mpmath.nsimplify`, which is essentially a wrapper for + :func:`~mpmath.identify`.) + + **Miscellaneous issues and limitations** + + The input `x` must be a real number. All base constants must be + positive real numbers and must not be rationals or rational linear + combinations of each other. + + The worst-case computation time grows quickly with the number of + base constants. Already with 3 or 4 base constants, + :func:`~mpmath.identify` may require several seconds to finish. To search + for relations among a large number of constants, you should + consider using :func:`~mpmath.pslq` directly. + + The extended transformations are applied to x, not the constants + separately. As a result, ``identify`` will for example be able to + recognize ``exp(2*pi+3)`` with ``pi`` given as a base constant, but + not ``2*exp(pi)+3``. It will be able to recognize the latter if + ``exp(pi)`` is given explicitly as a base constant. + + """ + + solutions = [] + + def addsolution(s): + if verbose: print("Found: ", s) + solutions.append(s) + + x = ctx.mpf(x) + + # Further along, x will be assumed positive + if x == 0: + if full: return ['0'] + else: return '0' + if x < 0: + sol = ctx.identify(-x, constants, tol, maxcoeff, full, verbose) + if sol is None: + return sol + if full: + return ["-(%s)"%s for s in sol] + else: + return "-(%s)" % sol + + if tol: + tol = ctx.mpf(tol) + else: + tol = ctx.eps**0.7 + M = maxcoeff + + if constants: + if isinstance(constants, dict): + constants = [(ctx.mpf(v), name) for (name, v) in sorted(constants.items())] + else: + namespace = dict((name, getattr(ctx,name)) for name in dir(ctx)) + constants = [(eval(p, namespace), p) for p in constants] + else: + constants = [] + + # We always want to find at least rational terms + if 1 not in [value for (name, value) in constants]: + constants = [(ctx.mpf(1), '1')] + constants + + # PSLQ with simple algebraic and functional transformations + for ft, ftn, red in transforms: + for c, cn in constants: + if red and cn == '1': + continue + t = ft(ctx,x,c) + # Prevent exponential transforms from wreaking havoc + if abs(t) > M**2 or abs(t) < tol: + continue + # Linear combination of base constants + r = ctx.pslq([t] + [a[0] for a in constants], tol, M) + s = None + if r is not None and max(abs(uw) for uw in r) <= M and r[0]: + s = pslqstring(r, constants) + # Quadratic algebraic numbers + else: + q = ctx.pslq([ctx.one, t, t**2], tol, M) + if q is not None and len(q) == 3 and q[2]: + aa, bb, cc = q + if max(abs(aa),abs(bb),abs(cc)) <= M: + s = quadraticstring(ctx,t,aa,bb,cc) + if s: + if cn == '1' and ('/$c' in ftn): + s = ftn.replace('$y', s).replace('/$c', '') + else: + s = ftn.replace('$y', s).replace('$c', cn) + addsolution(s) + if not full: return solutions[0] + + if verbose: + print(".") + + # Check for a direct multiplicative formula + if x != 1: + # Allow fractional powers of fractions + ilogs = [2,3,5,7] + # Watch out for existing fractional powers of fractions + logs = [] + for a, s in constants: + if not sum(bool(ctx.findpoly(ctx.ln(a)/ctx.ln(i),1)) for i in ilogs): + logs.append((ctx.ln(a), s)) + logs = [(ctx.ln(i),str(i)) for i in ilogs] + logs + r = ctx.pslq([ctx.ln(x)] + [a[0] for a in logs], tol, M) + if r is not None and max(abs(uw) for uw in r) <= M and r[0]: + addsolution(prodstring(r, logs)) + if not full: return solutions[0] + + if full: + return sorted(solutions, key=len) + else: + return None + +IdentificationMethods.pslq = pslq +IdentificationMethods.findpoly = findpoly +IdentificationMethods.identify = identify + + +if __name__ == '__main__': + import doctest + doctest.testmod() diff --git a/venv/lib/python3.12/site-packages/mpmath/libmp/__init__.py b/venv/lib/python3.12/site-packages/mpmath/libmp/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..1573114afc4fbce73f2ba9d2ddc99882c00027c0 --- /dev/null +++ b/venv/lib/python3.12/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) diff 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b/venv/lib/python3.12/site-packages/mpmath/libmp/backend.py new file mode 100644 index 0000000000000000000000000000000000000000..5610221290a05078f21f09df3c1a76b0e4ccdc02 --- /dev/null +++ b/venv/lib/python3.12/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) diff --git a/venv/lib/python3.12/site-packages/mpmath/libmp/gammazeta.py b/venv/lib/python3.12/site-packages/mpmath/libmp/gammazeta.py new file mode 100644 index 0000000000000000000000000000000000000000..3b05cc63c5f00e6c76d8383853dba06f15e46030 --- /dev/null +++ b/venv/lib/python3.12/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<>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<>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<>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 + 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) diff --git a/venv/lib/python3.12/site-packages/mpmath/libmp/libelefun.py b/venv/lib/python3.12/site-packages/mpmath/libmp/libelefun.py new file mode 100644 index 0000000000000000000000000000000000000000..3de2e5aaef02296ed03dec3df3021b56823f3728 --- /dev/null +++ b/venv/lib/python3.12/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)<> 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)<> 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<> 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-2) + s = (s*isqrt_fast(x<>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 + # 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 + 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< 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)) + 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 + 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<>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<= 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") diff --git a/venv/lib/python3.12/site-packages/mpmath/libmp/libhyper.py b/venv/lib/python3.12/site-packages/mpmath/libmp/libhyper.py new file mode 100644 index 0000000000000000000000000000000000000000..04f52d59710be77819066aea5c1cf4b0883f72d7 --- /dev/null +++ b/venv/lib/python3.12/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<= 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 + 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< 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) diff --git a/venv/lib/python3.12/site-packages/mpmath/libmp/libintmath.py b/venv/lib/python3.12/site-packages/mpmath/libmp/libintmath.py new file mode 100644 index 0000000000000000000000000000000000000000..7880546e135639208d136488408b102ad41682a2 --- /dev/null +++ b/venv/lib/python3.12/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<>> 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<> (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<= '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) diff --git a/venv/lib/python3.12/site-packages/mpmath/libmp/libmpc.py b/venv/lib/python3.12/site-packages/mpmath/libmp/libmpc.py new file mode 100644 index 0000000000000000000000000000000000000000..cc22d0e73674676c8a9249ebc2d48da7f3be8b0d --- /dev/null +++ b/venv/lib/python3.12/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< 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") diff --git a/venv/lib/python3.12/site-packages/mpmath/libmp/libmpf.py b/venv/lib/python3.12/site-packages/mpmath/libmp/libmpf.py new file mode 100644 index 0000000000000000000000000000000000000000..5c162e17d4f688c71dc3476b944e2d31c65faab7 --- /dev/null +++ b/venv/lib/python3.12/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<= 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< 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<= 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 / + 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<= 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<>= 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) diff --git a/venv/lib/python3.12/site-packages/mpmath/math2.py b/venv/lib/python3.12/site-packages/mpmath/math2.py new file mode 100644 index 0000000000000000000000000000000000000000..302e25f509c18b2c76a2b62611f2765db84ab13e --- /dev/null +++ b/venv/lib/python3.12/site-packages/mpmath/math2.py @@ -0,0 +1,672 @@ +""" +This module complements the math and cmath builtin modules by providing +fast machine precision versions of some additional functions (gamma, ...) +and wrapping math/cmath functions so that they can be called with either +real or complex arguments. +""" + +import operator +import math +import cmath + +# Irrational (?) constants +pi = 3.1415926535897932385 +e = 2.7182818284590452354 +sqrt2 = 1.4142135623730950488 +sqrt5 = 2.2360679774997896964 +phi = 1.6180339887498948482 +ln2 = 0.69314718055994530942 +ln10 = 2.302585092994045684 +euler = 0.57721566490153286061 +catalan = 0.91596559417721901505 +khinchin = 2.6854520010653064453 +apery = 1.2020569031595942854 + +logpi = 1.1447298858494001741 + +def _mathfun_real(f_real, f_complex): + def f(x, **kwargs): + if type(x) is float: + return f_real(x) + if type(x) is complex: + return f_complex(x) + try: + x = float(x) + return f_real(x) + except (TypeError, ValueError): + x = complex(x) + return f_complex(x) + f.__name__ = f_real.__name__ + return f + +def _mathfun(f_real, f_complex): + def f(x, **kwargs): + if type(x) is complex: + return f_complex(x) + try: + return f_real(float(x)) + except (TypeError, ValueError): + return f_complex(complex(x)) + f.__name__ = f_real.__name__ + return f + +def _mathfun_n(f_real, f_complex): + def f(*args, **kwargs): + try: + return f_real(*(float(x) for x in args)) + except (TypeError, ValueError): + return f_complex(*(complex(x) for x in args)) + f.__name__ = f_real.__name__ + return f + +# Workaround for non-raising log and sqrt in Python 2.5 and 2.4 +# on Unix system +try: + math.log(-2.0) + def math_log(x): + if x <= 0.0: + raise ValueError("math domain error") + return math.log(x) + def math_sqrt(x): + if x < 0.0: + raise ValueError("math domain error") + return math.sqrt(x) +except (ValueError, TypeError): + math_log = math.log + math_sqrt = math.sqrt + +pow = _mathfun_n(operator.pow, lambda x, y: complex(x)**y) +log = _mathfun_n(math_log, cmath.log) +sqrt = _mathfun(math_sqrt, cmath.sqrt) +exp = _mathfun_real(math.exp, cmath.exp) + +cos = _mathfun_real(math.cos, cmath.cos) +sin = _mathfun_real(math.sin, cmath.sin) +tan = _mathfun_real(math.tan, cmath.tan) + +acos = _mathfun(math.acos, cmath.acos) +asin = _mathfun(math.asin, cmath.asin) +atan = _mathfun_real(math.atan, cmath.atan) + +cosh = _mathfun_real(math.cosh, cmath.cosh) +sinh = _mathfun_real(math.sinh, cmath.sinh) +tanh = _mathfun_real(math.tanh, cmath.tanh) + +floor = _mathfun_real(math.floor, + lambda z: complex(math.floor(z.real), math.floor(z.imag))) +ceil = _mathfun_real(math.ceil, + lambda z: complex(math.ceil(z.real), math.ceil(z.imag))) + + +cos_sin = _mathfun_real(lambda x: (math.cos(x), math.sin(x)), + lambda z: (cmath.cos(z), cmath.sin(z))) + +cbrt = _mathfun(lambda x: x**(1./3), lambda z: z**(1./3)) + +def nthroot(x, n): + r = 1./n + try: + return float(x) ** r + except (ValueError, TypeError): + return complex(x) ** r + +def _sinpi_real(x): + if x < 0: + return -_sinpi_real(-x) + n, r = divmod(x, 0.5) + r *= pi + n %= 4 + if n == 0: return math.sin(r) + if n == 1: return math.cos(r) + if n == 2: return -math.sin(r) + if n == 3: return -math.cos(r) + +def _cospi_real(x): + if x < 0: + x = -x + n, r = divmod(x, 0.5) + r *= pi + n %= 4 + if n == 0: return math.cos(r) + if n == 1: return -math.sin(r) + if n == 2: return -math.cos(r) + if n == 3: return math.sin(r) + +def _sinpi_complex(z): + if z.real < 0: + return -_sinpi_complex(-z) + n, r = divmod(z.real, 0.5) + z = pi*complex(r, z.imag) + n %= 4 + if n == 0: return cmath.sin(z) + if n == 1: return cmath.cos(z) + if n == 2: return -cmath.sin(z) + if n == 3: return -cmath.cos(z) + +def _cospi_complex(z): + if z.real < 0: + z = -z + n, r = divmod(z.real, 0.5) + z = pi*complex(r, z.imag) + n %= 4 + if n == 0: return cmath.cos(z) + if n == 1: return -cmath.sin(z) + if n == 2: return -cmath.cos(z) + if n == 3: return cmath.sin(z) + +cospi = _mathfun_real(_cospi_real, _cospi_complex) +sinpi = _mathfun_real(_sinpi_real, _sinpi_complex) + +def tanpi(x): + try: + return sinpi(x) / cospi(x) + except OverflowError: + if complex(x).imag > 10: + return 1j + if complex(x).imag < 10: + return -1j + raise + +def cotpi(x): + try: + return cospi(x) / sinpi(x) + except OverflowError: + if complex(x).imag > 10: + return -1j + if complex(x).imag < 10: + return 1j + raise + +INF = 1e300*1e300 +NINF = -INF +NAN = INF-INF +EPS = 2.2204460492503131e-16 + +_exact_gamma = (INF, 1.0, 1.0, 2.0, 6.0, 24.0, 120.0, 720.0, 5040.0, 40320.0, + 362880.0, 3628800.0, 39916800.0, 479001600.0, 6227020800.0, 87178291200.0, + 1307674368000.0, 20922789888000.0, 355687428096000.0, 6402373705728000.0, + 121645100408832000.0, 2432902008176640000.0) + +_max_exact_gamma = len(_exact_gamma)-1 + +# Lanczos coefficients used by the GNU Scientific Library +_lanczos_g = 7 +_lanczos_p = (0.99999999999980993, 676.5203681218851, -1259.1392167224028, + 771.32342877765313, -176.61502916214059, 12.507343278686905, + -0.13857109526572012, 9.9843695780195716e-6, 1.5056327351493116e-7) + +def _gamma_real(x): + _intx = int(x) + if _intx == x: + if _intx <= 0: + #return (-1)**_intx * INF + raise ZeroDivisionError("gamma function pole") + if _intx <= _max_exact_gamma: + return _exact_gamma[_intx] + if x < 0.5: + # TODO: sinpi + return pi / (_sinpi_real(x)*_gamma_real(1-x)) + else: + x -= 1.0 + r = _lanczos_p[0] + for i in range(1, _lanczos_g+2): + r += _lanczos_p[i]/(x+i) + t = x + _lanczos_g + 0.5 + return 2.506628274631000502417 * t**(x+0.5) * math.exp(-t) * r + +def _gamma_complex(x): + if not x.imag: + return complex(_gamma_real(x.real)) + if x.real < 0.5: + # TODO: sinpi + return pi / (_sinpi_complex(x)*_gamma_complex(1-x)) + else: + x -= 1.0 + r = _lanczos_p[0] + for i in range(1, _lanczos_g+2): + r += _lanczos_p[i]/(x+i) + t = x + _lanczos_g + 0.5 + return 2.506628274631000502417 * t**(x+0.5) * cmath.exp(-t) * r + +gamma = _mathfun_real(_gamma_real, _gamma_complex) + +def rgamma(x): + try: + return 1./gamma(x) + except ZeroDivisionError: + return x*0.0 + +def factorial(x): + return gamma(x+1.0) + +def arg(x): + if type(x) is float: + return math.atan2(0.0,x) + return math.atan2(x.imag,x.real) + +# XXX: broken for negatives +def loggamma(x): + if type(x) not in (float, complex): + try: + x = float(x) + except (ValueError, TypeError): + x = complex(x) + try: + xreal = x.real + ximag = x.imag + except AttributeError: # py2.5 + xreal = x + ximag = 0.0 + # Reflection formula + # http://functions.wolfram.com/GammaBetaErf/LogGamma/16/01/01/0003/ + if xreal < 0.0: + if abs(x) < 0.5: + v = log(gamma(x)) + if ximag == 0: + v = v.conjugate() + return v + z = 1-x + try: + re = z.real + im = z.imag + except AttributeError: # py2.5 + re = z + im = 0.0 + refloor = floor(re) + if im == 0.0: + imsign = 0 + elif im < 0.0: + imsign = -1 + else: + imsign = 1 + return (-pi*1j)*abs(refloor)*(1-abs(imsign)) + logpi - \ + log(sinpi(z-refloor)) - loggamma(z) + 1j*pi*refloor*imsign + if x == 1.0 or x == 2.0: + return x*0 + p = 0. + while abs(x) < 11: + p -= log(x) + x += 1.0 + s = 0.918938533204672742 + (x-0.5)*log(x) - x + r = 1./x + r2 = r*r + s += 0.083333333333333333333*r; r *= r2 + s += -0.0027777777777777777778*r; r *= r2 + s += 0.00079365079365079365079*r; r *= r2 + s += -0.0005952380952380952381*r; r *= r2 + s += 0.00084175084175084175084*r; r *= r2 + s += -0.0019175269175269175269*r; r *= r2 + s += 0.0064102564102564102564*r; r *= r2 + s += -0.02955065359477124183*r + return s + p + +_psi_coeff = [ +0.083333333333333333333, +-0.0083333333333333333333, +0.003968253968253968254, +-0.0041666666666666666667, +0.0075757575757575757576, +-0.021092796092796092796, +0.083333333333333333333, +-0.44325980392156862745, +3.0539543302701197438, +-26.456212121212121212] + +def _digamma_real(x): + _intx = int(x) + if _intx == x: + if _intx <= 0: + raise ZeroDivisionError("polygamma pole") + if x < 0.5: + x = 1.0-x + s = pi*cotpi(x) + else: + s = 0.0 + while x < 10.0: + s -= 1.0/x + x += 1.0 + x2 = x**-2 + t = x2 + for c in _psi_coeff: + s -= c*t + if t < 1e-20: + break + t *= x2 + return s + math_log(x) - 0.5/x + +def _digamma_complex(x): + if not x.imag: + return complex(_digamma_real(x.real)) + if x.real < 0.5: + x = 1.0-x + s = pi*cotpi(x) + else: + s = 0.0 + while abs(x) < 10.0: + s -= 1.0/x + x += 1.0 + x2 = x**-2 + t = x2 + for c in _psi_coeff: + s -= c*t + if abs(t) < 1e-20: + break + t *= x2 + return s + cmath.log(x) - 0.5/x + +digamma = _mathfun_real(_digamma_real, _digamma_complex) + +# TODO: could implement complex erf and erfc here. Need +# to find an accurate method (avoiding cancellation) +# for approx. 1 < abs(x) < 9. + +_erfc_coeff_P = [ + 1.0000000161203922312, + 2.1275306946297962644, + 2.2280433377390253297, + 1.4695509105618423961, + 0.66275911699770787537, + 0.20924776504163751585, + 0.045459713768411264339, + 0.0063065951710717791934, + 0.00044560259661560421715][::-1] + +_erfc_coeff_Q = [ + 1.0000000000000000000, + 3.2559100272784894318, + 4.9019435608903239131, + 4.4971472894498014205, + 2.7845640601891186528, + 1.2146026030046904138, + 0.37647108453729465912, + 0.080970149639040548613, + 0.011178148899483545902, + 0.00078981003831980423513][::-1] + +def _polyval(coeffs, x): + p = coeffs[0] + for c in coeffs[1:]: + p = c + x*p + return p + +def _erf_taylor(x): + # Taylor series assuming 0 <= x <= 1 + x2 = x*x + s = t = x + n = 1 + while abs(t) > 1e-17: + t *= x2/n + s -= t/(n+n+1) + n += 1 + t *= x2/n + s += t/(n+n+1) + n += 1 + return 1.1283791670955125739*s + +def _erfc_mid(x): + # Rational approximation assuming 0 <= x <= 9 + return exp(-x*x)*_polyval(_erfc_coeff_P,x)/_polyval(_erfc_coeff_Q,x) + +def _erfc_asymp(x): + # Asymptotic expansion assuming x >= 9 + x2 = x*x + v = exp(-x2)/x*0.56418958354775628695 + r = t = 0.5 / x2 + s = 1.0 + for n in range(1,22,4): + s -= t + t *= r * (n+2) + s += t + t *= r * (n+4) + if abs(t) < 1e-17: + break + return s * v + +def erf(x): + """ + erf of a real number. + """ + x = float(x) + if x != x: + return x + if x < 0.0: + return -erf(-x) + if x >= 1.0: + if x >= 6.0: + return 1.0 + return 1.0 - _erfc_mid(x) + return _erf_taylor(x) + +def erfc(x): + """ + erfc of a real number. + """ + x = float(x) + if x != x: + return x + if x < 0.0: + if x < -6.0: + return 2.0 + return 2.0-erfc(-x) + if x > 9.0: + return _erfc_asymp(x) + if x >= 1.0: + return _erfc_mid(x) + return 1.0 - _erf_taylor(x) + +gauss42 = [\ +(0.99839961899006235, 0.0041059986046490839), +(-0.99839961899006235, 0.0041059986046490839), +(0.9915772883408609, 0.009536220301748501), +(-0.9915772883408609,0.009536220301748501), +(0.97934250806374812, 0.014922443697357493), +(-0.97934250806374812, 0.014922443697357493), +(0.96175936533820439,0.020227869569052644), +(-0.96175936533820439, 0.020227869569052644), +(0.93892355735498811, 0.025422959526113047), +(-0.93892355735498811,0.025422959526113047), +(0.91095972490412735, 0.030479240699603467), +(-0.91095972490412735, 0.030479240699603467), +(0.87802056981217269,0.03536907109759211), +(-0.87802056981217269, 0.03536907109759211), +(0.8402859832618168, 0.040065735180692258), +(-0.8402859832618168,0.040065735180692258), +(0.7979620532554873, 0.044543577771965874), +(-0.7979620532554873, 0.044543577771965874), +(0.75127993568948048,0.048778140792803244), +(-0.75127993568948048, 0.048778140792803244), +(0.70049459055617114, 0.052746295699174064), +(-0.70049459055617114,0.052746295699174064), +(0.64588338886924779, 0.056426369358018376), +(-0.64588338886924779, 0.056426369358018376), +(0.58774459748510932, 0.059798262227586649), +(-0.58774459748510932, 0.059798262227586649), +(0.5263957499311922, 0.062843558045002565), +(-0.5263957499311922, 0.062843558045002565), +(0.46217191207042191, 0.065545624364908975), +(-0.46217191207042191, 0.065545624364908975), +(0.39542385204297503, 0.067889703376521934), +(-0.39542385204297503, 0.067889703376521934), +(0.32651612446541151, 0.069862992492594159), +(-0.32651612446541151, 0.069862992492594159), +(0.25582507934287907, 0.071454714265170971), +(-0.25582507934287907, 0.071454714265170971), +(0.18373680656485453, 0.072656175243804091), +(-0.18373680656485453, 0.072656175243804091), +(0.11064502720851986, 0.073460813453467527), +(-0.11064502720851986, 0.073460813453467527), +(0.036948943165351772, 0.073864234232172879), +(-0.036948943165351772, 0.073864234232172879)] + +EI_ASYMP_CONVERGENCE_RADIUS = 40.0 + +def ei_asymp(z, _e1=False): + r = 1./z + s = t = 1.0 + k = 1 + while 1: + t *= k*r + s += t + if abs(t) < 1e-16: + break + k += 1 + v = s*exp(z)/z + if _e1: + if type(z) is complex: + zreal = z.real + zimag = z.imag + else: + zreal = z + zimag = 0.0 + if zimag == 0.0 and zreal > 0.0: + v += pi*1j + else: + if type(z) is complex: + if z.imag > 0: + v += pi*1j + if z.imag < 0: + v -= pi*1j + return v + +def ei_taylor(z, _e1=False): + s = t = z + k = 2 + while 1: + t = t*z/k + term = t/k + if abs(term) < 1e-17: + break + s += term + k += 1 + s += euler + if _e1: + s += log(-z) + else: + if type(z) is float or z.imag == 0.0: + s += math_log(abs(z)) + else: + s += cmath.log(z) + return s + +def ei(z, _e1=False): + typez = type(z) + if typez not in (float, complex): + try: + z = float(z) + typez = float + except (TypeError, ValueError): + z = complex(z) + typez = complex + if not z: + return -INF + absz = abs(z) + if absz > EI_ASYMP_CONVERGENCE_RADIUS: + return ei_asymp(z, _e1) + elif absz <= 2.0 or (typez is float and z > 0.0): + return ei_taylor(z, _e1) + # Integrate, starting from whichever is smaller of a Taylor + # series value or an asymptotic series value + if typez is complex and z.real > 0.0: + zref = z / absz + ref = ei_taylor(zref, _e1) + else: + zref = EI_ASYMP_CONVERGENCE_RADIUS * z / absz + ref = ei_asymp(zref, _e1) + C = (zref-z)*0.5 + D = (zref+z)*0.5 + s = 0.0 + if type(z) is complex: + _exp = cmath.exp + else: + _exp = math.exp + for x,w in gauss42: + t = C*x+D + s += w*_exp(t)/t + ref -= C*s + return ref + +def e1(z): + # hack to get consistent signs if the imaginary part if 0 + # and signed + typez = type(z) + if type(z) not in (float, complex): + try: + z = float(z) + typez = float + except (TypeError, ValueError): + z = complex(z) + typez = complex + if typez is complex and not z.imag: + z = complex(z.real, 0.0) + # end hack + return -ei(-z, _e1=True) + +_zeta_int = [\ +-0.5, +0.0, +1.6449340668482264365,1.2020569031595942854,1.0823232337111381915, +1.0369277551433699263,1.0173430619844491397,1.0083492773819228268, +1.0040773561979443394,1.0020083928260822144,1.0009945751278180853, +1.0004941886041194646,1.0002460865533080483,1.0001227133475784891, +1.0000612481350587048,1.0000305882363070205,1.0000152822594086519, +1.0000076371976378998,1.0000038172932649998,1.0000019082127165539, +1.0000009539620338728,1.0000004769329867878,1.0000002384505027277, +1.0000001192199259653,1.0000000596081890513,1.0000000298035035147, +1.0000000149015548284] + +_zeta_P = [-3.50000000087575873, -0.701274355654678147, +-0.0672313458590012612, -0.00398731457954257841, +-0.000160948723019303141, -4.67633010038383371e-6, +-1.02078104417700585e-7, -1.68030037095896287e-9, +-1.85231868742346722e-11][::-1] + +_zeta_Q = [1.00000000000000000, -0.936552848762465319, +-0.0588835413263763741, -0.00441498861482948666, +-0.000143416758067432622, -5.10691659585090782e-6, +-9.58813053268913799e-8, -1.72963791443181972e-9, +-1.83527919681474132e-11][::-1] + +_zeta_1 = [3.03768838606128127e-10, -1.21924525236601262e-8, +2.01201845887608893e-7, -1.53917240683468381e-6, +-5.09890411005967954e-7, 0.000122464707271619326, +-0.000905721539353130232, -0.00239315326074843037, +0.084239750013159168, 0.418938517907442414, 0.500000001921884009] + +_zeta_0 = [-3.46092485016748794e-10, -6.42610089468292485e-9, +1.76409071536679773e-7, -1.47141263991560698e-6, -6.38880222546167613e-7, +0.000122641099800668209, -0.000905894913516772796, -0.00239303348507992713, +0.0842396947501199816, 0.418938533204660256, 0.500000000000000052] + +def zeta(s): + """ + Riemann zeta function, real argument + """ + if not isinstance(s, (float, int)): + try: + s = float(s) + except (ValueError, TypeError): + try: + s = complex(s) + if not s.imag: + return complex(zeta(s.real)) + except (ValueError, TypeError): + pass + raise NotImplementedError + if s == 1: + raise ValueError("zeta(1) pole") + if s >= 27: + return 1.0 + 2.0**(-s) + 3.0**(-s) + n = int(s) + if n == s: + if n >= 0: + return _zeta_int[n] + if not (n % 2): + return 0.0 + if s <= 0.0: + return 2.**s*pi**(s-1)*_sinpi_real(0.5*s)*_gamma_real(1-s)*zeta(1-s) + if s <= 2.0: + if s <= 1.0: + return _polyval(_zeta_0,s)/(s-1) + return _polyval(_zeta_1,s)/(s-1) + z = _polyval(_zeta_P,s) / _polyval(_zeta_Q,s) + return 1.0 + 2.0**(-s) + 3.0**(-s) + 4.0**(-s)*z diff --git a/venv/lib/python3.12/site-packages/mpmath/matrices/__init__.py b/venv/lib/python3.12/site-packages/mpmath/matrices/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..293697b9fcf8bd82d58ac4ff45acd73fadac82f9 --- /dev/null +++ b/venv/lib/python3.12/site-packages/mpmath/matrices/__init__.py @@ -0,0 +1,2 @@ +from . import eigen # to set methods +from . import eigen_symmetric # to set methods diff --git a/venv/lib/python3.12/site-packages/mpmath/matrices/__pycache__/__init__.cpython-312.pyc b/venv/lib/python3.12/site-packages/mpmath/matrices/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..71aa45753e7b5314967f43f2f2c2c7cba2dfa0e1 Binary files /dev/null and b/venv/lib/python3.12/site-packages/mpmath/matrices/__pycache__/__init__.cpython-312.pyc differ diff --git a/venv/lib/python3.12/site-packages/mpmath/matrices/__pycache__/calculus.cpython-312.pyc b/venv/lib/python3.12/site-packages/mpmath/matrices/__pycache__/calculus.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..413048e7ac6c774be854761dd8578bb1396d1d99 Binary files /dev/null and b/venv/lib/python3.12/site-packages/mpmath/matrices/__pycache__/calculus.cpython-312.pyc differ diff --git a/venv/lib/python3.12/site-packages/mpmath/matrices/__pycache__/eigen.cpython-312.pyc b/venv/lib/python3.12/site-packages/mpmath/matrices/__pycache__/eigen.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..e8531149ea8ca3cce36d5b32b8d203aa3b2e0c89 Binary files /dev/null and b/venv/lib/python3.12/site-packages/mpmath/matrices/__pycache__/eigen.cpython-312.pyc differ diff --git a/venv/lib/python3.12/site-packages/mpmath/matrices/__pycache__/eigen_symmetric.cpython-312.pyc b/venv/lib/python3.12/site-packages/mpmath/matrices/__pycache__/eigen_symmetric.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..b454c5b3e53374b6da0d95bfa74171e60efe0eca Binary files /dev/null and b/venv/lib/python3.12/site-packages/mpmath/matrices/__pycache__/eigen_symmetric.cpython-312.pyc differ diff --git a/venv/lib/python3.12/site-packages/mpmath/matrices/__pycache__/linalg.cpython-312.pyc b/venv/lib/python3.12/site-packages/mpmath/matrices/__pycache__/linalg.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..6052143c0635b56e14cbf84b2eae18e119d04a7a Binary files /dev/null and b/venv/lib/python3.12/site-packages/mpmath/matrices/__pycache__/linalg.cpython-312.pyc differ diff --git a/venv/lib/python3.12/site-packages/mpmath/matrices/__pycache__/matrices.cpython-312.pyc b/venv/lib/python3.12/site-packages/mpmath/matrices/__pycache__/matrices.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..b3391eb35a809d1500b1d12d4cdd8b5e8b695218 Binary files /dev/null and b/venv/lib/python3.12/site-packages/mpmath/matrices/__pycache__/matrices.cpython-312.pyc differ diff --git a/venv/lib/python3.12/site-packages/mpmath/matrices/calculus.py b/venv/lib/python3.12/site-packages/mpmath/matrices/calculus.py new file mode 100644 index 0000000000000000000000000000000000000000..7fae2a7a9a29898241ed41810331b480ff70798f --- /dev/null +++ b/venv/lib/python3.12/site-packages/mpmath/matrices/calculus.py @@ -0,0 +1,531 @@ +from ..libmp.backend import xrange + +# TODO: should use diagonalization-based algorithms + +class MatrixCalculusMethods(object): + + def _exp_pade(ctx, a): + """ + Exponential of a matrix using Pade approximants. + + See G. H. Golub, C. F. van Loan 'Matrix Computations', + third Ed., page 572 + + TODO: + - find a good estimate for q + - reduce the number of matrix multiplications to improve + performance + """ + def eps_pade(p): + return ctx.mpf(2)**(3-2*p) * \ + ctx.factorial(p)**2/(ctx.factorial(2*p)**2 * (2*p + 1)) + q = 4 + extraq = 8 + while 1: + if eps_pade(q) < ctx.eps: + break + q += 1 + q += extraq + j = int(max(1, ctx.mag(ctx.mnorm(a,'inf')))) + extra = q + prec = ctx.prec + ctx.dps += extra + 3 + try: + a = a/2**j + na = a.rows + den = ctx.eye(na) + num = ctx.eye(na) + x = ctx.eye(na) + c = ctx.mpf(1) + for k in range(1, q+1): + c *= ctx.mpf(q - k + 1)/((2*q - k + 1) * k) + x = a*x + cx = c*x + num += cx + den += (-1)**k * cx + f = ctx.lu_solve_mat(den, num) + for k in range(j): + f = f*f + finally: + ctx.prec = prec + return f*1 + + def expm(ctx, A, method='taylor'): + r""" + Computes the matrix exponential of a square matrix `A`, which is defined + by the power series + + .. math :: + + \exp(A) = I + A + \frac{A^2}{2!} + \frac{A^3}{3!} + \ldots + + With method='taylor', the matrix exponential is computed + using the Taylor series. With method='pade', Pade approximants + are used instead. + + **Examples** + + Basic examples:: + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = True + >>> expm(zeros(3)) + [1.0 0.0 0.0] + [0.0 1.0 0.0] + [0.0 0.0 1.0] + >>> expm(eye(3)) + [2.71828182845905 0.0 0.0] + [ 0.0 2.71828182845905 0.0] + [ 0.0 0.0 2.71828182845905] + >>> expm([[1,1,0],[1,0,1],[0,1,0]]) + [ 3.86814500615414 2.26812870852145 0.841130841230196] + [ 2.26812870852145 2.44114713886289 1.42699786729125] + [0.841130841230196 1.42699786729125 1.6000162976327] + >>> expm([[1,1,0],[1,0,1],[0,1,0]], method='pade') + [ 3.86814500615414 2.26812870852145 0.841130841230196] + [ 2.26812870852145 2.44114713886289 1.42699786729125] + [0.841130841230196 1.42699786729125 1.6000162976327] + >>> expm([[1+j, 0], [1+j,1]]) + [(1.46869393991589 + 2.28735528717884j) 0.0] + [ (1.03776739863568 + 3.536943175722j) (2.71828182845905 + 0.0j)] + + Matrices with large entries are allowed:: + + >>> expm(matrix([[1,2],[2,3]])**25) + [5.65024064048415e+2050488462815550 9.14228140091932e+2050488462815550] + [9.14228140091932e+2050488462815550 1.47925220414035e+2050488462815551] + + The identity `\exp(A+B) = \exp(A) \exp(B)` does not hold for + noncommuting matrices:: + + >>> A = hilbert(3) + >>> B = A + eye(3) + >>> chop(mnorm(A*B - B*A)) + 0.0 + >>> chop(mnorm(expm(A+B) - expm(A)*expm(B))) + 0.0 + >>> B = A + ones(3) + >>> mnorm(A*B - B*A) + 1.8 + >>> mnorm(expm(A+B) - expm(A)*expm(B)) + 42.0927851137247 + + """ + if method == 'pade': + prec = ctx.prec + try: + A = ctx.matrix(A) + ctx.prec += 2*A.rows + res = ctx._exp_pade(A) + finally: + ctx.prec = prec + return res + A = ctx.matrix(A) + prec = ctx.prec + j = int(max(1, ctx.mag(ctx.mnorm(A,'inf')))) + j += int(0.5*prec**0.5) + try: + ctx.prec += 10 + 2*j + tol = +ctx.eps + A = A/2**j + T = A + Y = A**0 + A + k = 2 + while 1: + T *= A * (1/ctx.mpf(k)) + if ctx.mnorm(T, 'inf') < tol: + break + Y += T + k += 1 + for k in xrange(j): + Y = Y*Y + finally: + ctx.prec = prec + Y *= 1 + return Y + + def cosm(ctx, A): + r""" + Gives the cosine of a square matrix `A`, defined in analogy + with the matrix exponential. + + Examples:: + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = True + >>> X = eye(3) + >>> cosm(X) + [0.54030230586814 0.0 0.0] + [ 0.0 0.54030230586814 0.0] + [ 0.0 0.0 0.54030230586814] + >>> X = hilbert(3) + >>> cosm(X) + [ 0.424403834569555 -0.316643413047167 -0.221474945949293] + [-0.316643413047167 0.820646708837824 -0.127183694770039] + [-0.221474945949293 -0.127183694770039 0.909236687217541] + >>> X = matrix([[1+j,-2],[0,-j]]) + >>> cosm(X) + [(0.833730025131149 - 0.988897705762865j) (1.07485840848393 - 0.17192140544213j)] + [ 0.0 (1.54308063481524 + 0.0j)] + """ + B = 0.5 * (ctx.expm(A*ctx.j) + ctx.expm(A*(-ctx.j))) + if not sum(A.apply(ctx.im).apply(abs)): + B = B.apply(ctx.re) + return B + + def sinm(ctx, A): + r""" + Gives the sine of a square matrix `A`, defined in analogy + with the matrix exponential. + + Examples:: + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = True + >>> X = eye(3) + >>> sinm(X) + [0.841470984807897 0.0 0.0] + [ 0.0 0.841470984807897 0.0] + [ 0.0 0.0 0.841470984807897] + >>> X = hilbert(3) + >>> sinm(X) + [0.711608512150994 0.339783913247439 0.220742837314741] + [0.339783913247439 0.244113865695532 0.187231271174372] + [0.220742837314741 0.187231271174372 0.155816730769635] + >>> X = matrix([[1+j,-2],[0,-j]]) + >>> sinm(X) + [(1.29845758141598 + 0.634963914784736j) (-1.96751511930922 + 0.314700021761367j)] + [ 0.0 (0.0 - 1.1752011936438j)] + """ + B = (-0.5j) * (ctx.expm(A*ctx.j) - ctx.expm(A*(-ctx.j))) + if not sum(A.apply(ctx.im).apply(abs)): + B = B.apply(ctx.re) + return B + + def _sqrtm_rot(ctx, A, _may_rotate): + # If the iteration fails to converge, cheat by performing + # a rotation by a complex number + u = ctx.j**0.3 + return ctx.sqrtm(u*A, _may_rotate) / ctx.sqrt(u) + + def sqrtm(ctx, A, _may_rotate=2): + r""" + Computes a square root of the square matrix `A`, i.e. returns + a matrix `B = A^{1/2}` such that `B^2 = A`. The square root + of a matrix, if it exists, is not unique. + + **Examples** + + Square roots of some simple matrices:: + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = True + >>> sqrtm([[1,0], [0,1]]) + [1.0 0.0] + [0.0 1.0] + >>> sqrtm([[0,0], [0,0]]) + [0.0 0.0] + [0.0 0.0] + >>> sqrtm([[2,0],[0,1]]) + [1.4142135623731 0.0] + [ 0.0 1.0] + >>> sqrtm([[1,1],[1,0]]) + [ (0.920442065259926 - 0.21728689675164j) (0.568864481005783 + 0.351577584254143j)] + [(0.568864481005783 + 0.351577584254143j) (0.351577584254143 - 0.568864481005783j)] + >>> sqrtm([[1,0],[0,1]]) + [1.0 0.0] + [0.0 1.0] + >>> sqrtm([[-1,0],[0,1]]) + [(0.0 - 1.0j) 0.0] + [ 0.0 (1.0 + 0.0j)] + >>> sqrtm([[j,0],[0,j]]) + [(0.707106781186547 + 0.707106781186547j) 0.0] + [ 0.0 (0.707106781186547 + 0.707106781186547j)] + + A square root of a rotation matrix, giving the corresponding + half-angle rotation matrix:: + + >>> t1 = 0.75 + >>> t2 = t1 * 0.5 + >>> A1 = matrix([[cos(t1), -sin(t1)], [sin(t1), cos(t1)]]) + >>> A2 = matrix([[cos(t2), -sin(t2)], [sin(t2), cos(t2)]]) + >>> sqrtm(A1) + [0.930507621912314 -0.366272529086048] + [0.366272529086048 0.930507621912314] + >>> A2 + [0.930507621912314 -0.366272529086048] + [0.366272529086048 0.930507621912314] + + The identity `(A^2)^{1/2} = A` does not necessarily hold:: + + >>> A = matrix([[4,1,4],[7,8,9],[10,2,11]]) + >>> sqrtm(A**2) + [ 4.0 1.0 4.0] + [ 7.0 8.0 9.0] + [10.0 2.0 11.0] + >>> sqrtm(A)**2 + [ 4.0 1.0 4.0] + [ 7.0 8.0 9.0] + [10.0 2.0 11.0] + >>> A = matrix([[-4,1,4],[7,-8,9],[10,2,11]]) + >>> sqrtm(A**2) + [ 7.43715112194995 -0.324127569985474 1.8481718827526] + [-0.251549715716942 9.32699765900402 2.48221180985147] + [ 4.11609388833616 0.775751877098258 13.017955697342] + >>> chop(sqrtm(A)**2) + [-4.0 1.0 4.0] + [ 7.0 -8.0 9.0] + [10.0 2.0 11.0] + + For some matrices, a square root does not exist:: + + >>> sqrtm([[0,1], [0,0]]) + Traceback (most recent call last): + ... + ZeroDivisionError: matrix is numerically singular + + Two examples from the documentation for Matlab's ``sqrtm``:: + + >>> mp.dps = 15; mp.pretty = True + >>> sqrtm([[7,10],[15,22]]) + [1.56669890360128 1.74077655955698] + [2.61116483933547 4.17786374293675] + >>> + >>> X = matrix(\ + ... [[5,-4,1,0,0], + ... [-4,6,-4,1,0], + ... [1,-4,6,-4,1], + ... [0,1,-4,6,-4], + ... [0,0,1,-4,5]]) + >>> Y = matrix(\ + ... [[2,-1,-0,-0,-0], + ... [-1,2,-1,0,-0], + ... [0,-1,2,-1,0], + ... [-0,0,-1,2,-1], + ... [-0,-0,-0,-1,2]]) + >>> mnorm(sqrtm(X) - Y) + 4.53155328326114e-19 + + """ + A = ctx.matrix(A) + # Trivial + if A*0 == A: + return A + prec = ctx.prec + if _may_rotate: + d = ctx.det(A) + if abs(ctx.im(d)) < 16*ctx.eps and ctx.re(d) < 0: + return ctx._sqrtm_rot(A, _may_rotate-1) + try: + ctx.prec += 10 + tol = ctx.eps * 128 + Y = A + Z = I = A**0 + k = 0 + # Denman-Beavers iteration + while 1: + Yprev = Y + try: + Y, Z = 0.5*(Y+ctx.inverse(Z)), 0.5*(Z+ctx.inverse(Y)) + except ZeroDivisionError: + if _may_rotate: + Y = ctx._sqrtm_rot(A, _may_rotate-1) + break + else: + raise + mag1 = ctx.mnorm(Y-Yprev, 'inf') + mag2 = ctx.mnorm(Y, 'inf') + if mag1 <= mag2*tol: + break + if _may_rotate and k > 6 and not mag1 < mag2 * 0.001: + return ctx._sqrtm_rot(A, _may_rotate-1) + k += 1 + if k > ctx.prec: + raise ctx.NoConvergence + finally: + ctx.prec = prec + Y *= 1 + return Y + + def logm(ctx, A): + r""" + Computes a logarithm of the square matrix `A`, i.e. returns + a matrix `B = \log(A)` such that `\exp(B) = A`. The logarithm + of a matrix, if it exists, is not unique. + + **Examples** + + Logarithms of some simple matrices:: + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = True + >>> X = eye(3) + >>> logm(X) + [0.0 0.0 0.0] + [0.0 0.0 0.0] + [0.0 0.0 0.0] + >>> logm(2*X) + [0.693147180559945 0.0 0.0] + [ 0.0 0.693147180559945 0.0] + [ 0.0 0.0 0.693147180559945] + >>> logm(expm(X)) + [1.0 0.0 0.0] + [0.0 1.0 0.0] + [0.0 0.0 1.0] + + A logarithm of a complex matrix:: + + >>> X = matrix([[2+j, 1, 3], [1-j, 1-2*j, 1], [-4, -5, j]]) + >>> B = logm(X) + >>> nprint(B) + [ (0.808757 + 0.107759j) (2.20752 + 0.202762j) (1.07376 - 0.773874j)] + [ (0.905709 - 0.107795j) (0.0287395 - 0.824993j) (0.111619 + 0.514272j)] + [(-0.930151 + 0.399512j) (-2.06266 - 0.674397j) (0.791552 + 0.519839j)] + >>> chop(expm(B)) + [(2.0 + 1.0j) 1.0 3.0] + [(1.0 - 1.0j) (1.0 - 2.0j) 1.0] + [ -4.0 -5.0 (0.0 + 1.0j)] + + A matrix `X` close to the identity matrix, for which + `\log(\exp(X)) = \exp(\log(X)) = X` holds:: + + >>> X = eye(3) + hilbert(3)/4 + >>> X + [ 1.25 0.125 0.0833333333333333] + [ 0.125 1.08333333333333 0.0625] + [0.0833333333333333 0.0625 1.05] + >>> logm(expm(X)) + [ 1.25 0.125 0.0833333333333333] + [ 0.125 1.08333333333333 0.0625] + [0.0833333333333333 0.0625 1.05] + >>> expm(logm(X)) + [ 1.25 0.125 0.0833333333333333] + [ 0.125 1.08333333333333 0.0625] + [0.0833333333333333 0.0625 1.05] + + A logarithm of a rotation matrix, giving back the angle of + the rotation:: + + >>> t = 3.7 + >>> A = matrix([[cos(t),sin(t)],[-sin(t),cos(t)]]) + >>> chop(logm(A)) + [ 0.0 -2.58318530717959] + [2.58318530717959 0.0] + >>> (2*pi-t) + 2.58318530717959 + + For some matrices, a logarithm does not exist:: + + >>> logm([[1,0], [0,0]]) + Traceback (most recent call last): + ... + ZeroDivisionError: matrix is numerically singular + + Logarithm of a matrix with large entries:: + + >>> logm(hilbert(3) * 10**20).apply(re) + [ 45.5597513593433 1.27721006042799 0.317662687717978] + [ 1.27721006042799 42.5222778973542 2.24003708791604] + [0.317662687717978 2.24003708791604 42.395212822267] + + """ + A = ctx.matrix(A) + prec = ctx.prec + try: + ctx.prec += 10 + tol = ctx.eps * 128 + I = A**0 + B = A + n = 0 + while 1: + B = ctx.sqrtm(B) + n += 1 + if ctx.mnorm(B-I, 'inf') < 0.125: + break + T = X = B-I + L = X*0 + k = 1 + while 1: + if k & 1: + L += T / k + else: + L -= T / k + T *= X + if ctx.mnorm(T, 'inf') < tol: + break + k += 1 + if k > ctx.prec: + raise ctx.NoConvergence + finally: + ctx.prec = prec + L *= 2**n + return L + + def powm(ctx, A, r): + r""" + Computes `A^r = \exp(A \log r)` for a matrix `A` and complex + number `r`. + + **Examples** + + Powers and inverse powers of a matrix:: + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = True + >>> A = matrix([[4,1,4],[7,8,9],[10,2,11]]) + >>> powm(A, 2) + [ 63.0 20.0 69.0] + [174.0 89.0 199.0] + [164.0 48.0 179.0] + >>> chop(powm(powm(A, 4), 1/4.)) + [ 4.0 1.0 4.0] + [ 7.0 8.0 9.0] + [10.0 2.0 11.0] + >>> powm(extraprec(20)(powm)(A, -4), -1/4.) + [ 4.0 1.0 4.0] + [ 7.0 8.0 9.0] + [10.0 2.0 11.0] + >>> chop(powm(powm(A, 1+0.5j), 1/(1+0.5j))) + [ 4.0 1.0 4.0] + [ 7.0 8.0 9.0] + [10.0 2.0 11.0] + >>> powm(extraprec(5)(powm)(A, -1.5), -1/(1.5)) + [ 4.0 1.0 4.0] + [ 7.0 8.0 9.0] + [10.0 2.0 11.0] + + A Fibonacci-generating matrix:: + + >>> powm([[1,1],[1,0]], 10) + [89.0 55.0] + [55.0 34.0] + >>> fib(10) + 55.0 + >>> powm([[1,1],[1,0]], 6.5) + [(16.5166626964253 - 0.0121089837381789j) (10.2078589271083 + 0.0195927472575932j)] + [(10.2078589271083 + 0.0195927472575932j) (6.30880376931698 - 0.0317017309957721j)] + >>> (phi**6.5 - (1-phi)**6.5)/sqrt(5) + (10.2078589271083 - 0.0195927472575932j) + >>> powm([[1,1],[1,0]], 6.2) + [ (14.3076953002666 - 0.008222855781077j) (8.81733464837593 + 0.0133048601383712j)] + [(8.81733464837593 + 0.0133048601383712j) (5.49036065189071 - 0.0215277159194482j)] + >>> (phi**6.2 - (1-phi)**6.2)/sqrt(5) + (8.81733464837593 - 0.0133048601383712j) + + """ + A = ctx.matrix(A) + r = ctx.convert(r) + prec = ctx.prec + try: + ctx.prec += 10 + if ctx.isint(r): + v = A ** int(r) + elif ctx.isint(r*2): + y = int(r*2) + v = ctx.sqrtm(A) ** y + else: + v = ctx.expm(r*ctx.logm(A)) + finally: + ctx.prec = prec + v *= 1 + return v diff --git a/venv/lib/python3.12/site-packages/mpmath/matrices/eigen.py b/venv/lib/python3.12/site-packages/mpmath/matrices/eigen.py new file mode 100644 index 0000000000000000000000000000000000000000..885d604203195b695183329acc637de91aeaf5ea --- /dev/null +++ b/venv/lib/python3.12/site-packages/mpmath/matrices/eigen.py @@ -0,0 +1,877 @@ +#!/usr/bin/python +# -*- coding: utf-8 -*- + +################################################################################################## +# module for the eigenvalue problem +# Copyright 2013 Timo Hartmann (thartmann15 at gmail.com) +# +# todo: +# - implement balancing +# - agressive early deflation +# +################################################################################################## + +""" +The eigenvalue problem +---------------------- + +This file contains routines for the eigenvalue problem. + +high level routines: + + hessenberg : reduction of a real or complex square matrix to upper Hessenberg form + schur : reduction of a real or complex square matrix to upper Schur form + eig : eigenvalues and eigenvectors of a real or complex square matrix + +low level routines: + + hessenberg_reduce_0 : reduction of a real or complex square matrix to upper Hessenberg form + hessenberg_reduce_1 : auxiliary routine to hessenberg_reduce_0 + qr_step : a single implicitly shifted QR step for an upper Hessenberg matrix + hessenberg_qr : Schur decomposition of an upper Hessenberg matrix + eig_tr_r : right eigenvectors of an upper triangular matrix + eig_tr_l : left eigenvectors of an upper triangular matrix +""" + +from ..libmp.backend import xrange + +class Eigen(object): + pass + +def defun(f): + setattr(Eigen, f.__name__, f) + return f + +def hessenberg_reduce_0(ctx, A, T): + """ + This routine computes the (upper) Hessenberg decomposition of a square matrix A. + Given A, an unitary matrix Q is calculated such that + + Q' A Q = H and Q' Q = Q Q' = 1 + + where H is an upper Hessenberg matrix, meaning that it only contains zeros + below the first subdiagonal. Here ' denotes the hermitian transpose (i.e. + transposition and conjugation). + + parameters: + A (input/output) On input, A contains the square matrix A of + dimension (n,n). On output, A contains a compressed representation + of Q and H. + T (output) An array of length n containing the first elements of + the Householder reflectors. + """ + + # internally we work with householder reflections from the right. + # let u be a row vector (i.e. u[i]=A[i,:i]). then + # Q is build up by reflectors of the type (1-v'v) where v is a suitable + # modification of u. these reflectors are applyed to A from the right. + # because we work with reflectors from the right we have to start with + # the bottom row of A and work then upwards (this corresponds to + # some kind of RQ decomposition). + # the first part of the vectors v (i.e. A[i,:(i-1)]) are stored as row vectors + # in the lower left part of A (excluding the diagonal and subdiagonal). + # the last entry of v is stored in T. + # the upper right part of A (including diagonal and subdiagonal) becomes H. + + + n = A.rows + if n <= 2: return + + for i in xrange(n-1, 1, -1): + + # scale the vector + + scale = 0 + for k in xrange(0, i): + scale += abs(ctx.re(A[i,k])) + abs(ctx.im(A[i,k])) + + scale_inv = 0 + if scale != 0: + scale_inv = 1 / scale + + if scale == 0 or ctx.isinf(scale_inv): + # sadly there are floating point numbers not equal to zero whose reciprocal is infinity + T[i] = 0 + A[i,i-1] = 0 + continue + + # calculate parameters for housholder transformation + + H = 0 + for k in xrange(0, i): + A[i,k] *= scale_inv + rr = ctx.re(A[i,k]) + ii = ctx.im(A[i,k]) + H += rr * rr + ii * ii + + F = A[i,i-1] + f = abs(F) + G = ctx.sqrt(H) + A[i,i-1] = - G * scale + + if f == 0: + T[i] = G + else: + ff = F / f + T[i] = F + G * ff + A[i,i-1] *= ff + + H += G * f + H = 1 / ctx.sqrt(H) + + T[i] *= H + for k in xrange(0, i - 1): + A[i,k] *= H + + for j in xrange(0, i): + # apply housholder transformation (from right) + + G = ctx.conj(T[i]) * A[j,i-1] + for k in xrange(0, i-1): + G += ctx.conj(A[i,k]) * A[j,k] + + A[j,i-1] -= G * T[i] + for k in xrange(0, i-1): + A[j,k] -= G * A[i,k] + + for j in xrange(0, n): + # apply housholder transformation (from left) + + G = T[i] * A[i-1,j] + for k in xrange(0, i-1): + G += A[i,k] * A[k,j] + + A[i-1,j] -= G * ctx.conj(T[i]) + for k in xrange(0, i-1): + A[k,j] -= G * ctx.conj(A[i,k]) + + + +def hessenberg_reduce_1(ctx, A, T): + """ + This routine forms the unitary matrix Q described in hessenberg_reduce_0. + + parameters: + A (input/output) On input, A is the same matrix as delivered by + hessenberg_reduce_0. On output, A is set to Q. + + T (input) On input, T is the same array as delivered by hessenberg_reduce_0. + """ + + n = A.rows + + if n == 1: + A[0,0] = 1 + return + + A[0,0] = A[1,1] = 1 + A[0,1] = A[1,0] = 0 + + for i in xrange(2, n): + if T[i] != 0: + + for j in xrange(0, i): + G = T[i] * A[i-1,j] + for k in xrange(0, i-1): + G += A[i,k] * A[k,j] + + A[i-1,j] -= G * ctx.conj(T[i]) + for k in xrange(0, i-1): + A[k,j] -= G * ctx.conj(A[i,k]) + + A[i,i] = 1 + for j in xrange(0, i): + A[j,i] = A[i,j] = 0 + + + +@defun +def hessenberg(ctx, A, overwrite_a = False): + """ + This routine computes the Hessenberg decomposition of a square matrix A. + Given A, an unitary matrix Q is determined such that + + Q' A Q = H and Q' Q = Q Q' = 1 + + where H is an upper right Hessenberg matrix. Here ' denotes the hermitian + transpose (i.e. transposition and conjugation). + + input: + A : a real or complex square matrix + overwrite_a : if true, allows modification of A which may improve + performance. if false, A is not modified. + + output: + Q : an unitary matrix + H : an upper right Hessenberg matrix + + example: + >>> from mpmath import mp + >>> A = mp.matrix([[3, -1, 2], [2, 5, -5], [-2, -3, 7]]) + >>> Q, H = mp.hessenberg(A) + >>> mp.nprint(H, 3) # doctest:+SKIP + [ 3.15 2.23 4.44] + [-0.769 4.85 3.05] + [ 0.0 3.61 7.0] + >>> print(mp.chop(A - Q * H * Q.transpose_conj())) + [0.0 0.0 0.0] + [0.0 0.0 0.0] + [0.0 0.0 0.0] + + return value: (Q, H) + """ + + n = A.rows + + if n == 1: + return (ctx.matrix([[1]]), A) + + if not overwrite_a: + A = A.copy() + + T = ctx.matrix(n, 1) + + hessenberg_reduce_0(ctx, A, T) + Q = A.copy() + hessenberg_reduce_1(ctx, Q, T) + + for x in xrange(n): + for y in xrange(x+2, n): + A[y,x] = 0 + + return Q, A + + +########################################################################### + + +def qr_step(ctx, n0, n1, A, Q, shift): + """ + This subroutine executes a single implicitly shifted QR step applied to an + upper Hessenberg matrix A. Given A and shift as input, first an QR + decomposition is calculated: + + Q R = A - shift * 1 . + + The output is then following matrix: + + R Q + shift * 1 + + parameters: + n0, n1 (input) Two integers which specify the submatrix A[n0:n1,n0:n1] + on which this subroutine operators. The subdiagonal elements + to the left and below this submatrix must be deflated (i.e. zero). + following restriction is imposed: n1>=n0+2 + A (input/output) On input, A is an upper Hessenberg matrix. + On output, A is replaced by "R Q + shift * 1" + Q (input/output) The parameter Q is multiplied by the unitary matrix + Q arising from the QR decomposition. Q can also be false, in which + case the unitary matrix Q is not computated. + shift (input) a complex number specifying the shift. idealy close to an + eigenvalue of the bottemmost part of the submatrix A[n0:n1,n0:n1]. + + references: + Stoer, Bulirsch - Introduction to Numerical Analysis. + Kresser : Numerical Methods for General and Structured Eigenvalue Problems + """ + + # implicitly shifted and bulge chasing is explained at p.398/399 in "Stoer, Bulirsch - Introduction to Numerical Analysis" + # for bulge chasing see also "Watkins - The Matrix Eigenvalue Problem" sec.4.5,p.173 + + # the Givens rotation we used is determined as follows: let c,s be two complex + # numbers. then we have following relation: + # + # v = sqrt(|c|^2 + |s|^2) + # + # 1/v [ c~ s~] [c] = [v] + # [-s c ] [s] [0] + # + # the matrix on the left is our Givens rotation. + + n = A.rows + + # first step + + # calculate givens rotation + c = A[n0 ,n0] - shift + s = A[n0+1,n0] + + v = ctx.hypot(ctx.hypot(ctx.re(c), ctx.im(c)), ctx.hypot(ctx.re(s), ctx.im(s))) + + if v == 0: + v = 1 + c = 1 + s = 0 + else: + c /= v + s /= v + + cc = ctx.conj(c) + cs = ctx.conj(s) + + for k in xrange(n0, n): + # apply givens rotation from the left + x = A[n0 ,k] + y = A[n0+1,k] + A[n0 ,k] = cc * x + cs * y + A[n0+1,k] = c * y - s * x + + for k in xrange(min(n1, n0+3)): + # apply givens rotation from the right + x = A[k,n0 ] + y = A[k,n0+1] + A[k,n0 ] = c * x + s * y + A[k,n0+1] = cc * y - cs * x + + if not isinstance(Q, bool): + for k in xrange(n): + # eigenvectors + x = Q[k,n0 ] + y = Q[k,n0+1] + Q[k,n0 ] = c * x + s * y + Q[k,n0+1] = cc * y - cs * x + + # chase the bulge + + for j in xrange(n0, n1 - 2): + # calculate givens rotation + + c = A[j+1,j] + s = A[j+2,j] + + v = ctx.hypot(ctx.hypot(ctx.re(c), ctx.im(c)), ctx.hypot(ctx.re(s), ctx.im(s))) + + if v == 0: + A[j+1,j] = 0 + v = 1 + c = 1 + s = 0 + else: + A[j+1,j] = v + c /= v + s /= v + + A[j+2,j] = 0 + + cc = ctx.conj(c) + cs = ctx.conj(s) + + for k in xrange(j+1, n): + # apply givens rotation from the left + x = A[j+1,k] + y = A[j+2,k] + A[j+1,k] = cc * x + cs * y + A[j+2,k] = c * y - s * x + + for k in xrange(0, min(n1, j+4)): + # apply givens rotation from the right + x = A[k,j+1] + y = A[k,j+2] + A[k,j+1] = c * x + s * y + A[k,j+2] = cc * y - cs * x + + if not isinstance(Q, bool): + for k in xrange(0, n): + # eigenvectors + x = Q[k,j+1] + y = Q[k,j+2] + Q[k,j+1] = c * x + s * y + Q[k,j+2] = cc * y - cs * x + + + +def hessenberg_qr(ctx, A, Q): + """ + This routine computes the Schur decomposition of an upper Hessenberg matrix A. + Given A, an unitary matrix Q is determined such that + + Q' A Q = R and Q' Q = Q Q' = 1 + + where R is an upper right triangular matrix. Here ' denotes the hermitian + transpose (i.e. transposition and conjugation). + + parameters: + A (input/output) On input, A contains an upper Hessenberg matrix. + On output, A is replace by the upper right triangluar matrix R. + + Q (input/output) The parameter Q is multiplied by the unitary + matrix Q arising from the Schur decomposition. Q can also be + false, in which case the unitary matrix Q is not computated. + """ + + n = A.rows + + norm = 0 + for x in xrange(n): + for y in xrange(min(x+2, n)): + norm += ctx.re(A[y,x]) ** 2 + ctx.im(A[y,x]) ** 2 + norm = ctx.sqrt(norm) / n + + if norm == 0: + return + + n0 = 0 + n1 = n + + eps = ctx.eps / (100 * n) + maxits = ctx.dps * 4 + + its = totalits = 0 + + while 1: + # kressner p.32 algo 3 + # the active submatrix is A[n0:n1,n0:n1] + + k = n0 + + while k + 1 < n1: + s = abs(ctx.re(A[k,k])) + abs(ctx.im(A[k,k])) + abs(ctx.re(A[k+1,k+1])) + abs(ctx.im(A[k+1,k+1])) + if s < eps * norm: + s = norm + if abs(A[k+1,k]) < eps * s: + break + k += 1 + + if k + 1 < n1: + # deflation found at position (k+1, k) + + A[k+1,k] = 0 + n0 = k + 1 + + its = 0 + + if n0 + 1 >= n1: + # block of size at most two has converged + n0 = 0 + n1 = k + 1 + if n1 < 2: + # QR algorithm has converged + return + else: + if (its % 30) == 10: + # exceptional shift + shift = A[n1-1,n1-2] + elif (its % 30) == 20: + # exceptional shift + shift = abs(A[n1-1,n1-2]) + elif (its % 30) == 29: + # exceptional shift + shift = norm + else: + # A = [ a b ] det(x-A)=x*x-x*tr(A)+det(A) + # [ c d ] + # + # eigenvalues bad: (tr(A)+sqrt((tr(A))**2-4*det(A)))/2 + # bad because of cancellation if |c| is small and |a-d| is small, too. + # + # eigenvalues good: (a+d+sqrt((a-d)**2+4*b*c))/2 + + t = A[n1-2,n1-2] + A[n1-1,n1-1] + s = (A[n1-1,n1-1] - A[n1-2,n1-2]) ** 2 + 4 * A[n1-1,n1-2] * A[n1-2,n1-1] + if ctx.re(s) > 0: + s = ctx.sqrt(s) + else: + s = ctx.sqrt(-s) * 1j + a = (t + s) / 2 + b = (t - s) / 2 + if abs(A[n1-1,n1-1] - a) > abs(A[n1-1,n1-1] - b): + shift = b + else: + shift = a + + its += 1 + totalits += 1 + + qr_step(ctx, n0, n1, A, Q, shift) + + if its > maxits: + raise RuntimeError("qr: failed to converge after %d steps" % its) + + +@defun +def schur(ctx, A, overwrite_a = False): + """ + This routine computes the Schur decomposition of a square matrix A. + Given A, an unitary matrix Q is determined such that + + Q' A Q = R and Q' Q = Q Q' = 1 + + where R is an upper right triangular matrix. Here ' denotes the + hermitian transpose (i.e. transposition and conjugation). + + input: + A : a real or complex square matrix + overwrite_a : if true, allows modification of A which may improve + performance. if false, A is not modified. + + output: + Q : an unitary matrix + R : an upper right triangular matrix + + return value: (Q, R) + + example: + >>> from mpmath import mp + >>> A = mp.matrix([[3, -1, 2], [2, 5, -5], [-2, -3, 7]]) + >>> Q, R = mp.schur(A) + >>> mp.nprint(R, 3) # doctest:+SKIP + [2.0 0.417 -2.53] + [0.0 4.0 -4.74] + [0.0 0.0 9.0] + >>> print(mp.chop(A - Q * R * Q.transpose_conj())) + [0.0 0.0 0.0] + [0.0 0.0 0.0] + [0.0 0.0 0.0] + + warning: The Schur decomposition is not unique. + """ + + n = A.rows + + if n == 1: + return (ctx.matrix([[1]]), A) + + if not overwrite_a: + A = A.copy() + + T = ctx.matrix(n, 1) + + hessenberg_reduce_0(ctx, A, T) + Q = A.copy() + hessenberg_reduce_1(ctx, Q, T) + + for x in xrange(n): + for y in xrange(x + 2, n): + A[y,x] = 0 + + hessenberg_qr(ctx, A, Q) + + return Q, A + + +def eig_tr_r(ctx, A): + """ + This routine calculates the right eigenvectors of an upper right triangular matrix. + + input: + A an upper right triangular matrix + + output: + ER a matrix whose columns form the right eigenvectors of A + + return value: ER + """ + + # this subroutine is inspired by the lapack routines ctrevc.f,clatrs.f + + n = A.rows + + ER = ctx.eye(n) + + eps = ctx.eps + + unfl = ctx.ldexp(ctx.one, -ctx.prec * 30) + # since mpmath effectively has no limits on the exponent, we simply scale doubles up + # original double has prec*20 + + smlnum = unfl * (n / eps) + simin = 1 / ctx.sqrt(eps) + + rmax = 1 + + for i in xrange(1, n): + s = A[i,i] + + smin = max(eps * abs(s), smlnum) + + for j in xrange(i - 1, -1, -1): + + r = 0 + for k in xrange(j + 1, i + 1): + r += A[j,k] * ER[k,i] + + t = A[j,j] - s + if abs(t) < smin: + t = smin + + r = -r / t + ER[j,i] = r + + rmax = max(rmax, abs(r)) + if rmax > simin: + for k in xrange(j, i+1): + ER[k,i] /= rmax + rmax = 1 + + if rmax != 1: + for k in xrange(0, i + 1): + ER[k,i] /= rmax + + return ER + +def eig_tr_l(ctx, A): + """ + This routine calculates the left eigenvectors of an upper right triangular matrix. + + input: + A an upper right triangular matrix + + output: + EL a matrix whose rows form the left eigenvectors of A + + return value: EL + """ + + n = A.rows + + EL = ctx.eye(n) + + eps = ctx.eps + + unfl = ctx.ldexp(ctx.one, -ctx.prec * 30) + # since mpmath effectively has no limits on the exponent, we simply scale doubles up + # original double has prec*20 + + smlnum = unfl * (n / eps) + simin = 1 / ctx.sqrt(eps) + + rmax = 1 + + for i in xrange(0, n - 1): + s = A[i,i] + + smin = max(eps * abs(s), smlnum) + + for j in xrange(i + 1, n): + + r = 0 + for k in xrange(i, j): + r += EL[i,k] * A[k,j] + + t = A[j,j] - s + if abs(t) < smin: + t = smin + + r = -r / t + EL[i,j] = r + + rmax = max(rmax, abs(r)) + if rmax > simin: + for k in xrange(i, j + 1): + EL[i,k] /= rmax + rmax = 1 + + if rmax != 1: + for k in xrange(i, n): + EL[i,k] /= rmax + + return EL + +@defun +def eig(ctx, A, left = False, right = True, overwrite_a = False): + """ + This routine computes the eigenvalues and optionally the left and right + eigenvectors of a square matrix A. Given A, a vector E and matrices ER + and EL are calculated such that + + A ER[:,i] = E[i] ER[:,i] + EL[i,:] A = EL[i,:] E[i] + + E contains the eigenvalues of A. The columns of ER contain the right eigenvectors + of A whereas the rows of EL contain the left eigenvectors. + + + input: + A : a real or complex square matrix of shape (n, n) + left : if true, the left eigenvectors are calculated. + right : if true, the right eigenvectors are calculated. + overwrite_a : if true, allows modification of A which may improve + performance. if false, A is not modified. + + output: + E : a list of length n containing the eigenvalues of A. + ER : a matrix whose columns contain the right eigenvectors of A. + EL : a matrix whose rows contain the left eigenvectors of A. + + return values: + E if left and right are both false. + (E, ER) if right is true and left is false. + (E, EL) if left is true and right is false. + (E, EL, ER) if left and right are true. + + + examples: + >>> from mpmath import mp + >>> A = mp.matrix([[3, -1, 2], [2, 5, -5], [-2, -3, 7]]) + >>> E, ER = mp.eig(A) + >>> print(mp.chop(A * ER[:,0] - E[0] * ER[:,0])) + [0.0] + [0.0] + [0.0] + + >>> E, EL, ER = mp.eig(A,left = True, right = True) + >>> E, EL, ER = mp.eig_sort(E, EL, ER) + >>> mp.nprint(E) + [2.0, 4.0, 9.0] + >>> print(mp.chop(A * ER[:,0] - E[0] * ER[:,0])) + [0.0] + [0.0] + [0.0] + >>> print(mp.chop( EL[0,:] * A - EL[0,:] * E[0])) + [0.0 0.0 0.0] + + warning: + - If there are multiple eigenvalues, the eigenvectors do not necessarily + span the whole vectorspace, i.e. ER and EL may have not full rank. + Furthermore in that case the eigenvectors are numerical ill-conditioned. + - In the general case the eigenvalues have no natural order. + + see also: + - eigh (or eigsy, eighe) for the symmetric eigenvalue problem. + - eig_sort for sorting of eigenvalues and eigenvectors + """ + + n = A.rows + + if n == 1: + if left and (not right): + return ([A[0]], ctx.matrix([[1]])) + + if right and (not left): + return ([A[0]], ctx.matrix([[1]])) + + return ([A[0]], ctx.matrix([[1]]), ctx.matrix([[1]])) + + if not overwrite_a: + A = A.copy() + + T = ctx.zeros(n, 1) + + hessenberg_reduce_0(ctx, A, T) + + if left or right: + Q = A.copy() + hessenberg_reduce_1(ctx, Q, T) + else: + Q = False + + for x in xrange(n): + for y in xrange(x + 2, n): + A[y,x] = 0 + + hessenberg_qr(ctx, A, Q) + + E = [0 for i in xrange(n)] + for i in xrange(n): + E[i] = A[i,i] + + if not (left or right): + return E + + if left: + EL = eig_tr_l(ctx, A) + EL = EL * Q.transpose_conj() + + if right: + ER = eig_tr_r(ctx, A) + ER = Q * ER + + if left and (not right): + return (E, EL) + + if right and (not left): + return (E, ER) + + return (E, EL, ER) + +@defun +def eig_sort(ctx, E, EL = False, ER = False, f = "real"): + """ + This routine sorts the eigenvalues and eigenvectors delivered by ``eig``. + + parameters: + E : the eigenvalues as delivered by eig + EL : the left eigenvectors as delivered by eig, or false + ER : the right eigenvectors as delivered by eig, or false + f : either a string ("real" sort by increasing real part, "imag" sort by + increasing imag part, "abs" sort by absolute value) or a function + mapping complexs to the reals, i.e. ``f = lambda x: -mp.re(x) `` + would sort the eigenvalues by decreasing real part. + + return values: + E if EL and ER are both false. + (E, ER) if ER is not false and left is false. + (E, EL) if EL is not false and right is false. + (E, EL, ER) if EL and ER are not false. + + example: + >>> from mpmath import mp + >>> A = mp.matrix([[3, -1, 2], [2, 5, -5], [-2, -3, 7]]) + >>> E, EL, ER = mp.eig(A,left = True, right = True) + >>> E, EL, ER = mp.eig_sort(E, EL, ER) + >>> mp.nprint(E) + [2.0, 4.0, 9.0] + >>> E, EL, ER = mp.eig_sort(E, EL, ER,f = lambda x: -mp.re(x)) + >>> mp.nprint(E) + [9.0, 4.0, 2.0] + >>> print(mp.chop(A * ER[:,0] - E[0] * ER[:,0])) + [0.0] + [0.0] + [0.0] + >>> print(mp.chop( EL[0,:] * A - EL[0,:] * E[0])) + [0.0 0.0 0.0] + """ + + if isinstance(f, str): + if f == "real": + f = ctx.re + elif f == "imag": + f = ctx.im + elif f == "abs": + f = abs + else: + raise RuntimeError("unknown function %s" % f) + + n = len(E) + + # Sort eigenvalues (bubble-sort) + + for i in xrange(n): + imax = i + s = f(E[i]) # s is the current maximal element + + for j in xrange(i + 1, n): + c = f(E[j]) + if c < s: + s = c + imax = j + + if imax != i: + # swap eigenvalues + + z = E[i] + E[i] = E[imax] + E[imax] = z + + if not isinstance(EL, bool): + for j in xrange(n): + z = EL[i,j] + EL[i,j] = EL[imax,j] + EL[imax,j] = z + + if not isinstance(ER, bool): + for j in xrange(n): + z = ER[j,i] + ER[j,i] = ER[j,imax] + ER[j,imax] = z + + if isinstance(EL, bool) and isinstance(ER, bool): + return E + + if isinstance(EL, bool) and not(isinstance(ER, bool)): + return (E, ER) + + if isinstance(ER, bool) and not(isinstance(EL, bool)): + return (E, EL) + + return (E, EL, ER) diff --git a/venv/lib/python3.12/site-packages/mpmath/matrices/eigen_symmetric.py b/venv/lib/python3.12/site-packages/mpmath/matrices/eigen_symmetric.py new file mode 100644 index 0000000000000000000000000000000000000000..c82c0bb061d22c37a89f82a0b9bdab3e9ba7ddde --- /dev/null +++ b/venv/lib/python3.12/site-packages/mpmath/matrices/eigen_symmetric.py @@ -0,0 +1,1807 @@ +#!/usr/bin/python +# -*- coding: utf-8 -*- + +################################################################################################## +# module for the symmetric eigenvalue problem +# Copyright 2013 Timo Hartmann (thartmann15 at gmail.com) +# +# todo: +# - implement balancing +# +################################################################################################## + +""" +The symmetric eigenvalue problem. +--------------------------------- + +This file contains routines for the symmetric eigenvalue problem. + +high level routines: + + eigsy : real symmetric (ordinary) eigenvalue problem + eighe : complex hermitian (ordinary) eigenvalue problem + eigh : unified interface for eigsy and eighe + svd_r : singular value decomposition for real matrices + svd_c : singular value decomposition for complex matrices + svd : unified interface for svd_r and svd_c + + +low level routines: + + r_sy_tridiag : reduction of real symmetric matrix to real symmetric tridiagonal matrix + c_he_tridiag_0 : reduction of complex hermitian matrix to real symmetric tridiagonal matrix + c_he_tridiag_1 : auxiliary routine to c_he_tridiag_0 + c_he_tridiag_2 : auxiliary routine to c_he_tridiag_0 + tridiag_eigen : solves the real symmetric tridiagonal matrix eigenvalue problem + svd_r_raw : raw singular value decomposition for real matrices + svd_c_raw : raw singular value decomposition for complex matrices +""" + +from ..libmp.backend import xrange +from .eigen import defun + + +def r_sy_tridiag(ctx, A, D, E, calc_ev = True): + """ + This routine transforms a real symmetric matrix A to a real symmetric + tridiagonal matrix T using an orthogonal similarity transformation: + Q' * A * Q = T (here ' denotes the matrix transpose). + The orthogonal matrix Q is build up from Householder reflectors. + + parameters: + A (input/output) On input, A contains the real symmetric matrix of + dimension (n,n). On output, if calc_ev is true, A contains the + orthogonal matrix Q, otherwise A is destroyed. + + D (output) real array of length n, contains the diagonal elements + of the tridiagonal matrix + + E (output) real array of length n, contains the offdiagonal elements + of the tridiagonal matrix in E[0:(n-1)] where is the dimension of + the matrix A. E[n-1] is undefined. + + calc_ev (input) If calc_ev is true, this routine explicitly calculates the + orthogonal matrix Q which is then returned in A. If calc_ev is + false, Q is not explicitly calculated resulting in a shorter run time. + + This routine is a python translation of the fortran routine tred2.f in the + software library EISPACK (see netlib.org) which itself is based on the algol + procedure tred2 described in: + - Num. Math. 11, p.181-195 (1968) by Martin, Reinsch and Wilkonson + - Handbook for auto. comp., Vol II, Linear Algebra, p.212-226 (1971) + + For a good introduction to Householder reflections, see also + Stoer, Bulirsch - Introduction to Numerical Analysis. + """ + + # note : the vector v of the i-th houshoulder reflector is stored in a[(i+1):,i] + # whereas v/ is stored in a[i,(i+1):] + + n = A.rows + for i in xrange(n - 1, 0, -1): + # scale the vector + + scale = 0 + for k in xrange(0, i): + scale += abs(A[k,i]) + + scale_inv = 0 + if scale != 0: + scale_inv = 1/scale + + # sadly there are floating point numbers not equal to zero whose reciprocal is infinity + + if i == 1 or scale == 0 or ctx.isinf(scale_inv): + E[i] = A[i-1,i] # nothing to do + D[i] = 0 + continue + + # calculate parameters for housholder transformation + + H = 0 + for k in xrange(0, i): + A[k,i] *= scale_inv + H += A[k,i] * A[k,i] + + F = A[i-1,i] + G = ctx.sqrt(H) + if F > 0: + G = -G + E[i] = scale * G + H -= F * G + A[i-1,i] = F - G + F = 0 + + # apply housholder transformation + + for j in xrange(0, i): + if calc_ev: + A[i,j] = A[j,i] / H + + G = 0 # calculate A*U + for k in xrange(0, j + 1): + G += A[k,j] * A[k,i] + for k in xrange(j + 1, i): + G += A[j,k] * A[k,i] + + E[j] = G / H # calculate P + F += E[j] * A[j,i] + + HH = F / (2 * H) + + for j in xrange(0, i): # calculate reduced A + F = A[j,i] + G = E[j] - HH * F # calculate Q + E[j] = G + + for k in xrange(0, j + 1): + A[k,j] -= F * E[k] + G * A[k,i] + + D[i] = H + + for i in xrange(1, n): # better for compatibility + E[i-1] = E[i] + E[n-1] = 0 + + if calc_ev: + D[0] = 0 + for i in xrange(0, n): + if D[i] != 0: + for j in xrange(0, i): # accumulate transformation matrices + G = 0 + for k in xrange(0, i): + G += A[i,k] * A[k,j] + for k in xrange(0, i): + A[k,j] -= G * A[k,i] + + D[i] = A[i,i] + A[i,i] = 1 + + for j in xrange(0, i): + A[j,i] = A[i,j] = 0 + else: + for i in xrange(0, n): + D[i] = A[i,i] + + + + + +def c_he_tridiag_0(ctx, A, D, E, T): + """ + This routine transforms a complex hermitian matrix A to a real symmetric + tridiagonal matrix T using an unitary similarity transformation: + Q' * A * Q = T (here ' denotes the hermitian matrix transpose, + i.e. transposition und conjugation). + The unitary matrix Q is build up from Householder reflectors and + an unitary diagonal matrix. + + parameters: + A (input/output) On input, A contains the complex hermitian matrix + of dimension (n,n). On output, A contains the unitary matrix Q + in compressed form. + + D (output) real array of length n, contains the diagonal elements + of the tridiagonal matrix. + + E (output) real array of length n, contains the offdiagonal elements + of the tridiagonal matrix in E[0:(n-1)] where is the dimension of + the matrix A. E[n-1] is undefined. + + T (output) complex array of length n, contains a unitary diagonal + matrix. + + This routine is a python translation (in slightly modified form) of the fortran + routine htridi.f in the software library EISPACK (see netlib.org) which itself + is a complex version of the algol procedure tred1 described in: + - Num. Math. 11, p.181-195 (1968) by Martin, Reinsch and Wilkonson + - Handbook for auto. comp., Vol II, Linear Algebra, p.212-226 (1971) + + For a good introduction to Householder reflections, see also + Stoer, Bulirsch - Introduction to Numerical Analysis. + """ + + n = A.rows + T[n-1] = 1 + for i in xrange(n - 1, 0, -1): + + # scale the vector + + scale = 0 + for k in xrange(0, i): + scale += abs(ctx.re(A[k,i])) + abs(ctx.im(A[k,i])) + + scale_inv = 0 + if scale != 0: + scale_inv = 1 / scale + + # sadly there are floating point numbers not equal to zero whose reciprocal is infinity + + if scale == 0 or ctx.isinf(scale_inv): + E[i] = 0 + D[i] = 0 + T[i-1] = 1 + continue + + if i == 1: + F = A[i-1,i] + f = abs(F) + E[i] = f + D[i] = 0 + if f != 0: + T[i-1] = T[i] * F / f + else: + T[i-1] = T[i] + continue + + # calculate parameters for housholder transformation + + H = 0 + for k in xrange(0, i): + A[k,i] *= scale_inv + rr = ctx.re(A[k,i]) + ii = ctx.im(A[k,i]) + H += rr * rr + ii * ii + + F = A[i-1,i] + f = abs(F) + G = ctx.sqrt(H) + H += G * f + E[i] = scale * G + if f != 0: + F = F / f + TZ = - T[i] * F # T[i-1]=-T[i]*F, but we need T[i-1] as temporary storage + G *= F + else: + TZ = -T[i] # T[i-1]=-T[i] + A[i-1,i] += G + F = 0 + + # apply housholder transformation + + for j in xrange(0, i): + A[i,j] = A[j,i] / H + + G = 0 # calculate A*U + for k in xrange(0, j + 1): + G += ctx.conj(A[k,j]) * A[k,i] + for k in xrange(j + 1, i): + G += A[j,k] * A[k,i] + + T[j] = G / H # calculate P + F += ctx.conj(T[j]) * A[j,i] + + HH = F / (2 * H) + + for j in xrange(0, i): # calculate reduced A + F = A[j,i] + G = T[j] - HH * F # calculate Q + T[j] = G + + for k in xrange(0, j + 1): + A[k,j] -= ctx.conj(F) * T[k] + ctx.conj(G) * A[k,i] + # as we use the lower left part for storage + # we have to use the transpose of the normal formula + + T[i-1] = TZ + D[i] = H + + for i in xrange(1, n): # better for compatibility + E[i-1] = E[i] + E[n-1] = 0 + + D[0] = 0 + for i in xrange(0, n): + zw = D[i] + D[i] = ctx.re(A[i,i]) + A[i,i] = zw + + + + + + + +def c_he_tridiag_1(ctx, A, T): + """ + This routine forms the unitary matrix Q described in c_he_tridiag_0. + + parameters: + A (input/output) On input, A is the same matrix as delivered by + c_he_tridiag_0. On output, A is set to Q. + + T (input) On input, T is the same array as delivered by c_he_tridiag_0. + + """ + + n = A.rows + + for i in xrange(0, n): + if A[i,i] != 0: + for j in xrange(0, i): + G = 0 + for k in xrange(0, i): + G += ctx.conj(A[i,k]) * A[k,j] + for k in xrange(0, i): + A[k,j] -= G * A[k,i] + + A[i,i] = 1 + + for j in xrange(0, i): + A[j,i] = A[i,j] = 0 + + for i in xrange(0, n): + for k in xrange(0, n): + A[i,k] *= T[k] + + + + +def c_he_tridiag_2(ctx, A, T, B): + """ + This routine applied the unitary matrix Q described in c_he_tridiag_0 + onto the the matrix B, i.e. it forms Q*B. + + parameters: + A (input) On input, A is the same matrix as delivered by c_he_tridiag_0. + + T (input) On input, T is the same array as delivered by c_he_tridiag_0. + + B (input/output) On input, B is a complex matrix. On output B is replaced + by Q*B. + + This routine is a python translation of the fortran routine htribk.f in the + software library EISPACK (see netlib.org). See c_he_tridiag_0 for more + references. + """ + + n = A.rows + + for i in xrange(0, n): + for k in xrange(0, n): + B[k,i] *= T[k] + + for i in xrange(0, n): + if A[i,i] != 0: + for j in xrange(0, n): + G = 0 + for k in xrange(0, i): + G += ctx.conj(A[i,k]) * B[k,j] + for k in xrange(0, i): + B[k,j] -= G * A[k,i] + + + + + +def tridiag_eigen(ctx, d, e, z = False): + """ + This subroutine find the eigenvalues and the first components of the + eigenvectors of a real symmetric tridiagonal matrix using the implicit + QL method. + + parameters: + + d (input/output) real array of length n. on input, d contains the diagonal + elements of the input matrix. on output, d contains the eigenvalues in + ascending order. + + e (input) real array of length n. on input, e contains the offdiagonal + elements of the input matrix in e[0:(n-1)]. On output, e has been + destroyed. + + z (input/output) If z is equal to False, no eigenvectors will be computed. + Otherwise on input z should have the format z[0:m,0:n] (i.e. a real or + complex matrix of dimension (m,n) ). On output this matrix will be + multiplied by the matrix of the eigenvectors (i.e. the columns of this + matrix are the eigenvectors): z --> z*EV + That means if z[i,j]={1 if j==j; 0 otherwise} on input, then on output + z will contain the first m components of the eigenvectors. That means + if m is equal to n, the i-th eigenvector will be z[:,i]. + + This routine is a python translation (in slightly modified form) of the + fortran routine imtql2.f in the software library EISPACK (see netlib.org) + which itself is based on the algol procudure imtql2 desribed in: + - num. math. 12, p. 377-383(1968) by matrin and wilkinson + - modified in num. math. 15, p. 450(1970) by dubrulle + - handbook for auto. comp., vol. II-linear algebra, p. 241-248 (1971) + See also the routine gaussq.f in netlog.org or acm algorithm 726. + """ + + n = len(d) + e[n-1] = 0 + iterlim = 2 * ctx.dps + + for l in xrange(n): + j = 0 + while 1: + m = l + while 1: + # look for a small subdiagonal element + if m + 1 == n: + break + if abs(e[m]) <= ctx.eps * (abs(d[m]) + abs(d[m + 1])): + break + m = m + 1 + if m == l: + break + + if j >= iterlim: + raise RuntimeError("tridiag_eigen: no convergence to an eigenvalue after %d iterations" % iterlim) + + j += 1 + + # form shift + + p = d[l] + g = (d[l + 1] - p) / (2 * e[l]) + r = ctx.hypot(g, 1) + + if g < 0: + s = g - r + else: + s = g + r + + g = d[m] - p + e[l] / s + + s, c, p = 1, 1, 0 + + for i in xrange(m - 1, l - 1, -1): + f = s * e[i] + b = c * e[i] + if abs(f) > abs(g): # this here is a slight improvement also used in gaussq.f or acm algorithm 726. + c = g / f + r = ctx.hypot(c, 1) + e[i + 1] = f * r + s = 1 / r + c = c * s + else: + s = f / g + r = ctx.hypot(s, 1) + e[i + 1] = g * r + c = 1 / r + s = s * c + g = d[i + 1] - p + r = (d[i] - g) * s + 2 * c * b + p = s * r + d[i + 1] = g + p + g = c * r - b + + if not isinstance(z, bool): + # calculate eigenvectors + for w in xrange(z.rows): + f = z[w,i+1] + z[w,i+1] = s * z[w,i] + c * f + z[w,i ] = c * z[w,i] - s * f + + d[l] = d[l] - p + e[l] = g + e[m] = 0 + + for ii in xrange(1, n): + # sort eigenvalues and eigenvectors (bubble-sort) + i = ii - 1 + k = i + p = d[i] + for j in xrange(ii, n): + if d[j] >= p: + continue + k = j + p = d[k] + if k == i: + continue + d[k] = d[i] + d[i] = p + + if not isinstance(z, bool): + for w in xrange(z.rows): + p = z[w,i] + z[w,i] = z[w,k] + z[w,k] = p + +######################################################################################## + +@defun +def eigsy(ctx, A, eigvals_only = False, overwrite_a = False): + """ + This routine solves the (ordinary) eigenvalue problem for a real symmetric + square matrix A. Given A, an orthogonal matrix Q is calculated which + diagonalizes A: + + Q' A Q = diag(E) and Q Q' = Q' Q = 1 + + Here diag(E) is a diagonal matrix whose diagonal is E. + ' denotes the transpose. + + The columns of Q are the eigenvectors of A and E contains the eigenvalues: + + A Q[:,i] = E[i] Q[:,i] + + + input: + + A: real matrix of format (n,n) which is symmetric + (i.e. A=A' or A[i,j]=A[j,i]) + + eigvals_only: if true, calculates only the eigenvalues E. + if false, calculates both eigenvectors and eigenvalues. + + overwrite_a: if true, allows modification of A which may improve + performance. if false, A is not modified. + + output: + + E: vector of format (n). contains the eigenvalues of A in ascending order. + + Q: orthogonal matrix of format (n,n). contains the eigenvectors + of A as columns. + + return value: + + E if eigvals_only is true + (E, Q) if eigvals_only is false + + example: + >>> from mpmath import mp + >>> A = mp.matrix([[3, 2], [2, 0]]) + >>> E = mp.eigsy(A, eigvals_only = True) + >>> print(E) + [-1.0] + [ 4.0] + + >>> A = mp.matrix([[1, 2], [2, 3]]) + >>> E, Q = mp.eigsy(A) + >>> print(mp.chop(A * Q[:,0] - E[0] * Q[:,0])) + [0.0] + [0.0] + + see also: eighe, eigh, eig + """ + + if not overwrite_a: + A = A.copy() + + d = ctx.zeros(A.rows, 1) + e = ctx.zeros(A.rows, 1) + + if eigvals_only: + r_sy_tridiag(ctx, A, d, e, calc_ev = False) + tridiag_eigen(ctx, d, e, False) + return d + else: + r_sy_tridiag(ctx, A, d, e, calc_ev = True) + tridiag_eigen(ctx, d, e, A) + return (d, A) + + +@defun +def eighe(ctx, A, eigvals_only = False, overwrite_a = False): + """ + This routine solves the (ordinary) eigenvalue problem for a complex + hermitian square matrix A. Given A, an unitary matrix Q is calculated which + diagonalizes A: + + Q' A Q = diag(E) and Q Q' = Q' Q = 1 + + Here diag(E) a is diagonal matrix whose diagonal is E. + ' denotes the hermitian transpose (i.e. ordinary transposition and + complex conjugation). + + The columns of Q are the eigenvectors of A and E contains the eigenvalues: + + A Q[:,i] = E[i] Q[:,i] + + + input: + + A: complex matrix of format (n,n) which is hermitian + (i.e. A=A' or A[i,j]=conj(A[j,i])) + + eigvals_only: if true, calculates only the eigenvalues E. + if false, calculates both eigenvectors and eigenvalues. + + overwrite_a: if true, allows modification of A which may improve + performance. if false, A is not modified. + + output: + + E: vector of format (n). contains the eigenvalues of A in ascending order. + + Q: unitary matrix of format (n,n). contains the eigenvectors + of A as columns. + + return value: + + E if eigvals_only is true + (E, Q) if eigvals_only is false + + example: + >>> from mpmath import mp + >>> A = mp.matrix([[1, -3 - 1j], [-3 + 1j, -2]]) + >>> E = mp.eighe(A, eigvals_only = True) + >>> print(E) + [-4.0] + [ 3.0] + + >>> A = mp.matrix([[1, 2 + 5j], [2 - 5j, 3]]) + >>> E, Q = mp.eighe(A) + >>> print(mp.chop(A * Q[:,0] - E[0] * Q[:,0])) + [0.0] + [0.0] + + see also: eigsy, eigh, eig + """ + + if not overwrite_a: + A = A.copy() + + d = ctx.zeros(A.rows, 1) + e = ctx.zeros(A.rows, 1) + t = ctx.zeros(A.rows, 1) + + if eigvals_only: + c_he_tridiag_0(ctx, A, d, e, t) + tridiag_eigen(ctx, d, e, False) + return d + else: + c_he_tridiag_0(ctx, A, d, e, t) + B = ctx.eye(A.rows) + tridiag_eigen(ctx, d, e, B) + c_he_tridiag_2(ctx, A, t, B) + return (d, B) + +@defun +def eigh(ctx, A, eigvals_only = False, overwrite_a = False): + """ + "eigh" is a unified interface for "eigsy" and "eighe". Depending on + whether A is real or complex the appropriate function is called. + + This routine solves the (ordinary) eigenvalue problem for a real symmetric + or complex hermitian square matrix A. Given A, an orthogonal (A real) or + unitary (A complex) matrix Q is calculated which diagonalizes A: + + Q' A Q = diag(E) and Q Q' = Q' Q = 1 + + Here diag(E) a is diagonal matrix whose diagonal is E. + ' denotes the hermitian transpose (i.e. ordinary transposition and + complex conjugation). + + The columns of Q are the eigenvectors of A and E contains the eigenvalues: + + A Q[:,i] = E[i] Q[:,i] + + input: + + A: a real or complex square matrix of format (n,n) which is symmetric + (i.e. A[i,j]=A[j,i]) or hermitian (i.e. A[i,j]=conj(A[j,i])). + + eigvals_only: if true, calculates only the eigenvalues E. + if false, calculates both eigenvectors and eigenvalues. + + overwrite_a: if true, allows modification of A which may improve + performance. if false, A is not modified. + + output: + + E: vector of format (n). contains the eigenvalues of A in ascending order. + + Q: an orthogonal or unitary matrix of format (n,n). contains the + eigenvectors of A as columns. + + return value: + + E if eigvals_only is true + (E, Q) if eigvals_only is false + + example: + >>> from mpmath import mp + >>> A = mp.matrix([[3, 2], [2, 0]]) + >>> E = mp.eigh(A, eigvals_only = True) + >>> print(E) + [-1.0] + [ 4.0] + + >>> A = mp.matrix([[1, 2], [2, 3]]) + >>> E, Q = mp.eigh(A) + >>> print(mp.chop(A * Q[:,0] - E[0] * Q[:,0])) + [0.0] + [0.0] + + >>> A = mp.matrix([[1, 2 + 5j], [2 - 5j, 3]]) + >>> E, Q = mp.eigh(A) + >>> print(mp.chop(A * Q[:,0] - E[0] * Q[:,0])) + [0.0] + [0.0] + + see also: eigsy, eighe, eig + """ + + iscomplex = any(type(x) is ctx.mpc for x in A) + + if iscomplex: + return ctx.eighe(A, eigvals_only = eigvals_only, overwrite_a = overwrite_a) + else: + return ctx.eigsy(A, eigvals_only = eigvals_only, overwrite_a = overwrite_a) + + +@defun +def gauss_quadrature(ctx, n, qtype = "legendre", alpha = 0, beta = 0): + """ + This routine calulates gaussian quadrature rules for different + families of orthogonal polynomials. Let (a, b) be an interval, + W(x) a positive weight function and n a positive integer. + Then the purpose of this routine is to calculate pairs (x_k, w_k) + for k=0, 1, 2, ... (n-1) which give + + int(W(x) * F(x), x = a..b) = sum(w_k * F(x_k),k = 0..(n-1)) + + exact for all polynomials F(x) of degree (strictly) less than 2*n. For all + integrable functions F(x) the sum is a (more or less) good approximation to + the integral. The x_k are called nodes (which are the zeros of the + related orthogonal polynomials) and the w_k are called the weights. + + parameters + n (input) The degree of the quadrature rule, i.e. its number of + nodes. + + qtype (input) The family of orthogonal polynmomials for which to + compute the quadrature rule. See the list below. + + alpha (input) real number, used as parameter for some orthogonal + polynomials + + beta (input) real number, used as parameter for some orthogonal + polynomials. + + return value + + (X, W) a pair of two real arrays where x_k = X[k] and w_k = W[k]. + + + orthogonal polynomials: + + qtype polynomial + ----- ---------- + + "legendre" Legendre polynomials, W(x)=1 on the interval (-1, +1) + "legendre01" shifted Legendre polynomials, W(x)=1 on the interval (0, +1) + "hermite" Hermite polynomials, W(x)=exp(-x*x) on (-infinity,+infinity) + "laguerre" Laguerre polynomials, W(x)=exp(-x) on (0,+infinity) + "glaguerre" generalized Laguerre polynomials, W(x)=exp(-x)*x**alpha + on (0, +infinity) + "chebyshev1" Chebyshev polynomials of the first kind, W(x)=1/sqrt(1-x*x) + on (-1, +1) + "chebyshev2" Chebyshev polynomials of the second kind, W(x)=sqrt(1-x*x) + on (-1, +1) + "jacobi" Jacobi polynomials, W(x)=(1-x)**alpha * (1+x)**beta on (-1, +1) + with alpha>-1 and beta>-1 + + examples: + >>> from mpmath import mp + >>> f = lambda x: x**8 + 2 * x**6 - 3 * x**4 + 5 * x**2 - 7 + >>> X, W = mp.gauss_quadrature(5, "hermite") + >>> A = mp.fdot([(f(x), w) for x, w in zip(X, W)]) + >>> B = mp.sqrt(mp.pi) * 57 / 16 + >>> C = mp.quad(lambda x: mp.exp(- x * x) * f(x), [-mp.inf, +mp.inf]) + >>> mp.nprint((mp.chop(A-B, tol = 1e-10), mp.chop(A-C, tol = 1e-10))) + (0.0, 0.0) + + >>> f = lambda x: x**5 - 2 * x**4 + 3 * x**3 - 5 * x**2 + 7 * x - 11 + >>> X, W = mp.gauss_quadrature(3, "laguerre") + >>> A = mp.fdot([(f(x), w) for x, w in zip(X, W)]) + >>> B = 76 + >>> C = mp.quad(lambda x: mp.exp(-x) * f(x), [0, +mp.inf]) + >>> mp.nprint(mp.chop(A-B, tol = 1e-10), mp.chop(A-C, tol = 1e-10)) + .0 + + # orthogonality of the chebyshev polynomials: + >>> f = lambda x: mp.chebyt(3, x) * mp.chebyt(2, x) + >>> X, W = mp.gauss_quadrature(3, "chebyshev1") + >>> A = mp.fdot([(f(x), w) for x, w in zip(X, W)]) + >>> print(mp.chop(A, tol = 1e-10)) + 0.0 + + references: + - golub and welsch, "calculations of gaussian quadrature rules", mathematics of + computation 23, p. 221-230 (1969) + - golub, "some modified matrix eigenvalue problems", siam review 15, p. 318-334 (1973) + - stroud and secrest, "gaussian quadrature formulas", prentice-hall (1966) + + See also the routine gaussq.f in netlog.org or ACM Transactions on + Mathematical Software algorithm 726. + """ + + d = ctx.zeros(n, 1) + e = ctx.zeros(n, 1) + z = ctx.zeros(1, n) + + z[0,0] = 1 + + if qtype == "legendre": + # legendre on the range -1 +1 , abramowitz, table 25.4, p.916 + w = 2 + for i in xrange(n): + j = i + 1 + e[i] = ctx.sqrt(j * j / (4 * j * j - ctx.mpf(1))) + elif qtype == "legendre01": + # legendre shifted to 0 1 , abramowitz, table 25.8, p.921 + w = 1 + for i in xrange(n): + d[i] = 1 / ctx.mpf(2) + j = i + 1 + e[i] = ctx.sqrt(j * j / (16 * j * j - ctx.mpf(4))) + elif qtype == "hermite": + # hermite on the range -inf +inf , abramowitz, table 25.10,p.924 + w = ctx.sqrt(ctx.pi) + for i in xrange(n): + j = i + 1 + e[i] = ctx.sqrt(j / ctx.mpf(2)) + elif qtype == "laguerre": + # laguerre on the range 0 +inf , abramowitz, table 25.9, p. 923 + w = 1 + for i in xrange(n): + j = i + 1 + d[i] = 2 * j - 1 + e[i] = j + elif qtype=="chebyshev1": + # chebyshev polynimials of the first kind + w = ctx.pi + for i in xrange(n): + e[i] = 1 / ctx.mpf(2) + e[0] = ctx.sqrt(1 / ctx.mpf(2)) + elif qtype == "chebyshev2": + # chebyshev polynimials of the second kind + w = ctx.pi / 2 + for i in xrange(n): + e[i] = 1 / ctx.mpf(2) + elif qtype == "glaguerre": + # generalized laguerre on the range 0 +inf + w = ctx.gamma(1 + alpha) + for i in xrange(n): + j = i + 1 + d[i] = 2 * j - 1 + alpha + e[i] = ctx.sqrt(j * (j + alpha)) + elif qtype == "jacobi": + # jacobi polynomials + alpha = ctx.mpf(alpha) + beta = ctx.mpf(beta) + ab = alpha + beta + abi = ab + 2 + w = (2**(ab+1)) * ctx.gamma(alpha + 1) * ctx.gamma(beta + 1) / ctx.gamma(abi) + d[0] = (beta - alpha) / abi + e[0] = ctx.sqrt(4 * (1 + alpha) * (1 + beta) / ((abi + 1) * (abi * abi))) + a2b2 = beta * beta - alpha * alpha + for i in xrange(1, n): + j = i + 1 + abi = 2 * j + ab + d[i] = a2b2 / ((abi - 2) * abi) + e[i] = ctx.sqrt(4 * j * (j + alpha) * (j + beta) * (j + ab) / ((abi * abi - 1) * abi * abi)) + elif isinstance(qtype, str): + raise ValueError("unknown quadrature rule \"%s\"" % qtype) + elif not isinstance(qtype, str): + w = qtype(d, e) + else: + assert 0 + + tridiag_eigen(ctx, d, e, z) + + for i in xrange(len(z)): + z[i] *= z[i] + + z = z.transpose() + return (d, w * z) + +################################################################################################## +################################################################################################## +################################################################################################## + +def svd_r_raw(ctx, A, V = False, calc_u = False): + """ + This routine computes the singular value decomposition of a matrix A. + Given A, two orthogonal matrices U and V are calculated such that + + A = U S V + + where S is a suitable shaped matrix whose off-diagonal elements are zero. + The diagonal elements of S are the singular values of A, i.e. the + squareroots of the eigenvalues of A' A or A A'. Here ' denotes the transpose. + Householder bidiagonalization and a variant of the QR algorithm is used. + + overview of the matrices : + + A : m*n A gets replaced by U + U : m*n U replaces A. If n>m then only the first m*m block of U is + non-zero. column-orthogonal: U' U = B + here B is a n*n matrix whose first min(m,n) diagonal + elements are 1 and all other elements are zero. + S : n*n diagonal matrix, only the diagonal elements are stored in + the array S. only the first min(m,n) diagonal elements are non-zero. + V : n*n orthogonal: V V' = V' V = 1 + + parameters: + A (input/output) On input, A contains a real matrix of shape m*n. + On output, if calc_u is true A contains the column-orthogonal + matrix U; otherwise A is simply used as workspace and thus destroyed. + + V (input/output) if false, the matrix V is not calculated. otherwise + V must be a matrix of shape n*n. + + calc_u (input) If true, the matrix U is calculated and replaces A. + if false, U is not calculated and A is simply destroyed + + return value: + S an array of length n containing the singular values of A sorted by + decreasing magnitude. only the first min(m,n) elements are non-zero. + + This routine is a python translation of the fortran routine svd.f in the + software library EISPACK (see netlib.org) which itself is based on the + algol procedure svd described in: + - num. math. 14, 403-420(1970) by golub and reinsch. + - wilkinson/reinsch: handbook for auto. comp., vol ii-linear algebra, 134-151(1971). + + """ + + m, n = A.rows, A.cols + + S = ctx.zeros(n, 1) + + # work is a temporary array of size n + work = ctx.zeros(n, 1) + + g = scale = anorm = 0 + maxits = 3 * ctx.dps + + for i in xrange(n): # householder reduction to bidiagonal form + work[i] = scale*g + g = s = scale = 0 + if i < m: + for k in xrange(i, m): + scale += ctx.fabs(A[k,i]) + if scale != 0: + for k in xrange(i, m): + A[k,i] /= scale + s += A[k,i] * A[k,i] + f = A[i,i] + g = -ctx.sqrt(s) + if f < 0: + g = -g + h = f * g - s + A[i,i] = f - g + for j in xrange(i+1, n): + s = 0 + for k in xrange(i, m): + s += A[k,i] * A[k,j] + f = s / h + for k in xrange(i, m): + A[k,j] += f * A[k,i] + for k in xrange(i,m): + A[k,i] *= scale + + S[i] = scale * g + g = s = scale = 0 + + if i < m and i != n - 1: + for k in xrange(i+1, n): + scale += ctx.fabs(A[i,k]) + if scale: + for k in xrange(i+1, n): + A[i,k] /= scale + s += A[i,k] * A[i,k] + f = A[i,i+1] + g = -ctx.sqrt(s) + if f < 0: + g = -g + h = f * g - s + A[i,i+1] = f - g + + for k in xrange(i+1, n): + work[k] = A[i,k] / h + + for j in xrange(i+1, m): + s = 0 + for k in xrange(i+1, n): + s += A[j,k] * A[i,k] + for k in xrange(i+1, n): + A[j,k] += s * work[k] + + for k in xrange(i+1, n): + A[i,k] *= scale + + anorm = max(anorm, ctx.fabs(S[i]) + ctx.fabs(work[i])) + + if not isinstance(V, bool): + for i in xrange(n-2, -1, -1): # accumulation of right hand transformations + V[i+1,i+1] = 1 + + if work[i+1] != 0: + for j in xrange(i+1, n): + V[i,j] = (A[i,j] / A[i,i+1]) / work[i+1] + for j in xrange(i+1, n): + s = 0 + for k in xrange(i+1, n): + s += A[i,k] * V[j,k] + for k in xrange(i+1, n): + V[j,k] += s * V[i,k] + + for j in xrange(i+1, n): + V[j,i] = V[i,j] = 0 + + V[0,0] = 1 + + if m= maxits: + raise RuntimeError("svd: no convergence to an eigenvalue after %d iterations" % its) + + x = S[l] # shift from bottom 2 by 2 minor + nm = k-1 + y = S[nm] + g = work[nm] + h = work[k] + f = ((y - z) * (y + z) + (g - h) * (g + h))/(2 * h * y) + g = ctx.hypot(f, 1) + if f >= 0: f = ((x - z) * (x + z) + h * ((y / (f + g)) - h)) / x + else: f = ((x - z) * (x + z) + h * ((y / (f - g)) - h)) / x + + c = s = 1 # next qt transformation + + for j in xrange(l, nm + 1): + g = work[j+1] + y = S[j+1] + h = s * g + g = c * g + z = ctx.hypot(f, h) + work[j] = z + c = f / z + s = h / z + f = x * c + g * s + g = g * c - x * s + h = y * s + y *= c + if not isinstance(V, bool): + for jj in xrange(n): + x = V[j ,jj] + z = V[j+1,jj] + V[j ,jj]= x * c + z * s + V[j+1 ,jj]= z * c - x * s + z = ctx.hypot(f, h) + S[j] = z + if z != 0: # rotation can be arbitray if z=0 + z = 1 / z + c = f * z + s = h * z + f = c * g + s * y + x = c * y - s * g + + if calc_u: + for jj in xrange(m): + y = A[jj,j ] + z = A[jj,j+1] + A[jj,j ] = y * c + z * s + A[jj,j+1 ] = z * c - y * s + + work[l] = 0 + work[k] = f + S[k] = x + + ########################## + + # Sort singular values into decreasing order (bubble-sort) + + for i in xrange(n): + imax = i + s = ctx.fabs(S[i]) # s is the current maximal element + + for j in xrange(i + 1, n): + c = ctx.fabs(S[j]) + if c > s: + s = c + imax = j + + if imax != i: + # swap singular values + + z = S[i] + S[i] = S[imax] + S[imax] = z + + if calc_u: + for j in xrange(m): + z = A[j,i] + A[j,i] = A[j,imax] + A[j,imax] = z + + if not isinstance(V, bool): + for j in xrange(n): + z = V[i,j] + V[i,j] = V[imax,j] + V[imax,j] = z + + return S + +####################### + +def svd_c_raw(ctx, A, V = False, calc_u = False): + """ + This routine computes the singular value decomposition of a matrix A. + Given A, two unitary matrices U and V are calculated such that + + A = U S V + + where S is a suitable shaped matrix whose off-diagonal elements are zero. + The diagonal elements of S are the singular values of A, i.e. the + squareroots of the eigenvalues of A' A or A A'. Here ' denotes the hermitian + transpose (i.e. transposition and conjugation). Householder bidiagonalization + and a variant of the QR algorithm is used. + + overview of the matrices : + + A : m*n A gets replaced by U + U : m*n U replaces A. If n>m then only the first m*m block of U is + non-zero. column-unitary: U' U = B + here B is a n*n matrix whose first min(m,n) diagonal + elements are 1 and all other elements are zero. + S : n*n diagonal matrix, only the diagonal elements are stored in + the array S. only the first min(m,n) diagonal elements are non-zero. + V : n*n unitary: V V' = V' V = 1 + + parameters: + A (input/output) On input, A contains a complex matrix of shape m*n. + On output, if calc_u is true A contains the column-unitary + matrix U; otherwise A is simply used as workspace and thus destroyed. + + V (input/output) if false, the matrix V is not calculated. otherwise + V must be a matrix of shape n*n. + + calc_u (input) If true, the matrix U is calculated and replaces A. + if false, U is not calculated and A is simply destroyed + + return value: + S an array of length n containing the singular values of A sorted by + decreasing magnitude. only the first min(m,n) elements are non-zero. + + This routine is a python translation of the fortran routine svd.f in the + software library EISPACK (see netlib.org) which itself is based on the + algol procedure svd described in: + - num. math. 14, 403-420(1970) by golub and reinsch. + - wilkinson/reinsch: handbook for auto. comp., vol ii-linear algebra, 134-151(1971). + + """ + + m, n = A.rows, A.cols + + S = ctx.zeros(n, 1) + + # work is a temporary array of size n + work = ctx.zeros(n, 1) + lbeta = ctx.zeros(n, 1) + rbeta = ctx.zeros(n, 1) + dwork = ctx.zeros(n, 1) + + g = scale = anorm = 0 + maxits = 3 * ctx.dps + + for i in xrange(n): # householder reduction to bidiagonal form + dwork[i] = scale * g # dwork are the side-diagonal elements + g = s = scale = 0 + if i < m: + for k in xrange(i, m): + scale += ctx.fabs(ctx.re(A[k,i])) + ctx.fabs(ctx.im(A[k,i])) + if scale != 0: + for k in xrange(i, m): + A[k,i] /= scale + ar = ctx.re(A[k,i]) + ai = ctx.im(A[k,i]) + s += ar * ar + ai * ai + f = A[i,i] + g = -ctx.sqrt(s) + if ctx.re(f) < 0: + beta = -g - ctx.conj(f) + g = -g + else: + beta = -g + ctx.conj(f) + beta /= ctx.conj(beta) + beta += 1 + h = 2 * (ctx.re(f) * g - s) + A[i,i] = f - g + beta /= h + lbeta[i] = (beta / scale) / scale + for j in xrange(i+1, n): + s = 0 + for k in xrange(i, m): + s += ctx.conj(A[k,i]) * A[k,j] + f = beta * s + for k in xrange(i, m): + A[k,j] += f * A[k,i] + for k in xrange(i, m): + A[k,i] *= scale + + S[i] = scale * g # S are the diagonal elements + g = s = scale = 0 + + if i < m and i != n - 1: + for k in xrange(i+1, n): + scale += ctx.fabs(ctx.re(A[i,k])) + ctx.fabs(ctx.im(A[i,k])) + if scale: + for k in xrange(i+1, n): + A[i,k] /= scale + ar = ctx.re(A[i,k]) + ai = ctx.im(A[i,k]) + s += ar * ar + ai * ai + f = A[i,i+1] + g = -ctx.sqrt(s) + if ctx.re(f) < 0: + beta = -g - ctx.conj(f) + g = -g + else: + beta = -g + ctx.conj(f) + + beta /= ctx.conj(beta) + beta += 1 + + h = 2 * (ctx.re(f) * g - s) + A[i,i+1] = f - g + + beta /= h + rbeta[i] = (beta / scale) / scale + + for k in xrange(i+1, n): + work[k] = A[i, k] + + for j in xrange(i+1, m): + s = 0 + for k in xrange(i+1, n): + s += ctx.conj(A[i,k]) * A[j,k] + f = s * beta + for k in xrange(i+1,n): + A[j,k] += f * work[k] + + for k in xrange(i+1, n): + A[i,k] *= scale + + anorm = max(anorm,ctx.fabs(S[i]) + ctx.fabs(dwork[i])) + + if not isinstance(V, bool): + for i in xrange(n-2, -1, -1): # accumulation of right hand transformations + V[i+1,i+1] = 1 + + if dwork[i+1] != 0: + f = ctx.conj(rbeta[i]) + for j in xrange(i+1, n): + V[i,j] = A[i,j] * f + for j in xrange(i+1, n): + s = 0 + for k in xrange(i+1, n): + s += ctx.conj(A[i,k]) * V[j,k] + for k in xrange(i+1, n): + V[j,k] += s * V[i,k] + + for j in xrange(i+1,n): + V[j,i] = V[i,j] = 0 + + V[0,0] = 1 + + if m < n : minnm = m + else : minnm = n + + if calc_u: + for i in xrange(minnm-1, -1, -1): # accumulation of left hand transformations + g = S[i] + for j in xrange(i+1, n): + A[i,j] = 0 + if g != 0: + g = 1 / g + for j in xrange(i+1, n): + s = 0 + for k in xrange(i+1, m): + s += ctx.conj(A[k,i]) * A[k,j] + f = s * ctx.conj(lbeta[i]) + for k in xrange(i, m): + A[k,j] += f * A[k,i] + for j in xrange(i, m): + A[j,i] *= g + else: + for j in xrange(i, m): + A[j,i] = 0 + A[i,i] += 1 + + for k in xrange(n-1, -1, -1): + # diagonalization of the bidiagonal form: + # loop over singular values, and over allowed itations + + its = 0 + while 1: + its += 1 + flag = True + + for l in xrange(k, -1, -1): + nm = l - 1 + + if ctx.fabs(dwork[l]) + anorm == anorm: + flag = False + break + + if ctx.fabs(S[nm]) + anorm == anorm: + break + + if flag: + c = 0 + s = 1 + for i in xrange(l, k+1): + f = s * dwork[i] + dwork[i] *= c + if ctx.fabs(f) + anorm == anorm: + break + g = S[i] + h = ctx.hypot(f, g) + S[i] = h + h = 1 / h + c = g * h + s = -f * h + + if calc_u: + for j in xrange(m): + y = A[j,nm] + z = A[j,i] + A[j,nm]= y * c + z * s + A[j,i] = z * c - y * s + + z = S[k] + + if l == k: # convergence + if z < 0: # singular value is made nonnegative + S[k] = -z + if not isinstance(V, bool): + for j in xrange(n): + V[k,j] = -V[k,j] + break + + if its >= maxits: + raise RuntimeError("svd: no convergence to an eigenvalue after %d iterations" % its) + + x = S[l] # shift from bottom 2 by 2 minor + nm = k-1 + y = S[nm] + g = dwork[nm] + h = dwork[k] + f = ((y - z) * (y + z) + (g - h) * (g + h)) / (2 * h * y) + g = ctx.hypot(f, 1) + if f >=0: f = (( x - z) *( x + z) + h *((y / (f + g)) - h)) / x + else: f = (( x - z) *( x + z) + h *((y / (f - g)) - h)) / x + + c = s = 1 # next qt transformation + + for j in xrange(l, nm + 1): + g = dwork[j+1] + y = S[j+1] + h = s * g + g = c * g + z = ctx.hypot(f, h) + dwork[j] = z + c = f / z + s = h / z + f = x * c + g * s + g = g * c - x * s + h = y * s + y *= c + if not isinstance(V, bool): + for jj in xrange(n): + x = V[j ,jj] + z = V[j+1,jj] + V[j ,jj]= x * c + z * s + V[j+1,jj ]= z * c - x * s + z = ctx.hypot(f, h) + S[j] = z + if z != 0: # rotation can be arbitray if z=0 + z = 1 / z + c = f * z + s = h * z + f = c * g + s * y + x = c * y - s * g + if calc_u: + for jj in xrange(m): + y = A[jj,j ] + z = A[jj,j+1] + A[jj,j ]= y * c + z * s + A[jj,j+1 ]= z * c - y * s + + dwork[l] = 0 + dwork[k] = f + S[k] = x + + ########################## + + # Sort singular values into decreasing order (bubble-sort) + + for i in xrange(n): + imax = i + s = ctx.fabs(S[i]) # s is the current maximal element + + for j in xrange(i + 1, n): + c = ctx.fabs(S[j]) + if c > s: + s = c + imax = j + + if imax != i: + # swap singular values + + z = S[i] + S[i] = S[imax] + S[imax] = z + + if calc_u: + for j in xrange(m): + z = A[j,i] + A[j,i] = A[j,imax] + A[j,imax] = z + + if not isinstance(V, bool): + for j in xrange(n): + z = V[i,j] + V[i,j] = V[imax,j] + V[imax,j] = z + + return S + +################################################################################################## + +@defun +def svd_r(ctx, A, full_matrices = False, compute_uv = True, overwrite_a = False): + """ + This routine computes the singular value decomposition of a matrix A. + Given A, two orthogonal matrices U and V are calculated such that + + A = U S V and U' U = 1 and V V' = 1 + + where S is a suitable shaped matrix whose off-diagonal elements are zero. + Here ' denotes the transpose. The diagonal elements of S are the singular + values of A, i.e. the squareroots of the eigenvalues of A' A or A A'. + + input: + A : a real matrix of shape (m, n) + full_matrices : if true, U and V are of shape (m, m) and (n, n). + if false, U and V are of shape (m, min(m, n)) and (min(m, n), n). + compute_uv : if true, U and V are calculated. if false, only S is calculated. + overwrite_a : if true, allows modification of A which may improve + performance. if false, A is not modified. + + output: + U : an orthogonal matrix: U' U = 1. if full_matrices is true, U is of + shape (m, m). ortherwise it is of shape (m, min(m, n)). + + S : an array of length min(m, n) containing the singular values of A sorted by + decreasing magnitude. + + V : an orthogonal matrix: V V' = 1. if full_matrices is true, V is of + shape (n, n). ortherwise it is of shape (min(m, n), n). + + return value: + + S if compute_uv is false + (U, S, V) if compute_uv is true + + overview of the matrices: + + full_matrices true: + A : m*n + U : m*m U' U = 1 + S as matrix : m*n + V : n*n V V' = 1 + + full_matrices false: + A : m*n + U : m*min(n,m) U' U = 1 + S as matrix : min(m,n)*min(m,n) + V : min(m,n)*n V V' = 1 + + examples: + + >>> from mpmath import mp + >>> A = mp.matrix([[2, -2, -1], [3, 4, -2], [-2, -2, 0]]) + >>> S = mp.svd_r(A, compute_uv = False) + >>> print(S) + [6.0] + [3.0] + [1.0] + + >>> U, S, V = mp.svd_r(A) + >>> print(mp.chop(A - U * mp.diag(S) * V)) + [0.0 0.0 0.0] + [0.0 0.0 0.0] + [0.0 0.0 0.0] + + + see also: svd, svd_c + """ + + m, n = A.rows, A.cols + + if not compute_uv: + if not overwrite_a: + A = A.copy() + S = svd_r_raw(ctx, A, V = False, calc_u = False) + S = S[:min(m,n)] + return S + + if full_matrices and n < m: + V = ctx.zeros(m, m) + A0 = ctx.zeros(m, m) + A0[:,:n] = A + S = svd_r_raw(ctx, A0, V, calc_u = True) + + S = S[:n] + V = V[:n,:n] + + return (A0, S, V) + else: + if not overwrite_a: + A = A.copy() + V = ctx.zeros(n, n) + S = svd_r_raw(ctx, A, V, calc_u = True) + + if n > m: + if full_matrices == False: + V = V[:m,:] + + S = S[:m] + A = A[:,:m] + + return (A, S, V) + +############################## + +@defun +def svd_c(ctx, A, full_matrices = False, compute_uv = True, overwrite_a = False): + """ + This routine computes the singular value decomposition of a matrix A. + Given A, two unitary matrices U and V are calculated such that + + A = U S V and U' U = 1 and V V' = 1 + + where S is a suitable shaped matrix whose off-diagonal elements are zero. + Here ' denotes the hermitian transpose (i.e. transposition and complex + conjugation). The diagonal elements of S are the singular values of A, + i.e. the squareroots of the eigenvalues of A' A or A A'. + + input: + A : a complex matrix of shape (m, n) + full_matrices : if true, U and V are of shape (m, m) and (n, n). + if false, U and V are of shape (m, min(m, n)) and (min(m, n), n). + compute_uv : if true, U and V are calculated. if false, only S is calculated. + overwrite_a : if true, allows modification of A which may improve + performance. if false, A is not modified. + + output: + U : an unitary matrix: U' U = 1. if full_matrices is true, U is of + shape (m, m). ortherwise it is of shape (m, min(m, n)). + + S : an array of length min(m, n) containing the singular values of A sorted by + decreasing magnitude. + + V : an unitary matrix: V V' = 1. if full_matrices is true, V is of + shape (n, n). ortherwise it is of shape (min(m, n), n). + + return value: + + S if compute_uv is false + (U, S, V) if compute_uv is true + + overview of the matrices: + + full_matrices true: + A : m*n + U : m*m U' U = 1 + S as matrix : m*n + V : n*n V V' = 1 + + full_matrices false: + A : m*n + U : m*min(n,m) U' U = 1 + S as matrix : min(m,n)*min(m,n) + V : min(m,n)*n V V' = 1 + + example: + >>> from mpmath import mp + >>> A = mp.matrix([[-2j, -1-3j, -2+2j], [2-2j, -1-3j, 1], [-3+1j,-2j,0]]) + >>> S = mp.svd_c(A, compute_uv = False) + >>> print(mp.chop(S - mp.matrix([mp.sqrt(34), mp.sqrt(15), mp.sqrt(6)]))) + [0.0] + [0.0] + [0.0] + + >>> U, S, V = mp.svd_c(A) + >>> print(mp.chop(A - U * mp.diag(S) * V)) + [0.0 0.0 0.0] + [0.0 0.0 0.0] + [0.0 0.0 0.0] + + see also: svd, svd_r + """ + + m, n = A.rows, A.cols + + if not compute_uv: + if not overwrite_a: + A = A.copy() + S = svd_c_raw(ctx, A, V = False, calc_u = False) + S = S[:min(m,n)] + return S + + if full_matrices and n < m: + V = ctx.zeros(m, m) + A0 = ctx.zeros(m, m) + A0[:,:n] = A + S = svd_c_raw(ctx, A0, V, calc_u = True) + + S = S[:n] + V = V[:n,:n] + + return (A0, S, V) + else: + if not overwrite_a: + A = A.copy() + V = ctx.zeros(n, n) + S = svd_c_raw(ctx, A, V, calc_u = True) + + if n > m: + if full_matrices == False: + V = V[:m,:] + + S = S[:m] + A = A[:,:m] + + return (A, S, V) + +@defun +def svd(ctx, A, full_matrices = False, compute_uv = True, overwrite_a = False): + """ + "svd" is a unified interface for "svd_r" and "svd_c". Depending on + whether A is real or complex the appropriate function is called. + + This routine computes the singular value decomposition of a matrix A. + Given A, two orthogonal (A real) or unitary (A complex) matrices U and V + are calculated such that + + A = U S V and U' U = 1 and V V' = 1 + + where S is a suitable shaped matrix whose off-diagonal elements are zero. + Here ' denotes the hermitian transpose (i.e. transposition and complex + conjugation). The diagonal elements of S are the singular values of A, + i.e. the squareroots of the eigenvalues of A' A or A A'. + + input: + A : a real or complex matrix of shape (m, n) + full_matrices : if true, U and V are of shape (m, m) and (n, n). + if false, U and V are of shape (m, min(m, n)) and (min(m, n), n). + compute_uv : if true, U and V are calculated. if false, only S is calculated. + overwrite_a : if true, allows modification of A which may improve + performance. if false, A is not modified. + + output: + U : an orthogonal or unitary matrix: U' U = 1. if full_matrices is true, U is of + shape (m, m). ortherwise it is of shape (m, min(m, n)). + + S : an array of length min(m, n) containing the singular values of A sorted by + decreasing magnitude. + + V : an orthogonal or unitary matrix: V V' = 1. if full_matrices is true, V is of + shape (n, n). ortherwise it is of shape (min(m, n), n). + + return value: + + S if compute_uv is false + (U, S, V) if compute_uv is true + + overview of the matrices: + + full_matrices true: + A : m*n + U : m*m U' U = 1 + S as matrix : m*n + V : n*n V V' = 1 + + full_matrices false: + A : m*n + U : m*min(n,m) U' U = 1 + S as matrix : min(m,n)*min(m,n) + V : min(m,n)*n V V' = 1 + + examples: + + >>> from mpmath import mp + >>> A = mp.matrix([[2, -2, -1], [3, 4, -2], [-2, -2, 0]]) + >>> S = mp.svd(A, compute_uv = False) + >>> print(S) + [6.0] + [3.0] + [1.0] + + >>> U, S, V = mp.svd(A) + >>> print(mp.chop(A - U * mp.diag(S) * V)) + [0.0 0.0 0.0] + [0.0 0.0 0.0] + [0.0 0.0 0.0] + + see also: svd_r, svd_c + """ + + iscomplex = any(type(x) is ctx.mpc for x in A) + + if iscomplex: + return ctx.svd_c(A, full_matrices = full_matrices, compute_uv = compute_uv, overwrite_a = overwrite_a) + else: + return ctx.svd_r(A, full_matrices = full_matrices, compute_uv = compute_uv, overwrite_a = overwrite_a) diff --git a/venv/lib/python3.12/site-packages/mpmath/matrices/linalg.py b/venv/lib/python3.12/site-packages/mpmath/matrices/linalg.py new file mode 100644 index 0000000000000000000000000000000000000000..e2fe643e809822e3d05a52b73c965edb622f9af9 --- /dev/null +++ b/venv/lib/python3.12/site-packages/mpmath/matrices/linalg.py @@ -0,0 +1,790 @@ +""" +Linear algebra +-------------- + +Linear equations +................ + +Basic linear algebra is implemented; you can for example solve the linear +equation system:: + + x + 2*y = -10 + 3*x + 4*y = 10 + +using ``lu_solve``:: + + >>> from mpmath import * + >>> mp.pretty = False + >>> A = matrix([[1, 2], [3, 4]]) + >>> b = matrix([-10, 10]) + >>> x = lu_solve(A, b) + >>> x + matrix( + [['30.0'], + ['-20.0']]) + +If you don't trust the result, use ``residual`` to calculate the residual ||A*x-b||:: + + >>> residual(A, x, b) + matrix( + [['3.46944695195361e-18'], + ['3.46944695195361e-18']]) + >>> str(eps) + '2.22044604925031e-16' + +As you can see, the solution is quite accurate. The error is caused by the +inaccuracy of the internal floating point arithmetic. Though, it's even smaller +than the current machine epsilon, which basically means you can trust the +result. + +If you need more speed, use NumPy, or ``fp.lu_solve`` for a floating-point computation. + + >>> fp.lu_solve(A, b) # doctest: +ELLIPSIS + matrix(...) + +``lu_solve`` accepts overdetermined systems. It is usually not possible to solve +such systems, so the residual is minimized instead. Internally this is done +using Cholesky decomposition to compute a least squares approximation. This means +that that ``lu_solve`` will square the errors. If you can't afford this, use +``qr_solve`` instead. It is twice as slow but more accurate, and it calculates +the residual automatically. + + +Matrix factorization +.................... + +The function ``lu`` computes an explicit LU factorization of a matrix:: + + >>> P, L, U = lu(matrix([[0,2,3],[4,5,6],[7,8,9]])) + >>> print(P) + [0.0 0.0 1.0] + [1.0 0.0 0.0] + [0.0 1.0 0.0] + >>> print(L) + [ 1.0 0.0 0.0] + [ 0.0 1.0 0.0] + [0.571428571428571 0.214285714285714 1.0] + >>> print(U) + [7.0 8.0 9.0] + [0.0 2.0 3.0] + [0.0 0.0 0.214285714285714] + >>> print(P.T*L*U) + [0.0 2.0 3.0] + [4.0 5.0 6.0] + [7.0 8.0 9.0] + +Interval matrices +----------------- + +Matrices may contain interval elements. This allows one to perform +basic linear algebra operations such as matrix multiplication +and equation solving with rigorous error bounds:: + + >>> a = iv.matrix([['0.1','0.3','1.0'], + ... ['7.1','5.5','4.8'], + ... ['3.2','4.4','5.6']]) + >>> + >>> b = iv.matrix(['4','0.6','0.5']) + >>> c = iv.lu_solve(a, b) + >>> print(c) + [ [5.2582327113062568605927528666, 5.25823271130625686059275702219]] + [[-13.1550493962678375411635581388, -13.1550493962678375411635540152]] + [ [7.42069154774972557628979076189, 7.42069154774972557628979190734]] + >>> print(a*c) + [ [3.99999999999999999999999844904, 4.00000000000000000000000155096]] + [[0.599999999999999999999968898009, 0.600000000000000000000031763736]] + [[0.499999999999999999999979320485, 0.500000000000000000000020679515]] +""" + +# TODO: +# *implement high-level qr() +# *test unitvector +# *iterative solving + +from copy import copy + +from ..libmp.backend import xrange + +class LinearAlgebraMethods(object): + + def LU_decomp(ctx, A, overwrite=False, use_cache=True): + """ + LU-factorization of a n*n matrix using the Gauss algorithm. + Returns L and U in one matrix and the pivot indices. + + Use overwrite to specify whether A will be overwritten with L and U. + """ + if not A.rows == A.cols: + raise ValueError('need n*n matrix') + # get from cache if possible + if use_cache and isinstance(A, ctx.matrix) and A._LU: + return A._LU + if not overwrite: + orig = A + A = A.copy() + tol = ctx.absmin(ctx.mnorm(A,1) * ctx.eps) # each pivot element has to be bigger + n = A.rows + p = [None]*(n - 1) + for j in xrange(n - 1): + # pivoting, choose max(abs(reciprocal row sum)*abs(pivot element)) + biggest = 0 + for k in xrange(j, n): + s = ctx.fsum([ctx.absmin(A[k,l]) for l in xrange(j, n)]) + if ctx.absmin(s) <= tol: + raise ZeroDivisionError('matrix is numerically singular') + current = 1/s * ctx.absmin(A[k,j]) + if current > biggest: # TODO: what if equal? + biggest = current + p[j] = k + # swap rows according to p + ctx.swap_row(A, j, p[j]) + if ctx.absmin(A[j,j]) <= tol: + raise ZeroDivisionError('matrix is numerically singular') + # calculate elimination factors and add rows + for i in xrange(j + 1, n): + A[i,j] /= A[j,j] + for k in xrange(j + 1, n): + A[i,k] -= A[i,j]*A[j,k] + if ctx.absmin(A[n - 1,n - 1]) <= tol: + raise ZeroDivisionError('matrix is numerically singular') + # cache decomposition + if not overwrite and isinstance(orig, ctx.matrix): + orig._LU = (A, p) + return A, p + + def L_solve(ctx, L, b, p=None): + """ + Solve the lower part of a LU factorized matrix for y. + """ + if L.rows != L.cols: + raise RuntimeError("need n*n matrix") + n = L.rows + if len(b) != n: + raise ValueError("Value should be equal to n") + b = copy(b) + if p: # swap b according to p + for k in xrange(0, len(p)): + ctx.swap_row(b, k, p[k]) + # solve + for i in xrange(1, n): + for j in xrange(i): + b[i] -= L[i,j] * b[j] + return b + + def U_solve(ctx, U, y): + """ + Solve the upper part of a LU factorized matrix for x. + """ + if U.rows != U.cols: + raise RuntimeError("need n*n matrix") + n = U.rows + if len(y) != n: + raise ValueError("Value should be equal to n") + x = copy(y) + for i in xrange(n - 1, -1, -1): + for j in xrange(i + 1, n): + x[i] -= U[i,j] * x[j] + x[i] /= U[i,i] + return x + + def lu_solve(ctx, A, b, **kwargs): + """ + Ax = b => x + + Solve a determined or overdetermined linear equations system. + Fast LU decomposition is used, which is less accurate than QR decomposition + (especially for overdetermined systems), but it's twice as efficient. + Use qr_solve if you want more precision or have to solve a very ill- + conditioned system. + + If you specify real=True, it does not check for overdeterminded complex + systems. + """ + prec = ctx.prec + try: + ctx.prec += 10 + # do not overwrite A nor b + A, b = ctx.matrix(A, **kwargs).copy(), ctx.matrix(b, **kwargs).copy() + if A.rows < A.cols: + raise ValueError('cannot solve underdetermined system') + if A.rows > A.cols: + # use least-squares method if overdetermined + # (this increases errors) + AH = A.H + A = AH * A + b = AH * b + if (kwargs.get('real', False) or + not sum(type(i) is ctx.mpc for i in A)): + # TODO: necessary to check also b? + x = ctx.cholesky_solve(A, b) + else: + x = ctx.lu_solve(A, b) + else: + # LU factorization + A, p = ctx.LU_decomp(A) + b = ctx.L_solve(A, b, p) + x = ctx.U_solve(A, b) + finally: + ctx.prec = prec + return x + + def improve_solution(ctx, A, x, b, maxsteps=1): + """ + Improve a solution to a linear equation system iteratively. + + This re-uses the LU decomposition and is thus cheap. + Usually 3 up to 4 iterations are giving the maximal improvement. + """ + if A.rows != A.cols: + raise RuntimeError("need n*n matrix") # TODO: really? + for _ in xrange(maxsteps): + r = ctx.residual(A, x, b) + if ctx.norm(r, 2) < 10*ctx.eps: + break + # this uses cached LU decomposition and is thus cheap + dx = ctx.lu_solve(A, -r) + x += dx + return x + + def lu(ctx, A): + """ + A -> P, L, U + + LU factorisation of a square matrix A. L is the lower, U the upper part. + P is the permutation matrix indicating the row swaps. + + P*A = L*U + + If you need efficiency, use the low-level method LU_decomp instead, it's + much more memory efficient. + """ + # get factorization + A, p = ctx.LU_decomp(A) + n = A.rows + L = ctx.matrix(n) + U = ctx.matrix(n) + for i in xrange(n): + for j in xrange(n): + if i > j: + L[i,j] = A[i,j] + elif i == j: + L[i,j] = 1 + U[i,j] = A[i,j] + else: + U[i,j] = A[i,j] + # calculate permutation matrix + P = ctx.eye(n) + for k in xrange(len(p)): + ctx.swap_row(P, k, p[k]) + return P, L, U + + def unitvector(ctx, n, i): + """ + Return the i-th n-dimensional unit vector. + """ + assert 0 < i <= n, 'this unit vector does not exist' + return [ctx.zero]*(i-1) + [ctx.one] + [ctx.zero]*(n-i) + + def inverse(ctx, A, **kwargs): + """ + Calculate the inverse of a matrix. + + If you want to solve an equation system Ax = b, it's recommended to use + solve(A, b) instead, it's about 3 times more efficient. + """ + prec = ctx.prec + try: + ctx.prec += 10 + # do not overwrite A + A = ctx.matrix(A, **kwargs).copy() + n = A.rows + # get LU factorisation + A, p = ctx.LU_decomp(A) + cols = [] + # calculate unit vectors and solve corresponding system to get columns + for i in xrange(1, n + 1): + e = ctx.unitvector(n, i) + y = ctx.L_solve(A, e, p) + cols.append(ctx.U_solve(A, y)) + # convert columns to matrix + inv = [] + for i in xrange(n): + row = [] + for j in xrange(n): + row.append(cols[j][i]) + inv.append(row) + result = ctx.matrix(inv, **kwargs) + finally: + ctx.prec = prec + return result + + def householder(ctx, A): + """ + (A|b) -> H, p, x, res + + (A|b) is the coefficient matrix with left hand side of an optionally + overdetermined linear equation system. + H and p contain all information about the transformation matrices. + x is the solution, res the residual. + """ + if not isinstance(A, ctx.matrix): + raise TypeError("A should be a type of ctx.matrix") + m = A.rows + n = A.cols + if m < n - 1: + raise RuntimeError("Columns should not be less than rows") + # calculate Householder matrix + p = [] + for j in xrange(0, n - 1): + s = ctx.fsum(abs(A[i,j])**2 for i in xrange(j, m)) + if not abs(s) > ctx.eps: + raise ValueError('matrix is numerically singular') + p.append(-ctx.sign(ctx.re(A[j,j])) * ctx.sqrt(s)) + kappa = ctx.one / (s - p[j] * A[j,j]) + A[j,j] -= p[j] + for k in xrange(j+1, n): + y = ctx.fsum(ctx.conj(A[i,j]) * A[i,k] for i in xrange(j, m)) * kappa + for i in xrange(j, m): + A[i,k] -= A[i,j] * y + # solve Rx = c1 + x = [A[i,n - 1] for i in xrange(n - 1)] + for i in xrange(n - 2, -1, -1): + x[i] -= ctx.fsum(A[i,j] * x[j] for j in xrange(i + 1, n - 1)) + x[i] /= p[i] + # calculate residual + if not m == n - 1: + r = [A[m-1-i, n-1] for i in xrange(m - n + 1)] + else: + # determined system, residual should be 0 + r = [0]*m # maybe a bad idea, changing r[i] will change all elements + return A, p, x, r + + #def qr(ctx, A): + # """ + # A -> Q, R + # + # QR factorisation of a square matrix A using Householder decomposition. + # Q is orthogonal, this leads to very few numerical errors. + # + # A = Q*R + # """ + # H, p, x, res = householder(A) + # TODO: implement this + + def residual(ctx, A, x, b, **kwargs): + """ + Calculate the residual of a solution to a linear equation system. + + r = A*x - b for A*x = b + """ + oldprec = ctx.prec + try: + ctx.prec *= 2 + A, x, b = ctx.matrix(A, **kwargs), ctx.matrix(x, **kwargs), ctx.matrix(b, **kwargs) + return A*x - b + finally: + ctx.prec = oldprec + + def qr_solve(ctx, A, b, norm=None, **kwargs): + """ + Ax = b => x, ||Ax - b|| + + Solve a determined or overdetermined linear equations system and + calculate the norm of the residual (error). + QR decomposition using Householder factorization is applied, which gives very + accurate results even for ill-conditioned matrices. qr_solve is twice as + efficient. + """ + if norm is None: + norm = ctx.norm + prec = ctx.prec + try: + ctx.prec += 10 + # do not overwrite A nor b + A, b = ctx.matrix(A, **kwargs).copy(), ctx.matrix(b, **kwargs).copy() + if A.rows < A.cols: + raise ValueError('cannot solve underdetermined system') + H, p, x, r = ctx.householder(ctx.extend(A, b)) + res = ctx.norm(r) + # calculate residual "manually" for determined systems + if res == 0: + res = ctx.norm(ctx.residual(A, x, b)) + return ctx.matrix(x, **kwargs), res + finally: + ctx.prec = prec + + def cholesky(ctx, A, tol=None): + r""" + Cholesky decomposition of a symmetric positive-definite matrix `A`. + Returns a lower triangular matrix `L` such that `A = L \times L^T`. + More generally, for a complex Hermitian positive-definite matrix, + a Cholesky decomposition satisfying `A = L \times L^H` is returned. + + The Cholesky decomposition can be used to solve linear equation + systems twice as efficiently as LU decomposition, or to + test whether `A` is positive-definite. + + The optional parameter ``tol`` determines the tolerance for + verifying positive-definiteness. + + **Examples** + + Cholesky decomposition of a positive-definite symmetric matrix:: + + >>> from mpmath import * + >>> mp.dps = 25; mp.pretty = True + >>> A = eye(3) + hilbert(3) + >>> nprint(A) + [ 2.0 0.5 0.333333] + [ 0.5 1.33333 0.25] + [0.333333 0.25 1.2] + >>> L = cholesky(A) + >>> nprint(L) + [ 1.41421 0.0 0.0] + [0.353553 1.09924 0.0] + [0.235702 0.15162 1.05899] + >>> chop(A - L*L.T) + [0.0 0.0 0.0] + [0.0 0.0 0.0] + [0.0 0.0 0.0] + + Cholesky decomposition of a Hermitian matrix:: + + >>> A = eye(3) + matrix([[0,0.25j,-0.5j],[-0.25j,0,0],[0.5j,0,0]]) + >>> L = cholesky(A) + >>> nprint(L) + [ 1.0 0.0 0.0] + [(0.0 - 0.25j) (0.968246 + 0.0j) 0.0] + [ (0.0 + 0.5j) (0.129099 + 0.0j) (0.856349 + 0.0j)] + >>> chop(A - L*L.H) + [0.0 0.0 0.0] + [0.0 0.0 0.0] + [0.0 0.0 0.0] + + Attempted Cholesky decomposition of a matrix that is not positive + definite:: + + >>> A = -eye(3) + hilbert(3) + >>> L = cholesky(A) + Traceback (most recent call last): + ... + ValueError: matrix is not positive-definite + + **References** + + 1. [Wikipedia]_ http://en.wikipedia.org/wiki/Cholesky_decomposition + + """ + if not isinstance(A, ctx.matrix): + raise RuntimeError("A should be a type of ctx.matrix") + if not A.rows == A.cols: + raise ValueError('need n*n matrix') + if tol is None: + tol = +ctx.eps + n = A.rows + L = ctx.matrix(n) + for j in xrange(n): + c = ctx.re(A[j,j]) + if abs(c-A[j,j]) > tol: + raise ValueError('matrix is not Hermitian') + s = c - ctx.fsum((L[j,k] for k in xrange(j)), + absolute=True, squared=True) + if s < tol: + raise ValueError('matrix is not positive-definite') + L[j,j] = ctx.sqrt(s) + for i in xrange(j, n): + it1 = (L[i,k] for k in xrange(j)) + it2 = (L[j,k] for k in xrange(j)) + t = ctx.fdot(it1, it2, conjugate=True) + L[i,j] = (A[i,j] - t) / L[j,j] + return L + + def cholesky_solve(ctx, A, b, **kwargs): + """ + Ax = b => x + + Solve a symmetric positive-definite linear equation system. + This is twice as efficient as lu_solve. + + Typical use cases: + * A.T*A + * Hessian matrix + * differential equations + """ + prec = ctx.prec + try: + ctx.prec += 10 + # do not overwrite A nor b + A, b = ctx.matrix(A, **kwargs).copy(), ctx.matrix(b, **kwargs).copy() + if A.rows != A.cols: + raise ValueError('can only solve determined system') + # Cholesky factorization + L = ctx.cholesky(A) + # solve + n = L.rows + if len(b) != n: + raise ValueError("Value should be equal to n") + for i in xrange(n): + b[i] -= ctx.fsum(L[i,j] * b[j] for j in xrange(i)) + b[i] /= L[i,i] + x = ctx.U_solve(L.T, b) + return x + finally: + ctx.prec = prec + + def det(ctx, A): + """ + Calculate the determinant of a matrix. + """ + prec = ctx.prec + try: + # do not overwrite A + A = ctx.matrix(A).copy() + # use LU factorization to calculate determinant + try: + R, p = ctx.LU_decomp(A) + except ZeroDivisionError: + return 0 + z = 1 + for i, e in enumerate(p): + if i != e: + z *= -1 + for i in xrange(A.rows): + z *= R[i,i] + return z + finally: + ctx.prec = prec + + def cond(ctx, A, norm=None): + """ + Calculate the condition number of a matrix using a specified matrix norm. + + The condition number estimates the sensitivity of a matrix to errors. + Example: small input errors for ill-conditioned coefficient matrices + alter the solution of the system dramatically. + + For ill-conditioned matrices it's recommended to use qr_solve() instead + of lu_solve(). This does not help with input errors however, it just avoids + to add additional errors. + + Definition: cond(A) = ||A|| * ||A**-1|| + """ + if norm is None: + norm = lambda x: ctx.mnorm(x,1) + return norm(A) * norm(ctx.inverse(A)) + + def lu_solve_mat(ctx, a, b): + """Solve a * x = b where a and b are matrices.""" + r = ctx.matrix(a.rows, b.cols) + for i in range(b.cols): + c = ctx.lu_solve(a, b.column(i)) + for j in range(len(c)): + r[j, i] = c[j] + return r + + def qr(ctx, A, mode = 'full', edps = 10): + """ + Compute a QR factorization $A = QR$ where + A is an m x n matrix of real or complex numbers where m >= n + + mode has following meanings: + (1) mode = 'raw' returns two matrixes (A, tau) in the + internal format used by LAPACK + (2) mode = 'skinny' returns the leading n columns of Q + and n rows of R + (3) Any other value returns the leading m columns of Q + and m rows of R + + edps is the increase in mp precision used for calculations + + **Examples** + + >>> from mpmath import * + >>> mp.dps = 15 + >>> mp.pretty = True + >>> A = matrix([[1, 2], [3, 4], [1, 1]]) + >>> Q, R = qr(A) + >>> Q + [-0.301511344577764 0.861640436855329 0.408248290463863] + [-0.904534033733291 -0.123091490979333 -0.408248290463863] + [-0.301511344577764 -0.492365963917331 0.816496580927726] + >>> R + [-3.3166247903554 -4.52267016866645] + [ 0.0 0.738548945875996] + [ 0.0 0.0] + >>> Q * R + [1.0 2.0] + [3.0 4.0] + [1.0 1.0] + >>> chop(Q.T * Q) + [1.0 0.0 0.0] + [0.0 1.0 0.0] + [0.0 0.0 1.0] + >>> B = matrix([[1+0j, 2-3j], [3+j, 4+5j]]) + >>> Q, R = qr(B) + >>> nprint(Q) + [ (-0.301511 + 0.0j) (0.0695795 - 0.95092j)] + [(-0.904534 - 0.301511j) (-0.115966 + 0.278318j)] + >>> nprint(R) + [(-3.31662 + 0.0j) (-5.72872 - 2.41209j)] + [ 0.0 (3.91965 + 0.0j)] + >>> Q * R + [(1.0 + 0.0j) (2.0 - 3.0j)] + [(3.0 + 1.0j) (4.0 + 5.0j)] + >>> chop(Q.T * Q.conjugate()) + [1.0 0.0] + [0.0 1.0] + + """ + + # check values before continuing + assert isinstance(A, ctx.matrix) + m = A.rows + n = A.cols + assert n >= 0 + assert m >= n + assert edps >= 0 + + # check for complex data type + cmplx = any(type(x) is ctx.mpc for x in A) + + # temporarily increase the precision and initialize + with ctx.extradps(edps): + tau = ctx.matrix(n,1) + A = A.copy() + + # --------------- + # FACTOR MATRIX A + # --------------- + if cmplx: + one = ctx.mpc('1.0', '0.0') + zero = ctx.mpc('0.0', '0.0') + rzero = ctx.mpf('0.0') + + # main loop to factor A (complex) + for j in xrange(0, n): + alpha = A[j,j] + alphr = ctx.re(alpha) + alphi = ctx.im(alpha) + + if (m-j) >= 2: + xnorm = ctx.fsum( A[i,j]*ctx.conj(A[i,j]) for i in xrange(j+1, m) ) + xnorm = ctx.re( ctx.sqrt(xnorm) ) + else: + xnorm = rzero + + if (xnorm == rzero) and (alphi == rzero): + tau[j] = zero + continue + + if alphr < rzero: + beta = ctx.sqrt(alphr**2 + alphi**2 + xnorm**2) + else: + beta = -ctx.sqrt(alphr**2 + alphi**2 + xnorm**2) + + tau[j] = ctx.mpc( (beta - alphr) / beta, -alphi / beta ) + t = -ctx.conj(tau[j]) + za = one / (alpha - beta) + + for i in xrange(j+1, m): + A[i,j] *= za + + A[j,j] = one + for k in xrange(j+1, n): + y = ctx.fsum(A[i,j] * ctx.conj(A[i,k]) for i in xrange(j, m)) + temp = t * ctx.conj(y) + for i in xrange(j, m): + A[i,k] += A[i,j] * temp + + A[j,j] = ctx.mpc(beta, '0.0') + else: + one = ctx.mpf('1.0') + zero = ctx.mpf('0.0') + + # main loop to factor A (real) + for j in xrange(0, n): + alpha = A[j,j] + + if (m-j) > 2: + xnorm = ctx.fsum( (A[i,j])**2 for i in xrange(j+1, m) ) + xnorm = ctx.sqrt(xnorm) + elif (m-j) == 2: + xnorm = abs( A[m-1,j] ) + else: + xnorm = zero + + if xnorm == zero: + tau[j] = zero + continue + + if alpha < zero: + beta = ctx.sqrt(alpha**2 + xnorm**2) + else: + beta = -ctx.sqrt(alpha**2 + xnorm**2) + + tau[j] = (beta - alpha) / beta + t = -tau[j] + da = one / (alpha - beta) + + for i in xrange(j+1, m): + A[i,j] *= da + + A[j,j] = one + for k in xrange(j+1, n): + y = ctx.fsum( A[i,j] * A[i,k] for i in xrange(j, m) ) + temp = t * y + for i in xrange(j,m): + A[i,k] += A[i,j] * temp + + A[j,j] = beta + + # return factorization in same internal format as LAPACK + if (mode == 'raw') or (mode == 'RAW'): + return A, tau + + # ---------------------------------- + # FORM Q USING BACKWARD ACCUMULATION + # ---------------------------------- + + # form R before the values are overwritten + R = A.copy() + for j in xrange(0, n): + for i in xrange(j+1, m): + R[i,j] = zero + + # set the value of p (number of columns of Q to return) + p = m + if (mode == 'skinny') or (mode == 'SKINNY'): + p = n + + # add columns to A if needed and initialize + A.cols += (p-n) + for j in xrange(0, p): + A[j,j] = one + for i in xrange(0, j): + A[i,j] = zero + + # main loop to form Q + for j in xrange(n-1, -1, -1): + t = -tau[j] + A[j,j] += t + + for k in xrange(j+1, p): + if cmplx: + y = ctx.fsum(A[i,j] * ctx.conj(A[i,k]) for i in xrange(j+1, m)) + temp = t * ctx.conj(y) + else: + y = ctx.fsum(A[i,j] * A[i,k] for i in xrange(j+1, m)) + temp = t * y + A[j,k] = temp + for i in xrange(j+1, m): + A[i,k] += A[i,j] * temp + + for i in xrange(j+1, m): + A[i, j] *= t + + return A, R[0:p,0:n] + + # ------------------ + # END OF FUNCTION QR + # ------------------ diff --git a/venv/lib/python3.12/site-packages/mpmath/matrices/matrices.py b/venv/lib/python3.12/site-packages/mpmath/matrices/matrices.py new file mode 100644 index 0000000000000000000000000000000000000000..a97d5a9ca7e173195386dc7cb60860a826ab6a97 --- /dev/null +++ b/venv/lib/python3.12/site-packages/mpmath/matrices/matrices.py @@ -0,0 +1,1005 @@ +from ..libmp.backend import xrange +import warnings + +# TODO: interpret list as vectors (for multiplication) + +rowsep = '\n' +colsep = ' ' + +class _matrix(object): + """ + Numerical matrix. + + Specify the dimensions or the data as a nested list. + Elements default to zero. + Use a flat list to create a column vector easily. + + The datatype of the context (mpf for mp, mpi for iv, and float for fp) is used to store the data. + + Creating matrices + ----------------- + + Matrices in mpmath are implemented using dictionaries. Only non-zero values + are stored, so it is cheap to represent sparse matrices. + + The most basic way to create one is to use the ``matrix`` class directly. + You can create an empty matrix specifying the dimensions: + + >>> from mpmath import * + >>> mp.dps = 15 + >>> matrix(2) + matrix( + [['0.0', '0.0'], + ['0.0', '0.0']]) + >>> matrix(2, 3) + matrix( + [['0.0', '0.0', '0.0'], + ['0.0', '0.0', '0.0']]) + + Calling ``matrix`` with one dimension will create a square matrix. + + To access the dimensions of a matrix, use the ``rows`` or ``cols`` keyword: + + >>> A = matrix(3, 2) + >>> A + matrix( + [['0.0', '0.0'], + ['0.0', '0.0'], + ['0.0', '0.0']]) + >>> A.rows + 3 + >>> A.cols + 2 + + You can also change the dimension of an existing matrix. This will set the + new elements to 0. If the new dimension is smaller than before, the + concerning elements are discarded: + + >>> A.rows = 2 + >>> A + matrix( + [['0.0', '0.0'], + ['0.0', '0.0']]) + + Internally ``mpmathify`` is used every time an element is set. This + is done using the syntax A[row,column], counting from 0: + + >>> A = matrix(2) + >>> A[1,1] = 1 + 1j + >>> A + matrix( + [['0.0', '0.0'], + ['0.0', mpc(real='1.0', imag='1.0')]]) + + A more comfortable way to create a matrix lets you use nested lists: + + >>> matrix([[1, 2], [3, 4]]) + matrix( + [['1.0', '2.0'], + ['3.0', '4.0']]) + + Convenient advanced functions are available for creating various standard + matrices, see ``zeros``, ``ones``, ``diag``, ``eye``, ``randmatrix`` and + ``hilbert``. + + Vectors + ....... + + Vectors may also be represented by the ``matrix`` class (with rows = 1 or cols = 1). + For vectors there are some things which make life easier. A column vector can + be created using a flat list, a row vectors using an almost flat nested list:: + + >>> matrix([1, 2, 3]) + matrix( + [['1.0'], + ['2.0'], + ['3.0']]) + >>> matrix([[1, 2, 3]]) + matrix( + [['1.0', '2.0', '3.0']]) + + Optionally vectors can be accessed like lists, using only a single index:: + + >>> x = matrix([1, 2, 3]) + >>> x[1] + mpf('2.0') + >>> x[1,0] + mpf('2.0') + + Other + ..... + + Like you probably expected, matrices can be printed:: + + >>> print randmatrix(3) # doctest:+SKIP + [ 0.782963853573023 0.802057689719883 0.427895717335467] + [0.0541876859348597 0.708243266653103 0.615134039977379] + [ 0.856151514955773 0.544759264818486 0.686210904770947] + + Use ``nstr`` or ``nprint`` to specify the number of digits to print:: + + >>> nprint(randmatrix(5), 3) # doctest:+SKIP + [2.07e-1 1.66e-1 5.06e-1 1.89e-1 8.29e-1] + [6.62e-1 6.55e-1 4.47e-1 4.82e-1 2.06e-2] + [4.33e-1 7.75e-1 6.93e-2 2.86e-1 5.71e-1] + [1.01e-1 2.53e-1 6.13e-1 3.32e-1 2.59e-1] + [1.56e-1 7.27e-2 6.05e-1 6.67e-2 2.79e-1] + + As matrices are mutable, you will need to copy them sometimes:: + + >>> A = matrix(2) + >>> A + matrix( + [['0.0', '0.0'], + ['0.0', '0.0']]) + >>> B = A.copy() + >>> B[0,0] = 1 + >>> B + matrix( + [['1.0', '0.0'], + ['0.0', '0.0']]) + >>> A + matrix( + [['0.0', '0.0'], + ['0.0', '0.0']]) + + Finally, it is possible to convert a matrix to a nested list. This is very useful, + as most Python libraries involving matrices or arrays (namely NumPy or SymPy) + support this format:: + + >>> B.tolist() + [[mpf('1.0'), mpf('0.0')], [mpf('0.0'), mpf('0.0')]] + + + Matrix operations + ----------------- + + You can add and subtract matrices of compatible dimensions:: + + >>> A = matrix([[1, 2], [3, 4]]) + >>> B = matrix([[-2, 4], [5, 9]]) + >>> A + B + matrix( + [['-1.0', '6.0'], + ['8.0', '13.0']]) + >>> A - B + matrix( + [['3.0', '-2.0'], + ['-2.0', '-5.0']]) + >>> A + ones(3) # doctest:+ELLIPSIS + Traceback (most recent call last): + ... + ValueError: incompatible dimensions for addition + + It is possible to multiply or add matrices and scalars. In the latter case the + operation will be done element-wise:: + + >>> A * 2 + matrix( + [['2.0', '4.0'], + ['6.0', '8.0']]) + >>> A / 4 + matrix( + [['0.25', '0.5'], + ['0.75', '1.0']]) + >>> A - 1 + matrix( + [['0.0', '1.0'], + ['2.0', '3.0']]) + + Of course you can perform matrix multiplication, if the dimensions are + compatible, using ``@`` (for Python >= 3.5) or ``*``. For clarity, ``@`` is + recommended (`PEP 465 `), because + the meaning of ``*`` is different in many other Python libraries such as NumPy. + + >>> A @ B # doctest:+SKIP + matrix( + [['8.0', '22.0'], + ['14.0', '48.0']]) + >>> A * B # same as A @ B + matrix( + [['8.0', '22.0'], + ['14.0', '48.0']]) + >>> matrix([[1, 2, 3]]) * matrix([[-6], [7], [-2]]) + matrix( + [['2.0']]) + + .. + COMMENT: TODO: the above "doctest:+SKIP" may be removed as soon as we + have dropped support for Python 3.5 and below. + + You can raise powers of square matrices:: + + >>> A**2 + matrix( + [['7.0', '10.0'], + ['15.0', '22.0']]) + + Negative powers will calculate the inverse:: + + >>> A**-1 + matrix( + [['-2.0', '1.0'], + ['1.5', '-0.5']]) + >>> A * A**-1 + matrix( + [['1.0', '1.0842021724855e-19'], + ['-2.16840434497101e-19', '1.0']]) + + + + Matrix transposition is straightforward:: + + >>> A = ones(2, 3) + >>> A + matrix( + [['1.0', '1.0', '1.0'], + ['1.0', '1.0', '1.0']]) + >>> A.T + matrix( + [['1.0', '1.0'], + ['1.0', '1.0'], + ['1.0', '1.0']]) + + Norms + ..... + + Sometimes you need to know how "large" a matrix or vector is. Due to their + multidimensional nature it's not possible to compare them, but there are + several functions to map a matrix or a vector to a positive real number, the + so called norms. + + For vectors the p-norm is intended, usually the 1-, the 2- and the oo-norm are + used. + + >>> x = matrix([-10, 2, 100]) + >>> norm(x, 1) + mpf('112.0') + >>> norm(x, 2) + mpf('100.5186549850325') + >>> norm(x, inf) + mpf('100.0') + + Please note that the 2-norm is the most used one, though it is more expensive + to calculate than the 1- or oo-norm. + + It is possible to generalize some vector norms to matrix norm:: + + >>> A = matrix([[1, -1000], [100, 50]]) + >>> mnorm(A, 1) + mpf('1050.0') + >>> mnorm(A, inf) + mpf('1001.0') + >>> mnorm(A, 'F') + mpf('1006.2310867787777') + + The last norm (the "Frobenius-norm") is an approximation for the 2-norm, which + is hard to calculate and not available. The Frobenius-norm lacks some + mathematical properties you might expect from a norm. + """ + + def __init__(self, *args, **kwargs): + self.__data = {} + # LU decompostion cache, this is useful when solving the same system + # multiple times, when calculating the inverse and when calculating the + # determinant + self._LU = None + if "force_type" in kwargs: + warnings.warn("The force_type argument was removed, it did not work" + " properly anyway. If you want to force floating-point or" + " interval computations, use the respective methods from `fp`" + " or `mp` instead, e.g., `fp.matrix()` or `iv.matrix()`." + " If you want to truncate values to integer, use .apply(int) instead.") + if isinstance(args[0], (list, tuple)): + if isinstance(args[0][0], (list, tuple)): + # interpret nested list as matrix + A = args[0] + self.__rows = len(A) + self.__cols = len(A[0]) + for i, row in enumerate(A): + for j, a in enumerate(row): + # note: this will call __setitem__ which will call self.ctx.convert() to convert the datatype. + self[i, j] = a + else: + # interpret list as row vector + v = args[0] + self.__rows = len(v) + self.__cols = 1 + for i, e in enumerate(v): + self[i, 0] = e + elif isinstance(args[0], int): + # create empty matrix of given dimensions + if len(args) == 1: + self.__rows = self.__cols = args[0] + else: + if not isinstance(args[1], int): + raise TypeError("expected int") + self.__rows = args[0] + self.__cols = args[1] + elif isinstance(args[0], _matrix): + A = args[0] + self.__rows = A._matrix__rows + self.__cols = A._matrix__cols + for i in xrange(A.__rows): + for j in xrange(A.__cols): + self[i, j] = A[i, j] + elif hasattr(args[0], 'tolist'): + A = self.ctx.matrix(args[0].tolist()) + self.__data = A._matrix__data + self.__rows = A._matrix__rows + self.__cols = A._matrix__cols + else: + raise TypeError('could not interpret given arguments') + + def apply(self, f): + """ + Return a copy of self with the function `f` applied elementwise. + """ + new = self.ctx.matrix(self.__rows, self.__cols) + for i in xrange(self.__rows): + for j in xrange(self.__cols): + new[i,j] = f(self[i,j]) + return new + + def __nstr__(self, n=None, **kwargs): + # Build table of string representations of the elements + res = [] + # Track per-column max lengths for pretty alignment + maxlen = [0] * self.cols + for i in range(self.rows): + res.append([]) + for j in range(self.cols): + if n: + string = self.ctx.nstr(self[i,j], n, **kwargs) + else: + string = str(self[i,j]) + res[-1].append(string) + maxlen[j] = max(len(string), maxlen[j]) + # Patch strings together + for i, row in enumerate(res): + for j, elem in enumerate(row): + # Pad each element up to maxlen so the columns line up + row[j] = elem.rjust(maxlen[j]) + res[i] = "[" + colsep.join(row) + "]" + return rowsep.join(res) + + def __str__(self): + return self.__nstr__() + + def _toliststr(self, avoid_type=False): + """ + Create a list string from a matrix. + + If avoid_type: avoid multiple 'mpf's. + """ + # XXX: should be something like self.ctx._types + typ = self.ctx.mpf + s = '[' + for i in xrange(self.__rows): + s += '[' + for j in xrange(self.__cols): + if not avoid_type or not isinstance(self[i,j], typ): + a = repr(self[i,j]) + else: + a = "'" + str(self[i,j]) + "'" + s += a + ', ' + s = s[:-2] + s += '],\n ' + s = s[:-3] + s += ']' + return s + + def tolist(self): + """ + Convert the matrix to a nested list. + """ + return [[self[i,j] for j in range(self.__cols)] for i in range(self.__rows)] + + def __repr__(self): + if self.ctx.pretty: + return self.__str__() + s = 'matrix(\n' + s += self._toliststr(avoid_type=True) + ')' + return s + + def __get_element(self, key): + ''' + Fast extraction of the i,j element from the matrix + This function is for private use only because is unsafe: + 1. Does not check on the value of key it expects key to be a integer tuple (i,j) + 2. Does not check bounds + ''' + if key in self.__data: + return self.__data[key] + else: + return self.ctx.zero + + def __set_element(self, key, value): + ''' + Fast assignment of the i,j element in the matrix + This function is unsafe: + 1. Does not check on the value of key it expects key to be a integer tuple (i,j) + 2. Does not check bounds + 3. Does not check the value type + 4. Does not reset the LU cache + ''' + if value: # only store non-zeros + self.__data[key] = value + elif key in self.__data: + del self.__data[key] + + + def __getitem__(self, key): + ''' + Getitem function for mp matrix class with slice index enabled + it allows the following assingments + scalar to a slice of the matrix + B = A[:,2:6] + ''' + # Convert vector to matrix indexing + if isinstance(key, int) or isinstance(key,slice): + # only sufficent for vectors + if self.__rows == 1: + key = (0, key) + elif self.__cols == 1: + key = (key, 0) + else: + raise IndexError('insufficient indices for matrix') + + if isinstance(key[0],slice) or isinstance(key[1],slice): + + #Rows + if isinstance(key[0],slice): + #Check bounds + if (key[0].start is None or key[0].start >= 0) and \ + (key[0].stop is None or key[0].stop <= self.__rows+1): + # Generate indices + rows = xrange(*key[0].indices(self.__rows)) + else: + raise IndexError('Row index out of bounds') + else: + # Single row + rows = [key[0]] + + # Columns + if isinstance(key[1],slice): + # Check bounds + if (key[1].start is None or key[1].start >= 0) and \ + (key[1].stop is None or key[1].stop <= self.__cols+1): + # Generate indices + columns = xrange(*key[1].indices(self.__cols)) + else: + raise IndexError('Column index out of bounds') + + else: + # Single column + columns = [key[1]] + + # Create matrix slice + m = self.ctx.matrix(len(rows),len(columns)) + + # Assign elements to the output matrix + for i,x in enumerate(rows): + for j,y in enumerate(columns): + m.__set_element((i,j),self.__get_element((x,y))) + + return m + + else: + # single element extraction + if key[0] >= self.__rows or key[1] >= self.__cols: + raise IndexError('matrix index out of range') + if key in self.__data: + return self.__data[key] + else: + return self.ctx.zero + + def __setitem__(self, key, value): + # setitem function for mp matrix class with slice index enabled + # it allows the following assingments + # scalar to a slice of the matrix + # A[:,2:6] = 2.5 + # submatrix to matrix (the value matrix should be the same size as the slice size) + # A[3,:] = B where A is n x m and B is n x 1 + # Convert vector to matrix indexing + if isinstance(key, int) or isinstance(key,slice): + # only sufficent for vectors + if self.__rows == 1: + key = (0, key) + elif self.__cols == 1: + key = (key, 0) + else: + raise IndexError('insufficient indices for matrix') + # Slice indexing + if isinstance(key[0],slice) or isinstance(key[1],slice): + # Rows + if isinstance(key[0],slice): + # Check bounds + if (key[0].start is None or key[0].start >= 0) and \ + (key[0].stop is None or key[0].stop <= self.__rows+1): + # generate row indices + rows = xrange(*key[0].indices(self.__rows)) + else: + raise IndexError('Row index out of bounds') + else: + # Single row + rows = [key[0]] + # Columns + if isinstance(key[1],slice): + # Check bounds + if (key[1].start is None or key[1].start >= 0) and \ + (key[1].stop is None or key[1].stop <= self.__cols+1): + # Generate column indices + columns = xrange(*key[1].indices(self.__cols)) + else: + raise IndexError('Column index out of bounds') + else: + # Single column + columns = [key[1]] + # Assign slice with a scalar + if isinstance(value,self.ctx.matrix): + # Assign elements to matrix if input and output dimensions match + if len(rows) == value.rows and len(columns) == value.cols: + for i,x in enumerate(rows): + for j,y in enumerate(columns): + self.__set_element((x,y), value.__get_element((i,j))) + else: + raise ValueError('Dimensions do not match') + else: + # Assign slice with scalars + value = self.ctx.convert(value) + for i in rows: + for j in columns: + self.__set_element((i,j), value) + else: + # Single element assingment + # Check bounds + if key[0] >= self.__rows or key[1] >= self.__cols: + raise IndexError('matrix index out of range') + # Convert and store value + value = self.ctx.convert(value) + if value: # only store non-zeros + self.__data[key] = value + elif key in self.__data: + del self.__data[key] + + if self._LU: + self._LU = None + return + + def __iter__(self): + for i in xrange(self.__rows): + for j in xrange(self.__cols): + yield self[i,j] + + def __mul__(self, other): + if isinstance(other, self.ctx.matrix): + # dot multiplication + if self.__cols != other.__rows: + raise ValueError('dimensions not compatible for multiplication') + new = self.ctx.matrix(self.__rows, other.__cols) + self_zero = self.ctx.zero + self_get = self.__data.get + other_zero = other.ctx.zero + other_get = other.__data.get + for i in xrange(self.__rows): + for j in xrange(other.__cols): + new[i, j] = self.ctx.fdot((self_get((i,k), self_zero), other_get((k,j), other_zero)) + for k in xrange(other.__rows)) + return new + else: + # try scalar multiplication + new = self.ctx.matrix(self.__rows, self.__cols) + for i in xrange(self.__rows): + for j in xrange(self.__cols): + new[i, j] = other * self[i, j] + return new + + def __matmul__(self, other): + return self.__mul__(other) + + def __rmul__(self, other): + # assume other is scalar and thus commutative + if isinstance(other, self.ctx.matrix): + raise TypeError("other should not be type of ctx.matrix") + return self.__mul__(other) + + def __pow__(self, other): + # avoid cyclic import problems + #from linalg import inverse + if not isinstance(other, int): + raise ValueError('only integer exponents are supported') + if not self.__rows == self.__cols: + raise ValueError('only powers of square matrices are defined') + n = other + if n == 0: + return self.ctx.eye(self.__rows) + if n < 0: + n = -n + neg = True + else: + neg = False + i = n + y = 1 + z = self.copy() + while i != 0: + if i % 2 == 1: + y = y * z + z = z*z + i = i // 2 + if neg: + y = self.ctx.inverse(y) + return y + + def __div__(self, other): + # assume other is scalar and do element-wise divison + assert not isinstance(other, self.ctx.matrix) + new = self.ctx.matrix(self.__rows, self.__cols) + for i in xrange(self.__rows): + for j in xrange(self.__cols): + new[i,j] = self[i,j] / other + return new + + __truediv__ = __div__ + + def __add__(self, other): + if isinstance(other, self.ctx.matrix): + if not (self.__rows == other.__rows and self.__cols == other.__cols): + raise ValueError('incompatible dimensions for addition') + new = self.ctx.matrix(self.__rows, self.__cols) + for i in xrange(self.__rows): + for j in xrange(self.__cols): + new[i,j] = self[i,j] + other[i,j] + return new + else: + # assume other is scalar and add element-wise + new = self.ctx.matrix(self.__rows, self.__cols) + for i in xrange(self.__rows): + for j in xrange(self.__cols): + new[i,j] += self[i,j] + other + return new + + def __radd__(self, other): + return self.__add__(other) + + def __sub__(self, other): + if isinstance(other, self.ctx.matrix) and not (self.__rows == other.__rows + and self.__cols == other.__cols): + raise ValueError('incompatible dimensions for subtraction') + return self.__add__(other * (-1)) + + def __pos__(self): + """ + +M returns a copy of M, rounded to current working precision. + """ + return (+1) * self + + def __neg__(self): + return (-1) * self + + def __rsub__(self, other): + return -self + other + + def __eq__(self, other): + return self.__rows == other.__rows and self.__cols == other.__cols \ + and self.__data == other.__data + + def __len__(self): + if self.rows == 1: + return self.cols + elif self.cols == 1: + return self.rows + else: + return self.rows # do it like numpy + + def __getrows(self): + return self.__rows + + def __setrows(self, value): + for key in self.__data.copy(): + if key[0] >= value: + del self.__data[key] + self.__rows = value + + rows = property(__getrows, __setrows, doc='number of rows') + + def __getcols(self): + return self.__cols + + def __setcols(self, value): + for key in self.__data.copy(): + if key[1] >= value: + del self.__data[key] + self.__cols = value + + cols = property(__getcols, __setcols, doc='number of columns') + + def transpose(self): + new = self.ctx.matrix(self.__cols, self.__rows) + for i in xrange(self.__rows): + for j in xrange(self.__cols): + new[j,i] = self[i,j] + return new + + T = property(transpose) + + def conjugate(self): + return self.apply(self.ctx.conj) + + def transpose_conj(self): + return self.conjugate().transpose() + + H = property(transpose_conj) + + def copy(self): + new = self.ctx.matrix(self.__rows, self.__cols) + new.__data = self.__data.copy() + return new + + __copy__ = copy + + def column(self, n): + m = self.ctx.matrix(self.rows, 1) + for i in range(self.rows): + m[i] = self[i,n] + return m + +class MatrixMethods(object): + + def __init__(ctx): + # XXX: subclass + ctx.matrix = type('matrix', (_matrix,), {}) + ctx.matrix.ctx = ctx + ctx.matrix.convert = ctx.convert + + def eye(ctx, n, **kwargs): + """ + Create square identity matrix n x n. + """ + A = ctx.matrix(n, **kwargs) + for i in xrange(n): + A[i,i] = 1 + return A + + def diag(ctx, diagonal, **kwargs): + """ + Create square diagonal matrix using given list. + + Example: + >>> from mpmath import diag, mp + >>> mp.pretty = False + >>> diag([1, 2, 3]) + matrix( + [['1.0', '0.0', '0.0'], + ['0.0', '2.0', '0.0'], + ['0.0', '0.0', '3.0']]) + """ + A = ctx.matrix(len(diagonal), **kwargs) + for i in xrange(len(diagonal)): + A[i,i] = diagonal[i] + return A + + def zeros(ctx, *args, **kwargs): + """ + Create matrix m x n filled with zeros. + One given dimension will create square matrix n x n. + + Example: + >>> from mpmath import zeros, mp + >>> mp.pretty = False + >>> zeros(2) + matrix( + [['0.0', '0.0'], + ['0.0', '0.0']]) + """ + if len(args) == 1: + m = n = args[0] + elif len(args) == 2: + m = args[0] + n = args[1] + else: + raise TypeError('zeros expected at most 2 arguments, got %i' % len(args)) + A = ctx.matrix(m, n, **kwargs) + for i in xrange(m): + for j in xrange(n): + A[i,j] = 0 + return A + + def ones(ctx, *args, **kwargs): + """ + Create matrix m x n filled with ones. + One given dimension will create square matrix n x n. + + Example: + >>> from mpmath import ones, mp + >>> mp.pretty = False + >>> ones(2) + matrix( + [['1.0', '1.0'], + ['1.0', '1.0']]) + """ + if len(args) == 1: + m = n = args[0] + elif len(args) == 2: + m = args[0] + n = args[1] + else: + raise TypeError('ones expected at most 2 arguments, got %i' % len(args)) + A = ctx.matrix(m, n, **kwargs) + for i in xrange(m): + for j in xrange(n): + A[i,j] = 1 + return A + + def hilbert(ctx, m, n=None): + """ + Create (pseudo) hilbert matrix m x n. + One given dimension will create hilbert matrix n x n. + + The matrix is very ill-conditioned and symmetric, positive definite if + square. + """ + if n is None: + n = m + A = ctx.matrix(m, n) + for i in xrange(m): + for j in xrange(n): + A[i,j] = ctx.one / (i + j + 1) + return A + + def randmatrix(ctx, m, n=None, min=0, max=1, **kwargs): + """ + Create a random m x n matrix. + + All values are >= min and >> from mpmath import randmatrix + >>> randmatrix(2) # doctest:+SKIP + matrix( + [['0.53491598236191806', '0.57195669543302752'], + ['0.85589992269513615', '0.82444367501382143']]) + """ + if not n: + n = m + A = ctx.matrix(m, n, **kwargs) + for i in xrange(m): + for j in xrange(n): + A[i,j] = ctx.rand() * (max - min) + min + return A + + def swap_row(ctx, A, i, j): + """ + Swap row i with row j. + """ + if i == j: + return + if isinstance(A, ctx.matrix): + for k in xrange(A.cols): + A[i,k], A[j,k] = A[j,k], A[i,k] + elif isinstance(A, list): + A[i], A[j] = A[j], A[i] + else: + raise TypeError('could not interpret type') + + def extend(ctx, A, b): + """ + Extend matrix A with column b and return result. + """ + if not isinstance(A, ctx.matrix): + raise TypeError("A should be a type of ctx.matrix") + if A.rows != len(b): + raise ValueError("Value should be equal to len(b)") + A = A.copy() + A.cols += 1 + for i in xrange(A.rows): + A[i, A.cols-1] = b[i] + return A + + def norm(ctx, x, p=2): + r""" + Gives the entrywise `p`-norm of an iterable *x*, i.e. the vector norm + `\left(\sum_k |x_k|^p\right)^{1/p}`, for any given `1 \le p \le \infty`. + + Special cases: + + If *x* is not iterable, this just returns ``absmax(x)``. + + ``p=1`` gives the sum of absolute values. + + ``p=2`` is the standard Euclidean vector norm. + + ``p=inf`` gives the magnitude of the largest element. + + For *x* a matrix, ``p=2`` is the Frobenius norm. + For operator matrix norms, use :func:`~mpmath.mnorm` instead. + + You can use the string 'inf' as well as float('inf') or mpf('inf') + to specify the infinity norm. + + **Examples** + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = False + >>> x = matrix([-10, 2, 100]) + >>> norm(x, 1) + mpf('112.0') + >>> norm(x, 2) + mpf('100.5186549850325') + >>> norm(x, inf) + mpf('100.0') + + """ + try: + iter(x) + except TypeError: + return ctx.absmax(x) + if type(p) is not int: + p = ctx.convert(p) + if p == ctx.inf: + return max(ctx.absmax(i) for i in x) + elif p == 1: + return ctx.fsum(x, absolute=1) + elif p == 2: + return ctx.sqrt(ctx.fsum(x, absolute=1, squared=1)) + elif p > 1: + return ctx.nthroot(ctx.fsum(abs(i)**p for i in x), p) + else: + raise ValueError('p has to be >= 1') + + def mnorm(ctx, A, p=1): + r""" + Gives the matrix (operator) `p`-norm of A. Currently ``p=1`` and ``p=inf`` + are supported: + + ``p=1`` gives the 1-norm (maximal column sum) + + ``p=inf`` gives the `\infty`-norm (maximal row sum). + You can use the string 'inf' as well as float('inf') or mpf('inf') + + ``p=2`` (not implemented) for a square matrix is the usual spectral + matrix norm, i.e. the largest singular value. + + ``p='f'`` (or 'F', 'fro', 'Frobenius, 'frobenius') gives the + Frobenius norm, which is the elementwise 2-norm. The Frobenius norm is an + approximation of the spectral norm and satisfies + + .. math :: + + \frac{1}{\sqrt{\mathrm{rank}(A)}} \|A\|_F \le \|A\|_2 \le \|A\|_F + + The Frobenius norm lacks some mathematical properties that might + be expected of a norm. + + For general elementwise `p`-norms, use :func:`~mpmath.norm` instead. + + **Examples** + + >>> from mpmath import * + >>> mp.dps = 15; mp.pretty = False + >>> A = matrix([[1, -1000], [100, 50]]) + >>> mnorm(A, 1) + mpf('1050.0') + >>> mnorm(A, inf) + mpf('1001.0') + >>> mnorm(A, 'F') + mpf('1006.2310867787777') + + """ + A = ctx.matrix(A) + if type(p) is not int: + if type(p) is str and 'frobenius'.startswith(p.lower()): + return ctx.norm(A, 2) + p = ctx.convert(p) + m, n = A.rows, A.cols + if p == 1: + return max(ctx.fsum((A[i,j] for i in xrange(m)), absolute=1) for j in xrange(n)) + elif p == ctx.inf: + return max(ctx.fsum((A[i,j] for j in xrange(n)), absolute=1) for i in xrange(m)) + else: + raise NotImplementedError("matrix p-norm for arbitrary p") + +if __name__ == '__main__': + import doctest + doctest.testmod() diff --git a/venv/lib/python3.12/site-packages/mpmath/rational.py b/venv/lib/python3.12/site-packages/mpmath/rational.py new file mode 100644 index 0000000000000000000000000000000000000000..58745205319ac3548ad5feb49371d2d154b2d3c8 --- /dev/null +++ b/venv/lib/python3.12/site-packages/mpmath/rational.py @@ -0,0 +1,240 @@ +import operator +import sys +from .libmp import int_types, mpf_hash, bitcount, from_man_exp, HASH_MODULUS + +new = object.__new__ + +def create_reduced(p, q, _cache={}): + key = p, q + if key in _cache: + return _cache[key] + x, y = p, q + while y: + x, y = y, x % y + if x != 1: + p //= x + q //= x + v = new(mpq) + v._mpq_ = p, q + # Speedup integers, half-integers and other small fractions + if q <= 4 and abs(key[0]) < 100: + _cache[key] = v + return v + +class mpq(object): + """ + Exact rational type, currently only intended for internal use. + """ + + __slots__ = ["_mpq_"] + + def __new__(cls, p, q=1): + if type(p) is tuple: + p, q = p + elif hasattr(p, '_mpq_'): + p, q = p._mpq_ + return create_reduced(p, q) + + def __repr__(s): + return "mpq(%s,%s)" % s._mpq_ + + def __str__(s): + return "(%s/%s)" % s._mpq_ + + def __int__(s): + a, b = s._mpq_ + return a // b + + def __nonzero__(s): + return bool(s._mpq_[0]) + + __bool__ = __nonzero__ + + def __hash__(s): + a, b = s._mpq_ + if sys.version_info >= (3, 2): + inverse = pow(b, HASH_MODULUS-2, HASH_MODULUS) + if not inverse: + h = sys.hash_info.inf + else: + h = (abs(a) * inverse) % HASH_MODULUS + if a < 0: h = -h + if h == -1: h = -2 + return h + else: + if b == 1: + return hash(a) + # Power of two: mpf compatible hash + if not (b & (b-1)): + return mpf_hash(from_man_exp(a, 1-bitcount(b))) + return hash((a,b)) + + def __eq__(s, t): + ttype = type(t) + if ttype is mpq: + return s._mpq_ == t._mpq_ + if ttype in int_types: + a, b = s._mpq_ + if b != 1: + return False + return a == t + return NotImplemented + + def __ne__(s, t): + ttype = type(t) + if ttype is mpq: + return s._mpq_ != t._mpq_ + if ttype in int_types: + a, b = s._mpq_ + if b != 1: + return True + return a != t + return NotImplemented + + def _cmp(s, t, op): + ttype = type(t) + if ttype in int_types: + a, b = s._mpq_ + return op(a, t*b) + if ttype is mpq: + a, b = s._mpq_ + c, d = t._mpq_ + return op(a*d, b*c) + return NotImplementedError + + def __lt__(s, t): return s._cmp(t, operator.lt) + def __le__(s, t): return s._cmp(t, operator.le) + def __gt__(s, t): return s._cmp(t, operator.gt) + def __ge__(s, t): return s._cmp(t, operator.ge) + + def __abs__(s): + a, b = s._mpq_ + if a >= 0: + return s + v = new(mpq) + v._mpq_ = -a, b + return v + + def __neg__(s): + a, b = s._mpq_ + v = new(mpq) + v._mpq_ = -a, b + return v + + def __pos__(s): + return s + + def __add__(s, t): + ttype = type(t) + if ttype is mpq: + a, b = s._mpq_ + c, d = t._mpq_ + return create_reduced(a*d+b*c, b*d) + if ttype in int_types: + a, b = s._mpq_ + v = new(mpq) + v._mpq_ = a+b*t, b + return v + return NotImplemented + + __radd__ = __add__ + + def __sub__(s, t): + ttype = type(t) + if ttype is mpq: + a, b = s._mpq_ + c, d = t._mpq_ + return create_reduced(a*d-b*c, b*d) + if ttype in int_types: + a, b = s._mpq_ + v = new(mpq) + v._mpq_ = a-b*t, b + return v + return NotImplemented + + def __rsub__(s, t): + ttype = type(t) + if ttype is mpq: + a, b = s._mpq_ + c, d = t._mpq_ + return create_reduced(b*c-a*d, b*d) + if ttype in int_types: + a, b = s._mpq_ + v = new(mpq) + v._mpq_ = b*t-a, b + return v + return NotImplemented + + def __mul__(s, t): + ttype = type(t) + if ttype is mpq: + a, b = s._mpq_ + c, d = t._mpq_ + return create_reduced(a*c, b*d) + if ttype in int_types: + a, b = s._mpq_ + return create_reduced(a*t, b) + return NotImplemented + + __rmul__ = __mul__ + + def __div__(s, t): + ttype = type(t) + if ttype is mpq: + a, b = s._mpq_ + c, d = t._mpq_ + return create_reduced(a*d, b*c) + if ttype in int_types: + a, b = s._mpq_ + return create_reduced(a, b*t) + return NotImplemented + + def __rdiv__(s, t): + ttype = type(t) + if ttype is mpq: + a, b = s._mpq_ + c, d = t._mpq_ + return create_reduced(b*c, a*d) + if ttype in int_types: + a, b = s._mpq_ + return create_reduced(b*t, a) + return NotImplemented + + def __pow__(s, t): + ttype = type(t) + if ttype in int_types: + a, b = s._mpq_ + if t: + if t < 0: + a, b, t = b, a, -t + v = new(mpq) + v._mpq_ = a**t, b**t + return v + raise ZeroDivisionError + return NotImplemented + + +mpq_1 = mpq((1,1)) +mpq_0 = mpq((0,1)) +mpq_1_2 = mpq((1,2)) +mpq_3_2 = mpq((3,2)) +mpq_1_4 = mpq((1,4)) +mpq_1_16 = mpq((1,16)) +mpq_3_16 = mpq((3,16)) +mpq_5_2 = mpq((5,2)) +mpq_3_4 = mpq((3,4)) +mpq_7_4 = mpq((7,4)) +mpq_5_4 = mpq((5,4)) + + +# Register with "numbers" ABC +# We do not subclass, hence we do not use the @abstractmethod checks. 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import * +from mpmath.libmp import ifac + +import sys +if "-dps" in sys.argv: + maxdps = int(sys.argv[sys.argv.index("-dps")+1]) +else: + maxdps = 1000 + +raise_ = "-raise" in sys.argv + +errcount = 0 + +def check(name, func, z, y): + global errcount + try: + x = func(z) + except: + errcount += 1 + if raise_: + raise + print() + print(name) + print("EXCEPTION") + import traceback + traceback.print_tb(sys.exc_info()[2]) + print() + return + xre = x.real + xim = x.imag + yre = y.real + yim = y.imag + tol = eps*8 + err = 0 + if abs(xre-yre) > abs(yre)*tol: + err = 1 + print() + print("Error! %s (re = %s, wanted %s, err=%s)" % (name, nstr(xre,10), nstr(yre,10), nstr(abs(xre-yre)))) + errcount += 1 + if raise_: + raise SystemExit + if abs(xim-yim) > abs(yim)*tol: + err = 1 + print() + print("Error! %s (im = %s, wanted %s, err=%s)" % (name, nstr(xim,10), nstr(yim,10), nstr(abs(xim-yim)))) + errcount += 1 + if raise_: + raise SystemExit + if not err: + sys.stdout.write("%s ok; " % name) + +def testcase(case): + z, result = case + print("Testing z =", z) + mp.dps = 1010 + z = eval(z) + mp.dps = maxdps + 50 + if result is None: + gamma_val = gamma(z) + loggamma_val = loggamma(z) + factorial_val = factorial(z) + rgamma_val = rgamma(z) + else: + loggamma_val = eval(result) + gamma_val = exp(loggamma_val) + factorial_val = z * gamma_val + rgamma_val = 1/gamma_val + for dps in [5, 10, 15, 25, 40, 60, 90, 120, 250, 600, 1000, 1800, 3600]: + if dps > maxdps: + break + mp.dps = dps + print("dps = %s" % dps) + check("gamma", gamma, z, gamma_val) + check("rgamma", rgamma, z, rgamma_val) + check("loggamma", loggamma, z, loggamma_val) + check("factorial", factorial, z, factorial_val) + print() + mp.dps = 15 + +testcases = [] + +# Basic values +for n in list(range(1,200)) + list(range(201,2000,17)): + testcases.append(["%s" % n, None]) +for n in range(-200,200): + testcases.append(["%s+0.5" % n, None]) + testcases.append(["%s+0.37" % n, None]) + +testcases += [\ +["(0.1+1j)", None], +["(-0.1+1j)", None], +["(0.1-1j)", None], +["(-0.1-1j)", None], +["10j", None], +["-10j", None], +["100j", None], +["10000j", None], +["-10000000j", None], +["(10**100)*j", None], +["125+(10**100)*j", None], +["-125+(10**100)*j", None], +["(10**10)*(1+j)", None], +["(10**10)*(-1+j)", None], +["(10**100)*(1+j)", None], +["(10**100)*(-1+j)", None], +["(1.5-1j)", None], +["(6+4j)", None], +["(4+1j)", None], +["(3.5+2j)", None], +["(1.5-1j)", None], +["(-6-4j)", None], +["(-2-3j)", None], +["(-2.5-2j)", None], +["(4+1j)", None], +["(3+3j)", None], +["(2-2j)", None], +["1", "0"], +["2", "0"], +["3", "log(2)"], +["4", "log(6)"], +["5", "log(24)"], +["0.5", "log(pi)/2"], +["1.5", "log(sqrt(pi)/2)"], +["2.5", "log(3*sqrt(pi)/4)"], +["mpf('0.37')", None], +["0.25", "log(sqrt(2*sqrt(2*pi**3)/agm(1,sqrt(2))))"], +["-0.4", None], +["mpf('-1.9')", None], +["mpf('12.8')", None], +["mpf('33.7')", None], +["mpf('95.2')", None], +["mpf('160.3')", None], +["mpf('2057.8')", None], +["25", "log(ifac(24))"], +["80", "log(ifac(79))"], +["500", "log(ifac(500-1))"], +["8000", "log(ifac(8000-1))"], +["8000.5", None], +["mpf('8000.1')", None], +["mpf('1.37e10')", None], +["mpf('1.37e10')*(1+j)", None], +["mpf('1.37e10')*(-1+j)", None], +["mpf('1.37e10')*(-1-j)", None], +["mpf('1.37e10')*(-1+j)", None], +["mpf('1.37e100')", None], +["mpf('1.37e100')*(1+j)", None], +["mpf('1.37e100')*(-1+j)", None], +["mpf('1.37e100')*(-1-j)", None], +["mpf('1.37e100')*(-1+j)", None], +["3+4j", +"mpc('" +"-1.7566267846037841105306041816232757851567066070613445016197619371316057169" +"4723618263960834804618463052988607348289672535780644470689771115236512106002" +"5970873471563240537307638968509556191696167970488390423963867031934333890838" +"8009531786948197210025029725361069435208930363494971027388382086721660805397" +"9163230643216054580167976201709951509519218635460317367338612500626714783631" +"7498317478048447525674016344322545858832610325861086336204591943822302971823" +"5161814175530618223688296232894588415495615809337292518431903058265147109853" +"1710568942184987827643886816200452860853873815413367529829631430146227470517" +"6579967222200868632179482214312673161276976117132204633283806161971389519137" +"1243359764435612951384238091232760634271570950240717650166551484551654327989" +"9360285030081716934130446150245110557038117075172576825490035434069388648124" +"6678152254554001586736120762641422590778766100376515737713938521275749049949" +"1284143906816424244705094759339932733567910991920631339597278805393743140853" +"391550313363278558195609260225928','" +"4.74266443803465792819488940755002274088830335171164611359052405215840070271" +"5906813009373171139767051863542508136875688550817670379002790304870822775498" +"2809996675877564504192565392367259119610438951593128982646945990372179860613" +"4294436498090428077839141927485901735557543641049637962003652638924845391650" +"9546290137755550107224907606529385248390667634297183361902055842228798984200" +"9591180450211798341715874477629099687609819466457990642030707080894518168924" +"6805549314043258530272479246115112769957368212585759640878745385160943755234" +"9398036774908108204370323896757543121853650025529763655312360354244898913463" +"7115955702828838923393113618205074162812089732064414530813087483533203244056" +"0546577484241423134079056537777170351934430586103623577814746004431994179990" +"5318522939077992613855205801498201930221975721246498720895122345420698451980" +"0051215797310305885845964334761831751370672996984756815410977750799748813563" +"8784405288158432214886648743541773208808731479748217023665577802702269468013" +"673719173759245720489020315779001')"], +] + +for z in [4, 14, 34, 64]: + testcases.append(["(2+j)*%s/3" % z, None]) + testcases.append(["(-2+j)*%s/3" % z, None]) + testcases.append(["(1+2*j)*%s/3" % z, None]) + testcases.append(["(2-j)*%s/3" % z, None]) + testcases.append(["(20+j)*%s/3" % z, None]) + testcases.append(["(-20+j)*%s/3" % z, None]) + testcases.append(["(1+20*j)*%s/3" % z, None]) + testcases.append(["(20-j)*%s/3" % z, None]) + testcases.append(["(200+j)*%s/3" % z, None]) + testcases.append(["(-200+j)*%s/3" % z, None]) + testcases.append(["(1+200*j)*%s/3" % z, None]) + testcases.append(["(200-j)*%s/3" % z, None]) + +# Poles +for n in [0,1,2,3,4,25,-1,-2,-3,-4,-20,-21,-50,-51,-200,-201,-20000,-20001]: + for t in ['1e-5', '1e-20', '1e-100', '1e-10000']: + testcases.append(["fadd(%s,'%s',exact=True)" % (n, t), None]) + testcases.append(["fsub(%s,'%s',exact=True)" % (n, t), None]) + testcases.append(["fadd(%s,'%sj',exact=True)" % (n, t), None]) + testcases.append(["fsub(%s,'%sj',exact=True)" % (n, t), None]) + +if __name__ == "__main__": + from timeit import default_timer as clock + tot_time = 0.0 + for case in testcases: + t1 = clock() + testcase(case) + t2 = clock() + print("Test time:", t2-t1) + print() + tot_time += (t2-t1) + print("Total time:", tot_time) + print("Errors:", errcount) diff --git a/venv/lib/python3.12/site-packages/mpmath/tests/extratest_zeta.py b/venv/lib/python3.12/site-packages/mpmath/tests/extratest_zeta.py new file mode 100644 index 0000000000000000000000000000000000000000..582b3d9cbd956b9cdf94309e0e718371fe716101 --- /dev/null +++ b/venv/lib/python3.12/site-packages/mpmath/tests/extratest_zeta.py @@ -0,0 +1,30 @@ +from mpmath import zetazero +from timeit import default_timer as clock + +def test_zetazero(): + cases = [\ + (399999999, 156762524.6750591511), + (241389216, 97490234.2276711795), + (526196239, 202950727.691229534), + (542964976, 209039046.578535272), + (1048449112, 388858885.231056486), + (1048449113, 388858885.384337406), + (1048449114, 388858886.002285122), + (1048449115, 388858886.00239369), + (1048449116, 388858886.690745053) + ] + for n, v in cases: + print(n, v) + t1 = clock() + ok = zetazero(n).ae(complex(0.5,v)) + t2 = clock() + print("ok =", ok, ("(time = %s)" % round(t2-t1,3))) + print("Now computing two huge zeros (this may take hours)") + print("Computing zetazero(8637740722917)") + ok = zetazero(8637740722917).ae(complex(0.5,2124447368584.39296466152)) + print("ok =", ok) + ok = zetazero(8637740722918).ae(complex(0.5,2124447368584.39298170604)) + print("ok =", ok) + +if __name__ == "__main__": + test_zetazero() diff --git a/venv/lib/python3.12/site-packages/mpmath/tests/runtests.py b/venv/lib/python3.12/site-packages/mpmath/tests/runtests.py new file mode 100644 index 0000000000000000000000000000000000000000..70fde272fdc0e05e3d8951edddca380bd36139ab --- /dev/null +++ b/venv/lib/python3.12/site-packages/mpmath/tests/runtests.py @@ -0,0 +1,161 @@ +#!/usr/bin/env python + +""" +python runtests.py -py + Use py.test to run tests (more useful for debugging) + +python runtests.py -coverage + Generate test coverage report. Statistics are written to /tmp + +python runtests.py -profile + Generate profile stats (this is much slower) + +python runtests.py -nogmpy + Run tests without using GMPY even if it exists + +python runtests.py -strict + Enforce extra tests in normalize() + +python runtests.py -local + Insert '../..' at the beginning of sys.path to use local mpmath + +python runtests.py -skip ... + Skip tests from the listed modules + +Additional arguments are used to filter the tests to run. Only files that have +one of the arguments in their name are executed. + +""" + +import sys, os, traceback + +profile = False +if "-profile" in sys.argv: + sys.argv.remove('-profile') + profile = True + +coverage = False +if "-coverage" in sys.argv: + sys.argv.remove('-coverage') + coverage = True + +if "-nogmpy" in sys.argv: + sys.argv.remove('-nogmpy') + os.environ['MPMATH_NOGMPY'] = 'Y' + +if "-strict" in sys.argv: + sys.argv.remove('-strict') + os.environ['MPMATH_STRICT'] = 'Y' + +if "-local" in sys.argv: + sys.argv.remove('-local') + importdir = os.path.abspath(os.path.join(os.path.dirname(sys.argv[0]), + '../..')) +else: + importdir = '' + +# TODO: add a flag for this +testdir = '' + +def testit(importdir='', testdir='', exit_on_fail=False): + """Run all tests in testdir while importing from importdir.""" + if importdir: + sys.path.insert(1, importdir) + if testdir: + sys.path.insert(1, testdir) + import os.path + import mpmath + print("mpmath imported from %s" % os.path.dirname(mpmath.__file__)) + print("mpmath backend: %s" % mpmath.libmp.backend.BACKEND) + print("mpmath mp class: %s" % repr(mpmath.mp)) + print("mpmath version: %s" % mpmath.__version__) + print("Python version: %s" % sys.version) + print("") + if "-py" in sys.argv: + sys.argv.remove('-py') + import py + py.test.cmdline.main() + else: + import glob + from timeit import default_timer as clock + modules = [] + args = sys.argv[1:] + excluded = [] + if '-skip' in args: + excluded = args[args.index('-skip')+1:] + args = args[:args.index('-skip')] + # search for tests in directory of this file if not otherwise specified + if not testdir: + pattern = os.path.dirname(sys.argv[0]) + else: + pattern = testdir + if pattern: + pattern += '/' + pattern += 'test*.py' + # look for tests (respecting specified filter) + for f in glob.glob(pattern): + name = os.path.splitext(os.path.basename(f))[0] + # If run as a script, only run tests given as args, if any are given + if args and __name__ == "__main__": + ok = False + for arg in args: + if arg in name: + ok = True + break + if not ok: + continue + elif name in excluded: + continue + module = __import__(name) + priority = module.__dict__.get('priority', 100) + if priority == 666: + modules = [[priority, name, module]] + break + modules.append([priority, name, module]) + # execute tests + modules.sort() + tstart = clock() + for priority, name, module in modules: + print(name) + for f in sorted(module.__dict__.keys()): + if f.startswith('test_'): + if coverage and ('numpy' in f): + continue + sys.stdout.write(" " + f[5:].ljust(25) + " ") + t1 = clock() + try: + module.__dict__[f]() + except: + etype, evalue, trb = sys.exc_info() + if etype in (KeyboardInterrupt, SystemExit): + raise + print("") + print("TEST FAILED!") + print("") + traceback.print_exc() + if exit_on_fail: + return + t2 = clock() + print("ok " + " " + ("%.7f" % (t2-t1)) + " s") + tend = clock() + print("") + print("finished tests in " + ("%.2f" % (tend-tstart)) + " seconds") + # clean sys.path + if importdir: + sys.path.remove(importdir) + if testdir: + sys.path.remove(testdir) + +if __name__ == '__main__': + if profile: + import cProfile + cProfile.run("testit('%s', '%s')" % (importdir, testdir), sort=1) + elif coverage: + import trace + tracer = trace.Trace(ignoredirs=[sys.prefix, sys.exec_prefix], + trace=0, count=1) + tracer.run('testit(importdir, testdir)') + r = tracer.results() + r.write_results(show_missing=True, summary=True, coverdir="/tmp") + else: + testit(importdir, testdir) diff --git a/venv/lib/python3.12/site-packages/mpmath/tests/test_basic_ops.py b/venv/lib/python3.12/site-packages/mpmath/tests/test_basic_ops.py new file mode 100644 index 0000000000000000000000000000000000000000..f577c7fa9f9734876b6767f6cc21144df305d82f --- /dev/null +++ b/venv/lib/python3.12/site-packages/mpmath/tests/test_basic_ops.py @@ -0,0 +1,451 @@ +import mpmath +from mpmath import * +from mpmath.libmp import * +import random +import sys + +try: + long = long +except NameError: + long = int + +def test_type_compare(): + assert mpf(2) == mpc(2,0) + assert mpf(0) == mpc(0) + assert mpf(2) != mpc(2, 0.00001) + assert mpf(2) == 2.0 + assert mpf(2) != 3.0 + assert mpf(2) == 2 + assert mpf(2) != '2.0' + assert mpc(2) != '2.0' + +def test_add(): + assert mpf(2.5) + mpf(3) == 5.5 + assert mpf(2.5) + 3 == 5.5 + assert mpf(2.5) + 3.0 == 5.5 + assert 3 + mpf(2.5) == 5.5 + assert 3.0 + mpf(2.5) == 5.5 + assert (3+0j) + mpf(2.5) == 5.5 + assert mpc(2.5) + mpf(3) == 5.5 + assert mpc(2.5) + 3 == 5.5 + assert mpc(2.5) + 3.0 == 5.5 + assert mpc(2.5) + (3+0j) == 5.5 + assert 3 + mpc(2.5) == 5.5 + assert 3.0 + mpc(2.5) == 5.5 + assert (3+0j) + mpc(2.5) == 5.5 + +def test_sub(): + assert mpf(2.5) - mpf(3) == -0.5 + assert mpf(2.5) - 3 == -0.5 + assert mpf(2.5) - 3.0 == -0.5 + assert 3 - mpf(2.5) == 0.5 + assert 3.0 - mpf(2.5) == 0.5 + assert (3+0j) - mpf(2.5) == 0.5 + assert mpc(2.5) - mpf(3) == -0.5 + assert mpc(2.5) - 3 == -0.5 + assert mpc(2.5) - 3.0 == -0.5 + assert mpc(2.5) - (3+0j) == -0.5 + assert 3 - mpc(2.5) == 0.5 + assert 3.0 - mpc(2.5) == 0.5 + assert (3+0j) - mpc(2.5) == 0.5 + +def test_mul(): + assert mpf(2.5) * mpf(3) == 7.5 + assert mpf(2.5) * 3 == 7.5 + assert mpf(2.5) * 3.0 == 7.5 + assert 3 * mpf(2.5) == 7.5 + assert 3.0 * mpf(2.5) == 7.5 + assert (3+0j) * mpf(2.5) == 7.5 + assert mpc(2.5) * mpf(3) == 7.5 + assert mpc(2.5) * 3 == 7.5 + assert mpc(2.5) * 3.0 == 7.5 + assert mpc(2.5) * (3+0j) == 7.5 + assert 3 * mpc(2.5) == 7.5 + assert 3.0 * mpc(2.5) == 7.5 + assert (3+0j) * mpc(2.5) == 7.5 + +def test_div(): + assert mpf(6) / mpf(3) == 2.0 + assert mpf(6) / 3 == 2.0 + assert mpf(6) / 3.0 == 2.0 + assert 6 / mpf(3) == 2.0 + assert 6.0 / mpf(3) == 2.0 + assert (6+0j) / mpf(3.0) == 2.0 + assert mpc(6) / mpf(3) == 2.0 + assert mpc(6) / 3 == 2.0 + assert mpc(6) / 3.0 == 2.0 + assert mpc(6) / (3+0j) == 2.0 + assert 6 / mpc(3) == 2.0 + assert 6.0 / mpc(3) == 2.0 + assert (6+0j) / mpc(3) == 2.0 + +def test_pow(): + assert mpf(6) ** mpf(3) == 216.0 + assert mpf(6) ** 3 == 216.0 + assert mpf(6) ** 3.0 == 216.0 + assert 6 ** mpf(3) == 216.0 + assert 6.0 ** mpf(3) == 216.0 + assert (6+0j) ** mpf(3.0) == 216.0 + assert mpc(6) ** mpf(3) == 216.0 + assert mpc(6) ** 3 == 216.0 + assert mpc(6) ** 3.0 == 216.0 + assert mpc(6) ** (3+0j) == 216.0 + assert 6 ** mpc(3) == 216.0 + assert 6.0 ** mpc(3) == 216.0 + assert (6+0j) ** mpc(3) == 216.0 + +def test_mixed_misc(): + assert 1 + mpf(3) == mpf(3) + 1 == 4 + assert 1 - mpf(3) == -(mpf(3) - 1) == -2 + assert 3 * mpf(2) == mpf(2) * 3 == 6 + assert 6 / mpf(2) == mpf(6) / 2 == 3 + assert 1.0 + mpf(3) == mpf(3) + 1.0 == 4 + assert 1.0 - mpf(3) == -(mpf(3) - 1.0) == -2 + assert 3.0 * mpf(2) == mpf(2) * 3.0 == 6 + assert 6.0 / mpf(2) == mpf(6) / 2.0 == 3 + +def test_add_misc(): + mp.dps = 15 + assert mpf(4) + mpf(-70) == -66 + assert mpf(1) + mpf(1.1)/80 == 1 + 1.1/80 + assert mpf((1, 10000000000)) + mpf(3) == mpf((1, 10000000000)) + assert mpf(3) + mpf((1, 10000000000)) == mpf((1, 10000000000)) + assert mpf((1, -10000000000)) + mpf(3) == mpf(3) + assert mpf(3) + mpf((1, -10000000000)) == mpf(3) + assert mpf(1) + 1e-15 != 1 + assert mpf(1) + 1e-20 == 1 + assert mpf(1.07e-22) + 0 == mpf(1.07e-22) + assert mpf(0) + mpf(1.07e-22) == mpf(1.07e-22) + +def test_complex_misc(): + # many more tests needed + assert 1 + mpc(2) == 3 + assert not mpc(2).ae(2 + 1e-13) + assert mpc(2+1e-15j).ae(2) + +def test_complex_zeros(): + for a in [0,2]: + for b in [0,3]: + for c in [0,4]: + for d in [0,5]: + assert mpc(a,b)*mpc(c,d) == complex(a,b)*complex(c,d) + +def test_hash(): + for i in range(-256, 256): + assert hash(mpf(i)) == hash(i) + assert hash(mpf(0.5)) == hash(0.5) + assert hash(mpc(2,3)) == hash(2+3j) + # Check that this doesn't fail + assert hash(inf) + # Check that overflow doesn't assign equal hashes to large numbers + assert hash(mpf('1e1000')) != hash('1e10000') + assert hash(mpc(100,'1e1000')) != hash(mpc(200,'1e1000')) + from mpmath.rational import mpq + assert hash(mp.mpq(1,3)) + assert hash(mp.mpq(0,1)) == 0 + assert hash(mp.mpq(-1,1)) == hash(-1) + assert hash(mp.mpq(1,1)) == hash(1) + assert hash(mp.mpq(5,1)) == hash(5) + assert hash(mp.mpq(1,2)) == hash(0.5) + if sys.version_info >= (3, 2): + assert hash(mpf(1)*2**2000) == hash(2**2000) + assert hash(mpf(1)/2**2000) == hash(mpq(1,2**2000)) + +# Advanced rounding test +def test_add_rounding(): + mp.dps = 15 + a = from_float(1e-50) + assert mpf_sub(mpf_add(fone, a, 53, round_up), fone, 53, round_up) == from_float(2.2204460492503131e-16) + assert mpf_sub(fone, a, 53, round_up) == fone + assert mpf_sub(fone, mpf_sub(fone, a, 53, round_down), 53, round_down) == from_float(1.1102230246251565e-16) + assert mpf_add(fone, a, 53, round_down) == fone + +def test_almost_equal(): + assert mpf(1.2).ae(mpf(1.20000001), 1e-7) + assert not mpf(1.2).ae(mpf(1.20000001), 1e-9) + assert not mpf(-0.7818314824680298).ae(mpf(-0.774695868667929)) + +def test_arithmetic_functions(): + import operator + ops = [(operator.add, fadd), (operator.sub, fsub), (operator.mul, fmul), + (operator.truediv, fdiv)] + a = mpf(0.27) + b = mpf(1.13) + c = mpc(0.51+2.16j) + d = mpc(1.08-0.99j) + for x in [a,b,c,d]: + for y in [a,b,c,d]: + for op, fop in ops: + if fop is not fdiv: + mp.prec = 200 + z0 = op(x,y) + mp.prec = 60 + z1 = op(x,y) + mp.prec = 53 + z2 = op(x,y) + assert fop(x, y, prec=60) == z1 + assert fop(x, y) == z2 + if fop is not fdiv: + assert fop(x, y, prec=inf) == z0 + assert fop(x, y, dps=inf) == z0 + assert fop(x, y, exact=True) == z0 + assert fneg(fneg(z1, exact=True), prec=inf) == z1 + assert fneg(z1) == -(+z1) + mp.dps = 15 + +def test_exact_integer_arithmetic(): + # XXX: re-fix this so that all operations are tested with all rounding modes + random.seed(0) + for prec in [6, 10, 25, 40, 100, 250, 725]: + for rounding in ['d', 'u', 'f', 'c', 'n']: + mp.dps = prec + M = 10**(prec-2) + M2 = 10**(prec//2-2) + for i in range(10): + a = random.randint(-M, M) + b = random.randint(-M, M) + assert mpf(a, rounding=rounding) == a + assert int(mpf(a, rounding=rounding)) == a + assert int(mpf(str(a), rounding=rounding)) == a + assert mpf(a) + mpf(b) == a + b + assert mpf(a) - mpf(b) == a - b + assert -mpf(a) == -a + a = random.randint(-M2, M2) + b = random.randint(-M2, M2) + assert mpf(a) * mpf(b) == a*b + assert mpf_mul(from_int(a), from_int(b), mp.prec, rounding) == from_int(a*b) + mp.dps = 15 + +def test_odd_int_bug(): + assert to_int(from_int(3), round_nearest) == 3 + +def test_str_1000_digits(): + mp.dps = 1001 + # last digit may be wrong + assert str(mpf(2)**0.5)[-10:-1] == '9518488472'[:9] + assert str(pi)[-10:-1] == '2164201989'[:9] + mp.dps = 15 + +def test_str_10000_digits(): + mp.dps = 10001 + # last digit may be wrong + assert str(mpf(2)**0.5)[-10:-1] == '5873258351'[:9] + assert str(pi)[-10:-1] == '5256375678'[:9] + mp.dps = 15 + +def test_monitor(): + f = lambda x: x**2 + a = [] + b = [] + g = monitor(f, a.append, b.append) + assert g(3) == 9 + assert g(4) == 16 + assert a[0] == ((3,), {}) + assert b[0] == 9 + +def test_nint_distance(): + assert nint_distance(mpf(-3)) == (-3, -inf) + assert nint_distance(mpc(-3)) == (-3, -inf) + assert nint_distance(mpf(-3.1)) == (-3, -3) + assert nint_distance(mpf(-3.01)) == (-3, -6) + assert nint_distance(mpf(-3.001)) == (-3, -9) + assert nint_distance(mpf(-3.0001)) == (-3, -13) + assert nint_distance(mpf(-2.9)) == (-3, -3) + assert nint_distance(mpf(-2.99)) == (-3, -6) + assert nint_distance(mpf(-2.999)) == (-3, -9) + assert nint_distance(mpf(-2.9999)) == (-3, -13) + assert nint_distance(mpc(-3+0.1j)) == (-3, -3) + assert nint_distance(mpc(-3+0.01j)) == (-3, -6) + assert nint_distance(mpc(-3.1+0.1j)) == (-3, -3) + assert nint_distance(mpc(-3.01+0.01j)) == (-3, -6) + assert nint_distance(mpc(-3.001+0.001j)) == (-3, -9) + assert nint_distance(mpf(0)) == (0, -inf) + assert nint_distance(mpf(0.01)) == (0, -6) + assert nint_distance(mpf('1e-100')) == (0, -332) + +def test_floor_ceil_nint_frac(): + mp.dps = 15 + for n in range(-10,10): + assert floor(n) == n + assert floor(n+0.5) == n + assert ceil(n) == n + assert ceil(n+0.5) == n+1 + assert nint(n) == n + # nint rounds to even + if n % 2 == 1: + assert nint(n+0.5) == n+1 + else: + assert nint(n+0.5) == n + assert floor(inf) == inf + assert floor(ninf) == ninf + assert isnan(floor(nan)) + assert ceil(inf) == inf + assert ceil(ninf) == ninf + assert isnan(ceil(nan)) + assert nint(inf) == inf + assert nint(ninf) == ninf + assert isnan(nint(nan)) + assert floor(0.1) == 0 + assert floor(0.9) == 0 + assert floor(-0.1) == -1 + assert floor(-0.9) == -1 + assert floor(10000000000.1) == 10000000000 + assert floor(10000000000.9) == 10000000000 + assert floor(-10000000000.1) == -10000000000-1 + assert floor(-10000000000.9) == -10000000000-1 + assert floor(1e-100) == 0 + assert floor(-1e-100) == -1 + assert floor(1e100) == 1e100 + assert floor(-1e100) == -1e100 + assert ceil(0.1) == 1 + assert ceil(0.9) == 1 + assert ceil(-0.1) == 0 + assert ceil(-0.9) == 0 + assert ceil(10000000000.1) == 10000000000+1 + assert ceil(10000000000.9) == 10000000000+1 + assert ceil(-10000000000.1) == -10000000000 + assert ceil(-10000000000.9) == -10000000000 + assert ceil(1e-100) == 1 + assert ceil(-1e-100) == 0 + assert ceil(1e100) == 1e100 + assert ceil(-1e100) == -1e100 + assert nint(0.1) == 0 + assert nint(0.9) == 1 + assert nint(-0.1) == 0 + assert nint(-0.9) == -1 + assert nint(10000000000.1) == 10000000000 + assert nint(10000000000.9) == 10000000000+1 + assert nint(-10000000000.1) == -10000000000 + assert nint(-10000000000.9) == -10000000000-1 + assert nint(1e-100) == 0 + assert nint(-1e-100) == 0 + assert nint(1e100) == 1e100 + assert nint(-1e100) == -1e100 + assert floor(3.2+4.6j) == 3+4j + assert ceil(3.2+4.6j) == 4+5j + assert nint(3.2+4.6j) == 3+5j + for n in range(-10,10): + assert frac(n) == 0 + assert frac(0.25) == 0.25 + assert frac(1.25) == 0.25 + assert frac(2.25) == 0.25 + assert frac(-0.25) == 0.75 + assert frac(-1.25) == 0.75 + assert frac(-2.25) == 0.75 + assert frac('1e100000000000000') == 0 + u = mpf('1e-100000000000000') + assert frac(u) == u + assert frac(-u) == 1 # rounding! + u = mpf('1e-400') + assert frac(-u, prec=0) == fsub(1, u, exact=True) + assert frac(3.25+4.75j) == 0.25+0.75j + +def test_isnan_etc(): + from mpmath.rational import mpq + assert isnan(nan) == True + assert isnan(3) == False + assert isnan(mpf(3)) == False + assert isnan(inf) == False + assert isnan(mpc(2,nan)) == True + assert isnan(mpc(2,nan)) == True + assert isnan(mpc(nan,nan)) == True + assert isnan(mpc(2,2)) == False + assert isnan(mpc(nan,inf)) == True + assert isnan(mpc(inf,inf)) == False + assert isnan(mpq((3,2))) == False + assert isnan(mpq((0,1))) == False + assert isinf(inf) == True + assert isinf(-inf) == True + assert isinf(3) == False + assert isinf(nan) == False + assert isinf(3+4j) == False + assert isinf(mpc(inf)) == True + assert isinf(mpc(3,inf)) == True + assert isinf(mpc(inf,3)) == True + assert isinf(mpc(inf,inf)) == True + assert isinf(mpc(nan,inf)) == True + assert isinf(mpc(inf,nan)) == True + assert isinf(mpc(nan,nan)) == False + assert isinf(mpq((3,2))) == False + assert isinf(mpq((0,1))) == False + assert isnormal(3) == True + assert isnormal(3.5) == True + assert isnormal(mpf(3.5)) == True + assert isnormal(0) == False + assert isnormal(mpf(0)) == False + assert isnormal(0.0) == False + assert isnormal(inf) == False + assert isnormal(-inf) == False + assert isnormal(nan) == False + assert isnormal(float(inf)) == False + assert isnormal(mpc(0,0)) == False + assert isnormal(mpc(3,0)) == True + assert isnormal(mpc(0,3)) == True + assert isnormal(mpc(3,3)) == True + assert isnormal(mpc(0,nan)) == False + assert isnormal(mpc(0,inf)) == False + assert isnormal(mpc(3,nan)) == False + assert isnormal(mpc(3,inf)) == False + assert isnormal(mpc(3,-inf)) == False + assert isnormal(mpc(nan,0)) == False + assert isnormal(mpc(inf,0)) == False + assert isnormal(mpc(nan,3)) == False + assert isnormal(mpc(inf,3)) == False + assert isnormal(mpc(inf,nan)) == False + assert isnormal(mpc(nan,inf)) == False + assert isnormal(mpc(nan,nan)) == False + assert isnormal(mpc(inf,inf)) == False + assert isnormal(mpq((3,2))) == True + assert isnormal(mpq((0,1))) == False + assert isint(3) == True + assert isint(0) == True + assert isint(long(3)) == True + assert isint(long(0)) == True + assert isint(mpf(3)) == True + assert isint(mpf(0)) == True + assert isint(mpf(-3)) == True + assert isint(mpf(3.2)) == False + assert isint(3.2) == False + assert isint(nan) == False + assert isint(inf) == False + assert isint(-inf) == False + assert isint(mpc(0)) == True + assert isint(mpc(3)) == True + assert isint(mpc(3.2)) == False + assert isint(mpc(3,inf)) == False + assert isint(mpc(inf)) == False + assert isint(mpc(3,2)) == False + assert isint(mpc(0,2)) == False + assert isint(mpc(3,2),gaussian=True) == True + assert isint(mpc(3,0),gaussian=True) == True + assert isint(mpc(0,3),gaussian=True) == True + assert isint(3+4j) == False + assert isint(3+4j, gaussian=True) == True + assert isint(3+0j) == True + assert isint(mpq((3,2))) == False + assert isint(mpq((3,9))) == False + assert isint(mpq((9,3))) == True + assert isint(mpq((0,4))) == True + assert isint(mpq((1,1))) == True + assert isint(mpq((-1,1))) == True + assert mp.isnpint(0) == True + assert mp.isnpint(1) == False + assert mp.isnpint(-1) == True + assert mp.isnpint(-1.1) == False + assert mp.isnpint(-1.0) == True + assert mp.isnpint(mp.mpq(1,2)) == False + assert mp.isnpint(mp.mpq(-1,2)) == False + assert mp.isnpint(mp.mpq(-3,1)) == True + assert mp.isnpint(mp.mpq(0,1)) == True + assert mp.isnpint(mp.mpq(1,1)) == False + assert mp.isnpint(0+0j) == True + assert mp.isnpint(-1+0j) == True + assert mp.isnpint(-1.1+0j) == False + assert mp.isnpint(-1+0.1j) == False + assert mp.isnpint(0+0.1j) == False + + +def test_issue_438(): + assert mpf(finf) == mpf('inf') + assert mpf(fninf) == mpf('-inf') + assert mpf(fnan)._mpf_ == mpf('nan')._mpf_ diff --git a/venv/lib/python3.12/site-packages/mpmath/tests/test_bitwise.py b/venv/lib/python3.12/site-packages/mpmath/tests/test_bitwise.py new file mode 100644 index 0000000000000000000000000000000000000000..4f61b69fc8819cf275abaedd98847c58c3b5924a --- /dev/null +++ b/venv/lib/python3.12/site-packages/mpmath/tests/test_bitwise.py @@ -0,0 +1,188 @@ +""" +Test bit-level integer and mpf operations +""" + +from mpmath import * +from mpmath.libmp import * + +def test_bitcount(): + assert bitcount(0) == 0 + assert bitcount(1) == 1 + assert bitcount(7) == 3 + assert bitcount(8) == 4 + assert bitcount(2**100) == 101 + assert bitcount(2**100-1) == 100 + +def test_trailing(): + assert trailing(0) == 0 + assert trailing(1) == 0 + assert trailing(2) == 1 + assert trailing(7) == 0 + assert trailing(8) == 3 + assert trailing(2**100) == 100 + assert trailing(2**100-1) == 0 + +def test_round_down(): + assert from_man_exp(0, -4, 4, round_down)[:3] == (0, 0, 0) + assert from_man_exp(0xf0, -4, 4, round_down)[:3] == (0, 15, 0) + assert from_man_exp(0xf1, -4, 4, round_down)[:3] == (0, 15, 0) + assert from_man_exp(0xff, -4, 4, round_down)[:3] == (0, 15, 0) + assert from_man_exp(-0xf0, -4, 4, round_down)[:3] == (1, 15, 0) + assert from_man_exp(-0xf1, -4, 4, round_down)[:3] == (1, 15, 0) + assert from_man_exp(-0xff, -4, 4, round_down)[:3] == (1, 15, 0) + +def test_round_up(): + assert from_man_exp(0, -4, 4, round_up)[:3] == (0, 0, 0) + assert from_man_exp(0xf0, -4, 4, round_up)[:3] == (0, 15, 0) + assert from_man_exp(0xf1, -4, 4, round_up)[:3] == (0, 1, 4) + assert from_man_exp(0xff, -4, 4, round_up)[:3] == (0, 1, 4) + assert from_man_exp(-0xf0, -4, 4, round_up)[:3] == (1, 15, 0) + assert from_man_exp(-0xf1, -4, 4, round_up)[:3] == (1, 1, 4) + assert from_man_exp(-0xff, -4, 4, round_up)[:3] == (1, 1, 4) + +def test_round_floor(): + assert from_man_exp(0, -4, 4, round_floor)[:3] == (0, 0, 0) + assert from_man_exp(0xf0, -4, 4, round_floor)[:3] == (0, 15, 0) + assert from_man_exp(0xf1, -4, 4, round_floor)[:3] == (0, 15, 0) + assert from_man_exp(0xff, -4, 4, round_floor)[:3] == (0, 15, 0) + assert from_man_exp(-0xf0, -4, 4, round_floor)[:3] == (1, 15, 0) + assert from_man_exp(-0xf1, -4, 4, round_floor)[:3] == (1, 1, 4) + assert from_man_exp(-0xff, -4, 4, round_floor)[:3] == (1, 1, 4) + +def test_round_ceiling(): + assert from_man_exp(0, -4, 4, round_ceiling)[:3] == (0, 0, 0) + assert from_man_exp(0xf0, -4, 4, round_ceiling)[:3] == (0, 15, 0) + assert from_man_exp(0xf1, -4, 4, round_ceiling)[:3] == (0, 1, 4) + assert from_man_exp(0xff, -4, 4, round_ceiling)[:3] == (0, 1, 4) + assert from_man_exp(-0xf0, -4, 4, round_ceiling)[:3] == (1, 15, 0) + assert from_man_exp(-0xf1, -4, 4, round_ceiling)[:3] == (1, 15, 0) + assert from_man_exp(-0xff, -4, 4, round_ceiling)[:3] == (1, 15, 0) + +def test_round_nearest(): + assert from_man_exp(0, -4, 4, round_nearest)[:3] == (0, 0, 0) + assert from_man_exp(0xf0, -4, 4, round_nearest)[:3] == (0, 15, 0) + assert from_man_exp(0xf7, -4, 4, round_nearest)[:3] == (0, 15, 0) + assert from_man_exp(0xf8, -4, 4, round_nearest)[:3] == (0, 1, 4) # 1111.1000 -> 10000.0 + assert from_man_exp(0xf9, -4, 4, round_nearest)[:3] == (0, 1, 4) # 1111.1001 -> 10000.0 + assert from_man_exp(0xe8, -4, 4, round_nearest)[:3] == (0, 7, 1) # 1110.1000 -> 1110.0 + assert from_man_exp(0xe9, -4, 4, round_nearest)[:3] == (0, 15, 0) # 1110.1001 -> 1111.0 + assert from_man_exp(-0xf0, -4, 4, round_nearest)[:3] == (1, 15, 0) + assert from_man_exp(-0xf7, -4, 4, round_nearest)[:3] == (1, 15, 0) + assert from_man_exp(-0xf8, -4, 4, round_nearest)[:3] == (1, 1, 4) + assert from_man_exp(-0xf9, -4, 4, round_nearest)[:3] == (1, 1, 4) + assert from_man_exp(-0xe8, -4, 4, round_nearest)[:3] == (1, 7, 1) + assert from_man_exp(-0xe9, -4, 4, round_nearest)[:3] == (1, 15, 0) + +def test_rounding_bugs(): + # 1 less than power-of-two cases + assert from_man_exp(72057594037927935, -56, 53, round_up) == (0, 1, 0, 1) + assert from_man_exp(73786976294838205979, -65, 53, round_nearest) == (0, 1, 1, 1) + assert from_man_exp(31, 0, 4, round_up) == (0, 1, 5, 1) + assert from_man_exp(-31, 0, 4, round_floor) == (1, 1, 5, 1) + assert from_man_exp(255, 0, 7, round_up) == (0, 1, 8, 1) + assert from_man_exp(-255, 0, 7, round_floor) == (1, 1, 8, 1) + +def test_rounding_issue_200(): + a = from_man_exp(9867,-100) + b = from_man_exp(9867,-200) + c = from_man_exp(-1,0) + z = (1, 1023, -10, 10) + assert mpf_add(a, c, 10, 'd') == z + assert mpf_add(b, c, 10, 'd') == z + assert mpf_add(c, a, 10, 'd') == z + assert mpf_add(c, b, 10, 'd') == z + +def test_perturb(): + a = fone + b = from_float(0.99999999999999989) + c = from_float(1.0000000000000002) + assert mpf_perturb(a, 0, 53, round_nearest) == a + assert mpf_perturb(a, 1, 53, round_nearest) == a + assert mpf_perturb(a, 0, 53, round_up) == c + assert mpf_perturb(a, 0, 53, round_ceiling) == c + assert mpf_perturb(a, 0, 53, round_down) == a + assert mpf_perturb(a, 0, 53, round_floor) == a + assert mpf_perturb(a, 1, 53, round_up) == a + assert mpf_perturb(a, 1, 53, round_ceiling) == a + assert mpf_perturb(a, 1, 53, round_down) == b + assert mpf_perturb(a, 1, 53, round_floor) == b + a = mpf_neg(a) + b = mpf_neg(b) + c = mpf_neg(c) + assert mpf_perturb(a, 0, 53, round_nearest) == a + assert mpf_perturb(a, 1, 53, round_nearest) == a + assert mpf_perturb(a, 0, 53, round_up) == a + assert mpf_perturb(a, 0, 53, round_floor) == a + assert mpf_perturb(a, 0, 53, round_down) == b + assert mpf_perturb(a, 0, 53, round_ceiling) == b + assert mpf_perturb(a, 1, 53, round_up) == c + assert mpf_perturb(a, 1, 53, round_floor) == c + assert mpf_perturb(a, 1, 53, round_down) == a + assert mpf_perturb(a, 1, 53, round_ceiling) == a + +def test_add_exact(): + ff = from_float + assert mpf_add(ff(3.0), ff(2.5)) == ff(5.5) + assert mpf_add(ff(3.0), ff(-2.5)) == ff(0.5) + assert mpf_add(ff(-3.0), ff(2.5)) == ff(-0.5) + assert mpf_add(ff(-3.0), ff(-2.5)) == ff(-5.5) + assert mpf_sub(mpf_add(fone, ff(1e-100)), fone) == ff(1e-100) + assert mpf_sub(mpf_add(ff(1e-100), fone), fone) == ff(1e-100) + assert mpf_sub(mpf_add(fone, ff(-1e-100)), fone) == ff(-1e-100) + assert mpf_sub(mpf_add(ff(-1e-100), fone), fone) == ff(-1e-100) + assert mpf_add(fone, fzero) == fone + assert mpf_add(fzero, fone) == fone + assert mpf_add(fzero, fzero) == fzero + +def test_long_exponent_shifts(): + mp.dps = 15 + # Check for possible bugs due to exponent arithmetic overflow + # in a C implementation + x = mpf(1) + for p in [32, 64]: + a = ldexp(1,2**(p-1)) + b = ldexp(1,2**p) + c = ldexp(1,2**(p+1)) + d = ldexp(1,-2**(p-1)) + e = ldexp(1,-2**p) + f = ldexp(1,-2**(p+1)) + assert (x+a) == a + assert (x+b) == b + assert (x+c) == c + assert (x+d) == x + assert (x+e) == x + assert (x+f) == x + assert (a+x) == a + assert (b+x) == b + assert (c+x) == c + assert (d+x) == x + assert (e+x) == x + assert (f+x) == x + assert (x-a) == -a + assert (x-b) == -b + assert (x-c) == -c + assert (x-d) == x + assert (x-e) == x + assert (x-f) == x + assert (a-x) == a + assert (b-x) == b + assert (c-x) == c + assert (d-x) == -x + assert (e-x) == -x + assert (f-x) == -x + +def test_float_rounding(): + mp.prec = 64 + for x in [mpf(1), mpf(1)+eps, mpf(1)-eps, -mpf(1)+eps, -mpf(1)-eps]: + fa = float(x) + fb = float(fadd(x,0,prec=53,rounding='n')) + assert fa == fb + z = mpc(x,x) + ca = complex(z) + cb = complex(fadd(z,0,prec=53,rounding='n')) + assert ca == cb + for rnd in ['n', 'd', 'u', 'f', 'c']: + fa = to_float(x._mpf_, rnd=rnd) + fb = to_float(fadd(x,0,prec=53,rounding=rnd)._mpf_, rnd=rnd) + assert fa == fb + mp.prec = 53 diff --git a/venv/lib/python3.12/site-packages/mpmath/tests/test_calculus.py b/venv/lib/python3.12/site-packages/mpmath/tests/test_calculus.py new file mode 100644 index 0000000000000000000000000000000000000000..f0a59773d672f0db20bb5072773472a5a3cc1d1f --- /dev/null +++ b/venv/lib/python3.12/site-packages/mpmath/tests/test_calculus.py @@ -0,0 +1,216 @@ +import pytest +from mpmath import * + +def test_approximation(): + mp.dps = 15 + f = lambda x: cos(2-2*x)/x + p, err = chebyfit(f, [2, 4], 8, error=True) + assert err < 1e-5 + for i in range(10): + x = 2 + i/5. + assert abs(polyval(p, x) - f(x)) < err + +def test_limits(): + mp.dps = 15 + assert limit(lambda x: (x-sin(x))/x**3, 0).ae(mpf(1)/6) + assert limit(lambda n: (1+1/n)**n, inf).ae(e) + +def test_polyval(): + assert polyval([], 3) == 0 + assert polyval([0], 3) == 0 + assert polyval([5], 3) == 5 + # 4x^3 - 2x + 5 + p = [4, 0, -2, 5] + assert polyval(p,4) == 253 + assert polyval(p,4,derivative=True) == (253, 190) + +def test_polyroots(): + p = polyroots([1,-4]) + assert p[0].ae(4) + p, q = polyroots([1,2,3]) + assert p.ae(-1 - sqrt(2)*j) + assert q.ae(-1 + sqrt(2)*j) + #this is not a real test, it only tests a specific case + assert polyroots([1]) == [] + pytest.raises(ValueError, lambda: polyroots([0])) + +def test_polyroots_legendre(): + n = 64 + coeffs = [11975573020964041433067793888190275875, 0, + -190100434726484311252477736051902332000, 0, + 1437919688271127330313741595496589239248, 0, + -6897338342113537600691931230430793911840, 0, + 23556405536185284408974715545252277554280, 0, + -60969520211303089058522793175947071316960, 0, + 124284021969194758465450309166353645376880, 0, + -204721258548015217049921875719981284186016, 0, + 277415422258095841688223780704620656114900, 0, + -313237834141273382807123548182995095192800, 0, + 297432255354328395601259515935229287637200, 0, + -239057700565161140389797367947941296605600, 0, + 163356095386193445933028201431093219347160, 0, + -95158890516229191805647495979277603503200, 0, + 47310254620162038075933656063247634556400, 0, + -20071017111583894941305187420771723751200, 0, + 7255051932731034189479516844750603752850, 0, + -2228176940331017311443863996901733412640, 0, + 579006552594977616773047095969088431600, 0, + -126584428502545713788439446082310831200, 0, + 23112325428835593809686977515028663000, 0, + -3491517141958743235617737161547844000, 0, + 431305058712550634988073414073557200, 0, + -42927166660756742088912492757452000, 0, + 3378527005707706553294038781836500, 0, + -205277590220215081719131470288800, 0, + 9330799555464321896324157740400, 0, + -304114948474392713657972548576, 0, + 6695289961520387531608984680, 0, + -91048139350447232095702560, 0, + 659769125727878493447120, 0, + -1905929106580294155360, 0, + 916312070471295267] + + with mp.workdps(3): + with pytest.raises(mp.NoConvergence): + polyroots(coeffs, maxsteps=5, cleanup=True, error=False, + extraprec=n*10) + + roots = polyroots(coeffs, maxsteps=50, cleanup=True, error=False, + extraprec=n*10) + roots = [str(r) for r in roots] + assert roots == \ + ['-0.999', '-0.996', '-0.991', '-0.983', '-0.973', '-0.961', + '-0.946', '-0.93', '-0.911', '-0.889', '-0.866', '-0.841', + '-0.813', '-0.784', '-0.753', '-0.72', '-0.685', '-0.649', + '-0.611', '-0.572', '-0.531', '-0.489', '-0.446', '-0.402', + '-0.357', '-0.311', '-0.265', '-0.217', '-0.17', '-0.121', + '-0.073', '-0.0243', '0.0243', '0.073', '0.121', '0.17', '0.217', + '0.265', '0.311', '0.357', '0.402', '0.446', '0.489', '0.531', + '0.572', '0.611', '0.649', '0.685', '0.72', '0.753', '0.784', + '0.813', '0.841', '0.866', '0.889', '0.911', '0.93', '0.946', + '0.961', '0.973', '0.983', '0.991', '0.996', '0.999'] + +def test_polyroots_legendre_init(): + extra_prec = 100 + coeffs = [11975573020964041433067793888190275875, 0, + -190100434726484311252477736051902332000, 0, + 1437919688271127330313741595496589239248, 0, + -6897338342113537600691931230430793911840, 0, + 23556405536185284408974715545252277554280, 0, + -60969520211303089058522793175947071316960, 0, + 124284021969194758465450309166353645376880, 0, + -204721258548015217049921875719981284186016, 0, + 277415422258095841688223780704620656114900, 0, + -313237834141273382807123548182995095192800, 0, + 297432255354328395601259515935229287637200, 0, + -239057700565161140389797367947941296605600, 0, + 163356095386193445933028201431093219347160, 0, + -95158890516229191805647495979277603503200, 0, + 47310254620162038075933656063247634556400, 0, + -20071017111583894941305187420771723751200, 0, + 7255051932731034189479516844750603752850, 0, + -2228176940331017311443863996901733412640, 0, + 579006552594977616773047095969088431600, 0, + -126584428502545713788439446082310831200, 0, + 23112325428835593809686977515028663000, 0, + -3491517141958743235617737161547844000, 0, + 431305058712550634988073414073557200, 0, + -42927166660756742088912492757452000, 0, + 3378527005707706553294038781836500, 0, + -205277590220215081719131470288800, 0, + 9330799555464321896324157740400, 0, + -304114948474392713657972548576, 0, + 6695289961520387531608984680, 0, + -91048139350447232095702560, 0, + 659769125727878493447120, 0, + -1905929106580294155360, 0, + 916312070471295267] + + roots_init = matrix(['-0.999', '-0.996', '-0.991', '-0.983', '-0.973', + '-0.961', '-0.946', '-0.93', '-0.911', '-0.889', + '-0.866', '-0.841', '-0.813', '-0.784', '-0.753', + '-0.72', '-0.685', '-0.649', '-0.611', '-0.572', + '-0.531', '-0.489', '-0.446', '-0.402', '-0.357', + '-0.311', '-0.265', '-0.217', '-0.17', '-0.121', + '-0.073', '-0.0243', '0.0243', '0.073', '0.121', + '0.17', '0.217', '0.265', ' 0.311', '0.357', + '0.402', '0.446', '0.489', '0.531', '0.572', + '0.611', '0.649', '0.685', '0.72', '0.753', + '0.784', '0.813', '0.841', '0.866', '0.889', + '0.911', '0.93', '0.946', '0.961', '0.973', + '0.983', '0.991', '0.996', '0.999', '1.0']) + with mp.workdps(2*mp.dps): + roots_exact = polyroots(coeffs, maxsteps=50, cleanup=True, error=False, + extraprec=2*extra_prec) + with pytest.raises(mp.NoConvergence): + polyroots(coeffs, maxsteps=5, cleanup=True, error=False, + extraprec=extra_prec) + roots,err = polyroots(coeffs, maxsteps=5, cleanup=True, error=True, + extraprec=extra_prec,roots_init=roots_init) + assert max(matrix(roots_exact)-matrix(roots).apply(abs)) < err + roots1,err1 = polyroots(coeffs, maxsteps=25, cleanup=True, error=True, + extraprec=extra_prec,roots_init=roots_init[:60]) + assert max(matrix(roots_exact)-matrix(roots1).apply(abs)) < err1 + +def test_pade(): + one = mpf(1) + mp.dps = 20 + N = 10 + a = [one] + k = 1 + for i in range(1, N+1): + k *= i + a.append(one/k) + p, q = pade(a, N//2, N//2) + for x in arange(0, 1, 0.1): + r = polyval(p[::-1], x)/polyval(q[::-1], x) + assert(r.ae(exp(x), 1.0e-10)) + mp.dps = 15 + +def test_fourier(): + mp.dps = 15 + c, s = fourier(lambda x: x+1, [-1, 2], 2) + #plot([lambda x: x+1, lambda x: fourierval((c, s), [-1, 2], x)], [-1, 2]) + assert c[0].ae(1.5) + assert c[1].ae(-3*sqrt(3)/(2*pi)) + assert c[2].ae(3*sqrt(3)/(4*pi)) + assert s[0] == 0 + assert s[1].ae(3/(2*pi)) + assert s[2].ae(3/(4*pi)) + assert fourierval((c, s), [-1, 2], 1).ae(1.9134966715663442) + +def test_differint(): + mp.dps = 15 + assert differint(lambda t: t, 2, -0.5).ae(8*sqrt(2/pi)/3) + +def test_invlap(): + mp.dps = 15 + t = 0.01 + fp = lambda p: 1/(p+1)**2 + ft = lambda t: t*exp(-t) + ftt = ft(t) + assert invertlaplace(fp,t,method='talbot').ae(ftt) + assert invertlaplace(fp,t,method='stehfest').ae(ftt) + assert invertlaplace(fp,t,method='dehoog').ae(ftt) + assert invertlaplace(fp,t,method='cohen').ae(ftt) + t = 1.0 + ftt = ft(t) + assert invertlaplace(fp,t,method='talbot').ae(ftt) + assert invertlaplace(fp,t,method='stehfest').ae(ftt) + assert invertlaplace(fp,t,method='dehoog').ae(ftt) + assert invertlaplace(fp,t,method='cohen').ae(ftt) + + t = 0.01 + fp = lambda p: log(p)/p + ft = lambda t: -euler-log(t) + ftt = ft(t) + assert invertlaplace(fp,t,method='talbot').ae(ftt) + assert invertlaplace(fp,t,method='stehfest').ae(ftt) + assert invertlaplace(fp,t,method='dehoog').ae(ftt) + assert invertlaplace(fp,t,method='cohen').ae(ftt) + t = 1.0 + ftt = ft(t) + assert invertlaplace(fp,t,method='talbot').ae(ftt) + assert invertlaplace(fp,t,method='stehfest').ae(ftt) + assert invertlaplace(fp,t,method='dehoog').ae(ftt) + assert invertlaplace(fp,t,method='cohen').ae(ftt) diff --git a/venv/lib/python3.12/site-packages/mpmath/tests/test_compatibility.py b/venv/lib/python3.12/site-packages/mpmath/tests/test_compatibility.py new file mode 100644 index 0000000000000000000000000000000000000000..f26d6044b521306b6d1eaeadc5c7839be226dc54 --- /dev/null +++ b/venv/lib/python3.12/site-packages/mpmath/tests/test_compatibility.py @@ -0,0 +1,77 @@ +from mpmath import * +from random import seed, randint, random +import math + +# Test compatibility with Python floats, which are +# IEEE doubles (53-bit) + +N = 5000 +seed(1) + +# Choosing exponents between roughly -140, 140 ensures that +# the Python floats don't overflow or underflow +xs = [(random()-1) * 10**randint(-140, 140) for x in range(N)] +ys = [(random()-1) * 10**randint(-140, 140) for x in range(N)] + +# include some equal values +ys[int(N*0.8):] = xs[int(N*0.8):] + +# Detect whether Python is compiled to use 80-bit floating-point +# instructions, in which case the double compatibility test breaks +uses_x87 = -4.1974624032366689e+117 / -8.4657370748010221e-47 \ + == 4.9581771393902231e+163 + +def test_double_compatibility(): + mp.prec = 53 + for x, y in zip(xs, ys): + mpx = mpf(x) + mpy = mpf(y) + assert mpf(x) == x + assert (mpx < mpy) == (x < y) + assert (mpx > mpy) == (x > y) + assert (mpx == mpy) == (x == y) + assert (mpx != mpy) == (x != y) + assert (mpx <= mpy) == (x <= y) + assert (mpx >= mpy) == (x >= y) + assert mpx == mpx + if uses_x87: + mp.prec = 64 + a = mpx + mpy + b = mpx * mpy + c = mpx / mpy + d = mpx % mpy + mp.prec = 53 + assert +a == x + y + assert +b == x * y + assert +c == x / y + assert +d == x % y + else: + assert mpx + mpy == x + y + assert mpx * mpy == x * y + assert mpx / mpy == x / y + assert mpx % mpy == x % y + assert abs(mpx) == abs(x) + assert mpf(repr(x)) == x + assert ceil(mpx) == math.ceil(x) + assert floor(mpx) == math.floor(x) + +def test_sqrt(): + # this fails quite often. it appers to be float + # that rounds the wrong way, not mpf + fail = 0 + mp.prec = 53 + for x in xs: + x = abs(x) + mp.prec = 100 + mp_high = mpf(x)**0.5 + mp.prec = 53 + mp_low = mpf(x)**0.5 + fp = x**0.5 + assert abs(mp_low-mp_high) <= abs(fp-mp_high) + fail += mp_low != fp + assert fail < N/10 + +def test_bugs(): + # particular bugs + assert mpf(4.4408920985006262E-16) < mpf(1.7763568394002505E-15) + assert mpf(-4.4408920985006262E-16) > mpf(-1.7763568394002505E-15) diff --git a/venv/lib/python3.12/site-packages/mpmath/tests/test_convert.py b/venv/lib/python3.12/site-packages/mpmath/tests/test_convert.py new file mode 100644 index 0000000000000000000000000000000000000000..cb1db5b55c89e980e08fc3fa43cc9715ad68cac9 --- /dev/null +++ b/venv/lib/python3.12/site-packages/mpmath/tests/test_convert.py @@ -0,0 +1,233 @@ +import random +from mpmath import * +from mpmath.libmp import * + + +def test_basic_string(): + """ + Test basic string conversion + """ + mp.dps = 15 + assert mpf('3') == mpf('3.0') == mpf('0003.') == mpf('0.03e2') == mpf(3.0) + assert mpf('30') == mpf('30.0') == mpf('00030.') == mpf(30.0) + for i in range(10): + for j in range(10): + assert mpf('%ie%i' % (i,j)) == i * 10**j + assert str(mpf('25000.0')) == '25000.0' + assert str(mpf('2500.0')) == '2500.0' + assert str(mpf('250.0')) == '250.0' + assert str(mpf('25.0')) == '25.0' + assert str(mpf('2.5')) == '2.5' + assert str(mpf('0.25')) == '0.25' + assert str(mpf('0.025')) == '0.025' + assert str(mpf('0.0025')) == '0.0025' + assert str(mpf('0.00025')) == '0.00025' + assert str(mpf('0.000025')) == '2.5e-5' + assert str(mpf(0)) == '0.0' + assert str(mpf('2.5e1000000000000000000000')) == '2.5e+1000000000000000000000' + assert str(mpf('2.6e-1000000000000000000000')) == '2.6e-1000000000000000000000' + assert str(mpf(1.23402834e-15)) == '1.23402834e-15' + assert str(mpf(-1.23402834e-15)) == '-1.23402834e-15' + assert str(mpf(-1.2344e-15)) == '-1.2344e-15' + assert repr(mpf(-1.2344e-15)) == "mpf('-1.2343999999999999e-15')" + assert str(mpf("2163048125L")) == '2163048125.0' + assert str(mpf("-2163048125l")) == '-2163048125.0' + assert str(mpf("-2163048125L/1088391168")) == '-1.98738118113799' + assert str(mpf("2163048125/1088391168l")) == '1.98738118113799' + +def test_pretty(): + mp.pretty = True + assert repr(mpf(2.5)) == '2.5' + assert repr(mpc(2.5,3.5)) == '(2.5 + 3.5j)' + mp.pretty = False + iv.pretty = True + assert repr(mpi(2.5,3.5)) == '[2.5, 3.5]' + iv.pretty = False + +def test_str_whitespace(): + assert mpf('1.26 ') == 1.26 + +def test_unicode(): + mp.dps = 15 + try: + unicode = unicode + except NameError: + unicode = str + assert mpf(unicode('2.76')) == 2.76 + assert mpf(unicode('inf')) == inf + +def test_str_format(): + assert to_str(from_float(0.1),15,strip_zeros=False) == '0.100000000000000' + assert to_str(from_float(0.0),15,show_zero_exponent=True) == '0.0e+0' + assert to_str(from_float(0.0),0,show_zero_exponent=True) == '.0e+0' + assert to_str(from_float(0.0),0,show_zero_exponent=False) == '.0' + assert to_str(from_float(0.0),1,show_zero_exponent=True) == '0.0e+0' + assert to_str(from_float(0.0),1,show_zero_exponent=False) == '0.0' + assert to_str(from_float(1.23),3,show_zero_exponent=True) == '1.23e+0' + assert to_str(from_float(1.23456789000000e-2),15,strip_zeros=False,min_fixed=0,max_fixed=0) == '1.23456789000000e-2' + assert to_str(from_float(1.23456789000000e+2),15,strip_zeros=False,min_fixed=0,max_fixed=0) == '1.23456789000000e+2' + assert to_str(from_float(2.1287e14), 15, max_fixed=1000) == '212870000000000.0' + assert to_str(from_float(2.1287e15), 15, max_fixed=1000) == '2128700000000000.0' + assert to_str(from_float(2.1287e16), 15, max_fixed=1000) == '21287000000000000.0' + assert to_str(from_float(2.1287e30), 15, max_fixed=1000) == '2128700000000000000000000000000.0' + +def test_tight_string_conversion(): + mp.dps = 15 + # In an old version, '0.5' wasn't recognized as representing + # an exact binary number and was erroneously rounded up or down + assert from_str('0.5', 10, round_floor) == fhalf + assert from_str('0.5', 10, round_ceiling) == fhalf + +def test_eval_repr_invariant(): + """Test that eval(repr(x)) == x""" + random.seed(123) + for dps in [10, 15, 20, 50, 100]: + mp.dps = dps + for i in range(1000): + a = mpf(random.random())**0.5 * 10**random.randint(-100, 100) + assert eval(repr(a)) == a + mp.dps = 15 + +def test_str_bugs(): + mp.dps = 15 + # Decimal rounding used to give the wrong exponent in some cases + assert str(mpf('1e600')) == '1.0e+600' + assert str(mpf('1e10000')) == '1.0e+10000' + +def test_str_prec0(): + assert to_str(from_float(1.234), 0) == '.0e+0' + assert to_str(from_float(1e-15), 0) == '.0e-15' + assert to_str(from_float(1e+15), 0) == '.0e+15' + assert to_str(from_float(-1e-15), 0) == '-.0e-15' + assert to_str(from_float(-1e+15), 0) == '-.0e+15' + +def test_convert_rational(): + mp.dps = 15 + assert from_rational(30, 5, 53, round_nearest) == (0, 3, 1, 2) + assert from_rational(-7, 4, 53, round_nearest) == (1, 7, -2, 3) + assert to_rational((0, 1, -1, 1)) == (1, 2) + +def test_custom_class(): + class mympf: + @property + def _mpf_(self): + return mpf(3.5)._mpf_ + class mympc: + @property + def _mpc_(self): + return mpf(3.5)._mpf_, mpf(2.5)._mpf_ + assert mpf(2) + mympf() == 5.5 + assert mympf() + mpf(2) == 5.5 + assert mpf(mympf()) == 3.5 + assert mympc() + mpc(2) == mpc(5.5, 2.5) + assert mpc(2) + mympc() == mpc(5.5, 2.5) + assert mpc(mympc()) == (3.5+2.5j) + +def test_conversion_methods(): + class SomethingRandom: + pass + class SomethingReal: + def _mpmath_(self, prec, rounding): + return mp.make_mpf(from_str('1.3', prec, rounding)) + class SomethingComplex: + def _mpmath_(self, prec, rounding): + return mp.make_mpc((from_str('1.3', prec, rounding), \ + from_str('1.7', prec, rounding))) + x = mpf(3) + z = mpc(3) + a = SomethingRandom() + y = SomethingReal() + w = SomethingComplex() + for d in [15, 45]: + mp.dps = d + assert (x+y).ae(mpf('4.3')) + assert (y+x).ae(mpf('4.3')) + assert (x+w).ae(mpc('4.3', '1.7')) + assert (w+x).ae(mpc('4.3', '1.7')) + assert (z+y).ae(mpc('4.3')) + assert (y+z).ae(mpc('4.3')) + assert (z+w).ae(mpc('4.3', '1.7')) + assert (w+z).ae(mpc('4.3', '1.7')) + x-y; y-x; x-w; w-x; z-y; y-z; z-w; w-z + x*y; y*x; x*w; w*x; z*y; y*z; z*w; w*z + x/y; y/x; x/w; w/x; z/y; y/z; z/w; w/z + x**y; y**x; x**w; w**x; z**y; y**z; z**w; w**z + x==y; y==x; x==w; w==x; z==y; y==z; z==w; w==z + mp.dps = 15 + assert x.__add__(a) is NotImplemented + assert x.__radd__(a) is NotImplemented + assert x.__lt__(a) is NotImplemented + assert x.__gt__(a) is NotImplemented + assert x.__le__(a) is NotImplemented + assert x.__ge__(a) is NotImplemented + assert x.__eq__(a) is NotImplemented + assert x.__ne__(a) is NotImplemented + # implementation detail + if hasattr(x, "__cmp__"): + assert x.__cmp__(a) is NotImplemented + assert x.__sub__(a) is NotImplemented + assert x.__rsub__(a) is NotImplemented + assert x.__mul__(a) is NotImplemented + assert x.__rmul__(a) is NotImplemented + assert x.__div__(a) is NotImplemented + assert x.__rdiv__(a) is NotImplemented + assert x.__mod__(a) is NotImplemented + assert x.__rmod__(a) is NotImplemented + assert x.__pow__(a) is NotImplemented + assert x.__rpow__(a) is NotImplemented + assert z.__add__(a) is NotImplemented + assert z.__radd__(a) is NotImplemented + assert z.__eq__(a) is NotImplemented + assert z.__ne__(a) is NotImplemented + assert z.__sub__(a) is NotImplemented + assert z.__rsub__(a) is NotImplemented + assert z.__mul__(a) is NotImplemented + assert z.__rmul__(a) is NotImplemented + assert z.__div__(a) is NotImplemented + assert z.__rdiv__(a) is NotImplemented + assert z.__pow__(a) is NotImplemented + assert z.__rpow__(a) is NotImplemented + +def test_mpmathify(): + assert mpmathify('1/2') == 0.5 + assert mpmathify('(1.0+1.0j)') == mpc(1, 1) + assert mpmathify('(1.2e-10 - 3.4e5j)') == mpc('1.2e-10', '-3.4e5') + assert mpmathify('1j') == mpc(1j) + +def test_issue548(): + try: + # This expression is invalid, but may trigger the ReDOS vulnerability + # in the regular expression for parsing complex numbers. + mpmathify('(' + '1' * 5000 + '!j') + except: + return + # The expression is invalid and should raise an exception. + assert False + +def test_compatibility(): + try: + import numpy as np + from fractions import Fraction + from decimal import Decimal + import decimal + except ImportError: + return + # numpy types + for nptype in np.core.numerictypes.typeDict.values(): + if issubclass(nptype, np.complexfloating): + x = nptype(complex(0.5, -0.5)) + elif issubclass(nptype, np.floating): + x = nptype(0.5) + elif issubclass(nptype, np.integer): + x = nptype(2) + # Handle the weird types + try: diff = np.abs(type(np.sqrt(x))(sqrt(x)) - np.sqrt(x)) + except: continue + assert diff < 2.0**-53 + #Fraction and Decimal + oldprec = mp.prec + mp.prec = 1000 + decimal.getcontext().prec = mp.dps + assert sqrt(Fraction(2, 3)).ae(sqrt(mpf('2/3'))) + assert sqrt(Decimal(2)/Decimal(3)).ae(sqrt(mpf('2/3'))) + mp.prec = oldprec diff --git a/venv/lib/python3.12/site-packages/mpmath/tests/test_diff.py b/venv/lib/python3.12/site-packages/mpmath/tests/test_diff.py new file mode 100644 index 0000000000000000000000000000000000000000..f5711609da38862eb4fd62c88d35f1704c9425a4 --- /dev/null +++ b/venv/lib/python3.12/site-packages/mpmath/tests/test_diff.py @@ -0,0 +1,61 @@ +from mpmath import * + +def test_diff(): + mp.dps = 15 + assert diff(log, 2.0, n=0).ae(log(2)) + assert diff(cos, 1.0).ae(-sin(1)) + assert diff(abs, 0.0) == 0 + assert diff(abs, 0.0, direction=1) == 1 + assert diff(abs, 0.0, direction=-1) == -1 + assert diff(exp, 1.0).ae(e) + assert diff(exp, 1.0, n=5).ae(e) + assert diff(exp, 2.0, n=5, direction=3*j).ae(e**2) + assert diff(lambda x: x**2, 3.0, method='quad').ae(6) + assert diff(lambda x: 3+x**5, 3.0, n=2, method='quad').ae(540) + assert diff(lambda x: 3+x**5, 3.0, n=2, method='step').ae(540) + assert diffun(sin)(2).ae(cos(2)) + assert diffun(sin, n=2)(2).ae(-sin(2)) + +def test_diffs(): + mp.dps = 15 + assert [chop(d) for d in diffs(sin, 0, 1)] == [0, 1] + assert [chop(d) for d in diffs(sin, 0, 1, method='quad')] == [0, 1] + assert [chop(d) for d in diffs(sin, 0, 2)] == [0, 1, 0] + assert [chop(d) for d in diffs(sin, 0, 2, method='quad')] == [0, 1, 0] + +def test_taylor(): + mp.dps = 15 + # Easy to test since the coefficients are exact in floating-point + assert taylor(sqrt, 1, 4) == [1, 0.5, -0.125, 0.0625, -0.0390625] + +def test_diff_partial(): + mp.dps = 15 + x,y,z = xyz = 2,3,7 + f = lambda x,y,z: 3*x**2 * (y+2)**3 * z**5 + assert diff(f, xyz, (0,0,0)).ae(25210500) + assert diff(f, xyz, (0,0,1)).ae(18007500) + assert diff(f, xyz, (0,0,2)).ae(10290000) + assert diff(f, xyz, (0,1,0)).ae(15126300) + assert diff(f, xyz, (0,1,1)).ae(10804500) + assert diff(f, xyz, (0,1,2)).ae(6174000) + assert diff(f, xyz, (0,2,0)).ae(6050520) + assert diff(f, xyz, (0,2,1)).ae(4321800) + assert diff(f, xyz, (0,2,2)).ae(2469600) + assert diff(f, xyz, (1,0,0)).ae(25210500) + assert diff(f, xyz, (1,0,1)).ae(18007500) + assert diff(f, xyz, (1,0,2)).ae(10290000) + assert diff(f, xyz, (1,1,0)).ae(15126300) + assert diff(f, xyz, (1,1,1)).ae(10804500) + assert diff(f, xyz, (1,1,2)).ae(6174000) + assert diff(f, xyz, (1,2,0)).ae(6050520) + assert diff(f, xyz, (1,2,1)).ae(4321800) + assert diff(f, xyz, (1,2,2)).ae(2469600) + assert diff(f, xyz, (2,0,0)).ae(12605250) + assert diff(f, xyz, (2,0,1)).ae(9003750) + assert diff(f, xyz, (2,0,2)).ae(5145000) + assert diff(f, xyz, (2,1,0)).ae(7563150) + assert diff(f, xyz, (2,1,1)).ae(5402250) + assert diff(f, xyz, (2,1,2)).ae(3087000) + assert diff(f, xyz, (2,2,0)).ae(3025260) + assert diff(f, xyz, (2,2,1)).ae(2160900) + assert diff(f, xyz, (2,2,2)).ae(1234800) diff --git a/venv/lib/python3.12/site-packages/mpmath/tests/test_division.py b/venv/lib/python3.12/site-packages/mpmath/tests/test_division.py new file mode 100644 index 0000000000000000000000000000000000000000..c704cadeb953793ac0a887aa09c4278cf68a2824 --- /dev/null +++ b/venv/lib/python3.12/site-packages/mpmath/tests/test_division.py @@ -0,0 +1,143 @@ +from mpmath.libmp import * +from mpmath import mpf, mp + +from random import randint, choice, seed + +all_modes = [round_floor, round_ceiling, round_down, round_up, round_nearest] + +fb = from_bstr +fi = from_int +ff = from_float + + +def test_div_1_3(): + a = fi(1) + b = fi(3) + c = fi(-1) + + # floor rounds down, ceiling rounds up + assert mpf_div(a, b, 7, round_floor) == fb('0.01010101') + assert mpf_div(a, b, 7, round_ceiling) == fb('0.01010110') + assert mpf_div(a, b, 7, round_down) == fb('0.01010101') + assert mpf_div(a, b, 7, round_up) == fb('0.01010110') + assert mpf_div(a, b, 7, round_nearest) == fb('0.01010101') + + # floor rounds up, ceiling rounds down + assert mpf_div(c, b, 7, round_floor) == fb('-0.01010110') + assert mpf_div(c, b, 7, round_ceiling) == fb('-0.01010101') + assert mpf_div(c, b, 7, round_down) == fb('-0.01010101') + assert mpf_div(c, b, 7, round_up) == fb('-0.01010110') + assert mpf_div(c, b, 7, round_nearest) == fb('-0.01010101') + +def test_mpf_divi_1_3(): + a = 1 + b = fi(3) + c = -1 + assert mpf_rdiv_int(a, b, 7, round_floor) == fb('0.01010101') + assert mpf_rdiv_int(a, b, 7, round_ceiling) == fb('0.01010110') + assert mpf_rdiv_int(a, b, 7, round_down) == fb('0.01010101') + assert mpf_rdiv_int(a, b, 7, round_up) == fb('0.01010110') + assert mpf_rdiv_int(a, b, 7, round_nearest) == fb('0.01010101') + assert mpf_rdiv_int(c, b, 7, round_floor) == fb('-0.01010110') + assert mpf_rdiv_int(c, b, 7, round_ceiling) == fb('-0.01010101') + assert mpf_rdiv_int(c, b, 7, round_down) == fb('-0.01010101') + assert mpf_rdiv_int(c, b, 7, round_up) == fb('-0.01010110') + assert mpf_rdiv_int(c, b, 7, round_nearest) == fb('-0.01010101') + + +def test_div_300(): + + q = fi(1000000) + a = fi(300499999) # a/q is a little less than a half-integer + b = fi(300500000) # b/q exactly a half-integer + c = fi(300500001) # c/q is a little more than a half-integer + + # Check nearest integer rounding (prec=9 as 2**8 < 300 < 2**9) + + assert mpf_div(a, q, 9, round_down) == fi(300) + assert mpf_div(b, q, 9, round_down) == fi(300) + assert mpf_div(c, q, 9, round_down) == fi(300) + assert mpf_div(a, q, 9, round_up) == fi(301) + assert mpf_div(b, q, 9, round_up) == fi(301) + assert mpf_div(c, q, 9, round_up) == fi(301) + + # Nearest even integer is down + assert mpf_div(a, q, 9, round_nearest) == fi(300) + assert mpf_div(b, q, 9, round_nearest) == fi(300) + assert mpf_div(c, q, 9, round_nearest) == fi(301) + + # Nearest even integer is up + a = fi(301499999) + b = fi(301500000) + c = fi(301500001) + assert mpf_div(a, q, 9, round_nearest) == fi(301) + assert mpf_div(b, q, 9, round_nearest) == fi(302) + assert mpf_div(c, q, 9, round_nearest) == fi(302) + + +def test_tight_integer_division(): + # Test that integer division at tightest possible precision is exact + N = 100 + seed(1) + for i in range(N): + a = choice([1, -1]) * randint(1, 1< 1: + print("original matrix (hessenberg):\n", A) + + n = A.rows + + Q, H = mp.hessenberg(A) + + if verbose > 1: + print("Q:\n",Q) + print("H:\n",H) + + B = Q * H * Q.transpose_conj() + + eps = mp.exp(0.8 * mp.log(mp.eps)) + + err0 = 0 + for x in xrange(n): + for y in xrange(n): + err0 += abs(A[y,x] - B[y,x]) + err0 /= n * n + + err1 = 0 + for x in xrange(n): + for y in xrange(x + 2, n): + err1 += abs(H[y,x]) + + if verbose > 0: + print("difference (H):", err0, err1) + + if verbose > 1: + print("B:\n", B) + + assert err0 < eps + assert err1 == 0 + + +def run_schur(A, verbose = 0): + if verbose > 1: + print("original matrix (schur):\n", A) + + n = A.rows + + Q, R = mp.schur(A) + + if verbose > 1: + print("Q:\n", Q) + print("R:\n", R) + + B = Q * R * Q.transpose_conj() + C = Q * Q.transpose_conj() + + eps = mp.exp(0.8 * mp.log(mp.eps)) + + err0 = 0 + for x in xrange(n): + for y in xrange(n): + err0 += abs(A[y,x] - B[y,x]) + err0 /= n * n + + err1 = 0 + for x in xrange(n): + for y in xrange(n): + if x == y: + C[y,x] -= 1 + err1 += abs(C[y,x]) + err1 /= n * n + + err2 = 0 + for x in xrange(n): + for y in xrange(x + 1, n): + err2 += abs(R[y,x]) + + if verbose > 0: + print("difference (S):", err0, err1, err2) + + if verbose > 1: + print("B:\n", B) + + assert err0 < eps + assert err1 < eps + assert err2 == 0 + +def run_eig(A, verbose = 0): + if verbose > 1: + print("original matrix (eig):\n", A) + + n = A.rows + + E, EL, ER = mp.eig(A, left = True, right = True) + + if verbose > 1: + print("E:\n", E) + print("EL:\n", EL) + print("ER:\n", ER) + + eps = mp.exp(0.8 * mp.log(mp.eps)) + + err0 = 0 + for i in xrange(n): + B = A * ER[:,i] - E[i] * ER[:,i] + err0 = max(err0, mp.mnorm(B)) + + B = EL[i,:] * A - EL[i,:] * E[i] + err0 = max(err0, mp.mnorm(B)) + + err0 /= n * n + + if verbose > 0: + print("difference (E):", err0) + + assert err0 < eps + +##################### + +def test_eig_dyn(): + v = 0 + for i in xrange(5): + n = 1 + int(mp.rand() * 5) + if mp.rand() > 0.5: + # real + A = 2 * mp.randmatrix(n, n) - 1 + if mp.rand() > 0.5: + A *= 10 + for x in xrange(n): + for y in xrange(n): + A[x,y] = int(A[x,y]) + else: + A = (2 * mp.randmatrix(n, n) - 1) + 1j * (2 * mp.randmatrix(n, n) - 1) + if mp.rand() > 0.5: + A *= 10 + for x in xrange(n): + for y in xrange(n): + A[x,y] = int(mp.re(A[x,y])) + 1j * int(mp.im(A[x,y])) + + run_hessenberg(A, verbose = v) + run_schur(A, verbose = v) + run_eig(A, verbose = v) + +def test_eig(): + v = 0 + AS = [] + + A = mp.matrix([[2, 1, 0], # jordan block of size 3 + [0, 2, 1], + [0, 0, 2]]) + AS.append(A) + AS.append(A.transpose()) + + A = mp.matrix([[2, 0, 0], # jordan block of size 2 + [0, 2, 1], + [0, 0, 2]]) + AS.append(A) + AS.append(A.transpose()) + + A = mp.matrix([[2, 0, 1], # jordan block of size 2 + [0, 2, 0], + [0, 0, 2]]) + AS.append(A) + AS.append(A.transpose()) + + A= mp.matrix([[0, 0, 1], # cyclic + [1, 0, 0], + [0, 1, 0]]) + AS.append(A) + AS.append(A.transpose()) + + for A in AS: + run_hessenberg(A, verbose = v) + run_schur(A, verbose = v) + run_eig(A, verbose = v) diff --git a/venv/lib/python3.12/site-packages/mpmath/tests/test_eigen_symmetric.py b/venv/lib/python3.12/site-packages/mpmath/tests/test_eigen_symmetric.py new file mode 100644 index 0000000000000000000000000000000000000000..aab3d8ea3142aada6e14ad6d3ea25a7e8293554d --- /dev/null +++ b/venv/lib/python3.12/site-packages/mpmath/tests/test_eigen_symmetric.py @@ -0,0 +1,357 @@ +#!/usr/bin/python +# -*- coding: utf-8 -*- + +from mpmath import mp +from mpmath import libmp + +xrange = libmp.backend.xrange + +def run_eigsy(A, verbose = False): + if verbose: + print("original matrix:\n", str(A)) + + D, Q = mp.eigsy(A) + B = Q * mp.diag(D) * Q.transpose() + C = A - B + E = Q * Q.transpose() - mp.eye(A.rows) + + if verbose: + print("eigenvalues:\n", D) + print("eigenvectors:\n", Q) + + NC = mp.mnorm(C) + NE = mp.mnorm(E) + + if verbose: + print("difference:", NC, "\n", C, "\n") + print("difference:", NE, "\n", E, "\n") + + eps = mp.exp( 0.8 * mp.log(mp.eps)) + + assert NC < eps + assert NE < eps + + return NC + +def run_eighe(A, verbose = False): + if verbose: + print("original matrix:\n", str(A)) + + D, Q = mp.eighe(A) + B = Q * mp.diag(D) * Q.transpose_conj() + C = A - B + E = Q * Q.transpose_conj() - mp.eye(A.rows) + + if verbose: + print("eigenvalues:\n", D) + print("eigenvectors:\n", Q) + + NC = mp.mnorm(C) + NE = mp.mnorm(E) + + if verbose: + print("difference:", NC, "\n", C, "\n") + print("difference:", NE, "\n", E, "\n") + + eps = mp.exp( 0.8 * mp.log(mp.eps)) + + assert NC < eps + assert NE < eps + + return NC + +def run_svd_r(A, full_matrices = False, verbose = True): + + m, n = A.rows, A.cols + + eps = mp.exp(0.8 * mp.log(mp.eps)) + + if verbose: + print("original matrix:\n", str(A)) + print("full", full_matrices) + + U, S0, V = mp.svd_r(A, full_matrices = full_matrices) + + S = mp.zeros(U.cols, V.rows) + for j in xrange(min(m, n)): + S[j,j] = S0[j] + + if verbose: + print("U:\n", str(U)) + print("S:\n", str(S0)) + print("V:\n", str(V)) + + C = U * S * V - A + err = mp.mnorm(C) + if verbose: + print("C\n", str(C), "\n", err) + assert err < eps + + D = V * V.transpose() - mp.eye(V.rows) + err = mp.mnorm(D) + if verbose: + print("D:\n", str(D), "\n", err) + assert err < eps + + E = U.transpose() * U - mp.eye(U.cols) + err = mp.mnorm(E) + if verbose: + print("E:\n", str(E), "\n", err) + assert err < eps + +def run_svd_c(A, full_matrices = False, verbose = True): + + m, n = A.rows, A.cols + + eps = mp.exp(0.8 * mp.log(mp.eps)) + + if verbose: + print("original matrix:\n", str(A)) + print("full", full_matrices) + + U, S0, V = mp.svd_c(A, full_matrices = full_matrices) + + S = mp.zeros(U.cols, V.rows) + for j in xrange(min(m, n)): + S[j,j] = S0[j] + + if verbose: + print("U:\n", str(U)) + print("S:\n", str(S0)) + print("V:\n", str(V)) + + C = U * S * V - A + err = mp.mnorm(C) + if verbose: + print("C\n", str(C), "\n", err) + assert err < eps + + D = V * V.transpose_conj() - mp.eye(V.rows) + err = mp.mnorm(D) + if verbose: + print("D:\n", str(D), "\n", err) + assert err < eps + + E = U.transpose_conj() * U - mp.eye(U.cols) + err = mp.mnorm(E) + if verbose: + print("E:\n", str(E), "\n", err) + assert err < eps + +def run_gauss(qtype, a, b): + eps = 1e-5 + + d, e = mp.gauss_quadrature(len(a), qtype) + d -= mp.matrix(a) + e -= mp.matrix(b) + + assert mp.mnorm(d) < eps + assert mp.mnorm(e) < eps + +def irandmatrix(n, range = 10): + """ + random matrix with integer entries + """ + A = mp.matrix(n, n) + for i in xrange(n): + for j in xrange(n): + A[i,j]=int( (2 * mp.rand() - 1) * range) + return A + +####################### + +def test_eighe_fixed_matrix(): + A = mp.matrix([[2, 3], [3, 5]]) + run_eigsy(A) + run_eighe(A) + + A = mp.matrix([[7, -11], [-11, 13]]) + run_eigsy(A) + run_eighe(A) + + A = mp.matrix([[2, 11, 7], [11, 3, 13], [7, 13, 5]]) + run_eigsy(A) + run_eighe(A) + + A = mp.matrix([[2, 0, 7], [0, 3, 1], [7, 1, 5]]) + run_eigsy(A) + run_eighe(A) + + # + + A = mp.matrix([[2, 3+7j], [3-7j, 5]]) + run_eighe(A) + + A = mp.matrix([[2, -11j, 0], [+11j, 3, 29j], [0, -29j, 5]]) + run_eighe(A) + + A = mp.matrix([[2, 11 + 17j, 7 + 19j], [11 - 17j, 3, -13 + 23j], [7 - 19j, -13 - 23j, 5]]) + run_eighe(A) + +def test_eigsy_randmatrix(): + N = 5 + + for a in xrange(10): + A = 2 * mp.randmatrix(N, N) - 1 + + for i in xrange(0, N): + for j in xrange(i + 1, N): + A[j,i] = A[i,j] + + run_eigsy(A) + +def test_eighe_randmatrix(): + N = 5 + + for a in xrange(10): + A = (2 * mp.randmatrix(N, N) - 1) + 1j * (2 * mp.randmatrix(N, N) - 1) + + for i in xrange(0, N): + A[i,i] = mp.re(A[i,i]) + for j in xrange(i + 1, N): + A[j,i] = mp.conj(A[i,j]) + + run_eighe(A) + +def test_eigsy_irandmatrix(): + N = 4 + R = 4 + + for a in xrange(10): + A=irandmatrix(N, R) + + for i in xrange(0, N): + for j in xrange(i + 1, N): + A[j,i] = A[i,j] + + run_eigsy(A) + +def test_eighe_irandmatrix(): + N = 4 + R = 4 + + for a in xrange(10): + A=irandmatrix(N, R) + 1j * irandmatrix(N, R) + + for i in xrange(0, N): + A[i,i] = mp.re(A[i,i]) + for j in xrange(i + 1, N): + A[j,i] = mp.conj(A[i,j]) + + run_eighe(A) + +def test_svd_r_rand(): + for i in xrange(5): + full = mp.rand() > 0.5 + m = 1 + int(mp.rand() * 10) + n = 1 + int(mp.rand() * 10) + A = 2 * mp.randmatrix(m, n) - 1 + if mp.rand() > 0.5: + A *= 10 + for x in xrange(m): + for y in xrange(n): + A[x,y]=int(A[x,y]) + + run_svd_r(A, full_matrices = full, verbose = False) + +def test_svd_c_rand(): + for i in xrange(5): + full = mp.rand() > 0.5 + m = 1 + int(mp.rand() * 10) + n = 1 + int(mp.rand() * 10) + A = (2 * mp.randmatrix(m, n) - 1) + 1j * (2 * mp.randmatrix(m, n) - 1) + if mp.rand() > 0.5: + A *= 10 + for x in xrange(m): + for y in xrange(n): + A[x,y]=int(mp.re(A[x,y])) + 1j * int(mp.im(A[x,y])) + + run_svd_c(A, full_matrices=full, verbose=False) + +def test_svd_test_case(): + # a test case from Golub and Reinsch + # (see wilkinson/reinsch: handbook for auto. comp., vol ii-linear algebra, 134-151(1971).) + + eps = mp.exp(0.8 * mp.log(mp.eps)) + + a = [[22, 10, 2, 3, 7], + [14, 7, 10, 0, 8], + [-1, 13, -1, -11, 3], + [-3, -2, 13, -2, 4], + [ 9, 8, 1, -2, 4], + [ 9, 1, -7, 5, -1], + [ 2, -6, 6, 5, 1], + [ 4, 5, 0, -2, 2]] + + a = mp.matrix(a) + b = mp.matrix([mp.sqrt(1248), 20, mp.sqrt(384), 0, 0]) + + S = mp.svd_r(a, compute_uv = False) + S -= b + assert mp.mnorm(S) < eps + + S = mp.svd_c(a, compute_uv = False) + S -= b + assert mp.mnorm(S) < eps + + +def test_gauss_quadrature_static(): + a = [-0.57735027, 0.57735027] + b = [ 1, 1] + run_gauss("legendre", a , b) + + a = [ -0.906179846, -0.538469310, 0, 0.538469310, 0.906179846] + b = [ 0.23692689, 0.47862867, 0.56888889, 0.47862867, 0.23692689] + run_gauss("legendre", a , b) + + a = [ 0.06943184, 0.33000948, 0.66999052, 0.93056816] + b = [ 0.17392742, 0.32607258, 0.32607258, 0.17392742] + run_gauss("legendre01", a , b) + + a = [-0.70710678, 0.70710678] + b = [ 0.88622693, 0.88622693] + run_gauss("hermite", a , b) + + a = [ -2.02018287, -0.958572465, 0, 0.958572465, 2.02018287] + b = [ 0.01995324, 0.39361932, 0.94530872, 0.39361932, 0.01995324] + run_gauss("hermite", a , b) + + a = [ 0.41577456, 2.29428036, 6.28994508] + b = [ 0.71109301, 0.27851773, 0.01038926] + run_gauss("laguerre", a , b) + +def test_gauss_quadrature_dynamic(verbose = False): + n = 5 + + A = mp.randmatrix(2 * n, 1) + + def F(x): + r = 0 + for i in xrange(len(A) - 1, -1, -1): + r = r * x + A[i] + return r + + def run(qtype, FW, R, alpha = 0, beta = 0): + X, W = mp.gauss_quadrature(n, qtype, alpha = alpha, beta = beta) + + a = 0 + for i in xrange(len(X)): + a += W[i] * F(X[i]) + + b = mp.quad(lambda x: FW(x) * F(x), R) + + c = mp.fabs(a - b) + + if verbose: + print(qtype, c, a, b) + + assert c < 1e-5 + + run("legendre", lambda x: 1, [-1, 1]) + run("legendre01", lambda x: 1, [0, 1]) + run("hermite", lambda x: mp.exp(-x*x), [-mp.inf, mp.inf]) + run("laguerre", lambda x: mp.exp(-x), [0, mp.inf]) + run("glaguerre", lambda x: mp.sqrt(x)*mp.exp(-x), [0, mp.inf], alpha = 1 / mp.mpf(2)) + run("chebyshev1", lambda x: 1/mp.sqrt(1-x*x), [-1, 1]) + run("chebyshev2", lambda x: mp.sqrt(1-x*x), [-1, 1]) + run("jacobi", lambda x: (1-x)**(1/mp.mpf(3)) * (1+x)**(1/mp.mpf(5)), [-1, 1], alpha = 1 / mp.mpf(3), beta = 1 / mp.mpf(5) ) diff --git a/venv/lib/python3.12/site-packages/mpmath/tests/test_elliptic.py b/venv/lib/python3.12/site-packages/mpmath/tests/test_elliptic.py new file mode 100644 index 0000000000000000000000000000000000000000..4dddc2df34b8d2fa7f2028b3501e5b7f140d8912 --- /dev/null +++ b/venv/lib/python3.12/site-packages/mpmath/tests/test_elliptic.py @@ -0,0 +1,670 @@ +""" +Limited tests of the elliptic functions module. A full suite of +extensive testing can be found in elliptic_torture_tests.py + +Author of the first version: M.T. Taschuk + +References: + +[1] Abramowitz & Stegun. 'Handbook of Mathematical Functions, 9th Ed.', + (Dover duplicate of 1972 edition) +[2] Whittaker 'A Course of Modern Analysis, 4th Ed.', 1946, + Cambridge University Press + +""" + +import mpmath +import random +import pytest + +from mpmath import * + +def mpc_ae(a, b, eps=eps): + res = True + res = res and a.real.ae(b.real, eps) + res = res and a.imag.ae(b.imag, eps) + return res + +zero = mpf(0) +one = mpf(1) + +jsn = ellipfun('sn') +jcn = ellipfun('cn') +jdn = ellipfun('dn') + +calculate_nome = lambda k: qfrom(k=k) + +def test_ellipfun(): + mp.dps = 15 + assert ellipfun('ss', 0, 0) == 1 + assert ellipfun('cc', 0, 0) == 1 + assert ellipfun('dd', 0, 0) == 1 + assert ellipfun('nn', 0, 0) == 1 + assert ellipfun('sn', 0.25, 0).ae(sin(0.25)) + assert ellipfun('cn', 0.25, 0).ae(cos(0.25)) + assert ellipfun('dn', 0.25, 0).ae(1) + assert ellipfun('ns', 0.25, 0).ae(csc(0.25)) + assert ellipfun('nc', 0.25, 0).ae(sec(0.25)) + assert ellipfun('nd', 0.25, 0).ae(1) + assert ellipfun('sc', 0.25, 0).ae(tan(0.25)) + assert ellipfun('sd', 0.25, 0).ae(sin(0.25)) + assert ellipfun('cd', 0.25, 0).ae(cos(0.25)) + assert ellipfun('cs', 0.25, 0).ae(cot(0.25)) + assert ellipfun('dc', 0.25, 0).ae(sec(0.25)) + assert ellipfun('ds', 0.25, 0).ae(csc(0.25)) + assert ellipfun('sn', 0.25, 1).ae(tanh(0.25)) + assert ellipfun('cn', 0.25, 1).ae(sech(0.25)) + assert ellipfun('dn', 0.25, 1).ae(sech(0.25)) + assert ellipfun('ns', 0.25, 1).ae(coth(0.25)) + assert ellipfun('nc', 0.25, 1).ae(cosh(0.25)) + assert ellipfun('nd', 0.25, 1).ae(cosh(0.25)) + assert ellipfun('sc', 0.25, 1).ae(sinh(0.25)) + assert ellipfun('sd', 0.25, 1).ae(sinh(0.25)) + assert ellipfun('cd', 0.25, 1).ae(1) + assert ellipfun('cs', 0.25, 1).ae(csch(0.25)) + assert ellipfun('dc', 0.25, 1).ae(1) + assert ellipfun('ds', 0.25, 1).ae(csch(0.25)) + assert ellipfun('sn', 0.25, 0.5).ae(0.24615967096986145833) + assert ellipfun('cn', 0.25, 0.5).ae(0.96922928989378439337) + assert ellipfun('dn', 0.25, 0.5).ae(0.98473484156599474563) + assert ellipfun('ns', 0.25, 0.5).ae(4.0624038700573130369) + assert ellipfun('nc', 0.25, 0.5).ae(1.0317476065024692949) + assert ellipfun('nd', 0.25, 0.5).ae(1.0155017958029488665) + assert ellipfun('sc', 0.25, 0.5).ae(0.25397465134058993408) + assert ellipfun('sd', 0.25, 0.5).ae(0.24997558792415733063) + assert ellipfun('cd', 0.25, 0.5).ae(0.98425408443195497052) + assert ellipfun('cs', 0.25, 0.5).ae(3.9374008182374110826) + assert ellipfun('dc', 0.25, 0.5).ae(1.0159978158253033913) + assert ellipfun('ds', 0.25, 0.5).ae(4.0003906313579720593) + + + + +def test_calculate_nome(): + mp.dps = 100 + + q = calculate_nome(zero) + assert(q == zero) + + mp.dps = 25 + # used Mathematica's EllipticNomeQ[m] + math1 = [(mpf(1)/10, mpf('0.006584651553858370274473060')), + (mpf(2)/10, mpf('0.01394285727531826872146409')), + (mpf(3)/10, mpf('0.02227743615715350822901627')), + (mpf(4)/10, mpf('0.03188334731336317755064299')), + (mpf(5)/10, mpf('0.04321391826377224977441774')), + (mpf(6)/10, mpf('0.05702025781460967637754953')), + (mpf(7)/10, mpf('0.07468994353717944761143751')), + (mpf(8)/10, mpf('0.09927369733882489703607378')), + (mpf(9)/10, mpf('0.1401731269542615524091055')), + (mpf(9)/10, mpf('0.1401731269542615524091055'))] + + for i in math1: + m = i[0] + q = calculate_nome(sqrt(m)) + assert q.ae(i[1]) + + mp.dps = 15 + +def test_jtheta(): + mp.dps = 25 + + z = q = zero + for n in range(1,5): + value = jtheta(n, z, q) + assert(value == (n-1)//2) + + for q in [one, mpf(2)]: + for n in range(1,5): + pytest.raises(ValueError, lambda: jtheta(n, z, q)) + + z = one/10 + q = one/11 + + # Mathematical N[EllipticTheta[1, 1/10, 1/11], 25] + res = mpf('0.1069552990104042681962096') + result = jtheta(1, z, q) + assert(result.ae(res)) + + # Mathematica N[EllipticTheta[2, 1/10, 1/11], 25] + res = mpf('1.101385760258855791140606') + result = jtheta(2, z, q) + assert(result.ae(res)) + + # Mathematica N[EllipticTheta[3, 1/10, 1/11], 25] + res = mpf('1.178319743354331061795905') + result = jtheta(3, z, q) + assert(result.ae(res)) + + # Mathematica N[EllipticTheta[4, 1/10, 1/11], 25] + res = mpf('0.8219318954665153577314573') + result = jtheta(4, z, q) + assert(result.ae(res)) + + # test for sin zeros for jtheta(1, z, q) + # test for cos zeros for jtheta(2, z, q) + z1 = pi + z2 = pi/2 + for i in range(10): + qstring = str(random.random()) + q = mpf(qstring) + result = jtheta(1, z1, q) + assert(result.ae(0)) + result = jtheta(2, z2, q) + assert(result.ae(0)) + mp.dps = 15 + + +def test_jtheta_issue_79(): + # near the circle of covergence |q| = 1 the convergence slows + # down; for |q| > Q_LIM the theta functions raise ValueError + mp.dps = 30 + mp.dps += 30 + q = mpf(6)/10 - one/10**6 - mpf(8)/10 * j + mp.dps -= 30 + # Mathematica run first + # N[EllipticTheta[3, 1, 6/10 - 10^-6 - 8/10*I], 2000] + # then it works: + # N[EllipticTheta[3, 1, 6/10 - 10^-6 - 8/10*I], 30] + res = mpf('32.0031009628901652627099524264') + \ + mpf('16.6153027998236087899308935624') * j + result = jtheta(3, 1, q) + # check that for abs(q) > Q_LIM a ValueError exception is raised + mp.dps += 30 + q = mpf(6)/10 - one/10**7 - mpf(8)/10 * j + mp.dps -= 30 + pytest.raises(ValueError, lambda: jtheta(3, 1, q)) + + # bug reported in issue 79 + mp.dps = 100 + z = (1+j)/3 + q = mpf(368983957219251)/10**15 + mpf(636363636363636)/10**15 * j + # Mathematica N[EllipticTheta[1, z, q], 35] + res = mpf('2.4439389177990737589761828991467471') + \ + mpf('0.5446453005688226915290954851851490') *j + mp.dps = 30 + result = jtheta(1, z, q) + assert(result.ae(res)) + mp.dps = 80 + z = 3 + 4*j + q = 0.5 + 0.5*j + r1 = jtheta(1, z, q) + mp.dps = 15 + r2 = jtheta(1, z, q) + assert r1.ae(r2) + mp.dps = 80 + z = 3 + j + q1 = exp(j*3) + # longer test + # for n in range(1, 6) + for n in range(1, 2): + mp.dps = 80 + q = q1*(1 - mpf(1)/10**n) + r1 = jtheta(1, z, q) + mp.dps = 15 + r2 = jtheta(1, z, q) + assert r1.ae(r2) + mp.dps = 15 + # issue 79 about high derivatives + assert jtheta(3, 4.5, 0.25, 9).ae(1359.04892680683) + assert jtheta(3, 4.5, 0.25, 50).ae(-6.14832772630905e+33) + mp.dps = 50 + r = jtheta(3, 4.5, 0.25, 9) + assert r.ae('1359.048926806828939547859396600218966947753213803') + r = jtheta(3, 4.5, 0.25, 50) + assert r.ae('-6148327726309051673317975084654262.4119215720343656') + +def test_jtheta_identities(): + """ + Tests the some of the jacobi identidies found in Abramowitz, + Sec. 16.28, Pg. 576. The identities are tested to 1 part in 10^98. + """ + mp.dps = 110 + eps1 = ldexp(eps, 30) + + for i in range(10): + qstring = str(random.random()) + q = mpf(qstring) + + zstring = str(10*random.random()) + z = mpf(zstring) + # Abramowitz 16.28.1 + # v_1(z, q)**2 * v_4(0, q)**2 = v_3(z, q)**2 * v_2(0, q)**2 + # - v_2(z, q)**2 * v_3(0, q)**2 + term1 = (jtheta(1, z, q)**2) * (jtheta(4, zero, q)**2) + term2 = (jtheta(3, z, q)**2) * (jtheta(2, zero, q)**2) + term3 = (jtheta(2, z, q)**2) * (jtheta(3, zero, q)**2) + equality = term1 - term2 + term3 + assert(equality.ae(0, eps1)) + + zstring = str(100*random.random()) + z = mpf(zstring) + # Abramowitz 16.28.2 + # v_2(z, q)**2 * v_4(0, q)**2 = v_4(z, q)**2 * v_2(0, q)**2 + # - v_1(z, q)**2 * v_3(0, q)**2 + term1 = (jtheta(2, z, q)**2) * (jtheta(4, zero, q)**2) + term2 = (jtheta(4, z, q)**2) * (jtheta(2, zero, q)**2) + term3 = (jtheta(1, z, q)**2) * (jtheta(3, zero, q)**2) + equality = term1 - term2 + term3 + assert(equality.ae(0, eps1)) + + # Abramowitz 16.28.3 + # v_3(z, q)**2 * v_4(0, q)**2 = v_4(z, q)**2 * v_3(0, q)**2 + # - v_1(z, q)**2 * v_2(0, q)**2 + term1 = (jtheta(3, z, q)**2) * (jtheta(4, zero, q)**2) + term2 = (jtheta(4, z, q)**2) * (jtheta(3, zero, q)**2) + term3 = (jtheta(1, z, q)**2) * (jtheta(2, zero, q)**2) + equality = term1 - term2 + term3 + assert(equality.ae(0, eps1)) + + # Abramowitz 16.28.4 + # v_4(z, q)**2 * v_4(0, q)**2 = v_3(z, q)**2 * v_3(0, q)**2 + # - v_2(z, q)**2 * v_2(0, q)**2 + term1 = (jtheta(4, z, q)**2) * (jtheta(4, zero, q)**2) + term2 = (jtheta(3, z, q)**2) * (jtheta(3, zero, q)**2) + term3 = (jtheta(2, z, q)**2) * (jtheta(2, zero, q)**2) + equality = term1 - term2 + term3 + assert(equality.ae(0, eps1)) + + # Abramowitz 16.28.5 + # v_2(0, q)**4 + v_4(0, q)**4 == v_3(0, q)**4 + term1 = (jtheta(2, zero, q))**4 + term2 = (jtheta(4, zero, q))**4 + term3 = (jtheta(3, zero, q))**4 + equality = term1 + term2 - term3 + assert(equality.ae(0, eps1)) + mp.dps = 15 + +def test_jtheta_complex(): + mp.dps = 30 + z = mpf(1)/4 + j/8 + q = mpf(1)/3 + j/7 + # Mathematica N[EllipticTheta[1, 1/4 + I/8, 1/3 + I/7], 35] + res = mpf('0.31618034835986160705729105731678285') + \ + mpf('0.07542013825835103435142515194358975') * j + r = jtheta(1, z, q) + assert(mpc_ae(r, res)) + + # Mathematica N[EllipticTheta[2, 1/4 + I/8, 1/3 + I/7], 35] + res = mpf('1.6530986428239765928634711417951828') + \ + mpf('0.2015344864707197230526742145361455') * j + r = jtheta(2, z, q) + assert(mpc_ae(r, res)) + + # Mathematica N[EllipticTheta[3, 1/4 + I/8, 1/3 + I/7], 35] + res = mpf('1.6520564411784228184326012700348340') + \ + mpf('0.1998129119671271328684690067401823') * j + r = jtheta(3, z, q) + assert(mpc_ae(r, res)) + + # Mathematica N[EllipticTheta[4, 1/4 + I/8, 1/3 + I/7], 35] + res = mpf('0.37619082382228348252047624089973824') - \ + mpf('0.15623022130983652972686227200681074') * j + r = jtheta(4, z, q) + assert(mpc_ae(r, res)) + + # check some theta function identities + mp.dos = 100 + z = mpf(1)/4 + j/8 + q = mpf(1)/3 + j/7 + mp.dps += 10 + a = [0,0, jtheta(2, 0, q), jtheta(3, 0, q), jtheta(4, 0, q)] + t = [0, jtheta(1, z, q), jtheta(2, z, q), jtheta(3, z, q), jtheta(4, z, q)] + r = [(t[2]*a[4])**2 - (t[4]*a[2])**2 + (t[1] *a[3])**2, + (t[3]*a[4])**2 - (t[4]*a[3])**2 + (t[1] *a[2])**2, + (t[1]*a[4])**2 - (t[3]*a[2])**2 + (t[2] *a[3])**2, + (t[4]*a[4])**2 - (t[3]*a[3])**2 + (t[2] *a[2])**2, + a[2]**4 + a[4]**4 - a[3]**4] + mp.dps -= 10 + for x in r: + assert(mpc_ae(x, mpc(0))) + mp.dps = 15 + +def test_djtheta(): + mp.dps = 30 + + z = one/7 + j/3 + q = one/8 + j/5 + # Mathematica N[EllipticThetaPrime[1, 1/7 + I/3, 1/8 + I/5], 35] + res = mpf('1.5555195883277196036090928995803201') - \ + mpf('0.02439761276895463494054149673076275') * j + result = jtheta(1, z, q, 1) + assert(mpc_ae(result, res)) + + # Mathematica N[EllipticThetaPrime[2, 1/7 + I/3, 1/8 + I/5], 35] + res = mpf('0.19825296689470982332701283509685662') - \ + mpf('0.46038135182282106983251742935250009') * j + result = jtheta(2, z, q, 1) + assert(mpc_ae(result, res)) + + # Mathematica N[EllipticThetaPrime[3, 1/7 + I/3, 1/8 + I/5], 35] + res = mpf('0.36492498415476212680896699407390026') - \ + mpf('0.57743812698666990209897034525640369') * j + result = jtheta(3, z, q, 1) + assert(mpc_ae(result, res)) + + # Mathematica N[EllipticThetaPrime[4, 1/7 + I/3, 1/8 + I/5], 35] + res = mpf('-0.38936892528126996010818803742007352') + \ + mpf('0.66549886179739128256269617407313625') * j + result = jtheta(4, z, q, 1) + assert(mpc_ae(result, res)) + + for i in range(10): + q = (one*random.random() + j*random.random())/2 + # identity in Wittaker, Watson &21.41 + a = jtheta(1, 0, q, 1) + b = jtheta(2, 0, q)*jtheta(3, 0, q)*jtheta(4, 0, q) + assert(a.ae(b)) + + # test higher derivatives + mp.dps = 20 + for q,z in [(one/3, one/5), (one/3 + j/8, one/5), + (one/3, one/5 + j/8), (one/3 + j/7, one/5 + j/8)]: + for n in [1, 2, 3, 4]: + r = jtheta(n, z, q, 2) + r1 = diff(lambda zz: jtheta(n, zz, q), z, n=2) + assert r.ae(r1) + r = jtheta(n, z, q, 3) + r1 = diff(lambda zz: jtheta(n, zz, q), z, n=3) + assert r.ae(r1) + + # identity in Wittaker, Watson &21.41 + q = one/3 + z = zero + a = [0]*5 + a[1] = jtheta(1, z, q, 3)/jtheta(1, z, q, 1) + for n in [2,3,4]: + a[n] = jtheta(n, z, q, 2)/jtheta(n, z, q) + equality = a[2] + a[3] + a[4] - a[1] + assert(equality.ae(0)) + mp.dps = 15 + +def test_jsn(): + """ + Test some special cases of the sn(z, q) function. + """ + mp.dps = 100 + + # trival case + result = jsn(zero, zero) + assert(result == zero) + + # Abramowitz Table 16.5 + # + # sn(0, m) = 0 + + for i in range(10): + qstring = str(random.random()) + q = mpf(qstring) + + equality = jsn(zero, q) + assert(equality.ae(0)) + + # Abramowitz Table 16.6.1 + # + # sn(z, 0) = sin(z), m == 0 + # + # sn(z, 1) = tanh(z), m == 1 + # + # It would be nice to test these, but I find that they run + # in to numerical trouble. I'm currently treating as a boundary + # case for sn function. + + mp.dps = 25 + arg = one/10 + #N[JacobiSN[1/10, 2^-100], 25] + res = mpf('0.09983341664682815230681420') + m = ldexp(one, -100) + result = jsn(arg, m) + assert(result.ae(res)) + + # N[JacobiSN[1/10, 1/10], 25] + res = mpf('0.09981686718599080096451168') + result = jsn(arg, arg) + assert(result.ae(res)) + mp.dps = 15 + +def test_jcn(): + """ + Test some special cases of the cn(z, q) function. + """ + mp.dps = 100 + + # Abramowitz Table 16.5 + # cn(0, q) = 1 + qstring = str(random.random()) + q = mpf(qstring) + cn = jcn(zero, q) + assert(cn.ae(one)) + + # Abramowitz Table 16.6.2 + # + # cn(u, 0) = cos(u), m == 0 + # + # cn(u, 1) = sech(z), m == 1 + # + # It would be nice to test these, but I find that they run + # in to numerical trouble. I'm currently treating as a boundary + # case for cn function. + + mp.dps = 25 + arg = one/10 + m = ldexp(one, -100) + #N[JacobiCN[1/10, 2^-100], 25] + res = mpf('0.9950041652780257660955620') + result = jcn(arg, m) + assert(result.ae(res)) + + # N[JacobiCN[1/10, 1/10], 25] + res = mpf('0.9950058256237368748520459') + result = jcn(arg, arg) + assert(result.ae(res)) + mp.dps = 15 + +def test_jdn(): + """ + Test some special cases of the dn(z, q) function. + """ + mp.dps = 100 + + # Abramowitz Table 16.5 + # dn(0, q) = 1 + mstring = str(random.random()) + m = mpf(mstring) + + dn = jdn(zero, m) + assert(dn.ae(one)) + + mp.dps = 25 + # N[JacobiDN[1/10, 1/10], 25] + res = mpf('0.9995017055025556219713297') + arg = one/10 + result = jdn(arg, arg) + assert(result.ae(res)) + mp.dps = 15 + + +def test_sn_cn_dn_identities(): + """ + Tests the some of the jacobi elliptic function identities found + on Mathworld. Haven't found in Abramowitz. + """ + mp.dps = 100 + N = 5 + for i in range(N): + qstring = str(random.random()) + q = mpf(qstring) + zstring = str(100*random.random()) + z = mpf(zstring) + + # MathWorld + # sn(z, q)**2 + cn(z, q)**2 == 1 + term1 = jsn(z, q)**2 + term2 = jcn(z, q)**2 + equality = one - term1 - term2 + assert(equality.ae(0)) + + # MathWorld + # k**2 * sn(z, m)**2 + dn(z, m)**2 == 1 + for i in range(N): + mstring = str(random.random()) + m = mpf(qstring) + k = m.sqrt() + zstring = str(10*random.random()) + z = mpf(zstring) + term1 = k**2 * jsn(z, m)**2 + term2 = jdn(z, m)**2 + equality = one - term1 - term2 + assert(equality.ae(0)) + + + for i in range(N): + mstring = str(random.random()) + m = mpf(mstring) + k = m.sqrt() + zstring = str(random.random()) + z = mpf(zstring) + + # MathWorld + # k**2 * cn(z, m)**2 + (1 - k**2) = dn(z, m)**2 + term1 = k**2 * jcn(z, m)**2 + term2 = 1 - k**2 + term3 = jdn(z, m)**2 + equality = term3 - term1 - term2 + assert(equality.ae(0)) + + K = ellipk(k**2) + # Abramowitz Table 16.5 + # sn(K, m) = 1; K is K(k), first complete elliptic integral + r = jsn(K, m) + assert(r.ae(one)) + + # Abramowitz Table 16.5 + # cn(K, q) = 0; K is K(k), first complete elliptic integral + equality = jcn(K, m) + assert(equality.ae(0)) + + # Abramowitz Table 16.6.3 + # dn(z, 0) = 1, m == 0 + z = m + value = jdn(z, zero) + assert(value.ae(one)) + + mp.dps = 15 + +def test_sn_cn_dn_complex(): + mp.dps = 30 + # N[JacobiSN[1/4 + I/8, 1/3 + I/7], 35] in Mathematica + res = mpf('0.2495674401066275492326652143537') + \ + mpf('0.12017344422863833381301051702823') * j + u = mpf(1)/4 + j/8 + m = mpf(1)/3 + j/7 + r = jsn(u, m) + assert(mpc_ae(r, res)) + + #N[JacobiCN[1/4 + I/8, 1/3 + I/7], 35] + res = mpf('0.9762691700944007312693721148331') - \ + mpf('0.0307203994181623243583169154824')*j + r = jcn(u, m) + #assert r.real.ae(res.real) + #assert r.imag.ae(res.imag) + assert(mpc_ae(r, res)) + + #N[JacobiDN[1/4 + I/8, 1/3 + I/7], 35] + res = mpf('0.99639490163039577560547478589753039') - \ + mpf('0.01346296520008176393432491077244994')*j + r = jdn(u, m) + assert(mpc_ae(r, res)) + mp.dps = 15 + +def test_elliptic_integrals(): + # Test cases from Carlson's paper + mp.dps = 15 + assert elliprd(0,2,1).ae(1.7972103521033883112) + assert elliprd(2,3,4).ae(0.16510527294261053349) + assert elliprd(j,-j,2).ae(0.65933854154219768919) + assert elliprd(0,j,-j).ae(1.2708196271909686299 + 2.7811120159520578777j) + assert elliprd(0,j-1,j).ae(-1.8577235439239060056 - 0.96193450888838559989j) + assert elliprd(-2-j,-j,-1+j).ae(1.8249027393703805305 - 1.2218475784827035855j) + # extra test cases + assert elliprg(0,0,0) == 0 + assert elliprg(0,0,16).ae(2) + assert elliprg(0,16,0).ae(2) + assert elliprg(16,0,0).ae(2) + assert elliprg(1,4,0).ae(1.2110560275684595248036) + assert elliprg(1,0,4).ae(1.2110560275684595248036) + assert elliprg(0,4,1).ae(1.2110560275684595248036) + # should be symmetric -- fixes a bug present in the paper + x,y,z = 1,1j,-1+1j + assert elliprg(x,y,z).ae(0.64139146875812627545 + 0.58085463774808290907j) + assert elliprg(x,z,y).ae(0.64139146875812627545 + 0.58085463774808290907j) + assert elliprg(y,x,z).ae(0.64139146875812627545 + 0.58085463774808290907j) + assert elliprg(y,z,x).ae(0.64139146875812627545 + 0.58085463774808290907j) + assert elliprg(z,x,y).ae(0.64139146875812627545 + 0.58085463774808290907j) + assert elliprg(z,y,x).ae(0.64139146875812627545 + 0.58085463774808290907j) + + for n in [5, 15, 30, 60, 100]: + mp.dps = n + assert elliprf(1,2,0).ae('1.3110287771460599052324197949455597068413774757158115814084108519003952935352071251151477664807145467230678763') + assert elliprf(0.5,1,0).ae('1.854074677301371918433850347195260046217598823521766905585928045056021776838119978357271861650371897277771871') + assert elliprf(j,-j,0).ae('1.854074677301371918433850347195260046217598823521766905585928045056021776838119978357271861650371897277771871') + assert elliprf(j-1,j,0).ae(mpc('0.79612586584233913293056938229563057846592264089185680214929401744498956943287031832657642790719940442165621412', + '-1.2138566698364959864300942567386038975419875860741507618279563735753073152507112254567291141460317931258599889')) + assert elliprf(2,3,4).ae('0.58408284167715170669284916892566789240351359699303216166309375305508295130412919665541330837704050454472379308') + assert elliprf(j,-j,2).ae('1.0441445654064360931078658361850779139591660747973017593275012615517220315993723776182276555339288363064476126') + assert elliprf(j-1,j,1-j).ae(mpc('0.93912050218619371196624617169781141161485651998254431830645241993282941057500174238125105410055253623847335313', + '-0.53296252018635269264859303449447908970360344322834582313172115220559316331271520508208025270300138589669326136')) + assert elliprc(0,0.25).ae(+pi) + assert elliprc(2.25,2).ae(+ln2) + assert elliprc(0,j).ae(mpc('1.1107207345395915617539702475151734246536554223439225557713489017391086982748684776438317336911913093408525532', + '-1.1107207345395915617539702475151734246536554223439225557713489017391086982748684776438317336911913093408525532')) + assert elliprc(-j,j).ae(mpc('1.2260849569072198222319655083097718755633725139745941606203839524036426936825652935738621522906572884239069297', + '-0.34471136988767679699935618332997956653521218571295874986708834375026550946053920574015526038040124556716711353')) + assert elliprc(0.25,-2).ae(ln2/3) + assert elliprc(j,-1).ae(mpc('0.77778596920447389875196055840799837589537035343923012237628610795937014001905822029050288316217145443865649819', + '0.1983248499342877364755170948292130095921681309577950696116251029742793455964385947473103628983664877025779304')) + assert elliprj(0,1,2,3).ae('0.77688623778582332014190282640545501102298064276022952731669118325952563819813258230708177398475643634103990878') + assert elliprj(2,3,4,5).ae('0.14297579667156753833233879421985774801466647854232626336218889885463800128817976132826443904216546421431528308') + assert elliprj(2,3,4,-1+j).ae(mpc('0.13613945827770535203521374457913768360237593025944342652613569368333226052158214183059386307242563164036672709', + '-0.38207561624427164249600936454845112611060375760094156571007648297226090050927156176977091273224510621553615189')) + assert elliprj(j,-j,0,2).ae('1.6490011662710884518243257224860232300246792717163891216346170272567376981346412066066050103935109581019055806') + assert elliprj(-1+j,-1-j,1,2).ae('0.94148358841220238083044612133767270187474673547917988681610772381758628963408843935027667916713866133196845063') + assert elliprj(j,-j,0,1-j).ae(mpc('1.8260115229009316249372594065790946657011067182850435297162034335356430755397401849070610280860044610878657501', + '1.2290661908643471500163617732957042849283739403009556715926326841959667290840290081010472716420690899886276961')) + assert elliprj(-1+j,-1-j,1,-3+j).ae(mpc('-0.61127970812028172123588152373622636829986597243716610650831553882054127570542477508023027578037045504958619422', + '-1.0684038390006807880182112972232562745485871763154040245065581157751693730095703406209466903752930797510491155')) + assert elliprj(-1+j,-2-j,-j,-1+j).ae(mpc('1.8249027393703805304622013339009022294368078659619988943515764258335975852685224202567854526307030593012768954', + '-1.2218475784827035854568450371590419833166777535029296025352291308244564398645467465067845461070602841312456831')) + + assert elliprg(0,16,16).ae(+pi) + assert elliprg(2,3,4).ae('1.7255030280692277601061148835701141842692457170470456590515892070736643637303053506944907685301315299153040991') + assert elliprg(0,j,-j).ae('0.42360654239698954330324956174109581824072295516347109253028968632986700241706737986160014699730561497106114281') + assert elliprg(j-1,j,0).ae(mpc('0.44660591677018372656731970402124510811555212083508861036067729944477855594654762496407405328607219895053798354', + '0.70768352357515390073102719507612395221369717586839400605901402910893345301718731499237159587077682267374159282')) + assert elliprg(-j,j-1,j).ae(mpc('0.36023392184473309033675652092928695596803358846377334894215349632203382573844427952830064383286995172598964266', + '0.40348623401722113740956336997761033878615232917480045914551915169013722542827052849476969199578321834819903921')) + assert elliprg(0, mpf('0.0796'), 4).ae('1.0284758090288040009838871385180217366569777284430590125081211090574701293154645750017813190805144572673802094') + mp.dps = 15 + + # more test cases for the branch of ellippi / elliprj + assert elliprj(-1-0.5j, -10-6j, -10-3j, -5+10j).ae(0.128470516743927699 + 0.102175950778504625j, abs_eps=1e-8) + assert elliprj(1.987, 4.463 - 1.614j, 0, -3.965).ae(-0.341575118513811305 - 0.394703757004268486j, abs_eps=1e-8) + assert elliprj(0.3068, -4.037+0.632j, 1.654, -0.9609).ae(-1.14735199581485639 - 0.134450158867472264j, abs_eps=1e-8) + assert elliprj(0.3068, -4.037-0.632j, 1.654, -0.9609).ae(1.758765901861727 - 0.161002343366626892j, abs_eps=1e-5) + assert elliprj(0.3068, -4.037+0.0632j, 1.654, -0.9609).ae(-1.17157627949475577 - 0.069182614173988811j, abs_eps=1e-8) + assert elliprj(0.3068, -4.037+0.00632j, 1.654, -0.9609).ae(-1.17337595670549633 - 0.0623069224526925j, abs_eps=1e-8) + + # these require accurate integration + assert elliprj(0.3068, -4.037-0.0632j, 1.654, -0.9609).ae(1.77940452391261626 + 0.0388711305592447234j) + assert elliprj(0.3068, -4.037-0.00632j, 1.654, -0.9609).ae(1.77806722756403055 + 0.0592749824572262329j) + # issue #571 + assert ellippi(2.1 + 0.94j, 2.3 + 0.98j, 2.5 + 0.01j).ae(-0.40652414240811963438 + 2.1547659461404749309j) + + assert ellippi(2.0-1.0j, 2.0+1.0j).ae(1.8578723151271115 - 1.18642180609983531j) + assert ellippi(2.0-0.5j, 0.5+1.0j).ae(0.936761970766645807 - 1.61876787838890786j) + assert ellippi(2.0, 1.0+1.0j).ae(0.999881420735506708 - 2.4139272867045391j) + assert ellippi(2.0+1.0j, 2.0-1.0j).ae(1.8578723151271115 + 1.18642180609983531j) + assert ellippi(2.0+1.0j, 2.0).ae(2.78474654927885845 + 2.02204728966993314j) + +def test_issue_238(): + assert isnan(qfrom(m=nan)) diff --git a/venv/lib/python3.12/site-packages/mpmath/tests/test_fp.py b/venv/lib/python3.12/site-packages/mpmath/tests/test_fp.py new file mode 100644 index 0000000000000000000000000000000000000000..99f3759c3071c4d55e0481472f3d16c1f5df1fef --- /dev/null +++ b/venv/lib/python3.12/site-packages/mpmath/tests/test_fp.py @@ -0,0 +1,1671 @@ +""" +Easy-to-use test-generating code: + +cases = ''' +exp 2.25 +log 2.25 +''' + +from mpmath import * +mp.dps = 20 +for test in cases.splitlines(): + if not test: + continue + words = test.split() + fname = words[0] + args = words[1:] + argstr = ", ".join(args) + testline = "%s(%s)" % (fname, argstr) + ans = str(eval(testline)) + print " assert ae(fp.%s, %s)" % (testline, ans) + +""" + +from mpmath import fp + +def ae(x, y, tol=1e-12): + if x == y: + return True + return abs(x-y) <= tol*abs(y) + +def test_conj(): + assert fp.conj(4) == 4 + assert fp.conj(3+4j) == 3-4j + assert fp.fdot([1,2],[3,2+1j], conjugate=True) == 7-2j + +def test_fp_number_parts(): + assert ae(fp.arg(3), 0.0) + assert ae(fp.arg(-3), 3.1415926535897932385) + assert ae(fp.arg(3j), 1.5707963267948966192) + assert ae(fp.arg(-3j), -1.5707963267948966192) + assert ae(fp.arg(2+3j), 0.98279372324732906799) + assert ae(fp.arg(-1-1j), -2.3561944901923449288) + assert ae(fp.re(2.5), 2.5) + assert ae(fp.re(2.5+3j), 2.5) + assert ae(fp.im(2.5), 0.0) + assert ae(fp.im(2.5+3j), 3.0) + assert ae(fp.floor(2.5), 2.0) + assert ae(fp.floor(2), 2.0) + assert ae(fp.floor(2.0+0j), (2.0 + 0.0j)) + assert ae(fp.floor(-1.5-0.5j), (-2.0 - 1.0j)) + assert ae(fp.ceil(2.5), 3.0) + assert ae(fp.ceil(2), 2.0) + assert ae(fp.ceil(2.0+0j), (2.0 + 0.0j)) + assert ae(fp.ceil(-1.5-0.5j), (-1.0 + 0.0j)) + +def test_fp_cospi_sinpi(): + assert ae(fp.sinpi(0), 0.0) + assert ae(fp.sinpi(0.25), 0.7071067811865475244) + assert ae(fp.sinpi(0.5), 1.0) + assert ae(fp.sinpi(0.75), 0.7071067811865475244) + assert ae(fp.sinpi(1), 0.0) + assert ae(fp.sinpi(1.25), -0.7071067811865475244) + assert ae(fp.sinpi(1.5), -1.0) + assert ae(fp.sinpi(1.75), -0.7071067811865475244) + assert ae(fp.sinpi(2), 0.0) + assert ae(fp.sinpi(2.25), 0.7071067811865475244) + assert ae(fp.sinpi(0+3j), (0.0 + 6195.8238636085899556j)) + assert ae(fp.sinpi(0.25+3j), (4381.1091260582448033 + 4381.1090689950686908j)) + assert ae(fp.sinpi(0.5+3j), (6195.8239443081075259 + 0.0j)) + assert ae(fp.sinpi(0.75+3j), (4381.1091260582448033 - 4381.1090689950686908j)) + assert ae(fp.sinpi(1+3j), (0.0 - 6195.8238636085899556j)) + assert ae(fp.sinpi(1.25+3j), (-4381.1091260582448033 - 4381.1090689950686908j)) + assert ae(fp.sinpi(1.5+3j), (-6195.8239443081075259 + 0.0j)) + assert ae(fp.sinpi(1.75+3j), (-4381.1091260582448033 + 4381.1090689950686908j)) + assert ae(fp.sinpi(2+3j), (0.0 + 6195.8238636085899556j)) + assert ae(fp.sinpi(2.25+3j), (4381.1091260582448033 + 4381.1090689950686908j)) + assert ae(fp.sinpi(-0.75), -0.7071067811865475244) + assert ae(fp.sinpi(-1e-10), -3.1415926535897933529e-10) + assert ae(fp.sinpi(1e-10), 3.1415926535897933529e-10) + assert ae(fp.sinpi(1e-10+1e-10j), (3.141592653589793353e-10 + 3.1415926535897933528e-10j)) + assert ae(fp.sinpi(1e-10-1e-10j), (3.141592653589793353e-10 - 3.1415926535897933528e-10j)) + assert ae(fp.sinpi(-1e-10+1e-10j), (-3.141592653589793353e-10 + 3.1415926535897933528e-10j)) + assert ae(fp.sinpi(-1e-10-1e-10j), (-3.141592653589793353e-10 - 3.1415926535897933528e-10j)) + assert ae(fp.cospi(0), 1.0) + assert ae(fp.cospi(0.25), 0.7071067811865475244) + assert ae(fp.cospi(0.5), 0.0) + assert ae(fp.cospi(0.75), -0.7071067811865475244) + assert ae(fp.cospi(1), -1.0) + assert ae(fp.cospi(1.25), -0.7071067811865475244) + assert ae(fp.cospi(1.5), 0.0) + assert ae(fp.cospi(1.75), 0.7071067811865475244) + assert ae(fp.cospi(2), 1.0) + assert ae(fp.cospi(2.25), 0.7071067811865475244) + assert ae(fp.cospi(0+3j), (6195.8239443081075259 + 0.0j)) + assert ae(fp.cospi(0.25+3j), (4381.1091260582448033 - 4381.1090689950686908j)) + assert ae(fp.cospi(0.5+3j), (0.0 - 6195.8238636085899556j)) + assert ae(fp.cospi(0.75+3j), (-4381.1091260582448033 - 4381.1090689950686908j)) + assert ae(fp.cospi(1+3j), (-6195.8239443081075259 + 0.0j)) + assert ae(fp.cospi(1.25+3j), (-4381.1091260582448033 + 4381.1090689950686908j)) + assert ae(fp.cospi(1.5+3j), (0.0 + 6195.8238636085899556j)) + assert ae(fp.cospi(1.75+3j), (4381.1091260582448033 + 4381.1090689950686908j)) + assert ae(fp.cospi(2+3j), (6195.8239443081075259 + 0.0j)) + assert ae(fp.cospi(2.25+3j), (4381.1091260582448033 - 4381.1090689950686908j)) + assert ae(fp.cospi(-0.75), -0.7071067811865475244) + assert ae(fp.sinpi(-0.7), -0.80901699437494750611) + assert ae(fp.cospi(-0.7), -0.5877852522924730163) + assert ae(fp.cospi(-3+2j), (-267.74676148374822225 + 0.0j)) + assert ae(fp.sinpi(-3+2j), (0.0 - 267.74489404101651426j)) + assert ae(fp.sinpi(-0.7+2j), (-216.6116802292079471 - 157.37650009392034693j)) + assert ae(fp.cospi(-0.7+2j), (-157.37759774921754565 + 216.61016943630197336j)) + +def test_fp_expj(): + assert ae(fp.expj(0), (1.0 + 0.0j)) + assert ae(fp.expj(1), (0.5403023058681397174 + 0.84147098480789650665j)) + assert ae(fp.expj(2), (-0.416146836547142387 + 0.9092974268256816954j)) + assert ae(fp.expj(0.75), (0.73168886887382088631 + 0.68163876002333416673j)) + assert ae(fp.expj(2+3j), (-0.020718731002242879378 + 0.045271253156092975488j)) + assert ae(fp.expjpi(0), (1.0 + 0.0j)) + assert ae(fp.expjpi(1), (-1.0 + 0.0j)) + assert ae(fp.expjpi(2), (1.0 + 0.0j)) + assert ae(fp.expjpi(0.75), (-0.7071067811865475244 + 0.7071067811865475244j)) + assert ae(fp.expjpi(2+3j), (0.000080699517570304599239 + 0.0j)) + +def test_fp_bernoulli(): + assert ae(fp.bernoulli(0), 1.0) + assert ae(fp.bernoulli(1), -0.5) + assert ae(fp.bernoulli(2), 0.16666666666666666667) + assert ae(fp.bernoulli(10), 0.075757575757575757576) + assert ae(fp.bernoulli(11), 0.0) + +def test_fp_gamma(): + assert ae(fp.gamma(1), 1.0) + assert ae(fp.gamma(1.5), 0.88622692545275801365) + assert ae(fp.gamma(10), 362880.0) + assert ae(fp.gamma(-0.5), -3.5449077018110320546) + assert ae(fp.gamma(-7.1), 0.0016478244570263333622) + assert ae(fp.gamma(12.3), 83385367.899970000963) + assert ae(fp.gamma(2+0j), (1.0 + 0.0j)) + assert ae(fp.gamma(-2.5+0j), (-0.94530872048294188123 + 0.0j)) + assert ae(fp.gamma(3+4j), (0.0052255384713692141947 - 0.17254707929430018772j)) + assert ae(fp.gamma(-3-4j), (0.00001460997305874775607 - 0.000020760733311509070396j)) + assert ae(fp.fac(0), 1.0) + assert ae(fp.fac(1), 1.0) + assert ae(fp.fac(20), 2432902008176640000.0) + assert ae(fp.fac(-3.5), -0.94530872048294188123) + assert ae(fp.fac(2+3j), (-0.44011340763700171113 - 0.06363724312631702183j)) + assert ae(fp.loggamma(1.0), 0.0) + assert ae(fp.loggamma(2.0), 0.0) + assert ae(fp.loggamma(3.0), 0.69314718055994530942) + assert ae(fp.loggamma(7.25), 7.0521854507385394449) + assert ae(fp.loggamma(1000.0), 5905.2204232091812118) + assert ae(fp.loggamma(1e50), 1.1412925464970229298e+52) + assert ae(fp.loggamma(1e25+1e25j), (5.6125802751733671621e+26 + 5.7696599078528568383e+26j)) + assert ae(fp.loggamma(3+4j), (-1.7566267846037841105 + 4.7426644380346579282j)) + assert ae(fp.loggamma(-0.5), (1.2655121234846453965 - 3.1415926535897932385j)) + assert ae(fp.loggamma(-1.25), (1.3664317612369762346 - 6.2831853071795864769j)) + assert ae(fp.loggamma(-2.75), (0.0044878975359557733115 - 9.4247779607693797154j)) + assert ae(fp.loggamma(-3.5), (-1.3090066849930420464 - 12.566370614359172954j)) + assert ae(fp.loggamma(-4.5), (-2.8130840817693161197 - 15.707963267948966192j)) + assert ae(fp.loggamma(-2+3j), (-6.776523813485657093 - 4.568791367260286402j)) + assert ae(fp.loggamma(-1000.3), (-5912.8440347785205041 - 3144.7342462433830317j)) + assert ae(fp.loggamma(-100-100j), (-632.35117666833135562 - 158.37641469650352462j)) + assert ae(fp.loggamma(1e-10), 23.025850929882735237) + assert ae(fp.loggamma(-1e-10), (23.02585092999817837 - 3.1415926535897932385j)) + assert ae(fp.loggamma(1e-10j), (23.025850929940456804 - 1.5707963268526181857j)) + assert ae(fp.loggamma(1e-10j-1e-10), (22.679277339718205716 - 2.3561944902500664954j)) + +def test_fp_psi(): + assert ae(fp.psi(0, 3.7), 1.1671535393615114409) + assert ae(fp.psi(0, 0.5), -1.9635100260214234794) + assert ae(fp.psi(0, 1), -0.57721566490153286061) + assert ae(fp.psi(0, -2.5), 1.1031566406452431872) + assert ae(fp.psi(0, 12.9), 2.5179671503279156347) + assert ae(fp.psi(0, 100), 4.6001618527380874002) + assert ae(fp.psi(0, 2500.3), 7.8239660143238547877) + assert ae(fp.psi(0, 1e40), 92.103403719761827391) + assert ae(fp.psi(0, 1e200), 460.51701859880913677) + assert ae(fp.psi(0, 3.7+0j), (1.1671535393615114409 + 0.0j)) + assert ae(fp.psi(1, 3), 0.39493406684822643647) + assert ae(fp.psi(3, 2+3j), (-0.05383196209159972116 + 0.0076890935247364805218j)) + assert ae(fp.psi(4, -0.5+1j), (1.2719531355492328195 - 18.211833410936276774j)) + assert ae(fp.harmonic(0), 0.0) + assert ae(fp.harmonic(1), 1.0) + assert ae(fp.harmonic(2), 1.5) + assert ae(fp.harmonic(100), 5.1873775176396202608) + assert ae(fp.harmonic(-2.5), 1.2803723055467760478) + assert ae(fp.harmonic(2+3j), (1.9390425294578375875 + 0.87336044981834544043j)) + assert ae(fp.harmonic(-5-4j), (2.3725754822349437733 - 2.4160904444801621j)) + +def test_fp_zeta(): + assert ae(fp.zeta(1e100), 1.0) + assert ae(fp.zeta(3), 1.2020569031595942854) + assert ae(fp.zeta(2+0j), (1.6449340668482264365 + 0.0j)) + assert ae(fp.zeta(0.93), -13.713619351638164784) + assert ae(fp.zeta(1.74), 1.9796863545771774095) + assert ae(fp.zeta(0.0), -0.5) + assert ae(fp.zeta(-1.0), -0.083333333333333333333) + assert ae(fp.zeta(-2.0), 0.0) + assert ae(fp.zeta(-3.0), 0.0083333333333333333333) + assert ae(fp.zeta(-500.0), 0.0) + assert ae(fp.zeta(-7.4), 0.0036537321227995882447) + assert ae(fp.zeta(2.1), 1.5602165335033620158) + assert ae(fp.zeta(26.9), 1.0000000079854809935) + assert ae(fp.zeta(26), 1.0000000149015548284) + assert ae(fp.zeta(27), 1.0000000074507117898) + assert ae(fp.zeta(28), 1.0000000037253340248) + assert ae(fp.zeta(27.1), 1.000000006951755045) + assert ae(fp.zeta(32.7), 1.0000000001433243232) + assert ae(fp.zeta(100), 1.0) + assert ae(fp.altzeta(3.5), 0.92755357777394803511) + assert ae(fp.altzeta(1), 0.69314718055994530942) + assert ae(fp.altzeta(2), 0.82246703342411321824) + assert ae(fp.altzeta(0), 0.5) + assert ae(fp.zeta(-2+3j, 1), (0.13297115587929864827 + 0.12305330040458776494j)) + assert ae(fp.zeta(-2+3j, 5), (18.384866151867576927 - 11.377015110597711009j)) + assert ae(fp.zeta(1.0000000001), 9999999173.1735741337) + assert ae(fp.zeta(0.9999999999), -9999999172.0191428039) + assert ae(fp.zeta(1+0.000000001j), (0.57721566490153286061 - 999999999.99999993765j)) + assert ae(fp.primezeta(2.5+4j), (-0.16922458243438033385 - 0.010847965298387727811j)) + assert ae(fp.primezeta(4), 0.076993139764246844943) + assert ae(fp.riemannr(3.7), 2.3034079839110855717) + assert ae(fp.riemannr(8), 3.9011860449341499474) + assert ae(fp.riemannr(3+4j), (2.2369653314259991796 + 1.6339943856990281694j)) + +def test_fp_hyp2f1(): + assert ae(fp.hyp2f1(1, (3,2), 3.25, 5.0), (-0.46600275923108143059 - 0.74393667908854842325j)) + assert ae(fp.hyp2f1(1+1j, (3,2), 3.25, 5.0), (-5.9208875603806515987 - 2.3813557707889590686j)) + assert ae(fp.hyp2f1(1+1j, (3,2), 3.25, 2+3j), (0.17174552030925080445 + 0.19589781970539389999j)) + +def test_fp_erf(): + assert fp.erf(2) == fp.erf(2.0) == fp.erf(2.0+0.0j) + assert fp.erf(fp.inf) == 1.0 + assert fp.erf(fp.ninf) == -1.0 + assert ae(fp.erf(0), 0.0) + assert ae(fp.erf(-0), -0.0) + assert ae(fp.erf(0.3), 0.32862675945912741619) + assert ae(fp.erf(-0.3), -0.32862675945912741619) + assert ae(fp.erf(0.9), 0.79690821242283213966) + assert ae(fp.erf(-0.9), -0.79690821242283213966) + assert ae(fp.erf(1.0), 0.84270079294971486934) + assert ae(fp.erf(-1.0), -0.84270079294971486934) + assert ae(fp.erf(1.1), 0.88020506957408172966) + assert ae(fp.erf(-1.1), -0.88020506957408172966) + assert ae(fp.erf(8.5), 1.0) + assert ae(fp.erf(-8.5), -1.0) + assert ae(fp.erf(9.1), 1.0) + assert ae(fp.erf(-9.1), -1.0) + assert ae(fp.erf(20.0), 1.0) + assert ae(fp.erf(-20.0), -1.0) + assert ae(fp.erf(10000.0), 1.0) + assert ae(fp.erf(-10000.0), -1.0) + assert ae(fp.erf(1e+50), 1.0) + assert ae(fp.erf(-1e+50), -1.0) + assert ae(fp.erf(1j), 1.650425758797542876j) + assert ae(fp.erf(-1j), -1.650425758797542876j) + assert ae(fp.erf((2+3j)), (-20.829461427614568389 + 8.6873182714701631444j)) + assert ae(fp.erf(-(2+3j)), -(-20.829461427614568389 + 8.6873182714701631444j)) + assert ae(fp.erf((8+9j)), (-1072004.2525062051158 + 364149.91954310255423j)) + assert ae(fp.erf(-(8+9j)), -(-1072004.2525062051158 + 364149.91954310255423j)) + assert fp.erfc(fp.inf) == 0.0 + assert fp.erfc(fp.ninf) == 2.0 + assert fp.erfc(0) == 1 + assert fp.erfc(-0.0) == 1 + assert fp.erfc(0+0j) == 1 + assert ae(fp.erfc(0.3), 0.67137324054087258381) + assert ae(fp.erfc(-0.3), 1.3286267594591274162) + assert ae(fp.erfc(0.9), 0.20309178757716786034) + assert ae(fp.erfc(-0.9), 1.7969082124228321397) + assert ae(fp.erfc(1.0), 0.15729920705028513066) + assert ae(fp.erfc(-1.0), 1.8427007929497148693) + assert ae(fp.erfc(1.1), 0.11979493042591827034) + assert ae(fp.erfc(-1.1), 1.8802050695740817297) + assert ae(fp.erfc(8.5), 2.7623240713337714461e-33) + assert ae(fp.erfc(-8.5), 2.0) + assert ae(fp.erfc(9.1), 6.6969004279886077452e-38) + assert ae(fp.erfc(-9.1), 2.0) + assert ae(fp.erfc(20.0), 5.3958656116079009289e-176) + assert ae(fp.erfc(-20.0), 2.0) + assert ae(fp.erfc(10000.0), 0.0) + assert ae(fp.erfc(-10000.0), 2.0) + assert ae(fp.erfc(1e+50), 0.0) + assert ae(fp.erfc(-1e+50), 2.0) + assert ae(fp.erfc(1j), (1.0 - 1.650425758797542876j)) + assert ae(fp.erfc(-1j), (1.0 + 1.650425758797542876j)) + assert ae(fp.erfc((2+3j)), (21.829461427614568389 - 8.6873182714701631444j), 1e-13) + assert ae(fp.erfc(-(2+3j)), (-19.829461427614568389 + 8.6873182714701631444j), 1e-13) + assert ae(fp.erfc((8+9j)), (1072005.2525062051158 - 364149.91954310255423j)) + assert ae(fp.erfc(-(8+9j)), (-1072003.2525062051158 + 364149.91954310255423j)) + assert ae(fp.erfc(20+0j), (5.3958656116079009289e-176 + 0.0j)) + +def test_fp_lambertw(): + assert ae(fp.lambertw(0.0), 0.0) + assert ae(fp.lambertw(1.0), 0.567143290409783873) + assert ae(fp.lambertw(7.5), 1.5662309537823875394) + assert ae(fp.lambertw(-0.25), -0.35740295618138890307) + assert ae(fp.lambertw(-10.0), (1.3699809685212708156 + 2.140194527074713196j)) + assert ae(fp.lambertw(0+0j), (0.0 + 0.0j)) + assert ae(fp.lambertw(4+0j), (1.2021678731970429392 + 0.0j)) + assert ae(fp.lambertw(1000.5), 5.2500227450408980127) + assert ae(fp.lambertw(1e100), 224.84310644511850156) + assert ae(fp.lambertw(-1000.0), (5.1501630246362515223 + 2.6641981432905204596j)) + assert ae(fp.lambertw(1e-10), 9.9999999990000003645e-11) + assert ae(fp.lambertw(1e-10j), (1.0000000000000000728e-20 + 1.0000000000000000364e-10j)) + assert ae(fp.lambertw(3+4j), (1.2815618061237758782 + 0.53309522202097107131j)) + assert ae(fp.lambertw(-3-4j), (1.0750730665692549276 - 1.3251023817343588823j)) + assert ae(fp.lambertw(10000+1000j), (7.2361526563371602186 + 0.087567810943839352034j)) + assert ae(fp.lambertw(0.0, -1), -fp.inf) + assert ae(fp.lambertw(1.0, -1), (-1.5339133197935745079 - 4.3751851530618983855j)) + assert ae(fp.lambertw(7.5, -1), (0.44125668415098614999 - 4.8039842008452390179j)) + assert ae(fp.lambertw(-0.25, -1), -2.1532923641103496492) + assert ae(fp.lambertw(-10.0, -1), (1.3699809685212708156 - 2.140194527074713196j)) + assert ae(fp.lambertw(0+0j, -1), -fp.inf) + assert ae(fp.lambertw(4+0j, -1), (-0.15730793189620765317 - 4.6787800704666656212j)) + assert ae(fp.lambertw(1000.5, -1), (4.9153765415404024736 - 5.4465682700815159569j)) + assert ae(fp.lambertw(1e100, -1), (224.84272130101601052 - 6.2553713838167244141j)) + assert ae(fp.lambertw(-1000.0, -1), (5.1501630246362515223 - 2.6641981432905204596j)) + assert ae(fp.lambertw(1e-10, -1), (-26.303186778379041521 - 3.2650939117038283975j)) + assert ae(fp.lambertw(1e-10j, -1), (-26.297238779529035028 - 1.6328071613455765135j)) + assert ae(fp.lambertw(3+4j, -1), (0.25856740686699741676 - 3.8521166861614355895j)) + assert ae(fp.lambertw(-3-4j, -1), (-0.32028750204310768396 - 6.8801677192091972343j)) + assert ae(fp.lambertw(10000+1000j, -1), (7.0255308742285435567 - 5.5177506835734067601j)) + assert ae(fp.lambertw(0.0, 2), -fp.inf) + assert ae(fp.lambertw(1.0, 2), (-2.4015851048680028842 + 10.776299516115070898j)) + assert ae(fp.lambertw(7.5, 2), (-0.38003357962843791529 + 10.960916473368746184j)) + assert ae(fp.lambertw(-0.25, 2), (-4.0558735269061511898 + 13.852334658567271386j)) + assert ae(fp.lambertw(-10.0, 2), (-0.34479123764318858696 + 14.112740596763592363j)) + assert ae(fp.lambertw(0+0j, 2), -fp.inf) + assert ae(fp.lambertw(4+0j, 2), (-1.0070343323804262788 + 10.903476551861683082j)) + assert ae(fp.lambertw(1000.5, 2), (4.4076185165459395295 + 11.365524591091402177j)) + assert ae(fp.lambertw(1e100, 2), (224.84156762724875878 + 12.510785262632255672j)) + assert ae(fp.lambertw(-1000.0, 2), (4.1984245610246530756 + 14.420478573754313845j)) + assert ae(fp.lambertw(1e-10, 2), (-26.362258095445866488 + 9.7800247407031482519j)) + assert ae(fp.lambertw(1e-10j, 2), (-26.384250801683084252 + 11.403535950607739763j)) + assert ae(fp.lambertw(3+4j, 2), (-0.86554679943333993562 + 11.849956798331992027j)) + assert ae(fp.lambertw(-3-4j, 2), (-0.55792273874679112639 + 8.7173627024159324811j)) + assert ae(fp.lambertw(10000+1000j, 2), (6.6223802254585662734 + 11.61348646825020766j)) + +def test_fp_stress_ei_e1(): + # Can be tightened on recent Pythons with more accurate math/cmath + ATOL = 1e-13 + PTOL = 1e-12 + v = fp.e1(1.1641532182693481445e-10) + assert ae(v, 22.296641293693077672, tol=ATOL) + assert type(v) is float + v = fp.e1(0.25) + assert ae(v, 1.0442826344437381945, tol=ATOL) + assert type(v) is float + v = fp.e1(1.0) + assert ae(v, 0.21938393439552027368, tol=ATOL) + assert type(v) is float + v = fp.e1(2.0) + assert ae(v, 0.048900510708061119567, tol=ATOL) + assert type(v) is float + v = fp.e1(5.0) + assert ae(v, 0.0011482955912753257973, tol=ATOL) + assert type(v) is float + v = fp.e1(20.0) + assert ae(v, 9.8355252906498816904e-11, tol=ATOL) + assert type(v) is float + v = fp.e1(30.0) + assert ae(v, 3.0215520106888125448e-15, tol=ATOL) + assert type(v) is float + v = fp.e1(40.0) + assert ae(v, 1.0367732614516569722e-19, tol=ATOL) + assert type(v) is float + v = fp.e1(50.0) + assert ae(v, 3.7832640295504590187e-24, tol=ATOL) + assert type(v) is float + v = fp.e1(80.0) + assert ae(v, 2.2285432586884729112e-37, tol=ATOL) + assert type(v) is float + v = fp.e1((1.1641532182693481445e-10 + 0.0j)) + assert ae(v, (22.296641293693077672 + 0.0j), tol=ATOL) + assert ae(v.real, 22.296641293693077672, tol=PTOL) + assert v.imag == 0 + v = fp.e1((0.25 + 0.0j)) + assert ae(v, (1.0442826344437381945 + 0.0j), tol=ATOL) + assert ae(v.real, 1.0442826344437381945, tol=PTOL) + assert v.imag == 0 + v = fp.e1((1.0 + 0.0j)) + assert ae(v, (0.21938393439552027368 + 0.0j), tol=ATOL) + assert ae(v.real, 0.21938393439552027368, tol=PTOL) + assert v.imag == 0 + v = fp.e1((2.0 + 0.0j)) + assert ae(v, (0.048900510708061119567 + 0.0j), tol=ATOL) + assert ae(v.real, 0.048900510708061119567, tol=PTOL) + assert v.imag == 0 + v = fp.e1((5.0 + 0.0j)) + assert ae(v, (0.0011482955912753257973 + 0.0j), tol=ATOL) + assert ae(v.real, 0.0011482955912753257973, tol=PTOL) + assert v.imag == 0 + v = fp.e1((20.0 + 0.0j)) + assert ae(v, (9.8355252906498816904e-11 + 0.0j), tol=ATOL) + assert ae(v.real, 9.8355252906498816904e-11, tol=PTOL) + assert v.imag == 0 + v = fp.e1((30.0 + 0.0j)) + assert ae(v, (3.0215520106888125448e-15 + 0.0j), tol=ATOL) + assert ae(v.real, 3.0215520106888125448e-15, tol=PTOL) + assert v.imag == 0 + v = fp.e1((40.0 + 0.0j)) + assert ae(v, (1.0367732614516569722e-19 + 0.0j), tol=ATOL) + assert ae(v.real, 1.0367732614516569722e-19, tol=PTOL) + assert v.imag == 0 + v = fp.e1((50.0 + 0.0j)) + assert ae(v, (3.7832640295504590187e-24 + 0.0j), tol=ATOL) + assert ae(v.real, 3.7832640295504590187e-24, tol=PTOL) + assert v.imag == 0 + v = fp.e1((80.0 + 0.0j)) + assert ae(v, (2.2285432586884729112e-37 + 0.0j), tol=ATOL) + assert ae(v.real, 2.2285432586884729112e-37, tol=PTOL) + assert v.imag == 0 + v = fp.e1((4.6566128730773925781e-10 + 1.1641532182693481445e-10j)) + assert ae(v, (20.880034622014215597 - 0.24497866301044883237j), tol=ATOL) + assert ae(v.real, 20.880034622014215597, tol=PTOL) + assert ae(v.imag, -0.24497866301044883237, tol=PTOL) + v = fp.e1((1.0 + 0.25j)) + assert ae(v, (0.19731063945004229095 - 0.087366045774299963672j), tol=ATOL) + assert ae(v.real, 0.19731063945004229095, tol=PTOL) + assert ae(v.imag, -0.087366045774299963672, tol=PTOL) + v = fp.e1((4.0 + 1.0j)) + assert ae(v, (0.0013106173980145506944 - 0.0034542480199350626699j), tol=ATOL) + assert ae(v.real, 0.0013106173980145506944, tol=PTOL) + assert ae(v.imag, -0.0034542480199350626699, tol=PTOL) + v = fp.e1((8.0 + 2.0j)) + assert ae(v, (-0.000022278049065270225945 - 0.000029191940456521555288j), tol=ATOL) + assert ae(v.real, -0.000022278049065270225945, tol=PTOL) + assert ae(v.imag, -0.000029191940456521555288, tol=PTOL) + v = fp.e1((20.0 + 5.0j)) + assert ae(v, (4.7711374515765346894e-11 + 8.2902652405126947359e-11j), tol=ATOL) + assert ae(v.real, 4.7711374515765346894e-11, tol=PTOL) + assert ae(v.imag, 8.2902652405126947359e-11, tol=PTOL) + v = fp.e1((80.0 + 20.0j)) + assert ae(v, (3.8353473865788235787e-38 - 2.129247592349605139e-37j), tol=ATOL) + assert ae(v.real, 3.8353473865788235787e-38, tol=PTOL) + assert ae(v.imag, -2.129247592349605139e-37, tol=PTOL) + v = fp.e1((120.0 + 30.0j)) + assert ae(v, (2.3836002337480334716e-55 + 5.6704043587126198306e-55j), tol=ATOL) + assert ae(v.real, 2.3836002337480334716e-55, tol=PTOL) + assert ae(v.imag, 5.6704043587126198306e-55, tol=PTOL) + v = fp.e1((160.0 + 40.0j)) + assert ae(v, (-1.6238022898654510661e-72 - 1.104172355572287367e-72j), tol=ATOL) + assert ae(v.real, -1.6238022898654510661e-72, tol=PTOL) + assert ae(v.imag, -1.104172355572287367e-72, tol=PTOL) + v = fp.e1((200.0 + 50.0j)) + assert ae(v, (6.6800061461666228487e-90 + 1.4473816083541016115e-91j), tol=ATOL) + assert ae(v.real, 6.6800061461666228487e-90, tol=PTOL) + assert ae(v.imag, 1.4473816083541016115e-91, tol=PTOL) + v = fp.e1((320.0 + 80.0j)) + assert ae(v, (4.2737871527778786157e-143 + 3.1789935525785660314e-142j), tol=ATOL) + assert ae(v.real, 4.2737871527778786157e-143, tol=PTOL) + assert ae(v.imag, 3.1789935525785660314e-142, tol=PTOL) + v = fp.e1((1.1641532182693481445e-10 + 1.1641532182693481445e-10j)) + assert ae(v, (21.950067703413105017 - 0.7853981632810329878j), tol=ATOL) + assert ae(v.real, 21.950067703413105017, tol=PTOL) + assert ae(v.imag, -0.7853981632810329878, tol=PTOL) + v = fp.e1((0.25 + 0.25j)) + assert ae(v, (0.71092525792923287894 - 0.56491812441304194711j), tol=ATOL) + assert ae(v.real, 0.71092525792923287894, tol=PTOL) + assert ae(v.imag, -0.56491812441304194711, tol=PTOL) + v = fp.e1((1.0 + 1.0j)) + assert ae(v, (0.00028162445198141832551 - 0.17932453503935894015j), tol=ATOL) + assert ae(v.real, 0.00028162445198141832551, tol=PTOL) + assert ae(v.imag, -0.17932453503935894015, tol=PTOL) + v = fp.e1((2.0 + 2.0j)) + assert ae(v, (-0.033767089606562004246 - 0.018599414169750541925j), tol=ATOL) + assert ae(v.real, -0.033767089606562004246, tol=PTOL) + assert ae(v.imag, -0.018599414169750541925, tol=PTOL) + v = fp.e1((5.0 + 5.0j)) + assert ae(v, (0.0007266506660356393891 + 0.00047102780163522245054j), tol=ATOL) + assert ae(v.real, 0.0007266506660356393891, tol=PTOL) + assert ae(v.imag, 0.00047102780163522245054, tol=PTOL) + v = fp.e1((20.0 + 20.0j)) + assert ae(v, (-2.3824537449367396579e-11 - 6.6969873156525615158e-11j), tol=ATOL) + assert ae(v.real, -2.3824537449367396579e-11, tol=PTOL) + assert ae(v.imag, -6.6969873156525615158e-11, tol=PTOL) + v = fp.e1((30.0 + 30.0j)) + assert ae(v, (1.7316045841744061617e-15 + 1.3065678019487308689e-15j), tol=ATOL) + assert ae(v.real, 1.7316045841744061617e-15, tol=PTOL) + assert ae(v.imag, 1.3065678019487308689e-15, tol=PTOL) + v = fp.e1((40.0 + 40.0j)) + assert ae(v, (-7.4001043002899232182e-20 - 4.991847855336816304e-21j), tol=ATOL) + assert ae(v.real, -7.4001043002899232182e-20, tol=PTOL) + assert ae(v.imag, -4.991847855336816304e-21, tol=PTOL) + v = fp.e1((50.0 + 50.0j)) + assert ae(v, (2.3566128324644641219e-24 - 1.3188326726201614778e-24j), tol=ATOL) + assert ae(v.real, 2.3566128324644641219e-24, tol=PTOL) + assert ae(v.imag, -1.3188326726201614778e-24, tol=PTOL) + v = fp.e1((80.0 + 80.0j)) + assert ae(v, (9.8279750572186526673e-38 + 1.243952841288868831e-37j), tol=ATOL) + assert ae(v.real, 9.8279750572186526673e-38, tol=PTOL) + assert ae(v.imag, 1.243952841288868831e-37, tol=PTOL) + v = fp.e1((1.1641532182693481445e-10 + 4.6566128730773925781e-10j)) + assert ae(v, (20.880034621664969632 - 1.3258176632023711778j), tol=ATOL) + assert ae(v.real, 20.880034621664969632, tol=PTOL) + assert ae(v.imag, -1.3258176632023711778, tol=PTOL) + v = fp.e1((0.25 + 1.0j)) + assert ae(v, (-0.16868306393667788761 - 0.4858011885947426971j), tol=ATOL) + assert ae(v.real, -0.16868306393667788761, tol=PTOL) + assert ae(v.imag, -0.4858011885947426971, tol=PTOL) + v = fp.e1((1.0 + 4.0j)) + assert ae(v, (0.03373591813926547318 + 0.073523452241083821877j), tol=ATOL) + assert ae(v.real, 0.03373591813926547318, tol=PTOL) + assert ae(v.imag, 0.073523452241083821877, tol=PTOL) + v = fp.e1((2.0 + 8.0j)) + assert ae(v, (-0.015392833434733785143 - 0.0031747121557605415914j), tol=ATOL) + assert ae(v.real, -0.015392833434733785143, tol=PTOL) + assert ae(v.imag, -0.0031747121557605415914, tol=PTOL) + v = fp.e1((5.0 + 20.0j)) + assert ae(v, (-0.00024419662286542966525 - 0.00021008322966152755674j), tol=ATOL) + assert ae(v.real, -0.00024419662286542966525, tol=PTOL) + assert ae(v.imag, -0.00021008322966152755674, tol=PTOL) + v = fp.e1((20.0 + 80.0j)) + assert ae(v, (2.3255552781051330088e-11 + 8.9463918891349438007e-12j), tol=ATOL) + assert ae(v.real, 2.3255552781051330088e-11, tol=PTOL) + assert ae(v.imag, 8.9463918891349438007e-12, tol=PTOL) + v = fp.e1((30.0 + 120.0j)) + assert ae(v, (-2.7068919097124652332e-16 - 7.0477762411705130239e-16j), tol=ATOL) + assert ae(v.real, -2.7068919097124652332e-16, tol=PTOL) + assert ae(v.imag, -7.0477762411705130239e-16, tol=PTOL) + v = fp.e1((40.0 + 160.0j)) + assert ae(v, (-1.1695597827678024687e-20 + 2.2907401455645736661e-20j), tol=ATOL) + assert ae(v.real, -1.1695597827678024687e-20, tol=PTOL) + assert ae(v.imag, 2.2907401455645736661e-20, tol=PTOL) + v = fp.e1((50.0 + 200.0j)) + assert ae(v, (9.0323746914410162531e-25 - 2.3950601790033530935e-25j), tol=ATOL) + assert ae(v.real, 9.0323746914410162531e-25, tol=PTOL) + assert ae(v.imag, -2.3950601790033530935e-25, tol=PTOL) + v = fp.e1((80.0 + 320.0j)) + assert ae(v, (3.4819106748728063576e-38 - 4.215653005615772724e-38j), tol=ATOL) + assert ae(v.real, 3.4819106748728063576e-38, tol=PTOL) + assert ae(v.imag, -4.215653005615772724e-38, tol=PTOL) + v = fp.e1((0.0 + 1.1641532182693481445e-10j)) + assert ae(v, (22.29664129357666235 - 1.5707963266784812974j), tol=ATOL) + assert ae(v.real, 22.29664129357666235, tol=PTOL) + assert ae(v.imag, -1.5707963266784812974, tol=PTOL) + v = fp.e1((0.0 + 0.25j)) + assert ae(v, (0.82466306258094565309 - 1.3216627564751394551j), tol=ATOL) + assert ae(v.real, 0.82466306258094565309, tol=PTOL) + assert ae(v.imag, -1.3216627564751394551, tol=PTOL) + v = fp.e1((0.0 + 1.0j)) + assert ae(v, (-0.33740392290096813466 - 0.62471325642771360429j), tol=ATOL) + assert ae(v.real, -0.33740392290096813466, tol=PTOL) + assert ae(v.imag, -0.62471325642771360429, tol=PTOL) + v = fp.e1((0.0 + 2.0j)) + assert ae(v, (-0.4229808287748649957 + 0.034616650007798229345j), tol=ATOL) + assert ae(v.real, -0.4229808287748649957, tol=PTOL) + assert ae(v.imag, 0.034616650007798229345, tol=PTOL) + v = fp.e1((0.0 + 5.0j)) + assert ae(v, (0.19002974965664387862 - 0.020865081850222481957j), tol=ATOL) + assert ae(v.real, 0.19002974965664387862, tol=PTOL) + assert ae(v.imag, -0.020865081850222481957, tol=PTOL) + v = fp.e1((0.0 + 20.0j)) + assert ae(v, (-0.04441982084535331654 - 0.022554625751456779068j), tol=ATOL) + assert ae(v.real, -0.04441982084535331654, tol=PTOL) + assert ae(v.imag, -0.022554625751456779068, tol=PTOL) + v = fp.e1((0.0 + 30.0j)) + assert ae(v, (0.033032417282071143779 - 0.0040397867645455082476j), tol=ATOL) + assert ae(v.real, 0.033032417282071143779, tol=PTOL) + assert ae(v.imag, -0.0040397867645455082476, tol=PTOL) + v = fp.e1((0.0 + 40.0j)) + assert ae(v, (-0.019020007896208766962 + 0.016188792559887887544j), tol=ATOL) + assert ae(v.real, -0.019020007896208766962, tol=PTOL) + assert ae(v.imag, 0.016188792559887887544, tol=PTOL) + v = fp.e1((0.0 + 50.0j)) + assert ae(v, (0.0056283863241163054402 - 0.019179254308960724503j), tol=ATOL) + assert ae(v.real, 0.0056283863241163054402, tol=PTOL) + assert ae(v.imag, -0.019179254308960724503, tol=PTOL) + v = fp.e1((0.0 + 80.0j)) + assert ae(v, (0.012402501155070958192 + 0.0015345601175906961199j), tol=ATOL) + assert ae(v.real, 0.012402501155070958192, tol=PTOL) + assert ae(v.imag, 0.0015345601175906961199, tol=PTOL) + v = fp.e1((-1.1641532182693481445e-10 + 4.6566128730773925781e-10j)) + assert ae(v, (20.880034621432138988 - 1.8157749894560994861j), tol=ATOL) + assert ae(v.real, 20.880034621432138988, tol=PTOL) + assert ae(v.imag, -1.8157749894560994861, tol=PTOL) + v = fp.e1((-0.25 + 1.0j)) + assert ae(v, (-0.59066621214766308594 - 0.74474454765205036972j), tol=ATOL) + assert ae(v.real, -0.59066621214766308594, tol=PTOL) + assert ae(v.imag, -0.74474454765205036972, tol=PTOL) + v = fp.e1((-1.0 + 4.0j)) + assert ae(v, (0.49739047283060471093 + 0.41543605404038863174j), tol=ATOL) + assert ae(v.real, 0.49739047283060471093, tol=PTOL) + assert ae(v.imag, 0.41543605404038863174, tol=PTOL) + v = fp.e1((-2.0 + 8.0j)) + assert ae(v, (-0.8705211147733730969 + 0.24099328498605539667j), tol=ATOL) + assert ae(v.real, -0.8705211147733730969, tol=PTOL) + assert ae(v.imag, 0.24099328498605539667, tol=PTOL) + v = fp.e1((-5.0 + 20.0j)) + assert ae(v, (-7.0789514293925893007 - 1.6102177171960790536j), tol=ATOL) + assert ae(v.real, -7.0789514293925893007, tol=PTOL) + assert ae(v.imag, -1.6102177171960790536, tol=PTOL) + v = fp.e1((-20.0 + 80.0j)) + assert ae(v, (5855431.4907298084434 - 720920.93315409165707j), tol=ATOL) + assert ae(v.real, 5855431.4907298084434, tol=PTOL) + assert ae(v.imag, -720920.93315409165707, tol=PTOL) + v = fp.e1((-30.0 + 120.0j)) + assert ae(v, (-65402491644.703470747 - 56697658399.657460294j), tol=ATOL) + assert ae(v.real, -65402491644.703470747, tol=PTOL) + assert ae(v.imag, -56697658399.657460294, tol=PTOL) + v = fp.e1((-40.0 + 160.0j)) + assert ae(v, (25504929379604.776769 + 1429035198630573.2463j), tol=ATOL) + assert ae(v.real, 25504929379604.776769, tol=PTOL) + assert ae(v.imag, 1429035198630573.2463, tol=PTOL) + v = fp.e1((-50.0 + 200.0j)) + assert ae(v, (18437746526988116954.0 - 17146362239046152345.0j), tol=ATOL) + assert ae(v.real, 18437746526988116954.0, tol=PTOL) + assert ae(v.imag, -17146362239046152345.0, tol=PTOL) + v = fp.e1((-80.0 + 320.0j)) + assert ae(v, (3.3464697299634526706e+31 - 1.6473152633843023919e+32j), tol=ATOL) + assert ae(v.real, 3.3464697299634526706e+31, tol=PTOL) + assert ae(v.imag, -1.6473152633843023919e+32, tol=PTOL) + v = fp.e1((-4.6566128730773925781e-10 + 1.1641532182693481445e-10j)) + assert ae(v, (20.880034621082893023 - 2.8966139903465137624j), tol=ATOL) + assert ae(v.real, 20.880034621082893023, tol=PTOL) + assert ae(v.imag, -2.8966139903465137624, tol=PTOL) + v = fp.e1((-1.0 + 0.25j)) + assert ae(v, (-1.8942716983721074932 - 2.4689102827070540799j), tol=ATOL) + assert ae(v.real, -1.8942716983721074932, tol=PTOL) + assert ae(v.imag, -2.4689102827070540799, tol=PTOL) + v = fp.e1((-4.0 + 1.0j)) + assert ae(v, (-14.806699492675420438 + 9.1384225230837893776j), tol=ATOL) + assert ae(v.real, -14.806699492675420438, tol=PTOL) + assert ae(v.imag, 9.1384225230837893776, tol=PTOL) + v = fp.e1((-8.0 + 2.0j)) + assert ae(v, (54.633252667426386294 + 413.20318163814670688j), tol=ATOL) + assert ae(v.real, 54.633252667426386294, tol=PTOL) + assert ae(v.imag, 413.20318163814670688, tol=PTOL) + v = fp.e1((-20.0 + 5.0j)) + assert ae(v, (-711836.97165402624643 - 24745250.939695900956j), tol=ATOL) + assert ae(v.real, -711836.97165402624643, tol=PTOL) + assert ae(v.imag, -24745250.939695900956, tol=PTOL) + v = fp.e1((-80.0 + 20.0j)) + assert ae(v, (-4.2139911108612653091e+32 + 5.3367124741918251637e+32j), tol=ATOL) + assert ae(v.real, -4.2139911108612653091e+32, tol=PTOL) + assert ae(v.imag, 5.3367124741918251637e+32, tol=PTOL) + v = fp.e1((-120.0 + 30.0j)) + assert ae(v, (9.7760616203707508892e+48 - 1.058257682317195792e+50j), tol=ATOL) + assert ae(v.real, 9.7760616203707508892e+48, tol=PTOL) + assert ae(v.imag, -1.058257682317195792e+50, tol=PTOL) + v = fp.e1((-160.0 + 40.0j)) + assert ae(v, (8.7065541466623638861e+66 + 1.6577106725141739889e+67j), tol=ATOL) + assert ae(v.real, 8.7065541466623638861e+66, tol=PTOL) + assert ae(v.imag, 1.6577106725141739889e+67, tol=PTOL) + v = fp.e1((-200.0 + 50.0j)) + assert ae(v, (-3.070744996327018106e+84 - 1.7243244846769415903e+84j), tol=ATOL) + assert ae(v.real, -3.070744996327018106e+84, tol=PTOL) + assert ae(v.imag, -1.7243244846769415903e+84, tol=PTOL) + v = fp.e1((-320.0 + 80.0j)) + assert ae(v, (9.9960598637998647276e+135 - 2.6855081527595608863e+136j), tol=ATOL) + assert ae(v.real, 9.9960598637998647276e+135, tol=PTOL) + assert ae(v.imag, -2.6855081527595608863e+136, tol=PTOL) + v = fp.e1(-1.1641532182693481445e-10) + assert ae(v, (22.296641293460247028 - 3.1415926535897932385j), tol=ATOL) + assert ae(v.real, 22.296641293460247028, tol=PTOL) + assert ae(v.imag, -3.1415926535897932385, tol=PTOL) + v = fp.e1(-0.25) + assert ae(v, (0.54254326466191372953 - 3.1415926535897932385j), tol=ATOL) + assert ae(v.real, 0.54254326466191372953, tol=PTOL) + assert ae(v.imag, -3.1415926535897932385, tol=PTOL) + v = fp.e1(-1.0) + assert ae(v, (-1.8951178163559367555 - 3.1415926535897932385j), tol=ATOL) + assert ae(v.real, -1.8951178163559367555, tol=PTOL) + assert ae(v.imag, -3.1415926535897932385, tol=PTOL) + v = fp.e1(-2.0) + assert ae(v, (-4.9542343560018901634 - 3.1415926535897932385j), tol=ATOL) + assert ae(v.real, -4.9542343560018901634, tol=PTOL) + assert ae(v.imag, -3.1415926535897932385, tol=PTOL) + v = fp.e1(-5.0) + assert ae(v, (-40.185275355803177455 - 3.1415926535897932385j), tol=ATOL) + assert ae(v.real, -40.185275355803177455, tol=PTOL) + assert ae(v.imag, -3.1415926535897932385, tol=PTOL) + v = fp.e1(-20.0) + assert ae(v, (-25615652.66405658882 - 3.1415926535897932385j), tol=ATOL) + assert ae(v.real, -25615652.66405658882, tol=PTOL) + assert ae(v.imag, -3.1415926535897932385, tol=PTOL) + v = fp.e1(-30.0) + assert ae(v, (-368973209407.27419706 - 3.1415926535897932385j), tol=ATOL) + assert ae(v.real, -368973209407.27419706, tol=PTOL) + assert ae(v.imag, -3.1415926535897932385, tol=PTOL) + v = fp.e1(-40.0) + assert ae(v, (-6039718263611241.5784 - 3.1415926535897932385j), tol=ATOL) + assert ae(v.real, -6039718263611241.5784, tol=PTOL) + assert ae(v.imag, -3.1415926535897932385, tol=PTOL) + v = fp.e1(-50.0) + assert ae(v, (-1.0585636897131690963e+20 - 3.1415926535897932385j), tol=ATOL) + assert ae(v.real, -1.0585636897131690963e+20, tol=PTOL) + assert ae(v.imag, -3.1415926535897932385, tol=PTOL) + v = fp.e1(-80.0) + assert ae(v, (-7.0146000049047999696e+32 - 3.1415926535897932385j), tol=ATOL) + assert ae(v.real, -7.0146000049047999696e+32, tol=PTOL) + assert ae(v.imag, -3.1415926535897932385, tol=PTOL) + v = fp.e1((-1.1641532182693481445e-10 + 0.0j)) + assert ae(v, (22.296641293460247028 - 3.1415926535897932385j), tol=ATOL) + assert ae(v.real, 22.296641293460247028, tol=PTOL) + assert ae(v.imag, -3.1415926535897932385, tol=PTOL) + v = fp.e1((-0.25 + 0.0j)) + assert ae(v, (0.54254326466191372953 - 3.1415926535897932385j), tol=ATOL) + assert ae(v.real, 0.54254326466191372953, tol=PTOL) + assert ae(v.imag, -3.1415926535897932385, tol=PTOL) + v = fp.e1((-1.0 + 0.0j)) + assert ae(v, (-1.8951178163559367555 - 3.1415926535897932385j), tol=ATOL) + assert ae(v.real, -1.8951178163559367555, tol=PTOL) + assert ae(v.imag, -3.1415926535897932385, tol=PTOL) + v = fp.e1((-2.0 + 0.0j)) + assert ae(v, (-4.9542343560018901634 - 3.1415926535897932385j), tol=ATOL) + assert ae(v.real, -4.9542343560018901634, tol=PTOL) + assert ae(v.imag, -3.1415926535897932385, tol=PTOL) + v = fp.e1((-5.0 + 0.0j)) + assert ae(v, (-40.185275355803177455 - 3.1415926535897932385j), tol=ATOL) + assert ae(v.real, -40.185275355803177455, tol=PTOL) + assert ae(v.imag, -3.1415926535897932385, tol=PTOL) + v = fp.e1((-20.0 + 0.0j)) + assert ae(v, (-25615652.66405658882 - 3.1415926535897932385j), tol=ATOL) + assert ae(v.real, -25615652.66405658882, tol=PTOL) + assert ae(v.imag, -3.1415926535897932385, tol=PTOL) + v = fp.e1((-30.0 + 0.0j)) + assert ae(v, (-368973209407.27419706 - 3.1415926535897932385j), tol=ATOL) + assert ae(v.real, -368973209407.27419706, tol=PTOL) + assert ae(v.imag, -3.1415926535897932385, tol=PTOL) + v = fp.e1((-40.0 + 0.0j)) + assert ae(v, (-6039718263611241.5784 - 3.1415926535897932385j), tol=ATOL) + assert ae(v.real, -6039718263611241.5784, tol=PTOL) + assert ae(v.imag, -3.1415926535897932385, tol=PTOL) + v = fp.e1((-50.0 + 0.0j)) + assert ae(v, (-1.0585636897131690963e+20 - 3.1415926535897932385j), tol=ATOL) + assert ae(v.real, -1.0585636897131690963e+20, tol=PTOL) + assert ae(v.imag, -3.1415926535897932385, tol=PTOL) + v = fp.e1((-80.0 + 0.0j)) + assert ae(v, (-7.0146000049047999696e+32 - 3.1415926535897932385j), tol=ATOL) + assert ae(v.real, -7.0146000049047999696e+32, tol=PTOL) + assert ae(v.imag, -3.1415926535897932385, tol=PTOL) + v = fp.e1((-4.6566128730773925781e-10 - 1.1641532182693481445e-10j)) + assert ae(v, (20.880034621082893023 + 2.8966139903465137624j), tol=ATOL) + assert ae(v.real, 20.880034621082893023, tol=PTOL) + assert ae(v.imag, 2.8966139903465137624, tol=PTOL) + v = fp.e1((-1.0 - 0.25j)) + assert ae(v, (-1.8942716983721074932 + 2.4689102827070540799j), tol=ATOL) + assert ae(v.real, -1.8942716983721074932, tol=PTOL) + assert ae(v.imag, 2.4689102827070540799, tol=PTOL) + v = fp.e1((-4.0 - 1.0j)) + assert ae(v, (-14.806699492675420438 - 9.1384225230837893776j), tol=ATOL) + assert ae(v.real, -14.806699492675420438, tol=PTOL) + assert ae(v.imag, -9.1384225230837893776, tol=PTOL) + v = fp.e1((-8.0 - 2.0j)) + assert ae(v, (54.633252667426386294 - 413.20318163814670688j), tol=ATOL) + assert ae(v.real, 54.633252667426386294, tol=PTOL) + assert ae(v.imag, -413.20318163814670688, tol=PTOL) + v = fp.e1((-20.0 - 5.0j)) + assert ae(v, (-711836.97165402624643 + 24745250.939695900956j), tol=ATOL) + assert ae(v.real, -711836.97165402624643, tol=PTOL) + assert ae(v.imag, 24745250.939695900956, tol=PTOL) + v = fp.e1((-80.0 - 20.0j)) + assert ae(v, (-4.2139911108612653091e+32 - 5.3367124741918251637e+32j), tol=ATOL) + assert ae(v.real, -4.2139911108612653091e+32, tol=PTOL) + assert ae(v.imag, -5.3367124741918251637e+32, tol=PTOL) + v = fp.e1((-120.0 - 30.0j)) + assert ae(v, (9.7760616203707508892e+48 + 1.058257682317195792e+50j), tol=ATOL) + assert ae(v.real, 9.7760616203707508892e+48, tol=PTOL) + assert ae(v.imag, 1.058257682317195792e+50, tol=PTOL) + v = fp.e1((-160.0 - 40.0j)) + assert ae(v, (8.7065541466623638861e+66 - 1.6577106725141739889e+67j), tol=ATOL) + assert ae(v.real, 8.7065541466623638861e+66, tol=PTOL) + assert ae(v.imag, -1.6577106725141739889e+67, tol=PTOL) + v = fp.e1((-200.0 - 50.0j)) + assert ae(v, (-3.070744996327018106e+84 + 1.7243244846769415903e+84j), tol=ATOL) + assert ae(v.real, -3.070744996327018106e+84, tol=PTOL) + assert ae(v.imag, 1.7243244846769415903e+84, tol=PTOL) + v = fp.e1((-320.0 - 80.0j)) + assert ae(v, (9.9960598637998647276e+135 + 2.6855081527595608863e+136j), tol=ATOL) + assert ae(v.real, 9.9960598637998647276e+135, tol=PTOL) + assert ae(v.imag, 2.6855081527595608863e+136, tol=PTOL) + v = fp.e1((-1.1641532182693481445e-10 - 1.1641532182693481445e-10j)) + assert ae(v, (21.950067703180274374 + 2.356194490075929607j), tol=ATOL) + assert ae(v.real, 21.950067703180274374, tol=PTOL) + assert ae(v.imag, 2.356194490075929607, tol=PTOL) + v = fp.e1((-0.25 - 0.25j)) + assert ae(v, (0.21441047326710323254 + 2.0732153554307936389j), tol=ATOL) + assert ae(v.real, 0.21441047326710323254, tol=PTOL) + assert ae(v.imag, 2.0732153554307936389, tol=PTOL) + v = fp.e1((-1.0 - 1.0j)) + assert ae(v, (-1.7646259855638540684 + 0.7538228020792708192j), tol=ATOL) + assert ae(v.real, -1.7646259855638540684, tol=PTOL) + assert ae(v.imag, 0.7538228020792708192, tol=PTOL) + v = fp.e1((-2.0 - 2.0j)) + assert ae(v, (-1.8920781621855474089 - 2.1753697842428647236j), tol=ATOL) + assert ae(v.real, -1.8920781621855474089, tol=PTOL) + assert ae(v.imag, -2.1753697842428647236, tol=PTOL) + v = fp.e1((-5.0 - 5.0j)) + assert ae(v, (13.470936071475245856 + 18.464085049321024206j), tol=ATOL) + assert ae(v.real, 13.470936071475245856, tol=PTOL) + assert ae(v.imag, 18.464085049321024206, tol=PTOL) + v = fp.e1((-20.0 - 20.0j)) + assert ae(v, (-16589317.398788971896 - 5831702.3296441771206j), tol=ATOL) + assert ae(v.real, -16589317.398788971896, tol=PTOL) + assert ae(v.imag, -5831702.3296441771206, tol=PTOL) + v = fp.e1((-30.0 - 30.0j)) + assert ae(v, (154596484273.69322527 + 204179357837.41389696j), tol=ATOL) + assert ae(v.real, 154596484273.69322527, tol=PTOL) + assert ae(v.imag, 204179357837.41389696, tol=PTOL) + v = fp.e1((-40.0 - 40.0j)) + assert ae(v, (-287512180321448.45408 - 4203502407932314.974j), tol=ATOL) + assert ae(v.real, -287512180321448.45408, tol=PTOL) + assert ae(v.imag, -4203502407932314.974, tol=PTOL) + v = fp.e1((-50.0 - 50.0j)) + assert ae(v, (-36128528616649268826.0 + 64648801861338741963.0j), tol=ATOL) + assert ae(v.real, -36128528616649268826.0, tol=PTOL) + assert ae(v.imag, 64648801861338741963.0, tol=PTOL) + v = fp.e1((-80.0 - 80.0j)) + assert ae(v, (3.8674816337930010217e+32 + 3.0540709639658071041e+32j), tol=ATOL) + assert ae(v.real, 3.8674816337930010217e+32, tol=PTOL) + assert ae(v.imag, 3.0540709639658071041e+32, tol=PTOL) + v = fp.e1((-1.1641532182693481445e-10 - 4.6566128730773925781e-10j)) + assert ae(v, (20.880034621432138988 + 1.8157749894560994861j), tol=ATOL) + assert ae(v.real, 20.880034621432138988, tol=PTOL) + assert ae(v.imag, 1.8157749894560994861, tol=PTOL) + v = fp.e1((-0.25 - 1.0j)) + assert ae(v, (-0.59066621214766308594 + 0.74474454765205036972j), tol=ATOL) + assert ae(v.real, -0.59066621214766308594, tol=PTOL) + assert ae(v.imag, 0.74474454765205036972, tol=PTOL) + v = fp.e1((-1.0 - 4.0j)) + assert ae(v, (0.49739047283060471093 - 0.41543605404038863174j), tol=ATOL) + assert ae(v.real, 0.49739047283060471093, tol=PTOL) + assert ae(v.imag, -0.41543605404038863174, tol=PTOL) + v = fp.e1((-2.0 - 8.0j)) + assert ae(v, (-0.8705211147733730969 - 0.24099328498605539667j), tol=ATOL) + assert ae(v.real, -0.8705211147733730969, tol=PTOL) + assert ae(v.imag, -0.24099328498605539667, tol=PTOL) + v = fp.e1((-5.0 - 20.0j)) + assert ae(v, (-7.0789514293925893007 + 1.6102177171960790536j), tol=ATOL) + assert ae(v.real, -7.0789514293925893007, tol=PTOL) + assert ae(v.imag, 1.6102177171960790536, tol=PTOL) + v = fp.e1((-20.0 - 80.0j)) + assert ae(v, (5855431.4907298084434 + 720920.93315409165707j), tol=ATOL) + assert ae(v.real, 5855431.4907298084434, tol=PTOL) + assert ae(v.imag, 720920.93315409165707, tol=PTOL) + v = fp.e1((-30.0 - 120.0j)) + assert ae(v, (-65402491644.703470747 + 56697658399.657460294j), tol=ATOL) + assert ae(v.real, -65402491644.703470747, tol=PTOL) + assert ae(v.imag, 56697658399.657460294, tol=PTOL) + v = fp.e1((-40.0 - 160.0j)) + assert ae(v, (25504929379604.776769 - 1429035198630573.2463j), tol=ATOL) + assert ae(v.real, 25504929379604.776769, tol=PTOL) + assert ae(v.imag, -1429035198630573.2463, tol=PTOL) + v = fp.e1((-50.0 - 200.0j)) + assert ae(v, (18437746526988116954.0 + 17146362239046152345.0j), tol=ATOL) + assert ae(v.real, 18437746526988116954.0, tol=PTOL) + assert ae(v.imag, 17146362239046152345.0, tol=PTOL) + v = fp.e1((-80.0 - 320.0j)) + assert ae(v, (3.3464697299634526706e+31 + 1.6473152633843023919e+32j), tol=ATOL) + assert ae(v.real, 3.3464697299634526706e+31, tol=PTOL) + assert ae(v.imag, 1.6473152633843023919e+32, tol=PTOL) + v = fp.e1((0.0 - 1.1641532182693481445e-10j)) + assert ae(v, (22.29664129357666235 + 1.5707963266784812974j), tol=ATOL) + assert ae(v.real, 22.29664129357666235, tol=PTOL) + assert ae(v.imag, 1.5707963266784812974, tol=PTOL) + v = fp.e1((0.0 - 0.25j)) + assert ae(v, (0.82466306258094565309 + 1.3216627564751394551j), tol=ATOL) + assert ae(v.real, 0.82466306258094565309, tol=PTOL) + assert ae(v.imag, 1.3216627564751394551, tol=PTOL) + v = fp.e1((0.0 - 1.0j)) + assert ae(v, (-0.33740392290096813466 + 0.62471325642771360429j), tol=ATOL) + assert ae(v.real, -0.33740392290096813466, tol=PTOL) + assert ae(v.imag, 0.62471325642771360429, tol=PTOL) + v = fp.e1((0.0 - 2.0j)) + assert ae(v, (-0.4229808287748649957 - 0.034616650007798229345j), tol=ATOL) + assert ae(v.real, -0.4229808287748649957, tol=PTOL) + assert ae(v.imag, -0.034616650007798229345, tol=PTOL) + v = fp.e1((0.0 - 5.0j)) + assert ae(v, (0.19002974965664387862 + 0.020865081850222481957j), tol=ATOL) + assert ae(v.real, 0.19002974965664387862, tol=PTOL) + assert ae(v.imag, 0.020865081850222481957, tol=PTOL) + v = fp.e1((0.0 - 20.0j)) + assert ae(v, (-0.04441982084535331654 + 0.022554625751456779068j), tol=ATOL) + assert ae(v.real, -0.04441982084535331654, tol=PTOL) + assert ae(v.imag, 0.022554625751456779068, tol=PTOL) + v = fp.e1((0.0 - 30.0j)) + assert ae(v, (0.033032417282071143779 + 0.0040397867645455082476j), tol=ATOL) + assert ae(v.real, 0.033032417282071143779, tol=PTOL) + assert ae(v.imag, 0.0040397867645455082476, tol=PTOL) + v = fp.e1((0.0 - 40.0j)) + assert ae(v, (-0.019020007896208766962 - 0.016188792559887887544j), tol=ATOL) + assert ae(v.real, -0.019020007896208766962, tol=PTOL) + assert ae(v.imag, -0.016188792559887887544, tol=PTOL) + v = fp.e1((0.0 - 50.0j)) + assert ae(v, (0.0056283863241163054402 + 0.019179254308960724503j), tol=ATOL) + assert ae(v.real, 0.0056283863241163054402, tol=PTOL) + assert ae(v.imag, 0.019179254308960724503, tol=PTOL) + v = fp.e1((0.0 - 80.0j)) + assert ae(v, (0.012402501155070958192 - 0.0015345601175906961199j), tol=ATOL) + assert ae(v.real, 0.012402501155070958192, tol=PTOL) + assert ae(v.imag, -0.0015345601175906961199, tol=PTOL) + v = fp.e1((1.1641532182693481445e-10 - 4.6566128730773925781e-10j)) + assert ae(v, (20.880034621664969632 + 1.3258176632023711778j), tol=ATOL) + assert ae(v.real, 20.880034621664969632, tol=PTOL) + assert ae(v.imag, 1.3258176632023711778, tol=PTOL) + v = fp.e1((0.25 - 1.0j)) + assert ae(v, (-0.16868306393667788761 + 0.4858011885947426971j), tol=ATOL) + assert ae(v.real, -0.16868306393667788761, tol=PTOL) + assert ae(v.imag, 0.4858011885947426971, tol=PTOL) + v = fp.e1((1.0 - 4.0j)) + assert ae(v, (0.03373591813926547318 - 0.073523452241083821877j), tol=ATOL) + assert ae(v.real, 0.03373591813926547318, tol=PTOL) + assert ae(v.imag, -0.073523452241083821877, tol=PTOL) + v = fp.e1((2.0 - 8.0j)) + assert ae(v, (-0.015392833434733785143 + 0.0031747121557605415914j), tol=ATOL) + assert ae(v.real, -0.015392833434733785143, tol=PTOL) + assert ae(v.imag, 0.0031747121557605415914, tol=PTOL) + v = fp.e1((5.0 - 20.0j)) + assert ae(v, (-0.00024419662286542966525 + 0.00021008322966152755674j), tol=ATOL) + assert ae(v.real, -0.00024419662286542966525, tol=PTOL) + assert ae(v.imag, 0.00021008322966152755674, tol=PTOL) + v = fp.e1((20.0 - 80.0j)) + assert ae(v, (2.3255552781051330088e-11 - 8.9463918891349438007e-12j), tol=ATOL) + assert ae(v.real, 2.3255552781051330088e-11, tol=PTOL) + assert ae(v.imag, -8.9463918891349438007e-12, tol=PTOL) + v = fp.e1((30.0 - 120.0j)) + assert ae(v, (-2.7068919097124652332e-16 + 7.0477762411705130239e-16j), tol=ATOL) + assert ae(v.real, -2.7068919097124652332e-16, tol=PTOL) + assert ae(v.imag, 7.0477762411705130239e-16, tol=PTOL) + v = fp.e1((40.0 - 160.0j)) + assert ae(v, (-1.1695597827678024687e-20 - 2.2907401455645736661e-20j), tol=ATOL) + assert ae(v.real, -1.1695597827678024687e-20, tol=PTOL) + assert ae(v.imag, -2.2907401455645736661e-20, tol=PTOL) + v = fp.e1((50.0 - 200.0j)) + assert ae(v, (9.0323746914410162531e-25 + 2.3950601790033530935e-25j), tol=ATOL) + assert ae(v.real, 9.0323746914410162531e-25, tol=PTOL) + assert ae(v.imag, 2.3950601790033530935e-25, tol=PTOL) + v = fp.e1((80.0 - 320.0j)) + assert ae(v, (3.4819106748728063576e-38 + 4.215653005615772724e-38j), tol=ATOL) + assert ae(v.real, 3.4819106748728063576e-38, tol=PTOL) + assert ae(v.imag, 4.215653005615772724e-38, tol=PTOL) + v = fp.e1((1.1641532182693481445e-10 - 1.1641532182693481445e-10j)) + assert ae(v, (21.950067703413105017 + 0.7853981632810329878j), tol=ATOL) + assert ae(v.real, 21.950067703413105017, tol=PTOL) + assert ae(v.imag, 0.7853981632810329878, tol=PTOL) + v = fp.e1((0.25 - 0.25j)) + assert ae(v, (0.71092525792923287894 + 0.56491812441304194711j), tol=ATOL) + assert ae(v.real, 0.71092525792923287894, tol=PTOL) + assert ae(v.imag, 0.56491812441304194711, tol=PTOL) + v = fp.e1((1.0 - 1.0j)) + assert ae(v, (0.00028162445198141832551 + 0.17932453503935894015j), tol=ATOL) + assert ae(v.real, 0.00028162445198141832551, tol=PTOL) + assert ae(v.imag, 0.17932453503935894015, tol=PTOL) + v = fp.e1((2.0 - 2.0j)) + assert ae(v, (-0.033767089606562004246 + 0.018599414169750541925j), tol=ATOL) + assert ae(v.real, -0.033767089606562004246, tol=PTOL) + assert ae(v.imag, 0.018599414169750541925, tol=PTOL) + v = fp.e1((5.0 - 5.0j)) + assert ae(v, (0.0007266506660356393891 - 0.00047102780163522245054j), tol=ATOL) + assert ae(v.real, 0.0007266506660356393891, tol=PTOL) + assert ae(v.imag, -0.00047102780163522245054, tol=PTOL) + v = fp.e1((20.0 - 20.0j)) + assert ae(v, (-2.3824537449367396579e-11 + 6.6969873156525615158e-11j), tol=ATOL) + assert ae(v.real, -2.3824537449367396579e-11, tol=PTOL) + assert ae(v.imag, 6.6969873156525615158e-11, tol=PTOL) + v = fp.e1((30.0 - 30.0j)) + assert ae(v, (1.7316045841744061617e-15 - 1.3065678019487308689e-15j), tol=ATOL) + assert ae(v.real, 1.7316045841744061617e-15, tol=PTOL) + assert ae(v.imag, -1.3065678019487308689e-15, tol=PTOL) + v = fp.e1((40.0 - 40.0j)) + assert ae(v, (-7.4001043002899232182e-20 + 4.991847855336816304e-21j), tol=ATOL) + assert ae(v.real, -7.4001043002899232182e-20, tol=PTOL) + assert ae(v.imag, 4.991847855336816304e-21, tol=PTOL) + v = fp.e1((50.0 - 50.0j)) + assert ae(v, (2.3566128324644641219e-24 + 1.3188326726201614778e-24j), tol=ATOL) + assert ae(v.real, 2.3566128324644641219e-24, tol=PTOL) + assert ae(v.imag, 1.3188326726201614778e-24, tol=PTOL) + v = fp.e1((80.0 - 80.0j)) + assert ae(v, (9.8279750572186526673e-38 - 1.243952841288868831e-37j), tol=ATOL) + assert ae(v.real, 9.8279750572186526673e-38, tol=PTOL) + assert ae(v.imag, -1.243952841288868831e-37, tol=PTOL) + v = fp.e1((4.6566128730773925781e-10 - 1.1641532182693481445e-10j)) + assert ae(v, (20.880034622014215597 + 0.24497866301044883237j), tol=ATOL) + assert ae(v.real, 20.880034622014215597, tol=PTOL) + assert ae(v.imag, 0.24497866301044883237, tol=PTOL) + v = fp.e1((1.0 - 0.25j)) + assert ae(v, (0.19731063945004229095 + 0.087366045774299963672j), tol=ATOL) + assert ae(v.real, 0.19731063945004229095, tol=PTOL) + assert ae(v.imag, 0.087366045774299963672, tol=PTOL) + v = fp.e1((4.0 - 1.0j)) + assert ae(v, (0.0013106173980145506944 + 0.0034542480199350626699j), tol=ATOL) + assert ae(v.real, 0.0013106173980145506944, tol=PTOL) + assert ae(v.imag, 0.0034542480199350626699, tol=PTOL) + v = fp.e1((8.0 - 2.0j)) + assert ae(v, (-0.000022278049065270225945 + 0.000029191940456521555288j), tol=ATOL) + assert ae(v.real, -0.000022278049065270225945, tol=PTOL) + assert ae(v.imag, 0.000029191940456521555288, tol=PTOL) + v = fp.e1((20.0 - 5.0j)) + assert ae(v, (4.7711374515765346894e-11 - 8.2902652405126947359e-11j), tol=ATOL) + assert ae(v.real, 4.7711374515765346894e-11, tol=PTOL) + assert ae(v.imag, -8.2902652405126947359e-11, tol=PTOL) + v = fp.e1((80.0 - 20.0j)) + assert ae(v, (3.8353473865788235787e-38 + 2.129247592349605139e-37j), tol=ATOL) + assert ae(v.real, 3.8353473865788235787e-38, tol=PTOL) + assert ae(v.imag, 2.129247592349605139e-37, tol=PTOL) + v = fp.e1((120.0 - 30.0j)) + assert ae(v, (2.3836002337480334716e-55 - 5.6704043587126198306e-55j), tol=ATOL) + assert ae(v.real, 2.3836002337480334716e-55, tol=PTOL) + assert ae(v.imag, -5.6704043587126198306e-55, tol=PTOL) + v = fp.e1((160.0 - 40.0j)) + assert ae(v, (-1.6238022898654510661e-72 + 1.104172355572287367e-72j), tol=ATOL) + assert ae(v.real, -1.6238022898654510661e-72, tol=PTOL) + assert ae(v.imag, 1.104172355572287367e-72, tol=PTOL) + v = fp.e1((200.0 - 50.0j)) + assert ae(v, (6.6800061461666228487e-90 - 1.4473816083541016115e-91j), tol=ATOL) + assert ae(v.real, 6.6800061461666228487e-90, tol=PTOL) + assert ae(v.imag, -1.4473816083541016115e-91, tol=PTOL) + v = fp.e1((320.0 - 80.0j)) + assert ae(v, (4.2737871527778786157e-143 - 3.1789935525785660314e-142j), tol=ATOL) + assert ae(v.real, 4.2737871527778786157e-143, tol=PTOL) + assert ae(v.imag, -3.1789935525785660314e-142, tol=PTOL) + v = fp.ei(1.1641532182693481445e-10) + assert ae(v, -22.296641293460247028, tol=ATOL) + assert type(v) is float + v = fp.ei(0.25) + assert ae(v, -0.54254326466191372953, tol=ATOL) + assert type(v) is float + v = fp.ei(1.0) + assert ae(v, 1.8951178163559367555, tol=ATOL) + assert type(v) is float + v = fp.ei(2.0) + assert ae(v, 4.9542343560018901634, tol=ATOL) + assert type(v) is float + v = fp.ei(5.0) + assert ae(v, 40.185275355803177455, tol=ATOL) + assert type(v) is float + v = fp.ei(20.0) + assert ae(v, 25615652.66405658882, tol=ATOL) + assert type(v) is float + v = fp.ei(30.0) + assert ae(v, 368973209407.27419706, tol=ATOL) + assert type(v) is float + v = fp.ei(40.0) + assert ae(v, 6039718263611241.5784, tol=ATOL) + assert type(v) is float + v = fp.ei(50.0) + assert ae(v, 1.0585636897131690963e+20, tol=ATOL) + assert type(v) is float + v = fp.ei(80.0) + assert ae(v, 7.0146000049047999696e+32, tol=ATOL) + assert type(v) is float + v = fp.ei((1.1641532182693481445e-10 + 0.0j)) + assert ae(v, (-22.296641293460247028 + 0.0j), tol=ATOL) + assert ae(v.real, -22.296641293460247028, tol=PTOL) + assert v.imag == 0 + v = fp.ei((0.25 + 0.0j)) + assert ae(v, (-0.54254326466191372953 + 0.0j), tol=ATOL) + assert ae(v.real, -0.54254326466191372953, tol=PTOL) + assert v.imag == 0 + v = fp.ei((1.0 + 0.0j)) + assert ae(v, (1.8951178163559367555 + 0.0j), tol=ATOL) + assert ae(v.real, 1.8951178163559367555, tol=PTOL) + assert v.imag == 0 + v = fp.ei((2.0 + 0.0j)) + assert ae(v, (4.9542343560018901634 + 0.0j), tol=ATOL) + assert ae(v.real, 4.9542343560018901634, tol=PTOL) + assert v.imag == 0 + v = fp.ei((5.0 + 0.0j)) + assert ae(v, (40.185275355803177455 + 0.0j), tol=ATOL) + assert ae(v.real, 40.185275355803177455, tol=PTOL) + assert v.imag == 0 + v = fp.ei((20.0 + 0.0j)) + assert ae(v, (25615652.66405658882 + 0.0j), tol=ATOL) + assert ae(v.real, 25615652.66405658882, tol=PTOL) + assert v.imag == 0 + v = fp.ei((30.0 + 0.0j)) + assert ae(v, (368973209407.27419706 + 0.0j), tol=ATOL) + assert ae(v.real, 368973209407.27419706, tol=PTOL) + assert v.imag == 0 + v = fp.ei((40.0 + 0.0j)) + assert ae(v, (6039718263611241.5784 + 0.0j), tol=ATOL) + assert ae(v.real, 6039718263611241.5784, tol=PTOL) + assert v.imag == 0 + v = fp.ei((50.0 + 0.0j)) + assert ae(v, (1.0585636897131690963e+20 + 0.0j), tol=ATOL) + assert ae(v.real, 1.0585636897131690963e+20, tol=PTOL) + assert v.imag == 0 + v = fp.ei((80.0 + 0.0j)) + assert ae(v, (7.0146000049047999696e+32 + 0.0j), tol=ATOL) + assert ae(v.real, 7.0146000049047999696e+32, tol=PTOL) + assert v.imag == 0 + v = fp.ei((4.6566128730773925781e-10 + 1.1641532182693481445e-10j)) + assert ae(v, (-20.880034621082893023 + 0.24497866324327947603j), tol=ATOL) + assert ae(v.real, -20.880034621082893023, tol=PTOL) + assert ae(v.imag, 0.24497866324327947603, tol=PTOL) + v = fp.ei((1.0 + 0.25j)) + assert ae(v, (1.8942716983721074932 + 0.67268237088273915854j), tol=ATOL) + assert ae(v.real, 1.8942716983721074932, tol=PTOL) + assert ae(v.imag, 0.67268237088273915854, tol=PTOL) + v = fp.ei((4.0 + 1.0j)) + assert ae(v, (14.806699492675420438 + 12.280015176673582616j), tol=ATOL) + assert ae(v.real, 14.806699492675420438, tol=PTOL) + assert ae(v.imag, 12.280015176673582616, tol=PTOL) + v = fp.ei((8.0 + 2.0j)) + assert ae(v, (-54.633252667426386294 + 416.34477429173650012j), tol=ATOL) + assert ae(v.real, -54.633252667426386294, tol=PTOL) + assert ae(v.imag, 416.34477429173650012, tol=PTOL) + v = fp.ei((20.0 + 5.0j)) + assert ae(v, (711836.97165402624643 - 24745247.798103247366j), tol=ATOL) + assert ae(v.real, 711836.97165402624643, tol=PTOL) + assert ae(v.imag, -24745247.798103247366, tol=PTOL) + v = fp.ei((80.0 + 20.0j)) + assert ae(v, (4.2139911108612653091e+32 + 5.3367124741918251637e+32j), tol=ATOL) + assert ae(v.real, 4.2139911108612653091e+32, tol=PTOL) + assert ae(v.imag, 5.3367124741918251637e+32, tol=PTOL) + v = fp.ei((120.0 + 30.0j)) + assert ae(v, (-9.7760616203707508892e+48 - 1.058257682317195792e+50j), tol=ATOL) + assert ae(v.real, -9.7760616203707508892e+48, tol=PTOL) + assert ae(v.imag, -1.058257682317195792e+50, tol=PTOL) + v = fp.ei((160.0 + 40.0j)) + assert ae(v, (-8.7065541466623638861e+66 + 1.6577106725141739889e+67j), tol=ATOL) + assert ae(v.real, -8.7065541466623638861e+66, tol=PTOL) + assert ae(v.imag, 1.6577106725141739889e+67, tol=PTOL) + v = fp.ei((200.0 + 50.0j)) + assert ae(v, (3.070744996327018106e+84 - 1.7243244846769415903e+84j), tol=ATOL) + assert ae(v.real, 3.070744996327018106e+84, tol=PTOL) + assert ae(v.imag, -1.7243244846769415903e+84, tol=PTOL) + v = fp.ei((320.0 + 80.0j)) + assert ae(v, (-9.9960598637998647276e+135 - 2.6855081527595608863e+136j), tol=ATOL) + assert ae(v.real, -9.9960598637998647276e+135, tol=PTOL) + assert ae(v.imag, -2.6855081527595608863e+136, tol=PTOL) + v = fp.ei((1.1641532182693481445e-10 + 1.1641532182693481445e-10j)) + assert ae(v, (-21.950067703180274374 + 0.78539816351386363145j), tol=ATOL) + assert ae(v.real, -21.950067703180274374, tol=PTOL) + assert ae(v.imag, 0.78539816351386363145, tol=PTOL) + v = fp.ei((0.25 + 0.25j)) + assert ae(v, (-0.21441047326710323254 + 1.0683772981589995996j), tol=ATOL) + assert ae(v.real, -0.21441047326710323254, tol=PTOL) + assert ae(v.imag, 1.0683772981589995996, tol=PTOL) + v = fp.ei((1.0 + 1.0j)) + assert ae(v, (1.7646259855638540684 + 2.3877698515105224193j), tol=ATOL) + assert ae(v.real, 1.7646259855638540684, tol=PTOL) + assert ae(v.imag, 2.3877698515105224193, tol=PTOL) + v = fp.ei((2.0 + 2.0j)) + assert ae(v, (1.8920781621855474089 + 5.3169624378326579621j), tol=ATOL) + assert ae(v.real, 1.8920781621855474089, tol=PTOL) + assert ae(v.imag, 5.3169624378326579621, tol=PTOL) + v = fp.ei((5.0 + 5.0j)) + assert ae(v, (-13.470936071475245856 - 15.322492395731230968j), tol=ATOL) + assert ae(v.real, -13.470936071475245856, tol=PTOL) + assert ae(v.imag, -15.322492395731230968, tol=PTOL) + v = fp.ei((20.0 + 20.0j)) + assert ae(v, (16589317.398788971896 + 5831705.4712368307104j), tol=ATOL) + assert ae(v.real, 16589317.398788971896, tol=PTOL) + assert ae(v.imag, 5831705.4712368307104, tol=PTOL) + v = fp.ei((30.0 + 30.0j)) + assert ae(v, (-154596484273.69322527 - 204179357834.2723043j), tol=ATOL) + assert ae(v.real, -154596484273.69322527, tol=PTOL) + assert ae(v.imag, -204179357834.2723043, tol=PTOL) + v = fp.ei((40.0 + 40.0j)) + assert ae(v, (287512180321448.45408 + 4203502407932318.1156j), tol=ATOL) + assert ae(v.real, 287512180321448.45408, tol=PTOL) + assert ae(v.imag, 4203502407932318.1156, tol=PTOL) + v = fp.ei((50.0 + 50.0j)) + assert ae(v, (36128528616649268826.0 - 64648801861338741960.0j), tol=ATOL) + assert ae(v.real, 36128528616649268826.0, tol=PTOL) + assert ae(v.imag, -64648801861338741960.0, tol=PTOL) + v = fp.ei((80.0 + 80.0j)) + assert ae(v, (-3.8674816337930010217e+32 - 3.0540709639658071041e+32j), tol=ATOL) + assert ae(v.real, -3.8674816337930010217e+32, tol=PTOL) + assert ae(v.imag, -3.0540709639658071041e+32, tol=PTOL) + v = fp.ei((1.1641532182693481445e-10 + 4.6566128730773925781e-10j)) + assert ae(v, (-20.880034621432138988 + 1.3258176641336937524j), tol=ATOL) + assert ae(v.real, -20.880034621432138988, tol=PTOL) + assert ae(v.imag, 1.3258176641336937524, tol=PTOL) + v = fp.ei((0.25 + 1.0j)) + assert ae(v, (0.59066621214766308594 + 2.3968481059377428687j), tol=ATOL) + assert ae(v.real, 0.59066621214766308594, tol=PTOL) + assert ae(v.imag, 2.3968481059377428687, tol=PTOL) + v = fp.ei((1.0 + 4.0j)) + assert ae(v, (-0.49739047283060471093 + 3.5570287076301818702j), tol=ATOL) + assert ae(v.real, -0.49739047283060471093, tol=PTOL) + assert ae(v.imag, 3.5570287076301818702, tol=PTOL) + v = fp.ei((2.0 + 8.0j)) + assert ae(v, (0.8705211147733730969 + 3.3825859385758486351j), tol=ATOL) + assert ae(v.real, 0.8705211147733730969, tol=PTOL) + assert ae(v.imag, 3.3825859385758486351, tol=PTOL) + v = fp.ei((5.0 + 20.0j)) + assert ae(v, (7.0789514293925893007 + 1.5313749363937141849j), tol=ATOL) + assert ae(v.real, 7.0789514293925893007, tol=PTOL) + assert ae(v.imag, 1.5313749363937141849, tol=PTOL) + v = fp.ei((20.0 + 80.0j)) + assert ae(v, (-5855431.4907298084434 - 720917.79156143806727j), tol=ATOL) + assert ae(v.real, -5855431.4907298084434, tol=PTOL) + assert ae(v.imag, -720917.79156143806727, tol=PTOL) + v = fp.ei((30.0 + 120.0j)) + assert ae(v, (65402491644.703470747 - 56697658396.51586764j), tol=ATOL) + assert ae(v.real, 65402491644.703470747, tol=PTOL) + assert ae(v.imag, -56697658396.51586764, tol=PTOL) + v = fp.ei((40.0 + 160.0j)) + assert ae(v, (-25504929379604.776769 + 1429035198630576.3879j), tol=ATOL) + assert ae(v.real, -25504929379604.776769, tol=PTOL) + assert ae(v.imag, 1429035198630576.3879, tol=PTOL) + v = fp.ei((50.0 + 200.0j)) + assert ae(v, (-18437746526988116954.0 - 17146362239046152342.0j), tol=ATOL) + assert ae(v.real, -18437746526988116954.0, tol=PTOL) + assert ae(v.imag, -17146362239046152342.0, tol=PTOL) + v = fp.ei((80.0 + 320.0j)) + assert ae(v, (-3.3464697299634526706e+31 - 1.6473152633843023919e+32j), tol=ATOL) + assert ae(v.real, -3.3464697299634526706e+31, tol=PTOL) + assert ae(v.imag, -1.6473152633843023919e+32, tol=PTOL) + v = fp.ei((0.0 + 1.1641532182693481445e-10j)) + assert ae(v, (-22.29664129357666235 + 1.5707963269113119411j), tol=ATOL) + assert ae(v.real, -22.29664129357666235, tol=PTOL) + assert ae(v.imag, 1.5707963269113119411, tol=PTOL) + v = fp.ei((0.0 + 0.25j)) + assert ae(v, (-0.82466306258094565309 + 1.8199298971146537833j), tol=ATOL) + assert ae(v.real, -0.82466306258094565309, tol=PTOL) + assert ae(v.imag, 1.8199298971146537833, tol=PTOL) + v = fp.ei((0.0 + 1.0j)) + assert ae(v, (0.33740392290096813466 + 2.5168793971620796342j), tol=ATOL) + assert ae(v.real, 0.33740392290096813466, tol=PTOL) + assert ae(v.imag, 2.5168793971620796342, tol=PTOL) + v = fp.ei((0.0 + 2.0j)) + assert ae(v, (0.4229808287748649957 + 3.1762093035975914678j), tol=ATOL) + assert ae(v.real, 0.4229808287748649957, tol=PTOL) + assert ae(v.imag, 3.1762093035975914678, tol=PTOL) + v = fp.ei((0.0 + 5.0j)) + assert ae(v, (-0.19002974965664387862 + 3.1207275717395707565j), tol=ATOL) + assert ae(v.real, -0.19002974965664387862, tol=PTOL) + assert ae(v.imag, 3.1207275717395707565, tol=PTOL) + v = fp.ei((0.0 + 20.0j)) + assert ae(v, (0.04441982084535331654 + 3.1190380278383364594j), tol=ATOL) + assert ae(v.real, 0.04441982084535331654, tol=PTOL) + assert ae(v.imag, 3.1190380278383364594, tol=PTOL) + v = fp.ei((0.0 + 30.0j)) + assert ae(v, (-0.033032417282071143779 + 3.1375528668252477302j), tol=ATOL) + assert ae(v.real, -0.033032417282071143779, tol=PTOL) + assert ae(v.imag, 3.1375528668252477302, tol=PTOL) + v = fp.ei((0.0 + 40.0j)) + assert ae(v, (0.019020007896208766962 + 3.157781446149681126j), tol=ATOL) + assert ae(v.real, 0.019020007896208766962, tol=PTOL) + assert ae(v.imag, 3.157781446149681126, tol=PTOL) + v = fp.ei((0.0 + 50.0j)) + assert ae(v, (-0.0056283863241163054402 + 3.122413399280832514j), tol=ATOL) + assert ae(v.real, -0.0056283863241163054402, tol=PTOL) + assert ae(v.imag, 3.122413399280832514, tol=PTOL) + v = fp.ei((0.0 + 80.0j)) + assert ae(v, (-0.012402501155070958192 + 3.1431272137073839346j), tol=ATOL) + assert ae(v.real, -0.012402501155070958192, tol=PTOL) + assert ae(v.imag, 3.1431272137073839346, tol=PTOL) + v = fp.ei((-1.1641532182693481445e-10 + 4.6566128730773925781e-10j)) + assert ae(v, (-20.880034621664969632 + 1.8157749903874220607j), tol=ATOL) + assert ae(v.real, -20.880034621664969632, tol=PTOL) + assert ae(v.imag, 1.8157749903874220607, tol=PTOL) + v = fp.ei((-0.25 + 1.0j)) + assert ae(v, (0.16868306393667788761 + 2.6557914649950505414j), tol=ATOL) + assert ae(v.real, 0.16868306393667788761, tol=PTOL) + assert ae(v.imag, 2.6557914649950505414, tol=PTOL) + v = fp.ei((-1.0 + 4.0j)) + assert ae(v, (-0.03373591813926547318 + 3.2151161058308770603j), tol=ATOL) + assert ae(v.real, -0.03373591813926547318, tol=PTOL) + assert ae(v.imag, 3.2151161058308770603, tol=PTOL) + v = fp.ei((-2.0 + 8.0j)) + assert ae(v, (0.015392833434733785143 + 3.1384179414340326969j), tol=ATOL) + assert ae(v.real, 0.015392833434733785143, tol=PTOL) + assert ae(v.imag, 3.1384179414340326969, tol=PTOL) + v = fp.ei((-5.0 + 20.0j)) + assert ae(v, (0.00024419662286542966525 + 3.1413825703601317109j), tol=ATOL) + assert ae(v.real, 0.00024419662286542966525, tol=PTOL) + assert ae(v.imag, 3.1413825703601317109, tol=PTOL) + v = fp.ei((-20.0 + 80.0j)) + assert ae(v, (-2.3255552781051330088e-11 + 3.1415926535987396304j), tol=ATOL) + assert ae(v.real, -2.3255552781051330088e-11, tol=PTOL) + assert ae(v.imag, 3.1415926535987396304, tol=PTOL) + v = fp.ei((-30.0 + 120.0j)) + assert ae(v, (2.7068919097124652332e-16 + 3.1415926535897925337j), tol=ATOL) + assert ae(v.real, 2.7068919097124652332e-16, tol=PTOL) + assert ae(v.imag, 3.1415926535897925337, tol=PTOL) + v = fp.ei((-40.0 + 160.0j)) + assert ae(v, (1.1695597827678024687e-20 + 3.1415926535897932385j), tol=ATOL) + assert ae(v.real, 1.1695597827678024687e-20, tol=PTOL) + assert ae(v.imag, 3.1415926535897932385, tol=PTOL) + v = fp.ei((-50.0 + 200.0j)) + assert ae(v, (-9.0323746914410162531e-25 + 3.1415926535897932385j), tol=ATOL) + assert ae(v.real, -9.0323746914410162531e-25, tol=PTOL) + assert ae(v.imag, 3.1415926535897932385, tol=PTOL) + v = fp.ei((-80.0 + 320.0j)) + assert ae(v, (-3.4819106748728063576e-38 + 3.1415926535897932385j), tol=ATOL) + assert ae(v.real, -3.4819106748728063576e-38, tol=PTOL) + assert ae(v.imag, 3.1415926535897932385, tol=PTOL) + v = fp.ei((-4.6566128730773925781e-10 + 1.1641532182693481445e-10j)) + assert ae(v, (-20.880034622014215597 + 2.8966139905793444061j), tol=ATOL) + assert ae(v.real, -20.880034622014215597, tol=PTOL) + assert ae(v.imag, 2.8966139905793444061, tol=PTOL) + v = fp.ei((-1.0 + 0.25j)) + assert ae(v, (-0.19731063945004229095 + 3.0542266078154932748j), tol=ATOL) + assert ae(v.real, -0.19731063945004229095, tol=PTOL) + assert ae(v.imag, 3.0542266078154932748, tol=PTOL) + v = fp.ei((-4.0 + 1.0j)) + assert ae(v, (-0.0013106173980145506944 + 3.1381384055698581758j), tol=ATOL) + assert ae(v.real, -0.0013106173980145506944, tol=PTOL) + assert ae(v.imag, 3.1381384055698581758, tol=PTOL) + v = fp.ei((-8.0 + 2.0j)) + assert ae(v, (0.000022278049065270225945 + 3.1415634616493367169j), tol=ATOL) + assert ae(v.real, 0.000022278049065270225945, tol=PTOL) + assert ae(v.imag, 3.1415634616493367169, tol=PTOL) + v = fp.ei((-20.0 + 5.0j)) + assert ae(v, (-4.7711374515765346894e-11 + 3.1415926536726958909j), tol=ATOL) + assert ae(v.real, -4.7711374515765346894e-11, tol=PTOL) + assert ae(v.imag, 3.1415926536726958909, tol=PTOL) + v = fp.ei((-80.0 + 20.0j)) + assert ae(v, (-3.8353473865788235787e-38 + 3.1415926535897932385j), tol=ATOL) + assert ae(v.real, -3.8353473865788235787e-38, tol=PTOL) + assert ae(v.imag, 3.1415926535897932385, tol=PTOL) + v = fp.ei((-120.0 + 30.0j)) + assert ae(v, (-2.3836002337480334716e-55 + 3.1415926535897932385j), tol=ATOL) + assert ae(v.real, -2.3836002337480334716e-55, tol=PTOL) + assert ae(v.imag, 3.1415926535897932385, tol=PTOL) + v = fp.ei((-160.0 + 40.0j)) + assert ae(v, (1.6238022898654510661e-72 + 3.1415926535897932385j), tol=ATOL) + assert ae(v.real, 1.6238022898654510661e-72, tol=PTOL) + assert ae(v.imag, 3.1415926535897932385, tol=PTOL) + v = fp.ei((-200.0 + 50.0j)) + assert ae(v, (-6.6800061461666228487e-90 + 3.1415926535897932385j), tol=ATOL) + assert ae(v.real, -6.6800061461666228487e-90, tol=PTOL) + assert ae(v.imag, 3.1415926535897932385, tol=PTOL) + v = fp.ei((-320.0 + 80.0j)) + assert ae(v, (-4.2737871527778786157e-143 + 3.1415926535897932385j), tol=ATOL) + assert ae(v.real, -4.2737871527778786157e-143, tol=PTOL) + assert ae(v.imag, 3.1415926535897932385, tol=PTOL) + v = fp.ei(-1.1641532182693481445e-10) + assert ae(v, -22.296641293693077672, tol=ATOL) + assert type(v) is float + v = fp.ei(-0.25) + assert ae(v, -1.0442826344437381945, tol=ATOL) + assert type(v) is float + v = fp.ei(-1.0) + assert ae(v, -0.21938393439552027368, tol=ATOL) + assert type(v) is float + v = fp.ei(-2.0) + assert ae(v, -0.048900510708061119567, tol=ATOL) + assert type(v) is float + v = fp.ei(-5.0) + assert ae(v, -0.0011482955912753257973, tol=ATOL) + assert type(v) is float + v = fp.ei(-20.0) + assert ae(v, -9.8355252906498816904e-11, tol=ATOL) + assert type(v) is float + v = fp.ei(-30.0) + assert ae(v, -3.0215520106888125448e-15, tol=ATOL) + assert type(v) is float + v = fp.ei(-40.0) + assert ae(v, -1.0367732614516569722e-19, tol=ATOL) + assert type(v) is float + v = fp.ei(-50.0) + assert ae(v, -3.7832640295504590187e-24, tol=ATOL) + assert type(v) is float + v = fp.ei(-80.0) + assert ae(v, -2.2285432586884729112e-37, tol=ATOL) + assert type(v) is float + v = fp.ei((-1.1641532182693481445e-10 + 0.0j)) + assert ae(v, (-22.296641293693077672 + 0.0j), tol=ATOL) + assert ae(v.real, -22.296641293693077672, tol=PTOL) + assert v.imag == 0 + v = fp.ei((-0.25 + 0.0j)) + assert ae(v, (-1.0442826344437381945 + 0.0j), tol=ATOL) + assert ae(v.real, -1.0442826344437381945, tol=PTOL) + assert v.imag == 0 + v = fp.ei((-1.0 + 0.0j)) + assert ae(v, (-0.21938393439552027368 + 0.0j), tol=ATOL) + assert ae(v.real, -0.21938393439552027368, tol=PTOL) + assert v.imag == 0 + v = fp.ei((-2.0 + 0.0j)) + assert ae(v, (-0.048900510708061119567 + 0.0j), tol=ATOL) + assert ae(v.real, -0.048900510708061119567, tol=PTOL) + assert v.imag == 0 + v = fp.ei((-5.0 + 0.0j)) + assert ae(v, (-0.0011482955912753257973 + 0.0j), tol=ATOL) + assert ae(v.real, -0.0011482955912753257973, tol=PTOL) + assert v.imag == 0 + v = fp.ei((-20.0 + 0.0j)) + assert ae(v, (-9.8355252906498816904e-11 + 0.0j), tol=ATOL) + assert ae(v.real, -9.8355252906498816904e-11, tol=PTOL) + assert v.imag == 0 + v = fp.ei((-30.0 + 0.0j)) + assert ae(v, (-3.0215520106888125448e-15 + 0.0j), tol=ATOL) + assert ae(v.real, -3.0215520106888125448e-15, tol=PTOL) + assert v.imag == 0 + v = fp.ei((-40.0 + 0.0j)) + assert ae(v, (-1.0367732614516569722e-19 + 0.0j), tol=ATOL) + assert ae(v.real, -1.0367732614516569722e-19, tol=PTOL) + assert v.imag == 0 + v = fp.ei((-50.0 + 0.0j)) + assert ae(v, (-3.7832640295504590187e-24 + 0.0j), tol=ATOL) + assert ae(v.real, -3.7832640295504590187e-24, tol=PTOL) + assert v.imag == 0 + v = fp.ei((-80.0 + 0.0j)) + assert ae(v, (-2.2285432586884729112e-37 + 0.0j), tol=ATOL) + assert ae(v.real, -2.2285432586884729112e-37, tol=PTOL) + assert v.imag == 0 + v = fp.ei((-4.6566128730773925781e-10 - 1.1641532182693481445e-10j)) + assert ae(v, (-20.880034622014215597 - 2.8966139905793444061j), tol=ATOL) + assert ae(v.real, -20.880034622014215597, tol=PTOL) + assert ae(v.imag, -2.8966139905793444061, tol=PTOL) + v = fp.ei((-1.0 - 0.25j)) + assert ae(v, (-0.19731063945004229095 - 3.0542266078154932748j), tol=ATOL) + assert ae(v.real, -0.19731063945004229095, tol=PTOL) + assert ae(v.imag, -3.0542266078154932748, tol=PTOL) + v = fp.ei((-4.0 - 1.0j)) + assert ae(v, (-0.0013106173980145506944 - 3.1381384055698581758j), tol=ATOL) + assert ae(v.real, -0.0013106173980145506944, tol=PTOL) + assert ae(v.imag, -3.1381384055698581758, tol=PTOL) + v = fp.ei((-8.0 - 2.0j)) + assert ae(v, (0.000022278049065270225945 - 3.1415634616493367169j), tol=ATOL) + assert ae(v.real, 0.000022278049065270225945, tol=PTOL) + assert ae(v.imag, -3.1415634616493367169, tol=PTOL) + v = fp.ei((-20.0 - 5.0j)) + assert ae(v, (-4.7711374515765346894e-11 - 3.1415926536726958909j), tol=ATOL) + assert ae(v.real, -4.7711374515765346894e-11, tol=PTOL) + assert ae(v.imag, -3.1415926536726958909, tol=PTOL) + v = fp.ei((-80.0 - 20.0j)) + assert ae(v, (-3.8353473865788235787e-38 - 3.1415926535897932385j), tol=ATOL) + assert ae(v.real, -3.8353473865788235787e-38, tol=PTOL) + assert ae(v.imag, -3.1415926535897932385, tol=PTOL) + v = fp.ei((-120.0 - 30.0j)) + assert ae(v, (-2.3836002337480334716e-55 - 3.1415926535897932385j), tol=ATOL) + assert ae(v.real, -2.3836002337480334716e-55, tol=PTOL) + assert ae(v.imag, -3.1415926535897932385, tol=PTOL) + v = fp.ei((-160.0 - 40.0j)) + assert ae(v, (1.6238022898654510661e-72 - 3.1415926535897932385j), tol=ATOL) + assert ae(v.real, 1.6238022898654510661e-72, tol=PTOL) + assert ae(v.imag, -3.1415926535897932385, tol=PTOL) + v = fp.ei((-200.0 - 50.0j)) + assert ae(v, (-6.6800061461666228487e-90 - 3.1415926535897932385j), tol=ATOL) + assert ae(v.real, -6.6800061461666228487e-90, tol=PTOL) + assert ae(v.imag, -3.1415926535897932385, tol=PTOL) + v = fp.ei((-320.0 - 80.0j)) + assert ae(v, (-4.2737871527778786157e-143 - 3.1415926535897932385j), tol=ATOL) + assert ae(v.real, -4.2737871527778786157e-143, tol=PTOL) + assert ae(v.imag, -3.1415926535897932385, tol=PTOL) + v = fp.ei((-1.1641532182693481445e-10 - 1.1641532182693481445e-10j)) + assert ae(v, (-21.950067703413105017 - 2.3561944903087602507j), tol=ATOL) + assert ae(v.real, -21.950067703413105017, tol=PTOL) + assert ae(v.imag, -2.3561944903087602507, tol=PTOL) + v = fp.ei((-0.25 - 0.25j)) + assert ae(v, (-0.71092525792923287894 - 2.5766745291767512913j), tol=ATOL) + assert ae(v.real, -0.71092525792923287894, tol=PTOL) + assert ae(v.imag, -2.5766745291767512913, tol=PTOL) + v = fp.ei((-1.0 - 1.0j)) + assert ae(v, (-0.00028162445198141832551 - 2.9622681185504342983j), tol=ATOL) + assert ae(v.real, -0.00028162445198141832551, tol=PTOL) + assert ae(v.imag, -2.9622681185504342983, tol=PTOL) + v = fp.ei((-2.0 - 2.0j)) + assert ae(v, (0.033767089606562004246 - 3.1229932394200426965j), tol=ATOL) + assert ae(v.real, 0.033767089606562004246, tol=PTOL) + assert ae(v.imag, -3.1229932394200426965, tol=PTOL) + v = fp.ei((-5.0 - 5.0j)) + assert ae(v, (-0.0007266506660356393891 - 3.1420636813914284609j), tol=ATOL) + assert ae(v.real, -0.0007266506660356393891, tol=PTOL) + assert ae(v.imag, -3.1420636813914284609, tol=PTOL) + v = fp.ei((-20.0 - 20.0j)) + assert ae(v, (2.3824537449367396579e-11 - 3.1415926535228233653j), tol=ATOL) + assert ae(v.real, 2.3824537449367396579e-11, tol=PTOL) + assert ae(v.imag, -3.1415926535228233653, tol=PTOL) + v = fp.ei((-30.0 - 30.0j)) + assert ae(v, (-1.7316045841744061617e-15 - 3.141592653589794545j), tol=ATOL) + assert ae(v.real, -1.7316045841744061617e-15, tol=PTOL) + assert ae(v.imag, -3.141592653589794545, tol=PTOL) + v = fp.ei((-40.0 - 40.0j)) + assert ae(v, (7.4001043002899232182e-20 - 3.1415926535897932385j), tol=ATOL) + assert ae(v.real, 7.4001043002899232182e-20, tol=PTOL) + assert ae(v.imag, -3.1415926535897932385, tol=PTOL) + v = fp.ei((-50.0 - 50.0j)) + assert ae(v, (-2.3566128324644641219e-24 - 3.1415926535897932385j), tol=ATOL) + assert ae(v.real, -2.3566128324644641219e-24, tol=PTOL) + assert ae(v.imag, -3.1415926535897932385, tol=PTOL) + v = fp.ei((-80.0 - 80.0j)) + assert ae(v, (-9.8279750572186526673e-38 - 3.1415926535897932385j), tol=ATOL) + assert ae(v.real, -9.8279750572186526673e-38, tol=PTOL) + assert ae(v.imag, -3.1415926535897932385, tol=PTOL) + v = fp.ei((-1.1641532182693481445e-10 - 4.6566128730773925781e-10j)) + assert ae(v, (-20.880034621664969632 - 1.8157749903874220607j), tol=ATOL) + assert ae(v.real, -20.880034621664969632, tol=PTOL) + assert ae(v.imag, -1.8157749903874220607, tol=PTOL) + v = fp.ei((-0.25 - 1.0j)) + assert ae(v, (0.16868306393667788761 - 2.6557914649950505414j), tol=ATOL) + assert ae(v.real, 0.16868306393667788761, tol=PTOL) + assert ae(v.imag, -2.6557914649950505414, tol=PTOL) + v = fp.ei((-1.0 - 4.0j)) + assert ae(v, (-0.03373591813926547318 - 3.2151161058308770603j), tol=ATOL) + assert ae(v.real, -0.03373591813926547318, tol=PTOL) + assert ae(v.imag, -3.2151161058308770603, tol=PTOL) + v = fp.ei((-2.0 - 8.0j)) + assert ae(v, (0.015392833434733785143 - 3.1384179414340326969j), tol=ATOL) + assert ae(v.real, 0.015392833434733785143, tol=PTOL) + assert ae(v.imag, -3.1384179414340326969, tol=PTOL) + v = fp.ei((-5.0 - 20.0j)) + assert ae(v, (0.00024419662286542966525 - 3.1413825703601317109j), tol=ATOL) + assert ae(v.real, 0.00024419662286542966525, tol=PTOL) + assert ae(v.imag, -3.1413825703601317109, tol=PTOL) + v = fp.ei((-20.0 - 80.0j)) + assert ae(v, (-2.3255552781051330088e-11 - 3.1415926535987396304j), tol=ATOL) + assert ae(v.real, -2.3255552781051330088e-11, tol=PTOL) + assert ae(v.imag, -3.1415926535987396304, tol=PTOL) + v = fp.ei((-30.0 - 120.0j)) + assert ae(v, (2.7068919097124652332e-16 - 3.1415926535897925337j), tol=ATOL) + assert ae(v.real, 2.7068919097124652332e-16, tol=PTOL) + assert ae(v.imag, -3.1415926535897925337, tol=PTOL) + v = fp.ei((-40.0 - 160.0j)) + assert ae(v, (1.1695597827678024687e-20 - 3.1415926535897932385j), tol=ATOL) + assert ae(v.real, 1.1695597827678024687e-20, tol=PTOL) + assert ae(v.imag, -3.1415926535897932385, tol=PTOL) + v = fp.ei((-50.0 - 200.0j)) + assert ae(v, (-9.0323746914410162531e-25 - 3.1415926535897932385j), tol=ATOL) + assert ae(v.real, -9.0323746914410162531e-25, tol=PTOL) + assert ae(v.imag, -3.1415926535897932385, tol=PTOL) + v = fp.ei((-80.0 - 320.0j)) + assert ae(v, (-3.4819106748728063576e-38 - 3.1415926535897932385j), tol=ATOL) + assert ae(v.real, -3.4819106748728063576e-38, tol=PTOL) + assert ae(v.imag, -3.1415926535897932385, tol=PTOL) + v = fp.ei((0.0 - 1.1641532182693481445e-10j)) + assert ae(v, (-22.29664129357666235 - 1.5707963269113119411j), tol=ATOL) + assert ae(v.real, -22.29664129357666235, tol=PTOL) + assert ae(v.imag, -1.5707963269113119411, tol=PTOL) + v = fp.ei((0.0 - 0.25j)) + assert ae(v, (-0.82466306258094565309 - 1.8199298971146537833j), tol=ATOL) + assert ae(v.real, -0.82466306258094565309, tol=PTOL) + assert ae(v.imag, -1.8199298971146537833, tol=PTOL) + v = fp.ei((0.0 - 1.0j)) + assert ae(v, (0.33740392290096813466 - 2.5168793971620796342j), tol=ATOL) + assert ae(v.real, 0.33740392290096813466, tol=PTOL) + assert ae(v.imag, -2.5168793971620796342, tol=PTOL) + v = fp.ei((0.0 - 2.0j)) + assert ae(v, (0.4229808287748649957 - 3.1762093035975914678j), tol=ATOL) + assert ae(v.real, 0.4229808287748649957, tol=PTOL) + assert ae(v.imag, -3.1762093035975914678, tol=PTOL) + v = fp.ei((0.0 - 5.0j)) + assert ae(v, (-0.19002974965664387862 - 3.1207275717395707565j), tol=ATOL) + assert ae(v.real, -0.19002974965664387862, tol=PTOL) + assert ae(v.imag, -3.1207275717395707565, tol=PTOL) + v = fp.ei((0.0 - 20.0j)) + assert ae(v, (0.04441982084535331654 - 3.1190380278383364594j), tol=ATOL) + assert ae(v.real, 0.04441982084535331654, tol=PTOL) + assert ae(v.imag, -3.1190380278383364594, tol=PTOL) + v = fp.ei((0.0 - 30.0j)) + assert ae(v, (-0.033032417282071143779 - 3.1375528668252477302j), tol=ATOL) + assert ae(v.real, -0.033032417282071143779, tol=PTOL) + assert ae(v.imag, -3.1375528668252477302, tol=PTOL) + v = fp.ei((0.0 - 40.0j)) + assert ae(v, (0.019020007896208766962 - 3.157781446149681126j), tol=ATOL) + assert ae(v.real, 0.019020007896208766962, tol=PTOL) + assert ae(v.imag, -3.157781446149681126, tol=PTOL) + v = fp.ei((0.0 - 50.0j)) + assert ae(v, (-0.0056283863241163054402 - 3.122413399280832514j), tol=ATOL) + assert ae(v.real, -0.0056283863241163054402, tol=PTOL) + assert ae(v.imag, -3.122413399280832514, tol=PTOL) + v = fp.ei((0.0 - 80.0j)) + assert ae(v, (-0.012402501155070958192 - 3.1431272137073839346j), tol=ATOL) + assert ae(v.real, -0.012402501155070958192, tol=PTOL) + assert ae(v.imag, -3.1431272137073839346, tol=PTOL) + v = fp.ei((1.1641532182693481445e-10 - 4.6566128730773925781e-10j)) + assert ae(v, (-20.880034621432138988 - 1.3258176641336937524j), tol=ATOL) + assert ae(v.real, -20.880034621432138988, tol=PTOL) + assert ae(v.imag, -1.3258176641336937524, tol=PTOL) + v = fp.ei((0.25 - 1.0j)) + assert ae(v, (0.59066621214766308594 - 2.3968481059377428687j), tol=ATOL) + assert ae(v.real, 0.59066621214766308594, tol=PTOL) + assert ae(v.imag, -2.3968481059377428687, tol=PTOL) + v = fp.ei((1.0 - 4.0j)) + assert ae(v, (-0.49739047283060471093 - 3.5570287076301818702j), tol=ATOL) + assert ae(v.real, -0.49739047283060471093, tol=PTOL) + assert ae(v.imag, -3.5570287076301818702, tol=PTOL) + v = fp.ei((2.0 - 8.0j)) + assert ae(v, (0.8705211147733730969 - 3.3825859385758486351j), tol=ATOL) + assert ae(v.real, 0.8705211147733730969, tol=PTOL) + assert ae(v.imag, -3.3825859385758486351, tol=PTOL) + v = fp.ei((5.0 - 20.0j)) + assert ae(v, (7.0789514293925893007 - 1.5313749363937141849j), tol=ATOL) + assert ae(v.real, 7.0789514293925893007, tol=PTOL) + assert ae(v.imag, -1.5313749363937141849, tol=PTOL) + v = fp.ei((20.0 - 80.0j)) + assert ae(v, (-5855431.4907298084434 + 720917.79156143806727j), tol=ATOL) + assert ae(v.real, -5855431.4907298084434, tol=PTOL) + assert ae(v.imag, 720917.79156143806727, tol=PTOL) + v = fp.ei((30.0 - 120.0j)) + assert ae(v, (65402491644.703470747 + 56697658396.51586764j), tol=ATOL) + assert ae(v.real, 65402491644.703470747, tol=PTOL) + assert ae(v.imag, 56697658396.51586764, tol=PTOL) + v = fp.ei((40.0 - 160.0j)) + assert ae(v, (-25504929379604.776769 - 1429035198630576.3879j), tol=ATOL) + assert ae(v.real, -25504929379604.776769, tol=PTOL) + assert ae(v.imag, -1429035198630576.3879, tol=PTOL) + v = fp.ei((50.0 - 200.0j)) + assert ae(v, (-18437746526988116954.0 + 17146362239046152342.0j), tol=ATOL) + assert ae(v.real, -18437746526988116954.0, tol=PTOL) + assert ae(v.imag, 17146362239046152342.0, tol=PTOL) + v = fp.ei((80.0 - 320.0j)) + assert ae(v, (-3.3464697299634526706e+31 + 1.6473152633843023919e+32j), tol=ATOL) + assert ae(v.real, -3.3464697299634526706e+31, tol=PTOL) + assert ae(v.imag, 1.6473152633843023919e+32, tol=PTOL) + v = fp.ei((1.1641532182693481445e-10 - 1.1641532182693481445e-10j)) + assert ae(v, (-21.950067703180274374 - 0.78539816351386363145j), tol=ATOL) + assert ae(v.real, -21.950067703180274374, tol=PTOL) + assert ae(v.imag, -0.78539816351386363145, tol=PTOL) + v = fp.ei((0.25 - 0.25j)) + assert ae(v, (-0.21441047326710323254 - 1.0683772981589995996j), tol=ATOL) + assert ae(v.real, -0.21441047326710323254, tol=PTOL) + assert ae(v.imag, -1.0683772981589995996, tol=PTOL) + v = fp.ei((1.0 - 1.0j)) + assert ae(v, (1.7646259855638540684 - 2.3877698515105224193j), tol=ATOL) + assert ae(v.real, 1.7646259855638540684, tol=PTOL) + assert ae(v.imag, -2.3877698515105224193, tol=PTOL) + v = fp.ei((2.0 - 2.0j)) + assert ae(v, (1.8920781621855474089 - 5.3169624378326579621j), tol=ATOL) + assert ae(v.real, 1.8920781621855474089, tol=PTOL) + assert ae(v.imag, -5.3169624378326579621, tol=PTOL) + v = fp.ei((5.0 - 5.0j)) + assert ae(v, (-13.470936071475245856 + 15.322492395731230968j), tol=ATOL) + assert ae(v.real, -13.470936071475245856, tol=PTOL) + assert ae(v.imag, 15.322492395731230968, tol=PTOL) + v = fp.ei((20.0 - 20.0j)) + assert ae(v, (16589317.398788971896 - 5831705.4712368307104j), tol=ATOL) + assert ae(v.real, 16589317.398788971896, tol=PTOL) + assert ae(v.imag, -5831705.4712368307104, tol=PTOL) + v = fp.ei((30.0 - 30.0j)) + assert ae(v, (-154596484273.69322527 + 204179357834.2723043j), tol=ATOL) + assert ae(v.real, -154596484273.69322527, tol=PTOL) + assert ae(v.imag, 204179357834.2723043, tol=PTOL) + v = fp.ei((40.0 - 40.0j)) + assert ae(v, (287512180321448.45408 - 4203502407932318.1156j), tol=ATOL) + assert ae(v.real, 287512180321448.45408, tol=PTOL) + assert ae(v.imag, -4203502407932318.1156, tol=PTOL) + v = fp.ei((50.0 - 50.0j)) + assert ae(v, (36128528616649268826.0 + 64648801861338741960.0j), tol=ATOL) + assert ae(v.real, 36128528616649268826.0, tol=PTOL) + assert ae(v.imag, 64648801861338741960.0, tol=PTOL) + v = fp.ei((80.0 - 80.0j)) + assert ae(v, (-3.8674816337930010217e+32 + 3.0540709639658071041e+32j), tol=ATOL) + assert ae(v.real, -3.8674816337930010217e+32, tol=PTOL) + assert ae(v.imag, 3.0540709639658071041e+32, tol=PTOL) + v = fp.ei((4.6566128730773925781e-10 - 1.1641532182693481445e-10j)) + assert ae(v, (-20.880034621082893023 - 0.24497866324327947603j), tol=ATOL) + assert ae(v.real, -20.880034621082893023, tol=PTOL) + assert ae(v.imag, -0.24497866324327947603, tol=PTOL) + v = fp.ei((1.0 - 0.25j)) + assert ae(v, (1.8942716983721074932 - 0.67268237088273915854j), tol=ATOL) + assert ae(v.real, 1.8942716983721074932, tol=PTOL) + assert ae(v.imag, -0.67268237088273915854, tol=PTOL) + v = fp.ei((4.0 - 1.0j)) + assert ae(v, (14.806699492675420438 - 12.280015176673582616j), tol=ATOL) + assert ae(v.real, 14.806699492675420438, tol=PTOL) + assert ae(v.imag, -12.280015176673582616, tol=PTOL) + v = fp.ei((8.0 - 2.0j)) + assert ae(v, (-54.633252667426386294 - 416.34477429173650012j), tol=ATOL) + assert ae(v.real, -54.633252667426386294, tol=PTOL) + assert ae(v.imag, -416.34477429173650012, tol=PTOL) + v = fp.ei((20.0 - 5.0j)) + assert ae(v, (711836.97165402624643 + 24745247.798103247366j), tol=ATOL) + assert ae(v.real, 711836.97165402624643, tol=PTOL) + assert ae(v.imag, 24745247.798103247366, tol=PTOL) + v = fp.ei((80.0 - 20.0j)) + assert ae(v, (4.2139911108612653091e+32 - 5.3367124741918251637e+32j), tol=ATOL) + assert ae(v.real, 4.2139911108612653091e+32, tol=PTOL) + assert ae(v.imag, -5.3367124741918251637e+32, tol=PTOL) + v = fp.ei((120.0 - 30.0j)) + assert ae(v, (-9.7760616203707508892e+48 + 1.058257682317195792e+50j), tol=ATOL) + assert ae(v.real, -9.7760616203707508892e+48, tol=PTOL) + assert ae(v.imag, 1.058257682317195792e+50, tol=PTOL) + v = fp.ei((160.0 - 40.0j)) + assert ae(v, (-8.7065541466623638861e+66 - 1.6577106725141739889e+67j), tol=ATOL) + assert ae(v.real, -8.7065541466623638861e+66, tol=PTOL) + assert ae(v.imag, -1.6577106725141739889e+67, tol=PTOL) + v = fp.ei((200.0 - 50.0j)) + assert ae(v, (3.070744996327018106e+84 + 1.7243244846769415903e+84j), tol=ATOL) + assert ae(v.real, 3.070744996327018106e+84, tol=PTOL) + assert ae(v.imag, 1.7243244846769415903e+84, tol=PTOL) + v = fp.ei((320.0 - 80.0j)) + assert ae(v, (-9.9960598637998647276e+135 + 2.6855081527595608863e+136j), tol=ATOL) + assert ae(v.real, -9.9960598637998647276e+135, tol=PTOL) + assert ae(v.imag, 2.6855081527595608863e+136, tol=PTOL) diff --git a/venv/lib/python3.12/site-packages/mpmath/tests/test_functions.py b/venv/lib/python3.12/site-packages/mpmath/tests/test_functions.py new file mode 100644 index 0000000000000000000000000000000000000000..3bfe852f008173eb636c147abc83d71dbdd4d23a --- /dev/null +++ b/venv/lib/python3.12/site-packages/mpmath/tests/test_functions.py @@ -0,0 +1,920 @@ +from mpmath.libmp import * +from mpmath import * +import random +import time +import math +import cmath + +def mpc_ae(a, b, eps=eps): + res = True + res = res and a.real.ae(b.real, eps) + res = res and a.imag.ae(b.imag, eps) + return res + +#---------------------------------------------------------------------------- +# Constants and functions +# + +tpi = "3.1415926535897932384626433832795028841971693993751058209749445923078\ +1640628620899862803482534211706798" +te = "2.71828182845904523536028747135266249775724709369995957496696762772407\ +663035354759457138217852516642743" +tdegree = "0.017453292519943295769236907684886127134428718885417254560971914\ +4017100911460344944368224156963450948221" +teuler = "0.5772156649015328606065120900824024310421593359399235988057672348\ +84867726777664670936947063291746749516" +tln2 = "0.693147180559945309417232121458176568075500134360255254120680009493\ +393621969694715605863326996418687542" +tln10 = "2.30258509299404568401799145468436420760110148862877297603332790096\ +757260967735248023599720508959829834" +tcatalan = "0.91596559417721901505460351493238411077414937428167213426649811\ +9621763019776254769479356512926115106249" +tkhinchin = "2.6854520010653064453097148354817956938203822939944629530511523\ +4555721885953715200280114117493184769800" +tglaisher = "1.2824271291006226368753425688697917277676889273250011920637400\ +2174040630885882646112973649195820237439420646" +tapery = "1.2020569031595942853997381615114499907649862923404988817922715553\ +4183820578631309018645587360933525815" +tphi = "1.618033988749894848204586834365638117720309179805762862135448622705\ +26046281890244970720720418939113748475" +tmertens = "0.26149721284764278375542683860869585905156664826119920619206421\ +3924924510897368209714142631434246651052" +ttwinprime = "0.660161815846869573927812110014555778432623360284733413319448\ +423335405642304495277143760031413839867912" + +def test_constants(): + for prec in [3, 7, 10, 15, 20, 37, 80, 100, 29]: + mp.dps = prec + assert pi == mpf(tpi) + assert e == mpf(te) + assert degree == mpf(tdegree) + assert euler == mpf(teuler) + assert ln2 == mpf(tln2) + assert ln10 == mpf(tln10) + assert catalan == mpf(tcatalan) + assert khinchin == mpf(tkhinchin) + assert glaisher == mpf(tglaisher) + assert phi == mpf(tphi) + if prec < 50: + assert mertens == mpf(tmertens) + assert twinprime == mpf(ttwinprime) + mp.dps = 15 + assert pi >= -1 + assert pi > 2 + assert pi > 3 + assert pi < 4 + +def test_exact_sqrts(): + for i in range(20000): + assert sqrt(mpf(i*i)) == i + random.seed(1) + for prec in [100, 300, 1000, 10000]: + mp.dps = prec + for i in range(20): + A = random.randint(10**(prec//2-2), 10**(prec//2-1)) + assert sqrt(mpf(A*A)) == A + mp.dps = 15 + for i in range(100): + for a in [1, 8, 25, 112307]: + assert sqrt(mpf((a*a, 2*i))) == mpf((a, i)) + assert sqrt(mpf((a*a, -2*i))) == mpf((a, -i)) + +def test_sqrt_rounding(): + for i in [2, 3, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15]: + i = from_int(i) + for dps in [7, 15, 83, 106, 2000]: + mp.dps = dps + a = mpf_pow_int(mpf_sqrt(i, mp.prec, round_down), 2, mp.prec, round_down) + b = mpf_pow_int(mpf_sqrt(i, mp.prec, round_up), 2, mp.prec, round_up) + assert mpf_lt(a, i) + assert mpf_gt(b, i) + random.seed(1234) + prec = 100 + for rnd in [round_down, round_nearest, round_ceiling]: + for i in range(100): + a = mpf_rand(prec) + b = mpf_mul(a, a) + assert mpf_sqrt(b, prec, rnd) == a + # Test some extreme cases + mp.dps = 100 + a = mpf(9) + 1e-90 + b = mpf(9) - 1e-90 + mp.dps = 15 + assert sqrt(a, rounding='d') == 3 + assert sqrt(a, rounding='n') == 3 + assert sqrt(a, rounding='u') > 3 + assert sqrt(b, rounding='d') < 3 + assert sqrt(b, rounding='n') == 3 + assert sqrt(b, rounding='u') == 3 + # A worst case, from the MPFR test suite + assert sqrt(mpf('7.0503726185518891')) == mpf('2.655253776675949') + +def test_float_sqrt(): + mp.dps = 15 + # These should round identically + for x in [0, 1e-7, 0.1, 0.5, 1, 2, 3, 4, 5, 0.333, 76.19]: + assert sqrt(mpf(x)) == float(x)**0.5 + assert sqrt(-1) == 1j + assert sqrt(-2).ae(cmath.sqrt(-2)) + assert sqrt(-3).ae(cmath.sqrt(-3)) + assert sqrt(-100).ae(cmath.sqrt(-100)) + assert sqrt(1j).ae(cmath.sqrt(1j)) + assert sqrt(-1j).ae(cmath.sqrt(-1j)) + assert sqrt(math.pi + math.e*1j).ae(cmath.sqrt(math.pi + math.e*1j)) + assert sqrt(math.pi - math.e*1j).ae(cmath.sqrt(math.pi - math.e*1j)) + +def test_hypot(): + assert hypot(0, 0) == 0 + assert hypot(0, 0.33) == mpf(0.33) + assert hypot(0.33, 0) == mpf(0.33) + assert hypot(-0.33, 0) == mpf(0.33) + assert hypot(3, 4) == mpf(5) + +def test_exact_cbrt(): + for i in range(0, 20000, 200): + assert cbrt(mpf(i*i*i)) == i + random.seed(1) + for prec in [100, 300, 1000, 10000]: + mp.dps = prec + A = random.randint(10**(prec//2-2), 10**(prec//2-1)) + assert cbrt(mpf(A*A*A)) == A + mp.dps = 15 + +def test_exp(): + assert exp(0) == 1 + assert exp(10000).ae(mpf('8.8068182256629215873e4342')) + assert exp(-10000).ae(mpf('1.1354838653147360985e-4343')) + a = exp(mpf((1, 8198646019315405, -53, 53))) + assert(a.bc == bitcount(a.man)) + mp.prec = 67 + a = exp(mpf((1, 1781864658064754565, -60, 61))) + assert(a.bc == bitcount(a.man)) + mp.prec = 53 + assert exp(ln2 * 10).ae(1024) + assert exp(2+2j).ae(cmath.exp(2+2j)) + +def test_issue_73(): + mp.dps = 512 + a = exp(-1) + b = exp(1) + mp.dps = 15 + assert (+a).ae(0.36787944117144233) + assert (+b).ae(2.7182818284590451) + +def test_log(): + mp.dps = 15 + assert log(1) == 0 + for x in [0.5, 1.5, 2.0, 3.0, 100, 10**50, 1e-50]: + assert log(x).ae(math.log(x)) + assert log(x, x) == 1 + assert log(1024, 2) == 10 + assert log(10**1234, 10) == 1234 + assert log(2+2j).ae(cmath.log(2+2j)) + # Accuracy near 1 + assert (log(0.6+0.8j).real*10**17).ae(2.2204460492503131) + assert (log(0.6-0.8j).real*10**17).ae(2.2204460492503131) + assert (log(0.8-0.6j).real*10**17).ae(2.2204460492503131) + assert (log(1+1e-8j).real*10**16).ae(0.5) + assert (log(1-1e-8j).real*10**16).ae(0.5) + assert (log(-1+1e-8j).real*10**16).ae(0.5) + assert (log(-1-1e-8j).real*10**16).ae(0.5) + assert (log(1j+1e-8).real*10**16).ae(0.5) + assert (log(1j-1e-8).real*10**16).ae(0.5) + assert (log(-1j+1e-8).real*10**16).ae(0.5) + assert (log(-1j-1e-8).real*10**16).ae(0.5) + assert (log(1+1e-40j).real*10**80).ae(0.5) + assert (log(1j+1e-40).real*10**80).ae(0.5) + # Huge + assert log(ldexp(1.234,10**20)).ae(log(2)*1e20) + assert log(ldexp(1.234,10**200)).ae(log(2)*1e200) + # Some special values + assert log(mpc(0,0)) == mpc(-inf,0) + assert isnan(log(mpc(nan,0)).real) + assert isnan(log(mpc(nan,0)).imag) + assert isnan(log(mpc(0,nan)).real) + assert isnan(log(mpc(0,nan)).imag) + assert isnan(log(mpc(nan,1)).real) + assert isnan(log(mpc(nan,1)).imag) + assert isnan(log(mpc(1,nan)).real) + assert isnan(log(mpc(1,nan)).imag) + +def test_trig_hyperb_basic(): + for x in (list(range(100)) + list(range(-100,0))): + t = x / 4.1 + assert cos(mpf(t)).ae(math.cos(t)) + assert sin(mpf(t)).ae(math.sin(t)) + assert tan(mpf(t)).ae(math.tan(t)) + assert cosh(mpf(t)).ae(math.cosh(t)) + assert sinh(mpf(t)).ae(math.sinh(t)) + assert tanh(mpf(t)).ae(math.tanh(t)) + assert sin(1+1j).ae(cmath.sin(1+1j)) + assert sin(-4-3.6j).ae(cmath.sin(-4-3.6j)) + assert cos(1+1j).ae(cmath.cos(1+1j)) + assert cos(-4-3.6j).ae(cmath.cos(-4-3.6j)) + +def test_degrees(): + assert cos(0*degree) == 1 + assert cos(90*degree).ae(0) + assert cos(180*degree).ae(-1) + assert cos(270*degree).ae(0) + assert cos(360*degree).ae(1) + assert sin(0*degree) == 0 + assert sin(90*degree).ae(1) + assert sin(180*degree).ae(0) + assert sin(270*degree).ae(-1) + assert sin(360*degree).ae(0) + +def random_complexes(N): + random.seed(1) + a = [] + for i in range(N): + x1 = random.uniform(-10, 10) + y1 = random.uniform(-10, 10) + x2 = random.uniform(-10, 10) + y2 = random.uniform(-10, 10) + z1 = complex(x1, y1) + z2 = complex(x2, y2) + a.append((z1, z2)) + return a + +def test_complex_powers(): + for dps in [15, 30, 100]: + # Check accuracy for complex square root + mp.dps = dps + a = mpc(1j)**0.5 + assert a.real == a.imag == mpf(2)**0.5 / 2 + mp.dps = 15 + random.seed(1) + for (z1, z2) in random_complexes(100): + assert (mpc(z1)**mpc(z2)).ae(z1**z2, 1e-12) + assert (e**(-pi*1j)).ae(-1) + mp.dps = 50 + assert (e**(-pi*1j)).ae(-1) + mp.dps = 15 + +def test_complex_sqrt_accuracy(): + def test_mpc_sqrt(lst): + for a, b in lst: + z = mpc(a + j*b) + assert mpc_ae(sqrt(z*z), z) + z = mpc(-a + j*b) + assert mpc_ae(sqrt(z*z), -z) + z = mpc(a - j*b) + assert mpc_ae(sqrt(z*z), z) + z = mpc(-a - j*b) + assert mpc_ae(sqrt(z*z), -z) + random.seed(2) + N = 10 + mp.dps = 30 + dps = mp.dps + test_mpc_sqrt([(random.uniform(0, 10),random.uniform(0, 10)) for i in range(N)]) + test_mpc_sqrt([(i + 0.1, (i + 0.2)*10**i) for i in range(N)]) + mp.dps = 15 + +def test_atan(): + mp.dps = 15 + assert atan(-2.3).ae(math.atan(-2.3)) + assert atan(1e-50) == 1e-50 + assert atan(1e50).ae(pi/2) + assert atan(-1e-50) == -1e-50 + assert atan(-1e50).ae(-pi/2) + assert atan(10**1000).ae(pi/2) + for dps in [25, 70, 100, 300, 1000]: + mp.dps = dps + assert (4*atan(1)).ae(pi) + mp.dps = 15 + pi2 = pi/2 + assert atan(mpc(inf,-1)).ae(pi2) + assert atan(mpc(inf,0)).ae(pi2) + assert atan(mpc(inf,1)).ae(pi2) + assert atan(mpc(1,inf)).ae(pi2) + assert atan(mpc(0,inf)).ae(pi2) + assert atan(mpc(-1,inf)).ae(-pi2) + assert atan(mpc(-inf,1)).ae(-pi2) + assert atan(mpc(-inf,0)).ae(-pi2) + assert atan(mpc(-inf,-1)).ae(-pi2) + assert atan(mpc(-1,-inf)).ae(-pi2) + assert atan(mpc(0,-inf)).ae(-pi2) + assert atan(mpc(1,-inf)).ae(pi2) + +def test_atan2(): + mp.dps = 15 + assert atan2(1,1).ae(pi/4) + assert atan2(1,-1).ae(3*pi/4) + assert atan2(-1,-1).ae(-3*pi/4) + assert atan2(-1,1).ae(-pi/4) + assert atan2(-1,0).ae(-pi/2) + assert atan2(1,0).ae(pi/2) + assert atan2(0,0) == 0 + assert atan2(inf,0).ae(pi/2) + assert atan2(-inf,0).ae(-pi/2) + assert isnan(atan2(inf,inf)) + assert isnan(atan2(-inf,inf)) + assert isnan(atan2(inf,-inf)) + assert isnan(atan2(3,nan)) + assert isnan(atan2(nan,3)) + assert isnan(atan2(0,nan)) + assert isnan(atan2(nan,0)) + assert atan2(0,inf) == 0 + assert atan2(0,-inf).ae(pi) + assert atan2(10,inf) == 0 + assert atan2(-10,inf) == 0 + assert atan2(-10,-inf).ae(-pi) + assert atan2(10,-inf).ae(pi) + assert atan2(inf,10).ae(pi/2) + assert atan2(inf,-10).ae(pi/2) + assert atan2(-inf,10).ae(-pi/2) + assert atan2(-inf,-10).ae(-pi/2) + +def test_areal_inverses(): + assert asin(mpf(0)) == 0 + assert asinh(mpf(0)) == 0 + assert acosh(mpf(1)) == 0 + assert isinstance(asin(mpf(0.5)), mpf) + assert isinstance(asin(mpf(2.0)), mpc) + assert isinstance(acos(mpf(0.5)), mpf) + assert isinstance(acos(mpf(2.0)), mpc) + assert isinstance(atanh(mpf(0.1)), mpf) + assert isinstance(atanh(mpf(1.1)), mpc) + + random.seed(1) + for i in range(50): + x = random.uniform(0, 1) + assert asin(mpf(x)).ae(math.asin(x)) + assert acos(mpf(x)).ae(math.acos(x)) + + x = random.uniform(-10, 10) + assert asinh(mpf(x)).ae(cmath.asinh(x).real) + assert isinstance(asinh(mpf(x)), mpf) + x = random.uniform(1, 10) + assert acosh(mpf(x)).ae(cmath.acosh(x).real) + assert isinstance(acosh(mpf(x)), mpf) + x = random.uniform(-10, 0.999) + assert isinstance(acosh(mpf(x)), mpc) + + x = random.uniform(-1, 1) + assert atanh(mpf(x)).ae(cmath.atanh(x).real) + assert isinstance(atanh(mpf(x)), mpf) + + dps = mp.dps + mp.dps = 300 + assert isinstance(asin(0.5), mpf) + mp.dps = 1000 + assert asin(1).ae(pi/2) + assert asin(-1).ae(-pi/2) + mp.dps = dps + +def test_invhyperb_inaccuracy(): + mp.dps = 15 + assert (asinh(1e-5)*10**5).ae(0.99999999998333333) + assert (asinh(1e-10)*10**10).ae(1) + assert (asinh(1e-50)*10**50).ae(1) + assert (asinh(-1e-5)*10**5).ae(-0.99999999998333333) + assert (asinh(-1e-10)*10**10).ae(-1) + assert (asinh(-1e-50)*10**50).ae(-1) + assert asinh(10**20).ae(46.744849040440862) + assert asinh(-10**20).ae(-46.744849040440862) + assert (tanh(1e-10)*10**10).ae(1) + assert (tanh(-1e-10)*10**10).ae(-1) + assert (atanh(1e-10)*10**10).ae(1) + assert (atanh(-1e-10)*10**10).ae(-1) + +def test_complex_functions(): + for x in (list(range(10)) + list(range(-10,0))): + for y in (list(range(10)) + list(range(-10,0))): + z = complex(x, y)/4.3 + 0.01j + assert exp(mpc(z)).ae(cmath.exp(z)) + assert log(mpc(z)).ae(cmath.log(z)) + assert cos(mpc(z)).ae(cmath.cos(z)) + assert sin(mpc(z)).ae(cmath.sin(z)) + assert tan(mpc(z)).ae(cmath.tan(z)) + assert sinh(mpc(z)).ae(cmath.sinh(z)) + assert cosh(mpc(z)).ae(cmath.cosh(z)) + assert tanh(mpc(z)).ae(cmath.tanh(z)) + +def test_complex_inverse_functions(): + mp.dps = 15 + iv.dps = 15 + for (z1, z2) in random_complexes(30): + # apparently cmath uses a different branch, so we + # can't use it for comparison + assert sinh(asinh(z1)).ae(z1) + # + assert acosh(z1).ae(cmath.acosh(z1)) + assert atanh(z1).ae(cmath.atanh(z1)) + assert atan(z1).ae(cmath.atan(z1)) + # the reason we set a big eps here is that the cmath + # functions are inaccurate + assert asin(z1).ae(cmath.asin(z1), rel_eps=1e-12) + assert acos(z1).ae(cmath.acos(z1), rel_eps=1e-12) + one = mpf(1) + for i in range(-9, 10, 3): + for k in range(-9, 10, 3): + a = 0.9*j*10**k + 0.8*one*10**i + b = cos(acos(a)) + assert b.ae(a) + b = sin(asin(a)) + assert b.ae(a) + one = mpf(1) + err = 2*10**-15 + for i in range(-9, 9, 3): + for k in range(-9, 9, 3): + a = -0.9*10**k + j*0.8*one*10**i + b = cosh(acosh(a)) + assert b.ae(a, err) + b = sinh(asinh(a)) + assert b.ae(a, err) + +def test_reciprocal_functions(): + assert sec(3).ae(-1.01010866590799375) + assert csc(3).ae(7.08616739573718592) + assert cot(3).ae(-7.01525255143453347) + assert sech(3).ae(0.0993279274194332078) + assert csch(3).ae(0.0998215696688227329) + assert coth(3).ae(1.00496982331368917) + assert asec(3).ae(1.23095941734077468) + assert acsc(3).ae(0.339836909454121937) + assert acot(3).ae(0.321750554396642193) + assert asech(0.5).ae(1.31695789692481671) + assert acsch(3).ae(0.327450150237258443) + assert acoth(3).ae(0.346573590279972655) + assert acot(0).ae(1.5707963267948966192) + assert acoth(0).ae(1.5707963267948966192j) + +def test_ldexp(): + mp.dps = 15 + assert ldexp(mpf(2.5), 0) == 2.5 + assert ldexp(mpf(2.5), -1) == 1.25 + assert ldexp(mpf(2.5), 2) == 10 + assert ldexp(mpf('inf'), 3) == mpf('inf') + +def test_frexp(): + mp.dps = 15 + assert frexp(0) == (0.0, 0) + assert frexp(9) == (0.5625, 4) + assert frexp(1) == (0.5, 1) + assert frexp(0.2) == (0.8, -2) + assert frexp(1000) == (0.9765625, 10) + +def test_aliases(): + assert ln(7) == log(7) + assert log10(3.75) == log(3.75,10) + assert degrees(5.6) == 5.6 / degree + assert radians(5.6) == 5.6 * degree + assert power(-1,0.5) == j + assert fmod(25,7) == 4.0 and isinstance(fmod(25,7), mpf) + +def test_arg_sign(): + assert arg(3) == 0 + assert arg(-3).ae(pi) + assert arg(j).ae(pi/2) + assert arg(-j).ae(-pi/2) + assert arg(0) == 0 + assert isnan(atan2(3,nan)) + assert isnan(atan2(nan,3)) + assert isnan(atan2(0,nan)) + assert isnan(atan2(nan,0)) + assert isnan(atan2(nan,nan)) + assert arg(inf) == 0 + assert arg(-inf).ae(pi) + assert isnan(arg(nan)) + #assert arg(inf*j).ae(pi/2) + assert sign(0) == 0 + assert sign(3) == 1 + assert sign(-3) == -1 + assert sign(inf) == 1 + assert sign(-inf) == -1 + assert isnan(sign(nan)) + assert sign(j) == j + assert sign(-3*j) == -j + assert sign(1+j).ae((1+j)/sqrt(2)) + +def test_misc_bugs(): + # test that this doesn't raise an exception + mp.dps = 1000 + log(1302) + mp.dps = 15 + +def test_arange(): + assert arange(10) == [mpf('0.0'), mpf('1.0'), mpf('2.0'), mpf('3.0'), + mpf('4.0'), mpf('5.0'), mpf('6.0'), mpf('7.0'), + mpf('8.0'), mpf('9.0')] + assert arange(-5, 5) == [mpf('-5.0'), mpf('-4.0'), mpf('-3.0'), + mpf('-2.0'), mpf('-1.0'), mpf('0.0'), + mpf('1.0'), mpf('2.0'), mpf('3.0'), mpf('4.0')] + assert arange(0, 1, 0.1) == [mpf('0.0'), mpf('0.10000000000000001'), + mpf('0.20000000000000001'), + mpf('0.30000000000000004'), + mpf('0.40000000000000002'), + mpf('0.5'), mpf('0.60000000000000009'), + mpf('0.70000000000000007'), + mpf('0.80000000000000004'), + mpf('0.90000000000000002')] + assert arange(17, -9, -3) == [mpf('17.0'), mpf('14.0'), mpf('11.0'), + mpf('8.0'), mpf('5.0'), mpf('2.0'), + mpf('-1.0'), mpf('-4.0'), mpf('-7.0')] + assert arange(0.2, 0.1, -0.1) == [mpf('0.20000000000000001')] + assert arange(0) == [] + assert arange(1000, -1) == [] + assert arange(-1.23, 3.21, -0.0000001) == [] + +def test_linspace(): + assert linspace(2, 9, 7) == [mpf('2.0'), mpf('3.166666666666667'), + mpf('4.3333333333333339'), mpf('5.5'), mpf('6.666666666666667'), + mpf('7.8333333333333339'), mpf('9.0')] + assert linspace(2, 9, 7, endpoint=0) == [mpf('2.0'), mpf('3.0'), mpf('4.0'), + mpf('5.0'), mpf('6.0'), mpf('7.0'), mpf('8.0')] + assert linspace(2, 7, 1) == [mpf(2)] + +def test_float_cbrt(): + mp.dps = 30 + for a in arange(0,10,0.1): + assert cbrt(a*a*a).ae(a, eps) + assert cbrt(-1).ae(0.5 + j*sqrt(3)/2) + one_third = mpf(1)/3 + for a in arange(0,10,2.7) + [0.1 + 10**5]: + a = mpc(a + 1.1j) + r1 = cbrt(a) + mp.dps += 10 + r2 = pow(a, one_third) + mp.dps -= 10 + assert r1.ae(r2, eps) + mp.dps = 100 + for n in range(100, 301, 100): + w = 10**n + j*10**-3 + z = w*w*w + r = cbrt(z) + assert mpc_ae(r, w, eps) + mp.dps = 15 + +def test_root(): + mp.dps = 30 + random.seed(1) + a = random.randint(0, 10000) + p = a*a*a + r = nthroot(mpf(p), 3) + assert r == a + for n in range(4, 10): + p = p*a + assert nthroot(mpf(p), n) == a + mp.dps = 40 + for n in range(10, 5000, 100): + for a in [random.random()*10000, random.random()*10**100]: + r = nthroot(a, n) + r1 = pow(a, mpf(1)/n) + assert r.ae(r1) + r = nthroot(a, -n) + r1 = pow(a, -mpf(1)/n) + assert r.ae(r1) + # XXX: this is broken right now + # tests for nthroot rounding + for rnd in ['nearest', 'up', 'down']: + mp.rounding = rnd + for n in [-5, -3, 3, 5]: + prec = 50 + for i in range(10): + mp.prec = prec + a = rand() + mp.prec = 2*prec + b = a**n + mp.prec = prec + r = nthroot(b, n) + assert r == a + mp.dps = 30 + for n in range(3, 21): + a = (random.random() + j*random.random()) + assert nthroot(a, n).ae(pow(a, mpf(1)/n)) + assert mpc_ae(nthroot(a, n), pow(a, mpf(1)/n)) + a = (random.random()*10**100 + j*random.random()) + r = nthroot(a, n) + mp.dps += 4 + r1 = pow(a, mpf(1)/n) + mp.dps -= 4 + assert r.ae(r1) + assert mpc_ae(r, r1, eps) + r = nthroot(a, -n) + mp.dps += 4 + r1 = pow(a, -mpf(1)/n) + mp.dps -= 4 + assert r.ae(r1) + assert mpc_ae(r, r1, eps) + mp.dps = 15 + assert nthroot(4, 1) == 4 + assert nthroot(4, 0) == 1 + assert nthroot(4, -1) == 0.25 + assert nthroot(inf, 1) == inf + assert nthroot(inf, 2) == inf + assert nthroot(inf, 3) == inf + assert nthroot(inf, -1) == 0 + assert nthroot(inf, -2) == 0 + assert nthroot(inf, -3) == 0 + assert nthroot(j, 1) == j + assert nthroot(j, 0) == 1 + assert nthroot(j, -1) == -j + assert isnan(nthroot(nan, 1)) + assert isnan(nthroot(nan, 0)) + assert isnan(nthroot(nan, -1)) + assert isnan(nthroot(inf, 0)) + assert root(2,3) == nthroot(2,3) + assert root(16,4,0) == 2 + assert root(16,4,1) == 2j + assert root(16,4,2) == -2 + assert root(16,4,3) == -2j + assert root(16,4,4) == 2 + assert root(-125,3,1) == -5 + +def test_issue_136(): + for dps in [20, 80]: + mp.dps = dps + r = nthroot(mpf('-1e-20'), 4) + assert r.ae(mpf(10)**(-5) * (1 + j) * mpf(2)**(-0.5)) + mp.dps = 80 + assert nthroot('-1e-3', 4).ae(mpf(10)**(-3./4) * (1 + j)/sqrt(2)) + assert nthroot('-1e-6', 4).ae((1 + j)/(10 * sqrt(20))) + # Check that this doesn't take eternity to compute + mp.dps = 20 + assert nthroot('-1e100000000', 4).ae((1+j)*mpf('1e25000000')/sqrt(2)) + mp.dps = 15 + +def test_mpcfun_real_imag(): + mp.dps = 15 + x = mpf(0.3) + y = mpf(0.4) + assert exp(mpc(x,0)) == exp(x) + assert exp(mpc(0,y)) == mpc(cos(y),sin(y)) + assert cos(mpc(x,0)) == cos(x) + assert sin(mpc(x,0)) == sin(x) + assert cos(mpc(0,y)) == cosh(y) + assert sin(mpc(0,y)) == mpc(0,sinh(y)) + assert cospi(mpc(x,0)) == cospi(x) + assert sinpi(mpc(x,0)) == sinpi(x) + assert cospi(mpc(0,y)).ae(cosh(pi*y)) + assert sinpi(mpc(0,y)).ae(mpc(0,sinh(pi*y))) + c, s = cospi_sinpi(mpc(x,0)) + assert c == cospi(x) + assert s == sinpi(x) + c, s = cospi_sinpi(mpc(0,y)) + assert c.ae(cosh(pi*y)) + assert s.ae(mpc(0,sinh(pi*y))) + c, s = cos_sin(mpc(x,0)) + assert c == cos(x) + assert s == sin(x) + c, s = cos_sin(mpc(0,y)) + assert c == cosh(y) + assert s == mpc(0,sinh(y)) + +def test_perturbation_rounding(): + mp.dps = 100 + a = pi/10**50 + b = -pi/10**50 + c = 1 + a + d = 1 + b + mp.dps = 15 + assert exp(a) == 1 + assert exp(a, rounding='c') > 1 + assert exp(b, rounding='c') == 1 + assert exp(a, rounding='f') == 1 + assert exp(b, rounding='f') < 1 + assert cos(a) == 1 + assert cos(a, rounding='c') == 1 + assert cos(b, rounding='c') == 1 + assert cos(a, rounding='f') < 1 + assert cos(b, rounding='f') < 1 + for f in [sin, atan, asinh, tanh]: + assert f(a) == +a + assert f(a, rounding='c') > a + assert f(a, rounding='f') < a + assert f(b) == +b + assert f(b, rounding='c') > b + assert f(b, rounding='f') < b + for f in [asin, tan, sinh, atanh]: + assert f(a) == +a + assert f(b) == +b + assert f(a, rounding='c') > a + assert f(b, rounding='c') > b + assert f(a, rounding='f') < a + assert f(b, rounding='f') < b + assert ln(c) == +a + assert ln(d) == +b + assert ln(c, rounding='c') > a + assert ln(c, rounding='f') < a + assert ln(d, rounding='c') > b + assert ln(d, rounding='f') < b + assert cosh(a) == 1 + assert cosh(b) == 1 + assert cosh(a, rounding='c') > 1 + assert cosh(b, rounding='c') > 1 + assert cosh(a, rounding='f') == 1 + assert cosh(b, rounding='f') == 1 + +def test_integer_parts(): + assert floor(3.2) == 3 + assert ceil(3.2) == 4 + assert floor(3.2+5j) == 3+5j + assert ceil(3.2+5j) == 4+5j + +def test_complex_parts(): + assert fabs('3') == 3 + assert fabs(3+4j) == 5 + assert re(3) == 3 + assert re(1+4j) == 1 + assert im(3) == 0 + assert im(1+4j) == 4 + assert conj(3) == 3 + assert conj(3+4j) == 3-4j + assert mpf(3).conjugate() == 3 + +def test_cospi_sinpi(): + assert sinpi(0) == 0 + assert sinpi(0.5) == 1 + assert sinpi(1) == 0 + assert sinpi(1.5) == -1 + assert sinpi(2) == 0 + assert sinpi(2.5) == 1 + assert sinpi(-0.5) == -1 + assert cospi(0) == 1 + assert cospi(0.5) == 0 + assert cospi(1) == -1 + assert cospi(1.5) == 0 + assert cospi(2) == 1 + assert cospi(2.5) == 0 + assert cospi(-0.5) == 0 + assert cospi(100000000000.25).ae(sqrt(2)/2) + a = cospi(2+3j) + assert a.real.ae(cos((2+3j)*pi).real) + assert a.imag == 0 + b = sinpi(2+3j) + assert b.imag.ae(sin((2+3j)*pi).imag) + assert b.real == 0 + mp.dps = 35 + x1 = mpf(10000) - mpf('1e-15') + x2 = mpf(10000) + mpf('1e-15') + x3 = mpf(10000.5) - mpf('1e-15') + x4 = mpf(10000.5) + mpf('1e-15') + x5 = mpf(10001) - mpf('1e-15') + x6 = mpf(10001) + mpf('1e-15') + x7 = mpf(10001.5) - mpf('1e-15') + x8 = mpf(10001.5) + mpf('1e-15') + mp.dps = 15 + M = 10**15 + assert (sinpi(x1)*M).ae(-pi) + assert (sinpi(x2)*M).ae(pi) + assert (cospi(x3)*M).ae(pi) + assert (cospi(x4)*M).ae(-pi) + assert (sinpi(x5)*M).ae(pi) + assert (sinpi(x6)*M).ae(-pi) + assert (cospi(x7)*M).ae(-pi) + assert (cospi(x8)*M).ae(pi) + assert 0.999 < cospi(x1, rounding='d') < 1 + assert 0.999 < cospi(x2, rounding='d') < 1 + assert 0.999 < sinpi(x3, rounding='d') < 1 + assert 0.999 < sinpi(x4, rounding='d') < 1 + assert -1 < cospi(x5, rounding='d') < -0.999 + assert -1 < cospi(x6, rounding='d') < -0.999 + assert -1 < sinpi(x7, rounding='d') < -0.999 + assert -1 < sinpi(x8, rounding='d') < -0.999 + assert (sinpi(1e-15)*M).ae(pi) + assert (sinpi(-1e-15)*M).ae(-pi) + assert cospi(1e-15) == 1 + assert cospi(1e-15, rounding='d') < 1 + +def test_expj(): + assert expj(0) == 1 + assert expj(1).ae(exp(j)) + assert expj(j).ae(exp(-1)) + assert expj(1+j).ae(exp(j*(1+j))) + assert expjpi(0) == 1 + assert expjpi(1).ae(exp(j*pi)) + assert expjpi(j).ae(exp(-pi)) + assert expjpi(1+j).ae(exp(j*pi*(1+j))) + assert expjpi(-10**15 * j).ae('2.22579818340535731e+1364376353841841') + +def test_sinc(): + assert sinc(0) == sincpi(0) == 1 + assert sinc(inf) == sincpi(inf) == 0 + assert sinc(-inf) == sincpi(-inf) == 0 + assert sinc(2).ae(0.45464871341284084770) + assert sinc(2+3j).ae(0.4463290318402435457-2.7539470277436474940j) + assert sincpi(2) == 0 + assert sincpi(1.5).ae(-0.212206590789193781) + +def test_fibonacci(): + mp.dps = 15 + assert [fibonacci(n) for n in range(-5, 10)] == \ + [5, -3, 2, -1, 1, 0, 1, 1, 2, 3, 5, 8, 13, 21, 34] + assert fib(2.5).ae(1.4893065462657091) + assert fib(3+4j).ae(-5248.51130728372 - 14195.962288353j) + assert fib(1000).ae(4.3466557686937455e+208) + assert str(fib(10**100)) == '6.24499112864607e+2089876402499787337692720892375554168224592399182109535392875613974104853496745963277658556235103534' + mp.dps = 2100 + a = fib(10000) + assert a % 10**10 == 9947366875 + mp.dps = 15 + assert fibonacci(inf) == inf + assert fib(3+0j) == 2 + +def test_call_with_dps(): + mp.dps = 15 + assert abs(exp(1, dps=30)-e(dps=35)) < 1e-29 + +def test_tanh(): + mp.dps = 15 + assert tanh(0) == 0 + assert tanh(inf) == 1 + assert tanh(-inf) == -1 + assert isnan(tanh(nan)) + assert tanh(mpc('inf', '0')) == 1 + +def test_atanh(): + mp.dps = 15 + assert atanh(0) == 0 + assert atanh(0.5).ae(0.54930614433405484570) + assert atanh(-0.5).ae(-0.54930614433405484570) + assert atanh(1) == inf + assert atanh(-1) == -inf + assert isnan(atanh(nan)) + assert isinstance(atanh(1), mpf) + assert isinstance(atanh(-1), mpf) + # Limits at infinity + jpi2 = j*pi/2 + assert atanh(inf).ae(-jpi2) + assert atanh(-inf).ae(jpi2) + assert atanh(mpc(inf,-1)).ae(-jpi2) + assert atanh(mpc(inf,0)).ae(-jpi2) + assert atanh(mpc(inf,1)).ae(jpi2) + assert atanh(mpc(1,inf)).ae(jpi2) + assert atanh(mpc(0,inf)).ae(jpi2) + assert atanh(mpc(-1,inf)).ae(jpi2) + assert atanh(mpc(-inf,1)).ae(jpi2) + assert atanh(mpc(-inf,0)).ae(jpi2) + assert atanh(mpc(-inf,-1)).ae(-jpi2) + assert atanh(mpc(-1,-inf)).ae(-jpi2) + assert atanh(mpc(0,-inf)).ae(-jpi2) + assert atanh(mpc(1,-inf)).ae(-jpi2) + +def test_expm1(): + mp.dps = 15 + assert expm1(0) == 0 + assert expm1(3).ae(exp(3)-1) + assert expm1(inf) == inf + assert expm1(1e-50).ae(1e-50) + assert (expm1(1e-10)*1e10).ae(1.00000000005) + +def test_log1p(): + mp.dps = 15 + assert log1p(0) == 0 + assert log1p(3).ae(log(1+3)) + assert log1p(inf) == inf + assert log1p(1e-50).ae(1e-50) + assert (log1p(1e-10)*1e10).ae(0.99999999995) + +def test_powm1(): + mp.dps = 15 + assert powm1(2,3) == 7 + assert powm1(-1,2) == 0 + assert powm1(-1,0) == 0 + assert powm1(-2,0) == 0 + assert powm1(3+4j,0) == 0 + assert powm1(0,1) == -1 + assert powm1(0,0) == 0 + assert powm1(1,0) == 0 + assert powm1(1,2) == 0 + assert powm1(1,3+4j) == 0 + assert powm1(1,5) == 0 + assert powm1(j,4) == 0 + assert powm1(-j,4) == 0 + assert (powm1(2,1e-100)*1e100).ae(ln2) + assert powm1(2,'1e-100000000000') != 0 + assert (powm1(fadd(1,1e-100,exact=True), 5)*1e100).ae(5) + +def test_unitroots(): + assert unitroots(1) == [1] + assert unitroots(2) == [1, -1] + a, b, c = unitroots(3) + assert a == 1 + assert b.ae(-0.5 + 0.86602540378443864676j) + assert c.ae(-0.5 - 0.86602540378443864676j) + assert unitroots(1, primitive=True) == [1] + assert unitroots(2, primitive=True) == [-1] + assert unitroots(3, primitive=True) == unitroots(3)[1:] + assert unitroots(4, primitive=True) == [j, -j] + assert len(unitroots(17, primitive=True)) == 16 + assert len(unitroots(16, primitive=True)) == 8 + +def test_cyclotomic(): + mp.dps = 15 + assert [cyclotomic(n,1) for n in range(31)] == [1,0,2,3,2,5,1,7,2,3,1,11,1,13,1,1,2,17,1,19,1,1,1,23,1,5,1,3,1,29,1] + assert [cyclotomic(n,-1) for n in range(31)] == [1,-2,0,1,2,1,3,1,2,1,5,1,1,1,7,1,2,1,3,1,1,1,11,1,1,1,13,1,1,1,1] + assert [cyclotomic(n,j) for n in range(21)] == [1,-1+j,1+j,j,0,1,-j,j,2,-j,1,j,3,1,-j,1,2,1,j,j,5] + assert [cyclotomic(n,-j) for n in range(21)] == [1,-1-j,1-j,-j,0,1,j,-j,2,j,1,-j,3,1,j,1,2,1,-j,-j,5] + assert cyclotomic(1624,j) == 1 + assert cyclotomic(33600,j) == 1 + u = sqrt(j, prec=500) + assert cyclotomic(8, u).ae(0) + assert cyclotomic(30, u).ae(5.8284271247461900976) + assert cyclotomic(2040, u).ae(1) + assert cyclotomic(0,2.5) == 1 + assert cyclotomic(1,2.5) == 2.5-1 + assert cyclotomic(2,2.5) == 2.5+1 + assert cyclotomic(3,2.5) == 2.5**2 + 2.5 + 1 + assert cyclotomic(7,2.5) == 406.234375 diff --git a/venv/lib/python3.12/site-packages/mpmath/tests/test_functions2.py b/venv/lib/python3.12/site-packages/mpmath/tests/test_functions2.py new file mode 100644 index 0000000000000000000000000000000000000000..2b2d57fcec9be0db4d921b013f24fd6a5e0e9930 --- /dev/null +++ b/venv/lib/python3.12/site-packages/mpmath/tests/test_functions2.py @@ -0,0 +1,2384 @@ +import math +import pytest +from mpmath import * + +def test_bessel(): + mp.dps = 15 + assert j0(1).ae(0.765197686557966551) + assert j0(pi).ae(-0.304242177644093864) + assert j0(1000).ae(0.0247866861524201746) + assert j0(-25).ae(0.0962667832759581162) + assert j1(1).ae(0.440050585744933516) + assert j1(pi).ae(0.284615343179752757) + assert j1(1000).ae(0.00472831190708952392) + assert j1(-25).ae(0.125350249580289905) + assert besselj(5,1).ae(0.000249757730211234431) + assert besselj(5+0j,1).ae(0.000249757730211234431) + assert besselj(5,pi).ae(0.0521411843671184747) + assert besselj(5,1000).ae(0.00502540694523318607) + assert besselj(5,-25).ae(0.0660079953984229934) + assert besselj(-3,2).ae(-0.128943249474402051) + assert besselj(-4,2).ae(0.0339957198075684341) + assert besselj(3,3+2j).ae(0.424718794929639595942 + 0.625665327745785804812j) + assert besselj(0.25,4).ae(-0.374760630804249715) + assert besselj(1+2j,3+4j).ae(0.319247428741872131 - 0.669557748880365678j) + assert (besselj(3, 10**10) * 10**5).ae(0.76765081748139204023) + assert bessely(-0.5, 0) == 0 + assert bessely(0.5, 0) == -inf + assert bessely(1.5, 0) == -inf + assert bessely(0,0) == -inf + assert bessely(-0.4, 0) == -inf + assert bessely(-0.6, 0) == inf + assert bessely(-1, 0) == inf + assert bessely(-1.4, 0) == inf + assert bessely(-1.6, 0) == -inf + assert bessely(-1, 0) == inf + assert bessely(-2, 0) == -inf + assert bessely(-3, 0) == inf + assert bessely(0.5, 0) == -inf + assert bessely(1, 0) == -inf + assert bessely(1.5, 0) == -inf + assert bessely(2, 0) == -inf + assert bessely(2.5, 0) == -inf + assert bessely(3, 0) == -inf + assert bessely(0,0.5).ae(-0.44451873350670655715) + assert bessely(1,0.5).ae(-1.4714723926702430692) + assert bessely(-1,0.5).ae(1.4714723926702430692) + assert bessely(3.5,0.5).ae(-138.86400867242488443) + assert bessely(0,3+4j).ae(4.6047596915010138655-8.8110771408232264208j) + assert bessely(0,j).ae(-0.26803248203398854876+1.26606587775200833560j) + assert (bessely(3, 10**10) * 10**5).ae(0.21755917537013204058) + assert besseli(0,0) == 1 + assert besseli(1,0) == 0 + assert besseli(2,0) == 0 + assert besseli(-1,0) == 0 + assert besseli(-2,0) == 0 + assert besseli(0,0.5).ae(1.0634833707413235193) + assert besseli(1,0.5).ae(0.25789430539089631636) + assert besseli(-1,0.5).ae(0.25789430539089631636) + assert besseli(3.5,0.5).ae(0.00068103597085793815863) + assert besseli(0,3+4j).ae(-3.3924877882755196097-1.3239458916287264815j) + assert besseli(0,j).ae(besselj(0,1)) + assert (besseli(3, 10**10) * mpf(10)**(-4342944813)).ae(4.2996028505491271875) + assert besselk(0,0) == inf + assert besselk(1,0) == inf + assert besselk(2,0) == inf + assert besselk(-1,0) == inf + assert besselk(-2,0) == inf + assert besselk(0,0.5).ae(0.92441907122766586178) + assert besselk(1,0.5).ae(1.6564411200033008937) + assert besselk(-1,0.5).ae(1.6564411200033008937) + assert besselk(3.5,0.5).ae(207.48418747548460607) + assert besselk(0,3+4j).ae(-0.007239051213570155013+0.026510418350267677215j) + assert besselk(0,j).ae(-0.13863371520405399968-1.20196971531720649914j) + assert (besselk(3, 10**10) * mpf(10)**4342944824).ae(1.1628981033356187851) + # test for issue 331, bug reported by Michael Hartmann + for n in range(10,100,10): + mp.dps = n + assert besseli(91.5,24.7708).ae("4.00830632138673963619656140653537080438462342928377020695738635559218797348548092636896796324190271316137982810144874264e-41") + +def test_bessel_zeros(): + mp.dps = 15 + assert besseljzero(0,1).ae(2.40482555769577276869) + assert besseljzero(2,1).ae(5.1356223018406825563) + assert besseljzero(1,50).ae(157.86265540193029781) + assert besseljzero(10,1).ae(14.475500686554541220) + assert besseljzero(0.5,3).ae(9.4247779607693797153) + assert besseljzero(2,1,1).ae(3.0542369282271403228) + assert besselyzero(0,1).ae(0.89357696627916752158) + assert besselyzero(2,1).ae(3.3842417671495934727) + assert besselyzero(1,50).ae(156.29183520147840108) + assert besselyzero(10,1).ae(12.128927704415439387) + assert besselyzero(0.5,3).ae(7.8539816339744830962) + assert besselyzero(2,1,1).ae(5.0025829314460639452) + +def test_hankel(): + mp.dps = 15 + assert hankel1(0,0.5).ae(0.93846980724081290423-0.44451873350670655715j) + assert hankel1(1,0.5).ae(0.2422684576748738864-1.4714723926702430692j) + assert hankel1(-1,0.5).ae(-0.2422684576748738864+1.4714723926702430692j) + assert hankel1(1.5,0.5).ae(0.0917016996256513026-2.5214655504213378514j) + assert hankel1(1.5,3+4j).ae(0.0066806866476728165382-0.0036684231610839127106j) + assert hankel2(0,0.5).ae(0.93846980724081290423+0.44451873350670655715j) + assert hankel2(1,0.5).ae(0.2422684576748738864+1.4714723926702430692j) + assert hankel2(-1,0.5).ae(-0.2422684576748738864-1.4714723926702430692j) + assert hankel2(1.5,0.5).ae(0.0917016996256513026+2.5214655504213378514j) + assert hankel2(1.5,3+4j).ae(14.783528526098567526-7.397390270853446512j) + +def test_struve(): + mp.dps = 15 + assert struveh(2,3).ae(0.74238666967748318564) + assert struveh(-2.5,3).ae(0.41271003220971599344) + assert struvel(2,3).ae(1.7476573277362782744) + assert struvel(-2.5,3).ae(1.5153394466819651377) + +def test_whittaker(): + mp.dps = 15 + assert whitm(2,3,4).ae(49.753745589025246591) + assert whitw(2,3,4).ae(14.111656223052932215) + +def test_kelvin(): + mp.dps = 15 + assert ber(2,3).ae(0.80836846563726819091) + assert ber(3,4).ae(-0.28262680167242600233) + assert ber(-3,2).ae(-0.085611448496796363669) + assert bei(2,3).ae(-0.89102236377977331571) + assert bei(-3,2).ae(-0.14420994155731828415) + assert ker(2,3).ae(0.12839126695733458928) + assert ker(-3,2).ae(-0.29802153400559142783) + assert ker(0.5,3).ae(-0.085662378535217097524) + assert kei(2,3).ae(0.036804426134164634000) + assert kei(-3,2).ae(0.88682069845786731114) + assert kei(0.5,3).ae(0.013633041571314302948) + +def test_hyper_misc(): + mp.dps = 15 + assert hyp0f1(1,0) == 1 + assert hyp1f1(1,2,0) == 1 + assert hyp1f2(1,2,3,0) == 1 + assert hyp2f1(1,2,3,0) == 1 + assert hyp2f2(1,2,3,4,0) == 1 + assert hyp2f3(1,2,3,4,5,0) == 1 + # Degenerate case: 0F0 + assert hyper([],[],0) == 1 + assert hyper([],[],-2).ae(exp(-2)) + # Degenerate case: 1F0 + assert hyper([2],[],1.5) == 4 + # + assert hyp2f1((1,3),(2,3),(5,6),mpf(27)/32).ae(1.6) + assert hyp2f1((1,4),(1,2),(3,4),mpf(80)/81).ae(1.8) + assert hyp2f1((2,3),(1,1),(3,2),(2+j)/3).ae(1.327531603558679093+0.439585080092769253j) + mp.dps = 25 + v = mpc('1.2282306665029814734863026', '-0.1225033830118305184672133') + assert hyper([(3,4),2+j,1],[1,5,j/3],mpf(1)/5+j/8).ae(v) + mp.dps = 15 + +def test_elliptic_integrals(): + mp.dps = 15 + assert ellipk(0).ae(pi/2) + assert ellipk(0.5).ae(gamma(0.25)**2/(4*sqrt(pi))) + assert ellipk(1) == inf + assert ellipk(1+0j) == inf + assert ellipk(-1).ae('1.3110287771460599052') + assert ellipk(-2).ae('1.1714200841467698589') + assert isinstance(ellipk(-2), mpf) + assert isinstance(ellipe(-2), mpf) + assert ellipk(-50).ae('0.47103424540873331679') + mp.dps = 30 + n1 = +fraction(99999,100000) + n2 = +fraction(100001,100000) + mp.dps = 15 + assert ellipk(n1).ae('7.1427724505817781901') + assert ellipk(n2).ae(mpc('7.1427417367963090109', '-1.5707923998261688019')) + assert ellipe(n1).ae('1.0000332138990829170') + v = ellipe(n2) + assert v.real.ae('0.999966786328145474069137') + assert (v.imag*10**6).ae('7.853952181727432') + assert ellipk(2).ae(mpc('1.3110287771460599052', '-1.3110287771460599052')) + assert ellipk(50).ae(mpc('0.22326753950210985451', '-0.47434723226254522087')) + assert ellipk(3+4j).ae(mpc('0.91119556380496500866', '0.63133428324134524388')) + assert ellipk(3-4j).ae(mpc('0.91119556380496500866', '-0.63133428324134524388')) + assert ellipk(-3+4j).ae(mpc('0.95357894880405122483', '0.23093044503746114444')) + assert ellipk(-3-4j).ae(mpc('0.95357894880405122483', '-0.23093044503746114444')) + assert isnan(ellipk(nan)) + assert isnan(ellipe(nan)) + assert ellipk(inf) == 0 + assert isinstance(ellipk(inf), mpc) + assert ellipk(-inf) == 0 + assert ellipk(1+0j) == inf + assert ellipe(0).ae(pi/2) + assert ellipe(0.5).ae(pi**(mpf(3)/2)/gamma(0.25)**2 +gamma(0.25)**2/(8*sqrt(pi))) + assert ellipe(1) == 1 + assert ellipe(1+0j) == 1 + assert ellipe(inf) == mpc(0,inf) + assert ellipe(-inf) == inf + assert ellipe(3+4j).ae(1.4995535209333469543-1.5778790079127582745j) + assert ellipe(3-4j).ae(1.4995535209333469543+1.5778790079127582745j) + assert ellipe(-3+4j).ae(2.5804237855343377803-0.8306096791000413778j) + assert ellipe(-3-4j).ae(2.5804237855343377803+0.8306096791000413778j) + assert ellipe(2).ae(0.59907011736779610372+0.59907011736779610372j) + assert ellipe('1e-1000000000').ae(pi/2) + assert ellipk('1e-1000000000').ae(pi/2) + assert ellipe(-pi).ae(2.4535865983838923) + mp.dps = 50 + assert ellipk(1/pi).ae('1.724756270009501831744438120951614673874904182624739673') + assert ellipe(1/pi).ae('1.437129808135123030101542922290970050337425479058225712') + assert ellipk(-10*pi).ae('0.5519067523886233967683646782286965823151896970015484512') + assert ellipe(-10*pi).ae('5.926192483740483797854383268707108012328213431657645509') + v = ellipk(pi) + assert v.real.ae('0.973089521698042334840454592642137667227167622330325225') + assert v.imag.ae('-1.156151296372835303836814390793087600271609993858798016') + v = ellipe(pi) + assert v.real.ae('0.4632848917264710404078033487934663562998345622611263332') + assert v.imag.ae('1.0637961621753130852473300451583414489944099504180510966') + mp.dps = 15 + +def test_exp_integrals(): + mp.dps = 15 + x = +e + z = e + sqrt(3)*j + assert ei(x).ae(8.21168165538361560) + assert li(x).ae(1.89511781635593676) + assert si(x).ae(1.82104026914756705) + assert ci(x).ae(0.213958001340379779) + assert shi(x).ae(4.11520706247846193) + assert chi(x).ae(4.09647459290515367) + assert fresnels(x).ae(0.437189718149787643) + assert fresnelc(x).ae(0.401777759590243012) + assert airyai(x).ae(0.0108502401568586681) + assert airybi(x).ae(8.98245748585468627) + assert ei(z).ae(3.72597969491314951 + 7.34213212314224421j) + assert li(z).ae(2.28662658112562502 + 1.50427225297269364j) + assert si(z).ae(2.48122029237669054 + 0.12684703275254834j) + assert ci(z).ae(0.169255590269456633 - 0.892020751420780353j) + assert shi(z).ae(1.85810366559344468 + 3.66435842914920263j) + assert chi(z).ae(1.86787602931970484 + 3.67777369399304159j) + assert fresnels(z/3).ae(0.034534397197008182 + 0.754859844188218737j) + assert fresnelc(z/3).ae(1.261581645990027372 + 0.417949198775061893j) + assert airyai(z).ae(-0.0162552579839056062 - 0.0018045715700210556j) + assert airybi(z).ae(-4.98856113282883371 + 2.08558537872180623j) + assert li(0) == 0.0 + assert li(1) == -inf + assert li(inf) == inf + assert isinstance(li(0.7), mpf) + assert si(inf).ae(pi/2) + assert si(-inf).ae(-pi/2) + assert ci(inf) == 0 + assert ci(0) == -inf + assert isinstance(ei(-0.7), mpf) + assert airyai(inf) == 0 + assert airybi(inf) == inf + assert airyai(-inf) == 0 + assert airybi(-inf) == 0 + assert fresnels(inf) == 0.5 + assert fresnelc(inf) == 0.5 + assert fresnels(-inf) == -0.5 + assert fresnelc(-inf) == -0.5 + assert shi(0) == 0 + assert shi(inf) == inf + assert shi(-inf) == -inf + assert chi(0) == -inf + assert chi(inf) == inf + +def test_ei(): + mp.dps = 15 + assert ei(0) == -inf + assert ei(inf) == inf + assert ei(-inf) == -0.0 + assert ei(20+70j).ae(6.1041351911152984397e6 - 2.7324109310519928872e6j) + # tests for the asymptotic expansion + # values checked with Mathematica ExpIntegralEi + mp.dps = 50 + r = ei(20000) + s = '3.8781962825045010930273870085501819470698476975019e+8681' + assert str(r) == s + r = ei(-200) + s = '-6.8852261063076355977108174824557929738368086933303e-90' + assert str(r) == s + r =ei(20000 + 10*j) + sre = '-3.255138234032069402493850638874410725961401274106e+8681' + sim = '-2.1081929993474403520785942429469187647767369645423e+8681' + assert str(r.real) == sre and str(r.imag) == sim + mp.dps = 15 + # More asymptotic expansions + assert chi(-10**6+100j).ae('1.3077239389562548386e+434288 + 7.6808956999707408158e+434287j') + assert shi(-10**6+100j).ae('-1.3077239389562548386e+434288 - 7.6808956999707408158e+434287j') + mp.dps = 15 + assert ei(10j).ae(-0.0454564330044553726+3.2291439210137706686j) + assert ei(100j).ae(-0.0051488251426104921+3.1330217936839529126j) + u = ei(fmul(10**20, j, exact=True)) + assert u.real.ae(-6.4525128526578084421345e-21, abs_eps=0, rel_eps=8*eps) + assert u.imag.ae(pi) + assert ei(-10j).ae(-0.0454564330044553726-3.2291439210137706686j) + assert ei(-100j).ae(-0.0051488251426104921-3.1330217936839529126j) + u = ei(fmul(-10**20, j, exact=True)) + assert u.real.ae(-6.4525128526578084421345e-21, abs_eps=0, rel_eps=8*eps) + assert u.imag.ae(-pi) + assert ei(10+10j).ae(-1576.1504265768517448+436.9192317011328140j) + u = ei(-10+10j) + assert u.real.ae(7.6698978415553488362543e-7, abs_eps=0, rel_eps=8*eps) + assert u.imag.ae(3.141595611735621062025) + +def test_e1(): + mp.dps = 15 + assert e1(0) == inf + assert e1(inf) == 0 + assert e1(-inf) == mpc(-inf, -pi) + assert e1(10j).ae(0.045456433004455372635 + 0.087551267423977430100j) + assert e1(100j).ae(0.0051488251426104921444 - 0.0085708599058403258790j) + assert e1(fmul(10**20, j, exact=True)).ae(6.4525128526578084421e-21 - 7.6397040444172830039e-21j, abs_eps=0, rel_eps=8*eps) + assert e1(-10j).ae(0.045456433004455372635 - 0.087551267423977430100j) + assert e1(-100j).ae(0.0051488251426104921444 + 0.0085708599058403258790j) + assert e1(fmul(-10**20, j, exact=True)).ae(6.4525128526578084421e-21 + 7.6397040444172830039e-21j, abs_eps=0, rel_eps=8*eps) + +def test_expint(): + mp.dps = 15 + assert expint(0,0) == inf + assert expint(0,1).ae(1/e) + assert expint(0,1.5).ae(2/exp(1.5)/3) + assert expint(1,1).ae(-ei(-1)) + assert expint(2,0).ae(1) + assert expint(3,0).ae(1/2.) + assert expint(4,0).ae(1/3.) + assert expint(-2, 0.5).ae(26/sqrt(e)) + assert expint(-1,-1) == 0 + assert expint(-2,-1).ae(-e) + assert expint(5.5, 0).ae(2/9.) + assert expint(2.00000001,0).ae(100000000./100000001) + assert expint(2+3j,4-j).ae(0.0023461179581675065414+0.0020395540604713669262j) + assert expint('1.01', '1e-1000').ae(99.9999999899412802) + assert expint('1.000000000001', 3.5).ae(0.00697013985754701819446) + assert expint(2,3).ae(3*ei(-3)+exp(-3)) + assert (expint(10,20)*10**10).ae(0.694439055541231353) + assert expint(3,inf) == 0 + assert expint(3.2,inf) == 0 + assert expint(3.2+2j,inf) == 0 + assert expint(1,3j).ae(-0.11962978600800032763 + 0.27785620120457163717j) + assert expint(1,3).ae(0.013048381094197037413) + assert expint(1,-3).ae(-ei(3)-pi*j) + #assert expint(3) == expint(1,3) + assert expint(1,-20).ae(-25615652.66405658882 - 3.1415926535897932385j) + assert expint(1000000,0).ae(1./999999) + assert expint(0,2+3j).ae(-0.025019798357114678171 + 0.027980439405104419040j) + assert expint(-1,2+3j).ae(-0.022411973626262070419 + 0.038058922011377716932j) + assert expint(-1.5,0) == inf + +def test_trig_integrals(): + mp.dps = 30 + assert si(mpf(1)/1000000).ae('0.000000999999999999944444444444446111') + assert ci(mpf(1)/1000000).ae('-13.2382948930629912435014366276') + assert si(10**10).ae('1.5707963267075846569685111517747537') + assert ci(10**10).ae('-4.87506025174822653785729773959e-11') + assert si(10**100).ae(pi/2) + assert (ci(10**100)*10**100).ae('-0.372376123661276688262086695553') + assert si(-3) == -si(3) + assert ci(-3).ae(ci(3) + pi*j) + # Test complex structure + mp.dps = 15 + assert mp.ci(50).ae(-0.0056283863241163054402) + assert mp.ci(50+2j).ae(-0.018378282946133067149+0.070352808023688336193j) + assert mp.ci(20j).ae(1.28078263320282943611e7+1.5707963267949j) + assert mp.ci(-2+20j).ae(-4.050116856873293505e6+1.207476188206989909e7j) + assert mp.ci(-50+2j).ae(-0.0183782829461330671+3.0712398455661049023j) + assert mp.ci(-50).ae(-0.0056283863241163054+3.1415926535897932385j) + assert mp.ci(-50-2j).ae(-0.0183782829461330671-3.0712398455661049023j) + assert mp.ci(-2-20j).ae(-4.050116856873293505e6-1.207476188206989909e7j) + assert mp.ci(-20j).ae(1.28078263320282943611e7-1.5707963267949j) + assert mp.ci(50-2j).ae(-0.018378282946133067149-0.070352808023688336193j) + assert mp.si(50).ae(1.5516170724859358947) + assert mp.si(50+2j).ae(1.497884414277228461-0.017515007378437448j) + assert mp.si(20j).ae(1.2807826332028294459e7j) + assert mp.si(-2+20j).ae(-1.20747603112735722103e7-4.050116856873293554e6j) + assert mp.si(-50+2j).ae(-1.497884414277228461-0.017515007378437448j) + assert mp.si(-50).ae(-1.5516170724859358947) + assert mp.si(-50-2j).ae(-1.497884414277228461+0.017515007378437448j) + assert mp.si(-2-20j).ae(-1.20747603112735722103e7+4.050116856873293554e6j) + assert mp.si(-20j).ae(-1.2807826332028294459e7j) + assert mp.si(50-2j).ae(1.497884414277228461+0.017515007378437448j) + assert mp.chi(50j).ae(-0.0056283863241163054+1.5707963267948966192j) + assert mp.chi(-2+50j).ae(-0.0183782829461330671+1.6411491348185849554j) + assert mp.chi(-20).ae(1.28078263320282943611e7+3.1415926535898j) + assert mp.chi(-20-2j).ae(-4.050116856873293505e6+1.20747571696809187053e7j) + assert mp.chi(-2-50j).ae(-0.0183782829461330671-1.6411491348185849554j) + assert mp.chi(-50j).ae(-0.0056283863241163054-1.5707963267948966192j) + assert mp.chi(2-50j).ae(-0.0183782829461330671-1.500443518771208283j) + assert mp.chi(20-2j).ae(-4.050116856873293505e6-1.20747603112735722951e7j) + assert mp.chi(20).ae(1.2807826332028294361e7) + assert mp.chi(2+50j).ae(-0.0183782829461330671+1.500443518771208283j) + assert mp.shi(50j).ae(1.5516170724859358947j) + assert mp.shi(-2+50j).ae(0.017515007378437448+1.497884414277228461j) + assert mp.shi(-20).ae(-1.2807826332028294459e7) + assert mp.shi(-20-2j).ae(4.050116856873293554e6-1.20747603112735722103e7j) + assert mp.shi(-2-50j).ae(0.017515007378437448-1.497884414277228461j) + assert mp.shi(-50j).ae(-1.5516170724859358947j) + assert mp.shi(2-50j).ae(-0.017515007378437448-1.497884414277228461j) + assert mp.shi(20-2j).ae(-4.050116856873293554e6-1.20747603112735722103e7j) + assert mp.shi(20).ae(1.2807826332028294459e7) + assert mp.shi(2+50j).ae(-0.017515007378437448+1.497884414277228461j) + def ae(x,y,tol=1e-12): + return abs(x-y) <= abs(y)*tol + assert fp.ci(fp.inf) == 0 + assert ae(fp.ci(fp.ninf), fp.pi*1j) + assert ae(fp.si(fp.inf), fp.pi/2) + assert ae(fp.si(fp.ninf), -fp.pi/2) + assert fp.si(0) == 0 + assert ae(fp.ci(50), -0.0056283863241163054402) + assert ae(fp.ci(50+2j), -0.018378282946133067149+0.070352808023688336193j) + assert ae(fp.ci(20j), 1.28078263320282943611e7+1.5707963267949j) + assert ae(fp.ci(-2+20j), -4.050116856873293505e6+1.207476188206989909e7j) + assert ae(fp.ci(-50+2j), -0.0183782829461330671+3.0712398455661049023j) + assert ae(fp.ci(-50), -0.0056283863241163054+3.1415926535897932385j) + assert ae(fp.ci(-50-2j), -0.0183782829461330671-3.0712398455661049023j) + assert ae(fp.ci(-2-20j), -4.050116856873293505e6-1.207476188206989909e7j) + assert ae(fp.ci(-20j), 1.28078263320282943611e7-1.5707963267949j) + assert ae(fp.ci(50-2j), -0.018378282946133067149-0.070352808023688336193j) + assert ae(fp.si(50), 1.5516170724859358947) + assert ae(fp.si(50+2j), 1.497884414277228461-0.017515007378437448j) + assert ae(fp.si(20j), 1.2807826332028294459e7j) + assert ae(fp.si(-2+20j), -1.20747603112735722103e7-4.050116856873293554e6j) + assert ae(fp.si(-50+2j), -1.497884414277228461-0.017515007378437448j) + assert ae(fp.si(-50), -1.5516170724859358947) + assert ae(fp.si(-50-2j), -1.497884414277228461+0.017515007378437448j) + assert ae(fp.si(-2-20j), -1.20747603112735722103e7+4.050116856873293554e6j) + assert ae(fp.si(-20j), -1.2807826332028294459e7j) + assert ae(fp.si(50-2j), 1.497884414277228461+0.017515007378437448j) + assert ae(fp.chi(50j), -0.0056283863241163054+1.5707963267948966192j) + assert ae(fp.chi(-2+50j), -0.0183782829461330671+1.6411491348185849554j) + assert ae(fp.chi(-20), 1.28078263320282943611e7+3.1415926535898j) + assert ae(fp.chi(-20-2j), -4.050116856873293505e6+1.20747571696809187053e7j) + assert ae(fp.chi(-2-50j), -0.0183782829461330671-1.6411491348185849554j) + assert ae(fp.chi(-50j), -0.0056283863241163054-1.5707963267948966192j) + assert ae(fp.chi(2-50j), -0.0183782829461330671-1.500443518771208283j) + assert ae(fp.chi(20-2j), -4.050116856873293505e6-1.20747603112735722951e7j) + assert ae(fp.chi(20), 1.2807826332028294361e7) + assert ae(fp.chi(2+50j), -0.0183782829461330671+1.500443518771208283j) + assert ae(fp.shi(50j), 1.5516170724859358947j) + assert ae(fp.shi(-2+50j), 0.017515007378437448+1.497884414277228461j) + assert ae(fp.shi(-20), -1.2807826332028294459e7) + assert ae(fp.shi(-20-2j), 4.050116856873293554e6-1.20747603112735722103e7j) + assert ae(fp.shi(-2-50j), 0.017515007378437448-1.497884414277228461j) + assert ae(fp.shi(-50j), -1.5516170724859358947j) + assert ae(fp.shi(2-50j), -0.017515007378437448-1.497884414277228461j) + assert ae(fp.shi(20-2j), -4.050116856873293554e6-1.20747603112735722103e7j) + assert ae(fp.shi(20), 1.2807826332028294459e7) + assert ae(fp.shi(2+50j), -0.017515007378437448+1.497884414277228461j) + +def test_airy(): + mp.dps = 15 + assert (airyai(10)*10**10).ae(1.1047532552898687) + assert (airybi(10)/10**9).ae(0.45564115354822515) + assert (airyai(1000)*10**9158).ae(9.306933063179556004) + assert (airybi(1000)/10**9154).ae(5.4077118391949465477) + assert airyai(-1000).ae(0.055971895773019918842) + assert airybi(-1000).ae(-0.083264574117080633012) + assert (airyai(100+100j)*10**188).ae(2.9099582462207032076 + 2.353013591706178756j) + assert (airybi(100+100j)/10**185).ae(1.7086751714463652039 - 3.1416590020830804578j) + +def test_hyper_0f1(): + mp.dps = 15 + v = 8.63911136507950465 + assert hyper([],[(1,3)],1.5).ae(v) + assert hyper([],[1/3.],1.5).ae(v) + assert hyp0f1(1/3.,1.5).ae(v) + assert hyp0f1((1,3),1.5).ae(v) + # Asymptotic expansion + assert hyp0f1(3,1e9).ae('4.9679055380347771271e+27455') + assert hyp0f1(3,1e9j).ae('-2.1222788784457702157e+19410 + 5.0840597555401854116e+19410j') + +def test_hyper_1f1(): + mp.dps = 15 + v = 1.2917526488617656673 + assert hyper([(1,2)],[(3,2)],0.7).ae(v) + assert hyper([(1,2)],[(3,2)],0.7+0j).ae(v) + assert hyper([0.5],[(3,2)],0.7).ae(v) + assert hyper([0.5],[1.5],0.7).ae(v) + assert hyper([0.5],[(3,2)],0.7+0j).ae(v) + assert hyper([0.5],[1.5],0.7+0j).ae(v) + assert hyper([(1,2)],[1.5+0j],0.7).ae(v) + assert hyper([0.5+0j],[1.5],0.7).ae(v) + assert hyper([0.5+0j],[1.5+0j],0.7+0j).ae(v) + assert hyp1f1(0.5,1.5,0.7).ae(v) + assert hyp1f1((1,2),1.5,0.7).ae(v) + # Asymptotic expansion + assert hyp1f1(2,3,1e10).ae('2.1555012157015796988e+4342944809') + assert (hyp1f1(2,3,1e10j)*10**10).ae(-0.97501205020039745852 - 1.7462392454512132074j) + # Shouldn't use asymptotic expansion + assert hyp1f1(-2, 1, 10000).ae(49980001) + # Bug + assert hyp1f1(1j,fraction(1,3),0.415-69.739j).ae(25.857588206024346592 + 15.738060264515292063j) + +def test_hyper_2f1(): + mp.dps = 15 + v = 1.0652207633823291032 + assert hyper([(1,2), (3,4)], [2], 0.3).ae(v) + assert hyper([(1,2), 0.75], [2], 0.3).ae(v) + assert hyper([0.5, 0.75], [2.0], 0.3).ae(v) + assert hyper([0.5, 0.75], [2.0], 0.3+0j).ae(v) + assert hyper([0.5+0j, (3,4)], [2.0], 0.3+0j).ae(v) + assert hyper([0.5+0j, (3,4)], [2.0], 0.3).ae(v) + assert hyper([0.5, (3,4)], [2.0+0j], 0.3).ae(v) + assert hyper([0.5+0j, 0.75+0j], [2.0+0j], 0.3+0j).ae(v) + v = 1.09234681096223231717 + 0.18104859169479360380j + assert hyper([(1,2),0.75+j], [2], 0.5).ae(v) + assert hyper([0.5,0.75+j], [2.0], 0.5).ae(v) + assert hyper([0.5,0.75+j], [2.0], 0.5+0j).ae(v) + assert hyper([0.5,0.75+j], [2.0+0j], 0.5+0j).ae(v) + v = 0.9625 - 0.125j + assert hyper([(3,2),-1],[4], 0.1+j/3).ae(v) + assert hyper([1.5,-1.0],[4], 0.1+j/3).ae(v) + assert hyper([1.5,-1.0],[4+0j], 0.1+j/3).ae(v) + assert hyper([1.5+0j,-1.0+0j],[4+0j], 0.1+j/3).ae(v) + v = 1.02111069501693445001 - 0.50402252613466859521j + assert hyper([(2,10),(3,10)],[(4,10)],1.5).ae(v) + assert hyper([0.2,(3,10)],[0.4+0j],1.5).ae(v) + assert hyper([0.2,(3,10)],[0.4+0j],1.5+0j).ae(v) + v = 0.76922501362865848528 + 0.32640579593235886194j + assert hyper([(2,10),(3,10)],[(4,10)],4+2j).ae(v) + assert hyper([0.2,(3,10)],[0.4+0j],4+2j).ae(v) + assert hyper([0.2,(3,10)],[(4,10)],4+2j).ae(v) + +def test_hyper_2f1_hard(): + mp.dps = 15 + # Singular cases + assert hyp2f1(2,-1,-1,3).ae(7) + assert hyp2f1(2,-1,-1,3,eliminate_all=True).ae(0.25) + assert hyp2f1(2,-2,-2,3).ae(34) + assert hyp2f1(2,-2,-2,3,eliminate_all=True).ae(0.25) + assert hyp2f1(2,-2,-3,3) == 14 + assert hyp2f1(2,-3,-2,3) == inf + assert hyp2f1(2,-1.5,-1.5,3) == 0.25 + assert hyp2f1(1,2,3,0) == 1 + assert hyp2f1(0,1,0,0) == 1 + assert hyp2f1(0,0,0,0) == 1 + assert isnan(hyp2f1(1,1,0,0)) + assert hyp2f1(2,-1,-5, 0.25+0.25j).ae(1.1+0.1j) + assert hyp2f1(2,-5,-5, 0.25+0.25j, eliminate=False).ae(163./128 + 125./128*j) + assert hyp2f1(0.7235, -1, -5, 0.3).ae(1.04341) + assert hyp2f1(0.7235, -5, -5, 0.3, eliminate=False).ae(1.2939225017815903812) + assert hyp2f1(-1,-2,4,1) == 1.5 + assert hyp2f1(1,2,-3,1) == inf + assert hyp2f1(-2,-2,1,1) == 6 + assert hyp2f1(1,-2,-4,1).ae(5./3) + assert hyp2f1(0,-6,-4,1) == 1 + assert hyp2f1(0,-3,-4,1) == 1 + assert hyp2f1(0,0,0,1) == 1 + assert hyp2f1(1,0,0,1,eliminate=False) == 1 + assert hyp2f1(1,1,0,1) == inf + assert hyp2f1(1,-6,-4,1) == inf + assert hyp2f1(-7.2,-0.5,-4.5,1) == 0 + assert hyp2f1(-7.2,-1,-2,1).ae(-2.6) + assert hyp2f1(1,-0.5,-4.5, 1) == inf + assert hyp2f1(1,0.5,-4.5, 1) == -inf + # Check evaluation on / close to unit circle + z = exp(j*pi/3) + w = (nthroot(2,3)+1)*exp(j*pi/12)/nthroot(3,4)**3 + assert hyp2f1('1/2','1/6','1/3', z).ae(w) + assert hyp2f1('1/2','1/6','1/3', z.conjugate()).ae(w.conjugate()) + assert hyp2f1(0.25, (1,3), 2, '0.999').ae(1.06826449496030635) + assert hyp2f1(0.25, (1,3), 2, '1.001').ae(1.06867299254830309446-0.00001446586793975874j) + assert hyp2f1(0.25, (1,3), 2, -1).ae(0.96656584492524351673) + assert hyp2f1(0.25, (1,3), 2, j).ae(0.99041766248982072266+0.03777135604180735522j) + assert hyp2f1(2,3,5,'0.99').ae(27.699347904322690602) + assert hyp2f1((3,2),-0.5,3,'0.99').ae(0.68403036843911661388) + assert hyp2f1(2,3,5,1j).ae(0.37290667145974386127+0.59210004902748285917j) + assert fsum([hyp2f1((7,10),(2,3),(-1,2), 0.95*exp(j*k)) for k in range(1,15)]).ae(52.851400204289452922+6.244285013912953225j) + assert fsum([hyp2f1((7,10),(2,3),(-1,2), 1.05*exp(j*k)) for k in range(1,15)]).ae(54.506013786220655330-3.000118813413217097j) + assert fsum([hyp2f1((7,10),(2,3),(-1,2), exp(j*k)) for k in range(1,15)]).ae(55.792077935955314887+1.731986485778500241j) + assert hyp2f1(2,2.5,-3.25,0.999).ae(218373932801217082543180041.33) + # Branches + assert hyp2f1(1,1,2,1.01).ae(4.5595744415723676911-3.1104877758314784539j) + assert hyp2f1(1,1,2,1.01+0.1j).ae(2.4149427480552782484+1.4148224796836938829j) + assert hyp2f1(1,1,2,3+4j).ae(0.14576709331407297807+0.48379185417980360773j) + assert hyp2f1(1,1,2,4).ae(-0.27465307216702742285 - 0.78539816339744830962j) + assert hyp2f1(1,1,2,-4).ae(0.40235947810852509365) + # Other: + # Cancellation with a large parameter involved (bug reported on sage-devel) + assert hyp2f1(112, (51,10), (-9,10), -0.99999).ae(-1.6241361047970862961e-24, abs_eps=0, rel_eps=eps*16) + +def test_hyper_3f2_etc(): + assert hyper([1,2,3],[1.5,8],-1).ae(0.67108992351533333030) + assert hyper([1,2,3,4],[5,6,7], -1).ae(0.90232988035425506008) + assert hyper([1,2,3],[1.25,5], 1).ae(28.924181329701905701) + assert hyper([1,2,3,4],[5,6,7],5).ae(1.5192307344006649499-1.1529845225075537461j) + assert hyper([1,2,3,4,5],[6,7,8,9],-1).ae(0.96288759462882357253) + assert hyper([1,2,3,4,5],[6,7,8,9],1).ae(1.0428697385885855841) + assert hyper([1,2,3,4,5],[6,7,8,9],5).ae(1.33980653631074769423-0.07143405251029226699j) + assert hyper([1,2.79,3.08,4.37],[5.2,6.1,7.3],5).ae(1.0996321464692607231-1.7748052293979985001j) + assert hyper([1,1,1],[1,2],1) == inf + assert hyper([1,1,1],[2,(101,100)],1).ae(100.01621213528313220) + # slow -- covered by doctests + #assert hyper([1,1,1],[2,3],0.9999).ae(1.2897972005319693905) + +def test_hyper_u(): + mp.dps = 15 + assert hyperu(2,-3,0).ae(0.05) + assert hyperu(2,-3.5,0).ae(4./99) + assert hyperu(2,0,0) == 0.5 + assert hyperu(-5,1,0) == -120 + assert hyperu(-5,2,0) == inf + assert hyperu(-5,-2,0) == 0 + assert hyperu(7,7,3).ae(0.00014681269365593503986) #exp(3)*gammainc(-6,3) + assert hyperu(2,-3,4).ae(0.011836478100271995559) + assert hyperu(3,4,5).ae(1./125) + assert hyperu(2,3,0.0625) == 256 + assert hyperu(-1,2,0.25+0.5j) == -1.75+0.5j + assert hyperu(0.5,1.5,7.25).ae(2/sqrt(29)) + assert hyperu(2,6,pi).ae(0.55804439825913399130) + assert (hyperu((3,2),8,100+201j)*10**4).ae(-0.3797318333856738798 - 2.9974928453561707782j) + assert (hyperu((5,2),(-1,2),-5000)*10**10).ae(-5.6681877926881664678j) + # XXX: fails because of undetected cancellation in low level series code + # Alternatively: could use asymptotic series here, if convergence test + # tweaked back to recognize this one + #assert (hyperu((5,2),(-1,2),-500)*10**7).ae(-1.82526906001593252847j) + +def test_hyper_2f0(): + mp.dps = 15 + assert hyper([1,2],[],3) == hyp2f0(1,2,3) + assert hyp2f0(2,3,7).ae(0.0116108068639728714668 - 0.0073727413865865802130j) + assert hyp2f0(2,3,0) == 1 + assert hyp2f0(0,0,0) == 1 + assert hyp2f0(-1,-1,1).ae(2) + assert hyp2f0(-4,1,1.5).ae(62.5) + assert hyp2f0(-4,1,50).ae(147029801) + assert hyp2f0(-4,1,0.0001).ae(0.99960011997600240000) + assert hyp2f0(0.5,0.25,0.001).ae(1.0001251174078538115) + assert hyp2f0(0.5,0.25,3+4j).ae(0.85548875824755163518 + 0.21636041283392292973j) + # Important: cancellation check + assert hyp2f0((1,6),(5,6),-0.02371708245126284498).ae(0.996785723120804309) + # Should be exact; polynomial case + assert hyp2f0(-2,1,0.5+0.5j,zeroprec=200) == 0 + assert hyp2f0(1,-2,0.5+0.5j,zeroprec=200) == 0 + # There used to be a bug in thresholds that made one of the following hang + for d in [15, 50, 80]: + mp.dps = d + assert hyp2f0(1.5, 0.5, 0.009).ae('1.006867007239309717945323585695344927904000945829843527398772456281301440034218290443367270629519483 + 1.238277162240704919639384945859073461954721356062919829456053965502443570466701567100438048602352623e-46j') + +def test_hyper_1f2(): + mp.dps = 15 + assert hyper([1],[2,3],4) == hyp1f2(1,2,3,4) + a1,b1,b2 = (1,10),(2,3),1./16 + assert hyp1f2(a1,b1,b2,10).ae(298.7482725554557568) + assert hyp1f2(a1,b1,b2,100).ae(224128961.48602947604) + assert hyp1f2(a1,b1,b2,1000).ae(1.1669528298622675109e+27) + assert hyp1f2(a1,b1,b2,10000).ae(2.4780514622487212192e+86) + assert hyp1f2(a1,b1,b2,100000).ae(1.3885391458871523997e+274) + assert hyp1f2(a1,b1,b2,1000000).ae('9.8851796978960318255e+867') + assert hyp1f2(a1,b1,b2,10**7).ae('1.1505659189516303646e+2746') + assert hyp1f2(a1,b1,b2,10**8).ae('1.4672005404314334081e+8685') + assert hyp1f2(a1,b1,b2,10**20).ae('3.6888217332150976493e+8685889636') + assert hyp1f2(a1,b1,b2,10*j).ae(-16.163252524618572878 - 44.321567896480184312j) + assert hyp1f2(a1,b1,b2,100*j).ae(61938.155294517848171 + 637349.45215942348739j) + assert hyp1f2(a1,b1,b2,1000*j).ae(8455057657257695958.7 + 6261969266997571510.6j) + assert hyp1f2(a1,b1,b2,10000*j).ae(-8.9771211184008593089e+60 + 4.6550528111731631456e+59j) + assert hyp1f2(a1,b1,b2,100000*j).ae(2.6398091437239324225e+193 + 4.1658080666870618332e+193j) + assert hyp1f2(a1,b1,b2,1000000*j).ae('3.5999042951925965458e+613 + 1.5026014707128947992e+613j') + assert hyp1f2(a1,b1,b2,10**7*j).ae('-8.3208715051623234801e+1939 - 3.6752883490851869429e+1941j') + assert hyp1f2(a1,b1,b2,10**8*j).ae('2.0724195707891484454e+6140 - 1.3276619482724266387e+6141j') + assert hyp1f2(a1,b1,b2,10**20*j).ae('-1.1734497974795488504e+6141851462 + 1.1498106965385471542e+6141851462j') + +def test_hyper_2f3(): + mp.dps = 15 + assert hyper([1,2],[3,4,5],6) == hyp2f3(1,2,3,4,5,6) + a1,a2,b1,b2,b3 = (1,10),(2,3),(3,10), 2, 1./16 + # Check asymptotic expansion + assert hyp2f3(a1,a2,b1,b2,b3,10).ae(128.98207160698659976) + assert hyp2f3(a1,a2,b1,b2,b3,1000).ae(6.6309632883131273141e25) + assert hyp2f3(a1,a2,b1,b2,b3,10000).ae(4.6863639362713340539e84) + assert hyp2f3(a1,a2,b1,b2,b3,100000).ae(8.6632451236103084119e271) + assert hyp2f3(a1,a2,b1,b2,b3,10**6).ae('2.0291718386574980641e865') + assert hyp2f3(a1,a2,b1,b2,b3,10**7).ae('7.7639836665710030977e2742') + assert hyp2f3(a1,a2,b1,b2,b3,10**8).ae('3.2537462584071268759e8681') + assert hyp2f3(a1,a2,b1,b2,b3,10**20).ae('1.2966030542911614163e+8685889627') + assert hyp2f3(a1,a2,b1,b2,b3,10*j).ae(-18.551602185587547854 - 13.348031097874113552j) + assert hyp2f3(a1,a2,b1,b2,b3,100*j).ae(78634.359124504488695 + 74459.535945281973996j) + assert hyp2f3(a1,a2,b1,b2,b3,1000*j).ae(597682550276527901.59 - 65136194809352613.078j) + assert hyp2f3(a1,a2,b1,b2,b3,10000*j).ae(-1.1779696326238582496e+59 + 1.2297607505213133872e+59j) + assert hyp2f3(a1,a2,b1,b2,b3,100000*j).ae(2.9844228969804380301e+191 + 7.5587163231490273296e+190j) + assert hyp2f3(a1,a2,b1,b2,b3,1000000*j).ae('7.4859161049322370311e+610 - 2.8467477015940090189e+610j') + assert hyp2f3(a1,a2,b1,b2,b3,10**7*j).ae('-1.7477645579418800826e+1938 - 1.7606522995808116405e+1938j') + assert hyp2f3(a1,a2,b1,b2,b3,10**8*j).ae('-1.6932731942958401784e+6137 - 2.4521909113114629368e+6137j') + assert hyp2f3(a1,a2,b1,b2,b3,10**20*j).ae('-2.0988815677627225449e+6141851451 + 5.7708223542739208681e+6141851452j') + +def test_hyper_2f2(): + mp.dps = 15 + assert hyper([1,2],[3,4],5) == hyp2f2(1,2,3,4,5) + a1,a2,b1,b2 = (3,10),4,(1,2),1./16 + assert hyp2f2(a1,a2,b1,b2,10).ae(448225936.3377556696) + assert hyp2f2(a1,a2,b1,b2,10000).ae('1.2012553712966636711e+4358') + assert hyp2f2(a1,a2,b1,b2,-20000).ae(-0.04182343755661214626) + assert hyp2f2(a1,a2,b1,b2,10**20).ae('1.1148680024303263661e+43429448190325182840') + +def test_orthpoly(): + mp.dps = 15 + assert jacobi(-4,2,3,0.7).ae(22800./4913) + assert jacobi(3,2,4,5.5) == 4133.125 + assert jacobi(1.5,5/6.,4,0).ae(-1.0851951434075508417) + assert jacobi(-2, 1, 2, 4).ae(-0.16) + assert jacobi(2, -1, 2.5, 4).ae(34.59375) + #assert jacobi(2, -1, 2, 4) == 28.5 + assert legendre(5, 7) == 129367 + assert legendre(0.5,0).ae(0.53935260118837935667) + assert legendre(-1,-1) == 1 + assert legendre(0,-1) == 1 + assert legendre(0, 1) == 1 + assert legendre(1, -1) == -1 + assert legendre(7, 1) == 1 + assert legendre(7, -1) == -1 + assert legendre(8,1.5).ae(15457523./32768) + assert legendre(j,-j).ae(2.4448182735671431011 + 0.6928881737669934843j) + assert chebyu(5,1) == 6 + assert chebyt(3,2) == 26 + assert legendre(3.5,-1) == inf + assert legendre(4.5,-1) == -inf + assert legendre(3.5+1j,-1) == mpc(inf,inf) + assert legendre(4.5+1j,-1) == mpc(-inf,-inf) + assert laguerre(4, -2, 3).ae(-1.125) + assert laguerre(3, 1+j, 0.5).ae(0.2291666666666666667 + 2.5416666666666666667j) + +def test_hermite(): + mp.dps = 15 + assert hermite(-2, 0).ae(0.5) + assert hermite(-1, 0).ae(0.88622692545275801365) + assert hermite(0, 0).ae(1) + assert hermite(1, 0) == 0 + assert hermite(2, 0).ae(-2) + assert hermite(0, 2).ae(1) + assert hermite(1, 2).ae(4) + assert hermite(1, -2).ae(-4) + assert hermite(2, -2).ae(14) + assert hermite(0.5, 0).ae(0.69136733903629335053) + assert hermite(9, 0) == 0 + assert hermite(4,4).ae(3340) + assert hermite(3,4).ae(464) + assert hermite(-4,4).ae(0.00018623860287512396181) + assert hermite(-3,4).ae(0.0016540169879668766270) + assert hermite(9, 2.5j).ae(13638725j) + assert hermite(9, -2.5j).ae(-13638725j) + assert hermite(9, 100).ae(511078883759363024000) + assert hermite(9, -100).ae(-511078883759363024000) + assert hermite(9, 100j).ae(512922083920643024000j) + assert hermite(9, -100j).ae(-512922083920643024000j) + assert hermite(-9.5, 2.5j).ae(-2.9004951258126778174e-6 + 1.7601372934039951100e-6j) + assert hermite(-9.5, -2.5j).ae(-2.9004951258126778174e-6 - 1.7601372934039951100e-6j) + assert hermite(-9.5, 100).ae(1.3776300722767084162e-22, abs_eps=0, rel_eps=eps) + assert hermite(-9.5, -100).ae('1.3106082028470671626e4355') + assert hermite(-9.5, 100j).ae(-9.7900218581864768430e-23 - 9.7900218581864768430e-23j, abs_eps=0, rel_eps=eps) + assert hermite(-9.5, -100j).ae(-9.7900218581864768430e-23 + 9.7900218581864768430e-23j, abs_eps=0, rel_eps=eps) + assert hermite(2+3j, -1-j).ae(851.3677063883687676 - 1496.4373467871007997j) + +def test_gegenbauer(): + mp.dps = 15 + assert gegenbauer(1,2,3).ae(12) + assert gegenbauer(2,3,4).ae(381) + assert gegenbauer(0,0,0) == 0 + assert gegenbauer(2,-1,3) == 0 + assert gegenbauer(-7, 0.5, 3).ae(8989) + assert gegenbauer(1, -0.5, 3).ae(-3) + assert gegenbauer(1, -1.5, 3).ae(-9) + assert gegenbauer(1, -0.5, 3).ae(-3) + assert gegenbauer(-0.5, -0.5, 3).ae(-2.6383553159023906245) + assert gegenbauer(2+3j, 1-j, 3+4j).ae(14.880536623203696780 + 20.022029711598032898j) + #assert gegenbauer(-2, -0.5, 3).ae(-12) + +def test_legenp(): + mp.dps = 15 + assert legenp(2,0,4) == legendre(2,4) + assert legenp(-2, -1, 0.5).ae(0.43301270189221932338) + assert legenp(-2, -1, 0.5, type=3).ae(0.43301270189221932338j) + assert legenp(-2, 1, 0.5).ae(-0.86602540378443864676) + assert legenp(2+j, 3+4j, -j).ae(134742.98773236786148 + 429782.72924463851745j) + assert legenp(2+j, 3+4j, -j, type=3).ae(802.59463394152268507 - 251.62481308942906447j) + assert legenp(2,4,3).ae(0) + assert legenp(2,4,3,type=3).ae(0) + assert legenp(2,1,0.5).ae(-1.2990381056766579701) + assert legenp(2,1,0.5,type=3).ae(1.2990381056766579701j) + assert legenp(3,2,3).ae(-360) + assert legenp(3,3,3).ae(240j*2**0.5) + assert legenp(3,4,3).ae(0) + assert legenp(0,0.5,2).ae(0.52503756790433198939 - 0.52503756790433198939j) + assert legenp(-1,-0.5,2).ae(0.60626116232846498110 + 0.60626116232846498110j) + assert legenp(-2,0.5,2).ae(1.5751127037129959682 - 1.5751127037129959682j) + assert legenp(-2,0.5,-0.5).ae(-0.85738275810499171286) + +def test_legenq(): + mp.dps = 15 + f = legenq + # Evaluation at poles + assert isnan(f(3,2,1)) + assert isnan(f(3,2,-1)) + assert isnan(f(3,2,1,type=3)) + assert isnan(f(3,2,-1,type=3)) + # Evaluation at 0 + assert f(0,1,0,type=2).ae(-1) + assert f(-2,2,0,type=2,zeroprec=200).ae(0) + assert f(1.5,3,0,type=2).ae(-2.2239343475841951023) + assert f(0,1,0,type=3).ae(j) + assert f(-2,2,0,type=3,zeroprec=200).ae(0) + assert f(1.5,3,0,type=3).ae(2.2239343475841951022*(1-1j)) + # Standard case, degree 0 + assert f(0,0,-1.5).ae(-0.8047189562170501873 + 1.5707963267948966192j) + assert f(0,0,-0.5).ae(-0.54930614433405484570) + assert f(0,0,0,zeroprec=200).ae(0) + assert f(0,0,0.5).ae(0.54930614433405484570) + assert f(0,0,1.5).ae(0.8047189562170501873 - 1.5707963267948966192j) + assert f(0,0,-1.5,type=3).ae(-0.80471895621705018730) + assert f(0,0,-0.5,type=3).ae(-0.5493061443340548457 - 1.5707963267948966192j) + assert f(0,0,0,type=3).ae(-1.5707963267948966192j) + assert f(0,0,0.5,type=3).ae(0.5493061443340548457 - 1.5707963267948966192j) + assert f(0,0,1.5,type=3).ae(0.80471895621705018730) + # Standard case, degree 1 + assert f(1,0,-1.5).ae(0.2070784343255752810 - 2.3561944901923449288j) + assert f(1,0,-0.5).ae(-0.72534692783297257715) + assert f(1,0,0).ae(-1) + assert f(1,0,0.5).ae(-0.72534692783297257715) + assert f(1,0,1.5).ae(0.2070784343255752810 - 2.3561944901923449288j) + # Standard case, degree 2 + assert f(2,0,-1.5).ae(-0.0635669991240192885 + 4.5160394395353277803j) + assert f(2,0,-0.5).ae(0.81866326804175685571) + assert f(2,0,0,zeroprec=200).ae(0) + assert f(2,0,0.5).ae(-0.81866326804175685571) + assert f(2,0,1.5).ae(0.0635669991240192885 - 4.5160394395353277803j) + # Misc orders and degrees + assert f(2,3,1.5,type=2).ae(-5.7243340223994616228j) + assert f(2,3,1.5,type=3).ae(-5.7243340223994616228) + assert f(2,3,0.5,type=2).ae(-12.316805742712016310) + assert f(2,3,0.5,type=3).ae(-12.316805742712016310j) + assert f(2,3,-1.5,type=2).ae(-5.7243340223994616228j) + assert f(2,3,-1.5,type=3).ae(5.7243340223994616228) + assert f(2,3,-0.5,type=2).ae(-12.316805742712016310) + assert f(2,3,-0.5,type=3).ae(-12.316805742712016310j) + assert f(2+3j, 3+4j, 0.5, type=3).ae(0.0016119404873235186807 - 0.0005885900510718119836j) + assert f(2+3j, 3+4j, -1.5, type=3).ae(0.008451400254138808670 + 0.020645193304593235298j) + assert f(-2.5,1,-1.5).ae(3.9553395527435335749j) + assert f(-2.5,1,-0.5).ae(1.9290561746445456908) + assert f(-2.5,1,0).ae(1.2708196271909686299) + assert f(-2.5,1,0.5).ae(-0.31584812990742202869) + assert f(-2.5,1,1.5).ae(-3.9553395527435335742 + 0.2993235655044701706j) + assert f(-2.5,1,-1.5,type=3).ae(0.29932356550447017254j) + assert f(-2.5,1,-0.5,type=3).ae(-0.3158481299074220287 - 1.9290561746445456908j) + assert f(-2.5,1,0,type=3).ae(1.2708196271909686292 - 1.2708196271909686299j) + assert f(-2.5,1,0.5,type=3).ae(1.9290561746445456907 + 0.3158481299074220287j) + assert f(-2.5,1,1.5,type=3).ae(-0.29932356550447017254) + +def test_agm(): + mp.dps = 15 + assert agm(0,0) == 0 + assert agm(0,1) == 0 + assert agm(1,1) == 1 + assert agm(7,7) == 7 + assert agm(j,j) == j + assert (1/agm(1,sqrt(2))).ae(0.834626841674073186) + assert agm(1,2).ae(1.4567910310469068692) + assert agm(1,3).ae(1.8636167832448965424) + assert agm(1,j).ae(0.599070117367796104+0.599070117367796104j) + assert agm(2) == agm(1,2) + assert agm(-3,4).ae(0.63468509766550907+1.3443087080896272j) + +def test_gammainc(): + mp.dps = 15 + assert gammainc(2,5).ae(6*exp(-5)) + assert gammainc(2,0,5).ae(1-6*exp(-5)) + assert gammainc(2,3,5).ae(-6*exp(-5)+4*exp(-3)) + assert gammainc(-2.5,-0.5).ae(-0.9453087204829418812-5.3164237738936178621j) + assert gammainc(0,2,4).ae(0.045121158298212213088) + assert gammainc(0,3).ae(0.013048381094197037413) + assert gammainc(0,2+j,1-j).ae(0.00910653685850304839-0.22378752918074432574j) + assert gammainc(0,1-j).ae(0.00028162445198141833+0.17932453503935894015j) + assert gammainc(3,4,5,True).ae(0.11345128607046320253) + assert gammainc(3.5,0,inf).ae(gamma(3.5)) + assert gammainc(-150.5,500).ae('6.9825435345798951153e-627') + assert gammainc(-150.5,800).ae('4.6885137549474089431e-788') + assert gammainc(-3.5, -20.5).ae(0.27008820585226911 - 1310.31447140574997636j) + assert gammainc(-3.5, -200.5).ae(0.27008820585226911 - 5.3264597096208368435e76j) # XXX real part + assert gammainc(0,0,2) == inf + assert gammainc(1,b=1).ae(0.6321205588285576784) + assert gammainc(3,2,2) == 0 + assert gammainc(2,3+j,3-j).ae(-0.28135485191849314194j) + assert gammainc(4+0j,1).ae(5.8860710587430771455) + # GH issue #301 + assert gammainc(-1,-1).ae(-0.8231640121031084799 + 3.1415926535897932385j) + assert gammainc(-2,-1).ae(1.7707229202810768576 - 1.5707963267948966192j) + assert gammainc(-3,-1).ae(-1.4963349162467073643 + 0.5235987755982988731j) + assert gammainc(-4,-1).ae(1.05365418617643814992 - 0.13089969389957471827j) + # Regularized upper gamma + assert isnan(gammainc(0, 0, regularized=True)) + assert gammainc(-1, 0, regularized=True) == inf + assert gammainc(1, 0, regularized=True) == 1 + assert gammainc(0, 5, regularized=True) == 0 + assert gammainc(0, 2+3j, regularized=True) == 0 + assert gammainc(0, 5000, regularized=True) == 0 + assert gammainc(0, 10**30, regularized=True) == 0 + assert gammainc(-1, 5, regularized=True) == 0 + assert gammainc(-1, 5000, regularized=True) == 0 + assert gammainc(-1, 10**30, regularized=True) == 0 + assert gammainc(-1, -5, regularized=True) == 0 + assert gammainc(-1, -5000, regularized=True) == 0 + assert gammainc(-1, -10**30, regularized=True) == 0 + assert gammainc(-1, 3+4j, regularized=True) == 0 + assert gammainc(1, 5, regularized=True).ae(exp(-5)) + assert gammainc(1, 5000, regularized=True).ae(exp(-5000)) + assert gammainc(1, 10**30, regularized=True).ae(exp(-10**30)) + assert gammainc(1, 3+4j, regularized=True).ae(exp(-3-4j)) + assert gammainc(-1000000,2).ae('1.3669297209397347754e-301037', abs_eps=0, rel_eps=8*eps) + assert gammainc(-1000000,2,regularized=True) == 0 + assert gammainc(-1000000,3+4j).ae('-1.322575609404222361e-698979 - 4.9274570591854533273e-698978j', abs_eps=0, rel_eps=8*eps) + assert gammainc(-1000000,3+4j,regularized=True) == 0 + assert gammainc(2+3j, 4+5j, regularized=True).ae(0.085422013530993285774-0.052595379150390078503j) + assert gammainc(1000j, 1000j, regularized=True).ae(0.49702647628921131761 + 0.00297355675013575341j) + # Generalized + assert gammainc(3,4,2) == -gammainc(3,2,4) + assert gammainc(4, 2, 3).ae(1.2593494302978947396) + assert gammainc(4, 2, 3, regularized=True).ae(0.20989157171631578993) + assert gammainc(0, 2, 3).ae(0.035852129613864082155) + assert gammainc(0, 2, 3, regularized=True) == 0 + assert gammainc(-1, 2, 3).ae(0.015219822548487616132) + assert gammainc(-1, 2, 3, regularized=True) == 0 + assert gammainc(0, 2, 3).ae(0.035852129613864082155) + assert gammainc(0, 2, 3, regularized=True) == 0 + # Should use upper gammas + assert gammainc(5, 10000, 12000).ae('1.1359381951461801687e-4327', abs_eps=0, rel_eps=8*eps) + # Should use lower gammas + assert gammainc(10000, 2, 3).ae('8.1244514125995785934e4765') + # GH issue 306 + assert gammainc(3,-1-1j) == 0 + assert gammainc(3,-1+1j) == 0 + assert gammainc(2,-1) == 0 + assert gammainc(2,-1+0j) == 0 + assert gammainc(2+0j,-1) == 0 + +def test_gammainc_expint_n(): + # These tests are intended to check all cases of the low-level code + # for upper gamma and expint with small integer index. + # Need to cover positive/negative arguments; small/large/huge arguments + # for both positive and negative indices, as well as indices 0 and 1 + # which may be special-cased + mp.dps = 15 + assert expint(-3,3.5).ae(0.021456366563296693987) + assert expint(-2,3.5).ae(0.014966633183073309405) + assert expint(-1,3.5).ae(0.011092916359219041088) + assert expint(0,3.5).ae(0.0086278238349481430685) + assert expint(1,3.5).ae(0.0069701398575483929193) + assert expint(2,3.5).ae(0.0058018939208991255223) + assert expint(3,3.5).ae(0.0049453773495857807058) + assert expint(-3,-3.5).ae(-4.6618170604073311319) + assert expint(-2,-3.5).ae(-5.5996974157555515963) + assert expint(-1,-3.5).ae(-6.7582555017739415818) + assert expint(0,-3.5).ae(-9.4615577024835182145) + assert expint(1,-3.5).ae(-13.925353995152335292 - 3.1415926535897932385j) + assert expint(2,-3.5).ae(-15.62328702434085977 - 10.995574287564276335j) + assert expint(3,-3.5).ae(-10.783026313250347722 - 19.242255003237483586j) + assert expint(-3,350).ae(2.8614825451252838069e-155, abs_eps=0, rel_eps=8*eps) + assert expint(-2,350).ae(2.8532837224504675901e-155, abs_eps=0, rel_eps=8*eps) + assert expint(-1,350).ae(2.8451316155828634555e-155, abs_eps=0, rel_eps=8*eps) + assert expint(0,350).ae(2.8370258275042797989e-155, abs_eps=0, rel_eps=8*eps) + assert expint(1,350).ae(2.8289659656701459404e-155, abs_eps=0, rel_eps=8*eps) + assert expint(2,350).ae(2.8209516419468505006e-155, abs_eps=0, rel_eps=8*eps) + assert expint(3,350).ae(2.8129824725501272171e-155, abs_eps=0, rel_eps=8*eps) + assert expint(-3,-350).ae(-2.8528796154044839443e+149) + assert expint(-2,-350).ae(-2.8610072121701264351e+149) + assert expint(-1,-350).ae(-2.8691813842677537647e+149) + assert expint(0,-350).ae(-2.8774025343659421709e+149) + u = expint(1,-350) + assert u.ae(-2.8856710698020863568e+149) + assert u.imag.ae(-3.1415926535897932385) + u = expint(2,-350) + assert u.ae(-2.8939874026504650534e+149) + assert u.imag.ae(-1099.5574287564276335) + u = expint(3,-350) + assert u.ae(-2.9023519497915044349e+149) + assert u.imag.ae(-192422.55003237483586) + assert expint(-3,350000000000000000000000).ae('2.1592908471792544286e-152003068666138139677919', abs_eps=0, rel_eps=8*eps) + assert expint(-2,350000000000000000000000).ae('2.1592908471792544286e-152003068666138139677919', abs_eps=0, rel_eps=8*eps) + assert expint(-1,350000000000000000000000).ae('2.1592908471792544286e-152003068666138139677919', abs_eps=0, rel_eps=8*eps) + assert expint(0,350000000000000000000000).ae('2.1592908471792544286e-152003068666138139677919', abs_eps=0, rel_eps=8*eps) + assert expint(1,350000000000000000000000).ae('2.1592908471792544286e-152003068666138139677919', abs_eps=0, rel_eps=8*eps) + assert expint(2,350000000000000000000000).ae('2.1592908471792544286e-152003068666138139677919', abs_eps=0, rel_eps=8*eps) + assert expint(3,350000000000000000000000).ae('2.1592908471792544286e-152003068666138139677919', abs_eps=0, rel_eps=8*eps) + assert expint(-3,-350000000000000000000000).ae('-3.7805306852415755699e+152003068666138139677871') + assert expint(-2,-350000000000000000000000).ae('-3.7805306852415755699e+152003068666138139677871') + assert expint(-1,-350000000000000000000000).ae('-3.7805306852415755699e+152003068666138139677871') + assert expint(0,-350000000000000000000000).ae('-3.7805306852415755699e+152003068666138139677871') + u = expint(1,-350000000000000000000000) + assert u.ae('-3.7805306852415755699e+152003068666138139677871') + assert u.imag.ae(-3.1415926535897932385) + u = expint(2,-350000000000000000000000) + assert u.imag.ae(-1.0995574287564276335e+24) + assert u.ae('-3.7805306852415755699e+152003068666138139677871') + u = expint(3,-350000000000000000000000) + assert u.imag.ae(-1.9242255003237483586e+47) + assert u.ae('-3.7805306852415755699e+152003068666138139677871') + # Small case; no branch cut + assert gammainc(-3,3.5).ae(0.00010020262545203707109) + assert gammainc(-2,3.5).ae(0.00040370427343557393517) + assert gammainc(-1,3.5).ae(0.0016576839773997501492) + assert gammainc(0,3.5).ae(0.0069701398575483929193) + assert gammainc(1,3.5).ae(0.03019738342231850074) + assert gammainc(2,3.5).ae(0.13588822540043325333) + assert gammainc(3,3.5).ae(0.64169439772426814072) + # Small case; with branch cut + assert gammainc(-3,-3.5).ae(0.03595832954467563286 + 0.52359877559829887308j) + assert gammainc(-2,-3.5).ae(-0.88024704597962022221 - 1.5707963267948966192j) + assert gammainc(-1,-3.5).ae(4.4637962926688170771 + 3.1415926535897932385j) + assert gammainc(0,-3.5).ae(-13.925353995152335292 - 3.1415926535897932385j) + assert gammainc(1,-3.5).ae(33.115451958692313751) + assert gammainc(2,-3.5).ae(-82.788629896730784377) + assert gammainc(3,-3.5).ae(240.08702670051927469) + # Asymptotic case; no branch cut + assert gammainc(-3,350).ae(6.5424095113340358813e-163, abs_eps=0, rel_eps=8*eps) + assert gammainc(-2,350).ae(2.296312222489899769e-160, abs_eps=0, rel_eps=8*eps) + assert gammainc(-1,350).ae(8.059861834133858573e-158, abs_eps=0, rel_eps=8*eps) + assert gammainc(0,350).ae(2.8289659656701459404e-155, abs_eps=0, rel_eps=8*eps) + assert gammainc(1,350).ae(9.9295903962649792963e-153, abs_eps=0, rel_eps=8*eps) + assert gammainc(2,350).ae(3.485286229089007733e-150, abs_eps=0, rel_eps=8*eps) + assert gammainc(3,350).ae(1.2233453960006379793e-147, abs_eps=0, rel_eps=8*eps) + # Asymptotic case; branch cut + u = gammainc(-3,-350) + assert u.ae(6.7889565783842895085e+141) + assert u.imag.ae(0.52359877559829887308) + u = gammainc(-2,-350) + assert u.ae(-2.3692668977889832121e+144) + assert u.imag.ae(-1.5707963267948966192) + u = gammainc(-1,-350) + assert u.ae(8.2685354361441858669e+146) + assert u.imag.ae(3.1415926535897932385) + u = gammainc(0,-350) + assert u.ae(-2.8856710698020863568e+149) + assert u.imag.ae(-3.1415926535897932385) + u = gammainc(1,-350) + assert u.ae(1.0070908870280797598e+152) + assert u.imag == 0 + u = gammainc(2,-350) + assert u.ae(-3.5147471957279983618e+154) + assert u.imag == 0 + u = gammainc(3,-350) + assert u.ae(1.2266568422179417091e+157) + assert u.imag == 0 + # Extreme asymptotic case + assert gammainc(-3,350000000000000000000000).ae('5.0362468738874738859e-152003068666138139677990', abs_eps=0, rel_eps=8*eps) + assert gammainc(-2,350000000000000000000000).ae('1.7626864058606158601e-152003068666138139677966', abs_eps=0, rel_eps=8*eps) + assert gammainc(-1,350000000000000000000000).ae('6.1694024205121555102e-152003068666138139677943', abs_eps=0, rel_eps=8*eps) + assert gammainc(0,350000000000000000000000).ae('2.1592908471792544286e-152003068666138139677919', abs_eps=0, rel_eps=8*eps) + assert gammainc(1,350000000000000000000000).ae('7.5575179651273905e-152003068666138139677896', abs_eps=0, rel_eps=8*eps) + assert gammainc(2,350000000000000000000000).ae('2.645131287794586675e-152003068666138139677872', abs_eps=0, rel_eps=8*eps) + assert gammainc(3,350000000000000000000000).ae('9.2579595072810533625e-152003068666138139677849', abs_eps=0, rel_eps=8*eps) + u = gammainc(-3,-350000000000000000000000) + assert u.ae('8.8175642804468234866e+152003068666138139677800') + assert u.imag.ae(0.52359877559829887308) + u = gammainc(-2,-350000000000000000000000) + assert u.ae('-3.0861474981563882203e+152003068666138139677824') + assert u.imag.ae(-1.5707963267948966192) + u = gammainc(-1,-350000000000000000000000) + assert u.ae('1.0801516243547358771e+152003068666138139677848') + assert u.imag.ae(3.1415926535897932385) + u = gammainc(0,-350000000000000000000000) + assert u.ae('-3.7805306852415755699e+152003068666138139677871') + assert u.imag.ae(-3.1415926535897932385) + assert gammainc(1,-350000000000000000000000).ae('1.3231857398345514495e+152003068666138139677895') + assert gammainc(2,-350000000000000000000000).ae('-4.6311500894209300731e+152003068666138139677918') + assert gammainc(3,-350000000000000000000000).ae('1.6209025312973255256e+152003068666138139677942') + +def test_incomplete_beta(): + mp.dps = 15 + assert betainc(-2,-3,0.5,0.75).ae(63.4305673311255413583969) + assert betainc(4.5,0.5+2j,2.5,6).ae(0.2628801146130621387903065 + 0.5162565234467020592855378j) + assert betainc(4,5,0,6).ae(90747.77142857142857142857) + +def test_erf(): + mp.dps = 15 + assert erf(0) == 0 + assert erf(1).ae(0.84270079294971486934) + assert erf(3+4j).ae(-120.186991395079444098 - 27.750337293623902498j) + assert erf(-4-3j).ae(-0.99991066178539168236 + 0.00004972026054496604j) + assert erf(pi).ae(0.99999112385363235839) + assert erf(1j).ae(1.6504257587975428760j) + assert erf(-1j).ae(-1.6504257587975428760j) + assert isinstance(erf(1), mpf) + assert isinstance(erf(-1), mpf) + assert isinstance(erf(0), mpf) + assert isinstance(erf(0j), mpc) + assert erf(inf) == 1 + assert erf(-inf) == -1 + assert erfi(0) == 0 + assert erfi(1/pi).ae(0.371682698493894314) + assert erfi(inf) == inf + assert erfi(-inf) == -inf + assert erf(1+0j) == erf(1) + assert erfc(1+0j) == erfc(1) + assert erf(0.2+0.5j).ae(1 - erfc(0.2+0.5j)) + assert erfc(0) == 1 + assert erfc(1).ae(1-erf(1)) + assert erfc(-1).ae(1-erf(-1)) + assert erfc(1/pi).ae(1-erf(1/pi)) + assert erfc(-10) == 2 + assert erfc(-1000000) == 2 + assert erfc(-inf) == 2 + assert erfc(inf) == 0 + assert isnan(erfc(nan)) + assert (erfc(10**4)*mpf(10)**43429453).ae('3.63998738656420') + assert erf(8+9j).ae(-1072004.2525062051158 + 364149.91954310255423j) + assert erfc(8+9j).ae(1072005.2525062051158 - 364149.91954310255423j) + assert erfc(-8-9j).ae(-1072003.2525062051158 + 364149.91954310255423j) + mp.dps = 50 + # This one does not use the asymptotic series + assert (erfc(10)*10**45).ae('2.0884875837625447570007862949577886115608181193212') + # This one does + assert (erfc(50)*10**1088).ae('2.0709207788416560484484478751657887929322509209954') + mp.dps = 15 + assert str(erfc(10**50)) == '3.66744826532555e-4342944819032518276511289189166050822943970058036665661144537831658646492088707747292249493384317534' + assert erfinv(0) == 0 + assert erfinv(0.5).ae(0.47693627620446987338) + assert erfinv(-0.5).ae(-0.47693627620446987338) + assert erfinv(1) == inf + assert erfinv(-1) == -inf + assert erf(erfinv(0.95)).ae(0.95) + assert erf(erfinv(0.999999999995)).ae(0.999999999995) + assert erf(erfinv(-0.999999999995)).ae(-0.999999999995) + mp.dps = 50 + assert erf(erfinv('0.99999999999999999999999999999995')).ae('0.99999999999999999999999999999995') + assert erf(erfinv('0.999999999999999999999999999999995')).ae('0.999999999999999999999999999999995') + assert erf(erfinv('-0.999999999999999999999999999999995')).ae('-0.999999999999999999999999999999995') + mp.dps = 15 + # Complex asymptotic expansions + v = erfc(50j) + assert v.real == 1 + assert v.imag.ae('-6.1481820666053078736e+1083') + assert erfc(-100+5j).ae(2) + assert (erfc(100+5j)*10**4335).ae(2.3973567853824133572 - 3.9339259530609420597j) + assert erfc(100+100j).ae(0.00065234366376857698698 - 0.0039357263629214118437j) + +def test_pdf(): + mp.dps = 15 + assert npdf(-inf) == 0 + assert npdf(inf) == 0 + assert npdf(5,0,2).ae(npdf(5+4,4,2)) + assert quadts(lambda x: npdf(x,-0.5,0.8), [-inf, inf]) == 1 + assert ncdf(0) == 0.5 + assert ncdf(3,3) == 0.5 + assert ncdf(-inf) == 0 + assert ncdf(inf) == 1 + assert ncdf(10) == 1 + # Verify that this is computed accurately + assert (ncdf(-10)*10**24).ae(7.619853024160526) + +def test_lambertw(): + mp.dps = 15 + assert lambertw(0) == 0 + assert lambertw(0+0j) == 0 + assert lambertw(inf) == inf + assert isnan(lambertw(nan)) + assert lambertw(inf,1).real == inf + assert lambertw(inf,1).imag.ae(2*pi) + assert lambertw(-inf,1).real == inf + assert lambertw(-inf,1).imag.ae(3*pi) + assert lambertw(0,-1) == -inf + assert lambertw(0,1) == -inf + assert lambertw(0,3) == -inf + assert lambertw(e).ae(1) + assert lambertw(1).ae(0.567143290409783873) + assert lambertw(-pi/2).ae(j*pi/2) + assert lambertw(-log(2)/2).ae(-log(2)) + assert lambertw(0.25).ae(0.203888354702240164) + assert lambertw(-0.25).ae(-0.357402956181388903) + assert lambertw(-1./10000,0).ae(-0.000100010001500266719) + assert lambertw(-0.25,-1).ae(-2.15329236411034965) + assert lambertw(0.25,-1).ae(-3.00899800997004620-4.07652978899159763j) + assert lambertw(-0.25,-1).ae(-2.15329236411034965) + assert lambertw(0.25,1).ae(-3.00899800997004620+4.07652978899159763j) + assert lambertw(-0.25,1).ae(-3.48973228422959210+7.41405453009603664j) + assert lambertw(-4).ae(0.67881197132094523+1.91195078174339937j) + assert lambertw(-4,1).ae(-0.66743107129800988+7.76827456802783084j) + assert lambertw(-4,-1).ae(0.67881197132094523-1.91195078174339937j) + assert lambertw(1000).ae(5.24960285240159623) + assert lambertw(1000,1).ae(4.91492239981054535+5.44652615979447070j) + assert lambertw(1000,-1).ae(4.91492239981054535-5.44652615979447070j) + assert lambertw(1000,5).ae(3.5010625305312892+29.9614548941181328j) + assert lambertw(3+4j).ae(1.281561806123775878+0.533095222020971071j) + assert lambertw(-0.4+0.4j).ae(-0.10396515323290657+0.61899273315171632j) + assert lambertw(3+4j,1).ae(-0.11691092896595324+5.61888039871282334j) + assert lambertw(3+4j,-1).ae(0.25856740686699742-3.85211668616143559j) + assert lambertw(-0.5,-1).ae(-0.794023632344689368-0.770111750510379110j) + assert lambertw(-1./10000,1).ae(-11.82350837248724344+6.80546081842002101j) + assert lambertw(-1./10000,-1).ae(-11.6671145325663544) + assert lambertw(-1./10000,-2).ae(-11.82350837248724344-6.80546081842002101j) + assert lambertw(-1./100000,4).ae(-14.9186890769540539+26.1856750178782046j) + assert lambertw(-1./100000,5).ae(-15.0931437726379218666+32.5525721210262290086j) + assert lambertw((2+j)/10).ae(0.173704503762911669+0.071781336752835511j) + assert lambertw((2+j)/10,1).ae(-3.21746028349820063+4.56175438896292539j) + assert lambertw((2+j)/10,-1).ae(-3.03781405002993088-3.53946629633505737j) + assert lambertw((2+j)/10,4).ae(-4.6878509692773249+23.8313630697683291j) + assert lambertw(-(2+j)/10).ae(-0.226933772515757933-0.164986470020154580j) + assert lambertw(-(2+j)/10,1).ae(-2.43569517046110001+0.76974067544756289j) + assert lambertw(-(2+j)/10,-1).ae(-3.54858738151989450-6.91627921869943589j) + assert lambertw(-(2+j)/10,4).ae(-4.5500846928118151+20.6672982215434637j) + mp.dps = 50 + assert lambertw(pi).ae('1.073658194796149172092178407024821347547745350410314531') + mp.dps = 15 + # Former bug in generated branch + assert lambertw(-0.5+0.002j).ae(-0.78917138132659918344 + 0.76743539379990327749j) + assert lambertw(-0.5-0.002j).ae(-0.78917138132659918344 - 0.76743539379990327749j) + assert lambertw(-0.448+0.4j).ae(-0.11855133765652382241 + 0.66570534313583423116j) + assert lambertw(-0.448-0.4j).ae(-0.11855133765652382241 - 0.66570534313583423116j) + assert lambertw(-0.65475+0.0001j).ae(-0.61053421111385310898+1.0396534993944097723803j) + # Huge branch index + w = lambertw(1,10**20) + assert w.real.ae(-47.889578926290259164) + assert w.imag.ae(6.2831853071795864769e+20) + +def test_lambertw_hard(): + def check(x,y): + y = convert(y) + type_ok = True + if isinstance(y, mpf): + type_ok = isinstance(x, mpf) + real_ok = abs(x.real-y.real) <= abs(y.real)*8*eps + imag_ok = abs(x.imag-y.imag) <= abs(y.imag)*8*eps + #print x, y, abs(x.real-y.real), abs(x.imag-y.imag) + return real_ok and imag_ok + # Evaluation near 0 + mp.dps = 15 + assert check(lambertw(1e-10), 9.999999999000000000e-11) + assert check(lambertw(-1e-10), -1.000000000100000000e-10) + assert check(lambertw(1e-10j), 9.999999999999999999733e-21 + 9.99999999999999999985e-11j) + assert check(lambertw(-1e-10j), 9.999999999999999999733e-21 - 9.99999999999999999985e-11j) + assert check(lambertw(1e-10,1), -26.303186778379041559 + 3.265093911703828397j) + assert check(lambertw(-1e-10,1), -26.326236166739163892 + 6.526183280686333315j) + assert check(lambertw(1e-10j,1), -26.312931726911421551 + 4.896366881798013421j) + assert check(lambertw(-1e-10j,1), -26.297238779529035066 + 1.632807161345576513j) + assert check(lambertw(1e-10,-1), -26.303186778379041559 - 3.265093911703828397j) + assert check(lambertw(-1e-10,-1), -26.295238819246925694) + assert check(lambertw(1e-10j,-1), -26.297238779529035028 - 1.6328071613455765135j) + assert check(lambertw(-1e-10j,-1), -26.312931726911421551 - 4.896366881798013421j) + # Test evaluation very close to the branch point -1/e + # on the -1, 0, and 1 branches + add = lambda x, y: fadd(x,y,exact=True) + sub = lambda x, y: fsub(x,y,exact=True) + addj = lambda x, y: fadd(x,fmul(y,1j,exact=True),exact=True) + subj = lambda x, y: fadd(x,fmul(y,-1j,exact=True),exact=True) + mp.dps = 1500 + a = -1/e + 10*eps + d3 = mpf('1e-3') + d10 = mpf('1e-10') + d20 = mpf('1e-20') + d40 = mpf('1e-40') + d80 = mpf('1e-80') + d300 = mpf('1e-300') + d1000 = mpf('1e-1000') + mp.dps = 15 + # ---- Branch 0 ---- + # -1/e + eps + assert check(lambertw(add(a,d3)), -0.92802015005456704876) + assert check(lambertw(add(a,d10)), -0.99997668374140088071) + assert check(lambertw(add(a,d20)), -0.99999999976683560186) + assert lambertw(add(a,d40)) == -1 + assert lambertw(add(a,d80)) == -1 + assert lambertw(add(a,d300)) == -1 + assert lambertw(add(a,d1000)) == -1 + # -1/e - eps + assert check(lambertw(sub(a,d3)), -0.99819016149860989001+0.07367191188934638577j) + assert check(lambertw(sub(a,d10)), -0.9999999998187812114595992+0.0000233164398140346109194j) + assert check(lambertw(sub(a,d20)), -0.99999999999999999998187+2.331643981597124203344e-10j) + assert check(lambertw(sub(a,d40)), -1.0+2.33164398159712420336e-20j) + assert check(lambertw(sub(a,d80)), -1.0+2.33164398159712420336e-40j) + assert check(lambertw(sub(a,d300)), -1.0+2.33164398159712420336e-150j) + assert check(lambertw(sub(a,d1000)), mpc(-1,'2.33164398159712420336e-500')) + # -1/e + eps*j + assert check(lambertw(addj(a,d3)), -0.94790387486938526634+0.05036819639190132490j) + assert check(lambertw(addj(a,d10)), -0.9999835127872943680999899+0.0000164870314895821225256j) + assert check(lambertw(addj(a,d20)), -0.999999999835127872929987+1.64872127051890935830e-10j) + assert check(lambertw(addj(a,d40)), -0.9999999999999999999835+1.6487212707001281468305e-20j) + assert check(lambertw(addj(a,d80)), -1.0 + 1.64872127070012814684865e-40j) + assert check(lambertw(addj(a,d300)), -1.0 + 1.64872127070012814684865e-150j) + assert check(lambertw(addj(a,d1000)), mpc(-1.0,'1.64872127070012814684865e-500')) + # -1/e - eps*j + assert check(lambertw(subj(a,d3)), -0.94790387486938526634-0.05036819639190132490j) + assert check(lambertw(subj(a,d10)), -0.9999835127872943680999899-0.0000164870314895821225256j) + assert check(lambertw(subj(a,d20)), -0.999999999835127872929987-1.64872127051890935830e-10j) + assert check(lambertw(subj(a,d40)), -0.9999999999999999999835-1.6487212707001281468305e-20j) + assert check(lambertw(subj(a,d80)), -1.0 - 1.64872127070012814684865e-40j) + assert check(lambertw(subj(a,d300)), -1.0 - 1.64872127070012814684865e-150j) + assert check(lambertw(subj(a,d1000)), mpc(-1.0,'-1.64872127070012814684865e-500')) + # ---- Branch 1 ---- + assert check(lambertw(addj(a,d3),1), -3.088501303219933378005990 + 7.458676867597474813950098j) + assert check(lambertw(addj(a,d80),1), -3.088843015613043855957087 + 7.461489285654254556906117j) + assert check(lambertw(addj(a,d300),1), -3.088843015613043855957087 + 7.461489285654254556906117j) + assert check(lambertw(addj(a,d1000),1), -3.088843015613043855957087 + 7.461489285654254556906117j) + assert check(lambertw(subj(a,d3),1), -1.0520914180450129534365906 + 0.0539925638125450525673175j) + assert check(lambertw(subj(a,d10),1), -1.0000164872127056318529390 + 0.000016487393927159250398333077j) + assert check(lambertw(subj(a,d20),1), -1.0000000001648721270700128 + 1.64872127088134693542628e-10j) + assert check(lambertw(subj(a,d40),1), -1.000000000000000000016487 + 1.64872127070012814686677e-20j) + assert check(lambertw(subj(a,d80),1), -1.0 + 1.64872127070012814684865e-40j) + assert check(lambertw(subj(a,d300),1), -1.0 + 1.64872127070012814684865e-150j) + assert check(lambertw(subj(a,d1000),1), mpc(-1.0, '1.64872127070012814684865e-500')) + # ---- Branch -1 ---- + # -1/e + eps + assert check(lambertw(add(a,d3),-1), -1.075608941186624989414945) + assert check(lambertw(add(a,d10),-1), -1.000023316621036696460620) + assert check(lambertw(add(a,d20),-1), -1.000000000233164398177834) + assert lambertw(add(a,d40),-1) == -1 + assert lambertw(add(a,d80),-1) == -1 + assert lambertw(add(a,d300),-1) == -1 + assert lambertw(add(a,d1000),-1) == -1 + # -1/e - eps + assert check(lambertw(sub(a,d3),-1), -0.99819016149860989001-0.07367191188934638577j) + assert check(lambertw(sub(a,d10),-1), -0.9999999998187812114595992-0.0000233164398140346109194j) + assert check(lambertw(sub(a,d20),-1), -0.99999999999999999998187-2.331643981597124203344e-10j) + assert check(lambertw(sub(a,d40),-1), -1.0-2.33164398159712420336e-20j) + assert check(lambertw(sub(a,d80),-1), -1.0-2.33164398159712420336e-40j) + assert check(lambertw(sub(a,d300),-1), -1.0-2.33164398159712420336e-150j) + assert check(lambertw(sub(a,d1000),-1), mpc(-1,'-2.33164398159712420336e-500')) + # -1/e + eps*j + assert check(lambertw(addj(a,d3),-1), -1.0520914180450129534365906 - 0.0539925638125450525673175j) + assert check(lambertw(addj(a,d10),-1), -1.0000164872127056318529390 - 0.0000164873939271592503983j) + assert check(lambertw(addj(a,d20),-1), -1.0000000001648721270700 - 1.64872127088134693542628e-10j) + assert check(lambertw(addj(a,d40),-1), -1.00000000000000000001648 - 1.6487212707001281468667726e-20j) + assert check(lambertw(addj(a,d80),-1), -1.0 - 1.64872127070012814684865e-40j) + assert check(lambertw(addj(a,d300),-1), -1.0 - 1.64872127070012814684865e-150j) + assert check(lambertw(addj(a,d1000),-1), mpc(-1.0,'-1.64872127070012814684865e-500')) + # -1/e - eps*j + assert check(lambertw(subj(a,d3),-1), -3.088501303219933378005990-7.458676867597474813950098j) + assert check(lambertw(subj(a,d10),-1), -3.088843015579260686911033-7.461489285372968780020716j) + assert check(lambertw(subj(a,d20),-1), -3.088843015613043855953708-7.461489285654254556877988j) + assert check(lambertw(subj(a,d40),-1), -3.088843015613043855957087-7.461489285654254556906117j) + assert check(lambertw(subj(a,d80),-1), -3.088843015613043855957087 - 7.461489285654254556906117j) + assert check(lambertw(subj(a,d300),-1), -3.088843015613043855957087 - 7.461489285654254556906117j) + assert check(lambertw(subj(a,d1000),-1), -3.088843015613043855957087 - 7.461489285654254556906117j) + # One more case, testing higher precision + mp.dps = 500 + x = -1/e + mpf('1e-13') + ans = "-0.99999926266961377166355784455394913638782494543377383"\ + "744978844374498153493943725364881490261187530235150668593869563"\ + "168276697689459394902153960200361935311512317183678882" + mp.dps = 15 + assert lambertw(x).ae(ans) + mp.dps = 50 + assert lambertw(x).ae(ans) + mp.dps = 150 + assert lambertw(x).ae(ans) + +def test_meijerg(): + mp.dps = 15 + assert meijerg([[2,3],[1]],[[0.5,2],[3,4]], 2.5).ae(4.2181028074787439386) + assert meijerg([[],[1+j]],[[1],[1]], 3+4j).ae(271.46290321152464592 - 703.03330399954820169j) + assert meijerg([[0.25],[1]],[[0.5],[2]],0) == 0 + assert meijerg([[0],[]],[[0,0,'1/3','2/3'], []], '2/27').ae(2.2019391389653314120) + # Verify 1/z series being used + assert meijerg([[-3],[-0.5]], [[-1],[-2.5]], -0.5).ae(-1.338096165935754898687431) + assert meijerg([[1-(-1)],[1-(-2.5)]], [[1-(-3)],[1-(-0.5)]], -2.0).ae(-1.338096165935754898687431) + assert meijerg([[-3],[-0.5]], [[-1],[-2.5]], -1).ae(-(pi+4)/(4*pi)) + a = 2.5 + b = 1.25 + for z in [mpf(0.25), mpf(2)]: + x1 = hyp1f1(a,b,z) + x2 = gamma(b)/gamma(a)*meijerg([[1-a],[]],[[0],[1-b]],-z) + x3 = gamma(b)/gamma(a)*meijerg([[1-0],[1-(1-b)]],[[1-(1-a)],[]],-1/z) + assert x1.ae(x2) + assert x1.ae(x3) + +def test_appellf1(): + mp.dps = 15 + assert appellf1(2,-2,1,1,2,3).ae(-1.75) + assert appellf1(2,1,-2,1,2,3).ae(-8) + assert appellf1(2,1,-2,1,0.5,0.25).ae(1.5) + assert appellf1(-2,1,3,2,3,3).ae(19) + assert appellf1(1,2,3,4,0.5,0.125).ae( 1.53843285792549786518) + +def test_coulomb(): + # Note: most tests are doctests + # Test for a bug: + mp.dps = 15 + assert coulombg(mpc(-5,0),2,3).ae(20.087729487721430394) + +def test_hyper_param_accuracy(): + mp.dps = 15 + As = [n+1e-10 for n in range(-5,-1)] + Bs = [n+1e-10 for n in range(-12,-5)] + assert hyper(As,Bs,10).ae(-381757055858.652671927) + assert legenp(0.5, 100, 0.25).ae(-2.4124576567211311755e+144) + assert (hyp1f1(1000,1,-100)*10**24).ae(5.2589445437370169113) + assert (hyp2f1(10, -900, 10.5, 0.99)*10**24).ae(1.9185370579660768203) + assert (hyp2f1(1000,1.5,-3.5,-1.5)*10**385).ae(-2.7367529051334000764) + assert hyp2f1(-5, 10, 3, 0.5, zeroprec=500) == 0 + assert (hyp1f1(-10000, 1000, 100)*10**424).ae(-3.1046080515824859974) + assert (hyp2f1(1000,1.5,-3.5,-0.75,maxterms=100000)*10**231).ae(-4.0534790813913998643) + assert legenp(2, 3, 0.25) == 0 + pytest.raises(ValueError, lambda: hypercomb(lambda a: [([],[],[],[],[a],[-a],0.5)], [3])) + assert hypercomb(lambda a: [([],[],[],[],[a],[-a],0.5)], [3], infprec=200) == inf + assert meijerg([[],[]],[[0,0,0,0],[]],0.1).ae(1.5680822343832351418) + assert (besselk(400,400)*10**94).ae(1.4387057277018550583) + mp.dps = 5 + (hyp1f1(-5000.5, 1500, 100)*10**185).ae(8.5185229673381935522) + (hyp1f1(-5000, 1500, 100)*10**185).ae(9.1501213424563944311) + mp.dps = 15 + (hyp1f1(-5000.5, 1500, 100)*10**185).ae(8.5185229673381935522) + (hyp1f1(-5000, 1500, 100)*10**185).ae(9.1501213424563944311) + assert hyp0f1(fadd(-20,'1e-100',exact=True), 0.25).ae(1.85014429040102783e+49) + assert hyp0f1((-20*10**100+1, 10**100), 0.25).ae(1.85014429040102783e+49) + +def test_hypercomb_zero_pow(): + # check that 0^0 = 1 + assert hypercomb(lambda a: (([0],[a],[],[],[],[],0),), [0]) == 1 + assert meijerg([[-1.5],[]],[[0],[-0.75]],0).ae(1.4464090846320771425) + +def test_spherharm(): + mp.dps = 15 + t = 0.5; r = 0.25 + assert spherharm(0,0,t,r).ae(0.28209479177387814347) + assert spherharm(1,-1,t,r).ae(0.16048941205971996369 - 0.04097967481096344271j) + assert spherharm(1,0,t,r).ae(0.42878904414183579379) + assert spherharm(1,1,t,r).ae(-0.16048941205971996369 - 0.04097967481096344271j) + assert spherharm(2,-2,t,r).ae(0.077915886919031181734 - 0.042565643022253962264j) + assert spherharm(2,-1,t,r).ae(0.31493387233497459884 - 0.08041582001959297689j) + assert spherharm(2,0,t,r).ae(0.41330596756220761898) + assert spherharm(2,1,t,r).ae(-0.31493387233497459884 - 0.08041582001959297689j) + assert spherharm(2,2,t,r).ae(0.077915886919031181734 + 0.042565643022253962264j) + assert spherharm(3,-3,t,r).ae(0.033640236589690881646 - 0.031339125318637082197j) + assert spherharm(3,-2,t,r).ae(0.18091018743101461963 - 0.09883168583167010241j) + assert spherharm(3,-1,t,r).ae(0.42796713930907320351 - 0.10927795157064962317j) + assert spherharm(3,0,t,r).ae(0.27861659336351639787) + assert spherharm(3,1,t,r).ae(-0.42796713930907320351 - 0.10927795157064962317j) + assert spherharm(3,2,t,r).ae(0.18091018743101461963 + 0.09883168583167010241j) + assert spherharm(3,3,t,r).ae(-0.033640236589690881646 - 0.031339125318637082197j) + assert spherharm(0,-1,t,r) == 0 + assert spherharm(0,-2,t,r) == 0 + assert spherharm(0,1,t,r) == 0 + assert spherharm(0,2,t,r) == 0 + assert spherharm(1,2,t,r) == 0 + assert spherharm(1,3,t,r) == 0 + assert spherharm(1,-2,t,r) == 0 + assert spherharm(1,-3,t,r) == 0 + assert spherharm(2,3,t,r) == 0 + assert spherharm(2,4,t,r) == 0 + assert spherharm(2,-3,t,r) == 0 + assert spherharm(2,-4,t,r) == 0 + assert spherharm(3,4.5,0.5,0.25).ae(-22.831053442240790148 + 10.910526059510013757j) + assert spherharm(2+3j, 1-j, 1+j, 3+4j).ae(-2.6582752037810116935 - 1.0909214905642160211j) + assert spherharm(-6,2.5,t,r).ae(0.39383644983851448178 + 0.28414687085358299021j) + assert spherharm(-3.5, 3, 0.5, 0.25).ae(0.014516852987544698924 - 0.015582769591477628495j) + assert spherharm(-3, 3, 0.5, 0.25) == 0 + assert spherharm(-6, 3, 0.5, 0.25).ae(-0.16544349818782275459 - 0.15412657723253924562j) + assert spherharm(-6, 1.5, 0.5, 0.25).ae(0.032208193499767402477 + 0.012678000924063664921j) + assert spherharm(3,0,0,1).ae(0.74635266518023078283) + assert spherharm(3,-2,0,1) == 0 + assert spherharm(3,-2,1,1).ae(-0.16270707338254028971 - 0.35552144137546777097j) + +def test_qfunctions(): + mp.dps = 15 + assert qp(2,3,100).ae('2.7291482267247332183e2391') + +def test_issue_239(): + mp.prec = 150 + x = ldexp(2476979795053773,-52) + assert betainc(206, 385, 0, 0.55, 1).ae('0.99999999999999999999996570910644857895771110649954') + mp.dps = 15 + pytest.raises(ValueError, lambda: hyp2f1(-5,5,0.5,0.5)) + +# Extra stress testing for Bessel functions +# Reference zeros generated with the aid of scipy.special +# jn_zero, jnp_zero, yn_zero, ynp_zero + +V = 15 +M = 15 + +jn_small_zeros = \ +[[2.4048255576957728, + 5.5200781102863106, + 8.6537279129110122, + 11.791534439014282, + 14.930917708487786, + 18.071063967910923, + 21.211636629879259, + 24.352471530749303, + 27.493479132040255, + 30.634606468431975, + 33.775820213573569, + 36.917098353664044, + 40.058425764628239, + 43.19979171317673, + 46.341188371661814], + [3.8317059702075123, + 7.0155866698156188, + 10.173468135062722, + 13.323691936314223, + 16.470630050877633, + 19.615858510468242, + 22.760084380592772, + 25.903672087618383, + 29.046828534916855, + 32.189679910974404, + 35.332307550083865, + 38.474766234771615, + 41.617094212814451, + 44.759318997652822, + 47.901460887185447], + [5.1356223018406826, + 8.4172441403998649, + 11.619841172149059, + 14.795951782351261, + 17.959819494987826, + 21.116997053021846, + 24.270112313573103, + 27.420573549984557, + 30.569204495516397, + 33.7165195092227, + 36.86285651128381, + 40.008446733478192, + 43.153453778371463, + 46.297996677236919, + 49.442164110416873], + [6.3801618959239835, + 9.7610231299816697, + 13.015200721698434, + 16.223466160318768, + 19.409415226435012, + 22.582729593104442, + 25.748166699294978, + 28.908350780921758, + 32.064852407097709, + 35.218670738610115, + 38.370472434756944, + 41.520719670406776, + 44.669743116617253, + 47.817785691533302, + 50.965029906205183], + [7.5883424345038044, + 11.064709488501185, + 14.37253667161759, + 17.615966049804833, + 20.826932956962388, + 24.01901952477111, + 27.199087765981251, + 30.371007667117247, + 33.537137711819223, + 36.699001128744649, + 39.857627302180889, + 43.01373772335443, + 46.167853512924375, + 49.320360686390272, + 52.471551398458023], + [8.771483815959954, + 12.338604197466944, + 15.700174079711671, + 18.980133875179921, + 22.217799896561268, + 25.430341154222704, + 28.626618307291138, + 31.811716724047763, + 34.988781294559295, + 38.159868561967132, + 41.326383254047406, + 44.489319123219673, + 47.649399806697054, + 50.80716520300633, + 53.963026558378149], + [9.9361095242176849, + 13.589290170541217, + 17.003819667816014, + 20.320789213566506, + 23.58608443558139, + 26.820151983411405, + 30.033722386570469, + 33.233041762847123, + 36.422019668258457, + 39.603239416075404, + 42.778481613199507, + 45.949015998042603, + 49.11577372476426, + 52.279453903601052, + 55.440592068853149], + [11.086370019245084, + 14.821268727013171, + 18.287582832481726, + 21.641541019848401, + 24.934927887673022, + 28.191188459483199, + 31.42279419226558, + 34.637089352069324, + 37.838717382853611, + 41.030773691585537, + 44.21540850526126, + 47.394165755570512, + 50.568184679795566, + 53.738325371963291, + 56.905249991978781], + [12.225092264004655, + 16.037774190887709, + 19.554536430997055, + 22.94517313187462, + 26.266814641176644, + 29.54565967099855, + 32.795800037341462, + 36.025615063869571, + 39.240447995178135, + 42.443887743273558, + 45.638444182199141, + 48.825930381553857, + 52.007691456686903, + 55.184747939289049, + 58.357889025269694], + [13.354300477435331, + 17.241220382489128, + 20.807047789264107, + 24.233885257750552, + 27.583748963573006, + 30.885378967696675, + 34.154377923855096, + 37.400099977156589, + 40.628553718964528, + 43.843801420337347, + 47.048700737654032, + 50.245326955305383, + 53.435227157042058, + 56.619580266508436, + 59.799301630960228], + [14.475500686554541, + 18.433463666966583, + 22.046985364697802, + 25.509450554182826, + 28.887375063530457, + 32.211856199712731, + 35.499909205373851, + 38.761807017881651, + 42.004190236671805, + 45.231574103535045, + 48.447151387269394, + 51.653251668165858, + 54.851619075963349, + 58.043587928232478, + 61.230197977292681], + [15.589847884455485, + 19.61596690396692, + 23.275853726263409, + 26.773322545509539, + 30.17906117878486, + 33.526364075588624, + 36.833571341894905, + 40.111823270954241, + 43.368360947521711, + 46.608132676274944, + 49.834653510396724, + 53.050498959135054, + 56.257604715114484, + 59.457456908388002, + 62.651217388202912], + [16.698249933848246, + 20.789906360078443, + 24.494885043881354, + 28.026709949973129, + 31.45996003531804, + 34.829986990290238, + 38.156377504681354, + 41.451092307939681, + 44.721943543191147, + 47.974293531269048, + 51.211967004101068, + 54.437776928325074, + 57.653844811906946, + 60.8618046824805, + 64.062937824850136], + [17.801435153282442, + 21.95624406783631, + 25.705103053924724, + 29.270630441874802, + 32.731053310978403, + 36.123657666448762, + 39.469206825243883, + 42.780439265447158, + 46.06571091157561, + 49.330780096443524, + 52.579769064383396, + 55.815719876305778, + 59.040934037249271, + 62.257189393731728, + 65.465883797232125], + [18.899997953174024, + 23.115778347252756, + 26.907368976182104, + 30.505950163896036, + 33.993184984781542, + 37.408185128639695, + 40.772827853501868, + 44.100590565798301, + 47.400347780543231, + 50.678236946479898, + 53.93866620912693, + 57.184898598119301, + 60.419409852130297, + 63.644117508962281, + 66.860533012260103]] + +jnp_small_zeros = \ +[[0.0, + 3.8317059702075123, + 7.0155866698156188, + 10.173468135062722, + 13.323691936314223, + 16.470630050877633, + 19.615858510468242, + 22.760084380592772, + 25.903672087618383, + 29.046828534916855, + 32.189679910974404, + 35.332307550083865, + 38.474766234771615, + 41.617094212814451, + 44.759318997652822], + [1.8411837813406593, + 5.3314427735250326, + 8.5363163663462858, + 11.706004902592064, + 14.863588633909033, + 18.015527862681804, + 21.16436985918879, + 24.311326857210776, + 27.457050571059246, + 30.601922972669094, + 33.746182898667383, + 36.889987409236811, + 40.033444053350675, + 43.176628965448822, + 46.319597561173912], + [3.0542369282271403, + 6.7061331941584591, + 9.9694678230875958, + 13.170370856016123, + 16.347522318321783, + 19.512912782488205, + 22.671581772477426, + 25.826037141785263, + 28.977672772993679, + 32.127327020443474, + 35.275535050674691, + 38.422654817555906, + 41.568934936074314, + 44.714553532819734, + 47.859641607992093], + [4.2011889412105285, + 8.0152365983759522, + 11.345924310743006, + 14.585848286167028, + 17.78874786606647, + 20.9724769365377, + 24.144897432909265, + 27.310057930204349, + 30.470268806290424, + 33.626949182796679, + 36.781020675464386, + 39.933108623659488, + 43.083652662375079, + 46.232971081836478, + 49.381300092370349], + [5.3175531260839944, + 9.2823962852416123, + 12.681908442638891, + 15.964107037731551, + 19.196028800048905, + 22.401032267689004, + 25.589759681386733, + 28.767836217666503, + 31.938539340972783, + 35.103916677346764, + 38.265316987088158, + 41.423666498500732, + 44.579623137359257, + 47.733667523865744, + 50.886159153182682], + [6.4156163757002403, + 10.519860873772308, + 13.9871886301403, + 17.312842487884625, + 20.575514521386888, + 23.803581476593863, + 27.01030789777772, + 30.20284907898166, + 33.385443901010121, + 36.560777686880356, + 39.730640230067416, + 42.896273163494417, + 46.058566273567043, + 49.218174614666636, + 52.375591529563596], + [7.501266144684147, + 11.734935953042708, + 15.268181461097873, + 18.637443009666202, + 21.931715017802236, + 25.183925599499626, + 28.409776362510085, + 31.617875716105035, + 34.81339298429743, + 37.999640897715301, + 41.178849474321413, + 44.352579199070217, + 47.521956905768113, + 50.687817781723741, + 53.85079463676896], + [8.5778364897140741, + 12.932386237089576, + 16.529365884366944, + 19.941853366527342, + 23.268052926457571, + 26.545032061823576, + 29.790748583196614, + 33.015178641375142, + 36.224380548787162, + 39.422274578939259, + 42.611522172286684, + 45.793999658055002, + 48.971070951900596, + 52.143752969301988, + 55.312820330403446], + [9.6474216519972168, + 14.115518907894618, + 17.774012366915256, + 21.229062622853124, + 24.587197486317681, + 27.889269427955092, + 31.155326556188325, + 34.39662855427218, + 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40.757072537673599, + 44.086572292170345, + 47.387688809191869, + 50.66667461073936, + 53.928009929563275, + 57.175005343085052, + 60.410169281219877, + 63.635442539153021, + 66.85235358587768]] + +@pytest.mark.slow +def test_bessel_zeros_extra(): + mp.dps = 15 + for v in range(V): + for m in range(1,M+1): + print(v, m, "of", V, M) + # Twice to test cache (if used) + assert besseljzero(v,m).ae(jn_small_zeros[v][m-1]) + assert besseljzero(v,m).ae(jn_small_zeros[v][m-1]) + assert besseljzero(v,m,1).ae(jnp_small_zeros[v][m-1]) + assert besseljzero(v,m,1).ae(jnp_small_zeros[v][m-1]) + assert besselyzero(v,m).ae(yn_small_zeros[v][m-1]) + assert besselyzero(v,m).ae(yn_small_zeros[v][m-1]) + assert besselyzero(v,m,1).ae(ynp_small_zeros[v][m-1]) + assert besselyzero(v,m,1).ae(ynp_small_zeros[v][m-1]) diff --git a/venv/lib/python3.12/site-packages/mpmath/tests/test_gammazeta.py b/venv/lib/python3.12/site-packages/mpmath/tests/test_gammazeta.py new file mode 100644 index 0000000000000000000000000000000000000000..6a18a7964d746561dfd5f81177cd78ccc46d2a5d --- /dev/null +++ b/venv/lib/python3.12/site-packages/mpmath/tests/test_gammazeta.py @@ -0,0 +1,698 @@ +from mpmath import * +from mpmath.libmp import round_up, from_float, mpf_zeta_int + +def test_zeta_int_bug(): + assert mpf_zeta_int(0, 10) == from_float(-0.5) + +def test_bernoulli(): + assert bernfrac(0) == (1,1) + assert bernfrac(1) == (-1,2) + assert bernfrac(2) == (1,6) + assert bernfrac(3) == (0,1) + assert bernfrac(4) == (-1,30) + assert bernfrac(5) == (0,1) + assert bernfrac(6) == (1,42) + assert bernfrac(8) == (-1,30) + assert bernfrac(10) == (5,66) + assert bernfrac(12) == (-691,2730) + assert bernfrac(18) == (43867,798) + p, q = bernfrac(228) + assert p % 10**10 == 164918161 + assert q == 625170 + p, q = bernfrac(1000) + assert p % 10**10 == 7950421099 + assert q == 342999030 + mp.dps = 15 + assert bernoulli(0) == 1 + assert bernoulli(1) == -0.5 + assert bernoulli(2).ae(1./6) + assert bernoulli(3) == 0 + assert bernoulli(4).ae(-1./30) + assert bernoulli(5) == 0 + assert bernoulli(6).ae(1./42) + assert str(bernoulli(10)) == '0.0757575757575758' + assert str(bernoulli(234)) == '7.62772793964344e+267' + assert str(bernoulli(10**5)) == '-5.82229431461335e+376755' + assert str(bernoulli(10**8+2)) == '1.19570355039953e+676752584' + + mp.dps = 50 + assert str(bernoulli(10)) == '0.075757575757575757575757575757575757575757575757576' + assert str(bernoulli(234)) == '7.6277279396434392486994969020496121553385863373331e+267' + assert str(bernoulli(10**5)) == '-5.8222943146133508236497045360612887555320691004308e+376755' + assert str(bernoulli(10**8+2)) == '1.1957035503995297272263047884604346914602088317782e+676752584' + + mp.dps = 1000 + assert bernoulli(10).ae(mpf(5)/66) + + mp.dps = 50000 + assert bernoulli(10).ae(mpf(5)/66) + + mp.dps = 15 + +def test_bernpoly_eulerpoly(): + mp.dps = 15 + assert bernpoly(0,-1).ae(1) + assert bernpoly(0,0).ae(1) + assert bernpoly(0,'1/2').ae(1) + assert bernpoly(0,'3/4').ae(1) + assert bernpoly(0,1).ae(1) + assert bernpoly(0,2).ae(1) + assert bernpoly(1,-1).ae('-3/2') + assert bernpoly(1,0).ae('-1/2') + assert bernpoly(1,'1/2').ae(0) + assert bernpoly(1,'3/4').ae('1/4') + assert bernpoly(1,1).ae('1/2') + assert bernpoly(1,2).ae('3/2') + assert bernpoly(2,-1).ae('13/6') + assert bernpoly(2,0).ae('1/6') + assert bernpoly(2,'1/2').ae('-1/12') + assert bernpoly(2,'3/4').ae('-1/48') + assert bernpoly(2,1).ae('1/6') + assert bernpoly(2,2).ae('13/6') + assert bernpoly(3,-1).ae(-3) + assert bernpoly(3,0).ae(0) + assert bernpoly(3,'1/2').ae(0) + assert bernpoly(3,'3/4').ae('-3/64') + assert bernpoly(3,1).ae(0) + assert bernpoly(3,2).ae(3) + assert bernpoly(4,-1).ae('119/30') + assert bernpoly(4,0).ae('-1/30') + assert bernpoly(4,'1/2').ae('7/240') + assert bernpoly(4,'3/4').ae('7/3840') + assert bernpoly(4,1).ae('-1/30') + assert bernpoly(4,2).ae('119/30') + assert bernpoly(5,-1).ae(-5) + assert bernpoly(5,0).ae(0) + assert bernpoly(5,'1/2').ae(0) + assert bernpoly(5,'3/4').ae('25/1024') + assert bernpoly(5,1).ae(0) + assert bernpoly(5,2).ae(5) + assert bernpoly(10,-1).ae('665/66') + assert bernpoly(10,0).ae('5/66') + assert bernpoly(10,'1/2').ae('-2555/33792') + assert bernpoly(10,'3/4').ae('-2555/34603008') + assert bernpoly(10,1).ae('5/66') + assert bernpoly(10,2).ae('665/66') + assert bernpoly(11,-1).ae(-11) + assert bernpoly(11,0).ae(0) + assert bernpoly(11,'1/2').ae(0) + assert bernpoly(11,'3/4').ae('-555731/4194304') + assert bernpoly(11,1).ae(0) + assert bernpoly(11,2).ae(11) + assert eulerpoly(0,-1).ae(1) + assert eulerpoly(0,0).ae(1) + assert eulerpoly(0,'1/2').ae(1) + assert eulerpoly(0,'3/4').ae(1) + assert eulerpoly(0,1).ae(1) + assert eulerpoly(0,2).ae(1) + assert eulerpoly(1,-1).ae('-3/2') + assert eulerpoly(1,0).ae('-1/2') + assert eulerpoly(1,'1/2').ae(0) + assert eulerpoly(1,'3/4').ae('1/4') + assert eulerpoly(1,1).ae('1/2') + assert eulerpoly(1,2).ae('3/2') + assert eulerpoly(2,-1).ae(2) + assert eulerpoly(2,0).ae(0) + assert eulerpoly(2,'1/2').ae('-1/4') + assert eulerpoly(2,'3/4').ae('-3/16') + assert eulerpoly(2,1).ae(0) + assert eulerpoly(2,2).ae(2) + assert eulerpoly(3,-1).ae('-9/4') + assert eulerpoly(3,0).ae('1/4') + assert eulerpoly(3,'1/2').ae(0) + assert eulerpoly(3,'3/4').ae('-11/64') + assert eulerpoly(3,1).ae('-1/4') + assert eulerpoly(3,2).ae('9/4') + assert eulerpoly(4,-1).ae(2) + assert eulerpoly(4,0).ae(0) + assert eulerpoly(4,'1/2').ae('5/16') + assert eulerpoly(4,'3/4').ae('57/256') + assert eulerpoly(4,1).ae(0) + assert eulerpoly(4,2).ae(2) + assert eulerpoly(5,-1).ae('-3/2') + assert eulerpoly(5,0).ae('-1/2') + assert eulerpoly(5,'1/2').ae(0) + assert eulerpoly(5,'3/4').ae('361/1024') + assert eulerpoly(5,1).ae('1/2') + assert eulerpoly(5,2).ae('3/2') + assert eulerpoly(10,-1).ae(2) + assert eulerpoly(10,0).ae(0) + assert eulerpoly(10,'1/2').ae('-50521/1024') + assert eulerpoly(10,'3/4').ae('-36581523/1048576') + assert eulerpoly(10,1).ae(0) + assert eulerpoly(10,2).ae(2) + assert eulerpoly(11,-1).ae('-699/4') + assert eulerpoly(11,0).ae('691/4') + assert eulerpoly(11,'1/2').ae(0) + assert eulerpoly(11,'3/4').ae('-512343611/4194304') + assert eulerpoly(11,1).ae('-691/4') + assert eulerpoly(11,2).ae('699/4') + # Potential accuracy issues + assert bernpoly(10000,10000).ae('5.8196915936323387117e+39999') + assert bernpoly(200,17.5).ae(3.8048418524583064909e244) + assert eulerpoly(200,17.5).ae(-3.7309911582655785929e275) + +def test_gamma(): + mp.dps = 15 + assert gamma(0.25).ae(3.6256099082219083119) + assert gamma(0.0001).ae(9999.4228832316241908) + assert gamma(300).ae('1.0201917073881354535e612') + assert gamma(-0.5).ae(-3.5449077018110320546) + assert gamma(-7.43).ae(0.00026524416464197007186) + #assert gamma(Rational(1,2)) == gamma(0.5) + #assert gamma(Rational(-7,3)).ae(gamma(mpf(-7)/3)) + assert gamma(1+1j).ae(0.49801566811835604271 - 0.15494982830181068512j) + assert gamma(-1+0.01j).ae(-0.422733904013474115 + 99.985883082635367436j) + assert gamma(20+30j).ae(-1453876687.5534810 + 1163777777.8031573j) + # Should always give exact factorials when they can + # be represented as mpfs under the current working precision + fact = 1 + for i in range(1, 18): + assert gamma(i) == fact + fact *= i + for dps in [170, 600]: + fact = 1 + mp.dps = dps + for i in range(1, 105): + assert gamma(i) == fact + fact *= i + mp.dps = 100 + assert gamma(0.5).ae(sqrt(pi)) + mp.dps = 15 + assert factorial(0) == fac(0) == 1 + assert factorial(3) == 6 + assert isnan(gamma(nan)) + assert gamma(1100).ae('4.8579168073569433667e2866') + assert rgamma(0) == 0 + assert rgamma(-1) == 0 + assert rgamma(2) == 1.0 + assert rgamma(3) == 0.5 + assert loggamma(2+8j).ae(-8.5205176753667636926 + 10.8569497125597429366j) + assert loggamma('1e10000').ae('2.302485092994045684017991e10004') + assert loggamma('1e10000j').ae(mpc('-1.570796326794896619231322e10000','2.302485092994045684017991e10004')) + +def test_fac2(): + mp.dps = 15 + assert [fac2(n) for n in range(10)] == [1,1,2,3,8,15,48,105,384,945] + assert fac2(-5).ae(1./3) + assert fac2(-11).ae(-1./945) + assert fac2(50).ae(5.20469842636666623e32) + assert fac2(0.5+0.75j).ae(0.81546769394688069176-0.34901016085573266889j) + assert fac2(inf) == inf + assert isnan(fac2(-inf)) + +def test_gamma_quotients(): + mp.dps = 15 + h = 1e-8 + ep = 1e-4 + G = gamma + assert gammaprod([-1],[-3,-4]) == 0 + assert gammaprod([-1,0],[-5]) == inf + assert abs(gammaprod([-1],[-2]) - G(-1+h)/G(-2+h)) < 1e-4 + assert abs(gammaprod([-4,-3],[-2,0]) - G(-4+h)*G(-3+h)/G(-2+h)/G(0+h)) < 1e-4 + assert rf(3,0) == 1 + assert rf(2.5,1) == 2.5 + assert rf(-5,2) == 20 + assert rf(j,j).ae(gamma(2*j)/gamma(j)) + assert rf('-255.5815971722918','-0.5119253100282322').ae('-0.1952720278805729485') # issue 421 + assert ff(-2,0) == 1 + assert ff(-2,1) == -2 + assert ff(4,3) == 24 + assert ff(3,4) == 0 + assert binomial(0,0) == 1 + assert binomial(1,0) == 1 + assert binomial(0,-1) == 0 + assert binomial(3,2) == 3 + assert binomial(5,2) == 10 + assert binomial(5,3) == 10 + assert binomial(5,5) == 1 + assert binomial(-1,0) == 1 + assert binomial(-2,-4) == 3 + assert binomial(4.5, 1.5) == 6.5625 + assert binomial(1100,1) == 1100 + assert binomial(1100,2) == 604450 + assert beta(1,1) == 1 + assert beta(0,0) == inf + assert beta(3,0) == inf + assert beta(-1,-1) == inf + assert beta(1.5,1).ae(2/3.) + assert beta(1.5,2.5).ae(pi/16) + assert (10**15*beta(10,100)).ae(2.3455339739604649879) + assert beta(inf,inf) == 0 + assert isnan(beta(-inf,inf)) + assert isnan(beta(-3,inf)) + assert isnan(beta(0,inf)) + assert beta(inf,0.5) == beta(0.5,inf) == 0 + assert beta(inf,-1.5) == inf + assert beta(inf,-0.5) == -inf + assert beta(1+2j,-1-j/2).ae(1.16396542451069943086+0.08511695947832914640j) + assert beta(-0.5,0.5) == 0 + assert beta(-3,3).ae(-1/3.) + assert beta('-255.5815971722918','-0.5119253100282322').ae('18.157330562703710339') # issue 421 + +def test_zeta(): + mp.dps = 15 + assert zeta(2).ae(pi**2 / 6) + assert zeta(2.0).ae(pi**2 / 6) + assert zeta(mpc(2)).ae(pi**2 / 6) + assert zeta(100).ae(1) + assert zeta(0).ae(-0.5) + assert zeta(0.5).ae(-1.46035450880958681) + assert zeta(-1).ae(-mpf(1)/12) + assert zeta(-2) == 0 + assert zeta(-3).ae(mpf(1)/120) + assert zeta(-4) == 0 + assert zeta(-100) == 0 + assert isnan(zeta(nan)) + assert zeta(1e-30).ae(-0.5) + assert zeta(-1e-30).ae(-0.5) + # Zeros in the critical strip + assert zeta(mpc(0.5, 14.1347251417346937904)).ae(0) + assert zeta(mpc(0.5, 21.0220396387715549926)).ae(0) + assert zeta(mpc(0.5, 25.0108575801456887632)).ae(0) + assert zeta(mpc(1e-30,1e-40)).ae(-0.5) + assert zeta(mpc(-1e-30,1e-40)).ae(-0.5) + mp.dps = 50 + im = '236.5242296658162058024755079556629786895294952121891237' + assert zeta(mpc(0.5, im)).ae(0, 1e-46) + mp.dps = 15 + # Complex reflection formula + assert (zeta(-60+3j) / 10**34).ae(8.6270183987866146+15.337398548226238j) + # issue #358 + assert zeta(0,0.5) == 0 + assert zeta(0,0) == 0.5 + assert zeta(0,0.5,1).ae(-0.34657359027997265) + # see issue #390 + assert zeta(-1.5,0.5j).ae(-0.13671400162512768475 + 0.11411333638426559139j) + +def test_altzeta(): + mp.dps = 15 + assert altzeta(-2) == 0 + assert altzeta(-4) == 0 + assert altzeta(-100) == 0 + assert altzeta(0) == 0.5 + assert altzeta(-1) == 0.25 + assert altzeta(-3) == -0.125 + assert altzeta(-5) == 0.25 + assert altzeta(-21) == 1180529130.25 + assert altzeta(1).ae(log(2)) + assert altzeta(2).ae(pi**2/12) + assert altzeta(10).ae(73*pi**10/6842880) + assert altzeta(50) < 1 + assert altzeta(60, rounding='d') < 1 + assert altzeta(60, rounding='u') == 1 + assert altzeta(10000, rounding='d') < 1 + assert altzeta(10000, rounding='u') == 1 + assert altzeta(3+0j) == altzeta(3) + s = 3+4j + assert altzeta(s).ae((1-2**(1-s))*zeta(s)) + s = -3+4j + assert altzeta(s).ae((1-2**(1-s))*zeta(s)) + assert altzeta(-100.5).ae(4.58595480083585913e+108) + assert altzeta(1.3).ae(0.73821404216623045) + assert altzeta(1e-30).ae(0.5) + assert altzeta(-1e-30).ae(0.5) + assert altzeta(mpc(1e-30,1e-40)).ae(0.5) + assert altzeta(mpc(-1e-30,1e-40)).ae(0.5) + +def test_zeta_huge(): + mp.dps = 15 + assert zeta(inf) == 1 + mp.dps = 50 + assert zeta(100).ae('1.0000000000000000000000000000007888609052210118073522') + assert zeta(40*pi).ae('1.0000000000000000000000000000000000000148407238666182') + mp.dps = 10000 + v = zeta(33000) + mp.dps = 15 + assert str(v-1) == '1.02363019598118e-9934' + assert zeta(pi*1000, rounding=round_up) > 1 + assert zeta(3000, rounding=round_up) > 1 + assert zeta(pi*1000) == 1 + assert zeta(3000) == 1 + +def test_zeta_negative(): + mp.dps = 150 + a = -pi*10**40 + mp.dps = 15 + assert str(zeta(a)) == '2.55880492708712e+1233536161668617575553892558646631323374078' + mp.dps = 50 + assert str(zeta(a)) == '2.5588049270871154960875033337384432038436330847333e+1233536161668617575553892558646631323374078' + mp.dps = 15 + +def test_polygamma(): + mp.dps = 15 + psi0 = lambda z: psi(0,z) + psi1 = lambda z: psi(1,z) + assert psi0(3) == psi(0,3) == digamma(3) + #assert psi2(3) == psi(2,3) == tetragamma(3) + #assert psi3(3) == psi(3,3) == pentagamma(3) + assert psi0(pi).ae(0.97721330794200673) + assert psi0(-pi).ae(7.8859523853854902) + assert psi0(-pi+1).ae(7.5676424992016996) + assert psi0(pi+j).ae(1.04224048313859376 + 0.35853686544063749j) + assert psi0(-pi-j).ae(1.3404026194821986 - 2.8824392476809402j) + assert findroot(psi0, 1).ae(1.4616321449683622) + assert psi0(1e-10).ae(-10000000000.57722) + assert psi0(1e-40).ae(-1.000000000000000e+40) + assert psi0(1e-10+1e-10j).ae(-5000000000.577215 + 5000000000.000000j) + assert psi0(1e-40+1e-40j).ae(-5.000000000000000e+39 + 5.000000000000000e+39j) + assert psi0(inf) == inf + assert psi1(inf) == 0 + assert psi(2,inf) == 0 + assert psi1(pi).ae(0.37424376965420049) + assert psi1(-pi).ae(53.030438740085385) + assert psi1(pi+j).ae(0.32935710377142464 - 0.12222163911221135j) + assert psi1(-pi-j).ae(-0.30065008356019703 + 0.01149892486928227j) + assert (10**6*psi(4,1+10*pi*j)).ae(-6.1491803479004446 - 0.3921316371664063j) + assert psi0(1+10*pi*j).ae(3.4473994217222650 + 1.5548808324857071j) + assert isnan(psi0(nan)) + assert isnan(psi0(-inf)) + assert psi0(-100.5).ae(4.615124601338064) + assert psi0(3+0j).ae(psi0(3)) + assert psi0(-100+3j).ae(4.6106071768714086321+3.1117510556817394626j) + assert isnan(psi(2,mpc(0,inf))) + assert isnan(psi(2,mpc(0,nan))) + assert isnan(psi(2,mpc(0,-inf))) + assert isnan(psi(2,mpc(1,inf))) + assert isnan(psi(2,mpc(1,nan))) + assert isnan(psi(2,mpc(1,-inf))) + assert isnan(psi(2,mpc(inf,inf))) + assert isnan(psi(2,mpc(nan,nan))) + assert isnan(psi(2,mpc(-inf,-inf))) + mp.dps = 30 + # issue #534 + assert digamma(-0.75+1j).ae(mpc('0.46317279488182026118963809283042317', '2.4821070143037957102007677817351115')) + mp.dps = 15 + +def test_polygamma_high_prec(): + mp.dps = 100 + assert str(psi(0,pi)) == "0.9772133079420067332920694864061823436408346099943256380095232865318105924777141317302075654362928734" + assert str(psi(10,pi)) == "-12.98876181434889529310283769414222588307175962213707170773803550518307617769657562747174101900659238" + +def test_polygamma_identities(): + mp.dps = 15 + psi0 = lambda z: psi(0,z) + psi1 = lambda z: psi(1,z) + psi2 = lambda z: psi(2,z) + assert psi0(0.5).ae(-euler-2*log(2)) + assert psi0(1).ae(-euler) + assert psi1(0.5).ae(0.5*pi**2) + assert psi1(1).ae(pi**2/6) + assert psi1(0.25).ae(pi**2 + 8*catalan) + assert psi2(1).ae(-2*apery) + mp.dps = 20 + u = -182*apery+4*sqrt(3)*pi**3 + mp.dps = 15 + assert psi(2,5/6.).ae(u) + assert psi(3,0.5).ae(pi**4) + +def test_foxtrot_identity(): + # A test of the complex digamma function. + # See http://mathworld.wolfram.com/FoxTrotSeries.html and + # http://mathworld.wolfram.com/DigammaFunction.html + psi0 = lambda z: psi(0,z) + mp.dps = 50 + a = (-1)**fraction(1,3) + b = (-1)**fraction(2,3) + x = -psi0(0.5*a) - psi0(-0.5*b) + psi0(0.5*(1+a)) + psi0(0.5*(1-b)) + y = 2*pi*sech(0.5*sqrt(3)*pi) + assert x.ae(y) + mp.dps = 15 + +def test_polygamma_high_order(): + mp.dps = 100 + assert str(psi(50, pi)) == "-1344100348958402765749252447726432491812.641985273160531055707095989227897753035823152397679626136483" + assert str(psi(50, pi + 14*e)) == "-0.00000000000000000189793739550804321623512073101895801993019919886375952881053090844591920308111549337295143780341396" + assert str(psi(50, pi + 14*e*j)) == ("(-0.0000000000000000522516941152169248975225472155683565752375889510631513244785" + "9377385233700094871256507814151956624433 - 0.00000000000000001813157041407010184" + "702414110218205348527862196327980417757665282244728963891298080199341480881811613j)") + mp.dps = 15 + assert str(psi(50, pi)) == "-1.34410034895841e+39" + assert str(psi(50, pi + 14*e)) == "-1.89793739550804e-18" + assert str(psi(50, pi + 14*e*j)) == "(-5.2251694115217e-17 - 1.81315704140701e-17j)" + +def test_harmonic(): + mp.dps = 15 + assert harmonic(0) == 0 + assert harmonic(1) == 1 + assert harmonic(2) == 1.5 + assert harmonic(3).ae(1. + 1./2 + 1./3) + assert harmonic(10**10).ae(23.603066594891989701) + assert harmonic(10**1000).ae(2303.162308658947) + assert harmonic(0.5).ae(2-2*log(2)) + assert harmonic(inf) == inf + assert harmonic(2+0j) == 1.5+0j + assert harmonic(1+2j).ae(1.4918071802755104+0.92080728264223022j) + +def test_gamma_huge_1(): + mp.dps = 500 + x = mpf(10**10) / 7 + mp.dps = 15 + assert str(gamma(x)) == "6.26075321389519e+12458010678" + mp.dps = 50 + assert str(gamma(x)) == "6.2607532138951929201303779291707455874010420783933e+12458010678" + mp.dps = 15 + +def test_gamma_huge_2(): + mp.dps = 500 + x = mpf(10**100) / 19 + mp.dps = 15 + assert str(gamma(x)) == (\ + "1.82341134776679e+5172997469323364168990133558175077136829182824042201886051511" + "9656908623426021308685461258226190190661") + mp.dps = 50 + assert str(gamma(x)) == (\ + "1.82341134776678875374414910350027596939980412984e+5172997469323364168990133558" + "1750771368291828240422018860515119656908623426021308685461258226190190661") + +def test_gamma_huge_3(): + mp.dps = 500 + x = 10**80 // 3 + 10**70*j / 7 + mp.dps = 15 + y = gamma(x) + assert str(y.real) == (\ + "-6.82925203918106e+2636286142112569524501781477865238132302397236429627932441916" + "056964386399485392600") + assert str(y.imag) == (\ + "8.54647143678418e+26362861421125695245017814778652381323023972364296279324419160" + "56964386399485392600") + mp.dps = 50 + y = gamma(x) + assert str(y.real) == (\ + "-6.8292520391810548460682736226799637356016538421817e+26362861421125695245017814" + "77865238132302397236429627932441916056964386399485392600") + assert str(y.imag) == (\ + "8.5464714367841748507479306948130687511711420234015e+263628614211256952450178147" + "7865238132302397236429627932441916056964386399485392600") + +def test_gamma_huge_4(): + x = 3200+11500j + mp.dps = 15 + assert str(gamma(x)) == \ + "(8.95783268539713e+5164 - 1.94678798329735e+5164j)" + mp.dps = 50 + assert str(gamma(x)) == (\ + "(8.9578326853971339570292952697675570822206567327092e+5164" + " - 1.9467879832973509568895402139429643650329524144794e+51" + "64j)") + mp.dps = 15 + +def test_gamma_huge_5(): + mp.dps = 500 + x = 10**60 * j / 3 + mp.dps = 15 + y = gamma(x) + assert str(y.real) == "-3.27753899634941e-227396058973640224580963937571892628368354580620654233316839" + assert str(y.imag) == "-7.1519888950416e-227396058973640224580963937571892628368354580620654233316841" + mp.dps = 50 + y = gamma(x) + assert str(y.real) == (\ + "-3.2775389963494132168950056995974690946983219123935e-22739605897364022458096393" + "7571892628368354580620654233316839") + assert str(y.imag) == (\ + "-7.1519888950415979749736749222530209713136588885897e-22739605897364022458096393" + "7571892628368354580620654233316841") + mp.dps = 15 + +def test_gamma_huge_7(): + mp.dps = 100 + a = 3 + j/mpf(10)**1000 + mp.dps = 15 + y = gamma(a) + assert str(y.real) == "2.0" + # wrong + #assert str(y.imag) == "2.16735365342606e-1000" + assert str(y.imag) == "1.84556867019693e-1000" + mp.dps = 50 + y = gamma(a) + assert str(y.real) == "2.0" + #assert str(y.imag) == "2.1673536534260596065418805612488708028522563689298e-1000" + assert str(y.imag) == "1.8455686701969342787869758198351951379156813281202e-1000" + +def test_stieltjes(): + mp.dps = 15 + assert stieltjes(0).ae(+euler) + mp.dps = 25 + assert stieltjes(1).ae('-0.07281584548367672486058637587') + assert stieltjes(2).ae('-0.009690363192872318484530386035') + assert stieltjes(3).ae('0.002053834420303345866160046543') + assert stieltjes(4).ae('0.002325370065467300057468170178') + mp.dps = 15 + assert stieltjes(1).ae(-0.07281584548367672486058637587) + assert stieltjes(2).ae(-0.009690363192872318484530386035) + assert stieltjes(3).ae(0.002053834420303345866160046543) + assert stieltjes(4).ae(0.0023253700654673000574681701775) + +def test_barnesg(): + mp.dps = 15 + assert barnesg(0) == barnesg(-1) == 0 + assert [superfac(i) for i in range(8)] == [1, 1, 2, 12, 288, 34560, 24883200, 125411328000] + assert str(superfac(1000)) == '3.24570818422368e+1177245' + assert isnan(barnesg(nan)) + assert isnan(superfac(nan)) + assert isnan(hyperfac(nan)) + assert barnesg(inf) == inf + assert superfac(inf) == inf + assert hyperfac(inf) == inf + assert isnan(superfac(-inf)) + assert barnesg(0.7).ae(0.8068722730141471) + assert barnesg(2+3j).ae(-0.17810213864082169+0.04504542715447838j) + assert [hyperfac(n) for n in range(7)] == [1, 1, 4, 108, 27648, 86400000, 4031078400000] + assert [hyperfac(n) for n in range(0,-7,-1)] == [1,1,-1,-4,108,27648,-86400000] + a = barnesg(-3+0j) + assert a == 0 and isinstance(a, mpc) + a = hyperfac(-3+0j) + assert a == -4 and isinstance(a, mpc) + +def test_polylog(): + mp.dps = 15 + zs = [mpmathify(z) for z in [0, 0.5, 0.99, 4, -0.5, -4, 1j, 3+4j]] + for z in zs: assert polylog(1, z).ae(-log(1-z)) + for z in zs: assert polylog(0, z).ae(z/(1-z)) + for z in zs: assert polylog(-1, z).ae(z/(1-z)**2) + for z in zs: assert polylog(-2, z).ae(z*(1+z)/(1-z)**3) + for z in zs: assert polylog(-3, z).ae(z*(1+4*z+z**2)/(1-z)**4) + assert polylog(3, 7).ae(5.3192579921456754382-5.9479244480803301023j) + assert polylog(3, -7).ae(-4.5693548977219423182) + assert polylog(2, 0.9).ae(1.2997147230049587252) + assert polylog(2, -0.9).ae(-0.75216317921726162037) + assert polylog(2, 0.9j).ae(-0.17177943786580149299+0.83598828572550503226j) + assert polylog(2, 1.1).ae(1.9619991013055685931-0.2994257606855892575j) + assert polylog(2, -1.1).ae(-0.89083809026228260587) + assert polylog(2, 1.1*sqrt(j)).ae(0.58841571107611387722+1.09962542118827026011j) + assert polylog(-2, 0.9).ae(1710) + assert polylog(-2, -0.9).ae(-90/6859.) + assert polylog(3, 0.9).ae(1.0496589501864398696) + assert polylog(-3, 0.9).ae(48690) + assert polylog(-3, -4).ae(-0.0064) + assert polylog(0.5+j/3, 0.5+j/2).ae(0.31739144796565650535 + 0.99255390416556261437j) + assert polylog(3+4j,1).ae(zeta(3+4j)) + assert polylog(3+4j,-1).ae(-altzeta(3+4j)) + # issue 390 + assert polylog(1.5, -48.910886523731889).ae(-6.272992229311817) + assert polylog(1.5, 200).ae(-8.349608319033686529 - 8.159694826434266042j) + assert polylog(-2+0j, -2).ae(mpf(1)/13.5) + assert polylog(-2+0j, 1.25).ae(-180) + +def test_bell_polyexp(): + mp.dps = 15 + # TODO: more tests for polyexp + assert (polyexp(0,1e-10)*10**10).ae(1.00000000005) + assert (polyexp(1,1e-10)*10**10).ae(1.0000000001) + assert polyexp(5,3j).ae(-607.7044517476176454+519.962786482001476087j) + assert polyexp(-1,3.5).ae(12.09537536175543444) + # bell(0,x) = 1 + assert bell(0,0) == 1 + assert bell(0,1) == 1 + assert bell(0,2) == 1 + assert bell(0,inf) == 1 + assert bell(0,-inf) == 1 + assert isnan(bell(0,nan)) + # bell(1,x) = x + assert bell(1,4) == 4 + assert bell(1,0) == 0 + assert bell(1,inf) == inf + assert bell(1,-inf) == -inf + assert isnan(bell(1,nan)) + # bell(2,x) = x*(1+x) + assert bell(2,-1) == 0 + assert bell(2,0) == 0 + # large orders / arguments + assert bell(10) == 115975 + assert bell(10,1) == 115975 + assert bell(10, -8) == 11054008 + assert bell(5,-50) == -253087550 + assert bell(50,-50).ae('3.4746902914629720259e74') + mp.dps = 80 + assert bell(50,-50) == 347469029146297202586097646631767227177164818163463279814268368579055777450 + assert bell(40,50) == 5575520134721105844739265207408344706846955281965031698187656176321717550 + assert bell(74) == 5006908024247925379707076470957722220463116781409659160159536981161298714301202 + mp.dps = 15 + assert bell(10,20j) == 7504528595600+15649605360020j + # continuity of the generalization + assert bell(0.5,0).ae(sinc(pi*0.5)) + +def test_primezeta(): + mp.dps = 15 + assert primezeta(0.9).ae(1.8388316154446882243 + 3.1415926535897932385j) + assert primezeta(4).ae(0.076993139764246844943) + assert primezeta(1) == inf + assert primezeta(inf) == 0 + assert isnan(primezeta(nan)) + +def test_rs_zeta(): + mp.dps = 15 + assert zeta(0.5+100000j).ae(1.0730320148577531321 + 5.7808485443635039843j) + assert zeta(0.75+100000j).ae(1.837852337251873704 + 1.9988492668661145358j) + assert zeta(0.5+1000000j, derivative=3).ae(1647.7744105852674733 - 1423.1270943036622097j) + assert zeta(1+1000000j, derivative=3).ae(3.4085866124523582894 - 18.179184721525947301j) + assert zeta(1+1000000j, derivative=1).ae(-0.10423479366985452134 - 0.74728992803359056244j) + assert zeta(0.5-1000000j, derivative=1).ae(11.636804066002521459 + 17.127254072212996004j) + # Additional sanity tests using fp arithmetic. + # Some more high-precision tests are found in the docstrings + def ae(x, y, tol=1e-6): + return abs(x-y) < tol*abs(y) + assert ae(fp.zeta(0.5-100000j), 1.0730320148577531321 - 5.7808485443635039843j) + assert ae(fp.zeta(0.75-100000j), 1.837852337251873704 - 1.9988492668661145358j) + assert ae(fp.zeta(0.5+1e6j), 0.076089069738227100006 + 2.8051021010192989554j) + assert ae(fp.zeta(0.5+1e6j, derivative=1), 11.636804066002521459 - 17.127254072212996004j) + assert ae(fp.zeta(1+1e6j), 0.94738726251047891048 + 0.59421999312091832833j) + assert ae(fp.zeta(1+1e6j, derivative=1), -0.10423479366985452134 - 0.74728992803359056244j) + assert ae(fp.zeta(0.5+100000j, derivative=1), 10.766962036817482375 - 30.92705282105996714j) + assert ae(fp.zeta(0.5+100000j, derivative=2), -119.40515625740538429 + 217.14780631141830251j) + assert ae(fp.zeta(0.5+100000j, derivative=3), 1129.7550282628460881 - 1685.4736895169690346j) + assert ae(fp.zeta(0.5+100000j, derivative=4), -10407.160819314958615 + 13777.786698628045085j) + assert ae(fp.zeta(0.75+100000j, derivative=1), -0.41742276699594321475 - 6.4453816275049955949j) + assert ae(fp.zeta(0.75+100000j, derivative=2), -9.214314279161977266 + 35.07290795337967899j) + assert ae(fp.zeta(0.75+100000j, derivative=3), 110.61331857820103469 - 236.87847130518129926j) + assert ae(fp.zeta(0.75+100000j, derivative=4), -1054.334275898559401 + 1769.9177890161596383j) + +def test_siegelz(): + mp.dps = 15 + assert siegelz(100000).ae(5.87959246868176504171) + assert siegelz(100000, derivative=2).ae(-54.1172711010126452832) + assert siegelz(100000, derivative=3).ae(-278.930831343966552538) + assert siegelz(100000+j,derivative=1).ae(678.214511857070283307-379.742160779916375413j) + + + +def test_zeta_near_1(): + # Test for a former bug in mpf_zeta and mpc_zeta + mp.dps = 15 + s1 = fadd(1, '1e-10', exact=True) + s2 = fadd(1, '-1e-10', exact=True) + s3 = fadd(1, '1e-10j', exact=True) + assert zeta(s1).ae(1.000000000057721566490881444e10) + assert zeta(s2).ae(-9.99999999942278433510574872e9) + z = zeta(s3) + assert z.real.ae(0.57721566490153286060) + assert z.imag.ae(-9.9999999999999999999927184e9) + mp.dps = 30 + s1 = fadd(1, '1e-50', exact=True) + s2 = fadd(1, '-1e-50', exact=True) + s3 = fadd(1, '1e-50j', exact=True) + assert zeta(s1).ae('1e50') + assert zeta(s2).ae('-1e50') + z = zeta(s3) + assert z.real.ae('0.57721566490153286060651209008240243104215933593992') + assert z.imag.ae('-1e50') diff --git a/venv/lib/python3.12/site-packages/mpmath/tests/test_hp.py b/venv/lib/python3.12/site-packages/mpmath/tests/test_hp.py new file mode 100644 index 0000000000000000000000000000000000000000..9eba0af798f64ac3f8d464e2d3bf231567a48c9b --- /dev/null +++ b/venv/lib/python3.12/site-packages/mpmath/tests/test_hp.py @@ -0,0 +1,291 @@ +""" +Check that the output from irrational functions is accurate for +high-precision input, from 5 to 200 digits. The reference values were +verified with Mathematica. +""" + +import time +from mpmath import * + +precs = [5, 15, 28, 35, 57, 80, 100, 150, 200] + +# sqrt(3) + pi/2 +a = \ +"3.302847134363773912758768033145623809041389953497933538543279275605"\ +"841220051904536395163599428307109666700184672047856353516867399774243594"\ +"67433521615861420725323528325327484262075464241255915238845599752675" + +# e + 1/euler**2 +b = \ +"5.719681166601007617111261398629939965860873957353320734275716220045750"\ +"31474116300529519620938123730851145473473708966080207482581266469342214"\ +"824842256999042984813905047895479210702109260221361437411947323431" + +# sqrt(a) +sqrt_a = \ +"1.817373691447021556327498239690365674922395036495564333152483422755"\ +"144321726165582817927383239308173567921345318453306994746434073691275094"\ +"484777905906961689902608644112196725896908619756404253109722911487" + +# sqrt(a+b*i).real +sqrt_abi_real = \ +"2.225720098415113027729407777066107959851146508557282707197601407276"\ +"89160998185797504198062911768240808839104987021515555650875977724230130"\ +"3584116233925658621288393930286871862273400475179312570274423840384" + +# sqrt(a+b*i).imag +sqrt_abi_imag = \ +"1.2849057639084690902371581529110949983261182430040898147672052833653668"\ +"0629534491275114877090834296831373498336559849050755848611854282001250"\ +"1924311019152914021365263161630765255610885489295778894976075186" + +# log(a) +log_a = \ +"1.194784864491089550288313512105715261520511949410072046160598707069"\ +"4336653155025770546309137440687056366757650909754708302115204338077595203"\ +"83005773986664564927027147084436553262269459110211221152925732612" + +# log(a+b*i).real +log_abi_real = \ +"1.8877985921697018111624077550443297276844736840853590212962006811663"\ +"04949387789489704203167470111267581371396245317618589339274243008242708"\ +"014251531496104028712866224020066439049377679709216784954509456421" + +# log(a+b*i).imag +log_abi_imag = \ +"1.0471204952840802663567714297078763189256357109769672185219334169734948"\ +"4265809854092437285294686651806426649541504240470168212723133326542181"\ +"8300136462287639956713914482701017346851009323172531601894918640" + +# exp(a) +exp_a = \ +"27.18994224087168661137253262213293847994194869430518354305430976149"\ +"382792035050358791398632888885200049857986258414049540376323785711941636"\ +"100358982497583832083513086941635049329804685212200507288797531143" + +# exp(a+b*i).real +exp_abi_real = \ +"22.98606617170543596386921087657586890620262522816912505151109385026"\ +"40160179326569526152851983847133513990281518417211964710397233157168852"\ +"4963130831190142571659948419307628119985383887599493378056639916701" + +# exp(a+b*i).imag +exp_abi_imag = \ +"-14.523557450291489727214750571590272774669907424478129280902375851196283"\ +"3377162379031724734050088565710975758824441845278120105728824497308303"\ +"6065619788140201636218705414429933685889542661364184694108251449" + +# a**b +pow_a_b = \ +"928.7025342285568142947391505837660251004990092821305668257284426997"\ +"361966028275685583421197860603126498884545336686124793155581311527995550"\ +"580229264427202446131740932666832138634013168125809402143796691154" + +# (a**(a+b*i)).real +pow_a_abi_real = \ +"44.09156071394489511956058111704382592976814280267142206420038656267"\ +"67707916510652790502399193109819563864568986234654864462095231138500505"\ +"8197456514795059492120303477512711977915544927440682508821426093455" + +# (a**(a+b*i)).imag +pow_a_abi_imag = \ +"27.069371511573224750478105146737852141664955461266218367212527612279886"\ +"9322304536553254659049205414427707675802193810711302947536332040474573"\ +"8166261217563960235014674118610092944307893857862518964990092301" + +# ((a+b*i)**(a+b*i)).real +pow_abi_abi_real = \ +"-0.15171310677859590091001057734676423076527145052787388589334350524"\ +"8084195882019497779202452975350579073716811284169068082670778986235179"\ +"0813026562962084477640470612184016755250592698408112493759742219150452"\ + +# ((a+b*i)**(a+b*i)).imag +pow_abi_abi_imag = \ +"1.2697592504953448936553147870155987153192995316950583150964099070426"\ +"4736837932577176947632535475040521749162383347758827307504526525647759"\ +"97547638617201824468382194146854367480471892602963428122896045019902" + +# sin(a) +sin_a = \ +"-0.16055653857469062740274792907968048154164433772938156243509084009"\ +"38437090841460493108570147191289893388608611542655654723437248152535114"\ +"528368009465836614227575701220612124204622383149391870684288862269631" + +# sin(1000*a) +sin_1000a = \ +"-0.85897040577443833776358106803777589664322997794126153477060795801"\ +"09151695416961724733492511852267067419573754315098042850381158563024337"\ +"216458577140500488715469780315833217177634490142748614625281171216863" + +# sin(a+b*i) +sin_abi_real = \ +"-24.4696999681556977743346798696005278716053366404081910969773939630"\ +"7149215135459794473448465734589287491880563183624997435193637389884206"\ +"02151395451271809790360963144464736839412254746645151672423256977064" + +sin_abi_imag = \ +"-150.42505378241784671801405965872972765595073690984080160750785565810981"\ +"8314482499135443827055399655645954830931316357243750839088113122816583"\ +"7169201254329464271121058839499197583056427233866320456505060735" + +# cos +cos_a = \ +"-0.98702664499035378399332439243967038895709261414476495730788864004"\ +"05406821549361039745258003422386169330787395654908532996287293003581554"\ +"257037193284199198069707141161341820684198547572456183525659969145501" + +cos_1000a = \ +"-0.51202523570982001856195696460663971099692261342827540426136215533"\ +"52686662667660613179619804463250686852463876088694806607652218586060613"\ +"951310588158830695735537073667299449753951774916401887657320950496820" + +# tan +tan_a = \ +"0.162666873675188117341401059858835168007137819495998960250142156848"\ +"639654718809412181543343168174807985559916643549174530459883826451064966"\ +"7996119428949951351938178809444268785629011625179962457123195557310" + +tan_abi_real = \ +"6.822696615947538488826586186310162599974827139564433912601918442911"\ +"1026830824380070400102213741875804368044342309515353631134074491271890"\ +"467615882710035471686578162073677173148647065131872116479947620E-6" + +tan_abi_imag = \ +"0.9999795833048243692245661011298447587046967777739649018690797625964167"\ +"1446419978852235960862841608081413169601038230073129482874832053357571"\ +"62702259309150715669026865777947502665936317953101462202542168429" + + +def test_hp(): + for dps in precs: + mp.dps = dps + 8 + aa = mpf(a) + bb = mpf(b) + a1000 = 1000*mpf(a) + abi = mpc(aa, bb) + mp.dps = dps + assert (sqrt(3) + pi/2).ae(aa) + assert (e + 1/euler**2).ae(bb) + + assert sqrt(aa).ae(mpf(sqrt_a)) + assert sqrt(abi).ae(mpc(sqrt_abi_real, sqrt_abi_imag)) + + assert log(aa).ae(mpf(log_a)) + assert log(abi).ae(mpc(log_abi_real, log_abi_imag)) + + assert exp(aa).ae(mpf(exp_a)) + assert exp(abi).ae(mpc(exp_abi_real, exp_abi_imag)) + + assert (aa**bb).ae(mpf(pow_a_b)) + assert (aa**abi).ae(mpc(pow_a_abi_real, pow_a_abi_imag)) + assert (abi**abi).ae(mpc(pow_abi_abi_real, pow_abi_abi_imag)) + + assert sin(a).ae(mpf(sin_a)) + assert sin(a1000).ae(mpf(sin_1000a)) + assert sin(abi).ae(mpc(sin_abi_real, sin_abi_imag)) + + assert cos(a).ae(mpf(cos_a)) + assert cos(a1000).ae(mpf(cos_1000a)) + + assert tan(a).ae(mpf(tan_a)) + assert tan(abi).ae(mpc(tan_abi_real, tan_abi_imag)) + + # check that complex cancellation is avoided so that both + # real and imaginary parts have high relative accuracy. + # abs_eps should be 0, but has to be set to 1e-205 to pass the + # 200-digit case, probably due to slight inaccuracy in the + # precomputed input + assert (tan(abi).real).ae(mpf(tan_abi_real), abs_eps=1e-205) + assert (tan(abi).imag).ae(mpf(tan_abi_imag), abs_eps=1e-205) + mp.dps = 460 + assert str(log(3))[-20:] == '02166121184001409826' + mp.dps = 15 + +# Since str(a) can differ in the last digit from rounded a, and I want +# to compare the last digits of big numbers with the results in Mathematica, +# I made this hack to get the last 20 digits of rounded a + +def last_digits(a): + r = repr(a) + s = str(a) + #dps = mp.dps + #mp.dps += 3 + m = 10 + r = r.replace(s[:-m],'') + r = r.replace("mpf('",'').replace("')",'') + num0 = 0 + for c in r: + if c == '0': + num0 += 1 + else: + break + b = float(int(r))/10**(len(r) - m) + if b >= 10**m - 0.5: # pragma: no cover + raise NotImplementedError + n = int(round(b)) + sn = str(n) + s = s[:-m] + '0'*num0 + sn + return s[-20:] + +# values checked with Mathematica +def test_log_hp(): + mp.dps = 2000 + a = mpf(10)**15000/3 + r = log(a) + res = last_digits(r) + # Mathematica N[Log[10^15000/3], 2000] + # ...7443804441768333470331 + assert res == '43804441768333470331' + + # see issue 145 + r = log(mpf(3)/2) + # Mathematica N[Log[3/2], 2000] + # ...69653749808140753263288 + res = last_digits(r) + assert res == '53749808140753263288' + + mp.dps = 10000 + r = log(2) + res = last_digits(r) + # Mathematica N[Log[2], 10000] + # ...695615913401856601359655561 + assert res == '13401856601359655561' + r = log(mpf(10)**10/3) + res = last_digits(r) + # Mathematica N[Log[10^10/3], 10000] + # ...587087654020631943060007154 + assert res == '54020631943060007154', res + r = log(mpf(10)**100/3) + res = last_digits(r) + # Mathematica N[Log[10^100/3], 10000] + # ,,,59246336539088351652334666 + assert res == '36539088351652334666', res + mp.dps += 10 + a = 1 - mpf(1)/10**10 + mp.dps -= 10 + r = log(a) + res = last_digits(r) + # ...3310334360482956137216724048322957404 + # 372167240483229574038733026370 + # Mathematica N[Log[1 - 10^-10]*10^10, 10000] + # ...60482956137216724048322957404 + assert res == '37216724048322957404', res + mp.dps = 10000 + mp.dps += 100 + a = 1 + mpf(1)/10**100 + mp.dps -= 100 + + r = log(a) + res = last_digits(+r) + # Mathematica N[Log[1 + 10^-100]*10^10, 10030] + # ...3994733877377412241546890854692521568292338268273 10^-91 + assert res == '39947338773774122415', res + + mp.dps = 15 + +def test_exp_hp(): + mp.dps = 4000 + r = exp(mpf(1)/10) + # IntegerPart[N[Exp[1/10] * 10^4000, 4000]] + # ...92167105162069688129 + assert int(r * 10**mp.dps) % 10**20 == 92167105162069688129 diff --git a/venv/lib/python3.12/site-packages/mpmath/tests/test_identify.py b/venv/lib/python3.12/site-packages/mpmath/tests/test_identify.py new file mode 100644 index 0000000000000000000000000000000000000000..f75ab0bc4f04ecb614011e7f4599989465cab785 --- /dev/null +++ b/venv/lib/python3.12/site-packages/mpmath/tests/test_identify.py @@ -0,0 +1,19 @@ +from mpmath import * + +def test_pslq(): + mp.dps = 15 + assert pslq([3*pi+4*e/7, pi, e, log(2)]) == [7, -21, -4, 0] + assert pslq([4.9999999999999991, 1]) == [1, -5] + assert pslq([2,1]) == [1, -2] + +def test_identify(): + mp.dps = 20 + assert identify(zeta(4), ['log(2)', 'pi**4']) == '((1/90)*pi**4)' + mp.dps = 15 + assert identify(exp(5)) == 'exp(5)' + assert identify(exp(4)) == 'exp(4)' + assert identify(log(5)) == 'log(5)' + assert identify(exp(3*pi), ['pi']) == 'exp((3*pi))' + assert identify(3, full=True) == ['3', '3', '1/(1/3)', 'sqrt(9)', + '1/sqrt((1/9))', '(sqrt(12)/2)**2', '1/(sqrt(12)/6)**2'] + assert identify(pi+1, {'a':+pi}) == '(1 + 1*a)' diff --git a/venv/lib/python3.12/site-packages/mpmath/tests/test_interval.py b/venv/lib/python3.12/site-packages/mpmath/tests/test_interval.py new file mode 100644 index 0000000000000000000000000000000000000000..251fd8b7ddb00074e8ae27cce4a01d8f4f8fe151 --- /dev/null +++ b/venv/lib/python3.12/site-packages/mpmath/tests/test_interval.py @@ -0,0 +1,453 @@ +from mpmath import * + +def test_interval_identity(): + iv.dps = 15 + assert mpi(2) == mpi(2, 2) + assert mpi(2) != mpi(-2, 2) + assert not (mpi(2) != mpi(2, 2)) + assert mpi(-1, 1) == mpi(-1, 1) + assert str(mpi('0.1')) == "[0.099999999999999991673, 0.10000000000000000555]" + assert repr(mpi('0.1')) == "mpi('0.099999999999999992', '0.10000000000000001')" + u = mpi(-1, 3) + assert -1 in u + assert 2 in u + assert 3 in u + assert -1.1 not in u + assert 3.1 not in u + assert mpi(-1, 3) in u + assert mpi(0, 1) in u + assert mpi(-1.1, 2) not in u + assert mpi(2.5, 3.1) not in u + w = mpi(-inf, inf) + assert mpi(-5, 5) in w + assert mpi(2, inf) in w + assert mpi(0, 2) in mpi(0, 10) + assert not (3 in mpi(-inf, 0)) + +def test_interval_hash(): + assert hash(mpi(3)) == hash(3) + assert hash(mpi(3.25)) == hash(3.25) + assert hash(mpi(3,4)) == hash(mpi(3,4)) + assert hash(iv.mpc(3)) == hash(3) + assert hash(iv.mpc(3,4)) == hash(3+4j) + assert hash(iv.mpc((1,3),(2,4))) == hash(iv.mpc((1,3),(2,4))) + +def test_interval_arithmetic(): + iv.dps = 15 + assert mpi(2) + mpi(3,4) == mpi(5,6) + assert mpi(1, 2)**2 == mpi(1, 4) + assert mpi(1) + mpi(0, 1e-50) == mpi(1, mpf('1.0000000000000002')) + x = 1 / (1 / mpi(3)) + assert x.a < 3 < x.b + x = mpi(2) ** mpi(0.5) + iv.dps += 5 + sq = iv.sqrt(2) + iv.dps -= 5 + assert x.a < sq < x.b + assert mpi(1) / mpi(1, inf) + assert mpi(2, 3) / inf == mpi(0, 0) + assert mpi(0) / inf == 0 + assert mpi(0) / 0 == mpi(-inf, inf) + assert mpi(inf) / 0 == mpi(-inf, inf) + assert mpi(0) * inf == mpi(-inf, inf) + assert 1 / mpi(2, inf) == mpi(0, 0.5) + assert str((mpi(50, 50) * mpi(-10, -10)) / 3) == \ + '[-166.66666666666668561, -166.66666666666665719]' + assert mpi(0, 4) ** 3 == mpi(0, 64) + assert mpi(2,4).mid == 3 + iv.dps = 30 + a = mpi(iv.pi) + iv.dps = 15 + b = +a + assert b.a < a.a + assert b.b > a.b + a = mpi(iv.pi) + assert a == +a + assert abs(mpi(-1,2)) == mpi(0,2) + assert abs(mpi(0.5,2)) == mpi(0.5,2) + assert abs(mpi(-3,2)) == mpi(0,3) + assert abs(mpi(-3,-0.5)) == mpi(0.5,3) + assert mpi(0) * mpi(2,3) == mpi(0) + assert mpi(2,3) * mpi(0) == mpi(0) + assert mpi(1,3).delta == 2 + assert mpi(1,2) - mpi(3,4) == mpi(-3,-1) + assert mpi(-inf,0) - mpi(0,inf) == mpi(-inf,0) + assert mpi(-inf,0) - mpi(-inf,inf) == mpi(-inf,inf) + assert mpi(0,inf) - mpi(-inf,1) == mpi(-1,inf) + +def test_interval_mul(): + assert mpi(-1, 0) * inf == mpi(-inf, 0) + assert mpi(-1, 0) * -inf == mpi(0, inf) + assert mpi(0, 1) * inf == mpi(0, inf) + assert mpi(0, 1) * mpi(0, inf) == mpi(0, inf) + assert mpi(-1, 1) * inf == mpi(-inf, inf) + assert mpi(-1, 1) * mpi(0, inf) == mpi(-inf, inf) + assert mpi(-1, 1) * mpi(-inf, inf) == mpi(-inf, inf) + assert mpi(-inf, 0) * mpi(0, 1) == mpi(-inf, 0) + assert mpi(-inf, 0) * mpi(0, 0) * mpi(-inf, 0) + assert mpi(-inf, 0) * mpi(-inf, inf) == mpi(-inf, inf) + assert mpi(-5,0)*mpi(-32,28) == mpi(-140,160) + assert mpi(2,3) * mpi(-1,2) == mpi(-3,6) + # Should be undefined? + assert mpi(inf, inf) * 0 == mpi(-inf, inf) + assert mpi(-inf, -inf) * 0 == mpi(-inf, inf) + assert mpi(0) * mpi(-inf,2) == mpi(-inf,inf) + assert mpi(0) * mpi(-2,inf) == mpi(-inf,inf) + assert mpi(-2,inf) * mpi(0) == mpi(-inf,inf) + assert mpi(-inf,2) * mpi(0) == mpi(-inf,inf) + +def test_interval_pow(): + assert mpi(3)**2 == mpi(9, 9) + assert mpi(-3)**2 == mpi(9, 9) + assert mpi(-3, 1)**2 == mpi(0, 9) + assert mpi(-3, -1)**2 == mpi(1, 9) + assert mpi(-3, -1)**3 == mpi(-27, -1) + assert mpi(-3, 1)**3 == mpi(-27, 1) + assert mpi(-2, 3)**2 == mpi(0, 9) + assert mpi(-3, 2)**2 == mpi(0, 9) + assert mpi(4) ** -1 == mpi(0.25, 0.25) + assert mpi(-4) ** -1 == mpi(-0.25, -0.25) + assert mpi(4) ** -2 == mpi(0.0625, 0.0625) + assert mpi(-4) ** -2 == mpi(0.0625, 0.0625) + assert mpi(0, 1) ** inf == mpi(0, 1) + assert mpi(0, 1) ** -inf == mpi(1, inf) + assert mpi(0, inf) ** inf == mpi(0, inf) + assert mpi(0, inf) ** -inf == mpi(0, inf) + assert mpi(1, inf) ** inf == mpi(1, inf) + assert mpi(1, inf) ** -inf == mpi(0, 1) + assert mpi(2, 3) ** 1 == mpi(2, 3) + assert mpi(2, 3) ** 0 == 1 + assert mpi(1,3) ** mpi(2) == mpi(1,9) + +def test_interval_sqrt(): + assert mpi(4) ** 0.5 == mpi(2) + +def test_interval_div(): + assert mpi(0.5, 1) / mpi(-1, 0) == mpi(-inf, -0.5) + assert mpi(0, 1) / mpi(0, 1) == mpi(0, inf) + assert mpi(inf, inf) / mpi(inf, inf) == mpi(0, inf) + assert mpi(inf, inf) / mpi(2, inf) == mpi(0, inf) + assert mpi(inf, inf) / mpi(2, 2) == mpi(inf, inf) + assert mpi(0, inf) / mpi(2, inf) == mpi(0, inf) + assert mpi(0, inf) / mpi(2, 2) == mpi(0, inf) + assert mpi(2, inf) / mpi(2, 2) == mpi(1, inf) + assert mpi(2, inf) / mpi(2, inf) == mpi(0, inf) + assert mpi(-4, 8) / mpi(1, inf) == mpi(-4, 8) + assert mpi(-4, 8) / mpi(0.5, inf) == mpi(-8, 16) + assert mpi(-inf, 8) / mpi(0.5, inf) == mpi(-inf, 16) + assert mpi(-inf, inf) / mpi(0.5, inf) == mpi(-inf, inf) + assert mpi(8, inf) / mpi(0.5, inf) == mpi(0, inf) + assert mpi(-8, inf) / mpi(0.5, inf) == mpi(-16, inf) + assert mpi(-4, 8) / mpi(inf, inf) == mpi(0, 0) + assert mpi(0, 8) / mpi(inf, inf) == mpi(0, 0) + assert mpi(0, 0) / mpi(inf, inf) == mpi(0, 0) + assert mpi(-inf, 0) / mpi(inf, inf) == mpi(-inf, 0) + assert mpi(-inf, 8) / mpi(inf, inf) == mpi(-inf, 0) + assert mpi(-inf, inf) / mpi(inf, inf) == mpi(-inf, inf) + assert mpi(-8, inf) / mpi(inf, inf) == mpi(0, inf) + assert mpi(0, inf) / mpi(inf, inf) == mpi(0, inf) + assert mpi(8, inf) / mpi(inf, inf) == mpi(0, inf) + assert mpi(inf, inf) / mpi(inf, inf) == mpi(0, inf) + assert mpi(-1, 2) / mpi(0, 1) == mpi(-inf, +inf) + assert mpi(0, 1) / mpi(0, 1) == mpi(0.0, +inf) + assert mpi(-1, 0) / mpi(0, 1) == mpi(-inf, 0.0) + assert mpi(-0.5, -0.25) / mpi(0, 1) == mpi(-inf, -0.25) + assert mpi(0.5, 1) / mpi(0, 1) == mpi(0.5, +inf) + assert mpi(0.5, 4) / mpi(0, 1) == mpi(0.5, +inf) + assert mpi(-1, -0.5) / mpi(0, 1) == mpi(-inf, -0.5) + assert mpi(-4, -0.5) / mpi(0, 1) == mpi(-inf, -0.5) + assert mpi(-1, 2) / mpi(-2, 0.5) == mpi(-inf, +inf) + assert mpi(0, 1) / mpi(-2, 0.5) == mpi(-inf, +inf) + assert mpi(-1, 0) / mpi(-2, 0.5) == mpi(-inf, +inf) + assert mpi(-0.5, -0.25) / mpi(-2, 0.5) == mpi(-inf, +inf) + assert mpi(0.5, 1) / mpi(-2, 0.5) == mpi(-inf, +inf) + assert mpi(0.5, 4) / mpi(-2, 0.5) == mpi(-inf, +inf) + assert mpi(-1, -0.5) / mpi(-2, 0.5) == mpi(-inf, +inf) + assert mpi(-4, -0.5) / mpi(-2, 0.5) == mpi(-inf, +inf) + assert mpi(-1, 2) / mpi(-1, 0) == mpi(-inf, +inf) + assert mpi(0, 1) / mpi(-1, 0) == mpi(-inf, 0.0) + assert mpi(-1, 0) / mpi(-1, 0) == mpi(0.0, +inf) + assert mpi(-0.5, -0.25) / mpi(-1, 0) == mpi(0.25, +inf) + assert mpi(0.5, 1) / mpi(-1, 0) == mpi(-inf, -0.5) + assert mpi(0.5, 4) / mpi(-1, 0) == mpi(-inf, -0.5) + assert mpi(-1, -0.5) / mpi(-1, 0) == mpi(0.5, +inf) + assert mpi(-4, -0.5) / mpi(-1, 0) == mpi(0.5, +inf) + assert mpi(-1, 2) / mpi(0.5, 1) == mpi(-2.0, 4.0) + assert mpi(0, 1) / mpi(0.5, 1) == mpi(0.0, 2.0) + assert mpi(-1, 0) / mpi(0.5, 1) == mpi(-2.0, 0.0) + assert mpi(-0.5, -0.25) / mpi(0.5, 1) == mpi(-1.0, -0.25) + assert mpi(0.5, 1) / mpi(0.5, 1) == mpi(0.5, 2.0) + assert mpi(0.5, 4) / mpi(0.5, 1) == mpi(0.5, 8.0) + assert mpi(-1, -0.5) / mpi(0.5, 1) == mpi(-2.0, -0.5) + assert mpi(-4, -0.5) / mpi(0.5, 1) == mpi(-8.0, -0.5) + assert mpi(-1, 2) / mpi(-2, -0.5) == mpi(-4.0, 2.0) + assert mpi(0, 1) / mpi(-2, -0.5) == mpi(-2.0, 0.0) + assert mpi(-1, 0) / mpi(-2, -0.5) == mpi(0.0, 2.0) + assert mpi(-0.5, -0.25) / mpi(-2, -0.5) == mpi(0.125, 1.0) + assert mpi(0.5, 1) / mpi(-2, -0.5) == mpi(-2.0, -0.25) + assert mpi(0.5, 4) / mpi(-2, -0.5) == mpi(-8.0, -0.25) + assert mpi(-1, -0.5) / mpi(-2, -0.5) == mpi(0.25, 2.0) + assert mpi(-4, -0.5) / mpi(-2, -0.5) == mpi(0.25, 8.0) + # Should be undefined? + assert mpi(0, 0) / mpi(0, 0) == mpi(-inf, inf) + assert mpi(0, 0) / mpi(0, 1) == mpi(-inf, inf) + +def test_interval_cos_sin(): + iv.dps = 15 + cos = iv.cos + sin = iv.sin + tan = iv.tan + pi = iv.pi + # Around 0 + assert cos(mpi(0)) == 1 + assert sin(mpi(0)) == 0 + assert cos(mpi(0,1)) == mpi(0.54030230586813965399, 1.0) + assert sin(mpi(0,1)) == mpi(0, 0.8414709848078966159) + assert cos(mpi(1,2)) == mpi(-0.4161468365471424069, 0.54030230586813976501) + assert sin(mpi(1,2)) == mpi(0.84147098480789650488, 1.0) + assert sin(mpi(1,2.5)) == mpi(0.59847214410395643824, 1.0) + assert cos(mpi(-1, 1)) == mpi(0.54030230586813965399, 1.0) + assert cos(mpi(-1, 0.5)) == mpi(0.54030230586813965399, 1.0) + assert cos(mpi(-1, 1.5)) == mpi(0.070737201667702906405, 1.0) + assert sin(mpi(-1,1)) == mpi(-0.8414709848078966159, 0.8414709848078966159) + assert sin(mpi(-1,0.5)) == mpi(-0.8414709848078966159, 0.47942553860420300538) + assert mpi(-0.8414709848078966159, 1.00000000000000002e-100) in sin(mpi(-1,1e-100)) + assert mpi(-2.00000000000000004e-100, 1.00000000000000002e-100) in sin(mpi(-2e-100,1e-100)) + # Same interval + assert cos(mpi(2, 2.5)) + assert cos(mpi(3.5, 4)) == mpi(-0.93645668729079634129, -0.65364362086361182946) + assert cos(mpi(5, 5.5)) == mpi(0.28366218546322624627, 0.70866977429126010168) + assert mpi(0.59847214410395654927, 0.90929742682568170942) in sin(mpi(2, 2.5)) + assert sin(mpi(3.5, 4)) == mpi(-0.75680249530792831347, -0.35078322768961983646) + assert sin(mpi(5, 5.5)) == mpi(-0.95892427466313856499, -0.70554032557039181306) + # Higher roots + iv.dps = 55 + w = 4*10**50 + mpi(0.5) + for p in [15, 40, 80]: + iv.dps = p + assert 0 in sin(4*mpi(pi)) + assert 0 in sin(4*10**50*mpi(pi)) + assert 0 in cos((4+0.5)*mpi(pi)) + assert 0 in cos(w*mpi(pi)) + assert 1 in cos(4*mpi(pi)) + assert 1 in cos(4*10**50*mpi(pi)) + iv.dps = 15 + assert cos(mpi(2,inf)) == mpi(-1,1) + assert sin(mpi(2,inf)) == mpi(-1,1) + assert cos(mpi(-inf,2)) == mpi(-1,1) + assert sin(mpi(-inf,2)) == mpi(-1,1) + u = tan(mpi(0.5,1)) + assert mpf(u.a).ae(mp.tan(0.5)) + assert mpf(u.b).ae(mp.tan(1)) + v = iv.cot(mpi(0.5,1)) + assert mpf(v.a).ae(mp.cot(1)) + assert mpf(v.b).ae(mp.cot(0.5)) + # Sanity check of evaluation at n*pi and (n+1/2)*pi + for n in range(-5,7,2): + x = iv.cos(n*iv.pi) + assert -1 in x + assert x >= -1 + assert x != -1 + x = iv.sin((n+0.5)*iv.pi) + assert -1 in x + assert x >= -1 + assert x != -1 + for n in range(-6,8,2): + x = iv.cos(n*iv.pi) + assert 1 in x + assert x <= 1 + if n: + assert x != 1 + x = iv.sin((n+0.5)*iv.pi) + assert 1 in x + assert x <= 1 + assert x != 1 + for n in range(-6,7): + x = iv.cos((n+0.5)*iv.pi) + assert x.a < 0 < x.b + x = iv.sin(n*iv.pi) + if n: + assert x.a < 0 < x.b + +def test_interval_complex(): + # TODO: many more tests + iv.dps = 15 + mp.dps = 15 + assert iv.mpc(2,3) == 2+3j + assert iv.mpc(2,3) != 2+4j + assert iv.mpc(2,3) != 1+3j + assert 1+3j in iv.mpc([1,2],[3,4]) + assert 2+5j not in iv.mpc([1,2],[3,4]) + assert iv.mpc(1,2) + 1j == 1+3j + assert iv.mpc([1,2],[2,3]) + 2+3j == iv.mpc([3,4],[5,6]) + assert iv.mpc([2,4],[4,8]) / 2 == iv.mpc([1,2],[2,4]) + assert iv.mpc([1,2],[2,4]) * 2j == iv.mpc([-8,-4],[2,4]) + assert iv.mpc([2,4],[4,8]) / 2j == iv.mpc([2,4],[-2,-1]) + assert iv.exp(2+3j).ae(mp.exp(2+3j)) + assert iv.log(2+3j).ae(mp.log(2+3j)) + assert (iv.mpc(2,3) ** iv.mpc(0.5,2)).ae(mp.mpc(2,3) ** mp.mpc(0.5,2)) + assert 1j in (iv.mpf(-1) ** 0.5) + assert 1j in (iv.mpc(-1) ** 0.5) + assert abs(iv.mpc(0)) == 0 + assert abs(iv.mpc(inf)) == inf + assert abs(iv.mpc(3,4)) == 5 + assert abs(iv.mpc(4)) == 4 + assert abs(iv.mpc(0,4)) == 4 + assert abs(iv.mpc(0,[2,3])) == iv.mpf([2,3]) + assert abs(iv.mpc(0,[-3,2])) == iv.mpf([0,3]) + assert abs(iv.mpc([3,5],[4,12])) == iv.mpf([5,13]) + assert abs(iv.mpc([3,5],[-4,12])) == iv.mpf([3,13]) + assert iv.mpc(2,3) ** 0 == 1 + assert iv.mpc(2,3) ** 1 == (2+3j) + assert iv.mpc(2,3) ** 2 == (2+3j)**2 + assert iv.mpc(2,3) ** 3 == (2+3j)**3 + assert iv.mpc(2,3) ** 4 == (2+3j)**4 + assert iv.mpc(2,3) ** 5 == (2+3j)**5 + assert iv.mpc(2,2) ** (-1) == (2+2j) ** (-1) + assert iv.mpc(2,2) ** (-2) == (2+2j) ** (-2) + assert iv.cos(2).ae(mp.cos(2)) + assert iv.sin(2).ae(mp.sin(2)) + assert iv.cos(2+3j).ae(mp.cos(2+3j)) + assert iv.sin(2+3j).ae(mp.sin(2+3j)) + +def test_interval_complex_arg(): + mp.dps = 15 + iv.dps = 15 + assert iv.arg(3) == 0 + assert iv.arg(0) == 0 + assert iv.arg([0,3]) == 0 + assert iv.arg(-3).ae(pi) + assert iv.arg(2+3j).ae(iv.arg(2+3j)) + z = iv.mpc([-2,-1],[3,4]) + t = iv.arg(z) + assert t.a.ae(mp.arg(-1+4j)) + assert t.b.ae(mp.arg(-2+3j)) + z = iv.mpc([-2,1],[3,4]) + t = iv.arg(z) + assert t.a.ae(mp.arg(1+3j)) + assert t.b.ae(mp.arg(-2+3j)) + z = iv.mpc([1,2],[3,4]) + t = iv.arg(z) + assert t.a.ae(mp.arg(2+3j)) + assert t.b.ae(mp.arg(1+4j)) + z = iv.mpc([1,2],[-2,3]) + t = iv.arg(z) + assert t.a.ae(mp.arg(1-2j)) + assert t.b.ae(mp.arg(1+3j)) + z = iv.mpc([1,2],[-4,-3]) + t = iv.arg(z) + assert t.a.ae(mp.arg(1-4j)) + assert t.b.ae(mp.arg(2-3j)) + z = iv.mpc([-1,2],[-4,-3]) + t = iv.arg(z) + assert t.a.ae(mp.arg(-1-3j)) + assert t.b.ae(mp.arg(2-3j)) + z = iv.mpc([-2,-1],[-4,-3]) + t = iv.arg(z) + assert t.a.ae(mp.arg(-2-3j)) + assert t.b.ae(mp.arg(-1-4j)) + z = iv.mpc([-2,-1],[-3,3]) + t = iv.arg(z) + assert t.a.ae(-mp.pi) + assert t.b.ae(mp.pi) + z = iv.mpc([-2,2],[-3,3]) + t = iv.arg(z) + assert t.a.ae(-mp.pi) + assert t.b.ae(mp.pi) + +def test_interval_ae(): + iv.dps = 15 + x = iv.mpf([1,2]) + assert x.ae(1) is None + assert x.ae(1.5) is None + assert x.ae(2) is None + assert x.ae(2.01) is False + assert x.ae(0.99) is False + x = iv.mpf(3.5) + assert x.ae(3.5) is True + assert x.ae(3.5+1e-15) is True + assert x.ae(3.5-1e-15) is True + assert x.ae(3.501) is False + assert x.ae(3.499) is False + assert x.ae(iv.mpf([3.5,3.501])) is None + assert x.ae(iv.mpf([3.5,4.5+1e-15])) is None + +def test_interval_nstr(): + iv.dps = n = 30 + x = mpi(1, 2) + # FIXME: error_dps should not be necessary + assert iv.nstr(x, n, mode='plusminus', error_dps=6) == '1.5 +- 0.5' + assert iv.nstr(x, n, mode='plusminus', use_spaces=False, error_dps=6) == '1.5+-0.5' + assert iv.nstr(x, n, mode='percent') == '1.5 (33.33%)' + assert iv.nstr(x, n, mode='brackets', use_spaces=False) == '[1.0,2.0]' + assert iv.nstr(x, n, mode='brackets' , brackets=('<', '>')) == '<1.0, 2.0>' + x = mpi('5.2582327113062393041', '5.2582327113062749951') + assert iv.nstr(x, n, mode='diff') == '5.2582327113062[393041, 749951]' + assert iv.nstr(iv.cos(mpi(1)), n, mode='diff', use_spaces=False) == '0.54030230586813971740093660744[2955,3053]' + assert iv.nstr(mpi('1e123', '1e129'), n, mode='diff') == '[1.0e+123, 1.0e+129]' + exp = iv.exp + assert iv.nstr(iv.exp(mpi('5000.1')), n, mode='diff') == '3.2797365856787867069110487[0926, 1191]e+2171' + iv.dps = 15 + +def test_mpi_from_str(): + iv.dps = 15 + assert iv.convert('1.5 +- 0.5') == mpi(mpf('1.0'), mpf('2.0')) + assert mpi(1, 2) in iv.convert('1.5 (33.33333333333333333333333333333%)') + assert iv.convert('[1, 2]') == mpi(1, 2) + assert iv.convert('1[2, 3]') == mpi(12, 13) + assert iv.convert('1.[23,46]e-8') == mpi('1.23e-8', '1.46e-8') + assert iv.convert('12[3.4,5.9]e4') == mpi('123.4e+4', '125.9e4') + +def test_interval_gamma(): + mp.dps = 15 + iv.dps = 15 + # TODO: need many more tests + assert iv.rgamma(0) == 0 + assert iv.fac(0) == 1 + assert iv.fac(1) == 1 + assert iv.fac(2) == 2 + assert iv.fac(3) == 6 + assert iv.gamma(0) == [-inf,inf] + assert iv.gamma(1) == 1 + assert iv.gamma(2) == 1 + assert iv.gamma(3) == 2 + assert -3.5449077018110320546 in iv.gamma(-0.5) + assert iv.loggamma(1) == 0 + assert iv.loggamma(2) == 0 + assert 0.69314718055994530942 in iv.loggamma(3) + # Test tight log-gamma endpoints based on monotonicity + xs = [iv.mpc([2,3],[1,4]), + iv.mpc([2,3],[-4,-1]), + iv.mpc([2,3],[-1,4]), + iv.mpc([2,3],[-4,1]), + iv.mpc([2,3],[-4,4]), + iv.mpc([-3,-2],[2,4]), + iv.mpc([-3,-2],[-4,-2])] + for x in xs: + ys = [mp.loggamma(mp.mpc(x.a,x.c)), + mp.loggamma(mp.mpc(x.b,x.c)), + mp.loggamma(mp.mpc(x.a,x.d)), + mp.loggamma(mp.mpc(x.b,x.d))] + if 0 in x.imag: + ys += [mp.loggamma(x.a), mp.loggamma(x.b)] + min_real = min([y.real for y in ys]) + max_real = max([y.real for y in ys]) + min_imag = min([y.imag for y in ys]) + max_imag = max([y.imag for y in ys]) + z = iv.loggamma(x) + assert z.a.ae(min_real) + assert z.b.ae(max_real) + assert z.c.ae(min_imag) + assert z.d.ae(max_imag) + +def test_interval_conversions(): + mp.dps = 15 + iv.dps = 15 + for a, b in ((-0.0, 0), (0.0, 0.5), (1.0, 1), \ + ('-inf', 20.5), ('-inf', float(sqrt(2)))): + r = mpi(a, b) + assert int(r.b) == int(b) + assert float(r.a) == float(a) + assert float(r.b) == float(b) + assert complex(r.a) == complex(a) + assert complex(r.b) == complex(b) diff --git a/venv/lib/python3.12/site-packages/mpmath/tests/test_levin.py b/venv/lib/python3.12/site-packages/mpmath/tests/test_levin.py new file mode 100644 index 0000000000000000000000000000000000000000..b14855df4de1a45da27080dcd239267842a4ac7a --- /dev/null +++ b/venv/lib/python3.12/site-packages/mpmath/tests/test_levin.py @@ -0,0 +1,153 @@ +#!/usr/bin/python +# -*- coding: utf-8 -*- + +from mpmath import mp +from mpmath import libmp + +xrange = libmp.backend.xrange + +# Attention: +# These tests run with 15-20 decimal digits precision. For higher precision the +# working precision must be raised. + +def test_levin_0(): + mp.dps = 17 + eps = mp.mpf(mp.eps) + with mp.extraprec(2 * mp.prec): + 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") + eps = mp.exp(0.9 * mp.log(eps)) + err = abs(v - mp.pi ** 2 / 6) + assert err < eps + w = mp.nsum(lambda n: 1/(n * n), [1, mp.inf], method = "levin", levin_variant = "u") + err = abs(v - w) + assert err < eps + +def test_levin_1(): + mp.dps = 17 + eps = mp.mpf(mp.eps) + with mp.extraprec(2 * mp.prec): + 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") + eps = mp.exp(0.9 * mp.log(eps)) + err = abs(v - mp.zeta(-2-3j)) + assert err < eps + w = mp.nsum(lambda n: n ** (2 + 3j), [1, mp.inf], method = "levin", levin_variant = "v") + err = abs(v - w) + assert err < eps + +def test_levin_2(): + # [2] A. Sidi - "Pratical Extrapolation Methods" p.373 + mp.dps = 17 + z=mp.mpf(10) + eps = mp.mpf(mp.eps) + with mp.extraprec(2 * mp.prec): + 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") + eps = mp.exp(0.9 * mp.log(eps)) + exact = mp.quad(lambda x: mp.exp(-x)/(1+x/z),[0,mp.inf]) + # there is also a symbolic expression for the integral: + # exact = z * mp.exp(z) * mp.expint(1,z) + err = abs(v - exact) + assert err < eps + w = mp.nsum(lambda n: (-1) ** n * mp.fac(n) * z ** (-n), [0, mp.inf], method = "sidi", levin_variant = "t") + assert err < eps + +def test_levin_3(): + mp.dps = 17 + z=mp.mpf(2) + eps = mp.mpf(mp.eps) + 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") + eps = mp.exp(0.8 * mp.log(eps)) + exact = mp.quad(lambda x: mp.exp( -x * x / 2 - z * x ** 4), [0,mp.inf]) * 2 / mp.sqrt(2 * mp.pi) + # there is also a symbolic expression for the integral: + # exact = mp.exp(mp.one / (32 * z)) * mp.besselk(mp.one / 4, mp.one / (32 * z)) / (4 * mp.sqrt(z * mp.pi)) + err = abs(v - exact) + assert err < eps + 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)]) + err = abs(v - w) + assert err < eps + +def test_levin_nsum(): + mp.dps = 17 + + with mp.extraprec(mp.prec): + z = mp.mpf(10) ** (-10) + a = mp.nsum(lambda n: n**(-(1+z)), [1, mp.inf], method = "l") - 1 / z + assert abs(a - mp.euler) < 1e-10 + + eps = mp.exp(0.8 * mp.log(mp.eps)) + + a = mp.nsum(lambda n: (-1)**(n-1) / n, [1, mp.inf], method = "sidi") + assert abs(a - mp.log(2)) < eps + + z = 2 + 1j + 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)]) + exact = mp.hyp2f1(2 / mp.mpf(3), 4 / mp.mpf(3), 1 / mp.mpf(3), z) + assert abs(exact - v) < eps + +def test_cohen_alt_0(): + mp.dps = 17 + 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") + eps = mp.exp(0.9 * mp.log(mp.eps)) + err = abs(v - mp.pi ** 2 / 12) + assert err < eps + +def test_cohen_alt_1(): + mp.dps = 17 + 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) + err = abs(v - 1.06215090557106) + assert err < 1e-12 diff --git a/venv/lib/python3.12/site-packages/mpmath/tests/test_linalg.py b/venv/lib/python3.12/site-packages/mpmath/tests/test_linalg.py new file mode 100644 index 0000000000000000000000000000000000000000..14256a79f8953d3e4ef8b296258560d48204f547 --- /dev/null +++ b/venv/lib/python3.12/site-packages/mpmath/tests/test_linalg.py @@ -0,0 +1,332 @@ +# TODO: don't use round + +from __future__ import division + +import pytest +from mpmath import * +xrange = libmp.backend.xrange + +# XXX: these shouldn't be visible(?) +LU_decomp = mp.LU_decomp +L_solve = mp.L_solve +U_solve = mp.U_solve +householder = mp.householder +improve_solution = mp.improve_solution + +A1 = matrix([[3, 1, 6], + [2, 1, 3], + [1, 1, 1]]) +b1 = [2, 7, 4] + +A2 = matrix([[ 2, -1, -1, 2], + [ 6, -2, 3, -1], + [-4, 2, 3, -2], + [ 2, 0, 4, -3]]) +b2 = [3, -3, -2, -1] + +A3 = matrix([[ 1, 0, -1, -1, 0], + [ 0, 1, 1, 0, -1], + [ 4, -5, 2, 0, 0], + [ 0, 0, -2, 9,-12], + [ 0, 5, 0, 0, 12]]) +b3 = [0, 0, 0, 0, 50] + +A4 = matrix([[10.235, -4.56, 0., -0.035, 5.67], + [-2.463, 1.27, 3.97, -8.63, 1.08], + [-6.58, 0.86, -0.257, 9.32, -43.6 ], + [ 9.83, 7.39, -17.25, 0.036, 24.86], + [-9.31, 34.9, 78.56, 1.07, 65.8 ]]) +b4 = [8.95, 20.54, 7.42, 5.60, 58.43] + +A5 = matrix([[ 1, 2, -4], + [-2, -3, 5], + [ 3, 5, -8]]) + +A6 = matrix([[ 1.377360, 2.481400, 5.359190], + [ 2.679280, -1.229560, 25.560210], + [-1.225280+1.e6, 9.910180, -35.049900-1.e6]]) +b6 = [23.500000, -15.760000, 2.340000] + +A7 = matrix([[1, -0.5], + [2, 1], + [-2, 6]]) +b7 = [3, 2, -4] + +A8 = matrix([[1, 2, 3], + [-1, 0, 1], + [-1, -2, -1], + [1, 0, -1]]) +b8 = [1, 2, 3, 4] + +A9 = matrix([[ 4, 2, -2], + [ 2, 5, -4], + [-2, -4, 5.5]]) +b9 = [10, 16, -15.5] + +A10 = matrix([[1.0 + 1.0j, 2.0, 2.0], + [4.0, 5.0, 6.0], + [7.0, 8.0, 9.0]]) +b10 = [1.0, 1.0 + 1.0j, 1.0] + + +def test_LU_decomp(): + A = A3.copy() + b = b3 + A, p = LU_decomp(A) + y = L_solve(A, b, p) + x = U_solve(A, y) + assert p == [2, 1, 2, 3] + assert [round(i, 14) for i in x] == [3.78953107960742, 2.9989094874591098, + -0.081788440567070006, 3.8713195201744801, 2.9171210468920399] + A = A4.copy() + b = b4 + A, p = LU_decomp(A) + y = L_solve(A, b, p) + x = U_solve(A, y) + assert p == [0, 3, 4, 3] + assert [round(i, 14) for i in x] == [2.6383625899619201, 2.6643834462368399, + 0.79208015947958998, -2.5088376454101899, -1.0567657691375001] + A = randmatrix(3) + bak = A.copy() + LU_decomp(A, overwrite=1) + assert A != bak + +def test_inverse(): + for A in [A1, A2, A5]: + inv = inverse(A) + assert mnorm(A*inv - eye(A.rows), 1) < 1.e-14 + +def test_householder(): + mp.dps = 15 + A, b = A8, b8 + H, p, x, r = householder(extend(A, b)) + assert H == matrix( + [[mpf('3.0'), mpf('-2.0'), mpf('-1.0'), 0], + [-1.0,mpf('3.333333333333333'),mpf('-2.9999999999999991'),mpf('2.0')], + [-1.0, mpf('-0.66666666666666674'),mpf('2.8142135623730948'), + mpf('-2.8284271247461898')], + [1.0, mpf('-1.3333333333333333'),mpf('-0.20000000000000018'), + mpf('4.2426406871192857')]]) + assert p == [-2, -2, mpf('-1.4142135623730949')] + assert round(norm(r, 2), 10) == 4.2426406870999998 + + y = [102.102, 58.344, 36.463, 24.310, 17.017, 12.376, 9.282, 7.140, 5.610, + 4.488, 3.6465, 3.003] + + def coeff(n): + # similiar to Hilbert matrix + A = [] + for i in range(1, 13): + A.append([1. / (i + j - 1) for j in range(1, n + 1)]) + return matrix(A) + + residuals = [] + refres = [] + for n in range(2, 7): + A = coeff(n) + H, p, x, r = householder(extend(A, y)) + x = matrix(x) + y = matrix(y) + residuals.append(norm(r, 2)) + refres.append(norm(residual(A, x, y), 2)) + assert [round(res, 10) for res in residuals] == [15.1733888877, + 0.82378073210000002, 0.302645887, 0.0260109244, + 0.00058653999999999998] + assert norm(matrix(residuals) - matrix(refres), inf) < 1.e-13 + + def hilbert_cmplx(n): + # Complexified Hilbert matrix + A = hilbert(2*n,n) + v = randmatrix(2*n, 2, min=-1, max=1) + v = v.apply(lambda x: exp(1J*pi()*x)) + A = diag(v[:,0])*A*diag(v[:n,1]) + return A + + residuals_cmplx = [] + refres_cmplx = [] + for n in range(2, 10): + A = hilbert_cmplx(n) + H, p, x, r = householder(A.copy()) + residuals_cmplx.append(norm(r, 2)) + refres_cmplx.append(norm(residual(A[:,:n-1], x, A[:,n-1]), 2)) + assert norm(matrix(residuals_cmplx) - matrix(refres_cmplx), inf) < 1.e-13 + +def test_factorization(): + A = randmatrix(5) + P, L, U = lu(A) + assert mnorm(P*A - L*U, 1) < 1.e-15 + +def test_solve(): + assert norm(residual(A6, lu_solve(A6, b6), b6), inf) < 1.e-10 + assert norm(residual(A7, lu_solve(A7, b7), b7), inf) < 1.5 + assert norm(residual(A8, lu_solve(A8, b8), b8), inf) <= 3 + 1.e-10 + assert norm(residual(A6, qr_solve(A6, b6)[0], b6), inf) < 1.e-10 + assert norm(residual(A7, qr_solve(A7, b7)[0], b7), inf) < 1.5 + assert norm(residual(A8, qr_solve(A8, b8)[0], b8), 2) <= 4.3 + assert norm(residual(A10, lu_solve(A10, b10), b10), 2) < 1.e-10 + assert norm(residual(A10, qr_solve(A10, b10)[0], b10), 2) < 1.e-10 + +def test_solve_overdet_complex(): + A = matrix([[1, 2j], [3, 4j], [5, 6]]) + b = matrix([1 + j, 2, -j]) + assert norm(residual(A, lu_solve(A, b), b)) < 1.0208 + +def test_singular(): + mp.dps = 15 + A = [[5.6, 1.2], [7./15, .1]] + B = repr(zeros(2)) + b = [1, 2] + for i in ['lu_solve(%s, %s)' % (A, b), 'lu_solve(%s, %s)' % (B, b), + 'qr_solve(%s, %s)' % (A, b), 'qr_solve(%s, %s)' % (B, b)]: + pytest.raises((ZeroDivisionError, ValueError), lambda: eval(i)) + +def test_cholesky(): + assert fp.cholesky(fp.matrix(A9)) == fp.matrix([[2, 0, 0], [1, 2, 0], [-1, -3/2, 3/2]]) + x = fp.cholesky_solve(A9, b9) + assert fp.norm(fp.residual(A9, x, b9), fp.inf) == 0 + +def test_det(): + assert det(A1) == 1 + assert round(det(A2), 14) == 8 + assert round(det(A3)) == 1834 + assert round(det(A4)) == 4443376 + assert det(A5) == 1 + assert round(det(A6)) == 78356463 + assert det(zeros(3)) == 0 + +def test_cond(): + mp.dps = 15 + A = matrix([[1.2969, 0.8648], [0.2161, 0.1441]]) + assert cond(A, lambda x: mnorm(x,1)) == mpf('327065209.73817754') + assert cond(A, lambda x: mnorm(x,inf)) == mpf('327065209.73817754') + assert cond(A, lambda x: mnorm(x,'F')) == mpf('249729266.80008656') + +@extradps(50) +def test_precision(): + A = randmatrix(10, 10) + assert mnorm(inverse(inverse(A)) - A, 1) < 1.e-45 + +def test_interval_matrix(): + mp.dps = 15 + iv.dps = 15 + a = iv.matrix([['0.1','0.3','1.0'],['7.1','5.5','4.8'],['3.2','4.4','5.6']]) + b = iv.matrix(['4','0.6','0.5']) + c = iv.lu_solve(a, b) + assert c[0].delta < 1e-13 + assert c[1].delta < 1e-13 + assert c[2].delta < 1e-13 + assert 5.25823271130625686059275 in c[0] + assert -13.155049396267837541163 in c[1] + assert 7.42069154774972557628979 in c[2] + +def test_LU_cache(): + A = randmatrix(3) + LU = LU_decomp(A) + assert A._LU == LU_decomp(A) + A[0,0] = -1000 + assert A._LU is None + +def test_improve_solution(): + A = randmatrix(5, min=1e-20, max=1e20) + b = randmatrix(5, 1, min=-1000, max=1000) + x1 = lu_solve(A, b) + randmatrix(5, 1, min=-1e-5, max=1.e-5) + x2 = improve_solution(A, x1, b) + assert norm(residual(A, x2, b), 2) < norm(residual(A, x1, b), 2) + +def test_exp_pade(): + for i in range(3): + dps = 15 + extra = 15 + mp.dps = dps + extra + dm = 0 + N = 3 + dg = range(1,N+1) + a = diag(dg) + expa = diag([exp(x) for x in dg]) + # choose a random matrix not close to be singular + # to avoid adding too much extra precision in computing + # m**-1 * M * m + while abs(dm) < 0.01: + m = randmatrix(N) + dm = det(m) + m = m/dm + a1 = m**-1 * a * m + e2 = m**-1 * expa * m + mp.dps = dps + e1 = expm(a1, method='pade') + mp.dps = dps + extra + d = e2 - e1 + #print d + mp.dps = dps + assert norm(d, inf).ae(0) + mp.dps = 15 + +def test_qr(): + mp.dps = 15 # used default value for dps + lowlimit = -9 # lower limit of matrix element value + uplimit = 9 # uppter limit of matrix element value + maxm = 4 # max matrix size + flg = False # toggle to create real vs complex matrix + zero = mpf('0.0') + + for k in xrange(0,10): + exdps = 0 + mode = 'full' + flg = bool(k % 2) + + # generate arbitrary matrix size (2 to maxm) + num1 = nint(maxm*rand()) + num2 = nint(maxm*rand()) + m = int(max(num1, num2)) + n = int(min(num1, num2)) + + # create matrix + A = mp.matrix(m,n) + + # populate matrix values with arbitrary integers + if flg: + flg = False + dtype = 'complex' + for j in xrange(0,n): + for i in xrange(0,m): + val = nint(lowlimit + (uplimit-lowlimit)*rand()) + val2 = nint(lowlimit + (uplimit-lowlimit)*rand()) + A[i,j] = mpc(val, val2) + else: + flg = True + dtype = 'real' + for j in xrange(0,n): + for i in xrange(0,m): + val = nint(lowlimit + (uplimit-lowlimit)*rand()) + A[i,j] = mpf(val) + + # perform A -> QR decomposition + Q, R = qr(A, mode, edps = exdps) + + #print('\n\n A = \n', nstr(A, 4)) + #print('\n Q = \n', nstr(Q, 4)) + #print('\n R = \n', nstr(R, 4)) + #print('\n Q*R = \n', nstr(Q*R, 4)) + + maxnorm = mpf('1.0E-11') + n1 = norm(A - Q * R) + #print '\n Norm of A - Q * R = ', n1 + assert n1 <= maxnorm + + if dtype == 'real': + n1 = norm(eye(m) - Q.T * Q) + #print ' Norm of I - Q.T * Q = ', n1 + assert n1 <= maxnorm + + n1 = norm(eye(m) - Q * Q.T) + #print ' Norm of I - Q * Q.T = ', n1 + assert n1 <= maxnorm + + if dtype == 'complex': + n1 = norm(eye(m) - Q.T * Q.conjugate()) + #print ' Norm of I - Q.T * Q.conjugate() = ', n1 + assert n1 <= maxnorm + + n1 = norm(eye(m) - Q.conjugate() * Q.T) + #print ' Norm of I - Q.conjugate() * Q.T = ', n1 + assert n1 <= maxnorm diff --git a/venv/lib/python3.12/site-packages/mpmath/tests/test_matrices.py b/venv/lib/python3.12/site-packages/mpmath/tests/test_matrices.py new file mode 100644 index 0000000000000000000000000000000000000000..1547b90664dba66a98a7f026a04a4ed1aa1ed3b4 --- /dev/null +++ b/venv/lib/python3.12/site-packages/mpmath/tests/test_matrices.py @@ -0,0 +1,253 @@ +import pytest +import sys +from mpmath import * + +def test_matrix_basic(): + A1 = matrix(3) + for i in range(3): + A1[i,i] = 1 + assert A1 == eye(3) + assert A1 == matrix(A1) + A2 = matrix(3, 2) + assert not A2._matrix__data + A3 = matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) + assert list(A3) == list(range(1, 10)) + A3[1,1] = 0 + assert not (1, 1) in A3._matrix__data + A4 = matrix([[1, 2, 3], [4, 5, 6]]) + A5 = matrix([[6, -1], [3, 2], [0, -3]]) + assert A4 * A5 == matrix([[12, -6], [39, -12]]) + assert A1 * A3 == A3 * A1 == A3 + pytest.raises(ValueError, lambda: A2*A2) + l = [[10, 20, 30], [40, 0, 60], [70, 80, 90]] + A6 = matrix(l) + assert A6.tolist() == l + assert A6 == eval(repr(A6)) + A6 = fp.matrix(A6) + assert A6 == eval(repr(A6)) + assert A6*1j == eval(repr(A6*1j)) + assert A3 * 10 == 10 * A3 == A6 + assert A2.rows == 3 + assert A2.cols == 2 + A3.rows = 2 + A3.cols = 2 + assert len(A3._matrix__data) == 3 + assert A4 + A4 == 2*A4 + pytest.raises(ValueError, lambda: A4 + A2) + assert sum(A1 - A1) == 0 + A7 = matrix([[1, 2], [3, 4], [5, 6], [7, 8]]) + x = matrix([10, -10]) + assert A7*x == matrix([-10, -10, -10, -10]) + A8 = ones(5) + assert sum((A8 + 1) - (2 - zeros(5))) == 0 + assert (1 + ones(4)) / 2 - 1 == zeros(4) + assert eye(3)**10 == eye(3) + pytest.raises(ValueError, lambda: A7**2) + A9 = randmatrix(3) + A10 = matrix(A9) + A9[0,0] = -100 + assert A9 != A10 + assert nstr(A9) + +def test_matmul(): + """ + Test the PEP465 "@" matrix multiplication syntax. + To avoid syntax errors when importing this file in Python 3.5 and below, we have to use exec() - sorry for that. + """ + # TODO remove exec() wrapper as soon as we drop support for Python <= 3.5 + if sys.hexversion < 0x30500f0: + # we are on Python < 3.5 + pytest.skip("'@' (__matmul__) is only supported in Python 3.5 or newer") + A4 = matrix([[1, 2, 3], [4, 5, 6]]) + A5 = matrix([[6, -1], [3, 2], [0, -3]]) + exec("assert A4 @ A5 == A4 * A5") + +def test_matrix_slices(): + A = matrix([ [1, 2, 3], + [4, 5 ,6], + [7, 8 ,9]]) + V = matrix([1,2,3,4,5]) + + # Get slice + assert A[:,:] == A + assert A[:,1] == matrix([[2],[5],[8]]) + assert A[2,:] == matrix([[7, 8 ,9]]) + assert A[1:3,1:3] == matrix([[5,6],[8,9]]) + assert V[2:4] == matrix([3,4]) + pytest.raises(IndexError, lambda: A[:,1:6]) + + # Assign slice with matrix + A1 = matrix(3) + A1[:,:] = A + assert A1[:,:] == matrix([[1, 2, 3], + [4, 5 ,6], + [7, 8 ,9]]) + A1[0,:] = matrix([[10, 11, 12]]) + assert A1 == matrix([ [10, 11, 12], + [4, 5 ,6], + [7, 8 ,9]]) + A1[:,2] = matrix([[13], [14], [15]]) + assert A1 == matrix([ [10, 11, 13], + [4, 5 ,14], + [7, 8 ,15]]) + A1[:2,:2] = matrix([[16, 17], [18 , 19]]) + assert A1 == matrix([ [16, 17, 13], + [18, 19 ,14], + [7, 8 ,15]]) + V[1:3] = 10 + assert V == matrix([1,10,10,4,5]) + with pytest.raises(ValueError): + A1[2,:] = A[:,1] + + with pytest.raises(IndexError): + A1[2,1:20] = A[:,:] + + # Assign slice with scalar + A1[:,2] = 10 + assert A1 == matrix([ [16, 17, 10], + [18, 19 ,10], + [7, 8 ,10]]) + A1[:,:] = 40 + for x in A1: + assert x == 40 + + +def test_matrix_power(): + A = matrix([[1, 2], [3, 4]]) + assert A**2 == A*A + assert A**3 == A*A*A + assert A**-1 == inverse(A) + assert A**-2 == inverse(A*A) + +def test_matrix_transform(): + A = matrix([[1, 2], [3, 4], [5, 6]]) + assert A.T == A.transpose() == matrix([[1, 3, 5], [2, 4, 6]]) + swap_row(A, 1, 2) + assert A == matrix([[1, 2], [5, 6], [3, 4]]) + l = [1, 2] + swap_row(l, 0, 1) + assert l == [2, 1] + assert extend(eye(3), [1,2,3]) == matrix([[1,0,0,1],[0,1,0,2],[0,0,1,3]]) + +def test_matrix_conjugate(): + A = matrix([[1 + j, 0], [2, j]]) + assert A.conjugate() == matrix([[mpc(1, -1), 0], [2, mpc(0, -1)]]) + assert A.transpose_conj() == A.H == matrix([[mpc(1, -1), 2], + [0, mpc(0, -1)]]) + +def test_matrix_creation(): + assert diag([1, 2, 3]) == matrix([[1, 0, 0], [0, 2, 0], [0, 0, 3]]) + A1 = ones(2, 3) + assert A1.rows == 2 and A1.cols == 3 + for a in A1: + assert a == 1 + A2 = zeros(3, 2) + assert A2.rows == 3 and A2.cols == 2 + for a in A2: + assert a == 0 + assert randmatrix(10) != randmatrix(10) + one = mpf(1) + assert hilbert(3) == matrix([[one, one/2, one/3], + [one/2, one/3, one/4], + [one/3, one/4, one/5]]) + +def test_norms(): + # matrix norms + A = matrix([[1, -2], [-3, -1], [2, 1]]) + assert mnorm(A,1) == 6 + assert mnorm(A,inf) == 4 + assert mnorm(A,'F') == sqrt(20) + # vector norms + assert norm(-3) == 3 + x = [1, -2, 7, -12] + assert norm(x, 1) == 22 + assert round(norm(x, 2), 10) == 14.0712472795 + assert round(norm(x, 10), 10) == 12.0054633727 + assert norm(x, inf) == 12 + +def test_vector(): + x = matrix([0, 1, 2, 3, 4]) + assert x == matrix([[0], [1], [2], [3], [4]]) + assert x[3] == 3 + assert len(x._matrix__data) == 4 + assert list(x) == list(range(5)) + x[0] = -10 + x[4] = 0 + assert x[0] == -10 + assert len(x) == len(x.T) == 5 + assert x.T*x == matrix([[114]]) + +def test_matrix_copy(): + A = ones(6) + B = A.copy() + C = +A + assert A == B + assert A == C + B[0,0] = 0 + assert A != B + C[0,0] = 42 + assert A != C + +def test_matrix_numpy(): + try: + import numpy + except ImportError: + return + l = [[1, 2], [3, 4], [5, 6]] + a = numpy.array(l) + assert matrix(l) == matrix(a) + +def test_interval_matrix_scalar_mult(): + """Multiplication of iv.matrix and any scalar type""" + a = mpi(-1, 1) + b = a + a * 2j + c = mpf(42) + d = c + c * 2j + e = 1.234 + f = fp.convert(e) + g = e + e * 3j + h = fp.convert(g) + M = iv.ones(1) + for x in [a, b, c, d, e, f, g, h]: + assert x * M == iv.matrix([x]) + assert M * x == iv.matrix([x]) + +@pytest.mark.xfail() +def test_interval_matrix_matrix_mult(): + """Multiplication of iv.matrix and other matrix types""" + A = ones(1) + B = fp.ones(1) + M = iv.ones(1) + for X in [A, B, M]: + assert X * M == iv.matrix(X) + assert X * M == X + assert M * X == iv.matrix(X) + assert M * X == X + +def test_matrix_conversion_to_iv(): + # Test that matrices with foreign datatypes are properly converted + for other_type_eye in [eye(3), fp.eye(3), iv.eye(3)]: + A = iv.matrix(other_type_eye) + B = iv.eye(3) + assert type(A[0,0]) == type(B[0,0]) + assert A.tolist() == B.tolist() + +def test_interval_matrix_mult_bug(): + # regression test for interval matrix multiplication: + # result must be nonzero-width and contain the exact result + x = convert('1.00000000000001') # note: this is implicitly rounded to some near mpf float value + A = matrix([[x]]) + B = iv.matrix(A) + C = iv.matrix([[x]]) + assert B == C + B = B * B + C = C * C + assert B == C + assert B[0, 0].delta > 1e-16 + assert B[0, 0].delta < 3e-16 + assert C[0, 0].delta > 1e-16 + assert C[0, 0].delta < 3e-16 + assert mp.mpf('1.00000000000001998401444325291756783368705994138804689654') in B[0, 0] + assert mp.mpf('1.00000000000001998401444325291756783368705994138804689654') in C[0, 0] + # the following caused an error before the bug was fixed + assert iv.matrix(mp.eye(2)) * (iv.ones(2) + mpi(1, 2)) == iv.matrix([[mpi(2, 3), mpi(2, 3)], [mpi(2, 3), mpi(2, 3)]]) diff --git a/venv/lib/python3.12/site-packages/mpmath/tests/test_mpmath.py b/venv/lib/python3.12/site-packages/mpmath/tests/test_mpmath.py new file mode 100644 index 0000000000000000000000000000000000000000..9f1fe36ae9b1b0feca4677eeb90396bfa7ed8f7a --- /dev/null +++ b/venv/lib/python3.12/site-packages/mpmath/tests/test_mpmath.py @@ -0,0 +1,7 @@ +from mpmath.libmp import * +from mpmath import * + +def test_newstyle_classes(): + for cls in [mp, fp, iv, mpf, mpc]: + for s in cls.__class__.__mro__: + assert isinstance(s, type) diff --git a/venv/lib/python3.12/site-packages/mpmath/tests/test_ode.py b/venv/lib/python3.12/site-packages/mpmath/tests/test_ode.py new file mode 100644 index 0000000000000000000000000000000000000000..6b6dbffa79cfd4ca6dbf14f8591296ee48b16682 --- /dev/null +++ b/venv/lib/python3.12/site-packages/mpmath/tests/test_ode.py @@ -0,0 +1,73 @@ +#from mpmath.calculus import ODE_step_euler, ODE_step_rk4, odeint, arange +from mpmath import odefun, cos, sin, mpf, sinc, mp + +''' +solvers = [ODE_step_euler, ODE_step_rk4] + +def test_ode1(): + """ + Let's solve: + + x'' + w**2 * x = 0 + + i.e. x1 = x, x2 = x1': + + x1' = x2 + x2' = -x1 + """ + def derivs((x1, x2), t): + return x2, -x1 + + for solver in solvers: + t = arange(0, 3.1415926, 0.005) + sol = odeint(derivs, (0., 1.), t, solver) + x1 = [a[0] for a in sol] + x2 = [a[1] for a in sol] + # the result is x1 = sin(t), x2 = cos(t) + # let's just check the end points for t = pi + assert abs(x1[-1]) < 1e-2 + assert abs(x2[-1] - (-1)) < 1e-2 + +def test_ode2(): + """ + Let's solve: + + x' - x = 0 + + i.e. x = exp(x) + + """ + def derivs((x), t): + return x + + for solver in solvers: + t = arange(0, 1, 1e-3) + sol = odeint(derivs, (1.,), t, solver) + x = [a[0] for a in sol] + # the result is x = exp(t) + # let's just check the end point for t = 1, i.e. x = e + assert abs(x[-1] - 2.718281828) < 1e-2 +''' + +def test_odefun_rational(): + mp.dps = 15 + # A rational function + f = lambda t: 1/(1+mpf(t)**2) + g = odefun(lambda x, y: [-2*x*y[0]**2], 0, [f(0)]) + assert f(2).ae(g(2)[0]) + +def test_odefun_sinc_large(): + mp.dps = 15 + # Sinc function; test for large x + f = sinc + g = odefun(lambda x, y: [(cos(x)-y[0])/x], 1, [f(1)], tol=0.01, degree=5) + assert abs(f(100) - g(100)[0])/f(100) < 0.01 + +def test_odefun_harmonic(): + mp.dps = 15 + # Harmonic oscillator + f = odefun(lambda x, y: [-y[1], y[0]], 0, [1, 0]) + for x in [0, 1, 2.5, 8, 3.7]: # we go back to 3.7 to check caching + c, s = f(x) + assert c.ae(cos(x)) + assert s.ae(sin(x)) diff --git a/venv/lib/python3.12/site-packages/mpmath/tests/test_pickle.py b/venv/lib/python3.12/site-packages/mpmath/tests/test_pickle.py new file mode 100644 index 0000000000000000000000000000000000000000..c3d96e73a53603e0fa3f9525c5c0059725bdffb7 --- /dev/null +++ b/venv/lib/python3.12/site-packages/mpmath/tests/test_pickle.py @@ -0,0 +1,27 @@ +import os +import tempfile +import pickle + +from mpmath import * + +def pickler(obj): + fn = tempfile.mktemp() + + f = open(fn, 'wb') + pickle.dump(obj, f) + f.close() + + f = open(fn, 'rb') + obj2 = pickle.load(f) + f.close() + os.remove(fn) + + return obj2 + +def test_pickle(): + + obj = mpf('0.5') + assert obj == pickler(obj) + + obj = mpc('0.5','0.2') + assert obj == pickler(obj) diff --git a/venv/lib/python3.12/site-packages/mpmath/tests/test_power.py b/venv/lib/python3.12/site-packages/mpmath/tests/test_power.py new file mode 100644 index 0000000000000000000000000000000000000000..7a2447a62c36f9e02df79b9a40a8603f8a69b1d8 --- /dev/null +++ b/venv/lib/python3.12/site-packages/mpmath/tests/test_power.py @@ -0,0 +1,156 @@ +from mpmath import * +from mpmath.libmp import * + +import random + +def test_fractional_pow(): + mp.dps = 15 + assert mpf(16) ** 2.5 == 1024 + assert mpf(64) ** 0.5 == 8 + assert mpf(64) ** -0.5 == 0.125 + assert mpf(16) ** -2.5 == 0.0009765625 + assert (mpf(10) ** 0.5).ae(3.1622776601683791) + assert (mpf(10) ** 2.5).ae(316.2277660168379) + assert (mpf(10) ** -0.5).ae(0.31622776601683794) + assert (mpf(10) ** -2.5).ae(0.0031622776601683794) + assert (mpf(10) ** 0.3).ae(1.9952623149688795) + assert (mpf(10) ** -0.3).ae(0.50118723362727224) + +def test_pow_integer_direction(): + """ + Test that inexact integer powers are rounded in the right + direction. + """ + random.seed(1234) + for prec in [10, 53, 200]: + for i in range(50): + a = random.randint(1<<(prec-1), 1< ab + + +def test_pow_epsilon_rounding(): + """ + Stress test directed rounding for powers with integer exponents. + Basically, we look at the following cases: + + >>> 1.0001 ** -5 # doctest: +SKIP + 0.99950014996500702 + >>> 0.9999 ** -5 # doctest: +SKIP + 1.000500150035007 + >>> (-1.0001) ** -5 # doctest: +SKIP + -0.99950014996500702 + >>> (-0.9999) ** -5 # doctest: +SKIP + -1.000500150035007 + + >>> 1.0001 ** -6 # doctest: +SKIP + 0.99940020994401269 + >>> 0.9999 ** -6 # doctest: +SKIP + 1.0006002100560125 + >>> (-1.0001) ** -6 # doctest: +SKIP + 0.99940020994401269 + >>> (-0.9999) ** -6 # doctest: +SKIP + 1.0006002100560125 + + etc. + + We run the tests with values a very small epsilon away from 1: + small enough that the result is indistinguishable from 1 when + rounded to nearest at the output precision. We check that the + result is not erroneously rounded to 1 in cases where the + rounding should be done strictly away from 1. + """ + + def powr(x, n, r): + return make_mpf(mpf_pow_int(x._mpf_, n, mp.prec, r)) + + for (inprec, outprec) in [(100, 20), (5000, 3000)]: + + mp.prec = inprec + + pos10001 = mpf(1) + mpf(2)**(-inprec+5) + pos09999 = mpf(1) - mpf(2)**(-inprec+5) + neg10001 = -pos10001 + neg09999 = -pos09999 + + mp.prec = outprec + r = round_up + assert powr(pos10001, 5, r) > 1 + assert powr(pos09999, 5, r) == 1 + assert powr(neg10001, 5, r) < -1 + assert powr(neg09999, 5, r) == -1 + assert powr(pos10001, 6, r) > 1 + assert powr(pos09999, 6, r) == 1 + assert powr(neg10001, 6, r) > 1 + assert powr(neg09999, 6, r) == 1 + + assert powr(pos10001, -5, r) == 1 + assert powr(pos09999, -5, r) > 1 + assert powr(neg10001, -5, r) == -1 + assert powr(neg09999, -5, r) < -1 + assert powr(pos10001, -6, r) == 1 + assert powr(pos09999, -6, r) > 1 + assert powr(neg10001, -6, r) == 1 + assert powr(neg09999, -6, r) > 1 + + r = round_down + assert powr(pos10001, 5, r) == 1 + assert powr(pos09999, 5, r) < 1 + assert powr(neg10001, 5, r) == -1 + assert powr(neg09999, 5, r) > -1 + assert powr(pos10001, 6, r) == 1 + assert powr(pos09999, 6, r) < 1 + assert powr(neg10001, 6, r) == 1 + assert powr(neg09999, 6, r) < 1 + + assert powr(pos10001, -5, r) < 1 + assert powr(pos09999, -5, r) == 1 + assert powr(neg10001, -5, r) > -1 + assert powr(neg09999, -5, r) == -1 + assert powr(pos10001, -6, r) < 1 + assert powr(pos09999, -6, r) == 1 + assert powr(neg10001, -6, r) < 1 + assert powr(neg09999, -6, r) == 1 + + r = round_ceiling + assert powr(pos10001, 5, r) > 1 + assert powr(pos09999, 5, r) == 1 + assert powr(neg10001, 5, r) == -1 + assert powr(neg09999, 5, r) > -1 + assert powr(pos10001, 6, r) > 1 + assert powr(pos09999, 6, r) == 1 + assert powr(neg10001, 6, r) > 1 + assert powr(neg09999, 6, r) == 1 + + assert powr(pos10001, -5, r) == 1 + assert powr(pos09999, -5, r) > 1 + assert powr(neg10001, -5, r) > -1 + assert powr(neg09999, -5, r) == -1 + assert powr(pos10001, -6, r) == 1 + assert powr(pos09999, -6, r) > 1 + assert powr(neg10001, -6, r) == 1 + assert powr(neg09999, -6, r) > 1 + + r = round_floor + assert powr(pos10001, 5, r) == 1 + assert powr(pos09999, 5, r) < 1 + assert powr(neg10001, 5, r) < -1 + assert powr(neg09999, 5, r) == -1 + assert powr(pos10001, 6, r) == 1 + assert powr(pos09999, 6, r) < 1 + assert powr(neg10001, 6, r) == 1 + assert powr(neg09999, 6, r) < 1 + + assert powr(pos10001, -5, r) < 1 + assert powr(pos09999, -5, r) == 1 + assert powr(neg10001, -5, r) == -1 + assert powr(neg09999, -5, r) < -1 + assert powr(pos10001, -6, r) < 1 + assert powr(pos09999, -6, r) == 1 + assert powr(neg10001, -6, r) < 1 + assert powr(neg09999, -6, r) == 1 + + mp.dps = 15 diff --git a/venv/lib/python3.12/site-packages/mpmath/tests/test_quad.py b/venv/lib/python3.12/site-packages/mpmath/tests/test_quad.py new file mode 100644 index 0000000000000000000000000000000000000000..fc71c5f5ef9c0ecd876c988e7d033b321f065cdc --- /dev/null +++ b/venv/lib/python3.12/site-packages/mpmath/tests/test_quad.py @@ -0,0 +1,95 @@ +import pytest +from mpmath import * + +def ae(a, b): + return abs(a-b) < 10**(-mp.dps+5) + +def test_basic_integrals(): + for prec in [15, 30, 100]: + mp.dps = prec + assert ae(quadts(lambda x: x**3 - 3*x**2, [-2, 4]), -12) + assert ae(quadgl(lambda x: x**3 - 3*x**2, [-2, 4]), -12) + assert ae(quadts(sin, [0, pi]), 2) + assert ae(quadts(sin, [0, 2*pi]), 0) + assert ae(quadts(exp, [-inf, -1]), 1/e) + assert ae(quadts(lambda x: exp(-x), [0, inf]), 1) + assert ae(quadts(lambda x: exp(-x*x), [-inf, inf]), sqrt(pi)) + assert ae(quadts(lambda x: 1/(1+x*x), [-1, 1]), pi/2) + assert ae(quadts(lambda x: 1/(1+x*x), [-inf, inf]), pi) + assert ae(quadts(lambda x: 2*sqrt(1-x*x), [-1, 1]), pi) + mp.dps = 15 + +def test_multiple_intervals(): + y,err = quad(lambda x: sign(x), [-0.5, 0.9, 1], maxdegree=2, error=True) + assert abs(y-0.5) < 2*err + +def test_quad_symmetry(): + assert quadts(sin, [-1, 1]) == 0 + assert quadgl(sin, [-1, 1]) == 0 + +def test_quad_infinite_mirror(): + # Check mirrored infinite interval + assert ae(quad(lambda x: exp(-x*x), [inf,-inf]), -sqrt(pi)) + assert ae(quad(lambda x: exp(x), [0,-inf]), -1) + +def test_quadgl_linear(): + assert quadgl(lambda x: x, [0, 1], maxdegree=1).ae(0.5) + +def test_complex_integration(): + assert quadts(lambda x: x, [0, 1+j]).ae(j) + +def test_quadosc(): + mp.dps = 15 + assert quadosc(lambda x: sin(x)/x, [0, inf], period=2*pi).ae(pi/2) + +# Double integrals +def test_double_trivial(): + assert ae(quadts(lambda x, y: x, [0, 1], [0, 1]), 0.5) + assert ae(quadts(lambda x, y: x, [-1, 1], [-1, 1]), 0.0) + +def test_double_1(): + assert ae(quadts(lambda x, y: cos(x+y/2), [-pi/2, pi/2], [0, pi]), 4) + +def test_double_2(): + assert ae(quadts(lambda x, y: (x-1)/((1-x*y)*log(x*y)), [0, 1], [0, 1]), euler) + +def test_double_3(): + assert ae(quadts(lambda x, y: 1/sqrt(1+x*x+y*y), [-1, 1], [-1, 1]), 4*log(2+sqrt(3))-2*pi/3) + +def test_double_4(): + assert ae(quadts(lambda x, y: 1/(1-x*x * y*y), [0, 1], [0, 1]), pi**2 / 8) + +def test_double_5(): + assert ae(quadts(lambda x, y: 1/(1-x*y), [0, 1], [0, 1]), pi**2 / 6) + +def test_double_6(): + assert ae(quadts(lambda x, y: exp(-(x+y)), [0, inf], [0, inf]), 1) + +def test_double_7(): + assert ae(quadts(lambda x, y: exp(-x*x-y*y), [-inf, inf], [-inf, inf]), pi) + + +# Test integrals from "Experimentation in Mathematics" by Borwein, +# Bailey & Girgensohn +def test_expmath_integrals(): + for prec in [15, 30, 50]: + mp.dps = prec + assert ae(quadts(lambda x: x/sinh(x), [0, inf]), pi**2 / 4) + assert ae(quadts(lambda x: log(x)**2 / (1+x**2), [0, inf]), pi**3 / 8) + assert ae(quadts(lambda x: (1+x**2)/(1+x**4), [0, inf]), pi/sqrt(2)) + assert ae(quadts(lambda x: log(x)/cosh(x)**2, [0, inf]), log(pi)-2*log(2)-euler) + assert ae(quadts(lambda x: log(1+x**3)/(1-x+x**2), [0, inf]), 2*pi*log(3)/sqrt(3)) + assert ae(quadts(lambda x: log(x)**2 / (x**2+x+1), [0, 1]), 8*pi**3 / (81*sqrt(3))) + assert ae(quadts(lambda x: log(cos(x))**2, [0, pi/2]), pi/2 * (log(2)**2+pi**2/12)) + assert ae(quadts(lambda x: x**2 / sin(x)**2, [0, pi/2]), pi*log(2)) + assert ae(quadts(lambda x: x**2/sqrt(exp(x)-1), [0, inf]), 4*pi*(log(2)**2 + pi**2/12)) + assert ae(quadts(lambda x: x*exp(-x)*sqrt(1-exp(-2*x)), [0, inf]), pi*(1+2*log(2))/8) + mp.dps = 15 + +# Do not reach full accuracy +@pytest.mark.xfail +def test_expmath_fail(): + assert ae(quadts(lambda x: sqrt(tan(x)), [0, pi/2]), pi*sqrt(2)/2) + assert ae(quadts(lambda x: atan(x)/(x*sqrt(1-x**2)), [0, 1]), pi*log(1+sqrt(2))/2) + assert ae(quadts(lambda x: log(1+x**2)/x**2, [0, 1]), pi/2-log(2)) + assert ae(quadts(lambda x: x**2/((1+x**4)*sqrt(1-x**4)), [0, 1]), pi/8) diff --git a/venv/lib/python3.12/site-packages/mpmath/tests/test_rootfinding.py b/venv/lib/python3.12/site-packages/mpmath/tests/test_rootfinding.py new file mode 100644 index 0000000000000000000000000000000000000000..7c3c06463682eb1fd60efeb75b809bbb932a241c --- /dev/null +++ b/venv/lib/python3.12/site-packages/mpmath/tests/test_rootfinding.py @@ -0,0 +1,91 @@ +import pytest +from mpmath import * +from mpmath.calculus.optimization import Secant, Muller, Bisection, Illinois, \ + Pegasus, Anderson, Ridder, ANewton, Newton, MNewton, MDNewton + +def test_findroot(): + # old tests, assuming secant + mp.dps = 15 + assert findroot(lambda x: 4*x-3, mpf(5)).ae(0.75) + assert findroot(sin, mpf(3)).ae(pi) + assert findroot(sin, (mpf(3), mpf(3.14))).ae(pi) + assert findroot(lambda x: x*x+1, mpc(2+2j)).ae(1j) + # test all solvers with 1 starting point + f = lambda x: cos(x) + for solver in [Newton, Secant, MNewton, Muller, ANewton]: + x = findroot(f, 2., solver=solver) + assert abs(f(x)) < eps + # test all solvers with interval of 2 points + for solver in [Secant, Muller, Bisection, Illinois, Pegasus, Anderson, + Ridder]: + x = findroot(f, (1., 2.), solver=solver) + assert abs(f(x)) < eps + # test types + f = lambda x: (x - 2)**2 + + assert isinstance(findroot(f, 1, tol=1e-10), mpf) + assert isinstance(iv.findroot(f, 1., tol=1e-10), iv.mpf) + assert isinstance(fp.findroot(f, 1, tol=1e-10), float) + assert isinstance(fp.findroot(f, 1+0j, tol=1e-10), complex) + + # issue 401 + with pytest.raises(ValueError): + with workprec(2): + findroot(lambda x: x**2 - 4456178*x + 60372201703370, + mpc(real='5.278e+13', imag='-5.278e+13')) + + # issue 192 + with pytest.raises(ValueError): + findroot(lambda x: -1, 0) + + # issue 387 + with pytest.raises(ValueError): + findroot(lambda p: (1 - p)**30 - 1, 0.9) + +def test_bisection(): + # issue 273 + assert findroot(lambda x: x**2-1,(0,2),solver='bisect') == 1 + +def test_mnewton(): + f = lambda x: polyval([1,3,3,1],x) + x = findroot(f, -0.9, solver='mnewton') + assert abs(f(x)) < eps + +def test_anewton(): + f = lambda x: (x - 2)**100 + x = findroot(f, 1., solver=ANewton) + assert abs(f(x)) < eps + +def test_muller(): + f = lambda x: (2 + x)**3 + 2 + x = findroot(f, 1., solver=Muller) + assert abs(f(x)) < eps + +def test_multiplicity(): + for i in range(1, 5): + assert multiplicity(lambda x: (x - 1)**i, 1) == i + assert multiplicity(lambda x: x**2, 1) == 0 + +def test_multidimensional(): + def f(*x): + return [3*x[0]**2-2*x[1]**2-1, x[0]**2-2*x[0]+x[1]**2+2*x[1]-8] + assert mnorm(jacobian(f, (1,-2)) - matrix([[6,8],[0,-2]]),1) < 1.e-7 + for x, error in MDNewton(mp, f, (1,-2), verbose=0, + norm=lambda x: norm(x, inf)): + pass + assert norm(f(*x), 2) < 1e-14 + # The Chinese mathematician Zhu Shijie was the very first to solve this + # nonlinear system 700 years ago + f1 = lambda x, y: -x + 2*y + f2 = lambda x, y: (x**2 + x*(y**2 - 2) - 4*y) / (x + 4) + f3 = lambda x, y: sqrt(x**2 + y**2) + def f(x, y): + f1x = f1(x, y) + return (f2(x, y) - f1x, f3(x, y) - f1x) + x = findroot(f, (10, 10)) + assert [int(round(i)) for i in x] == [3, 4] + +def test_trivial(): + assert findroot(lambda x: 0, 1) == 1 + assert findroot(lambda x: x, 0) == 0 + #assert findroot(lambda x, y: x + y, (1, -1)) == (1, -1) diff --git a/venv/lib/python3.12/site-packages/mpmath/tests/test_special.py b/venv/lib/python3.12/site-packages/mpmath/tests/test_special.py new file mode 100644 index 0000000000000000000000000000000000000000..30825abd89ada00f937260cb51ef649546be7021 --- /dev/null +++ b/venv/lib/python3.12/site-packages/mpmath/tests/test_special.py @@ -0,0 +1,113 @@ +from mpmath import * + +def test_special(): + assert inf == inf + assert inf != -inf + assert -inf == -inf + assert inf != nan + assert nan != nan + assert isnan(nan) + assert --inf == inf + assert abs(inf) == inf + assert abs(-inf) == inf + assert abs(nan) != abs(nan) + + assert isnan(inf - inf) + assert isnan(inf + (-inf)) + assert isnan(-inf - (-inf)) + + assert isnan(inf + nan) + assert isnan(-inf + nan) + + assert mpf(2) + inf == inf + assert 2 + inf == inf + assert mpf(2) - inf == -inf + assert 2 - inf == -inf + + assert inf > 3 + assert 3 < inf + assert 3 > -inf + assert -inf < 3 + assert inf > mpf(3) + assert mpf(3) < inf + assert mpf(3) > -inf + assert -inf < mpf(3) + + assert not (nan < 3) + assert not (nan > 3) + + assert isnan(inf * 0) + assert isnan(-inf * 0) + assert inf * 3 == inf + assert inf * -3 == -inf + assert -inf * 3 == -inf + assert -inf * -3 == inf + assert inf * inf == inf + assert -inf * -inf == inf + + assert isnan(nan / 3) + assert inf / -3 == -inf + assert inf / 3 == inf + assert 3 / inf == 0 + assert -3 / inf == 0 + assert 0 / inf == 0 + assert isnan(inf / inf) + assert isnan(inf / -inf) + assert isnan(inf / nan) + + assert mpf('inf') == mpf('+inf') == inf + assert mpf('-inf') == -inf + assert isnan(mpf('nan')) + + assert isinf(inf) + assert isinf(-inf) + assert not isinf(mpf(0)) + assert not isinf(nan) + +def test_special_powers(): + assert inf**3 == inf + assert isnan(inf**0) + assert inf**-3 == 0 + assert (-inf)**2 == inf + assert (-inf)**3 == -inf + assert isnan((-inf)**0) + assert (-inf)**-2 == 0 + assert (-inf)**-3 == 0 + assert isnan(nan**5) + assert isnan(nan**0) + +def test_functions_special(): + assert exp(inf) == inf + assert exp(-inf) == 0 + assert isnan(exp(nan)) + assert log(inf) == inf + assert isnan(log(nan)) + assert isnan(sin(inf)) + assert isnan(sin(nan)) + assert atan(inf).ae(pi/2) + assert atan(-inf).ae(-pi/2) + assert isnan(sqrt(nan)) + assert sqrt(inf) == inf + +def test_convert_special(): + float_inf = 1e300 * 1e300 + float_ninf = -float_inf + float_nan = float_inf/float_ninf + assert mpf(3) * float_inf == inf + assert mpf(3) * float_ninf == -inf + assert isnan(mpf(3) * float_nan) + assert not (mpf(3) < float_nan) + assert not (mpf(3) > float_nan) + assert not (mpf(3) <= float_nan) + assert not (mpf(3) >= float_nan) + assert float(mpf('1e1000')) == float_inf + assert float(mpf('-1e1000')) == float_ninf + assert float(mpf('1e100000000000000000')) == float_inf + assert float(mpf('-1e100000000000000000')) == float_ninf + assert float(mpf('1e-100000000000000000')) == 0.0 + +def test_div_bug(): + assert isnan(nan/1) + assert isnan(nan/2) + assert inf/2 == inf + assert (-inf)/2 == -inf diff --git a/venv/lib/python3.12/site-packages/mpmath/tests/test_str.py b/venv/lib/python3.12/site-packages/mpmath/tests/test_str.py new file mode 100644 index 0000000000000000000000000000000000000000..569244f252c057ec1029b7efbd8b0ffbfbc47522 --- /dev/null +++ b/venv/lib/python3.12/site-packages/mpmath/tests/test_str.py @@ -0,0 +1,14 @@ +from mpmath import nstr, matrix, inf + +def test_nstr(): + m = matrix([[0.75, 0.190940654, -0.0299195971], + [0.190940654, 0.65625, 0.205663228], + [-0.0299195971, 0.205663228, 0.64453125e-20]]) + assert nstr(m, 4, min_fixed=-inf) == \ + '''[ 0.75 0.1909 -0.02992] +[ 0.1909 0.6563 0.2057] +[-0.02992 0.2057 0.000000000000000000006445]''' + assert nstr(m, 4) == \ + '''[ 0.75 0.1909 -0.02992] +[ 0.1909 0.6563 0.2057] +[-0.02992 0.2057 6.445e-21]''' diff --git a/venv/lib/python3.12/site-packages/mpmath/tests/test_summation.py b/venv/lib/python3.12/site-packages/mpmath/tests/test_summation.py new file mode 100644 index 0000000000000000000000000000000000000000..04ffd29f994e1e6310678eec292c0e03f2d6c725 --- /dev/null +++ b/venv/lib/python3.12/site-packages/mpmath/tests/test_summation.py @@ -0,0 +1,53 @@ +from mpmath import * + +def test_sumem(): + mp.dps = 15 + assert sumem(lambda k: 1/k**2.5, [50, 100]).ae(0.0012524505324784962) + assert sumem(lambda k: k**4 + 3*k + 1, [10, 100]).ae(2050333103) + +def test_nsum(): + mp.dps = 15 + assert nsum(lambda x: x**2, [1, 3]) == 14 + assert nsum(lambda k: 1/factorial(k), [0, inf]).ae(e) + assert nsum(lambda k: (-1)**(k+1) / k, [1, inf]).ae(log(2)) + assert nsum(lambda k: (-1)**(k+1) / k**2, [1, inf]).ae(pi**2 / 12) + assert nsum(lambda k: (-1)**k / log(k), [2, inf]).ae(0.9242998972229388) + assert nsum(lambda k: 1/k**2, [1, inf]).ae(pi**2 / 6) + assert nsum(lambda k: 2**k/fac(k), [0, inf]).ae(exp(2)) + assert nsum(lambda k: 1/k**2, [4, inf], method='e').ae(0.2838229557371153) + assert abs(fp.nsum(lambda k: 1/k**4, [1, fp.inf]) - 1.082323233711138) < 1e-5 + assert abs(fp.nsum(lambda k: 1/k**4, [1, fp.inf], method='e') - 1.082323233711138) < 1e-4 + +def test_nprod(): + mp.dps = 15 + assert nprod(lambda k: exp(1/k**2), [1,inf], method='r').ae(exp(pi**2/6)) + assert nprod(lambda x: x**2, [1, 3]) == 36 + +def test_fsum(): + mp.dps = 15 + assert fsum([]) == 0 + assert fsum([-4]) == -4 + assert fsum([2,3]) == 5 + assert fsum([1e-100,1]) == 1 + assert fsum([1,1e-100]) == 1 + assert fsum([1e100,1]) == 1e100 + assert fsum([1,1e100]) == 1e100 + assert fsum([1e-100,0]) == 1e-100 + assert fsum([1e-100,1e100,1e-100]) == 1e100 + assert fsum([2,1+1j,1]) == 4+1j + assert fsum([2,inf,3]) == inf + assert fsum([2,-1], absolute=1) == 3 + assert fsum([2,-1], squared=1) == 5 + assert fsum([1,1+j], squared=1) == 1+2j + assert fsum([1,3+4j], absolute=1) == 6 + assert fsum([1,2+3j], absolute=1, squared=1) == 14 + assert isnan(fsum([inf,-inf])) + assert fsum([inf,-inf], absolute=1) == inf + assert fsum([inf,-inf], squared=1) == inf + assert fsum([inf,-inf], absolute=1, squared=1) == inf + assert iv.fsum([1,mpi(2,3)]) == mpi(3,4) + +def test_fprod(): + mp.dps = 15 + assert fprod([]) == 1 + assert fprod([2,3]) == 6 diff --git a/venv/lib/python3.12/site-packages/mpmath/tests/test_trig.py b/venv/lib/python3.12/site-packages/mpmath/tests/test_trig.py new file mode 100644 index 0000000000000000000000000000000000000000..c70a2a0ff4c44c784404ecdb15357d5b91a992d6 --- /dev/null +++ b/venv/lib/python3.12/site-packages/mpmath/tests/test_trig.py @@ -0,0 +1,136 @@ +from mpmath import * +from mpmath.libmp import * + +def test_trig_misc_hard(): + mp.prec = 53 + # Worst-case input for an IEEE double, from a paper by Kahan + x = ldexp(6381956970095103,797) + assert cos(x) == mpf('-4.6871659242546277e-19') + assert sin(x) == 1 + + mp.prec = 150 + a = mpf(10**50) + mp.prec = 53 + assert sin(a).ae(-0.7896724934293100827) + assert cos(a).ae(-0.6135286082336635622) + + # Check relative accuracy close to x = zero + assert sin(1e-100) == 1e-100 # when rounding to nearest + assert sin(1e-6).ae(9.999999999998333e-007, rel_eps=2e-15, abs_eps=0) + assert sin(1e-6j).ae(1.0000000000001666e-006j, rel_eps=2e-15, abs_eps=0) + assert sin(-1e-6j).ae(-1.0000000000001666e-006j, rel_eps=2e-15, abs_eps=0) + assert cos(1e-100) == 1 + assert cos(1e-6).ae(0.9999999999995) + assert cos(-1e-6j).ae(1.0000000000005) + assert tan(1e-100) == 1e-100 + assert tan(1e-6).ae(1.0000000000003335e-006, rel_eps=2e-15, abs_eps=0) + assert tan(1e-6j).ae(9.9999999999966644e-007j, rel_eps=2e-15, abs_eps=0) + assert tan(-1e-6j).ae(-9.9999999999966644e-007j, rel_eps=2e-15, abs_eps=0) + +def test_trig_near_zero(): + mp.dps = 15 + + for r in [round_nearest, round_down, round_up, round_floor, round_ceiling]: + assert sin(0, rounding=r) == 0 + assert cos(0, rounding=r) == 1 + + a = mpf('1e-100') + b = mpf('-1e-100') + + assert sin(a, rounding=round_nearest) == a + assert sin(a, rounding=round_down) < a + assert sin(a, rounding=round_floor) < a + assert sin(a, rounding=round_up) >= a + assert sin(a, rounding=round_ceiling) >= a + assert sin(b, rounding=round_nearest) == b + assert sin(b, rounding=round_down) > b + assert sin(b, rounding=round_floor) <= b + assert sin(b, rounding=round_up) <= b + assert sin(b, rounding=round_ceiling) > b + + assert cos(a, rounding=round_nearest) == 1 + assert cos(a, rounding=round_down) < 1 + assert cos(a, rounding=round_floor) < 1 + assert cos(a, rounding=round_up) == 1 + assert cos(a, rounding=round_ceiling) == 1 + assert cos(b, rounding=round_nearest) == 1 + assert cos(b, rounding=round_down) < 1 + assert cos(b, rounding=round_floor) < 1 + assert cos(b, rounding=round_up) == 1 + assert cos(b, rounding=round_ceiling) == 1 + + +def test_trig_near_n_pi(): + + mp.dps = 15 + a = [n*pi for n in [1, 2, 6, 11, 100, 1001, 10000, 100001]] + mp.dps = 135 + a.append(10**100 * pi) + mp.dps = 15 + + assert sin(a[0]) == mpf('1.2246467991473531772e-16') + assert sin(a[1]) == mpf('-2.4492935982947063545e-16') + assert sin(a[2]) == mpf('-7.3478807948841190634e-16') + assert sin(a[3]) == mpf('4.8998251578625894243e-15') + assert sin(a[4]) == mpf('1.9643867237284719452e-15') + assert sin(a[5]) == mpf('-8.8632615209684813458e-15') + assert sin(a[6]) == mpf('-4.8568235395684898392e-13') + assert sin(a[7]) == mpf('3.9087342299491231029e-11') + assert sin(a[8]) == mpf('-1.369235466754566993528e-36') + + r = round_nearest + assert cos(a[0], rounding=r) == -1 + assert cos(a[1], rounding=r) == 1 + assert cos(a[2], rounding=r) == 1 + assert cos(a[3], rounding=r) == -1 + assert cos(a[4], rounding=r) == 1 + assert cos(a[5], rounding=r) == -1 + assert cos(a[6], rounding=r) == 1 + assert cos(a[7], rounding=r) == -1 + assert cos(a[8], rounding=r) == 1 + + r = round_up + assert cos(a[0], rounding=r) == -1 + assert cos(a[1], rounding=r) == 1 + assert cos(a[2], rounding=r) == 1 + assert cos(a[3], rounding=r) == -1 + assert cos(a[4], rounding=r) == 1 + assert cos(a[5], rounding=r) == -1 + assert cos(a[6], rounding=r) == 1 + assert cos(a[7], rounding=r) == -1 + assert cos(a[8], rounding=r) == 1 + + r = round_down + assert cos(a[0], rounding=r) > -1 + assert cos(a[1], rounding=r) < 1 + assert cos(a[2], rounding=r) < 1 + assert cos(a[3], rounding=r) > -1 + assert cos(a[4], rounding=r) < 1 + assert cos(a[5], rounding=r) > -1 + assert cos(a[6], rounding=r) < 1 + assert cos(a[7], rounding=r) > -1 + assert cos(a[8], rounding=r) < 1 + + r = round_floor + assert cos(a[0], rounding=r) == -1 + assert cos(a[1], rounding=r) < 1 + assert cos(a[2], rounding=r) < 1 + assert cos(a[3], rounding=r) == -1 + assert cos(a[4], rounding=r) < 1 + assert cos(a[5], rounding=r) == -1 + assert cos(a[6], rounding=r) < 1 + assert cos(a[7], rounding=r) == -1 + assert cos(a[8], rounding=r) < 1 + + r = round_ceiling + assert cos(a[0], rounding=r) > -1 + assert cos(a[1], rounding=r) == 1 + assert cos(a[2], rounding=r) == 1 + assert cos(a[3], rounding=r) > -1 + assert cos(a[4], rounding=r) == 1 + assert cos(a[5], rounding=r) > -1 + assert cos(a[6], rounding=r) == 1 + assert cos(a[7], rounding=r) > -1 + assert cos(a[8], rounding=r) == 1 + + mp.dps = 15 diff --git a/venv/lib/python3.12/site-packages/mpmath/tests/test_visualization.py b/venv/lib/python3.12/site-packages/mpmath/tests/test_visualization.py new file mode 100644 index 0000000000000000000000000000000000000000..81ffd05194322f00e4c75dc02bc862b383468bff --- /dev/null +++ b/venv/lib/python3.12/site-packages/mpmath/tests/test_visualization.py @@ -0,0 +1,32 @@ +""" +Limited tests of the visualization module. Right now it just makes +sure that passing custom Axes works. + +""" + +from mpmath import mp, fp + +def test_axes(): + try: + import matplotlib + version = matplotlib.__version__.split("-")[0] + version = version.split(".")[:2] + if [int(_) for _ in version] < [0,99]: + raise ImportError + import pylab + except ImportError: + print("\nSkipping test (pylab not available or too old version)\n") + return + fig = pylab.figure() + axes = fig.add_subplot(111) + for ctx in [mp, fp]: + ctx.plot(lambda x: x**2, [0, 3], axes=axes) + assert axes.get_xlabel() == 'x' + assert axes.get_ylabel() == 'f(x)' + + fig = pylab.figure() + axes = fig.add_subplot(111) + for ctx in [mp, fp]: + ctx.cplot(lambda z: z, [-2, 2], [-10, 10], axes=axes) + assert axes.get_xlabel() == 'Re(z)' + assert axes.get_ylabel() == 'Im(z)' diff --git a/venv/lib/python3.12/site-packages/mpmath/tests/torture.py b/venv/lib/python3.12/site-packages/mpmath/tests/torture.py new file mode 100644 index 0000000000000000000000000000000000000000..845d5c6d7d017e51e1ed9a8fe3106cfa32fd967f --- /dev/null +++ b/venv/lib/python3.12/site-packages/mpmath/tests/torture.py @@ -0,0 +1,224 @@ +""" +Torture tests for asymptotics and high precision evaluation of +special functions. + +(Other torture tests may also be placed here.) + +Running this file (gmpy recommended!) takes several CPU minutes. +With Python 2.6+, multiprocessing is used automatically to run tests +in parallel if many cores are available. (A single test may take between +a second and several minutes; possibly more.) + +The idea: + +* We evaluate functions at positive, negative, imaginary, 45- and 135-degree + complex values with magnitudes between 10^-20 to 10^20, at precisions between + 5 and 150 digits (we can go even higher for fast functions). + +* Comparing the result from two different precision levels provides + a strong consistency check (particularly for functions that use + different algorithms at different precision levels). + +* That the computation finishes at all (without failure), within reasonable + time, provides a check that evaluation works at all: that the code runs, + that it doesn't get stuck in an infinite loop, and that it doesn't use + some extremely slowly algorithm where it could use a faster one. + +TODO: + +* Speed up those functions that take long to finish! +* Generalize to test more cases; more options. +* Implement a timeout mechanism. +* Some functions are notably absent, including the following: + * inverse trigonometric functions (some become inaccurate for complex arguments) + * ci, si (not implemented properly for large complex arguments) + * zeta functions (need to modify test not to try too large imaginary values) + * and others... + +""" + + +import sys, os +from timeit import default_timer as clock + +if "-nogmpy" in sys.argv: + sys.argv.remove('-nogmpy') + os.environ['MPMATH_NOGMPY'] = 'Y' + +filt = '' +if not sys.argv[-1].endswith(".py"): + filt = sys.argv[-1] + +from mpmath import * +from mpmath.libmp.backend import exec_ + +def test_asymp(f, maxdps=150, verbose=False, huge_range=False): + dps = [5,15,25,50,90,150,500,1500,5000,10000] + dps = [p for p in dps if p <= maxdps] + def check(x,y,p,inpt): + if abs(x-y)/abs(y) < workprec(20)(power)(10, -p+1): + return + print() + print("Error!") + print("Input:", inpt) + print("dps =", p) + print("Result 1:", x) + print("Result 2:", y) + print("Absolute error:", abs(x-y)) + print("Relative error:", abs(x-y)/abs(y)) + raise AssertionError + exponents = range(-20,20) + if huge_range: + exponents += [-1000, -100, -50, 50, 100, 1000] + for n in exponents: + if verbose: + sys.stdout.write(". ") + mp.dps = 25 + xpos = mpf(10)**n / 1.1287 + xneg = -xpos + ximag = xpos*j + xcomplex1 = xpos*(1+j) + xcomplex2 = xpos*(-1+j) + for i in range(len(dps)): + if verbose: + print("Testing dps = %s" % dps[i]) + mp.dps = dps[i] + new = f(xpos), f(xneg), f(ximag), f(xcomplex1), f(xcomplex2) + if i != 0: + p = dps[i-1] + check(prev[0], new[0], p, xpos) + check(prev[1], new[1], p, xneg) + check(prev[2], new[2], p, ximag) + check(prev[3], new[3], p, xcomplex1) + check(prev[4], new[4], p, xcomplex2) + prev = new + if verbose: + print() + +a1, a2, a3, a4, a5 = 1.5, -2.25, 3.125, 4, 2 + +def test_bernoulli_huge(): + p, q = bernfrac(9000) + assert p % 10**10 == 9636701091 + assert q == 4091851784687571609141381951327092757255270 + mp.dps = 15 + assert str(bernoulli(10**100)) == '-2.58183325604736e+987675256497386331227838638980680030172857347883537824464410652557820800494271520411283004120790908623' + mp.dps = 50 + assert str(bernoulli(10**100)) == '-2.5818332560473632073252488656039475548106223822913e+987675256497386331227838638980680030172857347883537824464410652557820800494271520411283004120790908623' + mp.dps = 15 + +cases = """\ +test_bernoulli_huge() +test_asymp(lambda z: +pi, maxdps=10000) +test_asymp(lambda z: +e, maxdps=10000) +test_asymp(lambda z: +ln2, maxdps=10000) +test_asymp(lambda z: +ln10, maxdps=10000) +test_asymp(lambda z: +phi, maxdps=10000) +test_asymp(lambda z: +catalan, maxdps=5000) +test_asymp(lambda z: +euler, maxdps=5000) +test_asymp(lambda z: +glaisher, maxdps=1000) +test_asymp(lambda z: +khinchin, maxdps=1000) +test_asymp(lambda z: +twinprime, maxdps=150) +test_asymp(lambda z: stieltjes(2), maxdps=150) +test_asymp(lambda z: +mertens, maxdps=150) +test_asymp(lambda z: +apery, maxdps=5000) +test_asymp(sqrt, maxdps=10000, huge_range=True) +test_asymp(cbrt, maxdps=5000, huge_range=True) +test_asymp(lambda z: root(z,4), maxdps=5000, huge_range=True) +test_asymp(lambda z: root(z,-5), maxdps=5000, huge_range=True) +test_asymp(exp, maxdps=5000, huge_range=True) +test_asymp(expm1, maxdps=1500) +test_asymp(ln, maxdps=5000, huge_range=True) +test_asymp(cosh, maxdps=5000) +test_asymp(sinh, maxdps=5000) +test_asymp(tanh, maxdps=1500) +test_asymp(sin, maxdps=5000, huge_range=True) +test_asymp(cos, maxdps=5000, huge_range=True) +test_asymp(tan, maxdps=1500) +test_asymp(agm, maxdps=1500, huge_range=True) +test_asymp(ellipk, maxdps=1500) +test_asymp(ellipe, maxdps=1500) +test_asymp(lambertw, huge_range=True) +test_asymp(lambda z: lambertw(z,-1)) +test_asymp(lambda z: lambertw(z,1)) +test_asymp(lambda z: lambertw(z,4)) +test_asymp(gamma) +test_asymp(loggamma) # huge_range=True ? +test_asymp(ei) +test_asymp(e1) +test_asymp(li, huge_range=True) +test_asymp(ci) +test_asymp(si) +test_asymp(chi) +test_asymp(shi) +test_asymp(erf) +test_asymp(erfc) +test_asymp(erfi) +test_asymp(lambda z: besselj(2, z)) +test_asymp(lambda z: bessely(2, z)) +test_asymp(lambda z: besseli(2, z)) +test_asymp(lambda z: besselk(2, z)) +test_asymp(lambda z: besselj(-2.25, z)) +test_asymp(lambda z: bessely(-2.25, z)) +test_asymp(lambda z: besseli(-2.25, z)) +test_asymp(lambda z: besselk(-2.25, z)) +test_asymp(airyai) +test_asymp(airybi) +test_asymp(lambda z: hyp0f1(a1, z)) +test_asymp(lambda z: hyp1f1(a1, a2, z)) +test_asymp(lambda z: hyp1f2(a1, a2, a3, z)) +test_asymp(lambda z: hyp2f0(a1, a2, z)) +test_asymp(lambda z: hyperu(a1, a2, z)) +test_asymp(lambda z: hyp2f1(a1, a2, a3, z)) +test_asymp(lambda z: hyp2f2(a1, a2, a3, a4, z)) +test_asymp(lambda z: hyp2f3(a1, a2, a3, a4, a5, z)) +test_asymp(lambda z: coulombf(a1, a2, z)) +test_asymp(lambda z: coulombg(a1, a2, z)) +test_asymp(lambda z: polylog(2,z)) +test_asymp(lambda z: polylog(3,z)) +test_asymp(lambda z: polylog(-2,z)) +test_asymp(lambda z: expint(4, z)) +test_asymp(lambda z: expint(-4, z)) +test_asymp(lambda z: expint(2.25, z)) +test_asymp(lambda z: gammainc(2.5, z, 5)) +test_asymp(lambda z: gammainc(2.5, 5, z)) +test_asymp(lambda z: hermite(3, z)) +test_asymp(lambda z: hermite(2.5, z)) +test_asymp(lambda z: legendre(3, z)) +test_asymp(lambda z: legendre(4, z)) +test_asymp(lambda z: legendre(2.5, z)) +test_asymp(lambda z: legenp(a1, a2, z)) +test_asymp(lambda z: legenq(a1, a2, z), maxdps=90) # abnormally slow +test_asymp(lambda z: jtheta(1, z, 0.5)) +test_asymp(lambda z: jtheta(2, z, 0.5)) +test_asymp(lambda z: jtheta(3, z, 0.5)) +test_asymp(lambda z: jtheta(4, z, 0.5)) +test_asymp(lambda z: jtheta(1, z, 0.5, 1)) +test_asymp(lambda z: jtheta(2, z, 0.5, 1)) +test_asymp(lambda z: jtheta(3, z, 0.5, 1)) +test_asymp(lambda z: jtheta(4, z, 0.5, 1)) +test_asymp(barnesg, maxdps=90) +""" + +def testit(line): + if filt in line: + print(line) + t1 = clock() + exec_(line, globals(), locals()) + t2 = clock() + elapsed = t2-t1 + print("Time:", elapsed, "for", line, "(OK)") + +if __name__ == '__main__': + try: + from multiprocessing import Pool + mapf = Pool(None).map + print("Running tests with multiprocessing") + except ImportError: + print("Not using multiprocessing") + mapf = map + t1 = clock() + tasks = cases.splitlines() + mapf(testit, tasks) + t2 = clock() + print("Cumulative wall time:", t2-t1) diff --git a/venv/lib/python3.12/site-packages/mpmath/usertools.py b/venv/lib/python3.12/site-packages/mpmath/usertools.py new file mode 100644 index 0000000000000000000000000000000000000000..8028a4c46f1c635a6857f1f2de48ac6675d3c6d3 --- /dev/null +++ b/venv/lib/python3.12/site-packages/mpmath/usertools.py @@ -0,0 +1,93 @@ + +def monitor(f, input='print', output='print'): + """ + Returns a wrapped copy of *f* that monitors evaluation by calling + *input* with every input (*args*, *kwargs*) passed to *f* and + *output* with every value returned from *f*. The default action + (specify using the special string value ``'print'``) is to print + inputs and outputs to stdout, along with the total evaluation + count:: + + >>> from mpmath import * + >>> mp.dps = 5; mp.pretty = False + >>> diff(monitor(exp), 1) # diff will eval f(x-h) and f(x+h) + in 0 (mpf('0.99999999906867742538452148'),) {} + out 0 mpf('2.7182818259274480055282064') + in 1 (mpf('1.0000000009313225746154785'),) {} + out 1 mpf('2.7182818309906424675501024') + mpf('2.7182808') + + To disable either the input or the output handler, you may + pass *None* as argument. + + Custom input and output handlers may be used e.g. to store + results for later analysis:: + + >>> mp.dps = 15 + >>> input = [] + >>> output = [] + >>> findroot(monitor(sin, input.append, output.append), 3.0) + mpf('3.1415926535897932') + >>> len(input) # Count number of evaluations + 9 + >>> print(input[3]); print(output[3]) + ((mpf('3.1415076583334066'),), {}) + 8.49952562843408e-5 + >>> print(input[4]); print(output[4]) + ((mpf('3.1415928201669122'),), {}) + -1.66577118985331e-7 + + """ + if not input: + input = lambda v: None + elif input == 'print': + incount = [0] + def input(value): + args, kwargs = value + print("in %s %r %r" % (incount[0], args, kwargs)) + incount[0] += 1 + if not output: + output = lambda v: None + elif output == 'print': + outcount = [0] + def output(value): + print("out %s %r" % (outcount[0], value)) + outcount[0] += 1 + def f_monitored(*args, **kwargs): + input((args, kwargs)) + v = f(*args, **kwargs) + output(v) + return v + return f_monitored + +def timing(f, *args, **kwargs): + """ + Returns time elapsed for evaluating ``f()``. Optionally arguments + may be passed to time the execution of ``f(*args, **kwargs)``. + + If the first call is very quick, ``f`` is called + repeatedly and the best time is returned. + """ + once = kwargs.get('once') + if 'once' in kwargs: + del kwargs['once'] + if args or kwargs: + if len(args) == 1 and not kwargs: + arg = args[0] + g = lambda: f(arg) + else: + g = lambda: f(*args, **kwargs) + else: + g = f + from timeit import default_timer as clock + t1=clock(); v=g(); t2=clock(); t=t2-t1 + if t > 0.05 or once: + return t + for i in range(3): + t1=clock(); + # Evaluate multiple times because the timer function + # has a significant overhead + g();g();g();g();g();g();g();g();g();g() + t2=clock() + t=min(t,(t2-t1)/10) + return t diff --git a/venv/lib/python3.12/site-packages/mpmath/visualization.py b/venv/lib/python3.12/site-packages/mpmath/visualization.py new file mode 100644 index 0000000000000000000000000000000000000000..17e12e97bead4f2977b59361a4de7672f0e9b75f --- /dev/null +++ b/venv/lib/python3.12/site-packages/mpmath/visualization.py @@ -0,0 +1,313 @@ +""" +Plotting (requires matplotlib) +""" + +from colorsys import hsv_to_rgb, hls_to_rgb +from .libmp import NoConvergence +from .libmp.backend import xrange + +class VisualizationMethods(object): + plot_ignore = (ValueError, ArithmeticError, ZeroDivisionError, NoConvergence) + +def plot(ctx, f, xlim=[-5,5], ylim=None, points=200, file=None, dpi=None, + singularities=[], axes=None): + r""" + Shows a simple 2D plot of a function `f(x)` or list of functions + `[f_0(x), f_1(x), \ldots, f_n(x)]` over a given interval + specified by *xlim*. Some examples:: + + plot(lambda x: exp(x)*li(x), [1, 4]) + plot([cos, sin], [-4, 4]) + plot([fresnels, fresnelc], [-4, 4]) + plot([sqrt, cbrt], [-4, 4]) + plot(lambda t: zeta(0.5+t*j), [-20, 20]) + plot([floor, ceil, abs, sign], [-5, 5]) + + Points where the function raises a numerical exception or + returns an infinite value are removed from the graph. + Singularities can also be excluded explicitly + as follows (useful for removing erroneous vertical lines):: + + plot(cot, ylim=[-5, 5]) # bad + plot(cot, ylim=[-5, 5], singularities=[-pi, 0, pi]) # good + + For parts where the function assumes complex values, the + real part is plotted with dashes and the imaginary part + is plotted with dots. + + .. note :: This function requires matplotlib (pylab). + """ + if file: + axes = None + fig = None + if not axes: + import pylab + fig = pylab.figure() + axes = fig.add_subplot(111) + if not isinstance(f, (tuple, list)): + f = [f] + a, b = xlim + colors = ['b', 'r', 'g', 'm', 'k'] + for n, func in enumerate(f): + x = ctx.arange(a, b, (b-a)/float(points)) + segments = [] + segment = [] + in_complex = False + for i in xrange(len(x)): + try: + if i != 0: + for sing in singularities: + if x[i-1] <= sing and x[i] >= sing: + raise ValueError + v = func(x[i]) + if ctx.isnan(v) or abs(v) > 1e300: + raise ValueError + if hasattr(v, "imag") and v.imag: + re = float(v.real) + im = float(v.imag) + if not in_complex: + in_complex = True + segments.append(segment) + segment = [] + segment.append((float(x[i]), re, im)) + else: + if in_complex: + in_complex = False + segments.append(segment) + segment = [] + if hasattr(v, "real"): + v = v.real + segment.append((float(x[i]), v)) + except ctx.plot_ignore: + if segment: + segments.append(segment) + segment = [] + if segment: + segments.append(segment) + for segment in segments: + x = [s[0] for s in segment] + y = [s[1] for s in segment] + if not x: + continue + c = colors[n % len(colors)] + if len(segment[0]) == 3: + z = [s[2] for s in segment] + axes.plot(x, y, '--'+c, linewidth=3) + axes.plot(x, z, ':'+c, linewidth=3) + else: + axes.plot(x, y, c, linewidth=3) + axes.set_xlim([float(_) for _ in xlim]) + if ylim: + axes.set_ylim([float(_) for _ in ylim]) + axes.set_xlabel('x') + axes.set_ylabel('f(x)') + axes.grid(True) + if fig: + if file: + pylab.savefig(file, dpi=dpi) + else: + pylab.show() + +def default_color_function(ctx, z): + if ctx.isinf(z): + return (1.0, 1.0, 1.0) + if ctx.isnan(z): + return (0.5, 0.5, 0.5) + pi = 3.1415926535898 + a = (float(ctx.arg(z)) + ctx.pi) / (2*ctx.pi) + a = (a + 0.5) % 1.0 + b = 1.0 - float(1/(1.0+abs(z)**0.3)) + return hls_to_rgb(a, b, 0.8) + +blue_orange_colors = [ + (-1.0, (0.0, 0.0, 0.0)), + (-0.95, (0.1, 0.2, 0.5)), # dark blue + (-0.5, (0.0, 0.5, 1.0)), # blueish + (-0.05, (0.4, 0.8, 0.8)), # cyanish + ( 0.0, (1.0, 1.0, 1.0)), + ( 0.05, (1.0, 0.9, 0.3)), # yellowish + ( 0.5, (0.9, 0.5, 0.0)), # orangeish + ( 0.95, (0.7, 0.1, 0.0)), # redish + ( 1.0, (0.0, 0.0, 0.0)), + ( 2.0, (0.0, 0.0, 0.0)), +] + +def phase_color_function(ctx, z): + if ctx.isinf(z): + return (1.0, 1.0, 1.0) + if ctx.isnan(z): + return (0.5, 0.5, 0.5) + pi = 3.1415926535898 + w = float(ctx.arg(z)) / pi + w = max(min(w, 1.0), -1.0) + for i in range(1,len(blue_orange_colors)): + if blue_orange_colors[i][0] > w: + a, (ra, ga, ba) = blue_orange_colors[i-1] + b, (rb, gb, bb) = blue_orange_colors[i] + s = (w-a) / (b-a) + return ra+(rb-ra)*s, ga+(gb-ga)*s, ba+(bb-ba)*s + +def cplot(ctx, f, re=[-5,5], im=[-5,5], points=2000, color=None, + verbose=False, file=None, dpi=None, axes=None): + """ + Plots the given complex-valued function *f* over a rectangular part + of the complex plane specified by the pairs of intervals *re* and *im*. + For example:: + + cplot(lambda z: z, [-2, 2], [-10, 10]) + cplot(exp) + cplot(zeta, [0, 1], [0, 50]) + + By default, the complex argument (phase) is shown as color (hue) and + the magnitude is show as brightness. You can also supply a + custom color function (*color*). This function should take a + complex number as input and return an RGB 3-tuple containing + floats in the range 0.0-1.0. + + Alternatively, you can select a builtin color function by passing + a string as *color*: + + * "default" - default color scheme + * "phase" - a color scheme that only renders the phase of the function, + with white for positive reals, black for negative reals, gold in the + upper half plane, and blue in the lower half plane. + + To obtain a sharp image, the number of points may need to be + increased to 100,000 or thereabout. Since evaluating the + function that many times is likely to be slow, the 'verbose' + option is useful to display progress. + + .. note :: This function requires matplotlib (pylab). + """ + if color is None or color == "default": + color = ctx.default_color_function + if color == "phase": + color = ctx.phase_color_function + import pylab + if file: + axes = None + fig = None + if not axes: + fig = pylab.figure() + axes = fig.add_subplot(111) + rea, reb = re + ima, imb = im + dre = reb - rea + dim = imb - ima + M = int(ctx.sqrt(points*dre/dim)+1) + N = int(ctx.sqrt(points*dim/dre)+1) + x = pylab.linspace(rea, reb, M) + y = pylab.linspace(ima, imb, N) + # Note: we have to be careful to get the right rotation. + # Test with these plots: + # cplot(lambda z: z if z.real < 0 else 0) + # cplot(lambda z: z if z.imag < 0 else 0) + w = pylab.zeros((N, M, 3)) + for n in xrange(N): + for m in xrange(M): + z = ctx.mpc(x[m], y[n]) + try: + v = color(f(z)) + except ctx.plot_ignore: + v = (0.5, 0.5, 0.5) + w[n,m] = v + if verbose: + print(str(n) + ' of ' + str(N)) + rea, reb, ima, imb = [float(_) for _ in [rea, reb, ima, imb]] + axes.imshow(w, extent=(rea, reb, ima, imb), origin='lower') + axes.set_xlabel('Re(z)') + axes.set_ylabel('Im(z)') + if fig: + if file: + pylab.savefig(file, dpi=dpi) + else: + pylab.show() + +def splot(ctx, f, u=[-5,5], v=[-5,5], points=100, keep_aspect=True, \ + wireframe=False, file=None, dpi=None, axes=None): + """ + Plots the surface defined by `f`. + + If `f` returns a single component, then this plots the surface + defined by `z = f(x,y)` over the rectangular domain with + `x = u` and `y = v`. + + If `f` returns three components, then this plots the parametric + surface `x, y, z = f(u,v)` over the pairs of intervals `u` and `v`. + + For example, to plot a simple function:: + + >>> from mpmath import * + >>> f = lambda x, y: sin(x+y)*cos(y) + >>> splot(f, [-pi,pi], [-pi,pi]) # doctest: +SKIP + + Plotting a donut:: + + >>> r, R = 1, 2.5 + >>> f = lambda u, v: [r*cos(u), (R+r*sin(u))*cos(v), (R+r*sin(u))*sin(v)] + >>> splot(f, [0, 2*pi], [0, 2*pi]) # doctest: +SKIP + + .. note :: This function requires matplotlib (pylab) 0.98.5.3 or higher. + """ + import pylab + import mpl_toolkits.mplot3d as mplot3d + if file: + axes = None + fig = None + if not axes: + fig = pylab.figure() + axes = mplot3d.axes3d.Axes3D(fig) + ua, ub = u + va, vb = v + du = ub - ua + dv = vb - va + if not isinstance(points, (list, tuple)): + points = [points, points] + M, N = points + u = pylab.linspace(ua, ub, M) + v = pylab.linspace(va, vb, N) + x, y, z = [pylab.zeros((M, N)) for i in xrange(3)] + xab, yab, zab = [[0, 0] for i in xrange(3)] + for n in xrange(N): + for m in xrange(M): + fdata = f(ctx.convert(u[m]), ctx.convert(v[n])) + try: + x[m,n], y[m,n], z[m,n] = fdata + except TypeError: + x[m,n], y[m,n], z[m,n] = u[m], v[n], fdata + for c, cab in [(x[m,n], xab), (y[m,n], yab), (z[m,n], zab)]: + if c < cab[0]: + cab[0] = c + if c > cab[1]: + cab[1] = c + if wireframe: + axes.plot_wireframe(x, y, z, rstride=4, cstride=4) + else: + axes.plot_surface(x, y, z, rstride=4, cstride=4) + axes.set_xlabel('x') + axes.set_ylabel('y') + axes.set_zlabel('z') + if keep_aspect: + dx, dy, dz = [cab[1] - cab[0] for cab in [xab, yab, zab]] + maxd = max(dx, dy, dz) + if dx < maxd: + delta = maxd - dx + axes.set_xlim3d(xab[0] - delta / 2.0, xab[1] + delta / 2.0) + if dy < maxd: + delta = maxd - dy + axes.set_ylim3d(yab[0] - delta / 2.0, yab[1] + delta / 2.0) + if dz < maxd: + delta = maxd - dz + axes.set_zlim3d(zab[0] - delta / 2.0, zab[1] + delta / 2.0) + if fig: + if file: + pylab.savefig(file, dpi=dpi) + else: + pylab.show() + + +VisualizationMethods.plot = plot +VisualizationMethods.default_color_function = default_color_function +VisualizationMethods.phase_color_function = phase_color_function +VisualizationMethods.cplot = cplot +VisualizationMethods.splot = splot diff --git a/venv/lib/python3.12/site-packages/networkx/__init__.py b/venv/lib/python3.12/site-packages/networkx/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..b975a1ff66b1b1e87de65a9966dafdb09cb00031 --- /dev/null +++ b/venv/lib/python3.12/site-packages/networkx/__init__.py @@ -0,0 +1,62 @@ +""" +NetworkX +======== + +NetworkX is a Python package for the creation, manipulation, and study of the +structure, dynamics, and functions of complex networks. + +See https://networkx.org for complete documentation. +""" + +__version__ = "3.6.1" + + +# These are imported in order as listed +from networkx.lazy_imports import _lazy_import + +from networkx.exception import * + +from networkx import utils +from networkx.utils import _clear_cache, _dispatchable + +# load_and_call entry_points, set configs +config = utils.backends._set_configs_from_environment() +utils.config = utils.configs.config = config # type: ignore[attr-defined] + +from networkx import classes +from networkx.classes import filters +from networkx.classes import * + +from networkx import convert +from networkx.convert import * + +from networkx import convert_matrix +from networkx.convert_matrix import * + +from networkx import relabel +from networkx.relabel import * + +from networkx import generators +from networkx.generators import * + +from networkx import readwrite +from networkx.readwrite import * + +# Need to test with SciPy, when available +from networkx import algorithms +from networkx.algorithms import * + +from networkx import linalg +from networkx.linalg import * + +from networkx import drawing +from networkx.drawing import * + + +def __getattr__(name): + if name == "random_tree": + raise AttributeError( + "nx.random_tree was removed in version 3.4. 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b/venv/lib/python3.12/site-packages/networkx/__pycache__/relabel.cpython-312.pyc differ diff --git a/venv/lib/python3.12/site-packages/networkx/algorithms/__init__.py b/venv/lib/python3.12/site-packages/networkx/algorithms/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..6439d56fe1364cf9b08e2a20affe4362cb56cbe8 --- /dev/null +++ b/venv/lib/python3.12/site-packages/networkx/algorithms/__init__.py @@ -0,0 +1,134 @@ +from networkx.algorithms.assortativity import * +from networkx.algorithms.asteroidal import * +from networkx.algorithms.boundary import * +from networkx.algorithms.broadcasting import * +from networkx.algorithms.bridges import * +from networkx.algorithms.chains import * +from networkx.algorithms.centrality import * +from networkx.algorithms.chordal import * +from networkx.algorithms.cluster import * +from networkx.algorithms.clique import * +from networkx.algorithms.communicability_alg import * +from networkx.algorithms.components import * +from networkx.algorithms.coloring import * +from networkx.algorithms.core import * +from networkx.algorithms.covering import * +from networkx.algorithms.cycles import * +from networkx.algorithms.cuts import * +from networkx.algorithms.d_separation import * +from networkx.algorithms.dag import * +from networkx.algorithms.distance_measures import * +from networkx.algorithms.distance_regular import * +from networkx.algorithms.dominance import * +from networkx.algorithms.dominating import * +from networkx.algorithms.efficiency_measures import * +from networkx.algorithms.euler import * +from networkx.algorithms.graphical import * +from networkx.algorithms.hierarchy import * +from networkx.algorithms.hybrid import * +from networkx.algorithms.link_analysis import * +from networkx.algorithms.link_prediction import * +from networkx.algorithms.lowest_common_ancestors import * +from networkx.algorithms.isolate import * +from networkx.algorithms.matching import * +from networkx.algorithms.minors import * +from networkx.algorithms.mis import * +from networkx.algorithms.moral import * +from networkx.algorithms.non_randomness import * +from networkx.algorithms.operators import * +from networkx.algorithms.planarity import * +from networkx.algorithms.planar_drawing import * +from networkx.algorithms.polynomials import * +from networkx.algorithms.perfect_graph import * +from networkx.algorithms.reciprocity import * +from networkx.algorithms.regular import * +from networkx.algorithms.richclub import * +from networkx.algorithms.shortest_paths import * +from networkx.algorithms.similarity import * +from networkx.algorithms.graph_hashing import * +from networkx.algorithms.simple_paths import * +from networkx.algorithms.smallworld import * +from networkx.algorithms.smetric import * +from networkx.algorithms.structuralholes import * +from networkx.algorithms.sparsifiers import * +from networkx.algorithms.summarization import * +from networkx.algorithms.swap import * +from networkx.algorithms.time_dependent import * +from networkx.algorithms.traversal import * +from networkx.algorithms.triads import * +from networkx.algorithms.vitality import * +from networkx.algorithms.voronoi import * +from networkx.algorithms.walks import * +from networkx.algorithms.wiener import * + +# Make certain subpackages available to the user as direct imports from +# the `networkx` namespace. +from networkx.algorithms import approximation +from networkx.algorithms import assortativity +from networkx.algorithms import bipartite +from networkx.algorithms import node_classification +from networkx.algorithms import centrality +from networkx.algorithms import chordal +from networkx.algorithms import cluster +from networkx.algorithms import clique +from networkx.algorithms import components +from networkx.algorithms import connectivity +from networkx.algorithms import community +from networkx.algorithms import coloring +from networkx.algorithms import flow +from networkx.algorithms import isomorphism +from networkx.algorithms import link_analysis +from networkx.algorithms import lowest_common_ancestors +from networkx.algorithms import operators +from networkx.algorithms import shortest_paths +from networkx.algorithms import tournament +from networkx.algorithms import traversal +from networkx.algorithms import tree + +# Make certain functions from some of the previous subpackages available +# to the user as direct imports from the `networkx` namespace. +from networkx.algorithms.bipartite import complete_bipartite_graph +from networkx.algorithms.bipartite import is_bipartite +from networkx.algorithms.bipartite import projected_graph +from networkx.algorithms.connectivity import all_pairs_node_connectivity +from networkx.algorithms.connectivity import all_node_cuts +from networkx.algorithms.connectivity import average_node_connectivity +from networkx.algorithms.connectivity import edge_connectivity +from networkx.algorithms.connectivity import edge_disjoint_paths +from networkx.algorithms.connectivity import k_components +from networkx.algorithms.connectivity import k_edge_components +from networkx.algorithms.connectivity import k_edge_subgraphs +from networkx.algorithms.connectivity import k_edge_augmentation +from networkx.algorithms.connectivity import is_k_edge_connected +from networkx.algorithms.connectivity import minimum_edge_cut +from networkx.algorithms.connectivity import minimum_node_cut +from networkx.algorithms.connectivity import node_connectivity +from networkx.algorithms.connectivity import node_disjoint_paths +from networkx.algorithms.connectivity import stoer_wagner +from networkx.algorithms.flow import capacity_scaling +from networkx.algorithms.flow import cost_of_flow +from networkx.algorithms.flow import gomory_hu_tree +from networkx.algorithms.flow import max_flow_min_cost +from networkx.algorithms.flow import maximum_flow +from networkx.algorithms.flow import maximum_flow_value +from networkx.algorithms.flow import min_cost_flow +from networkx.algorithms.flow import min_cost_flow_cost +from networkx.algorithms.flow import minimum_cut +from networkx.algorithms.flow import minimum_cut_value +from networkx.algorithms.flow import network_simplex +from networkx.algorithms.isomorphism import could_be_isomorphic +from networkx.algorithms.isomorphism import fast_could_be_isomorphic +from networkx.algorithms.isomorphism import faster_could_be_isomorphic +from networkx.algorithms.isomorphism import is_isomorphic +from networkx.algorithms.isomorphism.vf2pp import * +from networkx.algorithms.tree.branchings import maximum_branching +from networkx.algorithms.tree.branchings import maximum_spanning_arborescence +from networkx.algorithms.tree.branchings import minimum_branching +from networkx.algorithms.tree.branchings import minimum_spanning_arborescence +from networkx.algorithms.tree.branchings import ArborescenceIterator +from networkx.algorithms.tree.coding import * +from networkx.algorithms.tree.decomposition import * +from networkx.algorithms.tree.mst import * +from networkx.algorithms.tree.operations import * +from networkx.algorithms.tree.recognition import * +from networkx.algorithms.tournament import is_tournament diff --git a/venv/lib/python3.12/site-packages/networkx/algorithms/__pycache__/__init__.cpython-312.pyc b/venv/lib/python3.12/site-packages/networkx/algorithms/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..09273a3f586ac1ca0ac81c576789974f1f1f2a3d Binary files /dev/null and b/venv/lib/python3.12/site-packages/networkx/algorithms/__pycache__/__init__.cpython-312.pyc differ diff --git a/venv/lib/python3.12/site-packages/networkx/algorithms/__pycache__/asteroidal.cpython-312.pyc b/venv/lib/python3.12/site-packages/networkx/algorithms/__pycache__/asteroidal.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a9ab1edba42e051540108f40f12a5b0627fa5467 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b/venv/lib/python3.12/site-packages/networkx/algorithms/approximation/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..b0b401583eb2a31ca600efd9cfdf43839e098a28 --- /dev/null +++ b/venv/lib/python3.12/site-packages/networkx/algorithms/approximation/__init__.py @@ -0,0 +1,26 @@ +"""Approximations of graph properties and Heuristic methods for optimization. + +The functions in this class are not imported into the top-level ``networkx`` +namespace so the easiest way to use them is with:: + + >>> from networkx.algorithms import approximation + +Another option is to import the specific function with +``from networkx.algorithms.approximation import function_name``. + +""" + +from networkx.algorithms.approximation.clustering_coefficient import * +from networkx.algorithms.approximation.clique import * +from networkx.algorithms.approximation.connectivity import * +from networkx.algorithms.approximation.distance_measures import * +from networkx.algorithms.approximation.dominating_set import * +from networkx.algorithms.approximation.kcomponents import * +from networkx.algorithms.approximation.matching import * +from networkx.algorithms.approximation.ramsey import * +from networkx.algorithms.approximation.steinertree import * +from networkx.algorithms.approximation.traveling_salesman import * +from networkx.algorithms.approximation.treewidth import * +from networkx.algorithms.approximation.vertex_cover import * +from networkx.algorithms.approximation.maxcut import * +from networkx.algorithms.approximation.density import * diff --git a/venv/lib/python3.12/site-packages/networkx/algorithms/approximation/__pycache__/__init__.cpython-312.pyc b/venv/lib/python3.12/site-packages/networkx/algorithms/approximation/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..73230ec71fc79a73b557963580853d73223edf48 Binary files /dev/null and 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