diff --git a/test/run_dios.py b/test/run_dios.py
index bde77740a68f42faed4e283906899c7b371db1af..1409433a032a55aef8feef0eb1c85843cc5e24d0 100644
--- a/test/run_dios.py
+++ b/test/run_dios.py
@@ -1,5 +1,5 @@
-from ..dios import *
+from dios import *
import numpy as np
if __name__ == '__main__':
diff --git a/test/test__getitem__.py b/test/test__getitem__.py
index d94ee4ed0964b57c592a398f4570660940dbda2f..2012b2e02b1bbd713a409c4f25efa6ca70261801 100644
--- a/test/test__getitem__.py
+++ b/test/test__getitem__.py
@@ -1,18 +1,15 @@
-from ..dios import *
from .test_setup import *
from pandas.core.dtypes.common import is_scalar
-# s1 = pd.Series(range(10), index=range(10))
-# s2 = pd.Series(range(5, 10), index=range(5, 10))
-# s3 = pd.Series(range(1, 30, 2), index=range(1, 30, 2))
-# s4 = pd.Series(np.linspace(7, 13, 9), index=range(3, 12))
-# s1.name, s2.name, s3.name, s4.name = 'a', 'b', 'c', 'd'
-# d1 = DictOfSeries(data=dict(a=s1.copy(), b=s2.copy(), c=s3.copy(), d=s4.copy()))
-#
-d1 = dios__()
+unal1 = pd.Series(range(10), index=range(10))
+unal2 = pd.Series(range(5, 10), index=range(5, 10))
+unal3 = pd.Series(range(1, 30, 2), index=range(1, 30, 2))
+unal4 = pd.Series(np.linspace(7, 13, 9), index=range(3, 12))
+unal1.name, unal2.name, unal3.name, unal4.name = 'a', 'b', 'c', 'd'
+d1 = DictOfSeries(data=dict(a=unal1.copy(), b=unal2.copy(), c=unal3.copy(), d=unal4.copy()))
-@pytest.mark.parametrize(('idxer', 'exp'), [('a', s1), ('c', s3)])
+@pytest.mark.parametrize(('idxer', 'exp'), [('a', unal1), ('c', unal3)])
def test__getitem_single(idxer, exp):
a = d1[idxer]
b = d1.loc[:, idxer]
@@ -22,31 +19,31 @@ def test__getitem_single(idxer, exp):
assert (b == exp).all()
-@pytest.mark.parametrize(('idxer', 'exp'), [((1, 'a'), s1), ((3, 'c'), s3)])
+@pytest.mark.parametrize(('idxer', 'exp'), [((1, 'a'), unal1), ((3, 'c'), unal3)])
def test__getitem_scalar_loc(idxer, exp):
a = d1.loc[idxer]
assert is_scalar(a)
assert a == exp.loc[idxer[0]]
-@pytest.mark.parametrize(('idxer', 'exp'), [(0, s1), (1, s2), (2, s3), (3, s4),
- (-1, s4), (-2, s3), (-3, s2), (-4, s1)])
+@pytest.mark.parametrize(('idxer', 'exp'), [(0, unal1), (1, unal2), (2, unal3), (3, unal4),
+ (-1, unal4), (-2, unal3), (-3, unal2), (-4, unal1)])
def test__getitem_single_iloc(idxer, exp):
a = d1.iloc[:, idxer]
assert isinstance(a, pd.Series)
assert (a == exp).all()
-@pytest.mark.parametrize(('idxer', 'exp'), [((1, 0), s1), ((3, -2), s3), ((-1, -1), s4)])
+@pytest.mark.parametrize(('idxer', 'exp'), [((1, 0), unal1), ((3, -2), unal3), ((-1, -1), unal4)])
def test__getitem_scalar_iloc(idxer, exp):
a = d1.iloc[idxer]
assert is_scalar(a)
assert a == exp.iloc[idxer[0]]
-@pytest.mark.parametrize('idxer', ['x', '2', 1, None, ])
+@pytest.mark.parametrize('idxer', ['x', '2', 1000, None, ])
def test__getitem_single_fail(idxer):
- with pytest.raises(KeyError):
+ with pytest.raises((KeyError, ValueError)):
a = d1[idxer]
print(idxer, a)
@@ -63,14 +60,14 @@ def test__getitem_single_iloc_fail(idxer):
a = d1.iloc[:, idxer]
-@pytest.mark.parametrize('idxer', INDEXERS)
-def test__getitem__(idxer):
+@pytest.mark.parametrize('idxer', INDEXERS_NOBOOL)
+def test__getitem_(idxer):
d = d1[idxer]
assert isinstance(d, DictOfSeries)
@pytest.mark.parametrize('idxer', FAIL_INDEXERS)
-def test__getitem__fail(idxer):
+def test__getitem_fail(idxer):
with pytest.raises((ValueError, KeyError)):
d1[idxer]
diff --git a/test/test_ops.py b/test/test__ops__.py
similarity index 98%
rename from test/test_ops.py
rename to test/test__ops__.py
index 1e835d40b06167bd18a157fc908f9b3285901f5c..67479b0230b7fc29b8dc09ee2bdec432565a17c5 100644
--- a/test/test_ops.py
+++ b/test/test__ops__.py
@@ -1,6 +1,5 @@
#!/usr/bin/env python
from .test_setup import *
-from ..dios.operators import *
__author__ = "Bert Palm"
diff --git a/test/test__setitem__.py b/test/test__setitem__.py
index 28dfe6177a2d8ebff1106732e4ef5d97dc471d28..5ec57e6fc5faac7e5b8ac06de4b10d340d2a2bca 100644
--- a/test/test__setitem__.py
+++ b/test/test__setitem__.py
@@ -1,4 +1,3 @@
-from ..dios import *
from .test_setup import *
import pytest
diff --git a/test/test_df_like.py b/test/test_dflike.py
similarity index 98%
rename from test/test_df_like.py
rename to test/test_dflike.py
index d706321cd06d77b2c3a01c7dbb16c981a30d6f57..a0b93a0673b8d5ee8e021c0dbb9c5b7baaaf17b9 100644
--- a/test/test_df_like.py
+++ b/test/test_dflike.py
@@ -1,6 +1,6 @@
#!/usr/bin/env python
import pytest
-from ..dios import *
+from .test_setup import *
import pandas as pd
from pandas.core.dtypes.common import is_dict_like, is_nested_list_like
diff --git a/test/test_dflike__setget__.py b/test/test_dflike__setget__.py
index 453a91490d48b2d66d8a4eb67e222f6b1afb0eda..bfd8640862f2e6bac77c960598827603c5ba4cc7 100644
--- a/test/test_dflike__setget__.py
+++ b/test/test_dflike__setget__.py
@@ -1,4 +1,3 @@
-from ..dios import *
from .test_setup import *
import pytest
diff --git a/test/test_dios_old.py b/test/test_dios_old.py
deleted file mode 100644
index 7e0a024df55c948aecd00435ba8324f2cf1ad0be..0000000000000000000000000000000000000000
--- a/test/test_dios_old.py
+++ /dev/null
@@ -1,306 +0,0 @@
-from ..dios import *
-from ..profiling import *
-import pandas as pd
-import datetime as dt
-import numpy as np
-import pytest
-
-pytestmark = pytest.mark.skip
-
-
-v0 = 'var0'
-v1 = 'var1'
-v2 = 'var2'
-v3 = 'var3'
-v4 = 'var4'
-v5 = 'var5'
-v6 = 'var6'
-v7 = 'var7'
-v8 = 'var8'
-v9 = 'var9'
-
-
-def gen_series(rows, randomize=True):
- start = dt.datetime.strptime("2000-01-10 00:00:00", "%Y-%m-%d %H:%M:%S")
- if randomize:
- sec = np.random.randint(1, 10)
- rows = np.random.randint(int(rows * 0.5), rows * 2)
- start += pd.Timedelta(sec)
- else:
- sec = 10
- times = pd.date_range(periods=rows, start=start, freq=f'{sec}s')
- d = np.random.randint(1, 9, rows)
- return pd.Series(data=d, index=times)
-
-
-def test_getitem():
- # prepare
- begin = dt.datetime.strptime("2000-01-10 00:00:00", "%Y-%m-%d %H:%M:%S")
- t0 = begin + pd.Timedelta('20s')
- t1 = t0 + pd.Timedelta('50s')
- dios_aligned = DictOfSeries()
- dios_rand = DictOfSeries()
-
- # fill
- vars = 10
- rows = 10
- for i in range(0, vars):
- dios_aligned[f'var{i}'] = gen_series(rows, randomize=False)
- dios_rand[f'var{i}'] = gen_series(rows, randomize=True)
-
- # testsets
- var = [v0, [], [v1], [v0, v0], [v0, v2]]
- tssl = [slice(None), slice(t0, None), slice(None, t1), slice(t0, t1), slice(t0, t0)]
- ts = [t0, [], [t0], [t0, t0], [t0, t1]]
-
- for v in var:
- print(v)
- dios_rand[v]
-
- # use aligned dios for time stamps instead of time-ranges
- for t in tssl:
- print(t)
- dios_rand[v]
-
- try:
- dios_aligned[v1, v2]
- except KeyError:
- pass
-
- try:
- dios_aligned[v1, v2, v3]
- except KeyError:
- pass
-
-
-def test_setitem():
- # prepare
- begin = dt.datetime.strptime("2000-01-10 00:00:00", "%Y-%m-%d %H:%M:%S")
- t0 = begin + pd.Timedelta('30s')
- t1 = t0 + pd.Timedelta('50s')
- dios_aligned = DictOfSeries()
- dios_aligned.name = 'aligned'
- dios_rand = DictOfSeries()
- dios_rand.name = 'rand'
-
- # fill
- vars = 10
- rows = 100
- for i in range(0, vars):
- dios_aligned[f'var{i}'] = gen_series(rows, randomize=False)
- dios_rand[f'var{i}'] = gen_series(rows, randomize=True)
-
- # testsets
- keys = [v0, [v1], [v0, v0], [v0, v2]]
- tssl = [slice(None), slice(t0, None), slice(None, t1), slice(t0, t1), slice(t0, t0)]
- scalars = [1, 'deadbeef']
- l = list(np.random.randint(0, 100, rows))
- dios = [dios_aligned.copy(), dios_rand.copy()]
- for d in dios:
- d.name = 'src-dios'
-
- # assign scalars
- for val in scalars:
- for v in keys:
- print(v, '=', val)
- dios_rand[v] = val
-
- # assign scalars
- for val in scalars:
- for t in tssl:
- print(t, '=', val)
- dios_rand[t] = val
-
- # assign list
- for v in keys:
- print(v, '=', l)
- dios_aligned[v] = l
-
- # assign series
- for v in keys:
- print(v, '=', 'series')
- dios_aligned[v] = dios_aligned[v4]
-
- # assign dios
- for v in keys:
- for d in dios:
- print(f'{v} = dios[{v}]')
- dios_aligned[v] = d[v]
-
- # probkeys = [[], slice(v0, v0), ]
- # for v in probkeys:
- # try:
- # dios_aligned[v] = l
- # except ValueError:
- # pass
- # else:
- # raise AssertionError("should return an error")
-
-
-def test_integrity():
- rows = 1000
- cols = 10
- df, _, _, dios = get_testset(1000, 10)
-
- v = var_prefix + str(np.random.randint(0, cols))
- t = find_index_range(dios)
- t0, t1 = gen_random_timestamps(*t)
-
- # originals
- dios_ = dios.copy(deep=False)
- df_ = df.copy()
- s_ = df[v].dropna()
-
- # identity
- assert (dios_ == dios).all().all()
- assert (dios_[v] == dios[v]).all().all()
- assert dios_ is not dios
- assert dios_[v] is not dios[v]
-
- # equal t0 df
- assert dios[v] is not df[v]
- assert (dios[v] == df[v].dropna()).all().all()
-
- # write
- dios = dios_.copy()
- s = s_.copy()
- dios[t0:t1] = 4
- s[t0:t1] = 4
- assert (dios[v] == s).all()
-
- # write all
- s = s_.copy()
- dios = dios_.copy()
- dios[v] = 111
- s[:] = 111
- assert (dios[v] == s).all()
-
- # multi variables - slice
- df = df_.copy()
- dios = dios_.copy()
- li = [v0, v1, v2]
- dios[t0:t1] = 222
- for x in li:
- s = df[x].dropna()
- s[t0:t1] = 222
- assert (dios[x] == s).all()
- # on all
- dios[t0:t1] = 222.111
- m = df.loc[t0:t1,:].notna()
- df[m] = 222.111
- for x in df:
- s = df[x].dropna()
- assert (dios[x] == s).all()
-
- # multi variables - list
- df = df_.copy()
- dios = dios_.copy()
- li = [v0, v5, v3, v9]
- dios[t0:t1] = 333
- for x in li:
- s = df[x].dropna()
- s[t0:t1] = 333
- assert (dios[x] == s).all()
-
- # dios to dios
- df = df_.copy()
- dios = dios_.copy()
- dios[v] = 444
- dios[v5] = dios[v] * 0.1
- s = df[v].dropna()
- s[:] = 444 * 0.1
- assert (dios[v5] == s).all()
-
-
-def test_foreach():
- # dios.foreach() is alias for dios.pipe()
-
- dios = DictOfSeries()
- ser = pd.Series([1,4,5,6,3,5,5,3,2,1,13])
- dios['a'] = ser
- dios['b'] = ser * 4
-
- # return type of function: Series
- d = dios.pipe(pd.Series.astype, float)
- assert isinstance(d, DictOfSeries)
- assert not d['a'].dtype == ser.dtype
- assert (d['a'] == ser.astype(float)).all()
-
- # return type of function: scalar
- d = dios.pipe(min)
- assert isinstance(d, pd.Series)
- assert d['a'] == ser.min()
- assert d['b'] == ser.min() * 4
-
- # return type of function: scalar
- # but with squeeze
- d = dios.copy()
- del d['a']
- d = d.pipe(min)
- assert isinstance(d, np.int64)
- assert d == 4
-
- # test inplace
- d = dios.copy()
- d1 = dios.copy()
- d2 = d.pipe(pd.Series.drop_duplicates, inplace=False)
- # original didtn change
- assert d is not d1 and (d == d1).all()
- # tests on d2
- assert d2 is not d
- assert isinstance(d2, DictOfSeries)
- assert (d2['a'] == ser.copy().drop_duplicates()).all()
- assert (d2['b'] == (ser * 4 ).copy().drop_duplicates()).all()
-
- d3 = d.pipe(pd.Series.drop_duplicates, inplace=True)
- assert d3 is None
- # original did (!) change
- assert d is not d1
- try: d == d1
- except ValueError: pass;
- else: raise AssertionError
- # check if gone well..
- assert (d['a'] == ser.copy().drop_duplicates()).all()
- assert (d['b'] == (ser * 4 ).copy().drop_duplicates()).all()
-
-
-def tmptest():
- # prepare
- begin = dt.datetime.strptime("2000-01-10 00:00:00", "%Y-%m-%d %H:%M:%S")
- t0 = begin + pd.Timedelta('20s')
- t1 = t0 + pd.Timedelta('50s')
- diosa = DictOfSeries()
- diosr = DictOfSeries()
-
- # fill
- vars = 10
- rows = 8
- for i in range(0, vars):
- diosa[f'v{i}'] = gen_series(rows, randomize=False)
- diosr[f'v{i}'] = gen_series(rows, randomize=True)
-
- diosa[[], slice(None)] = 99
- exit(9)
-
- print(diosa)
- d = diosa[t0:t1, v1]
- d = d * 1000
- print(d)
- print()
- print(diosa[t0:t1, v1])
- l1 = len(diosa[t0:t1, v1])
- l2 = len(d)
- print(l1, l2)
- diosa[t0:t1, v1] = d
- print(diosa[v1])
- exit(3)
-
-
-if __name__ == '__main__':
- dios_options['disp_max_rows'] = 20
-
- # tmptest()
- test_getitem()
- test_setitem()
- test_integrity()
- test_foreach()
diff --git a/test/test_methods.py b/test/test_methods.py
index 95dfb347f301550e560f510d54047410a562ee3c..60a2b297c47e3b04733edf0f3b74fc20cd5ebda8 100644
--- a/test/test_methods.py
+++ b/test/test_methods.py
@@ -24,9 +24,9 @@ def test_copy_copy_empty(getDtDiosAligned):
@pytest.mark.parametrize('left', diosFromMatr(DATA_UNALIGNED))
+# we use comp ops just to get some noise in the data
@pytest.mark.parametrize('op', OPCOMP)
def test_all(left, op):
- # we use comp ops just to get some noise in the data
a = left
ser = (op(a, a)).all()
assert isinstance(ser, pd.Series)
diff --git a/test/test_setup.py b/test/test_setup.py
index b98ace0a58f1322e9f376a756e13406cde578c1d..29e9ffb4094dca67e3a80fd415ddd25318669c07 100644
--- a/test/test_setup.py
+++ b/test/test_setup.py
@@ -1,6 +1,6 @@
import pytest
-from ..dios import *
-from ..dios.operators import _OP1_MAP, _OP2_DIV_MAP, _OP2_ARITH_MAP, _OP2_BOOL_MAP, _OP2_COMP_MAP
+from dios.dios import *
+from dios.operators import OP_MAP, _OP1_MAP, _OP2_DIV_MAP, _OP2_ARITH_MAP, _OP2_BOOL_MAP, _OP2_COMP_MAP
import pandas as pd
import numpy as np
from copy import deepcopy
@@ -133,12 +133,13 @@ def dios_eq_df(dios, df, dios_dropped_empty_colums=False, with_msg=False, raisea
BLIST = [True, False, False, False, True] * 2
LISTIDXER = [['a'], ['a', 'c'], pd.Series(['a', 'c'])]
-BOOLIDXER = [BLIST, pd.Series(BLIST), df__() > 10]
+BOOLIDXER = [BLIST, pd.Series(BLIST), pd.Series(BLIST).values]
SLICEIDXER = [slice(None), slice(4), slice(-3, -1), slice(-1, 3), slice(None, None, 3)]
-MULTIIDXER = [df__() > 9, df__() != df__(), df__() == df__()]
+MULTIIDXER = [df__() > 9, df__() != df__(), df__() == df__(), df__() % 3 == 0]
EMPTYIDEXER = [[], pd.Series(), slice(3, 3), slice(3, -1), pd.DataFrame(), []]
INDEXERS = LISTIDXER + BOOLIDXER + SLICEIDXER + MULTIIDXER + EMPTYIDEXER
+INDEXERS_NOBOOL = LISTIDXER + SLICEIDXER + MULTIIDXER + EMPTYIDEXER
LOC_L = [slice(None), slice(2, 8), pd.Series(BLIST), BLIST, [6, 5], 2]
LOC_R = [slice(None), slice('a', 'c'), pd.Series([False, False, True, False], index=list("abcd")),
@@ -149,7 +150,7 @@ IEMPTY = [[], slice(3, 3), slice(3, -1), []]
ILOC_L = [slice(None), slice(2, 8), BLIST, ] + ILIST
ILOC_R = [slice(None), slice(1, 3), [False, False, True, False], ] + ILIST
-FAIL_INDEXERS = [['z'], ['a', 'z'], pd.Series(['a', 'z']), BLIST, pd.DataFrame(dict(a=[1, 2, 3]))]
+FAIL_INDEXERS = [['z'], ['a', 'z'], pd.Series(['a', 'z']), pd.DataFrame(dict(a=[1, 2, 3]))]
O = [[0, 0, 0], [0, 0, 0]]
I = [[1, 1, 1], [1, 1, 1]]