""" python _generate_pyx.py Generate Ufunc definition source files for scipy.special. Produces files '_ufuncs.c' and '_ufuncs_cxx.c' by first producing Cython. This will generate both calls to PyUFunc_FromFuncAndData and the required ufunc inner loops. The functions signatures are contained in 'functions.json', the syntax for a function signature is : ':' '*' '->' '*' : * : * : ? : ? : [',' ]* The input parameter types are denoted by single character type codes, according to 'f': 'float' 'd': 'double' 'g': 'long double' 'F': 'float complex' 'D': 'double complex' 'G': 'long double complex' 'i': 'int' 'l': 'long' 'v': 'void' If multiple kernel functions are given for a single ufunc, the one which is used is determined by the standard ufunc mechanism. Kernel functions that are listed first are also matched first against the ufunc input types, so functions listed earlier take precedence. In addition, versions with casted variables, such as d->f,D->F and i->d are automatically generated. There should be either a single header that contains all of the kernel functions listed, or there should be one header for each kernel function. Cython pxd files are allowed in addition to .h files. Cython functions may use fused types, but the names in the list should be the specialized ones, such as 'somefunc[float]'. Function coming from C++ should have ``++`` appended to the name of the header. Floating-point exceptions inside these Ufuncs are converted to special function errors --- which are separately controlled by the user, and off by default, as they are usually not especially useful for the user. The C++ module -------------- In addition to ``_ufuncs`` module, a second module ``_ufuncs_cxx`` is generated. This module only exports function pointers that are to be used when constructing some of the ufuncs in ``_ufuncs``. The function pointers are exported via Cython's standard mechanism. This mainly avoids build issues --- Python distutils has no way to figure out what to do if you want to link both C++ and Fortran code in the same shared library. """ #--------------------------------------------------------------------------------- # Extra code #--------------------------------------------------------------------------------- UFUNCS_EXTRA_CODE_COMMON = """\ # This file is automatically generated by _generate_pyx.py. # Do not edit manually! include "_ufuncs_extra_code_common.pxi" """ UFUNCS_EXTRA_CODE = """\ include "_ufuncs_extra_code.pxi" """ UFUNCS_EXTRA_CODE_BOTTOM = """\ # # Aliases # jn = jv """ CYTHON_SPECIAL_PXD = """\ # This file is automatically generated by _generate_pyx.py. # Do not edit manually! ctypedef fused number_t: double complex double cpdef number_t spherical_jn(long n, number_t z, bint derivative=*) nogil cpdef number_t spherical_yn(long n, number_t z, bint derivative=*) nogil cpdef number_t spherical_in(long n, number_t z, bint derivative=*) nogil cpdef number_t spherical_kn(long n, number_t z, bint derivative=*) nogil """ CYTHON_SPECIAL_PYX = """\ # This file is automatically generated by _generate_pyx.py. # Do not edit manually! \"\"\" .. highlight:: cython Cython API for special functions ================================ Scalar, typed versions of many of the functions in ``scipy.special`` can be accessed directly from Cython; the complete list is given below. Functions are overloaded using Cython fused types so their names match their Python counterpart. The module follows the following conventions: - If a function's Python counterpart returns multiple values, then the function returns its outputs via pointers in the final arguments. - If a function's Python counterpart returns a single value, then the function's output is returned directly. The module is usable from Cython via:: cimport scipy.special.cython_special Error handling -------------- Functions can indicate an error by returning ``nan``; however they cannot emit warnings like their counterparts in ``scipy.special``. Available functions ------------------- FUNCLIST Custom functions ---------------- Some functions in ``scipy.special`` which are not ufuncs have custom Cython wrappers. Spherical Bessel functions ~~~~~~~~~~~~~~~~~~~~~~~~~~ The optional ``derivative`` boolean argument is replaced with an optional Cython ``bint``, leading to the following signatures. - :py:func:`~scipy.special.spherical_jn`:: double complex spherical_jn(long, double complex) double complex spherical_jn(long, double complex, bint) double spherical_jn(long, double) double spherical_jn(long, double, bint) - :py:func:`~scipy.special.spherical_yn`:: double complex spherical_yn(long, double complex) double complex spherical_yn(long, double complex, bint) double spherical_yn(long, double) double spherical_yn(long, double, bint) - :py:func:`~scipy.special.spherical_in`:: double complex spherical_in(long, double complex) double complex spherical_in(long, double complex, bint) double spherical_in(long, double) double spherical_in(long, double, bint) - :py:func:`~scipy.special.spherical_kn`:: double complex spherical_kn(long, double complex) double complex spherical_kn(long, double complex, bint) double spherical_kn(long, double) double spherical_kn(long, double, bint) \"\"\" include "_cython_special.pxi" include "_cython_special_custom.pxi" """ STUBS = """\ from typing import Any, Dict import numpy as np __all__ = [ 'geterr', 'seterr', 'errstate', {ALL} ] def geterr() -> Dict[str, str]: ... def seterr(**kwargs: str) -> Dict[str, str]: ... class errstate: def __init__(self, **kargs: str) -> None: ... def __enter__(self) -> None: ... def __exit__( self, exc_type: Any, # Unused exc_value: Any, # Unused traceback: Any, # Unused ) -> None: ... {STUBS} """ #--------------------------------------------------------------------------------- # Code generation #--------------------------------------------------------------------------------- import itertools import json import os import optparse import re import textwrap from typing import List import numpy BASE_DIR = os.path.abspath(os.path.dirname(__file__)) add_newdocs = __import__('add_newdocs') CY_TYPES = { 'f': 'float', 'd': 'double', 'g': 'long double', 'F': 'float complex', 'D': 'double complex', 'G': 'long double complex', 'i': 'int', 'l': 'long', 'v': 'void', } C_TYPES = { 'f': 'npy_float', 'd': 'npy_double', 'g': 'npy_longdouble', 'F': 'npy_cfloat', 'D': 'npy_cdouble', 'G': 'npy_clongdouble', 'i': 'npy_int', 'l': 'npy_long', 'v': 'void', } TYPE_NAMES = { 'f': 'NPY_FLOAT', 'd': 'NPY_DOUBLE', 'g': 'NPY_LONGDOUBLE', 'F': 'NPY_CFLOAT', 'D': 'NPY_CDOUBLE', 'G': 'NPY_CLONGDOUBLE', 'i': 'NPY_INT', 'l': 'NPY_LONG', } CYTHON_SPECIAL_BENCHFUNCS = { 'airy': ['d*dddd', 'D*DDDD'], 'beta': ['dd'], 'erf': ['d', 'D'], 'exprel': ['d'], 'gamma': ['d', 'D'], 'jv': ['dd', 'dD'], 'loggamma': ['D'], 'logit': ['d'], 'psi': ['d', 'D'], } def underscore(arg): return arg.replace(" ", "_") def cast_order(c): return ['ilfdgFDG'.index(x) for x in c] # These downcasts will cause the function to return NaNs, unless the # values happen to coincide exactly. DANGEROUS_DOWNCAST = set([ ('F', 'i'), ('F', 'l'), ('F', 'f'), ('F', 'd'), ('F', 'g'), ('D', 'i'), ('D', 'l'), ('D', 'f'), ('D', 'd'), ('D', 'g'), ('G', 'i'), ('G', 'l'), ('G', 'f'), ('G', 'd'), ('G', 'g'), ('f', 'i'), ('f', 'l'), ('d', 'i'), ('d', 'l'), ('g', 'i'), ('g', 'l'), ('l', 'i'), ]) NAN_VALUE = { 'f': 'NPY_NAN', 'd': 'NPY_NAN', 'g': 'NPY_NAN', 'F': 'NPY_NAN', 'D': 'NPY_NAN', 'G': 'NPY_NAN', 'i': '0xbad0bad0', 'l': '0xbad0bad0', } def generate_loop(func_inputs, func_outputs, func_retval, ufunc_inputs, ufunc_outputs): """ Generate a UFunc loop function that calls a function given as its data parameter with the specified input and output arguments and return value. This function can be passed to PyUFunc_FromFuncAndData. Parameters ---------- func_inputs, func_outputs, func_retval : str Signature of the function to call, given as type codes of the input, output and return value arguments. These 1-character codes are given according to the CY_TYPES and TYPE_NAMES lists above. The corresponding C function signature to be called is: retval func(intype1 iv1, intype2 iv2, ..., outtype1 *ov1, ...); If len(ufunc_outputs) == len(func_outputs)+1, the return value is treated as the first output argument. Otherwise, the return value is ignored. ufunc_inputs, ufunc_outputs : str Ufunc input and output signature. This does not have to exactly match the function signature, as long as the type casts work out on the C level. Returns ------- loop_name Name of the generated loop function. loop_body Generated C code for the loop. """ if len(func_inputs) != len(ufunc_inputs): raise ValueError("Function and ufunc have different number of inputs") if len(func_outputs) != len(ufunc_outputs) and not ( func_retval != "v" and len(func_outputs)+1 == len(ufunc_outputs)): raise ValueError("Function retval and ufunc outputs don't match") name = "loop_%s_%s_%s_As_%s_%s" % ( func_retval, func_inputs, func_outputs, ufunc_inputs, ufunc_outputs ) body = "cdef void %s(char **args, np.npy_intp *dims, np.npy_intp *steps, void *data) nogil:\n" % name body += " cdef np.npy_intp i, n = dims[0]\n" body += " cdef void *func = (data)[0]\n" body += " cdef char *func_name = (data)[1]\n" for j in range(len(ufunc_inputs)): body += " cdef char *ip%d = args[%d]\n" % (j, j) for j in range(len(ufunc_outputs)): body += " cdef char *op%d = args[%d]\n" % (j, j + len(ufunc_inputs)) ftypes = [] fvars = [] outtypecodes = [] for j in range(len(func_inputs)): ftypes.append(CY_TYPES[func_inputs[j]]) fvars.append("<%s>(<%s*>ip%d)[0]" % ( CY_TYPES[func_inputs[j]], CY_TYPES[ufunc_inputs[j]], j)) if len(func_outputs)+1 == len(ufunc_outputs): func_joff = 1 outtypecodes.append(func_retval) body += " cdef %s ov0\n" % (CY_TYPES[func_retval],) else: func_joff = 0 for j, outtype in enumerate(func_outputs): body += " cdef %s ov%d\n" % (CY_TYPES[outtype], j+func_joff) ftypes.append("%s *" % CY_TYPES[outtype]) fvars.append("&ov%d" % (j+func_joff)) outtypecodes.append(outtype) body += " for i in range(n):\n" if len(func_outputs)+1 == len(ufunc_outputs): rv = "ov0 = " else: rv = "" funcall = " %s(<%s(*)(%s) nogil>func)(%s)\n" % ( rv, CY_TYPES[func_retval], ", ".join(ftypes), ", ".join(fvars)) # Cast-check inputs and call function input_checks = [] for j in range(len(func_inputs)): if (ufunc_inputs[j], func_inputs[j]) in DANGEROUS_DOWNCAST: chk = "<%s>(<%s*>ip%d)[0] == (<%s*>ip%d)[0]" % ( CY_TYPES[func_inputs[j]], CY_TYPES[ufunc_inputs[j]], j, CY_TYPES[ufunc_inputs[j]], j) input_checks.append(chk) if input_checks: body += " if %s:\n" % (" and ".join(input_checks)) body += " " + funcall body += " else:\n" body += " sf_error.error(func_name, sf_error.DOMAIN, \"invalid input argument\")\n" for j, outtype in enumerate(outtypecodes): body += " ov%d = <%s>%s\n" % ( j, CY_TYPES[outtype], NAN_VALUE[outtype]) else: body += funcall # Assign and cast-check output values for j, (outtype, fouttype) in enumerate(zip(ufunc_outputs, outtypecodes)): if (fouttype, outtype) in DANGEROUS_DOWNCAST: body += " if ov%d == <%s>ov%d:\n" % (j, CY_TYPES[outtype], j) body += " (<%s *>op%d)[0] = <%s>ov%d\n" % ( CY_TYPES[outtype], j, CY_TYPES[outtype], j) body += " else:\n" body += " sf_error.error(func_name, sf_error.DOMAIN, \"invalid output\")\n" body += " (<%s *>op%d)[0] = <%s>%s\n" % ( CY_TYPES[outtype], j, CY_TYPES[outtype], NAN_VALUE[outtype]) else: body += " (<%s *>op%d)[0] = <%s>ov%d\n" % ( CY_TYPES[outtype], j, CY_TYPES[outtype], j) for j in range(len(ufunc_inputs)): body += " ip%d += steps[%d]\n" % (j, j) for j in range(len(ufunc_outputs)): body += " op%d += steps[%d]\n" % (j, j + len(ufunc_inputs)) body += " sf_error.check_fpe(func_name)\n" return name, body def generate_fused_type(codes): """ Generate name of and cython code for a fused type. Parameters ---------- typecodes : str Valid inputs to CY_TYPES (i.e. f, d, g, ...). """ cytypes = map(lambda x: CY_TYPES[x], codes) name = codes + "_number_t" declaration = ["ctypedef fused " + name + ":"] for cytype in cytypes: declaration.append(" " + cytype) declaration = "\n".join(declaration) return name, declaration def generate_bench(name, codes): tab = " "*4 top, middle, end = [], [], [] tmp = codes.split("*") if len(tmp) > 1: incodes = tmp[0] outcodes = tmp[1] else: incodes = tmp[0] outcodes = "" inargs, inargs_and_types = [], [] for n, code in enumerate(incodes): arg = "x{}".format(n) inargs.append(arg) inargs_and_types.append("{} {}".format(CY_TYPES[code], arg)) line = "def {{}}(int N, {}):".format(", ".join(inargs_and_types)) top.append(line) top.append(tab + "cdef int n") outargs = [] for n, code in enumerate(outcodes): arg = "y{}".format(n) outargs.append("&{}".format(arg)) line = "cdef {} {}".format(CY_TYPES[code], arg) middle.append(tab + line) end.append(tab + "for n in range(N):") end.append(2*tab + "{}({})") pyfunc = "_bench_{}_{}_{}".format(name, incodes, "py") cyfunc = "_bench_{}_{}_{}".format(name, incodes, "cy") pytemplate = "\n".join(top + end) cytemplate = "\n".join(top + middle + end) pybench = pytemplate.format(pyfunc, "_ufuncs." + name, ", ".join(inargs)) cybench = cytemplate.format(cyfunc, name, ", ".join(inargs + outargs)) return pybench, cybench def generate_doc(name, specs): tab = " "*4 doc = ["- :py:func:`~scipy.special.{}`::\n".format(name)] for spec in specs: incodes, outcodes = spec.split("->") incodes = incodes.split("*") intypes = list(map(lambda x: CY_TYPES[x], incodes[0])) if len(incodes) > 1: types = map(lambda x: "{} *".format(CY_TYPES[x]), incodes[1]) intypes.extend(types) outtype = CY_TYPES[outcodes] line = "{} {}({})".format(outtype, name, ", ".join(intypes)) doc.append(2*tab + line) doc[-1] = "{}\n".format(doc[-1]) doc = "\n".join(doc) return doc def npy_cdouble_from_double_complex(var): """Cast a Cython double complex to a NumPy cdouble.""" res = "_complexstuff.npy_cdouble_from_double_complex({})".format(var) return res def double_complex_from_npy_cdouble(var): """Cast a NumPy cdouble to a Cython double complex.""" res = "_complexstuff.double_complex_from_npy_cdouble({})".format(var) return res def iter_variants(inputs, outputs): """ Generate variants of UFunc signatures, by changing variable types, within the limitation that the corresponding C types casts still work out. This does not generate all possibilities, just the ones required for the ufunc to work properly with the most common data types. Parameters ---------- inputs, outputs : str UFunc input and output signature strings Yields ------ new_input, new_output : str Modified input and output strings. Also the original input/output pair is yielded. """ maps = [ # always use long instead of int (more common type on 64-bit) ('i', 'l'), ] # float32-preserving signatures if not ('i' in inputs or 'l' in inputs): # Don't add float32 versions of ufuncs with integer arguments, as this # can lead to incorrect dtype selection if the integer arguments are # arrays, but float arguments are scalars. # For instance sph_harm(0,[0],0,0).dtype == complex64 # This may be a NumPy bug, but we need to work around it. # cf. gh-4895, https://github.com/numpy/numpy/issues/5895 maps = maps + [(a + 'dD', b + 'fF') for a, b in maps] # do the replacements for src, dst in maps: new_inputs = inputs new_outputs = outputs for a, b in zip(src, dst): new_inputs = new_inputs.replace(a, b) new_outputs = new_outputs.replace(a, b) yield new_inputs, new_outputs class Func(object): """ Base class for Ufunc and FusedFunc. """ def __init__(self, name, signatures): self.name = name self.signatures = [] self.function_name_overrides = {} for header in signatures.keys(): for name, sig in signatures[header].items(): inarg, outarg, ret = self._parse_signature(sig) self.signatures.append((name, inarg, outarg, ret, header)) def _parse_signature(self, sig): m = re.match(r"\s*([fdgFDGil]*)\s*\*\s*([fdgFDGil]*)\s*->\s*([*fdgFDGil]*)\s*$", sig) if m: inarg, outarg, ret = [x.strip() for x in m.groups()] if ret.count('*') > 1: raise ValueError("{}: Invalid signature: {}".format(self.name, sig)) return inarg, outarg, ret m = re.match(r"\s*([fdgFDGil]*)\s*->\s*([fdgFDGil]?)\s*$", sig) if m: inarg, ret = [x.strip() for x in m.groups()] return inarg, "", ret raise ValueError("{}: Invalid signature: {}".format(self.name, sig)) def get_prototypes(self, nptypes_for_h=False): prototypes = [] for func_name, inarg, outarg, ret, header in self.signatures: ret = ret.replace('*', '') c_args = ([C_TYPES[x] for x in inarg] + [C_TYPES[x] + ' *' for x in outarg]) cy_args = ([CY_TYPES[x] for x in inarg] + [CY_TYPES[x] + ' *' for x in outarg]) c_proto = "%s (*)(%s)" % (C_TYPES[ret], ", ".join(c_args)) if header.endswith("h") and nptypes_for_h: cy_proto = c_proto + "nogil" else: cy_proto = "%s (*)(%s) nogil" % (CY_TYPES[ret], ", ".join(cy_args)) prototypes.append((func_name, c_proto, cy_proto, header)) return prototypes def cython_func_name(self, c_name, specialized=False, prefix="_func_", override=True): # act on function name overrides if override and c_name in self.function_name_overrides: c_name = self.function_name_overrides[c_name] prefix = "" # support fused types m = re.match(r'^(.*?)(\[.*\])$', c_name) if m: c_base_name, fused_part = m.groups() else: c_base_name, fused_part = c_name, "" if specialized: return "%s%s%s" % (prefix, c_base_name, fused_part.replace(' ', '_')) else: return "%s%s" % (prefix, c_base_name,) class Ufunc(Func): """ Ufunc signature, restricted format suitable for special functions. Parameters ---------- name Name of the ufunc to create signature String of form 'func: fff*ff->f, func2: ddd->*i' describing the C-level functions and types of their input arguments and return values. The syntax is 'function_name: inputparams*outputparams->output_retval*ignored_retval' Attributes ---------- name : str Python name for the Ufunc signatures : list of (func_name, inarg_spec, outarg_spec, ret_spec, header_name) List of parsed signatures doc : str Docstring, obtained from add_newdocs function_name_overrides : dict of str->str Overrides for the function names in signatures """ def __init__(self, name, signatures): super(Ufunc, self).__init__(name, signatures) self.doc = add_newdocs.get(name) if self.doc is None: raise ValueError("No docstring for ufunc %r" % name) self.doc = textwrap.dedent(self.doc).strip() def _get_signatures_and_loops(self, all_loops): inarg_num = None outarg_num = None seen = set() variants = [] def add_variant(func_name, inarg, outarg, ret, inp, outp): if inp in seen: return seen.add(inp) sig = (func_name, inp, outp) if "v" in outp: raise ValueError("%s: void signature %r" % (self.name, sig)) if len(inp) != inarg_num or len(outp) != outarg_num: raise ValueError("%s: signature %r does not have %d/%d input/output args" % ( self.name, sig, inarg_num, outarg_num)) loop_name, loop = generate_loop(inarg, outarg, ret, inp, outp) all_loops[loop_name] = loop variants.append((func_name, loop_name, inp, outp)) # First add base variants for func_name, inarg, outarg, ret, header in self.signatures: outp = re.sub(r'\*.*', '', ret) + outarg ret = ret.replace('*', '') if inarg_num is None: inarg_num = len(inarg) outarg_num = len(outp) inp, outp = list(iter_variants(inarg, outp))[0] add_variant(func_name, inarg, outarg, ret, inp, outp) # Then the supplementary ones for func_name, inarg, outarg, ret, header in self.signatures: outp = re.sub(r'\*.*', '', ret) + outarg ret = ret.replace('*', '') for inp, outp in iter_variants(inarg, outp): add_variant(func_name, inarg, outarg, ret, inp, outp) # Then sort variants to input argument cast order # -- the sort is stable, so functions earlier in the signature list # are still preferred variants.sort(key=lambda v: cast_order(v[2])) return variants, inarg_num, outarg_num def generate(self, all_loops): toplevel = "" variants, inarg_num, outarg_num = self._get_signatures_and_loops(all_loops) loops = [] funcs = [] types = [] for func_name, loop_name, inputs, outputs in variants: for x in inputs: types.append(TYPE_NAMES[x]) for x in outputs: types.append(TYPE_NAMES[x]) loops.append(loop_name) funcs.append(func_name) toplevel += "cdef np.PyUFuncGenericFunction ufunc_%s_loops[%d]\n" % (self.name, len(loops)) toplevel += "cdef void *ufunc_%s_ptr[%d]\n" % (self.name, 2*len(funcs)) toplevel += "cdef void *ufunc_%s_data[%d]\n" % (self.name, len(funcs)) toplevel += "cdef char ufunc_%s_types[%d]\n" % (self.name, len(types)) toplevel += 'cdef char *ufunc_%s_doc = (\n "%s")\n' % ( self.name, self.doc.replace("\\", "\\\\").replace('"', '\\"').replace('\n', '\\n\"\n "') ) for j, function in enumerate(loops): toplevel += "ufunc_%s_loops[%d] = %s\n" % (self.name, j, function) for j, type in enumerate(types): toplevel += "ufunc_%s_types[%d] = %s\n" % (self.name, j, type) for j, func in enumerate(funcs): toplevel += "ufunc_%s_ptr[2*%d] = %s\n" % (self.name, j, self.cython_func_name(func, specialized=True)) toplevel += "ufunc_%s_ptr[2*%d+1] = (\"%s\")\n" % (self.name, j, self.name) for j, func in enumerate(funcs): toplevel += "ufunc_%s_data[%d] = &ufunc_%s_ptr[2*%d]\n" % ( self.name, j, self.name, j) toplevel += ('@ = np.PyUFunc_FromFuncAndData(ufunc_@_loops, ' 'ufunc_@_data, ufunc_@_types, %d, %d, %d, 0, ' '"@", ufunc_@_doc, 0)\n' % (len(types)/(inarg_num+outarg_num), inarg_num, outarg_num) ).replace('@', self.name) return toplevel class FusedFunc(Func): """ Generate code for a fused-type special function that can be cimported in Cython. """ def __init__(self, name, signatures): super(FusedFunc, self).__init__(name, signatures) self.doc = "See the documentation for scipy.special." + self.name # "codes" are the keys for CY_TYPES self.incodes, self.outcodes = self._get_codes() self.fused_types = set() self.intypes, infused_types = self._get_types(self.incodes) self.fused_types.update(infused_types) self.outtypes, outfused_types = self._get_types(self.outcodes) self.fused_types.update(outfused_types) self.invars, self.outvars = self._get_vars() def _get_codes(self): inarg_num, outarg_num = None, None all_inp, all_outp = [], [] for _, inarg, outarg, ret, _ in self.signatures: outp = re.sub(r'\*.*', '', ret) + outarg if inarg_num is None: inarg_num = len(inarg) outarg_num = len(outp) inp, outp = list(iter_variants(inarg, outp))[0] all_inp.append(inp) all_outp.append(outp) incodes = [] for n in range(inarg_num): codes = unique(map(lambda x: x[n], all_inp)) codes.sort() incodes.append(''.join(codes)) outcodes = [] for n in range(outarg_num): codes = unique(map(lambda x: x[n], all_outp)) codes.sort() outcodes.append(''.join(codes)) return tuple(incodes), tuple(outcodes) def _get_types(self, codes): all_types = [] fused_types = set() for code in codes: if len(code) == 1: # It's not a fused type all_types.append((CY_TYPES[code], code)) else: # It's a fused type fused_type, dec = generate_fused_type(code) fused_types.add(dec) all_types.append((fused_type, code)) return all_types, fused_types def _get_vars(self): invars = ["x{}".format(n) for n in range(len(self.intypes))] outvars = ["y{}".format(n) for n in range(len(self.outtypes))] return invars, outvars def _get_conditional(self, types, codes, adverb): """Generate an if/elif/else clause that selects a specialization of fused types. """ clauses = [] seen = set() for (typ, typcode), code in zip(types, codes): if len(typcode) == 1: continue if typ not in seen: clauses.append("{} is {}".format(typ, underscore(CY_TYPES[code]))) seen.add(typ) if clauses and adverb != "else": line = "{} {}:".format(adverb, " and ".join(clauses)) elif clauses and adverb == "else": line = "else:" else: line = None return line def _get_incallvars(self, intypes, c): """Generate pure input variables to a specialization, i.e., variables that aren't used to return a value. """ incallvars = [] for n, intype in enumerate(intypes): var = self.invars[n] if c and intype == "double complex": var = npy_cdouble_from_double_complex(var) incallvars.append(var) return incallvars def _get_outcallvars(self, outtypes, c): """Generate output variables to a specialization, i.e., pointers that are used to return values. """ outcallvars, tmpvars, casts = [], [], [] # If there are more out variables than out types, we want the # tail of the out variables start = len(self.outvars) - len(outtypes) outvars = self.outvars[start:] for n, (var, outtype) in enumerate(zip(outvars, outtypes)): if c and outtype == "double complex": tmp = "tmp{}".format(n) tmpvars.append(tmp) outcallvars.append("&{}".format(tmp)) tmpcast = double_complex_from_npy_cdouble(tmp) casts.append("{}[0] = {}".format(var, tmpcast)) else: outcallvars.append("{}".format(var)) return outcallvars, tmpvars, casts def _get_nan_decs(self): """Set all variables to nan for specializations of fused types for which don't have signatures. """ # Set non fused-type variables to nan tab = " "*4 fused_types, lines = [], [tab + "else:"] seen = set() for outvar, outtype, code in zip(self.outvars, self.outtypes, self.outcodes): if len(code) == 1: line = "{}[0] = {}".format(outvar, NAN_VALUE[code]) lines.append(2*tab + line) else: fused_type = outtype name, _ = fused_type if name not in seen: fused_types.append(fused_type) seen.add(name) if not fused_types: return lines # Set fused-type variables to nan all_codes = tuple([codes for _unused, codes in fused_types]) codelens = list(map(lambda x: len(x), all_codes)) last = numpy.prod(codelens) - 1 for m, codes in enumerate(itertools.product(*all_codes)): fused_codes, decs = [], [] for n, fused_type in enumerate(fused_types): code = codes[n] fused_codes.append(underscore(CY_TYPES[code])) for nn, outvar in enumerate(self.outvars): if self.outtypes[nn] == fused_type: line = "{}[0] = {}".format(outvar, NAN_VALUE[code]) decs.append(line) if m == 0: adverb = "if" elif m == last: adverb = "else" else: adverb = "elif" cond = self._get_conditional(fused_types, codes, adverb) lines.append(2*tab + cond) lines.extend(map(lambda x: 3*tab + x, decs)) return lines def _get_tmp_decs(self, all_tmpvars): """Generate the declarations of any necessary temporary variables. """ tab = " "*4 tmpvars = list(all_tmpvars) tmpvars.sort() tmpdecs = [tab + "cdef npy_cdouble {}".format(tmpvar) for tmpvar in tmpvars] return tmpdecs def _get_python_wrap(self): """Generate a Python wrapper for functions which pass their arguments as pointers. """ tab = " "*4 body, callvars = [], [] for (intype, _), invar in zip(self.intypes, self.invars): callvars.append("{} {}".format(intype, invar)) line = "def _{}_pywrap({}):".format(self.name, ", ".join(callvars)) body.append(line) for (outtype, _), outvar in zip(self.outtypes, self.outvars): line = "cdef {} {}".format(outtype, outvar) body.append(tab + line) addr_outvars = map(lambda x: "&{}".format(x), self.outvars) line = "{}({}, {})".format(self.name, ", ".join(self.invars), ", ".join(addr_outvars)) body.append(tab + line) line = "return {}".format(", ".join(self.outvars)) body.append(tab + line) body = "\n".join(body) return body def _get_common(self, signum, sig): """Generate code common to all the _generate_* methods.""" tab = " "*4 func_name, incodes, outcodes, retcode, header = sig # Convert ints to longs; cf. iter_variants() incodes = incodes.replace('i', 'l') outcodes = outcodes.replace('i', 'l') retcode = retcode.replace('i', 'l') if header.endswith("h"): c = True else: c = False if header.endswith("++"): cpp = True else: cpp = False intypes = list(map(lambda x: CY_TYPES[x], incodes)) outtypes = list(map(lambda x: CY_TYPES[x], outcodes)) retcode = re.sub(r'\*.*', '', retcode) if not retcode: retcode = 'v' rettype = CY_TYPES[retcode] if cpp: # Functions from _ufuncs_cxx are exported as a void* # pointers; cast them to the correct types func_name = "scipy.special._ufuncs_cxx._export_{}".format(func_name) func_name = "(<{}(*)({}) nogil>{})"\ .format(rettype, ", ".join(intypes + outtypes), func_name) else: func_name = self.cython_func_name(func_name, specialized=True) if signum == 0: adverb = "if" else: adverb = "elif" cond = self._get_conditional(self.intypes, incodes, adverb) if cond: lines = [tab + cond] sp = 2*tab else: lines = [] sp = tab return func_name, incodes, outcodes, retcode, \ intypes, outtypes, rettype, c, lines, sp def _generate_from_return_and_no_outargs(self): tab = " "*4 specs, body = [], [] for signum, sig in enumerate(self.signatures): func_name, incodes, outcodes, retcode, intypes, outtypes, \ rettype, c, lines, sp = self._get_common(signum, sig) body.extend(lines) # Generate the call to the specialized function callvars = self._get_incallvars(intypes, c) call = "{}({})".format(func_name, ", ".join(callvars)) if c and rettype == "double complex": call = double_complex_from_npy_cdouble(call) line = sp + "return {}".format(call) body.append(line) sig = "{}->{}".format(incodes, retcode) specs.append(sig) if len(specs) > 1: # Return nan for signatures without a specialization body.append(tab + "else:") outtype, outcodes = self.outtypes[0] last = len(outcodes) - 1 if len(outcodes) == 1: line = "return {}".format(NAN_VALUE[outcodes]) body.append(2*tab + line) else: for n, code in enumerate(outcodes): if n == 0: adverb = "if" elif n == last: adverb = "else" else: adverb = "elif" cond = self._get_conditional(self.outtypes, code, adverb) body.append(2*tab + cond) line = "return {}".format(NAN_VALUE[code]) body.append(3*tab + line) # Generate the head of the function callvars, head = [], [] for n, (intype, _) in enumerate(self.intypes): callvars.append("{} {}".format(intype, self.invars[n])) (outtype, _) = self.outtypes[0] dec = "cpdef {} {}({}) nogil".format(outtype, self.name, ", ".join(callvars)) head.append(dec + ":") head.append(tab + '"""{}"""'.format(self.doc)) src = "\n".join(head + body) return dec, src, specs def _generate_from_outargs_and_no_return(self): tab = " "*4 all_tmpvars = set() specs, body = [], [] for signum, sig in enumerate(self.signatures): func_name, incodes, outcodes, retcode, intypes, outtypes, \ rettype, c, lines, sp = self._get_common(signum, sig) body.extend(lines) # Generate the call to the specialized function callvars = self._get_incallvars(intypes, c) outcallvars, tmpvars, casts = self._get_outcallvars(outtypes, c) callvars.extend(outcallvars) all_tmpvars.update(tmpvars) call = "{}({})".format(func_name, ", ".join(callvars)) body.append(sp + call) body.extend(map(lambda x: sp + x, casts)) if len(outcodes) == 1: sig = "{}->{}".format(incodes, outcodes) specs.append(sig) else: sig = "{}*{}->v".format(incodes, outcodes) specs.append(sig) if len(specs) > 1: lines = self._get_nan_decs() body.extend(lines) if len(self.outvars) == 1: line = "return {}[0]".format(self.outvars[0]) body.append(tab + line) # Generate the head of the function callvars, head = [], [] for invar, (intype, _) in zip(self.invars, self.intypes): callvars.append("{} {}".format(intype, invar)) if len(self.outvars) > 1: for outvar, (outtype, _) in zip(self.outvars, self.outtypes): callvars.append("{} *{}".format(outtype, outvar)) if len(self.outvars) == 1: outtype, _ = self.outtypes[0] dec = "cpdef {} {}({}) nogil".format(outtype, self.name, ", ".join(callvars)) else: dec = "cdef void {}({}) nogil".format(self.name, ", ".join(callvars)) head.append(dec + ":") head.append(tab + '"""{}"""'.format(self.doc)) if len(self.outvars) == 1: outvar = self.outvars[0] outtype, _ = self.outtypes[0] line = "cdef {} {}".format(outtype, outvar) head.append(tab + line) head.extend(self._get_tmp_decs(all_tmpvars)) src = "\n".join(head + body) return dec, src, specs def _generate_from_outargs_and_return(self): tab = " "*4 all_tmpvars = set() specs, body = [], [] for signum, sig in enumerate(self.signatures): func_name, incodes, outcodes, retcode, intypes, outtypes, \ rettype, c, lines, sp = self._get_common(signum, sig) body.extend(lines) # Generate the call to the specialized function callvars = self._get_incallvars(intypes, c) outcallvars, tmpvars, casts = self._get_outcallvars(outtypes, c) callvars.extend(outcallvars) all_tmpvars.update(tmpvars) call = "{}({})".format(func_name, ", ".join(callvars)) if c and rettype == "double complex": call = double_complex_from_npy_cdouble(call) call = "{}[0] = {}".format(self.outvars[0], call) body.append(sp + call) body.extend(map(lambda x: sp + x, casts)) sig = "{}*{}->v".format(incodes, outcodes + retcode) specs.append(sig) if len(specs) > 1: lines = self._get_nan_decs() body.extend(lines) # Generate the head of the function callvars, head = [], [] for invar, (intype, _) in zip(self.invars, self.intypes): callvars.append("{} {}".format(intype, invar)) for outvar, (outtype, _) in zip(self.outvars, self.outtypes): callvars.append("{} *{}".format(outtype, outvar)) dec = "cdef void {}({}) nogil".format(self.name, ", ".join(callvars)) head.append(dec + ":") head.append(tab + '"""{}"""'.format(self.doc)) head.extend(self._get_tmp_decs(all_tmpvars)) src = "\n".join(head + body) return dec, src, specs def generate(self): _, _, outcodes, retcode, _ = self.signatures[0] retcode = re.sub(r'\*.*', '', retcode) if not retcode: retcode = 'v' if len(outcodes) == 0 and retcode != 'v': dec, src, specs = self._generate_from_return_and_no_outargs() elif len(outcodes) > 0 and retcode == 'v': dec, src, specs = self._generate_from_outargs_and_no_return() elif len(outcodes) > 0 and retcode != 'v': dec, src, specs = self._generate_from_outargs_and_return() else: raise ValueError("Invalid signature") if len(self.outvars) > 1: wrap = self._get_python_wrap() else: wrap = None return dec, src, specs, self.fused_types, wrap def get_declaration(ufunc, c_name, c_proto, cy_proto, header, proto_h_filename): """ Construct a Cython declaration of a function coming either from a pxd or a header file. Do sufficient tricks to enable compile-time type checking against the signature expected by the ufunc. """ defs = [] defs_h = [] var_name = c_name.replace('[', '_').replace(']', '_').replace(' ', '_') if header.endswith('.pxd'): defs.append("from .%s cimport %s as %s" % ( header[:-4], ufunc.cython_func_name(c_name, prefix=""), ufunc.cython_func_name(c_name))) # check function signature at compile time proto_name = '_proto_%s_t' % var_name defs.append("ctypedef %s" % (cy_proto.replace('(*)', proto_name))) defs.append("cdef %s *%s_var = &%s" % ( proto_name, proto_name, ufunc.cython_func_name(c_name, specialized=True))) else: # redeclare the function, so that the assumed # signature is checked at compile time new_name = "%s \"%s\"" % (ufunc.cython_func_name(c_name), c_name) defs.append("cdef extern from \"%s\":" % proto_h_filename) defs.append(" cdef %s" % (cy_proto.replace('(*)', new_name))) defs_h.append("#include \"%s\"" % header) defs_h.append("%s;" % (c_proto.replace('(*)', c_name))) return defs, defs_h, var_name def generate_ufuncs(fn_prefix, cxx_fn_prefix, ufuncs): filename = fn_prefix + ".pyx" proto_h_filename = fn_prefix + '_defs.h' cxx_proto_h_filename = cxx_fn_prefix + '_defs.h' cxx_pyx_filename = cxx_fn_prefix + ".pyx" cxx_pxd_filename = cxx_fn_prefix + ".pxd" toplevel = "" # for _ufuncs* defs = [] defs_h = [] all_loops = {} # for _ufuncs_cxx* cxx_defs = [] cxx_pxd_defs = [ "from . cimport sf_error", "cdef void _set_action(sf_error.sf_error_t, sf_error.sf_action_t) nogil" ] cxx_defs_h = [] ufuncs.sort(key=lambda u: u.name) for ufunc in ufuncs: # generate function declaration and type checking snippets cfuncs = ufunc.get_prototypes() for c_name, c_proto, cy_proto, header in cfuncs: if header.endswith('++'): header = header[:-2] # for the CXX module item_defs, item_defs_h, var_name = get_declaration(ufunc, c_name, c_proto, cy_proto, header, cxx_proto_h_filename) cxx_defs.extend(item_defs) cxx_defs_h.extend(item_defs_h) cxx_defs.append("cdef void *_export_%s = %s" % ( var_name, ufunc.cython_func_name(c_name, specialized=True, override=False))) cxx_pxd_defs.append("cdef void *_export_%s" % (var_name,)) # let cython grab the function pointer from the c++ shared library ufunc.function_name_overrides[c_name] = "scipy.special._ufuncs_cxx._export_" + var_name else: # usual case item_defs, item_defs_h, _ = get_declaration(ufunc, c_name, c_proto, cy_proto, header, proto_h_filename) defs.extend(item_defs) defs_h.extend(item_defs_h) # ufunc creation code snippet t = ufunc.generate(all_loops) toplevel += t + "\n" # Produce output toplevel = "\n".join(sorted(all_loops.values()) + defs + [toplevel]) # Generate an `__all__` for the module all_ufuncs = ( [ "'{}'".format(ufunc.name) for ufunc in ufuncs if not ufunc.name.startswith('_') ] + ["'geterr'", "'seterr'", "'errstate'", "'jn'"] ) module_all = '__all__ = [{}]'.format(', '.join(all_ufuncs)) with open(filename, 'w') as f: f.write(UFUNCS_EXTRA_CODE_COMMON) f.write(UFUNCS_EXTRA_CODE) f.write(module_all) f.write("\n") f.write(toplevel) f.write(UFUNCS_EXTRA_CODE_BOTTOM) defs_h = unique(defs_h) with open(proto_h_filename, 'w') as f: f.write("#ifndef UFUNCS_PROTO_H\n#define UFUNCS_PROTO_H 1\n") f.write("\n".join(defs_h)) f.write("\n#endif\n") cxx_defs_h = unique(cxx_defs_h) with open(cxx_proto_h_filename, 'w') as f: f.write("#ifndef UFUNCS_PROTO_H\n#define UFUNCS_PROTO_H 1\n") f.write("\n".join(cxx_defs_h)) f.write("\n#endif\n") with open(cxx_pyx_filename, 'w') as f: f.write(UFUNCS_EXTRA_CODE_COMMON) f.write("\n") f.write("\n".join(cxx_defs)) f.write("\n# distutils: language = c++\n") with open(cxx_pxd_filename, 'w') as f: f.write("\n".join(cxx_pxd_defs)) def generate_fused_funcs(modname, ufunc_fn_prefix, fused_funcs): pxdfile = modname + ".pxd" pyxfile = modname + ".pyx" proto_h_filename = ufunc_fn_prefix + '_defs.h' sources = [] declarations = [] # Code for benchmarks bench_aux = [] fused_types = set() # Parameters for the tests doc = [] defs = [] for func in fused_funcs: if func.name.startswith("_"): # Don't try to deal with functions that have extra layers # of wrappers. continue # Get the function declaration for the .pxd and the source # code for the .pyx dec, src, specs, func_fused_types, wrap = func.generate() declarations.append(dec) sources.append(src) if wrap: sources.append(wrap) fused_types.update(func_fused_types) # Declare the specializations cfuncs = func.get_prototypes(nptypes_for_h=True) for c_name, c_proto, cy_proto, header in cfuncs: if header.endswith('++'): # We grab the c++ functions from the c++ module continue item_defs, _, _ = get_declaration(func, c_name, c_proto, cy_proto, header, proto_h_filename) defs.extend(item_defs) # Add a line to the documentation doc.append(generate_doc(func.name, specs)) # Generate code for benchmarks if func.name in CYTHON_SPECIAL_BENCHFUNCS: for codes in CYTHON_SPECIAL_BENCHFUNCS[func.name]: pybench, cybench = generate_bench(func.name, codes) bench_aux.extend([pybench, cybench]) fused_types = list(fused_types) fused_types.sort() with open(pxdfile, 'w') as f: f.write(CYTHON_SPECIAL_PXD) f.write("\n") f.write("\n\n".join(fused_types)) f.write("\n\n") f.write("\n".join(declarations)) with open(pyxfile, 'w') as f: header = CYTHON_SPECIAL_PYX header = header.replace("FUNCLIST", "\n".join(doc)) f.write(header) f.write("\n") f.write("\n".join(defs)) f.write("\n\n") f.write("\n\n".join(sources)) f.write("\n\n") f.write("\n\n".join(bench_aux)) def generate_ufuncs_type_stubs(module_name: str, ufuncs: List[Ufunc]): stubs, module_all = [], [] for ufunc in ufuncs: stubs.append(f'{ufunc.name}: np.ufunc') if not ufunc.name.startswith('_'): module_all.append(f"'{ufunc.name}'") # jn is an alias for jv. module_all.append("'jn'") stubs.append('jn: np.ufunc') module_all.sort() stubs.sort() contents = STUBS.format( ALL=',\n '.join(module_all), STUBS='\n'.join(stubs), ) stubs_file = f'{module_name}.pyi' with open(stubs_file, 'w') as f: f.write(contents) def unique(lst): """ Return a list without repeated entries (first occurrence is kept), preserving order. """ seen = set() new_lst = [] for item in lst: if item in seen: continue seen.add(item) new_lst.append(item) return new_lst def all_newer(src_files, dst_files): from distutils.dep_util import newer return all(os.path.exists(dst) and newer(dst, src) for dst in dst_files for src in src_files) def main(): p = optparse.OptionParser(usage=(__doc__ or '').strip()) options, args = p.parse_args() if len(args) != 0: p.error('invalid number of arguments') pwd = os.path.dirname(__file__) src_files = (os.path.abspath(__file__), os.path.abspath(os.path.join(pwd, 'functions.json')), os.path.abspath(os.path.join(pwd, 'add_newdocs.py'))) dst_files = ('_ufuncs.pyx', '_ufuncs_defs.h', '_ufuncs_cxx.pyx', '_ufuncs_cxx.pxd', '_ufuncs_cxx_defs.h', '_ufuncs.pyi', 'cython_special.pyx', 'cython_special.pxd') os.chdir(BASE_DIR) if all_newer(src_files, dst_files): print("scipy/special/_generate_pyx.py: all files up-to-date") return ufuncs, fused_funcs = [], [] with open('functions.json') as data: functions = json.load(data) for f, sig in functions.items(): ufuncs.append(Ufunc(f, sig)) fused_funcs.append(FusedFunc(f, sig)) generate_ufuncs("_ufuncs", "_ufuncs_cxx", ufuncs) generate_ufuncs_type_stubs("_ufuncs", ufuncs) generate_fused_funcs("cython_special", "_ufuncs", fused_funcs) if __name__ == "__main__": main()