# Copyright (c) 2020, TU Wien, Department of Geodesy and Geoinformation.
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"""
"""
import numpy as np
import netCDF4
import datetime
import pandas as pd
from pynetcf.time_series import OrthoMultiTs
from pynetcf.base import Dataset
import pygeogrids.grids as grids
[docs]class ArrayStack(OrthoMultiTs):
"""
Class for writing stacks of arrays (1D) into netCDF.
Array stacks are basically orthogonal multidimensional
array representation netCDF files.
"""
def __init__(self, filename, grid=None, times=None,
mode='r', name=''):
self.grid = grid
self.filename = filename
self.times = times
self.variables = []
self.time_var = 'time'
self.time_units = "days since 1900-01-01"
self.time_chunksize = 1
self.lon_chunksize = 1
if mode in ['a', 'r']:
super(ArrayStack, self).__init__(
filename, name=name, mode=mode, read_dates=False)
self._load_grid()
self._load_times()
if mode == 'w':
if grid is None:
raise IOError("grid needs to be defined")
super(ArrayStack, self).__init__(
filename, n_loc=len(self.grid.activegpis),
name=name, mode=mode, read_dates=False)
self.dataset.variables[self.lon_var][:] = self.grid.activearrlon
self.dataset.variables[self.lat_var][:] = self.grid.activearrlat
self.dataset.variables[self.loc_ids_name][:] = self.grid.activegpis
self.lat_chunksize = len(self.grid.activegpis)
def _load_grid(self):
lons = self.dataset.variables[self.lon_var][:]
lats = self.dataset.variables[self.lat_var][:]
self.grid = grids.BasicGrid(lons, lats)
def _load_times(self):
self.times = netCDF4.num2date(self.dataset.variables['time'][:],
self.time_units)
[docs] def write_ts(self, gpi, data):
"""
write a time series into the imagestack
at the given gpi
Parameters
----------
self: type
description
gpi: int or numpy.array
grid point indices to write to
data: dictionary
dictionary of int or numpy.array for each variable
that should be written
shape must be (len(gpi), len(times))
"""
gpi = np.atleast_1d(gpi)
for i, gp in enumerate(gpi):
for var in data:
super(ArrayStack, self).write_ts(
gp, {var: np.atleast_1d(np.atleast_2d(data[var])[i, :])},
np.array(self.times))
def __setitem__(self, gpi, data):
"""
write a time series into the imagestack
at the given gpi
Parameters
----------
self: type
description
gpi: int or numpy.array
grid point indices to write to
data: dictionary
dictionary of int or numpy.array for each variable
that should be written
shape must be (len(gpi), len(times))
"""
self.write_ts(gpi, data)
def __getitem__(self, key):
if type(key) == datetime.datetime:
index = netCDF4.date2index(
key, self.dataset.variables[self.time_var])
data = {}
for var in self._get_all_ts_variables():
data[var] = self.dataset.variables[var][:, index]
return data
else:
gpi = np.atleast_1d(key)
for i, gp in enumerate(gpi):
data = super(ArrayStack, self).read_all_ts(gp)
return pd.DataFrame(data, index=self.times)
[docs]class ImageStack(Dataset):
"""
Class for writing stacks of 2D images into netCDF.
"""
def __init__(self, filename, grid=None, times=None,
mode='r', name=''):
self.grid = grid
self.filename = filename
self.times = times
self.variables = []
self.time_var = 'time'
self.time_units = "days since 1900-01-01"
self.time_chunksize = 1
self.lon_chunksize = 1
self.lat_chunksize = self.grid.lat2d.shape[1]
super(ImageStack, self).__init__(filename, name=name, mode=mode)
if self.mode == 'w':
self._init_dimensions()
self._init_time()
self._init_location_variables()
elif self.mode in ['a', 'r']:
self._load_grid()
self._load_variables()
def _init_dimensions(self):
self.create_dim('lon', self.grid.lon2d.shape[0])
self.create_dim('lat', self.grid.lat2d.shape[1])
self.create_dim('time', len(self.times))
def _load_grid(self):
lons = self.dataset.variables['lon'][:]
lats = self.dataset.variables['lat'][:]
self.grid = grids.gridfromdims(lons, lats)
def _load_variables(self):
for var in self.dataset.variables:
if self.dataset.variables[var].dimensions == ('time', 'lat', 'lon'):
self.variables.append(var)
def _load_times(self):
self.times = netCDF4.num2date(self.dataset.variables['time'][:],
self.time_units)
def _init_time(self):
"""
initialize the dimensions and variables that are the basis of
the format
"""
# initialize time variable
time_data = netCDF4.date2num(self.times, self.time_units)
self.write_var(self.time_var, data=time_data, dim='time',
attr={'standard_name': 'time',
'long_name': 'time of measurement',
'units': self.time_units},
dtype=np.double,
chunksizes=[self.time_chunksize])
def _init_location_variables(self):
# write station information, longitude, latitude and altitude
self.write_var('lon', data=self.grid.lon2d[:, 0], dim='lon',
attr={'standard_name': 'longitude',
'long_name': 'location longitude',
'units': 'degrees_east',
'valid_range': (-180.0, 180.0)},
dtype=np.float)
self.write_var('lat', data=self.grid.lat2d[0, :], dim='lat',
attr={'standard_name': 'latitude',
'long_name': 'location latitude',
'units': 'degrees_north',
'valid_range': (-90.0, 90.0)},
dtype=np.float)
[docs] def init_variable(self, var):
self.write_var(var, data=None, dim=('time', 'lat', 'lon'),
dtype=np.float,
attr={'_FillValue': -9999.})
[docs] def write_ts(self, gpi, data):
"""
write a time series into the imagestack
at the given gpi
Parameters
----------
self: type
description
gpi: int or numpy.array
grid point indices to write to
data: dictionary
dictionary of int or numpy.array for each variable
that should be written
shape must be (len(gpi), len(times))
"""
gpi = np.atleast_1d(gpi)
for i, gp in enumerate(gpi):
row, column = self.grid.gpi2rowcol(gp)
for var in data:
if var not in self.variables:
self.variables.append(var)
self.init_variable(var)
self.dataset.variables[var][
:, row, column] = np.atleast_2d(data[var])[i, :]
def __setitem__(self, gpi, data):
"""
write a time series into the imagestack
at the given gpi
Parameters
----------
self: type
description
gpi: int or numpy.array
grid point indices to write to
data: dictionary
dictionary of int or numpy.array for each variable
that should be written
shape must be (len(gpi), len(times))
"""
self.write_ts(gpi, data)
def __getitem__(self, key):
gpi = np.atleast_1d(key)
data = {}
for i, gp in enumerate(gpi):
row, column = self.grid.gpi2rowcol(gp)
for var in self.variables:
data[var] = self.dataset.variables[var][
:, row, column]
return pd.DataFrame(data, index=self.times)