From 87d67259d9c1d756244c67a42d3c04c9e0226907 Mon Sep 17 00:00:00 2001 From: mcflugen Date: Fri, 22 Jun 2018 17:10:09 -0600 Subject: [PATCH] Add initial tutorial for Flexure1D. --- flexure/flexure_1d.ipynb | 513 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 513 insertions(+) create mode 100644 flexure/flexure_1d.ipynb diff --git a/flexure/flexure_1d.ipynb b/flexure/flexure_1d.ipynb new file mode 100644 index 0000000..d8d7133 --- /dev/null +++ b/flexure/flexure_1d.ipynb @@ -0,0 +1,513 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Using the Landlab 1D flexure component\n", + "\n", + "
\n", + " For instructions on how to run an interactive iPython notebook, click here: https://github.com/landlab/tutorials/blob/master/README.md
\n", + "For more Landlab tutorials, click here: https://github.com/landlab/landlab/wiki/Tutorials\n", + "
\n", + "\n", + "In this example we will:\n", + "* create a Landlab component that solves the (1D) flexure equation\n", + "* apply a point load\n", + "* run the component\n", + "* plot some output\n", + "* apply a distributed load" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A bit of magic so that we can plot within this notebook." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# %matplotlib inline\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create the grid\n", + "\n", + "We are going to build a uniform rectilinear grid with a node spacing of 100 km in the *y*-direction and 10 km in the *x*-direction on which we will solve the flexure equation.\n", + "\n", + "First we nee to import *RasterModelGrid*." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "from landlab import RasterModelGrid" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create a rectilinear grid with a spacing of 100 km between rows and 10 km between columns. The numbers of rows and columms are provided as a `tuple` of `(n_rows, n_cols)`, in the same manner as similar numpy functions. The spacing is also a `tuple`, `(dy, dx)`." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "grid = RasterModelGrid((3, 800), spacing=(100e3, 10e3))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(100000.0, 10000.0)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "grid.dy, grid.dx" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create the component" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we create the flexure component and tell it to use our newly-created grid. First, though, we'll examine the Flexure component a bit." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "from landlab.components.flexure import Flexure1D" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The Flexure component, as with most landlab components, will require our grid to have some data that it will use. We can get the names of these data fields with the `intput_var_names` attribute of the component *class*." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "('lithosphere__increment_of_overlying_pressure',)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Flexure1D.input_var_names" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see that flexure uses just 1 data field: the change in lithospheric loading. landlab component classes can provide additional information about each of these fields. For instance, to the the units for a field, use the `var_units` method." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Pa'" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Flexure1D.var_units('lithosphere__increment_of_overlying_pressure')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To print a more detailed description of a field, use `var_help`." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "name: lithosphere__increment_of_overlying_pressure\n", + "description:\n", + " Applied pressure to the lithosphere over a time step\n", + "units: Pa\n", + "at: node\n", + "intent: in\n" + ] + } + ], + "source": [ + "Flexure1D.var_help('lithosphere__increment_of_overlying_pressure')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What about the data that `Flexure` provides? Use the `output_var_names` attribute." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "('lithosphere_surface__increment_of_elevation',)" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Flexure1D.output_var_names" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "name: lithosphere_surface__increment_of_elevation\n", + "description:\n", + " The change in elevation of the top of the lithosphere (the land\n", + " surface) in one timestep\n", + "units: m\n", + "at: node\n", + "intent: out\n" + ] + } + ], + "source": [ + "Flexure1D.var_help('lithosphere_surface__increment_of_elevation')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we understand the component a little more, create it using our grid." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "flex = Flexure1D(grid, method='flexure')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Add a point load" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First we'll add just a single point load to the grid. We need to call the *update* method of the component to calculate the resulting deflection (if we don't run *update* the deflections would still be all zeros).\n", + "\n", + "Use the *load_at_node* attribute of *Flexure1D* to set the loads. Notice that *load_at_node* has the same shape as the grid. Likewise, *x_at_node* and *dz_at_node* also reshaped." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "flex.load_at_node[1, 200] = 1e6\n", + "flex.update()\n", + "plt.plot(flex.x_at_node[1, :400] / 1000., flex.dz_at_node[1, :400])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Before we make any changes, reset the deflections to zero." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "flex.dz_at_node[:] = 0." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we will double the effective elastic thickness but keep the same point load. Notice that, as expected, the deflections are more spread out." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "flex.eet *= 2.\n", + "flex.update()\n", + "plt.plot(flex.x_at_node[1, :400] / 1000., flex.dz_at_node[1, :400])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Add some loading\n", + "We will now add a distributed load. As we saw above, for this component, the name of the attribute that hold the applied loads is call, `load_at_node`. For this example we create a loading that increases linearly of the center portion of the grid until some maximum. This could by thought of as the water load following a sea-level rise over a (linear) continental shelf." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "flex.load_at_node[1, :100] = 0.\n", + "flex.load_at_node[1, 100:300] = np.arange(200) * 1e6 / 200.\n", + "flex.load_at_node[1, 300:] = 1e6" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(flex.load_at_node[1, :400])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Update the component to solve for deflection\n", + "Clear the current deflections, and run *update* to get the new deflections." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "flex.dz_at_node[:] = 0.\n", + "flex.update()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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b1IcgUgOiMef6aXPZuK2UGdecQMvGmWFHEjkgFQSRGvCnf37Kvz7dxO/+3zEM6Nwi7DgiCdEhI5Fq9k5+EX96+1MuHNKZUcO6hB1HJGEqCCLVqHDzTn4+bR59jmjGXT84Wjetk6SigiBSTXaXRxk3ZQ4xdx4ZM4SGmbr4TJKL+hBEqskd/7OEhetKmHTZULLbNA47jshB0x6CSDV4MbeQqbM/45rvHsnZ/Y8IO47IIVFBEDlMS9Zv5dd/XcQJPVpzw1lHhR1H5JCpIIgchpJd5Vz9XB4tGmXw4OjBpEf0kpLkpT4EkUMUizk3TJ/H+uJdvHDV8bRpkhV2JJHDoo8zIofokfdW8NbSIn59fl+GdmsVdhyRw6aCIHIIPijYxH1v5vP9gR0Ze2J22HFEqoUKgshBWl+8i2unzuXItk34r387RhefScpQQRA5CGUVMcZNmUNpeZRHLh1K4yx1w0nq0H+zyEG469UlzCss5uExQ+jZrknYcUSqVUJ7CGY23MzyzazAzG6uYnqWmb0QTJ9lZtmVpt0SjM83s3OCcV3M7B0zW2pmi83s+upaIZGa8te563j2ozX89OTunHdMh7DjiFS7AxYEM4sAE4FzgX7AKDPrt1ezK4At7t4TuB+4O5i3HzAS6A8MBx4OllcB3ODufYHjgfFVLFOkzsj/Yhu3vLyQYdmt+NW5fcKOI1IjEtlDGAYUuPtKdy8DpgEj9mozAngmGJ4BnGHxnrYRwDR3L3X3VUABMMzdP3f3OQDuvg1YCnQ6/NURqX7bdscvPmvSIJ2HRg8mQxefSYpK5D+7E1BY6fFavv3m/VUbd68ASoDWicwbHF4aDMxKPLZI7XB3fvniAj7bvJOHRg2mXbMGYUcSqTGJFISqzqnzBNvsd14zawK8BPzc3bdW+eRmV5pZrpnlbty4MYG4ItXnsX+t5PXFX3Dz8D4c16N12HFEalQiBWEtUPlrnzoD6/fVxszSgebA5v3Na2YZxIvBFHd/eV9P7u6T3D3H3XPatm2bQFyR6vHxyi+5+/V8zjvmCH76ne5hxxGpcYkUhE+AXmbW3cwyiXcSz9yrzUxgbDB8EfC2u3swfmRwFlJ3oBcwO+hfeAJY6u5/qI4VEalOG7buZsLzc+nWuhF3XzhAF59JvXDA6xDcvcLMJgBvABHgSXdfbGZ3ArnuPpP4m/tkMysgvmcwMph3sZlNB5YQP7NovLtHzexk4DJgoZnNC57qVnd/rbpXUORglUdjjJ8yhx2lFTz/s+No2iAj7EgitcLiH+STQ05Ojufm5oYdQ1Lcnf+zhCc/WMUfRw5ixCCd/CbJzczy3D0nkbY6f06kklcWrOfJD1bx4xOzVQyk3lFBEAkUFG3jphkLGNK1Bbee1zfsOCK1TgVBBNheWsFVk/NomBFh4pghZKbrpSH1j25uJ/Weu/OrlxawatMOnrviODo0bxh2JJFQ6GOQ1HtPfbCaVxd8zo3n9ObEnm3CjiMSGhUEqddyV2/md68t5ax+7bnm1CPDjiMSKhUEqbeKtu1m3JQ5dGrZkHt/OFAXn0m9p4Ig9VJFNMZ1U+eydXc5j146lOYNdfGZiDqVpV66/63lfLxyM/f+cCB9OzQLO45InaA9BKl33llWxMR3VnBJThcuGto57DgidYYKgtQr64p38Yvp8+jboRl3jOgfdhyROkUFQeqNsor4Tesqos7DY4bQICMSdiSROkV9CFJv/O61pcwrLOaRMUPo3qZx2HFE6hztIUi98OqCz3n6w9VcflI25x7TIew4InWSCoKkvJUbt/OrlxYwuGsLbjlXN60T2RcVBElpu8ujjJsyh/SI8dBo3bROZH/UhyAp7bd/W8yyL7bx1OXH0qmFblonsj/6uCQpa0beWl7ILWT8aUdyWu92YccRqfNUECQlLftiK7/+60KO79GKX5x5VNhxRJKCCoKknO2lFYybMoemDTL406jBpEf0by6SCL1SJKW4Oze/tIDVm3bwp5GDade0QdiRRJKGCoKklMkfr+GVBZ9zw9m9OeHI1mHHEUkqKgiSMuYXFvP/X1nCab3b6stuRA6BCoKkhOKdZYybMod2TRvwh4sHkZamL7sROVi6DkGSXizm3DB9PkXbdvPi1SfSsnFm2JFEkpL2ECTp/fn9lfxzWRG3ndeXQV1ahB1HJGmpIEhSm7XyS+59M5/zj+nA2BOzw44jktRUECRpbdxWyrVT59K1VSN+f+ExmKnfQORwqCBIUorGnOunzaVkVzkPjxlC0wYZYUcSSXrqVJak9Me3lvPhii+556IB9O3QLOw4IilBewiSdN5bvpEH3yngh0M7c3FOl7DjiKQMFQRJKuuLd/HzaXPp3b4pd444Ouw4IilFBUGSRnk0xoTn51BWEWPimCE0zIyEHUkkpagPQZLG3X9fxpzPinlo9GCObNsk7DgiKUd7CJIUXl/0BY//7yrGntCNCwZ0DDuOSEpKqCCY2XAzyzezAjO7uYrpWWb2QjB9lpllV5p2SzA+38zOqTT+STMrMrNF1bEikrrWfLmDX744n4Gdm3Pr+X3DjiOSsg5YEMwsAkwEzgX6AaPMrN9eza4Atrh7T+B+4O5g3n7ASKA/MBx4OFgewNPBOJF92l0eZdyUOaSlGQ+NHkJWuvoNRGpKInsIw4ACd1/p7mXANGDEXm1GAM8EwzOAMyx+2egIYJq7l7r7KqAgWB7u/j6wuRrWQVLYna8sYfH6rfzh4oF0adUo7DgiKS2RgtAJKKz0eG0wrso27l4BlACtE5x3v8zsSjPLNbPcjRs3HsyskuT+Oncdz8/6jKtPPZIz+rYPO45IykukIFR1gxhPsE0i8+6Xu09y9xx3z2nbtu3BzCpJ7NMN27jl5YUMy27FjWcfFXYckXohkYKwFqh8OWhnYP2+2phZOtCc+OGgROYV+YYdpRVcM2UOjbMiPDh6MOkRnQwnUhsSeaV9AvQys+5mlkm8k3jmXm1mAmOD4YuAt93dg/Ejg7OQugO9gNnVE11Skbtz218WsmLjdv44cjDtmzUIO5JIvXHAghD0CUwA3gCWAtPdfbGZ3Wlm3w+aPQG0NrMC4N+Bm4N5FwPTgSXA68B4d48CmNlU4COgt5mtNbMrqnfVJBlNnV3IX+et5xdnHsVJPduEHUekXrH4B/nkkJOT47m5uWHHkBqyaF0J//bIhxzXvRXPXD5M34ssUg3MLM/dcxJpq4OzUids3V3OuClzaNUokwcuGaRiIBIC3ctIQheLOTdOn8/64l28cNXxtG6SFXYkkXpJewgSukfeW8GbSzZw87l9GNqtVdhxROotFQQJ1b8+3ch9b+ZzwYAOXHFy97DjiNRrKggSmrVbdnLd1Ln0bNeEuy8cQPxuJyISFhUECcXu8ijXPDeHiqjz58tyaJyl7iyRsOlVKLXO3fmPvy1i4boSHvtRDt3bNA47koigPQQJwdTZhUzPXcu1p/fkrH66aZ1IXaGCILVqXmExt89czClHteXnZ+qmdSJ1iQqC1Jqirbu5enIe7Zpl8aeRg4jo4jOROkV9CFIrdpdHuXJyHiW7ynnpmhNp0Sgz7EgishcVBKlx7s6tLy9kXmExj146lH4dm4UdSUSqoENGUuMmvb+Sl+eu49/POorhRx8RdhwR2QcVBKlRby/bwO9fX8b5Azpw7ek9w44jIvuhgiA1ZvmGbVw3dR79Ozbj3osG6kpkkTpOBUFqRNHW3Vz+1Cc0zIww6bIcGmZGwo4kIgeggiDVbntpBZc//Qlbdpbx1I+PpWOLhmFHEpEE6CwjqVYV0Rjjp8xh2RfbeHxsDkd3ah52JBFJkPYQpNq4O7/52yLeW76R//zB0ZzWu13YkUTkIKggSLW59818ps4uZMJpPRk5rGvYcUTkIKkgSLV4+N0CJr6zglHDunLD2bpHkUgyUkGQw/bMh6u55/V8RgzqyF0/OFqnl4okKRUEOSwz8tby25mLOatfe+794UDdsE4kiakgyCGbnlvIL2fM5zu92vDQ6MFkRPTvJJLM9AqWQ/LsR6u5acYCTu7ZhkmX5ZCVrgvPRJKdrkOQgzbp/RX87rVlnNm3PRPHDFYxEEkRKgiSsGjMuevVJTz1wWouGNCB+y8ZpMNEIilEBUESsrOsguumzuOtpRu44uTu3HpeX3Ugi6QYFQQ5oLVbdnLNc3NYvL6EO0f050cnZIcdSURqgAqC7NdbSzZww4vzicWcx36Uwxl924cdSURqiAqCVKm0Isp9by5n0vsr6d+xGQ+PGUK31o3DjiUiNUgFQb5lXmExN82Yz/IN2xlzXFd+c0E/GmToTCKRVKeCIF/ZsqOMB95azuSP19C+WQOe+vGxnNZHdywVqS9UEIQdpRU89/EaJr5TwI6yKJce340bz+lNswYZYUcTkVqUUEEws+HAH4EI8Li7/36v6VnAs8BQ4EvgEndfHUy7BbgCiALXufsbiSxTat6GrbuZNruQpz5cRfHOck49qi23nd+Xo9o3DTuaiITggAXBzCLAROAsYC3wiZnNdPcllZpdAWxx955mNhK4G7jEzPoBI4H+QEfgLTPbc2/kAy1TasDu8ij/+nQTL3xSyDv5RURjzul92jHh9J4M6doy7HgiEqJE9hCGAQXuvhLAzKYBI4DKb94jgNuD4RnAQxa/B/IIYJq7lwKrzKwgWB4JLLPaVERjpJlhRr27NXM05izfsI28NVt4N7+I/y3YxO7yGG2aZHHlKT24JKcL2W109pCIJFYQOgGFlR6vBY7bVxt3rzCzEqB1MP7jvebtFAwfaJnV5pjb32RXeRQAM4iYfVUg0sxIC36bQVqafTXOKk1LjxgZkTQyI2lkpaeRGfzsGbfncVYwLis9jQYZEbLS08hKj9AgI/47q9LvBl89rrptRsQSKmA7yyr4cnsZX+4oY+O2UtZ8uYOVm3awomg7i9aVsKMsvu6dWjTkkpwunN63PSce2Vq3nRCRb0ikIFT1juQJttnX+KreifZeZnzBZlcCVwJ07XpoX8t47Rk9Ka9wou64OzF3Yg4xd9whFqv8+OvhmMe/JzgacypiTlk0RllF/Kc8GqO0PMa23RXxcZWm7RneXR4lVuVaJSbN+FbxSDOjrCJGaUWM0ooopcFz7q1lowyy2zTmwqGdGdSlBYO6tKB7m8b1bg9JRBKXSEFYC3Sp9LgzsH4fbdaaWTrQHNh8gHkPtEwA3H0SMAkgJyfnkN5ex32356HMVi3Ko/E3793l8Tfv0vIou8vjb+Z7fu89vbTim/PsLo9SGrSNOvG9lIyv91ZaNMqkdeNMWjfJpHWTLLq1akTLxpmhrbOIJKdECsInQC8z6w6sI95JPHqvNjOBscBHwEXA2+7uZjYTeN7M/kC8U7kXMJv4nsOBlpkSMiLxQ0hNsnSGr4jUbQd8lwr6BCYAbxA/RfRJd19sZncCue4+E3gCmBx0Gm8m/gZP0G468c7iCmC8u0cBqlpm9a+eiIgkytwP4yB3LcvJyfHc3NywY4iIJA0zy3P3nETa6jQTEREBVBBERCSggiAiIoAKgoiIBFQQREQEUEEQEZFAUp12amYbgTWHOHsbYFM1xqkuynVwlOvg1NVcUHezpVqubu7eNpGGSVUQDoeZ5SZ6Lm5tUq6Do1wHp67mgrqbrT7n0iEjEREBVBBERCRQnwrCpLAD7INyHRzlOjh1NRfU3Wz1Nle96UMQEZH9q097CCIish8pXxDMbLiZ5ZtZgZndHMLzrzazhWY2z8xyg3GtzOwfZvZp8LtlMN7M7E9B1gVmNqSaszxpZkVmtqjSuIPOYmZjg/afmtnYGsp1u5mtC7bbPDM7r9K0W4Jc+WZ2TqXx1fq3NrMuZvaOmS01s8Vmdn0wPtRttp9coW4zM2tgZrPNbH6Q645gfHczmxWs+wtmlhmMzwoeFwTTsw+Ut5pzPW1mqyptr0HB+Fr73w+WGTGzuWb2SvA4vO3lwddGpuIP8e9aWAH0ADKB+UC/Ws6wGmiz17h7gJuD4ZuBu4Ph84C/E/8CoeOBWdWc5RRgCLDoULMArYCVwe+WwXDLGsh1O3BjFW37BX/HLKB78PeN1MTfGugADAmGmwLLg+cPdZvtJ1eo2yxY7ybBcAYwK9gO04GRwfhHgWuC4XHAo8HwSOBDRC6tAAADoklEQVSF/eWtgVxPAxdV0b7W/veD5f478DzwSvA4tO2V6nsIw4ACd1/p7mXANGBEyJkgnuGZYPgZ4AeVxj/rcR8DLcysQ3U9qbu/T/wLjA4nyznAP9x9s7tvAf4BDK+BXPsyApjm7qXuvgooIP53rva/tbt/7u5zguFtwFKgEyFvs/3k2pda2WbBem8PHmYEPw6cDswIxu+9vfZsxxnAGWZm+8lb3bn2pdb+982sM3A+8Hjw2Ahxe6V6QegEFFZ6vJb9v3BqggNvmlmemV0ZjGvv7p9D/MUNtAvGh5H3YLPUZsYJwS77k3sOy4SVK9g9H0z802Wd2WZ75YKQt1lw+GMeUET8DXMFUOzuFVU8x1fPH0wvAVrXRi5337O9/jPYXvebWdbeufZ6/pr4Oz4A3ATEgsetCXF7pXpBsCrG1fZpVSe5+xDgXGC8mZ2yn7Z1Ie8e+8pSWxkfAY4EBgGfA/eFlcvMmgAvAT939637a1qb2arIFfo2c/eouw8COhP/lNp3P88RWi4zOxq4BegDHEv8MNCvajOXmV0AFLl7XuXR+3mOGs+V6gVhLdCl0uPOwPraDODu64PfRcBfiL9INuw5FBT8Lgqah5H3YLPUSkZ33xC8iGPAY3y9C1yrucwsg/ib7hR3fzkYHfo2qypXXdlmQZZi4F3ix+BbmNme72+v/BxfPX8wvTnxQ4e1kWt4cOjN3b0UeIra314nAd83s9XED9edTnyPIbztdTidIXX9B0gn3vHTna87zfrX4vM3BppWGv6Q+DHH/+abnZL3BMPn883OrNk1kCmbb3beHlQW4p+kVhHvVGsZDLeqgVwdKg3/gvgxUoD+fLMDbSXxztFq/1sH6/4s8MBe40PdZvvJFeo2A9oCLYLhhsC/gAuAF/lmJ+m4YHg83+wknb6/vDWQq0Ol7fkA8Psw/veDZX+XrzuVQ9te1fpmUxd/iJ8xsJz4sczbavm5ewR/qPnA4j3PT/y43z+BT4PfrSr9Y04Msi4Ecqo5z1TihxLKiX+quOJQsgA/Id5xVQBcXkO5JgfPuwCYyTff7G4LcuUD59bU3xo4mfiu9wJgXvBzXtjbbD+5Qt1mwABgbvD8i4D/qPQ6mB2s+4tAVjC+QfC4IJje40B5qznX28H2WgQ8x9dnItXa/36l5X6XrwtCaNtLVyqLiAiQ+n0IIiKSIBUEEREBVBBERCSggiAiIoAKgoiIBFQQREQEUEEQEZGACoKIiADwf6O4w0UKE0IxAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(flex.x_at_node[1, :400] / 1000., flex.dz_at_node[1, :400])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Maintain the same loading distribution but double the effective elastic thickness." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Click here for more Landlab tutorials" + ] + } + ], + "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": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +}