{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Duffing-stuffing parameter estimation\n", "\n", "Goals:\n", "\n", " * Estimate parameters for a Duffing-like model such that it describes the behavior of the system with low error in different experimental schemes (varying resonnance frequencies, degradation, etc.).\n", " * Simulate the system and perform various analyses (sensitivity, stability, etc.)\n", " \n", "\n", "## Table of contents\n", "\n", " 1. [Empirical data](#empiric-data)\n", " 2. [Model](#model)\n", " 3. [Loss function](#loss-function)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib notebook\n", "\n", "import os\n", "import numpy as np\n", "import random\n", "import itertools\n", "from tqdm import tqdm_notebook as tqdm\n", "from toolz import curry\n", "from scipy import signal\n", "from scipy.optimize import minimize\n", "from scipy.io import loadmat\n", "\n", "# PyDSTool requires scipy 0.X\n", "# However, solve_ivp was introduced in scipy 1.X.\n", "from scipy.integrate import odeint, solve_ivp\n", "#from pydstool_integrator import simulate as ds_simulate\n", "\n", "import matplotlib.pyplot as plt\n", "#from matplotlib import animation\n", "#plt.rcParams[\"animation.html\"] = \"jshtml\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "## Empirical data\n", "\n", "Data is measured through frequency scans at lab.\n", "\n", "Read data from `.mat`-files as a dict of numpy arrays. We focus primarily on the XY-trace data, containing a stable-state period of 100 observations per frequency, for 5 experiments total with variying resonnance frequencies." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "def read_xy(matfile, experiment_no):\n", " \"\"\"Experiment number in [0, 5]\"\"\"\n", " xy = loadmat(matfile)['XYPost'][0, experiment_no]\n", " print(\"Variables (rows x observations): \", xy.dtype.names)\n", " xy_data = dict([(k, xy[i]) for i, k in enumerate(xy.dtype.names)])\n", " t_min, t_max = xy_data['t'][0,0], xy_data['t'][-1,0]\n", " f_min, f_max = xy_data['f'][0,0], xy_data['f'][-1,0]\n", " print(\"Resonnance frequencies: (%d, %d)\" % (xy_data['XResfFreq'][0,0], xy_data['YResfFreq'][0,0]))\n", " print(\"Resonnance amplitudes: (%.2f, %.2f)\" % (xy_data['XResAmp'][0,0], xy_data['YResAmp'][0,0]))\n", " print(\"T = %.2f\" % (t_max - t_min,))\n", " print(\"t in [%.2f, %.2f]\" % (t_min, t_max))\n", " print(\"f in [%.1f, %.1f]\" % (f_min, f_max))\n", " print(\"x shape: %d x %d\" % xy_data['x'].shape)\n", " print(\"y shape: %d x %d\" % xy_data['y'].shape)\n", " return xy_data\n", "\n", "\n", "def read_amp(matfile):\n", " ds_name = os.path.splitext(os.path.basename(matfile))[0]\n", " print(\"Reading ds '%s'\" % ds_name)\n", " amp = loadmat(matfile)[ds_name]\n", " _, n_vars = amp.shape\n", " amp_data = dict([(amp[0,i][1][0][0][0], amp[0,i][0][:,0]) for i in range(n_vars)])\n", " print(\"Variables: %s\" % ','.join(amp_data.keys()))\n", " return amp_data" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Reading first experiment\n", "Variables (rows x observations): ('x', 'y', 's', 'f', 'v', 't', 'XResAmp', 'XResfFreq', 'YResAmp', 'YResfFreq')\n", "Resonnance frequencies: (7600, 8100)\n", "Resonnance amplitudes: (1.03, 1.10)\n", "T = 55.78\n", "t in [0.00, 55.78]\n", "f in [0.0, 10000.0]\n", "x shape: 101 x 100\n", "y shape: 101 x 100\n" ] } ], "source": [ "# Read first experiment\n", "print(\"Reading first experiment\")\n", "xy_data = read_xy('data/XYPost.mat', 0)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "scrolled": false }, "outputs": [], "source": [ "#amp_pre = read_amp('data/APre.mat')\n", "#amp_post = read_amp('data/APost.mat')\n", "#amp_postb = read_amp('data/APostB.mat')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Plotting\n", "\n", "Three types of plots:\n", "\n", " * Frequency scan with amplitude mean/std.\n", " * Trajectory plot in (x, y)-plane.\n", " * Trajectory over time for x and y." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "def plot_std_freqscan(xy_data):\n", " linewidth = 0.6\n", " fig, (ax11, ax12) = plt.subplots(\n", " nrows=1,\n", " ncols=2,\n", " figsize=(8, 4)\n", " )\n", " rows = list(filter(\n", " lambda r: xy_data['f'][r,0] != 0,\n", " range(xy_data['f'].shape[0])\n", " ))\n", " ax11.errorbar(\n", " xy_data['f'][rows,0],\n", " np.mean(xy_data['x'][rows, :], axis=1),\n", " np.std(xy_data['x'][rows, :], axis=1),\n", " linewidth=linewidth\n", " )\n", " ax11.axvline(x=xy_data['XResfFreq'][0,0], linestyle='--', color='red', linewidth=0.8)\n", " ax11.axvline(x=xy_data['YResfFreq'][0,0], linestyle='--', color='red', linewidth=0.8)\n", " ax11.set_xlabel(r'$f$')\n", " ax11.set_ylabel(r'$x$')\n", " ax12.errorbar(\n", " xy_data['f'][rows,0],\n", " np.mean(xy_data['y'][rows, :], axis=1),\n", " np.std(xy_data['y'][rows, :], axis=1),\n", " linewidth=linewidth\n", " )\n", " ax12.axvline(x=xy_data['XResfFreq'][0,0], linestyle='--', color='red', linewidth=0.8)\n", " ax12.axvline(x=xy_data['YResfFreq'][0,0], linestyle='--', color='red', linewidth=0.8)\n", " ax12.set_xlabel(r'$f$')\n", " ax12.set_ylabel(r'$y$')\n", " plt.suptitle(\"Amplitude mean and standard deviation per frequency\\nRed lines are resonnance frequencies\")\n", " #plt.tight_layout()\n", " \n", " \n", "def plot_xy(rows, xy_data):\n", " linewidth = 0.6\n", " fig, ((ax11, ax12), (ax21, ax22)) = plt.subplots(\n", " nrows=2,\n", " ncols=2,\n", " figsize=(8, 6)\n", " )\n", " for row in rows:\n", " ax11.plot(xy_data['x'][row, :], linewidth=linewidth)\n", " ax12.plot(xy_data['y'][row, :], linewidth=linewidth)\n", " ax21.plot(xy_data['x'][row, :], xy_data['y'][row, :], linewidth=linewidth)\n", " ax22.plot(xy_data['f'][row, :])\n", " \n", " ax11.set_ylabel(r'$x$')\n", " ax12.set_ylabel(r'$y$')\n", " ax21.set_xlabel(r'$x$')\n", " ax21.set_ylabel(r'$y$')\n", " ax22.set_ylabel(r'$f$')\n", " plt.suptitle(\"XY-data plots for given frequencies\")\n", " #plt.tight_layout()\n", " \n", " \n", "def plot_xyt(rows, xy_data, normalizer=lambda x: x, sim_xy_data=None):\n", " N = len(rows)\n", " linewidth = 0.6\n", " fig, axes = plt.subplots(\n", " nrows=N,\n", " ncols=2,\n", " #sharey=True,\n", " #sharex=True,\n", " figsize=(8, 3*N)\n", " )\n", " for i in range(N):\n", " axes[i,0].plot(normalizer(xy_data['x'][rows[i], :]), linewidth=linewidth)\n", " axes[i,1].plot(normalizer(xy_data['y'][rows[i], :]), linewidth=linewidth)\n", " if sim_xy_data is not None:\n", " axes[i,0].plot(normalizer(sim_xy_data['x'][rows[i], :]), linewidth=linewidth)\n", " axes[i,1].plot(normalizer(sim_xy_data['y'][rows[i], :]), linewidth=linewidth)\n", " axes[i,0].set_ylabel('$x$ ($f$=%d)' % xy_data['f'][rows[i], 0])\n", " axes[i,1].set_ylabel('$y$ ($f$=%d)' % xy_data['f'][rows[i], 0])\n", " plt.suptitle(\"Stable-state XY-data plots for given frequencies\")\n", " #plt.tight_layout()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Variables (rows x observations): ('x', 'y', 's', 'f', 'v', 't', 'XResAmp', 'XResfFreq', 'YResAmp', 'YResfFreq')\n", "Resonnance frequencies: (7600, 8100)\n", "Resonnance amplitudes: (1.03, 1.10)\n", "T = 55.78\n", "t in [0.00, 55.78]\n", "f in [0.0, 10000.0]\n", "x shape: 101 x 100\n", "y shape: 101 x 100\n" ] }, { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\n", "\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('
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');\n", " var titletext = $(\n", " '
');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('
');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var backingStore = this.context.backingStorePixelRatio ||\n", "\tthis.context.webkitBackingStorePixelRatio ||\n", "\tthis.context.mozBackingStorePixelRatio ||\n", "\tthis.context.msBackingStorePixelRatio ||\n", "\tthis.context.oBackingStorePixelRatio ||\n", "\tthis.context.backingStorePixelRatio || 1;\n", "\n", " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n", "\n", " var rubberband = $('');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width * mpl.ratio);\n", " canvas.attr('height', height * mpl.ratio);\n", " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('
')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('
');\n", " var button = $('');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " event.shiftKey = false;\n", " // Send a \"J\" for go to next cell\n", " event.which = 74;\n", " event.keyCode = 74;\n", " manager.command_mode();\n", " manager.handle_keydown(event);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\n", "\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('
');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " if (mpl.ratio != 1) {\n", " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n", " }\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " fig.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '
');\n", " var titletext = $(\n", " '
');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('
');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var backingStore = this.context.backingStorePixelRatio ||\n", "\tthis.context.webkitBackingStorePixelRatio ||\n", "\tthis.context.mozBackingStorePixelRatio ||\n", "\tthis.context.msBackingStorePixelRatio ||\n", "\tthis.context.oBackingStorePixelRatio ||\n", "\tthis.context.backingStorePixelRatio || 1;\n", "\n", " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n", "\n", " var rubberband = $('');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width * mpl.ratio);\n", " canvas.attr('height', height * mpl.ratio);\n", " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('
')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('
');\n", " var button = $('');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " event.shiftKey = false;\n", " // Send a \"J\" for go to next cell\n", " event.which = 74;\n", " event.keyCode = 74;\n", " manager.command_mode();\n", " manager.handle_keydown(event);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "exp_no = 0\n", "print(\"Read data, experiment %d\" % exp_no)\n", "xy_data = read_xy('data/XYPost.mat', exp_no)\n", "\n", "def gc(c1, c2):\n", " return 0.0\n", "\n", "def gk(k1, k2):\n", " return 0.0\n", "\n", "def f1(k1, k2):\n", " return 0.0\n", "\n", "def f2(k1, k2):\n", " return 0.0\n", "\n", "def ga(k1, k2):\n", " return 0.0\n", "\n", "rows = [50, 65, 70, 75, 80, 85, 90, 100]\n", "zeta1 = 0.1\n", "zeta2 = 0.1\n", "xyhat_data = simulate_experiment(xy_data, rows, zeta1, zeta2, gc, gk, ga, f1, f2, verbose=True)\n", "\n", "plot_std_freqscan(xyhat_data)\n", "plot_xy(rows, xyhat_data)\n", "plot_xyt(rows, xy_data, sim_xy_data=xyhat_data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### With duffing term\n", "\n", "The duffing term has to be pretty large to see any stiffening effect. Set $\\alpha_1 = 1500k_1$ and $\\alpha_2 = 1500k_2$ (still without coupling)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": false }, "outputs": [], "source": [ "exp_no = 0\n", "print(\"Read data, experiment %d\" % exp_no)\n", "xy_data = read_xy('data/XYPost.mat', exp_no)\n", "\n", "def gc(c1, c2):\n", " return 0.0\n", "\n", "def gk(k1, k2):\n", " return 0.0\n", "\n", "def f1(k1, k2):\n", " return 1.5e3*k1\n", "\n", "def f2(k1, k2):\n", " return 1.5e3*k2\n", "\n", "def ga(k1, k2):\n", " return 0.0\n", "\n", "rows = [50, 65, 70, 75, 80, 85, 90, 100]\n", "zeta1 = 0.1\n", "zeta2 = 0.1\n", "xyhat_data = simulate_experiment(xy_data, rows, zeta1, zeta2, gc, gk, ga, f1, f2, verbose=True)\n", "\n", "plot_std_freqscan(xyhat_data)\n", "plot_xy(rows, xyhat_data)\n", "plot_xyt(rows, xy_data, sim_xy_data=xyhat_data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### With coupling" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": false }, "outputs": [], "source": [ "exp_no = 0\n", "print(\"Read data, experiment %d\" % exp_no)\n", "xy_data = read_xy('data/XYPost.mat', exp_no)\n", "\n", "def gc(c1, c2):\n", " return 0.05*(c1 + c2)\n", "\n", "def gk(k1, k2):\n", " return 0.05*(k1 + k2)\n", "\n", "def f1(k1, k2):\n", " return 1.5e3*k1\n", "\n", "def f2(k1, k2):\n", " return 1.5e3*k2\n", "\n", "def ga(k1, k2):\n", " return 0.05*(a1 + a2)\n", "\n", "rows = [50, 65, 70, 75, 80, 85, 90, 100]\n", "zeta1 = 0.1\n", "zeta2 = 0.1\n", "xyhat_data = simulate_experiment(xy_data, rows, zeta1, zeta2, gc, gk, ga, f1, f2, verbose=True)\n", "\n", "plot_std_freqscan(xyhat_data)\n", "plot_xy(rows, xyhat_data)\n", "plot_xyt(rows, xy_data, sim_xy_data=xyhat_data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "## Loss function\n", "\n", "Given the two multivariate signals, one empirical and one simulated, we need a distance metric $d(\\mathbf{S}(\\omega), \\hat{\\mathbf{S}}(\\omega))$ that quantifies the error of our simulation.\n", "\n", "Consider first a single frequency $\\mathbf{S}(\\omega=x) \\in \\mathbb{R}^2$. Treat each component individually, perform autocorrelation do find the shift, then simply use mean squared error as the distance metric between the two common periods of $\\mathbf{S}$ and $\\hat{\\mathbf{S}}$. We extend this to the multivariate case by simply averaging the loss for each frequency.\n", "\n", "TODO: Assert that both components of $\\mathbf{S}(\\omega=x)$ have the same shift." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def normalize_signal(s):\n", " return (s - np.mean(s)) / np.std(s)\n", "\n", "def autocorrelate_1d(s1, s2):\n", " corr = np.correlate(s1, s2, mode='same') / (np.linalg.norm(s1)*np.linalg.norm(s2))\n", " corr_half = corr[int(len(s1)/2):]\n", " idx = np.argmax(corr_half)\n", " return idx, corr_half[idx]\n", "\n", "def loss_1d(s1, s2, normalize=True):\n", " assert len(s1) == len(s2)\n", " N = len(s1)\n", " if normalize:\n", " _s1 = normalize_signal(s1)\n", " _s2 = normalize_signal(s2)\n", " else:\n", " _s1 = s1\n", " _s2 = s2\n", " idx, coeff = autocorrelate_1d(_s1, _s2)\n", " return idx, coeff, np.mean((_s1[idx:]-_s2[:N-idx])**2)\n", "\n", "def xy_loss(rows, xy_data, xyhat_data, normalize=True, verbose=False):\n", " # Calculate correlation coefficients and MSE for both x and y for the specified set of rows\n", " x_idxs, x_coeffs, x_mses = zip(*[loss_1d(xy_data['x'][i,:], xyhat_data['x'][i,:], normalize=normalize) for i in rows])\n", " y_idxs, y_coeffs, y_mses = zip(*[loss_1d(xy_data['y'][i,:], xyhat_data['y'][i,:], normalize=normalize) for i in rows])\n", " # Print some statistics\n", " if verbose:\n", " print('\\n'.join(map(\n", " lambda var: \"%s: %.4f mean, %.4f std\" % (var[0], np.mean(var[1]), np.std(var[1])),\n", " [\n", " ('X idx', x_idxs),\n", " ('Y idx', y_idxs),\n", " ('X coeffs', x_coeffs),\n", " ('Y coeffs', y_coeffs),\n", " ('X MSEs', x_mses),\n", " ('Y MSEs', y_mses)\n", " ]\n", " )))\n", " # Return the sum of means of both components\n", " return np.mean(x_mses) + np.mean(y_mses)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Loss function test\n", "\n", "Random signals and sines." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": false }, "outputs": [], "source": [ "# Test loss function\n", "t = np.linspace(0, 100, 100)\n", "s1 = {\n", " 'f': np.array([\n", " np.ones((100,)),\n", " 2*np.ones((100,))\n", " ]),\n", " 'x': np.array([\n", " np.random.randn(100),\n", " np.array([1*np.sin(i/np.pi) for i in t]),\n", " ]),\n", " 'y': np.array([\n", " np.random.randn(100),\n", " np.array([1*np.cos(i/np.pi) for i in t]),\n", " ])\n", "}\n", "s2 = {\n", " 'f': np.array([\n", " np.ones((100,)),\n", " 2*np.ones((100,))\n", " ]),\n", " 'x': np.array([\n", " np.random.randn(100),\n", " np.array([1*np.cos(i/np.pi) for i in t]),\n", " ]),\n", " 'y': np.array([\n", " np.random.randn(100),\n", " np.array([1*np.sin(i/np.pi) for i in t]),\n", " ])\n", "}\n", "\n", "plot_xyt([0,1], s1, sim_xy_data=s2)\n", "\n", "print(\"Loss: %.4f\\n\" % xy_loss([0], s1, s2, verbose=True))\n", "print(\"Loss: %.4f\\n\" % xy_loss([1], s1, s2, verbose=True))\n", "print(\"Loss: %.4f\" % xy_loss([0, 1], s1, s2, verbose=True))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Loss for model\n", "\n", "Plotting normalized signals." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": false }, "outputs": [], "source": [ "print(\"Loss: %.4f\\n\" % xy_loss(rows, xy_data, xyhat_data, verbose=True))\n", "plot_xyt(rows, xy_data, normalizer=normalize_signal, sim_xy_data=xyhat_data)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": false }, "outputs": [], "source": [ "#C_grid = [1e1] # Amplitude of driving force (gamma)\n", "#c_grid = [1e2] # Damping (delta)\n", "#a_grid = [1e-1] # Non-linear restoring force (beta)\n", "#k_grid = [1e6] # Linear stiffness (alpha)\n", "\n", "#xy_data = read_xy('data/XYPost.mat', 0)\n", "#rows = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100]\n", "#rows = [10, 65, 70, 75, 80, 85, 90]\n", "\n", "#losses = []\n", "#best_loss = 1e9\n", "#best_p = None\n", "#for c, a, k, C in tqdm.tqdm(itertools.product(*[c_grid, a_grid, k_grid, C_grid])):\n", "# print(\"c1=c2=c3=%.4f, a1=a2=a3=%.4f, k1=k2=k3=%.4f, C1=C2=%.4f\" % (c, a, k, C))\n", "# _damping = 0.001\n", "# _omegar = 8e3\n", "# _omega0 = _omegar/(np.sqrt(1-2*_damping**2))\n", "# C = 1e5\n", "# c2 = 2*_damping*_omega0\n", "# k2 = _omega0**2\n", "# a = 0.5\n", "# p = (C, C, 0.03*c2, c2, 0.01*c2, 0.01*a, 0.9*a, 0.02*a, 0.5*k2, k2, 0.1*k2)\n", "# #p = (C, C, c, c, c, a, a, a, k, k, k)\n", "# xyhat_data = simulate_xy_data(rows, xy_data, p)\n", "# loss = xy_loss(rows, xy_data, xyhat_data, verbose=True)\n", "# losses.append(loss)\n", "# if loss < best_loss:\n", "# best_loss = loss\n", "# best_p = p\n", "# print(\"Loss: %.4f\\n\" % loss)\n", "# plot_xyt(rows, xy_data, sim_xy_data=xyhat_data)\n", "# break" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "@curry\n", "def objective(omega, p):\n", " obj = 0.0\n", " # Simulate and return MSE(xy_data, sim_xy_data)\n", " return obj\n", "\n", "p0 = (0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1)\n", "bounds = [\n", " (0.1, 0.2),\n", " (0.1, 0.2),\n", " (0.1, 0.2),\n", " (0.1, 0.2),\n", " (0.1, 0.2),\n", " (0.1, 0.2),\n", " (0.1, 0.2),\n", " (0.1, 0.2),\n", " (0.1, 0.2)\n", "]\n", "#omegas = xy_data['f'][:,0].tolist()\n", "#solution = minimize(objective(omega), p0, method='SLSQP', bounds=bounds)\n", "#p = solution.x\n", "\n", "# Simulate with updated values\n", "#t, X, dt, pstep = model(T, t_trans, dt_per_period, x0, v0, omega, p)" ] } ], "metadata": { "anaconda-cloud": {}, "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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 1 }