From 3b37500dd921fec11f8313192137b37f3432d60d Mon Sep 17 00:00:00 2001 From: Hannah Heesen Date: Tue, 10 Mar 2026 18:11:03 +0700 Subject: [PATCH 1/7] Update comparison --- .../confined1_oude_korendijk.ipynb | 182 ++++++- .../04pumpingtests/confined2_grindley.ipynb | 168 +++++- .../04pumpingtests/confined3_sioux.ipynb | 174 +++++- .../04pumpingtests/data/schroth_obs3.txt | 11 + docs/transient/04pumpingtests/figs/Nevada.png | Bin 0 -> 33173 bytes .../transient/04pumpingtests/figs/Schroth.png | Bin 0 -> 34946 bytes .../04pumpingtests/leaky1_dalem.ipynb | 284 +++++++++- .../04pumpingtests/leaky2_hardixveld.ipynb | 239 ++++++++- .../04pumpingtests/leaky3_texashill.ipynb | 175 +++++- .../04pumpingtests/slug1_pratt_county.ipynb | 173 +++++- .../04pumpingtests/slug2_multiwell.ipynb | 192 ++++++- .../04pumpingtests/slug3_falling_head.ipynb | 502 ++++++++++++++++++ 12 files changed, 1925 insertions(+), 175 deletions(-) create mode 100644 docs/transient/04pumpingtests/data/schroth_obs3.txt create mode 100644 docs/transient/04pumpingtests/figs/Nevada.png create mode 100644 docs/transient/04pumpingtests/figs/Schroth.png create mode 100644 docs/transient/04pumpingtests/slug3_falling_head.ipynb diff --git a/docs/transient/04pumpingtests/confined1_oude_korendijk.ipynb b/docs/transient/04pumpingtests/confined1_oude_korendijk.ipynb index acb713c..97f72b6 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": [ @@ -67,7 +67,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -92,7 +92,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -105,9 +105,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "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", @@ -125,9 +134,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".................................\n", + "Fit succeeded.\n" + ] + } + ], "source": [ "# unknown parameters: kaq, Saq\n", "cal = tft.Calibrate(ml)\n", @@ -140,9 +158,63 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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optimal
kaq_0_066.089354
Saq_0_00.000025
\n", + "
" + ], + "text/plain": [ + " optimal\n", + "kaq_0_0 66.089354\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\")" @@ -150,7 +222,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": { "editable": true, "slideshow": { @@ -158,7 +230,18 @@ }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "hm1 = ml.head(ro1, 0, to1)\n", "hm2 = ml.head(ro2, 0, to2)\n", @@ -184,12 +267,16 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The performance of `timflow` was compared with AQTESOLV (Duffield, 2007), MLU (Carlson and Randall, 2012), and Kruseman and de Ridder (1970), here abbreviated to K&dR. Results from `timflow` and AQTESOLV are identical, those from MLU are very similar, while the solution of Kruseman and de Ridder shows small differences." + "The performance of `timflow` was compared with MLU (Hemker & Post, 2014), and Kruseman and de Ridder (1970), here abbreviated to K&dR. \n", + "\n", + "MLU included, compared to `timflow`, three observation wells in the calibration, located at 30, 90, and 215 m from the pumping well. The fit remains poor for the observations at 215 m, which explains the relatively large RMSE. The late-time drawdowns increase more slowly than the computed Theis drawdowns, which is likely caused by leakage (Kruseman and de Ridder, 1970; Hemker & Post, 2014).\n", + "\n", + "Since `timflow` only includes the first two observation wells (30 and 90 m), the fit to the data is better. However, the estimated parameters are quite similar to those obtained with MLU, while the solution reported by K&dR shows differences." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "metadata": { "editable": true, "slideshow": { @@ -199,17 +286,61 @@ "hide-input" ] }, - "outputs": [], + "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]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": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "t = pd.DataFrame(\n", " columns=[\"k [m/d]\", \"Ss [1/m]\", \"RMSE [m]\"],\n", - " index=[\"timflow\", \"AQTESOLV\", \"MLU\", \"K&dR\"],\n", + " index=[\"timflow\", \"MLU\", \"K&dR\"],\n", ")\n", "\n", "t.loc[\"timflow\"] = np.append(cal.parameters[\"optimal\"].values, cal.rmse())\n", - "t.loc[\"AQTESOLV\"] = [66.086, 2.541e-05, 0.05006]\n", - "t.loc[\"MLU\"] = [66.850, 2.400e-05, 0.05083]\n", - "t.loc[\"K&dR\"] = [55.71429, 1.7e-4, \"-\"]\n", + "t.loc[\"MLU\"] = [63.51428571, 3.58e-5, 0.832886547]\n", + "t.loc[\"K&dR\"] = [55.71429, 1.7e-4, 1.292710331]\n", "\n", "t_formatted = t.style.format(\n", " {\n", @@ -234,17 +365,24 @@ "## References\n", "\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", - "* Carlson, F. and Randall, J. (2012), MLU: a Windows application for the analysis of aquifer tests and the design of well fields in layered systems, Ground Water 50(4):504–510\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", "* 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." ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python [conda env:base] *", "language": "python", - "name": "python3" + "name": "conda-base-py" }, "language_info": { "codemirror_mode": { @@ -256,7 +394,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.13.5" + "version": "3.12.2" } }, "nbformat": 4, diff --git a/docs/transient/04pumpingtests/confined2_grindley.ipynb b/docs/transient/04pumpingtests/confined2_grindley.ipynb index db6c4ec..beb4b03 100644 --- a/docs/transient/04pumpingtests/confined2_grindley.ipynb +++ b/docs/transient/04pumpingtests/confined2_grindley.ipynb @@ -22,7 +22,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": { "editable": true, "slideshow": { @@ -99,7 +99,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": { "editable": true, "slideshow": { @@ -135,7 +135,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": { "editable": true, "slideshow": { @@ -171,7 +171,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": { "editable": true, "slideshow": { @@ -179,7 +179,16 @@ }, "tags": [] }, - "outputs": [], + "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", @@ -214,7 +223,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "metadata": { "editable": true, "slideshow": { @@ -222,7 +231,16 @@ }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "..........................\n", + "Fit succeeded.\n" + ] + } + ], "source": [ "# unknown parameters: kaq, Saq\n", "cal = tft.Calibrate(ml) # create Calibrate object\n", @@ -237,7 +255,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "metadata": { "editable": true, "slideshow": { @@ -245,7 +263,61 @@ }, "tags": [] }, - "outputs": [], + "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_038.087248
Saq_0_00.000001
\n", + "
" + ], + "text/plain": [ + " optimal\n", + "kaq_0_0 38.087248\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\")" @@ -253,7 +325,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 36, "metadata": { "editable": true, "slideshow": { @@ -261,7 +333,18 @@ }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "hm1 = ml.head(r, 0, to1)\n", "hm2 = w.headinside(to2)\n", @@ -299,12 +382,14 @@ "tags": [] }, "source": [ - "The performance of `timflow` with two datasets simultaneously was compared with AQTESOLV (Duffield, 2007) and MLU (Carlson and Randall, 2012). Results from `timflow` with added wellbore storage and MLU are identical, while those from AQTESOLV show small differences." + "The performance of `timflow` is compared to the results based on Theis’ analytical solution (Theis, 1935), implemented in the software AQTESOLV (Duffield, 2007). \n", + "\n", + "Results from `timflow` with added wellbore storage differ largely from those from AQTESOLV. This difference is due to the missing wellborage storage in the AQTESOLV model." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 40, "metadata": { "editable": true, "slideshow": { @@ -314,18 +399,59 @@ "hide-input" ] }, - "outputs": [], + "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]
timflow38.091.19e-060.259
AQTESOLV22.953.72e-06-
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "t = pd.DataFrame(\n", - " columns=[\"k [m/d]\", \"Ss [1/m]\", \"RMSE [m]\"], index=[\"timflow\", \"AQTESOLV\", \"MLU\"]\n", + " columns=[\"k [m/d]\", \"Ss [1/m]\", \"RMSE [m]\"], index=[\"timflow\", \"AQTESOLV\"]\n", ")\n", "\n", "t.loc[\"timflow\"] = np.append(cal.parameters[\"optimal\"].values, cal.rmse())\n", - "t.loc[\"MLU\"] = [38.094, 1.193e-06, 0.259]\n", - "t.loc[\"AQTESOLV\"] = [37.803, 1.356e-06, 0.270]\n", + "t.loc[\"AQTESOLV\"] = [22.95, 0.000003722, \"-\"]\n", "\n", "t_formatted = t.style.format(\n", - " {\"k [m/d]\": \"{:.2f}\", \"Ss [1/m]\": \"{:.2e}\", \"RMSE [m]\": \"{:.3f}\"}\n", + " {\"k [m/d]\": \"{:.2f}\", \n", + " \"Ss [1/m]\": \"{:.2e}\", \n", + " \"RMSE [m]\": lambda x: \"-\" if x == \"-\" else f\"{float(x):.3f}\",\n", + " }\n", ")\n", "t_formatted" ] @@ -355,9 +481,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python [conda env:base] *", "language": "python", - "name": "python3" + "name": "conda-base-py" }, "language_info": { "codemirror_mode": { @@ -369,7 +495,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.13.5" + "version": "3.12.2" } }, "nbformat": 4, diff --git a/docs/transient/04pumpingtests/confined3_sioux.ipynb b/docs/transient/04pumpingtests/confined3_sioux.ipynb index 153e37f..06ce372 100644 --- a/docs/transient/04pumpingtests/confined3_sioux.ipynb +++ b/docs/transient/04pumpingtests/confined3_sioux.ipynb @@ -28,7 +28,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": { "editable": true, "slideshow": { @@ -103,7 +103,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": { "editable": true, "slideshow": { @@ -144,7 +144,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": { "editable": true, "slideshow": { @@ -167,7 +167,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": { "editable": true, "slideshow": { @@ -175,7 +175,16 @@ }, "tags": [] }, - "outputs": [], + "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", @@ -198,7 +207,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": { "editable": true, "slideshow": { @@ -206,7 +215,16 @@ }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "...............................................\n", + "Fit succeeded.\n" + ] + } + ], "source": [ "# unknown parameters: k, Saq\n", "cal = tft.Calibrate(ml)\n", @@ -220,7 +238,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": { "editable": true, "slideshow": { @@ -228,7 +246,61 @@ }, "tags": [] }, - "outputs": [], + "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\")" @@ -236,7 +308,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": { "editable": true, "slideshow": { @@ -244,7 +316,18 @@ }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "hm1 = ml.head(r1, 0, t1)\n", "hm2 = ml.head(r2, 0, t2)\n", @@ -273,12 +356,14 @@ }, "source": [ "### Comparison of results\n", - "The performance of `timflow` was compared with AQTESOLV (Duffield, 2007) and MLU (Carlson and Randall, 2012).`timflow` achieved similar results as the other software. " + "The performance of `timflow` is compared to the results based on Theis’ analytical solution (Theis, 1935), implemented in the software AQTESOLV (Duffield, 2007). \n", + "\n", + "`timflow` achieved similar results as AQTESOLV. " ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": { "editable": true, "slideshow": { @@ -288,18 +373,57 @@ "hide-input" ] }, - "outputs": [], + "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]
timflow282.804.21e-030.004
AQTESOLV282.664.04e-03-
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "t = pd.DataFrame(\n", - " columns=[\"k [m/d]\", \"Ss [1/m]\", \"RMSE [m]\"], index=[\"timflow\", \"AQTESOLV\", \"MLU\"]\n", + " columns=[\"k [m/d]\", \"Ss [1/m]\", \"RMSE [m]\"], index=[\"timflow\", \"AQTESOLV\"]\n", ")\n", "\n", "t.loc[\"timflow\"] = np.append(cal.parameters[\"optimal\"].values, cal.rmse())\n", - "t.loc[\"AQTESOLV\"] = [282.659, 4.211e-03, 0.003925]\n", - "t.loc[\"MLU\"] = [282.684, 4.209e-03, 0.003897]\n", + "t.loc[\"AQTESOLV\"] = [282.659, 4.0355e-03, \"-\"]\n", "\n", "t_formatted = t.style.format(\n", - " {\"k [m/d]\": \"{:.2f}\", \"Ss [1/m]\": \"{:.2e}\", \"RMSE [m]\": \"{:.3f}\"}\n", + " {\"k [m/d]\": \"{:.2f}\", \"Ss [1/m]\": \"{:.2e}\", \n", + " \"RMSE [m]\": lambda x: \"-\" if x == \"-\" else f\"{float(x):.3f}\",}\n", ")\n", "t_formatted" ] @@ -327,17 +451,23 @@ "tags": [] }, "source": [ - "* Carlson, F. and Randall, J. (2012), MLU: a Windows application for the analysis of aquifer tests and the design of well fields in layered systems, Ground Water 50(4):504–510\n", "* 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 (ipykernel)", + "display_name": "Python [conda env:base] *", "language": "python", - "name": "python3" + "name": "conda-base-py" }, "language_info": { "codemirror_mode": { @@ -349,7 +479,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.13.5" + "version": "3.12.2" } }, "nbformat": 4, diff --git a/docs/transient/04pumpingtests/data/schroth_obs3.txt b/docs/transient/04pumpingtests/data/schroth_obs3.txt new file mode 100644 index 0000000..365a502 --- /dev/null +++ b/docs/transient/04pumpingtests/data/schroth_obs3.txt @@ -0,0 +1,11 @@ +# time (d), head (m) +0.013889 -0.010 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"execution_count": null, + "execution_count": 8, "metadata": { "editable": true, "slideshow": { @@ -137,7 +137,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": { "editable": true, "slideshow": { @@ -175,7 +175,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": { "editable": true, "slideshow": { @@ -209,7 +209,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": { "editable": true, "slideshow": { @@ -217,7 +217,16 @@ }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "..............................................\n", + "Fit succeeded.\n" + ] + } + ], "source": [ "cal = tft.Calibrate(ml)\n", "cal.set_parameter(name=\"kaq\", initial=10, layers=0)\n", @@ -233,7 +242,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": { "editable": true, "slideshow": { @@ -241,7 +250,66 @@ }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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optimal
kaq_0_045.332066
Saq_0_00.000048
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\n", + "
" + ], + "text/plain": [ + " optimal\n", + "kaq_0_0 45.332066\n", + "Saq_0_0 0.000048\n", + "c_0_0 331.177727" + ] + }, + "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\")" @@ -249,7 +317,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 41, "metadata": { "editable": true, "slideshow": { @@ -257,7 +325,18 @@ }, "tags": [] }, - "outputs": [], + "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", @@ -298,9 +377,35 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 44, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "........................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 37\n", + " # data points = 51\n", + " # variables = 3\n", + " chi-square = 0.06328840\n", + " reduced chi-square = 0.00131851\n", + " Akaike info crit = -335.285820\n", + " Bayesian info crit = -329.490343\n", + "[[Variables]]\n", + " kaq_0_0: 48.0405082 +/- 1.42053102 (2.96%) (init = 10)\n", + " Saq_0_0: 4.3598e-05 +/- 2.8635e-06 (6.57%) (init = 0.0001)\n", + " c_0_0: 539.739712 +/- 180.224614 (33.39%) (init = 500)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq_0_0, Saq_0_0) = -0.7790\n", + " C(kaq_0_0, c_0_0) = +0.7623\n", + " C(Saq_0_0, c_0_0) = -0.2964\n" + ] + } + ], "source": [ "cal2 = tft.Calibrate(ml)\n", "cal2.set_parameter(name=\"kaq\", initial=10, layers=0)\n", @@ -316,9 +421,72 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 46, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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layersoptimal
kaq_0_0048.040508
Saq_0_000.000044
c_0_00539.739712
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" + ], + "text/plain": [ + " layers optimal\n", + "kaq_0_0 0 48.040508\n", + "Saq_0_0 0 0.000044\n", + "c_0_0 0 539.739712" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.00624 m\n" + ] + } + ], "source": [ "display(cal2.parameters[[\"layers\", \"optimal\"]])\n", "print(f\"RMSE: {cal2.rmse(weighted=False):.5f} m\")" @@ -335,14 +503,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The performance of `timflow` is compared with other models and with parameter values reported in Kruseman and de Ridder (1970). The published values were obtained by graphical matching to a Hantush type curve (Hantush, 1955). In addition to the `timflow` results and the literature values, results from AQTESOLV (Duffield, 2007) and MLU (Carlson & Randall, 2012) are included for comparison. Both AQTESOLV and MLU were applied to minimize the sum of the squared log-transformed drawdowns. \n", + "The performance of `timflow` is compared with other models and with parameter values reported in Kruseman and de Ridder (1970). The published values were obtained by graphical matching to a Hantush type curve (Hantush, 1955). In addition to the `timflow` results and the literature values, results from AQTESOLV (Duffield, 2007) and MLU (Hemker & Post, 2014) are included for comparison. Both AQTESOLV and MLU were applied to minimize the sum of the squared log-transformed drawdowns. AQTESOLV only fitted the model to the observation well at r = 90 m, whereas the other models were calibrated using data from multiple observation wells.\n", "\n", "Overall, all models yield similar estimates of the hydraulic conductivity and specific storage coefficient. The fitted value of the resistance differs. However, the `timflow` results differ substantially from the MLU and AQTESOLV solutions with respect to aquitard storage and hydraulic resistance." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 58, "metadata": { "editable": true, "slideshow": { @@ -350,7 +518,70 @@ }, "tags": [] }, - "outputs": [], + "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-053280.0382
MLU48.114.32e-055390.0424
Hantush45.334.76e-053310.0059
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "t = pd.DataFrame(\n", " columns=[\"k [m/d]\", \"Ss [1/m]\", \"c [d]\", \"RMSE [m]\"],\n", @@ -361,8 +592,8 @@ "t.loc[\"timflow weights\"] = np.append(\n", " cal2.parameters[\"optimal\"].values, cal2.rmse(weighted=False)\n", ")\n", - "t.loc[\"AQTESOLV\"] = [49.286, 4.559e-05, 745.156, 0.007245]\n", - "t.loc[\"MLU\"] = [45.186, 3.941e-05, 769.200, 0.005941]\n", + "t.loc[\"AQTESOLV\"] = [41.5575, 4.4625e-05, 327.920, 0.03818]\n", + "t.loc[\"MLU\"] = [48.108, 4.324e-05, 539, 0.042426]\n", "t.loc[\"Hantush\"] = [45.332, 4.762e-5, 331.141, 0.005917]\n", "\n", "t_formatted = t.style.format(\n", @@ -378,18 +609,25 @@ "## References\n", "\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", - "* Carlson, F. and Randall, J. (2012), MLU: a Windows application for the analysis of aquifer tests and the design of well fields in layered systems, Ground Water 50(4):504–510.\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", "* 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 (ipykernel)", + "display_name": "Python [conda env:base] *", "language": "python", - "name": "python3" + "name": "conda-base-py" }, "language_info": { "codemirror_mode": { @@ -401,7 +639,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.13.5" + "version": "3.12.2" } }, "nbformat": 4, diff --git a/docs/transient/04pumpingtests/leaky2_hardixveld.ipynb b/docs/transient/04pumpingtests/leaky2_hardixveld.ipynb index 930504f..464f953 100644 --- a/docs/transient/04pumpingtests/leaky2_hardixveld.ipynb +++ b/docs/transient/04pumpingtests/leaky2_hardixveld.ipynb @@ -28,7 +28,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": { "editable": true, "slideshow": { @@ -92,7 +92,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": { "editable": true, "slideshow": { @@ -122,7 +122,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": { "editable": true, "slideshow": { @@ -143,7 +143,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": { "editable": true, "slideshow": { @@ -151,7 +151,16 @@ }, "tags": [] }, - "outputs": [], + "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", @@ -168,7 +177,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": { "editable": true, "slideshow": { @@ -176,7 +185,16 @@ }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".....................................................................................\n", + "Fit succeeded.\n" + ] + } + ], "source": [ "cal = tft.Calibrate(ml)\n", "cal.set_parameter(name=\"kaq\", initial=50, pmin=0, layers=0)\n", @@ -189,7 +207,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": { "editable": true, "slideshow": { @@ -197,7 +215,66 @@ }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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optimal
kaq_0_048.630582
Saq_0_00.000667
res0.025542
\n", + "
" + ], + "text/plain": [ + " optimal\n", + "kaq_0_0 48.630582\n", + "Saq_0_0 0.000667\n", + "res 0.025542" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.00875 m\n" + ] + } + ], "source": [ "display(cal.parameters.loc[:, [\"optimal\"]])\n", "print(f\"RMSE: {cal.rmse():.5f} m\")" @@ -205,7 +282,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": { "editable": true, "slideshow": { @@ -213,7 +290,16 @@ }, "tags": [] }, - "outputs": [], + "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", @@ -231,7 +317,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": { "editable": true, "slideshow": { @@ -239,7 +325,16 @@ }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "..................................................................................................................\n", + "Fit succeeded.\n" + ] + } + ], "source": [ "cal1 = tft.Calibrate(ml1)\n", "cal1.set_parameter(name=\"kaq\", initial=50, pmin=0, layers=0)\n", @@ -252,7 +347,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": { "editable": true, "slideshow": { @@ -260,7 +355,27 @@ }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "kaq_0_0 45.905358\n", + "Saq_0_0 0.000003\n", + "res 0.015366\n", + "Name: optimal, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.00606 m\n" + ] + } + ], "source": [ "display(cal1.parameters[\"optimal\"])\n", "print(f\"RMSE: {cal1.rmse():.5f} m\")" @@ -268,7 +383,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": { "editable": true, "slideshow": { @@ -276,7 +391,18 @@ }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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timflow semi-confined45.912.60e-060.0150.0061
MLU48.941.04e-050.0010.0076
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "t = pd.DataFrame(\n", " columns=[\"k [m/d]\", \"Ss [1/m]\", \"res\", \"RMSE [m]\"],\n", @@ -338,7 +519,7 @@ "\n", "t.loc[\"timflow confined\"] = np.append(cal.parameters[\"optimal\"].values, cal.rmse())\n", "t.loc[\"timflow semi-confined\"] = np.append(cal1.parameters[\"optimal\"].values, cal1.rmse())\n", - "t.loc[\"MLU\"] = [51.530, 8.16e-04, 0.022, 0.00756]\n", + "t.loc[\"MLU\"] = [48.94241, 1.037e-05, 0.0007349, 0.00756]\n", "\n", "t_formatted = t.style.format(\n", " {\"k [m/d]\": \"{:.2f}\", \"Ss [1/m]\": \"{:.2e}\", \"res\": \"{:.3f}\", \"RMSE [m]\": \"{:.4f}\"}\n", @@ -352,17 +533,23 @@ "source": [ "## References\n", "\n", - "* Carlson F. and Randall J. (2012), MLU: a Windows application for the analysis of aquifer tests and the design of well fields in layered systems, Ground Water 50(4):504–510\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", "* 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 (ipykernel)", + "display_name": "Python [conda env:base] *", "language": "python", - "name": "python3" + "name": "conda-base-py" }, "language_info": { "codemirror_mode": { @@ -374,7 +561,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.13.5" + "version": "3.12.2" } }, "nbformat": 4, diff --git a/docs/transient/04pumpingtests/leaky3_texashill.ipynb b/docs/transient/04pumpingtests/leaky3_texashill.ipynb index 7ed25dc..aa6bd25 100644 --- a/docs/transient/04pumpingtests/leaky3_texashill.ipynb +++ b/docs/transient/04pumpingtests/leaky3_texashill.ipynb @@ -22,7 +22,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "editable": true, "slideshow": { @@ -86,7 +86,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { "editable": true, "slideshow": { @@ -130,7 +130,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": { "editable": true, "slideshow": { @@ -164,7 +164,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": { "editable": true, "slideshow": { @@ -172,7 +172,16 @@ }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], "source": [ "ml = tft.ModelMaq(\n", " kaq=10,\n", @@ -204,7 +213,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": { "editable": true, "slideshow": { @@ -212,7 +221,16 @@ }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "......................................................\n", + "Fit succeeded.\n" + ] + } + ], "source": [ "# unknown parameters: kaq, Saq, c\n", "cal = tft.Calibrate(ml)\n", @@ -227,7 +245,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": { "editable": true, "slideshow": { @@ -235,7 +253,66 @@ }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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optimal
kaq_0_0224.628907
Saq_0_00.000213
c_0_043.882076
\n", + "
" + ], + "text/plain": [ + " optimal\n", + "kaq_0_0 224.628907\n", + "Saq_0_0 0.000213\n", + "c_0_0 43.882076" + ] + }, + "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\")" @@ -243,7 +320,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": { "editable": true, "slideshow": { @@ -251,7 +328,18 @@ }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
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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": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "t = pd.DataFrame(\n", " columns=[\"k [m/d]\", \"Ss [1/m]\", \"c [d]\", \"RMSE [m]\"],\n", " index=[\"timflow\", \"AQTESOLV\"],\n", ")\n", "\n", - "t.loc[\"AQTESOLV\"] = [224.726, 2.125e-4, 43.964, 0.059627]\n", + "t.loc[\"AQTESOLV\"] = [224.723, 2.124e-4, 43.998, \"-\"]\n", "t.loc[\"timflow\"] = np.append(cal.parameters[\"optimal\"].values, cal.rmse())\n", "\n", "t_formatted = t.style.format(\n", - " {\"k [m/d]\": \"{:.2f}\", \"Ss [1/m]\": \"{:.2e}\", \"c [d]\": \"{:.2f}\", \"RMSE [m]\": \"{:.3f}\"}\n", + " {\"k [m/d]\": \"{:.2f}\", \"Ss [1/m]\": \"{:.2e}\", \"c [d]\": \"{:.2f}\", \n", + " \"RMSE [m]\": lambda x: \"-\" if x == \"-\" else f\"{float(x):.3f}\",}\n", ")\n", "t_formatted" ] @@ -329,17 +460,23 @@ "source": [ "## References\n", "\n", - "* Carlson F. and Randall J. (2012), MLU: a Windows application for the analysis of aquifer tests and the design of well fields in layered systems, Ground Water 50(4):504–510.\n", "* 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 (ipykernel)", + "display_name": "Python [conda env:base] *", "language": "python", - "name": "python3" + "name": "conda-base-py" }, "language_info": { "codemirror_mode": { @@ -351,7 +488,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.13.5" + "version": "3.12.2" } }, "nbformat": 4, diff --git a/docs/transient/04pumpingtests/slug1_pratt_county.ipynb b/docs/transient/04pumpingtests/slug1_pratt_county.ipynb index 92a1eb6..d5a6a97 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": 34, "metadata": { "editable": true, "slideshow": { @@ -102,7 +102,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 40, "metadata": { "editable": true, "slideshow": { @@ -132,7 +132,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 43, "metadata": { "editable": true, "slideshow": { @@ -166,7 +166,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 46, "metadata": { "editable": true, "slideshow": { @@ -174,7 +174,15 @@ }, "tags": [] }, - "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\")" @@ -196,7 +204,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 49, "metadata": { "editable": true, "slideshow": { @@ -204,7 +212,16 @@ }, "tags": [] }, - "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", @@ -227,7 +244,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 52, "metadata": { "editable": true, "slideshow": { @@ -235,7 +252,16 @@ }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".......................\n", + "Fit succeeded.\n" + ] + } + ], "source": [ "cal = tft.Calibrate(ml)\n", "cal.set_parameter(name=\"kaq\", layers=[0, 1, 2], initial=10)\n", @@ -246,7 +272,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 54, "metadata": { "editable": true, "slideshow": { @@ -254,7 +280,61 @@ }, "tags": [] }, - "outputs": [], + "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_26.048673
Saq_0_20.000213
\n", + "
" + ], + "text/plain": [ + " optimal\n", + "kaq_0_2 6.048673\n", + "Saq_0_2 0.000213" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.00286 m\n" + ] + } + ], "source": [ "display(cal.parameters.loc[:, [\"optimal\"]])\n", "print(f\"RMSE: {cal.rmse():.5f} m\")" @@ -262,7 +342,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 56, "metadata": { "editable": true, "slideshow": { @@ -270,7 +350,18 @@ }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", 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timflow6.052.13e-040.0029
AQTESOLV4.033.83e-040.0030
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "t = pd.DataFrame(\n", " columns=[\"k [m/d]\", \"Ss [1/m]\", \"RMSE [m]\"],\n", @@ -353,13 +483,20 @@ "* 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 (ipykernel)", + "display_name": "Python [conda env:base] *", "language": "python", - "name": "python3" + "name": "conda-base-py" }, "language_info": { "codemirror_mode": { @@ -371,7 +508,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.13.5" + "version": "3.12.2" } }, "nbformat": 4, diff --git a/docs/transient/04pumpingtests/slug2_multiwell.ipynb b/docs/transient/04pumpingtests/slug2_multiwell.ipynb index 90fee63..e93089e 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": { "editable": true, "slideshow": { @@ -59,7 +59,7 @@ "source": [ "## Introduction and Conceptual Model\n", "\n", - "A well (Ln-2) fully penetrates a sandy confined aquifer of 6.1 m thickness. Additionally, a fully penetrating observation well (Ln-3) is located 6.45 m away from the test well. The slug displacement is 2.798 m. Head change was recorded at the slug well and the observation well. The well and casing radii of the slug well are 0.102 and 0.051 m, respectively. For the observation well, they are 0.051 and 0.025 m, respectively." + "A well (Ln-2) fully penetrates a sandy confined aquifer of 6.1 m thickness. Additionally, a fully penetrating observation well (Ln-3) is located 6.45 m away from the test well. The slug displacement is 2.798 m. Head change was recorded at the slug well and the observation well. The well and casing radii of the slug well are 0.102 and 0.051 m, respectively. For the observation well, they are 0.071 and 0.025 m, respectively." ] }, { @@ -90,7 +90,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": { "editable": true, "slideshow": { @@ -124,7 +124,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": { "editable": true, "slideshow": { @@ -158,7 +158,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": { "editable": true, "slideshow": { @@ -166,7 +166,15 @@ }, "tags": [] }, - "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\")" @@ -174,7 +182,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": { "editable": true, "slideshow": { @@ -182,7 +190,16 @@ }, "tags": [] }, - "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", @@ -205,7 +222,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": { "editable": true, "slideshow": { @@ -213,7 +230,16 @@ }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "...........................................\n", + "Fit succeeded.\n" + ] + } + ], "source": [ "# unknown parameters: kaq, Saq\n", "cal = tft.Calibrate(ml)\n", @@ -226,7 +252,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": { "editable": true, "slideshow": { @@ -234,7 +260,61 @@ }, "tags": [] }, - "outputs": [], + "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_01.166111
Saq_0_00.000009
\n", + "
" + ], + "text/plain": [ + " optimal\n", + "kaq_0_0 1.166111\n", + "Saq_0_0 0.000009" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.01024 m\n" + ] + } + ], "source": [ "display(cal.parameters.loc[:, [\"optimal\"]])\n", "print(f\"RMSE: {cal.rmse():.5f} m\")" @@ -242,7 +322,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": { "editable": true, "slideshow": { @@ -250,7 +330,18 @@ }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "hm1 = w.headinside(t1)\n", "hm2 = ml.head(r, 0, t2, layers=0)\n", @@ -277,12 +368,12 @@ "source": [ "### Comparison of results\n", "\n", - "The values in `timflow` are compared with the KGS analytical model (Hyder et al. 1994) implemented in AQTESOLV (Duffield, 2007), and to the MLU model (Carlson & Randall, 2012). The parameters of all three models are very similar." + "The values in `timflow` are compared with the KGS analytical model (Hyder et al. 1994) implemented in AQTESOLV (Duffield, 2007), and to the MLU model (Hemker & Post, 2014). The parameters of `timflow`and AQTESOLV are very similar. MLU differs a bit, this is likely due to the added skin resistance (skin factor of 2.42). " ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 41, "metadata": { "editable": true, "slideshow": { @@ -292,7 +383,52 @@ "hide-input" ] }, - "outputs": [], + "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]Ss [1/m]RMSE [m]
timflow1.179.38e-060.010
AQTESOLV1.179.37e-06-
MLU1.318.20e-06-
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "t = pd.DataFrame(\n", " columns=[\"k [m/d]\", \"Ss [1/m]\", \"RMSE [m]\"],\n", @@ -300,11 +436,12 @@ ")\n", "\n", "t.loc[\"timflow\"] = np.append(cal.parameters[\"optimal\"].values, cal.rmse())\n", - "t.loc[\"AQTESOLV\"] = [1.166, 9.368e-06, 0.009151]\n", - "t.loc[\"MLU\"] = [1.311, 8.197e-06, 0.010373]\n", + "t.loc[\"AQTESOLV\"] = [1.166, 9.369e-06, \"-\"]\n", + "t.loc[\"MLU\"] = [1.311, 8.197e-06, \"-\"]\n", "\n", "t_formatted = t.style.format(\n", - " {\"k [m/d]\": \"{:.2f}\", \"Ss [1/m]\": \"{:.2e}\", \"RMSE [m]\": \"{:.3f}\"}\n", + " {\"k [m/d]\": \"{:.2f}\", \"Ss [1/m]\": \"{:.2e}\", \n", + " \"RMSE [m]\": lambda x: \"-\" if x == \"-\" else f\"{float(x):.3f}\",}\n", ")\n", "t_formatted" ] @@ -322,17 +459,24 @@ "### References\n", "\n", "* Butler Jr., J.J. (1998), The Design, Performance, and Analysis of Slug Tests, Lewis Publishers, Boca Raton, Florida, 252p.\n", - "* Carlson F. and Randall J. (2012), MLU: a Windows application for the analysis of aquifer tests and the design of well fields in layered systems, Ground Water 50(4):504–510.\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", "* 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 (ipykernel)", + "display_name": "Python [conda env:base] *", "language": "python", - "name": "python3" + "name": "conda-base-py" }, "language_info": { "codemirror_mode": { @@ -344,7 +488,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.13.5" + "version": "3.12.2" } }, "nbformat": 4, diff --git a/docs/transient/04pumpingtests/slug3_falling_head.ipynb b/docs/transient/04pumpingtests/slug3_falling_head.ipynb new file mode 100644 index 0000000..d0b251f --- /dev/null +++ b/docs/transient/04pumpingtests/slug3_falling_head.ipynb @@ -0,0 +1,502 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "# 3. Slug Test - Falling Head" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import packages" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "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]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Introduction and Conceptual Model\n", + "\n", + "A well partially penetrates a sandy unconfined aquifer that has a saturated depth of 32.57 ft. The top of the screen is located 0.47 ft below the water table and has 13.8 ft in length. The well and casing radii are 5 and 2 inches, respectively. The slug displacement is 1.48 ft. Head change has been recorded at the slug well. This slug test, taken from the AQTESOLV examples (Duffield, 2007), was reported in Batu (1998). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load data" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "data = np.loadtxt(\"data/falling_head.txt\", skiprows=2)\n", + "to = data[:, 0] / 60 / 60 / 24 # convert time from seconds to days\n", + "ho = (10 - data[:, 1]) * 0.3048 # convert drawdown from ft to meters" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Parameters and model" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "rw = 5 * 0.0254 # well radius, m\n", + "rc = 2 * 0.0254 # well casing radius, m\n", + "L = 13.8 * 0.3048 # screen length, m\n", + "b = 32.57 * 0.3048 # aquifer thickness, m\n", + "zt = 0 # top of aquifer, m\n", + "zst = -0.47 * 0.3048 # top of screen, m\n", + "zsb = zst - L # bottom of screen, m\n", + "zb = zt - b # bottom of aquifer, m\n", + "H0 = 1.48 * 0.3048 # initial displacement in well, m" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "convert measured displacement into volume" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "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\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will create a multi-layer model. For this, we divide the second and third layers into 0.5 m thick layers. " + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "18" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#z = np.array([zt, zt - 0.1, zst, zsb, zb])\n", + "z = np.hstack((zt, \n", + " np.linspace(zt - 0.01, zst, 5)[:-1], \n", + " np.linspace(zst, zsb, 5)[:-1],\n", + " np.linspace(zsb, zb, 10)))\n", + "nlay = len(z) - 1\n", + "nlay" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0. , -0.01 , -0.043314, -0.076628, -0.109942, -0.143256,\n", + " -1.194816, -2.246376, -3.297936, -4.349496, -4.969256, -5.589016,\n", + " -6.208776, -6.828536, -7.448296, -8.068056, -8.687816, -9.307576,\n", + " -9.927336])" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "z" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 5\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml = tft.Model3D(\n", + " kaq=10, z=z, Saq=[0.1] + (nlay - 1) * [1e-4], kzoverkh=1, tmin=1e-5, tmax=0.01, phreatictop=True,\n", + ")\n", + "w = tft.Well(\n", + " ml,\n", + " xw=0,\n", + " yw=0,\n", + " rw=rw,\n", + " tsandQ=[(0, -Q)],\n", + " layers=range(6, 6 + 5),\n", + " rc=rc,\n", + " wbstype=\"slug\",\n", + ")\n", + "ml.solve()" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "r = np.linspace(-10, 10, 100)\n", + "h = ml.headalongline(r, 0, 0.01).squeeze()\n", + "plt.plot(h[0])\n", + "plt.plot(h[1])\n", + "plt.plot(h[2])" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "t = np.logspace(-5, -2, 100)\n", + "h = ml.head(0, 0, t)\n", + "zc = 0.5 * (z[:-1] + z[1:])\n", + "plt.plot(zc, h[:, 10])" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Estimate aquifer parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".....................................\n", + "Fit succeeded.\n" + ] + }, + { + "data": { + "text/plain": [ + "kaq_1_17 0.439172\n", + "Saq_1_17 0.000461\n", + "Name: optimal, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.006011434252180949\n" + ] + } + ], + "source": [ + "cal = tft.Calibrate(ml)\n", + "cal.set_parameter(name=\"kaq\", layers=list(range(1, nlay)), initial=10, pmin=0)\n", + "cal.set_parameter(name=\"Saq\", layers=list(range(1, nlay)), initial=1e-4, pmin=0)\n", + "cal.seriesinwell(name=\"obs\", element=w, t=to, h=ho)\n", + "cal.fit(report=False)\n", + "display(cal.parameters[\"optimal\"])\n", + "print(\"RMSE:\", cal.rmse())" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "tm = np.logspace(np.log10(to[0]), np.log10(to[-1]), 100)\n", + "hm = w.headinside(tm)\n", + "plt.semilogx(to, ho / H0, \".\", label=\"obs\")\n", + "plt.semilogx(tm, hm[0] / H0, label=\"timflow\")\n", + "plt.xlabel(\"time [d]\")\n", + "plt.ylabel(\"Normalized head (h/H0)\")\n", + "plt.title(\"Model results - multi-layer model\")\n", + "plt.legend()\n", + "plt.grid()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Comparison of results\n", + "Here, the `timflow` performance in analysing slug tests is checked. The solution in `timflow` is compared with the KGS analytical model (Hyder et al. 1994) implemented in AQTESOLV (Duffield, 2007). The parameters of `timflow` and AQTESOLV are similar, even though AQTESOLV only used one layer." + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "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]
timflow0.444.61e-040.006
AQTESOLV0.425.70e-04-
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "t = pd.DataFrame(\n", + " columns=[\"k [m/d]\", \"Ss [1/m]\", \"RMSE [m]\"],\n", + " index=[\"timflow\", \"AQTESOLV\"],\n", + ")\n", + "\n", + "t.loc[\"timflow\"] = np.append(cal.parameters[\"optimal\"].values, cal.rmse())\n", + "t.loc[\"AQTESOLV\"] = [0.4211, 5.70E-4, \"-\"]\n", + "\n", + "t_formatted = t.style.format(\n", + " {\"k [m/d]\": \"{:.2f}\", \"Ss [1/m]\": \"{:.2e}\",\n", + " \"RMSE [m]\": lambda x: \"-\" if x == \"-\" else f\"{float(x):.3f}\",}\n", + ")\n", + "t_formatted" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## References\n", + "\n", + "* Batu, V. (1998), Aquifer hydraulics: a comprehensive guide to hydrogeologic data analysis, John Wiley & Sons\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 [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": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From e45a28bb166c6fb98b92b375527a2a27fbc6a575 Mon Sep 17 00:00:00 2001 From: Hannah Heesen Date: Wed, 11 Mar 2026 20:43:43 +0700 Subject: [PATCH 2/7] fix notebook output --- .../02examples/meandering_river.ipynb | 16 +------- .../04pumpingtests/confined2_grindley.ipynb | 7 ++-- .../04pumpingtests/confined3_sioux.ipynb | 7 +++- .../04pumpingtests/leaky3_texashill.ipynb | 8 +++- .../04pumpingtests/slug2_multiwell.ipynb | 7 +++- .../04pumpingtests/slug3_falling_head.ipynb | 37 +++++++++++++------ 6 files changed, 46 insertions(+), 36 deletions(-) diff --git a/docs/transient/02examples/meandering_river.ipynb b/docs/transient/02examples/meandering_river.ipynb index c5d158d..5c9f585 100644 --- a/docs/transient/02examples/meandering_river.ipynb +++ b/docs/transient/02examples/meandering_river.ipynb @@ -77,22 +77,8 @@ } ], "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.5" + "name": "python" } }, "nbformat": 4, diff --git a/docs/transient/04pumpingtests/confined2_grindley.ipynb b/docs/transient/04pumpingtests/confined2_grindley.ipynb index beb4b03..3ef6008 100644 --- a/docs/transient/04pumpingtests/confined2_grindley.ipynb +++ b/docs/transient/04pumpingtests/confined2_grindley.ipynb @@ -448,9 +448,10 @@ "t.loc[\"AQTESOLV\"] = [22.95, 0.000003722, \"-\"]\n", "\n", "t_formatted = t.style.format(\n", - " {\"k [m/d]\": \"{:.2f}\", \n", - " \"Ss [1/m]\": \"{:.2e}\", \n", - " \"RMSE [m]\": lambda x: \"-\" if x == \"-\" else f\"{float(x):.3f}\",\n", + " {\n", + " \"k [m/d]\": \"{:.2f}\",\n", + " \"Ss [1/m]\": \"{:.2e}\",\n", + " \"RMSE [m]\": lambda x: \"-\" if x == \"-\" else f\"{float(x):.3f}\",\n", " }\n", ")\n", "t_formatted" diff --git a/docs/transient/04pumpingtests/confined3_sioux.ipynb b/docs/transient/04pumpingtests/confined3_sioux.ipynb index 06ce372..1040394 100644 --- a/docs/transient/04pumpingtests/confined3_sioux.ipynb +++ b/docs/transient/04pumpingtests/confined3_sioux.ipynb @@ -422,8 +422,11 @@ "t.loc[\"AQTESOLV\"] = [282.659, 4.0355e-03, \"-\"]\n", "\n", "t_formatted = t.style.format(\n", - " {\"k [m/d]\": \"{:.2f}\", \"Ss [1/m]\": \"{:.2e}\", \n", - " \"RMSE [m]\": lambda x: \"-\" if x == \"-\" else f\"{float(x):.3f}\",}\n", + " {\n", + " \"k [m/d]\": \"{:.2f}\",\n", + " \"Ss [1/m]\": \"{:.2e}\",\n", + " \"RMSE [m]\": lambda x: \"-\" if x == \"-\" else f\"{float(x):.3f}\",\n", + " }\n", ")\n", "t_formatted" ] diff --git a/docs/transient/04pumpingtests/leaky3_texashill.ipynb b/docs/transient/04pumpingtests/leaky3_texashill.ipynb index aa6bd25..cffb403 100644 --- a/docs/transient/04pumpingtests/leaky3_texashill.ipynb +++ b/docs/transient/04pumpingtests/leaky3_texashill.ipynb @@ -448,8 +448,12 @@ "t.loc[\"timflow\"] = np.append(cal.parameters[\"optimal\"].values, cal.rmse())\n", "\n", "t_formatted = t.style.format(\n", - " {\"k [m/d]\": \"{:.2f}\", \"Ss [1/m]\": \"{:.2e}\", \"c [d]\": \"{:.2f}\", \n", - " \"RMSE [m]\": lambda x: \"-\" if x == \"-\" else f\"{float(x):.3f}\",}\n", + " {\n", + " \"k [m/d]\": \"{:.2f}\",\n", + " \"Ss [1/m]\": \"{:.2e}\",\n", + " \"c [d]\": \"{:.2f}\",\n", + " \"RMSE [m]\": lambda x: \"-\" if x == \"-\" else f\"{float(x):.3f}\",\n", + " }\n", ")\n", "t_formatted" ] diff --git a/docs/transient/04pumpingtests/slug2_multiwell.ipynb b/docs/transient/04pumpingtests/slug2_multiwell.ipynb index e93089e..0cef705 100644 --- a/docs/transient/04pumpingtests/slug2_multiwell.ipynb +++ b/docs/transient/04pumpingtests/slug2_multiwell.ipynb @@ -440,8 +440,11 @@ "t.loc[\"MLU\"] = [1.311, 8.197e-06, \"-\"]\n", "\n", "t_formatted = t.style.format(\n", - " {\"k [m/d]\": \"{:.2f}\", \"Ss [1/m]\": \"{:.2e}\", \n", - " \"RMSE [m]\": lambda x: \"-\" if x == \"-\" else f\"{float(x):.3f}\",}\n", + " {\n", + " \"k [m/d]\": \"{:.2f}\",\n", + " \"Ss [1/m]\": \"{:.2e}\",\n", + " \"RMSE [m]\": lambda x: \"-\" if x == \"-\" else f\"{float(x):.3f}\",\n", + " }\n", ")\n", "t_formatted" ] diff --git a/docs/transient/04pumpingtests/slug3_falling_head.ipynb b/docs/transient/04pumpingtests/slug3_falling_head.ipynb index d0b251f..e711cfa 100644 --- a/docs/transient/04pumpingtests/slug3_falling_head.ipynb +++ b/docs/transient/04pumpingtests/slug3_falling_head.ipynb @@ -92,9 +92,9 @@ "rc = 2 * 0.0254 # well casing radius, m\n", "L = 13.8 * 0.3048 # screen length, m\n", "b = 32.57 * 0.3048 # aquifer thickness, m\n", - "zt = 0 # top of aquifer, m\n", - "zst = -0.47 * 0.3048 # top of screen, m\n", - "zsb = zst - L # bottom of screen, m\n", + "zt = 0 # top of aquifer, m\n", + "zst = -0.47 * 0.3048 # top of screen, m\n", + "zsb = zst - L # bottom of screen, m\n", "zb = zt - b # bottom of aquifer, m\n", "H0 = 1.48 * 0.3048 # initial displacement in well, m" ] @@ -148,11 +148,15 @@ } ], "source": [ - "#z = np.array([zt, zt - 0.1, zst, zsb, zb])\n", - "z = np.hstack((zt, \n", - " np.linspace(zt - 0.01, zst, 5)[:-1], \n", - " np.linspace(zst, zsb, 5)[:-1],\n", - " np.linspace(zsb, zb, 10)))\n", + "# z = np.array([zt, zt - 0.1, zst, zsb, zb])\n", + "z = np.hstack(\n", + " (\n", + " zt,\n", + " np.linspace(zt - 0.01, zst, 5)[:-1],\n", + " np.linspace(zst, zsb, 5)[:-1],\n", + " np.linspace(zsb, zb, 10),\n", + " )\n", + ")\n", "nlay = len(z) - 1\n", "nlay" ] @@ -196,7 +200,13 @@ ], "source": [ "ml = tft.Model3D(\n", - " kaq=10, z=z, Saq=[0.1] + (nlay - 1) * [1e-4], kzoverkh=1, tmin=1e-5, tmax=0.01, phreatictop=True,\n", + " kaq=10,\n", + " z=z,\n", + " Saq=[0.1] + (nlay - 1) * [1e-4],\n", + " kzoverkh=1,\n", + " tmin=1e-5,\n", + " tmax=0.01,\n", + " phreatictop=True,\n", ")\n", "w = tft.Well(\n", " ml,\n", @@ -444,11 +454,14 @@ ")\n", "\n", "t.loc[\"timflow\"] = np.append(cal.parameters[\"optimal\"].values, cal.rmse())\n", - "t.loc[\"AQTESOLV\"] = [0.4211, 5.70E-4, \"-\"]\n", + "t.loc[\"AQTESOLV\"] = [0.4211, 5.70e-4, \"-\"]\n", "\n", "t_formatted = t.style.format(\n", - " {\"k [m/d]\": \"{:.2f}\", \"Ss [1/m]\": \"{:.2e}\",\n", - " \"RMSE [m]\": lambda x: \"-\" if x == \"-\" else f\"{float(x):.3f}\",}\n", + " {\n", + " \"k [m/d]\": \"{:.2f}\",\n", + " \"Ss [1/m]\": \"{:.2e}\",\n", + " \"RMSE [m]\": lambda x: \"-\" if x == \"-\" else f\"{float(x):.3f}\",\n", + " }\n", ")\n", "t_formatted" ] From b637e348b7c1dccd50016a8c7705472aa0aa7cc9 Mon Sep 17 00:00:00 2001 From: dbrakenhoff Date: Thu, 12 Mar 2026 12:49:14 +0100 Subject: [PATCH 3/7] properly remove all notebook metadata and set default kernel name --- .github/workflows/ci-transient.yml | 2 +- .../howtos/howto_convert_timml_model.ipynb | 6 +- .../howtos/howto_select_a_model.ipynb | 6 +- .../tutorial0_well_single_layer_aquifer.ipynb | 6 +- .../tutorial1_well_multi_layer_aquifer.ipynb | 6 +- .../02examples/00_single_layer_model.ipynb | 6 +- .../02examples/01_well_in_uniform_flow.ipynb | 6 +- .../02_wells_rivers_and_recharge.ipynb | 6 +- .../03a_modeling_inhomogeneities.ipynb | 6 +- .../03b_modeling_inhomogeneities3D.ipynb | 6 +- .../02examples/04_horizontal_wells.ipynb | 6 +- .../02examples/05_impermeable_walls.ipynb | 6 +- docs/steady/02examples/building_pit.ipynb | 6 +- .../02examples/circular_area_sink.ipynb | 6 +- docs/steady/02examples/collector_wells.ipynb | 6 +- docs/steady/02examples/connected_wells.ipynb | 6 +- .../03xsections/cross_section_models.ipynb | 6 +- .../large_diameter_experimental.ipynb | 6 +- .../test_circular_buildingpit.ipynb | 6 +- .../test_linesink_discharge.ipynb | 6 +- .../04benchmarks/test_linesinkstrings.ipynb | 6 +- .../04benchmarks/test_normal_flux.ipynb | 6 +- .../04benchmarks/test_polygon_areasink.ipynb | 6 +- .../04benchmarks/test_well_near_lake.ipynb | 6 +- docs/steady/04benchmarks/test_wells.ipynb | 6 +- .../test_wells_with_resistance.ipynb | 6 +- .../howtos/howto_convert_ttim_model.ipynb | 6 +- .../howto_fluctuating_head_boundary.ipynb | 6 +- .../howtos/howto_pumpingtest.ipynb | 6 +- .../howtos/howto_select_a_model.ipynb | 6 +- .../howtos/howto_xsection_model.ipynb | 6 +- .../tutorials/tutorial0_start_a_model.ipynb | 6 +- .../02examples/circareasink_example.ipynb | 6 +- .../higher_order_head_linesink.ipynb | 6 +- .../02examples/line_sink_well_sol.ipynb | 6 +- .../02examples/meandering_river.ipynb | 6 +- .../transient/02examples/pathline_trace.ipynb | 6 +- .../well_in_multilayer_system.ipynb | 6 +- .../transient/02examples/well_near_wall.ipynb | 6 +- .../wells_in_different_systems.ipynb | 6 +- docs/transient/03xsections/1d_elements.ipynb | 6 +- .../03xsections/1d_given_linesink.ipynb | 6 +- .../03xsections/1d_inhom_elements.ipynb | 6 +- .../03xsections/river_in_cross_section.ipynb | 56 ++- .../transient/03xsections/stripareasink.ipynb | 6 +- .../confined1_oude_korendijk.ipynb | 188 +--------- .../04pumpingtests/confined2_grindley.ipynb | 230 ++---------- .../04pumpingtests/confined3_sioux.ipynb | 230 ++---------- .../04pumpingtests/leaky1_dalem.ipynb | 343 ++---------------- .../04pumpingtests/leaky2_hardixveld.ipynb | 319 ++-------------- .../04pumpingtests/leaky3_texashill.ipynb | 238 ++---------- .../04pumpingtests/slug1_pratt_county.ipynb | 250 ++----------- .../04pumpingtests/slug2_multiwell.ipynb | 256 ++----------- .../04pumpingtests/slug3_falling_head.ipynb | 252 ++----------- .../05benchmarks/compare_wells_linesink.ipynb | 6 +- .../05benchmarks/line-sink-ditch.ipynb | 6 +- .../05benchmarks/synthetic0_data.ipynb | 6 +- .../synthetic_calibrate_2aquifers.ipynb | 6 +- .../synthetic_test_calibrate.ipynb | 6 +- .../05benchmarks/test_line_elements.ipynb | 6 +- .../05benchmarks/theis_storage.ipynb | 6 +- .../05benchmarks/ttim_neuman_comparison.ipynb | 6 +- ...validation_tidal_wave_with_Bruggeman.ipynb | 6 +- .../05benchmarks/well_benchmarks.ipynb | 6 +- .../well_near_river_or_wall.ipynb | 6 +- utils/clear_notebooks.py | 38 +- 66 files changed, 500 insertions(+), 2226 deletions(-) diff --git a/.github/workflows/ci-transient.yml b/.github/workflows/ci-transient.yml index 0fcce94..5579893 100644 --- a/.github/workflows/ci-transient.yml +++ b/.github/workflows/ci-transient.yml @@ -50,4 +50,4 @@ jobs: - name: Test benchmark notebooks run: | - uv run pytest --nbval-lax --cov-append --dist loadscope -n auto --ignore=docs/transient/05benchmarks/besselnumbanew_timing.ipynb docs/transient/05benchmarks/ + uv run pytest --nbval-lax --cov-append --dist loadscope -n auto docs/transient/05benchmarks/ diff --git a/docs/steady/00userguide/howtos/howto_convert_timml_model.ipynb b/docs/steady/00userguide/howtos/howto_convert_timml_model.ipynb index c7744c5..2542c40 100644 --- a/docs/steady/00userguide/howtos/howto_convert_timml_model.ipynb +++ b/docs/steady/00userguide/howtos/howto_convert_timml_model.ipynb @@ -178,8 +178,10 @@ } ], "metadata": { - "language_info": { - "name": "python" + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" } }, "nbformat": 4, diff --git a/docs/steady/00userguide/howtos/howto_select_a_model.ipynb b/docs/steady/00userguide/howtos/howto_select_a_model.ipynb index b392e64..87eb6e2 100644 --- a/docs/steady/00userguide/howtos/howto_select_a_model.ipynb +++ b/docs/steady/00userguide/howtos/howto_select_a_model.ipynb @@ -207,8 +207,10 @@ } ], "metadata": { - "language_info": { - "name": "python" + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" } }, "nbformat": 4, diff --git a/docs/steady/00userguide/tutorials/tutorial0_well_single_layer_aquifer.ipynb b/docs/steady/00userguide/tutorials/tutorial0_well_single_layer_aquifer.ipynb index 440f5ea..c79a062 100644 --- a/docs/steady/00userguide/tutorials/tutorial0_well_single_layer_aquifer.ipynb +++ b/docs/steady/00userguide/tutorials/tutorial0_well_single_layer_aquifer.ipynb @@ -302,8 +302,10 @@ } ], "metadata": { - "language_info": { - "name": "python" + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" } }, "nbformat": 4, diff --git a/docs/steady/00userguide/tutorials/tutorial1_well_multi_layer_aquifer.ipynb b/docs/steady/00userguide/tutorials/tutorial1_well_multi_layer_aquifer.ipynb index 72beb7f..8f9b4e7 100755 --- a/docs/steady/00userguide/tutorials/tutorial1_well_multi_layer_aquifer.ipynb +++ b/docs/steady/00userguide/tutorials/tutorial1_well_multi_layer_aquifer.ipynb @@ -260,8 +260,10 @@ } ], "metadata": { - "language_info": { - "name": "python" + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" } }, "nbformat": 4, diff --git a/docs/steady/02examples/00_single_layer_model.ipynb b/docs/steady/02examples/00_single_layer_model.ipynb index bf326c0..cbee9ae 100644 --- a/docs/steady/02examples/00_single_layer_model.ipynb +++ b/docs/steady/02examples/00_single_layer_model.ipynb @@ -395,8 +395,10 @@ } ], "metadata": { - "language_info": { - "name": "python" + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" } }, "nbformat": 4, diff --git a/docs/steady/02examples/01_well_in_uniform_flow.ipynb b/docs/steady/02examples/01_well_in_uniform_flow.ipynb index 954e270..df628cb 100755 --- a/docs/steady/02examples/01_well_in_uniform_flow.ipynb +++ b/docs/steady/02examples/01_well_in_uniform_flow.ipynb @@ -225,8 +225,10 @@ } ], "metadata": { - "language_info": { - "name": "python" + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" } }, "nbformat": 4, diff --git a/docs/steady/02examples/02_wells_rivers_and_recharge.ipynb b/docs/steady/02examples/02_wells_rivers_and_recharge.ipynb index 4753b60..e4420cc 100755 --- a/docs/steady/02examples/02_wells_rivers_and_recharge.ipynb +++ b/docs/steady/02examples/02_wells_rivers_and_recharge.ipynb @@ -214,8 +214,10 @@ } ], "metadata": { - "language_info": { - "name": "python" + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" } }, "nbformat": 4, diff --git a/docs/steady/02examples/03a_modeling_inhomogeneities.ipynb b/docs/steady/02examples/03a_modeling_inhomogeneities.ipynb index 3522da4..d5365bb 100755 --- a/docs/steady/02examples/03a_modeling_inhomogeneities.ipynb +++ b/docs/steady/02examples/03a_modeling_inhomogeneities.ipynb @@ -421,8 +421,10 @@ } ], "metadata": { - "language_info": { - "name": "python" + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" } }, "nbformat": 4, diff --git a/docs/steady/02examples/03b_modeling_inhomogeneities3D.ipynb b/docs/steady/02examples/03b_modeling_inhomogeneities3D.ipynb index 506555a..e90f640 100755 --- a/docs/steady/02examples/03b_modeling_inhomogeneities3D.ipynb +++ b/docs/steady/02examples/03b_modeling_inhomogeneities3D.ipynb @@ -267,8 +267,10 @@ } ], "metadata": { - "language_info": { - "name": "python" + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" } }, "nbformat": 4, diff --git a/docs/steady/02examples/04_horizontal_wells.ipynb b/docs/steady/02examples/04_horizontal_wells.ipynb index 4c04f94..629931b 100644 --- a/docs/steady/02examples/04_horizontal_wells.ipynb +++ b/docs/steady/02examples/04_horizontal_wells.ipynb @@ -418,8 +418,10 @@ } ], "metadata": { - "language_info": { - "name": "python" + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" } }, "nbformat": 4, diff --git a/docs/steady/02examples/05_impermeable_walls.ipynb b/docs/steady/02examples/05_impermeable_walls.ipynb index 655c428..f516605 100644 --- a/docs/steady/02examples/05_impermeable_walls.ipynb +++ b/docs/steady/02examples/05_impermeable_walls.ipynb @@ -113,8 +113,10 @@ } ], "metadata": { - "language_info": { - "name": "python" + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" } }, "nbformat": 4, diff --git a/docs/steady/02examples/building_pit.ipynb b/docs/steady/02examples/building_pit.ipynb index b1d5787..3da27f3 100644 --- a/docs/steady/02examples/building_pit.ipynb +++ b/docs/steady/02examples/building_pit.ipynb @@ -670,8 +670,10 @@ } ], "metadata": { - "language_info": { - "name": "python" + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" } }, "nbformat": 4, diff --git a/docs/steady/02examples/circular_area_sink.ipynb b/docs/steady/02examples/circular_area_sink.ipynb index f1be9ea..c3ef663 100644 --- a/docs/steady/02examples/circular_area_sink.ipynb +++ b/docs/steady/02examples/circular_area_sink.ipynb @@ -152,8 +152,10 @@ } ], "metadata": { - "language_info": { - "name": "python" + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" } }, "nbformat": 4, diff --git a/docs/steady/02examples/collector_wells.ipynb b/docs/steady/02examples/collector_wells.ipynb index b9fd1b8..ab0de1e 100644 --- a/docs/steady/02examples/collector_wells.ipynb +++ b/docs/steady/02examples/collector_wells.ipynb @@ -380,8 +380,10 @@ } ], "metadata": { - "language_info": { - "name": "python" + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" } }, "nbformat": 4, diff --git a/docs/steady/02examples/connected_wells.ipynb b/docs/steady/02examples/connected_wells.ipynb index 1f152b0..8a6f489 100755 --- a/docs/steady/02examples/connected_wells.ipynb +++ b/docs/steady/02examples/connected_wells.ipynb @@ -1170,8 +1170,10 @@ } ], "metadata": { - "language_info": { - "name": "python" + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" } }, "nbformat": 4, diff --git a/docs/steady/03xsections/cross_section_models.ipynb b/docs/steady/03xsections/cross_section_models.ipynb index 75a31b1..ba54d6c 100644 --- a/docs/steady/03xsections/cross_section_models.ipynb +++ b/docs/steady/03xsections/cross_section_models.ipynb @@ -512,8 +512,10 @@ } ], "metadata": { - "language_info": { - "name": "python" + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" } }, "nbformat": 4, diff --git a/docs/steady/04benchmarks/large_diameter_experimental.ipynb b/docs/steady/04benchmarks/large_diameter_experimental.ipynb index 9f0fbdc..030ba54 100644 --- a/docs/steady/04benchmarks/large_diameter_experimental.ipynb +++ b/docs/steady/04benchmarks/large_diameter_experimental.ipynb @@ -97,8 +97,10 @@ } ], "metadata": { - "language_info": { - "name": "python" + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" } }, "nbformat": 4, diff --git a/docs/steady/04benchmarks/test_circular_buildingpit.ipynb b/docs/steady/04benchmarks/test_circular_buildingpit.ipynb index 7b3e6b3..a8f6503 100644 --- a/docs/steady/04benchmarks/test_circular_buildingpit.ipynb +++ b/docs/steady/04benchmarks/test_circular_buildingpit.ipynb @@ -486,8 +486,10 @@ } ], "metadata": { - "language_info": { - "name": "python" + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" } }, "nbformat": 4, diff --git a/docs/steady/04benchmarks/test_linesink_discharge.ipynb b/docs/steady/04benchmarks/test_linesink_discharge.ipynb index 80dc7b9..426a84f 100644 --- a/docs/steady/04benchmarks/test_linesink_discharge.ipynb +++ b/docs/steady/04benchmarks/test_linesink_discharge.ipynb @@ -738,8 +738,10 @@ } ], "metadata": { - "language_info": { - "name": "python" + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" } }, "nbformat": 4, diff --git a/docs/steady/04benchmarks/test_linesinkstrings.ipynb b/docs/steady/04benchmarks/test_linesinkstrings.ipynb index cdab826..6e2f9d5 100644 --- a/docs/steady/04benchmarks/test_linesinkstrings.ipynb +++ b/docs/steady/04benchmarks/test_linesinkstrings.ipynb @@ -156,8 +156,10 @@ } ], "metadata": { - "language_info": { - "name": "python" + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" } }, "nbformat": 4, diff --git a/docs/steady/04benchmarks/test_normal_flux.ipynb b/docs/steady/04benchmarks/test_normal_flux.ipynb index 722416e..9300ccd 100644 --- a/docs/steady/04benchmarks/test_normal_flux.ipynb +++ b/docs/steady/04benchmarks/test_normal_flux.ipynb @@ -275,8 +275,10 @@ } ], "metadata": { - "language_info": { - "name": "python" + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" } }, "nbformat": 4, diff --git a/docs/steady/04benchmarks/test_polygon_areasink.ipynb b/docs/steady/04benchmarks/test_polygon_areasink.ipynb index b462ef0..dd49d6d 100644 --- a/docs/steady/04benchmarks/test_polygon_areasink.ipynb +++ b/docs/steady/04benchmarks/test_polygon_areasink.ipynb @@ -95,8 +95,10 @@ } ], "metadata": { - "language_info": { - "name": "python" + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" } }, "nbformat": 4, diff --git a/docs/steady/04benchmarks/test_well_near_lake.ipynb b/docs/steady/04benchmarks/test_well_near_lake.ipynb index fe95eb8..ff673fe 100644 --- a/docs/steady/04benchmarks/test_well_near_lake.ipynb +++ b/docs/steady/04benchmarks/test_well_near_lake.ipynb @@ -51,8 +51,10 @@ } ], "metadata": { - "language_info": { - "name": "python" + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" } }, "nbformat": 4, diff --git a/docs/steady/04benchmarks/test_wells.ipynb b/docs/steady/04benchmarks/test_wells.ipynb index 462557d..843d09a 100644 --- a/docs/steady/04benchmarks/test_wells.ipynb +++ b/docs/steady/04benchmarks/test_wells.ipynb @@ -112,8 +112,10 @@ } ], "metadata": { - "language_info": { - "name": "python" + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" } }, "nbformat": 4, diff --git a/docs/steady/04benchmarks/test_wells_with_resistance.ipynb b/docs/steady/04benchmarks/test_wells_with_resistance.ipynb index 33c953b..07409e7 100644 --- a/docs/steady/04benchmarks/test_wells_with_resistance.ipynb +++ b/docs/steady/04benchmarks/test_wells_with_resistance.ipynb @@ -379,8 +379,10 @@ } ], "metadata": { - "language_info": { - "name": "python" + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" } }, "nbformat": 4, diff --git a/docs/transient/00userguide/howtos/howto_convert_ttim_model.ipynb b/docs/transient/00userguide/howtos/howto_convert_ttim_model.ipynb index 48501e5..1a50eaf 100644 --- a/docs/transient/00userguide/howtos/howto_convert_ttim_model.ipynb +++ b/docs/transient/00userguide/howtos/howto_convert_ttim_model.ipynb @@ -75,8 +75,10 @@ } ], "metadata": { - "language_info": { - "name": "python" + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" } }, "nbformat": 4, diff --git a/docs/transient/00userguide/howtos/howto_fluctuating_head_boundary.ipynb b/docs/transient/00userguide/howtos/howto_fluctuating_head_boundary.ipynb index 6e6d037..397a4b1 100644 --- a/docs/transient/00userguide/howtos/howto_fluctuating_head_boundary.ipynb +++ b/docs/transient/00userguide/howtos/howto_fluctuating_head_boundary.ipynb @@ -236,8 +236,10 @@ } ], "metadata": { - "language_info": { - "name": "python" + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" } }, "nbformat": 4, diff --git a/docs/transient/00userguide/howtos/howto_pumpingtest.ipynb b/docs/transient/00userguide/howtos/howto_pumpingtest.ipynb index 22897f3..36b2fef 100755 --- a/docs/transient/00userguide/howtos/howto_pumpingtest.ipynb +++ b/docs/transient/00userguide/howtos/howto_pumpingtest.ipynb @@ -436,8 +436,10 @@ } ], "metadata": { - "language_info": { - "name": "python" + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" } }, "nbformat": 4, diff --git a/docs/transient/00userguide/howtos/howto_select_a_model.ipynb b/docs/transient/00userguide/howtos/howto_select_a_model.ipynb index 503dbbf..1429fc7 100644 --- a/docs/transient/00userguide/howtos/howto_select_a_model.ipynb +++ b/docs/transient/00userguide/howtos/howto_select_a_model.ipynb @@ -83,8 +83,10 @@ } ], "metadata": { - "language_info": { - "name": "python" + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" } }, "nbformat": 4, diff --git a/docs/transient/00userguide/howtos/howto_xsection_model.ipynb b/docs/transient/00userguide/howtos/howto_xsection_model.ipynb index b96248f..8b8250e 100644 --- a/docs/transient/00userguide/howtos/howto_xsection_model.ipynb +++ b/docs/transient/00userguide/howtos/howto_xsection_model.ipynb @@ -606,8 +606,10 @@ } ], "metadata": { - "language_info": { - "name": "python" + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" } }, "nbformat": 4, diff --git a/docs/transient/00userguide/tutorials/tutorial0_start_a_model.ipynb b/docs/transient/00userguide/tutorials/tutorial0_start_a_model.ipynb index 376bef9..94da71e 100755 --- a/docs/transient/00userguide/tutorials/tutorial0_start_a_model.ipynb +++ b/docs/transient/00userguide/tutorials/tutorial0_start_a_model.ipynb @@ -139,8 +139,10 @@ } ], "metadata": { - "language_info": { - "name": "python" + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" } }, "nbformat": 4, diff --git a/docs/transient/02examples/circareasink_example.ipynb b/docs/transient/02examples/circareasink_example.ipynb index 5a5de2b..0c410e3 100644 --- a/docs/transient/02examples/circareasink_example.ipynb +++ b/docs/transient/02examples/circareasink_example.ipynb @@ -182,8 +182,10 @@ } ], "metadata": { - "language_info": { - "name": "python" + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" } }, "nbformat": 4, diff --git a/docs/transient/02examples/higher_order_head_linesink.ipynb b/docs/transient/02examples/higher_order_head_linesink.ipynb index 3cf09d9..6a46222 100644 --- a/docs/transient/02examples/higher_order_head_linesink.ipynb +++ b/docs/transient/02examples/higher_order_head_linesink.ipynb @@ -65,8 +65,10 @@ } ], "metadata": { - "language_info": { - "name": "python" + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" } }, "nbformat": 4, diff --git a/docs/transient/02examples/line_sink_well_sol.ipynb b/docs/transient/02examples/line_sink_well_sol.ipynb index 458a924..fdaa999 100755 --- a/docs/transient/02examples/line_sink_well_sol.ipynb +++ b/docs/transient/02examples/line_sink_well_sol.ipynb @@ -177,8 +177,10 @@ } ], "metadata": { - "language_info": { - "name": "python" + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" } }, "nbformat": 4, diff --git a/docs/transient/02examples/meandering_river.ipynb b/docs/transient/02examples/meandering_river.ipynb index 5c9f585..dc22044 100644 --- a/docs/transient/02examples/meandering_river.ipynb +++ b/docs/transient/02examples/meandering_river.ipynb @@ -77,8 +77,10 @@ } ], "metadata": { - "language_info": { - "name": "python" + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" } }, "nbformat": 4, diff --git a/docs/transient/02examples/pathline_trace.ipynb b/docs/transient/02examples/pathline_trace.ipynb index ee60db3..35fdda4 100644 --- a/docs/transient/02examples/pathline_trace.ipynb +++ b/docs/transient/02examples/pathline_trace.ipynb @@ -521,8 +521,10 @@ } ], "metadata": { - "language_info": { - "name": "python" + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" } }, "nbformat": 4, diff --git a/docs/transient/02examples/well_in_multilayer_system.ipynb b/docs/transient/02examples/well_in_multilayer_system.ipynb index 28853be..a4925c8 100755 --- a/docs/transient/02examples/well_in_multilayer_system.ipynb +++ b/docs/transient/02examples/well_in_multilayer_system.ipynb @@ -222,8 +222,10 @@ } ], "metadata": { - "language_info": { - "name": "python" + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" } }, "nbformat": 4, diff --git a/docs/transient/02examples/well_near_wall.ipynb b/docs/transient/02examples/well_near_wall.ipynb index 26e33ad..af4c2e4 100644 --- a/docs/transient/02examples/well_near_wall.ipynb +++ b/docs/transient/02examples/well_near_wall.ipynb @@ -108,8 +108,10 @@ } ], "metadata": { - "language_info": { - "name": "python" + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" } }, "nbformat": 4, diff --git a/docs/transient/02examples/wells_in_different_systems.ipynb b/docs/transient/02examples/wells_in_different_systems.ipynb index 8f4fc8c..201c21b 100644 --- a/docs/transient/02examples/wells_in_different_systems.ipynb +++ b/docs/transient/02examples/wells_in_different_systems.ipynb @@ -266,8 +266,10 @@ } ], "metadata": { - "language_info": { - "name": "python" + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" } }, "nbformat": 4, diff --git a/docs/transient/03xsections/1d_elements.ipynb b/docs/transient/03xsections/1d_elements.ipynb index e0120d2..9063f7e 100644 --- a/docs/transient/03xsections/1d_elements.ipynb +++ b/docs/transient/03xsections/1d_elements.ipynb @@ -596,8 +596,10 @@ } ], "metadata": { - "language_info": { - "name": "python" + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" } }, "nbformat": 4, diff --git a/docs/transient/03xsections/1d_given_linesink.ipynb b/docs/transient/03xsections/1d_given_linesink.ipynb index 087be43..cb788f2 100755 --- a/docs/transient/03xsections/1d_given_linesink.ipynb +++ b/docs/transient/03xsections/1d_given_linesink.ipynb @@ -149,8 +149,10 @@ } ], "metadata": { - "language_info": { - "name": "python" + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" } }, "nbformat": 4, diff --git a/docs/transient/03xsections/1d_inhom_elements.ipynb b/docs/transient/03xsections/1d_inhom_elements.ipynb index 3ee8450..6f347ff 100644 --- a/docs/transient/03xsections/1d_inhom_elements.ipynb +++ b/docs/transient/03xsections/1d_inhom_elements.ipynb @@ -630,8 +630,10 @@ } ], "metadata": { - "language_info": { - "name": "python" + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" } }, "nbformat": 4, diff --git a/docs/transient/03xsections/river_in_cross_section.ipynb b/docs/transient/03xsections/river_in_cross_section.ipynb index d25609a..1c0a2d4 100644 --- a/docs/transient/03xsections/river_in_cross_section.ipynb +++ b/docs/transient/03xsections/river_in_cross_section.ipynb @@ -291,9 +291,24 @@ "metadata": {}, "outputs": [], "source": [ + "fig, ax = plt.subplots(1, 1, figsize=(10, 3))\n", + "ax.set_ylim(-30, 24)\n", "ml.plots.xsection(\n", - " xy=[(x0 - 25, 0), (100, 0)], labels=False, params=True, names=True, hstar=10, sep=\"\\n\"\n", - ")" + " xy=[(x0 - 0, 0), (60, 0)],\n", + " labels=True,\n", + " params=False,\n", + " names=True,\n", + " hstar=10,\n", + " sep=\"\\n\",\n", + " ax=ax,\n", + " units={\"k\": \"m/d\", \"Ss\": \"m$^{-1}$\", \"c\": \"d\"},\n", + " fontsize=8,\n", + ")\n", + "cal.xsection(ax=ax)\n", + "ax.set_xlabel(\"x-coordinate [m]\")\n", + "ax.set_ylabel(\"elevation [m]\")\n", + "fig.suptitle(\"Cross-section across a river of a semi-confined aquifer with 2 observation wells\")\n", + "fig.savefig(\"river_cross_section.png\", dpi=150, bbox_inches=\"tight\")" ] }, { @@ -440,7 +455,7 @@ "\n", "# set observation time series\n", "cal.series(\n", - " \"kruin\",\n", + " \"ObsWell 1\",\n", " xpb_kr,\n", " 0.0,\n", " layer=0,\n", @@ -448,7 +463,7 @@ " h=data_norm.loc[tstart:, \"kruin\"].values,\n", ")\n", "cal.series(\n", - " \"binnenteen\",\n", + " \"ObsWell 2\",\n", " xpb_bite,\n", " 0.0,\n", " layer=0,\n", @@ -551,14 +566,17 @@ "metadata": {}, "outputs": [], "source": [ - "ax = river.plot(labels=False, params=False, names=True)\n", - "foreland.plot(ax=ax, names=True)\n", - "hinterland.plot(ax=ax, params=False, names=True)\n", + "fig, ax = plt.subplots(1, 1, figsize=(10, 3))\n", + "river.plot(labels=False, params=True, names=True, ax=ax, sep=\"\\n\", fmt=\".1f\")\n", + "foreland.plot(ax=ax, params=True, names=True, sep=\"\\n\", fmt=\".1f\")\n", + "hinterland.plot(ax=ax, params=True, names=True, sep=\"\\n\", fmt=\".1f\")\n", "ml.elementlist[0].plot(ax=ax, hstar=10.0)\n", "ml.elementlist[1].plot(ax=ax, hstar=10.0)\n", "ax.set_xlim(x0, 100.0)\n", - "ax.set_ylim(-30, 20)\n", - "cal.xsection(ax=ax);" + "ax.set_ylim(-30, 27)\n", + "cal.xsection(ax=ax);\n", + "ax.set_ylabel(\"elevation [m]\")\n", + "ax.set_xlabel(\"x-coordinate [m]\")" ] }, { @@ -581,16 +599,22 @@ "\n", "t = data_norm.index.to_numpy()\n", "\n", - "f, axes = plt.subplots(len(xlocs), 1, figsize=(10, 6), sharex=True, sharey=True)\n", + "f, axes = plt.subplots(len(xlocs), 1, figsize=(10, 5), sharex=True, sharey=True)\n", "\n", "for i in range(len(xlocs)):\n", " h = ml.head(xlocs[i], 0.0, t)\n", - " axes[i].plot(t, h[0], label=\"model\")\n", + " axes[i].plot(t, h[0], label=\"timflow\")\n", " hobs = data_norm.loc[tstart:].iloc[:, i + 1] # showing calibration selection\n", " axes[i].plot(hobs.index, hobs, label=\"observed\")\n", - " axes[i].set_title(f\"x = {xlocs[i]} m\", loc=\"right\")\n", + " axes[i].set_title(f\"Observation well {i + 1} (x = {xlocs[i]} m)\", loc=\"right\")\n", " axes[i].legend(loc=(0, 1), frameon=False, ncol=2)\n", - " axes[i].grid()" + " axes[i].grid()\n", + " \n", + "for iax in axes:\n", + " iax.set_ylabel(\"head change [m]\")\n", + "axes[-1].set_xlabel(\"time [d]\")\n", + "axes[-1].figure.suptitle(\"Calibration result of timflow cross-section model compared to observed heads.\")\n", + "axes[-1].figure.savefig(\"calibrated_head_response.png\", bbox_inches=\"tight\", dpi=150)" ] }, { @@ -633,8 +657,10 @@ } ], "metadata": { - "language_info": { - "name": "python" + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" } }, "nbformat": 4, diff --git a/docs/transient/03xsections/stripareasink.ipynb b/docs/transient/03xsections/stripareasink.ipynb index 8810cef..6512f9d 100644 --- a/docs/transient/03xsections/stripareasink.ipynb +++ b/docs/transient/03xsections/stripareasink.ipynb @@ -690,8 +690,10 @@ } ], "metadata": { - "language_info": { - "name": "python" + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" } }, "nbformat": 4, diff --git a/docs/transient/04pumpingtests/confined1_oude_korendijk.ipynb b/docs/transient/04pumpingtests/confined1_oude_korendijk.ipynb index 97f72b6..555473c 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": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -67,7 +67,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -92,7 +92,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -105,18 +105,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "self.neq 1\n", - "solution complete\n" - ] - } - ], + "outputs": [], "source": [ "# timflow model\n", "ml = tft.ModelMaq(kaq=60, z=[zt, zb], Saq=1e-4, tmin=1e-5, tmax=1)\n", @@ -134,18 +125,9 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - ".................................\n", - "Fit succeeded.\n" - ] - } - ], + "outputs": [], "source": [ "# unknown parameters: kaq, Saq\n", "cal = tft.Calibrate(ml)\n", @@ -158,63 +140,9 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
optimal
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" - ], - "text/plain": [ - " optimal\n", - "kaq_0_0 66.089354\n", - "Saq_0_0 0.000025" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RMSE: 0.050 m\n" - ] - } - ], + "outputs": [], "source": [ "display(cal.parameters.loc[:, [\"optimal\"]])\n", "print(f\"RMSE: {cal.rmse():.3f} m\")" @@ -222,26 +150,9 @@ }, { "cell_type": "code", - "execution_count": 18, - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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timflow66.092.54e-050.050
MLU63.513.58e-050.833
K&dR55.711.70e-041.293
\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "t = pd.DataFrame(\n", " columns=[\"k [m/d]\", \"Ss [1/m]\", \"RMSE [m]\"],\n", @@ -380,21 +238,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python [conda env:base] *", + "display_name": "Python 3", "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": "3.12.2" + "name": "python3" } }, "nbformat": 4, diff --git a/docs/transient/04pumpingtests/confined2_grindley.ipynb b/docs/transient/04pumpingtests/confined2_grindley.ipynb index 3ef6008..b1a6713 100644 --- a/docs/transient/04pumpingtests/confined2_grindley.ipynb +++ b/docs/transient/04pumpingtests/confined2_grindley.ipynb @@ -22,14 +22,8 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", @@ -99,14 +93,8 @@ }, { "cell_type": "code", - "execution_count": 22, - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "# Loading Observation well (Well 1)\n", @@ -135,14 +123,8 @@ }, { "cell_type": "code", - "execution_count": 25, - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "# known parameters\n", @@ -171,24 +153,9 @@ }, { "cell_type": "code", - "execution_count": 28, - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "self.neq 1\n", - "solution complete\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "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", @@ -223,24 +190,9 @@ }, { "cell_type": "code", - "execution_count": 32, - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "..........................\n", - "Fit succeeded.\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# unknown parameters: kaq, Saq\n", "cal = tft.Calibrate(ml) # create Calibrate object\n", @@ -255,69 +207,9 @@ }, { "cell_type": "code", - "execution_count": 34, - "metadata": { - "editable": true, - "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", - "
optimal
kaq_0_038.087248
Saq_0_00.000001
\n", - "
" - ], - "text/plain": [ - " optimal\n", - "kaq_0_0 38.087248\n", - "Saq_0_0 0.000001" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RMSE: 0.259 m\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "display(cal.parameters.loc[:, [\"optimal\"]])\n", "print(f\"RMSE: {cal.rmse():.3f} m\")" @@ -325,26 +217,9 @@ }, { "cell_type": "code", - "execution_count": 36, - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
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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": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "t = pd.DataFrame(\n", " columns=[\"k [m/d]\", \"Ss [1/m]\", \"RMSE [m]\"], index=[\"timflow\", \"AQTESOLV\"]\n", @@ -482,21 +310,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python [conda env:base] *", + "display_name": "Python 3", "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": "3.12.2" + "name": "python3" } }, "nbformat": 4, diff --git a/docs/transient/04pumpingtests/confined3_sioux.ipynb b/docs/transient/04pumpingtests/confined3_sioux.ipynb index 1040394..6a5530c 100644 --- a/docs/transient/04pumpingtests/confined3_sioux.ipynb +++ b/docs/transient/04pumpingtests/confined3_sioux.ipynb @@ -28,14 +28,8 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", @@ -103,14 +97,8 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "# time and drawdown of piezometer 100ft away from pumping well\n", @@ -144,14 +132,8 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "# known parameters\n", @@ -167,24 +149,9 @@ }, { "cell_type": "code", - "execution_count": 18, - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "self.neq 1\n", - "solution complete\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# timflow model\n", "ml = tft.ModelMaq(kaq=10, z=[0, -b], Saq=0.001, tmin=0.001, tmax=10, topboundary=\"conf\")\n", @@ -207,24 +174,9 @@ }, { "cell_type": "code", - "execution_count": 21, - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "...............................................\n", - "Fit succeeded.\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# unknown parameters: k, Saq\n", "cal = tft.Calibrate(ml)\n", @@ -238,69 +190,9 @@ }, { "cell_type": "code", - "execution_count": 22, - "metadata": { - "editable": true, - "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", - "
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" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "display(cal.parameters.loc[:, [\"optimal\"]])\n", "print(f\"RMSE: {cal.rmse():.3f} m\")" @@ -308,26 +200,9 @@ }, { "cell_type": "code", - "execution_count": 25, - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "hm1 = ml.head(r1, 0, t1)\n", "hm2 = ml.head(r2, 0, t2)\n", @@ -363,56 +238,9 @@ }, { "cell_type": "code", - "execution_count": 28, - "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]
timflow282.804.21e-030.004
AQTESOLV282.664.04e-03-
\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "t = pd.DataFrame(\n", " columns=[\"k [m/d]\", \"Ss [1/m]\", \"RMSE [m]\"], index=[\"timflow\", \"AQTESOLV\"]\n", @@ -468,21 +296,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python [conda env:base] *", + "display_name": "Python 3", "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": "3.12.2" + "name": "python3" } }, "nbformat": 4, diff --git a/docs/transient/04pumpingtests/leaky1_dalem.ipynb b/docs/transient/04pumpingtests/leaky1_dalem.ipynb index de325ab..d37aab9 100644 --- a/docs/transient/04pumpingtests/leaky1_dalem.ipynb +++ b/docs/transient/04pumpingtests/leaky1_dalem.ipynb @@ -28,14 +28,8 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", @@ -91,14 +85,8 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "# data of observation well 30 m away from pumping well\n", @@ -137,14 +125,8 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "# known parameters\n", @@ -175,14 +157,8 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "# unkonwn parameters: kaq, Saq, c\n", @@ -209,24 +185,9 @@ }, { "cell_type": "code", - "execution_count": 17, - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "..............................................\n", - "Fit succeeded.\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "cal = tft.Calibrate(ml)\n", "cal.set_parameter(name=\"kaq\", initial=10, layers=0)\n", @@ -242,74 +203,9 @@ }, { "cell_type": "code", - "execution_count": 19, - "metadata": { - "editable": true, - "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", - "
optimal
kaq_0_045.332066
Saq_0_00.000048
c_0_0331.177727
\n", - "
" - ], - "text/plain": [ - " optimal\n", - "kaq_0_0 45.332066\n", - "Saq_0_0 0.000048\n", - "c_0_0 331.177727" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RMSE: 0.00592 m\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "display(cal.parameters.loc[:, [\"optimal\"]])\n", "print(f\"RMSE: {cal.rmse():.5f} m\")" @@ -317,26 +213,9 @@ }, { "cell_type": "code", - "execution_count": 41, - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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zZjFgwAD1jP9t27aRnZ1N//790dfXJzw8nA8++IDXXntNfe3p06fz+eef8/rrrzN16lQOHDjATz/9xI4dO9rktbhfSZJEREQEhYWFyGQynJ2d8fLywtvbG0tLy7buniAIQodSk5MqvvQL7VXNe1OhUHT8QBVg06ZNzJw5k+HDh6sL/q9atUq9X6FQEBcXp5H/s3z5cnXb2gX/a+jo6PDf//6XuXPnIkkSXbt25T//+Q8vvviiuo2bmxs7duxg7ty5rFy5EkdHR7766itRmqqZVVVV0atXL2JjY8nOziYtLY20tDT27t2LjY0N3t7eeHl5ifWxBUEQmkDc7hfaq8a+N2VS7VpMQosoLCzEzMyMgoICkaPagKqCCqpulFGsU0HitRTi4uJITU3VKBdmYmKCp6cn3t7euLm5oa3dYb5rCe2YQqFg586djB49Wtz6F1pUa30elJeXk5KSgpubG/r6+i12HUG4W419j4pPeaFdKInKIm9rAkiADHqM96D/pP6UlZWRkJBAbGwsiYmJFBUVcfr0aU6fPo2uri5du3bFy8sLT0/Peie3CYIgCPePiIgIgoKCyMvLq1OCUrj/iHuoQpurKqj4O0gFkCBvawJVBRUYGBjg5+fHE088weuvv84zzzxDQEAAJiYmVFZWEh0dzW+//cbHH3/M+vXrOXHiBHl5eW36fARBEIT7S0REBDKZ7I6lvuLi4ggKCsLOzg59fX3c3d15++2369Sx/fnnn/H29kZfX58ePXrUmbQt/E2MqAptrupG2d9Bag1JtV3b7O/qCdra2nh4eODh4cHo0aPJzMwkNjaWuLg4cnJySE1NJTU1ld27d2NnZ6eejNWpUyeRpyUIgiC0OB0dHZ5//nl69+6Nubk558+f58UXX0SpVPLBBx8AcOzYMZ566ik+/PBDHnnkETZv3szjjz/OmTNn6N69exs/g/ZHjKgKbU7b2gBujSNlf22vh1wup3PnzgwfPpxXXnmF2bNnExISgouLCzKZjOzsbA4fPszatWtZvnw5O3bsIDExkaqqqpZ9MoIgCPehzIIyjiXdILOgrMWvVVFRwezZs7G1tUVfX5+HHnqIqKioOu2OHj2Kn58f+vr69O/fn0uXLqn3paWlMXbsWCwsLDAyMqJbt24Njlpu3LhRfbfO3t6ep59+Wl3nMzU1laCgIAAsLCyQyWT11oB3d3dnypQp9OzZExcXFx599FGeeeYZ/vzzT3WblStXEhoayoIFC/Dx8eG9996jd+/efP755/X275133sHf359vvvkGZ2dnjI2NeeWVV6iurubjjz/G3t4eW1tb3n///QZf245IjKgKbU7bTA+L8R4aOaoW4z00RlPvxNLSkgEDBjBgwABKS0uJj48nLi6OxMRECgsLiYqKIioqCj09Pbp27Yq3tzceHh5ikoEgCMId/BiVzqKtF1FKIJfBh+N7MLGvc4td7/XXX+fXX3/lu+++w8XFhY8//piQkBASExM1yhUuWLCAlStXYm9vz5tvvsnYsWOJj49HR0eHGTNmUFlZyeHDhzEyMiI6OhpjY+N6r6lQKHjvvffw8vIiJyeHefPmMXnyZHbu3ImTkxO//vorYWFhxMXFNbjgz60SExPZvXs348ePV287fvy4xqJAACEhIfz+++8NnispKYldu3axe/dukpKSmDBhAsnJyXh6enLo0CGOHTvG1KlTCQ4OrrfGfEckAlWhXTDqa4+ep4Xqdr+1QZOC1FsZGhri7++Pv78/CoWClJQUdYpASUkJly9f5vLly8jlctzc3PD29sbb2xsTE5NmfEaCIAgdX2ZBmTpIBVBK8ObWSwzxtKGTWfNPYC0pKeHLL79k/fr1jBo1CoB169YRHh7O119/zYIFC9RtlyxZwogRIwD47rvvcHR05LfffuOJJ54gPT2dsLAwevToAahGOhsydepU9b/d3d1ZtWoVffv2pbi4GGNjY3WAbGtr26gJXAMHDuTMmTNUVFTw0ksv8e6776r3ZWVlYWdnp9Hezs6OrKysBs+pVCr55ptvMDExwdfXl6CgIOLi4ti5cydyuRwvLy8++ugjDh48KAJVQWgJ2mZ69xSg3o6Ojg6enp54enqiVCq5evUqMReiiYuL5WZhHklJSSQlJbFjxw4cHR3VQau1tXWz9kMQBKEjSrlRog5Sa1RLEqk3SlskUE1KSkKhUDBo0CD1Nh0dHfr160dMTIxG29orRFpaWuLl5aVuM3v2bF5++WX27t1LcHAwYWFh+Pn51Xvd06dP884773D+/Hny8vJQKpUApKen4+vr2+Tn8eOPP1JUVMT58+dZsGABn376Ka+//nqTz1Obq6urxoCKnZ0dWlpaGrXF7ezs6iw539GJQFV4YMjlciyzdPA9Yoiv1Jt8eQk5fhLJxVe5cuWK+rFv3z6sra3VQatYZEAQhAeVm7URchkawaqWTIardfte8eqFF14gJCSEHTt2sHfvXj788EM+++wzZs2aVadtSUkJISEhhISEsGnTJmxsbEhPTyckJITKysq7ur6TkxMAvr6+VFdX89JLLzF//ny0tLSwt7cnOztbo312djb29vYNnvPWGs8ymey222qC7PuF+PQVHhi3lsEyVxrhed6Yyf94jnnz5vHII4/QpUsX5HI5N27c4MiRI3z11VcsX76c7du3i8lYgiA8cDqZGfDh+B5o/VU5RUsm44Px3VtkNBWgS5cu6OrqcvToUfU2hUJBVFRUnZHNEydOqP+dl5dHfHw8Pj4+6m1OTk5Mnz6drVu3Mn/+fNatW3fba8bGxnLz5k2WLVvG4MGD8fb2rjMqqaurC0B1dXWTn5NSqUShUKgDyAEDBrB//36NNuHh4RojxMLfxIiq8MBoqAyWaRdzAgICCAgIoLy8XL3IQEJCAkVFRZw6dYpTp06hp6eHh4eHejKWnl7zpioIgiC0NxP7OjPE04bUG6W4Whu2WJAKYGRkxMsvv8yCBQuwtLTE2dmZjz/+mNLSUqZNm6bR9t1338XKygo7OzveeustrK2tefzxxwGYM2cOo0aNwtPTk7y8PA4ePKgRxNbm7OyMrq4uq1evZvr06Vy6dIn33ntPo01NRZnt27czevRoDAwMbjs5a9OmTejo6NCjRw/09PQ4deoUixYtYuLEierRz1dffZWhQ4fy2WefMWbMGLZs2cKpU6dYu3ZtM7yC9x8RqAoPDHUZrNrB6m3KYNUUYO7RowdVVVXqyVixsbGUlJRw6dIlLl26hJaWFu7u7nh7e+Pp6SkmYwmCcN/qZGbQogFqbcuWLUOpVPLcc89RVFREQEAAe/bswcLCok67V199lYSEBPz9/dm2bZvGyOeMGTO4cuUKpqamhIaGsnz58ttez8bGhvXr1/Pmm2+yatUqevfuzaeffsqjjz6qbtO5c2eWLl3KwoULmTJlCs8//zzr16+vcy5tbW0++ugj4uPjkSQJFxcXZs6cydy5c9VtBg4cyObNm3n77bd588038fDw4Pfffxc1VOshk2ovpC60iNZa21m4s1uXarUY74FR34bzgmrUTMaKjY0lJiaG3Nxcjf1OTk7qvFYrK6sW6L3QUhQKBTt37mT06NF1cr4EoTm11udBY9dRF4S20tj3qBhRFR4o91IGSy6X4+TkhJOTE8HBwdy4cYOYmBhiY2O5du0aGRkZZGRkEB4ejo2VNT7dfMXKWIIgCIJwD0SgKrSp4rxy8nPKMLc1wNiidb71N0cZLJlMho2NDTY2NgwZMoSCggIu7o0i+mI0mbI8rt+8wfXDhzl8+DBmZmbqkVZnZ2e0tLSa6ZkIgiAIwv1NBKpCm4k+eo2I72ORJJDJYNiz3vgOcmjrbt0VI/RxPaOHq9SLChSky2+QpnWdq3r5FBQUEBkZSWRkJIaGhnh6euLj44O7u7u4zSwIgiAIDRCBqtAmivPK1UEqgCRBxKZYnH0tW21ktTnVriighw4eyk54KDth9qw3V6WbxMTEEB8fT2lpKefOnePcuXPo6OioKwh4enqKPDJBEARBuIUIVIU2kZ9ThiRBdWUsci0HZFqmSEooyCnrkIFqfRUFDOxN8Tazwdvbm+rqatLS0tQVBAoLC4mOjiY6Olos5yoIgiAItyECVaFNmNsagFSComQnADItO7T0PEByAywaPrgd0jbTw2K8R52KArVzYWvKWbm7uzNq1CiuXbumriBw48aNOsu5+vj4iAoCgiAIwgNNBKpCmzC20CdgjD3HfnREWXUVqTqbqtJstiw5grWzKx79BuIZOBArJ5cOM2O+KRUFZDIZnTt3pnPnzgwfPlyjgsDVq38v6RoeHo6tra16pFVUEBAEQRAeJB2mjmpubi6zZs1i27ZtyOVywsLCWLly5W1XhqhRXl7O/Pnz2bJlCxUVFYSEhPDFF19gZ2cHwPr165kyZcptj83OzsbW1paIiAiCgoLq7M/MzLzjurw1RB3V+hXnlZOdnMXNqxdJv3iSjMsXUNZaos6iU2c8AgfiGTgIW7cuD0SQVlBQQFxcHLGxsaSmpmqs21xTQcDHxwdnZ2fkcrEKcnMQdVSF1iLqqAqCSmPfox0mUB01ahSZmZn873//Q6FQMGXKFPr27cvmzZvrPebll19mx44drF+/HjMzM2bOnIlcLlevIVxWVkZBQYHGMZMnT6a8vJyIiAgAdaAaFxen8UfF1ta20UGCCFQbr6y4iOTTJ4mPPErahbNUKxTqfaY2dnj0G4BH4CAcPLyQPQBBWllZGfHx8cTExJCYmEhVVZV6n6GhIV5eXnh7e4sKAvdIBKpCaxGBqiCo3FeBakxMDL6+vkRFRREQEADA7t27GT16NFeuXMHBoW5Jo4KCAmxsbNi8eTMTJkwAIDY2Fh8fH44fP07//v3rHHP9+nU6d+7M119/zXPPPQf8Hajm5eVhbm5+V/0XgerdqSwrJflMFAmRx0g+d4qqigr1PmMLS7r2G4BHv0E4+nRD/gDUJq2srCQpKYnY2Fji4uIoLy9X79PR0cG9kys+3Xzw7ukrPpiaSASqQmsRgerdaY7PYoDS0lKee+45wsPDKSoqIi8vD39/f+bMmcOcOXOarb/CnTX2PdohhqSOHz+Oubm5OkgFCA4ORi6XExkZedtjTp8+jUKhIDg4WL2tpuD68ePHb3vMhg0bMDQ0VAe2tfn7+9OpUydGjBihHpEVWpaugSHeg4Yydt4iXlm3iUfnv4nPQ8PQNTCkOC+Xc3t28PN7b7Jm+vPsXbua1HOnqa5S3PnEHZSuri4+Pj6MGzeOBQsW8Pzzz9O3b1+M9Y1QKBTEpSfw+67/4+OPPmbjxo2cOnWKoqKitu62IAhCkwwbNqxO0Dhw4EAyMzMxMzO7p3N/9913/Pnnnxw7dqxZztcSUlNTkclknDt3rsF2N2/eJDQ0FAcHB/T09HBycmLmzJkUFhZqtIuIiKB3797o6enRtWtX1q9f33KdbwEdYjJVVlYWtra2Gtu0tbWxtLQkKyur3mN0dXXrfPOys7Or95ivv/6ap59+GgMDA/W2Tp06sWbNGgICAqioqOCrr75i2LBhREZG0rt379uep6Kigopao381bxqFQoFCcf8GUi1KroVrr7649upLkEJBxqXzJEUdJ/nMScoKC7i4fw8X9+9Bz9AIt9596dp3AM49/NHW1QWgOK+CwutlmNoYYGxxb6tStRdOTk44mNrid8SEGxSRqpVDqvw6BfJSdQWB7du34+joiJeXF15eXlhYdLyKCq2h5vdS/H4KLU28x+6Orq5uo+eFNCQpKQkfHx+6d+/eDL1qW3K5nMcee4x///vf2NjYkJiYyIwZM8jNzVWnRaakpDBmzBimT5/Opk2b2L9/Py+88AKdOnUiJCSkjZ9B47RpoLpw4UI++uijBtvExMS0Sl+OHz9OTEwMGzdu1Nhe8wFfY+DAgSQlJbF8+fI6bWt8+OGHLF26tM72vXv3Ymho2Lwdf5A5dqGzgxtlOZkUp6dQciWVitISYo9EEHskApm2DkYOTmgbdqUsywOZTA+QsOhegZHT/fFhYVygjZdkig2m2FSZ0peu5MtKOGWfQU5FHqWlpeoKAvv370dfXx9zc3PMzMwwMDB4ICanNUV4eHhbd0G4z5WWlrZ1F9q1yZMnc+jQIQ4dOsTKlSsBVbCVmpqqcet//fr1zJkzh++//5758+eTkZHB6NGj2bBhAz///DNLliyhoKCA5557juXLl6OlpcWwYcM4dOgQoKq8MnToUPV8lNrS09OZNWsW+/fvRy6XExoayurVq7Gzs6OgoABLS0siIyMJCAhAqVRibW2Np6cnJ06cAOD7779n0aJFZGRk3PY57t69m3//+99cunQJLS0tBgwYwMqVK+nSpQsAbm5uAPTq1Qug3n5aWFjw8ssvq392cXHhlVde4ZNPPlFvW7NmDW5ubnz22WcA+Pj4cOTIEZYvX15voHo3r21LatNAdf78+UyePLnBNu7u7tjb25OTk6Oxvaqqitzc3Hq/Ydnb21NZWUl+fr7GqGp2dvZtj/nqq6/w9/enT58+d+x3v379OHLkSL37Fy1axLx589Q/FxYW4uTkxMiRI0WOagtSKqvJjI8jKeo4iadOUHzzBsXpyUAysB+5jitaOp7kXXRn1BPD74uR1eqCCm7EnNVYaMAcI5585im0zPQoLCwkLi6OuLg40tPTKS8vJysri6ysLMzNzdVfxDp37vxAVxBQKBSEh4czYsQIkaMqtKhbb8u2KkkCRRsFyjqGqrWy72DlypXEx8fTvXt33n33XQBsbGxITU2t07a0tJRVq1axZcsWioqKGD9+POPGjcPc3JydO3eSnJxMWFgYgwYNYuLEiWzdupWFCxdy6dIltm7diu5fd9xqUyqVPPbYYxgbG3Po0CGqqqqYMWMGEydOJCIiAjMzM/z9/YmIiCAgIICLFy8ik8k4e/YsxcXF6uOGDh1a73MsKSlh3rx5+Pn5UVxczOLFixk3bhznzp1DLpdz8uRJ+vXrx759++jWrdtt+3k7165dY+vWrRrXPn78uEYKJEBISMgd83Gb+tq2pDYNVG1sbLCxsbljuwEDBpCfn8/p06fVgeSBAwdQKpUEBgbe9pg+ffqgo6PD/v37CQsLA1B/WA8YMECjbXFxMT/99BMffvhho/p97tw5OnXqVO9+PT099PTqBkE6OjriQ7BF6eDaoyeuPXry8JR/kpUUz5ldB4g7dgRJWYBSkYRSkYSiVIvdq4/S4+GhdOnbHwPjjrsKlI61zm0XGtC3VpVts7KyYuDAgQwcOJDS0lJ12aukpCTy8/OJjIwkMjISIyMjvLy88PHxwc3NDW3tDpEV1OzE76jQ0tr0/aUohQ/qTj5uFW9eA12jOzYzMzNDV1cXQ0PDO97qVygUfPnll+qRyAkTJrBx40ays7MxNjbG19eXoKAgDh48yMSJE7G0tMTQ0LDBNIL9+/dz8eJFUlJScHJyAlTzV7p160ZUVBR9+/Zl2LBhRERE8NprrxEREcGIESOIjY3lyJEjhIaGEhERweuvv15vv2tikhrffPMNNjY2REdH0717d3VcZGVl1ah0h6eeeoo//viDsrIyxo4dy1dffaXel5WVpS7JWcPOzo7CwkLKyso0Uh1ra+pr25I6xKeRj48PoaGhvPjii6xZswaFQsHMmTN58skn1TP+r169yvDhw9mwYQP9+vXDzMyMadOmMW/ePCwtLTE1NWXWrFkMGDCgzoz/H3/8kaqqKp599tk6116xYgVubm5069aN8vJyvvrqKw4cOMDevXtb5bkLd0cmk9GpqxdDn3Uh9bInyqrrVCsSUFYmIClzuRZ/jmvx55Cv+xynbn54Bg6ia9/+GJqZt3XXm6yxCw0YGhrSq1cvevXqRWVlJYmJicTExBAfH09JSQlnzpzhzJkz6Orq4unpibe3Nx4eHrf90iUIgtDWDA0N1YEUqAIwV1dXjfrqdnZ2de7INiQmJgYnJyd1kArg6+uLubk5MTEx9O3bl6FDh/L1119TXV3NoUOHGDlyJPb29kRERODn50diYiLDhg2r9xoJCQksXryYyMhIbty4oa6VnZ6efle5s8uXL2fJkiXEx8er7+h+8cUXTT5PbS3x2t6tDhGoAmzatImZM2cyfPhwdcH/VatWqfcrFAri4uI08n+WL1+ublu74P+tvv76a8aPH3/bkheVlZXMnz+fq1evYmhoiJ+fH/v27bvtIgAdXVZJFumF6TibOmNvdO9J6+2BsYU+Qc/5ELFJhlzbFpnRIPqEmqJUJJBw4ijX01NJu3CWtAtn2ffVFzj6dMMjcCAe/QZibNlxli7VNtNrcCWsW+nq6uLr64uvry9VVVWkpqYSGxtLbGwsxcXFXLp0SZ0/5e7ujo+PD15eXhgZ3XlERBCEdkrHUDWy2VbXbu5T3jI6LZPJbrut9qIpzWHIkCEUFRVx5swZDh8+zAcffIC9vT3Lli2jZ8+eODg44OHhUe/xY8eOxcXFhXXr1uHg4IBSqaR79+5UVlbeVX/s7e2xt7fH29sbS0tLBg8ezL/+9S86deqEvb092dnZGu2zs7MxNTWtdzQV2u61vZ0OE6haWlo2WNzf1dWVW0vC6uvr89///pf//ve/DZ772LFj9e57/fXXGxzCv19sTdjK0uNLUUpK5DI5SwYsYbzH+LbuVrPwHeSAs68lBTllmNkaYGyhDwQwIOwp8jKvEh95jITIY2QnJ5ARfZGM6Isc+PZ/OHj64Nl/EB6BAzG1tr3jdToqbW1tunbtSteuXRk9ejRXr15VL+eam5tLQkICCQkJyGQynJ2d1Stj3UstQ0EQ2oBM1qjb721NV1eX6lorFLYmHx8fMjIyyMjIUI+qRkdHk5+fj6+vLwDm5ub4+fnx+eefo6Ojg7e3N7a2tkycOJHt27c3mJ968+ZN4uLiWLduHYMHDwaoM+elJif1bl6DmsCxpvLQgAED2Llzp0ab8PDwOimQ7VmHCVSFlpNVkqUOUgGUkpKlx5cy0GHgfTWyqgpQNVl06kzg4/8g8PF/UJCTTcLJY8RHHiUzPpZr8TFci48hYsNX2HfxwCNwEJ6BgzC3r5ufXJxXTn5OGebqQLhjksvl6tteI0aMICcnh9jYWGJiYsjKyiItLY20tDT27NmDvb09Pj4+6j/SooKAIAjNwdXVlcjISFJTUzE2NsbS0rLVrh0cHEyPHj145plnWLFiBVVVVbzyyisMHTpUo5b7sGHDWL16tbruuqWlJT4+Pvz4448NDo5ZWFhgZWXF2rVr6dSpE+np6SxcuFCjja2tLQYGBuzevRtHR0f09fVvW+91586dZGdnq+ppGxtz+fJlFixYwKBBg3B1dQVg+vTpfP7557z++utMnTqVAwcO8NNPP7Fjx45meLVahwhUBdIL09VBag2lpCSjKOO+CVQbw8zWjoBHxhHwyDiKcm+QEHmchJNHuRJzmaykBLKSEvhz83psXNzwDByER+AgrBydiD56jYjvY5Ek1YDFsGe98R3URhMWmpFMJsPOzg47OzuGDh1KXl6eOj0gPT1dXT3g4MGDWFpaqkdaH/QKAoIg3JvXXnuNSZMm4evrS1lZGSkpKa12bZlMxh9//MGsWbMYMmSIRnmq2oYOHcqKFSs0clGHDRvG+fPnG8xPlcvlbNmyhdmzZ9O9e3e8vLxYtWqVxjHa2tqsWrWKd999l8WLFzN48ODblqcyMDBg3bp1zJ07l4qKCpycnBg/frxG4Ovm5saOHTuYO3cuK1euxNHRka+++qrD1FCFDrKEakfX3pdQzSrJIuTXEI1gVS6TsydszwMVqNanJD+PxKgTxEceJePyBaRaOTkWnRwpyuuMXMcTmZY1MpkMmRyef39ghx5ZvZOSkhLi4uKIiYkhOTlZ4xaVsbEx3t7eeHt74+rq2iEqCIglVIXWIpZQFQSVxr5H2/8niNDi7I3sWTJgSZ0cVRGkqhiZW9BzxCh6jhhFWVEhiadOkBB5jLQL58jLvAJcgfJIZHJz5LoeaOl4kp9del8HqkZGRvTu3ZvevXtTUVFBQkICsbGxxMfHU1xczKlTpzh16hR6enrqCgJdu3YVFQQEQRCEJhEjqq2gvY+o1sgqySKjKAMnEycRpDZCeUkx0X8e49D3O1AqUoG/RxVNrGzxHKDKae3U1RPZA3IrvKqqipSUFKLPXyYuMZ7S8r+rcNSuIODp6alR5qStiRFVobWIEVVBUBEjqkKT2RvZiwC1CfSNjOkdOhJ9k+4c/P4C1RUpKBUJoEyl6GYOp7f/xuntv2FsaYVH4EA8Awfh4OWDXN6yy821JW1tbRzyTTA4bUUvqT858gKyvatIzEsnLy9PXUEAUFcQqCmpIgiCIAi3EoGqINyjv8tfBWJma4CeIaSeO0N85FGSz5ykOPcmZ3dt4+yubRiamePRbyAegQNx8u2BvIXXSG5tVQUV6lWy5MiwV5pjHwMj3xhFbkWBejJWZmYm6enppKens3fvXmxtbdUVBOzt7UUFAUEQBAEQgaogNItby195BKqC0arKStIuniMh8iiJp05QWpDP+fCdnA/fib6JKR59++MROAjn7n5oaXf8W85VN8pUS7nWJkH1zXLsuvxdQSA/P1+9nGtqaio5OTnk5ORw6NAhzMzM1COtzs7OaN1nwbwgCILQeCJQFQAoPXWK7E8+wTR0FKahIeh0qlsrtLncjytg1UdbV5cuffrRpU8/qqsUZFy6QHzkURKjTlBWVMjFA3u5eGAvekZGdOkTiGf/Qbj06IX2XwWfOxptawOQoRmsyv7aXou5uTmBgYEEBgZSWlpKfHw8sbGxJCYmUlBQQGRkJJGRkRgYGODl5YW3tzfu7u7qQtiCIAjCg0FMpmoFHWEyVdZ7/yZv0yb1zwa9emE6KhSTkBB07Oya7Tr38wpYTaGsruZKzCXiTxwlMeo4Jfl56n26Bga49+6HZ+AgXP17o6PXsSZClERlqW//IwOL8R4Y9W3cF5LKykqSk5OJiYkhPj6esrIy9b6aFbS8vb3x9PTE0LD5lmQUk6mE1iImUwmCSmPfoyJQbQUdIVBV5ORQtDecwt27KDt9Bmq9LQz69ME0NBSTkSPRsbv7pURFvdbbUyqruRYXQ0KkalWs4tyb6n3aenq4+wfgETgQW3c/SgvpEKtfVRVUUHWjDG1rA7TN7q4kVXV1Nenp6eq81oKCAvU+mUyGq6sr3t7eeHl53fNyriJQFVqLCFQFQUUEqu1IRwhUa1NkZ1O0Zy+Fu3dTdubM3ztkMgz79MFkVCimI0eibWPTpPOezDzJtL3T6mz/JuQb+tr3vddu3xckpZLMxHjVUq4njlJ4PbvWXi3kOq5o6Xow9NlQej7ctc362dokSSIrK4uYmBhiY2PJycnR2N+pUyd1XqutrS3VhZVNCpRFoCq0FhGoCoKKCFTbkY4WqNamyMqiaM8eCnftpuzcub93yOUY9u2rSg8YMQJtK6s7nkuMqDaNJEnkpCRx6dAhzu89iKTMr7VXjlM3P7wHDaZr3/4YmtZdB/p+lpubq7Gca23mhqY4FZrjUm2DLWZYjfe8Y+qBCFSF1iIC1XsXERFBUFAQeXl593w3RWg7jX2PPhhVyIW7pmNvj+WkSbhu+YGuB/Zj+8Yb6Pf0A6WS0shIst5ZSsLgIaRNmULejz9RlZtb77lqVsCSy1RvO7ECVsNkMhl27l3xGjgeXdMp6Jo8h5Z+f2RyK0BJxuVzhK9dzZqXnuOnd9/k3J4dFOfV//rfTywtLRk4cCBTp07ltddeY+zYsXh4eKClpUV+aSEXtdPZrneazbp/8sf//cHlMxeprKxs624LgtBBRUREIJPJyM/Pv2NbSZL49NNP8fT0RE9Pj86dO/P+++/XOV/v3r3R09Oja9eurF+/vmU6fh8Qs/6FRtNxcMBqymSspkym8spV1Ujr7t2UX7xI6fETlB4/Qda772IUGIjJqFBMgoPRtrDQOMd4j/EMdBjYaitg3Q8VBsxtDZDLZUgyG+TaNmAwEEnKxW9oBWkXTpKTkkTG5QtkXL7A/m/X4ODpg2fgQDz6DcTU5u5zijsKY2Nj+vTpQ58+fSiIzeH8hsOkaV0nQ36TcpmCeK1M4v/vV7R3auPu7q6ejNWeVsYSBOH+8eqrr7J3714+/fRTevToQW5uLrm1BnFSUlIYM2YM06dPZ9OmTezfv58XXniBTp06ERIS0oY9b5+adOs/Pz+f3377jT///JO0tDRKS0uxsbGhV69ehISEMHDgwJbsa4fVkW/9N0ZlRoY6PaD88uW/d2hrY9S/P6ajRmESPBwts9a9PX0/VRiIPnqNiE2xSEqQyWHYM974DnIAoCAni/jIYyREHiUzIU7jODt3j79WxRqIRafObdH1VlVVUEHWspMggRIlmfJ80uTXuWpZREFhgUZbR0dH9WQsMzMzdu3aJW79Cy2uo976b80v/RUVFSxYsIAtW7ZQWFhIQEAAy5cvp29f1VyGmlv/27dvZ9GiRcTHx+Pv789XX31F9+7dAUhLS2PmzJkcOXKEyspKXF1d+eSTTxg9evRtr7lx40ZWrlxJXFwcRkZGPPzww6xYsQJbW1tSU1Nxc3PTaD9p0qTbjoLGxMTg5+fHpUuX8PLyuu213njjDXbs2MGlS5fU25588kny8/PZvXv3bY9Zv349c+bM4fvvv2f+/PlkZGQwevRoNmzYwM8//8ySJUsoKCjgueeeY/ny5R2i/nSz5qheu3aNxYsXs2nTJhwcHOjXrx8ODg4YGBiQm5vLpUuXOH36NC4uLixZsoSJEyc265Pp6O73QLW2yrQ0CnerRlorYmL+3qGjg9HAAZiGjsJk+MNotfDrcD/mwxbnlVOQU4ZZA7P+i27eIOHkcRJOHuVqTDRSredv4+yKR+AgPAIHYuXofN+u/nS78liGAXZkZ2erFxnIzMzUOMbS0hJtbW1CQkJwc3NDLhdZUULL6IiBamt/6X/11Vf55Zdf+Oqrr3BxceHjjz/m//7v/0hMTMTS0lIdqPr4+LBy5Urs7e158803uXTpEvHx8ejo6PDII49QWVnJZ599hpGREdHR0ZiamjJkyJDbXvObb76hU6dOeHl5kZOTw7x58zA3N2fnzp1UV1fzxx9/EBYWRlxcHKamphgYGGB2m8GXjz/+mK+//pqXXnqJzz//HEmSCA4O5uOPP1YvFT1kyBB69+7NihUr1Md9++23zJkzR6O6SW3r16/npZdeYtiwYXz00UcUFRUxfvx4+vTpg7m5OUuWLCE5OZmwsDA2bNjQIeKwZg1U7ezsmDRpEpMnT8bX1/e2bcrKyvj9999ZtWoVYWFhvPbaa3ff+/vMgxSo1laRkqIeaa2IqzXSp6OD8aBBmI4KxXj4cLRa4BasqDAAJfl5JEadIOHkMdIvnUdS/h20Wjg4qtMDbN263HdB653KYxUUFKgXGUhJSUFZ67UxNDTE09MTLy8vunTpIhYZEJpVRwtUW/tLf0lJCRYWFqxfv56nn34aUE12dHV1Zc6cOSxYsEAdqG7ZskUdkOXm5uLo6Mj69et54okn8PPzIywsjCVLltxVP06dOkXfvn0pKirC2Ni40RO4pk+fzvr16/H39+eTTz6hurqauXPnYmFhwYEDBwDw9PRkypQpLFq0SH3czp07GTNmDKWlpRgYGNQ57/r165kyZQqJiYl06dJFfa2NGzeSnZ2tTmUKDQ3F1dWVNWvW3NXzbk2NfY82Kkc1OjoaqzvM6jYwMOCpp57iqaee4ubNmw22FR4Mem5u6E2fjvX06VQkJ1O4ezdFu3ZTkZBAcUQExRERyHR1MRo8GNPQUIyDgtAyNmqWazubOiOXyev8cXUycWqW83cERuYW9Bwxip4jRlFWXETSqUgSIo+SduEsedeuEPnbT0T+9hNmtnaqkdZ+A+nU1RPZfTCaqG2m12BZKjMzM/r27Uvfvn0pLy8nPj6egwcPUlZWRmlpKefOnePcuXNoa6vyWr28vPD09MTExKQVn4UgtL30wnSNv6MASklJRlFGiwSqSUlJKBQKBg0apN6mo6NDv379iKl9lw4YMGCA+t+WlpZ4eXmp28yePZuXX36ZvXv3EhwcTFhYGH5+fvVe9/Tp07zzzjucP3+evLw89ZfX9PT0egfobkepVFJRUcGGDRvw9PQE4Ouvv6ZPnz7ExcXVmw7QGIaGhuogFVSDiK6urhr59nZ2dnXK93V0jfpEulOQeq/tGyM3N5dnnnkGU1NTzM3NmTZtGsXFxQ0es3btWoYNG4apqWm9s/Uac94LFy4wePBg9PX1cXJy4uOPP27Op/ZA0HN3x+aVV3Df9n+4b9+G9YwZ6Lq7I1VWUrx/P9cWLCBh4ECuzJpFwY4dKEtK7ul6osKAJgNjE7oPC2bcG0t4ed1mRs96DY9+A9HW1aMgJ5tT27byw79eY+2MKRz49n9kRF9Eqaxu6263Cn19fXx8fNQjNpMmTSIwMBBzc3OqqqqIj49n27ZtfPbZZ6xbt44///yTnJwcRGU/4UFQ86W/to7wpf+FF14gOTmZ5557josXLxIQEMDq1atv27akpISQkBBMTU3ZtGkTUVFR/PbbbwBNrhbSqVMntLW11UEqgI+PD4C6lJ69vT3Z2dkax2VnZ6tTCupza/68TCa77bbad4juB3c16//atWscOXKEnJycOi/I7Nmzm6Vjt3rmmWfIzMwkPDwchULBlClTeOmll9i8eXO9x5SWlhIaGkpoaKjGEHtTzltYWMjIkSMJDg5mzZo1XLx4kalTp2Jubs5LL73UIs/1fqfXtSs2s2ZiPXMGFQkJFO3eTeHOXVSmplIUvo+i8H3I9PUxHjpUlR4wZAjyu1gus7UrDHQUeoaG+Dw0DJ+HhqEoLyfl/GkSIo+RfOYkxbk3Obt7G2d3b8PQzJyuffvjETgIJ98eaGnf/0VCtLS0cHNzw83NjdDQUHJycoiNjSUuLo5r165x9epVrl69yv79+7GwsFBPxnJycuoQkxcEoalqvvTfmqPaUn9Pa9Jtjh49iouLC6C69R8VFcWcOXM02p44cQJnZ2cA8vLyiI+PVweFAE5OTkyfPp3p06ezaNEi1q1bx6xZs+pcMzY2lps3b7Js2TKcnFQB+KlTpzTa1KQAVVc3/AV+0KBBVFVVkZSUpB79jI+PB1A/nwEDBrBz506N48LDwzVGiIW/NfmTZ/369fzzn/9EV1cXKysrjdw2mUzWIoFqTEwMu3fvJioqioCAAABWr17N6NGj+fTTT3FwcLjtcTVv6oiIiLs+76ZNm6isrOSbb75BV1eXbt26ce7cOf7zn/+IQPUeyWQy9D090ff0xHrWLCri4ijctZvCXbtQpKdTtGcPRXv2IDMwwCRoGCahfwWtTci3sjeyFwFqA3T09fEMHIRn4CCqKitJu3iOhMhjJJ06QWlBPhf27ebCvt3oG5vQpU8gHoEDcfHrhXYDM+OL88rJzynrEEu9NkQmk2FnZ4ednR1Dhw6lsLCQuLg44uLiSElJIS8vj+PHj3P8+HEMDAw08lr19O5u2VhBaI9a80u/kZERL7/8MgsWLMDS0hJnZ2c+/vhjSktLmTZNc97Bu+++i5WVFXZ2drz11ltYW1vz+OOPA6rP/1GjRuHp6UleXh4HDx7UCGJrc3Z2RldXl9WrVzN9+nQuXbrEe++9p9HGxcUFmUzG9u3bGT16NAYGBrctcRccHEzv3r2ZOnUqK1asQKlUMmPGDEaMGKEeZZ0+fTqff/45r7/+OlOnTuXAgQP89NNP7NixoxlewftPkwPVf/3rXyxevJhFixa12szY48ePY25urg4mQfVmkMvlREZGMm7cuBY77/HjxxkyZIjGhIqQkBA++ugj8vLysLilTqhwd2QyGfre3uh7e2Mz51UqYmL+DlqvXKFw5y4Kd+5CbmiIcVAQpqNHYfTQQ8jbMCC4H2q01qatq0uXPv3o0qcf1VVVZFy+QELkMRKijlNWWMDlQ/u4fGgfugaGuPfui2fgIFz9e6Oj93cwGn30GhHfxyJJIJPBsGf/LqPV0ZmamqrzWisqKkhKSiI2NpaEhATKyso4f/4858+fR0tLS53X6uXlJfJahftCa37pX7ZsGUqlkueee46ioiICAgLYs2dPnc/bZcuW8eqrr5KQkIC/vz/btm3TGPmcMWMGV65cwdTUlNDQUJYvX37b69nY2LB+/XrefPNNVq1aRe/evfn000959NFH1W06d+7M0qVLWbhwIVOmTOH555+/bXkquVzOtm3bmDVrFkOGDMHIyIhRo0bx2Wefqdu4ubmxY8cO5s6dy8qVK3F0dOSrr74SNVTr0eQlVK2srDh58qRGQm9L++CDD/juu++Ii9OsEWlra8vSpUt5+eWXGzy+vtl6jTnvyJEjcXNz43//+596f3R0NN26dSM6Ovq239AqKiqoqKhQ/1xYWIiTkxM3btx4oGb9NwdJkqiIjqZ4zx6K9+yl6to19T6ZkRHGQUEYh4zEcOBAZK04O/v3pN/5d+S/UaJEjpy3A9/m8S6Pt9r1W5NSWc21uBiSok6QGHWcklqrX2nr6eHi15uufQdg49qdX5ddovZfFJkMnlraD2OL9j3CqFAoCA8PZ8SIEU2uo6pUKsnIyCA+Pp74+Pg6ufAODg54eHjg6emJjY3NfVdhQWiawsJCrK2tO8ysf0FoKc0667+2adOm8fPPP7Nw4cJ76iDAwoUL+eijjxpsc+ssv47gww8/ZOnSpXW27927F8O7yLUUAG9v8PJCPyMD4wsXMLlwEZ2CAoq2b6do+3aq9fUp7uZLsZ8fJV27QgvmUxYoC/i08FMkVBGZEiXvRb5HWUwZZvLWXdSgVVk7YB86nvIbOZRkpFCckUJVSTFJUcdJijoOci3kWi5o6Xgg13FHJjdAkmDv9gj0rTrGxKzw8PB7Ot7V1ZXy8nIKCgooKCigtLSUa9euce3aNQ4dOoSuri5mZmaYmppibGws6rU+gEpLS9u6C4LQoTT50/zDDz/kkUceYffu3fTo0aPO6MN//vOfRp9r/vz5TJ48ucE27u7u2Nvb1ym3UFVVRW5uLvb2d38rojHnrW92Xs2+21m0aBHz5s1T/1wzojpy5EgxotpMJKWS8gsXVSOte/dCTg5mp89gdvoMchMTjIYPxzg0BMN+/ZA180pDUdlRSPs1b0RISHj09SDALqCeo+4/kiRxPTWZxKjjJJ48Tn7WNZTKZJSKZECOXNsRLV0PHho8AVvX9p0acS8jqg0pKioiISGB+Ph4UlNTqays5Pr161y/fh1dXV3c3Nzo2rUrXbt2FUu6PiAKCwvbuguC0KHcVaC6Z88edS2wWydTNYWNjQ02NjZ3bDdgwADy8/M5ffo0ffr0AeDAgQMolUoCAwObdM2mnnfAgAG89dZbKBQK9QdYeHg4Xl5e9ean6unp3XYyhY6OjliesRnp9g3AtG8A0puLKDt7VpXTumc31ddvUPT77xT9/jtaZmaYjByBSWgoRoGByJphpNXdwv22NVrdLNweuP+/nT296ezpzZCnJ3MzI42jv+wh6dRxpOobKKvSUVals+VfB3Dw8Maj3wC69huIuV37DVqb+3fU0tKSwMBAAgMDqaioIDk5mfj4eBISEiguLlZPzgJVioCnpyeenp506tRJpAjcpx60vxGCcK+anKNqYWHB8uXL7zgS2txGjRpFdnY2a9asUZeRCggIUJeRunr1KsOHD2fDhg3069cPgKysLLKysjh16hQvvvgihw8fxsTEBGdnZ/VSZnc6b0FBAV5eXowcOZI33niDS5cuMXXqVJYvX97oWf8P6spUbUGqrqb09GlVyas9e6mutfiEloUFJiNGYDp6FIYBAfcUtLb2koIdSXFeOVdiUrmRcZ70iyfJTNDMAbdxdcej7wA8+g3AysmlXQRkCoWCnTt3Mnr06BYLJGqvliU30SEzM1MdtF6rlXsNYGJios5rdXd3F6tj3Uc62spUgtBSmnUJ1drs7e35888/8fDwuOdONkVubi4zZ85k27ZtyOVywsLCWLVqlfp2WWpqKm5ubhw8eJBhw4YB8M4779w2V/Tbb79VB9p3Oi+oCv7PmDGDqKgorK2tmTVrFm+88Uaj+y4C1bYhVVdTGhWlWhFrbzjVuX9PAtKyssJk5AhMQ0dhGNAH2V3UwMwqyRI1WhuhKPcGiVEnSDx5nIzoi5pLuXZyoGu/gXj0G4B9F882C1pbOlAticoib2sCSIAMLMZ7YNT37/dM7RSBmpV5atTUdq0ZbW1o+Uah/ROBqiCotFig+uGHH5KZmcmqVavuuZMPChGotj2pqorSkycp3LWbovBwqmvNzNaytsZ05EhMR4Vi0Lv3XQWtQuOUFRWSdPokCSePkXbhLNW1AjJjK2s8+g6ga98BOPp0Q96K/x9aMlCtKqgga9lJqP2XVgb2C/vddplXhUJBWlpavVUEbG1t1UGro6OjmJDVwYhAVRBUWixQHTduHAcOHMDKyopu3brV+aO+devWu+vxfUwEqu2LpFBQEnmSwt27KArfh7KgQL1P28YGk5AQTEePwsDf/75Y9769qiwrJeXcX6tinT2ForxMvU/fxJSuAYF49BuIcw//BhcYaA4tGaiWJ+VzY93FOtutX+yBfhfzBo+VJInr16+rg9aMjAyNpVsNDAzUKQJdunRpcPlFoX0QgaogqLRYoDplypQG93/77bdNOd0DQQSq7ZekUFBy4oRqpHXfPpS1ZuRq29lhGhqCSWgoBj17iqC1BVVVVpJ+6TwJJ4+ReCqS8qK//z/oGhjg1qsvHv0G4tarD7r6zR+MtacR1YaUlpaSmJhIfHw8iYmJlJeX/31KmQwXFxf1aOutKwcK7YMIVAVBpcUCVaHpRKDaMUiVlRQfO0bRrt0U7d+PsrhYvU+7UydMQ0IwHRWKvp9fmwUA99tqWLejrK7mSsxlVdAadZzi3FoT4nR0cPHrhUe/gXTp0w8Dk7q/T3ezhGtb56jejerqao2FBm7cuKGx39LSUj3a6uLignYL1hYWGk8EqoKgIgLVdkQEqh2PsrKSkiNHKdy9i+L9B1CWlKj36Tg4YDIqFNPQUeh379ZqQeuDWGlAUirJSkogIeo4CZFHyc/KVO+TyeU4+fbAo99Auvbtj7Gl1V0v4dras/6bOpLaGLm5uRo1W6ur/15kQVdXly5duuDp6YmHh4eo2dqGRKB6d+pbYbKpSktLee655wgPD6eoqIi8vDz8/f2ZM2cOc+bMabb+CnfWrIFqaGgo77zzDv3792+wXVFREV988QXGxsbMmDGj6b2+T4lAtWNTVlRQcuQIhbt2U3zgAMpaK8voODr+lR4wCv1uvi0WtGaVZBHya0id2q17wvbctyOrt5IkiZsZaSScPE7CyWNcT0vR2G/n7snNTDvkOh7ItcwBkMnh+fcH3nFktTUC1dZUu2ZrfHw8JbW+aIGqZmvNQgNiQlbrEoHqnQ0bNgx/f39WrFih3lZZWUlubi52dnb39Hf2yy+/ZMmSJRw4cABra2vs7Oxwc3NrV4FqTRWjs2fP4u/vf8f269ev5z//+Q/x8fGYmpryj3/8g//+97/q/bUrF9nY2DBr1ixef/31FnwGjdOsS6j+4x//ICwsDDMzM8aOHUtAQAAODg7o6+uTl5dHdHQ0R44cYefOnYwZM4ZPPvmk2Z6IILQ1uZ4eJsOHYzJ8OMrycooPH6Zo926KDkaguHKFm199zc2vvkbHyUmd06rv27xBa3phukaQCqCUlGQUZTwwgapMJsPa2RVrZ1cGTHiK/KxMEv5aFetafAzZyfFAPJT9iUzLGrlOV7R0upCfXdroFID7hZ6eHj4+Pvj4+KBUKtU1W+Pj48nMzFQv63r48GH09fXp0qWLOnA1MTFp6+4LQh26urr3tBJljaSkJHx8fOjevXsz9Krt/ec//+Gzzz7jk08+ITAwkJKSElJTU9X7CwsLGTlyJMHBwaxZs4aLFy8ydepUzM3NG10Lvs1JjVReXi5t3LhReuSRRyRzc3NJJpNJMplMksvlUvfu3aX58+dL0dHRjT3dA6WgoEACpIKCgrbuitCMqktLpYJdu6SMV+dIMf69pGgvb/UjYcRIKfuz/0hlly9LSqXynq+VWZwp+X3nJ3Vf31398PvOT8oszmyGZ9LxFd28IZ347XfpP0+/LH36xCPSp0+MUT/WTJ8k7f9mjZR28ZxUpVDc9vjKykrp999/lyorK1u5561LkV8uXb9wRTp19KT0008/SR9++KG0ZMkSjceXX34phYeHS6mpqVJVVVVbd/m+01qfB2VlZVJ0dLRUVlbWotdpbpMmTZJQZXOrHykpKdLBgwclQMrLy5MkSZK+/fZbyczMTNq2bZvk6ekpGRgYSGFhYVJJSYm0fv16ycXFRTI3N5dmzZqlfh8PHTpU47xDhw6VJEmSXFxcpOXLl6v7kJaWJj366KOSkZGRZGJiIv3jH/+QsrKyJEmSpPz8fEkul0tRUVGSJElSdXW1ZGFhIQUGBqqP37hxo+To6Fjvc9y1a5c0aNAgyczMTLK0tJTGjBkjJSYmqvff+vxr+nmr3NxcycDAQNq3b1+91/riiy8kCwsLqaKiQr3tjTfekLy8vOo9pua13r17t+Tv7y/p6+tLQUFBUnZ2trRz507J29tbMjExkZ566imppKSk3vPcSWPfo43OrtfT0+PZZ5/l2WefBVQrNpWVlWFlZXVf3CoThKaSGxhgGhqKaWgoytJSig8fVqUHHDqEIj2dm2vXcnPtWnRcnDENHYVpaAh63t53NdJqb2TPkgFL6uSoPiijqXdibGlF4OOPYWLTl4Mbz1JdkUJ1VSIyZTrFuTc4u3sbZ3dvQ9/IGLfefenatz+uPXu3SAWB9qr2hC47GXiPH4z++PFcvXqVxMREEhMTuXbtmnpFvyNHjqCnp4e7uzseHh507dpVpC51cJIkIZWV3blhC5AZGDTqb9/KlSuJj4+ne/fuvPvuu4BqufXao4Q1SktLWbVqFVu2bKGoqIjx48czbtw4zM3N2blzJ8nJyYSFhTFo0CAmTpzI1q1bWbhwIZcuXWLr1q23XfFNqVTy2GOPYWxszKFDh6iqqmLGjBlMnDiRiIgIzMzM8Pf3JyIigoCAAC5evIhMJuPs2bMUFxerjxs6dGi9z7GkpIR58+bh5+dHcXExixcvZty4cZw7dw65XM7Jkyfp168f+/bto1u3bvWuTBceHo5SqeTq1av4+PhQVFTEwIED+eyzz3BycgLg+PHjDBkyROMcISEhfPTRR+Tl5dW7FDyoFk36/PPPMTQ05IknnuCJJ55AT0+PzZs3U1xczLhx41i9enWTFkC6G3c9DdTMzAwzM7Pm7IsgdFhyQ8O/g9aSEs2gNS2dm//7Hzf/9z90XVz+mogVip6XV5OC1vEe4xnoMFCshtUA30EOOPtaUpBThpmtAXpGMtIvniMxKpKk05GUFRYQ8+dBYv48qKog0MOfLgH9cenZu6273qKqCir+rjoAIEHe1gTsPS1wdnbG2dmZhx9+mOLiYpKSkkhISCApKYmysjJiYmKIiYkBVIsN1AStTk5OopJAByOVlRHXu0+bXNvrzGlkhoZ3bGdmZoauri6GhoZ3vNWvUCj48ssv6dKlCwATJkxg48aNZGdnY2xsjK+vL0FBQRw8eJCJEydiaWmJoaFhg2kE+/fv5+LFi6SkpKiDvQ0bNtCtWzeioqLo27cvw4YNIyIigtdee42IiAhGjBhBbGwsR44cITQ0lIiIiAZzQMPCwjR+/uabb7CxsSE6Opru3btjY2MDgJWVVYOvQXJyMkqlkg8++ICVK1diZmbG22+/zYgRI7hw4QK6urpkZWXh5uamcZydnR2gWma+oUD13//+N4MGDQJg2rRpLFq0iKSkJNzd3dWv98GDB9tvoCoIwu3JjYwwHTUK01GjVEHroUOqoPXwYSrT0ri55n/cXPM/dF1d/w5aPRu3fKi9kb0IUO/A2EJfIye1S59AuvQJRKms5lp8LIlRJ0iKOkF+dibJZ6JIPhMFMhn61rackSrxDByIRafObfgMml/VjTLNOq4Akmp77QoExsbG9OzZk549e6JUKrl27RoJCQkkJiZy9epVcnJyyMnJ4ejRo+jq6uLu7q7ObRVLuwqtzdDQUB2kgioAc3V11ahqYWdnR05OTqPPGRMTg5OTkzpIBfD19cXc3JyYmBj69u3L0KFD+frrr6murubQoUOMHDkSe3t7IiIi8PPzIzExUb2U++0kJCSwePFiIiMjuXHjBsq/lpVOT09vUu6sUqlEoVCwatUqRo4cCcAPP/yAvb09Bw8eJCQkpNHnuh0/Pz/1v+3s7DA0NFQHqTXbTp48eU/XaAwRqApCC5IbGWE6ejSmo0dTXVxC8aEIinbvpvjQYSpTU7n55RpufrkGXTc3TEeFYhIaip6HhyjU3gLkci0cvbvh6N2Noc9O5eaVdBKjTpAYdYLs5ATKr2dz5IfvOPLDd1g5OtO1b3+6BvTHzr1rh1/sQdvaAGTUWXRA27r+1Ae5XI6joyOOjo4EBQVRUlJCUlKSOk2gtLSU2NhYYmNjAdXt2ZqgVdRtbZ9kBgZ4nTndZtdubremHcpksttuqwkEm8uQIUMoKirizJkzHD58mA8++AB7e3uWLVtGz549cXBwwMPDo97jx44di4uLC+vWrcPBwQGlUkn37t2prKxsUj86deoEqALpGjY2NlhbW5Oeng6Avb092dnZGsfV/HynEevar2Vrvba3I/6SCEIr0TI2wmzMGMzGjFEFrQcPUrhnNyWH/6QyJYUbX3zJjS++RNfdXZVGMEoVtArNTyaTYe3kgrWTC/3HTyQ3K5PtG9ejX17M1ZhL3LySzs0r6UT+9hPGFpZ0CehP1779cerWAy3tjpeTr22mh8V4jzqLDjSlnquRkRF+fn74+fmpKwnUBK1Xrlzh+vXrXL9+nePHj6Ojo4Orq6u6moBYJat9kMlkjbr93tZ0dXU16gC3Jh8fHzIyMsjIyFCPqkZHR5Ofn68OCM3NzfHz8+Pzzz9HR0cHb29vbG1tmThxItu3b28wP/XmzZvExcWxbt06Bg8eDMCRI0c02tTkk97pNai5LR8XF4ejoyOgqqd848YNXFxcABgwYABvvfUWCoVCHWiGh4fj5eXV4G3/9kQEqoLQBrSMjTAb+whmYx+hurhYFbTu3kPJ4cNUJidz44svuPHFF+h27YJpyF9Ba9eubd3t+5aJlTVmnr6MHj2a6soKUs6eIvFUJClnT1Gcl8v58J2cD9+JroEhbr0C6Nq3P27+Aeh1gA/9GkZ97dHztGiWRQfkcjmdO3emc+fODB06lNLSUpKTk9WBa3FxMQkJCSQkJACqvMMuXbrQpUsX3NzcMOxAr5vQ+lxdXYmMjCQ1NRVjY2MsLS1b7drBwcH06NGDZ555hhUrVlBVVcUrr7zC0KFDCQgIULcbNmwYq1evZsKECYBqJTgfHx9+/PFHjRqmt7KwsMDKyoq1a9fSqVMn0tPTWbhwoUYbW1tbDAwM2L17N46Ojujr6992TpCnpyePPfYYr776KmvXrsXU1JRFixbh7e1NUFAQAE8//TRLly5l2rRpvPHGG1y6dImVK1eyfPny5ni5WsVdBar5+fn88ssvJCUlsWDBAiwtLTlz5gx2dnZ07nx/5XYJQkvTMjbGbOxYzMaOpbqoSBW07tpNyZEjVCYmcSPxv9z4739VQWvoKFXQWisvS2he+kbG+Dw0DJ+HhlGlUJBx6bwqr/V0JCX5ecQdO0zcscPItbRx7tGTrgH96RIQiLFF632Y3i1tM70WWRXL0NCQ7t270717d5RKJdnZ2SQlJZGUlER6ejoFBQWcOXOGM2fOIJPJcHBwUAeujo6OaGlpNXufhI7rtddeY9KkSfj6+lJWVkZKSsqdD2omMpmMP/74g1mzZjFkyBDkcjmhoaGsXr1ao93QoUNZsWKFRi7qsGHDOH/+fIP5qXK5nC1btjB79my6d++Ol5cXq1at0jhGW1ubVatW8e6777J48WIGDx5MRETEbc+3YcMG5s6dy5gxY5DL5QwdOpTdu3erR0/NzMzYu3cvM2bMoE+fPlhbW7N48eKOU0OVu1hC9cKFCwQHB2NmZkZqaipxcXG4u7vz9ttvk56ezoYNG1qqrx2WWJlKuBvVRUUUHzigmoh19CgoFOp9NSOtJiEjRU5rM2jMylSSUklmYjyJp1R5rXnXrmjs79TViy59VSkCVp2dbnuOB1FlZSVpaWnqwPX69esa+/X09HB1dcXd3R03NzdsbGzu6/ezWJlKEFSadQnV2oKDg+nduzcff/wxJiYmnD9/Hnd3d44dO8bTTz9921pnDzoRqAr3qrqwkKIDByjatZviY8c0g1Z3d9WKWCGh6Hm2XdCaVZJFemE6zqbOHa4ywd0soXrzagZJpyJJjDpOZkKcxj6LTp1Vk7H69qdTVy9kcjnFeeXk55RhbmvwwK2UVVtBQYE6aE1OTqbslrqexsbG6qDV3d39viuDKAJVQVBpsUDVzMyMM2fO0KVLF41ANS0tDS8vL8rLy++58/cbEagKzam6sPDvnNYjR5BqB62urpiEhtxVndZ7sTVha53FCMZ7jG+VazeHuwlUayvOyyX59EkSo46Tfuk81VVV6n2GZuZYOfYgO80KmbYzcrk2w571xneQQ3M+hQ6pZlJWcnIyycnJpKen15lAYmlpibu7O+7u7ri6unb4/FYRqAqCSmPfo03OUdXT06OwsLDO9vj4eHWRWkEQWo6WqSlmjz2G2WOPqdIDIiJUQeuff6pKXv1Vp1XHxVk1ESs0BD0fnxYLWrNKstRBKoBSUrL0+FIGOgzscCOrd8vYwhK/4FD8gkOpKC0l9fxpEqNOkHL2FKUF+ZQW/PlXSx3kOi7s+/oS1o5PYOty/70+VQUVjZ6wVXtS1uDBg1EoFGRkZJCSkkJycjLXrl0jNzeX3NxcTp06BahK8tSMuDo7O9e7ao8gCPeHJo+ovvDCC9y8eZOffvoJS0tLLly4gJaWFo8//jhDhgxhxYoVLdTVjkuMqAqtobq4mOKIQxTt2U3x4T+RKirU+3ScnTENGYlJSCj63XybNWg9mXmSaXun1dn+Tcg39LXv22zXaUn3OqJan+oqBWf3HOfIj3uorkwCqfjvnTIZnbp44t6nH+69+2Lj4tbhczNrL9NaUwLLqO/dB+NlZWWkpaWRnJxMSkpKnfxWLS0tHB0d1SOuDg4O7X5ilhhRFQSVFrv1X1BQwIQJEzh16hRFRUU4ODiQlZXFgAED2LlzJ0ZGRvfc+fuNCFSF1lZdXELJ4UMU7t5D8eHDSLVScnQcHTEJGYlpaCj63bvfc3CUVZJFyK8h6hFVALlMzp6wPR1mRLWlAlWA4rxyNrx5DKVSQqrOQalIplqRjFStWYTbxNoG99796NK7L07d/NDuYCOFVQUVZC07WWdRAfuF/Zqt0kBhYSEpKSnqEddb7+7p6uri6uqqzm+1tbVtd8G/CFQFQaXFAtUaR44c4cKFCxQXF9O7d2+Cg4PvurONkZuby6xZs9i2bRtyuZywsDBWrlypsVTardauXcvmzZs5c+YMRUVF5OXlaSzzl5qaynvvvceBAwfIysrCwcGBZ599lrfeekt9Oyk1NbXOOrkAx48fp3///o3quwhUhbakLCmh+PBhCvfspfjQIaRak1d0HBwwCQnBNDQEfT+/u/5Qf9BzVO8k+ug1IjbFIilBJodhz3jj7KNH8tkokk6fJP3ieaoqa42A6+nj4uePe2/VaKuRefsvzF2elM+NdRfrbLd+sQf6Xcyb/XqSJJGbm6sebU1JSakzMcvIyEgdtLq5ubWLAuciUBUElRYPVFvbqFGjyMzM5H//+x8KhYIpU6bQt29fNm/eXO8xK1asUE/uWrRoUZ1Adffu3fz444889dRTdO3alUuXLvHiiy/y3HPP8emnnwJ/B6r79u2jW7du6mOtrKwa/YEmAlWhvVCWllJ8+E+K9u6hKOIQUmmpep92p06YjhyJSWgIBj17NnnZ0KySLDKKMnAyceowI6k1WjpQBdXIakFOGWa3mfWvqKwg49IFkk5HknwmiuLcmxr77bt64t67L136BLbbFIG7HVFtSk5rQ5RKJVlZWerR1vT0dBS1JhqCqti6u7s7Li4uuLq6tsnfYxGoCoJKiwWqq1atuv2JZDL09fXp2rUrQ4YMadY8oZiYGHx9fYmKilKvDLF7925Gjx7NlStXcHBoePZsREQEQUFBdQLV2/nkk0/48ssvSU5OBv4OVM+ePYu/v/9d9V8EqkJ7pCwro/jPPynas5figwdR1g5a7e0xGTkC09BQDPz9O/xa93fSGoFqY0mSRE5qMsmnT5J0+iTZyQka+42trOnSuy/uffrh1M0PHd3mL+B/t5qao9rcOa21VVVVceXKFfWI65UrV7j1487S0hJXV1d14NoapbBEoCoIKi0263/58uVcv36d0tJS9W2UvLw8DA0NMTY2JicnB3d3dw4ePKheJ/deHT9+HHNzc43ly4KDg5HL5URGRjJu3LhmuQ6ocnBvt1zbo48+Snl5OZ6enrz++us8+uij9Z6joqKCiloTWWryqBQKRZ1v+ILQZrS1MQgKwiAoCOvyckqPHqM4PJySiAiqsrLI27CRvA0b0bK1xTg4GOMRwej36oWsnU5WyS7NJr0oHWcTZ+wM7Zp0bM3vZXv5/bR0dMbS0ZmAxyZQkpdLyrnTpJ47Rfql8xTfvMH58F2cD9+Ftp4eTt38cOvVFzf/Phi18epYuv5WWLsZU51bjpalPlpmevW+ptUFFX8HqQAS5G1NQMvNGK1mymmtXVGgoqKC1NgkUhNSuJKbSfaNHHVFgTNnzgCqNdydnZ1xcXHBxcWlRQLX9vIeE4SOosmB6gcffMDatWv56quv6PLXMo6JiYn885//5KWXXmLQoEE8+eSTzJ07l19++aVZOpmVlYWtra1mx7W1sbS0JCsrq1muAarnsXr1avVtf1AVn/7ss88YNGgQcrmcX3/9lccff5zff/+93mD1ww8/ZOnSpXW27927t8PXABTuc0MGIxvQH8OEBEwuXsTocjTk5FCweTMFmzdTZWJCcffuFPXoTpmbG7STkdZTFaf4o+wPJCRkyHjM4DEC9ALufOAtwsPDW6B3zUPu1RPnLt0oy8mk5GoapVfTqSotIeVMFClnogDQs7TGqLMLhp2d0bOwapcpAjWMC7Txkm4ZUZTg6M5DFJtV3f6ge2CVrYtLshH+WNETS5JcHUk3zKO4uJji4mJKS0vJz88nPz+fCxcuAKrJWcbGxuqHrq7uPb+mpbXuXAh3pyl3SYWOr8m3/rt06cKvv/5a5zb42bNnCQsLIzk5mWPHjhEWFkZmZmaD51q4cCEfffRRg21iYmLYunUr3333HXFxmqu/2NrasnTpUl5++eUGz9GYN/XVq1cZOnQow4YN46uvvmrwfM8//zwpKSn8+eeft91/uxFVJycnbty4cU+3euQH30Myd0XyHgsGt38egtCcpMpKSo8fp3hvOCUHD6IsKlLv07Kywih4OMYjRmDQpw8y7SZ/720W2aXZjPl9DEo0qw7seGxHo0dWFQoF4eHhjBgxos1v/TeWJEncSE8l5ewpUs5GkZ2kmSJgZGGJm38Abr0D/qoi0H5SBEA1onrjs7N1clqt5/dqthHVplyroqKCK1eukJaWRnp6OteuXauTKmBqakpwcDA+Pj533ZfCwkKsra3Frf970BaBalOu+dNPP/HBBx+o68vPnDmTBQsW1DnfvHnzuHz5Mk5OTrz99ttMnjy55Z5AO9Rit/4zMzOpqqr7bbeqqko9uung4EBRrQ+0+syfP/+O/2Pc3d2xt7cnJyenzvVyc3Oxt7/3fKZr164RFBTEwIEDWbt27R3bBwYGNjjyoqenh55e3T+0Ojo6d/8hWHwdjq8GSQm7X4euwdBjAniGgl79lQ8E4Z7o6KAbHIx5cDBSZSUlx49TuGcvRfv3U33zJoU//kThjz+hZWmJyYgRmIaMxLBfv1YNWq+VXtMIUkG16EBmWSaOZo5NOtc9/Y62AYeunjh09WTQP56mJD+P5LNRJJ8+SdqFc5Tk5XLp4F4uHdyLtq4ezt396OzTG4tO3ejU1aHNl3HVsdbBYrxHnRxVfevm/3tWXVCiGaSC6ueCKnT+up6Ojg7e3t54e3sDqsA1IyOD1NRU0tLSuHr1KoWFhehxb++RjvT+Eppu165dPPPMM6xevZqRI0cSExPDiy++iIGBATNnzgQgJSWFMWPGMH36dDZt2sT+/ft54YUX6NSpEyEhIW38DNqfJt+3CwoK4p///Cdnz55Vbzt79iwvv/wyDz/8MAAXL168bUmnW9nY2Kj/MNT30NXVZcCAAeTn53P69Gn1sQcOHECpVBIYGNjUp6Dh6tWrDBs2jD59+vDtt98ib8StzHPnztGpU6d7um6TybXg4bfBthsoFRC/C36dBp96wM9TIHYHVFXc+TyCcJdkuroYDx2Kwwfv4/nnYZzWrcVsQhhaZmZU5+aS/+OPpE+dRsJDg7n21lsUHzqEsrKyxfvlbOqMXKb5eyuXyXEyaZ4c+Y7CyNyCHkEjeey1t3nlq82MX7SUniPHYGJlQ1VlBclnovhz0//4v09ns3bGy/z2yX+5EnMJ5S1LlrZqn/vaY7+wH9Yv9sB+Yb9mm0h1K21rA7j1jr3sr+310NPTo2vXrgQHB/Ok3xieKxvCqMpe6P6QTUlU86WcdRTFeeVcicujOK/ll0mvqKhg9uzZ2Nraoq+vz0MPPURUVFSddkePHsXPzw99fX369+/PpUuX1PvS0tIYO3YsFhYWGBkZ0a1bN3bu3FnvNTdu3EhAQAAmJibY29vz9NNPqwfIUlNTCQoKAlSVI2QyWb2DbBs3buTxxx9n+vTpuLu7M2bMGBYtWsRHH32kHqFfs2YNbm5ufPbZZ/j4+DBz5kwmTJjA8uXL6+3f+vXrMTc3Z/v27Xh5eWFoaMiECRMoLS3lu+++w9XVFQsLC2bPnl1nGeKOrsnDHl9//TXPPfccffr0UX8zrKqqYvjw4Xz99dfA33mdzcXHx4fQ0FBefPFF1qxZg0KhYObMmTz55JPqGf9Xr15l+PDhbNiwgX79+gGq3NasrCwSExMBVQBtYmKCs7MzlpaW6iDVxcWFTz/9VGPVk5qR2u+++w5dXV169eoFwNatW/nmm2/umB7Q7AwtYfB81SMnBi79Chd/gbwUuLxV9dA3A5+x0H0CuA1RBbeC0AJkuroYDx6M8eDBSEuWUBJ5kqI9eyjat4/qvDwKft1Kwa9bkRsbYxwUhMnIERg/9BByg/oDg7tlb2TPkgFL6tRx7WglspqTtq4ubv59cPPvgzR1OumX4vj9s9+orkxGqs5Eqr5O8qldJJ/ahZ6hES49/HHrFYCrfx+MW3lClraZXrMtCNDQNW43etuY61b9NelLR9Kis6R6bfK2JqDnadHi/W4voo9eI+L7WCQJZDIY9qw3voMarrZzL15//XV+/fVXvvvuO1xcXPj4448JCQkhMTFRY7LzggULWLlyJfb29rz55puMHTuW+Ph4dHR0mDFjBpWVlRw+fBgjIyOio6MbrLuuUCh477338PLyIicnh3nz5jF58mR27tyJk5MTv/76K2FhYcTFxWFqaopBPX/LKioq6sxFMTAwUKeVuLq6cvz48Tq150NCQpgzZ06Dr0tpaSmrVq1iy5YtFBUVMX78eMaNG4e5uTk7d+4kOTmZsLAwBg0axMSJE+/wKnccTQ5U7e3tCQ8PJzY2lvj4eAC8vLzw8vJSt6n55tGcNm3axMyZMxk+fLi64H/tUlkKhYK4uDiNRPU1a9ZoTGoaMmQIAN9++y2TJ08mPDycxMREEhMTcXTUvEVYOzfpvffeIy0tDW1tbby9vfnxxx+ZMGFCsz/HRrP1UY2uBr0F187AxV9VgWpRJpz9XvUwsoVuj6uCVqd+qr8ugtACZDo6GD80COOHBmG/ZDGlp05RtHcvReH7qLp+ncJt2yjctg2ZgQHGQ4aogtahw9Aybr5V7MZ7jGegw8AOW8e1JclkMrR07dDWD0RbPxBJWYpSkUp1VSra2leoKC0mPvIo8ZFHAbBxdcf9r6DVwcMbeTut8tBURn3t0fO0aHLN1qobZbdNG6i6UfZABKrFeeXqIBVAkiBiUyzOvpYtkj5SUlLCl19+yfr16xk1ahQA69atIzw8nK+//loj13PJkiWMGDECUA0qOTo68ttvv/HEE0+Qnp5OWFgYPXr0AFRphA2ZOnWq+t/u7u6sWrWKvn37UlxcjLGxsTpAtrW1bTBHNSQkhLlz5zJ58mSCgoJITExUD9xlZmbi6upKVlYWdnaa+fN2dnYUFhZSVlZWbxCsUCj48ssv1RPZJ0yYwMaNG8nOzsbY2BhfX1+CgoI4ePDggx2o1qidy9MaLC0tGyzu7+rqWifx/Z133uGdd96p95jJkyffMUd20qRJTJo0qSldbT0yGXTuo3qMfA/SjsGlXyD6DyjJgZNrVQ8zZ+g+XpXTatddBK1Ci5Fpa2PUvz9G/ftj9/bblJ07R9GevRSG76XqWqZq1HXPHmS6uhgNGoTJyJGYPByEVjOUAbI3shcBaj3MbQ2QyVRBhkxuiJaeL9oGvjz7XiAluVdIPnuK1HOnyEpO5HpqMtdTk4n87Sf0jIxw6dELt14BqvJXHWCFrIbczeitOm3glolYDaUN3E/yc8q4dcq1pISCnLIWCVSTkpJQKBQMGjRIvU1HR4d+/foRExOj0XbAgAHqf1taWuLl5aVuM3v2bF5++WX27t1LcHAwYWFh+Pn51Xvd06dP884773D+/Hny8vJQKlV57+np6fj6+ja6/y+++CJJSUk88sgjKBQKTE1NefXVV3nnnXcalVrYEENDQ3WQCqrg1tXVVWOk2M7Ors6cno6uyYFqdXU169evZ//+/eTk5Kj/Z9Y4cOBAs3VOaAK5FrgNVj1GfwpJB1VBa+wOKEiHoytUD2svVcDaPQysutzprIJw12RyOYa9e2PYuze2C9+g/NJl1Ujr3r1UpqVRfPAgxQcPkqmtjVFgoCpoDR6OtpVVW3f9vmNsoc+wZ73rLONqamWEqZUXnTy8GPTEM5QW5JN6/oyqbuv5M5QXFxF/4gjxJ44AYOvaBbdefXDzD6CTh9d9M9rakHtJG7gf1P6SU0MmBzPb9h2ov/DCC4SEhLBjxw727t3Lhx9+yGeffcasWbPqtC0pKSEkJISQkBA2bdqEjY0N6enphISEUNnEPHuZTMZHH33EBx98QFZWFjY2Nuzfvx/4e1TX3t6e7OxsjeOys7MbTCmAuhPxZDLZbbfdGpd1dE0OVF999VXWr1/PmDFj6N69e7uu0/fA0tIBz5GqR2UpJOxR5bMmhMONODj4vurRyV8VtHYbD2ad27rXwn1MJpNh0KM7Bj26YzNvLhXxCeqgtSIhgZKjRyk5epSspUsx7NNHFbSOHIGOXdMK9wv18x3kgLOvZb3LuAIYmpnjO+RhfIc8jFJZTVZiAinnTpFy9jTZyQnkpCaRk5r092irX29VmkDP3h1+tLUhd5s2cD+o70tOS1WN6NKlC7q6uhw9ehQXFxdAdcs7KiqqTg7niRMncHZ2BlQLD8XHx2uUDnNycmL69OlMnz6dRYsWsW7dutsGqrGxsdy8eZNly5apFyo6deqURhtdXV2ARk9U0tLSonNn1efqDz/8wIABA7CxsQFUI8G3TuwKDw/XGCEW/tbkQHXLli389NNPjB49uiX6IzQ3XUPoNk71KC9QjbBe/AWSIyDznOqx91/gMlCVHuD7OBhZt22fhfuaTCZD38sTfS9PbGbNpCIlhaK94RTt3Uv55cuURkVRGhVF9vvvY9CzpypoDRmJrmPTSk0JdRlb6Dc6wJDLtXDw9MbB05tBTzyrHm1NPnuKtAtnVaOtx/8k/riqnrStWxdV3dZeAXTy8ER+n03mbI1JX+1VY77kNBcjIyNefvllFixYgKWlJc7Oznz88ceUlpYybdo0jbbvvvsuVlZW2NnZ8dZbb2Ftbc3jjz8OwJw5cxg1ahSenp7k5eVx8ODBeuvfOjs7o6ury+rVq5k+fTqXLl3ivffe02jj4uKCTCZj+/btjB49GgMDg9tOzrpx4wa//PILw4YNo7y8nG+//Zaff/6ZQ4cOqdtMnz6dzz//nNdff52pU6dy4MABfvrpJ3bs2HGPr979qcmBqq6uLl27dm2JvggtTd8M/J9WPYqvQ/TvcGkrpB+DtKOqx87XwX2YaqTVe4zqGEFoQXpubuj98yWs//kSlVeuUhSuClrLzp6l7Px5ys6fJ+eTT9D39f1rpHUkeu53Ln8nNK+6o63xpJw7TcrZU2QnJ5KTkkROShKRv/2IroERjj5+dAkIwLVnL0ytbe98AaFda8qXnHu1bNkylEolzz33HEVFRQQEBLBnzx71su2127366qskJCTg7+/Ptm3bNEY+Z8yYwZUrVzA1NSU0NLTe8k82NjasX7+eN998k1WrVtG7d28+/fRTjdUnO3fuzNKlS1m4cCFTpkzh+eefZ/369bc933fffcdrr72GJEkMGDCAiIgIdTUiADc3N3bs2MHcuXNZuXIljo6OfPXVV6KGaj2avDLVZ599RnJyMp9//rm47d9IhYWFmJmZtfhKJHet4IoqYL30q2qEtYaWHniM+HthAZ32nZMk3F8U2TkU7QunaG84pVFRUCvvSs+jKyYjVCOtep6e9/y3SKFQsHPnTkaPHt1iBdmzSrJIL0zH2dT5vpv0VZKfR+r5M5zfd4TMhAsgadZ0tnBwxNWvFy5+vXDy7Y6uwYO7lHRrfR7czytTCfeHxr5Hmxyojhs3joMHD2JpaUm3bt3q/FHfunXr3fX4PtZcf5gyC8pIuVGCm7URncxaKGi8kagKWC/9Ajfi/96uawxeo1VBq3sQaOu2zPUF4TaqcnMp2r+for3hlJw4AQqFep+OizOmf4206t9l3nxLB6pbE7bWqfM63mN8s1+nLRXnlbPhzWMolUqk6iyUijSUVWlIyiykWl8y5FpaOHj64OLXC1e/Xti6d7nv0gQaIgJVQVBpsUB1ypQpDe7/9ttvm3K6B0Jz/GH6MSqdRVsvopRALoMPx/dgYl/nZu5pLZIE2ZdU+ayXtqoqB9QwsACfR1VBq8sgsbCA0KqqCwspPniQwr3hlPz5J1KtWbnaDp0wGR6MSXAwhn16N3op15YMVLNKsgj5NQSlVCtYk8nZE7bnvhpZvRKXxx/Lz9bZPvplL6oq00k7f5bUC2coyNZc1Unf2ATnHv7qEVdTa5vW6nKbEIGqIKg09j3a5BxVEYi2vsyCMnWQCqCU4M2tlxjiadNyI6syGdj3UD2C34ErUX+NtG5V1Wg9853qYWyvmqjVY4KqnqtIBxFamJapKWaPPYbZY49RXVxCyeFDFO4Np/jwYaquZZK3cSN5GzeiZW6uWhUreDhGgwYhb6MP6/TCdI0gFUApKckoyrivAtX6yhjZOFthbNEZj76qGc35WZmkXTxL6vmzpF86X2dSlqWDIy49e+Hq1xtH3+7o6ouUI0F4kN11wX+h9aTcKFEHqTWqJYnUG6UtF6jWJpOpVrdy6gchH0Dqn6qgNfr/oDgLIr9UPcxdVPVZe0wAu24t3y/hgadlbITp6NGYjh6NsryckqNHKdq3n+IDB6jOz6fgt98o+O031apYDz2ESfBwjIcNa5YFBhrL2dQZuUxeZ0TVycSp1frQGhpbxsjcvhPm9p3oOWI0yupqMhPjSbtwhtQLZ8lKiCf32hVyr13h7K5tyLW06eylShNw8euFrZv7A5UmIAjCXdz6B/jll1/46aefSE9Pr1MM98yZM83WufvFvd7qySwoY9CyAxrBqpZMxpGFQXcdqDZLvmtVJSTtVwWtsTtBUfL3Phsf6BGmClwtG166ThCam1RVRenpMxTt20fR/n1UXcv8e6e2NoZ9AzAJVqUI6NjZiRzVZlScV37XZYzKi4vJuHyB1AtnSD1/lsLrmkXRdQ2M6OzdHbdevXDu3hNLB8cON6lX3PoXBJUWy1FdtWoVb731FpMnT2bt2rVMmTKFpKQkoqKimDFjBu+///49d/5+01w5qm9uvUS1JKElk/HB+O53naPaIvmulSUQv1uVGpCwF6prfYFx6K0KWLuPB1OHe7uOIDSRJEmUR0dTvH8/ReH7qEhI0Niv36MHhg8HcVpbmxGTJ7forP+MogycTJzuq1v+LUWSJPKzM0k7f5aLEcfISY4BNAdGjC0sce7eE6fuPXHu3vOO+a3FeeXk55Rh3sK1QBsiAlVBUGmxQNXb25slS5bw1FNPYWJiwvnz53F3d2fx4sXk5uby+eef33Pn7zfNOes/9UYprtaG9zSS2tyjs3WU5UPsdtVErJRDqoWhAZCJhQWENleZlkbRvv0U7dtH2blzGkmVOq6umI4YgUnwcPR79EB2j2tzC/eubjWBDJRV6cjIorpKodHWopODKnDt1hOnbj0wNP07xSP66DUivo9FklTZTMOe9cZ3UOO/ODdXkCsCVUFQabFA1dDQkJiYGFxcXLC1tSU8PJyePXuSkJBA//79uXnz5j13/n7TnuqoHku6wdPrIuts/+HF/gzo0gJrrKsXFvgV0o//vV2mpVpYoHsY+DwiFhYQ2kTV9esUHThI4d69lJw4gazW8ojatrYYD38Yk+BgjPr2RaYrSrK1hfqqCTwysxtImaRfOk/6pfNkJyUi3TJpzcbVHefuPbF19eHgpkLg7/+HMjk8//7ARgWd9xrk1iYCVUFQabFZ//b29uTm5uLi4oKzszMnTpygZ8+epKSkcBfprkIrc7M2Qi6jzoiqq3ULFeA2toF+L6oeBVfg8m+qkdbMc6r81qT9sH0OeIxUjbR6hoKuUcv0RRBuoW1jg8XEJzAeP47dW7cyyMCA0oMHKTl0mKqcHPJ/2EL+D1uQm5hgPGwYJsHBGD80CLmReI+2lvqqCVh1NsPYwg6XHv4AlJcUcyXmMumXzpF+8Tw3r6RzPTWZ66nJwG+AHJmWPXIdZ+Tajsi1O1GQU3bHQLU4r1wdpIKqHxGbYnH2tWyz9AFBeJA0OVB9+OGH+b//+z969erFlClTmDt3Lr/88gunTp1i/Pj7c3LA/aSTmQEfju9RJ9+1VaoHmDnCwFmqx82kv1bD+gWux6pSBWK3g44ReI1SjbR2HQ7aD+ba2kLrU+rrYzJqFJaPPoqyspLS48dVKQIHDlB98yaF27ZRuG0bMj09jAYOVAWtDwehfcuyjkLzamw1AX0jY7oGBNI1IBBQrZaVfvkC6RfPk3bhHEU3c5Cqr1FdfQ3VuLkWERv24dqzJ06+Pejk4YWOXt3AMz+njFvHYCQljQpyheYTERFBUFAQeXl5mJub3/V5SktLee655wgPD6eoqIi8vDz8/f2ZM2cOc+bMabb+Cs2nybf+lUolSqUS7b8KaW/ZsoVjx47h4eHBP//5T/U6u8Lf2tOt/xrNke/aLCQJcqL/qtH6K+Sl/r1P3wx8xqqCVtchoCWqqQkto6FZ/1J1NWXnzqnzWhUZGX/vlMsx7NNHVfZqeDC6jp1buecPjnupJgBwaucFjv1ykOrKdJRVV0Aq0dgv19Kmk4cnTr49cPTpgYOXNzp6+uoc2VtHdBubNnArcev/zoYNG4a/vz8rVqxQb6usrCQ3Nxc7O7t7qvTw5ZdfsmTJEg4cOIC1tTV2dna4ubm1q0A1NTUVNzc3zp49i7+/f4Nt9+/fz7/+9S8uXryIkZERkyZN4v3331fHaAAXLlxgxowZREVFYWNjw6xZs3j99ddb+FncWYvd+pfL5chrTTB48sknefLJJ++ul0Kb6WRm0LYBag2ZTFVz1a4bPPwvuHpGFbBe3gpFmXD2e9XDyEY1Aat7GDgFgpjkIrQSmZYWhn36YNinD7avL6AiPoGifeEU7d9PRXQMpVFRlEZFkf3hMvR8fTAZPhyT4BHoeXp0uNJJ7Zmxhf49jWAGjPbDe4AnBTllmNroU1WZR8bli1yJvkhG9EWKc29yNTaaq7HRwI/ItbSx7+qJk293ug9x4OLhapB06h3RFVqWrq4u9vb3Xi0jKSkJHx8funfv3gy9alvnz59n9OjRvPXWW2zYsIGrV68yffp0qqur+fTTTwHVF6ORI0cSHBzMmjVruHjxIlOnTsXc3JyXXnqpjZ9BI0l3IS8vT9qzZ4+0ceNG6bvvvtN4CHUVFBRIgFRQUNDWXek4qqslKeWIJG2bI0nLXCVpienfj898JWnPW5J09YwkKZVt3VPhPlBZWSn9/vvvUmVlZZOOq8i4It1cv15KffY5KdrHV4r28lY/EoJHSFnLPpJKTp2SlFVVLdTzpskszpQir0VKmcWZbd2VdkWpVEp5mdekC/v3SDtWfyqteXmS9OkTYzQe/3nyUenb+a9Klw8dvadrtdbnQVlZmRQdHS2VlZWptymVSqmyrKxNHspG/q2eNGmSBGg8UlJSpIMHD0qAlJeXJ0mSJH377beSmZmZtG3bNsnT01MyMDCQwsLCpJKSEmn9+vWSi4uLZG5uLs2aNUuq+uv3b+jQoRrnHTp0qCRJkuTi4iItX75c3Ye0tDTp0UcflYyMjCQTExPpH//4h5SVlSVJkiTl5+dLcrlcioqKkiRJkqqrqyULCwspMDBQffzGjRslR0fHep/jrl27pEGDBklmZmaSpaWlNGbMGCkxMVG9/9bnX9PPWy1atEgKCAjQ2PZ///d/kr6+vlRYWChJkiR98cUXkoWFhVRRUaFu88Ybb0heXl719q/mtd69e7fk7+8v6evrS0FBQVJ2dra0c+dOydvbWzIxMZGeeuopqaSkpN7z3Mnt3qO30+QR1W3btvHMM89QXFyMqampxoiBTCbj+eefv7fIWRBANWLqOkj1GPWxqszVxV9VeayFV+DYatXD0v2vGq0TwNa7rXstPGB0HTtjOWkSlpMmUZWbS/HBCIr27aPk6FEUGRnkfvstud9+i5aVFSYPP4xJ8HAM+/dHrtf6udcP0qIDTSWTydQrZvV4eCSSJFGQk01G9AWuXL5IRvQlim5e52ZGInpGHTcFqaqiglWTJrTJtWd/9ws6jUhBWLlyJfHx8XTv3p13330XABsbG1JTU+u0LS0tZdWqVWzZsoWioiLGjx/PuHHjMDc3Z+fOnSQnJxMWFsagQYOYOHEiW7duZeHChVy6dImtW7feNlVRqVTy2GOPYWxszKFDh6iqqmLGjBlMnDiRiIgIzMzM8Pf3JyIigoCAAC5evIhMJuPs2bMUFxerjxs6dGi9z7GkpIR58+bh5+dHcXExixcvZty4cZw7dw65XM7Jkyfp168f+/bto1u3bvWmVFZUVNS5ZW5gYEB5eTmnT59m2LBhHD9+nCFDhmicIyQkhI8++oi8vDwsGsixf+edd/j8888xNDTkiSee4IknnkBPT4/NmzdTXFzMuHHjWL16NW+88Ua952gOTb5/On/+fKZOnUpxcTH5+fnk5eWpH7m5uS3RRwByc3N55plnMDU1xdzcnGnTplFcXNzgMWvXrmXYsGHqgDo/P79OG1dXV2QymcZj2bJlGm0uXLjA4MGD0dfXx8nJiY8//rg5n5pwJ1o60DUYxn0JryXAxE3QbTxoG0BuMhz+BL4IhC8GwuFPITelrXssPIC0LS0xDxuP05df4Hn8GJ1XrsR07FjkJiZU37xJ/s8/k/HP6cT3H0DGzJnk//orVTdutErfskqy1EEqgFJSsvT4UrJKslrl+h2NTCbD3M6eHkEjGTVzPi/+9xteWP0VIdNfxdFHLA/dkszMzNDV1cXQ0BB7e3vs7e3R0rr9srkKhYIvv/ySXr16MWTIECZMmMCRI0f4+uuv8fX15ZFHHiEoKIiDBw8CYGlpiaGhoTqNwNLSss459+/fz8WLF9m8eTN9+vQhMDCQDRs2cOjQIaKiogBVDm1ERASgmuQ1YsQIfHx8OHLkiHpbQ4FqWFgY48ePp2vXrvj7+/PNN99w8eJFoqOjAVVgDmBlZVVvP0EVcB47dowffviB6upqrl69qg7uMzNVq/FlZWVhZ2encVzNz1lZDf/+//vf/2bQoEH06tWLadOmcejQIfXrPXjwYCZMmKB+bVtSk78aXr16ldmzZ2No2ELljOrxzDPPkJmZSXh4OAqFgilTpvDSSy+xefPmeo8pLS0lNDSU0NBQFi1aVG+7d999lxdffFH9s4mJifrf90V+RxtrluVaa+joq+qu+jwCFcV/rYb1KySEQ85lOHAZDrwHnfuoRlp9HwczMcFFaF1yIyNMQ0ZiGjISqbKSkqgoivbto/jAQaqysynet5/ifftBJkPfrwcmQQ9jHBTUYnmt6YXp6iC1hlJSklGUIVbJagSZTIaZrT1mth37tdLW02P2d7+02bWbm6GhIV26dFH/bGdnh6urK8bGxhrbcnJyGn3OmJgYnJyccHJyUm/z9fXF3NycmJgY+vbty9ChQ/n666+prq7m0KFDjBw5Ent7eyIiIvDz8yMxMZFhw4bVe42EhAQWL15MZGQkN27cQKlU/W6mp6c3KXd25MiRfPLJJ0yfPp3nnnsOPT09/vWvf/Hnn39qzCW6W35+fup/29nZYWhoiLu7u8a2kydP3vN17qTJgWpISAinTp3S6GxLi4mJYffu3URFRREQEADA6tWrGT16NJ9++ikODrcvvFwzg6/mm099TExM6k3S3rRpE5WVlXzzzTfo6urSrVs3zp07x3/+8x8RqDZCiyzXWkPPGHpMUD3K8iBmuypoTTkEV0+rHnveBOdaq2EZN7zEoiA0N5muLsaDBmE8aBDS4sWq5VwPRlB88CDlly9Tfv4C5ecvcH3FCnQcHDAOCsI4KAjDfn2RN1MVFWdTZ+QyuUawKpfJcTJxauAo4X4jk8kadfu9o7i1QodMJrvttppAsLkMGTKEoqIizpw5w+HDh/nggw+wt7dn2bJl9OzZEwcHBzw8POo9fuzYsbi4uLBu3TocHBxQKpV0796dysrKeo+pz7x585g7dy6ZmZlYWFiQmprKokWL1DGavb092dnZGsfU/HynyWm1X8vWem1vp1GB6v/93/+p/z1mzBgWLFhAdHQ0PXr0qNPxRx99tHl7CBw/fhxzc3N1kAoQHByMXC4nMjKScePG3dP5ly1bxnvvvYezszNPP/00c+fOVZd2uJv8joqKCioqKtQ/FxYWAqrbFAqFok77+1VmQbk6SAXVIgOLtl5kgJsFncya+Y+ltjH0eFL1KM5BHrsdWfRW5BknIP0YpB9D2vU6kutglL7jkLzGgIGofymo1Pxetsbvp7anJ+aenpj/8yWqsrMpOfwnJRERlEVGorh2jbxNm8jbtAmZkRGGAwdiNGwoRoMHo3UP9VqtdK14u9/b/Pvkv9U5qm/3exsrXasH6m9SeyBe7zvT1dWlutYqca3Jx8eHjIwMMjIy1KOq0dHR5Ofn4+vrC4C5uTl+fn58/vnn6Ojo4O3tja2tLRMnTmT79u0N3va/efMmcXFxrFu3jsGDBwOoUwZq1MQbjX0NZDKZesDuhx9+wMnJid69ewMwYMAA3nrrLRQKhTpeCw8Px8vLq8H81PakUYHq448/XmdbTR5EbTKZrEXeXFlZWdja2mps09bWxtLS8o45Fncye/ZsevfujaWlJceOHWPRokVkZmbyn//8R31tNzc3jWNq53fc7n/0hx9+yNKlS+ts37t3b6unTLSlhAIZSkkzt0gpwU87D+Jh1tKrmNmD9Svom06kc/5JOudFYlGajCzlEPKUQyh3zifHpAdXLQLJMutNlVY7KNUltLnw8PDWv6iRIYwZjWxEMIaJiRhHx2AUG4t2UREl4eGUhIcjyWSUuzhT7ONDiY8vlbY2qtJuTaCLLvNN5nOz+iZWWlboxumyM25nCz0poT6lpaVt3YV2z9XVlcjISFJTUzE2Nq43R7MlBAcH06NHD5555hlWrFhBVVUVr7zyCkOHDtUYLBs2bBirV69mwgTV5DRLS0t8fHz48ccf+e9//1vv+S0sLLCysmLt2rV06tSJ9PR0Fi5cqNHG1tYWAwMDdu/ejaOjI/r6+piZ3X6Z8U8++YTQ0FDkcjlbt25l2bJl/PTTT+q83qeffpqlS5cybdo03njjDS5dusTKlStZvnz5vb5UraZRgWpLDe0uXLiQjz76qME2MTExLXLtGvPmzVP/28/PD11dXf75z3/y4YcfoneXOTWLFi3SOG9hYSFOTk6MHDmy3RT8bw2ZBeV8EXNYY7lWuQyeGB3U/COqDXoOAEVeKvLo35FH/4Y85zL2heewLzyHpK2P1HWEaqS1azDoPDhfJgQVhUJBeHg4I0aMqHOXqC1ISiUVly9TEnGIkkOHqIyLwyA1DYPUNGx27UbHyQnDoUMxChqGQa9eyNpBn4XGqbnDJtTvtddeY9KkSfj6+lJWVkZKSutNkJXJZPzxxx/MmjWLIUOGIJfLCQ0NZfXq1Rrthg4dyooVKzRyUYcNG8b58+cbzE+Vy+Vs2bKF2bNn0717d7y8vFi1apXGMdra2qxatYp3332XxYsXM3jw4HpTGHft2sX7779PRUUFPXv25I8//mDUqFHq/WZmZuzdu5cZM2bQp08frK2tWbx4cYdKXWzyylTN6fr169y8ebPBNu7u7nz//ffMnz+fvLw89faqqir09fX5+eef73jrvylLr12+fJnu3f+/vTuPi7LaHzj+mWHfEUGWRHFFcF8JTXFBcV8rK28uuZQ3ta6Waff2c6m0ui1mdbVNM8urNzMzRZMQXFKRNHPfEERlk5AdWZ/fH4+MTgiyDQzyfb9e55XzzHnOc2YamC/nOed72nHu3Dm8vb2ZOHEi6enpbN26VVcnLCyM/v37k5KSUq6hc2PcmaqmbIqMLbFda7XNUa2KG+fvbOH656U7x81soM1QdSFWi/6yhWs9UdbOVMYgPy6OjPBwMsPCyT58GOWu28daOztse/dW57b26Y1JKSMvwjjIzlRCqAy2M9WcOXNo2bIlc+bM0Tv+8ccfc+nSJb0tz+7HxcVFl4ahLP7+/qSmpnL06FG6du0KwJ49eygqKsLPz69C/b+f4jxmxVMNHoT5HbVpfPcm9GntYhzbtd7NxRv6LYS+CyDh5O0tXLdAWiyc/E4txVu4th0LzQJkC1dRa8w8PHB66imcnnqKwswssg7+qi7I2ruXwpQU0oODSQ8Ohtu7aNn264ddv76Ye3nVeF8TshKITY+liX0TySgghKiyCo+oPvTQQ2zbtk0XMBY7duwYI0eO5Nq1a9XawWJDhgwhMTGR1atX69JTdevWTZee6vr16wwYMICvv/6aHj16AOoc0oSEBH777TemT5/Ovn37sLOzo0mTJjg5OXHo0CEiIiLo168fdnZ2HDp0iH/84x8MGTKEdevWAZCWloa3tzeDBg3Sze945pln+OCDD8o9dF6fR1RrQ6XTYSmKmimgOGjNvGv+s7Uz+I5SR1qb+MsWrg8YYx9RLY1SWEjOiRO3swjsIffiJb3nzZs10wWtVp07ozE17B9bsqnA/cmIqhCq8n5GKxyoWlpacurUKVq2bKl3/NKlS7Rr145bt25Vrsf3kZKSwqxZs/jpp5/QarWMGzeOlStX6vKlxcTE0KxZM8LCwnRzPRYvXnzPRU1r165l8uTJHDt2jL///e+cO3eO3NxcmjVrxtNPP83cuXP15qeeOHGC559/nsjISJydnZk9e3aFdmKQQLXmVFs6rKJCiD2kBq1nfoTsu6ao2LlD2zHqSGvjbhVe1CKMT10NVP8q79o1MveEkRkeRlbkb3DXFAETBwdsAvpg168fNr16YVLNv4sSshII+j6oRAqsn8f9LCOrd5FAVQiVwQLVdu3a8dxzzzFr1iy94x999BGrVq3S7awg7pBAtWbEp+XQ6609eou3TDQaDizoV7UpB4UFam7WU1vg7E+Qm3bnOYcm0Ha0mqfVvZMErXXUgxKo3q0wI4OsAwfICAsja+8+CtPu+tyamGDVuRO2fQKwDeiDRevWVd5o4Ej8Eabunlri+JqgNXR3616ltuHBmVIggaoQKoPNUZ07dy6zZs3ixo0b9O/fH1C3HHvvvfcqND9ViOoWnZylF6QCFCoKMcnZVQtUTUyh5QC1DH8fLoXC6S1wfqc6p/XgSrU4Nb8z0uraVoJWUatM7OywHzIE+yFDUAoKyDl+nIywMDLD95IXFUXOb0fJ+e0oN95/H1NXV2z79Mamd29sevbE5K6dfcrLkJsKyJQCIeqvSq36X7VqFW+++SZxcXGAmvNs8eLFTJw4sdo7+CCQEdWaYbAR1dLk58DF3epI64WfoSDnznPOrdWAtd1YdeGWMGoP4ohqWfKuXSdr/z4y9+4jKyICJeeuz66pqbogq09vbPv0wbxly3KPthoioHzQphTIiKoQKoPd+r/bjRs3sLKy0ttXV5QkgWrNqbV0WLmZcGGXGrReCoHCu7bCa9QW2t0eaW3YovQ2RK2pb4Hq3Ypyc8mO/I3MfXvJ2ruPvCtX9J439XDHtncfbAP6YOPnh9bGpsz2ErISuJpxFU87z2oJJKtzSoExTB+QQFUIVY0EqqJ8JFCtWfFpObWbDutWGpwLVqcHRO2BooI7z7l3VAPWtmOgQdOa75u4p/ocqP5V3pUrZO7bT+a+fWQfOYJy13bQGjMzrLt3w6ZPH3W0tVmzKs9tvZ/qGlE1lukDEqgKoZJA1YhIoFqPZafAue3qSGv0PlDu2mK4cffbQetosPeotS4KCVRLU5STQ/aRI2Tu3Ufmvn3k/yX9oFnjxtj26YNNn97qaKuVYf4wrGqQaUzTByRQrbqKbOIjjFd5P6OSDFKICopPy+FgVDLxaTn3r2ztBF0mwsSt8NIFGPY+ePUGNHAtEn5eCO/7wpohcORzyEwydPeFKDetlRW2AQG4/d9rtAjZTfPgYBoteAWbnj3RmJmRf+0aNzds4NpzM7ng9zCx02eQ8vX6EtMHqmpsq7H8PO5n1gSt4edxP1d4JDQ2PVYvSAUoUoq4mnG1OrspHmDh4eFoNBpSU1PLrHfr1i0mT55M+/btMTU1ZfTo0SXqbNmyhYEDB+Li4oK9vT3+/v78/PPPJep98skneHl5YWlpiZ+fH0eOHKmmV1O3yFY7QlRAlfK02jhD96lqyUhQ87Oe2gJXD0PsQbXsnA9ej6gjrT4jwaahYV+QEOWk0WiwaN4Mi+bNaDh5MkVZWWRFRJC5Tx1tLYiLJ2v/frL27ydx2TLMPD2xeaQXtr16Ye3nh4mdXZWu72bjVunRT0NmJBDiboWFhVhZWTFnzhy+//77e9bZt28fAwcOZNmyZTg6OrJ27VpGjBhBREQEnTt3BmDTpk3MnTuX1atX4+fnx4oVKwgKCuL8+fO6nTPrCxlRFaKc4tNydEEqQJECr245Vb6R1b+ycwO/Z2Hqz/CP0zDoDfDoAkqROkVg+4vwbitYPxZ+/wZyblbraxGiqrQ2Ntj174/74sW0DA2l+U/baPTyy1j7+YGpKflXr5L6341cmzWbCw/7E/PUBG785z/k/PEHSmHh/S9Qjdxs3FjkvwitRv3KK54+UJHANyErgSPxR0jISrh/5QdQQVout6JSKUjLvX/lKsrNzWXOnDk0atQIS0tLHnnkESIjI0vU+/XXX+nQoQOWlpY8/PDDnDp1SvfclStXGDFiBA0aNMDGxoa2bdsSHBxc6jXXr19Pt27dsLOzw83NjaeeeoqkJPUOV0xMDP369QOgQYMGaDQaJk+efM92bGxsWLVqFdOnT8fN7d6frxUrVjB//ny6d+9Oq1atWLZsGa1ateKnn37S1Xn//feZPn06U6ZMwdfXl9WrV2Ntbc2aNWtKfQ2TJ09m9OjRLFu2DFdXVxwdHVm6dCkFBQW8/PLLODk50bhxY9auXVtqG8aoXCOqK1euLHeDc+bMqXRnhDBmBsvT6tAYes5WS0o0nP5BXYiVcBKiQtXy04vQop+6CMt7KFg5VuWlCFGtNBoNFq1aYdGqFQ2nPkNhZhbZR46Q9euvZP36K3kxMeQcO0bOsWMkr/wIrYMDNv7+2PTqiW2vXph5GH6O9thWY+np0bNSGQmMZSFWbcmKTODmlougABpoMLYVNt0NN7d3/vz5fP/996xbt46mTZvyzjvvEBQUxKVLl3ByctLVe/nll/nwww9xc3Pj1VdfZcSIEVy4cAEzMzOef/558vLy2LdvHzY2Npw5c6bMDEX5+fm8/vrreHt7k5SUxNy5c5k8eTLBwcF4enry/fffM27cOM6fP4+9vT1W1Tgfu6ioiIyMDN1ry8vL4+jRoyxcuFBXR6vVEhgYyKFDh8psa8+ePTRu3Jh9+/bx66+/MnXqVA4ePEifPn2IiIhg06ZNPPvsswwcOJDGjRtX22swpHIFqh988IHe4xs3bpCdna2bxJyamoq1tTWNGjWSQFU8sJo526DVUCJPq5ezdfVdxKkZ9J6rluRLasB6agvcOKvmbL24G7Rm0KK/GrS2GQqWDtV3fSGqgYmtDXb9+2HXXx2Fyrt2jaxfD6qB66FDFKWlkbFrFxm7dgFg3rw5Nr16YdOrJzbdu983BVZlVWb6QEJWgi5IBXVu65JDS+jp0bNO5nGtqIK03DtBKoACN7dcxKJ1A0wdLMo8tzKysrJYtWoVX331FUOGDAHg888/JyQkhC+//JKXX35ZV3fRokUMHDgQgHXr1tG4cWN++OEHHn/8cWJjYxk3bhzt27cHoHnz5mVe95lnntH9u3nz5qxcuZLu3buTmZmJra2tLohs1KhRtS/gevfdd8nMzOTxxx8HIDk5mcLCQlxdXfXqubq6cu7cuTLbcnJyYuXKlWi1Wry9vXnnnXfIzs7m1VdfBWDhwoW89dZbHDhwgCeeeKJaX4ehlCtQjY6O1v17w4YN/Oc//+HLL7/E21tNZH7+/HmmT5/Os88+a5heCmEE3B2sWD62fYk8rQZLgeXcEgLmqyXpHJzZCqe33g5af1aLiTm0GHB7pHUIWEpWCWF8zBs3xnz84zQY/7i6S9bJk7rANeePP8i7fJm8y5e5uX49mJlh3aWLLnC19PFBo629WWplLcSqF4Fqcs6dILWYoh43RKAaFRVFfn4+vXr10h0zMzOjR48enD17Vq+uv7+/7t9OTk54e3vr6syZM4eZM2eye/duAgMDGTduHB06dCj1ukePHmXx4sX88ccf3Lx5k6Ii9f95bGwsvr6+1fkS9WzYsIElS5bw448/Vsvc07Zt26K96+fF1dWVdu3a6R6bmJjQsGFD3bSGuqDCi6lee+01Nm/erAtSAby9vfnggw949NFHmTBhQrV2UAhjMr57E/q0dqn5PK2N2kCjBdB3ASSdVQPW0z9A8nm4sFMtJubQMlANWlsPlqBVGCWNqSnWnTtj3bkzLrOepzA9nazDh9XA9cAB8q9fJzsiguyICG68/z4mTk7Y9OypBq49/TH7yyiTodX3hVimzlagQT9Y1dw+bsSmTZtGUFAQO3bsYPfu3Sxfvpz33nuP2bNnl6iblZVFUFAQQUFBfPvtt7i4uBAbG0tQUBB5eXn3aL16bNy4kWnTpvHdd98RGBioO+7s7IyJiQmJiYl69RMTE0ud91rsr+n1NBrNPY8VB+J1QYX/TI2Pj6egoKDE8cLCwhJvqhAPIncHK/xbNKydzQQAGvlAv4Uw6wj8/TAEvKJu2VqYB+eDYct0+HdL+O9TcOI7yM2onX4KUQ4m9vbYDxqE+5LFtPglhBa7duL62r+w7d8frbU1hSkppG/fTvzChVwK6EvUkKEkLF1K+u7dFN4nVVB1qI6FWHWZqYMFDca2UoNV0M1RNcRoKkCLFi0wNzfn119/1R3Lz88nMjKyxMjm4cOHdf++efMmFy5cwMfHR3fM09OT5557ji1btjBv3jw+//zze17z3Llz/Pnnn7z11lv07t2bNm3alBhxNDc3B9RYpzr897//ZcqUKfz3v/9l2LBhJa7VtWtXQkNDdceKiooIDQ3VG0WuLyo8ojpgwACeffZZvvjiC7p06QKoQ+YzZ87U+4tACFEDGvmope9CSDpzZ6T1z4twfodaTCyg1cDbI61BYFG1NEFCGIpGo8HcywsnLy+cJkxAycsj548/yPz1V7J+Pcit06fJi44mLzqamxv+CxoNlr6+2Pg/jPXD/lh37WKQTQeqshDrQWDT3Q2L1g3U2/3OVgYLUkFdNT9z5kzdKvUmTZro5llOnaq/le7SpUtp2LAhrq6u/POf/8TZ2VmXt/TFF19kyJAhtG7dmps3bxIWFqYXxN6tSZMmmJub89FHH/Hcc89x6tQpXn/9db06TZs2RaPRsH37doYOHVrm9vFnzpwhLy+PlJQUMjIyOH78OACdOnUC1Nv9kyZN4sMPP8TPz4+EBDWThJWVFQ4O6pqDuXPnMmnSJLp160aPHj1YsWIFWVlZTJkypTJva92mVFBSUpIyZMgQRaPRKObm5oq5ubmi1WqVIUOGKImJiRVtrl5IS0tTACUtLa22uyLqg6IiRYk/qSihryvKyi6Kssj+Tnm9kaL89ylFOfGdotzKqO2eGo28vDxl69atSl5eXm13RZShIC1NSQ8JUeKXvq5cGjpMOePdRq+cbddeifnb00rSJ58oWUePKUVG+P+zpr4PcnJylDNnzig5OTkGvY4h5OTkKLNnz1acnZ0VCwsLpVevXsqRI0d0z4eFhSmA8tNPPylt27ZVzM3NlR49eih//PGHrs6sWbOUFi1aKBYWFoqLi4vy9NNPK8nJyaVec8OGDYqXl5diYWGh+Pv7K9u2bVMA5ffff9fVWbp0qeLm5qZoNBpl0qRJpbbVtGlTBXWyhF4pFhAQcM/n/9rmRx99pDRp0kT3+g4fPlzm+zZp0iRl1KhRescCAgKUF154oUT/PvjggzLbqgnl/YxWegvVCxcu6FaftWnThtatW1cxZH5wyRaqorLi03KITs6imbNN5aYaKAoknro90roFUi7fec7UEloNujPSam6YldZ1gWyhWjflJyaRHXGYrEOHyTp8mIL4eL3ntdbWWHfvjrX/w9j4+2PRqlWtLswC2UJViGLl/YxWOlAV5SeBqqiMKu2CdS+KouZmPf2DmkFAL2i1gta3g9ZWg+pd0CqBat2nKAr5V66oC7MOHSb78GEK09L06pg4OWHzsB/WPXpg3aMH5s2aodFoSmnRMCRQFUJl0ED12rVrbNu2jdjY2BIr4t5///2K9/YBJ4GqqKj4tBx6vbWnRM7WAwv6Vc8iLkWBhBO3NxfYCjfvpKDDzPrOSGurQWBejXlijZQEqg8epaiI3HPndKOt2b/9hpKjv4ucibMzNj2612jgKoGqEKryfkYrvJgqNDSUkSNH0rx5c86dO0e7du2IiYlBURTd4iohRNUYbBesYhoNuHdUy4BFEP/HnZHWmzHqf89sVYPW1oOh7WhoObBeBK3iwaDRarH09cXS15eGU59RF2adOEHW4QiyIyPJ+f13CpOTSQ/eSXrwTqB2AlchRNkqPFln4cKFvPTSS5w8eRJLS0u+//57rl69SkBAAI899pgh+ghASkoKEyZMwN7eHkdHR6ZOnUpmZmaZ53z22Wf07dsXe3t7NBoNqX9JZRIeHo5Go7lnKd5XOCYm5p7P350WQ4jqVrwL1t2qfResYhoNeHSCgUtgznGYEQ69XgDHJpCfrc5t/d9ENeXV5mfgzDbIz7lPo0IYF425OdbduuEy63marvuK1pFHaLr+a5xnz8Lazw+NubkucE1YvITLQ4dxsXcfrs+dy82NG8m9fBmZKSdEzavwrX87OzuOHz9OixYtaNCgAQcOHKBt27b88ccfjBo1ipiYGIN0dMiQIcTHx/Ppp5+Sn5/PlClT6N69Oxs2bCj1nBUrVnDr1i1ADbBv3rypt/VZcfqIu7322muEhoYSFRWFRqMhJiaGZs2a8csvv9C2bVtdvYYNG5b7FqHc+heVsSkytsQuWFWao1pRigJxv9+ZHpAWe+c5c9vbI61j1E0GzOr2rUW59S+KcnO5deIEWUeOkB35Gzm//46Sm6tXx8TZGdeFC3D4S97LipBb/0KoDHbr38bGRjcv1d3dnaioKF0Al5ycXMnulu3s2bPs2rWLyMhIunXrBsBHH33E0KFDeffdd/Hw8LjneS+++CKgjpzei7m5ud4uD/n5+fz444/Mnj27xO2ehg0b3ndHCCGqU63tglVMo4GHuqhl4FK4fgzOFAetV+HUZrWY26rbt/qOhpYDwMy4d6wR4l60FhZqhoDu3QEoysu7E7geuTNVwPT2nu9CiJpR4UD14Ycf5sCBA/j4+DB06FDmzZvHyZMn2bJlCw8//LAh+sihQ4dwdHTUBakAgYGBaLVaIiIiGDNmTLVcZ9u2bfz555/3TKg7cuRIbt26RevWrZk/fz4jR44stZ3c3Fxy7/pLPD09HVAD4fz8/Grpq6gfnK1NcW6ijrrU+mfHtYNa+v4fmrhjaM7+iPbsj2jSr8PJ7+DkdyjmNigtB1LUZiRKiwF1JntA8Xtb6++xMB4aDWYdO+LYsSOO06ej5OVx6+RJTNu2rdLnRD5jQlRMhQPV999/Xzc3dMmSJWRmZrJp0yZatWplsBX/CQkJNGrUSO+YqakpTk5Ouh0dqsOXX35JUFAQjRs31h2ztbXlvffeo1evXmi1Wr7//ntGjx7N1q1bSw1Wly9fzpIlS0oc3717N9bWshhFPCgehuZ+NMiOwuNmBB6pkVjnpaA5sxXtma0UaMxJcuhInGN3Eu07UmBi/COtISEhtd0FYeyquFV4dnZ2NXVEiPqhwoFq8+bNdf+2sbFh9erVlb74ggULePvtt8usc/bs2Uq3XxHXrl3j559/5n//+5/ecWdnZ+bOnat73L17d+Li4vj3v/9daqC6cOFCvXPS09Px9PRk0KBBMkdVPLgUhYK439Gc/wnt2Z8wTY3BIzUSj9RIFBMLlOb9KPIZidIqCCwdaru3evLz8wkJCWHgwIEyR1UYVPEdNiFE+VQ4UAVITU1l8+bNREVF6fbjPXbsGK6urjz00EPlbmfevHlMnjy5zDrNmzfHzc2NpKQkveMFBQWkpKRU27zRtWvX0rBhwzJv6Rfz8/Mrc+TFwsICC4uSeyGbmZnJl6B4sHn5qWXQ62qe1jM/wumtaFKi0FzchfbiLtCaQYt+4DsKvIeCtfHM+ZOfUWFo8vmqnPDwcPr161diUXRFZWdn8/TTTxMSEkJGRgY3b96kU6dOvPjii7p1LcK4VDg91YkTJ2jdujVvv/027777ri7l05YtW1i4cGGF2nJxcaFNmzZlFnNzc/z9/UlNTeXo0aO6c/fs2UNRURF+fn4VfQklKIrC2rVrmThxYrl+iRw/fhx3d/cqX1eIB0V8Wg4Ho5KJT7udtqo4T+uA/4PZR2HmQQh4BVx8oCgfLu6GH59XU159PRp+WwuZN2r1NQghjEPfvn1LBI09e/YkPj4eB4eq3Y1Zt24d+/fv5+DBg9XSniEUp8U8fvz4fevOmTOHrl27YmFhQadOnUo8Hx4ezqhRo3B3d8fGxoZOnTrx7bfflqj33Xff0aZNGywtLWnfvj3BwcHV8EqqR4VHVOfOncvkyZN55513sLOz0x0fOnQoTz31VLV2rpiPjw+DBw9m+vTprF69mvz8fGbNmsUTTzyhW/F//fp1BgwYwNdff02PHj0AdW5rQkICly5dAuDkyZPY2dnRpEkTnO5aublnzx6io6OZNm1aiWuvW7cOc3NzOnfuDKgB+Zo1a/jiiy8M8lqFqGvuu9WrRgOubdXS71W4cV7NxXrmR0g8CZfD1LJjLjTtpY60+owAO8myIYRQ/TVLT2VFRUXh4+NDu3btqqFXxuGZZ54hIiKCEydOlHju4MGDdOjQgVdeeQVXV1e2b9/OxIkTcXBwYPjw4bo6Tz75JMuXL2f48OFs2LCB0aNHc+zYMeN4n5QKsre3Vy5duqQoiqLY2toqUVFRiqIoSkxMjGJhYVHR5srtzz//VJ588knF1tZWsbe3V6ZMmaJkZGTono+OjlYAJSwsTHds0aJFClCirF27Vq/tJ598UunZs+c9r/vVV18pPj4+irW1tWJvb6/06NFD+e677yrU97S0NAVQ0tLSKnSeEMYuLjVbabZgu9L0lTul+YIdSlxqdvkaSL6kKPvfV5RPAxRlkf1dxUFRvgxSlEP/UZTUq4Z8CYqiKEpeXp6ydetWJS8vz+DXEvVbTX0f5OTkKGfOnFFycnIMep3qNmnSpBLf2dHR0UpYWJgCKDdv3lQURVHWrl2rODg4KD/99JPSunVrxcrKShk3bpySlZWlfPXVV0rTpk0VR0dHZfbs2UpBQYGiKIoSEBCg125AQICiKIrStGlT5YMPPtD14cqVK8rIkSMVGxsbxc7OTnnssceUhIQERVEUJTU1VdFqtUpkZKSiKIpSWFioNGjQQPHz89Odv379eqVx48alvsadO3cqvXr1UhwcHBQnJydl2LBhurhKUZQSr7+4n2VZtGiR0rFjx3K8w4oydOhQZcqUKbrHjz/+uDJs2DC9On5+fsqzzz573+t9+eWXiqenp2JjY6PMnDlTKSgoUN5++23F1dVVcXFxUd54441S2yjvZ7TCI6oWFhb3nAx+4cIFXFxcKhwol5eTk1OZyf29vLxK7BqyePFiFi9efN+2y2p30qRJTJo0qdz9FKI+qfJWrw1bwCP/UMvNK3D29kjrtUiIPaSWXQvgoW7qSKvvSGjgZZDXIsSDTlGUWkuPZWZmVq7taD/88EMuXLhAu3btWLp0KaBOE7zXZkLZ2dmsXLmSjRs3kpGRwdixYxkzZgyOjo4EBwdz+fJlxo0bR69evRg/fjxbtmxhwYIFnDp1ii1btmBubl6izaKiIkaNGoWtrS179+6loKCA559/nvHjxxMeHo6DgwOdOnUiPDycbt26cfLkSTQaDb///juZmZm68wICAkp9jVlZWcydO5cOHTqQmZnJ//3f/zFmzBiOHz+OVqvlyJEj9OjRQ7fR0L36WRVpaWn4+PjoHh86dEhvAThAUFAQW7duLbOdqKgodu7cya5du4iKiuLRRx/l8uXLtG7dmr1793Lw4EGeeeYZAgMDqzRNs8KB6siRI1m6dKludbxGoyE2NpZXXnmFcePGVbojQoi6p3ir17uD1Upv9dqgKfScrZa0a3D2J3WKQOwhuP6bWkJeA/dOt4PWUWqgK4Qol/z8fJYtW1Yr13711VfLFXA5ODhgbm6OtbX1fW/15+fns2rVKlq0UH8PPProo6xfv57ExERsbW3x9fWlX79+hIWFMX78eJycnLC2ti5zGkFoaCgnT54kOjoaT09PAL7++mvatm1LZGQk3bt3p2/fvoSHh/PSSy8RHh7OwIEDOXfuHAcOHGDw4MGEh4czf/78Uvv911hpzZo1uLi4cObMGdq1a6cb9DPERkP/+9//iIyM5NNPP9UdS0hIwNXVVa+eq6vrfdN/FhUVsWbNGuzs7HTv9fnz5wkODkar1eLt7c3bb79NWFhYlQLVCi+meu+998jMzKRRo0bk5OQQEBBAy5YtsbOz480336x0R4QQdY+7gxXLx7bH5PZISfFWr1XeRcuhMTw8E57ZCfPOwdB3was3aLQQfxxCl8BHXWBVL9j7jjrvVQhRr1hbW+uCVFCDKy8vL2xtbfWO/TVrUFnOnj2Lp6enLkgF8PX1xdHRUZcuMyAggAMHDlBYWMjevXvp27evLniNi4vj0qVL9O3bt9RrXLx4kSeffJLmzZtjb2+Pl5cXALGxsaWeUx3CwsKYMmUKn3/+ud6W8JXl5eWlt1bJ1dUVX19ftFqt3rGKvP/3UuERVQcHB0JCQjhw4AAnTpwgMzOTLl26EBgYWKWOCCHqJoNv9WrnBj2mqyXzBpzfoU4PuLwXEk+pJexNcG4NbYaDz3Dw6KIu4hJC6JiZmfHqq6/W2rUN3aZGo7nnsaKiomq9bp8+fcjIyODYsWPs27ePZcuW4ebmxltvvUXHjh3x8PCgVatWpZ4/YsQImjZtyueff46HhwdFRUW0a9dOtz29Iezdu5cRI0bwwQcfMHHiRL3n3NzcSPzLRhaJiYn3Hc2tqfe/UnlUAR555BEeeeSRKl1cCPFgcHewqv4A9V5sXaDrZLVkp8D5YDVojQqD5Atw4H212D8EbYapgWvTXmBS6V91QjwwNBpNtc93NARzc3MKCwtr5do+Pj5cvXqVq1ev6kZVz5w5Q2pqKr6+vgA4OjrSoUMHPv74Y8zMzGjTpg2NGjVi/PjxbN++vcz5qX/++Sfnz5/n888/p3fv3gAcOHBAr07x/6Pqeg/Cw8MZPnw4b7/9NjNmzCjxvL+/P6GhoXopwUJCQvD396+W61dVpX57h4aGEhoaSlJSUolIec2aNdXSMSGEKJO1E3T+m1pupcHFEHVe68UQSL8ORz5Ti1UDaD1EHWlt0R/MjH8rVyHqMy8vLyIiIoiJicHW1lYvnaShBQYG0r59eyZMmMCKFSsoKCjg73//OwEBAXTr1k1Xr2/fvnz00Uc8+uijgLrg28fHh02bNvHJJ5+U2n6DBg1o2LAhn332Ge7u7sTGxrJgwQK9Oo0aNcLKyopdu3bRuHFjLC0tS833eunSJTIzM0lISCAnJ0eXe9XX1xdzc3PCwsIYPnw4L7zwAuPGjdPNOzU3N9e9ry+88AIBAQG89957DBs2jI0bN/Lbb7/x2WefVfp9rE4VnqO6ZMkSBg0aRGhoKMnJydy8eVOvCCFEjbN0gPaPwuPrYH4UPLkROv0NrJwg5yb8sQE2PgXvNIdNT8OJ/0FOam33WghxDy+99BImJib4+vri4uJi8Lmbd9NoNPz44480aNCAPn36EBgYSPPmzdm0aZNevYCAAAoLC/Xmovbt27fEsb/SarVs3LiRo0eP0q5dO/7xj3/w73//W6+OqakpK1eu5NNPP8XDw4NRo0aV2t60adPo3Lkzn376KRcuXKBz58507tyZuLg4QM0Fn52dzfLly3F3d9eVsWPH6tro2bMnGzZs4LPPPqNjx45s3ryZrVu3GkcOVUCj/DWn0324u7vzzjvv8PTTTxuqTw+c9PR0HBwcSEtLw97evra7I0T9UVigZg04tx3Obof0a3ee05pBs97QZjj5LQYRvP8YQ4cOlS0uhUHV1PfBrVu3iI6OplmzZlhaWhrsOkJUVnk/oxW+9Z+Xl0fPnj2r1DkhhKgRJqZqMNqsNwx+C+J+vxO0Jp+HqD0QtQdTNDxi0xKt02VoOwqcmtd2z4UQQlCJW//Tpk0rM0G+EELUtPi0HA5GJROfllN6JY0GHuoCA/4PZh2BWb/BgEXwUFc0KDTMuohJ6GJY2Rn+0xPClkH8CajYTSchhBDVqFwjqnfvWFBUVMRnn33GL7/8QocOHUrcJnv//fert4dCCFGGTZGxLNxykiIFtBpYPrY947s3uf+Jzq2g91zoPZf8P69w9od3aWcag/bKr5B0Wi173wbHpuAzQs0g4NkDtCaGf1FCCCGAcgaqv//+u97jTp06AXDq1Cm94+XZHk0IIapLfFqOLkgFdYesV7ecok9rl4qly7L3INolEJ+hQ9HmZ8CFXer0gKhQSL0Chz5Wi40LeA9VA9dmfcDUwjAvTAghBFDOQDUsLMzQ/RBCiAqLTs7S274VoFBRiEnOrnxeV2sn6PSUWvKy4FKoOq/1/C7IugHH1qnFwh5aDVRHWlsNBAu7+7cthBCiQiQLthCizmrmbINWg16waqLR4OVsXT0XMLcB35FqKciDmP1q0HpuB2Qmwqnv1WJiAS36qUGr91CwaVg91xeiiiqY2EeIGlPez6YEqkKIOsvdwYrlY9vz6pZTFCoKJhoNy8a2M8wuWabm0HKAWoa+B9d/UzcYOLcdUi6r0wUu7AKNFpr0VDcYaDMcHD3v37YQ1ax4/Uh2djZWVrLJhTA+2dnZwP23161wHlVRcZJHVQjDik/LISY5Gy9n60oFqfn5+QQHB1cuj6qiQNLZ22mvtkHCSf3n3TvdDlpHgIu3mn1A1Fs1+X0QHx9PamoqjRo1wtraWtaRCKOgKArZ2dkkJSXh6OiIu7t7mfVlRFUIUee5O1gZZhS1PDQacPVVS8B8uBmjTg04u13dbCD+uFr2vAENW4L3EHVLV08/Nc+rEAbi5uYGQFJSUi33RIiSHB0ddZ/RssiIag2QEVUhjFuVRlTLknkDzgero62Xw6Ew785zVg2g1SA1cG0xACzld0N9UBvfB4WFheTn59fItYQoDzMzM0xMypfqT/6cF0IIQ7F1ga6T1HIrXU13dX4nXNwNOTfhxCa1aM3A6xF1IZb3YHAsRx5YIcrJxMSk3EGBEMZGAlUhhKgJlvbQdoxaCgvgagRc2KkGrn9egsthatn5Mri2g9aD1cDVozNoK7yJoBBCPBDqzG+/lJQUJkyYgL29PY6OjkydOpXMzMwy68+ePRtvb2+srKxo0qQJc+bMIS0tTa9ebGwsw4YNw9ramkaNGvHyyy9TUFCgVyc8PJwuXbpgYWFBy5Yt+eqrrwzxEoUQ9YWJKXj1gkFvwOyj6nauA19XswVotJB4Cva/C1/0h/fbwLbZcC4Y8rJru+dCCFGj6syI6oQJE4iPjyckJIT8/HymTJnCjBkz2LBhwz3rx8XFERcXx7vvvouvry9XrlzhueeeIy4ujs2bNwPqvJ1hw4bh5ubGwYMHiY+PZ+LEiZiZmbFs2TIAoqOjGTZsGM899xzffvstoaGhTJs2DXd3d4KCgmrs9QshHmDOrdTSaw5kp6hTA87vVDcbyEyEY1+rxdQSmve9vSBrMNjdfyGCEELUZXViMdXZs2fx9fUlMjKSbt26AbBr1y6GDh3KtWvX8PDwKFc73333HX/729/IysrC1NSUnTt3Mnz4cOLi4nB1dQVg9erVvPLKK9y4cQNzc3NeeeUVduzYobdd7BNPPEFqaiq7du0q13VlMZUQxs1gi6nuEp+WQ3RyFs2cbcqfoaAgD64cUIPW87sgLVb/eY8uatDqPUSdLiDph4yefB8IUTF1YkT10KFDODo66oJUgMDAQLRaLREREYwZM6Zc7RT/YjA1NdW12759e12QChAUFMTMmTM5ffo0nTt35tChQwQGBuq1ExQUxIsvvljqdXJzc8nNzdU9Tk9PB9QvQ1l5KYTxKf65NNTP53dHr/GvH89QpIBWA2+M8uWxro3LcaYGmvRWS+CbkHQG7cWf0VzchTbuGBSXsDdR7B+iqNVglFZBKE17gamFQV6LqBr5DhCiYupEoJqQkECjRo30jpmamuLk5ERCQkK52khOTub1119nxowZeu3eHaQCusfF7ZZWJz09nZycnHvu+LF8+XKWLFlS4vju3buxtq6mrR2FENUuJCSk2ttMzYXFx0xQUEc7ixT459bT5MeewLFSsWQbcG2DhVMqrmnHcUv/HZf005imX8fk6Jdw9EsKtJYk2bcnwb4ziQ4dyTO1q9bXJCqveDceIUT51GqgumDBAt5+++0y65w9e7bK10lPT2fYsGH4+vqyePHiKrd3PwsXLmTu3Ll61/f09GTQoEFyq0cII5Sfn09ISAgDBw6s9lv/hy+noBz7Te+YgoYWnR7Gr5lTFVt/Sm0vP5uCmP1oL+xCc2k3ppmJeKRG4pEaiaLRojTugdJqEEWthqibDsgUgVpTfIdNCFE+tRqozps3j8mTJ5dZp3nz5ri5uZXYWaOgoICUlJT77mqQkZHB4MGDsbOz44cfftD7EnJzc+PIkSN69RMTE3XPFf+3+Njddezt7UvdP9nCwgILi5JDJWZmZgab/yaEqDpD/Iy2dLNHq1FHUouZaDS0cLWvvmuZOYDvcLUUFUH87+qc1vM70SSeRHP1MFw9jMmepeDUHFoFQauB0LQXmFlWTx9Euch3gBAVU6uBqouLCy4uLvet5+/vT2pqKkePHqVr164A7Nmzh6KiIvz8/Eo9Lz09naCgICwsLNi2bRuWlvq/kP39/XnzzTdJSkrSTS0ICQnB3t4eX19fXZ3g4GC980JCQvD396/QaxVC1E/uDlYsH9ueV7ecolBRMNFoWDa2neG2fNVq4aGuaun/T0iNhQs/qztkRe+HlMsQsUotZtbQLEANWlsNlI0GhBBGp06s+gcYMmQIiYmJrF69Wpeeqlu3brr0VNevX2fAgAF8/fXX9OjRg/T0dAYNGkR2djY//PADNjY2urZcXFwwMTGhsLCQTp064eHhwTvvvENCQgJPP/0006ZN00tP1a5dO55//nmeeeYZ9uzZw5w5c9ixY0e501PJKk8hjFtNrfqPSc7Gy9nacEHq/eRmQNQeuBiilsy/zPF38YFWgerWrp4Pg6l57fTzASbfB0JUTJ1YTAXw7bffMmvWLAYMGIBWq2XcuHGsXLlS93x+fj7nz5/XTVQ/duwYERERALRs2VKvrejoaLy8vDAxMWH79u3MnDkTf39/bGxsmDRpEkuXLtXVbdasGTt27OAf//gHH374IY0bN+aLL76QHKpCiApxd7CqvQC1mIUd+I5Si6KoGwtc3K0GrVcj4MZZtRz8CMztoEVfNWhtORDs3Wu370KIeqnOjKjWZfIXtBDGrSZGVI1edoq6hWvxaGt2sv7zbu3VoLXVIHiom7q7lqgw+T4QomLkN40QQgiwdoJ249RSvCDrYog64nr9GCScVMv+98DSEVoOUIPWFgPA9v5rDYQQojIkUBVCCKHv7gVZfRdA5g2IClWD1kuhcCsVTn2vFjTg0fnOaKtHZ/V8IYSoBhKoCiFEPVGpbVxBHTHt+IRaCgvg+tHbc1t3Q8KJOztk7X0LrJ2hZaA64tq8n4y2CiGqRAJVIYSoBzZFxrJwy0ndNq7Lx7ZnfPdKpKMyMYUmfmoZ8Bqkx8OlX9SgNSpMndt6YqNaANw6qEFri/6SSUAIUWESqAohxAMuPi1HF6SCuvnAq1tO0ae1S9UzEdi7Q5en1VKQp2YPuBSipsFKOKmOuCacgAMfgJkNeD1yJ3CVXbKEEPchgaoQQjzgopOz9HbGAihUFGKSs6s3ZZapOTTrrZaBSyEjES6Hq/Nbo/ZA1g24+LNaAByaQIt+auDarA9YNai+vgghHggSqAohxAOumbPNPbdx9XK2NuyF7Vyh43i1FBWpeVuj9qiBa+xhSIuFY+vUotGqaa9a9FcDV48ukgJLCCGBqhBCPOhqfBvXe9Fqwb2DWh55EfKyIObX24HrHkg+D9eOqGXvW2DpoG7vWhy4yvauQtRLEqgKIUQ9ML57E/q0dqn9bVyLmdtA60FqAUi9qm44cClUnS5wKxXOblMLqPNZm/dVg9dmvWWagBD1hASqQghRTxjFNq6lcfSELhPVUlQIcb+rI62XQuFaJPx5SS2RX6Dmbu2kBq3NA9RsAuYGnsYghKgVEqgKIYSoNpXO1Xo3rQk07qaWgPlwKw2i90P0Xri8V50mEPe7Wn5dASbm4Ol3J3CV+a1CPDDkJ1kIIUS1qLZcrX9l6QA+w9UCau7W4qA1ei+kX4eY/WoJewMs7KFpLzVobRYAjXwkDZYQdZQEqkIIIarMoLla/8re/c5OWYoCf0ZBdLgauMbsh5ybcGGnWgBsGqnpr4oD1wZNq7c/QgiDkUBVCCFEldVYrta/0mjAuaVauk9T02AlnLg94hoOVw5BVhKc2qwWUDMIePVWNx/wekQyCghhxCRQFUIIUWWGztVa7rmvWq260MqjE/R6AQpy1cVYxdMErv0GqbFw/Fu1gASuQhgxCVSFEEJUmSFztVZp7qupxZ0AlH9CbiZcPQwxB9Ry/ZgErkIYMY2iKMr9q4mqSE9Px8HBgbS0NOzt7Wu7O0KIv8jPzyc4OJihQ4diZmZW292p0+LTcqo1V2t8Wg693tpTYqT2wIJ+1TOl4F6Bq1KoX+fuwLVFf7Bzq/Tl5PtAiIqREVUhhBDVprpztRp87quFLbQMVAtAbgZcjSh9xHX4Cug2perXFUKUiwSqQgghjJah576WYGFXduDq1dsw1xVC3JO2tjtQXikpKUyYMAF7e3scHR2ZOnUqmZmZZdafPXs23t7eWFlZ0aRJE+bMmUNaWpquzh9//MGTTz6Jp6cnVlZW+Pj48OGHH+q1Ex4ejkajKVESEhIM9lqFEEKoiue+mtzOg1qVua/xaTkcjEomPi2n/CcVB66Bi2HaL2p2ASFEjakzI6oTJkwgPj6ekJAQ8vPzmTJlCjNmzGDDhg33rB8XF0dcXBzvvvsuvr6+XLlyheeee464uDg2b1ZTlBw9epRGjRrxzTff4OnpycGDB5kxYwYmJibMmjVLr73z58/rzSdq1KiR4V6sEEIInfHdm9CntUuV5r4abDMCIYRB1YnFVGfPnsXX15fIyEi6desGwK5duxg6dCjXrl3Dw8OjXO189913/O1vfyMrKwtT03vH6M8//zxnz55lz549gDqi2q9fP27evImjo2Ol+i+T54UwbrKY6sFm8AVZFSDfB0JUTJ0YUT106BCOjo66IBUgMDAQrVZLREQEY8aMKVc7xb8YSgtSi+s4OTmVON6pUydyc3Np164dixcvplevXqW2kZubS25uru5xeno6oH4Z5ufnl6uvQoiaU/xzKT+fD6ZLCen3XJAVlZiOs3XNfg3KZ0yIiqkTgWpCQkKJW+2mpqY4OTmVe65ocnIyr7/+OjNmzCi1zsGDB9m0aRM7duzQHXN3d2f16tV069aN3NxcvvjiC/r27UtERARdunS5ZzvLly9nyZIlJY7v3r0ba2sDLQAQQlRZSEhIbXdBGEBqLmgwQUGjO6ZBIer4Yf48W7N9yc7OrtkLClHH1WqgumDBAt5+++0y65w9W/XfIunp6QwbNgxfX18WL158zzqnTp1i1KhRLFq0iEGDBumOe3t74+3trXvcs2dPoqKi+OCDD1i/fv0921q4cCFz587Vu76npyeDBg2SWz1CGKH8/HxCQkIYOHCg3Pp/QJk1uca/fjyjm6P6xqi2PNa1cbnPj0+7xZU/s2na0Bp3B8tK96P4DpsQonxqNVCdN28ekydPLrNO8+bNcXNzIykpSe94QUEBKSkpuLmVnXg5IyODwYMHY2dnxw8//HDPL6EzZ84wYMAAZsyYwb/+9a/79rtHjx4cOHCg1OctLCywsLAocdzMzEy+BIUwYvIz+uB66uFm9PNxq9SCrOpciCWfLyEqplYDVRcXF1xcXO5bz9/fn9TUVI4ePUrXrl0B2LNnD0VFRfj5+ZV6Xnp6OkFBQVhYWLBt2zYsLUv+FXz69Gn69+/PpEmTePPNN8vV7+PHj+Pu7l6uukIIIYxDZTYjiE/L0QWpoOZzfXXLKfq0dqnxhVhC1Ed1Yo6qj48PgwcPZvr06axevZr8/HxmzZrFE088oVvxf/36dQYMGMDXX39Njx49SE9PZ9CgQWRnZ/PNN9+Qnp6uu+Xi4uKCiYkJp06don///gQFBTF37lzdfFcTExNdAL1ixQqaNWtG27ZtuXXrFl988QV79uxh9+7dtfNmCCGEqDEG3xlLCFGmOhGoAnz77bfMmjWLAQMGoNVqGTduHCtXrtQ9n5+fz/nz53UT1Y8dO0ZERAQALVvqJ2iOjo7Gy8uLzZs3c+PGDb755hu++eYb3fNNmzYlJiYGgLy8PObNm8f169extramQ4cO/PLLL/Tr18/Ar1gIIURtq/GdsYQQeupEHtW6TvLmCWHcJI+qKMumyFhe3XKKQkXR7YxV2Tmq8n0gRMXUmRFVIYQQojZUx85YQojKkUBVCCGEuI/KLMQSQlSdtrY7IIQQQgghxL1IoCqEEEIIIYySBKpCCCGEEMIoSaAqhBBCCCGMkiymqgHFGcBkj2chjFN+fj7Z2dmkp6dLeiphUMXfA5IZUojykUC1BmRkZADg6elZyz0RQghhDDIyMnBwcKjtbghh9CThfw0oKioiLi4OOzs7NBpNbXen0rp3705kZGRtd6PCjKHfNdEHQ1yjutqsSjuVPbci56Wnp+Pp6cnVq1clCXslGMPPWGXURr8VRSEjIwMPDw+0Wpl9J8T9yIhqDdBqtTRu3Li2u1FlJiYmdfJL3Bj6XRN9MMQ1qqvNqrRT2XMrc569vX2tf1bqImP4GauM2uq3jKQKUX7y55wot+eff762u1ApxtDvmuiDIa5RXW1WpZ3KnmsM/9/ri7r6XtfVfgtRn8itfyFEvSf7rwshhHGSEVUhRL1nYWHBokWLsLCwqO2uCCGEuIuMqAohhBBCCKMkI6pCCCGEEMIoSaAqhBBCCCGMkgSqQgghhBDCKEmgKoQQQgghjJIEqkIIIYQQwihJoCqEEBUwZswYGjRowKOPPlrbXRFCiAeeBKpCCFEBL7zwAl9//XVtd0MIIeoFCVSFEKIC+vbti52dXW13Qwgh6gUJVIUQD4x9+/YxYsQIPDw80Gg0bN26tUSdTz75BC8vLywtLfHz8+PIkSM131EhhBDlIoGqEOKBkZWVRceOHfnkk0/u+fymTZuYO3cuixYt4tixY3Ts2JGgoCCSkpJ0dTp16kS7du1KlLi4uJp6GUIIIW6TLVSFEA8kjUbDDz/8wOjRo3XH/Pz86N69Ox9//DEARUVFeHp6Mnv2bBYsWFDutsPDw/n444/ZvHlzdXdbCCHEXWREVQhRL+Tl5XH06FECAwN1x7RaLYGBgRw6dKgWeyaEEKI0EqgKIeqF5ORkCgsLcXV11Tvu6upKQkJCudsJDAzkscceIzg4mMaNG0uQK4QQBmRa2x0QQoi65JdffqntLgghRL0hI6pCiHrB2dkZExMTEhMT9Y4nJibi5uZWS70SQghRFglUhRD1grm5OV27diU0NFR3rKioiNDQUPz9/WuxZ0IIIUojt/6FEA+MzMxMLl26pHscHR3N8ePHcXJyokmTJsydO5dJkybRrVs3evTowYoVK8jKymLKlCm12GshhBClkfRUQogHRnh4OP369StxfNKkSXz11VcAfPzxx/z73/8mISGBTp06sXLlSvz8/Gq4p0IIIcpDAlUhhBBCCGGUZI6qEEIIIYQwShKoCiGEEEIIoySBqhBCCCGEMEoSqAohhBBCCKMkgaoQQgghhDBKEqgKIYQQQgijJIGqEEIIIYQwShKoCiGEEEIIoySBqhBCCCGEMEoSqAohSggPD0ej0ZCamlor1w8NDcXHx4fCwsJS6yxevJhOnTrpHi9YsIDZs2fXQO+EEELUFAlUhajn+vbty4svvqh3rGfPnsTHx+Pg4FArfZo/fz7/+te/MDExKfc5L730EuvWrePy5csG7JkQQoiaJIGqEKIEc3Nz3Nzc0Gg0NX7tAwcOEBUVxbhx4yp0nrOzM0FBQaxatcpAPRNCCFHTJFAVoh6bPHkye/fu5cMPP0Sj0aDRaIiJiSlx6/+rr77C0dGR7du34+3tjbW1NY8++ijZ2dmsW7cOLy8vGjRowJw5c/Ru1+fm5vLSSy/x0EMPYWNjg5+fH+Hh4WX2aePGjQwcOBBLS0u942+99Raurq7Y2dkxdepUbt26VeLcESNGsHHjxiq/L0IIIYyDBKpC1GMffvgh/v7+TJ8+nfj4eOLj4/H09Lxn3ezsbFauXMnGjRvZtWsX4eHhjBkzhuDgYIKDg1m/fj2ffvopmzdv1p0za9YsDh06xMaNGzlx4gSPPfYYgwcP5uLFi6X2af/+/XTr1k3v2P/+9z8WL17MsmXL+O2333B3d+c///lPiXN79OjBtWvXiImJqdwbIoQQwqiY1nYHhBC1x8HBAXNzc6ytrXFzcyuzbn5+PqtWraJFixYAPProo6xfv57ExERsbW3x9fWlX79+hIWFMX78eGJjY1m7di2xsbF4eHgA6jzSXbt2sXbtWpYtW3bP61y5ckVXv9iKFSuYOnUqU6dOBeCNN97gl19+KTGqWnzelStX8PLyqvD7IYQQwrjIiKoQolysra11QSqAq6srXl5e2Nra6h1LSkoC4OTJkxQWFtK6dWtsbW11Ze/evURFRZV6nZycnBK3/c+ePYufn5/eMX9//xLnWllZAerorxBCiLpPRlSFEOViZmam91ij0dzzWFFREQCZmZmYmJhw9OjREqv37w5u/8rZ2ZmbN29Wqo8pKSkAuLi4VOp8IYQQxkUCVSHqOXNz8zLzlVZW586dKSwsJCkpid69e1fovDNnzugd8/HxISIigokTJ+qOHT58uMS5p06dwszMjLZt21a+40IIIYyG3PoXop7z8vIiIiKCmJgYkpOTdSOiVdW6dWsmTJjAxIkT2bJlC9HR0Rw5coTly5ezY8eOUs8LCgriwIEDesdeeOEF1qxZw9q1a7lw4QKLFi3i9OnTJc7dv38/vXv31k0BEEIIUbdJoCpEPffSSy9hYmKCr68vLi4uxMbGVlvba9euZeLEicybNw9vb29Gjx5NZGQkTZo0KfWcCRMmcPr0ac6fP687Nn78eF577TXmz59P165duXLlCjNnzixx7saNG5k+fXq19V8IIUTt0iiKotR2J4QQ4m4vv/wy6enpfPrpp+U+Z+fOncybN48TJ05gaiqzmoQQ4kEgI6pCCKPzz3/+k6ZNm1ZoGkJWVhZr166VIFUIIR4gMqIqhBBCCCGMkoyoCiGEEEIIoySBqhBCCCGEMEoSqAohhBBCCKMkgaoQQgghhDBKEqgKIYQQQgijJIGqEEIIIYQwShKoCiGEEEIIoySBqhBCCCGEMEoSqAohhBBCCKP0/2paPf/fjIVwAAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "hm_1 = ml.head(r1, 0, t1)\n", "plt.semilogx(t1, h1, \".\", label=\"obs at 30 m\")\n", @@ -377,35 +256,9 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "........................................\n", - "Fit succeeded.\n", - "[[Fit Statistics]]\n", - " # fitting method = leastsq\n", - " # function evals = 37\n", - " # data points = 51\n", - " # variables = 3\n", - " chi-square = 0.06328840\n", - " reduced chi-square = 0.00131851\n", - " Akaike info crit = -335.285820\n", - " Bayesian info crit = -329.490343\n", - "[[Variables]]\n", - " kaq_0_0: 48.0405082 +/- 1.42053102 (2.96%) (init = 10)\n", - " Saq_0_0: 4.3598e-05 +/- 2.8635e-06 (6.57%) (init = 0.0001)\n", - " c_0_0: 539.739712 +/- 180.224614 (33.39%) (init = 500)\n", - "[[Correlations]] (unreported correlations are < 0.100)\n", - " C(kaq_0_0, Saq_0_0) = -0.7790\n", - " C(kaq_0_0, c_0_0) = +0.7623\n", - " C(Saq_0_0, c_0_0) = -0.2964\n" - ] - } - ], + "outputs": [], "source": [ "cal2 = tft.Calibrate(ml)\n", "cal2.set_parameter(name=\"kaq\", initial=10, layers=0)\n", @@ -421,72 +274,9 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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layersoptimal
kaq_0_0048.040508
Saq_0_000.000044
c_0_00539.739712
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" - ], - "text/plain": [ - " layers optimal\n", - "kaq_0_0 0 48.040508\n", - "Saq_0_0 0 0.000044\n", - "c_0_0 0 539.739712" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RMSE: 0.00624 m\n" - ] - } - ], + "outputs": [], "source": [ "display(cal2.parameters[[\"layers\", \"optimal\"]])\n", "print(f\"RMSE: {cal2.rmse(weighted=False):.5f} m\")" @@ -510,78 +300,9 @@ }, { "cell_type": "code", - "execution_count": 58, - "metadata": { - "editable": true, - "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-053280.0382
MLU48.114.32e-055390.0424
Hantush45.334.76e-053310.0059
\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 58, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "t = pd.DataFrame(\n", " columns=[\"k [m/d]\", \"Ss [1/m]\", \"c [d]\", \"RMSE [m]\"],\n", @@ -625,21 +346,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python [conda env:base] *", + "display_name": "Python 3", "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": "3.12.2" + "name": "python3" } }, "nbformat": 4, diff --git a/docs/transient/04pumpingtests/leaky2_hardixveld.ipynb b/docs/transient/04pumpingtests/leaky2_hardixveld.ipynb index 464f953..08d9c43 100644 --- a/docs/transient/04pumpingtests/leaky2_hardixveld.ipynb +++ b/docs/transient/04pumpingtests/leaky2_hardixveld.ipynb @@ -28,14 +28,8 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", @@ -92,14 +86,8 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "data = np.loadtxt(\"data/recovery.txt\", skiprows=1)\n", @@ -122,14 +110,8 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "# known parameters\n", @@ -143,24 +125,9 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "self.neq 1\n", - "solution complete\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# model with impermeable top and bottom\n", "ml = tft.ModelMaq(\n", @@ -177,24 +144,9 @@ }, { "cell_type": "code", - "execution_count": 15, - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - ".....................................................................................\n", - "Fit succeeded.\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "cal = tft.Calibrate(ml)\n", "cal.set_parameter(name=\"kaq\", initial=50, pmin=0, layers=0)\n", @@ -207,74 +159,9 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": { - "editable": true, - "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", - "
optimal
kaq_0_048.630582
Saq_0_00.000667
res0.025542
\n", - "
" - ], - "text/plain": [ - " optimal\n", - "kaq_0_0 48.630582\n", - "Saq_0_0 0.000667\n", - "res 0.025542" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RMSE: 0.00875 m\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "display(cal.parameters.loc[:, [\"optimal\"]])\n", "print(f\"RMSE: {cal.rmse():.5f} m\")" @@ -282,24 +169,9 @@ }, { "cell_type": "code", - "execution_count": 19, - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "self.neq 1\n", - "solution complete\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# model with semi-confining top\n", "ml1 = tft.ModelMaq(\n", @@ -317,24 +189,9 @@ }, { "cell_type": "code", - "execution_count": 21, - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "..................................................................................................................\n", - "Fit succeeded.\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "cal1 = tft.Calibrate(ml1)\n", "cal1.set_parameter(name=\"kaq\", initial=50, pmin=0, layers=0)\n", @@ -347,35 +204,9 @@ }, { "cell_type": "code", - "execution_count": 23, - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "kaq_0_0 45.905358\n", - "Saq_0_0 0.000003\n", - "res 0.015366\n", - "Name: optimal, dtype: float64" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RMSE: 0.00606 m\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "display(cal1.parameters[\"optimal\"])\n", "print(f\"RMSE: {cal1.rmse():.5f} m\")" @@ -383,26 +214,9 @@ }, { "cell_type": "code", - "execution_count": 25, - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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timflow semi-confined45.912.60e-060.0150.0061
MLU48.941.04e-050.0010.0076
\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "t = pd.DataFrame(\n", " columns=[\"k [m/d]\", \"Ss [1/m]\", \"res\", \"RMSE [m]\"],\n", @@ -547,21 +304,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python [conda env:base] *", + "display_name": "Python 3", "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": "3.12.2" + "name": "python3" } }, "nbformat": 4, diff --git a/docs/transient/04pumpingtests/leaky3_texashill.ipynb b/docs/transient/04pumpingtests/leaky3_texashill.ipynb index cffb403..a4abfec 100644 --- a/docs/transient/04pumpingtests/leaky3_texashill.ipynb +++ b/docs/transient/04pumpingtests/leaky3_texashill.ipynb @@ -22,14 +22,8 @@ }, { "cell_type": "code", - "execution_count": 1, - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", @@ -86,14 +80,8 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "# data of OW1\n", @@ -130,14 +118,8 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "# known parameters\n", @@ -164,24 +146,9 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "self.neq 1\n", - "solution complete\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "ml = tft.ModelMaq(\n", " kaq=10,\n", @@ -213,24 +180,9 @@ }, { "cell_type": "code", - "execution_count": 15, - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "......................................................\n", - "Fit succeeded.\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# unknown parameters: kaq, Saq, c\n", "cal = tft.Calibrate(ml)\n", @@ -245,74 +197,9 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": { - "editable": true, - "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", - "
optimal
kaq_0_0224.628907
Saq_0_00.000213
c_0_043.882076
\n", - "
" - ], - "text/plain": [ - " optimal\n", - "kaq_0_0 224.628907\n", - "Saq_0_0 0.000213\n", - "c_0_0 43.882076" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RMSE: 0.060 m\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "display(cal.parameters.loc[:, [\"optimal\"]])\n", "print(f\"RMSE: {cal.rmse():.3f} m\")" @@ -320,26 +207,9 @@ }, { "cell_type": "code", - "execution_count": 19, - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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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": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "t = pd.DataFrame(\n", " columns=[\"k [m/d]\", \"Ss [1/m]\", \"c [d]\", \"RMSE [m]\"],\n", @@ -478,21 +298,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python [conda env:base] *", + "display_name": "Python 3", "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": "3.12.2" + "name": "python3" } }, "nbformat": 4, diff --git a/docs/transient/04pumpingtests/slug1_pratt_county.ipynb b/docs/transient/04pumpingtests/slug1_pratt_county.ipynb index d5a6a97..070ad14 100644 --- a/docs/transient/04pumpingtests/slug1_pratt_county.ipynb +++ b/docs/transient/04pumpingtests/slug1_pratt_county.ipynb @@ -28,14 +28,8 @@ }, { "cell_type": "code", - "execution_count": 34, - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", @@ -102,14 +96,8 @@ }, { "cell_type": "code", - "execution_count": 40, - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "data = np.loadtxt(\"data/slug.txt\", skiprows=1)\n", @@ -132,14 +120,8 @@ }, { "cell_type": "code", - "execution_count": 43, - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "rw = 0.125 # well radius, m\n", @@ -166,23 +148,9 @@ }, { "cell_type": "code", - "execution_count": 46, - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "volume of slug: 0.00863 m^3\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "Q = np.pi * rc**2 * H0 # instantaneous discharge\n", "print(f\"volume of slug: {Q:.5f} m^3\")" @@ -204,24 +172,9 @@ }, { "cell_type": "code", - "execution_count": 49, - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "self.neq 1\n", - "solution complete\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "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", @@ -244,24 +197,9 @@ }, { "cell_type": "code", - "execution_count": 52, - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - ".......................\n", - "Fit succeeded.\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "cal = tft.Calibrate(ml)\n", "cal.set_parameter(name=\"kaq\", layers=[0, 1, 2], initial=10)\n", @@ -272,69 +210,9 @@ }, { "cell_type": "code", - "execution_count": 54, - "metadata": { - "editable": true, - "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", - "
optimal
kaq_0_26.048673
Saq_0_20.000213
\n", - "
" - ], - "text/plain": [ - " optimal\n", - "kaq_0_2 6.048673\n", - "Saq_0_2 0.000213" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RMSE: 0.00286 m\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "display(cal.parameters.loc[:, [\"optimal\"]])\n", "print(f\"RMSE: {cal.rmse():.5f} m\")" @@ -342,26 +220,9 @@ }, { "cell_type": "code", - "execution_count": 56, - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", 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timflow6.052.13e-040.0029
AQTESOLV4.033.83e-040.0030
\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 65, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "t = pd.DataFrame(\n", " columns=[\"k [m/d]\", \"Ss [1/m]\", \"RMSE [m]\"],\n", @@ -494,21 +308,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python [conda env:base] *", + "display_name": "Python 3", "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": "3.12.2" + "name": "python3" } }, "nbformat": 4, diff --git a/docs/transient/04pumpingtests/slug2_multiwell.ipynb b/docs/transient/04pumpingtests/slug2_multiwell.ipynb index 0cef705..52773ae 100644 --- a/docs/transient/04pumpingtests/slug2_multiwell.ipynb +++ b/docs/transient/04pumpingtests/slug2_multiwell.ipynb @@ -28,14 +28,8 @@ }, { "cell_type": "code", - "execution_count": 1, - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", @@ -90,14 +84,8 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "data1 = np.loadtxt(\"data/ln-2.txt\")\n", @@ -124,14 +112,8 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "# known parameters\n", @@ -158,23 +140,9 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Slug: 0.02286 m^3\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "Q = np.pi * rc1**2 * H0\n", "print(f\"Slug: {Q:.5f} m^3\")" @@ -182,24 +150,9 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "self.neq 1\n", - "solution complete\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "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", @@ -222,24 +175,9 @@ }, { "cell_type": "code", - "execution_count": 17, - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "...........................................\n", - "Fit succeeded.\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# unknown parameters: kaq, Saq\n", "cal = tft.Calibrate(ml)\n", @@ -252,69 +190,9 @@ }, { "cell_type": "code", - "execution_count": 19, - "metadata": { - "editable": true, - "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", - "
optimal
kaq_0_01.166111
Saq_0_00.000009
\n", - "
" - ], - "text/plain": [ - " optimal\n", - "kaq_0_0 1.166111\n", - "Saq_0_0 0.000009" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RMSE: 0.01024 m\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "display(cal.parameters.loc[:, [\"optimal\"]])\n", "print(f\"RMSE: {cal.rmse():.5f} m\")" @@ -322,26 +200,9 @@ }, { "cell_type": "code", - "execution_count": 21, - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "hm1 = w.headinside(t1)\n", "hm2 = ml.head(r, 0, t2, layers=0)\n", @@ -373,62 +234,9 @@ }, { "cell_type": "code", - "execution_count": 41, - "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]Ss [1/m]RMSE [m]
timflow1.179.38e-060.010
AQTESOLV1.179.37e-06-
MLU1.318.20e-06-
\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "t = pd.DataFrame(\n", " columns=[\"k [m/d]\", \"Ss [1/m]\", \"RMSE [m]\"],\n", @@ -477,21 +285,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python [conda env:base] *", + "display_name": "Python 3", "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": "3.12.2" + "name": "python3" } }, "nbformat": 4, diff --git a/docs/transient/04pumpingtests/slug3_falling_head.ipynb b/docs/transient/04pumpingtests/slug3_falling_head.ipynb index e711cfa..afb000f 100644 --- a/docs/transient/04pumpingtests/slug3_falling_head.ipynb +++ b/docs/transient/04pumpingtests/slug3_falling_head.ipynb @@ -22,14 +22,8 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", @@ -66,7 +60,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -84,7 +78,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -108,17 +102,9 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "slug: 0.00366 m^3\n" - ] - } - ], + "outputs": [], "source": [ "Q = np.pi * rc**2 * H0\n", "print(f\"slug: {Q:.5f} m^3\")" @@ -133,20 +119,9 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "18" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# z = np.array([zt, zt - 0.1, zst, zsb, zb])\n", "z = np.hstack(\n", @@ -163,41 +138,18 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0. , -0.01 , -0.043314, -0.076628, -0.109942, -0.143256,\n", - " -1.194816, -2.246376, -3.297936, -4.349496, -4.969256, -5.589016,\n", - " -6.208776, -6.828536, -7.448296, -8.068056, -8.687816, -9.307576,\n", - " -9.927336])" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "z" ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "self.neq 5\n", - "solution complete\n" - ] - } - ], + "outputs": [], "source": [ "ml = tft.Model3D(\n", " kaq=10,\n", @@ -223,30 +175,9 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "r = np.linspace(-10, 10, 100)\n", "h = ml.headalongline(r, 0, 0.01).squeeze()\n", @@ -257,30 +188,9 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "t = np.logspace(-5, -2, 100)\n", "h = ml.head(0, 0, t)\n", @@ -290,30 +200,9 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.plot(z)" ] @@ -327,36 +216,9 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - ".....................................\n", - "Fit succeeded.\n" - ] - }, - { - "data": { - "text/plain": [ - "kaq_1_17 0.439172\n", - "Saq_1_17 0.000461\n", - "Name: optimal, dtype: float64" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RMSE: 0.006011434252180949\n" - ] - } - ], + "outputs": [], "source": [ "cal = tft.Calibrate(ml)\n", "cal.set_parameter(name=\"kaq\", layers=list(range(1, nlay)), initial=10, pmin=0)\n", @@ -369,20 +231,9 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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timflow0.444.61e-040.006
AQTESOLV0.425.70e-04-
\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "t = pd.DataFrame(\n", " columns=[\"k [m/d]\", \"Ss [1/m]\", \"RMSE [m]\"],\n", @@ -493,21 +305,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python [conda env:base] *", + "display_name": "Python 3", "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": "3.12.2" + "name": "python3" } }, "nbformat": 4, diff --git a/docs/transient/05benchmarks/compare_wells_linesink.ipynb b/docs/transient/05benchmarks/compare_wells_linesink.ipynb index 9a76af1..61d629d 100644 --- a/docs/transient/05benchmarks/compare_wells_linesink.ipynb +++ b/docs/transient/05benchmarks/compare_wells_linesink.ipynb @@ -116,8 +116,10 @@ } ], "metadata": { - "language_info": { - "name": "python" + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" } }, "nbformat": 4, diff --git a/docs/transient/05benchmarks/line-sink-ditch.ipynb b/docs/transient/05benchmarks/line-sink-ditch.ipynb index 8ba4b73..d663af7 100644 --- a/docs/transient/05benchmarks/line-sink-ditch.ipynb +++ b/docs/transient/05benchmarks/line-sink-ditch.ipynb @@ -89,8 +89,10 @@ } ], "metadata": { - "language_info": { - "name": "python" + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" } }, "nbformat": 4, diff --git a/docs/transient/05benchmarks/synthetic0_data.ipynb b/docs/transient/05benchmarks/synthetic0_data.ipynb index da29fe6..a8ede69 100644 --- a/docs/transient/05benchmarks/synthetic0_data.ipynb +++ b/docs/transient/05benchmarks/synthetic0_data.ipynb @@ -638,8 +638,10 @@ } ], "metadata": { - "language_info": { - "name": "python" + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" } }, "nbformat": 4, diff --git a/docs/transient/05benchmarks/synthetic_calibrate_2aquifers.ipynb b/docs/transient/05benchmarks/synthetic_calibrate_2aquifers.ipynb index e4ff8e1..9d764cf 100644 --- a/docs/transient/05benchmarks/synthetic_calibrate_2aquifers.ipynb +++ b/docs/transient/05benchmarks/synthetic_calibrate_2aquifers.ipynb @@ -258,8 +258,10 @@ } ], "metadata": { - "language_info": { - "name": "python" + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" } }, "nbformat": 4, diff --git a/docs/transient/05benchmarks/synthetic_test_calibrate.ipynb b/docs/transient/05benchmarks/synthetic_test_calibrate.ipynb index a1a4a8e..41ce949 100644 --- a/docs/transient/05benchmarks/synthetic_test_calibrate.ipynb +++ b/docs/transient/05benchmarks/synthetic_test_calibrate.ipynb @@ -268,8 +268,10 @@ } ], "metadata": { - "language_info": { - "name": "python" + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" } }, "nbformat": 4, diff --git a/docs/transient/05benchmarks/test_line_elements.ipynb b/docs/transient/05benchmarks/test_line_elements.ipynb index cf8366c..a6f2b78 100644 --- a/docs/transient/05benchmarks/test_line_elements.ipynb +++ b/docs/transient/05benchmarks/test_line_elements.ipynb @@ -96,8 +96,10 @@ } ], "metadata": { - "language_info": { - "name": "python" + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" } }, "nbformat": 4, diff --git a/docs/transient/05benchmarks/theis_storage.ipynb b/docs/transient/05benchmarks/theis_storage.ipynb index 360029d..5012ac3 100644 --- a/docs/transient/05benchmarks/theis_storage.ipynb +++ b/docs/transient/05benchmarks/theis_storage.ipynb @@ -222,8 +222,10 @@ } ], "metadata": { - "language_info": { - "name": "python" + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" } }, "nbformat": 4, diff --git a/docs/transient/05benchmarks/ttim_neuman_comparison.ipynb b/docs/transient/05benchmarks/ttim_neuman_comparison.ipynb index c89bcfe..cbe973b 100644 --- a/docs/transient/05benchmarks/ttim_neuman_comparison.ipynb +++ b/docs/transient/05benchmarks/ttim_neuman_comparison.ipynb @@ -117,8 +117,10 @@ } ], "metadata": { - "language_info": { - "name": "python" + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" } }, "nbformat": 4, diff --git a/docs/transient/05benchmarks/validation_tidal_wave_with_Bruggeman.ipynb b/docs/transient/05benchmarks/validation_tidal_wave_with_Bruggeman.ipynb index 22f493e..d8b145b 100644 --- a/docs/transient/05benchmarks/validation_tidal_wave_with_Bruggeman.ipynb +++ b/docs/transient/05benchmarks/validation_tidal_wave_with_Bruggeman.ipynb @@ -505,8 +505,10 @@ } ], "metadata": { - "language_info": { - "name": "python" + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" } }, "nbformat": 4, diff --git a/docs/transient/05benchmarks/well_benchmarks.ipynb b/docs/transient/05benchmarks/well_benchmarks.ipynb index b5de1d6..058ab7b 100644 --- a/docs/transient/05benchmarks/well_benchmarks.ipynb +++ b/docs/transient/05benchmarks/well_benchmarks.ipynb @@ -488,8 +488,10 @@ } ], "metadata": { - "language_info": { - "name": "python" + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" } }, "nbformat": 4, diff --git a/docs/transient/05benchmarks/well_near_river_or_wall.ipynb b/docs/transient/05benchmarks/well_near_river_or_wall.ipynb index 3a86e1b..26eeae6 100644 --- a/docs/transient/05benchmarks/well_near_river_or_wall.ipynb +++ b/docs/transient/05benchmarks/well_near_river_or_wall.ipynb @@ -117,8 +117,10 @@ } ], "metadata": { - "language_info": { - "name": "python" + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" } }, "nbformat": 4, diff --git a/utils/clear_notebooks.py b/utils/clear_notebooks.py index e562cda..24b51f2 100644 --- a/utils/clear_notebooks.py +++ b/utils/clear_notebooks.py @@ -6,20 +6,7 @@ ClearOutputPreprocessor, ) -nbdirs = [ - # steady - Path("docs/steady/00userguide/tutorials"), - Path("docs/steady/00userguide/howtos"), - Path("docs/steady/02examples"), - Path("docs/steady/03xsections"), - Path("docs/steady/04benchmarks"), - # transient - Path("docs/transient/00userguide/tutorials"), - Path("docs/transient/00userguide/howtos"), - Path("docs/transient/02examples"), - Path("docs/transient/03xsections"), - Path("docs/transient/05benchmarks"), -] +nbroots = [Path("docs/steady"), Path("docs/transient")] skip = [ "benchmarking_besselaes.ipynb", @@ -30,13 +17,21 @@ def get_notebooks(): nblist = [] - for nbdir in nbdirs: - nblist += [nb for nb in nbdir.glob("*.ipynb") if nb.name not in skip] - return nblist + for root in nbroots: + for nb in root.rglob("*.ipynb"): + if nb.name in skip: + continue + if "_build" in nb.parts: + continue + nblist.append(nb) + return sorted(nblist) clear_output = ClearOutputPreprocessor() -clear_metadata = ClearMetadataPreprocessor() + +# 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) @@ -46,4 +41,11 @@ def get_notebooks(): 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) From 36aadd2523780270ed4553d7fdd879b6341520b5 Mon Sep 17 00:00:00 2001 From: dbrakenhoff Date: Thu, 12 Mar 2026 12:51:27 +0100 Subject: [PATCH 4/7] ruff --- .../03xsections/river_in_cross_section.ipynb | 12 ++++++++---- 1 file changed, 8 insertions(+), 4 deletions(-) diff --git a/docs/transient/03xsections/river_in_cross_section.ipynb b/docs/transient/03xsections/river_in_cross_section.ipynb index 1c0a2d4..c9296da 100644 --- a/docs/transient/03xsections/river_in_cross_section.ipynb +++ b/docs/transient/03xsections/river_in_cross_section.ipynb @@ -307,7 +307,9 @@ "cal.xsection(ax=ax)\n", "ax.set_xlabel(\"x-coordinate [m]\")\n", "ax.set_ylabel(\"elevation [m]\")\n", - "fig.suptitle(\"Cross-section across a river of a semi-confined aquifer with 2 observation wells\")\n", + "fig.suptitle(\n", + " \"Cross-section across a river of a semi-confined aquifer with 2 observation wells\"\n", + ")\n", "fig.savefig(\"river_cross_section.png\", dpi=150, bbox_inches=\"tight\")" ] }, @@ -574,7 +576,7 @@ "ml.elementlist[1].plot(ax=ax, hstar=10.0)\n", "ax.set_xlim(x0, 100.0)\n", "ax.set_ylim(-30, 27)\n", - "cal.xsection(ax=ax);\n", + "cal.xsection(ax=ax)\n", "ax.set_ylabel(\"elevation [m]\")\n", "ax.set_xlabel(\"x-coordinate [m]\")" ] @@ -609,11 +611,13 @@ " axes[i].set_title(f\"Observation well {i + 1} (x = {xlocs[i]} m)\", loc=\"right\")\n", " axes[i].legend(loc=(0, 1), frameon=False, ncol=2)\n", " axes[i].grid()\n", - " \n", + "\n", "for iax in axes:\n", " iax.set_ylabel(\"head change [m]\")\n", "axes[-1].set_xlabel(\"time [d]\")\n", - "axes[-1].figure.suptitle(\"Calibration result of timflow cross-section model compared to observed heads.\")\n", + "axes[-1].figure.suptitle(\n", + " \"Calibration result of timflow cross-section model compared to observed heads.\"\n", + ")\n", "axes[-1].figure.savefig(\"calibrated_head_response.png\", bbox_inches=\"tight\", dpi=150)" ] }, From 342c870b71e912da50a6438b5c90b7f977abc417 Mon Sep 17 00:00:00 2001 From: dbrakenhoff Date: Thu, 12 Mar 2026 12:54:38 +0100 Subject: [PATCH 5/7] forgot I had messed around in the xsection nb --- .../03xsections/river_in_cross_section.ipynb | 13 ++++--------- 1 file changed, 4 insertions(+), 9 deletions(-) diff --git a/docs/transient/03xsections/river_in_cross_section.ipynb b/docs/transient/03xsections/river_in_cross_section.ipynb index c9296da..57b1034 100644 --- a/docs/transient/03xsections/river_in_cross_section.ipynb +++ b/docs/transient/03xsections/river_in_cross_section.ipynb @@ -304,12 +304,9 @@ " units={\"k\": \"m/d\", \"Ss\": \"m$^{-1}$\", \"c\": \"d\"},\n", " fontsize=8,\n", ")\n", - "cal.xsection(ax=ax)\n", "ax.set_xlabel(\"x-coordinate [m]\")\n", "ax.set_ylabel(\"elevation [m]\")\n", - "fig.suptitle(\n", - " \"Cross-section across a river of a semi-confined aquifer with 2 observation wells\"\n", - ")\n", + "fig.suptitle(\"Cross-section across a river of a semi-confined aquifer\")\n", "fig.savefig(\"river_cross_section.png\", dpi=150, bbox_inches=\"tight\")" ] }, @@ -576,7 +573,7 @@ "ml.elementlist[1].plot(ax=ax, hstar=10.0)\n", "ax.set_xlim(x0, 100.0)\n", "ax.set_ylim(-30, 27)\n", - "cal.xsection(ax=ax)\n", + "cal.xsection(ax=ax);\n", "ax.set_ylabel(\"elevation [m]\")\n", "ax.set_xlabel(\"x-coordinate [m]\")" ] @@ -611,13 +608,11 @@ " axes[i].set_title(f\"Observation well {i + 1} (x = {xlocs[i]} m)\", loc=\"right\")\n", " axes[i].legend(loc=(0, 1), frameon=False, ncol=2)\n", " axes[i].grid()\n", - "\n", + " \n", "for iax in axes:\n", " iax.set_ylabel(\"head change [m]\")\n", "axes[-1].set_xlabel(\"time [d]\")\n", - "axes[-1].figure.suptitle(\n", - " \"Calibration result of timflow cross-section model compared to observed heads.\"\n", - ")\n", + "axes[-1].figure.suptitle(\"Calibration result of timflow cross-section model compared to observed heads.\")\n", "axes[-1].figure.savefig(\"calibrated_head_response.png\", bbox_inches=\"tight\", dpi=150)" ] }, From 3037b61cfdb3b4fc3a80dd7779c9e425d1361361 Mon Sep 17 00:00:00 2001 From: dbrakenhoff Date: Thu, 12 Mar 2026 12:55:38 +0100 Subject: [PATCH 6/7] more ruff --- docs/transient/03xsections/river_in_cross_section.ipynb | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/docs/transient/03xsections/river_in_cross_section.ipynb b/docs/transient/03xsections/river_in_cross_section.ipynb index 57b1034..375fa94 100644 --- a/docs/transient/03xsections/river_in_cross_section.ipynb +++ b/docs/transient/03xsections/river_in_cross_section.ipynb @@ -573,7 +573,7 @@ "ml.elementlist[1].plot(ax=ax, hstar=10.0)\n", "ax.set_xlim(x0, 100.0)\n", "ax.set_ylim(-30, 27)\n", - "cal.xsection(ax=ax);\n", + "cal.xsection(ax=ax)\n", "ax.set_ylabel(\"elevation [m]\")\n", "ax.set_xlabel(\"x-coordinate [m]\")" ] @@ -608,11 +608,13 @@ " axes[i].set_title(f\"Observation well {i + 1} (x = {xlocs[i]} m)\", loc=\"right\")\n", " axes[i].legend(loc=(0, 1), frameon=False, ncol=2)\n", " axes[i].grid()\n", - " \n", + "\n", "for iax in axes:\n", " iax.set_ylabel(\"head change [m]\")\n", "axes[-1].set_xlabel(\"time [d]\")\n", - "axes[-1].figure.suptitle(\"Calibration result of timflow cross-section model compared to observed heads.\")\n", + "axes[-1].figure.suptitle(\n", + " \"Calibration result of timflow cross-section model compared to observed heads.\"\n", + ")\n", "axes[-1].figure.savefig(\"calibrated_head_response.png\", bbox_inches=\"tight\", dpi=150)" ] }, From 43165f2053aa35bd21f3171e812794f7bd31ed3b Mon Sep 17 00:00:00 2001 From: Hannah Heesen Date: Sun, 15 Mar 2026 22:12:56 +0700 Subject: [PATCH 7/7] More pumping tests examples --- .../confined1_oude_korendijk.ipynb | 166 +++++- .../04pumpingtests/confined2_grindley.ipynb | 20 +- .../04pumpingtests/confined3_sioux.ipynb | 12 + .../04pumpingtests/confined4_nevada.ipynb | 526 ++++++++++++++++++ .../04pumpingtests/figs/Dawsonville.png | Bin 0 -> 24690 bytes docs/transient/04pumpingtests/figs/Moench.png | Bin 0 -> 31379 bytes .../04pumpingtests/figs/Vennebulten.png | Bin 0 -> 20566 bytes .../04pumpingtests/leaky1_dalem.ipynb | 14 +- .../04pumpingtests/leaky2_hardixveld.ipynb | 12 + .../04pumpingtests/leaky3_texashill.ipynb | 12 + .../04pumpingtests/slug1_pratt_county.ipynb | 12 + .../04pumpingtests/slug2_multiwell.ipynb | 178 +++++- .../04pumpingtests/slug3_falling_head.ipynb | 12 + .../04pumpingtests/slug4_dawsonville.ipynb | 409 ++++++++++++++ .../04pumpingtests/unconfined2_moench.ipynb | 420 ++++++++++++++ 15 files changed, 1763 insertions(+), 30 deletions(-) create mode 100644 docs/transient/04pumpingtests/confined4_nevada.ipynb create mode 100644 docs/transient/04pumpingtests/figs/Dawsonville.png create mode 100644 docs/transient/04pumpingtests/figs/Moench.png create mode 100644 docs/transient/04pumpingtests/figs/Vennebulten.png create mode 100644 docs/transient/04pumpingtests/slug4_dawsonville.ipynb create mode 100644 docs/transient/04pumpingtests/unconfined2_moench.ipynb diff --git a/docs/transient/04pumpingtests/confined1_oude_korendijk.ipynb b/docs/transient/04pumpingtests/confined1_oude_korendijk.ipynb index 555473c..e0b771b 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": 3, "metadata": {}, "outputs": [], "source": [ @@ -67,7 +67,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -92,7 +92,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -105,9 +105,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "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", @@ -125,9 +134,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".................................\n", + "Fit succeeded.\n" + ] + } + ], "source": [ "# unknown parameters: kaq, Saq\n", "cal = tft.Calibrate(ml)\n", @@ -140,9 +158,63 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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optimal
kaq_0_066.089354
Saq_0_00.000025
\n", + "
" + ], + "text/plain": [ + " optimal\n", + "kaq_0_0 66.089354\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 +222,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "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": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "t = pd.DataFrame(\n", " columns=[\"k [m/d]\", \"Ss [1/m]\", \"RMSE [m]\"],\n", @@ -241,6 +369,18 @@ "display_name": "Python 3", "language": "python", "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.3" } }, "nbformat": 4, diff --git a/docs/transient/04pumpingtests/confined2_grindley.ipynb b/docs/transient/04pumpingtests/confined2_grindley.ipynb index b1a6713..805dec5 100644 --- a/docs/transient/04pumpingtests/confined2_grindley.ipynb +++ b/docs/transient/04pumpingtests/confined2_grindley.ipynb @@ -302,10 +302,16 @@ "tags": [] }, "source": [ - "* Carlson, F. and Randall, J. (2012), MLU: a Windows application for the analysis of aquifer tests and the design of well fields in layered systems, Ground Water 50(4):504–510\n", "* 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": { @@ -313,6 +319,18 @@ "display_name": "Python 3", "language": "python", "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.3" } }, "nbformat": 4, diff --git a/docs/transient/04pumpingtests/confined3_sioux.ipynb b/docs/transient/04pumpingtests/confined3_sioux.ipynb index 6a5530c..d14cd69 100644 --- a/docs/transient/04pumpingtests/confined3_sioux.ipynb +++ b/docs/transient/04pumpingtests/confined3_sioux.ipynb @@ -299,6 +299,18 @@ "display_name": "Python 3", "language": "python", "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.3" } }, "nbformat": 4, diff --git a/docs/transient/04pumpingtests/confined4_nevada.ipynb b/docs/transient/04pumpingtests/confined4_nevada.ipynb new file mode 100644 index 0000000..0e8310b --- /dev/null +++ b/docs/transient/04pumpingtests/confined4_nevada.ipynb @@ -0,0 +1,526 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 4. Confined Aquifer Test - Nevada" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import packages" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "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": {}, + "source": [ + "This example is taken from Kruseman and de Ridder (1990). It is based on a pumping test conducted in a fractured tertiary volcanic aquifer near Yucca Mountains, Nevada, US. \n", + "\n", + "The borehole extends to 1219 m depth and penetrates a 400 m thick sequence of fractured volcanic rocks with water entry points. At every entry point, the head is more or less the same, so they have good hydraulic connection. The water table is about 470 m below land surface, which indicates confined conditions. Drawdown data was monitored at the well, named ***UE-25b#1*** and at an observation well, named ***UE-25a#1***, 110 m away. Three pumping tests were conducted at the site and will be the object of our investigation. \n", + "\n", + "Although conventional Darcy flow does not apply to flow in discrete fractures, the fracture network at this site is sufficiently pervasive to justify the application of Darcy’s law at the aquifer scale. Groundwater flow to the well occurs through fractures, while the matrix exchanges water with the fracture network. For implementation in `timflow`, the fractured aquifer system is approximated using a ModelMaq configuration. This is achieved by representing the matrix as an upper aquifer layer, separated by a 1 m thick aquitard from a lower aquifer layer representing the fracture network." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "scrolled": true, + "tags": [ + "hide-input" + ] + }, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load Data" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# time and drawdown of pumped well UE-25b#1\n", + "data1 = np.loadtxt(\"data/double-porosity-pumpingwell.txt\", skiprows=1)\n", + "t1 = data1[:, 0] # days\n", + "h1 = data1[:, 1] # m\n", + "\n", + "# time and drawdown of observation well UE-25a#1\n", + "data2 = np.loadtxt(\"data/double-porosity-110m.txt\", skiprows=1)\n", + "t2 = data2[:, 0]\n", + "h2 = data2[:, 1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Paramaters and model" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# known parameters\n", + "H = 400 # aquifer thickness in m\n", + "Q = 3093.12 # constant pumping rate in m^3/d\n", + "r = 110 # distance from pumping well to observation well in m" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For the `timflow` model, we will adopt the parameters for the first layer from the results obtained from Kruseman and de Ridder (1970). " + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "# parameters calculated by Kruseman and de Ridder (1970)\n", + "km = 0.1 / H # hydraulic conductivity of matrix\n", + "Sm = 3.75e-4 # specific storage of matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml = tft.ModelMaq(\n", + " kaq=[km, 1],\n", + " z=[0, -400, -401, -801],\n", + " c=5,\n", + " Saq=[Sm, 1e-3],\n", + " Sll=0,\n", + " topboundary=\"conf\",\n", + " tmin=1e-5,\n", + " tmax=3,\n", + ")\n", + "w = tft.Well(ml, xw=0, yw=0, rw=0.11, rc=0, tsandQ=[0, Q], layers=1)\n", + "ml.solve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Estimate aquifer parameters\n", + "The aquifer parameters are estimated using head observations at both wells." + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".........................................................................................................................................................................................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 198\n", + " # data points = 138\n", + " # variables = 4\n", + " chi-square = 5.38947309\n", + " reduced chi-square = 0.04021995\n", + " Akaike info crit = -439.507237\n", + " Bayesian info crit = -427.798222\n", + "[[Variables]]\n", + " kaq_1_1: 0.87713604 +/- 0.00687861 (0.78%) (init = 10)\n", + " Saq_1_1: 5.0658e-06 +/- 4.9878e-07 (9.85%) (init = 0.0001)\n", + " c_1_1: 12.9124741 +/- 1.56932257 (12.15%) (init = 10)\n", + " rc: 0.10567818 +/- 0.00318208 (3.01%) (init = 0)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq_1_1, c_1_1) = +0.8555\n", + " C(kaq_1_1, Saq_1_1) = -0.7271\n", + " C(Saq_1_1, c_1_1) = -0.5393\n", + " C(Saq_1_1, rc) = -0.4022\n" + ] + } + ], + "source": [ + "cal = tft.Calibrate(ml)\n", + "cal.set_parameter(name=\"kaq\", initial=10, layers=1)\n", + "cal.set_parameter(name=\"Saq\", initial=1e-4, pmin=0, layers=1)\n", + "cal.set_parameter(name=\"c\", initial=10, layers=1)\n", + "cal.set_parameter_by_reference(name=\"rc\", parameter=w.rc, initial=0)\n", + "\n", + "cal.seriesinwell(name=\"UE-25b#1\", element=w, t=t1, h=h1)\n", + "cal.series(name=\"UE-25a#1\", x=r, y=0, t=t2, h=h2, layer=1)\n", + "cal.fit(report=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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layersoptimalstdperc_stdpminpmaxinitialinhomsparray
kaq_1_11.00.8771366.878610e-030.784212-infinf10.0000None[[0.8771360363656927]]
Saq_1_11.00.0000054.987801e-079.8459950.0inf0.0001None[[5.0658166983463815e-06]]
c_1_11.012.9124741.569323e+0012.153539-infinf10.0000None[[12.912474111111717]]
rcNaN0.1056783.182079e-033.011103-infinf0.0000NaN[[0.1056781836698406]]
\n", + "
" + ], + "text/plain": [ + " layers optimal std perc_std pmin pmax initial \\\n", + "kaq_1_1 1.0 0.877136 6.878610e-03 0.784212 -inf inf 10.0000 \n", + "Saq_1_1 1.0 0.000005 4.987801e-07 9.845995 0.0 inf 0.0001 \n", + "c_1_1 1.0 12.912474 1.569323e+00 12.153539 -inf inf 10.0000 \n", + "rc NaN 0.105678 3.182079e-03 3.011103 -inf inf 0.0000 \n", + "\n", + " inhoms parray \n", + "kaq_1_1 None [[0.8771360363656927]] \n", + "Saq_1_1 None [[5.0658166983463815e-06]] \n", + "c_1_1 None [[12.912474111111717]] \n", + "rc NaN [[0.1056781836698406]] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.19762123575324378\n" + ] + } + ], + "source": [ + "display(cal.parameters)\n", + "print(\"RMSE:\", cal.rmse())" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hm1 = w.headinside(t1)\n", + "hm2 = ml.head(r, 0, t2)\n", + "plt.figure(figsize=(8, 5))\n", + "plt.semilogx(t1, -h1, \".\", label=\"obs UE-25b#1\")\n", + "plt.semilogx(t1, -hm1[-1], label=\"timflow UE-25b#1\")\n", + "plt.semilogx(t2, -h2, \".\", label=\"obs UE-25a#1\")\n", + "plt.semilogx(t2, -hm2[-1], label=\"timflow UE-25a#1\")\n", + "plt.xlabel(\"time [d]\")\n", + "plt.ylabel(\"drawdown [m]\")\n", + "plt.title(\"Model Results\")\n", + "plt.legend()\n", + "plt.grid();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Comparison of results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The performance of `timflow` was evaluated by comparison to the results obtained from the software AQTESOLV (Duffield, 2007), MLU (Hemker & Post, 2014), and Kruseman and de Ridder (1990), here abbreviated to K&dR.\n", + "\n", + "The problem is solved in AQTESOLV using a double-porosity analytical solution proposed by Moench (1984). The MLU (Hemker & Post, 2014) solution used a similar approach to our `timflow` model by simulating the matrix as a very-low transmissivity aquifer on top of the fractured aquifer and separated by a zero-storage aquitard. However, the calibrated MLU model only fits the observations of well UE-25b#1. When the observations of well UE-25a#1 are included, the fit is not as good as initially thought. Kruseman et al. (1990) solved the problem using a graphical method, where the transmissivity was calculated as one aquifer and the storativity was separated between matrix and fractures. \n", + "\n", + "Overall, `timflow` showed similar results to MLU but with a better fit since both observation wells are taken into account with the `timflow` model. While the AQTESOLV obtained the best fit using Moench's analytical solution." + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "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", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 km [m/d]Sm [1/m]kf [m/d]Sf [1/m]c [d]rc [m]RMSE [m]
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": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "t = pd.DataFrame(\n", + " columns=[\n", + " \"km [m/d]\",\n", + " \"Sm [1/m]\",\n", + " \"kf [m/d]\",\n", + " \"Sf [1/m]\",\n", + " \"c [d]\",\n", + " \"rc [m]\",\n", + " \"RMSE [m]\",\n", + " ],\n", + " index=[\"timflow\", \"AQTESOLV\", \"MLU\", \"K&dR\"],\n", + ")\n", + "\n", + "t.loc[\"timflow\"] = np.concatenate(\n", + " [np.array([0.00025, 3.750e-04]), cal.parameters[\"optimal\"].values, [cal.rmse()]]\n", + ")\n", + "t.loc[\"AQTESOLV\"] = [0.1513, 5.4800e-4, 0.9366, 1.9508e-3, \"-\", 0.11, \"-\"]\n", + "t.loc[\"MLU\"] = [0.00025, 3.750e-04, 0.8351125, 8.05e-6, 5.2035, \"-\", \"-\"]\n", + "t.loc[\"K&dR\"] = [0.8325, 3.750e-4, 0.8325, 4.000e-6, \"-\", \"-\", \"-\"]\n", + "\n", + "t_formatted = t.style.format(\n", + " {\n", + " \"km [m/d]\": \"{:.2f}\",\n", + " \"Sm [1/m]\": \"{:.2e}\",\n", + " \"kf [m/d]\": \"{:.2f}\",\n", + " \"Sf [1/m]\": \"{:.2e}\",\n", + " \"c [d]\": lambda x: \"-\" if x == \"-\" else f\"{float(x):.2f}\",\n", + " \"rc [m]\": lambda x: \"-\" if x == \"-\" else f\"{float(x):.2f}\",\n", + " \"RMSE [m]\": lambda x: \"-\" if x == \"-\" else f\"{float(x):.3f}\",\n", + " }\n", + ")\n", + "t_formatted" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 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", + "* 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." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/transient/04pumpingtests/figs/Dawsonville.png b/docs/transient/04pumpingtests/figs/Dawsonville.png new file mode 100644 index 0000000000000000000000000000000000000000..b5464b26cfd331dfb43793d9661a5c975b9af4e8 GIT binary patch literal 24690 zcmeFZbySsow=TTIphQqY5d=Xx1OX{27a$!f-AYOeB3+N9NOwz@fV4DKw#Z_AuKE}AuN1b-paz@g{eLQA?_O=VXN?TkjT5E 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"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", @@ -349,6 +349,18 @@ "display_name": "Python 3", "language": "python", "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.3" } }, "nbformat": 4, diff --git a/docs/transient/04pumpingtests/leaky2_hardixveld.ipynb b/docs/transient/04pumpingtests/leaky2_hardixveld.ipynb index 08d9c43..3e51f70 100644 --- a/docs/transient/04pumpingtests/leaky2_hardixveld.ipynb +++ b/docs/transient/04pumpingtests/leaky2_hardixveld.ipynb @@ -307,6 +307,18 @@ "display_name": "Python 3", "language": "python", "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.3" } }, "nbformat": 4, diff --git a/docs/transient/04pumpingtests/leaky3_texashill.ipynb b/docs/transient/04pumpingtests/leaky3_texashill.ipynb index a4abfec..351f2fd 100644 --- a/docs/transient/04pumpingtests/leaky3_texashill.ipynb +++ b/docs/transient/04pumpingtests/leaky3_texashill.ipynb @@ -301,6 +301,18 @@ "display_name": "Python 3", "language": "python", "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.3" } }, "nbformat": 4, diff --git a/docs/transient/04pumpingtests/slug1_pratt_county.ipynb b/docs/transient/04pumpingtests/slug1_pratt_county.ipynb index 070ad14..2dbe096 100644 --- a/docs/transient/04pumpingtests/slug1_pratt_county.ipynb +++ b/docs/transient/04pumpingtests/slug1_pratt_county.ipynb @@ -311,6 +311,18 @@ "display_name": "Python 3", "language": "python", "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.3" } }, "nbformat": 4, diff --git a/docs/transient/04pumpingtests/slug2_multiwell.ipynb b/docs/transient/04pumpingtests/slug2_multiwell.ipynb index 52773ae..50f2705 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": 5, "metadata": {}, "outputs": [], "source": [ @@ -112,7 +112,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -140,9 +140,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "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": 14, "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 +192,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "...................................\n", + "Fit succeeded.\n" + ] + } + ], "source": [ "# unknown parameters: kaq, Saq\n", "cal = tft.Calibrate(ml)\n", @@ -190,9 +216,63 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "

" + ], + "text/plain": [ + " optimal\n", + "kaq_0_0 1.166107\n", + "Saq_0_0 0.000009" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.01024 m\n" + ] + } + ], "source": [ "display(cal.parameters.loc[:, [\"optimal\"]])\n", "print(f\"RMSE: {cal.rmse():.5f} m\")" @@ -200,9 +280,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "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": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "t = pd.DataFrame(\n", " columns=[\"k [m/d]\", \"Ss [1/m]\", \"RMSE [m]\"],\n", @@ -288,6 +424,18 @@ "display_name": "Python 3", "language": "python", "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.3" } }, "nbformat": 4, diff --git a/docs/transient/04pumpingtests/slug3_falling_head.ipynb b/docs/transient/04pumpingtests/slug3_falling_head.ipynb index afb000f..3c4bb4a 100644 --- a/docs/transient/04pumpingtests/slug3_falling_head.ipynb +++ b/docs/transient/04pumpingtests/slug3_falling_head.ipynb @@ -308,6 +308,18 @@ "display_name": "Python 3", "language": "python", "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.3" } }, "nbformat": 4, diff --git a/docs/transient/04pumpingtests/slug4_dawsonville.ipynb b/docs/transient/04pumpingtests/slug4_dawsonville.ipynb new file mode 100644 index 0000000..667d2fe --- /dev/null +++ b/docs/transient/04pumpingtests/slug4_dawsonville.ipynb @@ -0,0 +1,409 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 4. Slug test for confined aquifer - Dawsonville" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import packages" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "\n", + "import timflow.transient as tft\n", + "\n", + "plt.rcParams[\"figure.figsize\"] = [5, 3]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "### Introduction and Conceptual Model\n", + "\n", + "This Slug Test, was reported by Cooper Jr et al. (1967), and it was performed in Dawsonville, Georgia, USA. \n", + "\n", + "A fully penetrated well (Ln-2) is screened in a confined aquifer, located between depths 24 and 122 (98 m thick). The volume of the slug is 10.16 litres. Head change has been recorded at the slug well. Both the well and the casing radii of the slug well is 0.076 m." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load data" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "data = np.loadtxt(\"data/dawsonville_slug.txt\")\n", + "to = data[:, 0]\n", + "ho = data[:, 1]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "### Parameters and model" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "# known parameters\n", + "b = 98 # aquifer thickness in m\n", + "zt = -24 # top of aquifer in m\n", + "zb = zt - b # bottom of aquifer in m\n", + "rw = 0.076 # well radius of Ln-2 Well in m\n", + "rc = 0.076 # casing radius of Ln-2 Well in m\n", + "Q = 10.16 / 1000 # slug volume in m^3 (10.16 l = 0.01016 m^3)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "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", + "ml.solve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Estimate aquifer parameters\n", + "\n", + "We calibrate hydraulic conductivity and specific storage, as in the KGS model (Hyder et al. 1994)." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".....................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 34\n", + " # data points = 22\n", + " # variables = 2\n", + " chi-square = 4.2779e-04\n", + " reduced chi-square = 2.1389e-05\n", + " Akaike info crit = -234.654459\n", + " Bayesian info crit = -232.472374\n", + "[[Variables]]\n", + " kaq0_0_0: 0.42101398 +/- 0.01839446 (4.37%) (init = 10)\n", + " Saq0_0_0: 1.6975e-05 +/- 5.2892e-06 (31.16%) (init = 0.0001)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0_0_0, Saq0_0_0) = -0.9853\n" + ] + } + ], + "source": [ + "# unknown parameters: kay, Saq\n", + "cal = tft.Calibrate(ml)\n", + "cal.set_parameter(name=\"kaq0\", initial=10, pmin=0, layers=0)\n", + "cal.set_parameter(name=\"Saq0\", initial=1e-4, layers=0)\n", + "cal.seriesinwell(name=\"obs\", element=w, t=to, h=ho)\n", + "cal.fit(report=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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layersoptimalstdperc_stdpminpmaxinitialinhomsparray
kaq0_0_000.4210140.0183944.3690860.0inf10.0000None[[0.42101397957109743]]
Saq0_0_000.0000170.00000531.159403-infinf0.0001None[[1.697465569017154e-05]]
\n", + "
" + ], + "text/plain": [ + " layers optimal std perc_std pmin pmax initial inhoms \\\n", + "kaq0_0_0 0 0.421014 0.018394 4.369086 0.0 inf 10.0000 None \n", + "Saq0_0_0 0 0.000017 0.000005 31.159403 -inf inf 0.0001 None \n", + "\n", + " parray \n", + "kaq0_0_0 [[0.42101397957109743]] \n", + "Saq0_0_0 [[1.697465569017154e-05]] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "rmse: 0.004409650627757543\n" + ] + } + ], + "source": [ + "display(cal.parameters)\n", + "print(\"rmse:\", cal.rmse())" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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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)\n", + "plt.semilogx(to, ho, \".\", label=\"obs\")\n", + "plt.semilogx(tm, hm[0], label=\"timflow\")\n", + "plt.xlabel(\"time [d]\")\n", + "plt.ylabel(\"drawdown [m]\")\n", + "plt.title(\"Model Results - Single-layer model\")\n", + "plt.legend()\n", + "plt.grid()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Comparison of results\n", + "\n", + "We now compare the values in `timflow` and add the results of the modelling done in MLU (Hemker & Post, 2014). Results are similar between both models. The RMSE of MLU is slightly better than the one from `timflow`." + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "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]
timflow0.421.70e-050.004
MLU0.411.94e-050.004
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "t = pd.DataFrame(\n", + " columns=[\"k [m/d]\", \"Ss [1/m]\", \"RMSE [m]\"],\n", + " index=[\"timflow\", \"MLU\"],\n", + ")\n", + "\n", + "t.loc[\"timflow\"] = np.append(cal.parameters[\"optimal\"].values, cal.rmse())\n", + "t.loc[\"MLU\"] = [0.4133, 1.9388e-05, 0.004264]\n", + "\n", + "t_formatted = t.style.format(\n", + " {\"k [m/d]\": \"{:.2f}\", \"Ss [1/m]\": \"{:.2e}\", \"RMSE [m]\": \"{:.3f}\"}\n", + ")\n", + "t_formatted" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### References\n", + "\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", + "* 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", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/transient/04pumpingtests/unconfined2_moench.ipynb b/docs/transient/04pumpingtests/unconfined2_moench.ipynb new file mode 100644 index 0000000..803ce19 --- /dev/null +++ b/docs/transient/04pumpingtests/unconfined2_moench.ipynb @@ -0,0 +1,420 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2.Test for an anisotropic water-table aquifer - Moench Example" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import required libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "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": { + "tags": [] + }, + "source": [ + "### Introduction and Conceptual Model\n", + "\n", + "This test is based on a synthetic example reported by Barlow and Moench (1999), utilizing an analytical solution developed by Moench and Allen (1997) for the transient flow of partially-penetrating wells in unconfined aquifers. \n", + "\n", + "The aquifer is partially saturated with water (10 m water table). A pumping well is screened from 5 to 10 m depth. The well and the well-casing radius is 0.1 m. \n", + "\n", + "Drawdown is recorded at the pumping well and four piezometers located at two different distances and two different depths. Two piezometers, PS1 and PS2, are located at one-meter depth below the water table and 3.16 and 31.6 m distance, respectively. Another two (PD1 and PD2) piezometers are at 7.5 m depth below the water table and the same distances, directly below the previous piezometers. The figure below shows the location of the well and the piezometers\n", + "\n", + "Pumping starts at time t = 0 at a constant rate of 172.8 m3/d. Drawdown is recorded until t = 3 days." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load data\n", + "\n", + "The dataset for each well consists of a column with the time data in seconds and drawdown in meters. We are loading it and converting it to days and meters." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "data0 = np.loadtxt(\"data/moench_pumped.txt\", skiprows=1)\n", + "t0 = data0[:, 0] / 60 / 60 / 24 # convert time from seconds to days\n", + "h0 = -data0[:, 1] # converting drawdown to heads\n", + "data1 = np.loadtxt(\"data/moench_ps1.txt\", skiprows=1)\n", + "t1 = data1[:, 0] / 60 / 60 / 24 # convert time from seconds to days\n", + "h1 = -data1[:, 1]\n", + "data2 = np.loadtxt(\"data/moench_pd1.txt\", skiprows=1)\n", + "t2 = data2[:, 0] / 60 / 60 / 24 # convert time from seconds to days\n", + "h2 = -data2[:, 1]\n", + "data3 = np.loadtxt(\"data/moench_ps2.txt\", skiprows=1)\n", + "t3 = data3[:, 0] / 60 / 60 / 24 # convert time from seconds to days\n", + "h3 = -data3[:, 1]\n", + "data4 = np.loadtxt(\"data/moench_pd2.txt\", skiprows=1)\n", + "t4 = data4[:, 0] / 60 / 60 / 24 # convert time from seconds to days\n", + "h4 = -data4[:, 1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Parameters and model" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "b = 10 # aquifer thickness, m\n", + "Q = 172.8 # constant discharge rate, m^3/d\n", + "rw = 0.1 # well radius, m\n", + "rc = 0.1 # casing radius, m\n", + "r1 = 3.16 # distance of closer wells, m\n", + "r2 = 31.6 # distance of wells more far away, m" + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml = tft.Model3D(\n", + " kaq=1,\n", + " z=[0, -0.1, -2.1, -5.1, -10.1],\n", + " Saq=[0.1, 1e-4, 1e-4, 1e-4],\n", + " kzoverkh=1,\n", + " tmin=1e-5,\n", + " tmax=3,\n", + " phreatictop=True,\n", + ")\n", + "w = tft.Well(ml, xw=0, yw=0, rw=rw, rc=rc, tsandQ=[(0, Q)], layers=3)\n", + "ml.solve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Estimate aquifer parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "............................................................\n", + "Fit succeeded.\n" + ] + } + ], + "source": [ + "cal = tft.Calibrate(ml)\n", + "cal.set_parameter(name=\"kaq\", initial=1, layers=[0, 1, 2, 3])\n", + "cal.set_parameter(name=\"Saq\", initial=0.2, layers=[0])\n", + "cal.set_parameter(name=\"Saq\", initial=1e-4, pmin=0, layers=[1, 2, 3])\n", + "cal.set_parameter(name=\"kzoverkh\", initial=0.1, pmin=0, layers=[0, 1, 2, 3])\n", + " \n", + "cal.seriesinwell(name=\"pumped\", element=w, t=t0, h=h0)\n", + " \n", + "cal.series(name=\"PS1\", x=r1, y=0, t=t1, h=h1, layer=1)\n", + "cal.series(name=\"PD1\", x=r1, y=0, t=t2, h=h2, layer=3)\n", + "cal.series(name=\"PS2\", x=r2, y=0, t=t3, h=h3, layer=1)\n", + "cal.series(name=\"PD2\", x=r2, y=0, t=t4, h=h4, layer=3)\n", + " \n", + "cal.fit()" + ] + }, + { + "cell_type": "code", + "execution_count": 164, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimal
kaq_0_39.067651
Saq_0_00.172880
Saq_1_30.000039
kzoverkh_0_30.535051
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" + ], + "text/plain": [ + " optimal\n", + "kaq_0_3 9.067651\n", + "Saq_0_0 0.172880\n", + "Saq_1_3 0.000039\n", + "kzoverkh_0_3 0.535051" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.01038 m\n" + ] + } + ], + "source": [ + "display(cal.parameters.loc[:, [\"optimal\"]])\n", + "print(f\"RMSE: {cal.rmse():.5f} m\")" + ] + }, + { + "cell_type": "code", + "execution_count": 158, + "metadata": {}, + "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", + "hm2 = ml.head(r1, 0, t2, layers=3)[0]\n", + "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", + "\n", + "plt.xlabel(\"time (d)\")\n", + "plt.ylabel(\"drawdown (m)\")\n", + "plt.title(\"Model Results\")\n", + "plt.legend(bbox_to_anchor=(1.05, 1))\n", + "plt.grid();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Comparison of results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The performance of `timflow` is compared with the stimulated results using Moench's parameters (Barlow and Moench, 1999). The RMSE decreased with the performance of `timflow`. " + ] + }, + { + "cell_type": "code", + "execution_count": 168, + "metadata": {}, + "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": 168, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "t = pd.DataFrame(\n", + " columns=[\"k [m/d]\", \"Sy [-]\", \"Ss [1/m]\", \"kz/kh\", \"RMSE [m]\"],\n", + " index= [\"timflow\", \"Moench\"],\n", + ")\n", + "\n", + "t.loc[\"timflow\"] = np.append(cal.parameters[\"optimal\"].values, cal.rmse())\n", + "t.loc[\"Moench\"] = [8.64, 0.2, 2e-5, 0.5, 0.061318]\n", + "\n", + "t_formatted = t.style.format(\n", + " {\"k [m/d]\": \"{:.2f}\", \n", + " \"Sy [-]\": \"{:.1f}\",\n", + " \"Ss [1/m]\": \"{:.2e}\", \n", + " \"kz/kh\": \"{:.1f}\", \n", + " \"RMSE [m]\": \"{:.4f}\"}\n", + ")\n", + "t_formatted" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "\n", + "* Barlow, P.M., Moench, A.F., 1999. WTAQ, a computer program for calculating drawdowns and estimating hydraulic properties for confined and water-table aquifers. 99-4225, US Dept. of the Interior, US Geological Survey\n", + "* Moench, Allen, F., 1997. Flow to a well of finite diameter in a homogeneous, anisotropic water table aquifer. Water Resources Research 34, 2431–2432." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +}