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Copernicus Marine Toolbox: ARMOR3D and GLORYS loaders
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| import pandas as pd | |
| import copernicusmarine | |
| class Armor3d: | |
| """Global Ocean 1/4° Multi Observation Product ARMOR3D | |
| Product description: | |
| 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 | |
| otherwise: | |
| Delivers the 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 | |
| ---------- | |
| 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 = box | |
| self.start_date = start_date | |
| self.n_days = n_days | |
| self.max_depth = max_depth | |
| 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 | |
| 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 start_date < self.time_axis.iloc[0]: | |
| raise ValueError('Date out of bounds') | |
| elif start_date + dt > self.time_axis.iloc[-1]: | |
| raise ValueError('Date out of bounds, %s > %s' % ( | |
| 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] | |
| 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, | |
| minimum_longitude=self.box[0], | |
| maximum_longitude=self.box[1], | |
| minimum_latitude=self.box[2], | |
| maximum_latitude=self.box[3], | |
| maximum_depth=self.max_depth, | |
| start_datetime=start_date.strftime("%Y-%m-%dT%H:%M:%S"), | |
| end_datetime=end_date.strftime("%Y-%m-%dT%H:%M:%S"), | |
| variables=['ugo', 'vgo'] | |
| ) | |
| return ds | |
| def _get_rep(self): | |
| """multi-year reprocessed (REP) weekly data | |
| Returns | |
| ------- | |
| :class:xarray.dataset | |
| """ | |
| return self._get_this("dataset-armor-3d-rep-weekly") | |
| def _get_nrt(self): | |
| """near-real-time (NRT) weekly data | |
| Returns | |
| ------- | |
| :class:xarray.dataset | |
| """ | |
| return self._get_this("dataset-armor-3d-nrt-weekly") | |
| def to_xarray(self): | |
| """Load and return data as a :class:`xarray.dataset` | |
| Returns | |
| ------- | |
| :class:xarray.dataset | |
| """ | |
| return self._loader() | |
| class Glorys: | |
| """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 | |
| otherwise: | |
| Delivers the near-real-time (NRT) Analysis and Forecast daily 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 | |
| ---------- | |
| 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 = box | |
| self.start_date = start_date | |
| self.n_days = n_days | |
| self.max_depth = max_depth | |
| 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 | |
| def _get_this(self, dataset_id, dates): | |
| ds = copernicusmarine.open_dataset( | |
| dataset_id=dataset_id, | |
| minimum_longitude=self.box[0], | |
| maximum_longitude=self.box[1], | |
| minimum_latitude=self.box[2], | |
| maximum_latitude=self.box[3], | |
| maximum_depth=self.max_depth, | |
| start_datetime=dates[0].strftime("%Y-%m-%dT%H:%M:%S"), | |
| end_datetime=dates[1].strftime("%Y-%m-%dT%H:%M:%S"), | |
| variables=['uo', 'vo'] | |
| ) | |
| return ds | |
| def _get_forecast(self): | |
| """ | |
| Returns | |
| ------- | |
| :class:`xarray.dataset` | |
| """ | |
| 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` | |
| """ | |
| start_date = self.start_date | |
| 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` | |
| Returns | |
| ------- | |
| :class:`xarray.dataset` | |
| """ | |
| return self._loader() |
gist updated with required imports
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Hi Guilaume,
Nice work.
I'm having a problem with the class script. When I try to run it, it seems that the pd module, which is an alias for pandas, is still being referenced in the Armor3d class. I've tried to fix this by importing the pandas module directly into my cat_utils.py script. But the same error appears.
Please could you assist me?
NameError Traceback (most recent call last)
Cell In[7], line 2
1 # Convert the date string to a datetime object using the datetime.strptime() method
----> 2 armor_data = Armor3d([-25, -13, 6.5, 13], datetime.strptime('20091130', '%Y%m%d'))
4 # Now you can proceed with your operations
5 armor_data.to_xarray()
File /Volumes/SWOT/Roshin/cmt_utils.py:39, in Armor3d.init(self, box, start_date, n_days, max_depth)
36 self.n_days = n_days
37 self.max_depth = max_depth
---> 39 dt = pd.Timedelta(n_days, 'D') if n_days > 1 else pd.Timedelta(0, 'D')
40 if start_date + dt <= pd.to_datetime('2022-12-28', utc=True):
41 self._loader = self._get_rep
NameError: name 'pd' is not defined