# Create a simple model with constant drift, noise, and bounds.
from ddm import Model
from ddm.models import DriftConstant, NoiseConstant, BoundConstant, OverlayNonDecision, ICPointSourceCenter
from ddm.functions import fit_adjust_model, display_model
model = Model(name='Simple model',
drift=DriftConstant(drift=2.2),
noise=NoiseConstant(noise=1.5),
bound=BoundConstant(B=1.1),
overlay=OverlayNonDecision(nondectime=.1),
dx=.001, dt=.01, T_dur=2)
# Solve the model, i.e. simulate the differential equations to
# generate a probability distribution solution.
display_model(model)
sol = model.solve()
# Now, sample from the model solution to create a new generated
# sample.
samp = sol.resample(1000)
# Fit a model identical to the one described above on the newly
# generated data so show that parameters can be recovered.
from ddm import Fittable, Fitted
from ddm.models import LossRobustBIC
from ddm.functions import fit_adjust_model
model_fit = Model(name='Simple model (fitted)',
drift=DriftConstant(drift=Fittable(minval=0, maxval=4)),
noise=NoiseConstant(noise=Fittable(minval=.5, maxval=4)),
bound=BoundConstant(B=1.1),
# resulting distribution of response times by
# `nondectime` seconds.
overlay=OverlayChain(overlays=[
OverlayNonDecision(nondectime=Fittable(minval=0, maxval=.4)),
OverlayPoissonMixture(pmixturecoef=.02, rate=1)
]),
dx=.001,
dt=.01,
T_dur=2)
# Fitting this will also be fast because PyDDM can automatically
# determine that DriftCoherence will allow an analytical solution.
fit_model_rs = fit_adjust_model(sample=roitman_sample,
model=model_rs,
verbose=False)
display_model(fit_model_rs)
fit_model_rs.parameters()
# Plot the model fit to the PDFs and save the file.
import ddm.plot
import matplotlib.pyplot as plt
ddm.plot.plot_fit_diagnostics(model=fit_model_rs, sample=roitman_sample)
plt.savefig("roitman-fit.png")
plt.show()
# To get an intuition for how parameters affect the fit, play with the
# parameters and task conditions in a GUI.
ddm.plot.model_gui(model=fit_model_rs, sample=roitman_sample)
# Let's try to improve the model fit.
### Fit the model to the dataset to find parameters
# Use the "differential_evolution" fitting method (default, recommanded fitting method)
fit_model_foodc = fit_adjust_model(sample=foodc_sample,
model=model_foodc,
method='differential_evolution')
# Use the "simple" fitting method (much faster)
# fit_model_foodc = fit_adjust_model(sample = foodc_sample, model = model_foodc, method = 'simple')
# To note, In documentation the key word "method" is documented as "fitting_method",
# which is actually invalid now.
# Display the fitting outcome (parameters)
display_model(fit_model_foodc)
### Plot
import ddm.plot
# import matplotlib.pyplot as plt
ddm.plot.plot_fit_diagnostics(model=fit_model_foodc,
sample=foodc_sample,
data_dt=.01)
plt.savefig("./foodc_PyDDM1.png")
# plt.savefig("./foodc_PyDDM1.png", bbox_inches='tight')
plt.show()
# At the moment the plot is incorrect
# The warning information from execution:
# /opt/anaconda3/lib/python3.7/site-packages/ddm/plot.py:232: