def test_summary_values_data_frame():
"""Test for the correct layout and size of the summary values data frame."""
summary_values = _summary_values(pd.DataFrame(__TEST_DATA))
assert list(
summary_values.columns == ["summary_key", "batch", "date", "key1", "key2"]
)
assert len(summary_values) == len(__TEST_DATA["simulation"])
assert set(summary_values["batch"]) == set(__TEST_DATA["batch"])
assert set(summary_values["date"]) == {
pd.Timestamp(date) for date in __TEST_DATA["date"]
}
def test_well_plot(app, dash_duo, mocker, caplog):
test_data = {
"realization":
range(12),
"simulation":
range(12),
"batch": [0] * 6 + [1] * 6,
"date": [
datetime(2000, 1, 1),
datetime(2000, 2, 1),
datetime(2000, 3, 1),
datetime(2000, 1, 1),
datetime(2000, 2, 1),
datetime(2000, 3, 1),
datetime(2000, 1, 1),
datetime(2000, 2, 1),
datetime(2000, 3, 1),
datetime(2000, 1, 1),
datetime(2000, 2, 1),
datetime(2000, 3, 1),
],
"WOPR:OP1":
range(1, 13),
"WOPRT:OP1":
range(10, 130, 10),
}
mocker.patch(
"everviz.plugins.summary_plot.wells_plot.get_data",
return_value=_summary_values(pd.DataFrame(test_data)),
)
plugin = WellsPlot(app, "values")
app.layout = plugin.layout
dash_duo.start_server(app)
# Tests warning label not there
dash_duo.wait_for_contains_text(f"#{plugin.label_id}", "")
dash_duo.clear_input("#{}".format(plugin.dropdown_key))
dash_duo.select_dcc_dropdown("#{}".format(plugin.dropdown_key), "WOPR:OP1")
result = dash_duo.find_element("#{}".format(
plugin.dropdown_key)).text.split()[0]
dash_duo.find_element("#{} label:nth-child({})".format(
plugin.radio_statistics, 2)).click()
# Tests warning label is there
dash_duo.wait_for_contains_text(f"#{plugin.label_id}",
"Statistics are calculated")
for record in caplog.records:
assert record.levelname != "ERROR"
def test_summary_statistics_content():
"""Test for the correct content of the summary statistics data frame."""
summary_values = _summary_values(pd.DataFrame(__TEST_DATA))
summary_statistics = calculate_statistics(summary_values, ["key1", "key2"])
mean = []
p10 = []
p90 = []
min_value = []
max_value = []
min_realization = []
max_realization = []
for key in ["key1", "key2"]:
zipped = list(
zip(
__TEST_DATA["batch"],
__TEST_DATA["date"],
__TEST_DATA[key],
__TEST_DATA["realization"],
))
for batch in [0, 1]:
for date in [
datetime(2000, 1, 1),
datetime(2000, 2, 1),
datetime(2000, 3, 1),
]:
values = [
v for b, d, v, _ in zipped if batch == b and date == d
]
realizations = [
r for b, d, _, r in zipped if batch == b and date == d
]
mean.append(np.mean(values))
p10.append(np.quantile(values, q=0.1))
p90.append(np.quantile(values, q=0.9))
min_value.append(np.min(values))
max_value.append(np.max(values))
min_realization.append(realizations[np.argmin(values)])
max_realization.append(realizations[np.argmax(values)])
assert summary_statistics["mean"].to_list() == mean
assert summary_statistics["P10"].to_list() == p10
assert summary_statistics["P90"].to_list() == p90
assert summary_statistics["min_value"].to_list() == min_value
assert summary_statistics["max_value"].to_list() == max_value
assert summary_statistics["min_realization"].to_list() == min_realization
assert summary_statistics["max_realization"].to_list() == max_realization
def test_summary_statistics_data_frame():
"""Test for the correct layout and size of the summary statistics data frame"""
summary_values = _summary_values(pd.DataFrame(__TEST_DATA))
summary_statistics = calculate_statistics(summary_values)
assert list(summary_statistics.columns) == [
"summary_key",
"batch",
"date",
"mean",
"P10",
"P90",
]
assert len(summary_statistics) == 2 * 3 * 2 # #batches * #dates * #keys
assert set(summary_statistics["summary_key"]) == set(["key1", "key2"])
assert set(summary_statistics["batch"]) == set(__TEST_DATA["batch"])
assert set(summary_statistics["date"]) == {
pd.Timestamp(date) for date in __TEST_DATA["date"]
}
def test_summary_statistics_content():
"""Test for the correct content of the summary statistics data frame."""
summary_values = _summary_values(pd.DataFrame(__TEST_DATA))
summary_statistics = calculate_statistics(summary_values)
mean = []
p10 = []
p90 = []
for key in ["key1", "key2"]:
zipped = list(zip(__TEST_DATA["batch"], __TEST_DATA["date"], __TEST_DATA[key]))
for batch in [0, 1]:
for date in [
datetime(2000, 1, 1),
datetime(2000, 2, 1),
datetime(2000, 3, 1),
]:
values = [v for b, d, v in zipped if batch == b and date == d]
mean.append(np.mean(values))
p10.append(np.quantile(values, q=0.1))
p90.append(np.quantile(values, q=0.9))
assert summary_statistics["mean"].to_list() == mean
assert summary_statistics["P10"].to_list() == p10
assert summary_statistics["P90"].to_list() == p90
def test_summary_plot_callback(app, dash_duo, mocker, caplog):
test_data = {
"realization":
range(12),
"simulation":
range(12),
"batch": [0] * 6 + [1] * 6,
"date": [
datetime(2000, 1, 1),
datetime(2000, 2, 1),
datetime(2000, 3, 1),
datetime(2000, 1, 1),
datetime(2000, 2, 1),
datetime(2000, 3, 1),
datetime(2000, 1, 1),
datetime(2000, 2, 1),
datetime(2000, 3, 1),
datetime(2000, 1, 1),
datetime(2000, 2, 1),
datetime(2000, 3, 1),
],
"key1":
range(1, 13),
"key2":
range(10, 130, 10),
}
mocker.patch(
"everviz.plugins.summary_plot.summary_plot.get_data",
return_value=_summary_values(pd.DataFrame(test_data)),
)
plugin = SummaryPlot(app, "values")
app.layout = plugin.layout
dash_duo.start_server(app)
# Test statistics plot.
dash_duo.find_element("#{} label:nth-child({})".format(plugin.radio_id,
1)).click()
dash_duo.select_dcc_dropdown("#{}".format(plugin.key_dropdown_id), "key1")
# Test data plot.
dash_duo.find_element("#{} label:nth-child({})".format(plugin.radio_id,
2)).click()
# Test filtering realizations.
dash_duo.find_element("#{} label:nth-child({})".format(
plugin.realization_filter_check_id, 1)).click()
dash_duo.find_element("#{}".format(
plugin.realization_filter_input_id)).send_keys("1, 3")
# Clear the axes dropdowns, which should not cause an error.
#
# NOTE: if you try to do this before the previous click action on a radio
# button, you get an error, because the the dropdown stays open and overlaps
# with the radio buttons.
dash_duo.clear_input("#{}".format(plugin.xaxis_dropdown_id))
dash_duo.clear_input("#{}".format(plugin.key_dropdown_id))
for record in caplog.records:
assert record.levelname != "ERROR"
def mock_get_data(data_type):
if data_type == "values":
result = _summary_values(pd.DataFrame(test_data))
else:
result = _summary_statistics(pd.DataFrame(test_data))
return result