api/pages/code/retention.py [180:252]:
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                size = 0,
                body = query
            )
        
        
        retained = 0
        added = 0
        lost = 0
        
        thisPeriod = []
        for bucket in res['aggregations']['by_author']['buckets']:
            who = bucket['key']
            thisPeriod.append(who)
            if who not in peopleSeen:
                peopleSeen[who] = tf
                added += 1
            activePeople[who] = tf
            if who not in allPeople:
                allPeople[who] = tf
        
        prune = []
        for k, v in activePeople.items():
            if v < (t - (hl*30.45*86400)):
                prune.append(k)
                lost += 1
        
        for who in prune:
            del activePeople[who]
            del peopleSeen[who]
        retained = len(activePeople) - added
        
        ts.append({
            'date': tf,
            'People who (re)joined': added,
            'People who quit': lost,
            'People retained': retained,
            'Active people': added + retained
        })
    
    groups = [
        ['More than 5 years', (5*365*86400)+1],
        ['2 - 5 years', (2*365*86400)+1],
        ['1 - 2 years', (365*86400)],
        ['Less than a year', 1]
    ]
    
    counts = {}
    totExp = 0
    for person, age in activePeople.items():
        totExp += time.time() - allPeople[person]
        for el in sorted(groups, key = lambda x: x[1], reverse = True):
            if allPeople[person] <= time.time() - el[1]:
                counts[el[0]] = counts.get(el[0], 0) + 1
                break
    avgyr = (totExp / (86400*365)) / max(len(activePeople),1)
    
    ts = sorted(ts, key = lambda x: x['date'])
    avgm = ""
    yr = int(avgyr)
    ym = round((avgyr-yr)*12)
    if yr >= 1:
        avgm += "%u year%s" % (yr, "s" if yr != 1 else "")
    if ym > 0:
        avgm += "%s%u month%s" % (", " if yr > 0 else "", ym, "s" if ym != 1 else "")
    JSON_OUT = {
        'text': "This shows Contributor retention as calculated over a %u month timespan. The average experience of currently active people is %s." % (hl, avgm),
        'timeseries': ts,
        'counts': counts,
        'averageYears': avgyr,
        'okay': True,
        'responseTime': time.time() - now,
    }
    yield json.dumps(JSON_OUT)
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api/pages/mail/retention.py [176:249]:
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                size = 0,
                body = query
            )
        
        
        retained = 0
        added = 0
        lost = 0
        
        thisPeriod = []
        for bucket in res['aggregations']['by_author']['buckets']:
            who = bucket['key']
            thisPeriod.append(who)
            if who not in peopleSeen:
                peopleSeen[who] = tf
                added += 1
            activePeople[who] = tf
            if who not in allPeople:
                allPeople[who] = tf
        
        prune = []
        for k, v in activePeople.items():
            if v < (t - (hl*30.45*86400)):
                prune.append(k)
                lost += 1
        
        for who in prune:
            del activePeople[who]
            del peopleSeen[who]
        retained = len(activePeople) - added
        
        ts.append({
            'date': tf,
            'People who (re)joined': added,
            'People who quit': lost,
            'People retained': retained,
            'Active people': added + retained
        })
    
    groups = [
        ['More than 5 years', (5*365*86400)+1],
        ['2 - 5 years', (2*365*86400)+1],
        ['1 - 2 years', (365*86400)],
        ['Less than a year', 1]
    ]
    
    counts = {}
    totExp = 0
    for person, age in activePeople.items():
        totExp += time.time() - allPeople[person]
        for el in sorted(groups, key = lambda x: x[1], reverse = True):
            if allPeople[person] <= time.time() - el[1]:
                counts[el[0]] = counts.get(el[0], 0) + 1
                break
    avgyr = (totExp / (86400*365)) / max(len(activePeople),1)
    
    ts = sorted(ts, key = lambda x: x['date'])
    
    avgm = ""
    yr = int(avgyr)
    ym = round((avgyr-yr)*12)
    if yr >= 1:
        avgm += "%u year%s" % (yr, "s" if yr != 1 else "")
    if ym > 0:
        avgm += "%s%u month%s" % (", " if yr > 0 else "", ym, "s" if ym != 1 else "")
    JSON_OUT = {
        'text': "This shows Contributor retention as calculated over a %u month timespan. The average experience of currently active people is %s." % (hl, avgm),
        'timeseries': ts,
        'counts': counts,
        'averageYears': avgyr,
        'okay': True,
        'responseTime': time.time() - now,
    }
    yield json.dumps(JSON_OUT)
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