3f538865b5
* lift node version restraint to allow more recent version if already installed * add node test for latest version * change python version * use purple for debug logs * update workflow * add playwright dev dependency * update workflow * change test * oops * improve test * update test * fix tests * mv units tests to a subfolder * reorganize tests * fix install * update test_state * revert node changes and only keep new tests organization * move integration tests in tests/integration * fix integration workflow * fix dockerfile workflow * fix dockerfile workflow 2 * fix shared_state
122 lines
3.6 KiB
Python
122 lines
3.6 KiB
Python
import pandas as pd
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import pytest
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import reflex as rx
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from reflex.components.gridjs.datatable import DataTable
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from reflex.utils import types
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from reflex.utils.serializers import serialize, serialize_dataframe
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@pytest.mark.parametrize(
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"data_table_state,expected",
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[
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pytest.param(
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{
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"data": pd.DataFrame(
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[["foo", "bar"], ["foo1", "bar1"]], columns=["column1", "column2"]
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)
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},
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"data",
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),
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pytest.param({"data": ["foo", "bar"]}, ""),
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pytest.param({"data": [["foo", "bar"], ["foo1", "bar1"]]}, ""),
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],
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indirect=["data_table_state"],
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)
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def test_validate_data_table(data_table_state: rx.State, expected):
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"""Test the str/render function.
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Args:
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data_table_state: The state fixture.
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expected: expected var name.
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"""
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if not types.is_dataframe(data_table_state.data._var_type):
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data_table_component = DataTable.create(
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data=data_table_state.data, columns=data_table_state.columns
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)
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else:
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data_table_component = DataTable.create(data=data_table_state.data)
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data_table_dict = data_table_component.render()
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# prefix expected with state name
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state_name = data_table_state.get_name()
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expected = f"{state_name}.{expected}" if expected else state_name
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assert data_table_dict["props"] == [
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f"columns={{{expected}.columns}}",
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f"data={{{expected}.data}}",
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]
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@pytest.mark.parametrize(
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"props",
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[
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{"data": [["foo", "bar"], ["foo1", "bar1"]]},
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{
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"data": pd.DataFrame([["foo", "bar"], ["foo1", "bar1"]]),
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"columns": ["column1", "column2"],
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},
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],
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)
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def test_invalid_props(props):
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"""Test if value error is thrown when invalid props are passed.
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Args:
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props: props to pass in component.
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"""
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with pytest.raises(ValueError):
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DataTable.create(**props)
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@pytest.mark.parametrize(
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"fixture, err_msg, is_data_frame",
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[
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(
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"data_table_state2",
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"Annotation of the computed var assigned to the data field should be provided.",
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True,
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),
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(
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"data_table_state3",
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"Annotation of the computed var assigned to the column field should be provided.",
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False,
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),
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(
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"data_table_state4",
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"Annotation of the computed var assigned to the data field should be provided.",
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False,
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),
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],
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)
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def test_computed_var_without_annotation(fixture, request, err_msg, is_data_frame):
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"""Test if value error is thrown when the computed var assigned to the data/column prop is not annotated.
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Args:
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fixture: the state.
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request: fixture request.
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err_msg: expected error message.
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is_data_frame: whether data field is a pandas dataframe.
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"""
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with pytest.raises(ValueError) as err:
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if is_data_frame:
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DataTable.create(data=request.getfixturevalue(fixture).data)
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else:
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DataTable.create(
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data=request.getfixturevalue(fixture).data,
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columns=request.getfixturevalue(fixture).columns,
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)
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assert err.value.args[0] == err_msg
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def test_serialize_dataframe():
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"""Test if dataframe is serialized correctly."""
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df = pd.DataFrame(
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[["foo", "bar"], ["foo1", "bar1"]], columns=["column1", "column2"]
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)
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value = serialize(df)
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assert value == serialize_dataframe(df)
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assert isinstance(value, dict)
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assert tuple(value) == ("columns", "data")
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