# This file is part of OpenDrift.
#
# OpenDrift is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, version 2
#
# OpenDrift is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with OpenDrift. If not, see <https://www.gnu.org/licenses/>.
#
# Copyright 2020, Knut-Frode Dagestad, MET Norway
from opendrift.readers.basereader import BaseReader, ContinuousReader
import numpy as np
[docs]
class Reader(BaseReader, ContinuousReader):
'''Reader providing the nearest value in time from a given time series.'''
def __init__(self, parameter_value_map):
'''init with a map {'time':, time array, 'variable_name': value, ...}'''
self.times = parameter_value_map['time']
if type(self.times) is not list:
raise ValueError('time must be a list of datetime objects')
del parameter_value_map['time']
for key, var in parameter_value_map.items():
parameter_value_map[key] = np.atleast_1d(var)
if len(parameter_value_map[key]) != len(self.times):
raise ValueError('All variables must have same length as time array')
self._parameter_value_map = parameter_value_map
self.variables = list(parameter_value_map.keys())
self.proj4 = '+proj=latlong'
self.xmin = -180
self.xmax = 180
self.ymin = -90
self.ymax = 90
self.start_time = self.times[0]
self.end_time = self.times[-1]
#self.end_time = None
self.time_step = self.times[1] - self.times[0]
self.name = 'reader_timeseries'
# Run constructor of parent Reader class
super(Reader, self).__init__()
[docs]
def get_variables(self, requestedVariables, time=None,
x=None, y=None, z=None):
nearestTime, dummy1, dummy2, indxTime, dummy3, dummy4 = \
self.nearest_time(time)
variables = {'time': time, 'x': x, 'y': y, 'z': z}
#variables.update(self._parameter_value_map)
for var in requestedVariables:
variables[var] = self._parameter_value_map[var][indxTime]*np.ones(x.shape)
return variables