def test_multiple_tiles_same_long(self):
tile1 = Tile()
tile1.latitudes = np.ma.array([0.0, 1.0, 2.0, 3.0])
tile1.longitudes = np.ma.array([0.0, -1.0, -2.0])
tile1.times = np.ma.array([0L])
tile1.data = np.ma.arange(12).reshape((1, 4, 3))
tile2 = Tile()
tile2.latitudes = np.ma.array([4.0, 5.0, 6.0, 7.0])
tile2.longitudes = np.ma.array([0.0, -1.0, -2.0])
tile2.times = np.ma.array([0L])
tile2.data = np.ma.arange(12, 24).reshape((1, 4, 3))
self.assertAlmostEqual(1, get_approximate_value_for_lat_lon([tile1, tile2], 0.4, -1))
def test_lat_exact_lon_approx(self):
tile = Tile()
tile.bbox = BBox(-1.0, 1.0, -2.0, 2.0)
tile.latitudes = np.ma.array([-1.0, -0.5, 0, .5, 1.0])
tile.longitudes = np.ma.array([-2.0, -1.0, 0, 1.0, 2.0])
tile.times = np.ma.array([0L])
tile.data = np.ma.arange(25.0).reshape((1, 5, 5))
# -2 -1 0 1 2
# -1.0 [[[0. 1. 2. 3. 4. ]
# -0.5 [5. 6. 7. 8. 9. ]
# 0. [10. 11. 12. 13. 14.]
# 0.5 [15. 16. 17. 18. 19.]
# 1.0 [20. 21. 22. 23. 24.]]]
self.assertAlmostEqual(18.0, get_approximate_value_for_lat_lon([tile], 0.5, 1.01))
def test_lon_greater_than_bounds(self):
tile = Tile()
tile.bbox = BBox(-1.0, 1.0, -2.0, 2.0)
tile.latitudes = np.ma.array([-1.0, -0.5, 0, .5, 1.0])
tile.longitudes = np.ma.array([-2.0, -1.0, 0, 1.0, 2.0])
tile.times = np.ma.array([0L])
tile.data = np.ma.arange(25.0).reshape((1, 5, 5))
# -2 -1 0 1 2
# -1.0 [[[0. 1. 2. 3. 4. ]
# -0.5 [5. 6. 7. 8. 9. ]
# 0. [10. 11. 12. 13. 14.]
# 0.5 [15. 16. 17. 18. 19.]
# 1.0 [20. 21. 22. 23. 24.]]]
self.assertTrue(np.isnan(get_approximate_value_for_lat_lon([tile], 0, 3)))
def calc(self, computeOptions, **args):
minLat = computeOptions.get_min_lat()
maxLat = computeOptions.get_max_lat()
minLon = computeOptions.get_min_lon()
maxLon = computeOptions.get_max_lon()
ds = computeOptions.get_dataset()
startTime = computeOptions.get_start_time()
endTime = computeOptions.get_end_time()
resolution = computeOptions.get_decimal_arg("res", default=1.0)
if not len(ds) == 2:
raise Exception("Requires two datasets for comparison. Specify request parameter ds=Dataset_1,Dataset_2")
ds1tiles = self._tile_service.find_tiles_in_polygon(box(minLon, minLat, maxLon, maxLat), ds[0], startTime,
endTime)
ds2tiles = self._tile_service.find_tiles_in_polygon(box(minLon, minLat, maxLon, maxLat), ds[1], startTime,
endTime)
matches = self._match_tiles(ds1tiles, ds2tiles)
if len(matches) == 0:
raise NexusProcessingException(reason="Could not find any data temporally co-located")
results = [[{
'cnt': 0,
'slope': 0,
'intercept': 0,
'r': 0,
'p': 0,
'stderr': 0,
'lat': float(lat),
'lon': float(lon)
} for lon in np.arange(minLon, maxLon, resolution)] for lat in
np.arange(minLat, maxLat, resolution)]
for stats in results:
for stat in stats:
values_x = []
values_y = []
for tile_matches in matches:
tile_1_list = tile_matches[0]
value_1 = get_approximate_value_for_lat_lon(tile_1_list, stat["lat"], stat["lon"])
tile_2_list = tile_matches[1]
value_2 = get_approximate_value_for_lat_lon(tile_2_list, stat["lat"], stat["lon"])
if not (math.isnan(value_1) or math.isnan(value_2)):
values_x.append(value_1)
values_y.append(value_2)
if len(values_x) > 2 and len(values_y) > 2:
stats = linregress(values_x, values_y)
stat["slope"] = stats[0] if not math.isnan(stats[0]) and not math.isinf(stats[0]) else str(stats[0])
stat["intercept"] = stats[1] if not math.isnan(stats[1]) and not math.isinf(stats[1]) else str(
stats[1])
stat["r"] = stats[2] if not math.isnan(stats[2]) and not math.isinf(stats[2]) else str(stats[2])
stat["p"] = stats[3] if not math.isnan(stats[3]) and not math.isinf(stats[3]) else str(stats[3])
stat["stderr"] = stats[4] if not math.isnan(stats[4]) and not math.isinf(stats[4]) else str(
stats[4])
stat["cnt"] = len(values_x)
return CorrelationResults(results)
def __getValueForLatLon(self, chunks, lat, lon, arrayName="data"):
value = get_approximate_value_for_lat_lon(chunks, lat, lon, arrayName)
return value
