import pandas as pd

country_mapping = {
    'NL': 'Netherlands'
    }   

# Read the input CSV file, assuming the second row is the header
read_df = pd.read_csv('../1/SCInstalledBaseLocation__c.csv', header=0, keep_default_na=False)
read_df_ib = pd.read_csv('../1/SCInstalledBase__c.csv', header=0, keep_default_na=False)
for row in read_df.to_dict('records'):
    try:
        # Your processing logic here
        pass
    except KeyError as e:
        print(f'KeyError: {e}')

# Columns for reindexing
reindex_columns = ['City__c','Country__c','Extension__c','FlatNo__c','Floor__c','GeoX__c','GeoY__c','HouseNo__c','Id','PostalCode__c','Street__c']
# ArticleNo__c,CommissioningDate__c,Id,InstallationDate__c,InstalledBaseLocation__c,InstalledBaseLocation__r.Id,Name,ProductEnergy__c,ProductUnitClass__c,ProductUnitType__c,SerialNo__c,SerialNoException__c
reindex_columns_ib = ['ArticleNo__c','CommissioningDate__c','Id','InstallationDate__c','InstalledBaseLocation__c','InstalledBaseLocation__r.Id','Name','ProductEnergy__c','ProductUnitClass__c','ProductUnitType__c','SerialNo__c','SerialNoException__c']

# Reindex the columns to match the desired format
df = read_df.reindex(reindex_columns, axis=1)
df_ib = read_df_ib.reindex(reindex_columns_ib, axis=1)

df['Street'] = (
    df['Street__c'].astype(str) + ' ' +
    df['HouseNo__c'].astype(str)
)

# Remove any trailing spaces that may result from missing values
df['Street'] = df['Street'].str.rstrip()

df['PKey__c'] = (
    df['Street'].astype(str) + ', ' +
    df['PostalCode__c'].astype(str) + ' ' +
    df['City__c'].astype(str) + ', ' +
    df['Country__c'].astype(str)
)

# Merge df_ib with df including additional columns
merged_df_ib = pd.merge(df_ib, 
                        df[['Id', 'PKey__c', 'Extension__c', 'FlatNo__c', 'Floor__c']], 
                        left_on='InstalledBaseLocation__c',
                        right_on='Id',
                        how='left')

# Handle missing values by setting them to None
merged_df_ib['Extension__c'] = merged_df_ib['Extension__c'].fillna('')
merged_df_ib['FlatNo__c'] = merged_df_ib['FlatNo__c'].fillna('')
merged_df_ib['Floor__c'] = merged_df_ib['Floor__c'].fillna('')

# If there are missing values (no match found), you can fill them with a placeholder
merged_df_ib['PKey__c'].fillna('Not Found', inplace=True)

## 1. Address.csv
# Columns needed for Address table based on the input CSV structure
address_columns = ['City__c', 'Country__c', 
                   'PostalCode__c', 'Street', 
                   'GeoY__c', 'GeoX__c', 'PKey__c']

# Extract data for Address
address_df = df[address_columns].copy()

address_df['CountryCode'] = address_df['Country__c'].map(country_mapping)

# Now create the 'Name' column without any warnings
address_df['Parent.Name'] = address_df['PKey__c']

# Rename columns to match the desired format
address_df.columns = ['City', 'CountryCode', 'PostalCode', 'Street', 'Latitude', 'Longitude', 'PKey__c', 'Country', 'Parent.Name']

# Check for duplicates in Address table based on OldId, City, CountryCode, PostalCode, and Street
address_df = address_df.drop_duplicates(subset=['City', 'Country', 'PostalCode', 'Street'], keep='first')

## 2. Parent_Location.csv
parent_columns = ['City__c', 'Country__c', 
                   'PostalCode__c', 'Street', 'PKey__c']
parent_df = df[parent_columns]
# Rename columns to match the desired format
parent_df.columns = ['City', 'Country',
                     'PostalCode', 'Street', 'Name']

parent_df['CountryCode'] = parent_df['Country'].map(country_mapping)

# Check for duplicates in Parent Location based on OldId and ConstructionEndDate
parent_df = parent_df.drop_duplicates(subset=['Name'], keep='first')

parent_df = parent_df.drop('Street', axis=1)
parent_df = parent_df.drop('PostalCode', axis=1)
parent_df = parent_df.drop('City', axis=1)
parent_df = parent_df.drop('CountryCode', axis=1)
parent_df = parent_df.drop('Country', axis=1)

parent_df['DuplicateCheck__c'] = 'false'
parent_df['IsInventoryLocation'] = 'false'
parent_df['IsMobile'] = 'false'
parent_df['LocationType'] = 'Site'

## 3. Child_Location.csv
child_columns = ['Extension__c', 'FlatNo__c', 'Floor__c', 'City__c', 'Country__c', 
                   'PostalCode__c', 'Street', 'PKey__c']
# Modify child_df by explicitly creating a new DataFrame
child_df = df[child_columns].copy()  # Add .copy() to create an explicit copy

# Create the 'Name' column with simplified logic
child_df['Name'] = (
    # Check if all three fields are not null; if so, concatenate them
    child_df['Floor__c'].astype(str) + '-' +
    child_df['FlatNo__c'].astype(str) + '-' +
    child_df['Extension__c'].astype(str)
)

# Replace any row where 'Floor__c', 'FlatNo__c', and 'Extension__c' are all empty with "HOME"
child_df.replace({'Name': {'--': 'HOME'}}, inplace=True)

# Rename columns to match the desired format
child_df.columns = ['Extension__c', 'Flat__c', 'Floor__c', 'City', 'Country',
                     'PostalCode', 'Street', 'PKey__c', 'Name']

child_df = child_df.drop_duplicates(subset=['Extension__c', 'Flat__c', 'Floor__c','City', 'Country', 'PostalCode', 'Street'], keep='first')

child_df = child_df.drop('Country', axis=1)
child_df = child_df.drop('PostalCode', axis=1)
child_df = child_df.drop('City', axis=1)
child_df = child_df.drop('Street', axis=1)

child_df['DuplicateCheck__c'] = 'false'
child_df['IsInventoryLocation'] = 'false'
child_df['IsMobile'] = 'false'
child_df['LocationType'] = 'Site'

## 4. Assets.csv
#ArticleNo__c,CommissioningDate__c,Id,InstallationDate__c,InstalledBaseLocation__c,InstalledBaseLocation__r.Extension__c,InstalledBaseLocation__r.FlatNo__c,InstalledBaseLocation__r.Floor__c,InstalledBaseLocation__r.Id,Name,ProductEnergy__c,ProductUnitClass__c,ProductUnitType__c,SerialNo__c,SerialNoException__c

merged_df_ib = merged_df_ib.drop('InstalledBaseLocation__c', axis=1)
merged_df_ib = merged_df_ib.drop('InstalledBaseLocation__r.Id', axis=1)
merged_df_ib = merged_df_ib.drop('Id_y', axis=1)
print(merged_df_ib.columns)
merged_df_ib.columns = ['Product2.EAN_Product_Code__c', 'FSL_1st_Ignition_Date__c', 'Id', 'InstallDate',  'Name', 'Kind_of_Energy__c', 'Kind_of_Installation__c', 'Main_Product_Group__c', 'SerialNumber', 'Serialnumber_Exception__c', 'Location.PKey__c', 'Location.Extension__c', 'Location.Flat__c', 'Location.Floor__c',] 

# Write each DataFrame to a separate CSV file
address_df.to_csv('../3/Address.csv', index=False)
parent_df.to_csv('../3/Location.csv', index=False)
child_df.to_csv('../5/Location.csv', index=False)
merged_df_ib.to_csv('../7/Asset.csv', index=False)

print('Data has been successfully split into Address.csv, Parent_Location.csv, and Child_Location.csv files with duplicate checks applied.')