import psycopg2

# --- SETUP ---
# The only things that need to be filled in to run the script:
# dbname, user, password
# and
# year, old data filename, category, empty string for later
input_for_script = (2016, 'ongeval.csv', 'ongevallen', '')

#tables = ["ongevallen_2016.csv", "ongevallen_2017.csv"]
#change input to
#old_data = ongeval.csv
#data_to_add = [data1, data2]

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

# --- EXECUTION ---

#This code adds a new year of RWS data to the old dataset.
#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' */

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

# 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 (
    year_to_add     INT,
    old_data        text,
    category        text,
    year_filename   text);
""")

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

cur.execute("""
UPDATE _rws_settings
SET year_filename = CONCAT(_rws_settings.category, '_', _rws_settings.year_to_add, '.csv');
""")


# 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 = (SELECT old_data FROM _rws_settings);
""")

# 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');
""")

# 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 _rws_settings.year_filename FROM _rws_settings)
AND (column_name LIKE '%\_ID' OR column_name LIKE '%\_CODE');
""")

#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()
#remove '(' and '),'
list_to_convert = [i[0] for i in list_to_convert]


# --- DIT IS WAT RUDY GEDAAN HEEFT OMG
#Query voor de loop, selecteert de juiste reference table gebaseerd op het iterable item in de loop
get_table_name_query = """
SELECT table_name from _rws_references WHERE column_name = '{0}'
"""

#Create tables (tables to convert/merge) ["ongevallen_2016.csv", "ongevallen_2017.csv"]
#create all data table and fill it with the old data
tables = ["ongevallen_2016.csv"]
all_data = cur.execute("""
DROP TABLE IF EXISTS _rws_all_temp;
CREATE TABLE _rws_all_temp AS SELECT * FROM (SELECT _rws_settings.old_data FROM _rws_settings) AS derived_temp
""")

#function that converts _ID or _CODE into _OMS
#0= data table (2016 for example) - table_to_convert
#1= the reference table
#2=item name (but the _OMS version, needs function turn_id_or_code_into_oms)
#3=item name, iterable (_ID or _CODE)
def run_rudy_query(table_to_convert, from_table, item_name):
    rudy_query = """
    SELECT "{0}".*, oms_table."{2}"
    FROM "{0}"
    LEFT JOIN "{1}" as oms_table
    ON "{0}"."{3}" = oms_table."{3}"
    """

    finished_query = rudy_query.format(table_to_convert, from_table, turn_id_or_code_into_oms(item_name), item_name)
    print(finished_query)
    cur.execute(rudy_query)

#changes extension _ID and _CODE into _OMS (for easy lookup in reference table)
def turn_id_or_code_into_oms(name):
    if(name.find("_CODE")):
        return name.replace("_CODE", "_OMS")
    else:
        return name.replace("_ID", "_OMS")

# Loop through all the tables and convertable items, find all reference tables and start function run_rudy_query
for table in tables:
    for item in list_to_convert:
        print('ref table name: ', get_table_name_query.format(item))
        from_table = cur.execute(get_table_name_query.format(item))
        run_rudy_query(table, from_table, turn_id_or_code_into_oms(item))

#def update_employee(id, name, birthdate):
#    cur.execute("UPDATE employees SET name={0}, birthdate={1} WHERE ID = {2}".format(name, birthdate, id))

# --- TOT HIER, OK?


# --- THIS IS NOT WORKING, KEEPING IT HERE FOR REFERENCE
# per column name to convert:
# find reference file to use
# use that file for a left join (create a new table the first time)
#for item in list_to_convert:
#    print(item)
#    reference_table = (("""SELECT _rws_references."table_name" FROM _rws_references WHERE _rws_references."column_name" LIKE %s """), item)
#    rws_data = ("""SELECT _rws_settings.year_filename FROM _rws_settings""")

#    cur.execute("""DROP TABLE IF EXISTS _year_to_add_oms;
#    CREATE TABLE _year_to_add_oms AS
#    SELECT * FROM (SELECT _rws_settings.year_filename FROM _rws_settings) AS _derived_table1, %s
#    LEFT JOIN (SELECT _rws_settings.year_filename FROM _rws_settings) AS _derived_table2 ON
#   (SELECT * FROM (SELECT _rws_columns_to_convert.%s FROM _rws_columns_to_convert) AS derived_table5)
#   = (SELECT * FROM (SELECT _rws_references.%s FROM _rws_references) AS _derived_table4)""", reference_table, , rws_data, rws_data, item)
# --- END OF NON-WORKING CODE

# --- TO DO
#change value from null-columns to correct datatype
#check for and add missing columns
#remove unnecessary columns
#union old and new data
#create index? aliases?
# create linking tables (maybe other script)

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

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