# This code adds a new year of RWS data to the old dataset.
# --- IMPORT ---
import psycopg2


# --- FUNCTIONS ---
# VKL_NUMMER <--- ID for rows
# Function to print some messages for easy debug
def run_query(query):
    if(DEBUG):
        print(query)
    cur.execute(query)

# Function to make a copy of the table to edit in
def copy_table(table_name):
    copy_table_query = """
    DROP TABLE IF EXISTS "{0}_copy";
    CREATE TABLE "{0}_copy" AS
    SELECT * FROM "{0}"
    """.format(table_name)

    run_query(copy_table_query)

# Function to add a column to a table
def add_column(table_name, column_name, datatype):
    add_column_query = """
    ALTER TABLE "{0}"
    ADD COLUMN "{1}" {2};
    """.format(table_name, column_name, datatype)

    run_query(add_column_query)

# Function that converts _ID or _CODE into _OMS
# 0= data table (2016 for example) - table_to_convert
# 1= the reference table
# 2=item name, iterable (_ID or _CODE)
# 3=item name (but the _OMS version, needs function turn_id_or_code_into_oms)
#HARDCODED: VKL_NUMMER (PTJ_ID for partij, SOR_ID for slachtoffer, PTJ_ID for voertuig)
#IDEA slachtoffer en voertuig: since the original data is here, maybe I should segment it in years and then put it on Ixiwa, solves the conversion problem and such
def run_queries(table_to_convert, from_table, item_name, item_oms):
    special_columns = ['BZD_','AGT_','BWG_','TDT_']

    if any(special_item in item_name for special_item in special_columns):
        print(item_name, ' is in special columns: TRUE')
        newcolname = item_name.replace("_ID", "")
        newcolname = newcolname + str("_OMS")
        newname = turn_id_or_code_into_oms(item)

        add_column(table_to_convert, newcolname, "VARCHAR")

        grab_oms_query = """
                DROP TABLE IF EXISTS "{3}_temp";
                CREATE TABLE "{3}_temp" AS
                SELECT "{0}"."{4}", "{0}"."{5}", oms_table."{3}"
                FROM "{0}"
                LEFT JOIN "{1}" as oms_table
                ON CAST("{0}"."{2}" AS VARCHAR) = CAST(oms_table."{3}" AS VARCHAR);
                ALTER TABLE "{3}_temp" RENAME COLUMN "{3}" TO "{5}";
            """.format(table_to_convert, from_table, item_name, newname, link_id, newcolname)

        fill_oms_column_query = """
            UPDATE "{0}"
            SET "{1}" = "{1}_temp"."{1}"
            FROM "{1}_temp"
            WHERE "{0}"."{2}" = "{1}_temp"."{2}"
        """.format(table_to_convert, newname, link_id)

        drop_temp_table_query = """
            DROP TABLE IF EXISTS "{0}";
        """.format(newname + "_temp")

        #run_query(grab_oms_query)
        #run_query(fill_oms_column_query)
        #run_query(drop_temp_table_query)
            #break

    else:
        print(item_name, ' is NOT in special columns: FALSE')
        add_column(table_to_convert, item_oms, "VARCHAR")

        grab_oms_query = """
            DROP TABLE IF EXISTS "{3}_temp";
            CREATE TABLE "{3}_temp" AS
            SELECT "{0}"."{4}", "{0}"."{2}", oms_table."{3}"
            FROM "{0}"
            LEFT JOIN "{1}" as oms_table
            ON CAST("{0}"."{2}" AS VARCHAR) = CAST(oms_table."{2}" AS VARCHAR);
        """.format(table_to_convert, from_table, item_name, item_oms, link_id)

        fill_oms_column_query = """
            UPDATE "{0}"
            SET "{1}" = "{1}_temp"."{1}"
            FROM "{1}_temp"
            WHERE "{0}"."{2}" = "{1}_temp"."{2}"
        """.format(table_to_convert, item_oms, link_id)

        drop_temp_table_query = """
            DROP TABLE IF EXISTS "{0}";
        """.format(item_oms + "_temp")

    run_query(grab_oms_query)
    run_query(fill_oms_column_query)
    run_query(drop_temp_table_query)
            #break

# changes extension _ID and _CODE into _OMS (for easy lookup in reference table)
def turn_id_or_code_into_oms(name):
    if "_CODE" in name:
        if name == "DAG_CODE" or name == "PVE_CODE":
            return name.replace("_CODE", "_NAAM")
        else:
            return name.replace("_CODE", "_OMS")
    #writing for BZD_ID_IF1 -> BZD_IF1_OMS
    elif "BZD_ID_" in name:
        #newname = name.replace("_ID", "")
        #newname = newname + str("_OMS")
        newname = "BZD_OMS"
        return newname
    elif "AGT_ID_" in name:
        newname = "AGT_OMS"
        return newname
    elif "BWG_ID_" in name:
        newname = "BWG_OMS"
        return newname
    elif "TDT_ID_" in name:
        newname = "TDT_OMS"
        return newname
    else:
        return name.replace("_ID", "_OMS")

# Concat column names for create_union_tables_query
def concat_column_names(column_names):
    all_column_names = ""
    for i in range(len(column_names) - 1):
        all_column_names += ("\"" + column_names[i] + "\"" + ", ")

    all_column_names += ("\"" + column_names[len(column_names) - 1] + "\"")
    return all_column_names

# create union query
def create_union_tables_query(tables):
    start = 'DROP TABLE IF EXISTS _all_data; CREATE TABLE _all_data AS SELECT {0} FROM "{1}" '.format(concat_column_names(all_columns), input_for_script[0] + '_copy')
    #union_query = 'UNION SELECT * FROM "{0}" '.format(tables[0])
    union_query  = ''
    for i in tables:
        union_query += 'UNION SELECT {0} FROM "{1}" '.format(concat_column_names(all_columns), i)
    union_query = start + union_query
    print(union_query)
    return union_query


# --- QUERIES ---
#space for queries, will restructure it later
# Query for the loop, selects the correct reference table based on the iteration
get_table_name_query = """
SELECT table_name from _rws_references WHERE column_name = '{0}'
"""

# --- SETUP ---
# for printing some messages in the console
DEBUG = True

#HARDCODED
input_for_script = ('partij.csv', 'partij')
if input_for_script[1] == 'partij':
        link_id = 'PTJ_ID'
elif input_for_script[1] == 'ongeval':
        link_id = 'VKL_NUMMER'

# The only things that need to be filled in to run the script is the variables 'tables' and 'input_for_script':
# List of tables that have ID/CODE to convert to OMS  ["ongevallen_2016.csv", "ongevallen_2017.csv"]
tables = ["partij_2016.csv"]
tables_copy = []
for table in tables:
    tables_copy.append(table + '_copy')
    print('tables copy: ', tables_copy)
# dbname, user, password and year, old data filename, category, empty string for later

# Connect to an existing database
conn = psycopg2.connect("dbname=rws_2011_2017 user=postgres password=Prandall19s!")

# Open a cursor to perform database operations
cur = conn.cursor()

copy_table(input_for_script[0])


#IDEA old table TIJDSTIP = (H)HMM, new table it's UUR = (H)H
#ADD COLUMN "TIJDSTIP_MODULO" integer
#SET "TIJDSTIP_MODULO" = (("TIJDSTIP"%100)/60.0+"TIJDSTIP"/100);



# --- EXECUTION ---
# Requirements: 1. all tables are located in the same schema (old years, new year to add, reference tables)
#               2. the table will only join the attributes from the old table
#               3. the names of the reference tables end in '.txt.csv' */

# Creates settings table. input it takes:
#   the year (to add),
#   the name of the old table (containing the previous years)
#   and category (partij/ongeval/slachtoffer/voertuig/etcetera)
cur.execute("""
DROP TABLE IF EXISTS _rws_settings;
CREATE TABLE _rws_settings (
    old_data        text,
    category        text);
""")
    #year_to_add        INT,

cur.execute("""
INSERT INTO _rws_settings
    (old_data, category)
VALUES
    (%s, %s);
    """,
    input_for_script)

# Creates lookup table _rws_datatypes, which states the column names and datatypes of old_data from _rws_settings
cur.execute("""
DROP TABLE IF EXISTS _rws_datatypes;
CREATE TABLE _rws_datatypes AS
SELECT column_name, data_type FROM INFORMATION_SCHEMA.columns
WHERE table_name = '{0}';
""".format(input_for_script[0] + '_copy'))

if input_for_script[0] == 'ongeval.csv':
    cur.execute("""INSERT INTO "_rws_datatypes" VALUES ('DAGTYPE', 'varchar');""")

# Creates lookup table _rws_references: It takes the columns ending in '_ID' or '_CODE' and the name of the reference table.
# With this, it'll be possible to look up whether columns in the new table (year to add) have to be transformed into '_OMS'.
cur.execute("""
DROP TABLE IF EXISTS _rws_references;
CREATE TABLE _rws_references AS
SELECT column_name, table_name FROM INFORMATION_SCHEMA.columns
WHERE table_name LIKE '%.txt.csv'
AND (column_name LIKE '%\_ID' OR column_name LIKE '%\_CODE' OR column_name LIKE '%_ID_%');
""")


# Loop through all the tables and convertable items, find all reference tables and start function run_queries
    # Runs the function to copy the table mentioned in the variable tables (list)
for table in tables:
    copy_table(table)

    # Selects all columns from new table that end in '_ID' or '_CODE', and outputs it into a Python list
    cur.execute("""
    DROP TABLE IF EXISTS _rws_columns_to_convert;
    CREATE TABLE _rws_columns_to_convert AS
    SELECT column_name FROM INFORMATION_SCHEMA.columns
    WHERE table_name LIKE (SELECT '{0}' FROM _rws_settings)
    AND (column_name LIKE '%\_ID' OR column_name LIKE '%\_CODE' OR column_name LIKE '%_ID_%');
    """.format(table))

    # Makes a list on what columns need to be converted with a reference table
    cur.execute("""
    SELECT _rws_columns_to_convert."column_name" FROM _rws_columns_to_convert;
    """)
    list_to_convert = cur.fetchall()
    # removes '(' and '),'
    list_to_convert = [i[0] for i in list_to_convert]
    # HARDCODED List of column names that do end in _ID or _CODE, but should not be converted (checked manually)
    #everything including and after AGT_ID_1 needs conversion 'AGT_ID_1', 'AGT_ID_2', 'BWG_ID_1', 'BWG_ID_2', 'TDT_ID_1', 'TDT_ID_2', 'TDT_ID_3'
    columns_not_to_convert = ['JTE_ID', 'WVK_ID', 'GME_ID', 'PVE_CODE', 'WSE_ID', 'PTJ_ID']

    # Loop to check if the name should not have a conversion to _OMS, and remove them from the conversion list that will be iterated through (not the table in pgAdmin)
    for name in columns_not_to_convert:
        if name in list_to_convert:
            list_to_convert.remove(name)
    print(list_to_convert)

    table_copy = table + "_copy"
    for item in list_to_convert:
        print("PROCESSING: " + table + " - item: " + item)
        if 'BZD_' in item:
            item_id = 'BZD_ID'
            from_table = cur.execute(get_table_name_query.format(item_id))
            from_table = cur.fetchone()[0] #ERROR: Nonetype object is not subscriptable
        elif 'AGT_' in item:
            item_id = 'AGT_ID'
            from_table = cur.execute(get_table_name_query.format(item_id))
            from_table = cur.fetchone()[0] #ERROR: Nonetype object is not subscriptable
        elif 'BWG_' in item:
            item_id = 'BWG_ID'
            from_table = cur.execute(get_table_name_query.format(item_id))
            from_table = cur.fetchone()[0] #ERROR: Nonetype object is not subscriptable
        elif 'TDT_' in item:
            item_id = 'TDT_ID'
            from_table = cur.execute(get_table_name_query.format(item_id))
            from_table = cur.fetchone()[0] #ERROR: Nonetype object is not subscriptable
        else:
            cur.execute(get_table_name_query.format(item))
            from_table = cur.fetchone()[0]

        run_queries(table_copy, from_table, item, turn_id_or_code_into_oms(item))

    #HARDCODED Adding dagtype to older data (will have to rewrite for Ixiwa, but this'll do for now)
    if input_for_script[0] == "ongeval.csv":
        print('input for script is ongeval.csv')
        cur.execute("""ALTER TABLE "ongeval.csv_copy" ADD COLUMN "DAGTYPE" varchar;""")
        cur.execute("""UPDATE "ongeval.csv_copy"
        SET "DAGTYPE" = 'MA-VR' WHERE ("DAG_CODE" = 'MA' OR "DAG_CODE" = 'DI' OR "DAG_CODE" = 'WO' OR "DAG_CODE" = 'DO' OR "DAG_CODE" = 'VR');
        UPDATE "ongeval.csv_copy"
        SET "DAGTYPE" = 'ZA-ZO' WHERE ("DAG_CODE" = 'ZA' OR "DAG_CODE" = 'ZO');""")


    # Get list with column names from new year datafile
    columns_in_new_data = cur.execute("""
    SELECT column_name FROM INFORMATION_SCHEMA.columns
    WHERE table_name = '{0}';
    """.format(table_copy))
    # Removes '(' and '),'
    columns_in_new_data = cur.fetchall()
    columns_in_new_data = [i[0] for i in columns_in_new_data]

    # Get list with column names from original datafile
    cur.execute("""
    SELECT _rws_datatypes."column_name" FROM _rws_datatypes;
    """)
    columns_for_union = cur.fetchall()
    # Removes '(' and '),'
    columns_for_union = [i[0] for i in columns_for_union]

    print('old: ', columns_for_union)
    print('2016:', columns_in_new_data)

    for column_name in columns_for_union:
        # If column exists in original datafile, but not in new datafile: add column with correct datatype
        if column_name not in columns_in_new_data:
            datatype_column = cur.execute("""SELECT data_type FROM _rws_datatypes WHERE column_name = '{0}'""".format(column_name))
            datatype_column = cur.fetchone()
            datatype_column = datatype_column[0]
            print(datatype_column)
            cur.execute("""
            ALTER TABLE "{0}"
            ADD COLUMN "{1}" {2};
            """.format(table_copy, column_name, datatype_column))
#    If column name exists in new datafile, but not in original datafile: drop column
    for column_name in columns_in_new_data:
            if column_name not in columns_for_union:
                cur.execute("""
                ALTER TABLE "{0}"
                DROP COLUMN "{1}";
                """.format(table_copy, column_name))




    # get all column names
    cur.execute("""SELECT column_name FROM _rws_datatypes""")
    all_columns = cur.fetchall()
    all_columns = [i[0] for i in all_columns]

    # Check if column names are same datatype (old and new dataset)
    for column_name in all_columns:
        wanted_datatype = cur.execute("""SELECT data_type FROM _rws_datatypes WHERE column_name = '{0}'""".format(column_name))
        wanted_datatype = cur.fetchone()
        wanted_datatype = wanted_datatype[0]
        print("column name: ", column_name, "datatype: ", wanted_datatype)
        current_datatype = cur.execute("""SELECT data_type FROM INFORMATION_SCHEMA.columns WHERE table_name = '{0}' AND column_name = '{1}';""".format(table_copy, column_name))
        current_datatype = cur.fetchone()
        current_datatype = current_datatype[0]
        print('current: ', current_datatype, 'wanted: ', wanted_datatype)

        # Changes a varchar column to the correct datatype and adds NULLS to empty values
        if current_datatype != wanted_datatype and current_datatype == 'character varying':
            cur.execute("""UPDATE "{0}" SET "{1}" = NULL WHERE "{1}" = '';""".format(table_copy, column_name))
            cur.execute("""
            ALTER TABLE "{0}"
            ALTER COLUMN "{1}" TYPE {2} USING "{1}"::{2};
            """.format(table_copy, column_name, wanted_datatype))
        # changes a non varchar column to the correct datatype - no NULLS changed
        elif current_datatype != wanted_datatype and current_datatype != 'character varying':
            cur.execute("""
            ALTER TABLE "{0}"
            ALTER COLUMN "{1}" TYPE {2} USING "{1}"::{2};
            """.format(table_copy, column_name, wanted_datatype))

    #ADD TIJDSTIP_MODULE (for nice graph on zonnestand)
    #cur.execute("""UPDATE "{0}" SET "TIJDSTIP_MODULO" = ({"TIJDSTIP"}%100)/60.0+{"TIJDSTIP"}/100;""".format(table))

#Union goes wrong because it sees a column with Null timestamps as varchar (even though it doesn't show..)
#If you add all column names manually instead of putting a star (*), it's all good
#Run union query
#cur.execute("""{0}""".format(create_union_tables_query(tables_copy)))

# --- TO DO
#-MELDDATAPK, VERVDATAPK, GEBDAT, DATOVERL to_date
#-  """({ongeval.TIJDSTIP}%100)/60.0+{ongeval.TIJDSTIP}/100""" DOES NOT WORK! OLD TABLE uses TIJDSTIP (H)HMM, new uses UUR (H)H
# New script: create linking tables
# make it work with partij
# make it work with voertuig
# make it work with slachtoffer
# create index? aliases?
#rewrite for use with Mozart


# TO DO in new scripts
#-ADD DAGTYPE TO YEAR < 2016
# new scripts - restructure for better readability
# 1. functions and queries
# 2. find and convert all txt to csv (reference tables) (input: main folder and year(s))
# 3. prepare datasets rws specific (coordinate system mainly) (input: main folder and year(s)) <-- maybe put this in 5.
# 4. import all data found in the folders to pgadmin (input: main folder, old data table(s) and year(s))
# 5. add new year's data ongeval/partij/voertuig/slachtoffer (also: convert coordinates, add DAGTYPE to original dataset)
# 6. make linking tables


# --- FINISH UP RUNNING THE SCRIPT
# Make the changes to the database persistent
conn.commit()

# Close communication with the database
cur.close()
conn.close()