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2016-02-26 14:27:04 -05:00
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<meta name="description" content="MathJax.Hub.Config({tex2jax: {inlineMath: [[&#39;$&#39;,&#39;$&#39;], [&#39;\(&#39;,&#39;\)&#39;]]}}); In [1]: from bokeh.plotting import figure, output_notebook, show from bokeh.palettes import PuBuGn9 as Palette import pandas ...">
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<title>Complaining about the Weather - Bradlee Speice</title>
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<h1 class="header-title">Complaining about the Weather</h1>
<p class="header-date"> <a href="/author/bradlee-speice.html">Bradlee Speice</a>, Fri 01 January 2016, <a href="/category/blog.html">Blog</a></p>
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<a href="/tag/weather.html">weather</a> </p>
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<div class=" highlight hl-ipython3"><pre><span class="kn">from</span> <span class="nn">bokeh.plotting</span> <span class="k">import</span> <span class="n">figure</span><span class="p">,</span> <span class="n">output_notebook</span><span class="p">,</span> <span class="n">show</span>
<span class="kn">from</span> <span class="nn">bokeh.palettes</span> <span class="k">import</span> <span class="n">PuBuGn9</span> <span class="k">as</span> <span class="n">Palette</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">datetime</span> <span class="k">import</span> <span class="n">datetime</span>
<span class="kn">import</span> <span class="nn">pickle</span>
<span class="n">output_notebook</span><span class="p">()</span>
</pre></div>
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outerWidth:t.fn.outerWidth,outerHeight:t.fn.outerHeight};t.fn["inner"+n]=function(i){return i===e?a["inner"+n].call(this):this.each(function(){t(this).css(o,r(this,i)+"px")})},t.fn["outer"+n]=function(e,i){return"number"!=typeof e?a["outer"+n].call(this,e):this.each(function(){t(this).css(o,r(this,e,!0,i)+"px")})}}),t.fn.addBack||(t.fn.addBack=function(t){return this.add(null==t?this.prevObject:this.prevObject.filter(t))}),t("<a>").data("a-b","a").removeData("a-b").data("a-b")&&(t.fn.removeData=function(e){return function(i){return arguments.length?e.call(this,t.camelCase(i)):e.call(this)}}(t.fn.removeData)),t.ui.ie=!!/msie [\w.]+/.exec(navigator.userAgent.toLowerCase()),t.support.selectstart="onselectstart"in document.createElement("div"),t.fn.extend({disableSelection:function(){return this.bind((t.support.selectstart?"selectstart":"mousedown")+".ui-disableSelection",function(t){t.preventDefault()})},enableSelection:function(){return this.unbind(".ui-disableSelection")}}),t.extend(t.ui,{plugin:{add:function(e,i,n){var r,s=t.ui[e].prototype;for(r in n)s.plugins[r]=s.plugins[r]||[],s.plugins[r].push([i,n[r]])},call:function(t,e,i){var n,r=t.plugins[e];if(r&&t.element[0].parentNode&&11!==t.element[0].parentNode.nodeType)for(n=0;n<r.length;n++)t.options[r[n][0]]&&r[n][1].apply(t.element,i)}},hasScroll:function(e,i){if("hidden"===t(e).css("overflow"))return!1;var n=i&&"left"===i?"scrollLeft":"scrollTop",r=!1;return e[n]>0?!0:(e[n]=1,r=e[n]>0,e[n]=0,r)}})}(n),function(t,e){var i=0,n=Array.prototype.slice,r=t.cleanData;t.cleanData=function(e){for(var i,n=0;null!=(i=e[n]);n++)try{t(i).triggerHandler("remove")}catch(s){}r(e)},t.widget=function(e,i,n){var r,s,o,a,l={},u=e.split(".")[0];e=e.split(".")[1],r=u+"-"+e,n||(n=i,i=t.Widget),t.expr[":"][r.toLowerCase()]=function(e){return!!t.data(e,r)},t[u]=t[u]||{},s=t[u][e],o=t[u][e]=function(t,e){return this._createWidget?void(arguments.length&&this._createWidget(t,e)):new o(t,e)},t.extend(o,s,{version:n.version,_proto:t.extend({},n),_childConstructors:[]}),a=new i,a.options=t.widget.extend({},a.options),t.each(n,function(e,n){return t.isFunction(n)?void(l[e]=function(){var t=function(){return i.prototype[e].apply(this,arguments)},r=function(t){return i.prototype[e].apply(this,t)};return function(){var e,i=this._super,s=this._superApply;return this._super=t,this._superApply=r,e=n.apply(this,arguments),this._super=i,this._superApply=s,e}}()):void(l[e]=n)}),o.prototype=t.widget.extend(a,{widgetEventPrefix:s?a.widgetEventPrefix:e},l,{constructor:o,namespace:u,widgetName:e,widgetFullName:r}),s?(t.each(s._childConstructors,function(e,i){var n=i.prototype;t.widget(n.namespace+"."+n.widgetName,o,i._proto)}),delete s._childConstructors):i._childConstructors.push(o),t.widget.bridge(e,o)},t.widget.extend=function(i){for(var r,s,o=n.call(arguments,1),a=0,l=o.length;l>a;a++)for(r in o[a])s=o[a][r],o[a].hasOwnProperty(r)&&s!==e&&(t.isPlainObject(s)?i[r]=t.isPlainObject(i[r])?t.widget.extend({},i[r],s):t.widget.extend({},s):i[r]=s);return i},t.widget.bridge=function(i,r){var s=r.prototype.widgetFullName||i;t.fn[i]=function(o){var a="string"==typeof o,l=n.call(arguments,1),u=this;return o=!a&&l.length?t.widget.extend.apply(null,[o].concat(l)):o,a?this.each(function(){var n,r=t.data(this,s);return r?t.isFunction(r[o])&&"_"!==o.charAt(0)?(n=r[o].apply(r,l),n!==r&&n!==e?(u=n&&n.jquery?u.pushStack(n.get()):n,!1):void 0):t.error("no such method '"+o+"' for "+i+" widget instance"):t.error("cannot call methods on "+i+" prior to initialization; attempted to call method '"+o+"'")}):this.each(function(){var e=t.data(this,s);e?e.option(o||{})._init():t.data(this,s,new r(o,this))}),u}},t.Widget=function(){},t.Widget._childConstructors=[],t.Widget.prototype={widgetName:"widget",widgetEventPrefix:"",defaultElement:"<div>",options:{disabled:!1,create:null},_createWidget:function(e,n){n=t(n||this.defaultElement||this)[0],this.element=t(n),this.uuid=i++,this.eventNamespace="."+this.widgetName+this.uuid,this.options=t.widget.extend({},this.options,this._getCreateOptions(),e),this.bindings=t(),this.hoverable=t
return o>=h&&c>=a&&l>=p&&d>=u;case"intersect":return h<o+t.helperProportions.width/2&&a-t.helperProportions.width/2<c&&p<l+t.helperProportions.height/2&&u-t.helperProportions.height/2<d;case"pointer":return r=(t.positionAbs||t.position.absolute).left+(t.clickOffset||t.offset.click).left,s=(t.positionAbs||t.position.absolute).top+(t.clickOffset||t.offset.click).top,i(s,p,e.proportions.height)&&i(r,h,e.proportions.width);case"touch":return(l>=p&&d>=l||u>=p&&d>=u||p>l&&u>d)&&(o>=h&&c>=o||a>=h&&c>=a||h>o&&a>c);default:return!1}},t.ui.ddmanager={current:null,droppables:{"default":[]},prepareOffsets:function(e,i){var n,r,s=t.ui.ddmanager.droppables[e.options.scope]||[],o=i?i.type:null,a=(e.currentItem||e.element).find(":data(ui-droppable)").addBack();t:for(n=0;n<s.length;n++)if(!(s[n].options.disabled||e&&!s[n].accept.call(s[n].element[0],e.currentItem||e.element))){for(r=0;r<a.length;r++)if(a[r]===s[n].element[0]){s[n].proportions.height=0;continue t}s[n].visible="none"!==s[n].element.css("display"),s[n].visible&&("mousedown"===o&&s[n]._activate.call(s[n],i),s[n].offset=s[n].element.offset(),s[n].proportions={width:s[n].element[0].offsetWidth,height:s[n].element[0].offsetHeight})}},drop:function(e,i){var n=!1;return t.each((t.ui.ddmanager.droppables[e.options.scope]||[]).slice(),function(){this.options&&(!this.options.disabled&&this.visible&&t.ui.intersect(e,this,this.options.tolerance)&&(n=this._drop.call(this,i)||n),!this.options.disabled&&this.visible&&this.accept.call(this.element[0],e.currentItem||e.element)&&(this.isout=!0,this.isover=!1,this._deactivate.call(this,i)))}),n},dragStart:function(e,i){e.element.parentsUntil("body").bind("scroll.droppable",function(){e.options.refreshPositions||t.ui.ddmanager.prepareOffsets(e,i)})},drag:function(e,i){e.options.refreshPositions&&t.ui.ddmanager.prepareOffsets(e,i),t.each(t.ui.ddmanager.droppables[e.options.scope]||[],function(){if(!this.options.disabled&&!this.greedyChild&&this.visible){var n,r,s,o=t.ui.intersect(e,this,this.options.tolerance),a=!o&&this.isover?"isout":o&&!this.isover?"isover":null;a&&(this.options.greedy&&(r=this.options.scope,s=this.element.parents(":data(ui-droppable)").filter(function(){return t.data(this,"ui-droppable").options.scope===r}),s.length&&(n=t.data(s[0],"ui-droppable"),n.greedyChild="isover"===a)),n&&"isover"===a&&(n.isover=!1,n.isout=!0,n._out.call(n,i)),this[a]=!0,this["isout"===a?"isover":"isout"]=!1,this["isover"===a?"_over":"_out"].call(this,i),n&&"isout"===a&&(n.isout=!1,n.isover=!0,n._over.call(n,i)))}})},dragStop:function(e,i){e.element.parentsUntil("body").unbind("scroll.droppable"),e.options.refreshPositions||t.ui.ddmanager.prepareOffsets(e,i)}}}(n),function(t,e){function i(t){return parseInt(t,10)||0}function n(t){return!isNaN(parseInt(t,10))}t.widget("ui.resizable",t.ui.mouse,{version:"1.10.3",widgetEventPrefix:"resize",options:{alsoResize:!1,animate:!1,animateDuration:"slow",animateEasing:"swing",aspectRatio:!1,autoHide:!1,containment:!1,ghost:!1,grid:!1,handles:"e,s,se",helper:!1,maxHeight:null,maxWidth:null,minHeight:10,minWidth:10,zIndex:90,resize:null,start:null,stop:null},_create:function(){var e,i,n,r,s,o=this,a=this.options;if(this.element.addClass("ui-resizable"),t.extend(this,{_aspectRatio:!!a.aspectRatio,aspectRatio:a.aspectRatio,originalElement:this.element,_proportionallyResizeElements:[],_helper:a.helper||a.ghost||a.animate?a.helper||"ui-resizable-helper":null}),this.element[0].nodeName.match(/canvas|textarea|input|select|button|img/i)&&(this.element.wrap(t("<div class='ui-wrapper' style='overflow: hidden;'></div>").css({position:this.element.css("position"),width:this.element.outerWidth(),height:this.element.outerHeight(),top:this.element.css("top"),left:this.element.css("left")})),this.element=this.element.parent().data("ui-resizable",this.element.data("ui-resizable")),this.elementIsWrapper=!0,this.element.css({marginLeft:this.originalElement.css("marginLeft"),marginTop:this.originalElement.css("marginTop"),marginRight:this.originalElement.css("marginRight"),marginBottom:this.originalElement.css("marginBotto
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t.datepicker._lastInput=e,t.datepicker._setDateFromField(i),t.datepicker._inDialog&&(e.value=""),t.datepicker._pos||(t.datepicker._pos=t.datepicker._findPos(e),t.datepicker._pos[1]+=e.offsetHeight),o=!1,t(e).parents().each(function(){return o|="fixed"===t(this).css("position"),!o}),a={left:t.datepicker._pos[0],top:t.datepicker._pos[1]},t.datepicker._pos=null,i.dpDiv.empty(),i.dpDiv.css({position:"absolute",display:"block",top:"-1000px"}),t.datepicker._updateDatepicker(i),a=t.datepicker._checkOffset(i,a,o),i.dpDiv.css({position:t.datepicker._inDialog&&t.blockUI?"static":o?"fixed":"absolute",display:"none",left:a.left+"px",top:a.top+"px"}),i.inline||(l=t.datepicker._get(i,"showAnim"),u=t.datepicker._get(i,"duration"),i.dpDiv.zIndex(t(e).zIndex()+1),t.datepicker._datepickerShowing=!0,t.effects&&t.effects.effect[l]?i.dpDiv.show(l,t.datepicker._get(i,"showOptions"),u):i.dpDiv[l||"show"](l?u:null),t.datepicker._shouldFocusInput(i)&&i.input.focus(),t.datepicker._curInst=i))}},_updateDatepicker:function(e){this.maxRows=4,s=e,e.dpDiv.empty().append(this._generateHTML(e)),this._attachHandlers(e),e.dpDiv.find("."+this._dayOverClass+" a").mouseover();var i,n=this._getNumberOfMonths(e),r=n[1],o=17;e.dpDiv.removeClass("ui-datepicker-multi-2 ui-datepicker-multi-3 ui-datepicker-multi-4").width(""),r>1&&e.dpDiv.addClass("ui-datepicker-multi-"+r).css("width",o*r+"em"),e.dpDiv[(1!==n[0]||1!==n[1]?"add":"remove")+"Class"]("ui-datepicker-multi"),e.dpDiv[(this._get(e,"isRTL")?"add":"remove")+"Class"]("ui-datepicker-rtl"),e===t.datepicker._curInst&&t.datepicker._datepickerShowing&&t.datepicker._shouldFocusInput(e)&&e.input.focus(),e.yearshtml&&(i=e.yearshtml,setTimeout(function(){i===e.yearshtml&&e.yearshtml&&e.dpDiv.find("select.ui-datepicker-year:first").replaceWith(e.yearshtml),i=e.yearshtml=null},0))},_shouldFocusInput:function(t){return t.input&&t.input.is(":visible")&&!t.input.is(":disabled")&&!t.input.is(":focus")},_checkOffset:function(e,i,n){var r=e.dpDiv.outerWidth(),s=e.dpDiv.outerHeight(),o=e.input?e.input.outerWidth():0,a=e.input?e.input.outerHeight():0,l=document.documentElement.clientWidth+(n?0:t(document).scrollLeft()),u=document.documentElement.clientHeight+(n?0:t(document).scrollTop());return i.left-=this._get(e,"isRTL")?r-o:0,i.left-=n&&i.left===e.input.offset().left?t(document).scrollLeft():0,i.top-=n&&i.top===e.input.offset().top+a?t(document).scrollTop():0,i.left-=Math.min(i.left,i.left+r>l&&l>r?Math.abs(i.left+r-l):0),i.top-=Math.min(i.top,i.top+s>u&&u>s?Math.abs(s+a):0),i},_findPos:function(e){for(var i,n=this._getInst(e),r=this._get(n,"isRTL");e&&("hidden"===e.type||1!==e.nodeType||t.expr.filters.hidden(e));)e=e[r?"previousSibling":"nextSibling"];return i=t(e).offset(),[i.left,i.top]},_hideDatepicker:function(e){var i,n,r,s,a=this._curInst;!a||e&&a!==t.data(e,o)||this._datepickerShowing&&(i=this._get(a,"showAnim"),n=this._get(a,"duration"),r=function(){t.datepicker._tidyDialog(a)},t.effects&&(t.effects.effect[i]||t.effects[i])?a.dpDiv.hide(i,t.datepicker._get(a,"showOptions"),n,r):a.dpDiv["slideDown"===i?"slideUp":"fadeIn"===i?"fadeOut":"hide"](i?n:null,r),i||r(),this._datepickerShowing=!1,s=this._get(a,"onClose"),s&&s.apply(a.input?a.input[0]:null,[a.input?a.input.val():"",a]),this._lastInput=null,this._inDialog&&(this._dialogInput.css({position:"absolute",left:"0",top:"-100px"}),t.blockUI&&(t.unblockUI(),t("body").append(this.dpDiv))),this._inDialog=!1)},_tidyDialog:function(t){t.dpDiv.removeClass(this._dialogClass).unbind(".ui-datepicker-calendar")},_checkExternalClick:function(e){if(t.datepicker._curInst){var i=t(e.target),n=t.datepicker._getInst(i[0]);(i[0].id!==t.datepicker._mainDivId&&0===i.parents("#"+t.datepicker._mainDivId).length&&!i.hasClass(t.datepicker.markerClassName)&&!i.closest("."+t.datepicker._triggerClass).length&&t.datepicker._datepickerShowing&&(!t.datepicker._inDialog||!t.blockUI)||i.hasClass(t.datepicker.markerClassName)&&t.datepicker._curInst!==n)&&t.datepicker._hideDatepicker()}},_adjustDate:function(e,i,n){var r=t(e),s=this._getInst(r[0]);this._isDisabledDatepicker(r[0])||(this._adju
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r.length>i&&(r.slice(i).remove(),r=r.slice(0,i)),e=r.length;i>e;e++)o.push(s);this.handles=r.add(t(o.join("")).appendTo(this.element)),this.handle=this.handles.eq(0),this.handles.each(function(e){t(this).data("ui-slider-handle-index",e)})},_createRange:function(){var e=this.options,i="";e.range?(e.range===!0&&(e.values?e.values.length&&2!==e.values.length?e.values=[e.values[0],e.values[0]]:t.isArray(e.values)&&(e.values=e.values.slice(0)):e.values=[this._valueMin(),this._valueMin()]),this.range&&this.range.length?this.range.removeClass("ui-slider-range-min ui-slider-range-max").css({left:"",bottom:""}):(this.range=t("<div></div>").appendTo(this.element),i="ui-slider-range ui-widget-header ui-corner-all"),this.range.addClass(i+("min"===e.range||"max"===e.range?" ui-slider-range-"+e.range:""))):this.range=t([])},_setupEvents:function(){var t=this.handles.add(this.range).filter("a");this._off(t),this._on(t,this._handleEvents),this._hoverable(t),this._focusable(t)},_destroy:function(){this.handles.remove(),this.range.remove(),this.element.removeClass("ui-slider ui-slider-horizontal ui-slider-vertical ui-widget ui-widget-content ui-corner-all"),this._mouseDestroy()},_mouseCapture:function(e){var i,n,r,s,o,a,l,u,h=this,c=this.options;return c.disabled?!1:(this.elementSize={width:this.element.outerWidth(),height:this.element.outerHeight()},this.elementOffset=this.element.offset(),i={x:e.pageX,y:e.pageY},n=this._normValueFromMouse(i),r=this._valueMax()-this._valueMin()+1,this.handles.each(function(e){var i=Math.abs(n-h.values(e));(r>i||r===i&&(e===h._lastChangedValue||h.values(e)===c.min))&&(r=i,s=t(this),o=e)}),a=this._start(e,o),a===!1?!1:(this._mouseSliding=!0,this._handleIndex=o,s.addClass("ui-state-active").focus(),l=s.offset(),u=!t(e.target).parents().addBack().is(".ui-slider-handle"),this._clickOffset=u?{left:0,top:0}:{left:e.pageX-l.left-s.width()/2,top:e.pageY-l.top-s.height()/2-(parseInt(s.css("borderTopWidth"),10)||0)-(parseInt(s.css("borderBottomWidth"),10)||0)+(parseInt(s.css("marginTop"),10)||0)},this.handles.hasClass("ui-state-hover")||this._slide(e,o,n),this._animateOff=!0,!0))},_mouseStart:function(){return!0},_mouseDrag:function(t){var e={x:t.pageX,y:t.pageY},i=this._normValueFromMouse(e);return this._slide(t,this._handleIndex,i),!1},_mouseStop:function(t){return this.handles.removeClass("ui-state-active"),this._mouseSliding=!1,this._stop(t,this._handleIndex),this._change(t,this._handleIndex),this._handleIndex=null,this._clickOffset=null,this._animateOff=!1,!1},_detectOrientation:function(){this.orientation="vertical"===this.options.orientation?"vertical":"horizontal"},_normValueFromMouse:function(t){var e,i,n,r,s;return"horizontal"===this.orientation?(e=this.elementSize.width,i=t.x-this.elementOffset.left-(this._clickOffset?this._clickOffset.left:0)):(e=this.elementSize.height,i=t.y-this.elementOffset.top-(this._clickOffset?this._clickOffset.top:0)),n=i/e,n>1&&(n=1),0>n&&(n=0),"vertical"===this.orientation&&(n=1-n),r=this._valueMax()-this._valueMin(),s=this._valueMin()+n*r,this._trimAlignValue(s)},_start:function(t,e){var i={handle:this.handles[e],value:this.value()};return this.options.values&&this.options.values.length&&(i.value=this.values(e),i.values=this.values()),this._trigger("start",t,i)},_slide:function(t,e,i){var n,r,s;this.options.values&&this.options.values.length?(n=this.values(e?0:1),2===this.options.values.length&&this.options.range===!0&&(0===e&&i>n||1===e&&n>i)&&(i=n),i!==this.values(e)&&(r=this.values(),r[e]=i,s=this._trigger("slide",t,{handle:this.handles[e],value:i,values:r}),n=this.values(e?0:1),s!==!1&&this.values(e,i,!0))):i!==this.value()&&(s=this._trigger("slide",t,{handle:this.handles[e],value:i}),s!==!1&&this.value(i))},_stop:function(t,e){var i={handle:this.handles[e],value:this.value()};this.options.values&&this.options.values.length&&(i.value=this.values(e),i.values=this.values()),this._trigger("stop",t,i)},_change:function(t,e){if(!this._keySliding&&!this._mouseSliding){var i={handle:this.handles[e],value:this.value()};this.options.values&&this.options.values.length&&(i.
t("./position"),function(t,e){t.widget("ui.menu",{version:"1.10.4",defaultElement:"<ul>",delay:300,options:{icons:{submenu:"ui-icon-carat-1-e"},menus:"ul",position:{my:"left top",at:"right top"},role:"menu",blur:null,focus:null,select:null},_create:function(){this.activeMenu=this.element,this.mouseHandled=!1,this.element.uniqueId().addClass("ui-menu ui-widget ui-widget-content ui-corner-all").toggleClass("ui-menu-icons",!!this.element.find(".ui-icon").length).attr({role:this.options.role,tabIndex:0}).bind("click"+this.eventNamespace,t.proxy(function(t){this.options.disabled&&t.preventDefault()},this)),this.options.disabled&&this.element.addClass("ui-state-disabled").attr("aria-disabled","true"),this._on({"mousedown .ui-menu-item > a":function(t){t.preventDefault()},"click .ui-state-disabled > a":function(t){t.preventDefault()},"click .ui-menu-item:has(a)":function(e){var i=t(e.target).closest(".ui-menu-item");!this.mouseHandled&&i.not(".ui-state-disabled").length&&(this.select(e),e.isPropagationStopped()||(this.mouseHandled=!0),i.has(".ui-menu").length?this.expand(e):!this.element.is(":focus")&&t(this.document[0].activeElement).closest(".ui-menu").length&&(this.element.trigger("focus",[!0]),this.active&&1===this.active.parents(".ui-menu").length&&clearTimeout(this.timer)))},"mouseenter .ui-menu-item":function(e){var i=t(e.currentTarget);i.siblings().children(".ui-state-active").removeClass("ui-state-active"),this.focus(e,i)},mouseleave:"collapseAll","mouseleave .ui-menu":"collapseAll",focus:function(t,e){var i=this.active||this.element.children(".ui-menu-item").eq(0);e||this.focus(t,i)},blur:function(e){this._delay(function(){t.contains(this.element[0],this.document[0].activeElement)||this.collapseAll(e)})},keydown:"_keydown"}),this.refresh(),this._on(this.document,{click:function(e){t(e.target).closest(".ui-menu").length||this.collapseAll(e),this.mouseHandled=!1}})},_destroy:function(){this.element.removeAttr("aria-activedescendant").find(".ui-menu").addBack().removeClass("ui-menu ui-widget ui-widget-content ui-corner-all ui-menu-icons").removeAttr("role").removeAttr("tabIndex").removeAttr("aria-labelledby").removeAttr("aria-expanded").removeAttr("aria-hidden").removeAttr("aria-disabled").removeUniqueId().show(),this.element.find(".ui-menu-item").removeClass("ui-menu-item").removeAttr("role").removeAttr("aria-disabled").children("a").removeUniqueId().removeClass("ui-corner-all ui-state-hover").removeAttr("tabIndex").removeAttr("role").removeAttr("aria-haspopup").children().each(function(){var e=t(this);e.data("ui-menu-submenu-carat")&&e.remove()}),this.element.find(".ui-menu-divider").removeClass("ui-menu-divider ui-widget-content")},_keydown:function(e){function i(t){return t.replace(/[\-\[\]{}()*+?.,\\\^$|#\s]/g,"\\$&")}var n,r,s,o,a,l=!0;switch(e.keyCode){case t.ui.keyCode.PAGE_UP:this.previousPage(e);break;case t.ui.keyCode.PAGE_DOWN:this.nextPage(e);break;case t.ui.keyCode.HOME:this._move("first","first",e);break;case t.ui.keyCode.END:this._move("last","last",e);break;case t.ui.keyCode.UP:this.previous(e);break;case t.ui.keyCode.DOWN:this.next(e);break;case t.ui.keyCode.LEFT:this.collapse(e);break;case t.ui.keyCode.RIGHT:this.active&&!this.active.is(".ui-state-disabled")&&this.expand(e);break;case t.ui.keyCode.ENTER:case t.ui.keyCode.SPACE:this._activate(e);break;case t.ui.keyCode.ESCAPE:this.collapse(e);break;default:l=!1,r=this.previousFilter||"",s=String.fromCharCode(e.keyCode),o=!1,clearTimeout(this.filterTimer),s===r?o=!0:s=r+s,a=new RegExp("^"+i(s),"i"),n=this.activeMenu.children(".ui-menu-item").filter(function(){return a.test(t(this).children("a").text())}),n=o&&-1!==n.index(this.active.next())?this.active.nextAll(".ui-menu-item"):n,n.length||(s=String.fromCharCode(e.keyCode),a=new RegExp("^"+i(s),"i"),n=this.activeMenu.children(".ui-menu-item").filter(function(){return a.test(t(this).children("a").text())})),n.length?(this.focus(e,n),n.length>1?(this.previousFilter=s,this.filterTimer=this._delay(function(){delete this.previousFilter},1e3)):delete this.previousFilter):delete this.previousFilter}l&&
}}),e.placeholder=t(n.placeholder.element.call(e.element,e.currentItem)),e.currentItem.after(e.placeholder),n.placeholder.update(e,e.placeholder)},_contactContainers:function(e){var r,s,o,a,l,u,h,c,p,d,f=null,g=null;for(r=this.containers.length-1;r>=0;r--)if(!t.contains(this.currentItem[0],this.containers[r].element[0]))if(this._intersectsWith(this.containers[r].containerCache)){if(f&&t.contains(this.containers[r].element[0],f.element[0]))continue;f=this.containers[r],g=r}else this.containers[r].containerCache.over&&(this.containers[r]._trigger("out",e,this._uiHash(this)),this.containers[r].containerCache.over=0);if(f)if(1===this.containers.length)this.containers[g].containerCache.over||(this.containers[g]._trigger("over",e,this._uiHash(this)),this.containers[g].containerCache.over=1);else{for(o=1e4,a=null,d=f.floating||n(this.currentItem),l=d?"left":"top",u=d?"width":"height",h=this.positionAbs[l]+this.offset.click[l],s=this.items.length-1;s>=0;s--)t.contains(this.containers[g].element[0],this.items[s].item[0])&&this.items[s].item[0]!==this.currentItem[0]&&(!d||i(this.positionAbs.top+this.offset.click.top,this.items[s].top,this.items[s].height))&&(c=this.items[s].item.offset()[l],p=!1,Math.abs(c-h)>Math.abs(c+this.items[s][u]-h)&&(p=!0,c+=this.items[s][u]),Math.abs(c-h)<o&&(o=Math.abs(c-h),a=this.items[s],this.direction=p?"up":"down"));if(!a&&!this.options.dropOnEmpty)return;if(this.currentContainer===this.containers[g])return;a?this._rearrange(e,a,null,!0):this._rearrange(e,null,this.containers[g].element,!0),this._trigger("change",e,this._uiHash()),this.containers[g]._trigger("change",e,this._uiHash(this)),this.currentContainer=this.containers[g],this.options.placeholder.update(this.currentContainer,this.placeholder),this.containers[g]._trigger("over",e,this._uiHash(this)),this.containers[g].containerCache.over=1}},_createHelper:function(e){var i=this.options,n=t.isFunction(i.helper)?t(i.helper.apply(this.element[0],[e,this.currentItem])):"clone"===i.helper?this.currentItem.clone():this.currentItem;return n.parents("body").length||t("parent"!==i.appendTo?i.appendTo:this.currentItem[0].parentNode)[0].appendChild(n[0]),n[0]===this.currentItem[0]&&(this._storedCSS={width:this.currentItem[0].style.width,height:this.currentItem[0].style.height,position:this.currentItem.css("position"),top:this.currentItem.css("top"),left:this.currentItem.css("left")}),(!n[0].style.width||i.forceHelperSize)&&n.width(this.currentItem.width()),(!n[0].style.height||i.forceHelperSize)&&n.height(this.currentItem.height()),n},_adjustOffsetFromHelper:function(e){"string"==typeof e&&(e=e.split(" ")),t.isArray(e)&&(e={left:+e[0],top:+e[1]||0}),"left"in e&&(this.offset.click.left=e.left+this.margins.left),"right"in e&&(this.offset.click.left=this.helperProportions.width-e.right+this.margins.left),"top"in e&&(this.offset.click.top=e.top+this.margins.top),"bottom"in e&&(this.offset.click.top=this.helperProportions.height-e.bottom+this.margins.top)},_getParentOffset:function(){this.offsetParent=this.helper.offsetParent();var e=this.offsetParent.offset();return"absolute"===this.cssPosition&&this.scrollParent[0]!==document&&t.contains(this.scrollParent[0],this.offsetParent[0])&&(e.left+=this.scrollParent.scrollLeft(),e.top+=this.scrollParent.scrollTop()),(this.offsetParent[0]===document.body||this.offsetParent[0].tagName&&"html"===this.offsetParent[0].tagName.toLowerCase()&&t.ui.ie)&&(e={top:0,left:0}),{top:e.top+(parseInt(this.offsetParent.css("borderTopWidth"),10)||0),left:e.left+(parseInt(this.offsetParent.css("borderLeftWidth"),10)||0)}},_getRelativeOffset:function(){if("relative"===this.cssPosition){var t=this.currentItem.position();return{top:t.top-(parseInt(this.helper.css("top"),10)||0)+this.scrollParent.scrollTop(),left:t.left-(parseInt(this.helper.css("left"),10)||0)+this.scrollParent.scrollLeft()}}return{top:0,left:0}},_cacheMargins:function(){this.margins={left:parseInt(this.currentItem.css("marginLeft"),10)||0,top:parseInt(this.currentItem.css("marginTop"),10)||0}},_cacheHelperProportions:function(){this.helperProportions={width:this
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<p>I'm originally from North Carolina, and I've been hearing a lot of people talking about how often it's been raining recently. They're excited for any day that has sun.</p>
<p>So I got a bit curious: Has North Carolina over the past few months actually had more cloudy and rainy days recently than in previous years? This shouldn't be a particularly challenging task, but I'm interested to know if people's perceptions actually reflect reality.</p>
<p>The data we'll use comes from <a href="http://forecast.io/">forecast.io</a>, since they can give us a cloud cover percentage. I've gone ahead and retrieved the data to a pickle file, and included the <a href="#Generating-the-Forecast-file">code that was used to generate it</a>. First up: What was the average cloud cover in North Carolina during August - November, and how many days were cloudy? We're going to assume that a "cloudy" day is defined as any day in which the cloud cover is above 50%.</p>
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<div class=" highlight hl-ipython3"><pre><span class="n">city_forecasts</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="nb">open</span><span class="p">(</span><span class="s1">&#39;city_forecasts.p&#39;</span><span class="p">,</span> <span class="s1">&#39;rb&#39;</span><span class="p">))</span>
<span class="n">forecast_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">from_dict</span><span class="p">(</span><span class="n">city_forecasts</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span class="n">cary_forecast</span> <span class="o">=</span> <span class="n">forecast_df</span><span class="p">[</span><span class="s1">&#39;cary&#39;</span><span class="p">]</span>
<span class="n">years</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1990</span><span class="p">,</span> <span class="mi">2016</span><span class="p">)</span>
<span class="n">months</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="mi">7</span><span class="p">,</span> <span class="mi">12</span><span class="p">)</span>
<span class="n">months_str</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;July&#39;</span><span class="p">,</span> <span class="s1">&#39;August&#39;</span><span class="p">,</span> <span class="s1">&#39;September&#39;</span><span class="p">,</span> <span class="s1">&#39;October&#39;</span><span class="p">,</span> <span class="s1">&#39;November&#39;</span><span class="p">]</span>
<span class="k">def</span> <span class="nf">safe_cover</span><span class="p">(</span><span class="n">frame</span><span class="p">):</span>
<span class="k">if</span> <span class="n">frame</span> <span class="ow">and</span> <span class="s1">&#39;cloudCover&#39;</span> <span class="ow">in</span> <span class="n">frame</span><span class="p">:</span>
<span class="k">return</span> <span class="n">frame</span><span class="p">[</span><span class="s1">&#39;cloudCover&#39;</span><span class="p">]</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">NaN</span>
<span class="k">def</span> <span class="nf">monthly_avg_cloudcover</span><span class="p">(</span><span class="n">year</span><span class="p">,</span> <span class="n">month</span><span class="p">):</span>
<span class="n">dates</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DatetimeIndex</span><span class="p">(</span><span class="n">start</span><span class="o">=</span><span class="n">datetime</span><span class="p">(</span><span class="n">year</span><span class="p">,</span> <span class="n">month</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">12</span><span class="p">),</span>
<span class="n">end</span><span class="o">=</span><span class="n">datetime</span><span class="p">(</span><span class="n">year</span><span class="p">,</span> <span class="n">month</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">12</span><span class="p">),</span>
<span class="n">freq</span><span class="o">=</span><span class="s1">&#39;D&#39;</span><span class="p">,</span> <span class="n">closed</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">)</span>
<span class="n">cloud_cover_vals</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">safe_cover</span><span class="p">(</span><span class="n">cary_forecast</span><span class="p">[</span><span class="n">x</span><span class="p">][</span><span class="s1">&#39;currently&#39;</span><span class="p">]),</span> <span class="n">dates</span><span class="p">))</span>
<span class="n">cloud_cover_samples</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">filter</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span> <span class="ow">is</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">NaN</span><span class="p">,</span> <span class="n">cloud_cover_vals</span><span class="p">)))</span>
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">cloud_cover_vals</span><span class="p">),</span> <span class="n">cloud_cover_samples</span>
<span class="n">monthly_cover_vals</span> <span class="o">=</span> <span class="p">[[</span><span class="n">monthly_avg_cloudcover</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">m</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">y</span> <span class="ow">in</span> <span class="n">years</span><span class="p">]</span> <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="n">months</span><span class="p">]</span>
<span class="n">f</span> <span class="o">=</span> <span class="n">figure</span><span class="p">(</span><span class="n">title</span><span class="o">=</span><span class="s1">&#39;Monthly Average Cloud Cover&#39;</span><span class="p">,</span>
<span class="n">x_range</span><span class="o">=</span><span class="p">(</span><span class="mi">1990</span><span class="p">,</span> <span class="mi">2015</span><span class="p">),</span>
<span class="n">x_axis_label</span><span class="o">=</span><span class="s1">&#39;Year&#39;</span><span class="p">)</span>
<span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">months</span><span class="p">)):</span>
<span class="n">f</span><span class="o">.</span><span class="n">line</span><span class="p">(</span><span class="n">years</span><span class="p">,</span> <span class="n">monthly_cover_vals</span><span class="p">[</span><span class="n">x</span><span class="p">],</span> <span class="n">legend</span><span class="o">=</span><span class="n">months_str</span><span class="p">[</span><span class="n">x</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="n">Palette</span><span class="p">[</span><span class="n">x</span><span class="p">])</span>
<span class="n">show</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
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<p>As we can see from the chart above, on the whole the monthly average cloud cover has been generally trending down over time. The average cloud cover is also lower than it was last year - it seems people are mostly just complaining. There are some data issues that start in 2012 that we need to be aware of - the cloud cover percentage doesn't exist for all days. Even so, the data that we have seems to reflect the wider trend, so we'll assume for now that the missing data doesn't skew our results.</p>
<p>There's one more metric we want to check though - how many cloudy days were there? This is probably a better gauge of sentiment than the average monthly cover.</p>
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<div class=" highlight hl-ipython3"><pre><span class="k">def</span> <span class="nf">monthly_cloudy_days</span><span class="p">(</span><span class="n">year</span><span class="p">,</span> <span class="n">month</span><span class="p">):</span>
<span class="n">dates</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DatetimeIndex</span><span class="p">(</span><span class="n">start</span><span class="o">=</span><span class="n">datetime</span><span class="p">(</span><span class="n">year</span><span class="p">,</span> <span class="n">month</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">12</span><span class="p">),</span>
<span class="n">end</span><span class="o">=</span><span class="n">datetime</span><span class="p">(</span><span class="n">year</span><span class="p">,</span> <span class="n">month</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">12</span><span class="p">),</span>
<span class="n">freq</span><span class="o">=</span><span class="s1">&#39;D&#39;</span><span class="p">,</span> <span class="n">closed</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">)</span>
<span class="n">cloud_cover_vals</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">safe_cover</span><span class="p">(</span><span class="n">cary_forecast</span><span class="p">[</span><span class="n">x</span><span class="p">][</span><span class="s1">&#39;currently&#39;</span><span class="p">]),</span> <span class="n">dates</span><span class="p">))</span>
<span class="n">cloud_cover_samples</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">filter</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span> <span class="ow">is</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">NaN</span><span class="p">,</span> <span class="n">cloud_cover_vals</span><span class="p">)))</span>
<span class="n">cloudy_days</span> <span class="o">=</span> <span class="p">[</span><span class="n">cover</span> <span class="o">&gt;</span> <span class="o">.</span><span class="mi">5</span> <span class="k">for</span> <span class="n">cover</span> <span class="ow">in</span> <span class="n">cloud_cover_vals</span><span class="p">]</span>
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">count_nonzero</span><span class="p">(</span><span class="n">cloudy_days</span><span class="p">),</span> <span class="n">cloud_cover_samples</span>
<span class="n">monthly_days_vals</span> <span class="o">=</span> <span class="p">[[</span><span class="n">monthly_cloudy_days</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">m</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">y</span> <span class="ow">in</span> <span class="n">years</span><span class="p">]</span> <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="n">months</span><span class="p">]</span>
<span class="n">monthly_cover_samples</span> <span class="o">=</span> <span class="p">[[</span><span class="n">monthly_cloudy_days</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">m</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">y</span> <span class="ow">in</span> <span class="n">years</span><span class="p">]</span> <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="n">months</span><span class="p">]</span>
<span class="n">f</span> <span class="o">=</span> <span class="n">figure</span><span class="p">(</span><span class="n">title</span><span class="o">=</span><span class="s1">&#39;Monthly Cloudy Days&#39;</span><span class="p">,</span>
<span class="n">x_range</span><span class="o">=</span><span class="p">(</span><span class="mi">1990</span><span class="p">,</span> <span class="mi">2015</span><span class="p">),</span>
<span class="n">x_axis_label</span><span class="o">=</span><span class="s1">&#39;Year&#39;</span><span class="p">)</span>
<span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">months</span><span class="p">)):</span>
<span class="n">f</span><span class="o">.</span><span class="n">line</span><span class="p">(</span><span class="n">years</span><span class="p">,</span> <span class="n">monthly_days_vals</span><span class="p">[</span><span class="n">x</span><span class="p">],</span> <span class="n">legend</span><span class="o">=</span><span class="n">months_str</span><span class="p">[</span><span class="n">x</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="n">Palette</span><span class="p">[</span><span class="n">x</span><span class="p">])</span>
<span class="n">show</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
<span class="n">f</span> <span class="o">=</span> <span class="n">figure</span><span class="p">(</span><span class="n">title</span><span class="o">=</span><span class="s1">&#39;Monthly Cloud Cover Samples&#39;</span><span class="p">,</span>
<span class="n">x_range</span><span class="o">=</span><span class="p">(</span><span class="mi">1990</span><span class="p">,</span> <span class="mi">2015</span><span class="p">),</span>
<span class="n">x_axis_label</span><span class="o">=</span><span class="s1">&#39;Year&#39;</span><span class="p">,</span>
<span class="n">height</span><span class="o">=</span><span class="mi">300</span><span class="p">)</span>
<span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">months</span><span class="p">)):</span>
<span class="n">f</span><span class="o">.</span><span class="n">line</span><span class="p">(</span><span class="n">years</span><span class="p">,</span> <span class="n">monthly_cover_samples</span><span class="p">[</span><span class="n">x</span><span class="p">],</span> <span class="n">legend</span><span class="o">=</span><span class="n">months_str</span><span class="p">[</span><span class="n">x</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="n">Palette</span><span class="p">[</span><span class="n">x</span><span class="p">])</span>
<span class="n">show</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
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<p>On the whole, the number of cloudy days seems to reflect the trend with average cloud cover - it's actually becoming more sunny as time progresses. That said, we need to be careful in how we view this number - because there weren't as many samples in 2015 as previous years, the number of days can get thrown off. In context though, even if most days not recorded were in fact cloudy, the overall count for 2015 would still be lower than previous years.</p>
<h1 id="Tracking-Precipitation-Chances">Tracking Precipitation Chances<a class="anchor-link" href="#Tracking-Precipitation-Chances">&#182;</a></h1><p>In addition to checking cloud cover, I wanted to check precipitation data as well - what is the average precipitation chance over a month, and how many days during a month is rain likely? The thinking is that days with a high-precipitation chance will also be days in which it is cloudy or depressing.</p>
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<div class=" highlight hl-ipython3"><pre><span class="k">def</span> <span class="nf">safe_precip</span><span class="p">(</span><span class="n">frame</span><span class="p">):</span>
<span class="k">if</span> <span class="n">frame</span> <span class="ow">and</span> <span class="s1">&#39;precipProbability&#39;</span> <span class="ow">in</span> <span class="n">frame</span><span class="p">:</span>
<span class="k">return</span> <span class="n">frame</span><span class="p">[</span><span class="s1">&#39;precipProbability&#39;</span><span class="p">]</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">NaN</span>
<span class="k">def</span> <span class="nf">monthly_avg_precip</span><span class="p">(</span><span class="n">year</span><span class="p">,</span> <span class="n">month</span><span class="p">):</span>
<span class="n">dates</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DatetimeIndex</span><span class="p">(</span><span class="n">start</span><span class="o">=</span><span class="n">datetime</span><span class="p">(</span><span class="n">year</span><span class="p">,</span> <span class="n">month</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">12</span><span class="p">),</span>
<span class="n">end</span><span class="o">=</span><span class="n">datetime</span><span class="p">(</span><span class="n">year</span><span class="p">,</span> <span class="n">month</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">12</span><span class="p">),</span>
<span class="n">freq</span><span class="o">=</span><span class="s1">&#39;D&#39;</span><span class="p">,</span> <span class="n">closed</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">)</span>
<span class="n">precip_vals</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">safe_precip</span><span class="p">(</span><span class="n">cary_forecast</span><span class="p">[</span><span class="n">x</span><span class="p">][</span><span class="s1">&#39;currently&#39;</span><span class="p">]),</span> <span class="n">dates</span><span class="p">))</span>
<span class="n">precip_samples</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">filter</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span> <span class="ow">is</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">NaN</span><span class="p">,</span> <span class="n">precip_vals</span><span class="p">)))</span>
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">precip_vals</span><span class="p">),</span> <span class="n">precip_samples</span>
<span class="n">monthly_avg_precip_vals</span> <span class="o">=</span> <span class="p">[[</span><span class="n">monthly_avg_precip</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">m</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">y</span> <span class="ow">in</span> <span class="n">years</span><span class="p">]</span> <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="n">months</span><span class="p">]</span>
<span class="n">f</span> <span class="o">=</span> <span class="n">figure</span><span class="p">(</span><span class="n">title</span><span class="o">=</span><span class="s1">&#39;Monthly Average Precipitation Chance&#39;</span><span class="p">,</span>
<span class="n">x_range</span><span class="o">=</span><span class="p">(</span><span class="mi">1990</span><span class="p">,</span> <span class="mi">2015</span><span class="p">),</span>
<span class="n">x_axis_label</span><span class="o">=</span><span class="s1">&#39;Year&#39;</span><span class="p">)</span>
<span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">months</span><span class="p">)):</span>
<span class="n">f</span><span class="o">.</span><span class="n">line</span><span class="p">(</span><span class="n">years</span><span class="p">,</span> <span class="n">monthly_avg_precip_vals</span><span class="p">[</span><span class="n">x</span><span class="p">],</span> <span class="n">legend</span><span class="o">=</span><span class="n">months_str</span><span class="p">[</span><span class="n">x</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="n">Palette</span><span class="p">[</span><span class="n">x</span><span class="p">])</span>
<span class="n">show</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
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<p>As we can see from the chart, the average chance of precipitation over a month more or less stays within a band of 0 - .1 for all months over all years. This is further evidence that the past few months are no more cloudy or rainy than previous years. Like the cloud cover though, we still want to get a count of all the rainy days, in addition to the average chance. We'll define a "rainy day" as any day in which the chance of rain is greater than 25%.</p>
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<div class=" highlight hl-ipython3"><pre><span class="k">def</span> <span class="nf">monthly_rainy_days</span><span class="p">(</span><span class="n">year</span><span class="p">,</span> <span class="n">month</span><span class="p">):</span>
<span class="n">dates</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DatetimeIndex</span><span class="p">(</span><span class="n">start</span><span class="o">=</span><span class="n">datetime</span><span class="p">(</span><span class="n">year</span><span class="p">,</span> <span class="n">month</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">12</span><span class="p">),</span>
<span class="n">end</span><span class="o">=</span><span class="n">datetime</span><span class="p">(</span><span class="n">year</span><span class="p">,</span> <span class="n">month</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">12</span><span class="p">),</span>
<span class="n">freq</span><span class="o">=</span><span class="s1">&#39;D&#39;</span><span class="p">,</span> <span class="n">closed</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">)</span>
<span class="n">precip_prob_vals</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">safe_precip</span><span class="p">(</span><span class="n">cary_forecast</span><span class="p">[</span><span class="n">x</span><span class="p">][</span><span class="s1">&#39;currently&#39;</span><span class="p">]),</span> <span class="n">dates</span><span class="p">))</span>
<span class="n">precip_prob_samples</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">filter</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span> <span class="ow">is</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">NaN</span><span class="p">,</span> <span class="n">precip_prob_vals</span><span class="p">)))</span>
<span class="n">precip_days</span> <span class="o">=</span> <span class="p">[</span><span class="n">prob</span> <span class="o">&gt;</span> <span class="o">.</span><span class="mi">25</span> <span class="k">for</span> <span class="n">prob</span> <span class="ow">in</span> <span class="n">precip_prob_vals</span><span class="p">]</span>
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">count_nonzero</span><span class="p">(</span><span class="n">precip_days</span><span class="p">),</span> <span class="n">precip_prob_samples</span>
<span class="n">monthly_precip_days_vals</span> <span class="o">=</span> <span class="p">[[</span><span class="n">monthly_rainy_days</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">m</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">y</span> <span class="ow">in</span> <span class="n">years</span><span class="p">]</span> <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="n">months</span><span class="p">]</span>
<span class="n">monthly_precip_samples</span> <span class="o">=</span> <span class="p">[[</span><span class="n">monthly_rainy_days</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">m</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">y</span> <span class="ow">in</span> <span class="n">years</span><span class="p">]</span> <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="n">months</span><span class="p">]</span>
<span class="n">f</span> <span class="o">=</span> <span class="n">figure</span><span class="p">(</span><span class="n">title</span><span class="o">=</span><span class="s1">&#39;Monthly Rainy Days&#39;</span><span class="p">,</span>
<span class="n">x_range</span><span class="o">=</span><span class="p">(</span><span class="mi">1990</span><span class="p">,</span> <span class="mi">2015</span><span class="p">),</span>
<span class="n">x_axis_label</span><span class="o">=</span><span class="s1">&#39;Year&#39;</span><span class="p">)</span>
<span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">months</span><span class="p">)):</span>
<span class="n">f</span><span class="o">.</span><span class="n">line</span><span class="p">(</span><span class="n">years</span><span class="p">,</span> <span class="n">monthly_precip_days_vals</span><span class="p">[</span><span class="n">x</span><span class="p">],</span> <span class="n">legend</span><span class="o">=</span><span class="n">months_str</span><span class="p">[</span><span class="n">x</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="n">Palette</span><span class="p">[</span><span class="n">x</span><span class="p">])</span>
<span class="n">show</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
<span class="n">f</span> <span class="o">=</span> <span class="n">figure</span><span class="p">(</span><span class="n">title</span><span class="o">=</span><span class="s1">&#39;Monthly Rainy Days Samples&#39;</span><span class="p">,</span>
<span class="n">x_range</span><span class="o">=</span><span class="p">(</span><span class="mi">1990</span><span class="p">,</span> <span class="mi">2015</span><span class="p">),</span>
<span class="n">x_axis_label</span><span class="o">=</span><span class="s1">&#39;Year&#39;</span><span class="p">,</span>
<span class="n">height</span><span class="o">=</span><span class="mi">300</span><span class="p">)</span>
<span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">months</span><span class="p">)):</span>
<span class="n">f</span><span class="o">.</span><span class="n">line</span><span class="p">(</span><span class="n">years</span><span class="p">,</span> <span class="n">monthly_precip_samples</span><span class="p">[</span><span class="n">x</span><span class="p">],</span> <span class="n">legend</span><span class="o">=</span><span class="n">months_str</span><span class="p">[</span><span class="n">x</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="n">Palette</span><span class="p">[</span><span class="n">x</span><span class="p">])</span>
<span class="n">show</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
</pre></div>
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<p>After trying to find the number of days that are rainy, we can see that November hit its max value for rainy days in 2015. However, that value is 6, as compared to a previous maximum of 5. While it is a new record, the value isn't actually all that different. And for other months, the values are mostly in-line with the averages.</p>
<h1 id="Summary-and-Conclusions">Summary and Conclusions<a class="anchor-link" href="#Summary-and-Conclusions">&#182;</a></h1><p>After having looked at forecast data for Cary, it appears that the months of July - November this year in terms of weather were at worst on par with prior years, if not slightly more sunny. This seems to be a case of confirmation bias: someone complains about a string of cloudy or rainy days, and suddenly you start noticing them more.</p>
<p>While this analysis doesn't take into account other areas of North Carolina, my initial guess would be to assume that other areas also will show similar results: nothing interesting is happening. Maybe that will be for another blog post later!</p>
<p>Coming soon: I'll compare rain/cloud conditions in North Carolina to some other places in the U.S.!</p>
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<h1 id="Generating-the-Forecast-file">Generating the Forecast file<a class="anchor-link" href="#Generating-the-Forecast-file">&#182;</a></h1><p>The following code was generates the file that was used throughout the blog post. Please note that I'm retrieving data for other cities to use in a future blog post, only Cary data was used for this post.</p>
<div class="highlight"><pre><span class="kn">import</span> <span class="nn">pandas</span> <span class="kn">as</span> <span class="nn">pd</span>
<span class="kn">from</span> <span class="nn">functools</span> <span class="kn">import</span> <span class="nb">reduce</span>
<span class="kn">import</span> <span class="nn">requests</span>
<span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">datetime</span>
<span class="c1"># Coordinate data from http://itouchmap.com/latlong.html</span>
<span class="n">cary_loc</span> <span class="o">=</span> <span class="p">(</span><span class="mf">35.79154</span><span class="p">,</span><span class="o">-</span><span class="mf">78.781117</span><span class="p">)</span>
<span class="n">nyc_loc</span> <span class="o">=</span> <span class="p">(</span><span class="mf">40.78306</span><span class="p">,</span><span class="o">-</span><span class="mf">73.971249</span><span class="p">)</span>
<span class="n">seattle_loc</span> <span class="o">=</span> <span class="p">(</span><span class="mf">47.60621</span><span class="p">,</span><span class="o">-</span><span class="mf">122.332071</span><span class="p">)</span>
<span class="n">binghamton_loc</span> <span class="o">=</span> <span class="p">(</span><span class="mf">42.098687</span><span class="p">,</span><span class="o">-</span><span class="mf">75.917974</span><span class="p">)</span>
<span class="n">cities</span> <span class="o">=</span> <span class="p">{</span>
<span class="s1">&#39;cary&#39;</span><span class="p">:</span> <span class="n">cary_loc</span><span class="p">,</span>
<span class="s1">&#39;nyc&#39;</span><span class="p">:</span> <span class="n">nyc_loc</span><span class="p">,</span>
<span class="s1">&#39;seattle&#39;</span><span class="p">:</span> <span class="n">seattle_loc</span><span class="p">,</span>
<span class="s1">&#39;binghamton&#39;</span><span class="p">:</span> <span class="n">binghamton_loc</span>
<span class="p">}</span>
<span class="n">apikey</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span> <span class="c1"># My super-secret API Key</span>
<span class="k">def</span> <span class="nf">get_forecast</span><span class="p">(</span><span class="n">lat</span><span class="p">,</span> <span class="nb">long</span><span class="p">,</span> <span class="n">date</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
<span class="n">forecast_base</span> <span class="o">=</span> <span class="s2">&quot;https://api.forecast.io/forecast/&quot;</span>
<span class="k">if</span> <span class="n">date</span> <span class="ow">is</span> <span class="bp">None</span><span class="p">:</span>
<span class="n">url</span> <span class="o">=</span> <span class="n">forecast_base</span> <span class="o">+</span> <span class="n">apikey</span> <span class="o">+</span> <span class="s1">&#39;/{},{}&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">lat</span><span class="p">,</span> <span class="nb">long</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">epoch</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">date</span><span class="o">.</span><span class="n">timestamp</span><span class="p">())</span>
<span class="n">url</span> <span class="o">=</span> <span class="n">forecast_base</span> <span class="o">+</span> <span class="n">apikey</span> <span class="o">+</span> <span class="s1">&#39;/{},{},{}&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">lat</span><span class="p">,</span> <span class="nb">long</span><span class="p">,</span> <span class="n">epoch</span><span class="p">)</span>
<span class="k">return</span> <span class="n">requests</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">url</span><span class="p">)</span><span class="o">.</span><span class="n">json</span><span class="p">()</span>
<span class="n">years</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1990</span><span class="p">,</span><span class="mi">2016</span><span class="p">)</span>
<span class="c1"># For datetimes, the 12 is for getting the weather at noon.</span>
<span class="c1"># We&#39;re doing this over midnight because we&#39;re more concerned</span>
<span class="c1"># with what people see, and people don&#39;t typically see the weather</span>
<span class="c1"># at midnight.</span>
<span class="n">dt_indices</span> <span class="o">=</span> <span class="p">[</span><span class="n">pd</span><span class="o">.</span><span class="n">date_range</span><span class="p">(</span><span class="n">start</span><span class="o">=</span><span class="n">datetime</span><span class="p">(</span><span class="n">year</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">12</span><span class="p">),</span>
<span class="n">end</span><span class="o">=</span><span class="n">datetime</span><span class="p">(</span><span class="n">year</span><span class="p">,</span> <span class="mi">11</span><span class="p">,</span> <span class="mi">30</span><span class="p">,</span> <span class="mi">12</span><span class="p">))</span>
<span class="k">for</span> <span class="n">year</span> <span class="ow">in</span> <span class="n">years</span><span class="p">]</span>
<span class="n">dt_merge</span> <span class="o">=</span> <span class="nb">reduce</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="n">x</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="n">y</span><span class="p">),</span> <span class="n">dt_indices</span><span class="p">)</span>
<span class="c1"># Because we have to pay a little bit to use the API, we use for loops here</span>
<span class="c1"># instead of a comprehension - if something breaks, we want to preserve the</span>
<span class="c1"># data already retrieved</span>
<span class="n">city_forecasts</span> <span class="o">=</span> <span class="p">{}</span>
<span class="k">for</span> <span class="n">city</span><span class="p">,</span> <span class="n">loc</span> <span class="ow">in</span> <span class="n">cities</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="k">print</span><span class="p">(</span><span class="s2">&quot;Retrieving data for {} starting at {}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">city</span><span class="p">,</span>
<span class="n">datetime</span><span class="o">.</span><span class="n">now</span><span class="p">()</span><span class="o">.</span><span class="n">strftime</span><span class="p">(</span><span class="s2">&quot;%I:%M:%S %p&quot;</span><span class="p">)))</span>
<span class="k">for</span> <span class="n">dt</span> <span class="ow">in</span> <span class="n">dt_merge</span><span class="p">:</span>
<span class="k">try</span><span class="p">:</span>
<span class="n">city_forecasts</span><span class="p">[(</span><span class="n">city</span><span class="p">,</span> <span class="n">dt</span><span class="p">)]</span> <span class="o">=</span> <span class="n">get_forecast</span><span class="p">(</span><span class="o">*</span><span class="n">loc</span><span class="p">,</span> <span class="n">dt</span><span class="p">)</span>
<span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
<span class="k">print</span><span class="p">(</span><span class="n">e</span><span class="p">)</span>
<span class="n">city_forecasts</span><span class="p">[(</span><span class="n">city</span><span class="p">,</span> <span class="n">dt</span><span class="p">)]</span> <span class="o">=</span> <span class="bp">None</span>
<span class="k">print</span><span class="p">(</span><span class="s2">&quot;End forecast retrieval: {}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">datetime</span><span class="o">.</span><span class="n">now</span><span class="p">()</span><span class="o">.</span><span class="n">strftime</span><span class="p">(</span><span class="s2">&quot;%I:%M:%S %p&quot;</span><span class="p">)))</span>
<span class="kn">import</span> <span class="nn">pickle</span>
<span class="n">pickle</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="n">city_forecasts</span><span class="p">,</span> <span class="nb">open</span><span class="p">(</span><span class="s1">&#39;city_forecasts.p&#39;</span><span class="p">,</span> <span class="s1">&#39;wb&#39;</span><span class="p">))</span>
<span class="c1">### Output:</span>
<span class="c1"># Retrieving data for binghamton starting at 05:13:42 PM</span>
<span class="c1"># Retrieving data for seattle starting at 05:30:51 PM</span>
<span class="c1"># Retrieving data for nyc starting at 05:48:30 PM</span>
<span class="c1"># Retrieving data for cary starting at 06:08:32 PM</span>
<span class="c1"># End forecast retrieval: 06:25:21 PM</span>
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