Merge pull request #10 from madgik/dev_refactor_result

Dev refactor result

galaxyCodeBase/tools/exaremeTools/CONFUSION_MATRIX_STATISTICS/CONFUSION_MATRIX_STATISTICS.py

@@ -147,7 +147,7 @@ def Rfunction_confusionmatrix(inputjson):
    "type": "application/vnd.dataresource+json"}
 
 
-    jsonResult = { "result": [ ResultOverall, ResultClassNames ] }
+    jsonResult = [ ResultOverall, ResultClassNames ]
     return jsonResult
 
 def main():
@@ -184,10 +184,13 @@ def main():
             print('  "type": "application/json"')
             print('   } , ... ')
             print('] }')
-        statistics.append(result)
+        statistics.append(result[0])
+        statistics.append(result[1])
+    
+    finalResult={ "result" : statistics }
     
     outputFile = open(opts.get("-o"), "w")
-    outputFile.write(json.dumps(statistics))
+    outputFile.write(json.dumps(finalResult))
     outputFile.close
 
 if __name__ == "__main__":

galaxyCodeBase/tools/exaremeTools/HEATMAP_HIGHCHART/HEATMAP_HIGHCHART.py

@@ -116,8 +116,8 @@ def main():
     results = []
     for i in xrange(len(inputJson)):
         try:
-            result = {'result': [{'data': heatmap(inputJson[i]['result'][0]['data'], title,
-                    xtitle, ytitle),'type': 'application/vnd.highcharts+json'}]}
+            result = {'data': heatmap(inputJson[i]['result'][0]['data'], title,
+                    xtitle, ytitle),'type': 'application/vnd.highcharts+json'}
         except ValueError:
             print("Input file should be:")
             print('[{ "result" : [{')
@@ -127,9 +127,11 @@ def main():
             print('] }')
 
         results.append(result)
-
+    
+    finalResult = { 'result' : results}
+    
     outputFile = open(opts.get('-o'), 'w')
-    outputFile.write(json.dumps(results))
+    outputFile.write(json.dumps(finalResult))
     outputFile.close
 
 

workflows/Galaxy-Workflow-Naive_Bayes_with_Cross_Validation.ga

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205}, "annotation": "{\n    \"name\": \"kfold\",\n    \"desc\": \"Number of pieces the dataset will be split\",\n    \"type\": \"other\",\n    \"value\": \"3\",\n    \"valueType\": \"integer\",\n    \"valueNotBlank\": true,\n    \"valueMultiple\": false\n}", "content_id": null, "type": "parameter_input"}, "5": {"tool_id": null, "tool_version": null, "outputs": [], "workflow_outputs": [{"output_name": "output", "uuid": "73e7abfc-92c6-45a0-a5c0-791b1fd0797d", "label": null}], "input_connections": {}, "tool_state": "{\"optional\": false, \"parameter_type\": \"text\"}", "id": 5, "uuid": "d8ded1b9-5e2a-4c55-9a9d-60d74ec940a2", "errors": null, "name": "Input parameter", "label": "alpha", "inputs": [], "position": {"top": 526.13330078125, "left": 203}, "annotation": "{\n    \"name\": \"alpha\",\n    \"desc\": \"Additive smoothing parameter (0 for no smoothing)\",\n    \"type\": \"other\",\n    \"value\": \"0.1\",\n    \"valueType\": \"real\",\n    \"valueNotBlank\": true,\n    \"valueMultiple\": false\n}", "content_id": null, "type": "parameter_input"}, "6": {"tool_id": null, "tool_version": null, "outputs": [], "workflow_outputs": [{"output_name": "output", "uuid": "f3c5695e-d0cf-404d-9cec-3331d510a6c2", "label": null}], "input_connections": {}, "tool_state": "{\"optional\": false, \"parameter_type\": \"text\"}", "id": 6, "uuid": "0f6fb62f-238c-404e-b636-230bd0b60207", "errors": null, "name": "Input parameter", "label": "filter", "inputs": [], "position": {"top": 595.13330078125, "left": 202}, "annotation": "{\n    \"name\": \"filter\",\n    \"desc\": \"\",\n    \"type\": \"filter\",\n    \"value\": \"\",\n    \"valueType\": \"string\",\n    \"valueNotBlank\": false,\n    \"valueMultiple\": true\n}", "content_id": null, "type": "parameter_input"}, "7": {"tool_id": "CROSS_VALIDATION_K_FOLD", "tool_version": "1.0.0", "outputs": [{"type": "json", "name": "output"}], "workflow_outputs": [], "input_connections": {"kfold": {"output_name": "output", "id": 4}, 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\ No newline at end of file

workflows/Galaxy-Workflow-Naive_Bayes_with_Hold_Out_Validation.ga

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If it is between 0.0 and 1.0, it represents the proportion of the dataset to include in the test split. If it is not given, the value is set to the complement of the train size.\",\n    \"type\": \"other\",\n    \"value\": \"0.25\",\n    \"valueType\": \"real\",\n    \"valueNotBlank\": false,\n    \"valueMultiple\": false,\n    \"valueMin\": 0.0,\n    \"valueMax\": 1.0\n}", "content_id": null, "type": "parameter_input"}, "7": {"tool_id": null, "tool_version": null, "outputs": [], "workflow_outputs": [{"output_name": "output", "uuid": "76c5bf8f-371b-4812-a31b-78a4abd2d4d3", "label": null}], "input_connections": {}, "tool_state": "{\"optional\": false, \"parameter_type\": \"text\"}", "id": 7, "uuid": "5739cbe8-b547-4c4e-ade0-93e5ca430c27", "errors": null, "name": "Input parameter", "label": "trainSize", "inputs": [], "position": {"top": 808, "left": 272.29998779296875}, "annotation": "{\n    \"name\": \"train_size\",\n    \"desc\": \"(Optional). If it is between 0.0 and 1.0, it represents the proportion of the dataset to include in the train split. If None, the value is automatically set to the complement of the test size.\",\n    \"type\": \"other\",\n    \"value\": \"\",\n    \"valueType\": \"real\",\n    \"valueNotBlank\": false,\n    \"valueMultiple\": false,\n    \"valueMin\": 0.0,\n    \"valueMax\": 1.0\n}", "content_id": null, "type": "parameter_input"}, "8": {"tool_id": null, "tool_version": null, "outputs": [], "workflow_outputs": [{"output_name": "output", "uuid": "d2838507-cedc-4a96-9388-3d9daf60e810", "label": null}], "input_connections": {}, "tool_state": "{\"optional\": false, \"parameter_type\": \"text\"}", "id": 8, "uuid": "b172eb6f-3bd1-4f85-b402-0de697a371dd", "errors": null, "name": "Input parameter", "label": "randomState", "inputs": [], "position": {"top": 873, "left": 316.29998779296875}, "annotation": "{\n    \"name\": \"random_state\",\n    \"desc\": \"(Optional). 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\ No newline at end of file