def add_chirps(
urls: Dict[Any, Any],
ds: xr.Dataset,
era: str,
training: bool = True,
dask_chunks: Dict[Any, Any] = {
"x": "auto",
"y": "auto"
},
) -> Optional[xr.Dataset]:
# load rainfall climatology
if era == "_S1":
chirps = rio_slurp_xarray(urls["chirps"][0])
if era == "_S2":
chirps = rio_slurp_xarray(urls["chirps"][1])
if chirps.size >= 2:
if training:
chirps = xr_reproject(chirps, ds.geobox, "bilinear")
ds["rain"] = chirps
else:
# Clip CHIRPS to ~ S2 tile boundaries so we can handle NaNs local to S2 tile
xmin, xmax = ds.x.values[0], ds.x.values[-1]
ymin, ymax = ds.y.values[0], ds.y.values[-1]
inProj = Proj("epsg:6933")
outProj = Proj("epsg:4326")
xmin, ymin = transform(inProj, outProj, xmin, ymin)
xmax, ymax = transform(inProj, outProj, xmax, ymax)
# create lat/lon indexing slices - buffer S2 bbox by 0.05deg
if (xmin < 0) & (xmax < 0):
x_slice = list(np.arange(xmin + 0.05, xmax - 0.05, -0.05))
else:
x_slice = list(np.arange(xmax - 0.05, xmin + 0.05, 0.05))
y_slice = list(np.arange(ymin - 0.05, ymax + 0.1, 0.05))
# index global chirps using buffered s2 tile bbox
chirps = assign_crs(
chirps.sel(longitude=y_slice,
latitude=x_slice,
method="nearest"))
# fill any NaNs in CHIRPS with local (s2-tile bbox) mean
chirps = chirps.fillna(chirps.mean())
chirps = xr_reproject(chirps, ds.geobox, "bilinear")
chirps = chirps.chunk(dask_chunks)
ds["rain"] = chirps
# rename bands to include era
for band in ds.data_vars:
ds = ds.rename({band: band + era})
return ds
return None
def annual_gm_mads_evi_training(ds):
dc = datacube.Datacube(app='training')
# grab gm+tmads
gm_mads=dc.load(product='ga_s2_gm',time='2019',like=ds.geobox,
measurements=['red', 'blue', 'green', 'nir',
'swir_1', 'swir_2', 'red_edge_1',
'red_edge_2', 'red_edge_3', 'SMAD',
'BCMAD','EMAD'])
gm_mads['SMAD'] = -np.log(gm_mads['SMAD'])
gm_mads['BCMAD'] = -np.log(gm_mads['BCMAD'])
gm_mads['EMAD'] = -np.log(gm_mads['EMAD']/10000)
#calculate band indices on gm
gm_mads = calculate_indices(gm_mads,
index=['EVI','LAI','MNDWI'],
drop=False,
collection='s2')
#normalise spectral GM bands 0-1
for band in gm_mads.data_vars:
if band not in ['SMAD', 'BCMAD','EMAD', 'EVI', 'LAI', 'MNDWI']:
gm_mads[band] = gm_mads[band] / 10000
#calculate EVI on annual timeseries
evi = calculate_indices(ds,index=['EVI'], drop=True, normalise=True, collection='s2')
# EVI stats
gm_mads['evi_std'] = evi.EVI.std(dim='time')
gm_mads['evi_10'] = evi.EVI.quantile(0.1, dim='time')
gm_mads['evi_25'] = evi.EVI.quantile(0.25, dim='time')
gm_mads['evi_75'] = evi.EVI.quantile(0.75, dim='time')
gm_mads['evi_90'] = evi.EVI.quantile(0.9, dim='time')
gm_mads['evi_range'] = gm_mads['evi_90'] - gm_mads['evi_10']
#rainfall climatology
chirps_S1 = xr_reproject(assign_crs(xr.open_rasterio('/g/data/CHIRPS/cumulative_alltime/CHPclim_jan_jun_cumulative_rainfall.nc'),
crs='epsg:4326'), ds.geobox,"bilinear")
chirps_S2 = xr_reproject(assign_crs(xr.open_rasterio('/g/data/CHIRPS/cumulative_alltime/CHPclim_jul_dec_cumulative_rainfall.nc'),
crs='epsg:4326'), ds.geobox,"bilinear")
gm_mads['rain_S1'] = chirps_S1
gm_mads['rain_S2'] = chirps_S2
#slope
url_slope = "https://deafrica-data.s3.amazonaws.com/ancillary/dem-derivatives/cog_slope_africa.tif"
slope = rio_slurp_xarray(url_slope, gbox=ds.geobox)
slope = slope.to_dataset(name='slope')#.chunk({'x':2000,'y':2000})
result = xr.merge([gm_mads,slope],compat='override')
return result.squeeze()
def add_chirps(ds, era, training=True, dask_chunks={'x': 'auto', 'y': 'auto'}):
# load rainfall climatology
if era == "_S1":
chirps = rio_slurp_xarray(
"s3://deafrica-input-datasets/rainfall/CHPclim_jan_jun_cumulative_rainfall.tif"
)
if era == "_S2":
chirps = rio_slurp_xarray(
"s3://deafrica-input-datasets/rainfall/CHPclim_jul_dec_cumulative_rainfall.tif"
)
if training:
chirps = xr_reproject(chirps, ds.geobox, "bilinear")
ds["rain"] = chirps
else:
# Clip CHIRPS to ~ S2 tile boundaries so we can handle NaNs local to S2 tile
xmin, xmax = ds.x.values[0], ds.x.values[-1]
ymin, ymax = ds.y.values[0], ds.y.values[-1]
inProj = Proj("epsg:6933")
outProj = Proj("epsg:4326")
xmin, ymin = transform(inProj, outProj, xmin, ymin)
xmax, ymax = transform(inProj, outProj, xmax, ymax)
# create lat/lon indexing slices - buffer S2 bbox by 0.05deg
if (xmin < 0) & (xmax < 0):
x_slice = list(np.arange(xmin + 0.05, xmax - 0.05, -0.05))
else:
x_slice = list(np.arange(xmax - 0.05, xmin + 0.05, 0.05))
if (ymin < 0) & (ymax < 0):
y_slice = list(np.arange(ymin + 0.05, ymax - 0.05, -0.05))
else:
y_slice = list(np.arange(ymin - 0.05, ymax + 0.05, 0.05))
# index global chirps using buffered s2 tile bbox
chirps = assign_crs(
chirps.sel(longitude=y_slice, latitude=x_slice, method="nearest"))
# fill any NaNs in CHIRPS with local (s2-tile bbox) mean
chirps = chirps.fillna(chirps.mean())
chirps = xr_reproject(chirps, ds.geobox, "bilinear")
chirps = chirps.chunk(dask_chunks)
ds["rain"] = chirps
#rename bands to include era
for band in ds.data_vars:
ds = ds.rename({band: band + era})
return ds
def fun(ds, era):
#geomedian and tmads
gm_mads = xr_geomedian_tmad(ds)
gm_mads = calculate_indices(gm_mads,
index=['NDVI','LAI','MNDWI'],
drop=False,
normalise=False,
collection='s2')
gm_mads['sdev'] = -np.log(gm_mads['sdev'])
gm_mads['bcdev'] = -np.log(gm_mads['bcdev'])
gm_mads['edev'] = -np.log(gm_mads['edev'])
#rainfall climatology
if era == '_S1':
chirps = assign_crs(xr.open_rasterio('/g/data/CHIRPS/cumulative_alltime/CHPclim_jan_jun_cumulative_rainfall.nc'), crs='epsg:4326')
if era == '_S2':
chirps = assign_crs(xr.open_rasterio('/g/data/CHIRPS/cumulative_alltime/CHPclim_jul_dec_cumulative_rainfall.nc'), crs='epsg:4326')
chirps = xr_reproject(chirps,ds.geobox,"bilinear")
gm_mads['rain'] = chirps
for band in gm_mads.data_vars:
gm_mads = gm_mads.rename({band:band+era})
return gm_mads
def complete_gm_mads(era_base_ds: xr.Dataset, geobox: GeoBox,
era: str) -> xr.Dataset:
"""
merge the geomedian and rainfall chirps data together
:param era_base_ds:
:param geobox:
:param era:
:return:
"""
# TODO: this is half year data, require integration tests
# TODO: load this data once use dask publish (?)
gm_mads = assign_crs(calculate_indices(era_base_ds))
rainfall = assign_crs(xr.open_rasterio(
FeaturePathConfig.rainfall_path[era]),
crs="epsg:4326")
rainfall = chirp_clip(gm_mads, rainfall)
rainfall = (xr_reproject(rainfall, geobox,
"bilinear").drop(["band",
"spatial_ref"]).squeeze())
gm_mads["rain"] = rainfall
return gm_mads.rename(
dict((var_name, str(var_name) + era.upper())
for var_name in gm_mads.data_vars))
def fun(ds, era):
# six-month geomedians
gm_mads = xr_geomedian(ds)
gm_mads = calculate_indices(
gm_mads,
index=["NDVI", "LAI", "MNDWI"],
drop=False,
normalise=False,
collection="s2",
)
# rainfall climatology
if era == "_S1":
chirps = assign_crs(
xr.open_rasterio(
"/g/data/CHIRPS/cumulative_alltime/CHPclim_jan_jun_cumulative_rainfall.nc"
),
crs="epsg:4326",
)
if era == "_S2":
chirps = assign_crs(
xr.open_rasterio(
"/g/data/CHIRPS/cumulative_alltime/CHPclim_jul_dec_cumulative_rainfall.nc"
),
crs="epsg:4326",
)
chirps = xr_reproject(chirps, ds.geobox, "bilinear")
gm_mads["rain"] = chirps
for band in gm_mads.data_vars:
gm_mads = gm_mads.rename({band: band + era})
return gm_mads
def fun(ds, era):
# geomedian and tmads
# gm_mads = xr_geomedian_tmad(ds)
gm_mads = xr_geomedian_tmad_new(ds).compute()
gm_mads = calculate_indices(
gm_mads,
index=["NDVI", "LAI", "MNDWI"],
drop=False,
normalise=False,
collection="s2",
)
gm_mads["sdev"] = -np.log(gm_mads["sdev"])
gm_mads["bcdev"] = -np.log(gm_mads["bcdev"])
gm_mads["edev"] = -np.log(gm_mads["edev"])
gm_mads = gm_mads.chunk({"x": 2000, "y": 2000})
# rainfall climatology
if era == "_S1":
chirps = assign_crs(
xr.open_rasterio(
"/g/data/CHIRPS/cumulative_alltime/CHPclim_jan_jun_cumulative_rainfall.nc"
),
crs="epsg:4326",
)
if era == "_S2":
chirps = assign_crs(
xr.open_rasterio(
"/g/data/CHIRPS/cumulative_alltime/CHPclim_jul_dec_cumulative_rainfall.nc"
),
crs="epsg:4326",
)
chirps = xr_reproject(chirps, ds.geobox, "bilinear")
chirps = chirps.chunk({"x": 2000, "y": 2000})
gm_mads["rain"] = chirps
for band in gm_mads.data_vars:
gm_mads = gm_mads.rename({band: band + era})
return gm_mads
def fun(ds, era):
# normalise SR and edev bands
for band in ds.data_vars:
if band not in ["sdev", "bcdev"]:
ds[band] = ds[band] / 10000
gm_mads = calculate_indices(
ds,
index=["NDVI", "LAI", "MNDWI"],
drop=False,
normalise=False,
collection="s2",
)
gm_mads["sdev"] = -np.log(gm_mads["sdev"])
gm_mads["bcdev"] = -np.log(gm_mads["bcdev"])
gm_mads["edev"] = -np.log(gm_mads["edev"])
# rainfall climatology
if era == "_S1":
chirps = assign_crs(
xr.open_rasterio(
"/g/data/CHIRPS/cumulative_alltime/CHPclim_jan_jun_cumulative_rainfall.nc"
),
crs="epsg:4326",
)
if era == "_S2":
chirps = assign_crs(
xr.open_rasterio(
"/g/data/CHIRPS/cumulative_alltime/CHPclim_jul_dec_cumulative_rainfall.nc"
),
crs="epsg:4326",
)
chirps = xr_reproject(chirps, ds.geobox, "bilinear")
gm_mads["rain"] = chirps
for band in gm_mads.data_vars:
gm_mads = gm_mads.rename({band: band + era})
return gm_mads
def _load_with_native_transform_1(
sources: xr.DataArray,
bands: Tuple[str, ...],
geobox: GeoBox,
native_transform: Callable[[xr.Dataset], xr.Dataset],
basis: Optional[str] = None,
groupby: Optional[str] = None,
fuser: Optional[Callable[[xr.Dataset], xr.Dataset]] = None,
resampling: str = "nearest",
chunks: Optional[Dict[str, int]] = None,
load_chunks: Optional[Dict[str, int]] = None,
pad: Optional[int] = None,
) -> xr.Dataset:
if basis is None:
basis = bands[0]
if load_chunks is None:
load_chunks = chunks
(ds, ) = sources.data[0]
load_geobox = compute_native_load_geobox(geobox, ds, basis)
if pad is not None:
load_geobox = gbox.pad(load_geobox, pad)
mm = ds.type.lookup_measurements(bands)
xx = Datacube.load_data(sources, load_geobox, mm, dask_chunks=load_chunks)
xx = native_transform(xx)
if groupby is not None:
if fuser is None:
fuser = _nodata_fuser
xx = xx.groupby(groupby).map(fuser)
_chunks = None
if chunks is not None:
_chunks = tuple(chunks.get(ax, -1) for ax in ("y", "x"))
return xr_reproject(xx, geobox, chunks=_chunks, resampling=resampling)
def gm_mads_evi_rainfall(ds):
"""
6 monthly and annual
gm + mads
evi stats (10, 50, 90 percentile, range, std)
rainfall actual stats (min, mean, max, range, std) from monthly data
rainfall clim stats (min, mean, max, range, std) from monthly data
"""
dc = datacube.Datacube(app='training')
ds = ds / 10000
ds = ds.rename({'nir_1':'nir_wide', 'nir_2':'nir'})
ds1 = ds.sel(time=slice('2019-01', '2019-06'))
ds2 = ds.sel(time=slice('2019-07', '2019-12'))
chirps = []
chpclim = []
for m in range(1,13):
chirps.append(xr_reproject(assign_crs(xr.open_rasterio(f'/g/data/CHIRPS/monthly_2019/chirps-v2.0.2019.{m:02d}.tif').squeeze().expand_dims({'time':[m]}), crs='epsg:4326'),
ds.geobox, "bilinear"))
chpclim.append(rio_slurp_xarray(f'https://deafrica-data-dev.s3.amazonaws.com/product-dev/deafrica_chpclim_50n_50s_{m:02d}.tif', gbox=ds.geobox,
resapling='bilinear').expand_dims({'time':[m]}))
chirps = xr.concat(chirps, dim='time')
chpclim = xr.concat(chpclim, dim='time')
def fun(ds, chirps, chpclim, era):
ds = calculate_indices(ds,
index=['EVI'],
drop=False,
normalise=False,
collection='s2')
#geomedian and tmads
gm_mads = xr_geomedian_tmad(ds)
gm_mads = calculate_indices(gm_mads,
index=['EVI','NDVI','LAI','MNDWI'],
drop=False,
normalise=False,
collection='s2')
gm_mads['sdev'] = -np.log(gm_mads['sdev'])
gm_mads['bcdev'] = -np.log(gm_mads['bcdev'])
gm_mads['edev'] = -np.log(gm_mads['edev'])
# EVI stats
gm_mads['evi_10'] = ds.EVI.quantile(0.1, dim='time')
gm_mads['evi_50'] = ds.EVI.quantile(0.5, dim='time')
gm_mads['evi_90'] = ds.EVI.quantile(0.9, dim='time')
gm_mads['evi_range'] = gm_mads['evi_90'] - gm_mads['evi_10']
gm_mads['evi_std'] = ds.EVI.std(dim='time')
# rainfall actual
gm_mads['rain_min'] = chirps.min(dim='time')
gm_mads['rain_mean'] = chirps.mean(dim='time')
gm_mads['rain_max'] = chirps.max(dim='time')
gm_mads['rain_range'] = gm_mads['rain_max'] - gm_mads['rain_min']
gm_mads['rain_std'] = chirps.std(dim='time')
# rainfall climatology
gm_mads['rainclim_min'] = chpclim.min(dim='time')
gm_mads['rainclim_mean'] = chpclim.mean(dim='time')
gm_mads['rainclim_max'] = chpclim.max(dim='time')
gm_mads['rainclim_range'] = gm_mads['rainclim_max'] - gm_mads['rainclim_min']
gm_mads['rainclim_std'] = chpclim.std(dim='time')
for band in gm_mads.data_vars:
gm_mads = gm_mads.rename({band:band+era})
return gm_mads
epoch0 = fun(ds, chirps, chpclim, era='_S0')
time, month = slice('2019-01', '2019-06'), slice(1, 6)
epoch1 = fun(ds.sel(time=time), chirps.sel(time=month), chpclim.sel(time=month), era='_S1')
time, month = slice('2019-07', '2019-12'), slice(7, 12)
epoch2 = fun(ds.sel(time=time), chirps.sel(time=month), chpclim.sel(time=month), era='_S2')
#slope
url_slope = "https://deafrica-data.s3.amazonaws.com/ancillary/dem-derivatives/cog_slope_africa.tif"
slope = rio_slurp_xarray(url_slope, gbox=ds.geobox)
slope = slope.to_dataset(name='slope')
result = xr.merge([epoch0,
epoch1,
epoch2,
slope],compat='override')
return result.squeeze()
def gm_mads_evi_rainfall(ds):
"""
6 monthly and annual
gm + mads
evi stats (10, 50, 90 percentile, range, std)
rainfall actual stats (min, mean, max, range, std) from monthly data
rainfall clim stats (min, mean, max, range, std) from monthly data
"""
dc = datacube.Datacube(app="training")
ds = ds / 10000
ds = ds.rename({"nir_1": "nir_wide", "nir_2": "nir"})
ds1 = ds.sel(time=slice("2019-01", "2019-06"))
ds2 = ds.sel(time=slice("2019-07", "2019-12"))
chirps = []
chpclim = []
for m in range(1, 13):
chirps.append(
xr_reproject(
assign_crs(
xr.open_rasterio(
f"/g/data/CHIRPS/monthly_2019/chirps-v2.0.2019.{m:02d}.tif"
)
.squeeze()
.expand_dims({"time": [m]}),
crs="epsg:4326",
),
ds.geobox,
"bilinear",
)
)
chpclim.append(
rio_slurp_xarray(
f"https://deafrica-data-dev.s3.amazonaws.com/product-dev/deafrica_chpclim_50n_50s_{m:02d}.tif",
gbox=ds.geobox,
resapling="bilinear",
).expand_dims({"time": [m]})
)
chirps = xr.concat(chirps, dim="time")
chpclim = xr.concat(chpclim, dim="time")
def fun(ds, chirps, chpclim, era):
ds = calculate_indices(
ds, index=["EVI"], drop=False, normalise=False, collection="s2"
)
# geomedian and tmads
gm_mads = xr_geomedian_tmad(ds)
gm_mads = calculate_indices(
gm_mads,
index=["EVI", "NDVI", "LAI", "MNDWI"],
drop=False,
normalise=False,
collection="s2",
)
gm_mads["sdev"] = -np.log(gm_mads["sdev"])
gm_mads["bcdev"] = -np.log(gm_mads["bcdev"])
gm_mads["edev"] = -np.log(gm_mads["edev"])
# EVI stats
gm_mads["evi_10"] = ds.EVI.quantile(0.1, dim="time")
gm_mads["evi_50"] = ds.EVI.quantile(0.5, dim="time")
gm_mads["evi_90"] = ds.EVI.quantile(0.9, dim="time")
gm_mads["evi_range"] = gm_mads["evi_90"] - gm_mads["evi_10"]
gm_mads["evi_std"] = ds.EVI.std(dim="time")
# rainfall actual
gm_mads["rain_min"] = chirps.min(dim="time")
gm_mads["rain_mean"] = chirps.mean(dim="time")
gm_mads["rain_max"] = chirps.max(dim="time")
gm_mads["rain_range"] = gm_mads["rain_max"] - gm_mads["rain_min"]
gm_mads["rain_std"] = chirps.std(dim="time")
# rainfall climatology
gm_mads["rainclim_min"] = chpclim.min(dim="time")
gm_mads["rainclim_mean"] = chpclim.mean(dim="time")
gm_mads["rainclim_max"] = chpclim.max(dim="time")
gm_mads["rainclim_range"] = gm_mads["rainclim_max"] - gm_mads["rainclim_min"]
gm_mads["rainclim_std"] = chpclim.std(dim="time")
for band in gm_mads.data_vars:
gm_mads = gm_mads.rename({band: band + era})
return gm_mads
epoch0 = fun(ds, chirps, chpclim, era="_S0")
time, month = slice("2019-01", "2019-06"), slice(1, 6)
epoch1 = fun(
ds.sel(time=time), chirps.sel(time=month), chpclim.sel(time=month), era="_S1"
)
time, month = slice("2019-07", "2019-12"), slice(7, 12)
epoch2 = fun(
ds.sel(time=time), chirps.sel(time=month), chpclim.sel(time=month), era="_S2"
)
# slope
url_slope = "https://deafrica-data.s3.amazonaws.com/ancillary/dem-derivatives/cog_slope_africa.tif"
slope = rio_slurp_xarray(url_slope, gbox=ds.geobox)
slope = slope.to_dataset(name="slope")
result = xr.merge([epoch0, epoch1, epoch2, slope], compat="override")
return result.squeeze()
def annual_gm_mads_evi_training(ds):
dc = datacube.Datacube(app="training")
# grab gm+tmads
gm_mads = dc.load(
product="ga_s2_gm",
time="2019",
like=ds.geobox,
measurements=[
"red",
"blue",
"green",
"nir",
"swir_1",
"swir_2",
"red_edge_1",
"red_edge_2",
"red_edge_3",
"SMAD",
"BCMAD",
"EMAD",
],
)
gm_mads["SMAD"] = -np.log(gm_mads["SMAD"])
gm_mads["BCMAD"] = -np.log(gm_mads["BCMAD"])
gm_mads["EMAD"] = -np.log(gm_mads["EMAD"] / 10000)
# calculate band indices on gm
gm_mads = calculate_indices(
gm_mads, index=["EVI", "LAI", "MNDWI"], drop=False, collection="s2"
)
# normalise spectral GM bands 0-1
for band in gm_mads.data_vars:
if band not in ["SMAD", "BCMAD", "EMAD", "EVI", "LAI", "MNDWI"]:
gm_mads[band] = gm_mads[band] / 10000
# calculate EVI on annual timeseries
evi = calculate_indices(
ds, index=["EVI"], drop=True, normalise=True, collection="s2"
)
# EVI stats
gm_mads["evi_std"] = evi.EVI.std(dim="time")
gm_mads["evi_10"] = evi.EVI.quantile(0.1, dim="time")
gm_mads["evi_25"] = evi.EVI.quantile(0.25, dim="time")
gm_mads["evi_75"] = evi.EVI.quantile(0.75, dim="time")
gm_mads["evi_90"] = evi.EVI.quantile(0.9, dim="time")
gm_mads["evi_range"] = gm_mads["evi_90"] - gm_mads["evi_10"]
# rainfall climatology
chirps_S1 = xr_reproject(
assign_crs(
xr.open_rasterio(
"/g/data/CHIRPS/cumulative_alltime/CHPclim_jan_jun_cumulative_rainfall.nc"
),
crs="epsg:4326",
),
ds.geobox,
"bilinear",
)
chirps_S2 = xr_reproject(
assign_crs(
xr.open_rasterio(
"/g/data/CHIRPS/cumulative_alltime/CHPclim_jul_dec_cumulative_rainfall.nc"
),
crs="epsg:4326",
),
ds.geobox,
"bilinear",
)
gm_mads["rain_S1"] = chirps_S1
gm_mads["rain_S2"] = chirps_S2
# slope
url_slope = "https://deafrica-data.s3.amazonaws.com/ancillary/dem-derivatives/cog_slope_africa.tif"
slope = rio_slurp_xarray(url_slope, gbox=ds.geobox)
slope = slope.to_dataset(name="slope") # .chunk({'x':2000,'y':2000})
result = xr.merge([gm_mads, slope], compat="override")
return result.squeeze()
def fun(ds, era):
# normalise SR and edev bands
for band in ds.data_vars:
if band not in ["sdev", "bcdev"]:
ds[band] = ds[band] / 10000
gm_mads = calculate_indices(
ds,
index=["NDVI", "LAI", "MNDWI"],
drop=False,
normalise=False,
collection="s2",
)
gm_mads["sdev"] = -np.log(gm_mads["sdev"])
gm_mads["bcdev"] = -np.log(gm_mads["bcdev"])
gm_mads["edev"] = -np.log(gm_mads["edev"])
# rainfall climatology
if era == "_S1":
chirps = assign_crs(
xr.open_rasterio(
"/g/data/CHIRPS/cumulative_alltime/CHPclim_jan_jun_cumulative_rainfall.nc"
),
crs="epsg:4326",
)
if era == "_S2":
chirps = assign_crs(
xr.open_rasterio(
"/g/data/CHIRPS/cumulative_alltime/CHPclim_jul_dec_cumulative_rainfall.nc"
),
crs="epsg:4326",
)
# Clip CHIRPS to ~ S2 tile boundaries so we can handle NaNs local to S2 tile
xmin, xmax = ds.x.values[0], ds.x.values[-1]
ymin, ymax = ds.y.values[0], ds.y.values[-1]
inProj = Proj("epsg:6933")
outProj = Proj("epsg:4326")
xmin, ymin = transform(inProj, outProj, xmin, ymin)
xmax, ymax = transform(inProj, outProj, xmax, ymax)
# create lat/lon indexing slices - buffer S2 bbox by 0.05deg
if (xmin < 0) & (xmax < 0):
x_slice = list(np.arange(xmin + 0.05, xmax - 0.05, -0.05))
else:
x_slice = list(np.arange(xmax - 0.05, xmin + 0.05, 0.05))
if (ymin < 0) & (ymax < 0):
y_slice = list(np.arange(ymin + 0.05, ymax - 0.05, -0.05))
else:
y_slice = list(np.arange(ymin - 0.05, ymax + 0.05, 0.05))
# index global chirps using buffered s2 tile bbox
chirps = assign_crs(chirps.sel(x=y_slice, y=x_slice, method="nearest"))
# fill any NaNs in CHIRPS with local (s2-tile bbox) mean
chirps = chirps.fillna(chirps.mean())
chirps = xr_reproject(chirps, ds.geobox, "bilinear")
gm_mads["rain"] = chirps
for band in gm_mads.data_vars:
gm_mads = gm_mads.rename({band: band + era})
return gm_mads
def features(ds, era):
#normalise SR and edev bands
for band in ds.data_vars:
if band not in ['sdev', 'bcdev']:
ds[band] = ds[band] / 10000
gm_mads = calculate_indices(ds,
index=['NDVI', 'LAI', 'MNDWI'],
drop=False,
normalise=False,
collection='s2')
gm_mads['sdev'] = -np.log(gm_mads['sdev'])
gm_mads['bcdev'] = -np.log(gm_mads['bcdev'])
gm_mads['edev'] = -np.log(gm_mads['edev'])
#rainfall climatology
if era == '_S1':
chirps = assign_crs(xr.open_rasterio(
'/g/data/CHIRPS/cumulative_alltime/CHPclim_jan_jun_cumulative_rainfall.nc'
),
crs='epsg:4326')
if era == '_S2':
chirps = assign_crs(xr.open_rasterio(
'/g/data/CHIRPS/cumulative_alltime/CHPclim_jul_dec_cumulative_rainfall.nc'
),
crs='epsg:4326')
#Clip CHIRPS to ~ S2 tile boundaries so we can handle NaNs local to S2 tile
xmin, xmax = ds.x.values[0], ds.x.values[-1]
ymin, ymax = ds.y.values[0], ds.y.values[-1]
inProj = Proj('epsg:6933')
outProj = Proj('epsg:4326')
xmin, ymin = transform(inProj, outProj, xmin, ymin)
xmax, ymax = transform(inProj, outProj, xmax, ymax)
#create lat/lon indexing slices - buffer S2 bbox by 0.05deg
if (xmin < 0) & (xmax < 0):
x_slice = list(np.arange(xmin + 0.05, xmax - 0.05, -0.05))
else:
x_slice = list(np.arange(xmax - 0.05, xmin + 0.05, 0.05))
if (ymin < 0) & (ymax < 0):
y_slice = list(np.arange(ymin + 0.05, ymax - 0.05, -0.05))
else:
y_slice = list(np.arange(ymin - 0.05, ymax + 0.05, 0.05))
#index global chirps using buffered s2 tile bbox
chirps = assign_crs(chirps.sel(x=y_slice, y=x_slice, method='nearest'))
#fill any NaNs in CHIRPS with local (s2-tile bbox) mean
chirps = chirps.fillna(chirps.mean())
#reproject to match satellite data
chirps = xr_reproject(chirps, ds.geobox, "bilinear")
gm_mads['rain'] = chirps
for band in gm_mads.data_vars:
gm_mads = gm_mads.rename({band: band + era})
return gm_mads
