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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 The 
Step1:  The 
Step2:  Notice that the reader function returned a list of evoked instances. This is
because you can store multiple categories into a single file. Here we have
categories of
['Left Auditory', 'Right Auditory', 'Left Visual', 'Right Visual'].
We can also use condition parameter to read in only one category.
Step3:  If you're gone through the tutorials of raw and epochs datasets, you're
probably already familiar with the 
Step4:  The evoked data structure also contains some new attributes easily
accessible
Step5:  The data is also easily accessible. Since the evoked data arrays are usually
much smaller than raw or epochs datasets, they are preloaded into the memory
when the evoked object is constructed. You can access the data as a numpy
array.
Step6:  The data is arranged in an array of shape (n_channels, n_times). Notice
that unlike epochs, evoked object does not support indexing. This means that
to access the data of a specific channel you must use the data array
directly.
Step7:  If you want to import evoked data from some other system and you have it in a
numpy array you can use 
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Python Code:
import os.path as op
import mne
Explanation: The :class:Evoked &lt;mne.Evoked&gt; data structure: evoked/averaged data
End of explanation
data_path = mne.datasets.sample.data_path()
fname = op.join(data_path, 'MEG', 'sample', 'sample_audvis-ave.fif')
evokeds = mne.read_evokeds(fname, baseline=(None, 0), proj=True)
print(evokeds)
Explanation: The :class:Evoked &lt;mne.Evoked&gt; data structure is mainly used for storing
averaged data over trials. In MNE the evoked objects are created by averaging
epochs data with :func:mne.Epochs.average. Here we read the evoked dataset
from a file.
End of explanation
evoked = mne.read_evokeds(fname, condition='Left Auditory')
evoked.apply_baseline((None, 0)).apply_proj()
print(evoked)
Explanation: Notice that the reader function returned a list of evoked instances. This is
because you can store multiple categories into a single file. Here we have
categories of
['Left Auditory', 'Right Auditory', 'Left Visual', 'Right Visual'].
We can also use condition parameter to read in only one category.
End of explanation
print(evoked.info)
print(evoked.times)
Explanation: If you're gone through the tutorials of raw and epochs datasets, you're
probably already familiar with the :class:Info &lt;mne.Info&gt; attribute.
There is nothing new or special with the evoked.info. All the relevant
info is still there.
End of explanation
print(evoked.nave)  # Number of averaged epochs.
print(evoked.first)  # First time sample.
print(evoked.last)  # Last time sample.
print(evoked.comment)  # Comment on dataset. Usually the condition.
print(evoked.kind)  # Type of data, either average or standard_error.
Explanation: The evoked data structure also contains some new attributes easily
accessible:
End of explanation
data = evoked.data
print(data.shape)
Explanation: The data is also easily accessible. Since the evoked data arrays are usually
much smaller than raw or epochs datasets, they are preloaded into the memory
when the evoked object is constructed. You can access the data as a numpy
array.
End of explanation
print('Data from channel {0}:'.format(evoked.ch_names[10]))
print(data[10])
Explanation: The data is arranged in an array of shape (n_channels, n_times). Notice
that unlike epochs, evoked object does not support indexing. This means that
to access the data of a specific channel you must use the data array
directly.
End of explanation
evoked = mne.EvokedArray(data, evoked.info, tmin=evoked.times[0])
evoked.plot()
Explanation: If you want to import evoked data from some other system and you have it in a
numpy array you can use :class:mne.EvokedArray for that. All you need is
the data and some info about the evoked data. For more information, see
tut_creating_data_structures.
End of explanation
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