{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# simulation" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import S1DBedloadSolver as S1Dbed\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 second\n", "18000 second\n", "36000 second\n", "54000 second\n", "72000 second\n", "90000 second\n", "108000 second\n", "126000 second\n", "144000 second\n", "162000 second\n", "180000 second\n", "198000 second\n", "216000 second\n", "234000 second\n", "252000 second\n", "270000 second\n", "288000 second\n", "306000 second\n", "324000 second\n", "342000 second\n", "360000 second\n", "378000 second\n", "396000 second\n", "414000 second\n", "432000 second\n", "450000 second\n", "468000 second\n", "486000 second\n", "504000 second\n", "522000 second\n", "540000 second\n", "558000 second\n", "576000 second\n", "594000 second\n", "612000 second\n", "630000 second\n", "648000 second\n", "666000 second\n", "684000 second\n", "702000 second\n", "720000 second\n", "738000 second\n", "756000 second\n", "774000 second\n", "792000 second\n", "810000 second\n", "828000 second\n", "846000 second\n", "864000 second\n", "882000 second\n", "900000 second\n", "918000 second\n", "936000 second\n", "954000 second\n", "972000 second\n", "990000 second\n", "1008000 second\n", "1026000 second\n", "1044000 second\n", "1062000 second\n", "Wall time: 37.5 s\n" ] } ], "source": [ "%%time\n", "length = 1000.0\n", "dx = 10.0\n", "imax = int(length/dx) + 1\n", "dt = 2.0\n", "totalTime = 300.0 * 3600.0\n", "outTimeStep = 5.0*3600.0\n", "RunUpTime = 3.0 * 3600.0\n", "hini = 1.0\n", "manning = 0.03\n", "ib = 1.0/1000.0\n", "outputfilename = '1D_case1.json'\n", "\n", "# grain diameter classification\n", "screenclass = np.array( [4.0], dtype=float )/1000\n", "dsize = np.array( [4.0/1000.0], dtype=float )\n", "\n", "# percentage of grain size under exchange layer\n", "dratioStandard1 = np.full_like(dsize, 1.0/len(dsize), dtype=float)\n", "dratioStandard = np.full( (imax, len(dsize) ), dratioStandard1, dtype=float)\n", "\n", "# initial percentage of grain size in exchange layer\n", "dratio = np.copy(dratioStandard)\n", "\n", "# thickness of exchange layer \n", "hExlayer = dsize[-1]\n", "\n", "# Initial & Boundary condition\n", "B = np.full(imax, 1.0, dtype=float)\n", "A = hini*B\n", "Q = ib**0.5*(hini)**(5.0/3.0)/manning*B #normal flow\n", "zb = np.zeros(imax)\n", "for i in range(1,imax):\n", "# zb[i] = zb[i-1] + ib2*dx if i < 50 else zb[i-1] + ib*dx\n", " zb[i] = zb[i-1] + ib*dx # if i < 50 else zb[i-1] + ib*dx\n", " \n", "zb = zb[::-1]\n", "zb[50:60] -= 0.1\n", "\n", "dAb = np.zeros(imax)\n", "\n", "Qup = Q[0]\n", "\n", "def Adown(time, Q, dzb, ib):\n", " return ( manning**2*Q**2/ib/B[-1]**2 )**0.3 * B[-1]\n", "\n", "def Qup(time):\n", " return Q[0]\n", "\n", "S1Dbed.bedvariation(\n", "dx,dt,manning,totalTime,outTimeStep,RunUpTime\n", ",dsize ,dratioStandard ,dratio ,hExlayer ,A ,Q ,B ,zb ,dAb ,Qup ,Adown\n", ",outputfilename, screenclass\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# json to NetCDF" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import xarray as xr\n", "import json\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "outputfile = '1D_case1.json'\n", "data = json.load( open(outputfile, 'r') )\n", "\n", "cond = data['condition']\n", "d = data['output']\n", "\n", "time = [ p['time']/3600 for p in d ]\n", "x = [ p['distance'] for p in d[0]['profile'] ]\n", "screenclass = np.array( cond['screenclass'] )*1000\n", "zb = np.array( cond['elevation'] )" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "dep = []\n", "dzb = []\n", "for dr in d :\n", " A = np.array( [ dd['A'] for dd in dr['profile'] ] )\n", " dAb = np.array( [ dd['dAb'] for dd in dr['profile'] ] )\n", " dep.append( zb + A + dAb)\n", " dzb.append( zb + dAb)\n", " \n", "dep = np.array(dep)\n", "dzb = np.array(dzb)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "ds = xr.Dataset({'H': (['t','x'], dep), 'zb': (['t','x'], dzb) }, coords={'x': x , 't': time})\n", "out = ds.to_netcdf('case1.nc')\n", "\n", "del out, ds" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# figure" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "application/javascript": [ "\n", "(function(root) {\n", " function now() {\n", " return new Date();\n", " }\n", "\n", " var force = true;\n", "\n", " if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n", " root._bokeh_onload_callbacks = [];\n", " root._bokeh_is_loading = undefined;\n", " }\n", "\n", " var JS_MIME_TYPE = 'application/javascript';\n", " var HTML_MIME_TYPE = 'text/html';\n", " var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", " var CLASS_NAME = 'output_bokeh rendered_html';\n", "\n", " /**\n", " * Render data to the DOM node\n", " */\n", " function render(props, node) {\n", " var script = document.createElement(\"script\");\n", " node.appendChild(script);\n", " }\n", "\n", " /**\n", " * Handle when an output is cleared or removed\n", " */\n", " function handleClearOutput(event, handle) {\n", " var cell = handle.cell;\n", "\n", " var id = cell.output_area._bokeh_element_id;\n", " var server_id = cell.output_area._bokeh_server_id;\n", " // Clean up Bokeh references\n", " if (id != null && id in Bokeh.index) {\n", " Bokeh.index[id].model.document.clear();\n", " delete Bokeh.index[id];\n", " }\n", "\n", " if (server_id !== undefined) {\n", " // Clean up Bokeh references\n", " var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", " cell.notebook.kernel.execute(cmd, {\n", " iopub: {\n", " output: function(msg) {\n", " var id = msg.content.text.trim();\n", " if (id in Bokeh.index) {\n", " Bokeh.index[id].model.document.clear();\n", " delete Bokeh.index[id];\n", " }\n", " }\n", " }\n", " });\n", " // Destroy server and session\n", " var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", " cell.notebook.kernel.execute(cmd);\n", " }\n", " }\n", "\n", " /**\n", " * Handle when a new output is added\n", " */\n", " function handleAddOutput(event, handle) {\n", " var output_area = handle.output_area;\n", " var output = handle.output;\n", "\n", " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", " if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", " return\n", " }\n", "\n", " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", "\n", " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", " // store reference to embed id on output_area\n", " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", " }\n", " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", " var bk_div = document.createElement(\"div\");\n", " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", " var script_attrs = bk_div.children[0].attributes;\n", " for (var i = 0; i < script_attrs.length; i++) {\n", " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", " }\n", " // store reference to server id on output_area\n", " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", " }\n", " }\n", "\n", " function register_renderer(events, OutputArea) {\n", "\n", " function append_mime(data, metadata, element) {\n", " // create a DOM node to render to\n", " var toinsert = this.create_output_subarea(\n", " metadata,\n", " CLASS_NAME,\n", " EXEC_MIME_TYPE\n", " );\n", " this.keyboard_manager.register_events(toinsert);\n", " // Render to node\n", " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", " render(props, toinsert[toinsert.length - 1]);\n", " element.append(toinsert);\n", " return toinsert\n", " }\n", "\n", " /* Handle when an output is cleared or removed */\n", " events.on('clear_output.CodeCell', handleClearOutput);\n", " events.on('delete.Cell', handleClearOutput);\n", "\n", " /* Handle when a new output is added */\n", " events.on('output_added.OutputArea', handleAddOutput);\n", "\n", " /**\n", " * Register the mime type and append_mime function with output_area\n", " */\n", " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", " /* Is output safe? */\n", " safe: true,\n", " /* Index of renderer in `output_area.display_order` */\n", " index: 0\n", " });\n", " }\n", "\n", " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", " if (root.Jupyter !== undefined) {\n", " var events = require('base/js/events');\n", " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", "\n", " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", " register_renderer(events, OutputArea);\n", " }\n", " }\n", "\n", " \n", " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", " root._bokeh_timeout = Date.now() + 5000;\n", " root._bokeh_failed_load = false;\n", " }\n", "\n", " var NB_LOAD_WARNING = {'data': {'text/html':\n", " \"
\\n\"+\n", " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", " \"
\\n\"+\n", " \"\\n\"+\n",
" \"from bokeh.resources import INLINE\\n\"+\n",
" \"output_notebook(resources=INLINE)\\n\"+\n",
" \"
\\n\"+\n",
" \"\"),e=0;e<7;e++)n.push(' | '+p(t,e,!0)+\" | \");return\"
---|