Update README.md

README.md

@@ -7,33 +7,33 @@ Tells user the available types of buildings and asks which one the user wants to
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 more_info(metadata , bdgs_selected) 
 Tells the user the top 20 sensor types of the buildings the user has selected along with their count. If a user prompts, they can see the whole list.
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 dict_builder(metadata, bdgs_selected)  
 Asks user which type of sensor they want to study in the chosen building type
 Builds a dictionary of the desired point/building combination sensor data, where keys are building names and values are dataframes. This eases indexing
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 df_builder (metadata, dict_data)
 Determine the first and last timestamp of data in the built dictionary
 Make a dataframe with a timestamp range, spanning between the two dates found with a frequency of 5 minutes
 Take the dataframe of every building within dictionary, and merge it with the previous dataframe until a final dataframe is constructed that includes all data points of the sensor-building combination
 In case of too many data points or NANs, resample the data to a larger period
 For each sensor within the dataframe, determine start and end times of recorded data and write them to a separate file names startendtimes, for later access
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 nan_detector (df, startendtimes, max_nan = 0.99)  
 Adds the sensor to a list, if the data recorded have more NANs that a threshold
 The threshold for maximum NAN content is 0.99 by default but user replaceable
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 constant_detector (df, startendtimes, max_constant = 0.99)
 For each sensor, constructs the differential dataframe for data, and if there are more zeros than the max_constant threshold, adds them to a list
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 negligible_detector (df, startendtimes, min_days = 1) 
 Adds the sensors with less than min_days of data points to a list
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 outlier_detector ( df, startendtimes, min_outlier=0.5, upr_parameter=1.5, lwr_parameter=1.5 )
 Utilizes the IQR method to determine an acceptable upper and  lower bound for data using given parameters
 Adds the sensors that have outlier data for more than 0.5 times of all their data points to a list 
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 show_results(df, startendtimes, filtered_list, constant_list, negligible_list, outlier_list)
 Combines all previously created lists into one dataframe that shows problemartic sensors and the reason for them being in the list
 Plots 5 of the problematic sensors data by random, to give the user a better visual understanding of the issue
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