import copy
import datetime
import os
import re
import sys
import time

import dateutil
import numpy as np
import pandas as pd

from crossnetwork_processing_functions import get_data_files, create_GHOST_stage_logfile
from GHOST_standards import get_standard_data, get_standard_metadata, country_ISO3166_to_name, standard_data_flag_name_to_data_flag_code, standard_QA_name_to_QA_code
from geographic_functions import local_timezone_name
from network_file_standardise import NetworkFileStandardise
from network_parsers import ParseMEXICOCDMXFile
from standardisation_mapping_functions import map_network_classifications_to_standard_classifications, map_network_measurement_information_to_standard_measurement_information, network_data_flag_to_standard_data_flag_mapping
from time_functions import round_to_minute, datetime_to_fractional_minutes_from_reference, get_datafile_download_timestamp
import unit_converter

#define dictionary defining defined unit codes to units
unit_code_dict = {'1':'nmol mol-1', '2':'ug m-3', '15':'umol mol-1'}

class StandardiseNetworkData:

    '''Convert one data provider original file for a specific year into GHOST standard numpy station specific files'''

    def __init__(self, year, run_details, parameter_details, network_details):
        
        #add passed variables to self
        self.year = year
        self.run_details = run_details
        self.parameter_details = parameter_details
        self.network_details = network_details
        

    def do_work(self):
        '''Function which does executes the functions of the class in a pipeline'''

        #gather data files for current year
        data_files = self.gather_data_files()

        #parse data files (writing standard files out per station)
        for data_file in data_files:
            self.parse_file(data_file)

        #write a valid process log file
        self.run_details['log_file_str'] += str((time.time() - self.run_details['stage_process_start_time'])/60.)
        create_GHOST_stage_logfile(0,self.run_details)


    def gather_data_files(self):

        '''Function to gather data files to parse per year'''

        #get specific data files for current year
        data_files = get_data_files(self.run_details, self.parameter_details, specific_year=self.year)
   
        #load all external metadata into memory 
        self.external_metadata = np.load(self.run_details['data_root']+'MEXICO_CDMX/metadata/processed/MEXICO_CDMX_META.npy', allow_pickle=True)
        #get standard measured unit quantity
        conv_obj = unit_converter.convert_units(self.parameter_details['standard_units'],self.parameter_details['standard_units'],1)
        self.measured_unit_quantity = conv_obj.output_represented_quantity

        return data_files

    def parse_file(self, data_file):

        '''Function to a specific annual data file to GHOST standard numpy station specific files'''

        self.run_details['log_file_str'] += 'PROCESSING FILE: {}\n'.format(data_file) 
        
        #create object for parsing MEXICO_CDMX annual file, and read file
        parsing_obj = ParseMEXICOCDMXFile(data_file)
        parsing_obj.read_data()

        #get unique station references
        unique_station_references = np.unique(parsing_obj.parsed_data['id_station'])

        #iterate over all unique station references
        for unique_station_reference in unique_station_references:

            #cut read data to just that associated with unique station reference & parameter being processed
            valid_inds = (parsing_obj.parsed_data['id_station'] == unique_station_reference) & (parsing_obj.parsed_data['id_parameter'] == self.run_details['parameter'])

            #get cut data variables for station
            start_dt = parsing_obj.parsed_data['start_dt'][valid_inds]
            end_dt = parsing_obj.parsed_data['end_dt'][valid_inds]
            cut_measurements = parsing_obj.parsed_data['value'][valid_inds]
            unit_codes = parsing_obj.parsed_data['unit'][valid_inds]

            #if no valid time then continue to next station reference
            if len(start_dt) == 0:
                continue

            self.run_details['log_file_str'] += '{} -- PARSING RAW DATA\n'.format(unique_station_reference)

            #create site instances of metadata/data dictionaries
            site_data =     get_standard_data(self.parameter_details)
            site_metadata = get_standard_metadata(self.parameter_details)

            #create a dictionary with unique units associated with indices in the file (for handling conversion separately later)
            inds_per_unit = {unit_code_dict[unit]:np.where(unit_codes == unit)[0] for unit in np.unique(unit_codes)}

            #------------------------------------------------------------------------#
            #PARSE METADATA
            #------------------------------------------------------------------------#

            #GHOST version
            site_metadata['GHOST_version']['value'].append(self.run_details['ghost_version'])

            #initialise string which will store all GHOST process warnings as empty            
            site_metadata['process_warnings']['value'].append('')

            #write known metadata  
            site_metadata['network']['value'].append(self.run_details['network'])
            site_metadata['data_level']['value'].append('none')   
            site_metadata['contact_institution']['value'].append('Sedema (La Secretaría del Medio Ambiente de la Ciudad de México)')      
            site_metadata['contact_email_address']['value'].append('calidadaire@sedema.cdmx.gob.mx')    
            site_metadata['data_licence']['value'].append('Rights reserved to La Secretaría del Medio Ambiente de la Ciudad de México: http://www.aire.cdmx.gob.mx/default.php') 
            #assume datum information
            site_metadata['ellipsoid']['value'].append('WGS 84') 
            site_metadata['horizontal_datum']['value'].append('World Geodetic System 1984')
            site_metadata['vertical_datum']['value'].append('tidal - mean sea level')
            site_metadata['projection']['value'].append('geographic')    

            #add external metadata
            cut_external_metadata = self.external_metadata[np.where(self.external_metadata['cve_estac'] == unique_station_reference)[0]]
            site_metadata['station_name']['value'].append(cut_external_metadata['nom_estac'][0])
            site_metadata['longitude']['value'].append(cut_external_metadata['longitud'][0])
            site_metadata['latitude']['value'].append(cut_external_metadata['latitud'][0])
            site_metadata['altitude']['value'].append(cut_external_metadata['alt'][0])

            #parse unique reported measurement unit
            #can be instances where have multiple units in a file, in this case write raw units as modal occurring unit (that is not blank or cannot be converted to standard units)
            #all raw units are taken into account when converting concentrations however
            unit_n = -1
            for unit_name in list(inds_per_unit.keys()):
                if len(inds_per_unit[unit_name]) > unit_n:
                    reported_units = unit_name   
                    unit_n = len(inds_per_unit[unit_name])        
            #convert raw reported units given to standard name
            conv_obj = unit_converter.convert_units(reported_units, reported_units, 1, measured_species = self.parameter_details['chemical_formula'])
            site_metadata['measuring_instrument_reported_units']['value'].append(conv_obj.output_standard_units)

            #parse station classifications (from external metadata), get measurement scale per species specific to parameter
            #for NO, take code for NO2
            #if PM10/PM2.5 codes are empty and the other is not take that
            if self.run_details['parameter'] == 'NO':
                code_param = 'NO2'
            else:
                code_param = copy.deepcopy(self.run_details['parameter'])
            m_scale_code = cut_external_metadata['{}r'.format(code_param)][0]
            if (m_scale_code == '') & (code_param == 'PM10'):
                m_scale_code = cut_external_metadata['PM2.5r'][0]
            elif (m_scale_code == '') & (code_param == 'PM2.5'):
                m_scale_code = cut_external_metadata['PM10r'][0]
            native_classifications = {'measurement_scale':m_scale_code}
            site_metadata = map_network_classifications_to_standard_classifications(native_classifications, site_metadata, self.run_details)

            #process measurement process information
            site_metadata, measurement_qa_flags, standard_method_abbreviation = map_network_measurement_information_to_standard_measurement_information('', site_metadata, self.run_details, self.parameter_details)
            #set measurement qa codes which are True as joined metadata str
            site_metadata['measurement_qa_flags'] = {'value':[], 'data_type':object, 'string_format':'lower_short'}
            measurement_qa_flag_codes = []
            for standard_flag_name in measurement_qa_flags.keys():
                if measurement_qa_flags[standard_flag_name]:
                    measurement_qa_flag_codes.append(str(standard_QA_name_to_QA_code[standard_flag_name]))
            site_metadata['measurement_qa_flags']['value'].append(','.join(measurement_qa_flag_codes))

            #iterate through metadata variables, if any columns have not been appended to add a NaN
            for column_header in list(site_metadata.keys()):
                n_column = len(site_metadata[column_header]['value'])
                #if column is empty, then append NaN
                if n_column < 1:
                    site_metadata[column_header]['value'].append(np.NaN)
    
            #set station_reference (provided network ID + standard measurement method abbreviation)
            site_metadata['station_reference']['value'][-1] = '{}_{}'.format(unique_station_reference,standard_method_abbreviation) 

            #get data file download timestamp
            site_metadata['data_download_stamp']['value'][-1] = get_datafile_download_timestamp(data_file)

            #add data revision timestamp, set as 0001-01-01 00:00 for all files
            data_revision_timestamp = datetime_to_fractional_minutes_from_reference(datetime.datetime(1,1,1,0,0))
            site_metadata['data_revision_stamp']['value'][-1] = data_revision_timestamp

            #------------------------------------------------------------------------#
            #PARSE TIME
            #------------------------------------------------------------------------#

            #get local timezone offset from UTC
            local_timezone = local_timezone_name(longitude=float(site_metadata['longitude']['value'][-1]), latitude=float(site_metadata['latitude']['value'][-1]))
            tzinfo = dateutil.tz.gettz(local_timezone)        

            #convert timezone to UTC
            start_dt = [round_to_minute(dt.replace(tzinfo=tzinfo).astimezone(dateutil.tz.tzutc())).replace(tzinfo=None) for dt in start_dt]
            end_dt = [round_to_minute(dt.replace(tzinfo=tzinfo).astimezone(dateutil.tz.tzutc())).replace(tzinfo=None) for dt in end_dt]

            #convert each start measurement datetime to minutes from reference time
            frac_time = [datetime_to_fractional_minutes_from_reference(dt) for dt in start_dt]
            
            #get timedeltas between start and end of measurement window
            temporal_resolutions = []
            for start, end in zip(start_dt,end_dt):
                dt_window = end - start
                #convert hours to minutes
                dt_window_minutes = int(dt_window.total_seconds()/60.)
                temporal_resolutions.append(dt_window_minutes)  

            #save meta_update_stamp, set as first sorted time processed (as minutes relative to reference time)
            site_metadata['meta_update_stamp']['value'][-1] = sorted(frac_time)[0]

            #------------------------------------------------------------------------#
            #PARSE MEASUREMENTS
            #------------------------------------------------------------------------#
            #measurement values may need converting (i.e. mass density to mixing ratio)
    
            #measurement temperature and pressure values are never explicitly provided by MEXICO_CDMX so are always assumed to be the network standard pressure and temperatures
            #these are defined as for 298.15 K temperature, and 1013.25 hPa for pressure here for all gas-phase compounds: http://www.aire.cdmx.gob.mx/descargas/monitoreo/normatividad/NOM-020-SSA1-2014.pdf (O3), http://www.aire.cdmx.gob.mx/descargas/monitoreo/normatividad/NOM-022-SSA1-2019.pdf (SO2), http://www.aire.cdmx.gob.mx/descargas/monitoreo/normatividad/NOM-021-SSA1-1993.pdf (CO), http://www.aire.cdmx.gob.mx/descargas/monitoreo/normatividad/NOM-023-SSA1-1993.pdf (NO2) 
            #for particulate matter the sampling volume refers to ambient conditions in terms of temperature and atmospheric pressure at the date of measurements: http://www.aire.cdmx.gob.mx/descargas/monitoreo/normatividad/NOM-025-SSA1-2014.pdf (PM10 and PM2.5)
            #if for some reason particulate measurements are given as mixing ratio and need converting to mass density, then the conversion is done using the same standard temperature/pressure as reported for gases.
            
            measurements = []
            #initialise qa as all having timezone doubt qa flag, as get timezone from longitude/latitude
            qa = [[standard_QA_name_to_QA_code['Timezone Doubt']] for i in range(len(frac_time))]

            standard_network_gas_volume = False
            unknown_gas_volume = False
            if self.parameter_details['matrix'] == 'gas':
                site_metadata['network_provided_volume_standard_temperature']['value'][-1] = '298.15'
                site_metadata['network_provided_volume_standard_pressure']['value'][-1] = '1013.25'
                standard_network_gas_volume = True
            else:
                site_metadata['network_provided_volume_standard_temperature']['value'][-1] = np.NaN
                site_metadata['network_provided_volume_standard_pressure']['value'][-1] = np.NaN
                unknown_gas_volume = True
    
            #check if reported standard units, match required standard units
            #take care to deal with multiple units in file
            order_n = []
            for reported_units in list(inds_per_unit.keys()):

                #set flags for writing QA flags associated with assumptions in unit conversion
                unit_conversion_standard_network = False
                unit_conversion_educated_guess = False

                #convert raw reported units given to standard name
                conv_obj = unit_converter.convert_units(reported_units, reported_units, 1, measured_species = self.parameter_details['chemical_formula'])
                standard_reported_units = conv_obj.output_standard_units
                measuring_instrument_reported_unit_quantity = conv_obj.output_represented_quantity  
    
                #cut concentration data to be that just associated with units
                unit_concentrations = cut_measurements[inds_per_unit[reported_units]]

                #check if reported standard units, match required standard units
                if standard_reported_units != site_data[self.parameter_details['bsc_parameter_name']]['units']:

                    #mass density to mixing ratio?
                    if (measuring_instrument_reported_unit_quantity == 'mass_density') & (self.measured_unit_quantity == 'mole_fraction'):
                        #get conversion factor
                        input_units = {'temperature':'K', 'pressure':'hPa', 'molar_mass':'kg mol-1', 'mass_density':standard_reported_units}
                        #do conversion with network provided sample gas temperature/pressure values
                        #if these are not set, then make educated guess (assume network standards for gases)
                        if (not pd.isnull(site_metadata['network_provided_volume_standard_temperature']['value'][-1])) & (not pd.isnull(site_metadata['network_provided_volume_standard_pressure']['value'][-1])):   
                            input_vals = {'temperature':site_metadata['network_provided_volume_standard_temperature']['value'][-1], 'pressure':site_metadata['network_provided_volume_standard_pressure']['value'][-1], 'molar_mass':unit_converter.get_molecular_mass(self.parameter_details['chemical_formula']), 'mass_density':1.0}
                            unit_conversion_standard_network = True
                        else:
                            input_vals = {'temperature':'298.15', 'pressure':'1013.25', 'molar_mass':unit_converter.get_molecular_mass(self.parameter_details['chemical_formula']), 'mass_density':1.0}
                            unit_conversion_educated_guess = True
                        conv_obj = unit_converter.convert_units(input_units,site_data[self.parameter_details['bsc_parameter_name']]['units'],input_vals,measured_species = self.parameter_details['chemical_formula'],conversion_input_quantity = 'mass_density')
            
                    #mixing ratio to mass density?
                    elif (measuring_instrument_reported_unit_quantity == 'mole_fraction') & (self.measured_unit_quantity == 'mass_density'):
                        input_units = {'temperature':'K', 'pressure':'hPa', 'molar_mass':'kg mol-1', 'mole_fraction':standard_reported_units}     
                        #do conversion with network provided sample gas temperature/pressure values
                        #if these are not set, then make educated guess (assume network standards)
                        if (not pd.isnull(site_metadata['network_provided_volume_standard_temperature']['value'][-1])) & (not pd.isnull(site_metadata['network_provided_volume_standard_pressure']['value'][-1])): 
                            input_vals = {'temperature':site_metadata['network_provided_volume_standard_temperature']['value'][-1], 'pressure':site_metadata['network_provided_volume_standard_pressure']['value'][-1], 'molar_mass':unit_converter.get_molecular_mass(self.parameter_details['chemical_formula']), 'mole_fraction':1.0}
                            unit_conversion_standard_network = True
                        else:
                            input_vals = {'temperature':'298.15', 'pressure':'1013.25', 'molar_mass':unit_converter.get_molecular_mass(self.parameter_details['chemical_formula']), 'mole_fraction':1.0}
                            unit_conversion_educated_guess = True
                        conv_obj = unit_converter.convert_units(input_units,site_data[self.parameter_details['bsc_parameter_name']]['units'],input_vals,measured_species = self.parameter_details['chemical_formula'],conversion_input_quantity = 'mole_fraction')
           
                    #otherwise simple unit conversion, i.e. pptv --> ppbv, ng/m3 --> ug/m3
                    else:
                        conv_obj = unit_converter.convert_units(standard_reported_units,site_data[self.parameter_details['bsc_parameter_name']]['units'],1.0, measured_species = self.parameter_details['chemical_formula'])
    
                    #process through each measurement and convert value using derived conversion factor
                    for n, meas in enumerate(unit_concentrations):
                        actual_n = inds_per_unit[reported_units][n]
                        order_n.append(actual_n)
                        #only do conversion if measurement value is above 0 and not infinite, and do not have data flag stating the measurement is missing 
                        try:
                            float_meas = float(meas)
                        except:
                            float_meas = np.NaN
                        if (float_meas > 0.0) & (np.isfinite(float_meas)):
                            measurements.append(float_meas*conv_obj.conversion_factor)
                        else:
                            measurements.append(float_meas)

                        #append sample gas volume/unit_conversion assumption QA flags if necessary
                        if standard_network_gas_volume:
                            qa[actual_n].append(standard_QA_name_to_QA_code['Sample Gas Volume - Network Standard'])
                        if unknown_gas_volume:
                            qa[actual_n].append(standard_QA_name_to_QA_code['Sample Gas Volume - Unknown'])
                        if unit_conversion_standard_network:
                            qa[actual_n].append(standard_QA_name_to_QA_code['Unit Conversion - Network Standard Sample Gas Volume Assumption'])
                        if unit_conversion_educated_guess:
                            qa[actual_n].append(standard_QA_name_to_QA_code['Unit Conversion - Educated Guess Sample Gas Volume Assumption'])
                
                #otherwise do nothing to measured values
                else:
                    for n, meas in enumerate(unit_concentrations):
                        order_n.append(inds_per_unit[reported_units][n])
                        try:
                            float_meas = float(meas)
                        except:
                            float_meas = np.NaN
                        measurements.append(float_meas)

            #resort measurements by original order
            measurements = [x for _,x in sorted(zip(order_n,measurements))]

            #keep only measurement windows that are >= 0 minute in temporal resolution, as well as all other associated variables across other fields
            valid_temporal_resolution_inds = [ii for ii, res in enumerate(temporal_resolutions) if res >= 0]
            if len(valid_temporal_resolution_inds) != len(frac_time):
                frac_time = [frac_time[x] for x in valid_temporal_resolution_inds]
                temporal_resolutions = [temporal_resolutions[x] for x in valid_temporal_resolution_inds]
                qa = [qa[x] for x in valid_temporal_resolution_inds]
                measurements = [measurements[x] for x in valid_temporal_resolution_inds]

            #----------------------------------------------------------------------------#
            #WRITE OUT DATA
            #----------------------------------------------------------------------------# 
            site_data['time']['value'] = frac_time
            site_data[self.parameter_details['bsc_parameter_name']]['value'] = measurements
            site_data['temporal_resolution']['value'] = temporal_resolutions   
            #set data flags as all un-flagged
            site_data['flag']['value'] = [[standard_data_flag_name_to_data_flag_code['Un-Flagged Data']] for i in range(len(site_data['time']['value']))]
            site_data['qa']['value'] = qa
            #iterate through data variables, if any columns have not been appended to, add a list of NaNs (equal to length of time array)
            for column_header in list(site_data.keys()):
                n_column = len(site_data[column_header]['value'])
                #if column is empty, then append NaN
                if n_column == 0:
                    if site_data[column_header]['data_type'] != np.uint8:
                        site_data[column_header]['value'] = [np.NaN]*len(site_data['time']['value'])
                    else:
                        site_data[column_header]['value'] = [255]*len(site_data['time']['value'])
            #------------------------------------------------------

            #standardise metadata/data formats and write out station data to numpy file
            write_obj = NetworkFileStandardise(site_metadata, site_data, self.run_details, self.parameter_details)
            if write_obj.written == False:
                self.run_details['log_file_str'] += 'DATA ALL MISSING FOR STATION: {}. SKIPPING.\n'.format(unique_station_reference)