from codegreen.decorators import init_experiment, time_shift, upload_cc_report

from codecarbon import track_emissions

from codegreen.queries import get_location_prediction, get_data

import numpy as np

from datetime import datetime

# Complete example to timeshift the computation, monitor the energy consumption using codecarbon and then upload the report.

#A. decorate the function

#@init_experiment(estimated_runtime_hours=1,estimated_runtime_minutes=30,percent_renewable=40,allowed_delay_hours= 24, area_code=["DE-9"],log_request=True,experiment_name="my_experiment",codecarbon_logfile="experiment.log",nextflow_logfile="nextflow.log",overwrite=True)

@init_experiment(experiment_name="my_experiment")

@time_shift("my_experiment")

@upload_cc_report("my_experiment")

@track_emissions(output_file='experiment.log')

def hello_random_matrix_generator():

for _ in range(1000):

np.random.random((1000, 1000))

print("Hello")

#B. run the function

hello_random_matrix_generator()

# Download the data you submitted.

data = get_data('codecarbon', False, 'my_experiment')

# Get a location prediction.

location = get_location_prediction(estimated_runtime_hours = 1,

estimated_run_time_in_minutes=12,

percent_renewable=40,

hard_finish_time = datetime.utcnow().replace(hour=18, minute=0, second=0).timestamp(),

area_code = ['DE-79117', 'CY-1', 'CZ-8'],

log_request = True,

process_id = '1',

experiment_name='my_experiment')