import numpy as np
import pandas as pd
from IPM_DLL.simple_refrig_api import Refifc

class RefProperties(object):
    """ 
    Class to define and handle the DLL calls 

    :param RERFRIG: Refrigerant type
    :type RERFRIG: str
    """

    def __init__(self, RERFRIG):
        """Constructor method"""
        self.refrig = Refifc(RERFRIG)

    def T_px(self, p, x):
        """ 
        Returns temperature given pressure and quality, along with additional properties 
        """
        temp = self.refrig.T_px(p, x)
        return self.calculate_all_properties(p=p, T=temp, x=x)

    def H_pT(self, p, t):
        """ 
        Returns enthalpy given pressure and temperature, along with additional properties 
        """
        enthalpy = self.refrig.h_pT(p, t)
        return self.calculate_all_properties(p=p, T=t)

    def calculate_all_properties(self, p=None, T=None, x=None, h=None):
        """
        Calculates and returns all thermodynamic properties based on given inputs.
        
        :param p: Pressure (optional)
        :param T: Temperature (optional)
        :param x: Quality (optional)
        :param h: Enthalpy (optional)
        :return: Dictionary of all thermodynamic properties
        """
        properties = {}

        if T is not None and x is not None:
            properties['Temperature (°C)'] = T - 273.15
            properties['Pressure (kPa)'] = p * 1e-3
            properties['Liquid Density (kg/m³)'] = self.refrig.rhosl_px(p, 0)
            properties['Vapor Density (kg/m³)'] = self.refrig.rhosv_px(p, 1)
            properties['Liquid Enthalpy (kJ/kg)'] = self.refrig.hsl_px(p, 0) * 1e-3
            properties['Vapor Enthalpy (kJ/kg)'] = self.refrig.hsv_px(p, 1) * 1e-3
            properties['Liquid Entropy (kJ/kg·K)'] = self.refrig.ssl_px(p, 0)
            properties['Vapor Entropy (kJ/kg·K)'] = self.refrig.ssv_px(p, 1)
            properties['Liquid Cp (kJ/kg·K)'] = self.refrig.Cpsl_px(p, 0)
            properties['Vapor Cp (kJ/kg·K)'] = self.refrig.Cpsv_px(p, 1)
        
        if p is not None and T is not None:
            properties['Temperature (°C)'] = T - 273.15
            properties['Pressure (kPa)'] = p * 1e-3
            properties['Enthalpy (kJ/kg)'] = self.refrig.h_pT(p, T) * 1e-3
            properties['Density (kg/m³)'] = self.refrig.rho_px(p, T)
            properties['Entropy (kJ/kg·K)'] = self.refrig.s_px(p, T)
            properties['Cp (kJ/kg·K)'] = self.refrig.Cp_px(p, T)
            properties['Cv (kJ/kg·K)'] = self.refrig.CpCv_px(p, T)

        if p is not None and h is not None:
            properties['Quality'] = self.refrig.x_ph(p, h)
            properties['Saturation Temperature (°C)'] = self.refrig.Ts_ph(p, h) - 273.15

        return properties

    def properties_to_dataframe(self, properties_dict):
        """
        Converts a dictionary of properties to a DataFrame.
        
        :param properties_dict: Dictionary of properties
        :type properties_dict: dict
        :return: DataFrame of properties
        :rtype: pd.DataFrame
        """
        df = pd.DataFrame([properties_dict])
        return df

    def saturation_table(self, temp_start, temp_end, step):
        """
        Generates a DataFrame of saturation properties based on temperature range and step.
        Includes temperature, pressure, densities, enthalpies, entropy, and specific heat capacities.

        :param temp_start: Starting temperature
        :param temp_end: Ending temperature
        :param step: Step size for temperature
        :return: DataFrame with saturation properties
        """
        temperatures = np.arange(temp_start, temp_end, step)
        pressures, liquid_densities, vapor_densities = [], [], []
        liquid_enthalpies, vapor_enthalpies = [], []
        liquid_entropies, vapor_entropies = [], []
        liquid_cp, vapor_cp = [], []
        
        for temp in temperatures:
            pressure = self.refrig.p_Tx(temp, 0)
            pressures.append(pressure * 1e-3)
            liquid_densities.append(self.refrig.rhosl_px(pressure, 0))
            vapor_densities.append(self.refrig.rhosv_px(pressure, 1))
            liquid_enthalpies.append(self.refrig.hsl_px(pressure, 0) * 1e-3)
            vapor_enthalpies.append(self.refrig.hsv_px(pressure, 1) * 1e-3)
            liquid_entropies.append(self.refrig.ssl_px(pressure, 0))
            vapor_entropies.append(self.refrig.ssv_px(pressure, 1))
            liquid_cp.append(self.refrig.Cpsl_px(pressure, 0))
            vapor_cp.append(self.refrig.Cpsv_px(pressure, 1))
        
        # Create and return DataFrame
        df = pd.DataFrame({
            'Temperature (°C)': temperatures - 273.15,
            'Pressure (kPa)': pressures,
            'Liquid Density (kg/m³)': liquid_densities,
            'Vapor Density (kg/m³)': vapor_densities,
            'Liquid Enthalpy (kJ/kg)': liquid_enthalpies,
            'Vapor Enthalpy (kJ/kg)': vapor_enthalpies,
            'Liquid Entropy (kJ/kg·K)': liquid_entropies,
            'Vapor Entropy (kJ/kg·K)': vapor_entropies,
            'Liquid Cp (kJ/kg·K)': liquid_cp,
            'Vapor Cp (kJ/kg·K)': vapor_cp
        })

        return df