def test_write_from_data_frame_different_types(tmpdir):
df = pd.read_csv(
"tests/testdata/co2-mm-mlo.csv",
parse_dates=True,
index_col="Date",
na_values=-99.99,
)
df = df[["Average", "Trend"]]
filename = os.path.join(tmpdir.dirname, "dataframe.dfs0")
items = [
ItemInfo("Average", EUMType.Concentration, EUMUnit.gram_per_meter_pow_3),
ItemInfo("Trend", EUMType.Undefined),
]
Dfs0.from_dataframe(df, filename, items=items)
ds = mikeio.read(filename)
assert len(ds.items) == 2
assert ds.items[0].type == EUMType.Concentration
assert ds.items[0].unit == EUMUnit.gram_per_meter_pow_3
assert ds.items[1].type == EUMType.Undefined
assert ds.items[1].unit == EUMUnit.undefined
def test_write_timestep_7days(tmpdir):
filename = os.path.join(tmpdir.dirname, "random.dfs0")
data = []
nt = 10
d1 = np.zeros(nt)
data.append(d1)
d2 = np.ones(nt)
data.append(d2)
items = [ItemInfo("Zeros"), ItemInfo("Ones")]
dfs = Dfs0()
dfs.write(
filename=filename,
data=data,
items=items,
title="Zeros and ones",
timeseries_unit=TimeStep.DAY,
dt=7,
)
assert os.path.exists(filename)
newdfs = Dfs0(filename)
res = newdfs.read()
dt = res.time[1] - res.time[0]
assert dt == timedelta(days=7)
assert res["Zeros"][-1] == 0.0
assert res["Ones"][-1] == 1.0
def test_read_dfs0_delete_value_conversion():
dfs0file = r"tests/testdata/da_diagnostic.dfs0"
dfs = Dfs0(dfs0file)
ds = dfs.read()
assert np.isnan(ds.data[3][1])
dfs0file = r"tests/testdata/random.dfs0"
dfs = Dfs0(dfs0file)
ds = dfs.read()
assert np.isnan(ds.data[0][2])
def test_create_non_equidistant_calendar():
dfs0file = r"neq.dfs0"
d1 = np.random.random([1000])
d2 = np.random.random([1000])
data = []
data.append(d1)
data.append(d2)
start_time = datetime.datetime(2017, 1, 1)
time_vector = []
for i in range(1000):
time_vector.append(start_time + datetime.timedelta(hours=i * 0.1))
title = "Hello Test"
items = [ItemInfo("VarFun01", 100000, 1000), ItemInfo("NotFun", 100000, 1000)]
data_value_type = [0, 1]
dfs = Dfs0()
dfs.create(
filename=dfs0file,
data=data,
datetimes=time_vector,
items=items,
title=title,
data_value_type=data_value_type,
)
assert True
os.remove(dfs0file)
def test_write_non_equidistant_calendar(tmpdir):
dfs0file = os.path.join(tmpdir.dirname, "neq.dfs0")
d1 = np.zeros(1000)
d2 = np.ones([1000])
data = []
data.append(d1)
data.append(d2)
start_time = datetime.datetime(2017, 1, 1)
time_vector = []
for i in range(1000):
time_vector.append(start_time + datetime.timedelta(hours=i * 0.1))
title = "Hello Test"
items = [ItemInfo("VarFun01", 100000, 1000), ItemInfo("NotFun", 100000, 1000)]
data_value_type = [0, 1]
dfs = Dfs0()
dfs.write(
filename=dfs0file,
data=data,
datetimes=time_vector,
items=items,
title=title,
data_value_type=data_value_type,
)
assert os.path.exists(dfs0file)
def test_read_dfs0_to_matrix():
dfs0file = r"tests/testdata/random.dfs0"
dfs = Dfs0()
(data, t, items) = dfs.read(filename=dfs0file)
assert len(data) == 2
def test_create_equidistant_calendar():
dfs0file = r"random.dfs0"
d1 = np.random.random([1000])
d2 = np.random.random([1000])
data = []
data.append(d1)
data.append(d2)
start_time = datetime.datetime(2017, 1, 1)
timeseries_unit = 1402
title = "Hello Test"
items = [ItemInfo("VarFun01", 100000, 1000), ItemInfo("NotFun", 100000, 1000)]
data_value_type = [0, 1] # TODO add data_value_type to ItemInfo
dt = 5
dfs = Dfs0()
dfs.create(
filename=dfs0file,
data=data,
start_time=start_time,
timeseries_unit=timeseries_unit,
dt=dt,
items=items,
title=title,
data_value_type=data_value_type,
)
os.remove(dfs0file)
assert True
def test_repr():
filename = os.path.join("tests", "testdata", "da_diagnostic.dfs0")
dfs = Dfs0(filename)
text = repr(dfs)
assert "NonEquidistant" in text
def test_create_timestep_7days():
dfs0File = r"zeros_ones.dfs0"
data = []
nt = 10
d1 = np.zeros(nt)
data.append(d1)
d2 = np.ones(nt)
data.append(d2)
items = [ItemInfo("Zeros"), ItemInfo("Ones")]
dfs = Dfs0()
dfs.create(
filename=dfs0File,
data=data,
items=items,
title="Zeros and ones",
timeseries_unit=TimeStep.DAY,
dt=7,
)
assert True
res = dfs.read(dfs0File)
dt = res.time[1] - res.time[0]
assert dt == timedelta(days=7)
os.remove(dfs0File)
def test_read_dfs0_to_matrix():
dfs0file = r"tests/testdata/random.dfs0"
dfs = Dfs0(dfs0file)
ds = dfs.read()
assert len(ds.data) == 2
def test_write_equidistant_calendar(tmpdir):
dfs0file = os.path.join(tmpdir.dirname, "random.dfs0")
d1 = np.random.random([1000])
d2 = np.random.random([1000])
data = []
data.append(d1)
data.append(d2)
start_time = datetime.datetime(2017, 1, 1)
timeseries_unit = 1402
title = "Hello Test"
items = [ItemInfo("VarFun01", 100000, 1000), ItemInfo("NotFun", 100000, 1000)]
data_value_type = [0, 1] # TODO add data_value_type to ItemInfo
dt = 5
dfs = Dfs0()
dfs.write(
filename=dfs0file,
data=data,
start_time=start_time,
timeseries_unit=timeseries_unit,
dt=dt,
items=items,
title=title,
data_value_type=data_value_type,
)
def test_read_relative_time_axis():
filename = r"tests/testdata/eq_relative.dfs0"
dfs0 = Dfs0(filename)
ds = dfs0.read()
assert len(ds) == 5
def test_read_non_eq_dfs0__temporal_subset():
dfs0file = r"tests/testdata/da_diagnostic.dfs0"
dfs = Dfs0(dfs0file)
ds = dfs.read(time_steps="2017-10-27 01:00,2017-10-27 02:00")
assert len(ds.time) == 7
def test_read_nonequidistant_dfs0_to_dataframe_no_freq():
dfs0file = r"tests/testdata/da_diagnostic.dfs0"
dfs = Dfs0(dfs0file)
df = dfs.to_dataframe()
assert df.index.freq is None
def test_read_dfs0_single_item():
dfs0file = r"tests/testdata/random.dfs0"
dfs = Dfs0()
(data, t, items) = dfs.read(dfs0file, item_numbers=[1])
assert len(data) == 1
def test_read_dfs0_to_dataframe():
dfs0file = r"tests/testdata/random.dfs0"
dfs = Dfs0(dfs0file)
pd = dfs.to_dataframe()
assert np.isnan(pd[pd.columns[0]][2])
def test_read_dfs0_single_item():
dfs0file = r"tests/testdata/random.dfs0"
dfs = Dfs0(dfs0file)
ds = dfs.read([1])
assert len(ds.data) == 1
def test_read_equidistant_dfs0_to_dataframe_fixed_freq():
dfs0file = r"tests/testdata/random.dfs0"
dfs = Dfs0()
df = dfs.to_dataframe(dfs0file)
assert df.index.freq is not None
def test_read_units_write_new(tmpdir):
dfs0file = r"tests/testdata/random.dfs0"
tmpfile = os.path.join(tmpdir.dirname, "random.dfs0")
dfs = Dfs0(dfs0file)
ds = dfs.read()
# write new file
dfs.write(tmpfile, ds)
# Verify that new file has same variables/units as original
newdfs = Dfs0(tmpfile)
ds2 = newdfs.read()
assert ds2.items[0].type == ds.items[0].type
assert ds2.items[0].unit == ds.items[0].unit
def test_read_equidistant_dfs0_to_dataframe_unit_in_name():
dfs0file = r"tests/testdata/random.dfs0"
dfs = Dfs0(dfs0file)
df = dfs.to_dataframe(unit_in_name=True)
assert "meter" in df.columns[0]
def test_read_dfs0_temporal_subset():
dfs0file = r"tests/testdata/random.dfs0"
dfs = Dfs0(dfs0file)
ds = dfs.read(time_steps=[1, 2])
assert len(ds.time) == 2
assert ds.time[0].strftime("%H") == "05"
def test_read_dfs0_single_item_named_access():
dfs0file = r"tests/testdata/random.dfs0"
dfs = Dfs0(dfs0file)
res = dfs.read(items=[1])
data = res.data
assert len(data) == 1
def test_repr_equidistant():
filename = os.path.join("tests", "testdata", "random.dfs0")
dfs = Dfs0(filename)
text = repr(dfs)
assert "Dfs0" in text
assert "Equidistant" in text
assert "NonEquidistant" not in text
def test_write_no_existing_file():
dfs0file = r"tests/testdata/random.dfs0"
dfs = Dfs0()
res = dfs.read(dfs0file)
data = res.data
# Overwrite the file
with pytest.raises(Exception):
dfs.write("not_a_file", data)
def test_read_dfs0_small_value_not_delete_value(tmpdir):
filename = os.path.join(tmpdir.dirname, "small.dfs0")
data = []
d = np.array([0.0, 0.0000001, -0.0001])
assert np.isclose(d, -1e-35, atol=1e-33).any()
data.append(d)
dfs = Dfs0()
dfs.write(filename=filename, data=data)
dfs = Dfs0(filename)
ds = dfs.read()
assert not np.isnan(ds.data[0]).any()
def test_create_from_data_frame(tmpdir):
df = pd.read_csv(
"tests/testdata/co2-mm-mlo.csv",
parse_dates=True,
index_col="Date",
na_values=-99.99,
)
filename = os.path.join(tmpdir.dirname, "dataframe.dfs0")
Dfs0.from_dataframe(
df, filename, itemtype=EUMType.Concentration, unit=EUMUnit.gram_per_meter_pow_3
) # Could not find better type
ds = mikeio.read(filename)
assert len(ds.items) == 5
assert ds.items[0].type == EUMType.Concentration
assert ds.items[0].unit == EUMUnit.gram_per_meter_pow_3
def test_write_default_datatype(tmpdir):
filename = os.path.join(tmpdir.dirname, "simple.dfs0")
data = []
d = np.random.random(100)
data.append(d)
dfs = Dfs0()
dfs.write(
filename=filename,
data=data,
start_time=datetime.datetime(2012, 1, 1),
dt=12,
items=[
ItemInfo("testing water level", EUMType.Water_Level, EUMUnit.meter)
],
title="test dfs0",
)
newdfs = Dfs0(filename)
assert newdfs.items[0].data_value_type == 0
def test_read_dfs0_single_item_read_by_name():
dfs0file = r"tests/testdata/random.dfs0"
dfs = Dfs0(dfs0file)
res = dfs.read(["NotFun", "VarFun01"]) # reversed order compare to original file
data = res.data
assert len(data) == 2
assert res.items[0].name == "NotFun"
assert res.items[0].type == EUMType.Water_Level
assert res.items[0].unit == EUMUnit.meter
assert repr(res.items[0].unit) == "meter"
def test_write_data_with_missing_values(tmpdir):
dfs0file = r"tests/testdata/random.dfs0"
tmpfile = os.path.join(tmpdir.dirname, "random.dfs0")
dfs = Dfs0(dfs0file)
ds = dfs.read()
# Do something with the data
ds.data[0] = np.zeros_like(ds.data[0])
ds.data[1] = np.ones_like(ds.data[0])
# Add some NaNs
ds.data[1][0:10] = np.nan
# Overwrite the file
dfs.write(tmpfile, ds)
# Write operation does not modify the data
assert np.isnan(ds.data[1][1])
moddfs = Dfs0(tmpfile)
modified = moddfs.read()
assert np.isnan(modified.data[1][5])
def test_write_int_not_possible(tmpdir):
filename = os.path.join(tmpdir.dirname, "simple_double.dfs0")
data = []
nt = 100
d = np.random.random([nt])
data.append(d)
dfs = Dfs0()
with pytest.raises(InvalidDataType):
dfs.write(filename=filename, data=data, dtype=np.int32)
