def test_fit_with_condition_parallel(self):
     """A simple one-parameter fit with conditions, parallelized"""
     ddm.set_N_cpus(2)
     self.test_fit_with_condition()
     ddm.set_N_cpus(1)
start_time = time.time()

import numpy as np
import pandas as pd
# np.random.seed(123)

import ddm.models
from ddm import ICPoint, Model, Fittable, Sample
from ddm.models import NoiseConstant, BoundConstant, OverlayChain, OverlayNonDecision, OverlayPoissonMixture, OverlayUniformMixture
from ddm.functions import fit_adjust_model, display_model
from ddm.models.loss import LossRobustLikelihood

# set up a parallel pool
from ddm import set_N_cpus

set_N_cpus(4)

# =============================================================================
# Load data and preprocessing
# =============================================================================

# load the full dataset and fitting reference table
data = pd.read_csv(
    '/Users/hutianqi/Desktop/Project Risk and Intelligence/01 Dataset/Trials.csv'
)
fit_ref = pd.read_csv(
    '/Users/hutianqi/Desktop/Project Risk and Intelligence/01 Dataset/Z_Fit_Ref2.csv'
)

# # for HPC fitting
# data = pd.read_csv('/home/bs/bsth4/DDM/Trials.csv')