Extra pumpingtest examples

docs/transient/04pumpingtests/index.rst

@@ -13,6 +13,7 @@ problems such as pumping tests and slug tests. More to come soon.
     confined1_oude_korendijk
     confined2_grindley
     confined3_sioux
+    confined4_nevada
 
 .. toctree::
     :maxdepth: 1
@@ -29,15 +30,24 @@ problems such as pumping tests and slug tests. More to come soon.
     :caption: Slug tests
 
     slug1_pratt_county
-    slug2_falling_head
+    slug2_multiwell
+    slug3_falling_head
+    slug4_dawsonville
 
+.. toctree::
+    :maxdepth: 1
+    :hidden:
+    :caption: unconfined aquifers
+
+    unconfined1_moench 
 
 Confined Pumping Tests
 ----------------------
 
 1. :doc:`Oude Korendijk <confined1_oude_korendijk>` - One layer confined pumping test with two observation wells
 2. :doc:`Grindley <confined2_grindley>` - One layer confined pumping test with data from both observation well and pumping well.
 3. :doc:`Sioux <confined3_sioux>` - One layer confined pumping test with three observation wells. 
+4. :doc:`Nevada <confined4_nevada>` - Multiple layer confined pumping test with two observation wells. 
 
 Leaky Pumping Tests (Semi-confined)
 -----------------------------------
@@ -51,3 +61,10 @@ Slug Tests
 
 1. :doc:`Pratt County <slug1_pratt_county>` - Slug test with data from the partially penetrating test well.
 2. :doc:`Multi well <slug2_multiwell>` - Slug test with data from both fully penetrating test well and observation well.
+3. :doc:`Falling head <slug3_falling_head>` - Slug test in an unconfined aquifer with a partially penetrating well.
+4. :doc:`Dawsonville <slug4_dawsonville>` - Slug test in a confined aquifer with a fully penetrating well.
+
+Unconfined Pumping Tests
+------------------------
+
+1. :doc:`Moench <unconfined1_moench>` - Unconfined pumping test with a partially penetrating well and observation wells at multiple depths.

docs/transient/04pumpingtests/leaky1_dalem.ipynb

@@ -29,7 +29,13 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
@@ -86,7 +92,13 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "# data of observation well 30 m away from pumping well\n",
@@ -126,7 +138,13 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "# known parameters\n",
@@ -158,7 +176,13 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "# unkonwn parameters: kaq, Saq, c\n",
@@ -186,7 +210,13 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "cal = tft.Calibrate(ml)\n",
@@ -204,7 +234,13 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "display(cal.parameters.loc[:, [\"optimal\"]])\n",
@@ -214,7 +250,13 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "hm_1 = ml.head(r1, 0, t1)\n",
@@ -257,7 +299,13 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "cal2 = tft.Calibrate(ml)\n",
@@ -269,29 +317,47 @@
     "cal2.series(name=\"obs2\", x=60, y=0, t=t2, h=h2, layer=0, weights=1 / np.max(np.abs(h2)))\n",
     "cal2.series(name=\"obs3\", x=90, y=0, t=t3, h=h3, layer=0, weights=1 / np.max(np.abs(h3)))\n",
     "cal2.series(name=\"obs4\", x=120, y=0, t=t4, h=h4, layer=0, weights=1 / np.max(np.abs(h4)))\n",
-    "cal2.fit(report=True)"
+    "cal2.fit(report=False)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "display(cal2.parameters[[\"layers\", \"optimal\"]])\n",
-    "print(f\"RMSE: {cal2.rmse(weighted=False):.5f} m\")"
+    "display(cal2.parameters[[\"optimal\"]])\n",
+    "print(f\"RMSE: {cal2.rmse(weighted=False):.3f} m\")"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "source": [
     "### Comparison of results"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "source": [
     "The performance of `timflow` is compared with other models and with parameter values reported in Kruseman and de Ridder (1970). The published values were obtained by graphical matching to a Hantush type curve (Hantush, 1955). In addition to the `timflow` results and the literature values, results from AQTESOLV (Duffield, 2007) and MLU (Hemker & Post, 2014) are included for comparison. Both AQTESOLV and MLU were applied to minimize the sum of the squared log-transformed drawdowns. AQTESOLV only fitted the model to the observation well at r = 90 m, whereas the other models were calibrated using data from multiple observation wells.\n",
     "\n",
@@ -301,7 +367,15 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": [
+     "hide-input"
+    ]
+   },
    "outputs": [],
    "source": [
     "t = pd.DataFrame(\n",
@@ -314,7 +388,7 @@
     "    cal2.parameters[\"optimal\"].values, cal2.rmse(weighted=False)\n",
     ")\n",
     "t.loc[\"AQTESOLV\"] = [41.5575, 4.4625e-05, 327.920, 0.03818]\n",
-    "t.loc[\"MLU\"] = [48.108, 4.324e-05, 539, 0.042426]\n",
+    "t.loc[\"MLU\"] = [45.297, 4.865e-05, 328, 0.042426]\n",
     "t.loc[\"Hantush\"] = [45.332, 4.762e-5, 331.141, 0.005917]\n",
     "\n",
     "t_formatted = t.style.format(\n",
@@ -325,7 +399,13 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "source": [
     "## References\n",
     "\n",
@@ -349,6 +429,18 @@
    "display_name": "Python 3",
    "language": "python",
    "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.3"
   }
  },
  "nbformat": 4,

docs/transient/04pumpingtests/leaky2_hardixveld.ipynb

@@ -164,7 +164,7 @@
    "outputs": [],
    "source": [
     "display(cal.parameters.loc[:, [\"optimal\"]])\n",
-    "print(f\"RMSE: {cal.rmse():.5f} m\")"
+    "print(f\"RMSE: {cal.rmse():.3f} m\")"
    ]
   },
   {
@@ -208,14 +208,20 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "display(cal1.parameters[\"optimal\"])\n",
-    "print(f\"RMSE: {cal1.rmse():.5f} m\")"
+    "display(cal1.parameters[[\"optimal\"]])\n",
+    "print(f\"RMSE: {cal1.rmse(weighted=False):.3f} m\")"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "tm = np.logspace(np.log10(to[0]), np.log10(to[-1]), 100)\n",
@@ -266,7 +272,15 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": [
+     "hide-input"
+    ]
+   },
    "outputs": [],
    "source": [
     "t = pd.DataFrame(\n",
@@ -307,6 +321,18 @@
    "display_name": "Python 3",
    "language": "python",
    "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.3"
   }
  },
  "nbformat": 4,