From 39aca51fa11f955fb0ea6249250cffef5cdcf744 Mon Sep 17 00:00:00 2001
From: JJ Aucouturier <aucouturier@gmail.com>
Date: Mon, 12 Feb 2024 19:35:57 +0100
Subject: [PATCH 01/17] added basic simulation classes

---
 python/palin/__init__.py                      |   2 +
 .../__pycache__/participant.cpython-38.pyc    | Bin 0 -> 1735 bytes
 .../__pycache__/simulation.cpython-38.pyc     | Bin 0 -> 4371 bytes
 python/palin/simulation/simulation.py         | 138 ++++++++++++++++++
 4 files changed, 140 insertions(+)
 create mode 100644 python/palin/simulation/__pycache__/participant.cpython-38.pyc
 create mode 100644 python/palin/simulation/__pycache__/simulation.cpython-38.pyc
 create mode 100644 python/palin/simulation/simulation.py

diff --git a/python/palin/__init__.py b/python/palin/__init__.py
index e24ccb2..206413c 100644
--- a/python/palin/__init__.py
+++ b/python/palin/__init__.py
@@ -3,3 +3,5 @@
 #from palin.utils import *
 #from palin.kernels import *
 #from palin.internal_noise import *
+
+from palin.simulation import simulation
diff --git a/python/palin/simulation/__pycache__/participant.cpython-38.pyc b/python/palin/simulation/__pycache__/participant.cpython-38.pyc
new file mode 100644
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diff --git a/python/palin/simulation/simulation.py b/python/palin/simulation/simulation.py
new file mode 100644
index 0000000..268d5cc
--- /dev/null
+++ b/python/palin/simulation/simulation.py
@@ -0,0 +1,138 @@
+#!/usr/bin/env python
+'''
+PALIN toolbox v0.1
+sep 2022, Aynaz Adl Zarrabi, JJ Aucouturier (CNRS/UBFC)
+
+Class to simulate participant in revcor experiment simulations
+'''
+
+import numpy as np
+import pandas as pd
+
+class Participant:
+    def __init__(self, kernel,internal_noise_std,criteria):
+        self.kernel = kernel
+        self.criteria = criteria
+        self.internal_noise_std = internal_noise_std
+
+    @classmethod
+    def with_random_kernel(cls, n_features, internal_noise_std,criteria):
+        return cls(np.random.uniform(-1,1,n_features),internal_noise_std,criteria)
+
+    def respond_to_stim(self, stim):
+        return np.dot(stim, self.kernel)
+    
+    def generate_internal_noise(self, external_noise_std): 
+        return np.random.normal(loc=0, scale=self.internal_noise_std)*external_noise_std
+        
+    def respond_to_trial(self, trial, external_noise_std): 
+
+        activity = self.respond_to_stim(trial.stims[0])
+        if trial.type == '2afc': 
+            activity -= self.respond_to_stim(trial.stims[1])
+        internal_noise = self.generate_internal_noise(external_noise_std)
+        response = 0 if (activity + internal_noise >= (self.criteria*external_noise_std)) else 1
+        return response
+
+    def respond_to_experiment(self,experiment):         
+        responses = []
+        for trial in experiment.trials: 
+            responses.append(self.respond_to_trial(trial, experiment.external_noise_std))
+        return responses
+
+class Trial: 
+    TYPES = ['1afc','2afc']
+    def __init__(self,stims):
+        self.stims = stims
+        self.type = Trial.TYPES[len(stims)-1]
+
+class Experiment: 
+    def __init__(self, n_trials, exp_type, n_features, external_noise_std): 
+        self.n_trials = n_trials
+        self.type = exp_type
+        self.n_features = n_features
+        self.external_noise_std = external_noise_std   
+
+    def create_stim_noise(self):
+        stim_noise = np.random.normal(loc=0, scale=self.external_noise_std, size=self.n_features)
+        return range(self.n_features),stim_noise
+
+    def generate_trials(self):
+        self.trials = []
+        for trial_number in range(self.n_trials):
+            stims = []
+            num_stim = (self.type == '2afc') + 1
+            for stim_order in range(num_stim):
+                stim_feature, stim_values = self.create_stim_noise()
+                stims.append(list(stim_values))
+            self.trials.append(Trial(stims))
+
+
+class Analyzer: 
+
+    
+    def set_kernel_method(self,kernel_method):
+        self.kernel_method = kernel_method
+
+    #self.noise_method = noise_method
+
+    @classmethod
+    def to_df(csl, experiment, responses): 
+
+        trial_ids = []
+        stim_orders = []
+        features = []
+        values = []
+        resps  = []
+    
+        for num_trial, trial in enumerate(experiment.trials):
+            response = responses[num_trial]
+            for num_stim, stim in enumerate(trial.stims): 
+                for num_feature, value in enumerate(stim):
+                    trial_ids.append(num_trial)
+                    stim_orders.append(num_stim)
+                    features.append(num_feature)
+                    values.append(value)
+                    resps.append(True if response == num_stim else False)
+    
+        return pd.DataFrame.from_dict({'trial_id': trial_ids,
+                                   'stim_order': stim_orders,
+                                   'feature': features,
+                                   'value': values, 
+                                   'response': resps})
+
+    def estimate_kernel(self, experiment, responses): 
+
+        if self.kernel_method == None: 
+            print('no kernel method',  file=sys.stderr)
+
+        responses_df = Analyzer.to_df(experiment, responses)
+
+        kernels_df = self.kernel_method(responses_df,
+            trial_ids=[],
+            dimension_ids=['feature'],
+            response_id='response', 
+            value_id='value',
+            normalize=True)
+
+        return list(kernels_df.kernel_value)
+
+
+
+
+
+
+
+
+        #response_df = trials_df.groupby('trial_id').apply(
+            #    lambda group: self.respond_to_trial(new Trial(type='2afc',
+            #                                                  stims=[group[group['stim_order'] == 0]['value'].values,
+            #                                                         group[group['stim_order'] == 1]['value'].values]),
+            #                                        experiment.external_noise_std))
+            # call this "response"
+            #response_df = response_df.reset_index().rename({0:'response'}, axis=1)
+            # merge with trial_data
+            #response_df = response_df.merge(trials_df, on='trial_id')
+            # and convert to True/false for the two stimuli of each trial
+            #response_df['response'] = response_df.apply(lambda row: True if row['response'] == row['stim_order'] else False, axis=1)
+        

From 32006725fd9866d2f7e6060a38c7d81d5b81b01b Mon Sep 17 00:00:00 2001
From: JJ Aucouturier <aucouturier@gmail.com>
Date: Mon, 12 Feb 2024 19:37:35 +0100
Subject: [PATCH 02/17] gitignore pycache

---
 python/palin/.gitignore                       | 1 +
 python/palin/internal_noise/.gitignore        | 1 +
 python/palin/kernels/.gitignore               | 1 +
 python/palin/kernels/classification_images.py | 6 +++---
 python/palin/simulation/.gitignore            | 1 +
 python/palin/utils/.gitignore                 | 1 +
 6 files changed, 8 insertions(+), 3 deletions(-)
 create mode 100644 python/palin/.gitignore
 create mode 100644 python/palin/internal_noise/.gitignore
 create mode 100644 python/palin/kernels/.gitignore
 create mode 100644 python/palin/simulation/.gitignore
 create mode 100644 python/palin/utils/.gitignore

diff --git a/python/palin/.gitignore b/python/palin/.gitignore
new file mode 100644
index 0000000..ed8ebf5
--- /dev/null
+++ b/python/palin/.gitignore
@@ -0,0 +1 @@
+__pycache__
\ No newline at end of file
diff --git a/python/palin/internal_noise/.gitignore b/python/palin/internal_noise/.gitignore
new file mode 100644
index 0000000..ed8ebf5
--- /dev/null
+++ b/python/palin/internal_noise/.gitignore
@@ -0,0 +1 @@
+__pycache__
\ No newline at end of file
diff --git a/python/palin/kernels/.gitignore b/python/palin/kernels/.gitignore
new file mode 100644
index 0000000..ed8ebf5
--- /dev/null
+++ b/python/palin/kernels/.gitignore
@@ -0,0 +1 @@
+__pycache__
\ No newline at end of file
diff --git a/python/palin/kernels/classification_images.py b/python/palin/kernels/classification_images.py
index 22708b3..fe0d792 100644
--- a/python/palin/kernels/classification_images.py
+++ b/python/palin/kernels/classification_images.py
@@ -9,15 +9,15 @@
 import pandas as pd
 import numpy as np
 
-def compute_kernel(data_df,trial_ids=['experimentor','type','subject','session'], dimension_ids=['segment'],response_id='response', value_id='pitch', normalize=True):
+def compute_kernel(data_df, trial_ids=['experimentor','type','subject','session'], dimension_ids=['segment'],response_id='response', value_id='pitch', normalize=True):
 	''' computes first-order temporal kernels for each participant using the classification image, ie. 
 	mean(stimulus features classified as positive) - mean(stimulus features classified as negative)'''
 
 	# for each participant, average stimulus features (e.g. mean pitch for each segment) separately for positive and negative responses, and subtract positives - negatives 
-	dimension_mean_value = data_df.groupby(trial_ids+[dimension_ids]+[response_id])[value_id].mean().reset_index()
+	dimension_mean_value = data_df.groupby(trial_ids+dimension_ids+[response_id])[value_id].mean().reset_index()
 	positives = dimension_mean_value.loc[dimension_mean_value[response_id] == True].reset_index()
 	negatives = dimension_mean_value.loc[dimension_mean_value[response_id] == False].reset_index()
-	kernels = pd.merge(positives, negatives, on=trial_ids+[dimension_ids])
+	kernels = pd.merge(positives, negatives, on=trial_ids+dimension_ids)
 	kernels['kernel_value'] = kernels['%s_x'%value_id] - kernels['%s_y'%value_id]
 
 	if(normalize):
diff --git a/python/palin/simulation/.gitignore b/python/palin/simulation/.gitignore
new file mode 100644
index 0000000..ed8ebf5
--- /dev/null
+++ b/python/palin/simulation/.gitignore
@@ -0,0 +1 @@
+__pycache__
\ No newline at end of file
diff --git a/python/palin/utils/.gitignore b/python/palin/utils/.gitignore
new file mode 100644
index 0000000..ed8ebf5
--- /dev/null
+++ b/python/palin/utils/.gitignore
@@ -0,0 +1 @@
+__pycache__
\ No newline at end of file

From 66ff6d921832c2cbeebbef75865244372c2d9cce Mon Sep 17 00:00:00 2001
From: JJ Aucouturier <aucouturier@gmail.com>
Date: Tue, 13 Feb 2024 09:23:08 +0100
Subject: [PATCH 03/17] added basic simulation api

---
 python/palin/simulation/simulation.py | 43 +++++++--------------------
 1 file changed, 11 insertions(+), 32 deletions(-)

diff --git a/python/palin/simulation/simulation.py b/python/palin/simulation/simulation.py
index 268d5cc..e0338c7 100644
--- a/python/palin/simulation/simulation.py
+++ b/python/palin/simulation/simulation.py
@@ -68,13 +68,10 @@ def generate_trials(self):
             self.trials.append(Trial(stims))
 
 
-class Analyzer: 
+class Analyser: 
 
-    
-    def set_kernel_method(self,kernel_method):
-        self.kernel_method = kernel_method
-
-    #self.noise_method = noise_method
+    def set_kernel_analyser(self,kernel_analyser):
+        self.kernel_analyser = kernel_analyser
 
     @classmethod
     def to_df(csl, experiment, responses): 
@@ -101,38 +98,20 @@ def to_df(csl, experiment, responses):
                                    'value': values, 
                                    'response': resps})
 
-    def estimate_kernel(self, experiment, responses): 
+    def estimate_kernel(self, experiment, participant_responses, normalize=True): 
 
-        if self.kernel_method == None: 
+        if self.kernel_analyser == None: 
             print('no kernel method',  file=sys.stderr)
 
-        responses_df = Analyzer.to_df(experiment, responses)
-
-        kernels_df = self.kernel_method(responses_df,
-            trial_ids=[],
-            dimension_ids=['feature'],
-            response_id='response', 
-            value_id='value',
-            normalize=True)
-
-        return list(kernels_df.kernel_value)
+        responses_df = Analyser.to_df(experiment, participant_responses)
 
+        kernel_df = self.kernel_analyser.extract_single_kernel(data_df = responses_df,
+        feature_id = 'feature', value_id = 'value', response_id = 'response')
 
+        if normalize: 
+            kernel_df = self.kernel_analyser.normalize_kernel(kernel_df)
 
+        return list(kernel_df.kernel_value)
 
 
 
-
-
-        #response_df = trials_df.groupby('trial_id').apply(
-            #    lambda group: self.respond_to_trial(new Trial(type='2afc',
-            #                                                  stims=[group[group['stim_order'] == 0]['value'].values,
-            #                                                         group[group['stim_order'] == 1]['value'].values]),
-            #                                        experiment.external_noise_std))
-            # call this "response"
-            #response_df = response_df.reset_index().rename({0:'response'}, axis=1)
-            # merge with trial_data
-            #response_df = response_df.merge(trials_df, on='trial_id')
-            # and convert to True/false for the two stimuli of each trial
-            #response_df['response'] = response_df.apply(lambda row: True if row['response'] == row['stim_order'] else False, axis=1)
-        

From 8127e0f96948f2f75fa09b16c135d44c37ac120f Mon Sep 17 00:00:00 2001
From: JJ Aucouturier <aucouturier@gmail.com>
Date: Tue, 13 Feb 2024 09:23:57 +0100
Subject: [PATCH 04/17] changed kernel api: object oriented with abstract class
 KernelAnalyser

---
 python/palin/__init__.py                      |   1 +
 python/palin/kernels/classification_images.py | 107 ++++++++----------
 python/palin/kernels/kernels.py               |  37 ++++++
 .../__pycache__/simulation.cpython-38.pyc     | Bin 4371 -> 4453 bytes
 4 files changed, 85 insertions(+), 60 deletions(-)
 create mode 100644 python/palin/kernels/kernels.py

diff --git a/python/palin/__init__.py b/python/palin/__init__.py
index 206413c..2257617 100644
--- a/python/palin/__init__.py
+++ b/python/palin/__init__.py
@@ -5,3 +5,4 @@
 #from palin.internal_noise import *
 
 from palin.simulation import simulation
+from palin.kernels import classification_images
diff --git a/python/palin/kernels/classification_images.py b/python/palin/kernels/classification_images.py
index fe0d792..de5818f 100644
--- a/python/palin/kernels/classification_images.py
+++ b/python/palin/kernels/classification_images.py
@@ -8,66 +8,53 @@
 
 import pandas as pd
 import numpy as np
-
-def compute_kernel(data_df, trial_ids=['experimentor','type','subject','session'], dimension_ids=['segment'],response_id='response', value_id='pitch', normalize=True):
-	''' computes first-order temporal kernels for each participant using the classification image, ie. 
-	mean(stimulus features classified as positive) - mean(stimulus features classified as negative)'''
-
-	# for each participant, average stimulus features (e.g. mean pitch for each segment) separately for positive and negative responses, and subtract positives - negatives 
-	dimension_mean_value = data_df.groupby(trial_ids+dimension_ids+[response_id])[value_id].mean().reset_index()
-	positives = dimension_mean_value.loc[dimension_mean_value[response_id] == True].reset_index()
-	negatives = dimension_mean_value.loc[dimension_mean_value[response_id] == False].reset_index()
-	kernels = pd.merge(positives, negatives, on=trial_ids+dimension_ids)
-	kernels['kernel_value'] = kernels['%s_x'%value_id] - kernels['%s_y'%value_id]
-
-	if(normalize):
-		# Kernel are then normalized for each participant/session by dividing them 
-		# by the square root of the sum of their squared values.
-		kernels['square_value'] = kernels['kernel_value']**2
-		if trial_ids: 
-			for_norm = kernels.groupby(trial_ids)['square_value'].mean().reset_index()
-			kernels = pd.merge(kernels, for_norm, on=trial_ids, suffixes=('', '_mean'))
-		else:
-			kernels['square_value_mean'] = kernels.square_value.mean()		 
-
-		kernels['kernel_value'] = kernels['kernel_value']/np.sqrt(kernels['square_value_mean'])
-		kernels.drop(columns=['square_value', 'square_value_mean'], inplace=True)
-		
-	kernels.drop(columns=['index_x','%s_x'%response_id,'%s_x'%value_id,'index_y','%s_y'%response_id,'%s_y'%value_id], inplace=True)
-
-	return kernels
-
-def compute_accuracy(data_df, control_kernel, session_identifiers = ['experimentor','type','subject','session'], trial_identifier = 'trial', stimulus_dimension='segment', stimulus_value = 'pitch', stimulus_response='response'): 
-	''' Computes participant's accuracy on the task as a measure of how well the participant performs 
-	compared to an ideal participant model having the control group as an internal representation and zero internal noise.
-	Accuracy therefore combines both internal representation and noise in a single measure.''' 
-
-	# for each participant, in each trial, compute the dot product of each stimulus with the control group kernel
-	
-	# create a df of positive and negative trial data for each participant
-	positives = data_df.loc[data_df[stimulus_response] == 1].reset_index()[session_identifiers + [trial_identifier,stimulus_dimension,stimulus_value]]
-	negatives = data_df.loc[data_df[stimulus_response] == 0].reset_index()[session_identifiers + [trial_identifier,stimulus_dimension,stimulus_value]]
-
-	trial_data = positives.merge(negatives, 
-                      on=session_identifiers + [trial_identifier,stimulus_dimension],
-                     suffixes=('_pos','_neg'))
-
-	# dot product of each positive and negative trial with the control group's kernel
-	def dot_control(x):
-		#print(list(x))
-		#print(control_kernel)
-		return np.dot(list(x), control_kernel)
-
-	trial_data = trial_data.groupby(session_identifiers+[trial_identifier]).agg({'%s_pos'%stimulus_value:dot_control,
-																										'%s_neg'%stimulus_value:dot_control}).reset_index()
-
-	# count hits as trials for which the positive stimuli is the one with higher dot product to control kernel
-	trial_data['hit'] = trial_data['%s_pos'%stimulus_value] > trial_data['%s_neg'%stimulus_value] 
-
-	# compute hit rate (average hit across trials) per participant
-	hit_rate = trial_data.groupby(session_identifiers, as_index=False).hit.mean()
-
-	return hit_rate
+from .kernels import KernelAnalyser
+
+class ClassificationImage(KernelAnalyser):
+
+    @classmethod
+    def extract_single_kernel(cls, data_df, feature_id = 'feature', value_id = 'value', response_id = 'response'):
+        feature_average = data_df.groupby([feature_id,response_id])[value_id].mean().reset_index()
+        positives = feature_average.loc[feature_average[response_id] == True].reset_index()
+        negatives = feature_average.loc[feature_average[response_id] == False].reset_index()
+        kernels = pd.merge(positives, negatives, on=feature_id, suffixes=('_true','_false'))
+        kernels['kernel_value'] = kernels['%s_true'%value_id] - kernels['%s_false'%value_id]
+        kernels = kernels[[feature_id,'kernel_value']].set_index(feature_id)
+        #kernels.index.names = ['feature']
+        return kernels
+
+        
+# def compute_accuracy(data_df, control_kernel, session_identifiers = ['experimentor','type','subject','session'], trial_identifier = 'trial', stimulus_dimension='segment', stimulus_value = 'pitch', stimulus_response='response'): 
+#   ''' Computes participant's accuracy on the task as a measure of how well the participant performs 
+#   compared to an ideal participant model having the control group as an internal representation and zero internal noise.
+#   Accuracy therefore combines both internal representation and noise in a single measure.''' 
+
+#   # for each participant, in each trial, compute the dot product of each stimulus with the control group kernel
+    
+#   # create a df of positive and negative trial data for each participant
+#   positives = data_df.loc[data_df[stimulus_response] == 1].reset_index()[session_identifiers + [trial_identifier,stimulus_dimension,stimulus_value]]
+#   negatives = data_df.loc[data_df[stimulus_response] == 0].reset_index()[session_identifiers + [trial_identifier,stimulus_dimension,stimulus_value]]
+
+#   trial_data = positives.merge(negatives, 
+#                       on=session_identifiers + [trial_identifier,stimulus_dimension],
+#                      suffixes=('_pos','_neg'))
+
+#   # dot product of each positive and negative trial with the control group's kernel
+#   def dot_control(x):
+#       #print(list(x))
+#       #print(control_kernel)
+#       return np.dot(list(x), control_kernel)
+
+#   trial_data = trial_data.groupby(session_identifiers+[trial_identifier]).agg({'%s_pos'%stimulus_value:dot_control,
+#                                                                                                       '%s_neg'%stimulus_value:dot_control}).reset_index()
+
+#   # count hits as trials for which the positive stimuli is the one with higher dot product to control kernel
+#   trial_data['hit'] = trial_data['%s_pos'%stimulus_value] > trial_data['%s_neg'%stimulus_value] 
+
+#   # compute hit rate (average hit across trials) per participant
+#   hit_rate = trial_data.groupby(session_identifiers, as_index=False).hit.mean()
+
+#   return hit_rate
 
 
 
diff --git a/python/palin/kernels/kernels.py b/python/palin/kernels/kernels.py
new file mode 100644
index 0000000..749f64f
--- /dev/null
+++ b/python/palin/kernels/kernels.py
@@ -0,0 +1,37 @@
+#!/usr/bin/env python
+'''
+PALIN toolbox v0.1
+Decemberr 2022, Aynaz Adl Zarrabi, JJ Aucouturier (CNRS/UBFC)
+
+Functions for kernel calculating method in Classification images
+'''
+
+import pandas as pd
+import numpy as np
+from abc import ABC, abstractmethod
+
+class KernelAnalyser(ABC):
+
+    @classmethod
+    @abstractmethod
+    def extract_single_kernel(cls,data_df, feature_id = 'feature', value_id = 'value', response_id = 'response'): 
+        raise NotImplementedError()
+
+    @classmethod
+    def extract_kernels(cls,data_df, group_ids, feature_id, value_id, response_id, normalize = True):
+        
+        # for each level in group, compute kernels
+        return data_df.groupby(group_ids).apply(lambda group: cls.normalize_kernel(cls.extract_single_kernel(group, feature_id, value_id, response_id),normalize)).reset_index()
+
+    @classmethod    
+    def normalize_kernel(cls,kernel, normalize=True):
+        if normalize: 
+            if isinstance(kernel,pd.DataFrame):
+                rms = np.sqrt((kernel.kernel_value**2).mean())
+                kernel.kernel_value /= rms
+            elif isinstance(kernel,(np.ndarray, list)):
+                rms = np.sqrt(np.mean(np.power(kernel,2)))
+                kernel = kernel/rms
+            else: 
+                raise TypeError('argument kernel is neither a pd.DataFrame or a np.ndarray')
+        return kernel
\ No newline at end of file
diff --git a/python/palin/simulation/__pycache__/simulation.cpython-38.pyc b/python/palin/simulation/__pycache__/simulation.cpython-38.pyc
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From b082b8d5bee7ea8161468ee8b47d2be43dacf33f Mon Sep 17 00:00:00 2001
From: JJ Aucouturier <aucouturier@gmail.com>
Date: Tue, 13 Feb 2024 09:32:25 +0100
Subject: [PATCH 05/17] added kernel normalizer to simulation

---
 .../__pycache__/simulation.cpython-38.pyc     | Bin 4453 -> 4866 bytes
 python/palin/simulation/simulation.py         |  17 ++++++++++++++++-
 2 files changed, 16 insertions(+), 1 deletion(-)

diff --git a/python/palin/simulation/__pycache__/simulation.cpython-38.pyc b/python/palin/simulation/__pycache__/simulation.cpython-38.pyc
index 03f2384ecaabeb75b3cd43420f509a0bccfec214..63150e0347d4400da2ef83d19d9b5ba479525fa9 100644
GIT binary patch
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diff --git a/python/palin/simulation/simulation.py b/python/palin/simulation/simulation.py
index e0338c7..c130e46 100644
--- a/python/palin/simulation/simulation.py
+++ b/python/palin/simulation/simulation.py
@@ -8,6 +8,7 @@
 
 import numpy as np
 import pandas as pd
+from ..kernels.kernels import KernelAnalyser
 
 class Participant:
     def __init__(self, kernel,internal_noise_std,criteria):
@@ -70,6 +71,9 @@ def generate_trials(self):
 
 class Analyser: 
 
+    def __init__(self):
+        self.kernel_analyser = None
+
     def set_kernel_analyser(self,kernel_analyser):
         self.kernel_analyser = kernel_analyser
 
@@ -101,7 +105,7 @@ def to_df(csl, experiment, responses):
     def estimate_kernel(self, experiment, participant_responses, normalize=True): 
 
         if self.kernel_analyser == None: 
-            print('no kernel method',  file=sys.stderr)
+            print('no kernel analyser',  file=sys.stderr)
 
         responses_df = Analyser.to_df(experiment, participant_responses)
 
@@ -113,5 +117,16 @@ def estimate_kernel(self, experiment, participant_responses, normalize=True):
 
         return list(kernel_df.kernel_value)
 
+    def normalize_kernel(self, kernel): 
+
+        if self.kernel_analyser == None: 
+            print('no kernel method, defaulting to KernelAnalyser')
+            self.kernel_analyser = KernelAnalyser
+        return self.kernel_analyser.normalize_kernel(kernel)
+
+
+
+
+
 
 

From 37035ae4e6ee38913f3a17b956d74d8868a8d260 Mon Sep 17 00:00:00 2001
From: JJ Aucouturier <aucouturier@gmail.com>
Date: Tue, 13 Feb 2024 15:25:59 +0100
Subject: [PATCH 06/17] added metrics (kernel distance)

---
 python/palin/__init__.py                      |   1 +
 python/palin/kernels/classification_images.py |   2 +-
 python/palin/metrics/.gitignore               |   1 +
 python/palin/metrics/metrics.py               |  39 ++++++++++++++++++
 .../__pycache__/simulation.cpython-38.pyc     | Bin 4866 -> 4866 bytes
 5 files changed, 42 insertions(+), 1 deletion(-)
 create mode 100644 python/palin/metrics/.gitignore
 create mode 100644 python/palin/metrics/metrics.py

diff --git a/python/palin/__init__.py b/python/palin/__init__.py
index 2257617..a903b7d 100644
--- a/python/palin/__init__.py
+++ b/python/palin/__init__.py
@@ -6,3 +6,4 @@
 
 from palin.simulation import simulation
 from palin.kernels import classification_images
+from palin.metrics import metrics
diff --git a/python/palin/kernels/classification_images.py b/python/palin/kernels/classification_images.py
index de5818f..a1c5744 100644
--- a/python/palin/kernels/classification_images.py
+++ b/python/palin/kernels/classification_images.py
@@ -20,7 +20,7 @@ def extract_single_kernel(cls, data_df, feature_id = 'feature', value_id = 'valu
         kernels = pd.merge(positives, negatives, on=feature_id, suffixes=('_true','_false'))
         kernels['kernel_value'] = kernels['%s_true'%value_id] - kernels['%s_false'%value_id]
         kernels = kernels[[feature_id,'kernel_value']].set_index(feature_id)
-        #kernels.index.names = ['feature']
+        kernels.index.names = ['feature']
         return kernels
 
         
diff --git a/python/palin/metrics/.gitignore b/python/palin/metrics/.gitignore
new file mode 100644
index 0000000..ed8ebf5
--- /dev/null
+++ b/python/palin/metrics/.gitignore
@@ -0,0 +1 @@
+__pycache__
\ No newline at end of file
diff --git a/python/palin/metrics/metrics.py b/python/palin/metrics/metrics.py
new file mode 100644
index 0000000..0dace42
--- /dev/null
+++ b/python/palin/metrics/metrics.py
@@ -0,0 +1,39 @@
+import pandas as pd
+import numpy as np
+
+def kernel_distance(kernel_1, kernel_2, type='CORR'): 
+    if type == 'RMS': 
+        return kernel_rms(kernel_1, kernel_2)
+    elif type == 'CORR': 
+        return kernel_correlation(kernel_1, kernel_2)
+    else: 
+        raise AttributeError('metric type %s unknown'%s)
+
+
+def kernel_rms(kernel_1, kernel_2):
+    if isinstance(kernel_1,pd.DataFrame):
+
+        rms = np.sqrt(np.mean((kernel_1.kernel_value - kernel_2.kernel_value_2)**2))
+
+    elif isinstance(kernel_1,(np.ndarray, list)):
+        rms = np.sqrt(np.mean(np.power(kernel_1-kernel_2,2)))
+    
+    else: 
+        raise TypeError('argument kernel is neither a pd.DataFrame or a np.ndarray')
+    
+    return rms    
+
+def kernel_correlation(kernel_1, kernel_2): 
+
+    if isinstance(kernel_1,pd.DataFrame):
+
+        correlation = np.corrcoef(kernel_1.kernel_value, kernel_2.kernel_value)[0, 1]
+
+    elif isinstance(kernel_1,(np.ndarray, list)):
+
+        correlation = np.corrcoef(kernel_1, kernel_2)[0, 1]
+    
+    else: 
+        raise TypeError('argument kernel is neither a pd.DataFrame or a np.ndarray')
+    
+    return correlation    
diff --git a/python/palin/simulation/__pycache__/simulation.cpython-38.pyc b/python/palin/simulation/__pycache__/simulation.cpython-38.pyc
index 63150e0347d4400da2ef83d19d9b5ba479525fa9..6afccbf4d2f542cb01a026f3df5eb45687eb5ff4 100644
GIT binary patch
delta 26
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delta 26
gcmZotYf|G1<>lpK0D^TI8@Uz>GA3?bCm6yA08M%ZLI3~&


From 3b3c9482daa1cc89d4758ae6a13b036fd7e66e0e Mon Sep 17 00:00:00 2001
From: JJ Aucouturier <aucouturier@gmail.com>
Date: Wed, 14 Feb 2024 08:16:25 +0100
Subject: [PATCH 07/17] refactored simulation api with abstract classes

---
 python/palin/kernels/kernels.py               |  28 ++--
 python/palin/metrics/metrics.py               |  13 +-
 .../__pycache__/simulation.cpython-38.pyc     | Bin 4866 -> 3596 bytes
 python/palin/simulation/analyser.py           |  36 +++++
 python/palin/simulation/experiment.py         |   9 ++
 python/palin/simulation/kernel_analyser.py    |  36 +++++
 python/palin/simulation/linear_observer.py    |  28 ++++
 python/palin/simulation/observer.py           |  18 +++
 python/palin/simulation/simple_experiment.py  |  29 ++++
 python/palin/simulation/simulation.py         | 132 ------------------
 python/palin/simulation/trial.py              |  39 ++++++
 11 files changed, 218 insertions(+), 150 deletions(-)
 create mode 100644 python/palin/simulation/analyser.py
 create mode 100644 python/palin/simulation/experiment.py
 create mode 100644 python/palin/simulation/kernel_analyser.py
 create mode 100644 python/palin/simulation/linear_observer.py
 create mode 100644 python/palin/simulation/observer.py
 create mode 100644 python/palin/simulation/simple_experiment.py
 delete mode 100644 python/palin/simulation/simulation.py
 create mode 100644 python/palin/simulation/trial.py

diff --git a/python/palin/kernels/kernels.py b/python/palin/kernels/kernels.py
index 749f64f..c2b6514 100644
--- a/python/palin/kernels/kernels.py
+++ b/python/palin/kernels/kernels.py
@@ -21,17 +21,23 @@ def extract_single_kernel(cls,data_df, feature_id = 'feature', value_id = 'value
     def extract_kernels(cls,data_df, group_ids, feature_id, value_id, response_id, normalize = True):
         
         # for each level in group, compute kernels
-        return data_df.groupby(group_ids).apply(lambda group: cls.normalize_kernel(cls.extract_single_kernel(group, feature_id, value_id, response_id),normalize)).reset_index()
+
+        def extract_normalize(group): 
+            kernel = cls.extract_single_kernel(group, feature_id, value_id, response_id)
+            if normalize: 
+                kernel = cls.normalize_kernel(kernel)
+            return kernel
+
+        return data_df.groupby(group_ids).apply(lambda group: extract_normalize(group)).reset_index()
 
     @classmethod    
-    def normalize_kernel(cls,kernel, normalize=True):
-        if normalize: 
-            if isinstance(kernel,pd.DataFrame):
-                rms = np.sqrt((kernel.kernel_value**2).mean())
-                kernel.kernel_value /= rms
-            elif isinstance(kernel,(np.ndarray, list)):
-                rms = np.sqrt(np.mean(np.power(kernel,2)))
-                kernel = kernel/rms
-            else: 
-                raise TypeError('argument kernel is neither a pd.DataFrame or a np.ndarray')
+    def normalize_kernel(cls,kernel):
+        if isinstance(kernel,pd.DataFrame):
+            rms = np.sqrt((kernel.kernel_value**2).mean())
+            kernel.kernel_value /= rms
+        elif isinstance(kernel,(np.ndarray, list)):
+            rms = np.sqrt(np.mean(np.power(kernel,2)))
+            kernel = kernel/rms
+        else: 
+            raise TypeError('argument kernel is neither a pd.DataFrame or a np.ndarray')
         return kernel
\ No newline at end of file
diff --git a/python/palin/metrics/metrics.py b/python/palin/metrics/metrics.py
index 0dace42..8998615 100644
--- a/python/palin/metrics/metrics.py
+++ b/python/palin/metrics/metrics.py
@@ -11,29 +11,28 @@ def kernel_distance(kernel_1, kernel_2, type='CORR'):
 
 
 def kernel_rms(kernel_1, kernel_2):
-    if isinstance(kernel_1,pd.DataFrame):
-
+    if isinstance(kernel_1,pd.DataFrame) & isinstance(kernel_2,pd.DataFrame):
         rms = np.sqrt(np.mean((kernel_1.kernel_value - kernel_2.kernel_value_2)**2))
 
-    elif isinstance(kernel_1,(np.ndarray, list)):
+    elif isinstance(kernel_1,(np.ndarray, list)) & isinstance(kernel_2,(np.ndarray, list)):
         rms = np.sqrt(np.mean(np.power(kernel_1-kernel_2,2)))
     
     else: 
-        raise TypeError('argument kernel is neither a pd.DataFrame or a np.ndarray')
+        raise TypeError('argument kernels are neither both pd.DataFrames or np.ndarrays')
     
     return rms    
 
 def kernel_correlation(kernel_1, kernel_2): 
 
-    if isinstance(kernel_1,pd.DataFrame):
+    if isinstance(kernel_1,pd.DataFrame) & isinstance(kernel_2,pd.DataFrame):
 
         correlation = np.corrcoef(kernel_1.kernel_value, kernel_2.kernel_value)[0, 1]
 
-    elif isinstance(kernel_1,(np.ndarray, list)):
+    elif isinstance(kernel_1,(np.ndarray, list)) & isinstance(kernel_2,(np.ndarray, list)):
 
         correlation = np.corrcoef(kernel_1, kernel_2)[0, 1]
     
     else: 
-        raise TypeError('argument kernel is neither a pd.DataFrame or a np.ndarray')
+        raise TypeError('argument kernels are neither both pd.DataFrames or np.ndarrays')
     
     return correlation    
diff --git a/python/palin/simulation/__pycache__/simulation.cpython-38.pyc b/python/palin/simulation/__pycache__/simulation.cpython-38.pyc
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diff --git a/python/palin/simulation/analyser.py b/python/palin/simulation/analyser.py
new file mode 100644
index 0000000..f887e67
--- /dev/null
+++ b/python/palin/simulation/analyser.py
@@ -0,0 +1,36 @@
+from abc import ABC, abstractmethod
+
+import pandas as pd
+
+
+class Analyser(ABC): 
+
+    @abstractmethod
+    def analyse(self,experiment, participant, participant_responses): 
+        raise NotImplementedError()
+
+    @classmethod
+    def to_df(csl, experiment, responses): 
+
+        trial_ids = []
+        stim_orders = []
+        features = []
+        values = []
+        resps  = []
+    
+        for num_trial, trial in enumerate(experiment.trials):
+            response = responses[num_trial]
+            for num_stim, stim in enumerate(trial.stims): 
+                for num_feature, value in enumerate(stim):
+                    trial_ids.append(num_trial)
+                    stim_orders.append(num_stim)
+                    features.append(num_feature)
+                    values.append(value)
+                    resps.append(True if response == num_stim else False)
+    
+        return pd.DataFrame.from_dict({'trial_id': trial_ids,
+                                   'stim_order': stim_orders,
+                                   'feature': features,
+                                   'value': values, 
+                                   'response': resps})
+
diff --git a/python/palin/simulation/experiment.py b/python/palin/simulation/experiment.py
new file mode 100644
index 0000000..002db6f
--- /dev/null
+++ b/python/palin/simulation/experiment.py
@@ -0,0 +1,9 @@
+from abc import ABC, abstractmethod
+
+class Experiment(ABC): 
+
+    # possibly yield next_trial(), if we want more abstraction ?
+    
+    @abstractmethod
+    def generate_trials(self):
+        raise NotImplementedError()
\ No newline at end of file
diff --git a/python/palin/simulation/kernel_analyser.py b/python/palin/simulation/kernel_analyser.py
new file mode 100644
index 0000000..b1b3c65
--- /dev/null
+++ b/python/palin/simulation/kernel_analyser.py
@@ -0,0 +1,36 @@
+
+from .analyser import Analyser
+from palin.metrics import metrics as me
+
+class KernelAnalyser(Analyser): 
+
+    def __init__(self, kernel_analyser):
+        self.kernel_analyser = kernel_analyser
+        
+    def analyse(self, experiment, participant, participant_responses): 
+
+        true_kernel = self.kernel_analyser.normalize_kernel(participant.kernel)
+
+        estimated_kernel = self.estimate_kernel(experiment, participant_responses)
+
+        return self.kernel_correlation(estimated_kernel, true_kernel)
+
+    def estimate_kernel(self, experiment, participant_responses, normalize=True): 
+
+        responses_df = self.to_df(experiment, participant_responses)
+
+        kernel_df = self.kernel_analyser.extract_single_kernel(data_df = responses_df,
+        feature_id = 'feature', value_id = 'value', response_id = 'response')
+
+        if normalize: 
+            kernel_df = self.kernel_analyser.normalize_kernel(kernel_df)
+
+        return list(kernel_df.kernel_value)
+
+    def normalize_kernel(self, kernel): 
+
+        return self.kernel_analyser.normalize_kernel(kernel)
+
+    def kernel_correlation(self, kernel_1, kernel_2): 
+        
+        return me.kernel_distance(kernel_1, kernel_2, type='CORR')
diff --git a/python/palin/simulation/linear_observer.py b/python/palin/simulation/linear_observer.py
new file mode 100644
index 0000000..a89cf2f
--- /dev/null
+++ b/python/palin/simulation/linear_observer.py
@@ -0,0 +1,28 @@
+
+import numpy as np
+from .observer import Observer
+
+class LinearObserver(Observer):
+
+    def __init__(self, kernel,internal_noise_std,criteria):
+        self.kernel = kernel
+        self.criteria = criteria
+        self.internal_noise_std = internal_noise_std
+
+    @classmethod
+    def with_random_kernel(cls, n_features, internal_noise_std,criteria):
+        return cls(np.random.uniform(-1,1,n_features),internal_noise_std,criteria)
+
+    def respond_to_stim(self, stim):
+        return np.dot(stim, self.kernel)
+    
+    def generate_internal_noise(self, external_noise_std): 
+        return np.random.normal(loc=0, scale=self.internal_noise_std)*external_noise_std
+        
+    def respond_to_trial(self, trial, experiment): 
+
+        trial_activity = trial.activate(self)
+        internal_noise = self.generate_internal_noise(experiment.external_noise_std)
+        response = 0 if (trial_activity + internal_noise >= (self.criteria*experiment.external_noise_std)) else 1
+        return response
+
diff --git a/python/palin/simulation/observer.py b/python/palin/simulation/observer.py
new file mode 100644
index 0000000..5c51d6e
--- /dev/null
+++ b/python/palin/simulation/observer.py
@@ -0,0 +1,18 @@
+from abc import ABC, abstractmethod
+
+
+class Observer(ABC):
+
+    @abstractmethod
+    def respond_to_stim(self, stim):
+        raise NotImplementedError()
+    
+    @abstractmethod
+    def respond_to_trial(self, trial, experiment): 
+        raise NotImplementedError()
+
+    def respond_to_experiment(self,experiment): 
+        responses = []
+        for trial in experiment.trials: 
+            responses.append(self.respond_to_trial(trial, experiment))
+        return responses       
diff --git a/python/palin/simulation/simple_experiment.py b/python/palin/simulation/simple_experiment.py
new file mode 100644
index 0000000..a5a702b
--- /dev/null
+++ b/python/palin/simulation/simple_experiment.py
@@ -0,0 +1,29 @@
+import numpy as np
+
+from .experiment import Experiment
+
+class SimpleExperiment(Experiment): 
+
+    def __init__(self, n_trials, trial_type, n_features, external_noise_std): 
+        # init parameters
+        self.n_trials = n_trials
+        self.trial_type = trial_type
+        self.n_features = n_features
+        self.external_noise_std = external_noise_std  
+        self.trials = None
+        # generate trials
+        self.generate_trials() 
+
+    def create_stim_noise(self):
+        stim_noise = np.random.normal(loc=0, scale=self.external_noise_std, size=self.n_features)
+        return range(self.n_features),stim_noise
+
+    def generate_trials(self):
+        self.trials = []
+        for trial_number in range(self.n_trials):
+            stims = []
+            num_stim = self.trial_type.n_stims()
+            for stim_order in range(num_stim):
+                stim_feature, stim_values = self.create_stim_noise()
+                stims.append(list(stim_values))
+            self.trials.append(self.trial_type(stims))
\ No newline at end of file
diff --git a/python/palin/simulation/simulation.py b/python/palin/simulation/simulation.py
deleted file mode 100644
index c130e46..0000000
--- a/python/palin/simulation/simulation.py
+++ /dev/null
@@ -1,132 +0,0 @@
-#!/usr/bin/env python
-'''
-PALIN toolbox v0.1
-sep 2022, Aynaz Adl Zarrabi, JJ Aucouturier (CNRS/UBFC)
-
-Class to simulate participant in revcor experiment simulations
-'''
-
-import numpy as np
-import pandas as pd
-from ..kernels.kernels import KernelAnalyser
-
-class Participant:
-    def __init__(self, kernel,internal_noise_std,criteria):
-        self.kernel = kernel
-        self.criteria = criteria
-        self.internal_noise_std = internal_noise_std
-
-    @classmethod
-    def with_random_kernel(cls, n_features, internal_noise_std,criteria):
-        return cls(np.random.uniform(-1,1,n_features),internal_noise_std,criteria)
-
-    def respond_to_stim(self, stim):
-        return np.dot(stim, self.kernel)
-    
-    def generate_internal_noise(self, external_noise_std): 
-        return np.random.normal(loc=0, scale=self.internal_noise_std)*external_noise_std
-        
-    def respond_to_trial(self, trial, external_noise_std): 
-
-        activity = self.respond_to_stim(trial.stims[0])
-        if trial.type == '2afc': 
-            activity -= self.respond_to_stim(trial.stims[1])
-        internal_noise = self.generate_internal_noise(external_noise_std)
-        response = 0 if (activity + internal_noise >= (self.criteria*external_noise_std)) else 1
-        return response
-
-    def respond_to_experiment(self,experiment):         
-        responses = []
-        for trial in experiment.trials: 
-            responses.append(self.respond_to_trial(trial, experiment.external_noise_std))
-        return responses
-
-class Trial: 
-    TYPES = ['1afc','2afc']
-    def __init__(self,stims):
-        self.stims = stims
-        self.type = Trial.TYPES[len(stims)-1]
-
-class Experiment: 
-    def __init__(self, n_trials, exp_type, n_features, external_noise_std): 
-        self.n_trials = n_trials
-        self.type = exp_type
-        self.n_features = n_features
-        self.external_noise_std = external_noise_std   
-
-    def create_stim_noise(self):
-        stim_noise = np.random.normal(loc=0, scale=self.external_noise_std, size=self.n_features)
-        return range(self.n_features),stim_noise
-
-    def generate_trials(self):
-        self.trials = []
-        for trial_number in range(self.n_trials):
-            stims = []
-            num_stim = (self.type == '2afc') + 1
-            for stim_order in range(num_stim):
-                stim_feature, stim_values = self.create_stim_noise()
-                stims.append(list(stim_values))
-            self.trials.append(Trial(stims))
-
-
-class Analyser: 
-
-    def __init__(self):
-        self.kernel_analyser = None
-
-    def set_kernel_analyser(self,kernel_analyser):
-        self.kernel_analyser = kernel_analyser
-
-    @classmethod
-    def to_df(csl, experiment, responses): 
-
-        trial_ids = []
-        stim_orders = []
-        features = []
-        values = []
-        resps  = []
-    
-        for num_trial, trial in enumerate(experiment.trials):
-            response = responses[num_trial]
-            for num_stim, stim in enumerate(trial.stims): 
-                for num_feature, value in enumerate(stim):
-                    trial_ids.append(num_trial)
-                    stim_orders.append(num_stim)
-                    features.append(num_feature)
-                    values.append(value)
-                    resps.append(True if response == num_stim else False)
-    
-        return pd.DataFrame.from_dict({'trial_id': trial_ids,
-                                   'stim_order': stim_orders,
-                                   'feature': features,
-                                   'value': values, 
-                                   'response': resps})
-
-    def estimate_kernel(self, experiment, participant_responses, normalize=True): 
-
-        if self.kernel_analyser == None: 
-            print('no kernel analyser',  file=sys.stderr)
-
-        responses_df = Analyser.to_df(experiment, participant_responses)
-
-        kernel_df = self.kernel_analyser.extract_single_kernel(data_df = responses_df,
-        feature_id = 'feature', value_id = 'value', response_id = 'response')
-
-        if normalize: 
-            kernel_df = self.kernel_analyser.normalize_kernel(kernel_df)
-
-        return list(kernel_df.kernel_value)
-
-    def normalize_kernel(self, kernel): 
-
-        if self.kernel_analyser == None: 
-            print('no kernel method, defaulting to KernelAnalyser')
-            self.kernel_analyser = KernelAnalyser
-        return self.kernel_analyser.normalize_kernel(kernel)
-
-
-
-
-
-
-
diff --git a/python/palin/simulation/trial.py b/python/palin/simulation/trial.py
new file mode 100644
index 0000000..1064d52
--- /dev/null
+++ b/python/palin/simulation/trial.py
@@ -0,0 +1,39 @@
+from abc import ABC, abstractmethod
+
+class Trial(ABC):
+
+    @abstractmethod
+    def activate(self,participant): 
+        raise NotImplementedError()
+    
+    @classmethod
+    @abstractmethod
+    def n_stims(self):
+        raise NotImplementedError()
+
+class Int1Trial(Trial): 
+
+    def __init__(self,stims):
+        self.stims = stims
+
+    def activate(self,obs): 
+        return obs.respond_to_stim(self.stims[0])
+
+    @classmethod
+    def n_stims(self):
+        return 1
+
+class Int2Trial(Trial): 
+
+    def __init__(self,stims):
+        self.stims = stims
+
+    def activate(self,obs): 
+        return obs.respond_to_stim(self.stims[0]) - obs.respond_to_stim(self.stims[1])
+
+    @classmethod
+    def n_stims(self):
+        return 2
+
+        
+

From f20392107a009ebe082e5bf1f4c6f7f892e93990 Mon Sep 17 00:00:00 2001
From: JJ Aucouturier <aucouturier@gmail.com>
Date: Wed, 14 Feb 2024 15:38:54 +0100
Subject: [PATCH 08/17] changed module name kernel analyser + created
 unimpleted LMAnalyser

---
 python/palin/__init__.py                      |  1 -
 python/palin/kernels/classification_images.py |  2 +-
 python/palin/kernels/kernel_analyser.py       | 43 +++++++++++++++++++
 python/palin/kernels/lm_analyser.py           | 21 +++++++++
 4 files changed, 65 insertions(+), 2 deletions(-)
 create mode 100644 python/palin/kernels/kernel_analyser.py
 create mode 100644 python/palin/kernels/lm_analyser.py

diff --git a/python/palin/__init__.py b/python/palin/__init__.py
index a903b7d..ac92014 100644
--- a/python/palin/__init__.py
+++ b/python/palin/__init__.py
@@ -4,6 +4,5 @@
 #from palin.kernels import *
 #from palin.internal_noise import *
 
-from palin.simulation import simulation
 from palin.kernels import classification_images
 from palin.metrics import metrics
diff --git a/python/palin/kernels/classification_images.py b/python/palin/kernels/classification_images.py
index a1c5744..012e1c7 100644
--- a/python/palin/kernels/classification_images.py
+++ b/python/palin/kernels/classification_images.py
@@ -8,7 +8,7 @@
 
 import pandas as pd
 import numpy as np
-from .kernels import KernelAnalyser
+from .kernel_analyser import KernelAnalyser
 
 class ClassificationImage(KernelAnalyser):
 
diff --git a/python/palin/kernels/kernel_analyser.py b/python/palin/kernels/kernel_analyser.py
new file mode 100644
index 0000000..c2b6514
--- /dev/null
+++ b/python/palin/kernels/kernel_analyser.py
@@ -0,0 +1,43 @@
+#!/usr/bin/env python
+'''
+PALIN toolbox v0.1
+Decemberr 2022, Aynaz Adl Zarrabi, JJ Aucouturier (CNRS/UBFC)
+
+Functions for kernel calculating method in Classification images
+'''
+
+import pandas as pd
+import numpy as np
+from abc import ABC, abstractmethod
+
+class KernelAnalyser(ABC):
+
+    @classmethod
+    @abstractmethod
+    def extract_single_kernel(cls,data_df, feature_id = 'feature', value_id = 'value', response_id = 'response'): 
+        raise NotImplementedError()
+
+    @classmethod
+    def extract_kernels(cls,data_df, group_ids, feature_id, value_id, response_id, normalize = True):
+        
+        # for each level in group, compute kernels
+
+        def extract_normalize(group): 
+            kernel = cls.extract_single_kernel(group, feature_id, value_id, response_id)
+            if normalize: 
+                kernel = cls.normalize_kernel(kernel)
+            return kernel
+
+        return data_df.groupby(group_ids).apply(lambda group: extract_normalize(group)).reset_index()
+
+    @classmethod    
+    def normalize_kernel(cls,kernel):
+        if isinstance(kernel,pd.DataFrame):
+            rms = np.sqrt((kernel.kernel_value**2).mean())
+            kernel.kernel_value /= rms
+        elif isinstance(kernel,(np.ndarray, list)):
+            rms = np.sqrt(np.mean(np.power(kernel,2)))
+            kernel = kernel/rms
+        else: 
+            raise TypeError('argument kernel is neither a pd.DataFrame or a np.ndarray')
+        return kernel
\ No newline at end of file
diff --git a/python/palin/kernels/lm_analyser.py b/python/palin/kernels/lm_analyser.py
new file mode 100644
index 0000000..84ee8ea
--- /dev/null
+++ b/python/palin/kernels/lm_analyser.py
@@ -0,0 +1,21 @@
+#!/usr/bin/env python
+'''
+PALIN toolbox v0.1
+Decemberr 2022, Aynaz Adl Zarrabi, JJ Aucouturier (CNRS/UBFC)
+
+Functions for kernel calculating method in Classification images
+'''
+
+import pandas as pd
+import numpy as np
+from .kernels import KernelAnalyser
+
+class LMAnalyser(KernelAnalyser):
+
+    @classmethod
+    def extract_single_kernel(cls, data_df, feature_id = 'feature', value_id = 'value', response_id = 'response'):
+        
+        raise NotImplementedError()
+
+        
+ 
\ No newline at end of file

From d79ec6c5508ef9b241f450dca08bc24980d97e39 Mon Sep 17 00:00:00 2001
From: Aynaz Adl Zarrabi <59262612+aynazadl@users.noreply.github.com>
Date: Mon, 18 Mar 2024 14:40:07 +0100
Subject: [PATCH 09/17] change of default value for parameter regex in
 str.replace

---
 python/palin/utils/utils.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/python/palin/utils/utils.py b/python/palin/utils/utils.py
index 76789d8..3950622 100644
--- a/python/palin/utils/utils.py
+++ b/python/palin/utils/utils.py
@@ -60,8 +60,8 @@ def compute_prob_agreement(data_df, session_identifiers=['experimentor', 'type',
     trial_responses_df = trial_responses_df.join(trial_responses_df[response_identifier].str.split(expand=True).rename(columns={0: '%s1' % response_identifier, 1: '%s2' % response_identifier}))
     
     #clean the response data by removing any non-numeric characters
-    trial_responses_df['%s1' % response_identifier] = trial_responses_df['%s1' % response_identifier].str.replace(r'\D', '')
-    trial_responses_df['%s2' % response_identifier] = trial_responses_df['%s2' % response_identifier].str.replace(r'\D', '')
+    trial_responses_df['%s1' % response_identifier] = trial_responses_df['%s1' % response_identifier].str.replace(r'\D', '', regex=True))
+    trial_responses_df['%s2' % response_identifier] = trial_responses_df['%s2' % response_identifier].str.replace(r'\D', '', regex=True))
     
     # drop the original response column
     trial_responses_df = trial_responses_df.drop(columns=[response_identifier])
@@ -133,4 +133,4 @@ def simulate_observer(internal_noise_sigma,criteria, n_trials, n_blocks=1):
 	#probability of agreement between pass
 	prob_agreement = np.mean(all_response_pass1==all_response_pass2) 
 	
-	return prob_agreement,prob_interval1
\ No newline at end of file
+	return prob_agreement,prob_interval1

From dcf6cbb2b1aa16b6459daccd5b3b54179c7235cc Mon Sep 17 00:00:00 2001
From: JJ Aucouturier <aucouturier@gmail.com>
Date: Tue, 9 Apr 2024 22:13:45 +0200
Subject: [PATCH 10/17] added simulation abstraction + sandbox notebook

---
 python/new_sim.ipynb                          | 132 +++---
 python/palin/kernels/kernels.py               |  43 --
 .../__pycache__/simulation.cpython-38.pyc     | Bin 3596 -> 2445 bytes
 .../correlation_with_true_kernel.py           |  36 ++
 python/palin/simulation/linear_observer.py    |   5 +
 python/palin/simulation/simulation.py         |  60 +++
 python/palin/simulation/trial.py              |   8 +-
 python/sandbox.ipynb                          | 385 ++++++++++++++++++
 8 files changed, 559 insertions(+), 110 deletions(-)
 delete mode 100644 python/palin/kernels/kernels.py
 create mode 100644 python/palin/simulation/correlation_with_true_kernel.py
 create mode 100644 python/palin/simulation/simulation.py
 create mode 100644 python/sandbox.ipynb

diff --git a/python/new_sim.ipynb b/python/new_sim.ipynb
index 17aef6e..9ee7e2a 100644
--- a/python/new_sim.ipynb
+++ b/python/new_sim.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "61ae16d1",
+   "id": "e99fe799",
    "metadata": {},
    "source": [
     "# Simulation Experiment"
@@ -11,7 +11,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "218cb1d4",
+   "id": "25ef725a",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-01-18T13:37:38.559326Z",
@@ -38,7 +38,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "67b5263d",
+   "id": "12b04338",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-01-18T13:37:49.595623Z",
@@ -94,7 +94,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7974d885",
+   "id": "b2a8e69b",
    "metadata": {},
    "source": [
     "## generate trials and responses"
@@ -102,7 +102,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "02b090af",
+   "id": "93deb009",
    "metadata": {},
    "source": [
     "### with IN"
@@ -111,7 +111,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "39d623ae",
+   "id": "c63479f5",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-01-09T11:51:05.326379Z",
@@ -407,7 +407,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "27f8230f",
+   "id": "95614290",
    "metadata": {},
    "source": [
     "### without IN"
@@ -416,7 +416,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "ff4070e0",
+   "id": "600f94be",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-01-10T13:48:42.038219Z",
@@ -734,7 +734,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "6ec4fa22",
+   "id": "d7e6f0ef",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-01-10T13:56:41.339132Z",
@@ -802,7 +802,7 @@
   {
    "cell_type": "code",
    "execution_count": 27,
-   "id": "06339766",
+   "id": "df82ef23",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-01-10T14:13:00.797724Z",
@@ -1058,7 +1058,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "64c4ff24",
+   "id": "dcd35e0e",
    "metadata": {},
    "source": [
     "## CI kernel computaion with palin"
@@ -1067,7 +1067,7 @@
   {
    "cell_type": "code",
    "execution_count": 51,
-   "id": "ea040695",
+   "id": "a1c2acec",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-01-11T10:48:37.990321Z",
@@ -1191,7 +1191,7 @@
   {
    "cell_type": "code",
    "execution_count": 52,
-   "id": "035c4e5d",
+   "id": "0d581ad6",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-01-11T10:50:16.700639Z",
@@ -1388,7 +1388,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "88da0057",
+   "id": "e669651c",
    "metadata": {},
    "source": [
     "## Simulate participant w/ CI kernel and IN 0 (graphs)"
@@ -1397,7 +1397,7 @@
   {
    "cell_type": "code",
    "execution_count": 92,
-   "id": "5dc2cc69",
+   "id": "c60393c8",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-01-11T13:33:09.670157Z",
@@ -1507,7 +1507,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2b1f9642",
+   "id": "7a218f2c",
    "metadata": {},
    "source": [
     "## Graphs "
@@ -1515,7 +1515,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4d682794",
+   "id": "b0045be8",
    "metadata": {},
    "source": [
     "### random kernel visualization"
@@ -1524,7 +1524,7 @@
   {
    "cell_type": "code",
    "execution_count": 100,
-   "id": "b8b8f7e4",
+   "id": "da3bfa43",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-01-11T14:00:48.775953Z",
@@ -1620,7 +1620,7 @@
   {
    "cell_type": "code",
    "execution_count": 93,
-   "id": "ebf36d1d",
+   "id": "2a59b2a0",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-01-11T13:33:34.410827Z",
@@ -1650,7 +1650,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9d7b4fe5",
+   "id": "8e391534",
    "metadata": {},
    "source": [
     "### Kernel value Evolution"
@@ -1659,7 +1659,7 @@
   {
    "cell_type": "code",
    "execution_count": 95,
-   "id": "d7432729",
+   "id": "59a5af6e",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-01-11T13:34:39.290061Z",
@@ -1847,7 +1847,7 @@
   {
    "cell_type": "code",
    "execution_count": 99,
-   "id": "2cdf0a8d",
+   "id": "83d9d01a",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-01-11T14:00:29.686409Z",
@@ -2030,7 +2030,7 @@
   {
    "cell_type": "code",
    "execution_count": 97,
-   "id": "7a060387",
+   "id": "12951875",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-01-11T13:35:06.438482Z",
@@ -2226,7 +2226,7 @@
   {
    "cell_type": "code",
    "execution_count": 84,
-   "id": "3896d726",
+   "id": "eead7e97",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-01-11T12:56:27.559484Z",
@@ -2407,7 +2407,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e5ff8723",
+   "id": "1160fd60",
    "metadata": {},
    "source": [
     "### correlation with participant random kernel"
@@ -2416,7 +2416,7 @@
   {
    "cell_type": "code",
    "execution_count": 102,
-   "id": "92cc553a",
+   "id": "3d440806",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-01-11T14:04:33.150155Z",
@@ -2492,7 +2492,7 @@
   {
    "cell_type": "code",
    "execution_count": 98,
-   "id": "0f68f4f8",
+   "id": "c5ff4a70",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-01-11T13:55:15.633617Z",
@@ -2530,7 +2530,7 @@
   {
    "cell_type": "code",
    "execution_count": 70,
-   "id": "c5dd2a7e",
+   "id": "ad3c31b7",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-01-11T12:34:32.110896Z",
@@ -2579,7 +2579,7 @@
   {
    "cell_type": "code",
    "execution_count": 36,
-   "id": "97d53d9c",
+   "id": "b7cf4832",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-01-10T14:38:54.912560Z",
@@ -2618,7 +2618,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8638d240",
+   "id": "2c09a8f3",
    "metadata": {},
    "source": [
     "## different values of IN and Criteria"
@@ -2627,7 +2627,7 @@
   {
    "cell_type": "code",
    "execution_count": 27,
-   "id": "efa0e863",
+   "id": "7a46844b",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-01-10T10:46:43.760310Z",
@@ -2696,7 +2696,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "5d3e5ecc",
+   "id": "29009e30",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2759,7 +2759,7 @@
   {
    "cell_type": "code",
    "execution_count": 28,
-   "id": "16b53e5c",
+   "id": "5e9a3d7c",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-01-10T10:46:51.758548Z",
@@ -2991,7 +2991,7 @@
   {
    "cell_type": "code",
    "execution_count": 29,
-   "id": "4b59cca3",
+   "id": "6e4fa100",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-01-10T10:47:14.696339Z",
@@ -3189,7 +3189,7 @@
   {
    "cell_type": "code",
    "execution_count": 34,
-   "id": "4ae835a9",
+   "id": "14cfea33",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-01-10T11:13:25.877519Z",
@@ -3267,7 +3267,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "b0756f7f",
+   "id": "a95aa8e0",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-12-22T14:58:54.514751Z",
@@ -3285,7 +3285,7 @@
   {
    "cell_type": "code",
    "execution_count": 147,
-   "id": "126a7881",
+   "id": "ed8bf8d5",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-12-22T16:04:45.627539Z",
@@ -3472,7 +3472,7 @@
   {
    "cell_type": "code",
    "execution_count": 148,
-   "id": "b70660e3",
+   "id": "8cbba7ef",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-12-22T16:04:57.636887Z",
@@ -3511,7 +3511,7 @@
   {
    "cell_type": "code",
    "execution_count": 149,
-   "id": "c971a1ee",
+   "id": "86bc796d",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-12-22T16:05:03.546637Z",
@@ -3689,7 +3689,7 @@
   {
    "cell_type": "code",
    "execution_count": 178,
-   "id": "d67c3612",
+   "id": "55f1c3a0",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-12-22T16:29:43.229712Z",
@@ -3776,7 +3776,7 @@
   {
    "cell_type": "code",
    "execution_count": 137,
-   "id": "dbdfbd70",
+   "id": "dbd37577",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-12-22T15:36:50.667399Z",
@@ -3794,7 +3794,7 @@
   {
    "cell_type": "code",
    "execution_count": 138,
-   "id": "8d6dcee0",
+   "id": "82fe1272",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-12-22T15:36:54.065249Z",
@@ -3820,7 +3820,7 @@
   {
    "cell_type": "code",
    "execution_count": 86,
-   "id": "5dfe6da3",
+   "id": "2d995dc4",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-12-22T10:16:30.123735Z",
@@ -4061,7 +4061,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "891a7421",
+   "id": "95f1f085",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4143,7 +4143,7 @@
   {
    "cell_type": "code",
    "execution_count": 87,
-   "id": "7e2923cc",
+   "id": "3ac37b58",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-12-22T10:19:29.722626Z",
@@ -4432,7 +4432,7 @@
   {
    "cell_type": "code",
    "execution_count": 59,
-   "id": "198bb0e4",
+   "id": "30fa36bf",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-12-22T09:57:26.120796Z",
@@ -4457,7 +4457,7 @@
   {
    "cell_type": "code",
    "execution_count": 60,
-   "id": "28912b02",
+   "id": "17efce54",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-12-22T09:57:26.951351Z",
@@ -4486,7 +4486,7 @@
   {
    "cell_type": "code",
    "execution_count": 88,
-   "id": "27eaa2be",
+   "id": "da7d3dac",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-12-22T10:20:56.141439Z",
@@ -4552,7 +4552,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "deee5314",
+   "id": "235143ae",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4684,7 +4684,7 @@
   {
    "cell_type": "code",
    "execution_count": 62,
-   "id": "4c7c4c0b",
+   "id": "44e7690d",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-12-22T09:57:40.477337Z",
@@ -4817,7 +4817,7 @@
   {
    "cell_type": "code",
    "execution_count": 46,
-   "id": "038a7310",
+   "id": "1fda96e6",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-12-21T21:31:26.672299Z",
@@ -4998,7 +4998,7 @@
   {
    "cell_type": "code",
    "execution_count": 186,
-   "id": "9f7bbfd0",
+   "id": "a38e4822",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-12-22T16:32:49.165346Z",
@@ -5036,7 +5036,7 @@
   {
    "cell_type": "code",
    "execution_count": 187,
-   "id": "15856aa4",
+   "id": "a3b326aa",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-12-22T16:32:49.908428Z",
@@ -5244,7 +5244,7 @@
   {
    "cell_type": "code",
    "execution_count": 89,
-   "id": "f1ef1711",
+   "id": "5a09b1c4",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-12-22T10:21:19.872108Z",
@@ -5420,7 +5420,7 @@
   {
    "cell_type": "code",
    "execution_count": 65,
-   "id": "4ea914db",
+   "id": "bdce1492",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-12-22T09:57:51.619598Z",
@@ -5579,7 +5579,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "4668ce88",
+   "id": "fd8f2de7",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -5638,7 +5638,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "92083cbb",
+   "id": "9768764e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -5648,7 +5648,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "1571041f",
+   "id": "7f71bf08",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -5665,7 +5665,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "4797d773",
+   "id": "a31c7ec0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -5721,7 +5721,7 @@
   {
    "cell_type": "code",
    "execution_count": 92,
-   "id": "f935090c",
+   "id": "4513b1da",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-12-22T10:21:52.587637Z",
@@ -5830,7 +5830,7 @@
   {
    "cell_type": "code",
    "execution_count": 80,
-   "id": "a460210d",
+   "id": "5f1dc0ae",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-12-22T10:09:46.311424Z",
@@ -5911,7 +5911,7 @@
   {
    "cell_type": "code",
    "execution_count": 94,
-   "id": "5652318b",
+   "id": "c727988c",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-12-22T10:23:53.589811Z",
@@ -5999,7 +5999,7 @@
   {
    "cell_type": "code",
    "execution_count": 93,
-   "id": "fe9945fc",
+   "id": "a3a9af36",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-12-22T10:22:00.580704Z",
@@ -6028,7 +6028,7 @@
   {
    "cell_type": "code",
    "execution_count": 90,
-   "id": "e3a77657",
+   "id": "cecf7185",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-12-22T10:21:32.745907Z",
@@ -6060,7 +6060,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "02347247",
+   "id": "ec589710",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -6082,7 +6082,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.3"
+   "version": "3.8.10"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/python/palin/kernels/kernels.py b/python/palin/kernels/kernels.py
deleted file mode 100644
index c2b6514..0000000
--- a/python/palin/kernels/kernels.py
+++ /dev/null
@@ -1,43 +0,0 @@
-#!/usr/bin/env python
-'''
-PALIN toolbox v0.1
-Decemberr 2022, Aynaz Adl Zarrabi, JJ Aucouturier (CNRS/UBFC)
-
-Functions for kernel calculating method in Classification images
-'''
-
-import pandas as pd
-import numpy as np
-from abc import ABC, abstractmethod
-
-class KernelAnalyser(ABC):
-
-    @classmethod
-    @abstractmethod
-    def extract_single_kernel(cls,data_df, feature_id = 'feature', value_id = 'value', response_id = 'response'): 
-        raise NotImplementedError()
-
-    @classmethod
-    def extract_kernels(cls,data_df, group_ids, feature_id, value_id, response_id, normalize = True):
-        
-        # for each level in group, compute kernels
-
-        def extract_normalize(group): 
-            kernel = cls.extract_single_kernel(group, feature_id, value_id, response_id)
-            if normalize: 
-                kernel = cls.normalize_kernel(kernel)
-            return kernel
-
-        return data_df.groupby(group_ids).apply(lambda group: extract_normalize(group)).reset_index()
-
-    @classmethod    
-    def normalize_kernel(cls,kernel):
-        if isinstance(kernel,pd.DataFrame):
-            rms = np.sqrt((kernel.kernel_value**2).mean())
-            kernel.kernel_value /= rms
-        elif isinstance(kernel,(np.ndarray, list)):
-            rms = np.sqrt(np.mean(np.power(kernel,2)))
-            kernel = kernel/rms
-        else: 
-            raise TypeError('argument kernel is neither a pd.DataFrame or a np.ndarray')
-        return kernel
\ No newline at end of file
diff --git a/python/palin/simulation/__pycache__/simulation.cpython-38.pyc b/python/palin/simulation/__pycache__/simulation.cpython-38.pyc
index 5a1e747d41cb08a8e34c68f191f724c3b4e85bcb..8882d368430b9b981482ea453d977f76c01a577c 100644
GIT binary patch
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diff --git a/python/palin/simulation/correlation_with_true_kernel.py b/python/palin/simulation/correlation_with_true_kernel.py
new file mode 100644
index 0000000..a097fb2
--- /dev/null
+++ b/python/palin/simulation/correlation_with_true_kernel.py
@@ -0,0 +1,36 @@
+
+from .analyser import Analyser
+from palin.metrics import metrics as me
+
+class CorrelationWithTrueKernel(Analyser): 
+
+    def __init__(self, kernel_analyser):
+        self.kernel_analyser = kernel_analyser
+        
+    def analyse(self, experiment, participant, participant_responses): 
+
+        true_kernel = self.kernel_analyser.normalize_kernel(participant.kernel)
+
+        estimated_kernel = self.estimate_kernel(experiment, participant_responses)
+
+        return self.kernel_correlation(estimated_kernel, true_kernel)
+
+    def estimate_kernel(self, experiment, participant_responses, normalize=True): 
+
+        responses_df = self.to_df(experiment, participant_responses)
+
+        kernel_df = self.kernel_analyser.extract_single_kernel(data_df = responses_df,
+        feature_id = 'feature', value_id = 'value', response_id = 'response')
+
+        if normalize: 
+            kernel_df = self.kernel_analyser.normalize_kernel(kernel_df)
+
+        return list(kernel_df.kernel_value)
+
+    def normalize_kernel(self, kernel): 
+
+        return self.kernel_analyser.normalize_kernel(kernel)
+
+    def kernel_correlation(self, kernel_1, kernel_2): 
+        
+        return me.kernel_distance(kernel_1, kernel_2, type='CORR')
diff --git a/python/palin/simulation/linear_observer.py b/python/palin/simulation/linear_observer.py
index a89cf2f..6326753 100644
--- a/python/palin/simulation/linear_observer.py
+++ b/python/palin/simulation/linear_observer.py
@@ -5,6 +5,7 @@
 class LinearObserver(Observer):
 
     def __init__(self, kernel,internal_noise_std,criteria):
+
         self.kernel = kernel
         self.criteria = criteria
         self.internal_noise_std = internal_noise_std
@@ -14,6 +15,10 @@ def with_random_kernel(cls, n_features, internal_noise_std,criteria):
         return cls(np.random.uniform(-1,1,n_features),internal_noise_std,criteria)
 
     def respond_to_stim(self, stim):
+        if isinstance(self.kernel, str):
+            if (self.kernel == 'random'):
+                # if initialized with random, obs creates a random kernel on first occurrence of stim
+                self.kernel = np.random.uniform(-1,1,len(stim))
         return np.dot(stim, self.kernel)
     
     def generate_internal_noise(self, external_noise_std): 
diff --git a/python/palin/simulation/simulation.py b/python/palin/simulation/simulation.py
new file mode 100644
index 0000000..51f9930
--- /dev/null
+++ b/python/palin/simulation/simulation.py
@@ -0,0 +1,60 @@
+from abc import ABC, abstractmethod
+import itertools
+import pandas as pd
+import numpy as np
+
+class Simulation(ABC): 
+
+    def __init__(self, experiment, experiment_params, observer, observer_params, analyser, analyser_params): 
+        
+        # store simulation classes
+        self.experiment = experiment
+        self.observer = observer
+        self.analyser = analyser
+        
+        # construct simulation plan
+        self.experiment_params = experiment_params
+        self.observer_params = observer_params
+        self.analyser_params = analyser_params
+        self.run_params = self.generate_runs(self.experiment_params,self.observer_params,self.analyser_params)
+        print("generated %d runs"%len(self.run_params))
+
+    @classmethod
+    def generate_runs(cls, experiment_params, observer_params, analyser_params): 
+        # construct simulation plan
+        sim_params ={}
+        for d in (experiment_params, observer_params, analyser_params): 
+            sim_params.update(d)
+        keys, values = zip(*sim_params.items())
+        return [dict(zip(keys, v)) for v in itertools.product(*values)]
+
+
+    def run_all(self, n_samples): 
+        runs = []
+        for run_param in self.run_params: 
+            print(run_param)
+            for sample in np.arange(n_samples): 
+                print('.',end='')
+                res = self.run(run_param)
+                run_res = run_param.copy() 
+                run_res.update({'sample':sample, 'metric':res})
+                runs.append(run_res)
+            print('')
+        return pd.DataFrame(runs)
+
+
+    def run(self, run_param): 
+
+        # separate this run's parameters into distinct sets
+        run_experiment_params = {k: v for k, v in run_param.items() if k in self.experiment_params}
+        run_observer_params = {k: v for k, v in run_param.items() if k in self.observer_params}
+        run_analyser_params = {k: v for k, v in run_param.items() if k in self.analyser_params}
+
+        exp = self.experiment(**run_experiment_params)
+        obs = self.observer(**run_observer_params)
+        ana = self.analyser(**run_analyser_params)
+
+        responses = obs.respond_to_experiment(exp)
+        
+        return ana.analyse(exp, obs, responses)
+
diff --git a/python/palin/simulation/trial.py b/python/palin/simulation/trial.py
index 1064d52..d60f1ed 100644
--- a/python/palin/simulation/trial.py
+++ b/python/palin/simulation/trial.py
@@ -23,6 +23,10 @@ def activate(self,obs):
     def n_stims(self):
         return 1
 
+    def __str__(self): 
+        return '1-interval'
+
+
 class Int2Trial(Trial): 
 
     def __init__(self,stims):
@@ -35,5 +39,7 @@ def activate(self,obs):
     def n_stims(self):
         return 2
 
-        
+    def __str__(self): 
+        return '2-interval'
+    
 
diff --git a/python/sandbox.ipynb b/python/sandbox.ipynb
new file mode 100644
index 0000000..e2f9fab
--- /dev/null
+++ b/python/sandbox.ipynb
@@ -0,0 +1,385 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "id": "9f82673e",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-04-09T18:19:01.978718Z",
+     "start_time": "2024-04-09T18:19:01.941618Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "id": "8fa531a7",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-04-09T18:34:55.185980Z",
+     "start_time": "2024-04-09T18:34:55.140103Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import os, sys\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "31fd8e73",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-04-09T16:44:59.134464Z",
+     "start_time": "2024-04-09T16:44:58.278104Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "sys.path.insert(0, os.path.abspath('../palin/python'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "id": "b2b99fd0",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-04-09T18:43:47.753878Z",
+     "start_time": "2024-04-09T18:43:47.708766Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from palin.simulation.simple_experiment import SimpleExperiment as Exp\n",
+    "from palin.simulation.trial import Int2Trial, Int1Trial \n",
+    "from palin.simulation.linear_observer import LinearObserver as Obs\n",
+    "from palin.simulation.kernel_analyser import KernelAnalyser as Analyser\n",
+    "from palin.kernels.classification_images import ClassificationImage\n",
+    "from palin.simulation.simulation import Simulation as Sim"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "22041899",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-04-09T16:49:31.299979Z",
+     "start_time": "2024-04-09T16:49:31.293995Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# single run: \n",
+    "exp = Exp(n_trials = 100,\n",
+    "          trial_type = Int2Trial, \n",
+    "          n_features = 5, \n",
+    "          external_noise_std = 100)\n",
+    "obs = Obs.with_random_kernel(n_features = 5, \n",
+    "                            internal_noise_std = 1, \n",
+    "                            criteria = 0)\n",
+    "responses = obs.respond_to_experiment(exp)\n",
+    "ka = Analyser(ClassificationImage)\n",
+    "ka.analyse(exp, obs, responses)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "386b6eb7",
+   "metadata": {
+    "ExecuteTime": {
+     "start_time": "2024-04-09T19:20:58.342Z"
+    },
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "generated 100 runs\n",
+      "{'n_trials': 1, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 2, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
+      "....................................................................................................\n",
+      "{'n_trials': 1, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 12, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
+      "....................................................................................................\n",
+      "{'n_trials': 1, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 22, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
+      "....................................................................................................\n",
+      "{'n_trials': 1, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 32, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
+      "....................................................................................................\n",
+      "{'n_trials': 1, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 42, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
+      "....................................................................................................\n",
+      "{'n_trials': 1, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 52, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
+      "....................................................................................................\n",
+      "{'n_trials': 1, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 62, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
+      "....................................................................................................\n",
+      "{'n_trials': 1, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 72, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
+      "....................................................................................................\n",
+      "{'n_trials': 1, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 82, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
+      "....................................................................................................\n",
+      "{'n_trials': 1, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 92, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
+      "....................................................................................................\n",
+      "{'n_trials': 101, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 2, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
+      "....................................................................................................\n",
+      "{'n_trials': 101, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 12, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
+      "....................................................................................................\n",
+      "{'n_trials': 101, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 22, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
+      "....................................................................................................\n",
+      "{'n_trials': 101, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 32, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
+      "....................................................................................................\n",
+      "{'n_trials': 101, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 42, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
+      "....................................................................................................\n",
+      "{'n_trials': 101, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 52, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
+      "....................................................................................................\n",
+      "{'n_trials': 101, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 62, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
+      "....................................................................................................\n",
+      "{'n_trials': 101, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 72, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
+      "....................................................................................................\n",
+      "{'n_trials': 101, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 82, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
+      "....................................................................................................\n",
+      "{'n_trials': 101, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 92, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
+      "....................................................................................................\n",
+      "{'n_trials': 201, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 2, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
+      "....................................................................................................\n",
+      "{'n_trials': 201, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 12, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
+      "....................................................................................................\n",
+      "{'n_trials': 201, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 22, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "....................................................................................................\n",
+      "{'n_trials': 201, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 32, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
+      "....................................................................................................\n",
+      "{'n_trials': 201, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 42, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
+      "....................................................................................................\n",
+      "{'n_trials': 201, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 52, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
+      "....................................................................................................\n",
+      "{'n_trials': 201, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 62, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
+      "....................................................................................................\n",
+      "{'n_trials': 201, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 72, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
+      "....................................................................................................\n",
+      "{'n_trials': 201, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 82, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
+      "....................................................................................................\n",
+      "{'n_trials': 201, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 92, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
+      "....................................................................................................\n",
+      "{'n_trials': 301, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 2, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
+      "....................................................................................................\n",
+      "{'n_trials': 301, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 12, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
+      "....................................................................................................\n",
+      "{'n_trials': 301, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 22, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
+      "....................................................................................................\n",
+      "{'n_trials': 301, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 32, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
+      "....................................................................................................\n",
+      "{'n_trials': 301, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 42, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
+      "....................................................................................................\n",
+      "{'n_trials': 301, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 52, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
+      "....................................................................................................\n",
+      "{'n_trials': 301, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 62, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
+      "....................................................................................................\n",
+      "{'n_trials': 301, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 72, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
+      "....................................................................................................\n",
+      "{'n_trials': 301, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 82, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
+      "....................................................................................................\n",
+      "{'n_trials': 301, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 92, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
+      "....................................................................................................\n",
+      "{'n_trials': 401, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 2, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
+      "....................................................................................................\n",
+      "{'n_trials': 401, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 12, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
+      "....................................................................................................\n",
+      "{'n_trials': 401, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 22, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
+      "....................................................................................................\n",
+      "{'n_trials': 401, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 32, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
+      "....................................................................................................\n",
+      "{'n_trials': 401, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 42, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
+      "....................................................................................................\n",
+      "{'n_trials': 401, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 52, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "....................................................................................................\n",
+      "{'n_trials': 401, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 62, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
+      "....................................................................................................\n",
+      "{'n_trials': 401, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 72, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
+      "....................................................................................................\n",
+      "{'n_trials': 401, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 82, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
+      "....................................................................................................\n",
+      "{'n_trials': 401, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 92, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
+      "....................................................................................................\n",
+      "{'n_trials': 501, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 2, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
+      "....................................................................................................\n",
+      "{'n_trials': 501, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 12, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
+      "....................................................................................................\n",
+      "{'n_trials': 501, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 22, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
+      "....................................................................................................\n",
+      "{'n_trials': 501, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 32, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
+      "....................................................................................................\n",
+      "{'n_trials': 501, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 42, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
+      "....................................................................................................\n",
+      "{'n_trials': 501, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 52, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
+      "....................................................................................................\n",
+      "{'n_trials': 501, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 62, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
+      "....................................................................................................\n",
+      "{'n_trials': 501, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 72, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
+      "....................................................................................................\n",
+      "{'n_trials': 501, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 82, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
+      "....................................................................................................\n",
+      "{'n_trials': 501, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 92, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
+      "....................................................................................................\n",
+      "{'n_trials': 601, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 2, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
+      "....................................................................................................\n",
+      "{'n_trials': 601, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 12, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
+      "....................................................................................................\n",
+      "{'n_trials': 601, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 22, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
+      ".........."
+     ]
+    }
+   ],
+   "source": [
+    "# obs = Obs.with_random_kernel(n_features=5, internal_noise_std=0, criteria=0)\n",
+    "\n",
+    "observer_params = {'kernel':['random'],\n",
+    "                   'internal_noise_std':[1], \n",
+    "                  'criteria':[0]}\n",
+    "experiment_params = {'n_trials':np.arange(1,1000,100),\n",
+    "                     'trial_type': [Int2Trial],\n",
+    "                     'n_features': np.arange(2,100,10),\n",
+    "                     'external_noise_std': [100]}\n",
+    "analyser_params = {'kernel_analyser':[ClassificationImage]}\n",
+    "\n",
+    "\n",
+    "sim = Sim(Exp, experiment_params, \n",
+    "          Obs, observer_params, \n",
+    "          Analyser, analyser_params)\n",
+    "sim_df = sim.run_all(n_samples=100)\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "id": "3c925055",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-04-09T19:19:35.346726Z",
+     "start_time": "2024-04-09T19:19:34.771230Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='n_trials', ylabel='metric'>"
+      ]
+     },
+     "execution_count": 151,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.lineplot(data=sim_df, \n",
+    "             x='n_trials',\n",
+    "             y='metric', hue='n_features')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "f65f5044",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-04-09T17:49:49.786914Z",
+     "start_time": "2024-04-09T17:49:49.783924Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "kernel\n",
+      "internal_noise_std\n",
+      "criteria\n",
+      "bla\n"
+     ]
+    }
+   ],
+   "source": [
+    "for k in observer_params: \n",
+    "    print(k)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "62579d26",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.10"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": true
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 972098b3cdad7eb4534dca4a9056b05b668e49f7 Mon Sep 17 00:00:00 2001
From: JJ Aucouturier <aucouturier@gmail.com>
Date: Wed, 10 Apr 2024 08:56:41 +0200
Subject: [PATCH 11/17] renamed analysers -> extractors

---
 python/palin/kernels/classification_images.py | 11 ++++--
 ...kernel_analyser.py => kernel_extractor.py} |  2 +-
 python/palin/kernels/linear_model.py          | 21 +++++++++++
 python/palin/kernels/lm_analyser.py           |  2 +-
 python/palin/metrics/metrics.py               |  2 +-
 python/palin/simulation/kernel_analyser.py    | 36 -------------------
 6 files changed, 33 insertions(+), 41 deletions(-)
 rename python/palin/kernels/{kernel_analyser.py => kernel_extractor.py} (98%)
 create mode 100644 python/palin/kernels/linear_model.py
 delete mode 100644 python/palin/simulation/kernel_analyser.py

diff --git a/python/palin/kernels/classification_images.py b/python/palin/kernels/classification_images.py
index 012e1c7..5dda088 100644
--- a/python/palin/kernels/classification_images.py
+++ b/python/palin/kernels/classification_images.py
@@ -8,12 +8,15 @@
 
 import pandas as pd
 import numpy as np
-from .kernel_analyser import KernelAnalyser
+from .kernel_extractor import KernelExtractor
 
-class ClassificationImage(KernelAnalyser):
+class ClassificationImage(KernelExtractor):
 
     @classmethod
     def extract_single_kernel(cls, data_df, feature_id = 'feature', value_id = 'value', response_id = 'response'):
+
+        ## note this doesn't work for 1-int data
+
         feature_average = data_df.groupby([feature_id,response_id])[value_id].mean().reset_index()
         positives = feature_average.loc[feature_average[response_id] == True].reset_index()
         negatives = feature_average.loc[feature_average[response_id] == False].reset_index()
@@ -23,6 +26,10 @@ def extract_single_kernel(cls, data_df, feature_id = 'feature', value_id = 'valu
         kernels.index.names = ['feature']
         return kernels
 
+    def __str__(self): 
+        return 'Classification Image'
+
+
         
 # def compute_accuracy(data_df, control_kernel, session_identifiers = ['experimentor','type','subject','session'], trial_identifier = 'trial', stimulus_dimension='segment', stimulus_value = 'pitch', stimulus_response='response'): 
 #   ''' Computes participant's accuracy on the task as a measure of how well the participant performs 
diff --git a/python/palin/kernels/kernel_analyser.py b/python/palin/kernels/kernel_extractor.py
similarity index 98%
rename from python/palin/kernels/kernel_analyser.py
rename to python/palin/kernels/kernel_extractor.py
index c2b6514..d75edae 100644
--- a/python/palin/kernels/kernel_analyser.py
+++ b/python/palin/kernels/kernel_extractor.py
@@ -10,7 +10,7 @@
 import numpy as np
 from abc import ABC, abstractmethod
 
-class KernelAnalyser(ABC):
+class KernelExtractor(ABC):
 
     @classmethod
     @abstractmethod
diff --git a/python/palin/kernels/linear_model.py b/python/palin/kernels/linear_model.py
new file mode 100644
index 0000000..c3f36fc
--- /dev/null
+++ b/python/palin/kernels/linear_model.py
@@ -0,0 +1,21 @@
+#!/usr/bin/env python
+'''
+PALIN toolbox v0.1
+Decemberr 2022, Aynaz Adl Zarrabi, JJ Aucouturier (CNRS/UBFC)
+
+Functions for kernel calculating method in Classification images
+'''
+
+import pandas as pd
+import numpy as np
+from .kernels import KernelAnalyser
+
+class LinearModel(KernelExtractor):
+
+    @classmethod
+    def extract_single_kernel(cls, data_df, feature_id = 'feature', value_id = 'value', response_id = 'response'):
+        
+        raise NotImplementedError()
+
+        
+ 
\ No newline at end of file
diff --git a/python/palin/kernels/lm_analyser.py b/python/palin/kernels/lm_analyser.py
index 84ee8ea..c3f36fc 100644
--- a/python/palin/kernels/lm_analyser.py
+++ b/python/palin/kernels/lm_analyser.py
@@ -10,7 +10,7 @@
 import numpy as np
 from .kernels import KernelAnalyser
 
-class LMAnalyser(KernelAnalyser):
+class LinearModel(KernelExtractor):
 
     @classmethod
     def extract_single_kernel(cls, data_df, feature_id = 'feature', value_id = 'value', response_id = 'response'):
diff --git a/python/palin/metrics/metrics.py b/python/palin/metrics/metrics.py
index 8998615..7c1c397 100644
--- a/python/palin/metrics/metrics.py
+++ b/python/palin/metrics/metrics.py
@@ -7,7 +7,7 @@ def kernel_distance(kernel_1, kernel_2, type='CORR'):
     elif type == 'CORR': 
         return kernel_correlation(kernel_1, kernel_2)
     else: 
-        raise AttributeError('metric type %s unknown'%s)
+        raise AttributeError('metric type %s unknown'%type)
 
 
 def kernel_rms(kernel_1, kernel_2):
diff --git a/python/palin/simulation/kernel_analyser.py b/python/palin/simulation/kernel_analyser.py
deleted file mode 100644
index b1b3c65..0000000
--- a/python/palin/simulation/kernel_analyser.py
+++ /dev/null
@@ -1,36 +0,0 @@
-
-from .analyser import Analyser
-from palin.metrics import metrics as me
-
-class KernelAnalyser(Analyser): 
-
-    def __init__(self, kernel_analyser):
-        self.kernel_analyser = kernel_analyser
-        
-    def analyse(self, experiment, participant, participant_responses): 
-
-        true_kernel = self.kernel_analyser.normalize_kernel(participant.kernel)
-
-        estimated_kernel = self.estimate_kernel(experiment, participant_responses)
-
-        return self.kernel_correlation(estimated_kernel, true_kernel)
-
-    def estimate_kernel(self, experiment, participant_responses, normalize=True): 
-
-        responses_df = self.to_df(experiment, participant_responses)
-
-        kernel_df = self.kernel_analyser.extract_single_kernel(data_df = responses_df,
-        feature_id = 'feature', value_id = 'value', response_id = 'response')
-
-        if normalize: 
-            kernel_df = self.kernel_analyser.normalize_kernel(kernel_df)
-
-        return list(kernel_df.kernel_value)
-
-    def normalize_kernel(self, kernel): 
-
-        return self.kernel_analyser.normalize_kernel(kernel)
-
-    def kernel_correlation(self, kernel_1, kernel_2): 
-        
-        return me.kernel_distance(kernel_1, kernel_2, type='CORR')

From 34dbb876b52775db03bf291248e0b34febdd6dde Mon Sep 17 00:00:00 2001
From: JJ Aucouturier <aucouturier@gmail.com>
Date: Wed, 10 Apr 2024 08:57:32 +0200
Subject: [PATCH 12/17] started refactoring internal noise

---
 .../results_subj_20111971.csv                 | 2101 +++++++++++++++++
 python/palin/internal_noise/double_pass.py    |  108 +
 .../internal_noise_extractor.py               |   27 +
 python/sandbox.ipynb                          |  412 ++--
 4 files changed, 2464 insertions(+), 184 deletions(-)
 create mode 100644 data/pitch_interrogation/results_subj_20111971.csv
 create mode 100644 python/palin/internal_noise/double_pass.py
 create mode 100644 python/palin/internal_noise/internal_noise_extractor.py

diff --git a/data/pitch_interrogation/results_subj_20111971.csv b/data/pitch_interrogation/results_subj_20111971.csv
new file mode 100644
index 0000000..dc7bf3d
--- /dev/null
+++ b/data/pitch_interrogation/results_subj_20111971.csv
@@ -0,0 +1,2101 @@
+subj,trial,block,date,stim,stim_order,response,rt,age,sex,param_index,segment_time,pitch
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diff --git a/python/palin/internal_noise/double_pass.py b/python/palin/internal_noise/double_pass.py
new file mode 100644
index 0000000..9fad4f2
--- /dev/null
+++ b/python/palin/internal_noise/double_pass.py
@@ -0,0 +1,108 @@
+#!/usr/bin/env python
+'''
+PALIN toolbox v0.1
+December 2022, Aynaz Adl Zarrabi, JJ Aucouturier (CNRS/UBFC)
+
+Functions for kernel calculating method in Classification images
+'''
+
+import pandas as pd
+import numpy as np
+from .internal_noise_extractor import InternalNoiseExtractor
+from ..simulation.linear_observer import LinearObserver
+from ..simulation.simple_experiment import SimpleExperiment
+from ..simulation.trial import Int2Trial, Int1Trial 
+
+class DoublePass(InternalNoiseExtractor):
+
+    @classmethod
+    def extract_single_internal_noise(cls,data_df, trial_id, stim_id = 'stim', feature_id = 'feature', value_id = 'value', response_id = 'response'):
+
+        double_pass_id = 'double_pass_id' # column by which to identify double pass trials
+
+        # index double pass trials
+        data_df = cls.index_double_pass_trials(data_df, trial_id=trial_id, value_id = value_id, double_pass_id = double_pass_id)
+        # compute probability of agreement over double pass
+        prob_agree = compute_prob_agreement(data_df, trial_id=trial_id, response_id=response_id, double_pass_id=double_pass_id)
+        # compute probability of choosing first response option
+        prob_first = compute_prob_first(data_df, trial_id=trial_id, response_id=response_id, stim_id=stim_id, double_pass_id=double_pass_id)
+
+        return 0
+
+    def __str__(self): 
+        return 'Double-Pass method'
+
+    @classmethod
+    def index_double_pass_trials(cls, data_df, trial_id='trial',double_pass_id='double_pass_id',value_id='stim_parameter_id'):
+    ''' identify repeated trials in experimental sessions (i.e. 'double pass trials'), and tag them with a unique id stored in a new column.
+    '''
+        # represent the several values of a given trial (ex. 6 features for interval 1, 6 features for interval 2) as a tuple 
+        frozen_set_df = data_df.groupby(trial_id).agg({value_id: lambda group: tuple(group)}).reset_index()
+
+        # count how many trials have each unique pair of stimuli
+        pass_count_df = frozen_set_df.groupby(value_id).agg({trial_id: ['nunique','first','last']})
+        pass_count_df.columns = ["_".join(x) for x in pass_count_df.columns.ravel()]
+        pass_count_df = pass_count_df.reset_index()
+
+        # identify pairs of stimuli that have 2 trials (i.e. for which there has been a double pass)
+        double_pass_df = pass_count_df[pass_count_df['%s_nunique'%trial_id]==2].reset_index(drop=True)
+        
+        # assign unique id
+        double_pass_df[double_pass_id] = double_pass_df.index
+
+        # join to base dataset
+        double_pass_df = double_pass_df.melt(id_vars=double_pass_id, 
+                                             value_vars=['%s_first'%trial_id,'%s_last'%trial_id], 
+                                             var_name='%s_type'%trial_id, 
+                                             value_name=trial_id)
+        data_df= pd.merge(data_df, double_pass_df[[trial_id, double_pass_id]], 
+                          how="left", on=trial_id)
+        return data_df  
+
+
+    @classmethod
+    def compute_prob_agreement(cls,data_df, trial_id='trial', response_id='response', double_pass_id='double_pass_id'):
+    # computes the probability of agreement between two responses to a repeated stimuli on the double pass trials 
+
+        # compute agreements for each double_pass trial
+        def same_answer(group, trial_id, response_id):    
+            d = group.groupby(trial_id).agg({response_id: lambda group: tuple(group)}).reset_index()
+            return d.response.nunique()==1
+        agrees = data_df.groupby(double_pass_id).apply(lambda group: same_answer(group, trial_id, response_id))
+    
+        # return agreement probability
+        return agrees.sum()/len(agrees)
+    
+    @classmethod
+    def compute_prob_first(cls, data_df, trial_id='trial', response_id='response', stim_id='stim_order', double_pass_id='double_pass_id'):
+    # Computes probability that responds true to the first interval across the subset of double_pass trialslumn (e.g. double_pass_id) identifying repeated trials. Use utils.index_double_pass_trials to create that column if doesn't exist. 
+    
+        # compute first response for each double_pass trial
+        def first_option(group, stim_id, response_id):    
+            resp = group.sort_values(by=stim_id)[response_id].iloc[0]
+            return resp==1
+        firsts = data_df[data_df[double_pass_id].notna()].groupby(trial_id).apply(lambda group: first_option(group, stim_id, response_id))
+    
+        return firsts.sum()/len(firsts)
+
+    @classmethod
+    def simulate_observer(cls,internal_noise_std,criteria, n_trials, n_blocks=1): 
+
+        # simulate observer with (criteria, internal_noise_sigma)
+        # in the midterm, this should be done with a Simulation object, for which we need a prob_a, prob_first analyser
+
+        obs = LinearObserver(kernel=[1], internal_noise_std=internal_noise_std, criteria=criteria)
+        exp = SimpleExperiment(n_trials=n_trials, trial_type=Int2Trial, n_features=1, external_noise_std=1)
+
+        responses_pass_1 = obs.respond_to_experiment(exp)
+        responses_pass_2 = obs.respond_to_experiment(exp)
+    
+        # probability interval 1 (average of prob in both pass)
+        prob_first = (np.mean(responses_pass_1) + np.mean(responses_pass_2))/2
+    
+        #probability of agreement between pass
+        prob_agree = np.mean(responses_pass_1==responses_pass_2) 
+    
+        return prob_agree,prob_first
+
+       
\ No newline at end of file
diff --git a/python/palin/internal_noise/internal_noise_extractor.py b/python/palin/internal_noise/internal_noise_extractor.py
new file mode 100644
index 0000000..be6fbb6
--- /dev/null
+++ b/python/palin/internal_noise/internal_noise_extractor.py
@@ -0,0 +1,27 @@
+#!/usr/bin/env python
+'''
+PALIN toolbox v0.1
+Decemberr 2022, Aynaz Adl Zarrabi, JJ Aucouturier (CNRS/UBFC)
+
+Functions for kernel calculating method in Classification images
+'''
+
+import pandas as pd
+import numpy as np
+from abc import ABC, abstractmethod
+
+class InternalNoiseExtractor(ABC):
+
+    @classmethod
+    @abstractmethod
+    def extract_single_internal_noise(cls,data_df, trial_id = 'trial_id', feature_id = 'feature', value_id = 'value', response_id = 'response'): 
+        raise NotImplementedError()
+
+    @classmethod
+    def extract_internal_noise(cls,data_df, group_ids, trial_id, feature_id, value_id, response_id, normalize = True):
+        
+        # for each level in group, extract internal_noise
+        return data_df.groupby(group_ids).apply(lambda group: cls.extract_single_internal_noise(group, 
+            trial_id, feature_id, value_id, response_id)).reset_index()
+
+    
\ No newline at end of file
diff --git a/python/sandbox.ipynb b/python/sandbox.ipynb
index e2f9fab..872ddbb 100644
--- a/python/sandbox.ipynb
+++ b/python/sandbox.ipynb
@@ -2,12 +2,12 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 94,
-   "id": "9f82673e",
+   "execution_count": 1,
+   "id": "264735f2",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-09T18:19:01.978718Z",
-     "start_time": "2024-04-09T18:19:01.941618Z"
+     "end_time": "2024-04-10T04:07:14.254091Z",
+     "start_time": "2024-04-10T04:07:14.212928Z"
     }
    },
    "outputs": [],
@@ -18,12 +18,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 113,
-   "id": "8fa531a7",
+   "execution_count": 2,
+   "id": "c7f458e5",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-09T18:34:55.185980Z",
-     "start_time": "2024-04-09T18:34:55.140103Z"
+     "end_time": "2024-04-10T04:07:15.786461Z",
+     "start_time": "2024-04-10T04:07:14.480236Z"
     }
    },
    "outputs": [],
@@ -37,11 +37,11 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "31fd8e73",
+   "id": "187bfd56",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-09T16:44:59.134464Z",
-     "start_time": "2024-04-09T16:44:58.278104Z"
+     "end_time": "2024-04-10T04:07:15.833288Z",
+     "start_time": "2024-04-10T04:07:15.787409Z"
     }
    },
    "outputs": [],
@@ -51,12 +51,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 128,
-   "id": "b2b99fd0",
+   "execution_count": 7,
+   "id": "edb2ad9c",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-09T18:43:47.753878Z",
-     "start_time": "2024-04-09T18:43:47.708766Z"
+     "end_time": "2024-04-10T04:13:23.133408Z",
+     "start_time": "2024-04-10T04:13:23.083499Z"
     }
    },
    "outputs": [],
@@ -64,7 +64,7 @@
     "from palin.simulation.simple_experiment import SimpleExperiment as Exp\n",
     "from palin.simulation.trial import Int2Trial, Int1Trial \n",
     "from palin.simulation.linear_observer import LinearObserver as Obs\n",
-    "from palin.simulation.kernel_analyser import KernelAnalyser as Analyser\n",
+    "from palin.simulation.kernel_distance import KernelDistance as Analyser\n",
     "from palin.kernels.classification_images import ClassificationImage\n",
     "from palin.simulation.simulation import Simulation as Sim"
    ]
@@ -72,7 +72,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "22041899",
+   "id": "a860c359",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-04-09T16:49:31.299979Z",
@@ -96,11 +96,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "386b6eb7",
+   "execution_count": 15,
+   "id": "8d87b6cc",
    "metadata": {
     "ExecuteTime": {
-     "start_time": "2024-04-09T19:20:58.342Z"
+     "end_time": "2024-04-10T04:42:23.899935Z",
+     "start_time": "2024-04-10T04:42:23.752329Z"
     },
     "scrolled": false
    },
@@ -109,145 +110,23 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "generated 100 runs\n",
-      "{'n_trials': 1, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 2, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
-      "....................................................................................................\n",
-      "{'n_trials': 1, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 12, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
-      "....................................................................................................\n",
-      "{'n_trials': 1, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 22, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
-      "....................................................................................................\n",
-      "{'n_trials': 1, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 32, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
-      "....................................................................................................\n",
-      "{'n_trials': 1, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 42, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
-      "....................................................................................................\n",
-      "{'n_trials': 1, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 52, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
-      "....................................................................................................\n",
-      "{'n_trials': 1, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 62, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
-      "....................................................................................................\n",
-      "{'n_trials': 1, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 72, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
-      "....................................................................................................\n",
-      "{'n_trials': 1, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 82, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
-      "....................................................................................................\n",
-      "{'n_trials': 1, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 92, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
-      "....................................................................................................\n",
-      "{'n_trials': 101, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 2, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
-      "....................................................................................................\n",
-      "{'n_trials': 101, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 12, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
-      "....................................................................................................\n",
-      "{'n_trials': 101, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 22, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
-      "....................................................................................................\n",
-      "{'n_trials': 101, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 32, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
-      "....................................................................................................\n",
-      "{'n_trials': 101, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 42, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
-      "....................................................................................................\n",
-      "{'n_trials': 101, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 52, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
-      "....................................................................................................\n",
-      "{'n_trials': 101, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 62, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
-      "....................................................................................................\n",
-      "{'n_trials': 101, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 72, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
-      "....................................................................................................\n",
-      "{'n_trials': 101, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 82, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
-      "....................................................................................................\n",
-      "{'n_trials': 101, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 92, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
-      "....................................................................................................\n",
-      "{'n_trials': 201, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 2, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
-      "....................................................................................................\n",
-      "{'n_trials': 201, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 12, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
-      "....................................................................................................\n",
-      "{'n_trials': 201, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 22, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "....................................................................................................\n",
-      "{'n_trials': 201, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 32, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
-      "....................................................................................................\n",
-      "{'n_trials': 201, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 42, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
-      "....................................................................................................\n",
-      "{'n_trials': 201, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 52, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
-      "....................................................................................................\n",
-      "{'n_trials': 201, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 62, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
-      "....................................................................................................\n",
-      "{'n_trials': 201, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 72, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
-      "....................................................................................................\n",
-      "{'n_trials': 201, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 82, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
-      "....................................................................................................\n",
-      "{'n_trials': 201, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 92, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
-      "....................................................................................................\n",
-      "{'n_trials': 301, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 2, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
-      "....................................................................................................\n",
-      "{'n_trials': 301, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 12, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
-      "....................................................................................................\n",
-      "{'n_trials': 301, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 22, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
-      "....................................................................................................\n",
-      "{'n_trials': 301, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 32, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
-      "....................................................................................................\n",
-      "{'n_trials': 301, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 42, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
-      "....................................................................................................\n",
-      "{'n_trials': 301, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 52, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
-      "....................................................................................................\n",
-      "{'n_trials': 301, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 62, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
-      "....................................................................................................\n",
-      "{'n_trials': 301, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 72, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
-      "....................................................................................................\n",
-      "{'n_trials': 301, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 82, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
-      "....................................................................................................\n",
-      "{'n_trials': 301, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 92, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
-      "....................................................................................................\n",
-      "{'n_trials': 401, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 2, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
-      "....................................................................................................\n",
-      "{'n_trials': 401, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 12, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
-      "....................................................................................................\n",
-      "{'n_trials': 401, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 22, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
-      "....................................................................................................\n",
-      "{'n_trials': 401, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 32, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
-      "....................................................................................................\n",
-      "{'n_trials': 401, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 42, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
-      "....................................................................................................\n",
-      "{'n_trials': 401, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 52, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "....................................................................................................\n",
-      "{'n_trials': 401, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 62, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
-      "....................................................................................................\n",
-      "{'n_trials': 401, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 72, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
-      "....................................................................................................\n",
-      "{'n_trials': 401, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 82, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
-      "....................................................................................................\n",
-      "{'n_trials': 401, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 92, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
-      "....................................................................................................\n",
-      "{'n_trials': 501, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 2, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
-      "....................................................................................................\n",
-      "{'n_trials': 501, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 12, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
-      "....................................................................................................\n",
-      "{'n_trials': 501, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 22, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
-      "....................................................................................................\n",
-      "{'n_trials': 501, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 32, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
-      "....................................................................................................\n",
-      "{'n_trials': 501, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 42, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
-      "....................................................................................................\n",
-      "{'n_trials': 501, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 52, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
-      "....................................................................................................\n",
-      "{'n_trials': 501, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 62, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
-      "....................................................................................................\n",
-      "{'n_trials': 501, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 72, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
-      "....................................................................................................\n",
-      "{'n_trials': 501, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 82, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
-      "....................................................................................................\n",
-      "{'n_trials': 501, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 92, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
-      "....................................................................................................\n",
-      "{'n_trials': 601, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 2, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
-      "....................................................................................................\n",
-      "{'n_trials': 601, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 12, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
-      "....................................................................................................\n",
-      "{'n_trials': 601, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 22, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_analyser': <class 'palin.kernels.classification_images.ClassificationImage'>}\n",
-      ".........."
+      "generated 8 runs\n",
+      "{'n_trials': 100, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 2, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_extractor': <class 'palin.kernels.classification_images.ClassificationImage'>, 'distance': 'CORR'}\n",
+      ".\n",
+      "{'n_trials': 100, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 3, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_extractor': <class 'palin.kernels.classification_images.ClassificationImage'>, 'distance': 'CORR'}\n",
+      ".\n",
+      "{'n_trials': 100, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 4, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_extractor': <class 'palin.kernels.classification_images.ClassificationImage'>, 'distance': 'CORR'}\n",
+      ".\n",
+      "{'n_trials': 100, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 5, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_extractor': <class 'palin.kernels.classification_images.ClassificationImage'>, 'distance': 'CORR'}\n",
+      ".\n",
+      "{'n_trials': 100, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 6, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_extractor': <class 'palin.kernels.classification_images.ClassificationImage'>, 'distance': 'CORR'}\n",
+      ".\n",
+      "{'n_trials': 100, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 7, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_extractor': <class 'palin.kernels.classification_images.ClassificationImage'>, 'distance': 'CORR'}\n",
+      ".\n",
+      "{'n_trials': 100, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 8, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_extractor': <class 'palin.kernels.classification_images.ClassificationImage'>, 'distance': 'CORR'}\n",
+      ".\n",
+      "{'n_trials': 100, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 9, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_extractor': <class 'palin.kernels.classification_images.ClassificationImage'>, 'distance': 'CORR'}\n",
+      ".\n"
      ]
     }
    ],
@@ -257,17 +136,18 @@
     "observer_params = {'kernel':['random'],\n",
     "                   'internal_noise_std':[1], \n",
     "                  'criteria':[0]}\n",
-    "experiment_params = {'n_trials':np.arange(1,1000,100),\n",
+    "experiment_params = {'n_trials':[100],#np.arange(1,1000,100),\n",
     "                     'trial_type': [Int2Trial],\n",
-    "                     'n_features': np.arange(2,100,10),\n",
+    "                     'n_features': np.arange(2,10,1),\n",
     "                     'external_noise_std': [100]}\n",
-    "analyser_params = {'kernel_analyser':[ClassificationImage]}\n",
+    "analyser_params = {'kernel_extractor':[ClassificationImage], \n",
+    "                  'distance':['CORR']}\n",
     "\n",
     "\n",
     "sim = Sim(Exp, experiment_params, \n",
     "          Obs, observer_params, \n",
     "          Analyser, analyser_params)\n",
-    "sim_df = sim.run_all(n_samples=100)\n",
+    "sim_df = sim.run_all(n_samples=1)\n",
     "\n",
     "\n",
     "\n",
@@ -276,28 +156,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 151,
-   "id": "3c925055",
+   "execution_count": 17,
+   "id": "59b42cc5",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-09T19:19:35.346726Z",
-     "start_time": "2024-04-09T19:19:34.771230Z"
+     "end_time": "2024-04-10T04:42:50.935778Z",
+     "start_time": "2024-04-10T04:42:50.744219Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<Axes: xlabel='n_trials', ylabel='metric'>"
+       "<Axes: xlabel='n_features', ylabel='metric'>"
       ]
      },
-     "execution_count": 151,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -308,41 +188,205 @@
    ],
    "source": [
     "sns.lineplot(data=sim_df, \n",
-    "             x='n_trials',\n",
-    "             y='metric', hue='n_features')"
+    "             x='n_features',\n",
+    "             y='metric')#, hue='n_features')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "1d2dda2d",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-04-10T05:45:07.904380Z",
+     "start_time": "2024-04-10T05:45:07.847512Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def index_double_pass_trials(data_df, trial_id='trial',double_pass_id='double_pass_id',value_id='stim_parameter_id'):\n",
+    "    \n",
+    "        # represent the several values of a given trial (ex. 6 features for interval 1, 6 features for interval 2) as a frozenset\n",
+    "        frozen_set_df = data_df.groupby(trial_id).agg({value_id: lambda group: tuple(group)}).reset_index()\n",
+    "\n",
+    "        # count how many trials have each unique pair of stimuli\n",
+    "        pass_count_df = frozen_set_df.groupby(value_id).agg({trial_id: ['nunique','first','last']})\n",
+    "        pass_count_df.columns = [\"_\".join(x) for x in pass_count_df.columns.ravel()]\n",
+    "        pass_count_df = pass_count_df.reset_index()\n",
+    "\n",
+    "        # identify pairs of stimuli that have 2 trials (i.e. for which there has been a double pass)\n",
+    "        double_pass_df = pass_count_df[pass_count_df['%s_nunique'%trial_id]==2].reset_index(drop=True)\n",
+    "        \n",
+    "        # assign unique id\n",
+    "        double_pass_df[double_pass_id] = double_pass_df.index\n",
+    "\n",
+    "        # join to base dataset\n",
+    "        double_pass_df = double_pass_df.melt(id_vars=double_pass_id, \n",
+    "                                             value_vars=['%s_first'%trial_id,'%s_last'%trial_id], \n",
+    "                                             var_name='%s_type'%trial_id, \n",
+    "                                             value_name=trial_id)\n",
+    "        data_df= pd.merge(data_df, double_pass_df[[trial_id, double_pass_id]], \n",
+    "                          how=\"left\", on=trial_id)\n",
+    "        return data_df  "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
-   "id": "f65f5044",
+   "execution_count": 75,
+   "id": "6cf44e37",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-09T17:49:49.786914Z",
-     "start_time": "2024-04-09T17:49:49.783924Z"
+     "end_time": "2024-04-10T05:45:08.457461Z",
+     "start_time": "2024-04-10T05:45:08.403620Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def compute_prob_agreement(data_df, trial_id='trial', response_id='response', order_id='stim_order', double_pass_id='double_pass_id'):\n",
+    "    # computes the probability of agreement between two responses to a repeated stimuli on the double pass trials \n",
+    "\n",
+    "    def same_answer(group, trial_id, response_id):    \n",
+    "        d = group.groupby(trial_id).agg({response_id: lambda group: tuple(group)}).reset_index()\n",
+    "        return d.response.nunique()==1\n",
+    "    \n",
+    "    # compute agreements for each double_pass trial\n",
+    "    agrees = data_df.groupby(double_pass_id).apply(lambda group: same_answer(group, trial_id, response_id))\n",
+    "    \n",
+    "    # return agreement probability\n",
+    "    return agrees.sum()/len(agrees)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "99e19273",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-04-10T06:12:08.125017Z",
+     "start_time": "2024-04-10T06:12:08.066174Z"
     }
    },
    "outputs": [
     {
-     "name": "stdout",
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_df.trial.iloc[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "id": "0712267b",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-04-10T06:14:59.604305Z",
+     "start_time": "2024-04-10T06:14:59.550411Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def compute_prob_interval1(data_df, trial_id='trial', response_id='response', stim_id='stim_order', double_pass_id='double_pass_id', p_int_1_identifier = 'p_int1'):\n",
+    "    ''' Computes probability that each subject defined unique in session_identifiers responds true to the first interval, i.e. to the stimulus in each trial identified by order_identifier = 0\n",
+    "    This computes p_int1 only on the subset of repeated trials, and assumes that the dataset already has a column (e.g. double_pass_id) identifying repeated trials. Use utils.index_double_pass_trials to create that column if doesn't exist. \n",
+    "    '''\n",
+    "\n",
+    "    def first_answer(group, stim_id, response_id):    \n",
+    "        resp = group.sort_values(by=stim_id)[response_id].iloc[0]\n",
+    "        return resp==1\n",
+    "    \n",
+    "    # compute agreements for each double_pass trial\n",
+    "    firsts = data_df[data_df[double_pass_id].notna()].groupby(trial_id).apply(lambda group: first_answer(group, stim_id, response_id))\n",
+    "    \n",
+    "    return firsts.sum()/len(firsts)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "id": "f93df38c",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-04-10T06:15:00.751582Z",
+     "start_time": "2024-04-10T06:15:00.656700Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
      "output_type": "stream",
      "text": [
-      "kernel\n",
-      "internal_noise_std\n",
-      "criteria\n",
-      "bla\n"
+      "C:\\Users\\Aucouturier\\AppData\\Local\\Temp\\ipykernel_18576\\1815507022.py:8: FutureWarning: Index.ravel returning ndarray is deprecated; in a future version this will return a view on self.\n",
+      "  pass_count_df.columns = [\"_\".join(x) for x in pass_count_df.columns.ravel()]\n"
      ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0.5"
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "for k in observer_params: \n",
-    "    print(k)"
+    "data_df = pd.read_csv('../data/pitch_interrogation/results_subj_20111971.csv')\n",
+    "data_df = index_double_pass_trials(data_df, trial_id='trial',double_pass_id='double_pass_id',value_id='pitch')\n",
+    "compute_prob_interval1(data_df, trial_id='trial', response_id='response', stim_id='stim_order', double_pass_id='double_pass_id')  \n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "3df8cfe1",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-04-10T05:29:57.912823Z",
+     "start_time": "2024-04-10T05:29:57.802132Z"
+    },
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.72"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def same_answer(group, trial_id='trial', response_id='response'):\n",
+    "    \n",
+    "    d = group.groupby(trial_id).agg({response_id: lambda group: tuple(group)}).reset_index()\n",
+    "    return d.response.nunique()==1\n",
+    "    \n",
+    "\n",
+    "d = data_df.groupby('double_pass_id').apply(lambda group: same_answer(group))\n",
+    "d.sum()/len(d)\n",
+    "\n",
+    "\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "62579d26",
+   "id": "612dc5a9",
    "metadata": {},
    "outputs": [],
    "source": []

From 3e42e89e6251203ad24b56f13668d480c61e8a0e Mon Sep 17 00:00:00 2001
From: JJ Aucouturier <aucouturier@gmail.com>
Date: Wed, 10 Apr 2024 08:58:09 +0200
Subject: [PATCH 13/17] made kernel_distance with keyword

---
 python/palin/simulation/kernel_distance.py | 39 ++++++++++++++++++++++
 1 file changed, 39 insertions(+)
 create mode 100644 python/palin/simulation/kernel_distance.py

diff --git a/python/palin/simulation/kernel_distance.py b/python/palin/simulation/kernel_distance.py
new file mode 100644
index 0000000..0789e27
--- /dev/null
+++ b/python/palin/simulation/kernel_distance.py
@@ -0,0 +1,39 @@
+
+from .analyser import Analyser
+from palin.metrics import metrics as me
+
+class KernelDistance(Analyser): 
+
+    def __init__(self, kernel_extractor, distance='CORR'):
+        self.kernel_extractor = kernel_extractor
+        self.distance = distance
+        
+    def analyse(self, experiment, participant, participant_responses): 
+
+        true_kernel = self.kernel_extractor.normalize_kernel(participant.kernel)
+
+        estimated_kernel = self.estimate_kernel(experiment, participant_responses)
+
+        return self.compute_distance(estimated_kernel, true_kernel)
+
+    def estimate_kernel(self, experiment, participant_responses, normalize=True): 
+
+        responses_df = self.to_df(experiment, participant_responses)
+
+        kernel_df = self.kernel_extractor.extract_single_kernel(data_df = responses_df,
+        feature_id = 'feature', value_id = 'value', response_id = 'response')
+
+        if normalize: 
+            kernel_df = self.kernel_extractor.normalize_kernel(kernel_df)
+
+        return list(kernel_df.kernel_value)
+
+    def normalize_kernel(self, kernel): 
+
+        return self.kernel_extractor.normalize_kernel(kernel)
+
+    def compute_distance(self, kernel_1, kernel_2): 
+        return me.kernel_distance(kernel_1, kernel_2, type=self.distance)
+
+        
+        
\ No newline at end of file

From efbaa7324177999dd4208026c3d27f2ef8c40dd6 Mon Sep 17 00:00:00 2001
From: JJ Aucouturier <aucouturier@gmail.com>
Date: Thu, 11 Apr 2024 06:22:00 +0200
Subject: [PATCH 14/17] added internal noise in sim

---
 python/model.csv                              | 5001 +++++++++++++++++
 python/palin/internal_noise/double_pass.py    |   78 +-
 .../__pycache__/simulation.cpython-38.pyc     |  Bin 2445 -> 2501 bytes
 python/palin/simulation/analyser.py           |    4 +-
 .../simulation/double_pass_experiment.py      |   23 +
 .../simulation/double_pass_statistics.py      |   20 +
 .../palin/simulation/internal_noise_value.py  |   32 +
 python/palin/simulation/simulation.py         |   45 +-
 python/sandbox.ipynb                          |  959 +++-
 9 files changed, 5963 insertions(+), 199 deletions(-)
 create mode 100644 python/model.csv
 create mode 100644 python/palin/simulation/double_pass_experiment.py
 create mode 100644 python/palin/simulation/double_pass_statistics.py
 create mode 100644 python/palin/simulation/internal_noise_value.py

diff --git a/python/model.csv b/python/model.csv
new file mode 100644
index 0000000..2c57d2a
--- /dev/null
+++ b/python/model.csv
@@ -0,0 +1,5001 @@
+,n_trials,n_repeated,trial_type,n_features,external_noise_std,kernel,internal_noise_std,criteria,run,metric
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diff --git a/python/palin/internal_noise/double_pass.py b/python/palin/internal_noise/double_pass.py
index 9fad4f2..f3c62ff 100644
--- a/python/palin/internal_noise/double_pass.py
+++ b/python/palin/internal_noise/double_pass.py
@@ -8,40 +8,50 @@
 
 import pandas as pd
 import numpy as np
+import os.path
+import warnings
+import ast
 from .internal_noise_extractor import InternalNoiseExtractor
 from ..simulation.linear_observer import LinearObserver
 from ..simulation.simple_experiment import SimpleExperiment
 from ..simulation.trial import Int2Trial, Int1Trial 
+from ..simulation.double_pass_experiment import DoublePassExperiment
+from ..simulation.trial import Int2Trial, Int1Trial 
+from ..simulation.linear_observer import LinearObserver
+from ..simulation.double_pass_statistics import DoublePassStatistics
+from ..simulation.simulation import Simulation as Sim
+
 
 class DoublePass(InternalNoiseExtractor):
 
     @classmethod
-    def extract_single_internal_noise(cls,data_df, trial_id, stim_id = 'stim', feature_id = 'feature', value_id = 'value', response_id = 'response'):
+    def extract_single_internal_noise(cls,data_df, trial_id, stim_id, feature_id, value_id, response_id, model_file, rebuild_model=False, internal_noise_range=np.arange(0,5,.1),criteria_range=np.arange(-5,5,1), n_repeated_trials=100, n_runs=10):
 
         double_pass_id = 'double_pass_id' # column by which to identify double pass trials
 
         # index double pass trials
         data_df = cls.index_double_pass_trials(data_df, trial_id=trial_id, value_id = value_id, double_pass_id = double_pass_id)
         # compute probability of agreement over double pass
-        prob_agree = compute_prob_agreement(data_df, trial_id=trial_id, response_id=response_id, double_pass_id=double_pass_id)
+        prob_agree = cls.compute_prob_agreement(data_df, trial_id=trial_id, response_id=response_id, double_pass_id=double_pass_id)
         # compute probability of choosing first response option
-        prob_first = compute_prob_first(data_df, trial_id=trial_id, response_id=response_id, stim_id=stim_id, double_pass_id=double_pass_id)
+        prob_first = cls.compute_prob_first(data_df, trial_id=trial_id, response_id=response_id, stim_id=stim_id, double_pass_id=double_pass_id)
 
-        return 0
+        internal_noise, criteria = cls.estimate_noise_criteria(prob_agree, prob_first, model_file, rebuild_model, internal_noise_range,criteria_range, n_repeated_trials, n_runs)
+
+        return internal_noise,criteria
 
     def __str__(self): 
         return 'Double-Pass method'
 
     @classmethod
-    def index_double_pass_trials(cls, data_df, trial_id='trial',double_pass_id='double_pass_id',value_id='stim_parameter_id'):
-    ''' identify repeated trials in experimental sessions (i.e. 'double pass trials'), and tag them with a unique id stored in a new column.
-    '''
+    def index_double_pass_trials(cls, data_df, trial_id='trial',double_pass_id='double_pass_id',value_id='value'):
+
         # represent the several values of a given trial (ex. 6 features for interval 1, 6 features for interval 2) as a tuple 
-        frozen_set_df = data_df.groupby(trial_id).agg({value_id: lambda group: tuple(group)}).reset_index()
+        set_df = data_df.groupby(trial_id).agg({value_id: lambda group: tuple(group)}).reset_index()
 
         # count how many trials have each unique pair of stimuli
-        pass_count_df = frozen_set_df.groupby(value_id).agg({trial_id: ['nunique','first','last']})
-        pass_count_df.columns = ["_".join(x) for x in pass_count_df.columns.ravel()]
+        pass_count_df = set_df.groupby(value_id).agg({trial_id: ['nunique','first','last']})
+        pass_count_df.columns = ["_".join(x) for x in pass_count_df.columns]
         pass_count_df = pass_count_df.reset_index()
 
         # identify pairs of stimuli that have 2 trials (i.e. for which there has been a double pass)
@@ -86,23 +96,41 @@ def first_option(group, stim_id, response_id):
         return firsts.sum()/len(firsts)
 
     @classmethod
-    def simulate_observer(cls,internal_noise_std,criteria, n_trials, n_blocks=1): 
+    def estimate_noise_criteria(cls,prob_agree, prob_first, model_file,rebuild_model=False, internal_noise_range=np.arange(0,5,.1),criteria_range=np.arange(-5,5,1), n_repeated_trials=100, n_runs=10): 
 
-        # simulate observer with (criteria, internal_noise_sigma)
-        # in the midterm, this should be done with a Simulation object, for which we need a prob_a, prob_first analyser
+        # load model or rebuild
+        if os.path.isfile(model_file) & ~rebuild_model: 
+            model_df = pd.read_csv(model_file, index_col=0)
+        else:
+            model_df = cls.build_model(internal_noise_range, criteria_range, n_repeated_trials, n_runs)
+            model_df.to_csv(model_file)
 
-        obs = LinearObserver(kernel=[1], internal_noise_std=internal_noise_std, criteria=criteria)
-        exp = SimpleExperiment(n_trials=n_trials, trial_type=Int2Trial, n_features=1, external_noise_std=1)
+        # find internal_noise & criteria settings that minimizes distance to prob_agree and prob_first 
 
-        responses_pass_1 = obs.respond_to_experiment(exp)
-        responses_pass_2 = obs.respond_to_experiment(exp)
-    
-        # probability interval 1 (average of prob in both pass)
-        prob_first = (np.mean(responses_pass_1) + np.mean(responses_pass_2))/2
-    
-        #probability of agreement between pass
-        prob_agree = np.mean(responses_pass_1==responses_pass_2) 
-    
-        return prob_agree,prob_first
+        model_df['dist'] = model_df.apply(lambda row: (ast.literal_eval(row.metric)[0]-prob_agree)**2 + (ast.literal_eval(row.metric)[1]-prob_first)**2, axis=1)
+
+        best_match = model_df[model_df.dist==model_df.dist.min()]
+
+        return best_match.internal_noise_std.iloc[0], best_match.criteria.iloc[0]
+
+    @classmethod
+    def build_model(cls,internal_noise_range=np.arange(0,5,.1),criteria_range=np.arange(-5,5,1), n_repeated_trials=100, n_runs=10): 
+
+        print('Rebuilding double-pass model')
+
+        observer_params = {'kernel':[[1]],
+                   'internal_noise_std':internal_noise_range, 
+                  'criteria':criteria_range}
+        experiment_params = {'n_trials':[n_repeated_trials],
+                     'n_repeated':[n_repeated_trials],
+                     'trial_type': [Int2Trial],
+                     'n_features': [1],
+                     'external_noise_std': [1]}
+        analyser_params = {}
+
+        sim = Sim(DoublePassExperiment, experiment_params,
+              LinearObserver, observer_params, 
+              DoublePassStatistics, analyser_params)
+        return sim.run_all(n_runs=n_runs, verbose=False)
 
        
\ No newline at end of file
diff --git a/python/palin/simulation/__pycache__/simulation.cpython-38.pyc b/python/palin/simulation/__pycache__/simulation.cpython-38.pyc
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diff --git a/python/palin/simulation/analyser.py b/python/palin/simulation/analyser.py
index f887e67..5b1c7e6 100644
--- a/python/palin/simulation/analyser.py
+++ b/python/palin/simulation/analyser.py
@@ -28,8 +28,8 @@ def to_df(csl, experiment, responses):
                     values.append(value)
                     resps.append(True if response == num_stim else False)
     
-        return pd.DataFrame.from_dict({'trial_id': trial_ids,
-                                   'stim_order': stim_orders,
+        return pd.DataFrame.from_dict({'trial': trial_ids,
+                                   'stim': stim_orders,
                                    'feature': features,
                                    'value': values, 
                                    'response': resps})
diff --git a/python/palin/simulation/double_pass_experiment.py b/python/palin/simulation/double_pass_experiment.py
new file mode 100644
index 0000000..bc00e1d
--- /dev/null
+++ b/python/palin/simulation/double_pass_experiment.py
@@ -0,0 +1,23 @@
+import numpy as np
+
+from .simple_experiment import SimpleExperiment
+
+
+class DoublePassExperiment(SimpleExperiment): 
+
+    def __init__(self, n_trials, n_repeated, trial_type, n_features, external_noise_std): 
+        # init parameters
+        self.n_repeated = n_repeated
+        super().__init__(n_trials, trial_type, n_features, external_noise_std)
+        # generate trials
+        self.generate_trials() 
+
+    def generate_trials(self):
+        
+        # generate n_trials
+        super().generate_trials()
+
+        # and then an extra n_repeated trials copied from the original trials (first n_repeated trials) 
+        if self.n_repeated > self.n_trials: 
+            self.n_repeated = self.n_trials
+        self.trials += self.trials[:self.n_repeated]
\ No newline at end of file
diff --git a/python/palin/simulation/double_pass_statistics.py b/python/palin/simulation/double_pass_statistics.py
new file mode 100644
index 0000000..b3e1f08
--- /dev/null
+++ b/python/palin/simulation/double_pass_statistics.py
@@ -0,0 +1,20 @@
+
+from .analyser import Analyser
+from palin.internal_noise.double_pass import DoublePass
+
+class DoublePassStatistics(Analyser): 
+
+    def analyse(self, experiment, participant, participant_responses): 
+
+        responses_df = self.to_df(experiment, participant_responses)
+
+        # index double_pass
+        responses_df = DoublePass.index_double_pass_trials(data_df = responses_df, 
+            trial_id='trial',value_id='value', double_pass_id='double_pass_id')
+
+        # compute probability of agreement over double pass
+        prob_agree = DoublePass.compute_prob_agreement(responses_df, trial_id='trial', response_id='response', double_pass_id='double_pass_id')
+        # compute probability of choosing first response option
+        prob_first = DoublePass.compute_prob_first(responses_df, trial_id='trial', response_id='response', stim_id='stim', double_pass_id='double_pass_id')
+
+        return prob_agree, prob_first
\ No newline at end of file
diff --git a/python/palin/simulation/internal_noise_value.py b/python/palin/simulation/internal_noise_value.py
new file mode 100644
index 0000000..ecece73
--- /dev/null
+++ b/python/palin/simulation/internal_noise_value.py
@@ -0,0 +1,32 @@
+
+from .analyser import Analyser
+import numpy as np
+
+class InternalNoiseValue(Analyser): 
+
+    def __init__(self, internal_noise_extractor, model_file, rebuild_model = False, internal_noise_range=np.arange(0,5,.1),criteria_range=np.arange(-5,5,1), n_repeated_trials=100, n_runs=10):
+        self.internal_noise_extractor = internal_noise_extractor
+        self.model_file = model_file
+        self.rebuild_model = rebuild_model
+        self.internal_noise_range = internal_noise_range
+        self.criteria_range = criteria_range
+        self.n_repeated_trials = n_repeated_trials
+        self.n_runs = n_runs
+        
+    def analyse(self, experiment, participant, participant_responses): 
+
+        #true_internal_noise = participant.internal_noise_std
+
+        return self.estimate_internal_noise(experiment, participant_responses)[0]
+
+    def estimate_internal_noise(self, experiment, participant_responses): 
+
+        responses_df = self.to_df(experiment, participant_responses)
+
+        internal_noise = self.internal_noise_extractor.extract_single_internal_noise(data_df = responses_df,
+         trial_id = 'trial', stim_id = 'stim', feature_id = 'feature', value_id = 'value', response_id = 'response', model_file = self.model_file,
+         internal_noise_range=self.internal_noise_range, criteria_range=self.criteria_range, n_repeated_trials=self.n_repeated_trials, n_runs=self.n_runs)
+
+        return internal_noise
+        
+        
\ No newline at end of file
diff --git a/python/palin/simulation/simulation.py b/python/palin/simulation/simulation.py
index 51f9930..e9e1960 100644
--- a/python/palin/simulation/simulation.py
+++ b/python/palin/simulation/simulation.py
@@ -16,11 +16,10 @@ def __init__(self, experiment, experiment_params, observer, observer_params, ana
         self.experiment_params = experiment_params
         self.observer_params = observer_params
         self.analyser_params = analyser_params
-        self.run_params = self.generate_runs(self.experiment_params,self.observer_params,self.analyser_params)
-        print("generated %d runs"%len(self.run_params))
-
+        self.config_params = self.generate_configs(self.experiment_params,self.observer_params,self.analyser_params)
+        
     @classmethod
-    def generate_runs(cls, experiment_params, observer_params, analyser_params): 
+    def generate_configs(cls, experiment_params, observer_params, analyser_params): 
         # construct simulation plan
         sim_params ={}
         for d in (experiment_params, observer_params, analyser_params): 
@@ -29,30 +28,36 @@ def generate_runs(cls, experiment_params, observer_params, analyser_params):
         return [dict(zip(keys, v)) for v in itertools.product(*values)]
 
 
-    def run_all(self, n_samples): 
+    def run_all(self, n_runs, verbose=True): 
+        if verbose: 
+            print("Running %d configs"%len(self.config_params))
+
         runs = []
-        for run_param in self.run_params: 
-            print(run_param)
-            for sample in np.arange(n_samples): 
-                print('.',end='')
-                res = self.run(run_param)
-                run_res = run_param.copy() 
-                run_res.update({'sample':sample, 'metric':res})
+        for config_param in self.config_params: 
+            if verbose: 
+                print(config_param)
+            for run in np.arange(n_runs): 
+                if verbose: 
+                    print('.',end='')
+                res = self.run(config_param)
+                run_res = config_param.copy() 
+                run_res.update({'run':run, 'metric':res})
                 runs.append(run_res)
-            print('')
+            if verbose: 
+                print(';')
         return pd.DataFrame(runs)
 
 
-    def run(self, run_param): 
+    def run(self, config_param): 
 
         # separate this run's parameters into distinct sets
-        run_experiment_params = {k: v for k, v in run_param.items() if k in self.experiment_params}
-        run_observer_params = {k: v for k, v in run_param.items() if k in self.observer_params}
-        run_analyser_params = {k: v for k, v in run_param.items() if k in self.analyser_params}
+        config_experiment_params = {k: v for k, v in config_param.items() if k in self.experiment_params}
+        config_observer_params = {k: v for k, v in config_param.items() if k in self.observer_params}
+        config_analyser_params = {k: v for k, v in config_param.items() if k in self.analyser_params}
 
-        exp = self.experiment(**run_experiment_params)
-        obs = self.observer(**run_observer_params)
-        ana = self.analyser(**run_analyser_params)
+        exp = self.experiment(**config_experiment_params)
+        obs = self.observer(**config_observer_params)
+        ana = self.analyser(**config_analyser_params)
 
         responses = obs.respond_to_experiment(exp)
         
diff --git a/python/sandbox.ipynb b/python/sandbox.ipynb
index 872ddbb..8e60145 100644
--- a/python/sandbox.ipynb
+++ b/python/sandbox.ipynb
@@ -2,15 +2,24 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "264735f2",
+   "execution_count": 51,
+   "id": "7de7295a",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-10T04:07:14.254091Z",
-     "start_time": "2024-04-10T04:07:14.212928Z"
+     "end_time": "2024-04-10T12:37:25.098785Z",
+     "start_time": "2024-04-10T12:37:25.041938Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The autoreload extension is already loaded. To reload it, use:\n",
+      "  %reload_ext autoreload\n"
+     ]
+    }
+   ],
    "source": [
     "%load_ext autoreload\n",
     "%autoreload 2"
@@ -18,12 +27,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "c7f458e5",
+   "execution_count": 179,
+   "id": "e244d137",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-10T04:07:15.786461Z",
-     "start_time": "2024-04-10T04:07:14.480236Z"
+     "end_time": "2024-04-11T04:19:40.595606Z",
+     "start_time": "2024-04-11T04:19:40.530803Z"
     }
    },
    "outputs": [],
@@ -36,12 +45,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "187bfd56",
+   "execution_count": 53,
+   "id": "c49b53cd",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-10T04:07:15.833288Z",
-     "start_time": "2024-04-10T04:07:15.787409Z"
+     "end_time": "2024-04-10T12:37:27.663173Z",
+     "start_time": "2024-04-10T12:37:27.618292Z"
     }
    },
    "outputs": [],
@@ -51,35 +60,346 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "edb2ad9c",
+   "execution_count": 123,
+   "id": "baa1e06a",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-10T04:13:23.133408Z",
-     "start_time": "2024-04-10T04:13:23.083499Z"
+     "end_time": "2024-04-10T12:56:54.870854Z",
+     "start_time": "2024-04-10T12:56:54.764155Z"
     }
    },
    "outputs": [],
    "source": [
-    "from palin.simulation.simple_experiment import SimpleExperiment as Exp\n",
+    "from palin.simulation.simple_experiment import SimpleExperiment\n",
+    "from palin.simulation.double_pass_experiment import DoublePassExperiment\n",
     "from palin.simulation.trial import Int2Trial, Int1Trial \n",
-    "from palin.simulation.linear_observer import LinearObserver as Obs\n",
-    "from palin.simulation.kernel_distance import KernelDistance as Analyser\n",
+    "from palin.simulation.linear_observer import LinearObserver\n",
+    "from palin.simulation.kernel_distance import KernelDistance\n",
+    "from palin.simulation.internal_noise_value import InternalNoiseValue\n",
+    "from palin.simulation.double_pass_statistics import DoublePassStatistics\n",
     "from palin.kernels.classification_images import ClassificationImage\n",
+    "from palin.internal_noise.double_pass import DoublePass\n",
     "from palin.simulation.simulation import Simulation as Sim"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "fa2978b0",
+   "metadata": {},
+   "source": [
+    "## Simulate with internal noise"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd21029c",
+   "metadata": {},
+   "source": [
+    "Single run"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "id": "a860c359",
+   "execution_count": 122,
+   "id": "2b2b91ae",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-09T16:49:31.299979Z",
-     "start_time": "2024-04-09T16:49:31.293995Z"
+     "end_time": "2024-04-10T12:42:09.804159Z",
+     "start_time": "2024-04-10T12:42:09.665497Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "E:\\WORK\\DO\\2022\\palin\\python\\palin\\internal_noise\\double_pass.py:43: FutureWarning: Index.ravel returning ndarray is deprecated; in a future version this will return a view on self.\n",
+      "  pass_count_df.columns = [\"_\".join(x) for x in pass_count_df.columns.ravel()]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0.24"
+      ]
+     },
+     "execution_count": 122,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# single run: \n",
+    "exp = DoublePassExperiment(n_trials = 100, n_repeated=50,\n",
+    "          trial_type = Int2Trial, \n",
+    "          n_features = 5, \n",
+    "          external_noise_std = 100)\n",
+    "obs = Obs.with_random_kernel(n_features = exp.n_features, \n",
+    "                            internal_noise_std = 0, \n",
+    "                            criteria = 1)\n",
+    "responses = obs.respond_to_experiment(exp)\n",
+    "ana = InternalNoiseValue(internal_noise_extractor = DoublePass)\n",
+    "ana.analyse(exp, obs, responses)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6396b632",
+   "metadata": {},
+   "source": [
+    "Simulation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "21b866b8",
+   "metadata": {
+    "ExecuteTime": {
+     "start_time": "2024-04-11T04:20:32.079Z"
+    },
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running 1 configs\n",
+      "{'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 100, 'kernel': [1], 'internal_noise_std': 0, 'criteria': 0, 'internal_noise_extractor': <class 'palin.internal_noise.double_pass.DoublePass'>, 'model_file': 'model_11_04_2024.csv', 'rebuild_model': False, 'internal_noise_range': array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. , 1.1, 1.2,\n",
+      "       1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2. , 2.1, 2.2, 2.3, 2.4, 2.5,\n",
+      "       2.6, 2.7, 2.8, 2.9, 3. , 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8,\n",
+      "       3.9, 4. , 4.1, 4.2, 4.3, 4.4, 4.5, 4.6, 4.7, 4.8, 4.9, 5. ]), 'criteria_range': array([-5.00000000e+00, -4.90000000e+00, -4.80000000e+00, -4.70000000e+00,\n",
+      "       -4.60000000e+00, -4.50000000e+00, -4.40000000e+00, -4.30000000e+00,\n",
+      "       -4.20000000e+00, -4.10000000e+00, -4.00000000e+00, -3.90000000e+00,\n",
+      "       -3.80000000e+00, -3.70000000e+00, -3.60000000e+00, -3.50000000e+00,\n",
+      "       -3.40000000e+00, -3.30000000e+00, -3.20000000e+00, -3.10000000e+00,\n",
+      "       -3.00000000e+00, -2.90000000e+00, -2.80000000e+00, -2.70000000e+00,\n",
+      "       -2.60000000e+00, -2.50000000e+00, -2.40000000e+00, -2.30000000e+00,\n",
+      "       -2.20000000e+00, -2.10000000e+00, -2.00000000e+00, -1.90000000e+00,\n",
+      "       -1.80000000e+00, -1.70000000e+00, -1.60000000e+00, -1.50000000e+00,\n",
+      "       -1.40000000e+00, -1.30000000e+00, -1.20000000e+00, -1.10000000e+00,\n",
+      "       -1.00000000e+00, -9.00000000e-01, -8.00000000e-01, -7.00000000e-01,\n",
+      "       -6.00000000e-01, -5.00000000e-01, -4.00000000e-01, -3.00000000e-01,\n",
+      "       -2.00000000e-01, -1.00000000e-01, -1.77635684e-14,  1.00000000e-01,\n",
+      "        2.00000000e-01,  3.00000000e-01,  4.00000000e-01,  5.00000000e-01,\n",
+      "        6.00000000e-01,  7.00000000e-01,  8.00000000e-01,  9.00000000e-01,\n",
+      "        1.00000000e+00,  1.10000000e+00,  1.20000000e+00,  1.30000000e+00,\n",
+      "        1.40000000e+00,  1.50000000e+00,  1.60000000e+00,  1.70000000e+00,\n",
+      "        1.80000000e+00,  1.90000000e+00,  2.00000000e+00,  2.10000000e+00,\n",
+      "        2.20000000e+00,  2.30000000e+00,  2.40000000e+00,  2.50000000e+00,\n",
+      "        2.60000000e+00,  2.70000000e+00,  2.80000000e+00,  2.90000000e+00,\n",
+      "        3.00000000e+00,  3.10000000e+00,  3.20000000e+00,  3.30000000e+00,\n",
+      "        3.40000000e+00,  3.50000000e+00,  3.60000000e+00,  3.70000000e+00,\n",
+      "        3.80000000e+00,  3.90000000e+00,  4.00000000e+00,  4.10000000e+00,\n",
+      "        4.20000000e+00,  4.30000000e+00,  4.40000000e+00,  4.50000000e+00,\n",
+      "        4.60000000e+00,  4.70000000e+00,  4.80000000e+00,  4.90000000e+00])}\n",
+      ".Rebuilding double-pass model\n"
+     ]
+    }
+   ],
+   "source": [
+    "observer_params = {'kernel':[[1]],\n",
+    "                   'internal_noise_std':[0], \n",
+    "                  'criteria':[0]}\n",
+    "experiment_params = {'n_trials':[1000],#np.arange(1,1000,100),\n",
+    "                     'n_repeated':[1000],\n",
+    "                     'trial_type': [Int2Trial],\n",
+    "                     'n_features': [1],\n",
+    "                     'external_noise_std': [100]}\n",
+    "analyser_params = {'internal_noise_extractor':[DoublePass], \n",
+    "                  'model_file': ['model_11_04_2024.csv'], \n",
+    "                  'rebuild_model': [False],\n",
+    "                   'internal_noise_range':[np.arange(0,5.1,0.1)],\n",
+    "                   'criteria_range':[np.arange(-5,5,0.1)]\n",
+    "                  }\n",
+    "\n",
+    "\n",
+    "sim = Sim(DoublePassExperiment, experiment_params, \n",
+    "          LinearObserver, observer_params, \n",
+    "          InternalNoiseValue, analyser_params)\n",
+    "sim_df = sim.run_all(n_runs=1)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 177,
+   "id": "0264e72a",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-04-11T04:11:28.822698Z",
+     "start_time": "2024-04-11T04:11:28.763833Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>n_trials</th>\n",
+       "      <th>n_repeated</th>\n",
+       "      <th>trial_type</th>\n",
+       "      <th>n_features</th>\n",
+       "      <th>external_noise_std</th>\n",
+       "      <th>kernel</th>\n",
+       "      <th>internal_noise_std</th>\n",
+       "      <th>criteria</th>\n",
+       "      <th>internal_noise_extractor</th>\n",
+       "      <th>model_file</th>\n",
+       "      <th>run</th>\n",
+       "      <th>metric</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1000</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>&lt;class 'palin.simulation.trial.Int2Trial'&gt;</td>\n",
+       "      <td>1</td>\n",
+       "      <td>100</td>\n",
+       "      <td>[1]</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>&lt;class 'palin.internal_noise.double_pass.Doubl...</td>\n",
+       "      <td>model.csv</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1000</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>&lt;class 'palin.simulation.trial.Int2Trial'&gt;</td>\n",
+       "      <td>1</td>\n",
+       "      <td>100</td>\n",
+       "      <td>[1]</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>&lt;class 'palin.internal_noise.double_pass.Doubl...</td>\n",
+       "      <td>model.csv</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1000</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>&lt;class 'palin.simulation.trial.Int2Trial'&gt;</td>\n",
+       "      <td>1</td>\n",
+       "      <td>100</td>\n",
+       "      <td>[1]</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>&lt;class 'palin.internal_noise.double_pass.Doubl...</td>\n",
+       "      <td>model.csv</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1000</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>&lt;class 'palin.simulation.trial.Int2Trial'&gt;</td>\n",
+       "      <td>1</td>\n",
+       "      <td>100</td>\n",
+       "      <td>[1]</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>&lt;class 'palin.internal_noise.double_pass.Doubl...</td>\n",
+       "      <td>model.csv</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.7</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   n_trials  n_repeated                                  trial_type  \\\n",
+       "0      1000        1000  <class 'palin.simulation.trial.Int2Trial'>   \n",
+       "1      1000        1000  <class 'palin.simulation.trial.Int2Trial'>   \n",
+       "2      1000        1000  <class 'palin.simulation.trial.Int2Trial'>   \n",
+       "3      1000        1000  <class 'palin.simulation.trial.Int2Trial'>   \n",
+       "\n",
+       "   n_features  external_noise_std kernel  internal_noise_std  criteria  \\\n",
+       "0           1                 100    [1]                   0        -1   \n",
+       "1           1                 100    [1]                   0         1   \n",
+       "2           1                 100    [1]                   1        -1   \n",
+       "3           1                 100    [1]                   1         1   \n",
+       "\n",
+       "                            internal_noise_extractor model_file  run  metric  \n",
+       "0  <class 'palin.internal_noise.double_pass.Doubl...  model.csv    0     0.0  \n",
+       "1  <class 'palin.internal_noise.double_pass.Doubl...  model.csv    0     0.0  \n",
+       "2  <class 'palin.internal_noise.double_pass.Doubl...  model.csv    0     1.0  \n",
+       "3  <class 'palin.internal_noise.double_pass.Doubl...  model.csv    0     0.7  "
+      ]
+     },
+     "execution_count": 177,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sim_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2629230e",
+   "metadata": {},
+   "source": [
+    "## Simulate with kernels"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d808e31a",
+   "metadata": {},
+   "source": [
+    "Single run"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "id": "52fc9460",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-04-10T12:41:07.781836Z",
+     "start_time": "2024-04-10T12:41:07.728949Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.948432263142621"
+      ]
+     },
+     "execution_count": 104,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# single run: \n",
     "exp = Exp(n_trials = 100,\n",
@@ -97,7 +417,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "8d87b6cc",
+   "id": "43c24287",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-04-10T04:42:23.899935Z",
@@ -154,10 +474,291 @@
     "\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "id": "918f61b8",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-04-10T15:36:24.243659Z",
+     "start_time": "2024-04-10T15:36:24.180798Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>n_trials</th>\n",
+       "      <th>n_repeated</th>\n",
+       "      <th>trial_type</th>\n",
+       "      <th>n_features</th>\n",
+       "      <th>external_noise_std</th>\n",
+       "      <th>kernel</th>\n",
+       "      <th>internal_noise_std</th>\n",
+       "      <th>criteria</th>\n",
+       "      <th>sample</th>\n",
+       "      <th>metric</th>\n",
+       "      <th>dist</th>\n",
+       "      <th>prob_agree</th>\n",
+       "      <th>prob_first</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>100</td>\n",
+       "      <td>50</td>\n",
+       "      <td>&lt;class 'palin.simulation.trial.Int2Trial'&gt;</td>\n",
+       "      <td>1</td>\n",
+       "      <td>100</td>\n",
+       "      <td>[1]</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>(1.0, 1.0)</td>\n",
+       "      <td>0.2900</td>\n",
+       "      <td>0.8</td>\n",
+       "      <td>0.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>100</td>\n",
+       "      <td>50</td>\n",
+       "      <td>&lt;class 'palin.simulation.trial.Int2Trial'&gt;</td>\n",
+       "      <td>1</td>\n",
+       "      <td>100</td>\n",
+       "      <td>[1]</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>(1.0, 1.0)</td>\n",
+       "      <td>0.2900</td>\n",
+       "      <td>0.8</td>\n",
+       "      <td>0.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>100</td>\n",
+       "      <td>50</td>\n",
+       "      <td>&lt;class 'palin.simulation.trial.Int2Trial'&gt;</td>\n",
+       "      <td>1</td>\n",
+       "      <td>100</td>\n",
+       "      <td>[1]</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>(1.0, 1.0)</td>\n",
+       "      <td>0.2900</td>\n",
+       "      <td>0.8</td>\n",
+       "      <td>0.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>100</td>\n",
+       "      <td>50</td>\n",
+       "      <td>&lt;class 'palin.simulation.trial.Int2Trial'&gt;</td>\n",
+       "      <td>1</td>\n",
+       "      <td>100</td>\n",
+       "      <td>[1]</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>(1.0, 0.88)</td>\n",
+       "      <td>0.1844</td>\n",
+       "      <td>0.8</td>\n",
+       "      <td>0.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>100</td>\n",
+       "      <td>50</td>\n",
+       "      <td>&lt;class 'palin.simulation.trial.Int2Trial'&gt;</td>\n",
+       "      <td>1</td>\n",
+       "      <td>100</td>\n",
+       "      <td>[1]</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>(1.0, 0.68)</td>\n",
+       "      <td>0.0724</td>\n",
+       "      <td>0.8</td>\n",
+       "      <td>0.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
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+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>495</th>\n",
+       "      <td>100</td>\n",
+       "      <td>50</td>\n",
+       "      <td>&lt;class 'palin.simulation.trial.Int2Trial'&gt;</td>\n",
+       "      <td>1</td>\n",
+       "      <td>100</td>\n",
+       "      <td>[1]</td>\n",
+       "      <td>4.9</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>(0.56, 0.5)</td>\n",
+       "      <td>0.0576</td>\n",
+       "      <td>0.8</td>\n",
+       "      <td>0.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>496</th>\n",
+       "      <td>100</td>\n",
+       "      <td>50</td>\n",
+       "      <td>&lt;class 'palin.simulation.trial.Int2Trial'&gt;</td>\n",
+       "      <td>1</td>\n",
+       "      <td>100</td>\n",
+       "      <td>[1]</td>\n",
+       "      <td>4.9</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>(0.56, 0.34)</td>\n",
+       "      <td>0.0832</td>\n",
+       "      <td>0.8</td>\n",
+       "      <td>0.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>497</th>\n",
+       "      <td>100</td>\n",
+       "      <td>50</td>\n",
+       "      <td>&lt;class 'palin.simulation.trial.Int2Trial'&gt;</td>\n",
+       "      <td>1</td>\n",
+       "      <td>100</td>\n",
+       "      <td>[1]</td>\n",
+       "      <td>4.9</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>(0.54, 0.41)</td>\n",
+       "      <td>0.0757</td>\n",
+       "      <td>0.8</td>\n",
+       "      <td>0.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>498</th>\n",
+       "      <td>100</td>\n",
+       "      <td>50</td>\n",
+       "      <td>&lt;class 'palin.simulation.trial.Int2Trial'&gt;</td>\n",
+       "      <td>1</td>\n",
+       "      <td>100</td>\n",
+       "      <td>[1]</td>\n",
+       "      <td>4.9</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>(0.62, 0.23)</td>\n",
+       "      <td>0.1053</td>\n",
+       "      <td>0.8</td>\n",
+       "      <td>0.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>499</th>\n",
+       "      <td>100</td>\n",
+       "      <td>50</td>\n",
+       "      <td>&lt;class 'palin.simulation.trial.Int2Trial'&gt;</td>\n",
+       "      <td>1</td>\n",
+       "      <td>100</td>\n",
+       "      <td>[1]</td>\n",
+       "      <td>4.9</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>(0.72, 0.16)</td>\n",
+       "      <td>0.1220</td>\n",
+       "      <td>0.8</td>\n",
+       "      <td>0.5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>500 rows × 13 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     n_trials  n_repeated                                  trial_type  \\\n",
+       "0         100          50  <class 'palin.simulation.trial.Int2Trial'>   \n",
+       "1         100          50  <class 'palin.simulation.trial.Int2Trial'>   \n",
+       "2         100          50  <class 'palin.simulation.trial.Int2Trial'>   \n",
+       "3         100          50  <class 'palin.simulation.trial.Int2Trial'>   \n",
+       "4         100          50  <class 'palin.simulation.trial.Int2Trial'>   \n",
+       "..        ...         ...                                         ...   \n",
+       "495       100          50  <class 'palin.simulation.trial.Int2Trial'>   \n",
+       "496       100          50  <class 'palin.simulation.trial.Int2Trial'>   \n",
+       "497       100          50  <class 'palin.simulation.trial.Int2Trial'>   \n",
+       "498       100          50  <class 'palin.simulation.trial.Int2Trial'>   \n",
+       "499       100          50  <class 'palin.simulation.trial.Int2Trial'>   \n",
+       "\n",
+       "     n_features  external_noise_std kernel  internal_noise_std  criteria  \\\n",
+       "0             1                 100    [1]                 0.0        -5   \n",
+       "1             1                 100    [1]                 0.0        -4   \n",
+       "2             1                 100    [1]                 0.0        -3   \n",
+       "3             1                 100    [1]                 0.0        -2   \n",
+       "4             1                 100    [1]                 0.0        -1   \n",
+       "..          ...                 ...    ...                 ...       ...   \n",
+       "495           1                 100    [1]                 4.9         0   \n",
+       "496           1                 100    [1]                 4.9         1   \n",
+       "497           1                 100    [1]                 4.9         2   \n",
+       "498           1                 100    [1]                 4.9         3   \n",
+       "499           1                 100    [1]                 4.9         4   \n",
+       "\n",
+       "     sample        metric    dist  prob_agree  prob_first  \n",
+       "0         0    (1.0, 1.0)  0.2900         0.8         0.5  \n",
+       "1         0    (1.0, 1.0)  0.2900         0.8         0.5  \n",
+       "2         0    (1.0, 1.0)  0.2900         0.8         0.5  \n",
+       "3         0   (1.0, 0.88)  0.1844         0.8         0.5  \n",
+       "4         0   (1.0, 0.68)  0.0724         0.8         0.5  \n",
+       "..      ...           ...     ...         ...         ...  \n",
+       "495       0   (0.56, 0.5)  0.0576         0.8         0.5  \n",
+       "496       0  (0.56, 0.34)  0.0832         0.8         0.5  \n",
+       "497       0  (0.54, 0.41)  0.0757         0.8         0.5  \n",
+       "498       0  (0.62, 0.23)  0.1053         0.8         0.5  \n",
+       "499       0  (0.72, 0.16)  0.1220         0.8         0.5  \n",
+       "\n",
+       "[500 rows x 13 columns]"
+      ]
+     },
+     "execution_count": 147,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sim_df"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "59b42cc5",
+   "id": "5f7ad1b1",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-04-10T04:42:50.935778Z",
@@ -194,199 +795,253 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
-   "id": "1d2dda2d",
+   "execution_count": 168,
+   "id": "ea12e662",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-10T05:45:07.904380Z",
-     "start_time": "2024-04-10T05:45:07.847512Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "def index_double_pass_trials(data_df, trial_id='trial',double_pass_id='double_pass_id',value_id='stim_parameter_id'):\n",
-    "    \n",
-    "        # represent the several values of a given trial (ex. 6 features for interval 1, 6 features for interval 2) as a frozenset\n",
-    "        frozen_set_df = data_df.groupby(trial_id).agg({value_id: lambda group: tuple(group)}).reset_index()\n",
-    "\n",
-    "        # count how many trials have each unique pair of stimuli\n",
-    "        pass_count_df = frozen_set_df.groupby(value_id).agg({trial_id: ['nunique','first','last']})\n",
-    "        pass_count_df.columns = [\"_\".join(x) for x in pass_count_df.columns.ravel()]\n",
-    "        pass_count_df = pass_count_df.reset_index()\n",
-    "\n",
-    "        # identify pairs of stimuli that have 2 trials (i.e. for which there has been a double pass)\n",
-    "        double_pass_df = pass_count_df[pass_count_df['%s_nunique'%trial_id]==2].reset_index(drop=True)\n",
-    "        \n",
-    "        # assign unique id\n",
-    "        double_pass_df[double_pass_id] = double_pass_df.index\n",
-    "\n",
-    "        # join to base dataset\n",
-    "        double_pass_df = double_pass_df.melt(id_vars=double_pass_id, \n",
-    "                                             value_vars=['%s_first'%trial_id,'%s_last'%trial_id], \n",
-    "                                             var_name='%s_type'%trial_id, \n",
-    "                                             value_name=trial_id)\n",
-    "        data_df= pd.merge(data_df, double_pass_df[[trial_id, double_pass_id]], \n",
-    "                          how=\"left\", on=trial_id)\n",
-    "        return data_df  "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "id": "6cf44e37",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-04-10T05:45:08.457461Z",
-     "start_time": "2024-04-10T05:45:08.403620Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "def compute_prob_agreement(data_df, trial_id='trial', response_id='response', order_id='stim_order', double_pass_id='double_pass_id'):\n",
-    "    # computes the probability of agreement between two responses to a repeated stimuli on the double pass trials \n",
-    "\n",
-    "    def same_answer(group, trial_id, response_id):    \n",
-    "        d = group.groupby(trial_id).agg({response_id: lambda group: tuple(group)}).reset_index()\n",
-    "        return d.response.nunique()==1\n",
-    "    \n",
-    "    # compute agreements for each double_pass trial\n",
-    "    agrees = data_df.groupby(double_pass_id).apply(lambda group: same_answer(group, trial_id, response_id))\n",
-    "    \n",
-    "    # return agreement probability\n",
-    "    return agrees.sum()/len(agrees)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "id": "99e19273",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-04-10T06:12:08.125017Z",
-     "start_time": "2024-04-10T06:12:08.066174Z"
+     "end_time": "2024-04-11T04:08:49.331441Z",
+     "start_time": "2024-04-11T04:08:49.277585Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0"
+       "['subj',\n",
+       " 'trial',\n",
+       " 'block',\n",
+       " 'date',\n",
+       " 'stim',\n",
+       " 'stim_order',\n",
+       " 'response',\n",
+       " 'rt',\n",
+       " 'age',\n",
+       " 'sex',\n",
+       " 'param_index',\n",
+       " 'segment_time',\n",
+       " 'pitch']"
       ]
      },
-     "execution_count": 79,
+     "execution_count": 168,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "data_df.trial.iloc[0]"
+    "list(data_df)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
-   "id": "0712267b",
+   "execution_count": 174,
+   "id": "be5177b7",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-10T06:14:59.604305Z",
-     "start_time": "2024-04-10T06:14:59.550411Z"
+     "end_time": "2024-04-11T04:10:11.894810Z",
+     "start_time": "2024-04-11T04:10:11.830804Z"
     }
    },
    "outputs": [],
    "source": [
-    "def compute_prob_interval1(data_df, trial_id='trial', response_id='response', stim_id='stim_order', double_pass_id='double_pass_id', p_int_1_identifier = 'p_int1'):\n",
-    "    ''' Computes probability that each subject defined unique in session_identifiers responds true to the first interval, i.e. to the stimulus in each trial identified by order_identifier = 0\n",
-    "    This computes p_int1 only on the subset of repeated trials, and assumes that the dataset already has a column (e.g. double_pass_id) identifying repeated trials. Use utils.index_double_pass_trials to create that column if doesn't exist. \n",
-    "    '''\n",
+    "data_df = pd.read_csv('../data/pitch_interrogation/results_subj_20111971.csv')\n",
+    "\n",
+    "\n",
+    "set_df = data_df.groupby('trial').agg({'pitch': lambda group: tuple(group)}).reset_index()\n",
+    "\n",
+    "# count how many trials have each unique pair of stimuli\n",
+    "pass_count_df = set_df.groupby('pitch').agg({'trial': ['nunique','first','last']})\n",
+    "pass_count_df.columns = [\"_\".join(x) for x in pass_count_df.columns]\n",
+    "\n",
     "\n",
-    "    def first_answer(group, stim_id, response_id):    \n",
-    "        resp = group.sort_values(by=stim_id)[response_id].iloc[0]\n",
-    "        return resp==1\n",
-    "    \n",
-    "    # compute agreements for each double_pass trial\n",
-    "    firsts = data_df[data_df[double_pass_id].notna()].groupby(trial_id).apply(lambda group: first_answer(group, stim_id, response_id))\n",
-    "    \n",
-    "    return firsts.sum()/len(firsts)"
+    "\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
-   "id": "f93df38c",
+   "execution_count": 173,
+   "id": "e657a304",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-10T06:15:00.751582Z",
-     "start_time": "2024-04-10T06:15:00.656700Z"
+     "end_time": "2024-04-11T04:09:56.002651Z",
+     "start_time": "2024-04-11T04:09:55.949793Z"
     }
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\Aucouturier\\AppData\\Local\\Temp\\ipykernel_18576\\1815507022.py:8: FutureWarning: Index.ravel returning ndarray is deprecated; in a future version this will return a view on self.\n",
-      "  pass_count_df.columns = [\"_\".join(x) for x in pass_count_df.columns.ravel()]\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
-       "0.5"
+       "['trial_nunique', 'trial_first', 'trial_last']"
       ]
      },
-     "execution_count": 90,
+     "execution_count": 173,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "data_df = pd.read_csv('../data/pitch_interrogation/results_subj_20111971.csv')\n",
-    "data_df = index_double_pass_trials(data_df, trial_id='trial',double_pass_id='double_pass_id',value_id='pitch')\n",
-    "compute_prob_interval1(data_df, trial_id='trial', response_id='response', stim_id='stim_order', double_pass_id='double_pass_id')  \n",
-    "\n",
-    "\n"
+    "[\"_\".join(x) for x in pass_count_df.columns]\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
-   "id": "3df8cfe1",
+   "execution_count": 175,
+   "id": "c4826227",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-10T05:29:57.912823Z",
-     "start_time": "2024-04-10T05:29:57.802132Z"
-    },
-    "scrolled": false
+     "end_time": "2024-04-11T04:10:14.250903Z",
+     "start_time": "2024-04-11T04:10:14.190027Z"
+    }
    },
    "outputs": [
     {
      "data": {
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+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>trial_nunique</th>\n",
+       "      <th>trial_first</th>\n",
+       "      <th>trial_last</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>pitch</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>(-140.284917, 56.043903, -2.142699, -41.999388, -69.522618, 29.697532, 40.565741, 23.45052, 102.584681, 38.2243, -16.427695, 7.421137, -9.369055, -89.176758)</th>\n",
+       "      <td>1</td>\n",
+       "      <td>18</td>\n",
+       "      <td>18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>(-135.344658, -76.172858, -130.999312, -146.306144, -78.266251, 30.254136, -114.179281, 102.170357, 20.715456, 81.972821, -125.018297, 103.374214, -81.704677, 85.109238)</th>\n",
+       "      <td>2</td>\n",
+       "      <td>82</td>\n",
+       "      <td>132</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>(-133.8014, 34.189717, -145.714669, 1.717896, 99.036025, 56.473042, 113.578937, 126.187147, 44.969184, -108.017243, 6.480803, 89.811831, 60.034007, 63.317816)</th>\n",
+       "      <td>2</td>\n",
+       "      <td>69</td>\n",
+       "      <td>119</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>(-133.486308, -51.704384, 123.73441, -122.063627, 105.373754, 42.489978, 65.5363, -67.009894, -124.385437, -32.338754, -145.840775, -6.786611, 89.278278, -47.823672)</th>\n",
+       "      <td>2</td>\n",
+       "      <td>72</td>\n",
+       "      <td>122</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>(-132.910898, -40.348113, -144.469254, -148.365603, 29.209812, -68.789467, 116.539457, 102.52713, 114.03702, -45.723055, -109.210749, -29.266312, -77.798514, -133.179439)</th>\n",
+       "      <td>2</td>\n",
+       "      <td>96</td>\n",
+       "      <td>146</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>(139.821339, -90.088416, -144.376143, -83.117215, -15.266959, -78.645111, 18.56671, 123.541183, -79.641893, -129.036775, -12.372422, 47.099802, 133.796504, 125.051187)</th>\n",
+       "      <td>1</td>\n",
+       "      <td>31</td>\n",
+       "      <td>31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>(141.243943, -34.322435, 10.018864, -24.066951, 45.502655, 107.276086, 58.11038, 46.973159, -61.71182, 14.836428, 30.862549, 28.459321, 2.114815, 86.651936)</th>\n",
+       "      <td>1</td>\n",
+       "      <td>11</td>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>(143.598511, 23.645136, -118.740497, -109.046436, 66.022521, -13.059175, 22.70066, 69.316576, -9.39536, -16.21002, 5.282415, -92.031474, -71.382142, -144.168558)</th>\n",
+       "      <td>2</td>\n",
+       "      <td>80</td>\n",
+       "      <td>130</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>(144.813706, 36.420023, -15.581887, -108.080315, 44.82704, -140.857646, -19.601185, -95.161618, -119.152564, 18.051976, -26.870716, 87.472746, -56.607347, 148.066508)</th>\n",
+       "      <td>1</td>\n",
+       "      <td>33</td>\n",
+       "      <td>33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>(147.824484, -35.75356, -17.671801, 145.037411, 17.430899, -34.827913, -148.430397, 20.473057, -103.427341, -58.67029, -88.181786, -145.892431, -121.19713, 3.215116)</th>\n",
+       "      <td>1</td>\n",
+       "      <td>26</td>\n",
+       "      <td>26</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>100 rows × 3 columns</p>\n",
+       "</div>"
+      ],
       "text/plain": [
-       "0.72"
+       "                                                    trial_nunique  \\\n",
+       "pitch                                                               \n",
+       "(-140.284917, 56.043903, -2.142699, -41.999388,...              1   \n",
+       "(-135.344658, -76.172858, -130.999312, -146.306...              2   \n",
+       "(-133.8014, 34.189717, -145.714669, 1.717896, 9...              2   \n",
+       "(-133.486308, -51.704384, 123.73441, -122.06362...              2   \n",
+       "(-132.910898, -40.348113, -144.469254, -148.365...              2   \n",
+       "...                                                           ...   \n",
+       "(139.821339, -90.088416, -144.376143, -83.11721...              1   \n",
+       "(141.243943, -34.322435, 10.018864, -24.066951,...              1   \n",
+       "(143.598511, 23.645136, -118.740497, -109.04643...              2   \n",
+       "(144.813706, 36.420023, -15.581887, -108.080315...              1   \n",
+       "(147.824484, -35.75356, -17.671801, 145.037411,...              1   \n",
+       "\n",
+       "                                                    trial_first  trial_last  \n",
+       "pitch                                                                        \n",
+       "(-140.284917, 56.043903, -2.142699, -41.999388,...           18          18  \n",
+       "(-135.344658, -76.172858, -130.999312, -146.306...           82         132  \n",
+       "(-133.8014, 34.189717, -145.714669, 1.717896, 9...           69         119  \n",
+       "(-133.486308, -51.704384, 123.73441, -122.06362...           72         122  \n",
+       "(-132.910898, -40.348113, -144.469254, -148.365...           96         146  \n",
+       "...                                                         ...         ...  \n",
+       "(139.821339, -90.088416, -144.376143, -83.11721...           31          31  \n",
+       "(141.243943, -34.322435, 10.018864, -24.066951,...           11          11  \n",
+       "(143.598511, 23.645136, -118.740497, -109.04643...           80         130  \n",
+       "(144.813706, 36.420023, -15.581887, -108.080315...           33          33  \n",
+       "(147.824484, -35.75356, -17.671801, 145.037411,...           26          26  \n",
+       "\n",
+       "[100 rows x 3 columns]"
       ]
      },
-     "execution_count": 70,
+     "execution_count": 175,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "def same_answer(group, trial_id='trial', response_id='response'):\n",
-    "    \n",
-    "    d = group.groupby(trial_id).agg({response_id: lambda group: tuple(group)}).reset_index()\n",
-    "    return d.response.nunique()==1\n",
-    "    \n",
-    "\n",
-    "d = data_df.groupby('double_pass_id').apply(lambda group: same_answer(group))\n",
-    "d.sum()/len(d)\n",
-    "\n",
-    "\n"
+    "pass_count_df"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "612dc5a9",
+   "id": "cca19c67",
    "metadata": {},
    "outputs": [],
    "source": []

From 63f20836155a4ef4f8ab12b6100b9e7282bf28fb Mon Sep 17 00:00:00 2001
From: JJ Aucouturier <aucouturier@gmail.com>
Date: Mon, 22 Apr 2024 16:12:53 +0200
Subject: [PATCH 15/17] latest batch

---
 python/palin/internal_noise/double_pass.py    |  46 +++++++---
 .../__pycache__/simulation.cpython-38.pyc     | Bin 2501 -> 4944 bytes
 python/palin/simulation/analyser.py           |   4 +
 .../simulation/double_pass_statistics.py      |   3 +
 python/palin/simulation/experiment.py         |   7 ++
 .../palin/simulation/internal_noise_value.py  |  11 +--
 python/palin/simulation/kernel_distance.py    |   3 +
 python/palin/simulation/simulation.py         |  84 +++++++++++++++---
 8 files changed, 130 insertions(+), 28 deletions(-)

diff --git a/python/palin/internal_noise/double_pass.py b/python/palin/internal_noise/double_pass.py
index f3c62ff..6fdec7a 100644
--- a/python/palin/internal_noise/double_pass.py
+++ b/python/palin/internal_noise/double_pass.py
@@ -23,12 +23,19 @@
 
 
 class DoublePass(InternalNoiseExtractor):
+    ''' 
+    This class provides methods to estimate internal noise and criteria from reverse correlation data, provided the data has double pass trials. 
+    Internal noise and criteria are computed using the Ponsot/Neri double-pass simulation method, by first computing prob_agree and prob_first from double-pass data, 
+    and then simulating an ideal observer with a range of internal noise and criteria values to find what duplet of values generates prob_agree and prob_first that are closest to those observed in the actual data. 
+    '''
 
     @classmethod
     def extract_single_internal_noise(cls,data_df, trial_id, stim_id, feature_id, value_id, response_id, model_file, rebuild_model=False, internal_noise_range=np.arange(0,5,.1),criteria_range=np.arange(-5,5,1), n_repeated_trials=100, n_runs=10):
-
+        '''
+        Extracts internal noise and criteria for a single observer/session. 
+        To extract for several users/sessions, use the superclass's method extract_internal_noise
+        '''
         double_pass_id = 'double_pass_id' # column by which to identify double pass trials
-
         # index double pass trials
         data_df = cls.index_double_pass_trials(data_df, trial_id=trial_id, value_id = value_id, double_pass_id = double_pass_id)
         # compute probability of agreement over double pass
@@ -45,7 +52,10 @@ def __str__(self):
 
     @classmethod
     def index_double_pass_trials(cls, data_df, trial_id='trial',double_pass_id='double_pass_id',value_id='value'):
-
+        '''
+        Runs over data by a single user, identifies any repeated trials based on the set of their values, and tags them with a column called double_pass_id.
+        At the end of the procedure, data-df[double_pass_id].max() is the total number of repeated trials found in the data.  
+        '''
         # represent the several values of a given trial (ex. 6 features for interval 1, 6 features for interval 2) as a tuple 
         set_df = data_df.groupby(trial_id).agg({value_id: lambda group: tuple(group)}).reset_index()
 
@@ -72,8 +82,9 @@ def index_double_pass_trials(cls, data_df, trial_id='trial',double_pass_id='doub
 
     @classmethod
     def compute_prob_agreement(cls,data_df, trial_id='trial', response_id='response', double_pass_id='double_pass_id'):
-    # computes the probability of agreement between two responses to a repeated stimuli on the double pass trials 
-
+        '''
+        Computes the probability of giving the same response over all pairs of repeated trials (as identified by double_pass_id, see index_double_pass_trials)
+        ''' 
         # compute agreements for each double_pass trial
         def same_answer(group, trial_id, response_id):    
             d = group.groupby(trial_id).agg({response_id: lambda group: tuple(group)}).reset_index()
@@ -85,7 +96,9 @@ def same_answer(group, trial_id, response_id):
     
     @classmethod
     def compute_prob_first(cls, data_df, trial_id='trial', response_id='response', stim_id='stim_order', double_pass_id='double_pass_id'):
-    # Computes probability that responds true to the first interval across the subset of double_pass trialslumn (e.g. double_pass_id) identifying repeated trials. Use utils.index_double_pass_trials to create that column if doesn't exist. 
+        '''
+        Computes probability to choose the first response option (i.e. a measure of response bias) across the subset of double_pass trials 
+        ''' 
     
         # compute first response for each double_pass trial
         def first_option(group, stim_id, response_id):    
@@ -97,7 +110,11 @@ def first_option(group, stim_id, response_id):
 
     @classmethod
     def estimate_noise_criteria(cls,prob_agree, prob_first, model_file,rebuild_model=False, internal_noise_range=np.arange(0,5,.1),criteria_range=np.arange(-5,5,1), n_repeated_trials=100, n_runs=10): 
-
+        '''
+        Estimates internal noise and criteria given a measure of prob_agree and prob_first. 
+        Either uses a prebuilt model (a dataframe previously generated by @build_model and stored as a .csv file), or rebuild a new model. 
+        Searches through a range of possible internal noise and criteria values
+        '''    
         # load model or rebuild
         if os.path.isfile(model_file) & ~rebuild_model: 
             model_df = pd.read_csv(model_file, index_col=0)
@@ -106,8 +123,7 @@ def estimate_noise_criteria(cls,prob_agree, prob_first, model_file,rebuild_model
             model_df.to_csv(model_file)
 
         # find internal_noise & criteria settings that minimizes distance to prob_agree and prob_first 
-
-        model_df['dist'] = model_df.apply(lambda row: (ast.literal_eval(row.metric)[0]-prob_agree)**2 + (ast.literal_eval(row.metric)[1]-prob_first)**2, axis=1)
+        model_df['dist'] = model_df.apply(lambda row: (row.prob_agree-prob_agree)**2 + (row.prob_first-prob_first)**2, axis=1)
 
         best_match = model_df[model_df.dist==model_df.dist.min()]
 
@@ -115,7 +131,10 @@ def estimate_noise_criteria(cls,prob_agree, prob_first, model_file,rebuild_model
 
     @classmethod
     def build_model(cls,internal_noise_range=np.arange(0,5,.1),criteria_range=np.arange(-5,5,1), n_repeated_trials=100, n_runs=10): 
-
+        '''
+        Build a model that associates a range of internal noise and criteria values with their corresponding (simulated) prob_agree and prob_first.
+        This uses a simulated LinearObserver, and returns the model as a dataframe 
+        '''
         print('Rebuilding double-pass model')
 
         observer_params = {'kernel':[[1]],
@@ -131,6 +150,11 @@ def build_model(cls,internal_noise_range=np.arange(0,5,.1),criteria_range=np.ara
         sim = Sim(DoublePassExperiment, experiment_params,
               LinearObserver, observer_params, 
               DoublePassStatistics, analyser_params)
-        return sim.run_all(n_runs=n_runs, verbose=False)
+
+        sim_df = sim.run_all(n_runs=n_runs, verbose=True)
+
+        # average measures over all runs
+        sim_df.groupby(['internal_noise_std','criteria'])[DoublePassStatistics.get_metric_names()].mean()
+        return sim_df
 
        
\ No newline at end of file
diff --git a/python/palin/simulation/__pycache__/simulation.cpython-38.pyc b/python/palin/simulation/__pycache__/simulation.cpython-38.pyc
index 08fb3cd1da29ab8b257603a405757e8d614ef581..1db8caf2783210af54ba6e2e1344e1c9199434d0 100644
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u1`rT9_X&+E2NFKy{-hG2{3)o!D~@jyLAEw>Wo}csR$GW=)>6ORH2((Fl5u<h

delta 1143
zcmZ`%y>App6rYd%zV-TS;6#Y6oQyy|84w6Ch*%aVr+_Gkghjzg5n65*dy~DrWp)n)
zT4N!*MT#|PBBi*i5=)Vb^r;X%Wi)>PN|!z~ytivUig2U*?acf5y*IykuTQ=@6x^Ab
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zkL}VdEn)u3CXiq9u~XW(dw2!1+mLM`@56TCj=hpkD2Qm^dOEvxdz7Yey52q^+Wler
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zDbFKi6M*m*g~faj$7dc>*JiJ5N%ZJOKMC_3-gsjus=<{Pz+9z&qW#B98Q{fcs@Zm!
zGsURqdWR+2JFe#8{k~+#fRf8&0NTpJRD`)ag$FYo4PZbP$uhVx>Hn%|XJs%Hqa<3T
N5K*bpgg1r7-T^=v?^*x=

diff --git a/python/palin/simulation/analyser.py b/python/palin/simulation/analyser.py
index 5b1c7e6..50a5cd3 100644
--- a/python/palin/simulation/analyser.py
+++ b/python/palin/simulation/analyser.py
@@ -9,6 +9,10 @@ class Analyser(ABC):
     def analyse(self,experiment, participant, participant_responses): 
         raise NotImplementedError()
 
+    @abstractmethod
+    def get_metric_names(self):
+        raise NotImplementedError()
+
     @classmethod
     def to_df(csl, experiment, responses): 
 
diff --git a/python/palin/simulation/double_pass_statistics.py b/python/palin/simulation/double_pass_statistics.py
index b3e1f08..677f97b 100644
--- a/python/palin/simulation/double_pass_statistics.py
+++ b/python/palin/simulation/double_pass_statistics.py
@@ -4,6 +4,9 @@
 
 class DoublePassStatistics(Analyser): 
 
+    def get_metric_names(self):
+        return ['prob_agree', 'prob_first']
+
     def analyse(self, experiment, participant, participant_responses): 
 
         responses_df = self.to_df(experiment, participant_responses)
diff --git a/python/palin/simulation/experiment.py b/python/palin/simulation/experiment.py
index 002db6f..b719b49 100644
--- a/python/palin/simulation/experiment.py
+++ b/python/palin/simulation/experiment.py
@@ -1,9 +1,16 @@
 from abc import ABC, abstractmethod
 
 class Experiment(ABC): 
+    '''
+        Abstract class that represents a simulated experimental paradigm, with trials that are then submitted to a simulated observer. 
+        See e.g. @SimpleExperiment for a example of implementation
+    '''
 
     # possibly yield next_trial(), if we want more abstraction ?
     
     @abstractmethod
     def generate_trials(self):
+        '''
+            Generates the experiment's trials, to be called upon __init__()
+        '''
         raise NotImplementedError()
\ No newline at end of file
diff --git a/python/palin/simulation/internal_noise_value.py b/python/palin/simulation/internal_noise_value.py
index ecece73..dace51d 100644
--- a/python/palin/simulation/internal_noise_value.py
+++ b/python/palin/simulation/internal_noise_value.py
@@ -12,21 +12,22 @@ def __init__(self, internal_noise_extractor, model_file, rebuild_model = False,
         self.criteria_range = criteria_range
         self.n_repeated_trials = n_repeated_trials
         self.n_runs = n_runs
+
+    def get_metric_names(self):
+        return ['estimated_internal_noise','estimated_criteria']
         
     def analyse(self, experiment, participant, participant_responses): 
 
-        #true_internal_noise = participant.internal_noise_std
-
-        return self.estimate_internal_noise(experiment, participant_responses)[0]
+        return self.estimate_internal_noise(experiment, participant_responses)
 
     def estimate_internal_noise(self, experiment, participant_responses): 
 
         responses_df = self.to_df(experiment, participant_responses)
 
-        internal_noise = self.internal_noise_extractor.extract_single_internal_noise(data_df = responses_df,
+        internal_noise, criteria = self.internal_noise_extractor.extract_single_internal_noise(data_df = responses_df,
          trial_id = 'trial', stim_id = 'stim', feature_id = 'feature', value_id = 'value', response_id = 'response', model_file = self.model_file,
          internal_noise_range=self.internal_noise_range, criteria_range=self.criteria_range, n_repeated_trials=self.n_repeated_trials, n_runs=self.n_runs)
 
-        return internal_noise
+        return internal_noise, criteria
         
         
\ No newline at end of file
diff --git a/python/palin/simulation/kernel_distance.py b/python/palin/simulation/kernel_distance.py
index 0789e27..6140879 100644
--- a/python/palin/simulation/kernel_distance.py
+++ b/python/palin/simulation/kernel_distance.py
@@ -7,6 +7,9 @@ class KernelDistance(Analyser):
     def __init__(self, kernel_extractor, distance='CORR'):
         self.kernel_extractor = kernel_extractor
         self.distance = distance
+
+    def get_metric_names(self):
+        return [distance.lower()]
         
     def analyse(self, experiment, participant, participant_responses): 
 
diff --git a/python/palin/simulation/simulation.py b/python/palin/simulation/simulation.py
index e9e1960..d0cb543 100644
--- a/python/palin/simulation/simulation.py
+++ b/python/palin/simulation/simulation.py
@@ -2,15 +2,51 @@
 import itertools
 import pandas as pd
 import numpy as np
+from .observer import Observer
+from .experiment import Experiment
+from .analyser import Analyser
 
 class Simulation(ABC): 
+    '''
+    Class that implements a simulation, i.e. a range of simulated @Observers that respond to @Experiments and whose results are analysed with an @Analyser.
+    Simulations are initiated with class names (e.g. SimpleExperiment, LinearObserver, KernelDistance) and a range of parameters that are looped across when run. 
+    Results are returned in a panda dataframe 
+    '''
 
-    def __init__(self, experiment, experiment_params, observer, observer_params, analyser, analyser_params): 
+    def __init__(self, experiment_class, experiment_params, observer_class, observer_params, analyser_class, analyser_params): 
+        '''
+        Initialize a Simulation with class names than implement Experiment, Observer and Analyser, and parameters that define the different configs in which the simulation is run.  
+        Upon running a config, one observer responds to one experiment, and their responses are analysed with one analyser. 
+        When initializing the simulation, each class name is associated with a dictionary of parameters whose keys are the arguments of the __init__() method of the corresponding class name.
+        For instance, if observer_class is LinearObserver, observer_params should be a dictionary with keys kernel,internal_noise_std and criteria.
+        Values for each key should be an iterable (typically a list) of values, which define the different configs. 
+
+        Example: 
+        observer_params = {'kernel':['random'],'internal_noise_std':[np.arange(0,10,1)],'criteria':[0]}
+        experiment_params = {'n_trials':[np.arange(1,1000,100)], 'n_repeated':[100], 'trial_type': [Int2Trial],
+                     'n_features': [5], 'external_noise_std': [100]}
+        analyser_params = {'internal_noise_extractor':[DoublePass], 'model_file': ['model.csv']}
+        sim = Sim(DoublePassExperiment, experiment_params,
+                  LinearObserver, observer_params, 
+                  InternalNoiseValue, analyser_params)
+        sim.run_all(n_runs=1) 
+
+        will run a simulation where LinearObservers respond to DoublePassExperiments, and computes their InternalNoiseValue, 
+        in 100 different configurations (observers with true internal noise values between 0..10; and experiments with 1..1000 trials).
+        Upon running, each of the configuration is run n_runs times, and separate results are stored for each run. 
+        '''
+        
+        if not issubclass(experiment_class,Experiment):
+            raise TypeError('argument experiment_class %s does not implement an Experiment')
+        self.experiment = experiment_class
         
-        # store simulation classes
-        self.experiment = experiment
-        self.observer = observer
-        self.analyser = analyser
+        if not issubclass(observer_class,Observer):
+            raise TypeError('argument observer_class %s does not implement an Observer')
+        self.observer = observer_class
+        
+        if not issubclass(analyser_class,Analyser):
+            raise TypeError('argument analyser_class %s does not implement an Analyser')
+        self.analyser = analyser_class
         
         # construct simulation plan
         self.experiment_params = experiment_params
@@ -18,8 +54,11 @@ def __init__(self, experiment, experiment_params, observer, observer_params, ana
         self.analyser_params = analyser_params
         self.config_params = self.generate_configs(self.experiment_params,self.observer_params,self.analyser_params)
         
-    @classmethod
-    def generate_configs(cls, experiment_params, observer_params, analyser_params): 
+    def generate_configs(self, experiment_params, observer_params, analyser_params): 
+        '''
+        Generates all combinations of parameters from the constructor's parameters. 
+        Each of these combinations will be individual configs that the Simulation will then run.
+        '''
         # construct simulation plan
         sim_params ={}
         for d in (experiment_params, observer_params, analyser_params): 
@@ -29,19 +68,26 @@ def generate_configs(cls, experiment_params, observer_params, analyser_params):
 
 
     def run_all(self, n_runs, verbose=True): 
+        '''
+        Run all configs stored in self.config_params. Each config is run n_runs times, and separate results are stored for each run. 
+        Each run instanciates one Observer, one Experiment and one Analyser (see @run).
+        Results are returned into a dataframe; each row is a run, and columns store config parameters, run number and analyser results. 
+        '''
         if verbose: 
             print("Running %d configs"%len(self.config_params))
 
         runs = []
-        for config_param in self.config_params: 
+        for index, config_param in enumerate(self.config_params): 
             if verbose: 
-                print(config_param)
+                print(str(index) + " : " +str(config_param))
             for run in np.arange(n_runs): 
                 if verbose: 
                     print('.',end='')
-                res = self.run(config_param)
+                # store run's config, run_number and results as a dict
                 run_res = config_param.copy() 
-                run_res.update({'run':run, 'metric':res})
+                run_res.update({'run':run})               
+                results = self.run(config_param) 
+                run_res.update(results)
                 runs.append(run_res)
             if verbose: 
                 print(';')
@@ -49,6 +95,12 @@ def run_all(self, n_runs, verbose=True):
 
 
     def run(self, config_param): 
+        '''
+        Perform individual run for a config defined by config_param. 
+        Each run instanciates one Observer, one Experiment and one Analyser. 
+        The observer responds to the experiment, and their responses are analysed with the analyser. 
+        Results are then returned in a dictionary of metric_name:value pairs. 
+        '''
 
         # separate this run's parameters into distinct sets
         config_experiment_params = {k: v for k, v in config_param.items() if k in self.experiment_params}
@@ -61,5 +113,13 @@ def run(self, config_param):
 
         responses = obs.respond_to_experiment(exp)
         
-        return ana.analyse(exp, obs, responses)
+        metrics =  ana.get_metric_names()
+        values = ana.analyse(exp, obs, responses)
+
+        # return the metrics as a dict of name:value pairs
+        results = {}
+        for metric,value in zip(metrics,values): 
+            results[metric] = value
+        return results
+
 

From d09bc5830fbd336f09e331daa897dd66d85e0051 Mon Sep 17 00:00:00 2001
From: JJ Aucouturier <aucouturier@gmail.com>
Date: Mon, 22 Apr 2024 16:13:22 +0200
Subject: [PATCH 16/17] latest batch, cted

---
 python/sandbox.ipynb | 2466 +++++++++++++++++++++++++++++++++++++++---
 1 file changed, 2298 insertions(+), 168 deletions(-)

diff --git a/python/sandbox.ipynb b/python/sandbox.ipynb
index 8e60145..40c5f81 100644
--- a/python/sandbox.ipynb
+++ b/python/sandbox.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": 51,
-   "id": "7de7295a",
+   "id": "87dd4fb9",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-04-10T12:37:25.098785Z",
@@ -28,7 +28,7 @@
   {
    "cell_type": "code",
    "execution_count": 179,
-   "id": "e244d137",
+   "id": "ab35c09e",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-04-11T04:19:40.595606Z",
@@ -46,7 +46,7 @@
   {
    "cell_type": "code",
    "execution_count": 53,
-   "id": "c49b53cd",
+   "id": "6a76823c",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-04-10T12:37:27.663173Z",
@@ -60,16 +60,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 123,
-   "id": "baa1e06a",
+   "execution_count": 208,
+   "id": "29b09a91",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-10T12:56:54.870854Z",
-     "start_time": "2024-04-10T12:56:54.764155Z"
+     "end_time": "2024-04-11T11:24:47.119855Z",
+     "start_time": "2024-04-11T11:24:47.063798Z"
     }
    },
    "outputs": [],
    "source": [
+    "from palin.simulation.experiment import Experiment\n",
     "from palin.simulation.simple_experiment import SimpleExperiment\n",
     "from palin.simulation.double_pass_experiment import DoublePassExperiment\n",
     "from palin.simulation.trial import Int2Trial, Int1Trial \n",
@@ -84,7 +85,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fa2978b0",
+   "id": "31bb6aee",
    "metadata": {},
    "source": [
     "## Simulate with internal noise"
@@ -92,7 +93,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bd21029c",
+   "id": "cd63729b",
    "metadata": {},
    "source": [
     "Single run"
@@ -100,30 +101,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 122,
-   "id": "2b2b91ae",
+   "execution_count": 189,
+   "id": "63e53f77",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-10T12:42:09.804159Z",
-     "start_time": "2024-04-10T12:42:09.665497Z"
+     "end_time": "2024-04-11T04:53:29.899499Z",
+     "start_time": "2024-04-11T04:53:29.371264Z"
     }
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "E:\\WORK\\DO\\2022\\palin\\python\\palin\\internal_noise\\double_pass.py:43: FutureWarning: Index.ravel returning ndarray is deprecated; in a future version this will return a view on self.\n",
-      "  pass_count_df.columns = [\"_\".join(x) for x in pass_count_df.columns.ravel()]\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
-       "0.24"
+       "(0.1, 1)"
       ]
      },
-     "execution_count": 122,
+     "execution_count": 189,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -138,13 +131,13 @@
     "                            internal_noise_std = 0, \n",
     "                            criteria = 1)\n",
     "responses = obs.respond_to_experiment(exp)\n",
-    "ana = InternalNoiseValue(internal_noise_extractor = DoublePass)\n",
+    "ana = InternalNoiseValue(internal_noise_extractor = DoublePass, model_file='model.csv')\n",
     "ana.analyse(exp, obs, responses)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "6396b632",
+   "id": "d90d2366",
    "metadata": {},
    "source": [
     "Simulation"
@@ -152,11 +145,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "21b866b8",
+   "execution_count": 200,
+   "id": "fc511049",
    "metadata": {
     "ExecuteTime": {
-     "start_time": "2024-04-11T04:20:32.079Z"
+     "end_time": "2024-04-11T06:57:34.621971Z",
+     "start_time": "2024-04-11T06:00:58.596353Z"
     },
     "scrolled": false
    },
@@ -166,7 +160,7 @@
      "output_type": "stream",
      "text": [
       "Running 1 configs\n",
-      "{'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 100, 'kernel': [1], 'internal_noise_std': 0, 'criteria': 0, 'internal_noise_extractor': <class 'palin.internal_noise.double_pass.DoublePass'>, 'model_file': 'model_11_04_2024.csv', 'rebuild_model': False, 'internal_noise_range': array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. , 1.1, 1.2,\n",
+      "{'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 100, 'kernel': [1], 'internal_noise_std': 0, 'criteria': 0, 'internal_noise_extractor': <class 'palin.internal_noise.double_pass.DoublePass'>, 'model_file': 'model_large.csv', 'rebuild_model': False, 'internal_noise_range': array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. , 1.1, 1.2,\n",
       "       1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2. , 2.1, 2.2, 2.3, 2.4, 2.5,\n",
       "       2.6, 2.7, 2.8, 2.9, 3. , 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8,\n",
       "       3.9, 4. , 4.1, 4.2, 4.3, 4.4, 4.5, 4.6, 4.7, 4.8, 4.9, 5. ]), 'criteria_range': array([-5.00000000e+00, -4.90000000e+00, -4.80000000e+00, -4.70000000e+00,\n",
@@ -193,9 +187,55 @@
       "        3.40000000e+00,  3.50000000e+00,  3.60000000e+00,  3.70000000e+00,\n",
       "        3.80000000e+00,  3.90000000e+00,  4.00000000e+00,  4.10000000e+00,\n",
       "        4.20000000e+00,  4.30000000e+00,  4.40000000e+00,  4.50000000e+00,\n",
-      "        4.60000000e+00,  4.70000000e+00,  4.80000000e+00,  4.90000000e+00])}\n",
+      "        4.60000000e+00,  4.70000000e+00,  4.80000000e+00,  4.90000000e+00]), 'n_runs': 2}\n",
       ".Rebuilding double-pass model\n"
      ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[200], line 19\u001b[0m\n\u001b[0;32m      9\u001b[0m analyser_params \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124minternal_noise_extractor\u001b[39m\u001b[38;5;124m'\u001b[39m:[DoublePass], \n\u001b[0;32m     10\u001b[0m                    \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmodel_file\u001b[39m\u001b[38;5;124m'\u001b[39m: [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmodel_large.csv\u001b[39m\u001b[38;5;124m'\u001b[39m], \n\u001b[0;32m     11\u001b[0m                    \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrebuild_model\u001b[39m\u001b[38;5;124m'\u001b[39m: [\u001b[38;5;28;01mFalse\u001b[39;00m],\n\u001b[0;32m     12\u001b[0m                    \u001b[38;5;124m'\u001b[39m\u001b[38;5;124minternal_noise_range\u001b[39m\u001b[38;5;124m'\u001b[39m:[np\u001b[38;5;241m.\u001b[39marange(\u001b[38;5;241m0\u001b[39m,\u001b[38;5;241m5.1\u001b[39m,\u001b[38;5;241m0.1\u001b[39m)],\n\u001b[0;32m     13\u001b[0m                    \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcriteria_range\u001b[39m\u001b[38;5;124m'\u001b[39m:[np\u001b[38;5;241m.\u001b[39marange(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m5\u001b[39m,\u001b[38;5;241m5\u001b[39m,\u001b[38;5;241m0.1\u001b[39m)],\n\u001b[0;32m     14\u001b[0m                    \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mn_runs\u001b[39m\u001b[38;5;124m'\u001b[39m:[\u001b[38;5;241m2\u001b[39m]}\n\u001b[0;32m     16\u001b[0m sim \u001b[38;5;241m=\u001b[39m Sim(DoublePassExperiment, experiment_params, \n\u001b[0;32m     17\u001b[0m           LinearObserver, observer_params, \n\u001b[0;32m     18\u001b[0m           InternalNoiseValue, analyser_params)\n\u001b[1;32m---> 19\u001b[0m sim_df \u001b[38;5;241m=\u001b[39m \u001b[43msim\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_all\u001b[49m\u001b[43m(\u001b[49m\u001b[43mn_runs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mE:\\WORK\\DO\\2022\\palin\\python\\palin\\simulation\\simulation.py:45\u001b[0m, in \u001b[0;36mSimulation.run_all\u001b[1;34m(self, n_runs, verbose)\u001b[0m\n\u001b[0;32m     43\u001b[0m run_res \u001b[38;5;241m=\u001b[39m config_param\u001b[38;5;241m.\u001b[39mcopy() \n\u001b[0;32m     44\u001b[0m run_res\u001b[38;5;241m.\u001b[39mupdate({\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrun\u001b[39m\u001b[38;5;124m'\u001b[39m:run})               \n\u001b[1;32m---> 45\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconfig_param\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     46\u001b[0m run_res\u001b[38;5;241m.\u001b[39mupdate(results)\n\u001b[0;32m     47\u001b[0m runs\u001b[38;5;241m.\u001b[39mappend(run_res)\n",
+      "File \u001b[1;32mE:\\WORK\\DO\\2022\\palin\\python\\palin\\simulation\\simulation.py:67\u001b[0m, in \u001b[0;36mSimulation.run\u001b[1;34m(self, config_param)\u001b[0m\n\u001b[0;32m     64\u001b[0m responses \u001b[38;5;241m=\u001b[39m obs\u001b[38;5;241m.\u001b[39mrespond_to_experiment(exp)\n\u001b[0;32m     66\u001b[0m metrics \u001b[38;5;241m=\u001b[39m  ana\u001b[38;5;241m.\u001b[39mget_metric_names()\n\u001b[1;32m---> 67\u001b[0m values \u001b[38;5;241m=\u001b[39m \u001b[43mana\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43manalyse\u001b[49m\u001b[43m(\u001b[49m\u001b[43mexp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mobs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mresponses\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     69\u001b[0m \u001b[38;5;66;03m# return the metrics as a dict of name:value pairs\u001b[39;00m\n\u001b[0;32m     70\u001b[0m results \u001b[38;5;241m=\u001b[39m {}\n",
+      "File \u001b[1;32mE:\\WORK\\DO\\2022\\palin\\python\\palin\\simulation\\internal_noise_value.py:21\u001b[0m, in \u001b[0;36mInternalNoiseValue.analyse\u001b[1;34m(self, experiment, participant, participant_responses)\u001b[0m\n\u001b[0;32m     19\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21manalyse\u001b[39m(\u001b[38;5;28mself\u001b[39m, experiment, participant, participant_responses): \n\u001b[1;32m---> 21\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mestimate_internal_noise\u001b[49m\u001b[43m(\u001b[49m\u001b[43mexperiment\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparticipant_responses\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mE:\\WORK\\DO\\2022\\palin\\python\\palin\\simulation\\internal_noise_value.py:27\u001b[0m, in \u001b[0;36mInternalNoiseValue.estimate_internal_noise\u001b[1;34m(self, experiment, participant_responses)\u001b[0m\n\u001b[0;32m     23\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mestimate_internal_noise\u001b[39m(\u001b[38;5;28mself\u001b[39m, experiment, participant_responses): \n\u001b[0;32m     25\u001b[0m     responses_df \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mto_df(experiment, participant_responses)\n\u001b[1;32m---> 27\u001b[0m     internal_noise, criteria \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minternal_noise_extractor\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mextract_single_internal_noise\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata_df\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mresponses_df\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m     28\u001b[0m \u001b[43m     \u001b[49m\u001b[43mtrial_id\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mtrial\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstim_id\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mstim\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfeature_id\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mfeature\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue_id\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mvalue\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mresponse_id\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mresponse\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel_file\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel_file\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m     29\u001b[0m \u001b[43m     \u001b[49m\u001b[43minternal_noise_range\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minternal_noise_range\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcriteria_range\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcriteria_range\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_repeated_trials\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mn_repeated_trials\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_runs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mn_runs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     31\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m internal_noise, criteria\n",
+      "File \u001b[1;32mE:\\WORK\\DO\\2022\\palin\\python\\palin\\internal_noise\\double_pass.py:46\u001b[0m, in \u001b[0;36mDoublePass.extract_single_internal_noise\u001b[1;34m(cls, data_df, trial_id, stim_id, feature_id, value_id, response_id, model_file, rebuild_model, internal_noise_range, criteria_range, n_repeated_trials, n_runs)\u001b[0m\n\u001b[0;32m     43\u001b[0m \u001b[38;5;66;03m# compute probability of choosing first response option\u001b[39;00m\n\u001b[0;32m     44\u001b[0m prob_first \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39mcompute_prob_first(data_df, trial_id\u001b[38;5;241m=\u001b[39mtrial_id, response_id\u001b[38;5;241m=\u001b[39mresponse_id, stim_id\u001b[38;5;241m=\u001b[39mstim_id, double_pass_id\u001b[38;5;241m=\u001b[39mdouble_pass_id)\n\u001b[1;32m---> 46\u001b[0m internal_noise, criteria \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mestimate_noise_criteria\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprob_agree\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprob_first\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel_file\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrebuild_model\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minternal_noise_range\u001b[49m\u001b[43m,\u001b[49m\u001b[43mcriteria_range\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_repeated_trials\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_runs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     48\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m internal_noise,criteria\n",
+      "File \u001b[1;32mE:\\WORK\\DO\\2022\\palin\\python\\palin\\internal_noise\\double_pass.py:122\u001b[0m, in \u001b[0;36mDoublePass.estimate_noise_criteria\u001b[1;34m(cls, prob_agree, prob_first, model_file, rebuild_model, internal_noise_range, criteria_range, n_repeated_trials, n_runs)\u001b[0m\n\u001b[0;32m    120\u001b[0m     model_df \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mread_csv(model_file, index_col\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\n\u001b[0;32m    121\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 122\u001b[0m     model_df \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbuild_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43minternal_noise_range\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcriteria_range\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_repeated_trials\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_runs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    123\u001b[0m     model_df\u001b[38;5;241m.\u001b[39mto_csv(model_file)\n\u001b[0;32m    125\u001b[0m \u001b[38;5;66;03m# find internal_noise & criteria settings that minimizes distance to prob_agree and prob_first \u001b[39;00m\n",
+      "File \u001b[1;32mE:\\WORK\\DO\\2022\\palin\\python\\palin\\internal_noise\\double_pass.py:154\u001b[0m, in \u001b[0;36mDoublePass.build_model\u001b[1;34m(cls, internal_noise_range, criteria_range, n_repeated_trials, n_runs)\u001b[0m\n\u001b[0;32m    148\u001b[0m analyser_params \u001b[38;5;241m=\u001b[39m {}\n\u001b[0;32m    150\u001b[0m sim \u001b[38;5;241m=\u001b[39m Sim(DoublePassExperiment, experiment_params,\n\u001b[0;32m    151\u001b[0m       LinearObserver, observer_params, \n\u001b[0;32m    152\u001b[0m       DoublePassStatistics, analyser_params)\n\u001b[1;32m--> 154\u001b[0m sim_df \u001b[38;5;241m=\u001b[39m \u001b[43msim\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_all\u001b[49m\u001b[43m(\u001b[49m\u001b[43mn_runs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mn_runs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[0;32m    156\u001b[0m \u001b[38;5;66;03m# average measures over all runs\u001b[39;00m\n\u001b[0;32m    157\u001b[0m sim_df\u001b[38;5;241m.\u001b[39mgroupby([\u001b[38;5;124m'\u001b[39m\u001b[38;5;124minternal_noise_std\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcriteria\u001b[39m\u001b[38;5;124m'\u001b[39m])[DoublePassStatistics\u001b[38;5;241m.\u001b[39mget_metric_names]\u001b[38;5;241m.\u001b[39mmean()\n",
+      "File \u001b[1;32mE:\\WORK\\DO\\2022\\palin\\python\\palin\\simulation\\simulation.py:45\u001b[0m, in \u001b[0;36mSimulation.run_all\u001b[1;34m(self, n_runs, verbose)\u001b[0m\n\u001b[0;32m     43\u001b[0m run_res \u001b[38;5;241m=\u001b[39m config_param\u001b[38;5;241m.\u001b[39mcopy() \n\u001b[0;32m     44\u001b[0m run_res\u001b[38;5;241m.\u001b[39mupdate({\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrun\u001b[39m\u001b[38;5;124m'\u001b[39m:run})               \n\u001b[1;32m---> 45\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconfig_param\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     46\u001b[0m run_res\u001b[38;5;241m.\u001b[39mupdate(results)\n\u001b[0;32m     47\u001b[0m runs\u001b[38;5;241m.\u001b[39mappend(run_res)\n",
+      "File \u001b[1;32mE:\\WORK\\DO\\2022\\palin\\python\\palin\\simulation\\simulation.py:67\u001b[0m, in \u001b[0;36mSimulation.run\u001b[1;34m(self, config_param)\u001b[0m\n\u001b[0;32m     64\u001b[0m responses \u001b[38;5;241m=\u001b[39m obs\u001b[38;5;241m.\u001b[39mrespond_to_experiment(exp)\n\u001b[0;32m     66\u001b[0m metrics \u001b[38;5;241m=\u001b[39m  ana\u001b[38;5;241m.\u001b[39mget_metric_names()\n\u001b[1;32m---> 67\u001b[0m values \u001b[38;5;241m=\u001b[39m \u001b[43mana\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43manalyse\u001b[49m\u001b[43m(\u001b[49m\u001b[43mexp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mobs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mresponses\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     69\u001b[0m \u001b[38;5;66;03m# return the metrics as a dict of name:value pairs\u001b[39;00m\n\u001b[0;32m     70\u001b[0m results \u001b[38;5;241m=\u001b[39m {}\n",
+      "File \u001b[1;32mE:\\WORK\\DO\\2022\\palin\\python\\palin\\simulation\\double_pass_statistics.py:19\u001b[0m, in \u001b[0;36mDoublePassStatistics.analyse\u001b[1;34m(self, experiment, participant, participant_responses)\u001b[0m\n\u001b[0;32m     15\u001b[0m responses_df \u001b[38;5;241m=\u001b[39m DoublePass\u001b[38;5;241m.\u001b[39mindex_double_pass_trials(data_df \u001b[38;5;241m=\u001b[39m responses_df, \n\u001b[0;32m     16\u001b[0m     trial_id\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtrial\u001b[39m\u001b[38;5;124m'\u001b[39m,value_id\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mvalue\u001b[39m\u001b[38;5;124m'\u001b[39m, double_pass_id\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdouble_pass_id\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m     18\u001b[0m \u001b[38;5;66;03m# compute probability of agreement over double pass\u001b[39;00m\n\u001b[1;32m---> 19\u001b[0m prob_agree \u001b[38;5;241m=\u001b[39m \u001b[43mDoublePass\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompute_prob_agreement\u001b[49m\u001b[43m(\u001b[49m\u001b[43mresponses_df\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrial_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mtrial\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mresponse_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mresponse\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdouble_pass_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mdouble_pass_id\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m     20\u001b[0m \u001b[38;5;66;03m# compute probability of choosing first response option\u001b[39;00m\n\u001b[0;32m     21\u001b[0m prob_first \u001b[38;5;241m=\u001b[39m DoublePass\u001b[38;5;241m.\u001b[39mcompute_prob_first(responses_df, trial_id\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtrial\u001b[39m\u001b[38;5;124m'\u001b[39m, response_id\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mresponse\u001b[39m\u001b[38;5;124m'\u001b[39m, stim_id\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mstim\u001b[39m\u001b[38;5;124m'\u001b[39m, double_pass_id\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdouble_pass_id\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
+      "File \u001b[1;32mE:\\WORK\\DO\\2022\\palin\\python\\palin\\internal_noise\\double_pass.py:92\u001b[0m, in \u001b[0;36mDoublePass.compute_prob_agreement\u001b[1;34m(cls, data_df, trial_id, response_id, double_pass_id)\u001b[0m\n\u001b[0;32m     90\u001b[0m     d \u001b[38;5;241m=\u001b[39m group\u001b[38;5;241m.\u001b[39mgroupby(trial_id)\u001b[38;5;241m.\u001b[39magg({response_id: \u001b[38;5;28;01mlambda\u001b[39;00m group: \u001b[38;5;28mtuple\u001b[39m(group)})\u001b[38;5;241m.\u001b[39mreset_index()\n\u001b[0;32m     91\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m d\u001b[38;5;241m.\u001b[39mresponse\u001b[38;5;241m.\u001b[39mnunique()\u001b[38;5;241m==\u001b[39m\u001b[38;5;241m1\u001b[39m\n\u001b[1;32m---> 92\u001b[0m agrees \u001b[38;5;241m=\u001b[39m \u001b[43mdata_df\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgroupby\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdouble_pass_id\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mgroup\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43msame_answer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgroup\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrial_id\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mresponse_id\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     94\u001b[0m \u001b[38;5;66;03m# return agreement probability\u001b[39;00m\n\u001b[0;32m     95\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m agrees\u001b[38;5;241m.\u001b[39msum()\u001b[38;5;241m/\u001b[39m\u001b[38;5;28mlen\u001b[39m(agrees)\n",
+      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\groupby\\groupby.py:1567\u001b[0m, in \u001b[0;36mGroupBy.apply\u001b[1;34m(self, func, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1559\u001b[0m     new_msg \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m   1560\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe operation \u001b[39m\u001b[38;5;132;01m{\u001b[39;00morig_func\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m failed on a column. If any error is \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1561\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mraised, this will raise an exception in a future version \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1562\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mof pandas. Drop these columns to avoid this warning.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1563\u001b[0m     )\n\u001b[0;32m   1564\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m rewrite_warning(\n\u001b[0;32m   1565\u001b[0m         old_msg, \u001b[38;5;167;01mFutureWarning\u001b[39;00m, new_msg\n\u001b[0;32m   1566\u001b[0m     ) \u001b[38;5;28;01mif\u001b[39;00m is_np_func \u001b[38;5;28;01melse\u001b[39;00m nullcontext():\n\u001b[1;32m-> 1567\u001b[0m         result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_python_apply_general\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_selected_obj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1568\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[0;32m   1569\u001b[0m     \u001b[38;5;66;03m# gh-20949\u001b[39;00m\n\u001b[0;32m   1570\u001b[0m     \u001b[38;5;66;03m# try again, with .apply acting as a filtering\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1574\u001b[0m     \u001b[38;5;66;03m# fails on *some* columns, e.g. a numeric operation\u001b[39;00m\n\u001b[0;32m   1575\u001b[0m     \u001b[38;5;66;03m# on a string grouper column\u001b[39;00m\n\u001b[0;32m   1577\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_group_selection_context():\n\u001b[0;32m   1578\u001b[0m         \u001b[38;5;66;03m# GH#50538\u001b[39;00m\n",
+      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\groupby\\groupby.py:1629\u001b[0m, in \u001b[0;36mGroupBy._python_apply_general\u001b[1;34m(self, f, data, not_indexed_same, is_transform, is_agg)\u001b[0m\n\u001b[0;32m   1592\u001b[0m \u001b[38;5;129m@final\u001b[39m\n\u001b[0;32m   1593\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_python_apply_general\u001b[39m(\n\u001b[0;32m   1594\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1599\u001b[0m     is_agg: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[0;32m   1600\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m NDFrameT:\n\u001b[0;32m   1601\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   1602\u001b[0m \u001b[38;5;124;03m    Apply function f in python space\u001b[39;00m\n\u001b[0;32m   1603\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1627\u001b[0m \u001b[38;5;124;03m        data after applying f\u001b[39;00m\n\u001b[0;32m   1628\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m-> 1629\u001b[0m     values, mutated \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgrouper\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1630\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m not_indexed_same \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   1631\u001b[0m         not_indexed_same \u001b[38;5;241m=\u001b[39m mutated \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmutated\n",
+      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\groupby\\ops.py:839\u001b[0m, in \u001b[0;36mBaseGrouper.apply\u001b[1;34m(self, f, data, axis)\u001b[0m\n\u001b[0;32m    837\u001b[0m \u001b[38;5;66;03m# group might be modified\u001b[39;00m\n\u001b[0;32m    838\u001b[0m group_axes \u001b[38;5;241m=\u001b[39m group\u001b[38;5;241m.\u001b[39maxes\n\u001b[1;32m--> 839\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgroup\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    840\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m mutated \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m _is_indexed_like(res, group_axes, axis):\n\u001b[0;32m    841\u001b[0m     mutated \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n",
+      "File \u001b[1;32mE:\\WORK\\DO\\2022\\palin\\python\\palin\\internal_noise\\double_pass.py:92\u001b[0m, in \u001b[0;36mDoublePass.compute_prob_agreement.<locals>.<lambda>\u001b[1;34m(group)\u001b[0m\n\u001b[0;32m     90\u001b[0m     d \u001b[38;5;241m=\u001b[39m group\u001b[38;5;241m.\u001b[39mgroupby(trial_id)\u001b[38;5;241m.\u001b[39magg({response_id: \u001b[38;5;28;01mlambda\u001b[39;00m group: \u001b[38;5;28mtuple\u001b[39m(group)})\u001b[38;5;241m.\u001b[39mreset_index()\n\u001b[0;32m     91\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m d\u001b[38;5;241m.\u001b[39mresponse\u001b[38;5;241m.\u001b[39mnunique()\u001b[38;5;241m==\u001b[39m\u001b[38;5;241m1\u001b[39m\n\u001b[1;32m---> 92\u001b[0m agrees \u001b[38;5;241m=\u001b[39m data_df\u001b[38;5;241m.\u001b[39mgroupby(double_pass_id)\u001b[38;5;241m.\u001b[39mapply(\u001b[38;5;28;01mlambda\u001b[39;00m group: \u001b[43msame_answer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgroup\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrial_id\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mresponse_id\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[0;32m     94\u001b[0m \u001b[38;5;66;03m# return agreement probability\u001b[39;00m\n\u001b[0;32m     95\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m agrees\u001b[38;5;241m.\u001b[39msum()\u001b[38;5;241m/\u001b[39m\u001b[38;5;28mlen\u001b[39m(agrees)\n",
+      "File \u001b[1;32mE:\\WORK\\DO\\2022\\palin\\python\\palin\\internal_noise\\double_pass.py:90\u001b[0m, in \u001b[0;36mDoublePass.compute_prob_agreement.<locals>.same_answer\u001b[1;34m(group, trial_id, response_id)\u001b[0m\n\u001b[0;32m     89\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21msame_answer\u001b[39m(group, trial_id, response_id):    \n\u001b[1;32m---> 90\u001b[0m     d \u001b[38;5;241m=\u001b[39m \u001b[43mgroup\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgroupby\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrial_id\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43magg\u001b[49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\u001b[43mresponse_id\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mgroup\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mtuple\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mgroup\u001b[49m\u001b[43m)\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mreset_index()\n\u001b[0;32m     91\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m d\u001b[38;5;241m.\u001b[39mresponse\u001b[38;5;241m.\u001b[39mnunique()\u001b[38;5;241m==\u001b[39m\u001b[38;5;241m1\u001b[39m\n",
+      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\groupby\\generic.py:895\u001b[0m, in \u001b[0;36mDataFrameGroupBy.aggregate\u001b[1;34m(self, func, engine, engine_kwargs, *args, **kwargs)\u001b[0m\n\u001b[0;32m    892\u001b[0m func \u001b[38;5;241m=\u001b[39m maybe_mangle_lambdas(func)\n\u001b[0;32m    894\u001b[0m op \u001b[38;5;241m=\u001b[39m GroupByApply(\u001b[38;5;28mself\u001b[39m, func, args, kwargs)\n\u001b[1;32m--> 895\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mop\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43magg\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    896\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_dict_like(func) \u001b[38;5;129;01mand\u001b[39;00m result \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    897\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
+      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\apply.py:172\u001b[0m, in \u001b[0;36mApply.agg\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    169\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply_str()\n\u001b[0;32m    171\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_dict_like(arg):\n\u001b[1;32m--> 172\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43magg_dict_like\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    173\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m is_list_like(arg):\n\u001b[0;32m    174\u001b[0m     \u001b[38;5;66;03m# we require a list, but not a 'str'\u001b[39;00m\n\u001b[0;32m    175\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39magg_list_like()\n",
+      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\apply.py:504\u001b[0m, in \u001b[0;36mApply.agg_dict_like\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    501\u001b[0m     results \u001b[38;5;241m=\u001b[39m {key: colg\u001b[38;5;241m.\u001b[39magg(how) \u001b[38;5;28;01mfor\u001b[39;00m key, how \u001b[38;5;129;01min\u001b[39;00m arg\u001b[38;5;241m.\u001b[39mitems()}\n\u001b[0;32m    502\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    503\u001b[0m     \u001b[38;5;66;03m# key used for column selection and output\u001b[39;00m\n\u001b[1;32m--> 504\u001b[0m     results \u001b[38;5;241m=\u001b[39m {\n\u001b[0;32m    505\u001b[0m         key: obj\u001b[38;5;241m.\u001b[39m_gotitem(key, ndim\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\u001b[38;5;241m.\u001b[39magg(how) \u001b[38;5;28;01mfor\u001b[39;00m key, how \u001b[38;5;129;01min\u001b[39;00m arg\u001b[38;5;241m.\u001b[39mitems()\n\u001b[0;32m    506\u001b[0m     }\n\u001b[0;32m    508\u001b[0m \u001b[38;5;66;03m# set the final keys\u001b[39;00m\n\u001b[0;32m    509\u001b[0m keys \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(arg\u001b[38;5;241m.\u001b[39mkeys())\n",
+      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\apply.py:505\u001b[0m, in \u001b[0;36m<dictcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    501\u001b[0m     results \u001b[38;5;241m=\u001b[39m {key: colg\u001b[38;5;241m.\u001b[39magg(how) \u001b[38;5;28;01mfor\u001b[39;00m key, how \u001b[38;5;129;01min\u001b[39;00m arg\u001b[38;5;241m.\u001b[39mitems()}\n\u001b[0;32m    502\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    503\u001b[0m     \u001b[38;5;66;03m# key used for column selection and output\u001b[39;00m\n\u001b[0;32m    504\u001b[0m     results \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m--> 505\u001b[0m         key: \u001b[43mobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_gotitem\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mndim\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43magg\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhow\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m key, how \u001b[38;5;129;01min\u001b[39;00m arg\u001b[38;5;241m.\u001b[39mitems()\n\u001b[0;32m    506\u001b[0m     }\n\u001b[0;32m    508\u001b[0m \u001b[38;5;66;03m# set the final keys\u001b[39;00m\n\u001b[0;32m    509\u001b[0m keys \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(arg\u001b[38;5;241m.\u001b[39mkeys())\n",
+      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\groupby\\generic.py:297\u001b[0m, in \u001b[0;36mSeriesGroupBy.aggregate\u001b[1;34m(self, func, engine, engine_kwargs, *args, **kwargs)\u001b[0m\n\u001b[0;32m    294\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_python_agg_general(func, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    296\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 297\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_python_agg_general\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    298\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n\u001b[0;32m    299\u001b[0m     \u001b[38;5;66;03m# TODO: KeyError is raised in _python_agg_general,\u001b[39;00m\n\u001b[0;32m    300\u001b[0m     \u001b[38;5;66;03m#  see test_groupby.test_basic\u001b[39;00m\n\u001b[0;32m    301\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_aggregate_named(func, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
+      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\groupby\\groupby.py:1673\u001b[0m, in \u001b[0;36mGroupBy._python_agg_general\u001b[1;34m(self, func, raise_on_typeerror, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1670\u001b[0m \u001b[38;5;66;03m# iterate through \"columns\" ex exclusions to populate output dict\u001b[39;00m\n\u001b[0;32m   1671\u001b[0m output: \u001b[38;5;28mdict\u001b[39m[base\u001b[38;5;241m.\u001b[39mOutputKey, ArrayLike] \u001b[38;5;241m=\u001b[39m {}\n\u001b[1;32m-> 1673\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mngroups\u001b[49m \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m   1674\u001b[0m     \u001b[38;5;66;03m# agg_series below assumes ngroups > 0\u001b[39;00m\n\u001b[0;32m   1675\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_python_apply_general(f, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_selected_obj, is_agg\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m   1677\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m idx, obj \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_iterate_slices()):\n",
+      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\groupby\\groupby.py:677\u001b[0m, in \u001b[0;36mBaseGroupBy.ngroups\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    674\u001b[0m \u001b[38;5;129m@final\u001b[39m\n\u001b[0;32m    675\u001b[0m \u001b[38;5;129m@property\u001b[39m\n\u001b[0;32m    676\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mngroups\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mint\u001b[39m:\n\u001b[1;32m--> 677\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgrouper\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mngroups\u001b[49m\n",
+      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\_libs\\properties.pyx:36\u001b[0m, in \u001b[0;36mpandas._libs.properties.CachedProperty.__get__\u001b[1;34m()\u001b[0m\n",
+      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\groupby\\ops.py:982\u001b[0m, in \u001b[0;36mBaseGrouper.ngroups\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    979\u001b[0m \u001b[38;5;129m@final\u001b[39m\n\u001b[0;32m    980\u001b[0m \u001b[38;5;129m@cache_readonly\u001b[39m\n\u001b[0;32m    981\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mngroups\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mint\u001b[39m:\n\u001b[1;32m--> 982\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresult_index\u001b[49m)\n",
+      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\_libs\\properties.pyx:36\u001b[0m, in \u001b[0;36mpandas._libs.properties.CachedProperty.__get__\u001b[1;34m()\u001b[0m\n",
+      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\groupby\\ops.py:993\u001b[0m, in \u001b[0;36mBaseGrouper.result_index\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    990\u001b[0m \u001b[38;5;129m@cache_readonly\u001b[39m\n\u001b[0;32m    991\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mresult_index\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Index:\n\u001b[0;32m    992\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgroupings) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m--> 993\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgroupings\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresult_index\u001b[49m\u001b[38;5;241m.\u001b[39mrename(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnames[\u001b[38;5;241m0\u001b[39m])\n\u001b[0;32m    995\u001b[0m     codes \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mreconstructed_codes\n\u001b[0;32m    996\u001b[0m     levels \u001b[38;5;241m=\u001b[39m [ping\u001b[38;5;241m.\u001b[39mresult_index \u001b[38;5;28;01mfor\u001b[39;00m ping \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgroupings]\n",
+      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\_libs\\properties.pyx:36\u001b[0m, in \u001b[0;36mpandas._libs.properties.CachedProperty.__get__\u001b[1;34m()\u001b[0m\n",
+      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\groupby\\grouper.py:647\u001b[0m, in \u001b[0;36mGrouping.result_index\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    645\u001b[0m     \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(group_idx, CategoricalIndex)\n\u001b[0;32m    646\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m recode_from_groupby(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_all_grouper, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sort, group_idx)\n\u001b[1;32m--> 647\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgroup_index\u001b[49m\n",
+      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\_libs\\properties.pyx:36\u001b[0m, in \u001b[0;36mpandas._libs.properties.CachedProperty.__get__\u001b[1;34m()\u001b[0m\n",
+      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\groupby\\grouper.py:655\u001b[0m, in \u001b[0;36mGrouping.group_index\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    651\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_group_index \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    652\u001b[0m     \u001b[38;5;66;03m# _group_index is set in __init__ for MultiIndex cases\u001b[39;00m\n\u001b[0;32m    653\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_group_index\n\u001b[1;32m--> 655\u001b[0m uniques \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_codes_and_uniques\u001b[49m[\u001b[38;5;241m1\u001b[39m]\n\u001b[0;32m    656\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m Index\u001b[38;5;241m.\u001b[39m_with_infer(uniques, name\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname)\n",
+      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\_libs\\properties.pyx:36\u001b[0m, in \u001b[0;36mpandas._libs.properties.CachedProperty.__get__\u001b[1;34m()\u001b[0m\n",
+      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\groupby\\grouper.py:692\u001b[0m, in \u001b[0;36mGrouping._codes_and_uniques\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    685\u001b[0m     uniques \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m    686\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgrouping_vector\u001b[38;5;241m.\u001b[39mresult_index\u001b[38;5;241m.\u001b[39m_values  \u001b[38;5;66;03m# type: ignore[assignment]\u001b[39;00m\n\u001b[0;32m    687\u001b[0m     )\n\u001b[0;32m    688\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    689\u001b[0m     \u001b[38;5;66;03m# GH35667, replace dropna=False with use_na_sentinel=False\u001b[39;00m\n\u001b[0;32m    690\u001b[0m     \u001b[38;5;66;03m# error: Incompatible types in assignment (expression has type \"Union[\u001b[39;00m\n\u001b[0;32m    691\u001b[0m     \u001b[38;5;66;03m# ndarray[Any, Any], Index]\", variable has type \"Categorical\")\u001b[39;00m\n\u001b[1;32m--> 692\u001b[0m     codes, uniques \u001b[38;5;241m=\u001b[39m \u001b[43malgorithms\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfactorize\u001b[49m\u001b[43m(\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# type: ignore[assignment]\u001b[39;49;00m\n\u001b[0;32m    693\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgrouping_vector\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msort\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_sort\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43muse_na_sentinel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_dropna\u001b[49m\n\u001b[0;32m    694\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    695\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m codes, uniques\n",
+      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\algorithms.py:832\u001b[0m, in \u001b[0;36mfactorize\u001b[1;34m(values, sort, na_sentinel, use_na_sentinel, size_hint)\u001b[0m\n\u001b[0;32m    829\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m na_sentinel \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    830\u001b[0m         \u001b[38;5;66;03m# TODO: Can remove when na_sentinel=na_sentinel as in TODO above\u001b[39;00m\n\u001b[0;32m    831\u001b[0m         na_sentinel \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m\n\u001b[1;32m--> 832\u001b[0m     uniques, codes \u001b[38;5;241m=\u001b[39m \u001b[43msafe_sort\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    833\u001b[0m \u001b[43m        \u001b[49m\u001b[43muniques\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcodes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mna_sentinel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mna_sentinel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43massume_unique\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mverify\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\n\u001b[0;32m    834\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    836\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m dropna \u001b[38;5;129;01mand\u001b[39;00m sort:\n\u001b[0;32m    837\u001b[0m     \u001b[38;5;66;03m# TODO: Can remove entire block when na_sentinel=na_sentinel as in TODO above\u001b[39;00m\n\u001b[0;32m    838\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m na_sentinel \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
+      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\algorithms.py:1875\u001b[0m, in \u001b[0;36msafe_sort\u001b[1;34m(values, codes, na_sentinel, assume_unique, verify)\u001b[0m\n\u001b[0;32m   1873\u001b[0m         ordered \u001b[38;5;241m=\u001b[39m original_values\u001b[38;5;241m.\u001b[39mtake(sorter)\n\u001b[0;32m   1874\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1875\u001b[0m         ordered \u001b[38;5;241m=\u001b[39m \u001b[43mvalues\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtake\u001b[49m\u001b[43m(\u001b[49m\u001b[43msorter\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1876\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[0;32m   1877\u001b[0m     \u001b[38;5;66;03m# Previous sorters failed or were not applicable, try `_sort_mixed`\u001b[39;00m\n\u001b[0;32m   1878\u001b[0m     \u001b[38;5;66;03m# which would work, but which fails for special case of 1d arrays\u001b[39;00m\n\u001b[0;32m   1879\u001b[0m     \u001b[38;5;66;03m# with tuples.\u001b[39;00m\n\u001b[0;32m   1880\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m values\u001b[38;5;241m.\u001b[39msize \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(values[\u001b[38;5;241m0\u001b[39m], \u001b[38;5;28mtuple\u001b[39m):\n",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
+     ]
     }
    ],
    "source": [
@@ -208,13 +248,12 @@
     "                     'n_features': [1],\n",
     "                     'external_noise_std': [100]}\n",
     "analyser_params = {'internal_noise_extractor':[DoublePass], \n",
-    "                  'model_file': ['model_11_04_2024.csv'], \n",
-    "                  'rebuild_model': [False],\n",
+    "                   'model_file': ['model_large.csv'], \n",
+    "                   'rebuild_model': [False],\n",
     "                   'internal_noise_range':[np.arange(0,5.1,0.1)],\n",
-    "                   'criteria_range':[np.arange(-5,5,0.1)]\n",
-    "                  }\n",
-    "\n",
-    "\n",
+    "                   'criteria_range':[np.arange(-5,5,0.1)],\n",
+    "                   'n_runs':[2]}\n",
+    "                  \n",
     "sim = Sim(DoublePassExperiment, experiment_params, \n",
     "          LinearObserver, observer_params, \n",
     "          InternalNoiseValue, analyser_params)\n",
@@ -224,147 +263,2238 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 177,
-   "id": "0264e72a",
+   "execution_count": 216,
+   "id": "d36ad464",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-11T04:11:28.822698Z",
-     "start_time": "2024-04-11T04:11:28.763833Z"
+     "end_time": "2024-04-11T11:28:06.869895Z",
+     "start_time": "2024-04-11T11:28:06.817000Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "text/html": [
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>n_trials</th>\n",
-       "      <th>n_repeated</th>\n",
-       "      <th>trial_type</th>\n",
-       "      <th>n_features</th>\n",
-       "      <th>external_noise_std</th>\n",
-       "      <th>kernel</th>\n",
-       "      <th>internal_noise_std</th>\n",
-       "      <th>criteria</th>\n",
-       "      <th>internal_noise_extractor</th>\n",
-       "      <th>model_file</th>\n",
-       "      <th>run</th>\n",
-       "      <th>metric</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000</td>\n",
-       "      <td>1000</td>\n",
-       "      <td>&lt;class 'palin.simulation.trial.Int2Trial'&gt;</td>\n",
-       "      <td>1</td>\n",
-       "      <td>100</td>\n",
-       "      <td>[1]</td>\n",
-       "      <td>0</td>\n",
-       "      <td>-1</td>\n",
-       "      <td>&lt;class 'palin.internal_noise.double_pass.Doubl...</td>\n",
-       "      <td>model.csv</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
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-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1000</td>\n",
-       "      <td>1000</td>\n",
-       "      <td>&lt;class 'palin.simulation.trial.Int2Trial'&gt;</td>\n",
-       "      <td>1</td>\n",
-       "      <td>100</td>\n",
-       "      <td>[1]</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>&lt;class 'palin.internal_noise.double_pass.Doubl...</td>\n",
-       "      <td>model.csv</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000</td>\n",
-       "      <td>1000</td>\n",
-       "      <td>&lt;class 'palin.simulation.trial.Int2Trial'&gt;</td>\n",
-       "      <td>1</td>\n",
-       "      <td>100</td>\n",
-       "      <td>[1]</td>\n",
-       "      <td>1</td>\n",
-       "      <td>-1</td>\n",
-       "      <td>&lt;class 'palin.internal_noise.double_pass.Doubl...</td>\n",
-       "      <td>model.csv</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000</td>\n",
-       "      <td>1000</td>\n",
-       "      <td>&lt;class 'palin.simulation.trial.Int2Trial'&gt;</td>\n",
-       "      <td>1</td>\n",
-       "      <td>100</td>\n",
-       "      <td>[1]</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>&lt;class 'palin.internal_noise.double_pass.Doubl...</td>\n",
-       "      <td>model.csv</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.7</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
       "text/plain": [
-       "   n_trials  n_repeated                                  trial_type  \\\n",
-       "0      1000        1000  <class 'palin.simulation.trial.Int2Trial'>   \n",
-       "1      1000        1000  <class 'palin.simulation.trial.Int2Trial'>   \n",
-       "2      1000        1000  <class 'palin.simulation.trial.Int2Trial'>   \n",
-       "3      1000        1000  <class 'palin.simulation.trial.Int2Trial'>   \n",
-       "\n",
-       "   n_features  external_noise_std kernel  internal_noise_std  criteria  \\\n",
-       "0           1                 100    [1]                   0        -1   \n",
-       "1           1                 100    [1]                   0         1   \n",
-       "2           1                 100    [1]                   1        -1   \n",
-       "3           1                 100    [1]                   1         1   \n",
-       "\n",
-       "                            internal_noise_extractor model_file  run  metric  \n",
-       "0  <class 'palin.internal_noise.double_pass.Doubl...  model.csv    0     0.0  \n",
-       "1  <class 'palin.internal_noise.double_pass.Doubl...  model.csv    0     0.0  \n",
-       "2  <class 'palin.internal_noise.double_pass.Doubl...  model.csv    0     1.0  \n",
-       "3  <class 'palin.internal_noise.double_pass.Doubl...  model.csv    0     0.7  "
+       "palin.simulation.simple_experiment.SimpleExperiment"
       ]
      },
-     "execution_count": 177,
+     "execution_count": 216,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "sim_df"
+    "SimpleExperiment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 219,
+   "id": "7aea1f12",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-04-11T20:09:16.840733Z",
+     "start_time": "2024-04-11T13:04:43.045583Z"
+    },
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rebuilding double-pass model\n",
+      "Running 1000 configs\n",
+      "0 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.0, 'criteria': -5.0}\n",
+      "..........;\n",
+      "1 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.0, 'criteria': -4.5}\n",
+      "..........;\n",
+      "2 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.0, 'criteria': -4.0}\n",
+      "..........;\n",
+      "3 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.0, 'criteria': -3.5}\n",
+      "..........;\n",
+      "4 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.0, 'criteria': -3.0}\n",
+      "..........;\n",
+      "5 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.0, 'criteria': -2.5}\n",
+      "..........;\n",
+      "6 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.0, 'criteria': -2.0}\n",
+      "..........;\n",
+      "7 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.0, 'criteria': -1.5}\n",
+      "..........;\n",
+      "8 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.0, 'criteria': -1.0}\n",
+      "..........;\n",
+      "9 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.0, 'criteria': -0.5}\n",
+      "..........;\n",
+      "10 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.0, 'criteria': 0.0}\n",
+      "..........;\n",
+      "11 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.0, 'criteria': 0.5}\n",
+      "..........;\n",
+      "12 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.0, 'criteria': 1.0}\n",
+      "..........;\n",
+      "13 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.0, 'criteria': 1.5}\n",
+      "..........;\n",
+      "14 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.0, 'criteria': 2.0}\n",
+      "..........;\n",
+      "15 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.0, 'criteria': 2.5}\n",
+      "..........;\n",
+      "16 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.0, 'criteria': 3.0}\n",
+      "..........;\n",
+      "17 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.0, 'criteria': 3.5}\n",
+      "..........;\n",
+      "18 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.0, 'criteria': 4.0}\n",
+      "..........;\n",
+      "19 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.0, 'criteria': 4.5}\n",
+      "..........;\n",
+      "20 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.1, 'criteria': -5.0}\n",
+      "..........;\n",
+      "21 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.1, 'criteria': -4.5}\n",
+      "..........;\n",
+      "22 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.1, 'criteria': -4.0}\n",
+      "..........;\n",
+      "23 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.1, 'criteria': -3.5}\n",
+      "..........;\n",
+      "24 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.1, 'criteria': -3.0}\n",
+      "..........;\n",
+      "25 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.1, 'criteria': -2.5}\n",
+      "..........;\n",
+      "26 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.1, 'criteria': -2.0}\n",
+      "..........;\n",
+      "27 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.1, 'criteria': -1.5}\n",
+      "..........;\n",
+      "28 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.1, 'criteria': -1.0}\n",
+      "..........;\n",
+      "29 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.1, 'criteria': -0.5}\n",
+      "..........;\n",
+      "30 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.1, 'criteria': 0.0}\n",
+      "..........;\n",
+      "31 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.1, 'criteria': 0.5}\n",
+      "..........;\n",
+      "32 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.1, 'criteria': 1.0}\n",
+      "..........;\n",
+      "33 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.1, 'criteria': 1.5}\n",
+      "..........;\n",
+      "34 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.1, 'criteria': 2.0}\n",
+      "..........;\n",
+      "35 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.1, 'criteria': 2.5}\n",
+      "..........;\n",
+      "36 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.1, 'criteria': 3.0}\n",
+      "..........;\n",
+      "37 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.1, 'criteria': 3.5}\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "..........;\n",
+      "38 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.1, 'criteria': 4.0}\n",
+      "..........;\n",
+      "39 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.1, 'criteria': 4.5}\n",
+      "..........;\n",
+      "40 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.2, 'criteria': -5.0}\n",
+      "..........;\n",
+      "41 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.2, 'criteria': -4.5}\n",
+      "..........;\n",
+      "42 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.2, 'criteria': -4.0}\n",
+      "..........;\n",
+      "43 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.2, 'criteria': -3.5}\n",
+      "..........;\n",
+      "44 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.2, 'criteria': -3.0}\n",
+      "..........;\n",
+      "45 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.2, 'criteria': -2.5}\n",
+      "..........;\n",
+      "46 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.2, 'criteria': -2.0}\n",
+      "..........;\n",
+      "47 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.2, 'criteria': -1.5}\n",
+      "..........;\n",
+      "48 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.2, 'criteria': -1.0}\n",
+      "..........;\n",
+      "49 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.2, 'criteria': -0.5}\n",
+      "..........;\n",
+      "50 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.2, 'criteria': 0.0}\n",
+      "..........;\n",
+      "51 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.2, 'criteria': 0.5}\n",
+      "..........;\n",
+      "52 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.2, 'criteria': 1.0}\n",
+      "..........;\n",
+      "53 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.2, 'criteria': 1.5}\n",
+      "..........;\n",
+      "54 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.2, 'criteria': 2.0}\n",
+      "..........;\n",
+      "55 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.2, 'criteria': 2.5}\n",
+      "..........;\n",
+      "56 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.2, 'criteria': 3.0}\n",
+      "..........;\n",
+      "57 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.2, 'criteria': 3.5}\n",
+      "..........;\n",
+      "58 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.2, 'criteria': 4.0}\n",
+      "..........;\n",
+      "59 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.2, 'criteria': 4.5}\n",
+      "..........;\n",
+      "60 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.30000000000000004, 'criteria': -5.0}\n",
+      "..........;\n",
+      "61 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.30000000000000004, 'criteria': -4.5}\n",
+      "..........;\n",
+      "62 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.30000000000000004, 'criteria': -4.0}\n",
+      "..........;\n",
+      "63 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.30000000000000004, 'criteria': -3.5}\n",
+      "..........;\n",
+      "64 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.30000000000000004, 'criteria': -3.0}\n",
+      "..........;\n",
+      "65 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.30000000000000004, 'criteria': -2.5}\n",
+      "..........;\n",
+      "66 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.30000000000000004, 'criteria': -2.0}\n",
+      "..........;\n",
+      "67 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.30000000000000004, 'criteria': -1.5}\n",
+      "..........;\n",
+      "68 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.30000000000000004, 'criteria': -1.0}\n",
+      "..........;\n",
+      "69 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.30000000000000004, 'criteria': -0.5}\n",
+      "..........;\n",
+      "70 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.30000000000000004, 'criteria': 0.0}\n",
+      "..........;\n",
+      "71 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.30000000000000004, 'criteria': 0.5}\n",
+      "..........;\n",
+      "72 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.30000000000000004, 'criteria': 1.0}\n",
+      "..........;\n",
+      "73 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.30000000000000004, 'criteria': 1.5}\n",
+      "..........;\n",
+      "74 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.30000000000000004, 'criteria': 2.0}\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "..........;\n",
+      "75 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.30000000000000004, 'criteria': 2.5}\n",
+      "..........;\n",
+      "76 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.30000000000000004, 'criteria': 3.0}\n",
+      "..........;\n",
+      "77 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.30000000000000004, 'criteria': 3.5}\n",
+      "..........;\n",
+      "78 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.30000000000000004, 'criteria': 4.0}\n",
+      "..........;\n",
+      "79 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.30000000000000004, 'criteria': 4.5}\n",
+      "..........;\n",
+      "80 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.4, 'criteria': -5.0}\n",
+      "..........;\n",
+      "81 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.4, 'criteria': -4.5}\n",
+      "..........;\n",
+      "82 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.4, 'criteria': -4.0}\n",
+      "..........;\n",
+      "83 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.4, 'criteria': -3.5}\n",
+      "..........;\n",
+      "84 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.4, 'criteria': -3.0}\n",
+      "..........;\n",
+      "85 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.4, 'criteria': -2.5}\n",
+      "..........;\n",
+      "86 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.4, 'criteria': -2.0}\n",
+      "..........;\n",
+      "87 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.4, 'criteria': -1.5}\n",
+      "..........;\n",
+      "88 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.4, 'criteria': -1.0}\n",
+      "..........;\n",
+      "89 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.4, 'criteria': -0.5}\n",
+      "..........;\n",
+      "90 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.4, 'criteria': 0.0}\n",
+      "..........;\n",
+      "91 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.4, 'criteria': 0.5}\n",
+      "..........;\n",
+      "92 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.4, 'criteria': 1.0}\n",
+      "..........;\n",
+      "93 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.4, 'criteria': 1.5}\n",
+      "..........;\n",
+      "94 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.4, 'criteria': 2.0}\n",
+      "..........;\n",
+      "95 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.4, 'criteria': 2.5}\n",
+      "..........;\n",
+      "96 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.4, 'criteria': 3.0}\n",
+      "..........;\n",
+      "97 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.4, 'criteria': 3.5}\n",
+      "..........;\n",
+      "98 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.4, 'criteria': 4.0}\n",
+      "..........;\n",
+      "99 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.4, 'criteria': 4.5}\n",
+      "..........;\n",
+      "100 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.5, 'criteria': -5.0}\n",
+      "..........;\n",
+      "101 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.5, 'criteria': -4.5}\n",
+      "..........;\n",
+      "102 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.5, 'criteria': -4.0}\n",
+      "..........;\n",
+      "103 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.5, 'criteria': -3.5}\n",
+      "..........;\n",
+      "104 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.5, 'criteria': -3.0}\n",
+      "..........;\n",
+      "105 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.5, 'criteria': -2.5}\n",
+      "..........;\n",
+      "106 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.5, 'criteria': -2.0}\n",
+      "..........;\n",
+      "107 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.5, 'criteria': -1.5}\n",
+      "..........;\n",
+      "108 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.5, 'criteria': -1.0}\n",
+      "..........;\n",
+      "109 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.5, 'criteria': -0.5}\n",
+      "..........;\n",
+      "110 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.5, 'criteria': 0.0}\n",
+      "..........;\n",
+      "111 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.5, 'criteria': 0.5}\n",
+      "..........;\n",
+      "112 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.5, 'criteria': 1.0}\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "..........;\n",
+      "113 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.5, 'criteria': 1.5}\n",
+      "..........;\n",
+      "114 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.5, 'criteria': 2.0}\n",
+      "..........;\n",
+      "115 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.5, 'criteria': 2.5}\n",
+      "..........;\n",
+      "116 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.5, 'criteria': 3.0}\n",
+      "..........;\n",
+      "117 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.5, 'criteria': 3.5}\n",
+      "..........;\n",
+      "118 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.5, 'criteria': 4.0}\n",
+      "..........;\n",
+      "119 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.5, 'criteria': 4.5}\n",
+      "..........;\n",
+      "120 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.6000000000000001, 'criteria': -5.0}\n",
+      "..........;\n",
+      "121 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.6000000000000001, 'criteria': -4.5}\n",
+      "..........;\n",
+      "122 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.6000000000000001, 'criteria': -4.0}\n",
+      "..........;\n",
+      "123 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.6000000000000001, 'criteria': -3.5}\n",
+      "..........;\n",
+      "124 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.6000000000000001, 'criteria': -3.0}\n",
+      "..........;\n",
+      "125 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.6000000000000001, 'criteria': -2.5}\n",
+      "..........;\n",
+      "126 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.6000000000000001, 'criteria': -2.0}\n",
+      "..........;\n",
+      "127 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.6000000000000001, 'criteria': -1.5}\n",
+      "..........;\n",
+      "128 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.6000000000000001, 'criteria': -1.0}\n",
+      "..........;\n",
+      "129 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.6000000000000001, 'criteria': -0.5}\n",
+      "..........;\n",
+      "130 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.6000000000000001, 'criteria': 0.0}\n",
+      "..........;\n",
+      "131 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.6000000000000001, 'criteria': 0.5}\n",
+      "..........;\n",
+      "132 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.6000000000000001, 'criteria': 1.0}\n",
+      "..........;\n",
+      "133 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.6000000000000001, 'criteria': 1.5}\n",
+      "..........;\n",
+      "134 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.6000000000000001, 'criteria': 2.0}\n",
+      "..........;\n",
+      "135 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.6000000000000001, 'criteria': 2.5}\n",
+      "..........;\n",
+      "136 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.6000000000000001, 'criteria': 3.0}\n",
+      "..........;\n",
+      "137 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.6000000000000001, 'criteria': 3.5}\n",
+      "..........;\n",
+      "138 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.6000000000000001, 'criteria': 4.0}\n",
+      "..........;\n",
+      "139 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.6000000000000001, 'criteria': 4.5}\n",
+      "..........;\n",
+      "140 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.7000000000000001, 'criteria': -5.0}\n",
+      "..........;\n",
+      "141 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.7000000000000001, 'criteria': -4.5}\n",
+      "..........;\n",
+      "142 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.7000000000000001, 'criteria': -4.0}\n",
+      "..........;\n",
+      "143 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.7000000000000001, 'criteria': -3.5}\n",
+      "..........;\n",
+      "144 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.7000000000000001, 'criteria': -3.0}\n",
+      "..........;\n",
+      "145 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.7000000000000001, 'criteria': -2.5}\n",
+      "..........;\n",
+      "146 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.7000000000000001, 'criteria': -2.0}\n",
+      "..........;\n",
+      "147 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.7000000000000001, 'criteria': -1.5}\n",
+      "..........;\n",
+      "148 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.7000000000000001, 'criteria': -1.0}\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "..........;\n",
+      "149 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.7000000000000001, 'criteria': -0.5}\n",
+      "..........;\n",
+      "150 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.7000000000000001, 'criteria': 0.0}\n",
+      "..........;\n",
+      "151 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.7000000000000001, 'criteria': 0.5}\n",
+      "..........;\n",
+      "152 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.7000000000000001, 'criteria': 1.0}\n",
+      "..........;\n",
+      "153 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.7000000000000001, 'criteria': 1.5}\n",
+      "..........;\n",
+      "154 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.7000000000000001, 'criteria': 2.0}\n",
+      "..........;\n",
+      "155 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.7000000000000001, 'criteria': 2.5}\n",
+      "..........;\n",
+      "156 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.7000000000000001, 'criteria': 3.0}\n",
+      "..........;\n",
+      "157 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.7000000000000001, 'criteria': 3.5}\n",
+      "..........;\n",
+      "158 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.7000000000000001, 'criteria': 4.0}\n",
+      "..........;\n",
+      "159 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.7000000000000001, 'criteria': 4.5}\n",
+      "..........;\n",
+      "160 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.8, 'criteria': -5.0}\n",
+      "..........;\n",
+      "161 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.8, 'criteria': -4.5}\n",
+      "..........;\n",
+      "162 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.8, 'criteria': -4.0}\n",
+      "..........;\n",
+      "163 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.8, 'criteria': -3.5}\n",
+      "..........;\n",
+      "164 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.8, 'criteria': -3.0}\n",
+      "..........;\n",
+      "165 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.8, 'criteria': -2.5}\n",
+      "..........;\n",
+      "166 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.8, 'criteria': -2.0}\n",
+      "..........;\n",
+      "167 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.8, 'criteria': -1.5}\n",
+      "..........;\n",
+      "168 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.8, 'criteria': -1.0}\n",
+      "..........;\n",
+      "169 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.8, 'criteria': -0.5}\n",
+      "..........;\n",
+      "170 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.8, 'criteria': 0.0}\n",
+      "..........;\n",
+      "171 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.8, 'criteria': 0.5}\n",
+      "..........;\n",
+      "172 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.8, 'criteria': 1.0}\n",
+      "..........;\n",
+      "173 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.8, 'criteria': 1.5}\n",
+      "..........;\n",
+      "174 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.8, 'criteria': 2.0}\n",
+      "..........;\n",
+      "175 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.8, 'criteria': 2.5}\n",
+      "..........;\n",
+      "176 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.8, 'criteria': 3.0}\n",
+      "..........;\n",
+      "177 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.8, 'criteria': 3.5}\n",
+      "..........;\n",
+      "178 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.8, 'criteria': 4.0}\n",
+      "..........;\n",
+      "179 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.8, 'criteria': 4.5}\n",
+      "..........;\n",
+      "180 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.9, 'criteria': -5.0}\n",
+      "..........;\n",
+      "181 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.9, 'criteria': -4.5}\n",
+      "..........;\n",
+      "182 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.9, 'criteria': -4.0}\n",
+      "..........;\n",
+      "183 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.9, 'criteria': -3.5}\n",
+      "..........;\n",
+      "184 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.9, 'criteria': -3.0}\n",
+      "..........;\n",
+      "185 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.9, 'criteria': -2.5}\n",
+      "..........;\n",
+      "186 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.9, 'criteria': -2.0}\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "..........;\n",
+      "187 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.9, 'criteria': -1.5}\n",
+      "..........;\n",
+      "188 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.9, 'criteria': -1.0}\n",
+      "..........;\n",
+      "189 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.9, 'criteria': -0.5}\n",
+      "..........;\n",
+      "190 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.9, 'criteria': 0.0}\n",
+      "..........;\n",
+      "191 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.9, 'criteria': 0.5}\n",
+      "..........;\n",
+      "192 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.9, 'criteria': 1.0}\n",
+      "..........;\n",
+      "193 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.9, 'criteria': 1.5}\n",
+      "..........;\n",
+      "194 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.9, 'criteria': 2.0}\n",
+      "..........;\n",
+      "195 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.9, 'criteria': 2.5}\n",
+      "..........;\n",
+      "196 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.9, 'criteria': 3.0}\n",
+      "..........;\n",
+      "197 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.9, 'criteria': 3.5}\n",
+      "..........;\n",
+      "198 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.9, 'criteria': 4.0}\n",
+      "..........;\n",
+      "199 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 0.9, 'criteria': 4.5}\n",
+      "..........;\n",
+      "200 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.0, 'criteria': -5.0}\n",
+      "..........;\n",
+      "201 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.0, 'criteria': -4.5}\n",
+      "..........;\n",
+      "202 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.0, 'criteria': -4.0}\n",
+      "..........;\n",
+      "203 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.0, 'criteria': -3.5}\n",
+      "..........;\n",
+      "204 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.0, 'criteria': -3.0}\n",
+      "..........;\n",
+      "205 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.0, 'criteria': -2.5}\n",
+      "..........;\n",
+      "206 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.0, 'criteria': -2.0}\n",
+      "..........;\n",
+      "207 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.0, 'criteria': -1.5}\n",
+      "..........;\n",
+      "208 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.0, 'criteria': -1.0}\n",
+      "..........;\n",
+      "209 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.0, 'criteria': -0.5}\n",
+      "..........;\n",
+      "210 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.0, 'criteria': 0.0}\n",
+      "..........;\n",
+      "211 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.0, 'criteria': 0.5}\n",
+      "..........;\n",
+      "212 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.0, 'criteria': 1.0}\n",
+      "..........;\n",
+      "213 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.0, 'criteria': 1.5}\n",
+      "..........;\n",
+      "214 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.0, 'criteria': 2.0}\n",
+      "..........;\n",
+      "215 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.0, 'criteria': 2.5}\n",
+      "..........;\n",
+      "216 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.0, 'criteria': 3.0}\n",
+      "..........;\n",
+      "217 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.0, 'criteria': 3.5}\n",
+      "..........;\n",
+      "218 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.0, 'criteria': 4.0}\n",
+      "..........;\n",
+      "219 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.0, 'criteria': 4.5}\n",
+      "..........;\n",
+      "220 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.1, 'criteria': -5.0}\n",
+      "..........;\n",
+      "221 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.1, 'criteria': -4.5}\n",
+      "..........;\n",
+      "222 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.1, 'criteria': -4.0}\n",
+      "..........;\n",
+      "223 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.1, 'criteria': -3.5}\n",
+      "..........;\n",
+      "224 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.1, 'criteria': -3.0}\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "..........;\n",
+      "225 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.1, 'criteria': -2.5}\n",
+      "..........;\n",
+      "226 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.1, 'criteria': -2.0}\n",
+      "..........;\n",
+      "227 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.1, 'criteria': -1.5}\n",
+      "..........;\n",
+      "228 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.1, 'criteria': -1.0}\n",
+      "..........;\n",
+      "229 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.1, 'criteria': -0.5}\n",
+      "..........;\n",
+      "230 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.1, 'criteria': 0.0}\n",
+      "..........;\n",
+      "231 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.1, 'criteria': 0.5}\n",
+      "..........;\n",
+      "232 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.1, 'criteria': 1.0}\n",
+      "..........;\n",
+      "233 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.1, 'criteria': 1.5}\n",
+      "..........;\n",
+      "234 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.1, 'criteria': 2.0}\n",
+      "..........;\n",
+      "235 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.1, 'criteria': 2.5}\n",
+      "..........;\n",
+      "236 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.1, 'criteria': 3.0}\n",
+      "..........;\n",
+      "237 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.1, 'criteria': 3.5}\n",
+      "..........;\n",
+      "238 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.1, 'criteria': 4.0}\n",
+      "..........;\n",
+      "239 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.1, 'criteria': 4.5}\n",
+      "..........;\n",
+      "240 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.2000000000000002, 'criteria': -5.0}\n",
+      "..........;\n",
+      "241 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.2000000000000002, 'criteria': -4.5}\n",
+      "..........;\n",
+      "242 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.2000000000000002, 'criteria': -4.0}\n",
+      "..........;\n",
+      "243 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.2000000000000002, 'criteria': -3.5}\n",
+      "..........;\n",
+      "244 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.2000000000000002, 'criteria': -3.0}\n",
+      "..........;\n",
+      "245 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.2000000000000002, 'criteria': -2.5}\n",
+      "..........;\n",
+      "246 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.2000000000000002, 'criteria': -2.0}\n",
+      "..........;\n",
+      "247 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.2000000000000002, 'criteria': -1.5}\n",
+      "..........;\n",
+      "248 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.2000000000000002, 'criteria': -1.0}\n",
+      "..........;\n",
+      "249 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.2000000000000002, 'criteria': -0.5}\n",
+      "..........;\n",
+      "250 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.2000000000000002, 'criteria': 0.0}\n",
+      "..........;\n",
+      "251 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.2000000000000002, 'criteria': 0.5}\n",
+      "..........;\n",
+      "252 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.2000000000000002, 'criteria': 1.0}\n",
+      "..........;\n",
+      "253 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.2000000000000002, 'criteria': 1.5}\n",
+      "..........;\n",
+      "254 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.2000000000000002, 'criteria': 2.0}\n",
+      "..........;\n",
+      "255 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.2000000000000002, 'criteria': 2.5}\n",
+      "..........;\n",
+      "256 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.2000000000000002, 'criteria': 3.0}\n",
+      "..........;\n",
+      "257 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.2000000000000002, 'criteria': 3.5}\n",
+      "..........;\n",
+      "258 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.2000000000000002, 'criteria': 4.0}\n",
+      "..........;\n",
+      "259 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.2000000000000002, 'criteria': 4.5}\n",
+      "..........;\n",
+      "260 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.3, 'criteria': -5.0}\n",
+      "..........;\n",
+      "261 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.3, 'criteria': -4.5}\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "..........;\n",
+      "262 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.3, 'criteria': -4.0}\n",
+      "..........;\n",
+      "263 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.3, 'criteria': -3.5}\n",
+      "..........;\n",
+      "264 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.3, 'criteria': -3.0}\n",
+      "..........;\n",
+      "265 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.3, 'criteria': -2.5}\n",
+      "..........;\n",
+      "266 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.3, 'criteria': -2.0}\n",
+      "..........;\n",
+      "267 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.3, 'criteria': -1.5}\n",
+      "..........;\n",
+      "268 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.3, 'criteria': -1.0}\n",
+      "..........;\n",
+      "269 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.3, 'criteria': -0.5}\n",
+      "..........;\n",
+      "270 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.3, 'criteria': 0.0}\n",
+      "..........;\n",
+      "271 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.3, 'criteria': 0.5}\n",
+      "..........;\n",
+      "272 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.3, 'criteria': 1.0}\n",
+      "..........;\n",
+      "273 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.3, 'criteria': 1.5}\n",
+      "..........;\n",
+      "274 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.3, 'criteria': 2.0}\n",
+      "..........;\n",
+      "275 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.3, 'criteria': 2.5}\n",
+      "..........;\n",
+      "276 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.3, 'criteria': 3.0}\n",
+      "..........;\n",
+      "277 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.3, 'criteria': 3.5}\n",
+      "..........;\n",
+      "278 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.3, 'criteria': 4.0}\n",
+      "..........;\n",
+      "279 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.3, 'criteria': 4.5}\n",
+      "..........;\n",
+      "280 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.4000000000000001, 'criteria': -5.0}\n",
+      "..........;\n",
+      "281 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.4000000000000001, 'criteria': -4.5}\n",
+      "..........;\n",
+      "282 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.4000000000000001, 'criteria': -4.0}\n",
+      "..........;\n",
+      "283 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.4000000000000001, 'criteria': -3.5}\n",
+      "..........;\n",
+      "284 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.4000000000000001, 'criteria': -3.0}\n",
+      "..........;\n",
+      "285 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.4000000000000001, 'criteria': -2.5}\n",
+      "..........;\n",
+      "286 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.4000000000000001, 'criteria': -2.0}\n",
+      "..........;\n",
+      "287 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.4000000000000001, 'criteria': -1.5}\n",
+      "..........;\n",
+      "288 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.4000000000000001, 'criteria': -1.0}\n",
+      "..........;\n",
+      "289 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.4000000000000001, 'criteria': -0.5}\n",
+      "..........;\n",
+      "290 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.4000000000000001, 'criteria': 0.0}\n",
+      "..........;\n",
+      "291 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.4000000000000001, 'criteria': 0.5}\n",
+      "..........;\n",
+      "292 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.4000000000000001, 'criteria': 1.0}\n",
+      "..........;\n",
+      "293 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.4000000000000001, 'criteria': 1.5}\n",
+      "..........;\n",
+      "294 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.4000000000000001, 'criteria': 2.0}\n",
+      "..........;\n",
+      "295 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.4000000000000001, 'criteria': 2.5}\n",
+      "..........;\n",
+      "296 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.4000000000000001, 'criteria': 3.0}\n",
+      "..........;\n",
+      "297 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.4000000000000001, 'criteria': 3.5}\n",
+      "..........;\n",
+      "298 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.4000000000000001, 'criteria': 4.0}\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "..........;\n",
+      "299 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.4000000000000001, 'criteria': 4.5}\n",
+      "..........;\n",
+      "300 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.5, 'criteria': -5.0}\n",
+      "..........;\n",
+      "301 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.5, 'criteria': -4.5}\n",
+      "..........;\n",
+      "302 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.5, 'criteria': -4.0}\n",
+      "..........;\n",
+      "303 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.5, 'criteria': -3.5}\n",
+      "..........;\n",
+      "304 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.5, 'criteria': -3.0}\n",
+      "..........;\n",
+      "305 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.5, 'criteria': -2.5}\n",
+      "..........;\n",
+      "306 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.5, 'criteria': -2.0}\n",
+      "..........;\n",
+      "307 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.5, 'criteria': -1.5}\n",
+      "..........;\n",
+      "308 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.5, 'criteria': -1.0}\n",
+      "..........;\n",
+      "309 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.5, 'criteria': -0.5}\n",
+      "..........;\n",
+      "310 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.5, 'criteria': 0.0}\n",
+      "..........;\n",
+      "311 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.5, 'criteria': 0.5}\n",
+      "..........;\n",
+      "312 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.5, 'criteria': 1.0}\n",
+      "..........;\n",
+      "313 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.5, 'criteria': 1.5}\n",
+      "..........;\n",
+      "314 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.5, 'criteria': 2.0}\n",
+      "..........;\n",
+      "315 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.5, 'criteria': 2.5}\n",
+      "..........;\n",
+      "316 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.5, 'criteria': 3.0}\n",
+      "..........;\n",
+      "317 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.5, 'criteria': 3.5}\n",
+      "..........;\n",
+      "318 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.5, 'criteria': 4.0}\n",
+      "..........;\n",
+      "319 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.5, 'criteria': 4.5}\n",
+      "..........;\n",
+      "320 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.6, 'criteria': -5.0}\n",
+      "..........;\n",
+      "321 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.6, 'criteria': -4.5}\n",
+      "..........;\n",
+      "322 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.6, 'criteria': -4.0}\n",
+      "..........;\n",
+      "323 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.6, 'criteria': -3.5}\n",
+      "..........;\n",
+      "324 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.6, 'criteria': -3.0}\n",
+      "..........;\n",
+      "325 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.6, 'criteria': -2.5}\n",
+      "..........;\n",
+      "326 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.6, 'criteria': -2.0}\n",
+      "..........;\n",
+      "327 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.6, 'criteria': -1.5}\n",
+      "..........;\n",
+      "328 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.6, 'criteria': -1.0}\n",
+      "..........;\n",
+      "329 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.6, 'criteria': -0.5}\n",
+      "..........;\n",
+      "330 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.6, 'criteria': 0.0}\n",
+      "..........;\n",
+      "331 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.6, 'criteria': 0.5}\n",
+      "..........;\n",
+      "332 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.6, 'criteria': 1.0}\n",
+      "..........;\n",
+      "333 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.6, 'criteria': 1.5}\n",
+      "..........;\n",
+      "334 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.6, 'criteria': 2.0}\n",
+      "..........;\n",
+      "335 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.6, 'criteria': 2.5}\n",
+      "..........;\n",
+      "336 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.6, 'criteria': 3.0}\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "..........;\n",
+      "337 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.6, 'criteria': 3.5}\n",
+      "..........;\n",
+      "338 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.6, 'criteria': 4.0}\n",
+      "..........;\n",
+      "339 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.6, 'criteria': 4.5}\n",
+      "..........;\n",
+      "340 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.7000000000000002, 'criteria': -5.0}\n",
+      "..........;\n",
+      "341 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.7000000000000002, 'criteria': -4.5}\n",
+      "..........;\n",
+      "342 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.7000000000000002, 'criteria': -4.0}\n",
+      "..........;\n",
+      "343 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.7000000000000002, 'criteria': -3.5}\n",
+      "..........;\n",
+      "344 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.7000000000000002, 'criteria': -3.0}\n",
+      "..........;\n",
+      "345 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.7000000000000002, 'criteria': -2.5}\n",
+      "..........;\n",
+      "346 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.7000000000000002, 'criteria': -2.0}\n",
+      "..........;\n",
+      "347 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.7000000000000002, 'criteria': -1.5}\n",
+      "..........;\n",
+      "348 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.7000000000000002, 'criteria': -1.0}\n",
+      "..........;\n",
+      "349 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.7000000000000002, 'criteria': -0.5}\n",
+      "..........;\n",
+      "350 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.7000000000000002, 'criteria': 0.0}\n",
+      "..........;\n",
+      "351 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.7000000000000002, 'criteria': 0.5}\n",
+      "..........;\n",
+      "352 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.7000000000000002, 'criteria': 1.0}\n",
+      "..........;\n",
+      "353 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.7000000000000002, 'criteria': 1.5}\n",
+      "..........;\n",
+      "354 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.7000000000000002, 'criteria': 2.0}\n",
+      "..........;\n",
+      "355 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.7000000000000002, 'criteria': 2.5}\n",
+      "..........;\n",
+      "356 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.7000000000000002, 'criteria': 3.0}\n",
+      "..........;\n",
+      "357 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.7000000000000002, 'criteria': 3.5}\n",
+      "..........;\n",
+      "358 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.7000000000000002, 'criteria': 4.0}\n",
+      "..........;\n",
+      "359 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.7000000000000002, 'criteria': 4.5}\n",
+      "..........;\n",
+      "360 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.8, 'criteria': -5.0}\n",
+      "..........;\n",
+      "361 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.8, 'criteria': -4.5}\n",
+      "..........;\n",
+      "362 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.8, 'criteria': -4.0}\n",
+      "..........;\n",
+      "363 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.8, 'criteria': -3.5}\n",
+      "..........;\n",
+      "364 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.8, 'criteria': -3.0}\n",
+      "..........;\n",
+      "365 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.8, 'criteria': -2.5}\n",
+      "..........;\n",
+      "366 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.8, 'criteria': -2.0}\n",
+      "..........;\n",
+      "367 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.8, 'criteria': -1.5}\n",
+      "..........;\n",
+      "368 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.8, 'criteria': -1.0}\n",
+      "..........;\n",
+      "369 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.8, 'criteria': -0.5}\n",
+      "..........;\n",
+      "370 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.8, 'criteria': 0.0}\n",
+      "..........;\n",
+      "371 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.8, 'criteria': 0.5}\n",
+      "..........;\n",
+      "372 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.8, 'criteria': 1.0}\n",
+      "..........;\n",
+      "373 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.8, 'criteria': 1.5}\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "..........;\n",
+      "374 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.8, 'criteria': 2.0}\n",
+      "..........;\n",
+      "375 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.8, 'criteria': 2.5}\n",
+      "..........;\n",
+      "376 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.8, 'criteria': 3.0}\n",
+      "..........;\n",
+      "377 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.8, 'criteria': 3.5}\n",
+      "..........;\n",
+      "378 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.8, 'criteria': 4.0}\n",
+      "..........;\n",
+      "379 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.8, 'criteria': 4.5}\n",
+      "..........;\n",
+      "380 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.9000000000000001, 'criteria': -5.0}\n",
+      "..........;\n",
+      "381 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.9000000000000001, 'criteria': -4.5}\n",
+      "..........;\n",
+      "382 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.9000000000000001, 'criteria': -4.0}\n",
+      "..........;\n",
+      "383 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.9000000000000001, 'criteria': -3.5}\n",
+      "..........;\n",
+      "384 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.9000000000000001, 'criteria': -3.0}\n",
+      "..........;\n",
+      "385 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.9000000000000001, 'criteria': -2.5}\n",
+      "..........;\n",
+      "386 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.9000000000000001, 'criteria': -2.0}\n",
+      "..........;\n",
+      "387 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.9000000000000001, 'criteria': -1.5}\n",
+      "..........;\n",
+      "388 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.9000000000000001, 'criteria': -1.0}\n",
+      "..........;\n",
+      "389 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.9000000000000001, 'criteria': -0.5}\n",
+      "..........;\n",
+      "390 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.9000000000000001, 'criteria': 0.0}\n",
+      "..........;\n",
+      "391 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.9000000000000001, 'criteria': 0.5}\n",
+      "..........;\n",
+      "392 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.9000000000000001, 'criteria': 1.0}\n",
+      "..........;\n",
+      "393 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.9000000000000001, 'criteria': 1.5}\n",
+      "..........;\n",
+      "394 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.9000000000000001, 'criteria': 2.0}\n",
+      "..........;\n",
+      "395 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.9000000000000001, 'criteria': 2.5}\n",
+      "..........;\n",
+      "396 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.9000000000000001, 'criteria': 3.0}\n",
+      "..........;\n",
+      "397 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.9000000000000001, 'criteria': 3.5}\n",
+      "..........;\n",
+      "398 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.9000000000000001, 'criteria': 4.0}\n",
+      "..........;\n",
+      "399 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 1.9000000000000001, 'criteria': 4.5}\n",
+      "..........;\n",
+      "400 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.0, 'criteria': -5.0}\n",
+      "..........;\n",
+      "401 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.0, 'criteria': -4.5}\n",
+      "..........;\n",
+      "402 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.0, 'criteria': -4.0}\n",
+      "..........;\n",
+      "403 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.0, 'criteria': -3.5}\n",
+      "..........;\n",
+      "404 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.0, 'criteria': -3.0}\n",
+      "..........;\n",
+      "405 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.0, 'criteria': -2.5}\n",
+      "..........;\n",
+      "406 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.0, 'criteria': -2.0}\n",
+      "..........;\n",
+      "407 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.0, 'criteria': -1.5}\n",
+      "..........;\n",
+      "408 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.0, 'criteria': -1.0}\n",
+      "..........;\n",
+      "409 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.0, 'criteria': -0.5}\n",
+      "..........;\n",
+      "410 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.0, 'criteria': 0.0}\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "..........;\n",
+      "411 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.0, 'criteria': 0.5}\n",
+      "..........;\n",
+      "412 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.0, 'criteria': 1.0}\n",
+      "..........;\n",
+      "413 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.0, 'criteria': 1.5}\n",
+      "..........;\n",
+      "414 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.0, 'criteria': 2.0}\n",
+      "..........;\n",
+      "415 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.0, 'criteria': 2.5}\n",
+      "..........;\n",
+      "416 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.0, 'criteria': 3.0}\n",
+      "..........;\n",
+      "417 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.0, 'criteria': 3.5}\n",
+      "..........;\n",
+      "418 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.0, 'criteria': 4.0}\n",
+      "..........;\n",
+      "419 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.0, 'criteria': 4.5}\n",
+      "..........;\n",
+      "420 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.1, 'criteria': -5.0}\n",
+      "..........;\n",
+      "421 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.1, 'criteria': -4.5}\n",
+      "..........;\n",
+      "422 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.1, 'criteria': -4.0}\n",
+      "..........;\n",
+      "423 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.1, 'criteria': -3.5}\n",
+      "..........;\n",
+      "424 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.1, 'criteria': -3.0}\n",
+      "..........;\n",
+      "425 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.1, 'criteria': -2.5}\n",
+      "..........;\n",
+      "426 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.1, 'criteria': -2.0}\n",
+      "..........;\n",
+      "427 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.1, 'criteria': -1.5}\n",
+      "..........;\n",
+      "428 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.1, 'criteria': -1.0}\n",
+      "..........;\n",
+      "429 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.1, 'criteria': -0.5}\n",
+      "..........;\n",
+      "430 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.1, 'criteria': 0.0}\n",
+      "..........;\n",
+      "431 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.1, 'criteria': 0.5}\n",
+      "..........;\n",
+      "432 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.1, 'criteria': 1.0}\n",
+      "..........;\n",
+      "433 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.1, 'criteria': 1.5}\n",
+      "..........;\n",
+      "434 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.1, 'criteria': 2.0}\n",
+      "..........;\n",
+      "435 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.1, 'criteria': 2.5}\n",
+      "..........;\n",
+      "436 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.1, 'criteria': 3.0}\n",
+      "..........;\n",
+      "437 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.1, 'criteria': 3.5}\n",
+      "..........;\n",
+      "438 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.1, 'criteria': 4.0}\n",
+      "..........;\n",
+      "439 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.1, 'criteria': 4.5}\n",
+      "..........;\n",
+      "440 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.2, 'criteria': -5.0}\n",
+      "..........;\n",
+      "441 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.2, 'criteria': -4.5}\n",
+      "..........;\n",
+      "442 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.2, 'criteria': -4.0}\n",
+      "..........;\n",
+      "443 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.2, 'criteria': -3.5}\n",
+      "..........;\n",
+      "444 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.2, 'criteria': -3.0}\n",
+      "..........;\n",
+      "445 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.2, 'criteria': -2.5}\n",
+      "..........;\n",
+      "446 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.2, 'criteria': -2.0}\n",
+      "..........;\n",
+      "447 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.2, 'criteria': -1.5}\n",
+      "..........;\n",
+      "448 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.2, 'criteria': -1.0}\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "..........;\n",
+      "449 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.2, 'criteria': -0.5}\n",
+      "..........;\n",
+      "450 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.2, 'criteria': 0.0}\n",
+      "..........;\n",
+      "451 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.2, 'criteria': 0.5}\n",
+      "..........;\n",
+      "452 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.2, 'criteria': 1.0}\n",
+      "..........;\n",
+      "453 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.2, 'criteria': 1.5}\n",
+      "..........;\n",
+      "454 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.2, 'criteria': 2.0}\n",
+      "..........;\n",
+      "455 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.2, 'criteria': 2.5}\n",
+      "..........;\n",
+      "456 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.2, 'criteria': 3.0}\n",
+      "..........;\n",
+      "457 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.2, 'criteria': 3.5}\n",
+      "..........;\n",
+      "458 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.2, 'criteria': 4.0}\n",
+      "..........;\n",
+      "459 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.2, 'criteria': 4.5}\n",
+      "..........;\n",
+      "460 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.3000000000000003, 'criteria': -5.0}\n",
+      "..........;\n",
+      "461 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.3000000000000003, 'criteria': -4.5}\n",
+      "..........;\n",
+      "462 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.3000000000000003, 'criteria': -4.0}\n",
+      "..........;\n",
+      "463 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.3000000000000003, 'criteria': -3.5}\n",
+      "..........;\n",
+      "464 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.3000000000000003, 'criteria': -3.0}\n",
+      "..........;\n",
+      "465 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.3000000000000003, 'criteria': -2.5}\n",
+      "..........;\n",
+      "466 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.3000000000000003, 'criteria': -2.0}\n",
+      "..........;\n",
+      "467 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.3000000000000003, 'criteria': -1.5}\n",
+      "..........;\n",
+      "468 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.3000000000000003, 'criteria': -1.0}\n",
+      "..........;\n",
+      "469 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.3000000000000003, 'criteria': -0.5}\n",
+      "..........;\n",
+      "470 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.3000000000000003, 'criteria': 0.0}\n",
+      "..........;\n",
+      "471 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.3000000000000003, 'criteria': 0.5}\n",
+      "..........;\n",
+      "472 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.3000000000000003, 'criteria': 1.0}\n",
+      "..........;\n",
+      "473 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.3000000000000003, 'criteria': 1.5}\n",
+      "..........;\n",
+      "474 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.3000000000000003, 'criteria': 2.0}\n",
+      "..........;\n",
+      "475 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.3000000000000003, 'criteria': 2.5}\n",
+      "..........;\n",
+      "476 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.3000000000000003, 'criteria': 3.0}\n",
+      "..........;\n",
+      "477 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.3000000000000003, 'criteria': 3.5}\n",
+      "..........;\n",
+      "478 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.3000000000000003, 'criteria': 4.0}\n",
+      "..........;\n",
+      "479 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.3000000000000003, 'criteria': 4.5}\n",
+      "..........;\n",
+      "480 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.4000000000000004, 'criteria': -5.0}\n",
+      "..........;\n",
+      "481 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.4000000000000004, 'criteria': -4.5}\n",
+      "..........;\n",
+      "482 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.4000000000000004, 'criteria': -4.0}\n",
+      "..........;\n",
+      "483 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.4000000000000004, 'criteria': -3.5}\n",
+      "..........;\n",
+      "484 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.4000000000000004, 'criteria': -3.0}\n",
+      "..........;\n",
+      "485 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.4000000000000004, 'criteria': -2.5}\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "..........;\n",
+      "486 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.4000000000000004, 'criteria': -2.0}\n",
+      "..........;\n",
+      "487 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.4000000000000004, 'criteria': -1.5}\n",
+      "..........;\n",
+      "488 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.4000000000000004, 'criteria': -1.0}\n",
+      "..........;\n",
+      "489 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.4000000000000004, 'criteria': -0.5}\n",
+      "..........;\n",
+      "490 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.4000000000000004, 'criteria': 0.0}\n",
+      "..........;\n",
+      "491 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.4000000000000004, 'criteria': 0.5}\n",
+      "..........;\n",
+      "492 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.4000000000000004, 'criteria': 1.0}\n",
+      "..........;\n",
+      "493 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.4000000000000004, 'criteria': 1.5}\n",
+      "..........;\n",
+      "494 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.4000000000000004, 'criteria': 2.0}\n",
+      "..........;\n",
+      "495 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.4000000000000004, 'criteria': 2.5}\n",
+      "..........;\n",
+      "496 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.4000000000000004, 'criteria': 3.0}\n",
+      "..........;\n",
+      "497 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.4000000000000004, 'criteria': 3.5}\n",
+      "..........;\n",
+      "498 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.4000000000000004, 'criteria': 4.0}\n",
+      "..........;\n",
+      "499 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.4000000000000004, 'criteria': 4.5}\n",
+      "..........;\n",
+      "500 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.5, 'criteria': -5.0}\n",
+      "..........;\n",
+      "501 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.5, 'criteria': -4.5}\n",
+      "..........;\n",
+      "502 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.5, 'criteria': -4.0}\n",
+      "..........;\n",
+      "503 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.5, 'criteria': -3.5}\n",
+      "..........;\n",
+      "504 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.5, 'criteria': -3.0}\n",
+      "..........;\n",
+      "505 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.5, 'criteria': -2.5}\n",
+      "..........;\n",
+      "506 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.5, 'criteria': -2.0}\n",
+      "..........;\n",
+      "507 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.5, 'criteria': -1.5}\n",
+      "..........;\n",
+      "508 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.5, 'criteria': -1.0}\n",
+      "..........;\n",
+      "509 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.5, 'criteria': -0.5}\n",
+      "..........;\n",
+      "510 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.5, 'criteria': 0.0}\n",
+      "..........;\n",
+      "511 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.5, 'criteria': 0.5}\n",
+      "..........;\n",
+      "512 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.5, 'criteria': 1.0}\n",
+      "..........;\n",
+      "513 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.5, 'criteria': 1.5}\n",
+      "..........;\n",
+      "514 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.5, 'criteria': 2.0}\n",
+      "..........;\n",
+      "515 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.5, 'criteria': 2.5}\n",
+      "..........;\n",
+      "516 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.5, 'criteria': 3.0}\n",
+      "..........;\n",
+      "517 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.5, 'criteria': 3.5}\n",
+      "..........;\n",
+      "518 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.5, 'criteria': 4.0}\n",
+      "..........;\n",
+      "519 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.5, 'criteria': 4.5}\n",
+      "..........;\n",
+      "520 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.6, 'criteria': -5.0}\n",
+      "..........;\n",
+      "521 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.6, 'criteria': -4.5}\n",
+      "..........;\n",
+      "522 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.6, 'criteria': -4.0}\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "..........;\n",
+      "523 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.6, 'criteria': -3.5}\n",
+      "..........;\n",
+      "524 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.6, 'criteria': -3.0}\n",
+      "..........;\n",
+      "525 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.6, 'criteria': -2.5}\n",
+      "..........;\n",
+      "526 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.6, 'criteria': -2.0}\n",
+      "..........;\n",
+      "527 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.6, 'criteria': -1.5}\n",
+      "..........;\n",
+      "528 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.6, 'criteria': -1.0}\n",
+      "..........;\n",
+      "529 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.6, 'criteria': -0.5}\n",
+      "..........;\n",
+      "530 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.6, 'criteria': 0.0}\n",
+      "..........;\n",
+      "531 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.6, 'criteria': 0.5}\n",
+      "..........;\n",
+      "532 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.6, 'criteria': 1.0}\n",
+      "..........;\n",
+      "533 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.6, 'criteria': 1.5}\n",
+      "..........;\n",
+      "534 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.6, 'criteria': 2.0}\n",
+      "..........;\n",
+      "535 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.6, 'criteria': 2.5}\n",
+      "..........;\n",
+      "536 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.6, 'criteria': 3.0}\n",
+      "..........;\n",
+      "537 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.6, 'criteria': 3.5}\n",
+      "..........;\n",
+      "538 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.6, 'criteria': 4.0}\n",
+      "..........;\n",
+      "539 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.6, 'criteria': 4.5}\n",
+      "..........;\n",
+      "540 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.7, 'criteria': -5.0}\n",
+      "..........;\n",
+      "541 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.7, 'criteria': -4.5}\n",
+      "..........;\n",
+      "542 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.7, 'criteria': -4.0}\n",
+      "..........;\n",
+      "543 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.7, 'criteria': -3.5}\n",
+      "..........;\n",
+      "544 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.7, 'criteria': -3.0}\n",
+      "..........;\n",
+      "545 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.7, 'criteria': -2.5}\n",
+      "..........;\n",
+      "546 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.7, 'criteria': -2.0}\n",
+      "..........;\n",
+      "547 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.7, 'criteria': -1.5}\n",
+      "..........;\n",
+      "548 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.7, 'criteria': -1.0}\n",
+      "..........;\n",
+      "549 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.7, 'criteria': -0.5}\n",
+      "..........;\n",
+      "550 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.7, 'criteria': 0.0}\n",
+      "..........;\n",
+      "551 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.7, 'criteria': 0.5}\n",
+      "..........;\n",
+      "552 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.7, 'criteria': 1.0}\n",
+      "..........;\n",
+      "553 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.7, 'criteria': 1.5}\n",
+      "..........;\n",
+      "554 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.7, 'criteria': 2.0}\n",
+      "..........;\n",
+      "555 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.7, 'criteria': 2.5}\n",
+      "..........;\n",
+      "556 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.7, 'criteria': 3.0}\n",
+      "..........;\n",
+      "557 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.7, 'criteria': 3.5}\n",
+      "..........;\n",
+      "558 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.7, 'criteria': 4.0}\n",
+      "..........;\n",
+      "559 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.7, 'criteria': 4.5}\n",
+      "..........;\n",
+      "560 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.8000000000000003, 'criteria': -5.0}\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "..........;\n",
+      "561 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.8000000000000003, 'criteria': -4.5}\n",
+      "..........;\n",
+      "562 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.8000000000000003, 'criteria': -4.0}\n",
+      "..........;\n",
+      "563 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.8000000000000003, 'criteria': -3.5}\n",
+      "..........;\n",
+      "564 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.8000000000000003, 'criteria': -3.0}\n",
+      "..........;\n",
+      "565 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.8000000000000003, 'criteria': -2.5}\n",
+      "..........;\n",
+      "566 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.8000000000000003, 'criteria': -2.0}\n",
+      "..........;\n",
+      "567 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.8000000000000003, 'criteria': -1.5}\n",
+      "..........;\n",
+      "568 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.8000000000000003, 'criteria': -1.0}\n",
+      "..........;\n",
+      "569 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.8000000000000003, 'criteria': -0.5}\n",
+      "..........;\n",
+      "570 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.8000000000000003, 'criteria': 0.0}\n",
+      "..........;\n",
+      "571 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.8000000000000003, 'criteria': 0.5}\n",
+      "..........;\n",
+      "572 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.8000000000000003, 'criteria': 1.0}\n",
+      "..........;\n",
+      "573 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.8000000000000003, 'criteria': 1.5}\n",
+      "..........;\n",
+      "574 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.8000000000000003, 'criteria': 2.0}\n",
+      "..........;\n",
+      "575 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.8000000000000003, 'criteria': 2.5}\n",
+      "..........;\n",
+      "576 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.8000000000000003, 'criteria': 3.0}\n",
+      "..........;\n",
+      "577 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.8000000000000003, 'criteria': 3.5}\n",
+      "..........;\n",
+      "578 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.8000000000000003, 'criteria': 4.0}\n",
+      "..........;\n",
+      "579 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.8000000000000003, 'criteria': 4.5}\n",
+      "..........;\n",
+      "580 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.9000000000000004, 'criteria': -5.0}\n",
+      "..........;\n",
+      "581 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.9000000000000004, 'criteria': -4.5}\n",
+      "..........;\n",
+      "582 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.9000000000000004, 'criteria': -4.0}\n",
+      "..........;\n",
+      "583 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.9000000000000004, 'criteria': -3.5}\n",
+      "..........;\n",
+      "584 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.9000000000000004, 'criteria': -3.0}\n",
+      "..........;\n",
+      "585 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.9000000000000004, 'criteria': -2.5}\n",
+      "..........;\n",
+      "586 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.9000000000000004, 'criteria': -2.0}\n",
+      "..........;\n",
+      "587 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.9000000000000004, 'criteria': -1.5}\n",
+      "..........;\n",
+      "588 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.9000000000000004, 'criteria': -1.0}\n",
+      "..........;\n",
+      "589 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.9000000000000004, 'criteria': -0.5}\n",
+      "..........;\n",
+      "590 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.9000000000000004, 'criteria': 0.0}\n",
+      "..........;\n",
+      "591 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.9000000000000004, 'criteria': 0.5}\n",
+      "..........;\n",
+      "592 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.9000000000000004, 'criteria': 1.0}\n",
+      "..........;\n",
+      "593 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.9000000000000004, 'criteria': 1.5}\n",
+      "..........;\n",
+      "594 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.9000000000000004, 'criteria': 2.0}\n",
+      "..........;\n",
+      "595 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.9000000000000004, 'criteria': 2.5}\n",
+      "..........;\n",
+      "596 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.9000000000000004, 'criteria': 3.0}\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "..........;\n",
+      "597 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.9000000000000004, 'criteria': 3.5}\n",
+      "..........;\n",
+      "598 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.9000000000000004, 'criteria': 4.0}\n",
+      "..........;\n",
+      "599 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 2.9000000000000004, 'criteria': 4.5}\n",
+      "..........;\n",
+      "600 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.0, 'criteria': -5.0}\n",
+      "..........;\n",
+      "601 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.0, 'criteria': -4.5}\n",
+      "..........;\n",
+      "602 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.0, 'criteria': -4.0}\n",
+      "..........;\n",
+      "603 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.0, 'criteria': -3.5}\n",
+      "..........;\n",
+      "604 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.0, 'criteria': -3.0}\n",
+      "..........;\n",
+      "605 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.0, 'criteria': -2.5}\n",
+      "..........;\n",
+      "606 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.0, 'criteria': -2.0}\n",
+      "..........;\n",
+      "607 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.0, 'criteria': -1.5}\n",
+      "..........;\n",
+      "608 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.0, 'criteria': -1.0}\n",
+      "..........;\n",
+      "609 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.0, 'criteria': -0.5}\n",
+      "..........;\n",
+      "610 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.0, 'criteria': 0.0}\n",
+      "..........;\n",
+      "611 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.0, 'criteria': 0.5}\n",
+      "..........;\n",
+      "612 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.0, 'criteria': 1.0}\n",
+      "..........;\n",
+      "613 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.0, 'criteria': 1.5}\n",
+      "..........;\n",
+      "614 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.0, 'criteria': 2.0}\n",
+      "..........;\n",
+      "615 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.0, 'criteria': 2.5}\n",
+      "..........;\n",
+      "616 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.0, 'criteria': 3.0}\n",
+      "..........;\n",
+      "617 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.0, 'criteria': 3.5}\n",
+      "..........;\n",
+      "618 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.0, 'criteria': 4.0}\n",
+      "..........;\n",
+      "619 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.0, 'criteria': 4.5}\n",
+      "..........;\n",
+      "620 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.1, 'criteria': -5.0}\n",
+      "..........;\n",
+      "621 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.1, 'criteria': -4.5}\n",
+      "..........;\n",
+      "622 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.1, 'criteria': -4.0}\n",
+      "..........;\n",
+      "623 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.1, 'criteria': -3.5}\n",
+      "..........;\n",
+      "624 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.1, 'criteria': -3.0}\n",
+      "..........;\n",
+      "625 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.1, 'criteria': -2.5}\n",
+      "..........;\n",
+      "626 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.1, 'criteria': -2.0}\n",
+      "..........;\n",
+      "627 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.1, 'criteria': -1.5}\n",
+      "..........;\n",
+      "628 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.1, 'criteria': -1.0}\n",
+      "..........;\n",
+      "629 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.1, 'criteria': -0.5}\n",
+      "..........;\n",
+      "630 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.1, 'criteria': 0.0}\n",
+      "..........;\n",
+      "631 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.1, 'criteria': 0.5}\n",
+      "..........;\n",
+      "632 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.1, 'criteria': 1.0}\n",
+      "..........;\n",
+      "633 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.1, 'criteria': 1.5}\n",
+      "..........;\n",
+      "634 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.1, 'criteria': 2.0}\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "..........;\n",
+      "635 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.1, 'criteria': 2.5}\n",
+      "..........;\n",
+      "636 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.1, 'criteria': 3.0}\n",
+      "..........;\n",
+      "637 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.1, 'criteria': 3.5}\n",
+      "..........;\n",
+      "638 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.1, 'criteria': 4.0}\n",
+      "..........;\n",
+      "639 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.1, 'criteria': 4.5}\n",
+      "..........;\n",
+      "640 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.2, 'criteria': -5.0}\n",
+      "..........;\n",
+      "641 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.2, 'criteria': -4.5}\n",
+      "..........;\n",
+      "642 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.2, 'criteria': -4.0}\n",
+      "..........;\n",
+      "643 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.2, 'criteria': -3.5}\n",
+      "..........;\n",
+      "644 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.2, 'criteria': -3.0}\n",
+      "..........;\n",
+      "645 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.2, 'criteria': -2.5}\n",
+      "..........;\n",
+      "646 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.2, 'criteria': -2.0}\n",
+      "..........;\n",
+      "647 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.2, 'criteria': -1.5}\n",
+      "..........;\n",
+      "648 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.2, 'criteria': -1.0}\n",
+      "..........;\n",
+      "649 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.2, 'criteria': -0.5}\n",
+      "..........;\n",
+      "650 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.2, 'criteria': 0.0}\n",
+      "..........;\n",
+      "651 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.2, 'criteria': 0.5}\n",
+      "..........;\n",
+      "652 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.2, 'criteria': 1.0}\n",
+      "..........;\n",
+      "653 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.2, 'criteria': 1.5}\n",
+      "..........;\n",
+      "654 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.2, 'criteria': 2.0}\n",
+      "..........;\n",
+      "655 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.2, 'criteria': 2.5}\n",
+      "..........;\n",
+      "656 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.2, 'criteria': 3.0}\n",
+      "..........;\n",
+      "657 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.2, 'criteria': 3.5}\n",
+      "..........;\n",
+      "658 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.2, 'criteria': 4.0}\n",
+      "..........;\n",
+      "659 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.2, 'criteria': 4.5}\n",
+      "..........;\n",
+      "660 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.3000000000000003, 'criteria': -5.0}\n",
+      "..........;\n",
+      "661 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.3000000000000003, 'criteria': -4.5}\n",
+      "..........;\n",
+      "662 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.3000000000000003, 'criteria': -4.0}\n",
+      "..........;\n",
+      "663 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.3000000000000003, 'criteria': -3.5}\n",
+      "..........;\n",
+      "664 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.3000000000000003, 'criteria': -3.0}\n",
+      "..........;\n",
+      "665 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.3000000000000003, 'criteria': -2.5}\n",
+      "..........;\n",
+      "666 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.3000000000000003, 'criteria': -2.0}\n",
+      "..........;\n",
+      "667 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.3000000000000003, 'criteria': -1.5}\n",
+      "..........;\n",
+      "668 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.3000000000000003, 'criteria': -1.0}\n",
+      "..........;\n",
+      "669 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.3000000000000003, 'criteria': -0.5}\n",
+      "..........;\n",
+      "670 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.3000000000000003, 'criteria': 0.0}\n",
+      "..........;\n",
+      "671 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.3000000000000003, 'criteria': 0.5}\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "..........;\n",
+      "672 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.3000000000000003, 'criteria': 1.0}\n",
+      "..........;\n",
+      "673 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.3000000000000003, 'criteria': 1.5}\n",
+      "..........;\n",
+      "674 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.3000000000000003, 'criteria': 2.0}\n",
+      "..........;\n",
+      "675 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.3000000000000003, 'criteria': 2.5}\n",
+      "..........;\n",
+      "676 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.3000000000000003, 'criteria': 3.0}\n",
+      "..........;\n",
+      "677 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.3000000000000003, 'criteria': 3.5}\n",
+      "..........;\n",
+      "678 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.3000000000000003, 'criteria': 4.0}\n",
+      "..........;\n",
+      "679 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.3000000000000003, 'criteria': 4.5}\n",
+      "..........;\n",
+      "680 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.4000000000000004, 'criteria': -5.0}\n",
+      "..........;\n",
+      "681 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.4000000000000004, 'criteria': -4.5}\n",
+      "..........;\n",
+      "682 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.4000000000000004, 'criteria': -4.0}\n",
+      "..........;\n",
+      "683 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.4000000000000004, 'criteria': -3.5}\n",
+      "..........;\n",
+      "684 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.4000000000000004, 'criteria': -3.0}\n",
+      "..........;\n",
+      "685 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.4000000000000004, 'criteria': -2.5}\n",
+      "..........;\n",
+      "686 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.4000000000000004, 'criteria': -2.0}\n",
+      "..........;\n",
+      "687 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.4000000000000004, 'criteria': -1.5}\n",
+      "..........;\n",
+      "688 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.4000000000000004, 'criteria': -1.0}\n",
+      "..........;\n",
+      "689 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.4000000000000004, 'criteria': -0.5}\n",
+      "..........;\n",
+      "690 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.4000000000000004, 'criteria': 0.0}\n",
+      "..........;\n",
+      "691 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.4000000000000004, 'criteria': 0.5}\n",
+      "..........;\n",
+      "692 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.4000000000000004, 'criteria': 1.0}\n",
+      "..........;\n",
+      "693 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.4000000000000004, 'criteria': 1.5}\n",
+      "..........;\n",
+      "694 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.4000000000000004, 'criteria': 2.0}\n",
+      "..........;\n",
+      "695 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.4000000000000004, 'criteria': 2.5}\n",
+      "..........;\n",
+      "696 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.4000000000000004, 'criteria': 3.0}\n",
+      "..........;\n",
+      "697 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.4000000000000004, 'criteria': 3.5}\n",
+      "..........;\n",
+      "698 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.4000000000000004, 'criteria': 4.0}\n",
+      "..........;\n",
+      "699 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.4000000000000004, 'criteria': 4.5}\n",
+      "..........;\n",
+      "700 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.5, 'criteria': -5.0}\n",
+      "..........;\n",
+      "701 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.5, 'criteria': -4.5}\n",
+      "..........;\n",
+      "702 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.5, 'criteria': -4.0}\n",
+      "..........;\n",
+      "703 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.5, 'criteria': -3.5}\n",
+      "..........;\n",
+      "704 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.5, 'criteria': -3.0}\n",
+      "..........;\n",
+      "705 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.5, 'criteria': -2.5}\n",
+      "..........;\n",
+      "706 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.5, 'criteria': -2.0}\n",
+      "..........;\n",
+      "707 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.5, 'criteria': -1.5}\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "..........;\n",
+      "708 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.5, 'criteria': -1.0}\n",
+      "..........;\n",
+      "709 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.5, 'criteria': -0.5}\n",
+      "..........;\n",
+      "710 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.5, 'criteria': 0.0}\n",
+      "..........;\n",
+      "711 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.5, 'criteria': 0.5}\n",
+      "..........;\n",
+      "712 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.5, 'criteria': 1.0}\n",
+      "..........;\n",
+      "713 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.5, 'criteria': 1.5}\n",
+      "..........;\n",
+      "714 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.5, 'criteria': 2.0}\n",
+      "..........;\n",
+      "715 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.5, 'criteria': 2.5}\n",
+      "..........;\n",
+      "716 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.5, 'criteria': 3.0}\n",
+      "..........;\n",
+      "717 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.5, 'criteria': 3.5}\n",
+      "..........;\n",
+      "718 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.5, 'criteria': 4.0}\n",
+      "..........;\n",
+      "719 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.5, 'criteria': 4.5}\n",
+      "..........;\n",
+      "720 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.6, 'criteria': -5.0}\n",
+      "..........;\n",
+      "721 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.6, 'criteria': -4.5}\n",
+      "..........;\n",
+      "722 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.6, 'criteria': -4.0}\n",
+      "..........;\n",
+      "723 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.6, 'criteria': -3.5}\n",
+      "..........;\n",
+      "724 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.6, 'criteria': -3.0}\n",
+      "..........;\n",
+      "725 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.6, 'criteria': -2.5}\n",
+      "..........;\n",
+      "726 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.6, 'criteria': -2.0}\n",
+      "..........;\n",
+      "727 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.6, 'criteria': -1.5}\n",
+      "..........;\n",
+      "728 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.6, 'criteria': -1.0}\n",
+      "..........;\n",
+      "729 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.6, 'criteria': -0.5}\n",
+      "..........;\n",
+      "730 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.6, 'criteria': 0.0}\n",
+      "..........;\n",
+      "731 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.6, 'criteria': 0.5}\n",
+      "..........;\n",
+      "732 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.6, 'criteria': 1.0}\n",
+      "..........;\n",
+      "733 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.6, 'criteria': 1.5}\n",
+      "..........;\n",
+      "734 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.6, 'criteria': 2.0}\n",
+      "..........;\n",
+      "735 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.6, 'criteria': 2.5}\n",
+      "..........;\n",
+      "736 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.6, 'criteria': 3.0}\n",
+      "..........;\n",
+      "737 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.6, 'criteria': 3.5}\n",
+      "..........;\n",
+      "738 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.6, 'criteria': 4.0}\n",
+      "..........;\n",
+      "739 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.6, 'criteria': 4.5}\n",
+      "..........;\n",
+      "740 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.7, 'criteria': -5.0}\n",
+      "..........;\n",
+      "741 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.7, 'criteria': -4.5}\n",
+      "..........;\n",
+      "742 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.7, 'criteria': -4.0}\n",
+      "..........;\n",
+      "743 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.7, 'criteria': -3.5}\n",
+      "..........;\n",
+      "744 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.7, 'criteria': -3.0}\n",
+      "..........;\n",
+      "745 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.7, 'criteria': -2.5}\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "..........;\n",
+      "746 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.7, 'criteria': -2.0}\n",
+      "..........;\n",
+      "747 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.7, 'criteria': -1.5}\n",
+      "..........;\n",
+      "748 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.7, 'criteria': -1.0}\n",
+      "..........;\n",
+      "749 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.7, 'criteria': -0.5}\n",
+      "..........;\n",
+      "750 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.7, 'criteria': 0.0}\n",
+      "..........;\n",
+      "751 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.7, 'criteria': 0.5}\n",
+      "..........;\n",
+      "752 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.7, 'criteria': 1.0}\n",
+      "..........;\n",
+      "753 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.7, 'criteria': 1.5}\n",
+      "..........;\n",
+      "754 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.7, 'criteria': 2.0}\n",
+      "..........;\n",
+      "755 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.7, 'criteria': 2.5}\n",
+      "..........;\n",
+      "756 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.7, 'criteria': 3.0}\n",
+      "..........;\n",
+      "757 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.7, 'criteria': 3.5}\n",
+      "..........;\n",
+      "758 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.7, 'criteria': 4.0}\n",
+      "..........;\n",
+      "759 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.7, 'criteria': 4.5}\n",
+      "..........;\n",
+      "760 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.8000000000000003, 'criteria': -5.0}\n",
+      "..........;\n",
+      "761 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.8000000000000003, 'criteria': -4.5}\n",
+      "..........;\n",
+      "762 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.8000000000000003, 'criteria': -4.0}\n",
+      "..........;\n",
+      "763 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.8000000000000003, 'criteria': -3.5}\n",
+      "..........;\n",
+      "764 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.8000000000000003, 'criteria': -3.0}\n",
+      "..........;\n",
+      "765 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.8000000000000003, 'criteria': -2.5}\n",
+      "..........;\n",
+      "766 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.8000000000000003, 'criteria': -2.0}\n",
+      "..........;\n",
+      "767 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.8000000000000003, 'criteria': -1.5}\n",
+      "..........;\n",
+      "768 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.8000000000000003, 'criteria': -1.0}\n",
+      "..........;\n",
+      "769 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.8000000000000003, 'criteria': -0.5}\n",
+      "..........;\n",
+      "770 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.8000000000000003, 'criteria': 0.0}\n",
+      "..........;\n",
+      "771 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.8000000000000003, 'criteria': 0.5}\n",
+      "..........;\n",
+      "772 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.8000000000000003, 'criteria': 1.0}\n",
+      "..........;\n",
+      "773 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.8000000000000003, 'criteria': 1.5}\n",
+      "..........;\n",
+      "774 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.8000000000000003, 'criteria': 2.0}\n",
+      "..........;\n",
+      "775 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.8000000000000003, 'criteria': 2.5}\n",
+      "..........;\n",
+      "776 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.8000000000000003, 'criteria': 3.0}\n",
+      "..........;\n",
+      "777 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.8000000000000003, 'criteria': 3.5}\n",
+      "..........;\n",
+      "778 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.8000000000000003, 'criteria': 4.0}\n",
+      "..........;\n",
+      "779 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.8000000000000003, 'criteria': 4.5}\n",
+      "..........;\n",
+      "780 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.9000000000000004, 'criteria': -5.0}\n",
+      "..........;\n",
+      "781 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.9000000000000004, 'criteria': -4.5}\n",
+      "..........;\n",
+      "782 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.9000000000000004, 'criteria': -4.0}\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "..........;\n",
+      "783 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.9000000000000004, 'criteria': -3.5}\n",
+      "..........;\n",
+      "784 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.9000000000000004, 'criteria': -3.0}\n",
+      "..........;\n",
+      "785 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.9000000000000004, 'criteria': -2.5}\n",
+      "..........;\n",
+      "786 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.9000000000000004, 'criteria': -2.0}\n",
+      "..........;\n",
+      "787 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.9000000000000004, 'criteria': -1.5}\n",
+      "..........;\n",
+      "788 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.9000000000000004, 'criteria': -1.0}\n",
+      "..........;\n",
+      "789 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.9000000000000004, 'criteria': -0.5}\n",
+      "..........;\n",
+      "790 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.9000000000000004, 'criteria': 0.0}\n",
+      "..........;\n",
+      "791 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.9000000000000004, 'criteria': 0.5}\n",
+      "..........;\n",
+      "792 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.9000000000000004, 'criteria': 1.0}\n",
+      "..........;\n",
+      "793 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.9000000000000004, 'criteria': 1.5}\n",
+      "..........;\n",
+      "794 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.9000000000000004, 'criteria': 2.0}\n",
+      "..........;\n",
+      "795 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.9000000000000004, 'criteria': 2.5}\n",
+      "..........;\n",
+      "796 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.9000000000000004, 'criteria': 3.0}\n",
+      "..........;\n",
+      "797 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.9000000000000004, 'criteria': 3.5}\n",
+      "..........;\n",
+      "798 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.9000000000000004, 'criteria': 4.0}\n",
+      "..........;\n",
+      "799 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.9000000000000004, 'criteria': 4.5}\n",
+      "..........;\n",
+      "800 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.0, 'criteria': -5.0}\n",
+      "..........;\n",
+      "801 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.0, 'criteria': -4.5}\n",
+      "..........;\n",
+      "802 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.0, 'criteria': -4.0}\n",
+      "..........;\n",
+      "803 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.0, 'criteria': -3.5}\n",
+      "..........;\n",
+      "804 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.0, 'criteria': -3.0}\n",
+      "..........;\n",
+      "805 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.0, 'criteria': -2.5}\n",
+      "..........;\n",
+      "806 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.0, 'criteria': -2.0}\n",
+      "..........;\n",
+      "807 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.0, 'criteria': -1.5}\n",
+      "..........;\n",
+      "808 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.0, 'criteria': -1.0}\n",
+      "..........;\n",
+      "809 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.0, 'criteria': -0.5}\n",
+      "..........;\n",
+      "810 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.0, 'criteria': 0.0}\n",
+      "..........;\n",
+      "811 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.0, 'criteria': 0.5}\n",
+      "..........;\n",
+      "812 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.0, 'criteria': 1.0}\n",
+      "..........;\n",
+      "813 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.0, 'criteria': 1.5}\n",
+      "..........;\n",
+      "814 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.0, 'criteria': 2.0}\n",
+      "..........;\n",
+      "815 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.0, 'criteria': 2.5}\n",
+      "..........;\n",
+      "816 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.0, 'criteria': 3.0}\n",
+      "..........;\n",
+      "817 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.0, 'criteria': 3.5}\n",
+      "..........;\n",
+      "818 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.0, 'criteria': 4.0}\n",
+      "..........;\n",
+      "819 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.0, 'criteria': 4.5}\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "..........;\n",
+      "820 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.1000000000000005, 'criteria': -5.0}\n",
+      "..........;\n",
+      "821 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.1000000000000005, 'criteria': -4.5}\n",
+      "..........;\n",
+      "822 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.1000000000000005, 'criteria': -4.0}\n",
+      "..........;\n",
+      "823 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.1000000000000005, 'criteria': -3.5}\n",
+      "..........;\n",
+      "824 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.1000000000000005, 'criteria': -3.0}\n",
+      "..........;\n",
+      "825 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.1000000000000005, 'criteria': -2.5}\n",
+      "..........;\n",
+      "826 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.1000000000000005, 'criteria': -2.0}\n",
+      "..........;\n",
+      "827 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.1000000000000005, 'criteria': -1.5}\n",
+      "..........;\n",
+      "828 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.1000000000000005, 'criteria': -1.0}\n",
+      "..........;\n",
+      "829 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.1000000000000005, 'criteria': -0.5}\n",
+      "..........;\n",
+      "830 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.1000000000000005, 'criteria': 0.0}\n",
+      "..........;\n",
+      "831 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.1000000000000005, 'criteria': 0.5}\n",
+      "..........;\n",
+      "832 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.1000000000000005, 'criteria': 1.0}\n",
+      "..........;\n",
+      "833 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.1000000000000005, 'criteria': 1.5}\n",
+      "..........;\n",
+      "834 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.1000000000000005, 'criteria': 2.0}\n",
+      "..........;\n",
+      "835 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.1000000000000005, 'criteria': 2.5}\n",
+      "..........;\n",
+      "836 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.1000000000000005, 'criteria': 3.0}\n",
+      "..........;\n",
+      "837 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.1000000000000005, 'criteria': 3.5}\n",
+      "..........;\n",
+      "838 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.1000000000000005, 'criteria': 4.0}\n",
+      "..........;\n",
+      "839 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.1000000000000005, 'criteria': 4.5}\n",
+      "..........;\n",
+      "840 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.2, 'criteria': -5.0}\n",
+      "..........;\n",
+      "841 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.2, 'criteria': -4.5}\n",
+      "..........;\n",
+      "842 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.2, 'criteria': -4.0}\n",
+      "..........;\n",
+      "843 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.2, 'criteria': -3.5}\n",
+      "..........;\n",
+      "844 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.2, 'criteria': -3.0}\n",
+      "..........;\n",
+      "845 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.2, 'criteria': -2.5}\n",
+      "..........;\n",
+      "846 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.2, 'criteria': -2.0}\n",
+      "..........;\n",
+      "847 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.2, 'criteria': -1.5}\n",
+      "..........;\n",
+      "848 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.2, 'criteria': -1.0}\n",
+      "..........;\n",
+      "849 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.2, 'criteria': -0.5}\n",
+      "..........;\n",
+      "850 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.2, 'criteria': 0.0}\n",
+      "..........;\n",
+      "851 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.2, 'criteria': 0.5}\n",
+      "..........;\n",
+      "852 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.2, 'criteria': 1.0}\n",
+      "..........;\n",
+      "853 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.2, 'criteria': 1.5}\n",
+      "..........;\n",
+      "854 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.2, 'criteria': 2.0}\n",
+      "..........;\n",
+      "855 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.2, 'criteria': 2.5}\n",
+      "..........;\n",
+      "856 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.2, 'criteria': 3.0}\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "..........;\n",
+      "857 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.2, 'criteria': 3.5}\n",
+      "..........;\n",
+      "858 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.2, 'criteria': 4.0}\n",
+      "..........;\n",
+      "859 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.2, 'criteria': 4.5}\n",
+      "..........;\n",
+      "860 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.3, 'criteria': -5.0}\n",
+      "..........;\n",
+      "861 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.3, 'criteria': -4.5}\n",
+      "..........;\n",
+      "862 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.3, 'criteria': -4.0}\n",
+      "..........;\n",
+      "863 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.3, 'criteria': -3.5}\n",
+      "..........;\n",
+      "864 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.3, 'criteria': -3.0}\n",
+      "..........;\n",
+      "865 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.3, 'criteria': -2.5}\n",
+      "..........;\n",
+      "866 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.3, 'criteria': -2.0}\n",
+      "..........;\n",
+      "867 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.3, 'criteria': -1.5}\n",
+      "..........;\n",
+      "868 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.3, 'criteria': -1.0}\n",
+      "..........;\n",
+      "869 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.3, 'criteria': -0.5}\n",
+      "..........;\n",
+      "870 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.3, 'criteria': 0.0}\n",
+      "..........;\n",
+      "871 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.3, 'criteria': 0.5}\n",
+      "..........;\n",
+      "872 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.3, 'criteria': 1.0}\n",
+      "..........;\n",
+      "873 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.3, 'criteria': 1.5}\n",
+      "..........;\n",
+      "874 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.3, 'criteria': 2.0}\n",
+      "..........;\n",
+      "875 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.3, 'criteria': 2.5}\n",
+      "..........;\n",
+      "876 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.3, 'criteria': 3.0}\n",
+      "..........;\n",
+      "877 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.3, 'criteria': 3.5}\n",
+      "..........;\n",
+      "878 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.3, 'criteria': 4.0}\n",
+      "..........;\n",
+      "879 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.3, 'criteria': 4.5}\n",
+      "..........;\n",
+      "880 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.4, 'criteria': -5.0}\n",
+      "..........;\n",
+      "881 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.4, 'criteria': -4.5}\n",
+      "..........;\n",
+      "882 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.4, 'criteria': -4.0}\n",
+      "..........;\n",
+      "883 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.4, 'criteria': -3.5}\n",
+      "..........;\n",
+      "884 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.4, 'criteria': -3.0}\n",
+      "..........;\n",
+      "885 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.4, 'criteria': -2.5}\n",
+      "..........;\n",
+      "886 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.4, 'criteria': -2.0}\n",
+      "..........;\n",
+      "887 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.4, 'criteria': -1.5}\n",
+      "..........;\n",
+      "888 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.4, 'criteria': -1.0}\n",
+      "..........;\n",
+      "889 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.4, 'criteria': -0.5}\n",
+      "..........;\n",
+      "890 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.4, 'criteria': 0.0}\n",
+      "..........;\n",
+      "891 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.4, 'criteria': 0.5}\n",
+      "..........;\n",
+      "892 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.4, 'criteria': 1.0}\n",
+      "..........;\n",
+      "893 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.4, 'criteria': 1.5}\n",
+      "..........;\n",
+      "894 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.4, 'criteria': 2.0}\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "..........;\n",
+      "895 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.4, 'criteria': 2.5}\n",
+      "..........;\n",
+      "896 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.4, 'criteria': 3.0}\n",
+      "..........;\n",
+      "897 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.4, 'criteria': 3.5}\n",
+      "..........;\n",
+      "898 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.4, 'criteria': 4.0}\n",
+      "..........;\n",
+      "899 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.4, 'criteria': 4.5}\n",
+      "..........;\n",
+      "900 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.5, 'criteria': -5.0}\n",
+      "..........;\n",
+      "901 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.5, 'criteria': -4.5}\n",
+      "..........;\n",
+      "902 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.5, 'criteria': -4.0}\n",
+      "..........;\n",
+      "903 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.5, 'criteria': -3.5}\n",
+      "..........;\n",
+      "904 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.5, 'criteria': -3.0}\n",
+      "..........;\n",
+      "905 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.5, 'criteria': -2.5}\n",
+      "..........;\n",
+      "906 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.5, 'criteria': -2.0}\n",
+      "..........;\n",
+      "907 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.5, 'criteria': -1.5}\n",
+      "..........;\n",
+      "908 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.5, 'criteria': -1.0}\n",
+      "..........;\n",
+      "909 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.5, 'criteria': -0.5}\n",
+      "..........;\n",
+      "910 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.5, 'criteria': 0.0}\n",
+      "..........;\n",
+      "911 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.5, 'criteria': 0.5}\n",
+      "..........;\n",
+      "912 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.5, 'criteria': 1.0}\n",
+      "..........;\n",
+      "913 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.5, 'criteria': 1.5}\n",
+      "..........;\n",
+      "914 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.5, 'criteria': 2.0}\n",
+      "..........;\n",
+      "915 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.5, 'criteria': 2.5}\n",
+      "..........;\n",
+      "916 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.5, 'criteria': 3.0}\n",
+      "..........;\n",
+      "917 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.5, 'criteria': 3.5}\n",
+      "..........;\n",
+      "918 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.5, 'criteria': 4.0}\n",
+      "..........;\n",
+      "919 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.5, 'criteria': 4.5}\n",
+      "..........;\n",
+      "920 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.6000000000000005, 'criteria': -5.0}\n",
+      "..........;\n",
+      "921 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.6000000000000005, 'criteria': -4.5}\n",
+      "..........;\n",
+      "922 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.6000000000000005, 'criteria': -4.0}\n",
+      "..........;\n",
+      "923 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.6000000000000005, 'criteria': -3.5}\n",
+      "..........;\n",
+      "924 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.6000000000000005, 'criteria': -3.0}\n",
+      "..........;\n",
+      "925 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.6000000000000005, 'criteria': -2.5}\n",
+      "..........;\n",
+      "926 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.6000000000000005, 'criteria': -2.0}\n",
+      "..........;\n",
+      "927 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.6000000000000005, 'criteria': -1.5}\n",
+      "..........;\n",
+      "928 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.6000000000000005, 'criteria': -1.0}\n",
+      "..........;\n",
+      "929 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.6000000000000005, 'criteria': -0.5}\n",
+      "..........;\n",
+      "930 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.6000000000000005, 'criteria': 0.0}\n",
+      "..........;\n",
+      "931 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.6000000000000005, 'criteria': 0.5}\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "..........;\n",
+      "932 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.6000000000000005, 'criteria': 1.0}\n",
+      "..........;\n",
+      "933 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.6000000000000005, 'criteria': 1.5}\n",
+      "..........;\n",
+      "934 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.6000000000000005, 'criteria': 2.0}\n",
+      "..........;\n",
+      "935 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.6000000000000005, 'criteria': 2.5}\n",
+      "..........;\n",
+      "936 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.6000000000000005, 'criteria': 3.0}\n",
+      "..........;\n",
+      "937 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.6000000000000005, 'criteria': 3.5}\n",
+      "..........;\n",
+      "938 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.6000000000000005, 'criteria': 4.0}\n",
+      "..........;\n",
+      "939 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.6000000000000005, 'criteria': 4.5}\n",
+      "..........;\n",
+      "940 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.7, 'criteria': -5.0}\n",
+      "..........;\n",
+      "941 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.7, 'criteria': -4.5}\n",
+      "..........;\n",
+      "942 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.7, 'criteria': -4.0}\n",
+      "..........;\n",
+      "943 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.7, 'criteria': -3.5}\n",
+      "..........;\n",
+      "944 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.7, 'criteria': -3.0}\n",
+      "..........;\n",
+      "945 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.7, 'criteria': -2.5}\n",
+      "..........;\n",
+      "946 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.7, 'criteria': -2.0}\n",
+      "..........;\n",
+      "947 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.7, 'criteria': -1.5}\n",
+      "..........;\n",
+      "948 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.7, 'criteria': -1.0}\n",
+      "..........;\n",
+      "949 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.7, 'criteria': -0.5}\n",
+      "..........;\n",
+      "950 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.7, 'criteria': 0.0}\n",
+      "..........;\n",
+      "951 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.7, 'criteria': 0.5}\n",
+      "..........;\n",
+      "952 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.7, 'criteria': 1.0}\n",
+      "..........;\n",
+      "953 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.7, 'criteria': 1.5}\n",
+      "..........;\n",
+      "954 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.7, 'criteria': 2.0}\n",
+      "..........;\n",
+      "955 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.7, 'criteria': 2.5}\n",
+      "..........;\n",
+      "956 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.7, 'criteria': 3.0}\n",
+      "..........;\n",
+      "957 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.7, 'criteria': 3.5}\n",
+      "..........;\n",
+      "958 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.7, 'criteria': 4.0}\n",
+      "..........;\n",
+      "959 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.7, 'criteria': 4.5}\n",
+      "..........;\n",
+      "960 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.800000000000001, 'criteria': -5.0}\n",
+      "..........;\n",
+      "961 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.800000000000001, 'criteria': -4.5}\n",
+      "..........;\n",
+      "962 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.800000000000001, 'criteria': -4.0}\n",
+      "..........;\n",
+      "963 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.800000000000001, 'criteria': -3.5}\n",
+      "..........;\n",
+      "964 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.800000000000001, 'criteria': -3.0}\n",
+      "..........;\n",
+      "965 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.800000000000001, 'criteria': -2.5}\n",
+      "..........;\n",
+      "966 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.800000000000001, 'criteria': -2.0}\n",
+      "..........;\n",
+      "967 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.800000000000001, 'criteria': -1.5}\n",
+      "..........;\n",
+      "968 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.800000000000001, 'criteria': -1.0}\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "..........;\n",
+      "969 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.800000000000001, 'criteria': -0.5}\n",
+      "..........;\n",
+      "970 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.800000000000001, 'criteria': 0.0}\n",
+      "..........;\n",
+      "971 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.800000000000001, 'criteria': 0.5}\n",
+      "..........;\n",
+      "972 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.800000000000001, 'criteria': 1.0}\n",
+      "..........;\n",
+      "973 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.800000000000001, 'criteria': 1.5}\n",
+      "..........;\n",
+      "974 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.800000000000001, 'criteria': 2.0}\n",
+      "..........;\n",
+      "975 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.800000000000001, 'criteria': 2.5}\n",
+      "..........;\n",
+      "976 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.800000000000001, 'criteria': 3.0}\n",
+      "..........;\n",
+      "977 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.800000000000001, 'criteria': 3.5}\n",
+      "..........;\n",
+      "978 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.800000000000001, 'criteria': 4.0}\n",
+      "..........;\n",
+      "979 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.800000000000001, 'criteria': 4.5}\n",
+      "..........;\n",
+      "980 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.9, 'criteria': -5.0}\n",
+      "..........;\n",
+      "981 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.9, 'criteria': -4.5}\n",
+      "..........;\n",
+      "982 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.9, 'criteria': -4.0}\n",
+      "..........;\n",
+      "983 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.9, 'criteria': -3.5}\n",
+      "..........;\n",
+      "984 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.9, 'criteria': -3.0}\n",
+      "..........;\n",
+      "985 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.9, 'criteria': -2.5}\n",
+      "..........;\n",
+      "986 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.9, 'criteria': -2.0}\n",
+      "..........;\n",
+      "987 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.9, 'criteria': -1.5}\n",
+      "..........;\n",
+      "988 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.9, 'criteria': -1.0}\n",
+      "..........;\n",
+      "989 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.9, 'criteria': -0.5}\n",
+      "..........;\n",
+      "990 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.9, 'criteria': 0.0}\n",
+      "..........;\n",
+      "991 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.9, 'criteria': 0.5}\n",
+      "..........;\n",
+      "992 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.9, 'criteria': 1.0}\n",
+      "..........;\n",
+      "993 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.9, 'criteria': 1.5}\n",
+      "..........;\n",
+      "994 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.9, 'criteria': 2.0}\n",
+      "..........;\n",
+      "995 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.9, 'criteria': 2.5}\n",
+      "..........;\n",
+      "996 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.9, 'criteria': 3.0}\n",
+      "..........;\n",
+      "997 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.9, 'criteria': 3.5}\n",
+      "..........;\n",
+      "998 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.9, 'criteria': 4.0}\n",
+      "..........;\n",
+      "999 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.9, 'criteria': 4.5}\n",
+      "..........;\n"
+     ]
+    },
+    {
+     "ename": "TypeError",
+     "evalue": "get_metric_names() missing 1 required positional argument: 'self'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[219], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[43mDoublePass\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbuild_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43minternal_noise_range\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marange\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m.1\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m      2\u001b[0m \u001b[43m                               \u001b[49m\u001b[43mcriteria_range\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marange\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m.5\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m      3\u001b[0m \u001b[43m                               \u001b[49m\u001b[43mn_repeated_trials\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1000\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_runs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mE:\\WORK\\DO\\2022\\palin\\python\\palin\\internal_noise\\double_pass.py:157\u001b[0m, in \u001b[0;36mDoublePass.build_model\u001b[1;34m(cls, internal_noise_range, criteria_range, n_repeated_trials, n_runs)\u001b[0m\n\u001b[0;32m    154\u001b[0m sim_df \u001b[38;5;241m=\u001b[39m sim\u001b[38;5;241m.\u001b[39mrun_all(n_runs\u001b[38;5;241m=\u001b[39mn_runs, verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m    156\u001b[0m \u001b[38;5;66;03m# average measures over all runs\u001b[39;00m\n\u001b[1;32m--> 157\u001b[0m sim_df\u001b[38;5;241m.\u001b[39mgroupby([\u001b[38;5;124m'\u001b[39m\u001b[38;5;124minternal_noise_std\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcriteria\u001b[39m\u001b[38;5;124m'\u001b[39m])[\u001b[43mDoublePassStatistics\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_metric_names\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m]\u001b[38;5;241m.\u001b[39mmean()\n\u001b[0;32m    158\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m sim_df\n",
+      "\u001b[1;31mTypeError\u001b[0m: get_metric_names() missing 1 required positional argument: 'self'"
+     ]
+    }
+   ],
+   "source": [
+    "model = DoublePass.build_model(internal_noise_range=np.arange(0,5,.1),\n",
+    "                               criteria_range=np.arange(-5,5,.5),\n",
+    "                               n_repeated_trials=1000, n_runs=10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "94a3c61d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.to_csv('model_large.csv')"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "2629230e",
+   "id": "f217823b",
    "metadata": {},
    "source": [
     "## Simulate with kernels"
@@ -372,7 +2502,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d808e31a",
+   "id": "26ee88e2",
    "metadata": {},
    "source": [
     "Single run"
@@ -381,7 +2511,7 @@
   {
    "cell_type": "code",
    "execution_count": 104,
-   "id": "52fc9460",
+   "id": "986cc8b8",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-04-10T12:41:07.781836Z",
@@ -417,7 +2547,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "43c24287",
+   "id": "85ac8a46",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-04-10T04:42:23.899935Z",
@@ -477,7 +2607,7 @@
   {
    "cell_type": "code",
    "execution_count": 147,
-   "id": "918f61b8",
+   "id": "1e0c8a43",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-04-10T15:36:24.243659Z",
@@ -758,7 +2888,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "5f7ad1b1",
+   "id": "3d71bdcd",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-04-10T04:42:50.935778Z",
@@ -796,7 +2926,7 @@
   {
    "cell_type": "code",
    "execution_count": 168,
-   "id": "ea12e662",
+   "id": "81eef14d",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-04-11T04:08:49.331441Z",
@@ -834,7 +2964,7 @@
   {
    "cell_type": "code",
    "execution_count": 174,
-   "id": "be5177b7",
+   "id": "caf9471b",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-04-11T04:10:11.894810Z",
@@ -859,7 +2989,7 @@
   {
    "cell_type": "code",
    "execution_count": 173,
-   "id": "e657a304",
+   "id": "ed2de38c",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-04-11T04:09:56.002651Z",
@@ -885,7 +3015,7 @@
   {
    "cell_type": "code",
    "execution_count": 175,
-   "id": "c4826227",
+   "id": "48a2e99c",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-04-11T04:10:14.250903Z",
@@ -1041,7 +3171,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "cca19c67",
+   "id": "f578f871",
    "metadata": {},
    "outputs": [],
    "source": []

From 3548eecd066c11be68d077d65d6722b1198e8d24 Mon Sep 17 00:00:00 2001
From: JJ Aucouturier <aucouturier@gmail.com>
Date: Mon, 22 Apr 2024 17:26:16 +0200
Subject: [PATCH 17/17] bug fixes

---
 python/model.csv                              | 10102 ++++++++--------
 python/new_sim.ipynb                          |   130 +-
 python/palin/internal_noise/double_pass.py    |     6 +-
 .../internal_noise_extractor.py               |     4 +-
 .../__pycache__/simulation.cpython-38.pyc     |   Bin 4944 -> 6135 bytes
 python/palin/simulation/kernel_distance.py    |     4 +-
 python/palin/simulation/simulation.py         |     2 +-
 python/sandbox.ipynb                          |   594 +-
 8 files changed, 5400 insertions(+), 5442 deletions(-)

diff --git a/python/model.csv b/python/model.csv
index 2c57d2a..549f6f6 100644
--- a/python/model.csv
+++ b/python/model.csv
@@ -1,5001 +1,5101 @@
-,n_trials,n_repeated,trial_type,n_features,external_noise_std,kernel,internal_noise_std,criteria,run,metric
-0,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],0.0,-5,0,"(1.0, 1.0)"
-1,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],0.0,-5,1,"(1.0, 1.0)"
-2,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],0.0,-5,2,"(1.0, 1.0)"
-3,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],0.0,-5,3,"(1.0, 1.0)"
-4,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],0.0,-5,4,"(1.0, 1.0)"
-5,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],0.0,-5,5,"(1.0, 1.0)"
-6,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],0.0,-5,6,"(1.0, 1.0)"
-7,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],0.0,-5,7,"(1.0, 1.0)"
-8,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],0.0,-5,8,"(1.0, 1.0)"
-9,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],0.0,-5,9,"(1.0, 1.0)"
-10,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],0.0,-4,0,"(1.0, 1.0)"
-11,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],0.0,-4,1,"(1.0, 1.0)"
-12,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],0.0,-4,2,"(1.0, 1.0)"
-13,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],0.0,-4,3,"(1.0, 1.0)"
-14,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],0.0,-4,4,"(1.0, 1.0)"
-15,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],0.0,-4,5,"(1.0, 0.99)"
-16,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],0.0,-4,6,"(1.0, 1.0)"
-17,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],0.0,-4,7,"(1.0, 1.0)"
-18,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],0.0,-4,8,"(1.0, 0.99)"
-19,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],0.0,-4,9,"(1.0, 0.97)"
-20,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],0.0,-3,0,"(1.0, 0.98)"
-21,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],0.0,-3,1,"(1.0, 1.0)"
-22,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],0.0,-3,2,"(1.0, 1.0)"
-23,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],0.0,-3,3,"(1.0, 0.99)"
-24,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],0.0,-3,4,"(1.0, 0.98)"
-25,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],0.0,-3,5,"(1.0, 0.99)"
-26,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],0.0,-3,6,"(1.0, 1.0)"
-27,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],0.0,-3,7,"(1.0, 0.99)"
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-29,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],0.0,-3,9,"(1.0, 0.98)"
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-1979,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],1.9000000000000001,2,9,"(0.79, 0.205)"
-1980,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],1.9000000000000001,3,0,"(0.82, 0.1)"
-1981,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],1.9000000000000001,3,1,"(0.75, 0.145)"
-1982,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],1.9000000000000001,3,2,"(0.9, 0.08)"
-1983,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],1.9000000000000001,3,3,"(0.83, 0.125)"
-1984,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],1.9000000000000001,3,4,"(0.88, 0.1)"
-1985,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],1.9000000000000001,3,5,"(0.84, 0.09)"
-1986,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],1.9000000000000001,3,6,"(0.92, 0.09)"
-1987,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],1.9000000000000001,3,7,"(0.83, 0.115)"
-1988,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],1.9000000000000001,3,8,"(0.88, 0.1)"
-1989,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],1.9000000000000001,3,9,"(0.81, 0.145)"
-1990,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],1.9000000000000001,4,0,"(0.9, 0.05)"
-1991,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],1.9000000000000001,4,1,"(0.92, 0.04)"
-1992,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],1.9000000000000001,4,2,"(0.86, 0.07)"
-1993,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],1.9000000000000001,4,3,"(0.93, 0.035)"
-1994,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],1.9000000000000001,4,4,"(0.94, 0.05)"
-1995,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],1.9000000000000001,4,5,"(0.91, 0.055)"
-1996,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],1.9000000000000001,4,6,"(0.96, 0.03)"
-1997,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],1.9000000000000001,4,7,"(0.89, 0.055)"
-1998,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],1.9000000000000001,4,8,"(0.96, 0.03)"
-1999,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],1.9000000000000001,4,9,"(0.97, 0.015)"
-2000,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],2.0,-5,0,"(0.99, 0.995)"
-2001,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],2.0,-5,1,"(0.97, 0.985)"
-2002,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],2.0,-5,2,"(1.0, 1.0)"
-2003,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],2.0,-5,3,"(0.95, 0.975)"
-2004,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],2.0,-5,4,"(0.97, 0.985)"
-2005,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],2.0,-5,5,"(0.95, 0.975)"
-2006,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],2.0,-5,6,"(0.92, 0.96)"
-2007,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],2.0,-5,7,"(0.97, 0.985)"
-2008,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],2.0,-5,8,"(0.96, 0.98)"
-2009,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],2.0,-5,9,"(0.95, 0.975)"
-2010,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],2.0,-4,0,"(0.82, 0.91)"
-2011,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],2.0,-4,1,"(0.92, 0.96)"
-2012,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],2.0,-4,2,"(0.9, 0.93)"
-2013,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],2.0,-4,3,"(0.91, 0.955)"
-2014,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],2.0,-4,4,"(0.88, 0.91)"
-2015,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],2.0,-4,5,"(0.94, 0.95)"
-2016,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],2.0,-4,6,"(0.91, 0.935)"
-2017,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],2.0,-4,7,"(0.95, 0.975)"
-2018,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],2.0,-4,8,"(0.91, 0.955)"
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+5059,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],5.0,0.899999999999979,0,0.51,0.435
+5060,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],5.0,0.9999999999999787,0,0.56,0.39
+5061,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],5.0,1.0999999999999783,0,0.66,0.38
+5062,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],5.0,1.199999999999978,0,0.54,0.39
+5063,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],5.0,1.2999999999999776,0,0.52,0.39
+5064,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],5.0,1.3999999999999773,0,0.37,0.395
+5065,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],5.0,1.499999999999977,0,0.54,0.37
+5066,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],5.0,1.5999999999999766,0,0.6,0.33
+5067,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],5.0,1.6999999999999762,0,0.6,0.36
+5068,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],5.0,1.7999999999999758,0,0.6,0.43
+5069,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],5.0,1.8999999999999755,0,0.63,0.375
+5070,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],5.0,1.9999999999999751,0,0.59,0.355
+5071,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],5.0,2.0999999999999748,0,0.52,0.38
+5072,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],5.0,2.1999999999999744,0,0.61,0.385
+5073,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],5.0,2.299999999999974,0,0.56,0.31
+5074,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],5.0,2.3999999999999737,0,0.6,0.34
+5075,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],5.0,2.4999999999999734,0,0.44,0.31
+5076,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],5.0,2.599999999999973,0,0.53,0.325
+5077,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],5.0,2.6999999999999726,0,0.59,0.285
+5078,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],5.0,2.7999999999999723,0,0.65,0.325
+5079,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],5.0,2.899999999999972,0,0.61,0.295
+5080,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],5.0,2.9999999999999716,0,0.56,0.3
+5081,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],5.0,3.0999999999999712,0,0.66,0.24
+5082,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],5.0,3.199999999999971,0,0.58,0.27
+5083,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],5.0,3.2999999999999705,0,0.59,0.255
+5084,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],5.0,3.39999999999997,0,0.63,0.235
+5085,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],5.0,3.49999999999997,0,0.7,0.27
+5086,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],5.0,3.5999999999999694,0,0.63,0.225
+5087,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],5.0,3.699999999999969,0,0.62,0.29
+5088,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],5.0,3.7999999999999687,0,0.68,0.25
+5089,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],5.0,3.8999999999999684,0,0.54,0.25
+5090,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],5.0,3.999999999999968,0,0.62,0.21
+5091,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],5.0,4.099999999999968,0,0.7,0.18
+5092,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],5.0,4.199999999999967,0,0.72,0.19
+5093,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],5.0,4.299999999999967,0,0.65,0.225
+5094,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],5.0,4.399999999999967,0,0.73,0.185
+5095,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],5.0,4.499999999999966,0,0.67,0.215
+5096,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],5.0,4.599999999999966,0,0.66,0.21
+5097,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],5.0,4.6999999999999655,0,0.8,0.16
+5098,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],5.0,4.799999999999965,0,0.73,0.175
+5099,100,100,<class 'palin.simulation.trial.Int2Trial'>,1,1,[1],5.0,4.899999999999965,0,0.71,0.165
diff --git a/python/new_sim.ipynb b/python/new_sim.ipynb
index 9ee7e2a..9f9f808 100644
--- a/python/new_sim.ipynb
+++ b/python/new_sim.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "e99fe799",
+   "id": "2dd1ed60",
    "metadata": {},
    "source": [
     "# Simulation Experiment"
@@ -11,7 +11,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "25ef725a",
+   "id": "58a4c6ba",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-01-18T13:37:38.559326Z",
@@ -38,7 +38,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "12b04338",
+   "id": "25579328",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-01-18T13:37:49.595623Z",
@@ -94,7 +94,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b2a8e69b",
+   "id": "fd7b1e24",
    "metadata": {},
    "source": [
     "## generate trials and responses"
@@ -102,7 +102,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "93deb009",
+   "id": "e2254d3c",
    "metadata": {},
    "source": [
     "### with IN"
@@ -111,7 +111,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "c63479f5",
+   "id": "56ec6410",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-01-09T11:51:05.326379Z",
@@ -407,7 +407,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "95614290",
+   "id": "299bc835",
    "metadata": {},
    "source": [
     "### without IN"
@@ -416,7 +416,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "600f94be",
+   "id": "eb167922",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-01-10T13:48:42.038219Z",
@@ -734,7 +734,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "d7e6f0ef",
+   "id": "54b03eab",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-01-10T13:56:41.339132Z",
@@ -802,7 +802,7 @@
   {
    "cell_type": "code",
    "execution_count": 27,
-   "id": "df82ef23",
+   "id": "0fe51e96",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-01-10T14:13:00.797724Z",
@@ -1058,7 +1058,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dcd35e0e",
+   "id": "c41676a5",
    "metadata": {},
    "source": [
     "## CI kernel computaion with palin"
@@ -1067,7 +1067,7 @@
   {
    "cell_type": "code",
    "execution_count": 51,
-   "id": "a1c2acec",
+   "id": "bac4b454",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-01-11T10:48:37.990321Z",
@@ -1191,7 +1191,7 @@
   {
    "cell_type": "code",
    "execution_count": 52,
-   "id": "0d581ad6",
+   "id": "84292476",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-01-11T10:50:16.700639Z",
@@ -1388,7 +1388,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e669651c",
+   "id": "226365e2",
    "metadata": {},
    "source": [
     "## Simulate participant w/ CI kernel and IN 0 (graphs)"
@@ -1397,7 +1397,7 @@
   {
    "cell_type": "code",
    "execution_count": 92,
-   "id": "c60393c8",
+   "id": "388a033c",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-01-11T13:33:09.670157Z",
@@ -1507,7 +1507,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7a218f2c",
+   "id": "2b1d7155",
    "metadata": {},
    "source": [
     "## Graphs "
@@ -1515,7 +1515,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b0045be8",
+   "id": "8fe92b13",
    "metadata": {},
    "source": [
     "### random kernel visualization"
@@ -1524,7 +1524,7 @@
   {
    "cell_type": "code",
    "execution_count": 100,
-   "id": "da3bfa43",
+   "id": "47d88d7c",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-01-11T14:00:48.775953Z",
@@ -1620,7 +1620,7 @@
   {
    "cell_type": "code",
    "execution_count": 93,
-   "id": "2a59b2a0",
+   "id": "2f72fecd",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-01-11T13:33:34.410827Z",
@@ -1650,7 +1650,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8e391534",
+   "id": "90d8c41c",
    "metadata": {},
    "source": [
     "### Kernel value Evolution"
@@ -1659,7 +1659,7 @@
   {
    "cell_type": "code",
    "execution_count": 95,
-   "id": "59a5af6e",
+   "id": "7d4ab019",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-01-11T13:34:39.290061Z",
@@ -1847,7 +1847,7 @@
   {
    "cell_type": "code",
    "execution_count": 99,
-   "id": "83d9d01a",
+   "id": "255f7d5d",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-01-11T14:00:29.686409Z",
@@ -2030,7 +2030,7 @@
   {
    "cell_type": "code",
    "execution_count": 97,
-   "id": "12951875",
+   "id": "98d79a54",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-01-11T13:35:06.438482Z",
@@ -2226,7 +2226,7 @@
   {
    "cell_type": "code",
    "execution_count": 84,
-   "id": "eead7e97",
+   "id": "41919892",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-01-11T12:56:27.559484Z",
@@ -2407,7 +2407,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1160fd60",
+   "id": "3e18dd39",
    "metadata": {},
    "source": [
     "### correlation with participant random kernel"
@@ -2416,7 +2416,7 @@
   {
    "cell_type": "code",
    "execution_count": 102,
-   "id": "3d440806",
+   "id": "7ceff7bd",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-01-11T14:04:33.150155Z",
@@ -2492,7 +2492,7 @@
   {
    "cell_type": "code",
    "execution_count": 98,
-   "id": "c5ff4a70",
+   "id": "c954b35d",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-01-11T13:55:15.633617Z",
@@ -2530,7 +2530,7 @@
   {
    "cell_type": "code",
    "execution_count": 70,
-   "id": "ad3c31b7",
+   "id": "da9f630f",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-01-11T12:34:32.110896Z",
@@ -2579,7 +2579,7 @@
   {
    "cell_type": "code",
    "execution_count": 36,
-   "id": "b7cf4832",
+   "id": "5b134ada",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-01-10T14:38:54.912560Z",
@@ -2618,7 +2618,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2c09a8f3",
+   "id": "e935609b",
    "metadata": {},
    "source": [
     "## different values of IN and Criteria"
@@ -2627,7 +2627,7 @@
   {
    "cell_type": "code",
    "execution_count": 27,
-   "id": "7a46844b",
+   "id": "3f12ad2b",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-01-10T10:46:43.760310Z",
@@ -2696,7 +2696,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "29009e30",
+   "id": "c4cbf32d",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2759,7 +2759,7 @@
   {
    "cell_type": "code",
    "execution_count": 28,
-   "id": "5e9a3d7c",
+   "id": "a1713c77",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-01-10T10:46:51.758548Z",
@@ -2991,7 +2991,7 @@
   {
    "cell_type": "code",
    "execution_count": 29,
-   "id": "6e4fa100",
+   "id": "17cf5ec9",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-01-10T10:47:14.696339Z",
@@ -3189,7 +3189,7 @@
   {
    "cell_type": "code",
    "execution_count": 34,
-   "id": "14cfea33",
+   "id": "5f8eea4b",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-01-10T11:13:25.877519Z",
@@ -3267,7 +3267,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "a95aa8e0",
+   "id": "0e0922c0",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-12-22T14:58:54.514751Z",
@@ -3285,7 +3285,7 @@
   {
    "cell_type": "code",
    "execution_count": 147,
-   "id": "ed8bf8d5",
+   "id": "8e715ca8",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-12-22T16:04:45.627539Z",
@@ -3472,7 +3472,7 @@
   {
    "cell_type": "code",
    "execution_count": 148,
-   "id": "8cbba7ef",
+   "id": "0657ae75",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-12-22T16:04:57.636887Z",
@@ -3511,7 +3511,7 @@
   {
    "cell_type": "code",
    "execution_count": 149,
-   "id": "86bc796d",
+   "id": "7de45c91",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-12-22T16:05:03.546637Z",
@@ -3689,7 +3689,7 @@
   {
    "cell_type": "code",
    "execution_count": 178,
-   "id": "55f1c3a0",
+   "id": "138348de",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-12-22T16:29:43.229712Z",
@@ -3776,7 +3776,7 @@
   {
    "cell_type": "code",
    "execution_count": 137,
-   "id": "dbd37577",
+   "id": "60504c48",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-12-22T15:36:50.667399Z",
@@ -3794,7 +3794,7 @@
   {
    "cell_type": "code",
    "execution_count": 138,
-   "id": "82fe1272",
+   "id": "7bbbdb44",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-12-22T15:36:54.065249Z",
@@ -3820,7 +3820,7 @@
   {
    "cell_type": "code",
    "execution_count": 86,
-   "id": "2d995dc4",
+   "id": "3d24da73",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-12-22T10:16:30.123735Z",
@@ -4061,7 +4061,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "95f1f085",
+   "id": "39cdd026",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4143,7 +4143,7 @@
   {
    "cell_type": "code",
    "execution_count": 87,
-   "id": "3ac37b58",
+   "id": "7e69bb8a",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-12-22T10:19:29.722626Z",
@@ -4432,7 +4432,7 @@
   {
    "cell_type": "code",
    "execution_count": 59,
-   "id": "30fa36bf",
+   "id": "19e5f4c0",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-12-22T09:57:26.120796Z",
@@ -4457,7 +4457,7 @@
   {
    "cell_type": "code",
    "execution_count": 60,
-   "id": "17efce54",
+   "id": "8f224379",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-12-22T09:57:26.951351Z",
@@ -4486,7 +4486,7 @@
   {
    "cell_type": "code",
    "execution_count": 88,
-   "id": "da7d3dac",
+   "id": "3092d4fa",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-12-22T10:20:56.141439Z",
@@ -4552,7 +4552,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "235143ae",
+   "id": "6b098b91",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4684,7 +4684,7 @@
   {
    "cell_type": "code",
    "execution_count": 62,
-   "id": "44e7690d",
+   "id": "2d699e4f",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-12-22T09:57:40.477337Z",
@@ -4817,7 +4817,7 @@
   {
    "cell_type": "code",
    "execution_count": 46,
-   "id": "1fda96e6",
+   "id": "caa4272f",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-12-21T21:31:26.672299Z",
@@ -4998,7 +4998,7 @@
   {
    "cell_type": "code",
    "execution_count": 186,
-   "id": "a38e4822",
+   "id": "4f78c81d",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-12-22T16:32:49.165346Z",
@@ -5036,7 +5036,7 @@
   {
    "cell_type": "code",
    "execution_count": 187,
-   "id": "a3b326aa",
+   "id": "e5afe516",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-12-22T16:32:49.908428Z",
@@ -5244,7 +5244,7 @@
   {
    "cell_type": "code",
    "execution_count": 89,
-   "id": "5a09b1c4",
+   "id": "99afc16c",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-12-22T10:21:19.872108Z",
@@ -5420,7 +5420,7 @@
   {
    "cell_type": "code",
    "execution_count": 65,
-   "id": "bdce1492",
+   "id": "fd7ed80e",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-12-22T09:57:51.619598Z",
@@ -5579,7 +5579,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "fd8f2de7",
+   "id": "7308b3fa",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -5638,7 +5638,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "9768764e",
+   "id": "c1a8f7af",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -5648,7 +5648,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "7f71bf08",
+   "id": "8da80e92",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -5665,7 +5665,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a31c7ec0",
+   "id": "578871f8",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -5721,7 +5721,7 @@
   {
    "cell_type": "code",
    "execution_count": 92,
-   "id": "4513b1da",
+   "id": "a0926d68",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-12-22T10:21:52.587637Z",
@@ -5830,7 +5830,7 @@
   {
    "cell_type": "code",
    "execution_count": 80,
-   "id": "5f1dc0ae",
+   "id": "ae56dee6",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-12-22T10:09:46.311424Z",
@@ -5911,7 +5911,7 @@
   {
    "cell_type": "code",
    "execution_count": 94,
-   "id": "c727988c",
+   "id": "4f504fb9",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-12-22T10:23:53.589811Z",
@@ -5999,7 +5999,7 @@
   {
    "cell_type": "code",
    "execution_count": 93,
-   "id": "a3a9af36",
+   "id": "b9c20834",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-12-22T10:22:00.580704Z",
@@ -6028,7 +6028,7 @@
   {
    "cell_type": "code",
    "execution_count": 90,
-   "id": "cecf7185",
+   "id": "6ee4dec9",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-12-22T10:21:32.745907Z",
@@ -6060,7 +6060,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ec589710",
+   "id": "6c1bd6ae",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/python/palin/internal_noise/double_pass.py b/python/palin/internal_noise/double_pass.py
index 6fdec7a..acfa0c1 100644
--- a/python/palin/internal_noise/double_pass.py
+++ b/python/palin/internal_noise/double_pass.py
@@ -18,7 +18,7 @@
 from ..simulation.double_pass_experiment import DoublePassExperiment
 from ..simulation.trial import Int2Trial, Int1Trial 
 from ..simulation.linear_observer import LinearObserver
-from ..simulation.double_pass_statistics import DoublePassStatistics
+from ..simulation import double_pass_statistics as dps
 from ..simulation.simulation import Simulation as Sim
 
 
@@ -149,12 +149,12 @@ def build_model(cls,internal_noise_range=np.arange(0,5,.1),criteria_range=np.ara
 
         sim = Sim(DoublePassExperiment, experiment_params,
               LinearObserver, observer_params, 
-              DoublePassStatistics, analyser_params)
+              dps.DoublePassStatistics, analyser_params)
 
         sim_df = sim.run_all(n_runs=n_runs, verbose=True)
 
         # average measures over all runs
-        sim_df.groupby(['internal_noise_std','criteria'])[DoublePassStatistics.get_metric_names()].mean()
+        sim_df.groupby(['internal_noise_std','criteria'])[dps.DoublePassStatistics.get_metric_names()].mean()
         return sim_df
 
        
\ No newline at end of file
diff --git a/python/palin/internal_noise/internal_noise_extractor.py b/python/palin/internal_noise/internal_noise_extractor.py
index be6fbb6..1365dec 100644
--- a/python/palin/internal_noise/internal_noise_extractor.py
+++ b/python/palin/internal_noise/internal_noise_extractor.py
@@ -18,10 +18,10 @@ def extract_single_internal_noise(cls,data_df, trial_id = 'trial_id', feature_id
         raise NotImplementedError()
 
     @classmethod
-    def extract_internal_noise(cls,data_df, group_ids, trial_id, feature_id, value_id, response_id, normalize = True):
+    def extract_internal_noise(cls,data_df, group_ids, trial_id, feature_id, value_id, response_id, model_file):
         
         # for each level in group, extract internal_noise
         return data_df.groupby(group_ids).apply(lambda group: cls.extract_single_internal_noise(group, 
-            trial_id, feature_id, value_id, response_id)).reset_index()
+            trial_id, feature_id, value_id, response_id, model_file)).reset_index()
 
     
\ No newline at end of file
diff --git a/python/palin/simulation/__pycache__/simulation.cpython-38.pyc b/python/palin/simulation/__pycache__/simulation.cpython-38.pyc
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GIT binary patch
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diff --git a/python/palin/simulation/kernel_distance.py b/python/palin/simulation/kernel_distance.py
index 6140879..87734f9 100644
--- a/python/palin/simulation/kernel_distance.py
+++ b/python/palin/simulation/kernel_distance.py
@@ -9,7 +9,7 @@ def __init__(self, kernel_extractor, distance='CORR'):
         self.distance = distance
 
     def get_metric_names(self):
-        return [distance.lower()]
+        return [self.distance.lower()]
         
     def analyse(self, experiment, participant, participant_responses): 
 
@@ -36,7 +36,7 @@ def normalize_kernel(self, kernel):
         return self.kernel_extractor.normalize_kernel(kernel)
 
     def compute_distance(self, kernel_1, kernel_2): 
-        return me.kernel_distance(kernel_1, kernel_2, type=self.distance)
+        return [me.kernel_distance(kernel_1, kernel_2, type=self.distance)]
 
         
         
\ No newline at end of file
diff --git a/python/palin/simulation/simulation.py b/python/palin/simulation/simulation.py
index d0cb543..2a39dc4 100644
--- a/python/palin/simulation/simulation.py
+++ b/python/palin/simulation/simulation.py
@@ -115,7 +115,7 @@ def run(self, config_param):
         
         metrics =  ana.get_metric_names()
         values = ana.analyse(exp, obs, responses)
-
+        
         # return the metrics as a dict of name:value pairs
         results = {}
         for metric,value in zip(metrics,values): 
diff --git a/python/sandbox.ipynb b/python/sandbox.ipynb
index 40c5f81..fc307f4 100644
--- a/python/sandbox.ipynb
+++ b/python/sandbox.ipynb
@@ -2,24 +2,15 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 51,
-   "id": "87dd4fb9",
+   "execution_count": 1,
+   "id": "dd024a9f",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-10T12:37:25.098785Z",
-     "start_time": "2024-04-10T12:37:25.041938Z"
+     "end_time": "2024-04-22T14:17:28.832662Z",
+     "start_time": "2024-04-22T14:17:28.792726Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The autoreload extension is already loaded. To reload it, use:\n",
-      "  %reload_ext autoreload\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%load_ext autoreload\n",
     "%autoreload 2"
@@ -27,12 +18,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 179,
-   "id": "ab35c09e",
+   "execution_count": 2,
+   "id": "af003d60",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-11T04:19:40.595606Z",
-     "start_time": "2024-04-11T04:19:40.530803Z"
+     "end_time": "2024-04-22T14:17:39.480077Z",
+     "start_time": "2024-04-22T14:17:38.354348Z"
     }
    },
    "outputs": [],
@@ -45,12 +36,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
-   "id": "6a76823c",
+   "execution_count": 3,
+   "id": "c00cdd7b",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-10T12:37:27.663173Z",
-     "start_time": "2024-04-10T12:37:27.618292Z"
+     "end_time": "2024-04-22T14:17:42.091684Z",
+     "start_time": "2024-04-22T14:17:42.039629Z"
     }
    },
    "outputs": [],
@@ -60,12 +51,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 208,
-   "id": "29b09a91",
+   "execution_count": 6,
+   "id": "1d4cca24",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-11T11:24:47.119855Z",
-     "start_time": "2024-04-11T11:24:47.063798Z"
+     "end_time": "2024-04-22T14:56:26.656143Z",
+     "start_time": "2024-04-22T14:56:26.596523Z"
     }
    },
    "outputs": [],
@@ -85,7 +76,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "31bb6aee",
+   "id": "a54e142f",
    "metadata": {},
    "source": [
     "## Simulate with internal noise"
@@ -93,7 +84,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cd63729b",
+   "id": "0296d613",
    "metadata": {},
    "source": [
     "Single run"
@@ -101,34 +92,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 189,
-   "id": "63e53f77",
+   "execution_count": 20,
+   "id": "9802f955",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-11T04:53:29.899499Z",
-     "start_time": "2024-04-11T04:53:29.371264Z"
+     "end_time": "2024-04-22T14:59:47.009015Z",
+     "start_time": "2024-04-22T14:59:46.740731Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(0.1, 1)"
+       "(2.1, 1.7999999999999758)"
       ]
      },
-     "execution_count": 189,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# single run: \n",
-    "exp = DoublePassExperiment(n_trials = 100, n_repeated=50,\n",
+    "exp = DoublePassExperiment(n_trials = 1000, n_repeated=50,\n",
     "          trial_type = Int2Trial, \n",
     "          n_features = 5, \n",
     "          external_noise_std = 100)\n",
-    "obs = Obs.with_random_kernel(n_features = exp.n_features, \n",
-    "                            internal_noise_std = 0, \n",
+    "obs = LinearObserver.with_random_kernel(n_features = exp.n_features, \n",
+    "                            internal_noise_std = 1, \n",
     "                            criteria = 1)\n",
     "responses = obs.respond_to_experiment(exp)\n",
     "ana = InternalNoiseValue(internal_noise_extractor = DoublePass, model_file='model.csv')\n",
@@ -137,7 +128,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d90d2366",
+   "id": "01c2f4d3",
    "metadata": {},
    "source": [
     "Simulation"
@@ -145,12 +136,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 200,
-   "id": "fc511049",
+   "execution_count": 26,
+   "id": "b0d2e495",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-11T06:57:34.621971Z",
-     "start_time": "2024-04-11T06:00:58.596353Z"
+     "end_time": "2024-04-22T15:10:31.367464Z",
+     "start_time": "2024-04-22T15:10:01.475677Z"
     },
     "scrolled": false
    },
@@ -159,138 +150,79 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Running 1 configs\n",
-      "{'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 100, 'kernel': [1], 'internal_noise_std': 0, 'criteria': 0, 'internal_noise_extractor': <class 'palin.internal_noise.double_pass.DoublePass'>, 'model_file': 'model_large.csv', 'rebuild_model': False, 'internal_noise_range': array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. , 1.1, 1.2,\n",
-      "       1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2. , 2.1, 2.2, 2.3, 2.4, 2.5,\n",
-      "       2.6, 2.7, 2.8, 2.9, 3. , 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8,\n",
-      "       3.9, 4. , 4.1, 4.2, 4.3, 4.4, 4.5, 4.6, 4.7, 4.8, 4.9, 5. ]), 'criteria_range': array([-5.00000000e+00, -4.90000000e+00, -4.80000000e+00, -4.70000000e+00,\n",
-      "       -4.60000000e+00, -4.50000000e+00, -4.40000000e+00, -4.30000000e+00,\n",
-      "       -4.20000000e+00, -4.10000000e+00, -4.00000000e+00, -3.90000000e+00,\n",
-      "       -3.80000000e+00, -3.70000000e+00, -3.60000000e+00, -3.50000000e+00,\n",
-      "       -3.40000000e+00, -3.30000000e+00, -3.20000000e+00, -3.10000000e+00,\n",
-      "       -3.00000000e+00, -2.90000000e+00, -2.80000000e+00, -2.70000000e+00,\n",
-      "       -2.60000000e+00, -2.50000000e+00, -2.40000000e+00, -2.30000000e+00,\n",
-      "       -2.20000000e+00, -2.10000000e+00, -2.00000000e+00, -1.90000000e+00,\n",
-      "       -1.80000000e+00, -1.70000000e+00, -1.60000000e+00, -1.50000000e+00,\n",
-      "       -1.40000000e+00, -1.30000000e+00, -1.20000000e+00, -1.10000000e+00,\n",
-      "       -1.00000000e+00, -9.00000000e-01, -8.00000000e-01, -7.00000000e-01,\n",
-      "       -6.00000000e-01, -5.00000000e-01, -4.00000000e-01, -3.00000000e-01,\n",
-      "       -2.00000000e-01, -1.00000000e-01, -1.77635684e-14,  1.00000000e-01,\n",
-      "        2.00000000e-01,  3.00000000e-01,  4.00000000e-01,  5.00000000e-01,\n",
-      "        6.00000000e-01,  7.00000000e-01,  8.00000000e-01,  9.00000000e-01,\n",
-      "        1.00000000e+00,  1.10000000e+00,  1.20000000e+00,  1.30000000e+00,\n",
-      "        1.40000000e+00,  1.50000000e+00,  1.60000000e+00,  1.70000000e+00,\n",
-      "        1.80000000e+00,  1.90000000e+00,  2.00000000e+00,  2.10000000e+00,\n",
-      "        2.20000000e+00,  2.30000000e+00,  2.40000000e+00,  2.50000000e+00,\n",
-      "        2.60000000e+00,  2.70000000e+00,  2.80000000e+00,  2.90000000e+00,\n",
-      "        3.00000000e+00,  3.10000000e+00,  3.20000000e+00,  3.30000000e+00,\n",
-      "        3.40000000e+00,  3.50000000e+00,  3.60000000e+00,  3.70000000e+00,\n",
-      "        3.80000000e+00,  3.90000000e+00,  4.00000000e+00,  4.10000000e+00,\n",
-      "        4.20000000e+00,  4.30000000e+00,  4.40000000e+00,  4.50000000e+00,\n",
-      "        4.60000000e+00,  4.70000000e+00,  4.80000000e+00,  4.90000000e+00]), 'n_runs': 2}\n",
-      ".Rebuilding double-pass model\n"
-     ]
-    },
-    {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[200], line 19\u001b[0m\n\u001b[0;32m      9\u001b[0m analyser_params \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124minternal_noise_extractor\u001b[39m\u001b[38;5;124m'\u001b[39m:[DoublePass], \n\u001b[0;32m     10\u001b[0m                    \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmodel_file\u001b[39m\u001b[38;5;124m'\u001b[39m: [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmodel_large.csv\u001b[39m\u001b[38;5;124m'\u001b[39m], \n\u001b[0;32m     11\u001b[0m                    \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrebuild_model\u001b[39m\u001b[38;5;124m'\u001b[39m: [\u001b[38;5;28;01mFalse\u001b[39;00m],\n\u001b[0;32m     12\u001b[0m                    \u001b[38;5;124m'\u001b[39m\u001b[38;5;124minternal_noise_range\u001b[39m\u001b[38;5;124m'\u001b[39m:[np\u001b[38;5;241m.\u001b[39marange(\u001b[38;5;241m0\u001b[39m,\u001b[38;5;241m5.1\u001b[39m,\u001b[38;5;241m0.1\u001b[39m)],\n\u001b[0;32m     13\u001b[0m                    \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcriteria_range\u001b[39m\u001b[38;5;124m'\u001b[39m:[np\u001b[38;5;241m.\u001b[39marange(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m5\u001b[39m,\u001b[38;5;241m5\u001b[39m,\u001b[38;5;241m0.1\u001b[39m)],\n\u001b[0;32m     14\u001b[0m                    \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mn_runs\u001b[39m\u001b[38;5;124m'\u001b[39m:[\u001b[38;5;241m2\u001b[39m]}\n\u001b[0;32m     16\u001b[0m sim \u001b[38;5;241m=\u001b[39m Sim(DoublePassExperiment, experiment_params, \n\u001b[0;32m     17\u001b[0m           LinearObserver, observer_params, \n\u001b[0;32m     18\u001b[0m           InternalNoiseValue, analyser_params)\n\u001b[1;32m---> 19\u001b[0m sim_df \u001b[38;5;241m=\u001b[39m \u001b[43msim\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_all\u001b[49m\u001b[43m(\u001b[49m\u001b[43mn_runs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32mE:\\WORK\\DO\\2022\\palin\\python\\palin\\simulation\\simulation.py:45\u001b[0m, in \u001b[0;36mSimulation.run_all\u001b[1;34m(self, n_runs, verbose)\u001b[0m\n\u001b[0;32m     43\u001b[0m run_res \u001b[38;5;241m=\u001b[39m config_param\u001b[38;5;241m.\u001b[39mcopy() \n\u001b[0;32m     44\u001b[0m run_res\u001b[38;5;241m.\u001b[39mupdate({\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrun\u001b[39m\u001b[38;5;124m'\u001b[39m:run})               \n\u001b[1;32m---> 45\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconfig_param\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     46\u001b[0m run_res\u001b[38;5;241m.\u001b[39mupdate(results)\n\u001b[0;32m     47\u001b[0m runs\u001b[38;5;241m.\u001b[39mappend(run_res)\n",
-      "File \u001b[1;32mE:\\WORK\\DO\\2022\\palin\\python\\palin\\simulation\\simulation.py:67\u001b[0m, in \u001b[0;36mSimulation.run\u001b[1;34m(self, config_param)\u001b[0m\n\u001b[0;32m     64\u001b[0m responses \u001b[38;5;241m=\u001b[39m obs\u001b[38;5;241m.\u001b[39mrespond_to_experiment(exp)\n\u001b[0;32m     66\u001b[0m metrics \u001b[38;5;241m=\u001b[39m  ana\u001b[38;5;241m.\u001b[39mget_metric_names()\n\u001b[1;32m---> 67\u001b[0m values \u001b[38;5;241m=\u001b[39m \u001b[43mana\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43manalyse\u001b[49m\u001b[43m(\u001b[49m\u001b[43mexp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mobs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mresponses\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     69\u001b[0m \u001b[38;5;66;03m# return the metrics as a dict of name:value pairs\u001b[39;00m\n\u001b[0;32m     70\u001b[0m results \u001b[38;5;241m=\u001b[39m {}\n",
-      "File \u001b[1;32mE:\\WORK\\DO\\2022\\palin\\python\\palin\\simulation\\internal_noise_value.py:21\u001b[0m, in \u001b[0;36mInternalNoiseValue.analyse\u001b[1;34m(self, experiment, participant, participant_responses)\u001b[0m\n\u001b[0;32m     19\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21manalyse\u001b[39m(\u001b[38;5;28mself\u001b[39m, experiment, participant, participant_responses): \n\u001b[1;32m---> 21\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mestimate_internal_noise\u001b[49m\u001b[43m(\u001b[49m\u001b[43mexperiment\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparticipant_responses\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32mE:\\WORK\\DO\\2022\\palin\\python\\palin\\simulation\\internal_noise_value.py:27\u001b[0m, in \u001b[0;36mInternalNoiseValue.estimate_internal_noise\u001b[1;34m(self, experiment, participant_responses)\u001b[0m\n\u001b[0;32m     23\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mestimate_internal_noise\u001b[39m(\u001b[38;5;28mself\u001b[39m, experiment, participant_responses): \n\u001b[0;32m     25\u001b[0m     responses_df \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mto_df(experiment, participant_responses)\n\u001b[1;32m---> 27\u001b[0m     internal_noise, criteria \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minternal_noise_extractor\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mextract_single_internal_noise\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata_df\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mresponses_df\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m     28\u001b[0m \u001b[43m     \u001b[49m\u001b[43mtrial_id\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mtrial\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstim_id\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mstim\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfeature_id\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mfeature\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue_id\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mvalue\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mresponse_id\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mresponse\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel_file\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel_file\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m     29\u001b[0m \u001b[43m     \u001b[49m\u001b[43minternal_noise_range\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minternal_noise_range\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcriteria_range\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcriteria_range\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_repeated_trials\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mn_repeated_trials\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_runs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mn_runs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     31\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m internal_noise, criteria\n",
-      "File \u001b[1;32mE:\\WORK\\DO\\2022\\palin\\python\\palin\\internal_noise\\double_pass.py:46\u001b[0m, in \u001b[0;36mDoublePass.extract_single_internal_noise\u001b[1;34m(cls, data_df, trial_id, stim_id, feature_id, value_id, response_id, model_file, rebuild_model, internal_noise_range, criteria_range, n_repeated_trials, n_runs)\u001b[0m\n\u001b[0;32m     43\u001b[0m \u001b[38;5;66;03m# compute probability of choosing first response option\u001b[39;00m\n\u001b[0;32m     44\u001b[0m prob_first \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39mcompute_prob_first(data_df, trial_id\u001b[38;5;241m=\u001b[39mtrial_id, response_id\u001b[38;5;241m=\u001b[39mresponse_id, stim_id\u001b[38;5;241m=\u001b[39mstim_id, double_pass_id\u001b[38;5;241m=\u001b[39mdouble_pass_id)\n\u001b[1;32m---> 46\u001b[0m internal_noise, criteria \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mestimate_noise_criteria\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprob_agree\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprob_first\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel_file\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrebuild_model\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minternal_noise_range\u001b[49m\u001b[43m,\u001b[49m\u001b[43mcriteria_range\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_repeated_trials\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_runs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     48\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m internal_noise,criteria\n",
-      "File \u001b[1;32mE:\\WORK\\DO\\2022\\palin\\python\\palin\\internal_noise\\double_pass.py:122\u001b[0m, in \u001b[0;36mDoublePass.estimate_noise_criteria\u001b[1;34m(cls, prob_agree, prob_first, model_file, rebuild_model, internal_noise_range, criteria_range, n_repeated_trials, n_runs)\u001b[0m\n\u001b[0;32m    120\u001b[0m     model_df \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mread_csv(model_file, index_col\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\n\u001b[0;32m    121\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 122\u001b[0m     model_df \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbuild_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43minternal_noise_range\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcriteria_range\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_repeated_trials\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_runs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    123\u001b[0m     model_df\u001b[38;5;241m.\u001b[39mto_csv(model_file)\n\u001b[0;32m    125\u001b[0m \u001b[38;5;66;03m# find internal_noise & criteria settings that minimizes distance to prob_agree and prob_first \u001b[39;00m\n",
-      "File \u001b[1;32mE:\\WORK\\DO\\2022\\palin\\python\\palin\\internal_noise\\double_pass.py:154\u001b[0m, in \u001b[0;36mDoublePass.build_model\u001b[1;34m(cls, internal_noise_range, criteria_range, n_repeated_trials, n_runs)\u001b[0m\n\u001b[0;32m    148\u001b[0m analyser_params \u001b[38;5;241m=\u001b[39m {}\n\u001b[0;32m    150\u001b[0m sim \u001b[38;5;241m=\u001b[39m Sim(DoublePassExperiment, experiment_params,\n\u001b[0;32m    151\u001b[0m       LinearObserver, observer_params, \n\u001b[0;32m    152\u001b[0m       DoublePassStatistics, analyser_params)\n\u001b[1;32m--> 154\u001b[0m sim_df \u001b[38;5;241m=\u001b[39m \u001b[43msim\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_all\u001b[49m\u001b[43m(\u001b[49m\u001b[43mn_runs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mn_runs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[0;32m    156\u001b[0m \u001b[38;5;66;03m# average measures over all runs\u001b[39;00m\n\u001b[0;32m    157\u001b[0m sim_df\u001b[38;5;241m.\u001b[39mgroupby([\u001b[38;5;124m'\u001b[39m\u001b[38;5;124minternal_noise_std\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcriteria\u001b[39m\u001b[38;5;124m'\u001b[39m])[DoublePassStatistics\u001b[38;5;241m.\u001b[39mget_metric_names]\u001b[38;5;241m.\u001b[39mmean()\n",
-      "File \u001b[1;32mE:\\WORK\\DO\\2022\\palin\\python\\palin\\simulation\\simulation.py:45\u001b[0m, in \u001b[0;36mSimulation.run_all\u001b[1;34m(self, n_runs, verbose)\u001b[0m\n\u001b[0;32m     43\u001b[0m run_res \u001b[38;5;241m=\u001b[39m config_param\u001b[38;5;241m.\u001b[39mcopy() \n\u001b[0;32m     44\u001b[0m run_res\u001b[38;5;241m.\u001b[39mupdate({\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrun\u001b[39m\u001b[38;5;124m'\u001b[39m:run})               \n\u001b[1;32m---> 45\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconfig_param\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     46\u001b[0m run_res\u001b[38;5;241m.\u001b[39mupdate(results)\n\u001b[0;32m     47\u001b[0m runs\u001b[38;5;241m.\u001b[39mappend(run_res)\n",
-      "File \u001b[1;32mE:\\WORK\\DO\\2022\\palin\\python\\palin\\simulation\\simulation.py:67\u001b[0m, in \u001b[0;36mSimulation.run\u001b[1;34m(self, config_param)\u001b[0m\n\u001b[0;32m     64\u001b[0m responses \u001b[38;5;241m=\u001b[39m obs\u001b[38;5;241m.\u001b[39mrespond_to_experiment(exp)\n\u001b[0;32m     66\u001b[0m metrics \u001b[38;5;241m=\u001b[39m  ana\u001b[38;5;241m.\u001b[39mget_metric_names()\n\u001b[1;32m---> 67\u001b[0m values \u001b[38;5;241m=\u001b[39m \u001b[43mana\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43manalyse\u001b[49m\u001b[43m(\u001b[49m\u001b[43mexp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mobs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mresponses\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     69\u001b[0m \u001b[38;5;66;03m# return the metrics as a dict of name:value pairs\u001b[39;00m\n\u001b[0;32m     70\u001b[0m results \u001b[38;5;241m=\u001b[39m {}\n",
-      "File \u001b[1;32mE:\\WORK\\DO\\2022\\palin\\python\\palin\\simulation\\double_pass_statistics.py:19\u001b[0m, in \u001b[0;36mDoublePassStatistics.analyse\u001b[1;34m(self, experiment, participant, participant_responses)\u001b[0m\n\u001b[0;32m     15\u001b[0m responses_df \u001b[38;5;241m=\u001b[39m DoublePass\u001b[38;5;241m.\u001b[39mindex_double_pass_trials(data_df \u001b[38;5;241m=\u001b[39m responses_df, \n\u001b[0;32m     16\u001b[0m     trial_id\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtrial\u001b[39m\u001b[38;5;124m'\u001b[39m,value_id\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mvalue\u001b[39m\u001b[38;5;124m'\u001b[39m, double_pass_id\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdouble_pass_id\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m     18\u001b[0m \u001b[38;5;66;03m# compute probability of agreement over double pass\u001b[39;00m\n\u001b[1;32m---> 19\u001b[0m prob_agree \u001b[38;5;241m=\u001b[39m \u001b[43mDoublePass\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompute_prob_agreement\u001b[49m\u001b[43m(\u001b[49m\u001b[43mresponses_df\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrial_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mtrial\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mresponse_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mresponse\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdouble_pass_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mdouble_pass_id\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m     20\u001b[0m \u001b[38;5;66;03m# compute probability of choosing first response option\u001b[39;00m\n\u001b[0;32m     21\u001b[0m prob_first \u001b[38;5;241m=\u001b[39m DoublePass\u001b[38;5;241m.\u001b[39mcompute_prob_first(responses_df, trial_id\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtrial\u001b[39m\u001b[38;5;124m'\u001b[39m, response_id\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mresponse\u001b[39m\u001b[38;5;124m'\u001b[39m, stim_id\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mstim\u001b[39m\u001b[38;5;124m'\u001b[39m, double_pass_id\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdouble_pass_id\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
-      "File \u001b[1;32mE:\\WORK\\DO\\2022\\palin\\python\\palin\\internal_noise\\double_pass.py:92\u001b[0m, in \u001b[0;36mDoublePass.compute_prob_agreement\u001b[1;34m(cls, data_df, trial_id, response_id, double_pass_id)\u001b[0m\n\u001b[0;32m     90\u001b[0m     d \u001b[38;5;241m=\u001b[39m group\u001b[38;5;241m.\u001b[39mgroupby(trial_id)\u001b[38;5;241m.\u001b[39magg({response_id: \u001b[38;5;28;01mlambda\u001b[39;00m group: \u001b[38;5;28mtuple\u001b[39m(group)})\u001b[38;5;241m.\u001b[39mreset_index()\n\u001b[0;32m     91\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m d\u001b[38;5;241m.\u001b[39mresponse\u001b[38;5;241m.\u001b[39mnunique()\u001b[38;5;241m==\u001b[39m\u001b[38;5;241m1\u001b[39m\n\u001b[1;32m---> 92\u001b[0m agrees \u001b[38;5;241m=\u001b[39m \u001b[43mdata_df\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgroupby\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdouble_pass_id\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mgroup\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43msame_answer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgroup\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrial_id\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mresponse_id\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     94\u001b[0m \u001b[38;5;66;03m# return agreement probability\u001b[39;00m\n\u001b[0;32m     95\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m agrees\u001b[38;5;241m.\u001b[39msum()\u001b[38;5;241m/\u001b[39m\u001b[38;5;28mlen\u001b[39m(agrees)\n",
-      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\groupby\\groupby.py:1567\u001b[0m, in \u001b[0;36mGroupBy.apply\u001b[1;34m(self, func, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1559\u001b[0m     new_msg \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m   1560\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe operation \u001b[39m\u001b[38;5;132;01m{\u001b[39;00morig_func\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m failed on a column. If any error is \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1561\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mraised, this will raise an exception in a future version \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1562\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mof pandas. Drop these columns to avoid this warning.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1563\u001b[0m     )\n\u001b[0;32m   1564\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m rewrite_warning(\n\u001b[0;32m   1565\u001b[0m         old_msg, \u001b[38;5;167;01mFutureWarning\u001b[39;00m, new_msg\n\u001b[0;32m   1566\u001b[0m     ) \u001b[38;5;28;01mif\u001b[39;00m is_np_func \u001b[38;5;28;01melse\u001b[39;00m nullcontext():\n\u001b[1;32m-> 1567\u001b[0m         result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_python_apply_general\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_selected_obj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1568\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[0;32m   1569\u001b[0m     \u001b[38;5;66;03m# gh-20949\u001b[39;00m\n\u001b[0;32m   1570\u001b[0m     \u001b[38;5;66;03m# try again, with .apply acting as a filtering\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1574\u001b[0m     \u001b[38;5;66;03m# fails on *some* columns, e.g. a numeric operation\u001b[39;00m\n\u001b[0;32m   1575\u001b[0m     \u001b[38;5;66;03m# on a string grouper column\u001b[39;00m\n\u001b[0;32m   1577\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_group_selection_context():\n\u001b[0;32m   1578\u001b[0m         \u001b[38;5;66;03m# GH#50538\u001b[39;00m\n",
-      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\groupby\\groupby.py:1629\u001b[0m, in \u001b[0;36mGroupBy._python_apply_general\u001b[1;34m(self, f, data, not_indexed_same, is_transform, is_agg)\u001b[0m\n\u001b[0;32m   1592\u001b[0m \u001b[38;5;129m@final\u001b[39m\n\u001b[0;32m   1593\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_python_apply_general\u001b[39m(\n\u001b[0;32m   1594\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1599\u001b[0m     is_agg: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[0;32m   1600\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m NDFrameT:\n\u001b[0;32m   1601\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   1602\u001b[0m \u001b[38;5;124;03m    Apply function f in python space\u001b[39;00m\n\u001b[0;32m   1603\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1627\u001b[0m \u001b[38;5;124;03m        data after applying f\u001b[39;00m\n\u001b[0;32m   1628\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m-> 1629\u001b[0m     values, mutated \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgrouper\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1630\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m not_indexed_same \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   1631\u001b[0m         not_indexed_same \u001b[38;5;241m=\u001b[39m mutated \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmutated\n",
-      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\groupby\\ops.py:839\u001b[0m, in \u001b[0;36mBaseGrouper.apply\u001b[1;34m(self, f, data, axis)\u001b[0m\n\u001b[0;32m    837\u001b[0m \u001b[38;5;66;03m# group might be modified\u001b[39;00m\n\u001b[0;32m    838\u001b[0m group_axes \u001b[38;5;241m=\u001b[39m group\u001b[38;5;241m.\u001b[39maxes\n\u001b[1;32m--> 839\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgroup\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    840\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m mutated \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m _is_indexed_like(res, group_axes, axis):\n\u001b[0;32m    841\u001b[0m     mutated \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n",
-      "File \u001b[1;32mE:\\WORK\\DO\\2022\\palin\\python\\palin\\internal_noise\\double_pass.py:92\u001b[0m, in \u001b[0;36mDoublePass.compute_prob_agreement.<locals>.<lambda>\u001b[1;34m(group)\u001b[0m\n\u001b[0;32m     90\u001b[0m     d \u001b[38;5;241m=\u001b[39m group\u001b[38;5;241m.\u001b[39mgroupby(trial_id)\u001b[38;5;241m.\u001b[39magg({response_id: \u001b[38;5;28;01mlambda\u001b[39;00m group: \u001b[38;5;28mtuple\u001b[39m(group)})\u001b[38;5;241m.\u001b[39mreset_index()\n\u001b[0;32m     91\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m d\u001b[38;5;241m.\u001b[39mresponse\u001b[38;5;241m.\u001b[39mnunique()\u001b[38;5;241m==\u001b[39m\u001b[38;5;241m1\u001b[39m\n\u001b[1;32m---> 92\u001b[0m agrees \u001b[38;5;241m=\u001b[39m data_df\u001b[38;5;241m.\u001b[39mgroupby(double_pass_id)\u001b[38;5;241m.\u001b[39mapply(\u001b[38;5;28;01mlambda\u001b[39;00m group: \u001b[43msame_answer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgroup\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrial_id\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mresponse_id\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[0;32m     94\u001b[0m \u001b[38;5;66;03m# return agreement probability\u001b[39;00m\n\u001b[0;32m     95\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m agrees\u001b[38;5;241m.\u001b[39msum()\u001b[38;5;241m/\u001b[39m\u001b[38;5;28mlen\u001b[39m(agrees)\n",
-      "File \u001b[1;32mE:\\WORK\\DO\\2022\\palin\\python\\palin\\internal_noise\\double_pass.py:90\u001b[0m, in \u001b[0;36mDoublePass.compute_prob_agreement.<locals>.same_answer\u001b[1;34m(group, trial_id, response_id)\u001b[0m\n\u001b[0;32m     89\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21msame_answer\u001b[39m(group, trial_id, response_id):    \n\u001b[1;32m---> 90\u001b[0m     d \u001b[38;5;241m=\u001b[39m \u001b[43mgroup\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgroupby\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrial_id\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43magg\u001b[49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\u001b[43mresponse_id\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mgroup\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mtuple\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mgroup\u001b[49m\u001b[43m)\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mreset_index()\n\u001b[0;32m     91\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m d\u001b[38;5;241m.\u001b[39mresponse\u001b[38;5;241m.\u001b[39mnunique()\u001b[38;5;241m==\u001b[39m\u001b[38;5;241m1\u001b[39m\n",
-      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\groupby\\generic.py:895\u001b[0m, in \u001b[0;36mDataFrameGroupBy.aggregate\u001b[1;34m(self, func, engine, engine_kwargs, *args, **kwargs)\u001b[0m\n\u001b[0;32m    892\u001b[0m func \u001b[38;5;241m=\u001b[39m maybe_mangle_lambdas(func)\n\u001b[0;32m    894\u001b[0m op \u001b[38;5;241m=\u001b[39m GroupByApply(\u001b[38;5;28mself\u001b[39m, func, args, kwargs)\n\u001b[1;32m--> 895\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mop\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43magg\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    896\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_dict_like(func) \u001b[38;5;129;01mand\u001b[39;00m result \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    897\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
-      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\apply.py:172\u001b[0m, in \u001b[0;36mApply.agg\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    169\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply_str()\n\u001b[0;32m    171\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_dict_like(arg):\n\u001b[1;32m--> 172\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43magg_dict_like\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    173\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m is_list_like(arg):\n\u001b[0;32m    174\u001b[0m     \u001b[38;5;66;03m# we require a list, but not a 'str'\u001b[39;00m\n\u001b[0;32m    175\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39magg_list_like()\n",
-      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\apply.py:504\u001b[0m, in \u001b[0;36mApply.agg_dict_like\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    501\u001b[0m     results \u001b[38;5;241m=\u001b[39m {key: colg\u001b[38;5;241m.\u001b[39magg(how) \u001b[38;5;28;01mfor\u001b[39;00m key, how \u001b[38;5;129;01min\u001b[39;00m arg\u001b[38;5;241m.\u001b[39mitems()}\n\u001b[0;32m    502\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    503\u001b[0m     \u001b[38;5;66;03m# key used for column selection and output\u001b[39;00m\n\u001b[1;32m--> 504\u001b[0m     results \u001b[38;5;241m=\u001b[39m {\n\u001b[0;32m    505\u001b[0m         key: obj\u001b[38;5;241m.\u001b[39m_gotitem(key, ndim\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\u001b[38;5;241m.\u001b[39magg(how) \u001b[38;5;28;01mfor\u001b[39;00m key, how \u001b[38;5;129;01min\u001b[39;00m arg\u001b[38;5;241m.\u001b[39mitems()\n\u001b[0;32m    506\u001b[0m     }\n\u001b[0;32m    508\u001b[0m \u001b[38;5;66;03m# set the final keys\u001b[39;00m\n\u001b[0;32m    509\u001b[0m keys \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(arg\u001b[38;5;241m.\u001b[39mkeys())\n",
-      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\apply.py:505\u001b[0m, in \u001b[0;36m<dictcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    501\u001b[0m     results \u001b[38;5;241m=\u001b[39m {key: colg\u001b[38;5;241m.\u001b[39magg(how) \u001b[38;5;28;01mfor\u001b[39;00m key, how \u001b[38;5;129;01min\u001b[39;00m arg\u001b[38;5;241m.\u001b[39mitems()}\n\u001b[0;32m    502\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    503\u001b[0m     \u001b[38;5;66;03m# key used for column selection and output\u001b[39;00m\n\u001b[0;32m    504\u001b[0m     results \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m--> 505\u001b[0m         key: \u001b[43mobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_gotitem\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mndim\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43magg\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhow\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m key, how \u001b[38;5;129;01min\u001b[39;00m arg\u001b[38;5;241m.\u001b[39mitems()\n\u001b[0;32m    506\u001b[0m     }\n\u001b[0;32m    508\u001b[0m \u001b[38;5;66;03m# set the final keys\u001b[39;00m\n\u001b[0;32m    509\u001b[0m keys \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(arg\u001b[38;5;241m.\u001b[39mkeys())\n",
-      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\groupby\\generic.py:297\u001b[0m, in \u001b[0;36mSeriesGroupBy.aggregate\u001b[1;34m(self, func, engine, engine_kwargs, *args, **kwargs)\u001b[0m\n\u001b[0;32m    294\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_python_agg_general(func, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    296\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 297\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_python_agg_general\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    298\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n\u001b[0;32m    299\u001b[0m     \u001b[38;5;66;03m# TODO: KeyError is raised in _python_agg_general,\u001b[39;00m\n\u001b[0;32m    300\u001b[0m     \u001b[38;5;66;03m#  see test_groupby.test_basic\u001b[39;00m\n\u001b[0;32m    301\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_aggregate_named(func, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
-      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\groupby\\groupby.py:1673\u001b[0m, in \u001b[0;36mGroupBy._python_agg_general\u001b[1;34m(self, func, raise_on_typeerror, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1670\u001b[0m \u001b[38;5;66;03m# iterate through \"columns\" ex exclusions to populate output dict\u001b[39;00m\n\u001b[0;32m   1671\u001b[0m output: \u001b[38;5;28mdict\u001b[39m[base\u001b[38;5;241m.\u001b[39mOutputKey, ArrayLike] \u001b[38;5;241m=\u001b[39m {}\n\u001b[1;32m-> 1673\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mngroups\u001b[49m \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m   1674\u001b[0m     \u001b[38;5;66;03m# agg_series below assumes ngroups > 0\u001b[39;00m\n\u001b[0;32m   1675\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_python_apply_general(f, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_selected_obj, is_agg\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m   1677\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m idx, obj \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_iterate_slices()):\n",
-      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\groupby\\groupby.py:677\u001b[0m, in \u001b[0;36mBaseGroupBy.ngroups\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    674\u001b[0m \u001b[38;5;129m@final\u001b[39m\n\u001b[0;32m    675\u001b[0m \u001b[38;5;129m@property\u001b[39m\n\u001b[0;32m    676\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mngroups\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mint\u001b[39m:\n\u001b[1;32m--> 677\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgrouper\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mngroups\u001b[49m\n",
-      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\_libs\\properties.pyx:36\u001b[0m, in \u001b[0;36mpandas._libs.properties.CachedProperty.__get__\u001b[1;34m()\u001b[0m\n",
-      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\groupby\\ops.py:982\u001b[0m, in \u001b[0;36mBaseGrouper.ngroups\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    979\u001b[0m \u001b[38;5;129m@final\u001b[39m\n\u001b[0;32m    980\u001b[0m \u001b[38;5;129m@cache_readonly\u001b[39m\n\u001b[0;32m    981\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mngroups\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mint\u001b[39m:\n\u001b[1;32m--> 982\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresult_index\u001b[49m)\n",
-      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\_libs\\properties.pyx:36\u001b[0m, in \u001b[0;36mpandas._libs.properties.CachedProperty.__get__\u001b[1;34m()\u001b[0m\n",
-      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\groupby\\ops.py:993\u001b[0m, in \u001b[0;36mBaseGrouper.result_index\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    990\u001b[0m \u001b[38;5;129m@cache_readonly\u001b[39m\n\u001b[0;32m    991\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mresult_index\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Index:\n\u001b[0;32m    992\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgroupings) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m--> 993\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgroupings\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresult_index\u001b[49m\u001b[38;5;241m.\u001b[39mrename(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnames[\u001b[38;5;241m0\u001b[39m])\n\u001b[0;32m    995\u001b[0m     codes \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mreconstructed_codes\n\u001b[0;32m    996\u001b[0m     levels \u001b[38;5;241m=\u001b[39m [ping\u001b[38;5;241m.\u001b[39mresult_index \u001b[38;5;28;01mfor\u001b[39;00m ping \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgroupings]\n",
-      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\_libs\\properties.pyx:36\u001b[0m, in \u001b[0;36mpandas._libs.properties.CachedProperty.__get__\u001b[1;34m()\u001b[0m\n",
-      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\groupby\\grouper.py:647\u001b[0m, in \u001b[0;36mGrouping.result_index\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    645\u001b[0m     \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(group_idx, CategoricalIndex)\n\u001b[0;32m    646\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m recode_from_groupby(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_all_grouper, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sort, group_idx)\n\u001b[1;32m--> 647\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgroup_index\u001b[49m\n",
-      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\_libs\\properties.pyx:36\u001b[0m, in \u001b[0;36mpandas._libs.properties.CachedProperty.__get__\u001b[1;34m()\u001b[0m\n",
-      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\groupby\\grouper.py:655\u001b[0m, in \u001b[0;36mGrouping.group_index\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    651\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_group_index \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    652\u001b[0m     \u001b[38;5;66;03m# _group_index is set in __init__ for MultiIndex cases\u001b[39;00m\n\u001b[0;32m    653\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_group_index\n\u001b[1;32m--> 655\u001b[0m uniques \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_codes_and_uniques\u001b[49m[\u001b[38;5;241m1\u001b[39m]\n\u001b[0;32m    656\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m Index\u001b[38;5;241m.\u001b[39m_with_infer(uniques, name\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname)\n",
-      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\_libs\\properties.pyx:36\u001b[0m, in \u001b[0;36mpandas._libs.properties.CachedProperty.__get__\u001b[1;34m()\u001b[0m\n",
-      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\groupby\\grouper.py:692\u001b[0m, in \u001b[0;36mGrouping._codes_and_uniques\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    685\u001b[0m     uniques \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m    686\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgrouping_vector\u001b[38;5;241m.\u001b[39mresult_index\u001b[38;5;241m.\u001b[39m_values  \u001b[38;5;66;03m# type: ignore[assignment]\u001b[39;00m\n\u001b[0;32m    687\u001b[0m     )\n\u001b[0;32m    688\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    689\u001b[0m     \u001b[38;5;66;03m# GH35667, replace dropna=False with use_na_sentinel=False\u001b[39;00m\n\u001b[0;32m    690\u001b[0m     \u001b[38;5;66;03m# error: Incompatible types in assignment (expression has type \"Union[\u001b[39;00m\n\u001b[0;32m    691\u001b[0m     \u001b[38;5;66;03m# ndarray[Any, Any], Index]\", variable has type \"Categorical\")\u001b[39;00m\n\u001b[1;32m--> 692\u001b[0m     codes, uniques \u001b[38;5;241m=\u001b[39m \u001b[43malgorithms\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfactorize\u001b[49m\u001b[43m(\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# type: ignore[assignment]\u001b[39;49;00m\n\u001b[0;32m    693\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgrouping_vector\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msort\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_sort\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43muse_na_sentinel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_dropna\u001b[49m\n\u001b[0;32m    694\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    695\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m codes, uniques\n",
-      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\algorithms.py:832\u001b[0m, in \u001b[0;36mfactorize\u001b[1;34m(values, sort, na_sentinel, use_na_sentinel, size_hint)\u001b[0m\n\u001b[0;32m    829\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m na_sentinel \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    830\u001b[0m         \u001b[38;5;66;03m# TODO: Can remove when na_sentinel=na_sentinel as in TODO above\u001b[39;00m\n\u001b[0;32m    831\u001b[0m         na_sentinel \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m\n\u001b[1;32m--> 832\u001b[0m     uniques, codes \u001b[38;5;241m=\u001b[39m \u001b[43msafe_sort\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    833\u001b[0m \u001b[43m        \u001b[49m\u001b[43muniques\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcodes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mna_sentinel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mna_sentinel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43massume_unique\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mverify\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\n\u001b[0;32m    834\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    836\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m dropna \u001b[38;5;129;01mand\u001b[39;00m sort:\n\u001b[0;32m    837\u001b[0m     \u001b[38;5;66;03m# TODO: Can remove entire block when na_sentinel=na_sentinel as in TODO above\u001b[39;00m\n\u001b[0;32m    838\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m na_sentinel \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
-      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\algorithms.py:1875\u001b[0m, in \u001b[0;36msafe_sort\u001b[1;34m(values, codes, na_sentinel, assume_unique, verify)\u001b[0m\n\u001b[0;32m   1873\u001b[0m         ordered \u001b[38;5;241m=\u001b[39m original_values\u001b[38;5;241m.\u001b[39mtake(sorter)\n\u001b[0;32m   1874\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1875\u001b[0m         ordered \u001b[38;5;241m=\u001b[39m \u001b[43mvalues\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtake\u001b[49m\u001b[43m(\u001b[49m\u001b[43msorter\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1876\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[0;32m   1877\u001b[0m     \u001b[38;5;66;03m# Previous sorters failed or were not applicable, try `_sort_mixed`\u001b[39;00m\n\u001b[0;32m   1878\u001b[0m     \u001b[38;5;66;03m# which would work, but which fails for special case of 1d arrays\u001b[39;00m\n\u001b[0;32m   1879\u001b[0m     \u001b[38;5;66;03m# with tuples.\u001b[39;00m\n\u001b[0;32m   1880\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m values\u001b[38;5;241m.\u001b[39msize \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(values[\u001b[38;5;241m0\u001b[39m], \u001b[38;5;28mtuple\u001b[39m):\n",
-      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
+      "Running 3 configs\n",
+      "0 : {'n_trials': 100, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 5, 'external_noise_std': 100, 'kernel': [0, 0, 0, 0, 10], 'internal_noise_std': 0, 'criteria': 0, 'internal_noise_extractor': <class 'palin.internal_noise.double_pass.DoublePass'>, 'model_file': 'model.csv', 'rebuild_model': False}\n",
+      "..........;\n",
+      "1 : {'n_trials': 500, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 5, 'external_noise_std': 100, 'kernel': [0, 0, 0, 0, 10], 'internal_noise_std': 0, 'criteria': 0, 'internal_noise_extractor': <class 'palin.internal_noise.double_pass.DoublePass'>, 'model_file': 'model.csv', 'rebuild_model': False}\n",
+      "..........;\n",
+      "2 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 5, 'external_noise_std': 100, 'kernel': [0, 0, 0, 0, 10], 'internal_noise_std': 0, 'criteria': 0, 'internal_noise_extractor': <class 'palin.internal_noise.double_pass.DoublePass'>, 'model_file': 'model.csv', 'rebuild_model': False}\n",
+      "..........;\n"
      ]
     }
    ],
    "source": [
-    "observer_params = {'kernel':[[1]],\n",
+    "observer_params = {'kernel':[[0,0,0,0,10]],\n",
     "                   'internal_noise_std':[0], \n",
     "                  'criteria':[0]}\n",
-    "experiment_params = {'n_trials':[1000],#np.arange(1,1000,100),\n",
+    "experiment_params = {'n_trials':[100,500,1000], #np.arange(1,1000,100),\n",
     "                     'n_repeated':[1000],\n",
     "                     'trial_type': [Int2Trial],\n",
-    "                     'n_features': [1],\n",
+    "                     'n_features': [5],\n",
     "                     'external_noise_std': [100]}\n",
     "analyser_params = {'internal_noise_extractor':[DoublePass], \n",
-    "                   'model_file': ['model_large.csv'], \n",
-    "                   'rebuild_model': [False],\n",
-    "                   'internal_noise_range':[np.arange(0,5.1,0.1)],\n",
-    "                   'criteria_range':[np.arange(-5,5,0.1)],\n",
-    "                   'n_runs':[2]}\n",
+    "                   'model_file': ['model.csv'], \n",
+    "                   'rebuild_model': [False]}\n",
+    "                   #'internal_noise_range':[np.arange(0,5.1,0.1)],\n",
+    "                   #'criteria_range':[np.arange(-5,5,0.1)],\n",
+    "                   #'n_runs':[2]}\n",
     "                  \n",
     "sim = Sim(DoublePassExperiment, experiment_params, \n",
     "          LinearObserver, observer_params, \n",
     "          InternalNoiseValue, analyser_params)\n",
-    "sim_df = sim.run_all(n_runs=1)\n",
+    "sim_df = sim.run_all(n_runs=10)\n",
     "\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 216,
-   "id": "d36ad464",
+   "execution_count": 29,
+   "id": "126511b3",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-11T11:28:06.869895Z",
-     "start_time": "2024-04-11T11:28:06.817000Z"
+     "end_time": "2024-04-22T15:12:06.333448Z",
+     "start_time": "2024-04-22T15:12:06.055674Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "palin.simulation.simple_experiment.SimpleExperiment"
+       "<Axes: xlabel='n_trials', ylabel='estimated_internal_noise'>"
       ]
      },
-     "execution_count": 216,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "SimpleExperiment"
+    "sns.pointplot(data=sim_df, x='n_trials', y='estimated_internal_noise')"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 219,
-   "id": "7aea1f12",
+   "id": "b64a77c2",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-04-11T20:09:16.840733Z",
@@ -1749,13 +1681,7 @@
       "..........;\n",
       "670 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.3000000000000003, 'criteria': 0.0}\n",
       "..........;\n",
-      "671 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.3000000000000003, 'criteria': 0.5}\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "671 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.3000000000000003, 'criteria': 0.5}\n",
       "..........;\n",
       "672 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 3.3000000000000003, 'criteria': 1.0}\n",
       "..........;\n",
@@ -2311,13 +2237,7 @@
       "..........;\n",
       "930 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.6000000000000005, 'criteria': 0.0}\n",
       "..........;\n",
-      "931 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.6000000000000005, 'criteria': 0.5}\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "931 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.6000000000000005, 'criteria': 0.5}\n",
       "..........;\n",
       "932 : {'n_trials': 1000, 'n_repeated': 1000, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 1, 'external_noise_std': 1, 'kernel': [1], 'internal_noise_std': 4.6000000000000005, 'criteria': 1.0}\n",
       "..........;\n",
@@ -2485,7 +2405,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "94a3c61d",
+   "id": "da967fc0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2494,7 +2414,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f217823b",
+   "id": "47c4c0c5",
    "metadata": {},
    "source": [
     "## Simulate with kernels"
@@ -2502,7 +2422,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "26ee88e2",
+   "id": "b6d62245",
    "metadata": {},
    "source": [
     "Single run"
@@ -2510,48 +2430,48 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
-   "id": "986cc8b8",
+   "execution_count": 32,
+   "id": "17c7e065",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-10T12:41:07.781836Z",
-     "start_time": "2024-04-10T12:41:07.728949Z"
+     "end_time": "2024-04-22T15:16:15.983628Z",
+     "start_time": "2024-04-22T15:16:15.907873Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.948432263142621"
+       "0.9799297710374408"
       ]
      },
-     "execution_count": 104,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# single run: \n",
-    "exp = Exp(n_trials = 100,\n",
+    "exp = SimpleExperiment(n_trials = 100,\n",
     "          trial_type = Int2Trial, \n",
     "          n_features = 5, \n",
     "          external_noise_std = 100)\n",
-    "obs = Obs.with_random_kernel(n_features = 5, \n",
+    "obs = LinearObserver.with_random_kernel(n_features = 5, \n",
     "                            internal_noise_std = 1, \n",
     "                            criteria = 0)\n",
     "responses = obs.respond_to_experiment(exp)\n",
-    "ka = Analyser(ClassificationImage)\n",
+    "ka = KernelDistance(ClassificationImage)\n",
     "ka.analyse(exp, obs, responses)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "id": "85ac8a46",
+   "execution_count": 41,
+   "id": "dc9c6a70",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-10T04:42:23.899935Z",
-     "start_time": "2024-04-10T04:42:23.752329Z"
+     "end_time": "2024-04-22T15:23:19.385125Z",
+     "start_time": "2024-04-22T15:23:19.185663Z"
     },
     "scrolled": false
    },
@@ -2560,23 +2480,39 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "generated 8 runs\n",
-      "{'n_trials': 100, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 2, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_extractor': <class 'palin.kernels.classification_images.ClassificationImage'>, 'distance': 'CORR'}\n",
-      ".\n",
-      "{'n_trials': 100, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 3, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_extractor': <class 'palin.kernels.classification_images.ClassificationImage'>, 'distance': 'CORR'}\n",
-      ".\n",
-      "{'n_trials': 100, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 4, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_extractor': <class 'palin.kernels.classification_images.ClassificationImage'>, 'distance': 'CORR'}\n",
-      ".\n",
-      "{'n_trials': 100, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 5, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_extractor': <class 'palin.kernels.classification_images.ClassificationImage'>, 'distance': 'CORR'}\n",
-      ".\n",
-      "{'n_trials': 100, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 6, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_extractor': <class 'palin.kernels.classification_images.ClassificationImage'>, 'distance': 'CORR'}\n",
-      ".\n",
-      "{'n_trials': 100, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 7, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_extractor': <class 'palin.kernels.classification_images.ClassificationImage'>, 'distance': 'CORR'}\n",
-      ".\n",
-      "{'n_trials': 100, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 8, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_extractor': <class 'palin.kernels.classification_images.ClassificationImage'>, 'distance': 'CORR'}\n",
-      ".\n",
-      "{'n_trials': 100, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 9, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_extractor': <class 'palin.kernels.classification_images.ClassificationImage'>, 'distance': 'CORR'}\n",
-      ".\n"
+      "Running 8 configs\n",
+      "0 : {'n_trials': 100, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 2, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_extractor': <class 'palin.kernels.classification_images.ClassificationImage'>, 'distance': 'CORR'}\n",
+      ".['corr']\n",
+      "[1.0]\n",
+      ";\n",
+      "1 : {'n_trials': 100, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 3, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_extractor': <class 'palin.kernels.classification_images.ClassificationImage'>, 'distance': 'CORR'}\n",
+      ".['corr']\n",
+      "[0.9997584847350102]\n",
+      ";\n",
+      "2 : {'n_trials': 100, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 4, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_extractor': <class 'palin.kernels.classification_images.ClassificationImage'>, 'distance': 'CORR'}\n",
+      ".['corr']\n",
+      "[0.9948042864707103]\n",
+      ";\n",
+      "3 : {'n_trials': 100, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 5, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_extractor': <class 'palin.kernels.classification_images.ClassificationImage'>, 'distance': 'CORR'}\n",
+      ".['corr']\n",
+      "[0.9713583847649068]\n",
+      ";\n",
+      "4 : {'n_trials': 100, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 6, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_extractor': <class 'palin.kernels.classification_images.ClassificationImage'>, 'distance': 'CORR'}\n",
+      ".['corr']\n",
+      "[0.8998782407812661]\n",
+      ";\n",
+      "5 : {'n_trials': 100, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 7, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_extractor': <class 'palin.kernels.classification_images.ClassificationImage'>, 'distance': 'CORR'}\n",
+      ".['corr']\n",
+      "[0.9226774771188183]\n",
+      ";\n",
+      "6 : {'n_trials': 100, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 8, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_extractor': <class 'palin.kernels.classification_images.ClassificationImage'>, 'distance': 'CORR'}\n",
+      ".['corr']\n",
+      "[0.9693352107369663]\n",
+      ";\n",
+      "7 : {'n_trials': 100, 'trial_type': <class 'palin.simulation.trial.Int2Trial'>, 'n_features': 9, 'external_noise_std': 100, 'kernel': 'random', 'internal_noise_std': 1, 'criteria': 0, 'kernel_extractor': <class 'palin.kernels.classification_images.ClassificationImage'>, 'distance': 'CORR'}\n",
+      ".['corr']\n",
+      "[0.9457780982693152]\n",
+      ";\n"
      ]
     }
    ],
@@ -2594,10 +2530,10 @@
     "                  'distance':['CORR']}\n",
     "\n",
     "\n",
-    "sim = Sim(Exp, experiment_params, \n",
-    "          Obs, observer_params, \n",
-    "          Analyser, analyser_params)\n",
-    "sim_df = sim.run_all(n_samples=1)\n",
+    "sim = Sim(SimpleExperiment, experiment_params, \n",
+    "          LinearObserver, observer_params, \n",
+    "          KernelDistance, analyser_params)\n",
+    "sim_df = sim.run_all(n_runs=1)\n",
     "\n",
     "\n",
     "\n",
@@ -2606,12 +2542,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 147,
-   "id": "1e0c8a43",
+   "execution_count": 42,
+   "id": "2115ae57",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-10T15:36:24.243659Z",
-     "start_time": "2024-04-10T15:36:24.180798Z"
+     "end_time": "2024-04-22T15:23:23.748491Z",
+     "start_time": "2024-04-22T15:23:23.682656Z"
     }
    },
    "outputs": [
@@ -2637,246 +2573,168 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>n_trials</th>\n",
-       "      <th>n_repeated</th>\n",
        "      <th>trial_type</th>\n",
        "      <th>n_features</th>\n",
        "      <th>external_noise_std</th>\n",
        "      <th>kernel</th>\n",
        "      <th>internal_noise_std</th>\n",
        "      <th>criteria</th>\n",
-       "      <th>sample</th>\n",
-       "      <th>metric</th>\n",
-       "      <th>dist</th>\n",
-       "      <th>prob_agree</th>\n",
-       "      <th>prob_first</th>\n",
+       "      <th>kernel_extractor</th>\n",
+       "      <th>distance</th>\n",
+       "      <th>run</th>\n",
+       "      <th>corr</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>100</td>\n",
-       "      <td>50</td>\n",
        "      <td>&lt;class 'palin.simulation.trial.Int2Trial'&gt;</td>\n",
-       "      <td>1</td>\n",
+       "      <td>2</td>\n",
        "      <td>100</td>\n",
-       "      <td>[1]</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-5</td>\n",
+       "      <td>random</td>\n",
+       "      <td>1</td>\n",
        "      <td>0</td>\n",
-       "      <td>(1.0, 1.0)</td>\n",
-       "      <td>0.2900</td>\n",
-       "      <td>0.8</td>\n",
-       "      <td>0.5</td>\n",
+       "      <td>&lt;class 'palin.kernels.classification_images.Cl...</td>\n",
+       "      <td>CORR</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>100</td>\n",
-       "      <td>50</td>\n",
        "      <td>&lt;class 'palin.simulation.trial.Int2Trial'&gt;</td>\n",
-       "      <td>1</td>\n",
+       "      <td>3</td>\n",
        "      <td>100</td>\n",
-       "      <td>[1]</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-4</td>\n",
+       "      <td>random</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>&lt;class 'palin.kernels.classification_images.Cl...</td>\n",
+       "      <td>CORR</td>\n",
        "      <td>0</td>\n",
-       "      <td>(1.0, 1.0)</td>\n",
-       "      <td>0.2900</td>\n",
-       "      <td>0.8</td>\n",
-       "      <td>0.5</td>\n",
+       "      <td>0.999758</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>100</td>\n",
-       "      <td>50</td>\n",
        "      <td>&lt;class 'palin.simulation.trial.Int2Trial'&gt;</td>\n",
-       "      <td>1</td>\n",
+       "      <td>4</td>\n",
        "      <td>100</td>\n",
-       "      <td>[1]</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-3</td>\n",
+       "      <td>random</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>&lt;class 'palin.kernels.classification_images.Cl...</td>\n",
+       "      <td>CORR</td>\n",
        "      <td>0</td>\n",
-       "      <td>(1.0, 1.0)</td>\n",
-       "      <td>0.2900</td>\n",
-       "      <td>0.8</td>\n",
-       "      <td>0.5</td>\n",
+       "      <td>0.994804</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>100</td>\n",
-       "      <td>50</td>\n",
        "      <td>&lt;class 'palin.simulation.trial.Int2Trial'&gt;</td>\n",
-       "      <td>1</td>\n",
+       "      <td>5</td>\n",
        "      <td>100</td>\n",
-       "      <td>[1]</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-2</td>\n",
+       "      <td>random</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>&lt;class 'palin.kernels.classification_images.Cl...</td>\n",
+       "      <td>CORR</td>\n",
        "      <td>0</td>\n",
-       "      <td>(1.0, 0.88)</td>\n",
-       "      <td>0.1844</td>\n",
-       "      <td>0.8</td>\n",
-       "      <td>0.5</td>\n",
+       "      <td>0.971358</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>100</td>\n",
-       "      <td>50</td>\n",
        "      <td>&lt;class 'palin.simulation.trial.Int2Trial'&gt;</td>\n",
-       "      <td>1</td>\n",
+       "      <td>6</td>\n",
        "      <td>100</td>\n",
-       "      <td>[1]</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>(1.0, 0.68)</td>\n",
-       "      <td>0.0724</td>\n",
-       "      <td>0.8</td>\n",
-       "      <td>0.5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>495</th>\n",
-       "      <td>100</td>\n",
-       "      <td>50</td>\n",
-       "      <td>&lt;class 'palin.simulation.trial.Int2Trial'&gt;</td>\n",
+       "      <td>random</td>\n",
        "      <td>1</td>\n",
-       "      <td>100</td>\n",
-       "      <td>[1]</td>\n",
-       "      <td>4.9</td>\n",
        "      <td>0</td>\n",
+       "      <td>&lt;class 'palin.kernels.classification_images.Cl...</td>\n",
+       "      <td>CORR</td>\n",
        "      <td>0</td>\n",
-       "      <td>(0.56, 0.5)</td>\n",
-       "      <td>0.0576</td>\n",
-       "      <td>0.8</td>\n",
-       "      <td>0.5</td>\n",
+       "      <td>0.899878</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>496</th>\n",
+       "      <th>5</th>\n",
        "      <td>100</td>\n",
-       "      <td>50</td>\n",
        "      <td>&lt;class 'palin.simulation.trial.Int2Trial'&gt;</td>\n",
-       "      <td>1</td>\n",
+       "      <td>7</td>\n",
        "      <td>100</td>\n",
-       "      <td>[1]</td>\n",
-       "      <td>4.9</td>\n",
+       "      <td>random</td>\n",
        "      <td>1</td>\n",
        "      <td>0</td>\n",
-       "      <td>(0.56, 0.34)</td>\n",
-       "      <td>0.0832</td>\n",
-       "      <td>0.8</td>\n",
-       "      <td>0.5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>497</th>\n",
-       "      <td>100</td>\n",
-       "      <td>50</td>\n",
-       "      <td>&lt;class 'palin.simulation.trial.Int2Trial'&gt;</td>\n",
-       "      <td>1</td>\n",
-       "      <td>100</td>\n",
-       "      <td>[1]</td>\n",
-       "      <td>4.9</td>\n",
-       "      <td>2</td>\n",
+       "      <td>&lt;class 'palin.kernels.classification_images.Cl...</td>\n",
+       "      <td>CORR</td>\n",
        "      <td>0</td>\n",
-       "      <td>(0.54, 0.41)</td>\n",
-       "      <td>0.0757</td>\n",
-       "      <td>0.8</td>\n",
-       "      <td>0.5</td>\n",
+       "      <td>0.922677</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>498</th>\n",
+       "      <th>6</th>\n",
        "      <td>100</td>\n",
-       "      <td>50</td>\n",
        "      <td>&lt;class 'palin.simulation.trial.Int2Trial'&gt;</td>\n",
-       "      <td>1</td>\n",
+       "      <td>8</td>\n",
        "      <td>100</td>\n",
-       "      <td>[1]</td>\n",
-       "      <td>4.9</td>\n",
-       "      <td>3</td>\n",
+       "      <td>random</td>\n",
+       "      <td>1</td>\n",
        "      <td>0</td>\n",
-       "      <td>(0.62, 0.23)</td>\n",
-       "      <td>0.1053</td>\n",
-       "      <td>0.8</td>\n",
-       "      <td>0.5</td>\n",
+       "      <td>&lt;class 'palin.kernels.classification_images.Cl...</td>\n",
+       "      <td>CORR</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.969335</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>499</th>\n",
+       "      <th>7</th>\n",
        "      <td>100</td>\n",
-       "      <td>50</td>\n",
        "      <td>&lt;class 'palin.simulation.trial.Int2Trial'&gt;</td>\n",
-       "      <td>1</td>\n",
+       "      <td>9</td>\n",
        "      <td>100</td>\n",
-       "      <td>[1]</td>\n",
-       "      <td>4.9</td>\n",
-       "      <td>4</td>\n",
+       "      <td>random</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>&lt;class 'palin.kernels.classification_images.Cl...</td>\n",
+       "      <td>CORR</td>\n",
        "      <td>0</td>\n",
-       "      <td>(0.72, 0.16)</td>\n",
-       "      <td>0.1220</td>\n",
-       "      <td>0.8</td>\n",
-       "      <td>0.5</td>\n",
+       "      <td>0.945778</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>500 rows × 13 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "     n_trials  n_repeated                                  trial_type  \\\n",
-       "0         100          50  <class 'palin.simulation.trial.Int2Trial'>   \n",
-       "1         100          50  <class 'palin.simulation.trial.Int2Trial'>   \n",
-       "2         100          50  <class 'palin.simulation.trial.Int2Trial'>   \n",
-       "3         100          50  <class 'palin.simulation.trial.Int2Trial'>   \n",
-       "4         100          50  <class 'palin.simulation.trial.Int2Trial'>   \n",
-       "..        ...         ...                                         ...   \n",
-       "495       100          50  <class 'palin.simulation.trial.Int2Trial'>   \n",
-       "496       100          50  <class 'palin.simulation.trial.Int2Trial'>   \n",
-       "497       100          50  <class 'palin.simulation.trial.Int2Trial'>   \n",
-       "498       100          50  <class 'palin.simulation.trial.Int2Trial'>   \n",
-       "499       100          50  <class 'palin.simulation.trial.Int2Trial'>   \n",
-       "\n",
-       "     n_features  external_noise_std kernel  internal_noise_std  criteria  \\\n",
-       "0             1                 100    [1]                 0.0        -5   \n",
-       "1             1                 100    [1]                 0.0        -4   \n",
-       "2             1                 100    [1]                 0.0        -3   \n",
-       "3             1                 100    [1]                 0.0        -2   \n",
-       "4             1                 100    [1]                 0.0        -1   \n",
-       "..          ...                 ...    ...                 ...       ...   \n",
-       "495           1                 100    [1]                 4.9         0   \n",
-       "496           1                 100    [1]                 4.9         1   \n",
-       "497           1                 100    [1]                 4.9         2   \n",
-       "498           1                 100    [1]                 4.9         3   \n",
-       "499           1                 100    [1]                 4.9         4   \n",
+       "   n_trials                                  trial_type  n_features  \\\n",
+       "0       100  <class 'palin.simulation.trial.Int2Trial'>           2   \n",
+       "1       100  <class 'palin.simulation.trial.Int2Trial'>           3   \n",
+       "2       100  <class 'palin.simulation.trial.Int2Trial'>           4   \n",
+       "3       100  <class 'palin.simulation.trial.Int2Trial'>           5   \n",
+       "4       100  <class 'palin.simulation.trial.Int2Trial'>           6   \n",
+       "5       100  <class 'palin.simulation.trial.Int2Trial'>           7   \n",
+       "6       100  <class 'palin.simulation.trial.Int2Trial'>           8   \n",
+       "7       100  <class 'palin.simulation.trial.Int2Trial'>           9   \n",
        "\n",
-       "     sample        metric    dist  prob_agree  prob_first  \n",
-       "0         0    (1.0, 1.0)  0.2900         0.8         0.5  \n",
-       "1         0    (1.0, 1.0)  0.2900         0.8         0.5  \n",
-       "2         0    (1.0, 1.0)  0.2900         0.8         0.5  \n",
-       "3         0   (1.0, 0.88)  0.1844         0.8         0.5  \n",
-       "4         0   (1.0, 0.68)  0.0724         0.8         0.5  \n",
-       "..      ...           ...     ...         ...         ...  \n",
-       "495       0   (0.56, 0.5)  0.0576         0.8         0.5  \n",
-       "496       0  (0.56, 0.34)  0.0832         0.8         0.5  \n",
-       "497       0  (0.54, 0.41)  0.0757         0.8         0.5  \n",
-       "498       0  (0.62, 0.23)  0.1053         0.8         0.5  \n",
-       "499       0  (0.72, 0.16)  0.1220         0.8         0.5  \n",
+       "   external_noise_std  kernel  internal_noise_std  criteria  \\\n",
+       "0                 100  random                   1         0   \n",
+       "1                 100  random                   1         0   \n",
+       "2                 100  random                   1         0   \n",
+       "3                 100  random                   1         0   \n",
+       "4                 100  random                   1         0   \n",
+       "5                 100  random                   1         0   \n",
+       "6                 100  random                   1         0   \n",
+       "7                 100  random                   1         0   \n",
        "\n",
-       "[500 rows x 13 columns]"
+       "                                    kernel_extractor distance  run      corr  \n",
+       "0  <class 'palin.kernels.classification_images.Cl...     CORR    0  1.000000  \n",
+       "1  <class 'palin.kernels.classification_images.Cl...     CORR    0  0.999758  \n",
+       "2  <class 'palin.kernels.classification_images.Cl...     CORR    0  0.994804  \n",
+       "3  <class 'palin.kernels.classification_images.Cl...     CORR    0  0.971358  \n",
+       "4  <class 'palin.kernels.classification_images.Cl...     CORR    0  0.899878  \n",
+       "5  <class 'palin.kernels.classification_images.Cl...     CORR    0  0.922677  \n",
+       "6  <class 'palin.kernels.classification_images.Cl...     CORR    0  0.969335  \n",
+       "7  <class 'palin.kernels.classification_images.Cl...     CORR    0  0.945778  "
       ]
      },
-     "execution_count": 147,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2887,28 +2745,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "id": "3d71bdcd",
+   "execution_count": 44,
+   "id": "26e8840e",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-10T04:42:50.935778Z",
-     "start_time": "2024-04-10T04:42:50.744219Z"
+     "end_time": "2024-04-22T15:23:54.807011Z",
+     "start_time": "2024-04-22T15:23:54.599975Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<Axes: xlabel='n_features', ylabel='metric'>"
+       "<Axes: xlabel='n_features', ylabel='corr'>"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 44,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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CqCB3/JhThsNnKlmAiIgsRNR1gKwV1wEiS1r6w3Es/eEE7usbiCVjY8SOQ0Rks2xiHSAiuqhpRehM3glGRGQxLEBEImtaCyivogZV9Y0ipyEisg8sQEQi83ZRINBdBUEAsop4OzwRkSWwABFZgSiuCE1EZFEsQERWgCtCExFZFgsQkRXgitBERJbFAkRkBSIvFaCiyjqcq9aJnIaIqONjASKyAm5KR4T6OAPgPCAiIktgASKyElwPiIjIcliAiKwE7wQjIrIcFiAiK/HnnWAa8Ak1RETtiwWIyEr0DnCDg1SCimodijX1YschIurQWICIrITSUYbuvq4AuB4QEVF7YwEisiJN84AyOQ+IiKhdsQARWRGuCE1EZBksQERW5K93ghmNnAhNRNReWICIrEgPP1coHKSoqtfj9LkaseMQEXVYLEBEVsRRJkWvADcAXA+IiKg9sQARWRmuCE1E1P5YgIisDFeEJiJqfyxARFam6U6wP85qoDcYxQ1DRNRBsQARWZlQb2e4KhxQ32jEibJqseMQEXVILEBEVkYqlSAysOkyWKW4YYiIOigWICIrFBXMFaGJiNoTCxCRFYoKdAfAESAiovbCAkRkhZruBMsprkJ9o0HkNEREHQ8LEJEVCvJQwdNZDr1RwNFirdhxiIg6HBYgIiskkUi4HhARUTtiASKyUlFcEZqIqN2wABFZqWiOABERtRsWICIr1TQCdLK8GtU6vbhhiIg6GNEL0MqVKxESEgKlUom4uDjs3bv3ivs2NjZi0aJFCAsLg1KpRHR0NNLS0sz2MRgMeP7559G1a1eoVCqEhYXhxRdfhCAI7X0qRG3Kx1WBALUSggBkcRSIiKhNiVqANm7ciOTkZCxYsAAHDx5EdHQ0EhMTUVZW1uz+qampWLVqFZYvX47s7GxMnToVo0ePRkZGhmmfV199FW+//TZWrFiBo0eP4tVXX8Vrr72G5cuXW+q0iNpM0yhQVlGlqDmIiDoaiSDi0EhcXBxiY2OxYsUKAIDRaERwcDBmzpyJuXPnXrZ/QEAA5s2bh+nTp5u2jRkzBiqVCuvXrwcA3H333fD19cX7779/xX2uRavVQq1WQ6PRwM3N7UZOkeiG/HdHLl5LO4YRUf5Y+XA/seMQEVm1lvz+Fm0EqKGhAQcOHEBCQsKfYaRSJCQkYPfu3c2+R6fTQalUmm1TqVTYtWuX6eNBgwYhPT0dx48fBwBkZmZi165dGDZs2BWz6HQ6aLVasxeRNYi+NALEFaGJiNqWaAWooqICBoMBvr6+Ztt9fX1RUlLS7HsSExOxZMkSnDhxAkajEdu3b8fmzZtRXFxs2mfu3Ll46KGHEBERAUdHR/Tt2xezZ8/G+PHjr5hl8eLFUKvVpldwcHDbnCTRDWp6KGrh+Tqcr2kQOQ0RUcch+iTolli2bBnCw8MREREBuVyOGTNmICkpCVLpn6fx2Wef4eOPP8aGDRtw8OBBrF27Fv/5z3+wdu3aKx43JSUFGo3G9CosLLTE6RBdk1rliK7ezgA4CkRE1JZEK0De3t6QyWQoLS01215aWgo/P79m3+Pj44OtW7eipqYG+fn5yMnJgYuLC0JDQ037PPPMM6ZRoD59+uDRRx/FU089hcWLF18xi0KhgJubm9mLyFpwRWgiorYnWgGSy+Xo378/0tPTTduMRiPS09MRHx9/1fcqlUoEBgZCr9dj06ZNGDVqlOlztbW1ZiNCACCTyWA0Gtv2BIgsJIrzgIiI2pyDmF88OTkZEydOxIABAzBw4EAsXboUNTU1SEpKAgBMmDABgYGBptGbPXv2oKioCDExMSgqKsLChQthNBoxZ84c0zFHjhyJl156CZ07d0bv3r2RkZGBJUuWYPLkyaKcI9GNaloROvOMBoIgQCKRiJyIiMj2iVqAxo4di/LycsyfPx8lJSWIiYlBWlqaaWJ0QUGB2WhOfX09UlNTkZeXBxcXFwwfPhzr1q2Du7u7aZ/ly5fj+eefxz//+U+UlZUhICAA//jHPzB//nxLnx5Rm+gdoIZMKkF5lQ4l2nr4q1ViRyIisnmirgNkrbgOEFmboUt/Rk5JFd55pD+GRjY/R46IyN7ZxDpARHT9uB4QEVHbYgEisgFRwbwTjIioLbEAEdmAv44A8ao1EdGNYwEisgE9/Fwhd5BCW69H/rlaseMQEdk8FiAiG+Aok6KX/8UJfZmcB0REdMNYgIhsBFeEJiJqOyxARDaCK0ITEbUdFiAiG9G0IvSRIi30Bj7ahYjoRrAAEdmIUB8XOMtlqGs0ILe8Wuw4REQ2jQWIyEbIpBJEBl6aB1TIeUBERDeCBYjIhkQHuwPgnWBERDeKBYjIhvBOMCKitsECRGRDmlaEzinRQqc3iBuGiMiGsQAR2ZAgDxU8nBzRaBCQU1wldhwiaoXjpVX4LquYj7URGQsQkQ2RSCRcD4jIhmWd0WDUil8x7eOD2LC3QOw4do0FiMjGNK0HlMl5QEQ2paiyDpPX7kNd48XL14u/zUGxpk7kVPaLBYjIxvThCBCRzdHWNyJpzV6UV+kQ4eeK6GB3VOv0SN1yhJfCRMICRGRjmkaAcsuqUaPTi5yGiK6l0WDEP9cfxPHSanRyVeCDSbF4/f4oOMokSM8pw1eHi8WOaJdYgIhsTCc3JfzclDAKwJEiXgYjsmaCICB1yxHsyq2Ak1yGDybFIsBdhe6+rpgxJBwA8MKXf+BCTYPISe0PCxCRDeJ6QES24b87TmLj/kJIJcCKh/uaVnMHgGn/F4Yevq44V9OAF7/OFjGlfWIBIrJBXBGayPp9cagIr287BgB44Z7euD3C1+zzcgcpXhnTBxIJsDmjCDuOlYkR026xABHZII4AEVm3vafO45n/HQYATLm5Kx6ND2l2v76dPTB5cFcAwLwtR1DNeX0WwwJEZIOiAt0BAAXnazl3gMjK5JVX44l1+9FgMGJobz88N7znVff/113dEeypQlFlHV5Py7FQSmIBIrJBaidHhHg5AQAOcyI0kdU4V61D0of7UFnbiOhgd7w5NgZSqeSq73GSO2Dx6CgAwEe/52P/6fOWiGr3WICIbFTTitBZnAdEZBXqGw14Yt0B5J+rRbCnCqsnDIBKLruu994c7o0H+gdBEIBnNx1GfSOf9dfeWICIbFQUV4QmshpGo4B//S8TB/IvwE3pgDWTYuHjqmjRMVJH9IK3iwIny2uw8qfcdkpKTViAiGxU051gXBGaSHyvbTuGbw4Xw1EmwapHB6BbJ9cWH0Pt5IgXR/UGALy94ySOFmvbOib9BQsQkY3qHeAGqQQo1epQqq0XOw6R3dqwpwDv7DwJAHjt/ijEh3m1+ljD+vgjsbcv9EYBz246DL3B2FYx6W9YgIhslJPcAeGX/pWZWVgpbhgiO7XjWBme/+IIAGB2QjhG9w264WO+OCoSrkoHHD6jwZpfT9/w8ah5LEBENozrARGJJ/usFjM2ZMBgFHBfv0A8eUd4mxy3k5sSqSMu3jr/xvZjyD9X0ybHJXMsQEQ2LIorQhOJokRTj8kf7kO1To/4UC+8cl8UJJKr3+7eEg8OCMagMC/UNxoxd1MWnxjfDqyiAK1cuRIhISFQKpWIi4vD3r17r7hvY2MjFi1ahLCwMCiVSkRHRyMtLc1sn5CQEEgkkste06dPb+9TIbKopifDZxVp+AOSyEKqdXpM/nAfSrT16NbJBe880h9yh7b9dSqRSPDKfVFQOkqxO+8cNu4rbNPjkxUUoI0bNyI5ORkLFizAwYMHER0djcTERJSVNf9MlNTUVKxatQrLly9HdnY2pk6ditGjRyMjI8O0z759+1BcXGx6bd++HQDwwAMPWOSciCwlws8NcpkUlbWNKDhfK3Ycog5PbzBi5oaDyC7WwttFjjWTYqF2cmyXr9XZywn/urMHAOClb4/yZoc2JnoBWrJkCR5//HEkJSWhV69eeOedd+Dk5IQPPvig2f3XrVuH5557DsOHD0doaCimTZuG4cOH44033jDt4+PjAz8/P9Pr66+/RlhYGG677TZLnRaRRcgdpOjpf2kiNOcBEbUrQRCw8Ks/8NOxcigdpVg9MRbBnk7t+jWTBocgOkiNqno9nt96hCO9bUjUAtTQ0IADBw4gISHBtE0qlSIhIQG7d+9u9j06nQ5KpdJsm0qlwq5du674NdavX4/Jkye36fVZImvRtCL0Yd4JRtSuVv9yCut/L4BEAiwd2xcxl+bgtScHmRSvjImCg1SC77NL8d2Rknb/mvZC1AJUUVEBg8EAX19fs+2+vr4oKWn+P3JiYiKWLFmCEydOwGg0Yvv27di8eTOKi4ub3X/r1q2orKzEpEmTrphDp9NBq9WavYhshelOMD4TjKjdfJdVjJe+PQoAmDe8J4ZG+lnsa/f0d8M//y8MADD/iz9QWcsHILcF0S+BtdSyZcsQHh6OiIgIyOVyzJgxA0lJSZBKmz+V999/H8OGDUNAQMAVj7l48WKo1WrTKzg4uL3iE7W5phWhjxRpYDByeJyorR0suIDZGw8BACbGd8FjN3e1eIbpt3dDt04uqKjW4d/fHLX41++IRC1A3t7ekMlkKC0tNdteWloKP7/m27WPjw+2bt2Kmpoa5OfnIycnBy4uLggNDb1s3/z8fPzwww+YMmXKVXOkpKRAo9GYXoWFnG1PtiPMxwVOchlqGww4WV4tdhyiDqXgXC0eX7sfOr0Rd0R0wvyRvUWZTqFwkOHVMX0gkQCfHziDX06UWzxDRyNqAZLL5ejfvz/S09NN24xGI9LT0xEfH3/V9yqVSgQGBkKv12PTpk0YNWrUZfusWbMGnTp1wogRI656LIVCATc3N7MXka2QSSWIDLj0YFTOAyJqM5W1DZj04V6cq2lAZKAb3hrXFzKpeHNJ+3fxxMT4EABAyuYs1DboRcvSEYh+CSw5ORnvvfce1q5di6NHj2LatGmoqalBUlISAGDChAlISUkx7b9nzx5s3rwZeXl5+OWXXzB06FAYjUbMmTPH7LhGoxFr1qzBxIkT4eDgYNFzIrI0rghN1LZ0egP+se4A8sprEKBW4oOJsXBWiP+75JnEHgh0V+HMhTr8Z9txsePYNNH/a44dOxbl5eWYP38+SkpKEBMTg7S0NNPE6IKCArP5PfX19UhNTUVeXh5cXFwwfPhwrFu3Du7u7mbH/eGHH1BQUIDJkydb8nSIRBHFJ8MTtRlBEDB3Uxb2nDoPF4UDPkiKRSc35bXfaAHOCge8NDoSk9bsw5rfTuHuaH/06+whdiybJBG4qMBltFot1Go1NBoNL4eRTcg/V4PbXt8BuUyKIy8ktvmqtET2ZMn243gr/QQcpBKsSYrFLeE+Yke6TPLGQ9icUYTuvi74euYt/Dt/SUt+f/M7RtQBdPZ0gruTIxoMRuSUcBkHotb63/5CvJV+AgDw0uhIqyw/APD83b3g5SzH8dJq/HdHrthxbBILEFEHIJFI0Cfw0kRozgMiapVfcyuQsjkLADB9SBjGxnYWOdGVeTjLsfCe3gCAlT/l4nhplciJbA8LEFEHEc0VoYla7URpFaauPwC9UcDI6ADTM7is2d1R/kjo2QmNBgFzPj/MdcBaiAWIqIPgnWBErVNWVY9Ja/ahql6PAV088Pr9UZCKeLv79ZJIJHjx3ki4KhxwqLASa387LXYkm8ICRNRBNK0IfaKsiuuDEF2n2gY9pqzdj6LKOnT1dsa7EwZA6SgTO9Z181erMHd4BADg9W3HUHi+VuREtoMFiKiD8HVTwtdNAaMA/HGWE6GJrsVgFPDkp4dw+IwGHk6OWDMpFp7OcrFjtdi42M6I6+qJukYDntuSxSfGXycWIKIOpOnJ8FwRmujaXvrmKLZnl0LuIMV7EwYgxNtZ7EitIpVK8MqYKCgcpPjlRAU+P3BG7Eg2gQWIqAOJCuQ8IKLr8eGvp/DBr6cAAEsejMaAEE+RE92Yrt7OeOrO7gCAf39zFOVVOpETWT8WIKIOhCtCE13b9uxSLPo6GwDw7NAI3B0VIHKitjHl5q6IDHSDpq4RC7/8Q+w4Vo8FiKgDaRoBOn2uFpraRpHTEFmfrDMazPokA0YBGDcwGFNvCxU7UptxkEnx6pgoyKQSfJNVjG1/lIgdyaqxABF1IB7OcnT2dAIAHC6qFDcMkZU5c6EWk9fuQ12jAbeEe2PRqEhIJNZ/u3tL9A5Q4x+3Xix1z289Ak0d/yF0JSxARB0M1wMiupy2vhGTP9yH8iodIvxc8d/x/eAo65i/AmfdEY5Qb2eUVemw+NujYsexWh3zvz6RHYvmnWBEZhoNRvxz/UEcL62Gr5sCH0yKhavSUexY7UbpKMMrY6IAAJ/uK8RvuRUiJ7JOLEBEHQxHgIj+JAgC5m3Jwq7cCjjJZXh/YiwC3FVix2p3A7t64pGbLj7LbO7mLNQ1GEROZH1YgIg6mMhANaQSoERbjzJtvdhxiES18qdcfLb/DKQSYOXD/RB56UYBe/Ds0Aj4q5UoOF+LN384LnYcq8MCRNTBOCsc0K2TCwA+GZ7s2xeHivCf7y/+4n/hnt4YEtFJ5ESW5ap0xEujIwEAq3/J4/IYf9OqAjRr1iy89dZbl21fsWIFZs+efaOZiOgGNa0IncUfeGSn9p46j2f+dxgA8PgtXfFofIi4gURye4Qv7okOgFEA5nx+GI0Go9iRrEarCtCmTZswePDgy7YPGjQIn3/++Q2HIqIbE31pHhBHgMge5ZVX44l1+9FgMGJobz+kDOspdiRRLRjZCx5OjsgpqcKqnSfFjmM1WlWAzp07B7X68uuobm5uqKjgbHMisfW5NAJ0+EwlH4xIduVctQ5JH+5DZW0jYoLd8ebYGEilHWutn5byclFgwcjeAIC30nORW1YlciLr0KoC1K1bN6SlpV22/bvvvkNoaMdZVZPIVvX0d4WjTIILtY04c6FO7DhEFlHfaMDjH+1H/rlaBHuqsHriAKjkMrFjWYVRMQEY0sMHDQYjnt2UBaOR/zByaM2bkpOTMWPGDJSXl+P2228HAKSnp+ONN97A0qVL2zIfEbWCwkGGCD83ZBVpkHmmEsGXVocm6qiMRgH/+iwTBwsq4aZ0wJpJA+HtohA7ltWQSCT49+g+uGvJThzIv4B1v+dj4qAQsWOJqlUjQJMnT8Ybb7yB999/H0OGDMGQIUOwfv16vP3223j88cfbOiMRtQLXAyJ78uq2HHyTVQxHmQTvThhguhOS/hTorsLcYREAgNfSclBUad+jw62+DX7atGk4c+YMSktLodVqkZeXhwkTJrRlNiK6AVwRmuzFx3vysWpnHgDgtfujcFOol8iJrNf4uC6IDfFATYMB87Zk2fUcwRteB8jHxwcuLmzaRNYmKvjiCNCRIg0MvN5PHdSOY2WY/8UfAICnErpjdN8gkRNZN6lUgsX3RUEuk2LHsXJsPVQkdiTRXPccoH79+iE9PR0eHh7o27fvVZ+ge/DgwTYJR0St183HBSpHGWoaDMgrr0a4r6vYkYjaVPZZLaZ/fBAGo4Ax/YIw645uYkeyCd06ueDJhHC8vu0YFn2VjVvCfexyvtR1F6BRo0ZBobj4Dbr33nvbKw8RtREHmRSRgW7Yd/oCMs9oWICoQynR1GPyh/tQ02BAfKgXFt/X56r/MCdzT9waiq8PF+NosRYvfJWN5eP6ih3J4q67AC1YsAAAYDAYMGTIEERFRcHd3b29chFRG4gKcse+0xdw+Ewl7u/PSwPUMVTr9Ej6cB9KtPXo1skF7zzaH3IHPtmpJRxlUrw2JgqjVu7CV5lncW9MAO7o6St2LItq8Z8YmUyGu+66CxcuXGiPPETUhngnGHU0eoMRMzYcxNFiLbxd5FgzKRZqlaPYsWxSnyA1Hr/l4tp987YcQVV9o8iJLKtVlTkyMhJ5eXltnYWI2ljTnWDZxVo06PkMILJtgiBgwZd/YMexcigdpVg9MZZrXN2g2Qnd0cXLCSXaerzyXY7YcSyqVQXo3//+N55++ml8/fXXKC4uhlarNXsRkXXo4uUEN6UDGvRGHC/l8vdk2977JQ8f7ymARAIse6gvYoLdxY5k81RyGRbf1wcA8PGeAuzJOydyIstpVQEaPnw4MjMzcc899yAoKAgeHh7w8PCAu7s7PDw82jojEbWSRCIxPRk+k0+GJxv2bVYxXv724ghF6oheSOztJ3KijmNQmDfGDQwGAMzdnIX6RoPIiSyjVY/C+Omnn9o6BxG1k6ggNXblVuBwoQbj48ROQ9RyBwsu4KmNhwAAE+O7YPLgEFHzdERzh/XEjzllOFVRg2XpJ/Ds0AixI7W7Vo0Ade3aFbfeeituu+02s9ett96Krl27tuhYK1euREhICJRKJeLi4rB3794r7tvY2IhFixYhLCwMSqUS0dHRzT6UtaioCI888gi8vLygUqnQp08f7N+/v8XnSdQRcASIbFn+uRo8vnY/dHojEnp2wvyRvXm7eztQqxzx4qhIAMC7P+fhSFHHv3Gi1QWovLz8su3nz59vUQHauHEjkpOTsWDBAhw8eBDR0dFITExEWVlZs/unpqZi1apVWL58ObKzszF16lSMHj0aGRkZpn0uXLiAwYMHw9HREd999x2ys7Pxxhtv8NIc2a3oSytCnyirRl2DfQxtU8dQWduApA/34VxNAyID3bDsob6QSVl+2stdvf0wIsofBqOAZzcdht7QsW+ckAiteBCIVCpFaWkpfHx8zLbn5+ejV69eqKmpua7jxMXFITY2FitWrAAAGI1GBAcHY+bMmZg7d+5l+wcEBGDevHmYPn26aduYMWOgUqmwfv16AMDcuXPx66+/4pdffmnpaZlotVqo1WpoNBq4ubm1+jhE1kAQBAx8OR3lVTp8PjUeA0I8xY5EdE06vQGPvr8Xe0+dR4Baia3TB6OTm1LsWB1eeZUOCUt2QlPXiGeHRmDa/4WJHalFWvL7u0VzgJKTkwFcnFj5/PPPw8npz9sPDQYD9uzZg5iYmOs6VkNDAw4cOICUlBTTNqlUioSEBOzevbvZ9+h0OiiV5n8BVCoVdu3aZfr4yy+/RGJiIh544AHs3LkTgYGB+Oc//3nVp9TrdDrodDrTx7yTjToSiUSC6CA1fjhahswzGhYgsnqCIGDO54ex99R5uCoc8EFSLMuPhfi4KvD83b3w9P8y8eYPx5HY2xehPh3zeZ8tugSWkZGBjIwMCIKArKws08cZGRnIyclBdHQ0Pvzww+s6VkVFBQwGA3x9zVee9PX1RUlJSbPvSUxMxJIlS3DixAkYjUZs374dmzdvRnFxsWmfvLw8vP322wgPD8e2bdswbdo0zJo1C2vXrr1ilsWLF0OtVptewcHB13UORLaiaR7QYc4DIhvw5vbj+OLQWThIJfjvI/0Q4ceReEsa0y8Qt4R7o0FvxNzNWTB20Icpt2gEqOnur6SkJCxbtszil4eWLVuGxx9/HBEREZBIJAgLC0NSUhI++OAD0z5GoxEDBgzAyy+/DADo27cvjhw5gnfeeQcTJ05s9rgpKSmm0S3g4ggQSxB1JFwRmmzF//YX4q0fcwEAL42OxC3hPtd4B7U1iUSCl0f3QeLSn7H31Hls2FuAR27qInasNteqSdBr1qyBm5sbcnNzsW3bNtTV1QG4OGx5vby9vSGTyVBaWmq2vbS0FH5+za/v4OPjg61bt6Kmpgb5+fnIycmBi4sLQkNDTfv4+/ujV69eZu/r2bMnCgoKrphFoVDAzc3N7EXUkTSNAJ2qqIGmzr6Wuyfb8WtuBVI2ZwEApg8Jw9jYziInsl/Bnk54JrEHAOCV73JQrKkTOVHba1UBOn/+PO644w50794dw4cPN12Ceuyxx/Cvf/3ruo4hl8vRv39/pKenm7YZjUakp6cjPj7+qu9VKpUIDAyEXq/Hpk2bMGrUKNPnBg8ejGPHjpntf/z4cXTp0vHaK9H18nSWI9hTBQB2cXsr2Z7jpVWYuv4A9EYBI6MD8K87e4gdye5NiA9B387uqNbpkbrlSIsGOWxBqwrQ7Nmz4ejoiIKCArOJ0GPHjm12XZ4rSU5OxnvvvYe1a9fi6NGjmDZtGmpqapCUlAQAmDBhgtkk6T179mDz5s3Iy8vDL7/8gqFDh8JoNGLOnDmmfZ566in8/vvvePnll5Gbm4sNGzbg3XffNbtzjMgecT0gslZlVfVIWrMPVfV6xIZ44PX7oyDl7e6ik0kleG1MFOQyKdJzyvDV4eJrv8mGtGol6O+//x7btm1DUFCQ2fbw8HDk5+df93HGjh2L8vJyzJ8/HyUlJYiJiUFaWpppYnRBQQGk0j87Wn19PVJTU5GXlwcXFxcMHz4c69atg7u7u2mf2NhYbNmyBSkpKVi0aBG6du2KpUuXYvz48a05VaIOIypQjW8OF+NwIUeAyHrUNugxZe1+FFXWoau3M959dACUjjKxY9El4b6umD6kG9784The+PIP3NzNG57OcrFjtYlWrQPk6uqKgwcPIjw8HK6ursjMzERoaCj279+PxMREnDtn2w9T4zpA1BHtPnkO4977HQFqJX5LuUPsOEQwGAX8Y90B/HC0FB5Ojtjyz8EI8XYWOxb9TYPeiJHLd+FYaRVG9w3Em2NjxI50RS35/d2qS2C33HILPvroI9PHEokERqMRr732GoYMGdKaQxJRO+sTpIZEApzV1KO8SnftNxC1s39/k40fjpZC7iDF6okDWH6slNxBilfvj4JUAmzJKMJPx5p/WoOtaVUBeu211/Duu+9i2LBhaGhowJw5cxAZGYmff/4Zr776altnJKI24KJwQNilBc24HhCJbc2vp7Dm19MAgCUPRqN/Fy7Qac1igt2RNPjio67mbc5CtU4vcqIb16oCFBkZiWPHjuHmm2/GqFGjUFNTg/vuuw8ZGRkIC7OtZbOJ7EnTekCZXA+IRLQ9uxSLvs4GADw7NAJ3RwWInIiux7/u6o5gTxXOaurxelqO2HFuWKsmQQMXb0W/8847ER0dDaPx4gPT9u3bBwC455572iYdEbWp6CB3bD5YxBEgEs2B/POY9UkGBAEYNzAYU28LvfabyCo4yR2weHQUHnl/Dz76PR8jowNs+tE6rSpAaWlpePTRR3H+/PnL1gWQSCQwGPjEaSJr9NcVoQVBgETCW43Jcg6fqcSkD/ahrtGA27r7YNGoSP4ZtDE3h3vjgf5B+N+BM3h202F8M+sWm71rr1WXwGbOnIkHH3wQZ8+ehdFoNHux/BBZr57+bnCQSnC+pgFnLnS8lV3JemWf1eLR9/eiSqfHwK6eeOeR/nCUtepXEIksdUQv+LgqcLK8Bit/yhU7Tqu16k9faWkpkpOTL3uQKRFZN6WjDBH+rgCALK4ITRZyorQKj76/B5q6RvTt7I4PJsVCJbfNUQMC1E6OWHRPbwDA2ztO4mixVuRErdOqAnT//fdjx44dbRyFiCyBK0KTJZ2qqMH41XtwrqYBkYFu+DBpIFwUrZ5+SlZiWB9/DO3tB71RwLObDkNvMIodqcVa9adwxYoVeOCBB/DLL7+gT58+cHR0NPv8rFmz2iQcEbW96CA1NuwBV4Smdld4vhbj3/sdZVU6RPi5Yt3kOKhVjtd+I9mERaN647eTFTh8RoMPfj2FJ261rbvAW1WAPvnkE3z//fdQKpXYsWOH2SQ2iUTCAkRkxfoEugO4+FBUo1HgM5eoXRRr6vDw6t9xVlOPMB9nrHssDh4d5BEKdFEnNyVSR/TCnE2HsWT7cST29kMXL9tZzLJVl8DmzZuHF154ARqNBqdPn8apU6dMr7y8vLbOSERtqLuvC5SOUlTp9MirqBE7DnVAZVX1GP/eHhSer0MXLyd8POUm+LgqxI5F7eCBAUEY3M0L9Y1GzN2UZVNPjG9VAWpoaMDYsWPNHlRKRLbBQSZF74Cm2+ErxQ1DHc75mgY8snoP8ipqEOiuwsdT4uCnVoodi9qJRCLB4tFRUDpKsTvvHDbuKxQ70nVrVYOZOHEiNm7c2NZZiMhC/roeEFFb0dQ24pHVe3C8tBq+bgpseDwOQR5OYseidtbZywlP39UDAPDSt0dRqq0XOdH1adUcIIPBgNdeew3btm1DVFTUZZOglyxZ0ibhiKh9RPNOMGpjVfWNmLBmL7KLtfB2kePjKTfZ1HwQujFJg7viq8yzyDyjwfNbj2DVo/2tfpHLVhWgrKws9O3bFwBw5MgRs89Z+wkT0Z8jQNlntWg0GLkgHd2Q2gY9Jn+4D5mFlXB3csT6KXHo1slF7FhkQTKpBK/eH4W739qF77NL8d2REgzv4y92rKtqVQH66aef2joHEVlQiJczXJUOqKrX41hJFSID1WJHIhtV32jAlLX7se/0BbgqHbD+sThE+LmJHYtEEOHnhn/+Xxje+jEX87/4A4PCvODuZL13/vGffUR2SCqVcB4Q3TCd3oCp6w/gt5Pn4CyXYe3kgSzTdm767d3QrZMLKqp1+Pc3R8WOc1UsQER2qmlF6KyiSlFzkG1qNBgxc0MGdhwrh9JRig8mxaJfZw+xY5HIFA4yvDomChIJ8PmBM/jlRLnYka6IBYjITkVfGgHK5IrQ1EIGo4DkzzLxfXYp5A5SrJ4Qi7hQL7FjkZXo38UDE+NDAAApm7NQo9OLG+gKWICI7FTTCNCx0irUNxrEDUM2w2gUMOfzw/gq8ywcZRK880g/3BzuLXYssjLPJPZAoLsKZy7U4Y3vj4sdp1ksQER2yl+thLeLHAajgD/O2ubTnMmyBEFA6hdHsOngGcikEiwf1xe3R/iKHYuskLPCAS/f1wcAsOa3UzhYcEHkRJdjASKyUxKJxDQKxBWh6VoEQcCir7OxYU8BJBJgyYPRGBpp3bc5k7hu6+6D+/oFQhCAuZsOo0FvXU+MZwEismO8E4yuhyAIeG3bMaz59TQA4NUxURgVEyhuKLIJz4/oBS9nOY6XVuO/O3LFjmOGBYjIjnFFaLoeb6Xn4u0dJwEAL94biQcHBIuciGyFh7McC+/pDQBY+VMujpdWiZzoTyxARHasaQQor7wG2vpGkdOQNXpn50m8+cPFSaypI3ri0Zu6iJyIbM3dUf5I6OmLRsPFCfQGo3U8MZ4FiMiOebkoEOiuAgAc4WUw+ps1v57CK9/lALh4V8+UW0JFTkS2SCKR4N/3RsJV4YBDhZX48LfTYkcCwAJEZPeigy+tB8QCRH+xYU8BXvgqGwAw6/ZumD6km8iJyJb5qZVIGd4TAPCfbcdQeL5W5EQsQER2j3eC0d9tOnAG87ZmAQCeuDUUT93ZXeRE1BE8FBuMuK6eqGs04LktWRAEcS+FsQAR2TneCUZ/9VXmWTzzeSYEAZgY3wUpwyIgkUjEjkUdgFQqwStjoqBwkOKXExX4/MAZcfOI+tWJSHR9AtWQSICiyjqcq9aJHYdE9P0fJZi98RCMwsV/rS8Y2Zvlh9pUV29n04ji+j0Foo4COYj2lYnIKrgqHRHq7YyT5TU4fEaDIRGdxI5EIthxrAwzNmTAYBQwum8gXhrdB1Ipyw+1vSk3d4WDVIJHbuoiasG2ihGglStXIiQkBEqlEnFxcdi7d+8V921sbMSiRYsQFhYGpVKJ6OhopKWlme2zcOFCSCQSs1dERER7nwaRzYriekB27bfcCvxj3QE0GIwY0ccfr98fBRnLD7UTB5kUU24JhdJRJmoO0QvQxo0bkZycjAULFuDgwYOIjo5GYmIiysrKmt0/NTUVq1atwvLly5GdnY2pU6di9OjRyMjIMNuvd+/eKC4uNr127dplidMhskmcB2S/9p0+j8fW7odOb0RCz05Y+lAMHGSi/2oganei/ylfsmQJHn/8cSQlJaFXr15455134OTkhA8++KDZ/detW4fnnnsOw4cPR2hoKKZNm4bhw4fjjTfeMNvPwcEBfn5+ppe3N59WTHQlf70TTOw7M8hyDhVWImnNPtQ1GnBLuDdWPNwPjiw/ZCdE/ZPe0NCAAwcOICEhwbRNKpUiISEBu3fvbvY9Op0OSqXSbJtKpbpshOfEiRMICAhAaGgoxo8fj4KCgivm0Ol00Gq1Zi8ie9I7wA0OUgkqqhtwVlMvdhyygD/OajDh/T2o1ulxU6gn3n10gOiXJIgsSdQCVFFRAYPBAF9fX7Ptvr6+KCkpafY9iYmJWLJkCU6cOAGj0Yjt27dj8+bNKC4uNu0TFxeHDz/8EGlpaXj77bdx6tQp3HLLLaiqav4ZJIsXL4ZarTa9goP5nBuyL0pHGbr7ugIADhdWihuG2t3x0io8+v5eaOv16N/FA+9PjIVKzvJD9sXmxjqXLVuG8PBwREREQC6XY8aMGUhKSoJU+uepDBs2DA888ACioqKQmJiIb7/9FpWVlfjss8+aPWZKSgo0Go3pVVhYaKnTIbIaXBHaPuSVV+Ph9/bgfE0DooLUWJMUC2cFbwgm+yNqAfL29oZMJkNpaanZ9tLSUvj5+TX7Hh8fH2zduhU1NTXIz89HTk4OXFxcEBp65WfUuLu7o3v37sjNzW328wqFAm5ubmYvInvDFaE7vsLztRi/eg8qqnWI8HPFR5MHwk3pKHYsIlGIWoDkcjn69++P9PR00zaj0Yj09HTEx8df9b1KpRKBgYHQ6/XYtGkTRo0adcV9q6urcfLkSfj7+7dZdqKOpulOsKwzGhit5GnN1HbOVtZh3Hu/o1hTj26dXLB+ShzcneRixyISjeiXwJKTk/Hee+9h7dq1OHr0KKZNm4aamhokJSUBACZMmICUlBTT/nv27MHmzZuRl5eHX375BUOHDoXRaMScOXNM+zz99NPYuXMnTp8+jd9++w2jR4+GTCbDuHHjLH5+RLaiu68rFA5SVOn0OHWuRuw41IbKtPUYv3oPzlyoQ4iXEzZMiYO3i0LsWESiEv3C79ixY1FeXo758+ejpKQEMTExSEtLM02MLigoMJvfU19fj9TUVOTl5cHFxQXDhw/HunXr4O7ubtrnzJkzGDduHM6dOwcfHx/cfPPN+P333+Hj42Pp0yOyGY4yKXoHuOFgQSWyzmgQ5uMidiRqA+eqdRi/eg9OVdQg0F2Fjx+/CZ3clNd+I1EHJxG46MdltFot1Go1NBoN5wORXVn45R/48LfTSBocggUje4sdh25QZW0Dxr23B0eLtfBzU+Kzf8Sjs5eT2LGI2k1Lfn+LfgmMiKwHV4TuOLT1jZj4wV4cLdbC20WBjx+PY/kh+gsWICIyaboT7I+zGugNRnHDUKvV6PSYvGYfMs9o4OHkiI+nxPGSJtHfsAARkUmotzNcFQ6obzTieGm12HGoFeobDZiydj/251+Am9IB6x6LQw8/V7FjEVkdFiAiMpFKJYgMbLoMViluGGoxnd6AJ9YdwO68c3BROGDt5IGm/55EZI4FiIjMRHFFaJvUaDBi+scZ+Pl4OVSOMnwwKRZ9O3uIHYvIarEAEZGZaK4IbXP0BiNmf3oIPxwthdxBitUTB2BgV0+xYxFZNRYgIjLTdCfYsZIq1DcaRE5D12I0Cpjz+WF8k1UMR5kEqx7tj8HdvMWORWT1WICIyEyguwpeznLojQKyi7Vix6GrEAQB87ZmYXNGEWRSCZaP64chPTqJHYvIJrAAEZEZiUTy53pAhZXihqErEgQBL3yVjU/2FkIqAZaOjcHQyOYfIk1El2MBIqLLmJ4MX8SJ0NZIEAS88l0OPvztNADgtfujMTI6QNxQRDaGBYiILhMdzBWhrdmbP5zAqp/zAAAvjY7E/f2DRE5EZHtYgIjoMn0C3QEAJ8urUa3TixuGzPx3Ry7eSj8BAJh/dy+Mj+siciIi28QCRESX8XFVIECthCAAWRwFshrv7zqF19KOAQCeHRqByTd3FTkRke1iASKiZkVxPSCrsv73fLz4dTYA4Mk7wjHt/8JETkRk21iAiKhZUZwHZDX+t78QqVuPAAD+cVsoZieEi5yIyPaxABFRs5pWhM7kCJCovsw8i2c3HQYATBoUgrlDIyCRSERORWT7WICIqFlND9E8c6EO56p1IqexT2lHSvDUxkMwCsC4gZ2xYGQvlh+iNsICRETNUqscEertDIDrAYnhp5wyzPzkIAxGAff1C8RL90ay/BC1IRYgIrqiP1eEZgGypF0nKvCP9QfQaBAwIsofr42JglTK8kPUlliAiOiKeCeY5e09dR5TPtqHBr0Rd/byxdKxMXCQ8Uc1UVvj3yoiuiLTitBFGgiCIHKaju9gwQUkrdmL+kYjbuvugxUP94Ujyw9Ru+DfLCK6ol7+asikEpRX6VCirRc7Tod2pEiDiR/sRU2DAYPCvLDq0f5QOMjEjkXUYbEAEdEVqeQydPd1BQBkch5QuzlWUoVH39+Dqno9BnTxwOqJA6B0ZPkhak8sQER0VVGBTQsiVoobpIM6WV6N8at/x4XaRkQHu2NNUiyc5A5ixyLq8FiAiOiquCJ0+8k/V4OH3/sdFdUN6OXvho+SBsJV6Sh2LCK7wAJERFcV/Zc7wTgRuu0UVdbh4ff2oFSrQ3gnF6x7bCDUTiw/RJbCAkREV9XDzxVyBym09XqcPlcrdpwOoVRbj4ff+x1FlXXo6u2Mjx+Pg5eLQuxYRHaFBYiIrspRJkUvfzcAnAfUFiqqdXj4vd+Rf64WwZ4qbHg8Dp1clWLHIrI7LEBEdE3Rl1aE5p1gN0ZvMGLa+gM4WV4Df7USG6bcBH+1SuxYRHaJBYiIrokrQreN5T/mYt/pC3BROGD9lDgEezqJHYnIbrEAEdE1Na0IfeSsBnqDUeQ0tmnvqfNY/uMJAMBLoyMR5uMiciIi+2YVBWjlypUICQmBUqlEXFwc9u7de8V9GxsbsWjRIoSFhUGpVCI6OhppaWlX3P+VV16BRCLB7Nmz2yE5kX0I9XaBi8IB9Y1G5JZXix3H5mhqGzH70wwYBeC+foEYFRModiQiuyd6Adq4cSOSk5OxYMECHDx4ENHR0UhMTERZWVmz+6empmLVqlVYvnw5srOzMXXqVIwePRoZGRmX7btv3z6sWrUKUVFR7X0aRB2aVCpBZOClidCcB9QigiAgZcthnNXUI8TLCYtGRYodiYhgBQVoyZIlePzxx5GUlIRevXrhnXfegZOTEz744INm91+3bh2ee+45DB8+HKGhoZg2bRqGDx+ON954w2y/6upqjB8/Hu+99x48PDwscSpEHVrTekCZnAfUIhv3FeLbrBI4SCVY9lBfuCi4yjORNRC1ADU0NODAgQNISEgwbZNKpUhISMDu3bubfY9Op4NSaX7LqEqlwq5du8y2TZ8+HSNGjDA7NhG1Xp8grgjdUrllVVj41R8AgKcTeyA62F3cQERkIuo/RSoqKmAwGODr62u23dfXFzk5Oc2+JzExEUuWLMGtt96KsLAwpKenY/PmzTAYDKZ9Pv30Uxw8eBD79u27rhw6nQ46nc70sVarbcXZEHVsTSNAOSVa6PQGPqn8GuobDZj5ySHUNxpxczdvPHFLqNiRiOgvRL8E1lLLli1DeHg4IiIiIJfLMWPGDCQlJUEqvXgqhYWFePLJJ/Hxxx9fNlJ0JYsXL4ZarTa9goOD2/MUiGxSkIcKHk6OaDQIOFpcJXYcq/dqWg6OFmvh6SzHkgejIZVKxI5ERH8hagHy9vaGTCZDaWmp2fbS0lL4+fk1+x4fHx9s3boVNTU1yM/PR05ODlxcXBAaevFfVwcOHEBZWRn69esHBwcHODg4YOfOnXjrrbfg4OBgNlLUJCUlBRqNxvQqLCxs+5MlsnESiYTrAV2nn3LKsObX0wCA/zwQhU5uXOmZyNqIWoDkcjn69++P9PR00zaj0Yj09HTEx8df9b1KpRKBgYHQ6/XYtGkTRo0aBQC44447kJWVhUOHDpleAwYMwPjx43Ho0CHIZJcP2ysUCri5uZm9iOhyXBH62sq09Xj6f5kAgEmDQnB7hO813kFEYhD9doTk5GRMnDgRAwYMwMCBA7F06VLU1NQgKSkJADBhwgQEBgZi8eLFAIA9e/agqKgIMTExKCoqwsKFC2E0GjFnzhwAgKurKyIjzW8zdXZ2hpeX12XbiahlOAJ0dUajgH/9LxPnahrQ098Nc4dFiB2JiK5A9AI0duxYlJeXY/78+SgpKUFMTAzS0tJME6MLCgpM83sAoL6+HqmpqcjLy4OLiwuGDx+OdevWwd3dXaQzILIfUZdWhM4tr0a1Ts9buv9m9a48/HKiAkpHKZaPi4HSkRPFiayVRBAEQewQ1kar1UKtVkOj0fByGNHfxC9OR7GmHp8+cRNuCvUSO47VOHymEmPe/g2NBgEvj+6Dh+M6ix2JyO605Pe3zd0FRkTiiro0DyiL6wGZVOv0mPVJBhoNAoZF+mHcQN5JSmTtWICIqEWiuCL0ZRZ88QdOn6tFgFqJV+6LgkTCW96JrB0LEBG1SLRpIjRHgADgi0NF2HTwDKQSYOlDfaF2chQ7EhFdBxYgImqRPoEXL4EVnK/FhZoGkdOIq/B8LVK3HAEAzLg9HAO7eoqciIiuFwsQEbWI2skRIV5OAIDDRfY7CtRoMGLWpxmo0ukxoIsHZt3eTexIRNQCLEBE1GKm9YAKK0XNIaZlP5xARkElXJUOWPpQDBxk/HFKZEv4N5aIWqzpTrBMO50HtPvkOazckQsAWHxfHwR5OImciIhaigWIiFosOtgdgH2uCH2hpgFPbTwEQQDGDgjG3VEBYkciolZgASKiFusd4AapBCir0qFEUy92HIsRBAFzNh1GibYeoT7OWHBPL7EjEVErsQARUYs5yR3Q3dcVgH2tB7R+TwG2Z5dCLpPirYf6wknOR4EQ2SoWICJqlaZ5QPZyGexYSRX+/XU2AGDO0B6IvLQcABHZJhYgImqVKDtaELG+0YBZn2RApzfitu4+mDy4q9iRiOgGsQARUav8dUXojv5M5Ze/PYpjpVXwdlHgPw9EQyrloy6IbB0LEBG1Sg8/V8hlUmjqGlFwvlbsOO1me3YpPtqdDwB448Fo+LgqRE5ERG2BBYiIWkXuIEVP/6aJ0B3zMliJph5zPs8EADx+S1fc1t1H5ERE1FZYgIio1TryitAGo4CnNh7ChdpGRAa64ZnECLEjEVEbYgEiolb7806wjjcC9M7Ok9iddw5Ochneeqgv5A78cUnUkfBvNBG1WtOK0EfOamAwdpyJ0BkFF7Bk+3EAwMJ7eiPUx0XkRETU1liAiKjVwnxc4CSXobbBgNyyarHjtImq+kbM+jQDBqOAu6P88UD/ILEjEVE7YAEiolaTSSWmBQE7yorQz289gsLzdQh0V+Gl0X0gkfCWd6KOiAWIiG5IdAdaEXrzwTPYeugsZFIJ3hoXA7XKUexIRNROWICI6IZ0lBWhT1fU4PmtRwAAs+8IR/8uniInIqL2xAJERDekaUXoo8Va6PQGccO0UoPeiFmfZqCmwYC4rp7455BuYkcionbGAkRENyTYUwUPJ0c0GgQcK6kSO06rvLH9GA6f0UCtcsSbY2Mg46MuiDo8FiAiuiESiQR9Lo0C2eKK0LtOVGDVzjwAwKtjohDgrhI5ERFZAgsQEd2wqEt3gtnaitDnqnV46rNDAICH4zpjaKSfuIGIyGJYgIjohtniitCCIOCZzw+jvEqH8E4ueH5EL7EjEZEFsQAR0Q1rWhH6RFkVahv04oa5Tmt/O40fc8ogd5DirXF9oZLLxI5ERBbEAkREN8zXTQlfNwWMAnCkSCt2nGvKPqvFy9/lAADmDe+Jnv5uIiciIktjASKiNvHnekCVoua4lroGA2Z+chANeiMSenbChPguYkciIhGwABFRm2haEdra7wRb9HU2TpbXoJOrAq/dH81HXRDZKRYgImoTtjAClHakGJ/sLYBEArw5NgaeznKxIxGRSKyiAK1cuRIhISFQKpWIi4vD3r17r7hvY2MjFi1ahLCwMCiVSkRHRyMtLc1sn7fffhtRUVFwc3ODm5sb4uPj8d1337X3aRDZtaY7wfLP1aKytkHkNJc7W1mHZzdlAQD+cWsYBnfzFjkREYlJ9AK0ceNGJCcnY8GCBTh48CCio6ORmJiIsrKyZvdPTU3FqlWrsHz5cmRnZ2Pq1KkYPXo0MjIyTPsEBQXhlVdewYEDB7B//37cfvvtGDVqFP744w9LnRaR3XF3kqOLlxMA67sd3mAUMHvjIWjqGhEdpMa/7uoudiQiEplEEARBzABxcXGIjY3FihUrAABGoxHBwcGYOXMm5s6de9n+AQEBmDdvHqZPn27aNmbMGKhUKqxfv/6KX8fT0xOvv/46HnvssWtm0mq1UKvV0Gg0cHPj3SFE12vmJxn4KvMsnknsgelW9Dytt9JPYMn243CWy/Dtk7egi5ez2JGIqB205Pe3qCNADQ0NOHDgABISEkzbpFIpEhISsHv37mbfo9PpoFQqzbapVCrs2rWr2f0NBgM+/fRT1NTUID4+/orH1Gq1Zi8iajnTRGgrWhH6QP55LEs/AQD49+hIlh8iAiByAaqoqIDBYICvr6/Zdl9fX5SUlDT7nsTERCxZsgQnTpyA0WjE9u3bsXnzZhQXF5vtl5WVBRcXFygUCkydOhVbtmxBr17Nr/S6ePFiqNVq0ys4OLhtTpDIzvQJtK4VoTV1jZj1ySEYjAJG9w3E6L5BYkciIish+hygllq2bBnCw8MREREBuVyOGTNmICkpCVKp+an06NEDhw4dwp49ezBt2jRMnDgR2dnZzR4zJSUFGo3G9CosLLTEqRB1OJGBakglQIm2HmXaelGzCIKA57ZkoaiyDp09nbBoVG9R8xCRdRG1AHl7e0Mmk6G0tNRse2lpKfz8mn8ooY+PD7Zu3Yqamhrk5+cjJycHLi4uCA0NNdtPLpejW7du6N+/PxYvXozo6GgsW7as2WMqFArTHWNNLyJqOWeFA7p1cgEg/npA/9t/Bt8cLoaDVIK3xvWFq9JR1DxEZF1ELUByuRz9+/dHenq6aZvRaER6evoV5+s0USqVCAwMhF6vx6ZNmzBq1Kir7m80GqHT6dokNxFdmTWsB3SyvBoLvrx412fyXd0Rc+lZZURETRzEDpCcnIyJEydiwIABGDhwIJYuXYqamhokJSUBACZMmIDAwEAsXrwYALBnzx4UFRUhJiYGRUVFWLhwIYxGI+bMmWM6ZkpKCoYNG4bOnTujqqoKGzZswI4dO7Bt2zZRzpHInkQHqfH5gTOijQDp9AbM+iQDdY0GDArzwtRbw0TJQUTWTfQCNHbsWJSXl2P+/PkoKSlBTEwM0tLSTBOjCwoKzOb31NfXIzU1FXl5eXBxccHw4cOxbt06uLu7m/YpKyvDhAkTUFxcDLVajaioKGzbtg133nmnpU+PyO78dQRIEASLP2ri9bRj+OOsFh5OjnhzbAykUj7qgoguJ/o6QNaI6wARtZ5Ob0Dkgm1oNAj4+Zkh6HxpcURL2HGsDJPW7AMArJ4wAAm9fK/xDiLqSGxmHSAi6ngUDjL09L/4gyfTgvOAyqt0ePp/mQCAifFdWH6I6KpYgIiozTU9F8xSE6GNRgH/+l8mKqobEOHnipThPS3ydYnIdrEAEVGb+3MekGUmQn/w6yn8fLwcCgcplo/rC6WjzCJfl4hsFwsQEbW56EsF6EiRBgZj+04zPFKkwatpOQCA5+/uhXBf13b9ekTUMbAAEVGb69bJBSpHGWoaDMgrr263r1Oj02PWJxloNAi4q5cvxsd1brevRUQdCwsQEbU5mVSCyMCmidDtdxnsha/+QF5FDfzclHh1TJTFb7knItvFAkRE7aK9V4T++vBZfLb/DCQSYOlDMfBwlrfL1yGijokFiIjaRdOdYO0xAlR4vhYpm7MAADOGdMNNoV5t/jWIqGNjASKidtE0EfroWS0a9MY2O67eYMSTn2agql6Pfp3d8eQd4W12bCKyHyxARNQuung5Qa1yRIPBiGMlVW123LfST+BgQSVcFQ5Y9lBfOMj4Y4yIWo4/OYioXUgkkr9cBqtsk2PuyTuHFT/lAgBeuq8Pgj0t95gNIupYWICIqN205YrQlbUNmL3xEIwCcH//INwTHXDDxyQi+8UCRETtpq1WhBYEAXM3ZaFYU4+u3s544Z7ebZCOiOwZCxARtZumidDHS6tQ12Bo9XE+2VuItD9K4CiT4K2H+sJZ4dBGCYnIXrEAEVG78VMr0clVAaMA/HG2daNAJ0qrsOjrPwAAcxIj0OfSZTUiohvBAkRE7arpMlhr1gOqbzRg5icZqG804pZwbzx2c9c2TkdE9ooFiIja1Y1MhH7luxzklFTB20WONx6MhlTKR10QUdtgASKidvVnAWrZCFD60VJ8+NtpAMDrD0Sjk6uyraMRkR1jASKidtV0CexURQ00dY3X9Z4ybT2e+fwwAGDy4K4Y0qNTe8UjIjvFAkRE7crTWY5gTxUAIOs6RoGMRgHJn2XifE0Devm74dlhPdo7IhHZIRYgImp3f06Errzmvu/+kodduRVQOcrw1ri+UDjI2jccEdklFiAianfR1zkROrOwEv/ZdgwAsPCeXujWyaW9oxGRnWIBIqJ2dz0rQlfr9Jj1aQb0RgEj+vjjwQHBFkpHRPaIBYiI2l1koBoSCVCsqUdZVX2z+8zfegT552oR6K7Cy/f1gUTCW96JqP2wABFRu3NROKCbz8XLWYcLLx8F2ppRhM0ZRZBKgGUPxUCtcrR0RCKyMyxARGQRpstgReYFqOBcLVK3HgEAzLojHANCPC0djYjsEAsQEVlEdPDlE6EbDUbM/DQD1To9YkM8MGNIN5HSEZG9YQEiIovoE/jnitCCIAAA3tx+HJmFlXBTOmDpQ33hIOOPJCKyDAexAxCRfejp7wYHqQTnaxpw5kIdCs/X4u2dJwEAr4yJQqC7SuSERGRP+M8tIrIIpaMMEf6uAICdx8vx1GeHIAjAuIHBGN7HX+R0RGRvWICIyGKaJkIv+iobpVodwnyc8fzdvcQNRUR2ySoK0MqVKxESEgKlUom4uDjs3bv3ivs2NjZi0aJFCAsLg1KpRHR0NNLS0sz2Wbx4MWJjY+Hq6opOnTrh3nvvxbFjx9r7NIjoGppWhG4wGCGXSbF8XD84yXklnogsT/QCtHHjRiQnJ2PBggU4ePAgoqOjkZiYiLKysmb3T01NxapVq7B8+XJkZ2dj6tSpGD16NDIyMkz77Ny5E9OnT8fvv/+O7du3o7GxEXfddRdqamosdVpE1IymESAAmDssAr0C3MQLQ0R2TSI03Y4hkri4OMTGxmLFihUAAKPRiODgYMycORNz5869bP+AgADMmzcP06dPN20bM2YMVCoV1q9f3+zXKC8vR6dOnbBz507ceuut18yk1WqhVquh0Wjg5sYf0ERtRRAEzNt6BE6OMswb0ZOrPRNRm2rJ729Rx54bGhpw4MABpKSkmLZJpVIkJCRg9+7dzb5Hp9NBqVSabVOpVNi1a9cVv45Gc3HhNU9PLrBGJCaJRIKXR/cROwYRkbiXwCoqKmAwGODr62u23dfXFyUlJc2+JzExEUuWLMGJEydgNBqxfft2bN68GcXFxc3ubzQaMXv2bAwePBiRkZHN7qPT6aDVas1eRERE1HGJPgeopZYtW4bw8HBERERALpdjxowZSEpKglTa/KlMnz4dR44cwaeffnrFYy5evBhqtdr0Cg7mU6iJiIg6MlELkLe3N2QyGUpLS822l5aWws/Pr9n3+Pj4YOvWraipqUF+fj5ycnLg4uKC0NDQy/adMWMGvv76a/z0008ICgq6Yo6UlBRoNBrTq7Cw8MZOjIiIiKyaqAVILpejf//+SE9PN20zGo1IT09HfHz8Vd+rVCoRGBgIvV6PTZs2YdSoUabPCYKAGTNmYMuWLfjxxx/RtWvXqx5LoVDAzc3N7EVEREQdl+gLcCQnJ2PixIkYMGAABg4ciKVLl6KmpgZJSUkAgAkTJiAwMBCLFy8GAOzZswdFRUWIiYlBUVERFi5cCKPRiDlz5piOOX36dGzYsAFffPEFXF1dTfOJ1Go1VCout09ERGTvRC9AY8eORXl5OebPn4+SkhLExMQgLS3NNDG6oKDAbH5PfX09UlNTkZeXBxcXFwwfPhzr1q2Du7u7aZ+3334bAPB///d/Zl9rzZo1mDRpUnufEhEREVk50dcBskZcB4iIiMj2tOT3t83dBUZERER0o1iAiIiIyO6wABEREZHdYQEiIiIiu8MCRERERHaHBYiIiIjsDgsQERER2R3RF0K0Rk1LI/Gp8ERERLaj6ff29SxxyALUjKqqKgDgU+GJiIhsUFVVFdRq9VX34UrQzTAajTh79ixcXV0hkUja9NharRbBwcEoLCy0y1Wm7f38AX4PeP72ff4Avwf2fv5A+30PBEFAVVUVAgICzB6j1RyOADVDKpUiKCioXb+GvT913t7PH+D3gOdv3+cP8Htg7+cPtM/34FojP004CZqIiIjsDgsQERER2R0WIAtTKBRYsGABFAqF2FFEYe/nD/B7wPO37/MH+D2w9/MHrON7wEnQREREZHc4AkRERER2hwWIiIiI7A4LEBEREdkdFiALWLx4MWJjY+Hq6opOnTrh3nvvxbFjx8SOZVFvv/02oqKiTGs+xMfH47vvvhM7lmheeeUVSCQSzJ49W+woFrNw4UJIJBKzV0REhNixLKqoqAiPPPIIvLy8oFKp0KdPH+zfv1/sWBYTEhJy2Z8BiUSC6dOnix3NIgwGA55//nl07doVKpUKYWFhePHFF6/rsQ0dRVVVFWbPno0uXbpApVJh0KBB2LdvnyhZuBCiBezcuRPTp09HbGws9Ho9nnvuOdx1113Izs6Gs7Oz2PEsIigoCK+88grCw8MhCALWrl2LUaNGISMjA7179xY7nkXt27cPq1atQlRUlNhRLK5379744YcfTB87ONjPj6ALFy5g8ODBGDJkCL777jv4+PjgxIkT8PDwEDuaxezbtw8Gg8H08ZEjR3DnnXfigQceEDGV5bz66qt4++23sXbtWvTu3Rv79+9HUlIS1Go1Zs2aJXY8i5gyZQqOHDmCdevWISAgAOvXr0dCQgKys7MRGBho2TACWVxZWZkAQNi5c6fYUUTl4eEhrF69WuwYFlVVVSWEh4cL27dvF2677TbhySefFDuSxSxYsECIjo4WO4Zonn32WeHmm28WO4ZVefLJJ4WwsDDBaDSKHcUiRowYIUyePNls23333SeMHz9epESWVVtbK8hkMuHrr782296vXz9h3rx5Fs/DS2Ai0Gg0AABPT0+Rk4jDYDDg008/RU1NDeLj48WOY1HTp0/HiBEjkJCQIHYUUZw4cQIBAQEIDQ3F+PHjUVBQIHYki/nyyy8xYMAAPPDAA+jUqRP69u2L9957T+xYomloaMD69esxefLkNn/morUaNGgQ0tPTcfz4cQBAZmYmdu3ahWHDhomczDL0ej0MBgOUSqXZdpVKhV27dlk+kMUrl50zGAzCiBEjhMGDB4sdxeIOHz4sODs7CzKZTFCr1cI333wjdiSL+uSTT4TIyEihrq5OEATB7kaAvv32W+Gzzz4TMjMzhbS0NCE+Pl7o3LmzoNVqxY5mEQqFQlAoFEJKSopw8OBBYdWqVYJSqRQ+/PBDsaOJYuPGjYJMJhOKiorEjmIxBoNBePbZZwWJRCI4ODgIEolEePnll8WOZVHx8fHCbbfdJhQVFQl6vV5Yt26dIJVKhe7du1s8CwuQhU2dOlXo0qWLUFhYKHYUi9PpdMKJEyeE/fv3C3PnzhW8vb2FP/74Q+xYFlFQUCB06tRJyMzMNG2ztwL0dxcuXBDc3Nzs5jKoo6OjEB8fb7Zt5syZwk033SRSInHdddddwt133y12DIv65JNPhKCgIOGTTz4RDh8+LHz00UeCp6enXZXg3Nxc4dZbbxUACDKZTIiNjRXGjx8vREREWDwLC5AFTZ8+XQgKChLy8vLEjmIV7rjjDuGJJ54QO4ZFbNmyxfQXvukFQJBIJIJMJhP0er3YEUUxYMAAYe7cuWLHsIjOnTsLjz32mNm2//73v0JAQIBIicRz+vRpQSqVClu3bhU7ikUFBQUJK1asMNv24osvCj169BApkXiqq6uFs2fPCoIgCA8++KAwfPhwi2fgHCALEAQBM2bMwJYtW/Djjz+ia9euYkeyCkajETqdTuwYFnHHHXcgKysLhw4dMr0GDBiA8ePH49ChQ5DJZGJHtLjq6mqcPHkS/v7+YkexiMGDB1+2/MXx48fRpUsXkRKJZ82aNejUqRNGjBghdhSLqq2thVRq/mtXJpPBaDSKlEg8zs7O8Pf3x4ULF7Bt2zaMGjXK4hns5x5UEU2fPh0bNmzAF198AVdXV5SUlAAA1Go1VCqVyOksIyUlBcOGDUPnzp1RVVWFDRs2YMeOHdi2bZvY0SzC1dUVkZGRZtucnZ3h5eV12faO6umnn8bIkSPRpUsXnD17FgsWLIBMJsO4cePEjmYRTz31FAYNGoSXX34ZDz74IPbu3Yt3330X7777rtjRLMpoNGLNmjWYOHGiXS2DAAAjR47ESy+9hM6dO6N3797IyMjAkiVLMHnyZLGjWcy2bdsgCAJ69OiB3NxcPPPMM4iIiEBSUpLlw1h8zMkOAWj2tWbNGrGjWczkyZOFLl26CHK5XPDx8RHuuOMO4fvvvxc7lqjsbQ7Q2LFjBX9/f0EulwuBgYHC2LFjhdzcXLFjWdRXX30lREZGCgqFQoiIiBDeffddsSNZ3LZt2wQAwrFjx8SOYnFarVZ48sknhc6dOwtKpVIIDQ0V5s2bJ+h0OrGjWczGjRuF0NBQQS6XC35+fsL06dOFyspKUbLwafBERERkdzgHiIiIiOwOCxARERHZHRYgIiIisjssQERERGR3WICIiIjI7rAAERERkd1hASIiIiK7wwJEREREdocFiIisXklJCe688044OzvD3d1d7DhE1AGwABGR1XvzzTdRXFyMQ4cO4fjx42123JCQECxdurTNjkdEtsO+nkRHRDbp5MmT6N+/P8LDw8WO0qyGhgbI5XKxYxBRC3AEiIgs4v/+7/8wa9YszJkzB56envDz88PChQuv+b6QkBBs2rQJH330ESQSCSZNmgQAqKysxJQpU+Dj4wM3NzfcfvvtyMzMNL3v5MmTGDVqFHx9feHi4oLY2Fj88MMPZnny8/Px1FNPQSKRQCKRAAAWLlyImJgYswxLly5FSEiI6eNJkybh3nvvxUsvvYSAgAD06NEDAFBYWIgHH3wQ7u7u8PT0xKhRo3D69GnT+3bs2IGBAweaLuUNHjwY+fn5LftGElGbYAEiIotZu3YtnJ2dsWfPHrz22mtYtGgRtm/fftX37Nu3D0OHDsWDDz6I4uJiLFu2DADwwAMPoKysDN999x0OHDiAfv364Y477sD58+cBANXV1Rg+fDjS09ORkZGBoUOHYuTIkSgoKAAAbN68GUFBQVi0aBGKi4tRXFzconNJT0/HsWPHsH37dnz99ddobGxEYmIiXF1d8csvv+DXX3+Fi4sLhg4dioaGBuj1etx777247bbbcPjwYezevRtPPPGEqXgRkWXxEhgRWUxUVBQWLFgAAAgPD8eKFSuQnp6OO++884rv8fHxgUKhgEqlgp+fHwBg165d2Lt3L8rKyqBQKAAA//nPf7B161Z8/vnneOKJJxAdHY3o6GjTcV588UVs2bIFX375JWbMmAFPT0/IZDK4urqajtsSzs7OWL16tenS1/r162E0GrF69WpTqVmzZg3c3d2xY8cODBgwABqNBnfffTfCwsIAAD179mzx1yWitsERICKymKioKLOP/f39UVZW1uLjZGZmorq6Gl5eXnBxcTG9Tp06hZMnTwK4OAL09NNPo2fPnnB3d4eLiwuOHj1qGgG6UX369DGb95OZmYnc3Fy4urqa8nh6eqK+vh4nT56Ep6cnJk2ahMTERIwcORLLli1r8agTEbUdjgARkcU4OjqafSyRSGA0Glt8nOrqavj7+2PHjh2Xfa7pNvmnn34a27dvx3/+8x9069YNKpUK999/PxoaGq56bKlUCkEQzLY1NjZetp+zs/Nlmfr374+PP/74sn19fHwAXBwRmjVrFtLS0rBx40akpqZi+/btuOmmm66aiYjaHgsQEdmcfv36oaSkBA4ODmaTk//q119/xaRJkzB69GgAFwvKXyckA4BcLofBYDDb5uPjg5KSEgiCYLqUdejQoevKtHHjRnTq1Alubm5X3K9v377o27cvUlJSEB8fjw0bNrAAEYmAl8CIyOYkJCQgPj4e9957L77//nucPn0av/32G+bNm4f9+/cDuDjHaPPmzTh06BAyMzPx8MMPXzbaFBISgp9//hlFRUWoqKgAcPHusPLycrz22ms4efIkVq5cie++++6amcaPHw9vb2+MGjUKv/zyC06dOoUdO3Zg1qxZOHPmDE6dOoWUlBTs3r0b+fn5+P7773HixAnOAyISCQsQEdkciUSCb7/9FrfeeiuSkpLQvXt3PPTQQ8jPz4evry8AYMmSJfDw8MCgQYMwcuRIJCYmol+/fmbHWbRoEU6fPo2wsDDTZaqePXviv//9L1auXIno6Gjs3bsXTz/99DUzOTk54eeff0bnzp1x3333oWfPnnjsscdQX18PNzc3ODk5IScnB2PGjEH37t3xxBNPYPr06fjHP/7R9t8gIromifD3i91EREREHRxHgIiIiMjusAARkag+/vhjs1vZ//rq3bu32PGIqIPiJTAiElVVVRVKS0ub/ZyjoyO6dOli4UREZA9YgIiIiMju8BIYERER2R0WICIiIrI7LEBERERkd1iAiIiIyO6wABEREZHdYQEiIiIiu8MCRERERHaHBYiIiIjszv8DZgkN4fc4/zsAAAAASUVORK5CYII=",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2920,13 +2778,13 @@
    "source": [
     "sns.lineplot(data=sim_df, \n",
     "             x='n_features',\n",
-    "             y='metric')#, hue='n_features')"
+    "             y='corr')#, hue='n_features')"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 168,
-   "id": "81eef14d",
+   "id": "7183e87f",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-04-11T04:08:49.331441Z",
@@ -2964,7 +2822,7 @@
   {
    "cell_type": "code",
    "execution_count": 174,
-   "id": "caf9471b",
+   "id": "5fa9b058",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-04-11T04:10:11.894810Z",
@@ -2989,7 +2847,7 @@
   {
    "cell_type": "code",
    "execution_count": 173,
-   "id": "ed2de38c",
+   "id": "87afe0c9",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-04-11T04:09:56.002651Z",
@@ -3015,7 +2873,7 @@
   {
    "cell_type": "code",
    "execution_count": 175,
-   "id": "48a2e99c",
+   "id": "09e9a387",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-04-11T04:10:14.250903Z",
@@ -3171,7 +3029,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "f578f871",
+   "id": "d5a96a45",
    "metadata": {},
    "outputs": [],
    "source": []