# -*- coding: utf-8 -*-
"""
Developed and tested with Python 2.7.15

@author: <NAME>
"""
import os
import pandas as pd 

def main():
    directory = r'D:\GLUE Datasets\Spectra\ecospeclib-1541789735754' # soil
    directory = r'D:\GLUE Datasets\Spectra\ecospeclib-1541790159942' # water
#    material = 'manmade'
#    material = 'soil'
    material = 'water'
    landsat_sensor = 5
    process_emissivity(directory,material,landsat_sensor)

def process_emissivity(directory,material,landsat_sensor):
    '''
    Description: 
        computes the average emissivity for material spectral response \n\
        curves from the ASTER Spectral Library. Emissivity values are used in the \n\
        calculation of land surface temperature.
    
    WARNING: only tested with soil and water spectra files; modify effective wavelength \n\
             values in try/except statements as necessary
    
    Args: 
        directory (str): specify directory of .txt files from ASTER Spectral Library 
        material (str): specify material type as given by the file from ASTER Spectral Library 
                       (e.g., if manmade.concrete.constructionconcrete.solid.all.0598uuucnc.jhu.becknic.spectrum, then 'manmade')
        landsat_sensor (int): specify landsat sensor; currently only accepts 5, 7, or 8
        
    Returns:
        print out of global (mean) emissivity of specified material at effective TIRS \n\
        wavelength for Landsat 5/7/8 
    
    $Future Implementation: modify targeted effective wavelengths into  own function 
                            due to multiple cases for different materials
    '''
  
    material_type = []
    df_list = []
    
    headers_mean = ['Material', 'wavelength', '% reflectance','reflectance','emissivity'] 
    df_select_mean = pd.DataFrame(columns=headers_mean)
    
    for root, dirnames, filenames in os.walk(directory):
        for file in range(len(filenames)):
            if filenames[file].endswith('spectrum.txt'):
    #            if 'tir' in filenames[file]:    #only for non-photosythethic or vegetation; not applicable for man-made
                if material in filenames[file]:  
                    count = 0
                    title = []
                    spectra_raw = []
                    spectra_cleaned = []
                    df_combine = pd.DataFrame()
                    
                    with open(os.path.join(root,filenames[file]) ,'r') as infile:
        
                        for line in infile:
                            if count == 0:
                                title.append(line.rstrip('\n')) 
                                material_type.append(title[0][6:])  
                                
                            elif count > 20:
                                spectra_raw.append('NaN' + '\t' + line)
                            count += 1
                          
                        headers = ['Material','wavelength','% reflectance']  
                          
                        #remove '\n' and convert '\t' to ','
                        for i in range(len(spectra_raw)):
                            #clean and separate
                            strip_n = spectra_raw[i].rstrip('\n')
                            replace_tab = strip_n.split('\t')
                            
                            # convert values and re-combine
                            title = replace_tab[0]
                            wavelength = float(replace_tab[1])
                            reflectance = float(replace_tab[2])
                            combined = [title, wavelength, reflectance]
                            
                            # make multiple lists 
                            spectra_cleaned.append(combined)
                            
                            #empty list for re-population
                            combined = []
                            

                        # while len of all lists not 0, continue 
                        df = pd.DataFrame(spectra_cleaned, columns=headers)
                        
                        #calculate emissivity: create new columns 
                        df['reflectance'] = df['% reflectance']/100
                        df['emissivity'] = 1 - df['reflectance']
                        
                        
                        ''' $Future Implementation: modify into own function due to multiple cases for different materials'''
                        # find effective wavelength; will be different, so factor for all cases
                        # range provided factors for global range from spectra files
                        try:
                            ## WARNING BELOW RANGES ARE SET BASED ON <MATERIAL SPECTRA> TXT WAVELENGTH SAMPLING INTERVALS; \n\
                            ## WILL CHANGE WITH OTHER ASTER SPECTRAL CURVE TXT DATASETS
                            if material == 'soil':
                                # ----- ASTER lib: soil spectra all: START ------ #    
                                if landsat_sensor == 8: # 10.895 um
                                    df_select = df[(df['wavelength'] >= 10.89) & (df['wavelength'] <= 10.9)]
    #                                print df_select
                                               
                                
                                # fix such that it takes the average value from 10.2 and 10.3; not necessary for this wavelength
                                elif landsat_sensor == 7: # 11.269 um (Revision of the Single-Channel Algorithm for Land Surface Temperature Retrieval From Landsat Thermal-Infrared Data)
                                    # only works for soil spectra
                                    df_select = df[(df['wavelength'] >= 11.26) & (df['wavelength'] <= 11.28)]
    #                                print df_select
                                
                                # fix such that it takes the average value from 11.4 and 11.5 
                                elif landsat_sensor == 5: # 11.457 um (Revision of the Single-Channel Algorithm for Land Surface Temperature Retrieval From Landsat Thermal-Infrared Data)
                                    # get average value between 2 values; saves as series and all rows become float; not as a dataframe 
                                    df_select = df[(df['wavelength'] >= 11.44) & (df['wavelength'] <= 11.47)].mean(axis = 0)
                                    # need to re-add name to series in order to be able to append to dataframe 
                                    df_select = df_select.rename(index = df[(df['wavelength'] >= 11.44) & (df['wavelength'] <= 11.47)].index[0])
                                    # append new row to list 
                                    df_select_mean = df_select_mean.append(df_select)   
                                    # at first column, convert row from float to string to allow for material name to added 
                                    df_select_mean[['Material']] = df_select_mean[['Material']].astype(str)
    
    #                                print df_select
                                    # grab index of first of two items that were averaged in previous step 
    #                                df_select_mean.index = df[(df['wavelength'] >= 11.44) & (df['wavelength'] <= 11.47)].head(1)
         
                                # ----- ASTER lib: soil spectra all: END ------ #  
        
                    
                            elif material == 'water':
                                
                                # ----- ASTER lib: water spectra all: START ------ #                            
                                if landsat_sensor == 8: # 10.896 um
                                    df_select = df[(df['wavelength'] >= 10.8900) & (df['wavelength'] <= 10.9140)]
    #                                print df_select                           
    
                                elif landsat_sensor == 7: # 11.269 um (Revision of the Single-Channel Algorithm for Land Surface Temperature Retrieval From Landsat Thermal-Infrared Data)

                                    df_select = df[(df['wavelength'] >= 11.2699) & (df['wavelength'] <= 11.2700)]
#                                    print df_select       
                                    
                                    if df_select.empty:
                                        
                                        df_select = df[(df['wavelength'] >= 11.2000) & (df['wavelength'] <= 11.3000)].mean(axis = 0)
                                        # need to re-add name to series in order to be able to append to dataframe 
                                        df_select = df_select.rename(index = df[(df['wavelength'] >= 11.2000) & (df['wavelength'] <= 11.3000)].index[0])
                                        # append new row to list 
                                        df_select_mean = df_select_mean.append(df_select)   
                                        # at first column, convert row from float to string to allow for material name to added 
                                        df_select_mean[['Material']] = df_select_mean[['Material']].astype(str)  

                                        
                                elif landsat_sensor == 5: # 11.457 um (Revision of the Single-Channel Algorithm for Land Surface Temperature Retrieval From Landsat Thermal-Infrared Data)
    
                                    df_select = df[(df['wavelength'] >= 11.4440) & (df['wavelength'] <= 11.4694)].mean(axis = 0)
                                    
                                    # if not NaN... continue
                                    if df_select.isnull().values.any() == False:
                                    
                                        # need to re-add name to series in order to be able to append to dataframe 
                                        df_select = df_select.rename(index = df[(df['wavelength'] >= 11.4440) & (df['wavelength'] <= 11.4694)].index[0])
                                        # append new row to list 
                                        df_select_mean = df_select_mean.append(df_select)   
                                        # at first column, convert row from float to string to allow for material name to added 
                                        df_select_mean[['Material']] = df_select_mean[['Material']].astype(str)   
                                    
                                    # if NaN, try searching at next hard-coded wavelength interval 
                                    elif df_select.isnull().values.any() == True:
                                        
                                        df_select = df[(df['wavelength'] >= 11.4400) & (df['wavelength'] <= 11.4700)].mean(axis = 0)
                                        
                                        # if not NaN... continue
                                        if df_select.isnull().values.any() == False:
                                            # need to re-add name to series in order to be able to append to dataframe 
                                            df_select = df_select.rename(index = df[(df['wavelength'] >= 11.4400) & (df['wavelength'] <= 11.4700)].index[0])
                                            # append new row to list 
                                            df_select_mean = df_select_mean.append(df_select)   
                                            # at first column, convert row from float to string to allow for material name to added 
                                            df_select_mean[['Material']] = df_select_mean[['Material']].astype(str) 
                                            
                                    # if NaN, try searching at next hard-coded wavelength interval         
                                    elif df_select.isnull().values.any() == True:
                                        
                                        df_select = df[(df['wavelength'] >= 11.4000) & (df['wavelength'] <= 11.500)].mean(axis = 0)   
                                       
                                        # if not NaN... continue        
                                        if df_select.isnull().values.any() == False:
                                            # need to re-add name to series in order to be able to append to dataframe 
                                            df_select = df_select.rename(index = df[(df['wavelength'] >= 11.4000) & (df['wavelength'] <= 11.500)].index[0])
                                            # append new row to list 
                                            df_select_mean = df_select_mean.append(df_select)   
                                            # at first column, convert row from float to string to allow for material name to added 
                                            df_select_mean[['Material']] = df_select_mean[['Material']].astype(str) 
    
                                # ----- ASTER lib: water spectra all: END ------ #   
       
                        except:
                            pass
                                                                           
                        ## set material name from previous list                    
                        # if NaN in df_select and df_select_mean is empty... 
                        if df_select.isnull().values.any() == False and df_select_mean.empty == True: 
                            # get value at index, and replace with material type name (index, col_name)
                            df_select.at[df_select.index[0], 'Material'] = material_type[0]
                            print df_select
                        
                            # convert values to list for processing
                            df_select_val_process = df_select.values.tolist()
                      
                        # else if no NaN in df_select and df_select_mean is filled...     
                        elif df_select.notnull().values.any() == True and df_select_mean.empty == False: 
                            # get value at index, and replace with material type name (index, col_name)
                            df_select_mean.at[df_select_mean.index[0], 'Material'] = material_type[0]
                            print df_select_mean
                            
                            # convert values to list for processing
                            df_select_val_process = df_select_mean.values.tolist()
       
                            
                        # remove each material name from end of list as it cycles through all txt files;
                        # such that each txt gets its original material name before processing end
                        material_type.pop(-1)
                        
                        df_list.append(df_select_val_process)
                    
                
                        # empty container for next val to be put into dataframe
                        df_select_mean = pd.DataFrame(columns=headers_mean)
                    
                        
                        # once all txt have cycled, append to new list for final output                    
                        df_list_cleaned = []
                        for i in range(len(df_list)):
                            df_list_cleaned.append(df_list[i][0])
                        
                    headers_final = ['Material','wavelength','% reflectance','reflectance','emissivity']  
                    df_analysis = pd.DataFrame(df_list_cleaned, columns=headers_final)
                    
                    # get mean value of emissivity for x material
                    df_analysis['emissivity'].mean()
                    
                    df_analysis['emissivity']
                    df_analysis[['Material','emissivity']]
                    df_analysis.head()  
    
    
    print (df_analysis[['Material','emissivity']])        
    print ('\n' + 'Average emissivity of all {0} materials is {1}'.format(material,round(df_analysis['emissivity'].mean(),4)))
    
    # empty dataframes 
    df_select = pd.DataFrame() 
    df_select_mean = pd.DataFrame()  
    
main() 
