def test_mod02_mix_order(
tmp_dir_path,
input_rows_lst,
df_expected_tests,
idx_ordered,
expected_label,
):
"""
Check the results are as expected for various alternative ordering
and repetitions of the input rows
"""
# Setup
in_filepath = tmp_dir_path / 'tmp_input.csv'
out_filepath = tmp_dir_path / 't11_output.csv'
# Given: Input data consisting of certain rows in a different order
_ = generate_input_data_csv([input_rows_lst[i] for i in idx_ordered],
in_filepath)
# When: Apply function
res_filepath = PCon.convert(in_filepath, out_filepath)
# Then: Result is as expected
df_reload_01 = PCon.load_formatted_file(res_filepath)
assert PCon.formatted_dfs_are_equal(
df_reload_01,
df_expected_tests[expected_label].iloc[idx_ordered, :].reset_index(
drop=True).pipe(add_one_to_index).rename_axis(
index=PCon.ROW_ID_NAME))
print(
"Correct: The reloaded values are equal, up to floating point tolerance"
)
def test_mod20_include_factors(
tmp_dir_path,
input_rows_lst,
df_expected_tests,
nrows,
include_factors,
expected_label,
):
"""Check that the `include_factors` argument produces the desired outcome"""
# Setup
in_filepath = tmp_dir_path / 'tmp_input.csv'
out_filepath = tmp_dir_path / 't10_output.csv'
# Given: Input data
_ = generate_input_data_csv(input_rows_lst, in_filepath)
# When: Apply function with limited rows and specify factors to include
res_filepath = PCon.convert(in_filepath,
out_filepath,
nrows=nrows,
include_factors=include_factors)
# Then: Result is as expected
df_reload_01 = PCon.load_formatted_file(res_filepath)
assert PCon.formatted_dfs_are_equal(df_reload_01,
df_expected_tests[expected_label])
print(
"Correct: The reloaded values are equal, up to floating point tolerance"
)
def test_CLI11_nrows(tmp_dir_path, input_rows_lst, df_expected_tests, nrows):
"""Give the argument to limit the number of rows"""
# Setup
in_filepath = tmp_dir_path / 'tmp_input.csv'
out_filepath = tmp_dir_path / 't05_output.csv'
# Given: Input data
_ = generate_input_data_csv(input_rows_lst, in_filepath)
# When: We run the CLI with option for limited number of rows
runner = CliRunner()
result = runner.invoke(PCon.cli, [
str(in_filepath), str(out_filepath),
'-r', nrows,
])
# Then: The CLI command completes successfully
# and the resulting output data is as expected
assert result.exit_code == 0
assert f"Output saved here: {out_filepath.absolute()}\n" in result.output
print("Correct: CLI has completed without error and with correct message")
df_reload_01 = PCon.load_formatted_file(out_filepath)
assert PCon.formatted_dfs_are_equal(
df_reload_01,
df_expected_tests[min(nrows if nrows is not None else 100, 5)]
)
print("Correct: The reloaded values are equal, up to floating point tolerance")
def test_CLI10_default_arguments(tmp_dir_path, input_rows_lst, df_expected_tests):
"""Default arguments"""
# Setup
in_filepath = tmp_dir_path / 'tmp_input.csv'
out_filepath = tmp_dir_path / 't04_output.csv'
# Given: Input data
_ = generate_input_data_csv(input_rows_lst, in_filepath)
# When: We run the CLI with default arguments
runner = CliRunner()
result = runner.invoke(
PCon.cli,
[str(in_filepath), str(out_filepath)] # Default arguments
)
# Then: The CLI command completes successfully
# and the resulting output data is as expected
assert result.exit_code == 0
assert f"Output saved here: {out_filepath.absolute()}\n" in result.output
for step_num in range(1, 6):
assert f"Step {step_num}" in result.output
print("Correct: CLI has completed without error and with correct message")
df_reload_01 = PCon.load_formatted_file(out_filepath)
assert PCon.formatted_dfs_are_equal(df_reload_01, df_expected_tests[5])
print("Correct: The reloaded values are equal, up to floating point tolerance")
def test_mod10_force_overwrite(tmp_dir_path, input_rows_lst,
df_expected_tests):
"""
Check that an existing file will not be overwritten unless force
is explicitly stated.
"""
# Setup
in_filepath = tmp_dir_path / 'tmp_input.csv'
out_filepath = tmp_dir_path / 't03_output.csv'
# Given: Input data and a file already exists in the output location
_ = generate_input_data_csv(input_rows_lst, in_filepath)
out_file_str = 'Some basic file contents'
_ = out_filepath.write_text(out_file_str)
assert out_filepath.read_text() == out_file_str # Check it has worked
# When: Apply function with default arguments (i.e. not force overwrite)
# Then: It throws an error and does not change the existing file
with pytest.raises(FileExistsError) as err:
PCon.convert(in_filepath, out_filepath)
assert err is not None # An error was thrown...
assert isinstance(err.value, FileExistsError) # ...of this specific type
assert 'File already exists' in str(
err.value) # The error message contains is helpful...
assert str(out_filepath.absolute()) in str(
err.value) # ...and contains the filepath
assert out_filepath.read_text(
) == out_file_str # The file contents are unchanged
print(
"Correct: File was not overwritten and helpful error message was thrown"
)
# When: Apply function force overwrite
res_filepath = PCon.convert(in_filepath,
out_filepath,
force_overwrite=True)
# Then: Result is as expected
df_reload_01 = PCon.load_formatted_file(
res_filepath) # Reload resulting data from workbook
assert PCon.formatted_dfs_are_equal(df_reload_01, df_expected_tests[5])
print(
"Correct: The reloaded values are equal, up to floating point tolerance"
)
def test_mod01_nrows(tmp_dir_path, input_rows_lst, df_expected_tests, nrows):
"""Give the argument to limit the number of rows"""
# Setup
in_filepath = tmp_dir_path / 'tmp_input.csv'
out_filepath = tmp_dir_path / 't02_output.csv'
# Given: Input data
_ = generate_input_data_csv(input_rows_lst, in_filepath)
# When: Apply function with limited rows
res_filepath = PCon.convert(in_filepath, out_filepath, nrows=nrows)
# Then: Result is as expected
df_reload_01 = PCon.load_formatted_file(res_filepath)
assert PCon.formatted_dfs_are_equal(
df_reload_01,
df_expected_tests[min(nrows if nrows is not None else 100, 5)])
print(
"Correct: The reloaded values are equal, up to floating point tolerance"
)
def test_mod00_default_arguments(tmp_dir_path, input_rows_lst,
df_expected_tests):
"""Default arguments"""
# Setup
in_filepath = tmp_dir_path / 'tmp_input.csv'
out_filepath = tmp_dir_path / 't01_output.csv'
# Given: Input data
_ = generate_input_data_csv(input_rows_lst, in_filepath)
# When: Apply function
res_filepath = PCon.convert(in_filepath, out_filepath)
# Then: Result is as expected
df_reload_01 = PCon.load_formatted_file(
res_filepath) # Reload resulting data from workbook
assert PCon.formatted_dfs_are_equal(df_reload_01, df_expected_tests[5])
print(
"Correct: The reloaded values are equal, up to floating point tolerance"
)
def test_CLI20_force_overwrite(tmp_dir_path, input_rows_lst, df_expected_tests):
"""
Check that an existing file will not be overwritten unless force
is explicitly stated.
"""
# Setup
in_filepath = tmp_dir_path / 'tmp_input.csv'
out_filepath = tmp_dir_path / 't07_output.csv'
# Given: Input data and a file already exists in the output location
_ = generate_input_data_csv(input_rows_lst, in_filepath)
out_file_str = 'Some basic file contents'
_ = out_filepath.write_text(out_file_str)
assert out_filepath.read_text() == out_file_str # Check it has worked
# When: We run the CLI with default arguments (i.e. not force overwrite)
runner = CliRunner()
result = runner.invoke(PCon.cli, [str(in_filepath), str(out_filepath)])
# Then: The CLI command exits with an error
assert result.exit_code == 1
assert result.exception # An error was thrown...
assert isinstance(result.exception, FileExistsError) # ...of this specific type
assert 'File already exists' in str(result.exception) # The error message contains is helpful..
assert str(out_filepath.absolute()) in str(result.exception) # ...and contains the filepath
assert out_filepath.read_text() == out_file_str # The file contents are unchanged
print("Correct: File was not overwritten and helpful error message was thrown")
# When: Run the CLI with force overwrite
result = runner.invoke(
PCon.cli, [str(in_filepath), str(out_filepath), '--force']
)
# Then: Result is as expected
df_reload_01 = PCon.load_formatted_file(out_filepath) # Reload resulting data from workbook
assert PCon.formatted_dfs_are_equal(df_reload_01, df_expected_tests[5])
print("Correct: The reloaded values are equal, up to floating point tolerance")
