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mxcp: 1
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tool:
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name: aggregate_by_category
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description: Aggregate data by category with statistics
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language: python
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source:
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file: ../python/data_analysis.py
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parameters:
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- name: table_name
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type: string
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description: Name of the table to aggregate
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return:
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type: array
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items:
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type: object
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properties:
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category:
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type: string
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count:
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type: integer
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avg_value:
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type: number
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total_value:
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type: number
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min_value:
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type: number
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max_value:
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type: number
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mxcp: 1
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tool:
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name: analyze_numbers
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description: Analyze a list of numbers with statistical operations
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language: python
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source:
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file: ../python/data_analysis.py
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parameters:
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- name: numbers
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type: array
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items:
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type: number
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description: List of numbers to analyze
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- name: operation
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type: string
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enum: ["mean", "median", "mode", "stdev", "sum", "min", "max"]
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default: "mean"
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description: Statistical operation to perform
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return:
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type: object
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properties:
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operation:
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type: string
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result:
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type: number
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count:
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type: integer
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mxcp: 1
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tool:
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name: create_sample_data
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description: Create a sample table with test data
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language: python
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source:
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file: ../python/data_analysis.py
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parameters:
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- name: table_name
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type: string
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description: Name of the table to create
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- name: row_count
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type: integer
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description: Number of rows to generate
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minimum: 1
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maximum: 10000
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return:
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type: object
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properties:
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status:
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type: string
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table:
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type: string
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rows_created:
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type: integer
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@@ -0,0 +1,21 @@
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mxcp: 1
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tool:
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name: process_time_series
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description: Process time series data with rolling window calculations (async)
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language: python
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source:
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file: ../python/data_analysis.py
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parameters:
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- name: table_name
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type: string
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description: Name of the table containing time series data
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- name: window_days
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type: integer
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default: 7
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description: Size of the rolling window in days
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minimum: 1
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maximum: 365
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return:
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type: array
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items:
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type: object
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