paulstey_ka.ipynb

{"cells": [{"source": "###############################################\n# Khan Academy Project\n# Using Julia Language and IJulia\n# April 5, 2015\n# Paul Stey\n###############################################\n\nusing DataFrames\nusing Gadfly\nusing Distributions\nusing Stan, Mamba, Compat\n\ncd(\"/Users/payforsoup/juliawd\")\n\n## read in our data\nd = readtable(\"ka-data-sample-0.9.1.csv\");\n\n", "execution_count": 3, "metadata": {"trusted": false, "collapsed": false}, "outputs": [], "cell_type": "code"}, {"source": "## quick function to count problems\n\nfunction count_prblms(x)\n   ## x is a vector of IDs\n   counts = Dict()\n    \n   for id = x\n\n     counts[id] = get(counts, id, 0) + 1\n   end\n\n   ## returns hash table of IDs and counts\n   return counts\nend\n\n\nproblem_hash = count_prblms(d[:, 1]);", "execution_count": 4, "metadata": {"trusted": false, "collapsed": false}, "outputs": [], "cell_type": "code"}, {"source": "## plot distribution of problems attempted\n\np = 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.forEach(function (element, i) {\n                if (element.attribute(\"gadfly:inscale\") == \"true\") {\n                    var cx = element.asPX(\"x\"),\n                        cy = element.asPX(\"y\");\n                    var st = element.data(\"static_transform\");\n                    unscale_t = new Snap.Matrix();\n                    unscale_t.scale(1, 1/scale, cx, cy).add(st);\n                    element.transform(unscale_t);\n\n                    var y = cy * scale + ty;\n                    element.attr(\"visibility\",\n                        bounds.y0 <= y && y <= bounds.y1 ? \"visible\" : \"hidden\");\n                }\n            });\n    }\n\n    if (xscalable) {\n        var yfixed_t = new Snap.Matrix().translate(tx, 0).scale(xscale, 1.0);\n        var xtrans = new Snap.Matrix().translate(tx, 0);\n        root.selectAll(\".yfixed\")\n            .forEach(function (element, i) {\n                element.transform(yfixed_t);\n            });\n\n        root.select(\".xlabels\")\n            .transform(yfixed_t)\n            .selectAll(\"text\")\n            .forEach(function (element, i) {\n                if (element.attribute(\"gadfly:inscale\") == \"true\") {\n                    var cx = element.asPX(\"x\"),\n                        cy = element.asPX(\"y\");\n                    var st = element.data(\"static_transform\");\n                    unscale_t = new Snap.Matrix();\n                    unscale_t.scale(1/scale, 1, cx, cy).add(st);\n\n                    element.transform(unscale_t);\n\n                    var x = cx * scale + tx;\n                    element.attr(\"visibility\",\n                        bounds.x0 <= x && x <= bounds.x1 ? \"visible\" : \"hidden\");\n                    }\n            });\n    }\n\n    // we must unscale anything that is scale invariance: widths, raiduses, etc.\n    var size_attribs = [\"font-size\"];\n    var unscaled_selection = \".geometry, .geometry *\";\n    if (xscalable) {\n        size_attribs.push(\"rx\");\n        unscaled_selection += \", .xgridlines\";\n    }\n    if (yscalable) {\n        size_attribs.push(\"ry\");\n        unscaled_selection += \", .ygridlines\";\n    }\n\n    root.selectAll(unscaled_selection)\n        .forEach(function (element, i) {\n            // circle need special help\n            if (element.node.nodeName == \"circle\") {\n                var cx = element.attribute(\"cx\"),\n                    cy = element.attribute(\"cy\");\n                unscale_t = new Snap.Matrix().scale(1/xscale, 1/yscale,\n                                                        cx, cy);\n                element.transform(unscale_t);\n                return;\n            }\n\n            for (i in size_attribs) {\n                var key = size_attribs[i];\n                var val = parseFloat(element.attribute(key));\n                if (val !== undefined && val != 0 && !isNaN(val)) {\n                    element.attribute(key, val * old_scale / scale);\n                }\n            }\n        });\n};\n\n\n// Find the most appropriate tick scale and update label visibility.\nvar update_tickscale = function(root, scale, axis) {\n    if (!root.hasClass(axis + \"scalable\")) return;\n\n    var tickscales = root.data(axis + \"tickscales\");\n    var best_tickscale = 1.0;\n    var best_tickscale_dist = Infinity;\n    for (tickscale in tickscales) {\n        var dist = Math.abs(Math.log(tickscale) - Math.log(scale));\n        if (dist < best_tickscale_dist) {\n            best_tickscale_dist = dist;\n            best_tickscale = tickscale;\n        }\n    }\n\n    if (best_tickscale != root.data(axis + \"tickscale\")) {\n        root.data(axis + \"tickscale\", best_tickscale);\n        var mark_inscale_gridlines = function (element, i) {\n            var inscale = element.attr(\"gadfly:scale\") == best_tickscale;\n            element.attribute(\"gadfly:inscale\", inscale);\n            element.attr(\"visibility\", inscale ? \"visible\" : \"hidden\");\n        };\n\n        var mark_inscale_labels = function (element, i) {\n            var inscale = element.attr(\"gadfly:scale\") == best_tickscale;\n            element.attribute(\"gadfly:inscale\", inscale);\n            element.attr(\"visibility\", inscale ? \"visible\" : \"hidden\");\n        };\n\n        root.select(\".\" + axis + \"gridlines\").selectAll(\"path\").forEach(mark_inscale_gridlines);\n        root.select(\".\" + axis + \"labels\").selectAll(\"text\").forEach(mark_inscale_labels);\n    }\n};\n\n\nvar set_plot_pan_zoom = function(root, tx, ty, scale) {\n    var old_scale = root.data(\"scale\");\n    var bounds = root.plotbounds();\n\n    var width = bounds.x1 - bounds.x0,\n        height = bounds.y1 - bounds.y0;\n\n    // compute the viewport derived from tx, ty, and scale\n    var x_min = -width * scale - (scale * width - width),\n        x_max = width * scale,\n        y_min = -height * scale - (scale * height - height),\n        y_max = height * scale;\n\n    var x0 = bounds.x0 - scale * bounds.x0,\n        y0 = bounds.y0 - scale * bounds.y0;\n\n    var tx = Math.max(Math.min(tx - x0, x_max), x_min),\n        ty = Math.max(Math.min(ty - y0, y_max), y_min);\n\n    tx += x0;\n    ty += y0;\n\n    // when the scale change, we may need to alter which set of\n    // ticks is being displayed\n    if (scale != old_scale) {\n        update_tickscale(root, scale, \"x\");\n        update_tickscale(root, scale, \"y\");\n    }\n\n    set_geometry_transform(root, tx, ty, scale);\n\n    root.data(\"scale\", scale);\n    root.data(\"tx\", tx);\n    root.data(\"ty\", ty);\n};\n\n\nvar scale_centered_translation = function(root, scale) {\n    var bounds = root.plotbounds();\n\n    var width = bounds.x1 - bounds.x0,\n        height = bounds.y1 - bounds.y0;\n\n    var tx0 = root.data(\"tx\"),\n        ty0 = root.data(\"ty\");\n\n    var scale0 = root.data(\"scale\");\n\n    // how off from center the current view is\n    var xoff = tx0 - (bounds.x0 * (1 - scale0) + (width * (1 - scale0)) / 2),\n        yoff = ty0 - (bounds.y0 * (1 - scale0) + (height * (1 - scale0)) / 2);\n\n    // rescale offsets\n    xoff = xoff * scale / scale0;\n    yoff = yoff * scale / scale0;\n\n    // adjust for the panel position being scaled\n    var x_edge_adjust = bounds.x0 * (1 - scale),\n        y_edge_adjust = bounds.y0 * (1 - scale);\n\n    return {\n        x: xoff + x_edge_adjust + (width - width * scale) / 2,\n        y: yoff + y_edge_adjust + (height - height * scale) / 2\n    };\n};\n\n\n// Initialize data for panning zooming if it isn't already.\nvar init_pan_zoom = function(root) {\n    if (root.data(\"zoompan-ready\")) {\n        return;\n    }\n\n    // The non-scaling-stroke trick. Rather than try to correct for the\n    // stroke-width when zooming, we force it to a fixed value.\n    var px_per_mm = root.node.getCTM().a;\n\n    // Drag events report deltas in pixels, which we'd like to convert to\n    // millimeters.\n    root.data(\"px_per_mm\", px_per_mm);\n\n    root.selectAll(\"path\")\n        .forEach(function (element, i) {\n        sw = element.asPX(\"stroke-width\") * px_per_mm;\n        if (sw > 0) {\n            element.attribute(\"stroke-width\", sw);\n            element.attribute(\"vector-effect\", \"non-scaling-stroke\");\n        }\n    });\n\n    // Store ticks labels original tranformation\n    root.selectAll(\".xlabels > text, .ylabels > text\")\n        .forEach(function (element, i) {\n            var lm = element.transform().localMatrix;\n            element.data(\"static_transform\",\n                new Snap.Matrix(lm.a, lm.b, lm.c, lm.d, lm.e, lm.f));\n        });\n\n    if (root.data(\"tx\") === undefined) root.data(\"tx\", 0);\n    if (root.data(\"ty\") === undefined) root.data(\"ty\", 0);\n    if (root.data(\"scale\") === undefined) root.data(\"scale\", 1.0);\n    if (root.data(\"xtickscales\") === undefined) {\n\n        // index all the tick scales that are listed\n        var xtickscales = {};\n        var ytickscales = {};\n        var add_x_tick_scales = function (element, i) {\n            xtickscales[element.attribute(\"gadfly:scale\")] = true;\n        };\n        var add_y_tick_scales = function (element, i) {\n            ytickscales[element.attribute(\"gadfly:scale\")] = true;\n        };\n\n        root.select(\".xgridlines\").selectAll(\"path\").forEach(add_x_tick_scales);\n        root.select(\".ygridlines\").selectAll(\"path\").forEach(add_y_tick_scales);\n        root.select(\".xlabels\").selectAll(\"text\").forEach(add_x_tick_scales);\n        root.select(\".ylabels\").selectAll(\"text\").forEach(add_y_tick_scales);\n\n        root.data(\"xtickscales\", xtickscales);\n        root.data(\"ytickscales\", ytickscales);\n        root.data(\"xtickscale\", 1.0);\n    }\n\n    var min_scale = 1.0, max_scale = 1.0;\n    for (scale in xtickscales) {\n        min_scale = Math.min(min_scale, scale);\n        max_scale = Math.max(max_scale, scale);\n    }\n    for (scale in ytickscales) {\n        min_scale = Math.min(min_scale, scale);\n        max_scale = Math.max(max_scale, scale);\n    }\n    root.data(\"min_scale\", min_scale);\n    root.data(\"max_scale\", max_scale);\n\n    // store the original positions of labels\n    root.select(\".xlabels\")\n        .selectAll(\"text\")\n        .forEach(function (element, i) {\n            element.data(\"x\", element.asPX(\"x\"));\n        });\n\n    root.select(\".ylabels\")\n        .selectAll(\"text\")\n        .forEach(function (element, i) {\n            element.data(\"y\", element.asPX(\"y\"));\n        });\n\n    // mark grid lines and ticks as in or out of scale.\n    var mark_inscale = function (element, i) {\n        element.attribute(\"gadfly:inscale\", element.attribute(\"gadfly:scale\") == 1.0);\n    };\n\n    root.select(\".xgridlines\").selectAll(\"path\").forEach(mark_inscale);\n    root.select(\".ygridlines\").selectAll(\"path\").forEach(mark_inscale);\n    root.select(\".xlabels\").selectAll(\"text\").forEach(mark_inscale);\n    root.select(\".ylabels\").selectAll(\"text\").forEach(mark_inscale);\n\n    // figure out the upper ond lower bounds on panning using the maximum\n    // and minum grid lines\n    var bounds = root.plotbounds();\n    var pan_bounds = {\n        x0: 0.0,\n        y0: 0.0,\n        x1: 0.0,\n        y1: 0.0\n    };\n\n    root.select(\".xgridlines\")\n        .selectAll(\"path\")\n        .forEach(function (element, i) {\n            if (element.attribute(\"gadfly:inscale\") == \"true\") {\n                var bbox = element.node.getBBox();\n                if (bounds.x1 - bbox.x < pan_bounds.x0) {\n                    pan_bounds.x0 = bounds.x1 - bbox.x;\n                }\n                if (bounds.x0 - bbox.x > pan_bounds.x1) {\n                    pan_bounds.x1 = bounds.x0 - bbox.x;\n                }\n            }\n        });\n\n    root.select(\".ygridlines\")\n        .selectAll(\"path\")\n        .forEach(function (element, i) {\n            if (element.attribute(\"gadfly:inscale\") == \"true\") {\n                var bbox = element.node.getBBox();\n                if (bounds.y1 - bbox.y < pan_bounds.y0) {\n                    pan_bounds.y0 = bounds.y1 - bbox.y;\n                }\n                if (bounds.y0 - bbox.y > pan_bounds.y1) {\n                    pan_bounds.y1 = bounds.y0 - bbox.y;\n                }\n            }\n        });\n\n    // nudge these values a little\n    pan_bounds.x0 -= 5;\n    pan_bounds.x1 += 5;\n    pan_bounds.y0 -= 5;\n    pan_bounds.y1 += 5;\n    root.data(\"pan_bounds\", pan_bounds);\n\n    // Set all grid lines at scale 1.0 to visible. Out of bounds lines\n    // will be clipped.\n    root.select(\".xgridlines\")\n        .selectAll(\"path\")\n        .forEach(function (element, i) {\n            if (element.attribute(\"gadfly:inscale\") == \"true\") {\n                element.attr(\"visibility\", \"visible\");\n            }\n        });\n\n    root.select(\".ygridlines\")\n        .selectAll(\"path\")\n        .forEach(function (element, i) {\n            if (element.attribute(\"gadfly:inscale\") == \"true\") {\n                element.attr(\"visibility\", \"visible\");\n            }\n        });\n\n    root.data(\"zoompan-ready\", true)\n};\n\n\n// Panning\nGadfly.guide_background_drag_onmove = function(dx, dy, x, y, event) {\n    var root = this.plotroot();\n    var px_per_mm = root.data(\"px_per_mm\");\n    dx /= px_per_mm;\n    dy /= px_per_mm;\n\n    var tx0 = root.data(\"tx\"),\n        ty0 = root.data(\"ty\");\n\n    var dx0 = root.data(\"dx\"),\n        dy0 = root.data(\"dy\");\n\n    root.data(\"dx\", dx);\n    root.data(\"dy\", dy);\n\n    dx = dx - dx0;\n    dy = dy - dy0;\n\n    var tx = tx0 + dx,\n        ty = ty0 + dy;\n\n    set_plot_pan_zoom(root, tx, ty, root.data(\"scale\"));\n};\n\n\nGadfly.guide_background_drag_onstart = function(x, y, event) {\n    var root = this.plotroot();\n    root.data(\"dx\", 0);\n    root.data(\"dy\", 0);\n    init_pan_zoom(root);\n};\n\n\nGadfly.guide_background_drag_onend = function(event) {\n    var root = this.plotroot();\n};\n\n\nGadfly.guide_background_scroll = function(event) {\n    if (event.shiftKey) {\n        var root = this.plotroot();\n        init_pan_zoom(root);\n        var new_scale = root.data(\"scale\") * Math.pow(2, 0.002 * event.wheelDelta);\n        new_scale = Math.max(\n            root.data(\"min_scale\"),\n            Math.min(root.data(\"max_scale\"), new_scale))\n        update_plot_scale(root, new_scale);\n        event.stopPropagation();\n    }\n};\n\n\nGadfly.zoomslider_button_mouseover = function(event) {\n    this.select(\".button_logo\")\n         .animate({fill: this.data(\"mouseover_color\")}, 100);\n};\n\n\nGadfly.zoomslider_button_mouseout = function(event) {\n     this.select(\".button_logo\")\n         .animate({fill: this.data(\"mouseout_color\")}, 100);\n};\n\n\nGadfly.zoomslider_zoomout_click = function(event) {\n    var root = this.plotroot();\n    init_pan_zoom(root);\n    var min_scale = root.data(\"min_scale\"),\n        scale = root.data(\"scale\");\n    Snap.animate(\n        scale,\n        Math.max(min_scale, scale / 1.5),\n        function (new_scale) {\n            update_plot_scale(root, new_scale);\n        },\n        200);\n};\n\n\nGadfly.zoomslider_zoomin_click = function(event) {\n    var root = this.plotroot();\n    init_pan_zoom(root);\n    var max_scale = root.data(\"max_scale\"),\n        scale = root.data(\"scale\");\n\n    Snap.animate(\n        scale,\n        Math.min(max_scale, scale * 1.5),\n        function (new_scale) {\n            update_plot_scale(root, new_scale);\n        },\n        200);\n};\n\n\nGadfly.zoomslider_track_click = function(event) {\n    // TODO\n};\n\n\nGadfly.zoomslider_thumb_mousedown = function(event) {\n    this.animate({fill: this.data(\"mouseover_color\")}, 100);\n};\n\n\nGadfly.zoomslider_thumb_mouseup = function(event) {\n    this.animate({fill: this.data(\"mouseout_color\")}, 100);\n};\n\n\n// compute the position in [0, 1] of the zoom slider thumb from the current scale\nvar slider_position_from_scale = function(scale, min_scale, max_scale) {\n    if (scale >= 1.0) {\n        return 0.5 + 0.5 * (Math.log(scale) / Math.log(max_scale));\n    }\n    else {\n        return 0.5 * (Math.log(scale) - Math.log(min_scale)) / (0 - Math.log(min_scale));\n    }\n}\n\n\nvar update_plot_scale = function(root, new_scale) {\n    var trans = scale_centered_translation(root, new_scale);\n    set_plot_pan_zoom(root, trans.x, trans.y, new_scale);\n\n    root.selectAll(\".zoomslider_thumb\")\n        .forEach(function (element, i) {\n            var min_pos = element.data(\"min_pos\"),\n                max_pos = element.data(\"max_pos\"),\n                min_scale = root.data(\"min_scale\"),\n                max_scale = root.data(\"max_scale\");\n            var xmid = (min_pos + max_pos) / 2;\n            var xpos = slider_position_from_scale(new_scale, min_scale, max_scale);\n            element.transform(new Snap.Matrix().translate(\n                Math.max(min_pos, Math.min(\n                         max_pos, min_pos + (max_pos - min_pos) * xpos)) - xmid, 0));\n    });\n};\n\n\nGadfly.zoomslider_thumb_dragmove = function(dx, dy, x, y) {\n    var root = this.plotroot();\n    var min_pos = this.data(\"min_pos\"),\n        max_pos = this.data(\"max_pos\"),\n        min_scale = root.data(\"min_scale\"),\n        max_scale = root.data(\"max_scale\"),\n        old_scale = root.data(\"old_scale\");\n\n    var px_per_mm = root.data(\"px_per_mm\");\n    dx /= px_per_mm;\n    dy /= px_per_mm;\n\n    var xmid = (min_pos + max_pos) / 2;\n    var xpos = slider_position_from_scale(old_scale, min_scale, max_scale) +\n                   dx / (max_pos - min_pos);\n\n    // compute the new scale\n    var new_scale;\n    if (xpos >= 0.5) {\n        new_scale = Math.exp(2.0 * (xpos - 0.5) * Math.log(max_scale));\n    }\n    else {\n        new_scale = Math.exp(2.0 * xpos * (0 - Math.log(min_scale)) +\n                        Math.log(min_scale));\n    }\n    new_scale = Math.min(max_scale, Math.max(min_scale, new_scale));\n\n    update_plot_scale(root, new_scale);\n};\n\n\nGadfly.zoomslider_thumb_dragstart = function(event) {\n    var root = this.plotroot();\n    init_pan_zoom(root);\n\n    // keep track of what the scale was when we started dragging\n    root.data(\"old_scale\", root.data(\"scale\"));\n};\n\n\nGadfly.zoomslider_thumb_dragend = function(event) {\n};\n\n\nvar toggle_color_class = function(root, color_class, ison) {\n    var guides = root.selectAll(\".guide.\" + color_class + \",.guide .\" + color_class);\n    var geoms = root.selectAll(\".geometry.\" + color_class + \",.geometry .\" + color_class);\n    if (ison) {\n        guides.animate({opacity: 0.5}, 250);\n        geoms.animate({opacity: 0.0}, 250);\n    } else {\n        guides.animate({opacity: 1.0}, 250);\n        geoms.animate({opacity: 1.0}, 250);\n    }\n};\n\n\nGadfly.colorkey_swatch_click = function(event) {\n    var root = this.plotroot();\n    var color_class = this.data(\"color_class\");\n\n    if (event.shiftKey) {\n        root.selectAll(\".colorkey text\")\n            .forEach(function (element) {\n                var other_color_class = element.data(\"color_class\");\n                if (other_color_class != color_class) {\n                    toggle_color_class(root, other_color_class,\n                                       element.attr(\"opacity\") == 1.0);\n                }\n            });\n    } else {\n        toggle_color_class(root, color_class, this.attr(\"opacity\") == 1.0);\n    }\n};\n\n\nreturn Gadfly;\n\n}));\n\n\n//@ sourceURL=gadfly.js\n\n(function (glob, factory) {\n    // AMD support\n      if (typeof require === \"function\" && typeof define === \"function\" && define.amd) {\n        require([\"Snap.svg\", \"Gadfly\"], function (Snap, Gadfly) {\n            factory(Snap, Gadfly);\n        });\n      } else {\n          factory(glob.Snap, glob.Gadfly);\n      }\n})(window, function (Snap, Gadfly) {\n    var fig = Snap(\"#fig-bd92986192a645ba83c07ce231853de9\");\nfig.select(\"#fig-bd92986192a645ba83c07ce231853de9-element-4\")\n   .mouseenter(Gadfly.plot_mouseover)\n.mouseleave(Gadfly.plot_mouseout)\n.mousewheel(Gadfly.guide_background_scroll)\n.drag(Gadfly.guide_background_drag_onmove,\n      Gadfly.guide_background_drag_onstart,\n      Gadfly.guide_background_drag_onend)\n;\nfig.select(\"#fig-bd92986192a645ba83c07ce231853de9-element-7\")\n   .plotroot().data(\"unfocused_ygrid_color\", \"#D0D0E0\")\n;\nfig.select(\"#fig-bd92986192a645ba83c07ce231853de9-element-7\")\n   .plotroot().data(\"focused_ygrid_color\", \"#A0A0A0\")\n;\nfig.select(\"#fig-bd92986192a645ba83c07ce231853de9-element-8\")\n   .plotroot().data(\"unfocused_xgrid_color\", \"#D0D0E0\")\n;\nfig.select(\"#fig-bd92986192a645ba83c07ce231853de9-element-8\")\n   .plotroot().data(\"focused_xgrid_color\", \"#A0A0A0\")\n;\nfig.select(\"#fig-bd92986192a645ba83c07ce231853de9-element-13\")\n   .data(\"mouseover_color\", \"#cd5c5c\")\n;\nfig.select(\"#fig-bd92986192a645ba83c07ce231853de9-element-13\")\n   .data(\"mouseout_color\", \"#6a6a6a\")\n;\nfig.select(\"#fig-bd92986192a645ba83c07ce231853de9-element-13\")\n   .click(Gadfly.zoomslider_zoomin_click)\n.mouseenter(Gadfly.zoomslider_button_mouseover)\n.mouseleave(Gadfly.zoomslider_button_mouseout)\n;\nfig.select(\"#fig-bd92986192a645ba83c07ce231853de9-element-15\")\n   .data(\"max_pos\", 120.42)\n;\nfig.select(\"#fig-bd92986192a645ba83c07ce231853de9-element-15\")\n   .data(\"min_pos\", 103.42)\n;\nfig.select(\"#fig-bd92986192a645ba83c07ce231853de9-element-15\")\n   .click(Gadfly.zoomslider_track_click);\nfig.select(\"#fig-bd92986192a645ba83c07ce231853de9-element-16\")\n   .data(\"max_pos\", 120.42)\n;\nfig.select(\"#fig-bd92986192a645ba83c07ce231853de9-element-16\")\n   .data(\"min_pos\", 103.42)\n;\nfig.select(\"#fig-bd92986192a645ba83c07ce231853de9-element-16\")\n   .data(\"mouseover_color\", \"#cd5c5c\")\n;\nfig.select(\"#fig-bd92986192a645ba83c07ce231853de9-element-16\")\n   .data(\"mouseout_color\", \"#6a6a6a\")\n;\nfig.select(\"#fig-bd92986192a645ba83c07ce231853de9-element-16\")\n   .drag(Gadfly.zoomslider_thumb_dragmove,\n     Gadfly.zoomslider_thumb_dragstart,\n     Gadfly.zoomslider_thumb_dragend)\n.mousedown(Gadfly.zoomslider_thumb_mousedown)\n.mouseup(Gadfly.zoomslider_thumb_mouseup)\n;\nfig.select(\"#fig-bd92986192a645ba83c07ce231853de9-element-17\")\n   .data(\"mouseover_color\", \"#cd5c5c\")\n;\nfig.select(\"#fig-bd92986192a645ba83c07ce231853de9-element-17\")\n   .data(\"mouseout_color\", \"#6a6a6a\")\n;\nfig.select(\"#fig-bd92986192a645ba83c07ce231853de9-element-17\")\n   .click(Gadfly.zoomslider_zoomout_click)\n.mouseenter(Gadfly.zoomslider_button_mouseover)\n.mouseleave(Gadfly.zoomslider_button_mouseout)\n;\n    });\n]]> </script>\n</svg>\n"}}], "cell_type": "code"}, {"metadata": {}, "cell_type": "markdown", "source": "<br>\n\n\nProblem 1.1\n \nFigure 1 above looks like lognormal distribution, with location parameter\nnear 1 and and a very large scale parameter. The vast majority of users are\ncompleted less than 40 problems; and very few completed more than 100. The \nx-axis is truncated to better see the shape of the graph, so we have left \nout the few users who complete several hundred problems.\n<br>\n<br>\n<br>\n<br>\n<br>\n<br>\n<br>"}, {"source": "## quick function to strip time stamp leaving only the date\n\nfunction strip_time(x)\n   ## x is vector of timestamp stings\n   n = length(x)\n   y = Array(String, n)\n\n   for i = 1:n\n\n      y[i] = x[i][1:10]\n   end\n\n   ## return vector of date strings\n   return y\nend\n\n\n\n\n## this function counts the days each user was active\n\nfunction count_alldays(ids, tmstamps)\n   ## ids is complete string vector of student IDs\n   ## tmstamps is string vector of time stamps\n   dates = strip_time(tmstamps)\n\n   uniq_ids = unique(ids)\n\n   n = length(uniq_ids)\n\n   days_cnt = Array(Int, n)\n\n   ## loop over unique IDs, count active days\n   for i = 1:n\n\n      indcs = find(ids .== uniq_ids[i])\n\n      days_cnt[i] = length(unique(dates[indcs]))\n   end\n\n   ## return matrix with unique IDs and days-active count\n   return hcat(uniq_ids, days_cnt)\nend\n\n\ndays_count = count_alldays(d[:, 1], d[:, 2]); ", "execution_count": 6, "metadata": {"trusted": false, "scrolled": true, "collapsed": false}, "outputs": [], "cell_type": "code"}, {"source": "## plotting distribution of days active\n\np = plot(\n   x = days_count[:, 2], \n   \n   Geom.histogram(bincount = 10),\n \n   Guide.ylabel(\"Frequency\"),\n   Guide.xlabel(\"Days Active\"),\n   Guide.title(\"Figure 2: Days Active for Each User\")  \n)", "execution_count": 7, "metadata": {"trusted": false, "scrolled": true, "collapsed": false}, "outputs": [{"metadata": {}, "output_type": "execute_result", "execution_count": 7, "data": {"image/svg+xml": "<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<svg xmlns=\"http://www.w3.org/2000/svg\"\n     xmlns:xlink=\"http://www.w3.org/1999/xlink\"\n     xmlns:gadfly=\"http://www.gadflyjl.org/ns\"\n     version=\"1.2\"\n     width=\"141.42mm\" height=\"100mm\" viewBox=\"0 0 141.42 100\"\n  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text-anchor=\"end\" dy=\"0.35em\">2.0\u00d710\u2074</text>\n  </g>\n  <g font-size=\"3.88\" font-family=\"'PT Sans','Helvetica Neue','Helvetica',sans-serif\" fill=\"#564A55\" stroke=\"none\" id=\"fig-bde438c7b30c4038ad775c4dd15d7819-element-13\">\n    <text x=\"8.81\" y=\"44.66\" text-anchor=\"middle\" dy=\"0.35em\" transform=\"rotate(-90, 8.81, 46.66)\">Frequency</text>\n  </g>\n  <g font-size=\"3.88\" font-family=\"'PT Sans','Helvetica Neue','Helvetica',sans-serif\" fill=\"#564A55\" stroke=\"none\" id=\"fig-bde438c7b30c4038ad775c4dd15d7819-element-14\">\n    <text x=\"81.57\" y=\"10.61\" text-anchor=\"middle\">Figure 2: Days Active for Each User</text>\n  </g>\n</g>\n<defs>\n<clipPath id=\"fig-bde438c7b30c4038ad775c4dd15d7819-element-5\">\n  <path d=\"M26.72,12.61 L 136.42 12.61 136.42 80.72 26.72 80.72\" />\n</clipPath\n></defs>\n</svg>\n", "image/png": 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this()};a.filter.brightness=function(d){null==d&&(d=1);return a.format('<feComponentTransfer><feFuncR type=\"linear\" slope=\"{amount}\"/><feFuncG type=\"linear\" slope=\"{amount}\"/><feFuncB type=\"linear\" slope=\"{amount}\"/></feComponentTransfer>',{amount:d})};a.filter.brightness.toString=function(){return this()};a.filter.contrast=function(d){null==d&&(d=1);return a.format('<feComponentTransfer><feFuncR type=\"linear\" slope=\"{amount}\" intercept=\"{amount2}\"/><feFuncG type=\"linear\" slope=\"{amount}\" intercept=\"{amount2}\"/><feFuncB type=\"linear\" slope=\"{amount}\" intercept=\"{amount2}\"/></feComponentTransfer>',\n{amount:d,amount2:0.5-d/2})};a.filter.contrast.toString=function(){return this()}});return C});\n\n]]> </script>\n<script> <![CDATA[\n\n(function (glob, factory) {\n    // AMD support\n    if (typeof define === \"function\" && define.amd) {\n        // Define as an anonymous module\n        define(\"Gadfly\", [\"Snap.svg\"], function (Snap) {\n            return factory(Snap);\n        });\n    } else {\n        // Browser globals (glob is window)\n        // Snap adds itself to window\n        glob.Gadfly = factory(glob.Snap);\n    }\n}(this, function (Snap) {\n\nvar Gadfly = {};\n\n// Get an x/y coordinate value in pixels\nvar xPX = function(fig, x) {\n    var client_box = fig.node.getBoundingClientRect();\n    return x * fig.node.viewBox.baseVal.width / client_box.width;\n};\n\nvar yPX = function(fig, y) {\n    var client_box = fig.node.getBoundingClientRect();\n    return y * fig.node.viewBox.baseVal.height / client_box.height;\n};\n\n\nSnap.plugin(function (Snap, Element, Paper, global) {\n    // Traverse upwards from a snap element to find and return the first\n    // note with the \"plotroot\" class.\n    Element.prototype.plotroot = function () {\n        var element = this;\n        while (!element.hasClass(\"plotroot\") && element.parent() != null) {\n            element = element.parent();\n        }\n        return element;\n    };\n\n    Element.prototype.svgroot = function () {\n        var element = this;\n        while (element.node.nodeName != \"svg\" && element.parent() != null) {\n            element = element.parent();\n        }\n        return element;\n    };\n\n    Element.prototype.plotbounds = function () {\n        var root = this.plotroot()\n        var bbox = root.select(\".guide.background\").node.getBBox();\n        return {\n            x0: bbox.x,\n            x1: bbox.x + bbox.width,\n            y0: bbox.y,\n            y1: bbox.y + bbox.height\n        };\n    };\n\n    Element.prototype.plotcenter = function () {\n        var root = this.plotroot()\n        var bbox = root.select(\".guide.background\").node.getBBox();\n        return {\n            x: bbox.x + bbox.width / 2,\n            y: bbox.y + bbox.height / 2\n        };\n    };\n\n    // Emulate IE style mouseenter/mouseleave events, since Microsoft always\n    // does everything right.\n    // See: http://www.dynamic-tools.net/toolbox/isMouseLeaveOrEnter/\n    var events = [\"mouseenter\", \"mouseleave\"];\n\n    for (i in events) {\n        (function (event_name) {\n            var event_name = events[i];\n            Element.prototype[event_name] = function (fn, scope) {\n                if (Snap.is(fn, \"function\")) {\n                    var fn2 = function (event) {\n                        if (event.type != \"mouseover\" && event.type != \"mouseout\") {\n                            return;\n                        }\n\n                        var reltg = event.relatedTarget ? event.relatedTarget :\n                            event.type == \"mouseout\" ? event.toElement : event.fromElement;\n                        while (reltg && reltg != this.node) reltg = reltg.parentNode;\n\n                        if (reltg != this.node) {\n                            return fn.apply(this, event);\n                        }\n                    };\n\n                    if (event_name == \"mouseenter\") {\n                        this.mouseover(fn2, scope);\n                    } else {\n                        this.mouseout(fn2, scope);\n                    }\n                }\n                return this;\n            };\n        })(events[i]);\n    }\n\n\n    Element.prototype.mousewheel = function (fn, scope) {\n        if (Snap.is(fn, \"function\")) {\n            var el = this;\n            var fn2 = function (event) {\n                fn.apply(el, [event]);\n            };\n        }\n\n        this.node.addEventListener(\n            /Firefox/i.test(navigator.userAgent) ? \"DOMMouseScroll\" : \"mousewheel\",\n            fn2);\n\n        return this;\n    };\n\n\n    // Snap's attr function can be too slow for things like panning/zooming.\n    // This is a function to directly update element attributes without going\n    // through eve.\n    Element.prototype.attribute = function(key, val) {\n        if (val === undefined) {\n            return this.node.getAttribute(key, val);\n        } else {\n            return this.node.setAttribute(key, val);\n        }\n    };\n});\n\n\n// When the plot is moused over, emphasize the grid lines.\nGadfly.plot_mouseover = function(event) {\n    var root = this.plotroot();\n    init_pan_zoom(root);\n\n    var xgridlines = root.select(\".xgridlines\"),\n        ygridlines = root.select(\".ygridlines\");\n\n    xgridlines.data(\"unfocused_strokedash\",\n                    xgridlines.attr(\"stroke-dasharray\").replace(/px/g, \"mm\"))\n    ygridlines.data(\"unfocused_strokedash\",\n                    ygridlines.attr(\"stroke-dasharray\").replace(/px/g, \"mm\"))\n\n    // emphasize grid lines\n    var destcolor = root.data(\"focused_xgrid_color\");\n    xgridlines.attr(\"stroke-dasharray\", \"none\")\n              .selectAll(\"path\")\n              .animate({stroke: destcolor}, 250);\n\n    destcolor = root.data(\"focused_ygrid_color\");\n    ygridlines.attr(\"stroke-dasharray\", \"none\")\n              .selectAll(\"path\")\n              .animate({stroke: destcolor}, 250);\n\n    // reveal zoom slider\n    root.select(\".zoomslider\")\n        .animate({opacity: 1.0}, 250);\n};\n\n\n// Unemphasize grid lines on mouse out.\nGadfly.plot_mouseout = function(event) {\n    var root = this.plotroot();\n    var xgridlines = root.select(\".xgridlines\"),\n        ygridlines = root.select(\".ygridlines\");\n\n    var destcolor = root.data(\"unfocused_xgrid_color\");\n\n    xgridlines.attr(\"stroke-dasharray\", xgridlines.data(\"unfocused_strokedash\"))\n              .selectAll(\"path\")\n              .animate({stroke: destcolor}, 250);\n\n    destcolor = root.data(\"unfocused_ygrid_color\");\n    ygridlines.attr(\"stroke-dasharray\", ygridlines.data(\"unfocused_strokedash\"))\n              .selectAll(\"path\")\n              .animate({stroke: destcolor}, 250);\n\n    // hide zoom slider\n    root.select(\".zoomslider\")\n        .animate({opacity: 0.0}, 250);\n};\n\n\nvar set_geometry_transform = function(root, tx, ty, scale) {\n    var xscalable = root.hasClass(\"xscalable\"),\n        yscalable = root.hasClass(\"yscalable\");\n\n    var old_scale = root.data(\"scale\");\n\n    var xscale = xscalable ? scale : 1.0,\n        yscale = yscalable ? scale : 1.0;\n\n    tx = xscalable ? tx : 0.0;\n    ty = yscalable ? ty : 0.0;\n\n    var t = new Snap.Matrix().translate(tx, ty).scale(xscale, yscale);\n\n    root.selectAll(\".geometry, image\")\n        .forEach(function (element, i) {\n            element.transform(t);\n        });\n\n    bounds = root.plotbounds();\n\n    if (yscalable) {\n        var xfixed_t = new Snap.Matrix().translate(0, ty).scale(1.0, yscale);\n        root.selectAll(\".xfixed\")\n            .forEach(function (element, i) {\n                element.transform(xfixed_t);\n            });\n\n        root.select(\".ylabels\")\n            .transform(xfixed_t)\n            .selectAll(\"text\")\n            .forEach(function (element, i) {\n                if (element.attribute(\"gadfly:inscale\") == \"true\") {\n                    var cx = element.asPX(\"x\"),\n                        cy = element.asPX(\"y\");\n                    var st = element.data(\"static_transform\");\n                    unscale_t = new Snap.Matrix();\n                    unscale_t.scale(1, 1/scale, cx, cy).add(st);\n                    element.transform(unscale_t);\n\n                    var y = cy * scale + ty;\n                    element.attr(\"visibility\",\n                        bounds.y0 <= y && y <= bounds.y1 ? \"visible\" : \"hidden\");\n                }\n            });\n    }\n\n    if (xscalable) {\n        var yfixed_t = new Snap.Matrix().translate(tx, 0).scale(xscale, 1.0);\n        var xtrans = new Snap.Matrix().translate(tx, 0);\n        root.selectAll(\".yfixed\")\n            .forEach(function (element, i) {\n                element.transform(yfixed_t);\n            });\n\n        root.select(\".xlabels\")\n            .transform(yfixed_t)\n            .selectAll(\"text\")\n            .forEach(function (element, i) {\n                if (element.attribute(\"gadfly:inscale\") == \"true\") {\n                    var cx = element.asPX(\"x\"),\n                        cy = element.asPX(\"y\");\n                    var st = element.data(\"static_transform\");\n                    unscale_t = new Snap.Matrix();\n                    unscale_t.scale(1/scale, 1, cx, cy).add(st);\n\n                    element.transform(unscale_t);\n\n                    var x = cx * scale + tx;\n                    element.attr(\"visibility\",\n                        bounds.x0 <= x && x <= bounds.x1 ? \"visible\" : \"hidden\");\n                    }\n            });\n    }\n\n    // we must unscale anything that is scale invariance: widths, raiduses, etc.\n    var size_attribs = [\"font-size\"];\n    var unscaled_selection = \".geometry, .geometry *\";\n    if (xscalable) {\n        size_attribs.push(\"rx\");\n        unscaled_selection += \", .xgridlines\";\n    }\n    if (yscalable) {\n        size_attribs.push(\"ry\");\n        unscaled_selection += \", .ygridlines\";\n    }\n\n    root.selectAll(unscaled_selection)\n        .forEach(function (element, i) {\n            // circle need special help\n            if (element.node.nodeName == \"circle\") {\n                var cx = element.attribute(\"cx\"),\n                    cy = element.attribute(\"cy\");\n                unscale_t = new Snap.Matrix().scale(1/xscale, 1/yscale,\n                                                        cx, cy);\n                element.transform(unscale_t);\n                return;\n            }\n\n            for (i in size_attribs) {\n                var key = size_attribs[i];\n                var val = parseFloat(element.attribute(key));\n                if (val !== undefined && val != 0 && !isNaN(val)) {\n                    element.attribute(key, val * old_scale / scale);\n                }\n            }\n        });\n};\n\n\n// Find the most appropriate tick scale and update label visibility.\nvar update_tickscale = function(root, scale, axis) {\n    if (!root.hasClass(axis + \"scalable\")) return;\n\n    var tickscales = root.data(axis + \"tickscales\");\n    var best_tickscale = 1.0;\n    var best_tickscale_dist = Infinity;\n    for (tickscale in tickscales) {\n        var dist = Math.abs(Math.log(tickscale) - Math.log(scale));\n        if (dist < best_tickscale_dist) {\n            best_tickscale_dist = dist;\n            best_tickscale = tickscale;\n        }\n    }\n\n    if (best_tickscale != root.data(axis + \"tickscale\")) {\n        root.data(axis + \"tickscale\", best_tickscale);\n        var mark_inscale_gridlines = function (element, i) {\n            var inscale = element.attr(\"gadfly:scale\") == best_tickscale;\n            element.attribute(\"gadfly:inscale\", inscale);\n            element.attr(\"visibility\", inscale ? \"visible\" : \"hidden\");\n        };\n\n        var mark_inscale_labels = function (element, i) {\n            var inscale = element.attr(\"gadfly:scale\") == best_tickscale;\n            element.attribute(\"gadfly:inscale\", inscale);\n            element.attr(\"visibility\", inscale ? \"visible\" : \"hidden\");\n        };\n\n        root.select(\".\" + axis + \"gridlines\").selectAll(\"path\").forEach(mark_inscale_gridlines);\n        root.select(\".\" + axis + \"labels\").selectAll(\"text\").forEach(mark_inscale_labels);\n    }\n};\n\n\nvar set_plot_pan_zoom = function(root, tx, ty, scale) {\n    var old_scale = root.data(\"scale\");\n    var bounds = root.plotbounds();\n\n    var width = bounds.x1 - bounds.x0,\n        height = bounds.y1 - bounds.y0;\n\n    // compute the viewport derived from tx, ty, and scale\n    var x_min = -width * scale - (scale * width - width),\n        x_max = width * scale,\n        y_min = -height * scale - (scale * height - height),\n        y_max = height * scale;\n\n    var x0 = bounds.x0 - scale * bounds.x0,\n        y0 = bounds.y0 - scale * bounds.y0;\n\n    var tx = Math.max(Math.min(tx - x0, x_max), x_min),\n        ty = Math.max(Math.min(ty - y0, y_max), y_min);\n\n    tx += x0;\n    ty += y0;\n\n    // when the scale change, we may need to alter which set of\n    // ticks is being displayed\n    if (scale != old_scale) {\n        update_tickscale(root, scale, \"x\");\n        update_tickscale(root, scale, \"y\");\n    }\n\n    set_geometry_transform(root, tx, ty, scale);\n\n    root.data(\"scale\", scale);\n    root.data(\"tx\", tx);\n    root.data(\"ty\", ty);\n};\n\n\nvar scale_centered_translation = function(root, scale) {\n    var bounds = root.plotbounds();\n\n    var width = bounds.x1 - bounds.x0,\n        height = bounds.y1 - bounds.y0;\n\n    var tx0 = root.data(\"tx\"),\n        ty0 = root.data(\"ty\");\n\n    var scale0 = root.data(\"scale\");\n\n    // how off from center the current view is\n    var xoff = tx0 - (bounds.x0 * (1 - scale0) + (width * (1 - scale0)) / 2),\n        yoff = ty0 - (bounds.y0 * (1 - scale0) + (height * (1 - scale0)) / 2);\n\n    // rescale offsets\n    xoff = xoff * scale / scale0;\n    yoff = yoff * scale / scale0;\n\n    // adjust for the panel position being scaled\n    var x_edge_adjust = bounds.x0 * (1 - scale),\n        y_edge_adjust = bounds.y0 * (1 - scale);\n\n    return {\n        x: xoff + x_edge_adjust + (width - width * scale) / 2,\n        y: yoff + y_edge_adjust + (height - height * scale) / 2\n    };\n};\n\n\n// Initialize data for panning zooming if it isn't already.\nvar init_pan_zoom = function(root) {\n    if (root.data(\"zoompan-ready\")) {\n        return;\n    }\n\n    // The non-scaling-stroke trick. Rather than try to correct for the\n    // stroke-width when zooming, we force it to a fixed value.\n    var px_per_mm = root.node.getCTM().a;\n\n    // Drag events report deltas in pixels, which we'd like to convert to\n    // millimeters.\n    root.data(\"px_per_mm\", px_per_mm);\n\n    root.selectAll(\"path\")\n        .forEach(function (element, i) {\n        sw = element.asPX(\"stroke-width\") * px_per_mm;\n        if (sw > 0) {\n            element.attribute(\"stroke-width\", sw);\n            element.attribute(\"vector-effect\", \"non-scaling-stroke\");\n        }\n    });\n\n    // Store ticks labels original tranformation\n    root.selectAll(\".xlabels > text, .ylabels > text\")\n        .forEach(function (element, i) {\n            var lm = element.transform().localMatrix;\n            element.data(\"static_transform\",\n                new Snap.Matrix(lm.a, lm.b, lm.c, lm.d, lm.e, lm.f));\n        });\n\n    if (root.data(\"tx\") === undefined) root.data(\"tx\", 0);\n    if (root.data(\"ty\") === undefined) root.data(\"ty\", 0);\n    if (root.data(\"scale\") === undefined) root.data(\"scale\", 1.0);\n    if (root.data(\"xtickscales\") === undefined) {\n\n        // index all the tick scales that are listed\n        var xtickscales = {};\n        var ytickscales = {};\n        var add_x_tick_scales = function (element, i) {\n            xtickscales[element.attribute(\"gadfly:scale\")] = true;\n        };\n        var add_y_tick_scales = function (element, i) {\n            ytickscales[element.attribute(\"gadfly:scale\")] = true;\n        };\n\n        root.select(\".xgridlines\").selectAll(\"path\").forEach(add_x_tick_scales);\n        root.select(\".ygridlines\").selectAll(\"path\").forEach(add_y_tick_scales);\n        root.select(\".xlabels\").selectAll(\"text\").forEach(add_x_tick_scales);\n        root.select(\".ylabels\").selectAll(\"text\").forEach(add_y_tick_scales);\n\n        root.data(\"xtickscales\", xtickscales);\n        root.data(\"ytickscales\", ytickscales);\n        root.data(\"xtickscale\", 1.0);\n    }\n\n    var min_scale = 1.0, max_scale = 1.0;\n    for (scale in xtickscales) {\n        min_scale = Math.min(min_scale, scale);\n        max_scale = Math.max(max_scale, scale);\n    }\n    for (scale in ytickscales) {\n        min_scale = Math.min(min_scale, scale);\n        max_scale = Math.max(max_scale, scale);\n    }\n    root.data(\"min_scale\", min_scale);\n    root.data(\"max_scale\", max_scale);\n\n    // store the original positions of labels\n    root.select(\".xlabels\")\n        .selectAll(\"text\")\n        .forEach(function (element, i) {\n            element.data(\"x\", element.asPX(\"x\"));\n        });\n\n    root.select(\".ylabels\")\n        .selectAll(\"text\")\n        .forEach(function (element, i) {\n            element.data(\"y\", element.asPX(\"y\"));\n        });\n\n    // mark grid lines and ticks as in or out of scale.\n    var mark_inscale = function (element, i) {\n        element.attribute(\"gadfly:inscale\", element.attribute(\"gadfly:scale\") == 1.0);\n    };\n\n    root.select(\".xgridlines\").selectAll(\"path\").forEach(mark_inscale);\n    root.select(\".ygridlines\").selectAll(\"path\").forEach(mark_inscale);\n    root.select(\".xlabels\").selectAll(\"text\").forEach(mark_inscale);\n    root.select(\".ylabels\").selectAll(\"text\").forEach(mark_inscale);\n\n    // figure out the upper ond lower bounds on panning using the maximum\n    // and minum grid lines\n    var bounds = root.plotbounds();\n    var pan_bounds = {\n        x0: 0.0,\n        y0: 0.0,\n        x1: 0.0,\n        y1: 0.0\n    };\n\n    root.select(\".xgridlines\")\n        .selectAll(\"path\")\n        .forEach(function (element, i) {\n            if (element.attribute(\"gadfly:inscale\") == \"true\") {\n                var bbox = element.node.getBBox();\n                if (bounds.x1 - bbox.x < pan_bounds.x0) {\n                    pan_bounds.x0 = bounds.x1 - bbox.x;\n                }\n                if (bounds.x0 - bbox.x > pan_bounds.x1) {\n                    pan_bounds.x1 = bounds.x0 - bbox.x;\n                }\n            }\n        });\n\n    root.select(\".ygridlines\")\n        .selectAll(\"path\")\n        .forEach(function (element, i) {\n            if (element.attribute(\"gadfly:inscale\") == \"true\") {\n                var bbox = element.node.getBBox();\n                if (bounds.y1 - bbox.y < pan_bounds.y0) {\n                    pan_bounds.y0 = bounds.y1 - bbox.y;\n                }\n                if (bounds.y0 - bbox.y > pan_bounds.y1) {\n                    pan_bounds.y1 = bounds.y0 - bbox.y;\n                }\n            }\n        });\n\n    // nudge these values a little\n    pan_bounds.x0 -= 5;\n    pan_bounds.x1 += 5;\n    pan_bounds.y0 -= 5;\n    pan_bounds.y1 += 5;\n    root.data(\"pan_bounds\", pan_bounds);\n\n    // Set all grid lines at scale 1.0 to visible. Out of bounds lines\n    // will be clipped.\n    root.select(\".xgridlines\")\n        .selectAll(\"path\")\n        .forEach(function (element, i) {\n            if (element.attribute(\"gadfly:inscale\") == \"true\") {\n                element.attr(\"visibility\", \"visible\");\n            }\n        });\n\n    root.select(\".ygridlines\")\n        .selectAll(\"path\")\n        .forEach(function (element, i) {\n            if (element.attribute(\"gadfly:inscale\") == \"true\") {\n                element.attr(\"visibility\", \"visible\");\n            }\n        });\n\n    root.data(\"zoompan-ready\", true)\n};\n\n\n// Panning\nGadfly.guide_background_drag_onmove = function(dx, dy, x, y, event) {\n    var root = this.plotroot();\n    var px_per_mm = root.data(\"px_per_mm\");\n    dx /= px_per_mm;\n    dy /= px_per_mm;\n\n    var tx0 = root.data(\"tx\"),\n        ty0 = root.data(\"ty\");\n\n    var dx0 = root.data(\"dx\"),\n        dy0 = root.data(\"dy\");\n\n    root.data(\"dx\", dx);\n    root.data(\"dy\", dy);\n\n    dx = dx - dx0;\n    dy = dy - dy0;\n\n    var tx = tx0 + dx,\n        ty = ty0 + dy;\n\n    set_plot_pan_zoom(root, tx, ty, root.data(\"scale\"));\n};\n\n\nGadfly.guide_background_drag_onstart = function(x, y, event) {\n    var root = this.plotroot();\n    root.data(\"dx\", 0);\n    root.data(\"dy\", 0);\n    init_pan_zoom(root);\n};\n\n\nGadfly.guide_background_drag_onend = function(event) {\n    var root = this.plotroot();\n};\n\n\nGadfly.guide_background_scroll = function(event) {\n    if (event.shiftKey) {\n        var root = this.plotroot();\n        init_pan_zoom(root);\n        var new_scale = root.data(\"scale\") * Math.pow(2, 0.002 * event.wheelDelta);\n        new_scale = Math.max(\n            root.data(\"min_scale\"),\n            Math.min(root.data(\"max_scale\"), new_scale))\n        update_plot_scale(root, new_scale);\n        event.stopPropagation();\n    }\n};\n\n\nGadfly.zoomslider_button_mouseover = function(event) {\n    this.select(\".button_logo\")\n         .animate({fill: this.data(\"mouseover_color\")}, 100);\n};\n\n\nGadfly.zoomslider_button_mouseout = function(event) {\n     this.select(\".button_logo\")\n         .animate({fill: this.data(\"mouseout_color\")}, 100);\n};\n\n\nGadfly.zoomslider_zoomout_click = function(event) {\n    var root = this.plotroot();\n    init_pan_zoom(root);\n    var min_scale = root.data(\"min_scale\"),\n        scale = root.data(\"scale\");\n    Snap.animate(\n        scale,\n        Math.max(min_scale, scale / 1.5),\n        function (new_scale) {\n            update_plot_scale(root, new_scale);\n        },\n        200);\n};\n\n\nGadfly.zoomslider_zoomin_click = function(event) {\n    var root = this.plotroot();\n    init_pan_zoom(root);\n    var max_scale = root.data(\"max_scale\"),\n        scale = root.data(\"scale\");\n\n    Snap.animate(\n        scale,\n        Math.min(max_scale, scale * 1.5),\n        function (new_scale) {\n            update_plot_scale(root, new_scale);\n        },\n        200);\n};\n\n\nGadfly.zoomslider_track_click = function(event) {\n    // TODO\n};\n\n\nGadfly.zoomslider_thumb_mousedown = function(event) {\n    this.animate({fill: this.data(\"mouseover_color\")}, 100);\n};\n\n\nGadfly.zoomslider_thumb_mouseup = function(event) {\n    this.animate({fill: this.data(\"mouseout_color\")}, 100);\n};\n\n\n// compute the position in [0, 1] of the zoom slider thumb from the current scale\nvar slider_position_from_scale = function(scale, min_scale, max_scale) {\n    if (scale >= 1.0) {\n        return 0.5 + 0.5 * (Math.log(scale) / Math.log(max_scale));\n    }\n    else {\n        return 0.5 * (Math.log(scale) - Math.log(min_scale)) / (0 - Math.log(min_scale));\n    }\n}\n\n\nvar update_plot_scale = function(root, new_scale) {\n    var trans = scale_centered_translation(root, new_scale);\n    set_plot_pan_zoom(root, trans.x, trans.y, new_scale);\n\n    root.selectAll(\".zoomslider_thumb\")\n        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px_per_mm;\n\n    var xmid = (min_pos + max_pos) / 2;\n    var xpos = slider_position_from_scale(old_scale, min_scale, max_scale) +\n                   dx / (max_pos - min_pos);\n\n    // compute the new scale\n    var new_scale;\n    if (xpos >= 0.5) {\n        new_scale = Math.exp(2.0 * (xpos - 0.5) * Math.log(max_scale));\n    }\n    else {\n        new_scale = Math.exp(2.0 * xpos * (0 - Math.log(min_scale)) +\n                        Math.log(min_scale));\n    }\n    new_scale = Math.min(max_scale, Math.max(min_scale, new_scale));\n\n    update_plot_scale(root, new_scale);\n};\n\n\nGadfly.zoomslider_thumb_dragstart = function(event) {\n    var root = this.plotroot();\n    init_pan_zoom(root);\n\n    // keep track of what the scale was when we started dragging\n    root.data(\"old_scale\", root.data(\"scale\"));\n};\n\n\nGadfly.zoomslider_thumb_dragend = function(event) {\n};\n\n\nvar toggle_color_class = function(root, color_class, ison) {\n    var guides = root.selectAll(\".guide.\" + color_class + \",.guide .\" + color_class);\n    var geoms = root.selectAll(\".geometry.\" + color_class + \",.geometry .\" + color_class);\n    if (ison) {\n        guides.animate({opacity: 0.5}, 250);\n        geoms.animate({opacity: 0.0}, 250);\n    } else {\n        guides.animate({opacity: 1.0}, 250);\n        geoms.animate({opacity: 1.0}, 250);\n    }\n};\n\n\nGadfly.colorkey_swatch_click = function(event) {\n    var root = this.plotroot();\n    var color_class = this.data(\"color_class\");\n\n    if (event.shiftKey) {\n        root.selectAll(\".colorkey text\")\n            .forEach(function (element) {\n                var other_color_class = element.data(\"color_class\");\n                if (other_color_class != color_class) {\n                    toggle_color_class(root, other_color_class,\n                                       element.attr(\"opacity\") == 1.0);\n                }\n            });\n    } else {\n        toggle_color_class(root, color_class, this.attr(\"opacity\") == 1.0);\n    }\n};\n\n\nreturn Gadfly;\n\n}));\n\n\n//@ sourceURL=gadfly.js\n\n(function (glob, factory) {\n    // AMD support\n      if (typeof require === \"function\" && typeof define === \"function\" && define.amd) {\n        require([\"Snap.svg\", \"Gadfly\"], function (Snap, Gadfly) {\n            factory(Snap, Gadfly);\n        });\n      } else {\n          factory(glob.Snap, glob.Gadfly);\n      }\n})(window, function (Snap, Gadfly) {\n    var fig = Snap(\"#fig-c43b58e06ae9464ca3db7c7fdc317fc2\");\nfig.select(\"#fig-c43b58e06ae9464ca3db7c7fdc317fc2-element-4\")\n   .mouseenter(Gadfly.plot_mouseover)\n.mouseleave(Gadfly.plot_mouseout)\n.mousewheel(Gadfly.guide_background_scroll)\n.drag(Gadfly.guide_background_drag_onmove,\n      Gadfly.guide_background_drag_onstart,\n      Gadfly.guide_background_drag_onend)\n;\nfig.select(\"#fig-c43b58e06ae9464ca3db7c7fdc317fc2-element-7\")\n   .plotroot().data(\"unfocused_ygrid_color\", \"#D0D0E0\")\n;\nfig.select(\"#fig-c43b58e06ae9464ca3db7c7fdc317fc2-element-7\")\n   .plotroot().data(\"focused_ygrid_color\", \"#A0A0A0\")\n;\nfig.select(\"#fig-c43b58e06ae9464ca3db7c7fdc317fc2-element-8\")\n   .plotroot().data(\"unfocused_xgrid_color\", \"#D0D0E0\")\n;\nfig.select(\"#fig-c43b58e06ae9464ca3db7c7fdc317fc2-element-8\")\n   .plotroot().data(\"focused_xgrid_color\", \"#A0A0A0\")\n;\nfig.select(\"#fig-c43b58e06ae9464ca3db7c7fdc317fc2-element-13\")\n   .data(\"mouseover_color\", \"#cd5c5c\")\n;\nfig.select(\"#fig-c43b58e06ae9464ca3db7c7fdc317fc2-element-13\")\n   .data(\"mouseout_color\", \"#6a6a6a\")\n;\nfig.select(\"#fig-c43b58e06ae9464ca3db7c7fdc317fc2-element-13\")\n   .click(Gadfly.zoomslider_zoomin_click)\n.mouseenter(Gadfly.zoomslider_button_mouseover)\n.mouseleave(Gadfly.zoomslider_button_mouseout)\n;\nfig.select(\"#fig-c43b58e06ae9464ca3db7c7fdc317fc2-element-15\")\n   .data(\"max_pos\", 120.42)\n;\nfig.select(\"#fig-c43b58e06ae9464ca3db7c7fdc317fc2-element-15\")\n   .data(\"min_pos\", 103.42)\n;\nfig.select(\"#fig-c43b58e06ae9464ca3db7c7fdc317fc2-element-15\")\n   .click(Gadfly.zoomslider_track_click);\nfig.select(\"#fig-c43b58e06ae9464ca3db7c7fdc317fc2-element-16\")\n   .data(\"max_pos\", 120.42)\n;\nfig.select(\"#fig-c43b58e06ae9464ca3db7c7fdc317fc2-element-16\")\n   .data(\"min_pos\", 103.42)\n;\nfig.select(\"#fig-c43b58e06ae9464ca3db7c7fdc317fc2-element-16\")\n   .data(\"mouseover_color\", \"#cd5c5c\")\n;\nfig.select(\"#fig-c43b58e06ae9464ca3db7c7fdc317fc2-element-16\")\n   .data(\"mouseout_color\", \"#6a6a6a\")\n;\nfig.select(\"#fig-c43b58e06ae9464ca3db7c7fdc317fc2-element-16\")\n   .drag(Gadfly.zoomslider_thumb_dragmove,\n     Gadfly.zoomslider_thumb_dragstart,\n     Gadfly.zoomslider_thumb_dragend)\n.mousedown(Gadfly.zoomslider_thumb_mousedown)\n.mouseup(Gadfly.zoomslider_thumb_mouseup)\n;\nfig.select(\"#fig-c43b58e06ae9464ca3db7c7fdc317fc2-element-17\")\n   .data(\"mouseover_color\", \"#cd5c5c\")\n;\nfig.select(\"#fig-c43b58e06ae9464ca3db7c7fdc317fc2-element-17\")\n   .data(\"mouseout_color\", \"#6a6a6a\")\n;\nfig.select(\"#fig-c43b58e06ae9464ca3db7c7fdc317fc2-element-17\")\n   .click(Gadfly.zoomslider_zoomout_click)\n.mouseenter(Gadfly.zoomslider_button_mouseover)\n.mouseleave(Gadfly.zoomslider_button_mouseout)\n;\n    });\n]]> </script>\n</svg>\n"}}], "cell_type": "code"}, {"metadata": {}, "cell_type": "markdown", "source": "\nProblem 1.2\n\nFigure 2 above again seems to generally have a lognormal shape. The vast\nmajority of users were active 1 or 2 days out of the week, and _very_ few \nwere active more than 5 days.\n<br>\n<br>\n<br>\n<br>\n<br>\n<br>\n\n"}, {"source": "## quick function to put together our two problem and day data\n\nfunction joindata(days_data, prbm_hash)\n   n = size(days_data)[1]\n   y = Array(Int, n)\n    \n   for i = 1:n\n\n      y[i] = prbm_hash[days_data[i, 1]]\n   end\n\n   return hcat(days_data, y)\nend\n\n\nd1 = joindata(days_count, problem_hash);", "execution_count": 8, "metadata": {"trusted": false, "scrolled": true, "collapsed": false}, "outputs": [], "cell_type": "code"}, {"source": "## operationalize engagement as problems-per-day\neng = d1[:, 3] ./ d1[:, 2]\n\n\n## plotting distribution of engagement\np = plot(\n   x = eng, \n   Geom.histogram(bincount = 200),\n   \n   Scale.x_continuous(minvalue = 0, maxvalue = 100),\n   Guide.xticks(ticks = [0, 20, 40, 60, 80, 100]),\n   Guide.ylabel(\"Frequency\"),\n   Guide.xlabel(\"Engagement Level\"),\n   Guide.title(\"Figure 3: Engagement Level by Each User\")  \n)", "execution_count": 9, "metadata": {"trusted": false, "collapsed": false}, "outputs": [{"metadata": {}, "output_type": "execute_result", "execution_count": 9, "data": {"image/svg+xml": "<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<svg xmlns=\"http://www.w3.org/2000/svg\"\n     xmlns:xlink=\"http://www.w3.org/1999/xlink\"\n     xmlns:gadfly=\"http://www.gadflyjl.org/ns\"\n     version=\"1.2\"\n     width=\"141.42mm\" height=\"100mm\" viewBox=\"0 0 141.42 100\"\n     stroke=\"none\"\n     fill=\"#000000\"\n     stroke-width=\"0.3\"\n     font-size=\"3.88\"\n>\n<g class=\"plotroot xscalable yscalable\" id=\"fig-79e7a75a0ec2480fb4efb645c1b7e046-element-1\">\n  <g font-size=\"3.88\" font-family=\"'PT Sans','Helvetica Neue','Helvetica',sans-serif\" fill=\"#564A55\" stroke=\"none\" id=\"fig-79e7a75a0ec2480fb4efb645c1b7e046-element-2\">\n    <text x=\"81.57\" y=\"92\" text-anchor=\"middle\">Engagement Level</text>\n  </g>\n  <g 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text-anchor=\"end\" dy=\"0.35em\">4.0\u00d710\u00b3</text>\n    <text x=\"25.73\" y=\"14.61\" text-anchor=\"end\" dy=\"0.35em\">5.0\u00d710\u00b3</text>\n  </g>\n  <g font-size=\"3.88\" font-family=\"'PT Sans','Helvetica Neue','Helvetica',sans-serif\" fill=\"#564A55\" stroke=\"none\" id=\"fig-79e7a75a0ec2480fb4efb645c1b7e046-element-13\">\n    <text x=\"8.81\" y=\"44.66\" text-anchor=\"middle\" dy=\"0.35em\" transform=\"rotate(-90, 8.81, 46.66)\">Frequency</text>\n  </g>\n  <g font-size=\"3.88\" font-family=\"'PT Sans','Helvetica Neue','Helvetica',sans-serif\" fill=\"#564A55\" stroke=\"none\" id=\"fig-79e7a75a0ec2480fb4efb645c1b7e046-element-14\">\n    <text x=\"81.57\" y=\"10.61\" text-anchor=\"middle\">Figure 3: Engagement Level by Each User</text>\n  </g>\n</g>\n<defs>\n<clipPath id=\"fig-79e7a75a0ec2480fb4efb645c1b7e046-element-5\">\n  <path d=\"M26.73,12.61 L 136.42 12.61 136.42 80.72 26.73 80.72\" />\n</clipPath\n></defs>\n</svg>\n", "image/png": 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lxSpfR/R/D/V+OdOcAtKUb7nu7niu2hgMBg/ck5KMvic+OplciOOSU+IMIsLrRFy+bNXy382ZNMd+b8ie3wJ0cvXq4AW9XV+qmeYheR57iouL7UGDBvlUM37KCsOrVwWn9HZNQiTIZRFxXAquCe5kxiJm+kVZqfPOe0Pe3QvQ6Fg8fkJ+9LFpHpLnsWdQ5qDR1IJWJgQIalFv1yOEECcSmnXh9HGzps0a2POiJwTTPCTPY8TixYvVZRdfNqy36xBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIcfyqra0v7u0a0pnkZ0byMyP5mZH8zKQyP/mEzrSj7uztCtKb5GdG8jMj+ZmR/MykLj+ZXKQZIv14b9eQziQ/M5KfGcnPjORnJpX5nZBfXDavfN5FCio38Tjqjz790EMf/Aa922bPzm1xs0qIKOpr8T17b/DeSE/t3uzcnBXrE5taUVUR7K6dhVcs7Ms+nsrEmyofqaw5unsnhBBC9K4T8swFgR4E+NzETzQatQ5+fc6cOb5WnfU3AA6AL8Sy21f01Oa8r8wbEevT/n0Q39xjOz5dAUYzgLuvv/r6U4+k9lCofu6RLC8+SPIzI/mZkfzMSH5mUpnfCTe5mH/1/AEg1FdUVd6Y+Kmqqmo7eBl/zD8ThMbKJZVORVXFfBANXHDVggIANK983tDEcosXL1Y3XXvTEABQmh4A1NmH1U6uug7AUGIMUTlq75HUz6wu/+h7LyQ/M5KfGcnPjORnJpX52ana0DHDwmnEOHlB+fznGNgBxkOVSyr/+oFlGOMACr//mF4hW0+af/V8H4AVN5bfOK1/fv/Xd9XvWAJS9QC+VbGkctr8q+d/iizc01M7aOaR2Vb70lY3u1i10vkAnj78HdB3fMQ9FwAkP1OSnxnJz4zkZyZ1+Z1wkwvSZBPxi6zoe3D5DCIE58yZM+6hhx7adWAh5iFE+PuBdYjfATC08tHK5QvLF17nwl25q2HXRibaWVlV0fWb1UU7YPWpCGddAuIhlm3/o/NqGzY0jorHXXfixFGNr732WkYk4j+VyG0ZP37UdtfVtRs2NI5yXb2nqKjgvXB4y2AiX05urn67oKCgbd2610f6/bY9fvzI+mAQauzYpjytVXsgMOKtcHhbH6L2IdGob9/kycN2bdz4Rn/X1SdZVtaOceMGN9fUNA2zbc6MRne+MXny5Fg43FDgulofXIdl6dZx4wq2/fOfr/XNzvYP7FxHc7N655xzRkTq6raMYPb5Dq6D2YoWFg5/c926t7P9/tjQznX4/ZGdY8eO3b9uXcMpfj9lHVyH1sxFRQUNGzdu9LtuzvDOdQB4d8KEvHdral4bZNv+3EPU0QAAdXVNTeHwm8MPriMej+6fOHHMzg0bGk8GcHLnOiyr+c1x48ZFa2vr8wHg4DqiUY5Mnpz/zqZNm3Kj0axBneuIRn3bJk8e1hoOvzmcyPUfVEc+USw2fvxpb7z00htZOTn6lM51tLZGd33yk2P2bdxYP9R1VfbBdShFVFiYX79u3Tqf3z9oROc6LEu9N27ciD3r1r090O+P9e1cx6ZNIxsdB7qurqmgcx3MsebCwtN21NbWn2RZqn+nOppee+21jDFjxrTX1GzNsyylDq4jHqe2iRNHvr1x444c140M7lwHc8b2wsKhLaHQG6cqpTMOriMajccnTx7dVF9fn7l/vxrWuY5oNL578uTRe+vqtg5htvpkZUXfStRh25Y1YULe1uefZ3vAgKaRneuIRKy9Z589fPe//vXmgKwst1/nOnbvHtl03nkU37ChcVSijs5jb9261/v5/faARB1djb0JE/K2VlezdaixF4lYdQDQ1dhL1HGosZeoo6uxl6jjUGMvUUdXY+9wjgFdjb1EHd2MvXoAVFfXlN/VMaCrsZeo46Cxt7ZzHV2NvUQdhxp7iTq6GnuJOg419hLHgK7GXqKOrsZeRx18qLGXqKOLsXegjkONvUQdXY29aNS3raho2NpDjb1EHYf9i/QwnHCXRSqWVLx4f1XlgoqHK96uXFL5ZwKezYj6Lj54GSZsYPCBeyE00J9cbASAAfkDXiTirQBfRKCfAuCuttVVO/dXVd7ign84MH/QF+771X3bO6+nNW5TiuYBQHOzfYrWepHr0iUAYFnW41rrRUT8CW9p+2Kt9aI9e+zh3uv29VrrRQBo5Mgtfbxl3S977UbGaK0X+Xzt5wNALOYWa60XxWLNZ3rr6su9dYf16yjlVqXUQgDYu1cN0VovikbpSwCQkWFP1FovUgqfBABma4rWelF2dnQEALiuPUdrvWj9+vX2uHE7s7TWi5jj5d66badprRdZVvQCAIhG4+dqrRe1tmaeBQC2zZdprRdlZPTv77WNrxHRjV7NfQd5NdMMAMjOtgJePnwOABDZpd66br5Xh3Wt1nrRli1bfOvXv5OpNa8C3GsAwOeLj/L2wX9Rx3Y+o7VeFIlkTfBepzLvcZ8BXtu4mQg3A0Ak0meAlyWVeY+zJnj7iM8AgFL+i7zX46O8KN1rvDzeydyyZYvPq9m61svDzffeJ7vUq5nP8bK0At4+0wxvn/sO8uqgG5nxNW/d/v211otsmy8DgNbWzLO89yl+rveeRi/w8mg7zdvHeLnWetG4cTuz1q9fb3t12HMAoE+f2EhvH6wp3j7gk9669kTvfaIvac2r9u5V3qVAr2/c6u3fsH5elnqWV3PzmV7NbrGXZfv5WutFWkfGePvgfllrvWjkyC19AJDXH+zrAWDPHnu41w/ti71l+RNeltYkLx+6RGu9qLnZPsWrg+ZpjdsAYPDgN/t2jI8rAaC9vW2s1npRZqZ7HgD4/e7nvNfbx3p1x6/SWi8aMKAp1xsjepFtq3lelr5hHe/TF73t2B/vqPMT3rK+L3p1WKd6/da+wcuPacyYhlxvO/Grve20ne5lGa8GgHjcPc97n9rO8PZRX+G952/1894nuo1ILQCA/futoV523tjLzPRN8tpGx2VYe5rWetF77+kRXtveMWDtWlgTJmzO9mpyy719aEscA77gvU/uZ7XWi+LxlrO81/Us7/WTTu4YE7copW709jcjcQyY7mVpF3nvEyXG3hSt9aKcnHie9z7Z12mtF7388su+DRu2dxwD3Nkd78PojmPAhR37e6431jLHe/2WZ2qtF2VlDegYe/Q1pdRNtbUNv2xvzx3o9S3lAEBbW1ahlw8+7a3rL/Hec13g1R27Vmu9qKGhwd/Q0OD3lo1d62WpC7w6/CXesvi01npRW1tWoZePcrTWi9rbcwd29PmbiOhrAJCVNWCAty7PBIBIJHO8t4+UGHsXaq0X+f3uaG8f3dla60UbNmzPevnllzuOAfZ1AJCTE8/rOAZM8d5TOsdb1y7qGHvTvbozEmPvRmbc4o2tk072+i1mee9/y1kdY++zHWPvC97YbBtTW9vwS2a3XGu9aMKEzdlr18Ly3n9v7B1NJ9xfiyz48oJriPis+6sqb54zZ47PH/WtJ1aXbmvd1jAwd2Degw8/uHXBlxd8GsTfraiqPO+Gy284WflVDTQmDxo16N3dDTurNNBKRNXQ/AvL0lPve/jBzQCQuCxS8UjlOR3bOmQ7lY9W7v6o9dfWNr5QVJT32aOVx4lG8jMj+ZmR/MxIfmZSmd8Jd+YiU2c+wYSzF3xl3mp/1N8I0N/uX3L/6wMyBwywtHodALa3bv8nwNsXls//t+VXryjGvZWPVu7e07BnNICdlVWVN1Q8UvEXEF2X+F/NoXTVjkn9zDTLZP0TneRnRvIzI/mZkfzMSH4p8NUr55zy1fKvntTdMgtmLxh22+zZud0tcziOVjtCCCGEEEddbW3jC71dQzqT/MxIfmYkPzOSn5lU5nfCXRZJd0T8dm/XkM4kPzOSnxnJz4zkZ0byE0IIIYQQqREObz29t2tIZ5KfGcnPjORnRvIzk8r85LJImtHaeqi3a0hnkp8Zyc+M5GdG8jOTyvxkcpFmiPTa3q4hnUl+ZiQ/M5KfGcnPjOQnhBBCCCFSo7a2Qb4V0IDkZ0byMyP5mZH8zKQyP7ksknZobm9XkN4kPzOSnxnJz4zkZyZ1+cnkIu3wL3q7gvQm+ZmR/MxIfmYkPzOSnxBCCCGESIVQqP7O3q4hnUl+ZiQ/M5KfGcnPTCrzk8siaYZZFfd2DelM8jMj+ZmR/MxIfmZSmZ9MLtIMM93S2zWkM8nPjORnRvIzI/mZkfyEEEIIIURqhEINS3u7hnQm+ZmR/MxIfmYkPzOpzE8ui6QZZhrW2zWkM8nPjORnRvIzI/mZkfyEEEIIIURqbNq0Kbe3a0hnkp8Zyc+M5GdG8jOTyvzkskiaiUSynuntGtKZ5GdG8jMj+ZmR/MykMj+ZXKQdfrW3K0hvkp8Zyc+M5GdG8jMj+QkhhBBCiFSora0v7u0a0pnkZ0byMyP5mZH8zKQyP7ksknaUfLa+EcnPjORnRvIzI/mZSV1+MrlIM0rxyt6uIZ1JfmYkPzOSnxnJz4zkJ4QQQgghUiMUqp/b2zWkM8nPjORnRvIzI/mZSWV+clkkzTCry3u7hnQm+ZmR/MxIfmYkPzOpzE8mF2lH39HbFaQ3yc+M5GdG8jMj+ZmR/IQQQgghRCqEQg0/6e0a0pnkZ0byMyP5mZH8zKQyP7kskmaYaVJv15DOJD8zkp8Zyc+M5GcmlfnZqdqQODqYadYHnljL1wH4bg+r/RTF9P2kFZVGPpSfOCKSnxnJz4zkZyaV+VGqNiSS5Hm+CcBPu12G8D0U0/9LTUFCCCFOdHJZJM3U1ja+0Ns1pDPJz4zkZ0byMyP5mUllfjK5SDNEvL+3a0hnkp8Zyc+M5GdG8jOTyvzkski6k8siQgghjjFy5iLNhMNbT+/tGtKZ5GdG8jMj+ZmR/MykMj+ZXKQZra2HeruGdCb5mZH8zEh+ZiQ/M6nM74T+U9QFX17waQCoWFLxYufXbps9O7fFzSohoqivxffsvcF7Iz21d7Nzc1asT2xqRVVFsLt2Fl6xsC/7eCoTb6p8pLLmSGom0muPZHnxQZKfGcnPjORnRvIzk8r8TtgzF1+9cs4pBH4aQHHn1+bMmeNr1Vl/A+AA+EIsu31FT+3N+8q8EbE+7d8H8c09tuPTFWA0A7j7+quvP/VI6g4ECuSz4Q1IfmYkPzOSnxnJz0wq8ztRJxcUt/2/ZOI/EzF3ftEf888EobFySaVTUVUxH0QDF1y1oAAAzSufNzSx3OLFi9VN1940BACUpgcAdfZhtZOrrgMwlBhDVI7aeySFh8ON0z7aLgtA8jMl+ZmR/MxIfmZSmd8JOblYWD7/VmL9PBOFD7kAYxz4oNeYXiFbT5p/9fwxBFp7Y/mNYxYvXqx21e9Y4sbd+QBQsaRyGmv+2uG0g+b4J7Ot1qVgCqtWdf6R1K41bj2S5cUHSX5mJD8zkp8Zyc9MKvM74SYXC2YvmMzE592/5IGuv8CFeQiBGxIPifgdAEMrH618VUFd58JduathV5CJWiqqKro+zdRFO2B1cURnfRvEQyzb+seR7QH/4siWFx8k+ZmR/MxIfmYkPzOpy+/Eu6FTYzGB/PPL5/+GGOMYoPlfmf9W5SOVSxKLMGEDwKe+vwr6Wy6eAYAB+QNe3NW4YysYFxFoEoAPXVbpqZ2fPVpZMa983tDB+YN3LF68WHdeLxSqXwbgvUCgYO769W+cZtvu95jxr0Ag/x4A74XDDdWA+m1h4cgVU3zNn10Vy+l2l8/3tZ73JwCh0NZPEKlbmfGHQCD/4drahiuUwheZ6WeBQN7fQ6H6O4noDKX0wvHjR22vrW14HEBLUVH+deFwQwGAH2pN64qK8n4UCtVfSETXaM2PFxUVPB0KNSwgwv8w052BQN7GUKjhZ0QYGovtuiInJyezvT3z1wAaCgvzb9uwoXESMy8C8FxhYf6vwuH6ywD6kuty5cSJBS+EQo3fIuKzYjHcNHly/juhUMNvAMQCgfzZNTVb8yxL/ZiZawKBgh/U1DSeb1k8B7z21VAAACAASURBVMCywsL8J2trG+YphWKl9HfHjx+1IRxuvBfgU/v25av37cuygLZLQqGGSYFA/i21tfUBpeh2Zv5zIFDwi1CosYyIZwD4eWFh/l9qaxv+n1IoBOyvFRYOf7O2tn4JABQVFXw5HH5zOBD/idYIFxXl3xUON3wOwPXMtDwQyKsOhernEtHntebvFRUVhEKhhnuIMALI/ErfvhF33z56FKC3Cgvzbq6r2zpBa/UNrbG2qCj/gXC4YTqAma5LD02cmPenUKj+f4loouvqr0+cOKoxFGp4GIAvEMi/at26hlN8PtzHTP8NBPK+XVNT/1nLovkAP1VYWPBEONxwLYALiOiHEybkrQ+HG34EID8jo+2a5ubmNp9v4GPM2BYI5N8YCjWOI+I7mPHXQCC/ora2/hKl6HJm/nUgUPBsbW3jbUrx5HC44R+Fhfn1tbUNvwTQp6go//K6uq1DtFb3a80vFxUVLA6FGj9DxDdqjd8XFeU/Fgo1zCbCRcz67kBg1L9DocbvE/ForXnOa6/l7z/99MalAHYWFubP37ChfiwzfRugFwsL8+4Lh5suBvSVWqOqqCh/dSjUcAsRzo7HrdsnTRqxJRSq/wWAkwKBgpnr1r090OeLPsCMzYFA/jdraxvOUQpfBeiZwsK8R2tr67+sFE0B+CeFhQX/rK1tuEspjGlrs284++zhu8Phhmpm2h0I5N1QW/v6GKWsu5jxz0Ag/ye1tQ1TlMKXmenRQCDvmdra+q8qRecA+puFhaM219Y2PKAUBk6YkDezrq7pJGb+hda0pago7/ZwuP5sgG7Rmld7Y7zxSiK+WGu6r6go78VQqOHbRBjrurH5EyeO2RkKNSwlwv7Cwvw5dXVNo7XW3wfw78LC/LvD4fqLAJqtNT9WVFTw+3C48UaAPxOP445Jk/JfCYUa7ifCkD178i7Pzd3ax+ezfsnM9YFAwaKamqbJlqVvA/BsYWH+r2trGy5XCpcQ6YoJE0b9tba2frFSdKZt843jxhVsC4cbHmNGWyCQf01tbX2+UvQjZlofCOT9MBTaegGRupYZTwQC+U/V1jbMVwqfBdS3CwtH/jcUqr+PiE7Jzo5dFY3288XjrQ9rjcaiovyvh8MNEwH8LzP9KRDIeygUqp9JRNO15geKigrWhkIN3yDCBGbr5kBgxFuhUMOjALmBQN5X6uq2jNDavgdAqLAw/3vhcOPnAZ4LUHVhYd7yUKj+eiL6nOvquyZOHBUOhRp+QoThLS3WlwGgTx93CTPeDATyv1ZTs7XQstT/Y+a/BAIFPw+HG2cAXAbQLwoL8/4cDjfcDiCgVPyW8eNPeyMUanwEYCsQyL86FHrjVCL3XmZsCATyv1tbW1+sFM1j5icDgYJloVDjHCI+H8APCgvza2prG36sFPJsO3u237831trq+w0zvxMIFNwUDjedBehvaY0XioryK0Ohhi8R4TJm/atAYNRzoVDjIiKepDXfVlRU0BAKNfyaCJmFhflXbNxYPzQep58BtLGwMO/ODRu2/g+zWqA1ni4qyn88HG64BsCFrqt+VFQ08vFQqP6HRFQQi7nX7d8/qqV//8bHmbE9EMhf2O0vkiN0wk0ubMQXxGHnEgAClzPgI6I/Oo5jDcwdmPfgww9uJab/gPBdAN+54fIbTgZQ7IJuXbx4sdrdsLOKQU1ENA2aV940+4ap9z384OZDbaurdgDggaoHtnVVo1Lq+mg0rgFg377hDQMGNM0FMqLeaziHiObm5roRAFgbz/kHgEu72+cXo5kvAUA8vqfW7x80NxbztQNAJGI9lZOjV9t2cwsAZGa23x2NZtlnnVWwFwAsi+a7rmYA2Lw5r2ns2Ka5fn9WDAD69ImvjUT8/z7pJI4AAFHmI0Ttj+3ZM3K/ty33m36/rSZNmhQH0FxX1zS3tTXqAkA0unOD3z9objTq1dHSYv8+J0c/m5HR0lFH5N5oNMveunXkPgBg5gOdfsuWgjfHjm2aa9vZMQDIzY3+LRLxr8/N1REvn8wlRO1L29v3NHv7GL0jO9tv5efntQNAONxQ39YWvwsAXHf3RssaNDcW87cDQGurWpmTo/+YlRVtBQC/P/s+1434Nm06dV/H5m9K1LF586nvHJxHdnbsxUjEX9vSotq8/c/4jd8fq3bdXc0A0NYWuzM722+NHz+kFQAOzqO9fc8rfv+guUp573Hfvrxq/371l9xcrw6fr0+F60Z8W7YUJPI4cPlt69a8HWPHNs3NyIjEASAjo/UfrpuzobnZagOAWMz/uN8fezIa3dmRR+y72dl+6/TTT29O1BGLeX3t3XdHbj64r510Ev6wfz+9kJ0d68gj64FYrOWbr76a3+Rtnb9uWYoA4KyzCnbW1TXNzcpqi3s1N//bdXPmRiKJPPxP+P2x38ViezryiH4/O9tvBQJ5+4qKSG/Y0Dg30ed3787fMmBA01zmRB7uc/v3q7/l5Hh5EGX+nKj94X37hnf0NX2b328rAJg06ZQ9dXXv5+H3t6xz3Zy5zc1eHZGIXZ2To1dYltfXsrLafhCNZtmf+MSpe7226UAe7703qv6DefCf9+9XL/Xtm+hrWb8kal+ye3fBfq8/uf9nWbYiImbmvV4eXh223VrrujlzAf56R197MidHr/L7E2Ov7cfRaJZdVHTaXq9tmpfI45VXRjSMHds017ISfS3+fCTi/1di7AEZvyZq/82+fQfG3jf8fluddx7FmXlfXV3T3Egk5gKA1jvCtv3+2ItErN/l5Og1lhXpyKP9nmg0yz7zzLy93vI8P9HXXnst/w1v7CWOAe4LkYj1n/fzyKwian989+4R+733OP7N7Gy/ddppp0UBROvqmua2t3t9PhbbVXfwsai11V6Rk6Of8/u998Xny/6p60Z8mzcPT/T5GwFGKFR/5yuv5H/7g3lE/x6J+GsSx4B4PONRvz+2TOtEX4stzs72W5/61PC2RJ+PRKIdeex52bbfPwb07auf2b9f/TlxDLDt7J+5bsT3yiujO+rQB27Yf/XV4dsOzsPvb33JdXPCLS1WR5/3/dbvjwUTY6+9Pfad7Gy/deaZg1qAQTj4fdmzZ8SmAQOaDhwD+vXj1fv3q+ezs92OY0BWpetGfK+9lpfI45bE3YJnnpm/o66uaa7f7/U1y4r803Vz6hJjLxbzL/X7Y09pvaM5FKq/s60tfld2tt+aNGnUPiLiDRsaD/T5o+mE/oTOheXzvw7Ad39V5feuv+r6wbZlba+oqiTHcawhfQY9RqBRDIwkxvfuX1L5sxvLbxzDcK+/v6ryVgC8oHxBMRifqFhS8SMAmH/1/E+RhXsqHqk8BwC6asek5traxheKivI+e+AJ+YTOI/Kh/MQRkfzMSH5mJD8zkt8xYsHsBcNumz0791hpBwBqapomf+CJ5/kmPM/c7c9avutobPt48KH8xBGR/MxIfmYkPzOpzO+EPnNxXJAzF0IIIY4xJ9xfi6S7UKhhaW/XkM4kPzOSnxnJz4zkZyaV+cnkIs0w07DeriGdSX5mJD8zkp8Zyc9MKvOTyyLpTi6LCCGEOMbImYs0s2nTpqNyY+iJSvIzI/mZkfzMSH5mUpmfTC7STCSS9Uxv15DOJD8zkp8Zyc+M5GcmlfnJ5CLt8Ku9XUF6k/zMSH5mJD8zkp+Z1OUn91ykO7nnQgghxDFGzlykmdra+uLeriGdSX5mJD8zkp8Zyc9MKvOTyUXaUXf2dgXpTfIzI/mZkfzMSH5mUpefTC7SjFK8srdrSGeSnxnJz4zkZ0byM5PK/OSei3Qn91wIIYQ4xsiZizQTCtXP7e0a0pnkZ0byMyP5mZH8zKQyP5lcpBlmdXlv15DOJD8zkp8Zyc+M5GcmlfnJ5CLNKIW7e7uGdCb5mZH8zEh+ZiQ/M6nMT+65SHdyz4UQQohjjJy5SDOhUMNPeruGdCb5mZH8zEh+ZiQ/M6nMTyYXaYaZJvV2DelM8jMj+ZmR/MxIfmZSmZ9MLtIMM83q7RrSmeRnRvIzI/mZkfzMpDI/ueci3ck9F0IIIY4xcuYizYRCDfKVwwYkPzOSnxnJz4zkZyaV+cnkQgghhBBHlVwWSXdyWUQIIcQxRs5cpJlweOvpvV1DOpP8zEh+ZiQ/M5KfmVTmJ5OLNKO19VBv15DOJD8zkp8Zyc+M5GcmlfnJ5CLNEPH63q4hnUl+ZiQ/M5KfGcnPTCrzk3su0p3ccyGEEOIYI2cu0kw43Ditt2tIZ5KfGcnPjORnRvIzk8r8ZHKRZrTGrb1dQzqT/MxIfmYkPzOSn5lU5ieTi7TDv+jtCtKb5GdG8jMj+ZmR/MykLj+55yLdyT0XQgghjjFy5iLNhMMNclrQgORnRvIzI/mZkfzMpDI/mVykGa1JbmgyIPmZkfzMSH5mJD8zqcxPJhdphplu6e0a0pnkZ0byMyP5mZH8zKQyv6Tdc+E4TlYwGIwkq33RQe65EEIIcYxJ2pkLauGqslLnzzNLZ3zlipIr+iZrOyeaUKhhaW/XkM4kPzOSnxnJz4zkZyaV+SVtcqEUHgR4O4Pui1F0+8xS5wlnijOluLjYTtY2TwTMNKy3a0hnkp8Zyc+M5GdG8jOTyvySNrl44pnla6tXL788u7XPYDDNAuASY+ng7EFvlZXM+PH0qdPHJGvbx7OsrMjU3q4hnUl+ZiQ/M5KfGcnPTCrzS/oNnVVrq9qq11Q/ndXa5xoingMgBqJbLa02l01xVs2cMvOsZNcghBBCiNRJ6uRi2rRp2WWlZdPLSsqWtvZp2cmgXwL0ZwYucJU+nTTeZNa/TmYNx5tIJOuZ3q4hnUl+ZiQ/M5KfGcnPTCrzS9r9D2WlM74Bl/4PYD8UngPTnIgV+f3KlStbE8s4jvNVakHrZRdfNuyJFU+8naxaOrth9g2jbE2fh7LqB+wf8NfFwcXRzsvcNnt2boubVUJEUV+L79l7g/f2+JcvNzs3Z8X6xKZWVFUEu2tn4RUL+7KPpzLxpspHKmuOrHp+9ciWFx8k+ZmR/MxIfmYkPzOpyy95Zy5YERH/byweP7V6VXBK9erqpQdPLABg586dMdYYO3bi2G1Jq6OTedfMO83S1t+Z1QTW7mW7+ux6rvMyc+bM8bXqrL8BcAB8IZbdvqLHdr8yb0SsT/v3QXxzj+34dAUYzQDuvv7q6089kvqLivKvO5LlxQdJfmYkPzOSnxnJz0wq80va5KJ6TfV3lq1afv/vnvvdjq6WWbt2bTz4h+DmxYsX62TV0Zly1RQCV1QsqVhYUfXAtQCfcdO1Nw05eBl/zD8ThMbKJZVORVXFfBANXHDVggIANK983tDEcosXL1aJdZWmBwB19mG1k6uuAzCUGENUjtp7JPXX1DRN/uh7LyQ/M5KfGcnPjORnJpX5Je2ySElJSUauyr2LmT8bXB38OADMLHUeZVB71B/9+tNPP/1esrbdnYqqivsAYOHshYWs+VIQv37fr+7b/oGFGOMACr//mF4hW0+af/V8H4AVN5bfOK1/fv/Xd9XvWAJS9QC+VbGkctr8q+d/iizc01M7aOaR2Vb70lY3u1i10vkAnj7c+on4HgCf/Wh7LyQ/M5KfGcnPjORnJpX5JW1y0Vf1uYaZrybQne8/y08C+GFG1FcJ4IpkbftwsMtng/hcMMdvuPyGkx98/MF333+RhxDh74mHRPwOgKGVj1YuX1i+8DoX7spdDbs2MtHOyqqKO7reyKHbAatPRTjrEhAPsWz7H51X27ChcRKRGxs/ftSGcHhbH6L2sQDenTAhb6tS+vmO198cP37U9hEUHfAG+7vd11MpNvAtALW19SdZlhpNFNsxfvxpb4RCb5yqlB5qWap+3LgRe8LhracTWTlZWdH/jhkzpj0cbpjoutqdOHFUeN26t7P9/tgZRPTe+PEjX6+peW2QbftHWpZ+a9y4gm21tfX5lqUG+P2RV8eOHbs/HG46i4gzxo8fWbt2LdSAAU2FsRi3TpqU/8q6da/38/vt06LR+M7Jk0c31dQ0DbNtPiUSsRrOPnv47lDo9Y8pZefm5uqNBQUFbaFQQ5HWWk+cOCr80ktvZOXk6DNjMbV30qQRWxJ1RKP89uTJ+e8k6mhtjb72yU+O2RcKNY5TCpnjx4+sDQZBSjXV1tQ0njlxYt7LiTricXfXxImjGhN1RKO+xsmTh+2qrX19jGXZfRN11NbWBwCgqKggVF9fn7l/vxrnuvF9RUWjX1u37u2Bfn8sLx6ndyZOHPl2Tc3WPNu2Bkaj8S2TJ4/eW1PTeKZtI2vTppEhxwHX1TUVaY22QCBv4z//+Vrf7Gz/GNfVu4uKChrWrWs4xe+nYfF4tGnixDE7169/4zSfT/drblYvn3POiEhNzdZCpZQKBPJrE3VoHd8fCIx+9V//enNAVpabn6hj3brXR/r99qBEHevXN5zh81H27t0jw8XF0HV1TUXM1F5YOPK/mzZtyo1Gsz6WqGPjxvqhrqtOTdRRV9c0WmuuXbfu7ezJk4e11tRsLbQsZRUW5te89tprGZGI/6xEHRs3vtHfdXWB1mpbIDDirbq6LSOYfYNdV79eVFTwXqKOaHTnhkmTJsXr6pomAio6YcKIuo0bd+S4buR0Zt5TWJhfX1e3dQizNZw59kZh4Wk7NmxoHAXgZOaMTYWFQ1vq6rZOYLZ8Eybkrd+4caPfdXPGM7vNhYWjNm/Y0HgygFHM1vbCwuFvhsNvDidyhwDYOmFC3rsbNtSPBVQfy2quGzduXHTDhsZJiTo6j71EHYmxl6jDsrI2jxs3uHnDhjfGA9o/YULe+nXr1vn8/kETAN0yYULBpvfr4L8BQKKOzmMvUUc43DBRKR0fP37Uhs5jLxzeMpjINyIx9sLhhgIi6p+o46CxV7N2LayDx17iGJAYe4ljQOexlzgGHDz2EnX0NPYSx4CDx17iGBCPI3Lw2OvqGNB57IVCDUXMzErxys7HgMTYS9SRGHudjwGbNo0MAcDYsU2BzmMvcQxIjL3EMaDz2KutrQ8QER089hLHgJ7GXuIYcPDYS9TReex1dQyIRn2vJMZe4hjQeewl6kiMvUQdrqtfV4pXdjoGuHV1TRMTx4Aefm0ekaRdFmGmaQDfW726ujLx3LLVy3/vKj2NgSmO43T/GzFJFsxeMHnOnDkDK5ZUPFRRVfl5MNpt3we/zIUJGxh84F4IDfQnFxsBYED+gBeJeCvAFxHopwC4q2111c79VZW3uOAfDswf9IUPnTUBoDVmaK2mAoBSzf211o7WOMd7Tf1Ra+0w43QAOM2O5/W0z3lWLB8AiNSpXlv+Iq9td4LW2olG4yMBgJmKvceZfTrqmK6UmgYAtu2e7K3Ln/Za9Z3mLYuxXts423ucNchbV5dorZ21a6H699+eobV2bJsu8Ja1hmmtHctSEzvqGK+1djIzvX0hsv5Ha+3s3p2R07ELX1LK+iIA9OsX76e1dpSKfwYALMsepbV2fD6c4bWlJmutnYwM1XGpiy/SWjsvv/yyPXz4m35m3aaUvhAAfD4a6rWlJnnb5XFeXfGCjjrO1Vo7+/a5fTv28VIiXAoA+/a5fbXWDpF1rldHvMB7zOM66pjk1UVDvcf6Qq21M3z4m/6XX37Z1lo7AF8EABkZakhHHZO9unCGV4c9yls3/hmttdOvX7yf99j6IoAvAcDu3Rk5HXX8DwBkZsbzvLbc8V5damLH68O895Eu0Fo7/ftvz1i7Fsp7T3UJAMRi2YO9ZXE2AESjGOvV6TvNe0/508y6zbbdkzv2cZrWmO4tm9nHW1ad5z2Oj+yoY4K3rr/Ia1ud6tWFz2utnZycnEwA1FFHqbduy8COfD7p9Uuc7r3uG9PRL8/x2m7u7z1WU7XGDG/d7GxvXfqc9565I7x144VeW26go87hAOC69DmttROJZGZ19FuHOT7Vy6NlgNbacV3+lLesNcZry/6Y95g/qbV22tubB3jrxqd46zPF4/2yvHXp8x1j69SOuv7VsewEb93YiI7Xz/OWz+kYezQjHvfGns+n+3fU0XEMsE/z1vWOAQCfrbV24vHWjrHHpVprJxiEGjx4Z6bXl+h8bzsqMfaKvHzi473x4h0DALtYa+3s2ZOV09HnpyulLu7ohyd1jL1Pd4y90d64ThwD1Ce01k5LS58hHeOpRGvtrF+/3srNfcfvvf/6AgDw+9Up3rrWpI62z/LGiy7o6OP/o7V23n03nuvVhUsBXFJYmH93bm6sb8c4Pdfr020dxwA6s6PPT/aOJ6rjEjZfqLV2ioq22EVFWxJj70JvvKihHXl0jD0606urbVTHuD5Xa+3k5sYSnzJ9SUctePfdeK6Xhzf2fD5d0JHPWV5d1iStteP3q1O8PPQFXlvv+NevX291HC9KAKClpc+QjvHxCW9db+xZlj26I59Pd7R9UsfYu5jIG3t79mTleP3O/iwAZGR4Y48onjgGFHmvq2GFhfl3Wxadr7V2Bg/emRkMJo4BXIqjLGnfLVJW6tQycF9wdbDq4OcvveDSwT7bbnItHXhy5ZObkrX9riwsn3+7ZtiVSyq/3fH431Dqum37t/13YO7AvAcffnDrgi8v+DSIv1tRVXneDZffcLLyqxpoTB40atC7uxt2VmmglYiqofkXlqWn3vfwg5sBIHFZpOKRynMAoKt2Kh+t3P1R6w+F6ucGAgW/OPCEfLfIEflQfuKISH5mJD8zkp+ZVOaXxL8W4WcVcLszxZmEjknMrGmzBvps3w8A7FeZ6rWkbbs7tnqUCOcsKF+wbsFX5tUw+KX7H74/PCBzwABLq9cBYHvr9n8CvH1h+fx/W371imLcW/lo5e49DXtGA9hZWVV5Q8UjFX8B0XWu6/2P+lC6asekfGZ1ucn6JzrJz4zkZ0byMyP5mUllfkk7c1FeXJ7ZmtXyNAgXgtAMYBsYowHsZ2BGcHXwj8na9uG46dqbhpy89+R3D/UZFwkLZi8Ylo3W/T96+OH9Jts6Wu0AQDjcOK2wMG/lgSfkzMUR+VB+4ohIfmYkPzOSn5lU5pe0yUXCZVNnFLuaPk6ggQDqLctavnTl0l3J3u4JQyYXQgghjjFJ/24RV9MmZak/k0XLyKJ/a+iRM6fNnJjs7R6vQqGGn/R2DelM8jMj+ZmR/MxIfmZSmV/S/hTVKXXGE7AKwAh2D/kZWUk/a3I8YqZJvV1DOpP8zEh+ZiQ/M5KfmVTml7TJBQF3ACDWONfSVmPUinb5J5vi8LH39fXiI5L8zEh+ZiQ/M5KfmVTml7SzB06p808C/ebgz7kQSXB491z8DIyl3S4TQxMuoJR9eZwQQojjV9LuuSDwS2Cclqz2T1ShUMORf2UuoxDAP7r98ePLR7HMY9ZHyk8cIPmZkfzMSH5mUplf8i6LkPVLhn62rLSsmVj/TTN94BtRg38I/r2rdYUQQgiRvpL3CZ1TnCDY+0jeQ6leHZQbOo+Gw7ksAryAnr6shnA7iun7R6ssIYQQJ67kfbdINq5us9v7dvWTrO0e78Lhraf3dg3pTPIzI/mZkfzMSH5mUplf0iYXwWAwsmLFiuZMN3NyRjzjuqyY/zNZsazBdruds2LFCuNPqjxRaW091Ns1pDPJz4zkZ0byMyP5mUllfkmbXJSUlGQ4pc4LYP4LAd+AolKtdLFtW6+WTSm7MFnbPd4R8freriGdSX5mJD8zkp8Zyc9MKvNL3j0XJWXfZOIZLrtTLLLKFKFg2argjWWlZXcReGa/7SePfWj9Q7Fkbf+EIfdcCCGEOMYk7+O/iT8P4N6n1jz15kHPsk32jxk45d1T3i1K2raPY+Fw47TeriGdSX5mJD8zkp8Zyc9MKvNL5neLRIh4TOcnYxTLBmArqPYkbvu4pTVu7e0a0pnkZ0byMyP5mZH8zKQyv6R9zgWYngLwg5lTZtSxpkwQY9aF08e5Ln0DxG9tb96+MWnbPo4R6cd7u4Z0JvmZkfzMSH5mJD8zqcwvqZ81MbPUuYeBm+CdIXHhTWaaWMEJPhP8dzK3fcKQey6EEEIcY5L6levLVgdvYRdjQXQ1QLcx4YsRq+0MmVh8dOFwg5wWNCD5mZH8zEh+ZiQ/M6nML2mTC+d8p9+si2cNsTPs/ZZl/dGyrcdty/5XDuXkzrp41pBkbfd4pzXJDU0GJD8zkp8Zyc+M5Gcmlfkl77tFMvArNx7v8uO/keRLMscrZrqlt2tIZ5KfGcnPjORnRvIzk8r8kja5YIXbFNSBa/g6rk8CcDoRbmPg/mRt93g3ceLIdb1dQzqT/MxIfmYkPzOSn5lU5pe8j/9eGaxftnJZTeInuCb4l+Ca4IOupUsImJes7R7vamsbftnbNaQzyc+M5GdG8jMj+ZlJZX5JvaHzUHS73gMgf2bJzNGp3vbxgT7W2xWkN8nPjORnRvIzI/mZSV1+SbssMn3a9LEqpgYe/BxZlAnm6wHorEjWW8na9vEsKysytbdrSGeSnxnJz4zkZ0byM5PK/JI2ubC0+g4UPnhDJzMAvMHANVVrq9qStW0hhBBC9J6kXRbxaf81zBj8gZ8oTqpeHRwZXB38TbK2e7yLRLKe6e0a0pnkZ0byMyP5mZH8zKQyv6SduWhTbQNtZZ/UeWszp8380LJnTDojtHjxYp2sWo4nRPx2b9eQziQ/M5KfGcnPjORnJpX5Je8r16c4QTC6+5yLA3zs7/fYmsf2JauW45p8/LcQQohjTNIuiyitvwegCUxzCOp0ZVungugLYPwD4EoGTkn8PLbmsf3JquN4U1PTNLm3a0hnkp8Zyc+M5GdG8jOTyvyS9yFaUNcD/PPqNcGD/6727cunXL4+zrHtfvbfLmcrjhwR34OezkKILkl+ZiQ/M5KfGcnPTCrzS9qZCwbGg9SH7qN4u+Xt/QDaohQdlaxtH8+U4pW9XUM6k/zMSH5mJD8zkp+ZVOaXvHsuSmb8AESzmeEgB/8IBoNRx3EsauU7wLSQ+2BYMBiMJGv7Jwy550IIIcQxJmlnCMAXwgAAIABJREFULvaj5Q4Q/kOEtdSCPc4U57/UghYw3ULE18jE4qOprW24vLdrSGeSnxnJz4zkZ0byM5PK/JI2uVizZk179argFDB9BkSLSfMzAH1Vxa2xy1YtfypZ2z3+0dzeriC9SX5mJD8zkp8Zyc9M6vJL2g2dCdVrql8E8GKyt3OiUAp393YN6UzyMyP5mZH8zEh+ZlKZX9LuuUi0XzalrJiZixTzK4D1akzHWp969ql3krzdE4fccyGEEOIYk7TLIiUlJRlOqfMCmP9CwDegqFQrXWzb1qtlU8ouTNZ2j3ehUMNPeruGdCb5mZH8zEh+ZiQ/M6nML2mXRXKRexuD+8XZHWGRVUZAQXBV8OGy0rLRBH5gzqQ5Yx9a/1AsWds/XjHTpOQ0jEI8z9U9LPNrfI6eTcr2UyRp+Z0gJD8zkp8Zyc9MKvNL3j0XxJ8HcO9Ta5560yl1Es+yTfaP4xz76runvFsE4N9J2/5xSil3TpKaHoqeL538FUBaTy6SmN8JQfIzI/mZkfzMpDK/pF0WARAh4jGdn4xRLBuAraDak7jt41Zh4ajNvV1DOpP8zEh+ZiQ/M5KfmVTml7wzF0xPAfjBzCkz6lhTJogx68Lp41yXvgHit7Y3b9+YtG334KZrbxqi47ESTfRuzBdb/dBDH748c9vs2bktblYJEUV9Lb5n7w3e2+Pnctzs3JwV6xObWlFVEeyunYVXLOzLPp7KxJsqH6msOZLaQ6GGZwKB/KlHso54n+RnRvIzI/mZkfzMpDK/pJ25qF5T/UsCqpjptyD+LjOudy31XxCfwwoz165dG0/Wtrsz/+r5A9x4/F8MXATgXH/Ut+W22bNzD15mzpw5vlad9TcADoAvxLLbV/TU7ryvzBsR69P+fRDf3GM7Pl0BRjOAu6+/+vpTj+LuCSGEEL0uaZOLmVOc+wF+hV2MBdHVAN3GhC9GrLYzgs8Ee+1eC2XxeQz8uaLqgcsqH6m8FcB/Im7mpQcv44/5Z4LQWLmk0qmoqpgPooELrlpQAIDmlc8bmlhu8eLF6qZrbxoCAErTA4A6+7DayVXXARhKjCEqR+09kvpl1m5G8jMj+ZmR/MxIfmZSmV/y7rnQ2MugW4PPBl+vXlX92+rV1fcGV/3/9u48TIrq3B/49z1V3bOz7wLTwyaC0N0zRHAHFRWUqNHGJYmOJBJlBr3eeM0vy43kJtckXo2JzrglUcC4wKAmsrmzuQvT3RAQFZgeNCigKMssvdR5f3/0tCJxFjzQTcv7eZ55Hrr71Km3vlOn+lBdXVPz9MKFCxsP2zo7IJ7QK23b/hkAzJw5M4eB4xPMoS81YowEU/iLx/Q22bqs4sqKoQRafn359UNnzZqlPq7bMcdJOBUAUDWnegpr/s+O9IN9iXH5VuNjYAqrRnXWwdRfW7u138Fus/iC5GdG8jMj+ZmR/MykM7/Dds2FJiwg4KKpkwJPkOLHNNNn+79es6Tm+cO17rbc9/B9OwBgZvl143ifcw9Bzb937r1rv9SIuTcRXk49JOIPAfSpnlu9YGb5zGscOAs/jny8nol2Vs+uuqXVlbXSD1id2MR5F4K4t2Xbrx1M/UT8GORPDn9tkp8Zyc+M5GdG8jOTzvwO2+SCCD8HYwQII5jpoq+4FejhvjtoqyrLZ8xg0JUgml71UNXqA19nwlqAP78WQgPdLAeLAKC7p/srH9fv2ALGuQQqA8Ctrae1fu6aW101o3xGn16eXjtmzZr1b3+WPhSqew5Qu3y+4svC4S3HAqqKmVb5fMX/Q6R3h8OR57WmB/z+4poL3Hsn/iNWdGAXX1JixYbWOe6OhdOGa/N2V14bquvp85XcEg5v/TagZwL8V6+35PFQqP4nRHzWHfu63D831nnfrPyPb8pV6Pa/zT1+2ZljOZV5e37eoNWHv27sdvefCneq8e7GGwFa6PUW3xUK1V1NRFdoTbf5/cXPh8OR2wD4Aftqr7f/B6FQZBGAmM/n+c7atfWDmPl+AG94vZ5fBIOR85TCf2iN2X6/55FwOHITgHMA/MTr9dSGQpEHiTCgocH6dufOOVYi0egJBiN/9vs91wSDW7+llL6VmZf4fCV3hsP1VwL8fYDv8HpLngmF6n9LxGMcR/+wtHRQfTBY9w8A8PtLLqit3VJsWeovzLTa5yv+aThcdy5APwboYa+3eG4oVHcjEU3WWv3M7x/4VjAY+bNS8Nh2/kW7d0edggLnaWa87/N5poXDkVIAvwfwrNfruT0YjHxXKZRrjT/6/Z7F4XDkNwDGEtGPRo8u3hIKRZ4E4Pb5POeHwx/0BxIPAQh6vZ6bg8H6iUrxzcz8qM9X8lA4XH89wFMA/m+vt+T1UKj+PiIeHI87l+TlNTclEgWLteZtfn/JVbW1W7yWpW5nphd8vuLfh8N1lwH0A0Dd7fUOfDoUqvsVEXuCwc1D/f7B7wWDkRoiFPp8nkmrV0f6ulyYy4y1Pp/nx+Fw5AwAPwUwz+v1/CUYjFQohQu1pll+f/Er4XCkGsAw27YuXb++/+5jj61/hhnbfT7P98LhrccD+k4Ay7xez63BYH1AKZ7OjHt8Ps9ToVD9L4n4VEBXer2D3gmF6h8HdDefr+TscHhTL8B+hJk3+HwlNwSDdeOVop8z8wKfr+T+UKjuWiK6mEj/evToQStDochdRDguHndfPmZMv4/D4cjzzPjY5/NcvmZN5Djbxl3MWOnzeX4dDkcuBnAtQPd7vcULgsHIz5XCeMehG0pLizeEw5FHAPQaPbr47Dff/Fe33NzE48x4x+fzVIbDW08F9C+ZOZEc4/XTiTjAjFt9Ps+ycLj+ToCPV0p/b9SoQdtDofpnAL3b5yu5NBTaPIzIqtaaX/H7S2YFg3UXKkUVzPwXn69kXjhc91OAziCy/nP06AHrQqHIXCL03bWreFKXLpFCpahGa7zn93tmBIORk5TCr5jpaZ+v+O5wOPIDAJc5Dv2+tLT4hVAocjsRvI6jriotHbgtHI4sZkaTz+e5ZN26rYO11vcx43Wfz/Pf4XD9FICv1xoP+f2eR4PB+puV4onMuNnn8wRDofqHiLh/fn78fMdx3NFo7pPMvMXnK/lROFw3FqDfaM2L/f6SPwaDdeVK0XeZ+Xafr+TZUKju90RUqlRi2qhRQ94PheqeBsgBeE0wWOdRiv6sNb3l9xf/LBiMTFYKNzLTXJ+v+OFQKPJjIpzrOOqnpaUDV4dCkb8QoTged18AAC5X7B/MqPf5PD+srd06xrL0b5nxjM/nuSMUqv8+EV+pNe70+z1LgsH6W5Xib2nN1/j9JZFQKPIUwJbPV/Ltdes2DdDafpCZa32+kp+EQnXnENFNWvMjfn/J7GCw7j+UovMA/oXXW/JGKFR3PxENyslp/o5lWbHGRtciZvrA5yu+OhSK+Ilwm9b0vN9ffFswGLlCKVwN0F1eb/HCUCjyayKMU0pdO2rUwM2hUGQBEfK8Xs95tbVb+1mWnsOMsM/nuam2tv4sy+KfAHjc6/X8NRSqn0nE39YatxDxmmAwco9SGKo1Bz77zLOvW7f6pcz40OfzXGn8JrGfw/YGHwgErJ07d7baf8Yu6CyvmAhg1o6GHafV1NQ4qecDgYDVo6hH8b0P3rul8qrKk0H8m6rZ1ROuu+K6rsqtaqExpuegnp9+Etk5WwONRDQfmu+3LH3+nx689x0AqLiy4kSycEfVQ9UnAUBr/VTPrf7kkG3Qobr9d8fazMQEqmqnntkArmqnnwAm0IJ22gghhMhSh/TMxaWTLpmkQYNrltZU7f/GfUQhnEOM43oX9KqrLK9IPkX8v/GEfsrSajMA2t64/fXeBT23zyyveJOBgcS49e651Z9cX379UAA7q2dX3wSAK8srr3Ec6wIAt33Vqlrrx6T8cLh+itdbvNCkj6OZ5GdG8jMj+ZmR/MykM79DOrlghVOIcTqAKgAoH1+e25TfEFRaX/rYM0+sbWfxtGj5hshNrbxMANAyMbqsclplv3w07r3twQf3AsBds+96D8CPU42rZlctB7D8877nVr8G4KTU49b6MaE1bgIgg+trkvzMSH5mJD8zkp+ZdOZ3WP/kekPPBqIGDGeFvMO5nsOl6sGqbUdSPwBApB89VH0djSQ/M5KfGcnPjORnJp35Hc7bf4vDwOcruT/TNWQzyc+M5GdG8jMj+ZlJZ34yucgyLd+GEF+T5GdG8jMj+ZmR/MykMz+ZXGQZrWlKpmvIZpKfGcnPjORnRvIzk878Dv01F4zhgcmBJwAADbAAwGH1u8DkwK79m9Usqbn4kK/7KMBMP26/lWiN5GdG8jMj+ZmR/MykM79D+20R0BYA68Do9sVzLd+m2O858fWVlg78t5t+iY6T/MxIfmYkPzOSn5l05ndIJxc1i2v+CuCvh7JP8WWpu0tmuo5sJfmZkfzMSH5mJD8z6cxPrrnIOjQs0xVkN8nPjORnRvIzI/mZSV9+MrnIMnl5TfInhw1IfmYkPzOSnxnJz0w685PJRZYZPny48V0+j2aSnxnJz4zkZ0byM5PO/GRykWWCwfoVma4hm0l+ZiQ/M5KfGcnPTDrzk8lFliHiQ3Yr8aOR5GdG8jMj+ZmR/MykMz+ZXGQZn89zeaZryGaSnxnJz4zkZ0byM5PO/GRykWVqa7eOyXQN2UzyMyP5mZH8zEh+ZtKZn0wusgwR35HpGrKZ5GdG8jMj+ZmR/MykMz+ZXGQZIr080zVkM8nPjORnRvIzI/mZSWd+lK4VicNkGd8A4I/ttFoB4HTjNhq/g8Ib7fTzXQCXtNMmgAm0oJ02QgghspScucgywWDkioyt3MJJAJ5q56dvxurrgIzm9w0g+ZmR/MxIfmbSmZ9MLrIO/SjTFWQ3yc+M5GdG8jMj+ZlJX34yucgySuH2TNeQzSQ/M5KfGcnPjORnJp35yTUX2S6d11wQVoJxWjv9vALg5HbayDUXQgjxDSZnLrJMKFT3q0zXkM0kPzOSnxnJz4zkZyad+cnkIsswq/GZriGbSX5mJD8zkp8Zyc9MOvOTyUWWUcqZnukaspnkZ0byMyP5mZH8zKQzP5lcZBmvd9A7ma4hm0l+ZiQ/M5KfGcnPTDrzk8lFlgmFIosyXUM2k/zMSH5mJD8zkp+ZdOYnk4ssw0xFma4hm0l+ZiQ/M5KfGcnPTDrzk6+iZjv5KqoQQogjjJy5yDK1tVv7ZbqGbCb5mZH8zEh+ZiQ/M+nMTyYXWYaIH8t0DdlM8jMj+ZmR/MxIfmbSmZ9MLrIMEa/JdA3ZTPIzI/mZkfzMSH5m0pmfXHOR7eSaCyGEEEcYOXORZYLBuvGZriGbSX5mJD8zkp8Zyc9MOvOTyUXWUXJvfSOSnxnJz4zkZ0byM5O+/GRykWWI9KOZriGbSX5mJD8zkp8Zyc9MOvOTay6ynVxzIYQQ4ggjZy6yTDgcuSnTNWQzyc+M5GdG8jMj+ZlJZ34yucgyWtOUTNeQzSQ/M5KfGcnPjORnJp35yeQi6+hbMl1BdpP8zEh+ZiQ/M5KfmfTlJ9dcZDu55kIIIcQR5qg9c3Fj4Ma8yvLKQGuv3zxtWlHFVRVTK8srL7wxcGPe1+3zq/qZ+d2ZnSrLK6+ouLqi9GDrDgYjfz7YZcQXJD8zkp8Zyc+M5GcmnfkdlZOLGVfPGBAviP4WxDd+1evTp093Neq8VQACACbG86NPf50+W+3HpavA2Afg9muvvPaYg6uehh1ce/Flkp8Zyc+M5GdG8jOTvvyOysmF0nQPoMa29ro77r4UhPrqOdWBqtlVFSDqUfn9yhIANKN8Rp9Uu1mzZqkbfnhD79b6bLWfInUNgD7E6K0K1e6DqT0vr+n8g9tasT/Jz4zkZ0byMyP5mUlnfna6VnQkqZpTPaXiyooTycIdX9mAMRKg8BeP6W2ydVnFlRUuAE9fX379lG6ebps/rtsxB6TqAPzyK/tspR/s44H5VvSxRid/vGqkswD8vaO1Dx8+fO9Bb/CRhnEClvHgNtsozMfpVHeoV/2NyC+DJD8zkp8Zyc9MOvM7KicX7WLuTYSXUw+J+EMAfarnVi+YWT7zGgfOwo8jH69nop3Vs6tav/q2lX7A6sQmzrsQxL0t237twMXC4fozAYp5vQNXvf76e53cbvcJLpezY9SoQWtDocjrWtMvAOe90tJB9cNUrO+72t3m5nRSumiPNj9J1QW662ftnOyywXaineuEXRbOiGuUtdUm4NpLP1m97a4xY/o1hkKRCVqzLi0tWbF+/Y7CaLRpHJH+2O8vCYVC7x+jtT7Osniz1+upq62tHwGgX06Oqh05csCu2trIiQAVbNo0cFnPnqDOnbfWWpae7vWWvP7GGx90d7kcPzP/q6zM8/batfWDEgkMAmhDaenAbaFQxK81dbesnNe83j4Nqfvy+/0ly8PhjwocJ3qiUvyJz+cJ1tZu7QfwCNvGltGji7esWRM5joiOicet4Nix/T8Jh+vGOY4q3L174PKdO8FDhmydAHBDaanntfXr3+8WjepSANtKS4s3hMOREsehwUqpt32+Af8KBut8zKpHTk7e6yNH9tpXW1t3ulKkfD7PstWrt+UrFT/JsniX1+upXb060lcpGulyUd2oUQM3r11bNzyRUP21doXGjOn3cThcN9ZxVJHWO1eUlZU5weDWM4i40e/3vLp2bX3XRAJlSuFDn694fTBY52FWQyzL2uj19v+gtnaLF7Aezs9vOnn48OF7167dchqzsr1ez0uvvvp+Xm6uPtm28eno0cVr1q+v6xONquOZVaSsbMCmcHjLsY5jDbCs+Fqvd8iOUGjLCVpbnXJy9q0cMWJEPBjceqZSaPb5il8OBuu6MKsxlkUfeb0D/1lbu6UYsIa6XPF3Ro0a8v66dVtGx+NWr1gs9ua4cUP3hMNbTwXY7fUWv1hXV5f76afqFCLa7fcPfGvdui2943FrFFGi3u8f/F4otHmY1vZAl8tZN2rUoO3B4NZvMXPnoqLYqqFDh0Zra+vPsiyKer0DV61evbmzUva3bFttHz16wLrVqzcPVMoelhp7a9e+PyqR0L21Trw1Zszg3aFQ/SlaI9fvH/jipk2b3Hv3uk9Vytnj8w16Mxze1MtxXKOVwq0+X/EJweDmocx2cU6O/ufIkSUf1dZuHQNwl+Zm9cpJJw1oCofrzyRy4qNHD1p54DFg3bpNA+Jx17FEepPfXxIJh7ce7zjch0iv9vtLPgsG609mRp7fP/ClDRs22NFo4WmWpfd6vSVv1Na+1xNwe21bfzB6dMnGNWveH0KkPVrz+jFjPB+uXVtflkigq9auV8eM6dcYDkfOcBx2SktLVmzcuLGosTFvbGrshcMf9HccZ3hq7IVC9SO1Rt/U2AsGIycxU/6mTQOXDRq0RinV83TL0vu83pLXV6/e1kOpuC819tat2zo4HueS1NgLhyOljkPdUmMvFIpMYGYG1K/c7rzzotGmcamxlzoGpMZe6hiQGnupY8Du3QOXA0DnzlvHp8Ze6hiQGnupY0Bq7KWOAamxFwzWjSci8vk8y1LHgNTYSx0DUmMvdQzYb+yNcxxVqPXOFVu2lOkhQ7ZOSI291DEgNfZSx4DU2EsdA/Lzm94YPnz43trautMtiyyv1/NS6hiQGnupY0Bq7KWOAUAsTOReoJS+2XFUUcvYSySPAWjy+4tf6dg7QccclR+LtIcJaxn8+bUQGuhGDtYDQHdP91eIeAvA5xLojwD4YPu5e3b1jx3w73t4ek7801/+tP3A5bRGmdZ6NADYtqtAKZQlEjQIAJhpt1IoUwq9AaCnpbu3tz15QP5BbH6r8okL22tjA1Z7bZTmdie1A5QzNC/PyW156Lcs5QOAaHR3XnL71RAAYE70VAplWqMvABCxRymUxWKJTgBgWRipFMpGjtxg9e+/ySJiDdBIAMjLczorhTLb5mIA0Br9lEKZy5XokVwtDVUKZfG4Tl3Q62v5QTyu85RCGUBDAcDlSvRoqaMfANg2FyuFsrw8p3NLXyOVQln//puskSM3WEqhzLIwEgBisUQnpVBGxJ6WOvoqhTLmRE8AUEoNUQpl0ejuvOQ2KR8Af8s25Cbr4GHJOlT3lmWPSfbFA5N9O12SeWFEMp/e9vLlUMnX6PiWOoqSfaEkuV70ST5O9GqpYzAR63jcnZ/s2/ICKE3WkchRCmWOw8cmf0+qW/L3lOgPAI5jDUjWRS110HHJ/bq7CwAl16OPT67HVdhSx6CWOnonfw/u3gCQSNCg5O/NVZCsQ4/WLZPV7dsT7pYsj21ZT9dkHdaA5P5h9W+po2vL/jI8WbfjbllXmdZ6VEsdBcnXEoOSubt6J/u2eyW3KdFSh7sw2Zce1VI3YrHOrmRba3jy96+6tvye9u1fR3Nzqg59rFIoc7tj7pbfW6nWajQAFBXZ+ck61ODkNrl67X8MALgkWYdT1NLX8UqhrKYG1NTU1W7ZL49Lvqa6tPTVkkfimOSy1C3Ztx6WrCOe07K/+C1LeZO/U7tl7NHg/cee46BP8nFy7EWj8aL99/lBg9ao7t27Wy25jwCAnJxE5+QY4IH7jz3bpu4tfQ1N5pNoOQawD4CPiLc1NSVyW/oaklw23qOljr77j73c3M+PASNSY69//02psTcCAHJzk2MvdQxwnOTY0zreo2X/GaIUypqaUnXA11ILmptTdfDQ5Hqp+/7HAMtKjr2cnETnlr5GKIWy7t27W8nJFspSx6JoNF7U0penpY4+Xz4G0OBktnbqGOBlTh4D3O54TrKtHtZSR7fkvpc8BjiOGpB8rLoQ8TatcVxym7raNTXJsUeUHHuH0lH7VdTURxhVD1WfBACBQMDqUdSj+N4H791SeVXlySD+TdXs6gnXXXFdV+VWtdAY03NQz08/ieycrYFGIpoPzfdblj7/Tw/e+85X9dlaP9Vzqz85ZBuSnV9FfQNAq9e8AAAYr4Iwqp02E3EGvdHOuoQQQqSZnLlo0T23e3dLq80AsL1x++sAb59ZXvGm5VZvK8ad1XOrP9kV2TUYwM7q2dXXVT1U9RKIrnEc64LW+mytH5M6k6dPjwIWcgAUtfNz0B/rHTX5HSaSnxnJz4zkZyad+R21Zy46onJaZb98NO697cEHjS6COVT9AEAwWL/C7y/+4gzDN/XMhcIatHNdBhin4Aw6qM8J/y0/cVAkPzOSnxnJz0w685MLOttQ9WDVtiOpHwAg0ssPVV9HI8nPjORnRvIzI/mZSWd+cuYi28mZi0N6hbMQQghzcs1FlgkGI1dkuoZsJvmZkfzMSH5mJD8z6cxPJhdZh36U6Qqym+RnRvIzI/mZkfzMpC8/ueYi6/D9ma4gqyzje0HJ+yUAwKQ9ia5Yzs9+qQ3jKkygj9JeW1aS/c+M5GdG8jOTvvxkcpFl/H7Po5muIcucDP7ifhkfJe/f9eX7ZziH5iZjRwPZ/8xIfmYkPzPpzE8+FskyoVDdrzJdgzh6yf5nRvIzI/mZSWd+MrnIMsxqfKZrEEcv2f/MSH5mJD8z6cxPJhdZRilneqZrEEcv2f/MSH5mJD8z6cxPJhdZxusd9E6maxBHL9n/zEh+ZiQ/M+nMTy7ozDKhUOQxn89zeabrOCIQAljOU9tso+GSW8UdOrL/mZH8zEh+ZtKZn0wusgwz9ct0DUcMwhn7fxOklTbvpamao4Lsf2YkPzOSn5l05icfi2QZ+aM9IpNk/zMj+ZmR/MykMz+ZXGSZ2tqtMnMXGSP7nxnJz4zkZyad+cnkIssQ8WOZrkEcvWT/MyP5mZH8zKQzP5lcZB1+N9MViKOZ7H9mJD8zkp+Z9OUnk4ss4/d7rsl0DeLoJfufGcnPjORnJp35yeQiywSDdeMzXYM4esn+Z0byMyP5mUlnfjK5yDpK7q0vMkj2PzOSnxnJz0z68pPJRZYh0vJXAUXGyP5nRvIzI/mZSWd+cu/CbLeMbwDwx3ZarQDQ3veb229DWAnGae308wqAk9tp8waAsW22UFgDjbJ26lnX7k20gPcADG2nzWYQBrRTT3+cRjvb6UcIIQTkzEXWCYXqfpTpGr6B3OB2fppkIg7I/mdK8jMj+ZlJZ35y++8sw6yuAHB/pus46tgYixe5oc02q7Acs0inqaKMkP3PjORnRvIzk878ZHKRdfQtma7gqER4CITubbYpQwGAxvQUlCmy/5mR/MxIfmbSl598LJJl/P6S5ZmuQRy9ZP8zI/mZkfzMpDM/mVxkmWAw8udM1yCOXrL/mZH8zEh+ZtKZn0wusg4Ny3QF4mgm+58Zyc+M5GcmffnJNRdZhpkuz3QN4mtiJqzAtW220YjiDHowTRUdNNn/zEh+ZiQ/M+nMTyYXWaa0dOC2TNcgWpGHPniOm1t9fTkUgHva7ENhD4AjdnIh+58Zyc+M5GcmnfnJxyJZJhisX5HpGkQrLKyHC/9q9YdRn+kSTcn+Z0byMyP5mUlnfjK5yDJELDN3kTGy/5mR/MxIfmbSmZ9MLrKMz+eRzxxFxsj+Z0byMyP5mUlnfjK5yDLh8JZjM12DOMyWcW6bP0s4J1Olyf5nRvIzI/mZSWd+ckFnltHaegDt/xEyka0YBKCpzTb52IZlPLLNNi44OIX2HsLKAMj+Z0ryMyP5mUlnfjK5yDJEenmmaxAZxrAAfNpmmwTeBXDI/5ci+58Zyc+M5GcmnfnJxyJZxucrkXvri4yR/c+M5GdG8jOTzvzkzEWWCQYjV/j9nkczXYc4wjHcWMa72mxD2AjG5nb6eRtn0K2ph7L/mZH8zEh+ZtKZn0wusg79CIAMLtERXdt5vQuA77XZgrACwK37PSH7nxHJz4zkZyZ9+cnkIuvw/ZmuQBxVFJ7ngakHr8V2Ltj/MQAgB004jXYar+lZLoBq58/a29iDCfSZ8boyRsavGcnPTPryk8lFmt08bVpRg5M3iYhirgbXs3fW3Nn2NwMOIKcERZp1gv3FnUVnNPYEbNz1pRYO3sJy9rbZi8Y8nEGwf/SeAAAUP0lEQVRXttkmB1eA8UA79fwRwI3ttDliyfg1I/mZSWd+ckFnGk2fPt3VqPNWAQgAmBjPjz59sH2EQnW/OvSVCWGEwHC3+UMowkt8Tps/QN9Mb8jhJuPXjORnJp35yZmLNHLH3ZeCuL56dnUAACrLK4KV368sqXq4qq6jfTCr8YerPiEOo94gPNNmC8bLHejnGCznO9psofEczqBn22yzjHMB5LbZJgGGDWqzTREaMIbibbbZj4xfM5KfmXTmJ5OLdGKMBCj8xWN6m2xdBqDDkwulnOmHozQhsgKjN5Jn/lpHGIVl/Ld2+gmDcGabbWysAnBqm2324hks4+HtrOsWWFgHAL9u+uyPWMH+f2ujkQMNd5v95CDY7o3RXuDusOBqs81O7MRUctpsc4SS45+ZdOYnk4t0Yu5N9MX/zoj4QwB9DqYLr3fQO4e8LiG+SZIfw/Rop9WhOfYxOoHgabMNYQY0xgLAk9EurbUJQuHfJx37i+ElLOMB7awLYAxts00P3Ill3N5H4jbaO7MDXAigrVvRxwHMbbMHRgzUzqSKsRuEhwDAuwtRLGPPv7VpRifktjPp1NgBC6vbabMHzdjUZps8OJhAiTbbHKHS+f4hk4s0YsJagI9JPdZAN8vBogPbhUJ1zwFql89XfFnyXvCqiplW+XzF/xMKRVYRoVlresDvL675ZcHHI4PxvK2rE7nLP3SsT852N0zKIeQvjhU8mU86Z4Kr6TyX0jlPRov+4FHxfqOs6Ekfsb3prURuqMxuHtWPEse+7bjf3JRwB093NRZ1UtxjZTzvmd1aNZyX03CRBseXRgsX9VROl3PdDafVadfrL8fyXhtuxQYPteO+iGP/c10i551xruZv9VTOwA2OO7g54XpzorvhnFxC4dJo3t9dgH1WTtP5jUyfvRjLf3G4FS0Yasf7bmd785uJ3KDfio7sr+LHbXTcb72n3fXjXY2n9VeJroujBX/+FNbe892NFzqs9dJ44dM9LafzONU08TO2Plzl5C09VsU2D7PipfXaXr82nrNxrLu5rBc5nmDCveoDx7XjgpyGCRq89Zl44d8VQBe69/zgE7Y3vxDPf34Axfv47Ogpu1m9szKRX+O1oiMGqviId7R79buOO3Kaq+mUznD6vBLPe24XWzsnu/dNBYAl8cJ/dIPT6WRX09m7YX20Mp738jAr5jnOipdFEvbGcCJnwwmu5tLeyikJJXJfft+xtp/lbphYoFD0TLRgoWbwuTkNFzQz7X0+lv9cfyvey2/HTt3BVuSNRO7ro63oqGIVH/mu4659R7u3nGo3ndyFnL6v67zndzrW7gtd+85LkGpYFMv/e1c4Rae4ms7ZC7VjeTx/5VAVKx5uxb61XVvr3nTynjzBbvb3psTgdTrnlYjj+vBMu/GsfNJdXojlL3Sgdp3jbriqmbHv+VjBs8eoeM9SV+y0ndra+no8961RdvTY4+3YscF4ztMbHffmU9xNJx6j4qNfihXM26mtzybl7DtfgVyLowVPd7b0mtPsxkl7We1cnshfMUTFBh5nxU7YxvY7axK568bbjUOLSG9d5+S8GtGubWe6Gs/Mh+66LJG/qJFV9DzXvotzCYU1zYV39rWc7mPs5vEfa/X+a/G8N4+3o8NKrMSozQk7vMHJef0Ud7PdFYn+bzp5L27X1qfnuhrOcxFyF8YKnuhETv4F7oaz3nfs4PJY/vLBdqL/CCs69l+O9V5tInftGFfz6L7KGRrR9rvr4jm1E9yNEwqJuy2L5S3Zx6ppSk7DxTFG07OxgiUDKa687ljxJ9r64NV47hsj7ejQQVZidJ12rf1n3P3eiXbT2MF2rPcL8cI5H7H1yTmuhslucP7CeOGCQkLuBHvf+Y1Qu16M5S8qUfH64+3YOJs456nmwkdLXdFRx6jEsPVx15tbtPv9Ke594wBsXZHIX7KHVeMUd8N3EsyxpfHCRb2V0/UEq+nMZib1fKxg0Qg7NniwFffVJex//tPJeedEV9MJPZQesDqeu+JDWJGJdsMluQoFi5sLnsq1tOtMV9P5DUyfvhTLf8ljxft9y918Yb3jiryZyA2W2tHjj6H48I069833HHvreLvx9CLSPV+O5S/8lNW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.forEach(function (element, i) {\n                if (element.attribute(\"gadfly:inscale\") == \"true\") {\n                    var cx = element.asPX(\"x\"),\n                        cy = element.asPX(\"y\");\n                    var st = element.data(\"static_transform\");\n                    unscale_t = new Snap.Matrix();\n                    unscale_t.scale(1, 1/scale, cx, cy).add(st);\n                    element.transform(unscale_t);\n\n                    var y = cy * scale + ty;\n                    element.attr(\"visibility\",\n                        bounds.y0 <= y && y <= bounds.y1 ? \"visible\" : \"hidden\");\n                }\n            });\n    }\n\n    if (xscalable) {\n        var yfixed_t = new Snap.Matrix().translate(tx, 0).scale(xscale, 1.0);\n        var xtrans = new Snap.Matrix().translate(tx, 0);\n        root.selectAll(\".yfixed\")\n            .forEach(function (element, i) {\n                element.transform(yfixed_t);\n            });\n\n        root.select(\".xlabels\")\n            .transform(yfixed_t)\n            .selectAll(\"text\")\n            .forEach(function (element, i) {\n                if (element.attribute(\"gadfly:inscale\") == \"true\") {\n                    var cx = element.asPX(\"x\"),\n                        cy = element.asPX(\"y\");\n                    var st = element.data(\"static_transform\");\n                    unscale_t = new Snap.Matrix();\n                    unscale_t.scale(1/scale, 1, cx, cy).add(st);\n\n                    element.transform(unscale_t);\n\n                    var x = cx * scale + tx;\n                    element.attr(\"visibility\",\n                        bounds.x0 <= x && x <= bounds.x1 ? \"visible\" : \"hidden\");\n                    }\n            });\n    }\n\n    // we must unscale anything that is scale invariance: widths, raiduses, etc.\n    var size_attribs = [\"font-size\"];\n    var unscaled_selection = \".geometry, .geometry *\";\n    if (xscalable) {\n        size_attribs.push(\"rx\");\n        unscaled_selection += \", .xgridlines\";\n    }\n    if (yscalable) {\n        size_attribs.push(\"ry\");\n        unscaled_selection += \", .ygridlines\";\n    }\n\n    root.selectAll(unscaled_selection)\n        .forEach(function (element, i) {\n            // circle need special help\n            if (element.node.nodeName == \"circle\") {\n                var cx = element.attribute(\"cx\"),\n                    cy = element.attribute(\"cy\");\n                unscale_t = new Snap.Matrix().scale(1/xscale, 1/yscale,\n                                                        cx, cy);\n                element.transform(unscale_t);\n                return;\n            }\n\n            for (i in size_attribs) {\n                var key = size_attribs[i];\n                var val = parseFloat(element.attribute(key));\n                if (val !== undefined && val != 0 && !isNaN(val)) {\n                    element.attribute(key, val * old_scale / scale);\n                }\n            }\n        });\n};\n\n\n// Find the most appropriate tick scale and update label visibility.\nvar update_tickscale = function(root, scale, axis) {\n    if (!root.hasClass(axis + \"scalable\")) return;\n\n    var tickscales = root.data(axis + \"tickscales\");\n    var best_tickscale = 1.0;\n    var best_tickscale_dist = Infinity;\n    for (tickscale in tickscales) {\n        var dist = Math.abs(Math.log(tickscale) - Math.log(scale));\n        if (dist < best_tickscale_dist) {\n            best_tickscale_dist = dist;\n            best_tickscale = tickscale;\n        }\n    }\n\n    if (best_tickscale != root.data(axis + \"tickscale\")) {\n        root.data(axis + \"tickscale\", best_tickscale);\n        var mark_inscale_gridlines = function (element, i) {\n            var inscale = element.attr(\"gadfly:scale\") == best_tickscale;\n            element.attribute(\"gadfly:inscale\", inscale);\n            element.attr(\"visibility\", inscale ? \"visible\" : \"hidden\");\n        };\n\n        var mark_inscale_labels = function (element, i) {\n            var inscale = element.attr(\"gadfly:scale\") == best_tickscale;\n            element.attribute(\"gadfly:inscale\", inscale);\n            element.attr(\"visibility\", inscale ? \"visible\" : \"hidden\");\n        };\n\n        root.select(\".\" + axis + \"gridlines\").selectAll(\"path\").forEach(mark_inscale_gridlines);\n        root.select(\".\" + axis + \"labels\").selectAll(\"text\").forEach(mark_inscale_labels);\n    }\n};\n\n\nvar set_plot_pan_zoom = function(root, tx, ty, scale) {\n    var old_scale = root.data(\"scale\");\n    var bounds = root.plotbounds();\n\n    var width = bounds.x1 - bounds.x0,\n        height = bounds.y1 - bounds.y0;\n\n    // compute the viewport derived from tx, ty, and scale\n    var x_min = -width * scale - (scale * width - width),\n        x_max = width * scale,\n        y_min = -height * scale - (scale * height - height),\n        y_max = height * scale;\n\n    var x0 = bounds.x0 - scale * bounds.x0,\n        y0 = bounds.y0 - scale * bounds.y0;\n\n    var tx = Math.max(Math.min(tx - x0, x_max), x_min),\n        ty = Math.max(Math.min(ty - y0, y_max), y_min);\n\n    tx += x0;\n    ty += y0;\n\n    // when the scale change, we may need to alter which set of\n    // ticks is being displayed\n    if (scale != old_scale) {\n        update_tickscale(root, scale, \"x\");\n        update_tickscale(root, scale, \"y\");\n    }\n\n    set_geometry_transform(root, tx, ty, scale);\n\n    root.data(\"scale\", scale);\n    root.data(\"tx\", tx);\n    root.data(\"ty\", ty);\n};\n\n\nvar scale_centered_translation = function(root, scale) {\n    var bounds = root.plotbounds();\n\n    var width = bounds.x1 - bounds.x0,\n        height = bounds.y1 - bounds.y0;\n\n    var tx0 = root.data(\"tx\"),\n        ty0 = root.data(\"ty\");\n\n    var scale0 = root.data(\"scale\");\n\n    // how off from center the current view is\n    var xoff = tx0 - (bounds.x0 * (1 - scale0) + (width * (1 - scale0)) / 2),\n        yoff = ty0 - (bounds.y0 * (1 - scale0) + (height * (1 - scale0)) / 2);\n\n    // rescale offsets\n    xoff = xoff * scale / scale0;\n    yoff = yoff * scale / scale0;\n\n    // adjust for the panel position being scaled\n    var x_edge_adjust = bounds.x0 * (1 - scale),\n        y_edge_adjust = bounds.y0 * (1 - scale);\n\n    return {\n        x: xoff + x_edge_adjust + (width - width * scale) / 2,\n        y: yoff + y_edge_adjust + (height - height * scale) / 2\n    };\n};\n\n\n// Initialize data for panning zooming if it isn't already.\nvar init_pan_zoom = function(root) {\n    if (root.data(\"zoompan-ready\")) {\n        return;\n    }\n\n    // The non-scaling-stroke trick. Rather than try to correct for the\n    // stroke-width when zooming, we force it to a fixed value.\n    var px_per_mm = root.node.getCTM().a;\n\n    // Drag events report deltas in pixels, which we'd like to convert to\n    // millimeters.\n    root.data(\"px_per_mm\", px_per_mm);\n\n    root.selectAll(\"path\")\n        .forEach(function (element, i) {\n        sw = element.asPX(\"stroke-width\") * px_per_mm;\n        if (sw > 0) {\n            element.attribute(\"stroke-width\", sw);\n            element.attribute(\"vector-effect\", \"non-scaling-stroke\");\n        }\n    });\n\n    // Store ticks labels original tranformation\n    root.selectAll(\".xlabels > text, .ylabels > text\")\n        .forEach(function (element, i) {\n            var lm = element.transform().localMatrix;\n            element.data(\"static_transform\",\n                new Snap.Matrix(lm.a, lm.b, lm.c, lm.d, lm.e, lm.f));\n        });\n\n    if (root.data(\"tx\") === undefined) root.data(\"tx\", 0);\n    if (root.data(\"ty\") === undefined) root.data(\"ty\", 0);\n    if (root.data(\"scale\") === undefined) root.data(\"scale\", 1.0);\n    if (root.data(\"xtickscales\") === undefined) {\n\n        // index all the tick scales that are listed\n        var xtickscales = {};\n        var ytickscales = {};\n        var add_x_tick_scales = function (element, i) {\n            xtickscales[element.attribute(\"gadfly:scale\")] = true;\n        };\n        var add_y_tick_scales = function (element, i) {\n            ytickscales[element.attribute(\"gadfly:scale\")] = true;\n        };\n\n        root.select(\".xgridlines\").selectAll(\"path\").forEach(add_x_tick_scales);\n        root.select(\".ygridlines\").selectAll(\"path\").forEach(add_y_tick_scales);\n        root.select(\".xlabels\").selectAll(\"text\").forEach(add_x_tick_scales);\n        root.select(\".ylabels\").selectAll(\"text\").forEach(add_y_tick_scales);\n\n        root.data(\"xtickscales\", xtickscales);\n        root.data(\"ytickscales\", ytickscales);\n        root.data(\"xtickscale\", 1.0);\n    }\n\n    var min_scale = 1.0, max_scale = 1.0;\n    for (scale in xtickscales) {\n        min_scale = Math.min(min_scale, scale);\n        max_scale = Math.max(max_scale, scale);\n    }\n    for (scale in ytickscales) {\n        min_scale = Math.min(min_scale, scale);\n        max_scale = Math.max(max_scale, scale);\n    }\n    root.data(\"min_scale\", min_scale);\n    root.data(\"max_scale\", max_scale);\n\n    // store the original positions of labels\n    root.select(\".xlabels\")\n        .selectAll(\"text\")\n        .forEach(function (element, i) {\n            element.data(\"x\", element.asPX(\"x\"));\n        });\n\n    root.select(\".ylabels\")\n        .selectAll(\"text\")\n        .forEach(function (element, i) {\n            element.data(\"y\", element.asPX(\"y\"));\n        });\n\n    // mark grid lines and ticks as in or out of scale.\n    var mark_inscale = function (element, i) {\n        element.attribute(\"gadfly:inscale\", element.attribute(\"gadfly:scale\") == 1.0);\n    };\n\n    root.select(\".xgridlines\").selectAll(\"path\").forEach(mark_inscale);\n    root.select(\".ygridlines\").selectAll(\"path\").forEach(mark_inscale);\n    root.select(\".xlabels\").selectAll(\"text\").forEach(mark_inscale);\n    root.select(\".ylabels\").selectAll(\"text\").forEach(mark_inscale);\n\n    // figure out the upper ond lower bounds on panning using the maximum\n    // and minum grid lines\n    var bounds = root.plotbounds();\n    var pan_bounds = {\n        x0: 0.0,\n        y0: 0.0,\n        x1: 0.0,\n        y1: 0.0\n    };\n\n    root.select(\".xgridlines\")\n        .selectAll(\"path\")\n        .forEach(function (element, i) {\n            if (element.attribute(\"gadfly:inscale\") == \"true\") {\n                var bbox = element.node.getBBox();\n                if (bounds.x1 - bbox.x < pan_bounds.x0) {\n                    pan_bounds.x0 = bounds.x1 - bbox.x;\n                }\n                if (bounds.x0 - bbox.x > pan_bounds.x1) {\n                    pan_bounds.x1 = bounds.x0 - bbox.x;\n                }\n            }\n        });\n\n    root.select(\".ygridlines\")\n        .selectAll(\"path\")\n        .forEach(function (element, i) {\n            if (element.attribute(\"gadfly:inscale\") == \"true\") {\n                var bbox = element.node.getBBox();\n                if (bounds.y1 - bbox.y < pan_bounds.y0) {\n                    pan_bounds.y0 = bounds.y1 - bbox.y;\n                }\n                if (bounds.y0 - bbox.y > pan_bounds.y1) {\n                    pan_bounds.y1 = bounds.y0 - bbox.y;\n                }\n            }\n        });\n\n    // nudge these values a little\n    pan_bounds.x0 -= 5;\n    pan_bounds.x1 += 5;\n    pan_bounds.y0 -= 5;\n    pan_bounds.y1 += 5;\n    root.data(\"pan_bounds\", pan_bounds);\n\n    // Set all grid lines at scale 1.0 to visible. Out of bounds lines\n    // will be clipped.\n    root.select(\".xgridlines\")\n        .selectAll(\"path\")\n        .forEach(function (element, i) {\n            if (element.attribute(\"gadfly:inscale\") == \"true\") {\n                element.attr(\"visibility\", \"visible\");\n            }\n        });\n\n    root.select(\".ygridlines\")\n        .selectAll(\"path\")\n        .forEach(function (element, i) {\n            if (element.attribute(\"gadfly:inscale\") == \"true\") {\n                element.attr(\"visibility\", \"visible\");\n            }\n        });\n\n    root.data(\"zoompan-ready\", true)\n};\n\n\n// Panning\nGadfly.guide_background_drag_onmove = function(dx, dy, x, y, event) {\n    var root = this.plotroot();\n    var px_per_mm = root.data(\"px_per_mm\");\n    dx /= px_per_mm;\n    dy /= px_per_mm;\n\n    var tx0 = root.data(\"tx\"),\n        ty0 = root.data(\"ty\");\n\n    var dx0 = root.data(\"dx\"),\n        dy0 = root.data(\"dy\");\n\n    root.data(\"dx\", dx);\n    root.data(\"dy\", dy);\n\n    dx = dx - dx0;\n    dy = dy - dy0;\n\n    var tx = tx0 + dx,\n        ty = ty0 + dy;\n\n    set_plot_pan_zoom(root, tx, ty, root.data(\"scale\"));\n};\n\n\nGadfly.guide_background_drag_onstart = function(x, y, event) {\n    var root = this.plotroot();\n    root.data(\"dx\", 0);\n    root.data(\"dy\", 0);\n    init_pan_zoom(root);\n};\n\n\nGadfly.guide_background_drag_onend = function(event) {\n    var root = this.plotroot();\n};\n\n\nGadfly.guide_background_scroll = function(event) {\n    if (event.shiftKey) {\n        var root = this.plotroot();\n        init_pan_zoom(root);\n        var new_scale = root.data(\"scale\") * Math.pow(2, 0.002 * event.wheelDelta);\n        new_scale = Math.max(\n            root.data(\"min_scale\"),\n            Math.min(root.data(\"max_scale\"), new_scale))\n        update_plot_scale(root, new_scale);\n        event.stopPropagation();\n    }\n};\n\n\nGadfly.zoomslider_button_mouseover = function(event) {\n    this.select(\".button_logo\")\n         .animate({fill: this.data(\"mouseover_color\")}, 100);\n};\n\n\nGadfly.zoomslider_button_mouseout = function(event) {\n     this.select(\".button_logo\")\n         .animate({fill: this.data(\"mouseout_color\")}, 100);\n};\n\n\nGadfly.zoomslider_zoomout_click = function(event) {\n    var root = this.plotroot();\n    init_pan_zoom(root);\n    var min_scale = root.data(\"min_scale\"),\n        scale = root.data(\"scale\");\n    Snap.animate(\n        scale,\n        Math.max(min_scale, scale / 1.5),\n        function (new_scale) {\n            update_plot_scale(root, new_scale);\n        },\n        200);\n};\n\n\nGadfly.zoomslider_zoomin_click = function(event) {\n    var root = this.plotroot();\n    init_pan_zoom(root);\n    var max_scale = root.data(\"max_scale\"),\n        scale = root.data(\"scale\");\n\n    Snap.animate(\n        scale,\n        Math.min(max_scale, scale * 1.5),\n        function (new_scale) {\n            update_plot_scale(root, new_scale);\n        },\n        200);\n};\n\n\nGadfly.zoomslider_track_click = function(event) {\n    // TODO\n};\n\n\nGadfly.zoomslider_thumb_mousedown = function(event) {\n    this.animate({fill: this.data(\"mouseover_color\")}, 100);\n};\n\n\nGadfly.zoomslider_thumb_mouseup = function(event) {\n    this.animate({fill: this.data(\"mouseout_color\")}, 100);\n};\n\n\n// compute the position in [0, 1] of the zoom slider thumb from the current scale\nvar slider_position_from_scale = function(scale, min_scale, max_scale) {\n    if (scale >= 1.0) {\n        return 0.5 + 0.5 * (Math.log(scale) / Math.log(max_scale));\n    }\n    else {\n        return 0.5 * (Math.log(scale) - Math.log(min_scale)) / (0 - Math.log(min_scale));\n    }\n}\n\n\nvar update_plot_scale = function(root, new_scale) {\n    var trans = scale_centered_translation(root, new_scale);\n    set_plot_pan_zoom(root, trans.x, trans.y, new_scale);\n\n    root.selectAll(\".zoomslider_thumb\")\n        .forEach(function (element, i) {\n            var min_pos = element.data(\"min_pos\"),\n                max_pos = element.data(\"max_pos\"),\n                min_scale = root.data(\"min_scale\"),\n                max_scale = root.data(\"max_scale\");\n            var xmid = (min_pos + max_pos) / 2;\n            var xpos = slider_position_from_scale(new_scale, min_scale, max_scale);\n            element.transform(new Snap.Matrix().translate(\n                Math.max(min_pos, Math.min(\n                         max_pos, min_pos + (max_pos - min_pos) * xpos)) - xmid, 0));\n    });\n};\n\n\nGadfly.zoomslider_thumb_dragmove = function(dx, dy, x, y) {\n    var root = this.plotroot();\n    var min_pos = this.data(\"min_pos\"),\n        max_pos = this.data(\"max_pos\"),\n        min_scale = root.data(\"min_scale\"),\n        max_scale = root.data(\"max_scale\"),\n        old_scale = root.data(\"old_scale\");\n\n    var px_per_mm = root.data(\"px_per_mm\");\n    dx /= px_per_mm;\n    dy /= px_per_mm;\n\n    var xmid = (min_pos + max_pos) / 2;\n    var xpos = slider_position_from_scale(old_scale, min_scale, max_scale) +\n                   dx / (max_pos - min_pos);\n\n    // compute the new scale\n    var new_scale;\n    if (xpos >= 0.5) {\n        new_scale = Math.exp(2.0 * (xpos - 0.5) * Math.log(max_scale));\n    }\n    else {\n        new_scale = Math.exp(2.0 * xpos * (0 - Math.log(min_scale)) +\n                        Math.log(min_scale));\n    }\n    new_scale = Math.min(max_scale, Math.max(min_scale, new_scale));\n\n    update_plot_scale(root, new_scale);\n};\n\n\nGadfly.zoomslider_thumb_dragstart = function(event) {\n    var root = this.plotroot();\n    init_pan_zoom(root);\n\n    // keep track of what the scale was when we started dragging\n    root.data(\"old_scale\", root.data(\"scale\"));\n};\n\n\nGadfly.zoomslider_thumb_dragend = function(event) {\n};\n\n\nvar toggle_color_class = function(root, color_class, ison) {\n    var guides = root.selectAll(\".guide.\" + color_class + \",.guide .\" + color_class);\n    var geoms = root.selectAll(\".geometry.\" + color_class + \",.geometry .\" + color_class);\n    if (ison) {\n        guides.animate({opacity: 0.5}, 250);\n        geoms.animate({opacity: 0.0}, 250);\n    } else {\n        guides.animate({opacity: 1.0}, 250);\n        geoms.animate({opacity: 1.0}, 250);\n    }\n};\n\n\nGadfly.colorkey_swatch_click = function(event) {\n    var root = this.plotroot();\n    var color_class = this.data(\"color_class\");\n\n    if (event.shiftKey) {\n        root.selectAll(\".colorkey text\")\n            .forEach(function (element) {\n                var other_color_class = element.data(\"color_class\");\n                if (other_color_class != color_class) {\n                    toggle_color_class(root, other_color_class,\n                                       element.attr(\"opacity\") == 1.0);\n                }\n            });\n    } else {\n        toggle_color_class(root, color_class, this.attr(\"opacity\") == 1.0);\n    }\n};\n\n\nreturn Gadfly;\n\n}));\n\n\n//@ sourceURL=gadfly.js\n\n(function (glob, factory) {\n    // AMD support\n      if (typeof require === \"function\" && typeof define === \"function\" && define.amd) {\n        require([\"Snap.svg\", \"Gadfly\"], function (Snap, Gadfly) {\n            factory(Snap, Gadfly);\n        });\n      } else {\n          factory(glob.Snap, glob.Gadfly);\n      }\n})(window, function (Snap, Gadfly) {\n    var fig = Snap(\"#fig-6f7c24f0cc6a42ab87ed85d1d0bc7d06\");\nfig.select(\"#fig-6f7c24f0cc6a42ab87ed85d1d0bc7d06-element-4\")\n   .mouseenter(Gadfly.plot_mouseover)\n.mouseleave(Gadfly.plot_mouseout)\n.mousewheel(Gadfly.guide_background_scroll)\n.drag(Gadfly.guide_background_drag_onmove,\n      Gadfly.guide_background_drag_onstart,\n      Gadfly.guide_background_drag_onend)\n;\nfig.select(\"#fig-6f7c24f0cc6a42ab87ed85d1d0bc7d06-element-7\")\n   .plotroot().data(\"unfocused_ygrid_color\", \"#D0D0E0\")\n;\nfig.select(\"#fig-6f7c24f0cc6a42ab87ed85d1d0bc7d06-element-7\")\n   .plotroot().data(\"focused_ygrid_color\", \"#A0A0A0\")\n;\nfig.select(\"#fig-6f7c24f0cc6a42ab87ed85d1d0bc7d06-element-8\")\n   .plotroot().data(\"unfocused_xgrid_color\", \"#D0D0E0\")\n;\nfig.select(\"#fig-6f7c24f0cc6a42ab87ed85d1d0bc7d06-element-8\")\n   .plotroot().data(\"focused_xgrid_color\", \"#A0A0A0\")\n;\nfig.select(\"#fig-6f7c24f0cc6a42ab87ed85d1d0bc7d06-element-13\")\n   .data(\"mouseover_color\", \"#cd5c5c\")\n;\nfig.select(\"#fig-6f7c24f0cc6a42ab87ed85d1d0bc7d06-element-13\")\n   .data(\"mouseout_color\", \"#6a6a6a\")\n;\nfig.select(\"#fig-6f7c24f0cc6a42ab87ed85d1d0bc7d06-element-13\")\n   .click(Gadfly.zoomslider_zoomin_click)\n.mouseenter(Gadfly.zoomslider_button_mouseover)\n.mouseleave(Gadfly.zoomslider_button_mouseout)\n;\nfig.select(\"#fig-6f7c24f0cc6a42ab87ed85d1d0bc7d06-element-15\")\n   .data(\"max_pos\", 120.42)\n;\nfig.select(\"#fig-6f7c24f0cc6a42ab87ed85d1d0bc7d06-element-15\")\n   .data(\"min_pos\", 103.42)\n;\nfig.select(\"#fig-6f7c24f0cc6a42ab87ed85d1d0bc7d06-element-15\")\n   .click(Gadfly.zoomslider_track_click);\nfig.select(\"#fig-6f7c24f0cc6a42ab87ed85d1d0bc7d06-element-16\")\n   .data(\"max_pos\", 120.42)\n;\nfig.select(\"#fig-6f7c24f0cc6a42ab87ed85d1d0bc7d06-element-16\")\n   .data(\"min_pos\", 103.42)\n;\nfig.select(\"#fig-6f7c24f0cc6a42ab87ed85d1d0bc7d06-element-16\")\n   .data(\"mouseover_color\", \"#cd5c5c\")\n;\nfig.select(\"#fig-6f7c24f0cc6a42ab87ed85d1d0bc7d06-element-16\")\n   .data(\"mouseout_color\", \"#6a6a6a\")\n;\nfig.select(\"#fig-6f7c24f0cc6a42ab87ed85d1d0bc7d06-element-16\")\n   .drag(Gadfly.zoomslider_thumb_dragmove,\n     Gadfly.zoomslider_thumb_dragstart,\n     Gadfly.zoomslider_thumb_dragend)\n.mousedown(Gadfly.zoomslider_thumb_mousedown)\n.mouseup(Gadfly.zoomslider_thumb_mouseup)\n;\nfig.select(\"#fig-6f7c24f0cc6a42ab87ed85d1d0bc7d06-element-17\")\n   .data(\"mouseover_color\", \"#cd5c5c\")\n;\nfig.select(\"#fig-6f7c24f0cc6a42ab87ed85d1d0bc7d06-element-17\")\n   .data(\"mouseout_color\", \"#6a6a6a\")\n;\nfig.select(\"#fig-6f7c24f0cc6a42ab87ed85d1d0bc7d06-element-17\")\n   .click(Gadfly.zoomslider_zoomout_click)\n.mouseenter(Gadfly.zoomslider_button_mouseover)\n.mouseleave(Gadfly.zoomslider_button_mouseout)\n;\n    });\n]]> </script>\n</svg>\n"}}], "cell_type": "code"}, {"source": "## plot of days active against problem attempts\np = plot(\n   y = d1[:, 2] .+ rand(Normal(0, 0.1), 20000),          # jitter with Gaussian noise\n   x = d1[:, 3] .+ rand(Normal(0, 5), 20000),            # jitter with Gaussian noise\n   color = eng,\n   Geom.point,\n   \n   Theme(\n      highlight_width = 0.0mm,\n      default_point_size = 0.4mm\n   ),\n   Guide.yticks(ticks = [1:7]),\n   Guide.xticks(ticks = [0, 300, 600, 900, 1200]),\n   Guide.xlabel(\"Problems Attempted\"),\n   Guide.ylabel(\"Days Active\"),\n   Guide.title(\"Figure 4: Problems Attempted and Days Active for Each User\"),\n   Guide.colorkey(\"Engagement\")\n)", "execution_count": 10, "metadata": {"trusted": false, "collapsed": false}, "outputs": [{"metadata": {}, "output_type": "execute_result", "execution_count": 10, "data": {"image/svg+xml": "<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<svg xmlns=\"http://www.w3.org/2000/svg\"\n     xmlns:xlink=\"http://www.w3.org/1999/xlink\"\n     xmlns:gadfly=\"http://www.gadflyjl.org/ns\"\n     version=\"1.2\"\n     width=\"141.42mm\" height=\"100mm\" viewBox=\"0 0 141.42 100\"\n     stroke=\"none\"\n     fill=\"#000000\"\n     stroke-width=\"0.3\"\n     font-size=\"3.88\"\n>\n<g class=\"plotroot xscalable yscalable\" id=\"fig-757fddc5089a4bec8de385a234bb3439-element-1\">\n  <g font-size=\"3.88\" font-family=\"'PT Sans','Helvetica Neue','Helvetica',sans-serif\" fill=\"#564A55\" stroke=\"none\" id=\"fig-757fddc5089a4bec8de385a234bb3439-element-2\">\n    <text x=\"65.94\" y=\"92\" text-anchor=\"middle\">Problems Attempted</text>\n  </g>\n  <g class=\"guide 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