Python numpy.NaN方法代码示例

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import NaN [as 别名]
def render(self, take_screenshot=False, output_type=0):
    # self.render_timer.tic()
    self._actual_render()
    # self.render_timer.toc(log_at=1000, log_str='render timer', type='time')

    np_rgb_img = None
    np_d_img = None
    c = 1000.
    if take_screenshot:
      if self.modality == 'rgb':
        screenshot_rgba = np.zeros((self.height, self.width, 4), dtype=np.uint8)
        glReadPixels(0, 0, self.width, self.height, GL_RGBA, GL_UNSIGNED_BYTE, screenshot_rgba)
        np_rgb_img = screenshot_rgba[::-1,:,:3];

      if self.modality == 'depth': 
        screenshot_d = np.zeros((self.height, self.width, 4), dtype=np.uint8)
        glReadPixels(0, 0, self.width, self.height, GL_RGBA, GL_UNSIGNED_BYTE, screenshot_d)
        np_d_img = screenshot_d[::-1,:,:3];
        np_d_img = np_d_img[:,:,2]*(255.*255./c) + np_d_img[:,:,1]*(255./c) + np_d_img[:,:,0]*(1./c)
        np_d_img = np_d_img.astype(np.float32)
        np_d_img[np_d_img == 0] = np.NaN
        np_d_img = np_d_img[:,:,np.newaxis]

    glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT)
    return np_rgb_img, np_d_img 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import NaN [as 别名]
def test_basic_equality(self):
        self.meta['new1'] = {'units': 'hey1', 'long_name': 'crew'}
        self.meta['new2'] = {'units': 'hey', 'long_name': 'boo',
                             'description': 'boohoo', 'fill': np.NaN}
        # ensure things are the same
        meta2 = self.meta.copy()
        assert (meta2 == self.meta)

        # different way to create meta object
        meta3 = pysat.Meta()
        meta3['new1'] = self.meta['new1']
        meta3['new2'] = self.meta['new2']
        assert (meta3 == self.meta)

        # make sure differences matter
        self.meta['new2'] = {'fill': 1}
        assert not (meta2 == self.meta) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import NaN [as 别名]
def _to_primitive(arr, string_max_len=None, forced_dtype=None):
    if arr.dtype.hasobject:
        if len(arr) > 0 and isinstance(arr[0], Timestamp):
            return np.array([t.value for t in arr], dtype=DTN64_DTYPE)

        if forced_dtype is not None:
            casted_arr = arr.astype(dtype=forced_dtype, copy=False)
        elif string_max_len is not None:
            casted_arr = np.array(arr.astype('U{:d}'.format(string_max_len)))
        else:
            casted_arr = np.array(list(arr))

        # Pick any unwanted data conversions (e.g. np.NaN to 'nan')
        if np.array_equal(arr, casted_arr):
            return casted_arr
    return arr 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import NaN [as 别名]
def assert_series_equal(self, left, right, *args, **kwargs):
        def convert(x):
            # need to convert array([Decimal(NaN)], dtype='object') to np.NaN
            # because Series[object].isnan doesn't recognize decimal(NaN) as
            # NA.
            try:
                return math.isnan(x)
            except TypeError:
                return False

        if left.dtype == 'object':
            left_na = left.apply(convert)
        else:
            left_na = left.isna()
        if right.dtype == 'object':
            right_na = right.apply(convert)
        else:
            right_na = right.isna()

        tm.assert_series_equal(left_na, right_na)
        return tm.assert_series_equal(left[~left_na],
                                      right[~right_na],
                                      *args, **kwargs) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import NaN [as 别名]
def test_rolling_skew_edge_cases(self):

        all_nan = Series([np.NaN] * 5)

        # yields all NaN (0 variance)
        d = Series([1] * 5)
        x = d.rolling(window=5).skew()
        tm.assert_series_equal(all_nan, x)

        # yields all NaN (window too small)
        d = Series(np.random.randn(5))
        x = d.rolling(window=2).skew()
        tm.assert_series_equal(all_nan, x)

        # yields [NaN, NaN, NaN, 0.177994, 1.548824]
        d = Series([-1.50837035, -0.1297039, 0.19501095, 1.73508164, 0.41941401
                    ])
        expected = Series([np.NaN, np.NaN, np.NaN, 0.177994, 1.548824])
        x = d.rolling(window=4).skew()
        tm.assert_series_equal(expected, x) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import NaN [as 别名]
def test_dense_to_sparse(self):
        series = self.bseries.to_dense()
        bseries = series.to_sparse(kind='block')
        iseries = series.to_sparse(kind='integer')
        tm.assert_sp_series_equal(bseries, self.bseries)
        tm.assert_sp_series_equal(iseries, self.iseries, check_names=False)
        assert iseries.name == self.bseries.name

        assert len(series) == len(bseries)
        assert len(series) == len(iseries)
        assert series.shape == bseries.shape
        assert series.shape == iseries.shape

        # non-NaN fill value
        series = self.zbseries.to_dense()
        zbseries = series.to_sparse(kind='block', fill_value=0)
        ziseries = series.to_sparse(kind='integer', fill_value=0)
        tm.assert_sp_series_equal(zbseries, self.zbseries)
        tm.assert_sp_series_equal(ziseries, self.ziseries, check_names=False)
        assert ziseries.name == self.zbseries.name

        assert len(series) == len(zbseries)
        assert len(series) == len(ziseries)
        assert series.shape == zbseries.shape
        assert series.shape == ziseries.shape 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import NaN [as 别名]
def test_constructor_from_series(self):

        # GH 2873
        x = Series(np.random.randn(10000), name='a')
        x = x.to_sparse(fill_value=0)
        assert isinstance(x, SparseSeries)
        df = SparseDataFrame(x)
        assert isinstance(df, SparseDataFrame)

        x = Series(np.random.randn(10000), name='a')
        y = Series(np.random.randn(10000), name='b')
        x2 = x.astype(float)
        x2.loc[:9998] = np.NaN
        # TODO: x_sparse is unused...fix
        x_sparse = x2.to_sparse(fill_value=np.NaN)  # noqa

        # Currently fails too with weird ufunc error
        # df1 = SparseDataFrame([x_sparse, y])

        y.loc[:9998] = 0
        # TODO: y_sparse is unsused...fix
        y_sparse = y.to_sparse(fill_value=0)  # noqa
        # without sparse value raises error
        # df2 = SparseDataFrame([x2_sparse, y]) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import NaN [as 别名]
def test_astype(self):
        # GH 13149, GH 13209
        idx = DatetimeIndex(['2016-05-16', 'NaT', NaT, np.NaN])

        result = idx.astype(object)
        expected = Index([Timestamp('2016-05-16')] + [NaT] * 3, dtype=object)
        tm.assert_index_equal(result, expected)

        result = idx.astype(int)
        expected = Int64Index([1463356800000000000] +
                              [-9223372036854775808] * 3, dtype=np.int64)
        tm.assert_index_equal(result, expected)

        rng = date_range('1/1/2000', periods=10)
        result = rng.astype('i8')
        tm.assert_index_equal(result, Index(rng.asi8))
        tm.assert_numpy_array_equal(result.values, rng.asi8) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import NaN [as 别名]
def test_astype_conversion(self):
        # GH#13149, GH#13209
        idx = PeriodIndex(['2016-05-16', 'NaT', NaT, np.NaN], freq='D')

        result = idx.astype(object)
        expected = Index([Period('2016-05-16', freq='D')] +
                         [Period(NaT, freq='D')] * 3, dtype='object')
        tm.assert_index_equal(result, expected)

        result = idx.astype(np.int64)
        expected = Int64Index([16937] + [-9223372036854775808] * 3,
                              dtype=np.int64)
        tm.assert_index_equal(result, expected)

        result = idx.astype(str)
        expected = Index(str(x) for x in idx)
        tm.assert_index_equal(result, expected)

        idx = period_range('1990', '2009', freq='A')
        result = idx.astype('i8')
        tm.assert_index_equal(result, Index(idx.asi8))
        tm.assert_numpy_array_equal(result.values, idx.asi8) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import NaN [as 别名]
def test_count(self, datetime_series):
        assert datetime_series.count() == len(datetime_series)

        datetime_series[::2] = np.NaN

        assert datetime_series.count() == np.isfinite(datetime_series).sum()

        mi = MultiIndex.from_arrays([list('aabbcc'), [1, 2, 2, nan, 1, 2]])
        ts = Series(np.arange(len(mi)), index=mi)

        left = ts.count(level=1)
        right = Series([2, 3, 1], index=[1, 2, nan])
        assert_series_equal(left, right)

        ts.iloc[[0, 3, 5]] = nan
        assert_series_equal(ts.count(level=1), right - 1) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import NaN [as 别名]
def test_scalar(self):

        N = 30
        rng = date_range('1/1/1990', periods=N, freq='53s')
        ts = Series(np.arange(N), index=rng)
        ts[5:10] = np.NaN
        ts[15:20] = np.NaN

        val1 = ts.asof(ts.index[7])
        val2 = ts.asof(ts.index[19])

        assert val1 == ts[4]
        assert val2 == ts[14]

        # accepts strings
        val1 = ts.asof(str(ts.index[7]))
        assert val1 == ts[4]

        # in there
        result = ts.asof(ts.index[3])
        assert result == ts[3]

        # no as of value
        d = ts.index[0] - offsets.BDay()
        assert np.isnan(ts.asof(d)) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import NaN [as 别名]
def test_fromValue(self, datetime_series):

        nans = Series(np.NaN, index=datetime_series.index)
        assert nans.dtype == np.float_
        assert len(nans) == len(datetime_series)

        strings = Series('foo', index=datetime_series.index)
        assert strings.dtype == np.object_
        assert len(strings) == len(datetime_series)

        d = datetime.now()
        dates = Series(d, index=datetime_series.index)
        assert dates.dtype == 'M8[ns]'
        assert len(dates) == len(datetime_series)

        # GH12336
        # Test construction of categorical series from value
        categorical = Series(0, index=datetime_series.index, dtype="category")
        expected = Series(0, index=datetime_series.index).astype("category")
        assert categorical.dtype == 'category'
        assert len(categorical) == len(datetime_series)
        tm.assert_series_equal(categorical, expected) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import NaN [as 别名]
def test_unstack_to_series(self):
        # check reversibility
        data = self.frame.unstack()

        assert isinstance(data, Series)
        undo = data.unstack().T
        assert_frame_equal(undo, self.frame)

        # check NA handling
        data = DataFrame({'x': [1, 2, np.NaN], 'y': [3.0, 4, np.NaN]})
        data.index = Index(['a', 'b', 'c'])
        result = data.unstack()

        midx = MultiIndex(levels=[['x', 'y'], ['a', 'b', 'c']],
                          codes=[[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 1, 2]])
        expected = Series([1, 2, np.NaN, 3, 4, np.NaN], index=midx)

        assert_series_equal(result, expected)

        # check composability of unstack
        old_data = data.copy()
        for _ in range(4):
            data = data.unstack()
        assert_frame_equal(old_data, data) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import NaN [as 别名]
def test_unstack_fill_frame_object():
    # GH12815 Test unstacking with object.
    data = pd.Series(['a', 'b', 'c', 'a'], dtype='object')
    data.index = pd.MultiIndex.from_tuples(
        [('x', 'a'), ('x', 'b'), ('y', 'b'), ('z', 'a')])

    # By default missing values will be NaN
    result = data.unstack()
    expected = pd.DataFrame(
        {'a': ['a', np.nan, 'a'], 'b': ['b', 'c', np.nan]},
        index=list('xyz')
    )
    assert_frame_equal(result, expected)

    # Fill with any value replaces missing values as expected
    result = data.unstack(fill_value='d')
    expected = pd.DataFrame(
        {'a': ['a', 'd', 'a'], 'b': ['b', 'c', 'd']},
        index=list('xyz')
    )
    assert_frame_equal(result, expected) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import NaN [as 别名]
def test_isna_lists(self):
        result = isna([[False]])
        exp = np.array([[False]])
        tm.assert_numpy_array_equal(result, exp)

        result = isna([[1], [2]])
        exp = np.array([[False], [False]])
        tm.assert_numpy_array_equal(result, exp)

        # list of strings / unicode
        result = isna(['foo', 'bar'])
        exp = np.array([False, False])
        tm.assert_numpy_array_equal(result, exp)

        result = isna([u('foo'), u('bar')])
        exp = np.array([False, False])
        tm.assert_numpy_array_equal(result, exp)

        # GH20675
        result = isna([np.NaN, 'world'])
        exp = np.array([True, False])
        tm.assert_numpy_array_equal(result, exp)