Python ma.masked_where方法代碼示例

# 需要導入模塊: from numpy import ma [as 別名]
# 或者: from numpy.ma import masked_where [as 別名]
def transform_non_affine(self, ll):
            longitude = ll[:, 0:1]
            latitude  = ll[:, 1:2]

            # Pre-compute some values
            half_long = longitude / 2.0
            cos_latitude = np.cos(latitude)

            alpha = np.arccos(cos_latitude * np.cos(half_long))
            # Mask this array or we'll get divide-by-zero errors
            alpha = ma.masked_where(alpha == 0.0, alpha)
            # The numerators also need to be masked so that masked
            # division will be invoked.
            # We want unnormalized sinc.  numpy.sinc gives us normalized
            sinc_alpha = ma.sin(alpha) / alpha

            x = (cos_latitude * ma.sin(half_long)) / sinc_alpha
            y = (ma.sin(latitude) / sinc_alpha)
            return np.concatenate((x.filled(0), y.filled(0)), 1) 

# 需要導入模塊: from numpy import ma [as 別名]
# 或者: from numpy.ma import masked_where [as 別名]
def _contour_args(self, args, kwargs):
        if self.filled:
            fn = 'contourf'
        else:
            fn = 'contour'
        Nargs = len(args)
        if Nargs <= 2:
            z = ma.asarray(args[0], dtype=np.float64)
            x, y = self._initialize_x_y(z)
            args = args[1:]
        elif Nargs <= 4:
            x, y, z = self._check_xyz(args[:3], kwargs)
            args = args[3:]
        else:
            raise TypeError("Too many arguments to %s; see help(%s)" %
                            (fn, fn))
        z = ma.masked_invalid(z, copy=False)
        self.zmax = ma.maximum(z)
        self.zmin = ma.minimum(z)
        if self.logscale and self.zmin <= 0:
            z = ma.masked_where(z <= 0, z)
            warnings.warn('Log scale: values of z <= 0 have been masked')
            self.zmin = z.min()
        self._contour_level_args(z, args)
        return (x, y, z) 

# 需要導入模塊: from numpy import ma [as 別名]
# 或者: from numpy.ma import masked_where [as 別名]
def test_mode(self):
        a1 = [0,0,0,1,1,1,2,3,3,3,3,4,5,6,7]
        a2 = np.reshape(a1, (3,5))
        a3 = np.array([1,2,3,4,5,6])
        a4 = np.reshape(a3, (3,2))
        ma1 = ma.masked_where(ma.array(a1) > 2, a1)
        ma2 = ma.masked_where(a2 > 2, a2)
        ma3 = ma.masked_where(a3 < 2, a3)
        ma4 = ma.masked_where(ma.array(a4) < 2, a4)
        assert_equal(mstats.mode(a1, axis=None), (3,4))
        assert_equal(mstats.mode(a1, axis=0), (3,4))
        assert_equal(mstats.mode(ma1, axis=None), (0,3))
        assert_equal(mstats.mode(a2, axis=None), (3,4))
        assert_equal(mstats.mode(ma2, axis=None), (0,3))
        assert_equal(mstats.mode(a3, axis=None), (1,1))
        assert_equal(mstats.mode(ma3, axis=None), (2,1))
        assert_equal(mstats.mode(a2, axis=0), ([[0,0,0,1,1]], [[1,1,1,1,1]]))
        assert_equal(mstats.mode(ma2, axis=0), ([[0,0,0,1,1]], [[1,1,1,1,1]]))
        assert_equal(mstats.mode(a2, axis=-1), ([[0],[3],[3]], [[3],[3],[1]]))
        assert_equal(mstats.mode(ma2, axis=-1), ([[0],[1],[0]], [[3],[1],[0]]))
        assert_equal(mstats.mode(ma4, axis=0), ([[3,2]], [[1,1]]))
        assert_equal(mstats.mode(ma4, axis=-1), ([[2],[3],[5]], [[1],[1],[1]])) 

# 需要導入模塊: from numpy import ma [as 別名]
# 或者: from numpy.ma import masked_where [as 別名]
def test_pcolormesh():
    n = 12
    x = np.linspace(-1.5, 1.5, n)
    y = np.linspace(-1.5, 1.5, n*2)
    X, Y = np.meshgrid(x, y)
    Qx = np.cos(Y) - np.cos(X)
    Qz = np.sin(Y) + np.sin(X)
    Qx = (Qx + 1.1)
    Z = np.sqrt(X**2 + Y**2)/5
    Z = (Z - Z.min()) / (Z.max() - Z.min())

    # The color array can include masked values:
    Zm = ma.masked_where(np.fabs(Qz) < 0.5*np.amax(Qz), Z)

    fig = plt.figure()
    ax = fig.add_subplot(131)
    ax.pcolormesh(Qx, Qz, Z, lw=0.5, edgecolors='k')

    ax = fig.add_subplot(132)
    ax.pcolormesh(Qx, Qz, Z, lw=2, edgecolors=['b', 'w'])

    ax = fig.add_subplot(133)
    ax.pcolormesh(Qx, Qz, Z, shading="gouraud") 

# 需要導入模塊: from numpy import ma [as 別名]
# 或者: from numpy.ma import masked_where [as 別名]
def _contour_args(self, args, kwargs):
        if self.filled:
            fn = 'contourf'
        else:
            fn = 'contour'
        Nargs = len(args)
        if Nargs <= 2:
            z = ma.asarray(args[0], dtype=np.float64)
            x, y = self._initialize_x_y(z)
            args = args[1:]
        elif Nargs <= 4:
            x, y, z = self._check_xyz(args[:3], kwargs)
            args = args[3:]
        else:
            raise TypeError("Too many arguments to %s; see help(%s)" %
                            (fn, fn))
        z = ma.masked_invalid(z, copy=False)
        self.zmax = float(z.max())
        self.zmin = float(z.min())
        if self.logscale and self.zmin <= 0:
            z = ma.masked_where(z <= 0, z)
            warnings.warn('Log scale: values of z <= 0 have been masked')
            self.zmin = float(z.min())
        self._contour_level_args(z, args)
        return (x, y, z) 

# 需要導入模塊: from numpy import ma [as 別名]
# 或者: from numpy.ma import masked_where [as 別名]
def transform_non_affine(self, a):
            """
            This transform takes an Nx1 ``numpy`` array and returns a
            transformed copy.  Since the range of the Mercator scale
            is limited by the user-specified threshold, the input
            array must be masked to contain only valid values.
            ``matplotlib`` will handle masked arrays and remove the
            out-of-range data from the plot.  Importantly, the
            ``transform`` method *must* return an array that is the
            same shape as the input array, since these values need to
            remain synchronized with values in the other dimension.
            """
            masked = ma.masked_where((a < -self.thresh) | (a > self.thresh), a)
            if masked.mask.any():
                return ma.log(np.abs(ma.tan(masked) + 1.0 / ma.cos(masked)))
            else:
                return np.log(np.abs(np.tan(a) + 1.0 / np.cos(a))) 

# 需要導入模塊: from numpy import ma [as 別名]
# 或者: from numpy.ma import masked_where [as 別名]
def test_pcolormesh():
    n = 12
    x = np.linspace(-1.5, 1.5, n)
    y = np.linspace(-1.5, 1.5, n*2)
    X, Y = np.meshgrid(x, y)
    Qx = np.cos(Y) - np.cos(X)
    Qz = np.sin(Y) + np.sin(X)
    Qx = (Qx + 1.1)
    Z = np.hypot(X, Y) / 5
    Z = (Z - Z.min()) / Z.ptp()

    # The color array can include masked values:
    Zm = ma.masked_where(np.abs(Qz) < 0.5 * np.max(Qz), Z)

    fig, (ax1, ax2, ax3) = plt.subplots(1, 3)
    ax1.pcolormesh(Qx, Qz, Z, lw=0.5, edgecolors='k')
    ax2.pcolormesh(Qx, Qz, Z, lw=2, edgecolors=['b', 'w'])
    ax3.pcolormesh(Qx, Qz, Z, shading="gouraud") 

# 需要導入模塊: from numpy import ma [as 別名]
# 或者: from numpy.ma import masked_where [as 別名]
def _plot_well_traj(self, zvals, hvals):
        """Plot the trajectory as a black line"""

        zvals_copy = ma.masked_where(zvals < self._zmin, zvals)
        hvals_copy = ma.masked_where(zvals < self._zmin, hvals)

        self._figure.add_trace(
            {
                "x": hvals_copy,
                "y": zvals_copy,
                "name": self._well.name,
                "marker": {"color": "black"},
            },
            self.main_trace_row,
            1,
        )

        # ax.plot(hvals_copy, zvals_copy, linewidth=6, c="black")

    # pylint: disable-too-many-locals 

# 需要導入模塊: from numpy import ma [as 別名]
# 或者: from numpy.ma import masked_where [as 別名]
def mask_tiles_to_bbox(self, min_lat, max_lat, min_lon, max_lon, tiles):

        for tile in tiles:
            tile.latitudes = ma.masked_outside(tile.latitudes, min_lat, max_lat)
            tile.longitudes = ma.masked_outside(tile.longitudes, min_lon, max_lon)

            # Or together the masks of the individual arrays to create the new mask
            data_mask = ma.getmaskarray(tile.times)[:, np.newaxis, np.newaxis] \
                        | ma.getmaskarray(tile.latitudes)[np.newaxis, :, np.newaxis] \
                        | ma.getmaskarray(tile.longitudes)[np.newaxis, np.newaxis, :]

            tile.data = ma.masked_where(data_mask, tile.data)

        tiles[:] = [tile for tile in tiles if not tile.data.mask.all()]

        return tiles 

# 需要導入模塊: from numpy import ma [as 別名]
# 或者: from numpy.ma import masked_where [as 別名]
def mask_tiles_to_bbox_and_time(self, min_lat, max_lat, min_lon, max_lon, start_time, end_time, tiles):
        for tile in tiles:
            tile.times = ma.masked_outside(tile.times, start_time, end_time)
            tile.latitudes = ma.masked_outside(tile.latitudes, min_lat, max_lat)
            tile.longitudes = ma.masked_outside(tile.longitudes, min_lon, max_lon)

            # Or together the masks of the individual arrays to create the new mask
            data_mask = ma.getmaskarray(tile.times)[:, np.newaxis, np.newaxis] \
                        | ma.getmaskarray(tile.latitudes)[np.newaxis, :, np.newaxis] \
                        | ma.getmaskarray(tile.longitudes)[np.newaxis, np.newaxis, :]

            tile.data = ma.masked_where(data_mask, tile.data)

        tiles[:] = [tile for tile in tiles if not tile.data.mask.all()]

        return tiles 

# 需要導入模塊: from numpy import ma [as 別名]
# 或者: from numpy.ma import masked_where [as 別名]
def mask_tiles_to_time_range(self, start_time, end_time, tiles):
        """
        Masks data in tiles to specified time range.
        :param start_time: The start time to search for tiles
        :param end_time: The end time to search for tiles
        :param tiles: List of tiles
        :return: A list tiles with data masked to specified time range
        """
        if 0 < start_time <= end_time:
            for tile in tiles:
                tile.times = ma.masked_outside(tile.times, start_time, end_time)

                # Or together the masks of the individual arrays to create the new mask
                data_mask = ma.getmaskarray(tile.times)[:, np.newaxis, np.newaxis] \
                            | ma.getmaskarray(tile.latitudes)[np.newaxis, :, np.newaxis] \
                            | ma.getmaskarray(tile.longitudes)[np.newaxis, np.newaxis, :]

                tile.data = ma.masked_where(data_mask, tile.data)

            tiles[:] = [tile for tile in tiles if not tile.data.mask.all()]

        return tiles 

# 需要導入模塊: from numpy import ma [as 別名]
# 或者: from numpy.ma import masked_where [as 別名]
def test_mem_masked_where(self):
        # Ticket #62
        from numpy.ma import masked_where, MaskType
        a = np.zeros((1, 1))
        b = np.zeros(a.shape, MaskType)
        c = masked_where(b, a)
        a-c 

# 需要導入模塊: from numpy import ma [as 別名]
# 或者: from numpy.ma import masked_where [as 別名]
def PlotRaster(RasterFile, Map, alpha=1.):

    """
    Description goes here...

    MDH
    """

    print "Plotting raster..."

    #Read data
    gdata = gdal.Open(RasterFile, gdal.GA_ReadOnly)
    geo = gdata.GetGeoTransform()
    Data = gdata.ReadAsArray()

    # make topodat a masked array, masking values lower than sea level.
    Data = ma.masked_where(Data < 0, Data)

    #setup meshgrid for raster plotting
    xres = geo[1]
    yres = geo[5]
    xmin = geo[0] + xres * 0.5
    xmax = geo[0] + (xres * gdata.RasterXSize) - xres * 0.5
    ymin = geo[3] + (yres * gdata.RasterYSize) + yres * 0.5
    ymax = geo[3] - yres * 0.5

    x,y = np.mgrid[xmin:xmax+xres:xres, ymax+yres:ymin:yres]
    x,y = Map(x,y)
    Map.pcolormesh(x, y, Data.T, cmap=plt.cm.Greys, alpha=alpha) 

# 需要導入模塊: from numpy import ma [as 別名]
# 或者: from numpy.ma import masked_where [as 別名]
def __init__(self,vals,vals_dmin,vals_dmax,mask=ma.nomask):
        super(UncertContainer, self).__init__()
        
        # If input data already masked arrays extract unmasked data
        if ma.isMaskedArray(vals): 
            vals = vals.data
        if ma.isMaskedArray(vals_dmin):
            vals_dmin = vals_dmin.data
        if ma.isMaskedArray(vals_dmax):
            vals_dmax = vals_dmax.data
        
        # Adjust negative values
        ineg = np.where(vals_dmin <= 0.0)
        vals_dmin[ineg] = TOL*vals[ineg]

        # Calculate weight based on fractional uncertainty 
        diff = vals_dmax - vals_dmin
        diff_m = ma.masked_where(vals_dmax == vals_dmin,diff)        

        self.vals = ma.masked_where(vals == 0.0,vals)

        self.wt = (self.vals/diff_m)**2
        self.uncert = diff_m/self.vals

        self.wt.fill_value = np.inf
        self.uncert.fill_vaule = np.inf

        assert np.all(self.wt.mask == self.uncert.mask)
        
        # Mask data if uncertainty is not finite or if any of the inputs were
        # already masked

        mm = ma.mask_or(self.wt.mask,mask)
        
        self.vals.mask = mm
        self.wt.mask = mm
        self.uncert.mask = mm
        self.dmin = ma.array(vals_dmin,mask=mm,fill_value=np.inf)
        self.dmax = ma.array(vals_dmax,mask=mm,fill_value=np.inf)

        self.mask = ma.getmaskarray(self.vals) 

# 需要導入模塊: from numpy import ma [as 別名]
# 或者: from numpy.ma import masked_where [as 別名]
def test_mem_masked_where(self,level=rlevel):
        # Ticket #62
        from numpy.ma import masked_where, MaskType
        a = np.zeros((1, 1))
        b = np.zeros(a.shape, MaskType)
        c = masked_where(b, a)
        a-c