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pyerrors/tests/pandas_test.py
Justus Kuhlmann 9489b87a7e
 fix nan to None test (#192)
2023-06-01 14:07:16 +01:00

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import numpy as np
import pandas as pd
import pyerrors as pe
import pytest
import warnings
def test_df_export_import(tmp_path):
my_dict = {"int": 1,
"float": -0.01,
"Obs1": pe.pseudo_Obs(87, 21, "test_ensemble"),
"Obs2": pe.pseudo_Obs(-87, 21, "test_ensemble2")}
for gz in [True, False]:
my_df = pd.DataFrame([my_dict] * 10)
pe.input.pandas.dump_df(my_df, (tmp_path / 'df_output').as_posix(), gz=gz)
reconstructed_df = pe.input.pandas.load_df((tmp_path / 'df_output').as_posix(), auto_gamma=True, gz=gz)
assert np.all(my_df == reconstructed_df)
pe.input.pandas.load_df((tmp_path / 'df_output.csv').as_posix(), gz=gz)
def test_null_first_line_df_export_import(tmp_path):
my_dict = {"int": 1,
"float": -0.01,
"Obs1": pe.pseudo_Obs(87, 21, "test_ensemble"),
"Obs2": pe.pseudo_Obs(-87, 21, "test_ensemble2")}
my_df = pd.DataFrame([my_dict] * 4)
my_df.loc[0, "Obs1"] = None
my_df.loc[2, "Obs1"] = None
for gz in [True, False]:
pe.input.pandas.dump_df(my_df, (tmp_path / 'df_output').as_posix(), gz=gz)
reconstructed_df = pe.input.pandas.load_df((tmp_path / 'df_output').as_posix(), auto_gamma=True, gz=gz)
assert reconstructed_df.loc[0, "Obs1"] is None
assert reconstructed_df.loc[2, "Obs1"] is None
assert np.all(reconstructed_df.loc[1] == my_df.loc[1])
assert np.all(reconstructed_df.loc[3] == my_df.loc[3])
def test_nan_df_export_import(tmp_path):
my_dict = {"int": 1,
"float": -0.01,
"Obs1": pe.pseudo_Obs(87, 21, "test_ensemble"),
"Obs2": pe.pseudo_Obs(-87, 21, "test_ensemble2")}
my_df = pd.DataFrame([my_dict] * 4)
my_df.loc[1, "int"] = np.nan
for gz in [True, False]:
with pytest.warns(UserWarning, match="nan value in column int will be replaced by None"):
pe.input.pandas.dump_df(my_df, (tmp_path / 'df_output').as_posix(), gz=gz)
reconstructed_df = pe.input.pandas.load_df((tmp_path / 'df_output').as_posix(), auto_gamma=True, gz=gz)
assert reconstructed_df.loc[1, "int"] is None
assert np.all(reconstructed_df.loc[:, "float"] == my_df.loc[:, "float"])
assert np.all(reconstructed_df.loc[:, "Obs1"] == my_df.loc[:, "Obs1"])
assert np.all(reconstructed_df.loc[:, "Obs2"] == my_df.loc[:, "Obs2"])
def test_null_second_line_df_export_import(tmp_path):
my_dict = {"int": 1,
"float": -0.01,
"Obs1": pe.pseudo_Obs(87, 21, "test_ensemble"),
"Obs2": pe.pseudo_Obs(-87, 21, "test_ensemble2")}
my_df = pd.DataFrame([my_dict] * 4)
my_df.loc[1, "Obs1"] = None
for gz in [True, False]:
pe.input.pandas.dump_df(my_df, (tmp_path / 'df_output').as_posix(), gz=gz)
reconstructed_df = pe.input.pandas.load_df((tmp_path / 'df_output').as_posix(), auto_gamma=True, gz=gz)
assert reconstructed_df.loc[1, "Obs1"] is None
assert np.all(reconstructed_df.loc[0] == my_df.loc[0])
assert np.all(reconstructed_df.loc[2:] == my_df.loc[2:])
def test_null_first_line_df_gzsql_export_import(tmp_path):
my_dict = {"int": 1,
"float": -0.01,
"Obs1": pe.pseudo_Obs(87, 21, "test_ensemble"),
"Obs2": pe.pseudo_Obs(-87, 21, "test_ensemble2")}
my_df = pd.DataFrame([my_dict] * 4)
my_df.loc[0, "Obs1"] = None
my_df.loc[2, "Obs1"] = None
gz = True
pe.input.pandas.to_sql(my_df, 'test', (tmp_path / 'test.db').as_posix(), gz=gz)
reconstructed_df = pe.input.pandas.read_sql('SELECT * FROM test', (tmp_path / 'test.db').as_posix(), auto_gamma=True)
assert reconstructed_df.loc[0, "Obs1"] is None
assert reconstructed_df.loc[2, "Obs1"] is None
assert np.all(reconstructed_df.loc[1] == my_df.loc[1])
assert np.all(reconstructed_df.loc[3] == my_df.loc[3])
def test_null_second_line_df_gzsql_export_import(tmp_path):
my_dict = {"int": 1,
"float": -0.01,
"Obs1": pe.pseudo_Obs(87, 21, "test_ensemble"),
"Obs2": pe.pseudo_Obs(-87, 21, "test_ensemble2")}
my_df = pd.DataFrame([my_dict] * 4)
my_df.loc[1, "Obs1"] = None
gz = True
pe.input.pandas.to_sql(my_df, 'test', (tmp_path / 'test.db').as_posix(), gz=gz)
reconstructed_df = pe.input.pandas.read_sql('SELECT * FROM test', (tmp_path / 'test.db').as_posix(), auto_gamma=True)
assert reconstructed_df.loc[1, "Obs1"] is None
assert np.all(reconstructed_df.loc[0] == my_df.loc[0])
assert np.all(reconstructed_df.loc[2:] == my_df.loc[2:])
def test_null_first_line_df_sql_export_import(tmp_path):
my_dict = {"int": 1,
"float": -0.01,
"Obs1": pe.pseudo_Obs(87, 21, "test_ensemble"),
"Obs2": pe.pseudo_Obs(-87, 21, "test_ensemble2")}
my_df = pd.DataFrame([my_dict] * 4)
my_df.loc[0, "Obs1"] = None
my_df.loc[2, "Obs1"] = None
gz = False
pe.input.pandas.to_sql(my_df, 'test', (tmp_path / 'test.db').as_posix(), gz=gz)
reconstructed_df = pe.input.pandas.read_sql('SELECT * FROM test', (tmp_path / 'test.db').as_posix(), auto_gamma=True)
assert reconstructed_df.loc[0, "Obs1"] is None
assert reconstructed_df.loc[2, "Obs1"] is None
assert np.all(reconstructed_df.loc[1] == my_df.loc[1])
assert np.all(reconstructed_df.loc[3] == my_df.loc[3])
def test_nan_sql_export_import(tmp_path):
my_dict = {"int": 1,
"float": -0.01,
"Obs1": pe.pseudo_Obs(87, 21, "test_ensemble"),
"Obs2": pe.pseudo_Obs(-87, 21, "test_ensemble2")}
my_df = pd.DataFrame([my_dict] * 4)
my_df.loc[1, "int"] = np.nan
gz = False
pe.input.pandas.to_sql(my_df, 'test', (tmp_path / 'test.db').as_posix(), gz=gz)
reconstructed_df = pe.input.pandas.read_sql('SELECT * FROM test', (tmp_path / 'test.db').as_posix(), auto_gamma=True)
with pytest.warns(UserWarning, match="nan value in column int will be replaced by None"):
warnings.warn("nan value in column int will be replaced by None", UserWarning)
assert np.isnan(reconstructed_df.loc[1, "int"])
assert np.all(reconstructed_df.loc[:, "float"] == my_df.loc[:, "float"])
assert np.all(reconstructed_df.loc[:, "Obs1"] == my_df.loc[:, "Obs1"])
assert np.all(reconstructed_df.loc[:, "Obs2"] == my_df.loc[:, "Obs2"])
def test_nan_gzsql_export_import(tmp_path):
my_dict = {"int": 1,
"float": -0.01,
"Obs1": pe.pseudo_Obs(87, 21, "test_ensemble"),
"Obs2": pe.pseudo_Obs(-87, 21, "test_ensemble2")}
my_df = pd.DataFrame([my_dict] * 4)
my_df.loc[1, "int"] = np.nan
gz = True
pe.input.pandas.to_sql(my_df, 'test', (tmp_path / 'test.db').as_posix(), gz=gz)
reconstructed_df = pe.input.pandas.read_sql('SELECT * FROM test', (tmp_path / 'test.db').as_posix(), auto_gamma=True)
assert np.isnan(reconstructed_df.loc[1, "int"])
assert np.all(reconstructed_df.loc[:, "float"] == my_df.loc[:, "float"])
assert np.all(reconstructed_df.loc[:, "Obs1"] == my_df.loc[:, "Obs1"])
assert np.all(reconstructed_df.loc[:, "Obs2"] == my_df.loc[:, "Obs2"])
def test_null_second_line_df_sql_export_import(tmp_path):
my_dict = {"int": 1,
"float": -0.01,
"Obs1": pe.pseudo_Obs(87, 21, "test_ensemble"),
"Obs2": pe.pseudo_Obs(-87, 21, "test_ensemble2")}
my_df = pd.DataFrame([my_dict] * 4)
my_df.loc[1, "Obs1"] = None
gz = False
pe.input.pandas.to_sql(my_df, 'test', (tmp_path / 'test.db').as_posix(), gz=gz)
reconstructed_df = pe.input.pandas.read_sql('SELECT * FROM test', (tmp_path / 'test.db').as_posix(), auto_gamma=True)
assert reconstructed_df.loc[1, "Obs1"] is None
assert np.all(reconstructed_df.loc[0] == my_df.loc[0])
assert np.all(reconstructed_df.loc[2:] == my_df.loc[2:])
def test_null_col_df_gzsql_export_import(tmp_path):
my_dict = {"int": 1,
"float": -0.01,
"Noneval": None,
"Obs1": pe.pseudo_Obs(87, 21, "test_ensemble"),
"Obs2": pe.pseudo_Obs(-87, 21, "test_ensemble2")}
my_df = pd.DataFrame([my_dict] * 4)
pe.input.pandas.to_sql(my_df, 'test', (tmp_path / 'test.db').as_posix(), gz=True)
reconstructed_df = pe.input.pandas.read_sql('SELECT * FROM test', (tmp_path / 'test.db').as_posix(), auto_gamma=True)
assert np.all(reconstructed_df["int"] == my_df["int"])
assert np.all(reconstructed_df["float"] == my_df["float"])
assert np.all([e is None for e in reconstructed_df["Noneval"]])
assert np.all(reconstructed_df["Obs1"] == my_df["Obs1"])
assert np.all(reconstructed_df["Obs2"] == my_df["Obs2"])
def test_df_Corr(tmp_path):
my_corr = pe.Corr([pe.pseudo_Obs(-0.48, 0.04, "test"), pe.pseudo_Obs(-0.154, 0.03, "test")])
my_dict = {"int": 1,
"float": -0.01,
"Corr": my_corr}
my_df = pd.DataFrame([my_dict] * 5)
pe.input.pandas.dump_df(my_df, (tmp_path / 'df_output').as_posix())
reconstructed_df = pe.input.pandas.load_df((tmp_path / 'df_output').as_posix(), auto_gamma=True)
def test_default_export_pe_import(tmp_path):
df = pd.DataFrame([{"Column1": 1.1, "Column2": 2, "Column3": "my string£"}])
df.to_csv((tmp_path / 'plain_df.csv').as_posix(), index=False)
re_df = pe.input.pandas.load_df((tmp_path / 'plain_df').as_posix(), gz=False)
assert np.all(df == re_df)
def test_pe_export_default_import(tmp_path):
df = pd.DataFrame([{"Column1": 1.1, "Column2": 2, "Column3": "my string£"}])
pe.input.pandas.dump_df(df, (tmp_path / 'pe_df').as_posix(), gz=False)
re_df = pd.read_csv((tmp_path / 'pe_df.csv').as_posix())
assert np.all(df == re_df)
def test_gz_serialization():
my_obs = pe.pseudo_Obs(0.1, 0.01, "pandas DataFrame ensemble only for test purposes.")
my_df = pd.DataFrame([{"Label": 1, "Obs": my_obs}])
for gz in [False, True]:
ser = pe.input.pandas._serialize_df(my_df, gz=gz)
deser = pe.input.pandas._deserialize_df(ser)
assert np.all(my_df == deser)
def test_sql(tmp_path):
my_list = [{"Label": i, "Obs": pe.pseudo_Obs(5 * np.exp(-0.2 * i), 0.01, "test_ensemble", 20)} for i in range(15)]
pe_df = pd.DataFrame(my_list)
my_db = (tmp_path / "test_db.sqlite").as_posix()
pe.input.pandas.to_sql(pe_df, "My_table", my_db)
for auto_gamma in [False, True]:
re_df = pe.input.pandas.read_sql("SELECT * from My_table", my_db, auto_gamma=auto_gamma)
assert np.all(re_df == pe_df)
def test_sql_if_exists_fail(tmp_path):
pe_df = pd.DataFrame([{"Label": 1, "Obs": pe.pseudo_Obs(5 * np.exp(-0.2), 0.01, "test_ensemble", 20)}])
my_db = (tmp_path / "test_db.sqlite").as_posix()
pe.input.pandas.to_sql(pe_df, "My_table", my_db)
with pytest.raises(ValueError):
pe.input.pandas.to_sql(pe_df, "My_table", my_db)
pe.input.pandas.to_sql(pe_df, "My_table", my_db, if_exists='append')
pe.input.pandas.to_sql(pe_df, "My_table", my_db, if_exists='replace')
def test_Obs_list_sql(tmp_path):
my_dict = {"int": 1,
"Obs1": pe.pseudo_Obs(17, 11, "test_sql_if_exists_failnsemble"),
"Obs_list": [[pe.pseudo_Obs(0.0, 0.1, "test_ensemble2"), pe.pseudo_Obs(3.2, 1.1, "test_ensemble2")]]}
pe_df = pd.DataFrame(my_dict)
my_db = (tmp_path / "test_db.sqlite").as_posix()
pe.input.pandas.to_sql(pe_df, "My_table", my_db)
for auto_gamma in [False, True]:
re_df = pe.input.pandas.read_sql("SELECT * from My_table", my_db, auto_gamma=auto_gamma)
assert np.all(re_df == pe_df)
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