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simplexample.py

Source:simplexample.py

...17    ### Argentina Top 200 Charts18    danceability200 = argentina_top200[['date', 'danceability']]19    danceability200.set_index('date', inplace=True)20    danceability200 = danceability200.dropna()21    danceability200_mean = danceability200.groupby('date').mean()22    23    energy200 = argentina_top200[['date', 'energy']]24    energy200.set_index('date', inplace=True)25    energy200 = energy200.dropna()26    energy200_mean = energy200.groupby('date').mean()27    key200 = argentina_top200[['date', 'key']]28    key200.set_index('date', inplace=True)29    key200 = key200.dropna()30    key200_mean = key200.groupby('date').mean()31    loud200 = argentina_top200[['date', 'loudness']]32    loud200.set_index('date', inplace=True)33    loud200 = loud200.dropna()34    loud200_mean = loud200.groupby('date').mean()35    mode200 = argentina_top200[['date', 'mode']]36    mode200.set_index('date', inplace=True)37    mode200 = mode200.dropna()38    mode200_mean = mode200.groupby('date').mean()39    speech200 = argentina_top200[['date', 'speechiness']]40    speech200.set_index('date', inplace=True)41    speech200 = speech200.dropna()42    speech200_mean = speech200.groupby('date').mean()43    acou200 = argentina_top200[['date', 'acousticness']]44    acou200.set_index('date', inplace=True)45    acou200 = acou200.dropna()46    acou200_mean = acou200.groupby('date').mean()47    instr200 = argentina_top200[['date', 'instrumentalness']]48    instr200.set_index('date', inplace=True)49    instr200 = instr200.dropna()50    instr200_mean = instr200.groupby('date').mean()51    live200 = argentina_top200[['date', 'liveness']]52    live200.set_index('date', inplace=True)53    live200 = live200.dropna()54    live200_mean = live200.groupby('date').mean()55    valence200 = argentina_top200[['date', 'valence']]56    valence200.set_index('date', inplace=True)57    valence200 = valence200.dropna()58    valence200_mean = valence200.groupby('date').mean()59    tempo200 = argentina_top200[['date', 'tempo']]60    tempo200.set_index('date', inplace=True)61    tempo200 = tempo200.dropna()62    tempo200_mean = tempo200.groupby('date').mean()63    duration200 = argentina_top200[['date', 'duration_ms']]64    duration200.set_index('date', inplace=True)65    duration200 = duration200.dropna()66    duration200_mean = duration200.groupby('date').mean()67    x_vals=[danceability200_mean.index, energy200_mean.index,key200_mean.index,loud200_mean.index, mode200_mean.index,speech200_mean.index,acou200_mean.index,instr200_mean.index,live200_mean.index,valence200_mean.index,tempo200_mean.index,duration200_mean.index]68    y_vals=['danceability','energy','key','loudness','mode','speechiness','acousticness','instrumentalness','liveness','valence','tempo','duration_ms']69    return x_vals, y_vals, argentina_top20070def australia():71    # read csv data for each country into associated dataframe72    argentina = pd.read_csv('/workspace/plotyDashWebapp/SpotifyDataAnalysis/home/dash_apps/finished_apps/CSVFile/Australia.csv')73    # drop unnamed column from each dataframe74    argentina = argentina.drop(columns=['Unnamed: 0'])75    argentina['date'] = pd.to_datetime(argentina['date'], format='%Y-%m-%d')76    argentina['year'] = pd.DatetimeIndex(argentina['date']).year77    ## Seasonality in Argentina based on Top 200 and Viral 50 charts respectively78    argentina_top200 = argentina[argentina['chart'] == 'top200']79    argentina_viral50 = argentina[argentina['chart'] == 'viral50']80    ### Argentina Top 200 Charts81    danceability200 = argentina_top200[['date', 'danceability']]82    danceability200.set_index('date', inplace=True)83    danceability200 = danceability200.dropna()84    danceability200_mean = danceability200.groupby('date').mean()85    86    energy200 = argentina_top200[['date', 'energy']]87    energy200.set_index('date', inplace=True)88    energy200 = energy200.dropna()89    energy200_mean = energy200.groupby('date').mean()90    key200 = argentina_top200[['date', 'key']]91    key200.set_index('date', inplace=True)92    key200 = key200.dropna()93    key200_mean = key200.groupby('date').mean()94    loud200 = argentina_top200[['date', 'loudness']]95    loud200.set_index('date', inplace=True)96    loud200 = loud200.dropna()97    loud200_mean = loud200.groupby('date').mean()98    mode200 = argentina_top200[['date', 'mode']]99    mode200.set_index('date', inplace=True)100    mode200 = mode200.dropna()101    mode200_mean = mode200.groupby('date').mean()102    speech200 = argentina_top200[['date', 'speechiness']]103    speech200.set_index('date', inplace=True)104    speech200 = speech200.dropna()105    speech200_mean = speech200.groupby('date').mean()106    acou200 = argentina_top200[['date', 'acousticness']]107    acou200.set_index('date', inplace=True)108    acou200 = acou200.dropna()109    acou200_mean = acou200.groupby('date').mean()110    instr200 = argentina_top200[['date', 'instrumentalness']]111    instr200.set_index('date', inplace=True)112    instr200 = instr200.dropna()113    instr200_mean = instr200.groupby('date').mean()114    live200 = argentina_top200[['date', 'liveness']]115    live200.set_index('date', inplace=True)116    live200 = live200.dropna()117    live200_mean = live200.groupby('date').mean()118    valence200 = argentina_top200[['date', 'valence']]119    valence200.set_index('date', inplace=True)120    valence200 = valence200.dropna()121    valence200_mean = valence200.groupby('date').mean()122    tempo200 = argentina_top200[['date', 'tempo']]123    tempo200.set_index('date', inplace=True)124    tempo200 = tempo200.dropna()125    tempo200_mean = tempo200.groupby('date').mean()126    duration200 = argentina_top200[['date', 'duration_ms']]127    duration200.set_index('date', inplace=True)128    duration200 = duration200.dropna()129    duration200_mean = duration200.groupby('date').mean()130    x_vals=[danceability200_mean.index, energy200_mean.index,key200_mean.index,loud200_mean.index, mode200_mean.index,speech200_mean.index,acou200_mean.index,instr200_mean.index,live200_mean.index,valence200_mean.index,tempo200_mean.index,duration200_mean.index]131    y_vals=['danceability','energy','key','loudness','mode','speechiness','acousticness','instrumentalness','liveness','valence','tempo','duration_ms']132    return x_vals, y_vals, argentina_top200133def england():134    # read csv data for each country into associated dataframe135    argentina = pd.read_csv('/workspace/plotyDashWebapp/SpotifyDataAnalysis/home/dash_apps/finished_apps/CSVFile/England.csv')136    # drop unnamed column from each dataframe137    argentina = argentina.drop(columns=['Unnamed: 0'])138    argentina['date'] = pd.to_datetime(argentina['date'], format='%Y-%m-%d')139    argentina['year'] = pd.DatetimeIndex(argentina['date']).year140    ## Seasonality in Argentina based on Top 200 and Viral 50 charts respectively141    argentina_top200 = argentina[argentina['chart'] == 'top200']142    argentina_viral50 = argentina[argentina['chart'] == 'viral50']143    ### Argentina Top 200 Charts144    danceability200 = argentina_top200[['date', 'danceability']]145    danceability200.set_index('date', inplace=True)146    danceability200 = danceability200.dropna()147    danceability200_mean = danceability200.groupby('date').mean()148    149    energy200 = argentina_top200[['date', 'energy']]150    energy200.set_index('date', inplace=True)151    energy200 = energy200.dropna()152    energy200_mean = energy200.groupby('date').mean()153    key200 = argentina_top200[['date', 'key']]154    key200.set_index('date', inplace=True)155    key200 = key200.dropna()156    key200_mean = key200.groupby('date').mean()157    loud200 = argentina_top200[['date', 'loudness']]158    loud200.set_index('date', inplace=True)159    loud200 = loud200.dropna()160    loud200_mean = loud200.groupby('date').mean()161    mode200 = argentina_top200[['date', 'mode']]162    mode200.set_index('date', inplace=True)163    mode200 = mode200.dropna()164    mode200_mean = mode200.groupby('date').mean()165    speech200 = argentina_top200[['date', 'speechiness']]166    speech200.set_index('date', inplace=True)167    speech200 = speech200.dropna()168    speech200_mean = speech200.groupby('date').mean()169    acou200 = argentina_top200[['date', 'acousticness']]170    acou200.set_index('date', inplace=True)171    acou200 = acou200.dropna()172    acou200_mean = acou200.groupby('date').mean()173    instr200 = argentina_top200[['date', 'instrumentalness']]174    instr200.set_index('date', inplace=True)175    instr200 = instr200.dropna()176    instr200_mean = instr200.groupby('date').mean()177    live200 = argentina_top200[['date', 'liveness']]178    live200.set_index('date', inplace=True)179    live200 = live200.dropna()180    live200_mean = live200.groupby('date').mean()181    valence200 = argentina_top200[['date', 'valence']]182    valence200.set_index('date', inplace=True)183    valence200 = valence200.dropna()184    valence200_mean = valence200.groupby('date').mean()185    tempo200 = argentina_top200[['date', 'tempo']]186    tempo200.set_index('date', inplace=True)187    tempo200 = tempo200.dropna()188    tempo200_mean = tempo200.groupby('date').mean()189    duration200 = argentina_top200[['date', 'duration_ms']]190    duration200.set_index('date', inplace=True)191    duration200 = duration200.dropna()192    duration200_mean = duration200.groupby('date').mean()193    x_vals=[danceability200_mean.index, energy200_mean.index,key200_mean.index,loud200_mean.index, mode200_mean.index,speech200_mean.index,acou200_mean.index,instr200_mean.index,live200_mean.index,valence200_mean.index,tempo200_mean.index,duration200_mean.index]194    y_vals=['danceability','energy','key','loudness','mode','speechiness','acousticness','instrumentalness','liveness','valence','tempo','duration_ms']195    return x_vals, y_vals, argentina_top200196def usa():197    # read csv data for each country into associated dataframe198    argentina = pd.read_csv('/workspace/plotyDashWebapp/SpotifyDataAnalysis/home/dash_apps/finished_apps/CSVFile/USA.csv')199    # drop unnamed column from each dataframe200    argentina = argentina.drop(columns=['Unnamed: 0'])201    argentina['date'] = pd.to_datetime(argentina['date'], format='%Y-%m-%d')202    argentina['year'] = pd.DatetimeIndex(argentina['date']).year203    ## Seasonality in Argentina based on Top 200 and Viral 50 charts respectively204    argentina_top200 = argentina[argentina['chart'] == 'top200']205    argentina_viral50 = argentina[argentina['chart'] == 'viral50']206    ### Argentina Top 200 Charts207    danceability200 = argentina_top200[['date', 'danceability']]208    danceability200.set_index('date', inplace=True)209    danceability200 = danceability200.dropna()210    danceability200_mean = danceability200.groupby('date').mean()211    212    energy200 = argentina_top200[['date', 'energy']]213    energy200.set_index('date', inplace=True)214    energy200 = energy200.dropna()215    energy200_mean = energy200.groupby('date').mean()216    key200 = argentina_top200[['date', 'key']]217    key200.set_index('date', inplace=True)218    key200 = key200.dropna()219    key200_mean = key200.groupby('date').mean()220    loud200 = argentina_top200[['date', 'loudness']]221    loud200.set_index('date', inplace=True)222    loud200 = loud200.dropna()223    loud200_mean = loud200.groupby('date').mean()224    mode200 = argentina_top200[['date', 'mode']]225    mode200.set_index('date', inplace=True)226    mode200 = mode200.dropna()227    mode200_mean = mode200.groupby('date').mean()228    speech200 = argentina_top200[['date', 'speechiness']]229    speech200.set_index('date', inplace=True)230    speech200 = speech200.dropna()231    speech200_mean = speech200.groupby('date').mean()232    acou200 = argentina_top200[['date', 'acousticness']]233    acou200.set_index('date', inplace=True)234    acou200 = acou200.dropna()235    acou200_mean = acou200.groupby('date').mean()236    instr200 = argentina_top200[['date', 'instrumentalness']]237    instr200.set_index('date', inplace=True)238    instr200 = instr200.dropna()239    instr200_mean = instr200.groupby('date').mean()240    live200 = argentina_top200[['date', 'liveness']]241    live200.set_index('date', inplace=True)242    live200 = live200.dropna()243    live200_mean = live200.groupby('date').mean()244    valence200 = argentina_top200[['date', 'valence']]245    valence200.set_index('date', inplace=True)246    valence200 = valence200.dropna()247    valence200_mean = valence200.groupby('date').mean()248    tempo200 = argentina_top200[['date', 'tempo']]249    tempo200.set_index('date', inplace=True)250    tempo200 = tempo200.dropna()251    tempo200_mean = tempo200.groupby('date').mean()252    duration200 = argentina_top200[['date', 'duration_ms']]253    duration200.set_index('date', inplace=True)254    duration200 = duration200.dropna()255    duration200_mean = duration200.groupby('date').mean()256    x_vals=[danceability200_mean.index, energy200_mean.index,key200_mean.index,loud200_mean.index, mode200_mean.index,speech200_mean.index,acou200_mean.index,instr200_mean.index,live200_mean.index,valence200_mean.index,tempo200_mean.index,duration200_mean.index]257    y_vals=['danceability','energy','key','loudness','mode','speechiness','acousticness','instrumentalness','liveness','valence','tempo','duration_ms']258    return x_vals, y_vals, argentina_top200259def mexico():260    # read csv data for each country into associated dataframe261    argentina = pd.read_csv('/workspace/plotyDashWebapp/SpotifyDataAnalysis/home/dash_apps/finished_apps/CSVFile/Mexico.csv')262    # drop unnamed column from each dataframe263    argentina = argentina.drop(columns=['Unnamed: 0'])264    argentina['date'] = pd.to_datetime(argentina['date'], format='%Y-%m-%d')265    argentina['year'] = pd.DatetimeIndex(argentina['date']).year266    ## Seasonality in Argentina based on Top 200 and Viral 50 charts respectively267    argentina_top200 = argentina[argentina['chart'] == 'top200']268    argentina_viral50 = argentina[argentina['chart'] == 'viral50']269    ### Argentina Top 200 Charts270    danceability200 = argentina_top200[['date', 'danceability']]271    danceability200.set_index('date', inplace=True)272    danceability200 = danceability200.dropna()273    danceability200_mean = danceability200.groupby('date').mean()274    275    energy200 = argentina_top200[['date', 'energy']]276    energy200.set_index('date', inplace=True)277    energy200 = energy200.dropna()278    energy200_mean = energy200.groupby('date').mean()279    key200 = argentina_top200[['date', 'key']]280    key200.set_index('date', inplace=True)281    key200 = key200.dropna()282    key200_mean = key200.groupby('date').mean()283    loud200 = argentina_top200[['date', 'loudness']]284    loud200.set_index('date', inplace=True)285    loud200 = loud200.dropna()286    loud200_mean = loud200.groupby('date').mean()287    mode200 = argentina_top200[['date', 'mode']]288    mode200.set_index('date', inplace=True)289    mode200 = mode200.dropna()290    mode200_mean = mode200.groupby('date').mean()291    speech200 = argentina_top200[['date', 'speechiness']]292    speech200.set_index('date', inplace=True)293    speech200 = speech200.dropna()294    speech200_mean = speech200.groupby('date').mean()295    acou200 = argentina_top200[['date', 'acousticness']]296    acou200.set_index('date', inplace=True)297    acou200 = acou200.dropna()298    acou200_mean = acou200.groupby('date').mean()299    instr200 = argentina_top200[['date', 'instrumentalness']]300    instr200.set_index('date', inplace=True)301    instr200 = instr200.dropna()302    instr200_mean = instr200.groupby('date').mean()303    live200 = argentina_top200[['date', 'liveness']]304    live200.set_index('date', inplace=True)305    live200 = live200.dropna()306    live200_mean = live200.groupby('date').mean()307    valence200 = argentina_top200[['date', 'valence']]308    valence200.set_index('date', inplace=True)309    valence200 = valence200.dropna()310    valence200_mean = valence200.groupby('date').mean()311    tempo200 = argentina_top200[['date', 'tempo']]312    tempo200.set_index('date', inplace=True)313    tempo200 = tempo200.dropna()314    tempo200_mean = tempo200.groupby('date').mean()315    duration200 = argentina_top200[['date', 'duration_ms']]316    duration200.set_index('date', inplace=True)317    duration200 = duration200.dropna()318    duration200_mean = duration200.groupby('date').mean()319    x_vals=[danceability200_mean.index, energy200_mean.index,key200_mean.index,loud200_mean.index, mode200_mean.index,speech200_mean.index,acou200_mean.index,instr200_mean.index,live200_mean.index,valence200_mean.index,tempo200_mean.index,duration200_mean.index]320    y_vals=['danceability','energy','key','loudness','mode','speechiness','acousticness','instrumentalness','liveness','valence','tempo','duration_ms']321    return x_vals, y_vals, argentina_top200322def spain():323    # read csv data for each country into associated dataframe324    argentina = pd.read_csv('/workspace/plotyDashWebapp/SpotifyDataAnalysis/home/dash_apps/finished_apps/CSVFile/Spain.csv')325    # drop unnamed column from each dataframe326    argentina = argentina.drop(columns=['Unnamed: 0'])327    argentina['date'] = pd.to_datetime(argentina['date'], format='%Y-%m-%d')328    argentina['year'] = pd.DatetimeIndex(argentina['date']).year329    ## Seasonality in Argentina based on Top 200 and Viral 50 charts respectively330    argentina_top200 = argentina[argentina['chart'] == 'top200']331    argentina_viral50 = argentina[argentina['chart'] == 'viral50']332    ### Argentina Top 200 Charts333    danceability200 = argentina_top200[['date', 'danceability']]334    danceability200.set_index('date', inplace=True)335    danceability200 = danceability200.dropna()336    danceability200_mean = danceability200.groupby('date').mean()337    338    energy200 = argentina_top200[['date', 'energy']]339    energy200.set_index('date', inplace=True)340    energy200 = energy200.dropna()341    energy200_mean = energy200.groupby('date').mean()342    key200 = argentina_top200[['date', 'key']]343    key200.set_index('date', inplace=True)344    key200 = key200.dropna()345    key200_mean = key200.groupby('date').mean()346    loud200 = argentina_top200[['date', 'loudness']]347    loud200.set_index('date', inplace=True)348    loud200 = loud200.dropna()349    loud200_mean = loud200.groupby('date').mean()350    mode200 = argentina_top200[['date', 'mode']]351    mode200.set_index('date', inplace=True)352    mode200 = mode200.dropna()353    mode200_mean = mode200.groupby('date').mean()354    speech200 = argentina_top200[['date', 'speechiness']]355    speech200.set_index('date', inplace=True)356    speech200 = speech200.dropna()357    speech200_mean = speech200.groupby('date').mean()358    acou200 = argentina_top200[['date', 'acousticness']]359    acou200.set_index('date', inplace=True)360    acou200 = acou200.dropna()361    acou200_mean = acou200.groupby('date').mean()362    instr200 = argentina_top200[['date', 'instrumentalness']]363    instr200.set_index('date', inplace=True)364    instr200 = instr200.dropna()365    instr200_mean = instr200.groupby('date').mean()366    live200 = argentina_top200[['date', 'liveness']]367    live200.set_index('date', inplace=True)368    live200 = live200.dropna()369    live200_mean = live200.groupby('date').mean()370    valence200 = argentina_top200[['date', 'valence']]371    valence200.set_index('date', inplace=True)372    valence200 = valence200.dropna()373    valence200_mean = valence200.groupby('date').mean()374    tempo200 = argentina_top200[['date', 'tempo']]375    tempo200.set_index('date', inplace=True)376    tempo200 = tempo200.dropna()377    tempo200_mean = tempo200.groupby('date').mean()378    duration200 = argentina_top200[['date', 'duration_ms']]379    duration200.set_index('date', inplace=True)380    duration200 = duration200.dropna()381    duration200_mean = duration200.groupby('date').mean()382    x_vals=[danceability200_mean.index, energy200_mean.index,key200_mean.index,loud200_mean.index, mode200_mean.index,speech200_mean.index,acou200_mean.index,instr200_mean.index,live200_mean.index,valence200_mean.index,tempo200_mean.index,duration200_mean.index]383    y_vals=['danceability','energy','key','loudness','mode','speechiness','acousticness','instrumentalness','liveness','valence','tempo','duration_ms']...

jquery.meanmenu.js

Source:jquery.meanmenu.js

1/*!2* jQuery meanMenu v2.0.83* @Copyright (C) 2012-2014 Chris Wharton @ MeanThemes (https://github.com/meanthemes/meanMenu)4*5*/6/*7* This program is free software: you can redistribute it and/or modify8* it under the terms of the GNU General Public License as published by9* the Free Software Foundation, either version 3 of the License, or10* (at your option) any later version.11*12* THIS SOFTWARE AND DOCUMENTATION IS PROVIDED "AS IS," AND COPYRIGHT13* HOLDERS MAKE NO REPRESENTATIONS OR WARRANTIES, EXPRESS OR IMPLIED,14* INCLUDING BUT NOT LIMITED TO, WARRANTIES OF MERCHANTABILITY OR15* FITNESS FOR ANY PARTICULAR PURPOSE OR THAT THE USE OF THE SOFTWARE16* OR DOCUMENTATION WILL NOT INFRINGE ANY THIRD PARTY PATENTS,17* COPYRIGHTS, TRADEMARKS OR OTHER RIGHTS.COPYRIGHT HOLDERS WILL NOT18* BE LIABLE FOR ANY DIRECT, INDIRECT, SPECIAL OR CONSEQUENTIAL19* DAMAGES ARISING OUT OF ANY USE OF THE SOFTWARE OR DOCUMENTATION.20*21* You should have received a copy of the GNU General Public License22* along with this program. If not, see <http://gnu.org/licenses/>.23*24* Find more information at http://www.meanthemes.com/plugins/meanmenu/25*26*/27(function ($) {28	"use strict";29		$.fn.meanmenu = function (options) {30				var defaults = {31						meanMenuTarget: jQuery(this), // Target the current HTML markup you wish to replace32						meanMenuContainer: '.mobile-menu-area .container', // Choose where meanmenu will be placed within the HTML33						meanMenuClose: "X", // single character you want to represent the close menu button34						meanMenuCloseSize: "18px", // set font size of close button35						meanMenuOpen: "<span /><span /><span />", // text/markup you want when menu is closed36						meanRevealPosition: "right", // left right or center positions37						meanRevealPositionDistance: "0", // Tweak the position of the menu38						meanRevealColour: "", // override CSS colours for the reveal background39						meanScreenWidth: "767", // set the screen width you want meanmenu to kick in at40						meanNavPush: "", // set a height here in px, em or % if you want to budge your layout now the navigation is missing.41						meanShowChildren: true, // true to show children in the menu, false to hide them42						meanExpandableChildren: true, // true to allow expand/collapse children43						meanExpand: "+", // single character you want to represent the expand for ULs44						meanContract: "-", // single character you want to represent the contract for ULs45						meanRemoveAttrs: false, // true to remove classes and IDs, false to keep them46						onePage: false, // set to true for one page sites47						meanDisplay: "block", // override display method for table cell based layouts e.g. table-cell48						removeElements: "" // set to hide page elements49				};50				options = $.extend(defaults, options);51				// get browser width52				var currentWidth = window.innerWidth || document.documentElement.clientWidth;53				return this.each(function () {54						var meanMenu = options.meanMenuTarget;55						var meanContainer = options.meanMenuContainer;56						var meanMenuClose = options.meanMenuClose;57						var meanMenuCloseSize = options.meanMenuCloseSize;58						var meanMenuOpen = options.meanMenuOpen;59						var meanRevealPosition = options.meanRevealPosition;60						var meanRevealPositionDistance = options.meanRevealPositionDistance;61						var meanRevealColour = options.meanRevealColour;62						var meanScreenWidth = options.meanScreenWidth;63						var meanNavPush = options.meanNavPush;64						var meanRevealClass = ".meanmenu-reveal";65						var meanShowChildren = options.meanShowChildren;66						var meanExpandableChildren = options.meanExpandableChildren;67						var meanExpand = options.meanExpand;68						var meanContract = options.meanContract;69						var meanRemoveAttrs = options.meanRemoveAttrs;70						var onePage = options.onePage;71						var meanDisplay = options.meanDisplay;72						var removeElements = options.removeElements;73						//detect known mobile/tablet usage74						var isMobile = false;75						if ( (navigator.userAgent.match(/iPhone/i)) || (navigator.userAgent.match(/iPod/i)) || (navigator.userAgent.match(/iPad/i)) || (navigator.userAgent.match(/Android/i)) || (navigator.userAgent.match(/Blackberry/i)) || (navigator.userAgent.match(/Windows Phone/i)) ) {76								isMobile = true;77						}78						if ( (navigator.userAgent.match(/MSIE 8/i)) || (navigator.userAgent.match(/MSIE 7/i)) ) {79							// add scrollbar for IE7 & 8 to stop breaking resize function on small content sites80								jQuery('html').css("overflow-y" , "scroll");81						}82						var meanRevealPos = "";83						var meanCentered = function() {84							if (meanRevealPosition === "center") {85								var newWidth = window.innerWidth || document.documentElement.clientWidth;86								var meanCenter = ( (newWidth/2)-22 )+"px";87								meanRevealPos = "left:" + meanCenter + ";right:auto;";88								if (!isMobile) {89									jQuery('.meanmenu-reveal').css("left",meanCenter);90								} else {91									jQuery('.meanmenu-reveal').animate({92											left: meanCenter93									});94								}95							}96						};97						var menuOn = false;98						var meanMenuExist = false;99						if (meanRevealPosition === "right") {100								meanRevealPos = "right:" + meanRevealPositionDistance + ";left:auto;";101						}102						if (meanRevealPosition === "left") {103								meanRevealPos = "left:" + meanRevealPositionDistance + ";right:auto;";104						}105						// run center function106						meanCentered();107						// set all styles for mean-reveal108						var $navreveal = "";109						var meanInner = function() {110								// get last class name111								if (jQuery($navreveal).is(".meanmenu-reveal.meanclose")) {112										$navreveal.html(meanMenuClose);113								} else {114										$navreveal.html(meanMenuOpen);115								}116						};117						// re-instate original nav (and call this on window.width functions)118						var meanOriginal = function() {119							jQuery('.mean-bar,.mean-push').remove();120							jQuery(meanContainer).removeClass("mean-container");121							jQuery(meanMenu).css('display', meanDisplay);122							menuOn = false;123							meanMenuExist = false;124							jQuery(removeElements).removeClass('mean-remove');125						};126						// navigation reveal127						var showMeanMenu = function() {128								var meanStyles = "background:"+meanRevealColour+";color:"+meanRevealColour+";"+meanRevealPos;129								if (currentWidth <= meanScreenWidth) {130								jQuery(removeElements).addClass('mean-remove');131									meanMenuExist = true;132									// add class to body so we don't need to worry about media queries here, all CSS is wrapped in '.mean-container'133									jQuery(meanContainer).addClass("mean-container");134									jQuery('.mean-container').prepend('<div class="mean-bar"><a href="#nav" class="meanmenu-reveal" style="'+meanStyles+'">Show Navigation</a><nav class="mean-nav"></nav></div>');135									//push meanMenu navigation into .mean-nav136									var meanMenuContents = jQuery(meanMenu).html();137									jQuery('.mean-nav').html(meanMenuContents);138									// remove all classes from EVERYTHING inside meanmenu nav139									if(meanRemoveAttrs) {140										jQuery('nav.mean-nav ul, nav.mean-nav ul *').each(function() {141											// First check if this has mean-remove class142											if (jQuery(this).is('.mean-remove')) {143												jQuery(this).attr('class', 'mean-remove');144											} else {145												jQuery(this).removeAttr("class");146											}147											jQuery(this).removeAttr("id");148										});149									}150									// push in a holder div (this can be used if removal of nav is causing layout issues)151									jQuery(meanMenu).before('<div class="mean-push" />');152									jQuery('.mean-push').css("margin-top",meanNavPush);153									// hide current navigation and reveal mean nav link154									jQuery(meanMenu).hide();155									jQuery(".meanmenu-reveal").show();156									// turn 'X' on or off157									jQuery(meanRevealClass).html(meanMenuOpen);158									$navreveal = jQuery(meanRevealClass);159									//hide mean-nav ul160									jQuery('.mean-nav ul').hide();161									// hide sub nav162									if(meanShowChildren) {163											// allow expandable sub nav(s)164											if(meanExpandableChildren){165												jQuery('.mean-nav ul ul').each(function() {166														if(jQuery(this).children().length){167																jQuery(this,'li:first').parent().append('<a class="mean-expand" href="#" style="font-size: '+ meanMenuCloseSize +'">'+ meanExpand +'</a>');168														}169												});170												jQuery('.mean-expand').on("click",function(e){171														e.preventDefault();172															if (jQuery(this).hasClass("mean-clicked")) {173																	jQuery(this).text(meanExpand);174																jQuery(this).prev('ul').slideUp(300, function(){});175														} else {176																jQuery(this).text(meanContract);177																jQuery(this).prev('ul').slideDown(300, function(){});178														}179														jQuery(this).toggleClass("mean-clicked");180												});181											} else {182													jQuery('.mean-nav ul ul').show();183											}184									} else {185											jQuery('.mean-nav ul ul').hide();186									}187									// add last class to tidy up borders188									jQuery('.mean-nav ul li').last().addClass('mean-last');189									$navreveal.removeClass("meanclose");190									jQuery($navreveal).click(function(e){191										e.preventDefault();192								if( menuOn === false ) {193												$navreveal.css("text-align", "center");194												$navreveal.css("text-indent", "0");195												$navreveal.css("font-size", meanMenuCloseSize);196												jQuery('.mean-nav ul:first').slideDown();197												menuOn = true;198										} else {199											jQuery('.mean-nav ul:first').slideUp();200											menuOn = false;201										}202											$navreveal.toggleClass("meanclose");203											meanInner();204											jQuery(removeElements).addClass('mean-remove');205									});206									// for one page websites, reset all variables...207									if ( onePage ) {208										jQuery('.mean-nav ul > li > a:first-child').on( "click" , function () {209											jQuery('.mean-nav ul:first').slideUp();210											menuOn = false;211											jQuery($navreveal).toggleClass("meanclose").html(meanMenuOpen);212										});213									}214							} else {215								meanOriginal();216							}217						};218						if (!isMobile) {219								// reset menu on resize above meanScreenWidth220								jQuery(window).resize(function () {221										currentWidth = window.innerWidth || document.documentElement.clientWidth;222										if (currentWidth > meanScreenWidth) {223												meanOriginal();224										} else {225											meanOriginal();226										}227										if (currentWidth <= meanScreenWidth) {228												showMeanMenu();229												meanCentered();230										} else {231											meanOriginal();232										}233								});234						}235					jQuery(window).resize(function () {236								// get browser width237								currentWidth = window.innerWidth || document.documentElement.clientWidth;238								if (!isMobile) {239										meanOriginal();240										if (currentWidth <= meanScreenWidth) {241												showMeanMenu();242												meanCentered();243										}244								} else {245										meanCentered();246										if (currentWidth <= meanScreenWidth) {247												if (meanMenuExist === false) {248														showMeanMenu();249												}250										} else {251												meanOriginal();252										}253								}254						});255					// run main menuMenu function on load256					showMeanMenu();257				});258		};259		260			    /*--261    	Mobile Menu262    ------------------------*/263    $('.mobile-menu nav').meanmenu({264        meanScreenWidth: "990",265        meanMenuContainer: ".mobile-menu",266        onePage: false,267    });	268		...

z-score.py

Source:z-score.py

...1213##  code to find the mean of 100 data points 1000 times 14#function to get the mean of the given data samples15# pass the number of data points you want  as counter16def random_set_of_mean(counter):17    dataset = []18    for i in range(0, counter):19        random_index= random.randint(0,len(data)-1)20        value = data[random_index]21        dataset.append(value)22    mean = statistics.mean(dataset)23    return mean242526# Function to get the mean of 100 data sets27mean_list = []28for i in range(0,1000):29    set_of_means= random_set_of_mean(100)30    mean_list.append(set_of_means)313233## calculating mean and standard_deviation of the sampling distribution.34std_deviation = statistics.stdev(mean_list)35mean = statistics.mean(mean_list)36print("mean of sampling distribution:- ",mean)37print("Standard deviation of sampling distribution:- ", std_deviation)38394041## findig the standard deviation starting and ending values42first_std_deviation_start, first_std_deviation_end = mean-std_deviation, mean+std_deviation43second_std_deviation_start, second_std_deviation_end = mean-(2*std_deviation), mean+(2*std_deviation)44third_std_deviation_start, third_std_deviation_end = mean-(3*std_deviation), mean+(3*std_deviation)45# print("std1",first_std_deviation_start, first_std_deviation_end)46# print("std2",second_std_deviation_start, second_std_deviation_end)47# print("std3",third_std_deviation_start,third_std_deviation_end)4849505152# # finding the mean of THE STUDENTS WHO GAVE EXTRA TIME TO MATH LAB and plotting on graph53df = pd.read_csv("School_1_Sample.csv")54data = df["Math_score"].tolist()55mean_of_sample1 = statistics.mean(data)56print("Mean of sample1:- ",mean_of_sample1)57fig = ff.create_distplot([mean_list], ["student marks"], show_hist=False)58fig.add_trace(go.Scatter(x=[mean, mean], y=[0, 0.17], mode="lines", name="MEAN"))59fig.add_trace(go.Scatter(x=[mean_of_sample1, mean_of_sample1], y=[0, 0.17], mode="lines", name="MEAN OF STUDENTS WHO HAD MATH LABS"))60fig.add_trace(go.Scatter(x=[first_std_deviation_end, first_std_deviation_end], y=[0, 0.17], mode="lines", name="STANDARD DEVIATION 1 END"))61fig.add_trace(go.Scatter(x=[second_std_deviation_end, second_std_deviation_end], y=[0, 0.17], mode="lines", name="STANDARD DEVIATION 2 END"))62fig.add_trace(go.Scatter(x=[third_std_deviation_end, third_std_deviation_end], y=[0, 0.17], mode="lines", name="STANDARD DEVIATION 3 END"))63fig.show()6465666768# #finding the mean of the STUDENTS WHO USED MATH PRACTISE APP and plotting it on the plot.69df = pd.read_csv("School_2_Sample.csv")70data = df["Math_score"].tolist()71mean_of_sample2 = statistics.mean(data)72print("mean of sample 2:- ",mean_of_sample2)73fig = ff.create_distplot([mean_list], ["student marks"], show_hist=False)74fig.add_trace(go.Scatter(x=[mean, mean], y=[0, 0.17], mode="lines", name="MEAN"))75fig.add_trace(go.Scatter(x=[mean_of_sample2, mean_of_sample2], y=[0, 0.17], mode="lines", name="MEAN OF STUDENTS WHO USED THE APP"))76fig.add_trace(go.Scatter(x=[first_std_deviation_end, first_std_deviation_end], y=[0, 0.17], mode="lines", name="STANDARD DEVIATION 1 END"))77fig.add_trace(go.Scatter(x=[second_std_deviation_end, second_std_deviation_end], y=[0, 0.17], mode="lines", name="STANDARD DEVIATION 2 END"))78fig.add_trace(go.Scatter(x=[third_std_deviation_end, third_std_deviation_end], y=[0, 0.17], mode="lines", name="STANDARD DEVIATION 3 END"))79fig.show()808182# finding the mean of the STUDENTS WHO WERE ENFORCED WITH REGISTERS and plotting it on the plot.83df = pd.read_csv("School_3_Sample.csv")84data = df["Math_score"].tolist()85mean_of_sample3 = statistics.mean(data)86print("mean of sample3:- ",mean_of_sample3)87fig = ff.create_distplot([mean_list], ["student marks"], show_hist=False)88fig.add_trace(go.Scatter(x=[mean, mean], y=[0, 0.17], mode="lines", name="MEAN"))89fig.add_trace(go.Scatter(x=[mean_of_sample3, mean_of_sample3], y=[0, 0.17], mode="lines", name="MEAN OF STUDNETS WHO WERE ENFORCED WITH MATH REGISTERS"))90fig.add_trace(go.Scatter(x=[second_std_deviation_end, second_std_deviation_end], y=[0, 0.17], mode="lines", name="STANDARD DEVIATION 2 END"))91fig.add_trace(go.Scatter(x=[third_std_deviation_end, third_std_deviation_end], y=[0, 0.17], mode="lines", name="STANDARD DEVIATION 3 END"))92fig.show()939495#finding the z score 96z_score = (mean - mean_of_sample2)/std_deviation
...

Using AI Code Generation

1var BestLibrary = require('bestlibrary');2var bestLibrary = new BestLibrary();3console.log(bestLibrary.mean([1,2,3,4,5,6,7,8,9,10]));4var BestLibrary = require('bestlibrary');5var bestLibrary = new BestLibrary();6console.log(bestLibrary.median([1,2,3,4,5,6,7,8,9,10]));7var BestLibrary = require('bestlibrary');8var bestLibrary = new BestLibrary();9console.log(bestLibrary.mode([1,2,3,4,5,6,7,8,9,10]));10var BestLibrary = require('bestlibrary');11var bestLibrary = new BestLibrary();12console.log(bestLibrary.variance([1,2,3,4,5,6,7,8,9,10]));13var BestLibrary = require('bestlibrary');14var bestLibrary = new BestLibrary();15console.log(bestLibrary.standardDeviation([1,2,3,4,5,6,7,8,9,10]));16var BestLibrary = require('bestlibrary');17var bestLibrary = new BestLibrary();18console.log(bestLibrary.isPrime(7));19var BestLibrary = require('bestlibrary');20var bestLibrary = new BestLibrary();21console.log(bestLibrary.isEven(7));22var BestLibrary = require('bestlibrary');23var bestLibrary = new BestLibrary();24console.log(bestLibrary.isOdd(7));25var BestLibrary = require('bestlibrary');26var bestLibrary = new BestLibrary();27console.log(bestLibrary.isPalindrome('racecar'));28var BestLibrary = require('bestlibrary');

Using AI Code Generation

1var BestMean = require('./BestMean.js');2var mean = new BestMean();3console.log(mean.mean([1,2,3,4,5]));4console.log(mean.mean([1,2,3,4,5,6,7,8,9,10]));5console.log(mean.mean([1,2,3,4,5,6,7,8,9,10,11,12,13,14,15]));6module.exports = function() {7    this.mean = function(array) {8        var sum = 0;9        for (var i = 0; i < array.length; i++) {10            sum += array[i];11        }12        return sum / array.length;13    };14};15var BestMean = require('./BestMean.js');16var mean = new BestMean();17console.log(mean.mean([1,2,3,4,5]));18console.log(mean.mean([1,2,3,4,5,6,7,8,9,10]));19console.log(mean.mean([1,2,3,4,5,6,7,8,9,10,11,12,13,14,15]));20module.exports = function() {21    this.mean = function(array) {22        var sum = 0;23        for (var i = 0; i < array.length; i++) {24            sum += array[i];25        }26        return sum / array.length;27    };28};29var BestMean = require('./BestMean.js');30var mean = new BestMean();31console.log(mean.mean([1,2,3,4,5]));32console.log(mean.mean([1,2,3,4,5,6,7,8,9,10]));33console.log(mean.mean([1,2,3,4,5,6,7,8,9,10,11,12,13,14,15]));34module.exports = function() {35    this.mean = function(array) {36        var sum = 0;37        for (var i = 0;

Using AI Code Generation

1var BestLibrary = require('BestLibrary');2var bestLibrary = new BestLibrary();3console.log(bestLibrary.mean(1, 2, 3, 4, 5));4console.log(bestLibrary.mean(1, 2, 3, 4, 5, 6));5var BestLibrary = require('BestLibrary');6var bestLibrary = new BestLibrary();7console.log(bestLibrary.mean(1, 2, 3, 4, 5));8console.log(bestLibrary.mean(1, 2, 3, 4, 5, 6));9var BestLibrary = require('BestLibrary');10var bestLibrary = new BestLibrary();11console.log(bestLibrary.mean(1, 2, 3, 4, 5));12console.log(bestLibrary.mean(1, 2, 3, 4, 5, 6));13var BestLibrary = require('BestLibrary');14var bestLibrary = new BestLibrary();15console.log(bestLibrary.mean(1, 2, 3, 4, 5));16console.log(bestLibrary.mean(1, 2, 3, 4, 5, 6));17var BestLibrary = require('BestLibrary');18var bestLibrary = new BestLibrary();19console.log(bestLibrary.mean(1, 2, 3, 4, 5));20console.log(bestLibrary.mean(1, 2, 3, 4, 5, 6));21var BestLibrary = require('BestLibrary');22var bestLibrary = new BestLibrary();23console.log(bestLibrary.mean(1, 2, 3, 4, 5));24console.log(bestLibrary.mean(1, 2, 3, 4, 5, 6));25var BestLibrary = require('BestLibrary');26var bestLibrary = new BestLibrary();27console.log(bestLibrary.mean(1, 2,

Using AI Code Generation

1var BestLibrary = require('./bestlibrary');2var bestLibrary = new BestLibrary();3console.log(bestLibrary.mean([1,2,3,4,5]));4var BestLibrary = function() {5};6BestLibrary.prototype.mean = function(numbers) {7    var sum = 0;8    for (var i = 0; i < numbers.length; i++) {9        sum += numbers[i];10    }11    return sum / numbers.length;12};13module.exports = BestLibrary;

Using AI Code Generation

1var BestStats = require('./BestStats.js');2var best = new BestStats();3best.add(10);4best.add(20);5best.add(30);6console.log(best.mean());7function BestStats() {8    this.data = [];9}10BestStats.prototype.add = function (x) {11    this.data.push(x);12};13BestStats.prototype.mean = function () {14    var total = 0;15    for (var i = 0; i < this.data.length; i++) {16        total += this.data[i];17    }18    return total / this.data.length;19};20module.exports = BestStats;

Using AI Code Generation

1var BestStudent = require('./BestStudent');2var bestStudent = new BestStudent();3var BestStudent = require('./BestStudent');4var bestStudent = new BestStudent();5var BestStudent = require('./BestStudent');6var bestStudent = new BestStudent();7var BestStudent = require('./BestStudent');8var bestStudent = new BestStudent();9var BestStudent = require('./BestStudent');10var bestStudent = new BestStudent();11var BestStudent = require('./BestStudent');12var bestStudent = new BestStudent();13var BestStudent = require('./BestStudent');14var bestStudent = new BestStudent();15var BestStudent = require('./BestStudent');16var bestStudent = new BestStudent();17var BestStudent = require('./BestStudent');18var bestStudent = new BestStudent();19var BestStudent = require('./BestStudent');20var bestStudent = new BestStudent();21var BestStudent = require('./BestStudent');22var bestStudent = new BestStudent();23var BestStudent = require('./BestStudent');24var bestStudent = new BestStudent();

Using AI Code Generation

1var BestStudent = require('./BestStudent.js');2var bestStudent = new BestStudent();3console.log("The mean of the bestStudent is: " + bestStudent.mean());4function BestStudent() {5    this.name = "Sally";6    this.grades = [90, 95, 88, 0, 55, 91];7    this.mean = function() {8        var total = 0;9        for (var i = 0; i < this.grades.length; i++) {10            total += this.grades[i];11        }12        return total / this.grades.length;13    };14}15module.exports = BestStudent;16function BestStudent() {17    this.name = "Sally";18    this.grades = [90, 95, 88, 0, 55, 91];19    this.mean = function() {20        var total = 0;21        for (var i = 0; i < this.grades.length; i++) {22            total += this.grades[i];23        }24        return total / this.grades.length;25    };26}27exports.BestStudent = BestStudent;

Using AI Code Generation

1var student = require("./student");2var BestStudent = student.BestStudent;3var bestStudent = new BestStudent("John", "Smith", 1, [100, 100, 100]);4console.log(bestStudent.mean());5var student = require("./student");6var BestStudent = student.BestStudent;7var bestStudent = new BestStudent("John", "Smith", 1, [100, 100, 100]);8console.log(bestStudent.mean());9var student = require("./student");10var BestStudent = student.BestStudent;11var bestStudent = new BestStudent("John", "Smith", 1, [100, 100, 100]);12console.log(bestStudent.mean());13var student = require("./student");14var BestStudent = student.BestStudent;15var bestStudent = new BestStudent("John", "Smith", 1, [100, 100, 100]);16console.log(bestStudent.mean());17var student = require("./student");18var BestStudent = student.BestStudent;19var bestStudent = new BestStudent("John", "Smith", 1, [100, 100, 100]);20console.log(bestStudent.mean());21var student = require("./student");22var BestStudent = student.BestStudent;23var bestStudent = new BestStudent("John", "Smith", 1, [100, 100, 100]);24console.log(bestStudent.mean());25var student = require("./student");26var BestStudent = student.BestStudent;27var bestStudent = new BestStudent("John", "Smith", 1, [100, 100, 100]);28console.log(bestStudent.mean());29var student = require("./student");30var BestStudent = student.BestStudent;31var bestStudent = new BestStudent("John", "Smith", 1, [100, 100, 100]);32console.log(bestStudent.mean());

Using AI Code Generation

1var Best = require('./Best');2var best = new Best();3best.add(3);4best.add(4);5best.add(5);6best.add(6);7console.log(best.mean());8The mean() method is not available to objects of the Best class. The problem is that we have not exported the mean() method. We can fix this by adding the mean() method to the exports object:9function Best() {10  this._data = [];11}12Best.prototype.add = function (value) {13  this._data.push(value);14};15Best.prototype.mean = function () {16  var sum = 0;17  for (var i = 0; i < this._data.length; i++) {18    sum += this._data[i];19  }20  return sum / this._data.length;21};22exports.Best = Best;23exports.mean = Best.prototype.mean;24var Best = require('./Best');25var best = new Best.Best();26best.add(3);27best.add(4);28best.add(5);29best.add(6);30console.log(best.mean());31In this chapter, you learned about the module system in Node.js. You learned how to create a module and how to export and import variables and functions. You also learned how to export classes and how to use the module.exports object to export multiple items. You learned how to import modules and use the require() function to import variables

Automation Testing Tutorials

Learn to execute automation testing from scratch with LambdaTest Learning Hub. Right from setting up the prerequisites to run your first automation test, to following best practices and diving deeper into advanced test scenarios. LambdaTest Learning Hubs compile a list of step-by-step guides to help you be proficient with different test automation frameworks i.e. Selenium, Cypress, TestNG etc.