tp.emsd TypeError: mean() got an unexpected keyword argument 'level'

[susuwataridib]

Trying to use the emsd function.

em = tp.emsd(tm, microns_per_pixel, frames_per_second)

But got following error, I'm pretty new Python to and don't know where start to fix this error. Any advice appreciated!

---

TypeError Traceback (most recent call last) Cell In[122], line 1 ----> 1 em = tp.emsd(tm, microns_per_pixel, frames_per_second) # microns per pixel = 100/285., frames per second = 24 2 fig, ax = plt.subplots() 3 ax.plot(em.index, em, 'o')

File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\trackpy\motion.py:235, in emsd(traj, mpp, fps, max_lagtime, detail, pos_columns) 233 ids.append(pid) 234 msds = pandas_concat(msds, keys=ids, names=['particle', 'frame']) --> 235 results = msds.mul(msds['N'], axis=0).mean(level=1) # weighted average 236 results = results.div(msds['N'].mean(level=1), axis=0) # weights normalized 237 # Above, lagt is lumped in with the rest for simplicity and speed. 238 # Here, rebuild it from the frame index.

File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\pandas\core\generic.py:11556, in NDFrame._add_numeric_operations..mean(self, axis, skipna, numeric_only, kwargs) 11539 @doc( 11540 _num_doc, 11541 desc="Return the mean of the values over the requested axis.", (...) 11554 kwargs, 11555 ):

11556 return NDFrame.mean(self, axis, skipna, numeric_only, **kwargs)

File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\pandas\core\generic.py:11201, in NDFrame.mean(self, axis, skipna, numeric_only, kwargs) 11194 def mean( 11195 self, 11196 axis: Axis | None = 0, (...) 11199 kwargs, 11200 ) -> Series | float:

11201 return self._stat_function( 11202 "mean", nanops.nanmean, axis, skipna, numeric_only, **kwargs 11203 )

File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\pandas\core\generic.py:11154, in NDFrame._stat_function(self, name, func, axis, skipna, numeric_only, **kwargs) 11152 nv.validate_median((), kwargs) 11153 else:

11154 nv.validate_stat_func((), kwargs, fname=name) 11156 validate_bool_kwarg(skipna, "skipna", none_allowed=False) 11158 return self._reduce( 11159 func, name=name, axis=axis, skipna=skipna, numeric_only=numeric_only 11160 )

File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\pandas\compat\numpy\function.py:80, in CompatValidator.call(self, args, kwargs, fname, max_fname_arg_count, method) 78 validate_args(fname, args, max_fname_arg_count, self.defaults) 79 elif method == "kwargs": ---> 80 validate_kwargs(fname, kwargs, self.defaults) 81 elif method == "both": 82 validate_args_and_kwargs( 83 fname, args, kwargs, max_fname_arg_count, self.defaults 84 )

File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\pandas\util_validators.py:162, in validate_kwargs(fname, kwargs, compat_args) 140 """ 141 Checks whether parameters passed to the *kwargs argument in a 142 function fname are valid parameters as specified in `compat_args (...) 159 map to the default values specified incompat_args` 160 """ 161 kwds = kwargs.copy() --> 162 _check_for_invalid_keys(fname, kwargs, compat_args) 163 _check_for_default_values(fname, kwds, compat_args)

File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\pandas\util_validators.py:136, in _check_for_invalid_keys(fname, kwargs, compat_args) 134 if diff: 135 bad_arg = list(diff)[0] --> 136 raise TypeError(f"{fname}() got an unexpected keyword argument '{bad_arg}'")

TypeError: mean() got an unexpected keyword argument 'level'

​

[b-grimaud]

Seeing this : 235 results = msds.mul(msds['N'], axis=0).mean(level=1) # weighted average and this : TypeError: mean() got an unexpected keyword argument 'level', it seems like a deprecation issue.

From the pandas documentation :

Deprecated since version 1.3.0: The level keyword is deprecated. Use groupby instead.

[susuwataridib]

Which version of pandas is compatible with trackpy 0.6.0rc1?

[nkeim]

Thanks for reporting this! Pandas 1.3 is the most recent version that I'm certain trackpy works with.

This needs a pull request, or a working version of that line of code.

[rodrigo-j-goncalves]

These are the only changes needed, both in motion.py: Line 235: results = msds.mul(msds['N'], axis=0).groupby(level=1).mean() # weighted average Line 236: results = results.div(msds['N'].groupby(level=1).mean(), axis=0) # weights normalized

I changed them in my own copy of motion.py within my virtual env and it works fine as expected. Thanks for this great software :)

[nkeim]

That's great! Thank you!!

[yichechang]

I think PR #735 might be related too.

[vivarose]

These are the only changes needed, both in motion.py: Line 235: results = msds.mul(msds['N'], axis=0).groupby(level=1).mean() # weighted average Line 236: results = results.div(msds['N'].groupby(level=1).mean(), axis=0) # weights normalized

I changed them in my own copy of motion.py within my virtual env and it works fine as expected. Thanks for this great software :)

I have submitted a pull request (#742) with this edit. (In the meantime, anyone wishing to access the updated version may use my fork: https://github.com/horowitz-lab/trackpy/tree/fixpandasmean2023