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        "author": "Maciej Janowski",
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        "description": "# CubeStories\nCubeStories allows querying Linked Open Statistical by Providing parameters in a form of Python dictionaries(JSON).\n\nReserach paper underpinning the implementation [Research paper](https://www.researchgate.net/publication/333227519_Mediating_Open_Data_Consumption_-_Identifying_Story_Patterns_for_Linked_Open_Statistical_Data)\n\nHigh-level encapsulation of datastories librabry [DataStories repo](https://github.com/MaciejJanowski/DataStoryPatternLibrary)\n\nTest sripts are available at: [Test repor](https://github.com/MaciejJanowski/CubeStoriesTesting)\n\n### Installation\n```python\npip install cubestories\n```\nRequirements will be automatically installed with package\n\n### Import/Usage \n```python\nfrom CubeStories import *\n\n```\n# Usage \nLibrary implements 3 artifacts required for Data analysis\n\n* Metadata Parameters - metadata required for SPARQL queries\n```json\n    {\n    \"sparqlEndPointUrl\":\"[SPARQL ENDPOINT URL]\", \n    \"jsonMetaDataFile\":\"[directory of JSON file with metadata]\" \n}\n```\n\n* Cube Parameters - what properties of cube to be retrieved from endpoint(based on JSON file provided in Metadata Parameters).\nValues highlighted as: ```--- --- `` has to be specified by a user - replaced to value only\n```json  \n    {\n    \"cube\":\"---Key of Cube ---\",  \n    \"dimensions\":[\"---List of dimensions---\"], \n    \"measures\":[\"---List of Measures---\"], \n    \"hierdimensions\": \n        {\"---DimKey---\":{ \n            \"selected_level\":\"---levelkey---\" \n            }\n        }\n```\n\n* Analysis Pipeline - JSON-based list of pattern analysis to be performed. Each Pattern will have such template provided\n```json\n{ \n    \"---PatternName---\": {  \n\n        \"parameter1\":[\"---list of values---\"],\n        \"parameter2\":\"---value---\"\n    },\n    \n    \"---PatternName----\":{\n        \"parameter1\":\"---pattern1 value---\",\n        \"parameter2\":[\"---list of values---\"]\n    }\n}\n\n```\n\n\n# JSON Template - one of the metadata parameters\n```json\n{\n    \"---cube_key---\" : {\n\t\t\"title\":\"---title of cube---\",\n\t\t\"dataset_structure\":\"---URI for cube structure---\",\n        \"dimensions\":{\n            \"---dimension_key---\":{\n                \"dimension_title\":\"---Title of diemnsion---\",\n                \"dimension_url\":\"---URI for dimension---\",\n                \"dimension_prefix\":\"---URI for dimension's values---\"\n            },\n            \"---dimension_key---\":{\n                \"dimension_title\":\"---Title of diemnsion---\",\n                \"dimension_url\":\"---URI for dimension---\",\n                \"dimension_prefix\":\"---URI for dimension's values---\"\n            }\n\t\t},\n\t\t\"hierarchical_dimensions\":{\n\t\t\t\"---dimension_key---\":{\n                \"dimension_title\":\"---Title of diemnsion---\",\n                \"dimension_url\":\"---URI for dimension---\",\n                \"dimension_prefix\":\"---URI for dimension's values---\",\n\t\t\t\t\"dimension_levels\":\n\t\t\t\t{\n\t\t\t\t\t\"---level_key---\":{\n              \"description\":\"---description of granularity level---\",\n              \"granularity\":\"---integer level of granularity---\"\n          },\n\t\t\t\t\t\"---level_key---\":{\n              \"description\":\"---description of granularity level---\",\n              \"granularity\":\"---integer level of granularity---\"\n          }\n\n\t\t\t\t}\n\t\t\t}\n\t\t},\n\t\t\"measures\":{\n\t\t\t\"---measure_key---\":{\n\t\t\t\"measure_title\":\"---Title of measure---\",\n\t\t\t\"measure_url\":\"---URI for measure---\"\n\t\t\t}\n\n\t\t}\n    }\n}\n```\n \n\n# Patterns Description\n## Comments after ## are just for descriptive purposes. REMOVE THEM WHEN SPECIFYING PIPELINE\n<!--ts-->\n   * [Measurement and Counting](#MCounting)\n   * [League Table](#LTable)\n   * [Internal Comprison](#InternalComparison)\n   * [Profile Outliers](#ProfileOutliers)\n   * [Dissect Factors](#DissectFactors)\n   * [Highlight Contrast](#HighlightContrast)\n   * [Start Big Drill Down](#StartBigDrillDown)\n   * [Start Small Zoom Out](#StartSmallZoomOut)\n   * [Analysis By Category](#AnalysisByCategory)\n   * [Explore Intersection](#ExploreIntersection)\n   * [Narrating Change Over Time](#NarratingChangeOverTime)\n<!--te-->\n# MCounting\n\n  Measurement and Counting\n  Arithemtical operators applied to whole dataset - basic information regarding data\n    \n### Attributes\n ```json\n \"MeasCount\":{\n     \"count_type\":\"count value\"\n }\n\n ```\n  Parameter                 | Type       | Description   |\t\n  | :------------------------ |:-------------:| :-------------|\n  | count_type\t       |\t```String```         | Type of Count to perform\n \n### Output\nBased on count_type value\n\n|Count_type                |  Description   |\t\n  | ------------------------ | -------------|\n  | raw| data without any analysis performed|\n  | sum| sum across all numeric columns|\n  | mean| mean across all numeric columns|\n  | min| minimum values from all numeric columns|\n  | max| maximum values from all numeric columns|\n  | count| amount of records|\n\n\n# LTable\n\n  LeagueTable - sorting and extraction specific amount of records\n    \n### Attributes\n ```json\n \"LeagueTab\":{  \n        \"columns_to_order\":[\"list of columns to order by\"],\n        \"order_type\":\"type of order by\",\n        \"number_of_records\":5\n    }\n ```\n  Parameter                 | Type       | Description   |\t\n  | :------------------------ |:-------------:| :-------------|\n  | columns_to_order\t       |\t```list[String]```         | Set of columns to order by\n  | order_type\t       |\t```String```         | Type of order (asc/desc)\n  | number_of_records\t       |\t```Integer```         | Amount of records to retrieve\n \n### Output\nBased on sort_type value\n\n|Sort_type                |  Description   |\t\n  | ------------------------ | -------------|\n  | asc|ascending order based on columns provided in ```columns_to_order```|\n  | desc|descending order based on columns provided in ```columns_to_order```|\n\n\n# InternalComparison\n\n  InternalComparison - comparison of numeric values related to textual values within one column\n    \n### Attributes\n```json\n \"IntComp\":{\n     \"dim_to_compare\":\"dimension to compare\",\n     \"meas_to_compare\":\"measure to compare\",\n     \"comp_type\":\"comparison type\"\n }\n\n ```\n \n  Parameter                 | Type       | Description   |\t\n  | :------------------------ |:-------------:| :-------------|\n  | dim_to_compare\t       |\t```String```         | Dimension, which values will be investigated\n  | meas_to_compare\t       |\t```String```         | Measure, which numeric values related to ```dim_to_compare``` will be processed\n  | comp_type\t       |\t```String```         | Type of comparison to perform\n \n### Output \nIndependent from ```comp_type``` selected, output data will have additional column with numerical column ```meas_to_compare``` processed in specific way.\n\nAvailable types of comparison ```comp_type```\n\n|Comp_type                |  Description   |\t\n  | ------------------------ | -------------|\n  | diffmax|difference with max value related to specific textual value|\n  | diffmean|difference with arithmetic mean related to specific textual values|\n  | diffmin|difference with minimum value related to specific textual value|\n\n# ProfileOutliers\n\n  ProfileOutliers - detection of unusual values within data (anomalies)\n    \n### Attributes\n```json\n    \"ProfOut\":{\n        \"display_type\":\"Gender\"\n    }\n```\n  Parameter                 | Type       | Description   |\t\n  | :------------------------ |:-------------:| :-------------|\n  | display_type       |\t```String```         | What information display are bound to display (with/without anomalies)\n\n### Output \nPattern analysis using ```python scipy``` library will perform quick exploration in serach of unusual values within data.\n\nBased on ```display_type``` parameter data will be displayed with/without ddetected unusual values.\n\nAvailable types of displaying ```display_type```\n\n|display_type                |  Description   |\t\n  | ------------------------ | -------------|\n  | outliers_only|returns rows from dataset where unusual values were detected |\n  | without_outliers|returns dataset with excluded rows where unusual values were detected |\n\n\n# DissectFactors\n\n  DissectFactors - decomposition of data based on values in dim_to_dissect\n    \n### Attributes\n```json\n    \"DissFact\":{\n        \"dim_to_dissect\":\"dimension to dissect\"\n    }\n```\n  Parameter                 | Type       | Description   |\t\n  | :------------------------ |:-------------:| :-------------|\n  | dim_to_dissect\t       |\t```String```         | Based on which dimension data should be decomposed\n\n### Output \nAs an output, data will be decomposed in a form of a dictionary, where each subset have values only related to specific value.\nDictionary of subdataset will be constructed as a series of paiers where key per each susbet will values from ```dim_to_dissect```\nand this key value will be data, where yhis key value was occurring.\n\n# HighlightContrast\n\n  HighlightContrast - partial difference within values related to one textual column\n    \n### Attributes\n```json\n\"HighCont\":{\n    \"dim_to_contrast\":\"dimension to contrast\",\n    \"meas_to_contrast\":\"measure to contrast\",\n    \"contrast_type\":\"type of contrast\"\n}\n```\n  Parameter                 | Type       | Description   |\t\n  | :------------------------ |:-------------:| :-------------|\n  | dim_to_contrast\t       |\t```String```         | Textual column, from which values will be contrasted\n  | meas_to_contrast\t       |\t```String```         | Numerical column, which values are contrasted\n  | contrast_type\t       |\t```String```         | Type of contrast to present\n \n### Output \nIndependent from ```contrast_type``` selected, output data will have additional column with numerical column ```meas_to_contrast``` processed in specific way.\n\nAvailable types of comparison ```contrast_type```\n\n|Contrast_type                |  Description   |\t\n  | ------------------------ | -------------|\n  | partofwhole| difference with max value related to specific textual value|\n  | partofmax| difference with arithmetic mean related to specific textual values|\n  | partofmin|difference with minimum value related to specific textual value|\n\n\n\n\n# StartBigDrillDown\n\n  StartBigDrillDown - data retrieval from multiple hierachical levels.\n\n  This pattern can be only applied to data not stored already in DataFrame\n    \n### Attributes\n\n```json\n\"StBigDrillDown\":{\n    \"hierdim_drill_down\":{ \n        \"Key of hierarchical dimension\":[\"dimlevel1key\",\"dimlevel2key\",\"dimlevel3key\"] \n        }\n}\n\n```\n  Parameter                 | Type       | Description   |\t\n  | :------------------------ |:-------------:| :-------------|\n  | hierdim_drill_down\t       |```  dict{hierdim:list[str]} ```        | Hierarchical dimension with list of hierarchy levels to inspect\n  \n\n### Output \nAs an output, data will be retrieved in a form of a dictionary, where each dataset will be retrieved from different hierachy level. List will be provided in```hierdim_drill_down```. Hierachy levels provided by in parameter will automatically sorted in order from most general to most detailed level based on metadata provided.\n\n\n# StartSmallZoomOut\n\n  StartSmallZoomOut - data retrieval from multiple hierachical levels.\n\n  This pattern can be only applied to data not stored already in DataFrame\n    \n### Attributes\n\n```json\n\"StSmallZoomOut\":{\n    \"hierdim_zoom_out\":{ \n        \"Key of hierarchical dimension\":[\"dimlevel1key\",\"dimlevel2key\",\"dimlevel3key\"] \n        }\n}\n ```\n\n  Parameter                 | Type       | Description   |\t\n  | :------------------------ |:-------------:| :-------------|\n  | hierdim_zoom_out\t       |```  dict{hierdim:list[str]} ```        | Hierarchical dimension with list of hierarchy levels to inspect\n  \n\n### Output \nAs an output, data will be retrieved in a form of a dictionary, where each dataset will be retrieved from different hierachy level. List will be provided in```hierdim_zoom_out```. Hierachy levels provided by in parameter will automatically sorted in order from most detaile to most general level based on metadata provided.\n\n\n# AnalysisByCategory\n\n  AnalysisByCategory - ecomposition of data based on values in dim_for_category with analysis performed on each susbet\n    \n### Attributes\n```json\n\"AByCategory\":{\n    \"dim_for_category\":\"dimension for categorisation\",\n    \"meas_to_analyse\":\"measure to perform analysis\",\n    \"analysis_type\":\"type of analysis\"\n}\n\n```\n  Parameter                 | Type       | Description   |\t\n  | :------------------------ |:-------------:| :-------------|\n  | dim_for_category\t       |\t```String```         |  Dimension, based on which input data will be categorised\n  | meas_to_analyse\t       |\t```String```         | Measure, which will be analysed\n  | analysis_type\t       |\t```String```         | Type of analysis to perform\n \n### Output \nAs an output, data will be decomposed in a form of a dictionary, where each subset have values only related to specific value. Such subset will get analysed based on ```analysis_type``` parameter\n\nAvailable types of analysis ```analysis_type```\n\n|Analysis_type                |  Description   |\t\n  | ------------------------ | -------------|\n  | min| Minimum per each category|\n  | max| Maximum per each category|\n  | mean|Arithmetical mean per each category|\n  | sum|Total value from each category|\n\n\n# ExploreIntersection\n\n\n### Attributes\n```json\n\"ExpInt\":{\n    \"dim_to_explore\":\"dimension to explroe across cubes\"\n}\n```\n  Parameter                 | Type       | Description   |\t\n  | :------------------------ |:-------------:| :-------------|\n  | dim_to_explore\t       |```\tString     ```   |  Dimension, which existence within enpoint is going to be investigated\n \n### Output \nPattern will return series of datasets, where each will represent occurence of ```dim_to_explore``` in one cube\n\n# NarratingChangeOverTime\n\nPresenting difference between 2 numerical properties of data\n### Attributes\n```json\n\"NarrChangeOT\":{\n    \"meas_to_narrate\":[\"list of two dimensions to present change\"],\n    \"narr_type\":\"type of narration\"\n}\n```\n\n  Parameter                 | Type       | Description   |\t\n  | :------------------------ |:-------------:| :-------------|\n  | meas_to_narrate\t       |\t```String```         |  Set of 2 measures, which change will be narrated\n  | narr_type\t       |\t```String```         | Type of narration to perform\n\n### Output \nIndependent from ```narr_type``` selected, output data will have additional column with numerical values processed in specific way.\n\nAvailable types of analysis ```narr_type```\n\n|Narr_type                |  Description   |\t\n  | ------------------------ | -------------|\n  | percchange| Percentage change between first nad second property|\n  | diffchange| Quantitive change between first and second property|",
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