forked from santosjorge/cufflinks
/
tests.py
261 lines (194 loc) · 6.13 KB
/
tests.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
import cufflinks as cf
import pandas as pd
import unittest
from nose.tools import assert_equals
##
## nosetests -xv tests.py --with-coverage --cover-package=cufflinks
##
class TestIPlot(unittest.TestCase):
def setUp(self):
self.df = pd.DataFrame(dict(x=[1, 2, 3], y=[4, 2, 1], c=[3, 1, 5]))
def _iplot(self, df, **kwargs):
return df.iplot(asFigure=True, **kwargs)
def _ta(self, df, study, **kwargs):
# print(study,kwargs)
return df.ta_figure(study,**kwargs)
def test_scatter_matrix(self):
self.df.scatter_matrix(asFigure=True)
def test_irregular_subplots():
df = cf.datagen.bubble(10, 50, mode='stocks')
figs = cf.figures(df, [
dict(kind='histogram', keys='x', color='blue'),
dict(kind='scatter', mode='markers', x='x', y='y', size=5),
dict(kind='scatter', mode='markers', x='x', y='y',
size=5, color='teal')],asList=True)
figs.append(cf.datagen.lines(1).figure(bestfit=False, colors=['blue'],
bestfit_colors=['pink']))
base_layout = cf.tools.get_base_layout(figs)
sp = cf.subplots(figs, shape=(3, 2), base_layout=base_layout,
vertical_spacing=.15, horizontal_spacing=.03,
specs=[[{'rowspan': 2}, {}], [None, {}],
[{'colspan': 2}, None]],
subplot_titles=['Histogram', 'Scatter 1',
'Scatter 2', 'Bestfit Line'])
sp['layout'].update(showlegend=False)
return sp
def bar_input_argument_tests():
options = {
'kind': ['bar', 'barh'],
'barmode': ['stack', 'overlay', 'group'],
'bargap': [0.1],
'subplots': [True]
}
def bar_test(self, **kwargs):
self._iplot(self.df, **kwargs)
_generate_tests(TestIPlot, bar_test, 'bar', options)
def bar_row_input_argument_tests():
options = {
'kind': ['bar', 'barh'],
'barmode': ['stack', 'overlay', 'group'],
'bargap': [0.1],
'subplots': [True]
}
def bar_row_test(self, **kwargs):
self._iplot(self.df.ix[1], **kwargs)
_generate_tests(TestIPlot, bar_row_test, 'bar_row', options)
def histogram_input_argument_tests():
options = {
'barmode': ['stack'],
'bins': [20],
'orientation': ['h', 'v', 'horizontal', 'vertical'],
'histnorm': ['probability','percent','density'],
'subplots': [True],
'line_color':['blue','#fa0']
}
def histogram_test(self, **kwargs):
self._iplot(self.df, kind='histogram', **kwargs)
_generate_tests(TestIPlot, histogram_test, 'histogram', options)
def heatmap_input_argument_tests():
options = {}
def heatmap_test(self, **kwargs):
self._iplot(self.df, kind='heatmap', **kwargs)
df=cf.datagen.heatmap()
df.index=cf.pd.period_range('1/1/2016',periods=5)
self._iplot(df,kind='heatmap', **kwargs)
# df.iplot(kind='heatmap')
_generate_tests(TestIPlot, heatmap_test, 'heatmap', options)
def box_input_argument_tests():
options = {}
def box_test(self, **kwargs):
self._iplot(self.df, kind='box', **kwargs)
_generate_tests(TestIPlot, box_test, 'box', options)
def area_plot_input_argument_tests():
options = {
'fill': [True],
'opacity': [1],
'kind': ['area']
}
def area_test(self, **kwargs):
self._iplot(self.df, **kwargs)
_generate_tests(TestIPlot, area_test, 'area', options)
def scatter_plot_input_argument_tests():
options = {
'x': ['x'],
'y': ['y'],
'mode': ['markers'],
'symbol': ['dot'],
'colors': [['orange', 'teal']],
'size': [10]
}
def scatter_test(self, **kwargs):
self._iplot(self.df, **kwargs)
_generate_tests(TestIPlot, scatter_test, 'scatter', options)
def bubble_chart_argument_tests():
options = {
'x': ['x'], 'y': ['y'], 'size': ['c']
}
def bubble_test(self, **kwargs):
self._iplot(self.df, **kwargs)
_generate_tests(TestIPlot, bubble_test, 'bubble', options)
def subplot_input_argument_tests():
options = {
'shape': [(3, 1)],
'shared_xaxes': [True],
'vertical_spacing': [0.02],
'fill': [True],
'subplot_titles': [True],
'legend': [False]
}
def subplot_test(self, **kwargs):
self._iplot(self.df, subplots=True, **kwargs)
_generate_tests(TestIPlot, subplot_test, 'subplots', options)
def shape_input_argument_tests():
df = cf.datagen.lines(3, columns=['a', 'b', 'c'])
options = {
'hline': [
[2, 4],
[dict(y=-1, color='blue', width=3),
dict(y=1, color='pink', dash='dash')]],
'vline': [['2015-02-10']],
'hspan': [[(-1, 1), (2, 5)]],
'vspan': [{
'x0': '2015-02-15', 'x1': '2015-03-15',
'color': 'teal', 'fill': True, 'opacity': .4}]
}
def shape_tests(self, **kwargs):
self._iplot(df, **kwargs)
_generate_tests(TestIPlot, shape_tests, 'shape', options)
# colors
def color_normalize_tests():
c=dict([(k.lower(),v.upper()) for k,v in list(cf.cnames.items())])
d={}
for k,v in list(c.items()):
assert_equals(v,cf.normalize(k).upper())
return 2
# technical analysis
def ta_tests():
df=cf.datagen.lines(1,500)
studies=['sma']
options = {
'periods' : [14]
}
def ta_tests(self, studies, **kwargs):
for study in studies:
self._ta(df, study, **kwargs)
_generate_tests(TestIPlot, ta_tests, 'ta', options)
# test generators
def _generate_tests(test_class, test_func, test_name, options):
from itertools import chain, combinations, product
def powerset(iterable):
"powerset([1,2,3]) --> () (1,) (2,) (3,) (1,2) (1,3) (2,3) (1,2,3)"
s = list(iterable)
return chain.from_iterable(combinations(s, r)
for r in range(len(s) + 1))
key_value_tuple = {}
for option, values in list(options.items()):
key_value_tuple[option] = [(option, i) for i in values]
for option_groups in powerset(key_value_tuple.values()):
for input_kwargs in product(*option_groups):
kwargs = {i[0]: i[1] for i in input_kwargs}
setattr(
test_class,
'test_{}_{}'.format(test_name, '__'.join([
'_'.join([str(s) for s in i])
for i in kwargs.items()])),
_generate_test(test_func, **kwargs))
def _generate_test(test_func, **kwargs):
def test(self):
test_func(self, **kwargs)
return test
bar_input_argument_tests()
bar_row_input_argument_tests()
histogram_input_argument_tests()
box_input_argument_tests()
heatmap_input_argument_tests()
area_plot_input_argument_tests()
scatter_plot_input_argument_tests()
bubble_chart_argument_tests()
subplot_input_argument_tests()
shape_input_argument_tests()
test_irregular_subplots()
color_normalize_tests()
# ta_tests()
if __name__ == '__main__':
unittest.main()