import json
import requests
import numpy as np
import pandas as pd

# Predico instance URL. Switch BASE_URL to the environment you want to use:
#   - Playground: https://playground.predico.inesctec.pt
#   - Production: https://predico.inesctec.pt
BASE_URL = "https://predico.inesctec.pt"
API_URL = f"{BASE_URL}/api/v1"

# Authenticate via `/token` endpoint
access_token = "your_access_token_here"
headers = {
    'Authorization': f'Bearer {access_token}',
    'Accept': 'application/json'
}

# Get the session id from `/market/session/` endpoint
open_market_session_id = "your_open_market_session_id"

# Challenge resource identifier (name):
resource_name = "wind_farm_1"

# Request the challenges for the open market session:
response = requests.get(
    url=f"{API_URL}/market/challenge",
    params={'market_session': int(open_market_session_id),
            'resource_name': resource_name},
    headers=headers,
    timeout=30
)

# Check if the request was successful
if response.status_code == 200:
    challenges = response.json()
else:
    print("Failed to retrieve challenges.")
    print(f"Status code: {response.status_code}")
    exit()

# Select the first challenge of the list of challenges previous retrieved
# Note that as we're filtering by resource_name, there should be only a
# single challenge per session
selected_challenge = challenges["data"][0]

# Unpack selected challenge information
resource_id = selected_challenge["resource"]  # challenge resource UUID
challenge_id = selected_challenge["id"] # challenge UUID
start_datetime = selected_challenge["start_datetime"] # first forecast leadtime
end_datetime = selected_challenge["end_datetime"] # last forecast leadtime
print(f"""
Challenge info:
ID: {challenge_id}
Resource ID: {resource_id}
First forecast leadtime): {start_datetime}
Last forecast leadtime): {end_datetime}
""")

# Create a random 24h submission (random values):
# Generate datetime values for the challenge period:
datetime_range = pd.date_range(start=start_datetime,
                               end=end_datetime,
                               freq='15min')
datetime_range = [x.strftime("%Y-%m-%dT%H:%M:%SZ") for x in datetime_range]
# Generate random Q50 (median) values, then derive Q10/Q90 around it so that
# Q10 <= Q50 <= Q90 holds at every timestamp (as required by the API).
q50 = np.random.uniform(low=0.1, high=0.9, size=len(datetime_range))
quantile_values = {
    "q10": [round(float(x), 3) for x in np.clip(q50 - 0.1, 0.0, 1.0)],
    "q50": [round(float(x), 3) for x in q50],
    "q90": [round(float(x), 3) for x in np.clip(q50 + 0.1, 0.0, 1.0)],
}

# Prepare submissions for the three quantiles Q10, Q50, Q90:
submission_list = []
for qt in ["q50", "q10", "q90"]:
    qt_forec = pd.DataFrame({
        'datetime': datetime_range,
        'value': quantile_values[qt],
    })
    submission_list.append({
        "variable": qt,
        "forecasts": qt_forec.to_dict(orient="records")
    })

# Your submissions:
print("Submission List:")
for i, submission in enumerate(submission_list):
    print("-"*79)
    print(f"Submission #{i+1}")
    print(json.dumps(submission, indent=3))

# Submit the forecasts:
for submission in submission_list:
    response = requests.post(url=f"{API_URL}/market/challenge/submission/{challenge_id}",
                            json=submission,
                            headers=headers,
                            timeout=30)

    # Check if the request was successful
    if response.status_code == 201:
        print(f"Forecast submission successful for {submission['variable']} quantile.")
    else:
        print(f"Failed to submit forecast for {submission['variable']} quantile.")
        print(f"Status code: {response.status_code}")