python类putmask()的实例源码

def _join_non_unique(self, other, how='left', return_indexers=False):
        from pandas.tools.merge import _get_join_indexers

        left_idx, right_idx = _get_join_indexers([self.values],
                                                 [other._values], how=how,
                                                 sort=True)

        left_idx = com._ensure_platform_int(left_idx)
        right_idx = com._ensure_platform_int(right_idx)

        join_index = self.values.take(left_idx)
        mask = left_idx == -1
        np.putmask(join_index, mask, other._values.take(right_idx))

        join_index = self._wrap_joined_index(join_index, other)

        if return_indexers:
            return join_index, left_idx, right_idx
        else:
            return join_index

def decons_group_index(comp_labels, shape):
    # reconstruct labels
    if _int64_overflow_possible(shape):
        # at some point group indices are factorized,
        # and may not be deconstructed here! wrong path!
        raise ValueError('cannot deconstruct factorized group indices!')

    label_list = []
    factor = 1
    y = 0
    x = comp_labels
    for i in reversed(range(len(shape))):
        labels = (x - y) % (factor * shape[i]) // factor
        np.putmask(labels, comp_labels < 0, -1)
        label_list.append(labels)
        y = labels * factor
        factor *= shape[i]
    return label_list[::-1]

def _get_counts_nanvar(mask, axis, ddof, dtype=float):
    dtype = _get_dtype(dtype)
    count = _get_counts(mask, axis, dtype=dtype)
    d = count - dtype.type(ddof)

    # always return NaN, never inf
    if lib.isscalar(count):
        if count <= ddof:
            count = np.nan
            d = np.nan
    else:
        mask2 = count <= ddof
        if mask2.any():
            np.putmask(d, mask2, np.nan)
            np.putmask(count, mask2, np.nan)
    return count, d

def make_nancomp(op):
    def f(x, y):
        xmask = isnull(x)
        ymask = isnull(y)
        mask = xmask | ymask

        result = op(x, y)

        if mask.any():
            if is_bool_dtype(result):
                result = result.astype('O')
            np.putmask(result, mask, np.nan)

        return result

    return f

def test_frame_getitem_setitem_boolean(self):
        df = self.frame.T.copy()
        values = df.values

        result = df[df > 0]
        expected = df.where(df > 0)
        assert_frame_equal(result, expected)

        df[df > 0] = 5
        values[values > 0] = 5
        assert_almost_equal(df.values, values)

        df[df == 5] = 0
        values[values == 5] = 0
        assert_almost_equal(df.values, values)

        # a df that needs alignment first
        df[df[:-1] < 0] = 2
        np.putmask(values[:-1], values[:-1] < 0, 2)
        assert_almost_equal(df.values, values)

        with assertRaisesRegexp(TypeError, 'boolean values only'):
            df[df * 0] = 2

def _sort_labels(uniques, left, right):
    if not isinstance(uniques, np.ndarray):
        # tuplesafe
        uniques = Index(uniques).values

    sorter = uniques.argsort()

    reverse_indexer = np.empty(len(sorter), dtype=np.int64)
    reverse_indexer.put(sorter, np.arange(len(sorter)))

    new_left = reverse_indexer.take(com._ensure_platform_int(left))
    np.putmask(new_left, left == -1, -1)

    new_right = reverse_indexer.take(com._ensure_platform_int(right))
    np.putmask(new_right, right == -1, -1)

    return new_left, new_right

def cloud_shadow_mask_array(image_array, image_difference_array, solar_zenith, solar_azimuth, resolution):
    '''
    This method creates a mask for clouds and shadows using a reference array.
    '''
    clouds = cloud_mask_array(image_difference_array)
    shadows = shadow_mask_array(image_difference_array)
    inbetween = calculate_cloud_shadow(clouds, shadows, solar_zenith, solar_azimuth, resolution)

    image_mask_array = outside_mask_array(image_array, outside_value=255)

    pixel_sizes = [250, 150, 50]
    number_of_sizes = len(pixel_sizes)
    for pixel_size in pixel_sizes:
        numpy.putmask(image_mask_array, morph_dilation(clouds, pixel_size) == 1, FMASK_CLOUD * 10 + number_of_sizes)
        number_of_sizes = number_of_sizes - 1
    numpy.putmask(image_mask_array, inbetween == 1, FMASK_CLOUD_SHADOW)
    numpy.putmask(image_mask_array, clouds == 1, FMASK_CLOUD)
    return image_mask_array

def filter_bytes(self, databytes, bit_mask):
        """
        Detect from a bit mask which bits
        to keep to recompose the signal.
        """
        n_bytes = len(databytes)
        mask = np.ones(n_bytes, dtype=int)
        np.putmask(mask, mask, bit_mask)
        to_keep = np.where(mask > 0)[0]
        return databytes.take(to_keep)

def get_tag_data(self, episode, tag_channel):
        #memorise some useful properties
        block = self.episode_block(episode)
        sample_size = self.sample_size(episode, tag_channel)
        sample_symbol = self.sample_symbol(episode, tag_channel)

        #create a bit mask to define which
        #sample to keep from the file
        channel_mask = self.create_channel_mask(episode)
        bit_mask = self.create_bit_mask(channel_mask, 1)

        #get bytes from the file
        data_block = self.data_blocks[episode - 1]
        n_bytes = data_block.size
        self.file.seek(data_block.start)
        databytes = np.frombuffer(self.file.read(n_bytes), '<i1')

        #detect which bits keep to recompose the tag
        ep_mask = np.ones(n_bytes, dtype=int)
        np.putmask(ep_mask, ep_mask, bit_mask)
        to_keep = np.where(ep_mask > 0)[0]
        raw = databytes.take(to_keep)
        raw = raw.reshape([len(raw) / sample_size, sample_size])

        #create a recarray containing data
        dt = np.dtype(numpy_map[sample_symbol])
        dt.newbyteorder('<')
        tag_mask = 0b01 if (tag_channel == 1) else 0b10
        y_data = np.frombuffer(raw, dt) & tag_mask
        x_data = np.arange(0, len(y_data)) * block.dX + block.X0
        data = np.recarray(len(y_data), dtype=[('x', b_float), ('y', b_int)])
        data['x'] = x_data
        data['y'] = y_data

        return data

def filter_bytes(self, databytes, bit_mask):
        """
        Detect from a bit mask which bits
        to keep to recompose the signal.
        """
        n_bytes = len(databytes)
        mask = np.ones(n_bytes, dtype=int)
        np.putmask(mask, mask, bit_mask)
        to_keep = np.where(mask > 0)[0]
        return databytes.take(to_keep)

def get_tag_data(self, episode, tag_channel):
        #memorise some useful properties
        block = self.episode_block(episode)
        sample_size = self.sample_size(episode, tag_channel)
        sample_symbol = self.sample_symbol(episode, tag_channel)

        #create a bit mask to define which
        #sample to keep from the file
        channel_mask = self.create_channel_mask(episode)
        bit_mask = self.create_bit_mask(channel_mask, 1)

        #get bytes from the file
        data_block = self.data_blocks[episode - 1]
        n_bytes = data_block.size
        self.file.seek(data_block.start)
        databytes = np.frombuffer(self.file.read(n_bytes), '<i1')

        #detect which bits keep to recompose the tag
        ep_mask = np.ones(n_bytes, dtype=int)
        np.putmask(ep_mask, ep_mask, bit_mask)
        to_keep = np.where(ep_mask > 0)[0]
        raw = databytes.take(to_keep)
        raw = raw.reshape([len(raw) / sample_size, sample_size])

        #create a recarray containing data
        dt = np.dtype(numpy_map[sample_symbol])
        dt.newbyteorder('<')
        tag_mask = 0b01 if (tag_channel == 1) else 0b10
        y_data = np.frombuffer(raw, dt) & tag_mask
        x_data = np.arange(0, len(y_data)) * block.dX + block.X0
        data = np.recarray(len(y_data), dtype=[('x', b_float), ('y', b_int)])
        data['x'] = x_data
        data['y'] = y_data

        return data

def tst_basic(self, x, T, mask, val):
        np.putmask(x, mask, val)
        assert_(np.all(x[mask] == T(val)))
        assert_(x.dtype == T)

def test_mask_size(self):
        assert_raises(ValueError, np.putmask, np.array([1, 2, 3]), [True], 5)

def tst_byteorder(self, dtype):
        x = np.array([1, 2, 3], dtype)
        np.putmask(x, [True, False, True], -1)
        assert_array_equal(x, [-1, 2, -1])

def test_record_array(self):
        # Note mixed byteorder.
        rec = np.array([(-5, 2.0, 3.0), (5.0, 4.0, 3.0)],
                      dtype=[('x', '<f8'), ('y', '>f8'), ('z', '<f8')])
        np.putmask(rec['x'], [True, False], 10)
        assert_array_equal(rec['x'], [10, 5])
        assert_array_equal(rec['y'], [2, 4])
        assert_array_equal(rec['z'], [3, 3])
        np.putmask(rec['y'], [True, False], 11)
        assert_array_equal(rec['x'], [10, 5])
        assert_array_equal(rec['y'], [11, 4])
        assert_array_equal(rec['z'], [3, 3])

def oppnorm_convert(arr, threshold=0.1):
    #assert(arr.min()>=0 and arr.max()<=1)
    #out = sp.empty_like(arr)
    arr = arr.astype('float32')
    out = np.empty(arr.shape[:2]+(2,), dtype='float32')

    print out.shape

    # red-green
    out[:,:,0] = arr[:,:,0] - arr[:,:,1]
    # blue-yellow
    out[:,:,1] = arr[:,:,2] - arr[:,:,[0,1]].min(2)
    # intensity
    denom = arr.max(2)

    mask = denom < threshold#*denom[:,:,2].mean()

    out[:,:,0] /= denom    
    out[:,:,1] /= denom

    np.putmask(out[:,:,0], mask, 0)
    np.putmask(out[:,:,1], mask, 0)

    return out

# ------------------------------------------------------------------------------

def radian2degree(x):
    """
    Convert radians angles to torsion angles.

    @param x: radian angle
    @return: torsion angle of x
    """
    x = x % (2 * numpy.pi)
    numpy.putmask(x, x > numpy.pi, x - 2 * numpy.pi)
    return x * 180. / numpy.pi

def degree2radian(x):
    """
    Convert randian angles to torsion angles.

    @param x: torsion angle
    @return: radian angle of x
    """
    numpy.putmask(x, x < 0., x + 360.)
    return x * numpy.pi / 180.

def tst_basic(self, x, T, mask, val):
        np.putmask(x, mask, val)
        assert_(np.all(x[mask] == T(val)))
        assert_(x.dtype == T)

def test_mask_size(self):
        assert_raises(ValueError, np.putmask, np.array([1, 2, 3]), [True], 5)

def tst_byteorder(self, dtype):
        x = np.array([1, 2, 3], dtype)
        np.putmask(x, [True, False, True], -1)
        assert_array_equal(x, [-1, 2, -1])

def test_record_array(self):
        # Note mixed byteorder.
        rec = np.array([(-5, 2.0, 3.0), (5.0, 4.0, 3.0)],
                      dtype=[('x', '<f8'), ('y', '>f8'), ('z', '<f8')])
        np.putmask(rec['x'], [True, False], 10)
        assert_array_equal(rec['x'], [10, 5])
        assert_array_equal(rec['y'], [2, 4])
        assert_array_equal(rec['z'], [3, 3])
        np.putmask(rec['y'], [True, False], 11)
        assert_array_equal(rec['x'], [10, 5])
        assert_array_equal(rec['y'], [11, 4])
        assert_array_equal(rec['z'], [3, 3])

def resize_save(path, size):
  path = os.path.join(os.getcwd(), path)
  images_files = os.listdir(path)
  el = (np.array([[0,1],[1,1]])).astype(np.float32)
  for i, image in enumerate(images_files):
    image_file =  os.path.join(path,image)
    try:
      im = (ndimage.imread(image_file)).astype(np.float32)
      np.putmask(im, im < 100, 0)
      im = ndimage.binary_dilation(im, structure=el)
      im = (misc.imresize(im, (size, size))).astype(np.uint8)
      new_im = Image.fromarray(im)
      new_im.save(image_file)
    except Exception as e:
      print('Could not read:', image_file, ':', e, '- it\'s ok, skipping.')

def putmask(self, mask, value):
        """
        return a new Index of the values set with the mask

        See also
        --------
        numpy.ndarray.putmask
        """
        values = self.values.copy()
        try:
            np.putmask(values, mask, self._convert_for_op(value))
            return self._shallow_copy(values)
        except (ValueError, TypeError):
            # coerces to object
            return self.astype(object).putmask(mask, value)

def fillna(self, value=None, downcast=None):
        self._assert_can_do_op(value)
        if self.hasnans:
            result = self.putmask(self._isnan, value)
            if downcast is None:
                # no need to care metadata other than name
                # because it can't have freq if
                return Index(result, name=self.name)
        return self._shallow_copy()

def _comp_method_PANEL(op, name, str_rep=None, masker=False):
    def na_op(x, y):
        try:
            result = expressions.evaluate(op, str_rep, x, y,
                                          raise_on_error=True)
        except TypeError:
            xrav = x.ravel()
            result = np.empty(x.size, dtype=bool)
            if isinstance(y, np.ndarray):
                yrav = y.ravel()
                mask = notnull(xrav) & notnull(yrav)
                result[mask] = op(np.array(list(xrav[mask])),
                                  np.array(list(yrav[mask])))
            else:
                mask = notnull(xrav)
                result[mask] = op(np.array(list(xrav[mask])), y)

            if op == operator.ne:  # pragma: no cover
                np.putmask(result, ~mask, True)
            else:
                np.putmask(result, ~mask, False)
            result = result.reshape(x.shape)

        return result

    @Appender('Wrapper for comparison method %s' % name)
    def f(self, other):
        if isinstance(other, self._constructor):
            return self._compare_constructor(other, na_op)
        elif isinstance(other, (self._constructor_sliced, pd.DataFrame,
                                ABCSeries)):
            raise Exception("input needs alignment for this object [%s]" %
                            self._constructor)
        else:
            return self._combine_const(other, na_op)

    f.__name__ = name

    return f

def pct_change(self, periods=1, fill_method='pad', limit=None, freq=None,
                   **kwargs):
        # TODO: Not sure if above is correct - need someone to confirm.
        axis = self._get_axis_number(kwargs.pop('axis', self._stat_axis_name))
        if fill_method is None:
            data = self
        else:
            data = self.fillna(method=fill_method, limit=limit, axis=axis)

        rs = (data.div(data.shift(periods=periods, freq=freq, axis=axis,
                                  **kwargs)) - 1)
        if freq is None:
            mask = com.isnull(_values_from_object(self))
            np.putmask(rs.values, mask, np.nan)
        return rs

def _make_cum_function(name, name1, name2, axis_descr, desc, accum_func,
                       mask_a, mask_b):
    @Substitution(outname=name, desc=desc, name1=name1, name2=name2,
                  axis_descr=axis_descr)
    @Appender("Return cumulative {0} over requested axis.".format(name) +
              _cnum_doc)
    def func(self, axis=None, dtype=None, out=None, skipna=True, **kwargs):
        _validate_kwargs(name, kwargs, 'out', 'dtype')
        if axis is None:
            axis = self._stat_axis_number
        else:
            axis = self._get_axis_number(axis)

        y = _values_from_object(self).copy()

        if (skipna and
                issubclass(y.dtype.type, (np.datetime64, np.timedelta64))):
            result = accum_func(y, axis)
            mask = isnull(self)
            np.putmask(result, mask, pd.tslib.iNaT)
        elif skipna and not issubclass(y.dtype.type, (np.integer, np.bool_)):
            mask = isnull(self)
            np.putmask(y, mask, mask_a)
            result = accum_func(y, axis)
            np.putmask(result, mask, mask_b)
        else:
            result = accum_func(y, axis)

        d = self._construct_axes_dict()
        d['copy'] = False
        return self._constructor(result, **d).__finalize__(self)

    func.__name__ = name
    return func

def nanvar(values, axis=None, skipna=True, ddof=1):

    dtype = values.dtype
    mask = isnull(values)
    if is_any_int_dtype(values):
        values = values.astype('f8')
        values[mask] = np.nan

    if is_float_dtype(values):
        count, d = _get_counts_nanvar(mask, axis, ddof, values.dtype)
    else:
        count, d = _get_counts_nanvar(mask, axis, ddof)

    if skipna:
        values = values.copy()
        np.putmask(values, mask, 0)

    # xref GH10242
    # Compute variance via two-pass algorithm, which is stable against
    # cancellation errors and relatively accurate for small numbers of
    # observations.
    #
    # See https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance
    avg = _ensure_numeric(values.sum(axis=axis, dtype=np.float64)) / count
    if axis is not None:
        avg = np.expand_dims(avg, axis)
    sqr = _ensure_numeric((avg - values)**2)
    np.putmask(sqr, mask, 0)
    result = sqr.sum(axis=axis, dtype=np.float64) / d

    # Return variance as np.float64 (the datatype used in the accumulator),
    # unless we were dealing with a float array, in which case use the same
    # precision as the original values array.
    if is_float_dtype(dtype):
        result = result.astype(dtype)
    return _wrap_results(result, values.dtype)

def _map(f, arr, na_mask=False, na_value=np.nan, dtype=object):
    from pandas.core.series import Series

    if not len(arr):
        return np.ndarray(0, dtype=dtype)

    if isinstance(arr, Series):
        arr = arr.values
    if not isinstance(arr, np.ndarray):
        arr = np.asarray(arr, dtype=object)
    if na_mask:
        mask = isnull(arr)
        try:
            result = lib.map_infer_mask(arr, f, mask.view(np.uint8))
        except (TypeError, AttributeError):

            def g(x):
                try:
                    return f(x)
                except (TypeError, AttributeError):
                    return na_value

            return _map(g, arr, dtype=dtype)
        if na_value is not np.nan:
            np.putmask(result, mask, na_value)
            if result.dtype == object:
                result = lib.maybe_convert_objects(result)
        return result
    else:
        return lib.map_infer(arr, f)