Implement Type Hints

.github/pull_request_template.md

@@ -5,5 +5,6 @@ Add a short description describing the pull request (PR) here.
 - [ ] closes issue #xxxx
 - [ ] is documented
 - [ ] PEP8 compliant code
+- [ ] type hints for functions and methods
 - [ ] tests added / passed
 - [ ] Example Notebook (for new features)

doc/concepts/armamodel.ipynb

@@ -1,13 +1,14 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "# ARMA(1,1) Noise Model for Pastas\n",
     "*R.A. Collenteur, University of Graz, May 2020*\n",
     "\n",
-    "In this notebook an Autoregressive-Moving-Average (ARMA(1,1)) noise model is developed for Pastas models. This new noise model is tested against synthetic data generated with Numpy or Statsmodels' ARMA model. This noise model is tested on head time series with a regular time step.\n",
+    "In this notebook an Autoregressive-Moving-Average (ARMA(1,1)) noise model is developed for Pastas models. This new noise model is tested against synthetic data generated with NumPy or Statsmodels' ARMA model. This noise model is tested on head time series with a regular time step.\n",
     "\n",
     "<div class=\"alert alert-info\">\n",
     "    \n",
@@ -82,11 +83,12 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## 3. Generate ARMA(1,1) noise and add it to the synthetic heads\n",
-    "In the following code-block, noise is generated using an ARMA(1,1) process using Numpy. An alternative procedure is available from Statsmodels (commented out now). More information about the ARMA model can be found on the [statsmodels website](https://www.statsmodels.org/dev/generated/statsmodels.tsa.arima_process.ArmaProcess.html). The noise is added to the head series generated in the previous code-block."
+    "In the following code-block, noise is generated using an ARMA(1,1) process using NumPy. An alternative procedure is available from Statsmodels (commented out now). More information about the ARMA model can be found on the [statsmodels website](https://www.statsmodels.org/dev/generated/statsmodels.tsa.arima_process.ArmaProcess.html). The noise is added to the head series generated in the previous code-block."
    ]
   },
   {
@@ -107,7 +109,7 @@
     "# arma = stats.tsa.ArmaProcess(ar, ma)\n",
     "# noise = arma.generate_sample(head[0].index.size)*np.std(head.values) * 0.1\n",
     "\n",
-    "# generate samples using Numpy\n",
+    "# generate samples using NumPy\n",
     "random_seed = np.random.RandomState(1234)\n",
     "\n",
     "noise = random_seed.normal(0,1,len(head)) * np.std(head.values) * 0.1\n",
@@ -281,7 +283,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "pastas_dev",
    "language": "python",
    "name": "python3"
   },
@@ -295,7 +297,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.9.15 | packaged by conda-forge | (main, Nov 22 2022, 08:41:22) [MSC v.1929 64 bit (AMD64)]"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "29475f8be425919747d373d827cb41e481e140756dd3c75aa328bf3399a0138e"
+   }
   }
  },
  "nbformat": 4,

doc/concepts/noisemodel.ipynb

@@ -1,13 +1,14 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "# Testing an AR(1) Noise Model for Pastas\n",
     "*R.A. Collenteur, University of Graz, May 2020*\n",
     "\n",
-    "In this notebook the classical Autoregressive AR1 noise model is tested for Pastas models. This noise model is tested against synthetic data generated with Numpy or Statsmodels' ARMA model. This noise model is tested on head time series with a regular time step."
+    "In this notebook the classical Autoregressive AR1 noise model is tested for Pastas models. This noise model is tested against synthetic data generated with NumPy or Statsmodels' ARMA model. This noise model is tested on head time series with a regular time step."
    ]
   },
   {
@@ -90,7 +91,7 @@
     "np.random.seed(1234)\n",
     "alpha= 0.8\n",
     "\n",
-    "# generate samples using Numpy\n",
+    "# generate samples using NumPy\n",
     "random_seed = np.random.RandomState(1234)\n",
     "noise = random_seed.normal(0,1,len(head)) * np.std(head.values) * 0.2\n",
     "a = np.zeros_like(head[0])\n",
@@ -239,7 +240,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "pastas_dev",
    "language": "python",
    "name": "python3"
   },
@@ -253,7 +254,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.9.15 | packaged by conda-forge | (main, Nov 22 2022, 08:41:22) [MSC v.1929 64 bit (AMD64)]"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "29475f8be425919747d373d827cb41e481e140756dd3c75aa328bf3399a0138e"
+   }
   }
  },
  "nbformat": 4,

doc/examples/example_spline.py

@@ -42,4 +42,4 @@
 ml3.solve(noise=noise, fit_constant=fit_constant)
 
 # Compare both models
-axes = ps.plots.compare([ml1, ml2, ml3])
+ax = ps.plots.compare([ml1, ml2, ml3])

doc/examples/example_uncertainty.py

@@ -27,14 +27,14 @@
 print('percentage inside:', np.count_nonzero(inside) / len(inside) * 100)
 
 # # Plot some results
-axes = ml.plots.results(tmin="2010", tmax="2015", figsize=(10, 6))
-axes[0].fill_between(df.index, df.iloc[:, 0], df.iloc[:, 1], color="gray",
-                     zorder=-1, alpha=0.5, label="95% Prediction interval")
-axes[0].legend(ncol=3)
+axs = ml.plots.results(tmin="2010", tmax="2015", figsize=(10, 6))
+axs[0].fill_between(df.index, df.iloc[:, 0], df.iloc[:, 1], color="gray",
+                    zorder=-1, alpha=0.5, label="95% Prediction interval")
+axs[0].legend(ncol=3)
 df = ml.fit.ci_contribution("recharge", tmin="2010", tmax="2015")
-axes[2].fill_between(df.index, df.iloc[:, 0], df.iloc[:, 1], color="gray",
-                     zorder=-1, alpha=0.5, label="95% confidence")
+axs[2].fill_between(df.index, df.iloc[:, 0], df.iloc[:, 1], color="gray",
+                    zorder=-1, alpha=0.5, label="95% confidence")
 
 df = ml.fit.ci_step_response("recharge", alpha=0.05, n=1000)
-axes[3].fill_between(df.index, df.iloc[:, 0], df.iloc[:, 1], color="gray",
-                     zorder=-1, alpha=0.5, label="95% confidence")
+axs[3].fill_between(df.index, df.iloc[:, 0], df.iloc[:, 1], color="gray",
+                    zorder=-1, alpha=0.5, label="95% confidence")

doc/examples/groundwater_paper/Ex1_simple_model/Example1.ipynb

@@ -666,7 +666,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.9.15 | packaged by conda-forge | (main, Nov 22 2022, 08:41:22) [MSC v.1929 64 bit (AMD64)]"
   },
   "vscode": {
    "interpreter": {

pastas/io/base.py

@@ -9,10 +9,13 @@
 import pastas as ps
 from pandas import to_numeric
 
+# Type Hinting
+from pastas.typing import Model
+
 logger = getLogger(__name__)
 
 
-def load(fname, **kwargs):
+def load(fname: str, **kwargs) -> Model:
     """Method to load a Pastas Model from file.
 
     Parameters
@@ -44,14 +47,18 @@ def load(fname, **kwargs):
 
     ml = _load_model(data)
 
-    logger.info("Pastas Model from file %s successfully loaded. This file "
-                "was created with Pastas %s. Your current version of Pastas "
-                "is: %s", fname, data["file_info"]["pastas_version"],
-                ps.__version__)
+    logger.info(
+        "Pastas Model from file %s successfully loaded. This file "
+        "was created with Pastas %s. Your current version of Pastas "
+        "is: %s",
+        fname,
+        data["file_info"]["pastas_version"],
+        ps.__version__,
+    )
     return ml
 
 
-def _load_model(data):
+def _load_model(data: dict) -> Model:
     """Internal method to create a model from a dictionary."""
     # Create model
     oseries = ps.TimeSeries(**data["oseries"])
@@ -76,8 +83,9 @@ def _load_model(data):
     else:
         noise = False
 
-    ml = ps.Model(oseries, constant=constant, noisemodel=noise, name=name,
-                  metadata=metadata)
+    ml = ps.Model(
+        oseries, constant=constant, noisemodel=noise, name=name, metadata=metadata
+    )
 
     if "settings" in data.keys():
         ml.settings.update(data["settings"])
@@ -88,13 +96,15 @@ def _load_model(data):
     for name, ts in data["stressmodels"].items():
         # Deal with old StressModel2 files for version 0.22.0. Remove in 0.23.0.
         if ts["stressmodel"] == "StressModel2":
-            logger.warning("StressModel2 is removed since Pastas 0.22.0 and "
-                           "is replaced by the RechargeModel using a Linear "
-                           "recharge model. Make sure to save this file "
-                           "again using Pastas version 0.22.0 as this file "
-                           "cannot be loaded in newer Pastas versions. This "
-                           "will automatically update your model to the newer "
-                           "RechargeModel stress model.")
+            logger.warning(
+                "StressModel2 is removed since Pastas 0.22.0 and "
+                "is replaced by the RechargeModel using a Linear "
+                "recharge model. Make sure to save this file "
+                "again using Pastas version 0.22.0 as this file "
+                "cannot be loaded in newer Pastas versions. This "
+                "will automatically update your model to the newer "
+                "RechargeModel stress model."
+            )
             ts["stressmodel"] = "RechargeModel"
             ts["recharge"] = "Linear"
             ts["prec"] = ts["stress"][0]
@@ -125,7 +135,7 @@ def _load_model(data):
             ts["rfunc"] = getattr(ps.rfunc, ts["rfunc"])(**rfunc_kwargs)
         if "recharge" in ts.keys():
             recharge_kwargs = {}
-            if 'recharge_kwargs' in ts:
+            if "recharge_kwargs" in ts:
                 recharge_kwargs = ts.pop("recharge_kwargs")
             ts["recharge"] = getattr(
                 ps.recharge, ts["recharge"])(**recharge_kwargs)
@@ -171,7 +181,7 @@ def _load_model(data):
     return ml
 
 
-def dump(fname, data, **kwargs):
+def dump(fname: str, data: dict, **kwargs):
     """Method to save a pastas-model to a file.
 
     Parameters

pastas/io/men.py

@@ -14,13 +14,17 @@
 from ..utils import datetime2matlab
 
 
-def load(fname):
+def load(fname: str) -> NotImplementedError:
     raise NotImplementedError("This is not implemented yet. See the "
                               "reads-section for a Menyanthes-read")
 
 
-def dump(fname, data, version=3, verbose=True):
-    # version can also be a specific version, like '2.x.g.t (beta)', or an integer (see below)
+def dump(fname: str,
+         data: dict,
+         version: int = 3,
+         verbose: bool = True) -> None:
+    # version can also be a specific version,
+    # like '2.x.g.t (beta)', or an integer (see below)
     if version == 3:
         version = '3.x.b.c (gamma)'
     elif version == 2:

pastas/io/pas.py

@@ -16,12 +16,12 @@
 logger = getLogger(__name__)
 
 
-def load(fname):
+def load(fname: str) -> dict:
     data = json.load(open(fname), object_hook=pastas_hook)
     return data
 
 
-def pastas_hook(obj):
+def pastas_hook(obj: dict):
     for key, value in obj.items():
         if key in ["tmin", "tmax", "date_modified", "date_created"]:
             val = Timestamp(value)
@@ -56,7 +56,7 @@ def pastas_hook(obj):
     return obj
 
 
-def dump(fname, data):
+def dump(fname: str, data: dict) -> None:
     json.dump(data, open(fname, 'w'), indent=4, cls=PastasEncoder)
     logger.info("%s file successfully exported", fname)
 
@@ -67,7 +67,7 @@ class PastasEncoder(json.JSONEncoder):
     Notes
     -----
     Currently supported formats are: DataFrame, Series,
-    Timedelta, TimeStamps.
+    Timedelta, Timestamps.
 
     see: https://docs.python.org/3/library/json.html
     """