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 in compat_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