{"group":{"id":1,"name":"Community","lockable":false,"created_at":"2012-01-18T18:02:15.000Z","updated_at":"2026-04-06T14:01:22.000Z","description":"Problems submitted by members of the MATLAB Central community.","is_default":true,"created_by":161519,"badge_id":null,"featured":false,"trending":false,"solution_count_in_trending_period":0,"trending_last_calculated":"2026-04-06T00:00:00.000Z","image_id":null,"published":true,"community_created":false,"status_id":2,"is_default_group_for_player":false,"deleted_by":null,"deleted_at":null,"restored_by":null,"restored_at":null,"description_opc":null,"description_html":null,"published_at":null},"problems":[{"id":3064,"title":"Cycling — Normalized Power","description":"In cycling, a power meter is an indispensable tool to record power output (in Watts) and measure fitness gains and performance metrics. When analyzing the data though, many different workouts can yield approximately the same average power, despite major differences between workouts (e.g., a long steady effort vs. sprints or intervals). Normalized power (NP) is a method to measure the effect of more intense efforts on the overall workout. NP is calculated by the following four steps (from Training and Racing with a Power Meter by Allen and Coggan):\r\n\r\n# Calculate a 30-second rolling average of the power data\r\n# Raise these values to the fourth power\r\n# Average the resulting values\r\n# Take the fourth root of the result\r\n\r\nYou will be provided with the 30-second rolling average power data set (vector). Write a function to return the average power (using the rolling average data) and the normalized power using steps 2–4 above. Round the values to the nearest integer.","description_html":"\u003cp\u003eIn cycling, a power meter is an indispensable tool to record power output (in Watts) and measure fitness gains and performance metrics. When analyzing the data though, many different workouts can yield approximately the same average power, despite major differences between workouts (e.g., a long steady effort vs. sprints or intervals). Normalized power (NP) is a method to measure the effect of more intense efforts on the overall workout. NP is calculated by the following four steps (from Training and Racing with a Power Meter by Allen and Coggan):\u003c/p\u003e\u003col\u003e\u003cli\u003eCalculate a 30-second rolling average of the power data\u003c/li\u003e\u003cli\u003eRaise these values to the fourth power\u003c/li\u003e\u003cli\u003eAverage the resulting values\u003c/li\u003e\u003cli\u003eTake the fourth root of the result\u003c/li\u003e\u003c/ol\u003e\u003cp\u003eYou will be provided with the 30-second rolling average power data set (vector). Write a function to return the average power (using the rolling average data) and the normalized power using steps 2–4 above. Round the values to the nearest integer.\u003c/p\u003e","function_template":"function [P_avg,NP] = cycling_norm_power(power)\r\n\r\nP_avg = 0;\r\n\r\nNP = 0;\r\n\r\nend\r\n","test_suite":"%% steady\r\npower = 200*ones(1,3600);\r\nP_avg_corr = 200;\r\nNP_corr = 200;\r\n[P_avg,NP] = cycling_norm_power(power);\r\nassert(isequal(P_avg_corr,P_avg))\r\nassert(isequal(NP_corr,NP))\r\n\r\n%% intervals\r\npower = 100*ones(1,60);\r\npower = [power 250*ones(1,240)];\r\npower = [power 100*ones(1,60)];\r\npower = repmat(power,[1,10]);\r\nP_avg_corr = 200;\r\nNP_corr = 227;\r\n[P_avg,NP] = cycling_norm_power(power);\r\nassert(isequal(P_avg_corr,P_avg))\r\nassert(isequal(NP_corr,NP))\r\n\r\n%% ramped intervals\r\npower = 100*ones(1,30);\r\npower = [power 100:249];\r\npower = [power 250:-1:101];\r\npower = [power 100*ones(1,30)];\r\npower = repmat(power,[1,10]);\r\nP_avg_corr = 163;\r\nNP_corr = 182;\r\n[P_avg,NP] = cycling_norm_power(power);\r\nassert(isequal(P_avg_corr,P_avg))\r\nassert(isequal(NP_corr,NP))\r\n\r\n%% medium sprints\r\npower = 100*ones(1,170);\r\npower = [power 500*ones(1,20)];\r\npower = [power 100*ones(1,170)];\r\npower = repmat(power,[1,10]);\r\nP_avg_corr = 122;\r\nNP_corr = 244;\r\n[P_avg,NP] = cycling_norm_power(power);\r\nassert(isequal(P_avg_corr,P_avg))\r\nassert(isequal(NP_corr,NP))\r\n\r\n%% intense sprints\r\npower = 100*ones(1,176);\r\npower = [power 1500*ones(1,8)];\r\npower = [power 100*ones(1,176)];\r\npower = repmat(power,[1,10]);\r\nP_avg_corr = 131;\r\nNP_corr = 579;\r\n[P_avg,NP] = cycling_norm_power(power);\r\nassert(isequal(P_avg_corr,P_avg))\r\nassert(isequal(NP_corr,NP))\r\n\r\n%% anti-cheating (random)\r\nind = randi(5);\r\nswitch ind\r\n\tcase 1\r\n\t\tpower = 200*ones(1,3600);\r\n\t\tP_avg_corr = 200;\r\n\t\tNP_corr = 200;\r\n\tcase 2\r\n\t\tpower = 100*ones(1,60);\r\n\t\tpower = [power 250*ones(1,240)];\r\n\t\tpower = [power 100*ones(1,60)];\r\n\t\tpower = repmat(power,[1,10]);\r\n\t\tP_avg_corr = 200;\r\n\t\tNP_corr = 227;\r\n\tcase 3\r\n\t\tpower = 100*ones(1,30);\r\n\t\tpower = [power 100:249];\r\n\t\tpower = [power 250:-1:101];\r\n\t\tpower = [power 100*ones(1,30)];\r\n\t\tpower = repmat(power,[1,10]);\r\n\t\tP_avg_corr = 163;\r\n\t\tNP_corr = 182;\r\n\tcase 4\r\n\t\tpower = 100*ones(1,170);\r\n\t\tpower = [power 500*ones(1,20)];\r\n\t\tpower = [power 100*ones(1,170)];\r\n\t\tpower = repmat(power,[1,10]);\r\n\t\tP_avg_corr = 122;\r\n\t\tNP_corr = 244;\r\n\tcase 5\r\n\t\tpower = 100*ones(1,176);\r\n\t\tpower = [power 1500*ones(1,8)];\r\n\t\tpower = [power 100*ones(1,176)];\r\n\t\tpower = repmat(power,[1,10]);\r\n\t\tP_avg_corr = 131;\r\n\t\tNP_corr = 579;\r\nend\r\n[P_avg,NP] = cycling_norm_power(power);\r\nassert(isequal(P_avg_corr,P_avg))\r\nassert(isequal(NP_corr,NP))\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":1,"created_by":26769,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":43,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2015-03-05T03:45:47.000Z","updated_at":"2026-03-31T11:12:20.000Z","published_at":"2015-03-05T03:45:47.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIn cycling, a power meter is an indispensable tool to record power output (in Watts) and measure fitness gains and performance metrics. When analyzing the data though, many different workouts can yield approximately the same average power, despite major differences between workouts (e.g., a long steady effort vs. sprints or intervals). Normalized power (NP) is a method to measure the effect of more intense efforts on the overall workout. NP is calculated by the following four steps (from Training and Racing with a Power Meter by Allen and Coggan):\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eCalculate a 30-second rolling average of the power data\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eRaise these values to the fourth power\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAverage the resulting values\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTake the fourth root of the result\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYou will be provided with the 30-second rolling average power data set (vector). Write a function to return the average power (using the rolling average data) and the normalized power using steps 2–4 above. Round the values to the nearest integer.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"}],"problem_search":{"errors":[],"problems":[{"id":3064,"title":"Cycling — Normalized Power","description":"In cycling, a power meter is an indispensable tool to record power output (in Watts) and measure fitness gains and performance metrics. When analyzing the data though, many different workouts can yield approximately the same average power, despite major differences between workouts (e.g., a long steady effort vs. sprints or intervals). Normalized power (NP) is a method to measure the effect of more intense efforts on the overall workout. NP is calculated by the following four steps (from Training and Racing with a Power Meter by Allen and Coggan):\r\n\r\n# Calculate a 30-second rolling average of the power data\r\n# Raise these values to the fourth power\r\n# Average the resulting values\r\n# Take the fourth root of the result\r\n\r\nYou will be provided with the 30-second rolling average power data set (vector). Write a function to return the average power (using the rolling average data) and the normalized power using steps 2–4 above. Round the values to the nearest integer.","description_html":"\u003cp\u003eIn cycling, a power meter is an indispensable tool to record power output (in Watts) and measure fitness gains and performance metrics. When analyzing the data though, many different workouts can yield approximately the same average power, despite major differences between workouts (e.g., a long steady effort vs. sprints or intervals). Normalized power (NP) is a method to measure the effect of more intense efforts on the overall workout. NP is calculated by the following four steps (from Training and Racing with a Power Meter by Allen and Coggan):\u003c/p\u003e\u003col\u003e\u003cli\u003eCalculate a 30-second rolling average of the power data\u003c/li\u003e\u003cli\u003eRaise these values to the fourth power\u003c/li\u003e\u003cli\u003eAverage the resulting values\u003c/li\u003e\u003cli\u003eTake the fourth root of the result\u003c/li\u003e\u003c/ol\u003e\u003cp\u003eYou will be provided with the 30-second rolling average power data set (vector). Write a function to return the average power (using the rolling average data) and the normalized power using steps 2–4 above. Round the values to the nearest integer.\u003c/p\u003e","function_template":"function [P_avg,NP] = cycling_norm_power(power)\r\n\r\nP_avg = 0;\r\n\r\nNP = 0;\r\n\r\nend\r\n","test_suite":"%% steady\r\npower = 200*ones(1,3600);\r\nP_avg_corr = 200;\r\nNP_corr = 200;\r\n[P_avg,NP] = cycling_norm_power(power);\r\nassert(isequal(P_avg_corr,P_avg))\r\nassert(isequal(NP_corr,NP))\r\n\r\n%% intervals\r\npower = 100*ones(1,60);\r\npower = [power 250*ones(1,240)];\r\npower = [power 100*ones(1,60)];\r\npower = repmat(power,[1,10]);\r\nP_avg_corr = 200;\r\nNP_corr = 227;\r\n[P_avg,NP] = cycling_norm_power(power);\r\nassert(isequal(P_avg_corr,P_avg))\r\nassert(isequal(NP_corr,NP))\r\n\r\n%% ramped intervals\r\npower = 100*ones(1,30);\r\npower = [power 100:249];\r\npower = [power 250:-1:101];\r\npower = [power 100*ones(1,30)];\r\npower = repmat(power,[1,10]);\r\nP_avg_corr = 163;\r\nNP_corr = 182;\r\n[P_avg,NP] = cycling_norm_power(power);\r\nassert(isequal(P_avg_corr,P_avg))\r\nassert(isequal(NP_corr,NP))\r\n\r\n%% medium sprints\r\npower = 100*ones(1,170);\r\npower = [power 500*ones(1,20)];\r\npower = [power 100*ones(1,170)];\r\npower = repmat(power,[1,10]);\r\nP_avg_corr = 122;\r\nNP_corr = 244;\r\n[P_avg,NP] = cycling_norm_power(power);\r\nassert(isequal(P_avg_corr,P_avg))\r\nassert(isequal(NP_corr,NP))\r\n\r\n%% intense sprints\r\npower = 100*ones(1,176);\r\npower = [power 1500*ones(1,8)];\r\npower = [power 100*ones(1,176)];\r\npower = repmat(power,[1,10]);\r\nP_avg_corr = 131;\r\nNP_corr = 579;\r\n[P_avg,NP] = cycling_norm_power(power);\r\nassert(isequal(P_avg_corr,P_avg))\r\nassert(isequal(NP_corr,NP))\r\n\r\n%% anti-cheating (random)\r\nind = randi(5);\r\nswitch ind\r\n\tcase 1\r\n\t\tpower = 200*ones(1,3600);\r\n\t\tP_avg_corr = 200;\r\n\t\tNP_corr = 200;\r\n\tcase 2\r\n\t\tpower = 100*ones(1,60);\r\n\t\tpower = [power 250*ones(1,240)];\r\n\t\tpower = [power 100*ones(1,60)];\r\n\t\tpower = repmat(power,[1,10]);\r\n\t\tP_avg_corr = 200;\r\n\t\tNP_corr = 227;\r\n\tcase 3\r\n\t\tpower = 100*ones(1,30);\r\n\t\tpower = [power 100:249];\r\n\t\tpower = [power 250:-1:101];\r\n\t\tpower = [power 100*ones(1,30)];\r\n\t\tpower = repmat(power,[1,10]);\r\n\t\tP_avg_corr = 163;\r\n\t\tNP_corr = 182;\r\n\tcase 4\r\n\t\tpower = 100*ones(1,170);\r\n\t\tpower = [power 500*ones(1,20)];\r\n\t\tpower = [power 100*ones(1,170)];\r\n\t\tpower = repmat(power,[1,10]);\r\n\t\tP_avg_corr = 122;\r\n\t\tNP_corr = 244;\r\n\tcase 5\r\n\t\tpower = 100*ones(1,176);\r\n\t\tpower = [power 1500*ones(1,8)];\r\n\t\tpower = [power 100*ones(1,176)];\r\n\t\tpower = repmat(power,[1,10]);\r\n\t\tP_avg_corr = 131;\r\n\t\tNP_corr = 579;\r\nend\r\n[P_avg,NP] = cycling_norm_power(power);\r\nassert(isequal(P_avg_corr,P_avg))\r\nassert(isequal(NP_corr,NP))\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":1,"created_by":26769,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":43,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2015-03-05T03:45:47.000Z","updated_at":"2026-03-31T11:12:20.000Z","published_at":"2015-03-05T03:45:47.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIn cycling, a power meter is an indispensable tool to record power output (in Watts) and measure fitness gains and performance metrics. When analyzing the data though, many different workouts can yield approximately the same average power, despite major differences between workouts (e.g., a long steady effort vs. sprints or intervals). Normalized power (NP) is a method to measure the effect of more intense efforts on the overall workout. NP is calculated by the following four steps (from Training and Racing with a Power Meter by Allen and Coggan):\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eCalculate a 30-second rolling average of the power data\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eRaise these values to the fourth power\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAverage the resulting values\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTake the fourth root of the result\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYou will be provided with the 30-second rolling average power data set (vector). Write a function to return the average power (using the rolling average data) and the normalized power using steps 2–4 above. Round the values to the nearest integer.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"}],"term":"tag:\"normalized\"","current_player_id":null,"fields":[{"name":"page","type":"integer","callback":null,"default":1,"directive":null,"facet":null,"facet_method":"and","operator":null,"param":null,"static":null,"prepend":true},{"name":"per_page","type":"integer","callback":null,"default":50,"directive":null,"facet":null,"facet_method":"and","operator":null,"param":null,"static":null,"prepend":true},{"name":"sort","type":"string","callback":null,"default":null,"directive":null,"facet":null,"facet_method":"and","operator":null,"param":null,"static":null,"prepend":true},{"name":"body","type":"text","callback":null,"default":"*:*","directive":null,"facet":null,"facet_method":"and","operator":null,"param":"term","static":null,"prepend":false},{"name":"group","type":"string","callback":null,"default":null,"directive":"group","facet":true,"facet_method":"and","operator":null,"param":"term","static":null,"prepend":true},{"name":"difficulty_rating_bin","type":"string","callback":null,"default":null,"directive":"difficulty_rating_bin","facet":true,"facet_method":"and","operator":null,"param":"term","static":null,"prepend":true},{"name":"id","type":"integer","callback":null,"default":null,"directive":"id","facet":null,"facet_method":"and","operator":null,"param":"term","static":null,"prepend":true},{"name":"tag","type":"string","callback":null,"default":null,"directive":"tag","facet":null,"facet_method":"and","operator":null,"param":"term","static":null,"prepend":true},{"name":"product","type":"string","callback":null,"default":null,"directive":"product","facet":null,"facet_method":"and","operator":null,"param":"term","static":null,"prepend":true},{"name":"created_at","type":"timeframe","callback":{},"default":null,"directive":"created_at","facet":null,"facet_method":"and","operator":null,"param":"term","static":null,"prepend":true},{"name":"profile_id","type":"integer","callback":null,"default":null,"directive":"author_id","facet":null,"facet_method":"and","operator":null,"param":"term","static":null,"prepend":true},{"name":"created_by","type":"string","callback":null,"default":null,"directive":"author","facet":null,"facet_method":"and","operator":null,"param":"term","static":null,"prepend":true},{"name":"player_id","type":"integer","callback":null,"default":null,"directive":"solver_id","facet":null,"facet_method":"and","operator":null,"param":"term","static":null,"prepend":true},{"name":"player","type":"string","callback":null,"default":null,"directive":"solver","facet":null,"facet_method":"and","operator":null,"param":"term","static":null,"prepend":true},{"name":"solvers_count","type":"integer","callback":null,"default":null,"directive":"solvers_count","facet":null,"facet_method":"and","operator":null,"param":"term","static":null,"prepend":true},{"name":"comments_count","type":"integer","callback":null,"default":null,"directive":"comments_count","facet":null,"facet_method":"and","operator":null,"param":"term","static":null,"prepend":true},{"name":"likes_count","type":"integer","callback":null,"default":null,"directive":"likes_count","facet":null,"facet_method":"and","operator":null,"param":"term","static":null,"prepend":true},{"name":"leader_id","type":"integer","callback":null,"default":null,"directive":"leader_id","facet":null,"facet_method":"and","operator":null,"param":"term","static":null,"prepend":true},{"name":"leading_solution","type":"integer","callback":null,"default":null,"directive":"leading_solution","facet":null,"facet_method":"and","operator":null,"param":"term","static":null,"prepend":true}],"filters":[{"name":"asset_type","type":"string","callback":null,"default":null,"directive":null,"facet":null,"facet_method":"and","operator":null,"param":null,"static":"\"cody:problem\"","prepend":true},{"name":"profile_id","type":"integer","callback":{},"default":null,"directive":null,"facet":null,"facet_method":"and","operator":null,"param":"author_id","static":null,"prepend":true}],"query":{"params":{"per_page":50,"term":"tag:\"normalized\"","current_player":null,"sort":"map(difficulty_value,0,0,999) asc"},"parser":"MathWorks::Search::Solr::QueryParser","directives":{"term":{"directives":{"tag":[["tag:\"normalized\"","","\"","normalized","\""]]}}},"facets":{"#\u003cMathWorks::Search::Field:0x00007f03d2760340\u003e":null,"#\u003cMathWorks::Search::Field:0x00007f03d27602a0\u003e":null},"filters":{"#\u003cMathWorks::Search::Field:0x00007f03d29df968\u003e":"\"cody:problem\""},"fields":{"#\u003cMathWorks::Search::Field:0x00007f03d27605c0\u003e":1,"#\u003cMathWorks::Search::Field:0x00007f03d2760520\u003e":50,"#\u003cMathWorks::Search::Field:0x00007f03d2760480\u003e":"map(difficulty_value,0,0,999) asc","#\u003cMathWorks::Search::Field:0x00007f03d27603e0\u003e":"tag:\"normalized\""},"user_query":{"#\u003cMathWorks::Search::Field:0x00007f03d27603e0\u003e":"tag:\"normalized\""},"queried_facets":{}},"query_backend":{"connection":{"configuration":{"index_url":"http://index-op-v2/solr/","query_url":"http://search-op-v2/solr/","direct_access_index_urls":["http://index-op-v2/solr/"],"direct_access_query_urls":["http://search-op-v2/solr/"],"timeout":10,"vhost":"search","exchange":"search.topic","heartbeat":30,"pre_index_mode":false,"host":"rabbitmq-eks","port":5672,"username":"cody-search","password":"78X075ddcV44","virtual_host":"search","indexer":"amqp","http_logging":"true","core":"cody"},"query_connection":{"uri":"http://search-op-v2/solr/cody/","proxy":null,"connection":{"parallel_manager":null,"headers":{"User-Agent":"Faraday v1.0.1"},"params":{},"options":{"params_encoder":"Faraday::FlatParamsEncoder","proxy":null,"bind":null,"timeout":null,"open_timeout":null,"read_timeout":null,"write_timeout":null,"boundary":null,"oauth":null,"context":null,"on_data":null},"ssl":{"verify":true,"ca_file":null,"ca_path":null,"verify_mode":null,"cert_store":null,"client_cert":null,"client_key":null,"certificate":null,"private_key":null,"verify_depth":null,"version":null,"min_version":null,"max_version":null},"default_parallel_manager":null,"builder":{"adapter":{"name":"Faraday::Adapter::NetHttp","args":[],"block":null},"handlers":[{"name":"Faraday::Response::RaiseError","args":[],"block":null}],"app":{"app":{"ssl_cert_store":{"verify_callback":null,"error":null,"error_string":null,"chain":null,"time":null},"app":{},"connection_options":{},"config_block":null}}},"url_prefix":"http://search-op-v2/solr/cody/","manual_proxy":false,"proxy":null},"update_format":"RSolr::JSON::Generator","update_path":"update","options":{"url":"http://search-op-v2/solr/cody"}}},"query":{"params":{"per_page":50,"term":"tag:\"normalized\"","current_player":null,"sort":"map(difficulty_value,0,0,999) asc"},"parser":"MathWorks::Search::Solr::QueryParser","directives":{"term":{"directives":{"tag":[["tag:\"normalized\"","","\"","normalized","\""]]}}},"facets":{"#\u003cMathWorks::Search::Field:0x00007f03d2760340\u003e":null,"#\u003cMathWorks::Search::Field:0x00007f03d27602a0\u003e":null},"filters":{"#\u003cMathWorks::Search::Field:0x00007f03d29df968\u003e":"\"cody:problem\""},"fields":{"#\u003cMathWorks::Search::Field:0x00007f03d27605c0\u003e":1,"#\u003cMathWorks::Search::Field:0x00007f03d2760520\u003e":50,"#\u003cMathWorks::Search::Field:0x00007f03d2760480\u003e":"map(difficulty_value,0,0,999) asc","#\u003cMathWorks::Search::Field:0x00007f03d27603e0\u003e":"tag:\"normalized\""},"user_query":{"#\u003cMathWorks::Search::Field:0x00007f03d27603e0\u003e":"tag:\"normalized\""},"queried_facets":{}},"options":{"fields":["id","difficulty_rating"]},"join":" "},"results":[{"id":3064,"difficulty_rating":"easy"}]}}