Update of project use

DOCUMENTATION.md

@@ -94,7 +94,7 @@ Example of loading or saving Python object (need of pickle):
 ```python
 # check if necessary to construct `quest` object or if backup exists
 if not os.path.exists(model_filepath):
-    qp = QuestPlus(stim_space, [thresholds, slopes], function=psychometric_fun)
+    qp = QuestPlus(stim_space, [stime_space, slopes], function=psychometric_fun)
 else:
     print('Load `qp` model')
     filehandler = open(model_filepath, 'rb') 

expe/config.py

@@ -51,7 +51,9 @@ expes_configuration            = {
         'params':{
             'iterations': 10
         },
-       
+        'slopes':{
+
+        },
         # if others custom session param are directly set for experiments
         'session_params': [
             'expe_data',

expe/expes/run.py

@@ -3,6 +3,7 @@ import os
 import time
 import numpy as np
 import pickle
+import sys
 
 # django imports
 from django.conf import settings
@@ -17,6 +18,10 @@ from .. import config as cfg
 from .classes.quest_plus import QuestPlus
 from .classes.quest_plus import psychometric_fun
 
+# other imports 
+from ipfml import utils
+from pprint import pprint
+
 
 def run_quest_one_image(request, model_filepath, output_file):
 
@@ -56,28 +61,45 @@ def run_quest_one_image(request, model_filepath, output_file):
     # 3. Load or create Quest instance
     # default params
     # TODO : add specific thresholds information for scene
-    thresholds = np.arange(50, 10000, 50)
+    #thresholds = np.arange(50, 10000, 50)
     stim_space = np.asarray(qualities)
-    slopes = np.arange(0.0001, 0.001, 0.00003)
+    #slopes = np.arange(0.0001, 0.001, 0.00003) # contemporary
+    slopes = np.arange(0.0005, 0.01, 0.0003) # bathroom
+
+    # TODO : update norm slopes
+    # stim_space = np.asarray(qualities)
+    # slopes = np.arange(0.0001, 0.001, 0.00003)
+
+    # # normalize stim_space and slopes for this current scene
+    # stim_space_norm = np.array(utils.normalize_arr_with_range(stim_space, stim_space.min(), stim_space.max()))
+    # slopes_norm = slopes * (slopes.max() - slopes.min()) 
 
     # check if necessary to construct `quest` object
     if not os.path.exists(model_filepath):
-        qp = QuestPlus(stim_space, [thresholds, slopes], function=psychometric_fun)
+        print('Creation of `qp` model')
+        #print(slopes_norm)
+        #qp = QuestPlus(stim_space_norm, [stim_space_norm, slopes_norm], function=psychometric_fun)
+        qp = QuestPlus(stim_space, [stim_space, slopes], function=psychometric_fun)
+
     else:
         print('Load `qp` model')
         filehandler = open(model_filepath, 'rb') 
         qp = pickle.load(filehandler)
+        pprint(qp)
     
     # 4. If expe started update and save experiments information and model
     # if experiments is already began
     if request.session.get('expe_started'):
 
-        # TODO : check `i` variable 
-        # update of `quest`
-        # qp.update(qualities[i], answer)
-        # Use of previous stim
-        qp.update(qualities[iteration], answer) 
+        # TODO : update norm slopes
+        #previous_stim_norm = (int(previous_stim) - stim_space.min()) / (stim_space.max() - stim_space.min() + sys.float_info.epsilon)
+
+        print(previous_stim)
+        #print(previous_stim_norm)
+
+        qp.update(int(previous_stim), answer) 
         entropy = qp.get_entropy()
+        print('chosen entropy', entropy)
 
         line = str(previous_stim) 
         line += ";" + scene_name 
@@ -92,20 +114,25 @@ def run_quest_one_image(request, model_filepath, output_file):
         output_file.write(line)
         output_file.flush()
 
-    # save `quest` model
-    file_pi = open(model_filepath, 'wb') 
-    pickle.dump(qp, file_pi)
-
     # 5. Contruct new image and save it
     # construct image 
     if iteration < cfg.expes_configuration[expe_name]['params']['iterations']:
         # process `quest`
+
         next_stim = qp.next_contrast()
-        print("Next quality ", next_stim)
+        print(next_stim)
+        #next_stim_img = int(next_stim*(stim_space.max()-stim_space.min())+stim_space.min())
+    
+        print('-------------------------------------------------')
+        print('Iteration', iteration)
+        print(next_stim)
+        #print('denorm', next_stim_img)
+        print('-------------------------------------------------')
 
-        # construct new image
+        #noisy_image = api.get_image(scene_name, next_stim_img)
         noisy_image = api.get_image(scene_name, next_stim)
 
+
         # reconstruct reference image from list stored into session
         ref_image = api.get_image(scene_name, 'max')
         img_merge, percentage, orientation, position = crop_images(noisy_image, ref_image)
@@ -127,6 +154,10 @@ def run_quest_one_image(request, model_filepath, output_file):
     if img_merge is not None:
         img_merge.save(filepath_img)
 
+    # save qp model at each iteration
+    file_pi = open(model_filepath, 'wb') 
+    pickle.dump(qp, file_pi)
+
     # 6. Prepare experiments data for current iteration and data for view
     
     # here you can save whatever you need for you experiments

expe/utils/api.py

@@ -23,6 +23,7 @@ def get_image(scene, img_quality):
     # Make a get request to get information of scene image with quality of 200
     response = requests.get(url)
     # Print the content of the response formatted into JSON
+
     content_json = json.loads(response.content)
     
     # Access to link of image using 'key' (data & link) from json data

expe/views.py

@@ -74,6 +74,8 @@ def expe_list(request):
 
 def indications(request):
 
+    random.seed(10)
+
     # get param 
     expe_name = request.GET.get('expe')
     scene_name = request.GET.get('scene')

requirements.txt

@@ -5,4 +5,5 @@ questplus
 psychopy
 pandas
 requests
-docker-compose
+docker-compose
+ipfml