| /dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/tools/validation/tests/ |
| H A D | test_validation.py | 21 def use_pandas(request): function
25 def gen_data(dim, use_pandas): argument
28 if use_pandas:
32 if use_pandas:
41 def test_1d(self, use_pandas): argument
42 data = gen_data(1, use_pandas)
61 def test_2d(self, use_pandas): argument
158 def test_dot(self, use_pandas): argument
188 def test_wrap_pandas(use_pandas): argument
189 a = gen_data(1, use_pandas)
[all …]
|
| /dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/tsa/tests/ |
| H A D | test_tsa_tools.py | 333 use_pandas=True,
377 use_pandas=True,
385 use_pandas=True,
393 use_pandas=True,
401 use_pandas=True,
439 self.series, 3, trim="both", original="in", use_pandas=True
443 self.series, 3, trim="both", original="ex", use_pandas=True
447 self.series, 3, trim="both", original="sep", use_pandas=True
743 lagmat = stattools.lagmat2ds(data, 2, use_pandas=True)
756 data.iloc[:, :2], 3, use_pandas=True, trim="both"
[all …]
|
| H A D | test_ar.py | 284 nexog, period, missing, use_pandas, hold_back = request.param[3:]
285 data = gen_data(250, nexog, use_pandas)
341 nexog, period, missing, use_pandas, hold_back = request.param[3:]
342 data = gen_data(250, nexog, use_pandas)
|
| /dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/tsa/ |
| H A D | tsatools.py | 300 use_pandas: bool=False
370 use_pandas = bool_like(use_pandas, "use_pandas")
382 is_pandas = _is_using_pandas(orig, None) and use_pandas
440 x, maxlag0, maxlagex=None, dropex=0, trim="forward", use_pandas=False argument
500 if is_pandas and use_pandas:
502 x.iloc[:, 0], maxlag, trim=trim, original="in", use_pandas=True
507 x.iloc[:, k], maxlag, trim=trim, original="in", use_pandas=True
|
| /dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/tsa/statespace/tests/ |
| H A D | test_dynamic_factor_mq.py | 1397 def test_simulate_standardized_1d(standardize, use_pandas):
1399 if use_pandas:
1424 desired_shape = (10, 1) if use_pandas else (10, 1, 1)
1430 desired_shape = (10, 2) if use_pandas else (10, 1, 2)
1440 def test_simulate_standardized_2d(standardize, use_pandas):
1442 if use_pandas:
1459 desired_nd = desired if use_pandas else desired[..., None]
1468 desired_shape = (10, 2) if use_pandas else (10, 2, 1)
1474 desired_shape = (10, 4) if use_pandas else (10, 2, 2)
1833 use_pandas):
[all …]
|
| H A D | test_cfa_tvpvar.py | 70 use_pandas=True)
|
| /dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/tsa/statespace/ |
| H A D | mlemodel.py | 1997 if use_pandas:
2004 if use_pandas:
2008 if use_pandas:
2011 elif use_pandas:
2200 if use_pandas:
2391 elif use_pandas:
2400 elif use_pandas:
2416 elif use_pandas:
2424 elif use_pandas:
2437 elif use_pandas:
[all …]
|
| H A D | dynamic_factor_mq.py | 3182 use_pandas = isinstance(self.data, PandasData)
3185 if use_pandas:
3297 use_pandas = isinstance(self.data, PandasData)
3300 if use_pandas:
|
| /dports/devel/py-numba/numba-0.51.2/docs/source/user/ |
| H A D | 5minguide.rst | 81 def use_pandas(a): # Function will not benefit from Numba jit
86 print(use_pandas(x))
105 ``nopython=True`` is not set (as seen in the ``use_pandas`` example above). In
|
| /dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/graphics/tests/ |
| H A D | test_tsaplots.py | 314 def test_predict_plot(use_pandas, model_and_args, alpha): argument
321 if use_pandas:
|
| /dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/tsa/exponential_smoothing/ |
| H A D | ets.py | 1256 if self.use_pandas:
1392 if self.model.use_pandas:
2230 self.use_pandas = results.model.use_pandas
2353 if self.use_pandas:
|
| H A D | base.py | 44 self.use_pandas = isinstance(self.data, PandasData)
212 if self.use_pandas:
|
| /dports/math/py-statsmodels/statsmodels-0.13.1/examples/python/ |
| H A D | statespace_tvpvar_mcmc_cfa.py | 384 use_pandas=True)
|
| /dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/tsa/vector_ar/tests/ |
| H A D | test_var.py | 126 return prp.convert_robj(r["result"], use_pandas=False)
|
| /dports/math/py-statsmodels/statsmodels-0.13.1/examples/notebooks/ |
| H A D | statespace_tvpvar_mcmc_cfa.ipynb | 368 " augmented = sm.tsa.lagmat(y, 1, trim='both', original='in', use_pandas=True)\n",
|