python类integer()的实例源码

def test_default_type_conversion(self):
        df = sql.read_sql_table("types_test_data", self.conn)

        self.assertTrue(issubclass(df.FloatCol.dtype.type, np.floating),
                        "FloatCol loaded with incorrect type")
        self.assertTrue(issubclass(df.IntCol.dtype.type, np.integer),
                        "IntCol loaded with incorrect type")
        self.assertTrue(issubclass(df.BoolCol.dtype.type, np.bool_),
                        "BoolCol loaded with incorrect type")

        # Int column with NA values stays as float
        self.assertTrue(issubclass(df.IntColWithNull.dtype.type, np.floating),
                        "IntColWithNull loaded with incorrect type")
        # Bool column with NA values becomes object
        self.assertTrue(issubclass(df.BoolColWithNull.dtype.type, np.object),
                        "BoolColWithNull loaded with incorrect type")

def test_default_type_conversion(self):
        df = sql.read_sql_table("types_test_data", self.conn)

        self.assertTrue(issubclass(df.FloatCol.dtype.type, np.floating),
                        "FloatCol loaded with incorrect type")
        self.assertTrue(issubclass(df.IntCol.dtype.type, np.integer),
                        "IntCol loaded with incorrect type")
        # sqlite has no boolean type, so integer type is returned
        self.assertTrue(issubclass(df.BoolCol.dtype.type, np.integer),
                        "BoolCol loaded with incorrect type")

        # Int column with NA values stays as float
        self.assertTrue(issubclass(df.IntColWithNull.dtype.type, np.floating),
                        "IntColWithNull loaded with incorrect type")
        # Non-native Bool column with NA values stays as float
        self.assertTrue(issubclass(df.BoolColWithNull.dtype.type, np.floating),
                        "BoolColWithNull loaded with incorrect type")

def test_default_type_conversion(self):
        df = sql.read_sql_table("types_test_data", self.conn)

        self.assertTrue(issubclass(df.FloatCol.dtype.type, np.floating),
                        "FloatCol loaded with incorrect type")
        self.assertTrue(issubclass(df.IntCol.dtype.type, np.integer),
                        "IntCol loaded with incorrect type")
        # MySQL has no real BOOL type (it's an alias for TINYINT)
        self.assertTrue(issubclass(df.BoolCol.dtype.type, np.integer),
                        "BoolCol loaded with incorrect type")

        # Int column with NA values stays as float
        self.assertTrue(issubclass(df.IntColWithNull.dtype.type, np.floating),
                        "IntColWithNull loaded with incorrect type")
        # Bool column with NA = int column with NA values => becomes float
        self.assertTrue(issubclass(df.BoolColWithNull.dtype.type, np.floating),
                        "BoolColWithNull loaded with incorrect type")

def __init__(self, f, colspecs, delimiter, comment):
        self.f = f
        self.buffer = None
        self.delimiter = '\r\n' + delimiter if delimiter else '\n\r\t '
        self.comment = comment
        if colspecs == 'infer':
            self.colspecs = self.detect_colspecs()
        else:
            self.colspecs = colspecs

        if not isinstance(self.colspecs, (tuple, list)):
            raise TypeError("column specifications must be a list or tuple, "
                            "input was a %r" % type(colspecs).__name__)

        for colspec in self.colspecs:

            if not (isinstance(colspec, (tuple, list)) and
                    len(colspec) == 2 and
                    isinstance(colspec[0], (int, np.integer, type(None))) and
                    isinstance(colspec[1], (int, np.integer, type(None)))):
                raise TypeError('Each column specification must be '
                                '2 element tuple or list of integers')

def set_atom_categorical(self, block, items, info=None, values=None):
        # currently only supports a 1-D categorical
        # in a 1-D block

        values = block.values
        codes = values.codes
        self.kind = 'integer'
        self.dtype = codes.dtype.name
        if values.ndim > 1:
            raise NotImplementedError("only support 1-d categoricals")
        if len(items) > 1:
            raise NotImplementedError("only support single block categoricals")

        # write the codes; must be in a block shape
        self.ordered = values.ordered
        self.typ = self.get_atom_data(block, kind=codes.dtype.name)
        self.set_data(_block_shape(codes))

        # write the categories
        self.meta = 'category'
        self.set_metadata(block.values.categories)

        # update the info
        self.update_info(info)

def _handle_date_column(col, format=None):
    if isinstance(format, dict):
        return to_datetime(col, errors='ignore', **format)
    else:
        if format in ['D', 's', 'ms', 'us', 'ns']:
            return to_datetime(col, errors='coerce', unit=format, utc=True)
        elif (issubclass(col.dtype.type, np.floating) or
              issubclass(col.dtype.type, np.integer)):
            # parse dates as timestamp
            format = 's' if format is None else format
            return to_datetime(col, errors='coerce', unit=format, utc=True)
        elif com.is_datetime64tz_dtype(col):
            # coerce to UTC timezone
            # GH11216
            return (to_datetime(col, errors='coerce')
                    .astype('datetime64[ns, UTC]'))
        else:
            return to_datetime(col, errors='coerce', format=format, utc=True)

def _get_dtype(self, sqltype):
        from sqlalchemy.types import (Integer, Float, Boolean, DateTime,
                                      Date, TIMESTAMP)

        if isinstance(sqltype, Float):
            return float
        elif isinstance(sqltype, Integer):
            # TODO: Refine integer size.
            return np.dtype('int64')
        elif isinstance(sqltype, TIMESTAMP):
            # we have a timezone capable type
            if not sqltype.timezone:
                return datetime
            return DatetimeTZDtype
        elif isinstance(sqltype, DateTime):
            # Caution: np.datetime64 is also a subclass of np.number.
            return datetime
        elif isinstance(sqltype, Date):
            return date
        elif isinstance(sqltype, Boolean):
            return bool
        return object

def _sql_type_name(self, col):
        dtype = self.dtype or {}
        if col.name in dtype:
            return dtype[col.name]

        col_type = self._get_notnull_col_dtype(col)
        if col_type == 'timedelta64':
            warnings.warn("the 'timedelta' type is not supported, and will be "
                          "written as integer values (ns frequency) to the "
                          "database.", UserWarning, stacklevel=8)
            col_type = "integer"

        elif col_type == "datetime64":
            col_type = "datetime"

        elif col_type == "empty":
            col_type = "string"

        elif col_type == "complex":
            raise ValueError('Complex datatypes not supported')

        if col_type not in _SQL_TYPES:
            col_type = "string"

        return _SQL_TYPES[col_type][self.pd_sql.flavor]

def __getitem__(self, key):
        """

        """
        try:
            return self._get_val_at(self.index.get_loc(key))

        except KeyError:
            if isinstance(key, (int, np.integer)):
                return self._get_val_at(key)
            raise Exception('Requested index not in this series!')

        except TypeError:
            # Could not hash item, must be array-like?
            pass

        # is there a case where this would NOT be an ndarray?
        # need to find an example, I took out the case for now

        key = _values_from_object(key)
        dataSlice = self.values[key]
        new_index = Index(self.index.view(ndarray)[key])
        return self._constructor(dataSlice, index=new_index).__finalize__(self)

def test_allclose(self):
        # Tests allclose on arrays
        a = np.random.rand(10)
        b = a + np.random.rand(10) * 1e-8
        self.assertTrue(allclose(a, b))
        # Test allclose w/ infs
        a[0] = np.inf
        self.assertTrue(not allclose(a, b))
        b[0] = np.inf
        self.assertTrue(allclose(a, b))
        # Test all close w/ masked
        a = masked_array(a)
        a[-1] = masked
        self.assertTrue(allclose(a, b, masked_equal=True))
        self.assertTrue(not allclose(a, b, masked_equal=False))
        # Test comparison w/ scalar
        a *= 1e-8
        a[0] = 0
        self.assertTrue(allclose(a, 0, masked_equal=True))

        # Test that the function works for MIN_INT integer typed arrays
        a = masked_array([np.iinfo(np.int_).min], dtype=np.int_)
        self.assertTrue(allclose(a, a))

def check_random_state(seed):
    """Turn seed into a np.random.RandomState instance
    Parameters
    ----------
    seed : None | int | instance of RandomState
        If seed is None, return the RandomState singleton used by np.random.
        If seed is an int, return a new RandomState instance seeded with seed.
        If seed is already a RandomState instance, return it.
        Otherwise raise ValueError.
    """
    if seed is None or seed is np.random:
        return np.random.mtrand._rand
    if isinstance(seed, (numbers.Integral, np.integer)):
        return np.random.RandomState(seed)
    if isinstance(seed, np.random.RandomState):
        return seed
    raise ValueError('%r cannot be used to seed a numpy.random.RandomState'
                     ' instance' % seed)

def guessCfgType( value ):
    # For guessing the data type (bool, integer, float, or string only) from ConfigParser
    if value.lower() == 'true':
        return True
    if value.lower() == 'false':
        return False
    try:
        value = np.int( value )
        return value
    except: 
        pass
    try: 
        value = np.float32( value )
        return value
    except: 
        pass 
    return value

def check_window_length(window_length):
    """
    Ensure the window length provided to a transform is valid.
    """
    if window_length is None:
        raise InvalidWindowLength("window_length must be provided")
    if not isinstance(window_length, Integral):
        raise InvalidWindowLength(
            "window_length must be an integer-like number")
    if window_length == 0:
        raise InvalidWindowLength("window_length must be non-zero")
    if window_length < 0:
        raise InvalidWindowLength("window_length must be positive")

def _extract_field_names(self, event):
        # extract field names from sids (price, volume etc), make sure
        # every sid has the same fields.
        sid_keys = []
        for sid in itervalues(event.data):
            keys = set([name for name, value in sid.items()
                        if isinstance(value,
                                      (int,
                                       float,
                                       numpy.integer,
                                       numpy.float,
                                       numpy.long))
                        ])
            sid_keys.append(keys)

        # with CUSTOM data events, there may be different fields
        # per sid. So the allowable keys are the union of all events.
        union = set.union(*sid_keys)
        unwanted_fields = {
            'portfolio',
            'sid',
            'dt',
            'type',
            'source_id',
            '_initial_len',
        }
        return union - unwanted_fields

def RATWriteArray(rat, array, field, start=0):
    """
    Pure Python implementation of writing a chunk of the RAT
    from a numpy array. Type of array is coerced to one of the types
    (int, double, string) supported. Called from RasterAttributeTable.WriteArray
    """
    if array is None:
        raise ValueError("Expected array of dim 1")

    # if not the array type convert it to handle lists etc
    if not isinstance(array, numpy.ndarray):
        array = numpy.array(array)

    if array.ndim != 1:
        raise ValueError("Expected array of dim 1")

    if (start + array.size) > rat.GetRowCount():
        raise ValueError("Array too big to fit into RAT from start position")

    if numpy.issubdtype(array.dtype, numpy.integer):
        # is some type of integer - coerce to standard int
        # TODO: must check this is fine on all platforms
        # confusingly numpy.int 64 bit even if native type 32 bit
        array = array.astype(numpy.int32)
    elif numpy.issubdtype(array.dtype, numpy.floating):
        # is some type of floating point - coerce to double
        array = array.astype(numpy.double)
    elif numpy.issubdtype(array.dtype, numpy.character):
        # cast away any kind of Unicode etc
        array = array.astype(numpy.character)
    else:
        raise ValueError("Array not of a supported type (integer, double or string)")

    return RATValuesIONumPyWrite(rat, field, start, array)

def default(self, obj):
    if isinstance(obj, np.integer):
      return int(obj)
    elif isinstance(obj, np.ndarray):
      return obj.tolist()
    elif isinstance(obj, np.floating):
      return float(obj)
    else:
      return super(MyEncoder, self).default(obj)

def writeHDF5Meta(self, root, name, data, **dsOpts):
        if isinstance(data, np.ndarray):
            dsOpts['maxshape'] = (None,) + data.shape[1:]
            root.create_dataset(name, data=data, **dsOpts)
        elif isinstance(data, list) or isinstance(data, tuple):
            gr = root.create_group(name)
            if isinstance(data, list):
                gr.attrs['_metaType_'] = 'list'
            else:
                gr.attrs['_metaType_'] = 'tuple'
            #n = int(np.log10(len(data))) + 1
            for i in range(len(data)):
                self.writeHDF5Meta(gr, str(i), data[i], **dsOpts)
        elif isinstance(data, dict):
            gr = root.create_group(name)
            gr.attrs['_metaType_'] = 'dict'
            for k, v in data.items():
                self.writeHDF5Meta(gr, k, v, **dsOpts)
        elif isinstance(data, int) or isinstance(data, float) or isinstance(data, np.integer) or isinstance(data, np.floating):
            root.attrs[name] = data
        else:
            try:   ## strings, bools, None are stored as repr() strings
                root.attrs[name] = repr(data)
            except:
                print("Can not store meta data of type '%s' in HDF5. (key is '%s')" % (str(type(data)), str(name)))
                raise

def writeHDF5Meta(self, root, name, data, **dsOpts):
        if isinstance(data, np.ndarray):
            dsOpts['maxshape'] = (None,) + data.shape[1:]
            root.create_dataset(name, data=data, **dsOpts)
        elif isinstance(data, list) or isinstance(data, tuple):
            gr = root.create_group(name)
            if isinstance(data, list):
                gr.attrs['_metaType_'] = 'list'
            else:
                gr.attrs['_metaType_'] = 'tuple'
            #n = int(np.log10(len(data))) + 1
            for i in range(len(data)):
                self.writeHDF5Meta(gr, str(i), data[i], **dsOpts)
        elif isinstance(data, dict):
            gr = root.create_group(name)
            gr.attrs['_metaType_'] = 'dict'
            for k, v in data.items():
                self.writeHDF5Meta(gr, k, v, **dsOpts)
        elif isinstance(data, int) or isinstance(data, float) or isinstance(data, np.integer) or isinstance(data, np.floating):
            root.attrs[name] = data
        else:
            try:   ## strings, bools, None are stored as repr() strings
                root.attrs[name] = repr(data)
            except:
                print("Can not store meta data of type '%s' in HDF5. (key is '%s')" % (str(type(data)), str(name)))
                raise

def repeat(self, nr_sites):
        """Construct a longer MP-POVM by repetition

        The resulting POVM will have length `nr_sites`. If `nr_sites`
        is not an integer multiple of `len(self)`, `self` must
        factorize (have leg  dimension one) at the position where it
        will be cut. For example, consider the tensor product MP-POVM
        of Pauli X and Pauli Y. Calling `repeat(nr_sites=5)` will
        construct the tensor product POVM XYXYX:

        >>> import mpnum as mp
        >>> import mpnum.povm as mpp
        >>> x, y = (mpp.MPPovm.from_local_povm(lp(3), 1) for lp in
        ...         (mpp.x_povm, mpp.y_povm))
        >>> xy = mp.chain([x, y])
        >>> xyxyx = mp.chain([x, y, x, y, x])
        >>> mp.norm(xyxyx - xy.repeat(5)) <= 1e-10
        True

        """
        n_repeat, n_last = nr_sites // len(self), nr_sites % len(self)
        if n_last > 0:
            assert self.ranks[n_last - 1] == 1, \
                "Partial repetition requires factorizing MP-POVM"
        return mp.chain([self] * n_repeat
                        + ([MPPovm(self.lt[:n_last])] if n_last > 0 else []))

def est_pmf(self, samples, normalize=True, eps=1e-10):
        """Estimate probability mass function from samples

        :param np.ndarray samples: `(n_samples, len(self.nsoutdims))`
            array of samples
        :param bool normalize: True: Return normalized probability
            estimates (default). False: Return integer outcome counts.
        :returns: Estimated probabilities as ndarray `est_pmf` with
            shape `self.nsoutdims`

        `n_samples * est_pmf[i1, ..., ik]` provides the number of
        occurences of outcome `(i1, ..., ik)` in `samples`.

        """
        n_samples = samples.shape[0]
        n_out = np.prod(self.nsoutdims)
        if samples.ndim > 1:
            samples = self.pack_samples(samples)
        counts = np.bincount(samples, minlength=n_out)
        assert counts.shape == (n_out,)
        counts = counts.reshape(self.nsoutdims)
        assert counts.sum() == n_samples
        if normalize:
            return counts / n_samples
        else:
            return counts