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// Licensed to the Apache Software Foundation (ASF) under one
// or more contributor license agreements. See the NOTICE file
// distributed with this work for additional information
// regarding copyright ownership. The ASF licenses this file
// to you under the Apache License, Version 2.0 (the
// "License"); you may not use this file except in compliance
// with the License. You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing,
// software distributed under the License is distributed on an
// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
// KIND, either express or implied. See the License for the
// specific language governing permissions and limitations
// under the License.
use arrow::datatypes::FieldRef;
use pyo3::prelude::*;
use crate::common::data_type::PyDataType;
#[pyclass(
from_py_object,
frozen,
name = "ScalarVariable",
module = "datafusion.expr",
subclass
)]
#[derive(Clone)]
pub struct PyScalarVariable {
field: FieldRef,
variables: Vec<String>,
}
impl PyScalarVariable {
pub fn new(field: &FieldRef, variables: &[String]) -> Self {
Self {
field: field.to_owned(),
variables: variables.to_vec(),
}
}
}
#[pymethods]
impl PyScalarVariable {
/// Get the data type
fn data_type(&self) -> PyResult<PyDataType> {
Ok(self.field.data_type().clone().into())
}
fn variables(&self) -> PyResult<Vec<String>> {
Ok(self.variables.clone())
}
fn __repr__(&self) -> PyResult<String> {
Ok(format!("{}{:?}", self.field.data_type(), self.variables))
}
}