benjamintli/cosqa-llm-filtered-hard-negatives huggingface dataset:
Query: check if singular matrix python

Relevant code:
def is_square_matrix(mat):
    """Test if an array is a square matrix."""
    mat = np.array(mat)
    if mat.ndim != 2:
        return False
    shape = mat.shape
    return shape[0] == shape[1]

Similar but not relevant code:
def full_s(self):
        """ Get the full singular value matrix of self

        Returns
        -------
        Matrix : Matrix

        """
        x = np.zeros((self.shape),dtype=np.float32)

        x[:self.s.shape[0],:self.s.shape[0]] = self.s.as_2d
        s = Matrix(x=x, row_names=self.row_names,
                          col_names=self.col_names, isdiagonal=False,
                          autoalign=False)
        return s

Similar but not relevant code:
def isSquare(matrix):
    """Check that ``matrix`` is square.

    Returns
    =======
    is_square : bool
        ``True`` if ``matrix`` is square, ``False`` otherwise.

    """
    try:
        try:
            dim1, dim2 = matrix.shape
        except AttributeError:
            dim1, dim2 = _np.array(matrix).shape
    except ValueError:
        return False
    if dim1 == dim2:
        return True
    return False

Similar but not relevant code:
def test_SVD(pca):
    """
    Function to test the validity of singular
    value decomposition by reconstructing original
    data.
    """
    _ = pca
    rec = N.dot(_.U,N.dot(_.sigma,_.V))
    assert N.allclose(_.arr,rec)

Similar but not relevant code:
def is_orthogonal(
        matrix: np.ndarray,
        *,
        rtol: float = 1e-5,
        atol: float = 1e-8) -> bool:
    """Determines if a matrix is approximately orthogonal.

    A matrix is orthogonal if it's square and real and its transpose is its
    inverse.

    Args:
        matrix: The matrix to check.
        rtol: The per-matrix-entry relative tolerance on equality.
        atol: The per-matrix-entry absolute tolerance on equality.

    Returns:
        Whether the matrix is orthogonal within the given tolerance.
    """
    return (matrix.shape[0] == matrix.shape[1] and
            np.all(np.imag(matrix) == 0) and
            np.allclose(matrix.dot(matrix.T), np.eye(matrix.shape[0]),
                        rtol=rtol,
                        atol=atol))

Similar but not relevant code:
def class_check(vector):
    """
    Check different items in matrix classes.

    :param vector: input vector
    :type vector : list
    :return: bool
    """
    for i in vector:
        if not isinstance(i, type(vector[0])):
            return False
    return True
Query: python expandvars replace non defined with empty string

Relevant code:
def _expand(self, str, local_vars={}):
        """Expand $vars in a string."""
        return ninja_syntax.expand(str, self.vars, local_vars)

Similar but not relevant code:
def format_vars(args):
    """Format the given vars in the form: 'flag=value'"""
    variables = []
    for key, value in args.items():
        if value:
            variables += ['{0}={1}'.format(key, value)]
    return variables

Similar but not relevant code:
def _match_space_at_line(line):
    """Return a re.match object if an empty comment was found on line."""
    regex = re.compile(r"^{0}$".format(_MDL_COMMENT))
    return regex.match(line)

Similar but not relevant code:
def replaceStrs(s, *args):
    r"""Replace all ``(frm, to)`` tuples in `args` in string `s`.

    >>> replaceStrs("nothing is better than warm beer",
    ...             ('nothing','warm beer'), ('warm beer','nothing'))
    'warm beer is better than nothing'

    """
    if args == (): return s
    mapping = dict((frm, to) for frm, to in args)
    return re.sub("|".join(map(re.escape, mapping.keys())),
                  lambda match:mapping[match.group(0)], s)
Query: check is end of file reached python

Relevant code:
def eof(fd):
    """Determine if end-of-file is reached for file fd."""
    b = fd.read(1)
    end = len(b) == 0
    if not end:
        curpos = fd.tell()
        fd.seek(curpos - 1)
    return end

Similar but not relevant code:
def tail(filename, number_of_bytes):
    """Returns the last number_of_bytes of filename"""
    with open(filename, "rb") as f:
        if os.stat(filename).st_size > number_of_bytes:
            f.seek(-number_of_bytes, 2)
        return f.read()

Similar but not relevant code:
def empty_line_count_at_the_end(self):
        """
        Return number of empty lines at the end of the document.
        """
        count = 0
        for line in self.lines[::-1]:
            if not line or line.isspace():
                count += 1
            else:
                break

        return count

Similar but not relevant code:
def last(self):
        """Get the last object in file."""
        # End of file
        self.__file.seek(0, 2)

        # Get the last struct
        data = self.get(self.length - 1)

        return data
Query: check variable type in python as double

Relevant code:
def contains_geometric_info(var):
    """ Check whether the passed variable is a tuple with two floats or integers """
    return isinstance(var, tuple) and len(var) == 2 and all(isinstance(val, (int, float)) for val in var)

Similar but not relevant code:
def is_array(type_):
    """returns True, if type represents C++ array type, False otherwise"""
    nake_type = remove_alias(type_)
    nake_type = remove_reference(nake_type)
    nake_type = remove_cv(nake_type)
    return isinstance(nake_type, cpptypes.array_t)

Similar but not relevant code:
def is_complex(dtype):
  """Returns whether this is a complex floating point type."""
  dtype = tf.as_dtype(dtype)
  if hasattr(dtype, 'is_complex'):
    return dtype.is_complex
  return np.issubdtype(np.dtype(dtype), np.complex)

Similar but not relevant code:
def is_int_type(val):
    """Return True if `val` is of integer type."""
    try:               # Python 2
        return isinstance(val, (int, long))
    except NameError:  # Python 3
        return isinstance(val, int)
Query: python fabric verbose logging

Relevant code:
def logv(msg, *args, **kwargs):
    """
    Print out a log message, only if verbose mode.
    """
    if settings.VERBOSE:
        log(msg, *args, **kwargs)

Similar but not relevant code:
def _configure_logger():
    """Configure the logging module."""
    if not app.debug:
        _configure_logger_for_production(logging.getLogger())
    elif not app.testing:
        _configure_logger_for_debugging(logging.getLogger())

Similar but not relevant code:
def handle_logging(self):
        """
        To allow devs to log as early as possible, logging will already be
        handled here
        """

        configure_logging(self.get_scrapy_options())

        # Disable duplicates
        self.__scrapy_options["LOG_ENABLED"] = False

        # Now, after log-level is correctly set, lets log them.
        for msg in self.log_output:
            if msg["level"] is "error":
                self.log.error(msg["msg"])
            elif msg["level"] is "info":
                self.log.info(msg["msg"])
            elif msg["level"] is "debug":
                self.log.debug(msg["msg"])

Similar but not relevant code:
def pylog(self, *args, **kwargs):
        """Display all available logging information."""
        printerr(self.name, args, kwargs, traceback.format_exc())

Similar but not relevant code:
def stop_logging():
    """Stop logging to logfile and console."""
    from . import log
    logger = logging.getLogger("gromacs")
    logger.info("GromacsWrapper %s STOPPED logging", get_version())
    log.clear_handlers(logger)
Query: python factorials using n!

Relevant code:
def computeFactorial(n):
    """
    computes factorial of n
    """
    sleep_walk(10)
    ret = 1
    for i in range(n):
        ret = ret * (i + 1)
    return ret

Similar but not relevant code:
def _cho_factor(A, lower=True, check_finite=True):
    """Implementaton of :func:`scipy.linalg.cho_factor` using
    a function supported in cupy."""

    return cp.linalg.cholesky(A), True

Similar but not relevant code:
def factors(n):
    """
    Computes all the integer factors of the number `n`

    Example:
        >>> # ENABLE_DOCTEST
        >>> from utool.util_alg import *  # NOQA
        >>> import utool as ut
        >>> result = sorted(ut.factors(10))
        >>> print(result)
        [1, 2, 5, 10]

    References:
        http://stackoverflow.com/questions/6800193/finding-all-the-factors
    """
    return set(reduce(list.__add__,
                      ([i, n // i] for i in range(1, int(n ** 0.5) + 1) if n % i == 0)))

Similar but not relevant code:
def Square(x, a, b, c):
    """Second order polynomial

    Inputs:
    -------
        ``x``: independent variable
        ``a``: coefficient of the second-order term
        ``b``: coefficient of the first-order term
        ``c``: additive constant

    Formula:
    --------
        ``a*x^2 + b*x + c``
    """
    return a * x ** 2 + b * x + c

Similar but not relevant code:
def _factor_generator(n):
    """
    From a given natural integer, returns the prime factors and their multiplicity
    :param n: Natural integer
    :return:
    """
    p = prime_factors(n)
    factors = {}
    for p1 in p:
        try:
            factors[p1] += 1
        except KeyError:
            factors[p1] = 1
    return factors
Query: python fast way to get random sample from list

Relevant code:
def get_randomized_guid_sample(self, item_count):
        """ Fetch a subset of randomzied GUIDs from the whitelist """
        dataset = self.get_whitelist()
        random.shuffle(dataset)
        return dataset[:item_count]

Similar but not relevant code:
def random_numbers(n):
    """
    Generate a random string from 0-9
    :param n: length of the string
    :return: the random string
    """
    return ''.join(random.SystemRandom().choice(string.digits) for _ in range(n))
Query: checking objects for equality python

Relevant code:
def __eq__(self, other):
        """Determine if two objects are equal."""
        return isinstance(other, self.__class__) \
            and self._freeze() == other._freeze()

Similar but not relevant code:
def are_equal_xml(a_xml, b_xml):
    """Normalize and compare XML documents for equality. The document may or may not be
    a DataONE type.

    Args:
      a_xml: str
      b_xml: str
        XML documents to compare for equality.

    Returns:
      bool: ``True`` if the XML documents are semantically equivalent.

    """
    a_dom = xml.dom.minidom.parseString(a_xml)
    b_dom = xml.dom.minidom.parseString(b_xml)
    return are_equal_elements(a_dom.documentElement, b_dom.documentElement)
Query: python fft takes long time

Relevant code:
def stft_magnitude(signal, fft_length,
                   hop_length=None,
                   window_length=None):
  """Calculate the short-time Fourier transform magnitude.

  Args:
    signal: 1D np.array of the input time-domain signal.
    fft_length: Size of the FFT to apply.
    hop_length: Advance (in samples) between each frame passed to FFT.
    window_length: Length of each block of samples to pass to FFT.

  Returns:
    2D np.array where each row contains the magnitudes of the fft_length/2+1
    unique values of the FFT for the corresponding frame of input samples.
  """
  frames = frame(signal, window_length, hop_length)
  # Apply frame window to each frame. We use a periodic Hann (cosine of period
  # window_length) instead of the symmetric Hann of np.hanning (period
  # window_length-1).
  window = periodic_hann(window_length)
  windowed_frames = frames * window
  return np.abs(np.fft.rfft(windowed_frames, int(fft_length)))

Similar but not relevant code:
def stft(func=None, **kwparams):
  """
  Short Time Fourier Transform for real data keeping the full FFT block.

  Same to the default STFT strategy, but with new defaults. This is the same
  to:

  .. code-block:: python

    stft.base(transform=numpy.fft.fft,
              inverse_transform=lambda *args: numpy.fft.ifft(*args).real)

  See ``stft.base`` docs for more.
  """
  from numpy.fft import fft, ifft
  ifft_r = lambda *args: ifft(*args).real
  return stft.base(transform=fft, inverse_transform=ifft_r)(func, **kwparams)

Similar but not relevant code:
def stft(func=None, **kwparams):
  """
  Short Time Fourier Transform for complex data.

  Same to the default STFT strategy, but with new defaults. This is the same
  to:

  .. code-block:: python

    stft.base(transform=numpy.fft.fft, inverse_transform=numpy.fft.ifft)

  See ``stft.base`` docs for more.
  """
  from numpy.fft import fft, ifft
  return stft.base(transform=fft, inverse_transform=ifft)(func, **kwparams)

Similar but not relevant code:
def fft_freqs(n_fft, fs):
    """Return frequencies for DFT

    Parameters
    ----------
    n_fft : int
        Number of points in the FFT.
    fs : float
        The sampling rate.
    """
    return np.arange(0, (n_fft // 2 + 1)) / float(n_fft) * float(fs)
Query: chek if type list python

Relevant code:
def is_iter_non_string(obj):
    """test if object is a list or tuple"""
    if isinstance(obj, list) or isinstance(obj, tuple):
        return True
    return False

Similar but not relevant code:
def all_strings(arr):
        """
        Ensures that the argument is a list that either is empty or contains only strings
        :param arr: list
        :return:
        """
        if not isinstance([], list):
            raise TypeError("non-list value found where list is expected")
        return all(isinstance(x, str) for x in arr)
Query: python fftt low pass filter

Relevant code:
def fft_bandpassfilter(data, fs, lowcut, highcut):
    """
    http://www.swharden.com/blog/2009-01-21-signal-filtering-with-python/#comment-16801
    """
    fft = np.fft.fft(data)
    # n = len(data)
    # timestep = 1.0 / fs
    # freq = np.fft.fftfreq(n, d=timestep)
    bp = fft.copy()

    # Zero out fft coefficients
    # bp[10:-10] = 0

    # Normalise
    # bp *= real(fft.dot(fft))/real(bp.dot(bp))

    bp *= fft.dot(fft) / bp.dot(bp)

    # must multipy by 2 to get the correct amplitude
    ibp = 12 * np.fft.ifft(bp)
    return ibp

Similar but not relevant code:
def highpass(cutoff):
  """
  This strategy uses an exponential approximation for cut-off frequency
  calculation, found by matching the one-pole Laplace lowpass filter
  and mirroring the resulting filter to get a highpass.
  """
  R = thub(exp(cutoff - pi), 2)
  return (1 - R) / (1 + R * z ** -1)
Query: python file auto flush

Relevant code:
def file_writelines_flush_sync(path, lines):
    """
    Fill file at @path with @lines then flush all buffers
    (Python and system buffers)
    """
    fp = open(path, 'w')
    try:
        fp.writelines(lines)
        flush_sync_file_object(fp)
    finally:
        fp.close()

Similar but not relevant code:
def writefile(openedfile, newcontents):
    """Set the contents of a file."""
    openedfile.seek(0)
    openedfile.truncate()
    openedfile.write(newcontents)

Similar but not relevant code:
def _save_file(self, filename, contents):
        """write the html file contents to disk"""
        with open(filename, 'w') as f:
            f.write(contents)
Query: python file check if writable

Relevant code:
def is_writable_by_others(filename):
    """Check if file or directory is world writable."""
    mode = os.stat(filename)[stat.ST_MODE]
    return mode & stat.S_IWOTH

Similar but not relevant code:
def is_readable(filename):
    """Check if file is a regular file and is readable."""
    return os.path.isfile(filename) and os.access(filename, os.R_OK)

Similar but not relevant code:
def check_exists(filename, oappend=False):
    """
    Avoid overwriting some files accidentally.
    """
    if op.exists(filename):
        if oappend:
            return oappend
        logging.error("`{0}` found, overwrite (Y/N)?".format(filename))
        overwrite = (raw_input() == 'Y')
    else:
        overwrite = True

    return overwrite
Query: clear a canvas python

Relevant code:
def _clear(self):
        """
        Helper that clears the composition.
        """
        draw = ImageDraw.Draw(self._background_image)
        draw.rectangle(self._device.bounding_box,
                       fill="black")
        del draw

Similar but not relevant code:
def forget_canvas(canvas):
    """ Forget about the given canvas. Used by the canvas when closed.
    """
    cc = [c() for c in canvasses if c() is not None]
    while canvas in cc:
        cc.remove(canvas)
    canvasses[:] = [weakref.ref(c) for c in cc]

Similar but not relevant code:
def clear_timeline(self):
        """
        Clear the contents of the TimeLine Canvas

        Does not modify the actual markers dictionary and thus after
        redrawing all markers are visible again.
        """
        self._timeline.delete(tk.ALL)
        self._canvas_ticks.delete(tk.ALL)

Similar but not relevant code:
def update(self):
        """Update all visuals in the attached canvas."""
        if not self.canvas:
            return
        for visual in self.canvas.visuals:
            self.update_program(visual.program)
        self.canvas.update()
Query: clearing python memory during a for loop

Relevant code:
def Flush(self):
    """Flush all items from cache."""
    while self._age:
      node = self._age.PopLeft()
      self.KillObject(node.data)

    self._hash = dict()

Similar but not relevant code:
def purge_cache(self, object_type):
        """ Purge the named cache of all values. If no cache exists for object_type, nothing is done """
        if object_type in self.mapping:
            cache = self.mapping[object_type]
            log.debug("Purging [{}] cache of {} values.".format(object_type, len(cache)))
            cache.purge()

Similar but not relevant code:
def get_memory(self, mode):
        """Return a smt bit vector that represents a memory location.
        """
        mem = {
            "pre": self._translator.get_memory_init(),
            "post": self._translator.get_memory_curr(),
        }

        return mem[mode]
Query: python fillna with other columns values

Relevant code:
def _maybe_fill(arr, fill_value=np.nan):
    """
    if we have a compatible fill_value and arr dtype, then fill
    """
    if _isna_compat(arr, fill_value):
        arr.fill(fill_value)
    return arr

Similar but not relevant code:
def fill_nulls(self, col: str):
        """
        Fill all null values with NaN values in a column.
        Null values are ``None`` or en empty string

        :param col: column name
        :type col: str

        :example: ``ds.fill_nulls("mycol")``
        """
        n = [None, ""]
        try:
            self.df[col] = self.df[col].replace(n, nan)
        except Exception as e:
            self.err(e)

Similar but not relevant code:
def fillna(series_or_arr, missing_value=0.0):
    """Fill missing values in pandas objects and numpy arrays.

    Arguments
    ---------
    series_or_arr : pandas.Series, numpy.ndarray
        The numpy array or pandas series for which the missing values
        need to be replaced.
    missing_value : float, int, str
        The value to replace the missing value with. Default 0.0.

    Returns
    -------
    pandas.Series, numpy.ndarray
        The numpy array or pandas series with the missing values
        filled.
    """

    if pandas.notnull(missing_value):
        if isinstance(series_or_arr, (numpy.ndarray)):
            series_or_arr[numpy.isnan(series_or_arr)] = missing_value
        else:
            series_or_arr.fillna(missing_value, inplace=True)

    return series_or_arr

Similar but not relevant code:
def clean_dataframe(df):
    """Fill NaNs with the previous value, the next value or if all are NaN then 1.0"""
    df = df.fillna(method='ffill')
    df = df.fillna(0.0)
    return df

Similar but not relevant code:
def add_blank_row(self, label):
        """
        Add a blank row with only an index value to self.df.
        This is done inplace.
        """
        col_labels = self.df.columns
        blank_item = pd.Series({}, index=col_labels, name=label)
        # use .loc to add in place (append won't do that)
        self.df.loc[blank_item.name] = blank_item
        return self.df
Query: column stack every item in a list of lists python

Relevant code:
def column_stack_2d(data):
    """Perform column-stacking on a list of 2d data blocks."""
    return list(list(itt.chain.from_iterable(_)) for _ in zip(*data))

Similar but not relevant code:
def _listify(collection):
        """This is a workaround where Collections are no longer iterable
        when using JPype."""
        new_list = []
        for index in range(len(collection)):
            new_list.append(collection[index])
        return new_list

Similar but not relevant code:
def itemlist(item, sep, suppress_trailing=True):
    """Create a list of items seperated by seps."""
    return condense(item + ZeroOrMore(addspace(sep + item)) + Optional(sep.suppress() if suppress_trailing else sep))

Similar but not relevant code:
def to_list(self):
        """Convert this confusion matrix into a 2x2 plain list of values."""
        return [[int(self.table.cell_values[0][1]), int(self.table.cell_values[0][2])],
                [int(self.table.cell_values[1][1]), int(self.table.cell_values[1][2])]]

Similar but not relevant code:
def list_depth(list_, func=max, _depth=0):
    """
    Returns the deepest level of nesting within a list of lists

    Args:
       list_  : a nested listlike object
       func   : depth aggregation strategy (defaults to max)
       _depth : internal var

    Example:
        >>> # ENABLE_DOCTEST
        >>> from utool.util_list import *  # NOQA
        >>> list_ = [[[[[1]]], [3]], [[1], [3]], [[1], [3]]]
        >>> result = (list_depth(list_, _depth=0))
        >>> print(result)

    """
    depth_list = [list_depth(item, func=func, _depth=_depth + 1)
                  for item in  list_ if util_type.is_listlike(item)]
    if len(depth_list) > 0:
        return func(depth_list)
    else:
        return _depth
Query: combine list elements python as one item

Relevant code:
def combinations(l):
    """Pure-Python implementation of itertools.combinations(l, 2)."""
    result = []
    for x in xrange(len(l) - 1):
        ls = l[x + 1:]
        for y in ls:
            result.append((l[x], y))
    return result

Similar but not relevant code:
def compose_all(tups):
  """Compose all given tuples together."""
  from . import ast  # I weep for humanity
  return functools.reduce(lambda x, y: x.compose(y), map(ast.make_tuple, tups), ast.make_tuple({}))

Similar but not relevant code:
def _ensure_element(tup, elem):
    """
    Create a tuple containing all elements of tup, plus elem.

    Returns the new tuple and the index of elem in the new tuple.
    """
    try:
        return tup, tup.index(elem)
    except ValueError:
        return tuple(chain(tup, (elem,))), len(tup)
Query: python filter lambda return none if empty

Relevant code:
def BROADCAST_FILTER_NOT(func):
        """
        Composes the passed filters into an and-joined filter.
        """
        return lambda u, command, *args, **kwargs: not func(u, command, *args, **kwargs)

Similar but not relevant code:
def exclude(self, *args, **kwargs) -> "QuerySet":
        """
        Same as .filter(), but with appends all args with NOT
        """
        return self._filter_or_exclude(negate=True, *args, **kwargs)

Similar but not relevant code:
def _not_none(items):
    """Whether the item is a placeholder or contains a placeholder."""
    if not isinstance(items, (tuple, list)):
        items = (items,)
    return all(item is not _none for item in items)

Similar but not relevant code:
def apply(filter):
    """Manufacture decorator that filters return value with given function.

    ``filter``:
      Callable that takes a single parameter.
    """
    def decorator(callable):
        return lambda *args, **kwargs: filter(callable(*args, **kwargs))
    return decorator
Query: compare filename in python regex

Relevant code:
def file_matches(filename, patterns):
    """Does this filename match any of the patterns?"""
    return any(fnmatch.fnmatch(filename, pat) for pat in patterns)

Similar but not relevant code:
def ILIKE(pattern):
    """Unix shell-style wildcards. Case-insensitive"""
    return P(lambda x: fnmatch.fnmatch(x.lower(), pattern.lower()))