Merge remote-tracking branch 'origin/dev' into py312

.github/workflows/ci.yml

@@ -93,5 +93,5 @@ jobs:
         if: ${{ github.repository == 'pastas/pastastore' && success() }}
         uses: codacy/codacy-coverage-reporter-action@master
         with:
-          project-token: ${{ secrets.CODACY_PROJECT_TOKEN }}
+          project-token: ${{ secrets.CODACY_API_TOKEN }}
           coverage-reports: coverage.xml

pastastore/base.py

@@ -1,6 +1,8 @@
 import functools
 import json
 import warnings
+
+# import weakref
 from abc import ABC, abstractmethod, abstractproperty
 from collections.abc import Iterable
 from itertools import chain
@@ -20,6 +22,34 @@
 warnings.showwarning = _custom_warning
 
 
+# def weak_lru(maxsize=128, typed=False):
+#     """LRU Cache decorator that keeps a weak reference to 'self'.
+
+#     From https://stackoverflow.com/a/68052994/10596229.
+
+#     Parameters
+#     ----------
+#     maxsize : int, optional
+#         maximum size of cache, by default 128
+#     typed : bool, optional
+#         whether to differentiate between types, by default False
+
+#     """
+
+#     def wrapper(func):
+#         @functools.lru_cache(maxsize, typed)
+#         def _func(_self, *args, **kwargs):
+#             return func(_self(), *args, **kwargs)
+
+#         @functools.wraps(func)
+#         def inner(self, *args, **kwargs):
+#             return _func(weakref.ref(self), *args, **kwargs)
+
+#         return inner
+
+#     return wrapper
+
+
 class BaseConnector(ABC):
     """Base Connector class.
 
@@ -312,6 +342,9 @@ def _update_series(
         self._validate_input_series(series)
         series = self._set_series_name(series, name)
         stored = self._get_series(libname, name, progressbar=False)
+        if self.conn_type == "pas" and (type(series) != type(stored)):
+            if isinstance(series, pd.DataFrame):
+                stored = stored.to_frame()
         # get union of index
         idx_union = stored.index.union(series.index)
         # update series with new values
@@ -659,6 +692,16 @@ def del_models(self, names: Union[list, str]) -> None:
             self._del_oseries_model_link(oname, n)
         self._clear_cache("_modelnames_cache")
 
+    def del_model(self, names: Union[list, str]) -> None:
+        """Delete model(s) from the database.
+
+        Parameters
+        ----------
+        names : str or list of str
+            name(s) of the model to delete
+        """
+        self.del_models(names=names)
+
     def del_oseries(self, names: Union[list, str], remove_models: bool = False):
         """Delete oseries from the database.
 
@@ -1247,11 +1290,13 @@ def _meta_list_to_frame(metalist: list, names: list):
             meta = pd.DataFrame(metalist)
         elif len(metalist) == 0:
             meta = pd.DataFrame()
+
         meta.index = names
+        meta.index.name = "name"
         return meta
 
     def _parse_model_dict(self, mdict: dict, update_ts_settings: bool = False):
-        """Internal method to parse dictionary describing pastas models.
+        """Parse dictionary describing pastas models (internal method).
 
         Parameters
         ----------
@@ -1276,7 +1321,7 @@ def _parse_model_dict(self, mdict: dict, update_ts_settings: bool = False):
             if name not in self.oseries.index:
                 msg = "oseries '{}' not present in library".format(name)
                 raise LookupError(msg)
-            mdict["oseries"]["series"] = self.get_oseries(name)
+            mdict["oseries"]["series"] = self.get_oseries(name).squeeze()
             # update tmin/tmax from time series
             if update_ts_settings:
                 mdict["oseries"]["settings"]["tmin"] = mdict["oseries"]["series"].index[
@@ -1296,7 +1341,7 @@ def _parse_model_dict(self, mdict: dict, update_ts_settings: bool = False):
                         if "series" not in stress:
                             name = str(stress["name"])
                             if name in self.stresses.index:
-                                stress["series"] = self.get_stresses(name)
+                                stress["series"] = self.get_stresses(name).squeeze()
                                 # update tmin/tmax from time series
                                 if update_ts_settings:
                                     stress["settings"]["tmin"] = stress["series"].index[
@@ -1311,7 +1356,7 @@ def _parse_model_dict(self, mdict: dict, update_ts_settings: bool = False):
                         if "series" not in stress:
                             name = str(stress["name"])
                             if name in self.stresses.index:
-                                stress["series"] = self.get_stresses(name)
+                                stress["series"] = self.get_stresses(name).squeeze()
                                 # update tmin/tmax from time series
                                 if update_ts_settings:
                                     stress["settings"]["tmin"] = stress["series"].index[
@@ -1327,7 +1372,7 @@ def _parse_model_dict(self, mdict: dict, update_ts_settings: bool = False):
                     if "series" not in stress:
                         name = str(stress["name"])
                         if name in self.stresses.index:
-                            stress["series"] = self.get_stresses(name)
+                            stress["series"] = self.get_stresses(name).squeeze()
                             # update tmin/tmax from time series
                             if update_ts_settings:
                                 stress["settings"]["tmin"] = stress["series"].index[0]
@@ -1717,23 +1762,27 @@ def _models_to_archive(self, archive, names=None, progressbar=True):
             archive.writestr(f"models/{n}.pas", jsondict)
 
     @staticmethod
-    def _series_from_json(fjson: str):
+    def _series_from_json(fjson: str, squeeze: bool = True):
         """Load time series from JSON.
 
         Parameters
         ----------
         fjson : str
             path to file
+        squeeze : bool, optional
+            squeeze time series object to obtain pandas Series
 
         Returns
         -------
         s : pd.DataFrame
             DataFrame containing time series
         """
-        s = pd.read_json(fjson, orient="columns", precise_float=True)
+        s = pd.read_json(fjson, orient="columns", precise_float=True, dtype=False)
         if not isinstance(s.index, pd.DatetimeIndex):
             s.index = pd.to_datetime(s.index, unit="ms")
         s = s.sort_index()  # needed for some reason ...
+        if squeeze:
+            return s.squeeze()
         return s
 
     @staticmethod

pastastore/connectors.py

@@ -29,6 +29,12 @@ def __init__(self, name: str, connstr: str):
         connstr : str
             connection string (e.g. 'mongodb://localhost:27017/')
         """
+        warnings.warn(
+            "ArcticConnector is deprecated. Please use a different "
+            "connector, e.g. `pst.ArcticDBConnector`.",
+            DeprecationWarning,
+            stacklevel=1,
+        )
         try:
             import arctic
         except ModuleNotFoundError as e:
@@ -392,6 +398,12 @@ def __init__(self, name: str, path: str):
         path : str
             path to the pystore directory
         """
+        warnings.warn(
+            "PystoreConnector is deprecated. Please use a different "
+            "connector, e.g. `pst.PasConnector`.",
+            DeprecationWarning,
+            stacklevel=1,
+        )
         try:
             import pystore
         except ModuleNotFoundError as e:

pastastore/store.py

@@ -1,7 +1,7 @@
 import json
 import os
 import warnings
-from typing import List, Optional, Tuple, Union
+from typing import List, Literal, Optional, Tuple, Union
 
 import numpy as np
 import pandas as pd
@@ -14,6 +14,7 @@
 from pastastore.connectors import DictConnector
 from pastastore.plotting import Maps, Plots
 from pastastore.util import _custom_warning
+from pastastore.version import PASTAS_GEQ_150
 from pastastore.yaml_interface import PastastoreYAML
 
 FrameorSeriesUnion = Union[pd.DataFrame, pd.Series]
@@ -384,7 +385,12 @@ def get_signatures(
 
         return signatures_df
 
-    def get_tmin_tmax(self, libname, names=None, progressbar=False):
+    def get_tmin_tmax(
+        self,
+        libname: Literal["oseries", "stresses", "models"],
+        names: Union[str, List[str], None] = None,
+        progressbar: bool = False,
+    ):
         """Get tmin and tmax for time series.
 
         Parameters
@@ -410,12 +416,22 @@ def get_tmin_tmax(self, libname, names=None, progressbar=False):
         )
         desc = f"Get tmin/tmax {libname}"
         for n in tqdm(names, desc=desc) if progressbar else names:
-            if libname == "oseries":
-                s = self.conn.get_oseries(n)
+            if libname == "models":
+                mld = self.conn.get_models(
+                    n,
+                    return_dict=True,
+                )
+                tmintmax.loc[n, "tmin"] = mld["settings"]["tmin"]
+                tmintmax.loc[n, "tmax"] = mld["settings"]["tmax"]
             else:
-                s = self.conn.get_stresses(n)
-            tmintmax.loc[n, "tmin"] = s.first_valid_index()
-            tmintmax.loc[n, "tmax"] = s.last_valid_index()
+                s = (
+                    self.conn.get_oseries(n)
+                    if libname == "oseries"
+                    else self.conn.get_stresses(n)
+                )
+                tmintmax.loc[n, "tmin"] = s.first_valid_index()
+                tmintmax.loc[n, "tmax"] = s.last_valid_index()
+
         return tmintmax
 
     def get_extent(self, libname, names=None, buffer=0.0):
@@ -558,6 +574,7 @@ def create_model(
         name: str,
         modelname: str = None,
         add_recharge: bool = True,
+        add_ar_noisemodel: bool = False,
         recharge_name: str = "recharge",
     ) -> ps.Model:
         """Create a pastas Model.
@@ -572,6 +589,8 @@ def create_model(
             add recharge to the model by looking for the closest
             precipitation and evaporation time series in the stresses
             library, by default True
+        add_ar1_noisemodel : bool, optional
+            add AR(1) noise model to the model, by default False
         recharge_name : str
             name of the RechargeModel
 
@@ -598,6 +617,8 @@ def create_model(
             ml = ps.Model(ts, name=modelname, metadata=meta)
             if add_recharge:
                 self.add_recharge(ml, recharge_name=recharge_name)
+            if add_ar_noisemodel and PASTAS_GEQ_150:
+                ml.add_noisemodel(ps.ArNoiseModel())
             return ml
         else:
             raise ValueError("Empty time series!")

pastastore/util.py

@@ -552,6 +552,7 @@ def frontiers_checks(
     check4_gain: bool = True,
     check5_parambounds: bool = False,
     csv_dir: Optional[str] = None,
+    progressbar: bool = False,
 ) -> pd.DataFrame:  # pragma: no cover
     """Check models in a PastaStore to see if they pass reliability criteria.
 
@@ -597,6 +598,8 @@ def frontiers_checks(
     csv_dir : string, optional
         directory to store CSV file with overview of checks for every
         model, by default None which will not store results
+    progressbar : bool, optional
+        show progressbar, by default False
 
     Returns
     -------
@@ -629,7 +632,9 @@ def frontiers_checks(
     else:
         models = pstore.model_names
 
-    for mlnam in tqdm(models, desc="Running model diagnostics"):
+    for mlnam in (
+        tqdm(models, desc="Running model diagnostics") if progressbar else models
+    ):
         ml = pstore.get_models(mlnam)
 
         if ml.parameters["optimal"].hasnans:

pastastore/version.py

@@ -1,7 +1,38 @@
+# ruff: noqa: D100
+from importlib import import_module, metadata
+from platform import python_version
+
 import pastas as ps
 from packaging.version import parse as parse_version
 
 PASTAS_VERSION = parse_version(ps.__version__)
 PASTAS_LEQ_022 = PASTAS_VERSION <= parse_version("0.22.0")
+PASTAS_GEQ_150 = PASTAS_VERSION >= parse_version("1.5.0")
+
+__version__ = "1.5.0"
+
+
+def show_versions(optional=False) -> None:
+    """Print the version of dependencies.
+
+    Parameters
+    ----------
+    optional : bool, optional
+        Print the version of optional dependencies, by default False
+    """
+    msg = (
+        f"Python version     : {python_version()}\n"
+        f"Pandas version     : {metadata.version('pandas')}\n"
+        f"Matplotlib version : {metadata.version('matplotlib')}\n"
+        f"Pastas version     : {metadata.version('pastas')}\n"
+        f"PyYAML version     : {metadata.version('pyyaml')}\n"
+    )
+    if optional:
+        msg += "\nArcticDB version   : "
+        try:
+            import_module("arcticdb")
+            msg += f"{metadata.version('arctidb')}"
+        except ImportError:
+            msg += "Not Installed"
 
-__version__ = "1.4.0"
+    print(msg)