from farmOS import farmOS

hostname = "farmos.wagframe.duckdns.org"
username = "matze"
password = "m6ChEHx5gMqgctr8dpb3fhATZbQS8hv4"

# Create the client.
farm_client = farmOS(
    hostname=hostname,
    client_id = "jupyter", # Optional. The default oauth client_id "farm" is enabled on all farmOS servers.
)
import json
# Authorize the client, save the token.
# A scope can be specified, but will default to the default scope set when initializing the client.
token = farm_client.authorize(username, password)

import pandas as pd


def get_related_logs_of_type(asset, type): 
    response = farm_client.resource.get('log', type)
    ret = []
    for log in response['data']:
        assets = log['relationships']['asset']['data']
        for asset in assets:
            if asset['id'] == id:
                ret.append(log)
    return ret

def get_related_quantities_of_measure(log, measure):
    quantities = log['relationships']['quantity']['data']
    ret = []
    for quant_ref in quantities:
        quant_id = quant_ref['id']
        quant = farm_client.resource.get_id('quantity', 'standard', quant_id)
        if quant['data']['attributes']['measure'] == measure:
            ret.append(quant)
    return ret

# Beispiel für eine API-Abfrage mit farm_client
response = farm_client.resource.get('log', 'harvest')

logs = []
for item in response['data']:
    # plant name und fläche holen
    assets = item['relationships']['asset']['data']
    plant_name = ''
    plant_area = 0.0
    for asset in assets:
        if asset['type'] == 'asset--plant':    
            plant_id = asset['id']
            seedings = get_related_logs_of_type(plant_id, 'seeding')
            for seeding in seedings:
                area_quants = get_related_quantities_of_measure(seeding, 'area')
                for area_quant in area_quants:
                    plant_area += float(area_quant['data']['attributes']['value']['decimal'])
            plant = farm_client.asset.get_id('plant', plant_id)
            plant_name = plant['data']['attributes']['name']
            
    
    # log aufbauen
    log = {
        'ID': item['id'],
        'Name': item['attributes']['name'],
        'Timestamp': item['attributes']['timestamp'],
        'Plant': plant_name,
        'Area': plant_area,
        'Status': item['attributes']['status'],
        'Revision Created': item['attributes']['revision_created'],
        'Link': item['links']['self']['href']
    }
    logs.append(log)

# Erstelle ein DataFrame aus den Harvest Logs
df = pd.DataFrame(logs)

# Zeige die Tabelle an
print(df)

# Speichere die Tabelle in einer CSV-Datei
csv_file_path = 'resources/harvest_logs.csv'
df.to_csv(csv_file_path, index=False)

print(f"Die Tabelle wurde erfolgreich in der Datei '{csv_file_path}' gespeichert.")