diff --git a/README.md b/README.md index 766e5353..700bbb10 100644 --- a/README.md +++ b/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). diff --git a/docs/transient/04pumpingtests/confined1_oude_korendijk.ipynb b/docs/transient/04pumpingtests/confined1_oude_korendijk.ipynb index 6633d963..555a058e 100644 --- a/docs/transient/04pumpingtests/confined1_oude_korendijk.ipynb +++ b/docs/transient/04pumpingtests/confined1_oude_korendijk.ipynb @@ -22,7 +22,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -53,21 +53,33 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, "source": [ "" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, "source": [ "### Load data" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -92,7 +104,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -105,9 +117,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], "source": [ "# timflow model\n", "ml = tft.ModelMaq(kaq=60, z=[zt, zb], Saq=1e-4, tmin=1e-5, tmax=1)\n", @@ -117,7 +138,13 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, "source": [ "### Estimate aquifer parameters\n", "The aquifer parameters are estimated using head observations at both piezometers." @@ -125,9 +152,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "................................\n", + "Fit succeeded.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".......................\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fit succeeded.\n" + ] + } + ], "source": [ "# unknown parameters: kaq, Saq\n", "cal = tft.Calibrate(ml)\n", @@ -140,9 +190,63 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " optimal\n", + "kaq_0_0 66.088542\n", + "Saq_0_0 0.000025" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.050 m\n" + ] + } + ], "source": [ "display(cal.parameters.loc[:, [\"optimal\"]])\n", "print(f\"RMSE: {cal.rmse():.3f} m\")" @@ -150,9 +254,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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 k [m/d]Ss [1/m]RMSE [m]
timflow66.092.54e-050.050
MLU63.513.58e-050.833
K&dR55.711.70e-041.293
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "t = pd.DataFrame(\n", " columns=[\"k [m/d]\", \"Ss [1/m]\", \"RMSE [m]\"],\n", @@ -224,16 +392,28 @@ "\n", "* Bakker, M. (2013), Semi-analytic modeling of transient multi-layer flow with TTim, Hydrogeol J 21, 935–943, https://doi.org/10.1007/s10040-013-0975-2\n", "* Duffield, G.M. (2007), AQTESOLV for Windows Version 4.5 User's Guide, HydroSOLVE, Inc., Reston, VA.\n", - "* Hemker, K. en Post V. (2014) MLU for Windows: well flow modeling in multilayer aquifer systems; [MLU User's guide](https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fmicrofem.com%2Fdownload%2Fmlu-user.pdf&data=05%7C02%7CMark.Bakker%40tudelft.nl%7Cad7f16364d2d4fd55dbf08de73832eaa%7C096e524d692940308cd38ab42de0887b%7C0%7C0%7C639075204580287861%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=OBoe8seXZUfoat89Dfr4g6lF%2Bn1FdtXqtp%2F18BMXCn0%3D&reserved=0)\n", + "* Hemker, K. en Post V. (2014) MLU for Windows: well flow modeling in multilayer aquifer systems; [MLU User's guide](https://microfem.com/download/mlu-user.pdf)\n", "* Kruseman, G.P., De Ridder, N.A. and Verweij, J.M. (1970), Analysis and evaluationof pumping test data, volume 11, International institute for land reclamation and improvement The Netherlands." ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python [conda env:base] *", "language": "python", - "name": "python3" + "name": "conda-base-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.5" } }, "nbformat": 4, diff --git a/docs/transient/04pumpingtests/confined2_grindley.ipynb b/docs/transient/04pumpingtests/confined2_grindley.ipynb index afb08f86..6ea8d204 100644 --- a/docs/transient/04pumpingtests/confined2_grindley.ipynb +++ b/docs/transient/04pumpingtests/confined2_grindley.ipynb @@ -22,8 +22,15 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-03T20:48:17.294372Z", + "iopub.status.busy": "2026-07-03T20:48:17.293985Z", + "iopub.status.idle": "2026-07-03T20:48:18.200722Z", + "shell.execute_reply": "2026-07-03T20:48:18.200438Z" + } + }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", @@ -93,8 +100,15 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-03T20:48:18.202066Z", + "iopub.status.busy": "2026-07-03T20:48:18.201966Z", + "iopub.status.idle": "2026-07-03T20:48:18.204192Z", + "shell.execute_reply": "2026-07-03T20:48:18.203956Z" + } + }, "outputs": [], "source": [ "# Loading Observation well (Well 1)\n", @@ -123,8 +137,15 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-03T20:48:18.205167Z", + "iopub.status.busy": "2026-07-03T20:48:18.205111Z", + "iopub.status.idle": "2026-07-03T20:48:18.206484Z", + "shell.execute_reply": "2026-07-03T20:48:18.206314Z" + } + }, "outputs": [], "source": [ "# known parameters\n", @@ -153,9 +174,25 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-03T20:48:18.207340Z", + "iopub.status.busy": "2026-07-03T20:48:18.207290Z", + "iopub.status.idle": "2026-07-03T20:48:18.271509Z", + "shell.execute_reply": "2026-07-03T20:48:18.271319Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], "source": [ "ml = tft.ModelMaq(kaq=10, z=[0, -H], Saq=0.001, tmin=0.001, tmax=1, topboundary=\"conf\")\n", "w = tft.Well(ml, xw=0, yw=0, rw=rw, tsandQ=[(0, Q)], rc=rw, layers=0)\n", @@ -190,9 +227,38 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-03T20:48:18.284860Z", + "iopub.status.busy": "2026-07-03T20:48:18.284758Z", + "iopub.status.idle": "2026-07-03T20:48:18.511580Z", + "shell.execute_reply": "2026-07-03T20:48:18.511385Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".............." + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".................\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fit succeeded.\n" + ] + } + ], "source": [ "# unknown parameters: kaq, Saq\n", "cal = tft.Calibrate(ml) # create Calibrate object\n", @@ -207,9 +273,70 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-03T20:48:18.512509Z", + "iopub.status.busy": "2026-07-03T20:48:18.512416Z", + "iopub.status.idle": "2026-07-03T20:48:18.518716Z", + "shell.execute_reply": "2026-07-03T20:48:18.518524Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
optimal
kaq_0_038.086666
Saq_0_00.000001
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" + ], + "text/plain": [ + " optimal\n", + "kaq_0_0 38.086666\n", + "Saq_0_0 0.000001" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.259 m\n" + ] + } + ], "source": [ "display(cal.parameters.loc[:, [\"optimal\"]])\n", "print(f\"RMSE: {cal.rmse():.3f} m\")" @@ -217,9 +344,27 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-03T20:48:18.519565Z", + "iopub.status.busy": "2026-07-03T20:48:18.519514Z", + "iopub.status.idle": "2026-07-03T20:48:18.628312Z", + "shell.execute_reply": "2026-07-03T20:48:18.628107Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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 k [m/d]Ss [1/m]RMSE [m]
timflow38.091.19e-060.259
AQTESOLV22.953.72e-06-
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "t = pd.DataFrame(\n", " columns=[\"k [m/d]\", \"Ss [1/m]\", \"RMSE [m]\"], index=[\"timflow\", \"AQTESOLV\"]\n", @@ -305,20 +503,25 @@ "* Duffield, G.M. (2007), AQTESOLV for Windows Version 4.5 User's Guide, HydroSOLVE, Inc., Reston, VA.\n", "* Walton, W.C. (1962), Selected analytical methods for well and aquifer evaluation, Illinois, department of Registration & Education.bulletin 49." ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python [conda env:base] *", "language": "python", - "name": "python3" + "name": "conda-base-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.5" } }, "nbformat": 4, diff --git a/docs/transient/04pumpingtests/confined3_sioux.ipynb b/docs/transient/04pumpingtests/confined3_sioux.ipynb index 6a5530c5..661cc5a1 100644 --- a/docs/transient/04pumpingtests/confined3_sioux.ipynb +++ b/docs/transient/04pumpingtests/confined3_sioux.ipynb @@ -28,8 +28,15 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-03T20:48:28.515714Z", + "iopub.status.busy": "2026-07-03T20:48:28.515271Z", + "iopub.status.idle": "2026-07-03T20:48:29.434523Z", + "shell.execute_reply": "2026-07-03T20:48:29.434237Z" + } + }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", @@ -97,8 +104,15 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-03T20:48:29.435821Z", + "iopub.status.busy": "2026-07-03T20:48:29.435727Z", + "iopub.status.idle": "2026-07-03T20:48:29.438147Z", + "shell.execute_reply": "2026-07-03T20:48:29.437928Z" + } + }, "outputs": [], "source": [ "# time and drawdown of piezometer 100ft away from pumping well\n", @@ -132,8 +146,15 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-03T20:48:29.439095Z", + "iopub.status.busy": "2026-07-03T20:48:29.439035Z", + "iopub.status.idle": "2026-07-03T20:48:29.440434Z", + "shell.execute_reply": "2026-07-03T20:48:29.440256Z" + } + }, "outputs": [], "source": [ "# known parameters\n", @@ -149,9 +170,25 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-03T20:48:29.441218Z", + "iopub.status.busy": "2026-07-03T20:48:29.441150Z", + "iopub.status.idle": "2026-07-03T20:48:29.504614Z", + "shell.execute_reply": "2026-07-03T20:48:29.504413Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], "source": [ "# timflow model\n", "ml = tft.ModelMaq(kaq=10, z=[0, -b], Saq=0.001, tmin=0.001, tmax=10, topboundary=\"conf\")\n", @@ -174,9 +211,38 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-03T20:48:29.517750Z", + "iopub.status.busy": "2026-07-03T20:48:29.517654Z", + "iopub.status.idle": "2026-07-03T20:48:29.789483Z", + "shell.execute_reply": "2026-07-03T20:48:29.789299Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".........." + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "............................\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fit succeeded.\n" + ] + } + ], "source": [ "# unknown parameters: k, Saq\n", "cal = tft.Calibrate(ml)\n", @@ -190,9 +256,70 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-03T20:48:29.790475Z", + "iopub.status.busy": "2026-07-03T20:48:29.790386Z", + "iopub.status.idle": "2026-07-03T20:48:29.797516Z", + "shell.execute_reply": "2026-07-03T20:48:29.797328Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimal
kaq_0_0282.795232
Saq_0_00.004209
\n", + "
" + ], + "text/plain": [ + " optimal\n", + "kaq_0_0 282.795232\n", + "Saq_0_0 0.004209" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.004 m\n" + ] + } + ], "source": [ "display(cal.parameters.loc[:, [\"optimal\"]])\n", "print(f\"RMSE: {cal.rmse():.3f} m\")" @@ -200,9 +327,27 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-03T20:48:29.798349Z", + "iopub.status.busy": "2026-07-03T20:48:29.798295Z", + "iopub.status.idle": "2026-07-03T20:48:29.919145Z", + "shell.execute_reply": "2026-07-03T20:48:29.918950Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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 k [m/d]Ss [1/m]RMSE [m]
timflow282.804.21e-030.004
AQTESOLV282.664.04e-03-
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "t = pd.DataFrame(\n", " columns=[\"k [m/d]\", \"Ss [1/m]\", \"RMSE [m]\"], index=[\"timflow\", \"AQTESOLV\"]\n", @@ -285,20 +483,25 @@ "* Duffield, G.M. (2007), AQTESOLV for Windows Version 4.5 User's Guide, HydroSOLVE, Inc., Reston, VA.\n", "* Newville, M., Stensitzki, T., Allen, D.B., Ingargiola, A. (2014), LMFIT: Non Linear Least-Squares Minimization and Curve Fitting for Python, https://dx.doi.org/10.5281/zenodo.11813, https://lmfit.github.io/lmfit-py/intro.html (last access: August,2021)." ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python [conda env:base] *", "language": "python", - "name": "python3" + "name": "conda-base-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.5" } }, "nbformat": 4, diff --git a/docs/transient/04pumpingtests/confined4_nevada.ipynb b/docs/transient/04pumpingtests/confined4_nevada.ipynb index 07ad4242..b26710c7 100644 --- a/docs/transient/04pumpingtests/confined4_nevada.ipynb +++ b/docs/transient/04pumpingtests/confined4_nevada.ipynb @@ -2,7 +2,13 @@ "cells": [ { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, "source": [ "# 4. Confined Aquifer Test - Nevada" ] @@ -16,7 +22,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -68,7 +74,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -92,7 +98,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -111,7 +117,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -122,9 +128,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], "source": [ "ml = tft.ModelMaq(\n", " kaq=[km, 1],\n", @@ -150,9 +165,88 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "...................................................................................................................................................................................................................\n", + "Fit succeeded.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "...................................." + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "......" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "......................................" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "....." + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "....................................." + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "......." + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "...................................." + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "......." + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".................................\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fit succeeded.\n" + ] + } + ], "source": [ "cal = tft.Calibrate(ml)\n", "cal.set_parameter(name=\"kaq\", initial=10, layers=1)\n", @@ -167,9 +261,73 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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rc0.105678
\n", + "
" + ], + "text/plain": [ + " optimal\n", + "kaq_1_1 0.877132\n", + "Saq_1_1 0.000005\n", + "c_1_1 12.911460\n", + "rc 0.105678" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.198 m\n" + ] + } + ], "source": [ "display(cal.parameters.loc[:, [\"optimal\"]])\n", "print(f\"RMSE: {cal.rmse():.3f} m\")" @@ -177,19 +335,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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7kxsQ4PATBeRqobb899Xd6TpWTHp6ulPt3GpENzs7G39/f/7v//6Pa665xr7/rrvuYsuWLaxevbrIazSiW7lq299Uj6Qf4fKvLsdaIIqYTWa+u+o76vvXd6pNScfnDZzHjUtvdGq/KS+yGRjFvocz/QWKHDNh4uvLv8bLy8vpPhbXl+L2VdQ7l7xD1/pdiTsSx6SfJhU5fm+ne3l588tO73++9/M8su4Rh89SWFnf81d7vuKpX5/CihUzZh7v/jhXN7va3vZI+hESUhOICYopcl0SUvYT7deAep6hGBYLRnY2RlY21qxMjKwsjMy8n1lZWLOyMDKzMLIysWZkYE1Px0hPx5qegTUtzfY4Ix1rWjq5p1PJTDqOl8WCUeC/dWfDXKcOHqGheISF4REWhmdYGB7hYQ77PMLC8QgPxxwcVOIAg5yd2vbfV3el61g5auWIrre3N126dGH58uUOQXf58uVcddVVxb7Gx8cHHx+fIvu9vLz0C1gBteX7axTSiGm9pjHjlxlYDStmk5lpPafRKKSR021KOt4pslO59gOl9sOZvhQ+NtR3KI1CGuHl5VXhvhTed0XTK/h277dOPS/MbDLTJLQJXl5eNA1titlkdmhnNpnpGtUV8xbn95s9zKWGXCg+qFsNK4kZiXh6evLUb0/Zz2HFylO/PcVF0RcRGRDJol2Linwnw1oMK3H/4bTDJKQcI6Z+DJEBkaX2qzQWi4UlS5YwZMgQPA2D3NOnsaakkJt6mtyUZKzJyeSmpJB7Kv/nqTPbyZP2xxgG1lOnsJ46hSW+6Oh+YSYvLzwiIvAMD8czIgKPCNtPz/AIPOva9ntEROBZty7mgACF4nKoLf99dXe6jhXj7HfnViO6YJtebPz48bz11lv07NmTd955h3fffZe//vqLxo0bl/n6lJQUQkJCyvwbghSv4P+p1qY/wIfTDnMg9QDRQdElhpKy2pR0vDz7nelHWe3yj0X5RbFx1UaHa1nRvhTe5+zzbUnbmL1pdpEwmK+8IbK4/b0a9GLQwkHFBut8prz/FR7ZXjp8KQkpCUxYNqHIa94f9D7RQdFFzm02mflo8Edc9/11Rfbf3fnuUj9vwe89ISWBmOCSw3Bl/Jk0cnPJTU4m98QJco6fIPfE8UI/T5Bz4gS5x4+Tc/w41tTUcp3f5OtrC8ERthDsER5uD8T2xxHheIaHYw4IKPuEbqq2/vfV3eg6Vg5n85pbjegCjB49muPHjzNz5kwSExNp164dS5YscSrkipytyIDIMkfdympT0vHy7HemH2W1yz9msRS9wa6ifSm8z9nn3SK7MbjJ4BLD+bAWw+jVoFeR4+XdP63ntFJHnUsarc5/fXEjy9FB0SSkJBQJ0FbDyuajm4vd/8rGV+yjx1bDyoxfZtCrQS+Hz11SiC/ocNph9p7cS7I1mYoweXjgmVeq4NO87PbWrCxb6E1KIifpODlJx/Ke5+9LIjfvpzUtDSMzE8u//2L599+y++LnZxsNDg/DM6zAz7BQWylFqK2UwjMsFI/QUMx+fhX67CJSs7ld0AWYMmUKU6ZMcXU3RKQSnYu/KBQXgO/sdGeRQFxcSI4MiCwSlMsKwZ3qdSqy34SpSImE1bByIPWAw8h4/vvkHy8chgsGYRMm/Pb4MbL1SPvrixsJdmaE2BlmHx/MDRrg1aBBmW2t6enkHD9OzrEkco7nBeBjx2yh+PhxeyDOOX7cVqeckeF0KAYw+fjgERKCR506Z37WOfPcHBKCR1AQ5qCgIj/NxZS1iUjN4pZBV0TkbJU1ylzSPih5pLikENy+bvsi+wuWLeTLHxnOV9IIcX4YLhyEDQx7vfD6Q+vPola44uG3JGZ/f7z9/fGOji61nWEYtpvrTuQF4BMnzvxMyvt58gS5J0/lPT4JeTfg5Rw9Ss7Ro+Xum8nb2zZ9m58fZl9fTL6+Jfz0wezrh8nPF7OPL2Y/X9u0cAV/+vph9vUp+tPPzzZ3smqURaqEgq6ISCUqbwgubn+IT0iJI8MAMcExJZZJQMlB+I9jfxQ7EtyiToti9ydnJTtVK1xQVQVjk8mER2AAHoEBeMfElNneHoxPnSI32XZDnTU5mZy8n7ab7JJttcenU7GmniY3NQVr6ml7jbGRnU1udjYkV6z0w4kPZ5sn2dvbvqCIffP2Ak8v2yIjZjONUlI4uGgRZk8v8MxbdMRstj328LTNkezpgcnsUfo+Tw/bXM35j80eRfflL2pSaL9twRPPM4udeHpC/qInngUWQMl7joK8uJCCrojIOeJsGUVJobhg+9LKJEoKwoZhVHqtcEHO1A1D1YXhggoGYxo1LNdrDasVa1oa1pQU2/RtmVkYmRlYMzLzpnQr+ac1K9M293H+z8xMjMzif5I/d7JhYGRkYGRklNk3fyDDiVkvqp384JsfhguvBOjthdnL2/bYxydvnzcmH2/M3t6YfHxtj319MXk7Lpxi9vXJ++mLyc8fs1/eSHuBkXiT2ezqb0BcREFXRKQaKqsmubQwXDgImzDx+AWP07Fex0qtFS7ImbphKDsMlxaCz0VABtvyzh55tbpVybBYHAOwxYJhycHIsUBOju15To59X05WNpvi4uh0fgc8IG9/DlhzMXJyMXJzbAuN2B9bz+zLtUJuju2YNRdycjFyc8/sy83Na5eb9/55j3NzHB9bcvLa5PUzrw8FN4q5kRWLxfZ5qvQbLZnJ1xezn1/e6oH5qwn6Y/bL+xkQkLedeewRGIg5b/MIDLTVbQcGYQ7w1wh1DaKgKyJSQ5UWhvODcPzJeHbF7eLqZlfb5kOuxFrhgsqqG4ayw3BpIbi0Y8UFYGdCcVltqjpYm7y88PDyAicDtcVi4XRWJkGDB1fraakMw7AF9fywXtyWnW0L6tnZGJbsM/uys20LomRnY2TbaqyN7LxFUrKybSPnmfkLp2Ta9mVm5i2ikvcXhowM+18g7H3KzCQ3M5Pckycr/gE9PGw3LIYE4xEccuZxUDAeIcGYg4PxDA3Nu/Exb5GVvJsfTZ6KXeeavnERETcVGRBJuHc4R81nbsSqzFrhgsqqG4bSwzBQYggu7VhxN9cVbF9SCUVZI8vOlmFUFmdGshv4lz2LRXVgMpnspQq4cHo3w2q1BeKMDKzpGRgZ6faVBK3p+T/TbD9Pp9nKVfK306expqXZFlg5bavbzj192lZukptrX0ilmLHrUpmDgvAICyPaBInLluMVEYFnuG1FQc/wsLy5pOvaF1KRilPQFRGpZSqrVrjwa0urG4bSw3BpIbik2uLibq6bvn66w6IexZVQlDWy7GwZRmVxeiQbM0P9hjKEIfbXlmequMoe5QbOSSnJ2TKZzbY6XT8/CKv4+Yy8Wurc1FRyk5Nt4Tc5xbbCYEoquSkpWFPzVhk8dYqcUyftNz1a825otKamYk1NxQ9I27e/1Pcz+/vjUdcWfL3q1cczMhKv+vXwrF8fz3r18WoQhWfdurabDaVECroiIlIiZxchgYrfRFfaiLCzN9cZef8rqHAJRVllFs6UYVSW0kI1FBrJxsrXGV8zKX0SjUIalWvlv4LnqoxRbhO2GlUDw6lSkuI+d8E256r+uiJMJpO9tterfv1yvdbIybEtsX3yJFnHjhH344+cHxuLcSrZvsKgbTGVY+QcS8JIT7eNNO9PwLI/gRJvU/T0xKt+fbyiovBsEIVXgwZ4RTXAO7oRXtHReEVF1fpyidr96UVEpFKd7U10ZYXg4o4Vd3NdScs0FyyhKKvMwpkyjMpS3pFsA4MDqQfw9PR0eqq4qhjlLviXibJKScoK1MWtQOjsDYrOtDmbY5UdvE2envbVBc0xMZw+coSQUpYAzj2dRm7SMXKOHcNy9Cg5R46Sc+QIlqNHbI8PH8Zy9Cjk5GA5eBDLwYPFv7GHB14N8oNvjO1no2i8m8Ti3bgxZl/fCn+26k5BV0REzqnyzjVc2rHiAjCUvExz/vuXFqqdKcOoLGWF6uKCfHmXla6qUe7CbUuap7msQL14z2KH8zh7g2K+s72JsTwj4udi8ZSC7HNGx8aW2MbIzbUF4UOJWBIPYTl0iJzERLIPHsTy70EsBw5gZGdjOXAAy4EDwC9FzuHZIAqf2CZ4x8bi3awpPs1b4NO8GZ7h4VX34c4xBV0REak2ShsRdnbZZih+mWZnXufs8cpS3pHsob5Dqe9fH09Pz3JNFVcVo9wFlTZPc1mBujBnblB0ZiS6tNeXdKyii6c4U+O89+Rekq3JTr3Oqf2dOwGdHI4dTkkgOrAREekeWA4cIDvhAJZ/bT+zExLI3rcPa0oKOYcSyTmUSNr69Q798QgNxadZM7xbNMenWXN8mjfHp1VLPENDS7121ZGCroiI1GjlWaa5PG3KU59cEc6OZEf5RbFx1UZ735ydKq4qRrmLq9EtaZ7msgJ1Yc7coOjMSHRpwbsqFk8pb42z3x4/RrYeWe6R5XKPUncdhn/XroAtBB9LSSA6KJq62T5k79tHdnw8J3ZuI23XDrz3H8V6KJHckydJ//130n//3eG78KxXD59WrWj02qs1puxBQVdERMTFnBnJthRaiKE8U8VB5Y9yA06VkpQVqIur0XXmBkUof+lHWcfOdvGUs6lxfuq3p2gd3rpcI8sl7S9rBLvEMpDOw/ghaB8z0hZhbWDbP73TE1xmbk/2nj1k7dpNys6/yNq1C/OhY+QcPUq2JYujuaeIpHreNFiYgq6IiEgN5exUcaW1LU+bwsfPpuSjuDZ3drqz3DcoOtOmvMfOdvGUs53Jo6QR5PLuL2sEG5wv1Zi++Vl6Dl9KZNu2eeF4PtZeVvyyPIk+ZhCUkcrmhYOqfG7pyqKgKyIiIpXmbAL12dyg6Eybszl2NounnO1MHsWNIJ/N/rJGsMsbtIsLxxk+sLORCTBBCeUb1ZHZ1R0QERERKUlkQCTdIruVOdJcUpuzOVZ4/7AWw1g6fCnvD3qfpcOXFhnJzB9ZNptssaqkGuf84yZMPH7B4/YR5MKvK+/+/L8olHQsP2gXVDA4F95fUjguqGAgrs40oisiIiJShrOdI7rw8fiT8eyK28XVza4u9XXl3V/asfLevFhSjXRBVTW3dGVT0BURERGpBM7UOId7h3PUfNSp15V3f2nHyhucnZlpo7qXLYCCroiIiEitUN7g7MxMG9Wdgq6IiIiIFKusmTaqO92MJiIiIiJuSUFXRERERNySgq6IiIiIuCUFXRERERFxSwq6IiIiIuKWFHRFRERExC0p6IqIiIiIW1LQFRERERG3pKArIiIiIm5JQVdERERE3JKCroiIiIi4JQVdEREREXFLCroiIiIi4pYUdEVERETELSnoioiIiIhbUtAVEREREbekoCsiIiIibklBV0RERETckoKuiIiIiLglBV0RERERcUsKuiIiIiLilhR0RURERMQtKeiKiIiIiFtS0BURERERt6SgKyIiIiJuSUFXRERERNySgq6IiIiIuCUFXRERERFxSwq6IiIiIuKWFHRFRERExC0p6IqIiIiIW1LQFRERERG3pKArIiIiIm5JQVdERERE3JKCroiIiIi4JQVdEREREXFLCroiIiIi4pbcJuju27ePCRMm0KRJE/z8/GjWrBnTpk0jOzvb1V0TERERERfwdHUHKsuOHTuwWq28/fbbNG/enG3btjFx4kTS0tKYNWuWq7snIiIiIueY2wTdyy67jMsuu8z+vGnTpvzzzz+8+eabCroiIiIitZDbBN3iJCcnExYWVmqbrKwssrKy7M9TUlIAsFgsWCyWKu2fO8r/zvTd1Xy6lu5B19F96Fq6B13HyuHs92cyDMOo4r64xJ49e+jcuTMvvfQSt9xyS4ntpk+fzowZM4rs/+STT/D396/KLoqIiIjIWUhPT2fs2LEkJycTHBxcYrtqH3RLCqIFxcXF0bVrV/vzQ4cO0bdvX/r27ct7771X6muLG9GNjo4mKSmp1C9OimexWFi+fDkDBgzAy8vL1d2RCtC1dA+6ju5D19I96DpWjpSUFCIiIsoMutW+dOGOO+5gzJgxpbaJjY21Pz506BD9+/enZ8+evPPOO2We38fHBx8fnyL7vby89AtYAfr+3IeupXvQdXQfupbuQdexYpz97qp90I2IiCAiIsKptgcPHqR///506dKFDz74ALO5BsyeZrWCyWTbRERERKTS1IAk6JxDhw7Rr18/oqOjmTVrFseOHePw4cMcPnzY1V0rWdZp+L/r4Zf/uronIiIiIm6n2o/oOmvZsmXs3r2b3bt306hRI4dj1bYM+e9vbNuO76BuK2gxwNU9EhEREXEbbjOie+ONN2IYRrFbtXX+GOg0HgwrfHEzHNvp6h6JiIiIuA23Cbo1kskEl78MMT0hKwU+HQMZJ13dKxERERG3oKDrap7eMOpDCImGE3vg/26E3BxX90pERESkxlPQrQ4C68KYT8DLH/augmWPu7pHIiIiIjWegm51EdUBrnnb9vjXN2HT/1zbHxEREZEaTkG3OmkzFPo9anv87b2w/xfX9kdERESkBlPQrW76PghtrgarBRZcB6cSXN0jERERkRpJQbe6MZng6jchsgOkJ8Gn19oWlhARERGRclHQdbHE5AzW70kiMTnjzE5vf7j2UwioB0e2wZeTbEsFi4iIiIjTFHRdaEFcAr2fW8HYd3+l93MrWBBXoEwhpBGM+Rg8vGHHt7DqWdd1VERERKQGUtB1kcTkDB5Z9CfWvIXbrAY8umib48hu9AVwxWzb4zUvwLZF57yfIiIiIjWVgq6LxCel2UNuvlzDYF9SuuPOTuOg5x22x19NgUNbzkn/RERERGo6BV0XaRIRgNnkuM/DZCI2wr9o4wEzofmlkJMBn42F1CPnppMiIiIiNZiCrotEhfjx7LD2eJhsadfDZOKZYe2ICvEr2tjsAcPnQngLSDlom3YsJ+sc91hERESkZvF0dQdqs9HdYujTsi77ktKJjfAvPuTm86sDYxfAu/3h39/gm7vh6jds05GJiIiISBEa0XWxqBA/ejYLLz3k5gtvBiPngckD/vgEfnm9yvsnIiIiUlMp6NY0zS6GQc/YHi9/AnYtd21/RERERKopBd2aqPsk6DQeDCt8cTMc2+nqHomIiIhUOwq6NZHJBJe/DDE9ISsFPh0DGSdd3SsRERGRakVBt6by9IZRH0JINJzYA/93I+TmuLpXIiIiItWGgm5NFlgXrv0UvAJg7ypY9pireyQiIiJSbSjo1nSR7WHY27bHv74FG+e7tj8iIiIi1YSCrjs470ronzea+919sH+9a/sjIiIiUg0o6LqLPg9Am6vBarGtnHYqwdU9EhEREXEpBV13YTLB1W9CZAdIPw6fXgtZp13dKxERERGXUdB1J97+tpvTAurBkW3w5SSwWl3dKxERERGXUNB1NyGNYMzH4OENO76FVc+6ukciIiIiLqGg646iL4Ar59ger3kBti1ybX9EREREXEBB1111HAs977A9/moKHNri0u6IiIiInGsKuu5swExoPgByMuCzsZB6xNU9EhERETlnFHTdmdkDRsyF8BaQchAWjIOcLFf3SkREROScUNB1d74hMHaB7ee/cfDN3WAYru6ViIiISJVT0K0NwpvByHlg8oA/PoFfXnd1j0RERESqnIJubdHsYhj0jO3x8idg13LX9kdERESkiino1ibdJ0Hn68Gwwhc3w7F/XN0jERERkSqjoFubmEww5CWI6QVZKfDpGEg/4epeiYiIiFQJBd3axtMbRn8IITFwYi98cRPk5ri6VyIiIiKVTkG3NgqIgGs/Ba8A2LsKlj3m6h6JiIiIVDoF3doqsh0Me9v2+Ne3YON81/ZHREREpJIp6NZm510J/fNGc7+7D/avd21/RERERCqRgm5t1+cBaHsNWC3wfzfB6WOu7pGIiIhIpVDQre1MJrjqv1C3NZw+DIsmgjXX1b0SERERqTAFXQHvABg5H7z8Ye9K+PllV/dIREREpMIUdMWmXmu4/CXb41XPQPzPru2PiIiISAV5OtMoLCysXCc1mUxs2rSJxo0bn1WnxEU6joV9a2HLx7BwAkxeC4H1XN0rERERkbPiVNA9deoUs2fPJiQkpMy2hmEwZcoUcnNV51kjDXkRDm6EYzts9brXLQKzh6t7JSIiIlJuTgVdgDFjxlCvnnOje3feeedZd0hcLL9e993+tsUkfn4Z+j7g6l6JiIiIlJtTNbpWq9XpkAuQmppK06ZNz7pT4mKq1xURERE3oJvRpHgdx0LHcWBYbfW6p4+6ukciIiIi5eJ06UJBBw8eZN26dRw9ehSr1epwbOrUqZXSMakGhsyCg5vg2N+q1xUREZEap9xB94MPPmDy5Ml4e3sTHh6OyWSyHzOZTAq6NVRicgbxSWk0iQggKsTPttPbH0bOK1Cv+xL0fdCV3RQRERFxWrmD7hNPPMETTzzBI488gtmsygd3sCAugUcW/YnVALMJnh3WntHdYmwH67WGy1+GrybDqmchpgc06ePaDouIiIg4odxJNT09nTFjxijkuonE5Ax7yAWwGvDoom0kJmecadTxWuh4XV697i2q1xUREZEaodxpdcKECfzf//1fVfRFXCA+Kc0ecvPlGgb7ktIddw55EeqeB6eP2Op1rZonWURERKq3cpcuPPvss1xxxRX88MMPtG/fHi8vL4fjL7/8cqV1Tqpek4gAzCYcwq6HyURshL9jQ9XrioiISA1T7qD7zDPPsHTpUlq1agVQ5GY0qVmiQvx4dlh7Hl20jVzDwMNk4plh7c7ckFaQ6nVFRESkBil30H355Zd5//33ufHGG6ugO+IKo7vF0KdlXfYlpRMb4V98yM3X8VrYtxa2fGSr1528FgKdX0xERERE5Fwpd42uj48PvXv3roq+VJqsrCw6duyIyWRiy5Ytru5OjRAV4kfPZuGlh9x8qtcVERGRGqDcQfeuu+7itddeq4q+VJoHH3yQBg0auLob7iu/XtfL31avu2aWq3skIiIiUkS5Sxd+++03VqxYwbfffkvbtm2L3Iy2aNGiSuvc2fj+++9ZtmwZCxcu5Pvvv3dpX9xacfW6Tfu6ulciIiIiduUOunXq1GHYsGFV0ZcKO3LkCBMnTuSrr77C39+/7BdgK3PIysqyP09JSQHAYrFgsViqpJ9uo+0IPOJ/xvzHxxgLbyHnlpVYfMIA9N25gfxrqGtZs+k6ug9dS/eg61g5nP3+TIZhGGU3q/4Mw2DIkCH07t2bxx9/nH379tGkSRM2b95Mx44dS3zd9OnTmTFjRpH9n3zyidNhuTbzsGbR55/pBGce5FhgG9Y3fxBMWkxEREREqk56ejpjx44lOTmZ4ODgEttV+6BbUhAtKC4ujvXr17NgwQLWrFmDh4eH00G3uBHd6OhokpKSSv3ipICknXi+fykmSzrZvR/g+/T2DBgwoEhZi9QsFouF5cuX61rWcLqO7kPX0j3oOlaOlJQUIiIiygy6TpUudO7cmZ9++onQ0FCn3vzCCy9kwYIFNGzY0LneluKOO+5gzJgxpbaJjY3lqaeeYsOGDfj4+Dgc69q1K+PGjWP+/PnFvtbHx6fIawC8vLz0C+isqLb2el2vdbOIaP4QXl5D9P25Cf1ZcA+6ju5D19I96DpWjLPfnVNBd8uWLfzxxx+EhYU5ddItW7Y4jJJWREREBBEREWW2e/XVV3nqqafszw8dOsSgQYNYsGAB3bt3r5S+SCk6Xgv712La/BFd9r0Jp8dDaCNX90pERERqMadvRrvkkktwtsrBFSukxcTEODwPDAwEoFmzZjRqpMB1Tgx+EePf3/E9tgPr17fB9V+B2cPVvRIREZFayqmgGx8fX+4TK1zWQt7+5Ax7H9O7/fDctwbWvAj9HnZ1r0RERKSWciroNm7cuKr7UeliY2OdHoGWShTRkq2NbqRzwjuw6jlo1A2aX+LqXomIiEgtpHmgpNIdCL8Qa8frAAMW3gKnElzdJREREamFFHSlSuQOeg6iOkLGCfj8erBkurpLIiIiUsso6ErV8PSFUf8Dv1A4tBl+eMjVPRIREZFaRkFXqk5oYxj+HmCCjfNg88eu7pGIiIjUImcVdE+dOsV7773HI488wokTJwDYtGkTBw8erNTOiRtofin0e8T2+Lt7IXGra/sjIiIitUa5g+7WrVtp2bIlzz//PLNmzeLUqVMAfPnllzzyyCOV3T9xB30egBYDIScTFlwHGSdd3SMRERGpBcoddO+9915uvPFGdu3aha+vr33/4MGDWbNmTaV2TtyE2QzXvA11GsOp/bDoVrBaXd0rERERcXPlDrpxcXFMmjSpyP6GDRty+PDhSumUuCH/MBj9oe0mtV3L4OdZru6RiIiIuLlyB11fX19SUlKK7P/nn3+oW7dupXRK3FTU+XD5S7bHK5+B3T+6tj8iIiLi1soddK+66ipmzpyJxWIBwGQykZCQwMMPP8zw4cMrvYPiZjpdB51vwL6YxMn9ru6RiIiIuKlyB91Zs2Zx7Ngx6tWrR0ZGBn379qV58+YEBQXx9NNPV0UfpQZKTM5k/Z4kEpMzih4c/ELeYhIntZiEiIiIVBnP8r4gODiYtWvXsmLFCjZt2oTVaqVz585ceumlVdE/qYF+OWLinpfWYDXAbIJnh7VndLeYMw28fG31um/3gcQt8P2DMPRVl/VXRERE3FO5g26+iy++mIsvvrgy+yJuIDE5kwV7zRh5z60GPLpoG31a1iUqxO9MwzoxMHwufDQcNs2H6AtsZQ0iIiIilaTcQffVV4sfeTOZTPj6+tK8eXP69OmDh4dHhTsnNc/+4+kYmBz25RoG+5LSHYMuQPNLoP+jsPJp+PZeqN8OGnQ8d50VERERt1buoPvKK69w7Ngx0tPTCQ0NxTAMTp06hb+/P4GBgRw9epSmTZuycuVKoqOjq6LPUo01DvfHhOEQdj1MJmIj/It/wUX3w7+/w66l8Pl4uHW1bSoyERERkQoq981ozzzzDN26dWPXrl0cP36cEydOsHPnTrp3786cOXNISEggMjKSe+65pyr6K9VcVIgvo5taMeflXA+TiWeGtSs6mpvPbIZhb0NoLJxKgC8naTEJERERqRTlHtF9/PHHWbhwIc2aNbPva968ObNmzWL48OHs3buXF154QVON1WI96xtMGdaHg8nZxEb4lxxy8/mFwqgPYe4A22ISa16Efg+dm86KiIiI2yr3iG5iYiI5OTlF9ufk5NhXRmvQoAGpqakV753UWFEhvvRsFl52yLW/oANc/rLt8apnYeeyquuciIiI1ArlDrr9+/dn0qRJbN682b5v8+bN3HbbbfZZGP7880+aNGlSeb2U2qHTOOhyE2DAFzfDke2u7pGIiIjUYOUOunPnziUsLIwuXbrg4+ODj48PXbt2JSwsjLlz5wIQGBjISy+9VOmdFfeX2HsGyfW7Q3YqfDoaTh9zdZdERESkhip3jW5kZCTLly9nx44d7Ny5E8MwaN26Na1atbK36d+/f6V2UmqHBXEJPLLoT4KNm/nKZx+xpxJgwXVww2Lw9HF190RERKSGOesFI1q3bk3r1q0rsy9SiyUmZ/DIoj+xGnCKICZk38+X3tMIPrABFk+Fa94Ck6nsE4mIiIjkKXfQzc3NZd68efz0008cPXoUa6GpoFasWFFpnZPaIz4pDatx5vkeoyFTLHfxoc8LmLZ+BnVbwkX3ua6DIiIiUuOUO+jeddddzJs3j8svv5x27dph0iibVIImEQGYTTiE3V+MDqT0f4qQFY/ATzMhvAW0Geq6ToqIiEiNUu6g+9lnn/H5558zZMiQquiP1FJRIX48O6w9jy7aRq5h2BeaCOk2BE7Hw2/v2BaTqBPjsExwYnIG8UlpNIkIcH4qMxEREakVyh10vb29ad68eVX0RWq50d1i6NOyLvuS0h0Xmhj0LBzfA3t+gk/HwMSVEBxlv3nNaoDZBM8Oa8/objGu/RAiIiJSbZR7erH77ruPOXPmYBhG2Y1FyikqxK/oQhMenjDyA4hoBamJ8OkYEpOO20Mu2EoeHl20jcTkDNd0XERERKqdco/orl27lpUrV/L999/Ttm1bvLy8HI4vWrSo0jonYucbAmM/g3cvgcQteC2+HcMYR8G/q+UaBvuS0lXCICIiIsBZBN06depwzTXXVEVfREoX1hTGfAzzhxKR8D33evrwUs4o+2EPk4nYCH+Hl6iGV0REpPYqd9D94IMPqqIfIs5p3AuunANfT+FOz6/YazTky9ze9pvXCoZZ1fCKiIjUbuWu0RVxuU7joPddALzs+x7fXOXF2of7O4TYggtQgGp4RUREaqOzWhntiy++4PPPPychIYHs7GyHY5s2baqUjomU6pLpkLQb0z/f0X7NrdDsB+DMSn2FF6CA4mt4VdogIiLivso9ovvqq69y0003Ua9ePTZv3swFF1xAeHg4e/fuZfDgwVXRR5GizGYY/i407AoZJ+GjYXDqgP1w/gIUBRWu4V0Ql0Dv51Yw9t1f6f3cChbEJZyr3ouIiMg5UO6g+8Ybb/DOO+/w+uuv4+3tzYMPPsjy5cuZOnUqycnJVdFHkeJ5B8C4/7NNO5Zy0BZ2044DZxag8Mhbua9wDa9KG0RERNxfuUsXEhIS6NWrFwB+fn6kpqYCMH78eHr06MHrr79euT0UKY1/GIxfBHMHQtJO+GQkXL8YfAJLXoAC50sbREREpOYq94huZGQkx4/bRs0aN27Mhg0bAIiPj9ciEuIaIY1g/JfgFwYHN8Ln4yHHVjte7AIUOFfaICIiIjVbuYPuxRdfzDfffAPAhAkTuOeeexgwYACjR4/W/LriOnVbwbgvwCsA9qyAryaD1Vpi87JKG0RERKTmK3fpwjvvvIM1L0BMnjyZsLAw1q5dy5VXXsnkyZMrvYMiTmvUBUZ/CJ+Mhm0LwT8cBr8AJlOxzUsrbQDNyCAiIlLTlTvoms1mzOYzA8GjRo1i1KhRpbxC5Bxqfglc8xYsvAV+ewcC6kLfB0tsHhXiV2yI1WITIiIiNd9ZzaN76tQpfvvtN44ePWof3c13/fXXV0rHRM5a+xGQfhy+fxBWPm0b2e02wemXlzQjQ5+WdTWyKyIiUoOUO+h+8803jBs3jrS0NIKCgjAV+Gdhk8mkoCvVQ/dJkJYEa16A7+4D3xBbAHaCZmQQERFxD+W+Ge2+++7j5ptvJjU1lVOnTnHy5En7duLEiaroo8jZ6f8odL0ZMGDRrbBtkVMv04wMIiIi7qHcQffgwYNMnToVf3/9n75UcyYTDHkJOo4DI9dWt/vXl2W+zJnFJtbvSdLiEiIiItVcuUsXBg0axO+//07Tpk2roj8ilctshqGvgWHAH5/AFxPAZIY2V5X6spJmZNBNaiIiIjWHU0F38eLF9seXX345DzzwANu3b6d9+/Z4eXk5tB06dGjl9lCkoswecNXrYFhh62fwxc0w4gNoU/rvauEZGXSTmoiISM3iVNC9+uqri+ybOXNmkX0mk4nc3NwKd0qk0pk94Oo3bI+3fgZf3AQj58F5Vzp9Ct2kJiIiUrM4VaNrtVqd2hRypVrLD7vtR4E1B/7vRvj7W6dfrpvUREREapZy34wmUqOZPWwLSrQfmRd2b4Ad3zn10tJuUtMNaiIiItVPuW9Gmzp1Ks2bN2fq1KkO+19//XV2797N7NmzK6tvIlXD7AFXv2W7QW3bF/D5DTD8XWh7TZkvLe4mNd2gJiIiUj2Ve0R34cKF9O7du8j+Xr168cUXX1RKp0SqnIcnXPM2tBsBVgv8302wcZ5TL40K8aNns3D7SG5xN6hpZFdERMT1yh10jx8/TkhISJH9wcHBJCUlVUqnRM4JD08Y9g50uQkw4Ju7YO3scp2itBvURERExLXKHXSbN2/ODz/8UGT/999/r7l1peYxe8AVr8CF99ie/zgNlk+zlTU4QTeoiYiIVF/lrtG99957ueOOOzh27BgXX3wxAD/99BMvvfSS6nOlZjKZ4NLp4BcKy5+AdbMh46QtAJs9Sn1p/g1qjy7aRq5hFFlFTURERFyn3EH35ptvJisri6effponn3wSgNjYWN58802uv/76Su+gyDnT+y7wrQPf3g2b5kNmMgx7Fzy9S31ZSauoiYiIiGuVO+gC3Hbbbdx2220cO3YMPz8/AgMDK7tfIq7R5QbwDYGFt8D2r2xhd9T/wDe41JcVXkVNREREXK9C8+jWrVtXIVfcT9urYewC8PKHvSvhg8GQfNDVvRIREZFycrsFI7777ju6d++On58fERERDBs2zNVdkpqo+SVw43cQUA+ObIP3LoHEra7ulYiIiJSDWwXdhQsXMn78eG666Sb++OMP1q1bx9ixY13dLampGnaGW36EiFaQmmgb2d3141mfTquniYiInFtnVaNbHeXk5HDXXXfx4osvMmHCBPv+Vq1aubBXUuOFNoYJy2DBdbDvZ/hkFFzxMnS5sVyn0eppIiIi557bBN1NmzZx8OBBzGYznTp14vDhw3Ts2JFZs2bRtm3bEl+XlZVFVlaW/XlKSgoAFosFi8VS5f12N/nfmVt9d54BMGYBHt/dg/nPBfDNXeQm7cHa77Eypx8DSEzOLLJ62iOL/qRnk1CiQnyruPNnzy2vZS2k6+g+dC3dg65j5XD2+zMZRtkz47/66qtOv/HUqVOdbluZPvvsM6699lpiYmJ4+eWXiY2N5aWXXmLZsmXs3LmTsLCwYl83ffp0ZsyYUWT/J598gr+/Jv2XAgyDVoe/ovXhLwE4HHw+G2NvI8ej9N+TXckmXt9eNBDf0SaXFiHOLUwhIiIiZ6SnpzN27FiSk5MJDi55ZiSngm6TJk0cnh87doz09HTq1KkDwKlTp/D396devXrs3bu3Yj0vpKQgWlBcXBw7d+5k3LhxvP3229x6662AbbS2UaNGPPXUU0yaNKnY1xY3ohsdHU1SUlKpX5wUz2KxsHz5cgYMGICXl5eru1MlTNu+wOO7uzHlZGKEtyBn1EcQ1qzE9onJmfR7aY3DUsFmE6y6r0+1H9F192tZG+g6ug9dS/eg61g5UlJSiIiIKDPoOlW6EB8fb3/8ySef8MYbbzB37lx7/es///zDxIkTSwyTFXHHHXcwZsyYUtvExsaSmpoKQJs2bez7fXx8aNq0KQkJCSW+1sfHBx8fnyL7vby89AtYAW79/XW6Fuq1gs/GYTq+C68PBsKID2wzNRQjJsKr2NXTYiKCznHHz45bX8taRNfRfehaugddx4px9rsrd43uf/7zH7744guHm7xatWrFK6+8wogRIxg3blx5T1mqiIgIIiIiymzXpUsXfHx8+Oeff7jwwgsB29+a9u3bR+PGjSu1TyI07Ay3rrLdpPbvb/DxCBgwE3reYVtSuBCtniYiInLulXt6scTExGILgHNzczly5EildOpsBAcHM3nyZKZNm8ayZcv4559/uO222wAYOXKky/olbiyoPtz4LXS6DgwrLHscPh9vW02tGFEhfvRsFl5iyNX0YyIiIpWr3CO6l1xyCRMnTmTu3Ll06dIFk8nE77//zqRJk7j00kuroo9Oe/HFF/H09GT8+PFkZGTQvXt3VqxYQWhoqEv7JW7M0weGvg5RHeGHR+Dvb+Dwn7Zlg6POd/o0mn5MRESk8pV7RPf999+nYcOGXHDBBfj6+uLj40P37t2Jiorivffeq4o+Os3Ly4tZs2Zx5MgRUlJSWL58ealTi4lUCpMJLpgIE5ZCnRg4uQ/eGwC/vw9l3+tJYnJGkenHHl20TSO7IiIiFVTuEd26deuyZMkSdu7cyY4dOzAMg/POO4+WLVtWRf9Eao6GXWDSGvjyNtj5PXx7D+xfD1fMBp/AEl8Wn5TmMCMDQK5hsC8pXbW8IiIiFXDWC0a0bNlS4VakML9QuPZTWP8q/DgD/vw/SPzDVspQ77xiX9IkIgCzCYew62EyERuheZxFREQq4qyC7r///svixYtJSEggOzvb4djLL79cKR0TqbFMJuh9FzS6AL64CZJ2wrsXwxWvwPlFp8qLCvErdvoxjeaKiIhUTLmD7k8//cTQoUNp0qQJ//zzD+3atWPfvn0YhkHnzp2roo8iNVPjnjDpZ1h0C+xdBV9Ogn1rYfDz4B3g0FTTj4mIiFS+ct+M9sgjj3Dfffexbds2fH19WbhwIQcOHKBv376axkuksMC6cN0i6PcIYILNH8JbF8G/G4s0LWn6MU07JiIicnbKHXT//vtvbrjhBgA8PT3JyMggMDCQmTNn8vzzz1d6B0VqPLMH9HsYrv8aghrAiT0wdwCseh5yc0p96YK4BHo/t4Kx7/5K7+dWsCCu5FX+RERExFG5g25AQABZWVkANGjQgD179tiPJSUlVV7PRNxN074wZT20Gw5GLqx6Bj64DI7vKba5ph0TERGpmHIH3R49erBu3ToALr/8cu677z6efvppbr75Znr06FHpHRRxK36hMOJ9GPYe+ITAv3G2UoaN84vMuVvatGMiIiJStnLfjPbyyy9z+vRpAKZPn87p06dZsGABzZs355VXXqn0Doq4pQ4jIaYHfHUb7PsZvpkKO3+AK1+11fWiacdEREQqqtxBt2nTpvbH/v7+vPHGG5XaIZFao040XL8YfnkdfpoJ/yyBA7/CkBeh7TBNOyYiIlJBZzWP7qlTp/jiiy/Ys2cPDzzwAGFhYWzatIn69evTsGHDyu6jiPsym6H3VGjWHxZNgqN/wRc3w7ZFcPnLpU47lpicQXxSGk0iAhR+RUREilHuoLt161YuvfRSQkJC2LdvHxMnTiQsLIwvv/yS/fv387///a8q+ini3iLbw62r4OeX4OdZsONb2L8OLnueqA6jigTZBXEJ9hvVzCZ4dlh7RneLcU3fRUREqqly34x27733cuONN7Jr1y58fX3t+wcPHsyaNWsqtXMitYqnN/R/xBZ4IztAxkn48lb4dAykHLI302wMIiIizil30I2Li2PSpElF9jds2JDDhw9XSqdEarXI9jBxBVz8OHh4225S+28P+P0DsFo1G4OIiIiTyh10fX19SUlJKbL/n3/+oW7dupXSKZFaz8ML+jwAk9ZAg86QlQzf3g3zhtDCdBCzqVBzzcYgIiJSRLmD7lVXXcXMmTOxWCwAmEwmEhISePjhhxk+fHild1CkVqt3HkxYDoOeBa8ASPiFuh9dwjdtV+Nnsv0ZLDgbg5YLFhEROaPcN6PNmjWLIUOGUK9ePTIyMujbty+HDx+mZ8+ePP3001XRR5HazcMTek6B866EJffDzh9ou/tt/oz8kX+6ziSs3cVEhfjpBjUREZFCyh10g4ODWbt2LStWrGDTpk1YrVY6d+7MpZdeWhX9E5F8daLh2s9g+9fw/UN4ntxD2+Xj4MgYjvR4rNgb1Pq0rKupx0REpNY6q3l0AS6++GIuvvjiyuyLiJTFZIK2V9vm3f1xBvz+Pmz9jPC/v+M68zA+yh2ANa8iKf8GNQVdERGprc4q6P7000/89NNPHD16FKvV6nDs/fffr5SOiUgpfEPgipeh0zj49l48E7cw02s+Iz1W8x/LzWwxmusGNRERqfXKHXRnzJjBzJkz6dq1K1FRUZhMprJfJCJVo2EX21Rkv79P9rIZtM/ZxyLvaXxh7Yv3oGkazRURkVqt3EH3rbfeYt68eYwfP74q+iMi5WX2gAsm4t3mKtK/exT/v/+PUR6r4OdNYH4QLphkW4xCRESklin39GLZ2dn06tWrKvoiIhURWA//0e/BzcsgqiNkpcCyx+HNXrBruat7JyIics6VO+jecsstfPLJJ1XRFxGpDDHdYeJKGPo6BNSF47vg4xHwyWg4vgdA8+2KiEit4FTpwr333mt/bLVaeeedd/jxxx/p0KEDXl5eDm1ffvnlyu2hiJSf2Qydx0ObobD6Bfj1LdtSwrt/4u/Y6xj194WkGn6ab1dERNyaU0F38+bNDs87duwIwLZt2xz268Y0kWrGNwQGPQ2db4Clj8DuHzlv7wf85P0lL+aMYmFuH823KyIibsupoLty5cqq7oeIVKW6LWHcF/y95v/w/elxmpiP8KLXO9zksZSnc8ayL6m7gq6IiLidctfoikgNZTJRp+OVDLa8wFOWcSQb/rQx7+dj72fptHYiHN3h6h6KiIhUKgVdkVokKsSPGcM684H1CvpmvcIHOZdhNXniG/+TbXaGb++B08dc3U0REZFKcdZLAItIzTS6Wwx9WtZlX1I6sRFXYc45BMufgB3f5i0p/H9w0T3Q5RZXd1VERKRCNKIrUgtFhfjRs1m4rS43vBmM+RhuXAINOkF2Kvw0E9N/LyDm+Gqw5ri6uyIiImdFQVdEbGJ7s6DjPO6xTOGAtS6eaYfplDAXz3f7wI7vwDBc3UMREZFyUdAVEcC2iMQjX/7Fl7kXckn2LGZaxnPCCMSUtBM+GwvvXwYJv7q6myIiIk5T0BURAOKT0rDmDdpm48X7uYPpmzWbA20ng6cfHNgA7w+ET8fCke2u7ayIiIgTFHRFBIAmEQGYC635cho/jP6Pw9RNtkUnTGb45zvbDA2LJsHJfS7pq4iIiDMUdEUEsN2g9uyw9njkrXBoNsHoplaiQnwhuAEMfZWj168mKWYwYMDWz+C1rvDd/ZB62LWdFxERKYamFxMRu4JTjzUM8WbzuhX2YwviEnhk0QGsxng6mHvzdoMlRCWth7h3YfNH0GMy9JoK/mEu/AQiIiJnaERXRBycmXrM174vMTmDRxb9aa/h3WptyoUH7+T4iIXQqBvkZMDaV2DO+bDqechMcVHvRUREzlDQFZEyFbxRLV+uYbDTrxNMWA7Xfgb120NWCqx6BuZ0gLWzITvNJf0VEREBBV0RcUJxN6p5mEzERviDyURiZD/WD1jEySFvQ3gLyDgJP06zjfCufx2y013TcRERqdUUdEWkTIVvVPMwmXhmWDuiQvxYEJdA7+dWMPa9OLp8GcTnF3wOV78FdRpD2jFY9pgt8P7yX7BkuPiTiIhIbaKb0UTEKQVvVIuN8CcqxK9I7a7VgEe+2sFFD19NVPsR8MensOZFOJUASx+FdXOg993Q9Sbw8nPp5xEREfenEV0RcdqZG9VsIbWk2t19Seng4QWdr4c7NsKVr0JIDJw+AksfsY3wbnhTI7wiIlKlFHRF5KyVVrubmJzB+j1JJKblQpcb4M6NcMVsCIm2Bd4fHoY5HWHDW2DJdEX3RUTEzSnoishZK6l2d83OY7a63Xd/pfdzK1gQlwCe3raShTs3wRWvQHAjOH0YfngIXu0Iv76tEV4REalUqtEVkQopXLsL0Pu5FQ51u48u2kaflnVtJQ+e3tD1Zug4zrbQxM8vQcpB+P5BWDMLet1pO+4T6MJPJSIi7kAjuiJSYQVrd0ut2y3I0we6TYCpm+Hyl2wlDWlHYfl/YHZ7W+jNTD53H0JERNyOgq6IVKpS59wtjqcPdLvFVtIw9HUIbQIZJ2DFk7bAu/IZSD9R9R0XERG3o6ArIpWqtDl3S+XpDZ3Hwx2/w7B3IaKVbUR39fO2wLt8Gpw+dg4+gYiIuAvV6IpIpStuzl2neXhCh1HQbgTs+MY2D+/hP2HdbNsNa53H2+p468RUWf9FRMQ9aERXRKpE4Tl3S2Ofiiy5wKwLZjO0uQom/QzXLoCGXSEnA357B17tBF9OhqM7qvATiIhITacRXRFxqQVxCfbV1cwmeHZYe0Z3KzBaazJBq8ug5SCIXwNrX4a9q2yrrv3xKbS+Ai68Fxp1cdlnEBGR6kkjuiLiMsUtIfzoom2OI7v5TCZo2heu/xomroDzrgRMsONbeO9imH8l7FkJhlH0tSIiUisp6IqIyzg9FVlhDbvA6I/g9l9t8/GaPW2jvR9eDW/3gT+/gNycKuu3iIjUDAq6IuIy5Z6KrLC6reDqN2DqFug+Gbz84fBWWDgBXusMv70L2WWEZhERcVsKuiLiMuWZiqzYG9by1YmGwc/DPX9Bv0fBPxxO7Ycl98PsdrDqOUg7XtUfR0REqhm3uhlt586dPPDAA6xbt47s7Gzat2/PU089Rf/+/V3dNREpgTNTkZV5w1o+/zDo95Bt+rEtH8P612yBd9WzsPYVOP9a6Hk7RLQ4B59MRERcza1GdC+//HJycnJYsWIFGzdupGPHjlxxxRUcPnzY1V0TkVKUNhVZuW5Yy+ftDxdMtK22NnwuRHWEnEzY+AG83hU+GQP71urGNRERN+c2QTcpKYndu3fz8MMP06FDB1q0aMFzzz1Heno6f/31l6u7JyJn6axvWAPb4hPtR8Ctq+DG76DlYNv+nd/DvMvhnX55N65ZKrvbIiJSDbhN0A0PD+e8887jf//7H2lpaeTk5PD2229Tv359unTR/JoiNZWzN6yVWsNrMkHshTD2M9sSw11uAk9fSNxiu3FtTkdY96ptyWEREXEbblOjazKZWL58OVdddRVBQUGYzWbq16/PDz/8QJ06dUp8XVZWFllZWfbnKSkpAFgsFiwWjfKUV/53pu+u5qsu1zLC35OnrmrD419vt9foPnnVeUT4e9r79n8b/3U4/tRVbRjZpVHxJwyJhctehIsewrxpHuaNczGl/AvL/4Ox+nmsHa/D2m0i1Gl87j5kFaou11EqTtfSPeg6Vg5nvz+TYVTvIrXp06czY8aMUtvExcXRpUsXrr76aiwWC4899hh+fn689957LF68mLi4OKKiosp1/k8++QR/fyenOBKRKncqC45lmqjra1DHx3H/9E0eGJwZ9jVhML1zrkO7kpit2TQ6+QvNjv5AcOZBAAxMHA7pzJ66Azke2No2IiwiItVGeno6Y8eOJTk5meDg4BLbVfugm5SURFJSUqltYmNjWbduHQMHDuTkyZMOH7hFixZMmDCBhx9+uNjXFjeiGx0dTVJSUqlfnBTPYrGwfPlyBgwYgJeXl6u7IxVQU67lhr0nGP/B70X2f3RzV7o3CSMxOZP9x9NpHO5PVIhvyScyDEx7V2D+9U3M8avO7K7XjtxuEzHaDbeVO9QwNeU6Stl0Ld2DrmPlSElJISIiosygW+1LFyIiIoiIiCizXXq67cYUs9mx7NhsNmO1Wkt8nY+PDz4+RYd9vLy8SvwFtFqtZGdnl9mn2ig3NxdPT09yc3OLXAtxLS8vLzw8PM7qddX5P8bNI4Mxm3C4Yc3DZKJZ/WAWbUl0blqyfK0vs21Hd8Bvb8Mfn2E6ug3P7+6ClTNttb3dboHg4v+FqDqr7tdRnKdr6R50HSvG2e+u2gddZ/Xs2ZPQ0FBuuOEGnnjiCfz8/Hj33XeJj4/n8ssvr7T3yc7OJj4+vtTwXJsZhkFkZCQHDhzApH/urXbq1KlDZGSkW12b/EUnHl20jVzDsC86ARQ7LVmflnWLncbMQb3WcMUrcMkTsOl/thXWkg/Az7Ng3Wxoc5VtJbZG3VTWICJSjblN0I2IiOCHH37gscce4+KLL8ZisdC2bVu+/vprzj///Ep5D8MwSExMxMPDg+joaI1YFsNqtXL69GkCAwP1/VQjhmGQnp7O0aNHAUqsWa+pilt0Yv2epBKnJSsz6ObzC4Xed0GP2+GfJfDrW7B/HWxbaNsiO9jm6203wjZ3r4iIVCtuE3QBunbtytKlS6vs/Dk5OaSnp9OgQQPdqFaC/LIOX19fBd1qxs/PFu6OHj1KvXr1zqqMoTqLCvFzCLD505IVLmkoPC1ZYYnJGcQnpdEkIuDM+Tw8oc1Q25b4B/z6Dmz7Ag5vhcV3wrL/QKfroOvNEN6sKj6eiIicBSWRcsjNzQXA29vbxT0ROTv5f0GrDdPa5Jc0eOSVFuSXNJQ2mrsgLoHez61g7Lu/0vu5FSyISyjmxOfD1f+Fe/+GAU/apiHLPAW/vA6vdYaPRsDOpWDNraJPJiIiznKrEd1zxZ3qG6V2qW2/u8WVNJSkpKWGi6vptY36WmnSbiJRPW+H3T/a6nh3Lz+zhURDlxug0/UQVL8qP6aIiJRAQVdE3FrhkoaSlLbUcMHXL4hLKGYmh0HQchCc2Atxc2HzR7ab11Y8Baueg9aX28oaYvuASnpERM4Z/RdXWLVqFSaTiVOnTrm6K+dUbf3cUjxnlhouadTXvuxwWFMY9DTctwOueRuiu4M1B7Z/Df+7Cl7vAuvmwOlj5+hTiYjUbgq6UiWaNm3K7Nmzi+yfPXs2sbGx9ufTp0/HZDIV2Vq3bl3iuU+cOMGdd95Jq1at8Pf3JyYmhqlTp5KcnOzQLjY2tsh5S1o4xFmXX34577zzDgATJ05k5syZDsf/+usvhg8fbn/v4r4DqZ6cqektbdQ3X2JyBusT0kiMvQomLIPb1kO3ieAdZBvxXf4EvHwefH497P4JNFWhiEiVUemCuFzbtm358ccfHfZ5epb8q3no0CEOHTrErFmzaNOmDfv372fy5MkcOnSIL774wqHtzJkzmThxov15YGDgWffTMAw2bNjACy+8AMDatWt5/fXXHdqkp6fTtGlTRo4cyT333HPW7yWuUVZNb1kzORRf1tAWLp8Fl063TUm2aT4c3Ggb5d3+NYTEQOfx0HEchDQ8h59WRMT9aUTXRRKTM1i/J+nMP3lWoaysLKZOnUq9evXw9fXlwgsvJC4urki7devWcf755+Pr60v37t35888/7cf279/PlVdeSWhoKAEBAbRt25YlS5ZUSv88PT2JjIx02EpbDa9du3YsXLiQK6+8kmbNmnHxxRfz9NNP880335CTk+PQNigoyOG8xQXd0j53Qf/88w9Wq5U2bdpw/Phxdu/eTffu3R3adOvWjRdffJExY8YUu+KeVH9RIX70bBZebF1vaaO+ZZY1+ASS2HwU6y/+nGPXrYALJoFvCCQnwMqnYXY7+Gg4/PUl5GQVeW8RESk/jei6QPGjPqUsS1pBDz74IAsXLmT+/Pk0btyYF154gUGDBrF7927CwsLs7R544AHmzJlDZGQkjz76KEOHDmXnzp14eXlx++23k52dzZo1awgICGD79u0VGh2tbPlrXRceCX7++ed58skniY6OZuTIkTzwwANFpocr7XMDXHHFFaxdu5acnBwyMjIIDQ0lNzeX3NxcGjVqBKA631qkpFHfsm5mK/rn/g5G3zcDti+2jfLuX2ebvWH3j7aFKtqPgk7jbItS1LLZMkREKotGdM+xMkd9KllaWhpvvvkmL774IoMHD6ZNmza8++67+Pn5MXfuXIe206ZNY8CAAbRv35758+dz5MgRvvzySwASEhLo3bs37du3p2nTplxxxRX06dOnUvr4559/EhgY6LDdcsstTr/++PHjPPnkk0yaNMlh/1133cVnn33GypUrueOOO5g9ezZTpkwp8vrSPjfAe++9x5YtW7jwwgv5z3/+w5YtW7jmmmu444472LJlC1u2bDnrzy41U3GjvqXdzFbin/t04PzRcNMSuHMTXHQ/BDWAjJPw29vwdh94s5ftBraUxHP3AUVE3IRGdM8xZ6cwqix79uzBYrHQu3dv+z4vLy8uuOAC/v77b4e2PXv2tD8OCwujVatW9jZTp07ltttuY9myZVx66aUMHz6cDh06VEofW7VqxeLFix32BQUFAfDMM8/wzDPP2Pdv376dmJgzo98pKSlcfvnltGnThmnTpjmco2CNbIcOHQgNDWXEiBE8//zzhIeH24+V9rkBIiMjsVgsbNiwgddee43Y2Fh++eUXPvjgA4cb66R2yy9reHTRNnINw6GsoazliBOTM4g/FUKTrvcT1f9R2LMSNn9oW3b46HbbDWw/Toem/eD8a23TlXkHuOJjiojUKAq659jZLkt6tgzD9kaFFwowDMOpxQPy29xyyy0MGjSI7777jmXLlvHss8/y0ksvceeddxb7uuDg4CKzIIDtn/hDQkIc9nl7e9O8efNizzN58mRGjRplf96gQQP749TUVC677DICAwP58ssv7aUGJenRowcAu3fvdgi6xcn/3PlB2zAM0tPT6dSpE2AbKR80aBAmk4nvv/+eiy66qNTzSe1QUllDaX/uiy9luhRaXGob2f3rK/jjMziwAfassG3egdDmKjh/DDS+UHPzioiUQP91PMfOZlnSimjevDne3t6sXbvWvs9isfD7779z3nnnObTdsGGD/fHJkyfZuXOnwzRf0dHRTJ48mUWLFnHffffx7rvvlvi+rVq1KvaGt7i4OFq1auV0/8PCwmjevLl9y6/BTUlJYeDAgXh7e7N48WJ8fX3LPNfmzZsBiIqKcthf2ueePHkyW7ZsYdKkSYwcOZItW7bw2GOP0a9fP/744w+2bNlC165dnf484v6KK2so6c89UGIpU2JyBusP5ZLYYgxMWApTN0O/RyA0FrJPw5aPYf6VtpvYlj4GhzaDYRTujohIraYRXRcoz7KkFRUQEMBtt93GAw88QFhYGDExMbzwwgukp6czYcIEh7YzZ84kPDyc+vXr89hjjxEREcHVV18NwN13383gwYNp2bIlJ0+eZMWKFUWCckH33HMPF110ETNnzmTEiBEALFy4kB9++IH169c7tM3JyeHw4cMO+0wmE/XrF79sampqKgMHDiQ9PZ2PPvqIlJQUUlJSAKhbty4eHh788ssvbNiwgf79+xMSEkJcXBz33HMPQ4cOdSh9KOtzh4WFERYWxvbt2xk9ejTNmzdn165dDBw4sNhR6OzsbLZv325/fPDgQbZs2UJgYGCJo9ZSOxT3576kkoYP1u7jvbV7C43yNoV+D0Pfh+DAr7ZR3r8WQcpB+OV12xbWDNoNh/YjoK7zf6EUEXFXCrou4uyypJXhueeew2q1Mn78eFJTU+natStLly4lNDS0SLu77rqLXbt2cf7557N48WL7DAW5ubncfvvt/PvvvwQHB3PZZZfxyiuvlPiePXr0YOnSpcycOdO+aELbtm1ZunRpkSm5/vrrryKjrD4+PmRmZhZ77o0bN/Lrr78CFAmP8fHxxMbG4uPjw4IFC5gxYwZZWVk0btyYiRMn8uCDDxb7/ZT0ucEWxNetW8d///tfAFavXl3kLwn5Dh06ZC9vAJg1axazZs2ib9++rFq1qtjXSO1R+M99cSUNZrCHXDgzytunZV3ba00miOkBMT1I7DWN5K3f0zjxe/z2LocTe2DNC7atfjtb6G115bn9kCIi1YjJMPRvXQWlpKQQEhJin66qoMzMTOLj42nSpIlT/1ReG1mtVlJSUggODsasusFqpzy/wxaLhSVLljBkyJAy65/l7C2IS3C4gW3ChbG883N8kXafTuxBz2bhDq8rWNv74tCmDA/407Yoxe4fbUsP5znuG0udHtfh0e4aiNC/LNRU+jPpHnQdK0dpea0gjeiKiLhQ4ZIGgPfWxpd6w2px05U9uDieXg9fSVSHUZB+grjv52H54wu6m7YTnrkPVj1l2+q1JbXZEHZHXExks45E1amaG2FFRKoDBV0RERcrXNJQ0jRl+cqapjDR4sfo31thNR4jjBQGeGxkiMev9PHcjunoXwQd/YtOvEi8NZK/mw/mvH5joFE3ElOziU9Ko0lEgP39EpMziuwTEakpFHRFRKqZsm5YLWuawoJB+ATBLMjtz4Lc/rx9ZRN+/Goel5l/40LznzQxH4a9H8DeD8j0DmNNRgeW5XbhF6Md04bZZhM5l6s4iohUNgVdEZFqqLQbVktbnAJKuMnNBBafOvxfbl/+L7cvAWTQ1/wHAz1+53LfP/HNPsFoj1WM9lhFpuHFL4vbstLaiSijIwepW/SmuBJoBFhEqhMFXRGRGqi0Ud/CQdiEwVNXtaVL41B7AE7DjyXWHiw1euJ1eVs++fxTBpg3cqnHJhqZkujvsYX+HluY6QU7rNGssXZgrbUdCYc7EBXSqNg+Fb/4RdER4NLCsIKyiFQmBV0RkRqqtFHf/CC850gKe7ZsYGSXRnh5eRU7Ety5SV3uNNqzLqc903NuoKXpXy4xb+Zij810Nu2ktfkArc0HuJXvMD5/BaK7Q7P+0LQ/RJ0PZo9ib5ArbgS4tDBcVlAuTwhWYBYRUNAVEXFbUSF+RPh7cvzvM/tKGgk+E4BhDzFMuHoIe4FJizbQ2/QHF3ls4/KAHQRkHoZ9P9u2n2aCXyg06UtacDcaEMC/1LO/V8Eb5KD42SLywzAUv0pcflB2drQYKmdk2ZnjIlL9KeiKiNQyxY0ElxSAbfsuITbCn4BgXzi+B/auhD0rbWE34yRs/4rmfMVaH9hnrc+v1vOIM1qxydqa2HDnZoswMEo8BqWH4IIqY2S5rOMlBWAFY5HqR0FXRESA4gNwkX0RzW3bBRMhNwcObbKF3r0rsR6II9Z8hFjzEUazytb+vefyVnLrRYuwTniZcrEYHvbTFZwtoqSZJMqaTq0gZ9qWFYZLO75m57FiA/DZBOOCFJJFqoaCbi21atUq+vfvz8mTJ6lTp85Znyc9PZ3x48ezfPlyUlNTOX78OB07duSee+7hnnvuqbwO13D79u2jSZMmbN68mY4dO7q6OyKVw8MToi+wbf0ewpyVyontK8nYvY66JzfhfWQLnD4C27+G7V9TF9geEMAv2c3YbG3GNqMZV1x2eTHlE0VnkihtOrWCypp6DcoOwyUd37jvZLEBuHVkULmDcUHO1iY3CvEp8nk1uixSOq3RWgv069ePu+++22Ffr169SExMJCQkpELnnj9/Pj///DPr16+vlPOdjeI+H8BXX32FyWSyP583bx4mk6nIVtpSuBaLhYceeoj27dsTEBBAgwYNuP766zl06FCRPhQ+75gxYyr0uW6//XYeffRRAJ5++mluvvlmh+OJiYmMHTuWVq1aYTabi/0ORM4pnyDCOg2l4cjn8b51OTx8AG76AS55AloMBJ8QvHLS6GPeyl2eX/Ku1yyu+qk/vNwGPhvH6Mz/47drPfj8hjasfbi/PezlzyLhkffnubhFNPI50zY/DBdUMAyXdJxCARpsAThu38lyBePE5Ax7u5JGj/PbLIhLoPdzKxj77q/0e2kNvxw507GCx3o/t4IFcQml7k9MzmD9niSH9xdxdxrRraW8vb2JjIys8Hn27NnDeeedR7t27QCwWq0VPmdVCg4O5p9//nHYVzAMF5aens6mTZv4z3/+w/nnn8/Jkye5++67GTp0KL///rtD24kTJzJz5kz7cz+/io2i/PLLLzz55JMArF27lhEjRjgcz8rKom7dujz22GO88sorFXovkSrh5QuNe9o2AGsuHP0bEn6BQ5vh4CY4tgNSDtq2Hd8SDoQDhMRAZDuo3xbqt2N00/b0eagv+45nFruIRkFlLbhR1jzEJR0vOD1bPg+TiW6xxe8vKRgXLKMobXQZitYmL9hrZkpyJl5eOeUaXT6VYeH573dU+AY9kZpGQdfN3XjjjaxevZrVq1czZ84cAOLj49m3b59D6cK8efO4++67+eijj7jvvvs4cOAAQ4YMYf78+XzxxRdMmzaN5ORkrrvuOmbPno2Hhwf9+vVj9erVgC0s9u3blxUrVhTpQ0JCAnfeeSc//fQTZrOZyy67jNdee4369euTnJxMWFgYv/32G126dMEwDMLDw2nWrBlxcXEAfPrpp9x7770kJiZW+PswmUzlCvghISEsX77cYd9rr73GBRdcQEJCAjExZ/6Pwt/fv8xz79ixgylTprBp0yaaNWvGf//7X/r161ekXVpaGtu2baNnz55YrVZ++eWXImE2NjbWfk3ff/99pz+TiMuYPWzhNbLdmX1ZqZD4hy30Htpk+3lqPyQn2LZ/ltibRnn5E1W3FYS3gIgWEN7c9jOsGXg7ljGUNvUalB2Gy56d4kwAPj86tFzBuGAZRWmlFsWFYAMTCSfSMXt4lGt0+bnvd2BU8Aa9whSKpSZQ0K0IwwBLumve28sfShmJzDdnzhx27txJu3bt7KONdevWZd++fUXapqen8+qrr/LZZ5+RmprKsGHDGDZsGHXq1GHJkiXs3buX4cOHc+GFFzJ69GgWLVrEww8/zLZt21i0aBHe3t5FzmkYBldffTUBAQGsXr2anJwcpkyZwujRo1m1ahUhISF07NiRVatW0aVLF7Zu3QrA1q1bSUlJITg4mFWrVtG3b9+KfV+VKDk5GZPJVKS2+eOPP+ajjz6ifv36DB48mGnTphEUFOTQ5oEHHmD27Nm0adOGl19+maFDhxIfH094eDgAU6ZM4ZNPPsFqtWKxWGjatCmGYZCSkkKPHj0A23dTMGCL1Gg+QRB7oW3Ll34Cjm6Hw9vgyJ9w5C/bSLAl3TYSfGhz0fOERJ8JvuEtbDfMhTWF4Ibg4VXsW5cVhsszO0V5gnFpi3uUVZtswiAmzB8vL0+nR5fNlD2y7OxsFfnKG4oLU0iWc0VBtyIs6fBMA9e896OHwDugzGYhISF4e3s7NdposVh48803adasGQAjRozgww8/5MiRIwQGBtKmTRv69+/PypUrGT16NGFhYfj7+zuUQRQuXfjxxx/ZunUr8fHxREdHA/Dhhx/Stm1b4uLi6NatG/369WPVqlXcd999rFq1iksuuYS9e/eydu1ahgwZwqpVqyrtxrbk5GQCAwMd9vXq1Ytly5Y59frMzEwefvhhxo4dS3BwsH3/uHHjaNKkCZGRkWzbto1HHnmEP/74o8ho8B133MHw4cMBePPNN/nhhx+YO3cuDz74IAAzZ87kwQcf5KmnngLg8ccf55133mHHjh28/PLLADRo4KLfOZFzxT+saPjNzYETe+DYP3B8FyTtzvu5CzJPQfIB27Z3peO5TGYIioKQRrYwXCc673FM3s+G4BPs1MBBvpICcnmCsTNtCodgswlGNbESFeJb4uIfxY0uP3hZK57/YUeFbtArqLyhuDCNHMu5pKArdv7+/vaQC1C/fn1iY2MdgmH9+vU5evSo0+f8+++/iY6OtodcgDZt2lCnTh3+/vtve9CdO3cuVquV1atXc8kllxATE8Pq1avp3LkzO3furLQR3aCgIDZt2uSwL7+W9uOPP2bSpEn2/d9//z0XXXSR/bnFYmHMmDFYrVbeeOMNh3NMnDjR/rhdu3a0aNGCrl27smnTJjp37mw/1rNnT/tjT09Punbtyt9/n5nNPyIigoiICNavX8+cOXOIjY0lLi6OG264gdjY2Ip9eJGazMMT6raybQUZBqQftwXe/OB7fLft56n9kJt9pgb4wK/Fn9vTDwLrQVCk7WdgfQjMexwQAf7hts0vDPzq2EowyqGskePS2hQMwQ1DvNm8bkWxx8oaXa7j71XqyLIzs1XkK08oLqyqRo61rLSUREG3Irz8bSOrrnrvyj6ll+M/75lMpmL3leeGM8Mwir3Zq+D+Pn36kJqayqZNm/j555958skniY6O5plnnqFjx47Uq1eP8847r8T3CA4OJjk5ucj+U6dOOYy6ApjNZpo3b17seYYOHUr37t3tzxs2bGh/bLFYGDVqFPHx8axYsaLIeQvr3LkzXl5e7Nq1yyHoFif/eygYtNPS0rj66qsxmUykp6ezbt06Jk+ezNtvv824ceNKPZ9IrWIy2cJoQMSZm97yWa2QduzMaO+pA5D8r+PzzFOQk2ELxaf2O/OGtrCbH3p964BviOPmE3Rm8w7M+xlg27z8bT89vJ0eRc4PwRaLhcJFG86OLlf0Br2CyhOKC6uKkeNztay01EwKuhVhMjlVPuBq3t7e5ObmuuS927RpQ0JCAgcOHLCP6m7fvp3k5GR7eM2v03399dcxmUy0adOGBg0asHnzZr799tsyR3Nbt27N999/X2R/XFwcrVq1KuYVxQsKCipSUwtnQu6uXbtYuXKlvZ62NH/99RcWi4WoqCiH/Rs2bKBPnz4A5OTksHHjRu644w7gTND++uuvWbRoEfPnz2f9+vU89dRTLFliuyGnfv36Tn8ekVrPbIag+ratUdfi22Sn2+b6PX0072eBLfWIbbQ4/bitbjgrGTBsq8FlnKxY30wethDs7Z8XfvN+evrafnr52kaavXzt+81mL5od2Yf598Pg4w9efuDpY2vn6VPgue+ZLf88Hp4VvkEvX3lCcWGVPXJ8rpaVlppLQbcWiI2N5ddff2Xfvn0EBgYSFhZ2zt770ksvpUOHDowbN47Zs2fbb0br27cvXbue+T+efv36MWfOHK655hpMJhOhoaG0adOGBQsW8Oqrr5b6HlOmTOH111/n9ttv59Zbb8XPz4/ly5czd+5cPvzwQ4e2hmFw+PDhIueoV68eZnPRaaVzcnIYMWIEmzZt4ttvvyU3N9f++rCwMLy9vdmzZw8ff/wxQ4YMISIigu3bt3PffffRqVMnevfu7XC+//73v7Ro0YLzzjuPV155hZMnT9rnx80P2rt27eLSSy+lefPmfPLJJ/Tv37/EUegtW7YAcPr0aY4dO8aWLVvw9vamTZs2pX5nIpLH2x/Cmti2suRabAE3P/hmJhfaTtl+ZqXatuzTBR6n2e7ryM22ncvItQXnrKL/GlUSD6AdwKFPy/85TR62IOzllxegHUO0LVz7EeXlS5SXP8QHgFdAgSBeMJQHMDomgP6Tm7I/1USjqHpEhYc61Y3KHjmuqmWlNdLrPhR0a4H777+fG264gTZt2pCRkUF8fPw5e2+TycRXX33FnXfeSZ8+fRymFyuof//+vPzyyw5TbfXt25ctW7aUOaIbGxvLzz//zGOPPcbAgQPJzMykZcuWzJs3j5EjRzq0TUlJKTLKCrbFF4q7We/ff/9l8eLFAEVWNFu5ciX9+vXD29ubn376iTlz5nD69Gmio6O5/PLLmTZtGh4ejrV8zz33HM8//zybN2+mWbNmfP3110RERDi0WbVqFf/9738BWL16dZGFIgrq1KmT/fHGjRv55JNPaNy4cbGzaohIBXl45dXw1jv7c+RazoTe7HRbGM5/nJMBlrwtJ7PA4wywZGLNTuPg/r00rB+OOTfbvp+cTMjJsj3Pycp7bSbkZp15XyPX9l7Zpyv+PeSpl7cBtlKMgiUbPoVKOHyCwDcYfIIZ7RPEgJE+HMr0pX79SOrW87b129Nx5TdnQnFZYfhslpUuaTU7hd+ayWQYhlF2s9ojJSWFkJAQkpOTi9RhZmZmEh8fT5MmTUpdTas2s1qt9mnBihshFdcqz++wxWJhyZIlDBkypEitttQcuo7uo9zX0mq1hd2CwTk/BFvS8x6nnwnU+aE6Oz0vfKed+VkwnFvynuc/rixe/uAXWmCrA36hnDYHkWQNpk7dKOpENMi7QTCvLtvLVoJQOAwXrNEt7lhicga9n1tRJAQvmtKTa95YX2T/g4NbObXghjP0Z7JylJbXCtKIroiIiDsym8GcV6ZQVazWAiUaKY4/M1POlG5kpeaVauQ9zkyGjFO2UpDMU2BY80J3um2GjAIC87ZieQcyOiCCa2LDOe0Zjk9oFAHp22BjfQiKYnSj+vS7sy170/yIrRdU5up3adm5Z73ghlRPCroiIiJydsxmW0mCbzDQsMzmxbJabeE489SZG/0KbuknIT3JNoNGWlLedgysFns5hjf7CAM4UPT09YH6Jo+8+ZQbQnADCG7I6OCGDBxelwO54dSPaUH9yEYkpmSe1YIbUn0p6IqIiIjrmM15ZQp1IDTWudcYhm1UOP24LfQWnDUj9fCZn6mHbceNXEj517YVEJq3AeDlT1SdGNY2imDFYV8SrHU5QCQX9+rOjLXpnDbOlHs5O52auJ6CroiIiNQsJtOZcBzerPS2uTmQdhRSDtnmUU456Pg4+V9ITbSVTRzbQQPgOg9s01wAxMFIH0gygok3Iok3GtCqbSeiEgFLC1s4z1tmWjesVT8KuiIiIuK+PDzzyhUalDyfck6WLfCeSsjb9sPJfXAiHk7GQ8ZJIkwpRJhS6MZO2LEKduS91uwJoU046NmQbw8GsctowC4jmvFDBzKih/NzuUvVUNAVERGR2s3TxzYyXNLocMYpW+A9vidviemdZ5abtqTD8V00ZBeTCqSq3O//Q86vTfGMbAv120K9NrafQY3OyUcSGwVdERERkdL41QG/TtCgk+N+qxVSD/HXnxv57PsVNDMdooXpIK3NCYSbUuHkHtv292L7Szy9A7jQqyFmz3XQsBNEnQ8RLW0jz1Lp9K2KiIiInA2zGUIaEdY+nI+/8ywwO4NBfVMKS64NI/z0bji6HY78hXF0B6bsNMKzd0LcTojLa+7pB5HtoGEXaNgVGnWB0Ca2WmSpEAVdERERkQooOi+vmXuHXUh4hzOLSiyIS+CxRX/QhEO0N+3l1haptDbi4fBW2zRp/8bZtnz+4XmhtxvEdLc99tZMD+WloCusWrWK/v37c/LkSerUqePq7oiIiNQ4o7vF0KdlXfYlpRMb4e8w60JickbessJmdtGIXUYjvtoJ6x6+mKggHzixBw5thn9/h4O/Q+JW29Rpu5baNrDd9BZ1PsT0hJgeENMLAsJd9GlrDq3RKlWiadOmzJ49u8j+2bNnExsba38+ffp0TCZTka1169YlnvvEiRPceeedtGrVCn9/f2JiYpg6dSrJyckO7WJjY4uc9+GHHz7rz2S1WgkODmbnzp0AtGjRgjVr1ji0WbRoEYMGDSIiIgKTycSWLVvO+v1ERKRmiQrxo2ez8CJTi8UnpRVZdMJqwL6kdDCbSfRqxPqAi0nsPQMmroBH/oUJP8Jlz0HbayCoAVhz4OBG+OV1WHAdvNgU3ugFSx6E7Ysh7fg5/KQ1h0Z0xeXatm3Ljz/+6LDP07PkX81Dhw5x6NAhZs2aRZs2bdi/fz+TJ0/m0KFDfPHFFw5tZ86cycSJE+3PAwNLXEiyTNu2bcPHx4eWLVty9OhREhIS6Natm0ObtLQ0evfuzciRIx3eV0REaq8mEQFFV1wzQWyEPwviEvJGe237nh3WntHdYiC6m23rcZttgYzkA5CwARJ+gf2/wLG/4ehftu23t20njewATfvZtpieKnVAI7ouczjtML8l/sbhtMNV/l5ZWVlMnTqVevXq4evry4UXXkhcXFyRduvWreP888/H19eX7t278+eff9qP7d+/nyuvvJLQ0FACAgJo27YtS5YsqZT+eXp6EhkZ6bBFRESU2L5du3YsXLiQK6+8kmbNmnHxxRfz9NNP880335CTk+PQNigoyOG8BYNubm4uEyZMoEmTJvj5+dGqVSvmzJlT4vuuX7+e3r17A/Dzzz/TqVMn/Pwc/9Y+fvx4nnjiCS699NKz+SpERMQN5dfweuTdXGbC4Kmr2gDYQy7YgvCji7aRmJzheAKTCerEQIdRcMUrcPsGeGAPjPofdJsIdc+ztTu8Fda/Ch8Ng+cbw/yhsO5VOLLdFpZrIY3ousCiXYuY8csMrIYVs8nMtJ7TGNZiWJW934MPPsjChQuZP38+jRs35oUXXmDQoEHs3r2bsLAwe7sHHniAOXPmEBkZyaOPPsrQoUPZuXMnXl5e3H777WRnZ7NmzRoCAgLYvn17hUZHK1tycjLBwcFFRoKff/55nnzySaKjoxk5ciQPPPAA3t7egK0UoVGjRnz++edERESwfv16br31VqKiohg1apT9HPl1y5mZmRiGQZ06dcjKyiI3N5c6depw4YUX8u23356zzyoiIjVPfg3vniMp7NmygZFdGhGXkFykpCHXMNiXlE5UiF/pK60FRECbq2wbQOoRiF8De1fB3pW2Vd/iV9u25f+xlT+0uBRaDLKN+PpUn/8Pr0oKuufY4bTD9pALYDWszPhlBr0a9CIyILLS3y8tLY0333yTefPmMXjwYADeffddli9fzty5c3nggQfsbadNm8aAAQMAmD9/Po0aNeLLL79k1KhRJCQkMHz4cNq3bw/YanAry59//lkkNI8ZM4b33nvPqdcfP36cJ598kkmTJjnsv+uuu+jcuTOhoaH89ttvPPLII8THx9vP6+XlxYwZM+ztmzRpwvr16/n8888dgu6WLVswDIMuXbrwySef0Lp1awYOHMj06dPp1asXvr6+iIiIlCUqxI8If0+O/217XlxJg4fJVHpJAyUsNRxUHzqMtG2GYVvQYs9PsPsn2PczpB6CTf+zbR7eEHshtBoCLS+DOtHn+Js4dxR0z7GElAR7yM1nNawcSD1QJUF3z549WCwW+z+5gy3gXXDBBfz9998ObXv27Gl/HBYWRqtWrextpk6dym233cayZcu49NJLGT58OB06dKiUPrZq1YrFixc77AsKCgLgmWee4ZlnnrHv3759OzExZ6ZrSUlJ4fLLL6dNmzZMmzbN4Rz33HOP/XGHDh0IDQ1lxIgRPP/884SH2+5Ufeutt3jvvffYv38/GRkZZGdn07FjR4fzxMbG8ttvv+Hv789ll13GwYMHOXToEMOHD8fHx6dSvgMREal9ik5LZuKZYe2A4ksa+rSsy5qdx4oE4D4t6zoGX5MJ6ra0bT1uA0sG7F8HO5fZZnE4uQ/2rLBtS+631fa2vgLaDIW6rd1q/l4F3XMsJjgGs8nsEHbNJjPRQVXztykjrybHVOiX1jCMIvuKk9/mlltuYdCgQXz33XcsW7aMZ599lpdeeok777yz2NcFBwcXmQUB4NSpU4SEhDjs8/b2pnnz5sWeZ/LkyQ6jqw0aNLA/Tk1N5bLLLiMwMJAvv/wSLy+vUj9Ljx49ANi9ezfh4eF8/vnn3HPPPbz00kv07NmToKAgXnzxRX799Vf7awYPHszPP/9MTk4OOTk5BAYGkpubS1ZWlj0snz59utT3FRERKUlx05Kt35NUbEnDxn0niwTghxf+iSlvVLjgyG+RUd/ml9o243nbaO/O7+Gf7+HAr7ba3sNbYdUzEN4iryRiqC0A1/DQq6B7jkUGRDKt57QiNbpVMZoL0Lx5c7y9vVm7di1jx44FwGKx8Pvvv3P33Xc7tN2wYYN9tPTkyZPs3LnTYZqv6OhoJk+ezOTJk3nkkUd49913Swy6rVq1KvaGt7i4OFq1auV0/8PCwhzqiPOlpKQwaNAgfHx8WLx4sVPlA5s3bwYgKioKsN1Q1qtXL6ZMmWJvs2fPHofXvPfee2RkZHDDDTcwbNgwrrrqKu6//35at27NLbfc4vTnEBERKUlUiJ9DDW5JJQ0U2gdgcOY+s/yR31MZFp7/fkexZQ8Oo72974K0JNj5A/z9jW2E9/gu+HmWbQuNhfOuhDZX21Ztq4GhV0HXBYa1GEavBr04kHqA6KDoKgu5AAEBAdx222088MADhIWFERMTwwsvvEB6ejoTJkxwaDtz5kzCw8OpX78+jz32GBEREVx99dUA3H333QwePJiWLVty8uRJVqxYwXnnnVfi+95zzz1cdNFFzJw5kxEjRgCwcOFCfvjhB9avX+/QNicnh8OHHWefMJlM1K9fv9hzp6amMnDgQNLT0/noo49ISUkhJSUFgLp16+Lh4cEvv/zChg0b6N+/PyEhIcTFxXHPPfcwdOhQe5hv3rw5//vf/1i6dClNmjThww8/JC4ujiZNmtjfq2HDhuTk5LB161Y++ugjmjRpwtatW3nooYeKHYU+ceIECQkJHDp0CIB//vkHwD7rg4iISFlKKmno0ji0SAAuLNcweO77HUXCb5+WdYve0Aa2m9o6XWfbMlNg51L4+2vY9aOtxGH9a7atTmNoNxzaj4T6barkc1cFBV0XiQyIrNKAW9Bzzz2H1Wpl/PjxpKam0rVrV5YuXUpoaGiRdnfddRe7du3i/PPPZ/HixfYZCnJzc7n99tv5999/CQ4O5rLLLuOVV14p8T179OjB0qVLmTlzpn3hiLZt27J06VK6d+/u0Pavv/6yj7Lm8/HxITMzs9hzb9y40V5eUDhsxsfHExsbi4+PDwsWLGDGjBlkZWXRuHFjJk6cyIMPPmhvO3nyZLZs2cLo0aMxmUxce+21TJkyhe+//97hnL///jt16tShSZMm/Pvvvxw5coSuXbsW27fFixdz00032Z+PGTMGsN3oN3369BK+LREREUclrbRWMACbyRvRLfA6M0WDcH7ZQ1hgCTM45PMNPnNDW3Ya7P4Rtn9tC7+n9sPal21bvTbQfoQt+IbGVv6Hr0Qmw6ilE6uVICUlhZCQEPt0VQVlZmYSHx9PkyZNdKd9CaxWKykpKQQHB2M2a5rm6qY8v8MWi4UlS5YwZMiQMuufpfrSdXQfupbuoTKuY2Jyhj0Ar9l5zGHk98HLWvH8Dzscwq4Jiq3jdVp2uq28YdtC2LUMcrPPHGt0AZw/xraCm3/RUsOqUlpeK6jGJJGnn36aXr164e/vb5/XtLCEhASuvPJKAgICiIiIYOrUqWRnZxfbVkRERKQmKrjU8OhuMax9uD+fTuzB2of7M6lvM4fFKfKDXpmLUpTG2x/aDYMxH8P9u2Do69CkL5jM8O9v8N298FIr+OnJyv2glaDGlC5kZ2czcuRIevbsydy5c4scz83N5fLLL6du3bqsXbuW48ePc8MNN2AYBq+99poLeiwiIiJS9QrfzFaw7OF4WhZ3fLLZoX3BRSnKza8OdB5v21IPw59fwB+fwZE/Iaj63YtSY4Ju/sT+8+bNK/b4smXL2L59OwcOHLBPQfXSSy9x44038vTTT5c6rC0iIiLiTvLDb2JyRpEb2MzA8bQsEpMzzi7s5guKhF532LbD2yCkYYX7XdlqTNAtyy+//EK7du0c5lkdNGgQWVlZbNy4kf79+xf7uqysLLKysuzP8+/et1gsWCwWh7YWiwXDMLBarVitjos+iE1+yXf+9yTVi9VqxTAMLBYLHh4epbbN//0v/OdAahZdR/eha+kezvV1jPD35Kmr2vD419uxGrZ6XQO445PNmE3w1FVtGNmlUcXfKDxv6tBz9Lmc/f7cJugePny4yHRUoaGheHt7F5m6qqBnn33WYRnYfMuWLcPf399hn6enJ5GRkZw+fVq1v2VITU11dRekGNnZ2WRkZLBmzRpycnKces3y5curuFdyLug6ug9dS/dwLq9jADCtE8Snmpi/y4yBrX7XasBjX/1F0u6tZFtN1PU1qFNDFvxMT093qp1Lg+706dOLDZkFxcXFlTiVU2HFrfRV1gpgjzzyCPfee6/9eUpKCtHR0QwcOLDYWRcOHDhAYGCgZl0ogWEYpKamEhQU5NTKa3JuZWZm4ufnR58+fZyadWH58uUMGDBAd3jXYLqO7kPX0j248jpu2HuCebt+d9hnYOKVvzwx8mZkqLQR3iqW/y/wZXFp0L3jjjvs84yWJDY21qlzRUZGOizdCrbVvSwWS4kLD4BtvlYfn6J/ffHy8iryC5ibm4vJZMJsNmvqrBLklyvkf09SvZjNZkwmU7G/3yUpT1upvnQd3YeupXtwxXVsHhlc7IITBReX+M/Xf9P/vMiK1e6eA85+dy4NuhEREURERFTKuXr27MnTTz9NYmKiffGBZcuW4ePjQ5cuXSrlPURERERqqsIrrpmBwnfTVGhGhmqoxtToJiQk2JdXzc3NZcuWLYBtZazAwEAGDhxImzZtGD9+PC+++CInTpzg/vvvZ+LEiZpxQURERATHqcf8vc1c88Z6hxFeD5OJ2Aj/kk9Qw9SYf1t+4okn6NSpE9OmTeP06dN06tSJTp068fvvtloTDw8PvvvuO3x9fenduzejRo3i6quvZtasWS7uefW0atUqTCYTp06dqtB50tPTGT58OMHBwfbzdejQgTlz5lROR0VERKRS5S84cX50qMPiEh4mE88Ma+c2o7lQg4LuvHnzMAyjyNavXz97m5iYGL799lvS09M5fvw4r732WrH1t7VNv379uPvuux329erVi8TEREJCQip07vnz5/Pzzz+zfv36Sjnf2Sju8wF89dVXDjfEzZs3D5PJVGQr7aYsi8XCQw89RPv27QkICKBBgwZcf/31HDp0qEgfCp+3rPrzsrRt25Zly5YBMGDAAP73v/85HF+zZg1XXnklDRo0wGQy8dVXX1Xo/UREpPYpvLJa4aWBE5MzWL8nqXwrqVUjNaZ0QSqXt7c3kZEVX8Fkz549nHfeebRr1w6g2s+dGxwczD///OOwr7TZIdLT09m0aRP/+c9/OP/88zl58iR33303Q4cOtf9rQr6JEycyc+ZM+3M/v7P/G/GpU6fYuXMnPXr0IDc3l19//ZW33nrLoU1aWhrnn38+N910E8OHDz/r9xIRkdqt8Mpq+RbEJfDIoj+x5s3I8Oyw9g5BODE5g/ikNJpEBFTbUeAaM6IrZ+fGG29k9erVzJkzxz7SuG/fviKlC/PmzaNOnTp8++23tGrVCn9/f0aMGEFaWhrz588nNjaW0NBQ7rzzTnJzcwHbKOZLL73EmjVrMJlMDqPrBSUkJHDVVVcRGBhIcHAwo0aN4siRIwAkJyfj4eHBxo0bAdv0ZGFhYXTr1s3++k8//dR+g2FFmUwmIiMjHbbSZuUICQlh+fLljBo1ilatWtGjRw9ee+01Nm7cSEJCgkNbf39/h/MWHt1+6KGHaNmyJf7+/jRt2pT//Oc/JU54vWHDBtq2bUtwcDBbtmzB39+fZs2aObQZPHgwTz31FMOGDTvLb0NERKR4ickZ9pALthkZHl20zT6yuyAugd7PrWDsu7/S+7kVLIhLKOVsrqMR3QowDAMjwzVD+SY/P6fmqZ0zZw47d+6kXbt29tHGunXrsm/fviJt09PTefXVV/nss89ITU1l2LBhDBs2jDp16rBkyRL27t3L8OHDufDCCxk9ejSLFi3i4YcfZtu2bSxatAhvb+8i5zQMg6uvvpqAgABWr15NTk4OU6ZMYfTo0axatYqQkBA6duzIqlWr6NKlC1u3bgVg69atpKSkEBwczKpVq+jbt2/FvrBKlJycjMlkok6dOg77P/74Yz766CPq16/P4MGDmTZtGkFBQfbjQUFBzJs3jwYNGvDnn38yceJEgoKCePDBB+1tOnToQEJCAtnZ2VgsFurUqYPFYiErK4s6deoQExNj/45ERESqSnxSWpFpyPJnZACKDcF9WtatdiO7CroVYGRk8E9n10xd1mrTRkz+Zd8VGRISgre3t320sTQWi4U333zTPnI4YsQIPvzwQ44cOUJgYCBt2rShf//+rFy5ktGjRxMWFoa/v79DGUTh0oUff/yRrVu3Eh8fT3R0NAAffvghbdu2JS4ujm7dutGvXz9WrVrFfffdx6pVq7jkkkvYu3cva9euZciQIaxatYp77rnnbL6mIpKTkwkMDHTY16tXL3stbFkyMzN5+OGHGTt2rMNsHuPGjaNJkyZERkaybds2HnnkEf744w+HlW8ef/xx++PY2Fjuu+8+FixY4BB0lyxZQk5ODldccQV33303l156KTfffDOXXXYZo0aNwtNTf2RFRKTqNYkIKDLnbv6MDKWFYAVdqbYK//N4/fr1iY2NdQiG9evX5+jRo06f8++//yY6OtoecgHatGlDnTp1+Pvvv+1Bd+7cuVitVlavXs0ll1xCTEwMq1evpnPnzuzcubPSRnSDgoLYtGmTw778WtqPP/6YSZMm2fd///33XHTRRfbnFouFMWPGYLVaeeONNxzOMXHiRPvjdu3a0aJFC7p27cqmTZvo3LkzAF988QWzZ89m9+7dnD59mpycnCJT3zVq1IgjR46wZ88exowZg5eXF3FxccyfP9/hOxQREalKhefcLTwjQ+EQbAb8vatfRayCbgWY/PxotWmjy967shVeZSR/Ba3C+8pzw1lJSzAX3N+nTx9SU1PZtGkTP//8M08++STR0dE888wzdOzYkXr16nHeeeeV+B7BwcEkJycX2X/q1KkiQdJsNtO8efNizzN06FC6d+9uf96wYUP7Y4vFwqhRo4iPj2fFihVlzs3cuXNnvLy82LVrF507d2bDhg2MGTOGGTNmMGjQIEJCQvjss8946aWX7K+ZPHkyH330EVarlaysLCIjIzEMg/T0dPvn3759OzExMSW9rYiISKUpOOdubIS/PeQWDsFgW3jimjfWF7lhzdUUdCvAZDI5VT7gat7e3vYbyM61Nm3akJCQwIEDB+wjktu3byc5Odke3vLrdF9//XVMJhNt2rShQYMGbN68mW+//bbM0dzWrVvz/fffF9kfFxdHq1atnO5rUFCQQ01tvvyQu2vXLlauXEl4eHiZ5/rrr7+wWCz2m+jWrVtH48aNeeyxx+xt9u/f7/CamTNncv/99/Poo4/SqFEjpkyZwiuvvEJKSgrTpk0DoEGDBk5/HhERkYoqaUa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timflow0.003.75e-040.885.07e-0612.910.110.198
AQTESOLV0.155.48e-040.941.95e-03-0.11-
MLU0.003.75e-040.848.05e-065.20--
K&dR0.833.75e-040.834.00e-06---
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "t = pd.DataFrame(\n", " columns=[\n", @@ -260,7 +506,7 @@ "## Reference\n", "\n", "* Duffield, G.M. (2007), AQTESOLV for Windows Version 4.5 User's Guide, HydroSOLVE, Inc., Reston, VA.\n", - "* Hemker, K. en Post V. (2014) MLU for Windows: well flow modeling in multilayer aquifer systems; MLU User's guide. https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fmicrofem.com%2Fdownload%2Fmlu-user.pdf&data=05%7C02%7CMark.Bakker%40tudelft.nl%7Cad7f16364d2d4fd55dbf08de73832eaa%7C096e524d692940308cd38ab42de0887b%7C0%7C0%7C639075204580287861%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=OBoe8seXZUfoat89Dfr4g6lF%2Bn1FdtXqtp%2F18BMXCn0%3D&reserved=0\n", + "* Hemker, K. en Post V. (2014) MLU for Windows: well flow modeling in multilayer aquifer systems; [MLU User's guide](https://microfem.com/download/mlu-user.pdf)\n", "* Kruseman, G.P. and De Ridder, N.A. (1990), Analysis and evaluation of pumping test data, 2nd edn. ILRI Publ. 47, ILRI, Wageningen, The Netherlands\n", "* Moench, A.F. (1984), Double-Porosity Models for a Fissured Groundwater Reservoir With Fracture Skin, Water Resour. Res., 20( 7), 831– 846, doi:10.1029/WR020i007p00831." ] @@ -268,9 +514,21 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python [conda env:base] *", "language": "python", - "name": "python3" + "name": "conda-base-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.5" } }, "nbformat": 4, diff --git a/docs/transient/04pumpingtests/index.rst b/docs/transient/04pumpingtests/index.rst index 0cf5a078..a699e63a 100644 --- a/docs/transient/04pumpingtests/index.rst +++ b/docs/transient/04pumpingtests/index.rst @@ -40,6 +40,7 @@ problems such as pumping tests and slug tests. More to come soon. :caption: unconfined aquifers unconfined1_moench + unconfined2_vennebulten Confined Pumping Tests ---------------------- @@ -68,3 +69,4 @@ Unconfined Pumping Tests ------------------------ 1. :doc:`Moench ` - Unconfined pumping test with a partially penetrating well and observation wells at multiple depths. +1. :doc:`Vennebulten ` - Unconfined pumping test with a partially penetrating well and observation wells at multiple depths. diff --git a/docs/transient/04pumpingtests/leaky1_dalem.ipynb b/docs/transient/04pumpingtests/leaky1_dalem.ipynb index 52b9a28a..1954b188 100644 --- a/docs/transient/04pumpingtests/leaky1_dalem.ipynb +++ b/docs/transient/04pumpingtests/leaky1_dalem.ipynb @@ -28,8 +28,15 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-03T20:48:07.512956Z", + "iopub.status.busy": "2026-07-03T20:48:07.512865Z", + "iopub.status.idle": "2026-07-03T20:48:08.425769Z", + "shell.execute_reply": "2026-07-03T20:48:08.425497Z" + } + }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", @@ -85,8 +92,15 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-03T20:48:08.427051Z", + "iopub.status.busy": "2026-07-03T20:48:08.426957Z", + "iopub.status.idle": "2026-07-03T20:48:08.431384Z", + "shell.execute_reply": "2026-07-03T20:48:08.431145Z" + } + }, "outputs": [], "source": [ "# data of observation well 30 m away from pumping well\n", @@ -125,8 +139,15 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-03T20:48:08.432343Z", + "iopub.status.busy": "2026-07-03T20:48:08.432286Z", + "iopub.status.idle": "2026-07-03T20:48:08.433754Z", + "shell.execute_reply": "2026-07-03T20:48:08.433551Z" + } + }, "outputs": [], "source": [ "# known parameters\n", @@ -157,8 +178,15 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-03T20:48:08.434588Z", + "iopub.status.busy": "2026-07-03T20:48:08.434538Z", + "iopub.status.idle": "2026-07-03T20:48:08.498260Z", + "shell.execute_reply": "2026-07-03T20:48:08.497994Z" + } + }, "outputs": [], "source": [ "# unkonwn parameters: kaq, Saq, c\n", @@ -185,9 +213,31 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-03T20:48:08.499389Z", + "iopub.status.busy": "2026-07-03T20:48:08.499325Z", + "iopub.status.idle": "2026-07-03T20:48:08.727480Z", + "shell.execute_reply": "2026-07-03T20:48:08.727262Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "...............................................\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fit succeeded.\n" + ] + } + ], "source": [ "cal = tft.Calibrate(ml)\n", "cal.set_parameter(name=\"kaq\", initial=10, layers=0)\n", @@ -203,9 +253,75 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-03T20:48:08.740953Z", + "iopub.status.busy": "2026-07-03T20:48:08.740823Z", + "iopub.status.idle": "2026-07-03T20:48:08.746731Z", + "shell.execute_reply": "2026-07-03T20:48:08.746532Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimal
kaq_0_045.331872
Saq_0_00.000048
c_0_0331.163422
\n", + "
" + ], + "text/plain": [ + " optimal\n", + "kaq_0_0 45.331872\n", + "Saq_0_0 0.000048\n", + "c_0_0 331.163422" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.00592 m\n" + ] + } + ], "source": [ "display(cal.parameters.loc[:, [\"optimal\"]])\n", "print(f\"RMSE: {cal.rmse():.5f} m\")" @@ -213,9 +329,27 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-03T20:48:08.747602Z", + "iopub.status.busy": "2026-07-03T20:48:08.747545Z", + "iopub.status.idle": "2026-07-03T20:48:08.862212Z", + "shell.execute_reply": "2026-07-03T20:48:08.862031Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "hm_1 = ml.head(r1, 0, t1)\n", "plt.semilogx(t1, h1, \".\", label=\"obs at 30 m\")\n", @@ -256,9 +390,31 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 8, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-03T20:48:08.863178Z", + "iopub.status.busy": "2026-07-03T20:48:08.863098Z", + "iopub.status.idle": "2026-07-03T20:48:08.925458Z", + "shell.execute_reply": "2026-07-03T20:48:08.925275Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".......................................\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fit succeeded.\n" + ] + } + ], "source": [ "cal2 = tft.Calibrate(ml)\n", "cal2.set_parameter(name=\"kaq\", initial=10, layers=0)\n", @@ -274,9 +430,75 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 9, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-03T20:48:08.926332Z", + "iopub.status.busy": "2026-07-03T20:48:08.926276Z", + "iopub.status.idle": "2026-07-03T20:48:08.930794Z", + "shell.execute_reply": "2026-07-03T20:48:08.930549Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimal
kaq_0_048.040547
Saq_0_00.000044
c_0_0539.737569
\n", + "
" + ], + "text/plain": [ + " optimal\n", + "kaq_0_0 48.040547\n", + "Saq_0_0 0.000044\n", + "c_0_0 539.737569" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.006 m\n" + ] + } + ], "source": [ "display(cal2.parameters[[\"optimal\"]])\n", "print(f\"RMSE: {cal2.rmse(weighted=False):.3f} m\")" @@ -312,9 +534,84 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 10, + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2026-07-03T20:48:08.931697Z", + "iopub.status.busy": "2026-07-03T20:48:08.931644Z", + "iopub.status.idle": "2026-07-03T20:48:08.956788Z", + "shell.execute_reply": "2026-07-03T20:48:08.956598Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 k [m/d]Ss [1/m]c [d]RMSE [m]
timflow45.334.76e-053310.0059
timflow weights48.044.36e-055400.0062
AQTESOLV41.564.46e-05328-
MLU45.304.86e-053280.0424
Hantush45.334.76e-053310.0059
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "t = pd.DataFrame(\n", " columns=[\"k [m/d]\", \"Ss [1/m]\", \"c [d]\", \"RMSE [m]\"],\n", @@ -325,12 +622,17 @@ "t.loc[\"timflow weights\"] = np.append(\n", " 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[\"AQTESOLV\"] = [41.5575, 4.4625e-05, 327.920, \"-\"]\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", - " {\"k [m/d]\": \"{:.2f}\", \"Ss [1/m]\": \"{:.2e}\", \"c [d]\": \"{:.0f}\", \"RMSE [m]\": \"{:.4f}\"}\n", + " {\n", + " \"k [m/d]\": \"{:.2f}\",\n", + " \"Ss [1/m]\": \"{:.2e}\",\n", + " \"c [d]\": \"{:.0f}\",\n", + " \"RMSE [m]\": lambda x: \"-\" if x == \"-\" else f\"{float(x):.4f}\",\n", + " }\n", ")\n", "t_formatted" ] @@ -349,24 +651,29 @@ "\n", "* Bakker, M. (2013), Semi-analytic modeling of transient multi-layer flow with TTim, Hydrogeol J 21, 935–943, https://doi.org/10.1007/s10040-013-0975-2 \n", "* Duffield, G.M., (2007), AQTESOLV for Windows Version 4.5 User's Guide, HydroSOLVE, Inc., Reston, VA.\n", - "* Hemker, K. en Post V. (2014) MLU for Windows: well flow modeling in multilayer aquifer systems; MLU User's guide. https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fmicrofem.com%2Fdownload%2Fmlu-user.pdf&data=05%7C02%7CMark.Bakker%40tudelft.nl%7Cad7f16364d2d4fd55dbf08de73832eaa%7C096e524d692940308cd38ab42de0887b%7C0%7C0%7C639075204580287861%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=OBoe8seXZUfoat89Dfr4g6lF%2Bn1FdtXqtp%2F18BMXCn0%3D&reserved=0\n", + "* Hemker, K. en Post V. (2014) MLU for Windows: well flow modeling in multilayer aquifer systems; [MLU User's guide](https://microfem.com/download/mlu-user.pdf)\n", "* Kruseman, G.P., De Ridder, N.A. and Verweij, J.M., (1970), Analysis and evaluationof pumping test data, volume 11, International institute for land reclamation and improvement The Netherlands.\n", "* Newville, M., Stensitzki, T., Allen, D.B. and Ingargiola, A. (2014), LMFIT: Non Linear Least-Squares Minimization and Curve Fitting for Python, https://dx.doi.org/10.5281/zenodo.11813, https://lmfit.github.io/lmfit-py/intro.html (last access: August,2021)." ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python [conda env:base] *", "language": "python", - "name": "python3" + "name": "conda-base-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.5" } }, "nbformat": 4, diff --git a/docs/transient/04pumpingtests/leaky2_hardixveld.ipynb b/docs/transient/04pumpingtests/leaky2_hardixveld.ipynb index 27a36604..a99c3d07 100644 --- a/docs/transient/04pumpingtests/leaky2_hardixveld.ipynb +++ b/docs/transient/04pumpingtests/leaky2_hardixveld.ipynb @@ -28,8 +28,15 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-03T20:48:44.019198Z", + "iopub.status.busy": "2026-07-03T20:48:44.018835Z", + "iopub.status.idle": "2026-07-03T20:48:44.921905Z", + "shell.execute_reply": "2026-07-03T20:48:44.921630Z" + } + }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", @@ -86,8 +93,15 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-03T20:48:44.923227Z", + "iopub.status.busy": "2026-07-03T20:48:44.923126Z", + "iopub.status.idle": "2026-07-03T20:48:44.925580Z", + "shell.execute_reply": "2026-07-03T20:48:44.925346Z" + } + }, "outputs": [], "source": [ "data = np.loadtxt(\"data/recovery.txt\", skiprows=1)\n", @@ -110,8 +124,15 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-03T20:48:44.926543Z", + "iopub.status.busy": "2026-07-03T20:48:44.926492Z", + "iopub.status.idle": "2026-07-03T20:48:44.927847Z", + "shell.execute_reply": "2026-07-03T20:48:44.927659Z" + } + }, "outputs": [], "source": [ "# known parameters\n", @@ -125,9 +146,25 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-03T20:48:44.928772Z", + "iopub.status.busy": "2026-07-03T20:48:44.928717Z", + "iopub.status.idle": "2026-07-03T20:48:44.994471Z", + "shell.execute_reply": "2026-07-03T20:48:44.994256Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], "source": [ "# model with impermeable top and bottom\n", "ml = tft.ModelMaq(\n", @@ -144,9 +181,45 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-03T20:48:45.008161Z", + "iopub.status.busy": "2026-07-03T20:48:45.008077Z", + "iopub.status.idle": "2026-07-03T20:48:45.372523Z", + "shell.execute_reply": "2026-07-03T20:48:45.372323Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "........" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".............................................................." + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fit succeeded.\n" + ] + } + ], "source": [ "cal = tft.Calibrate(ml)\n", "cal.set_parameter(name=\"kaq\", initial=50, pmin=0, layers=0)\n", @@ -159,9 +232,75 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-03T20:48:45.373518Z", + "iopub.status.busy": "2026-07-03T20:48:45.373418Z", + "iopub.status.idle": "2026-07-03T20:48:45.380749Z", + "shell.execute_reply": "2026-07-03T20:48:45.380567Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimal
kaq_0_048.648440
Saq_0_00.000201
res0.023634
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" + ], + "text/plain": [ + " optimal\n", + "kaq_0_0 48.648440\n", + "Saq_0_0 0.000201\n", + "res 0.023634" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.009 m\n" + ] + } + ], "source": [ "display(cal.parameters.loc[:, [\"optimal\"]])\n", "print(f\"RMSE: {cal.rmse():.3f} m\")" @@ -169,9 +308,25 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-03T20:48:45.381631Z", + "iopub.status.busy": "2026-07-03T20:48:45.381575Z", + "iopub.status.idle": "2026-07-03T20:48:45.386718Z", + "shell.execute_reply": "2026-07-03T20:48:45.386544Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], "source": [ "# model with semi-confining top\n", "ml1 = tft.ModelMaq(\n", @@ -189,9 +344,437 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 8, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-03T20:48:45.387554Z", + "iopub.status.busy": "2026-07-03T20:48:45.387504Z", + "iopub.status.idle": "2026-07-03T20:48:49.747580Z", + "shell.execute_reply": "2026-07-03T20:48:49.747402Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "..." + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".............................................................." + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".." + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".." + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "..." + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "." + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + 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"stdout", + "output_type": "stream", + "text": [ + "..................................................................." + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "....." + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "...................................................................." + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "....." + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "...........\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fit succeeded.\n" + ] + } + ], "source": [ "cal1 = tft.Calibrate(ml1)\n", "cal1.set_parameter(name=\"kaq\", initial=50, pmin=0, layers=0)\n", @@ -204,9 +787,75 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 9, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-03T20:48:49.748471Z", + "iopub.status.busy": "2026-07-03T20:48:49.748412Z", + "iopub.status.idle": "2026-07-03T20:48:49.754224Z", + "shell.execute_reply": "2026-07-03T20:48:49.754041Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimal
kaq_0_044.611467
Saq_0_00.000002
res0.013883
\n", + "
" + ], + "text/plain": [ + " optimal\n", + "kaq_0_0 44.611467\n", + "Saq_0_0 0.000002\n", + "res 0.013883" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.006 m\n" + ] + } + ], "source": [ "display(cal1.parameters[[\"optimal\"]])\n", "print(f\"RMSE: {cal1.rmse(weighted=False):.3f} m\")" @@ -214,9 +863,27 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 10, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-03T20:48:49.755093Z", + "iopub.status.busy": "2026-07-03T20:48:49.755035Z", + "iopub.status.idle": "2026-07-03T20:48:49.882243Z", + "shell.execute_reply": "2026-07-03T20:48:49.882050Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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 k [m/d]Ss [1/m]resRMSE [m]
timflow confined48.652.01e-040.0240.0087
timflow semi-confined44.611.64e-060.0140.0055
MLU48.941.04e-050.0010.0076
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "t = pd.DataFrame(\n", " columns=[\"k [m/d]\", \"Ss [1/m]\", \"res\", \"RMSE [m]\"],\n", @@ -290,23 +1020,28 @@ "source": [ "## References\n", "\n", - "* Hemker, K. en Post V. (2014) MLU for Windows: well flow modeling in multilayer aquifer systems; MLU User's guide. https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fmicrofem.com%2Fdownload%2Fmlu-user.pdf&data=05%7C02%7CMark.Bakker%40tudelft.nl%7Cad7f16364d2d4fd55dbf08de73832eaa%7C096e524d692940308cd38ab42de0887b%7C0%7C0%7C639075204580287861%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=OBoe8seXZUfoat89Dfr4g6lF%2Bn1FdtXqtp%2F18BMXCn0%3D&reserved=0\n", + "* Hemker, K. en Post V. (2014) MLU for Windows: well flow modeling in multilayer aquifer systems; [MLU User's guide](https://microfem.com/download/mlu-user.pdf)\n", "* Newville, M., Stensitzki, T., Allen, D.B. and Ingargiola, A. (2014), LMFIT: Non Linear Least-Squares Minimization and Curve Fitting for Python, https://dx.doi.org/10.5281/zenodo.11813, https://lmfit.github.io/lmfit-py/intro.html (last access: August,2021)." ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python [conda env:base] *", "language": "python", - "name": "python3" + "name": "conda-base-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.5" } }, "nbformat": 4, diff --git a/docs/transient/04pumpingtests/leaky3_texashill.ipynb b/docs/transient/04pumpingtests/leaky3_texashill.ipynb index a4abfec5..f75fbfab 100644 --- a/docs/transient/04pumpingtests/leaky3_texashill.ipynb +++ b/docs/transient/04pumpingtests/leaky3_texashill.ipynb @@ -22,8 +22,15 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-03T20:48:04.258768Z", + "iopub.status.busy": "2026-07-03T20:48:04.258353Z", + "iopub.status.idle": "2026-07-03T20:48:05.390252Z", + "shell.execute_reply": "2026-07-03T20:48:05.389995Z" + } + }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", @@ -80,8 +87,15 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-03T20:48:05.391504Z", + "iopub.status.busy": "2026-07-03T20:48:05.391395Z", + "iopub.status.idle": "2026-07-03T20:48:05.394868Z", + "shell.execute_reply": "2026-07-03T20:48:05.394653Z" + } + }, "outputs": [], "source": [ "# data of OW1\n", @@ -118,8 +132,15 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-03T20:48:05.395778Z", + "iopub.status.busy": "2026-07-03T20:48:05.395717Z", + "iopub.status.idle": "2026-07-03T20:48:05.397086Z", + "shell.execute_reply": "2026-07-03T20:48:05.396930Z" + } + }, "outputs": [], "source": [ "# known parameters\n", @@ -146,9 +167,25 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-03T20:48:05.397847Z", + "iopub.status.busy": "2026-07-03T20:48:05.397789Z", + "iopub.status.idle": "2026-07-03T20:48:05.478630Z", + "shell.execute_reply": "2026-07-03T20:48:05.478425Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], "source": [ "ml = tft.ModelMaq(\n", " kaq=10,\n", @@ -180,9 +217,38 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-03T20:48:05.492073Z", + "iopub.status.busy": "2026-07-03T20:48:05.491972Z", + "iopub.status.idle": "2026-07-03T20:48:05.787181Z", + "shell.execute_reply": "2026-07-03T20:48:05.786987Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "." + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".................................................\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fit succeeded.\n" + ] + } + ], "source": [ "# unknown parameters: kaq, Saq, c\n", "cal = tft.Calibrate(ml)\n", @@ -197,9 +263,75 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-03T20:48:05.788184Z", + "iopub.status.busy": "2026-07-03T20:48:05.788086Z", + "iopub.status.idle": "2026-07-03T20:48:05.795801Z", + "shell.execute_reply": "2026-07-03T20:48:05.795631Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimal
kaq_0_0224.628611
Saq_0_00.000213
c_0_043.881709
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" + ], + "text/plain": [ + " optimal\n", + "kaq_0_0 224.628611\n", + "Saq_0_0 0.000213\n", + "c_0_0 43.881709" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.060 m\n" + ] + } + ], "source": [ "display(cal.parameters.loc[:, [\"optimal\"]])\n", "print(f\"RMSE: {cal.rmse():.3f} m\")" @@ -207,9 +339,27 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-03T20:48:05.796643Z", + "iopub.status.busy": "2026-07-03T20:48:05.796577Z", + "iopub.status.idle": "2026-07-03T20:48:05.903223Z", + "shell.execute_reply": "2026-07-03T20:48:05.903013Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", 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 k [m/d]Ss [1/m]c [d]RMSE [m]
timflow224.632.13e-0443.880.060
AQTESOLV224.722.12e-0444.00-
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "t = pd.DataFrame(\n", " columns=[\"k [m/d]\", \"Ss [1/m]\", \"c [d]\", \"RMSE [m]\"],\n", @@ -287,20 +493,25 @@ "* Duffield, G.M. (2007), AQTESOLV for Windows Version 4.5 User's Guide, HydroSOLVE, Inc., Reston, VA.\n", "* Newville, M.,Stensitzki, T., Allen, D.B. and Ingargiola, A. (2014), LMFIT: Non Linear Least-Squares Minimization and Curve Fitting for Python, https://dx.doi.org/10.5281/zenodo.11813, https://lmfit.github.io/lmfit-py/intro.html (last access: August,2021)." ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python [conda env:base] *", "language": "python", - "name": "python3" + "name": "conda-base-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.5" } }, "nbformat": 4, diff --git a/docs/transient/04pumpingtests/slug1_pratt_county.ipynb b/docs/transient/04pumpingtests/slug1_pratt_county.ipynb index 89fdb581..339ae414 100644 --- a/docs/transient/04pumpingtests/slug1_pratt_county.ipynb +++ b/docs/transient/04pumpingtests/slug1_pratt_county.ipynb @@ -28,7 +28,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -96,7 +96,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -120,7 +120,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -148,9 +148,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "volume of slug: 0.00863 m^3\n" + ] + } + ], "source": [ "Q = np.pi * rc**2 * H0 # instantaneous discharge\n", "print(f\"volume of slug: {Q:.5f} m^3\")" @@ -172,9 +180,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], "source": [ "ml = tft.Model3D(kaq=10, z=[0, zt, zb, -b], Saq=1e-4, kzoverkh=1, tmin=1e-6, tmax=0.01)\n", "w = tft.Well(ml, xw=0, yw=0, rw=rw, rc=rc, tsandQ=[(0, -Q)], layers=1, wbstype=\"slug\")\n", @@ -197,9 +214,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "........................\n", + "Fit succeeded.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "............\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fit succeeded.\n" + ] + } + ], "source": [ "cal = tft.Calibrate(ml)\n", "cal.set_parameter(name=\"kaq\", layers=[0, 1, 2], initial=10)\n", @@ -210,9 +250,63 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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optimal
kaq_0_26.048929
Saq_0_20.000213
\n", + "
" + ], + "text/plain": [ + " optimal\n", + "kaq_0_2 6.048929\n", + "Saq_0_2 0.000213" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.003 m\n" + ] + } + ], "source": [ "display(cal.parameters.loc[:, [\"optimal\"]])\n", "print(f\"RMSE: {cal.rmse():.3f} m\")" @@ -220,9 +314,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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Kg4OD4uPjo0ybNk159OiRoiiK8uOPPyr+/v6Ki4uLYmJiolSsWFHp06ePcubMmVz7PnLkiNK8eXPF0tJSqVSpkjJ9+nRl2bJlzxz1qiiKsmPHDqVhw4aKqampAigDBw5UUlJSlJEjRyr16tVTbGxsFHNzc6VmzZrK9OnTlcTExAI/h8LSZNTryy+/nKf8yff0rO/Ypk2bFH9/f8XGxkYxNTVVPD09ld69eys7duzIVe/06dNKnz59FGdnZ8XY2FhxdXVV2rVrpyxduvSZ7+XJ71pqaqoyceJEpVKlSoqZmZnSqFEjZdOmTcrAgQMVT0/PfPcxY8YMBVAOHz6c7+uPHj1SPvzwQ6VmzZqKiYmJYmtrq9StW1cZN26ccufOnVyxvPPOO3m21/S7JXRHpSg6HNomhBDlXOPGjVGpVHmu+YrSS069CiHEc4qPj+fcuXP8+eefHD9+nI0bN+o7JKFDkiiFEOI5nThxAn9/fypUqMD06dPp3r27vkMSOiSnXoUQQogC6HXCgb1799K1a1cqVqyISqVi06ZNz9wmNDQUHx8fzMzMqFKlCkuXLi36QIUQQpRbek2UiYmJ1K9fX30z97OEh4fTpUsXWrVqxcmTJ5k6dSqjR49m/fr1RRypEEKI8qrEnHpVqVRs3LixwHP7H3zwAZs3b841x+LIkSM5ffo0hw4dKoYohRBClDelajDPoUOHCAgIyFXWsWNHli9fTnp6OsbGxnm2SU1NzbXuYVZWFrGxsVSoUEEWgxVCiHJMURQSEhKoWLEiBgZPP8FaqhLlnTt3cHFxyVXm4uJCRkYGMTEx+U4gHBQUpJ61QwghhHjSzZs3qVy58lNfL1WJEvJOEpxz5vhpvcMpU6Ywfvx49fO4uDg8PDwIDw/H2tr6mcdLT09n9+7d+Pv7q3usfxyNoHdn3c6LqRgagqlp9sPcHMzMwMQEJednMzP+TYWzMakkG5mQamxCw5oVqe7plF3fwiK77n8/Y24OVlbZZZaW//8wM4NS2pPOry2EfkhblBzSFoWXkJCAt7f3M3NBqUqUrq6ueVaTiI6Oznd5phympqZ5JjUGcHBwwMbG5pnHTE9Px8LCggoVKmBsbExUXDJzd97EpEFnjDMzMM7KwCQrk5eq2GJGFqSlZT9SU3P/nN8jK+v/D5SZCUlJ2Y8HD/KNxQ6o/XjB6WeGn5ehIVmWVqSbW2Boa4ORjTXY2IC1dfbDxib7YWv7///a2hJjZM6tLGPcPN1w8XTLTrpPJNyouGTCYxLxdrTEzdY8z6Gf9fqzPNkWQn+kLUoOaYvCy/m8nnUZrlQlSj8/vzwrK2zfvp3GjRsX2xckPCaRFENjPuyYe4mnNcOb4Vc1/2SdL0WBjIz/T5opKdn/Jidn/5yS8v8/Jydz5UY0P+y4gFl6GmYZqZhmpGGenkq3Gva4GmdBYmJ2kn3838REePQo+9+cVSYyMzGIj8M0Pg7uRmkcruN/DzVDQ7C3Vz+iDM05Egex5jYcNbOiVbOaNPKpARUqgKMjW26n8n5oFMlGphioIKhnXQJ9/38h3+dNorpQEmIQQpQ8ek2Ujx494urVq+rn4eHhnDp1CgcHBzw8PJgyZQq3b99WL8k0cuRIvvnmG8aPH8/w4cM5dOgQy5cvZ82aNcUWs7ejJQYqyHpsrLChSoWXo4V2O1KpwNg4+2Fl9czqVnHJ/Hp/V57jdp3sD5r8Us/M5E7UfV79bBsWqclYpCVjlZaMdXoKn3eugn1GCiQkQFwcxMdnP+LiSL3/gEuXbmGd+gjr1CRsUx5hnJWZ3QOOicl+AG7Aq48f74kVnbr+90g0NuO+hS33f7IjpX41zCq5cT7TjN9upRNtYc99K3sG9PLjlY4+2aeQi8nao5FM2XCWLIV8E7kQovzSa6I8duwY/v7+6uc51xIHDhzIypUriYqKIjIyUv26t7c3wcHBjBs3jkWLFlGxYkUWLlxY4Bp1uuZma05Qz7pM3XCOTEXBUKViTs86Rd4Dee7jGhpyPdWAu5YOYJn7pYttnt4bPn4thr7f//P/BYqCWUYqq3vWxMcWePCAC2E3WPHHMWxSHmGfkoB9cjx2yQm0tFdhmxhH2t1oiLmPSVYGlukpWMal4BF3F/69BMCLQK7hVqv/+9fWlnRXNxIdXTHxrIypR2U84+J4kJTOv7bOuL5YHVfPvAO4tO0ZRsUlq5MkZP8RNGX9WV5wtaa+u2ZrTgohyi69Jsq2bdtS0G2cK1euzFPWpk0bTpw4UYRRPVugrwetazgREZOEl6NFsZ2me97jFqY3nGcblYp0E3Mq1qmq7sna1WvM+gj7PPvdP9kfW1tz7scl0yJoJ5apSTgkxVEhMQ7n5Dg+be1CfMRtdu89i9OjBzglPsQ5MRbnRw8wz0iFuDiM4+Kwu3RR3UNtALBkCTmr/KVZWmNSxQs8PcHLi5OGdnwbmcUNO1du2rvxv9eb0LqGU4GJMzwmMVfsAFlA98UH+VR6lqIQMjMzSU9PL5ZjpaenY2RkREpKCpmZmcVyzNLC2NgYQ0PD595PqbpGWZK42Zrr5TrW8xy3ML1STbZ5Vh03W3OCetVj6oZzJJhacsuhEnN61sHO14PkuGRmfvrEKWVgU/86jJ//N04J93F9dB/XhPu4JcRkP+Kz/3VIjsckMQHOns1+AA2Bxyc1jFlqxw17V6Ls3DjkUAm/Ts2o3rIR1+wr4unhjJuteb5/QED2ZeSpG87RuoYTbrbmcg1TPJOiKNy5c4eHDx8W6zFdXV25efOm3BueDzs7O1xdXZ/rs5FEWc4UpleqyTbPqvO015+WZBPMLLhSwZ0rFdyfGpd5WgoV4+/xTXMHaqXFcutkGKf2nqJy3F3cH96hQnI8jkkPcUx6iM/ti9kb7fsZACfglo0zUbVr4dasIWstXPnshiGXHd2JN/v/a8aZikJETBJ7L9/Lcw3zWT1VUf7kJElnZ2csLCyKJXFlZWXx6NEjrKysCrxpvrxRFIWkpCSio6MB8r3PXlOSKMuhwvRKNdnmWXWe9np+STQqLjlPLy/7V46C8t9PySZmRDh5YNcre0CTYVwyox/rnVqnJuLxIArPh3fwevAv3rH/4v3gNlVib+OQHE/l+Gg4HA2HQ/EFfv/vOLetnbjk5MlFZy8uOVfBPtKR/lvvkqXKPoWTpcDk9WdR/RefDP4RkH26NSdJPu12taKQlZVFWloaZmZmkiifYG6e/fsmOjoaZ2fnQp+GlUQpSoQnk2h+Pc2PX63FmTNn+S3ckCyF/E/xPrZNoqklYa7VOO9aLc/x7JPiqBJ7m2r3bzLGLZ2K/4bD+fNw6xaVEu5RKeEe7a4fy668eS5njE254OTNWddqnHWtzlnXqlyt4A4G2bE8fopWlE851yQtinG0tni2nPZIT0+XRCnKnid7mo4WRljePcOonq25HZem0SnevZfvqROnAaD893hgYctxC1tOub/I2MdvsXn4kJjDx4n/5wQuNy5heeE8yunTWCQn4/PvRXz+vag+VqKxGWddq3Gy4gucqliD2xe8cGtWW65llnNynbBk0UV7SKIUJdrjPc2cv9jdbM3wcHz6lFOPb1NQ4sx3MJOdHY6d2uPYqb26SJWZSfCGULav2sqLd65Q785VXrx7Dau0ZJrdPEezm+eyK26cQ0JlTw7aVeVopdoc9ajDW8M6EdjEU5KnEKWYJEpR5hWUODVKWoaGdHmtHQ0D/IiIScLD0YLgC3dYsWwrdf+9iM+/l+iUeAPba5ewvnWDXrdu0OvcLgCif7HnWhM/fjDxYr9HfSIdKhLUq55czxSlyp49e/D39+fBgwfY2dnpO5xiJ4lSlDuFvcXm8e36NPOmVa1B6oRrZ2vOP6eus3jOz/jcCqPJrfM0/PcSzokPcN4dzOz/9nHLxomDfzfgwdj+2L/6Mjg46PCdCSGKgiRKIQrpyYTr4e3Gvqo+hFbxAcA0I40GUZdpGnmW5jdO0/Dfi1SOv0efMyEwJATFwIBH9RpB505Y9+5BVJUXCL+fJKdnhShhZCyxEDqSM+rW8L/BAxnGprR7qzfftHyD1/t+Sv0xv9K/zyyWNenBfe8aqLKysD51DOug2eDjg8rdnYhe/Zny1uesO5g9B3JUXDIHr8UQFZesz7cm9KC42z41NZXRo0fj7OyMmZkZLVu25OjRo7nqHDhwgPr162NmZkbTpk05+99EHwA3btyga9eu2NvbY2lpyYsvvkhwcHCxxF7UpEcphA7ldw3UzsKYqRvOkWJsxsEqPrQc1RffrRdxjouhTfhx2l87SsuIk7g+iqXv6a30Pb2V+D/mcqV1e760qsNubx/STUzlXs1yRB+T9L///vusX7+eH3/8EU9PT+bOnUvHjh1zLVwxadIkFixYgKurK1OnTqVbt25cvnwZY2Nj3nnnHdLS0ti7dy+WlpaEhYVhpcGCD6WBJEohdOzJU7JPJs+cuWXv2Diytn5H1tbviGlGGn43zvDS1X8IuHIY58QH2IRsZimbeWRiTki1pgRfaU3rJeNxc7LV47sTRS2/SfqL+j7dxMRElixZwsqVK+ncuTMA33//PSEhISxfvhxfX18Apk+fTocOHQD48ccfqVy5Mhs3bqRPnz5ERkbSq1cv6tatC0CVKlWKJFZ9kEQpRDF4Mnk+OetQqpEJe6o2Zk/Vxvwv4G18bl8i4PJBulzaT+X4e/QI20OPsD2k7/qGxF69uRbQHacObXCzk5vby5r8JunPmUqxqBLltWvXSE9Pp0WLFuoyY2NjmjRpwoULF9SJ0s/PT/26g4MDNWvW5MKFCwCMHj2at99+m+3bt/PSSy/Rq1cv6tWrVyTxFje5RilEMXvyWqahSkWvRpXUzw0MDOkwvAefth9Kq5HL6fnm56zw6Ua0pT3GD2KxXPYd9fp0IaVKdU6/OxmiouRaZhmSM0n/4wq15q0WclZxevLmfEVRnnnDfs7rw4YN4/r16/Tv35+zZ8/SuHFjvv7666IJuJhJohRCDwJ9Pdg/2Z81w5uxf7I/X/ZpkOv5iDZVCepZFwMDQ05UqsUnHUawafNhBgTOYsOL/iQZm+L94F/qL/qMLHd3zjduy7IPvqbVnBDWHo18dgCixMrvD6miXvO2WrVqmJiYsH//fnVZeno6x44do1atWuqyw4cPq39+8OABly9f5oUXXlCXubu7M3LkSDZs2MCECRP4/vvviyzm4iSnXoXQk/zmt33Wtc05Xo3Y69WID9NG8fLF/fQ5E4Lv7TBeunqEl64e4ZaNM7/805m7y2biUs1TH29L6EBxr3lraWnJ22+/zaRJk3BwcMDDw4O5c+eSlJTE0KFDOX36NACzZs2iQoUKuLi4MG3aNBwdHenevTsAY8eOpXPnztSoUYMHDx6wa9euXEm2NJNEKUQJ9rRrm0km5qyr14H19Trgff8mgae389rZHVSOj+b90B/Jqr2GpN59uNx3KC6tmsp9maVQca95++mnn5KVlUX//v1JSEigcePGbNu2DXt7+1x1xowZw5UrV6hfvz6bN2/GxMQEyF495Z133uHWrVvY2NjQqVMnvvrqq2KLvyiplJyT0+VEfHw8tra2xMXFYWNj88z66enpBAcH06VLF4yNjYshQvE00hbZtw08Plft+51q8tnWi2QpYJqeyisX9zPg5F/Uj7qs3uagRz0yxoyl9diBoKNlmKQt8kpJSSE8PBxvb2/MzMyK7bhZWVnEx8djY2Mjy2zlo6B20TQfSI9SiFKkoPs0U41N2VTvJWq8/w4zl29i8NE/6HzpAM0jz8CEIaQv/5LEMeO50LozXm520ssUQkOSKIUoZTS5lhlU8QVOvPoCbvH3GHR8C31P/Y112HnsRgzF3caZb/xeo8FHY3mteTVZ2USIZ5BEKUQZ8LRrmVE2TgT5D2GxXx/ePBXMoKObqRwfzSfbFnH70G/sHT6a4QZ1SDUwLrYZYIQobeSEthBlTH63FwR2qMuiZn1oOXI5M9q/xV0rByrF36P1l/9j57dv0evsTsjMZOqGc3IvphBPkB6lEGXQk6djAZbtDyfV2JSVjbuxpn5H+p7exsh/fqdy/D2+DP6Kt46s5/PWA4m41wRATscK8R9JlEKUUU+ejg3qWVc9YjbDxAzX/72P/5YA+h//k1GH1lEzJpJlGz7m32tbGdGwH2dcqsnpWCGQRClEufHUEbPG5qyp34mRRzYw/MRmKp4+yubTR1lfpx1zWw8o8gm5hSjpJFEKUY4UPGK2G6fPXCby7XH0PL+bXud20fnSARY2f4Mb/zaURCnKLRnMI0Q552Zrjl/VCrjZmlOpXg0mdp3Aq/2/5FilWlikpzI5dCWNu/tzf3OwTLwuyiVJlEIItZwRs+cqvcBr/T5j0svjSHFwxOjyJSq8+jLR3XrT9aONrDt+S9+hiiK0Z88eVCoVDx8+fK79JCUl0atXL2xsbNT78/LyYv78+TqJs7gU6tRreno6d+7cISkpCScnJxwcHHQdlxBCT3Kfjn2Jhw8nsbXXCPqf+IvuYaG0Dj/J7OvDqP9mK6LiUrgVFyejY0u5tm3b0qBBA3UCa968OVFRUdjaPt8i4T/++CP79u3j4MGDODo6Pvf+9EXjRPno0SNWr17NmjVrOHLkCKmpqerXKleuTEBAAG+99ZZ6gU8hROn1+LXMgzGJzHhpBBte9OezvxdS614E8/6cx7kru3lj70NuWzvJ6NgyxsTEBFdX1+fez7Vr16hVqxZ16tTRQVT6o9Gp16+++govLy++//572rVrx4YNGzh16hSXLl3i0KFDTJ8+nYyMDDp06ECnTp24cuVKUccthCgmOQsJn3GrQdeB85nbegCphsbUuXSSv5e9Q6+zO8nKUmSyglJq0KBBhIaGsmDBAlQqFSqVipUrV+Y69bpy5Urs7Oz4888/qVmzJhYWFvTu3ZvExER+/PFHvLy8sLe357333iMzMxPI7qV++eWX7N27F5VKRdu2bfM9fmRkJK+++ipWVlbY2NjQp08f7t69C0BcXByGhoYcP34cyF5I2sHBIVeHbM2aNbi5uRXdB4SGPcqDBw+ye/du6tatm+/rTZo0YciQISxdupTly5cTGhpK9erVdRqoEEI/cq5bTt1wjgxDI75tHojlG6/h9/FEGv17iS+Dv6LT5YNM6fQuxyMe4GAlExWoKQokJRXtMbKyIDERDA1zrw5jYQH/zc5UkAULFnD58mXq1KnDrFmzADh//nyeeklJSSxcuJBff/2VhIQEevbsSc+ePbGzsyM4OJjr16/Tq1cvWrZsSWBgIBs2bGDy5MmcO3eODRs2qJfjepyiKHTv3h1LS0tCQ0PJyMhg1KhRBAYGsmfPHmxtbWnQoAF79uzBx8eHM2fOAHDmzBn1iil79uyhTZs2hfzwNKNRoly3bp1GOzM1NWXUqFHPFZAQouR58h7M9PQM2l3/jOFHNjJu32o6XP2Hhisu8v7dseyq4iunYnMkJYGVVZEewgCwy++FR4/A0vKZ29va2mJiYoKFhYX6dOvFixfz1EtPT2fJkiVUrVoVgN69e7Nq1Sru3r2LlZUVtWvXxt/fn927dxMYGIiDgwMWFhYFnsbdsWMHZ86cITw8HHd3dwBWrVrFiy++yNGjR/H19aVt27bs2bOHCRMmsGfPHtq3b8/169fZv38/Xbp0Yc+ePYwbN06Tj6rQZNSrEEIjj99G4mZrRu9qKr71e42ug+ZzwckLx6Q4VqybyUc7vsMoPV1OxZYxFhYW6iQJ4OLigpeXF1aP/SHg4uJCdHS0xvu8cOEC7u7u6iQJULt2bezs7Lhw4QKQfQp33759ZGVlERoaStu2bWnbti2hoaHcuXOHy5cvF3mPUqtEefToUfr164e3tzfm5uZYWFjg7e1Nv379OHbsWFHFKIQogfxcFPZMaM2Mqa8T/tdOfvDpCsCQ45vZtGo8HvdvERFTxKcdSzoLi+yeXRE+suLjeXjrFlnx8blfs7DQ6Vt5coFulUqVb1lWVpbG+1QUBVU+p4cfL2/dujUJCQmcOHGCffv20bZtW9q0aUNoaCi7d+/G2dmZWrVqFeIdaU7jRLlp0yZatGhBbGwsY8aMYcWKFSxbtowxY8bw4MEDWrRowR9//KF1AIsXL1avPO3j48O+ffsKrL969Wrq16+PhYUFbm5uDB48mPv372t9XCHE83OzNcOvagUa1nDj4w4jGNx7OvfNbagdHc7mH8fywoFt+g5Rv1Sq7NOf+nhocH0yh4mJiXoQTnGqXbs2kZGR3Lx5U10WFhZGXFycOvnlXKf85ptvUKlU1K5dm1atWnHy5En+/PPPIu9NghaJ8sMPP2TWrFn8/fffjB07ljfeeIO+ffsyduxYgoODmTVrFlOnTtXq4GvXrmXs2LFMmzaNkydP0qpVKzp37kxkZGS+9ffv38+AAQMYOnQo58+fZ926dRw9epRhw4ZpdVwhhG7lDPjZW60JnYZ8wz/udbBOS8Z+YD8YP56omHiZ1acE8/Ly4p9//iEiIoKYmBiteoXP46WXXqJevXr069ePEydOcOTIEQYMGECbNm1o3Lixul7btm35+eefadOmDSqVCnt7e2rXrs3atWufOppWlzROlFevXqVnz55Pfb179+5cu3ZNq4PPmzePoUOHMmzYMGrVqsX8+fNxd3dnyZIl+dY/fPgwXl5ejB49Gm9vb1q2bMmIESPktK8QJUCgrwf7J/uzcFwXPE4ehPffz37hq6+43dCP0fO30uLTXaw9mv8fwkJ/Jk6ciKGhIbVr18bJyempnRVdU6lUbNq0CXt7e1q3bs1LL71ElSpVWLt2ba56/v7+ZGZm5kqKbdq0ITMzs1h6lCpFURRNKr744osMHDiQ93O+/E+YO3cuK1euJCwsTKMDp6WlYWFhwbp16+jRo4e6fMyYMZw6dYrQ0NA82xw8eBB/f382btxI586diY6Opk+fPtSqVYulS5fme5zU1NRckyPEx8fj7u5OTEwMNjY2z4wzPT2dkJAQOnTokOd8vChe0hYlh6Zt8XDtBkyHDcUmNZF/rR0Z0WMa5ytW57e3mpKclolnBQvcbM2KMfKik5KSws2bN/Hy8sLMrPjek6IoJCQkYG1tne/1vvIuJSWFiIgI3N3d87RLfHw8jo6OxMXFFZgPNJ6ZZ9asWbz++uuEhoYSEBCAi4sLKpWKO3fuEBISwvbt2/n11181Dj4mJobMzExcXFxylbu4uHDnzp18t2nevDmrV68mMDCQlJQUMjIy6NatG19//fVTjxMUFMTMmTPzlG/fvh0LLS52h4SEaFxXFC1pi5LjWW1xJcucvwbM4/v1H1Mt9hbrfvmADzq9x2vfKiioUKEQWCULPxeN/l4v0YyMjHB1deXRo0ekpaUV+/ETEhKK/ZilQVpaGsnJyezdu5eMjIxcryVpeI+rxj1KgEOHDrFgwQIOHTqkTmaurq74+fkxZswY/Pz8NA7+33//pVKlShw8eDDXdp988gmrVq3K9z6esLAwXnrpJcaNG0fHjh2Jiopi0qRJ+Pr6snz58nyPIz3KskPaouTQtC2i4lJo++VeLFMSmb/lC9pfOwrAN359+LLVmygqAwxUsGdC61Lfs5QeZclUrD1KAD8/P62SYUEcHR0xNDTM03uMjo7O08vMERQURIsWLZg0aRIA9erVw9LSklatWjF79ux8pzEyNTXF1NQ0T7mxsbFWv2y1rS+KjrRFyfGstvBwNFbP6jO854dM3Pczow6v491Dv+Hx8A6Tuowl1ciE23FpeDhaF2PkupeZmYlKpcLAwAADg+K7RT1n4E3OsUVuBgYG6ltZnvyuavp7RG+fqomJCT4+PnlO3YSEhNC8efN8t0lKSsrzRTA0NASy/6oSQpQ8OYN8Vo9oQYvfvmVSl7GkGxjS7cJeVv86DafkeLwcdXvPnxC6pHGPsmHDhhp160+cOKHxwcePH0///v1p3Lgxfn5+fPfdd0RGRjJy5EgApkyZwu3bt/npp58A6Nq1K8OHD2fJkiXqU69jx46lSZMmVKxYUePjCiGK1+OrkVycMY7BNs4s3vgJjW9fYNfGqViPaQq2VYiKSyY8pnTPFSt/tJcsumgPjRNl9+7dcx04KCiIkSNHPtdalIGBgdy/f59Zs2YRFRVFnTp1CA4OxtPTE4CoqKhcw5QHDRpEQkIC33zzDRMmTMDOzo527drx2WefFToGIUTxCvT1oPXSsVwf1JY6b72BdWQEtGjBti9+4O1zmWQplMq5YnNO4yUlJWFuXjqTfFmUM2DneS7XaDWY53HW1tacPn2aKlWqFPrg+hAfH4+tre0zL97mSE9PJzg4mC5dush1MT2Ttig5dNYWUVHQqROcOUOCqQVDe33EEffstQsNVSr2T/YvVT3LqKgoHj58iLOzMxYWFsUyuCYrK4tHjx5hZWUl1ygfoygKSUlJREdHY2dnl+8YFk3zgVaDeYQQQqfc3CA0lLiAztgePcyqtf/jnVcns6N6UzIVhYiYpFKVKHNWydBmYvDnpSgKycnJmJuby6jXfNjZ2T33ItSSKIUQ+mVnR9Lmv/in9csEXDnMkk1zGNN1EttqtSp1g3xUKhVubm44OzuTnp5eLMdMT09n7969tG7dWs60PMHY2Fg94PN5SKIUQuidm6sD+35czR/DhvJq2B6+3jyXow1dcLPtou/QCsXQ0FAnv6A1PVZGRgZmZmaSKIuIxoly4cKFuZ5nZGSwcuVKHB0dc5WPHj1aN5EJIcqVPn5ViNq3hegRI3H+fTXNZo7nob0xYS8HlupRsKL00zhRfvXVV7meu7q6smrVqlxlKpVKEqUQotDcHKxg7U8w2g4WLcJuzLtsDr7Ibw07lbpRsKLs0DhRhoeHF2UcQgiRzcCAqNlzCT5yi6FH/+DTbd+QaWDIVFS0ruEkPUtR7DQeS9y3b19+++03mXhXCFHkwu8n8bH/MH7w6QrAZ38vpNvZnUTEaDaJtRC6pHGirFGjBp999hlOTk4EBASwaNGiXKtSCyGErng7WmJgoGJm+7dY1bALBih8ETyfF0L/0ndoohzSOFHOmDGD48ePc/XqVbp3787mzZupXr06jRo1YsaMGZw8ebIo4xRClCNutuYE9ayLoYEBH3UYyZoGnTBUsrAfMZTY3zZw8FoMUXHJ+g5TlBNaT+NQuXJlRo0axbZt27h37x6TJ0/mypUrtG/fHk9PT959913Onz9fFLEKIcqRnMnUf3mrOW13/Q79+kFGBhb93uCrj5bT4tNdrD0a+ewdCfGcnmu+I2tra/r06cPq1au5d+8eK1aswNDQkEOHDukqPiFEOeZma45f1Qq42VsSNX8xO6v6YpaRxvLfZ1HrzjWmbjgnPUtR5HQ2MaChoSHt27dnwYIFDBs2TFe7FUIIAMLj0hj16mT+ca+DTVoSP/72Ee73b8kAH1HktE6Ud+/epX///lSsWBEjIyP1DBTFOROFEKL88Xa0JN3ElKG9PuKsS1Uck+L4cd0MqiiP9B2aKOO0nsJu0KBBREZG8r///Q83NzeZhFcIUSxyBvhM3XCOwa/NYMOqiXg+jIK+r8Hu3WBpqe8QRRmldaLcv38/+/bto0GDBkUQjhBCPF2grwetazgREZOE2aB60LEdHD1KSq/XOLFgBd6utjIhgdA5rU+9uru7ywreQgi9yRng4+xTDzZvJsPUFLNtf3P99SG0CNopI2GFzmmdKOfPn8/kyZOJiIgognCEEEJzUS825N0uE8hCxZun/mbgsc0yElbonEanXu3t7XNdi0xMTKRq1apYWFjkWdYlNjZWtxEKIcRThMcksrVGc4LaDmbanhV8uGs54faViIhpKqdghc5olCjnz59fxGEIIYT2vB0tMVDB9016UO3+TQLPhvD15s9IGf8yVK2g7/BEGaFRohw4cGBRxyGEEFp7fCTshx1H4fnwDs1unsW672tw5Ag4Oek7RFEGaHSNMjExUaudaltfCCEKK2equ59GtsIrNBiqVoWICFJ79ubQxSi5Ximem0aJslq1asyZM4d///33qXUURSEkJITOnTuzcOFCnQUohBDPkjMS1tW7MmzeTLqFJab793LhzZEyJ6x4bhqdet2zZw8ffvghM2fOpEGDBjRu3JiKFStiZmbGgwcPCAsL49ChQxgbGzNlyhTeeuutoo5bCCHyFVXJm5kdx7B04xyGHN/MWddqsuizeC4aJcqaNWuybt06bt26xbp169i7dy8HDx4kOTkZR0dHGjZsyPfff0+XLl0wMNDZ9LFCCKG1nJGwC/0CGX1oLUHbvuGKo4eMhBWFptXMPJUrV2bcuHGMGzeuqOIRQojnkjMS9qtW/Xgx+jrtrx3l242fYDi9l75DE6WUdP+EEGVKzkhYAwNDxr0ygQj7ilSKv4fruyOIepAoiz4LrWk916sQQpR0j88Ja9lrPQT4Q3Awq7uP4hu/PhioIKhnXQJ9PfQdqigFpEcphCiTckbCOrVqxsO58wAYt+9n/G6cIUtBproTGpNEKYQo88I692ZdnZcwVLJYuGUuTo9iyVQUWfRZaEQSpRCizPN2smJ6x5FccPLCKfEhX/35JUYoeDla6Ds0UQpodI3yzJkzGu+wXr16hQ5GCCGKgputOdMDfXkvfgqbV46m5Y3TbEjYh5vtK/oOTZQCGiXKBg0aoFKpUBQl1yoi+cnMzNRJYEIIoUvZA3z682/VVKpNHk29b7+EN7uDn5++QxMlnEanXsPDw7l+/Trh4eGsX78eb29vFi9ezMmTJzl58iSLFy+matWqrF+/vqjjFUKIQnOzNafa++/CG29AZmb2vw8f6jssUcJp1KP09PRU//zaa6+xcOFCunTpoi6rV68e7u7u/O9//6N79+46D1IIIXRGpYKlS+HwYQgPJ3nwUE5+vhRvJyuZuUfkS+vBPGfPnsXb2ztPube3N2FhYToJSgghipSNDfz6K1mGRphv2sDvY4Nk8nTxVFonylq1ajF79mxSUlLUZampqcyePZtatWppHcDixYvx9vbGzMwMHx8f9u3bV2D91NRUpk2bhqenJ6amplStWpUVK1ZofVwhRPkWVbMu81r0BWBmyFIqPrwr91aKfGk9M8/SpUvp2rUr7u7u1K9fH4DTp0+jUqn4888/tdrX2rVrGTt2LIsXL6ZFixZ8++23dO7cmbCwMDw88p8xo0+fPty9e5fly5dTrVo1oqOjycjI0PZtCCHKufCYRBY37UWba8fwvR3Gl3/O44035hARkySnYEUuWifKJk2aEB4ezs8//8zFixdRFIXAwED69u2LpaWlVvuaN28eQ4cOZdiwYQDMnz+fbdu2sWTJEoKCgvLU37p1K6GhoVy/fh0HBwcAvLy8tH0LQgiBt6MlGBoy7pXxbP3hPZreOs/IIxvwmtZB36GJEqZQEw5YWFjw1ltvMW/ePL766iuGDx+udZJMS0vj+PHjBAQE5CoPCAjg4MGD+W6zefNmGjduzNy5c6lUqRI1atRg4sSJJCfLqRIhhHZyJk+PsndjZvvsNXQnHPgFt+sX9RyZKGkKPSl6WFgYkZGRpKWl5Srv1q2bRtvHxMSQmZmJi4tLrnIXFxfu3LmT7zbXr19n//79mJmZsXHjRmJiYhg1ahSxsbFPvU6ZmppKamqq+nl8fDwA6enppKenPzPOnDqa1BVFS9qi5CgrbdGzgRt+3vZE3m9EksF1LP7agtK/PxmHD4Opqb7D00hZaQt90PQz0zpRXr9+nR49enD27Fn1JASAeiICbScceHICg4ImNcjKykKlUrF69WpsbW2B7NO3vXv3ZtGiRZib572uEBQUxMyZM/OUb9++HQsLzaevCgkJ0biuKFrSFiVHWWqL0D69abd/L6bnzxM+eDAX3nxT3yFppSy1RXFJStJsrl+tE+WYMWPw9vZmx44dVKlShSNHjnD//n0mTJjAF198ofF+HB0dMTQ0zNN7jI6OztPLzOHm5kalSpXUSRKyR+EqisKtW7eoXr16nm2mTJnC+PHj1c/j4+Nxd3cnICAAGxubZ8aZnp5OSEgIHTp0wNjYWNO3J4qAtEXJUVbbQmVqCq+/TvUNG0gMHEqFti1wszXTd1gFKqttURxyzjA+i9aJ8tChQ+zatQsnJycMDAwwMDCgZcuWBAUFMXr0aE6ePKnRfkxMTPDx8SEkJIQePXqoy0NCQnj11Vfz3aZFixasW7eOR48eYWVlBcDly5cxMDCgcuXK+W5jamqKaT6nUIyNjbX6UmlbXxQdaYuSo8y1RWAgN1b8guf2zViNfIuAwQuY2cenVKxbWebaohho+nlpPZgnMzNTnaQcHR35999/gezZey5duqTVvsaPH8+yZctYsWIFFy5cYNy4cURGRjJy5Egguzc4YMAAdf2+fftSoUIFBg8eTFhYGHv37mXSpEkMGTIk39OuQgihjai4ZHrUep17lnZUv3+TsftWy72VQvtEWadOHfVqIk2bNmXu3LkcOHCAWbNmUaVKFa32FRgYyPz585k1axYNGjRg7969BAcHq6fMi4qKIjLy/2fKsLKyIiQkhIcPH9K4cWP69etH165dWbhwobZvQwgh8giPSSTWzIapHd8FYPiRjdS9fVHWrSzntD71+uGHH5KYmAjA7NmzeeWVV2jVqhUVKlRg7dq1WgcwatQoRo0ale9rK1euzFP2wgsvyEVrIUSR8Ha0xEAFIdWbsbF2W3qE7eGzrQux+XKovkMTeqR1j7Jjx4707NkTgCpVqhAWFkZMTAzR0dG0a9dO5wEKIURxybm30lClYlb74dw3t6HmvRu4LVmg79CEHhVqwgGAq1evsm3bNpKTk9Wz5AghRGkX6OvB/sn+LB7TEYNvvs4unD0bzp/Xb2BCb7ROlPfv36d9+/bUqFGDLl26EBUVBcCwYcOYMGGCzgMUQoji5mZrjl/VCtgP7g9du0J6OmmDh3Dw8l0Z2FMOaZ0ox40bh7GxMZGRkblu2A8MDGTr1q06DU4IIfRKpYLFi0m3tMLk6BFCRv1PluMqh7ROlNu3b+ezzz7Lc99i9erVuXHjhs4CE0KIkiDKugIzWg4EYOLeVbjE3ZNbRsoZrRNlYmJivlO/xcTE5HtjvxBClGbhMYn8Ur8jRyvVxjI9hZk7viVTUeSWkXJE60TZunVrfvrpJ/VzlUpFVlYWn3/+Of7+/joNTggh9M3b0RKVgQFTO75DuoEhAVcO0+nKYbwcNZ8rWpRuWt9H+fnnn9O2bVuOHTtGWloa77//PufPnyc2NpYDBw4URYxCCKE3ObeMTN2g4tumvXj30G/M278MC4P39R2aKCZa9yhr167NmTNnaNKkCR06dCAxMZGePXty8uRJqlatWhQxCiGEXuXcMuL7/ZdkeHljEX0H/vc/fYclikmh1qN0dXXNd+kqIYQoq9xszXGzrQzfLoWOHeHrr6F/f/Dx0XdooogVKlE+fPiQI0eOEB0dTVZWVq7XHp/EXAghypyAAHjjDVizhrQRIzm25i+8na1xs5WFGcoqrRPlli1b6NevH4mJiVhbW+daZFmlUkmiFEKUfV9+SfrmLZgcP8aW9z5mbcNOBPWsWyqW4xLa0/oa5YQJExgyZAgJCQk8fPiQBw8eqB+xsbFFEaMQQpQoURZ2BDV7A4APQldilxgn91aWYVonytu3bzN69Oh876UUQojyIDwmkR8bvUKYszd2KY/4YM9KubeyDCvU6iHHjh0riliEEKJU8Ha0RDE05MMO2UsEBp4Nwff2Bbm3sozS6Brl5s2b1T+//PLLTJo0ibCwMOrWrYuxsXGuut26ddNthEIIUcI8fm/lb3Vfos/ZHXx35AfsrcbpOzRRBDRKlN27d89TNmvWrDxlKpWKzMzM5w5KCCFKukBfD1rXcOJWj6pkBTTD/nIYfPcdvP22vkMTOqbRqdesrCyNHpIkhRDliZutOb5NamLw8X8dhw8/hPv39RuU0LlCL9wshBDiP2+/DXXrQmyszNhTBkmiFEKI52VklD1TD6B8+y2ntuyWW0XKEEmUQgihC23aENmhK6qsLNJGvUuLoJ2ywHMZIYlSCCF0ICoumder9yLJ2JQmt8J4JSxUJiEoIyRRCiGEDoTHJPKvtSOLmvUBYPKelZikJsskBGWARreHxMfHa7xDGxubQgcjhBCllbejJQYqWObbndfPbMc97i5vH1mP1/Qu+g5NPCeNepR2dnbY29tr9BBCiPIoZxKCDBMz5rQdDMCoYxtwi7un58jE89KoR7l79271zxEREUyePJlBgwbh5+cHwKFDh/jxxx8JCgoqmiiFEKIUyJmEIOJeE1JjDmB6YB988AGsWaPv0MRz0ChRtmnTRv3zrFmzmDdvHm+88Ya6rFu3btStW5fvvvuOgQMH6j5KIYQoJbIXeDaHbxZCo0bw66/wzjvQsqW+QxOFpPVgnkOHDtG4ceM85Y0bN+bIkSM6CUoIIUq9Bg1g+PDsn8eMgScWuRelh9aJ0t3dnaVLl+Yp//bbb3F3d9dJUEIIUSZ8/DHY2MCJE1z5crHcKlJKaXTq9XFfffUVvXr1Ytu2bTRr1gyAw4cPc+3aNdavX6/zAIUQotRydubUwHdo8HUQ1rOm0/6OGx+97kugr4e+IxNa0LpH2aVLFy5fvky3bt2IjY3l/v37vPrqq1y+fJkuXWQYtBBC5IiKS+Z10yZE2rrg+iiW4f+sl0kISiGte5SQffp1zpw5uo5FCCHKlPCYRFIMjQlqO5glf3zKiH82sKZ+RyJikrIH/IhSoVAz8+zbt48333yT5s2bc/v2bQBWrVrF/v37dRqcEEKUZjmTEPxdswVHKtfGPCOVD/auwsvRQt+hCS1onSjXr19Px44dMTc358SJE6SmpgKQkJAgvUwhhHhMziQEhgYGzG43DICe53biduW8niMT2tA6Uc6ePZulS5fy/fffY2xsrC5v3rw5J06c0GlwQghR2gX6erB/sj9TPhpAUp//7j+fMAEURb+BCY1pnSgvXbpE69at85Tb2Njw8OFDrQNYvHgx3t7emJmZ4ePjw759+zTa7sCBAxgZGdGgQQOtjymEEMXJzdYcv6oVsPjiMzAzg717iV2zjoPXYmRgTymgdaJ0c3Pj6tWrecr3799PlSpVtNrX2rVrGTt2LNOmTePkyZO0atWKzp07ExlZ8BpucXFxDBgwgPbt22t1PCGE0Ct3dxg/HoCH741nwNIDtPh0l6xbWcJpnShHjBjBmDFj+Oeff1CpVPz777+sXr2aiRMnMmrUKK32NW/ePIYOHcqwYcOoVasW8+fPx93dnSVLljwzhr59+6rnmhVCiNLizsjRxFjYUSX2Nn1P/U2WgtwyUsJpnSjff/99unfvjr+/P48ePaJ169YMGzaMESNG8O6772q8n7S0NI4fP05AQECu8oCAAA4ePPjU7X744QeuXbvG9OnTtQ1dCCH07nqaIV+17AvA2ANrsEl5RKaiyLqVJVih7qP85JNPmDZtGmFhYWRlZVG7dm2srKy02kdMTAyZmZm4uLjkKndxceHOnTv5bnPlyhUmT57Mvn37MDLSLPTU1FT1yFz4/7U109PTSU9Pf+b2OXU0qSuKlrRFySFtUXiVbU35rUFHBh3fQvX7Nxl1eB1z/QdTydakUJ+ntEXhafqZaZ0of/rpJ3x9falVq1auydFTUlL47bffGDBggFb7U6lUuZ4ripKnDCAzM5O+ffsyc+ZMatSoofH+g4KCmDlzZp7y7du3Y2Gh+b1MISEhGtcVRUvaouSQtiicXlVVBPkPZsXvsxh87A/iunXk5IFdnHyOfUpbaC8pSbNevEpRtBujbGBggKWlJStXrqRXr17q8rt371KxYkUyMzM12k9aWhoWFhasW7eOHj16qMvHjBnDqVOnCA0NzVX/4cOH2NvbY2hoqC7LyspCURQMDQ3Zvn077dq1y3Oc/HqU7u7uxMTEYGNj88w409PTCQkJoUOHDrluhxHFT9qi5JC2eH5RD5Ox6fYydof3k/X662T+9FOh9iNtUXjx8fE4OjoSFxdXYD4o1KnXmTNn0r9/f86ePcuMGTMKFaCJiQk+Pj6EhITkSpQhISG8+uqreerb2Nhw9uzZXGWLFy9m165d/P7773h7e+d7HFNTU0xNTfOUGxsba/Wl0ra+KDrSFiWHtEXheTgZw6IF4OODwa+/YjBhAuSzhKGmpC20p+nnVahEmTN9XY8ePTh37hyrVq0qzG4YP348/fv3p3Hjxvj5+fHdd98RGRnJyJEjAZgyZQq3b9/mp59+wsDAgDp16uTa3tnZGTMzszzlQghRKjRqBG++CT//DJMmwa5dkM+lJ6FfWo96zbl+2KxZM/755x+uXr1K8+bNiYiI0PrggYGBzJ8/n1mzZtGgQQP27t1LcHAwnp6eAERFRT3znkohhCjVZs8GU1PYswf++kvf0Yh8aJ0oH7+k6eHhwcGDB/Hy8qJDhw6FCmDUqFFERESQmprK8ePHc836s3LlSvbs2fPUbWfMmMGpU6cKdVwhhCgRPD1hzBgA0idO4uClO3JPZQmjdaKcPn16rltBLCws2LhxI+PGjct3ajshhBDPMGUKqbb2GF+6yJYxn8hsPSVMoRJlfrdVzJw5k927d+skKCGEKE+iVKZ86vsaAOP3/4xZarLM1lOCaDSYZ/PmzXTu3BljY2M2b9781HoqlYquXbvqLDghhCgPwmMS+blBZwYe24LXwyiGH9nIgpZ9ZYHnEkKjRNm9e3fu3LmDs7Mz3bt3f2o9lUql8X2UQgghsnk7WpJpZMzcNgNZ/MenvHVkA7827CwLPJcQGp16zcrKwtnZWf3z0x6SJIUQQns5Czxve6ElJ91qYpmewi+3/5beZAmh9TVKIYQQuhfo68H+Ke0w/upLAKr+8StcuKDnqARoeOp14cKFGu9w9OjRhQ5GCCHKMzdbc9wCX4Zfu8OmTfDBB1DAuBBRPDRKlF999ZVGO1OpVJIohRDieX36KWzZAlu2cP+v7Vx6oRHejpZyKlZPNEqU4eHhRR2HEEKIHDVrwltvwZIl3B72Lv0GfInKwICgnnUJ9PXQd3TljlyjFEKIEujuuPd5ZGJOvTtX6HphH1kKcm+lnhRqUvRbt26xefNmIiMjSUtLy/XavHnzdBKYEEKUZ9cMrDjYtBcT9/3MpL0/sbVGc9KMjOXeSj3QOlHu3LmTbt264e3tzaVLl6hTpw4REREoikKjRo2KIkYhhCh3vB0tGdakO2+eDMY97i4DTmzhh6a95N5KPdD61OuUKVOYMGEC586dw8zMjPXr13Pz5k3atGnDa6+9VhQxCiFEueNma870QF++atUfgPcOruWL9pWlN6kHWifKCxcuMHDgQACMjIxITk7GysqKWbNm8dlnn+k8QCGEKK8CfT0Y+/NsEmvWxjY1kR7BK/UdUrmkdaK0tLQkNTUVgIoVK3Lt2jX1azExMbqLTAghBG4OVljOz56EgG++gevX9RtQOaR1omzWrBkHDhwA4OWXX2bChAl88sknDBkyhGbNmuk8QCGEKPc6doQOHSA9HSZP1nc05Y7Wg3nmzZvHo0ePgOyFkx89esTatWupVq2axhMTCCGE0IJKBV98AQ0awLp1cOgQ+PnpO6pyQ+tEWaVKFfXPFhYWLF68WKcBCSGEyEe9ejB4MKxYQdrYcRxbvQVvJyscLQp1l5/QwnNNOPDo0SPi4+NzPYQQQhSRjz8mw8wckyP/sGriPFp8uot1x2/pO6oyT+tEGR4ezssvv4ylpSW2trbY29tjb2+PnZ0d9vb2RRGjEEIIIMrSnkU+3QGYHLoSo/R0PvwjjIep+o2rrNO6z96vXz8AVqxYgYuLCyqVSudBCSGEyCs8JpFvm/Tk9dPb8Hx4h/4n/mR5kx7cS5Hfw0VJ60R55swZjh8/Ts2aNYsiHiGEEE/h7WhJiqk5X7R6k8//XsiYg7+yqW47nMws9R1amab1qVdfX19u3rxZFLEIIYQogJutOUE967Kp7kucd66CTWoia25vxc5U35GVbVr3KJctW8bIkSO5ffs2derUwdjYONfr9erV01lwQgghcgv09aB1DSdi634J/XtQfdMv3GrbRN9hlWlaJ8p79+5x7do1Bg8erC5TqVQoioJKpSIzM1OnAQohhMjNzdYctze7w++vovrjD1784QcYMULfYZVZWifKIUOG0LBhQ9asWSODeYQQQp8+/xwlOBiXEyfI2L4dXn5Z3xGVSVonyhs3brB582aqVatWFPEIIYTQVPXqZI0aheGCBWRNmMjBaj54u9rKCiM6pvVgnnbt2nH69OmiiEUIIYSWsqZOJdHSGpNLF/n7vZm0+HQXa49G6jusMkXrHmXXrl0ZN24cZ8+epW7dunkG83Tr1k1nwQkhhChYlIE53zfvx8chSxm/bzV/1G7L1A3naF3DSXqWOqJ1ohw5ciQAs2bNyvOaDOYRQojideN+Er806MybJ4OpGRPJ2P2/MOult4iISZJEqSNan3rNysp66kOSpBBCFC/PChZkGRjwcbvhAPQ/+RfVY2/h5Wih58jKDq0SZUZGBkZGRpw7d66o4hFCCKEFN1szAqtkcbBKQ0KqNcE4K5Mfw9ZKb1KHtEqURkZGeHp6Ss9RCCFKED8XhT0TWuO49GsUY2MqHtgNf/+t77DKDK1PvX744YdMmTKF2NjYoohHCCFEIbjZmtGwfRNUo0dnF4weDamyrIguaD2YZ+HChVy9epWKFSvi6emJpWXuyXhPnDihs+CEEEJo6aOP4Jdf4OpV+OILmDZN3xGVelonyu7duxdBGEIIIXTCxiY7Qfbrh/LJJ5xo1YWK9V+Qa5bPQetEOX36dJ0GsHjxYj7//HOioqJ48cUXmT9/Pq1atcq37oYNG1iyZAmnTp0iNTWVF198kRkzZtCxY0edxiSEEKXaG28Q/eXXOJ84zL3h7/Baz2kE9axLoK+HviMrlbS+Rpnj+PHj/Pzzz6xevZqTJ08Wah9r165l7NixTJs2jZMnT9KqVSs6d+5MZGT+s0rs3buXDh06EBwczPHjx/H396dr166FPr4QQpRFUfEpDGj4JhkqAzpdPkSra8eZuuEcUXHJ+g6tVNI6UUZHR9OuXTt8fX0ZPXo07777Lj4+PrRv35579+5pta958+YxdOhQhg0bRq1atZg/fz7u7u4sWbIk3/rz58/n/fffx9fXl+rVqzNnzhyqV6/Oli1btH0bQghRZoXHJHLR0YsfGmfPlDZjx1KM0lKIiEnSc2Slk9anXt977z3i4+M5f/48tWrVAiAsLIyBAwcyevRo1qxZo9F+0tLSOH78OJMnT85VHhAQwMGDBzXaR1ZWFgkJCTg4ODy1TmpqKqmPjfyKj48HID09nfT09GceI6eOJnVF0ZK2KDmkLUqO/Nqisq0pBipY0KIvXS/sxftBFO8d/o1KUzpImz1G089C60S5detWduzYoU6SALVr12bRokUEBARovJ+YmBgyMzNxcXHJVe7i4sKdO3c02seXX35JYmIiffr0eWqdoKAgZs6cmad8+/btWFhoPnNFSEiIxnVF0ZK2KDmkLUqOJ9uij7eKtdfNmd5hJN9unMPbh38n9JfmnPT01FOEJU9SkmY9bK0TZVZWVp6J0AGMjY3JysrSdnd51rPMWQD6WdasWcOMGTP4448/cHZ2fmq9KVOmMH78ePXz+Ph43N3dCQgIwMbG5pnHSU9PJyQkhA4dOuT7vkXxkbYoOaQtSo6ntUUXYFRcCpGxviSlnMbi779o+8svZIaGgkGhh6eUKTlnGJ9F60TZrl07xowZw5o1a6hYsSIAt2/fZty4cbRv317j/Tg6OmJoaJin9xgdHZ2nl/mktWvXMnToUNatW8dLL71UYF1TU1NMTU3zlBsbG2v1H1zb+qLoSFuUHNIWJUd+beHhaIyHozV8txRq1cLgn39I+GYJ57u/ibejZbm/ZUTT767Wf1Z88803JCQk4OXlRdWqValWrRre3t4kJCTw9ddfa7wfExMTfHx88pwuCAkJoXnz5k/dbs2aNQwaNIhffvmFl2U1byGEeLbKlSEoCACDadMYN+8vWbdSC1r3KN3d3Tlx4gQhISFcvHgRRVGoXbv2M3t2+Rk/fjz9+/encePG+Pn58d133xEZGaleymvKlCncvn2bn376CchOkgMGDGDBggU0a9ZM3Rs1NzfH1tZW6+MLIUR5EdV3EHeCvqHhv5eYvX0xw3v+T9at1JDWiTJHhw4d6NChw3MdPDAwkPv37zNr1iyioqKoU6cOwcHBeP53sTkqKirXPZXffvstGRkZvPPOO7zzzjvq8oEDB7Jy5crnikUIIcqy8AcpzOj0Hn+uHEuHq0d4NWwPf7zoL+tWaqBQiXLnzp3s3LmT6OjoPAN4VqxYodW+Ro0axahRo/J97cnkt2fPHq32LYQQIpu3oyVXnb1Y2OJ1Ju77mRk7vuOwVwNZt1IDWl+jnDlzJgEBAezcuZOYmBgePHiQ6yGEEKLkcbM1J6hnXb5v9hrnXKpin5LA7+dX42Zjpu/QSjyte5RLly5l5cqV9O/fvyjiEUIIUUQCfT1oXcOJ6NbLUXq8hPvurbBuHRRwL7ooRI8yLS2twFGpQgghSi43W3Pqv9IGVc7yW++8w93L4Ry8FiNzwT6F1oly2LBh/PLLL0URixBCiOIydSrUrw8xMYR16UPf7w7LLSNPofWp15SUFL777jt27NhBvXr18tywOW/ePJ0FJ4QQooiYmHBv6XJsWvrhf+0Yb54M5udGL8stI/nQOlGeOXOGBg0aAHDu3Llcr2ky9ZwQQoiS4YqTJ9vbDmbGzu/4cPdyDnnU45qju9wy8gStE+Xu3buLIg4hhBDFzNvRklWNX6HdtaO0jjjJ/D+/4LUBX8otI0+QmXGFEKKccrM1Z06v+nzw8jgemFlT9+41Nkb9Lb3JJ0iiFEKIcizQ14MNn/Qmet43ANT6+VvYsoWouGQZCfufQk9hJ4QQomxwszXH7e0BcOkELFhA6pv96dN3HjdtXTBQQVDPugT6eug7TL2RHqUQQohsc+eS1tgX0/g4vtn0KSYZ6WQpMHXDuXLds5REKYQQIpuJCWfmfccDM2vq37nCtN3LAMhUFCJikvQcnP5IohRCCKFWqV5NJnQdD8DAE3/x2pkQDFWqcj0SVhKlEEIINTdbczpOGsLCFm8A8Mm2RXxXJblcj4SVRCmEECKXQF8PXtvyPfc7dsUkK4P2U0dCRIS+w9IbSZRCCCHycLO3pML6NdCwIcTEQNeukJCg77D0QhKlEEKI/FlawubN4OoK586R0qs3hy5GlbsRsJIohRBCPF3lyvDHH2SYmmEWsp27vd6gZdCOcrXKiCRKIYQQBYqqWZfh3SaTbmBI97BQPgr5lqnrz5abnqUkSiGEEAUKj0lkd5XGTHh5PFmoGHjiL97bv5qImKRyMdWdTGEnhBCiQN6OlhioYHPtNtikPGJ2yBLGHljDkU/daOHYjiyFMj3VnfQohRBCFMjN1pygnnUxVKn4udHLfNF6AABNls1jwp4fQVHK9FR30qMUQgjxTIG+HrSu4URETBJeU9oR8WlVvD6dzjuH12GRnsLM9m+RCWVy0WfpUQohhNCIm605flUr4GZrjunkSfwvYBQAg49vYe7fCzDLysDCxKDMXbOUHqUQQgitudmaU2f2B0wyNuXT4AX0ObuDRpkPCEwdz31z2zJ1zVJ6lEIIIQol0NeD8as/4fKyX8i0sqJa2HE2/DSBajGRZeqapSRKIYQQheZma06tIYGc+e1vIm1d8Hx4hw2rJtL54v4yszyXJEohhBDPzbW5Dz0GzuOfyi9ik5bEkj8+Ze7fC/A2y9J3aM9NEqUQQojn5mZrzvtvtmTAG3NY1Ow1slDR50wIrm2bE7Nrb6ke4COJUgghhE4E+nqwZ1oHGv28hAdb/s6eJ/bqVRzat+XaawPpMv2PUjlHrCRKIYQQOpNzC0mFVzpyZ99h/qjdBgMU+p8MZsd3Izg5/UuiHiSWqqnvJFEKIYQoEtczTRnTdRJvvD6HyxU8qJAcz6d/L8SoQX0+HTCdN789SItPd6l7mSU1ecp9lEIIIYpEzhyxhzzr0WXwQgYe38KYg7/iFHmNBZGfM9phDUua9WZmehoPk9P57O+LJXLeWOlRCiGEKBKPzxGbYWjEyqY9+f6nnXzR6k0emllRNfYWXwTP59A3/TEbN5bq0REAue7BzK+XWdw9T+lRCiGEKDK55oh1tACgxcnXWenTjTdPBvPmyWAqx0cz8MSfDDzxJ5ccPdhe3Y+Q6k35Ya8nyw5G5OplAkzZcLZYe55671EuXrwYb29vzMzM8PHxYd++fQXWDw0NxcfHBzMzM6pUqcLSpUuLKVIhhBCF8fgcsTm9zGQzS5Y2603bt5fz1+c/8HfN5qQbGFIzJpL3Dq1l80/jGfZGKxZu/JSBx7fwwp3rfLjulDpJAsU2+49ee5Rr165l7NixLF68mBYtWvDtt9/SuXNnwsLC8PDI+xdCeHg4Xbp0Yfjw4fz8888cOHCAUaNG4eTkRK9evfTwDoQQQmjryV6mm605a9u0o+nqQ7S+dpSAK4dpH3kS58QHvHJpP69c2g9AqqExVxw9uOTkyUVHL3ZWa8L1CpWLfMUSvSbKefPmMXToUIYNGwbA/Pnz2bZtG0uWLCEoKChP/aVLl+Lh4cH8+fMBqFWrFseOHeOLL76QRCmEEKVITu8yx/8nz5fwcrQgNiWFseO/o/HN8zS5dZ5Gty9gnZZMnbvXqHP3GgD3rOy54eiuPqVbVPSWKNPS0jh+/DiTJ0/OVR4QEMDBgwfz3ebQoUMEBATkKuvYsSPLly8nPT0dY2PjPNukpqaSmpqqfh4XFwdAbGws6enpz4wzPT2dpKQk7t+/n+/+RfGRtig5pC1KjrLUFiZADTsgIwmMoMOQzsz+qwpf+3TFkCzmNLLG7vplTm47RLV7kZyqUIkpL1XGJCOJ+/e1n1M2ISEBAEVRCqynt0QZExNDZmYmLi4uucpdXFy4c+dOvtvcuXMn3/oZGRnExMTg5uaWZ5ugoCBmzpyZp9zb2/s5ohdCCFHc+j5ZcPUwoT/C8Ofcb0JCAra2tk99Xe+jXlUqVa7niqLkKXtW/fzKc0yZMoXx48ern2dlZREbG0uFChVo0qQJR48ezVXf19c3V1l8fDzu7u7cvHkTGxsbzd6UjjwZS3HsQ9P6z6pX0Ov5vaZJWXlrC0230XVbPK388bLS3haF2Y8u/m9IW+hmP7r6PdW4cWN27dpFxYoVC9yP3hKlo6MjhoaGeXqP0dHReXqNOVxdXfOtb2RkRIUKFfLdxtTUFFNT01xldnZ2ABgaGub5YuVXBmBjY1PsX8KnxVKU+9C0/rPqFfS6pp97eW8LTbfRdVs8rTy/stLaFoXZjy7+b0hb6GY/uvo9ZWRkROXKlZ+5H73dHmJiYoKPjw8hISG5ykNCQmjevHm+2/j5+eWpv337dho3blyoc/PvvPOORmX6ootYtN2HpvWfVa+g1zX93Mt7W2i6ja7b4mnlJaU9dBWHPv5vSFvoZj/F8XsqF0WPfv31V8XY2FhZvny5EhYWpowdO1axtLRUIiIiFEVRlMmTJyv9+/dX179+/bpiYWGhjBs3TgkLC1OWL1+uGBsbK7///nuRxRgXF6cASlxcXJEdQ2hG2qLkkLYoOaQtip5er1EGBgZy//59Zs2aRVRUFHXq1CE4OBhPT08AoqKiiIz8/yVZvL29CQ4OZty4cSxatIiKFSuycOHCIr01xNTUlOnTp+c5fSuKn7RFySFtUXJIWxQ9laI8Y1ysEEIIUY7pfQo7IYQQoiSTRCmEEEIUQBKlEEIIUQBJlEIIIUQBJFEKIYQQBZBEqUPh4eH4+/tTu3Zt6tatS2Jior5DKreMjIxo0KABDRo0UK9OI/QnKSkJT09PJk6cqO9QyrWEhAR8fX1p0KABdevW5fvvv9d3SKWC3B6iQ23atGH27Nm0atWK2NhYbGxsMDLS+3S65ZKjoyMxMTH6DkP8Z9q0aVy5cgUPDw+++OILfYdTbmVmZpKamoqFhQVJSUnUqVOHo0ePPnUKUJFNepQ6cv78eYyNjWnVqhUADg4OkiSFAK5cucLFixfp0qWLvkMp9wwNDbGwyF67MSUlhczMzGcuMSXKUaLcu3cvXbt2pWLFiqhUKjZt2pSnzuLFi/H29sbMzAwfHx/27dun8f6vXLmClZUV3bp1o1GjRsyZM0eH0ZctRd0WkL2igo+PDy1btiQ0NFRHkZc9xdEWEydOzHchdpFXcbTHw4cPqV+/PpUrV+b999/H0dFRR9GXXeWmy5OYmEj9+vUZPHhwvlPerV27lrFjx7J48WJatGjBt99+S+fOnQkLC8PDwwMAHx+fXItA59i+fTvp6ens27ePU6dO4ezsTKdOnfD19aVDhw5F/t5Km6Jui4oVKxIREUHFihU5d+4cL7/8MmfPni32lRVKg6Jui6NHj1KjRg1q1Kjx1AXZxf8rjv8bdnZ2nD59mrt379KzZ0969+791BWbxH/0OtOsngDKxo0bc5U1adJEGTlyZK6yF154QZk8ebJG+zx48KDSsWNH9fO5c+cqc+fOfe5Yy7qiaIsnderUSTl69GhhQyw3iqItJk+erFSuXFnx9PRUKlSooNjY2CgzZ87UVchlWnH83xg5cqTy22+/FTbEcqPcnHotSFpaGsePHycgICBXeUBAgMZ/Bfv6+nL37l0ePHhAVlYWe/fupVatWkURbpmmi7Z48OCB+i/qW7duERYWRpUqVXQea1mni7YICgri5s2bRERE8MUXXzB8+HA++uijogi3zNNFe9y9e5f4+Hgg+/LE3r17qVmzps5jLWvKzanXgsTExJCZmZnn9IOLi0uehaKfxsjIiDlz5tC6dWsURSEgIIBXXnmlKMIt03TRFhcuXGDEiBEYGBigUqlYsGABDg4ORRFumaaLthC6o4v2uHXrFkOHDkVRFBRF4d1336VevXpFEW6ZIonyMSqVKtdzRVHylBWkc+fOdO7cWddhlUvP0xbNmzfn7NmzRRFWufS8/y9yDBo0SEcRlW/P0x4+Pj6cOnWqCKIq2+TUK9n33BkaGub5qyw6OlouchczaYuSQ9qiZJH20B9JlICJiQk+Pj6EhITkKg8JCaF58+Z6iqp8krYoOaQtShZpD/0pN6deHz16xNWrV9XPw8PDOXXqFA4ODnh4eDB+/Hj69+9P48aN8fPz47vvviMyMpKRI0fqMeqySdqi5JC2KFmkPUooPY64LVa7d+9WgDyPgQMHqussWrRI8fT0VExMTJRGjRopoaGh+gu4DJO2KDmkLUoWaY+SSeZ6FUIIIQog1yiFEEKIAkiiFEIIIQogiVIIIYQogCRKIYQQogCSKIUQQogCSKIUQgghCiCJUgghhCiAJEohhBCiAJIohSjh9uzZg0ql4uHDh8V+bJVKhUqlws7OrsB6M2bMoEGDBrme52w7f/78Io1RiKImiVKIEqRt27aMHTs2V1nz5s2JiorC1tZWLzH98MMPXL58WattJk6cSFRUFJUrVy6iqIQoPuVmUnQhSisTExNcXV31dnw7OzucnZ212sbKygorKysMDQ2LKCohio/0KIUoIQYNGkRoaCgLFixQn7aMiIjIc+p15cqV2NnZ8eeff1KzZk0sLCzo3bs3iYmJ/Pjjj3h5eWFvb897771HZmamev9paWm8//77VKpUCUtLS5o2bcqePXsKFeunn36Ki4sL1tbWDB06lJSUFB18AkKUTJIohSghFixYgJ+fH8OHDycqKoqoqCjc3d3zrZuUlMTChQv59ddf2bp1K3v27KFnz54EBwcTHBzMqlWr+O677/j999/V2wwePJgDBw7w66+/cubMGV577TU6derElStXtIrzt99+Y/r06XzyySccO3YMNzc3Fi9e/FzvXYiSTE69ClFC2NraYmJigoWFxTNPtaanp7NkyRKqVq0KQO/evVm1ahV3797FysqK2rVr4+/vz+7duwkMDOTatWusWbOGW7duUbFiRSD7OuLWrVv54YcfmDNnjsZxzp8/nyFDhjBs2DAAZs+ezY4dO6RXKcos6VEKUQpZWFiokySAi4sLXl5eWFlZ5SqLjo4G4MSJEyiKQo0aNdTXD62srAgNDeXatWtaHfvChQv4+fnlKnvyuRBlifQohSiFjI2Ncz1XqVT5lmVlZQGQlZWFoaEhx48fzzPA5vHkKoTISxKlECWIiYlJrgE4utKwYUMyMzOJjo6mVatWz7WvWrVqcfjwYQYMGKAuO3z48POGKESJJYlSiBLEy8uLf/75h4iICKysrHBwcNDJfmvUqEG/fv0YMGAAX375JQ0bNiQmJoZdu3ZRt25dunTpovG+xowZw8CBA2ncuDEtW7Zk9erVnD9/nipVqugkViFKGrlGKUQJMnHiRAwNDalduzZOTk5ERkbqbN8//PADAwYMYMKECdSsWZNu3brxzz//PHVk7dMEBgby0Ucf8cEHH+Dj48ONGzd4++23dRanECWNSlEURd9BCCFKJpVKxcaNG+nevXuhtvfy8mLs2LF5ZhsSojSRHqUQokBvvPGG1lPRzZkzBysrK532iIXQF+lRCiGe6urVqwAYGhri7e2t8XaxsbHExsYC4OTkpLd5aoXQBUmUQgghRAHk1KsQQghRAEmUQgghRAEkUQohhBAFkEQphBBCFEASpRBCCFEASZRCCCFEASRRCiGEEAWQRCmEEEIUQBKlEEIIUYD/A7p5sTUxV0+uAAAAAElFTkSuQmCC", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "tm = np.logspace(np.log10(to[0]), np.log10(to[-1]), 100)\n", "hm = ml.head(0, 0, tm, layers=1)\n", @@ -230,7 +335,7 @@ "plt.semilogx(tm, hm[-1] / H0, \"r\", label=\"timflow\")\n", "plt.ylim([0, 1])\n", "plt.xlabel(\"time [d]\")\n", - "plt.ylabel(\"h/H0\")\n", + "plt.ylabel(\"normalized head (h/H0)\")\n", "plt.title(\"Model Results - Three layers\")\n", "plt.legend()\n", "plt.grid()" @@ -264,9 +369,56 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 9, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 k [m/d]Ss [1/m]RMSE [m]
timflow6.052.13e-040.0029
AQTESOLV4.033.83e-040.0030
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "t = pd.DataFrame(\n", " columns=[\"k [m/d]\", \"Ss [1/m]\", \"RMSE [m]\"],\n", @@ -297,20 +449,25 @@ "* Duffield, G.M. (2007), AQTESOLV for Windows Version 4.5 User's Guide, HydroSOLVE, Inc., Reston, VA.\n", "* Hyder, Z., Butler Jr, J.J., McElwee, C.D. and Liu, W. (1994), Slug tests in partially penetrating wells, Water Resources Research 30, 2945–2957." ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python [conda env:base] *", "language": "python", - "name": "python3" + "name": "conda-base-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.5" } }, "nbformat": 4, diff --git a/docs/transient/04pumpingtests/slug2_multiwell.ipynb b/docs/transient/04pumpingtests/slug2_multiwell.ipynb index be3d4e57..18b45b9f 100644 --- a/docs/transient/04pumpingtests/slug2_multiwell.ipynb +++ b/docs/transient/04pumpingtests/slug2_multiwell.ipynb @@ -28,7 +28,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -84,7 +84,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -112,7 +112,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -140,9 +140,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Slug: 0.02286 m^3\n" + ] + } + ], "source": [ "Q = np.pi * rc1**2 * H0\n", "print(f\"Slug: {Q:.5f} m^3\")" @@ -150,9 +158,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], "source": [ "ml = tft.ModelMaq(kaq=10, z=[0, -b], Saq=1e-4, tmin=1e-5, tmax=0.01)\n", "w = tft.Well(ml, xw=0, yw=0, rw=rw1, rc=rc1, tsandQ=[(0, -Q)], layers=0, wbstype=\"slug\")\n", @@ -175,9 +192,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "....................................\n", + "Fit succeeded.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "..................\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fit succeeded.\n" + ] + } + ], "source": [ "# unknown parameters: kaq, Saq\n", "cal = tft.Calibrate(ml)\n", @@ -190,9 +230,63 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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optimal
kaq_0_01.166115
Saq_0_00.000009
\n", + "
" + ], + "text/plain": [ + " optimal\n", + "kaq_0_0 1.166115\n", + "Saq_0_0 0.000009" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.010 m\n" + ] + } + ], "source": [ "display(cal.parameters.loc[:, [\"optimal\"]])\n", "print(f\"RMSE: {cal.rmse():.3f} m\")" @@ -200,9 +294,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", 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 k [m/d]Ss [1/m]RMSE [m]
timflow1.179.38e-060.010
AQTESOLV1.179.37e-06-
MLU1.318.20e-06-
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "t = pd.DataFrame(\n", " columns=[\"k [m/d]\", \"Ss [1/m]\", \"RMSE [m]\"],\n", @@ -270,24 +428,29 @@ "### References\n", "\n", "* Butler Jr., J.J. (1998), The Design, Performance, and Analysis of Slug Tests, Lewis Publishers, Boca Raton, Florida, 252p.\n", - "* Hemker, K. and Post V. (2014) MLU for Windows: well flow modeling in multilayer aquifer systems; MLU User's guide. https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fmicrofem.com%2Fdownload%2Fmlu-user.pdf&data=05%7C02%7CMark.Bakker%40tudelft.nl%7Cad7f16364d2d4fd55dbf08de73832eaa%7C096e524d692940308cd38ab42de0887b%7C0%7C0%7C639075204580287861%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=OBoe8seXZUfoat89Dfr4g6lF%2Bn1FdtXqtp%2F18BMXCn0%3D&reserved=0\n", + "* Hemker, K. and Post V. (2014) MLU for Windows: well flow modeling in multilayer aquifer systems; [MLU User's guide](https://microfem.com/download/mlu-user.pdf)\n", "* Duffield, G.M. (2007), AQTESOLV for Windows Version 4.5 User's Guide, HydroSOLVE, Inc., Reston, VA.\n", "* Hyder, Z., Butler Jr, J.J., McElwee, C.D. and Liu, W. (1994), Slug tests in partially penetrating wells, Water Resources Research 30, 2945–2957." ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python [conda env:base] *", "language": "python", - "name": "python3" + "name": "conda-base-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.5" } }, "nbformat": 4, diff --git a/docs/transient/04pumpingtests/slug3_falling_head.ipynb b/docs/transient/04pumpingtests/slug3_falling_head.ipynb index c33ce676..1194843f 100644 --- a/docs/transient/04pumpingtests/slug3_falling_head.ipynb +++ b/docs/transient/04pumpingtests/slug3_falling_head.ipynb @@ -22,7 +22,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -60,7 +60,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -78,7 +78,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -102,9 +102,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "slug: 0.00366 m^3\n" + ] + } + ], "source": [ "Q = np.pi * rc**2 * H0\n", "print(f\"slug: {Q:.5f} m^3\")" @@ -119,7 +127,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -137,9 +145,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 5\n", + "solution complete\n" + ] + } + ], "source": [ "ml = tft.Model3D(\n", " kaq=10,\n", @@ -172,9 +189,46 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".....................................\n", + "Fit succeeded.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "......................." + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "......" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "..\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fit succeeded.\n" + ] + } + ], "source": [ "cal = tft.Calibrate(ml)\n", "cal.set_parameter(name=\"kaq\", layers=list(range(1, nlay)), initial=10, pmin=0)\n", @@ -185,9 +239,63 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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optimal
kaq_1_170.439169
Saq_1_170.000461
\n", + "
" + ], + "text/plain": [ + " optimal\n", + "kaq_1_17 0.439169\n", + "Saq_1_17 0.000461" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.006 m\n" + ] + } + ], "source": [ "display(cal.parameters.loc[:, [\"optimal\"]])\n", "print(f\"RMSE: {cal.rmse():.3f} m\")" @@ -195,16 +303,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
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 k [m/d]Ss [1/m]RMSE [m]
timflow0.444.61e-040.006
AQTESOLV0.425.70e-04-
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "t = pd.DataFrame(\n", " columns=[\"k [m/d]\", \"Ss [1/m]\", \"RMSE [m]\"],\n", @@ -262,9 +428,21 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python [conda env:base] *", "language": "python", - "name": "python3" + "name": "conda-base-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.5" } }, "nbformat": 4, diff --git a/docs/transient/04pumpingtests/slug4_dawsonville.ipynb b/docs/transient/04pumpingtests/slug4_dawsonville.ipynb index 312fc066..f38da21b 100644 --- a/docs/transient/04pumpingtests/slug4_dawsonville.ipynb +++ b/docs/transient/04pumpingtests/slug4_dawsonville.ipynb @@ -22,7 +22,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -74,7 +74,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -98,7 +98,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -113,9 +113,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], "source": [ "ml = tft.ModelMaq(kaq=10, z=[zt, zb], Saq=1e-4, tmin=1e-6, tmax=1e-3, topboundary=\"conf\")\n", "w = tft.Well(ml, xw=0, yw=0, rw=rw, rc=rc, tsandQ=[(0, -Q)], layers=0, wbstype=\"slug\")\n", @@ -133,9 +142,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "...........................\n", + "Fit succeeded.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "..............\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fit succeeded.\n" + ] + } + ], "source": [ "# unknown parameters: kay, Saq\n", "cal = tft.Calibrate(ml)\n", @@ -147,9 +179,85 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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layersoptimalpminpmaxinitialinhomsparray
kaq0_0_000.4210840.0inf10.0000None[[0.4210837066362776]]
Saq0_0_000.000017-infinf0.0001None[[1.6952353239503044e-05]]
\n", + "
" + ], + "text/plain": [ + " layers optimal pmin pmax initial inhoms \\\n", + "kaq0_0_0 0 0.421084 0.0 inf 10.0000 None \n", + "Saq0_0_0 0 0.000017 -inf inf 0.0001 None \n", + "\n", + " parray \n", + "kaq0_0_0 [[0.4210837066362776]] \n", + "Saq0_0_0 [[1.6952353239503044e-05]] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "rmse: 0.004409597805393658\n" + ] + } + ], "source": [ "display(cal.parameters)\n", "print(\"rmse:\", cal.rmse())" @@ -157,16 +265,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", 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 k [m/d]Ss [1/m]RMSE [m]
timflow0.421.70e-050.004
MLU0.411.94e-050.004
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "t = pd.DataFrame(\n", " columns=[\"k [m/d]\", \"Ss [1/m]\", \"RMSE [m]\"],\n", @@ -209,23 +375,28 @@ "\n", "* Cooper Jr, H.H., Bredehoeft, J.D. and Papadopulos, I.S. (1967) Response of a finite diameter well to an instantaneous charge of water, Water Resources Research 3, 263–269\n", "* Duffield, G.M. (2007), AQTESOLV for Windows Version 4.5 User's Guide, HydroSOLVE, Inc., Reston, VA.\n", - "* Hemker, K. en Post V. (2014) MLU for Windows: well flow modeling in multilayer aquifer systems; MLU User's guide. https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fmicrofem.com%2Fdownload%2Fmlu-user.pdf&data=05%7C02%7CMark.Bakker%40tudelft.nl%7Cad7f16364d2d4fd55dbf08de73832eaa%7C096e524d692940308cd38ab42de0887b%7C0%7C0%7C639075204580287861%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=OBoe8seXZUfoat89Dfr4g6lF%2Bn1FdtXqtp%2F18BMXCn0%3D&reserved=0\n", + "* Hemker, K. en Post V. (2014) MLU for Windows: well flow modeling in multilayer aquifer systems; [MLU User's guide](https://microfem.com/download/mlu-user.pdf)\n", "* Hyder, Z., Butler Jr, J.J., McElwee, C.D. and Liu, W. (1994) Slug tests in partially penetrating wells, Water Resources Research 30, 2945–2957." ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python [conda env:base] *", "language": "python", - "name": "python3" + "name": "conda-base-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.5" } }, "nbformat": 4, diff --git a/docs/transient/04pumpingtests/unconfined1_moench.ipynb b/docs/transient/04pumpingtests/unconfined1_moench.ipynb index 3afd4a54..4909971e 100644 --- a/docs/transient/04pumpingtests/unconfined1_moench.ipynb +++ b/docs/transient/04pumpingtests/unconfined1_moench.ipynb @@ -2,9 +2,15 @@ "cells": [ { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, "source": [ - "# 1.Test for an anisotropic water-table aquifer - Moench Example" + "# 1. Test for an anisotropic water-table aquifer - Moench Example" ] }, { @@ -16,7 +22,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -66,7 +72,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -96,7 +102,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -110,9 +116,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], "source": [ "ml = tft.Model3D(\n", " kaq=1,\n", @@ -136,9 +151,46 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".............................................................\n", + "Fit succeeded.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "............................." + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "......." + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "....................\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fit succeeded.\n" + ] + } + ], "source": [ "cal = tft.Calibrate(ml)\n", "cal.set_parameter(name=\"kaq\", initial=1, layers=[0, 1, 2, 3])\n", @@ -158,9 +210,73 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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optimal
kaq_0_39.067639
Saq_0_00.172885
Saq_1_30.000039
kzoverkh_0_30.535059
\n", + "
" + ], + "text/plain": [ + " optimal\n", + "kaq_0_3 9.067639\n", + "Saq_0_0 0.172885\n", + "Saq_1_3 0.000039\n", + "kzoverkh_0_3 0.535059" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.010 m\n" + ] + } + ], "source": [ "display(cal.parameters.loc[:, [\"optimal\"]])\n", "print(f\"RMSE: {cal.rmse():.3f} m\")" @@ -168,9 +284,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "hm0 = ml.head(0, 0, t0, layers=3)[0]\n", "hm1 = ml.head(r1, 0, t1, layers=1)[0]\n", @@ -178,19 +305,19 @@ "hm3 = ml.head(r2, 0, t3, layers=1)[0]\n", "hm4 = ml.head(r2, 0, t4, layers=3)[0]\n", "\n", - "plt.semilogx(t0, -h0, \".\", label=\"pumped\")\n", - "plt.semilogx(t0, -hm0, label=\"ttim pumped\")\n", - "plt.semilogx(t1, -h1, \".\", label=\"PS1\")\n", - "plt.semilogx(t1, -hm1, label=\"ttim PS1\")\n", - "plt.semilogx(t2, -h2, \".\", label=\"PD1\")\n", - "plt.semilogx(t2, -hm2, label=\"ttim PD1\")\n", - "plt.semilogx(t3, -h3, \".\", label=\"PS2\")\n", - "plt.semilogx(t3, -hm3, label=\"ttim PS2\")\n", - "plt.semilogx(t4, -h4, \".\", label=\"PD2\")\n", - "plt.semilogx(t4, -hm4, label=\"ttim PD2\")\n", + "plt.semilogx(t0, h0, \".\", label=\"pumped\")\n", + "plt.semilogx(t0, hm0, label=\"ttim pumped\")\n", + "plt.semilogx(t1, h1, \".\", label=\"PS1\")\n", + "plt.semilogx(t1, hm1, label=\"ttim PS1\")\n", + "plt.semilogx(t2, h2, \".\", label=\"PD1\")\n", + "plt.semilogx(t2, hm2, label=\"ttim PD1\")\n", + "plt.semilogx(t3, h3, \".\", label=\"PS2\")\n", + "plt.semilogx(t3, hm3, label=\"ttim PS2\")\n", + "plt.semilogx(t4, h4, \".\", label=\"PD2\")\n", + "plt.semilogx(t4, hm4, label=\"ttim PD2\")\n", "\n", "plt.xlabel(\"time (d)\")\n", - "plt.ylabel(\"drawdown (m)\")\n", + "plt.ylabel(\"head change (m)\")\n", "plt.title(\"Model Results\")\n", "plt.legend(bbox_to_anchor=(1.05, 1))\n", "plt.grid();" @@ -212,9 +339,62 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 8, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 k [m/d]Sy [-]Ss [1/m]kz/khRMSE [m]
timflow9.070.23.87e-050.50.0104
Moench8.640.22.00e-050.50.0613
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "t = pd.DataFrame(\n", " columns=[\"k [m/d]\", \"Sy [-]\", \"Ss [1/m]\", \"kz/kh\", \"RMSE [m]\"],\n", @@ -249,9 +429,21 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python [conda env:base] *", "language": "python", - "name": "python3" + "name": "conda-base-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.5" } }, "nbformat": 4, diff --git a/docs/transient/04pumpingtests/unconfined2_vennebulten.ipynb b/docs/transient/04pumpingtests/unconfined2_vennebulten.ipynb new file mode 100644 index 00000000..41c5f59b --- /dev/null +++ b/docs/transient/04pumpingtests/unconfined2_vennebulten.ipynb @@ -0,0 +1,656 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "# 2. Unconfined Aquifer Test - Vennebulten" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "### Import packages" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "import timflow.transient as tft\n", + "\n", + "plt.rcParams[\"figure.figsize\"] = (5, 3) # default figure size" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Introduction and Conceptual Model" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "In aquifer tests in unconfined aquifers, there is also the vertical component to flow to the well. The drawdown data shows the delayed water table response, a distinguishable S-shape in the log-log plot. In the early times of the drawdown, the drawdown behaves as a confined aquifer: when the aquifer releases the elastic storage. However, as pumping continues, the water table storage begins to be released, generating further drawdown and the S-shape.\n", + "\n", + "This test conducted in Vennebulten, the Netherlands, is reported in Kruseman et al. (1970). The cross-section consists of a first layer up to 6 m depth of very fine and loamy sands, followed by coarse sands until 21 m deep.\n", + "\n", + "The screen of the pumping well is placed between 10 and 21 meters depth, and pumping has taken place for 25 hours at a rate of 873 m3/d. The available drawdown data comes from two piezometers, a shallow one, screened at 3 m depth, and a deeper one, screened in the depths between 12 to 19 m. Both wells are located 90 m from the pumping well." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load data " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "data1 = np.loadtxt(\"data/venne_shallow.txt\", skiprows=1)\n", + "ts = data1[:, 0] / 60 / 24 # convert min to days\n", + "hs = data1[:, 1]\n", + "\n", + "data2 = np.loadtxt(\"data/venne_deep.txt\", skiprows=1)\n", + "td = data2[:, 0] / 60 / 24 # convert min to days\n", + "hd = data2[:, 1]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "### Parameters and model" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "b = -21 # aquifer thickness, m\n", + "r = 90 # distance from observation wells to pumping well, m\n", + "Q = 873 # constant discharge, m^3/d\n", + "k = 12 * [0.5] + 15 * [150]\n", + "z = np.hstack((np.arange(0, -6, -0.5), np.arange(-6, -21.1, -1)))\n", + "Ss = [0.2] + 26 * [1e-4]\n", + "kzoverkh = 0.1" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 20\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml = tft.Model3D(\n", + " kaq=k,\n", + " z=z,\n", + " Saq=Ss,\n", + " kzoverkh=kzoverkh,\n", + " topboundary=\"phreatic\",\n", + " tmin=1e-4,\n", + " tmax=1.1,\n", + ")\n", + "w = tft.Well(ml, xw=0, yw=0, rw=0.1, tsandQ=[(0, Q)], layers=range(10, 21))\n", + "wobs = tft.Well(ml, xw=90, yw=0, rw=0.1, tsandQ=[(0, 0)], layers=range(18, 27))\n", + "ml.solve()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "### Estimate aquifer parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "...............................................................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 76\n", + " # data points = 48\n", + " # variables = 4\n", + " chi-square = 0.00319397\n", + " reduced chi-square = 7.2590e-05\n", + " Akaike info crit = -453.649150\n", + " Bayesian info crit = -446.164346\n", + "[[Variables]]\n", + " Saq_1_11: 1.3997e-04 (init = 0.0001)\n", + " kaq_0_11: 0.08061669 (init = 1)\n", + " Saq_12_26: 2.9428e-05 (init = 0.0001)\n", + " kaq_12_26: 121.610702 (init = 150)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "......" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "." + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "......." + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "." + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "......." + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "." + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "......" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "." + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "......." + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "." + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "......" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "." + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "......." + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "." + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "......." + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "." + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "......." + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "." + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "......." + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "." + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 76\n", + " # data points = 48\n", + " # variables = 4\n", + " chi-square = 0.00319397\n", + " reduced chi-square = 7.2590e-05\n", + " Akaike info crit = -453.649150\n", + " Bayesian info crit = -446.164346\n", + "[[Variables]]\n", + " Saq_1_11: 1.3997e-04 (init = 0.0001)\n", + " kaq_0_11: 0.08061669 (init = 1)\n", + " Saq_12_26: 2.9428e-05 (init = 0.0001)\n", + " kaq_12_26: 121.610702 (init = 150)\n" + ] + } + ], + "source": [ + "cal = tft.Calibrate(ml)\n", + "\n", + "cal.set_parameter(\n", + " name=\"Saq\", initial=1e-4, layers=list(range(1, 12)), pmin=1e-6, pmax=1e-2\n", + ")\n", + "cal.set_parameter(name=\"kaq\", initial=150, layers=list(range(0, 12)), pmin=0.05, pmax=1)\n", + "\n", + "cal.set_parameter(\n", + " name=\"Saq\", initial=1e-4, layers=list(range(12, 27)), pmin=1e-6, pmax=1e-2\n", + ")\n", + "cal.set_parameter(name=\"kaq\", initial=150, layers=list(range(12, 27)), pmin=100, pmax=200)\n", + "\n", + "cal.series(name=\"shallow_obs\", x=r, y=0, t=ts, h=hs, layer=5)\n", + "cal.seriesinwell(name=\"wobs\", element=wobs, t=td, h=hd)\n", + "\n", + "cal.fit(report=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " optimal\n", + "Saq_1_11 0.000140\n", + "kaq_0_11 0.080617\n", + "Saq_12_26 0.000029\n", + "kaq_12_26 121.610702" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.008 m\n" + ] + } + ], + "source": [ + "display(cal.parameters.loc[:, [\"optimal\"]])\n", + "print(f\"RMSE: {cal.rmse():.3f} m\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hs_1 = ml.head(r, 0, ts, layers=3)\n", + "hd_1 = wobs.headinside(td)\n", + "\n", + "plt.semilogx(ts, hs, \".\", label=\"shallow obs\")\n", + "plt.semilogx(ts, hs_1[0], label=\"shallow ttim\")\n", + "plt.semilogx(td, hd, \".\", label=\"deep obs\")\n", + "plt.semilogx(td, hd_1[0], \"--\", label=\"deep ttim\")\n", + "plt.xlabel(\"time [d]\")\n", + "plt.ylabel(\"head change [m]\")\n", + "plt.title(\"Timflow Unconfined Model Results - Shallow Piezometer\")\n", + "plt.legend()\n", + "plt.grid()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Comparison of results\n", + "The performance of `timflow` is compared with a model performed with MLU (Hemker & Post, 2014). The schematization of the MLU model differs considerably. The model consists of three layers: a very thin fine-grained upper aquifer layer with horizontal flow and two deeper sublayers with a thickness of 1 and 10 meters. In total, four parameters were estimated (Ss0, c2, Ss2, k2), while fixed values were chosen for the remaining ones. \n", + "\n", + "The RMSE of MLU is smaller than that of timflow, but MLU uses more layers with fixed parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "t = pd.DataFrame(\n", + " columns=[\n", + " \"Ss0 [1/m]\",\n", + " \"k0 [m/d]\",\n", + " \"c1 [d]\",\n", + " \"Ss1 [1/m]\",\n", + " \"k1 [m/d]\",\n", + " \"c2 [d]\",\n", + " \"Ss2 [1/m]\",\n", + " \"k2 [m/d]\",\n", + " \"RMSE [m]\",\n", + " ],\n", + " index=[\"timflow\", \"MLU\"],\n", + ")\n", + "\n", + "t.loc[\"timflow\"] = [\n", + " cal.parameters[\"optimal\"].values[0],\n", + " cal.parameters[\"optimal\"].values[1],\n", + " \"-\",\n", + " cal.parameters[\"optimal\"].values[2],\n", + " cal.parameters[\"optimal\"].values[3],\n", + " \"-\",\n", + " \"-\",\n", + " \"-\",\n", + " cal.rmse(),\n", + "]\n", + "t.loc[\"MLU\"] = [0.38, 1, 100, 1e-6, 50, 209, 0.00558, 165.9, 0.0005]\n", + "\n", + "t_formatted = t.style.format(\n", + " {\n", + " \"Ss0 [1/m]\": \"{:.2e}\",\n", + " \"k0 [m/d]\": \"{:.2f}\",\n", + " \"c1 [d]\": lambda x: \"-\" if x == \"-\" else f\"{float(x):.0f}\",\n", + " \"Ss1 [1/m]\": \"{:.2e}\",\n", + " \"k1 [m/d]\": \"{:.2f}\",\n", + " \"c2 [d]\": lambda x: \"-\" if x == \"-\" else f\"{float(x):.0f}\",\n", + " \"Ss2 [1/m]\": lambda x: \"-\" if x == \"-\" else f\"{float(x):.2e}\",\n", + " \"k2 [m/d]\": lambda x: \"-\" if x == \"-\" else f\"{float(x):.2f}\",\n", + " \"RMSE [m]\": \"{:.4f}\",\n", + " }\n", + ")\n", + "t_formatted" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## References\n", + "* Hemker, K. en Post V. (2014) MLU for Windows: well flow modeling in multilayer aquifer systems; [MLU User's guide](https://microfem.com/download/mlu-user.pdf)\n", + "* Kruseman, G.P., De Ridder, N.A., Verweij, J.M., 1970. Analysis and evaluationof pumping test data. volume 11. International institute for land reclamation and improvement The Netherlands.\n", + "* Neuman, S.P., Witherspoon, P.A., 1969. Applicability of current theories of flow in leaky aquifers. Water Resources Research 5, 817–829." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:base] *", + "language": "python", + "name": "conda-base-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/papers/2003_bakker_strack_jofh.pdf b/papers/2003_bakker_strack_jofh.pdf new file mode 100755 index 00000000..f33a93f6 Binary files /dev/null and b/papers/2003_bakker_strack_jofh.pdf differ diff --git a/papers/2013_bakker_ttim_hgj.pdf b/papers/2013_bakker_ttim_hgj.pdf new file mode 100755 index 00000000..5f8a8582 Binary files /dev/null and b/papers/2013_bakker_ttim_hgj.pdf differ diff --git a/timflow/version.py b/timflow/version.py index fdcffeb7..177eea05 100644 --- a/timflow/version.py +++ b/timflow/version.py @@ -2,7 +2,7 @@ from importlib import import_module, metadata from platform import python_version -__version__ = "0.4.0" +__version__ = "0.4.1" def show_versions(optional=True) -> None: @@ -21,13 +21,14 @@ def show_versions(optional=True) -> None: f"Scipy version : {metadata.version('scipy')}\n" f"Pandas version : {metadata.version('pandas')}\n" f"Matplotlib version : {metadata.version('matplotlib')}" + # f"tqdm version : {metadata.version('tqdm')}" ) if optional: msg += "\nLmFit version : " try: import_module("lmfit") msg += f"{metadata.version('lmfit')}" - except ImportError: + except ModuleNotFoundError: msg += "Not Installed" print(msg) diff --git a/utils/clear_pumping_test_notebooks.py b/utils/clear_pumping_test_notebooks.py new file mode 100644 index 00000000..cf561aea --- /dev/null +++ b/utils/clear_pumping_test_notebooks.py @@ -0,0 +1,43 @@ +from pathlib import Path + +import nbformat +from nbconvert.preprocessors import ( + ClearMetadataPreprocessor, + ClearOutputPreprocessor, +) + +nbroots = [Path("docs/transient/04pumpingtests")] + + +def get_notebooks(): + nblist = [] + for root in nbroots: + for nb in root.glob("*.ipynb"): + if "_build" in nb.parts: + continue + nblist.append(nb) + return sorted(nblist) + + +clear_output = ClearOutputPreprocessor() + +# By default nbconvert keeps `metadata.language_info.name`. Use an empty preserve +# mask so kernel/language metadata is stripped from notebook files. +clear_metadata = ClearMetadataPreprocessor(preserve_nb_metadata_mask=set()) + +for notebook in get_notebooks(): + print("Clearing notebook:", notebook) + nb = nbformat.read(notebook, as_version=4) + + # run nbconvert preprocessors to clear outputs and metadata + clear_output.preprocess(nb, {}) + clear_metadata.preprocess(nb, {}) + + # Ensure a portable kernel is defined for CI/ReadTheDocs notebook execution. + nb.metadata["kernelspec"] = { + "name": "python3", + "display_name": "Python 3", + "language": "python", + } + + nbformat.write(nb, notebook)