Release v0.5.0

README.md

@@ -22,7 +22,7 @@ existing part of the code. `timflow` is coded in Python and uses `numba` to spee
 evaluation of the line elements and inverse Laplace transforms.
 The `transient` submodule is based on the Laplace-transform analytic element
 method. The solution is computed analytically in the Laplace domain and converted back
-to the time domain numerically usig the algorithm of De Hoog, Stokes, and Knight.
+to the time domain numerically using the algorithm of De Hoog, Stokes, and Knight.
 
 ## Installation
 
@@ -49,10 +49,10 @@ Some of the papers that you may want to cite when using `timflow` are:
 
 * Steady-state flow:
   * Bakker, M., and O.D.L. Strack. 2003. Analytic Elements for Multiaquifer Flow.
-  Journal of Hydrology, 271(1-4), 119-129. [https://doi.org/10.1016/S0022-1694(02)00319-0](https://doi.org/10.1016/S0022-1694(02)00319-0)
+  Journal of Hydrology, 271(1-4), 119-129. [https://doi.org/10.1016/S0022-1694(02)00319-0](https://doi.org/10.1016/S0022-1694(02)00319-0) or [here](https://github.com/timflow-org/timflow/blob/main/2003_bakker_strack_jofh.pdf).
 * Transient flow:
   * M. Bakker. 2013. Semi-analytic modeling of transient multi-layer flow with TTim.
-  Hydrogeology Journal, 21: 935-943. [https://doi.org/10.1007/s10040-013-0975-2](https://doi.org/10.1007/s10040-013-0975-2)
+  Hydrogeology Journal, 21: 935-943. [https://doi.org/10.1007/s10040-013-0975-2](https://doi.org/10.1007/s10040-013-0975-2) or [here](https://github.com/timflow-org/timflow/blob/main/papers/2013_bakker_ttim_hgj.pdf).
   * M .Bakker. 2013. Analytic modeling of transient multi-layer flow. In: Advances in
   Hydrogeology, edited by P Mishra and K Kuhlman, Springer, Heidelberg, 95-114. Available [here](https://github.com/timflow-org/timflow/blob/main/papers/2013_bakker_ttim_theory.pdf).
 

docs/steady/02examples/collector_wells.ipynb

@@ -24,6 +24,20 @@
     "import timflow.steady as tfs"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5d1e632f",
+   "metadata": {
+    "tags": [
+     "parameters"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "NGR = 101"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "35500d1a-fa15-4e42-8e86-4e2d2a633c79",
@@ -104,7 +118,7 @@
    "source": [
     "ml.plots.contour(\n",
     "    win=[-60, 60, -60, 60],\n",
-    "    ngr=50,\n",
+    "    ngr=NGR,\n",
     "    layers=7,\n",
     "    levels=np.arange(20, 24.1, 0.2),\n",
     "    decimals=1,\n",
@@ -133,7 +147,7 @@
    "source": [
     "ml.plots.contour(\n",
     "    win=[-60, 60, -60, 60],\n",
-    "    ngr=50,\n",
+    "    ngr=NGR,\n",
     "    layers=7,\n",
     "    levels=np.arange(20, 24.1, 0.2),\n",
     "    decimals=1,\n",
@@ -200,7 +214,7 @@
     "ml.solve()\n",
     "ml.plots.contour(\n",
     "    win=[-100, 100, -100, 100],\n",
-    "    ngr=101,\n",
+    "    ngr=NGR,\n",
     "    layers=7,\n",
     "    levels=np.arange(21, 30, 0.5),\n",
     "    decimals=1,\n",
@@ -247,7 +261,7 @@
     "ml.solve()\n",
     "ml.plots.contour(\n",
     "    win=[-100, 100, -100, 100],\n",
-    "    ngr=101,\n",
+    "    ngr=NGR,\n",
     "    layers=7,\n",
     "    levels=np.arange(21, 30, 0.5),\n",
     "    decimals=1,\n",
@@ -284,7 +298,7 @@
     "ml.solve()\n",
     "ml.plots.contour(\n",
     "    win=[-100, 100, -100, 100],\n",
-    "    ngr=101,\n",
+    "    ngr=NGR,\n",
     "    layers=7,\n",
     "    levels=np.arange(21, 30, 0.5),\n",
     "    decimals=1,\n",
@@ -322,7 +336,7 @@
     "# contour in layer 0\n",
     "ax = ml.plots.contour(\n",
     "    win=[-100, 100, -100, 100],\n",
-    "    ngr=101,\n",
+    "    ngr=NGR,\n",
     "    layers=0,\n",
     "    levels=np.arange(21, 30, 0.5),\n",
     "    decimals=1,\n",
@@ -333,7 +347,7 @@
     "# contour in layer 7\n",
     "ax = ml.plots.contour(\n",
     "    win=[-100, 100, -100, 100],\n",
-    "    ngr=101,\n",
+    "    ngr=NGR,\n",
     "    layers=7,\n",
     "    levels=np.arange(21, 30, 0.5),\n",
     "    decimals=1,\n",
@@ -381,9 +395,21 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "timflow (3.13.x)",
    "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.-1"
   }
  },
  "nbformat": 4,

docs/steady/02examples/vertical_anisotropy.ipynb

@@ -169,6 +169,19 @@
     "So 1000 realizations takes on the order of 3000 seconds (on this machine), so around 50 minutes. "
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "parameters"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "NTOT = 1000"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -185,10 +198,9 @@
    ],
    "source": [
     "kaq = np.array(80 * [1, 3.16, 10, 31.6, 100])\n",
-    "ntot = 1000\n",
-    "aniso = np.zeros(ntot)\n",
+    "aniso = np.zeros(NTOT)\n",
     "\n",
-    "for i in range(ntot):\n",
+    "for i in range(NTOT):\n",
     "    k = generatek(kaq, nsections=1, seed=i)\n",
     "    h0 = makemodel(k, k)\n",
     "    kx = np.mean(k)\n",