Python ma.concatenate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 concatenate [as 別名]
def transform_non_affine(self, points):
        if self._x.is_affine and self._y.is_affine:
            return points
        x = self._x
        y = self._y

        if x == y and x.input_dims == 2:
            return x.transform_non_affine(points)

        if x.input_dims == 2:
            x_points = x.transform_non_affine(points)[:, 0:1]
        else:
            x_points = x.transform_non_affine(points[:, 0])
            x_points = x_points.reshape((len(x_points), 1))

        if y.input_dims == 2:
            y_points = y.transform_non_affine(points)[:, 1:]
        else:
            y_points = y.transform_non_affine(points[:, 1])
            y_points = y_points.reshape((len(y_points), 1))

        if isinstance(x_points, MaskedArray) or isinstance(y_points, MaskedArray):
            return ma.concatenate((x_points, y_points), 1)
        else:
            return np.concatenate((x_points, y_points), 1) 

# 需要導入模塊: from numpy import ma [as 別名]
# 或者: from numpy.ma import concatenate [as 別名]
def read(self, indexes=None, **kwargs):
        """
        Read reprojected & resampled input data.

        Parameters
        ----------
        indexes : integer or list
            band number or list of band numbers

        Returns
        -------
        data : array
        """
        band_indexes = self._get_band_indexes(indexes)
        arr = self.process.get_raw_output(self.tile)
        return (
            arr[band_indexes[0] - 1]
            if len(band_indexes) == 1
            else ma.concatenate([ma.expand_dims(arr[i - 1], 0) for i in band_indexes])
        ) 

# 需要導入模塊: from numpy import ma [as 別名]
# 或者: from numpy.ma import concatenate [as 別名]
def concatenate(self,value,axis=0):
        """ Concatentate UncertContainer value to self.
            Assumes that if dimensions of self and value do not match, to 
            add a np.newaxis along axis of value
        """

        if isinstance(value,UncertContainer):
            if value.vals.ndim == self.vals.ndim:
                vals = value.vals
                dmin = value.dmin
                dmax = value.dmax
                wt = value.wt
                uncert = value.uncert
                mask = value.mask
            elif (value.vals.ndim + 1) == self.vals.ndim:
                vals =  ma.expand_dims(value.vals,axis)
                dmin =  ma.expand_dims(value.dmin,axis)
                dmax =  ma.expand_dims(value.dmax,axis)
                wt =  ma.expand_dims(value.wt,axis)
                uncert =  ma.expand_dims(value.uncert,axis)
                mask =  np.expand_dims(value.mask,axis)
            else:
                raise ValueError('Could not propery match dimensionality')
                
            self.vals = ma.concatenate((self.vals,vals),axis=axis)
            self.dmin = ma.concatenate((self.dmin,dmin),axis=axis)
            self.dmax = ma.concatenate((self.dmax,dmax),axis=axis)
            self.wt = ma.concatenate((self.wt,wt),axis=axis)
            self.uncert = ma.concatenate((self.uncert,uncert),axis=axis)
            
            self.mask = np.concatenate((self.mask,mask),axis=axis)
        else:
            raise ValueError('Can only concatenate with an UncertContainer object') 

# 需要導入模塊: from numpy import ma [as 別名]
# 或者: from numpy.ma import concatenate [as 別名]
def segment_hits(cx, cy, x, y, radius):
    """
    Determine if any line segments are within radius of a
    point. Returns the list of line segments that are within that
    radius.
    """
    # Process single points specially
    if len(x) < 2:
        res, = np.nonzero((cx - x) ** 2 + (cy - y) ** 2 <= radius ** 2)
        return res

    # We need to lop the last element off a lot.
    xr, yr = x[:-1], y[:-1]

    # Only look at line segments whose nearest point to C on the line
    # lies within the segment.
    dx, dy = x[1:] - xr, y[1:] - yr
    Lnorm_sq = dx ** 2 + dy ** 2  # Possibly want to eliminate Lnorm==0
    u = ((cx - xr) * dx + (cy - yr) * dy) / Lnorm_sq
    candidates = (u >= 0) & (u <= 1)
    #if any(candidates): print "candidates",xr[candidates]

    # Note that there is a little area near one side of each point
    # which will be near neither segment, and another which will
    # be near both, depending on the angle of the lines.  The
    # following radius test eliminates these ambiguities.
    point_hits = (cx - x) ** 2 + (cy - y) ** 2 <= radius ** 2
    #if any(point_hits): print "points",xr[candidates]
    candidates = candidates & ~(point_hits[:-1] | point_hits[1:])

    # For those candidates which remain, determine how far they lie away
    # from the line.
    px, py = xr + u * dx, yr + u * dy
    line_hits = (cx - px) ** 2 + (cy - py) ** 2 <= radius ** 2
    #if any(line_hits): print "lines",xr[candidates]
    line_hits = line_hits & candidates
    points, = point_hits.ravel().nonzero()
    lines, = line_hits.ravel().nonzero()
    #print points,lines
    return np.concatenate((points, lines)) 

# 需要導入模塊: from numpy import ma [as 別名]
# 或者: from numpy.ma import concatenate [as 別名]
def find_duplicates(a, key=None, ignoremask=True, return_index=False):
    """
    Find the duplicates in a structured array along a given key

    Parameters
    ----------
    a : array-like
        Input array
    key : {string, None}, optional
        Name of the fields along which to check the duplicates.
        If None, the search is performed by records
    ignoremask : {True, False}, optional
        Whether masked data should be discarded or considered as duplicates.
    return_index : {False, True}, optional
        Whether to return the indices of the duplicated values.

    Examples
    --------
    >>> from numpy.lib import recfunctions as rfn
    >>> ndtype = [('a', int)]
    >>> a = np.ma.array([1, 1, 1, 2, 2, 3, 3],
    ...         mask=[0, 0, 1, 0, 0, 0, 1]).view(ndtype)
    >>> rfn.find_duplicates(a, ignoremask=True, return_index=True)
    ... # XXX: judging by the output, the ignoremask flag has no effect
    """
    a = np.asanyarray(a).ravel()
    # Get a dictionary of fields
    fields = get_fieldstructure(a.dtype)
    # Get the sorting data (by selecting the corresponding field)
    base = a
    if key:
        for f in fields[key]:
            base = base[f]
        base = base[key]
    # Get the sorting indices and the sorted data
    sortidx = base.argsort()
    sortedbase = base[sortidx]
    sorteddata = sortedbase.filled()
    # Compare the sorting data
    flag = (sorteddata[:-1] == sorteddata[1:])
    # If masked data must be ignored, set the flag to false where needed
    if ignoremask:
        sortedmask = sortedbase.recordmask
        flag[sortedmask[1:]] = False
    flag = np.concatenate(([False], flag))
    # We need to take the point on the left as well (else we're missing it)
    flag[:-1] = flag[:-1] + flag[1:]
    duplicates = a[sortidx][flag]
    if return_index:
        return (duplicates, sortidx[flag])
    else:
        return duplicates 

# 需要導入模塊: from numpy import ma [as 別名]
# 或者: from numpy.ma import concatenate [as 別名]
def mannwhitneyu(x,y, use_continuity=True):
    """
    Computes the Mann-Whitney statistic

    Missing values in `x` and/or `y` are discarded.

    Parameters
    ----------
    x : sequence
        Input
    y : sequence
        Input
    use_continuity : {True, False}, optional
        Whether a continuity correction (1/2.) should be taken into account.

    Returns
    -------
    statistic : float
        The Mann-Whitney statistics
    pvalue : float
        Approximate p-value assuming a normal distribution.

    """
    x = ma.asarray(x).compressed().view(ndarray)
    y = ma.asarray(y).compressed().view(ndarray)
    ranks = rankdata(np.concatenate([x,y]))
    (nx, ny) = (len(x), len(y))
    nt = nx + ny
    U = ranks[:nx].sum() - nx*(nx+1)/2.
    U = max(U, nx*ny - U)
    u = nx*ny - U

    mu = (nx*ny)/2.
    sigsq = (nt**3 - nt)/12.
    ties = count_tied_groups(ranks)
    sigsq -= np.sum(v*(k**3-k) for (k,v) in iteritems(ties))/12.
    sigsq *= nx*ny/float(nt*(nt-1))

    if use_continuity:
        z = (U - 1/2. - mu) / ma.sqrt(sigsq)
    else:
        z = (U - mu) / ma.sqrt(sigsq)

    prob = special.erfc(abs(z)/np.sqrt(2))
    return MannwhitneyuResult(u, prob) 

# 需要導入模塊: from numpy import ma [as 別名]
# 或者: from numpy.ma import concatenate [as 別名]
def ks_twosamp(data1, data2, alternative="two-sided"):
    """
    Computes the Kolmogorov-Smirnov test on two samples.

    Missing values are discarded.

    Parameters
    ----------
    data1 : array_like
        First data set
    data2 : array_like
        Second data set
    alternative : {'two-sided', 'less', 'greater'}, optional
        Indicates the alternative hypothesis.  Default is 'two-sided'.

    Returns
    -------
    d : float
        Value of the Kolmogorov Smirnov test
    p : float
        Corresponding p-value.

    """
    (data1, data2) = (ma.asarray(data1), ma.asarray(data2))
    (n1, n2) = (data1.count(), data2.count())
    n = (n1*n2/float(n1+n2))
    mix = ma.concatenate((data1.compressed(), data2.compressed()))
    mixsort = mix.argsort(kind='mergesort')
    csum = np.where(mixsort < n1, 1./n1, -1./n2).cumsum()
    # Check for ties
    if len(np.unique(mix)) < (n1+n2):
        csum = csum[np.r_[np.diff(mix[mixsort]).nonzero()[0],-1]]

    alternative = str(alternative).lower()[0]
    if alternative == 't':
        d = ma.abs(csum).max()
        prob = special.kolmogorov(np.sqrt(n)*d)
    elif alternative == 'l':
        d = -csum.min()
        prob = np.exp(-2*n*d**2)
    elif alternative == 'g':
        d = csum.max()
        prob = np.exp(-2*n*d**2)
    else:
        raise ValueError("Invalid value for the alternative hypothesis: "
                         "should be in 'two-sided', 'less' or 'greater'")

    return (d, prob) 

# 需要導入模塊: from numpy import ma [as 別名]
# 或者: from numpy.ma import concatenate [as 別名]
def recache(self, always=False):
        if always or self._invalidx:
            xconv = self.convert_xunits(self._xorig)
            if ma.isMaskedArray(self._xorig):
                x = ma.asarray(xconv, np.float_)
            else:
                x = np.asarray(xconv, np.float_)
            x = x.ravel()
        else:
            x = self._x
        if always or self._invalidy:
            yconv = self.convert_yunits(self._yorig)
            if ma.isMaskedArray(self._yorig):
                y = ma.asarray(yconv, np.float_)
            else:
                y = np.asarray(yconv, np.float_)
            y = y.ravel()
        else:
            y = self._y

        if len(x) == 1 and len(y) > 1:
            x = x * np.ones(y.shape, np.float_)
        if len(y) == 1 and len(x) > 1:
            y = y * np.ones(x.shape, np.float_)

        if len(x) != len(y):
            raise RuntimeError('xdata and ydata must be the same length')

        x = x.reshape((len(x), 1))
        y = y.reshape((len(y), 1))

        if ma.isMaskedArray(x) or ma.isMaskedArray(y):
            self._xy = ma.concatenate((x, y), 1)
        else:
            self._xy = np.concatenate((x, y), 1)
        self._x = self._xy[:, 0]  # just a view
        self._y = self._xy[:, 1]  # just a view

        self._subslice = False
        if (self.axes and len(x) > 100 and self._is_sorted(x) and
                self.axes.name == 'rectilinear' and
                self.axes.get_xscale() == 'linear' and
                self._markevery is None):
            self._subslice = True
        if hasattr(self, '_path'):
            interpolation_steps = self._path._interpolation_steps
        else:
            interpolation_steps = 1
        self._path = Path(self._xy, None, interpolation_steps)
        self._transformed_path = None
        self._invalidx = False
        self._invalidy = False 

# 需要導入模塊: from numpy import ma [as 別名]
# 或者: from numpy.ma import concatenate [as 別名]
def mannwhitneyu(x,y, use_continuity=True):
    """
    Computes the Mann-Whitney statistic

    Missing values in `x` and/or `y` are discarded.

    Parameters
    ----------
    x : sequence
        Input
    y : sequence
        Input
    use_continuity : {True, False}, optional
        Whether a continuity correction (1/2.) should be taken into account.

    Returns
    -------
    u : float
        The Mann-Whitney statistics
    prob : float
        Approximate p-value assuming a normal distribution.

    """
    x = ma.asarray(x).compressed().view(ndarray)
    y = ma.asarray(y).compressed().view(ndarray)
    ranks = rankdata(np.concatenate([x,y]))
    (nx, ny) = (len(x), len(y))
    nt = nx + ny
    U = ranks[:nx].sum() - nx*(nx+1)/2.
    U = max(U, nx*ny - U)
    u = nx*ny - U
    #
    mu = (nx*ny)/2.
    sigsq = (nt**3 - nt)/12.
    ties = count_tied_groups(ranks)
    sigsq -= np.sum(v*(k**3-k) for (k,v) in iteritems(ties))/12.
    sigsq *= nx*ny/float(nt*(nt-1))
    #
    if use_continuity:
        z = (U - 1/2. - mu) / ma.sqrt(sigsq)
    else:
        z = (U - mu) / ma.sqrt(sigsq)
    prob = special.erfc(abs(z)/np.sqrt(2))
    return (u, prob) 

# 需要導入模塊: from numpy import ma [as 別名]
# 或者: from numpy.ma import concatenate [as 別名]
def recache(self, always=False):
        if always or self._invalidx:
            xconv = self.convert_xunits(self._xorig)
            if ma.isMaskedArray(self._xorig):
                x = ma.asarray(xconv, np.float_)
            else:
                x = np.asarray(xconv, np.float_)
            x = x.ravel()
        else:
            x = self._x
        if always or self._invalidy:
            yconv = self.convert_yunits(self._yorig)
            if ma.isMaskedArray(self._yorig):
                y = ma.asarray(yconv, np.float_)
            else:
                y = np.asarray(yconv, np.float_)
            y = y.ravel()
        else:
            y = self._y

        if len(x) == 1 and len(y) > 1:
            x = x * np.ones(y.shape, np.float_)
        if len(y) == 1 and len(x) > 1:
            y = y * np.ones(x.shape, np.float_)

        if len(x) != len(y):
            raise RuntimeError('xdata and ydata must be the same length')

        x = x.reshape((len(x), 1))
        y = y.reshape((len(y), 1))

        if ma.isMaskedArray(x) or ma.isMaskedArray(y):
            self._xy = ma.concatenate((x, y), 1)
        else:
            self._xy = np.concatenate((x, y), 1)
        self._x = self._xy[:, 0]  # just a view
        self._y = self._xy[:, 1]  # just a view

        self._subslice = False
        if (self.axes and len(x) > 100 and self._is_sorted(x) and
                self.axes.name == 'rectilinear' and
                self.axes.get_xscale() == 'linear' and
                self._markevery is None and
                self.get_clip_on() is True):
            self._subslice = True
        if hasattr(self, '_path'):
            interpolation_steps = self._path._interpolation_steps
        else:
            interpolation_steps = 1
        self._path = Path(self._xy, None, interpolation_steps)
        self._transformed_path = None
        self._invalidx = False
        self._invalidy = False 

# 需要導入模塊: from numpy import ma [as 別名]
# 或者: from numpy.ma import concatenate [as 別名]
def mannwhitneyu(x,y, use_continuity=True):
    """
    Computes the Mann-Whitney statistic

    Missing values in `x` and/or `y` are discarded.

    Parameters
    ----------
    x : sequence
        Input
    y : sequence
        Input
    use_continuity : {True, False}, optional
        Whether a continuity correction (1/2.) should be taken into account.

    Returns
    -------
    statistic : float
        The Mann-Whitney statistics
    pvalue : float
        Approximate p-value assuming a normal distribution.

    """
    x = ma.asarray(x).compressed().view(ndarray)
    y = ma.asarray(y).compressed().view(ndarray)
    ranks = rankdata(np.concatenate([x,y]))
    (nx, ny) = (len(x), len(y))
    nt = nx + ny
    U = ranks[:nx].sum() - nx*(nx+1)/2.
    U = max(U, nx*ny - U)
    u = nx*ny - U

    mu = (nx*ny)/2.
    sigsq = (nt**3 - nt)/12.
    ties = count_tied_groups(ranks)
    sigsq -= sum(v*(k**3-k) for (k,v) in iteritems(ties))/12.
    sigsq *= nx*ny/float(nt*(nt-1))

    if use_continuity:
        z = (U - 1/2. - mu) / ma.sqrt(sigsq)
    else:
        z = (U - mu) / ma.sqrt(sigsq)

    prob = special.erfc(abs(z)/np.sqrt(2))
    return MannwhitneyuResult(u, prob)