Do not add NoiseModel to Model-class by default anymore (#678)

.github/workflows/python-publish.yml

@@ -22,10 +22,10 @@ jobs:
       run: |
         python -m pip install --upgrade pip
         pip install wheel twine build
-    - name: Build and publish
-      env:
-        TWINE_USERNAME: ${{ secrets.PYPI_USERNAME }}
-        TWINE_PASSWORD: ${{ secrets.PYPI_PASSWORD }}
-      run: |
-        python3 -m build
-        python3 -m twine upload --repository pypi dist/*
+    - name: Build package
+      run: python -m build
+    - name: Publish package
+      uses: pypa/gh-action-pypi-publish@27b31702a0e7fc50959f5ad993c78deac1bdfc29
+      with:
+        user: __token__
+        password: ${{ secrets.PYPI_TOKEN }}

doc/benchmarks/autocorrelation.ipynb

@@ -394,7 +394,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.10.12"
   },
   "vscode": {
    "interpreter": {

doc/benchmarks/check_response_functions.ipynb

@@ -976,7 +976,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.13"
+   "version": "3.10.12"
   },
   "vscode": {
    "interpreter": {

doc/benchmarks/noisemodel.ipynb

@@ -54,7 +54,7 @@
     "\n",
     "$$\\upsilon(t_i) = r(t_i) - r(t_{i-1}) \\text{e}^{-\\Delta t_i / \\alpha}$$\n",
     "\n",
-    "where $\\upsilon$ is the noise, $\\Delta t_i$ the time step between the residuals ($r$), and $\\alpha$ [days] the AR parameter of the model. The model is named `NoiseModel` and can be found in `noisemodel.py`. This is the default noise model that is added to a Pastas model, but it can also be added manually as follows: `ml.add_noisemodel(ps.NoiseModel())`\n",
+    "where $\\upsilon$ is the noise, $\\Delta t_i$ the time step between the residuals ($r$), and $\\alpha$ [days] the AR parameter of the model. The model is named `NoiseModel` and can be found in `noisemodel.py`. It can be added to a Pastas model as follows: `ml.add_noisemodel(ps.ArNoiseModel())`\n",
     "\n",
     "## 2. Generate synthetic head time series"
    ]
@@ -169,9 +169,9 @@
     "ml = ps.Model(head_noise)\n",
     "sm = ps.StressModel(rain, ps.Gamma(), name=\"recharge\", settings=\"prec\")\n",
     "ml.add_stressmodel(sm)\n",
-    "ml.add_noisemodel(ps.NoiseModel())\n",
+    "ml.add_noisemodel(ps.ArNoiseModel())\n",
     "\n",
-    "ml.solve(tmin=\"1991\", tmax=\"2015-06-29\", noise=True, report=False)\n",
+    "ml.solve(tmin=\"1991\", tmax=\"2015-06-29\", report=False)\n",
     "\n",
     "# Plot the results\n",
     "axes = ml.plots.results(figsize=(10, 5))\n",
@@ -295,14 +295,14 @@
     "ml = ps.Model(head_irregular)\n",
     "sm = ps.StressModel(rain, ps.Gamma(cutoff=0.99), name=\"recharge\", settings=\"prec\")\n",
     "ml.add_stressmodel(sm)\n",
-    "ml.add_noisemodel(ps.NoiseModel())\n",
+    "ml.add_noisemodel(ps.ArNoiseModel())\n",
     "\n",
     "# ml.set_parameter(\"noise_alpha\", 9.5, vary=False)\n",
     "# ml.set_parameter(\"recharge_A\", 800, vary=False)\n",
     "# ml.set_parameter(\"recharge_n\", 1.2, vary=False)\n",
     "# ml.set_parameter(\"recharge_a\", 200, vary=False)\n",
     "\n",
-    "ml.solve(tmin=\"1991\", tmax=\"2015-06-29\", noise=True, report=False)\n",
+    "ml.solve(tmin=\"1991\", tmax=\"2015-06-29\", report=False)\n",
     "axes = ml.plots.results(figsize=(10, 5))\n",
     "axes[-2].plot(ps.Gamma().step([Atrue, ntrue, atrue]))\n",
     "\n",
@@ -376,7 +376,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.10.12"
   },
   "vscode": {
    "interpreter": {

doc/benchmarks/pastas_synthetic.ipynb

@@ -230,7 +230,7 @@
     "ml = ps.Model(head)\n",
     "sm = ps.StressModel(rain, ps.Gamma(), name=\"recharge\", settings=\"prec\")\n",
     "ml.add_stressmodel(sm)\n",
-    "ml.solve(noise=False, ftol=1e-8)\n",
+    "ml.solve(ftol=1e-8)\n",
     "ml.plots.results();"
    ]
   },
@@ -345,9 +345,10 @@
    ],
    "source": [
     "ml2 = ps.Model(head_noise)\n",
+    "ml2.add_noisemodel(ps.ArNoiseModel())\n",
     "sm2 = ps.StressModel(rain, ps.Gamma(), name=\"recharge\", settings=\"prec\")\n",
     "ml2.add_stressmodel(sm2)\n",
-    "ml2.solve(noise=True)\n",
+    "ml2.solve()\n",
     "ml2.plots.results();"
    ]
   },
@@ -465,9 +466,10 @@
    ],
    "source": [
     "ml3 = ps.Model(head_noise_corr)\n",
+    "ml3.add_noisemodel(ps.ArNoiseModel())\n",
     "sm3 = ps.StressModel(rain, ps.Gamma(), name=\"recharge\", settings=\"prec\")\n",
     "ml3.add_stressmodel(sm3)\n",
-    "ml3.solve(noise=True)\n",
+    "ml3.solve()\n",
     "ml3.plots.results();"
    ]
   },
@@ -524,7 +526,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.5"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

doc/conf.py

@@ -99,6 +99,7 @@
             "type": "fontawesome",  # Default is fontawesome
         }
     ],
+    "announcement": "The default behavior for adding and solving with noisemodels has changed from Pastas 1.5. <a href='https://github.com/pastas/pastas/issues/735'>Find more information here</a>",
 }
 
 html_context = {

doc/examples/adding_rivers.ipynb

@@ -63,6 +63,7 @@
    "outputs": [],
    "source": [
     "ml = ps.Model(oseries.resample(\"D\").mean().dropna(), name=\"River\")\n",
+    "ml.add_noisemodel(ps.ArNoiseModel())\n",
     "\n",
     "sm = ps.RechargeModel(rain, evap, rfunc=ps.Exponential(), name=\"recharge\")\n",
     "ml.add_stressmodel(sm)\n",
@@ -103,7 +104,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "pastas_dev",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -117,7 +118,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.15 | packaged by conda-forge | (main, Nov 22 2022, 08:41:22) [MSC v.1929 64 bit (AMD64)]"
+   "version": "3.10.12"
   },
   "vscode": {
    "interpreter": {

doc/examples/adding_trends.ipynb

@@ -45,15 +45,17 @@
     "\n",
     "# Create a Pastas model\n",
     "ml = ps.Model(head)\n",
+    "ml.add_noisemodel(ps.ArNoiseModel())\n",
     "\n",
     "# Add a recharge model\n",
     "rch = ps.rch.FlexModel()\n",
     "rm = ps.RechargeModel(rain, evap, recharge=rch, rfunc=ps.Exponential(), name=\"rch\")\n",
     "ml.add_stressmodel(rm)\n",
     "\n",
     "# Solve and plot the model\n",
-    "ml.solve(noise=False, tmin=\"1990\", report=False)  # Get better initial estimated first\n",
-    "ml.solve(noise=True, tmin=\"1990\", initial=False, report=False)\n",
+    "ml.solve(tmin=\"1990\", report=False)  # Get better initial estimated first\n",
+    "ml.add_noisemodel(ps.ArNoiseModel())\n",
+    "ml.solve(tmin=\"1990\", initial=False, report=False)\n",
     "ml.plots.results(figsize=(10, 6));"
    ]
   },
@@ -76,8 +78,7 @@
     "ml.add_stressmodel(tm)\n",
     "\n",
     "# Solve the model\n",
-    "ml.solve(noise=False, tmin=\"1990\", report=False)  # Get better initial estimated first\n",
-    "ml.solve(noise=True, tmin=\"1990\", initial=False, report=False)\n",
+    "ml.solve(tmin=\"1990\", initial=False, report=False)\n",
     "ml.plots.results(figsize=(10, 6));"
    ]
   },
@@ -103,6 +104,7 @@
     "h = pd.read_csv(\"data/nb18_head.csv\", index_col=0, parse_dates=True).squeeze()\n",
     "\n",
     "ml = ps.Model(h.iloc[::10])\n",
+    "ml.add_noisemodel(ps.ArNoiseModel())\n",
     "\n",
     "sm = ps.RechargeModel(\n",
     "    p, e, name=\"recharge\", rfunc=ps.Exponential(), recharge=ps.rch.Linear()\n",
@@ -191,6 +193,7 @@
    "source": [
     "# method 1\n",
     "ml_before = ps.Model(h.iloc[::10][:\"2012-07-01\"])\n",
+    "ml_before.add_noisemodel(ps.ArNoiseModel())\n",
     "\n",
     "sm = ps.RechargeModel(\n",
     "    p, e, name=\"recharge\", rfunc=ps.Exponential(), recharge=ps.rch.Linear()\n",
@@ -216,6 +219,7 @@
    "source": [
     "# method 2\n",
     "ml_after = ps.Model(h.iloc[::10][pd.Timestamp(\"2012-07-01\") :])\n",
+    "ml_after.add_noisemodel(ps.ArNoiseModel())\n",
     "\n",
     "sm = ps.RechargeModel(\n",
     "    p, e, name=\"recharge\", rfunc=ps.Exponential(), recharge=ps.rch.Linear()\n",
@@ -265,7 +269,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.5"
+   "version": "3.11.3"
   }
  },
  "nbformat": 4,

doc/examples/adding_wells.ipynb

@@ -80,13 +80,14 @@
    "source": [
     "# Create the time series model\n",
     "ml = ps.Model(head, name=\"groundwater\")\n",
+    "ml.add_noisemodel(ps.ArNoiseModel())\n",
     "\n",
     "# Add the stres model for the net recharge\n",
     "rm = ps.RechargeModel(\n",
     "    rain, evap, name=\"recharge\", rfunc=ps.Exponential(), recharge=ps.rch.Linear()\n",
     ")\n",
     "ml.add_stressmodel(rm)\n",
-    "ml.solve(noise=True)\n",
+    "ml.solve()\n",
     "ml.plot(figsize=(10, 4))\n",
     "\n",
     "# Let's store the simulated values to compare later\n",
@@ -165,7 +166,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.5"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

doc/examples/basic_model.ipynb

@@ -123,7 +123,7 @@
    "metadata": {},
    "source": [
     "### 4. Solve the model\n",
-    "The next step is to compute the optimal model parameters. The default solver  uses a non-linear least squares method for the optimization. The python package `scipy` is used (info on `scipy's` least_squares solver can be found [here](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.least_squares.html)). Some standard optimization statistics are reported along with the optimized parameter values and correlations."
+    "The next step is to compute the optimal model parameters. The default solver uses a non-linear least squares method for the optimization. The python package `scipy` is used (info on `scipy's` least_squares solver can be found [here](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.least_squares.html)). Some standard optimization statistics are reported along with the optimized parameter values and correlations."
    ]
   },
   {
@@ -248,7 +248,7 @@
   },
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "pastas_dev",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -262,7 +262,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.15"
+   "version": "3.10.12"
   },
   "vscode": {
    "interpreter": {

doc/examples/calibration_options.ipynb

@@ -84,6 +84,7 @@
    "outputs": [],
    "source": [
     "ml = ps.Model(head)\n",
+    "ml.add_noisemodel(ps.ArNoiseModel())\n",
     "rch = ps.rch.FlexModel()\n",
     "rm = ps.RechargeModel(rain, evap, recharge=rch, rfunc=ps.Gamma(), name=\"rch\")\n",
     "ml.add_stressmodel(rm)"
@@ -142,6 +143,7 @@
    "outputs": [],
    "source": [
     "ml = ps.Model(head)\n",
+    "ml.add_noisemodel(ps.ArNoiseModel())\n",
     "rch = ps.rch.FlexModel()\n",
     "rm = ps.RechargeModel(\n",
     "    rain * 1e3, evap * 1e3, recharge=rch, rfunc=ps.Gamma(), name=\"rch\"\n",
@@ -172,7 +174,7 @@
    "outputs": [],
    "source": [
     "ml.set_parameter(\"rch_n\", initial=15)  # Clearly wrong, just for educational purposes\n",
-    "ml.solve(noise=True, tmin=\"1986\", tmax=\"2003\", report=True)"
+    "ml.solve(tmin=\"1986\", tmax=\"2003\", report=True)"
    ]
   },
   {
@@ -190,12 +192,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "ml.solve(\n",
-    "    noise=False, report=False, tmin=\"1986\", tmax=\"2003\"\n",
-    ")  # First solve without noise model\n",
-    "ml.solve(\n",
-    "    noise=True, initial=False, tmin=\"1986\", tmax=\"2003\", report=True\n",
-    ")  # Then solve with noise model, but do not initialize the parameters\n",
+    "# First solve without noise model\n",
+    "ml.del_noisemodel()\n",
+    "ml.solve(report=False, tmin=\"1986\", tmax=\"2003\")\n",
+    "# Then solve with noise model, but do not initialize the parameters\n",
+    "ml.add_noisemodel(ps.ArNoiseModel())\n",
+    "ml.solve(initial=False, tmin=\"1986\", tmax=\"2003\", report=True)\n",
     "axes = ml.plots.results(figsize=(10, 6))"
    ]
   },
@@ -257,7 +259,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "pastas_dev",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -271,7 +273,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.15"
+   "version": "3.10.12"
   },
   "vscode": {
    "interpreter": {

doc/examples/changing_responses.ipynb

@@ -111,16 +111,18 @@
    "source": [
     "# Normal Model\n",
     "ml = ps.Model(head, name=\"linear\")\n",
+    "ml.add_noisemodel(ps.ArNoiseModel())\n",
     "\n",
     "sm = ps.StressModel(river, ps.Exponential(), name=\"test\")\n",
     "step = ps.StepModel(\"2012-01-01\", rfunc=ps.One(), name=\"step\")\n",
     "\n",
     "ml.add_stressmodel([sm, step])\n",
-    "ml.solve(report=False, tmin=\"2004\", tmax=\"2017-12-31\", noise=True)\n",
+    "ml.solve(report=False, tmin=\"2004\", tmax=\"2017-12-31\")\n",
     "ml.plots.results(figsize=(10, 6))\n",
     "\n",
     "# ChangeModel\n",
     "ml2 = ps.Model(head, name=\"linear\")\n",
+    "ml2.add_noisemodel(ps.ArNoiseModel())\n",
     "\n",
     "cm = ps.ChangeModel(\n",
     "    river,\n",
@@ -131,7 +133,7 @@
     ")\n",
     "\n",
     "ml2.add_stressmodel([cm, step])\n",
-    "ml2.solve(report=False, tmin=\"2004\", tmax=\"2017-12-31\", noise=True)\n",
+    "ml2.solve(report=False, tmin=\"2004\", tmax=\"2017-12-31\")\n",
     "ml2.plots.results(figsize=(10, 6));"
    ]
   },
@@ -210,6 +212,7 @@
    "outputs": [],
    "source": [
     "ml3 = ps.Model(head, name=\"linear\")\n",
+    "ml3.add_noisemodel(ps.ArNoiseModel())\n",
     "\n",
     "river1 = river.copy()\n",
     "river1.loc[\"2012\":] = 0\n",
@@ -222,7 +225,7 @@
     "step = ps.StepModel(\"2012-01-01\", rfunc=ps.One(), name=\"step\")\n",
     "\n",
     "ml3.add_stressmodel([r1, r2, step])\n",
-    "ml3.solve(report=False, tmin=\"2004\", tmax=\"2017-12-31\", noise=True)\n",
+    "ml3.solve(report=False, tmin=\"2004\", tmax=\"2017-12-31\")\n",
     "ml3.plots.results(figsize=(10, 6));"
    ]
   },
@@ -278,7 +281,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "pastas_dev",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -292,7 +295,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.15"
+   "version": "3.10.12"
   },
   "vscode": {
    "interpreter": {