End of night commit

matrix_test_thread.py

@@ -6,7 +6,7 @@ import dill
 import base64
 import os
 import random
-from scipy.optimize import minimize
+from scipy.optimize import minimize, least_squares
 import csv
 from shutil import copy2
 import sys
@@ -171,6 +171,7 @@ class MatTestThread(BaseThread):
 
         while not self.finishTest and not self._stopevent.isSet() and len(self.availableSentenceInds) and len(self.trackOrder):
             # Plot SNR of current trial to the clinician screen
+            plt.clf()
             for at in self.adaptiveTracks:
                 at.plotSNR()
             self.renderSNRPlot()
@@ -212,6 +213,7 @@ class MatTestThread(BaseThread):
             self.socketio.emit('processing-complete', {'data': ''}, namespace='/main')
             self.waitForPageLoad()
             # Plot SNR of current trial to the clinician screen
+            plt.clf()
             for at in self.adaptiveTracks:
                 at.plotSNR()
             self.renderSNRPlot()
@@ -219,14 +221,13 @@ class MatTestThread(BaseThread):
             self.waitForFinalise()
 
 
-
     @staticmethod
-    def logisticFunction(L, L_50, s_50):
+    def logisticFunction(L, L_50, s_50, minima=0.0, maxima=1.0):
         '''
         Calculate logistic function for SNRs L, 50% SRT point L_50, and slope
         s_50
         '''
-        return 1./(1.+np.exp(4.*s_50*(L_50-L)))
+        return (minima+((maxima-minima))*(1./(1.+np.exp(4*s_50*(L_50-L)))))
 
 
     def logisticFuncLiklihood(self, args):
@@ -251,7 +252,7 @@ class MatTestThread(BaseThread):
         with np.errstate(divide='raise'):
             try:
                 a = np.concatenate(res)
-                a[a == 0] = np.finfo(float).eps
+                a[np.logical_or(a == 0.0, np.isnan(a))] = np.finfo(float).eps
                 out = -np.sum(np.log(a))
             except:
                 set_trace()
@@ -263,11 +264,21 @@ class MatTestThread(BaseThread):
         '''
         self.wordsCorrect = np.concatenate([x.getWordsCorrect() for x in self.adaptiveTracks])
         self.trackSNR = np.concatenate([x.getSNRTrack() for x in self.adaptiveTracks])
-        res = minimize(self.logisticFuncLiklihood, np.array([np.mean(self.trackSNR),1.0]))
+
+        res = least_squares(
+            self.logisticFuncLiklihood,
+            np.array([np.median(self.trackSNR),1.0]),
+            args=()
+        )
+        if not res.success:
+            logger.error("Logistic function fitting failed. SRT and slope estimate results will be incorrect")
+        breakpoint()
+        #res = minimize(self.logisticFuncLiklihood, np.array([np.mean(self.trackSNR),1.0]))
         percent_correct = (np.sum(self.wordsCorrect, axis=1)/self.wordsCorrect.shape[1])*100.
         sortedSNRind = np.argsort(-self.trackSNR)
         sortedSNR = self.trackSNR[sortedSNRind]
         sortedPC = percent_correct[sortedSNRind]
+
         x = np.linspace(np.min(sortedSNR)-5, np.max(sortedSNR)+3, 3000)
         srt_50, s_50 = res.x
         x_y = self.logisticFunction(x, srt_50, s_50)
@@ -281,21 +292,28 @@ class MatTestThread(BaseThread):
 
         #plt.plot(sortedSNR, sortedPC, "x")
         #sbnplot = sns.relplot(data=pd.DataFrame(x_y*100., x), kind="line")
-        plt.clf()
         axes = plt.gca()
-        psycLine, = axes.plot(x, x_y)
-        plt.title("Predicted psychometric function")
-        plt.xlabel("SNR (dB)")
-        plt.ylabel("% Correct")
         srtLine, = axes.plot([srt_50,srt_50], [-50,50.], 'r--')
         axes.plot([-50.,srt_50], [50.,50.], 'r--')
-        plt.xlim(x.min(), x.max())
-        plt.ylim(x_y.min(), x_y.max())
-        plt.yticks(np.arange(5)*25.)
         x_vals = np.array(axes.get_xlim())
         s_50 *= 100.
         b = 50. - s_50 * srt_50
         y_vals = s_50 * x_vals + b
+
+
+        wc = self.wordsCorrect.sum(axis=1)*(100/5.)
+        #wc = words_correct*(100)
+        plt.clf()
+        axes = plt.gca()
+        points = plt.plot(self.trackSNR, wc, marker='x', color='r',
+            linestyle='None')
+        psycLine, = axes.plot(x, x_y)
+        plt.title("Predicted psychometric function")
+        plt.xlabel("SNR (dB)")
+        plt.ylabel("% Correct")
+        plt.xlim(x.min(), x.max())
+        plt.ylim(x_y.min(), x_y.max())
+        plt.yticks(np.arange(5)*25.)
         slopeLine, = axes.plot(x_vals, y_vals, '--')
         ticks = (np.arange((x.max()-x.min())/2.5)*2.5)+(2.5 * round(float(x.min())/2.5))
         ticks[find_nearest_idx(ticks, srt_50)] = srt_50

templates/matrix_test_setup.html

@@ -15,7 +15,6 @@
                 <select class="form-control" name="mode" id="mode">
                     <option>Familiarisation</option>
                     <option>Testing</option>
-                    <option>Testingssjks</option>
                 </select>
             </div>
             <div class="form-group d-flex justify-content-center">