def fixture_dataframe():
"""
Loads the sample ICESat-2 ATL11 data, and processes it into an suitable
pandas.DataFrame format.
"""
dataset: xr.Dataset = catalog.test_data.atl11_test_case.to_dask()
dataset["utc_time"] = deltatime_to_utctime(dataarray=dataset.delta_time)
with tempfile.TemporaryDirectory() as tmpdir:
df: pd.DataFrame = ndarray_to_parquet(
ndarray=dataset,
parquetpath=os.path.join(tmpdir, "temp.parquet"),
variables=["longitude", "latitude", "h_corr", "utc_time"],
use_deprecated_int96_timestamps=True,
)
dataframe: pd.DataFrame = wide_to_long(
df=df, stubnames=["h_corr", "utc_time"], j="cycle_number"
)
dataframe: pd.DataFrame = dataframe.reset_index(drop=True)
# Mock up a dummy track1_track2 column based on the cycle_number
dataframe["track1_track2"] = np.where(
dataframe["cycle_number"] == 1, "0111_pt1x0222_pt2", "0333pt3x0111_pt1"
)
return dataframe
def test_deltatime_to_utctime_xarray_dataarray():
"""
Test that converting from ICESat-2 delta_time to utc_time works on an
xarray.DataArray, and that the dimensions are preserved in the process.
"""
atl11_dataset: xr.Dataset = catalog.test_data.atl11_test_case.to_dask()
utc_time: xr.DataArray = deltatime_to_utctime(
dataarray=atl11_dataset.delta_time)
assert utc_time.shape == (1404, 2)
assert utc_time.dims == ("ref_pt", "cycle_number")
assert dask.is_dask_collection(utc_time)
utc_time = utc_time.compute()
npt.assert_equal(
actual=utc_time.data.min(),
desired=np.datetime64("2019-05-19T20:53:51.039891534"),
)
npt.assert_equal(
actual=np.datetime64(pd.DataFrame(utc_time.data)[0].mean()),
desired=np.datetime64("2019-05-19 20:54:00.925868"),
)
npt.assert_equal(
actual=np.datetime64(pd.DataFrame(utc_time.data)[1].mean()),
desired=np.datetime64("2019-08-18 16:33:47.791226"),
)
npt.assert_equal(
actual=utc_time.data.max(),
desired=np.datetime64("2019-08-18T16:33:57.834610209"),
)
def test_deltatime_to_utctime_pandas_series():
"""
Test that converting from ICESat-2 delta_time to utc_time works on a
dask.dataframe.core.Series.
"""
atl11_dataset: xr.Dataset = catalog.test_data.atl11_test_case.to_dask()
atl11_dataframe: pd.DataFrame = atl11_dataset.to_dataframe()
utc_time: pd.Series = deltatime_to_utctime(
dataarray=atl11_dataframe.delta_time)
assert utc_time.shape == (2808, )
npt.assert_equal(actual=utc_time.min(),
desired=pd.Timestamp("2019-05-19T20:53:51.039891534"))
npt.assert_equal(
actual=utc_time.loc[3].mean(),
desired=pd.Timestamp("2019-05-19 20:54:00.925868800"),
)
npt.assert_equal(
actual=utc_time.loc[4].mean(),
desired=pd.Timestamp("2019-08-18 16:33:47.791226368"),
)
npt.assert_equal(actual=utc_time.max(),
desired=pd.Timestamp("2019-08-18T16:33:57.834610209"))
def test_deltatime_to_utctime_numpy_timedelta64():
"""
Test that converting from ICESat-2 delta_time to utc_time works on a
single numpy.timedelta object.
"""
delta_time = np.timedelta64(24731275413287379, "ns")
utc_time: np.datetime64 = deltatime_to_utctime(dataarray=delta_time)
npt.assert_equal(actual=utc_time,
desired=np.datetime64("2018-10-14T05:47:55.413287379"))
# ### Retrieve some basic information for plots later
#
# Simply getting the number of cycles and date range
# to put into our plots later on
# %%
# Get number of ICESat-2 cycles used
num_cycles: int = len(ds.cycle_number)
# %%
# Get first and last dates to put into our plots
min_date, max_date = ("2018-10-14", "2020-04-04")
if min_date is None:
min_delta_time = np.nanmin(
ds.delta_time.isel(cycle_number=0).data).compute()
min_utc_time = deepicedrain.deltatime_to_utctime(min_delta_time)
min_date: str = np.datetime_as_string(arr=min_utc_time, unit="D")
if max_date is None:
max_delta_time = np.nanmax(
ds.delta_time.isel(cycle_number=-1).data).compute()
max_utc_time = deepicedrain.deltatime_to_utctime(max_delta_time)
max_date: str = np.datetime_as_string(arr=max_utc_time, unit="D")
print(f"Handling {num_cycles} ICESat-2 cycles from {min_date} to {max_date}")
# %%
# %% [markdown]
# # Calculate height range (h_range)
#
# A simple way of finding active subglacial lakes is to see where
# there has been a noticeably rapid change in elevation over
# ### Retrieve some basic information for plots later
#
# Simply getting the number of cycles and date range
# to put into our plots later on
# %%
# Get number of ICESat-2 cycles used
num_cycles: int = len(ds.cycle_number)
# %%
# Get first and last dates to put into our plots
min_date, max_date = ("2018-10-14", "2020-05-13")
if min_date is None:
min_delta_time = np.nanmin(
ds.delta_time.isel(cycle_number=0).data).compute()
min_utc_time = deepicedrain.deltatime_to_utctime(min_delta_time)
min_date: str = np.datetime_as_string(arr=min_utc_time, unit="D")
if max_date is None:
max_delta_time = np.nanmax(
ds.delta_time.isel(cycle_number=-1).data).compute()
max_utc_time = deepicedrain.deltatime_to_utctime(max_delta_time)
max_date: str = np.datetime_as_string(arr=max_utc_time, unit="D")
print(f"Handling {num_cycles} ICESat-2 cycles from {min_date} to {max_date}")
# %%
# %% [markdown]
# # Calculate height range (h_range)
#
# A simple way of finding active subglacial lakes is to see where
# there has been a noticeably rapid change in elevation over
# %% [markdown] # ## Convert delta_time to utc_time # # To get more human-readable datetimes, # we'll convert the delta_time attribute from the original GPS time format # (nanoseconds since the beginning of ICESat-2 starting epoch) # to Coordinated Universal Time (UTC). # The reference date for the ICESat-2 Epoch is 2018 January 1st according to # https://github.com/SmithB/pointCollection/blob/master/is2_calendar.py#L11-L15 # # TODO: Account for [leap seconds](https://en.wikipedia.org/wiki/Leap_second) # in the future. # %% ds["utc_time"] = deepicedrain.deltatime_to_utctime(dataarray=ds.delta_time) # %% [markdown] # ## Mask out low quality height data # # Good quality data has value 0, not so good is > 0. # Look at the 'fit_quality' attribute in `ds` # for more information on what this quality flag means. # # We'll mask out values other than 0 with NaN using xarray's # [where](http://xarray.pydata.org/en/v0.15.1/indexing.html#masking-with-where). # %% ds["h_corr"] = ds.h_corr.where(cond=ds.fit_quality == 0) # %%
# ### Retrieve some basic information for plots later
#
# Simply getting the number of cycles and date range
# to put into our plots later on
# %%
# Get number of ICESat-2 cycles used
num_cycles: int = len(ds.cycle_number)
# %%
# Get first and last dates to put into our plots
min_date, max_date = ("2018-10-14", "2020-07-16")
if min_date is None:
min_delta_time = np.nanmin(
ds.delta_time.isel(cycle_number=0).data).compute()
min_utc_time = deepicedrain.deltatime_to_utctime(min_delta_time)
min_date: str = np.datetime_as_string(arr=min_utc_time, unit="D")
if max_date is None:
max_delta_time = np.nanmax(
ds.delta_time.isel(cycle_number=-1).data).compute()
max_utc_time = deepicedrain.deltatime_to_utctime(max_delta_time)
max_date: str = np.datetime_as_string(arr=max_utc_time, unit="D")
print(f"Handling {num_cycles} ICESat-2 cycles from {min_date} to {max_date}")
# %%
# %% [markdown]
# # Calculate height range (h_range)
#
# A simple way of finding active subglacial lakes is to see where
# there has been a noticeably rapid change in elevation over
