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6db4d17567
All per-method local imports in the 7 chart plugins were moved to module-level. None of them create circular imports: schemas.py, chart_utils.py, and dataset_validator.py are safe to import at plugin load time because those modules guard their own registry lookups with local imports. - big_number: add map_big_number_config, _big_number_chart_what, _summarize_filters, DatasetValidator to top-level imports - pie: add map_pie_config, _pie_chart_what, _summarize_filters, PieChartConfig, DatasetValidator to top-level imports - xy: add map_xy_config, _xy_chart_what/context, XYChartConfig, DatasetValidator, FormatTypeValidator, CardinalityValidator to top-level imports - table: add map_table_config, _table_chart_what, _summarize_filters, TableChartConfig, DatasetValidator to top-level imports - pivot_table: add map_pivot_table_config, _pivot_table_what, _summarize_filters, PivotTableChartConfig, DatasetValidator to top-level imports - mixed_timeseries: add map_mixed_timeseries_config, _mixed_timeseries_what, _summarize_filters, MixedTimeseriesChartConfig, DatasetValidator to top-level - handlebars: add map_handlebars_config, _handlebars_chart_what, _summarize_filters, HandlebarsChartConfig, DatasetValidator to top-level imports Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
112 lines
4.1 KiB
Python
112 lines
4.1 KiB
Python
# Licensed to the Apache Software Foundation (ASF) under one
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# or more contributor license agreements. See the NOTICE file
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# distributed with this work for additional information
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# regarding copyright ownership. The ASF licenses this file
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# to you under the Apache License, Version 2.0 (the
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# "License"); you may not use this file except in compliance
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# with the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing,
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# software distributed under the License is distributed on an
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# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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# KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations
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# under the License.
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"""Pie chart type plugin."""
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from __future__ import annotations
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from typing import Any
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from superset.mcp_service.chart.chart_utils import (
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_pie_chart_what,
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_summarize_filters,
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map_pie_config,
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)
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from superset.mcp_service.chart.plugin import BaseChartPlugin
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from superset.mcp_service.chart.schemas import ColumnRef, PieChartConfig
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from superset.mcp_service.chart.validation.dataset_validator import DatasetValidator
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from superset.mcp_service.common.error_schemas import ChartGenerationError
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class PieChartPlugin(BaseChartPlugin):
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"""Plugin for pie chart type."""
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chart_type = "pie"
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display_name = "Pie / Donut Chart"
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native_viz_types = {
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"pie": "Pie Chart",
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}
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def pre_validate(
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self,
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config: dict[str, Any],
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) -> ChartGenerationError | None:
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missing_fields = []
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if "dimension" not in config:
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missing_fields.append("'dimension' (category column for slices)")
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if "metric" not in config:
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missing_fields.append("'metric' (value metric for slice sizes)")
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if missing_fields:
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return ChartGenerationError(
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error_type="missing_pie_fields",
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message=(
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f"Pie chart missing required fields: {', '.join(missing_fields)}"
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),
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details=(
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"Pie charts require a dimension (categories) and a metric (values)"
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),
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suggestions=[
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"Add 'dimension' field: {'name': 'category_column'}",
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"Add 'metric' field: {'name': 'value_column', 'aggregate': 'SUM'}",
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"Example: {'chart_type': 'pie', 'dimension': {'name': 'product'}, "
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"'metric': {'name': 'revenue', 'aggregate': 'SUM'}}",
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],
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error_code="MISSING_PIE_FIELDS",
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)
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return None
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def extract_column_refs(self, config: Any) -> list[ColumnRef]:
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if not isinstance(config, PieChartConfig):
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return []
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refs: list[ColumnRef] = [config.dimension, config.metric]
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if config.filters:
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for f in config.filters:
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refs.append(ColumnRef(name=f.column))
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return refs
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def to_form_data(
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self, config: Any, dataset_id: int | str | None = None
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) -> dict[str, Any]:
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return map_pie_config(config)
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def generate_name(self, config: Any, dataset_name: str | None = None) -> str:
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what = _pie_chart_what(config)
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context = _summarize_filters(config.filters)
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return self._with_context(what, context)
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def resolve_viz_type(self, config: Any) -> str:
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return "pie"
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def normalize_column_refs(self, config: Any, dataset_context: Any) -> Any:
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config_dict = config.model_dump()
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if config_dict.get("dimension"):
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config_dict["dimension"]["name"] = (
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DatasetValidator._get_canonical_column_name(
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config_dict["dimension"]["name"], dataset_context
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)
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)
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if config_dict.get("metric") and not config_dict["metric"].get("saved_metric"):
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config_dict["metric"]["name"] = DatasetValidator._get_canonical_column_name(
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config_dict["metric"]["name"], dataset_context
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)
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DatasetValidator._normalize_filters(config_dict, dataset_context)
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return PieChartConfig.model_validate(config_dict)
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