def test_seasonal_get_grouping(self):
dates = get_date_list(dt(2012,4,1),dt(2012,10,31),1)
td = TemporalDimension(value=dates)
## standard seasonal group
calc_grouping = [[6,7,8]]
tg = td.get_grouping(calc_grouping)
self.assertEqual(len(tg.value),1)
selected_months = [s.month for s in td.value[tg.dgroups[0]].flat]
not_selected_months = [s.month for s in td.value[np.invert(tg.dgroups[0])]]
self.assertEqual(set(calc_grouping[0]),set(selected_months))
self.assertFalse(set(not_selected_months).issubset(set(calc_grouping[0])))
## seasons different sizes
calc_grouping = [[4,5,6,7],[8,9,10]]
tg = td.get_grouping(calc_grouping)
self.assertEqual(len(tg.value),2)
self.assertNumpyAll(tg.dgroups[0],np.invert(tg.dgroups[1]))
## crosses year boundary
calc_grouping = [[11,12,1]]
dates = get_date_list(dt(2012,10,1),dt(2013,3,31),1)
td = TemporalDimension(value=dates)
tg = td.get_grouping(calc_grouping)
selected_months = [s.month for s in td.value[tg.dgroups[0]].flat]
self.assertEqual(set(calc_grouping[0]),set(selected_months))
self.assertEqual(tg.value[0],dt(2012,12,16))
## grab real data
rd = self.test_data.get_rd('cancm4_tas')
field = rd.get()
td = TemporalDimension(value=field.temporal.value_datetime)
tg = td.get_grouping([[3,4,5]])
self.assertEqual(tg.value[0],dt(2005,4,16))
def test_seasonal_get_grouping_unique_flag(self):
## test with year flag
dates = get_date_list(dt(2012,1,1),dt(2013,12,31),1)
td = TemporalDimension(value=dates)
calc_grouping = [[6,7,8],'unique']
tg = td.get_grouping(calc_grouping)
time_region = {'year':[2012],'month':[6,7,8]}
sub1,idx1 = td.get_time_region(time_region,return_indices=True)
time_region = {'year':[2013],'month':[6,7,8]}
sub2,idx2 = td.get_time_region(time_region,return_indices=True)
base_select = np.zeros(td.shape[0],dtype=bool)
dgroups = deque()
for software,manual in itertools.izip(tg.dgroups,dgroups):
self.assertNumpyAll(software,manual)
self.assertEqual(len(tg.dgroups),2)
self.assertEqual(tg.value.tolist(),[datetime.datetime(2012, 7, 17, 0, 0), datetime.datetime(2013, 7, 17, 0, 0)])
self.assertEqual(tg.bounds.tolist(),[[datetime.datetime(2012, 6, 1, 0, 0), datetime.datetime(2012, 8, 31, 0, 0)], [datetime.datetime(2013, 6, 1, 0, 0), datetime.datetime(2013, 8, 31, 0, 0)]])
dgroup1 = base_select.copy()
dgroup1[idx1] = True
dgroup2 = base_select.copy()
dgroup2[idx2] = True
dgroups.append(dgroup1)
dgroups.append(dgroup2)
tg = td.get_grouping([[6,7,8],'year'])
for ii in range(len(tg.dgroups)):
self.assertNumpyAll(tg.dgroups[ii],dgroups[ii])
self.assertEqual(len(tg.dgroups),len(dgroups))
def test_get_bounds_from_1d(self):
sdim = self.get_sdim(bounds=False)
test_sdim = self.get_sdim(bounds=True)
row_bounds = get_bounds_from_1d(sdim.grid.row.value)
col_bounds = get_bounds_from_1d(sdim.grid.col.value)
self.assertNumpyAll(row_bounds, test_sdim.grid.row.bounds)
self.assertNumpyAll(col_bounds, test_sdim.grid.col.bounds)
across_180 = np.array([-180, -90, 0, 90, 180], dtype=float)
bounds_180 = get_bounds_from_1d(across_180)
self.assertEqual(bounds_180.tostring(), '\x00\x00\x00\x00\x00 l\xc0\x00\x00\x00\x00\x00\xe0`\xc0\x00\x00\x00\x00\x00\xe0`\xc0\x00\x00\x00\x00\x00\x80F\xc0\x00\x00\x00\x00\x00\x80F\xc0\x00\x00\x00\x00\x00\x80F@\x00\x00\x00\x00\x00\x80F@\x00\x00\x00\x00\x00\xe0`@\x00\x00\x00\x00\x00\xe0`@\x00\x00\x00\x00\x00 l@')
dates = get_date_list(datetime(2000, 1, 31), datetime(2002, 12, 31), 1)
with self.assertRaises(NotImplementedError):
get_bounds_from_1d(np.array(dates))
with self.assertRaises(ValueError):
get_bounds_from_1d(np.array([0], dtype=float))
just_two = get_bounds_from_1d(np.array([50, 75], dtype=float))
self.assertEqual(just_two.tostring(), '\x00\x00\x00\x00\x00\xc0B@\x00\x00\x00\x00\x00@O@\x00\x00\x00\x00\x00@O@\x00\x00\x00\x00\x00\xe0U@')
just_two_reversed = get_bounds_from_1d(np.array([75, 50], dtype=float))
self.assertEqual(just_two_reversed.tostring(), '\x00\x00\x00\x00\x00\xe0U@\x00\x00\x00\x00\x00@O@\x00\x00\x00\x00\x00@O@\x00\x00\x00\x00\x00\xc0B@')
zero_origin = get_bounds_from_1d(np.array([0, 50, 100], dtype=float))
self.assertEqual(zero_origin.tostring(), '\x00\x00\x00\x00\x00\x009\xc0\x00\x00\x00\x00\x00\x009@\x00\x00\x00\x00\x00\x009@\x00\x00\x00\x00\x00\xc0R@\x00\x00\x00\x00\x00\xc0R@\x00\x00\x00\x00\x00@_@')
def test_seasonal_get_grouping_unique_flag_winter_season(self):
dt1 = datetime.datetime(1900,01,01)
dt2 = datetime.datetime(1902,12,31)
dates = get_date_list(dt1,dt2,days=1)
td = TemporalDimension(value=dates)
group = [[12,1,2],'unique']
tg = td.get_grouping(group)
self.assertEqual(tg.value.shape[0],2)
self.assertEqual(tg.bounds.tolist(),[[datetime.datetime(1901, 1, 1, 0, 0), datetime.datetime(1901, 2, 28, 0, 0)], [datetime.datetime(1902, 1, 1, 0, 0), datetime.datetime(1902, 2, 28, 0, 0)]])
def test_empty_season_with_year_missing_month(self):
dt1 = datetime.datetime(1900,01,01)
dt2 = datetime.datetime(1903,1,31)
dates = get_date_list(dt1,dt2,days=1)
td = TemporalDimension(value=dates)
group = [[12,1,2],'unique']
tg = td.get_grouping(group)
## there should be a month missing from the last season (february) and it should not be
## considered complete
self.assertEqual(tg.value.shape[0],2)
def get_temporal_dimension(self,add_bounds=True,start=None,stop=None,days=1):
start = start or datetime.datetime(1899,1,1,12)
stop = stop or datetime.datetime(1901,12,31,12)
dates = get_date_list(start,stop,days=days)
if add_bounds:
delta = datetime.timedelta(hours=12)
lower = np.array(dates) - delta
upper = np.array(dates) + delta
bounds = np.empty((lower.shape[0],2),dtype=object)
bounds[:,0] = lower
bounds[:,1] = upper
else:
bounds = None
td = TemporalDimension(value=dates,bounds=bounds)
return(td)
def test_get_boolean_groups_from_time_regions(self):
dates = get_date_list(dt(2012,1,1),dt(2013,12,31),1)
seasons = [[3,4,5],[6,7,8],[9,10,11],[12,1,2]]
td = TemporalDimension(value=dates)
time_regions = get_time_regions(seasons,dates,raise_if_incomplete=False)
dgroups = list(iter_boolean_groups_from_time_regions(time_regions,td))
## the last winter season is not complete as it does not have enough years
self.assertEqual(len(dgroups),7)
to_test = []
for dgroup in dgroups:
sub = td[dgroup]
## (upper and lower values of time vector, count of elements in time group, the middle value of the vector)
to_test.append([sub.extent, sub.shape[0], sub[sub.shape[0]/2].value[0]])
correct = [[(datetime.datetime(2012, 3, 1, 0, 0), datetime.datetime(2012, 5, 31, 0, 0)), 92, datetime.datetime(2012, 4, 16, 0, 0)], [(datetime.datetime(2012, 6, 1, 0, 0), datetime.datetime(2012, 8, 31, 0, 0)), 92, datetime.datetime(2012, 7, 17, 0, 0)], [(datetime.datetime(2012, 9, 1, 0, 0), datetime.datetime(2012, 11, 30, 0, 0)), 91, datetime.datetime(2012, 10, 16, 0, 0)], [(datetime.datetime(2012, 12, 1, 0, 0), datetime.datetime(2013, 2, 28, 0, 0)), 90, datetime.datetime(2013, 1, 15, 0, 0)], [(datetime.datetime(2013, 3, 1, 0, 0), datetime.datetime(2013, 5, 31, 0, 0)), 92, datetime.datetime(2013, 4, 16, 0, 0)], [(datetime.datetime(2013, 6, 1, 0, 0), datetime.datetime(2013, 8, 31, 0, 0)), 92, datetime.datetime(2013, 7, 17, 0, 0)], [(datetime.datetime(2013, 9, 1, 0, 0), datetime.datetime(2013, 11, 30, 0, 0)), 91, datetime.datetime(2013, 10, 16, 0, 0)]]
self.assertEqual(to_test,correct)
def test_time_range_subset(self):
dt1 = datetime.datetime(1950,01,01,12)
dt2 = datetime.datetime(1950,12,31,12)
dates = np.array(get_date_list(dt1,dt2,1))
r1 = datetime.datetime(1950,01,01)
r2 = datetime.datetime(1950,12,31)
td = TemporalDimension(value=dates)
ret = td.get_between(r1,r2)
self.assertEqual(ret.value[-1],datetime.datetime(1950,12,30,12,0))
delta = datetime.timedelta(hours=12)
lower = dates - delta
upper = dates + delta
bounds = np.empty((lower.shape[0],2),dtype=object)
bounds[:,0] = lower
bounds[:,1] = upper
td = TemporalDimension(value=dates,bounds=bounds)
ret = td.get_between(r1,r2)
self.assertEqual(ret.value[-1],datetime.datetime(1950,12,31,12,0))
def test_get_time_region_value_only(self):
dates = get_date_list(dt(2002,1,31),dt(2009,12,31),1)
td = TemporalDimension(value=dates)
ret,indices = td.get_time_region({'month':[8]},return_indices=True)
self.assertEqual(set([8]),set([d.month for d in ret.value.flat]))
ret,indices = td.get_time_region({'year':[2008,2004]},return_indices=True)
self.assertEqual(set([2008,2004]),set([d.year for d in ret.value.flat]))
ret,indices = td.get_time_region({'day':[20,31]},return_indices=True)
self.assertEqual(set([20,31]),set([d.day for d in ret.value.flat]))
ret,indices = td.get_time_region({'day':[20,31],'month':[9,10],'year':[2003]},return_indices=True)
self.assertNumpyAll(ret.value,np.array([dt(2003,9,20),dt(2003,10,20),dt(2003,10,31,)]))
self.assertEqual(ret.shape,indices.shape)
self.assertEqual(ret.extent,(datetime.datetime(2003,9,20),datetime.datetime(2003,10,31)))
def test_seasonal_get_grouping_year_flag(self):
## test with year flag
dates = get_date_list(dt(2012,1,1),dt(2013,12,31),1)
td = TemporalDimension(value=dates)
calc_grouping = [[6,7,8],'year']
tg = td.get_grouping(calc_grouping)
self.assertEqual(tg.value.shape[0],2)
self.assertEqual(str(tg.value),'[2012-07-16 00:00:00 2013-07-16 00:00:00]')
sub0 = td.value[tg.dgroups[0]]
self.assertEqual(str(sub0),'[2012-06-01 00:00:00 2012-06-02 00:00:00 2012-06-03 00:00:00\n 2012-06-04 00:00:00 2012-06-05 00:00:00 2012-06-06 00:00:00\n 2012-06-07 00:00:00 2012-06-08 00:00:00 2012-06-09 00:00:00\n 2012-06-10 00:00:00 2012-06-11 00:00:00 2012-06-12 00:00:00\n 2012-06-13 00:00:00 2012-06-14 00:00:00 2012-06-15 00:00:00\n 2012-06-16 00:00:00 2012-06-17 00:00:00 2012-06-18 00:00:00\n 2012-06-19 00:00:00 2012-06-20 00:00:00 2012-06-21 00:00:00\n 2012-06-22 00:00:00 2012-06-23 00:00:00 2012-06-24 00:00:00\n 2012-06-25 00:00:00 2012-06-26 00:00:00 2012-06-27 00:00:00\n 2012-06-28 00:00:00 2012-06-29 00:00:00 2012-06-30 00:00:00\n 2012-07-01 00:00:00 2012-07-02 00:00:00 2012-07-03 00:00:00\n 2012-07-04 00:00:00 2012-07-05 00:00:00 2012-07-06 00:00:00\n 2012-07-07 00:00:00 2012-07-08 00:00:00 2012-07-09 00:00:00\n 2012-07-10 00:00:00 2012-07-11 00:00:00 2012-07-12 00:00:00\n 2012-07-13 00:00:00 2012-07-14 00:00:00 2012-07-15 00:00:00\n 2012-07-16 00:00:00 2012-07-17 00:00:00 2012-07-18 00:00:00\n 2012-07-19 00:00:00 2012-07-20 00:00:00 2012-07-21 00:00:00\n 2012-07-22 00:00:00 2012-07-23 00:00:00 2012-07-24 00:00:00\n 2012-07-25 00:00:00 2012-07-26 00:00:00 2012-07-27 00:00:00\n 2012-07-28 00:00:00 2012-07-29 00:00:00 2012-07-30 00:00:00\n 2012-07-31 00:00:00 2012-08-01 00:00:00 2012-08-02 00:00:00\n 2012-08-03 00:00:00 2012-08-04 00:00:00 2012-08-05 00:00:00\n 2012-08-06 00:00:00 2012-08-07 00:00:00 2012-08-08 00:00:00\n 2012-08-09 00:00:00 2012-08-10 00:00:00 2012-08-11 00:00:00\n 2012-08-12 00:00:00 2012-08-13 00:00:00 2012-08-14 00:00:00\n 2012-08-15 00:00:00 2012-08-16 00:00:00 2012-08-17 00:00:00\n 2012-08-18 00:00:00 2012-08-19 00:00:00 2012-08-20 00:00:00\n 2012-08-21 00:00:00 2012-08-22 00:00:00 2012-08-23 00:00:00\n 2012-08-24 00:00:00 2012-08-25 00:00:00 2012-08-26 00:00:00\n 2012-08-27 00:00:00 2012-08-28 00:00:00 2012-08-29 00:00:00\n 2012-08-30 00:00:00 2012-08-31 00:00:00]')
sub1 = td.value[tg.dgroups[1]]
self.assertEqual(str(sub1),'[2013-06-01 00:00:00 2013-06-02 00:00:00 2013-06-03 00:00:00\n 2013-06-04 00:00:00 2013-06-05 00:00:00 2013-06-06 00:00:00\n 2013-06-07 00:00:00 2013-06-08 00:00:00 2013-06-09 00:00:00\n 2013-06-10 00:00:00 2013-06-11 00:00:00 2013-06-12 00:00:00\n 2013-06-13 00:00:00 2013-06-14 00:00:00 2013-06-15 00:00:00\n 2013-06-16 00:00:00 2013-06-17 00:00:00 2013-06-18 00:00:00\n 2013-06-19 00:00:00 2013-06-20 00:00:00 2013-06-21 00:00:00\n 2013-06-22 00:00:00 2013-06-23 00:00:00 2013-06-24 00:00:00\n 2013-06-25 00:00:00 2013-06-26 00:00:00 2013-06-27 00:00:00\n 2013-06-28 00:00:00 2013-06-29 00:00:00 2013-06-30 00:00:00\n 2013-07-01 00:00:00 2013-07-02 00:00:00 2013-07-03 00:00:00\n 2013-07-04 00:00:00 2013-07-05 00:00:00 2013-07-06 00:00:00\n 2013-07-07 00:00:00 2013-07-08 00:00:00 2013-07-09 00:00:00\n 2013-07-10 00:00:00 2013-07-11 00:00:00 2013-07-12 00:00:00\n 2013-07-13 00:00:00 2013-07-14 00:00:00 2013-07-15 00:00:00\n 2013-07-16 00:00:00 2013-07-17 00:00:00 2013-07-18 00:00:00\n 2013-07-19 00:00:00 2013-07-20 00:00:00 2013-07-21 00:00:00\n 2013-07-22 00:00:00 2013-07-23 00:00:00 2013-07-24 00:00:00\n 2013-07-25 00:00:00 2013-07-26 00:00:00 2013-07-27 00:00:00\n 2013-07-28 00:00:00 2013-07-29 00:00:00 2013-07-30 00:00:00\n 2013-07-31 00:00:00 2013-08-01 00:00:00 2013-08-02 00:00:00\n 2013-08-03 00:00:00 2013-08-04 00:00:00 2013-08-05 00:00:00\n 2013-08-06 00:00:00 2013-08-07 00:00:00 2013-08-08 00:00:00\n 2013-08-09 00:00:00 2013-08-10 00:00:00 2013-08-11 00:00:00\n 2013-08-12 00:00:00 2013-08-13 00:00:00 2013-08-14 00:00:00\n 2013-08-15 00:00:00 2013-08-16 00:00:00 2013-08-17 00:00:00\n 2013-08-18 00:00:00 2013-08-19 00:00:00 2013-08-20 00:00:00\n 2013-08-21 00:00:00 2013-08-22 00:00:00 2013-08-23 00:00:00\n 2013-08-24 00:00:00 2013-08-25 00:00:00 2013-08-26 00:00:00\n 2013-08-27 00:00:00 2013-08-28 00:00:00 2013-08-29 00:00:00\n 2013-08-30 00:00:00 2013-08-31 00:00:00]')
## test crossing year boundary
for calc_grouping in [[[12,1,2],'year'],['year',[12,1,2]]]:
tg = td.get_grouping(calc_grouping)
self.assertEqual(str(tg.value),'[2012-01-16 00:00:00 2013-01-16 00:00:00]')
self.assertEqual(str(tg.bounds),'[[2012-01-01 00:00:00 2012-12-31 00:00:00]\n [2013-01-01 00:00:00 2013-12-31 00:00:00]]')
self.assertEqual(str(td.value[tg.dgroups[1]]),'[2013-01-01 00:00:00 2013-01-02 00:00:00 2013-01-03 00:00:00\n 2013-01-04 00:00:00 2013-01-05 00:00:00 2013-01-06 00:00:00\n 2013-01-07 00:00:00 2013-01-08 00:00:00 2013-01-09 00:00:00\n 2013-01-10 00:00:00 2013-01-11 00:00:00 2013-01-12 00:00:00\n 2013-01-13 00:00:00 2013-01-14 00:00:00 2013-01-15 00:00:00\n 2013-01-16 00:00:00 2013-01-17 00:00:00 2013-01-18 00:00:00\n 2013-01-19 00:00:00 2013-01-20 00:00:00 2013-01-21 00:00:00\n 2013-01-22 00:00:00 2013-01-23 00:00:00 2013-01-24 00:00:00\n 2013-01-25 00:00:00 2013-01-26 00:00:00 2013-01-27 00:00:00\n 2013-01-28 00:00:00 2013-01-29 00:00:00 2013-01-30 00:00:00\n 2013-01-31 00:00:00 2013-02-01 00:00:00 2013-02-02 00:00:00\n 2013-02-03 00:00:00 2013-02-04 00:00:00 2013-02-05 00:00:00\n 2013-02-06 00:00:00 2013-02-07 00:00:00 2013-02-08 00:00:00\n 2013-02-09 00:00:00 2013-02-10 00:00:00 2013-02-11 00:00:00\n 2013-02-12 00:00:00 2013-02-13 00:00:00 2013-02-14 00:00:00\n 2013-02-15 00:00:00 2013-02-16 00:00:00 2013-02-17 00:00:00\n 2013-02-18 00:00:00 2013-02-19 00:00:00 2013-02-20 00:00:00\n 2013-02-21 00:00:00 2013-02-22 00:00:00 2013-02-23 00:00:00\n 2013-02-24 00:00:00 2013-02-25 00:00:00 2013-02-26 00:00:00\n 2013-02-27 00:00:00 2013-02-28 00:00:00 2013-12-01 00:00:00\n 2013-12-02 00:00:00 2013-12-03 00:00:00 2013-12-04 00:00:00\n 2013-12-05 00:00:00 2013-12-06 00:00:00 2013-12-07 00:00:00\n 2013-12-08 00:00:00 2013-12-09 00:00:00 2013-12-10 00:00:00\n 2013-12-11 00:00:00 2013-12-12 00:00:00 2013-12-13 00:00:00\n 2013-12-14 00:00:00 2013-12-15 00:00:00 2013-12-16 00:00:00\n 2013-12-17 00:00:00 2013-12-18 00:00:00 2013-12-19 00:00:00\n 2013-12-20 00:00:00 2013-12-21 00:00:00 2013-12-22 00:00:00\n 2013-12-23 00:00:00 2013-12-24 00:00:00 2013-12-25 00:00:00\n 2013-12-26 00:00:00 2013-12-27 00:00:00 2013-12-28 00:00:00\n 2013-12-29 00:00:00 2013-12-30 00:00:00 2013-12-31 00:00:00]')
def test_seasonal_get_time_regions(self):
dates = get_date_list(dt(2012,1,1),dt(2013,12,31),1)
## two simple seasons
calc_grouping = [[6,7,8],[9,10,11]]
time_regions = get_time_regions(calc_grouping,dates)
correct = [[{'month': [6, 7, 8], 'year': [2012]}], [{'month': [9, 10, 11], 'year': [2012]}], [{'month': [6, 7, 8], 'year': [2013]}], [{'month': [9, 10, 11], 'year': [2013]}]]
self.assertEqual(time_regions,correct)
## add an interannual season at the back
calc_grouping = [[6,7,8],[9,10,11],[12,1,2]]
with self.assertRaises(IncompleteSeasonError):
get_time_regions(calc_grouping,dates)
time_regions = get_time_regions(calc_grouping,dates,raise_if_incomplete=False)
correct = [[{'month': [6, 7, 8], 'year': [2012]}], [{'month': [9, 10, 11], 'year': [2012]}], [{'month': [12], 'year': [2012]}, {'month': [2, 1], 'year': [2013]}], [{'month': [6, 7, 8], 'year': [2013]}], [{'month': [9, 10, 11], 'year': [2013]}]]
self.assertEqual(time_regions,correct)
## put the interannual season in the middle
calc_grouping = [[9,10,11],[12,1,2],[6,7,8]]
with self.assertRaises(IncompleteSeasonError):
get_time_regions(calc_grouping,dates)
time_regions = get_time_regions(calc_grouping,dates,raise_if_incomplete=False)
correct = [[{'month': [9, 10, 11], 'year': [2012]}], [{'month': [12], 'year': [2012]}, {'month': [2, 1], 'year': [2013]}], [{'month': [6, 7, 8], 'year': [2012]}], [{'month': [9, 10, 11], 'year': [2013]}], [{'month': [6, 7, 8], 'year': [2013]}]]
self.assertEqual(time_regions,correct)
## odd seasons, but covering the whole year
calc_grouping = [[1,2,3],[4,5,6],[7,8,9],[10,11,12]]
time_regions = get_time_regions(calc_grouping,dates)
correct = [[{'month': [1, 2, 3], 'year': [2012]}], [{'month': [4, 5, 6], 'year': [2012]}], [{'month': [7, 8, 9], 'year': [2012]}], [{'month': [10, 11, 12], 'year': [2012]}], [{'month': [1, 2, 3], 'year': [2013]}], [{'month': [4, 5, 6], 'year': [2013]}], [{'month': [7, 8, 9], 'year': [2013]}], [{'month': [10, 11, 12], 'year': [2013]}]]
self.assertEqual(time_regions,correct)
## standard seasons
calc_grouping = [[3,4,5],[6,7,8],[9,10,11],[12,1,2]]
time_regions = get_time_regions(calc_grouping,dates,raise_if_incomplete=False)
correct = [[{'month': [3, 4, 5], 'year': [2012]}], [{'month': [6, 7, 8], 'year': [2012]}], [{'month': [9, 10, 11], 'year': [2012]}], [{'month': [12], 'year': [2012]}, {'month': [2, 1], 'year': [2013]}], [{'month': [3, 4, 5], 'year': [2013]}], [{'month': [6, 7, 8], 'year': [2013]}], [{'month': [9, 10, 11], 'year': [2013]}]]
self.assertEqual(time_regions,correct)
def get_field(self,with_bounds=True,with_value=False,with_level=True,with_temporal=True,
with_realization=True,month_count=1,name='tmax'):
if with_temporal:
temporal_start = dt(2000,1,1,12)
if month_count == 1:
temporal_stop = dt(2000,1,31,12)
elif month_count == 2:
temporal_stop = dt(2000,2,29,12)
else:
raise(NotImplementedError)
temporal_value = get_date_list(temporal_start,temporal_stop,1)
delta_bounds = datetime.timedelta(hours=12)
if with_bounds:
temporal_bounds = [[v-delta_bounds,v+delta_bounds] for v in temporal_value]
else:
temporal_bounds = None
temporal = TemporalDimension(value=temporal_value,bounds=temporal_bounds,name='time',
units='days')
t_shape = temporal.shape[0]
else:
temporal = None
t_shape = 1
if with_level:
level_value = [50,150]
if with_bounds:
level_bounds = [[0,100],[100,200]]
else:
level_bounds = None
level = VectorDimension(value=level_value,bounds=level_bounds,name='level',
units='meters')
l_shape = level.shape[0]
else:
level = None
l_shape = 1
row = self.get_row(bounds=with_bounds)
col = self.get_col(bounds=with_bounds)
grid = SpatialGridDimension(row=row,col=col)
spatial = SpatialDimension(grid=grid)
row_shape = row.shape[0]
col_shape = col.shape[0]
if with_realization:
realization = VectorDimension(value=[1,2],name='realization')
r_shape = realization.shape[0]
else:
realization = None
r_shape = 1
if with_value:
value = np.random.rand(r_shape,t_shape,l_shape,row_shape,col_shape)
else:
value = None
var = Variable(name,units='C',debug=True,data=None,value=value)
vc = VariableCollection(variables=var)
field = Field(variables=vc,temporal=temporal,level=level,realization=realization,
spatial=spatial)
return(field)
def get_field(self, with_bounds=True, with_value=False, with_level=True, with_temporal=True, with_realization=True,
month_count=1, name='tmax', units='kelvin', field_name=None, crs=None, with_dimension_names=True):
if with_temporal:
temporal_start = dt(2000, 1, 1, 12)
if month_count == 1:
temporal_stop = dt(2000, 1, 31, 12)
elif month_count == 2:
temporal_stop = dt(2000, 2, 29, 12)
else:
raise NotImplementedError
temporal_value = get_date_list(temporal_start, temporal_stop, 1)
delta_bounds = datetime.timedelta(hours=12)
if with_bounds:
temporal_bounds = [[v - delta_bounds, v + delta_bounds] for v in temporal_value]
else:
temporal_bounds = None
dname = 'time' if with_dimension_names else None
temporal = TemporalDimension(value=temporal_value, bounds=temporal_bounds, name=dname)
t_shape = temporal.shape[0]
else:
temporal = None
t_shape = 1
if with_level:
level_value = [50, 150]
if with_bounds:
level_bounds = [[0, 100], [100, 200]]
else:
level_bounds = None
dname = 'level' if with_dimension_names else None
level = VectorDimension(value=level_value, bounds=level_bounds, name=dname, units='meters')
l_shape = level.shape[0]
else:
level = None
l_shape = 1
with_name = True if with_dimension_names else False
row = self.get_row(bounds=with_bounds, with_name=with_name)
col = self.get_col(bounds=with_bounds, with_name=with_name)
grid = SpatialGridDimension(row=row, col=col)
spatial = SpatialDimension(grid=grid, crs=crs)
row_shape = row.shape[0]
col_shape = col.shape[0]
if with_realization:
dname = 'realization' if with_dimension_names else None
realization = VectorDimension(value=[1, 2], name=dname)
r_shape = realization.shape[0]
else:
realization = None
r_shape = 1
if with_value:
value = np.random.rand(r_shape, t_shape, l_shape, row_shape, col_shape)
data = None
else:
value = None
data = 'foo'
var = Variable(name, units=units, request_dataset=data, value=value)
vc = VariableCollection(variables=var)
field = Field(variables=vc, temporal=temporal, level=level, realization=realization, spatial=spatial,
name=field_name)
return field