1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166

// 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 std::any::Any;
use std::sync::Arc;

use arrow::array::{ArrayRef, Float32Array, Float64Array};
use arrow::datatypes::DataType;
use arrow::datatypes::DataType::{Float32, Float64};

use datafusion_common::{exec_err, DataFusionError, Result};
use datafusion_expr::ColumnarValue;
use datafusion_expr::{ScalarUDFImpl, Signature, Volatility};

use crate::utils::make_scalar_function;

#[derive(Debug)]
pub struct CotFunc {
    signature: Signature,
}

impl Default for CotFunc {
    fn default() -> Self {
        CotFunc::new()
    }
}

impl CotFunc {
    pub fn new() -> Self {
        use DataType::*;
        Self {
            // math expressions expect 1 argument of type f64 or f32
            // priority is given to f64 because e.g. `sqrt(1i32)` is in IR (real numbers) and thus we
            // return the best approximation for it (in f64).
            // We accept f32 because in this case it is clear that the best approximation
            // will be as good as the number of digits in the number
            signature: Signature::uniform(
                1,
                vec![Float64, Float32],
                Volatility::Immutable,
            ),
        }
    }
}

impl ScalarUDFImpl for CotFunc {
    fn as_any(&self) -> &dyn Any {
        self
    }

    fn name(&self) -> &str {
        "cot"
    }

    fn signature(&self) -> &Signature {
        &self.signature
    }

    fn return_type(&self, arg_types: &[DataType]) -> Result<DataType> {
        match arg_types[0] {
            Float32 => Ok(Float32),
            _ => Ok(Float64),
        }
    }

    fn invoke(&self, args: &[ColumnarValue]) -> Result<ColumnarValue> {
        make_scalar_function(cot, vec![])(args)
    }
}

///cot SQL function
fn cot(args: &[ArrayRef]) -> Result<ArrayRef> {
    match args[0].data_type() {
        Float64 => Ok(Arc::new(make_function_scalar_inputs!(
            &args[0],
            "x",
            Float64Array,
            { compute_cot64 }
        )) as ArrayRef),
        Float32 => Ok(Arc::new(make_function_scalar_inputs!(
            &args[0],
            "x",
            Float32Array,
            { compute_cot32 }
        )) as ArrayRef),
        other => exec_err!("Unsupported data type {other:?} for function cot"),
    }
}

fn compute_cot32(x: f32) -> f32 {
    let a = f32::tan(x);
    1.0 / a
}

fn compute_cot64(x: f64) -> f64 {
    let a = f64::tan(x);
    1.0 / a
}

#[cfg(test)]
mod test {
    use crate::math::cot::cot;
    use arrow::array::{ArrayRef, Float32Array, Float64Array};
    use datafusion_common::cast::{as_float32_array, as_float64_array};
    use std::sync::Arc;

    #[test]
    fn test_cot_f32() {
        let args: Vec<ArrayRef> =
            vec![Arc::new(Float32Array::from(vec![12.1, 30.0, 90.0, -30.0]))];
        let result = cot(&args).expect("failed to initialize function cot");
        let floats =
            as_float32_array(&result).expect("failed to initialize function cot");

        let expected = Float32Array::from(vec![
            -1.986_460_4,
            -0.156_119_96,
            -0.501_202_8,
            0.156_119_96,
        ]);

        let eps = 1e-6;
        assert_eq!(floats.len(), 4);
        assert!((floats.value(0) - expected.value(0)).abs() < eps);
        assert!((floats.value(1) - expected.value(1)).abs() < eps);
        assert!((floats.value(2) - expected.value(2)).abs() < eps);
        assert!((floats.value(3) - expected.value(3)).abs() < eps);
    }

    #[test]
    fn test_cot_f64() {
        let args: Vec<ArrayRef> =
            vec![Arc::new(Float64Array::from(vec![12.1, 30.0, 90.0, -30.0]))];
        let result = cot(&args).expect("failed to initialize function cot");
        let floats =
            as_float64_array(&result).expect("failed to initialize function cot");

        let expected = Float64Array::from(vec![
            -1.986_458_685_881_4,
            -0.156_119_952_161_6,
            -0.501_202_783_380_1,
            0.156_119_952_161_6,
        ]);

        let eps = 1e-12;
        assert_eq!(floats.len(), 4);
        assert!((floats.value(0) - expected.value(0)).abs() < eps);
        assert!((floats.value(1) - expected.value(1)).abs() < eps);
        assert!((floats.value(2) - expected.value(2)).abs() < eps);
        assert!((floats.value(3) - expected.value(3)).abs() < eps);
    }
}