def test_extract_items(tmpdir):
infile = "tests/testdata/oresund_vertical_slice.dfsu"
outfile = os.path.join(tmpdir.dirname, "extracted_vertical_slice.dfsu")
extract(infile, outfile, items="Temperature")
extracted = mikeio.read(outfile)
assert extracted.n_items == 1
extract(infile, outfile, items=[0, 2])
extracted = mikeio.read(outfile)
assert extracted.n_items == 2
extract(infile, outfile, items=["Salinity", 1])
extracted = mikeio.read(outfile)
assert extracted.n_items == 2
with pytest.raises(Exception):
# must be unique
extract(infile, outfile, items=["Salinity", 2])
with pytest.raises(Exception):
# no negative numbers
extract(infile, outfile, items=[0, 2, -1])
with pytest.raises(Exception):
extract(infile, outfile, items=[0, "not_an_item"])
def test_extract_non_equidistant(tmpdir):
infile = "tests/testdata/da_diagnostic.dfs0"
outfile = os.path.join(tmpdir.dirname, "diagnostic_subset.dfs0")
extract(infile, outfile, start="2017-10-27 01:58", end="2017-10-27 04:32")
extracted = mikeio.read(outfile)
assert extracted.n_timesteps == 16
assert extracted.time[0].hour == 2
extract(infile, outfile, end=3600.0)
extracted = mikeio.read(outfile)
assert extracted.n_timesteps == 7
assert extracted.time[0].hour == 0
assert extracted.time[-1].minute == 0
extract(infile, outfile, start=1800.0, end=237981231.232)
extracted = mikeio.read(outfile)
assert extracted.n_timesteps > 0
extract(infile, outfile, "2017-10-27,2017-10-28")
extracted = mikeio.read(outfile)
assert extracted.n_timesteps > 0
with pytest.raises(Exception):
extract(infile, outfile, start=7200.0, end=1800.0)
def test_extract_equidistant(tmpdir):
infile = "tests/testdata/waves.dfs2"
outfile = os.path.join(tmpdir.dirname, "waves_subset.dfs2")
extract(infile, outfile, start=1, end=-1)
orig = mikeio.read(infile)
extracted = mikeio.read(outfile)
assert extracted.n_timesteps == orig.n_timesteps - 1
assert extracted.time[0] == orig.time[1]
def test_write_xyz(tmpdir):
outfilename = tmpdir.join("foo.xyz")
filename = "tests/testdata/positions.xyz"
df1 = mikeio.read(filename)
df1.to_xyz(outfilename)
df2 = mikeio.read(outfilename)
assert df1.shape == df2.shape
def test_dfs2_to_dfs1_x_direction(tmpdir):
dfs2file = "tests/testdata/eq.dfs2"
ds2 = mikeio.read(dfs2file)
assert ds2.data[0].shape == (25, 10, 20)
dfs1file1 = os.path.join(tmpdir.dirname, "eq_ax_x.dfs1")
dfs2todfs1(dfs2file, dfs1file1, axis=2)
ds1 = mikeio.read(dfs1file1)
assert ds1.data[0].shape == (25, 10)
def test_diff_dfsu(tmpdir):
infilename_a = "tests/testdata/HD2D.dfsu"
infilename_b = "tests/testdata/HD2D.dfsu"
outfilename = os.path.join(tmpdir.dirname, "diff.dfsu")
mikeio.generic.diff(infilename_a, infilename_b, outfilename)
org = mikeio.read(infilename_a)
diffed = mikeio.read(outfilename)
expected = 0.0
scaledvalue = diffed.data[0][0]
assert scaledvalue == pytest.approx(expected)
def test_multiply_constant(tmpdir):
infilename = "tests/testdata/random.dfs0"
outfilename = os.path.join(tmpdir.dirname, "mult.dfs0")
scale(infilename, outfilename, factor=1.5)
org = mikeio.read(infilename)
scaled = mikeio.read(outfilename)
orgvalue = org.data[0][0]
expected = orgvalue * 1.5
scaledvalue = scaled.data[0][0]
assert scaledvalue == pytest.approx(expected)
def test_add_constant(tmpdir):
infilename = "tests/testdata/random.dfs0"
outfilename = os.path.join(tmpdir.dirname, "add.dfs0")
scale(infilename, outfilename, offset=100.0)
org = mikeio.read(infilename)
scaled = mikeio.read(outfilename)
orgvalue = org.data[0][0]
expected = orgvalue + 100.0
scaledvalue = scaled.data[0][0]
assert scaledvalue == pytest.approx(expected)
def test_dfs2_to_dfs1_y_direction(tmpdir):
dfs2file = "tests/testdata/eq.dfs2"
ds2 = mikeio.read(dfs2file)
assert ds2.data[0].shape == (25, 10, 20) # t, y, x
dfs1file1 = os.path.join(tmpdir.dirname, "eq_ax_y.dfs1")
dfs2todfs1(dfs2file, dfs1file1) # default aggregation is over y axis
ds1 = mikeio.read(dfs1file1)
assert ds1.data[0].shape == (25, 20)
def test_dfsu_to_dfs0(tmpdir):
dfsufile = "tests/testdata/HD2D.dfsu"
ds_in = mikeio.read(dfsufile)
assert ds_in.data[0].shape == (9, 884)
dfs0file1 = os.path.join(tmpdir.dirname, "HD2D_mean.dfs0")
dfstodfs0(dfsufile, dfs0file1)
ds0 = mikeio.read(dfs0file1)
assert ds0.data[0].shape == (9, )
def test_linear_transform_dfsu(tmpdir):
infilename = "tests/testdata/HD2D.dfsu"
outfilename = os.path.join(tmpdir.dirname, "linear.dfsu")
scale(infilename, outfilename, offset=-20.0, factor=1.5)
org = mikeio.read(infilename)
scaled = mikeio.read(outfilename)
orgvalue = org.data[0][0]
expected = orgvalue * 1.5 - 20.0
scaledvalue = scaled.data[0][0]
assert scaledvalue == pytest.approx(expected)
def test_extract_non_equidistant(tmpdir):
infile = "tests/testdata/da_diagnostic.dfs0"
outfile = os.path.join(tmpdir.dirname, "diagnostic_subset.dfs0")
extract(infile, outfile, start="2017-10-27 01:58", end="2017-10-27 04:32")
extracted = mikeio.read(outfile)
assert extracted.n_timesteps == 16
assert extracted.time[0].hour == 2
extract(infile, outfile, end=3600.0)
extracted = mikeio.read(outfile)
assert extracted.n_timesteps == 7
assert extracted.time[0].hour == 0
assert extracted.time[-1].minute == 0
def test_sum_dfsu(tmpdir):
infilename_a = "tests/testdata/HD2D.dfsu"
infilename_b = "tests/testdata/HD2D.dfsu"
outfilename = os.path.join(tmpdir.dirname, "sum.dfsu")
mikeio.generic.sum(infilename_a, infilename_b, outfilename)
org = mikeio.read(infilename_a)
summed = mikeio.read(outfilename)
orgvalue = org.data[0][0]
expected = orgvalue * 2
scaledvalue = summed.data[0][0]
assert scaledvalue == pytest.approx(expected)
def test_sum_itself(tmpdir):
infilename_1 = "tests/testdata/gebco_sound.dfs2"
infilename_2 = "tests/testdata/gebco_sound.dfs2"
outfilename = os.path.join(tmpdir.dirname, "diff.dfs2")
sum(infilename_1, infilename_2, outfilename)
org = mikeio.read(infilename_1)
assert np.isnan(org["Elevation"][0][0,-1])
summed = mikeio.read(outfilename)
assert np.isnan(summed["Elevation"][0][0,-1])
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_read_dfs1_generic_read():
filename = r"tests/testdata/random.dfs1"
res = mikeio.read(filename)
data = res.data[0]
assert data.shape == (100, 3) # time, x
def test_read_dfsu_generic_read():
filename = "tests/testdata/HD2D.dfsu"
ds = mikeio.read(filename)
assert len(ds) == 4
def test_read_dfsu_generic_read_single_item_number():
filename = "tests/testdata/HD2D.dfsu"
ds = mikeio.read(filename, item_numbers=[1])
assert len(ds) == 1
def test_write_from_data_frame_monkey_patched_data_value_not_default(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")
items = []
for col in df.columns:
items.append(
ItemInfo(col,
EUMType.Concentration,
EUMUnit.gram_per_meter_pow_3,
data_value_type="MeanStepBackward"))
df.to_dfs0(filename, items=items)
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
assert np.isnan(ds["Average"][3])
assert ds.time[0].year == 1958
assert ds.items[0].data_value_type == 3
def test_diff_itself(tmpdir):
infilename_1 = "tests/testdata/gebco_sound.dfs2"
infilename_2 = "tests/testdata/gebco_sound.dfs2"
outfilename = os.path.join(tmpdir.dirname, "diff.dfs2")
diff(infilename_1, infilename_2, outfilename)
org = mikeio.read(infilename_1)
assert np.isnan(org["Elevation"][0][0,-1])
diffed = mikeio.read(outfilename)
diffedvalue = diffed["Elevation"][0, 0, 0]
assert diffedvalue == pytest.approx(0.0)
assert np.isnan(diffed["Elevation"][0][0,-1])
def test_read_dfs0_generic_read():
filename = r"tests/testdata/random.dfs0"
res = mikeio.read(filename)
data = res.data
assert len(data) == 2
def test_read_dfs2_generic_read():
filename = r"tests/testdata/random.dfs2"
res = mikeio.read(filename, item_names=["testing water level"])
data = res.data[0]
assert data[0, 11, 0] == 0
assert data.shape == (3, 100, 2) # time, y, x
def test_read_dfsu_generic_read():
filename = "tests/testdata/HD2D.dfsu"
(data, t, names) = mikeio.read(filename)
assert len(data) == 4
assert len(names) == 4
def test_concat_overlapping(tmpdir):
infilename_a = "tests/testdata/tide1.dfs1"
infilename_b = "tests/testdata/tide2.dfs1"
outfilename = os.path.join(tmpdir.dirname, "concat.dfs1")
mikeio.generic.concat([infilename_a, infilename_b], outfilename)
ds = mikeio.read(outfilename)
assert len(ds.time) == 145
def test_multiply_constant_single_item_name(tmpdir):
infilename = "tests/testdata/wind_north_sea.dfsu"
outfilename = os.path.join(tmpdir.dirname, "multname.dfsu")
scale(infilename, outfilename, factor=1.5, items=["Wind speed"])
org = mikeio.read(infilename)
scaled = mikeio.read(outfilename)
orgvalue_speed = org["Wind speed"][0, 0]
expected_speed = orgvalue_speed * 1.5
scaledvalue_speed = scaled["Wind speed"][0, 0]
assert scaledvalue_speed == pytest.approx(expected_speed)
orgvalue_dir = org["Wind direction"][0, 0]
expected_dir = orgvalue_dir
scaledvalue_dir = scaled["Wind direction"][0, 0]
assert scaledvalue_dir == pytest.approx(expected_dir)
def test_multiply_constant_single_item_number(tmpdir):
infilename = "tests/testdata/wind_north_sea.dfsu"
outfilename = os.path.join(tmpdir.dirname, "mult.dfsu")
scale(infilename, outfilename, factor=1.5, item_numbers=[0])
org = mikeio.read(infilename)
scaled = mikeio.read(outfilename)
orgvalue_speed = org.data[0][0][0]
expected_speed = orgvalue_speed * 1.5
scaledvalue_speed = scaled.data[0][0][0]
assert scaledvalue_speed == pytest.approx(expected_speed)
orgvalue_dir = org.data[1][0][0]
expected_dir = orgvalue_dir
scaledvalue_dir = scaled.data[1][0][0]
assert scaledvalue_dir == pytest.approx(expected_dir)
def test_add_constant_delete_values_unchanged(tmpdir):
infilename = "tests/testdata/gebco_sound.dfs2"
outfilename = os.path.join(tmpdir.dirname, "adj.dfs2")
scale(infilename, outfilename, offset=-2.1, items=["Elevation"])
org = mikeio.read(infilename)
scaled = mikeio.read(outfilename)
orgvalue = org["Elevation"][0, 0, 0]
scaledvalue = scaled["Elevation"][0, 0, 0]
assert scaledvalue == pytest.approx(orgvalue - 2.1)
orgvalue = org["Elevation"][0, 100, 0]
assert np.isnan(orgvalue)
scaledvalue = scaled["Elevation"][0, 100, 0]
assert np.isnan(scaledvalue)
def test_concat_three_files(tmpdir):
infiles = [
"tests/testdata/tide1.dfs1",
"tests/testdata/tide2.dfs1",
"tests/testdata/tide4.dfs1",
]
outfilename = os.path.join(tmpdir.dirname, "concat.dfs1")
mikeio.generic.concat(infiles, outfilename)
ds = mikeio.read(outfilename)
assert len(ds.time) == (5 * 48 + 1)
def test_multiply_constant_delete_values_unchanged_2(tmpdir):
infilename = "tests/testdata/random_two_item.dfs2"
outfilename = os.path.join(tmpdir.dirname, "adj.dfs2")
item_name = "testing water level"
scale(infilename, outfilename, factor=1000.0, items=[item_name])
org = mikeio.read(infilename)
scaled = mikeio.read(outfilename)
orgvalue = org[item_name][0, 0, 0]
scaledvalue = scaled[item_name][0, 0, 0]
assert scaledvalue == pytest.approx(orgvalue * 1000.0)
orgvalue = org[item_name][0, 10, 0]
assert np.isnan(orgvalue)
scaledvalue = scaled[item_name][0, 10, 0]
assert np.isnan(scaledvalue)
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
