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39 lines
1.8 KiB
Python
39 lines
1.8 KiB
Python
from app.tool.chart_visualization.python_execute import NormalPythonExecute
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class VisualizationPrepare(NormalPythonExecute):
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"""A tool for Chart Generation Preparation"""
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name: str = "visualization_preparation"
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description: str = "Using Python code to generates metadata of data_visualization tool. Outputs: 1) JSON Information. 2) Cleaned CSV data files (Optional)."
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parameters: dict = {
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"type": "object",
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"properties": {
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"code_type": {
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"description": "code type, visualization: csv -> chart; insight: choose insight into chart",
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"type": "string",
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"default": "visualization",
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"enum": ["visualization", "insight"],
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},
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"code": {
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"type": "string",
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"description": """Python code for data_visualization prepare.
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## Visualization Type
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1. Data loading logic
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2. Csv Data and chart description generate
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2.1 Csv data (The data you want to visulazation, cleaning / transform from origin data, saved in .csv)
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2.2 Chart description of csv data (The chart title or description should be concise and clear. Examples: 'Product sales distribution', 'Monthly revenue trend'.)
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3. Save information in json file.( format: {"csvFilePath": string, "chartTitle": string}[])
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## Insight Type
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1. Select the insights from the data_visualization results that you want to add to the chart.
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2. Save information in json file.( format: {"chartPath": string, "insights_id": number[]}[])
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# Note
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1. You can generate one or multiple csv data with different visualization needs.
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2. Make each chart data esay, clean and different.
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3. Json file saving in utf-8 with path print: print(json_path)
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""",
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},
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},
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"required": ["code", "code_type"],
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}
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