JT 9068093081
Improve type hovers (#9515)
# Description

This PR does a few things to help improve type hovers and, in the
process, fixes a few outstanding issues in the type system. Here's a
list of the changes:

* `for` now will try to infer the type of the iteration variable based
on the expression it's given. This fixes things like `for x in [1, 2, 3]
{ }` where `x` now properly gets the int type.
* Removed old input/output type fields from the signature, focuses on
the vec of signatures. Updated a bunch of dataframe commands that hadn't
moved over. This helps tie things together a bit better
* Fixed inference of types from subexpressions to use the last
expression in the block
* Fixed handling of explicit types in `let` and `mut` calls, so we now
respect that as the authoritative type

I also tried to add `def` input/output type inference, but unfortunately
we only know the predecl types universally, which means we won't have
enough information to properly know what the types of the custom
commands are.

# User-Facing Changes

Script typechecking will get tighter in some cases
Hovers should be more accurate in some cases that previously resorted to
any.

# Tests + Formatting
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---------

Co-authored-by: Darren Schroeder <343840+fdncred@users.noreply.github.com>
2023-06-29 05:19:48 +12:00

87 lines
2.3 KiB
Rust

use nu_protocol::{
ast::Call,
engine::{Command, EngineState, Stack},
Category, Example, PipelineData, ShellError, Signature, Span, Type, Value,
};
use crate::dataframe::values::Column;
use super::super::values::NuDataFrame;
#[derive(Clone)]
pub struct ShapeDF;
impl Command for ShapeDF {
fn name(&self) -> &str {
"dfr shape"
}
fn usage(&self) -> &str {
"Shows column and row size for a dataframe."
}
fn signature(&self) -> Signature {
Signature::build(self.name())
.input_output_type(
Type::Custom("dataframe".into()),
Type::Custom("dataframe".into()),
)
.category(Category::Custom("dataframe".into()))
}
fn examples(&self) -> Vec<Example> {
vec![Example {
description: "Shows row and column shape",
example: "[[a b]; [1 2] [3 4]] | dfr into-df | dfr shape",
result: Some(
NuDataFrame::try_from_columns(vec![
Column::new("rows".to_string(), vec![Value::test_int(2)]),
Column::new("columns".to_string(), vec![Value::test_int(2)]),
])
.expect("simple df for test should not fail")
.into_value(Span::test_data()),
),
}]
}
fn run(
&self,
engine_state: &EngineState,
stack: &mut Stack,
call: &Call,
input: PipelineData,
) -> Result<PipelineData, ShellError> {
command(engine_state, stack, call, input)
}
}
fn command(
_engine_state: &EngineState,
_stack: &mut Stack,
call: &Call,
input: PipelineData,
) -> Result<PipelineData, ShellError> {
let df = NuDataFrame::try_from_pipeline(input, call.head)?;
let rows = Value::int(df.as_ref().height() as i64, call.head);
let cols = Value::int(df.as_ref().width() as i64, call.head);
let rows_col = Column::new("rows".to_string(), vec![rows]);
let cols_col = Column::new("columns".to_string(), vec![cols]);
NuDataFrame::try_from_columns(vec![rows_col, cols_col])
.map(|df| PipelineData::Value(df.into_value(call.head), None))
}
#[cfg(test)]
mod test {
use super::super::super::test_dataframe::test_dataframe;
use super::*;
#[test]
fn test_examples() {
test_dataframe(vec![Box::new(ShapeDF {})])
}
}