Skip to content

Instantly share code, notes, and snippets.

@rohanp

rohanp/gist:1082ad5a5e199f6b1cb8ade965c4519f Secret

Created August 8, 2017 05:01
Show Gist options
  • Star 0 You must be signed in to star a gist
  • Fork 0 You must be signed in to fork a gist
  • Save rohanp/1082ad5a5e199f6b1cb8ade965c4519f to your computer and use it in GitHub Desktop.
Save rohanp/1082ad5a5e199f6b1cb8ade965c4519f to your computer and use it in GitHub Desktop.
Download ZIP
Raw
gistfile1.txt
(pandas) ➜ pandas git:(rohanp) ✗ pytest pandas/tests/test_nanops.py -k test_nanmedian --pdb
============================================= test session starts ==============================================
platform darwin -- Python 3.5.2, pytest-3.2.0, py-1.4.34, pluggy-0.4.0
rootdir: /Users/rohanp/pandas, inifile: setup.cfg
collected 55 items
pandas/tests/test_nanops.py
>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> PDB set_trace (IO-capturing turned off) >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
> /Users/rohanp/pandas/pandas/tests/test_nanops.py(196)check_fun_data()
-> self.check_results(targ, res, axis,
(Pdb) ll
183 def check_fun_data(self, testfunc, targfunc, testarval, targarval,
184 targarnanval, check_dtype=True, **kwargs):
185 for axis in list(range(targarval.ndim)) + [None]:
186 for skipna in [False, True]:
187 targartempval = targarval if skipna else targarnanval
188 try:
189 targ = targfunc(targartempval, axis=axis, **kwargs)
190 res = testfunc(testarval, axis=axis, skipna=skipna,
191 **kwargs)
192
193 if not np.allclose(targ, res):
194 import pdb; pdb.set_trace()
195
196 -> self.check_results(targ, res, axis,
197 check_dtype=check_dtype)
198 if skipna:
199 res = testfunc(testarval, axis=axis, **kwargs)
200 self.check_results(targ, res, axis,
201 check_dtype=check_dtype)
202 if axis is None:
203 res = testfunc(testarval, skipna=skipna, **kwargs)
204 self.check_results(targ, res, axis,
205 check_dtype=check_dtype)
206 if skipna and axis is None:
207 res = testfunc(testarval, **kwargs)
208 self.check_results(targ, res, axis,
209 check_dtype=check_dtype)
210 except BaseException as exc:
211 exc.args += ('axis: %s of %s' % (axis, testarval.ndim - 1),
212 'skipna: %s' % skipna, 'kwargs: %s' % kwargs)
213
214 raise
215
216 if testarval.ndim <= 1:
217 return
218
219 try:
220 testarval2 = np.take(testarval, 0, axis=-1)
221 targarval2 = np.take(targarval, 0, axis=-1)
222 targarnanval2 = np.take(targarnanval, 0, axis=-1)
223 except ValueError:
224 return
225 self.check_fun_data(testfunc, targfunc, testarval2, targarval2,
226 targarnanval2, check_dtype=check_dtype, **kwargs)
(Pdb) targ
array([[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan]])
(Pdb) res
array([[ 0.05118711, -0.45872435, -0.02085726, 0.00471621, -0.48745034],
[ 0.25417631, -0.03097218, -0.0738097 , -0.26466025, 0.0678109 ],
[ 0.16513667, -0.92211551, -0.33636526, 0.63226656, -0.27500906],
[ 0.00760543, -0.0346826 , -0.08828393, 0.2677941 , -0.03389868],
[ 0.1601678 , -0.18806439, -0.88011583, -0.14156705, 0.75968485],
[-0.10196248, 0.13446948, -0.41102928, -0.27228986, 0.17492847],
[-0.43148189, 0.24527084, 0.14005599, -0.0727601 , 0.23868236]])
(Pdb) import inspect
(Pdb) print(inspect.getsource(targfunc))
def median(a, axis=None, out=None, overwrite_input=False, keepdims=False):
"""
Compute the median along the specified axis.
Returns the median of the array elements.
Parameters
----------
a : array_like
Input array or object that can be converted to an array.
axis : {int, sequence of int, None}, optional
Axis or axes along which the medians are computed. The default
is to compute the median along a flattened version of the array.
A sequence of axes is supported since version 1.9.0.
out : ndarray, optional
Alternative output array in which to place the result. It must
have the same shape and buffer length as the expected output,
but the type (of the output) will be cast if necessary.
overwrite_input : bool, optional
If True, then allow use of memory of input array `a` for
calculations. The input array will be modified by the call to
`median`. This will save memory when you do not need to preserve
the contents of the input array. Treat the input as undefined,
but it will probably be fully or partially sorted. Default is
False. If `overwrite_input` is ``True`` and `a` is not already an
`ndarray`, an error will be raised.
keepdims : bool, optional
If this is set to True, the axes which are reduced are left
in the result as dimensions with size one. With this option,
the result will broadcast correctly against the original `arr`.
.. versionadded:: 1.9.0
Returns
-------
median : ndarray
A new array holding the result. If the input contains integers
or floats smaller than ``float64``, then the output data-type is
``np.float64``. Otherwise, the data-type of the output is the
same as that of the input. If `out` is specified, that array is
returned instead.
See Also
--------
mean, percentile
Notes
-----
Given a vector ``V`` of length ``N``, the median of ``V`` is the
middle value of a sorted copy of ``V``, ``V_sorted`` - i
e., ``V_sorted[(N-1)/2]``, when ``N`` is odd, and the average of the
two middle values of ``V_sorted`` when ``N`` is even.
Examples
--------
>>> a = np.array([[10, 7, 4], [3, 2, 1]])
>>> a
array([[10, 7, 4],
[ 3, 2, 1]])
>>> np.median(a)
3.5
>>> np.median(a, axis=0)
array([ 6.5, 4.5, 2.5])
>>> np.median(a, axis=1)
array([ 7., 2.])
>>> m = np.median(a, axis=0)
>>> out = np.zeros_like(m)
>>> np.median(a, axis=0, out=m)
array([ 6.5, 4.5, 2.5])
>>> m
array([ 6.5, 4.5, 2.5])
>>> b = a.copy()
>>> np.median(b, axis=1, overwrite_input=True)
array([ 7., 2.])
>>> assert not np.all(a==b)
>>> b = a.copy()
>>> np.median(b, axis=None, overwrite_input=True)
3.5
>>> assert not np.all(a==b)
"""
r, k = _ureduce(a, func=_median, axis=axis, out=out,
overwrite_input=overwrite_input)
if keepdims:
return r.reshape(k)
else:
return r
(Pdb) print(inspect.getsource(testfunc))
@disallow('M8')
@bottleneck_switch()
def nanmedian(values, axis=None, skipna=True):
#import pdb; pdb.set_trace()
values, mask, dtype, dtype_max = _get_values(values, skipna)
np_nanmedian_available = StrictVersion(np.version.version) >= StrictVersion("1.9")
#print("values!")
#print(values)
def get_median(x):
mask = notnull(x)
if not skipna and not mask.all():
return np.nan
return algos.median(_values_from_object(x[mask]))
if not is_float_dtype(values):
values = values.astype('f8')
values[mask] = np.nan
if axis is None:
values = values.ravel()
notempty = values.size
# an array from a frame
if values.ndim > 1:
# there's a non-empty array to apply over otherwise numpy raises
if notempty:
if np_nanmedian_available:
return np.nanmedian(values, axis)
else:
#print("NOT HERE")
return _wrap_results(
np.apply_along_axis(get_median, axis, values), dtype)
# must return the correct shape, but median is not defined for the
# empty set so return nans of shape "everything but the passed axis"
# since "axis" is where the reduction would occur if we had a nonempty
# array
shp = np.array(values.shape)
dims = np.arange(values.ndim)
ret = np.empty(shp[dims != axis])
ret.fill(np.nan)
return _wrap_results(ret, dtype)
# otherwise return a scalar value
if np_nanmedian_available:
return _wrap_results(np.nanmedian(values) if notempty else np.nan, dtype)
else:
return _wrap_results(get_median(values) if notempty else np.nan, dtype)
(Pdb) targartempval
array([[[-0.38825224, -0.45872435, 1.05486525, 0.31956083, 1.14809371],
[-0.00578802, -0.14351561, -1.40698905, -0.34134135, -0.14323561],
[ 0.16513667, -0.48747916, 0.63898755, 1.56494777, -0.7443864 ],
[ 0.66294031, -0.8317759 , 1.31468696, 0.95759531, -0.03389868],
[ 0.73086499, 0.70585301, 1.91718117, 2.27577977, 1.89121214],
[-1.07458971, -1.08979394, -1.76423455, 0.80586292, 0.30584617],
[ 0.92739786, -2.69452768, 2.04372204, 0.48859691, -1.57612352]],
[[ 2.25028687, 0.63779974, 0.68342731, 0.00471621, -0.65399029],
[-0.19476041, 1.81552545, 0.51889123, 0.87562612, -0.3892774 ],
[ 0.01635034, -1.24305638, -0.36368474, -0.36445098, 0.42753209],
[-0.4729695 , -0.01745559, 0.89452566, -1.4390385 , -1.26780395],
[ 1.18201306, -1.1622051 , -2.71979787, 0.41992428, 0.88180422],
[ 0.27834578, 0.13446948, 0.55212485, -0.98854351, 0.99006069],
[-0.80094851, -0.08638208, -0.34687719, -0.0727601 , 1.3633947 ]],
[[ 0.97792431, -0.50685917, -0.49734799, 0.30445786, 0.62872697],
[ 0.67838492, 0.26102178, 1.61255621, -0.85968821, -2.05477625],
[-0.89228212, 0.7917057 , -0.30265329, 0.35312641, -0.34641609],
[-1.22003801, -0.84863456, 0.98217849, -1.53655233, -0.80637798],
[-0.33071735, -1.89204786, -1.93527651, 0.19684946, -1.14932361],
[ 0.43355292, 0.96918435, -0.41102928, 0.24262618, -1.13047781],
[-0.84918425, 0.33501427, -1.69681832, -0.33944707, -0.55422857]],
[[ 0.05118711, -0.57524952, -1.11626042, -0.97951706, 0.8940154 ],
[ 0.31769978, 0.71076409, 1.7592069 , -1.09807486, 0.0678109 ],
[-0.49852146, -0.22318572, -0.33636526, -0.91411015, -1.12652678],
[ 0.64227766, 0.13517118, 0.10704381, 1.28589488, 0.69870227],
[-0.87857642, 0.15461119, -1.02891304, 0.35705758, 0.12209429],
[-0.32618092, -1.1629626 , 0.98892129, -0.27018407, 0.06803624],
[-1.03207802, -0.69316651, 0.14005599, -0.11922739, -0.07816359]],
[[-0.38908183, -0.81040345, 0.68370322, 0.90188594, -1.80321481],
[ 0.12745242, 1.46988529, 1.15539722, 2.36676976, -0.23269785],
[ 2.08116059, 3.36348586, -0.95406058, 0.33430095, 1.28731917],
[-0.22204977, -0.0346826 , -0.84104312, 0.95147879, -0.52867337],
[ 0.67371056, 0.21330956, -0.66596554, -1.08538032, 2.57846358],
[-0.43980808, -1.02388464, -1.72123434, 0.6026453 , 1.34109299],
[ 1.41656069, 1.7624381 , 0.20258937, -0.74805922, 0.72920807]],
[[-1.25383019, -0.86054481, 1.25054603, 1.14458702, -0.26085491],
[ 1.55586896, 0.02332803, -0.0738097 , 0.93617682, 0.48228872],
[ 2.4965577 , -0.94128041, -1.22715694, 0.7998703 , 0.3431659 ],
[ 0.57162177, -0.62947045, -1.32218205, 1.15827038, 0.58420174],
[ 1.1200749 , -1.2067002 , -0.73592999, -0.71823314, 0.75968485],
[-0.29155435, 1.34940108, 0.25466987, -1.57469254, 0.5947282 ],
[ 1.28683932, 1.65044268, 0.20319533, 1.64323562, 0.42682942]],
[[-0.97858595, 0.08474185, -1.62431349, 0.57411943, -1.02951143],
[ 1.09678942, -0.87360806, -0.50266793, -0.79779213, 0.19842459],
[ 0.31413511, -1.18696502, -1.16598118, -0.5097998 , 0.85985869],
[-1.32148743, -0.32531782, -0.90164102, -0.26826046, 1.35848258],
[ 0.33896427, -1.27730814, -1.78411941, -0.14156705, 1.67153113],
[-0.13255231, -0.02392706, -1.15117929, -0.48422198, -1.13779698],
[-0.43148189, -0.84148705, 0.8029512 , -1.02114507, 1.54490774]],
[[ 0.50348946, 0.99861864, -0.22948088, -0.33243308, -1.19982988],
[ 0.12821233, -0.77641173, -0.68633081, 0.542418 , -0.71827667],
[-0.01899879, -0.94336419, -0.22560816, 0.63226656, 0.28848488],
[ 0.00760543, 0.32084445, -0.08828393, -1.28400062, -2.24612778],
[ 0.1601678 , -1.34652894, -0.88011583, -0.90701249, -0.67519159],
[ 2.23324348, 0.19140225, -0.05528151, -0.27228986, 1.07504245],
[-1.37579119, 0.85146862, -0.38325804, 1.9058229 , -0.02567673]],
[[ 0.91971294, 0.14128946, -0.92976 , -1.27721231, 0.89395778],
[ 0.25417631, -0.03097218, 0.66371714, 1.29501118, 0.90261116],
[ 0.69055382, -0.92211551, -0.52063755, 0.67925763, -0.29163266],
[-0.45724346, -0.39291401, 0.36451466, -1.63039786, 0.89032433],
[-0.28970788, -0.13039404, 0.62613009, -1.25539236, 0.08363484],
[ 0.36769137, -0.29022357, 0.41889391, -0.74074718, -0.40273736],
[ 0.49654374, 0.24527084, -1.01815164, -0.51220951, 0.3033394 ]],
[[ 0.18107761, -0.58778822, -0.02085726, -0.04116001, -0.88875231],
[-0.38750674, -0.40634409, -0.62407662, -0.26466025, 0.30117429],
[ 0.10072977, -2.04403817, 0.562087 , 1.04582238, -1.40481056],
[ 1.75650654, 0.27738833, -0.86271809, 0.2677941 , 1.15538839],
[-0.01359106, -0.18806439, -1.67248932, 0.41075649, 0.11251433],
[-0.10196248, 2.57257769, -0.7059846 , -0.06504558, 0.17492847],
[-1.58738364, -0.58217846, -1.30481354, 0.54497637, 0.23868236]],
[[-0.97499552, 1.1338151 , 0.3757687 , -0.6285383 , -0.48745034],
[ 1.17234195, -0.9577289 , -1.0635969 , -0.9060959 , 0.82134947],
[ 1.10549235, 1.19218029, 0.78660926, 1.81719581, -0.27500906],
[ 0.13658901, 1.63647576, -0.93375975, 1.21100674, -0.51331421],
[ 0.03228562, 0.7437696 , -0.80044138, -0.29710109, 1.13082706],
[ 1.02375849, 1.47633042, -0.49172581, -0.82026711, -0.13627042],
[ 0.42524962, 0.58666376, 1.17249809, 1.10854989, -0.67451349]],
[[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan]],
[[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan]],
[[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan]],
[[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan]],
[[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan]],
[[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan]],
[[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan]],
[[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan]],
[[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan]],
[[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan]],
[[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan]]])
(Pdb) testarval
array([[[-0.38825224, -0.45872435, 1.05486525, 0.31956083, 1.14809371],
[-0.00578802, -0.14351561, -1.40698905, -0.34134135, -0.14323561],
[ 0.16513667, -0.48747916, 0.63898755, 1.56494777, -0.7443864 ],
[ 0.66294031, -0.8317759 , 1.31468696, 0.95759531, -0.03389868],
[ 0.73086499, 0.70585301, 1.91718117, 2.27577977, 1.89121214],
[-1.07458971, -1.08979394, -1.76423455, 0.80586292, 0.30584617],
[ 0.92739786, -2.69452768, 2.04372204, 0.48859691, -1.57612352]],
[[ 2.25028687, 0.63779974, 0.68342731, 0.00471621, -0.65399029],
[-0.19476041, 1.81552545, 0.51889123, 0.87562612, -0.3892774 ],
[ 0.01635034, -1.24305638, -0.36368474, -0.36445098, 0.42753209],
[-0.4729695 , -0.01745559, 0.89452566, -1.4390385 , -1.26780395],
[ 1.18201306, -1.1622051 , -2.71979787, 0.41992428, 0.88180422],
[ 0.27834578, 0.13446948, 0.55212485, -0.98854351, 0.99006069],
[-0.80094851, -0.08638208, -0.34687719, -0.0727601 , 1.3633947 ]],
[[ 0.97792431, -0.50685917, -0.49734799, 0.30445786, 0.62872697],
[ 0.67838492, 0.26102178, 1.61255621, -0.85968821, -2.05477625],
[-0.89228212, 0.7917057 , -0.30265329, 0.35312641, -0.34641609],
[-1.22003801, -0.84863456, 0.98217849, -1.53655233, -0.80637798],
[-0.33071735, -1.89204786, -1.93527651, 0.19684946, -1.14932361],
[ 0.43355292, 0.96918435, -0.41102928, 0.24262618, -1.13047781],
[-0.84918425, 0.33501427, -1.69681832, -0.33944707, -0.55422857]],
[[ 0.05118711, -0.57524952, -1.11626042, -0.97951706, 0.8940154 ],
[ 0.31769978, 0.71076409, 1.7592069 , -1.09807486, 0.0678109 ],
[-0.49852146, -0.22318572, -0.33636526, -0.91411015, -1.12652678],
[ 0.64227766, 0.13517118, 0.10704381, 1.28589488, 0.69870227],
[-0.87857642, 0.15461119, -1.02891304, 0.35705758, 0.12209429],
[-0.32618092, -1.1629626 , 0.98892129, -0.27018407, 0.06803624],
[-1.03207802, -0.69316651, 0.14005599, -0.11922739, -0.07816359]],
[[-0.38908183, -0.81040345, 0.68370322, 0.90188594, -1.80321481],
[ 0.12745242, 1.46988529, 1.15539722, 2.36676976, -0.23269785],
[ 2.08116059, 3.36348586, -0.95406058, 0.33430095, 1.28731917],
[-0.22204977, -0.0346826 , -0.84104312, 0.95147879, -0.52867337],
[ 0.67371056, 0.21330956, -0.66596554, -1.08538032, 2.57846358],
[-0.43980808, -1.02388464, -1.72123434, 0.6026453 , 1.34109299],
[ 1.41656069, 1.7624381 , 0.20258937, -0.74805922, 0.72920807]],
[[-1.25383019, -0.86054481, 1.25054603, 1.14458702, -0.26085491],
[ 1.55586896, 0.02332803, -0.0738097 , 0.93617682, 0.48228872],
[ 2.4965577 , -0.94128041, -1.22715694, 0.7998703 , 0.3431659 ],
[ 0.57162177, -0.62947045, -1.32218205, 1.15827038, 0.58420174],
[ 1.1200749 , -1.2067002 , -0.73592999, -0.71823314, 0.75968485],
[-0.29155435, 1.34940108, 0.25466987, -1.57469254, 0.5947282 ],
[ 1.28683932, 1.65044268, 0.20319533, 1.64323562, 0.42682942]],
[[-0.97858595, 0.08474185, -1.62431349, 0.57411943, -1.02951143],
[ 1.09678942, -0.87360806, -0.50266793, -0.79779213, 0.19842459],
[ 0.31413511, -1.18696502, -1.16598118, -0.5097998 , 0.85985869],
[-1.32148743, -0.32531782, -0.90164102, -0.26826046, 1.35848258],
[ 0.33896427, -1.27730814, -1.78411941, -0.14156705, 1.67153113],
[-0.13255231, -0.02392706, -1.15117929, -0.48422198, -1.13779698],
[-0.43148189, -0.84148705, 0.8029512 , -1.02114507, 1.54490774]],
[[ 0.50348946, 0.99861864, -0.22948088, -0.33243308, -1.19982988],
[ 0.12821233, -0.77641173, -0.68633081, 0.542418 , -0.71827667],
[-0.01899879, -0.94336419, -0.22560816, 0.63226656, 0.28848488],
[ 0.00760543, 0.32084445, -0.08828393, -1.28400062, -2.24612778],
[ 0.1601678 , -1.34652894, -0.88011583, -0.90701249, -0.67519159],
[ 2.23324348, 0.19140225, -0.05528151, -0.27228986, 1.07504245],
[-1.37579119, 0.85146862, -0.38325804, 1.9058229 , -0.02567673]],
[[ 0.91971294, 0.14128946, -0.92976 , -1.27721231, 0.89395778],
[ 0.25417631, -0.03097218, 0.66371714, 1.29501118, 0.90261116],
[ 0.69055382, -0.92211551, -0.52063755, 0.67925763, -0.29163266],
[-0.45724346, -0.39291401, 0.36451466, -1.63039786, 0.89032433],
[-0.28970788, -0.13039404, 0.62613009, -1.25539236, 0.08363484],
[ 0.36769137, -0.29022357, 0.41889391, -0.74074718, -0.40273736],
[ 0.49654374, 0.24527084, -1.01815164, -0.51220951, 0.3033394 ]],
[[ 0.18107761, -0.58778822, -0.02085726, -0.04116001, -0.88875231],
[-0.38750674, -0.40634409, -0.62407662, -0.26466025, 0.30117429],
[ 0.10072977, -2.04403817, 0.562087 , 1.04582238, -1.40481056],
[ 1.75650654, 0.27738833, -0.86271809, 0.2677941 , 1.15538839],
[-0.01359106, -0.18806439, -1.67248932, 0.41075649, 0.11251433],
[-0.10196248, 2.57257769, -0.7059846 , -0.06504558, 0.17492847],
[-1.58738364, -0.58217846, -1.30481354, 0.54497637, 0.23868236]],
[[-0.97499552, 1.1338151 , 0.3757687 , -0.6285383 , -0.48745034],
[ 1.17234195, -0.9577289 , -1.0635969 , -0.9060959 , 0.82134947],
[ 1.10549235, 1.19218029, 0.78660926, 1.81719581, -0.27500906],
[ 0.13658901, 1.63647576, -0.93375975, 1.21100674, -0.51331421],
[ 0.03228562, 0.7437696 , -0.80044138, -0.29710109, 1.13082706],
[ 1.02375849, 1.47633042, -0.49172581, -0.82026711, -0.13627042],
[ 0.42524962, 0.58666376, 1.17249809, 1.10854989, -0.67451349]],
[[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan]],
[[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan]],
[[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan]],
[[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan]],
[[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan]],
[[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan]],
[[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan]],
[[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan]],
[[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan]],
[[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan]],
[[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan],
[ nan, nan, nan, nan, nan]]])
(Pdb) skipna
False
(Pdb)
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment