| /dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/base/tests/ |
| H A D | test_predict.py | 22 fitted = res.fittedvalues.iloc[1:10:2]
24 pred = res.predict(data.iloc[1:10:2])
29 xd = dict(zip(data.columns, data.iloc[1:10:2].values.T))
39 pred = res.predict(data.iloc[:1])
40 pdt.assert_index_equal(pred.index, data.iloc[:1].index)
63 fitted = res.fittedvalues.iloc[1:10:2]
82 pred = res.predict(x.iloc[0])
124 fitted = res.fittedvalues.iloc[1:10:2]
139 data2 = data.iloc[1:10:2].copy()
146 data2 = data.iloc[1:10:2].copy()
[all …]
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| /dports/math/py-pandas/pandas-1.2.5/pandas/tests/reshape/concat/ |
| H A D | test_index.py | 148 tm.assert_frame_equal(result.iloc[:, :4], df)
149 tm.assert_frame_equal(result.iloc[:, 4:], df)
152 tm.assert_frame_equal(result.iloc[:10], df)
153 tm.assert_frame_equal(result.iloc[10:], df)
167 tm.assert_frame_equal(result.iloc[:, :6], df)
171 tm.assert_frame_equal(result.iloc[:10], df)
172 tm.assert_frame_equal(result.iloc[10:], df)
175 result = df.iloc[0:8, :].append(df.iloc[8:])
178 result = df.iloc[0:8, :].append(df.iloc[8:9]).append(df.iloc[9:10])
237 res = concat([df.iloc[[2, 3, 4], :], df.iloc[[5], :]])
[all …]
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| /dports/math/py-pandas/pandas-1.2.5/pandas/tests/indexing/ |
| H A D | test_indexing.py | 33 def iloc(x): function
34 return x.iloc
512 df.iloc[1, 0] = TO(1)
513 df.iloc[1, 0] = TO(2)
522 df.iloc[1, 0] = TO(1)
556 df.iloc[:, 0:2] = df.iloc[:, 0:2].astype(np.int64)
563 df.iloc[:, 0:2] = df.iloc[:, 0:2]._convert(datetime=True, numeric=True)
752 df2 = df.iloc[[], :]
973 result = df.iloc[0]
986 result = df.iloc[0]
[all …]
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| /dports/math/py-pandas/pandas-1.2.5/pandas/tests/indexes/multi/ |
| H A D | test_partial_indexing.py | 38 expected = df.loc[IndexSlice[:, "a"], :].iloc[0:2]
55 expected = df_swap.iloc[[0, 1, 5, 6, 10, 11]]
65 expected = df.iloc[0:6]
70 expected = df.iloc[9:12]
75 expected = df_swap.iloc[[2, 3, 7, 8, 12, 13]]
80 expected = df.iloc[[0, 3]]
100 expected = df.iloc[118:180]
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| /dports/math/py-point-annotator/point-annotator-2.0.0/pointannotator/tests/ |
| H A D | test_annotate_samples.py | 77 self.assertEqual(len(annotations.iloc[0]), 2) # two types in the data
100 self.assertEqual(len(annotations.iloc[0]), 2) # two types in the data
109 self.assertEqual(len(annotations.iloc[0]), 3) # two types in the data
126 self.assertEqual(len(annotations.iloc[0]), 2) # two types in the data
133 self.data = self.data.iloc[:2]
145 self.assertGreaterEqual(z.iloc[0, 0], 1)
146 self.assertGreaterEqual(z.iloc[0, 1], 1)
147 self.assertGreaterEqual(z.iloc[0, 3], 1)
192 self.assertEqual(len(annotations.iloc[0]), 2) # two types in the data
208 self.assertEqual(annotations.iloc[0, 0], self.data.iloc[0].sum())
[all …]
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| /dports/science/nwchem-data/nwchem-7.0.2-release/src/ddscf/ |
| H A D | localize.F | 37 integer iloc(maxnloc) local
119 iloc(i) = i
129 iloc(i-ncore) = i
138 iloc(i-nclosed) = i
188 iloc(i) = i
198 iloc(i-ncore) = i
287 s = iloc(ss)
335 s = iloc(ss)
402 s = iloc(ss)
500 s = iloc(ss)
[all …]
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| /dports/science/nwchem/nwchem-7b21660b82ebd85ef659f6fba7e1e73433b0bd0a/src/ddscf/ |
| H A D | localize.F | 37 integer iloc(maxnloc)
119 iloc(i) = i
129 iloc(i-ncore) = i
138 iloc(i-nclosed) = i
188 iloc(i) = i
198 iloc(i-ncore) = i
287 s = iloc(ss)
335 s = iloc(ss)
402 s = iloc(ss)
500 s = iloc(ss)
[all …]
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| /dports/science/nwchem-data/nwchem-7.0.2-release/src/dntmc/ |
| H A D | dntmc_setgeom.F | 339 ccluster(1, iloc) = clocal(1, j)
340 ccluster(2, iloc) = clocal(2, j)
341 ccluster(3, iloc) = clocal(3, j)
342 qcluster(iloc) = qlocal(j)
343 mcluster(iloc) = mlocal(j)
344 tcluster(iloc) = tlocal(j)
345 tag(iloc) = tlocal(j)
346 iloc = iloc + 1
375 rinit(iloc, i, 1) = clocal(1, i)*scale
376 rinit(iloc, i, 2) = clocal(2, i)*scale
[all …]
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| /dports/science/nwchem/nwchem-7b21660b82ebd85ef659f6fba7e1e73433b0bd0a/src/dntmc/ |
| H A D | dntmc_setgeom.F | 339 ccluster(1, iloc) = clocal(1, j)
340 ccluster(2, iloc) = clocal(2, j)
341 ccluster(3, iloc) = clocal(3, j)
342 qcluster(iloc) = qlocal(j)
343 mcluster(iloc) = mlocal(j)
344 tcluster(iloc) = tlocal(j)
345 tag(iloc) = tlocal(j)
346 iloc = iloc + 1
375 rinit(iloc, i, 1) = clocal(1, i)*scale
376 rinit(iloc, i, 2) = clocal(2, i)*scale
[all …]
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| /dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/stats/tests/ |
| H A D | test_anova_rm.py | 86 df = AnovaRM(data.iloc[:16, :], 'DV', 'id', within=['B']).fit()
88 assert_array_almost_equal(df.anova_table.iloc[:, [1, 2, 0, 3]].values,
97 df = AnovaRM(data.iloc[:48, :], 'DV', 'id', within=['A', 'B']).fit()
101 assert_array_almost_equal(df.anova_table.iloc[:, [1, 2, 0, 3]].values,
118 assert_array_almost_equal(df.anova_table.iloc[:, [1, 2, 0, 3]].values,
126 assert_raises(ValueError, AnovaRM, data.iloc[:16, :], 'DV', 'id',
131 data1 = data.iloc[:48, :].copy()
137 assert_raises(ValueError, AnovaRM, data.iloc[1:48, :], 'DV', 'id',
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| /dports/graphics/py-plotly/plotly-4.14.3/plotly/figure_factory/ |
| H A D | _bullet.py | 89 for idx in range(len(df.iloc[row]["ranges"])):
94 [sorted(df.iloc[row]["ranges"])[-1 - idx]]
101 else [sorted(df.iloc[row]["ranges"])[-1 - idx]]
118 for idx in range(len(df.iloc[row]["measures"])):
122 len(df.iloc[row]["measures"]),
126 [sorted(df.iloc[row]["measures"])[-1 - idx]]
133 else [sorted(df.iloc[row]["measures"])[-1 - idx]]
150 x = df.iloc[row]["markers"] if orientation == "h" else [0.5]
151 y = [0.5] if orientation == "h" else df.iloc[row]["markers"]
165 title = df.iloc[row]["titles"]
[all …]
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| /dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/tsa/statespace/tests/ |
| H A D | test_simulation_smoothing.py | 38 obs.iloc[0:50, :] = np.nan
40 obs.iloc[0:50, 0] = np.nan
42 obs.iloc[0:50, 0] = np.nan
43 obs.iloc[19:70, 1] = np.nan
44 obs.iloc[39:90, 2] = np.nan
45 obs.iloc[119:130, 0] = np.nan
46 obs.iloc[119:130, 2] = np.nan
47 obs.iloc[-10:, :] = np.nan
50 obs = obs.iloc[:9]
441 obs.iloc[:50, :] = np.nan
[all …]
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| /dports/math/py-statsmodels/statsmodels-0.13.1/examples/python/ |
| H A D | statespace_forecasting.py | 259 init_training_endog = endog.iloc[:n_init_training]
269 updated_endog = endog.iloc[t:t + 1]
278 print(forecasts.iloc[:5, :5])
288 print(forecast_errors.iloc[:5, :5])
304 print(flattened.iloc[:3, :5])
327 init_training_endog = endog.iloc[:n_init_training]
337 updated_endog = endog.iloc[t:t + 1]
346 print(forecasts.iloc[:5, :5])
352 print(forecast_errors.iloc[:5, :5])
363 print(flattened.iloc[:3, :5])
|
| H A D | markov_regression.py | 114 mod_fedfunds2 = sm.tsa.MarkovRegression(dta_fedfunds.iloc[1:],
116 exog=dta_fedfunds.iloc[:-1])
159 exog = pd.concat((dta_fedfunds.shift(), dta_ogap, dta_inf), axis=1).iloc[4:]
162 mod_fedfunds3 = sm.tsa.MarkovRegression(dta_fedfunds.iloc[4:],
169 mod_fedfunds4 = sm.tsa.MarkovRegression(dta_fedfunds.iloc[4:],
227 dta_areturns.iloc[1:],
229 exog=dta_areturns.iloc[:-1],
|
| /dports/math/py-pandas/pandas-1.2.5/pandas/tests/frame/ |
| H A D | conftest.py | 43 df.iloc[5:10] = np.nan
44 df.iloc[15:20, -2:] = np.nan
77 df.iloc[5:10] = np.nan
78 df.iloc[15:20, -2:] = np.nan
83 df.iloc[i, i] = True
223 df.iloc[1, 1] = NaT
224 df.iloc[1, 2] = NaT
|
| /dports/devel/py-dask/dask-2021.11.2/dask/dataframe/tests/ |
| H A D | test_indexing.py | 582 result = ddf.iloc[:, indexer]
592 ds.iloc[:]
600 ddf.iloc[[0, 1], :]
603 ddf.iloc[[0, 1], [0, 1]]
609 ddf.iloc[:, [5, 6]]
618 selection = ddf.iloc[:, 2]
622 select_first = ddf.iloc[:, 1]
639 selection = ddf.iloc[:, 2]
661 a = ddf.iloc[:, 0]
662 b = ddf.iloc[:, 1]
[all …]
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| /dports/science/py-lifelines/lifelines-0.19.5/lifelines/ |
| H A D | plotting.py | 263 def create_dataframe_slicer(iloc, loc): argument
264 user_did_not_specify_certain_indexes = (iloc is None) and (loc is None)
265 …user_submitted_slice = slice(None) if user_did_not_specify_certain_indexes else coalesce(loc, iloc)
271 def plot_loglogs(cls, loc=None, iloc=None, show_censors=False, censor_styles=None, **kwargs): argument
279 if (loc is not None) and (iloc is not None):
290 dataframe_slicer = create_dataframe_slicer(iloc, loc)
316 iloc=None, argument
366 cls, estimate, confidence_intervals, loc, iloc, show_censors, censor_styles, **kwargs
369 dataframe_slicer = create_dataframe_slicer(iloc, loc)
437 self.iloc = iloc
[all …]
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| /dports/math/py-pandas/pandas-1.2.5/pandas/tests/window/moments/ |
| H A D | test_moments_rolling_apply.py | 27 tm.assert_almost_equal(result.iloc[-1], np.mean(series[-50:]))
34 result.iloc[-1, :],
35 frame.iloc[-50:, :].apply(np.mean, axis=0, raw=raw),
74 tm.assert_almost_equal(result.iloc[-1], np.mean(obj[10:-10]))
78 assert isna(result.iloc[23])
79 assert not isna(result.iloc[24])
81 assert not isna(result.iloc[-6])
82 assert isna(result.iloc[-5])
86 assert isna(result.iloc[3])
87 assert notna(result.iloc[4])
|
| H A D | test_moments_rolling_quantile.py | 35 tm.assert_almost_equal(result.iloc[-1], compare_func(series[-50:]))
44 result.iloc[-1, :],
45 frame.iloc[-50:, :].apply(compare_func, axis=0, raw=raw),
88 tm.assert_almost_equal(result.iloc[-1], compare_func(obj[10:-10]))
92 assert isna(result.iloc[23])
93 assert not isna(result.iloc[24])
95 assert not isna(result.iloc[-6])
96 assert isna(result.iloc[-5])
100 assert isna(result.iloc[3])
101 assert notna(result.iloc[4])
|
| H A D | test_moments_rolling_skew_kurt.py | 22 tm.assert_almost_equal(result.iloc[-1], compare_func(series[-50:]))
34 result.iloc[-1, :],
35 frame.iloc[-50:, :].apply(compare_func, axis=0, raw=raw),
87 tm.assert_almost_equal(result.iloc[-1], compare_func(obj[10:-10]))
91 assert isna(result.iloc[23])
92 assert not isna(result.iloc[24])
94 assert not isna(result.iloc[-6])
95 assert isna(result.iloc[-5])
99 assert isna(result.iloc[3])
100 assert notna(result.iloc[4])
|
| /dports/math/py-pandas-datareader/pandas-datareader-0.9.0/pandas_datareader/tests/av/ |
| H A D | test_av_time_series.py | 137 assert df.iloc[0].name == "2015-02-13"
138 assert df.iloc[-1].name == "2017-05-19"
153 assert df.iloc[0].name == "2015-02-13"
154 assert df.iloc[-1].name == "2017-05-19"
169 assert df.iloc[0].name == "2015-02-27"
170 assert df.iloc[-1].name == "2017-04-28"
186 assert df.iloc[0].name == "2015-02-27"
187 assert df.iloc[-1].name == "2017-04-28"
|
| /dports/math/py-pandas/pandas-1.2.5/pandas/tests/frame/indexing/ |
| H A D | test_indexing.py | 480 df.iloc[0] = np.nan
780 cp.iloc[4:10] = 0
785 cp.iloc[3:11] = 0
999 df.iloc[1.0:5]
1015 assert (cp.iloc[0:1] == df.iloc[0:1]).values.all()
1018 cp.iloc[4:5] = 0
1020 assert (cp.iloc[0:4] == df.iloc[0:4]).values.all()
1272 result = df.iloc[1]
1276 result = df.iloc[2]
1334 result = df.iloc[0]
[all …]
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| /dports/science/gromacs/gromacs-2021.4/src/gromacs/nbnxm/ |
| H A D | gpu_common_utils.h | 67 static inline bool canSkipNonbondedWork(const NbnxmGpu& nb, InteractionLocality iloc) in canSkipNonbondedWork() argument
69 assert(nb.plist[iloc]); in canSkipNonbondedWork()
70 return (iloc == InteractionLocality::NonLocal && nb.plist[iloc]->nsci == 0); in canSkipNonbondedWork()
|
| /dports/devel/ga/ga-5.8/global/examples/scf/ |
| H A D | ft-scf.F | 204 iloc = i - lo(1) + 1
208 work(iloc,jloc) = sqrt(gg)
338 iloc = i - lo(1) + 1
379 iloc = i - lo(1) + 1
534 iloc = i-lo(1) + 1
548 f_ij(iloc,jloc) = f_ij(iloc,jloc)
550 f_ik(iloc,kloc) = f_ik(iloc,kloc)
654 iloc = i - lo(1) + 1
690 iloc = i - lo(1) + 1
692 work(iloc,jloc) = work(iloc,jloc) + shift
[all …]
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| /dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/multivariate/tests/ |
| H A D | test_factor.py | 51 mod = Factor(None, 2, corr=np.corrcoef(X.iloc[:, 1:-1], rowvar=0),
60 mod.endog_names = X.iloc[:, 1:-1].columns
65 X.iloc[:, :1].columns)
70 mod = Factor(X.iloc[:, 1:-1], 2, method='ab')
103 mod = Factor(X.iloc[:, 1:-1], 2, smc=False)
113 mod = Factor(X.iloc[:, 1:-1], 2, smc=True)
195 mod = Factor(X.iloc[:, 1:], 3)
207 mod = Factor(X.iloc[:, 1:-1], 2, smc=True)
240 xm = X.iloc[:, 1:-1].copy()
242 xm.iloc[2,2] = np.nan
|