mirror of https://github.com/morpheus65535/bazarr
265 lines
8.9 KiB
Python
265 lines
8.9 KiB
Python
from tqdm import tqdm
|
|
from tests_tqdm import with_setup, pretest, posttest, SkipTest, \
|
|
StringIO, closing
|
|
|
|
|
|
@with_setup(pretest, posttest)
|
|
def test_pandas_setup():
|
|
"""Test tqdm.pandas()"""
|
|
try:
|
|
from numpy.random import randint
|
|
import pandas as pd
|
|
except ImportError:
|
|
raise SkipTest
|
|
|
|
with closing(StringIO()) as our_file:
|
|
tqdm.pandas(file=our_file, leave=True, ascii=True, total=123)
|
|
series = pd.Series(randint(0, 50, (100,)))
|
|
series.progress_apply(lambda x: x + 10)
|
|
res = our_file.getvalue()
|
|
assert '100/123' in res
|
|
|
|
|
|
@with_setup(pretest, posttest)
|
|
def test_pandas_rolling_expanding():
|
|
"""Test pandas.(Series|DataFrame).(rolling|expanding)"""
|
|
try:
|
|
from numpy.random import randint
|
|
import pandas as pd
|
|
except ImportError:
|
|
raise SkipTest
|
|
|
|
with closing(StringIO()) as our_file:
|
|
tqdm.pandas(file=our_file, leave=True, ascii=True)
|
|
|
|
series = pd.Series(randint(0, 50, (123,)))
|
|
res1 = series.rolling(10).progress_apply(lambda x: 1, raw=True)
|
|
res2 = series.rolling(10).apply(lambda x: 1, raw=True)
|
|
assert res1.equals(res2)
|
|
|
|
res3 = series.expanding(10).progress_apply(lambda x: 2, raw=True)
|
|
res4 = series.expanding(10).apply(lambda x: 2, raw=True)
|
|
assert res3.equals(res4)
|
|
|
|
expects = ['114it'] # 123-10+1
|
|
for exres in expects:
|
|
our_file.seek(0)
|
|
if our_file.getvalue().count(exres) < 2:
|
|
our_file.seek(0)
|
|
raise AssertionError(
|
|
"\nExpected:\n{0}\nIn:\n{1}\n".format(
|
|
exres + " at least twice.", our_file.read()))
|
|
|
|
|
|
@with_setup(pretest, posttest)
|
|
def test_pandas_series():
|
|
"""Test pandas.Series.progress_apply and .progress_map"""
|
|
try:
|
|
from numpy.random import randint
|
|
import pandas as pd
|
|
except ImportError:
|
|
raise SkipTest
|
|
|
|
with closing(StringIO()) as our_file:
|
|
tqdm.pandas(file=our_file, leave=True, ascii=True)
|
|
|
|
series = pd.Series(randint(0, 50, (123,)))
|
|
res1 = series.progress_apply(lambda x: x + 10)
|
|
res2 = series.apply(lambda x: x + 10)
|
|
assert res1.equals(res2)
|
|
|
|
res3 = series.progress_map(lambda x: x + 10)
|
|
res4 = series.map(lambda x: x + 10)
|
|
assert res3.equals(res4)
|
|
|
|
expects = ['100%', '123/123']
|
|
for exres in expects:
|
|
our_file.seek(0)
|
|
if our_file.getvalue().count(exres) < 2:
|
|
our_file.seek(0)
|
|
raise AssertionError(
|
|
"\nExpected:\n{0}\nIn:\n{1}\n".format(
|
|
exres + " at least twice.", our_file.read()))
|
|
|
|
|
|
@with_setup(pretest, posttest)
|
|
def test_pandas_data_frame():
|
|
"""Test pandas.DataFrame.progress_apply and .progress_applymap"""
|
|
try:
|
|
from numpy.random import randint
|
|
import pandas as pd
|
|
except ImportError:
|
|
raise SkipTest
|
|
|
|
with closing(StringIO()) as our_file:
|
|
tqdm.pandas(file=our_file, leave=True, ascii=True)
|
|
df = pd.DataFrame(randint(0, 50, (100, 200)))
|
|
|
|
def task_func(x):
|
|
return x + 1
|
|
|
|
# applymap
|
|
res1 = df.progress_applymap(task_func)
|
|
res2 = df.applymap(task_func)
|
|
assert res1.equals(res2)
|
|
|
|
# apply unhashable
|
|
res1 = []
|
|
df.progress_apply(res1.extend)
|
|
assert len(res1) == df.size
|
|
|
|
# apply
|
|
for axis in [0, 1, 'index', 'columns']:
|
|
res3 = df.progress_apply(task_func, axis=axis)
|
|
res4 = df.apply(task_func, axis=axis)
|
|
assert res3.equals(res4)
|
|
|
|
our_file.seek(0)
|
|
if our_file.read().count('100%') < 3:
|
|
our_file.seek(0)
|
|
raise AssertionError("\nExpected:\n{0}\nIn:\n{1}\n".format(
|
|
'100% at least three times', our_file.read()))
|
|
|
|
# apply_map, apply axis=0, apply axis=1
|
|
expects = ['20000/20000', '200/200', '100/100']
|
|
for exres in expects:
|
|
our_file.seek(0)
|
|
if our_file.getvalue().count(exres) < 1:
|
|
our_file.seek(0)
|
|
raise AssertionError(
|
|
"\nExpected:\n{0}\nIn:\n {1}\n".format(
|
|
exres + " at least once.", our_file.read()))
|
|
|
|
|
|
@with_setup(pretest, posttest)
|
|
def test_pandas_groupby_apply():
|
|
"""Test pandas.DataFrame.groupby(...).progress_apply"""
|
|
try:
|
|
from numpy.random import randint, rand
|
|
import pandas as pd
|
|
except ImportError:
|
|
raise SkipTest
|
|
|
|
with closing(StringIO()) as our_file:
|
|
tqdm.pandas(file=our_file, leave=False, ascii=True)
|
|
|
|
df = pd.DataFrame(randint(0, 50, (500, 3)))
|
|
df.groupby(0).progress_apply(lambda x: None)
|
|
|
|
dfs = pd.DataFrame(randint(0, 50, (500, 3)), columns=list('abc'))
|
|
dfs.groupby(['a']).progress_apply(lambda x: None)
|
|
|
|
df2 = df = pd.DataFrame(dict(a=randint(1, 8, 10000), b=rand(10000)))
|
|
res1 = df2.groupby("a").apply(max)
|
|
res2 = df2.groupby("a").progress_apply(max)
|
|
assert res1.equals(res2)
|
|
|
|
our_file.seek(0)
|
|
|
|
# don't expect final output since no `leave` and
|
|
# high dynamic `miniters`
|
|
nexres = '100%|##########|'
|
|
if nexres in our_file.read():
|
|
our_file.seek(0)
|
|
raise AssertionError("\nDid not expect:\n{0}\nIn:{1}\n".format(
|
|
nexres, our_file.read()))
|
|
|
|
with closing(StringIO()) as our_file:
|
|
tqdm.pandas(file=our_file, leave=True, ascii=True)
|
|
|
|
dfs = pd.DataFrame(randint(0, 50, (500, 3)), columns=list('abc'))
|
|
dfs.loc[0] = [2, 1, 1]
|
|
dfs['d'] = 100
|
|
|
|
expects = ['500/500', '1/1', '4/4', '2/2']
|
|
dfs.groupby(dfs.index).progress_apply(lambda x: None)
|
|
dfs.groupby('d').progress_apply(lambda x: None)
|
|
dfs.groupby(dfs.columns, axis=1).progress_apply(lambda x: None)
|
|
dfs.groupby([2, 2, 1, 1], axis=1).progress_apply(lambda x: None)
|
|
|
|
our_file.seek(0)
|
|
if our_file.read().count('100%') < 4:
|
|
our_file.seek(0)
|
|
raise AssertionError("\nExpected:\n{0}\nIn:\n{1}\n".format(
|
|
'100% at least four times', our_file.read()))
|
|
|
|
for exres in expects:
|
|
our_file.seek(0)
|
|
if our_file.getvalue().count(exres) < 1:
|
|
our_file.seek(0)
|
|
raise AssertionError(
|
|
"\nExpected:\n{0}\nIn:\n {1}\n".format(
|
|
exres + " at least once.", our_file.read()))
|
|
|
|
|
|
@with_setup(pretest, posttest)
|
|
def test_pandas_leave():
|
|
"""Test pandas with `leave=True`"""
|
|
try:
|
|
from numpy.random import randint
|
|
import pandas as pd
|
|
except ImportError:
|
|
raise SkipTest
|
|
|
|
with closing(StringIO()) as our_file:
|
|
df = pd.DataFrame(randint(0, 100, (1000, 6)))
|
|
tqdm.pandas(file=our_file, leave=True, ascii=True)
|
|
df.groupby(0).progress_apply(lambda x: None)
|
|
|
|
our_file.seek(0)
|
|
|
|
exres = '100%|##########| 100/100'
|
|
if exres not in our_file.read():
|
|
our_file.seek(0)
|
|
raise AssertionError(
|
|
"\nExpected:\n{0}\nIn:{1}\n".format(exres, our_file.read()))
|
|
|
|
|
|
@with_setup(pretest, posttest)
|
|
def test_pandas_apply_args_deprecation():
|
|
"""Test warning info in
|
|
`pandas.Dataframe(Series).progress_apply(func, *args)`"""
|
|
try:
|
|
from numpy.random import randint
|
|
from tqdm import tqdm_pandas
|
|
import pandas as pd
|
|
except ImportError:
|
|
raise SkipTest
|
|
|
|
with closing(StringIO()) as our_file:
|
|
tqdm_pandas(tqdm(file=our_file, leave=False, ascii=True, ncols=20))
|
|
df = pd.DataFrame(randint(0, 50, (500, 3)))
|
|
df.progress_apply(lambda x: None, 1) # 1 shall cause a warning
|
|
# Check deprecation message
|
|
res = our_file.getvalue()
|
|
assert all([i in res for i in (
|
|
"TqdmDeprecationWarning", "not supported",
|
|
"keyword arguments instead")])
|
|
|
|
|
|
@with_setup(pretest, posttest)
|
|
def test_pandas_deprecation():
|
|
"""Test bar object instance as argument deprecation"""
|
|
try:
|
|
from numpy.random import randint
|
|
from tqdm import tqdm_pandas
|
|
import pandas as pd
|
|
except ImportError:
|
|
raise SkipTest
|
|
|
|
with closing(StringIO()) as our_file:
|
|
tqdm_pandas(tqdm(file=our_file, leave=False, ascii=True, ncols=20))
|
|
df = pd.DataFrame(randint(0, 50, (500, 3)))
|
|
df.groupby(0).progress_apply(lambda x: None)
|
|
# Check deprecation message
|
|
assert "TqdmDeprecationWarning" in our_file.getvalue()
|
|
assert "instead of `tqdm_pandas(tqdm(...))`" in our_file.getvalue()
|
|
|
|
with closing(StringIO()) as our_file:
|
|
tqdm_pandas(tqdm, file=our_file, leave=False, ascii=True, ncols=20)
|
|
df = pd.DataFrame(randint(0, 50, (500, 3)))
|
|
df.groupby(0).progress_apply(lambda x: None)
|
|
# Check deprecation message
|
|
assert "TqdmDeprecationWarning" in our_file.getvalue()
|
|
assert "instead of `tqdm_pandas(tqdm, ...)`" in our_file.getvalue()
|