Python numpy.fromiter方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import fromiter [as 別名]
def make2d(array, cols=None, dtype=None):
    '''
    Make a 2D array from an array of arrays.  The `cols' and `dtype'
    arguments can be omitted if the array is not empty.

    '''
    if (cols is None or dtype is None) and not len(array):
        raise RuntimeError("cols and dtype must be specified for empty "
                           "array")

    if cols is None:
        cols = len(array[0])

    if dtype is None:
        dtype = array[0].dtype

    return _np.fromiter(array, [('_', dtype, (cols,))],
                        count=len(array))['_'] 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import fromiter [as 別名]
def test_constructor_object_dtype(self):
        # GH 11856
        arr = SparseArray(['A', 'A', np.nan, 'B'], dtype=np.object)
        assert arr.dtype == SparseDtype(np.object)
        assert np.isnan(arr.fill_value)

        arr = SparseArray(['A', 'A', np.nan, 'B'], dtype=np.object,
                          fill_value='A')
        assert arr.dtype == SparseDtype(np.object, 'A')
        assert arr.fill_value == 'A'

        # GH 17574
        data = [False, 0, 100.0, 0.0]
        arr = SparseArray(data, dtype=np.object, fill_value=False)
        assert arr.dtype == SparseDtype(np.object, False)
        assert arr.fill_value is False
        arr_expected = np.array(data, dtype=np.object)
        it = (type(x) == type(y) and x == y for x, y in zip(arr, arr_expected))
        assert np.fromiter(it, dtype=np.bool).all() 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import fromiter [as 別名]
def decons_obs_group_ids(comp_ids, obs_ids, shape, labels, xnull):
    """
    reconstruct labels from observed group ids

    Parameters
    ----------
    xnull: boolean,
        if nulls are excluded; i.e. -1 labels are passed through
    """

    if not xnull:
        lift = np.fromiter(((a == -1).any() for a in labels), dtype='i8')
        shape = np.asarray(shape, dtype='i8') + lift

    if not is_int64_overflow_possible(shape):
        # obs ids are deconstructable! take the fast route!
        out = decons_group_index(obs_ids, shape)
        return out if xnull or not lift.any() \
            else [x - y for x, y in zip(out, lift)]

    i = unique_label_indices(comp_ids)
    i8copy = lambda a: a.astype('i8', subok=False, copy=True)
    return [i8copy(lab[i]) for lab in labels] 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import fromiter [as 別名]
def divide_qualities_into_bins(qualities, n_bins=4):
    """Divides the raw quality scores in bins according to the mean phred
    quality of the sequence they come from

    Args:
        qualities (list): raw count of all the phred scores and mean sequence
            quality
        n_bins (int): number of bins to create (default: 4)

    Returns:
        list: a list of lists containing the binned quality scores
    """
    logger = logging.getLogger(__name__)
    logger.debug('Dividing qualities into mean clusters')
    bin_lists = [[] for _ in range(n_bins)]  # create list of `n_bins` list
    ranges = np.split(np.array(range(40)), n_bins)
    for quality in qualities:
        mean = int(quality[0][1])  # mean is at 1 and same regardless of b pos
        which_array = 0
        for array in ranges:
            if mean in array:
                read = np.fromiter((q[0] for q in quality), dtype=np.float)
                bin_lists[which_array].append(read)
            which_array += 1
    return bin_lists 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import fromiter [as 別名]
def make2d(array, cols=None, dtype=None):
    '''
    Make a 2D array from an array of arrays.  The `cols' and `dtype'
    arguments can be omitted if the array is not empty.
    '''
    if (cols is None or dtype is None) and not len(array):
        raise RuntimeError("cols and dtype must be specified for empty "
                           "array")

    if cols is None:
        cols = len(array[0])

    if dtype is None:
        dtype = array[0].dtype

    return _np.fromiter(array, [('_', dtype, (cols,))],
                        count=len(array))['_'] 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import fromiter [as 別名]
def create_array(width, height, background=None):
    """returns an array.array or numpy.array of the correct size and
    with the given background color"""
    if numpy is not None:
        if background is None:
            return numpy.zeros(width * height, dtype=numpy.uint32)
        else:
            iterable = (background for _ in range(width * height))
            return numpy.fromiter(iterable, numpy.uint32)
    else:
        # Use the smallest typecode that can store a 32-bit unsigned integer
        typecode = "I" if array.array("I").itemsize >= 4 else "L"
        background = (background if background is not None else
                      ColorForName["transparent"])
        return array.array(typecode, [background] * width * height)


# Taken from rgb.txt and converted to ARGB (with the addition of
# transparent). Default is solid black. 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import fromiter [as 別名]
def test_constructor_object_dtype(self):
        # GH 11856
        arr = SparseArray(['A', 'A', np.nan, 'B'], dtype=np.object)
        assert arr.dtype == np.object
        assert np.isnan(arr.fill_value)

        arr = SparseArray(['A', 'A', np.nan, 'B'], dtype=np.object,
                          fill_value='A')
        assert arr.dtype == np.object
        assert arr.fill_value == 'A'

        # GH 17574
        data = [False, 0, 100.0, 0.0]
        arr = SparseArray(data, dtype=np.object, fill_value=False)
        assert arr.dtype == np.object
        assert arr.fill_value is False
        arr_expected = np.array(data, dtype=np.object)
        it = (type(x) == type(y) and x == y for x, y in zip(arr, arr_expected))
        assert np.fromiter(it, dtype=np.bool).all() 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import fromiter [as 別名]
def load_word2vec(word2vec_model_path,embed_size):
    """
    load pretrained word2vec in txt format
    :param word2vec_model_path:
    :return: word2vec_dict. word2vec_dict[word]=vector
    """
    #word2vec_object = codecs.open(word2vec_model_path,'r','utf-8') #open(word2vec_model_path,'r')
    #lines=word2vec_object.readlines()
    #word2vec_dict={}
    #for i,line in enumerate(lines):
    #    if i==0: continue
    #    string_list=line.strip().split(" ")
    #    word=string_list[0]
    #    vector=string_list[1:][0:embed_size]
    #    word2vec_dict[word]=vector
    ######################
    word2vec_dict = {}
    with open(word2vec_model_path, errors='ignore') as f:
        meta = f.readline()
        for line in f.readlines():
            items = line.split(' ')
            #if len(items[0]) > 1 and items[0] in vocab:
            word2vec_dict[items[0]] = np.fromiter(items[1:][0:embed_size], dtype=float)
    return word2vec_dict 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import fromiter [as 別名]
def cartesian_product(X):
    '''
    Numpy version of itertools.product or pandas.compat.product.
    Sometimes faster (for large inputs)...

    Examples
    --------
    >>> cartesian_product([list('ABC'), [1, 2]])
    [array(['A', 'A', 'B', 'B', 'C', 'C'], dtype='|S1'),
 	array([1, 2, 1, 2, 1, 2])]

    '''

    lenX = np.fromiter((len(x) for x in X), dtype=int)
    cumprodX = np.cumproduct(lenX)

    a = np.roll(cumprodX, 1)
    a[0] = 1

    b = cumprodX[-1] / cumprodX

    return [np.tile(np.repeat(x, b[i]), 
                    np.product(a[i]))
               for i, x in enumerate(X)] 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import fromiter [as 別名]
def __load(fh):
        return numpy.fromiter(Obj.__read(fh), dtype=Obj.obj_dtype) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import fromiter [as 別名]
def __load_ascii(fh, header):
        return numpy.fromiter(Stl.__ascii_reader(fh, header), dtype=Stl.stl_dtype) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import fromiter [as 別名]
def to_timestamps(values):
    try:
        if len(values) == 0:
            return []
        if isinstance(values[0], (numpy.datetime64, datetime.datetime)):
            times = numpy.array(values)
        else:
            try:
                # Try to convert to float. If it works, then we consider
                # timestamps to be number of seconds since Epoch
                # e.g. 123456 or 129491.1293
                float(values[0])
            except ValueError:
                try:
                    # Try to parse the value as a string of ISO timestamp
                    # e.g. 2017-10-09T23:23:12.123
                    numpy.datetime64(values[0])
                except ValueError:
                    # Last chance: it can be relative timestamp, so convert
                    # to timedelta relative to now()
                    # e.g. "-10 seconds" or "5 minutes"
                    times = numpy.fromiter(
                        numpy.add(numpy.datetime64(utcnow()),
                                  [to_timespan(v, True) for v in values]),
                        dtype='datetime64[ns]', count=len(values))
                else:
                    times = numpy.array(values, dtype='datetime64[ns]')
            else:
                times = numpy.array(values, dtype='float') * 10e8
    except ValueError:
        raise ValueError("Unable to convert timestamps")

    times = times.astype('datetime64[ns]')

    if (times < unix_universal_start64).any():
        raise ValueError('Timestamp must be after Epoch')

    return times 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import fromiter [as 別名]
def _encode_measures(self, measures):
        return numpy.fromiter(measures,
                              dtype=TIMESERIES_ARRAY_DTYPE).tobytes() 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import fromiter [as 別名]
def _transitions_matrix(self):
        """ Return a matrix of transition log probabilities. """
        trans_iter = (self._transitions[sj].logprob(si)
                      for sj in self._states
                      for si in self._states)

        transitions_logprob = np.fromiter(trans_iter, dtype=np.float64)
        N = len(self._states)
        return transitions_logprob.reshape((N, N)).T 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import fromiter [as 別名]
def _outputs_vector(self, symbol):
        """
        Return a vector with log probabilities of emitting a symbol
        when entering states.
        """
        out_iter = (self._output_logprob(sj, symbol) for sj in self._states)
        return np.fromiter(out_iter, dtype=np.float64)