def show_tile_bounds( location: str, year, time_range, download = True ):
dat_url = "https://floodmap.modaps.eosdis.nasa.gov/Products"
dataMgr = MWPDataManager( DATA_DIR, dat_url )
dataMgr.setDefaults(product="2D2OT", download=download, year=year, start_day=time_range[0], end_day=time_range[1])
file_paths = dataMgr.get_tile(location)
data_arrays: List[xr.DataArray ] = XGeo.loadRasterFiles(file_paths[0:2], band=1 )
print( f"Reading location: {location}")
print( TileLocator.get_bounds(data_arrays[0]) )
print( TileLocator.infer_tiles_xa( data_arrays[0] ) )
def show_roi( xbounds, ybounds, year, time_range ):
dat_url = "https://floodmap.modaps.eosdis.nasa.gov/Products"
dataMgr = MWPDataManager( DATA_DIR, dat_url )
dataMgr.setDefaults(product="2D2OT", year=year, start_day=time_range[0], end_day=time_range[1])
location = TileLocator.get_tile( *xbounds, *ybounds )
file_paths = dataMgr.get_tile(location)
roi_bounds = xbounds + ybounds
time_values = np.array([ get_date_from_filename(os.path.basename(path)) for path in file_paths], dtype='datetime64[ns]')
cropped_data: xr.DataArray = XRio.load(file_paths, mask=roi_bounds, band=0, index=time_values)
cropped_data.attrs['cmap'] = dict(colors=colors3)
animation = SliceAnimation( cropped_data )
animation.show()
def get_mpw_data(self, **kwargs ) -> Tuple[Optional[xr.DataArray],Optional[ np.array]]:
print( "reading mpw data")
t0 = time.time()
results_dir = kwargs.get('results_dir')
lake_id = kwargs.get('lake_index')
download = kwargs.get( 'download', True )
from geoproc.data.mwp import MWPDataManager
from geoproc.xext.xrio import XRio
source_spec = kwargs.get('source')
data_url = source_spec.get('url')
product = source_spec.get('product')
locations = source_spec.get( 'location', self.infer_tile_locations() )
if not locations:
print( "NO LOCATION DATA. ABORTING")
return None, None
year_range = kwargs.get('year_range')
day_range = kwargs.get('day_range',[0,365])
dataMgr = MWPDataManager(results_dir, data_url)
cropped_tiles: Dict[str,xr.DataArray] = {}
file_paths = []
time_values = None
cropped_data = None
for location in locations:
try:
print( f"Reading Location {location}" )
dataMgr.setDefaults(product=product, download=download, years=range(int(year_range[0]),int(year_range[1])+1), start_day=int(day_range[0]), end_day=int(day_range[1]))
file_paths = dataMgr.get_tile(location)
time_values = np.array([ self.get_date_from_filename(os.path.basename(path)) for path in file_paths], dtype='datetime64[ns]')
cropped_tiles[location] = XRio.load( file_paths, mask=self.roi_bounds, band=0, mask_value=self.mask_value, index=time_values )
except Exception as err:
print( f"Error reading mpw data for location {location}, first file paths = {file_paths[0:10]} ")
for file in file_paths:
if not os.path.isfile( file ): print( f" --> File {file} does not exist!")
exc = traceback.format_exc()
print( f"Error: {err}: \n{exc}" )
XRio.print_array_dims( file_paths )
nTiles = len( cropped_tiles.keys() )
if nTiles > 0:
print( f"Merging {nTiles} Tiles ")
cropped_data = self.merge_tiles( cropped_tiles)
cropped_data.attrs.update( roi = self.roi_bounds )
cropped_data = cropped_data.persist()
print(f"Done reading mpw data for lake {lake_id} in time {time.time()-t0}, nTiles = {nTiles}")
return cropped_data, time_values
data_vars.append(key)
return data_vars
if __name__ == "__main__":
from geoproc.util.configuration import Region
from geoproc.data.mwp import MWPDataManager
colors = [(0, 0, 0), (0, 1, 0), (0, 0, 1), (1, 1, 0)]
locations = ["120W050N", "100W040N"]
products = ["1D1OS", "2D2OT", "3D3OT"]
DATA_DIR = "/Users/tpmaxwel/Dropbox/Tom/Data/Birkitt"
location: str = locations[0]
product = products[0]
year = 2019
download = False
roi = None
bbox = Region([3000, 3500], 750)
time_index_range = [0, 365]
dataMgr = MWPDataManager(
DATA_DIR, "https://floodmap.modaps.eosdis.nasa.gov/Products")
dataMgr.setDefaults(product=product,
download=download,
year=2019,
start_day=time_index_range[0],
end_day=time_index_range[1]) # , bbox=bbox )
data_array: xr.DataArray = dataMgr.get_tile_data(location, True)
data_array.xplot.animate(title='MPW Time Slice Animation', colors=colors)
data_arrays = [xa.open_rasterio(file)[0] for file in files]
else:
data_arrays = [
xa.open_rasterio(file)[0,
bbox.origin[0]:bbox.bounds[0],
bbox.origin[1]:bbox.bounds[1]]
for file in files
]
else:
dset = xa.open_mfdataset(
files) if len(files) > 1 else xa.open_dataset(files[0])
data_arrays = dset.data_vars.values()
return [array.to_iris() for array in data_arrays]
if __name__ == '__main__':
DATA_DIR = "/Users/tpmaxwel/Dropbox/Tom/Data/Birkitt"
location = "120W050N"
dataMgr = MWPDataManager(
DATA_DIR, "https://floodmap.modaps.eosdis.nasa.gov/Products")
dataMgr.setDefaults(product="1D1OS",
download=True,
year=2019,
start_day=1,
end_day=365)
files = dataMgr.get_tile(location, download=False)
irisDataMgr = IrisDataManager(DATA_DIR)
cubes = irisDataMgr.get_cube_data(files)
print(cubes)
dtype='datetime64[ns]')
water_prob = water_prob.assign_coords(year=time_values).rename(
year='time')
return water_prob
lake_mask: gpd.GeoSeries = gpd.read_file(SHAPEFILE)
cropped_data_file = DATA_DIR + f"/SaltLake_cropped_data.nc"
if use_existing_cropped_data:
cropped_data_dataset: xr.Dataset = xr.open_dataset(cropped_data_file)
cropped_data: xr.DataArray = cropped_data_dataset.cropped_data
cropped_data.attrs['cmap'] = dict(colors=colors4)
else:
dataMgr = MWPDataManager(DATA_DIR, dat_url)
dataMgr.setDefaults(product=product,
download=download,
years=range(*year_range),
start_day=time_range[0],
end_day=time_range[1])
file_paths = dataMgr.get_tile(location)
dem_arrays: List[xr.DataArray] = dataMgr.get_array_data(glob(DEMs))
dem_array: xr.DataArray = dem_arrays[0]
xc = dem_array.coords['x']
yc = dem_array.coords['y']
time_values = np.array([
get_date_from_filename(os.path.basename(path)) for path in file_paths
],
location: str = locations[0]
product: str = products[0]
year = 2019
download = False
shpManager = ShapefileManager()
locPoint: Point = shpManager.parseLocation(location)
minH20 = 2
threshold = 0.5
binSize = 8
LakeIndex = 6
time_range = [0, 365] # [200,216] #
# subset = [500,100]
subset = None
anim_running = True
dataMgr = MWPDataManager(DATA_DIR, dat_url)
dataMgr.setDefaults(product=product,
download=download,
year=year,
start_day=time_range[0],
end_day=time_range[1])
file_paths = dataMgr.get_tile(location)
waterMapGenerator = WaterMapGenerator()
data_array: xr.DataArray = waterMapGenerator.createDataset(file_paths,
subset=subset)
print(
f" Data Array {data_array.name}: shape = {data_array.shape}, dims = {data_array.dims}"
)
masking_results: xr.Dataset = waterMapGenerator.get_water_masks(
import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap, Normalize
locations = ["120W050N", "100W040N"]
products = ["1D1OS", "3D3OT"]
DATA_DIR = "/Users/tpmaxwel/Dropbox/Tom/Data/Birkitt"
data_source_url = "https://floodmap.modaps.eosdis.nasa.gov/Products"
location: str = locations[1]
product: str = products[0]
year = 2019
start_day = 171
num_days = 4
download = False
south = location[-1] == "S"
dataMgr = MWPDataManager(DATA_DIR, data_source_url)
dataMgr.setDefaults(product=product,
download=download,
year=year,
start_day=start_day,
end_day=start_day + num_days - 1)
file_paths = dataMgr.get_tile(location)
arrays: List[xr.DataArray] = dataMgr.get_array_data(file_paths)
data_array = arrays[0]
osgeoManager = OsgeoManager(DATA_DIR)
result: np.ndarray = osgeoManager.reprojectXarray(arrays[0], 250)
print(result.shape)
colors = [(0, 0, 0), (0.5, 1, 0.25), (0, 0, 1), (1, 1, 0)]