def test_conflict_on_conflicting_rows(A, B2):
"""Returns the two sets of two rows that are in conflict"""
result = h.conflict(A, B2, [0])
expected = pd.DataFrame(
[
(0, 0, "left"),
(2, 4, "left"),
(0, 99, "right"),
(2, 99, "right"),
],
index=[0, 2, 0, 1],
columns=[0, 1, "_merge"],
)
assert result.equals(expected)
# print(ksads.shape, added.shape, deleted.shape, modified.shape)
# %%
import changes.accepted
df = ksads.set_index('id')
changes.accepted.execute(df) # ACTION!
ksads = df.reset_index()
# %% [markdown]
# # Capture Changes
# %%
# print new shapes
added = h.diff(ksads, current_redcap.id)
deleted = h.diff(current_redcap, ksads.id)
modified = h.conflict(current_redcap, ksads, intersect_on="id", sources=("redcap", "ksads.net"))
print(ksads.shape, added.shape, deleted.shape, modified.shape)
# %%
# deleted is always empty, if not throw error
assert deleted.empty, "KSADS.net data has been deleted, take a look at `deleted` dataframe and figure out whether to delete those rows from redcap, Invalidate, mark a flag, or notify users etc."
deleted
file = open("changes/to_review.py", "w")
file.write("from changes.utils import block, modify\n\n\ndef execute(df):\n pass\n")
# %%
def generate_blocking_code_for_added_rows(added):
for _, row in added.iterrows():
info = row.additionalinfo
def test_conflict_on_nonconflicting_rows(A, B):
"""Since there is no conflict, should be empty"""
result = h.conflict(A, B, [0])
assert result.empty