import pandas as pd
import numpy as np

class DataParser:
    def __init__(self, scaler = 1):
        self.data = pd.DataFrame({})
        self.scaler = scaler

    def setData(self, path):
        self.data = pd.read_csv(path)

    def getBoolDict (self):
        boolDict = {}
        for signalName in self.data.columns:
            if type (self.data[signalName].iloc[0]) == np.bool:
                boolDict[signalName] = self._getBoolChanges(signalName)
        return boolDict
    
    def getFloatDict (self):
        floatDict = {}
        for signalName in self.data.columns:
            if type (self.data[signalName].iloc[0]) == np.float64:
                floatDict[signalName] = self._getFloatChanges(signalName)
        return floatDict

    
    def _getBoolChanges(self, signalName):
        timeCode = self.data['time']
        signal_values = self.data[signalName]
        changes = []

        if len(signal_values) > 0:
            changes.append([float(timeCode.iloc[0]), 'high' if bool(signal_values.iloc[0]) else 'low'])

            for i in range(1, len(signal_values)):
                if signal_values.iloc[i] != signal_values.iloc[i - 1]:
                    changes.append([float(timeCode.iloc[i])*self.scaler, 'high' if bool(signal_values.iloc[i]) else 'low'])
        
        return changes
    

    def _getFloatChanges(self, signalName):
        timeCode = self.data['time']
        signal_values = self.data[signalName]
        changes = []

        if len(signal_values) > 0:
            for i in range (len(signal_values)):
                changes.append([float(timeCode.iloc[i]), float(signal_values.iloc[i])])
        
        return changes