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Update recovery_prediction.py
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- minor update to Armor3d and Glorys classes
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@gmaze
gmaze committed Feb 6, 2024
1 parent 415328c commit 4a4f950
Showing 1 changed file with 85 additions and 47 deletions.
132 changes: 85 additions & 47 deletions cli/recovery_prediction.py
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Original file line number Diff line number Diff line change
Expand Up @@ -225,47 +225,68 @@ def preprocess(this_ds):


class Armor3d:
"""Global Ocean 1/4° Multi Observation Product ARMOR3D
def __init__(self, a_box, a_start_date, n_days=1, max_depth=2500):
"""Load Global Ocean 1/4° Multi Observation Product ARMOR3D
Product description:
https://data.marine.copernicus.eu/product/MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012
https://data.marine.copernicus.eu/product/MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012
If start_date + n_days <= 2022-12-28:
Delivers the multi-year reprocessed (REP) weekly data
If ``a_start_date+n_days`` < 2022-12-28:
delivers the multi-year reprocessed (REP) weekly data
otherwise:
Delivers the near-real-time (NRT) weekly data
otherwise:
delivers near-real-time (NRT) weekly data
Examples
--------
>>> Armor3d([-25, -13, 6.5, 13], pd.to_datetime('20091130', utc=True)).to_xarray()
>>> Armor3d([-25, -13, 6.5, 13], pd.to_datetime('20231121', utc=True), n_days=10).to_xarray()
"""

def __init__(self, box, start_date, n_days=1, max_depth=2500):
"""
Parameters
----------
a_box
a_start_date
n_days
box: list(float)
Define domain to load: [lon_min, lon_max, lat_min, lat_max]
start_date: :class:`pandas.Timestamp`
Starting date of the time series to load. Since ARMOR3D is weekly, the effective starting
date will be the first weekly period including the user-defined ``start_date``
n_days: int (default=1)
Number of days to load data for.
max_depth: float (default=2500)
Maximum depth levels to load data for.
"""
self.box = a_box
self.start_date = a_start_date
self.box = box
self.start_date = start_date
self.n_days = n_days
self.max_depth = max_depth

if a_start_date + pd.Timedelta(n_days, 'D') < pd.to_datetime('2022-12-28', utc=True):
self.loader = self._get_rep
dt = pd.Timedelta(n_days, 'D') if n_days > 1 else pd.Timedelta(0, 'D')
if start_date + dt <= pd.to_datetime('2022-12-28', utc=True):
self._loader = self._get_rep
self.time_axis = pd.Series(pd.date_range('19930106', '20221228', freq='7D').tz_localize("UTC"))
else:
self.loader = self._get_nrt # 'now' + 10 days
self._loader = self._get_nrt
self.time_axis = pd.Series(
pd.date_range('20190102', pd.to_datetime('now', utc=True).strftime("%Y%m%d"), freq='7D').tz_localize(
"UTC")[0:-1])

if a_start_date < self.time_axis.iloc[0]:
if start_date < self.time_axis.iloc[0]:
raise ValueError('Date out of bounds')
elif a_start_date + (self.n_days + 1) * pd.Timedelta(1, 'D') > self.time_axis.iloc[-1]:
elif start_date + dt > self.time_axis.iloc[-1]:
# target delivery time: Near Real Time: Tuesday at 16:00
# Eg: on Tuesday Feb. 6th is published the last 7 days nrt analysis, dated Wed. Jan. 31st
raise ValueError('Date out of bounds, %s > %s' % (
a_start_date + (self.n_days + 1) * pd.Timedelta(1, 'D'), self.time_axis.iloc[-1]))
start_date + dt, self.time_axis.iloc[-1]))

def _get_this(self, dataset_id):
start_date = self.time_axis[self.time_axis <= self.start_date].iloc[-1]
end_date = self.time_axis[self.time_axis <= self.start_date + (self.n_days + 1) * pd.Timedelta(1, 'D')].iloc[-1]
if self.n_days == 1:
end_date = start_date
else:
end_date = \
self.time_axis[self.time_axis <= self.start_date + (self.n_days + 1) * pd.Timedelta(1, 'D')].iloc[-1]

ds = copernicusmarine.open_dataset(
dataset_id=dataset_id,
Expand Down Expand Up @@ -299,43 +320,56 @@ def _get_nrt(self):
return self._get_this("dataset-armor-3d-nrt-weekly")

def to_xarray(self):
""" Load and return data as a :class:xarray.dataset
"""Load and return data as a :class:`xarray.dataset`
Returns
-------
:class:xarray.dataset
"""
return self.loader()
return self._loader()


class Glorys:
"""Global Ocean 1/12° Physics Re-Analysis or Forecast
def __init__(self, a_box, a_start_date, n_days=1, max_depth=2500):
"""Load Global Ocean 1/12° Physics Re-Analysis or Forecast
If start_date + n_days <= 2021-01-09:
Delivers the multi-year reprocessed (REP) daily data
https://resources.marine.copernicus.eu/product-detail/GLOBAL_MULTIYEAR_PHY_001_030
If ``a_start_date+n_days`` < 2023-10-24:
delivers the multi-year reprocessed (REP) daily data
https://resources.marine.copernicus.eu/product-detail/GLOBAL_MULTIYEAR_PHY_001_030
otherwise:
Delivers the near-real-time (NRT) Analysis and Forecast daily data
https://resources.marine.copernicus.eu/product-detail/GLOBAL_ANALYSISFORECAST_PHY_001_024
otherwise:
delivers near-real-time (NRT) Analysis and Forecast 6-hourly data
https://resources.marine.copernicus.eu/product-detail/GLOBAL_ANALYSISFORECAST_PHY_001_024
Examples
--------
>>> Glorys([-25, -13, 6.5, 13], pd.to_datetime('20091130', utc=True)).to_xarray()
>>> Glorys([-25, -13, 6.5, 13], pd.to_datetime('20231121', utc=True), n_days=10).to_xarray()
"""

def __init__(self, box, start_date, n_days=1, max_depth=2500):
"""
Parameters
----------
a_box
a_start_date
n_days
box: list(float)
Define domain to load: [lon_min, lon_max, lat_min, lat_max]
start_date: :class:`pandas.Timestamp`
Starting date of the time series to load.
n_days: int (default=1)
Number of days to load data for.
max_depth: float (default=2500)
Maximum depth levels to load data for.
"""
self.box = a_box
self.start_date = a_start_date
self.box = box
self.start_date = start_date
self.n_days = n_days
self.max_depth = max_depth

if a_start_date + pd.Timedelta(n_days, 'D') <= pd.to_datetime('2023-10-24', utc=True):
self.loader = self._get_reanalysis
dt = pd.Timedelta(n_days, 'D') if n_days > 1 else pd.Timedelta(0, 'D')
if start_date + dt <= pd.to_datetime('2021-01-09', utc=True):
self._loader = self._get_reanalysis
else:
self.loader = self._get_forecast # 'now' + 10 days
self._loader = self._get_forecast # 'now' + 10 days

def _get_this(self, dataset_id, dates):
ds = copernicusmarine.open_dataset(
Expand All @@ -351,35 +385,39 @@ def _get_this(self, dataset_id, dates):
)
return ds


def _get_forecast(self):
"""
Returns
-------
:class:xarray.dataset
:class:`xarray.dataset`
"""
start_date = self.start_date - pd.Timedelta(1, 'D')
end_date = self.start_date + (self.n_days + 1) * pd.Timedelta(1, 'D')
start_date = self.start_date
if self.n_days == 1:
end_date = start_date
else:
end_date = start_date + pd.Timedelta(self.n_days - 1, 'D')
return self._get_this("cmems_mod_glo_phy-cur_anfc_0.083deg_P1D-m", [start_date, end_date])

def _get_reanalysis(self):
"""
Returns
-------
:class:xarray.dataset
:class:`xarray.dataset`
"""
start_date = self.start_date
end_date = self.start_date + (self.n_days + 1) * pd.Timedelta(1, 'D')
if self.n_days == 1:
end_date = start_date
else:
end_date = self.start_date + pd.Timedelta(self.n_days - 1, 'D')
return self._get_this("cmems_mod_glo_phy_my_0.083_P1D-m", [start_date, end_date])

def to_xarray(self):
""" Load and return data as a :class:xarray.dataset
""" Load and return data as a :class:`xarray.dataset`
Returns
-------
:class:xarray.dataset
:class:`xarray.dataset`
"""
return self.loader()
return self._loader()


def get_velocity_field(a_box, a_date, n_days=1, output='.', dataset='ARMOR3D'):
Expand Down

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