# Cython backend¶

Cython code is much more complicated than Pythran code… We won’t be able to support all Cython features!

However, a descent set of Cython can be supported. We need to find Python syntaxes for the most useful Cython special syntaxes.

Note that some Cython features are useless in Pythran (for example cdef of
local variables, or `nogil`

).

Note that ideally, we want to write Cython code that can be executed without the Cython package and without Cython compilation. It is possible with the “pure Python mode” of Cython. Therefore, we first need examples of Cython code written in this mode.

Note however, that this mode is currently still experimental and that we hit simple Cython bugs which limit a lot what can be done in practice with the Cython backend. For example:

Pure-Python mode and fused types https://github.com/cython/cython/issues/3142

`cython.locals(arr=np.ndarray[...])`

https://github.com/cython/cython/issues/3129Incompatibility ccall/nogil in pure-Python mode: https://github.com/cython/cython/issues/3169

nogil and pxd in pure-Python mode: https://github.com/cython/cython/issues/3170

More generally, there are many known bugs in Cython which do not help! For example:

`ctypedef`

and buffer https://github.com/cython/cython/issues/754Defining a fused type using a fused type https://stackoverflow.com/questions/57887972

I think at least some of these bugs have to be solved upstream…

## Cython syntaxes already supported¶

`cpdef`

signature with simple (basic and array) types for arguments¶

`cdef`

for type declaration of local variables: `cython.locals`

¶

In “pure Python mode”, one can write

```
@cython.locals(result=np.float64_t, i=cython.int, n=cython.int)
cpdef mysum(np.float64_t[:] arr_input)
```

With variables annotations (which are removed for Pythran / Numba):

```
from transonic import boost
@boost
def mysum(arr_input: "float[]"):
i: int
n: int = arr_input.shape[0]
result: float = 0.
for i in range(n):
result += arr_input[i]
return result
```

### Cython decorators¶

```
@cython.boundscheck(False)
@cython.wraparound(False)
```

Currently only for simple functions (no methods).

## Cython syntaxes partly supported¶

### Function definition (cdef, cpdef, inline, nogil, return type)¶

```
from transonic import boost
@boost(inline=True, nogil=True)
def func(a: "float[]", n: int) -> "void":
...
```

which would translate in Cython as something like:

```
cpdef inline void func(np.ndarray[np.float_t, ndim=1] a, cython.int n) nogil
```

all function signatures use

`cpdef`

(?)`boost(inline=True)`

is supported for functions, see this example.Return type is supported and there is a void type (

`"void"`

or`np.void`

).

### Fused types¶

We already have fused types in Transonic. With Transonic, we can already do:

```
import numpy as np
from transonic import Array, Type, NDim
np_floats = Type(np.float32, np.float64)
N = NDim(2, 3, 4)
A = Array[np_floats, N]
A1 = Array[np_floats, N + 1]
# or simply
A3d = Array[np_floats, "3d"]
```

However, Cython Fused types are currently very limited.

Even with something as simple as that

```
from transonic import Array, Type
A = Array[Type(np.float64, np.complex128), "1d"]
def mysum(arr: A):
result: A.dtype = arr.dtype.type(0.)
i: int
for i in range(arr.shape[0]):
result += arr[i]
return result
```

should be translated to this (not supported, see https://github.com/cython/cython/issues/754) Cython code:

```
import cython
import numpy as np
cimport numpy as np
ctypedef fused T0:
np.complex128_t
np.float64_t
ctypedef np.ndarray[T0, ndim=1] A
def mysum(A arr):
cdef T0 ret = arr.dtype.type(0.)
cdef cython.int i
for i in range(arr.shape[0]):
ret += arr[i]
return ret
```

Note that it works with a memoryview… (but not in pure-Python mode!)

Note that another working alternative is:

```
import cython
import numpy as np
cimport numpy as np
ctypedef fused T0:
np.complex128_t
np.float64_t
def mysum(np.ndarray[T0, ndim=1] arr):
cdef T0 ret = arr.dtype.type(0.)
cdef cython.int i
for i in range(arr.shape[0]):
ret += arr[i]
return ret
```

But the corresponding pure-Python version does not work!

### More array types (contiguous arrays, C or F order, memoryviews)¶

I think we should support:

```
Array[int, NDim(3), "C"]
Array[int, "3d", "C"]
transonic.typeof(np.empty((2, 2, 2)))
Array["int[:, :, ::1]"]
Array[int, "[:, :, ::1]"]
transonic.str2type("int[:, :, ::1]")
```

and maybe also:

```
transonic.int64[:, :, ::1]
```

I tend to think that the default (`"int[:,:]"`

) should correspond to
`"order=C"`

. “Fortran” order and “any” order (contiguous C or F) could be
obtained with `"order=F"`

and `"order=any"`

.

Strided arrays could be obtained with `Array[int, NDim(3), "strided"]`

or
`str2type("int[::, ::, ::]")`

.

Note that for Pythran, we could also support:

```
A_fixed_dim = Array[Type(np.float32, float), "[:, :, 3]"]
```

For Cython, we need to be able to specify if an array is a `np.ndarray`

or a
`memoryview`

. By default, we will use `np.ndarray`

and `memoryview`

could be
obtained with:

```
Array[int, "[:, :, ::1]", "memview"]
```

### Special C types¶

For example `Py_ssize_t`

(nearly `np.intp`

, which is supported) and `void`

(supported)

```
from ctypes import c_ssize_t as Py_ssize_t
from transonic import boost
@boost
def func(arr: "float[]", index: Py_ssize_t):
if n > 1:
a[n-1] = 0
```

or just:

```
from transonic import boost
@boost
def func(arr: "float[]", index: "Py_ssize_t"):
...
```

## Cython syntaxes that can be supported quite easily¶

`with nogil:`

¶

We could support something like

```
from transonic import boost, nogil
@boost
def func(n: int):
with nogil:
result = n**2
return result
```

Of course there is no equivalent in Pythran, so the Pythran backend would have
to suppress the `with nogil()`

.

### Cast¶

```
return <DTYPE_t*> myvar
```

I guess we should follow Cython and its pure Python mode function
`cython.cast(type, myvar)`

.

## Cython syntaxes that will be difficult to support¶

### Pointers and addresses¶

```
cdef Py_ssize_t *p_indexer
```

### Definition `struct`

, `enum`

, `class`

¶

```
cdef struct Heap:
Py_ssize_t items
Py_ssize_t space
Heapitem *data
Heapitem **ptrs
```

or

```
cdef class Foo:
```

### C allocation¶

```
features_carr = <MBLBP*>malloc(features_number * sizeof(MBLBP))
```