Laba1/2

python_env_practice/.gitignore

@@ -1,4 +1,4 @@
-.venv/
+../../.venv/
 __pycache__/
 *.pyc
 output.txt

python_env_practice/.venv/Lib/site-packages/dateutil/__init__.py

@@ -0,0 +1,24 @@
+# -*- coding: utf-8 -*-
+import sys
+
+try:
+    from ._version import version as __version__
+except ImportError:
+    __version__ = 'unknown'
+
+__all__ = ['easter', 'parser', 'relativedelta', 'rrule', 'tz',
+           'utils', 'zoneinfo']
+
+def __getattr__(name):
+    import importlib
+
+    if name in __all__:
+        return importlib.import_module("." + name, __name__)
+    raise AttributeError(
+        "module {!r} has not attribute {!r}".format(__name__, name)
+    )
+
+
+def __dir__():
+    # __dir__ should include all the lazy-importable modules as well.
+    return [x for x in globals() if x not in sys.modules] + __all__

python_env_practice/.venv/Lib/site-packages/dateutil/_common.py

@@ -0,0 +1,43 @@
+"""
+Common code used in multiple modules.
+"""
+
+
+class weekday(object):
+    __slots__ = ["weekday", "n"]
+
+    def __init__(self, weekday, n=None):
+        self.weekday = weekday
+        self.n = n
+
+    def __call__(self, n):
+        if n == self.n:
+            return self
+        else:
+            return self.__class__(self.weekday, n)
+
+    def __eq__(self, other):
+        try:
+            if self.weekday != other.weekday or self.n != other.n:
+                return False
+        except AttributeError:
+            return False
+        return True
+
+    def __hash__(self):
+        return hash((
+          self.weekday,
+          self.n,
+        ))
+
+    def __ne__(self, other):
+        return not (self == other)
+
+    def __repr__(self):
+        s = ("MO", "TU", "WE", "TH", "FR", "SA", "SU")[self.weekday]
+        if not self.n:
+            return s
+        else:
+            return "%s(%+d)" % (s, self.n)
+
+# vim:ts=4:sw=4:et

python_env_practice/.venv/Lib/site-packages/dateutil/_version.py

@@ -0,0 +1,4 @@
+# file generated by setuptools_scm
+# don't change, don't track in version control
+__version__ = version = '2.9.0.post0'
+__version_tuple__ = version_tuple = (2, 9, 0)

python_env_practice/.venv/Lib/site-packages/dateutil/easter.py

@@ -0,0 +1,89 @@
+# -*- coding: utf-8 -*-
+"""
+This module offers a generic Easter computing method for any given year, using
+Western, Orthodox or Julian algorithms.
+"""
+
+import datetime
+
+__all__ = ["easter", "EASTER_JULIAN", "EASTER_ORTHODOX", "EASTER_WESTERN"]
+
+EASTER_JULIAN = 1
+EASTER_ORTHODOX = 2
+EASTER_WESTERN = 3
+
+
+def easter(year, method=EASTER_WESTERN):
+    """
+    This method was ported from the work done by GM Arts,
+    on top of the algorithm by Claus Tondering, which was
+    based in part on the algorithm of Ouding (1940), as
+    quoted in "Explanatory Supplement to the Astronomical
+    Almanac", P.  Kenneth Seidelmann, editor.
+
+    This algorithm implements three different Easter
+    calculation methods:
+
+    1. Original calculation in Julian calendar, valid in
+       dates after 326 AD
+    2. Original method, with date converted to Gregorian
+       calendar, valid in years 1583 to 4099
+    3. Revised method, in Gregorian calendar, valid in
+       years 1583 to 4099 as well
+
+    These methods are represented by the constants:
+
+    * ``EASTER_JULIAN   = 1``
+    * ``EASTER_ORTHODOX = 2``
+    * ``EASTER_WESTERN  = 3``
+
+    The default method is method 3.
+
+    More about the algorithm may be found at:
+
+    `GM Arts: Easter Algorithms <http://www.gmarts.org/index.php?go=415>`_
+
+    and
+
+    `The Calendar FAQ: Easter <https://www.tondering.dk/claus/cal/easter.php>`_
+
+    """
+
+    if not (1 <= method <= 3):
+        raise ValueError("invalid method")
+
+    # g - Golden year - 1
+    # c - Century
+    # h - (23 - Epact) mod 30
+    # i - Number of days from March 21 to Paschal Full Moon
+    # j - Weekday for PFM (0=Sunday, etc)
+    # p - Number of days from March 21 to Sunday on or before PFM
+    #     (-6 to 28 methods 1 & 3, to 56 for method 2)
+    # e - Extra days to add for method 2 (converting Julian
+    #     date to Gregorian date)
+
+    y = year
+    g = y % 19
+    e = 0
+    if method < 3:
+        # Old method
+        i = (19*g + 15) % 30
+        j = (y + y//4 + i) % 7
+        if method == 2:
+            # Extra dates to convert Julian to Gregorian date
+            e = 10
+            if y > 1600:
+                e = e + y//100 - 16 - (y//100 - 16)//4
+    else:
+        # New method
+        c = y//100
+        h = (c - c//4 - (8*c + 13)//25 + 19*g + 15) % 30
+        i = h - (h//28)*(1 - (h//28)*(29//(h + 1))*((21 - g)//11))
+        j = (y + y//4 + i + 2 - c + c//4) % 7
+
+    # p can be from -6 to 56 corresponding to dates 22 March to 23 May
+    # (later dates apply to method 2, although 23 May never actually occurs)
+    p = i - j + e
+    d = 1 + (p + 27 + (p + 6)//40) % 31
+    m = 3 + (p + 26)//30
+    return datetime.date(int(y), int(m), int(d))

python_env_practice/.venv/Lib/site-packages/dateutil/parser/__init__.py

@@ -0,0 +1,61 @@
+# -*- coding: utf-8 -*-
+from ._parser import parse, parser, parserinfo, ParserError
+from ._parser import DEFAULTPARSER, DEFAULTTZPARSER
+from ._parser import UnknownTimezoneWarning
+
+from ._parser import __doc__
+
+from .isoparser import isoparser, isoparse
+
+__all__ = ['parse', 'parser', 'parserinfo',
+           'isoparse', 'isoparser',
+           'ParserError',
+           'UnknownTimezoneWarning']
+
+
+###
+# Deprecate portions of the private interface so that downstream code that
+# is improperly relying on it is given *some* notice.
+
+
+def __deprecated_private_func(f):
+    from functools import wraps
+    import warnings
+
+    msg = ('{name} is a private function and may break without warning, '
+           'it will be moved and or renamed in future versions.')
+    msg = msg.format(name=f.__name__)
+
+    @wraps(f)
+    def deprecated_func(*args, **kwargs):
+        warnings.warn(msg, DeprecationWarning)
+        return f(*args, **kwargs)
+
+    return deprecated_func
+
+def __deprecate_private_class(c):
+    import warnings
+
+    msg = ('{name} is a private class and may break without warning, '
+           'it will be moved and or renamed in future versions.')
+    msg = msg.format(name=c.__name__)
+
+    class private_class(c):
+        __doc__ = c.__doc__
+
+        def __init__(self, *args, **kwargs):
+            warnings.warn(msg, DeprecationWarning)
+            super(private_class, self).__init__(*args, **kwargs)
+
+    private_class.__name__ = c.__name__
+
+    return private_class
+
+
+from ._parser import _timelex, _resultbase
+from ._parser import _tzparser, _parsetz
+
+_timelex = __deprecate_private_class(_timelex)
+_tzparser = __deprecate_private_class(_tzparser)
+_resultbase = __deprecate_private_class(_resultbase)
+_parsetz = __deprecated_private_func(_parsetz)

python_env_practice/.venv/Lib/site-packages/dateutil/parser/isoparser.py

@@ -0,0 +1,416 @@
+# -*- coding: utf-8 -*-
+"""
+This module offers a parser for ISO-8601 strings
+
+It is intended to support all valid date, time and datetime formats per the
+ISO-8601 specification.
+
+..versionadded:: 2.7.0
+"""
+from datetime import datetime, timedelta, time, date
+import calendar
+from dateutil import tz
+
+from functools import wraps
+
+import re
+import six
+
+__all__ = ["isoparse", "isoparser"]
+
+
+def _takes_ascii(f):
+    @wraps(f)
+    def func(self, str_in, *args, **kwargs):
+        # If it's a stream, read the whole thing
+        str_in = getattr(str_in, 'read', lambda: str_in)()
+
+        # If it's unicode, turn it into bytes, since ISO-8601 only covers ASCII
+        if isinstance(str_in, six.text_type):
+            # ASCII is the same in UTF-8
+            try:
+                str_in = str_in.encode('ascii')
+            except UnicodeEncodeError as e:
+                msg = 'ISO-8601 strings should contain only ASCII characters'
+                six.raise_from(ValueError(msg), e)
+
+        return f(self, str_in, *args, **kwargs)
+
+    return func
+
+
+class isoparser(object):
+    def __init__(self, sep=None):
+        """
+        :param sep:
+            A single character that separates date and time portions. If
+            ``None``, the parser will accept any single character.
+            For strict ISO-8601 adherence, pass ``'T'``.
+        """
+        if sep is not None:
+            if (len(sep) != 1 or ord(sep) >= 128 or sep in '0123456789'):
+                raise ValueError('Separator must be a single, non-numeric ' +
+                                 'ASCII character')
+
+            sep = sep.encode('ascii')
+
+        self._sep = sep
+
+    @_takes_ascii
+    def isoparse(self, dt_str):
+        """
+        Parse an ISO-8601 datetime string into a :class:`datetime.datetime`.
+
+        An ISO-8601 datetime string consists of a date portion, followed
+        optionally by a time portion - the date and time portions are separated
+        by a single character separator, which is ``T`` in the official
+        standard. Incomplete date formats (such as ``YYYY-MM``) may *not* be
+        combined with a time portion.
+
+        Supported date formats are:
+
+        Common:
+
+        - ``YYYY``
+        - ``YYYY-MM``
+        - ``YYYY-MM-DD`` or ``YYYYMMDD``
+
+        Uncommon:
+
+        - ``YYYY-Www`` or ``YYYYWww`` - ISO week (day defaults to 0)
+        - ``YYYY-Www-D`` or ``YYYYWwwD`` - ISO week and day
+
+        The ISO week and day numbering follows the same logic as
+        :func:`datetime.date.isocalendar`.
+
+        Supported time formats are:
+
+        - ``hh``
+        - ``hh:mm`` or ``hhmm``
+        - ``hh:mm:ss`` or ``hhmmss``
+        - ``hh:mm:ss.ssssss`` (Up to 6 sub-second digits)
+
+        Midnight is a special case for `hh`, as the standard supports both
+        00:00 and 24:00 as a representation. The decimal separator can be
+        either a dot or a comma.
+
+
+        .. caution::
+
+            Support for fractional components other than seconds is part of the
+            ISO-8601 standard, but is not currently implemented in this parser.
+
+        Supported time zone offset formats are:
+
+        - `Z` (UTC)
+        - `±HH:MM`
+        - `±HHMM`
+        - `±HH`
+
+        Offsets will be represented as :class:`dateutil.tz.tzoffset` objects,
+        with the exception of UTC, which will be represented as
+        :class:`dateutil.tz.tzutc`. Time zone offsets equivalent to UTC (such
+        as `+00:00`) will also be represented as :class:`dateutil.tz.tzutc`.
+
+        :param dt_str:
+            A string or stream containing only an ISO-8601 datetime string
+
+        :return:
+            Returns a :class:`datetime.datetime` representing the string.
+            Unspecified components default to their lowest value.
+
+        .. warning::
+
+            As of version 2.7.0, the strictness of the parser should not be
+            considered a stable part of the contract. Any valid ISO-8601 string
+            that parses correctly with the default settings will continue to
+            parse correctly in future versions, but invalid strings that
+            currently fail (e.g. ``2017-01-01T00:00+00:00:00``) are not
+            guaranteed to continue failing in future versions if they encode
+            a valid date.
+
+        .. versionadded:: 2.7.0
+        """
+        components, pos = self._parse_isodate(dt_str)
+
+        if len(dt_str) > pos:
+            if self._sep is None or dt_str[pos:pos + 1] == self._sep:
+                components += self._parse_isotime(dt_str[pos + 1:])
+            else:
+                raise ValueError('String contains unknown ISO components')
+
+        if len(components) > 3 and components[3] == 24:
+            components[3] = 0
+            return datetime(*components) + timedelta(days=1)
+
+        return datetime(*components)
+
+    @_takes_ascii
+    def parse_isodate(self, datestr):
+        """
+        Parse the date portion of an ISO string.
+
+        :param datestr:
+            The string portion of an ISO string, without a separator
+
+        :return:
+            Returns a :class:`datetime.date` object
+        """
+        components, pos = self._parse_isodate(datestr)
+        if pos < len(datestr):
+            raise ValueError('String contains unknown ISO ' +
+                             'components: {!r}'.format(datestr.decode('ascii')))
+        return date(*components)
+
+    @_takes_ascii
+    def parse_isotime(self, timestr):
+        """
+        Parse the time portion of an ISO string.
+
+        :param timestr:
+            The time portion of an ISO string, without a separator
+
+        :return:
+            Returns a :class:`datetime.time` object
+        """
+        components = self._parse_isotime(timestr)
+        if components[0] == 24:
+            components[0] = 0
+        return time(*components)
+
+    @_takes_ascii
+    def parse_tzstr(self, tzstr, zero_as_utc=True):
+        """
+        Parse a valid ISO time zone string.
+
+        See :func:`isoparser.isoparse` for details on supported formats.
+
+        :param tzstr:
+            A string representing an ISO time zone offset
+
+        :param zero_as_utc:
+            Whether to return :class:`dateutil.tz.tzutc` for zero-offset zones
+
+        :return:
+            Returns :class:`dateutil.tz.tzoffset` for offsets and
+            :class:`dateutil.tz.tzutc` for ``Z`` and (if ``zero_as_utc`` is
+            specified) offsets equivalent to UTC.
+        """
+        return self._parse_tzstr(tzstr, zero_as_utc=zero_as_utc)
+
+    # Constants
+    _DATE_SEP = b'-'
+    _TIME_SEP = b':'
+    _FRACTION_REGEX = re.compile(b'[\\.,]([0-9]+)')
+
+    def _parse_isodate(self, dt_str):
+        try:
+            return self._parse_isodate_common(dt_str)
+        except ValueError:
+            return self._parse_isodate_uncommon(dt_str)
+
+    def _parse_isodate_common(self, dt_str):
+        len_str = len(dt_str)
+        components = [1, 1, 1]
+
+        if len_str < 4:
+            raise ValueError('ISO string too short')
+
+        # Year
+        components[0] = int(dt_str[0:4])
+        pos = 4
+        if pos >= len_str:
+            return components, pos
+
+        has_sep = dt_str[pos:pos + 1] == self._DATE_SEP
+        if has_sep:
+            pos += 1
+
+        # Month
+        if len_str - pos < 2:
+            raise ValueError('Invalid common month')
+
+        components[1] = int(dt_str[pos:pos + 2])
+        pos += 2
+
+        if pos >= len_str:
+            if has_sep:
+                return components, pos
+            else:
+                raise ValueError('Invalid ISO format')
+
+        if has_sep:
+            if dt_str[pos:pos + 1] != self._DATE_SEP:
+                raise ValueError('Invalid separator in ISO string')
+            pos += 1
+
+        # Day
+        if len_str - pos < 2:
+            raise ValueError('Invalid common day')
+        components[2] = int(dt_str[pos:pos + 2])
+        return components, pos + 2
+
+    def _parse_isodate_uncommon(self, dt_str):
+        if len(dt_str) < 4:
+            raise ValueError('ISO string too short')
+
+        # All ISO formats start with the year
+        year = int(dt_str[0:4])
+
+        has_sep = dt_str[4:5] == self._DATE_SEP
+
+        pos = 4 + has_sep       # Skip '-' if it's there
+        if dt_str[pos:pos + 1] == b'W':
+            # YYYY-?Www-?D?
+            pos += 1
+            weekno = int(dt_str[pos:pos + 2])
+            pos += 2
+
+            dayno = 1
+            if len(dt_str) > pos:
+                if (dt_str[pos:pos + 1] == self._DATE_SEP) != has_sep:
+                    raise ValueError('Inconsistent use of dash separator')
+
+                pos += has_sep
+
+                dayno = int(dt_str[pos:pos + 1])
+                pos += 1
+
+            base_date = self._calculate_weekdate(year, weekno, dayno)
+        else:
+            # YYYYDDD or YYYY-DDD
+            if len(dt_str) - pos < 3:
+                raise ValueError('Invalid ordinal day')
+
+            ordinal_day = int(dt_str[pos:pos + 3])
+            pos += 3
+
+            if ordinal_day < 1 or ordinal_day > (365 + calendar.isleap(year)):
+                raise ValueError('Invalid ordinal day' +
+                                 ' {} for year {}'.format(ordinal_day, year))
+
+            base_date = date(year, 1, 1) + timedelta(days=ordinal_day - 1)
+
+        components = [base_date.year, base_date.month, base_date.day]
+        return components, pos
+
+    def _calculate_weekdate(self, year, week, day):
+        """
+        Calculate the day of corresponding to the ISO year-week-day calendar.
+
+        This function is effectively the inverse of
+        :func:`datetime.date.isocalendar`.
+
+        :param year:
+            The year in the ISO calendar
+
+        :param week:
+            The week in the ISO calendar - range is [1, 53]
+
+        :param day:
+            The day in the ISO calendar - range is [1 (MON), 7 (SUN)]
+
+        :return:
+            Returns a :class:`datetime.date`
+        """
+        if not 0 < week < 54:
+            raise ValueError('Invalid week: {}'.format(week))
+
+        if not 0 < day < 8:     # Range is 1-7
+            raise ValueError('Invalid weekday: {}'.format(day))
+
+        # Get week 1 for the specific year:
+        jan_4 = date(year, 1, 4)   # Week 1 always has January 4th in it
+        week_1 = jan_4 - timedelta(days=jan_4.isocalendar()[2] - 1)
+
+        # Now add the specific number of weeks and days to get what we want
+        week_offset = (week - 1) * 7 + (day - 1)
+        return week_1 + timedelta(days=week_offset)
+
+    def _parse_isotime(self, timestr):
+        len_str = len(timestr)
+        components = [0, 0, 0, 0, None]
+        pos = 0
+        comp = -1
+
+        if len_str < 2:
+            raise ValueError('ISO time too short')
+
+        has_sep = False
+
+        while pos < len_str and comp < 5:
+            comp += 1
+
+            if timestr[pos:pos + 1] in b'-+Zz':
+                # Detect time zone boundary
+                components[-1] = self._parse_tzstr(timestr[pos:])
+                pos = len_str
+                break
+
+            if comp == 1 and timestr[pos:pos+1] == self._TIME_SEP:
+                has_sep = True
+                pos += 1
+            elif comp == 2 and has_sep:
+                if timestr[pos:pos+1] != self._TIME_SEP:
+                    raise ValueError('Inconsistent use of colon separator')
+                pos += 1
+
+            if comp < 3:
+                # Hour, minute, second
+                components[comp] = int(timestr[pos:pos + 2])
+                pos += 2
+
+            if comp == 3:
+                # Fraction of a second
+                frac = self._FRACTION_REGEX.match(timestr[pos:])
+                if not frac:
+                    continue
+
+                us_str = frac.group(1)[:6]  # Truncate to microseconds
+                components[comp] = int(us_str) * 10**(6 - len(us_str))
+                pos += len(frac.group())
+
+        if pos < len_str:
+            raise ValueError('Unused components in ISO string')
+
+        if components[0] == 24:
+            # Standard supports 00:00 and 24:00 as representations of midnight
+            if any(component != 0 for component in components[1:4]):
+                raise ValueError('Hour may only be 24 at 24:00:00.000')
+
+        return components
+
+    def _parse_tzstr(self, tzstr, zero_as_utc=True):
+        if tzstr == b'Z' or tzstr == b'z':
+            return tz.UTC
+
+        if len(tzstr) not in {3, 5, 6}:
+            raise ValueError('Time zone offset must be 1, 3, 5 or 6 characters')
+
+        if tzstr[0:1] == b'-':
+            mult = -1
+        elif tzstr[0:1] == b'+':
+            mult = 1
+        else:
+            raise ValueError('Time zone offset requires sign')
+
+        hours = int(tzstr[1:3])
+        if len(tzstr) == 3:
+            minutes = 0
+        else:
+            minutes = int(tzstr[(4 if tzstr[3:4] == self._TIME_SEP else 3):])
+
+        if zero_as_utc and hours == 0 and minutes == 0:
+            return tz.UTC
+        else:
+            if minutes > 59:
+                raise ValueError('Invalid minutes in time zone offset')
+
+            if hours > 23:
+                raise ValueError('Invalid hours in time zone offset')
+
+            return tz.tzoffset(None, mult * (hours * 60 + minutes) * 60)
+
+
+DEFAULT_ISOPARSER = isoparser()
+isoparse = DEFAULT_ISOPARSER.isoparse

python_env_practice/.venv/Lib/site-packages/dateutil/relativedelta.py

@@ -0,0 +1,599 @@
+# -*- coding: utf-8 -*-
+import datetime
+import calendar
+
+import operator
+from math import copysign
+
+from six import integer_types
+from warnings import warn
+
+from ._common import weekday
+
+MO, TU, WE, TH, FR, SA, SU = weekdays = tuple(weekday(x) for x in range(7))
+
+__all__ = ["relativedelta", "MO", "TU", "WE", "TH", "FR", "SA", "SU"]
+
+
+class relativedelta(object):
+    """
+    The relativedelta type is designed to be applied to an existing datetime and
+    can replace specific components of that datetime, or represents an interval
+    of time.
+
+    It is based on the specification of the excellent work done by M.-A. Lemburg
+    in his
+    `mx.DateTime <https://www.egenix.com/products/python/mxBase/mxDateTime/>`_ extension.
+    However, notice that this type does *NOT* implement the same algorithm as
+    his work. Do *NOT* expect it to behave like mx.DateTime's counterpart.
+
+    There are two different ways to build a relativedelta instance. The
+    first one is passing it two date/datetime classes::
+
+        relativedelta(datetime1, datetime2)
+
+    The second one is passing it any number of the following keyword arguments::
+
+        relativedelta(arg1=x,arg2=y,arg3=z...)
+
+        year, month, day, hour, minute, second, microsecond:
+            Absolute information (argument is singular); adding or subtracting a
+            relativedelta with absolute information does not perform an arithmetic
+            operation, but rather REPLACES the corresponding value in the
+            original datetime with the value(s) in relativedelta.
+
+        years, months, weeks, days, hours, minutes, seconds, microseconds:
+            Relative information, may be negative (argument is plural); adding
+            or subtracting a relativedelta with relative information performs
+            the corresponding arithmetic operation on the original datetime value
+            with the information in the relativedelta.
+
+        weekday:
+            One of the weekday instances (MO, TU, etc) available in the
+            relativedelta module. These instances may receive a parameter N,
+            specifying the Nth weekday, which could be positive or negative
+            (like MO(+1) or MO(-2)). Not specifying it is the same as specifying
+            +1. You can also use an integer, where 0=MO. This argument is always
+            relative e.g. if the calculated date is already Monday, using MO(1)
+            or MO(-1) won't change the day. To effectively make it absolute, use
+            it in combination with the day argument (e.g. day=1, MO(1) for first
+            Monday of the month).
+
+        leapdays:
+            Will add given days to the date found, if year is a leap
+            year, and the date found is post 28 of february.
+
+        yearday, nlyearday:
+            Set the yearday or the non-leap year day (jump leap days).
+            These are converted to day/month/leapdays information.
+
+    There are relative and absolute forms of the keyword
+    arguments. The plural is relative, and the singular is
+    absolute. For each argument in the order below, the absolute form
+    is applied first (by setting each attribute to that value) and
+    then the relative form (by adding the value to the attribute).
+
+    The order of attributes considered when this relativedelta is
+    added to a datetime is:
+
+    1. Year
+    2. Month
+    3. Day
+    4. Hours
+    5. Minutes
+    6. Seconds
+    7. Microseconds
+
+    Finally, weekday is applied, using the rule described above.
+
+    For example
+
+    >>> from datetime import datetime
+    >>> from dateutil.relativedelta import relativedelta, MO
+    >>> dt = datetime(2018, 4, 9, 13, 37, 0)
+    >>> delta = relativedelta(hours=25, day=1, weekday=MO(1))
+    >>> dt + delta
+    datetime.datetime(2018, 4, 2, 14, 37)
+
+    First, the day is set to 1 (the first of the month), then 25 hours
+    are added, to get to the 2nd day and 14th hour, finally the
+    weekday is applied, but since the 2nd is already a Monday there is
+    no effect.
+
+    """
+
+    def __init__(self, dt1=None, dt2=None,
+                 years=0, months=0, days=0, leapdays=0, weeks=0,
+                 hours=0, minutes=0, seconds=0, microseconds=0,
+                 year=None, month=None, day=None, weekday=None,
+                 yearday=None, nlyearday=None,
+                 hour=None, minute=None, second=None, microsecond=None):
+
+        if dt1 and dt2:
+            # datetime is a subclass of date. So both must be date
+            if not (isinstance(dt1, datetime.date) and
+                    isinstance(dt2, datetime.date)):
+                raise TypeError("relativedelta only diffs datetime/date")
+
+            # We allow two dates, or two datetimes, so we coerce them to be
+            # of the same type
+            if (isinstance(dt1, datetime.datetime) !=
+                    isinstance(dt2, datetime.datetime)):
+                if not isinstance(dt1, datetime.datetime):
+                    dt1 = datetime.datetime.fromordinal(dt1.toordinal())
+                elif not isinstance(dt2, datetime.datetime):
+                    dt2 = datetime.datetime.fromordinal(dt2.toordinal())
+
+            self.years = 0
+            self.months = 0
+            self.days = 0
+            self.leapdays = 0
+            self.hours = 0
+            self.minutes = 0
+            self.seconds = 0
+            self.microseconds = 0
+            self.year = None
+            self.month = None
+            self.day = None
+            self.weekday = None
+            self.hour = None
+            self.minute = None
+            self.second = None
+            self.microsecond = None
+            self._has_time = 0
+
+            # Get year / month delta between the two
+            months = (dt1.year - dt2.year) * 12 + (dt1.month - dt2.month)
+            self._set_months(months)
+
+            # Remove the year/month delta so the timedelta is just well-defined
+            # time units (seconds, days and microseconds)
+            dtm = self.__radd__(dt2)
+
+            # If we've overshot our target, make an adjustment
+            if dt1 < dt2:
+                compare = operator.gt
+                increment = 1
+            else:
+                compare = operator.lt
+                increment = -1
+
+            while compare(dt1, dtm):
+                months += increment
+                self._set_months(months)
+                dtm = self.__radd__(dt2)
+
+            # Get the timedelta between the "months-adjusted" date and dt1
+            delta = dt1 - dtm
+            self.seconds = delta.seconds + delta.days * 86400
+            self.microseconds = delta.microseconds
+        else:
+            # Check for non-integer values in integer-only quantities
+            if any(x is not None and x != int(x) for x in (years, months)):
+                raise ValueError("Non-integer years and months are "
+                                 "ambiguous and not currently supported.")
+
+            # Relative information
+            self.years = int(years)
+            self.months = int(months)
+            self.days = days + weeks * 7
+            self.leapdays = leapdays
+            self.hours = hours
+            self.minutes = minutes
+            self.seconds = seconds
+            self.microseconds = microseconds
+
+            # Absolute information
+            self.year = year
+            self.month = month
+            self.day = day
+            self.hour = hour
+            self.minute = minute
+            self.second = second
+            self.microsecond = microsecond
+
+            if any(x is not None and int(x) != x
+                   for x in (year, month, day, hour,
+                             minute, second, microsecond)):
+                # For now we'll deprecate floats - later it'll be an error.
+                warn("Non-integer value passed as absolute information. " +
+                     "This is not a well-defined condition and will raise " +
+                     "errors in future versions.", DeprecationWarning)
+
+            if isinstance(weekday, integer_types):
+                self.weekday = weekdays[weekday]
+            else:
+                self.weekday = weekday
+
+            yday = 0
+            if nlyearday:
+                yday = nlyearday
+            elif yearday:
+                yday = yearday
+                if yearday > 59:
+                    self.leapdays = -1
+            if yday:
+                ydayidx = [31, 59, 90, 120, 151, 181, 212,
+                           243, 273, 304, 334, 366]
+                for idx, ydays in enumerate(ydayidx):
+                    if yday <= ydays:
+                        self.month = idx+1
+                        if idx == 0:
+                            self.day = yday
+                        else:
+                            self.day = yday-ydayidx[idx-1]
+                        break
+                else:
+                    raise ValueError("invalid year day (%d)" % yday)
+
+        self._fix()
+
+    def _fix(self):
+        if abs(self.microseconds) > 999999:
+            s = _sign(self.microseconds)
+            div, mod = divmod(self.microseconds * s, 1000000)
+            self.microseconds = mod * s
+            self.seconds += div * s
+        if abs(self.seconds) > 59:
+            s = _sign(self.seconds)
+            div, mod = divmod(self.seconds * s, 60)
+            self.seconds = mod * s
+            self.minutes += div * s
+        if abs(self.minutes) > 59:
+            s = _sign(self.minutes)
+            div, mod = divmod(self.minutes * s, 60)
+            self.minutes = mod * s
+            self.hours += div * s
+        if abs(self.hours) > 23:
+            s = _sign(self.hours)
+            div, mod = divmod(self.hours * s, 24)
+            self.hours = mod * s
+            self.days += div * s
+        if abs(self.months) > 11:
+            s = _sign(self.months)
+            div, mod = divmod(self.months * s, 12)
+            self.months = mod * s
+            self.years += div * s
+        if (self.hours or self.minutes or self.seconds or self.microseconds
+                or self.hour is not None or self.minute is not None or
+                self.second is not None or self.microsecond is not None):
+            self._has_time = 1
+        else:
+            self._has_time = 0
+
+    @property
+    def weeks(self):
+        return int(self.days / 7.0)
+
+    @weeks.setter
+    def weeks(self, value):
+        self.days = self.days - (self.weeks * 7) + value * 7
+
+    def _set_months(self, months):
+        self.months = months
+        if abs(self.months) > 11:
+            s = _sign(self.months)
+            div, mod = divmod(self.months * s, 12)
+            self.months = mod * s
+            self.years = div * s
+        else:
+            self.years = 0
+
+    def normalized(self):
+        """
+        Return a version of this object represented entirely using integer
+        values for the relative attributes.
+
+        >>> relativedelta(days=1.5, hours=2).normalized()
+        relativedelta(days=+1, hours=+14)
+
+        :return:
+            Returns a :class:`dateutil.relativedelta.relativedelta` object.
+        """
+        # Cascade remainders down (rounding each to roughly nearest microsecond)
+        days = int(self.days)
+
+        hours_f = round(self.hours + 24 * (self.days - days), 11)
+        hours = int(hours_f)
+
+        minutes_f = round(self.minutes + 60 * (hours_f - hours), 10)
+        minutes = int(minutes_f)
+
+        seconds_f = round(self.seconds + 60 * (minutes_f - minutes), 8)
+        seconds = int(seconds_f)
+
+        microseconds = round(self.microseconds + 1e6 * (seconds_f - seconds))
+
+        # Constructor carries overflow back up with call to _fix()
+        return self.__class__(years=self.years, months=self.months,
+                              days=days, hours=hours, minutes=minutes,
+                              seconds=seconds, microseconds=microseconds,
+                              leapdays=self.leapdays, year=self.year,
+                              month=self.month, day=self.day,
+                              weekday=self.weekday, hour=self.hour,
+                              minute=self.minute, second=self.second,
+                              microsecond=self.microsecond)
+
+    def __add__(self, other):
+        if isinstance(other, relativedelta):
+            return self.__class__(years=other.years + self.years,
+                                 months=other.months + self.months,
+                                 days=other.days + self.days,
+                                 hours=other.hours + self.hours,
+                                 minutes=other.minutes + self.minutes,
+                                 seconds=other.seconds + self.seconds,
+                                 microseconds=(other.microseconds +
+                                               self.microseconds),
+                                 leapdays=other.leapdays or self.leapdays,
+                                 year=(other.year if other.year is not None
+                                       else self.year),
+                                 month=(other.month if other.month is not None
+                                        else self.month),
+                                 day=(other.day if other.day is not None
+                                      else self.day),
+                                 weekday=(other.weekday if other.weekday is not None
+                                          else self.weekday),
+                                 hour=(other.hour if other.hour is not None
+                                       else self.hour),
+                                 minute=(other.minute if other.minute is not None
+                                         else self.minute),
+                                 second=(other.second if other.second is not None
+                                         else self.second),
+                                 microsecond=(other.microsecond if other.microsecond
+                                              is not None else
+                                              self.microsecond))
+        if isinstance(other, datetime.timedelta):
+            return self.__class__(years=self.years,
+                                  months=self.months,
+                                  days=self.days + other.days,
+                                  hours=self.hours,
+                                  minutes=self.minutes,
+                                  seconds=self.seconds + other.seconds,
+                                  microseconds=self.microseconds + other.microseconds,
+                                  leapdays=self.leapdays,
+                                  year=self.year,
+                                  month=self.month,
+                                  day=self.day,
+                                  weekday=self.weekday,
+                                  hour=self.hour,
+                                  minute=self.minute,
+                                  second=self.second,
+                                  microsecond=self.microsecond)
+        if not isinstance(other, datetime.date):
+            return NotImplemented
+        elif self._has_time and not isinstance(other, datetime.datetime):
+            other = datetime.datetime.fromordinal(other.toordinal())
+        year = (self.year or other.year)+self.years
+        month = self.month or other.month
+        if self.months:
+            assert 1 <= abs(self.months) <= 12
+            month += self.months
+            if month > 12:
+                year += 1
+                month -= 12
+            elif month < 1:
+                year -= 1
+                month += 12
+        day = min(calendar.monthrange(year, month)[1],
+                  self.day or other.day)
+        repl = {"year": year, "month": month, "day": day}
+        for attr in ["hour", "minute", "second", "microsecond"]:
+            value = getattr(self, attr)
+            if value is not None:
+                repl[attr] = value
+        days = self.days
+        if self.leapdays and month > 2 and calendar.isleap(year):
+            days += self.leapdays
+        ret = (other.replace(**repl)
+               + datetime.timedelta(days=days,
+                                    hours=self.hours,
+                                    minutes=self.minutes,
+                                    seconds=self.seconds,
+                                    microseconds=self.microseconds))
+        if self.weekday:
+            weekday, nth = self.weekday.weekday, self.weekday.n or 1
+            jumpdays = (abs(nth) - 1) * 7
+            if nth > 0:
+                jumpdays += (7 - ret.weekday() + weekday) % 7
+            else:
+                jumpdays += (ret.weekday() - weekday) % 7
+                jumpdays *= -1
+            ret += datetime.timedelta(days=jumpdays)
+        return ret
+
+    def __radd__(self, other):
+        return self.__add__(other)
+
+    def __rsub__(self, other):
+        return self.__neg__().__radd__(other)
+
+    def __sub__(self, other):
+        if not isinstance(other, relativedelta):
+            return NotImplemented   # In case the other object defines __rsub__
+        return self.__class__(years=self.years - other.years,
+                             months=self.months - other.months,
+                             days=self.days - other.days,
+                             hours=self.hours - other.hours,
+                             minutes=self.minutes - other.minutes,
+                             seconds=self.seconds - other.seconds,
+                             microseconds=self.microseconds - other.microseconds,
+                             leapdays=self.leapdays or other.leapdays,
+                             year=(self.year if self.year is not None
+                                   else other.year),
+                             month=(self.month if self.month is not None else
+                                    other.month),
+                             day=(self.day if self.day is not None else
+                                  other.day),
+                             weekday=(self.weekday if self.weekday is not None else
+                                      other.weekday),
+                             hour=(self.hour if self.hour is not None else
+                                   other.hour),
+                             minute=(self.minute if self.minute is not None else
+                                     other.minute),
+                             second=(self.second if self.second is not None else
+                                     other.second),
+                             microsecond=(self.microsecond if self.microsecond
+                                          is not None else
+                                          other.microsecond))
+
+    def __abs__(self):
+        return self.__class__(years=abs(self.years),
+                              months=abs(self.months),
+                              days=abs(self.days),
+                              hours=abs(self.hours),
+                              minutes=abs(self.minutes),
+                              seconds=abs(self.seconds),
+                              microseconds=abs(self.microseconds),
+                              leapdays=self.leapdays,
+                              year=self.year,
+                              month=self.month,
+                              day=self.day,
+                              weekday=self.weekday,
+                              hour=self.hour,
+                              minute=self.minute,
+                              second=self.second,
+                              microsecond=self.microsecond)
+
+    def __neg__(self):
+        return self.__class__(years=-self.years,
+                             months=-self.months,
+                             days=-self.days,
+                             hours=-self.hours,
+                             minutes=-self.minutes,
+                             seconds=-self.seconds,
+                             microseconds=-self.microseconds,
+                             leapdays=self.leapdays,
+                             year=self.year,
+                             month=self.month,
+                             day=self.day,
+                             weekday=self.weekday,
+                             hour=self.hour,
+                             minute=self.minute,
+                             second=self.second,
+                             microsecond=self.microsecond)
+
+    def __bool__(self):
+        return not (not self.years and
+                    not self.months and
+                    not self.days and
+                    not self.hours and
+                    not self.minutes and
+                    not self.seconds and
+                    not self.microseconds and
+                    not self.leapdays and
+                    self.year is None and
+                    self.month is None and
+                    self.day is None and
+                    self.weekday is None and
+                    self.hour is None and
+                    self.minute is None and
+                    self.second is None and
+                    self.microsecond is None)
+    # Compatibility with Python 2.x
+    __nonzero__ = __bool__
+
+    def __mul__(self, other):
+        try:
+            f = float(other)
+        except TypeError:
+            return NotImplemented
+
+        return self.__class__(years=int(self.years * f),
+                             months=int(self.months * f),
+                             days=int(self.days * f),
+                             hours=int(self.hours * f),
+                             minutes=int(self.minutes * f),
+                             seconds=int(self.seconds * f),
+                             microseconds=int(self.microseconds * f),
+                             leapdays=self.leapdays,
+                             year=self.year,
+                             month=self.month,
+                             day=self.day,
+                             weekday=self.weekday,
+                             hour=self.hour,
+                             minute=self.minute,
+                             second=self.second,
+                             microsecond=self.microsecond)
+
+    __rmul__ = __mul__
+
+    def __eq__(self, other):
+        if not isinstance(other, relativedelta):
+            return NotImplemented
+        if self.weekday or other.weekday:
+            if not self.weekday or not other.weekday:
+                return False
+            if self.weekday.weekday != other.weekday.weekday:
+                return False
+            n1, n2 = self.weekday.n, other.weekday.n
+            if n1 != n2 and not ((not n1 or n1 == 1) and (not n2 or n2 == 1)):
+                return False
+        return (self.years == other.years and
+                self.months == other.months and
+                self.days == other.days and
+                self.hours == other.hours and
+                self.minutes == other.minutes and
+                self.seconds == other.seconds and
+                self.microseconds == other.microseconds and
+                self.leapdays == other.leapdays and
+                self.year == other.year and
+                self.month == other.month and
+                self.day == other.day and
+                self.hour == other.hour and
+                self.minute == other.minute and
+                self.second == other.second and
+                self.microsecond == other.microsecond)
+
+    def __hash__(self):
+        return hash((
+            self.weekday,
+            self.years,
+            self.months,
+            self.days,
+            self.hours,
+            self.minutes,
+            self.seconds,
+            self.microseconds,
+            self.leapdays,
+            self.year,
+            self.month,
+            self.day,
+            self.hour,
+            self.minute,
+            self.second,
+            self.microsecond,
+        ))
+
+    def __ne__(self, other):
+        return not self.__eq__(other)
+
+    def __div__(self, other):
+        try:
+            reciprocal = 1 / float(other)
+        except TypeError:
+            return NotImplemented
+
+        return self.__mul__(reciprocal)
+
+    __truediv__ = __div__
+
+    def __repr__(self):
+        l = []
+        for attr in ["years", "months", "days", "leapdays",
+                     "hours", "minutes", "seconds", "microseconds"]:
+            value = getattr(self, attr)
+            if value:
+                l.append("{attr}={value:+g}".format(attr=attr, value=value))
+        for attr in ["year", "month", "day", "weekday",
+                     "hour", "minute", "second", "microsecond"]:
+            value = getattr(self, attr)
+            if value is not None:
+                l.append("{attr}={value}".format(attr=attr, value=repr(value)))
+        return "{classname}({attrs})".format(classname=self.__class__.__name__,
+                                             attrs=", ".join(l))
+
+
+def _sign(x):
+    return int(copysign(1, x))
+
+# vim:ts=4:sw=4:et

python_env_practice/.venv/Lib/site-packages/dateutil/tz/__init__.py

@@ -0,0 +1,12 @@
+# -*- coding: utf-8 -*-
+from .tz import *
+from .tz import __doc__
+
+__all__ = ["tzutc", "tzoffset", "tzlocal", "tzfile", "tzrange",
+           "tzstr", "tzical", "tzwin", "tzwinlocal", "gettz",
+           "enfold", "datetime_ambiguous", "datetime_exists",
+           "resolve_imaginary", "UTC", "DeprecatedTzFormatWarning"]
+
+
+class DeprecatedTzFormatWarning(Warning):
+    """Warning raised when time zones are parsed from deprecated formats."""

python_env_practice/.venv/Lib/site-packages/dateutil/tz/_common.py

@@ -0,0 +1,419 @@
+from six import PY2
+
+from functools import wraps
+
+from datetime import datetime, timedelta, tzinfo
+
+
+ZERO = timedelta(0)
+
+__all__ = ['tzname_in_python2', 'enfold']
+
+
+def tzname_in_python2(namefunc):
+    """Change unicode output into bytestrings in Python 2
+
+    tzname() API changed in Python 3. It used to return bytes, but was changed
+    to unicode strings
+    """
+    if PY2:
+        @wraps(namefunc)
+        def adjust_encoding(*args, **kwargs):
+            name = namefunc(*args, **kwargs)
+            if name is not None:
+                name = name.encode()
+
+            return name
+
+        return adjust_encoding
+    else:
+        return namefunc
+
+
+# The following is adapted from Alexander Belopolsky's tz library
+# https://github.com/abalkin/tz
+if hasattr(datetime, 'fold'):
+    # This is the pre-python 3.6 fold situation
+    def enfold(dt, fold=1):
+        """
+        Provides a unified interface for assigning the ``fold`` attribute to
+        datetimes both before and after the implementation of PEP-495.
+
+        :param fold:
+            The value for the ``fold`` attribute in the returned datetime. This
+            should be either 0 or 1.
+
+        :return:
+            Returns an object for which ``getattr(dt, 'fold', 0)`` returns
+            ``fold`` for all versions of Python. In versions prior to
+            Python 3.6, this is a ``_DatetimeWithFold`` object, which is a
+            subclass of :py:class:`datetime.datetime` with the ``fold``
+            attribute added, if ``fold`` is 1.
+
+        .. versionadded:: 2.6.0
+        """
+        return dt.replace(fold=fold)
+
+else:
+    class _DatetimeWithFold(datetime):
+        """
+        This is a class designed to provide a PEP 495-compliant interface for
+        Python versions before 3.6. It is used only for dates in a fold, so
+        the ``fold`` attribute is fixed at ``1``.
+
+        .. versionadded:: 2.6.0
+        """
+        __slots__ = ()
+
+        def replace(self, *args, **kwargs):
+            """
+            Return a datetime with the same attributes, except for those
+            attributes given new values by whichever keyword arguments are
+            specified. Note that tzinfo=None can be specified to create a naive
+            datetime from an aware datetime with no conversion of date and time
+            data.
+
+            This is reimplemented in ``_DatetimeWithFold`` because pypy3 will
+            return a ``datetime.datetime`` even if ``fold`` is unchanged.
+            """
+            argnames = (
+                'year', 'month', 'day', 'hour', 'minute', 'second',
+                'microsecond', 'tzinfo'
+            )
+
+            for arg, argname in zip(args, argnames):
+                if argname in kwargs:
+                    raise TypeError('Duplicate argument: {}'.format(argname))
+
+                kwargs[argname] = arg
+
+            for argname in argnames:
+                if argname not in kwargs:
+                    kwargs[argname] = getattr(self, argname)
+
+            dt_class = self.__class__ if kwargs.get('fold', 1) else datetime
+
+            return dt_class(**kwargs)
+
+        @property
+        def fold(self):
+            return 1
+
+    def enfold(dt, fold=1):
+        """
+        Provides a unified interface for assigning the ``fold`` attribute to
+        datetimes both before and after the implementation of PEP-495.
+
+        :param fold:
+            The value for the ``fold`` attribute in the returned datetime. This
+            should be either 0 or 1.
+
+        :return:
+            Returns an object for which ``getattr(dt, 'fold', 0)`` returns
+            ``fold`` for all versions of Python. In versions prior to
+            Python 3.6, this is a ``_DatetimeWithFold`` object, which is a
+            subclass of :py:class:`datetime.datetime` with the ``fold``
+            attribute added, if ``fold`` is 1.
+
+        .. versionadded:: 2.6.0
+        """
+        if getattr(dt, 'fold', 0) == fold:
+            return dt
+
+        args = dt.timetuple()[:6]
+        args += (dt.microsecond, dt.tzinfo)
+
+        if fold:
+            return _DatetimeWithFold(*args)
+        else:
+            return datetime(*args)
+
+
+def _validate_fromutc_inputs(f):
+    """
+    The CPython version of ``fromutc`` checks that the input is a ``datetime``
+    object and that ``self`` is attached as its ``tzinfo``.
+    """
+    @wraps(f)
+    def fromutc(self, dt):
+        if not isinstance(dt, datetime):
+            raise TypeError("fromutc() requires a datetime argument")
+        if dt.tzinfo is not self:
+            raise ValueError("dt.tzinfo is not self")
+
+        return f(self, dt)
+
+    return fromutc
+
+
+class _tzinfo(tzinfo):
+    """
+    Base class for all ``dateutil`` ``tzinfo`` objects.
+    """
+
+    def is_ambiguous(self, dt):
+        """
+        Whether or not the "wall time" of a given datetime is ambiguous in this
+        zone.
+
+        :param dt:
+            A :py:class:`datetime.datetime`, naive or time zone aware.
+
+
+        :return:
+            Returns ``True`` if ambiguous, ``False`` otherwise.
+
+        .. versionadded:: 2.6.0
+        """
+
+        dt = dt.replace(tzinfo=self)
+
+        wall_0 = enfold(dt, fold=0)
+        wall_1 = enfold(dt, fold=1)
+
+        same_offset = wall_0.utcoffset() == wall_1.utcoffset()
+        same_dt = wall_0.replace(tzinfo=None) == wall_1.replace(tzinfo=None)
+
+        return same_dt and not same_offset
+
+    def _fold_status(self, dt_utc, dt_wall):
+        """
+        Determine the fold status of a "wall" datetime, given a representation
+        of the same datetime as a (naive) UTC datetime. This is calculated based
+        on the assumption that ``dt.utcoffset() - dt.dst()`` is constant for all
+        datetimes, and that this offset is the actual number of hours separating
+        ``dt_utc`` and ``dt_wall``.
+
+        :param dt_utc:
+            Representation of the datetime as UTC
+
+        :param dt_wall:
+            Representation of the datetime as "wall time". This parameter must
+            either have a `fold` attribute or have a fold-naive
+            :class:`datetime.tzinfo` attached, otherwise the calculation may
+            fail.
+        """
+        if self.is_ambiguous(dt_wall):
+            delta_wall = dt_wall - dt_utc
+            _fold = int(delta_wall == (dt_utc.utcoffset() - dt_utc.dst()))
+        else:
+            _fold = 0
+
+        return _fold
+
+    def _fold(self, dt):
+        return getattr(dt, 'fold', 0)
+
+    def _fromutc(self, dt):
+        """
+        Given a timezone-aware datetime in a given timezone, calculates a
+        timezone-aware datetime in a new timezone.
+
+        Since this is the one time that we *know* we have an unambiguous
+        datetime object, we take this opportunity to determine whether the
+        datetime is ambiguous and in a "fold" state (e.g. if it's the first
+        occurrence, chronologically, of the ambiguous datetime).
+
+        :param dt:
+            A timezone-aware :class:`datetime.datetime` object.
+        """
+
+        # Re-implement the algorithm from Python's datetime.py
+        dtoff = dt.utcoffset()
+        if dtoff is None:
+            raise ValueError("fromutc() requires a non-None utcoffset() "
+                             "result")
+
+        # The original datetime.py code assumes that `dst()` defaults to
+        # zero during ambiguous times. PEP 495 inverts this presumption, so
+        # for pre-PEP 495 versions of python, we need to tweak the algorithm.
+        dtdst = dt.dst()
+        if dtdst is None:
+            raise ValueError("fromutc() requires a non-None dst() result")
+        delta = dtoff - dtdst
+
+        dt += delta
+        # Set fold=1 so we can default to being in the fold for
+        # ambiguous dates.
+        dtdst = enfold(dt, fold=1).dst()
+        if dtdst is None:
+            raise ValueError("fromutc(): dt.dst gave inconsistent "
+                             "results; cannot convert")
+        return dt + dtdst
+
+    @_validate_fromutc_inputs
+    def fromutc(self, dt):
+        """
+        Given a timezone-aware datetime in a given timezone, calculates a
+        timezone-aware datetime in a new timezone.
+
+        Since this is the one time that we *know* we have an unambiguous
+        datetime object, we take this opportunity to determine whether the
+        datetime is ambiguous and in a "fold" state (e.g. if it's the first
+        occurrence, chronologically, of the ambiguous datetime).
+
+        :param dt:
+            A timezone-aware :class:`datetime.datetime` object.
+        """
+        dt_wall = self._fromutc(dt)
+
+        # Calculate the fold status given the two datetimes.
+        _fold = self._fold_status(dt, dt_wall)
+
+        # Set the default fold value for ambiguous dates
+        return enfold(dt_wall, fold=_fold)
+
+
+class tzrangebase(_tzinfo):
+    """
+    This is an abstract base class for time zones represented by an annual
+    transition into and out of DST. Child classes should implement the following
+    methods:
+
+        * ``__init__(self, *args, **kwargs)``
+        * ``transitions(self, year)`` - this is expected to return a tuple of
+          datetimes representing the DST on and off transitions in standard
+          time.
+
+    A fully initialized ``tzrangebase`` subclass should also provide the
+    following attributes:
+        * ``hasdst``: Boolean whether or not the zone uses DST.
+        * ``_dst_offset`` / ``_std_offset``: :class:`datetime.timedelta` objects
+          representing the respective UTC offsets.
+        * ``_dst_abbr`` / ``_std_abbr``: Strings representing the timezone short
+          abbreviations in DST and STD, respectively.
+        * ``_hasdst``: Whether or not the zone has DST.
+
+    .. versionadded:: 2.6.0
+    """
+    def __init__(self):
+        raise NotImplementedError('tzrangebase is an abstract base class')
+
+    def utcoffset(self, dt):
+        isdst = self._isdst(dt)
+
+        if isdst is None:
+            return None
+        elif isdst:
+            return self._dst_offset
+        else:
+            return self._std_offset
+
+    def dst(self, dt):
+        isdst = self._isdst(dt)
+
+        if isdst is None:
+            return None
+        elif isdst:
+            return self._dst_base_offset
+        else:
+            return ZERO
+
+    @tzname_in_python2
+    def tzname(self, dt):
+        if self._isdst(dt):
+            return self._dst_abbr
+        else:
+            return self._std_abbr
+
+    def fromutc(self, dt):
+        """ Given a datetime in UTC, return local time """
+        if not isinstance(dt, datetime):
+            raise TypeError("fromutc() requires a datetime argument")
+
+        if dt.tzinfo is not self:
+            raise ValueError("dt.tzinfo is not self")
+
+        # Get transitions - if there are none, fixed offset
+        transitions = self.transitions(dt.year)
+        if transitions is None:
+            return dt + self.utcoffset(dt)
+
+        # Get the transition times in UTC
+        dston, dstoff = transitions
+
+        dston -= self._std_offset
+        dstoff -= self._std_offset
+
+        utc_transitions = (dston, dstoff)
+        dt_utc = dt.replace(tzinfo=None)
+
+        isdst = self._naive_isdst(dt_utc, utc_transitions)
+
+        if isdst:
+            dt_wall = dt + self._dst_offset
+        else:
+            dt_wall = dt + self._std_offset
+
+        _fold = int(not isdst and self.is_ambiguous(dt_wall))
+
+        return enfold(dt_wall, fold=_fold)
+
+    def is_ambiguous(self, dt):
+        """
+        Whether or not the "wall time" of a given datetime is ambiguous in this
+        zone.
+
+        :param dt:
+            A :py:class:`datetime.datetime`, naive or time zone aware.
+
+
+        :return:
+            Returns ``True`` if ambiguous, ``False`` otherwise.
+
+        .. versionadded:: 2.6.0
+        """
+        if not self.hasdst:
+            return False
+
+        start, end = self.transitions(dt.year)
+
+        dt = dt.replace(tzinfo=None)
+        return (end <= dt < end + self._dst_base_offset)
+
+    def _isdst(self, dt):
+        if not self.hasdst:
+            return False
+        elif dt is None:
+            return None
+
+        transitions = self.transitions(dt.year)
+
+        if transitions is None:
+            return False
+
+        dt = dt.replace(tzinfo=None)
+
+        isdst = self._naive_isdst(dt, transitions)
+
+        # Handle ambiguous dates
+        if not isdst and self.is_ambiguous(dt):
+            return not self._fold(dt)
+        else:
+            return isdst
+
+    def _naive_isdst(self, dt, transitions):
+        dston, dstoff = transitions
+
+        dt = dt.replace(tzinfo=None)
+
+        if dston < dstoff:
+            isdst = dston <= dt < dstoff
+        else:
+            isdst = not dstoff <= dt < dston
+
+        return isdst
+
+    @property
+    def _dst_base_offset(self):
+        return self._dst_offset - self._std_offset
+
+    __hash__ = None
+
+    def __ne__(self, other):
+        return not (self == other)
+
+    def __repr__(self):
+        return "%s(...)" % self.__class__.__name__
+
+    __reduce__ = object.__reduce__

python_env_practice/.venv/Lib/site-packages/dateutil/tz/_factories.py

@@ -0,0 +1,80 @@
+from datetime import timedelta
+import weakref
+from collections import OrderedDict
+
+from six.moves import _thread
+
+
+class _TzSingleton(type):
+    def __init__(cls, *args, **kwargs):
+        cls.__instance = None
+        super(_TzSingleton, cls).__init__(*args, **kwargs)
+
+    def __call__(cls):
+        if cls.__instance is None:
+            cls.__instance = super(_TzSingleton, cls).__call__()
+        return cls.__instance
+
+
+class _TzFactory(type):
+    def instance(cls, *args, **kwargs):
+        """Alternate constructor that returns a fresh instance"""
+        return type.__call__(cls, *args, **kwargs)
+
+
+class _TzOffsetFactory(_TzFactory):
+    def __init__(cls, *args, **kwargs):
+        cls.__instances = weakref.WeakValueDictionary()
+        cls.__strong_cache = OrderedDict()
+        cls.__strong_cache_size = 8
+
+        cls._cache_lock = _thread.allocate_lock()
+
+    def __call__(cls, name, offset):
+        if isinstance(offset, timedelta):
+            key = (name, offset.total_seconds())
+        else:
+            key = (name, offset)
+
+        instance = cls.__instances.get(key, None)
+        if instance is None:
+            instance = cls.__instances.setdefault(key,
+                                                  cls.instance(name, offset))
+
+        # This lock may not be necessary in Python 3. See GH issue #901
+        with cls._cache_lock:
+            cls.__strong_cache[key] = cls.__strong_cache.pop(key, instance)
+
+            # Remove an item if the strong cache is overpopulated
+            if len(cls.__strong_cache) > cls.__strong_cache_size:
+                cls.__strong_cache.popitem(last=False)
+
+        return instance
+
+
+class _TzStrFactory(_TzFactory):
+    def __init__(cls, *args, **kwargs):
+        cls.__instances = weakref.WeakValueDictionary()
+        cls.__strong_cache = OrderedDict()
+        cls.__strong_cache_size = 8
+
+        cls.__cache_lock = _thread.allocate_lock()
+
+    def __call__(cls, s, posix_offset=False):
+        key = (s, posix_offset)
+        instance = cls.__instances.get(key, None)
+
+        if instance is None:
+            instance = cls.__instances.setdefault(key,
+                cls.instance(s, posix_offset))
+
+        # This lock may not be necessary in Python 3. See GH issue #901
+        with cls.__cache_lock:
+            cls.__strong_cache[key] = cls.__strong_cache.pop(key, instance)
+
+            # Remove an item if the strong cache is overpopulated
+            if len(cls.__strong_cache) > cls.__strong_cache_size:
+                cls.__strong_cache.popitem(last=False)
+
+        return instance
+

python_env_practice/.venv/Lib/site-packages/dateutil/tz/win.py

@@ -0,0 +1,370 @@
+# -*- coding: utf-8 -*-
+"""
+This module provides an interface to the native time zone data on Windows,
+including :py:class:`datetime.tzinfo` implementations.
+
+Attempting to import this module on a non-Windows platform will raise an
+:py:obj:`ImportError`.
+"""
+# This code was originally contributed by Jeffrey Harris.
+import datetime
+import struct
+
+from six.moves import winreg
+from six import text_type
+
+try:
+    import ctypes
+    from ctypes import wintypes
+except ValueError:
+    # ValueError is raised on non-Windows systems for some horrible reason.
+    raise ImportError("Running tzwin on non-Windows system")
+
+from ._common import tzrangebase
+
+__all__ = ["tzwin", "tzwinlocal", "tzres"]
+
+ONEWEEK = datetime.timedelta(7)
+
+TZKEYNAMENT = r"SOFTWARE\Microsoft\Windows NT\CurrentVersion\Time Zones"
+TZKEYNAME9X = r"SOFTWARE\Microsoft\Windows\CurrentVersion\Time Zones"
+TZLOCALKEYNAME = r"SYSTEM\CurrentControlSet\Control\TimeZoneInformation"
+
+
+def _settzkeyname():
+    handle = winreg.ConnectRegistry(None, winreg.HKEY_LOCAL_MACHINE)
+    try:
+        winreg.OpenKey(handle, TZKEYNAMENT).Close()
+        TZKEYNAME = TZKEYNAMENT
+    except WindowsError:
+        TZKEYNAME = TZKEYNAME9X
+    handle.Close()
+    return TZKEYNAME
+
+
+TZKEYNAME = _settzkeyname()
+
+
+class tzres(object):
+    """
+    Class for accessing ``tzres.dll``, which contains timezone name related
+    resources.
+
+    .. versionadded:: 2.5.0
+    """
+    p_wchar = ctypes.POINTER(wintypes.WCHAR)        # Pointer to a wide char
+
+    def __init__(self, tzres_loc='tzres.dll'):
+        # Load the user32 DLL so we can load strings from tzres
+        user32 = ctypes.WinDLL('user32')
+
+        # Specify the LoadStringW function
+        user32.LoadStringW.argtypes = (wintypes.HINSTANCE,
+                                       wintypes.UINT,
+                                       wintypes.LPWSTR,
+                                       ctypes.c_int)
+
+        self.LoadStringW = user32.LoadStringW
+        self._tzres = ctypes.WinDLL(tzres_loc)
+        self.tzres_loc = tzres_loc
+
+    def load_name(self, offset):
+        """
+        Load a timezone name from a DLL offset (integer).
+
+        >>> from dateutil.tzwin import tzres
+        >>> tzr = tzres()
+        >>> print(tzr.load_name(112))
+        'Eastern Standard Time'
+
+        :param offset:
+            A positive integer value referring to a string from the tzres dll.
+
+        .. note::
+
+            Offsets found in the registry are generally of the form
+            ``@tzres.dll,-114``. The offset in this case is 114, not -114.
+
+        """
+        resource = self.p_wchar()
+        lpBuffer = ctypes.cast(ctypes.byref(resource), wintypes.LPWSTR)
+        nchar = self.LoadStringW(self._tzres._handle, offset, lpBuffer, 0)
+        return resource[:nchar]
+
+    def name_from_string(self, tzname_str):
+        """
+        Parse strings as returned from the Windows registry into the time zone
+        name as defined in the registry.
+
+        >>> from dateutil.tzwin import tzres
+        >>> tzr = tzres()
+        >>> print(tzr.name_from_string('@tzres.dll,-251'))
+        'Dateline Daylight Time'
+        >>> print(tzr.name_from_string('Eastern Standard Time'))
+        'Eastern Standard Time'
+
+        :param tzname_str:
+            A timezone name string as returned from a Windows registry key.
+
+        :return:
+            Returns the localized timezone string from tzres.dll if the string
+            is of the form `@tzres.dll,-offset`, else returns the input string.
+        """
+        if not tzname_str.startswith('@'):
+            return tzname_str
+
+        name_splt = tzname_str.split(',-')
+        try:
+            offset = int(name_splt[1])
+        except:
+            raise ValueError("Malformed timezone string.")
+
+        return self.load_name(offset)
+
+
+class tzwinbase(tzrangebase):
+    """tzinfo class based on win32's timezones available in the registry."""
+    def __init__(self):
+        raise NotImplementedError('tzwinbase is an abstract base class')
+
+    def __eq__(self, other):
+        # Compare on all relevant dimensions, including name.
+        if not isinstance(other, tzwinbase):
+            return NotImplemented
+
+        return  (self._std_offset == other._std_offset and
+                 self._dst_offset == other._dst_offset and
+                 self._stddayofweek == other._stddayofweek and
+                 self._dstdayofweek == other._dstdayofweek and
+                 self._stdweeknumber == other._stdweeknumber and
+                 self._dstweeknumber == other._dstweeknumber and
+                 self._stdhour == other._stdhour and
+                 self._dsthour == other._dsthour and
+                 self._stdminute == other._stdminute and
+                 self._dstminute == other._dstminute and
+                 self._std_abbr == other._std_abbr and
+                 self._dst_abbr == other._dst_abbr)
+
+    @staticmethod
+    def list():
+        """Return a list of all time zones known to the system."""
+        with winreg.ConnectRegistry(None, winreg.HKEY_LOCAL_MACHINE) as handle:
+            with winreg.OpenKey(handle, TZKEYNAME) as tzkey:
+                result = [winreg.EnumKey(tzkey, i)
+                          for i in range(winreg.QueryInfoKey(tzkey)[0])]
+        return result
+
+    def display(self):
+        """
+        Return the display name of the time zone.
+        """
+        return self._display
+
+    def transitions(self, year):
+        """
+        For a given year, get the DST on and off transition times, expressed
+        always on the standard time side. For zones with no transitions, this
+        function returns ``None``.
+
+        :param year:
+            The year whose transitions you would like to query.
+
+        :return:
+            Returns a :class:`tuple` of :class:`datetime.datetime` objects,
+            ``(dston, dstoff)`` for zones with an annual DST transition, or
+            ``None`` for fixed offset zones.
+        """
+
+        if not self.hasdst:
+            return None
+
+        dston = picknthweekday(year, self._dstmonth, self._dstdayofweek,
+                               self._dsthour, self._dstminute,
+                               self._dstweeknumber)
+
+        dstoff = picknthweekday(year, self._stdmonth, self._stddayofweek,
+                                self._stdhour, self._stdminute,
+                                self._stdweeknumber)
+
+        # Ambiguous dates default to the STD side
+        dstoff -= self._dst_base_offset
+
+        return dston, dstoff
+
+    def _get_hasdst(self):
+        return self._dstmonth != 0
+
+    @property
+    def _dst_base_offset(self):
+        return self._dst_base_offset_
+
+
+class tzwin(tzwinbase):
+    """
+    Time zone object created from the zone info in the Windows registry
+
+    These are similar to :py:class:`dateutil.tz.tzrange` objects in that
+    the time zone data is provided in the format of a single offset rule
+    for either 0 or 2 time zone transitions per year.
+
+    :param: name
+        The name of a Windows time zone key, e.g. "Eastern Standard Time".
+        The full list of keys can be retrieved with :func:`tzwin.list`.
+    """
+
+    def __init__(self, name):
+        self._name = name
+
+        with winreg.ConnectRegistry(None, winreg.HKEY_LOCAL_MACHINE) as handle:
+            tzkeyname = text_type("{kn}\\{name}").format(kn=TZKEYNAME, name=name)
+            with winreg.OpenKey(handle, tzkeyname) as tzkey:
+                keydict = valuestodict(tzkey)
+
+        self._std_abbr = keydict["Std"]
+        self._dst_abbr = keydict["Dlt"]
+
+        self._display = keydict["Display"]
+
+        # See http://ww_winreg.jsiinc.com/SUBA/tip0300/rh0398.htm
+        tup = struct.unpack("=3l16h", keydict["TZI"])
+        stdoffset = -tup[0]-tup[1]          # Bias + StandardBias * -1
+        dstoffset = stdoffset-tup[2]        # + DaylightBias * -1
+        self._std_offset = datetime.timedelta(minutes=stdoffset)
+        self._dst_offset = datetime.timedelta(minutes=dstoffset)
+
+        # for the meaning see the win32 TIME_ZONE_INFORMATION structure docs
+        # http://msdn.microsoft.com/en-us/library/windows/desktop/ms725481(v=vs.85).aspx
+        (self._stdmonth,
+         self._stddayofweek,   # Sunday = 0
+         self._stdweeknumber,  # Last = 5
+         self._stdhour,
+         self._stdminute) = tup[4:9]
+
+        (self._dstmonth,
+         self._dstdayofweek,   # Sunday = 0
+         self._dstweeknumber,  # Last = 5
+         self._dsthour,
+         self._dstminute) = tup[12:17]
+
+        self._dst_base_offset_ = self._dst_offset - self._std_offset
+        self.hasdst = self._get_hasdst()
+
+    def __repr__(self):
+        return "tzwin(%s)" % repr(self._name)
+
+    def __reduce__(self):
+        return (self.__class__, (self._name,))
+
+
+class tzwinlocal(tzwinbase):
+    """
+    Class representing the local time zone information in the Windows registry
+
+    While :class:`dateutil.tz.tzlocal` makes system calls (via the :mod:`time`
+    module) to retrieve time zone information, ``tzwinlocal`` retrieves the
+    rules directly from the Windows registry and creates an object like
+    :class:`dateutil.tz.tzwin`.
+
+    Because Windows does not have an equivalent of :func:`time.tzset`, on
+    Windows, :class:`dateutil.tz.tzlocal` instances will always reflect the
+    time zone settings *at the time that the process was started*, meaning
+    changes to the machine's time zone settings during the run of a program
+    on Windows will **not** be reflected by :class:`dateutil.tz.tzlocal`.
+    Because ``tzwinlocal`` reads the registry directly, it is unaffected by
+    this issue.
+    """
+    def __init__(self):
+        with winreg.ConnectRegistry(None, winreg.HKEY_LOCAL_MACHINE) as handle:
+            with winreg.OpenKey(handle, TZLOCALKEYNAME) as tzlocalkey:
+                keydict = valuestodict(tzlocalkey)
+
+            self._std_abbr = keydict["StandardName"]
+            self._dst_abbr = keydict["DaylightName"]
+
+            try:
+                tzkeyname = text_type('{kn}\\{sn}').format(kn=TZKEYNAME,
+                                                          sn=self._std_abbr)
+                with winreg.OpenKey(handle, tzkeyname) as tzkey:
+                    _keydict = valuestodict(tzkey)
+                    self._display = _keydict["Display"]
+            except OSError:
+                self._display = None
+
+        stdoffset = -keydict["Bias"]-keydict["StandardBias"]
+        dstoffset = stdoffset-keydict["DaylightBias"]
+
+        self._std_offset = datetime.timedelta(minutes=stdoffset)
+        self._dst_offset = datetime.timedelta(minutes=dstoffset)
+
+        # For reasons unclear, in this particular key, the day of week has been
+        # moved to the END of the SYSTEMTIME structure.
+        tup = struct.unpack("=8h", keydict["StandardStart"])
+
+        (self._stdmonth,
+         self._stdweeknumber,  # Last = 5
+         self._stdhour,
+         self._stdminute) = tup[1:5]
+
+        self._stddayofweek = tup[7]
+
+        tup = struct.unpack("=8h", keydict["DaylightStart"])
+
+        (self._dstmonth,
+         self._dstweeknumber,  # Last = 5
+         self._dsthour,
+         self._dstminute) = tup[1:5]
+
+        self._dstdayofweek = tup[7]
+
+        self._dst_base_offset_ = self._dst_offset - self._std_offset
+        self.hasdst = self._get_hasdst()
+
+    def __repr__(self):
+        return "tzwinlocal()"
+
+    def __str__(self):
+        # str will return the standard name, not the daylight name.
+        return "tzwinlocal(%s)" % repr(self._std_abbr)
+
+    def __reduce__(self):
+        return (self.__class__, ())
+
+
+def picknthweekday(year, month, dayofweek, hour, minute, whichweek):
+    """ dayofweek == 0 means Sunday, whichweek 5 means last instance """
+    first = datetime.datetime(year, month, 1, hour, minute)
+
+    # This will work if dayofweek is ISO weekday (1-7) or Microsoft-style (0-6),
+    # Because 7 % 7 = 0
+    weekdayone = first.replace(day=((dayofweek - first.isoweekday()) % 7) + 1)
+    wd = weekdayone + ((whichweek - 1) * ONEWEEK)
+    if (wd.month != month):
+        wd -= ONEWEEK
+
+    return wd
+
+
+def valuestodict(key):
+    """Convert a registry key's values to a dictionary."""
+    dout = {}
+    size = winreg.QueryInfoKey(key)[1]
+    tz_res = None
+
+    for i in range(size):
+        key_name, value, dtype = winreg.EnumValue(key, i)
+        if dtype == winreg.REG_DWORD or dtype == winreg.REG_DWORD_LITTLE_ENDIAN:
+            # If it's a DWORD (32-bit integer), it's stored as unsigned - convert
+            # that to a proper signed integer
+            if value & (1 << 31):
+                value = value - (1 << 32)
+        elif dtype == winreg.REG_SZ:
+            # If it's a reference to the tzres DLL, load the actual string
+            if value.startswith('@tzres'):
+                tz_res = tz_res or tzres()
+                value = tz_res.name_from_string(value)
+
+            value = value.rstrip('\x00')    # Remove trailing nulls
+
+        dout[key_name] = value
+
+    return dout

python_env_practice/.venv/Lib/site-packages/dateutil/tzwin.py

@@ -0,0 +1,2 @@
+# tzwin has moved to dateutil.tz.win
+from .tz.win import *

python_env_practice/.venv/Lib/site-packages/dateutil/utils.py

@@ -0,0 +1,71 @@
+# -*- coding: utf-8 -*-
+"""
+This module offers general convenience and utility functions for dealing with
+datetimes.
+
+.. versionadded:: 2.7.0
+"""
+from __future__ import unicode_literals
+
+from datetime import datetime, time
+
+
+def today(tzinfo=None):
+    """
+    Returns a :py:class:`datetime` representing the current day at midnight
+
+    :param tzinfo:
+        The time zone to attach (also used to determine the current day).
+
+    :return:
+        A :py:class:`datetime.datetime` object representing the current day
+        at midnight.
+    """
+
+    dt = datetime.now(tzinfo)
+    return datetime.combine(dt.date(), time(0, tzinfo=tzinfo))
+
+
+def default_tzinfo(dt, tzinfo):
+    """
+    Sets the ``tzinfo`` parameter on naive datetimes only
+
+    This is useful for example when you are provided a datetime that may have
+    either an implicit or explicit time zone, such as when parsing a time zone
+    string.
+
+    .. doctest::
+
+        >>> from dateutil.tz import tzoffset
+        >>> from dateutil.parser import parse
+        >>> from dateutil.utils import default_tzinfo
+        >>> dflt_tz = tzoffset("EST", -18000)
+        >>> print(default_tzinfo(parse('2014-01-01 12:30 UTC'), dflt_tz))
+        2014-01-01 12:30:00+00:00
+        >>> print(default_tzinfo(parse('2014-01-01 12:30'), dflt_tz))
+        2014-01-01 12:30:00-05:00
+
+    :param dt:
+        The datetime on which to replace the time zone
+
+    :param tzinfo:
+        The :py:class:`datetime.tzinfo` subclass instance to assign to
+        ``dt`` if (and only if) it is naive.
+
+    :return:
+        Returns an aware :py:class:`datetime.datetime`.
+    """
+    if dt.tzinfo is not None:
+        return dt
+    else:
+        return dt.replace(tzinfo=tzinfo)
+
+
+def within_delta(dt1, dt2, delta):
+    """
+    Useful for comparing two datetimes that may have a negligible difference
+    to be considered equal.
+    """
+    delta = abs(delta)
+    difference = dt1 - dt2
+    return -delta <= difference <= delta

python_env_practice/.venv/Lib/site-packages/dateutil/zoneinfo/__init__.py

@@ -0,0 +1,167 @@
+# -*- coding: utf-8 -*-
+import warnings
+import json
+
+from tarfile import TarFile
+from pkgutil import get_data
+from io import BytesIO
+
+from dateutil.tz import tzfile as _tzfile
+
+__all__ = ["get_zonefile_instance", "gettz", "gettz_db_metadata"]
+
+ZONEFILENAME = "dateutil-zoneinfo.tar.gz"
+METADATA_FN = 'METADATA'
+
+
+class tzfile(_tzfile):
+    def __reduce__(self):
+        return (gettz, (self._filename,))
+
+
+def getzoneinfofile_stream():
+    try:
+        return BytesIO(get_data(__name__, ZONEFILENAME))
+    except IOError as e:  # TODO  switch to FileNotFoundError?
+        warnings.warn("I/O error({0}): {1}".format(e.errno, e.strerror))
+        return None
+
+
+class ZoneInfoFile(object):
+    def __init__(self, zonefile_stream=None):
+        if zonefile_stream is not None:
+            with TarFile.open(fileobj=zonefile_stream) as tf:
+                self.zones = {zf.name: tzfile(tf.extractfile(zf), filename=zf.name)
+                              for zf in tf.getmembers()
+                              if zf.isfile() and zf.name != METADATA_FN}
+                # deal with links: They'll point to their parent object. Less
+                # waste of memory
+                links = {zl.name: self.zones[zl.linkname]
+                         for zl in tf.getmembers() if
+                         zl.islnk() or zl.issym()}
+                self.zones.update(links)
+                try:
+                    metadata_json = tf.extractfile(tf.getmember(METADATA_FN))
+                    metadata_str = metadata_json.read().decode('UTF-8')
+                    self.metadata = json.loads(metadata_str)
+                except KeyError:
+                    # no metadata in tar file
+                    self.metadata = None
+        else:
+            self.zones = {}
+            self.metadata = None
+
+    def get(self, name, default=None):
+        """
+        Wrapper for :func:`ZoneInfoFile.zones.get`. This is a convenience method
+        for retrieving zones from the zone dictionary.
+
+        :param name:
+            The name of the zone to retrieve. (Generally IANA zone names)
+
+        :param default:
+            The value to return in the event of a missing key.
+
+        .. versionadded:: 2.6.0
+
+        """
+        return self.zones.get(name, default)
+
+
+# The current API has gettz as a module function, although in fact it taps into
+# a stateful class. So as a workaround for now, without changing the API, we
+# will create a new "global" class instance the first time a user requests a
+# timezone. Ugly, but adheres to the api.
+#
+# TODO: Remove after deprecation period.
+_CLASS_ZONE_INSTANCE = []
+
+
+def get_zonefile_instance(new_instance=False):
+    """
+    This is a convenience function which provides a :class:`ZoneInfoFile`
+    instance using the data provided by the ``dateutil`` package. By default, it
+    caches a single instance of the ZoneInfoFile object and returns that.
+
+    :param new_instance:
+        If ``True``, a new instance of :class:`ZoneInfoFile` is instantiated and
+        used as the cached instance for the next call. Otherwise, new instances
+        are created only as necessary.
+
+    :return:
+        Returns a :class:`ZoneInfoFile` object.
+
+    .. versionadded:: 2.6
+    """
+    if new_instance:
+        zif = None
+    else:
+        zif = getattr(get_zonefile_instance, '_cached_instance', None)
+
+    if zif is None:
+        zif = ZoneInfoFile(getzoneinfofile_stream())
+
+        get_zonefile_instance._cached_instance = zif
+
+    return zif
+
+
+def gettz(name):
+    """
+    This retrieves a time zone from the local zoneinfo tarball that is packaged
+    with dateutil.
+
+    :param name:
+        An IANA-style time zone name, as found in the zoneinfo file.
+
+    :return:
+        Returns a :class:`dateutil.tz.tzfile` time zone object.
+
+    .. warning::
+        It is generally inadvisable to use this function, and it is only
+        provided for API compatibility with earlier versions. This is *not*
+        equivalent to ``dateutil.tz.gettz()``, which selects an appropriate
+        time zone based on the inputs, favoring system zoneinfo. This is ONLY
+        for accessing the dateutil-specific zoneinfo (which may be out of
+        date compared to the system zoneinfo).
+
+    .. deprecated:: 2.6
+        If you need to use a specific zoneinfofile over the system zoneinfo,
+        instantiate a :class:`dateutil.zoneinfo.ZoneInfoFile` object and call
+        :func:`dateutil.zoneinfo.ZoneInfoFile.get(name)` instead.
+
+        Use :func:`get_zonefile_instance` to retrieve an instance of the
+        dateutil-provided zoneinfo.
+    """
+    warnings.warn("zoneinfo.gettz() will be removed in future versions, "
+                  "to use the dateutil-provided zoneinfo files, instantiate a "
+                  "ZoneInfoFile object and use ZoneInfoFile.zones.get() "
+                  "instead. See the documentation for details.",
+                  DeprecationWarning)
+
+    if len(_CLASS_ZONE_INSTANCE) == 0:
+        _CLASS_ZONE_INSTANCE.append(ZoneInfoFile(getzoneinfofile_stream()))
+    return _CLASS_ZONE_INSTANCE[0].zones.get(name)
+
+
+def gettz_db_metadata():
+    """ Get the zonefile metadata
+
+    See `zonefile_metadata`_
+
+    :returns:
+        A dictionary with the database metadata
+
+    .. deprecated:: 2.6
+        See deprecation warning in :func:`zoneinfo.gettz`. To get metadata,
+        query the attribute ``zoneinfo.ZoneInfoFile.metadata``.
+    """
+    warnings.warn("zoneinfo.gettz_db_metadata() will be removed in future "
+                  "versions, to use the dateutil-provided zoneinfo files, "
+                  "ZoneInfoFile object and query the 'metadata' attribute "
+                  "instead. See the documentation for details.",
+                  DeprecationWarning)
+
+    if len(_CLASS_ZONE_INSTANCE) == 0:
+        _CLASS_ZONE_INSTANCE.append(ZoneInfoFile(getzoneinfofile_stream()))
+    return _CLASS_ZONE_INSTANCE[0].metadata

python_env_practice/.venv/Lib/site-packages/dateutil/zoneinfo/rebuild.py

@@ -0,0 +1,75 @@
+import logging
+import os
+import tempfile
+import shutil
+import json
+from subprocess import check_call, check_output
+from tarfile import TarFile
+
+from dateutil.zoneinfo import METADATA_FN, ZONEFILENAME
+
+
+def rebuild(filename, tag=None, format="gz", zonegroups=[], metadata=None):
+    """Rebuild the internal timezone info in dateutil/zoneinfo/zoneinfo*tar*
+
+    filename is the timezone tarball from ``ftp.iana.org/tz``.
+
+    """
+    tmpdir = tempfile.mkdtemp()
+    zonedir = os.path.join(tmpdir, "zoneinfo")
+    moduledir = os.path.dirname(__file__)
+    try:
+        with TarFile.open(filename) as tf:
+            for name in zonegroups:
+                tf.extract(name, tmpdir)
+            filepaths = [os.path.join(tmpdir, n) for n in zonegroups]
+
+            _run_zic(zonedir, filepaths)
+
+        # write metadata file
+        with open(os.path.join(zonedir, METADATA_FN), 'w') as f:
+            json.dump(metadata, f, indent=4, sort_keys=True)
+        target = os.path.join(moduledir, ZONEFILENAME)
+        with TarFile.open(target, "w:%s" % format) as tf:
+            for entry in os.listdir(zonedir):
+                entrypath = os.path.join(zonedir, entry)
+                tf.add(entrypath, entry)
+    finally:
+        shutil.rmtree(tmpdir)
+
+
+def _run_zic(zonedir, filepaths):
+    """Calls the ``zic`` compiler in a compatible way to get a "fat" binary.
+
+    Recent versions of ``zic`` default to ``-b slim``, while older versions
+    don't even have the ``-b`` option (but default to "fat" binaries). The
+    current version of dateutil does not support Version 2+ TZif files, which
+    causes problems when used in conjunction with "slim" binaries, so this
+    function is used to ensure that we always get a "fat" binary.
+    """
+
+    try:
+        help_text = check_output(["zic", "--help"])
+    except OSError as e:
+        _print_on_nosuchfile(e)
+        raise
+
+    if b"-b " in help_text:
+        bloat_args = ["-b", "fat"]
+    else:
+        bloat_args = []
+
+    check_call(["zic"] + bloat_args + ["-d", zonedir] + filepaths)
+
+
+def _print_on_nosuchfile(e):
+    """Print helpful troubleshooting message
+
+    e is an exception raised by subprocess.check_call()
+
+    """
+    if e.errno == 2:
+        logging.error(
+            "Could not find zic. Perhaps you need to install "
+            "libc-bin or some other package that provides it, "
+            "or it's not in your PATH?")

python_env_practice/.venv/Lib/site-packages/numpy-2.4.4.dist-info/DELVEWHEEL

@@ -0,0 +1,2 @@
+Version: 1.11.2
+Arguments: ['C:\\Users\\runneradmin\\AppData\\Local\\Temp\\cibw-run-kstykkss\\cp313-win_amd64\\build\\venv\\Scripts\\delvewheel', 'repair', '--add-path', 'D:/a/numpy-release/numpy-release/.openblas/lib', '-w', 'C:\\Users\\runneradmin\\AppData\\Local\\Temp\\cibw-run-kstykkss\\cp313-win_amd64\\repaired_wheel', 'C:\\Users\\runneradmin\\AppData\\Local\\Temp\\cibw-run-kstykkss\\cp313-win_amd64\\built_wheel\\numpy-2.4.4-cp313-cp313-win_amd64.whl']

python_env_practice/.venv/Lib/site-packages/numpy-2.4.4.dist-info/INSTALLER

@@ -0,0 +1 @@
+pip

python_env_practice/.venv/Lib/site-packages/numpy-2.4.4.dist-info/METADATA

@@ -0,0 +1,139 @@
+Metadata-Version: 2.4
+Name: numpy
+Version: 2.4.4
+Summary: Fundamental package for array computing in Python
+Author: Travis E. Oliphant et al.
+Maintainer-Email: NumPy Developers <numpy-discussion@python.org>
+License-Expression: BSD-3-Clause AND 0BSD AND MIT AND Zlib AND CC0-1.0
+License-File: LICENSE.txt
+License-File: numpy/_core/include/numpy/libdivide/LICENSE.txt
+License-File: numpy/_core/src/common/pythoncapi-compat/COPYING
+License-File: numpy/_core/src/highway/LICENSE
+License-File: numpy/_core/src/multiarray/dragon4_LICENSE.txt
+License-File: numpy/_core/src/npysort/x86-simd-sort/LICENSE.md
+License-File: numpy/_core/src/umath/svml/LICENSE
+License-File: numpy/fft/pocketfft/LICENSE.md
+License-File: numpy/linalg/lapack_lite/LICENSE.txt
+License-File: numpy/ma/LICENSE
+License-File: numpy/random/LICENSE.md
+License-File: numpy/random/src/distributions/LICENSE.md
+License-File: numpy/random/src/mt19937/LICENSE.md
+License-File: numpy/random/src/pcg64/LICENSE.md
+License-File: numpy/random/src/philox/LICENSE.md
+License-File: numpy/random/src/sfc64/LICENSE.md
+License-File: numpy/random/src/splitmix64/LICENSE.md
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Intended Audience :: Science/Research
+Classifier: Intended Audience :: Developers
+Classifier: Programming Language :: C
+Classifier: Programming Language :: Python
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3.12
+Classifier: Programming Language :: Python :: 3.13
+Classifier: Programming Language :: Python :: 3.14
+Classifier: Programming Language :: Python :: 3 :: Only
+Classifier: Programming Language :: Python :: Implementation :: CPython
+Classifier: Topic :: Software Development
+Classifier: Topic :: Scientific/Engineering
+Classifier: Typing :: Typed
+Classifier: Operating System :: Microsoft :: Windows
+Classifier: Operating System :: POSIX
+Classifier: Operating System :: Unix
+Classifier: Operating System :: MacOS
+Project-URL: homepage, https://numpy.org
+Project-URL: documentation, https://numpy.org/doc/
+Project-URL: source, https://github.com/numpy/numpy
+Project-URL: download, https://pypi.org/project/numpy/#files
+Project-URL: tracker, https://github.com/numpy/numpy/issues
+Project-URL: release notes, https://numpy.org/doc/stable/release
+Requires-Python: >=3.11
+Description-Content-Type: text/markdown
+
+<h1 align="center">
+<img src="https://raw.githubusercontent.com/numpy/numpy/main/branding/logo/primary/numpylogo.svg" width="300">
+</h1><br>
+
+
+[![Powered by NumFOCUS](https://img.shields.io/badge/powered%20by-NumFOCUS-orange.svg?style=flat&colorA=E1523D&colorB=007D8A)](
+https://numfocus.org)
+[![PyPI Downloads](https://img.shields.io/pypi/dm/numpy.svg?label=PyPI%20downloads)](
+https://pypi.org/project/numpy/)
+[![Conda Downloads](https://img.shields.io/conda/dn/conda-forge/numpy.svg?label=Conda%20downloads)](
+https://anaconda.org/conda-forge/numpy)
+[![Stack Overflow](https://img.shields.io/badge/stackoverflow-Ask%20questions-blue.svg)](
+https://stackoverflow.com/questions/tagged/numpy)
+[![Nature Paper](https://img.shields.io/badge/DOI-10.1038%2Fs41586--020--2649--2-blue)](
+https://doi.org/10.1038/s41586-020-2649-2)
+[![LFX Health Score](https://insights.linuxfoundation.org/api/badge/health-score?project=numpy)](https://insights.linuxfoundation.org/project/numpy)
+[![OpenSSF Scorecard](https://api.securityscorecards.dev/projects/github.com/numpy/numpy/badge)](https://securityscorecards.dev/viewer/?uri=github.com/numpy/numpy)
+[![Typing](https://img.shields.io/pypi/types/numpy)](https://pypi.org/project/numpy/)
+
+
+NumPy is the fundamental package for scientific computing with Python.
+
+- **Website:** https://numpy.org
+- **Documentation:** https://numpy.org/doc
+- **Mailing list:** https://mail.python.org/mailman/listinfo/numpy-discussion
+- **Source code:** https://github.com/numpy/numpy
+- **Contributing:** https://numpy.org/devdocs/dev/index.html
+- **Bug reports:** https://github.com/numpy/numpy/issues
+- **Report a security vulnerability:** https://tidelift.com/docs/security
+
+It provides:
+
+- a powerful N-dimensional array object
+- sophisticated (broadcasting) functions
+- tools for integrating C/C++ and Fortran code
+- useful linear algebra, Fourier transform, and random number capabilities
+
+Testing:
+
+NumPy requires `pytest` and `hypothesis`.  Tests can then be run after installation with:
+
+    python -c "import numpy, sys; sys.exit(numpy.test() is False)"
+
+Code of Conduct
+----------------------
+
+NumPy is a community-driven open source project developed by a diverse group of
+[contributors](https://numpy.org/teams/). The NumPy leadership has made a strong
+commitment to creating an open, inclusive, and positive community. Please read the
+[NumPy Code of Conduct](https://numpy.org/code-of-conduct/) for guidance on how to interact
+with others in a way that makes our community thrive.
+
+Call for Contributions
+----------------------
+
+The NumPy project welcomes your expertise and enthusiasm!
+
+Small improvements or fixes are always appreciated. If you are considering larger contributions
+to the source code, please contact us through the [mailing
+list](https://mail.python.org/mailman/listinfo/numpy-discussion) first.
+
+Writing code isn’t the only way to contribute to NumPy. You can also:
+- review pull requests
+- help us stay on top of new and old issues
+- develop tutorials, presentations, and other educational materials
+- maintain and improve [our website](https://github.com/numpy/numpy.org)
+- develop graphic design for our brand assets and promotional materials
+- translate website content
+- help with outreach and onboard new contributors
+- write grant proposals and help with other fundraising efforts
+
+For more information about the ways you can contribute to NumPy, visit [our website](https://numpy.org/contribute/). 
+If you’re unsure where to start or how your skills fit in, reach out! You can
+ask on the mailing list or here, on GitHub, by opening a new issue or leaving a
+comment on a relevant issue that is already open.
+
+Our preferred channels of communication are all public, but if you’d like to
+speak to us in private first, contact our community coordinators at
+numpy-team@googlegroups.com or on Slack (write numpy-team@googlegroups.com for
+an invitation).
+
+We also have a biweekly community call, details of which are announced on the
+mailing list. You are very welcome to join.
+
+If you are new to contributing to open source, [this
+guide](https://opensource.guide/how-to-contribute/) helps explain why, what,
+and how to successfully get involved.

python_env_practice/.venv/Lib/site-packages/numpy-2.4.4.dist-info/WHEEL

@@ -0,0 +1,4 @@
+Wheel-Version: 1.0
+Generator: meson
+Root-Is-Purelib: false
+Tag: cp313-cp313-win_amd64

python_env_practice/.venv/Lib/site-packages/numpy-2.4.4.dist-info/entry_points.txt

@@ -0,0 +1,13 @@
+[pkg_config]
+numpy = numpy._core.lib.pkgconfig
+
+[array_api]
+numpy = numpy
+
+[pyinstaller40]
+hook-dirs = numpy:_pyinstaller_hooks_dir
+
+[console_scripts]
+f2py = numpy.f2py.f2py2e:main
+numpy-config = numpy._configtool:main
+

python_env_practice/.venv/Lib/site-packages/numpy-2.4.4.dist-info/licenses/LICENSE.txt

@@ -0,0 +1,914 @@
+Copyright (c) 2005-2025, NumPy Developers.
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are
+met:
+
+    * Redistributions of source code must retain the above copyright
+       notice, this list of conditions and the following disclaimer.
+
+    * 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.
+
+    * Neither the name of the NumPy Developers nor the names of any
+       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
+OWNER 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.
+
+----
+
+
+----
+
+This binary distribution of NumPy also bundles the following software:
+
+
+Name: OpenBLAS
+Files: numpy.libs\libscipy_openblas*.dll
+Description: bundled as a dynamically linked library
+Availability: https://github.com/OpenMathLib/OpenBLAS/
+License: BSD-3-Clause
+  Copyright (c) 2011-2014, The OpenBLAS Project
+  All rights reserved.
+
+  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 OpenBLAS project 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 OWNER 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.
+
+
+Name: LAPACK
+Files: numpy.libs\libscipy_openblas*.dll
+Description: bundled in OpenBLAS
+Availability: https://github.com/OpenMathLib/OpenBLAS/
+License: BSD-3-Clause-Open-MPI
+  Copyright (c) 1992-2013 The University of Tennessee and The University
+                          of Tennessee Research Foundation.  All rights
+                          reserved.
+  Copyright (c) 2000-2013 The University of California Berkeley. All
+                          rights reserved.
+  Copyright (c) 2006-2013 The University of Colorado Denver.  All rights
+                          reserved.
+
+  $COPYRIGHT$
+
+  Additional copyrights may follow
+
+  $HEADER$
+
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+----
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+----
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+GCC RUNTIME LIBRARY EXCEPTION
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+----
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python_env_practice/.venv/Lib/site-packages/numpy-2.4.4.dist-info/licenses/numpy/_core/include/numpy/libdivide/LICENSE.txt

@@ -0,0 +1,21 @@
+  zlib License
+  ------------
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+  Copyright (C) 2016 - 2019 Kim Walisch, <kim.walisch@gmail.com>
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python_env_practice/.venv/Lib/site-packages/numpy-2.4.4.dist-info/licenses/numpy/_core/src/common/pythoncapi-compat/COPYING

@@ -0,0 +1,14 @@
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python_env_practice/.venv/Lib/site-packages/numpy-2.4.4.dist-info/licenses/numpy/_core/src/highway/LICENSE

@@ -0,0 +1,371 @@
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+irrevocable and unconditional license to exercise Affirmer's Copyright and
+Related Rights in the Work (i) in all territories worldwide, (ii) for the
+maximum duration provided by applicable law or treaty (including future
+time extensions), (iii) in any current or future medium and for any number
+of copies, and (iv) for any purpose whatsoever, including without
+limitation commercial, advertising or promotional purposes (the
+"License"). The License shall be deemed effective as of the date CC0 was
+applied by Affirmer to the Work. Should any part of the License for any
+reason be judged legally invalid or ineffective under applicable law, such
+partial invalidity or ineffectiveness shall not invalidate the remainder
+of the License, and in such case Affirmer hereby affirms that he or she
+will not (i) exercise any of his or her remaining Copyright and Related
+Rights in the Work or (ii) assert any associated claims and causes of
+action with respect to the Work, in either case contrary to Affirmer's
+express Statement of Purpose.
+
+4. Limitations and Disclaimers.
+
+ a. No trademark or patent rights held by Affirmer are waived, abandoned,
+    surrendered, licensed or otherwise affected by this document.
+ b. Affirmer offers the Work as-is and makes no representations or
+    warranties of any kind concerning the Work, express, implied,
+    statutory or otherwise, including without limitation warranties of
+    title, merchantability, fitness for a particular purpose, non
+    infringement, or the absence of latent or other defects, accuracy, or
+    the present or absence of errors, whether or not discoverable, all to
+    the greatest extent permissible under applicable law.
+ c. Affirmer disclaims responsibility for clearing rights of other persons
+    that may apply to the Work or any use thereof, including without
+    limitation any person's Copyright and Related Rights in the Work.
+    Further, Affirmer disclaims responsibility for obtaining any necessary
+    consents, permissions or other rights required for any use of the
+    Work.
+ d. Affirmer understands and acknowledges that Creative Commons is not a
+    party to this document and has no duty or obligation with respect to
+    this CC0 or use of the Work.

python_env_practice/.venv/Lib/site-packages/numpy-2.4.4.dist-info/licenses/numpy/_core/src/multiarray/dragon4_LICENSE.txt

@@ -0,0 +1,27 @@
+Copyright (c) 2014 Ryan Juckett
+
+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.
+
+dragon4.c|h h contains a modified version of Ryan Juckett's Dragon4
+implementation, obtained from https://www.ryanjuckett.com,
+which has been ported from C++ to C and which has
+modifications specific to printing floats in numpy.
+
+Ryan Juckett's original code was under the Zlib license; he gave numpy
+permission to include it under the MIT license instead.

python_env_practice/.venv/Lib/site-packages/numpy-2.4.4.dist-info/licenses/numpy/_core/src/npysort/x86-simd-sort/LICENSE.md

@@ -0,0 +1,28 @@
+BSD 3-Clause License
+
+Copyright (c) 2022, Intel. All rights reserved.
+
+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.

python_env_practice/.venv/Lib/site-packages/numpy-2.4.4.dist-info/licenses/numpy/_core/src/umath/svml/LICENSE

@@ -0,0 +1,30 @@
+Copyright (c) 2005-2021, NumPy Developers.
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are
+met:
+
+    * Redistributions of source code must retain the above copyright
+       notice, this list of conditions and the following disclaimer.
+
+    * 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.
+
+    * Neither the name of the NumPy Developers nor the names of any
+       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
+OWNER 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.

python_env_practice/.venv/Lib/site-packages/numpy-2.4.4.dist-info/licenses/numpy/fft/pocketfft/LICENSE.md

@@ -0,0 +1,25 @@
+Copyright (C) 2010-2018 Max-Planck-Society
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without modification,
+are permitted provided that the following conditions are met:
+
+* Redistributions of source code must retain the above copyright notice, this
+  list of conditions and the following disclaimer.
+* 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.
+* 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.

python_env_practice/.venv/Lib/site-packages/numpy-2.4.4.dist-info/licenses/numpy/linalg/lapack_lite/LICENSE.txt

@@ -0,0 +1,48 @@
+Copyright (c) 1992-2013 The University of Tennessee and The University
+                        of Tennessee Research Foundation.  All rights
+                        reserved.
+Copyright (c) 2000-2013 The University of California Berkeley. All
+                        rights reserved.
+Copyright (c) 2006-2013 The University of Colorado Denver.  All rights
+                        reserved.
+
+$COPYRIGHT$
+
+Additional copyrights may follow
+
+$HEADER$
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are
+met:
+
+- Redistributions of source code must retain the above copyright
+  notice, this list of conditions and the following disclaimer.
+
+- Redistributions in binary form must reproduce the above copyright
+  notice, this list of conditions and the following disclaimer listed
+  in this license in the documentation and/or other materials
+  provided with the distribution.
+
+- Neither the name of the copyright holders nor the names of its
+  contributors may be used to endorse or promote products derived from
+  this software without specific prior written permission.
+
+The copyright holders provide no reassurances that the source code
+provided does not infringe any patent, copyright, or any other
+intellectual property rights of third parties.  The copyright holders
+disclaim any liability to any recipient for claims brought against
+recipient by any third party for infringement of that parties
+intellectual property rights.
+
+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
+OWNER 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.

python_env_practice/.venv/Lib/site-packages/numpy-2.4.4.dist-info/licenses/numpy/ma/LICENSE

@@ -0,0 +1,24 @@
+* Copyright (c) 2006, University of Georgia and Pierre G.F. Gerard-Marchant
+* All rights reserved.
+* Redistribution and use in source and binary forms, with or without
+* modification, are permitted provided that the following conditions are met:
+*
+*     * Redistributions of source code must retain the above copyright
+*       notice, this list of conditions and the following disclaimer.
+*     * 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.
+*     * Neither the name of the University of Georgia 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 REGENTS 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.

python_env_practice/.venv/Lib/site-packages/numpy-2.4.4.dist-info/licenses/numpy/random/LICENSE.md

@@ -0,0 +1,71 @@
+**This software is dual-licensed under the The University of Illinois/NCSA
+Open Source License (NCSA) and The 3-Clause BSD License**
+
+# NCSA Open Source License
+**Copyright (c) 2019 Kevin Sheppard. All rights reserved.**
+
+Developed by: Kevin Sheppard (<kevin.sheppard@economics.ox.ac.uk>,
+<kevin.k.sheppard@gmail.com>)
+[http://www.kevinsheppard.com](http://www.kevinsheppard.com)
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of
+this software and associated documentation files (the "Software"), to deal with
+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:
+
+Redistributions of source code must retain the above copyright notice, this
+list of conditions and the following disclaimers.
+
+Redistributions in binary form must reproduce the above copyright notice, this
+list of conditions and the following disclaimers in the documentation and/or
+other materials provided with the distribution.
+
+Neither the names of Kevin Sheppard, nor the names of any contributors may be
+used to endorse or promote products derived from this Software without specific
+prior written permission.
+
+**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
+CONTRIBUTORS 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 WITH
+THE SOFTWARE.**
+
+
+# 3-Clause BSD License
+**Copyright (c) 2019 Kevin Sheppard. All rights reserved.**
+
+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.**
+
+# Components
+
+Many parts of this module have been derived from original sources, 
+often the algorithm's designer. Component licenses are located with 
+the component code.

python_env_practice/.venv/Lib/site-packages/numpy-2.4.4.dist-info/licenses/numpy/random/src/distributions/LICENSE.md

@@ -0,0 +1,61 @@
+## NumPy
+
+Copyright (c) 2005-2017, NumPy Developers.
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are
+met:
+
+* Redistributions of source code must retain the above copyright
+   notice, this list of conditions and the following disclaimer.
+
+* 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.
+
+* Neither the name of the NumPy Developers nor the names of any
+   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
+OWNER 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.
+
+
+## Julia
+
+The ziggurat methods were derived from Julia.
+
+Copyright (c) 2009-2019: Jeff Bezanson, Stefan Karpinski, Viral B. Shah,
+and other contributors:
+
+https://github.com/JuliaLang/julia/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.

python_env_practice/.venv/Lib/site-packages/numpy-2.4.4.dist-info/licenses/numpy/random/src/mt19937/LICENSE.md

@@ -0,0 +1,61 @@
+# MT19937
+
+Copyright (c) 2003-2005, Jean-Sebastien Roy (js@jeannot.org)
+
+The rk_random and rk_seed functions algorithms and the original design of
+the Mersenne Twister RNG:
+
+  Copyright (C) 1997 - 2002, Makoto Matsumoto and Takuji Nishimura,
+  All rights reserved.
+
+  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. The names of its contributors may not 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 OWNER
+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.
+
+Original algorithm for the implementation of rk_interval function from
+Richard J. Wagner's implementation of the Mersenne Twister RNG, optimised by
+Magnus Jonsson.
+
+Constants used in the rk_double implementation by Isaku Wada.
+
+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.

python_env_practice/.venv/Lib/site-packages/numpy-2.4.4.dist-info/licenses/numpy/random/src/pcg64/LICENSE.md

@@ -0,0 +1,22 @@
+# PCG64
+
+## The MIT License
+
+PCG Random Number Generation for C.
+
+Copyright 2014 Melissa O'Neill <oneill@pcg-random.org>
+
+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.

python_env_practice/.venv/Lib/site-packages/numpy-2.4.4.dist-info/licenses/numpy/random/src/philox/LICENSE.md

@@ -0,0 +1,31 @@
+# PHILOX
+
+Copyright 2010-2012, D. E. Shaw Research.
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are
+met:
+
+* Redistributions of source code must retain the above copyright
+  notice, this list of conditions, and the following disclaimer.
+
+* 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.
+
+* Neither the name of D. E. Shaw Research 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
+OWNER 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.

python_env_practice/.venv/Lib/site-packages/numpy-2.4.4.dist-info/licenses/numpy/random/src/sfc64/LICENSE.md

@@ -0,0 +1,27 @@
+# SFC64
+
+## The MIT License
+
+Adapted from a C++ implementation of Chris Doty-Humphrey's SFC PRNG.
+
+https://gist.github.com/imneme/f1f7821f07cf76504a97f6537c818083
+
+Copyright (c) 2018 Melissa E. O'Neill
+
+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.

python_env_practice/.venv/Lib/site-packages/numpy-2.4.4.dist-info/licenses/numpy/random/src/splitmix64/LICENSE.md

@@ -0,0 +1,9 @@
+# SPLITMIX64
+
+Written in 2015 by Sebastiano Vigna (vigna@acm.org)
+
+To the extent possible under law, the author has dedicated all copyright
+and related and neighboring rights to this software to the public domain
+worldwide. This software is distributed without any warranty.
+
+See <http://creativecommons.org/publicdomain/zero/1.0/>.

python_env_practice/.venv/Lib/site-packages/numpy/__config__.py

@@ -0,0 +1,170 @@
+# This file is generated by numpy's build process
+# It contains system_info results at the time of building this package.
+from enum import Enum
+from numpy._core._multiarray_umath import (
+    __cpu_features__,
+    __cpu_baseline__,
+    __cpu_dispatch__,
+)
+
+__all__ = ["show_config"]
+_built_with_meson = True
+
+
+class DisplayModes(Enum):
+    stdout = "stdout"
+    dicts = "dicts"
+
+
+def _cleanup(d):
+    """
+    Removes empty values in a `dict` recursively
+    This ensures we remove values that Meson could not provide to CONFIG
+    """
+    if isinstance(d, dict):
+        return {k: _cleanup(v) for k, v in d.items() if v and _cleanup(v)}
+    else:
+        return d
+
+
+CONFIG = _cleanup(
+    {
+        "Compilers": {
+            "c": {
+                "name": "msvc",
+                "linker": r"link",
+                "version": "19.44.35225",
+                "commands": r"cl",
+                "args": r"",
+                "linker args": r"",
+            },
+            "cython": {
+                "name": "cython",
+                "linker": r"cython",
+                "version": "3.2.4",
+                "commands": r"cython",
+                "args": r"",
+                "linker args": r"",
+            },
+            "c++": {
+                "name": "msvc",
+                "linker": r"link",
+                "version": "19.44.35225",
+                "commands": r"cl",
+                "args": r"",
+                "linker args": r"",
+            },
+        },
+        "Machine Information": {
+            "host": {
+                "cpu": "x86_64",
+                "family": "x86_64",
+                "endian": "little",
+                "system": "windows",
+            },
+            "build": {
+                "cpu": "x86_64",
+                "family": "x86_64",
+                "endian": "little",
+                "system": "windows",
+            },
+            "cross-compiled": bool("False".lower().replace("false", "")),
+        },
+        "Build Dependencies": {
+            "blas": {
+                "name": "scipy-openblas",
+                "found": bool("True".lower().replace("false", "")),
+                "version": "0.3.31.188.0",
+                "detection method": "pkgconfig",
+                "include directory": r"C:/Users/runneradmin/AppData/Local/Temp/cibw-run-kstykkss/cp313-win_amd64/build/venv/Lib/site-packages/scipy_openblas64/include",
+                "lib directory": r"C:/Users/runneradmin/AppData/Local/Temp/cibw-run-kstykkss/cp313-win_amd64/build/venv/Lib/site-packages/scipy_openblas64/lib",
+                "openblas configuration": r"OpenBLAS 0.3.31.188.0  USE64BITINT DYNAMIC_ARCH NO_AFFINITY Haswell MAX_THREADS=24",
+                "pc file directory": r"D:/a/numpy-release/numpy-release/.openblas",
+            },
+            "lapack": {
+                "name": "scipy-openblas",
+                "found": bool("True".lower().replace("false", "")),
+                "version": "0.3.31.188.0",
+                "detection method": "pkgconfig",
+                "include directory": r"C:/Users/runneradmin/AppData/Local/Temp/cibw-run-kstykkss/cp313-win_amd64/build/venv/Lib/site-packages/scipy_openblas64/include",
+                "lib directory": r"C:/Users/runneradmin/AppData/Local/Temp/cibw-run-kstykkss/cp313-win_amd64/build/venv/Lib/site-packages/scipy_openblas64/lib",
+                "openblas configuration": r"OpenBLAS 0.3.31.188.0  USE64BITINT DYNAMIC_ARCH NO_AFFINITY Haswell MAX_THREADS=24",
+                "pc file directory": r"D:/a/numpy-release/numpy-release/.openblas",
+            },
+        },
+        "Python Information": {
+            "path": r"C:\Users\runneradmin\AppData\Local\Temp\build-env-xd03mtse\Scripts\python.exe",
+            "version": "3.13",
+        },
+        "SIMD Extensions": {
+            "baseline": __cpu_baseline__,
+            "found": [
+                feature for feature in __cpu_dispatch__ if __cpu_features__[feature]
+            ],
+            "not found": [
+                feature for feature in __cpu_dispatch__ if not __cpu_features__[feature]
+            ],
+        },
+    }
+)
+
+
+def _check_pyyaml():
+    import yaml
+
+    return yaml
+
+
+def show(mode=DisplayModes.stdout.value):
+    """
+    Show libraries and system information on which NumPy was built
+    and is being used
+
+    Parameters
+    ----------
+    mode : {`'stdout'`, `'dicts'`}, optional.
+        Indicates how to display the config information.
+        `'stdout'` prints to console, `'dicts'` returns a dictionary
+        of the configuration.
+
+    Returns
+    -------
+    out : {`dict`, `None`}
+        If mode is `'dicts'`, a dict is returned, else None
+
+    See Also
+    --------
+    get_include : Returns the directory containing NumPy C
+                  header files.
+
+    Notes
+    -----
+    1. The `'stdout'` mode will give more readable
+       output if ``pyyaml`` is installed
+
+    """
+    if mode == DisplayModes.stdout.value:
+        try:  # Non-standard library, check import
+            yaml = _check_pyyaml()
+
+            print(yaml.dump(CONFIG))
+        except ModuleNotFoundError:
+            import warnings
+            import json
+
+            warnings.warn("Install `pyyaml` for better output", stacklevel=1)
+            print(json.dumps(CONFIG, indent=2))
+    elif mode == DisplayModes.dicts.value:
+        return CONFIG
+    else:
+        raise AttributeError(
+            f"Invalid `mode`, use one of: {', '.join([e.value for e in DisplayModes])}"
+        )
+
+
+def show_config(mode=DisplayModes.stdout.value):
+    return show(mode)
+
+
+show_config.__doc__ = show.__doc__
+show_config.__module__ = "numpy"

python_env_practice/.venv/Lib/site-packages/numpy/__config__.pyi

@@ -0,0 +1,108 @@
+from enum import Enum
+from types import ModuleType
+from typing import (
+    Final,
+    Literal as L,
+    NotRequired,
+    TypedDict,
+    overload,
+    type_check_only,
+)
+
+_CompilerConfigDictValue = TypedDict(
+    "_CompilerConfigDictValue",
+    {
+        "name": str,
+        "linker": str,
+        "version": str,
+        "commands": str,
+        "args": str,
+        "linker args": str,
+    },
+)
+_CompilerConfigDict = TypedDict(
+    "_CompilerConfigDict",
+    {
+        "c": _CompilerConfigDictValue,
+        "cython": _CompilerConfigDictValue,
+        "c++": _CompilerConfigDictValue,
+    },
+)
+_MachineInformationDict = TypedDict(
+    "_MachineInformationDict",
+    {
+        "host": _MachineInformationDictValue,
+        "build": _MachineInformationDictValue,
+        "cross-compiled": NotRequired[L[True]],
+    },
+)
+
+@type_check_only
+class _MachineInformationDictValue(TypedDict):
+    cpu: str
+    family: str
+    endian: L["little", "big"]
+    system: str
+
+_BuildDependenciesDictValue = TypedDict(
+    "_BuildDependenciesDictValue",
+    {
+        "name": str,
+        "found": NotRequired[L[True]],
+        "version": str,
+        "include directory": str,
+        "lib directory": str,
+        "openblas configuration": str,
+        "pc file directory": str,
+    },
+)
+
+class _BuildDependenciesDict(TypedDict):
+    blas: _BuildDependenciesDictValue
+    lapack: _BuildDependenciesDictValue
+
+class _PythonInformationDict(TypedDict):
+    path: str
+    version: str
+
+_SIMDExtensionsDict = TypedDict(
+    "_SIMDExtensionsDict",
+    {
+        "baseline": list[str],
+        "found": list[str],
+        "not found": list[str],
+    },
+)
+
+_ConfigDict = TypedDict(
+    "_ConfigDict",
+    {
+        "Compilers": _CompilerConfigDict,
+        "Machine Information": _MachineInformationDict,
+        "Build Dependencies": _BuildDependenciesDict,
+        "Python Information": _PythonInformationDict,
+        "SIMD Extensions": _SIMDExtensionsDict,
+    },
+)
+
+###
+
+__all__ = ["show_config"]
+
+CONFIG: Final[_ConfigDict] = ...
+
+class DisplayModes(Enum):
+    stdout = "stdout"
+    dicts = "dicts"
+
+def _check_pyyaml() -> ModuleType: ...
+
+@overload
+def show(mode: L["stdout"] = "stdout") -> None: ...
+@overload
+def show(mode: L["dicts"]) -> _ConfigDict: ...
+
+@overload
+def show_config(mode: L["stdout"] = "stdout") -> None: ...
+@overload
+def show_config(mode: L["dicts"]) -> _ConfigDict: ...

python_env_practice/.venv/Lib/site-packages/numpy/__init__.py

@@ -0,0 +1,955 @@
+"""
+NumPy
+=====
+
+Provides
+  1. An array object of arbitrary homogeneous items
+  2. Fast mathematical operations over arrays
+  3. Linear Algebra, Fourier Transforms, Random Number Generation
+
+How to use the documentation
+----------------------------
+Documentation is available in two forms: docstrings provided
+with the code, and a loose standing reference guide, available from
+`the NumPy homepage <https://numpy.org>`_.
+
+We recommend exploring the docstrings using
+`IPython <https://ipython.org>`_, an advanced Python shell with
+TAB-completion and introspection capabilities.  See below for further
+instructions.
+
+The docstring examples assume that `numpy` has been imported as ``np``::
+
+  >>> import numpy as np
+
+Code snippets are indicated by three greater-than signs::
+
+  >>> x = 42
+  >>> x = x + 1
+
+Use the built-in ``help`` function to view a function's docstring::
+
+  >>> help(np.sort)
+  ... # doctest: +SKIP
+
+For some objects, ``np.info(obj)`` may provide additional help.  This is
+particularly true if you see the line "Help on ufunc object:" at the top
+of the help() page.  Ufuncs are implemented in C, not Python, for speed.
+The native Python help() does not know how to view their help, but our
+np.info() function does.
+
+Available subpackages
+---------------------
+lib
+    Basic functions used by several sub-packages.
+random
+    Core Random Tools
+linalg
+    Core Linear Algebra Tools
+fft
+    Core FFT routines
+polynomial
+    Polynomial tools
+testing
+    NumPy testing tools
+distutils
+    Enhancements to distutils with support for
+    Fortran compilers support and more (for Python <= 3.11)
+
+Utilities
+---------
+test
+    Run numpy unittests
+show_config
+    Show numpy build configuration
+__version__
+    NumPy version string
+
+Viewing documentation using IPython
+-----------------------------------
+
+Start IPython and import `numpy` usually under the alias ``np``: `import
+numpy as np`.  Then, directly past or use the ``%cpaste`` magic to paste
+examples into the shell.  To see which functions are available in `numpy`,
+type ``np.<TAB>`` (where ``<TAB>`` refers to the TAB key), or use
+``np.*cos*?<ENTER>`` (where ``<ENTER>`` refers to the ENTER key) to narrow
+down the list.  To view the docstring for a function, use
+``np.cos?<ENTER>`` (to view the docstring) and ``np.cos??<ENTER>`` (to view
+the source code).
+
+Copies vs. in-place operation
+-----------------------------
+Most of the functions in `numpy` return a copy of the array argument
+(e.g., `np.sort`).  In-place versions of these functions are often
+available as array methods, i.e. ``x = np.array([1,2,3]); x.sort()``.
+Exceptions to this rule are documented.
+
+"""
+
+
+# start delvewheel patch
+def _delvewheel_patch_1_11_2():
+    import os
+    if os.path.isdir(libs_dir := os.path.abspath(os.path.join(os.path.dirname(__file__), os.pardir, 'numpy.libs'))):
+        os.add_dll_directory(libs_dir)
+
+
+_delvewheel_patch_1_11_2()
+del _delvewheel_patch_1_11_2
+# end delvewheel patch
+
+import os
+import sys
+import warnings
+
+# If a version with git hash was stored, use that instead
+from . import version
+from ._expired_attrs_2_0 import __expired_attributes__
+from ._globals import _CopyMode, _NoValue
+from .version import __version__
+
+# We first need to detect if we're being called as part of the numpy setup
+# procedure itself in a reliable manner.
+try:
+    __NUMPY_SETUP__  # noqa: B018
+except NameError:
+    __NUMPY_SETUP__ = False
+
+if __NUMPY_SETUP__:
+    sys.stderr.write('Running from numpy source directory.\n')
+else:
+    # Allow distributors to run custom init code before importing numpy._core
+    from . import _distributor_init
+
+    try:
+        from numpy.__config__ import show_config
+    except ImportError as e:
+        if isinstance(e, ModuleNotFoundError) and e.name == "numpy.__config__":
+            # The __config__ module itself was not found, so add this info:
+            msg = """Error importing numpy: you should not try to import numpy from
+            its source directory; please exit the numpy source tree, and relaunch
+            your python interpreter from there."""
+            raise ImportError(msg) from e
+        raise
+
+    from . import _core
+    from ._core import (
+        False_,
+        ScalarType,
+        True_,
+        abs,
+        absolute,
+        acos,
+        acosh,
+        add,
+        all,
+        allclose,
+        amax,
+        amin,
+        any,
+        arange,
+        arccos,
+        arccosh,
+        arcsin,
+        arcsinh,
+        arctan,
+        arctan2,
+        arctanh,
+        argmax,
+        argmin,
+        argpartition,
+        argsort,
+        argwhere,
+        around,
+        array,
+        array2string,
+        array_equal,
+        array_equiv,
+        array_repr,
+        array_str,
+        asanyarray,
+        asarray,
+        ascontiguousarray,
+        asfortranarray,
+        asin,
+        asinh,
+        astype,
+        atan,
+        atan2,
+        atanh,
+        atleast_1d,
+        atleast_2d,
+        atleast_3d,
+        base_repr,
+        binary_repr,
+        bitwise_and,
+        bitwise_count,
+        bitwise_invert,
+        bitwise_left_shift,
+        bitwise_not,
+        bitwise_or,
+        bitwise_right_shift,
+        bitwise_xor,
+        block,
+        bool,
+        bool_,
+        broadcast,
+        busday_count,
+        busday_offset,
+        busdaycalendar,
+        byte,
+        bytes_,
+        can_cast,
+        cbrt,
+        cdouble,
+        ceil,
+        character,
+        choose,
+        clip,
+        clongdouble,
+        complex64,
+        complex128,
+        complexfloating,
+        compress,
+        concat,
+        concatenate,
+        conj,
+        conjugate,
+        convolve,
+        copysign,
+        copyto,
+        correlate,
+        cos,
+        cosh,
+        count_nonzero,
+        cross,
+        csingle,
+        cumprod,
+        cumsum,
+        cumulative_prod,
+        cumulative_sum,
+        datetime64,
+        datetime_as_string,
+        datetime_data,
+        deg2rad,
+        degrees,
+        diagonal,
+        divide,
+        divmod,
+        dot,
+        double,
+        dtype,
+        e,
+        einsum,
+        einsum_path,
+        empty,
+        empty_like,
+        equal,
+        errstate,
+        euler_gamma,
+        exp,
+        exp2,
+        expm1,
+        fabs,
+        finfo,
+        flatiter,
+        flatnonzero,
+        flexible,
+        float16,
+        float32,
+        float64,
+        float_power,
+        floating,
+        floor,
+        floor_divide,
+        fmax,
+        fmin,
+        fmod,
+        format_float_positional,
+        format_float_scientific,
+        frexp,
+        from_dlpack,
+        frombuffer,
+        fromfile,
+        fromfunction,
+        fromiter,
+        frompyfunc,
+        fromstring,
+        full,
+        full_like,
+        gcd,
+        generic,
+        geomspace,
+        get_printoptions,
+        getbufsize,
+        geterr,
+        geterrcall,
+        greater,
+        greater_equal,
+        half,
+        heaviside,
+        hstack,
+        hypot,
+        identity,
+        iinfo,
+        indices,
+        inexact,
+        inf,
+        inner,
+        int8,
+        int16,
+        int32,
+        int64,
+        int_,
+        intc,
+        integer,
+        intp,
+        invert,
+        is_busday,
+        isclose,
+        isdtype,
+        isfinite,
+        isfortran,
+        isinf,
+        isnan,
+        isnat,
+        isscalar,
+        issubdtype,
+        lcm,
+        ldexp,
+        left_shift,
+        less,
+        less_equal,
+        lexsort,
+        linspace,
+        little_endian,
+        log,
+        log1p,
+        log2,
+        log10,
+        logaddexp,
+        logaddexp2,
+        logical_and,
+        logical_not,
+        logical_or,
+        logical_xor,
+        logspace,
+        long,
+        longdouble,
+        longlong,
+        matmul,
+        matrix_transpose,
+        matvec,
+        max,
+        maximum,
+        may_share_memory,
+        mean,
+        memmap,
+        min,
+        min_scalar_type,
+        minimum,
+        mod,
+        modf,
+        moveaxis,
+        multiply,
+        nan,
+        ndarray,
+        ndim,
+        nditer,
+        negative,
+        nested_iters,
+        newaxis,
+        nextafter,
+        nonzero,
+        not_equal,
+        number,
+        object_,
+        ones,
+        ones_like,
+        outer,
+        partition,
+        permute_dims,
+        pi,
+        positive,
+        pow,
+        power,
+        printoptions,
+        prod,
+        promote_types,
+        ptp,
+        put,
+        putmask,
+        rad2deg,
+        radians,
+        ravel,
+        recarray,
+        reciprocal,
+        record,
+        remainder,
+        repeat,
+        require,
+        reshape,
+        resize,
+        result_type,
+        right_shift,
+        rint,
+        roll,
+        rollaxis,
+        round,
+        sctypeDict,
+        searchsorted,
+        set_printoptions,
+        setbufsize,
+        seterr,
+        seterrcall,
+        shape,
+        shares_memory,
+        short,
+        sign,
+        signbit,
+        signedinteger,
+        sin,
+        single,
+        sinh,
+        size,
+        sort,
+        spacing,
+        sqrt,
+        square,
+        squeeze,
+        stack,
+        std,
+        str_,
+        subtract,
+        sum,
+        swapaxes,
+        take,
+        tan,
+        tanh,
+        tensordot,
+        timedelta64,
+        trace,
+        transpose,
+        true_divide,
+        trunc,
+        typecodes,
+        ubyte,
+        ufunc,
+        uint,
+        uint8,
+        uint16,
+        uint32,
+        uint64,
+        uintc,
+        uintp,
+        ulong,
+        ulonglong,
+        unsignedinteger,
+        unstack,
+        ushort,
+        var,
+        vdot,
+        vecdot,
+        vecmat,
+        void,
+        vstack,
+        where,
+        zeros,
+        zeros_like,
+    )
+
+    # NOTE: It's still under discussion whether these aliases
+    # should be removed.
+    for ta in ["float96", "float128", "complex192", "complex256"]:
+        try:
+            globals()[ta] = getattr(_core, ta)
+        except AttributeError:
+            pass
+    del ta
+
+    from . import lib, matrixlib as _mat
+    from .lib import scimath as emath
+    from .lib._arraypad_impl import pad
+    from .lib._arraysetops_impl import (
+        ediff1d,
+        intersect1d,
+        isin,
+        setdiff1d,
+        setxor1d,
+        union1d,
+        unique,
+        unique_all,
+        unique_counts,
+        unique_inverse,
+        unique_values,
+    )
+    from .lib._function_base_impl import (
+        angle,
+        append,
+        asarray_chkfinite,
+        average,
+        bartlett,
+        bincount,
+        blackman,
+        copy,
+        corrcoef,
+        cov,
+        delete,
+        diff,
+        digitize,
+        extract,
+        flip,
+        gradient,
+        hamming,
+        hanning,
+        i0,
+        insert,
+        interp,
+        iterable,
+        kaiser,
+        median,
+        meshgrid,
+        percentile,
+        piecewise,
+        place,
+        quantile,
+        rot90,
+        select,
+        sinc,
+        sort_complex,
+        trapezoid,
+        trim_zeros,
+        unwrap,
+        vectorize,
+    )
+    from .lib._histograms_impl import histogram, histogram_bin_edges, histogramdd
+    from .lib._index_tricks_impl import (
+        c_,
+        diag_indices,
+        diag_indices_from,
+        fill_diagonal,
+        index_exp,
+        ix_,
+        mgrid,
+        ndenumerate,
+        ndindex,
+        ogrid,
+        r_,
+        ravel_multi_index,
+        s_,
+        unravel_index,
+    )
+    from .lib._nanfunctions_impl import (
+        nanargmax,
+        nanargmin,
+        nancumprod,
+        nancumsum,
+        nanmax,
+        nanmean,
+        nanmedian,
+        nanmin,
+        nanpercentile,
+        nanprod,
+        nanquantile,
+        nanstd,
+        nansum,
+        nanvar,
+    )
+    from .lib._npyio_impl import (
+        fromregex,
+        genfromtxt,
+        load,
+        loadtxt,
+        packbits,
+        save,
+        savetxt,
+        savez,
+        savez_compressed,
+        unpackbits,
+    )
+    from .lib._polynomial_impl import (
+        poly,
+        poly1d,
+        polyadd,
+        polyder,
+        polydiv,
+        polyfit,
+        polyint,
+        polymul,
+        polysub,
+        polyval,
+        roots,
+    )
+    from .lib._shape_base_impl import (
+        apply_along_axis,
+        apply_over_axes,
+        array_split,
+        column_stack,
+        dsplit,
+        dstack,
+        expand_dims,
+        hsplit,
+        kron,
+        put_along_axis,
+        row_stack,
+        split,
+        take_along_axis,
+        tile,
+        vsplit,
+    )
+    from .lib._stride_tricks_impl import (
+        broadcast_arrays,
+        broadcast_shapes,
+        broadcast_to,
+    )
+    from .lib._twodim_base_impl import (
+        diag,
+        diagflat,
+        eye,
+        fliplr,
+        flipud,
+        histogram2d,
+        mask_indices,
+        tri,
+        tril,
+        tril_indices,
+        tril_indices_from,
+        triu,
+        triu_indices,
+        triu_indices_from,
+        vander,
+    )
+    from .lib._type_check_impl import (
+        common_type,
+        imag,
+        iscomplex,
+        iscomplexobj,
+        isreal,
+        isrealobj,
+        mintypecode,
+        nan_to_num,
+        real,
+        real_if_close,
+        typename,
+    )
+    from .lib._ufunclike_impl import fix, isneginf, isposinf
+    from .lib._utils_impl import get_include, info, show_runtime
+    from .matrixlib import asmatrix, bmat, matrix
+
+    # public submodules are imported lazily, therefore are accessible from
+    # __getattr__. Note that `distutils` (deprecated) and `array_api`
+    # (experimental label) are not added here, because `from numpy import *`
+    # must not raise any warnings - that's too disruptive.
+    __numpy_submodules__ = {
+        "linalg", "fft", "dtypes", "random", "polynomial", "ma",
+        "exceptions", "lib", "ctypeslib", "testing", "typing",
+        "f2py", "test", "rec", "char", "core", "strings",
+    }
+
+    # We build warning messages for former attributes
+    _msg = (
+        "module 'numpy' has no attribute '{n}'.\n"
+        "`np.{n}` was a deprecated alias for the builtin `{n}`. "
+        "To avoid this error in existing code, use `{n}` by itself. "
+        "Doing this will not modify any behavior and is safe. {extended_msg}\n"
+        "The aliases was originally deprecated in NumPy 1.20; for more "
+        "details and guidance see the original release note at:\n"
+        "    https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations")
+
+    _specific_msg = (
+        "If you specifically wanted the numpy scalar type, use `np.{}` here.")
+
+    _int_extended_msg = (
+        "When replacing `np.{}`, you may wish to use e.g. `np.int64` "
+        "or `np.int32` to specify the precision. If you wish to review "
+        "your current use, check the release note link for "
+        "additional information.")
+
+    _type_info = [
+        ("object", ""),  # The NumPy scalar only exists by name.
+        ("float", _specific_msg.format("float64")),
+        ("complex", _specific_msg.format("complex128")),
+        ("str", _specific_msg.format("str_")),
+        ("int", _int_extended_msg.format("int"))]
+
+    __former_attrs__ = {
+         n: _msg.format(n=n, extended_msg=extended_msg)
+         for n, extended_msg in _type_info
+     }
+
+    # Some of these could be defined right away, but most were aliases to
+    # the Python objects and only removed in NumPy 1.24.  Defining them should
+    # probably wait for NumPy 1.26 or 2.0.
+    # When defined, these should possibly not be added to `__all__` to avoid
+    # import with `from numpy import *`.
+    __future_scalars__ = {"str", "bytes", "object"}
+
+    __array_api_version__ = "2024.12"
+
+    from ._array_api_info import __array_namespace_info__
+
+    __all__ = list(
+        __numpy_submodules__ |
+        set(_core.__all__) |
+        set(_mat.__all__) |
+        set(lib._histograms_impl.__all__) |
+        set(lib._nanfunctions_impl.__all__) |
+        set(lib._function_base_impl.__all__) |
+        set(lib._twodim_base_impl.__all__) |
+        set(lib._shape_base_impl.__all__) |
+        set(lib._type_check_impl.__all__) |
+        set(lib._arraysetops_impl.__all__) |
+        set(lib._ufunclike_impl.__all__) |
+        set(lib._arraypad_impl.__all__) |
+        set(lib._utils_impl.__all__) |
+        set(lib._stride_tricks_impl.__all__) |
+        set(lib._polynomial_impl.__all__) |
+        set(lib._npyio_impl.__all__) |
+        set(lib._index_tricks_impl.__all__) |
+        {"emath", "show_config", "__version__", "__array_namespace_info__"}
+    )
+
+    # Filter out Cython harmless warnings
+    warnings.filterwarnings("ignore", message="numpy.dtype size changed")
+    warnings.filterwarnings("ignore", message="numpy.ufunc size changed")
+    warnings.filterwarnings("ignore", message="numpy.ndarray size changed")
+
+    def __getattr__(attr):
+        # Warn for expired attributes
+        import warnings
+
+        if attr == "linalg":
+            import numpy.linalg as linalg
+            return linalg
+        elif attr == "fft":
+            import numpy.fft as fft
+            return fft
+        elif attr == "dtypes":
+            import numpy.dtypes as dtypes
+            return dtypes
+        elif attr == "random":
+            import numpy.random as random
+            return random
+        elif attr == "polynomial":
+            import numpy.polynomial as polynomial
+            return polynomial
+        elif attr == "ma":
+            import numpy.ma as ma
+            return ma
+        elif attr == "ctypeslib":
+            import numpy.ctypeslib as ctypeslib
+            return ctypeslib
+        elif attr == "exceptions":
+            import numpy.exceptions as exceptions
+            return exceptions
+        elif attr == "testing":
+            import numpy.testing as testing
+            return testing
+        elif attr == "matlib":
+            import numpy.matlib as matlib
+            return matlib
+        elif attr == "f2py":
+            import numpy.f2py as f2py
+            return f2py
+        elif attr == "typing":
+            import numpy.typing as typing
+            return typing
+        elif attr == "rec":
+            import numpy.rec as rec
+            return rec
+        elif attr == "char":
+            import numpy.char as char
+            return char
+        elif attr == "array_api":
+            raise AttributeError("`numpy.array_api` is not available from "
+                                 "numpy 2.0 onwards", name=None)
+        elif attr == "core":
+            import numpy.core as core
+            return core
+        elif attr == "strings":
+            import numpy.strings as strings
+            return strings
+        elif attr == "distutils":
+            if 'distutils' in __numpy_submodules__:
+                import numpy.distutils as distutils
+                return distutils
+            else:
+                raise AttributeError("`numpy.distutils` is not available from "
+                                     "Python 3.12 onwards", name=None)
+
+        if attr in __future_scalars__:
+            # And future warnings for those that will change, but also give
+            # the AttributeError
+            warnings.warn(
+                f"In the future `np.{attr}` will be defined as the "
+                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
+
+        if attr in __former_attrs__:
+            raise AttributeError(__former_attrs__[attr], name=None)
+
+        if attr in __expired_attributes__:
+            raise AttributeError(
+                f"`np.{attr}` was removed in the NumPy 2.0 release. "
+                f"{__expired_attributes__[attr]}",
+                name=None
+            )
+
+        if attr == "chararray":
+            warnings.warn(
+                "`np.chararray` is deprecated and will be removed from "
+                "the main namespace in the future. Use an array with a string "
+                "or bytes dtype instead.", DeprecationWarning, stacklevel=2)
+            import numpy.char as char
+            return char.chararray
+
+        raise AttributeError(f"module {__name__!r} has no attribute {attr!r}")
+
+    def __dir__():
+        public_symbols = (
+            globals().keys() | __numpy_submodules__
+        )
+        public_symbols -= {
+            "matrixlib", "matlib", "tests", "conftest", "version",
+            "distutils", "array_api"
+        }
+        return list(public_symbols)
+
+    # Pytest testing
+    from numpy._pytesttester import PytestTester
+    test = PytestTester(__name__)
+    del PytestTester
+
+    def _sanity_check():
+        """
+        Quick sanity checks for common bugs caused by environment.
+        There are some cases e.g. with wrong BLAS ABI that cause wrong
+        results under specific runtime conditions that are not necessarily
+        achieved during test suite runs, and it is useful to catch those early.
+
+        See https://github.com/numpy/numpy/issues/8577 and other
+        similar bug reports.
+
+        """
+        try:
+            x = ones(2, dtype=float32)
+            if not abs(x.dot(x) - float32(2.0)) < 1e-5:
+                raise AssertionError
+        except AssertionError:
+            msg = ("The current Numpy installation ({!r}) fails to "
+                   "pass simple sanity checks. This can be caused for example "
+                   "by incorrect BLAS library being linked in, or by mixing "
+                   "package managers (pip, conda, apt, ...). Search closed "
+                   "numpy issues for similar problems.")
+            raise RuntimeError(msg.format(__file__)) from None
+
+    _sanity_check()
+    del _sanity_check
+
+    def _mac_os_check():
+        """
+        Quick Sanity check for Mac OS look for accelerate build bugs.
+        Testing numpy polyfit calls init_dgelsd(LAPACK)
+        """
+        try:
+            c = array([3., 2., 1.])
+            x = linspace(0, 2, 5)
+            y = polyval(c, x)
+            _ = polyfit(x, y, 2, cov=True)
+        except ValueError:
+            pass
+
+    if sys.platform == "darwin":
+        from . import exceptions
+        with warnings.catch_warnings(record=True) as w:
+            _mac_os_check()
+            # Throw runtime error, if the test failed
+            # Check for warning and report the error_message
+            if len(w) > 0:
+                for _wn in w:
+                    if _wn.category is exceptions.RankWarning:
+                        # Ignore other warnings, they may not be relevant (see gh-25433)
+                        error_message = (
+                            f"{_wn.category.__name__}: {_wn.message}"
+                        )
+                        msg = (
+                            "Polyfit sanity test emitted a warning, most likely due "
+                            "to using a buggy Accelerate backend."
+                            "\nIf you compiled yourself, more information is available at:"  # noqa: E501
+                            "\nhttps://numpy.org/devdocs/building/index.html"
+                            "\nOtherwise report this to the vendor "
+                            f"that provided NumPy.\n\n{error_message}\n")
+                        raise RuntimeError(msg)
+                del _wn
+            del w
+    del _mac_os_check
+
+    def blas_fpe_check():
+        # Check if BLAS adds spurious FPEs, mostly seen on M4 arms with Accelerate.
+        with errstate(all='raise'):
+            x = ones((20, 20))
+            try:
+                x @ x
+            except FloatingPointError:
+                res = _core._multiarray_umath._blas_supports_fpe(False)
+                if res:  # res was not modified (hardcoded to True for now)
+                    warnings.warn(
+                        "Spurious warnings given by blas but suppression not "
+                        "set up on this platform. Please open a NumPy issue.",
+                        UserWarning, stacklevel=2)
+
+    blas_fpe_check()
+    del blas_fpe_check
+
+    def hugepage_setup():
+        """
+        We usually use madvise hugepages support, but on some old kernels it
+        is slow and thus better avoided. Specifically kernel version 4.6
+        had a bug fix which probably fixed this:
+        https://github.com/torvalds/linux/commit/7cf91a98e607c2f935dbcc177d70011e95b8faff
+        """
+        use_hugepage = os.environ.get("NUMPY_MADVISE_HUGEPAGE", None)
+        if sys.platform == "linux" and use_hugepage is None:
+            # If there is an issue with parsing the kernel version,
+            # set use_hugepage to 0. Usage of LooseVersion will handle
+            # the kernel version parsing better, but avoided since it
+            # will increase the import time.
+            # See: #16679 for related discussion.
+            try:
+                use_hugepage = 1
+                kernel_version = os.uname().release.split(".")[:2]
+                kernel_version = tuple(int(v) for v in kernel_version)
+                if kernel_version < (4, 6):
+                    use_hugepage = 0
+            except ValueError:
+                use_hugepage = 0
+        elif use_hugepage is None:
+            # This is not Linux, so it should not matter, just enable anyway
+            use_hugepage = 1
+        else:
+            use_hugepage = int(use_hugepage)
+        return use_hugepage
+
+    # Note that this will currently only make a difference on Linux
+    _core.multiarray._set_madvise_hugepage(hugepage_setup())
+    del hugepage_setup
+
+    # Give a warning if NumPy is reloaded or imported on a sub-interpreter
+    # We do this from python, since the C-module may not be reloaded and
+    # it is tidier organized.
+    _core.multiarray._multiarray_umath._reload_guard()
+
+    # TODO: Remove the environment variable entirely now that it is "weak"
+    if (os.environ.get("NPY_PROMOTION_STATE", "weak") != "weak"):
+        warnings.warn(
+            "NPY_PROMOTION_STATE was a temporary feature for NumPy 2.0 "
+            "transition and is ignored after NumPy 2.2.",
+            UserWarning, stacklevel=2)
+
+    # Tell PyInstaller where to find hook-numpy.py
+    def _pyinstaller_hooks_dir():
+        from pathlib import Path
+        return [str(Path(__file__).with_name("_pyinstaller").resolve())]
+
+
+# Remove symbols imported for internal use
+del os, sys, warnings

python_env_practice/.venv/Lib/site-packages/numpy/_array_api_info.py

@@ -0,0 +1,346 @@
+"""
+Array API Inspection namespace
+
+This is the namespace for inspection functions as defined by the array API
+standard. See
+https://data-apis.org/array-api/latest/API_specification/inspection.html for
+more details.
+
+"""
+from numpy._core import (
+    bool,
+    complex64,
+    complex128,
+    dtype,
+    float32,
+    float64,
+    int8,
+    int16,
+    int32,
+    int64,
+    intp,
+    uint8,
+    uint16,
+    uint32,
+    uint64,
+)
+from numpy._utils import set_module
+
+
+@set_module('numpy')
+class __array_namespace_info__:
+    """
+    Get the array API inspection namespace for NumPy.
+
+    The array API inspection namespace defines the following functions:
+
+    - capabilities()
+    - default_device()
+    - default_dtypes()
+    - dtypes()
+    - devices()
+
+    See
+    https://data-apis.org/array-api/latest/API_specification/inspection.html
+    for more details.
+
+    Returns
+    -------
+    info : ModuleType
+        The array API inspection namespace for NumPy.
+
+    Examples
+    --------
+    >>> info = np.__array_namespace_info__()
+    >>> info.default_dtypes()
+    {'real floating': numpy.float64,
+     'complex floating': numpy.complex128,
+     'integral': numpy.int64,
+     'indexing': numpy.int64}
+
+    """
+
+    def capabilities(self):
+        """
+        Return a dictionary of array API library capabilities.
+
+        The resulting dictionary has the following keys:
+
+        - **"boolean indexing"**: boolean indicating whether an array library
+          supports boolean indexing. Always ``True`` for NumPy.
+
+        - **"data-dependent shapes"**: boolean indicating whether an array
+          library supports data-dependent output shapes. Always ``True`` for
+          NumPy.
+
+        See
+        https://data-apis.org/array-api/latest/API_specification/generated/array_api.info.capabilities.html
+        for more details.
+
+        See Also
+        --------
+        __array_namespace_info__.default_device,
+        __array_namespace_info__.default_dtypes,
+        __array_namespace_info__.dtypes,
+        __array_namespace_info__.devices
+
+        Returns
+        -------
+        capabilities : dict
+            A dictionary of array API library capabilities.
+
+        Examples
+        --------
+        >>> info = np.__array_namespace_info__()
+        >>> info.capabilities()
+        {'boolean indexing': True,
+         'data-dependent shapes': True,
+         'max dimensions': 64}
+
+        """
+        return {
+            "boolean indexing": True,
+            "data-dependent shapes": True,
+            "max dimensions": 64,
+        }
+
+    def default_device(self):
+        """
+        The default device used for new NumPy arrays.
+
+        For NumPy, this always returns ``'cpu'``.
+
+        See Also
+        --------
+        __array_namespace_info__.capabilities,
+        __array_namespace_info__.default_dtypes,
+        __array_namespace_info__.dtypes,
+        __array_namespace_info__.devices
+
+        Returns
+        -------
+        device : str
+            The default device used for new NumPy arrays.
+
+        Examples
+        --------
+        >>> info = np.__array_namespace_info__()
+        >>> info.default_device()
+        'cpu'
+
+        """
+        return "cpu"
+
+    def default_dtypes(self, *, device=None):
+        """
+        The default data types used for new NumPy arrays.
+
+        For NumPy, this always returns the following dictionary:
+
+        - **"real floating"**: ``numpy.float64``
+        - **"complex floating"**: ``numpy.complex128``
+        - **"integral"**: ``numpy.intp``
+        - **"indexing"**: ``numpy.intp``
+
+        Parameters
+        ----------
+        device : str, optional
+            The device to get the default data types for. For NumPy, only
+            ``'cpu'`` is allowed.
+
+        Returns
+        -------
+        dtypes : dict
+            A dictionary describing the default data types used for new NumPy
+            arrays.
+
+        See Also
+        --------
+        __array_namespace_info__.capabilities,
+        __array_namespace_info__.default_device,
+        __array_namespace_info__.dtypes,
+        __array_namespace_info__.devices
+
+        Examples
+        --------
+        >>> info = np.__array_namespace_info__()
+        >>> info.default_dtypes()
+        {'real floating': numpy.float64,
+         'complex floating': numpy.complex128,
+         'integral': numpy.int64,
+         'indexing': numpy.int64}
+
+        """
+        if device not in ["cpu", None]:
+            raise ValueError(
+                'Device not understood. Only "cpu" is allowed, but received:'
+                f' {device}'
+            )
+        return {
+            "real floating": dtype(float64),
+            "complex floating": dtype(complex128),
+            "integral": dtype(intp),
+            "indexing": dtype(intp),
+        }
+
+    def dtypes(self, *, device=None, kind=None):
+        """
+        The array API data types supported by NumPy.
+
+        Note that this function only returns data types that are defined by
+        the array API.
+
+        Parameters
+        ----------
+        device : str, optional
+            The device to get the data types for. For NumPy, only ``'cpu'`` is
+            allowed.
+        kind : str or tuple of str, optional
+            The kind of data types to return. If ``None``, all data types are
+            returned. If a string, only data types of that kind are returned.
+            If a tuple, a dictionary containing the union of the given kinds
+            is returned. The following kinds are supported:
+
+            - ``'bool'``: boolean data types (i.e., ``bool``).
+            - ``'signed integer'``: signed integer data types (i.e., ``int8``,
+              ``int16``, ``int32``, ``int64``).
+            - ``'unsigned integer'``: unsigned integer data types (i.e.,
+              ``uint8``, ``uint16``, ``uint32``, ``uint64``).
+            - ``'integral'``: integer data types. Shorthand for ``('signed
+              integer', 'unsigned integer')``.
+            - ``'real floating'``: real-valued floating-point data types
+              (i.e., ``float32``, ``float64``).
+            - ``'complex floating'``: complex floating-point data types (i.e.,
+              ``complex64``, ``complex128``).
+            - ``'numeric'``: numeric data types. Shorthand for ``('integral',
+              'real floating', 'complex floating')``.
+
+        Returns
+        -------
+        dtypes : dict
+            A dictionary mapping the names of data types to the corresponding
+            NumPy data types.
+
+        See Also
+        --------
+        __array_namespace_info__.capabilities,
+        __array_namespace_info__.default_device,
+        __array_namespace_info__.default_dtypes,
+        __array_namespace_info__.devices
+
+        Examples
+        --------
+        >>> info = np.__array_namespace_info__()
+        >>> info.dtypes(kind='signed integer')
+        {'int8': numpy.int8,
+         'int16': numpy.int16,
+         'int32': numpy.int32,
+         'int64': numpy.int64}
+
+        """
+        if device not in ["cpu", None]:
+            raise ValueError(
+                'Device not understood. Only "cpu" is allowed, but received:'
+                f' {device}'
+            )
+        if kind is None:
+            return {
+                "bool": dtype(bool),
+                "int8": dtype(int8),
+                "int16": dtype(int16),
+                "int32": dtype(int32),
+                "int64": dtype(int64),
+                "uint8": dtype(uint8),
+                "uint16": dtype(uint16),
+                "uint32": dtype(uint32),
+                "uint64": dtype(uint64),
+                "float32": dtype(float32),
+                "float64": dtype(float64),
+                "complex64": dtype(complex64),
+                "complex128": dtype(complex128),
+            }
+        if kind == "bool":
+            return {"bool": bool}
+        if kind == "signed integer":
+            return {
+                "int8": dtype(int8),
+                "int16": dtype(int16),
+                "int32": dtype(int32),
+                "int64": dtype(int64),
+            }
+        if kind == "unsigned integer":
+            return {
+                "uint8": dtype(uint8),
+                "uint16": dtype(uint16),
+                "uint32": dtype(uint32),
+                "uint64": dtype(uint64),
+            }
+        if kind == "integral":
+            return {
+                "int8": dtype(int8),
+                "int16": dtype(int16),
+                "int32": dtype(int32),
+                "int64": dtype(int64),
+                "uint8": dtype(uint8),
+                "uint16": dtype(uint16),
+                "uint32": dtype(uint32),
+                "uint64": dtype(uint64),
+            }
+        if kind == "real floating":
+            return {
+                "float32": dtype(float32),
+                "float64": dtype(float64),
+            }
+        if kind == "complex floating":
+            return {
+                "complex64": dtype(complex64),
+                "complex128": dtype(complex128),
+            }
+        if kind == "numeric":
+            return {
+                "int8": dtype(int8),
+                "int16": dtype(int16),
+                "int32": dtype(int32),
+                "int64": dtype(int64),
+                "uint8": dtype(uint8),
+                "uint16": dtype(uint16),
+                "uint32": dtype(uint32),
+                "uint64": dtype(uint64),
+                "float32": dtype(float32),
+                "float64": dtype(float64),
+                "complex64": dtype(complex64),
+                "complex128": dtype(complex128),
+            }
+        if isinstance(kind, tuple):
+            res = {}
+            for k in kind:
+                res.update(self.dtypes(kind=k))
+            return res
+        raise ValueError(f"unsupported kind: {kind!r}")
+
+    def devices(self):
+        """
+        The devices supported by NumPy.
+
+        For NumPy, this always returns ``['cpu']``.
+
+        Returns
+        -------
+        devices : list of str
+            The devices supported by NumPy.
+
+        See Also
+        --------
+        __array_namespace_info__.capabilities,
+        __array_namespace_info__.default_device,
+        __array_namespace_info__.default_dtypes,
+        __array_namespace_info__.dtypes
+
+        Examples
+        --------
+        >>> info = np.__array_namespace_info__()
+        >>> info.devices()
+        ['cpu']
+
+        """
+        return ["cpu"]

python_env_practice/.venv/Lib/site-packages/numpy/_array_api_info.pyi

@@ -0,0 +1,206 @@
+from typing import (
+    Literal,
+    Never,
+    TypeAlias,
+    TypedDict,
+    TypeVar,
+    final,
+    overload,
+    type_check_only,
+)
+
+import numpy as np
+
+_Device: TypeAlias = Literal["cpu"]
+_DeviceLike: TypeAlias = _Device | None
+
+_Capabilities = TypedDict(
+    "_Capabilities",
+    {
+        "boolean indexing": Literal[True],
+        "data-dependent shapes": Literal[True],
+    },
+)
+
+_DefaultDTypes = TypedDict(
+    "_DefaultDTypes",
+    {
+        "real floating": np.dtype[np.float64],
+        "complex floating": np.dtype[np.complex128],
+        "integral": np.dtype[np.intp],
+        "indexing": np.dtype[np.intp],
+    },
+)
+
+_KindBool: TypeAlias = Literal["bool"]
+_KindInt: TypeAlias = Literal["signed integer"]
+_KindUInt: TypeAlias = Literal["unsigned integer"]
+_KindInteger: TypeAlias = Literal["integral"]
+_KindFloat: TypeAlias = Literal["real floating"]
+_KindComplex: TypeAlias = Literal["complex floating"]
+_KindNumber: TypeAlias = Literal["numeric"]
+_Kind: TypeAlias = (
+    _KindBool
+    | _KindInt
+    | _KindUInt
+    | _KindInteger
+    | _KindFloat
+    | _KindComplex
+    | _KindNumber
+)
+
+_T1 = TypeVar("_T1")
+_T2 = TypeVar("_T2")
+_T3 = TypeVar("_T3")
+_Permute1: TypeAlias = _T1 | tuple[_T1]
+_Permute2: TypeAlias = tuple[_T1, _T2] | tuple[_T2, _T1]
+_Permute3: TypeAlias = (
+    tuple[_T1, _T2, _T3] | tuple[_T1, _T3, _T2]
+    | tuple[_T2, _T1, _T3] | tuple[_T2, _T3, _T1]
+    | tuple[_T3, _T1, _T2] | tuple[_T3, _T2, _T1]
+)
+
+@type_check_only
+class _DTypesBool(TypedDict):
+    bool: np.dtype[np.bool]
+
+@type_check_only
+class _DTypesInt(TypedDict):
+    int8: np.dtype[np.int8]
+    int16: np.dtype[np.int16]
+    int32: np.dtype[np.int32]
+    int64: np.dtype[np.int64]
+
+@type_check_only
+class _DTypesUInt(TypedDict):
+    uint8: np.dtype[np.uint8]
+    uint16: np.dtype[np.uint16]
+    uint32: np.dtype[np.uint32]
+    uint64: np.dtype[np.uint64]
+
+@type_check_only
+class _DTypesInteger(_DTypesInt, _DTypesUInt): ...
+
+@type_check_only
+class _DTypesFloat(TypedDict):
+    float32: np.dtype[np.float32]
+    float64: np.dtype[np.float64]
+
+@type_check_only
+class _DTypesComplex(TypedDict):
+    complex64: np.dtype[np.complex64]
+    complex128: np.dtype[np.complex128]
+
+@type_check_only
+class _DTypesNumber(_DTypesInteger, _DTypesFloat, _DTypesComplex): ...
+
+@type_check_only
+class _DTypes(_DTypesBool, _DTypesNumber): ...
+
+@type_check_only
+class _DTypesUnion(TypedDict, total=False):
+    bool: np.dtype[np.bool]
+    int8: np.dtype[np.int8]
+    int16: np.dtype[np.int16]
+    int32: np.dtype[np.int32]
+    int64: np.dtype[np.int64]
+    uint8: np.dtype[np.uint8]
+    uint16: np.dtype[np.uint16]
+    uint32: np.dtype[np.uint32]
+    uint64: np.dtype[np.uint64]
+    float32: np.dtype[np.float32]
+    float64: np.dtype[np.float64]
+    complex64: np.dtype[np.complex64]
+    complex128: np.dtype[np.complex128]
+
+_EmptyDict: TypeAlias = dict[Never, Never]
+
+@final
+class __array_namespace_info__:
+    __module__: Literal["numpy"] = "numpy"
+
+    def capabilities(self) -> _Capabilities: ...
+    def default_device(self) -> _Device: ...
+    def default_dtypes(
+        self,
+        *,
+        device: _DeviceLike = None,
+    ) -> _DefaultDTypes: ...
+    def devices(self) -> list[_Device]: ...
+
+    @overload
+    def dtypes(
+        self,
+        *,
+        device: _DeviceLike = None,
+        kind: None = None,
+    ) -> _DTypes: ...
+    @overload
+    def dtypes(
+        self,
+        *,
+        device: _DeviceLike = None,
+        kind: _Permute1[_KindBool],
+    ) -> _DTypesBool: ...
+    @overload
+    def dtypes(
+        self,
+        *,
+        device: _DeviceLike = None,
+        kind: _Permute1[_KindInt],
+    ) -> _DTypesInt: ...
+    @overload
+    def dtypes(
+        self,
+        *,
+        device: _DeviceLike = None,
+        kind: _Permute1[_KindUInt],
+    ) -> _DTypesUInt: ...
+    @overload
+    def dtypes(
+        self,
+        *,
+        device: _DeviceLike = None,
+        kind: _Permute1[_KindFloat],
+    ) -> _DTypesFloat: ...
+    @overload
+    def dtypes(
+        self,
+        *,
+        device: _DeviceLike = None,
+        kind: _Permute1[_KindComplex],
+    ) -> _DTypesComplex: ...
+    @overload
+    def dtypes(
+        self,
+        *,
+        device: _DeviceLike = None,
+        kind: (
+            _Permute1[_KindInteger]
+            | _Permute2[_KindInt, _KindUInt]
+        ),
+    ) -> _DTypesInteger: ...
+    @overload
+    def dtypes(
+        self,
+        *,
+        device: _DeviceLike = None,
+        kind: (
+            _Permute1[_KindNumber]
+            | _Permute3[_KindInteger, _KindFloat, _KindComplex]
+        ),
+    ) -> _DTypesNumber: ...
+    @overload
+    def dtypes(
+        self,
+        *,
+        device: _DeviceLike = None,
+        kind: tuple[()],
+    ) -> _EmptyDict: ...
+    @overload
+    def dtypes(
+        self,
+        *,
+        device: _DeviceLike = None,
+        kind: tuple[_Kind, ...],
+    ) -> _DTypesUnion: ...

python_env_practice/.venv/Lib/site-packages/numpy/_configtool.py

@@ -0,0 +1,39 @@
+import argparse
+import sys
+from pathlib import Path
+
+from .lib._utils_impl import get_include
+from .version import __version__
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--version",
+        action="version",
+        version=__version__,
+        help="Print the version and exit.",
+    )
+    parser.add_argument(
+        "--cflags",
+        action="store_true",
+        help="Compile flag needed when using the NumPy headers.",
+    )
+    parser.add_argument(
+        "--pkgconfigdir",
+        action="store_true",
+        help=("Print the pkgconfig directory in which `numpy.pc` is stored "
+              "(useful for setting $PKG_CONFIG_PATH)."),
+    )
+    args = parser.parse_args()
+    if not sys.argv[1:]:
+        parser.print_help()
+    if args.cflags:
+        print("-I" + get_include())
+    if args.pkgconfigdir:
+        _path = Path(get_include()) / '..' / 'lib' / 'pkgconfig'
+        print(_path.resolve())
+
+
+if __name__ == "__main__":
+    main()

python_env_practice/.venv/Lib/site-packages/numpy/_configtool.pyi

@@ -0,0 +1 @@
+def main() -> None: ...

python_env_practice/.venv/Lib/site-packages/numpy/_core/__init__.py

@@ -0,0 +1,203 @@
+"""
+Contains the core of NumPy: ndarray, ufuncs, dtypes, etc.
+
+Please note that this module is private.  All functions and objects
+are available in the main ``numpy`` namespace - use that instead.
+
+"""
+
+import os
+
+from numpy.version import version as __version__
+
+# disables OpenBLAS affinity setting of the main thread that limits
+# python threads or processes to one core
+env_added = []
+for envkey in ['OPENBLAS_MAIN_FREE']:
+    if envkey not in os.environ:
+        # Note: using `putenv` (and `unsetenv` further down) instead of updating
+        # `os.environ` on purpose to avoid a race condition, see gh-30627.
+        os.putenv(envkey, '1')
+        env_added.append(envkey)
+
+try:
+    from . import multiarray
+except ImportError as exc:
+    import sys
+
+    # Bypass for the module re-initialization opt-out
+    if exc.msg == "cannot load module more than once per process":
+        raise
+
+    # Basically always, the problem should be that the C module is wrong/missing...
+    if (
+        isinstance(exc, ModuleNotFoundError)
+        and exc.name == "numpy._core._multiarray_umath"
+    ):
+        import sys
+        candidates = []
+        for path in __path__:
+            candidates.extend(
+                f for f in os.listdir(path) if f.startswith("_multiarray_umath"))
+        if len(candidates) == 0:
+            bad_c_module_info = (
+                "We found no compiled module, did NumPy build successfully?\n")
+        else:
+            candidate_str = '\n  * '.join(candidates)
+            # cache_tag is documented to be possibly None, so just use name if it is
+            # this guesses at cache_tag being the same as the extension module scheme
+            tag = sys.implementation.cache_tag or sys.implementation.name
+            bad_c_module_info = (
+                f"The following compiled module files exist, but seem incompatible\n"
+                f"with with either python '{tag}' or the "
+                f"platform '{sys.platform}':\n\n  * {candidate_str}\n"
+            )
+    else:
+        bad_c_module_info = ""
+
+    major, minor, *_ = sys.version_info
+    msg = f"""
+
+IMPORTANT: PLEASE READ THIS FOR ADVICE ON HOW TO SOLVE THIS ISSUE!
+
+Importing the numpy C-extensions failed. This error can happen for
+many reasons, often due to issues with your setup or how NumPy was
+installed.
+{bad_c_module_info}
+We have compiled some common reasons and troubleshooting tips at:
+
+    https://numpy.org/devdocs/user/troubleshooting-importerror.html
+
+Please note and check the following:
+
+  * The Python version is: Python {major}.{minor} from "{sys.executable}"
+  * The NumPy version is: "{__version__}"
+
+and make sure that they are the versions you expect.
+
+Please carefully study the information and documentation linked above.
+This is unlikely to be a NumPy issue but will be caused by a bad install
+or environment on your machine.
+
+Original error was: {exc}
+"""
+
+    raise ImportError(msg) from exc
+finally:
+    for envkey in env_added:
+        os.unsetenv(envkey)
+del envkey
+del env_added
+del os
+
+from . import umath
+
+# Check that multiarray,umath are pure python modules wrapping
+# _multiarray_umath and not either of the old c-extension modules
+if not (hasattr(multiarray, '_multiarray_umath') and
+        hasattr(umath, '_multiarray_umath')):
+    import sys
+    path = sys.modules['numpy'].__path__
+    msg = ("Something is wrong with the numpy installation. "
+        "While importing we detected an older version of "
+        "numpy in {}. One method of fixing this is to repeatedly uninstall "
+        "numpy until none is found, then reinstall this version.")
+    raise ImportError(msg.format(path))
+
+from . import numerictypes as nt
+from .numerictypes import sctypeDict, sctypes
+
+multiarray.set_typeDict(nt.sctypeDict)
+from . import einsumfunc, fromnumeric, function_base, getlimits, numeric, shape_base
+from .einsumfunc import *
+from .fromnumeric import *
+from .function_base import *
+from .getlimits import *
+
+# Note: module name memmap is overwritten by a class with same name
+from .memmap import *
+from .numeric import *
+from .records import recarray, record
+from .shape_base import *
+
+del nt
+
+# do this after everything else, to minimize the chance of this misleadingly
+# appearing in an import-time traceback
+# add these for module-freeze analysis (like PyInstaller)
+from . import (
+    _add_newdocs,
+    _add_newdocs_scalars,
+    _dtype,
+    _dtype_ctypes,
+    _internal,
+    _methods,
+)
+from .numeric import absolute as abs
+
+acos = numeric.arccos
+acosh = numeric.arccosh
+asin = numeric.arcsin
+asinh = numeric.arcsinh
+atan = numeric.arctan
+atanh = numeric.arctanh
+atan2 = numeric.arctan2
+concat = numeric.concatenate
+bitwise_left_shift = numeric.left_shift
+bitwise_invert = numeric.invert
+bitwise_right_shift = numeric.right_shift
+permute_dims = numeric.transpose
+pow = numeric.power
+
+__all__ = [
+    "abs", "acos", "acosh", "asin", "asinh", "atan", "atanh", "atan2",
+    "bitwise_invert", "bitwise_left_shift", "bitwise_right_shift", "concat",
+    "pow", "permute_dims", "memmap", "sctypeDict", "record", "recarray"
+]
+__all__ += numeric.__all__
+__all__ += function_base.__all__
+__all__ += getlimits.__all__
+__all__ += shape_base.__all__
+__all__ += einsumfunc.__all__
+
+
+def _ufunc_reduce(func):
+    # Report the `__name__`. pickle will try to find the module. Note that
+    # pickle supports for this `__name__` to be a `__qualname__`. It may
+    # make sense to add a `__qualname__` to ufuncs, to allow this more
+    # explicitly (Numba has ufuncs as attributes).
+    # See also: https://github.com/dask/distributed/issues/3450
+    return func.__name__
+
+
+def _DType_reconstruct(scalar_type):
+    # This is a work-around to pickle type(np.dtype(np.float64)), etc.
+    # and it should eventually be replaced with a better solution, e.g. when
+    # DTypes become HeapTypes.
+    return type(dtype(scalar_type))
+
+
+def _DType_reduce(DType):
+    # As types/classes, most DTypes can simply be pickled by their name:
+    if not DType._legacy or DType.__module__ == "numpy.dtypes":
+        return DType.__name__
+
+    # However, user defined legacy dtypes (like rational) do not end up in
+    # `numpy.dtypes` as module and do not have a public class at all.
+    # For these, we pickle them by reconstructing them from the scalar type:
+    scalar_type = DType.type
+    return _DType_reconstruct, (scalar_type,)
+
+
+import copyreg
+
+copyreg.pickle(ufunc, _ufunc_reduce)
+copyreg.pickle(type(dtype), _DType_reduce, _DType_reconstruct)
+
+# Unclutter namespace (must keep _*_reconstruct for unpickling)
+del copyreg, _ufunc_reduce, _DType_reduce
+
+from numpy._pytesttester import PytestTester
+
+test = PytestTester(__name__)
+del PytestTester

python_env_practice/.venv/Lib/site-packages/numpy/_core/__init__.pyi

@@ -0,0 +1,666 @@
+# keep in sync with https://github.com/numpy/numtype/blob/main/src/numpy-stubs/_core/__init__.pyi
+
+from ._asarray import require
+from ._ufunc_config import (
+    errstate,
+    getbufsize,
+    geterr,
+    geterrcall,
+    setbufsize,
+    seterr,
+    seterrcall,
+)
+from .arrayprint import (
+    array2string,
+    array_repr,
+    array_str,
+    format_float_positional,
+    format_float_scientific,
+    get_printoptions,
+    printoptions,
+    set_printoptions,
+)
+from .einsumfunc import einsum, einsum_path
+from .fromnumeric import (
+    all,
+    amax,
+    amin,
+    any,
+    argmax,
+    argmin,
+    argpartition,
+    argsort,
+    around,
+    choose,
+    clip,
+    compress,
+    cumprod,
+    cumsum,
+    cumulative_prod,
+    cumulative_sum,
+    diagonal,
+    matrix_transpose,
+    max,
+    mean,
+    min,
+    ndim,
+    nonzero,
+    partition,
+    prod,
+    ptp,
+    put,
+    ravel,
+    repeat,
+    reshape,
+    resize,
+    round,
+    searchsorted,
+    shape,
+    size,
+    sort,
+    squeeze,
+    std,
+    sum,
+    swapaxes,
+    take,
+    trace,
+    transpose,
+    transpose as permute_dims,
+    var,
+)
+from .function_base import geomspace, linspace, logspace
+from .getlimits import finfo, iinfo
+from .memmap import memmap
+from .numeric import (
+    False_,
+    True_,
+    allclose,
+    arange,
+    argwhere,
+    array,
+    array_equal,
+    array_equiv,
+    asanyarray,
+    asarray,
+    ascontiguousarray,
+    asfortranarray,
+    astype,
+    base_repr,
+    binary_repr,
+    bitwise_not,
+    broadcast,
+    can_cast,
+    concatenate,
+    concatenate as concat,
+    convolve,
+    copyto,
+    correlate,
+    count_nonzero,
+    cross,
+    dot,
+    dtype,
+    empty,
+    empty_like,
+    flatiter,
+    flatnonzero,
+    from_dlpack,
+    frombuffer,
+    fromfile,
+    fromfunction,
+    fromiter,
+    fromstring,
+    full,
+    full_like,
+    identity,
+    indices,
+    inf,
+    inner,
+    isclose,
+    isfortran,
+    isscalar,
+    lexsort,
+    little_endian,
+    matmul,
+    may_share_memory,
+    min_scalar_type,
+    moveaxis,
+    nan,
+    ndarray,
+    nditer,
+    nested_iters,
+    newaxis,
+    ones,
+    ones_like,
+    outer,
+    promote_types,
+    putmask,
+    result_type,
+    roll,
+    rollaxis,
+    shares_memory,
+    tensordot,
+    ufunc,
+    vdot,
+    vecdot,
+    where,
+    zeros,
+    zeros_like,
+)
+from .numerictypes import (
+    ScalarType,
+    bool,
+    bool_,
+    busday_count,
+    busday_offset,
+    busdaycalendar,
+    byte,
+    bytes_,
+    cdouble,
+    character,
+    clongdouble,
+    complex64,
+    complex128,
+    complex192,
+    complex256,
+    complexfloating,
+    csingle,
+    datetime64,
+    datetime_as_string,
+    datetime_data,
+    double,
+    flexible,
+    float16,
+    float32,
+    float64,
+    float96,
+    float128,
+    floating,
+    generic,
+    half,
+    inexact,
+    int8,
+    int16,
+    int32,
+    int64,
+    int_,
+    intc,
+    integer,
+    intp,
+    is_busday,
+    isdtype,
+    issubdtype,
+    long,
+    longdouble,
+    longlong,
+    number,
+    object_,
+    sctypeDict,
+    short,
+    signedinteger,
+    single,
+    str_,
+    timedelta64,
+    typecodes,
+    ubyte,
+    uint,
+    uint8,
+    uint16,
+    uint32,
+    uint64,
+    uintc,
+    uintp,
+    ulong,
+    ulonglong,
+    unsignedinteger,
+    ushort,
+    void,
+)
+from .records import recarray, record
+from .shape_base import (
+    atleast_1d,
+    atleast_2d,
+    atleast_3d,
+    block,
+    hstack,
+    stack,
+    unstack,
+    vstack,
+)
+from .umath import (
+    absolute,
+    absolute as abs,
+    add,
+    arccos,
+    arccos as acos,
+    arccosh,
+    arccosh as acosh,
+    arcsin,
+    arcsin as asin,
+    arcsinh,
+    arcsinh as asinh,
+    arctan,
+    arctan as atan,
+    arctan2,
+    arctan2 as atan2,
+    arctanh,
+    arctanh as atanh,
+    bitwise_and,
+    bitwise_count,
+    bitwise_or,
+    bitwise_xor,
+    cbrt,
+    ceil,
+    conj,
+    conjugate,
+    copysign,
+    cos,
+    cosh,
+    deg2rad,
+    degrees,
+    divide,
+    divmod,
+    e,
+    equal,
+    euler_gamma,
+    exp,
+    exp2,
+    expm1,
+    fabs,
+    float_power,
+    floor,
+    floor_divide,
+    fmax,
+    fmin,
+    fmod,
+    frexp,
+    frompyfunc,
+    gcd,
+    greater,
+    greater_equal,
+    heaviside,
+    hypot,
+    invert,
+    invert as bitwise_invert,
+    isfinite,
+    isinf,
+    isnan,
+    isnat,
+    lcm,
+    ldexp,
+    left_shift,
+    left_shift as bitwise_left_shift,
+    less,
+    less_equal,
+    log,
+    log1p,
+    log2,
+    log10,
+    logaddexp,
+    logaddexp2,
+    logical_and,
+    logical_not,
+    logical_or,
+    logical_xor,
+    matvec,
+    maximum,
+    minimum,
+    mod,
+    modf,
+    multiply,
+    negative,
+    nextafter,
+    not_equal,
+    pi,
+    positive,
+    power,
+    power as pow,
+    rad2deg,
+    radians,
+    reciprocal,
+    remainder,
+    right_shift,
+    right_shift as bitwise_right_shift,
+    rint,
+    sign,
+    signbit,
+    sin,
+    sinh,
+    spacing,
+    sqrt,
+    square,
+    subtract,
+    tan,
+    tanh,
+    true_divide,
+    trunc,
+    vecmat,
+)
+
+__all__ = [
+    "False_",
+    "ScalarType",
+    "True_",
+    "abs",
+    "absolute",
+    "acos",
+    "acosh",
+    "add",
+    "all",
+    "allclose",
+    "amax",
+    "amin",
+    "any",
+    "arange",
+    "arccos",
+    "arccosh",
+    "arcsin",
+    "arcsinh",
+    "arctan",
+    "arctan2",
+    "arctanh",
+    "argmax",
+    "argmin",
+    "argpartition",
+    "argsort",
+    "argwhere",
+    "around",
+    "array",
+    "array2string",
+    "array_equal",
+    "array_equiv",
+    "array_repr",
+    "array_str",
+    "asanyarray",
+    "asarray",
+    "ascontiguousarray",
+    "asfortranarray",
+    "asin",
+    "asinh",
+    "astype",
+    "atan",
+    "atan2",
+    "atanh",
+    "atleast_1d",
+    "atleast_2d",
+    "atleast_3d",
+    "base_repr",
+    "binary_repr",
+    "bitwise_and",
+    "bitwise_count",
+    "bitwise_invert",
+    "bitwise_left_shift",
+    "bitwise_not",
+    "bitwise_or",
+    "bitwise_right_shift",
+    "bitwise_xor",
+    "block",
+    "bool",
+    "bool_",
+    "broadcast",
+    "busday_count",
+    "busday_offset",
+    "busdaycalendar",
+    "byte",
+    "bytes_",
+    "can_cast",
+    "cbrt",
+    "cdouble",
+    "ceil",
+    "character",
+    "choose",
+    "clip",
+    "clongdouble",
+    "complex64",
+    "complex128",
+    "complex192",
+    "complex256",
+    "complexfloating",
+    "compress",
+    "concat",
+    "concatenate",
+    "conj",
+    "conjugate",
+    "convolve",
+    "copysign",
+    "copyto",
+    "correlate",
+    "cos",
+    "cosh",
+    "count_nonzero",
+    "cross",
+    "csingle",
+    "cumprod",
+    "cumsum",
+    "cumulative_prod",
+    "cumulative_sum",
+    "datetime64",
+    "datetime_as_string",
+    "datetime_data",
+    "deg2rad",
+    "degrees",
+    "diagonal",
+    "divide",
+    "divmod",
+    "dot",
+    "double",
+    "dtype",
+    "e",
+    "einsum",
+    "einsum_path",
+    "empty",
+    "empty_like",
+    "equal",
+    "errstate",
+    "euler_gamma",
+    "exp",
+    "exp2",
+    "expm1",
+    "fabs",
+    "finfo",
+    "flatiter",
+    "flatnonzero",
+    "flexible",
+    "float16",
+    "float32",
+    "float64",
+    "float96",
+    "float128",
+    "float_power",
+    "floating",
+    "floor",
+    "floor_divide",
+    "fmax",
+    "fmin",
+    "fmod",
+    "format_float_positional",
+    "format_float_scientific",
+    "frexp",
+    "from_dlpack",
+    "frombuffer",
+    "fromfile",
+    "fromfunction",
+    "fromiter",
+    "frompyfunc",
+    "fromstring",
+    "full",
+    "full_like",
+    "gcd",
+    "generic",
+    "geomspace",
+    "get_printoptions",
+    "getbufsize",
+    "geterr",
+    "geterrcall",
+    "greater",
+    "greater_equal",
+    "half",
+    "heaviside",
+    "hstack",
+    "hypot",
+    "identity",
+    "iinfo",
+    "indices",
+    "inexact",
+    "inf",
+    "inner",
+    "int8",
+    "int16",
+    "int32",
+    "int64",
+    "int_",
+    "intc",
+    "integer",
+    "intp",
+    "invert",
+    "is_busday",
+    "isclose",
+    "isdtype",
+    "isfinite",
+    "isfortran",
+    "isinf",
+    "isnan",
+    "isnat",
+    "isscalar",
+    "issubdtype",
+    "lcm",
+    "ldexp",
+    "left_shift",
+    "less",
+    "less_equal",
+    "lexsort",
+    "linspace",
+    "little_endian",
+    "log",
+    "log1p",
+    "log2",
+    "log10",
+    "logaddexp",
+    "logaddexp2",
+    "logical_and",
+    "logical_not",
+    "logical_or",
+    "logical_xor",
+    "logspace",
+    "long",
+    "longdouble",
+    "longlong",
+    "matmul",
+    "matrix_transpose",
+    "matvec",
+    "max",
+    "maximum",
+    "may_share_memory",
+    "mean",
+    "memmap",
+    "min",
+    "min_scalar_type",
+    "minimum",
+    "mod",
+    "modf",
+    "moveaxis",
+    "multiply",
+    "nan",
+    "ndarray",
+    "ndim",
+    "nditer",
+    "negative",
+    "nested_iters",
+    "newaxis",
+    "nextafter",
+    "nonzero",
+    "not_equal",
+    "number",
+    "object_",
+    "ones",
+    "ones_like",
+    "outer",
+    "partition",
+    "permute_dims",
+    "pi",
+    "positive",
+    "pow",
+    "power",
+    "printoptions",
+    "prod",
+    "promote_types",
+    "ptp",
+    "put",
+    "putmask",
+    "rad2deg",
+    "radians",
+    "ravel",
+    "recarray",
+    "reciprocal",
+    "record",
+    "remainder",
+    "repeat",
+    "require",
+    "reshape",
+    "resize",
+    "result_type",
+    "right_shift",
+    "rint",
+    "roll",
+    "rollaxis",
+    "round",
+    "sctypeDict",
+    "searchsorted",
+    "set_printoptions",
+    "setbufsize",
+    "seterr",
+    "seterrcall",
+    "shape",
+    "shares_memory",
+    "short",
+    "sign",
+    "signbit",
+    "signedinteger",
+    "sin",
+    "single",
+    "sinh",
+    "size",
+    "sort",
+    "spacing",
+    "sqrt",
+    "square",
+    "squeeze",
+    "stack",
+    "std",
+    "str_",
+    "subtract",
+    "sum",
+    "swapaxes",
+    "take",
+    "tan",
+    "tanh",
+    "tensordot",
+    "timedelta64",
+    "trace",
+    "transpose",
+    "true_divide",
+    "trunc",
+    "typecodes",
+    "ubyte",
+    "ufunc",
+    "uint",
+    "uint8",
+    "uint16",
+    "uint32",
+    "uint64",
+    "uintc",
+    "uintp",
+    "ulong",
+    "ulonglong",
+    "unsignedinteger",
+    "unstack",
+    "ushort",
+    "var",
+    "vdot",
+    "vecdot",
+    "vecmat",
+    "void",
+    "vstack",
+    "where",
+    "zeros",
+    "zeros_like",
+]

python_env_practice/.venv/Lib/site-packages/numpy/_core/_add_newdocs.pyi

@@ -0,0 +1,2 @@
+from .function_base import add_newdoc as add_newdoc
+from .overrides import get_array_function_like_doc as get_array_function_like_doc

python_env_practice/.venv/Lib/site-packages/numpy/_core/_add_newdocs_scalars.py

@@ -0,0 +1,381 @@
+"""
+This file is separate from ``_add_newdocs.py`` so that it can be mocked out by
+our sphinx ``conf.py`` during doc builds, where we want to avoid showing
+platform-dependent information.
+"""
+import os
+import sys
+
+from numpy._core import dtype, numerictypes as _numerictypes
+from numpy._core.function_base import add_newdoc
+
+##############################################################################
+#
+# Documentation for concrete scalar classes
+#
+##############################################################################
+
+def numeric_type_aliases(aliases):
+    def type_aliases_gen():
+        for alias, doc in aliases:
+            try:
+                alias_type = getattr(_numerictypes, alias)
+            except AttributeError:
+                # The set of aliases that actually exist varies between platforms
+                pass
+            else:
+                yield (alias_type, alias, doc)
+    return list(type_aliases_gen())
+
+
+possible_aliases = numeric_type_aliases([
+    ('int8', '8-bit signed integer (``-128`` to ``127``)'),
+    ('int16', '16-bit signed integer (``-32_768`` to ``32_767``)'),
+    ('int32', '32-bit signed integer (``-2_147_483_648`` to ``2_147_483_647``)'),
+    ('int64', '64-bit signed integer (``-9_223_372_036_854_775_808`` to ``9_223_372_036_854_775_807``)'),
+    ('intp', 'Signed integer large enough to fit pointer, compatible with C ``intptr_t``'),
+    ('uint8', '8-bit unsigned integer (``0`` to ``255``)'),
+    ('uint16', '16-bit unsigned integer (``0`` to ``65_535``)'),
+    ('uint32', '32-bit unsigned integer (``0`` to ``4_294_967_295``)'),
+    ('uint64', '64-bit unsigned integer (``0`` to ``18_446_744_073_709_551_615``)'),
+    ('uintp', 'Unsigned integer large enough to fit pointer, compatible with C ``uintptr_t``'),
+    ('float16', '16-bit-precision floating-point number type: sign bit, 5 bits exponent, 10 bits mantissa'),
+    ('float32', '32-bit-precision floating-point number type: sign bit, 8 bits exponent, 23 bits mantissa'),
+    ('float64', '64-bit precision floating-point number type: sign bit, 11 bits exponent, 52 bits mantissa'),
+    ('float96', '96-bit extended-precision floating-point number type'),
+    ('float128', '128-bit extended-precision floating-point number type'),
+    ('complex64', 'Complex number type composed of 2 32-bit-precision floating-point numbers'),
+    ('complex128', 'Complex number type composed of 2 64-bit-precision floating-point numbers'),
+    ('complex192', 'Complex number type composed of 2 96-bit extended-precision floating-point numbers'),
+    ('complex256', 'Complex number type composed of 2 128-bit extended-precision floating-point numbers'),
+])
+
+
+def _get_platform_and_machine():
+    try:
+        system, _, _, _, machine = os.uname()
+    except AttributeError:
+        system = sys.platform
+        if system == 'win32':
+            machine = os.environ.get('PROCESSOR_ARCHITEW6432', '') \
+                    or os.environ.get('PROCESSOR_ARCHITECTURE', '')
+        else:
+            machine = 'unknown'
+    return system, machine
+
+
+_system, _machine = _get_platform_and_machine()
+_doc_alias_string = f":Alias on this platform ({_system} {_machine}):"
+
+# docstring prefix that cpython uses to populate `__text_signature__`
+_ARGUMENT_CLINIC_TEMPLATE = """{name}{signature}
+--
+
+{docstring}"""
+
+def add_newdoc_for_scalar_type(name: str, text_signature: str, doc: str) -> None:
+    # note: `:field: value` is rST syntax which renders as field lists.
+    cls = getattr(_numerictypes, name)
+    module = cls.__module__
+
+    lines_extra = [
+        "",  # blank line after main doc
+        f":Character code: ``{dtype(cls).char!r}``",
+    ]
+
+    if name != cls.__name__:
+        lines_extra.append(f":Canonical name: `{module}.{name}`")
+
+    lines_extra.extend(
+        f"{_doc_alias_string} `{module}.{alias}`: {doc}."
+        for alias_type, alias, doc in possible_aliases
+        if alias_type is cls
+    )
+
+    docstring = _ARGUMENT_CLINIC_TEMPLATE.format(
+        name=cls.__name__,  # must match the class name
+        signature=text_signature,
+        docstring="\n".join([doc.strip(), *lines_extra]),
+    )
+    add_newdoc('numpy._core.numerictypes', name, docstring)
+
+
+for bool_name in ('bool', 'bool_'):
+    add_newdoc_for_scalar_type(bool_name, '(value=False, /)', """
+Boolean type (True or False), stored as a byte.
+
+.. warning::
+
+    The :class:`bool` type is not a subclass of the :class:`int_` type
+    (the :class:`bool` is not even a number type). This is different
+    than Python's default implementation of :class:`bool` as a
+    sub-class of :class:`int`.
+""")
+
+add_newdoc_for_scalar_type('byte', '(value=0, /)', """
+Signed integer type, compatible with C ``char``.
+""")
+
+add_newdoc_for_scalar_type('short', '(value=0, /)', """
+Signed integer type, compatible with C ``short``.
+""")
+
+add_newdoc_for_scalar_type('intc', '(value=0, /)', """
+Signed integer type, compatible with C ``int``.
+""")
+
+add_newdoc_for_scalar_type('long', '(value=0, /)', """
+Signed integer type, compatible with C ``long``.
+""")
+
+# TODO: These docs probably need an if to highlight the default rather than
+#       the C-types (and be correct).
+add_newdoc_for_scalar_type('int_', '(value=0, /)', """
+Default signed integer type, 64bit on 64bit systems and 32bit on 32bit systems.
+""")
+
+add_newdoc_for_scalar_type('longlong', '(value=0, /)', """
+Signed integer type, compatible with C ``long long``.
+""")
+
+add_newdoc_for_scalar_type('ubyte', '(value=0, /)', """
+Unsigned integer type, compatible with C ``unsigned char``.
+""")
+
+add_newdoc_for_scalar_type('ushort', '(value=0, /)', """
+Unsigned integer type, compatible with C ``unsigned short``.
+""")
+
+add_newdoc_for_scalar_type('uintc', '(value=0, /)', """
+Unsigned integer type, compatible with C ``unsigned int``.
+""")
+
+add_newdoc_for_scalar_type('uint', '(value=0, /)', """
+Unsigned signed integer type, 64bit on 64bit systems and 32bit on 32bit systems.
+""")
+
+add_newdoc_for_scalar_type('ulong', '(value=0, /)', """
+Unsigned integer type, compatible with C ``unsigned long``.
+""")
+
+add_newdoc_for_scalar_type('ulonglong', '(value=0, /)', """
+Unsigned integer type, compatible with C ``unsigned long long``.
+""")
+
+add_newdoc_for_scalar_type('half', '(value=0, /)', """
+Half-precision floating-point number type.
+""")
+
+add_newdoc_for_scalar_type('single', '(value=0, /)', """
+Single-precision floating-point number type, compatible with C ``float``.
+""")
+
+add_newdoc_for_scalar_type('double', '(value=0, /)', """
+Double-precision floating-point number type, compatible with Python :class:`float` and C ``double``.
+""")
+
+add_newdoc_for_scalar_type('longdouble', '(value=0, /)', """
+Extended-precision floating-point number type, compatible with C ``long double``
+but not necessarily with IEEE 754 quadruple-precision.
+""")
+
+add_newdoc_for_scalar_type('csingle', '(real=0, imag=0, /)', """
+Complex number type composed of two single-precision floating-point numbers.
+""")
+
+add_newdoc_for_scalar_type('cdouble', '(real=0, imag=0, /)', """
+Complex number type composed of two double-precision floating-point numbers,
+compatible with Python :class:`complex`.
+""")
+
+add_newdoc_for_scalar_type('clongdouble', '(real=0, imag=0, /)', """
+Complex number type composed of two extended-precision floating-point numbers.
+""")
+
+add_newdoc_for_scalar_type('object_', '(value=None, /)', """
+Any Python object.
+""")
+
+add_newdoc_for_scalar_type('str_', '(value="", /, *args, **kwargs)', r"""
+A unicode string.
+
+This type strips trailing null codepoints.
+
+>>> s = np.str_("abc\x00")
+>>> s
+'abc'
+
+Unlike the builtin :class:`str`, this supports the
+:ref:`python:bufferobjects`, exposing its contents as UCS4:
+
+>>> m = memoryview(np.str_("abc"))
+>>> m.format
+'3w'
+>>> m.tobytes()
+b'a\x00\x00\x00b\x00\x00\x00c\x00\x00\x00'
+""")
+
+add_newdoc_for_scalar_type('bytes_', '(value="", /, *args, **kwargs)', r"""
+A byte string.
+
+When used in arrays, this type strips trailing null bytes.
+""")
+
+add_newdoc_for_scalar_type('void', '(length_or_data, /, dtype=None)', r"""
+np.void(length_or_data, /, dtype=None)
+
+Create a new structured or unstructured void scalar.
+
+Parameters
+----------
+length_or_data : int, array-like, bytes-like, object
+    One of multiple meanings (see notes).  The length or
+    bytes data of an unstructured void.  Or alternatively,
+    the data to be stored in the new scalar when `dtype`
+    is provided.
+    This can be an array-like, in which case an array may
+    be returned.
+dtype : dtype, optional
+    If provided the dtype of the new scalar.  This dtype must
+    be "void" dtype (i.e. a structured or unstructured void,
+    see also :ref:`defining-structured-types`).
+
+    .. versionadded:: 1.24
+
+Notes
+-----
+For historical reasons and because void scalars can represent both
+arbitrary byte data and structured dtypes, the void constructor
+has three calling conventions:
+
+1. ``np.void(5)`` creates a ``dtype="V5"`` scalar filled with five
+    ``\0`` bytes.  The 5 can be a Python or NumPy integer.
+2. ``np.void(b"bytes-like")`` creates a void scalar from the byte string.
+    The dtype itemsize will match the byte string length, here ``"V10"``.
+3. When a ``dtype=`` is passed the call is roughly the same as an
+    array creation.  However, a void scalar rather than array is returned.
+
+Please see the examples which show all three different conventions.
+
+Examples
+--------
+>>> np.void(5)
+np.void(b'\x00\x00\x00\x00\x00')
+>>> np.void(b'abcd')
+np.void(b'\x61\x62\x63\x64')
+>>> np.void((3.2, b'eggs'), dtype="d,S5")
+np.void((3.2, b'eggs'), dtype=[('f0', '<f8'), ('f1', 'S5')])
+>>> np.void(3, dtype=[('x', np.int8), ('y', np.int8)])
+np.void((3, 3), dtype=[('x', 'i1'), ('y', 'i1')])
+""")
+
+add_newdoc_for_scalar_type('datetime64', '(value=None, /, *args)', """
+If created from a 64-bit integer, it represents an offset from ``1970-01-01T00:00:00``.
+If created from string, the string can be in ISO 8601 date or datetime format.
+
+When parsing a string to create a datetime object, if the string contains
+a trailing timezone (A 'Z' or a timezone offset), the timezone will be
+dropped and a User Warning is given.
+
+Datetime64 objects should be considered to be UTC and therefore have an
+offset of +0000.
+
+>>> np.datetime64(10, 'Y')
+np.datetime64('1980')
+>>> np.datetime64('1980', 'Y')
+np.datetime64('1980')
+>>> np.datetime64(10, 'D')
+np.datetime64('1970-01-11')
+
+See :ref:`arrays.datetime` for more information.
+""")
+
+add_newdoc_for_scalar_type('timedelta64', '(value=0, /, *args)', """
+A timedelta stored as a 64-bit integer.
+
+See :ref:`arrays.datetime` for more information.
+""")
+
+add_newdoc('numpy._core.numerictypes', "integer", ('is_integer',
+    """
+    is_integer($self, /)
+    --
+
+    integer.is_integer() -> bool
+
+    Return ``True`` if the number is finite with integral value.
+
+    .. versionadded:: 1.22
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> np.int64(-2).is_integer()
+    True
+    >>> np.uint32(5).is_integer()
+    True
+    """))
+
+# TODO: work out how to put this on the base class, np.floating
+for float_name in ('half', 'single', 'double', 'longdouble'):
+    add_newdoc('numpy._core.numerictypes', float_name, ('as_integer_ratio',
+        f"""
+        as_integer_ratio($self, /)
+        --
+
+        {float_name}.as_integer_ratio() -> (int, int)
+
+        Return a pair of integers, whose ratio is exactly equal to the original
+        floating point number, and with a positive denominator.
+        Raise `OverflowError` on infinities and a `ValueError` on NaNs.
+
+        >>> np.{float_name}(10.0).as_integer_ratio()
+        (10, 1)
+        >>> np.{float_name}(0.0).as_integer_ratio()
+        (0, 1)
+        >>> np.{float_name}(-.25).as_integer_ratio()
+        (-1, 4)
+        """))
+
+    add_newdoc('numpy._core.numerictypes', float_name, ('is_integer',
+        f"""
+        is_integer($self, /)
+        --
+
+        {float_name}.is_integer() -> bool
+
+        Return ``True`` if the floating point number is finite with integral
+        value, and ``False`` otherwise.
+
+        .. versionadded:: 1.22
+
+        Examples
+        --------
+        >>> np.{float_name}(-2.0).is_integer()
+        True
+        >>> np.{float_name}(3.2).is_integer()
+        False
+        """))
+
+for int_name in ('int8', 'uint8', 'int16', 'uint16', 'int32', 'uint32',
+                 'int64', 'uint64', 'int64', 'uint64', 'int64', 'uint64',
+                 'longlong', 'ulonglong'):
+    # Add negative examples for signed cases by checking typecode
+    add_newdoc('numpy._core.numerictypes', int_name, ('bit_count',
+        f"""
+        bit_count($self, /)
+        --
+
+        {int_name}.bit_count() -> int
+
+        Computes the number of 1-bits in the absolute value of the input.
+        Analogous to the builtin `int.bit_count` or ``popcount`` in C++.
+
+        Examples
+        --------
+        >>> np.{int_name}(127).bit_count()
+        7""" +
+        (f"""
+        >>> np.{int_name}(-127).bit_count()
+        7
+        """ if dtype(int_name).char.islower() else "")))

python_env_practice/.venv/Lib/site-packages/numpy/_core/_add_newdocs_scalars.pyi

@@ -0,0 +1,16 @@
+from typing import Final
+
+import numpy as np
+
+possible_aliases: Final[list[tuple[type[np.number], str, str]]] = ...
+_system: Final[str] = ...
+_machine: Final[str] = ...
+_doc_alias_string: Final[str] = ...
+_bool_docstring: Final[str] = ...
+bool_name: str = ...
+int_name: str = ...
+float_name: str = ...
+
+def numeric_type_aliases(aliases: list[tuple[str, str]]) -> list[tuple[type[np.number], str, str]]: ...
+def add_newdoc_for_scalar_type(name: str, text_signature: str, doc: str) -> None: ...
+def _get_platform_and_machine() -> tuple[str, str]: ...

python_env_practice/.venv/Lib/site-packages/numpy/_core/_asarray.py

@@ -0,0 +1,130 @@
+"""
+Functions in the ``as*array`` family that promote array-likes into arrays.
+
+`require` fits this category despite its name not matching this pattern.
+"""
+from .multiarray import array, asanyarray
+from .overrides import array_function_dispatch, finalize_array_function_like, set_module
+
+__all__ = ["require"]
+
+
+POSSIBLE_FLAGS = {
+    'C': 'C', 'C_CONTIGUOUS': 'C', 'CONTIGUOUS': 'C',
+    'F': 'F', 'F_CONTIGUOUS': 'F', 'FORTRAN': 'F',
+    'A': 'A', 'ALIGNED': 'A',
+    'W': 'W', 'WRITEABLE': 'W',
+    'O': 'O', 'OWNDATA': 'O',
+    'E': 'E', 'ENSUREARRAY': 'E'
+}
+
+
+@finalize_array_function_like
+@set_module('numpy')
+def require(a, dtype=None, requirements=None, *, like=None):
+    """
+    Return an ndarray of the provided type that satisfies requirements.
+
+    This function is useful to be sure that an array with the correct flags
+    is returned for passing to compiled code (perhaps through ctypes).
+
+    Parameters
+    ----------
+    a : array_like
+       The object to be converted to a type-and-requirement-satisfying array.
+    dtype : data-type
+       The required data-type. If None preserve the current dtype. If your
+       application requires the data to be in native byteorder, include
+       a byteorder specification as a part of the dtype specification.
+    requirements : str or sequence of str
+       The requirements list can be any of the following
+
+       * 'F_CONTIGUOUS' ('F') - ensure a Fortran-contiguous array
+       * 'C_CONTIGUOUS' ('C') - ensure a C-contiguous array
+       * 'ALIGNED' ('A')      - ensure a data-type aligned array
+       * 'WRITEABLE' ('W')    - ensure a writable array
+       * 'OWNDATA' ('O')      - ensure an array that owns its own data
+       * 'ENSUREARRAY', ('E') - ensure a base array, instead of a subclass
+    ${ARRAY_FUNCTION_LIKE}
+
+        .. versionadded:: 1.20.0
+
+    Returns
+    -------
+    out : ndarray
+        Array with specified requirements and type if given.
+
+    See Also
+    --------
+    asarray : Convert input to an ndarray.
+    asanyarray : Convert to an ndarray, but pass through ndarray subclasses.
+    ascontiguousarray : Convert input to a contiguous array.
+    asfortranarray : Convert input to an ndarray with column-major
+                     memory order.
+    ndarray.flags : Information about the memory layout of the array.
+
+    Notes
+    -----
+    The returned array will be guaranteed to have the listed requirements
+    by making a copy if needed.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> x = np.arange(6).reshape(2,3)
+    >>> x.flags
+      C_CONTIGUOUS : True
+      F_CONTIGUOUS : False
+      OWNDATA : False
+      WRITEABLE : True
+      ALIGNED : True
+      WRITEBACKIFCOPY : False
+
+    >>> y = np.require(x, dtype=np.float32, requirements=['A', 'O', 'W', 'F'])
+    >>> y.flags
+      C_CONTIGUOUS : False
+      F_CONTIGUOUS : True
+      OWNDATA : True
+      WRITEABLE : True
+      ALIGNED : True
+      WRITEBACKIFCOPY : False
+
+    """
+    if like is not None:
+        return _require_with_like(
+            like,
+            a,
+            dtype=dtype,
+            requirements=requirements,
+        )
+
+    if not requirements:
+        return asanyarray(a, dtype=dtype)
+
+    requirements = {POSSIBLE_FLAGS[x.upper()] for x in requirements}
+
+    if 'E' in requirements:
+        requirements.remove('E')
+        subok = False
+    else:
+        subok = True
+
+    order = 'A'
+    if requirements >= {'C', 'F'}:
+        raise ValueError('Cannot specify both "C" and "F" order')
+    elif 'F' in requirements:
+        order = 'F'
+        requirements.remove('F')
+    elif 'C' in requirements:
+        order = 'C'
+        requirements.remove('C')
+
+    arr = array(a, dtype=dtype, order=order, copy=None, subok=subok)
+
+    for prop in requirements:
+        if not arr.flags[prop]:
+            return arr.copy(order)
+    return arr
+
+
+_require_with_like = array_function_dispatch()(require)

python_env_practice/.venv/Lib/site-packages/numpy/_core/_asarray.pyi

@@ -0,0 +1,43 @@
+from collections.abc import Iterable
+from typing import Any, Literal, TypeAlias, TypeVar, overload
+
+from numpy._typing import DTypeLike, NDArray, _SupportsArrayFunc
+
+__all__ = ["require"]
+
+_ArrayT = TypeVar("_ArrayT", bound=NDArray[Any])
+
+_Requirements: TypeAlias = Literal[
+    "C", "C_CONTIGUOUS", "CONTIGUOUS",
+    "F", "F_CONTIGUOUS", "FORTRAN",
+    "A", "ALIGNED",
+    "W", "WRITEABLE",
+    "O", "OWNDATA"
+]
+_E: TypeAlias = Literal["E", "ENSUREARRAY"]
+_RequirementsWithE: TypeAlias = _Requirements | _E
+
+@overload
+def require(
+    a: _ArrayT,
+    dtype: None = None,
+    requirements: _Requirements | Iterable[_Requirements] | None = None,
+    *,
+    like: _SupportsArrayFunc | None = None
+) -> _ArrayT: ...
+@overload
+def require(
+    a: object,
+    dtype: DTypeLike | None = None,
+    requirements: _E | Iterable[_RequirementsWithE] | None = None,
+    *,
+    like: _SupportsArrayFunc | None = None
+) -> NDArray[Any]: ...
+@overload
+def require(
+    a: object,
+    dtype: DTypeLike | None = None,
+    requirements: _Requirements | Iterable[_Requirements] | None = None,
+    *,
+    like: _SupportsArrayFunc | None = None
+) -> NDArray[Any]: ...

python_env_practice/.venv/Lib/site-packages/numpy/_core/_dtype.py

@@ -0,0 +1,366 @@
+"""
+A place for code to be called from the implementation of np.dtype
+
+String handling is much easier to do correctly in python.
+"""
+import numpy as np
+
+_kind_to_stem = {
+    'u': 'uint',
+    'i': 'int',
+    'c': 'complex',
+    'f': 'float',
+    'b': 'bool',
+    'V': 'void',
+    'O': 'object',
+    'M': 'datetime',
+    'm': 'timedelta',
+    'S': 'bytes',
+    'U': 'str',
+}
+
+
+def _kind_name(dtype):
+    try:
+        return _kind_to_stem[dtype.kind]
+    except KeyError as e:
+        raise RuntimeError(
+            f"internal dtype error, unknown kind {dtype.kind!r}"
+        ) from None
+
+
+def __str__(dtype):
+    if dtype.fields is not None:
+        return _struct_str(dtype, include_align=True)
+    elif dtype.subdtype:
+        return _subarray_str(dtype)
+    elif issubclass(dtype.type, np.flexible) or not dtype.isnative:
+        return dtype.str
+    else:
+        return dtype.name
+
+
+def __repr__(dtype):
+    arg_str = _construction_repr(dtype, include_align=False)
+    if dtype.isalignedstruct:
+        arg_str = arg_str + ", align=True"
+    return f"dtype({arg_str})"
+
+
+def _unpack_field(dtype, offset, title=None):
+    """
+    Helper function to normalize the items in dtype.fields.
+
+    Call as:
+
+    dtype, offset, title = _unpack_field(*dtype.fields[name])
+    """
+    return dtype, offset, title
+
+
+def _isunsized(dtype):
+    # PyDataType_ISUNSIZED
+    return dtype.itemsize == 0
+
+
+def _construction_repr(dtype, include_align=False, short=False):
+    """
+    Creates a string repr of the dtype, excluding the 'dtype()' part
+    surrounding the object. This object may be a string, a list, or
+    a dict depending on the nature of the dtype. This
+    is the object passed as the first parameter to the dtype
+    constructor, and if no additional constructor parameters are
+    given, will reproduce the exact memory layout.
+
+    Parameters
+    ----------
+    short : bool
+        If true, this creates a shorter repr using 'kind' and 'itemsize',
+        instead of the longer type name.
+
+    include_align : bool
+        If true, this includes the 'align=True' parameter
+        inside the struct dtype construction dict when needed. Use this flag
+        if you want a proper repr string without the 'dtype()' part around it.
+
+        If false, this does not preserve the
+        'align=True' parameter or sticky NPY_ALIGNED_STRUCT flag for
+        struct arrays like the regular repr does, because the 'align'
+        flag is not part of first dtype constructor parameter. This
+        mode is intended for a full 'repr', where the 'align=True' is
+        provided as the second parameter.
+    """
+    if dtype.fields is not None:
+        return _struct_str(dtype, include_align=include_align)
+    elif dtype.subdtype:
+        return _subarray_str(dtype)
+    else:
+        return _scalar_str(dtype, short=short)
+
+
+def _scalar_str(dtype, short):
+    byteorder = _byte_order_str(dtype)
+
+    if dtype.type == np.bool:
+        if short:
+            return "'?'"
+        else:
+            return "'bool'"
+
+    elif dtype.type == np.object_:
+        # The object reference may be different sizes on different
+        # platforms, so it should never include the itemsize here.
+        return "'O'"
+
+    elif dtype.type == np.bytes_:
+        if _isunsized(dtype):
+            return "'S'"
+        else:
+            return "'S%d'" % dtype.itemsize
+
+    elif dtype.type == np.str_:
+        if _isunsized(dtype):
+            return f"'{byteorder}U'"
+        else:
+            return "'%sU%d'" % (byteorder, dtype.itemsize / 4)
+
+    elif dtype.type == str:
+        return "'T'"
+
+    elif not type(dtype)._legacy:
+        return f"'{byteorder}{type(dtype).__name__}{dtype.itemsize * 8}'"
+
+    # unlike the other types, subclasses of void are preserved - but
+    # historically the repr does not actually reveal the subclass
+    elif issubclass(dtype.type, np.void):
+        if _isunsized(dtype):
+            return "'V'"
+        else:
+            return "'V%d'" % dtype.itemsize
+
+    elif dtype.type == np.datetime64:
+        return f"'{byteorder}M8{_datetime_metadata_str(dtype)}'"
+
+    elif dtype.type == np.timedelta64:
+        return f"'{byteorder}m8{_datetime_metadata_str(dtype)}'"
+
+    elif dtype.isbuiltin == 2:
+        return dtype.type.__name__
+
+    elif np.issubdtype(dtype, np.number):
+        # Short repr with endianness, like '<f8'
+        if short or dtype.byteorder not in ('=', '|'):
+            return "'%s%c%d'" % (byteorder, dtype.kind, dtype.itemsize)
+
+        # Longer repr, like 'float64'
+        else:
+            return "'%s%d'" % (_kind_name(dtype), 8 * dtype.itemsize)
+
+    else:
+        raise RuntimeError(
+            "Internal error: NumPy dtype unrecognized type number")
+
+
+def _byte_order_str(dtype):
+    """ Normalize byteorder to '<' or '>' """
+    # hack to obtain the native and swapped byte order characters
+    swapped = np.dtype(int).newbyteorder('S')
+    native = swapped.newbyteorder('S')
+
+    byteorder = dtype.byteorder
+    if byteorder == '=':
+        return native.byteorder
+    if byteorder == 'S':
+        # TODO: this path can never be reached
+        return swapped.byteorder
+    elif byteorder == '|':
+        return ''
+    else:
+        return byteorder
+
+
+def _datetime_metadata_str(dtype):
+    # TODO: this duplicates the C metastr_to_unicode functionality
+    unit, count = np.datetime_data(dtype)
+    if unit == 'generic':
+        return ''
+    elif count == 1:
+        return f'[{unit}]'
+    else:
+        return f'[{count}{unit}]'
+
+
+def _struct_dict_str(dtype, includealignedflag):
+    # unpack the fields dictionary into ls
+    names = dtype.names
+    fld_dtypes = []
+    offsets = []
+    titles = []
+    for name in names:
+        fld_dtype, offset, title = _unpack_field(*dtype.fields[name])
+        fld_dtypes.append(fld_dtype)
+        offsets.append(offset)
+        titles.append(title)
+
+    # Build up a string to make the dictionary
+
+    if np._core.arrayprint._get_legacy_print_mode() <= 121:
+        colon = ":"
+        fieldsep = ","
+    else:
+        colon = ": "
+        fieldsep = ", "
+
+    # First, the names
+    ret = "{'names'%s[" % colon
+    ret += fieldsep.join(repr(name) for name in names)
+
+    # Second, the formats
+    ret += f"], 'formats'{colon}["
+    ret += fieldsep.join(
+        _construction_repr(fld_dtype, short=True) for fld_dtype in fld_dtypes)
+
+    # Third, the offsets
+    ret += f"], 'offsets'{colon}["
+    ret += fieldsep.join("%d" % offset for offset in offsets)
+
+    # Fourth, the titles
+    if any(title is not None for title in titles):
+        ret += f"], 'titles'{colon}["
+        ret += fieldsep.join(repr(title) for title in titles)
+
+    # Fifth, the itemsize
+    ret += "], 'itemsize'%s%d" % (colon, dtype.itemsize)
+
+    if (includealignedflag and dtype.isalignedstruct):
+        # Finally, the aligned flag
+        ret += ", 'aligned'%sTrue}" % colon
+    else:
+        ret += "}"
+
+    return ret
+
+
+def _aligned_offset(offset, alignment):
+    # round up offset:
+    return - (-offset // alignment) * alignment
+
+
+def _is_packed(dtype):
+    """
+    Checks whether the structured data type in 'dtype'
+    has a simple layout, where all the fields are in order,
+    and follow each other with no alignment padding.
+
+    When this returns true, the dtype can be reconstructed
+    from a list of the field names and dtypes with no additional
+    dtype parameters.
+
+    Duplicates the C `is_dtype_struct_simple_unaligned_layout` function.
+    """
+    align = dtype.isalignedstruct
+    max_alignment = 1
+    total_offset = 0
+    for name in dtype.names:
+        fld_dtype, fld_offset, title = _unpack_field(*dtype.fields[name])
+
+        if align:
+            total_offset = _aligned_offset(total_offset, fld_dtype.alignment)
+            max_alignment = max(max_alignment, fld_dtype.alignment)
+
+        if fld_offset != total_offset:
+            return False
+        total_offset += fld_dtype.itemsize
+
+    if align:
+        total_offset = _aligned_offset(total_offset, max_alignment)
+
+    return total_offset == dtype.itemsize
+
+
+def _struct_list_str(dtype):
+    items = []
+    for name in dtype.names:
+        fld_dtype, fld_offset, title = _unpack_field(*dtype.fields[name])
+
+        item = "("
+        if title is not None:
+            item += f"({title!r}, {name!r}), "
+        else:
+            item += f"{name!r}, "
+        # Special case subarray handling here
+        if fld_dtype.subdtype is not None:
+            base, shape = fld_dtype.subdtype
+            item += f"{_construction_repr(base, short=True)}, {shape}"
+        else:
+            item += _construction_repr(fld_dtype, short=True)
+
+        item += ")"
+        items.append(item)
+
+    return "[" + ", ".join(items) + "]"
+
+
+def _struct_str(dtype, include_align):
+    # The list str representation can't include the 'align=' flag,
+    # so if it is requested and the struct has the aligned flag set,
+    # we must use the dict str instead.
+    if not (include_align and dtype.isalignedstruct) and _is_packed(dtype):
+        sub = _struct_list_str(dtype)
+
+    else:
+        sub = _struct_dict_str(dtype, include_align)
+
+    # If the data type isn't the default, void, show it
+    if dtype.type != np.void:
+        return f"({dtype.type.__module__}.{dtype.type.__name__}, {sub})"
+    else:
+        return sub
+
+
+def _subarray_str(dtype):
+    base, shape = dtype.subdtype
+    return f"({_construction_repr(base, short=True)}, {shape})"
+
+
+def _name_includes_bit_suffix(dtype):
+    if dtype.type == np.object_:
+        # pointer size varies by system, best to omit it
+        return False
+    elif dtype.type == np.bool:
+        # implied
+        return False
+    elif dtype.type is None:
+        return True
+    elif np.issubdtype(dtype, np.flexible) and _isunsized(dtype):
+        # unspecified
+        return False
+    else:
+        return True
+
+
+def _name_get(dtype):
+    # provides dtype.name.__get__, documented as returning a "bit name"
+
+    if dtype.isbuiltin == 2:
+        # user dtypes don't promise to do anything special
+        return dtype.type.__name__
+
+    if not type(dtype)._legacy:
+        name = type(dtype).__name__
+
+    elif issubclass(dtype.type, np.void):
+        # historically, void subclasses preserve their name, eg `record64`
+        name = dtype.type.__name__
+    else:
+        name = _kind_name(dtype)
+
+    # append bit counts
+    if _name_includes_bit_suffix(dtype):
+        name += f"{dtype.itemsize * 8}"
+
+    # append metadata to datetimes
+    if dtype.type in (np.datetime64, np.timedelta64):
+        name += _datetime_metadata_str(dtype)
+
+    return name

python_env_practice/.venv/Lib/site-packages/numpy/_core/_dtype.pyi

@@ -0,0 +1,56 @@
+from typing import Final, Literal as L, TypeAlias, TypedDict, overload, type_check_only
+from typing_extensions import ReadOnly, TypeVar
+
+import numpy as np
+
+###
+
+_T = TypeVar("_T")
+
+_Name: TypeAlias = L["uint", "int", "complex", "float", "bool", "void", "object", "datetime", "timedelta", "bytes", "str"]
+
+@type_check_only
+class _KindToStemType(TypedDict):
+    u: ReadOnly[L["uint"]]
+    i: ReadOnly[L["int"]]
+    c: ReadOnly[L["complex"]]
+    f: ReadOnly[L["float"]]
+    b: ReadOnly[L["bool"]]
+    V: ReadOnly[L["void"]]
+    O: ReadOnly[L["object"]]
+    M: ReadOnly[L["datetime"]]
+    m: ReadOnly[L["timedelta"]]
+    S: ReadOnly[L["bytes"]]
+    U: ReadOnly[L["str"]]
+
+###
+
+_kind_to_stem: Final[_KindToStemType] = ...
+
+#
+def _kind_name(dtype: np.dtype) -> _Name: ...
+def __str__(dtype: np.dtype) -> str: ...
+def __repr__(dtype: np.dtype) -> str: ...
+
+#
+def _isunsized(dtype: np.dtype) -> bool: ...
+def _is_packed(dtype: np.dtype) -> bool: ...
+def _name_includes_bit_suffix(dtype: np.dtype) -> bool: ...
+
+#
+def _construction_repr(dtype: np.dtype, include_align: bool = False, short: bool = False) -> str: ...
+def _scalar_str(dtype: np.dtype, short: bool) -> str: ...
+def _byte_order_str(dtype: np.dtype) -> str: ...
+def _datetime_metadata_str(dtype: np.dtype) -> str: ...
+def _struct_dict_str(dtype: np.dtype, includealignedflag: bool) -> str: ...
+def _struct_list_str(dtype: np.dtype) -> str: ...
+def _struct_str(dtype: np.dtype, include_align: bool) -> str: ...
+def _subarray_str(dtype: np.dtype) -> str: ...
+def _name_get(dtype: np.dtype) -> str: ...
+
+#
+@overload
+def _unpack_field(dtype: np.dtype, offset: int, title: _T) -> tuple[np.dtype, int, _T]: ...
+@overload
+def _unpack_field(dtype: np.dtype, offset: int, title: None = None) -> tuple[np.dtype, int, None]: ...
+def _aligned_offset(offset: int, alignment: int) -> int: ...

python_env_practice/.venv/Lib/site-packages/numpy/_core/_dtype_ctypes.py

@@ -0,0 +1,120 @@
+"""
+Conversion from ctypes to dtype.
+
+In an ideal world, we could achieve this through the PEP3118 buffer protocol,
+something like::
+
+    def dtype_from_ctypes_type(t):
+        # needed to ensure that the shape of `t` is within memoryview.format
+        class DummyStruct(ctypes.Structure):
+            _fields_ = [('a', t)]
+
+        # empty to avoid memory allocation
+        ctype_0 = (DummyStruct * 0)()
+        mv = memoryview(ctype_0)
+
+        # convert the struct, and slice back out the field
+        return _dtype_from_pep3118(mv.format)['a']
+
+Unfortunately, this fails because:
+
+* ctypes cannot handle length-0 arrays with PEP3118 (bpo-32782)
+* PEP3118 cannot represent unions, but both numpy and ctypes can
+* ctypes cannot handle big-endian structs with PEP3118 (bpo-32780)
+"""
+
+# We delay-import ctypes for distributions that do not include it.
+# While this module is not used unless the user passes in ctypes
+# members, it is eagerly imported from numpy/_core/__init__.py.
+import numpy as np
+
+
+def _from_ctypes_array(t):
+    return np.dtype((dtype_from_ctypes_type(t._type_), (t._length_,)))
+
+
+def _from_ctypes_structure(t):
+    for item in t._fields_:
+        if len(item) > 2:
+            raise TypeError(
+                "ctypes bitfields have no dtype equivalent")
+
+    if hasattr(t, "_pack_"):
+        import ctypes
+        formats = []
+        offsets = []
+        names = []
+        current_offset = 0
+        for fname, ftyp in t._fields_:
+            names.append(fname)
+            formats.append(dtype_from_ctypes_type(ftyp))
+            # Each type has a default offset, this is platform dependent
+            # for some types.
+            effective_pack = min(t._pack_, ctypes.alignment(ftyp))
+            current_offset = (
+                (current_offset + effective_pack - 1) // effective_pack
+            ) * effective_pack
+            offsets.append(current_offset)
+            current_offset += ctypes.sizeof(ftyp)
+
+        return np.dtype({
+            "formats": formats,
+            "offsets": offsets,
+            "names": names,
+            "itemsize": ctypes.sizeof(t)})
+    else:
+        fields = []
+        for fname, ftyp in t._fields_:
+            fields.append((fname, dtype_from_ctypes_type(ftyp)))
+
+        # by default, ctypes structs are aligned
+        return np.dtype(fields, align=True)
+
+
+def _from_ctypes_scalar(t):
+    """
+    Return the dtype type with endianness included if it's the case
+    """
+    if getattr(t, '__ctype_be__', None) is t:
+        return np.dtype('>' + t._type_)
+    elif getattr(t, '__ctype_le__', None) is t:
+        return np.dtype('<' + t._type_)
+    else:
+        return np.dtype(t._type_)
+
+
+def _from_ctypes_union(t):
+    import ctypes
+    formats = []
+    offsets = []
+    names = []
+    for fname, ftyp in t._fields_:
+        names.append(fname)
+        formats.append(dtype_from_ctypes_type(ftyp))
+        offsets.append(0)  # Union fields are offset to 0
+
+    return np.dtype({
+        "formats": formats,
+        "offsets": offsets,
+        "names": names,
+        "itemsize": ctypes.sizeof(t)})
+
+
+def dtype_from_ctypes_type(t):
+    """
+    Construct a dtype object from a ctypes type
+    """
+    import _ctypes
+    if issubclass(t, _ctypes.Array):
+        return _from_ctypes_array(t)
+    elif issubclass(t, _ctypes._Pointer):
+        raise TypeError("ctypes pointers have no dtype equivalent")
+    elif issubclass(t, _ctypes.Structure):
+        return _from_ctypes_structure(t)
+    elif issubclass(t, _ctypes.Union):
+        return _from_ctypes_union(t)
+    elif isinstance(getattr(t, '_type_', None), str):
+        return _from_ctypes_scalar(t)
+    else:
+        raise NotImplementedError(
+            f"Unknown ctypes type {t.__name__}")

python_env_practice/.venv/Lib/site-packages/numpy/_core/_dtype_ctypes.pyi

@@ -0,0 +1,83 @@
+import _ctypes
+import ctypes as ct
+from typing import Any, overload
+
+import numpy as np
+
+#
+@overload
+def dtype_from_ctypes_type(t: type[_ctypes.Array[Any] | _ctypes.Structure]) -> np.dtype[np.void]: ...
+@overload
+def dtype_from_ctypes_type(t: type[ct.c_bool]) -> np.dtype[np.bool]: ...
+@overload
+def dtype_from_ctypes_type(t: type[ct.c_int8 | ct.c_byte]) -> np.dtype[np.int8]: ...
+@overload
+def dtype_from_ctypes_type(t: type[ct.c_uint8 | ct.c_ubyte]) -> np.dtype[np.uint8]: ...
+@overload
+def dtype_from_ctypes_type(t: type[ct.c_int16 | ct.c_short]) -> np.dtype[np.int16]: ...
+@overload
+def dtype_from_ctypes_type(t: type[ct.c_uint16 | ct.c_ushort]) -> np.dtype[np.uint16]: ...
+@overload
+def dtype_from_ctypes_type(t: type[ct.c_int32 | ct.c_int]) -> np.dtype[np.int32]: ...
+@overload
+def dtype_from_ctypes_type(t: type[ct.c_uint32 | ct.c_uint]) -> np.dtype[np.uint32]: ...
+@overload
+def dtype_from_ctypes_type(t: type[ct.c_ssize_t | ct.c_long]) -> np.dtype[np.int32 | np.int64]: ...
+@overload
+def dtype_from_ctypes_type(t: type[ct.c_size_t | ct.c_ulong]) -> np.dtype[np.uint32 | np.uint64]: ...
+@overload
+def dtype_from_ctypes_type(t: type[ct.c_int64 | ct.c_longlong]) -> np.dtype[np.int64]: ...
+@overload
+def dtype_from_ctypes_type(t: type[ct.c_uint64 | ct.c_ulonglong]) -> np.dtype[np.uint64]: ...
+@overload
+def dtype_from_ctypes_type(t: type[ct.c_float]) -> np.dtype[np.float32]: ...
+@overload
+def dtype_from_ctypes_type(t: type[ct.c_double]) -> np.dtype[np.float64]: ...
+@overload
+def dtype_from_ctypes_type(t: type[ct.c_longdouble]) -> np.dtype[np.longdouble]: ...
+@overload
+def dtype_from_ctypes_type(t: type[ct.c_char]) -> np.dtype[np.bytes_]: ...
+@overload
+def dtype_from_ctypes_type(t: type[ct.py_object[Any]]) -> np.dtype[np.object_]: ...
+
+# NOTE: the complex ctypes on python>=3.14 are not yet supported at runtim, see
+# https://github.com/numpy/numpy/issues/28360
+
+#
+def _from_ctypes_array(t: type[_ctypes.Array[Any]]) -> np.dtype[np.void]: ...
+def _from_ctypes_structure(t: type[_ctypes.Structure]) -> np.dtype[np.void]: ...
+def _from_ctypes_union(t: type[_ctypes.Union]) -> np.dtype[np.void]: ...
+
+# keep in sync with `dtype_from_ctypes_type` (minus the first overload)
+@overload
+def _from_ctypes_scalar(t: type[ct.c_bool]) -> np.dtype[np.bool]: ...
+@overload
+def _from_ctypes_scalar(t: type[ct.c_int8 | ct.c_byte]) -> np.dtype[np.int8]: ...
+@overload
+def _from_ctypes_scalar(t: type[ct.c_uint8 | ct.c_ubyte]) -> np.dtype[np.uint8]: ...
+@overload
+def _from_ctypes_scalar(t: type[ct.c_int16 | ct.c_short]) -> np.dtype[np.int16]: ...
+@overload
+def _from_ctypes_scalar(t: type[ct.c_uint16 | ct.c_ushort]) -> np.dtype[np.uint16]: ...
+@overload
+def _from_ctypes_scalar(t: type[ct.c_int32 | ct.c_int]) -> np.dtype[np.int32]: ...
+@overload
+def _from_ctypes_scalar(t: type[ct.c_uint32 | ct.c_uint]) -> np.dtype[np.uint32]: ...
+@overload
+def _from_ctypes_scalar(t: type[ct.c_ssize_t | ct.c_long]) -> np.dtype[np.int32 | np.int64]: ...
+@overload
+def _from_ctypes_scalar(t: type[ct.c_size_t | ct.c_ulong]) -> np.dtype[np.uint32 | np.uint64]: ...
+@overload
+def _from_ctypes_scalar(t: type[ct.c_int64 | ct.c_longlong]) -> np.dtype[np.int64]: ...
+@overload
+def _from_ctypes_scalar(t: type[ct.c_uint64 | ct.c_ulonglong]) -> np.dtype[np.uint64]: ...
+@overload
+def _from_ctypes_scalar(t: type[ct.c_float]) -> np.dtype[np.float32]: ...
+@overload
+def _from_ctypes_scalar(t: type[ct.c_double]) -> np.dtype[np.float64]: ...
+@overload
+def _from_ctypes_scalar(t: type[ct.c_longdouble]) -> np.dtype[np.longdouble]: ...
+@overload
+def _from_ctypes_scalar(t: type[ct.c_char]) -> np.dtype[np.bytes_]: ...
+@overload
+def _from_ctypes_scalar(t: type[ct.py_object[Any]]) -> np.dtype[np.object_]: ...

python_env_practice/.venv/Lib/site-packages/numpy/_core/_exceptions.py

@@ -0,0 +1,162 @@
+"""
+Various richly-typed exceptions, that also help us deal with string formatting
+in python where it's easier.
+
+By putting the formatting in `__str__`, we also avoid paying the cost for
+users who silence the exceptions.
+"""
+
+def _unpack_tuple(tup):
+    if len(tup) == 1:
+        return tup[0]
+    else:
+        return tup
+
+
+def _display_as_base(cls):
+    """
+    A decorator that makes an exception class look like its base.
+
+    We use this to hide subclasses that are implementation details - the user
+    should catch the base type, which is what the traceback will show them.
+
+    Classes decorated with this decorator are subject to removal without a
+    deprecation warning.
+    """
+    assert issubclass(cls, Exception)
+    cls.__name__ = cls.__base__.__name__
+    return cls
+
+
+class UFuncTypeError(TypeError):
+    """ Base class for all ufunc exceptions """
+    def __init__(self, ufunc):
+        self.ufunc = ufunc
+
+
+@_display_as_base
+class _UFuncNoLoopError(UFuncTypeError):
+    """ Thrown when a ufunc loop cannot be found """
+    def __init__(self, ufunc, dtypes):
+        super().__init__(ufunc)
+        self.dtypes = tuple(dtypes)
+
+    def __str__(self):
+        return (
+            f"ufunc {self.ufunc.__name__!r} did not contain a loop with signature "
+            f"matching types {_unpack_tuple(self.dtypes[:self.ufunc.nin])!r} "
+            f"-> {_unpack_tuple(self.dtypes[self.ufunc.nin:])!r}"
+        )
+
+
+@_display_as_base
+class _UFuncBinaryResolutionError(_UFuncNoLoopError):
+    """ Thrown when a binary resolution fails """
+    def __init__(self, ufunc, dtypes):
+        super().__init__(ufunc, dtypes)
+        assert len(self.dtypes) == 2
+
+    def __str__(self):
+        return (
+            "ufunc {!r} cannot use operands with types {!r} and {!r}"
+        ).format(
+            self.ufunc.__name__, *self.dtypes
+        )
+
+
+@_display_as_base
+class _UFuncCastingError(UFuncTypeError):
+    def __init__(self, ufunc, casting, from_, to):
+        super().__init__(ufunc)
+        self.casting = casting
+        self.from_ = from_
+        self.to = to
+
+
+@_display_as_base
+class _UFuncInputCastingError(_UFuncCastingError):
+    """ Thrown when a ufunc input cannot be casted """
+    def __init__(self, ufunc, casting, from_, to, i):
+        super().__init__(ufunc, casting, from_, to)
+        self.in_i = i
+
+    def __str__(self):
+        # only show the number if more than one input exists
+        i_str = f"{self.in_i} " if self.ufunc.nin != 1 else ""
+        return (
+            f"Cannot cast ufunc {self.ufunc.__name__!r} input {i_str}from "
+            f"{self.from_!r} to {self.to!r} with casting rule {self.casting!r}"
+        )
+
+
+@_display_as_base
+class _UFuncOutputCastingError(_UFuncCastingError):
+    """ Thrown when a ufunc output cannot be casted """
+    def __init__(self, ufunc, casting, from_, to, i):
+        super().__init__(ufunc, casting, from_, to)
+        self.out_i = i
+
+    def __str__(self):
+        # only show the number if more than one output exists
+        i_str = f"{self.out_i} " if self.ufunc.nout != 1 else ""
+        return (
+            f"Cannot cast ufunc {self.ufunc.__name__!r} output {i_str}from "
+            f"{self.from_!r} to {self.to!r} with casting rule {self.casting!r}"
+        )
+
+
+@_display_as_base
+class _ArrayMemoryError(MemoryError):
+    """ Thrown when an array cannot be allocated"""
+    def __init__(self, shape, dtype):
+        self.shape = shape
+        self.dtype = dtype
+
+    @property
+    def _total_size(self):
+        num_bytes = self.dtype.itemsize
+        for dim in self.shape:
+            num_bytes *= dim
+        return num_bytes
+
+    @staticmethod
+    def _size_to_string(num_bytes):
+        """ Convert a number of bytes into a binary size string """
+
+        # https://en.wikipedia.org/wiki/Binary_prefix
+        LOG2_STEP = 10
+        STEP = 1024
+        units = ['bytes', 'KiB', 'MiB', 'GiB', 'TiB', 'PiB', 'EiB']
+
+        unit_i = max(num_bytes.bit_length() - 1, 1) // LOG2_STEP
+        unit_val = 1 << (unit_i * LOG2_STEP)
+        n_units = num_bytes / unit_val
+        del unit_val
+
+        # ensure we pick a unit that is correct after rounding
+        if round(n_units) == STEP:
+            unit_i += 1
+            n_units /= STEP
+
+        # deal with sizes so large that we don't have units for them
+        if unit_i >= len(units):
+            new_unit_i = len(units) - 1
+            n_units *= 1 << ((unit_i - new_unit_i) * LOG2_STEP)
+            unit_i = new_unit_i
+
+        unit_name = units[unit_i]
+        # format with a sensible number of digits
+        if unit_i == 0:
+            # no decimal point on bytes
+            return f'{n_units:.0f} {unit_name}'
+        elif round(n_units) < 1000:
+            # 3 significant figures, if none are dropped to the left of the .
+            return f'{n_units:#.3g} {unit_name}'
+        else:
+            # just give all the digits otherwise
+            return f'{n_units:#.0f} {unit_name}'
+
+    def __str__(self):
+        size_str = self._size_to_string(self._total_size)
+        return (f"Unable to allocate {size_str} for an array with shape "
+                f"{self.shape} and data type {self.dtype}")

python_env_practice/.venv/Lib/site-packages/numpy/_core/_exceptions.pyi

@@ -0,0 +1,54 @@
+from collections.abc import Iterable
+from typing import Any, Final, TypeVar, overload
+
+import numpy as np
+from numpy import _CastingKind
+
+###
+
+_T = TypeVar("_T")
+_TupleT = TypeVar("_TupleT", bound=tuple[()] | tuple[Any, Any, *tuple[Any, ...]])
+_ExceptionT = TypeVar("_ExceptionT", bound=Exception)
+
+###
+
+class UFuncTypeError(TypeError):
+    ufunc: Final[np.ufunc]
+    def __init__(self, /, ufunc: np.ufunc) -> None: ...
+
+class _UFuncNoLoopError(UFuncTypeError):
+    dtypes: tuple[np.dtype, ...]
+    def __init__(self, /, ufunc: np.ufunc, dtypes: Iterable[np.dtype]) -> None: ...
+
+class _UFuncBinaryResolutionError(_UFuncNoLoopError):
+    dtypes: tuple[np.dtype, np.dtype]
+    def __init__(self, /, ufunc: np.ufunc, dtypes: Iterable[np.dtype]) -> None: ...
+
+class _UFuncCastingError(UFuncTypeError):
+    casting: Final[_CastingKind]
+    from_: Final[np.dtype]
+    to: Final[np.dtype]
+    def __init__(self, /, ufunc: np.ufunc, casting: _CastingKind, from_: np.dtype, to: np.dtype) -> None: ...
+
+class _UFuncInputCastingError(_UFuncCastingError):
+    in_i: Final[int]
+    def __init__(self, /, ufunc: np.ufunc, casting: _CastingKind, from_: np.dtype, to: np.dtype, i: int) -> None: ...
+
+class _UFuncOutputCastingError(_UFuncCastingError):
+    out_i: Final[int]
+    def __init__(self, /, ufunc: np.ufunc, casting: _CastingKind, from_: np.dtype, to: np.dtype, i: int) -> None: ...
+
+class _ArrayMemoryError(MemoryError):
+    shape: tuple[int, ...]
+    dtype: np.dtype
+    def __init__(self, /, shape: tuple[int, ...], dtype: np.dtype) -> None: ...
+    @property
+    def _total_size(self) -> int: ...
+    @staticmethod
+    def _size_to_string(num_bytes: int) -> str: ...
+
+@overload
+def _unpack_tuple(tup: tuple[_T]) -> _T: ...
+@overload
+def _unpack_tuple(tup: _TupleT) -> _TupleT: ...
+def _display_as_base(cls: type[_ExceptionT]) -> type[_ExceptionT]: ...

python_env_practice/.venv/Lib/site-packages/numpy/_core/_internal.py

@@ -0,0 +1,968 @@
+"""
+A place for internal code
+
+Some things are more easily handled Python.
+
+"""
+import ast
+import math
+import re
+import sys
+import warnings
+
+from numpy import _NoValue
+from numpy.exceptions import DTypePromotionError
+
+from .multiarray import StringDType, array, dtype, promote_types
+
+try:
+    import ctypes
+except ImportError:
+    ctypes = None
+
+IS_PYPY = sys.implementation.name == 'pypy'
+
+if sys.byteorder == 'little':
+    _nbo = '<'
+else:
+    _nbo = '>'
+
+def _makenames_list(adict, align):
+    allfields = []
+
+    for fname, obj in adict.items():
+        n = len(obj)
+        if not isinstance(obj, tuple) or n not in (2, 3):
+            raise ValueError("entry not a 2- or 3- tuple")
+        if n > 2 and obj[2] == fname:
+            continue
+        num = int(obj[1])
+        if num < 0:
+            raise ValueError("invalid offset.")
+        format = dtype(obj[0], align=align)
+        if n > 2:
+            title = obj[2]
+        else:
+            title = None
+        allfields.append((fname, format, num, title))
+    # sort by offsets
+    allfields.sort(key=lambda x: x[2])
+    names = [x[0] for x in allfields]
+    formats = [x[1] for x in allfields]
+    offsets = [x[2] for x in allfields]
+    titles = [x[3] for x in allfields]
+
+    return names, formats, offsets, titles
+
+# Called in PyArray_DescrConverter function when
+#  a dictionary without "names" and "formats"
+#  fields is used as a data-type descriptor.
+def _usefields(adict, align):
+    try:
+        names = adict[-1]
+    except KeyError:
+        names = None
+    if names is None:
+        names, formats, offsets, titles = _makenames_list(adict, align)
+    else:
+        formats = []
+        offsets = []
+        titles = []
+        for name in names:
+            res = adict[name]
+            formats.append(res[0])
+            offsets.append(res[1])
+            if len(res) > 2:
+                titles.append(res[2])
+            else:
+                titles.append(None)
+
+    return dtype({"names": names,
+                  "formats": formats,
+                  "offsets": offsets,
+                  "titles": titles}, align)
+
+
+# construct an array_protocol descriptor list
+#  from the fields attribute of a descriptor
+# This calls itself recursively but should eventually hit
+#  a descriptor that has no fields and then return
+#  a simple typestring
+
+def _array_descr(descriptor):
+    fields = descriptor.fields
+    if fields is None:
+        subdtype = descriptor.subdtype
+        if subdtype is None:
+            if descriptor.metadata is None:
+                return descriptor.str
+            else:
+                new = descriptor.metadata.copy()
+                if new:
+                    return (descriptor.str, new)
+                else:
+                    return descriptor.str
+        else:
+            return (_array_descr(subdtype[0]), subdtype[1])
+
+    names = descriptor.names
+    ordered_fields = [fields[x] + (x,) for x in names]
+    result = []
+    offset = 0
+    for field in ordered_fields:
+        if field[1] > offset:
+            num = field[1] - offset
+            result.append(('', f'|V{num}'))
+            offset += num
+        elif field[1] < offset:
+            raise ValueError(
+                "dtype.descr is not defined for types with overlapping or "
+                "out-of-order fields")
+        if len(field) > 3:
+            name = (field[2], field[3])
+        else:
+            name = field[2]
+        if field[0].subdtype:
+            tup = (name, _array_descr(field[0].subdtype[0]),
+                   field[0].subdtype[1])
+        else:
+            tup = (name, _array_descr(field[0]))
+        offset += field[0].itemsize
+        result.append(tup)
+
+    if descriptor.itemsize > offset:
+        num = descriptor.itemsize - offset
+        result.append(('', f'|V{num}'))
+
+    return result
+
+
+# format_re was originally from numarray by J. Todd Miller
+
+format_re = re.compile(r'(?P<order1>[<>|=]?)'
+                       r'(?P<repeats> *[(]?[ ,0-9]*[)]? *)'
+                       r'(?P<order2>[<>|=]?)'
+                       r'(?P<dtype>[A-Za-z0-9.?]*(?:\[[a-zA-Z0-9,.]+\])?)')
+sep_re = re.compile(r'\s*,\s*')
+space_re = re.compile(r'\s+$')
+
+# astr is a string (perhaps comma separated)
+
+_convorder = {'=': _nbo}
+
+def _commastring(astr):
+    startindex = 0
+    result = []
+    islist = False
+    while startindex < len(astr):
+        mo = format_re.match(astr, pos=startindex)
+        try:
+            (order1, repeats, order2, dtype) = mo.groups()
+        except (TypeError, AttributeError):
+            raise ValueError(
+                f'format number {len(result) + 1} of "{astr}" is not recognized'
+                ) from None
+        startindex = mo.end()
+        # Separator or ending padding
+        if startindex < len(astr):
+            if space_re.match(astr, pos=startindex):
+                startindex = len(astr)
+            else:
+                mo = sep_re.match(astr, pos=startindex)
+                if not mo:
+                    raise ValueError(
+                        'format number %d of "%s" is not recognized' %
+                        (len(result) + 1, astr))
+                startindex = mo.end()
+                islist = True
+
+        if order2 == '':
+            order = order1
+        elif order1 == '':
+            order = order2
+        else:
+            order1 = _convorder.get(order1, order1)
+            order2 = _convorder.get(order2, order2)
+            if (order1 != order2):
+                raise ValueError(
+                    f'inconsistent byte-order specification {order1} and {order2}')
+            order = order1
+
+        if order in ('|', '=', _nbo):
+            order = ''
+        dtype = order + dtype
+        if repeats == '':
+            newitem = dtype
+        else:
+            if (repeats[0] == "(" and repeats[-1] == ")"
+                    and repeats[1:-1].strip() != ""
+                    and "," not in repeats):
+                warnings.warn(
+                    'Passing in a parenthesized single number for repeats '
+                    'is deprecated; pass either a single number or indicate '
+                    'a tuple with a comma, like "(2,)".', DeprecationWarning,
+                    stacklevel=2)
+            newitem = (dtype, ast.literal_eval(repeats))
+
+        result.append(newitem)
+
+    return result if islist else result[0]
+
+class dummy_ctype:
+
+    def __init__(self, cls):
+        self._cls = cls
+
+    def __mul__(self, other):
+        return self
+
+    def __call__(self, *other):
+        return self._cls(other)
+
+    def __eq__(self, other):
+        return self._cls == other._cls
+
+    def __ne__(self, other):
+        return self._cls != other._cls
+
+def _getintp_ctype():
+    val = _getintp_ctype.cache
+    if val is not None:
+        return val
+    if ctypes is None:
+        import numpy as np
+        val = dummy_ctype(np.intp)
+    else:
+        char = dtype('n').char
+        if char == 'i':
+            val = ctypes.c_int
+        elif char == 'l':
+            val = ctypes.c_long
+        elif char == 'q':
+            val = ctypes.c_longlong
+        else:
+            val = ctypes.c_long
+    _getintp_ctype.cache = val
+    return val
+
+
+_getintp_ctype.cache = None
+
+# Used for .ctypes attribute of ndarray
+
+class _missing_ctypes:
+    def cast(self, num, obj):
+        return num.value
+
+    class c_void_p:
+        def __init__(self, ptr):
+            self.value = ptr
+
+
+class _ctypes:
+    def __init__(self, array, ptr=None):
+        self._arr = array
+
+        if ctypes:
+            self._ctypes = ctypes
+            self._data = self._ctypes.c_void_p(ptr)
+        else:
+            # fake a pointer-like object that holds onto the reference
+            self._ctypes = _missing_ctypes()
+            self._data = self._ctypes.c_void_p(ptr)
+            self._data._objects = array
+
+        if self._arr.ndim == 0:
+            self._zerod = True
+        else:
+            self._zerod = False
+
+    def data_as(self, obj):
+        """
+        Return the data pointer cast to a particular c-types object.
+        For example, calling ``self._as_parameter_`` is equivalent to
+        ``self.data_as(ctypes.c_void_p)``. Perhaps you want to use
+        the data as a pointer to a ctypes array of floating-point data:
+        ``self.data_as(ctypes.POINTER(ctypes.c_double))``.
+
+        The returned pointer will keep a reference to the array.
+        """
+        # _ctypes.cast function causes a circular reference of self._data in
+        # self._data._objects. Attributes of self._data cannot be released
+        # until gc.collect is called. Make a copy of the pointer first then
+        # let it hold the array reference. This is a workaround to circumvent
+        # the CPython bug https://bugs.python.org/issue12836.
+        ptr = self._ctypes.cast(self._data, obj)
+        ptr._arr = self._arr
+        return ptr
+
+    def shape_as(self, obj):
+        """
+        Return the shape tuple as an array of some other c-types
+        type. For example: ``self.shape_as(ctypes.c_short)``.
+        """
+        if self._zerod:
+            return None
+        return (obj * self._arr.ndim)(*self._arr.shape)
+
+    def strides_as(self, obj):
+        """
+        Return the strides tuple as an array of some other
+        c-types type. For example: ``self.strides_as(ctypes.c_longlong)``.
+        """
+        if self._zerod:
+            return None
+        return (obj * self._arr.ndim)(*self._arr.strides)
+
+    @property
+    def data(self):
+        """
+        A pointer to the memory area of the array as a Python integer.
+        This memory area may contain data that is not aligned, or not in
+        correct byte-order. The memory area may not even be writeable.
+        The array flags and data-type of this array should be respected
+        when passing this attribute to arbitrary C-code to avoid trouble
+        that can include Python crashing. User Beware! The value of this
+        attribute is exactly the same as:
+        ``self._array_interface_['data'][0]``.
+
+        Note that unlike ``data_as``, a reference won't be kept to the array:
+        code like ``ctypes.c_void_p((a + b).ctypes.data)`` will result in a
+        pointer to a deallocated array, and should be spelt
+        ``(a + b).ctypes.data_as(ctypes.c_void_p)``
+        """
+        return self._data.value
+
+    @property
+    def shape(self):
+        """
+        (c_intp*self.ndim): A ctypes array of length self.ndim where
+        the basetype is the C-integer corresponding to ``dtype('p')`` on this
+        platform (see `~numpy.ctypeslib.c_intp`). This base-type could be
+        `ctypes.c_int`, `ctypes.c_long`, or `ctypes.c_longlong` depending on
+        the platform. The ctypes array contains the shape of
+        the underlying array.
+        """
+        return self.shape_as(_getintp_ctype())
+
+    @property
+    def strides(self):
+        """
+        (c_intp*self.ndim): A ctypes array of length self.ndim where
+        the basetype is the same as for the shape attribute. This ctypes
+        array contains the strides information from the underlying array.
+        This strides information is important for showing how many bytes
+        must be jumped to get to the next element in the array.
+        """
+        return self.strides_as(_getintp_ctype())
+
+    @property
+    def _as_parameter_(self):
+        """
+        Overrides the ctypes semi-magic method
+
+        Enables `c_func(some_array.ctypes)`
+        """
+        return self.data_as(ctypes.c_void_p)
+
+
+def _newnames(datatype, order):
+    """
+    Given a datatype and an order object, return a new names tuple, with the
+    order indicated
+    """
+    oldnames = datatype.names
+    nameslist = list(oldnames)
+    if isinstance(order, str):
+        order = [order]
+    seen = set()
+    if isinstance(order, (list, tuple)):
+        for name in order:
+            try:
+                nameslist.remove(name)
+            except ValueError:
+                if name in seen:
+                    raise ValueError(f"duplicate field name: {name}") from None
+                else:
+                    raise ValueError(f"unknown field name: {name}") from None
+            seen.add(name)
+        return tuple(list(order) + nameslist)
+    raise ValueError(f"unsupported order value: {order}")
+
+def _copy_fields(ary):
+    """Return copy of structured array with padding between fields removed.
+
+    Parameters
+    ----------
+    ary : ndarray
+       Structured array from which to remove padding bytes
+
+    Returns
+    -------
+    ary_copy : ndarray
+       Copy of ary with padding bytes removed
+    """
+    dt = ary.dtype
+    copy_dtype = {'names': dt.names,
+                  'formats': [dt.fields[name][0] for name in dt.names]}
+    return array(ary, dtype=copy_dtype, copy=True)
+
+def _promote_fields(dt1, dt2):
+    """ Perform type promotion for two structured dtypes.
+
+    Parameters
+    ----------
+    dt1 : structured dtype
+        First dtype.
+    dt2 : structured dtype
+        Second dtype.
+
+    Returns
+    -------
+    out : dtype
+        The promoted dtype
+
+    Notes
+    -----
+    If one of the inputs is aligned, the result will be.  The titles of
+    both descriptors must match (point to the same field).
+    """
+    # Both must be structured and have the same names in the same order
+    if (dt1.names is None or dt2.names is None) or dt1.names != dt2.names:
+        raise DTypePromotionError(
+                f"field names `{dt1.names}` and `{dt2.names}` mismatch.")
+
+    # if both are identical, we can (maybe!) just return the same dtype.
+    identical = dt1 is dt2
+    new_fields = []
+    for name in dt1.names:
+        field1 = dt1.fields[name]
+        field2 = dt2.fields[name]
+        new_descr = promote_types(field1[0], field2[0])
+        identical = identical and new_descr is field1[0]
+
+        # Check that the titles match (if given):
+        if field1[2:] != field2[2:]:
+            raise DTypePromotionError(
+                    f"field titles of field '{name}' mismatch")
+        if len(field1) == 2:
+            new_fields.append((name, new_descr))
+        else:
+            new_fields.append(((field1[2], name), new_descr))
+
+    res = dtype(new_fields, align=dt1.isalignedstruct or dt2.isalignedstruct)
+
+    # Might as well preserve identity (and metadata) if the dtype is identical
+    # and the itemsize, offsets are also unmodified.  This could probably be
+    # sped up, but also probably just be removed entirely.
+    if identical and res.itemsize == dt1.itemsize:
+        for name in dt1.names:
+            if dt1.fields[name][1] != res.fields[name][1]:
+                return res  # the dtype changed.
+        return dt1
+
+    return res
+
+
+def _getfield_is_safe(oldtype, newtype, offset):
+    """ Checks safety of getfield for object arrays.
+
+    As in _view_is_safe, we need to check that memory containing objects is not
+    reinterpreted as a non-object datatype and vice versa.
+
+    Parameters
+    ----------
+    oldtype : data-type
+        Data type of the original ndarray.
+    newtype : data-type
+        Data type of the field being accessed by ndarray.getfield
+    offset : int
+        Offset of the field being accessed by ndarray.getfield
+
+    Raises
+    ------
+    TypeError
+        If the field access is invalid
+
+    """
+    if newtype.hasobject or oldtype.hasobject:
+        if offset == 0 and newtype == oldtype:
+            return
+        if oldtype.names is not None:
+            for name in oldtype.names:
+                if (oldtype.fields[name][1] == offset and
+                        oldtype.fields[name][0] == newtype):
+                    return
+        raise TypeError("Cannot get/set field of an object array")
+    return
+
+def _view_is_safe(oldtype, newtype):
+    """ Checks safety of a view involving object arrays, for example when
+    doing::
+
+        np.zeros(10, dtype=oldtype).view(newtype)
+
+    Parameters
+    ----------
+    oldtype : data-type
+        Data type of original ndarray
+    newtype : data-type
+        Data type of the view
+
+    Raises
+    ------
+    TypeError
+        If the new type is incompatible with the old type.
+
+    """
+
+    # if the types are equivalent, there is no problem.
+    # for example: dtype((np.record, 'i4,i4')) == dtype((np.void, 'i4,i4'))
+    if oldtype == newtype:
+        return
+
+    if newtype.hasobject or oldtype.hasobject:
+        raise TypeError("Cannot change data-type for array of references.")
+    return
+
+
+# Given a string containing a PEP 3118 format specifier,
+# construct a NumPy dtype
+
+_pep3118_native_map = {
+    '?': '?',
+    'c': 'S1',
+    'b': 'b',
+    'B': 'B',
+    'h': 'h',
+    'H': 'H',
+    'i': 'i',
+    'I': 'I',
+    'l': 'l',
+    'L': 'L',
+    'q': 'q',
+    'Q': 'Q',
+    'e': 'e',
+    'f': 'f',
+    'd': 'd',
+    'g': 'g',
+    'Zf': 'F',
+    'Zd': 'D',
+    'Zg': 'G',
+    's': 'S',
+    'w': 'U',
+    'O': 'O',
+    'x': 'V',  # padding
+}
+_pep3118_native_typechars = ''.join(_pep3118_native_map.keys())
+
+_pep3118_standard_map = {
+    '?': '?',
+    'c': 'S1',
+    'b': 'b',
+    'B': 'B',
+    'h': 'i2',
+    'H': 'u2',
+    'i': 'i4',
+    'I': 'u4',
+    'l': 'i4',
+    'L': 'u4',
+    'q': 'i8',
+    'Q': 'u8',
+    'e': 'f2',
+    'f': 'f',
+    'd': 'd',
+    'Zf': 'F',
+    'Zd': 'D',
+    's': 'S',
+    'w': 'U',
+    'O': 'O',
+    'x': 'V',  # padding
+}
+_pep3118_standard_typechars = ''.join(_pep3118_standard_map.keys())
+
+_pep3118_unsupported_map = {
+    'u': 'UCS-2 strings',
+    '&': 'pointers',
+    't': 'bitfields',
+    'X': 'function pointers',
+}
+
+class _Stream:
+    def __init__(self, s):
+        self.s = s
+        self.byteorder = '@'
+
+    def advance(self, n):
+        res = self.s[:n]
+        self.s = self.s[n:]
+        return res
+
+    def consume(self, c):
+        if self.s[:len(c)] == c:
+            self.advance(len(c))
+            return True
+        return False
+
+    def consume_until(self, c):
+        if callable(c):
+            i = 0
+            while i < len(self.s) and not c(self.s[i]):
+                i = i + 1
+            return self.advance(i)
+        else:
+            i = self.s.index(c)
+            res = self.advance(i)
+            self.advance(len(c))
+            return res
+
+    @property
+    def next(self):
+        return self.s[0]
+
+    def __bool__(self):
+        return bool(self.s)
+
+
+def _dtype_from_pep3118(spec):
+    stream = _Stream(spec)
+    dtype, align = __dtype_from_pep3118(stream, is_subdtype=False)
+    return dtype
+
+def __dtype_from_pep3118(stream, is_subdtype):
+    field_spec = {
+        'names': [],
+        'formats': [],
+        'offsets': [],
+        'itemsize': 0
+    }
+    offset = 0
+    common_alignment = 1
+    is_padding = False
+
+    # Parse spec
+    while stream:
+        value = None
+
+        # End of structure, bail out to upper level
+        if stream.consume('}'):
+            break
+
+        # Sub-arrays (1)
+        shape = None
+        if stream.consume('('):
+            shape = stream.consume_until(')')
+            shape = tuple(map(int, shape.split(',')))
+
+        # Byte order
+        if stream.next in ('@', '=', '<', '>', '^', '!'):
+            byteorder = stream.advance(1)
+            if byteorder == '!':
+                byteorder = '>'
+            stream.byteorder = byteorder
+
+        # Byte order characters also control native vs. standard type sizes
+        if stream.byteorder in ('@', '^'):
+            type_map = _pep3118_native_map
+            type_map_chars = _pep3118_native_typechars
+        else:
+            type_map = _pep3118_standard_map
+            type_map_chars = _pep3118_standard_typechars
+
+        # Item sizes
+        itemsize_str = stream.consume_until(lambda c: not c.isdigit())
+        if itemsize_str:
+            itemsize = int(itemsize_str)
+        else:
+            itemsize = 1
+
+        # Data types
+        is_padding = False
+
+        if stream.consume('T{'):
+            value, align = __dtype_from_pep3118(
+                stream, is_subdtype=True)
+        elif stream.next in type_map_chars:
+            if stream.next == 'Z':
+                typechar = stream.advance(2)
+            else:
+                typechar = stream.advance(1)
+
+            is_padding = (typechar == 'x')
+            dtypechar = type_map[typechar]
+            if dtypechar in 'USV':
+                dtypechar += '%d' % itemsize
+                itemsize = 1
+            numpy_byteorder = {'@': '=', '^': '='}.get(
+                stream.byteorder, stream.byteorder)
+            value = dtype(numpy_byteorder + dtypechar)
+            align = value.alignment
+        elif stream.next in _pep3118_unsupported_map:
+            desc = _pep3118_unsupported_map[stream.next]
+            raise NotImplementedError(
+                f"Unrepresentable PEP 3118 data type {stream.next!r} ({desc})")
+        else:
+            raise ValueError(
+                f"Unknown PEP 3118 data type specifier {stream.s!r}"
+            )
+
+        #
+        # Native alignment may require padding
+        #
+        # Here we assume that the presence of a '@' character implicitly
+        # implies that the start of the array is *already* aligned.
+        #
+        extra_offset = 0
+        if stream.byteorder == '@':
+            start_padding = (-offset) % align
+            intra_padding = (-value.itemsize) % align
+
+            offset += start_padding
+
+            if intra_padding != 0:
+                if itemsize > 1 or (shape is not None and _prod(shape) > 1):
+                    # Inject internal padding to the end of the sub-item
+                    value = _add_trailing_padding(value, intra_padding)
+                else:
+                    # We can postpone the injection of internal padding,
+                    # as the item appears at most once
+                    extra_offset += intra_padding
+
+            # Update common alignment
+            common_alignment = _lcm(align, common_alignment)
+
+        # Convert itemsize to sub-array
+        if itemsize != 1:
+            value = dtype((value, (itemsize,)))
+
+        # Sub-arrays (2)
+        if shape is not None:
+            value = dtype((value, shape))
+
+        # Field name
+        if stream.consume(':'):
+            name = stream.consume_until(':')
+        else:
+            name = None
+
+        if not (is_padding and name is None):
+            if name is not None and name in field_spec['names']:
+                raise RuntimeError(
+                    f"Duplicate field name '{name}' in PEP3118 format"
+                )
+            field_spec['names'].append(name)
+            field_spec['formats'].append(value)
+            field_spec['offsets'].append(offset)
+
+        offset += value.itemsize
+        offset += extra_offset
+
+        field_spec['itemsize'] = offset
+
+    # extra final padding for aligned types
+    if stream.byteorder == '@':
+        field_spec['itemsize'] += (-offset) % common_alignment
+
+    # Check if this was a simple 1-item type, and unwrap it
+    if (field_spec['names'] == [None]
+            and field_spec['offsets'][0] == 0
+            and field_spec['itemsize'] == field_spec['formats'][0].itemsize
+            and not is_subdtype):
+        ret = field_spec['formats'][0]
+    else:
+        _fix_names(field_spec)
+        ret = dtype(field_spec)
+
+    # Finished
+    return ret, common_alignment
+
+def _fix_names(field_spec):
+    """ Replace names which are None with the next unused f%d name """
+    names = field_spec['names']
+    for i, name in enumerate(names):
+        if name is not None:
+            continue
+
+        j = 0
+        while True:
+            name = f'f{j}'
+            if name not in names:
+                break
+            j = j + 1
+        names[i] = name
+
+def _add_trailing_padding(value, padding):
+    """Inject the specified number of padding bytes at the end of a dtype"""
+    if value.fields is None:
+        field_spec = {
+            'names': ['f0'],
+            'formats': [value],
+            'offsets': [0],
+            'itemsize': value.itemsize
+        }
+    else:
+        fields = value.fields
+        names = value.names
+        field_spec = {
+            'names': names,
+            'formats': [fields[name][0] for name in names],
+            'offsets': [fields[name][1] for name in names],
+            'itemsize': value.itemsize
+        }
+
+    field_spec['itemsize'] += padding
+    return dtype(field_spec)
+
+def _prod(a):
+    p = 1
+    for x in a:
+        p *= x
+    return p
+
+def _gcd(a, b):
+    """Calculate the greatest common divisor of a and b"""
+    if not (math.isfinite(a) and math.isfinite(b)):
+        raise ValueError('Can only find greatest common divisor of '
+                         f'finite arguments, found "{a}" and "{b}"')
+    while b:
+        a, b = b, a % b
+    return a
+
+def _lcm(a, b):
+    return a // _gcd(a, b) * b
+
+def array_ufunc_errmsg_formatter(dummy, ufunc, method, *inputs, **kwargs):
+    """ Format the error message for when __array_ufunc__ gives up. """
+    args_string = ', '.join([f'{arg!r}' for arg in inputs] +
+                            [f'{k}={v!r}'
+                             for k, v in kwargs.items()])
+    args = inputs + kwargs.get('out', ())
+    types_string = ', '.join(repr(type(arg).__name__) for arg in args)
+    return ('operand type(s) all returned NotImplemented from '
+            f'__array_ufunc__({ufunc!r}, {method!r}, {args_string}): {types_string}'
+            )
+
+
+def array_function_errmsg_formatter(public_api, types):
+    """ Format the error message for when __array_ufunc__ gives up. """
+    func_name = f'{public_api.__module__}.{public_api.__name__}'
+    return (f"no implementation found for '{func_name}' on types that implement "
+            f'__array_function__: {list(types)}')
+
+
+def _ufunc_doc_signature_formatter(ufunc):
+    """
+    Builds a signature string which resembles PEP 457
+
+    This is used to construct the first line of the docstring
+
+    Keep in sync with `_ufunc_inspect_signature_builder`.
+    """
+
+    # input arguments are simple
+    if ufunc.nin == 1:
+        in_args = 'x'
+    else:
+        in_args = ', '.join(f'x{i + 1}' for i in range(ufunc.nin))
+
+    # output arguments are both keyword or positional
+    if ufunc.nout == 0:
+        out_args = ', /, out=()'
+    elif ufunc.nout == 1:
+        out_args = ', /, out=None'
+    else:
+        out_args = '[, {positional}], / [, out={default}]'.format(
+            positional=', '.join(
+                f'out{i + 1}' for i in range(ufunc.nout)),
+            default=repr((None,) * ufunc.nout)
+        )
+
+    # keyword only args depend on whether this is a gufunc
+    kwargs = (
+        ", casting='same_kind'"
+        ", order='K'"
+        ", dtype=None"
+        ", subok=True"
+    )
+
+    # NOTE: gufuncs may or may not support the `axis` parameter
+    if ufunc.signature is None:
+        kwargs = f", where=True{kwargs}[, signature]"
+    else:
+        kwargs += "[, signature, axes, axis]"
+
+    # join all the parts together
+    return f'{ufunc.__name__}({in_args}{out_args}, *{kwargs})'
+
+
+def _ufunc_inspect_signature_builder(ufunc):
+    """
+    Builds a ``__signature__`` string.
+
+    Should be kept in sync with `_ufunc_doc_signature_formatter`.
+    """
+
+    from inspect import Parameter, Signature
+
+    params = []
+
+    # positional-only input parameters
+    if ufunc.nin == 1:
+        params.append(Parameter("x", Parameter.POSITIONAL_ONLY))
+    else:
+        params.extend(
+            Parameter(f"x{i}", Parameter.POSITIONAL_ONLY)
+            for i in range(1, ufunc.nin + 1)
+        )
+
+    # for the sake of simplicity, we only consider a single output parameter
+    if ufunc.nout == 1:
+        out_default = None
+    else:
+        out_default = (None,) * ufunc.nout
+    params.append(
+        Parameter("out", Parameter.POSITIONAL_OR_KEYWORD, default=out_default),
+    )
+
+    if ufunc.signature is None:
+        params.append(Parameter("where", Parameter.KEYWORD_ONLY, default=True))
+    else:
+        # NOTE: not all gufuncs support the `axis` parameters
+        params.append(Parameter("axes", Parameter.KEYWORD_ONLY, default=_NoValue))
+        params.append(Parameter("axis", Parameter.KEYWORD_ONLY, default=_NoValue))
+        params.append(Parameter("keepdims", Parameter.KEYWORD_ONLY, default=False))
+
+    params.extend((
+        Parameter("casting", Parameter.KEYWORD_ONLY, default='same_kind'),
+        Parameter("order", Parameter.KEYWORD_ONLY, default='K'),
+        Parameter("dtype", Parameter.KEYWORD_ONLY, default=None),
+        Parameter("subok", Parameter.KEYWORD_ONLY, default=True),
+        Parameter("signature", Parameter.KEYWORD_ONLY, default=None),
+    ))
+
+    return Signature(params)
+
+
+def npy_ctypes_check(cls):
+    # determine if a class comes from ctypes, in order to work around
+    # a bug in the buffer protocol for those objects, bpo-10746
+    try:
+        # ctypes class are new-style, so have an __mro__. This probably fails
+        # for ctypes classes with multiple inheritance.
+        if IS_PYPY:
+            # (..., _ctypes.basics._CData, Bufferable, object)
+            ctype_base = cls.__mro__[-3]
+        else:
+            # # (..., _ctypes._CData, object)
+            ctype_base = cls.__mro__[-2]
+        # right now, they're part of the _ctypes module
+        return '_ctypes' in ctype_base.__module__
+    except Exception:
+        return False
+
+# used to handle the _NoValue default argument for na_object
+# in the C implementation of the __reduce__ method for stringdtype
+def _convert_to_stringdtype_kwargs(coerce, na_object=_NoValue):
+    if na_object is _NoValue:
+        return StringDType(coerce=coerce)
+    return StringDType(coerce=coerce, na_object=na_object)

python_env_practice/.venv/Lib/site-packages/numpy/_core/_internal.pyi

@@ -0,0 +1,61 @@
+import ctypes as ct
+import re
+from collections.abc import Callable, Iterable
+from typing import Any, Final, Generic, Self, overload
+from typing_extensions import TypeVar
+
+import numpy as np
+import numpy.typing as npt
+from numpy.ctypeslib import c_intp
+
+_CastT = TypeVar("_CastT", bound=ct._CanCastTo)
+_T_co = TypeVar("_T_co", covariant=True)
+_CT = TypeVar("_CT", bound=ct._CData)
+_PT_co = TypeVar("_PT_co", bound=int | None, default=None, covariant=True)
+
+###
+
+IS_PYPY: Final[bool] = ...
+
+format_re: Final[re.Pattern[str]] = ...
+sep_re: Final[re.Pattern[str]] = ...
+space_re: Final[re.Pattern[str]] = ...
+
+###
+
+# TODO: Let the likes of `shape_as` and `strides_as` return `None`
+# for 0D arrays once we've got shape-support
+
+class _ctypes(Generic[_PT_co]):
+    @overload
+    def __init__(self: _ctypes[None], /, array: npt.NDArray[Any], ptr: None = None) -> None: ...
+    @overload
+    def __init__(self, /, array: npt.NDArray[Any], ptr: _PT_co) -> None: ...
+
+    #
+    @property
+    def data(self) -> _PT_co: ...
+    @property
+    def shape(self) -> ct.Array[c_intp]: ...
+    @property
+    def strides(self) -> ct.Array[c_intp]: ...
+    @property
+    def _as_parameter_(self) -> ct.c_void_p: ...
+
+    #
+    def data_as(self, /, obj: type[_CastT]) -> _CastT: ...
+    def shape_as(self, /, obj: type[_CT]) -> ct.Array[_CT]: ...
+    def strides_as(self, /, obj: type[_CT]) -> ct.Array[_CT]: ...
+
+class dummy_ctype(Generic[_T_co]):
+    _cls: type[_T_co]
+
+    def __init__(self, /, cls: type[_T_co]) -> None: ...
+    def __eq__(self, other: Self, /) -> bool: ...  # type: ignore[override]  # pyright: ignore[reportIncompatibleMethodOverride]
+    def __ne__(self, other: Self, /) -> bool: ...  # type: ignore[override]  # pyright: ignore[reportIncompatibleMethodOverride]
+    def __mul__(self, other: object, /) -> Self: ...
+    def __call__(self, /, *other: object) -> _T_co: ...
+
+def array_ufunc_errmsg_formatter(dummy: object, ufunc: np.ufunc, method: str, *inputs: object, **kwargs: object) -> str: ...
+def array_function_errmsg_formatter(public_api: Callable[..., object], types: Iterable[str]) -> str: ...
+def npy_ctypes_check(cls: type) -> bool: ...

python_env_practice/.venv/Lib/site-packages/numpy/_core/_methods.py

@@ -0,0 +1,252 @@
+"""
+Array methods which are called by both the C-code for the method
+and the Python code for the NumPy-namespace function
+
+"""
+import os
+import pickle
+import warnings
+from contextlib import nullcontext
+
+import numpy as np
+from numpy._core import multiarray as mu, numerictypes as nt, umath as um
+from numpy._core.multiarray import asanyarray
+from numpy._globals import _NoValue
+
+# save those O(100) nanoseconds!
+bool_dt = mu.dtype("bool")
+umr_maximum = um.maximum.reduce
+umr_minimum = um.minimum.reduce
+umr_sum = um.add.reduce
+umr_prod = um.multiply.reduce
+umr_bitwise_count = um.bitwise_count
+umr_any = um.logical_or.reduce
+umr_all = um.logical_and.reduce
+
+# Complex types to -> (2,)float view for fast-path computation in _var()
+_complex_to_float = {
+    nt.dtype(nt.csingle): nt.dtype(nt.single),
+    nt.dtype(nt.cdouble): nt.dtype(nt.double),
+}
+# Special case for windows: ensure double takes precedence
+if nt.dtype(nt.longdouble) != nt.dtype(nt.double):
+    _complex_to_float.update({
+        nt.dtype(nt.clongdouble): nt.dtype(nt.longdouble),
+    })
+
+# avoid keyword arguments to speed up parsing, saves about 15%-20% for very
+# small reductions
+def _amax(a, axis=None, out=None, keepdims=False,
+          initial=_NoValue, where=True):
+    return umr_maximum(a, axis, None, out, keepdims, initial, where)
+
+def _amin(a, axis=None, out=None, keepdims=False,
+          initial=_NoValue, where=True):
+    return umr_minimum(a, axis, None, out, keepdims, initial, where)
+
+def _sum(a, axis=None, dtype=None, out=None, keepdims=False,
+         initial=_NoValue, where=True):
+    return umr_sum(a, axis, dtype, out, keepdims, initial, where)
+
+def _prod(a, axis=None, dtype=None, out=None, keepdims=False,
+          initial=_NoValue, where=True):
+    return umr_prod(a, axis, dtype, out, keepdims, initial, where)
+
+def _any(a, axis=None, dtype=None, out=None, keepdims=False, *, where=True):
+    # By default, return a boolean for any and all
+    if dtype is None:
+        dtype = bool_dt
+    # Parsing keyword arguments is currently fairly slow, so avoid it for now
+    if where is True:
+        return umr_any(a, axis, dtype, out, keepdims)
+    return umr_any(a, axis, dtype, out, keepdims, where=where)
+
+def _all(a, axis=None, dtype=None, out=None, keepdims=False, *, where=True):
+    # By default, return a boolean for any and all
+    if dtype is None:
+        dtype = bool_dt
+    # Parsing keyword arguments is currently fairly slow, so avoid it for now
+    if where is True:
+        return umr_all(a, axis, dtype, out, keepdims)
+    return umr_all(a, axis, dtype, out, keepdims, where=where)
+
+def _count_reduce_items(arr, axis, keepdims=False, where=True):
+    # fast-path for the default case
+    if where is True:
+        # no boolean mask given, calculate items according to axis
+        if axis is None:
+            axis = tuple(range(arr.ndim))
+        elif not isinstance(axis, tuple):
+            axis = (axis,)
+        items = 1
+        for ax in axis:
+            items *= arr.shape[mu.normalize_axis_index(ax, arr.ndim)]
+        items = nt.intp(items)
+    else:
+        # TODO: Optimize case when `where` is broadcast along a non-reduction
+        # axis and full sum is more excessive than needed.
+
+        # guarded to protect circular imports
+        from numpy.lib._stride_tricks_impl import broadcast_to
+        # count True values in (potentially broadcasted) boolean mask
+        items = umr_sum(broadcast_to(where, arr.shape), axis, nt.intp, None,
+                        keepdims)
+    return items
+
+def _clip(a, min=None, max=None, out=None, **kwargs):
+    if a.dtype.kind in "iu":
+        # If min/max is a Python integer, deal with out-of-bound values here.
+        # (This enforces NEP 50 rules as no value based promotion is done.)
+        if type(min) is int and min <= np.iinfo(a.dtype).min:
+            min = None
+        if type(max) is int and max >= np.iinfo(a.dtype).max:
+            max = None
+
+    if min is None and max is None:
+        # return identity
+        return um.positive(a, out=out, **kwargs)
+    elif min is None:
+        return um.minimum(a, max, out=out, **kwargs)
+    elif max is None:
+        return um.maximum(a, min, out=out, **kwargs)
+    else:
+        return um.clip(a, min, max, out=out, **kwargs)
+
+def _mean(a, axis=None, dtype=None, out=None, keepdims=False, *, where=True):
+    arr = asanyarray(a)
+
+    is_float16_result = False
+
+    rcount = _count_reduce_items(arr, axis, keepdims=keepdims, where=where)
+    if rcount == 0 if where is True else umr_any(rcount == 0, axis=None):
+        warnings.warn("Mean of empty slice", RuntimeWarning, stacklevel=2)
+
+    # Cast bool, unsigned int, and int to float64 by default
+    if dtype is None:
+        if issubclass(arr.dtype.type, (nt.integer, nt.bool)):
+            dtype = mu.dtype('f8')
+        elif issubclass(arr.dtype.type, nt.float16):
+            dtype = mu.dtype('f4')
+            is_float16_result = True
+
+    ret = umr_sum(arr, axis, dtype, out, keepdims, where=where)
+    if isinstance(ret, mu.ndarray):
+        ret = um.true_divide(
+                ret, rcount, out=ret, casting='unsafe', subok=False)
+        if is_float16_result and out is None:
+            ret = arr.dtype.type(ret)
+    elif hasattr(ret, 'dtype'):
+        if is_float16_result:
+            ret = arr.dtype.type(ret / rcount)
+        else:
+            ret = ret.dtype.type(ret / rcount)
+    else:
+        ret = ret / rcount
+
+    return ret
+
+def _var(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False, *,
+         where=True, mean=None):
+    arr = asanyarray(a)
+
+    rcount = _count_reduce_items(arr, axis, keepdims=keepdims, where=where)
+    # Make this warning show up on top.
+    if ddof >= rcount if where is True else umr_any(ddof >= rcount, axis=None):
+        warnings.warn("Degrees of freedom <= 0 for slice", RuntimeWarning,
+                      stacklevel=2)
+
+    # Cast bool, unsigned int, and int to float64 by default
+    if dtype is None and issubclass(arr.dtype.type, (nt.integer, nt.bool)):
+        dtype = mu.dtype('f8')
+
+    if mean is not None:
+        arrmean = mean
+    else:
+        # Compute the mean.
+        # Note that if dtype is not of inexact type then arraymean will
+        # not be either.
+        arrmean = umr_sum(arr, axis, dtype, keepdims=True, where=where)
+        # The shape of rcount has to match arrmean to not change the shape of
+        # out in broadcasting. Otherwise, it cannot be stored back to arrmean.
+        if rcount.ndim == 0:
+            # fast-path for default case when where is True
+            div = rcount
+        else:
+            # matching rcount to arrmean when where is specified as array
+            div = rcount.reshape(arrmean.shape)
+        if isinstance(arrmean, mu.ndarray):
+            arrmean = um.true_divide(arrmean, div, out=arrmean,
+                                     casting='unsafe', subok=False)
+        elif hasattr(arrmean, "dtype"):
+            arrmean = arrmean.dtype.type(arrmean / rcount)
+        else:
+            arrmean = arrmean / rcount
+
+    # Compute sum of squared deviations from mean
+    # Note that x may not be inexact and that we need it to be an array,
+    # not a scalar.
+    x = um.subtract(arr, arrmean, out=...)
+    if issubclass(arr.dtype.type, (nt.floating, nt.integer)):
+        x = um.square(x, out=x)
+    # Fast-paths for built-in complex types
+    elif (_float_dtype := _complex_to_float.get(x.dtype)) is not None:
+        xv = x.view(dtype=(_float_dtype, (2,)))
+        um.square(xv, out=xv)
+        x = um.add(xv[..., 0], xv[..., 1], out=x.real)
+    # Most general case; includes handling object arrays containing imaginary
+    # numbers and complex types with non-native byteorder
+    else:
+        x = um.multiply(x, um.conjugate(x), out=x).real
+
+    ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where)
+
+    # Compute degrees of freedom and make sure it is not negative.
+    rcount = um.maximum(rcount - ddof, 0)
+
+    # divide by degrees of freedom
+    if isinstance(ret, mu.ndarray):
+        ret = um.true_divide(
+                ret, rcount, out=ret, casting='unsafe', subok=False)
+    elif hasattr(ret, 'dtype'):
+        ret = ret.dtype.type(ret / rcount)
+    else:
+        ret = ret / rcount
+
+    return ret
+
+def _std(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False, *,
+         where=True, mean=None):
+    ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,
+               keepdims=keepdims, where=where, mean=mean)
+
+    if isinstance(ret, mu.ndarray):
+        ret = um.sqrt(ret, out=ret)
+    elif hasattr(ret, 'dtype'):
+        ret = ret.dtype.type(um.sqrt(ret))
+    else:
+        ret = um.sqrt(ret)
+
+    return ret
+
+def _ptp(a, axis=None, out=None, keepdims=False):
+    return um.subtract(
+        umr_maximum(a, axis, None, out, keepdims),
+        umr_minimum(a, axis, None, None, keepdims),
+        out
+    )
+
+def _dump(self, file, protocol=2):
+    if hasattr(file, 'write'):
+        ctx = nullcontext(file)
+    else:
+        ctx = open(os.fspath(file), "wb")
+    with ctx as f:
+        pickle.dump(self, f, protocol=protocol)
+
+def _dumps(self, protocol=2):
+    return pickle.dumps(self, protocol=protocol)
+
+def _bitwise_count(a, out=None, *, where=True, casting='same_kind',
+          order='K', dtype=None, subok=True):
+    return umr_bitwise_count(a, out, where=where, casting=casting,
+            order=order, dtype=dtype, subok=subok)

python_env_practice/.venv/Lib/site-packages/numpy/_core/_methods.pyi

@@ -0,0 +1,22 @@
+from collections.abc import Callable
+from typing import Any, Concatenate, TypeAlias
+
+import numpy as np
+
+from . import _exceptions as _exceptions
+
+###
+
+_Reduce2: TypeAlias = Callable[Concatenate[object, ...], Any]
+
+###
+
+bool_dt: np.dtype[np.bool] = ...
+umr_maximum: _Reduce2 = ...
+umr_minimum: _Reduce2 = ...
+umr_sum: _Reduce2 = ...
+umr_prod: _Reduce2 = ...
+umr_bitwise_count = np.bitwise_count
+umr_any: _Reduce2 = ...
+umr_all: _Reduce2 = ...
+_complex_to_float: dict[np.dtype[np.complexfloating], np.dtype[np.floating]] = ...

python_env_practice/.venv/Lib/site-packages/numpy/_core/_simd.pyi

@@ -0,0 +1,35 @@
+from types import ModuleType
+from typing import TypedDict, type_check_only
+
+# NOTE: these 5 are only defined on systems with an intel processor
+SSE42: ModuleType | None = ...
+FMA3: ModuleType | None = ...
+AVX2: ModuleType | None = ...
+AVX512F: ModuleType | None = ...
+AVX512_SKX: ModuleType | None = ...
+
+# NOTE: these 2 are only defined on systems with an arm processor
+ASIMD: ModuleType | None = ...
+NEON: ModuleType | None = ...
+
+# NOTE: This is only defined on systems with an riscv64 processor.
+RVV: ModuleType | None = ...
+
+baseline: ModuleType | None = ...
+
+@type_check_only
+class SimdTargets(TypedDict):
+    SSE42: ModuleType | None
+    AVX2: ModuleType | None
+    FMA3: ModuleType | None
+    AVX512F: ModuleType | None
+    AVX512_SKX: ModuleType | None
+    ASIMD: ModuleType | None
+    NEON: ModuleType | None
+    RVV: ModuleType | None
+    baseline: ModuleType | None
+
+targets: SimdTargets = ...
+
+def clear_floatstatus() -> None: ...
+def get_floatstatus() -> int: ...

python_env_practice/.venv/Lib/site-packages/numpy/_core/_string_helpers.py

@@ -0,0 +1,100 @@
+"""
+String-handling utilities to avoid locale-dependence.
+
+Used primarily to generate type name aliases.
+"""
+# "import string" is costly to import!
+# Construct the translation tables directly
+#   "A" = chr(65), "a" = chr(97)
+_all_chars = tuple(map(chr, range(256)))
+_ascii_upper = _all_chars[65:65 + 26]
+_ascii_lower = _all_chars[97:97 + 26]
+LOWER_TABLE = _all_chars[:65] + _ascii_lower + _all_chars[65 + 26:]
+UPPER_TABLE = _all_chars[:97] + _ascii_upper + _all_chars[97 + 26:]
+
+
+def english_lower(s):
+    """ Apply English case rules to convert ASCII strings to all lower case.
+
+    This is an internal utility function to replace calls to str.lower() such
+    that we can avoid changing behavior with changing locales. In particular,
+    Turkish has distinct dotted and dotless variants of the Latin letter "I" in
+    both lowercase and uppercase. Thus, "I".lower() != "i" in a "tr" locale.
+
+    Parameters
+    ----------
+    s : str
+
+    Returns
+    -------
+    lowered : str
+
+    Examples
+    --------
+    >>> from numpy._core.numerictypes import english_lower
+    >>> english_lower('ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789_')
+    'abcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyz0123456789_'
+    >>> english_lower('')
+    ''
+    """
+    lowered = s.translate(LOWER_TABLE)
+    return lowered
+
+
+def english_upper(s):
+    """ Apply English case rules to convert ASCII strings to all upper case.
+
+    This is an internal utility function to replace calls to str.upper() such
+    that we can avoid changing behavior with changing locales. In particular,
+    Turkish has distinct dotted and dotless variants of the Latin letter "I" in
+    both lowercase and uppercase. Thus, "i".upper() != "I" in a "tr" locale.
+
+    Parameters
+    ----------
+    s : str
+
+    Returns
+    -------
+    uppered : str
+
+    Examples
+    --------
+    >>> from numpy._core.numerictypes import english_upper
+    >>> english_upper('ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789_')
+    'ABCDEFGHIJKLMNOPQRSTUVWXYZABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789_'
+    >>> english_upper('')
+    ''
+    """
+    uppered = s.translate(UPPER_TABLE)
+    return uppered
+
+
+def english_capitalize(s):
+    """ Apply English case rules to convert the first character of an ASCII
+    string to upper case.
+
+    This is an internal utility function to replace calls to str.capitalize()
+    such that we can avoid changing behavior with changing locales.
+
+    Parameters
+    ----------
+    s : str
+
+    Returns
+    -------
+    capitalized : str
+
+    Examples
+    --------
+    >>> from numpy._core.numerictypes import english_capitalize
+    >>> english_capitalize('int8')
+    'Int8'
+    >>> english_capitalize('Int8')
+    'Int8'
+    >>> english_capitalize('')
+    ''
+    """
+    if s:
+        return english_upper(s[0]) + s[1:]
+    else:
+        return s

python_env_practice/.venv/Lib/site-packages/numpy/_core/_string_helpers.pyi

@@ -0,0 +1,12 @@
+from typing import Final
+
+_all_chars: Final[tuple[str, ...]] = ...
+_ascii_upper: Final[tuple[str, ...]] = ...
+_ascii_lower: Final[tuple[str, ...]] = ...
+
+LOWER_TABLE: Final[tuple[str, ...]] = ...
+UPPER_TABLE: Final[tuple[str, ...]] = ...
+
+def english_lower(s: str) -> str: ...
+def english_upper(s: str) -> str: ...
+def english_capitalize(s: str) -> str: ...

python_env_practice/.venv/Lib/site-packages/numpy/_core/_type_aliases.py

@@ -0,0 +1,131 @@
+"""
+Due to compatibility, numpy has a very large number of different naming
+conventions for the scalar types (those subclassing from `numpy.generic`).
+This file produces a convoluted set of dictionaries mapping names to types,
+and sometimes other mappings too.
+
+.. data:: allTypes
+    A dictionary of names to types that will be exposed as attributes through
+    ``np._core.numerictypes.*``
+
+.. data:: sctypeDict
+    Similar to `allTypes`, but maps a broader set of aliases to their types.
+
+.. data:: sctypes
+    A dictionary keyed by a "type group" string, providing a list of types
+    under that group.
+
+"""
+
+import numpy._core.multiarray as ma
+from numpy._core.multiarray import dtype, typeinfo
+
+######################################
+# Building `sctypeDict` and `allTypes`
+######################################
+
+sctypeDict = {}
+allTypes = {}
+c_names_dict = {}
+
+_abstract_type_names = {
+    "generic", "integer", "inexact", "floating", "number",
+    "flexible", "character", "complexfloating", "unsignedinteger",
+    "signedinteger"
+}
+
+for _abstract_type_name in _abstract_type_names:
+    allTypes[_abstract_type_name] = getattr(ma, _abstract_type_name)
+    del _abstract_type_name
+
+for k, v in typeinfo.items():
+    if k.startswith("NPY_") and v not in c_names_dict:
+        c_names_dict[k[4:]] = v
+    else:
+        concrete_type = v.type
+        allTypes[k] = concrete_type
+        sctypeDict[k] = concrete_type
+        del concrete_type
+    del k, v
+
+_aliases = {
+    "double": "float64",
+    "cdouble": "complex128",
+    "single": "float32",
+    "csingle": "complex64",
+    "half": "float16",
+    "bool_": "bool",
+    # Default integer:
+    "int_": "intp",
+    "uint": "uintp",
+}
+
+for k, v in _aliases.items():
+    sctypeDict[k] = allTypes[v]
+    allTypes[k] = allTypes[v]
+    del k, v
+
+# extra aliases are added only to `sctypeDict`
+# to support dtype name access, such as`np.dtype("float")`
+_extra_aliases = {
+    "float": "float64",
+    "complex": "complex128",
+    "object": "object_",
+    "bytes": "bytes_",
+    "a": "bytes_",
+    "int": "int_",
+    "str": "str_",
+    "unicode": "str_",
+}
+
+for k, v in _extra_aliases.items():
+    sctypeDict[k] = allTypes[v]
+    del k, v
+
+# include extended precision sized aliases
+for is_complex, full_name in [(False, "longdouble"), (True, "clongdouble")]:
+    longdouble_type = allTypes[full_name]
+
+    bits = dtype(longdouble_type).itemsize * 8
+    base_name = "complex" if is_complex else "float"
+    extended_prec_name = f"{base_name}{bits}"
+    if extended_prec_name not in allTypes:
+        sctypeDict[extended_prec_name] = longdouble_type
+        allTypes[extended_prec_name] = longdouble_type
+
+    del is_complex, full_name, longdouble_type, bits, base_name, extended_prec_name
+
+
+####################
+# Building `sctypes`
+####################
+
+sctypes = {"int": set(), "uint": set(), "float": set(),
+           "complex": set(), "others": set()}
+
+for type_info in typeinfo.values():
+    if type_info.kind in ["M", "m"]:  # exclude timedelta and datetime
+        continue
+
+    concrete_type = type_info.type
+
+    # find proper group for each concrete type
+    for type_group, abstract_type in [
+        ("int", ma.signedinteger), ("uint", ma.unsignedinteger),
+        ("float", ma.floating), ("complex", ma.complexfloating),
+        ("others", ma.generic)
+    ]:
+        if issubclass(concrete_type, abstract_type):
+            sctypes[type_group].add(concrete_type)
+            del type_group, abstract_type
+            break
+
+    del type_info, concrete_type
+
+# sort sctype groups by bitsize
+for sctype_key in sctypes.keys():
+    sctype_list = list(sctypes[sctype_key])
+    sctype_list.sort(key=lambda x: dtype(x).itemsize)
+    sctypes[sctype_key] = sctype_list
+
+    del sctype_key, sctype_list

python_env_practice/.venv/Lib/site-packages/numpy/_core/_type_aliases.pyi

@@ -0,0 +1,86 @@
+from collections.abc import Collection
+from typing import Final, Literal as L, TypeAlias, TypedDict, type_check_only
+
+import numpy as np
+
+sctypeDict: Final[dict[str, type[np.generic]]]
+allTypes: Final[dict[str, type[np.generic]]]
+
+@type_check_only
+class _CNamesDict(TypedDict):
+    BOOL: np.dtype[np.bool]
+    HALF: np.dtype[np.half]
+    FLOAT: np.dtype[np.single]
+    DOUBLE: np.dtype[np.double]
+    LONGDOUBLE: np.dtype[np.longdouble]
+    CFLOAT: np.dtype[np.csingle]
+    CDOUBLE: np.dtype[np.cdouble]
+    CLONGDOUBLE: np.dtype[np.clongdouble]
+    STRING: np.dtype[np.bytes_]
+    UNICODE: np.dtype[np.str_]
+    VOID: np.dtype[np.void]
+    OBJECT: np.dtype[np.object_]
+    DATETIME: np.dtype[np.datetime64]
+    TIMEDELTA: np.dtype[np.timedelta64]
+    BYTE: np.dtype[np.byte]
+    UBYTE: np.dtype[np.ubyte]
+    SHORT: np.dtype[np.short]
+    USHORT: np.dtype[np.ushort]
+    INT: np.dtype[np.intc]
+    UINT: np.dtype[np.uintc]
+    LONG: np.dtype[np.long]
+    ULONG: np.dtype[np.ulong]
+    LONGLONG: np.dtype[np.longlong]
+    ULONGLONG: np.dtype[np.ulonglong]
+
+c_names_dict: Final[_CNamesDict]
+
+_AbstractTypeName: TypeAlias = L[
+    "generic",
+    "flexible",
+    "character",
+    "number",
+    "integer",
+    "inexact",
+    "unsignedinteger",
+    "signedinteger",
+    "floating",
+    "complexfloating",
+]
+_abstract_type_names: Final[set[_AbstractTypeName]]
+
+@type_check_only
+class _AliasesType(TypedDict):
+    double: L["float64"]
+    cdouble: L["complex128"]
+    single: L["float32"]
+    csingle: L["complex64"]
+    half: L["float16"]
+    bool_: L["bool"]
+    int_: L["intp"]
+    uint: L["intp"]
+
+_aliases: Final[_AliasesType]
+
+@type_check_only
+class _ExtraAliasesType(TypedDict):
+    float: L["float64"]
+    complex: L["complex128"]
+    object: L["object_"]
+    bytes: L["bytes_"]
+    a: L["bytes_"]
+    int: L["int_"]
+    str: L["str_"]
+    unicode: L["str_"]
+
+_extra_aliases: Final[_ExtraAliasesType]
+
+@type_check_only
+class _SCTypes(TypedDict):
+    int: Collection[type[np.signedinteger]]
+    uint: Collection[type[np.unsignedinteger]]
+    float: Collection[type[np.floating]]
+    complex: Collection[type[np.complexfloating]]
+    others: Collection[type[np.flexible | np.bool | np.object_]]
+
+sctypes: Final[_SCTypes]

python_env_practice/.venv/Lib/site-packages/numpy/_core/_ufunc_config.py

@@ -0,0 +1,515 @@
+"""
+Functions for changing global ufunc configuration
+
+This provides helpers which wrap `_get_extobj_dict` and `_make_extobj`, and
+`_extobj_contextvar` from umath.
+"""
+import functools
+
+from numpy._utils import set_module
+
+from .umath import _extobj_contextvar, _get_extobj_dict, _make_extobj
+
+__all__ = [
+    "seterr", "geterr", "setbufsize", "getbufsize", "seterrcall", "geterrcall",
+    "errstate"
+]
+
+
+@set_module('numpy')
+def seterr(all=None, divide=None, over=None, under=None, invalid=None):
+    """
+    Set how floating-point errors are handled.
+
+    Note that operations on integer scalar types (such as `int16`) are
+    handled like floating point, and are affected by these settings.
+
+    Parameters
+    ----------
+    all : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional
+        Set treatment for all types of floating-point errors at once:
+
+        - ignore: Take no action when the exception occurs.
+        - warn: Print a :exc:`RuntimeWarning` (via the Python `warnings`
+          module).
+        - raise: Raise a :exc:`FloatingPointError`.
+        - call: Call a function specified using the `seterrcall` function.
+        - print: Print a warning directly to ``stdout``.
+        - log: Record error in a Log object specified by `seterrcall`.
+
+        The default is not to change the current behavior.
+    divide : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional
+        Treatment for division by zero.
+    over : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional
+        Treatment for floating-point overflow.
+    under : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional
+        Treatment for floating-point underflow.
+    invalid : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional
+        Treatment for invalid floating-point operation.
+
+    Returns
+    -------
+    old_settings : dict
+        Dictionary containing the old settings.
+
+    See also
+    --------
+    seterrcall : Set a callback function for the 'call' mode.
+    geterr, geterrcall, errstate
+
+
+    Notes
+    -----
+    The floating-point exceptions are defined in the IEEE 754 standard [1]_:
+
+    - Division by zero: infinite result obtained from finite numbers.
+    - Overflow: result too large to be expressed.
+    - Underflow: result so close to zero that some precision
+      was lost.
+    - Invalid operation: result is not an expressible number, typically
+      indicates that a NaN was produced.
+
+    **Concurrency note:** see  :ref:`fp_error_handling`
+
+    .. [1] https://en.wikipedia.org/wiki/IEEE_754
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> orig_settings = np.seterr(all='ignore')  # seterr to known value
+    >>> np.int16(32000) * np.int16(3)
+    np.int16(30464)
+    >>> np.seterr(over='raise')
+    {'divide': 'ignore', 'over': 'ignore', 'under': 'ignore', 'invalid': 'ignore'}
+    >>> old_settings = np.seterr(all='warn', over='raise')
+    >>> np.int16(32000) * np.int16(3)
+    Traceback (most recent call last):
+      File "<stdin>", line 1, in <module>
+    FloatingPointError: overflow encountered in scalar multiply
+
+    >>> old_settings = np.seterr(all='print')
+    >>> np.geterr()
+    {'divide': 'print', 'over': 'print', 'under': 'print', 'invalid': 'print'}
+    >>> np.int16(32000) * np.int16(3)
+    np.int16(30464)
+    >>> np.seterr(**orig_settings)  # restore original
+    {'divide': 'print', 'over': 'print', 'under': 'print', 'invalid': 'print'}
+
+    """
+
+    old = _get_extobj_dict()
+    # The errstate doesn't include call and bufsize, so pop them:
+    old.pop("call", None)
+    old.pop("bufsize", None)
+
+    extobj = _make_extobj(
+            all=all, divide=divide, over=over, under=under, invalid=invalid)
+    _extobj_contextvar.set(extobj)
+    return old
+
+
+@set_module('numpy')
+def geterr():
+    """
+    Get the current way of handling floating-point errors.
+
+    Returns
+    -------
+    res : dict
+        A dictionary with keys "divide", "over", "under", and "invalid",
+        whose values are from the strings "ignore", "print", "log", "warn",
+        "raise", and "call". The keys represent possible floating-point
+        exceptions, and the values define how these exceptions are handled.
+
+    See Also
+    --------
+    geterrcall, seterr, seterrcall
+
+    Notes
+    -----
+    For complete documentation of the types of floating-point exceptions and
+    treatment options, see `seterr`.
+
+    **Concurrency note:** see :doc:`/reference/routines.err`
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> np.geterr()
+    {'divide': 'warn', 'over': 'warn', 'under': 'ignore', 'invalid': 'warn'}
+    >>> np.arange(3.) / np.arange(3.)  # doctest: +SKIP
+    array([nan,  1.,  1.])
+    RuntimeWarning: invalid value encountered in divide
+
+    >>> oldsettings = np.seterr(all='warn', invalid='raise')
+    >>> np.geterr()
+    {'divide': 'warn', 'over': 'warn', 'under': 'warn', 'invalid': 'raise'}
+    >>> np.arange(3.) / np.arange(3.)
+    Traceback (most recent call last):
+      ...
+    FloatingPointError: invalid value encountered in divide
+    >>> oldsettings = np.seterr(**oldsettings)  # restore original
+
+    """
+    res = _get_extobj_dict()
+    # The "geterr" doesn't include call and bufsize,:
+    res.pop("call", None)
+    res.pop("bufsize", None)
+    return res
+
+
+@set_module('numpy')
+def setbufsize(size):
+    """
+    Set the size of the buffer used in ufuncs.
+
+    .. versionchanged:: 2.0
+        The scope of setting the buffer is tied to the `numpy.errstate`
+        context.  Exiting a ``with errstate():`` will also restore the bufsize.
+
+    Parameters
+    ----------
+    size : int
+        Size of buffer.
+
+    Returns
+    -------
+    bufsize : int
+        Previous size of ufunc buffer in bytes.
+
+    Notes
+    -----
+    **Concurrency note:** see :doc:`/reference/routines.err`
+
+    Examples
+    --------
+    When exiting a `numpy.errstate` context manager the bufsize is restored:
+
+    >>> import numpy as np
+    >>> with np.errstate():
+    ...     np.setbufsize(4096)
+    ...     print(np.getbufsize())
+    ...
+    8192
+    4096
+    >>> np.getbufsize()
+    8192
+
+    """
+    if size < 0:
+        raise ValueError("buffer size must be non-negative")
+    old = _get_extobj_dict()["bufsize"]
+    extobj = _make_extobj(bufsize=size)
+    _extobj_contextvar.set(extobj)
+    return old
+
+
+@set_module('numpy')
+def getbufsize():
+    """
+    Return the size of the buffer used in ufuncs.
+
+    Returns
+    -------
+    getbufsize : int
+        Size of ufunc buffer in bytes.
+
+    Notes
+    -----
+
+    **Concurrency note:** see :doc:`/reference/routines.err`
+
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> np.getbufsize()
+    8192
+
+    """
+    return _get_extobj_dict()["bufsize"]
+
+
+@set_module('numpy')
+def seterrcall(func):
+    """
+    Set the floating-point error callback function or log object.
+
+    There are two ways to capture floating-point error messages.  The first
+    is to set the error-handler to 'call', using `seterr`.  Then, set
+    the function to call using this function.
+
+    The second is to set the error-handler to 'log', using `seterr`.
+    Floating-point errors then trigger a call to the 'write' method of
+    the provided object.
+
+    Parameters
+    ----------
+    func : callable f(err, flag) or object with write method
+        Function to call upon floating-point errors ('call'-mode) or
+        object whose 'write' method is used to log such message ('log'-mode).
+
+        The call function takes two arguments. The first is a string describing
+        the type of error (such as "divide by zero", "overflow", "underflow",
+        or "invalid value"), and the second is the status flag.  The flag is a
+        byte, whose four least-significant bits indicate the type of error, one
+        of "divide", "over", "under", "invalid"::
+
+          [0 0 0 0 divide over under invalid]
+
+        In other words, ``flags = divide + 2*over + 4*under + 8*invalid``.
+
+        If an object is provided, its write method should take one argument,
+        a string.
+
+    Returns
+    -------
+    h : callable, log instance or None
+        The old error handler.
+
+    See Also
+    --------
+    seterr, geterr, geterrcall
+
+    Notes
+    -----
+
+    **Concurrency note:** see :doc:`/reference/routines.err`
+
+    Examples
+    --------
+    Callback upon error:
+
+    >>> def err_handler(type, flag):
+    ...     print("Floating point error (%s), with flag %s" % (type, flag))
+    ...
+
+    >>> import numpy as np
+
+    >>> orig_handler = np.seterrcall(err_handler)
+    >>> orig_err = np.seterr(all='call')
+
+    >>> np.array([1, 2, 3]) / 0.0
+    Floating point error (divide by zero), with flag 1
+    array([inf, inf, inf])
+
+    >>> np.seterrcall(orig_handler)
+    <function err_handler at 0x...>
+    >>> np.seterr(**orig_err)
+    {'divide': 'call', 'over': 'call', 'under': 'call', 'invalid': 'call'}
+
+    Log error message:
+
+    >>> class Log:
+    ...     def write(self, msg):
+    ...         print("LOG: %s" % msg)
+    ...
+
+    >>> log = Log()
+    >>> saved_handler = np.seterrcall(log)
+    >>> save_err = np.seterr(all='log')
+
+    >>> np.array([1, 2, 3]) / 0.0
+    LOG: Warning: divide by zero encountered in divide
+    array([inf, inf, inf])
+
+    >>> np.seterrcall(orig_handler)
+    <numpy.Log object at 0x...>
+    >>> np.seterr(**orig_err)
+    {'divide': 'log', 'over': 'log', 'under': 'log', 'invalid': 'log'}
+
+    """
+    old = _get_extobj_dict()["call"]
+    extobj = _make_extobj(call=func)
+    _extobj_contextvar.set(extobj)
+    return old
+
+
+@set_module('numpy')
+def geterrcall():
+    """
+    Return the current callback function used on floating-point errors.
+
+    When the error handling for a floating-point error (one of "divide",
+    "over", "under", or "invalid") is set to 'call' or 'log', the function
+    that is called or the log instance that is written to is returned by
+    `geterrcall`. This function or log instance has been set with
+    `seterrcall`.
+
+    Returns
+    -------
+    errobj : callable, log instance or None
+        The current error handler. If no handler was set through `seterrcall`,
+        ``None`` is returned.
+
+    See Also
+    --------
+    seterrcall, seterr, geterr
+
+    Notes
+    -----
+    For complete documentation of the types of floating-point exceptions and
+    treatment options, see `seterr`.
+
+    **Concurrency note:** see :ref:`fp_error_handling`
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> np.geterrcall()  # we did not yet set a handler, returns None
+
+    >>> orig_settings = np.seterr(all='call')
+    >>> def err_handler(type, flag):
+    ...     print("Floating point error (%s), with flag %s" % (type, flag))
+    >>> old_handler = np.seterrcall(err_handler)
+    >>> np.array([1, 2, 3]) / 0.0
+    Floating point error (divide by zero), with flag 1
+    array([inf, inf, inf])
+
+    >>> cur_handler = np.geterrcall()
+    >>> cur_handler is err_handler
+    True
+    >>> old_settings = np.seterr(**orig_settings)  # restore original
+    >>> old_handler = np.seterrcall(None)  # restore original
+
+    """
+    return _get_extobj_dict()["call"]
+
+
+class _unspecified:
+    pass
+
+
+_Unspecified = _unspecified()
+
+
+@set_module('numpy')
+class errstate:
+    """
+    errstate(**kwargs)
+
+    Context manager for floating-point error handling.
+
+    Using an instance of `errstate` as a context manager allows statements in
+    that context to execute with a known error handling behavior. Upon entering
+    the context the error handling is set with `seterr` and `seterrcall`, and
+    upon exiting it is reset to what it was before.
+
+    ..  versionchanged:: 1.17.0
+        `errstate` is also usable as a function decorator, saving
+        a level of indentation if an entire function is wrapped.
+
+    .. versionchanged:: 2.0
+        `errstate` is now fully thread and asyncio safe, but may not be
+        entered more than once.
+        It is not safe to decorate async functions using ``errstate``.
+
+    Parameters
+    ----------
+    kwargs : {divide, over, under, invalid}
+        Keyword arguments. The valid keywords are the possible floating-point
+        exceptions. Each keyword should have a string value that defines the
+        treatment for the particular error. Possible values are
+        {'ignore', 'warn', 'raise', 'call', 'print', 'log'}.
+
+    See Also
+    --------
+    seterr, geterr, seterrcall, geterrcall
+
+    Notes
+    -----
+    For complete documentation of the types of floating-point exceptions and
+    treatment options, see `seterr`.
+
+    **Concurrency note:** see :ref:`fp_error_handling`
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> olderr = np.seterr(all='ignore')  # Set error handling to known state.
+
+    >>> np.arange(3) / 0.
+    array([nan, inf, inf])
+    >>> with np.errstate(divide='ignore'):
+    ...     np.arange(3) / 0.
+    array([nan, inf, inf])
+
+    >>> np.sqrt(-1)
+    np.float64(nan)
+    >>> with np.errstate(invalid='raise'):
+    ...     np.sqrt(-1)
+    Traceback (most recent call last):
+      File "<stdin>", line 2, in <module>
+    FloatingPointError: invalid value encountered in sqrt
+
+    Outside the context the error handling behavior has not changed:
+
+    >>> np.geterr()
+    {'divide': 'ignore', 'over': 'ignore', 'under': 'ignore', 'invalid': 'ignore'}
+    >>> olderr = np.seterr(**olderr)  # restore original state
+
+    """
+    __slots__ = (
+        "_all",
+        "_call",
+        "_divide",
+        "_invalid",
+        "_over",
+        "_token",
+        "_under",
+    )
+
+    def __init__(self, *, call=_Unspecified,
+                 all=None, divide=None, over=None, under=None, invalid=None):
+        self._token = None
+        self._call = call
+        self._all = all
+        self._divide = divide
+        self._over = over
+        self._under = under
+        self._invalid = invalid
+
+    def __enter__(self):
+        # Note that __call__ duplicates much of this logic
+        if self._token is not None:
+            raise TypeError("Cannot enter `np.errstate` twice.")
+        if self._call is _Unspecified:
+            extobj = _make_extobj(
+                    all=self._all, divide=self._divide, over=self._over,
+                    under=self._under, invalid=self._invalid)
+        else:
+            extobj = _make_extobj(
+                    call=self._call,
+                    all=self._all, divide=self._divide, over=self._over,
+                    under=self._under, invalid=self._invalid)
+
+        self._token = _extobj_contextvar.set(extobj)
+
+    def __exit__(self, *exc_info):
+        _extobj_contextvar.reset(self._token)
+
+    def __call__(self, func):
+        # We need to customize `__call__` compared to `ContextDecorator`
+        # because we must store the token per-thread so cannot store it on
+        # the instance (we could create a new instance for this).
+        # This duplicates the code from `__enter__`.
+        @functools.wraps(func)
+        def inner(*args, **kwargs):
+            if self._call is _Unspecified:
+                extobj = _make_extobj(
+                        all=self._all, divide=self._divide, over=self._over,
+                        under=self._under, invalid=self._invalid)
+            else:
+                extobj = _make_extobj(
+                        call=self._call,
+                        all=self._all, divide=self._divide, over=self._over,
+                        under=self._under, invalid=self._invalid)
+
+            _token = _extobj_contextvar.set(extobj)
+            try:
+                # Call the original, decorated, function:
+                return func(*args, **kwargs)
+            finally:
+                _extobj_contextvar.reset(_token)
+
+        return inner

python_env_practice/.venv/Lib/site-packages/numpy/_core/_ufunc_config.pyi

@@ -0,0 +1,69 @@
+from _typeshed import SupportsWrite
+from collections.abc import Callable
+from types import TracebackType
+from typing import Any, Final, Literal, TypeAlias, TypedDict, TypeVar, type_check_only
+
+__all__ = [
+    "seterr",
+    "geterr",
+    "setbufsize",
+    "getbufsize",
+    "seterrcall",
+    "geterrcall",
+    "errstate",
+]
+
+_ErrKind: TypeAlias = Literal["ignore", "warn", "raise", "call", "print", "log"]
+_ErrCall: TypeAlias = Callable[[str, int], Any] | SupportsWrite[str]
+
+_CallableT = TypeVar("_CallableT", bound=Callable[..., object])
+
+@type_check_only
+class _ErrDict(TypedDict):
+    divide: _ErrKind
+    over: _ErrKind
+    under: _ErrKind
+    invalid: _ErrKind
+
+###
+
+class _unspecified: ...
+
+_Unspecified: Final[_unspecified]
+
+class errstate:
+    __slots__ = "_all", "_call", "_divide", "_invalid", "_over", "_token", "_under"
+
+    def __init__(
+        self,
+        /,
+        *,
+        call: _ErrCall | _unspecified = ...,  # = _Unspecified
+        all: _ErrKind | None = None,
+        divide: _ErrKind | None = None,
+        over: _ErrKind | None = None,
+        under: _ErrKind | None = None,
+        invalid: _ErrKind | None = None,
+    ) -> None: ...
+    def __call__(self, /, func: _CallableT) -> _CallableT: ...
+    def __enter__(self) -> None: ...
+    def __exit__(
+        self,
+        exc_type: type[BaseException] | None,
+        exc_value: BaseException | None,
+        traceback: TracebackType | None,
+        /,
+    ) -> None: ...
+
+def seterr(
+    all: _ErrKind | None = None,
+    divide: _ErrKind | None = None,
+    over: _ErrKind | None = None,
+    under: _ErrKind | None = None,
+    invalid: _ErrKind | None = None,
+) -> _ErrDict: ...
+def geterr() -> _ErrDict: ...
+def setbufsize(size: int) -> int: ...
+def getbufsize() -> int: ...
+def seterrcall(func: _ErrCall | None) -> _ErrCall | None: ...
+def geterrcall() -> _ErrCall | None: ...

python_env_practice/.venv/Lib/site-packages/numpy/_core/_umath_tests.pyi

@@ -0,0 +1,47 @@
+# undocumented internal testing module for ufunc features, defined in
+# numpy/_core/src/umath/_umath_tests.c.src
+
+from typing import Final, Literal as L, TypedDict, type_check_only
+
+import numpy as np
+from numpy._typing import _GUFunc_Nin2_Nout1, _UFunc_Nin1_Nout1, _UFunc_Nin2_Nout1
+
+@type_check_only
+class _TestDispatchResult(TypedDict):
+    func: str  # e.g. 'func_AVX2'
+    var: str  # e.g. 'var_AVX2'
+    func_xb: str  # e.g. 'func_AVX2'
+    var_xb: str  # e.g. 'var_AVX2'
+    all: list[str]  # e.g. ['func_AVX2', 'func_SSE41', 'func']
+
+###
+
+# undocumented
+def test_signature(
+    nin: int, nout: int, signature: str, /
+) -> tuple[
+    L[0, 1],  # core_enabled (0 for scalar ufunc; 1 for generalized ufunc)
+    tuple[int, ...] | None,  # core_num_dims
+    tuple[int, ...] | None,  # core_dim_ixs
+    tuple[int, ...] | None,  # core_dim_flags
+    tuple[int, ...] | None,  # core_dim_sizes
+]: ...
+
+# undocumented
+def test_dispatch() -> _TestDispatchResult: ...
+
+# undocumented ufuncs and gufuncs
+always_error: Final[_UFunc_Nin2_Nout1[L["always_error"], L[1], None]] = ...
+always_error_unary: Final[_UFunc_Nin1_Nout1[L["always_error_unary"], L[1], None]] = ...
+always_error_gufunc: Final[_GUFunc_Nin2_Nout1[L["always_error_gufunc"], L[1], None, L["(i),()->()"]]] = ...
+inner1d: Final[_GUFunc_Nin2_Nout1[L["inner1d"], L[2], None, L["(i),(i)->()"]]] = ...
+innerwt: Final[np.ufunc] = ...  # we have no specialized type for 3->1 gufuncs
+matrix_multiply: Final[_GUFunc_Nin2_Nout1[L["matrix_multiply"], L[3], None, L["(m,n),(n,p)->(m,p)"]]] = ...
+matmul: Final[_GUFunc_Nin2_Nout1[L["matmul"], L[3], None, L["(m?,n),(n,p?)->(m?,p?)"]]] = ...
+euclidean_pdist: Final[_GUFunc_Nin2_Nout1[L["euclidean_pdist"], L[2], None, L["(n,d)->(p)"]]] = ...
+cumsum: Final[np.ufunc] = ...  # we have no specialized type for 1->1 gufuncs
+inner1d_no_doc: Final[_GUFunc_Nin2_Nout1[L["inner1d_no_doc"], L[2], None, L["(i),(i)->()"]]] = ...
+cross1d: Final[_GUFunc_Nin2_Nout1[L["cross1d"], L[2], None, L["(3),(3)->(3)"]]] = ...
+_pickleable_module_global_ufunc: Final[np.ufunc] = ...  # 0->0 ufunc; segfaults if called
+indexed_negative: Final[_UFunc_Nin1_Nout1[L["indexed_negative"], L[0], L[0]]] = ...  # ntypes=0; can't be called
+conv1d_full: Final[_GUFunc_Nin2_Nout1[L["conv1d_full"], L[1], None, L["(m),(n)->(p)"]]] = ...

python_env_practice/.venv/Lib/site-packages/numpy/_core/arrayprint.pyi

@@ -0,0 +1,158 @@
+from collections.abc import Callable
+
+# Using a private class is by no means ideal, but it is simply a consequence
+# of a `contextlib.context` returning an instance of aforementioned class
+from contextlib import _GeneratorContextManager
+from typing import (
+    Any,
+    Final,
+    Literal,
+    SupportsIndex,
+    TypeAlias,
+    TypedDict,
+    type_check_only,
+)
+
+import numpy as np
+from numpy._typing import NDArray, _CharLike_co, _FloatLike_co
+
+__all__ = [
+    "array2string",
+    "array_repr",
+    "array_str",
+    "format_float_positional",
+    "format_float_scientific",
+    "get_printoptions",
+    "printoptions",
+    "set_printoptions",
+]
+
+###
+
+_FloatMode: TypeAlias = Literal["fixed", "unique", "maxprec", "maxprec_equal"]
+_LegacyNoStyle: TypeAlias = Literal["1.21", "1.25", "2.1", False]
+_Legacy: TypeAlias = Literal["1.13", _LegacyNoStyle]
+_Sign: TypeAlias = Literal["-", "+", " "]
+_Trim: TypeAlias = Literal["k", ".", "0", "-"]
+_ReprFunc: TypeAlias = Callable[[NDArray[Any]], str]
+
+@type_check_only
+class _FormatDict(TypedDict, total=False):
+    bool: Callable[[np.bool], str]
+    int: Callable[[np.integer], str]
+    timedelta: Callable[[np.timedelta64], str]
+    datetime: Callable[[np.datetime64], str]
+    float: Callable[[np.floating], str]
+    longfloat: Callable[[np.longdouble], str]
+    complexfloat: Callable[[np.complexfloating], str]
+    longcomplexfloat: Callable[[np.clongdouble], str]
+    void: Callable[[np.void], str]
+    numpystr: Callable[[_CharLike_co], str]
+    object: Callable[[object], str]
+    all: Callable[[object], str]
+    int_kind: Callable[[np.integer], str]
+    float_kind: Callable[[np.floating], str]
+    complex_kind: Callable[[np.complexfloating], str]
+    str_kind: Callable[[_CharLike_co], str]
+
+@type_check_only
+class _FormatOptions(TypedDict):
+    precision: int
+    threshold: int
+    edgeitems: int
+    linewidth: int
+    suppress: bool
+    nanstr: str
+    infstr: str
+    formatter: _FormatDict | None
+    sign: _Sign
+    floatmode: _FloatMode
+    legacy: _Legacy
+
+###
+
+__docformat__: Final = "restructuredtext"  # undocumented
+
+def set_printoptions(
+    precision: SupportsIndex | None = None,
+    threshold: int | None = None,
+    edgeitems: int | None = None,
+    linewidth: int | None = None,
+    suppress: bool | None = None,
+    nanstr: str | None = None,
+    infstr: str | None = None,
+    formatter: _FormatDict | None = None,
+    sign: _Sign | None = None,
+    floatmode: _FloatMode | None = None,
+    *,
+    legacy: _Legacy | None = None,
+    override_repr: _ReprFunc | None = None,
+) -> None: ...
+def get_printoptions() -> _FormatOptions: ...
+
+# public numpy export
+def array2string(
+    a: NDArray[Any],
+    max_line_width: int | None = None,
+    precision: SupportsIndex | None = None,
+    suppress_small: bool | None = None,
+    separator: str = " ",
+    prefix: str = "",
+    *,
+    formatter: _FormatDict | None = None,
+    threshold: int | None = None,
+    edgeitems: int | None = None,
+    sign: _Sign | None = None,
+    floatmode: _FloatMode | None = None,
+    suffix: str = "",
+    legacy: _Legacy | None = None,
+) -> str: ...
+
+def format_float_scientific(
+    x: _FloatLike_co,
+    precision: int | None = None,
+    unique: bool = True,
+    trim: _Trim = "k",
+    sign: bool = False,
+    pad_left: int | None = None,
+    exp_digits: int | None = None,
+    min_digits: int | None = None,
+) -> str: ...
+def format_float_positional(
+    x: _FloatLike_co,
+    precision: int | None = None,
+    unique: bool = True,
+    fractional: bool = True,
+    trim: _Trim = "k",
+    sign: bool = False,
+    pad_left: int | None = None,
+    pad_right: int | None = None,
+    min_digits: int | None = None,
+) -> str: ...
+def array_repr(
+    arr: NDArray[Any],
+    max_line_width: int | None = None,
+    precision: SupportsIndex | None = None,
+    suppress_small: bool | None = None,
+) -> str: ...
+def array_str(
+    a: NDArray[Any],
+    max_line_width: int | None = None,
+    precision: SupportsIndex | None = None,
+    suppress_small: bool | None = None,
+) -> str: ...
+def printoptions(
+    precision: SupportsIndex | None = ...,
+    threshold: int | None = ...,
+    edgeitems: int | None = ...,
+    linewidth: int | None = ...,
+    suppress: bool | None = ...,
+    nanstr: str | None = ...,
+    infstr: str | None = ...,
+    formatter: _FormatDict | None = ...,
+    sign: _Sign | None = None,
+    floatmode: _FloatMode | None = None,
+    *,
+    legacy: _Legacy | None = None,
+    override_repr: _ReprFunc | None = None,
+) -> _GeneratorContextManager[_FormatOptions]: ...

python_env_practice/.venv/Lib/site-packages/numpy/_core/cversions.py

@@ -0,0 +1,13 @@
+"""Simple script to compute the api hash of the current API.
+
+The API has is defined by numpy_api_order and ufunc_api_order.
+
+"""
+from os.path import dirname
+
+from code_generators.genapi import fullapi_hash
+from code_generators.numpy_api import full_api
+
+if __name__ == '__main__':
+    curdir = dirname(__file__)
+    print(fullapi_hash(full_api))