File: C:/Users/fred/anaconda3/Lib/site-packages/spyder_kernels/utils/tests/test_nsview.py
# -*- coding: utf-8 -*-
# -----------------------------------------------------------------------------
# Copyright (c) 2009- Spyder Kernels Contributors
#
# Licensed under the terms of the MIT License
# (see spyder_kernels/__init__.py for details)
# -----------------------------------------------------------------------------
"""
Tests for utils.py
"""
# Standard library imports
from collections import defaultdict
import datetime
import sys
# Third party imports
import numpy as np
import pandas as pd
import pytest
import xarray as xr
import PIL.Image
# Local imports
from spyder_kernels.py3compat import PY2
from spyder_kernels.utils.nsview import (
sort_against, is_supported, value_to_display, get_size,
get_supported_types, get_type_string, get_numpy_type_string,
is_editable_type)
def generate_complex_object():
"""Taken from issue #4221."""
bug = defaultdict(list)
for i in range(50000):
a = {j:np.random.rand(10) for j in range(10)}
bug[i] = a
return bug
COMPLEX_OBJECT = generate_complex_object()
DF = pd.DataFrame([1,2,3])
DATASET = xr.Dataset({0: pd.DataFrame([1,2]), 1:pd.DataFrame([3,4])})
# --- Tests
# -----------------------------------------------------------------------------
def test_get_size():
"""Test that the size of all values is returned correctly"""
class RecursionClassNoLen():
def __getattr__(self, name):
if name=='size': return self.name
else:
return super(object, self).__getattribute__(name)
length = [list([1,2,3]), tuple([1,2,3]), set([1,2,3]), '123',
{1:1, 2:2, 3:3}]
for obj in length:
assert get_size(obj) == 3
df = pd.DataFrame([[1,2,3], [1,2,3]])
assert get_size(df) == (2, 3)
df = pd.Series([1,2,3])
assert get_size(df) == (3,)
df = pd.Index([1,2,3])
assert get_size(df) == (3,)
arr = np.array([[1,2,3], [1,2,3]], dtype=np.complex128)
assert get_size(arr) == (2, 3)
img = PIL.Image.new('RGB', (256,256))
assert get_size(img) == (256,256)
obj = RecursionClassNoLen()
assert get_size(obj) == 1
def test_sort_against():
lista = [5, 6, 7]
listb = [2, 3, 1]
res = sort_against(lista, listb)
assert res == [7, 5, 6]
def test_sort_against_is_stable():
lista = [3, 0, 1]
listb = [1, 1, 1]
res = sort_against(lista, listb)
assert res == lista
def test_none_values_are_supported():
"""Tests that None values are displayed by default"""
supported_types = get_supported_types()
mode = 'editable'
none_var = None
none_list = [2, None, 3, None]
none_dict = {'a': None, 'b': 4}
none_tuple = (None, [3, None, 4], 'eggs')
assert is_supported(none_var, filters=tuple(supported_types[mode]))
assert is_supported(none_list, filters=tuple(supported_types[mode]))
assert is_supported(none_dict, filters=tuple(supported_types[mode]))
assert is_supported(none_tuple, filters=tuple(supported_types[mode]))
def test_str_subclass_display():
"""Test for value_to_display of subclasses of str/basestring."""
class Test(str):
def __repr__(self):
return 'test'
value = Test()
value_display = value_to_display(value)
assert 'Test object' in value_display
def test_default_display():
"""Tests for default_display."""
# Display of defaultdict
assert (value_to_display(COMPLEX_OBJECT) ==
'defaultdict object of collections module')
# Display of array of COMPLEX_OBJECT
assert (value_to_display(np.array(COMPLEX_OBJECT)) ==
'ndarray object of numpy module')
# Display of Dataset
assert (value_to_display(DATASET) ==
'Dataset object of xarray.core.dataset module')
@pytest.mark.skipif(
sys.platform == 'darwin' and sys.version_info[:2] == (3, 8),
reason="Fails on Mac with Python 3.8")
def test_list_display():
"""Tests for display of lists."""
long_list = list(range(100))
# Simple list
assert value_to_display([1, 2, 3]) == '[1, 2, 3]'
# Long list
assert (value_to_display(long_list) ==
'[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, ...]')
# Short list of lists
assert (value_to_display([long_list] * 3) ==
'[[0, 1, 2, 3, 4, ...], [0, 1, 2, 3, 4, ...], [0, 1, 2, 3, 4, ...]]')
# Long list of lists
result = '[' + ''.join('[0, 1, 2, 3, 4, ...], '*10)[:-2] + ']'
assert value_to_display([long_list] * 10) == result[:70] + ' ...'
# Multiple level lists
assert (value_to_display([[1, 2, 3, [4], 5]] + long_list) ==
'[[1, 2, 3, [...], 5], 0, 1, 2, 3, 4, 5, 6, 7, 8, ...]')
assert value_to_display([1, 2, [DF]]) == '[1, 2, [Dataframe]]'
assert value_to_display([1, 2, [[DF], DATASET]]) == '[1, 2, [[...], Dataset]]'
# List of complex object
assert value_to_display([COMPLEX_OBJECT]) == '[defaultdict]'
# List of composed objects
li = [COMPLEX_OBJECT, DATASET, 1, {1:2, 3:4}, DF]
result = '[defaultdict, Dataset, 1, {1:2, 3:4}, Dataframe]'
assert value_to_display(li) == result
# List starting with a non-supported object (#5313)
supported_types = tuple(get_supported_types()['editable'])
li = [len, 1]
assert value_to_display(li) == '[builtin_function_or_method, 1]'
assert is_supported(li, filters=supported_types)
@pytest.mark.skipif(
sys.platform == 'darwin' and sys.version_info[:2] == (3, 8),
reason="Fails on Mac with Python 3.8")
def test_dict_display():
"""Tests for display of dicts."""
long_list = list(range(100))
long_dict = dict(zip(list(range(100)), list(range(100))))
# Simple dict
assert value_to_display({0:0, 'a':'b'}) == "{0:0, 'a':'b'}"
# Long dict
assert (value_to_display(long_dict) ==
'{0:0, 1:1, 2:2, 3:3, 4:4, 5:5, 6:6, 7:7, 8:8, 9:9, ...}')
# Short list of lists
assert (value_to_display({1:long_dict, 2:long_dict}) ==
'{1:{0:0, 1:1, 2:2, 3:3, 4:4, ...}, 2:{0:0, 1:1, 2:2, 3:3, 4:4, ...}}')
# Long dict of dicts
result = ('{(0, 0, 0, 0, 0, ...):[0, 1, 2, 3, 4, ...], '
'(1, 1, 1, 1, 1, ...):[0, 1, 2, 3, 4, ...]}')
assert value_to_display({(0,)*100:long_list, (1,)*100:long_list}) == result[:70] + ' ...'
# Multiple level dicts
assert (value_to_display({0: {1:1, 2:2, 3:3, 4:{0:0}, 5:5}, 1:1}) ==
'{0:{1:1, 2:2, 3:3, 4:{...}, 5:5}, 1:1}')
assert value_to_display({0:0, 1:1, 2:2, 3:DF}) == '{0:0, 1:1, 2:2, 3:Dataframe}'
assert value_to_display({0:0, 1:1, 2:[[DF], DATASET]}) == '{0:0, 1:1, 2:[[...], Dataset]}'
# Dict of complex object
assert value_to_display({0:COMPLEX_OBJECT}) == '{0:defaultdict}'
# Dict of composed objects
li = {0:COMPLEX_OBJECT, 1:DATASET, 2:2, 3:{0:0, 1:1}, 4:DF}
result = '{0:defaultdict, 1:Dataset, 2:2, 3:{0:0, 1:1}, 4:Dataframe}'
assert value_to_display(li) == result
# Dict starting with a non-supported object (#5313)
supported_types = tuple(get_supported_types()['editable'])
di = {max: len, 1: 1}
assert value_to_display(di) in (
'{builtin_function_or_method:builtin_function_or_method, 1:1}',
'{1:1, builtin_function_or_method:builtin_function_or_method}')
assert is_supported(di, filters=supported_types)
def test_set_display():
"""Tests for display of sets."""
long_set = {i for i in range(100)}
# Simple set
assert value_to_display({1, 2, 3}) == '{1, 2, 3}'
# Long set
disp = '{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, ...}'
assert value_to_display(long_set) == disp
# Short list of sets
disp = '[{0, 1, 2, 3, 4, ...}, {0, 1, 2, 3, 4, ...}, {0, 1, 2, 3, 4, ...}]'
assert value_to_display([long_set] * 3) == disp
# Long list of sets
disp = '[' + ''.join('{0, 1, 2, 3, 4, ...}, '*10)[:-2] + ']'
assert value_to_display([long_set] * 10) == disp[:70] + ' ...'
def test_datetime_display():
"""Simple tests that dates, datetimes and timedeltas display correctly."""
test_date = datetime.date(2017, 12, 18)
test_date_2 = datetime.date(2017, 2, 2)
test_datetime = datetime.datetime(2017, 12, 18, 13, 43, 2)
test_datetime_2 = datetime.datetime(2017, 8, 18, 0, 41, 27)
test_timedelta = datetime.timedelta(-1, 2000)
test_timedelta_2 = datetime.timedelta(0, 3600)
# Simple dates/datetimes/timedeltas
assert value_to_display(test_date) == '2017-12-18'
assert value_to_display(test_datetime) == '2017-12-18 13:43:02'
assert value_to_display(test_timedelta) == '-1 day, 0:33:20'
# Lists of dates/datetimes/timedeltas
assert (value_to_display([test_date, test_date_2]) ==
'[2017-12-18, 2017-02-02]')
assert (value_to_display([test_datetime, test_datetime_2]) ==
'[2017-12-18 13:43:02, 2017-08-18 00:41:27]')
assert (value_to_display([test_timedelta, test_timedelta_2]) ==
'[-1 day, 0:33:20, 1:00:00]')
# Tuple of dates/datetimes/timedeltas
assert (value_to_display((test_date, test_datetime, test_timedelta)) ==
'(2017-12-18, 2017-12-18 13:43:02, -1 day, 0:33:20)')
# Dict of dates/datetimes/timedeltas
assert (value_to_display({0: test_date,
1: test_datetime,
2: test_timedelta_2}) ==
("{0:2017-12-18, 1:2017-12-18 13:43:02, 2:1:00:00}"))
def test_str_in_container_display():
"""Test that strings are displayed correctly inside lists or dicts."""
# Assert that both bytes and unicode return the right display
assert value_to_display([b'a', u'b']) == "['a', 'b']"
# Encoded unicode gives bytes and it can't be transformed to
# unicode again. So this test the except part of
# is_binary_string(value) in value_to_display
if PY2:
assert value_to_display([u'Э'.encode('cp1251')]) == "['\xdd']"
def test_ellipses(tmpdir):
"""
Test that we're adding a binary ellipses when value_to_display of
a collection is too long and binary.
For issue 6942
"""
# Create binary file with all bytes
file = tmpdir.new(basename='bytes.txt')
file.write_binary(bytearray(list(range(255))))
# Read bytes back
buffer = file.read(mode='rb')
# Assert that there's a binary ellipses in the representation
assert b' ...' in value_to_display(buffer)
def test_get_type_string():
"""Test for get_type_string."""
# Bools
assert get_type_string(True) == 'bool'
# Numeric types (PY2 has long, which disappeared in PY3)
if not PY2:
expected = ['int', 'float', 'complex']
numeric_types = [1, 1.5, 1 + 2j]
assert [get_type_string(t) for t in numeric_types] == expected
# Lists
assert get_type_string([1, 2, 3]) == 'list'
# Sets
assert get_type_string({1, 2, 3}) == 'set'
# Dictionaries
assert get_type_string({'a': 1, 'b': 2}) == 'dict'
# Tuples
assert get_type_string((1, 2, 3)) == 'tuple'
# Strings
if not PY2:
assert get_type_string('foo') == 'str'
# Numpy objects
assert get_type_string(np.array([1, 2, 3])) == 'NDArray'
masked_array = np.ma.MaskedArray([1, 2, 3], mask=[True, False, True])
assert get_type_string(masked_array) == 'MaskedArray'
matrix = np.matrix([[1, 2], [3, 4]])
assert get_type_string(matrix) == 'Matrix'
# Pandas objects
df = pd.DataFrame([1, 2, 3])
assert get_type_string(df) == 'DataFrame'
series = pd.Series([1, 2, 3])
assert get_type_string(series) == 'Series'
index = pd.Index([1, 2, 3])
assert get_type_string(index) in ['Int64Index', 'Index']
# PIL images
img = PIL.Image.new('RGB', (256,256))
assert get_type_string(img) == 'PIL.Image.Image'
# Datetime objects
date = datetime.date(2010, 10, 1)
assert get_type_string(date) == 'datetime.date'
date = datetime.timedelta(-1, 2000)
assert get_type_string(date) == 'datetime.timedelta'
def test_is_editable_type():
"""Test for get_type_string."""
# Bools
assert is_editable_type(True)
# Numeric type
numeric_types = [1, 1.5, 1 + 2j]
assert all([is_editable_type(t) for t in numeric_types])
# Lists
assert is_editable_type([1, 2, 3])
# Sets
assert is_editable_type({1, 2, 3})
# Dictionaries
assert is_editable_type({'a': 1, 'b': 2})
# Tuples
assert is_editable_type((1, 2, 3))
# Strings
assert is_editable_type('foo')
# Numpy objects
assert is_editable_type(np.array([1, 2, 3]))
masked_array = np.ma.MaskedArray([1, 2, 3], mask=[True, False, True])
assert is_editable_type(masked_array)
matrix = np.matrix([[1, 2], [3, 4]])
assert is_editable_type(matrix)
# Pandas objects
df = pd.DataFrame([1, 2, 3])
assert is_editable_type(df)
series = pd.Series([1, 2, 3])
assert is_editable_type(series)
index = pd.Index([1, 2, 3])
assert is_editable_type(index)
# PIL images
img = PIL.Image.new('RGB', (256,256))
assert is_editable_type(img)
# Datetime objects
date = datetime.date(2010, 10, 1)
assert is_editable_type(date)
date = datetime.timedelta(-1, 2000)
assert is_editable_type(date)
# Other objects
class MyClass:
a = 1
assert not is_editable_type(MyClass)
my_instance = MyClass()
assert not is_editable_type(my_instance)
def test_get_numpy_type():
"""Test for get_numpy_type_string."""
# Numpy objects
assert get_numpy_type_string(np.array([1, 2, 3])) == 'Array'
matrix = np.matrix([[1, 2], [3, 4]])
assert get_numpy_type_string(matrix) == 'Array'
assert get_numpy_type_string(np.int32(1)) == 'Scalar'
# Regular Python objects
assert get_numpy_type_string(1.5) == 'Unknown'
assert get_numpy_type_string([1, 2, 3]) == 'Unknown'
assert get_numpy_type_string({1: 2}) == 'Unknown'
# PIL images
img = PIL.Image.new('RGB', (256,256))
assert get_numpy_type_string(img) == 'Unknown'
# Pandas objects
df = pd.DataFrame([1, 2, 3])
assert get_numpy_type_string(df) == 'Unknown'
if __name__ == "__main__":
pytest.main()