Python netCDF4.Dataset方法代碼示例

# 需要導入模塊: import netCDF4 [as 別名]
# 或者: from netCDF4 import Dataset [as 別名]
def dataValidation(self, in_nc, messages):
        """Check the necessary dimensions and variables in the input netcdf data"""
        data_nc = NET.Dataset(in_nc)
        dims = data_nc.dimensions.keys()
        globalattrs = data_nc.__dict__.keys()
        for each in self.dimensions:
            if each not in dims:
                messages.addErrorMessage(self.errorMessages[1].format(each))
                raise arcpy.ExecuteError
        for each in self.globalattributes:
            if each not in globalattrs:
                messages.addErrorMessage(self.errorMessages[2].format(each))
                raise arcpy.ExecuteError

        data_nc.close()

        return 

# 需要導入模塊: import netCDF4 [as 別名]
# 或者: from netCDF4 import Dataset [as 別名]
def dataValidation(self, in_nc, messages):
        """Check the necessary dimensions and variables in the input netcdf data"""
        data_nc = NET.Dataset(in_nc)
        vars = data_nc.variables.keys()
        for each in self.vars_oi:
            if each not in vars:
                messages.addErrorMessage(self.errorMessages[3].format(each))
                raise arcpy.ExecuteError
            else:
                dims = data_nc.variables[each].dimensions
                if self.dims_var != dims:
                    messages.addErrorMessage(self.errorMessages[4].format(each))
                    raise arcpy.ExecuteError

        data_nc.close()

        return 

# 需要導入模塊: import netCDF4 [as 別名]
# 或者: from netCDF4 import Dataset [as 別名]
def updateMessages(self, parameters):
        """Modify the messages created by internal validation for each tool
        parameter.  This method is called after internal validation."""
        if parameters[0].altered:
            in_nc = parameters[0].valueAsText
            try:
                data_nc = NET.Dataset(in_nc)
                data_nc.close()
            except Exception as e:
                parameters[0].setErrorMessage(e.message)

        if parameters[1].altered:
            (dirnm, basenm) = os.path.split(parameters[1].valueAsText)
            if not basenm.endswith(".csv"):
                parameters[1].setErrorMessage("The weight table must be in CSV format")

        return 

# 需要導入模塊: import netCDF4 [as 別名]
# 或者: from netCDF4 import Dataset [as 別名]
def createUniqueIDTable(self, in_nc, out_table):
        """Create a table of unique stream IDs"""
        data_nc = NET.Dataset(in_nc)
        comid_arr = data_nc.variables[self.vars_oi[0]][:]
        comid_size = len(comid_arr)
        comid_arr = comid_arr.reshape(comid_size, 1)
        arcpy.AddMessage(comid_arr.transpose())
        arcpy.AddMessage(self.vars_oi[0])

        #convert to numpy structured array
        str_arr = NUM.core.records.fromarrays(comid_arr.transpose(), NUM.dtype([(self.vars_oi[0], NUM.int32)]))

        # numpy structured array to table
        arcpy.da.NumPyArrayToTable(str_arr, out_table)

        data_nc.close()

        return 

# 需要導入模塊: import netCDF4 [as 別名]
# 或者: from netCDF4 import Dataset [as 別名]
def __init__(self, **kwargs):
        """Initialise a Dataset object.

        All keyword arguments will be translated into attributes.
        Does not take positional arguments.

        Note that if you create a dataset with a name that already exists,
        the existing object is returned, but __init__ is still called
        (Python does this, see
        https://docs.python.org/3.7/reference/datamodel.html#object.__new__).
        """
        self.mandatory_fields = set()
        for (k, v) in kwargs.items():
            setattr(self, k, v)
        self.setlocal()
        if self.my_pseudo_fields is None:
            self.my_pseudo_fields = collections.OrderedDict() 

# 需要導入模塊: import netCDF4 [as 別名]
# 或者: from netCDF4 import Dataset [as 別名]
def _apply_limits_and_filters(self, cont, limits, simple_filters):
        if isinstance(cont, xarray.Dataset):
            if len(limits)>0:
                raise NotImplementedError( 
                    "limits not implemented on xarray datasets")
            oldsize = cont[self.time_field].size
            for f in simple_filters:
                cont = f(cont)
            logger.debug("Filters reduced number from "
                "{:d} to {:d}".format(oldsize, cont[self.time_field].size))
            return cont
        oldsize = cont.size
        cont = tpmath.array.limit_ndarray(cont, limits)
        for f in simple_filters:
            cont = f(cont)
        if cont.size < oldsize:
            logger.debug("Applying limitations, reducing "
                "{:d} to {:d}".format(oldsize, cont.size))
        return cont 

# 需要導入模塊: import netCDF4 [as 別名]
# 或者: from netCDF4 import Dataset [as 別名]
def _add_cont_to_arr(self, arr, N, cont):
        """Changes arr in-situ, does not return"""
        if isinstance(cont, xarray.Dataset):
            # we should already know it's large enough
            # for arr[self.time_field] I start at N
            # for the other time coordinates at the relative "speed" they
            # are behind N
            # but this is not guaranteed to be regular so I would need to
            # keep trac of each individually, or inspect it on-the-fly
            # this approximation may be good enough for pre-allocation
            # (which is approximate anyway), when actually storing we need
            # to do a better job… for each time coordinate, check when it
            # “dies”
            raise NotImplementedError("This is not used for xarrays. "
                "But see comment in source-code for some thoughts.")
        else:
            arr[N:(N+cont.size)] = cont
            #arr = self._finalise_arr(arr, N) 

# 需要導入模塊: import netCDF4 [as 別名]
# 或者: from netCDF4 import Dataset [as 別名]
def get_additional_field(self, M, fld):
        """Get additional field.

        Get field from other dataset, original objects, or otherwise.
        To be implemented by subclass implementations.

        Exact fields depend on subclass.

        Arguments:

            M (ndarray): ndarray with existing data
                A (masked) array with a dtype such as returned from
                `self.read <Dataset.read>`.

            fld (str): Additional field to read from original data

        Returns:
            
            ndarray with fields of M + fld.
        """
        raise NotImplementedError("Must be implemented by child-class") 

# 需要導入模塊: import netCDF4 [as 別名]
# 或者: from netCDF4 import Dataset [as 別名]
def read(self, file_info, fields=None, **kwargs):
        """Read a CSV file and return an xarray.Dataset with its content

        Args:
            file_info: Path and name of the file as string or FileInfo object.
            fields: Field that you want to extract from the file. If not given,
                all fields are going to be extracted.
            **kwargs: Additional keyword arguments for the pandas function
                `pandas.read_csv`. See for more details:
                https://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html

        Returns:
            A xarray.Dataset object.
        """

        data = pd.read_csv(file_info.path, **kwargs).to_xarray()

        if fields is None:
            return data
        else:
            return data[fields] 

# 需要導入模塊: import netCDF4 [as 別名]
# 或者: from netCDF4 import Dataset [as 別名]
def __init__(self, filename):
        self.data = netCDF4.Dataset(filename)
        # data variables and dimensions
        variables = set(self.data.variables.keys())
        dimensions = set(self.data.dimensions.keys())
        self.keys = tuple(variables - dimensions)
        # size of lat/lon dimensions
        self.lat_size = self.data.dimensions['latitude'].size
        self.lon_size = self.data.dimensions['longitude'].size
        # spatial resolution in degrees
        self.delta_lat = -180.0 / (self.lat_size - 1)  # from north to south
        self.delta_lon = 360.0 / self.lon_size  # from west to east
        # time resolution in hours
        self.time_size = self.data.dimensions['time'].size
        self.start_time = self.data['time'][0]
        self.stop_time = self.data['time'][-1]
        self.time_range = self.stop_time - self.start_time
        self.delta_time = self.time_range / (self.time_size - 1) 

# 需要導入模塊: import netCDF4 [as 別名]
# 或者: from netCDF4 import Dataset [as 別名]
def __init__(self, filename, mode='w'):
        """Create NetCDF storage layer, creating or appending to an existing file.

        Parameters
        ----------
        filename : str
           Name of storage file to bind to.
        mode : str, optional, default='w'
           File open mode, 'w' for (over)write, 'a' for append.

        """
        self._filename = filename
        self._ncfile = netcdf.Dataset(self._filename, mode=mode)
        self._envname = None
        self._modname = None

        # Create standard dimensions.
        if 'iterations' not in self._ncfile.dimensions:
            self._ncfile.createDimension('iterations', size=None)
        if 'spatial' not in self._ncfile.dimensions:
            self._ncfile.createDimension('spatial', size=3) 

# 需要導入模塊: import netCDF4 [as 別名]
# 或者: from netCDF4 import Dataset [as 別名]
def nc_var_has_attr_vals(ds, var_name, att_dict):
    """
    Checks that the variable, var_name, as the attributes (and values)
    in the att_dict.

    """
    if not isinstance(ds, netCDF4.Dataset):
        ds = netCDF4.Dataset(ds)

    for key, val in att_dict.items():
        try:
            if val != getattr(ds.variables[var_name], key):
                return False
        except AttributeError:
            return False
    return True 

# 需要導入模塊: import netCDF4 [as 別名]
# 或者: from netCDF4 import Dataset [as 別名]
def load_grid(nc):
    """
    Get a SGrid object from a netCDF4.Dataset or file/URL.

    :param str or netCDF4.Dataset nc: a netCDF4 Dataset or URL/filepath
                                       to the netCDF file
    :return: SGrid object
    :rtype: sgrid.SGrid

    """
    if isinstance(nc, Dataset):
        pass
    else:
        nc = Dataset(nc, 'r')

    return SGrid.load_grid(nc) 

# 需要導入模塊: import netCDF4 [as 別名]
# 或者: from netCDF4 import Dataset [as 別名]
def get_dataset(ncfile, dataset=None):
    """
    Utility to create a netCDF4 Dataset from a filename, list of filenames,
    or just pass it through if it's already a netCDF4.Dataset

    if dataset is not None, it should be a valid netCDF4 Dataset object,
    and it will simply be returned
    """
    if dataset is not None:
        return dataset
    if isinstance(ncfile, nc4.Dataset):
        return ncfile
    elif isinstance(ncfile, Iterable) and len(ncfile) == 1:
        return nc4.Dataset(ncfile[0])
    elif isstring(ncfile):
        return nc4.Dataset(ncfile)
    else:
        return nc4.MFDataset(ncfile) 

# 需要導入模塊: import netCDF4 [as 別名]
# 或者: from netCDF4 import Dataset [as 別名]
def get_writable_dataset(ncfile, format="netcdf4"):
    """
    Utility to create a writable netCDF4 Dataset from a filename, list of filenames,
    or just pass it through if it's already a netCDF4.Dataset

    if dataset is not None, it should be a valid netCDF4 Dataset object,
    and it will simply be returned
    """
    if isinstance(ncfile, nc4.Dataset):
        # fixme: check for writable
        return ncfile
    elif isstring(ncfile):  # Fixme: should be pathlike...
        print("filename is:", ncfile)
        return nc4.Dataset(ncfile,
                           mode="w",
                           clobber=True,
                           format="NETCDF4")
    else:
        raise ValueError("Must be a string path or a netcdf4 Dataset")