(function(globals) {

  var django = globals.django || (globals.django = {});

  
  django.pluralidx = function(n) {
    var v=(n != 1);
    if (typeof(v) == 'boolean') {
      return v ? 1 : 0;
    } else {
      return v;
    }
  };
  

  /* gettext library */

  django.catalog = django.catalog || {};
  
  var newcatalog = {
    "AboutContactButtonTitle": "contact us",
    "AboutMarkKrakow": "Krakow - Europe",
    "AboutMarkLimassol": "Limassol - Cyprus",
    "AboutMarkMoscow": "Moscow - Russia & CIS",
    "AboutMarkSpain": "UK",
    "AboutMarkWilmington": "UK",
    "AboutUsDescription": "We are an experienced team of data scientist experts, initially started from applying ML to retail chain network optimization. Today we are fully focused on solving problems of consumer good companies with ML/AI-driven solutions \u2014 immersing ourselves into customer\u2019s problems and the specifics of every client needs.",
    "AboutUsHeader": "About us",
    "AllRightsReserved": "All Rights Reserved",
    "BlogListDescription": "Hello everyone! In the life of our company, there are many interesting discoveries and observations, and we are happy to share them with you.",
    "BlogListHeader": "Blog",
    "CPGPointBeach": "Beach",
    "CPGPointBusStop": "Bus Stop",
    "CPGPointBusinessOffice": "Business Office",
    "CPGPointFootballStadium": "Football Stadium",
    "CPGPointGym": "Gym",
    "CPGPointMall": "Mall",
    "CPGPointMetro": "Metro",
    "CPGPointPark": "Park",
    "CPGPointSuperMarket": "Super Market",
    "CPGPointVillage": "Village",
    "CPGPointsDescription": "BestPlace uses machine learning to convert the specific area into its digital replica. It enables predicting current and future customer behaviour and preferences in the real world.",
    "CPGPointsHeader": "Inside look at the territory as holistic purchasing environment",
    "CPGProfitableLocationsDescription": "BestPlace processes 250+ sources of social, geospatial and historical data to identify 100+ category-specific patterns in consumer behaviour and adjust store assortment with customers needs.",
    "CPGProfitableLocationsHeader": "Consumer brands can\u2019t afford irrelevance",
    "CPGProfitableLocationsParty": "Party",
    "CPGProfitableLocationsPicnic": "Picnic",
    "CPGProfitableLocationsShop": "Shop",
    "CPGProfitableLocationsSport": "Sport",
    "CPGProfitableLocationsWork": "Work",
    "CPGShopperPatternsDescription": "BestPlace is a proven research and predictive geoanalytical tool that provides end-to-end visibility of local shopper patterns",
    "CPGShopperPatternsHeader": "Grow your sales in rapidly changing environment",
    "CPGShopperPatternsROI": "ROI growth by field force activities optimization",
    "CPGShopperPatternsSalesLift": "sales uplift from BestPlace recommendations",
    "CallbackButtonTitle": "get started",
    "CallbackPopupHeader": "Request a call",
    "CaseDetailAllPublications": "all publications",
    "CaseDetailBackButton": "back to publications",
    "CaseDetailCompanyLabel": "company",
    "CaseDetailDateLabel": "date",
    "CaseDetailIdeaLabel": "Solution Idea",
    "CaseDetailOjbectiveLabel": "Objective",
    "CaseDetailOtherPublications": "Other publications",
    "CasesListDescription": "Hello everyone! In the life of our company, there are many interesting discoveries and observations, and we are happy to share them with you.",
    "CasesListDetailButton": "watch case",
    "CasesListHeader": "Cases",
    "CasesListLoadMoreButton": "load more",
    "CpgMapFirstDescription": "Identify stores with the highest sales potential and optimize product mix based on local consumers behaviour",
    "CpgMapFirstHeader": "Find uplift potential in your existing channels",
    "CpgMapSecondHeader": "<b>To \u21161</b> market share in less than <b>4 months</b>",
    "CpgShopsDescription": "Reach your customers near their actual demand through the actively growing e-comm channels",
    "CpgShopsHeader": "Increase your market share with the direct to consumer opportunity",
    "CyprusContactName": "Cyprus",
    "FooterAgreementText": "By clicking on the \"Send\" button, you consent to the processing of personal data and agree to the policy regarding the processing of personal data of the company",
    "FormCompanyLabel": "Company / Job title",
    "FormEmailLabel": "E-mail",
    "FormMessageLabel": "Message",
    "FormNameLabel": "Name",
    "FormPhoneLabel": "Phone number",
    "FormSubmitButton": "send",
    "MapBoxHeader": "Contacts",
    "MenuAbout": "about us",
    "MenuBlog": "blog",
    "MenuCPG": "cpg",
    "MenuRetail": "retail",
    "NotFoundDescription": "We suggest that you return to MAIN PAGE",
    "NotFoundHeader": "Page not found",
    "NotFoundMainButtonTitle": "main screen",
    "PolandContactName": "Poland",
    "PrivacyPolicy": "Privacy Policy",
    "RetailCirclesBlue": "Consumer<br />behaviour<br />patterns",
    "RetailCirclesDescription": "End-to-end visibility of consumers, stores, channels and demand",
    "RetailCirclesGreen": "Client data",
    "RetailCirclesHeader": "Provide compelling location insights",
    "RetailCirclesYellow": "Machine learning",
    "RetailClientsHeader": "Our clients",
    "RetailDataSourcesGPS": "traffic flows<br />routing",
    "RetailDataSourcesGovernmentData": "statistics<br />research",
    "RetailDataSourcesHeader": "Research data sources",
    "RetailDataSourcesMapsTransport": "points of interest<br/>transport",
    "RetailDataSourcesMorphText": "Shopper patterns<br />detailed to micro<br />location level",
    "RetailDataSourcesRealEstate": "housing<br />offices",
    "RetailHeatMapBlueText": "monthly increased sales volume over existing stores",
    "RetailHeatMapDescription": " ",
    "RetailHeatMapHeader": "Focus your expansion on the areas with the largest unmet demand",
    "RetailPredictionModelBlueText": "ROI growth on the new sites spent",
    "RetailPredictionModelDescription": " ",
    "RetailPredictionModelHeader": "Make accurate investment decisions with the help of tailored AI model",
    "RetailProfitableLocationsDescription": "BestPlace is a proven research and predictive geoanalytical tool that provides 90% accurate sales forecast for the new stores",
    "RetailProfitableLocationsHeader": "Find the most profitable locations",
    "RetailWorkflowBlueText": "time-saving for the field force and management team",
    "RetailWorkflowDescription": " ",
    "RetailWorkflowHeader": "Increase operational efficiency by digitalizing your workflow",
    "RussiaContactName": "Russia & CIS",
    "ScheduleMeetingDescription": "with our team to calculate potential profits for your business",
    "ScheduleMeetingHeader": "Schedule a meeting",
    "SpainContactName": "UK",
    "ThankYouDescription": "Thank you for your request. We will get back as soon as it possible.",
    "ThankYouHeader": "Thank you",
    "USContactName": "US"
  };
  for (var key in newcatalog) {
    django.catalog[key] = newcatalog[key];
  }
  

  if (!django.jsi18n_initialized) {
    django.gettext = function(msgid) {
      var value = django.catalog[msgid];
      if (typeof(value) == 'undefined') {
        return msgid;
      } else {
        return (typeof(value) == 'string') ? value : value[0];
      }
    };

    django.ngettext = function(singular, plural, count) {
      var value = django.catalog[singular];
      if (typeof(value) == 'undefined') {
        return (count == 1) ? singular : plural;
      } else {
        return value.constructor === Array ? value[django.pluralidx(count)] : value;
      }
    };

    django.gettext_noop = function(msgid) { return msgid; };

    django.pgettext = function(context, msgid) {
      var value = django.gettext(context + '\x04' + msgid);
      if (value.indexOf('\x04') != -1) {
        value = msgid;
      }
      return value;
    };

    django.npgettext = function(context, singular, plural, count) {
      var value = django.ngettext(context + '\x04' + singular, context + '\x04' + plural, count);
      if (value.indexOf('\x04') != -1) {
        value = django.ngettext(singular, plural, count);
      }
      return value;
    };

    django.interpolate = function(fmt, obj, named) {
      if (named) {
        return fmt.replace(/%\(\w+\)s/g, function(match){return String(obj[match.slice(2,-2)])});
      } else {
        return fmt.replace(/%s/g, function(match){return String(obj.shift())});
      }
    };


    /* formatting library */

    django.formats = {
    "DATETIME_FORMAT": "N j, Y, P",
    "DATETIME_INPUT_FORMATS": [
      "%Y-%m-%d %H:%M:%S",
      "%Y-%m-%d %H:%M:%S.%f",
      "%Y-%m-%d %H:%M",
      "%m/%d/%Y %H:%M:%S",
      "%m/%d/%Y %H:%M:%S.%f",
      "%m/%d/%Y %H:%M",
      "%m/%d/%y %H:%M:%S",
      "%m/%d/%y %H:%M:%S.%f",
      "%m/%d/%y %H:%M",
      "%Y-%m-%d"
    ],
    "DATE_FORMAT": "N j, Y",
    "DATE_INPUT_FORMATS": [
      "%Y-%m-%d",
      "%m/%d/%Y",
      "%m/%d/%y"
    ],
    "DECIMAL_SEPARATOR": ".",
    "FIRST_DAY_OF_WEEK": 0,
    "MONTH_DAY_FORMAT": "F j",
    "NUMBER_GROUPING": 3,
    "SHORT_DATETIME_FORMAT": "m/d/Y P",
    "SHORT_DATE_FORMAT": "m/d/Y",
    "THOUSAND_SEPARATOR": ",",
    "TIME_FORMAT": "P",
    "TIME_INPUT_FORMATS": [
      "%H:%M:%S",
      "%H:%M:%S.%f",
      "%H:%M"
    ],
    "YEAR_MONTH_FORMAT": "F Y"
  };

    django.get_format = function(format_type) {
      var value = django.formats[format_type];
      if (typeof(value) == 'undefined') {
        return format_type;
      } else {
        return value;
      }
    };

    /* add to global namespace */
    globals.pluralidx = django.pluralidx;
    globals.gettext = django.gettext;
    globals.ngettext = django.ngettext;
    globals.gettext_noop = django.gettext_noop;
    globals.pgettext = django.pgettext;
    globals.npgettext = django.npgettext;
    globals.interpolate = django.interpolate;
    globals.get_format = django.get_format;

    django.jsi18n_initialized = true;
  }

}(this));