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"""
Test deserialization routines that read from file
"""
import unittest
import param
import sys
from unittest import skipIf
from tempfile import mkdtemp
from shutil import rmtree
try:
import numpy as np
ndarray = np.array([[1,2,3],[4,5,6]])
except:
np = ndarray = None
try:
import pandas as pd
pd_ver = pd.__version__.split('.')
df = pd.DataFrame({'A':[1,2,3], 'B':[1.1,2.2,3.3]})
modern_pd = pd if (int(pd_ver[0]) >= 1 and int(pd_ver[1]) >= 2) else None
except:
pd = df1 = df2 = modern_pd = None
# The writer could be xlsxwriter, but the sufficient condition is the presence of
# openpyxl
try:
import openpyxl as xlsxm
except:
xlsxm = None
try:
import odf as ods
except:
ods = None
# prior to pandas version 1.2, xlrd was always the default excel reader (though it
# had to be be of a version before xlrd's 2.0).
xls = None
try:
import xlrd as xls
if int(xls.__version__.split('.')[0]) > 2:
raise Exception()
except:
if modern_pd is None:
xlsxm = None
try:
import pyarrow.feather as feather
except:
feather = None
try:
import fastparquet as parquet
except:
parquet = None
try:
import pyarrow as parquet
except:
pass
try:
import tables as hdf5
except:
hdf5 = None
np_skip = skipIf(np is None, "NumPy is not available")
pd_skip = skipIf(pd is None, "pandas is not available")
modern_pd_skip = skipIf(modern_pd is None, "pandas is too old")
xlsxm_skip = skipIf(xlsxm is None, "openpyxl is not available")
ods_skip = skipIf(ods is None, "odfpy is not available")
xls_skip = skipIf(xls is None, "xlrd is not available")
feather_skip = skipIf(feather is None, "pyarrow.feather is not available")
parquet_skip = skipIf(parquet is None, "fastparquet and pyarrow are not available")
hdf5_skip = skipIf(hdf5 is None, "pytables is not available")
class P(param.Parameterized):
array = None if np is None else param.Array(default=ndarray)
data_frame = None if pd is None else param.DataFrame(default=df)
class TestFileDeserialization(unittest.TestCase):
def run(self, result):
self.temp_dir = mkdtemp().replace('\\', '/')
try:
return super(TestFileDeserialization, self).run(result=result)
finally:
rmtree(self.temp_dir, ignore_errors=True)
@np_skip
def _test_deserialize_array(self, obj, path, pname, check=True):
# assumes the parameter has already been serialized to path!
deserialized = obj.param.deserialize_value(
pname, '"{}"'.format(path), mode='json')
if check:
self.assertTrue(np.array_equal(deserialized, getattr(obj, pname)))
@np_skip
def test_fail_to_deserialize(self):
path = '{}/val.foo'.format(self.temp_dir)
with self.assertRaisesRegex(IOError, "does not exist or is not a file"):
self._test_deserialize_array(P, path, 'array')
with open(path, 'w'):
pass
with self.assertRaisesRegex(ValueError, "no deserialization method for files"):
self._test_deserialize_array(P, path, 'array')
path = '{}/val.npy'.format(self.temp_dir)
with open(path, 'w'):
pass
with self.assertRaises(Exception):
self._test_deserialize_array(P, path, 'array')
@np_skip
def test_array_npy(self):
path = '{}/val.npy'.format(self.temp_dir)
np.save(path, P.array)
self._test_deserialize_array(P, path, 'array')
@np_skip
def test_array_txt(self):
path = '{}/val.txt'.format(self.temp_dir)
np.savetxt(path, P.array)
self._test_deserialize_array(P, path, 'array')
@np_skip
def test_array_txt_gz(self):
path = '{}/val.txt.gz'.format(self.temp_dir)
np.savetxt(path, P.array)
self._test_deserialize_array(P, path, 'array')
@pd_skip
def test_data_frame_pkl(self):
path = '{}/val.pkl.zip'.format(self.temp_dir)
P.data_frame.to_pickle(path)
self._test_deserialize_array(P, path, 'data_frame')
@pd_skip
def test_data_frame_csv(self):
path = '{}/val.csv.bz2'.format(self.temp_dir)
P.data_frame.to_csv(path, index=False)
self._test_deserialize_array(P, path, 'data_frame')
@pd_skip
def test_data_frame_tsv(self):
path = '{}/val.tsv'.format(self.temp_dir)
P.data_frame.to_csv(path, index=False, sep='\t')
self._test_deserialize_array(P, path, 'data_frame')
@pd_skip
def test_data_frame_json(self):
path = '{}/val.json'.format(self.temp_dir)
P.data_frame.to_json(path)
self._test_deserialize_array(P, path, 'data_frame')
# FIXME(sdrobert): xls are old-style excel files. There are two distinct engines for
# reading and writing these, and the writer engine is deprecated by pandas. We could
# store the serialized file as a byte array to future-proof somewhat, but that would
# break if we ever decided to change the default data_frame value. Who cares.
@pd_skip
@xlsxm_skip
def test_data_frame_xlsm(self):
path = '{}/val.xlsm'.format(self.temp_dir)
P.data_frame.to_excel(path, index=False)
self._test_deserialize_array(P, path, 'data_frame')
@pd_skip
@xlsxm_skip
def test_data_frame_xlsx(self):
path = '{}/val.xlsx'.format(self.temp_dir)
P.data_frame.to_excel(path, index=False)
self._test_deserialize_array(P, path, 'data_frame')
@pd_skip
@ods_skip
@skipIf(sys.version_info[0] < 3, "py2k pandas does not support 'ods'")
def test_data_frame_ods(self):
path = '{}/val.ods'.format(self.temp_dir)
P.data_frame.to_excel(path, index=False)
self._test_deserialize_array(P, path, 'data_frame')
@pd_skip
@feather_skip
def test_data_frame_feather(self):
path = '{}/val.feather'.format(self.temp_dir)
P.data_frame.to_feather(path)
self._test_deserialize_array(P, path, 'data_frame')
@pd_skip
@parquet_skip
def test_data_frame_parquet(self):
path = '{}/val.parquet'.format(self.temp_dir)
P.data_frame.to_parquet(path)
self._test_deserialize_array(P, path, 'data_frame')
@pd_skip
def test_data_frame_stata(self):
path = '{}/val.dta'.format(self.temp_dir)
P.data_frame.to_stata(path, write_index=False)
self._test_deserialize_array(P, path, 'data_frame')
@pd_skip
@hdf5_skip
def test_data_frame_hdf5(self):
path = '{}/val.h5'.format(self.temp_dir)
P.data_frame.to_hdf(path, key='df')
self._test_deserialize_array(P, path, 'data_frame')
path = '{}/val.hdf5'.format(self.temp_dir)
P.data_frame.to_hdf(path, key='df')
self._test_deserialize_array(P, path, 'data_frame')
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