debug.py

"""
This file is to debug while developing, but also serves as a demonstration
as how the dssatservice package is used to setup and operate the service.

Some functions are examples of basic setup funcionalities like: adding a new 
domain, adding the soil data, adding the cultivars information, etc.
"""
import dssatservice.data.ingest as ing
import dssatservice.database as db
from dssatservice.dssat import run_spatial_dssat
from datetime import datetime
import psycopg2 as pg

import time
import numpy as np
import re

INPUT_PATH = "/home/diego/dssat_service_data"
with open('psswd', 'r') as f:
    psswd = f.readline().strip()
con = pg.connect(dbname="dssatserv", password=psswd, user='diego', host='localhost')

def add_country():
    """
    Adds a new domain to the service database
    """
    shp = f"{INPUT_PATH}/rwanda_admin1/rwa_adm1.shp"
    db.add_country(con, "Rwanda", shp, "NAME_1")

def ingest_soil_data():
    """
    Ingest the Soil data for the domain. ALl soil profiles to be ingested must
    be in the same Soil DSSAT file.
    """
    soil_path = f"{INPUT_PATH}/RW.SOL"
    mask1 = f"{INPUT_PATH}/subsaharanAfrica-maize.tif"
    mask2 = f"{INPUT_PATH}/subsaharanAfrica-suitableAg-v2.tif"
    ing.ingest_soil(
        con=con,
        schema="rwanda",
        soilfile=soil_path,
        mask1=mask1,
        mask2=mask2
    )

def ingest_static_data():
    """
    Ingest static data. In this exmple two DSSAT weather parameters are ingested:
    Average temperature (TAV), and Average seasonal temperature range (TAMP).
    """
    ing.ingest_static(
        con=con,
        schema="rwanda",
        rast=f"{INPUT_PATH}/tamp_rwanda_2m.tif",
        parname="tamp"
    )
    ing.ingest_static(
        con=con,
        schema="rwanda",
        rast=f"{INPUT_PATH}/tav_rwanda_2m.tif",
        parname="tav"
    )

def ingest_era5_data():
    """
    Ingests the ERA5 data
    """
    ing.ingest_era5_series(
        con, "rwanda", 
        datetime(2024, 1, 1), 
        datetime(2025, 3, 1)
    )

def run_model():
    """
    Runs a end-of-season simulation (using only ERA5 historical data)
    """
    time0 = time.time()
    df, overview = run_spatial_dssat(
        con=con, 
        schema="rwanda", 
        admin1="Amajyepfo",
        plantingdate=datetime(2024, 2, 1),
        cultivar="990002",
        nitrogen=[(5, 20), (30, 10), (50, 10)],
        overview=True
    )
    df = df.iloc[:, 3:].astype(int).replace(-99, np.nan)
    print(df.describe())
    from collections import Counter
    print(Counter([l[:7] for l in overview if "Sowing" in l]))
    N_uptake = [
        int(re.findall("(\d+)", l)[0]) 
        for l in overview 
        if "N uptake during growing season" in l
    ]
    print(np.mean(N_uptake), np.std(N_uptake))
    print(f"{(time.time() - time0):.3f} seconds running one season")

def era5_climatology():
    """
    Creates the ERA5 climatology table. It estimates the monthly stats using
    all the ERA5 data available in the database.
    """
    ing.calculate_climatology(con, 'rwanda')

def ingest_cultivars():
    """
    Ingest the cultivar options. These cultivars are the ones avaible for the 
    user selection. 
    """
    ing.ingest_cultivars(con, "rwanda", f"{INPUT_PATH}/cultivar_table.csv")

def ingest_nmme_data():
    """
    Ingest the NMME climate prediction data.
    """
    ing.ingest_nmme(con, 'rwanda')

def run_model_forecast_onthefly():
    """
    Runs a in-season simulation (Using future weather data from NMME).
    This is just running the model as one user would do it by getting NMME data.
    It is not the function to run the operative forecast!!!
    """
    time0 = time.time()
    df, overview = run_spatial_dssat(
        con=con, 
        schema="rwanda", 
        admin1="Amajyepfo",
        plantingdate=datetime(2024, 10, 1),
        cultivar="990002",
        nitrogen=[(5, 20), (30, 10), (50, 10)],
        overview=True
    )
    df = df.iloc[:, 3:].astype(int).replace(-99, np.nan)
    print(df.describe())
    # parse_overview("".join(overview))
    from collections import Counter
    print(Counter([l[:7] for l in overview if "Sowing" in l]))
    N_uptake = [
        int(re.findall("(\d+)", l)[0]) 
        for l in overview 
        if "N uptake during growing season" in l
    ]
    print(np.mean(N_uptake), np.std(N_uptake))
    print(f"{(time.time() - time0):.3f} seconds running one season")

if __name__ == "__main__":
    # add_country()
    # ingest_soil_data()
    # ingest_static_data()
    # ingest_era5_data()
    # run_model() # Run only end-of-season (no climate forecast)
    # era5_climatology()
    # ingest_cultivars()
    # ingest_nmme_data()
    # run_model_forecast_onthefly() # Run in-season (with climate forecast)
    con.close()