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zEPfc4v2lO8I|2gHfgBYVJ%5KYZ2~OYrd?TD1PsPZmnP{JqE9!9>NgVISjZ*bgv=Em?9NS&&>P*QMHJ#aLyj=3CHao6`_gu z40rF|9YahlQBml%@DUdYd4QD+i{dQoG~je!HcGT7gC+>}A;BRbGXSg|t60oKukPA{ zFhV><2w+%M4kP1=)IT>#gsYdcmaWiH$IqAaFB=G50?>B z0W3>S!&Y+f{V9?PyZLi54DTQkn*xAlYL}(LJ~RFi2xT4TA;BReLV~D%h$Gp+5rHi@ z!M5-q%e(rkCI}p`ufPl3gc#@`U8gZv0j)fsJIWP^S3ChFSq|}D896zeoBaGy;0cF< z_nriIUIU9`6V?ebyJsxQ#&N;M^QS?P!(Ot-UV(-tE2=@7H*g^qFOzKp_VY$P9v$y| zw+Vz}XONz7|E~Xc1edo_<$p(J5!r!U|Gx!uPv|;z&grT&NYSource code for cyclops.report.report

     _raise_if_not_dict_with_str_keys,
     create_metric_cards,
     empty,
+    get_histories,
     get_names,
     get_passed,
-    get_plots,
     get_slices,
     get_thresholds,
+    get_timestamps,
     get_trends,
     regex_replace,
     regex_search,
@@ -1316,8 +1317,10 @@ 
Source code for cyclops.report.report
         # write to file
         if synthetic_timestamp is not None:
             today = synthetic_timestamp
+            today_now = synthetic_timestamp
         else:
             today = dt_date.today().strftime("%Y-%m-%d")
+            today_now = dt_datetime.now().strftime("%Y-%m-%d %H:%M:%S")
 
         current_report_metrics: List[List[PerformanceMetric]] = []
         sweep_metrics(self._model_card, current_report_metrics)
@@ -1348,6 +1351,7 @@ 
Source code for cyclops.report.report
             # compare tests
             metrics, tooltips, slices, values, metric_cards = create_metric_cards(
                 current_report_metrics_set,
+                today_now,
                 latest_report_metric_cards_set,
             )
             self._log_metric_card_collection(
@@ -1365,11 +1369,12 @@ 
Source code for cyclops.report.report
             "sweep_tests": sweep_tests,
             "sweep_graphics": sweep_graphics,
             "get_slices": get_slices,
-            "get_plots": get_plots,
             "get_thresholds": get_thresholds,
             "get_trends": get_trends,
             "get_passed": get_passed,
             "get_names": get_names,
+            "get_histories": get_histories,
+            "get_timestamps": get_timestamps,
         }
         template.globals.update(func_dict)
 
diff --git a/api/_sources/tutorials/kaggle/heart_failure_prediction.ipynb.txt b/api/_sources/tutorials/kaggle/heart_failure_prediction.ipynb.txt
index 1a35c1186..d415fbe56 100644
--- a/api/_sources/tutorials/kaggle/heart_failure_prediction.ipynb.txt
+++ b/api/_sources/tutorials/kaggle/heart_failure_prediction.ipynb.txt
@@ -570,7 +570,7 @@
    "source": [
     "## Task Creation\n",
     "\n",
-    "We use Cyclops tasks to define our model's task (in this case, MortalityPrediction), train the model, make predictions, and evaluate performance. Cyclops task classes encapsulate the entire ML pipeline into a single, cohesive structure, making the process smooth and easy to manage."
+    "We use Cyclops tasks to define our model's task (in this case, heart failure prediction), train the model, make predictions, and evaluate performance. Cyclops task classes encapsulate the entire ML pipeline into a single, cohesive structure, making the process smooth and easy to manage."
    ]
   },
   {
@@ -581,7 +581,7 @@
    },
    "outputs": [],
    "source": [
-    "mortality_task = BinaryTabularClassificationTask(\n",
+    "heart_failure_prediction_task = BinaryTabularClassificationTask(\n",
     "    {model_name: model},\n",
     "    task_features=features_list,\n",
     "    task_target=\"outcome\",\n",
@@ -596,7 +596,7 @@
    },
    "outputs": [],
    "source": [
-    "mortality_task.list_models()"
+    "heart_failure_prediction_task.list_models()"
    ]
   },
   {
@@ -626,7 +626,7 @@
     "    \"method\": \"grid\",\n",
     "}\n",
     "\n",
-    "mortality_task.train(\n",
+    "heart_failure_prediction_task.train(\n",
     "    dataset[\"train\"],\n",
     "    model_name=model_name,\n",
     "    transforms=preprocessor,\n",
@@ -640,7 +640,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "model_params = mortality_task.list_models_params()[model_name]\n",
+    "model_params = heart_failure_prediction_task.list_models_params()[model_name]\n",
     "print(model_params)"
    ]
   },
@@ -679,7 +679,7 @@
    },
    "outputs": [],
    "source": [
-    "y_pred = mortality_task.predict(\n",
+    "y_pred = heart_failure_prediction_task.predict(\n",
     "    dataset[\"test\"],\n",
     "    model_name=model_name,\n",
     "    transforms=preprocessor,\n",
@@ -836,9 +836,9 @@
    },
    "outputs": [],
    "source": [
-    "results, dataset_with_preds = mortality_task.evaluate(\n",
-    "    dataset[\"test\"],\n",
-    "    metric_collection,\n",
+    "results, dataset_with_preds = heart_failure_prediction_task.evaluate(\n",
+    "    dataset=dataset[\"test\"],\n",
+    "    metrics=metric_collection,\n",
     "    model_names=model_name,\n",
     "    transforms=preprocessor,\n",
     "    prediction_column_prefix=\"preds\",\n",
@@ -849,6 +849,25 @@
     ")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results_female, _ = heart_failure_prediction_task.evaluate(\n",
+    "    dataset=dataset[\"test\"],\n",
+    "    metrics=MetricCollection(\n",
+    "        {\"BinaryAccuracy\": create_metric(metric_name=\"accuracy\", task=\"binary\")},\n",
+    "    ),\n",
+    "    model_names=model_name,\n",
+    "    transforms=preprocessor,\n",
+    "    prediction_column_prefix=\"preds\",\n",
+    "    slice_spec=SliceSpec([{\"Sex\": {\"value\": \"F\"}}], include_overall=False),\n",
+    "    batch_size=32,\n",
+    ")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -872,6 +891,30 @@
     "    model_name=model_name,\n",
     ")\n",
     "\n",
+    "results_female_flat = flatten_results_dict(\n",
+    "    results=results_female,\n",
+    "    model_name=model_name,\n",
+    ")\n",
+    "\n",
+    "for name, metric in results_female_flat.items():\n",
+    "    split, name = name.split(\"/\")  # noqa: PLW2901\n",
+    "    descriptions = {\n",
+    "        \"BinaryPrecision\": \"The proportion of predicted positive instances that are correctly predicted.\",\n",
+    "        \"BinaryRecall\": \"The proportion of actual positive instances that are correctly predicted. Also known as recall or true positive rate.\",\n",
+    "        \"BinaryAccuracy\": \"The proportion of all instances that are correctly predicted.\",\n",
+    "        \"BinaryAUROC\": \"The area under the receiver operating characteristic curve (AUROC) is a measure of the performance of a binary classification model.\",\n",
+    "        \"BinaryF1Score\": \"The harmonic mean of precision and recall.\",\n",
+    "    }\n",
+    "    report.log_quantitative_analysis(\n",
+    "        \"performance\",\n",
+    "        name=name,\n",
+    "        value=metric,\n",
+    "        description=descriptions[name],\n",
+    "        metric_slice=split,\n",
+    "        pass_fail_thresholds=0.7,\n",
+    "        pass_fail_threshold_fns=lambda x, threshold: bool(x >= threshold),\n",
+    "    )\n",
+    "\n",
     "for name, metric in results_flat.items():\n",
     "    split, name = name.split(\"/\")  # noqa: PLW2901\n",
     "    descriptions = {\n",
@@ -1174,9 +1217,20 @@
    },
    "outputs": [],
    "source": [
-    "report_path = report.export(output_filename=\"heart_failure_report_periodic.html\")\n",
+    "synthetic_timestamps = [\n",
+    "    \"2021-09-01\",\n",
+    "    \"2021-10-01\",\n",
+    "    \"2021-11-01\",\n",
+    "    \"2021-12-01\",\n",
+    "    \"2022-01-01\",\n",
+    "]\n",
+    "report._model_card.overview = None\n",
+    "report_path = report.export(\n",
+    "    output_filename=\"heart_failure_report_periodic.html\",\n",
+    "    synthetic_timestamp=synthetic_timestamps[0],\n",
+    ")\n",
     "shutil.copy(f\"{report_path}\", \".\")\n",
-    "for _ in range(5):\n",
+    "for i in range(4):\n",
     "    report._model_card.overview = None\n",
     "    report._model_card.quantitative_analysis = None\n",
     "    results_flat = flatten_results_dict(\n",
@@ -1185,6 +1239,25 @@
     "        model_name=model_name,\n",
     "    )\n",
     "\n",
+    "    for name, metric in results_female_flat.items():\n",
+    "        split, name = name.split(\"/\")  # noqa: PLW2901\n",
+    "        descriptions = {\n",
+    "            \"BinaryPrecision\": \"The proportion of predicted positive instances that are correctly predicted.\",\n",
+    "            \"BinaryRecall\": \"The proportion of actual positive instances that are correctly predicted. Also known as recall or true positive rate.\",\n",
+    "            \"BinaryAccuracy\": \"The proportion of all instances that are correctly predicted.\",\n",
+    "            \"BinaryAUROC\": \"The area under the receiver operating characteristic curve (AUROC) is a measure of the performance of a binary classification model.\",\n",
+    "            \"BinaryF1Score\": \"The harmonic mean of precision and recall.\",\n",
+    "        }\n",
+    "        report.log_quantitative_analysis(\n",
+    "            \"performance\",\n",
+    "            name=name,\n",
+    "            value=np.clip(metric + np.random.normal(0, 0.1), 0, 1),\n",
+    "            description=descriptions[name],\n",
+    "            metric_slice=split,\n",
+    "            pass_fail_thresholds=0.7,\n",
+    "            pass_fail_threshold_fns=lambda x, threshold: bool(x >= threshold),\n",
+    "        )\n",
+    "\n",
     "    for name, metric in results_flat.items():\n",
     "        split, name = name.split(\"/\")  # noqa: PLW2901\n",
     "        descriptions = {\n",
@@ -1203,8 +1276,12 @@
     "            pass_fail_thresholds=0.7,\n",
     "            pass_fail_threshold_fns=lambda x, threshold: bool(x >= threshold),\n",
     "        )\n",
-    "    report_path = report.export(output_filename=\"heart_failure_report_periodic.html\")\n",
-    "    shutil.copy(f\"{report_path}\", \".\")"
+    "    report_path = report.export(\n",
+    "        output_filename=\"heart_failure_report_periodic.html\",\n",
+    "        synthetic_timestamp=synthetic_timestamps[i + 1],\n",
+    "    )\n",
+    "    shutil.copy(f\"{report_path}\", \".\")\n",
+    "shutil.rmtree(\"./cyclops_reports\")"
    ]
   },
   {
diff --git a/api/searchindex.js b/api/searchindex.js
index f4beb9c2d..4296cf0dd 100644
--- a/api/searchindex.js
+++ b/api/searchindex.js
@@ -1 +1 @@
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"Import-Libraries-and-Load-NIHCXR-Dataset"]], "Example 1. Generate Source/Target Dataset for Experiments (1-2)": [[133, "Example-1.-Generate-Source/Target-Dataset-for-Experiments-(1-2)"]], "Example 2. Sensitivity test experiment with 3 dimensionality reduction techniques": [[133, "Example-2.-Sensitivity-test-experiment-with-3-dimensionality-reduction-techniques"]], "Example 3. Sensitivity test experiment with models trained on different datasets": [[133, "Example-3.-Sensitivity-test-experiment-with-models-trained-on-different-datasets"]], "Example 4. Sensitivity test experiment with different clinical shifts": [[133, "Example-4.-Sensitivity-test-experiment-with-different-clinical-shifts"]], "Example 5. Rolling window experiment with synthetic timestamps using biweekly window": [[133, "Example-5.-Rolling-window-experiment-with-synthetic-timestamps-using-biweekly-window"]], "Prolonged Length of Stay Prediction": [[134, "Prolonged-Length-of-Stay-Prediction"]], "Data Querying": [[134, "Data-Querying"]], "Compute length of stay (labels)": [[134, "Compute-length-of-stay-(labels)"]], "Data Inspection and Preprocessing": [[134, "Data-Inspection-and-Preprocessing"]], "Drop NaNs based on the NAN_THRESHOLD": [[134, "Drop-NaNs-based-on-the-NAN_THRESHOLD"]], "Length of stay distribution": [[134, "Length-of-stay-distribution"]], "Gender distribution": [[134, "Gender-distribution"]], "monitor API": [[135, "monitor-api"]], "Example use cases": [[136, "example-use-cases"]], "Tabular data": [[136, "tabular-data"]], "Kaggle Heart Failure Prediction": [[136, "kaggle-heart-failure-prediction"]], "Synthea Prolonged Length of Stay Prediction": [[136, "synthea-prolonged-length-of-stay-prediction"]], "Image 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"Heart-Failure-Prediction"]], "Import Libraries": [[131, "Import-Libraries"], [132, "Import-Libraries"], [134, "Import-Libraries"]], "Constants": [[131, "Constants"], [134, "Constants"]], "Data Loading": [[131, "Data-Loading"]], "Sex values": [[131, "Sex-values"]], "Age distribution": [[131, "Age-distribution"], [134, "Age-distribution"]], "Outcome distribution": [[131, "Outcome-distribution"], [134, "Outcome-distribution"]], "Identifying feature types": [[131, "Identifying-feature-types"], [134, "Identifying-feature-types"]], "Creating data preprocessors": [[131, "Creating-data-preprocessors"], [134, "Creating-data-preprocessors"]], "Creating Hugging Face Dataset": [[131, "Creating-Hugging-Face-Dataset"], [134, "Creating-Hugging-Face-Dataset"]], "Model Creation": [[131, "Model-Creation"], [132, "Model-Creation"], [134, "Model-Creation"]], "Task Creation": [[131, "Task-Creation"], [134, "Task-Creation"]], "Training": [[131, "Training"], [134, "Training"]], "Prediction": [[131, 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Dataset": [[133, "Import-Libraries-and-Load-NIHCXR-Dataset"]], "Example 1. Generate Source/Target Dataset for Experiments (1-2)": [[133, "Example-1.-Generate-Source/Target-Dataset-for-Experiments-(1-2)"]], "Example 2. Sensitivity test experiment with 3 dimensionality reduction techniques": [[133, "Example-2.-Sensitivity-test-experiment-with-3-dimensionality-reduction-techniques"]], "Example 3. Sensitivity test experiment with models trained on different datasets": [[133, "Example-3.-Sensitivity-test-experiment-with-models-trained-on-different-datasets"]], "Example 4. Sensitivity test experiment with different clinical shifts": [[133, "Example-4.-Sensitivity-test-experiment-with-different-clinical-shifts"]], "Example 5. Rolling window experiment with synthetic timestamps using biweekly window": [[133, "Example-5.-Rolling-window-experiment-with-synthetic-timestamps-using-biweekly-window"]], "Prolonged Length of Stay Prediction": [[134, "Prolonged-Length-of-Stay-Prediction"]], "Data Querying": [[134, "Data-Querying"]], "Compute length of stay (labels)": [[134, "Compute-length-of-stay-(labels)"]], "Data Inspection and Preprocessing": [[134, "Data-Inspection-and-Preprocessing"]], "Drop NaNs based on the NAN_THRESHOLD": [[134, "Drop-NaNs-based-on-the-NAN_THRESHOLD"]], "Length of stay distribution": [[134, "Length-of-stay-distribution"]], "Gender distribution": [[134, "Gender-distribution"]], "monitor API": [[135, "monitor-api"]], "Example use cases": [[136, "example-use-cases"]], "Tabular data": [[136, "tabular-data"]], "Kaggle Heart Failure Prediction": [[136, "kaggle-heart-failure-prediction"]], "Synthea Prolonged Length of Stay Prediction": [[136, "synthea-prolonged-length-of-stay-prediction"]], "Image 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\ No newline at end of file
diff --git a/api/tutorials/kaggle/heart_failure_prediction.html b/api/tutorials/kaggle/heart_failure_prediction.html
index 20b756cd1..00eea26e2 100644
--- a/api/tutorials/kaggle/heart_failure_prediction.html
+++ b/api/tutorials/kaggle/heart_failure_prediction.html
@@ -484,7 +484,7 @@ 
Import Libraries
 
-/home/amritk/.cache/pypoetry/virtualenvs/pycyclops-mhx6UJW0-py3.10/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
+/home/amritk/.cache/pypoetry/virtualenvs/pycyclops-wIzUAwxh-py3.10/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
   from .autonotebook import tqdm as notebook_tqdm
 

 

@@ -556,7 +556,7 @@ 
Data Loading
 
-2023-11-20 15:32:06,461 INFO cyclops.utils.file - Loading DataFrame from ./data/heart.csv
+2023-11-21 10:24:43,545 INFO cyclops.utils.file - Loading DataFrame from ./data/heart.csv
@@ -638,9 +638,9 @@

Sex values

-
-
[40]:
+
[41]:
 

 

 
# Extracting the overall classification metric values.
@@ -1483,7 +1536,7 @@ 
Evaluation
-
[41]:
+
[42]:
 

 

 
# Plotting the overall classification metric values.
@@ -1504,9 +1557,9 @@ 
Evaluation
 

 

-
                            
                    

 

 

-
[42]:
+
[43]:
 

 

 
# Extracting the metric values for all the slices.
@@ -1547,7 +1600,7 @@ 
Evaluation
-
[43]:
+
[44]:
 

 

 
# Plotting the metric values for all the slices.
@@ -1565,9 +1618,9 @@ 
Evaluation
 

 

-
                            
                    

 

 

-
[44]:
+
[45]:
 

 

 
# Reformating the fairness metrics
@@ -1606,7 +1659,7 @@ 
Evaluation
-
[45]:
+
[46]:
 

 

 
# Plotting the fairness metrics
@@ -1627,9 +1680,9 @@ 
Evaluation
 

 

-
                            
                    

-  
-

-
-      
+      

+      

     

   

 

@@ -585,7 +644,7 @@ 
A quick glance of your most import
     
     

       
-        0.91
+        0.9
       
       
         
@@ -595,15 +654,8 @@ 
A quick glance of your most import
       
         0.7
minimum
threshold
       
-      
-        
-

-  
-    
                            
                    

-  
-

-
-      
+      

+      

     

   

 

@@ -625,7 +677,7 @@ 
A quick glance of your most import
     
     

       
-        0.71
+        0.79
       
       
         
@@ -635,15 +687,8 @@ 
A quick glance of your most import
       
         0.7
minimum
threshold
       
-      
-        
-

-  
-    
                            
                    

-  
-

-
-      
+      

+      

     

   

 

@@ -665,7 +710,7 @@ 
A quick glance of your most import
     
     

       
-        0.74
+        0.82
       
       
         
@@ -675,15 +720,8 @@ 
A quick glance of your most import
       
         0.7
minimum
threshold
       
-      
-        
-

-  
-    
                            
                    

-  
-

-
-      
+      

+      

     

   

 

@@ -705,7 +743,7 @@ 
A quick glance of your most import
     
     

       
-        0.84
+        1.0
       
       
         
@@ -715,15 +753,8 @@ 
A quick glance of your most import
       
         0.7
minimum
threshold
       
-      
-        
-

-  
-    
                            
                    

-  
-

-
-      
+      

+      

     

   

 

@@ -789,6 +820,10 @@ 
A quick glance of your most import
     
       
     
+
+    
+      
+    
 
     
       
@@ -796,13 +831,23 @@ 
A quick glance of your most import
 
 
   
-

+

   
How is your model doing over time?


   
See how your model is performing over several metrics and subgroups over time.

+  
+  

+    
Multi-plot Selection:

+    

+      
+      
+      
+      
+    

+  

   

     

       
Metrics

-      

+      

           
           
             

@@ -865,9 +910,21 @@ 
Metrics

       

     

     
+      

+        
Sex

+        

+          
+          
+          
+            
+            
+          
+        

+      

+    
       

         
Age

-        

+        

           
           
           
@@ -882,141 +939,7 @@ 
Age

     
   

   

-    

-      
-
-    

+    

   

 

 
@@ -1319,23 +1242,23 @@ 
Sensitive Data

   
     
     

-      
Reference

+      
Citation

       
    
       
 
    
   
+  
  • 
     
-    
  • 
       
-        
-          
-            https://www.kaggle.com/datasets/fedesoriano/heart-failure-prediction
-          
-          
    
-        
+        @misc{fedesoriano,
+  title={Heart Failure Prediction Dataset.},
+  author={Fedesoriano, F},
+  year={2021},
+  publisher={Kaggle}
+}
       
-    
    • 
     
+  
   
 
    
 
@@ -1347,23 +1270,23 @@ 
    Reference
    
   
     
     
    
-      
    Citation
    
+      
    Reference
    
       
      
       
 
 
@@ -1422,7 +1345,7 @@ 
      Graphics
      
     
 
      
   
-    
                                  
                          
      
+    
                                  
                          
      
   
 
      
 
@@ -1430,7 +1353,7 @@ 
      Graphics
      
     
 
      
   
-    
                                  
                          
      
+    
                                  
                          
      
   
 
      
 
@@ -1438,7 +1361,7 @@ 
      Graphics
      
     
 
      
   
-    
                                  
                          
      
+    
                                  
                          
      
   
 
      
 
@@ -1482,6 +1405,8 @@ 
      Quantitative Analysis
      
       
         
       
+        
+      
         
       
         
@@ -1499,7 +1424,7 @@ 
      Quantitative Analysis
      
     
     
      
       
-        0.84
+        0.76
       
       
         
@@ -1509,15 +1434,8 @@ 
      Quantitative Analysis
      
       
         0.7
      minimum
      threshold      
             
-      
-        
-
      
-  
-    
                                  
                          
      
-  
-
      
-
-      
+      
      
+      
      
     
      
   
    
 

@@ -1539,7 +1457,7 @@ 
Quantitative Analysis

     
     

       
-        0.91
+        0.9
       
       
         
@@ -1549,15 +1467,8 @@ 
Quantitative Analysis

       
         0.7
minimum
threshold
       
-      
-        
-

-  
-    
                            
                    

-  
-

-
-      
+      

+      

     

   

 

@@ -1579,7 +1490,7 @@ 
Quantitative Analysis

     
     

       
-        0.71
+        0.79
       
       
         
@@ -1589,15 +1500,8 @@ 
Quantitative Analysis

       
         0.7
minimum
threshold
       
-      
-        
-

-  
-    
                            
                    

-  
-

-
-      
+      

+      

     

   

 

@@ -1619,7 +1523,7 @@ 
Quantitative Analysis

     
     

       
-        0.74
+        0.82
       
       
         
@@ -1629,15 +1533,8 @@ 
Quantitative Analysis

       
         0.7
minimum
threshold
       
-      
-        
-

-  
-    
                            
                    

-  
-

-
-      
+      

+      

     

   

 

@@ -1659,7 +1556,7 @@ 
Quantitative Analysis

     
     

       
-        0.84
+        1.0
       
       
         
@@ -1669,15 +1566,8 @@ 
Quantitative Analysis

       
         0.7
minimum
threshold
       
-      
-        
-

-  
-    
                            
                    

-  
-

-
-      
+      

+      

     

   

 

@@ -1724,7 +1614,7 @@ 
Graphics

     
 

   
-    
                            
                    

+    
                            
                    

   
 

 
@@ -1779,7 +1669,7 @@ 
Version

         
           
             
-              Date: 2023-11-20
+              Date: 2023-11-21
             
           
           

@@ -1997,8 +1887,8 @@ 
Model Parameters

   
     
     

-      
Class_weight

-      balanced
+      
L1_ratio

+      0.15
     

     
   
@@ -2006,8 +1896,8 @@ 
Class_weight

   
     
     

-      
Tol

-      0.001
+      
Average

+      False
     

     
   
@@ -2015,8 +1905,8 @@ 
Tol

   
     
     

-      
Verbose

-      0
+      
Epsilon

+      0.1
     

     
   
@@ -2024,8 +1914,8 @@ 
Verbose

   
     
     

-      
Validation_fraction

-      0.1
+      
Verbose

+      0
     

     
   
@@ -2033,8 +1923,8 @@ 
Validation_fraction

   
     
     

-      
L1_ratio

-      0.15
+      
Penalty

+      l2
     

     
   
@@ -2042,8 +1932,8 @@ 
L1_ratio

   
     
     

-      
Max_iter

-      1000
+      
Shuffle

+      True
     

     
   
@@ -2051,8 +1941,8 @@ 
Max_iter

   
     
     

-      
Average

-      False
+      
Class_weight

+      balanced
     

     
   
@@ -2060,8 +1950,8 @@ 
Average

   
     
     

-      
Alpha

-      0.001
+      
Random_state

+      123
     

     
   
@@ -2069,31 +1959,31 @@ 
Alpha

   
     
     

-      
Loss

-      log_loss
+      
Fit_intercept

+      True
     

     
   
 
   
     
+    

+      
Max_iter

+      1000
+    

     
   
 
   
     
-    

-      
Shuffle

-      True
-    

     
   
 
   
     
     

-      
Early_stopping

-      True
+      
Validation_fraction

+      0.1
     

     
   
@@ -2101,8 +1991,8 @@ 
Early_stopping

   
     
     

-      
Random_state

-      123
+      
Early_stopping

+      True
     

     
   
@@ -2110,8 +2000,8 @@ 
Random_state

   
     
     

-      
Power_t

-      0.5
+      
Eta0

+      0.01
     

     
   
@@ -2119,8 +2009,8 @@ 
Power_t

   
     
     

-      
Epsilon

-      0.1
+      
Learning_rate

+      adaptive
     

     
   
@@ -2137,8 +2027,8 @@ 
Warm_start

   
     
     

-      
Fit_intercept

-      True
+      
N_iter_no_change

+      5
     

     
   
@@ -2146,8 +2036,8 @@ 
Fit_intercept

   
     
     

-      
Eta0

-      0.01
+      
Power_t

+      0.5
     

     
   
@@ -2155,8 +2045,8 @@ 
Eta0

   
     
     

-      
Penalty

-      l2
+      
Tol

+      0.001
     

     
   
@@ -2164,8 +2054,8 @@ 
Penalty

   
     
     

-      
N_iter_no_change

-      5
+      
Loss

+      log_loss
     

     
   
@@ -2173,8 +2063,8 @@ 
N_iter_no_change

   
     
     

-      
Learning_rate

-      adaptive
+      
Alpha

+      0.001
     

     
   
@@ -2450,7 +2340,6 @@ 
Ethical Considerations

       return true;
     }
   }
-
   function setActiveButton() {
     const buttons = document.querySelectorAll('#contents li');
     const sections = document.querySelectorAll('.card');
@@ -2471,7 +2360,781 @@ 
Ethical Considerations

       }
     }
   }
-
   document.addEventListener('scroll', setActiveButton);
   setActiveButton();
+
+  function generate_model_card_plot() {
+    var model_card_plots = []
+    var overall_indices = [11, 12, 13, 14, 15]
+    var histories = JSON.parse("{\"0\": [\"0.8421052631578947\", \"0.9443366297070744\", \"0.6696966658997907\", \"0.7917884636345517\", \"1.0\"], \"1\": [\"0.796875\", \"0.8032692280474679\", \"0.8104086880694173\", \"0.7662504408321089\", \"0.733254320528702\"], \"2\": [\"0.8260869565217391\", \"0.8801965037090079\", \"0.9088616256065348\", \"0.9005887032719392\", \"0.7318415609625138\"], \"3\": [\"0.6785714285714286\", \"0.7061788871876555\", \"0.8234200856028526\", \"0.6689744509810135\", \"0.7620675719240091\"], \"4\": [\"0.7450980392156863\", \"0.8232575960858443\", \"0.5498675985686183\", \"0.8574174525837635\", \"0.6937614116110762\"], \"5\": [\"0.8819444444444444\", \"0.6529999183975839\", \"0.8503495058201004\", \"1.0\", \"0.9920904453877579\"], \"6\": [\"0.8623853211009175\", \"0.7451503351764498\", \"0.7575696615631983\", \"0.8128392007041036\", \"0.8669919022480744\"], \"7\": [\"0.8676470588235294\", \"0.8505926531332457\", \"0.7755402584280724\", \"0.7046570917647931\", \"0.8368618989726452\"], \"8\": [\"0.9076923076923077\", \"0.9027325794047122\", \"0.9145744539589293\", \"0.9795187743625171\", \"0.8281839998952537\"], \"9\": [\"0.8872180451127819\", \"0.8303960872133204\", \"0.844145250696786\", \"0.868645924442111\", \"0.7165689140230025\"], \"10\": [\"0.927972027972028\", \"0.9917891023297087\", \"0.924922111379513\", \"0.991110811175645\", \"0.8625594142253621\"], \"11\": [\"0.842391304347826\", \"0.7825740292592875\", \"0.9243205295960827\", \"0.9133802204641654\", \"0.7641960608195242\"], \"12\": [\"0.8686868686868687\", \"1.0\", \"0.9423804438863684\", \"0.889084104518579\", \"0.8975897083031747\"], \"13\": [\"0.8431372549019608\", \"0.7795144694830195\", \"0.9426669241322884\", \"0.6806287006654718\", \"0.785614301768915\"], \"14\": [\"0.8557213930348259\", \"0.9757521710384467\", \"0.9555559061064605\", \"0.9615911087123208\", \"0.8243079817735177\"], \"15\": [\"0.9152319464371114\", \"0.9113294244106782\", \"0.8458731419792791\", \"0.8605328254392286\", \"1.0\"]}");
+    var thresholds = JSON.parse("{\"0\": \"0.7\", \"1\": \"0.7\", \"2\": \"0.7\", \"3\": \"0.7\", \"4\": \"0.7\", \"5\": \"0.7\", \"6\": \"0.7\", \"7\": \"0.7\", \"8\": \"0.7\", \"9\": \"0.7\", \"10\": \"0.7\", \"11\": \"0.7\", \"12\": \"0.7\", \"13\": \"0.7\", \"14\": \"0.7\", \"15\": \"0.7\"}");
+    var timestamps = JSON.parse("{\"0\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"1\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"2\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"3\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"4\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"5\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"6\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"7\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"8\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"9\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"10\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"11\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"12\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"13\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"14\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"15\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"]}");
+
+    for (let i = 0; i < overall_indices.length; i++) {
+      var idx = overall_indices[i];
+      var model_card_plot = "model-card-plot-" + idx;
+      var threshold = thresholds[idx];
+      var history_data = [];
+      for (let i = 0; i < histories[idx].length; i++) {
+        history_data.push(parseFloat(histories[idx][i]));
+      }
+      var timestamp_data = [];
+      for (let i = 0; i < timestamps[idx].length; i++) {
+        timestamp_data.push(timestamps[idx][i]);
+      }
+
+      var model_card_fig = {
+        data: [
+          {
+            x: timestamp_data,
+            y: history_data,
+            mode: "lines+markers",
+            marker: { color: "rgb(31,111,235)" },
+            line: { color: "rgb(31,111,235)" },
+            showlegend: false,
+            type: "scatter",
+            name: ""
+          },
+          {
+            x: timestamp_data,
+            y: Array(history_data.length).fill(threshold),
+            mode: "lines",
+            line: { color: "black", dash: "dot" },
+            showlegend: false,
+            type: "scatter",
+            name: ""
+          }
+        ],
+        layout: {
+          paper_bgcolor: "rgba(0,0,0,0)",
+          plot_bgcolor: "rgba(0,0,0,0)",
+          xaxis: {
+            zeroline: false,
+            showticklabels: false,
+            showgrid: false
+          },
+          yaxis: {
+            gridcolor: "#ffffff"
+          },
+          margin: { l: 0, r: 0, t: 0, b: 0 },
+          height: 125,
+          width: 250
+        }
+      };
+      if (history.length > 0) {
+        Plotly.newPlot(model_card_plot, model_card_fig.data, model_card_fig.layout, {displayModeBar: false});
+      }
+    }
+  }
+  generate_model_card_plot();
+
+  const plot = document.getElementById('plot');
+  const inputs_all = document.querySelectorAll('#slice-selection input[type="radio"]');
+  const plot_selection = document.querySelectorAll('#plot-selection input[type="radio"]');
+  var selections = [null, null, null, null, null, null, null, null, null, null, null];
+  var plot_colors = [
+    "rgb(0, 115, 228)",
+    "rgb(31, 119, 180)",
+    "rgb(255, 127, 14)",
+    "rgb(44, 160, 44)",
+    "rgb(214, 39, 40)",
+    "rgb(148, 103, 189)",
+    "rgb(140, 86, 75)",
+    "rgb(227, 119, 194)",
+    "rgb(127, 127, 127)",
+    "rgb(188, 189, 34)",
+    "rgb(23, 190, 207)"
+  ];
+
+  function deletePlotSelection(plot_number) {
+    var plot_selection = document.querySelectorAll('#plot-selection input[type="radio"]');
+    var label_selection = document.querySelectorAll('#plot-selection label');
+    var label_slice_selection = document.querySelectorAll('#slice-selection label');
+    var button_plot_selection = document.querySelectorAll('#plot-selection button');
+
+    // set last plot to checked
+    // get plot_selection with name "Plot N" where N is plot_number
+    for (let i = 0; i < plot_selection.length; i++) {
+      var plot_name = "Plot " + (plot_number+1)
+      if (plot_selection[i].value === plot_name) {
+        plot_number = i;
+      }
+    }
+    plot_selection[plot_number].checked = false;
+    plot_selection[plot_number-1].checked = true;
+
+    // delete plot_selected and label
+    plot_selection[plot_number].remove();
+    label_selection[plot_number].remove();
+
+    selections[plot_number] = null;
+
+    // set selection to last plot
+    selection = selections[plot_number-1];
+    plot_color = plot_colors[plot_number-1];
+
+    // set current plot selection color to plot_color
+    const [r, g, b] = plot_color.match(/\d+/g);
+    const rgbaColor = `rgba(${r}, ${g}, ${b}, 0.2)`;
+    plot_selection[plot_number-1].style.backgroundColor = rgbaColor;
+    plot_selection[plot_number-1].style.border = "2px solid " + plot_color;
+    plot_selection[plot_number-1].style.color = plot_color;
+
+    // make visibility of delete button from last plot visible
+    if (button_plot_selection.length >= 2) {
+      button_plot_selection[button_plot_selection.length-2].style.visibility = "visible";
+    }
+
+    for (let i = 0; i < selection.length; i++) {
+      // use selection to set label_slice_selection background color
+      for (let j = 0; j < inputs_all.length; j++) {
+        if (inputs_all[j].name === selection[i].split(":")[0]) {
+          if (inputs_all[j].value == selection[i].split(":")[1]) {
+            inputs_all[j].checked = true;
+            label_slice_selection[j].style.backgroundColor = rgbaColor;
+            label_slice_selection[j].style.border = "2px solid " + plot_color;
+            label_slice_selection[j].style.color = plot_color;
+          }
+        }
+      }
+    }
+    updatePlot();
+  }
+
+  function updatePlotSelection() {
+    const inputs = document.querySelectorAll('#slice-selection input[type="radio"]:checked');
+    var plot_selection = document.querySelectorAll('#plot-selection input[type="radio"]');
+    var plot_selected = document.querySelectorAll('#plot-selection input[type="radio"]:checked')[0];
+    // get number from value in plot_selected "Plot 1" -> 1
+    var plot_number = parseInt(plot_selected.value.split(" ")[1]);
+    var label_selection = document.querySelectorAll('#plot-selection label');
+    var label_slice_selection = document.querySelectorAll('#slice-selection label');
+    var button_plot_selection = document.querySelectorAll('#plot-selection button');
+
+    // if plot_selected is "+" then add new radio button to plot_selection called "Plot N" where last plot is N-1 but keep "+" at end and set new radio button to checked for second last element
+    if (plot_selected.value === "+") {
+      // if 10 plots already exist, don't add new plot and gray out "+"
+      if (plot_selection.length === 11) {
+        plot_selected.checked = false;
+        label_selection[label_selection.length-1].style.color = "gray";
+        return;
+      }
+      // plot_name should be name of last plot + 1
+      if (plot_selection.length === 2) {
+        var plot_name = "Plot 2"
+      } else {
+        var plot_name = "Plot " + (parseInt(plot_selection[plot_selection.length - 2].value.split(" ")[1]) + 1);
+      }
+      var new_plot = document.createElement("input");
+      new_plot.type = "radio";
+      new_plot.id = plot_name;
+      new_plot.name = "plot";
+      new_plot.value = plot_name;
+      new_plot.checked = true;
+      var new_label = document.createElement("label");
+      new_label.htmlFor = plot_name;
+      new_label.innerHTML = plot_name;
+
+      // Parse plot_color to get r, g, b values
+      var plot_color = plot_colors[plot_selection.length]
+      const [r, g, b] = plot_color.match(/\d+/g);
+      const rgbaColor = `rgba(${r}, ${g}, ${b}, 0.2)`;
+      // set background color of new radio button to plot_color
+      new_label.style.backgroundColor = rgbaColor;
+      new_label.style.border = "2px solid " + plot_color;
+      new_label.style.color = plot_color;
+
+      // add button to delete plot
+      var delete_button = document.createElement("button");
+      delete_button.id = "button";
+      delete_button.innerHTML = "×";
+      delete_button.style.backgroundColor = "transparent";
+      delete_button.style.border = "none";
+      new_label.style.padding = "1.5px 0px";
+      new_label.style.paddingLeft = "10px";
+
+      new_label.appendChild(delete_button)
+
+      // make delete button from last plot invisible if not Plot 1
+      if (plot_selection.length > 2) {
+        button_plot_selection[button_plot_selection.length-1].style.visibility = "hidden";
+      }
+      // add on_click event to delete button and send plot number to deletePlotSelection
+      delete_button.onclick = function() {deletePlotSelection(plot_number)};
+
+      // insert new radio button and label before "+" radio button and after last radio button
+      plot_selected.insertAdjacentElement("beforebegin", new_plot);
+      plot_selected.insertAdjacentElement("beforebegin", new_label);
+
+      // Add event listener to new radio button
+      new_plot.addEventListener('change', updatePlotSelection);
+
+      // set plot_selected to new plot
+      var plot_selected = new_plot
+
+      for (let i = 0; i < label_selection.length-1; i++) {
+        plot_selection[i].checked = false;
+        label_selection[i].style.backgroundColor = "#ffffff";
+        label_selection[i].style.border = "2px solid #DADCE0";
+        label_selection[i].style.color = "#000000";
+        }
+
+      selections[parseInt(plot_selected.value.split(" ")[1]-1)] = selections[parseInt(plot_selected.value.split(" ")[1]-2)]
+      selection = selections[parseInt(plot_selected.value.split(" ")[1]-1)];
+      plot_color = plot_colors[parseInt(plot_selected.value.split(" ")[1])];
+
+      for (let i = 0; i < selection.length; i++) {
+        // use selection to set label_slice_selection background color
+        for (let j = 0; j < inputs_all.length; j++) {
+          if (inputs_all[j].name === selection[i].split(":")[0]) {
+            if (inputs_all[j].value == selection[i].split(":")[1]) {
+              const [r, g, b] = plot_color.match(/\d+/g);
+              const rgbaColor = `rgba(${r}, ${g}, ${b}, 0.2)`;
+              inputs_all[j].checked = true;
+              label_slice_selection[j].style.backgroundColor = rgbaColor;
+              label_slice_selection[j].style.border = "2px solid " + plot_color;
+              label_slice_selection[j].style.color = plot_color;
+            }
+            else {
+              inputs_all[j].checked = false;
+              label_slice_selection[j].style.backgroundColor = "#ffffff";
+              label_slice_selection[j].style.border = "2px solid #DADCE0";
+              label_slice_selection[j].style.color = "#000000";
+            }
+          }
+        }
+      }
+    } else {
+      for (let i = 0; i < plot_selection.length-1; i++) {
+        if (plot_selection[i].value !== plot_selected.value) {
+          plot_selection[i].checked = false;
+          label_selection[i].style.backgroundColor = "#ffffff";
+          label_selection[i].style.border = "2px solid #DADCE0";
+          label_selection[i].style.color = "#000000";
+        }
+        else {
+          var plot_color = plot_colors[i+1]
+          const [r, g, b] = plot_color.match(/\d+/g);
+          const rgbaColor = `rgba(${r}, ${g}, ${b}, 0.2)`;
+          plot_selected.checked = true;
+          label_selection[i].style.backgroundColor = rgbaColor;
+          label_selection[i].style.border = "2px solid " + plot_color;
+          label_selection[i].style.color = plot_color;
+        }
+      }
+      selection = selections[parseInt(plot_selected.value.split(" ")[1]-1)];
+      plot_color = plot_colors[parseInt(plot_selected.value.split(" ")[1])];
+      for (let i = 0; i < selection.length; i++) {
+        // use selection to set label_slice_selection background color
+        for (let j = 0; j < inputs_all.length; j++) {
+          if (inputs_all[j].name === selection[i].split(":")[0]) {
+            if (inputs_all[j].value == selection[i].split(":")[1]) {
+              inputs_all[j].checked = true;
+              const [r, g, b] = plot_color.match(/\d+/g);
+              const rgbaColor = `rgba(${r}, ${g}, ${b}, 0.2)`;
+              label_slice_selection[j].style.backgroundColor = rgbaColor;
+              label_slice_selection[j].style.border = "2px solid " + plot_color;
+              label_slice_selection[j].style.color = plot_color;
+            }
+            else {
+              inputs_all[j].checked = false;
+              label_slice_selection[j].style.backgroundColor = "#ffffff";
+              label_slice_selection[j].style.border = "2px solid #DADCE0";
+              label_slice_selection[j].style.color = "#000000";
+            }
+          }
+        }
+      }
+    }
+    var slices_all = JSON.parse("{\"0\": [\"metric:Accuracy\", \"Sex:F\", \"Age:overall_Age\"], \"1\": [\"metric:Accuracy\", \"Age:[30 - 50)\", \"Sex:overall_Sex\"], \"2\": [\"metric:Precision\", \"Age:[30 - 50)\", \"Sex:overall_Sex\"], \"3\": [\"metric:Recall\", \"Age:[30 - 50)\", \"Sex:overall_Sex\"], \"4\": [\"metric:F1 Score\", \"Age:[30 - 50)\", \"Sex:overall_Sex\"], \"5\": [\"metric:AUROC\", \"Age:[30 - 50)\", \"Sex:overall_Sex\"], \"6\": [\"metric:Accuracy\", \"Age:[50 - 70)\", \"Sex:overall_Sex\"], \"7\": [\"metric:Precision\", \"Age:[50 - 70)\", \"Sex:overall_Sex\"], \"8\": [\"metric:Recall\", \"Age:[50 - 70)\", \"Sex:overall_Sex\"], \"9\": [\"metric:F1 Score\", \"Age:[50 - 70)\", \"Sex:overall_Sex\"], \"10\": [\"metric:AUROC\", \"Age:[50 - 70)\", \"Sex:overall_Sex\"], \"11\": [\"metric:Accuracy\", \"Sex:overall_Sex\", \"Age:overall_Age\"], \"12\": [\"metric:Precision\", \"Sex:overall_Sex\", \"Age:overall_Age\"], \"13\": [\"metric:Recall\", \"Sex:overall_Sex\", \"Age:overall_Age\"], \"14\": [\"metric:F1 Score\", \"Sex:overall_Sex\", \"Age:overall_Age\"], \"15\": [\"metric:AUROC\", \"Sex:overall_Sex\", \"Age:overall_Age\"]}");
+    var histories_all = JSON.parse("{\"0\": [\"0.8421052631578947\", \"0.9443366297070744\", \"0.6696966658997907\", \"0.7917884636345517\", \"1.0\"], \"1\": [\"0.796875\", \"0.8032692280474679\", \"0.8104086880694173\", \"0.7662504408321089\", \"0.733254320528702\"], \"2\": [\"0.8260869565217391\", \"0.8801965037090079\", \"0.9088616256065348\", \"0.9005887032719392\", \"0.7318415609625138\"], \"3\": [\"0.6785714285714286\", \"0.7061788871876555\", \"0.8234200856028526\", \"0.6689744509810135\", \"0.7620675719240091\"], \"4\": [\"0.7450980392156863\", \"0.8232575960858443\", \"0.5498675985686183\", \"0.8574174525837635\", \"0.6937614116110762\"], \"5\": [\"0.8819444444444444\", \"0.6529999183975839\", \"0.8503495058201004\", \"1.0\", \"0.9920904453877579\"], \"6\": [\"0.8623853211009175\", \"0.7451503351764498\", \"0.7575696615631983\", \"0.8128392007041036\", \"0.8669919022480744\"], \"7\": [\"0.8676470588235294\", \"0.8505926531332457\", \"0.7755402584280724\", \"0.7046570917647931\", \"0.8368618989726452\"], \"8\": [\"0.9076923076923077\", \"0.9027325794047122\", \"0.9145744539589293\", \"0.9795187743625171\", \"0.8281839998952537\"], \"9\": [\"0.8872180451127819\", \"0.8303960872133204\", \"0.844145250696786\", \"0.868645924442111\", \"0.7165689140230025\"], \"10\": [\"0.927972027972028\", \"0.9917891023297087\", \"0.924922111379513\", \"0.991110811175645\", \"0.8625594142253621\"], \"11\": [\"0.842391304347826\", \"0.7825740292592875\", \"0.9243205295960827\", \"0.9133802204641654\", \"0.7641960608195242\"], \"12\": [\"0.8686868686868687\", \"1.0\", \"0.9423804438863684\", \"0.889084104518579\", \"0.8975897083031747\"], \"13\": [\"0.8431372549019608\", \"0.7795144694830195\", \"0.9426669241322884\", \"0.6806287006654718\", \"0.785614301768915\"], \"14\": [\"0.8557213930348259\", \"0.9757521710384467\", \"0.9555559061064605\", \"0.9615911087123208\", \"0.8243079817735177\"], \"15\": [\"0.9152319464371114\", \"0.9113294244106782\", \"0.8458731419792791\", \"0.8605328254392286\", \"1.0\"]}");
+    var thresholds_all = JSON.parse("{\"0\": \"0.7\", \"1\": \"0.7\", \"2\": \"0.7\", \"3\": \"0.7\", \"4\": \"0.7\", \"5\": \"0.7\", \"6\": \"0.7\", \"7\": \"0.7\", \"8\": \"0.7\", \"9\": \"0.7\", \"10\": \"0.7\", \"11\": \"0.7\", \"12\": \"0.7\", \"13\": \"0.7\", \"14\": \"0.7\", \"15\": \"0.7\"}");
+    var trends_all = JSON.parse("{\"0\": \"positive\", \"1\": \"negative\", \"2\": \"negative\", \"3\": \"positive\", \"4\": \"neutral\", \"5\": \"positive\", \"6\": \"neutral\", \"7\": \"negative\", \"8\": \"neutral\", \"9\": \"negative\", \"10\": \"negative\", \"11\": \"neutral\", \"12\": \"neutral\", \"13\": \"negative\", \"14\": \"neutral\", \"15\": \"positive\"}");
+    var passed_all = JSON.parse("{\"0\": true, \"1\": true, \"2\": true, \"3\": true, \"4\": false, \"5\": true, \"6\": true, \"7\": true, \"8\": true, \"9\": true, \"10\": true, \"11\": true, \"12\": true, \"13\": true, \"14\": true, \"15\": true}");
+    var names_all = JSON.parse("{\"0\": \"Accuracy\", \"1\": \"Accuracy\", \"2\": \"Precision\", \"3\": \"Recall\", \"4\": \"F1 Score\", \"5\": \"AUROC\", \"6\": \"Accuracy\", \"7\": \"Precision\", \"8\": \"Recall\", \"9\": \"F1 Score\", \"10\": \"AUROC\", \"11\": \"Accuracy\", \"12\": \"Precision\", \"13\": \"Recall\", \"14\": \"F1 Score\", \"15\": \"AUROC\"}");
+    var timestamps_all = JSON.parse("{\"0\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"1\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"2\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"3\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"4\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"5\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"6\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"7\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"8\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"9\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"10\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"11\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"12\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"13\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"14\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"15\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"]}");
+
+    var radioGroups = {};
+    var labelGroups = {};
+    for (let i = 0; i < inputs_all.length; i++) {
+      var input = inputs_all[i];
+      var label = label_slice_selection[i];
+      var groupName = input.name;
+      if (!radioGroups[groupName]) {
+        radioGroups[groupName] = [];
+        labelGroups[groupName] = [];
+      }
+      radioGroups[groupName].push(input);
+      labelGroups[groupName].push(label);
+    }
+
+    // use radioGroups to loop through selection changing only one element at a time
+    for (let i = 0; i < selection.length; i++) {
+      for (let j = 0; j < inputs_all.length; j++) {
+        if (inputs_all[j].name === selection[i].split(":")[0]) {
+          radio_group = radioGroups[selection[i].split(":")[0]];
+          label_group = labelGroups[selection[i].split(":")[0]];
+          for (let k = 0; k < radio_group.length; k++) {
+            selection_copy = selection.slice();
+            selection_copy[i] = selection[i].split(":")[0] + ":" + radio_group[k].value;
+            // get idx of slices where all elements match
+            var idx = Object.keys(slices_all).find(key => JSON.stringify(slices_all[key].sort()) === JSON.stringify(selection_copy.sort()));
+            if (idx === undefined) {
+              // set radio button to disabled and cursor to not allowed and color to gray if idx is undefined
+              radio_group[k].disabled = true;
+              label_group[k].style.cursor = "not-allowed";
+              label_group[k].style.color = "gray";
+              label_group[k].style.backgroundColor = "rgba(125, 125, 125, 0.2)";
+            }
+            else {
+              radio_group[k].disabled = false;
+              label_group[k].style.cursor = "pointer";
+            }
+          }
+        }
+      }
+    }
+
+    traces = [];
+    var plot_number = parseInt(plot_selected.value.split(" ")[1]-1);
+    for (let i = 0; i < selections.length; i++) {
+      if (selections[i] === null) {
+        continue;
+      }
+      selection = selections[i]
+
+      // get idx of slices where all elements match
+      var idx = Object.keys(slices_all).find(key => JSON.stringify(slices_all[key].sort()) === JSON.stringify(selection));
+      var history_data = [];
+      for (let i = 0; i < histories_all[idx].length; i++) {
+        history_data.push(parseFloat(histories_all[idx][i]));
+      }
+      var timestamp_data = [];
+      for (let i = 0; i < timestamps_all[idx].length; i++) {
+        timestamp_data.push(timestamps_all[idx][i]);
+      }
+      threshold = parseFloat(thresholds_all[idx]);
+      trend = trends_all[idx];
+      passed = passed_all[idx];
+      name = names_all[idx];
+
+      // if trend is "positive" set keyword to upwards, if trend is "negative" set keyword to downwards, else set keyword to flat
+      if (trend === "positive") {
+        var trend_keyword = "upwards";
+      } else if (trend === "negative") {
+        var trend_keyword = "downwards";
+      } else {
+        var trend_keyword = "flat";
+      }
+
+      // if passed is true set keyword to Above, if passed is false set keyword to Below
+      if (passed) {
+        var passed_keyword = "above";
+      }
+      else {
+        var passed_keyword = "below";
+      }
+
+      // create title for plot: Current {metric name} is trending {trend_keyword} and is {passed_keyword} the threshold.
+      // get number of nulls in selections, if 9 then plot title, else don't plot title
+      console.log(selections)
+      var nulls = 0;
+      for (let i = 0; i < selections.length; i++) {
+        if (selections[i] === null) {
+          nulls += 1;
+        }
+      }
+      if (nulls === 10) {
+        var plot_title = "Current " + name + " is trending " + trend_keyword + " and is " + passed_keyword + " the threshold.";
+        var showlegend = false;
+      }
+      else {
+        var plot_title = "";
+        var showlegend = true;
+      }
+      name = ""
+      suffix = " ( "
+      for (let i = 0; i < selection.length; i++) {
+        if (selection[i].split(":")[0] === "metric") {
+          name += selection[i].split(":")[1];
+        }
+        else {
+          if (selection[i].split(":")[1].includes("overall")) {
+            continue;
+          } else {
+            suffix += selection[i];
+            suffix += ", ";
+          }
+        }
+      }
+      if (suffix === " ( ") {
+        name += "";
+      }
+      else {
+        suffix = suffix.slice(0, -2);
+        name += suffix + " )";
+      }
+
+      var trace = {
+        // range of x is the length of the list of floats
+        x: timestamp_data,
+        y: history_data,
+        mode: 'lines+markers',
+        type: 'scatter',
+        marker: {color: plot_colors[i+1]},
+        line: {color: plot_colors[i+1]},
+        name: name,
+      };
+      traces.push(trace);
+    }
+
+    if (nulls === 10) {
+      var threshold_trace = {
+        x: timestamp_data,
+        y: Array.from({length: history_data.length}, (_, i) => threshold),
+        mode: 'lines',
+        type: 'scatter',
+        marker: {color: 'rgb(0,0,0)'},
+        line: {color: 'rgb(0,0,0)', dash: 'dot'},
+        name: '',
+      };
+      traces.push(threshold_trace);
+    }
+    var layout = {
+      title: {
+        text: plot_title,
+        font: {
+          family:  'Arial, Helvetica, sans-serif',
+          size: 18,
+        }
+      },
+      paper_bgcolor: 'rgba(0,0,0,0)',
+      plot_bgcolor: 'rgba(0,0,0,0)',
+      xaxis: {
+        zeroline: false,
+        showticklabels: false,
+        showgrid: false,
+      },
+      yaxis: {
+        gridcolor: '#ffffff',
+      },
+      showlegend: showlegend,
+      margin: {
+        l: 50,
+        r: 50,
+        b: 50,
+        t: 50,
+        pad: 4
+      },
+      // set height and width of plot to extra-wide to fit the plot
+      height: 500,
+      width: 900,
+    }
+    Plotly.newPlot(plot, traces, layout, {displayModeBar: false});
+  }
+
+
+
+  // Define a function to update the plot based on selected filters
+  function updatePlot() {
+    const inputs = document.querySelectorAll('#slice-selection input[type="radio"]:checked');
+    var plot_selection = document.querySelectorAll('#plot-selection input[type="radio"]');
+    var plot_selected = document.querySelectorAll('#plot-selection input[type="radio"]:checked')[0];
+    // get number from value in plot_selected "Plot 1" -> 1
+    var label_selection = document.querySelectorAll('#plot-selection label');
+    var label_slice_selection = document.querySelectorAll('#slice-selection label');
+
+    // get all inputs values from div class radio-buttons
+    // get name of inputs
+    var inputs_name = [];
+    var inputs_value = [];
+    for (let i = 0; i < inputs.length; i++) {
+      inputs_name.push(inputs[i].name);
+      inputs_value.push(inputs[i].value);
+    }
+
+    var plot_number = parseInt(plot_selected.value.split(" ")[1]-1);
+    var selection = [];
+    for (let i = 0; i < inputs_value.length; i++) {
+      selection.push(inputs_name[i] + ":" + inputs_value[i]);
+    }
+    selection.sort();
+    selections[plot_number] = selection;
+
+    // if plot_selected is "+" then add new radio button to plot_selection called "Plot N" where last plot is N-1 but keep "+" at end and set new radio button to checked for second last element
+    if (plot_selected.value === "+") {
+      // if 10 plots already exist, don't add new plot and gray out "+"
+      if (plot_selection.length === 13) {
+        plot_selected.checked = false;
+        label_selection[-1].style.color = "gray";
+        return;
+      }
+      var new_plot = document.createElement("input");
+      new_plot.type = "radio";
+      new_plot.id = "Plot " + (plot_selection.length);
+      new_plot.name = "plot";
+      new_plot.value = "Plot " + (plot_selection.length);
+      new_plot.checked = true;
+      var new_label = document.createElement("label");
+      new_label.htmlFor = "Plot " + (plot_selection.length);
+      new_label.innerHTML = "Plot " + (plot_selection.length);
+
+      // Parse plot_color to get r, g, b values
+      var plot_color = plot_colors[plot_selection.length]
+      const [r, g, b] = plot_color.match(/\d+/g);
+      const rgbaColor = `rgba(${r}, ${g}, ${b}, 0.2)`;
+      // set background color of new radio button to plot_color
+      new_label.style.backgroundColor = rgbaColor;
+      new_label.style.border = "2px solid " + plot_color;
+      new_label.style.color = plot_color;
+
+      // insert new radio button and label before "+" radio button and after last radio button
+      plot_selected.insertAdjacentElement("beforebegin", new_plot);
+      plot_selected.insertAdjacentElement("beforebegin", new_label);
+      // Add event listener to new radio button
+      new_plot.addEventListener('change', updatePlot);
+
+      // set plot_selected to new plot
+      plot_selected = new_plot
+
+      for (let i = 0; i < label_selection.length-1; i++) {
+        plot_selection[i].checked = false;
+        label_selection[i].style.backgroundColor = "#ffffff";
+        label_selection[i].style.border = "2px solid #DADCE0";
+        label_selection[i].style.color = "#000000";
+        }
+    } else {
+      for (let i = 0; i < plot_selection.length-1; i++) {
+        if (plot_selection[i].value !== plot_selected.value) {
+          plot_selection[i].checked = false;
+          label_selection[i].style.backgroundColor = "#ffffff";
+          label_selection[i].style.border = "2px solid #DADCE0";
+          label_selection[i].style.color = "#000000";
+        }
+        else {
+          var plot_color = plot_colors[i+1]
+          const [r, g, b] = plot_color.match(/\d+/g);
+          const rgbaColor = `rgba(${r}, ${g}, ${b}, 0.2)`;
+          plot_selected.checked = true;
+          label_selection[i].style.backgroundColor = rgbaColor;
+          label_selection[i].style.border = "2px solid " + plot_color;
+          label_selection[i].style.color = plot_color;
+        }
+      }
+    }
+    var slices_all = JSON.parse("{\"0\": [\"metric:Accuracy\", \"Sex:F\", \"Age:overall_Age\"], \"1\": [\"metric:Accuracy\", \"Age:[30 - 50)\", \"Sex:overall_Sex\"], \"2\": [\"metric:Precision\", \"Age:[30 - 50)\", \"Sex:overall_Sex\"], \"3\": [\"metric:Recall\", \"Age:[30 - 50)\", \"Sex:overall_Sex\"], \"4\": [\"metric:F1 Score\", \"Age:[30 - 50)\", \"Sex:overall_Sex\"], \"5\": [\"metric:AUROC\", \"Age:[30 - 50)\", \"Sex:overall_Sex\"], \"6\": [\"metric:Accuracy\", \"Age:[50 - 70)\", \"Sex:overall_Sex\"], \"7\": [\"metric:Precision\", \"Age:[50 - 70)\", \"Sex:overall_Sex\"], \"8\": [\"metric:Recall\", \"Age:[50 - 70)\", \"Sex:overall_Sex\"], \"9\": [\"metric:F1 Score\", \"Age:[50 - 70)\", \"Sex:overall_Sex\"], \"10\": [\"metric:AUROC\", \"Age:[50 - 70)\", \"Sex:overall_Sex\"], \"11\": [\"metric:Accuracy\", \"Sex:overall_Sex\", \"Age:overall_Age\"], \"12\": [\"metric:Precision\", \"Sex:overall_Sex\", \"Age:overall_Age\"], \"13\": [\"metric:Recall\", \"Sex:overall_Sex\", \"Age:overall_Age\"], \"14\": [\"metric:F1 Score\", \"Sex:overall_Sex\", \"Age:overall_Age\"], \"15\": [\"metric:AUROC\", \"Sex:overall_Sex\", \"Age:overall_Age\"]}");
+    var histories_all = JSON.parse("{\"0\": [\"0.8421052631578947\", \"0.9443366297070744\", \"0.6696966658997907\", \"0.7917884636345517\", \"1.0\"], \"1\": [\"0.796875\", \"0.8032692280474679\", \"0.8104086880694173\", \"0.7662504408321089\", \"0.733254320528702\"], \"2\": [\"0.8260869565217391\", \"0.8801965037090079\", \"0.9088616256065348\", \"0.9005887032719392\", \"0.7318415609625138\"], \"3\": [\"0.6785714285714286\", \"0.7061788871876555\", \"0.8234200856028526\", \"0.6689744509810135\", \"0.7620675719240091\"], \"4\": [\"0.7450980392156863\", \"0.8232575960858443\", \"0.5498675985686183\", \"0.8574174525837635\", \"0.6937614116110762\"], \"5\": [\"0.8819444444444444\", \"0.6529999183975839\", \"0.8503495058201004\", \"1.0\", \"0.9920904453877579\"], \"6\": [\"0.8623853211009175\", \"0.7451503351764498\", \"0.7575696615631983\", \"0.8128392007041036\", \"0.8669919022480744\"], \"7\": [\"0.8676470588235294\", \"0.8505926531332457\", \"0.7755402584280724\", \"0.7046570917647931\", \"0.8368618989726452\"], \"8\": [\"0.9076923076923077\", \"0.9027325794047122\", \"0.9145744539589293\", \"0.9795187743625171\", \"0.8281839998952537\"], \"9\": [\"0.8872180451127819\", \"0.8303960872133204\", \"0.844145250696786\", \"0.868645924442111\", \"0.7165689140230025\"], \"10\": [\"0.927972027972028\", \"0.9917891023297087\", \"0.924922111379513\", \"0.991110811175645\", \"0.8625594142253621\"], \"11\": [\"0.842391304347826\", \"0.7825740292592875\", \"0.9243205295960827\", \"0.9133802204641654\", \"0.7641960608195242\"], \"12\": [\"0.8686868686868687\", \"1.0\", \"0.9423804438863684\", \"0.889084104518579\", \"0.8975897083031747\"], \"13\": [\"0.8431372549019608\", \"0.7795144694830195\", \"0.9426669241322884\", \"0.6806287006654718\", \"0.785614301768915\"], \"14\": [\"0.8557213930348259\", \"0.9757521710384467\", \"0.9555559061064605\", \"0.9615911087123208\", \"0.8243079817735177\"], \"15\": [\"0.9152319464371114\", \"0.9113294244106782\", \"0.8458731419792791\", \"0.8605328254392286\", \"1.0\"]}");
+    var thresholds_all = JSON.parse("{\"0\": \"0.7\", \"1\": \"0.7\", \"2\": \"0.7\", \"3\": \"0.7\", \"4\": \"0.7\", \"5\": \"0.7\", \"6\": \"0.7\", \"7\": \"0.7\", \"8\": \"0.7\", \"9\": \"0.7\", \"10\": \"0.7\", \"11\": \"0.7\", \"12\": \"0.7\", \"13\": \"0.7\", \"14\": \"0.7\", \"15\": \"0.7\"}");
+    var trends_all = JSON.parse("{\"0\": \"positive\", \"1\": \"negative\", \"2\": \"negative\", \"3\": \"positive\", \"4\": \"neutral\", \"5\": \"positive\", \"6\": \"neutral\", \"7\": \"negative\", \"8\": \"neutral\", \"9\": \"negative\", \"10\": \"negative\", \"11\": \"neutral\", \"12\": \"neutral\", \"13\": \"negative\", \"14\": \"neutral\", \"15\": \"positive\"}");
+    var passed_all = JSON.parse("{\"0\": true, \"1\": true, \"2\": true, \"3\": true, \"4\": false, \"5\": true, \"6\": true, \"7\": true, \"8\": true, \"9\": true, \"10\": true, \"11\": true, \"12\": true, \"13\": true, \"14\": true, \"15\": true}");
+    var names_all = JSON.parse("{\"0\": \"Accuracy\", \"1\": \"Accuracy\", \"2\": \"Precision\", \"3\": \"Recall\", \"4\": \"F1 Score\", \"5\": \"AUROC\", \"6\": \"Accuracy\", \"7\": \"Precision\", \"8\": \"Recall\", \"9\": \"F1 Score\", \"10\": \"AUROC\", \"11\": \"Accuracy\", \"12\": \"Precision\", \"13\": \"Recall\", \"14\": \"F1 Score\", \"15\": \"AUROC\"}");
+    var timestamps_all = JSON.parse("{\"0\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"1\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"2\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"3\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"4\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"5\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"6\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"7\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"8\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"9\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"10\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"11\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"12\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"13\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"14\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"], \"15\": [\"2021-09-01\", \"2021-10-01\", \"2021-11-01\", \"2021-12-01\", \"2022-01-01\"]}");
+
+    for (let i = 0; i < selection.length; i++) {
+      // use selection to set label_slice_selection background color
+      for (let j = 0; j < inputs_all.length; j++) {
+        if (inputs_all[j].name === selection[i].split(":")[0]) {
+          if (inputs_all[j].value == selection[i].split(":")[1]) {
+            inputs_all[j].checked = true;
+            const [r, g, b] = plot_color.match(/\d+/g);
+            const rgbaColor = `rgba(${r}, ${g}, ${b}, 0.2)`;
+            label_slice_selection[j].style.backgroundColor = rgbaColor;
+            label_slice_selection[j].style.border = "2px solid " + plot_color;
+            label_slice_selection[j].style.color = plot_color;
+          }
+          else {
+            inputs_all[j].checked = false;
+            label_slice_selection[j].style.backgroundColor = "#ffffff";
+            label_slice_selection[j].style.border = "2px solid #DADCE0";
+            label_slice_selection[j].style.color = "#000000";
+          }
+        }
+      }
+    }
+
+    var radioGroups = {};
+    var labelGroups = {};
+    for (let i = 0; i < inputs_all.length; i++) {
+      var input = inputs_all[i];
+      var label = label_slice_selection[i];
+      var groupName = input.name;
+      if (!radioGroups[groupName]) {
+        radioGroups[groupName] = [];
+        labelGroups[groupName] = [];
+      }
+      radioGroups[groupName].push(input);
+      labelGroups[groupName].push(label);
+    }
+
+    // use radioGroups to loop through selection changing only one element at a time
+    for (let i = 0; i < selection.length; i++) {
+      for (let j = 0; j < inputs_all.length; j++) {
+        if (inputs_all[j].name === selection[i].split(":")[0]) {
+          radio_group = radioGroups[selection[i].split(":")[0]];
+          label_group = labelGroups[selection[i].split(":")[0]];
+          for (let k = 0; k < radio_group.length; k++) {
+            selection_copy = selection.slice();
+            selection_copy[i] = selection[i].split(":")[0] + ":" + radio_group[k].value;
+            // get idx of slices where all elements match
+            var idx = Object.keys(slices_all).find(key => JSON.stringify(slices_all[key].sort()) === JSON.stringify(selection_copy.sort()));
+            if (idx === undefined) {
+              // set radio button to disabled and cursor to not allowed and color to gray if idx is undefined
+              radio_group[k].disabled = true;
+              label_group[k].style.cursor = "not-allowed";
+              label_group[k].style.color = "gray";
+              label_group[k].style.backgroundColor = "rgba(125, 125, 125, 0.2)";
+            }
+            else {
+              radio_group[k].disabled = false;
+              label_group[k].style.cursor = "pointer";
+            }
+          }
+        }
+      }
+    }
+
+    traces = [];
+    for (let i = 0; i < selections.length; i++) {
+      if (selections[i] === null) {
+        continue;
+      }
+      selection = selections[i]
+      // get idx of slices where all elements match
+      var idx = Object.keys(slices_all).find(key => JSON.stringify(slices_all[key].sort()) === JSON.stringify(selection));
+      var history_data = [];
+      for (let i = 0; i < histories_all[idx].length; i++) {
+        history_data.push(parseFloat(histories_all[idx][i]));
+      }
+      var timestamp_data = [];
+      for (let i = 0; i < timestamps_all[idx].length; i++) {
+        timestamp_data.push(timestamps_all[idx][i]);
+      }
+      threshold = parseFloat(thresholds_all[idx]);
+      trend = trends_all[idx];
+      passed = passed_all[idx];
+      name = names_all[idx];
+
+      // if trend is "positive" set keyword to upwards, if trend is "negative" set keyword to downwards, else set keyword to flat
+      if (trend === "positive") {
+        var trend_keyword = "upwards";
+      } else if (trend === "negative") {
+        var trend_keyword = "downwards";
+      } else {
+        var trend_keyword = "flat";
+      }
+
+      // if passed is true set keyword to Above, if passed is false set keyword to Below
+      if (passed) {
+        var passed_keyword = "above";
+      }
+      else {
+        var passed_keyword = "below";
+      }
+
+      // create title for plot: Current {metric name} is trending {trend_keyword} and is {passed_keyword} the threshold.
+      // get number of nulls in selections, if 9 then plot title, else don't plot title
+      var nulls = 0;
+      for (let i = 0; i < selections.length; i++) {
+        if (selections[i] === null) {
+          nulls += 1;
+        }
+      }
+      if (nulls === 10) {
+        var plot_title = "Current " + name + " is trending " + trend_keyword + " and is " + passed_keyword + " the threshold.";
+        var showlegend = false;
+      }
+      else {
+        var plot_title = "";
+        var showlegend = true;
+      }
+      name = ""
+      suffix = " ( "
+      for (let i = 0; i < selection.length; i++) {
+        if (selection[i].split(":")[0] === "metric") {
+          name += selection[i].split(":")[1];
+        }
+        else {
+          if (selection[i].split(":")[1].includes("overall")) {
+            continue;
+          } else {
+            suffix += selection[i];
+            suffix += ", ";
+          }
+        }
+      }
+      if (suffix === " ( ") {
+        name += "";
+      }
+      else {
+        suffix = suffix.slice(0, -2);
+        name += suffix + " )";
+      }
+      var trace = {
+        // range of x is the length of the list of floats
+        x: timestamp_data,
+        y: history_data,
+        mode: 'lines+markers',
+        type: 'scatter',
+        marker: {color: plot_colors[i+1]},
+        line: {color: plot_colors[i+1]},
+        name: name,
+        //name: selection.toString(),
+      };
+      traces.push(trace);
+    }
+
+    if (nulls === 10) {
+      var threshold_trace = {
+        x: timestamp_data,
+        y: Array.from({length: history_data.length}, (_, i) => threshold),
+        mode: 'lines',
+        type: 'scatter',
+        marker: {color: 'rgb(0,0,0)'},
+        line: {color: 'rgb(0,0,0)', dash: 'dot'},
+        name: '',
+      };
+      traces.push(threshold_trace);
+    }
+    var layout = {
+      title: {
+        text: plot_title,
+        font: {
+          family:  'Arial, Helvetica, sans-serif',
+          size: 18,
+        }
+      },
+      paper_bgcolor: 'rgba(0,0,0,0)',
+      plot_bgcolor: 'rgba(0,0,0,0)',
+      xaxis: {
+        zeroline: false,
+        showticklabels: false,
+        showgrid: false,
+      },
+      yaxis: {
+        gridcolor: '#ffffff',
+      },
+      showlegend: showlegend,
+      margin: {
+        l: 50,
+        r: 50,
+        b: 50,
+        t: 50,
+        pad: 4
+      },
+      // set height and width of plot to extra-wide to fit the plot
+      height: 500,
+      width: 900,
+    }
+    Plotly.newPlot(plot, traces, layout, {displayModeBar: false});
+  }
+  // Add event listeners to radio buttons
+  for (let input of inputs_all) {
+    input.addEventListener('change', updatePlot);
+  }
+  for (let selection of plot_selection) {
+    selection.addEventListener('change', updatePlotSelection);
+  }
+  // Initial update when the page loads
+  updatePlot();
+
 
\ No newline at end of file
diff --git a/api/tutorials/nihcxr/cxr_classification.html b/api/tutorials/nihcxr/cxr_classification.html
index a2651db2e..ba19bbd66 100644
--- a/api/tutorials/nihcxr/cxr_classification.html
+++ b/api/tutorials/nihcxr/cxr_classification.html
@@ -478,7 +478,7 @@ 
Import Libraries
 
-/home/amritk/.cache/pypoetry/virtualenvs/pycyclops-mhx6UJW0-py3.10/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
+/home/amritk/.cache/pypoetry/virtualenvs/pycyclops-wIzUAwxh-py3.10/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
   from .autonotebook import tqdm as notebook_tqdm
 

 

@@ -503,74 +503,74 @@ 
Generate Historical Reports
 
-Flattening the indices: 100%|████████| 1000/1000 [00:59<00:00, 16.77 examples/s]
-Flattening the indices: 100%|████| 1000/1000 [00:00<00:00, 504123.08 examples/s]
-Filter: 100%|████████████████████| 1000/1000 [00:00<00:00, 201107.79 examples/s]
-Map: 100%|███████████████████████████| 400/400 [00:00<00:00, 1509.44 examples/s]
-Filter -> Patient Gender:M: 100%|███| 400/400 [00:00<00:00, 41562.74 examples/s]
-Filter -> Patient Gender:F: 100%|███| 400/400 [00:00<00:00, 41742.68 examples/s]
-Filter -> overall: 100%|████████████| 400/400 [00:00<00:00, 43866.59 examples/s]
-Filter -> Patient Age:[19 - 35]: 100%|█| 400/400 [00:00<00:00, 42852.59 examples
-Filter -> Patient Age:[35 - 65]: 100%|█| 400/400 [00:00<00:00, 43904.47 examples
-Filter -> Patient Age:[65 - 100]: 100%|█| 400/400 [00:00<00:00, 43300.51 example
+Flattening the indices: 100%|████████| 1000/1000 [00:57<00:00, 17.36 examples/s]
+Flattening the indices: 100%|████| 1000/1000 [00:00<00:00, 530320.39 examples/s]
+Filter: 100%|████████████████████| 1000/1000 [00:00<00:00, 215092.51 examples/s]
+Map: 100%|███████████████████████████| 400/400 [00:00<00:00, 1612.86 examples/s]
+Filter -> Patient Gender:M: 100%|███| 400/400 [00:00<00:00, 37072.62 examples/s]
+Filter -> Patient Gender:F: 100%|███| 400/400 [00:00<00:00, 26745.98 examples/s]
+Filter -> overall: 100%|████████████| 400/400 [00:00<00:00, 28224.51 examples/s]
+Filter -> Patient Age:[19 - 35]: 100%|█| 400/400 [00:00<00:00, 26132.74 examples
+Filter -> Patient Age:[35 - 65]: 100%|█| 400/400 [00:00<00:00, 28789.24 examples
+Filter -> Patient Age:[65 - 100]: 100%|█| 400/400 [00:00<00:00, 27377.07 example
 Filter -> Patient Age:[19 - 35]&Patient Gender:M: 100%|█| 400/400 [00:00<00:00,
 Filter -> Patient Age:[19 - 35]&Patient Gender:F: 100%|█| 400/400 [00:00<00:00,
 Filter -> Patient Age:[35 - 65]&Patient Gender:M: 100%|█| 400/400 [00:00<00:00,
 Filter -> Patient Age:[35 - 65]&Patient Gender:F: 100%|█| 400/400 [00:00<00:00,
 Filter -> Patient Age:[65 - 100]&Patient Gender:M: 100%|█| 400/400 [00:00<00:00,
 Filter -> Patient Age:[65 - 100]&Patient Gender:F: 100%|█| 400/400 [00:00<00:00,
-Filter -> overall: 100%|████████████| 400/400 [00:00<00:00, 31870.40 examples/s]
-Flattening the indices: 100%|████████| 1000/1000 [00:57<00:00, 17.24 examples/s]
-Flattening the indices: 100%|████| 1000/1000 [00:00<00:00, 493157.44 examples/s]
-Filter: 100%|████████████████████| 1000/1000 [00:00<00:00, 226633.38 examples/s]
-Map: 100%|███████████████████████████| 396/396 [00:00<00:00, 1859.37 examples/s]
-Filter -> Patient Gender:M: 100%|███| 396/396 [00:00<00:00, 42283.66 examples/s]
-Filter -> Patient Gender:F: 100%|███| 396/396 [00:00<00:00, 42090.78 examples/s]
-Filter -> overall: 100%|████████████| 396/396 [00:00<00:00, 43437.01 examples/s]
-Filter -> Patient Age:[19 - 35]: 100%|█| 396/396 [00:00<00:00, 41593.28 examples
-Filter -> Patient Age:[35 - 65]: 100%|█| 396/396 [00:00<00:00, 42478.31 examples
-Filter -> Patient Age:[65 - 100]: 100%|█| 396/396 [00:00<00:00, 42016.25 example
+Filter -> overall: 100%|████████████| 400/400 [00:00<00:00, 45724.45 examples/s]
+Flattening the indices: 100%|████████| 1000/1000 [01:11<00:00, 13.97 examples/s]
+Flattening the indices: 100%|████| 1000/1000 [00:00<00:00, 571431.06 examples/s]
+Filter: 100%|████████████████████| 1000/1000 [00:00<00:00, 241287.69 examples/s]
+Map: 100%|███████████████████████████| 396/396 [00:00<00:00, 1886.01 examples/s]
+Filter -> Patient Gender:M: 100%|███| 396/396 [00:00<00:00, 19850.66 examples/s]
+Filter -> Patient Gender:F: 100%|███| 396/396 [00:00<00:00, 43247.00 examples/s]
+Filter -> overall: 100%|████████████| 396/396 [00:00<00:00, 45546.50 examples/s]
+Filter -> Patient Age:[19 - 35]: 100%|█| 396/396 [00:00<00:00, 45133.13 examples
+Filter -> Patient Age:[35 - 65]: 100%|█| 396/396 [00:00<00:00, 45285.72 examples
+Filter -> Patient Age:[65 - 100]: 100%|█| 396/396 [00:00<00:00, 45249.94 example
 Filter -> Patient Age:[19 - 35]&Patient Gender:M: 100%|█| 396/396 [00:00<00:00,
 Filter -> Patient Age:[19 - 35]&Patient Gender:F: 100%|█| 396/396 [00:00<00:00,
 Filter -> Patient Age:[35 - 65]&Patient Gender:M: 100%|█| 396/396 [00:00<00:00,
 Filter -> Patient Age:[35 - 65]&Patient Gender:F: 100%|█| 396/396 [00:00<00:00,
 Filter -> Patient Age:[65 - 100]&Patient Gender:M: 100%|█| 396/396 [00:00<00:00,
 Filter -> Patient Age:[65 - 100]&Patient Gender:F: 100%|█| 396/396 [00:00<00:00,
-Filter -> overall: 100%|████████████| 396/396 [00:00<00:00, 43402.96 examples/s]
-Flattening the indices: 100%|████████| 1000/1000 [00:57<00:00, 17.26 examples/s]
-Flattening the indices: 100%|████| 1000/1000 [00:00<00:00, 530119.31 examples/s]
-Filter: 100%|████████████████████| 1000/1000 [00:00<00:00, 224702.88 examples/s]
-Map: 100%|███████████████████████████| 383/383 [00:00<00:00, 1797.29 examples/s]
-Filter -> Patient Gender:M: 100%|███| 383/383 [00:00<00:00, 40789.64 examples/s]
-Filter -> Patient Gender:F: 100%|███| 383/383 [00:00<00:00, 30004.64 examples/s]
-Filter -> overall: 100%|████████████| 383/383 [00:00<00:00, 42906.48 examples/s]
-Filter -> Patient Age:[19 - 35]: 100%|█| 383/383 [00:00<00:00, 43211.17 examples
-Filter -> Patient Age:[35 - 65]: 100%|█| 383/383 [00:00<00:00, 44180.92 examples
-Filter -> Patient Age:[65 - 100]: 100%|█| 383/383 [00:00<00:00, 42533.85 example
+Filter -> overall: 100%|████████████| 396/396 [00:00<00:00, 47140.39 examples/s]
+Flattening the indices: 100%|████████| 1000/1000 [00:57<00:00, 17.53 examples/s]
+Flattening the indices: 100%|████| 1000/1000 [00:00<00:00, 560061.96 examples/s]
+Filter: 100%|████████████████████| 1000/1000 [00:00<00:00, 241357.12 examples/s]
+Map: 100%|███████████████████████████| 383/383 [00:00<00:00, 1921.87 examples/s]
+Filter -> Patient Gender:M: 100%|███| 383/383 [00:00<00:00, 35777.69 examples/s]
+Filter -> Patient Gender:F: 100%|███| 383/383 [00:00<00:00, 43020.23 examples/s]
+Filter -> overall: 100%|████████████| 383/383 [00:00<00:00, 45167.25 examples/s]
+Filter -> Patient Age:[19 - 35]: 100%|█| 383/383 [00:00<00:00, 43747.78 examples
+Filter -> Patient Age:[35 - 65]: 100%|█| 383/383 [00:00<00:00, 44947.35 examples
+Filter -> Patient Age:[65 - 100]: 100%|█| 383/383 [00:00<00:00, 44381.10 example
 Filter -> Patient Age:[19 - 35]&Patient Gender:M: 100%|█| 383/383 [00:00<00:00,
 Filter -> Patient Age:[19 - 35]&Patient Gender:F: 100%|█| 383/383 [00:00<00:00,
 Filter -> Patient Age:[35 - 65]&Patient Gender:M: 100%|█| 383/383 [00:00<00:00,
 Filter -> Patient Age:[35 - 65]&Patient Gender:F: 100%|█| 383/383 [00:00<00:00,
 Filter -> Patient Age:[65 - 100]&Patient Gender:M: 100%|█| 383/383 [00:00<00:00,
 Filter -> Patient Age:[65 - 100]&Patient Gender:F: 100%|█| 383/383 [00:00<00:00,
-Filter -> overall: 100%|████████████| 383/383 [00:00<00:00, 45386.74 examples/s]
-Flattening the indices: 100%|████████| 1000/1000 [01:08<00:00, 14.55 examples/s]
-Flattening the indices: 100%|████| 1000/1000 [00:00<00:00, 290665.56 examples/s]
-Filter: 100%|████████████████████| 1000/1000 [00:00<00:00, 105820.57 examples/s]
-Map: 100%|███████████████████████████| 411/411 [00:00<00:00, 1763.35 examples/s]
-Filter -> Patient Gender:M: 100%|███| 411/411 [00:00<00:00, 37239.61 examples/s]
-Filter -> Patient Gender:F: 100%|███| 411/411 [00:00<00:00, 38503.92 examples/s]
-Filter -> overall: 100%|████████████| 411/411 [00:00<00:00, 28800.10 examples/s]
-Filter -> Patient Age:[19 - 35]: 100%|█| 411/411 [00:00<00:00, 34093.96 examples
-Filter -> Patient Age:[35 - 65]: 100%|█| 411/411 [00:00<00:00, 29990.59 examples
-Filter -> Patient Age:[65 - 100]: 100%|█| 411/411 [00:00<00:00, 33812.43 example
+Filter -> overall: 100%|████████████| 383/383 [00:00<00:00, 44512.69 examples/s]
+Flattening the indices: 100%|████████| 1000/1000 [00:56<00:00, 17.84 examples/s]
+Flattening the indices: 100%|████| 1000/1000 [00:00<00:00, 572444.93 examples/s]
+Filter: 100%|████████████████████| 1000/1000 [00:00<00:00, 238380.45 examples/s]
+Map: 100%|███████████████████████████| 411/411 [00:00<00:00, 1835.56 examples/s]
+Filter -> Patient Gender:M: 100%|███| 411/411 [00:00<00:00, 44467.17 examples/s]
+Filter -> Patient Gender:F: 100%|███| 411/411 [00:00<00:00, 43879.73 examples/s]
+Filter -> overall: 100%|████████████| 411/411 [00:00<00:00, 45320.58 examples/s]
+Filter -> Patient Age:[19 - 35]: 100%|█| 411/411 [00:00<00:00, 44772.07 examples
+Filter -> Patient Age:[35 - 65]: 100%|█| 411/411 [00:00<00:00, 46457.69 examples
+Filter -> Patient Age:[65 - 100]: 100%|█| 411/411 [00:00<00:00, 45263.46 example
 Filter -> Patient Age:[19 - 35]&Patient Gender:M: 100%|█| 411/411 [00:00<00:00,
 Filter -> Patient Age:[19 - 35]&Patient Gender:F: 100%|█| 411/411 [00:00<00:00,
 Filter -> Patient Age:[35 - 65]&Patient Gender:M: 100%|█| 411/411 [00:00<00:00,
 Filter -> Patient Age:[35 - 65]&Patient Gender:F: 100%|█| 411/411 [00:00<00:00,
 Filter -> Patient Age:[65 - 100]&Patient Gender:M: 100%|█| 411/411 [00:00<00:00,
 Filter -> Patient Age:[65 - 100]&Patient Gender:F: 100%|█| 411/411 [00:00<00:00,
-Filter -> overall: 100%|████████████| 411/411 [00:00<00:00, 42585.45 examples/s]
+Filter -> overall: 100%|████████████| 411/411 [00:00<00:00, 46223.49 examples/s]
 

 

 
CyclOps offers a package for documentation of the model through a model report. The ModelCardReport class is used to populate and generate the model report as an HTML file. The model report has the following sections:

@@ -667,8 +667,8 @@ 
Model Creation
 
-Filter: 100%|██████████| 1000/1000 [00:00<00:00, 79064.71 examples/s]
-Map: 100%|██████████| 661/661 [00:00<00:00, 1251.18 examples/s]
+Filter: 100%|██████████| 1000/1000 [00:00<00:00, 114726.99 examples/s]
+Map: 100%|██████████| 661/661 [00:00<00:00, 1729.15 examples/s]
 

 

 

@@ -795,9 +795,9 @@ 
Multilabel AUROC by Pathology and Sex
 
-Filter -> Patient Gender:M: 100%|██████████| 661/661 [00:00<00:00, 45281.99 examples/s]
-Filter -> Patient Gender:F: 100%|██████████| 661/661 [00:00<00:00, 45183.84 examples/s]
-Filter -> overall: 100%|██████████| 661/661 [00:00<00:00, 46246.56 examples/s]
+Filter -> Patient Gender:M: 100%|██████████| 661/661 [00:00<00:00, 41450.77 examples/s]
+Filter -> Patient Gender:F: 100%|██████████| 661/661 [00:00<00:00, 47011.94 examples/s]
+Filter -> overall: 100%|██████████| 661/661 [00:00<00:00, 46982.46 examples/s]
@@ -858,16 +858,16 @@

Multilabel AUROC by Pathology and Age 
-Filter -> Patient Age:[19 - 35]: 100%|██████████| 661/661 [00:00<00:00, 47232.19 examples/s]
-Filter -> Patient Age:[35 - 65]: 100%|██████████| 661/661 [00:00<00:00, 44932.66 examples/s]
-Filter -> Patient Age:[65 - 100]: 100%|██████████| 661/661 [00:00<00:00, 44363.22 examples/s]
-Filter -> Patient Age:[19 - 35]&Patient Gender:M: 100%|██████████| 661/661 [00:00<00:00, 42913.63 examples/s]
-Filter -> Patient Age:[19 - 35]&Patient Gender:F: 100%|██████████| 661/661 [00:00<00:00, 37359.32 examples/s]
-Filter -> Patient Age:[35 - 65]&Patient Gender:M: 100%|██████████| 661/661 [00:00<00:00, 45026.07 examples/s]
-Filter -> Patient Age:[35 - 65]&Patient Gender:F: 100%|██████████| 661/661 [00:00<00:00, 42832.08 examples/s]
-Filter -> Patient Age:[65 - 100]&Patient Gender:M: 100%|██████████| 661/661 [00:00<00:00, 42517.44 examples/s]
-Filter -> Patient Age:[65 - 100]&Patient Gender:F: 100%|██████████| 661/661 [00:00<00:00, 44203.36 examples/s]
-Filter -> overall: 100%|██████████| 661/661 [00:00<00:00, 41788.15 examples/s]
+Filter -> Patient Age:[19 - 35]: 100%|██████████| 661/661 [00:00<00:00, 46581.45 examples/s]
+Filter -> Patient Age:[35 - 65]: 100%|██████████| 661/661 [00:00<00:00, 48143.42 examples/s]
+Filter -> Patient Age:[65 - 100]: 100%|██████████| 661/661 [00:00<00:00, 47470.76 examples/s]
+Filter -> Patient Age:[19 - 35]&Patient Gender:M: 100%|██████████| 661/661 [00:00<00:00, 45817.04 examples/s]
+Filter -> Patient Age:[19 - 35]&Patient Gender:F: 100%|██████████| 661/661 [00:00<00:00, 45001.95 examples/s]
+Filter -> Patient Age:[35 - 65]&Patient Gender:M: 100%|██████████| 661/661 [00:00<00:00, 46226.51 examples/s]
+Filter -> Patient Age:[35 - 65]&Patient Gender:F: 100%|██████████| 661/661 [00:00<00:00, 46525.17 examples/s]
+Filter -> Patient Age:[65 - 100]&Patient Gender:M: 100%|██████████| 661/661 [00:00<00:00, 44062.86 examples/s]
+Filter -> Patient Age:[65 - 100]&Patient Gender:F: 100%|██████████| 661/661 [00:00<00:00, 42838.04 examples/s]
+Filter -> overall: 100%|██████████| 661/661 [00:00<00:00, 45220.68 examples/s]

@@ -924,9 +924,9 @@

Multilabel AUROC by Pathology and Age

-
- -
- - +
+
@@ -1675,15 +3138,8 @@

A quick glance of your most import 0.7
minimum
threshold - - -
- -
- -
- - +
+
@@ -1743,15 +3199,8 @@

A quick glance of your most import 0.7
minimum
threshold - - -
- -
- -
- - +
+
@@ -1811,15 +3260,8 @@

A quick glance of your most import 0.7
minimum
threshold - - -
- -
- -
- - +
+
@@ -4980,13 +6422,23 @@

A quick glance of your most import -
+

How is your model doing over time?


See how your model is performing over several metrics and subgroups over time.

+ +
+

Multi-plot Selection:

+
+ + + + +
+

Metrics

-
+
@@ -5041,7 +6493,7 @@

Metrics

Patient Age

-
+
@@ -5059,7 +6511,7 @@

Patient Age

Pathology

-
+
@@ -5110,7 +6562,7 @@

Pathology

Patient Gender

-
+
@@ -5125,141 +6577,7 @@

Patient Gender

-
- - -
+
@@ -5289,7 +6607,7 @@

Graphics

-
+
@@ -5297,7 +6615,7 @@

Graphics

-
+
@@ -5305,7 +6623,7 @@

Graphics

-
+
@@ -6436,15 +7754,8 @@

Quantitative Analysis

0.7
minimum
threshold - - -
- -
- -
- - +
+
@@ -6504,15 +7815,8 @@

Quantitative Analysis

0.7
minimum
threshold - - -
- -
- -
- - +
+
@@ -6572,15 +7876,8 @@

Quantitative Analysis

0.7
minimum
threshold - - -
- -
- -
- - +
+
@@ -6640,15 +7937,8 @@

Quantitative Analysis

0.7
minimum
threshold - - -
- -
- -
- - +
+
@@ -7415,7 +8705,6 @@

Tradeoffs

return true; } } - function setActiveButton() { const buttons = document.querySelectorAll('#contents li'); const sections = document.querySelectorAll('.card'); @@ -7436,7 +8725,781 @@

Tradeoffs

} } } - document.addEventListener('scroll', setActiveButton); setActiveButton(); + + function generate_model_card_plot() { + var model_card_plots = [] + var overall_indices = [540, 555, 570, 585] + var histories = JSON.parse("{\"0\": [\"0.15167980673960318\", \"0.14129280028530336\", \"0.15995072768185709\", \"0.1258001490759194\"], \"1\": [\"0.30952380952380953\", \"0.3939393939393939\", \"0.32653061224489793\", \"0.20689655172413793\"], \"2\": [\"0.10714285714285714\", \"0.17777777777777778\", \"0.06896551724137931\", \"0.026845637583892617\"], \"3\": [\"0.4426229508196721\", \"0.42592592592592593\", \"0.647887323943662\", \"0.3712121212121212\"], \"4\": [\"0.047619047619047616\", \"0.05\", \"0.06\", \"0.4112903225806452\"], \"5\": [\"0.1276595744680851\", \"0.023255813953488372\", \"0.020833333333333332\", \"0.06422018348623854\"], \"6\": [\"0.027777777777777776\", \"0.0\", \"0.027777777777777776\", \"0.01834862385321101\"], \"7\": [\"0.041666666666666664\", \"0.0\", \"0.09375\", \"0.05102040816326531\"], \"8\": [\"0.36363636363636365\", \"0.48148148148148145\", \"0.43902439024390244\", \"0.17708333333333334\"], \"9\": [\"0.0\", \"0.02857142857142857\", \"0.045454545454545456\", \"0.03787878787878788\"], \"10\": [\"0.06451612903225806\", \"0.07407407407407407\", \"0.05\", \"0.16822429906542055\"], \"11\": [\"0.2962962962962963\", \"0.15789473684210525\", \"0.12\", \"0.1\"], \"12\": [\"0.05263157894736842\", \"0.09375\", \"0.175\", \"0.06818181818181818\"], \"13\": [\"0.24242424242424243\", \"0.07142857142857142\", \"0.10526315789473684\", \"0.06\"], \"14\": [\"0.0\", \"0.0\", \"0.058823529411764705\", \"0.0\"], \"15\": [\"0.948161213119298\", \"0.9395606461176224\", \"0.937037604089962\", \"0.9041296306770732\"], \"16\": [\"0.9285714285714286\", \"0.9642857142857143\", \"0.8823529411764706\", \"0.9230769230769231\"], \"17\": [\"1.0\", \"1.0\", \"1.0\", \"0.8333333333333334\"], \"18\": [\"0.7777777777777778\", \"0.5714285714285714\", \"0.5833333333333334\", \"0.6521739130434783\"], \"19\": [\"0.9285714285714286\", \"0.9047619047619048\", \"1.0\", \"0.7096774193548387\"], \"20\": [\"1.0\", \"1.0\", \"0.9714285714285714\", \"1.0\"], \"21\": [\"0.9705882352941176\", \"0.96875\", \"0.9787234042553191\", \"0.8260869565217391\"], \"22\": [\"0.9347826086956522\", \"1.0\", \"0.9803921568627451\", \"0.9649122807017544\"], \"23\": [\"0.9615384615384616\", \"0.9117647058823529\", \"0.8809523809523809\", \"0.9491525423728814\"], \"24\": [\"0.967741935483871\", \"0.9615384615384616\", \"1.0\", \"0.9130434782608695\"], \"25\": [\"0.9487179487179487\", \"0.9705882352941176\", \"1.0\", \"0.9791666666666666\"], \"26\": [\"0.9767441860465116\", \"1.0\", \"1.0\", \"1.0\"], \"27\": [\"0.90625\", \"0.9310344827586207\", \"0.9302325581395349\", \"0.9253731343283582\"], \"28\": [\"0.972972972972973\", \"0.9696969696969697\", \"0.9111111111111111\", \"0.9818181818181818\"], \"29\": [\"1.0\", \"1.0\", \"1.0\", \"1.0\"], \"30\": [\"0.61904681412795\", \"0.6113585818942961\", \"0.816754962854707\", \"0.7305053759940978\"], \"31\": [\"0.8666666666666667\", \"0.9285714285714286\", \"0.8\", \"0.8888888888888888\"], \"32\": [\"1.0\", \"1.0\", \"1.0\", \"0.8\"], \"33\": [\"0.9310344827586207\", \"0.8846153846153846\", \"0.9019607843137255\", \"0.8596491228070176\"], \"34\": [\"0.5\", \"0.5\", \"1.0\", \"0.85\"], \"35\": [\"1.0\", \"1.0\", \"0.5\", \"1.0\"], \"36\": [\"0.5\", \"0.0\", \"0.5\", \"0.2\"], \"37\": [\"0.25\", \"0.0\", \"0.75\", \"0.7142857142857143\"], \"38\": [\"0.9411764705882353\", \"0.8125\", \"0.782608695652174\", \"0.85\"], \"39\": [\"0.0\", \"0.5\", \"1.0\", \"0.7142857142857143\"], \"40\": [\"0.5\", \"0.6666666666666666\", \"1.0\", \"0.9473684210526315\"], \"41\": [\"0.8888888888888888\", \"1.0\", \"1.0\", \"1.0\"], \"42\": [\"0.4\", \"0.6\", \"0.7\", \"0.5454545454545454\"], \"43\": [\"0.8888888888888888\", \"0.6666666666666666\", \"0.5\", \"0.8571428571428571\"], \"44\": [\"0.0\", \"0.0\", \"1.0\", \"0.0\"], \"45\": [\"0.46336563851831164\", \"0.48289690046395356\", \"0.5070017410868425\", \"0.3276357598055654\"], \"46\": [\"0.4727272727272727\", \"0.574468085106383\", \"0.47619047619047616\", \"0.28125\"], \"47\": [\"0.21875\", \"0.3018867924528302\", \"0.31645569620253167\", \"0.03333333333333333\"], \"48\": [\"0.17073170731707318\", \"0.11428571428571428\", \"0.21875\", \"0.15306122448979592\"], \"49\": [\"0.3939393939393939\", \"0.3333333333333333\", \"0.4125\", \"0.23157894736842105\"], \"50\": [\"0.359375\", \"0.3\", \"0.41975308641975306\", \"0.3108108108108108\"], \"51\": [\"0.4852941176470588\", \"0.5166666666666667\", \"0.5679012345679012\", \"0.2620689655172414\"], \"52\": [\"0.6515151515151515\", \"0.5245901639344263\", \"0.6329113924050633\", \"0.3716216216216216\"], \"53\": [\"0.4716981132075472\", \"0.6888888888888889\", \"0.6166666666666667\", \"0.4148148148148148\"], \"54\": [\"0.43478260869565216\", \"0.423728813559322\", \"0.48148148148148145\", \"0.14189189189189189\"], \"55\": [\"0.5606060606060606\", \"0.5689655172413793\", \"0.5308641975308642\", \"0.34558823529411764\"], \"56\": [\"0.6885245901639344\", \"0.7241379310344828\", \"0.725\", \"0.7615894039735099\"], \"57\": [\"0.4461538461538462\", \"0.48214285714285715\", \"0.547945205479452\", \"0.4305555555555556\"], \"58\": [\"0.5901639344262295\", \"0.5517241379310345\", \"0.5466666666666666\", \"0.36486486486486486\"], \"59\": [\"0.5428571428571428\", \"0.6557377049180327\", \"0.6049382716049383\", \"0.4838709677419355\"], \"60\": [\"0.1390022332304764\", \"0.12461397321555136\", \"0.14317050475245158\", \"0.14359228947856187\"], \"61\": [\"0.27218934911242604\", \"0.22981366459627328\", \"0.29931972789115646\", \"0.24516129032258063\"], \"62\": [\"0.11578947368421053\", \"0.08994708994708994\", \"0.09142857142857143\", \"0.1206896551724138\"], \"63\": [\"0.38388625592417064\", \"0.3736842105263158\", \"0.40641711229946526\", \"0.3813559322033898\"], \"64\": [\"0.0425531914893617\", \"0.08\", \"0.06944444444444445\", \"0.13653136531365315\"], \"65\": [\"0.030303030303030304\", \"0.078125\", \"0.08870967741935484\", \"0.10380622837370242\"], \"66\": [\"0.03571428571428571\", \"0.041379310344827586\", \"0.05714285714285714\", \"0.0821917808219178\"], \"67\": [\"0.04929577464788732\", \"0.02877697841726619\", \"0.048\", \"0.05855855855855856\"], \"68\": [\"0.3969465648854962\", \"0.3492063492063492\", \"0.35\", \"0.30111524163568776\"], \"69\": [\"0.03164556962025317\", \"0.014492753623188406\", \"0.023255813953488372\", \"0.030508474576271188\"], \"70\": [\"0.06338028169014084\", \"0.06818181818181818\", \"0.1\", \"0.07228915662650602\"], \"71\": [\"0.15294117647058825\", \"0.0958904109589041\", \"0.08064516129032258\", \"0.34615384615384615\"], \"72\": [\"0.16417910447761194\", \"0.16176470588235295\", \"0.2028985507246377\", \"0.045081967213114756\"], \"73\": [\"0.2072072072072072\", \"0.13333333333333333\", \"0.17073170731707318\", \"0.08298755186721991\"], \"74\": 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\"2023-10-30\", \"2023-11-06\"], \"719\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"]}"); + + for (let i = 0; i < overall_indices.length; i++) { + var idx = overall_indices[i]; + var model_card_plot = "model-card-plot-" + idx; + var threshold = thresholds[idx]; + var history_data = []; + for (let i = 0; i < histories[idx].length; i++) { + history_data.push(parseFloat(histories[idx][i])); + } + var timestamp_data = []; + for (let i = 0; i < timestamps[idx].length; i++) { + timestamp_data.push(timestamps[idx][i]); + } + + var model_card_fig = { + data: [ + { + x: timestamp_data, + y: history_data, + mode: "lines+markers", + marker: { color: "rgb(31,111,235)" }, + line: { color: "rgb(31,111,235)" }, + showlegend: false, + type: "scatter", + name: "" + }, + { + x: timestamp_data, + y: Array(history_data.length).fill(threshold), + mode: "lines", + line: { color: "black", dash: "dot" }, + showlegend: false, + type: "scatter", + name: "" + } + ], + layout: { + paper_bgcolor: "rgba(0,0,0,0)", + plot_bgcolor: "rgba(0,0,0,0)", + xaxis: { + zeroline: false, + showticklabels: false, + showgrid: false + }, + yaxis: { + gridcolor: "#ffffff" + }, + margin: { l: 0, r: 0, t: 0, b: 0 }, + height: 125, + width: 250 + } + }; + if (history.length > 0) { + Plotly.newPlot(model_card_plot, model_card_fig.data, model_card_fig.layout, {displayModeBar: false}); + } + } + } + generate_model_card_plot(); + + const plot = document.getElementById('plot'); + const inputs_all = document.querySelectorAll('#slice-selection input[type="radio"]'); + const plot_selection = document.querySelectorAll('#plot-selection input[type="radio"]'); + var selections = [null, null, null, null, null, null, null, null, null, null, null]; + var plot_colors = [ + "rgb(0, 115, 228)", + "rgb(31, 119, 180)", + "rgb(255, 127, 14)", + "rgb(44, 160, 44)", + "rgb(214, 39, 40)", + "rgb(148, 103, 189)", + "rgb(140, 86, 75)", + "rgb(227, 119, 194)", + "rgb(127, 127, 127)", + "rgb(188, 189, 34)", + "rgb(23, 190, 207)" + ]; + + function deletePlotSelection(plot_number) { + var plot_selection = document.querySelectorAll('#plot-selection input[type="radio"]'); + var label_selection = document.querySelectorAll('#plot-selection label'); + var label_slice_selection = document.querySelectorAll('#slice-selection label'); + var button_plot_selection = document.querySelectorAll('#plot-selection button'); + + // set last plot to checked + // get plot_selection with name "Plot N" where N is plot_number + for (let i = 0; i < plot_selection.length; i++) { + var plot_name = "Plot " + (plot_number+1) + if (plot_selection[i].value === plot_name) { + plot_number = i; + } + } + plot_selection[plot_number].checked = false; + plot_selection[plot_number-1].checked = true; + + // delete plot_selected and label + plot_selection[plot_number].remove(); + label_selection[plot_number].remove(); + + selections[plot_number] = null; + + // set selection to last plot + selection = selections[plot_number-1]; + plot_color = plot_colors[plot_number-1]; + + // set current plot selection color to plot_color + const [r, g, b] = plot_color.match(/\d+/g); + const rgbaColor = `rgba(${r}, ${g}, ${b}, 0.2)`; + plot_selection[plot_number-1].style.backgroundColor = rgbaColor; + plot_selection[plot_number-1].style.border = "2px solid " + plot_color; + plot_selection[plot_number-1].style.color = plot_color; + + // make visibility of delete button from last plot visible + if (button_plot_selection.length >= 2) { + button_plot_selection[button_plot_selection.length-2].style.visibility = "visible"; + } + + for (let i = 0; i < selection.length; i++) { + // use selection to set label_slice_selection background color + for (let j = 0; j < inputs_all.length; j++) { + if (inputs_all[j].name === selection[i].split(":")[0]) { + if (inputs_all[j].value == selection[i].split(":")[1]) { + inputs_all[j].checked = true; + label_slice_selection[j].style.backgroundColor = rgbaColor; + label_slice_selection[j].style.border = "2px solid " + plot_color; + label_slice_selection[j].style.color = plot_color; + } + } + } + } + updatePlot(); + } + + function updatePlotSelection() { + const inputs = document.querySelectorAll('#slice-selection input[type="radio"]:checked'); + var plot_selection = document.querySelectorAll('#plot-selection input[type="radio"]'); + var plot_selected = document.querySelectorAll('#plot-selection input[type="radio"]:checked')[0]; + // get number from value in plot_selected "Plot 1" -> 1 + var plot_number = parseInt(plot_selected.value.split(" ")[1]); + var label_selection = document.querySelectorAll('#plot-selection label'); + var label_slice_selection = document.querySelectorAll('#slice-selection label'); + var button_plot_selection = document.querySelectorAll('#plot-selection button'); + + // if plot_selected is "+" then add new radio button to plot_selection called "Plot N" where last plot is N-1 but keep "+" at end and set new radio button to checked for second last element + if (plot_selected.value === "+") { + // if 10 plots already exist, don't add new plot and gray out "+" + if (plot_selection.length === 11) { + plot_selected.checked = false; + label_selection[label_selection.length-1].style.color = "gray"; + return; + } + // plot_name should be name of last plot + 1 + if (plot_selection.length === 2) { + var plot_name = "Plot 2" + } else { + var plot_name = "Plot " + (parseInt(plot_selection[plot_selection.length - 2].value.split(" ")[1]) + 1); + } + var new_plot = document.createElement("input"); + new_plot.type = "radio"; + new_plot.id = plot_name; + new_plot.name = "plot"; + new_plot.value = plot_name; + new_plot.checked = true; + var new_label = document.createElement("label"); + new_label.htmlFor = plot_name; + new_label.innerHTML = plot_name; + + // Parse plot_color to get r, g, b values + var plot_color = plot_colors[plot_selection.length] + const [r, g, b] = plot_color.match(/\d+/g); + const rgbaColor = `rgba(${r}, ${g}, ${b}, 0.2)`; + // set background color of new radio button to plot_color + new_label.style.backgroundColor = rgbaColor; + new_label.style.border = "2px solid " + plot_color; + new_label.style.color = plot_color; + + // add button to delete plot + var delete_button = document.createElement("button"); + delete_button.id = "button"; + delete_button.innerHTML = "×"; + delete_button.style.backgroundColor = "transparent"; + delete_button.style.border = "none"; + new_label.style.padding = "1.5px 0px"; + new_label.style.paddingLeft = "10px"; + + new_label.appendChild(delete_button) + + // make delete button from last plot invisible if not Plot 1 + if (plot_selection.length > 2) { + button_plot_selection[button_plot_selection.length-1].style.visibility = "hidden"; + } + // add on_click event to delete button and send plot number to deletePlotSelection + delete_button.onclick = function() {deletePlotSelection(plot_number)}; + + // insert new radio button and label before "+" radio button and after last radio button + plot_selected.insertAdjacentElement("beforebegin", new_plot); + plot_selected.insertAdjacentElement("beforebegin", new_label); + + // Add event listener to new radio button + new_plot.addEventListener('change', updatePlotSelection); + + // set plot_selected to new plot + var plot_selected = new_plot + + for (let i = 0; i < label_selection.length-1; i++) { + plot_selection[i].checked = false; + label_selection[i].style.backgroundColor = "#ffffff"; + label_selection[i].style.border = "2px solid #DADCE0"; + label_selection[i].style.color = "#000000"; + } + + selections[parseInt(plot_selected.value.split(" ")[1]-1)] = selections[parseInt(plot_selected.value.split(" ")[1]-2)] + selection = selections[parseInt(plot_selected.value.split(" ")[1]-1)]; + plot_color = plot_colors[parseInt(plot_selected.value.split(" ")[1])]; + + for (let i = 0; i < selection.length; i++) { + // use selection to set label_slice_selection background color + for (let j = 0; j < inputs_all.length; j++) { + if (inputs_all[j].name === selection[i].split(":")[0]) { + if (inputs_all[j].value == selection[i].split(":")[1]) { + const [r, g, b] = plot_color.match(/\d+/g); + const rgbaColor = `rgba(${r}, ${g}, ${b}, 0.2)`; + inputs_all[j].checked = true; + label_slice_selection[j].style.backgroundColor = rgbaColor; + label_slice_selection[j].style.border = "2px solid " + plot_color; + label_slice_selection[j].style.color = plot_color; + } + else { + inputs_all[j].checked = false; + label_slice_selection[j].style.backgroundColor = "#ffffff"; + label_slice_selection[j].style.border = "2px solid #DADCE0"; + label_slice_selection[j].style.color = "#000000"; + } + } + } + } + } else { + for (let i = 0; i < plot_selection.length-1; i++) { + if (plot_selection[i].value !== plot_selected.value) { + plot_selection[i].checked = false; + label_selection[i].style.backgroundColor = "#ffffff"; + label_selection[i].style.border = "2px solid #DADCE0"; + label_selection[i].style.color = "#000000"; + } + else { + var plot_color = plot_colors[i+1] + const [r, g, b] = plot_color.match(/\d+/g); + const rgbaColor = `rgba(${r}, ${g}, ${b}, 0.2)`; + plot_selected.checked = true; + label_selection[i].style.backgroundColor = rgbaColor; + label_selection[i].style.border = "2px solid " + plot_color; + label_selection[i].style.color = plot_color; + } + } + selection = selections[parseInt(plot_selected.value.split(" ")[1]-1)]; + plot_color = plot_colors[parseInt(plot_selected.value.split(" ")[1])]; + for (let i = 0; i < selection.length; i++) { + // use selection to set label_slice_selection background color + for (let j = 0; j < inputs_all.length; j++) { + if (inputs_all[j].name === selection[i].split(":")[0]) { + if (inputs_all[j].value == selection[i].split(":")[1]) { + inputs_all[j].checked = true; + const [r, g, b] = plot_color.match(/\d+/g); + const rgbaColor = `rgba(${r}, ${g}, ${b}, 0.2)`; + label_slice_selection[j].style.backgroundColor = rgbaColor; + label_slice_selection[j].style.border = "2px solid " + plot_color; + label_slice_selection[j].style.color = plot_color; + } + else { + inputs_all[j].checked = false; + label_slice_selection[j].style.backgroundColor = "#ffffff"; + label_slice_selection[j].style.border = "2px solid #DADCE0"; + label_slice_selection[j].style.color = "#000000"; + } + } + } + } + } + var slices_all = JSON.parse("{\"0\": [\"metric:Positive Predictive Value (PPV)\", \"Patient Age:[19 - 35]\", \"pathology:overall_pathology\", \"Patient Gender:overall_Patient Gender\"], \"1\": [\"metric:Positive Predictive Value (PPV)\", \"Patient Age:[19 - 35]\", \"pathology:Atelectasis\", \"Patient Gender:overall_Patient Gender\"], \"2\": [\"metric:Positive Predictive Value (PPV)\", \"Patient Age:[19 - 35]\", \"pathology:Consolidation\", \"Patient Gender:overall_Patient Gender\"], \"3\": [\"metric:Positive Predictive Value (PPV)\", \"Patient Age:[19 - 35]\", \"pathology:Infiltration\", \"Patient Gender:overall_Patient Gender\"], \"4\": [\"metric:Positive Predictive Value (PPV)\", \"Patient Age:[19 - 35]\", \"pathology:Pneumothorax\", \"Patient Gender:overall_Patient Gender\"], \"5\": [\"metric:Positive Predictive Value (PPV)\", \"Patient Age:[19 - 35]\", \"pathology:Edema\", \"Patient Gender:overall_Patient Gender\"], \"6\": [\"metric:Positive Predictive Value (PPV)\", \"Patient Age:[19 - 35]\", \"pathology:Emphysema\", \"Patient Gender:overall_Patient Gender\"], \"7\": [\"metric:Positive Predictive Value (PPV)\", \"Patient Age:[19 - 35]\", \"pathology:Fibrosis\", \"Patient Gender:overall_Patient Gender\"], \"8\": [\"metric:Positive Predictive Value (PPV)\", \"Patient Age:[19 - 35]\", \"pathology:Effusion\", \"Patient Gender:overall_Patient Gender\"], \"9\": [\"metric:Positive Predictive Value (PPV)\", \"Patient Age:[19 - 35]\", \"pathology:Pneumonia\", \"Patient Gender:overall_Patient Gender\"], \"10\": [\"metric:Positive Predictive Value (PPV)\", \"Patient Age:[19 - 35]\", \"pathology:Pleural_Thickening\", \"Patient Gender:overall_Patient Gender\"], \"11\": [\"metric:Positive Predictive Value (PPV)\", \"Patient Age:[19 - 35]\", \"pathology:Cardiomegaly\", \"Patient Gender:overall_Patient Gender\"], \"12\": [\"metric:Positive Predictive Value (PPV)\", \"Patient Age:[19 - 35]\", \"pathology:Nodule\", \"Patient Gender:overall_Patient Gender\"], \"13\": [\"metric:Positive Predictive Value (PPV)\", \"Patient Age:[19 - 35]\", \"pathology:Mass\", \"Patient Gender:overall_Patient Gender\"], \"14\": [\"metric:Positive Predictive Value (PPV)\", \"Patient Age:[19 - 35]\", \"pathology:Hernia\", \"Patient Gender:overall_Patient Gender\"], \"15\": [\"metric:Negative Predictive Value (NPV)\", \"Patient Age:[19 - 35]\", \"pathology:overall_pathology\", \"Patient Gender:overall_Patient Gender\"], \"16\": [\"metric:Negative Predictive Value (NPV)\", \"Patient Age:[19 - 35]\", \"pathology:Atelectasis\", \"Patient Gender:overall_Patient Gender\"], \"17\": [\"metric:Negative Predictive Value (NPV)\", \"Patient Age:[19 - 35]\", 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\"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"700\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"701\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"702\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"703\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"704\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"705\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"706\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"707\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"708\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"709\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"710\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"711\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"712\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"713\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"714\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"715\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"716\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"717\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"718\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"719\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"]}"); + + var radioGroups = {}; + var labelGroups = {}; + for (let i = 0; i < inputs_all.length; i++) { + var input = inputs_all[i]; + var label = label_slice_selection[i]; + var groupName = input.name; + if (!radioGroups[groupName]) { + radioGroups[groupName] = []; + labelGroups[groupName] = []; + } + radioGroups[groupName].push(input); + labelGroups[groupName].push(label); + } + + // use radioGroups to loop through selection changing only one element at a time + for (let i = 0; i < selection.length; i++) { + for (let j = 0; j < inputs_all.length; j++) { + if (inputs_all[j].name === selection[i].split(":")[0]) { + radio_group = radioGroups[selection[i].split(":")[0]]; + label_group = labelGroups[selection[i].split(":")[0]]; + for (let k = 0; k < radio_group.length; k++) { + selection_copy = selection.slice(); + selection_copy[i] = selection[i].split(":")[0] + ":" + radio_group[k].value; + // get idx of slices where all elements match + var idx = Object.keys(slices_all).find(key => JSON.stringify(slices_all[key].sort()) === JSON.stringify(selection_copy.sort())); + if (idx === undefined) { + // set radio button to disabled and cursor to not allowed and color to gray if idx is undefined + radio_group[k].disabled = true; + label_group[k].style.cursor = "not-allowed"; + label_group[k].style.color = "gray"; + label_group[k].style.backgroundColor = "rgba(125, 125, 125, 0.2)"; + } + else { + radio_group[k].disabled = false; + label_group[k].style.cursor = "pointer"; + } + } + } + } + } + + traces = []; + var plot_number = parseInt(plot_selected.value.split(" ")[1]-1); + for (let i = 0; i < selections.length; i++) { + if (selections[i] === null) { + continue; + } + selection = selections[i] + + // get idx of slices where all elements match + var idx = Object.keys(slices_all).find(key => JSON.stringify(slices_all[key].sort()) === JSON.stringify(selection)); + var history_data = []; + for (let i = 0; i < histories_all[idx].length; i++) { + history_data.push(parseFloat(histories_all[idx][i])); + } + var timestamp_data = []; + for (let i = 0; i < timestamps_all[idx].length; i++) { + timestamp_data.push(timestamps_all[idx][i]); + } + threshold = parseFloat(thresholds_all[idx]); + trend = trends_all[idx]; + passed = passed_all[idx]; + name = names_all[idx]; + + // if trend is "positive" set keyword to upwards, if trend is "negative" set keyword to downwards, else set keyword to flat + if (trend === "positive") { + var trend_keyword = "upwards"; + } else if (trend === "negative") { + var trend_keyword = "downwards"; + } else { + var trend_keyword = "flat"; + } + + // if passed is true set keyword to Above, if passed is false set keyword to Below + if (passed) { + var passed_keyword = "above"; + } + else { + var passed_keyword = "below"; + } + + // create title for plot: Current {metric name} is trending {trend_keyword} and is {passed_keyword} the threshold. + // get number of nulls in selections, if 9 then plot title, else don't plot title + console.log(selections) + var nulls = 0; + for (let i = 0; i < selections.length; i++) { + if (selections[i] === null) { + nulls += 1; + } + } + if (nulls === 10) { + var plot_title = "Current " + name + " is trending " + trend_keyword + " and is " + passed_keyword + " the threshold."; + var showlegend = false; + } + else { + var plot_title = ""; + var showlegend = true; + } + name = "" + suffix = " ( " + for (let i = 0; i < selection.length; i++) { + if (selection[i].split(":")[0] === "metric") { + name += selection[i].split(":")[1]; + } + else { + if (selection[i].split(":")[1].includes("overall")) { + continue; + } else { + suffix += selection[i]; + suffix += ", "; + } + } + } + if (suffix === " ( ") { + name += ""; + } + else { + suffix = suffix.slice(0, -2); + name += suffix + " )"; + } + + var trace = { + // range of x is the length of the list of floats + x: timestamp_data, + y: history_data, + mode: 'lines+markers', + type: 'scatter', + marker: {color: plot_colors[i+1]}, + line: {color: plot_colors[i+1]}, + name: name, + }; + traces.push(trace); + } + + if (nulls === 10) { + var threshold_trace = { + x: timestamp_data, + y: Array.from({length: history_data.length}, (_, i) => threshold), + mode: 'lines', + type: 'scatter', + marker: {color: 'rgb(0,0,0)'}, + line: {color: 'rgb(0,0,0)', dash: 'dot'}, + name: '', + }; + traces.push(threshold_trace); + } + var layout = { + title: { + text: plot_title, + font: { + family: 'Arial, Helvetica, sans-serif', + size: 18, + } + }, + paper_bgcolor: 'rgba(0,0,0,0)', + plot_bgcolor: 'rgba(0,0,0,0)', + xaxis: { + zeroline: false, + showticklabels: false, + showgrid: false, + }, + yaxis: { + gridcolor: '#ffffff', + }, + showlegend: showlegend, + margin: { + l: 50, + r: 50, + b: 50, + t: 50, + pad: 4 + }, + // set height and width of plot to extra-wide to fit the plot + height: 500, + width: 900, + } + Plotly.newPlot(plot, traces, layout, {displayModeBar: false}); + } + + + + // Define a function to update the plot based on selected filters + function updatePlot() { + const inputs = document.querySelectorAll('#slice-selection input[type="radio"]:checked'); + var plot_selection = document.querySelectorAll('#plot-selection input[type="radio"]'); + var plot_selected = document.querySelectorAll('#plot-selection input[type="radio"]:checked')[0]; + // get number from value in plot_selected "Plot 1" -> 1 + var label_selection = document.querySelectorAll('#plot-selection label'); + var label_slice_selection = document.querySelectorAll('#slice-selection label'); + + // get all inputs values from div class radio-buttons + // get name of inputs + var inputs_name = []; + var inputs_value = []; + for (let i = 0; i < inputs.length; i++) { + inputs_name.push(inputs[i].name); + inputs_value.push(inputs[i].value); + } + + var plot_number = parseInt(plot_selected.value.split(" ")[1]-1); + var selection = []; + for (let i = 0; i < inputs_value.length; i++) { + selection.push(inputs_name[i] + ":" + inputs_value[i]); + } + selection.sort(); + selections[plot_number] = selection; + + // if plot_selected is "+" then add new radio button to plot_selection called "Plot N" where last plot is N-1 but keep "+" at end and set new radio button to checked for second last element + if (plot_selected.value === "+") { + // if 10 plots already exist, don't add new plot and gray out "+" + if (plot_selection.length === 13) { + plot_selected.checked = false; + label_selection[-1].style.color = "gray"; + return; + } + var new_plot = document.createElement("input"); + new_plot.type = "radio"; + new_plot.id = "Plot " + (plot_selection.length); + new_plot.name = "plot"; + new_plot.value = "Plot " + (plot_selection.length); + new_plot.checked = true; + var new_label = document.createElement("label"); + new_label.htmlFor = "Plot " + (plot_selection.length); + new_label.innerHTML = "Plot " + (plot_selection.length); + + // Parse plot_color to get r, g, b values + var plot_color = plot_colors[plot_selection.length] + const [r, g, b] = plot_color.match(/\d+/g); + const rgbaColor = `rgba(${r}, ${g}, ${b}, 0.2)`; + // set background color of new radio button to plot_color + new_label.style.backgroundColor = rgbaColor; + new_label.style.border = "2px solid " + plot_color; + new_label.style.color = plot_color; + + // insert new radio button and label before "+" radio button and after last radio button + plot_selected.insertAdjacentElement("beforebegin", new_plot); + plot_selected.insertAdjacentElement("beforebegin", new_label); + // Add event listener to new radio button + new_plot.addEventListener('change', updatePlot); + + // set plot_selected to new plot + plot_selected = new_plot + + for (let i = 0; i < label_selection.length-1; i++) { + plot_selection[i].checked = false; + label_selection[i].style.backgroundColor = "#ffffff"; + label_selection[i].style.border = "2px solid #DADCE0"; + label_selection[i].style.color = "#000000"; + } + } else { + for (let i = 0; i < plot_selection.length-1; i++) { + if (plot_selection[i].value !== plot_selected.value) { + plot_selection[i].checked = false; + label_selection[i].style.backgroundColor = "#ffffff"; + label_selection[i].style.border = "2px solid #DADCE0"; + label_selection[i].style.color = "#000000"; + } + else { + var plot_color = plot_colors[i+1] + const [r, g, b] = plot_color.match(/\d+/g); + const rgbaColor = `rgba(${r}, ${g}, ${b}, 0.2)`; + plot_selected.checked = true; + label_selection[i].style.backgroundColor = rgbaColor; + label_selection[i].style.border = "2px solid " + plot_color; + label_selection[i].style.color = plot_color; + } + } + } + var slices_all = JSON.parse("{\"0\": [\"metric:Positive Predictive Value (PPV)\", \"Patient Age:[19 - 35]\", \"pathology:overall_pathology\", \"Patient Gender:overall_Patient Gender\"], \"1\": [\"metric:Positive Predictive Value (PPV)\", \"Patient Age:[19 - 35]\", \"pathology:Atelectasis\", \"Patient Gender:overall_Patient Gender\"], \"2\": [\"metric:Positive Predictive Value (PPV)\", \"Patient Age:[19 - 35]\", \"pathology:Consolidation\", \"Patient Gender:overall_Patient Gender\"], \"3\": [\"metric:Positive Predictive Value (PPV)\", \"Patient Age:[19 - 35]\", \"pathology:Infiltration\", \"Patient Gender:overall_Patient Gender\"], \"4\": [\"metric:Positive Predictive Value (PPV)\", \"Patient Age:[19 - 35]\", \"pathology:Pneumothorax\", \"Patient Gender:overall_Patient Gender\"], \"5\": [\"metric:Positive Predictive Value (PPV)\", \"Patient Age:[19 - 35]\", \"pathology:Edema\", \"Patient Gender:overall_Patient Gender\"], \"6\": [\"metric:Positive Predictive Value (PPV)\", \"Patient Age:[19 - 35]\", \"pathology:Emphysema\", \"Patient Gender:overall_Patient Gender\"], \"7\": [\"metric:Positive Predictive Value (PPV)\", \"Patient Age:[19 - 35]\", \"pathology:Fibrosis\", \"Patient Gender:overall_Patient Gender\"], \"8\": [\"metric:Positive Predictive Value (PPV)\", \"Patient Age:[19 - 35]\", \"pathology:Effusion\", \"Patient Gender:overall_Patient Gender\"], \"9\": [\"metric:Positive Predictive Value (PPV)\", \"Patient Age:[19 - 35]\", \"pathology:Pneumonia\", \"Patient Gender:overall_Patient Gender\"], \"10\": [\"metric:Positive Predictive Value (PPV)\", \"Patient Age:[19 - 35]\", \"pathology:Pleural_Thickening\", \"Patient Gender:overall_Patient Gender\"], \"11\": [\"metric:Positive Predictive Value (PPV)\", \"Patient Age:[19 - 35]\", \"pathology:Cardiomegaly\", \"Patient Gender:overall_Patient Gender\"], \"12\": [\"metric:Positive Predictive Value (PPV)\", \"Patient Age:[19 - 35]\", \"pathology:Nodule\", \"Patient Gender:overall_Patient Gender\"], \"13\": [\"metric:Positive Predictive Value (PPV)\", \"Patient Age:[19 - 35]\", \"pathology:Mass\", \"Patient Gender:overall_Patient Gender\"], \"14\": [\"metric:Positive Predictive Value (PPV)\", \"Patient Age:[19 - 35]\", \"pathology:Hernia\", \"Patient Gender:overall_Patient Gender\"], \"15\": [\"metric:Negative Predictive Value (NPV)\", \"Patient Age:[19 - 35]\", \"pathology:overall_pathology\", \"Patient Gender:overall_Patient Gender\"], \"16\": [\"metric:Negative Predictive Value (NPV)\", \"Patient Age:[19 - 35]\", \"pathology:Atelectasis\", \"Patient Gender:overall_Patient Gender\"], \"17\": [\"metric:Negative Predictive Value (NPV)\", \"Patient Age:[19 - 35]\", \"pathology:Consolidation\", \"Patient Gender:overall_Patient Gender\"], \"18\": [\"metric:Negative Predictive Value (NPV)\", \"Patient Age:[19 - 35]\", \"pathology:Infiltration\", \"Patient Gender:overall_Patient Gender\"], \"19\": [\"metric:Negative Predictive Value (NPV)\", \"Patient Age:[19 - 35]\", \"pathology:Pneumothorax\", \"Patient Gender:overall_Patient Gender\"], \"20\": [\"metric:Negative Predictive Value (NPV)\", \"Patient Age:[19 - 35]\", \"pathology:Edema\", \"Patient Gender:overall_Patient Gender\"], \"21\": [\"metric:Negative Predictive Value (NPV)\", \"Patient Age:[19 - 35]\", \"pathology:Emphysema\", \"Patient Gender:overall_Patient Gender\"], \"22\": [\"metric:Negative Predictive Value (NPV)\", \"Patient Age:[19 - 35]\", \"pathology:Fibrosis\", \"Patient Gender:overall_Patient Gender\"], \"23\": [\"metric:Negative Predictive Value (NPV)\", \"Patient Age:[19 - 35]\", \"pathology:Effusion\", \"Patient Gender:overall_Patient Gender\"], \"24\": [\"metric:Negative Predictive Value (NPV)\", \"Patient Age:[19 - 35]\", \"pathology:Pneumonia\", \"Patient Gender:overall_Patient Gender\"], \"25\": [\"metric:Negative Predictive Value (NPV)\", \"Patient Age:[19 - 35]\", \"pathology:Pleural_Thickening\", \"Patient Gender:overall_Patient Gender\"], \"26\": [\"metric:Negative Predictive Value (NPV)\", \"Patient Age:[19 - 35]\", \"pathology:Cardiomegaly\", \"Patient Gender:overall_Patient Gender\"], \"27\": [\"metric:Negative Predictive Value (NPV)\", \"Patient Age:[19 - 35]\", \"pathology:Nodule\", \"Patient Gender:overall_Patient Gender\"], \"28\": [\"metric:Negative Predictive Value (NPV)\", \"Patient Age:[19 - 35]\", \"pathology:Mass\", \"Patient Gender:overall_Patient Gender\"], \"29\": [\"metric:Negative Predictive Value (NPV)\", \"Patient Age:[19 - 35]\", \"pathology:Hernia\", \"Patient Gender:overall_Patient Gender\"], \"30\": [\"metric:Sensitivity\", \"Patient Age:[19 - 35]\", \"pathology:overall_pathology\", \"Patient Gender:overall_Patient Gender\"], \"31\": [\"metric:Sensitivity\", \"Patient Age:[19 - 35]\", \"pathology:Atelectasis\", \"Patient Gender:overall_Patient Gender\"], \"32\": [\"metric:Sensitivity\", \"Patient Age:[19 - 35]\", \"pathology:Consolidation\", \"Patient Gender:overall_Patient Gender\"], \"33\": [\"metric:Sensitivity\", \"Patient Age:[19 - 35]\", \"pathology:Infiltration\", \"Patient Gender:overall_Patient Gender\"], \"34\": [\"metric:Sensitivity\", \"Patient Age:[19 - 35]\", \"pathology:Pneumothorax\", \"Patient Gender:overall_Patient Gender\"], \"35\": [\"metric:Sensitivity\", \"Patient Age:[19 - 35]\", \"pathology:Edema\", \"Patient Gender:overall_Patient Gender\"], \"36\": [\"metric:Sensitivity\", \"Patient Age:[19 - 35]\", \"pathology:Emphysema\", \"Patient Gender:overall_Patient Gender\"], \"37\": [\"metric:Sensitivity\", \"Patient Age:[19 - 35]\", \"pathology:Fibrosis\", \"Patient Gender:overall_Patient Gender\"], \"38\": [\"metric:Sensitivity\", \"Patient Age:[19 - 35]\", \"pathology:Effusion\", \"Patient Gender:overall_Patient Gender\"], \"39\": [\"metric:Sensitivity\", \"Patient Age:[19 - 35]\", \"pathology:Pneumonia\", \"Patient Gender:overall_Patient Gender\"], \"40\": [\"metric:Sensitivity\", \"Patient Age:[19 - 35]\", \"pathology:Pleural_Thickening\", \"Patient Gender:overall_Patient Gender\"], \"41\": [\"metric:Sensitivity\", \"Patient Age:[19 - 35]\", \"pathology:Cardiomegaly\", \"Patient Gender:overall_Patient Gender\"], \"42\": [\"metric:Sensitivity\", \"Patient Age:[19 - 35]\", \"pathology:Nodule\", \"Patient Gender:overall_Patient Gender\"], \"43\": [\"metric:Sensitivity\", \"Patient Age:[19 - 35]\", \"pathology:Mass\", \"Patient Gender:overall_Patient Gender\"], \"44\": [\"metric:Sensitivity\", \"Patient Age:[19 - 35]\", \"pathology:Hernia\", \"Patient Gender:overall_Patient Gender\"], \"45\": [\"metric:Specificity\", \"Patient Age:[19 - 35]\", \"pathology:overall_pathology\", \"Patient Gender:overall_Patient Gender\"], \"46\": [\"metric:Specificity\", \"Patient Age:[19 - 35]\", \"pathology:Atelectasis\", \"Patient Gender:overall_Patient Gender\"], \"47\": [\"metric:Specificity\", \"Patient Age:[19 - 35]\", \"pathology:Consolidation\", \"Patient Gender:overall_Patient Gender\"], \"48\": [\"metric:Specificity\", \"Patient Age:[19 - 35]\", \"pathology:Infiltration\", \"Patient Gender:overall_Patient Gender\"], \"49\": [\"metric:Specificity\", \"Patient Age:[19 - 35]\", \"pathology:Pneumothorax\", \"Patient Gender:overall_Patient Gender\"], \"50\": [\"metric:Specificity\", \"Patient Age:[19 - 35]\", \"pathology:Edema\", \"Patient Gender:overall_Patient Gender\"], \"51\": [\"metric:Specificity\", \"Patient Age:[19 - 35]\", \"pathology:Emphysema\", \"Patient Gender:overall_Patient Gender\"], \"52\": [\"metric:Specificity\", \"Patient Age:[19 - 35]\", \"pathology:Fibrosis\", \"Patient Gender:overall_Patient Gender\"], \"53\": [\"metric:Specificity\", \"Patient Age:[19 - 35]\", \"pathology:Effusion\", \"Patient Gender:overall_Patient Gender\"], \"54\": [\"metric:Specificity\", \"Patient Age:[19 - 35]\", \"pathology:Pneumonia\", \"Patient Gender:overall_Patient Gender\"], \"55\": [\"metric:Specificity\", \"Patient Age:[19 - 35]\", \"pathology:Pleural_Thickening\", \"Patient Gender:overall_Patient Gender\"], \"56\": [\"metric:Specificity\", \"Patient Age:[19 - 35]\", \"pathology:Cardiomegaly\", \"Patient Gender:overall_Patient Gender\"], \"57\": [\"metric:Specificity\", \"Patient Age:[19 - 35]\", \"pathology:Nodule\", \"Patient Gender:overall_Patient Gender\"], \"58\": [\"metric:Specificity\", \"Patient Age:[19 - 35]\", \"pathology:Mass\", \"Patient Gender:overall_Patient Gender\"], \"59\": [\"metric:Specificity\", \"Patient Age:[19 - 35]\", \"pathology:Hernia\", \"Patient Gender:overall_Patient Gender\"], \"60\": [\"metric:Positive Predictive Value (PPV)\", \"Patient Age:[35 - 65]\", \"pathology:overall_pathology\", \"Patient Gender:overall_Patient Gender\"], \"61\": [\"metric:Positive Predictive Value (PPV)\", \"Patient Age:[35 - 65]\", \"pathology:Atelectasis\", \"Patient Gender:overall_Patient Gender\"], \"62\": [\"metric:Positive Predictive Value (PPV)\", \"Patient Age:[35 - 65]\", \"pathology:Consolidation\", \"Patient Gender:overall_Patient Gender\"], \"63\": [\"metric:Positive Predictive Value (PPV)\", \"Patient Age:[35 - 65]\", \"pathology:Infiltration\", \"Patient Gender:overall_Patient Gender\"], \"64\": [\"metric:Positive Predictive Value (PPV)\", \"Patient Age:[35 - 65]\", \"pathology:Pneumothorax\", \"Patient Gender:overall_Patient Gender\"], \"65\": [\"metric:Positive Predictive Value (PPV)\", \"Patient Age:[35 - 65]\", \"pathology:Edema\", \"Patient Gender:overall_Patient Gender\"], \"66\": [\"metric:Positive Predictive Value (PPV)\", \"Patient Age:[35 - 65]\", \"pathology:Emphysema\", \"Patient Gender:overall_Patient Gender\"], \"67\": [\"metric:Positive Predictive Value (PPV)\", \"Patient Age:[35 - 65]\", \"pathology:Fibrosis\", \"Patient Gender:overall_Patient Gender\"], \"68\": [\"metric:Positive Predictive Value (PPV)\", \"Patient Age:[35 - 65]\", \"pathology:Effusion\", \"Patient Gender:overall_Patient Gender\"], \"69\": [\"metric:Positive Predictive Value (PPV)\", \"Patient Age:[35 - 65]\", \"pathology:Pneumonia\", \"Patient Gender:overall_Patient Gender\"], \"70\": [\"metric:Positive Predictive Value (PPV)\", \"Patient Age:[35 - 65]\", \"pathology:Pleural_Thickening\", \"Patient Gender:overall_Patient Gender\"], \"71\": [\"metric:Positive Predictive Value (PPV)\", \"Patient Age:[35 - 65]\", \"pathology:Cardiomegaly\", \"Patient Gender:overall_Patient Gender\"], \"72\": [\"metric:Positive Predictive Value (PPV)\", \"Patient Age:[35 - 65]\", \"pathology:Nodule\", \"Patient Gender:overall_Patient Gender\"], \"73\": [\"metric:Positive Predictive Value (PPV)\", \"Patient Age:[35 - 65]\", \"pathology:Mass\", \"Patient Gender:overall_Patient Gender\"], \"74\": [\"metric:Positive Predictive Value (PPV)\", \"Patient Age:[35 - 65]\", \"pathology:Hernia\", \"Patient Gender:overall_Patient Gender\"], \"75\": [\"metric:Negative Predictive Value (NPV)\", \"Patient Age:[35 - 65]\", \"pathology:overall_pathology\", \"Patient Gender:overall_Patient Gender\"], \"76\": [\"metric:Negative Predictive Value (NPV)\", \"Patient Age:[35 - 65]\", \"pathology:Atelectasis\", \"Patient Gender:overall_Patient Gender\"], \"77\": [\"metric:Negative Predictive Value (NPV)\", \"Patient Age:[35 - 65]\", \"pathology:Consolidation\", \"Patient Gender:overall_Patient Gender\"], \"78\": [\"metric:Negative Predictive Value (NPV)\", \"Patient Age:[35 - 65]\", \"pathology:Infiltration\", \"Patient Gender:overall_Patient Gender\"], \"79\": [\"metric:Negative Predictive Value (NPV)\", \"Patient Age:[35 - 65]\", \"pathology:Pneumothorax\", \"Patient Gender:overall_Patient Gender\"], \"80\": [\"metric:Negative Predictive Value (NPV)\", \"Patient Age:[35 - 65]\", \"pathology:Edema\", \"Patient Gender:overall_Patient Gender\"], \"81\": [\"metric:Negative Predictive Value (NPV)\", \"Patient Age:[35 - 65]\", \"pathology:Emphysema\", \"Patient Gender:overall_Patient Gender\"], \"82\": [\"metric:Negative Predictive Value (NPV)\", \"Patient Age:[35 - 65]\", \"pathology:Fibrosis\", \"Patient Gender:overall_Patient Gender\"], \"83\": [\"metric:Negative Predictive Value (NPV)\", \"Patient Age:[35 - 65]\", \"pathology:Effusion\", \"Patient Gender:overall_Patient Gender\"], \"84\": [\"metric:Negative Predictive Value (NPV)\", \"Patient Age:[35 - 65]\", \"pathology:Pneumonia\", \"Patient Gender:overall_Patient Gender\"], \"85\": [\"metric:Negative Predictive Value (NPV)\", \"Patient Age:[35 - 65]\", \"pathology:Pleural_Thickening\", \"Patient Gender:overall_Patient Gender\"], \"86\": [\"metric:Negative Predictive Value (NPV)\", \"Patient Age:[35 - 65]\", \"pathology:Cardiomegaly\", \"Patient Gender:overall_Patient Gender\"], \"87\": [\"metric:Negative Predictive Value (NPV)\", \"Patient Age:[35 - 65]\", \"pathology:Nodule\", \"Patient Gender:overall_Patient Gender\"], \"88\": [\"metric:Negative Predictive Value (NPV)\", \"Patient Age:[35 - 65]\", \"pathology:Mass\", \"Patient Gender:overall_Patient Gender\"], \"89\": [\"metric:Negative Predictive Value (NPV)\", \"Patient Age:[35 - 65]\", \"pathology:Hernia\", \"Patient Gender:overall_Patient Gender\"], \"90\": [\"metric:Sensitivity\", \"Patient Age:[35 - 65]\", \"pathology:overall_pathology\", \"Patient Gender:overall_Patient Gender\"], \"91\": [\"metric:Sensitivity\", \"Patient Age:[35 - 65]\", \"pathology:Atelectasis\", \"Patient Gender:overall_Patient Gender\"], \"92\": [\"metric:Sensitivity\", \"Patient Age:[35 - 65]\", \"pathology:Consolidation\", \"Patient Gender:overall_Patient Gender\"], \"93\": [\"metric:Sensitivity\", \"Patient Age:[35 - 65]\", \"pathology:Infiltration\", \"Patient Gender:overall_Patient Gender\"], \"94\": [\"metric:Sensitivity\", \"Patient Age:[35 - 65]\", \"pathology:Pneumothorax\", \"Patient Gender:overall_Patient Gender\"], \"95\": [\"metric:Sensitivity\", \"Patient Age:[35 - 65]\", \"pathology:Edema\", \"Patient Gender:overall_Patient Gender\"], \"96\": [\"metric:Sensitivity\", \"Patient Age:[35 - 65]\", \"pathology:Emphysema\", \"Patient Gender:overall_Patient Gender\"], \"97\": [\"metric:Sensitivity\", \"Patient Age:[35 - 65]\", \"pathology:Fibrosis\", \"Patient Gender:overall_Patient Gender\"], \"98\": [\"metric:Sensitivity\", \"Patient Age:[35 - 65]\", \"pathology:Effusion\", \"Patient Gender:overall_Patient Gender\"], \"99\": [\"metric:Sensitivity\", \"Patient Age:[35 - 65]\", \"pathology:Pneumonia\", \"Patient Gender:overall_Patient Gender\"], \"100\": [\"metric:Sensitivity\", \"Patient Age:[35 - 65]\", \"pathology:Pleural_Thickening\", \"Patient Gender:overall_Patient Gender\"], \"101\": [\"metric:Sensitivity\", \"Patient Age:[35 - 65]\", \"pathology:Cardiomegaly\", \"Patient Gender:overall_Patient Gender\"], \"102\": [\"metric:Sensitivity\", \"Patient Age:[35 - 65]\", \"pathology:Nodule\", \"Patient Gender:overall_Patient Gender\"], \"103\": [\"metric:Sensitivity\", \"Patient Age:[35 - 65]\", \"pathology:Mass\", \"Patient Gender:overall_Patient Gender\"], \"104\": [\"metric:Sensitivity\", \"Patient Age:[35 - 65]\", \"pathology:Hernia\", \"Patient Gender:overall_Patient Gender\"], \"105\": [\"metric:Specificity\", \"Patient Age:[35 - 65]\", \"pathology:overall_pathology\", \"Patient Gender:overall_Patient Gender\"], \"106\": [\"metric:Specificity\", \"Patient Age:[35 - 65]\", \"pathology:Atelectasis\", \"Patient Gender:overall_Patient Gender\"], \"107\": [\"metric:Specificity\", \"Patient Age:[35 - 65]\", \"pathology:Consolidation\", \"Patient Gender:overall_Patient Gender\"], \"108\": [\"metric:Specificity\", \"Patient Age:[35 - 65]\", \"pathology:Infiltration\", \"Patient Gender:overall_Patient Gender\"], \"109\": [\"metric:Specificity\", \"Patient Age:[35 - 65]\", \"pathology:Pneumothorax\", \"Patient Gender:overall_Patient Gender\"], \"110\": [\"metric:Specificity\", \"Patient Age:[35 - 65]\", \"pathology:Edema\", \"Patient Gender:overall_Patient Gender\"], \"111\": [\"metric:Specificity\", \"Patient Age:[35 - 65]\", \"pathology:Emphysema\", \"Patient Gender:overall_Patient Gender\"], \"112\": [\"metric:Specificity\", \"Patient Age:[35 - 65]\", \"pathology:Fibrosis\", \"Patient Gender:overall_Patient Gender\"], \"113\": [\"metric:Specificity\", \"Patient Age:[35 - 65]\", \"pathology:Effusion\", \"Patient Gender:overall_Patient Gender\"], \"114\": [\"metric:Specificity\", \"Patient Age:[35 - 65]\", \"pathology:Pneumonia\", \"Patient Gender:overall_Patient Gender\"], \"115\": [\"metric:Specificity\", \"Patient Age:[35 - 65]\", \"pathology:Pleural_Thickening\", \"Patient Gender:overall_Patient Gender\"], \"116\": [\"metric:Specificity\", \"Patient Age:[35 - 65]\", \"pathology:Cardiomegaly\", \"Patient Gender:overall_Patient Gender\"], \"117\": [\"metric:Specificity\", \"Patient Age:[35 - 65]\", \"pathology:Nodule\", \"Patient Gender:overall_Patient Gender\"], \"118\": [\"metric:Specificity\", \"Patient Age:[35 - 65]\", \"pathology:Mass\", \"Patient Gender:overall_Patient Gender\"], \"119\": [\"metric:Specificity\", \"Patient Age:[35 - 65]\", \"pathology:Hernia\", \"Patient Gender:overall_Patient Gender\"], \"120\": [\"metric:Positive Predictive Value (PPV)\", \"Patient Age:[65 - 100]\", \"pathology:overall_pathology\", \"Patient Gender:overall_Patient Gender\"], \"121\": [\"metric:Positive Predictive Value (PPV)\", \"Patient Age:[65 - 100]\", \"pathology:Atelectasis\", \"Patient Gender:overall_Patient Gender\"], \"122\": [\"metric:Positive Predictive Value (PPV)\", \"Patient Age:[65 - 100]\", \"pathology:Consolidation\", \"Patient Gender:overall_Patient Gender\"], \"123\": [\"metric:Positive Predictive Value (PPV)\", \"Patient Age:[65 - 100]\", \"pathology:Infiltration\", \"Patient Gender:overall_Patient Gender\"], \"124\": [\"metric:Positive Predictive Value (PPV)\", \"Patient Age:[65 - 100]\", \"pathology:Pneumothorax\", \"Patient Gender:overall_Patient Gender\"], \"125\": [\"metric:Positive Predictive Value (PPV)\", \"Patient Age:[65 - 100]\", \"pathology:Edema\", \"Patient Gender:overall_Patient Gender\"], \"126\": [\"metric:Positive Predictive Value (PPV)\", \"Patient Age:[65 - 100]\", \"pathology:Emphysema\", \"Patient Gender:overall_Patient Gender\"], \"127\": [\"metric:Positive Predictive Value (PPV)\", \"Patient Age:[65 - 100]\", \"pathology:Fibrosis\", \"Patient Gender:overall_Patient Gender\"], \"128\": [\"metric:Positive Predictive Value (PPV)\", \"Patient Age:[65 - 100]\", \"pathology:Effusion\", \"Patient Gender:overall_Patient Gender\"], \"129\": [\"metric:Positive Predictive Value (PPV)\", \"Patient Age:[65 - 100]\", \"pathology:Pneumonia\", \"Patient Gender:overall_Patient Gender\"], \"130\": [\"metric:Positive Predictive Value (PPV)\", \"Patient Age:[65 - 100]\", \"pathology:Pleural_Thickening\", \"Patient Gender:overall_Patient Gender\"], \"131\": [\"metric:Positive Predictive Value (PPV)\", \"Patient Age:[65 - 100]\", \"pathology:Cardiomegaly\", \"Patient Gender:overall_Patient Gender\"], \"132\": [\"metric:Positive Predictive Value (PPV)\", \"Patient Age:[65 - 100]\", \"pathology:Nodule\", \"Patient Gender:overall_Patient Gender\"], \"133\": [\"metric:Positive Predictive Value (PPV)\", \"Patient Age:[65 - 100]\", \"pathology:Mass\", \"Patient Gender:overall_Patient Gender\"], \"134\": [\"metric:Positive Predictive Value (PPV)\", \"Patient Age:[65 - 100]\", \"pathology:Hernia\", \"Patient Gender:overall_Patient Gender\"], \"135\": [\"metric:Negative Predictive Value (NPV)\", \"Patient Age:[65 - 100]\", \"pathology:overall_pathology\", \"Patient Gender:overall_Patient Gender\"], \"136\": [\"metric:Negative Predictive Value (NPV)\", \"Patient Age:[65 - 100]\", \"pathology:Atelectasis\", \"Patient Gender:overall_Patient Gender\"], \"137\": [\"metric:Negative Predictive Value (NPV)\", \"Patient Age:[65 - 100]\", \"pathology:Consolidation\", \"Patient Gender:overall_Patient Gender\"], \"138\": [\"metric:Negative Predictive Value (NPV)\", \"Patient Age:[65 - 100]\", \"pathology:Infiltration\", \"Patient Gender:overall_Patient Gender\"], \"139\": [\"metric:Negative Predictive Value (NPV)\", \"Patient Age:[65 - 100]\", \"pathology:Pneumothorax\", \"Patient Gender:overall_Patient Gender\"], \"140\": [\"metric:Negative Predictive Value (NPV)\", \"Patient Age:[65 - 100]\", \"pathology:Edema\", \"Patient Gender:overall_Patient Gender\"], \"141\": [\"metric:Negative Predictive Value (NPV)\", \"Patient Age:[65 - 100]\", \"pathology:Emphysema\", \"Patient Gender:overall_Patient Gender\"], \"142\": [\"metric:Negative Predictive Value (NPV)\", \"Patient Age:[65 - 100]\", \"pathology:Fibrosis\", \"Patient Gender:overall_Patient Gender\"], \"143\": [\"metric:Negative Predictive Value (NPV)\", \"Patient Age:[65 - 100]\", \"pathology:Effusion\", \"Patient Gender:overall_Patient Gender\"], \"144\": [\"metric:Negative Predictive Value (NPV)\", \"Patient Age:[65 - 100]\", \"pathology:Pneumonia\", \"Patient Gender:overall_Patient Gender\"], \"145\": [\"metric:Negative Predictive Value (NPV)\", \"Patient Age:[65 - 100]\", \"pathology:Pleural_Thickening\", \"Patient Gender:overall_Patient Gender\"], \"146\": [\"metric:Negative Predictive Value (NPV)\", \"Patient Age:[65 - 100]\", \"pathology:Cardiomegaly\", \"Patient Gender:overall_Patient Gender\"], \"147\": [\"metric:Negative Predictive Value (NPV)\", \"Patient Age:[65 - 100]\", \"pathology:Nodule\", \"Patient Gender:overall_Patient Gender\"], \"148\": [\"metric:Negative Predictive Value (NPV)\", \"Patient Age:[65 - 100]\", \"pathology:Mass\", \"Patient Gender:overall_Patient Gender\"], \"149\": [\"metric:Negative Predictive Value (NPV)\", \"Patient Age:[65 - 100]\", \"pathology:Hernia\", \"Patient Gender:overall_Patient Gender\"], \"150\": [\"metric:Sensitivity\", \"Patient Age:[65 - 100]\", \"pathology:overall_pathology\", \"Patient Gender:overall_Patient Gender\"], \"151\": [\"metric:Sensitivity\", \"Patient Age:[65 - 100]\", \"pathology:Atelectasis\", \"Patient 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[\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"636\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"637\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"638\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"639\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"640\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"641\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"642\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"643\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"644\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"645\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"646\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"647\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"648\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"649\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"650\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"651\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"652\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"653\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"654\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"655\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"656\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"657\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"658\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"659\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"660\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"661\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"662\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"663\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"664\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"665\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"666\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"667\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"668\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"669\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"670\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"671\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"672\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"673\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"674\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"675\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"676\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"677\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"678\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"679\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"680\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"681\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"682\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"683\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"684\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"685\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"686\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"687\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"688\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"689\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"690\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"691\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"692\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"693\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"694\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"695\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"696\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"697\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"698\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"699\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"700\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"701\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"702\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"703\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"704\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"705\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"706\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"707\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"708\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"709\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"710\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"711\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"712\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"713\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"714\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"715\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"716\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"717\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"718\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"], \"719\": [\"2023-10-19\", \"2023-10-22\", \"2023-10-30\", \"2023-11-06\"]}"); + + for (let i = 0; i < selection.length; i++) { + // use selection to set label_slice_selection background color + for (let j = 0; j < inputs_all.length; j++) { + if (inputs_all[j].name === selection[i].split(":")[0]) { + if (inputs_all[j].value == selection[i].split(":")[1]) { + inputs_all[j].checked = true; + const [r, g, b] = plot_color.match(/\d+/g); + const rgbaColor = `rgba(${r}, ${g}, ${b}, 0.2)`; + label_slice_selection[j].style.backgroundColor = rgbaColor; + label_slice_selection[j].style.border = "2px solid " + plot_color; + label_slice_selection[j].style.color = plot_color; + } + else { + inputs_all[j].checked = false; + label_slice_selection[j].style.backgroundColor = "#ffffff"; + label_slice_selection[j].style.border = "2px solid #DADCE0"; + label_slice_selection[j].style.color = "#000000"; + } + } + } + } + + var radioGroups = {}; + var labelGroups = {}; + for (let i = 0; i < inputs_all.length; i++) { + var input = inputs_all[i]; + var label = label_slice_selection[i]; + var groupName = input.name; + if (!radioGroups[groupName]) { + radioGroups[groupName] = []; + labelGroups[groupName] = []; + } + radioGroups[groupName].push(input); + labelGroups[groupName].push(label); + } + + // use radioGroups to loop through selection changing only one element at a time + for (let i = 0; i < selection.length; i++) { + for (let j = 0; j < inputs_all.length; j++) { + if (inputs_all[j].name === selection[i].split(":")[0]) { + radio_group = radioGroups[selection[i].split(":")[0]]; + label_group = labelGroups[selection[i].split(":")[0]]; + for (let k = 0; k < radio_group.length; k++) { + selection_copy = selection.slice(); + selection_copy[i] = selection[i].split(":")[0] + ":" + radio_group[k].value; + // get idx of slices where all elements match + var idx = Object.keys(slices_all).find(key => JSON.stringify(slices_all[key].sort()) === JSON.stringify(selection_copy.sort())); + if (idx === undefined) { + // set radio button to disabled and cursor to not allowed and color to gray if idx is undefined + radio_group[k].disabled = true; + label_group[k].style.cursor = "not-allowed"; + label_group[k].style.color = "gray"; + label_group[k].style.backgroundColor = "rgba(125, 125, 125, 0.2)"; + } + else { + radio_group[k].disabled = false; + label_group[k].style.cursor = "pointer"; + } + } + } + } + } + + traces = []; + for (let i = 0; i < selections.length; i++) { + if (selections[i] === null) { + continue; + } + selection = selections[i] + // get idx of slices where all elements match + var idx = Object.keys(slices_all).find(key => JSON.stringify(slices_all[key].sort()) === JSON.stringify(selection)); + var history_data = []; + for (let i = 0; i < histories_all[idx].length; i++) { + history_data.push(parseFloat(histories_all[idx][i])); + } + var timestamp_data = []; + for (let i = 0; i < timestamps_all[idx].length; i++) { + timestamp_data.push(timestamps_all[idx][i]); + } + threshold = parseFloat(thresholds_all[idx]); + trend = trends_all[idx]; + passed = passed_all[idx]; + name = names_all[idx]; + + // if trend is "positive" set keyword to upwards, if trend is "negative" set keyword to downwards, else set keyword to flat + if (trend === "positive") { + var trend_keyword = "upwards"; + } else if (trend === "negative") { + var trend_keyword = "downwards"; + } else { + var trend_keyword = "flat"; + } + + // if passed is true set keyword to Above, if passed is false set keyword to Below + if (passed) { + var passed_keyword = "above"; + } + else { + var passed_keyword = "below"; + } + + // create title for plot: Current {metric name} is trending {trend_keyword} and is {passed_keyword} the threshold. + // get number of nulls in selections, if 9 then plot title, else don't plot title + var nulls = 0; + for (let i = 0; i < selections.length; i++) { + if (selections[i] === null) { + nulls += 1; + } + } + if (nulls === 10) { + var plot_title = "Current " + name + " is trending " + trend_keyword + " and is " + passed_keyword + " the threshold."; + var showlegend = false; + } + else { + var plot_title = ""; + var showlegend = true; + } + name = "" + suffix = " ( " + for (let i = 0; i < selection.length; i++) { + if (selection[i].split(":")[0] === "metric") { + name += selection[i].split(":")[1]; + } + else { + if (selection[i].split(":")[1].includes("overall")) { + continue; + } else { + suffix += selection[i]; + suffix += ", "; + } + } + } + if (suffix === " ( ") { + name += ""; + } + else { + suffix = suffix.slice(0, -2); + name += suffix + " )"; + } + var trace = { + // range of x is the length of the list of floats + x: timestamp_data, + y: history_data, + mode: 'lines+markers', + type: 'scatter', + marker: {color: plot_colors[i+1]}, + line: {color: plot_colors[i+1]}, + name: name, + //name: selection.toString(), + }; + traces.push(trace); + } + + if (nulls === 10) { + var threshold_trace = { + x: timestamp_data, + y: Array.from({length: history_data.length}, (_, i) => threshold), + mode: 'lines', + type: 'scatter', + marker: {color: 'rgb(0,0,0)'}, + line: {color: 'rgb(0,0,0)', dash: 'dot'}, + name: '', + }; + traces.push(threshold_trace); + } + var layout = { + title: { + text: plot_title, + font: { + family: 'Arial, Helvetica, sans-serif', + size: 18, + } + }, + paper_bgcolor: 'rgba(0,0,0,0)', + plot_bgcolor: 'rgba(0,0,0,0)', + xaxis: { + zeroline: false, + showticklabels: false, + showgrid: false, + }, + yaxis: { + gridcolor: '#ffffff', + }, + showlegend: showlegend, + margin: { + l: 50, + r: 50, + b: 50, + t: 50, + pad: 4 + }, + // set height and width of plot to extra-wide to fit the plot + height: 500, + width: 900, + } + Plotly.newPlot(plot, traces, layout, {displayModeBar: false}); + } + // Add event listeners to radio buttons + for (let input of inputs_all) { + input.addEventListener('change', updatePlot); + } + for (let selection of plot_selection) { + selection.addEventListener('change', updatePlotSelection); + } + // Initial update when the page loads + updatePlot(); + \ No newline at end of file diff --git a/api/tutorials/synthea/length_of_stay_report_periodic.html b/api/tutorials/synthea/length_of_stay_report_periodic.html index 5e38f2eb6..7efec01e2 100644 --- a/api/tutorials/synthea/length_of_stay_report_periodic.html +++ b/api/tutorials/synthea/length_of_stay_report_periodic.html @@ -3,6 +3,70 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + @@ -55,6 +119,7 @@ .card { display: flex; flex-wrap: wrap; + flex-basis: 100%; justify-content: left; padding: 1em; border: 1px solid #DADCE0; @@ -420,6 +485,23 @@ } + .radio-buttons #button { + padding-right: 5px; + margin-left: 5px; + margin-right: 0px; + font-size:18px; + font-weight: bold; + cursor: pointer; + color: black; + background-color: #ffffff; + border: 2px solid #DADCE0; + } + + .radio-buttons #button:hover { + color: #0073e4; + } + +
- - - - +
+
@@ -605,7 +680,7 @@

A quick glance of your most import
- 1.0 + 0.87 @@ -615,15 +690,8 @@

A quick glance of your most import 0.7
minimum
threshold - - -
- -
- -
- - +
+

@@ -645,7 +713,7 @@

A quick glance of your most import
- 0.74 + 0.96 @@ -655,15 +723,8 @@

A quick glance of your most import 0.7
minimum
threshold - - -
- -
- -
- - +
+

@@ -685,7 +746,7 @@

A quick glance of your most import
- 0.87 + 0.95 @@ -695,15 +756,8 @@

A quick glance of your most import 0.7
minimum
threshold - - -
- -
- -
- - +
+

@@ -725,7 +779,7 @@

A quick glance of your most import
- 0.93 + 1.0 @@ -735,15 +789,8 @@

A quick glance of your most import 0.7
minimum
threshold - - -
- -
- -
- - +
+

@@ -856,13 +903,23 @@

A quick glance of your most import -
+

How is your model doing over time?


See how your model is performing over several metrics and subgroups over time.

+ +
+

Multi-plot Selection:

+
+ + + + +
+

Metrics

-
+
@@ -927,7 +984,7 @@

Metrics

Age

-
+
@@ -942,7 +999,7 @@

Age

Gender

-
+
@@ -957,141 +1014,7 @@

Gender

-
- - -
+
@@ -1121,7 +1044,7 @@

Graphics

-
+
@@ -1129,7 +1052,7 @@

Graphics

-
+
@@ -1137,7 +1060,7 @@

Graphics

-
+
@@ -1145,7 +1068,7 @@

Graphics

-
+
@@ -1153,7 +1076,7 @@

Graphics

-
+
@@ -1234,7 +1157,7 @@

Quantitative Analysis

- 0.93 + 0.72 @@ -1244,15 +1167,8 @@

Quantitative Analysis

0.7
minimum
threshold - - -
- -
- -
- - +
+
@@ -1274,7 +1190,7 @@

Quantitative Analysis

- 1.0 + 0.87 @@ -1284,15 +1200,8 @@

Quantitative Analysis

0.7
minimum
threshold - - -
- -
- -
- - +
+
@@ -1314,7 +1223,7 @@

Quantitative Analysis

- 0.74 + 0.96 @@ -1324,15 +1233,8 @@

Quantitative Analysis

0.7
minimum
threshold - - -
- -
- -
- - +
+
@@ -1354,7 +1256,7 @@

Quantitative Analysis

- 0.87 + 0.95 @@ -1364,15 +1266,8 @@

Quantitative Analysis

0.7
minimum
threshold - - -
- -
- -
- - +
+
@@ -1394,7 +1289,7 @@

Quantitative Analysis

- 0.93 + 1.0 @@ -1404,15 +1299,8 @@

Quantitative Analysis

0.7
minimum
threshold - - -
- -
- -
- - +
+
@@ -1459,7 +1347,7 @@

Graphics

-
+
@@ -1514,7 +1402,7 @@

Version

- Date: 2023-11-20 + Date: 2023-11-21
@@ -1736,19 +1624,18 @@

Model Parameters

- - - - - +
+

Eval_metric

+ logloss +
-

Seed

- 123 +

Max_depth

+ 2
@@ -1770,19 +1657,11 @@

Seed

-
-

Missing

- nan -
-
-

Eval_metric

- logloss -
@@ -1804,32 +1683,32 @@

Eval_metric

-

Objective

- binary:logistic +

Enable_categorical

+ False
+
+

Learning_rate

+ 0.1 +
-

Gamma

- 10 +

Colsample_bytree

+ 0.7
-
-

Max_depth

- 5 -
@@ -1856,8 +1735,8 @@

Max_depth

-

Min_child_weight

- 3 +

Reg_lambda

+ 1
@@ -1869,6 +1748,11 @@

Min_child_weight

+ + + + +

Random_state

123 @@ -1878,10 +1762,6 @@

Random_state

-
-

Learning_rate

- 0.01 -
@@ -1889,7 +1769,7 @@

Learning_rate

N_estimators

- 250 + 500
@@ -1901,14 +1781,18 @@

N_estimators

+
+

Objective

+ binary:logistic +
-

Enable_categorical

- False +

Missing

+ nan
@@ -1936,18 +1820,14 @@

Enable_categorical

-

Reg_lambda

- 1 +

Min_child_weight

+ 3
-
-

Colsample_bytree

- 1 -
@@ -1958,6 +1838,10 @@

Colsample_bytree

+
+

Gamma

+ 0 +
@@ -1978,6 +1862,10 @@

Colsample_bytree

+
+

Seed

+ 123 +
@@ -2227,7 +2115,6 @@

Ethical Considerations

return true; } } - function setActiveButton() { const buttons = document.querySelectorAll('#contents li'); const sections = document.querySelectorAll('.card'); @@ -2248,7 +2135,781 @@

Ethical Considerations

} } } - document.addEventListener('scroll', setActiveButton); setActiveButton(); + + function generate_model_card_plot() { + var model_card_plots = [] + var overall_indices = [20, 21, 22, 23, 24] + var histories = JSON.parse("{\"0\": [\"0.8793103448275862\", \"0.9112260325449034\", \"0.8575824835984872\", \"0.9230144744587516\", \"0.8175329936926595\", \"0.916584170845128\"], \"1\": [\"0.8450704225352113\", \"0.8883412445283754\", \"0.7961352302411743\", \"0.9208934112141931\", \"0.8666950924061437\", \"0.8012599385166602\"], \"2\": [\"0.9523809523809523\", \"0.9490165279323434\", \"1.0\", \"0.8409656018676719\", \"0.8870390354301704\", \"0.9045440972725609\"], \"3\": [\"0.8955223880597015\", \"0.7809847260980638\", \"1.0\", \"0.8365585183544693\", \"0.9068393699311781\", \"0.9860835232523685\"], \"4\": [\"0.9743935309973046\", \"0.8701731770203791\", \"1.0\", \"1.0\", \"0.9073900095895263\", \"1.0\"], \"5\": [\"0.88\", \"0.8791842592739592\", \"0.8262115616349736\", \"0.9373940238476682\", \"0.8240361248834713\", \"0.8595082519104404\"], \"6\": [\"0.92\", \"0.8476042678401939\", \"0.9652052791499265\", \"0.9222768447320873\", \"0.9612635163689724\", \"0.7668058750835873\"], \"7\": [\"0.8518518518518519\", \"0.6812784060705941\", \"0.6685072239097107\", \"0.773556052849631\", \"0.9131247310352374\", \"0.6604382153005658\"], \"8\": [\"0.8846153846153846\", \"1.0\", \"0.7373973430239029\", \"0.8088822187245996\", \"0.812440929890144\", \"0.7718804326076331\"], \"9\": [\"0.9541062801932366\", \"0.9712658753145598\", \"1.0\", \"1.0\", \"0.7975653265168696\", \"0.934565405819743\"], \"10\": [\"0.8934426229508197\", \"0.7700653475175312\", \"0.8268806521162448\", \"1.0\", \"0.9180948185879803\", \"1.0\"], \"11\": [\"0.8777777777777778\", \"0.7876920965127165\", \"0.7493400363894769\", \"0.9257337904514158\", \"0.7872600500482494\", \"0.9661021479330794\"], \"12\": [\"0.9753086419753086\", \"0.9487799988771382\", \"1.0\", \"0.9030564913453146\", \"1.0\", \"1.0\"], \"13\": [\"0.9239766081871345\", \"0.9803446664763811\", \"0.9659185381219713\", \"1.0\", \"0.7892416791208378\", \"0.9336551556383622\"], \"14\": [\"0.9795242396868412\", \"0.9492236556240479\", \"0.8815904798440254\", \"0.9586394168401164\", \"1.0\", \"1.0\"], \"15\": [\"0.8942307692307693\", \"0.9934397483886594\", \"0.9317532146760295\", \"0.7428577701148424\", \"0.944805631985229\", \"0.8227196994553121\"], \"16\": [\"0.9491525423728814\", \"0.9331639033724253\", \"0.896429628610939\", \"0.9791293390143264\", \"0.9791112207162269\", \"1.0\"], \"17\": [\"0.875\", \"0.7082479018940693\", \"0.802566665945898\", \"0.9714202211916472\", \"0.9315912971695113\", \"0.9467323855074238\"], \"18\": [\"0.9105691056910569\", \"0.7983763656056397\", \"0.8358465799622541\", \"0.8870219049839082\", \"1.0\", \"0.8788616294782583\"], \"19\": [\"0.9568359375\", \"1.0\", \"1.0\", \"1.0\", \"0.9985570257337495\", \"1.0\"], \"20\": [\"0.8938053097345132\", \"0.8438629068410547\", \"0.9780655953026155\", \"0.7787351086407908\", \"0.7891063411584547\", \"0.7244072548738367\"], \"21\": [\"0.9060402684563759\", \"1.0\", \"0.926476632075494\", \"0.8524988854351541\", \"0.7639817203390269\", \"0.8709932381579796\"], \"22\": [\"0.9310344827586207\", \"1.0\", \"0.8126051965537983\", \"0.8653925852663946\", \"0.8302727026390117\", \"0.9627765850564689\"], \"23\": [\"0.9183673469387755\", \"0.9694880736660227\", \"0.7878158795207733\", \"0.721868688061021\", \"0.9269148799390148\", \"0.9546774457015843\"], \"24\": [\"0.9649638143891016\", \"0.9211517538236319\", \"1.0\", \"0.8381534535529309\", \"0.905752056962576\", \"0.9987995055231192\"]}"); + var thresholds = JSON.parse("{\"0\": \"0.7\", \"1\": \"0.7\", \"2\": \"0.7\", \"3\": \"0.7\", \"4\": \"0.7\", \"5\": \"0.7\", \"6\": \"0.7\", \"7\": \"0.7\", \"8\": \"0.7\", \"9\": \"0.7\", \"10\": \"0.7\", \"11\": \"0.7\", \"12\": \"0.7\", \"13\": \"0.7\", \"14\": \"0.7\", \"15\": \"0.7\", \"16\": \"0.7\", \"17\": \"0.7\", \"18\": \"0.7\", \"19\": \"0.7\", \"20\": \"0.7\", \"21\": \"0.7\", \"22\": \"0.7\", \"23\": \"0.7\", \"24\": \"0.7\"}"); + var timestamps = JSON.parse("{\"0\": [\"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:33\", \"2023-11-21 10:32:33\"], \"1\": [\"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:33\", \"2023-11-21 10:32:33\"], \"2\": [\"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:33\", \"2023-11-21 10:32:33\"], \"3\": [\"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:33\", \"2023-11-21 10:32:33\"], \"4\": [\"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:33\", \"2023-11-21 10:32:33\"], \"5\": [\"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:33\", \"2023-11-21 10:32:33\"], \"6\": [\"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:33\", \"2023-11-21 10:32:33\"], \"7\": [\"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:33\", \"2023-11-21 10:32:33\"], \"8\": [\"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:33\", \"2023-11-21 10:32:33\"], \"9\": [\"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:33\", \"2023-11-21 10:32:33\"], \"10\": [\"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:33\", \"2023-11-21 10:32:33\"], \"11\": [\"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:33\", \"2023-11-21 10:32:33\"], \"12\": [\"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:33\", \"2023-11-21 10:32:33\"], \"13\": [\"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:33\", \"2023-11-21 10:32:33\"], \"14\": [\"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:33\", \"2023-11-21 10:32:33\"], \"15\": [\"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:33\", \"2023-11-21 10:32:33\"], \"16\": [\"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:33\", \"2023-11-21 10:32:33\"], \"17\": [\"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:33\", \"2023-11-21 10:32:33\"], \"18\": [\"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:33\", \"2023-11-21 10:32:33\"], \"19\": [\"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:33\", \"2023-11-21 10:32:33\"], \"20\": [\"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:33\", \"2023-11-21 10:32:33\"], \"21\": [\"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:33\", \"2023-11-21 10:32:33\"], \"22\": [\"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:33\", \"2023-11-21 10:32:33\"], \"23\": [\"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:33\", \"2023-11-21 10:32:33\"], \"24\": [\"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:33\", \"2023-11-21 10:32:33\"]}"); + + for (let i = 0; i < overall_indices.length; i++) { + var idx = overall_indices[i]; + var model_card_plot = "model-card-plot-" + idx; + var threshold = thresholds[idx]; + var history_data = []; + for (let i = 0; i < histories[idx].length; i++) { + history_data.push(parseFloat(histories[idx][i])); + } + var timestamp_data = []; + for (let i = 0; i < timestamps[idx].length; i++) { + timestamp_data.push(timestamps[idx][i]); + } + + var model_card_fig = { + data: [ + { + x: timestamp_data, + y: history_data, + mode: "lines+markers", + marker: { color: "rgb(31,111,235)" }, + line: { color: "rgb(31,111,235)" }, + showlegend: false, + type: "scatter", + name: "" + }, + { + x: timestamp_data, + y: Array(history_data.length).fill(threshold), + mode: "lines", + line: { color: "black", dash: "dot" }, + showlegend: false, + type: "scatter", + name: "" + } + ], + layout: { + paper_bgcolor: "rgba(0,0,0,0)", + plot_bgcolor: "rgba(0,0,0,0)", + xaxis: { + zeroline: false, + showticklabels: false, + showgrid: false + }, + yaxis: { + gridcolor: "#ffffff" + }, + margin: { l: 0, r: 0, t: 0, b: 0 }, + height: 125, + width: 250 + } + }; + if (history.length > 0) { + Plotly.newPlot(model_card_plot, model_card_fig.data, model_card_fig.layout, {displayModeBar: false}); + } + } + } + generate_model_card_plot(); + + const plot = document.getElementById('plot'); + const inputs_all = document.querySelectorAll('#slice-selection input[type="radio"]'); + const plot_selection = document.querySelectorAll('#plot-selection input[type="radio"]'); + var selections = [null, null, null, null, null, null, null, null, null, null, null]; + var plot_colors = [ + "rgb(0, 115, 228)", + "rgb(31, 119, 180)", + "rgb(255, 127, 14)", + "rgb(44, 160, 44)", + "rgb(214, 39, 40)", + "rgb(148, 103, 189)", + "rgb(140, 86, 75)", + "rgb(227, 119, 194)", + "rgb(127, 127, 127)", + "rgb(188, 189, 34)", + "rgb(23, 190, 207)" + ]; + + function deletePlotSelection(plot_number) { + var plot_selection = document.querySelectorAll('#plot-selection input[type="radio"]'); + var label_selection = document.querySelectorAll('#plot-selection label'); + var label_slice_selection = document.querySelectorAll('#slice-selection label'); + var button_plot_selection = document.querySelectorAll('#plot-selection button'); + + // set last plot to checked + // get plot_selection with name "Plot N" where N is plot_number + for (let i = 0; i < plot_selection.length; i++) { + var plot_name = "Plot " + (plot_number+1) + if (plot_selection[i].value === plot_name) { + plot_number = i; + } + } + plot_selection[plot_number].checked = false; + plot_selection[plot_number-1].checked = true; + + // delete plot_selected and label + plot_selection[plot_number].remove(); + label_selection[plot_number].remove(); + + selections[plot_number] = null; + + // set selection to last plot + selection = selections[plot_number-1]; + plot_color = plot_colors[plot_number-1]; + + // set current plot selection color to plot_color + const [r, g, b] = plot_color.match(/\d+/g); + const rgbaColor = `rgba(${r}, ${g}, ${b}, 0.2)`; + plot_selection[plot_number-1].style.backgroundColor = rgbaColor; + plot_selection[plot_number-1].style.border = "2px solid " + plot_color; + plot_selection[plot_number-1].style.color = plot_color; + + // make visibility of delete button from last plot visible + if (button_plot_selection.length >= 2) { + button_plot_selection[button_plot_selection.length-2].style.visibility = "visible"; + } + + for (let i = 0; i < selection.length; i++) { + // use selection to set label_slice_selection background color + for (let j = 0; j < inputs_all.length; j++) { + if (inputs_all[j].name === selection[i].split(":")[0]) { + if (inputs_all[j].value == selection[i].split(":")[1]) { + inputs_all[j].checked = true; + label_slice_selection[j].style.backgroundColor = rgbaColor; + label_slice_selection[j].style.border = "2px solid " + plot_color; + label_slice_selection[j].style.color = plot_color; + } + } + } + } + updatePlot(); + } + + function updatePlotSelection() { + const inputs = document.querySelectorAll('#slice-selection input[type="radio"]:checked'); + var plot_selection = document.querySelectorAll('#plot-selection input[type="radio"]'); + var plot_selected = document.querySelectorAll('#plot-selection input[type="radio"]:checked')[0]; + // get number from value in plot_selected "Plot 1" -> 1 + var plot_number = parseInt(plot_selected.value.split(" ")[1]); + var label_selection = document.querySelectorAll('#plot-selection label'); + var label_slice_selection = document.querySelectorAll('#slice-selection label'); + var button_plot_selection = document.querySelectorAll('#plot-selection button'); + + // if plot_selected is "+" then add new radio button to plot_selection called "Plot N" where last plot is N-1 but keep "+" at end and set new radio button to checked for second last element + if (plot_selected.value === "+") { + // if 10 plots already exist, don't add new plot and gray out "+" + if (plot_selection.length === 11) { + plot_selected.checked = false; + label_selection[label_selection.length-1].style.color = "gray"; + return; + } + // plot_name should be name of last plot + 1 + if (plot_selection.length === 2) { + var plot_name = "Plot 2" + } else { + var plot_name = "Plot " + (parseInt(plot_selection[plot_selection.length - 2].value.split(" ")[1]) + 1); + } + var new_plot = document.createElement("input"); + new_plot.type = "radio"; + new_plot.id = plot_name; + new_plot.name = "plot"; + new_plot.value = plot_name; + new_plot.checked = true; + var new_label = document.createElement("label"); + new_label.htmlFor = plot_name; + new_label.innerHTML = plot_name; + + // Parse plot_color to get r, g, b values + var plot_color = plot_colors[plot_selection.length] + const [r, g, b] = plot_color.match(/\d+/g); + const rgbaColor = `rgba(${r}, ${g}, ${b}, 0.2)`; + // set background color of new radio button to plot_color + new_label.style.backgroundColor = rgbaColor; + new_label.style.border = "2px solid " + plot_color; + new_label.style.color = plot_color; + + // add button to delete plot + var delete_button = document.createElement("button"); + delete_button.id = "button"; + delete_button.innerHTML = "×"; + delete_button.style.backgroundColor = "transparent"; + delete_button.style.border = "none"; + new_label.style.padding = "1.5px 0px"; + new_label.style.paddingLeft = "10px"; + + new_label.appendChild(delete_button) + + // make delete button from last plot invisible if not Plot 1 + if (plot_selection.length > 2) { + button_plot_selection[button_plot_selection.length-1].style.visibility = "hidden"; + } + // add on_click event to delete button and send plot number to deletePlotSelection + delete_button.onclick = function() {deletePlotSelection(plot_number)}; + + // insert new radio button and label before "+" radio button and after last radio button + plot_selected.insertAdjacentElement("beforebegin", new_plot); + plot_selected.insertAdjacentElement("beforebegin", new_label); + + // Add event listener to new radio button + new_plot.addEventListener('change', updatePlotSelection); + + // set plot_selected to new plot + var plot_selected = new_plot + + for (let i = 0; i < label_selection.length-1; i++) { + plot_selection[i].checked = false; + label_selection[i].style.backgroundColor = "#ffffff"; + label_selection[i].style.border = "2px solid #DADCE0"; + label_selection[i].style.color = "#000000"; + } + + selections[parseInt(plot_selected.value.split(" ")[1]-1)] = selections[parseInt(plot_selected.value.split(" ")[1]-2)] + selection = selections[parseInt(plot_selected.value.split(" ")[1]-1)]; + plot_color = plot_colors[parseInt(plot_selected.value.split(" ")[1])]; + + for (let i = 0; i < selection.length; i++) { + // use selection to set label_slice_selection background color + for (let j = 0; j < inputs_all.length; j++) { + if (inputs_all[j].name === selection[i].split(":")[0]) { + if (inputs_all[j].value == selection[i].split(":")[1]) { + const [r, g, b] = plot_color.match(/\d+/g); + const rgbaColor = `rgba(${r}, ${g}, ${b}, 0.2)`; + inputs_all[j].checked = true; + label_slice_selection[j].style.backgroundColor = rgbaColor; + label_slice_selection[j].style.border = "2px solid " + plot_color; + label_slice_selection[j].style.color = plot_color; + } + else { + inputs_all[j].checked = false; + label_slice_selection[j].style.backgroundColor = "#ffffff"; + label_slice_selection[j].style.border = "2px solid #DADCE0"; + label_slice_selection[j].style.color = "#000000"; + } + } + } + } + } else { + for (let i = 0; i < plot_selection.length-1; i++) { + if (plot_selection[i].value !== plot_selected.value) { + plot_selection[i].checked = false; + label_selection[i].style.backgroundColor = "#ffffff"; + label_selection[i].style.border = "2px solid #DADCE0"; + label_selection[i].style.color = "#000000"; + } + else { + var plot_color = plot_colors[i+1] + const [r, g, b] = plot_color.match(/\d+/g); + const rgbaColor = `rgba(${r}, ${g}, ${b}, 0.2)`; + plot_selected.checked = true; + label_selection[i].style.backgroundColor = rgbaColor; + label_selection[i].style.border = "2px solid " + plot_color; + label_selection[i].style.color = plot_color; + } + } + selection = selections[parseInt(plot_selected.value.split(" ")[1]-1)]; + plot_color = plot_colors[parseInt(plot_selected.value.split(" ")[1])]; + for (let i = 0; i < selection.length; i++) { + // use selection to set label_slice_selection background color + for (let j = 0; j < inputs_all.length; j++) { + if (inputs_all[j].name === selection[i].split(":")[0]) { + if (inputs_all[j].value == selection[i].split(":")[1]) { + inputs_all[j].checked = true; + const [r, g, b] = plot_color.match(/\d+/g); + const rgbaColor = `rgba(${r}, ${g}, ${b}, 0.2)`; + label_slice_selection[j].style.backgroundColor = rgbaColor; + label_slice_selection[j].style.border = "2px solid " + plot_color; + label_slice_selection[j].style.color = plot_color; + } + else { + inputs_all[j].checked = false; + label_slice_selection[j].style.backgroundColor = "#ffffff"; + label_slice_selection[j].style.border = "2px solid #DADCE0"; + label_slice_selection[j].style.color = "#000000"; + } + } + } + } + } + var slices_all = JSON.parse("{\"0\": [\"metric:Accuracy\", \"age:[20 - 50)\", \"gender:overall_gender\"], \"1\": [\"metric:Precision\", \"age:[20 - 50)\", \"gender:overall_gender\"], \"2\": [\"metric:Recall\", \"age:[20 - 50)\", \"gender:overall_gender\"], \"3\": [\"metric:F1 Score\", \"age:[20 - 50)\", \"gender:overall_gender\"], \"4\": [\"metric:AUROC\", \"age:[20 - 50)\", \"gender:overall_gender\"], \"5\": [\"metric:Accuracy\", \"age:[50 - 80)\", \"gender:overall_gender\"], \"6\": [\"metric:Precision\", \"age:[50 - 80)\", \"gender:overall_gender\"], \"7\": [\"metric:Recall\", \"age:[50 - 80)\", \"gender:overall_gender\"], \"8\": [\"metric:F1 Score\", \"age:[50 - 80)\", \"gender:overall_gender\"], \"9\": [\"metric:AUROC\", \"age:[50 - 80)\", \"gender:overall_gender\"], \"10\": [\"metric:Accuracy\", \"gender:M\", \"age:overall_age\"], \"11\": [\"metric:Precision\", \"gender:M\", \"age:overall_age\"], \"12\": [\"metric:Recall\", \"gender:M\", \"age:overall_age\"], \"13\": [\"metric:F1 Score\", \"gender:M\", \"age:overall_age\"], \"14\": [\"metric:AUROC\", \"gender:M\", \"age:overall_age\"], \"15\": [\"metric:Accuracy\", \"gender:F\", \"age:overall_age\"], \"16\": [\"metric:Precision\", \"gender:F\", \"age:overall_age\"], \"17\": [\"metric:Recall\", \"gender:F\", \"age:overall_age\"], \"18\": [\"metric:F1 Score\", \"gender:F\", \"age:overall_age\"], \"19\": [\"metric:AUROC\", \"gender:F\", \"age:overall_age\"], \"20\": [\"metric:Accuracy\", \"age:overall_age\", \"gender:overall_gender\"], \"21\": [\"metric:Precision\", \"age:overall_age\", \"gender:overall_gender\"], \"22\": [\"metric:Recall\", \"age:overall_age\", \"gender:overall_gender\"], \"23\": [\"metric:F1 Score\", \"age:overall_age\", \"gender:overall_gender\"], \"24\": [\"metric:AUROC\", \"age:overall_age\", \"gender:overall_gender\"]}"); + var histories_all = JSON.parse("{\"0\": [\"0.8793103448275862\", \"0.9112260325449034\", \"0.8575824835984872\", \"0.9230144744587516\", \"0.8175329936926595\", \"0.916584170845128\"], \"1\": [\"0.8450704225352113\", \"0.8883412445283754\", \"0.7961352302411743\", \"0.9208934112141931\", \"0.8666950924061437\", \"0.8012599385166602\"], \"2\": [\"0.9523809523809523\", \"0.9490165279323434\", \"1.0\", \"0.8409656018676719\", \"0.8870390354301704\", \"0.9045440972725609\"], \"3\": [\"0.8955223880597015\", \"0.7809847260980638\", \"1.0\", \"0.8365585183544693\", \"0.9068393699311781\", \"0.9860835232523685\"], \"4\": [\"0.9743935309973046\", \"0.8701731770203791\", \"1.0\", \"1.0\", \"0.9073900095895263\", \"1.0\"], \"5\": [\"0.88\", \"0.8791842592739592\", \"0.8262115616349736\", \"0.9373940238476682\", \"0.8240361248834713\", \"0.8595082519104404\"], \"6\": [\"0.92\", \"0.8476042678401939\", \"0.9652052791499265\", \"0.9222768447320873\", \"0.9612635163689724\", \"0.7668058750835873\"], \"7\": [\"0.8518518518518519\", \"0.6812784060705941\", \"0.6685072239097107\", \"0.773556052849631\", \"0.9131247310352374\", \"0.6604382153005658\"], \"8\": [\"0.8846153846153846\", \"1.0\", \"0.7373973430239029\", \"0.8088822187245996\", \"0.812440929890144\", \"0.7718804326076331\"], \"9\": [\"0.9541062801932366\", \"0.9712658753145598\", \"1.0\", \"1.0\", \"0.7975653265168696\", \"0.934565405819743\"], \"10\": [\"0.8934426229508197\", \"0.7700653475175312\", \"0.8268806521162448\", \"1.0\", \"0.9180948185879803\", \"1.0\"], \"11\": [\"0.8777777777777778\", \"0.7876920965127165\", \"0.7493400363894769\", \"0.9257337904514158\", \"0.7872600500482494\", \"0.9661021479330794\"], \"12\": [\"0.9753086419753086\", \"0.9487799988771382\", \"1.0\", \"0.9030564913453146\", \"1.0\", \"1.0\"], \"13\": [\"0.9239766081871345\", \"0.9803446664763811\", \"0.9659185381219713\", \"1.0\", \"0.7892416791208378\", \"0.9336551556383622\"], \"14\": [\"0.9795242396868412\", \"0.9492236556240479\", \"0.8815904798440254\", \"0.9586394168401164\", \"1.0\", \"1.0\"], \"15\": [\"0.8942307692307693\", \"0.9934397483886594\", \"0.9317532146760295\", \"0.7428577701148424\", \"0.944805631985229\", \"0.8227196994553121\"], \"16\": [\"0.9491525423728814\", \"0.9331639033724253\", \"0.896429628610939\", \"0.9791293390143264\", \"0.9791112207162269\", \"1.0\"], \"17\": [\"0.875\", \"0.7082479018940693\", \"0.802566665945898\", \"0.9714202211916472\", \"0.9315912971695113\", \"0.9467323855074238\"], \"18\": [\"0.9105691056910569\", \"0.7983763656056397\", \"0.8358465799622541\", \"0.8870219049839082\", \"1.0\", \"0.8788616294782583\"], \"19\": [\"0.9568359375\", \"1.0\", \"1.0\", \"1.0\", \"0.9985570257337495\", \"1.0\"], \"20\": [\"0.8938053097345132\", \"0.8438629068410547\", \"0.9780655953026155\", \"0.7787351086407908\", \"0.7891063411584547\", \"0.7244072548738367\"], \"21\": [\"0.9060402684563759\", \"1.0\", \"0.926476632075494\", \"0.8524988854351541\", \"0.7639817203390269\", \"0.8709932381579796\"], \"22\": [\"0.9310344827586207\", \"1.0\", \"0.8126051965537983\", \"0.8653925852663946\", \"0.8302727026390117\", \"0.9627765850564689\"], \"23\": [\"0.9183673469387755\", \"0.9694880736660227\", \"0.7878158795207733\", \"0.721868688061021\", \"0.9269148799390148\", \"0.9546774457015843\"], \"24\": [\"0.9649638143891016\", \"0.9211517538236319\", \"1.0\", \"0.8381534535529309\", \"0.905752056962576\", \"0.9987995055231192\"]}"); + var thresholds_all = JSON.parse("{\"0\": \"0.7\", \"1\": \"0.7\", \"2\": \"0.7\", \"3\": \"0.7\", \"4\": \"0.7\", \"5\": \"0.7\", \"6\": \"0.7\", \"7\": \"0.7\", \"8\": \"0.7\", \"9\": \"0.7\", \"10\": \"0.7\", \"11\": \"0.7\", \"12\": \"0.7\", \"13\": \"0.7\", \"14\": \"0.7\", \"15\": \"0.7\", \"16\": \"0.7\", \"17\": \"0.7\", \"18\": \"0.7\", \"19\": \"0.7\", \"20\": \"0.7\", \"21\": \"0.7\", \"22\": \"0.7\", \"23\": \"0.7\", \"24\": \"0.7\"}"); + var trends_all = JSON.parse("{\"0\": \"neutral\", \"1\": \"neutral\", \"2\": \"negative\", \"3\": \"positive\", \"4\": \"neutral\", \"5\": \"neutral\", \"6\": \"negative\", \"7\": \"neutral\", \"8\": \"negative\", \"9\": \"negative\", \"10\": \"positive\", \"11\": \"positive\", \"12\": \"neutral\", \"13\": \"negative\", \"14\": \"neutral\", \"15\": \"negative\", \"16\": \"positive\", \"17\": \"positive\", \"18\": \"positive\", \"19\": \"neutral\", \"20\": \"negative\", \"21\": \"negative\", \"22\": \"neutral\", \"23\": \"neutral\", \"24\": \"neutral\"}"); + var passed_all = JSON.parse("{\"0\": true, \"1\": true, \"2\": true, \"3\": true, \"4\": true, \"5\": true, \"6\": true, \"7\": false, \"8\": true, \"9\": true, \"10\": true, \"11\": true, \"12\": true, \"13\": true, \"14\": true, \"15\": true, \"16\": true, \"17\": true, \"18\": true, \"19\": true, \"20\": true, \"21\": true, \"22\": true, \"23\": true, \"24\": true}"); + var names_all = JSON.parse("{\"0\": \"Accuracy\", \"1\": \"Precision\", \"2\": \"Recall\", \"3\": \"F1 Score\", \"4\": \"AUROC\", \"5\": \"Accuracy\", \"6\": \"Precision\", \"7\": \"Recall\", \"8\": \"F1 Score\", \"9\": \"AUROC\", \"10\": \"Accuracy\", \"11\": \"Precision\", \"12\": \"Recall\", \"13\": \"F1 Score\", \"14\": \"AUROC\", \"15\": \"Accuracy\", \"16\": \"Precision\", \"17\": \"Recall\", \"18\": \"F1 Score\", \"19\": \"AUROC\", \"20\": \"Accuracy\", \"21\": \"Precision\", \"22\": \"Recall\", \"23\": \"F1 Score\", \"24\": \"AUROC\"}"); + var timestamps_all = JSON.parse("{\"0\": [\"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:33\", \"2023-11-21 10:32:33\"], \"1\": [\"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:33\", \"2023-11-21 10:32:33\"], \"2\": [\"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:33\", \"2023-11-21 10:32:33\"], \"3\": [\"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:33\", \"2023-11-21 10:32:33\"], \"4\": [\"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:33\", \"2023-11-21 10:32:33\"], \"5\": [\"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:33\", \"2023-11-21 10:32:33\"], \"6\": [\"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:33\", \"2023-11-21 10:32:33\"], \"7\": [\"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:33\", \"2023-11-21 10:32:33\"], \"8\": [\"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:33\", \"2023-11-21 10:32:33\"], \"9\": [\"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:33\", \"2023-11-21 10:32:33\"], \"10\": [\"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:33\", \"2023-11-21 10:32:33\"], \"11\": [\"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:33\", \"2023-11-21 10:32:33\"], \"12\": [\"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:33\", \"2023-11-21 10:32:33\"], \"13\": [\"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:33\", \"2023-11-21 10:32:33\"], \"14\": [\"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:33\", \"2023-11-21 10:32:33\"], \"15\": [\"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:33\", \"2023-11-21 10:32:33\"], \"16\": [\"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:33\", \"2023-11-21 10:32:33\"], \"17\": [\"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:33\", \"2023-11-21 10:32:33\"], \"18\": [\"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:33\", \"2023-11-21 10:32:33\"], \"19\": [\"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:33\", \"2023-11-21 10:32:33\"], \"20\": [\"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:33\", \"2023-11-21 10:32:33\"], \"21\": [\"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:33\", \"2023-11-21 10:32:33\"], \"22\": [\"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:33\", \"2023-11-21 10:32:33\"], \"23\": [\"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:33\", \"2023-11-21 10:32:33\"], \"24\": [\"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:33\", \"2023-11-21 10:32:33\"]}"); + + var radioGroups = {}; + var labelGroups = {}; + for (let i = 0; i < inputs_all.length; i++) { + var input = inputs_all[i]; + var label = label_slice_selection[i]; + var groupName = input.name; + if (!radioGroups[groupName]) { + radioGroups[groupName] = []; + labelGroups[groupName] = []; + } + radioGroups[groupName].push(input); + labelGroups[groupName].push(label); + } + + // use radioGroups to loop through selection changing only one element at a time + for (let i = 0; i < selection.length; i++) { + for (let j = 0; j < inputs_all.length; j++) { + if (inputs_all[j].name === selection[i].split(":")[0]) { + radio_group = radioGroups[selection[i].split(":")[0]]; + label_group = labelGroups[selection[i].split(":")[0]]; + for (let k = 0; k < radio_group.length; k++) { + selection_copy = selection.slice(); + selection_copy[i] = selection[i].split(":")[0] + ":" + radio_group[k].value; + // get idx of slices where all elements match + var idx = Object.keys(slices_all).find(key => JSON.stringify(slices_all[key].sort()) === JSON.stringify(selection_copy.sort())); + if (idx === undefined) { + // set radio button to disabled and cursor to not allowed and color to gray if idx is undefined + radio_group[k].disabled = true; + label_group[k].style.cursor = "not-allowed"; + label_group[k].style.color = "gray"; + label_group[k].style.backgroundColor = "rgba(125, 125, 125, 0.2)"; + } + else { + radio_group[k].disabled = false; + label_group[k].style.cursor = "pointer"; + } + } + } + } + } + + traces = []; + var plot_number = parseInt(plot_selected.value.split(" ")[1]-1); + for (let i = 0; i < selections.length; i++) { + if (selections[i] === null) { + continue; + } + selection = selections[i] + + // get idx of slices where all elements match + var idx = Object.keys(slices_all).find(key => JSON.stringify(slices_all[key].sort()) === JSON.stringify(selection)); + var history_data = []; + for (let i = 0; i < histories_all[idx].length; i++) { + history_data.push(parseFloat(histories_all[idx][i])); + } + var timestamp_data = []; + for (let i = 0; i < timestamps_all[idx].length; i++) { + timestamp_data.push(timestamps_all[idx][i]); + } + threshold = parseFloat(thresholds_all[idx]); + trend = trends_all[idx]; + passed = passed_all[idx]; + name = names_all[idx]; + + // if trend is "positive" set keyword to upwards, if trend is "negative" set keyword to downwards, else set keyword to flat + if (trend === "positive") { + var trend_keyword = "upwards"; + } else if (trend === "negative") { + var trend_keyword = "downwards"; + } else { + var trend_keyword = "flat"; + } + + // if passed is true set keyword to Above, if passed is false set keyword to Below + if (passed) { + var passed_keyword = "above"; + } + else { + var passed_keyword = "below"; + } + + // create title for plot: Current {metric name} is trending {trend_keyword} and is {passed_keyword} the threshold. + // get number of nulls in selections, if 9 then plot title, else don't plot title + console.log(selections) + var nulls = 0; + for (let i = 0; i < selections.length; i++) { + if (selections[i] === null) { + nulls += 1; + } + } + if (nulls === 10) { + var plot_title = "Current " + name + " is trending " + trend_keyword + " and is " + passed_keyword + " the threshold."; + var showlegend = false; + } + else { + var plot_title = ""; + var showlegend = true; + } + name = "" + suffix = " ( " + for (let i = 0; i < selection.length; i++) { + if (selection[i].split(":")[0] === "metric") { + name += selection[i].split(":")[1]; + } + else { + if (selection[i].split(":")[1].includes("overall")) { + continue; + } else { + suffix += selection[i]; + suffix += ", "; + } + } + } + if (suffix === " ( ") { + name += ""; + } + else { + suffix = suffix.slice(0, -2); + name += suffix + " )"; + } + + var trace = { + // range of x is the length of the list of floats + x: timestamp_data, + y: history_data, + mode: 'lines+markers', + type: 'scatter', + marker: {color: plot_colors[i+1]}, + line: {color: plot_colors[i+1]}, + name: name, + }; + traces.push(trace); + } + + if (nulls === 10) { + var threshold_trace = { + x: timestamp_data, + y: Array.from({length: history_data.length}, (_, i) => threshold), + mode: 'lines', + type: 'scatter', + marker: {color: 'rgb(0,0,0)'}, + line: {color: 'rgb(0,0,0)', dash: 'dot'}, + name: '', + }; + traces.push(threshold_trace); + } + var layout = { + title: { + text: plot_title, + font: { + family: 'Arial, Helvetica, sans-serif', + size: 18, + } + }, + paper_bgcolor: 'rgba(0,0,0,0)', + plot_bgcolor: 'rgba(0,0,0,0)', + xaxis: { + zeroline: false, + showticklabels: false, + showgrid: false, + }, + yaxis: { + gridcolor: '#ffffff', + }, + showlegend: showlegend, + margin: { + l: 50, + r: 50, + b: 50, + t: 50, + pad: 4 + }, + // set height and width of plot to extra-wide to fit the plot + height: 500, + width: 900, + } + Plotly.newPlot(plot, traces, layout, {displayModeBar: false}); + } + + + + // Define a function to update the plot based on selected filters + function updatePlot() { + const inputs = document.querySelectorAll('#slice-selection input[type="radio"]:checked'); + var plot_selection = document.querySelectorAll('#plot-selection input[type="radio"]'); + var plot_selected = document.querySelectorAll('#plot-selection input[type="radio"]:checked')[0]; + // get number from value in plot_selected "Plot 1" -> 1 + var label_selection = document.querySelectorAll('#plot-selection label'); + var label_slice_selection = document.querySelectorAll('#slice-selection label'); + + // get all inputs values from div class radio-buttons + // get name of inputs + var inputs_name = []; + var inputs_value = []; + for (let i = 0; i < inputs.length; i++) { + inputs_name.push(inputs[i].name); + inputs_value.push(inputs[i].value); + } + + var plot_number = parseInt(plot_selected.value.split(" ")[1]-1); + var selection = []; + for (let i = 0; i < inputs_value.length; i++) { + selection.push(inputs_name[i] + ":" + inputs_value[i]); + } + selection.sort(); + selections[plot_number] = selection; + + // if plot_selected is "+" then add new radio button to plot_selection called "Plot N" where last plot is N-1 but keep "+" at end and set new radio button to checked for second last element + if (plot_selected.value === "+") { + // if 10 plots already exist, don't add new plot and gray out "+" + if (plot_selection.length === 13) { + plot_selected.checked = false; + label_selection[-1].style.color = "gray"; + return; + } + var new_plot = document.createElement("input"); + new_plot.type = "radio"; + new_plot.id = "Plot " + (plot_selection.length); + new_plot.name = "plot"; + new_plot.value = "Plot " + (plot_selection.length); + new_plot.checked = true; + var new_label = document.createElement("label"); + new_label.htmlFor = "Plot " + (plot_selection.length); + new_label.innerHTML = "Plot " + (plot_selection.length); + + // Parse plot_color to get r, g, b values + var plot_color = plot_colors[plot_selection.length] + const [r, g, b] = plot_color.match(/\d+/g); + const rgbaColor = `rgba(${r}, ${g}, ${b}, 0.2)`; + // set background color of new radio button to plot_color + new_label.style.backgroundColor = rgbaColor; + new_label.style.border = "2px solid " + plot_color; + new_label.style.color = plot_color; + + // insert new radio button and label before "+" radio button and after last radio button + plot_selected.insertAdjacentElement("beforebegin", new_plot); + plot_selected.insertAdjacentElement("beforebegin", new_label); + // Add event listener to new radio button + new_plot.addEventListener('change', updatePlot); + + // set plot_selected to new plot + plot_selected = new_plot + + for (let i = 0; i < label_selection.length-1; i++) { + plot_selection[i].checked = false; + label_selection[i].style.backgroundColor = "#ffffff"; + label_selection[i].style.border = "2px solid #DADCE0"; + label_selection[i].style.color = "#000000"; + } + } else { + for (let i = 0; i < plot_selection.length-1; i++) { + if (plot_selection[i].value !== plot_selected.value) { + plot_selection[i].checked = false; + label_selection[i].style.backgroundColor = "#ffffff"; + label_selection[i].style.border = "2px solid #DADCE0"; + label_selection[i].style.color = "#000000"; + } + else { + var plot_color = plot_colors[i+1] + const [r, g, b] = plot_color.match(/\d+/g); + const rgbaColor = `rgba(${r}, ${g}, ${b}, 0.2)`; + plot_selected.checked = true; + label_selection[i].style.backgroundColor = rgbaColor; + label_selection[i].style.border = "2px solid " + plot_color; + label_selection[i].style.color = plot_color; + } + } + } + var slices_all = JSON.parse("{\"0\": [\"metric:Accuracy\", \"age:[20 - 50)\", \"gender:overall_gender\"], \"1\": [\"metric:Precision\", \"age:[20 - 50)\", \"gender:overall_gender\"], \"2\": [\"metric:Recall\", \"age:[20 - 50)\", \"gender:overall_gender\"], \"3\": [\"metric:F1 Score\", \"age:[20 - 50)\", \"gender:overall_gender\"], \"4\": [\"metric:AUROC\", \"age:[20 - 50)\", \"gender:overall_gender\"], \"5\": [\"metric:Accuracy\", \"age:[50 - 80)\", \"gender:overall_gender\"], \"6\": [\"metric:Precision\", \"age:[50 - 80)\", \"gender:overall_gender\"], \"7\": [\"metric:Recall\", \"age:[50 - 80)\", \"gender:overall_gender\"], \"8\": [\"metric:F1 Score\", \"age:[50 - 80)\", \"gender:overall_gender\"], \"9\": [\"metric:AUROC\", \"age:[50 - 80)\", \"gender:overall_gender\"], \"10\": [\"metric:Accuracy\", \"gender:M\", \"age:overall_age\"], \"11\": [\"metric:Precision\", \"gender:M\", \"age:overall_age\"], \"12\": [\"metric:Recall\", \"gender:M\", \"age:overall_age\"], \"13\": [\"metric:F1 Score\", \"gender:M\", \"age:overall_age\"], \"14\": [\"metric:AUROC\", \"gender:M\", \"age:overall_age\"], \"15\": [\"metric:Accuracy\", \"gender:F\", \"age:overall_age\"], \"16\": [\"metric:Precision\", \"gender:F\", \"age:overall_age\"], \"17\": [\"metric:Recall\", \"gender:F\", \"age:overall_age\"], \"18\": [\"metric:F1 Score\", \"gender:F\", \"age:overall_age\"], \"19\": [\"metric:AUROC\", \"gender:F\", \"age:overall_age\"], \"20\": [\"metric:Accuracy\", \"age:overall_age\", \"gender:overall_gender\"], \"21\": [\"metric:Precision\", \"age:overall_age\", \"gender:overall_gender\"], \"22\": [\"metric:Recall\", \"age:overall_age\", \"gender:overall_gender\"], \"23\": [\"metric:F1 Score\", \"age:overall_age\", \"gender:overall_gender\"], \"24\": [\"metric:AUROC\", \"age:overall_age\", \"gender:overall_gender\"]}"); + var histories_all = JSON.parse("{\"0\": [\"0.8793103448275862\", \"0.9112260325449034\", \"0.8575824835984872\", \"0.9230144744587516\", \"0.8175329936926595\", \"0.916584170845128\"], \"1\": [\"0.8450704225352113\", \"0.8883412445283754\", \"0.7961352302411743\", \"0.9208934112141931\", \"0.8666950924061437\", \"0.8012599385166602\"], \"2\": [\"0.9523809523809523\", \"0.9490165279323434\", \"1.0\", \"0.8409656018676719\", \"0.8870390354301704\", \"0.9045440972725609\"], \"3\": [\"0.8955223880597015\", \"0.7809847260980638\", \"1.0\", \"0.8365585183544693\", \"0.9068393699311781\", \"0.9860835232523685\"], \"4\": [\"0.9743935309973046\", \"0.8701731770203791\", \"1.0\", \"1.0\", \"0.9073900095895263\", \"1.0\"], \"5\": [\"0.88\", \"0.8791842592739592\", \"0.8262115616349736\", \"0.9373940238476682\", \"0.8240361248834713\", \"0.8595082519104404\"], \"6\": [\"0.92\", \"0.8476042678401939\", \"0.9652052791499265\", \"0.9222768447320873\", \"0.9612635163689724\", \"0.7668058750835873\"], \"7\": [\"0.8518518518518519\", \"0.6812784060705941\", \"0.6685072239097107\", \"0.773556052849631\", \"0.9131247310352374\", \"0.6604382153005658\"], \"8\": [\"0.8846153846153846\", \"1.0\", \"0.7373973430239029\", \"0.8088822187245996\", \"0.812440929890144\", \"0.7718804326076331\"], \"9\": [\"0.9541062801932366\", \"0.9712658753145598\", \"1.0\", \"1.0\", \"0.7975653265168696\", \"0.934565405819743\"], \"10\": [\"0.8934426229508197\", \"0.7700653475175312\", \"0.8268806521162448\", \"1.0\", \"0.9180948185879803\", \"1.0\"], \"11\": [\"0.8777777777777778\", \"0.7876920965127165\", \"0.7493400363894769\", \"0.9257337904514158\", \"0.7872600500482494\", \"0.9661021479330794\"], \"12\": [\"0.9753086419753086\", \"0.9487799988771382\", \"1.0\", \"0.9030564913453146\", \"1.0\", \"1.0\"], \"13\": [\"0.9239766081871345\", \"0.9803446664763811\", \"0.9659185381219713\", \"1.0\", \"0.7892416791208378\", \"0.9336551556383622\"], \"14\": [\"0.9795242396868412\", \"0.9492236556240479\", \"0.8815904798440254\", \"0.9586394168401164\", \"1.0\", \"1.0\"], \"15\": [\"0.8942307692307693\", \"0.9934397483886594\", \"0.9317532146760295\", \"0.7428577701148424\", \"0.944805631985229\", \"0.8227196994553121\"], \"16\": [\"0.9491525423728814\", \"0.9331639033724253\", \"0.896429628610939\", \"0.9791293390143264\", \"0.9791112207162269\", \"1.0\"], \"17\": [\"0.875\", \"0.7082479018940693\", \"0.802566665945898\", \"0.9714202211916472\", \"0.9315912971695113\", \"0.9467323855074238\"], \"18\": [\"0.9105691056910569\", \"0.7983763656056397\", \"0.8358465799622541\", \"0.8870219049839082\", \"1.0\", \"0.8788616294782583\"], \"19\": [\"0.9568359375\", \"1.0\", \"1.0\", \"1.0\", \"0.9985570257337495\", \"1.0\"], \"20\": [\"0.8938053097345132\", \"0.8438629068410547\", \"0.9780655953026155\", \"0.7787351086407908\", \"0.7891063411584547\", \"0.7244072548738367\"], \"21\": [\"0.9060402684563759\", \"1.0\", \"0.926476632075494\", \"0.8524988854351541\", \"0.7639817203390269\", \"0.8709932381579796\"], \"22\": [\"0.9310344827586207\", \"1.0\", \"0.8126051965537983\", \"0.8653925852663946\", \"0.8302727026390117\", \"0.9627765850564689\"], \"23\": [\"0.9183673469387755\", \"0.9694880736660227\", \"0.7878158795207733\", \"0.721868688061021\", \"0.9269148799390148\", \"0.9546774457015843\"], \"24\": [\"0.9649638143891016\", \"0.9211517538236319\", \"1.0\", \"0.8381534535529309\", \"0.905752056962576\", \"0.9987995055231192\"]}"); + var thresholds_all = JSON.parse("{\"0\": \"0.7\", \"1\": \"0.7\", \"2\": \"0.7\", \"3\": \"0.7\", \"4\": \"0.7\", \"5\": \"0.7\", \"6\": \"0.7\", \"7\": \"0.7\", \"8\": \"0.7\", \"9\": \"0.7\", \"10\": \"0.7\", \"11\": \"0.7\", \"12\": \"0.7\", \"13\": \"0.7\", \"14\": \"0.7\", \"15\": \"0.7\", \"16\": \"0.7\", \"17\": \"0.7\", \"18\": \"0.7\", \"19\": \"0.7\", \"20\": \"0.7\", \"21\": \"0.7\", \"22\": \"0.7\", \"23\": \"0.7\", \"24\": \"0.7\"}"); + var trends_all = JSON.parse("{\"0\": \"neutral\", \"1\": \"neutral\", \"2\": \"negative\", \"3\": \"positive\", \"4\": \"neutral\", \"5\": \"neutral\", \"6\": \"negative\", \"7\": \"neutral\", \"8\": \"negative\", \"9\": \"negative\", \"10\": \"positive\", \"11\": \"positive\", \"12\": \"neutral\", \"13\": \"negative\", \"14\": \"neutral\", \"15\": \"negative\", \"16\": \"positive\", \"17\": \"positive\", \"18\": \"positive\", \"19\": \"neutral\", \"20\": \"negative\", \"21\": \"negative\", \"22\": \"neutral\", \"23\": \"neutral\", \"24\": \"neutral\"}"); + var passed_all = JSON.parse("{\"0\": true, \"1\": true, \"2\": true, \"3\": true, \"4\": true, \"5\": true, \"6\": true, \"7\": false, \"8\": true, \"9\": true, \"10\": true, \"11\": true, \"12\": true, \"13\": true, \"14\": true, \"15\": true, \"16\": true, \"17\": true, \"18\": true, \"19\": true, \"20\": true, \"21\": true, \"22\": true, \"23\": true, \"24\": true}"); + var names_all = JSON.parse("{\"0\": \"Accuracy\", \"1\": \"Precision\", \"2\": \"Recall\", \"3\": \"F1 Score\", \"4\": \"AUROC\", \"5\": \"Accuracy\", \"6\": \"Precision\", \"7\": \"Recall\", \"8\": \"F1 Score\", \"9\": \"AUROC\", \"10\": \"Accuracy\", \"11\": \"Precision\", \"12\": \"Recall\", \"13\": \"F1 Score\", \"14\": \"AUROC\", \"15\": \"Accuracy\", \"16\": \"Precision\", \"17\": \"Recall\", \"18\": \"F1 Score\", \"19\": \"AUROC\", \"20\": \"Accuracy\", \"21\": \"Precision\", \"22\": \"Recall\", \"23\": \"F1 Score\", \"24\": \"AUROC\"}"); + var timestamps_all = JSON.parse("{\"0\": [\"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:33\", \"2023-11-21 10:32:33\"], \"1\": [\"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:33\", \"2023-11-21 10:32:33\"], \"2\": [\"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:33\", \"2023-11-21 10:32:33\"], \"3\": [\"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:33\", \"2023-11-21 10:32:33\"], \"4\": [\"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:33\", \"2023-11-21 10:32:33\"], \"5\": [\"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:33\", \"2023-11-21 10:32:33\"], \"6\": [\"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:33\", \"2023-11-21 10:32:33\"], \"7\": [\"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:33\", \"2023-11-21 10:32:33\"], \"8\": [\"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:33\", \"2023-11-21 10:32:33\"], \"9\": [\"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:33\", \"2023-11-21 10:32:33\"], \"10\": [\"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:33\", \"2023-11-21 10:32:33\"], \"11\": [\"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:33\", \"2023-11-21 10:32:33\"], \"12\": [\"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:33\", \"2023-11-21 10:32:33\"], \"13\": [\"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:33\", \"2023-11-21 10:32:33\"], \"14\": [\"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:33\", \"2023-11-21 10:32:33\"], \"15\": [\"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:33\", \"2023-11-21 10:32:33\"], \"16\": [\"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:33\", \"2023-11-21 10:32:33\"], \"17\": [\"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:33\", \"2023-11-21 10:32:33\"], \"18\": [\"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:33\", \"2023-11-21 10:32:33\"], \"19\": [\"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:33\", \"2023-11-21 10:32:33\"], \"20\": [\"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:33\", \"2023-11-21 10:32:33\"], \"21\": [\"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:33\", \"2023-11-21 10:32:33\"], \"22\": [\"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:33\", \"2023-11-21 10:32:33\"], \"23\": [\"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:33\", \"2023-11-21 10:32:33\"], \"24\": [\"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:32\", \"2023-11-21 10:32:33\", \"2023-11-21 10:32:33\"]}"); + + for (let i = 0; i < selection.length; i++) { + // use selection to set label_slice_selection background color + for (let j = 0; j < inputs_all.length; j++) { + if (inputs_all[j].name === selection[i].split(":")[0]) { + if (inputs_all[j].value == selection[i].split(":")[1]) { + inputs_all[j].checked = true; + const [r, g, b] = plot_color.match(/\d+/g); + const rgbaColor = `rgba(${r}, ${g}, ${b}, 0.2)`; + label_slice_selection[j].style.backgroundColor = rgbaColor; + label_slice_selection[j].style.border = "2px solid " + plot_color; + label_slice_selection[j].style.color = plot_color; + } + else { + inputs_all[j].checked = false; + label_slice_selection[j].style.backgroundColor = "#ffffff"; + label_slice_selection[j].style.border = "2px solid #DADCE0"; + label_slice_selection[j].style.color = "#000000"; + } + } + } + } + + var radioGroups = {}; + var labelGroups = {}; + for (let i = 0; i < inputs_all.length; i++) { + var input = inputs_all[i]; + var label = label_slice_selection[i]; + var groupName = input.name; + if (!radioGroups[groupName]) { + radioGroups[groupName] = []; + labelGroups[groupName] = []; + } + radioGroups[groupName].push(input); + labelGroups[groupName].push(label); + } + + // use radioGroups to loop through selection changing only one element at a time + for (let i = 0; i < selection.length; i++) { + for (let j = 0; j < inputs_all.length; j++) { + if (inputs_all[j].name === selection[i].split(":")[0]) { + radio_group = radioGroups[selection[i].split(":")[0]]; + label_group = labelGroups[selection[i].split(":")[0]]; + for (let k = 0; k < radio_group.length; k++) { + selection_copy = selection.slice(); + selection_copy[i] = selection[i].split(":")[0] + ":" + radio_group[k].value; + // get idx of slices where all elements match + var idx = Object.keys(slices_all).find(key => JSON.stringify(slices_all[key].sort()) === JSON.stringify(selection_copy.sort())); + if (idx === undefined) { + // set radio button to disabled and cursor to not allowed and color to gray if idx is undefined + radio_group[k].disabled = true; + label_group[k].style.cursor = "not-allowed"; + label_group[k].style.color = "gray"; + label_group[k].style.backgroundColor = "rgba(125, 125, 125, 0.2)"; + } + else { + radio_group[k].disabled = false; + label_group[k].style.cursor = "pointer"; + } + } + } + } + } + + traces = []; + for (let i = 0; i < selections.length; i++) { + if (selections[i] === null) { + continue; + } + selection = selections[i] + // get idx of slices where all elements match + var idx = Object.keys(slices_all).find(key => JSON.stringify(slices_all[key].sort()) === JSON.stringify(selection)); + var history_data = []; + for (let i = 0; i < histories_all[idx].length; i++) { + history_data.push(parseFloat(histories_all[idx][i])); + } + var timestamp_data = []; + for (let i = 0; i < timestamps_all[idx].length; i++) { + timestamp_data.push(timestamps_all[idx][i]); + } + threshold = parseFloat(thresholds_all[idx]); + trend = trends_all[idx]; + passed = passed_all[idx]; + name = names_all[idx]; + + // if trend is "positive" set keyword to upwards, if trend is "negative" set keyword to downwards, else set keyword to flat + if (trend === "positive") { + var trend_keyword = "upwards"; + } else if (trend === "negative") { + var trend_keyword = "downwards"; + } else { + var trend_keyword = "flat"; + } + + // if passed is true set keyword to Above, if passed is false set keyword to Below + if (passed) { + var passed_keyword = "above"; + } + else { + var passed_keyword = "below"; + } + + // create title for plot: Current {metric name} is trending {trend_keyword} and is {passed_keyword} the threshold. + // get number of nulls in selections, if 9 then plot title, else don't plot title + var nulls = 0; + for (let i = 0; i < selections.length; i++) { + if (selections[i] === null) { + nulls += 1; + } + } + if (nulls === 10) { + var plot_title = "Current " + name + " is trending " + trend_keyword + " and is " + passed_keyword + " the threshold."; + var showlegend = false; + } + else { + var plot_title = ""; + var showlegend = true; + } + name = "" + suffix = " ( " + for (let i = 0; i < selection.length; i++) { + if (selection[i].split(":")[0] === "metric") { + name += selection[i].split(":")[1]; + } + else { + if (selection[i].split(":")[1].includes("overall")) { + continue; + } else { + suffix += selection[i]; + suffix += ", "; + } + } + } + if (suffix === " ( ") { + name += ""; + } + else { + suffix = suffix.slice(0, -2); + name += suffix + " )"; + } + var trace = { + // range of x is the length of the list of floats + x: timestamp_data, + y: history_data, + mode: 'lines+markers', + type: 'scatter', + marker: {color: plot_colors[i+1]}, + line: {color: plot_colors[i+1]}, + name: name, + //name: selection.toString(), + }; + traces.push(trace); + } + + if (nulls === 10) { + var threshold_trace = { + x: timestamp_data, + y: Array.from({length: history_data.length}, (_, i) => threshold), + mode: 'lines', + type: 'scatter', + marker: {color: 'rgb(0,0,0)'}, + line: {color: 'rgb(0,0,0)', dash: 'dot'}, + name: '', + }; + traces.push(threshold_trace); + } + var layout = { + title: { + text: plot_title, + font: { + family: 'Arial, Helvetica, sans-serif', + size: 18, + } + }, + paper_bgcolor: 'rgba(0,0,0,0)', + plot_bgcolor: 'rgba(0,0,0,0)', + xaxis: { + zeroline: false, + showticklabels: false, + showgrid: false, + }, + yaxis: { + gridcolor: '#ffffff', + }, + showlegend: showlegend, + margin: { + l: 50, + r: 50, + b: 50, + t: 50, + pad: 4 + }, + // set height and width of plot to extra-wide to fit the plot + height: 500, + width: 900, + } + Plotly.newPlot(plot, traces, layout, {displayModeBar: false}); + } + // Add event listeners to radio buttons + for (let input of inputs_all) { + input.addEventListener('change', updatePlot); + } + for (let selection of plot_selection) { + selection.addEventListener('change', updatePlotSelection); + } + // Initial update when the page loads + updatePlot(); + \ No newline at end of file diff --git a/api/tutorials/synthea/los_prediction.html b/api/tutorials/synthea/los_prediction.html index 155a1effd..899c57fce 100644 --- a/api/tutorials/synthea/los_prediction.html +++ b/api/tutorials/synthea/los_prediction.html @@ -489,7 +489,7 @@

Import Libraries 
-/home/amritk/.cache/pypoetry/virtualenvs/pycyclops-mhx6UJW0-py3.10/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
+/home/amritk/.cache/pypoetry/virtualenvs/pycyclops-wIzUAwxh-py3.10/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
   from .autonotebook import tqdm as notebook_tqdm

@@ -681,17 +681,17 @@

Compute length of stay (labels) 
-2023-11-20 15:40:06,463 INFO cycquery.orm    - Database setup, ready to run queries!
-2023-11-20 15:40:10,609 INFO cycquery.orm    - Query returned successfully!
-2023-11-20 15:40:10,610 INFO cycquery.utils.profile - Finished executing function run_query in 3.522007 s
-2023-11-20 15:40:12,455 INFO cycquery.orm    - Query returned successfully!
-2023-11-20 15:40:12,457 INFO cycquery.utils.profile - Finished executing function run_query in 1.845816 s
-2023-11-20 15:40:13,745 INFO cycquery.orm    - Query returned successfully!
-2023-11-20 15:40:13,746 INFO cycquery.utils.profile - Finished executing function run_query in 0.357999 s
-2023-11-20 15:40:14,189 INFO cycquery.orm    - Query returned successfully!
-2023-11-20 15:40:14,190 INFO cycquery.utils.profile - Finished executing function run_query in 0.439785 s
-2023-11-20 15:40:14,277 INFO cycquery.orm    - Query returned successfully!
-2023-11-20 15:40:14,278 INFO cycquery.utils.profile - Finished executing function run_query in 0.087144 s
+2023-11-21 10:32:16,868 INFO cycquery.orm    - Database setup, ready to run queries!
+2023-11-21 10:32:20,273 INFO cycquery.orm    - Query returned successfully!
+2023-11-21 10:32:20,274 INFO cycquery.utils.profile - Finished executing function run_query in 2.435702 s
+2023-11-21 10:32:22,077 INFO cycquery.orm    - Query returned successfully!
+2023-11-21 10:32:22,078 INFO cycquery.utils.profile - Finished executing function run_query in 1.802668 s
+2023-11-21 10:32:23,282 INFO cycquery.orm    - Query returned successfully!
+2023-11-21 10:32:23,283 INFO cycquery.utils.profile - Finished executing function run_query in 0.342421 s
+2023-11-21 10:32:23,708 INFO cycquery.orm    - Query returned successfully!
+2023-11-21 10:32:23,709 INFO cycquery.utils.profile - Finished executing function run_query in 0.422492 s
+2023-11-21 10:32:23,788 INFO cycquery.orm    - Query returned successfully!
+2023-11-21 10:32:23,788 INFO cycquery.utils.profile - Finished executing function run_query in 0.078192 s
@@ -778,9 +778,9 @@

Drop NaNs based on the
-
- + +

Archive

Archive

2023​

diff --git a/blog/cyclops-alpha-release/index.html b/blog/cyclops-alpha-release/index.html index a96a88116..b9b80cfad 100644 --- a/blog/cyclops-alpha-release/index.html +++ b/blog/cyclops-alpha-release/index.html @@ -5,8 +5,8 @@ CyclOps Alpha Release | CyclOps - - + +

CyclOps Alpha Release

· One min read
Amrit Krishnan

Developing machine learning (ML) systems for clinical use cases is difficult. Furthermore, evaluating ML models diff --git a/blog/index.html b/blog/index.html index d9409d6eb..c65d968be 100644 --- a/blog/index.html +++ b/blog/index.html @@ -5,8 +5,8 @@ Blog | CyclOps - - + +

· One min read
Amrit Krishnan

Developing machine learning (ML) systems for clinical use cases is difficult. Furthermore, evaluating ML models diff --git a/blog/tags/alpha/index.html b/blog/tags/alpha/index.html index 19611f588..734449f87 100644 --- a/blog/tags/alpha/index.html +++ b/blog/tags/alpha/index.html @@ -5,8 +5,8 @@ One post tagged with "alpha" | CyclOps - - + +

One post tagged with "alpha"

View All Tags

· One min read
Amrit Krishnan

Developing machine learning (ML) systems for clinical use cases is difficult. Furthermore, evaluating ML models diff --git a/blog/tags/index.html b/blog/tags/index.html index 7c1f732e6..1d3ecb26e 100644 --- a/blog/tags/index.html +++ b/blog/tags/index.html @@ -5,8 +5,8 @@ Tags | CyclOps - - + +

Tags

diff --git a/docs/intro/index.html b/docs/intro/index.html index 0858bed17..882502cb0 100644 --- a/docs/intro/index.html +++ b/docs/intro/index.html @@ -5,8 +5,8 @@ intro | CyclOps - - + +

intro

Getting Started​

diff --git a/index.html b/index.html index de8749247..fc871b86d 100644 --- a/index.html +++ b/index.html @@ -5,8 +5,8 @@ CyclOps | CyclOps - - + +

CyclOps

Cyclical development towards Operationalizing ML models for healthcare

Rigorous Evaluation

CyclOps APIs support rigorous evaluation across patient sub-populations

Deployment and Operationalization

By leveraging powerful open source tools, CyclOps provides a modular and extensible MLOps platform for healthcare

Monitoring

CyclOps supports monitoring of clinical ML models for dataset shifts

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Markdown page example