MRG: Tracker dealer example

examples/stimuli/tracker_dealer.py

@@ -0,0 +1,81 @@
+# -*- coding: utf-8 -*-
+"""
+======================
+Tracker Dealer Example
+======================
+
+This file shows how to interleave multiple Tracker objects using
+:class:`expyfun.stimuli.TrackerDealer`.
+
+In this case, a modeled human subject generates two curves (one for each trial
+type: 1 & 2)
+
+@author: maddycapp27
+"""
+
+import numpy as np
+from expyfun.stimuli import TrackerUD, TrackerDealer
+from expyfun.analyze import sigmoid
+import matplotlib.pyplot as plt
+
+# define parameters of modeled subject (using sigmoid probability)
+true_thresh = [30, 40]
+slope = 0.1
+chance = 0.5
+
+# define parameters for each tracker (assuming each tracker uses same rules)
+up = 1
+down = 1
+step_size_up = [9, 3]
+step_size_down = [3, 1]
+stop_criterion = 30
+stop_rule = 'reversals'
+start_value = 45
+change_criteria = [0, 5]
+change_rule = 'reversals'
+x_min = 0
+x_max = 90
+
+# parameters for the tracker dealer
+max_lag = 2
+rand = None
+
+
+# callback function that prints to console
+def callback(event_type, value=None, timestamp=None):
+    print((str(event_type) + ':').ljust(40) + str(value))
+
+
+# initialize two tracker objects--one for each trial type
+tr_UD = [TrackerUD(callback, up, down, step_size_up, step_size_down,
+                   stop_criterion, stop_rule, start_value,
+                   change_criteria, change_rule, x_min, x_max) for i in [0, 1]]
+
+# initialize TrackerDealer object
+tr = TrackerDealer(tr_UD, max_lag, rand)
+
+# Initialize human state
+rng = np.random.RandomState(1)
+
+while not tr.stopped:
+    # Get information of which trial type is next and what the level is at
+    # that time from TrackerDealer
+    ss, level = tr.get_trial()
+    ss = sum(ss)
+    tr_UD[ss].respond(rng.rand() < sigmoid(level - true_thresh[ss],
+                                           lower=chance, slope=slope))
+
+# Plot the results
+axes = [plt.subplot(211), plt.subplot(212)]
+for i in [0, 1]:
+    fig, ax, lines = tr[i].plot(ax=axes[i])
+    lines += tr[i].plot_thresh(4, ax=ax)
+
+    lines[0].set_label('Trials')
+    lines[1].set_label('Reversals')
+    lines[2].set_label('Estimated threshold')
+
+    ax.legend(loc='best')
+    ax.set_title('Adaptive track of model human trial type {} (true threshold '
+                 'is {})'.format(i + 1, true_thresh[i]))
+    plt.tight_layout()

examples/stimuli/tracker_staircase.py

@@ -22,7 +22,6 @@
 def callback(event_type, value=None, timestamp=None):
     print((str(event_type) + ':').ljust(40) + str(value))
 
-
 # Define parameters for modeled human subject (sigmoid probability)
 true_thresh = 35
 slope = 0.1
@@ -37,7 +36,7 @@ def callback(event_type, value=None, timestamp=None):
 # Do the task until the tracker stops
 while not tr.stopped:
     tr.respond(rng.rand() < sigmoid(tr.x_current - true_thresh,
-                                    lower=0.5, slope=0.1))
+                                    lower=chance, slope=slope))
 
 # Plot the results
 fig, ax, lines = tr.plot()

expyfun/stimuli/_tracker.py

@@ -41,7 +41,7 @@ def _check_callback(callback):
 class TrackerUD(object):
     """Up-down adaptive tracker
 
-    This class implements a standard up-down adative tracker object. Based on
+    This class implements a standard up-down adaptive tracker object. Based on
     how it is configured, it can be used to run a fixed-step m-down n-up
     tracker (staircase), or it can implement a weighted up-down procedure.