# Copyright (c) 2015,Vienna University of Technology,
# Department of Geodesy and Geoinformation
# All rights reserved.
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL VIENNA UNIVERSITY OF TECHNOLOGY,
# DEPARTMENT OF GEODESY AND GEOINFORMATION BE LIABLE FOR ANY
# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
'''
Module contains the Class for reading ESA CCI data in netCDF Format
Created on Fri Mar 27 15:12:18 2015
@author: Christoph.Paulik@geo.tuwien.ac.at
'''
import netCDF4 as nc
import numpy as np
import os
import pygeogrids.grids as grids
class ESACCI_grid(grids.BasicGrid):
"""
ESA CCI grid class
Attributes
----------
land_ind: numpy.ndarray
indices of the land points
"""
def __init__(self, lsmaskfile=None):
if lsmaskfile is None:
lsmaskfile = os.path.join(os.path.dirname(__file__), "..", "bin",
"esa-cci",
"ESACCI-SOILMOISTURE-LANDMASK_V0.4.nc")
with nc.Dataset(lsmaskfile) as ls:
# flip along the latitude axis to fit together with the images from the
# CCI data. This inconsitency was already reported to the CCI team.
land = ls.variables['land'][::-1, :].data == 1
all_ind = np.arange(land.size)
land_ind = all_ind[land.flat == True]
self.land_ind = land_ind
longrid, latgrid = np.meshgrid(ls.variables['lon'][:],
ls.variables['lat'][::-1])
super(ESACCI_grid, self).__init__(longrid.flatten(), latgrid.flatten(),
subset=self.land_ind, shape=(1440, 720))
[docs]class ESACCI_netcdf(object):
"""
Class for reading ESA CCI data from netCDF files
Caches the following:
- time variable
- keeps the dataset open as long as the instance exists
"""
def __init__(self, fname, variables=None, avg_var=None, time_var='time', lat_var='lat', lon_var='lon'):
"""
Parameters
----------
self: type
description
fname: string
filename
variables: list, optional
if given only these variables will be read
avg_var: list, optional
list of variables for which to calculate the average if not given
it is calculated for all variables
time_var: string, optional
name of the time variable in the netCDF file
lat_var: string, optional
name of the latitude variable in the netCDF file
lon_var: string, optional
name of the longitude variable in the netCDF file
"""
self.fname = fname
self.ds = nc.Dataset(fname)
self.lat_var = lat_var
self.lon_var = lon_var
self.time_var = time_var
self.avg_var = avg_var
if variables is None:
self.variables = self.ds.variables.keys()
# exclude time, lat and lon from variable list
self.variables.remove(self.time_var)
self.variables.remove(self.lat_var)
self.variables.remove(self.lon_var)
else:
self.variables = variables
self._init_grid()
def _init_grid(self):
"""
initialize the grid of the dataset
"""
self.grid = ESACCI_grid()
[docs] def get_timeseries(self, locationid, date_start=None, date_end=None):
"""
Parameters
----------
locationid: int
location id as lat_index * row_length + lon_index
date_start: datetime, optional
start date of the time series
date_end: datetime, optional
end date of the time series
Returns
-------
ts : dict
"""
start_index, end_index = None, None
if date_start is not None:
start_index = nc.netcdftime.date2index(date_start,
self.ds.variables[self.time_var])
if date_end is not None:
end_index = nc.netcdftime.date2index(date_end,
self.ds.variables[self.time_var])
date_slice = slice(start_index, end_index, None)
# get row, column from location id
row, col = self.grid.gpi2rowcol(locationid)
ts = {}
for v in self.variables:
ts[v] = self.ds.variables[v][date_slice, row, col]
return ts
[docs] def get_avg_image(self, date_start, date_end=None, cellID=None):
"""
Reads image from dataset, takes the average if more than one value is in the result array.
Parameters
----------
date_start: datetime
start date of the image to get. If only one date is given then
the whole day of this date is read
date_end: datetime, optional
end date of the averaged image to get
cellID: int, optional
cell id to which the image should be limited, for ESA CCI this is
not defined at the moment.
"""
if date_end is None:
date_end = date_start
img = self.get_data(date_start, date_end)
# calculate average
for v in img:
if self.avg_var is not None:
if v in self.avg_var:
img[v] = img[v].mean(axis=0)
else:
img[v] = img[v].mean(axis=0)
return img
[docs] def get_data(self, date_start, date_end, cellID=1):
"""
Reads date cube from dataset
Parameters
----------
date_start: datetime
start date of the image to get. If only one date is given then
the whole day of this date is read
date_end: datetime
end date of the averaged image to get
cellID: int
cell id to which the image should be limited, for ESA CCI this is
not defined at the moment.
"""
start_index = nc.netcdftime.date2index(date_start,
self.ds.variables[self.time_var])
end_index = nc.netcdftime.date2index(date_end,
self.ds.variables[self.time_var])
date_slice = slice(start_index, end_index + 1, None)
img = {}
for v in self.variables:
img[v] = self.ds.variables[v][date_slice, :, :]
return img