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upgrade to current hdf5-sys and rust #8

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94e7f20
upgrade to current hdf5-sys and rust
HerrMuellerluedenscheid May 2, 2021
ce22d4d
wip: fix casting
HerrMuellerluedenscheid May 2, 2021
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14 changes: 7 additions & 7 deletions Cargo.toml
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Original file line number Diff line number Diff line change
@@ -1,6 +1,6 @@
[package]
name = "numeric"
version = "0.1.4"
version = "0.2.0"
authors = ["Gustav Larsson <[email protected]>"]
description = "N-dimensional matrix class for Rust"
repository = "https://github.com/numeric-rust/numeric"
Expand All @@ -11,12 +11,12 @@ keywords = ["numeric", "tensor", "matrix", "vector", "hdf5"]
license = "MIT"

[dependencies]
blas = "0.9.1"
lapack = "0.8.1"
num = "0.1.29"
rand = "0.3.12"
libc = "0.2.4"
hdf5-sys = "0.3.2"
blas = "0.22.0"
lapack = "0.19.0"
num = "0.4.0"
rand = "0.8.3"
libc = "0.2.94"
hdf5-sys = "0.7.1"

[[test]]
name = "numeric"
Expand Down
146 changes: 73 additions & 73 deletions src/io/mod.rs
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Original file line number Diff line number Diff line change
Expand Up @@ -43,11 +43,11 @@ extern crate std;

use libc::{c_char, c_void};
use std::path::Path;
use hdf5_sys as ffi;

// use hdf5_sys as ffi;
use hdf5_sys::{h5d, h5t, h5p, h5f, h5e, h5s, h5i};
use tensor::Tensor;

extern fn error_handler(_: ffi::hid_t, _: *const c_void) {
extern fn error_handler(_: h5i::hid_t, _: *const c_void) {
// Suppress errors. We will rely on return statuses alone.
}

Expand All @@ -71,30 +71,30 @@ macro_rules! add_save {
let group = "data";

unsafe {
let filename_cstr = try!(::std::ffi::CString::new(filename));
let group_cstr = try!(::std::ffi::CString::new(group));
let filename_cstr = ::std::ffi::CString::new(filename)?;
let group_cstr = ::std::ffi::CString::new(group)?;

//ffi::H5Eset_auto2(0, error_handler, 0 as *const c_void);
//h5e::H5Eset_auto2(0, error_handler, 0 as *const c_void);

let file = ffi::H5Fcreate(filename_cstr.as_ptr() as *const c_char,
ffi::H5F_ACC_TRUNC, ffi::H5P_DEFAULT, ffi::H5P_DEFAULT);
let file = h5f::H5Fcreate(filename_cstr.as_ptr() as *const c_char,
h5f::H5F_ACC_TRUNC, h5p::H5P_DEFAULT, h5p::H5P_DEFAULT);

let mut shape: Vec<u64> = Vec::new();
for s in self.shape().iter() {
shape.push(*s as u64);
}

let space = ffi::H5Screate_simple(shape.len() as i32, shape.as_ptr(),
let space = h5s::H5Screate_simple(shape.len() as i32, shape.as_ptr(),
std::ptr::null());

let dset = ffi::H5Dcreate2(file, group_cstr.as_ptr() as *const c_char,
let dset = h5d::H5Dcreate2(file, group_cstr.as_ptr() as *const c_char,
$h5type, space,
ffi::H5P_DEFAULT,
ffi::H5P_DEFAULT,
ffi::H5P_DEFAULT);
h5p::H5P_DEFAULT,
h5p::H5P_DEFAULT,
h5p::H5P_DEFAULT);

let status = ffi::H5Dwrite(dset, $h5type, ffi::H5S_ALL, ffi::H5S_ALL,
ffi::H5P_DEFAULT, self.as_ptr() as * const c_void);
let status = h5d::H5Dwrite(dset, $h5type, h5s::H5S_ALL, h5s::H5S_ALL,
h5p::H5P_DEFAULT, self.as_ptr() as * const c_void);

if status < 0 {
let msg = format!("Failed to write '{}': {:?}", group, path);
Expand All @@ -103,25 +103,25 @@ macro_rules! add_save {
}


ffi::H5Dclose(dset);
ffi::H5Fclose(file);
h5d::H5Dclose(dset);
h5f::H5Fclose(file);
}
Ok(())
}
}
)
}

add_save!(u8, ffi::H5T_NATIVE_UINT8);
add_save!(u16, ffi::H5T_NATIVE_UINT16);
add_save!(u32, ffi::H5T_NATIVE_UINT32);
add_save!(u64, ffi::H5T_NATIVE_UINT64);
add_save!(i8, ffi::H5T_NATIVE_INT8);
add_save!(i16, ffi::H5T_NATIVE_INT16);
add_save!(i32, ffi::H5T_NATIVE_INT32);
add_save!(i64, ffi::H5T_NATIVE_INT64);
add_save!(f32, ffi::H5T_NATIVE_FLOAT);
add_save!(f64, ffi::H5T_NATIVE_DOUBLE);
add_save!(u8, h5t::H5T_NATIVE_UINT8);
add_save!(u16, h5t::H5T_NATIVE_UINT16);
add_save!(u32, h5t::H5T_NATIVE_UINT32);
add_save!(u64, h5t::H5T_NATIVE_UINT64);
add_save!(i8, h5t::H5T_NATIVE_INT8);
add_save!(i16, h5t::H5T_NATIVE_INT16);
add_save!(i32, h5t::H5T_NATIVE_INT32);
add_save!(i64, h5t::H5T_NATIVE_INT64);
add_save!(f32, h5t::H5T_NATIVE_FLOAT);
add_save!(f64, h5t::H5T_NATIVE_DOUBLE);


macro_rules! add_load {
Expand All @@ -137,38 +137,38 @@ macro_rules! add_load {
},
};
unsafe {
let filename_cstr = try!(::std::ffi::CString::new(filename));
let group_cstr = try!(::std::ffi::CString::new(group));
let filename_cstr = ::std::ffi::CString::new(filename)?;
let group_cstr = ::std::ffi::CString::new(group)?;

ffi::H5Eset_auto2(0, error_handler, 0 as *const c_void);
h5e::H5Eset_auto2(0, error_handler, 0 as *const c_void);

let file = ffi::H5Fopen(filename_cstr.as_ptr() as *const c_char,
ffi::H5F_ACC_RDONLY, ffi::H5P_DEFAULT);
let file = h5f::H5Fopen(filename_cstr.as_ptr() as *const c_char,
h5f::H5F_ACC_RDONLY, h5p::H5P_DEFAULT);

if file < 0 {
let msg = format!("File not found: {:?}", path);
let err = std::io::Error::new(std::io::ErrorKind::NotFound, msg);
return Err(err);
}

let dset = ffi::H5Dopen2(file, group_cstr.as_ptr() as *const c_char,
ffi::H5P_DEFAULT);
let dset = h5d::H5Dopen2(file, group_cstr.as_ptr() as *const c_char,
h5p::H5P_DEFAULT);

if dset < 0 {
let msg = format!("Group '{}' not found: {}", group, filename);
let err = std::io::Error::new(std::io::ErrorKind::NotFound, msg);
return Err(err);
}

let datatype = ffi::H5Dget_type(dset);
let datatype = h5d::H5Dget_type(dset);

let space = ffi::H5Dget_space(dset);
let ndims = ffi::H5Sget_simple_extent_ndims(space);
let space = h5d::H5Dget_space(dset);
let ndims = h5s::H5Sget_simple_extent_ndims(space);

let mut shape: Tensor<ffi::hsize_t> = Tensor::zeros(&[ndims as usize]);
let mut shape: Tensor<h5d::hsize_t> = Tensor::zeros(&[ndims as usize]);

if ffi::H5Sget_simple_extent_dims(space, shape.as_mut_ptr(),
0 as *mut ffi::hsize_t) != ndims {
if h5s::H5Sget_simple_extent_dims(space, shape.as_mut_ptr(),
0 as *mut h5d::hsize_t) != ndims {
let msg = format!("Could not read shape of tesor: {}", filename);
let err = std::io::Error::new(std::io::ErrorKind::InvalidData, msg);
return Err(err);
Expand All @@ -179,65 +179,65 @@ macro_rules! add_load {
let unsigned_shape = &unsigned_tensor.data();

let data: Tensor<$t> = {
if ffi::H5Tequal(datatype, ffi::H5T_NATIVE_UINT8) == 1 {
if h5t::H5Tequal(datatype, h5t::H5T_NATIVE_UINT8) == 1 {
let mut native_data: Tensor<u8> = Tensor::empty(&unsigned_shape[..]);
// Finally load the actual data
ffi::H5Dread(dset, ffi::H5T_NATIVE_UINT8, ffi::H5S_ALL, ffi::H5S_ALL,
ffi::H5P_DEFAULT, native_data.as_mut_ptr() as *mut c_void);
h5d::H5Dread(dset, h5t::H5T_NATIVE_UINT8, h5s::H5S_ALL, h5s::H5S_ALL,
h5p::H5P_DEFAULT, native_data.as_mut_ptr() as *mut c_void);
native_data.convert::<$t>()
} else if ffi::H5Tequal(datatype, ffi::H5T_NATIVE_INT8) == 1 {
} else if h5t::H5Tequal(datatype, h5t::H5T_NATIVE_INT8) == 1 {
let mut native_data: Tensor<i8> = Tensor::empty(&unsigned_shape[..]);
// Finally load the actual data
ffi::H5Dread(dset, ffi::H5T_NATIVE_INT8, ffi::H5S_ALL, ffi::H5S_ALL,
ffi::H5P_DEFAULT, native_data.as_mut_ptr() as *mut c_void);
h5d::H5Dread(dset, h5t::H5T_NATIVE_INT8, h5s::H5S_ALL, h5s::H5S_ALL,
h5p::H5P_DEFAULT, native_data.as_mut_ptr() as *mut c_void);
native_data.convert::<$t>()
} else if ffi::H5Tequal(datatype, ffi::H5T_NATIVE_UINT16) == 1 {
} else if h5t::H5Tequal(datatype, h5t::H5T_NATIVE_UINT16) == 1 {
let mut native_data: Tensor<u16> = Tensor::empty(&unsigned_shape[..]);
// Finally load the actual data
ffi::H5Dread(dset, ffi::H5T_NATIVE_UINT16, ffi::H5S_ALL, ffi::H5S_ALL,
ffi::H5P_DEFAULT, native_data.as_mut_ptr() as *mut c_void);
h5d::H5Dread(dset, h5t::H5T_NATIVE_UINT16, h5s::H5S_ALL, h5s::H5S_ALL,
h5p::H5P_DEFAULT, native_data.as_mut_ptr() as *mut c_void);
native_data.convert::<$t>()
} else if ffi::H5Tequal(datatype, ffi::H5T_NATIVE_INT16) == 1 {
} else if h5t::H5Tequal(datatype, h5t::H5T_NATIVE_INT16) == 1 {
let mut native_data: Tensor<i16> = Tensor::empty(&unsigned_shape[..]);
// Finally load the actual data
ffi::H5Dread(dset, ffi::H5T_NATIVE_INT16, ffi::H5S_ALL, ffi::H5S_ALL,
ffi::H5P_DEFAULT, native_data.as_mut_ptr() as *mut c_void);
h5d::H5Dread(dset, h5t::H5T_NATIVE_INT16, h5s::H5S_ALL, h5s::H5S_ALL,
h5p::H5P_DEFAULT, native_data.as_mut_ptr() as *mut c_void);
native_data.convert::<$t>()
} else if ffi::H5Tequal(datatype, ffi::H5T_NATIVE_UINT32) == 1 {
} else if h5t::H5Tequal(datatype, h5t::H5T_NATIVE_UINT32) == 1 {
let mut native_data: Tensor<u32> = Tensor::empty(&unsigned_shape[..]);
// Finally load the actual data
ffi::H5Dread(dset, ffi::H5T_NATIVE_UINT32, ffi::H5S_ALL, ffi::H5S_ALL,
ffi::H5P_DEFAULT, native_data.as_mut_ptr() as *mut c_void);
h5d::H5Dread(dset, h5t::H5T_NATIVE_UINT32, h5s::H5S_ALL, h5s::H5S_ALL,
h5p::H5P_DEFAULT, native_data.as_mut_ptr() as *mut c_void);
native_data.convert::<$t>()
} else if ffi::H5Tequal(datatype, ffi::H5T_NATIVE_INT32) == 1 {
} else if h5t::H5Tequal(datatype, h5t::H5T_NATIVE_INT32) == 1 {
let mut native_data: Tensor<i32> = Tensor::empty(&unsigned_shape[..]);
// Finally load the actual data
ffi::H5Dread(dset, ffi::H5T_NATIVE_INT32, ffi::H5S_ALL, ffi::H5S_ALL,
ffi::H5P_DEFAULT, native_data.as_mut_ptr() as *mut c_void);
h5d::H5Dread(dset, h5t::H5T_NATIVE_INT32, h5s::H5S_ALL, h5s::H5S_ALL,
h5p::H5P_DEFAULT, native_data.as_mut_ptr() as *mut c_void);
native_data.convert::<$t>()
} else if ffi::H5Tequal(datatype, ffi::H5T_NATIVE_UINT64) == 1 {
} else if h5t::H5Tequal(datatype, h5t::H5T_NATIVE_UINT64) == 1 {
let mut native_data: Tensor<u64> = Tensor::empty(&unsigned_shape[..]);
// Finally load the actual data
ffi::H5Dread(dset, ffi::H5T_NATIVE_UINT64, ffi::H5S_ALL, ffi::H5S_ALL,
ffi::H5P_DEFAULT, native_data.as_mut_ptr() as *mut c_void);
h5d::H5Dread(dset, h5t::H5T_NATIVE_UINT64, h5s::H5S_ALL, h5s::H5S_ALL,
h5p::H5P_DEFAULT, native_data.as_mut_ptr() as *mut c_void);
native_data.convert::<$t>()
} else if ffi::H5Tequal(datatype, ffi::H5T_NATIVE_INT64) == 1 {
} else if h5t::H5Tequal(datatype, h5t::H5T_NATIVE_INT64) == 1 {
let mut native_data: Tensor<i64> = Tensor::empty(&unsigned_shape[..]);
// Finally load the actual data
ffi::H5Dread(dset, ffi::H5T_NATIVE_INT64, ffi::H5S_ALL, ffi::H5S_ALL,
ffi::H5P_DEFAULT, native_data.as_mut_ptr() as *mut c_void);
h5d::H5Dread(dset, h5t::H5T_NATIVE_INT64, h5s::H5S_ALL, h5s::H5S_ALL,
h5p::H5P_DEFAULT, native_data.as_mut_ptr() as *mut c_void);
native_data.convert::<$t>()
} else if ffi::H5Tequal(datatype, ffi::H5T_NATIVE_FLOAT) == 1 {
} else if h5t::H5Tequal(datatype, h5t::H5T_NATIVE_FLOAT) == 1 {
let mut native_data: Tensor<f32> = Tensor::empty(&unsigned_shape[..]);
// Finally load the actual data
ffi::H5Dread(dset, ffi::H5T_NATIVE_FLOAT, ffi::H5S_ALL, ffi::H5S_ALL,
ffi::H5P_DEFAULT, native_data.as_mut_ptr() as *mut c_void);
h5d::H5Dread(dset, h5t::H5T_NATIVE_FLOAT, h5s::H5S_ALL, h5s::H5S_ALL,
h5p::H5P_DEFAULT, native_data.as_mut_ptr() as *mut c_void);
native_data.convert::<$t>()
} else if ffi::H5Tequal(datatype, ffi::H5T_NATIVE_DOUBLE) == 1 {
} else if h5t::H5Tequal(datatype, h5t::H5T_NATIVE_DOUBLE) == 1 {
let mut native_data: Tensor<f64> = Tensor::empty(&unsigned_shape[..]);
// Finally load the actual data
ffi::H5Dread(dset, ffi::H5T_NATIVE_DOUBLE, ffi::H5S_ALL, ffi::H5S_ALL,
ffi::H5P_DEFAULT, native_data.as_mut_ptr() as *mut c_void);
h5d::H5Dread(dset, h5t::H5T_NATIVE_DOUBLE, h5s::H5S_ALL, h5s::H5S_ALL,
h5p::H5P_DEFAULT, native_data.as_mut_ptr() as *mut c_void);
native_data.convert::<$t>()
} else {
let msg = format!("Unable to convert '{}' to {}: {}",
Expand All @@ -247,9 +247,9 @@ macro_rules! add_load {
}
};

ffi::H5Tclose(datatype);
ffi::H5Dclose(dset);
ffi::H5Fclose(file);
h5t::H5Tclose(datatype);
h5d::H5Dclose(dset);
h5f::H5Fclose(file);

Ok(data)
}
Expand Down
9 changes: 5 additions & 4 deletions src/random/mod.rs
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Original file line number Diff line number Diff line change
Expand Up @@ -16,8 +16,9 @@
//! // 0.702227 0.346673 0.737954
//! // [Tensor<f64> of shape 3x3]
//! ```
use rand::{Rng, SeedableRng, StdRng};
use rand::distributions::range::SampleRange;
use rand::{Rng, SeedableRng};
use rand::rngs::StdRng;
use rand::distributions::uniform::SampleRange;
use num::traits::Float;
use std::f64;

Expand All @@ -40,7 +41,7 @@ impl RandomState {
/// Generates a tensor by independently drawing samples from a uniform distribution in the
/// range [`low`, `high`). This is appropriate for integer types as well.
pub fn uniform<T>(&mut self, low: T, high: T, shape: &[usize]) -> Tensor<T>
where T: NumericTrait + SampleRange {
where T: NumericTrait + SampleRange<T> {
let mut t = Tensor::zeros(shape);
{
let n = t.size();
Expand All @@ -54,7 +55,7 @@ impl RandomState {

/// Generates a tensor by independently drawing samples from a standard normal.
pub fn normal<T>(&mut self, shape: &[usize]) -> Tensor<T>
where T: NumericTrait + SampleRange + Float {
where T: NumericTrait + SampleRange<T> + Float {
let u1 = self.uniform(T::zero(), T::one(), shape);
let u2 = self.uniform(T::zero(), T::one(), shape);

Expand Down
16 changes: 8 additions & 8 deletions src/tensor/dot.rs
View file
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Original file line number Diff line number Diff line change
Expand Up @@ -25,8 +25,8 @@ macro_rules! add_impl {
} else {
let t1 = self.canonize();
let t2 = rhs.canonize();
blas::$gemv(b'T', t1.shape[1], t1.shape[0], 1.0, &t1.data,
t1.shape[1], &t2.data, 1, 0.0, data, 1);
blas::$gemv(b'T', t1.shape[1] as i32, t1.shape[0] as i32, 1.0, &t1.data,
t1.shape[1] as i32, &t2.data, 1, 0.0, data, 1);
}
}
t3
Expand All @@ -47,8 +47,8 @@ macro_rules! add_impl {
} else {
let t1 = self.canonize();
let t2 = rhs.canonize();
blas::$gemv(b'N', t2.shape[1], t2.shape[0], 1.0, &t2.data,
t2.shape[1], &t1.data, 1, 0.0, data, 1);
blas::$gemv(b'N', t2.shape[1] as i32, t2.shape[0] as i32, 1.0, &t2.data,
t2.shape[1] as i32, &t1.data, 1, 0.0, data, 1);
}
}
t3
Expand All @@ -72,9 +72,9 @@ macro_rules! add_impl {
let t1 = self.canonize();
let t2 = rhs.canonize();
let mut data = t3.slice_mut();
blas::$gemm(b'N', b'N', t2.shape[1], t1.shape[0], t2.shape[0], 1.0,
&t2.data, t2.shape[1], &t1.data, t2.shape[0], 0.0,
data, t2.shape[1]);
blas::$gemm(b'N', b'N', t2.shape[1] as i32, t1.shape[0] as i32, t2.shape[0] as i32, 1.0,
&t2.data, t2.shape[1] as i32, &t1.data, t2.shape[0] as i32, 0.0,
data, t2.shape[1] as i32);
}
t3
} else if self.ndim() == 1 && rhs.ndim() == 1 { // scalar product
Expand All @@ -88,7 +88,7 @@ macro_rules! add_impl {
} else {
let t1 = self.canonize();
let t2 = rhs.canonize();
v = blas::$dot(t1.size(), &t1.data, 1, &t2.data, 1);
v = blas::$dot(t1.size() as i32, &t1.data, 1, &t2.data, 1);
}
Tensor::scalar(v)
} else {
Expand Down