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Fixed psychometric function plotting for behavioural test, trial 1 behavioural results saving bug still exists

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Sam Perry
2019-12-13 14:54:56 +00:00
parent 36e9aa9f9c
commit a9aed2f51b
1 changed files with 33 additions and 22 deletions
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matrix_test_thread.py
+33 -22
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@@ -6,7 +6,7 @@ import dill
import base64
import os
import random
from scipy.optimize import minimize, least_squares
from scipy.optimize import minimize, least_squares, curve_fit
import csv
from shutil import copy2
import sys
@@ -253,7 +253,9 @@ class MatTestThread(BaseThread):
try:
a = np.concatenate(res)
a[np.logical_or(a == 0.0, np.isnan(a))] = np.finfo(float).eps
out = -np.sum(np.log(a))
out = -np.log(np.sum(np.log(a)))
if np.isnan(out):
out = 999999999.0
except:
set_trace()
return out
@@ -262,25 +264,32 @@ class MatTestThread(BaseThread):
def fitLogistic(self):
'''
'''
self.wordsCorrect = np.concatenate([x.getWordsCorrect() for x in self.adaptiveTracks])
self.trackSNR = np.concatenate([x.getSNRTrack() for x in self.adaptiveTracks])
self.wordsCorrect = np.concatenate([x.getWordsCorrect() for x in self.adaptiveTracks])[1:]
self.trackSNR = np.concatenate([x.getSNRTrack() for x in self.adaptiveTracks])[:-1]
inds = np.argsort(self.trackSNR)
wcs = self.wordsCorrect[inds]
stsnr = self.trackSNR[inds]
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]))
x = stsnr
y = wcs.sum(axis=1)/5.
popt, pcov = curve_fit(self.logisticFunction, x, y, p0=[np.median(self.trackSNR), 0.1], bounds=([-30.0, 0.0001], [30.0, 100.0]), method='dogbox')
srt_50, s_50 = popt
# res = least_squares(
# self.logisticFuncLiklihood,
# np.array([np.median(self.trackSNR),0.01]),
# args=()
# )
# srt_50, s_50 = res.x
# if not res.success:
# logger.error("Logistic function fitting failed. SRT and slope estimate results will be incorrect")
# 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)
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)
x_y *= 100.
# np.savez('./plot.npz', x, x_y*100., sortedSNR, sortedPC)
@@ -292,28 +301,29 @@ 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()
srtLine, = axes.plot([srt_50,srt_50], [-50,50.], 'r--')
axes.plot([-50.,srt_50], [50.,50.], 'r--')
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',
points = plt.plot(sortedSNR, sortedPC, 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.ylim(x_y.min()-5, x_y.max()+5)
plt.yticks(np.arange(5)*25.)
x_vals = np.array(axes.get_xlim())
y_point = self.logisticFunction(srt_50, srt_50, s_50)*100.
s_50 *= 100
c = y_point - s_50 * srt_50
y_vals = s_50 * x_vals + c
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
@@ -327,6 +337,7 @@ class MatTestThread(BaseThread):
plot_url = "data:image/png;base64,{}".format(plot_url)
self.srt_50, self.s_50 = srt_50, s_50
self.socketio.emit("mat_mle_plot_ready", {'data': plot_url}, namespace="/main")
breakpoint()