Internal refactoring to use new utility class for static assets loading

argopy/extensions/canyon_b.py

@@ -38,7 +38,8 @@ def decorator(func):
     delayed = None
 
 from ..errors import InvalidDatasetStructure, DataNotFound
-from ..utils import path2assets, to_list, point_in_polygon
+from ..utils import to_list, point_in_polygon
+from argopy.utils.assets import Asset
 from . import register_argo_accessor, ArgoAccessorExtension
 
 
@@ -170,9 +171,6 @@ def __init__(self, *args, **kwargs):
         if self._argo.N_POINTS == 0:
             raise DataNotFound("Empty dataset, no data to transform !")
 
-        self.path2coef = Path(path2assets).joinpath(
-            "canyon-b"
-        )  # Path to CANYON-B assets
 
     def get_param_attrs(self, param: str) -> dict:
         """
@@ -447,22 +445,16 @@ def load_weights(self, param: str) -> pd.DataFrame:
 
         Returns
         -------
-        pd.DataFrame
+        :class:`pd.DataFrame`
             DataFrame containing the neural network weights for the specified parameter.
         """
 
         if param in ["AT", "pCO2", "NO3", "PO4", "SiOH4"]:
-            weights = pd.read_csv(
-                self.path2coef.joinpath(f"wgts_{param}.txt"), header=None, sep="\t"
-            )
+            weights = Asset.load(f"wgts_{param}.txt", header=None, sep="\t")
         elif param == "DIC":
-            weights = pd.read_csv(
-                self.path2coef.joinpath("wgts_CT.txt"), header=None, sep="\t"
-            )
+            weights = Asset.load("wgts_CT.txt", header=None, sep="\t")
         else:
-            weights = pd.read_csv(
-                self.path2coef.joinpath("wgts_pH.txt"), header=None, sep="\t"
-            )
+            weights = Asset.load("wgts_pH.txt", header=None, sep="\t")
 
         return weights
 

argopy/extensions/canyon_med.py

@@ -5,7 +5,8 @@
 from typing import Union, List
 
 from ..errors import InvalidDatasetStructure, DataNotFound
-from ..utils import path2assets, to_list
+from ..utils import to_list
+from argopy.utils.assets import Asset
 from . import register_argo_accessor, ArgoAccessorExtension
 
 
@@ -78,7 +79,6 @@ def __init__(self, *args, **kwargs):
             raise DataNotFound("Empty dataset, no data to transform !")
 
         self.n_list = 5
-        self.path2coef = Path(path2assets).joinpath("canyon-med")
         self._input = None  # Private CANYON-MED input dataframe
 
     @property
@@ -186,50 +186,21 @@ def isin_medsea(row):
     def load_normalisation_factors(self, param, subset="F"):
         suff = self.param2suff(param)
 
-        moy_sub = pd.read_table(
-            self.path2coef.joinpath("moy_%s_%s.txt" % (suff, subset)),
-            sep=" {3}",
-            header=None,
-            engine="python",
-        ).values
-        std_sub = pd.read_table(
-            self.path2coef.joinpath("std_%s_%s.txt" % (suff, subset)),
-            sep=" {3}",
-            header=None,
-            engine="python",
-        ).values
+        moy_sub = Asset.load(f"canyon-med:moy_{suff}_{subset}.txt", sep=" {3}", header=None, engine="python").values
+        std_sub = Asset.load(f"canyon-med:std_{suff}_{subset}.txt", sep=" {3}", header=None, engine="python").values
+
         return moy_sub, std_sub
 
     def load_weights(self, param, subset, i):
         suff = self.param2suff(param)
 
-        b1 = pd.read_csv(
-            self.path2coef.joinpath("poids_%s_b1_%s_%i.txt" % (suff, subset, i)),
-            header=None,
-        )
-        b2 = pd.read_csv(
-            self.path2coef.joinpath("poids_%s_b2_%s_%i.txt" % (suff, subset, i)),
-            header=None,
-        )
-        b3 = pd.read_csv(
-            self.path2coef.joinpath("poids_%s_b3_%s_%i.txt" % (suff, subset, i)),
-            header=None,
-        )
-        IW = pd.read_csv(
-            self.path2coef.joinpath("poids_%s_IW_%s_%i.txt" % (suff, subset, i)),
-            sep=r"\s+",
-            header=None,
-        )
-        LW1 = pd.read_csv(
-            self.path2coef.joinpath("poids_%s_LW1_%s_%i.txt" % (suff, subset, i)),
-            sep=r"\s+",
-            header=None,
-        )
-        LW2 = pd.read_csv(
-            self.path2coef.joinpath("poids_%s_LW2_%s_%i.txt" % (suff, subset, i)),
-            sep=r"\s+",
-            header=None,
-        )
+        b1 = Asset.load(f"canyon-med:poids_{suff}_b1_{subset}_{i}.txt", header=None)
+        b2 = Asset.load(f"canyon-med:poids_{suff}_b2_{subset}_{i}.txt", header=None)
+        b3 = Asset.load(f"canyon-med:poids_{suff}_b3_{subset}_{i}.txt", header=None)
+
+        IW = Asset.load(f"canyon-med:poids_{suff}_IW_{subset}_{i}.txt", header=None, sep=r"\s+")
+        LW1 = Asset.load(f"canyon-med:poids_{suff}_LW1_{subset}_{i}.txt", header=None, sep=r"\s+")
+        LW2 = Asset.load(f"canyon-med:poids_{suff}_LW2_{subset}_{i}.txt", header=None, sep=r"\s+")
 
         # Using float128 arrays avoid the error or warning "overflow encountered in exp" raised by the
         # activation function

argopy/plot/plot.py

@@ -8,15 +8,12 @@
 #
 import warnings
 import logging
-import os
-import json
 from copy import copy
 
 import xarray as xr
 import pandas as pd
 import numpy as np
 from typing import Union
-import importlib
 
 from ..options import OPTIONS
 from ..utils.loggers import warnUnless
@@ -25,6 +22,7 @@
 from ..utils.lists import subsample_list
 from ..utils.casting import to_list
 from ..errors import InvalidDatasetStructure
+from argopy.utils.assets import Asset
 
 from .utils import STYLE, has_seaborn, has_mpl, has_cartopy, has_ipython, has_ipywidgets
 from .utils import axes_style, latlongrid, land_feature
@@ -50,13 +48,6 @@
 
 log = logging.getLogger("argopy.plot.plot")
 
-path2assets = importlib.util.find_spec(
-    "argopy.static.assets"
-).submodule_search_locations[0]
-
-with open(os.path.join(path2assets, "data_types.json"), "r") as f:
-    DATA_TYPES = json.load(f)
-
 
 def guess_cmap(hue: str) -> str | None:
     """Try to guess the ArgoColors colormap name to use as a function of the variable to plot
@@ -756,6 +747,8 @@ def scatter_plot(
     """
     warnUnless(has_mpl, "requires matplotlib installed")
 
+
+
     #deprecation
     if 'this_param' in kwargs:
         warnings.warn(
@@ -779,8 +772,8 @@ def scatter_plot(
         )
         y = kwargs['this_y']  # Safe fallback on new argument
 
-    if param in DATA_TYPES["data"]["str"]:
-        raise ValueError("scatter_plot does not support parameter of string type (yet !)")
+    if param in Asset.load('data_types')["data"]["str"]:
+        raise ValueError("scatter_plot does not support string data type (yet !)")        
 
     # Transform the 'cmap' argument into a mpl.colors.Colormap instance
     a_color = None

argopy/related/argo_documentation.py

@@ -1,17 +1,14 @@
-import os
-import json
 import pandas as pd
 from functools import lru_cache
 import requests
 
 from ..stores import httpstore, memorystore
 from ..options import OPTIONS
-from .utils import path2assets
+from argopy.utils.assets import Asset
 
 
 # Load the ADMT documentation catalogue:
-with open(os.path.join(path2assets, "admt_documentation_catalogue.json"), "rb") as f:
-    ADMT_CATALOGUE = json.load(f)['data']['catalogue']
+ADMT_CATALOGUE = Asset.load('admt_documentation_catalogue')['data']['catalogue']
 
 
 class ArgoDocs:

argopy/related/reference_tables.py

@@ -1,14 +1,13 @@
 import pandas as pd
 from functools import lru_cache
 import collections
-from pathlib import Path
 
-from ..stores import httpstore, filestore
+from ..stores import httpstore
 from ..options import OPTIONS
-from ..utils import path2assets
+from argopy.utils.assets import Asset
 
 
-VALID_REF = filestore(cache=True).open_json(Path(path2assets).joinpath("nvs_reference_tables.json"))['data']['valid_ref']
+VALID_REF = Asset.load('nvs_reference_tables')['data']['valid_ref']
 
 
 class ArgoNVSReferenceTables:

argopy/related/utils.py

@@ -1,14 +1,10 @@
-import importlib
-import os
-import json
 import logging
+
+from argopy.utils.assets import Asset
 from . import ArgoNVSReferenceTables
 
 
 log = logging.getLogger("argopy.related.utils")
-path2assets = importlib.util.find_spec(
-    "argopy.static.assets"
-).submodule_search_locations[0]
 
 
 def load_dict(ptype):
@@ -21,8 +17,7 @@ def load_dict(ptype):
             profilers = dict(sorted(profilers.items()))
             return profilers
         except Exception:
-            with open(os.path.join(path2assets, "profilers.json"), "rb") as f:
-                jsdata = json.load(f)
+            jsdata = Asset.load('profilers')
             log.debug(
                 "Failed to load the ArgoNVSReferenceTables R08 for profiler types, fall back on static assets last updated on %s"
                 % jsdata["last_update"]
@@ -37,8 +32,7 @@ def load_dict(ptype):
             institutions = dict(sorted(institutions.items()))
             return institutions
         except Exception:
-            with open(os.path.join(path2assets, "institutions.json"), "rb") as f:
-                jsdata = json.load(f)
+            jsdata = Asset.load('institutions')
             log.debug(
                 "Failed to load the ArgoNVSReferenceTables R04 for institutions name, fall back on static assets last updated on %s"
                 % jsdata["last_update"]

argopy/tests/test_utils_assets.py

@@ -0,0 +1,22 @@
+import pandas as pd
+import pytest
+from argopy.utils.assets import Asset
+
+
+class Test_Asset():
+    assets = ['gdac_servers.json', 'data_types', 'schema:argo.sensor.schema.json', 'schema:argo.float.schema']
+    assets_id = [f"{a}" for a in assets]
+    @pytest.mark.parametrize("asset", assets, indirect=False, ids=assets_id)
+    def test_load_json(self, asset):
+        data = Asset.load(asset)
+        assert isinstance(data, dict)
+
+    assets = ['canyon-b:wgts_AT.txt']
+    assets_id = [f"{a}" for a in assets]
+    @pytest.mark.parametrize("asset", assets, indirect=False, ids=assets_id)
+    def test_load_csv(self, asset):
+        data = Asset.load(asset)
+        assert isinstance(data, pd.DataFrame)
+
+        data = Asset.load(asset, header=None, sep="\t")
+        assert isinstance(data, pd.DataFrame)

argopy/tests/test_utils_locals.py

@@ -1,10 +1,8 @@
 import os
-
-import pandas as pd
 import pytest
 import io
 import argopy
-from argopy.utils.locals import modified_environ, Asset
+from argopy.utils.locals import modified_environ
 
 
 @pytest.mark.parametrize("conda", [False, True],
@@ -22,22 +20,3 @@ def test_modified_environ():
         assert os.environ['DUMMY_ENV_ARGOPY'] == 'toto'
     assert os.environ['DUMMY_ENV_ARGOPY'] == 'initial'
     os.environ.pop('DUMMY_ENV_ARGOPY')
-
-
-class Test_Asset():
-    assets = ['gdac_servers.json', 'data_types', 'schema:argo.sensor.schema.json', 'schema:argo.float.schema']
-    assets_id = [f"{a}" for a in assets]
-    @pytest.mark.parametrize("asset", assets, indirect=False, ids=assets_id)
-    def test_load_json(self, asset):
-        data = Asset.load(asset)
-        assert isinstance(data, dict)
-
-    assets = ['canyon-b:wgts_AT.txt']
-    assets_id = [f"{a}" for a in assets]
-    @pytest.mark.parametrize("asset", assets, indirect=False, ids=assets_id)
-    def test_load_csv(self, asset):
-        data = Asset.load(asset)
-        assert isinstance(data, pd.DataFrame)
-
-        data = Asset.load(asset, header=None, sep="\t")
-        assert isinstance(data, pd.DataFrame)

argopy/utils/__init__.py

@@ -43,7 +43,6 @@
     modified_environ,
     get_sys_info,  # noqa: F401
     netcdf_and_hdf5_versions,  # noqa: F401
-    Asset,
 )
 from .monitors import monitor_status, badge, fetch_status  # noqa: F401
 from .geo import (
@@ -69,9 +68,6 @@
 from . import optical_modeling
 from .carbonate import calculate_uncertainties, error_propagation
 
-import importlib
-path2assets = importlib.util.find_spec('argopy.static.assets').submodule_search_locations[0]
-
 
 __all__ = (
     # Checkers:
@@ -121,12 +117,10 @@
     # Accessories classes (specific objects):
     "Registry",
     "float_wmo",
-    # Locals (environments, versions, systems):
-    "path2assets",
+    # Locals (environments, versions, systems, assets):
     "show_versions",
     "show_options",
     "modified_environ",
-    "Asset",
     # Monitors
     "monitor_status",
     # Geo (space/time data utilities)