benjamintli/cosqa-llm-filtered-hard-negatives huggingface dataset:
Query: apply a function ona series column python

Relevant code:
def ts_func(f):
    """
    This wraps a function that would normally only accept an array
    and allows it to operate on a DataFrame. Useful for applying
    numpy functions to DataFrames.
    """
    def wrap_func(df, *args):
        # TODO: should vectorize to apply over all columns?
        return Chromatogram(f(df.values, *args), df.index, df.columns)
    return wrap_func

Similar but not relevant code:
def fourier_series(x, f, n=0):
    """
    Returns a symbolic fourier series of order `n`.

    :param n: Order of the fourier series.
    :param x: Independent variable
    :param f: Frequency of the fourier series
    """
    # Make the parameter objects for all the terms
    a0, *cos_a = parameters(','.join(['a{}'.format(i) for i in range(0, n + 1)]))
    sin_b = parameters(','.join(['b{}'.format(i) for i in range(1, n + 1)]))
    # Construct the series
    series = a0 + sum(ai * cos(i * f * x) + bi * sin(i * f * x)
                     for i, (ai, bi) in enumerate(zip(cos_a, sin_b), start=1))
    return series

Similar but not relevant code:
def from_series(series):
        """
        Deseralize a PercentRankTransform the given pandas.Series, as returned
        by `to_series()`.

        Parameters
        ----------
        series : pandas.Series

        Returns
        -------
        PercentRankTransform

        """
        result = PercentRankTransform()
        result.cdf = series.values
        result.bin_edges = series.index.values[1:-1]
        return result
Query: python code for finding magnitude of a vector

Relevant code:
def mag(z):
    """Get the magnitude of a vector."""
    if isinstance(z[0], np.ndarray):
        return np.array(list(map(np.linalg.norm, z)))
    else:
        return np.linalg.norm(z)

Similar but not relevant code:
def normalized(vector):
    """
    Get unit vector for a given one.

    :param vector:
        Numpy vector as coordinates in Cartesian space, or an array of such.
    :returns:
        Numpy array of the same shape and structure where all vectors are
        normalized. That is, each coordinate component is divided by its
        vector's length.
    """
    length = numpy.sum(vector * vector, axis=-1)
    length = numpy.sqrt(length.reshape(length.shape + (1, )))
    return vector / length

Similar but not relevant code:
def normalize_vector(v):
    """Take a vector and return the normalized vector
    :param v: a vector v
    :returns : normalized vector v
    """
    norm = np.linalg.norm(v)
    return v/norm if not norm == 0 else v
Query: apply function to each row of data frame python

Relevant code:
def transform(self, df):
        """
        Transforms a DataFrame in place. Computes all outputs of the DataFrame.

        Args:
            df (pandas.DataFrame): DataFrame to transform.
        """
        for name, function in self.outputs:
            df[name] = function(df)

Similar but not relevant code:
def apply_filters(df, filters):
        """Basic filtering for a dataframe."""
        idx = pd.Series([True]*df.shape[0])
        for k, v in list(filters.items()):
            if k not in df.columns:
                continue
            idx &= (df[k] == v)

        return df.loc[idx]

Similar but not relevant code:
def to_dataframe(products):
        """Return the products from a query response as a Pandas DataFrame
        with the values in their appropriate Python types.
        """
        try:
            import pandas as pd
        except ImportError:
            raise ImportError("to_dataframe requires the optional dependency Pandas.")

        return pd.DataFrame.from_dict(products, orient='index')

Similar but not relevant code:
def intersect(self, other):
        """ Return a new :class:`DataFrame` containing rows only in
        both this frame and another frame.

        This is equivalent to `INTERSECT` in SQL.
        """
        return DataFrame(self._jdf.intersect(other._jdf), self.sql_ctx)
Query: apply styling on individual elements of a particular column in python

Relevant code:
def apply_conditional_styles(self, cbfct):
        """
        Ability to provide dynamic styling of the cell based on its value.
        :param cbfct: function(cell_value) should return a dict of format commands to apply to that cell
        :return: self
        """
        for ridx in range(self.nrows):
            for cidx in range(self.ncols):
                fmts = cbfct(self.actual_values.iloc[ridx, cidx])
                fmts and self.iloc[ridx, cidx].apply_styles(fmts)
        return self

Similar but not relevant code:
def OnCellBackgroundColor(self, event):
        """Cell background color event handler"""

        with undo.group(_("Background color")):
            self.grid.actions.set_attr("bgcolor", event.color)

        self.grid.ForceRefresh()

        self.grid.update_attribute_toolbar()

        event.Skip()

Similar but not relevant code:
def styles(self, dictobj):
		"""
		Add or update styles
		"""
		for k in dictobj:
			self.chart_style[k] = dictobj[k]

Similar but not relevant code:
def _column_resized(self, col, old_width, new_width):
        """Update the column width."""
        self.dataTable.setColumnWidth(col, new_width)
        self._update_layout()

Similar but not relevant code:
def _get_printable_columns(columns, row):
    """Return only the part of the row which should be printed.
    """
    if not columns:
        return row

    # Extract the column values, in the order specified.
    return tuple(row[c] for c in columns)

Similar but not relevant code:
def alter_change_column(self, table, column, field):
        """Support change columns."""
        return self._update_column(table, column, lambda a, b: b)
Query: asign all id's the max value python

Relevant code:
def maxId(self):
        """int: current max id of objects"""
        if len(self.model.db) == 0:
            return 0

        return max(map(lambda obj: obj["id"], self.model.db))

Similar but not relevant code:
def max(self):
        """
        The maximum integer value of a value-set. It is only defined when there is exactly one region.

        :return: A integer that represents the maximum integer value of this value-set.
        :rtype:  int
        """

        if len(self.regions) != 1:
            raise ClaripyVSAOperationError("'max()' onlly works on single-region value-sets.")

        return self.get_si(next(iter(self.regions))).max

Similar but not relevant code:
def argmax(self, rows: List[Row], column: ComparableColumn) -> List[Row]:
        """
        Takes a list of rows and a column name and returns a list containing a single row (dict from
        columns to cells) that has the maximum numerical value in the given column. We return a list
        instead of a single dict to be consistent with the return type of ``select`` and
        ``all_rows``.
        """
        if not rows:
            return []
        value_row_pairs = [(row.values[column.name], row) for row in rows]
        if not value_row_pairs:
            return []
        # Returns a list containing the row with the max cell value.
        return [sorted(value_row_pairs, key=lambda x: x[0], reverse=True)[0][1]]
Query: python code to read a file and tokenize it

Relevant code:
def listified_tokenizer(source):
    """Tokenizes *source* and returns the tokens as a list of lists."""
    io_obj = io.StringIO(source)
    return [list(a) for a in tokenize.generate_tokens(io_obj.readline)]

Similar but not relevant code:
def from_file(cls, path, encoding, dialect, fields, converters, field_index):
        """Read delimited text from a text file."""

        return cls(open(path, 'r', encoding=encoding), dialect, fields, converters, field_index)
Query: python codecs decode encoding

Relevant code:
def b(s):
	""" Encodes Unicode strings to byte strings, if necessary. """

	return s if isinstance(s, bytes) else s.encode(locale.getpreferredencoding())

Similar but not relevant code:
def get_best_encoding(stream):
    """Returns the default stream encoding if not found."""
    rv = getattr(stream, 'encoding', None) or sys.getdefaultencoding()
    if is_ascii_encoding(rv):
        return 'utf-8'
    return rv

Similar but not relevant code:
def get_encoding(binary):
    """Return the encoding type."""

    try:
        from chardet import detect
    except ImportError:
        LOGGER.error("Please install the 'chardet' module")
        sys.exit(1)

    encoding = detect(binary).get('encoding')

    return 'iso-8859-1' if encoding == 'CP949' else encoding

Similar but not relevant code:
def decode_unicode_string(string):
    """
    Decode string encoded by `unicode_string`
    """
    if string.startswith('[BASE64-DATA]') and string.endswith('[/BASE64-DATA]'):
        return base64.b64decode(string[len('[BASE64-DATA]'):-len('[/BASE64-DATA]')])
    return string

Similar but not relevant code:
def process_module(self, module):
        """inspect the source file to find encoding problem"""
        if module.file_encoding:
            encoding = module.file_encoding
        else:
            encoding = "ascii"

        with module.stream() as stream:
            for lineno, line in enumerate(stream):
                self._check_encoding(lineno + 1, line, encoding)
Query: python color not in tuple

Relevant code:
def rgba_bytes_tuple(self, x):
        """Provides the color corresponding to value `x` in the
        form of a tuple (R,G,B,A) with int values between 0 and 255.
        """
        return tuple(int(u*255.9999) for u in self.rgba_floats_tuple(x))

Similar but not relevant code:
def _get_tuple(self, fields):
    """
    :param fields: a list which contains either 0,1,or 2 values
    :return: a tuple with default values of '';
    """
    v1 = ''
    v2 = ''
    if len(fields) > 0:
      v1 = fields[0]
    if len(fields) > 1:
      v2 = fields[1]
    return v1, v2

Similar but not relevant code:
def tuple_check(*args, func=None):
    """Check if arguments are tuple type."""
    func = func or inspect.stack()[2][3]
    for var in args:
        if not isinstance(var, (tuple, collections.abc.Sequence)):
            name = type(var).__name__
            raise TupleError(
                f'Function {func} expected tuple, {name} got instead.')

Similar but not relevant code:
def option2tuple(opt):
    """Return a tuple of option, taking possible presence of level into account"""

    if isinstance(opt[0], int):
        tup = opt[1], opt[2:]
    else:
        tup = opt[0], opt[1:]

    return tup

Similar but not relevant code:
def hex_color_to_tuple(hex):
    """ convent hex color to tuple
    "#ffffff"   ->  (255, 255, 255)
    "#ffff00ff" ->  (255, 255, 0, 255)
    """
    hex = hex[1:]
    length = len(hex) // 2
    return tuple(int(hex[i*2:i*2+2], 16) for i in range(length))

Similar but not relevant code:
def tuple(self, var, cast=None, default=NOTSET):
        """
        :rtype: tuple
        """
        return self.get_value(var, cast=tuple if not cast else (cast,), default=default)
Query: python colors converter cmyk

Relevant code:
def cmyk(c, m, y, k):
    """
    Create a spectra.Color object in the CMYK color space.

    :param float c: c coordinate.
    :param float m: m coordinate.
    :param float y: y coordinate.
    :param float k: k coordinate.

    :rtype: Color
    :returns: A spectra.Color object in the CMYK color space.
    """
    return Color("cmyk", c, m, y, k)

Similar but not relevant code:
def _value_to_color(value, cmap):
    """Convert a value in the range [0,1] to an RGB tuple using a colormap."""
    cm = plt.get_cmap(cmap)
    rgba = cm(value)
    return [int(round(255*v)) for v in rgba[0:3]]

Similar but not relevant code:
def hex_to_hsv(color):
    """
    Converts from hex to hsv

    Parameters:
    -----------
            color : string
                    Color representation on color

    Example:
            hex_to_hsv('#ff9933')
    """
    color = normalize(color)
    color = color[1:]
    # color=tuple(ord(c)/255.0 for c in color.decode('hex'))
    color = (int(color[0:2], base=16) / 255.0, int(color[2:4],
                                                   base=16) / 255.0, int(color[4:6], base=16) / 255.0)
    return colorsys.rgb_to_hsv(*color)

Similar but not relevant code:
def name2rgb(hue):
    """Originally used to calculate color based on module name.
    """
    r, g, b = colorsys.hsv_to_rgb(hue / 360.0, .8, .7)
    return tuple(int(x * 256) for x in [r, g, b])

Similar but not relevant code:
def colorbar(height, length, colormap):
    """Return the channels of a colorbar.
    """
    cbar = np.tile(np.arange(length) * 1.0 / (length - 1), (height, 1))
    cbar = (cbar * (colormap.values.max() - colormap.values.min())
            + colormap.values.min())

    return colormap.colorize(cbar)

Similar but not relevant code:
def apply_color_map(name: str, mat: np.ndarray = None):
    """returns an RGB matrix scaled by a matplotlib color map"""
    def apply_map(mat):
        return (cm.get_cmap(name)(_normalize(mat))[:, :, :3] * 255).astype(np.uint8)
        
    return apply_map if mat is None else apply_map(mat)
Query: python combine multiple models

Relevant code:
def _merge_meta(model1, model2):
    """Simple merge of samplesets."""
    w1 = _get_meta(model1)
    w2 = _get_meta(model2)
    return metadata.merge(w1, w2, metadata_conflicts='silent')

Similar but not relevant code:
def _merge_maps(m1, m2):
    """merge two Mapping objects, keeping the type of the first mapping"""
    return type(m1)(chain(m1.items(), m2.items()))

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 merge(database=None, directory=None, verbose=None):
    """Merge migrations into one."""
    router = get_router(directory, database, verbose)
    router.merge()

Similar but not relevant code:
def _join(verb):
    """
    Join helper
    """
    data = pd.merge(verb.x, verb.y, **verb.kwargs)

    # Preserve x groups
    if isinstance(verb.x, GroupedDataFrame):
        data.plydata_groups = list(verb.x.plydata_groups)
    return data
Query: black cursor block python

Relevant code:
def hidden_cursor(self):
        """Return a context manager that hides the cursor while inside it and
        makes it visible on leaving."""
        self.stream.write(self.hide_cursor)
        try:
            yield
        finally:
            self.stream.write(self.normal_cursor)

Similar but not relevant code:
def erase_lines(n=1):
    """ Erases n lines from the screen and moves the cursor up to follow
    """
    for _ in range(n):
        print(codes.cursor["up"], end="")
        print(codes.cursor["eol"], end="")

Similar but not relevant code:
def off(self):
        """Turn off curses"""
        self.win.keypad(0)
        curses.nocbreak()
        curses.echo()
        try:
            curses.curs_set(1)
        except:
            pass
        curses.endwin()
Query: python constructor taking iterable

Relevant code:
def fromiterable(cls, itr):
        """Initialize from iterable"""
        x, y, z = itr
        return cls(x, y, z)

Similar but not relevant code:
def ensure_iterable(inst):
    """
    Wraps scalars or string types as a list, or returns the iterable instance.
    """
    if isinstance(inst, string_types):
        return [inst]
    elif not isinstance(inst, collections.Iterable):
        return [inst]
    else:
        return inst

Similar but not relevant code:
def force_iterable(f):
    """Will make any functions return an iterable objects by wrapping its result in a list."""
    def wrapper(*args, **kwargs):
        r = f(*args, **kwargs)
        if hasattr(r, '__iter__'):
            return r
        else:
            return [r]
    return wrapper
Query: calculate n choose k python

Relevant code:
def n_choose_k(n, k):
    """ get the number of quartets as n-choose-k. This is used
    in equal splits to decide whether a split should be exhaustively sampled
    or randomly sampled. Edges near tips can be exhaustive while highly nested
    edges probably have too many quartets
    """
    return int(reduce(MUL, (Fraction(n-i, i+1) for i in range(k)), 1))

Similar but not relevant code:
def _weighted_selection(l, n):
    """
        Selects  n random elements from a list of (weight, item) tuples.
        Based on code snippet by Nick Johnson
    """
    cuml = []
    items = []
    total_weight = 0.0
    for weight, item in l:
        total_weight += weight
        cuml.append(total_weight)
        items.append(item)

    return [items[bisect.bisect(cuml, random.random()*total_weight)] for _ in range(n)]

Similar but not relevant code:
def cric__decision_tree():
    """ Decision Tree
    """
    model = sklearn.tree.DecisionTreeClassifier(random_state=0, max_depth=4)

    # we want to explain the raw probability outputs of the trees
    model.predict = lambda X: model.predict_proba(X)[:,1]
    
    return model

Similar but not relevant code:
def EvalBinomialPmf(k, n, p):
    """Evaluates the binomial pmf.

    Returns the probabily of k successes in n trials with probability p.
    """
    return scipy.stats.binom.pmf(k, n, p)

Similar but not relevant code:
def random_choice(sequence):
    """ Same as :meth:`random.choice`, but also supports :class:`set` type to be passed as sequence. """
    return random.choice(tuple(sequence) if isinstance(sequence, set) else sequence)

Similar but not relevant code:
def human__decision_tree():
    """ Decision Tree
    """

    # build data
    N = 1000000
    M = 3
    X = np.zeros((N,M))
    X.shape
    y = np.zeros(N)
    X[0, 0] = 1
    y[0] = 8
    X[1, 1] = 1
    y[1] = 8
    X[2, 0:2] = 1
    y[2] = 4

    # fit model
    xor_model = sklearn.tree.DecisionTreeRegressor(max_depth=2)
    xor_model.fit(X, y)

    return xor_model
Query: calculate standard deviation in python ignoring null

Relevant code:
def stddev(values, meanval=None):  #from AI: A Modern Appproach
    """The standard deviation of a set of values.
    Pass in the mean if you already know it."""
    if meanval == None: meanval = mean(values)
    return math.sqrt( sum([(x - meanval)**2 for x in values]) / (len(values)-1) )

Similar but not relevant code:
def standard_deviation(numbers):
    """Return standard deviation."""
    numbers = list(numbers)
    if not numbers:
        return 0
    mean = sum(numbers) / len(numbers)
    return (sum((n - mean) ** 2 for n in numbers) /
            len(numbers)) ** .5

Similar but not relevant code:
def weighted_std(values, weights):
    """ Calculate standard deviation weighted by errors """
    average = np.average(values, weights=weights)
    variance = np.average((values-average)**2, weights=weights)
    return np.sqrt(variance)

Similar but not relevant code:
def circstd(dts, axis=2):
    """Circular standard deviation"""
    R = np.abs(np.exp(1.0j * dts).mean(axis=axis))
    return np.sqrt(-2.0 * np.log(R))

Similar but not relevant code:
def sem(inlist):
    """
Returns the estimated standard error of the mean (sx-bar) of the
values in the passed list.  sem = stdev / sqrt(n)

Usage:   lsem(inlist)
"""
    sd = stdev(inlist)
    n = len(inlist)
    return sd / math.sqrt(n)
Query: python covariance of two array

Relevant code:
def covariance(self,pt0,pt1):
        """ get the covarince between two points implied by Vario2d

        Parameters
        ----------
        pt0 : (iterable of len 2)
            first point x and y
        pt1 : (iterable of len 2)
            second point x and y

        Returns
        -------
        cov : float
            covariance between pt0 and pt1

        """

        x = np.array([pt0[0],pt1[0]])
        y = np.array([pt0[1],pt1[1]])
        names = ["n1","n2"]
        return self.covariance_matrix(x,y,names=names).x[0,1]

Similar but not relevant code:
def Cinv(self):
        """Inverse of the noise covariance."""
        try:
            return np.linalg.inv(self.c)
        except np.linalg.linalg.LinAlgError:
            print('Warning: non-invertible noise covariance matrix c.')
            return np.eye(self.c.shape[0])

Similar but not relevant code:
def _covariance_matrix(self, type='noise'):
        """
        Constructs the covariance matrix from PCA
        residuals
        """
        if type == 'sampling':
            return self.sigma**2/(self.n-1)
        elif type == 'noise':
            return 4*self.sigma*N.var(self.rotated(), axis=0)

Similar but not relevant code:
def cross_product_matrix(vec):
    """Returns a 3x3 cross-product matrix from a 3-element vector."""
    return np.array([[0, -vec[2], vec[1]],
                     [vec[2], 0, -vec[0]],
                     [-vec[1], vec[0], 0]])
Query: python create a array of datetime

Relevant code:
def get_dt_list(fn_list):
    """Get list of datetime objects, extracted from a filename
    """
    dt_list = np.array([fn_getdatetime(fn) for fn in fn_list])
    return dt_list

Similar but not relevant code:
def _possibly_convert_objects(values):
    """Convert arrays of datetime.datetime and datetime.timedelta objects into
    datetime64 and timedelta64, according to the pandas convention.
    """
    return np.asarray(pd.Series(values.ravel())).reshape(values.shape)

Similar but not relevant code:
def A(*a):
    """convert iterable object into numpy array"""
    return np.array(a[0]) if len(a)==1 else [np.array(o) for o in a]

Similar but not relevant code:
def to_array(self):
        """Convert the table to a structured NumPy array."""
        dt = np.dtype(list(zip(self.labels, (c.dtype for c in self.columns))))
        arr = np.empty_like(self.columns[0], dt)
        for label in self.labels:
            arr[label] = self[label]
        return arr

Similar but not relevant code:
def toArray(self):
        """
        Returns a copy of this SparseVector as a 1-dimensional NumPy array.
        """
        arr = np.zeros((self.size,), dtype=np.float64)
        arr[self.indices] = self.values
        return arr
Query: can python play sounds

Relevant code:
def pause(self):
        """Pause the music"""
        mixer.music.pause()
        self.pause_time = self.get_time()
        self.paused = True

Similar but not relevant code:
def OnTogglePlay(self, event):
        """Toggles the video status between play and hold"""

        if self.player.get_state() == vlc.State.Playing:
            self.player.pause()
        else:
            self.player.play()

        event.Skip()

Similar but not relevant code:
def write_wave(path, audio, sample_rate):
    """Writes a .wav file.

    Takes path, PCM audio data, and sample rate.
    """
    with contextlib.closing(wave.open(path, 'wb')) as wf:
        wf.setnchannels(1)
        wf.setsampwidth(2)
        wf.setframerate(sample_rate)
        wf.writeframes(audio)
Query: python create instance from type intance

Relevant code:
def construct_from_string(cls, string):
        """
        Construction from a string, raise a TypeError if not
        possible
        """
        if string == cls.name:
            return cls()
        raise TypeError("Cannot construct a '{}' from "
                        "'{}'".format(cls, string))

Similar but not relevant code:
def __init__(self, enumtype, index, key):
        """ Set up a new instance. """
        self._enumtype = enumtype
        self._index = index
        self._key = key

Similar but not relevant code:
def simple_generate(cls, create, **kwargs):
        """Generate a new instance.

        The instance will be either 'built' or 'created'.

        Args:
            create (bool): whether to 'build' or 'create' the instance.

        Returns:
            object: the generated instance
        """
        strategy = enums.CREATE_STRATEGY if create else enums.BUILD_STRATEGY
        return cls.generate(strategy, **kwargs)

Similar but not relevant code:
def __init__(self, find, subcon):
        """Initialize."""
        Subconstruct.__init__(self, subcon)
        self.find = find