def test_csv():
def time_parse(value):
return int(value)
def value_parse(value):
try:
return int(value)
except ValueError:
return None
filename = 'sample.csv'
with open(filename, 'w') as csvfile:
writer = csv.writer(csvfile)
writer.writerow(['hour', 'value'])
writer.writerow(['10', '15'])
writer.writerow(['11', '34'])
writer.writerow(['12', '19'])
writer.writerow(['13', 'nan'])
writer.writerow(['14', '18'])
writer.writerow(['15', 'nan'])
ts = TimeSeries.from_csv(filename,
time_column=0,
time_transform=time_parse,
value_column=1,
value_transform=value_parse,
default=None)
os.remove(filename)
assert ts[9] is None
assert ts[20] is None
assert ts[13.5] is None
histogram = ts.distribution()
nose.tools.assert_almost_equal(histogram.mean(), (15 + 34 + 19 + 18) / 4.0)
filename = 'sample.csv'
with open(filename, 'w') as csvfile:
writer = csv.writer(csvfile)
writer.writerow(['hour', 'value'])
writer.writerow(['2000-01-01 10:00:00', '15'])
writer.writerow(['2000-01-01 11:00:00', '34'])
writer.writerow(['2000-01-01 12:00:00', '19'])
writer.writerow(['2000-01-01 13:00:00', 'nan'])
writer.writerow(['2000-01-01 14:00:00', '18'])
writer.writerow(['2000-01-01 15:00:00', 'nan'])
ts = TimeSeries.from_csv(filename)
os.remove(filename)
assert ts[datetime(2000, 1, 1, 9)] is None
assert ts[datetime(2000, 1, 1, 10, 30)] == '15'
assert ts[datetime(2000, 1, 1, 20)] == 'nan'
def add_annotation(self, file: os.PathLike, **kwargs):
self.file_list.append(file)
# setup default kwargs #
if self.ordinal:
kwargs.update({'value_transform': lambda cls: self.order.get(cls)})
if not kwargs.get('time_transform', None):
kwargs.update({'time_transform': lambda t: float(t)})
if not kwargs.get('skip_header', None):
kwargs.update({'skip_header': False})
if not os.path.isfile(file):
raise FileNotFoundError(f'File {file} not found.')
if os.path.getsize(file):
ts = TimeSeries.from_csv(file,
**kwargs).slice(self.start_time,
self.end_time)
meta = pd.read_csv(file, names=COLUMNS.keys())
meta.drop(meta[meta['time'] < self.start_time].index)
meta.drop(meta[meta['time'] > self.end_time].index)
else:
ts = TimeSeries()
meta = pd.DataFrame()
self.metadata.append(meta)
self.series.append(ts)
self._data = self._report()
def __init__(self, file_list):
"""
Takes a list of csv files and spits out a behavior index
"""
self.ts_list = list()
self._dict = dict()
for file in file_list:
ts = TimeSeries.from_csv(file)
self.ts_list.append(ts)
self.file_list = file_list
