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jonmmeasejonmmease
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Fix JSON encoding tests
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‎plotly/tests/test_optional/test_utils/test_utils.py

Lines changed: 104 additions & 105 deletions
Original file line numberDiff line numberDiff line change
@@ -28,6 +28,21 @@
2828
from plotly.matplotlylib import Exporter, PlotlyRenderer
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## JSON encoding
32+
numeric_list = [1, 2, 3]
33+
np_list = np.array([1, 2, 3, np.NaN, np.NAN, np.Inf, dt(2014, 1, 5)])
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mixed_list = [1, 'A', dt(2014, 1, 5), dt(2014, 1, 5, 1, 1, 1),
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dt(2014, 1, 5, 1, 1, 1, 1)]
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dt_list = [dt(2014, 1, 5), dt(2014, 1, 5, 1, 1, 1),
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dt(2014, 1, 5, 1, 1, 1, 1)]
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df = pd.DataFrame(columns=['col 1'],
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data=[1, 2, 3, dt(2014, 1, 5), pd.NaT, np.NaN, np.Inf])
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42+
rng = pd.date_range('1/1/2011', periods=2, freq='H')
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ts = pd.Series([1.5, 2.5], index=rng)
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3146
class TestJSONEncoder(TestCase):
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3348
def test_encode_as_plotly(self):
@@ -161,104 +176,100 @@ def test_encode_as_decimal(self):
161176
self.assertAlmostEqual(res, 1.023452) # Checks upto 7 decimal places
162177
self.assertIsInstance(res, float)
163178

164-
## JSON encoding
165-
numeric_list = [1, 2, 3]
166-
np_list = np.array([1, 2, 3, np.NaN, np.NAN, np.Inf, dt(2014, 1, 5)])
167-
mixed_list = [1, 'A', dt(2014, 1, 5), dt(2014, 1, 5, 1, 1, 1),
168-
dt(2014, 1, 5, 1, 1, 1, 1)]
169-
dt_list = [dt(2014, 1, 5), dt(2014, 1, 5, 1, 1, 1),
170-
dt(2014, 1, 5, 1, 1, 1, 1)]
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172-
df = pd.DataFrame(columns=['col 1'],
173-
data=[1, 2, 3, dt(2014, 1, 5), pd.NaT, np.NaN, np.Inf])
174-
175-
rng = pd.date_range('1/1/2011', periods=2, freq='H')
176-
ts = pd.Series([1.5, 2.5], index=rng)
177-
178-
179-
def test_figure_json_encoding():
180-
df = pd.DataFrame(columns=['col 1'], data=[1, 2, 3])
181-
s1 = Scatter3d(x=numeric_list, y=np_list, z=mixed_list)
182-
s2 = Scatter(x=df['col 1'])
183-
data = Data([s1, s2])
184-
figure = Figure(data=data)
185-
186-
js1 = _json.dumps(s1, cls=utils.PlotlyJSONEncoder, sort_keys=True)
187-
js2 = _json.dumps(s2, cls=utils.PlotlyJSONEncoder, sort_keys=True)
188-
189-
assert(js1 == '{"type": "scatter3d", "x": [1, 2, 3], '
190-
'"y": [1, 2, 3, null, null, null, "2014-01-05"], '
191-
'"z": [1, "A", "2014-01-05", '
192-
'"2014-01-05 01:01:01", "2014-01-05 01:01:01.000001"]}')
193-
assert(js2 == '{"type": "scatter", "x": [1, 2, 3]}')
194-
195-
# Test JSON encoding works
196-
_json.dumps(data, cls=utils.PlotlyJSONEncoder, sort_keys=True)
197-
_json.dumps(figure, cls=utils.PlotlyJSONEncoder, sort_keys=True)
198-
199-
# Test data wasn't mutated
200-
np_array = np.array(
201-
[1, 2, 3, np.NaN, np.NAN, np.Inf, dt(2014, 1, 5)]
202-
)
203-
for k in range(len(np_array)):
204-
if k in [3, 4]:
205-
# check NaN
206-
assert np.isnan(np_list[k]) and np.isnan(np_array[k])
207-
else:
208-
# non-NaN
209-
assert np_list[k] == np_array[k]
210-
211-
assert(set(data[0]['z']) ==
212-
set([1, 'A', dt(2014, 1, 5), dt(2014, 1, 5, 1, 1, 1),
213-
dt(2014, 1, 5, 1, 1, 1, 1)]))
180+
def test_figure_json_encoding(self):
181+
df = pd.DataFrame(columns=['col 1'], data=[1, 2, 3])
182+
s1 = Scatter3d(x=numeric_list, y=np_list, z=mixed_list)
183+
s2 = Scatter(x=df['col 1'])
184+
data = Data([s1, s2])
185+
figure = Figure(data=data)
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187+
js1 = _json.dumps(s1, cls=utils.PlotlyJSONEncoder, sort_keys=True)
188+
js2 = _json.dumps(s2, cls=utils.PlotlyJSONEncoder, sort_keys=True)
215189

216-
def test_datetime_json_encoding():
217-
j1 = _json.dumps(dt_list, cls=utils.PlotlyJSONEncoder)
218-
assert(j1 == '["2014-01-05", '
219-
'"2014-01-05 01:01:01", '
220-
'"2014-01-05 01:01:01.000001"]')
221-
j2 = _json.dumps({"x": dt_list}, cls=utils.PlotlyJSONEncoder)
222-
assert(j2 == '{"x": ["2014-01-05", '
223-
'"2014-01-05 01:01:01", '
224-
'"2014-01-05 01:01:01.000001"]}')
190+
assert(js1 == '{"type": "scatter3d", "x": [1, 2, 3], '
191+
'"y": [1, 2, 3, null, null, null, "2014-01-05T00:00:00"], '
192+
'"z": [1, "A", "2014-01-05T00:00:00", '
193+
'"2014-01-05T01:01:01", "2014-01-05T01:01:01.000001"]}')
194+
assert(js2 == '{"type": "scatter", "x": [1, 2, 3]}')
225195

196+
# Test JSON encoding works
197+
_json.dumps(data, cls=utils.PlotlyJSONEncoder, sort_keys=True)
198+
_json.dumps(figure, cls=utils.PlotlyJSONEncoder, sort_keys=True)
226199

227-
def test_pandas_json_encoding():
228-
j1 = _json.dumps(df['col 1'], cls=utils.PlotlyJSONEncoder)
229-
print(j1)
230-
print('\n')
231-
assert(j1 == '[1, 2, 3, "2014-01-05", null, null, null]')
232-
233-
# Test that data wasn't mutated
234-
assert_series_equal(df['col 1'],
235-
pd.Series([1, 2, 3, dt(2014, 1, 5),
236-
pd.NaT, np.NaN, np.Inf], name='col 1'))
237-
238-
j2 = _json.dumps(df.index, cls=utils.PlotlyJSONEncoder)
239-
assert(j2 == '[0, 1, 2, 3, 4, 5, 6]')
240-
241-
nat = [pd.NaT]
242-
j3 = _json.dumps(nat, cls=utils.PlotlyJSONEncoder)
243-
assert(j3 == '[null]')
244-
assert(nat[0] is pd.NaT)
245-
246-
j4 = _json.dumps(rng, cls=utils.PlotlyJSONEncoder)
247-
assert(j4 == '["2011-01-01", "2011-01-01 01:00:00"]')
248-
249-
j5 = _json.dumps(ts, cls=utils.PlotlyJSONEncoder)
250-
assert(j5 == '[1.5, 2.5]')
251-
assert_series_equal(ts, pd.Series([1.5, 2.5], index=rng))
252-
253-
j6 = _json.dumps(ts.index, cls=utils.PlotlyJSONEncoder)
254-
assert(j6 == '["2011-01-01", "2011-01-01 01:00:00"]')
255-
256-
257-
def test_numpy_masked_json_encoding():
258-
l = [1, 2, np.ma.core.masked]
259-
j1 = _json.dumps(l, cls=utils.PlotlyJSONEncoder)
260-
print(j1)
261-
assert(j1 == '[1, 2, null]')
200+
# Test data wasn't mutated
201+
np_array = np.array(
202+
[1, 2, 3, np.NaN, np.NAN, np.Inf, dt(2014, 1, 5)]
203+
)
204+
for k in range(len(np_array)):
205+
if k in [3, 4]:
206+
# check NaN
207+
assert np.isnan(np_list[k]) and np.isnan(np_array[k])
208+
else:
209+
# non-NaN
210+
assert np_list[k] == np_array[k]
211+
212+
assert(set(data[0]['z']) ==
213+
set([1, 'A', dt(2014, 1, 5), dt(2014, 1, 5, 1, 1, 1),
214+
dt(2014, 1, 5, 1, 1, 1, 1)]))
215+
216+
def test_datetime_json_encoding(self):
217+
j1 = _json.dumps(dt_list, cls=utils.PlotlyJSONEncoder)
218+
assert(j1 == '["2014-01-05T00:00:00", '
219+
'"2014-01-05T01:01:01", '
220+
'"2014-01-05T01:01:01.000001"]')
221+
j2 = _json.dumps({"x": dt_list}, cls=utils.PlotlyJSONEncoder)
222+
assert(j2 == '{"x": ["2014-01-05T00:00:00", '
223+
'"2014-01-05T01:01:01", '
224+
'"2014-01-05T01:01:01.000001"]}')
225+
226+
def test_pandas_json_encoding(self):
227+
j1 = _json.dumps(df['col 1'], cls=utils.PlotlyJSONEncoder)
228+
print(j1)
229+
print('\n')
230+
assert(j1 == '[1, 2, 3, "2014-01-05T00:00:00", null, null, null]')
231+
232+
# Test that data wasn't mutated
233+
assert_series_equal(df['col 1'],
234+
pd.Series([1, 2, 3, dt(2014, 1, 5),
235+
pd.NaT, np.NaN, np.Inf], name='col 1'))
236+
237+
j2 = _json.dumps(df.index, cls=utils.PlotlyJSONEncoder)
238+
assert(j2 == '[0, 1, 2, 3, 4, 5, 6]')
239+
240+
nat = [pd.NaT]
241+
j3 = _json.dumps(nat, cls=utils.PlotlyJSONEncoder)
242+
assert(j3 == '[null]')
243+
assert(nat[0] is pd.NaT)
244+
245+
j4 = _json.dumps(rng, cls=utils.PlotlyJSONEncoder)
246+
assert(j4 == '["2011-01-01T00:00:00", "2011-01-01T01:00:00"]')
247+
248+
j5 = _json.dumps(ts, cls=utils.PlotlyJSONEncoder)
249+
assert(j5 == '[1.5, 2.5]')
250+
assert_series_equal(ts, pd.Series([1.5, 2.5], index=rng))
251+
252+
j6 = _json.dumps(ts.index, cls=utils.PlotlyJSONEncoder)
253+
assert(j6 == '["2011-01-01T00:00:00", "2011-01-01T01:00:00"]')
254+
255+
def test_numpy_masked_json_encoding(self):
256+
l = [1, 2, np.ma.core.masked]
257+
j1 = _json.dumps(l, cls=utils.PlotlyJSONEncoder)
258+
print(j1)
259+
assert(j1 == '[1, 2, null]')
260+
261+
def test_numpy_dates(self):
262+
a = np.arange(np.datetime64('2011-07-11'), np.datetime64('2011-07-18'))
263+
j1 = _json.dumps(a, cls=utils.PlotlyJSONEncoder)
264+
assert(j1 == '["2011-07-11", "2011-07-12", "2011-07-13", '
265+
'"2011-07-14", "2011-07-15", "2011-07-16", '
266+
'"2011-07-17"]')
267+
268+
269+
def test_datetime_dot_date(self):
270+
a = [datetime.date(2014, 1, 1), datetime.date(2014, 1, 2)]
271+
j1 = _json.dumps(a, cls=utils.PlotlyJSONEncoder)
272+
assert(j1 == '["2014-01-01", "2014-01-02"]')
262273

263274

264275
if matplotlylib:
@@ -290,15 +301,3 @@ def test_masked_constants_example():
290301
assert(array == [-398.11793027, -398.11792966, -398.11786308, None])
291302

292303

293-
def test_numpy_dates():
294-
a = np.arange(np.datetime64('2011-07-11'), np.datetime64('2011-07-18'))
295-
j1 = _json.dumps(a, cls=utils.PlotlyJSONEncoder)
296-
assert(j1 == '["2011-07-11", "2011-07-12", "2011-07-13", '
297-
'"2011-07-14", "2011-07-15", "2011-07-16", '
298-
'"2011-07-17"]')
299-
300-
301-
def test_datetime_dot_date():
302-
a = [datetime.date(2014, 1, 1), datetime.date(2014, 1, 2)]
303-
j1 = _json.dumps(a, cls=utils.PlotlyJSONEncoder)
304-
assert(j1 == '["2014-01-01", "2014-01-02"]')

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