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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:
 Working with the fcs parser
ATTENTION The parser returns the data exactly as recorded in the file. This means that no transformations or compensation have been applied to this data. It is up to you to transform the data from this point for doing analysis and visualization.
Step1:  Unpack meta only
Step2:  Unpack meta and reformat
Step3:  Unpack meta and data
Step4:  Untransformed data
The plot below is a plot of the raw data as recorded in the file. Specifically, no compensation or transformation has been applied to this data.
To better visualize this data you might want to take a look at either hlog or logicle transformations.
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Python Code:
%pylab inline
import fcsparser
path = fcsparser.test_sample_path # path to a test data file that is included with the package
Explanation: Working with the fcs parser
ATTENTION The parser returns the data exactly as recorded in the file. This means that no transformations or compensation have been applied to this data. It is up to you to transform the data from this point for doing analysis and visualization.
End of explanation
meta = fcsparser.parse(path, meta_data_only=True)
print type(meta)
print meta.keys()
Explanation: Unpack meta only
End of explanation
meta = fcsparser.parse(path, meta_data_only=True, reformat_meta=True)
meta['_channels_']
Explanation: Unpack meta and reformat
End of explanation
meta, data = fcsparser.parse(path, meta_data_only=False, reformat_meta=True)
print type(meta)
print type(data)
data
Explanation: Unpack meta and data
End of explanation
scatter(data['FITC-A'], data['AmCyan-A'], alpha=0.8, color='gray')
Explanation: Untransformed data
The plot below is a plot of the raw data as recorded in the file. Specifically, no compensation or transformation has been applied to this data.
To better visualize this data you might want to take a look at either hlog or logicle transformations.
End of explanation
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