def test_convert_to_json_under_10():
"""
Confirm that totals under $10 are aggregated.
"""
opportunities = DataFrame({
"AccountId": ["A01", "B01", "A01", "B01"],
"Amount": [5, 20, 4, 4000],
"CloseDate": ['2009-01-02', '2009-01-03', '2009-01-04', '2010-01-02']
})
accounts = DataFrame({
"AccountId": ["A01", "B01"],
"Text_For_Donor_Wall__c": ["Donor A", "Donor B"]
})
expected = """
[
{
"donations": [
{
"amount": "Less than $10",
"year": 2009
},
{
"amount": "Less than $10",
"year": "all-time"
}
],
"name": "Donor A"
},
{
"donations": [
{
"amount": "$20",
"year": 2009
},
{
"amount": "$4,000",
"year": 2010
},
{
"amount": "$4,020",
"year": "all-time"
}
],
"name": "Donor B"
}
]
"""
actual = convert_donors(opportunities=opportunities, accounts=accounts)
assert json.loads(actual) == json.loads(expected)
def test_convert_to_json_normal():
"""
Check the normal case.
"""
opportunities = DataFrame({
"AccountId": ["A01", "B01", "A01", "B01"],
"Amount": [10, 20, 30, 4000],
"CloseDate": ['2009-01-02', '2009-01-03', '2009-01-04', '2010-01-02']
})
accounts = DataFrame({
"AccountId": ["A01", "B01"],
"Text_For_Donor_Wall__c": ["Donor A", "Donor B"]
})
expected = """
[
{
"donations": [
{
"amount": "$40",
"year": 2009
},
{
"amount": "$40",
"year": "all-time"
}
],
"name": "Donor A"
},
{
"donations": [
{
"amount": "$20",
"year": 2009
},
{
"amount": "$4,000",
"year": 2010
},
{
"amount": "$4,020",
"year": "all-time"
}
],
"name": "Donor B"
}
]
"""
actual = convert_donors(opportunities=opportunities, accounts=accounts)
assert json.loads(actual) == json.loads(expected)
def test_convert_to_json_with_empty_amount():
"""
Verify that blank values are ignored.
"""
opportunities = DataFrame({
"AccountId": ["A01", "B01", "A01", "B01"],
"Amount": [10, 20, 30, ''],
"CloseDate": ['2009-01-02', '2009-01-03', '2009-01-04', '2010-01-02']
})
accounts = DataFrame({
"AccountId": ["A01", "B01"],
"Text_For_Donor_Wall__c": ["Donor A", "Donor B"]
})
actual = convert_donors(opportunities=opportunities, accounts=accounts)
expected = """
[
{
"donations": [
{
"amount": "$40",
"year": 2009
},
{
"amount": "$40",
"year": "all-time"
}
],
"name": "Donor A"
},
{
"donations": [
{
"amount": "$20",
"year": 2009
},
{
"amount": "$20",
"year": "all-time"
}
],
"name": "Donor B"
}
]
"""
assert json.loads(actual) == json.loads(expected)
sleep(3)
bulk.close_job(job)
rows = bulk.get_batch_result_iter(job, batch, parse_csv=True)
accts = DataFrame.from_dict(list(rows))
accts.rename(columns={'Id': 'AccountId'}, inplace=True)
return opps, accts
# Circles
print "Fetching Circle data..."
generate_circle_data()
# Sponsors
opps, accts = sf_data(sponsors_query)
print "Transforming and exporting to JSON..."
json_output = convert_sponsors(opportunities=opps, accounts=accts)
print "Saving sponsors to S3..."
push_to_s3(filename='sponsors.json', contents=json_output)
# Donors
opps, accounts = sf_data(donors_query)
print "Transforming and exporting to JSON..."
json_output = convert_donors(opportunities=opps, accounts=accts)
print "Saving donors to S3..."
push_to_s3(filename='donors.json', contents=json_output)
