{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Changes to Rainy River Flows 1970-2010" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "* Flow Data for Rainy River\n", "* Mean Annual Flow, 1970-2000 vs 2000-2010\n", "* Distribution of Flowrates, 1970-2000 vs 2000-2010\n", "* Stage-Frequency, 1970-2000 vs 2000-2010\n", "* Discharge Characteristics, 1970-2000 vs 2000-2010\n", "* Conclusions" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Initialize Notebook" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "# Display graphics inline with the notebook\n", "%matplotlib inline\n", "\n", "# Standard Python modules\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import os\n", "import datetime\n", "import seaborn as sns\n", "\n", "# Directories\n", "dir = '../data/'\n", "img = '../images/'\n", "\n", "sns.set_context('talk')" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Flow Data for Rainy River" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Historical data for flows on upper Rainy River was obtained at station 05PC019 from the HYDAT database maintained by the Water Survey of Canada. The data was extracted and stored in a Python Pandas Series object `RR` that is loaded and displayed in the following cell." ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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JthzMTnc6gLvMeq0lhPwUbOPb4QAWAvgRNA6Gs//P0YLD7uHUFvm4V7fNC+Ig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\noNbI/4S+AAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "RR = pd.read_pickle(dir + 'RR.pkl')\n", "WSC_FLOWS = pd.read_pickle(dir + 'WSC_FLOWS')\n", "WSC_STATIONS = pd.read_pickle(dir + 'WSC_STATIONS')\n", "\n", "s_FtFrancis = '05PC019'\n", "s_Manitou = '05PC018'\n", "\n", "plt.figure(figsize=(10,6))\n", "plt.subplot(2,1,1)\n", "WSC_FLOWS[s_FtFrancis].plot()\n", "plt.title(WSC_STATIONS.ix[s_FtFrancis,'STATION_NAME'])\n", "plt.ylabel('Flow [cubic meters/sec]');\n", "plt.xlabel('Year');\n", "\n", "plt.subplot(2,1,2)\n", "RR.hist(bins=100)\n", "plt.xlabel('Flow [cubic meters/sec]')\n", "plt.ylabel('Count')\n", "plt.title('Histogram of Daily Flows on Rainy River')\n", "\n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Some contextual comparisons of flow through Rainy River." ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mean Annual Flow of Rainy River at Fort Frances = 7.11 million acre-feet\n" ] }, { "data": { "image/png": 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Xbt26ceGFY7noonP466+tDBx4VrXyhkpN/Xfmmf0pLCz0BqL26tWb0tJSBgwY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\nj4dyXIbDL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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "annualflow = (0.000810713194*3600*24/1000000)*RR.groupby(RR.index.year).aggregate(np.sum)\n", "annualflow = annualflow.loc[1906:]\n", "\n", "plt.figure(figsize=(10,6))\n", "annualflow.plot()\n", "plt.title('Annual Flow of Rainy River at Fort Frances')\n", "plt.xlabel('Year')\n", "plt.ylabel('Annual Flow in Millions of Acre Feet')\n", "plt.ylim([0,16])\n", "\n", "ya = annualflow.index.min()\n", "yb = annualflow.index.max()\n", "plt.plot([ya,yb],[annualflow.mean(),annualflow.mean()],'r--')\n", "\n", "plt.legend(['Total Annual Flow of Rainy River',\n", " 'Mean Rainy River = 7.1 MAF'])\n", "\n", "plt.gcf().set_size_inches(9,4)\n", "plt.savefig(img + 'RainyRiverMeanAnnualFlowMAF.png')\n", "\n", "print(\"Mean Annual Flow of Rainy River at Fort Frances = {0:5.2f} million acre-feet\".format(annualflow.mean()))" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Mean Flows, 1970-2000 vs 2000-2010" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "The mean annual flow is a proxy for the total flow through upper Rainy River in a given year. " ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\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", " 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", " this.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 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);\n", " canvas.attr('height', height);\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" }, { "name": "stdout", "output_type": "stream", "text": [ "Mean Flow 288.357834\n", "dtype: float64\n", "Mean Flow 276.752584\n", "dtype: float64\n" ] } ], "source": [ "annualMeanFlow = [RR[RR.index.year == yr].mean() for yr in range(1970,2013)]\n", "print RR.mean()\n", "\n", "Qmean = pd.DataFrame(annualMeanFlow,index=range(1970,2013),columns=['Mean Flow'])\n", "Qmean.plot(kind='bar')\n", "plt.title('Mean Flow by Year')\n", "plt.ylabel('[cubic meters/sec')\n", "\n", "print Qmean.ix[1970:2000].mean()\n", "plt.hold(True)\n", "plt.plot([1970,2000],[Qmean.ix[1970:2000].mean(),Qmean.ix[1970:2000].mean()])\n", "plt.plot([2000,2013],[Qmean.ix[2000:].mean(),Qmean.ix[2000:].mean()])\n", "plt.hold(False)\n", "print Qmean.ix[2000:].mean()\n" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "The key observation is that the annual mean flow through upper Rainy River was virtually identical during the period 1970-2000 and 2000-2010. But let's look at the distribution of daily flows." ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Distribution of Flowrates, 1970-2000 vs 2000-2010" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\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", " 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", " this.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 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);\n", " canvas.attr('height', height);\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": [ "plt.figure()\n", "\n", "plt.hold(True)\n", "\n", "hist,bins = np.histogram([q for q in RR['1970':'2000'] if pd.notnull(q)],\n", " bins = 100)\n", "c = np.cumsum(hist)\n", "c = [1-float(x)/c.max() for x in c]\n", "plt.semilogx(c,bins[:-1])\n", "\n", "hist,bins = np.histogram([q for q in RR['2000':] if pd.notnull(q)],\n", " bins = 100)\n", "c = np.cumsum(hist)\n", "c = [1-float(x)/c.max() for x in c]\n", "plt.semilogx(c,bins[:-1])\n", "plt.gca().invert_xaxis()\n", "plt.legend(['1970-2000','2000-2010'])\n", "\n", "plt.xlabel('Probability to Exceed a Given Flowrate')\n", "plt.ylabel('Flow [cubic meters/sec')\n", "plt.title('Stage-Frequency Diagram for Flows on Upper Rainy River')\n", "\n", "plt.hold(False)\n", "plt.savefig(img + 'RainyRiverStageFrequency.png')" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Discharge Characteristics" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Upper Rainy River is the sole outlet of Rainy Lake, so the discharge characteristics (i.e., the relationship between lake level and flow in the Upper Rainy River) is critical to determination of the lake level dynamics.\n" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\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", " 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", " this.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 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);\n", " canvas.attr('height', height);\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": [ "# Get historical flowrates and levels on RR and RL\n", "RR = pd.read_pickle(dir+'RR.pkl')['1948':]\n", "RL = pd.read_pickle(dir+'RL.pkl')['1948':]\n", "\n", "Q = pd.concat([RR,RL],axis=1)\n", "Q.columns = ['RR','RL']\n", "\n", "A = Q['1948':'1970']\n", "\n", "plt.figure(figsize=(10,8))\n", "plt.hold(True)\n", "plt.scatter(A['RR'],A['RL'],marker='.',color='b',alpha = 0.5,s=15)\n", "B = Q['1970':'2000']\n", "plt.scatter(B['RR'],B['RL'],marker='.',color='g',alpha = 0.5,s=15)\n", "C = Q['2000':]\n", "plt.scatter(C['RR'],C['RL'],marker='.',color='r',alpha = 0.5,s=15)\n", "\n", "\n", "ax = plt.axis()\n", "plt.plot([ax[0],ax[1]],[336.70,336.70],'y--')\n", "plt.plot([ax[0],ax[1]],[337.20,337.20],'y--')\n", "plt.plot([ax[0],ax[1]],[337.75,337.75],'m--')\n", "plt.plot([ax[0],ax[1]],[337.90,337.90],'c--')\n", "\n", "plt.xlabel('Upper Rainy River Flow [cubic meters/sec]')\n", "plt.ylabel('Rainy Lake Level [meters]')\n", "plt.title('Upper Rainy River Discharge Characteristics')\n", "plt.xlim(0,1400)\n", "plt.ylim(336.25,339.5)\n", "plt.legend(['Winter Drought Line', \\\n", " 'Summer Drought Line', \\\n", " 'URC_max','All Gates Open', \\\n", " '1948-1970','1970-2000','2000-2010',''],loc=\"upper left\")\n", "\n", "plt.hold(False)\n", "\n", "fname = img + 'RainyRiverDischarge.png'\n", "\n", "plt.savefig(fname)\n", "!convert $fname -trim $fname" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\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", " 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", " this.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 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);\n", " canvas.attr('height', height);\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": [ "# Get historical flowrates and levels on RR and RL\n", "RR = pd.read_pickle(dir+'RR.pkl')['1948':]\n", "RL = pd.read_pickle(dir+'RL.pkl')['1948':]\n", "\n", "Q = pd.concat([RR,RL],axis=1)\n", "Q.columns = ['RR','RL']\n", "\n", "A = Q['1948':'1970']\n", "\n", "plt.figure(figsize=(10,8))\n", "plt.hold(True)\n", "plt.scatter(A['RR'],A['RL'],marker='.',color='b',alpha = 0.5,s=15)\n", "B = Q['1970':'2000']\n", "plt.scatter(B['RR'],B['RL'],marker='.',color='g',alpha = 0.5,s=15)\n", "C = Q['2000':]\n", "plt.scatter(C['RR'],C['RL'],marker='.',color='r',alpha = 0.5,s=15)\n", "\n", "plt.plot(RLdischarge[1],RLdischarge[0])\n", "\n", "h = np.linspace(336.2,339.0)\n", "q = 650.0*(h-336.2)**0.7\n", "plt.plot(q,h,'r')\n", "\n", "ax = plt.axis()\n", "plt.plot([ax[0],ax[1]],[336.70,336.70],'y--')\n", "plt.plot([ax[0],ax[1]],[337.20,337.20],'y--')\n", "plt.plot([ax[0],ax[1]],[337.75,337.75],'m--')\n", "plt.plot([ax[0],ax[1]],[337.90,337.90],'c--')\n", "\n", "plt.xlabel('Upper Rainy River Flow [cubic meters/sec]')\n", "plt.ylabel('Rainy Lake Level [meters]')\n", "plt.title('Upper Rainy River Discharge Characteristics')\n", "plt.xlim(0,1400)\n", "plt.ylim(336.25,339.5)\n", "plt.legend(['IJC Hydralic','Fitted Hydralic','Winter Drought Line', \\\n", " 'Summer Drought Line', \\\n", " 'URC_max','All Gates Open', \\\n", " '1948-1970','1970-2000','2000-2010',''],loc=\"upper left\")\n", "\n", "plt.hold(False)\n", "\n", "fname = img + 'RainyRiverDischarge_Fit.png'\n", "\n", "plt.savefig(fname)\n", "!convert $fname -trim $fname" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Conclusions" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [conda root]", "language": "python", "name": "conda-root-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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }