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conversion.py works on the xarray datamodel
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2 files changed

+282
-108
lines changed

2 files changed

+282
-108
lines changed

‎pysteps/tests/test_utils_conversion.py

Lines changed: 273 additions & 100 deletions
Original file line numberDiff line numberDiff line change
@@ -635,115 +635,288 @@ def test_to_raindepth(dataset, expected):
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np.array([23.01029996]),
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{
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"units": "dBZ",
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"transform": "dB",
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"accutime": 5,
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"threshold": 23.01029996,
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"zerovalue": 18.01029996,
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},
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)
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},
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attrs={"precip_var": "reflectivity"},
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),
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),
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(
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xr.Dataset(
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data_vars={
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"precip_accum": (
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["x"],
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np.array([1.0]),
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{
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"units": "mm",
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"transform": None,
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"accutime": 5,
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"threshold": 1.0,
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"zerovalue": 1.0,
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},
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)
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},
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attrs={"precip_var": "precip_accum"},
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),
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xr.Dataset(
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data_vars={
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"reflectivity": (
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["x"],
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np.array([40.27719989]),
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{
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"units": "dBZ",
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"transform": "dB",
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"accutime": 5,
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"threshold": 40.27719989,
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"zerovalue": 35.27719989,
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},
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)
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},
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attrs={"precip_var": "reflectivity"},
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),
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),
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(
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xr.Dataset(
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data_vars={
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"precip_intensity": (
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["x"],
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np.array([1.0]),
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{
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"units": "mm/h",
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"transform": "dB",
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"accutime": 5,
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"threshold": 1.0,
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"zerovalue": 1.0,
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},
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)
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},
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attrs={"precip_var": "precip_intensity"},
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),
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xr.Dataset(
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data_vars={
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"reflectivity": (
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["x"],
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np.array([24.61029996]),
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{
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"units": "dBZ",
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"transform": "dB",
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"accutime": 5,
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"threshold": 24.61029996,
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"zerovalue": 19.61029996,
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},
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)
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},
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attrs={"precip_var": "reflectivity"},
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),
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),
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(
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xr.Dataset(
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data_vars={
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"precip_accum": (
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["x"],
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np.array([1.0]),
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{
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"units": "mm",
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"transform": "dB",
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"accutime": 5,
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"threshold": 1.0,
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"zerovalue": 1.0,
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},
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)
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},
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attrs={"precip_var": "precip_accum"},
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),
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xr.Dataset(
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data_vars={
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"reflectivity": (
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["x"],
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np.array([41.87719989]),
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{
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"units": "dBZ",
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"transform": "dB",
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"accutime": 5,
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"threshold": 41.87719989,
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"zerovalue": 36.87719989,
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},
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)
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},
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attrs={"precip_var": "reflectivity"},
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),
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),
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(
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xr.Dataset(
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data_vars={
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"reflectivity": (
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["x"],
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np.array([1.0]),
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{
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"units": "dBZ",
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"transform": "dB",
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"accutime": 5,
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"threshold": 1.0,
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"zerovalue": 1.0,
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},
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)
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},
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attrs={"precip_var": "reflectivity"},
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),
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xr.Dataset(
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data_vars={
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"reflectivity": (
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["x"],
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np.array([1.0]),
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{
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"units": "dBZ",
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"transform": "dB",
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"accutime": 5,
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"threshold": 1.0,
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"zerovalue": -4.0,
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},
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)
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},
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attrs={"precip_var": "reflectivity"},
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),
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),
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(
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xr.Dataset(
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data_vars={
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"precip_intensity": (
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["x"],
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np.array([1.0]),
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{
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"units": "mm/h",
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"transform": "log",
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"accutime": 5,
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"threshold": 1.0,
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"zerovalue": 1.0,
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},
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)
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},
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attrs={"precip_var": "precip_intensity"},
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),
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xr.Dataset(
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data_vars={
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"reflectivity": (
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["x"],
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np.array([29.95901167]),
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{
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"units": "dBZ",
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"transform": "dB",
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"accutime": 5,
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"threshold": 29.95901167,
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"zerovalue": 24.95901167,
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},
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)
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},
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attrs={"precip_var": "reflectivity"},
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),
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),
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(
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xr.Dataset(
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data_vars={
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"precip_accum": (
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["x"],
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np.array([1.0]),
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{
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"units": "mm",
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"transform": "log",
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"accutime": 5,
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"threshold": 1.0,
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"zerovalue": 1.0,
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},
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)
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},
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attrs={"precip_var": "precip_accum"},
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),
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xr.Dataset(
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data_vars={
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"reflectivity": (
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["x"],
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np.array([47.2259116]),
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{
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"units": "dBZ",
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"transform": "dB",
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"accutime": 5,
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"threshold": 47.2259116,
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"zerovalue": 42.2259116,
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},
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)
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},
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attrs={"precip_var": "reflectivity"},
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),
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),
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(
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xr.Dataset(
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data_vars={
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"precip_intensity": (
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["x"],
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np.array([1.0]),
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{
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"units": "mm/h",
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"transform": "sqrt",
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"accutime": 5,
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"threshold": 1.0,
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"zerovalue": 1.0,
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},
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)
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},
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attrs={"precip_var": "precip_intensity"},
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),
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xr.Dataset(
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data_vars={
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"reflectivity": (
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["x"],
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np.array([23.01029996]),
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{
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"units": "dBZ",
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"transform": "dB",
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"accutime": 5,
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"threshold": 23.01029996,
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"zerovalue": 23.01029996,
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"zerovalue": 18.01029996,
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},
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)
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},
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attrs={"precip_var": "reflectivity"},
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),
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),
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(
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xr.Dataset(
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data_vars={
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"precip_accum": (
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["x"],
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np.array([1.0]),
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{
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"units": "mm",
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"transform": "sqrt",
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"accutime": 5,
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"threshold": 1.0,
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"zerovalue": 1.0,
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},
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)
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},
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attrs={"precip_var": "precip_accum"},
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),
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xr.Dataset(
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data_vars={
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"reflectivity": (
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["x"],
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np.array([40.27719989]),
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{
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"units": "dBZ",
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"transform": "dB",
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"accutime": 5,
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"threshold": 40.27719989,
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"zerovalue": 35.27719989,
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},
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)
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},
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attrs={"precip_var": "reflectivity"},
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),
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),
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# (
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# np.array([1]),
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# {
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# "accutime": 5,
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# "transform": None,
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# "unit": "mm/h",
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# "threshold": 0,
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# "zerovalue": 0,
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# },
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# np.array([23.01029996]),
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# ),
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# (
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# np.array([1]),
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# {
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# "accutime": 5,
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# "transform": None,
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# "unit": "mm",
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# "threshold": 0,
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# "zerovalue": 0,
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# },
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# np.array([40.27719989]),
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# ),
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# (
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# np.array([1]),
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# {
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# "accutime": 5,
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# "transform": "dB",
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# "unit": "mm/h",
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# "threshold": 0,
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# "zerovalue": 0,
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# },
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# np.array([24.61029996]),
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# ),
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# (
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# np.array([1]),
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# {
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# "accutime": 5,
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# "transform": "dB",
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# "unit": "mm",
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# "threshold": 0,
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# "zerovalue": 0,
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# },
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# np.array([41.87719989]),
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# ),
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# (
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# np.array([1]),
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# {
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# "accutime": 5,
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# "transform": "dB",
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# "unit": "dBZ",
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# "threshold": 0,
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# "zerovalue": 0,
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# },
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# np.array([1]),
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# ),
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# (
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# np.array([1]),
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# {
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# "accutime": 5,
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# "transform": "log",
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# "unit": "mm/h",
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# "threshold": 0,
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# "zerovalue": 0,
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# },
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# np.array([29.95901167]),
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# ),
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# (
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# np.array([1.0]),
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# {
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# "accutime": 5,
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# "transform": "log",
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# "unit": "mm",
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# "threshold": 0,
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# "zerovalue": 0,
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# },
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# np.array([47.2259116]),
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# ),
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# (
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# np.array([1]),
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# {
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# "accutime": 5,
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# "transform": "sqrt",
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# "unit": "mm/h",
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# "threshold": 0,
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# "zerovalue": 0,
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# },
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# np.array([23.01029996]),
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# ),
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# (
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# np.array([1.0]),
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# {
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# "accutime": 5,
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# "transform": "sqrt",
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# "unit": "mm",
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# "threshold": 0,
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# "zerovalue": 0,
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# },
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# np.array([40.27719989]),
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# ),
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]
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