def test_time_series_ref(self):
result = ReactTable(slicer.metrics.votes) \
.transform(cont_uni_dim_ref_df,
slicer,
[
slicer.dimensions.timestamp,
slicer.dimensions.state
], [
ElectionOverElection(slicer.dimensions.timestamp)
])
self.assertIn('data', result)
result['data'] = result['data'][:2] # shorten the results to make the test easier to read
self.assertEqual({
'columns': [{'Header': 'Timestamp', 'accessor': '$d$timestamp'},
{'Header': 'State', 'accessor': '$d$state'},
{'Header': 'Votes', 'accessor': '$m$votes'},
{'Header': 'Votes (EoE)', 'accessor': '$m$votes_eoe'}],
'data': [{
'$d$state': {'display': 'Texas', 'raw': '1'},
'$d$timestamp': {'raw': '2000-01-01'},
'$m$votes': {'display': '6,233,385', 'raw': 6233385},
'$m$votes_eoe': {'display': '5,574,387', 'raw': 5574387}
}, {
'$d$state': {'display': 'California', 'raw': '2'},
'$d$timestamp': {'raw': '2000-01-01'},
'$m$votes': {'display': '10,428,632', 'raw': 10428632},
'$m$votes_eoe': {'display': '9,646,062', 'raw': 9646062}
}]
}, result)
def test_operations_evaluated_for_each_reference(self,
mock_fetch_data: Mock,
*mocks):
eoe = ElectionOverElection(slicer.dimensions.timestamp)
mock_operation = Mock(name='mock_operation ', spec=f.Operation)
mock_operation.key, mock_operation.definition = 'mock_operation', slicer.table.abc
mock_operation.metrics = []
mock_widget = f.Widget(mock_operation)
mock_widget.transform = Mock()
mock_df = {}
mock_fetch_data.return_value = mock_df
# Need to keep widget the last call in the chain otherwise the object gets cloned and the assertion won't work
slicer.data \
.dimension(slicer.dimensions.timestamp) \
.reference(eoe) \
.widget(mock_widget) \
.fetch()
mock_operation.apply.assert_has_calls([
call(mock_df, None),
call(mock_df, eoe),
])
def test_apply_to_timeseries_with_uni_dim_and_ref(self):
cumprod = CumProd(slicer.metrics.wins)
result = cumprod.apply(
cont_uni_dim_ref_df,
ElectionOverElection(slicer.dimensions.timestamp))
expected = pd.Series([1., 1, 1, 1, 1, 1, 1, 1, 1, 1],
name='$m$wins_eoe',
index=cont_uni_dim_ref_df.index)
pandas.testing.assert_series_equal(expected, result)
def test_apply_to_timeseries_with_uni_dim_and_ref(self):
rolling_mean = RollingMean(slicer.metrics.wins, 3)
result = rolling_mean.apply(
cont_uni_dim_ref_df,
ElectionOverElection(slicer.dimensions.timestamp))
expected = pd.Series(
[np.nan, np.nan, np.nan, np.nan, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
name='$m$wins_eoe',
index=cont_uni_dim_ref_df.index)
pandas.testing.assert_series_equal(expected, result)
def test_apply_to_timeseries_with_uni_dim_and_ref(self):
cummean = CumMean(slicer.metrics.votes)
result = cummean.apply(
cont_uni_dim_ref_df,
ElectionOverElection(slicer.dimensions.timestamp))
expected = pd.Series([
5574387.0, 9646062.0, 5903886.0, 10037347.0, 6389131.0,
10776668.333333334, 6793838.5, 11404064.75, 7010664.2, 11662100.4
],
name='$m$votes_eoe',
index=cont_uni_dim_ref_df.index)
pandas.testing.assert_series_equal(expected, result)
def test_ref_item_with_delta_percentage_formats_prefix_suffix(self):
exp_ref_item = {
'key': 'wins_with_suffix_and_prefix_eoe_delta_percent',
'label': 'Wins (EoE Δ%)',
'prefix': None,
'suffix': '%',
'precision': None,
}
ref = ElectionOverElection(slicer.dimensions.timestamp, delta=True, delta_percent=True)
ref_item = ReferenceItem(slicer.metrics.wins_with_suffix_and_prefix, ref)
self.assert_object_dict(ref_item, exp_ref_item, self.ref_item_attrs)
def test_base_ref_item(self):
exp_ref_item = {
'key': 'wins_with_suffix_and_prefix_eoe',
'label': 'Wins (EoE)',
'prefix': '$',
'suffix': '€',
'precision': None,
}
ref = ElectionOverElection(slicer.dimensions.timestamp)
ref_item = ReferenceItem(slicer.metrics.wins_with_suffix_and_prefix, ref)
self.assert_object_dict(ref_item, exp_ref_item, self.ref_item_attrs)
def test_time_series_ref(self):
result = CSV(slicer.metrics.votes) \
.transform(cont_uni_dim_ref_df,
slicer,
[
slicer.dimensions.timestamp,
slicer.dimensions.state
], [
ElectionOverElection(slicer.dimensions.timestamp)
])
expected = cont_uni_dim_ref_df.copy() \
.set_index(fd('state_display'), append=True) \
.reset_index(fd('state'), drop=True)[[fm('votes'), fm('votes_eoe')]]
expected.index.names = ['Timestamp', 'State']
expected.columns = ['Votes', 'Votes (EoE)']
self.assertEqual(expected.to_csv(), result)
def test_pivot_second_dimension_with_multiple_metrics_and_references(self):
result = ReactTable(slicer.metrics.votes, slicer.metrics.wins, pivot=[slicer.dimensions.state]) \
.transform(cont_uni_dim_ref_df,
slicer, [
slicer.dimensions.timestamp, slicer.dimensions.state
], [
ElectionOverElection(slicer.dimensions.timestamp)
])
self.assertIn('data', result)
result['data'] = result['data'][:2] # shorten the results to make the test easier to read
self.assertEqual({
'columns': [{'Header': 'Timestamp', 'accessor': '$d$timestamp'},
{
'Header': 'Votes',
'columns': [{'Header': 'Texas', 'accessor': '$m$votes.1'},
{'Header': 'California', 'accessor': '$m$votes.2'}]
}, {
'Header': 'Votes (EoE)',
'columns': [{'Header': 'Texas', 'accessor': '$m$votes_eoe.1'},
{
'Header': 'California',
'accessor': '$m$votes_eoe.2'
}]
}, {
'Header': 'Wins',
'columns': [{'Header': 'Texas', 'accessor': '$m$wins.1'},
{'Header': 'California', 'accessor': '$m$wins.2'}]
}, {
'Header': 'Wins (EoE)',
'columns': [{'Header': 'Texas', 'accessor': '$m$wins_eoe.1'},
{
'Header': 'California',
'accessor': '$m$wins_eoe.2'
}]
}],
'data': [{
'$d$timestamp': {'raw': '2000-01-01'},
'$m$votes': {
'1': {'display': '6,233,385', 'raw': 6233385},
'2': {'display': '10,428,632', 'raw': 10428632}
},
'$m$votes_eoe': {
'1': {'display': '5,574,387', 'raw': 5574387},
'2': {'display': '9,646,062', 'raw': 9646062}
},
'$m$wins': {
'1': {'display': '1', 'raw': 1},
'2': {'display': '1', 'raw': 1}
},
'$m$wins_eoe': {
'1': {'display': '1', 'raw': 1},
'2': {'display': '1', 'raw': 1}
}
}, {
'$d$timestamp': {'raw': '2004-01-01'},
'$m$votes': {
'1': {'display': '7,359,621', 'raw': 7359621},
'2': {'display': '12,255,311', 'raw': 12255311}
},
'$m$votes_eoe': {
'1': {'display': '6,233,385', 'raw': 6233385},
'2': {'display': '10,428,632', 'raw': 10428632}
},
'$m$wins': {
'1': {'display': '1', 'raw': 1},
'2': {'display': '1', 'raw': 1}
},
'$m$wins_eoe': {
'1': {'display': '1', 'raw': 1},
'2': {'display': '1', 'raw': 1}
}
}]
}, result)