Python Dataset.__getattribute__方法代码示例

# 需要导入模块: from netCDF4 import Dataset [as 别名]
# 或者: from netCDF4.Dataset import __getattribute__ [as 别名]
def getrealization(f):
    """ Returns the realization from a filename and directory path.
        This is dependant on the cmip naming convention

    Parameters
    ----------
    string : name of file including path

    Returns
    -------
    string of realization number
    """
    nc = Dataset(f, 'r')
    return str(nc.__getattribute__('realization'))

# 需要导入模块: from netCDF4 import Dataset [as 别名]
# 或者: from netCDF4.Dataset import __getattribute__ [as 别名]
def getfrequency(f):
    """ Returns the frequency from a filename and directory path.
        ex. 'day', 'mon', 'yr', 'fx'
        This is dependant on a specific directory organization

    Parameters
    ----------
    string : name of file including path

    Returns
    -------
    string of frequency
    """
    nc = Dataset(f, 'r')
    return str(nc.__getattribute__('frequency'))

# 需要导入模块: from netCDF4 import Dataset [as 别名]
# 或者: from netCDF4.Dataset import __getattribute__ [as 别名]
def getrealm(f):
    """ Returns the realm from a filename and directory path.
        This is dependant on a specific directory organization

    Parameters
    ----------
    string : name of file including path

    Returns
    -------
    string of realm
    """
    nc = Dataset(f, 'r')
    realm = str(nc.__getattribute__('modeling_realm'))
    if 'seaIce' in realm:
        realm = 'seaIce'
    return realm

# 需要导入模块: from netCDF4 import Dataset [as 别名]
# 或者: from netCDF4.Dataset import __getattribute__ [as 别名]
def getexperiment(f):
    nc = Dataset(f, 'r')
    return str(nc.__getattribute__('experiment'))    

# 需要导入模块: from netCDF4 import Dataset [as 别名]
# 或者: from netCDF4.Dataset import __getattribute__ [as 别名]
class NetCDFFacade(object):

    def __init__(self, filename=None, dataset=None):
        if filename is not None:
            try:
                self.data_set = Dataset(filename, 'r', format='NETCDF4_CLASSIC')
            except RuntimeError as re:
                raise ValueError('%s: %s' % (re.args[0], filename))
        elif dataset is not None:
            self.data_set = dataset
        else:
            raise ValueError('Either filename or dataset must be provided')


    def get_dim_size(self, dimName):
        dimensions = self.data_set.dimensions
        for currentDimName in dimensions:
            if currentDimName == dimName:
                return len(dimensions[currentDimName])


    def get_global_attribute(self, attributeName):
        globalAttributes = self.data_set.ncattrs
        for currentAttribute in globalAttributes():
            if currentAttribute == attributeName:
                return self.data_set.__getattribute__(attributeName)


    def get_variable(self, variableName):
        variables = self.data_set.variables
        for currentVarName in variables:
            if currentVarName == variableName:
                return variables[currentVarName]
        return None


    def get_variable_attribute(self, variableName, attributeName):
        variable = self.get_variable(variableName)
        if hasattr(variable, attributeName):
            return variable.__getattribute__(attributeName)
        return None


    def get_dimension_string(self, variableName):
        variable = self.get_variable(variableName)
        dimensionString = ""
        for dimName in variable._getdims():
            dimensionString = "%s%s " % (dimensionString, dimName)
        dimensionString = dimensionString.strip()
        return dimensionString


    def get_dim_length(self, variableName, index=0):
        variable = self.get_variable(variableName)
        variableDimensions = variable._getdims()
        for i in range(len(variableDimensions)):
            if i == index:
                dimName = variableDimensions[i]
                return self.get_dim_size(dimName)


    def get_data(self, variableName, origin, shape):
        variable = self.get_variable(variableName)
        dimCount = len(variable._getdims())
        if dimCount != len(origin) or dimCount != len(shape):
            raise ValueError("len(origin) and len(shape) must be equal to number of dimensions of variable '" + variableName + "'")
        index_array = []
        for dimIndex in range(0, dimCount):
            current_index = range(origin[dimIndex], origin[dimIndex] + shape[dimIndex])
            index_array.append(current_index)
        # ensure that resulting array has same dimension as variable
        # unfortunately, the netcdf lib reduces the array's rank in some cases
        array = variable[index_array]
        if len(array.shape) < len(variable._getdims()):
            new_shape = []
            for d in range(dimCount - len(array.shape)):
                new_shape.append(1)
            for i in array.shape:
                new_shape.append(i)
            array = array.reshape(new_shape)
        return array


    def close(self):
        self.data_set.close()


    def get_dimensions(self, variable_name=None):
        result = []
        if variable_name is None:
            for dimension in self.data_set.dimensions:
                result.append(dimension)
            return result
        return (self.get_variable(variable_name))._getdims()


    def is_coordinate_or_reference_variable(self, ncVariable):
        return len(ncVariable._getdims()) == 1


#.........这里部分代码省略.........