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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:  
Step2:  List all the fields in the info object
Step3:  Obtain the sampling rate of the data
Step4:  List all information about the first data channel
Step5:  Obtaining subsets of channels
There are a number of convenience functions to obtain channel indices, given
an 
Step6:  Get channel indices by regular expression
Step7:  Channel types
MNE supports different channel types
Step8:  MEG gradiometers and EEG channels
Step9:  Get a dictionary of channel indices, grouped by channel type
Step10:  Obtaining information about channels
Step11:  Channel types of a collection of channels
Step12:  Dropping channels from an info structure
It is possible to limit the info structure to only include a subset of
channels with the 
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Python Code:
import mne
import os.path as op
Explanation: The :class:~mne.Info data structure
The :class:~mne.Info data object is typically created
when data is imported into MNE-Python and contains details such as:
date, subject information, and other recording details
the sampling rate
information about the data channels (name, type, position, etc.)
digitized points
sensor–head coordinate transformation matrices
and so forth. See the :class:the API reference &lt;mne.Info&gt;
for a complete list of all data fields. Once created, this object is passed
around throughout the data analysis pipeline.
End of explanation
# Read the info object from an example recording
info = mne.io.read_info(
    op.join(mne.datasets.sample.data_path(), 'MEG', 'sample',
            'sample_audvis_raw.fif'), verbose=False)
Explanation: :class:mne.Info behaves as a nested Python dictionary:
End of explanation
print('Keys in info dictionary:\n', info.keys())
Explanation: List all the fields in the info object
End of explanation
print(info['sfreq'], 'Hz')
Explanation: Obtain the sampling rate of the data
End of explanation
print(info['chs'][0])
Explanation: List all information about the first data channel
End of explanation
channel_indices_two = mne.pick_channels(
    info['ch_names'], ['MEG 0312', 'EEG 005'])
Explanation: Obtaining subsets of channels
There are a number of convenience functions to obtain channel indices, given
an :class:mne.Info object.
Get channel indices by name
End of explanation
channel_indices_meg_re = mne.pick_channels_regexp(info['ch_names'], 'MEG *')
Explanation: Get channel indices by regular expression
End of explanation
channel_indices_meg = mne.pick_types(info, meg=True)  # MEG only
channel_indices_eeg = mne.pick_types(info, meg=False, eeg=True)  # EEG only
Explanation: Channel types
MNE supports different channel types:
eeg : For EEG channels with data stored in Volts (V)
meg (mag) : For MEG magnetometers channels stored in Tesla (T)
meg (grad) : For MEG gradiometers channels stored in Tesla/Meter (T/m)
ecg : For ECG channels stored in Volts (V)
seeg : For Stereotactic EEG channels in Volts (V).
ecog : For Electrocorticography (ECoG) channels in Volts (V).
fnirs (HBO) : Functional near-infrared spectroscopy oxyhemoglobin data.
fnirs (HBR) : Functional near-infrared spectroscopy deoxyhemoglobin data.
emg : For EMG channels stored in Volts (V)
bio : For biological channels (AU).
stim : For the stimulus (a.k.a. trigger) channels (AU)
resp : For the response-trigger channel (AU)
chpi : For HPI coil channels (T).
exci : Flux excitation channel used to be a stimulus channel.
ias : For Internal Active Shielding data (maybe on Triux only).
syst : System status channel information (on Triux systems only).
Get channel indices by type
End of explanation
channel_indices_grad_eeg = mne.pick_types(info, meg='grad', eeg=True)
Explanation: MEG gradiometers and EEG channels
End of explanation
channel_indices_by_type = mne.io.pick.channel_indices_by_type(info)
print('The first three magnetometers:', channel_indices_by_type['mag'][:3])
Explanation: Get a dictionary of channel indices, grouped by channel type
End of explanation
# Channel type of a specific channel
channel_type = mne.io.pick.channel_type(info, 75)
print('Channel #75 is of type:', channel_type)
Explanation: Obtaining information about channels
End of explanation
meg_channels = mne.pick_types(info, meg=True)[:10]
channel_types = [mne.io.pick.channel_type(info, ch) for ch in meg_channels]
print('First 10 MEG channels are of type:\n', channel_types)
Explanation: Channel types of a collection of channels
End of explanation
# Only keep EEG channels
eeg_indices = mne.pick_types(info, meg=False, eeg=True)
reduced_info = mne.pick_info(info, eeg_indices)
print(reduced_info)
Explanation: Dropping channels from an info structure
It is possible to limit the info structure to only include a subset of
channels with the :func:mne.pick_info function:
End of explanation
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