def test_get_move(self):
"""
Test the get_move(...) results in WindMover match the expected delta
"""
for ix in range(2):
curr_time = sec_to_date(date_to_sec(self.model_time) +
self.time_step * ix)
self.wm.prepare_for_model_step(self.sc, self.time_step, curr_time)
delta = self.wm.get_move(self.sc, self.time_step, curr_time)
actual = self._expected_move()
# the results should be independent of model time
tol = 1e-8
msg = ('{0} is not within a tolerance of '
'{1}'.format('WindMover.get_move()', tol))
np.testing.assert_allclose(delta, actual, tol, tol, msg, 0)
assert self.wm.active
ts = date_to_sec(curr_time) - date_to_sec(self.model_time)
print ('Time step [sec]:\t{0}'
'C++ delta-move:\n{1}'
'Expected delta-move:\n{2}'
''.format(ts, delta, actual))
self.wm.model_step_is_done()
def test_variable_wind_after_model_time(self):
'''
test to make sure the wind mover is behaving properly with
out-of-bounds winds.
A variable wind should not extrapolate if it is out of bounds,
so prepare_for_model_step() should fail with an exception
in this case.
'''
wind_time = datetime(2012, 8, 21, 13) # one day after model time
time_series = (np.zeros((3, ), dtype=datetime_value_2d)
.view(dtype=np.recarray))
time_series.time = [sec_to_date(date_to_sec(wind_time) +
self.time_step * i)
for i in range(3)]
time_series.value = np.array(((2., 25.), (2., 25.), (2., 25.)))
wind = Wind(timeseries=time_series.reshape(3),
units='meter per second')
wm = WindMover(wind)
wm.prepare_for_model_run()
for ix in range(2):
curr_time = sec_to_date(date_to_sec(self.model_time) +
self.time_step * ix)
with raises(RuntimeError):
wm.prepare_for_model_step(self.sc, self.time_step, curr_time)
def test_get_move(self):
"""
Test the get_move(...) results in WindMover match the expected delta
"""
for ix in range(2):
curr_time = sec_to_date(date_to_sec(self.model_time) +
self.time_step * ix)
self.wm.prepare_for_model_step(self.sc, self.time_step, curr_time)
delta = self.wm.get_move(self.sc, self.time_step, curr_time)
actual = self._expected_move()
# the results should be independent of model time
tol = 1e-8
msg = ('{0} is not within a tolerance of '
'{1}'.format('WindMover.get_move()', tol))
np.testing.assert_allclose(delta, actual, tol, tol, msg, 0)
assert self.wm.active
ts = date_to_sec(curr_time) - date_to_sec(self.model_time)
print ('Time step [sec]:\t{0}'
'C++ delta-move:\n{1}'
'Expected delta-move:\n{2}'
''.format(ts, delta, actual))
self.wm.model_step_is_done()
def test_variable_wind_after_model_time(self):
'''
test to make sure the wind mover is behaving properly with
out-of-bounds winds.
A variable wind should not extrapolate if it is out of bounds,
so prepare_for_model_step() should fail with an exception
in this case.
'''
wind_time = datetime(2012, 8, 21, 13) # one day after model time
time_series = (np.zeros((3, ), dtype=datetime_value_2d)
.view(dtype=np.recarray))
time_series.time = [sec_to_date(date_to_sec(wind_time) +
self.time_step * i)
for i in range(3)]
time_series.value = np.array(((2., 25.), (2., 25.), (2., 25.)))
wind = Wind(timeseries=time_series.reshape(3),
units='meter per second')
wm = WindMover(wind)
wm.prepare_for_model_run()
for ix in range(2):
curr_time = sec_to_date(date_to_sec(self.model_time) +
self.time_step * ix)
with raises(RuntimeError):
wm.prepare_for_model_step(self.sc, self.time_step, curr_time)
def write_output(self, step_num, islast_step=False):
"""
Generate image from data
"""
# I don't think we need this for this outputter:
# - it does stuff with cache initialization
super(IceImageOutput, self).write_output(step_num, islast_step)
if (self.on is False or
not self._write_step or
len(self.ice_movers) == 0):
return None
# fixme -- doing all this cache stuff just to get the timestep..
# maybe timestep should be passed in.
for sc in self.cache.load_timestep(step_num).items():
model_time = date_to_sec(sc.current_time_stamp)
iso_time = sc.current_time_stamp.isoformat()
thick_image, conc_image, bb = self.render_images(model_time)
# info to return to the caller
web_mercator = 'EPSG:3857'
equirectangular = 'EPSG:32662'
output_dict = {'step_num': step_num,
'time_stamp': iso_time,
'thickness_image': thick_image,
'concentration_image': conc_image,
'bounding_box': bb,
'projection': equirectangular,
}
return output_dict
def write_output(self, step_num, islast_step=False):
"""
Generate image from data
"""
# I don't think we need this for this outputter:
# - it does stuff with cache initialization
super(IceImageOutput, self).write_output(step_num, islast_step)
if self.on is False or not self._write_step or self.ice_mover is None:
return None
## fixme -- doing all this cache stuff just to get the timestep..
## maybe timestep should be passed in.
for sc in self.cache.load_timestep(step_num).items():
pass
model_time = date_to_sec(sc.current_time_stamp)
thick_image, conc_image = self.render_images(model_time)
## fixme: Can we really loop through the movers?
## or should there be one IceImage outputter for each Ice Mover.
## here is where we render....
# do something with self.get_coverage_fc(ice_coverage, mover_triangles))
# do somethign with self.get_thickness_fc(ice_thickness, mover_triangles))
# info to return to the caller
output_dict = {'step_num': step_num,
'time_stamp': sc.current_time_stamp.isoformat(),
'thickness_image': thick_image,
'concentration_image': conc_image,
'bounding_box': ((-85.0, 20.0),(-55.0, 45.0)),
'projection': ("EPSG:3857"),
}
return output_dict
def check_time(self, wind, model_time):
'''
Should have an option to extrapolate but for now we do by default
TODO, FIXME: This function does not appear to be used by anything.
Removing it does not break any of the unit tests.
If it is not used, it should probably go away.
'''
new_model_time = model_time
if wind is not None:
if model_time is not None:
timeval = date_to_sec(model_time)
start_time = wind.get_start_time()
end_time = wind.get_end_time()
if end_time == start_time:
return model_time
if timeval < start_time:
new_model_time = sec_to_datetime(start_time)
if timeval > end_time:
new_model_time = sec_to_datetime(end_time)
else:
return model_time
return new_model_time
def test_move_gridcur_series(self):
"""
test move for a gridCur file series (first time in first file)
"""
time = datetime.datetime(2002, 1, 30, 1)
self.cm.model_time = time_utils.date_to_sec(time)
time_grid_file = testdata['GridCurrentMover']['series_gridCur']
topology_file = r""
self.gcm.text_read(time_grid_file, topology_file)
self.cm.ref[:]['long'] = -119.933264 # for gridCur test
self.cm.ref[:]['lat'] = 34.138736
self.check_move()
actual = np.empty((self.cm.num_le, ), dtype=world_point)
actual[:]['lat'] = -0.0034527536849574456
actual[:]['long'] = 0.005182449331779978
actual[:]['z'] = 0.
tol = 1e-5
msg = r"{0} move is not within a tolerance of {1}"
np.testing.assert_allclose(self.cm.delta['lat'], actual['lat'], tol,
tol, msg.format('gridcur series', tol), 0)
np.testing.assert_allclose(self.cm.delta['long'], actual['long'], tol,
tol, msg.format('gridcur series', tol), 0)
# np.testing.assert_equal(self.cm.delta, actual,
# "test_move_gridcur_series() failed", 0)
np.all(self.cm.delta['z'] == 0)
def test_move_ptcur_extrapolate(self):
"""
test move for a ptCur grid (first time in file)
"""
# time before first time in file
time = datetime.datetime(2000, 2, 14, 8)
self.cm.model_time = time_utils.date_to_sec(time)
time_grid_file = testdata['GridCurrentMover']['ptCur']
self.gcm.text_read(time_grid_file)
# result of move should be same as first step for ptCur test
self.gcm.extrapolate_in_time(True)
self.cm.ref[:]['long'] = -124.686928
self.cm.ref[:]['lat'] = 48.401124
self.check_move()
actual = np.empty((self.cm.num_le, ), dtype=world_point)
actual[:]['lat'] = .0161987
actual[:]['long'] = -.02439887
tol = 1e-5
msg = r"{0} move is not within a tolerance of {1}"
np.testing.assert_allclose(self.cm.delta['lat'], actual['lat'], tol,
tol, msg.format('ptcur', tol), 0)
np.testing.assert_allclose(self.cm.delta['long'], actual['long'], tol,
tol, msg.format('ptcur', tol), 0)
def test_move_curv_series(self):
"""
Test a curvilinear file series
- time in first file
- time in second file
"""
# time = datetime.datetime(2009, 8, 2, 0) # first file
time = datetime.datetime(2009, 8, 9, 0) # second file
self.cm.model_time = time_utils.date_to_sec(time)
time_grid_file = testdata['GridCurrentMover']['series_curv']
topology_file = testdata['GridCurrentMover']['series_top']
self.gcm.text_read(time_grid_file, topology_file)
self.cm.ref[:]['long'] = -157.795728 # for HiROMS
self.cm.ref[:]['lat'] = 21.069288
self.check_move()
actual = np.empty((self.cm.num_le, ), dtype=world_point)
# actual[:]['lat'] = -.003850193 # file 2
# actual[:]['long'] = .000152012
# updated to new curvilinear algorithm
actual[:]['lat'] = .00292 # file 2
actual[:]['long'] = .00051458
tol = 1e-5
msg = r"{0} move is not within a tolerance of {1}"
np.testing.assert_allclose(self.cm.delta['lat'], actual['lat'], tol,
tol, msg.format('HiROMS', tol), 0)
np.testing.assert_allclose(self.cm.delta['long'], actual['long'], tol,
tol, msg.format('HiROMS', tol), 0)
def test_move_curv_no_top(self):
"""
test move for a curvilinear grid (first time in file)
"""
time = datetime.datetime(2008, 1, 29, 17)
self.cm.model_time = time_utils.date_to_sec(time)
time_grid_file = testdata['GridCurrentMover']['curr_curv']
self.gcm.text_read(time_grid_file, topology_file=None)
topology_file2 = os.path.join(
os.path.split(time_grid_file)[0], 'NYTopologyNew.dat')
self.gcm.export_topology(topology_file2)
self.cm.ref[:]['long'] = -74.03988 # for NY
self.cm.ref[:]['lat'] = 40.536092
self.check_move()
actual = np.empty((self.cm.num_le, ), dtype=world_point)
actual[:]['lat'] = .000911
actual[:]['long'] = -.001288
tol = 1e-5
msg = r"{0} move is not within a tolerance of {1}"
np.testing.assert_allclose(self.cm.delta['lat'], actual['lat'], tol,
tol, msg.format('ny_cg.nc', tol), 0)
np.testing.assert_allclose(self.cm.delta['long'], actual['long'], tol,
tol, msg.format('ny_cg.nc', tol), 0)
def test_move_reg(self):
"""
test move for a regular grid (first time in file)
"""
time = datetime.datetime(1999, 11, 29, 21)
self.cm.model_time = time_utils.date_to_sec(time)
time_grid_file = testdata['GridCurrentMover']['curr_reg']
self.gcm.text_read(time_grid_file)
self.cm.ref[:]['long'] = 3.104588 # for simple example
self.cm.ref[:]['lat'] = 52.016468
self.check_move()
actual = np.empty((self.cm.num_le, ), dtype=world_point)
actual[:]['lat'] = .003354610952486354
actual[:]['long'] = .0010056182923228838
actual[:]['z'] = 0.
tol = 1e-5
msg = r"{0} move is not within a tolerance of {1}"
np.testing.assert_allclose(self.cm.delta['lat'], actual['lat'], tol,
tol, msg.format('test.cdf', tol), 0)
np.testing.assert_allclose(self.cm.delta['long'], actual['long'], tol,
tol, msg.format('test.cdf', tol), 0)
# np.testing.assert_equal(self.cm.delta['z'], actual['z'],
# "test_move_reg() failed", 0)
np.all(self.cm.delta['z'] == 0)
def write_output(self, step_num, islast_step=False):
'dump data in geojson format'
super(CurrentJsonOutput, self).write_output(step_num, islast_step)
if self.on is False or not self._write_step:
return None
for sc in self.cache.load_timestep(step_num).items():
model_time = date_to_sec(sc.current_time_stamp)
iso_time = sc.current_time_stamp.isoformat()
json_ = {}
for cm in self.current_movers:
velocities = cm.get_scaled_velocities(model_time)
velocities = self.get_rounded_velocities(velocities)
x = velocities[:, 0]
y = velocities[:, 1]
direction = np.arctan2(y, x) - np.pi / 2
magnitude = np.sqrt(x**2 + y**2)
direction = np.round(direction, 2)
magnitude = np.round(magnitude, 2)
json_[cm.id] = {
'magnitude': magnitude.tolist(),
'direction': direction.tolist()
}
return json_
def test_constant_wind_after_model_time(self):
'''
test to make sure the wind mover is behaving properly with
out-of-bounds winds.
A constant wind should extrapolate if it is out of bounds,
so prepare_for_model_step() should not fail.
We are testing that the wind extrapolates properly, so the
windages should be updated in the same way as the in-bounds test
'''
wind_time = datetime(2012, 8, 21, 13) # one day after model time
wind = Wind(timeseries=np.array((wind_time, (2., 25.)),
dtype=datetime_value_2d).reshape(1),
units='meter per second')
wm = WindMover(wind)
wm.prepare_for_model_run()
for ix in range(2):
curr_time = sec_to_date(date_to_sec(self.model_time) +
self.time_step * ix)
print 'curr_time = ', curr_time
old_windages = np.copy(self.sc['windages'])
wm.prepare_for_model_step(self.sc, self.time_step, curr_time)
mask = self.sc['windage_persist'] == -1
assert np.all(self.sc['windages'][mask] == old_windages[mask])
mask = self.sc['windage_persist'] > 0
assert np.all(self.sc['windages'][mask] != old_windages[mask])
def write_output(self, step_num, islast_step=False):
'dump data in geojson format'
super(IceGeoJsonOutput, self).write_output(step_num, islast_step)
if self.on is False or not self._write_step:
return None
for sc in self.cache.load_timestep(step_num).items():
pass
model_time = date_to_sec(sc.current_time_stamp)
geojson = {}
for mover in self.ice_movers:
grid_data = mover.get_grid_data()
ice_coverage, ice_thickness = mover.get_ice_fields(model_time)
geojson[mover.id] = []
geojson[mover.id].append(
self.get_coverage_fc(ice_coverage, grid_data))
geojson[mover.id].append(
self.get_thickness_fc(ice_thickness, grid_data))
# default geojson should not output data to file
output_info = {
'time_stamp': sc.current_time_stamp.isoformat(),
'feature_collections': geojson
}
return output_info
def _convert(x):
"""
helper method for the next 4 tests
"""
y = time_utils.date_to_sec(x)
return time_utils.sec_to_date(y)
def test_move_curv_no_top(self):
"""
test move for a curvilinear grid (first time in file)
"""
time = datetime.datetime(2008, 1, 29, 17)
self.cm.model_time = time_utils.date_to_sec(time)
time_grid_file = testdata['GridCurrentMover']['curr_curv']
self.gcm.text_read(time_grid_file, topology_file=None)
topology_file2 = os.path.join(os.path.split(time_grid_file)[0],
'NYTopologyNew.dat')
self.gcm.export_topology(topology_file2)
self.cm.ref[:]['long'] = -74.03988 # for NY
self.cm.ref[:]['lat'] = 40.536092
self.check_move()
actual = np.empty((self.cm.num_le, ), dtype=world_point)
actual[:]['lat'] = .000911
actual[:]['long'] = -.001288
tol = 1e-5
msg = r"{0} move is not within a tolerance of {1}"
np.testing.assert_allclose(self.cm.delta['lat'], actual['lat'],
tol, tol,
msg.format('ny_cg.nc', tol),
0)
np.testing.assert_allclose(self.cm.delta['long'], actual['long'],
tol, tol,
msg.format('ny_cg.nc', tol),
0)
def check_time(self, wind, model_time):
'''
Should have an option to extrapolate but for now we do by default
TODO, FIXME: This function does not appear to be used by anything.
Removing it does not break any of the unit tests.
If it is not used, it should probably go away.
'''
new_model_time = model_time
if wind is not None:
if model_time is not None:
timeval = date_to_sec(model_time)
start_time = wind.get_start_time()
end_time = wind.get_end_time()
if end_time == start_time:
return model_time
if timeval < start_time:
new_model_time = sec_to_datetime(start_time)
if timeval > end_time:
new_model_time = sec_to_datetime(end_time)
else:
return model_time
return new_model_time
def test_move_ptcur_extrapolate(self):
"""
test move for a ptCur grid (first time in file)
"""
# time before first time in file
time = datetime.datetime(2000, 2, 14, 8)
self.cm.model_time = time_utils.date_to_sec(time)
time_grid_file = testdata['GridCurrentMover']['ptCur']
self.gcm.text_read(time_grid_file)
# result of move should be same as first step for ptCur test
self.gcm.extrapolate_in_time(True)
self.cm.ref[:]['long'] = -124.686928
self.cm.ref[:]['lat'] = 48.401124
self.check_move()
actual = np.empty((self.cm.num_le, ), dtype=world_point)
actual[:]['lat'] = .0161987
actual[:]['long'] = -.02439887
tol = 1e-5
msg = r"{0} move is not within a tolerance of {1}"
np.testing.assert_allclose(self.cm.delta['lat'], actual['lat'],
tol, tol,
msg.format('ptcur', tol),
0)
np.testing.assert_allclose(self.cm.delta['long'], actual['long'],
tol, tol,
msg.format('ptcur', tol),
0)
def test_grid_wind_curv():
# curvlinear grid
curv = Grid(wind_file, topology_file, grid_type=2)
time = date_to_sec(datetime(2006, 3, 31, 21))
vel = curv.get_value(time, (-122.934656, 38.27594))
print "Curv grid - vel: {0}\n".format(vel)
assert vel.item() != 0
def _convert(x):
"""
helper method for the next 4 tests
"""
y = time_utils.date_to_sec(x)
return time_utils.sec_to_date(y)
def test_get_move_exceptions(self):
curr_time = sec_to_date(date_to_sec(self.model_time) + self.time_step)
tmp_windages = self.sc._data_arrays['windages']
del self.sc._data_arrays['windages']
with pytest.raises(KeyError):
self.wm.get_move(self.sc, self.time_step, curr_time)
self.sc._data_arrays['windages'] = tmp_windages
def test_prepare_for_model_step():
"""
explicitly test to make sure windages are being updated for persistence
!= 0 and windages are not being changed for persistance == -1
"""
time_step = 15 * 60 # seconds
model_time = datetime(2012, 8, 20, 13) # yyyy/month/day/hr/min/sec
sc = sample_sc_release(5, (3., 6., 0.), model_time)
sc['windage_persist'][:2] = -1
wind = Wind(timeseries=np.array((model_time, (2., 25.)),
dtype=datetime_value_2d).reshape(1),
units='meter per second')
wm = WindMover(wind)
wm.prepare_for_model_run()
for ix in range(2):
curr_time = sec_to_date(date_to_sec(model_time) + time_step * ix)
old_windages = np.copy(sc['windages'])
wm.prepare_for_model_step(sc, time_step, curr_time)
mask = [sc['windage_persist'] == -1]
assert np.all(sc['windages'][mask] == old_windages[mask])
mask = [sc['windage_persist'] > 0]
assert np.all(sc['windages'][mask] != old_windages[mask])
def _convert(x):
"""
helper method for the next 4 tests
"""
y = date_to_sec(x)
return sec_to_date(y)
def write_output(self, step_num, islast_step=False):
'dump data in geojson format'
super(IceGeoJsonOutput, self).write_output(step_num, islast_step)
if self.on is False or not self._write_step:
return None
for sc in self.cache.load_timestep(step_num).items():
pass
model_time = date_to_sec(sc.current_time_stamp)
geojson = {}
for mover in self.ice_movers:
mover_triangles = self.get_triangles(mover)
ice_coverage, ice_thickness = mover.get_ice_fields(model_time)
geojson[mover.id] = []
geojson[mover.id].append(self.get_coverage_fc(ice_coverage,
mover_triangles))
geojson[mover.id].append(self.get_thickness_fc(ice_thickness,
mover_triangles))
# default geojson should not output data to file
output_info = {'time_stamp': sc.current_time_stamp.isoformat(),
'feature_collections': geojson
}
return output_info
def write_output(self, step_num, islast_step=False):
'dump data in geojson format'
super(IceJsonOutput, self).write_output(step_num, islast_step)
if self.on is False or not self._write_step:
return None
for sc in self.cache.load_timestep(step_num).items():
pass
model_time = date_to_sec(sc.current_time_stamp)
raw_json = {}
for mover in self.ice_movers:
ice_coverage, ice_thickness = mover.get_ice_fields(model_time)
raw_json[mover.id] = {"thickness": [],
"concentration": []}
raw_json[mover.id]["thickness"] = ice_thickness.tolist()
raw_json[mover.id]["concentration"] = ice_coverage.tolist()
output_info = {'time_stamp': sc.current_time_stamp.isoformat(),
'data': raw_json}
return output_info
def write_output(self, step_num, islast_step=False):
'dump data in geojson format'
super(CurrentJsonOutput, self).write_output(step_num, islast_step)
if self.on is False or not self._write_step:
return None
for sc in self.cache.load_timestep(step_num).items():
model_time = date_to_sec(sc.current_time_stamp)
iso_time = sc.current_time_stamp.isoformat()
json_ = {}
for cm in self.current_movers:
velocities = cm.get_scaled_velocities(model_time)
velocities = self.get_rounded_velocities(velocities)
x = velocities[:,0]
y = velocities[:,1]
direction = np.arctan2(y,x) - np.pi/2
magnitude = np.sqrt(x**2 + y**2)
direction = np.round(direction,2)
magnitude = np.round(magnitude,2)
json_[cm.id]={'magnitude':magnitude.tolist(),
'direction':direction.tolist()
}
return json_
def test_prepare_for_model_step():
"""
explicitly test to make sure windages are being updated for persistence
!= 0 and windages are not being changed for persistance == -1
"""
time_step = 15 * 60 # seconds
model_time = datetime(2012, 8, 20, 13) # yyyy/month/day/hr/min/sec
sc = sample_sc_release(5, (3., 6., 0.), model_time)
sc['windage_persist'][:2] = -1
wind = Wind(timeseries=np.array((model_time, (2., 25.)),
dtype=datetime_value_2d).reshape(1),
units='meter per second')
wm = WindMover(wind)
wm.prepare_for_model_run()
for ix in range(2):
curr_time = sec_to_date(date_to_sec(model_time) + time_step * ix)
old_windages = np.copy(sc['windages'])
wm.prepare_for_model_step(sc, time_step, curr_time)
mask = [sc['windage_persist'] == -1]
assert np.all(sc['windages'][mask] == old_windages[mask])
mask = [sc['windage_persist'] > 0]
assert np.all(sc['windages'][mask] != old_windages[mask])
def datetime_to_seconds(self, model_time):
"""
Put the time conversion call here - in case we decide to change it, it
only updates here
"""
return time_utils.date_to_sec(model_time)
def get_timeseries(self, datetime=None, format='uv'):
"""
Returns the timeseries in requested format. If datetime=None,
then the original timeseries that was entered is returned.
If datetime is a list containing datetime objects, then the value
for each of those date times is determined by the underlying
C++ object and the timeseries is returned.
The output format is defined by the strings 'r-theta', 'uv'
:param datetime: [optional] datetime object or list of datetime
objects for which the value is desired
:type datetime: datetime object
:param format: output format for the times series:
either 'r-theta' or 'uv'
:type format: either string or integer value defined by
basic_types.ts_format.* (see cy_basic_types.pyx)
:returns: numpy array containing dtype=basic_types.datetime_value_2d.
Contains user specified datetime and the corresponding
values in user specified ts_format
"""
if datetime is None:
datetimeval = to_datetime_value_2d(self.ossm.timeseries, format)
else:
datetime = np.asarray(datetime, dtype='datetime64[s]').reshape(-1)
timeval = np.zeros((len(datetime), ),
dtype=basic_types.time_value_pair)
timeval['time'] = date_to_sec(datetime)
timeval['value'] = self.ossm.get_time_value(timeval['time'])
datetimeval = to_datetime_value_2d(timeval, format)
return datetimeval
def get_timeseries(self, datetime=None, format='uv'):
"""
Returns the timeseries in requested format. If datetime=None,
then the original timeseries that was entered is returned.
If datetime is a list containing datetime objects, then the value
for each of those date times is determined by the underlying
C++ object and the timeseries is returned.
The output format is defined by the strings 'r-theta', 'uv'
:param datetime: [optional] datetime object or list of datetime
objects for which the value is desired
:type datetime: datetime object
:param format: output format for the times series:
either 'r-theta' or 'uv'
:type format: either string or integer value defined by
basic_types.ts_format.* (see cy_basic_types.pyx)
:returns: numpy array containing dtype=basic_types.datetime_value_2d.
Contains user specified datetime and the corresponding
values in user specified ts_format
"""
if datetime is None:
datetimeval = to_datetime_value_2d(self.ossm.timeseries, format)
else:
datetime = np.asarray(datetime, dtype='datetime64[s]').reshape(-1)
timeval = np.zeros((len(datetime), ),
dtype=basic_types.time_value_pair)
timeval['time'] = date_to_sec(datetime)
timeval['value'] = self.ossm.get_time_value(timeval['time'])
datetimeval = to_datetime_value_2d(timeval, format)
return datetimeval
def write_output(self, step_num, islast_step=False):
"""
Generate image from data
"""
# I don't think we need this for this outputter:
# - it does stuff with cache initialization
super(IceImageOutput, self).write_output(step_num, islast_step)
if (self.on is False or not self._write_step
or len(self.ice_movers) == 0):
return None
# fixme -- doing all this cache stuff just to get the timestep..
# maybe timestep should be passed in.
for sc in self.cache.load_timestep(step_num).items():
model_time = date_to_sec(sc.current_time_stamp)
iso_time = sc.current_time_stamp.isoformat()
thick_image, conc_image, bb = self.render_images(model_time)
# web_mercator = 'EPSG:3857'
equirectangular = 'EPSG:32662'
# info to return to the caller
output_dict = {
'step_num': step_num,
'time_stamp': iso_time,
'thickness_image': thick_image,
'concentration_image': conc_image,
'bounding_box': bb,
'projection': equirectangular,
}
return output_dict
def write_output(self, step_num, islast_step=False):
'dump data in geojson format'
super(IceJsonOutput, self).write_output(step_num, islast_step)
if self.on is False or not self._write_step:
return None
for sc in self.cache.load_timestep(step_num).items():
pass
model_time = date_to_sec(sc.current_time_stamp)
raw_json = {}
for mover in self.ice_movers:
ice_coverage, ice_thickness = mover.get_ice_fields(model_time)
raw_json[mover.id] = {"thickness": [], "concentration": []}
raw_json[mover.id]["thickness"] = ice_thickness.tolist()
raw_json[mover.id]["concentration"] = ice_coverage.tolist()
output_info = {
'time_stamp': sc.current_time_stamp.isoformat(),
'data': raw_json
}
return output_info
def test_constant_wind_after_model_time(self):
'''
test to make sure the wind mover is behaving properly with
out-of-bounds winds.
A constant wind should extrapolate if it is out of bounds,
so prepare_for_model_step() should not fail.
We are testing that the wind extrapolates properly, so the
windages should be updated in the same way as the in-bounds test
'''
wind_time = datetime(2012, 8, 21, 13) # one day after model time
wind = Wind(timeseries=np.array((wind_time, (2., 25.)),
dtype=datetime_value_2d).reshape(1),
units='meter per second')
wm = WindMover(wind)
wm.prepare_for_model_run()
for ix in range(2):
curr_time = sec_to_date(date_to_sec(self.model_time) +
self.time_step * ix)
print 'curr_time = ', curr_time
old_windages = np.copy(self.sc['windages'])
wm.prepare_for_model_step(self.sc, self.time_step, curr_time)
mask = self.sc['windage_persist'] == -1
assert np.all(self.sc['windages'][mask] == old_windages[mask])
mask = self.sc['windage_persist'] > 0
assert np.all(self.sc['windages'][mask] != old_windages[mask])
def test_grid_wind_curv():
# curvlinear grid
curv = Grid(wind_file, topology_file, grid_type=2)
time = date_to_sec(datetime(2006, 3, 31, 21))
vel = curv.get_value(time, (-122.934656, 38.27594))
print "Curv grid - vel: {0}\n".format(vel)
assert vel.item() != 0
def write_output(self, step_num, islast_step=False):
'dump data in geojson format'
super(TrajectoryGeoJsonOutput,
self).write_output(step_num, islast_step)
if not self._write_step:
return None
# one feature per element client; replaced with multipoint
# because client performance is much more stable with one
# feature per step rather than (n) features per step.features = []
features = []
for sc in self.cache.load_timestep(step_num).items():
time = date_to_sec(sc.current_time_stamp)
position = self._dataarray_p_types(sc['positions'])
status = self._dataarray_p_types(sc['status_codes'])
sc_type = 'uncertain' if sc.uncertain else 'forecast'
# break elements into multipoint features based on their status code
# evaporated : 10
# in_water : 2
# not_released : 0
# off_maps : 7
# on_land : 3
# to_be_removed : 12
points = {}
for ix, pos in enumerate(position):
st_code = status[ix]
if st_code not in points:
points[st_code] = []
points[st_code].append(pos[:2])
for k in points:
feature = Feature(geometry=MultiPoint(points[k]),
id="1",
properties={
'sc_type': sc_type,
'status_code': k,
})
if sc.uncertain:
features.insert(0, feature)
else:
features.append(feature)
geojson = FeatureCollection(features)
# default geojson should not output data to file
# read data from file and send it to web client
output_info = {
'time_stamp': sc.current_time_stamp.isoformat(),
'feature_collection': geojson
}
if self.output_dir:
output_filename = self.output_to_file(geojson, step_num)
output_info.update({'output_filename': output_filename})
return output_info
def test_get_time_value():
'make sure get_time_value goes to correct C++ derived class function'
shio = CyShioTime(shio_file)
t = time_utils.date_to_sec(datetime(2012, 8, 20, 13))
time = [t + 3600.*dt for dt in range(10)]
vel_rec = shio.get_time_value(time)
assert all(vel_rec['u'] != 0)
assert all(vel_rec['v'] == 0)
def test_grid_wind_curv():
# curvlinear grid
curv = CyTimeGridWindCurv(testdata['GridWindMover']['wind_curv'],
testdata['GridWindMover']['top_curv'])
time = date_to_sec(datetime(2006, 3, 31, 21))
vel = curv.get_value(time, (-122.934656, 38.27594))
print "Curv grid - vel: {0}\n".format(vel)
assert vel.item() != 0
def test_grid_wind_curv():
# curvlinear grid
curv = CyTimeGridWindCurv(testdata['GridWindMover']['wind_curv'],
testdata['GridWindMover']['top_curv'])
time = date_to_sec(datetime(2006, 3, 31, 21))
vel = curv.get_value(time, (-122.934656, 38.27594))
print "Curv grid - vel: {0}\n".format(vel)
assert vel.item() != 0
class TestRandomMover:
"""
gnome.RandomMover() test
"""
num_le = 5
# start_pos = np.zeros((num_le,3), dtype=basic_types.world_point_type)
start_pos = (0., 0., 0.)
rel_time = datetime.datetime(2012, 8, 20, 13) # yyyy/month/day/hr/min/sec
model_time = sec_to_date(date_to_sec(rel_time) + 1)
time_step = 15 * 60 # seconds
mover = RandomMover()
def reset_pos(self):
self.pSpill['positions'] = (0., 0., 0.)
print self.pSpill['positions']
def test_string_representation_matches_repr_method(self):
"""
Just print repr and str
"""
print
print repr(self.mover)
print str(self.mover)
assert True
def test_id_matches_builtin_id(self):
# It is not a good assumption that the obj.id property
# will always contain the id(obj) value. For example it could
# have been overloaded with, say, a uuid1() generator.
# assert id(self.mover) == self.mover.id
pass
def test_change_diffusion_coef(self):
self.mover.diffusion_coef = 200000
assert self.mover.diffusion_coef == 200000
def test_change_uncertain_factor(self):
self.mover.uncertain_factor = 3
assert self.mover.uncertain_factor == 3
def test_prepare_for_model_step(self):
"""
Simply tests the method executes without exceptions
"""
pSpill = sample_sc_release(self.num_le, self.start_pos)
self.mover.prepare_for_model_step(pSpill, self.time_step,
self.model_time)
assert True
def write_output(self, step_num, islast_step=False):
'dump data in geojson format'
super(TrajectoryGeoJsonOutput, self).write_output(step_num,
islast_step)
if not self._write_step:
return None
# one feature per element client; replaced with multipoint
# because client performance is much more stable with one
# feature per step rather than (n) features per step.features = []
features = []
for sc in self.cache.load_timestep(step_num).items():
time = date_to_sec(sc.current_time_stamp)
position = self._dataarray_p_types(sc['positions'])
status = self._dataarray_p_types(sc['status_codes'])
sc_type = 'uncertain' if sc.uncertain else 'forecast'
# break elements into multipoint features based on their
# status code
# evaporated : 10
# in_water : 2
# not_released : 0
# off_maps : 7
# on_land : 3
# to_be_removed : 12
points = {}
for ix, pos in enumerate(position):
st_code = status[ix]
if st_code not in points:
points[st_code] = []
points[st_code].append(pos[:2])
for k in points:
feature = Feature(geometry=MultiPoint(points[k]), id="1",
properties={
'sc_type': sc_type,
'status_code': k,
})
if sc.uncertain:
features.insert(0, feature)
else:
features.append(feature)
geojson = FeatureCollection(features)
# default geojson should not output data to file
# read data from file and send it to web client
output_info = {'time_stamp': sc.current_time_stamp.isoformat(),
'feature_collection': geojson
}
if self.output_dir:
output_filename = self.output_to_file(geojson, step_num)
output_info.update({'output_filename': output_filename})
return output_info
def test_get_move_exceptions(self):
curr_time = sec_to_date(date_to_sec(self.model_time) + self.time_step)
tmp_windages = self.sc._data_arrays['windages']
del self.sc._data_arrays['windages']
with raises(KeyError):
self.wm.get_move(self.sc, self.time_step, curr_time)
self.sc._data_arrays['windages'] = tmp_windages
def test_move_tri_tide(self):
"""
test move for a triangular grid (first time in file)
"""
time = datetime.datetime(2014, 6, 9, 0)
self.cm.model_time = time_utils.date_to_sec(time)
self.cm.uncertain = True
time_grid_file = get_datafile(os.path.join(cur_dir, 'PQBayCur.nc4'
))
topology_file = get_datafile(os.path.join(cur_dir, 'PassamaquoddyTOP.dat'
))
tide_file = get_datafile(os.path.join(tide_dir, 'EstesHead.txt'
))
yeardata_path = os.path.join(os.path.dirname(gnome.__file__),
'data/yeardata/')
self.shio = cy_shio_time.CyShioTime(tide_file)
self.ccm.set_shio(self.shio)
self.ccm.text_read(time_grid_file, topology_file)
self.shio.set_shio_yeardata_path(yeardata_path)
self.cm.ref[:]['long'] = -66.991344 # for Passamaquoddy
self.cm.ref[:]['lat'] = 45.059316
#self.check_move()
self.check_move_certain_uncertain(self.ccm.uncertain_time_delay)
actual = np.empty((self.cm.num_le, ), dtype=world_point)
actual[:]['lat'] = -.000440779
actual[:]['long'] = .00016611
tol = 1e-5
msg = r"{0} move is not within a tolerance of {1}"
np.testing.assert_allclose(
self.cm.delta['lat'],
actual['lat'],
tol,
tol,
msg.format('ches_bay', tol),
0,
)
np.testing.assert_allclose(
self.cm.delta['long'],
actual['long'],
tol,
tol,
msg.format('ches_bay', tol),
0,
)
#check that certain and uncertain are the same if uncertainty is time delayed
#self.ccm.uncertain_time_delay = 3
self.ccm.uncertain_time_delay = 10800 # cython expects time_delay in seconds
self.check_move_certain_uncertain(self.ccm.uncertain_time_delay)
def get_timeseries(
self,
datetime=None,
units=None,
format='r-theta',
):
"""
Returns the timeseries in the requested format. If datetime=None,
then the original timeseries that was entered is returned.
If datetime is a list containing datetime objects, then the wind value
for each of those date times is determined by the underlying
CyOSSMTime object and the timeseries is returned.
The output format is defined by the strings 'r-theta', 'uv'
:param datetime: [optional] datetime object or list of datetime
objects for which the value is desired
:type datetime: datetime object
:param units: [optional] outputs data in these units. Default is to
output data in units
:type units: string. Uses the hazpy.unit_conversion module.
hazpy.unit_conversion throws error for invalid units
:param format: output format for the times series:
either 'r-theta' or 'uv'
:type format: either string or integer value defined by
basic_types.ts_format.* (see cy_basic_types.pyx)
:returns: numpy array containing dtype=basic_types.datetime_value_2d.
Contains user specified datetime and the corresponding
values in user specified ts_format
"""
if datetime is None:
datetimeval = \
convert.to_datetime_value_2d(self.ossm.timeseries,
format)
else:
datetime = np.asarray(datetime, dtype='datetime64[s]'
).reshape(-1)
timeval = np.zeros((len(datetime), ),
dtype=basic_types.time_value_pair)
timeval['time'] = time_utils.date_to_sec(datetime)
timeval['value'] = self.ossm.get_time_value(timeval['time'])
datetimeval = convert.to_datetime_value_2d(timeval, format)
if units is not None:
datetimeval['value'] = \
self._convert_units(datetimeval['value'], format,
'meter per second', units)
else:
datetimeval['value'] = \
self._convert_units(datetimeval['value'], format,
'meter per second', self.units)
return datetimeval
def prepare_for_model_step(self, model_time):
"""
Make sure we are up to date with the referenced time series
"""
model_time = date_to_sec(model_time)
if self.ossm.check_time_in_range(model_time):
return
self.create_running_average_timeseries(self._past_hours_to_average,
model_time)
def __init__(self):
time = datetime.datetime(2012, 8, 20, 13)
self.model_time = time_utils.date_to_sec(time)
# ###############
# init. arrays #
# ###############
self.ref[:] = 1.
self.ref[:]['z'] = 0 # on surface by default
self.status[:] = oil_status.in_water
def prepare_for_model_step(self, model_time):
"""
Make sure we are up to date with the referenced time series
"""
model_time = date_to_sec(model_time)
if self.ossm.check_time_in_range(model_time):
return
self.create_running_average_timeseries(self._past_hours_to_average,
model_time)
def __init__(self):
time = datetime.datetime(2012, 8, 20, 13)
self.model_time = time_utils.date_to_sec(time)
# ###############
# init. arrays #
# ###############
self.ref[:] = 1.
self.ref[:]['z'] = 0 # on surface by default
self.status[:] = oil_status.in_water
def prepare_for_model_run(self, model_time):
"""
Make sure we are up to date with the referenced time series
"""
if self.wind is None:
msg = "wind object not defined for WindMover"
raise ReferencedObjectNotSet(msg)
model_time = date_to_sec(model_time)
self.create_running_average_timeseries(self._past_hours_to_average,
model_time)
def to_time_value_pair(datetime_value, in_ts_format=None):
"""
converts a numpy array containing basic_types.datetime_value_2d in
user specified basic_types.ts_format into a time_value_pair array
or it takes a basic_types.datetime_value_1d array and converts it to
a time_value_pair array -- for 1d data, assume the ['value'] contains
the 'u' component and set the 'v' component to 0.0
:param datetime_value: numpy array of type basic_types.datetime_value_2d or
basic_types.datetime_value_1d
:param in_ts_format=None: format of the datetime_value_2d array - not
required when converting from datetime_value_1d. Can be defined by a
string 'r-theta', 'uv' or by an integer defined by one of the options
given in basic_types.ts_format.
"""
if(datetime_value.dtype != basic_types.datetime_value_2d and
datetime_value.dtype != basic_types.datetime_value_1d):
raise ValueError('Method expects a numpy array containing '
'basic_types.datetime_value_2d or basic_types.datetime_value_1d')
# convert datetime_value_2d to time_value_pair
time_value_pair = np.zeros((len(datetime_value), ),
dtype=basic_types.time_value_pair)
time_value_pair['time'] = \
time_utils.date_to_sec(datetime_value['time'])
if datetime_value.dtype == basic_types.datetime_value_1d:
time_value_pair['value']['u'] = datetime_value['value'][:, 0]
else:
if in_ts_format is None:
raise ValueError("for datetime_value_2d data conversion, the "
"format defined by 'in_ts_format', cannot be None ")
if isinstance(in_ts_format, basestring):
in_ts_format = tsformat(in_ts_format)
if in_ts_format == basic_types.ts_format.magnitude_direction:
uv = transforms.r_theta_to_uv_wind(datetime_value['value'])
time_value_pair['value']['u'] = uv[:, 0]
time_value_pair['value']['v'] = uv[:, 1]
elif in_ts_format == basic_types.ts_format.uv:
time_value_pair['value']['u'] = datetime_value['value'][:, 0]
time_value_pair['value']['v'] = datetime_value['value'][:, 1]
else:
raise ValueError('in_ts_format is not one of the two supported '
'types: basic_types.ts_format.magnitude_direction, '
'basic_types.ts_format.uv')
return time_value_pair
def prepare_for_model_run(self, model_time):
"""
Make sure we are up to date with the referenced time series
"""
if self.wind is None:
msg = "wind object not defined for WindMover"
raise ReferencedObjectNotSet(msg)
model_time = date_to_sec(model_time)
self.create_running_average_timeseries(self._past_hours_to_average,
model_time)
def to_time_value_pair(datetime_value, in_ts_format=None):
"""
converts a numpy array containing basic_types.datetime_value_2d in
user specified basic_types.ts_format into a time_value_pair array
or it takes a basic_types.datetime_value_1d array and converts it to
a time_value_pair array -- for 1d data, assume the ['value'] contains
the 'u' component and set the 'v' component to 0.0
:param datetime_value: numpy array of type basic_types.datetime_value_2d or
basic_types.datetime_value_1d
:param in_ts_format=None: format of the datetime_value_2d array - not
required when converting from datetime_value_1d. Can be defined by a
string 'r-theta', 'uv' or by an integer defined by one of the options
given in basic_types.ts_format.
"""
if (datetime_value.dtype not in (basic_types.datetime_value_2d,
basic_types.datetime_value_1d)):
raise ValueError('Method expects a numpy array containing '
'basic_types.datetime_value_2d or '
'basic_types.datetime_value_1d')
# convert datetime_value_2d to time_value_pair
time_value_pair = np.zeros((len(datetime_value), ),
dtype=basic_types.time_value_pair)
time_value_pair['time'] = time_utils.date_to_sec(datetime_value['time'])
if datetime_value.dtype == basic_types.datetime_value_1d:
time_value_pair['value']['u'] = datetime_value['value'][:]
else:
if in_ts_format is None:
raise ValueError("for datetime_value_2d data conversion, "
"the format defined by 'in_ts_format' "
"cannot be None ")
if isinstance(in_ts_format, basestring):
in_ts_format = tsformat(in_ts_format)
if in_ts_format == basic_types.ts_format.magnitude_direction:
uv = transforms.r_theta_to_uv_wind(datetime_value['value'])
time_value_pair['value']['u'] = uv[:, 0]
time_value_pair['value']['v'] = uv[:, 1]
elif in_ts_format == basic_types.ts_format.uv:
time_value_pair['value']['u'] = datetime_value['value'][:, 0]
time_value_pair['value']['v'] = datetime_value['value'][:, 1]
else:
raise ValueError('in_ts_format is not one of the two supported '
'types: '
'basic_types.ts_format.magnitude_direction, '
'basic_types.ts_format.uv')
return time_value_pair
def test_move_curv(self):
"""
test move for a curvilinear grid (first time in file)
"""
time = datetime.datetime(2006, 3, 31, 21)
self.cm.model_time = date_to_sec(time)
self.cm.uncertain = True
time_grid_file = get_datafile(os.path.join(winds_dir,
'WindSpeedDirSubset.nc'))
topology_file = get_datafile(os.path.join(winds_dir,
'WindSpeedDirSubsetTop.dat'))
self.gcm.text_read(time_grid_file, topology_file)
self.cm.ref[:]['long'] = -122.934656 # for NWS off CA
self.cm.ref[:]['lat'] = 38.27594
#self.check_move()
self.check_move_certain_uncertain(self.gcm.uncertain_time_delay)
actual = np.empty((self.cm.num_le, ), dtype=world_point)
actual[:]['lat'] = 0.0009890068148185598
actual[:]['long'] = 0.0012165959734995123
actual[:]['z'] = 0.
tol = 1e-5
msg = '{0} move is not within a tolerance of {1}'
np.testing.assert_allclose(
self.cm.delta['lat'],
actual['lat'],
tol,
tol,
msg.format('WindSpeedDirSubset.nc', tol),
0,
)
np.testing.assert_allclose(
self.cm.delta['long'],
actual['long'],
tol,
tol,
msg.format('WindSpeedDirSubset.nc', tol),
0,
)
#check that certain and uncertain are the same if uncertainty is time delayed
#self.gcm.uncertain_time_delay = 3
self.gcm.uncertain_time_delay = 10800 # cython expects time_delay in seconds
self.check_move_certain_uncertain(self.gcm.uncertain_time_delay)
# np.testing.assert_equal(self.cm.delta, actual,
# "test_move_curv() failed", 0)
np.all(self.cm.delta['z'] == 0)
def to_time_value_pair(datetime_value_2d, in_ts_format):
"""
converts a numpy array containing basic_types.datetime_value_2d in
user specified basic_types.ts_format into a time_value_pair array
:param datetime_value_2d: numpy array of type basic_types.datetime_value_2d
:param in_ts_format: format of the array. Can be defined by a string
'r-theta', 'uv' or by an integer defined by one of the
options given in basic_types.ts_format
"""
# print (datetime_value_2d.dtype, basic_types.datetime_value_2d)
if datetime_value_2d.dtype != basic_types.datetime_value_2d:
raise ValueError("Method expects a numpy array containing basic_types.datetime_value_2d")
# convert datetime_value_2d to time_value_pair
time_value_pair = np.zeros((len(datetime_value_2d),), dtype=basic_types.time_value_pair)
if type(in_ts_format) is str:
in_ts_format = tsformat(in_ts_format)
if in_ts_format == basic_types.ts_format.magnitude_direction:
time_value_pair["time"] = time_utils.date_to_sec(datetime_value_2d["time"])
uv = transforms.r_theta_to_uv_wind(datetime_value_2d["value"])
time_value_pair["value"]["u"] = uv[:, 0]
time_value_pair["value"]["v"] = uv[:, 1]
elif in_ts_format == basic_types.ts_format.uv:
time_value_pair["time"] = time_utils.date_to_sec(datetime_value_2d["time"])
time_value_pair["value"]["u"] = datetime_value_2d["value"][:, 0]
time_value_pair["value"]["v"] = datetime_value_2d["value"][:, 1]
else:
raise ValueError(
"in_ts_format is not one of the two supported types: basic_types.ts_format.magnitude_direction, basic_types.ts_format.uv"
)
return time_value_pair
def test_variable_wind_after_model_time_with_extrapolation(self):
'''
test to make sure the wind mover is behaving properly with
out-of-bounds winds.
A variable wind can extrapolate if it is configured to do so,
so prepare_for_model_step() should succeed in this case.
We are testing that the wind extrapolates properly, so the
windages should be updated in the same way as the in-bounds test
'''
wind_time = datetime(2012, 8, 21, 13) # one day after model time
time_series = (np.zeros((3, ), dtype=datetime_value_2d)
.view(dtype=np.recarray))
time_series.time = [sec_to_date(date_to_sec(wind_time) +
self.time_step * i)
for i in range(3)]
time_series.value = np.array(((2., 25.), (2., 25.), (2., 25.)))
wind = Wind(timeseries=time_series.reshape(3),
extrapolation_is_allowed=True,
units='meter per second')
wm = WindMover(wind)
wm.prepare_for_model_run()
for ix in range(2):
curr_time = sec_to_date(date_to_sec(self.model_time) +
self.time_step * ix)
old_windages = np.copy(self.sc['windages'])
wm.prepare_for_model_step(self.sc, self.time_step, curr_time)
mask = self.sc['windage_persist'] == -1
assert np.all(self.sc['windages'][mask] == old_windages[mask])
mask = self.sc['windage_persist'] > 0
assert np.all(self.sc['windages'][mask] != old_windages[mask])
def test_variable_wind_after_model_time_with_extrapolation(self):
'''
test to make sure the wind mover is behaving properly with
out-of-bounds winds.
A variable wind can extrapolate if it is configured to do so,
so prepare_for_model_step() should succeed in this case.
We are testing that the wind extrapolates properly, so the
windages should be updated in the same way as the in-bounds test
'''
wind_time = datetime(2012, 8, 21, 13) # one day after model time
time_series = (np.zeros((3, ), dtype=datetime_value_2d)
.view(dtype=np.recarray))
time_series.time = [sec_to_date(date_to_sec(wind_time) +
self.time_step * i)
for i in range(3)]
time_series.value = np.array(((2., 25.), (2., 25.), (2., 25.)))
wind = Wind(timeseries=time_series.reshape(3),
extrapolation_is_allowed=True,
units='meter per second')
wm = WindMover(wind)
wm.prepare_for_model_run()
for ix in range(2):
curr_time = sec_to_date(date_to_sec(self.model_time) +
self.time_step * ix)
old_windages = np.copy(self.sc['windages'])
wm.prepare_for_model_step(self.sc, self.time_step, curr_time)
mask = self.sc['windage_persist'] == -1
assert np.all(self.sc['windages'][mask] == old_windages[mask])
mask = self.sc['windage_persist'] > 0
assert np.all(self.sc['windages'][mask] != old_windages[mask])
def prepare_for_model_step(self, model_time):
"""
Make sure we are up to date with the referenced time series
"""
model_time = date_to_sec(model_time)
if self.ossm.check_time_in_range(model_time):
return
else:
if self.wind.ossm.check_time_in_range(model_time):
# there is wind data for this time so create
# a new running average
self.create_running_average_timeseries(self._past_hours_to_average, model_time)
self.create_running_average_timeseries(self._past_hours_to_average,
model_time)
def test_move_curv(self):
"""
test move for a curvilinear grid (first time in file)
"""
time = datetime.datetime(2006, 3, 31, 21)
self.cm.model_time = date_to_sec(time)
self.cm.uncertain = True
self.gcm.text_read(testdata['GridWindMover']['wind_curv'],
testdata['GridWindMover']['top_curv'])
self.cm.ref[:]['long'] = -122.934656 # for NWS off CA
self.cm.ref[:]['lat'] = 38.27594
#self.check_move()
self.check_move_certain_uncertain(self.gcm.uncertain_time_delay)
actual = np.empty((self.cm.num_le, ), dtype=world_point)
actual[:]['lat'] = 0.0009890068148185598
actual[:]['long'] = 0.0012165959734995123
actual[:]['z'] = 0.
tol = 1e-5
msg = '{0} move is not within a tolerance of {1}'
np.testing.assert_allclose(
self.cm.delta['lat'],
actual['lat'],
tol,
tol,
msg.format('WindSpeedDirSubset.nc', tol),
0,
)
np.testing.assert_allclose(
self.cm.delta['long'],
actual['long'],
tol,
tol,
msg.format('WindSpeedDirSubset.nc', tol),
0,
)
#check that certain and uncertain are the same if uncertainty is time delayed
#self.gcm.uncertain_time_delay = 3
self.gcm.uncertain_time_delay = 10800 # cython expects time_delay in seconds
self.check_move_certain_uncertain(self.gcm.uncertain_time_delay)
# np.testing.assert_equal(self.cm.delta, actual,
# "test_move_curv() failed", 0)
np.all(self.cm.delta['z'] == 0)
