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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:
 Visualizing
EQTransformer comes with a few visualization tools to get a better sense of data that is beinig processed and the results. 
1) continouty of the seismic data being processed
Step1:  Check your current directory for 'data_chart.png'
2) continous waveforms
Step2:  Now you can mark those events that you have detected in your helicorder plot to check if you have catched most of them or you are missing too many of them (high false negativ). This togather with the events plots (in the Figure subfolders in your station_output folders) can give you a sense that if you are using a too high or too low threshold levels.
Step3:  3) map plot
Step4:  check for 'station_map.png'
4) histograms
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Python Code:
from EQTransformer.utils.plot import plot_data_chart
plot_data_chart('preproc/time_tracks.pkl',  time_interval=10)
Explanation: Visualizing
EQTransformer comes with a few visualization tools to get a better sense of data that is beinig processed and the results. 
1) continouty of the seismic data being processed:
Both prepocessor and mseed_predictor output a "time_tracks.pkl" file that contains the time info of data slices and their number of components. You can use this file to visualize the continuity and type of your data using the following module:
End of explanation
from EQTransformer.utils.plot import plot_detections, plot_helicorder
plot_helicorder(input_mseed='downloads_mseeds/CA06/GS.CA06.00.HHZ__20190901T000000Z__20190902T000000Z.mseed', 
                input_csv=None, save_plot=True)
Explanation: Check your current directory for 'data_chart.png'
2) continous waveforms:
It is always a good idea to check the raw data:
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plot_helicorder(input_mseed='downloads_mseeds/CA06/GS.CA06.00.HH2__20190901T000000Z__20190902T000000Z.mseed', 
                input_csv='detections1/CA06_outputs/X_prediction_results.csv', save_plot=True)
Explanation: Now you can mark those events that you have detected in your helicorder plot to check if you have catched most of them or you are missing too many of them (high false negativ). This togather with the events plots (in the Figure subfolders in your station_output folders) can give you a sense that if you are using a too high or too low threshold levels.
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plot_detections(input_dir ="detections1", 
                input_json="json/station_list.json", 
                plot_type='station_map', 
                marker_size=50)
Explanation: 3) map plot:
You can also visulaize the number of detections over your network using this:
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plot_detections(input_dir ="detections1",  
                input_json="json/station_list.json", 
                plot_type='hist', 
                time_window=120)
Explanation: check for 'station_map.png'
4) histograms:
And this command will automatically generate detection histograms for each station in your detection folder:
End of explanation
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