Python ma.masked_less方法代码示例

# 需要导入模块: from numpy import ma [as 别名]
# 或者: from numpy.ma import masked_less [as 别名]
def mask_to_limits(a, limits, inclusive):
    """Mask an array for values outside of given limits.

    This is primarily a utility function.

    Parameters
    ----------
    a : array
    limits : (float or None, float or None)
        A tuple consisting of the (lower limit, upper limit).  Values in the
        input array less than the lower limit or greater than the upper limit
        will be masked out. None implies no limit.
    inclusive : (bool, bool)
        A tuple consisting of the (lower flag, upper flag).  These flags
        determine whether values exactly equal to lower or upper are allowed.

    Returns
    -------
    A MaskedArray.

    Raises
    ------
    A ValueError if there are no values within the given limits.
    """
    lower_limit, upper_limit = limits
    lower_include, upper_include = inclusive
    am = ma.MaskedArray(a)
    if lower_limit is not None:
        if lower_include:
            am = ma.masked_less(am, lower_limit)
        else:
            am = ma.masked_less_equal(am, lower_limit)
    if upper_limit is not None:
        if upper_include:
            am = ma.masked_greater(am, upper_limit)
        else:
            am = ma.masked_greater_equal(am, upper_limit)
    if am.count() == 0:
        raise ValueError("No array values within given limits")
    return am 

# 需要导入模块: from numpy import ma [as 别名]
# 或者: from numpy.ma import masked_less [as 别名]
def operation(self, opname, value):
        """Do operation on map values.

        Do operations on the current map values. Valid operations are:

        * 'elilt' or 'eliminatelessthan': Eliminate less than <value>

        * 'elile' or 'eliminatelessequal': Eliminate less or equal than <value>

        Args:
            opname (str): Name of operation. See list above.
            values (*): A scalar number (float) or a tuple of two floats,
                dependent on operation opname.

        Examples::

            surf.operation('elilt', 200)  # set all values < 200 as undef
        """

        if opname in ("elilt", "eliminatelessthan"):
            self._values = ma.masked_less(self._values, value)
        elif opname in ("elile", "eliminatelessequal"):
            self._values = ma.masked_less_equal(self._values, value)
        else:
            raise ValueError("Invalid operation name")

    # ==================================================================================
    # Operations restricted to inside/outside polygons
    # ================================================================================== 

# 需要导入模块: from numpy import ma [as 别名]
# 或者: from numpy.ma import masked_less [as 别名]
def _mask_to_limits(a, limits, inclusive):
    """Mask an array for values outside of given limits.

    This is primarily a utility function.

    Parameters
    ----------
    a : array
    limits : (float or None, float or None)
        A tuple consisting of the (lower limit, upper limit).  Values in the
        input array less than the lower limit or greater than the upper limit
        will be masked out. None implies no limit.
    inclusive : (bool, bool)
        A tuple consisting of the (lower flag, upper flag).  These flags
        determine whether values exactly equal to lower or upper are allowed.

    Returns
    -------
    A MaskedArray.

    Raises
    ------
    A ValueError if there are no values within the given limits.
    """
    lower_limit, upper_limit = limits
    lower_include, upper_include = inclusive
    am = ma.MaskedArray(a)
    if lower_limit is not None:
        if lower_include:
            am = ma.masked_less(am, lower_limit)
        else:
            am = ma.masked_less_equal(am, lower_limit)

    if upper_limit is not None:
        if upper_include:
            am = ma.masked_greater(am, upper_limit)
        else:
            am = ma.masked_greater_equal(am, upper_limit)

    if am.count() == 0:
        raise ValueError("No array values within given limits")

    return am 

# 需要导入模块: from numpy import ma [as 别名]
# 或者: from numpy.ma import masked_less [as 别名]
def _mask_to_limits(a, limits, inclusive):
    """Mask an array for values outside of given limits.

    This is primarily a utility function.

    Parameters
    ----------
    a : array
    limits : (float or None, float or None)
    A tuple consisting of the (lower limit, upper limit).  Values in the
    input array less than the lower limit or greater than the upper limit
    will be masked out. None implies no limit.
    inclusive : (bool, bool)
    A tuple consisting of the (lower flag, upper flag).  These flags
    determine whether values exactly equal to lower or upper are allowed.

    Returns
    -------
    A MaskedArray.

    Raises
    ------
    A ValueError if there are no values within the given limits.
    """
    lower_limit, upper_limit = limits
    lower_include, upper_include = inclusive
    am = ma.MaskedArray(a)
    if lower_limit is not None:
        if lower_include:
            am = ma.masked_less(am, lower_limit)
        else:
            am = ma.masked_less_equal(am, lower_limit)

    if upper_limit is not None:
        if upper_include:
            am = ma.masked_greater(am, upper_limit)
        else:
            am = ma.masked_greater_equal(am, upper_limit)

    if am.count() == 0:
        raise ValueError("No array values within given limits")

    return am 

# 需要导入模块: from numpy import ma [as 别名]
# 或者: from numpy.ma import masked_less [as 别名]
def _attvalues(attribute, stacked):  # pylint: disable=too-many-branches
    """Attribute values computed in numpy.ma stack."""
    if attribute == "max":
        attvalues = ma.max(stacked, axis=2)
    elif attribute == "min":
        attvalues = ma.min(stacked, axis=2)
    elif attribute == "rms":
        attvalues = np.sqrt(ma.mean(np.square(stacked), axis=2))
    elif attribute == "var":
        attvalues = ma.var(stacked, axis=2)
    elif attribute == "mean":
        attvalues = ma.mean(stacked, axis=2)
    elif attribute == "maxpos":
        stacked = ma.masked_less(stacked, 0.0, copy=True)
        attvalues = ma.max(stacked, axis=2)
    elif attribute == "maxneg":  # ~ minimum of negative values?
        stacked = ma.masked_greater_equal(stacked, 0.0, copy=True)
        attvalues = ma.min(stacked, axis=2)
    elif attribute == "maxabs":
        attvalues = ma.max(abs(stacked), axis=2)
    elif attribute == "sumpos":
        stacked = ma.masked_less(stacked, 0.0, copy=True)
        attvalues = ma.sum(stacked, axis=2)
    elif attribute == "sumneg":
        stacked = ma.masked_greater_equal(stacked, 0.0, copy=True)
        attvalues = ma.sum(stacked, axis=2)
    elif attribute == "sumabs":
        attvalues = ma.sum(abs(stacked), axis=2)
    elif attribute == "meanabs":
        attvalues = ma.mean(abs(stacked), axis=2)
    elif attribute == "meanpos":
        stacked = ma.masked_less(stacked, 0.0, copy=True)
        attvalues = ma.mean(stacked, axis=2)
    elif attribute == "meanneg":
        stacked = ma.masked_greater_equal(stacked, 0.0, copy=True)
        attvalues = ma.mean(stacked, axis=2)
    else:
        etxt = "Invalid attribute applied: {}".format(attribute)
        raise ValueError(etxt)

    if not attvalues.flags["C_CONTIGUOUS"]:
        mask = ma.getmaskarray(attvalues)
        mask = np.asanyarray(mask, order="C")
        attvalues = np.asanyarray(attvalues, order="C")
        attvalues = ma.array(attvalues, mask=mask, order="C")

    return attvalues