def test_robust_normalise_returns_dataframe():
# create test DataFrame
x = np.random.randn(50).tolist()
y = np.random.randn(50).tolist()
z = np.random.randn(50).tolist()
plate = (["plate1"] * 10 + ["plate2"] * 10 + ["plate3"] * 10 +
["plate4"] * 10 + ["plate5"] * 10)
compound = (["drug"] * 8 + ["DMSO"] * 2) * 5
colnames = ["A", "B", "C", "Metadata_plate", "Metadata_compound"]
df = pd.DataFrame(list(zip(x, y, z, plate, compound)), columns=colnames)
out = normalise.robust_normalise(df, plate_id="Metadata_plate")
assert isinstance(out, pd.DataFrame)
def test_robust_normalise_returns_dataframe():
# create test DataFrame
x = np.random.randn(50).tolist()
y = np.random.randn(50).tolist()
z = np.random.randn(50).tolist()
plate = (
["plate1"] * 10
+ ["plate2"] * 10
+ ["plate3"] * 10
+ ["plate4"] * 10
+ ["plate5"] * 10
)
compound = (["drug"] * 8 + ["DMSO"] * 2) * 5
colnames = ["A", "B", "C", "Metadata_plate", "Metadata_compound"]
df = pd.DataFrame(list(zip(x, y, z, plate, compound)), columns=colnames)
out = normalise.robust_normalise(df, plate_id="Metadata_plate")
assert isinstance(out, pd.DataFrame)
def test_robust_normalise_extra_metadata_cols():
# dataframe with weird columns names
x = np.random.randn(50).tolist()
y = np.random.randn(50).tolist()
z = np.random.randn(50).tolist()
plate = (["plate1"] * 10 + ["plate2"] * 10 + ["plate3"] * 10 +
["plate4"] * 10 + ["plate5"] * 10)
compound = (["drug"] * 8 + ["DMSO"] * 2) * 5
extra_metadata = ["A", "B"] * 25
colnames = ["A", "B", "C", "meta_plate", "meta_cmpd", "metadata_extra"]
df = pd.DataFrame(list(zip(x, y, z, plate, compound, extra_metadata)))
df.columns = colnames
out = normalise.robust_normalise(df,
metadata_string="meta",
compound="meta_cmpd",
plate_id="meta_plate")
assert df.shape == out.shape
assert df.columns.tolist() == out.columns.tolist()
def test_robust_normalise_non_default_cols():
# dataframe with weird columns names
x = np.random.randn(50).tolist()
y = np.random.randn(50).tolist()
z = np.random.randn(50).tolist()
plate = ["plate1"] * 10 + ["plate2"] * 10 + ["plate3"] * 10 + [
"plate4"
] * 10 + ["plate5"] * 10
compound = (["drug"] * 8 + ["DMSO"] * 2) * 5
colnames = ["A", "B", "C", "meta_plate", "meta_cmpd"]
non_default_df = pd.DataFrame(list(zip(x, y, z, plate, compound)),
columns=colnames)
out = normalise.robust_normalise(non_default_df,
metadata_string="meta",
compound="meta_cmpd",
plate_id="meta_plate")
assert isinstance(out, pd.DataFrame)
assert out.shape == non_default_df.shape
def test_robust_normalise_extra_metadata_cols():
# dataframe with weird columns names
x = np.random.randn(50).tolist()
y = np.random.randn(50).tolist()
z = np.random.randn(50).tolist()
plate = (
["plate1"] * 10
+ ["plate2"] * 10
+ ["plate3"] * 10
+ ["plate4"] * 10
+ ["plate5"] * 10
)
compound = (["drug"] * 8 + ["DMSO"] * 2) * 5
extra_metadata = ["A", "B"] * 25
colnames = ["A", "B", "C", "meta_plate", "meta_cmpd", "metadata_extra"]
df = pd.DataFrame(list(zip(x, y, z, plate, compound, extra_metadata)))
df.columns = colnames
out = normalise.robust_normalise(
df, metadata_string="meta", compound="meta_cmpd", plate_id="meta_plate"
)
assert df.shape == out.shape
assert df.columns.tolist() == out.columns.tolist()
def test_robust_normalise_non_default_cols():
# dataframe with weird columns names
x = np.random.randn(50).tolist()
y = np.random.randn(50).tolist()
z = np.random.randn(50).tolist()
plate = (
["plate1"] * 10
+ ["plate2"] * 10
+ ["plate3"] * 10
+ ["plate4"] * 10
+ ["plate5"] * 10
)
compound = (["drug"] * 8 + ["DMSO"] * 2) * 5
colnames = ["A", "B", "C", "meta_plate", "meta_cmpd"]
non_default_df = pd.DataFrame(list(zip(x, y, z, plate, compound)), columns=colnames)
out = normalise.robust_normalise(
non_default_df,
metadata_string="meta",
compound="meta_cmpd",
plate_id="meta_plate",
)
assert isinstance(out, pd.DataFrame)
assert out.shape == non_default_df.shape