python类int_()的实例源码

def test_output_shape(self):
        # see also gh-616
        a = np.ones((10, 5))
        # Check some simple shape mismatches
        out = np.ones(11, dtype=np.int_)
        assert_raises(ValueError, a.argmax, -1, out)

        out = np.ones((2, 5), dtype=np.int_)
        assert_raises(ValueError, a.argmax, -1, out)

        # these could be relaxed possibly (used to allow even the previous)
        out = np.ones((1, 10), dtype=np.int_)
        assert_raises(ValueError, a.argmax, -1, np.ones((1, 10)))

        out = np.ones(10, dtype=np.int_)
        a.argmax(-1, out=out)
        assert_equal(out, a.argmax(-1))

def test_output_shape(self):
        # see also gh-616
        a = np.ones((10, 5))
        # Check some simple shape mismatches
        out = np.ones(11, dtype=np.int_)
        assert_raises(ValueError, a.argmin, -1, out)

        out = np.ones((2, 5), dtype=np.int_)
        assert_raises(ValueError, a.argmin, -1, out)

        # these could be relaxed possibly (used to allow even the previous)
        out = np.ones((1, 10), dtype=np.int_)
        assert_raises(ValueError, a.argmin, -1, np.ones((1, 10)))

        out = np.ones(10, dtype=np.int_)
        a.argmin(-1, out=out)
        assert_equal(out, a.argmin(-1))

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 console_fill_foreground(con,r,g,b) :
    if len(r) != len(g) or len(r) != len(b):
        raise TypeError('R, G and B must all have the same size.')

    if (numpy_available and isinstance(r, numpy.ndarray) and
        isinstance(g, numpy.ndarray) and isinstance(b, numpy.ndarray)):
        #numpy arrays, use numpy's ctypes functions
        r = numpy.ascontiguousarray(r, dtype=numpy.int_)
        g = numpy.ascontiguousarray(g, dtype=numpy.int_)
        b = numpy.ascontiguousarray(b, dtype=numpy.int_)
        cr = r.ctypes.data_as(POINTER(c_int))
        cg = g.ctypes.data_as(POINTER(c_int))
        cb = b.ctypes.data_as(POINTER(c_int))
    else:
        # otherwise convert using ctypes arrays
        cr = (c_int * len(r))(*r)
        cg = (c_int * len(g))(*g)
        cb = (c_int * len(b))(*b)

    _lib.TCOD_console_fill_foreground(con, cr, cg, cb)

def console_fill_background(con,r,g,b) :
    if len(r) != len(g) or len(r) != len(b):
        raise TypeError('R, G and B must all have the same size.')

    if (numpy_available and isinstance(r, numpy.ndarray) and
        isinstance(g, numpy.ndarray) and isinstance(b, numpy.ndarray)):
        #numpy arrays, use numpy's ctypes functions
        r = numpy.ascontiguousarray(r, dtype=numpy.int_)
        g = numpy.ascontiguousarray(g, dtype=numpy.int_)
        b = numpy.ascontiguousarray(b, dtype=numpy.int_)
        cr = r.ctypes.data_as(POINTER(c_int))
        cg = g.ctypes.data_as(POINTER(c_int))
        cb = b.ctypes.data_as(POINTER(c_int))
    else:
        # otherwise convert using ctypes arrays
        cr = (c_int * len(r))(*r)
        cg = (c_int * len(g))(*g)
        cb = (c_int * len(b))(*b)

    _lib.TCOD_console_fill_background(con, cr, cg, cb)

def test_get_all_route_shapes(self):
        res = self.gtfs.get_all_route_shapes()
        self.assertTrue(isinstance(res, list))
        el = res[0]
        keys = u"name type agency lats lons".split()
        for key in keys:
            self.assertTrue(key in el)

        for el in res:
            self.assertTrue(isinstance(el[u"name"], string_types), type(el[u"name"]))
            self.assertTrue(isinstance(el[u"type"], (int, numpy.int_)), type(el[u'type']))
            self.assertTrue(isinstance(el[u"agency"], string_types))
            self.assertTrue(isinstance(el[u"lats"], list), type(el[u'lats']))
            self.assertTrue(isinstance(el[u"lons"], list))
            self.assertTrue(isinstance(el[u'lats'][0], float))
            self.assertTrue(isinstance(el[u'lons'][0], float))

def test_get_stop_count_data(self):
        dt_start_query = datetime.datetime(2007, 1, 1, 7, 59, 59)
        dt_end_query = datetime.datetime(2007, 1, 1, 10, 2, 1)
        start_query = self.gtfs.unlocalized_datetime_to_ut_seconds(dt_start_query)
        end_query = self.gtfs.unlocalized_datetime_to_ut_seconds(dt_end_query)
        df = self.gtfs.get_stop_count_data(start_query, end_query)
        self.assertTrue(isinstance(df, pandas.DataFrame))
        columns = ["stop_I", "count", "lat", "lon", "name"]
        for c in columns:
            self.assertTrue(c in df.columns)
            el = df[c].iloc[0]
            if c in ["stop_I", "count"]:
                self.assertTrue(isinstance(el, (int, numpy.int_)))
            if c in ["lat", "lon"]:
                self.assertTrue(isinstance(el, float))
            if c in ["name"]:
                self.assertTrue(isinstance(el, string_types), type(el))
        self.assertTrue((df['count'].values > 0).any())

def get_reads_base_sds(chrm_strand_reads, chrm_len, rev_strand):
    base_sd_sums = np.zeros(chrm_len)
    base_cov = np.zeros(chrm_len, dtype=np.int_)
    for r_data in chrm_strand_reads:
        # extract read means data so data across all chrms is not
        # in RAM at one time
        try:
            read_data = h5py.File(r_data.fn, 'r')
        except IOError:
            # probably truncated file
            continue
        events_slot = '/'.join((
            '/Analyses', r_data.corr_group, 'Events'))
        if events_slot not in read_data:
            continue
        read_sds = read_data[events_slot]['norm_stdev']

        if rev_strand:
            read_sds = read_sds[::-1]
        base_sd_sums[r_data.start:
                     r_data.start + len(read_sds)] += read_sds
        base_cov[r_data.start:r_data.start + len(read_sds)] += 1

    return base_sd_sums / base_cov

def get_reads_base_lengths(chrm_strand_reads, chrm_len, rev_strand):
    base_length_sums = np.zeros(chrm_len)
    base_cov = np.zeros(chrm_len, dtype=np.int_)
    for r_data in chrm_strand_reads:
        # extract read means data so data across all chrms is not
        # in RAM at one time
        try:
            read_data = h5py.File(r_data.fn, 'r')
        except IOError:
            # probably truncated file
            continue
        events_slot = '/'.join((
            '/Analyses', r_data.corr_group, 'Events'))
        if events_slot not in read_data:
            continue
        read_lengths = read_data[events_slot]['length']

        if rev_strand:
            read_lengths = read_lengths[::-1]
        base_length_sums[
            r_data.start:
            r_data.start + len(read_lengths)] += read_lengths
        base_cov[r_data.start:r_data.start + len(read_lengths)] += 1

    return base_length_sums / base_cov

def test_empty_tuple_index(self):
        # Empty tuple index creates a view
        a = np.array([1, 2, 3])
        assert_equal(a[()], a)
        assert_(a[()].base is a)
        a = np.array(0)
        assert_(isinstance(a[()], np.int_))

        # Regression, it needs to fall through integer and fancy indexing
        # cases, so need the with statement to ignore the non-integer error.
        with warnings.catch_warnings():
            warnings.filterwarnings('ignore', '', DeprecationWarning)
            a = np.array([1.])
            assert_(isinstance(a[0.], np.float_))

            a = np.array([np.array(1)], dtype=object)
            assert_(isinstance(a[0.], np.ndarray))

def test_output_shape(self):
        # see also gh-616
        a = np.ones((10, 5))
        # Check some simple shape mismatches
        out = np.ones(11, dtype=np.int_)
        assert_raises(ValueError, a.argmax, -1, out)

        out = np.ones((2, 5), dtype=np.int_)
        assert_raises(ValueError, a.argmax, -1, out)

        # these could be relaxed possibly (used to allow even the previous)
        out = np.ones((1, 10), dtype=np.int_)
        assert_raises(ValueError, a.argmax, -1, out)

        out = np.ones(10, dtype=np.int_)
        a.argmax(-1, out=out)
        assert_equal(out, a.argmax(-1))

def test_output_shape(self):
        # see also gh-616
        a = np.ones((10, 5))
        # Check some simple shape mismatches
        out = np.ones(11, dtype=np.int_)
        assert_raises(ValueError, a.argmin, -1, out)

        out = np.ones((2, 5), dtype=np.int_)
        assert_raises(ValueError, a.argmin, -1, out)

        # these could be relaxed possibly (used to allow even the previous)
        out = np.ones((1, 10), dtype=np.int_)
        assert_raises(ValueError, a.argmin, -1, out)

        out = np.ones(10, dtype=np.int_)
        a.argmin(-1, out=out)
        assert_equal(out, a.argmin(-1))

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 allclose 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 default(self, obj):
        # convert dates and numpy objects in a json serializable format
        if isinstance(obj, datetime):
            return obj.strftime('%Y-%m-%dT%H:%M:%SZ')
        elif isinstance(obj, date):
            return obj.strftime('%Y-%m-%d')
        elif type(obj) in [np.int_, np.intc, np.intp, np.int8, np.int16,
                           np.int32, np.int64, np.uint8, np.uint16,
                           np.uint32, np.uint64]:
            return int(obj)
        elif type(obj) in [np.bool_]:
            return bool(obj)
        elif type(obj) in [np.float_, np.float16, np.float32, np.float64,
                           np.complex_, np.complex64, np.complex128]:
            return float(obj)

        # Let the base class default method raise the TypeError
        return json.JSONEncoder.default(self, obj)

def test_int_subclassing(self):
        # Regression test for https://github.com/numpy/numpy/pull/3526

        numpy_int = np.int_(0)

        if sys.version_info[0] >= 3:
            # On Py3k int_ should not inherit from int, because it's not
            # fixed-width anymore
            assert_equal(isinstance(numpy_int, int), False)
        else:
            # Otherwise, it should inherit from int...
            assert_equal(isinstance(numpy_int, int), True)

            # ... and fast-path checks on C-API level should also work
            from numpy.core.multiarray_tests import test_int_subclass
            assert_equal(test_int_subclass(numpy_int), True)

def test_output_shape(self):
        # see also gh-616
        a = np.ones((10, 5))
        # Check some simple shape mismatches
        out = np.ones(11, dtype=np.int_)
        assert_raises(ValueError, a.argmax, -1, out)

        out = np.ones((2, 5), dtype=np.int_)
        assert_raises(ValueError, a.argmax, -1, out)

        # these could be relaxed possibly (used to allow even the previous)
        out = np.ones((1, 10), dtype=np.int_)
        assert_raises(ValueError, a.argmax, -1, out)

        out = np.ones(10, dtype=np.int_)
        a.argmax(-1, out=out)
        assert_equal(out, a.argmax(-1))

def test_output_shape(self):
        # see also gh-616
        a = np.ones((10, 5))
        # Check some simple shape mismatches
        out = np.ones(11, dtype=np.int_)
        assert_raises(ValueError, a.argmin, -1, out)

        out = np.ones((2, 5), dtype=np.int_)
        assert_raises(ValueError, a.argmin, -1, out)

        # these could be relaxed possibly (used to allow even the previous)
        out = np.ones((1, 10), dtype=np.int_)
        assert_raises(ValueError, a.argmin, -1, out)

        out = np.ones(10, dtype=np.int_)
        a.argmin(-1, out=out)
        assert_equal(out, a.argmin(-1))

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 allclose 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 data_style_func(df):
        '''
        Default value that can be used as callback for data_style_func

        Args:
            df: the dataframe that will be used to build the presentation model

        Returns:
            a function table takes idx, col as arguments and returns a dictionary of html style attributes
        '''
        def _style_func(r, c):
            if isinstance(df.at[r,c], (np.int_, np.float, np.uint)):
                return td_style_to_str(default_numeric_td_style)
            return td_style_to_str(default_td_style)
        return _style_func