171 lines
7.9 KiB
Python
171 lines
7.9 KiB
Python
from django.core.management.base import BaseCommand
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import django
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from regluit.core.models import Campaign
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from regluit.payment.models import Transaction
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from django.db.models import Q, F, Count, Sum, Max
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from django.contrib.auth.models import User
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from regluit.payment.manager import PaymentManager
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from decimal import Decimal as D
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from regluit.experimental.gutenberg import unicode_csv
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def amazon_payments(fname=r"/Users/raymondyee/Downloads/All-Activity-Jan-01-2012-Jul-17-2012.csv"):
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r0 = unicode_csv.UnicodeReader(f=open(fname), encoding="iso-8859-1")
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# grab the header
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header = r0.next()
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print "header", header
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r = unicode_csv.UnicodeDictReader(f=open(fname), fieldnames=header, encoding="iso-8859-1")
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return dict([(k['Transaction ID'],k) for k in r])
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def transactions_with_payment_info(transactions, payments=None):
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"""an iterator for transactions / if a dict representing the actual payment data is provided, correlate the two"""
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for t in transactions:
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data = {"id": t.id,
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"user_id": t.user.id,
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"username":t.user.username,
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"email":t.user.email,
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"campaign_id": t.campaign.id if t.campaign is not None else "NULL",
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"amount":t.amount,
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"status":t.status,
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"local_status":t.local_status,
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"premium_id": t.premium.id if t.premium is not None else "NULL",
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"premium_amount": t.premium.amount if t.premium is not None else "NULL",
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"premium_description": t.premium.description if t.premium is not None else "NULL",
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"preapproval_key":t.preapproval_key,
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"approved":t.approved,
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"error":t.error}
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if payments is not None:
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payment = payments.get(t.preapproval_key, None)
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if payment is not None:
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data.update({'payment_transaction_id':payment['Transaction ID'],
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'payment_name': payment['Name'],
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'payment_status': payment['Status'],
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'payment_amount': D(payment['Amount'].replace("$","")),
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'payment_fees': D(payment['Fees'].replace("$",""))
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})
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else:
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data.update({'payment_transaction_id':"NULL",
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'payment_name': "NULL",
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'payment_status': "NULL",
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'payment_amount': "NULL",
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'payment_fees': "NULL"
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})
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else:
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data.update({'payment_transaction_id':"NULL",
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'payment_name': "NULL",
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'payment_status': "NULL",
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'payment_amount': "NULL",
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'payment_fees': "NULL"
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})
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yield data
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def stats_for_campaign(c):
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print "transactions by statuses", c.transaction_set.values('status').annotate(count_status=Count('status'))
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# distinguish among campaigns that are Active, Successful, or Closed
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if c.status == 'ACTIVE':
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# nothing collected yet -- can tally all Active transactions for a total
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valid_trans = c.transaction_set.filter(Q(status='Complete') | Q(status='Active') | Q(status='Pending') | Q(status='Error') | Q(status='Failed') )
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total_amount = c.transaction_set.filter(Q(status='Active')).aggregate(Sum('amount'))['amount__sum']
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print "expected amount for active campaign", total_amount
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elif c.status == 'SUCCESSFUL':
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# how to not double count transactions wih Error or Failed status that have an Active or Completed followup?
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valid_trans = c.transaction_set.filter(Q(status='Complete') | Q(status='Active') )
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uid_ok_trans = set([x[0] for x in c.transaction_set.filter(Q(status='Complete') | Q(status='Active')).values_list('user')])
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cleared_amount = c.transaction_set.filter(Q(status='Complete')).aggregate(Sum('amount'))['amount__sum']
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failed_errored_amount = sum([t.amount for t in c.transaction_set.filter(Q(status='Pending') | Q(status='Error') | Q(status='Failed') ) if t.user.id not in uid_ok_trans])
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print "cleared amount for Successful campaign", cleared_amount
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print "failed_errored_amount for Successful campaign", failed_errored_amount
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elif c.status == 'CLOSED':
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valid_trans = c.transaction_set.filter(Q(status='Complete'))
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total_amount = c.transaction_set.filter(Q(status='Complete')).aggregate(Sum('amount'))['amount__sum']
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print "total amount for Closed campaign", total_amount
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else:
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valid_trans = c.transaction_set.filter(Q(status='Complete') | Q(status='Active') | Q(status='Pending') | Q(status='Error') | Q(status='Failed') )
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# distribution?
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clusters = valid_trans.values('amount').annotate(count_amount=Count('amount')).order_by('-amount')
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for t in clusters:
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print "{0}\t{1}\t{2}".format(t['amount'], t['count_amount'], t['count_amount']*t['amount'])
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c_users = set([u[0] for u in c.transaction_set.values_list('user__username')])
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# 0 or 1 Active
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# 0 or 1 Complete
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# Created? -- outmoded
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# any number of NONE
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# any number of Canceled
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# if there is an Active transaction, it should be the very last one.
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c_users_active_t = set([u[0] for u in c.transaction_set.filter(status='Active').values_list('user__username')])
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[(u, c.transaction_set.filter(user__username=u).values_list('status').order_by('-date_created')[0]) for u in c_users_active_t]
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# pull up latest transaction by user
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# is there only one and only one active transaction for a user who has pledged?
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# users who have more than one transaction with c
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users_multi_trans = c.transaction_set.values_list('user__username').annotate(user_count=Count('user')).filter(user_count__gt=1)
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# NONE status -- follow up on?
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# tally up different classes of statuses we have for c
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# who has modified a transaction and never cancelled it?
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class Command(BaseCommand):
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help = "Displays data about how the campaigns are progressing"
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# args = "<filename> <username>"
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def handle(self, **options):
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# what campaigns are active
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# List the campaigns
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for c in Campaign.objects.all():
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print c.name, c.status
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print
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# tally up the number of Campaigns with various statuses
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print Campaign.objects.values('status').annotate(count_status=Count('status'))
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print
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# breakdown of Transactions by status
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print "transactions by status", Transaction.objects.values('status').annotate(count_status=Count('status'))
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print
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# distribution of donations for *Oral Literature*
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print "stats for *Oral Literature in Africa*"
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c3 = Campaign.objects.get(id=3)
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stats_for_campaign(c3)
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out_fname = "/Users/raymondyee/Downloads/unglue_it_trans.csv"
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out_headers = ["id",
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"user_id",
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"username",
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"email",
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"campaign_id",
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"amount",
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"status",
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"local_status",
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"premium_id",
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"premium_amount",
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"premium_description",
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"preapproval_key",
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"approved",
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"error",
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"payment_transaction_id",
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"payment_name",
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"payment_fees",
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"payment_status",
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"payment_amount"
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]
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f = open(out_fname, "wb")
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transactions = c3.transaction_set.filter(Q(status='Complete') | Q(status='Active') | Q(status='Pending') | Q(status='Error') | Q(status='Failed') )
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f = unicode_csv.output_to_csv(f, out_headers, transactions_with_payment_info(transactions, payments = amazon_payments()))
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