diff --git a/helper_scripts/dtw/cdtw.c b/helper_scripts/dtw/cdtw.c index 27723c2..9de6162 100644 --- a/helper_scripts/dtw/cdtw.c +++ b/helper_scripts/dtw/cdtw.c @@ -1,227 +1,266 @@ /* This code is written by Davide Albanese . (C) mlpy Developers. This program is free software: you can redistribute it and/or modify it underthe terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see . */ #include #include #include #include #include "cdtw.h" double min3(double a, double b, double c) { double min; min = a; if (b < min) min = b; if (c < min) min = c; return min; } // Paliwal adjustment window used for restricting the warping function // r: window length int paliwal_window(int i, int j, int n, int m, int r) { double s, f; s = ((double) m) / n; f = fabs(i - (((double) j) / s)); if (f <= r) return 1; else return 0; } // euclidean distance double e_dist(double x, double y) { return fabs(x - y); } // squared euclidean distance double se_dist(double x, double y) { return pow(x - y, 2); } // Returns the (unnormalized) minimum-distance warp path // between time series x and y and the cost matrix C double std(double *x, double *y, int n, int m, double *cost, int squared) { int i, j; double (*dist)(double, double); if (squared == 0) dist = &e_dist; else dist = &se_dist; cost[0] = (*dist)(x[0], y[0]); for (i=1; i startx = n-1; if starty = -1 -> starty = m-1 int path(double *cost, int n, int m, int startx, int starty, Path *p) { int i, j, k, z1, z2; int *px; int *py; double min_cost; if ((startx >= n) || (starty >= m)) return 0; if (startx < 0) startx = n - 1; if (starty < 0) starty = m - 1; i = startx; j = starty; k = 1; // allocate path for the worst case px = (int *) malloc ((startx+1) * (starty+1) * sizeof(int)); py = (int *) malloc ((startx+1) * (starty+1) * sizeof(int)); px[0] = i; py[0] = j; while ((i > 0) || (j > 0)) { if (i == 0) j--; else if (j == 0) i--; else { min_cost = min3(cost[(i-1)*m+j], cost[(i-1)*m+(j-1)], cost[i*m+(j-1)]); if (cost[(i-1)*m+(j-1)] == min_cost) { i--; j--; } else if (cost[i*m+(j-1)] == min_cost) j--; else i--; } px[k] = i; py[k] = j; k++; } p->px = (int *) malloc (k * sizeof(int)); p->py = (int *) malloc (k * sizeof(int)); for (z1=0, z2=k-1; z1px[z1] = px[z2]; p->py[z1] = py[z2]; } p->k = k; free(px); free(py); return 1; } // void subsequence(double *x, double *y, int n, int m, double *cost) { int i, j; cost[0] = fabs(x[0]-y[0]); for (i=1; ik; i++) if (p->px[i] == 0) a_star++; else break; // rebuild path tmpx = p->px; tmpy = p->py; p->px = (int *) malloc ((p->k-a_star) * sizeof(int)); p->py = (int *) malloc ((p->k-a_star) * sizeof(int)); for (z1=0, z2=a_star; z2k; z1++, z2++) { p->px[z1] = tmpx[z2]; p->py[z1] = tmpy[z2]; } p->k = p->k-a_star; free(tmpx); free(tmpy); return 1; } + +//modified dtw to work with events and not equispaced values + +double +e_dist_tv(double tx,double x,double ty,double y){ + return fabs(x - y)*(1+fabs(tx - ty)); +} + +double +se_dist_tv(double tx,double x,double ty,double y){ + return pow(x - y, 2)*(1+fabs(tx - ty)); +} + +double +tvstd(double *tx, double *x, double *ty, double *y, int n, int m, double *cost, int squared) +{ + int i, j; + double (*dist)(double, double, double, double); + + if (squared == 0) + dist = &e_dist_tv; + else + dist = &se_dist_tv; + + cost[0] = (*dist)(tx[0], x[0], ty[0], y[0]); + + for (i=1; i typedef struct Path { int k; int *px; int *py; } Path; double std(double *x, double *y, int n, int m, double *cost, int squared); +double tvstd(double *tx, double *x, double *ty, double *y, int n, int m, double *cost, int squared); int path(double *cost, int n, int m, int startx, int starty, Path *p); void subsequence(double *x, double *y, int n, int m, double *cost); -int subsequence_path(double *cost, int n, int m, int starty, Path *p); +int subsequence_path(double *cost, int n, int m, int starty, Path *p); \ No newline at end of file diff --git a/helper_scripts/dtw/cdtw.pxd b/helper_scripts/dtw/cdtw.pxd index 535fd7c..236b7a3 100644 --- a/helper_scripts/dtw/cdtw.pxd +++ b/helper_scripts/dtw/cdtw.pxd @@ -1,12 +1,13 @@ cdef extern from "cdtw.h": ctypedef struct Path: int k int *px int *py double std(double *x, double *y, int n, int m, double *cost, int squared) + double tvstd(double *tx, double *x, double *ty, double *y, int n, int m, double *cost, int squared) int path(double *cost, int n, int m, int startx, int starty, Path *p) void subsequence(double *x, double *y, int n, int m, double *cost) int subsequence_path(double *cost, int n, int m, int starty, Path *p) diff --git a/helper_scripts/dtw/dtw.c b/helper_scripts/dtw/dtw.c index 2dcbb43..9190215 100644 --- a/helper_scripts/dtw/dtw.c +++ b/helper_scripts/dtw/dtw.c @@ -1,7842 +1,8863 @@ /* Generated by Cython 0.29.23 */ /* BEGIN: Cython Metadata { "distutils": { "depends": [ "cdtw.h" ], "include_dirs": [ "." ], "name": "dtw", "sources": [ "dtw.pyx", "cdtw.c" ] }, "module_name": "dtw" } END: Cython Metadata */ #ifndef PY_SSIZE_T_CLEAN #define PY_SSIZE_T_CLEAN #endif /* PY_SSIZE_T_CLEAN */ #include "Python.h" #ifndef Py_PYTHON_H #error Python headers needed to compile C extensions, please install development version of Python. #elif PY_VERSION_HEX < 0x02060000 || (0x03000000 <= PY_VERSION_HEX && PY_VERSION_HEX < 0x03030000) #error Cython requires Python 2.6+ or Python 3.3+. #else #define CYTHON_ABI "0_29_23" #define CYTHON_HEX_VERSION 0x001D17F0 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((void)(klass), PyMethod_New(func, self)) : __Pyx_NewRef(func)) #else #define __Pyx_PyMethod_New(func, self, klass) PyMethod_New(func, self, klass) #endif #if CYTHON_USE_ASYNC_SLOTS #if PY_VERSION_HEX >= 0x030500B1 #define __Pyx_PyAsyncMethodsStruct PyAsyncMethods #define __Pyx_PyType_AsAsync(obj) (Py_TYPE(obj)->tp_as_async) #else #define __Pyx_PyType_AsAsync(obj) ((__Pyx_PyAsyncMethodsStruct*) (Py_TYPE(obj)->tp_reserved)) #endif #else #define __Pyx_PyType_AsAsync(obj) NULL #endif #ifndef __Pyx_PyAsyncMethodsStruct typedef struct { unaryfunc am_await; unaryfunc am_aiter; unaryfunc am_anext; } __Pyx_PyAsyncMethodsStruct; #endif #if defined(WIN32) || defined(MS_WINDOWS) #define _USE_MATH_DEFINES #endif #include #ifdef NAN #define __PYX_NAN() ((float) NAN) #else static CYTHON_INLINE float __PYX_NAN() { float value; memset(&value, 0xFF, sizeof(value)); return value; } #endif #if defined(__CYGWIN__) && defined(_LDBL_EQ_DBL) #define __Pyx_truncl trunc #else #define __Pyx_truncl truncl #endif #define __PYX_MARK_ERR_POS(f_index, lineno) \ { __pyx_filename = __pyx_f[f_index]; (void)__pyx_filename; __pyx_lineno = lineno; (void)__pyx_lineno; __pyx_clineno = __LINE__; (void)__pyx_clineno; } #define __PYX_ERR(f_index, lineno, Ln_error) \ { __PYX_MARK_ERR_POS(f_index, lineno) goto Ln_error; } #ifndef __PYX_EXTERN_C #ifdef __cplusplus #define __PYX_EXTERN_C extern "C" #else #define __PYX_EXTERN_C extern #endif #endif #define __PYX_HAVE__dtw #define __PYX_HAVE_API__dtw /* Early includes */ #include #include #include "numpy/arrayobject.h" #include "numpy/ndarrayobject.h" #include "numpy/ndarraytypes.h" #include "numpy/arrayscalars.h" #include "numpy/ufuncobject.h" /* NumPy API declarations from "numpy/__init__.pxd" */ #include #include "cdtw.h" #ifdef _OPENMP #include #endif /* _OPENMP */ #if defined(PYREX_WITHOUT_ASSERTIONS) && !defined(CYTHON_WITHOUT_ASSERTIONS) #define CYTHON_WITHOUT_ASSERTIONS #endif typedef struct {PyObject **p; const char *s; const Py_ssize_t n; const char* encoding; const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry; #define __PYX_DEFAULT_STRING_ENCODING_IS_ASCII 0 #define __PYX_DEFAULT_STRING_ENCODING_IS_UTF8 0 #define __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT (PY_MAJOR_VERSION >= 3 && __PYX_DEFAULT_STRING_ENCODING_IS_UTF8) #define __PYX_DEFAULT_STRING_ENCODING "" #define __Pyx_PyObject_FromString __Pyx_PyBytes_FromString #define __Pyx_PyObject_FromStringAndSize __Pyx_PyBytes_FromStringAndSize #define __Pyx_uchar_cast(c) ((unsigned char)c) #define __Pyx_long_cast(x) ((long)x) #define __Pyx_fits_Py_ssize_t(v, type, is_signed) (\ (sizeof(type) < sizeof(Py_ssize_t)) ||\ (sizeof(type) > sizeof(Py_ssize_t) &&\ likely(v < (type)PY_SSIZE_T_MAX ||\ v == (type)PY_SSIZE_T_MAX) &&\ (!is_signed || likely(v > (type)PY_SSIZE_T_MIN ||\ v == (type)PY_SSIZE_T_MIN))) ||\ (sizeof(type) == sizeof(Py_ssize_t) &&\ (is_signed || likely(v < (type)PY_SSIZE_T_MAX ||\ v == (type)PY_SSIZE_T_MAX))) ) static CYTHON_INLINE int __Pyx_is_valid_index(Py_ssize_t i, Py_ssize_t limit) { return (size_t) i < (size_t) limit; } #if defined (__cplusplus) && __cplusplus >= 201103L #include #define __Pyx_sst_abs(value) std::abs(value) #elif SIZEOF_INT >= SIZEOF_SIZE_T #define __Pyx_sst_abs(value) abs(value) #elif SIZEOF_LONG >= SIZEOF_SIZE_T #define __Pyx_sst_abs(value) labs(value) #elif defined (_MSC_VER) #define __Pyx_sst_abs(value) ((Py_ssize_t)_abs64(value)) #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L #define __Pyx_sst_abs(value) llabs(value) #elif defined (__GNUC__) #define __Pyx_sst_abs(value) __builtin_llabs(value) #else #define __Pyx_sst_abs(value) ((value<0) ? -value : value) #endif static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject*); static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject*, Py_ssize_t* length); #define __Pyx_PyByteArray_FromString(s) PyByteArray_FromStringAndSize((const char*)s, strlen((const char*)s)) #define __Pyx_PyByteArray_FromStringAndSize(s, l) PyByteArray_FromStringAndSize((const char*)s, l) #define __Pyx_PyBytes_FromString PyBytes_FromString #define __Pyx_PyBytes_FromStringAndSize PyBytes_FromStringAndSize static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char*); #if PY_MAJOR_VERSION < 3 #define __Pyx_PyStr_FromString __Pyx_PyBytes_FromString #define __Pyx_PyStr_FromStringAndSize __Pyx_PyBytes_FromStringAndSize #else #define __Pyx_PyStr_FromString __Pyx_PyUnicode_FromString #define __Pyx_PyStr_FromStringAndSize __Pyx_PyUnicode_FromStringAndSize #endif #define __Pyx_PyBytes_AsWritableString(s) ((char*) PyBytes_AS_STRING(s)) #define __Pyx_PyBytes_AsWritableSString(s) ((signed char*) PyBytes_AS_STRING(s)) #define __Pyx_PyBytes_AsWritableUString(s) ((unsigned char*) PyBytes_AS_STRING(s)) #define __Pyx_PyBytes_AsString(s) ((const char*) PyBytes_AS_STRING(s)) #define __Pyx_PyBytes_AsSString(s) ((const signed char*) PyBytes_AS_STRING(s)) #define __Pyx_PyBytes_AsUString(s) ((const unsigned char*) PyBytes_AS_STRING(s)) #define __Pyx_PyObject_AsWritableString(s) ((char*) __Pyx_PyObject_AsString(s)) #define __Pyx_PyObject_AsWritableSString(s) ((signed char*) __Pyx_PyObject_AsString(s)) #define __Pyx_PyObject_AsWritableUString(s) ((unsigned char*) __Pyx_PyObject_AsString(s)) #define __Pyx_PyObject_AsSString(s) ((const signed char*) __Pyx_PyObject_AsString(s)) #define __Pyx_PyObject_AsUString(s) ((const unsigned char*) __Pyx_PyObject_AsString(s)) #define __Pyx_PyObject_FromCString(s) __Pyx_PyObject_FromString((const char*)s) #define __Pyx_PyBytes_FromCString(s) __Pyx_PyBytes_FromString((const char*)s) #define __Pyx_PyByteArray_FromCString(s) __Pyx_PyByteArray_FromString((const char*)s) #define __Pyx_PyStr_FromCString(s) __Pyx_PyStr_FromString((const char*)s) #define __Pyx_PyUnicode_FromCString(s) __Pyx_PyUnicode_FromString((const char*)s) static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const Py_UNICODE *u) { const Py_UNICODE *u_end = u; while (*u_end++) ; return (size_t)(u_end - u - 1); } #define __Pyx_PyUnicode_FromUnicode(u) PyUnicode_FromUnicode(u, __Pyx_Py_UNICODE_strlen(u)) #define __Pyx_PyUnicode_FromUnicodeAndLength PyUnicode_FromUnicode #define __Pyx_PyUnicode_AsUnicode PyUnicode_AsUnicode #define __Pyx_NewRef(obj) (Py_INCREF(obj), obj) #define __Pyx_Owned_Py_None(b) __Pyx_NewRef(Py_None) static CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b); static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*); static CYTHON_INLINE int __Pyx_PyObject_IsTrueAndDecref(PyObject*); static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x); #define __Pyx_PySequence_Tuple(obj)\ (likely(PyTuple_CheckExact(obj)) ? __Pyx_NewRef(obj) : PySequence_Tuple(obj)) static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*); static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t); #if CYTHON_ASSUME_SAFE_MACROS #define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x)) #else #define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x) #endif #define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x)) #if PY_MAJOR_VERSION >= 3 #define __Pyx_PyNumber_Int(x) (PyLong_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Long(x)) #else #define __Pyx_PyNumber_Int(x) (PyInt_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Int(x)) #endif #define __Pyx_PyNumber_Float(x) (PyFloat_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Float(x)) #if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII static int __Pyx_sys_getdefaultencoding_not_ascii; static int __Pyx_init_sys_getdefaultencoding_params(void) { PyObject* sys; PyObject* default_encoding = NULL; PyObject* ascii_chars_u = NULL; PyObject* ascii_chars_b = NULL; const char* default_encoding_c; sys = PyImport_ImportModule("sys"); if (!sys) goto bad; default_encoding = PyObject_CallMethod(sys, (char*) "getdefaultencoding", NULL); Py_DECREF(sys); if (!default_encoding) goto bad; default_encoding_c = PyBytes_AsString(default_encoding); if (!default_encoding_c) goto bad; if (strcmp(default_encoding_c, "ascii") == 0) { __Pyx_sys_getdefaultencoding_not_ascii = 0; } else { char ascii_chars[128]; int c; for (c = 0; c < 128; c++) { ascii_chars[c] = c; } __Pyx_sys_getdefaultencoding_not_ascii = 1; ascii_chars_u = PyUnicode_DecodeASCII(ascii_chars, 128, NULL); if (!ascii_chars_u) goto bad; ascii_chars_b = PyUnicode_AsEncodedString(ascii_chars_u, default_encoding_c, NULL); if (!ascii_chars_b || !PyBytes_Check(ascii_chars_b) || memcmp(ascii_chars, PyBytes_AS_STRING(ascii_chars_b), 128) != 0) { PyErr_Format( PyExc_ValueError, "This module compiled with c_string_encoding=ascii, but default encoding '%.200s' is not a superset of ascii.", default_encoding_c); goto bad; } Py_DECREF(ascii_chars_u); Py_DECREF(ascii_chars_b); } Py_DECREF(default_encoding); return 0; bad: Py_XDECREF(default_encoding); Py_XDECREF(ascii_chars_u); Py_XDECREF(ascii_chars_b); return -1; } #endif #if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT && PY_MAJOR_VERSION >= 3 #define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_DecodeUTF8(c_str, size, NULL) #else #define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_Decode(c_str, size, __PYX_DEFAULT_STRING_ENCODING, NULL) #if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT static char* __PYX_DEFAULT_STRING_ENCODING; static int __Pyx_init_sys_getdefaultencoding_params(void) { PyObject* sys; PyObject* default_encoding = NULL; char* default_encoding_c; sys = PyImport_ImportModule("sys"); if (!sys) goto bad; default_encoding = PyObject_CallMethod(sys, (char*) (const char*) "getdefaultencoding", NULL); Py_DECREF(sys); if (!default_encoding) goto bad; default_encoding_c = PyBytes_AsString(default_encoding); if (!default_encoding_c) goto bad; __PYX_DEFAULT_STRING_ENCODING = (char*) malloc(strlen(default_encoding_c) + 1); if (!__PYX_DEFAULT_STRING_ENCODING) goto bad; strcpy(__PYX_DEFAULT_STRING_ENCODING, default_encoding_c); Py_DECREF(default_encoding); return 0; bad: Py_XDECREF(default_encoding); return -1; } #endif #endif /* Test for GCC > 2.95 */ #if defined(__GNUC__) && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95))) #define likely(x) __builtin_expect(!!(x), 1) #define unlikely(x) __builtin_expect(!!(x), 0) #else /* !__GNUC__ or GCC < 2.95 */ #define likely(x) (x) #define unlikely(x) (x) #endif /* __GNUC__ */ static CYTHON_INLINE void __Pyx_pretend_to_initialize(void* ptr) { (void)ptr; } static PyObject *__pyx_m = NULL; static PyObject *__pyx_d; static PyObject *__pyx_b; static PyObject *__pyx_cython_runtime = NULL; static PyObject *__pyx_empty_tuple; static PyObject *__pyx_empty_bytes; static PyObject *__pyx_empty_unicode; static int __pyx_lineno; static int __pyx_clineno = 0; static const char * __pyx_cfilenm= __FILE__; static const char *__pyx_filename; /* Header.proto */ #if !defined(CYTHON_CCOMPLEX) #if defined(__cplusplus) #define CYTHON_CCOMPLEX 1 #elif defined(_Complex_I) #define CYTHON_CCOMPLEX 1 #else #define CYTHON_CCOMPLEX 0 #endif #endif #if CYTHON_CCOMPLEX #ifdef __cplusplus #include #else #include #endif #endif #if CYTHON_CCOMPLEX && !defined(__cplusplus) && defined(__sun__) && defined(__GNUC__) #undef _Complex_I #define _Complex_I 1.0fj #endif static const char *__pyx_f[] = { "dtw.pyx", "__init__.pxd", "type.pxd", }; /* BufferFormatStructs.proto */ #define IS_UNSIGNED(type) (((type) -1) > 0) struct __Pyx_StructField_; #define __PYX_BUF_FLAGS_PACKED_STRUCT (1 << 0) typedef struct { const char* name; 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-/* "../../../../../../anaconda3/lib/python3.7/site-packages/numpy/__init__.pxd":692 +/* "../../../../../anaconda3/lib/python3.7/site-packages/numpy/__init__.pxd":692 * ctypedef npy_int8 int8_t * ctypedef npy_int16 int16_t * ctypedef npy_int32 int32_t # <<<<<<<<<<<<<< * ctypedef npy_int64 int64_t * #ctypedef npy_int96 int96_t */ typedef npy_int32 __pyx_t_5numpy_int32_t; -/* "../../../../../../anaconda3/lib/python3.7/site-packages/numpy/__init__.pxd":693 +/* "../../../../../anaconda3/lib/python3.7/site-packages/numpy/__init__.pxd":693 * ctypedef npy_int16 int16_t * ctypedef npy_int32 int32_t * ctypedef npy_int64 int64_t # <<<<<<<<<<<<<< * #ctypedef npy_int96 int96_t * #ctypedef npy_int128 int128_t */ typedef npy_int64 __pyx_t_5numpy_int64_t; -/* "../../../../../../anaconda3/lib/python3.7/site-packages/numpy/__init__.pxd":697 +/* "../../../../../anaconda3/lib/python3.7/site-packages/numpy/__init__.pxd":697 * #ctypedef npy_int128 int128_t * * ctypedef npy_uint8 uint8_t # <<<<<<<<<<<<<< * ctypedef npy_uint16 uint16_t * ctypedef npy_uint32 uint32_t */ typedef npy_uint8 __pyx_t_5numpy_uint8_t; 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-/* "../../../../../../anaconda3/lib/python3.7/site-packages/numpy/__init__.pxd":704 +/* "../../../../../anaconda3/lib/python3.7/site-packages/numpy/__init__.pxd":704 * #ctypedef npy_uint128 uint128_t * * ctypedef npy_float32 float32_t # <<<<<<<<<<<<<< * ctypedef npy_float64 float64_t * #ctypedef npy_float80 float80_t */ typedef npy_float32 __pyx_t_5numpy_float32_t; -/* "../../../../../../anaconda3/lib/python3.7/site-packages/numpy/__init__.pxd":705 +/* "../../../../../anaconda3/lib/python3.7/site-packages/numpy/__init__.pxd":705 * * ctypedef npy_float32 float32_t * ctypedef npy_float64 float64_t # <<<<<<<<<<<<<< * #ctypedef npy_float80 float80_t * #ctypedef npy_float128 float128_t */ typedef npy_float64 __pyx_t_5numpy_float64_t; -/* "../../../../../../anaconda3/lib/python3.7/site-packages/numpy/__init__.pxd":714 +/* "../../../../../anaconda3/lib/python3.7/site-packages/numpy/__init__.pxd":714 * # The int types are mapped a bit surprising -- * # numpy.int corresponds to 'l' and numpy.long to 'q' * ctypedef npy_long int_t # <<<<<<<<<<<<<< * ctypedef npy_longlong long_t * ctypedef npy_longlong longlong_t */ typedef npy_long __pyx_t_5numpy_int_t; 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static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); #if CYTHON_COMPILING_IN_CPYTHON #define __Pyx_PyErr_SetNone(exc) (Py_INCREF(exc), __Pyx_ErrRestore((exc), NULL, NULL)) #else #define __Pyx_PyErr_SetNone(exc) PyErr_SetNone(exc) #endif #else #define __Pyx_PyErr_Clear() PyErr_Clear() #define __Pyx_PyErr_SetNone(exc) PyErr_SetNone(exc) #define __Pyx_ErrRestoreWithState(type, value, tb) PyErr_Restore(type, value, tb) #define __Pyx_ErrFetchWithState(type, value, tb) PyErr_Fetch(type, value, tb) #define __Pyx_ErrRestoreInState(tstate, type, value, tb) PyErr_Restore(type, value, tb) #define __Pyx_ErrFetchInState(tstate, type, value, tb) PyErr_Fetch(type, value, tb) #define __Pyx_ErrRestore(type, value, tb) PyErr_Restore(type, value, tb) #define __Pyx_ErrFetch(type, value, tb) PyErr_Fetch(type, value, tb) #endif /* GetItemInt.proto */ #define __Pyx_GetItemInt(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ __Pyx_GetItemInt_Fast(o, (Py_ssize_t)i, is_list, wraparound, boundscheck) :\ (is_list ? (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL) :\ __Pyx_GetItemInt_Generic(o, to_py_func(i)))) #define __Pyx_GetItemInt_List(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ __Pyx_GetItemInt_List_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) :\ (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL)) static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i, int wraparound, int boundscheck); #define __Pyx_GetItemInt_Tuple(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ __Pyx_GetItemInt_Tuple_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) :\ (PyErr_SetString(PyExc_IndexError, "tuple index out of range"), (PyObject*)NULL)) static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i, int wraparound, int boundscheck); static PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j); static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, int is_list, int wraparound, int boundscheck); /* ObjectGetItem.proto */ #if CYTHON_USE_TYPE_SLOTS static CYTHON_INLINE PyObject *__Pyx_PyObject_GetItem(PyObject *obj, PyObject* key); #else #define __Pyx_PyObject_GetItem(obj, key) PyObject_GetItem(obj, key) #endif /* PyCFunctionFastCall.proto */ #if CYTHON_FAST_PYCCALL static CYTHON_INLINE PyObject *__Pyx_PyCFunction_FastCall(PyObject *func, PyObject **args, Py_ssize_t nargs); #else #define __Pyx_PyCFunction_FastCall(func, args, nargs) (assert(0), NULL) #endif /* PyFunctionFastCall.proto */ #if CYTHON_FAST_PYCALL #define __Pyx_PyFunction_FastCall(func, args, nargs)\ __Pyx_PyFunction_FastCallDict((func), (args), (nargs), NULL) #if 1 || PY_VERSION_HEX < 0x030600B1 static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, Py_ssize_t nargs, PyObject *kwargs); #else #define __Pyx_PyFunction_FastCallDict(func, args, nargs, kwargs) _PyFunction_FastCallDict(func, args, nargs, kwargs) #endif #define __Pyx_BUILD_ASSERT_EXPR(cond)\ (sizeof(char [1 - 2*!(cond)]) - 1) #ifndef Py_MEMBER_SIZE #define Py_MEMBER_SIZE(type, member) sizeof(((type *)0)->member) #endif static size_t __pyx_pyframe_localsplus_offset = 0; #include "frameobject.h" #define __Pxy_PyFrame_Initialize_Offsets()\ ((void)__Pyx_BUILD_ASSERT_EXPR(sizeof(PyFrameObject) == offsetof(PyFrameObject, f_localsplus) + Py_MEMBER_SIZE(PyFrameObject, f_localsplus)),\ (void)(__pyx_pyframe_localsplus_offset = ((size_t)PyFrame_Type.tp_basicsize) - Py_MEMBER_SIZE(PyFrameObject, f_localsplus))) #define __Pyx_PyFrame_GetLocalsplus(frame)\ (assert(__pyx_pyframe_localsplus_offset), (PyObject **)(((char *)(frame)) + __pyx_pyframe_localsplus_offset)) #endif /* PyObjectCall2Args.proto */ static CYTHON_UNUSED PyObject* __Pyx_PyObject_Call2Args(PyObject* function, PyObject* arg1, PyObject* arg2); /* PyObjectCallMethO.proto */ #if CYTHON_COMPILING_IN_CPYTHON static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg); #endif /* PyObjectCallOneArg.proto */ static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg); /* GetTopmostException.proto */ #if CYTHON_USE_EXC_INFO_STACK static _PyErr_StackItem * __Pyx_PyErr_GetTopmostException(PyThreadState *tstate); #endif /* SaveResetException.proto */ #if CYTHON_FAST_THREAD_STATE #define __Pyx_ExceptionSave(type, value, tb) __Pyx__ExceptionSave(__pyx_tstate, type, value, tb) static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); #define __Pyx_ExceptionReset(type, value, tb) __Pyx__ExceptionReset(__pyx_tstate, type, value, tb) static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb); #else #define __Pyx_ExceptionSave(type, value, tb) PyErr_GetExcInfo(type, value, tb) #define __Pyx_ExceptionReset(type, value, tb) PyErr_SetExcInfo(type, value, tb) #endif /* PyErrExceptionMatches.proto */ #if CYTHON_FAST_THREAD_STATE #define __Pyx_PyErr_ExceptionMatches(err) __Pyx_PyErr_ExceptionMatchesInState(__pyx_tstate, err) static CYTHON_INLINE int __Pyx_PyErr_ExceptionMatchesInState(PyThreadState* tstate, PyObject* err); #else #define __Pyx_PyErr_ExceptionMatches(err) PyErr_ExceptionMatches(err) #endif /* GetException.proto */ #if CYTHON_FAST_THREAD_STATE #define __Pyx_GetException(type, value, tb) __Pyx__GetException(__pyx_tstate, type, value, tb) static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); #else static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb); #endif /* RaiseException.proto */ static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause); /* TypeImport.proto */ #ifndef __PYX_HAVE_RT_ImportType_proto #define __PYX_HAVE_RT_ImportType_proto enum __Pyx_ImportType_CheckSize { __Pyx_ImportType_CheckSize_Error = 0, __Pyx_ImportType_CheckSize_Warn = 1, __Pyx_ImportType_CheckSize_Ignore = 2 }; static PyTypeObject *__Pyx_ImportType(PyObject* module, const char *module_name, const char *class_name, size_t size, enum __Pyx_ImportType_CheckSize check_size); #endif /* Import.proto */ static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level); /* CLineInTraceback.proto */ #ifdef CYTHON_CLINE_IN_TRACEBACK #define __Pyx_CLineForTraceback(tstate, c_line) (((CYTHON_CLINE_IN_TRACEBACK)) ? c_line : 0) #else static int __Pyx_CLineForTraceback(PyThreadState *tstate, int c_line); #endif /* CodeObjectCache.proto */ typedef struct { PyCodeObject* code_object; int code_line; } __Pyx_CodeObjectCacheEntry; struct __Pyx_CodeObjectCache { int count; int max_count; __Pyx_CodeObjectCacheEntry* entries; }; static struct __Pyx_CodeObjectCache __pyx_code_cache = {0,0,NULL}; static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line); static PyCodeObject *__pyx_find_code_object(int code_line); static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object); /* AddTraceback.proto */ static void __Pyx_AddTraceback(const char *funcname, int c_line, int py_line, const char *filename); /* BufferStructDeclare.proto */ typedef struct { Py_ssize_t shape, strides, suboffsets; } __Pyx_Buf_DimInfo; typedef struct { size_t refcount; Py_buffer pybuffer; } __Pyx_Buffer; typedef struct { __Pyx_Buffer *rcbuffer; char *data; __Pyx_Buf_DimInfo diminfo[8]; } __Pyx_LocalBuf_ND; #if PY_MAJOR_VERSION < 3 static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags); static void __Pyx_ReleaseBuffer(Py_buffer *view); #else #define __Pyx_GetBuffer PyObject_GetBuffer #define __Pyx_ReleaseBuffer PyBuffer_Release #endif /* GCCDiagnostics.proto */ #if defined(__GNUC__) && (__GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ >= 6)) #define __Pyx_HAS_GCC_DIAGNOSTIC #endif /* RealImag.proto */ #if CYTHON_CCOMPLEX #ifdef __cplusplus #define __Pyx_CREAL(z) ((z).real()) #define __Pyx_CIMAG(z) ((z).imag()) #else #define __Pyx_CREAL(z) (__real__(z)) #define __Pyx_CIMAG(z) (__imag__(z)) #endif #else #define __Pyx_CREAL(z) ((z).real) #define __Pyx_CIMAG(z) ((z).imag) #endif #if defined(__cplusplus) && CYTHON_CCOMPLEX\ && (defined(_WIN32) || defined(__clang__) || (defined(__GNUC__) && (__GNUC__ >= 5 || __GNUC__ == 4 && __GNUC_MINOR__ >= 4 )) || __cplusplus >= 201103) #define __Pyx_SET_CREAL(z,x) ((z).real(x)) #define __Pyx_SET_CIMAG(z,y) ((z).imag(y)) #else #define __Pyx_SET_CREAL(z,x) __Pyx_CREAL(z) = (x) #define __Pyx_SET_CIMAG(z,y) __Pyx_CIMAG(z) = (y) #endif /* Arithmetic.proto */ #if CYTHON_CCOMPLEX #define __Pyx_c_eq_float(a, b) ((a)==(b)) #define __Pyx_c_sum_float(a, b) ((a)+(b)) #define __Pyx_c_diff_float(a, b) ((a)-(b)) #define __Pyx_c_prod_float(a, b) ((a)*(b)) #define __Pyx_c_quot_float(a, b) ((a)/(b)) #define __Pyx_c_neg_float(a) (-(a)) #ifdef __cplusplus #define __Pyx_c_is_zero_float(z) ((z)==(float)0) #define __Pyx_c_conj_float(z) (::std::conj(z)) #if 1 #define __Pyx_c_abs_float(z) (::std::abs(z)) #define __Pyx_c_pow_float(a, b) (::std::pow(a, b)) #endif #else #define __Pyx_c_is_zero_float(z) ((z)==0) #define __Pyx_c_conj_float(z) (conjf(z)) #if 1 #define __Pyx_c_abs_float(z) (cabsf(z)) #define __Pyx_c_pow_float(a, b) (cpowf(a, b)) #endif #endif #else static CYTHON_INLINE int __Pyx_c_eq_float(__pyx_t_float_complex, __pyx_t_float_complex); static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sum_float(__pyx_t_float_complex, __pyx_t_float_complex); static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_diff_float(__pyx_t_float_complex, __pyx_t_float_complex); static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prod_float(__pyx_t_float_complex, __pyx_t_float_complex); static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex, __pyx_t_float_complex); static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_neg_float(__pyx_t_float_complex); static CYTHON_INLINE int __Pyx_c_is_zero_float(__pyx_t_float_complex); static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conj_float(__pyx_t_float_complex); #if 1 static CYTHON_INLINE float __Pyx_c_abs_float(__pyx_t_float_complex); static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_pow_float(__pyx_t_float_complex, __pyx_t_float_complex); #endif #endif /* Arithmetic.proto */ #if CYTHON_CCOMPLEX #define __Pyx_c_eq_double(a, b) ((a)==(b)) #define __Pyx_c_sum_double(a, b) ((a)+(b)) #define __Pyx_c_diff_double(a, b) ((a)-(b)) #define __Pyx_c_prod_double(a, b) ((a)*(b)) #define __Pyx_c_quot_double(a, b) ((a)/(b)) #define __Pyx_c_neg_double(a) (-(a)) #ifdef __cplusplus #define __Pyx_c_is_zero_double(z) ((z)==(double)0) #define __Pyx_c_conj_double(z) (::std::conj(z)) #if 1 #define __Pyx_c_abs_double(z) (::std::abs(z)) #define __Pyx_c_pow_double(a, b) (::std::pow(a, b)) #endif #else #define __Pyx_c_is_zero_double(z) ((z)==0) #define __Pyx_c_conj_double(z) (conj(z)) #if 1 #define __Pyx_c_abs_double(z) (cabs(z)) #define __Pyx_c_pow_double(a, b) (cpow(a, b)) #endif #endif #else static CYTHON_INLINE int __Pyx_c_eq_double(__pyx_t_double_complex, __pyx_t_double_complex); static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum_double(__pyx_t_double_complex, __pyx_t_double_complex); static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff_double(__pyx_t_double_complex, __pyx_t_double_complex); static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod_double(__pyx_t_double_complex, __pyx_t_double_complex); static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex, __pyx_t_double_complex); static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg_double(__pyx_t_double_complex); static CYTHON_INLINE int __Pyx_c_is_zero_double(__pyx_t_double_complex); static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj_double(__pyx_t_double_complex); #if 1 static CYTHON_INLINE double __Pyx_c_abs_double(__pyx_t_double_complex); static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow_double(__pyx_t_double_complex, __pyx_t_double_complex); #endif #endif /* CIntToPy.proto */ static CYTHON_INLINE PyObject* __Pyx_PyInt_From_Py_intptr_t(Py_intptr_t value); /* CIntToPy.proto */ static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value); /* CIntFromPy.proto */ static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *); /* CIntToPy.proto */ static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value); /* CIntFromPy.proto */ static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *); /* FastTypeChecks.proto */ #if CYTHON_COMPILING_IN_CPYTHON #define __Pyx_TypeCheck(obj, type) __Pyx_IsSubtype(Py_TYPE(obj), (PyTypeObject *)type) static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b); static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches(PyObject *err, PyObject *type); static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObject *type1, PyObject *type2); #else #define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type) #define __Pyx_PyErr_GivenExceptionMatches(err, type) PyErr_GivenExceptionMatches(err, type) #define __Pyx_PyErr_GivenExceptionMatches2(err, type1, type2) (PyErr_GivenExceptionMatches(err, type1) || PyErr_GivenExceptionMatches(err, type2)) #endif #define __Pyx_PyException_Check(obj) __Pyx_TypeCheck(obj, PyExc_Exception) /* CheckBinaryVersion.proto */ static int __Pyx_check_binary_version(void); /* InitStrings.proto */ static int __Pyx_InitStrings(__Pyx_StringTabEntry *t); /* Module declarations from 'cpython.buffer' */ /* Module declarations from 'libc.string' */ /* Module declarations from 'libc.stdio' */ /* Module declarations from '__builtin__' */ /* Module declarations from 'cpython.type' */ static PyTypeObject *__pyx_ptype_7cpython_4type_type = 0; /* Module declarations from 'cpython' */ /* Module declarations from 'cpython.object' */ /* Module declarations from 'cpython.ref' */ /* Module declarations from 'cpython.mem' */ /* Module declarations from 'numpy' */ /* Module declarations from 'numpy' */ static PyTypeObject *__pyx_ptype_5numpy_dtype = 0; static PyTypeObject *__pyx_ptype_5numpy_flatiter = 0; static PyTypeObject *__pyx_ptype_5numpy_broadcast = 0; static PyTypeObject *__pyx_ptype_5numpy_ndarray = 0; static PyTypeObject *__pyx_ptype_5numpy_generic = 0; static PyTypeObject *__pyx_ptype_5numpy_number = 0; static PyTypeObject *__pyx_ptype_5numpy_integer = 0; static PyTypeObject *__pyx_ptype_5numpy_signedinteger = 0; static PyTypeObject *__pyx_ptype_5numpy_unsignedinteger = 0; static PyTypeObject *__pyx_ptype_5numpy_inexact = 0; static PyTypeObject *__pyx_ptype_5numpy_floating = 0; static PyTypeObject *__pyx_ptype_5numpy_complexfloating = 0; static PyTypeObject *__pyx_ptype_5numpy_flexible = 0; static PyTypeObject *__pyx_ptype_5numpy_character = 0; static PyTypeObject *__pyx_ptype_5numpy_ufunc = 0; static CYTHON_INLINE int __pyx_f_5numpy_import_array(void); /*proto*/ /* Module declarations from 'libc.stdlib' */ /* Module declarations from 'cdtw' */ /* Module declarations from 'dtw' */ static __Pyx_TypeInfo __Pyx_TypeInfo_nn___pyx_t_5numpy_float_t = { "float_t", NULL, sizeof(__pyx_t_5numpy_float_t), { 0 }, 0, 'R', 0, 0 }; static __Pyx_TypeInfo __Pyx_TypeInfo_nn___pyx_t_5numpy_int_t = { "int_t", NULL, sizeof(__pyx_t_5numpy_int_t), { 0 }, 0, IS_UNSIGNED(__pyx_t_5numpy_int_t) ? 'U' : 'I', IS_UNSIGNED(__pyx_t_5numpy_int_t), 0 }; #define __Pyx_MODULE_NAME "dtw" extern int __pyx_module_is_main_dtw; int __pyx_module_is_main_dtw = 0; /* Implementation of 'dtw' */ static PyObject *__pyx_builtin_range; +static PyObject *__pyx_builtin_max; static PyObject *__pyx_builtin_ImportError; static const char __pyx_k_i[] = "i"; static const char __pyx_k_p[] = "p"; static const char __pyx_k_x[] = "x"; static const char __pyx_k_y[] = "y"; static const char __pyx_k_np[] = "np"; static const char __pyx_k_sq[] = "sq"; +static const char __pyx_k_tx[] = "tx"; +static const char __pyx_k_ty[] = "ty"; static const char __pyx_k_dtw[] = "dtw"; static const char __pyx_k_idx[] = "idx"; static const char __pyx_k_int[] = "int"; +static const char __pyx_k_max[] = "max"; static const char __pyx_k_dist[] = "dist"; static const char __pyx_k_main[] = "__main__"; static const char __pyx_k_name[] = "__name__"; static const char __pyx_k_test[] = "__test__"; static const char __pyx_k_dtype[] = "dtype"; static const char __pyx_k_empty[] = "empty"; static const char __pyx_k_float[] = "float"; static const char __pyx_k_numpy[] = "numpy"; static const char __pyx_k_range[] = "range"; static const char __pyx_k_x_arr[] = "x_arr"; static const char __pyx_k_y_arr[] = "y_arr"; static const char __pyx_k_argmin[] = "argmin"; +static const char __pyx_k_dtw_tv[] = "dtw_tv"; static const char __pyx_k_import[] = "__import__"; static const char __pyx_k_px_arr[] = "px_arr"; static const char __pyx_k_py_arr[] = "py_arr"; +static const char __pyx_k_tx_arr[] = "tx_arr"; +static const char __pyx_k_ty_arr[] = "ty_arr"; static const char __pyx_k_dtw_pyx[] = "dtw.pyx"; static const char __pyx_k_dtw_std[] = "dtw_std"; +static const char __pyx_k_max_tot[] = "max_tot"; static const char __pyx_k_squared[] = "squared"; static const char __pyx_k_cost_arr[] = "cost_arr"; static const char __pyx_k_dist_only[] = "dist_only"; static const char __pyx_k_ImportError[] = "ImportError"; static const char __pyx_k_dtw_subsequence[] = "dtw_subsequence"; static const char __pyx_k_ascontiguousarray[] = "ascontiguousarray"; static const char __pyx_k_cline_in_traceback[] = "cline_in_traceback"; static const char __pyx_k_numpy_core_multiarray_failed_to[] = "numpy.core.multiarray failed to import"; static const char __pyx_k_numpy_core_umath_failed_to_impor[] = "numpy.core.umath failed to import"; static PyObject *__pyx_n_s_ImportError; static PyObject *__pyx_n_s_argmin; static PyObject *__pyx_n_s_ascontiguousarray; static PyObject *__pyx_n_s_cline_in_traceback; static PyObject *__pyx_n_s_cost_arr; static PyObject *__pyx_n_s_dist; static PyObject *__pyx_n_s_dist_only; static PyObject *__pyx_n_s_dtw; static PyObject *__pyx_kp_s_dtw_pyx; static PyObject *__pyx_n_s_dtw_std; static PyObject *__pyx_n_s_dtw_subsequence; +static PyObject *__pyx_n_s_dtw_tv; static PyObject *__pyx_n_s_dtype; static PyObject *__pyx_n_s_empty; static PyObject *__pyx_n_s_float; static PyObject *__pyx_n_s_i; static PyObject *__pyx_n_s_idx; static PyObject *__pyx_n_s_import; static PyObject *__pyx_n_s_int; static PyObject *__pyx_n_s_main; +static PyObject *__pyx_n_s_max; +static PyObject *__pyx_n_s_max_tot; static PyObject *__pyx_n_s_name; static PyObject *__pyx_n_s_np; static PyObject *__pyx_n_s_numpy; static PyObject *__pyx_kp_s_numpy_core_multiarray_failed_to; static PyObject *__pyx_kp_s_numpy_core_umath_failed_to_impor; static PyObject *__pyx_n_s_p; static PyObject *__pyx_n_s_px_arr; static PyObject *__pyx_n_s_py_arr; static PyObject *__pyx_n_s_range; static PyObject *__pyx_n_s_sq; static PyObject *__pyx_n_s_squared; static PyObject *__pyx_n_s_test; +static PyObject *__pyx_n_s_tx; +static PyObject *__pyx_n_s_tx_arr; +static PyObject *__pyx_n_s_ty; +static PyObject *__pyx_n_s_ty_arr; static PyObject *__pyx_n_s_x; static PyObject *__pyx_n_s_x_arr; static PyObject *__pyx_n_s_y; static PyObject *__pyx_n_s_y_arr; static PyObject *__pyx_pf_3dtw_dtw_std(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_x, PyObject *__pyx_v_y, PyObject *__pyx_v_dist_only, PyObject *__pyx_v_squared); /* proto */ static PyObject *__pyx_pf_3dtw_2dtw_subsequence(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_x, PyObject *__pyx_v_y); /* proto */ +static PyObject *__pyx_pf_3dtw_4dtw_tv(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_tx, PyObject *__pyx_v_x, PyObject *__pyx_v_ty, PyObject *__pyx_v_y, PyObject *__pyx_v_dist_only, PyObject *__pyx_v_squared); /* proto */ +static PyObject *__pyx_int_1; static PyObject *__pyx_int_neg_1; static PyObject *__pyx_slice_; static PyObject *__pyx_tuple__2; static PyObject *__pyx_tuple__3; static PyObject *__pyx_tuple__4; static PyObject *__pyx_tuple__5; static PyObject *__pyx_tuple__7; +static PyObject *__pyx_tuple__9; static PyObject *__pyx_codeobj__6; static PyObject *__pyx_codeobj__8; +static PyObject *__pyx_codeobj__10; /* Late includes */ /* "dtw.pyx":26 * * * def dtw_std(x, y, dist_only=True, squared=False): # <<<<<<<<<<<<<< * """Standard DTW as described in [Muller07]_, * using the Euclidean distance (absolute value */ /* Python wrapper */ static PyObject *__pyx_pw_3dtw_1dtw_std(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ static char __pyx_doc_3dtw_dtw_std[] = "Standard DTW as described in [Muller07]_,\n using the Euclidean distance (absolute value \n of the difference) or squared Euclidean distance\n (as in [Keogh01]_) as local cost measure.\n\n :Parameters:\n x : 1d array_like object (N)\n first sequence\n y : 1d array_like object (M)\n second sequence\n dist_only : bool\n compute only the distance\n squared : bool\n squared Euclidean distance\n\n :Returns:\n dist : float\n unnormalized minimum-distance warp path \n between sequences\n cost : 2d numpy array (N,M) [if dist_only=False]\n accumulated cost matrix\n path : tuple of two 1d numpy array (path_x, path_y) [if dist_only=False]\n warp path\n \n .. [Muller07] M Muller. Information Retrieval for Music and Motion. Springer, 2007.\n .. [Keogh01] E J Keogh, M J Pazzani. Derivative Dynamic Time Warping. 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} /* RaiseArgTupleInvalid */ static void __Pyx_RaiseArgtupleInvalid( const char* func_name, int exact, Py_ssize_t num_min, Py_ssize_t num_max, Py_ssize_t num_found) { Py_ssize_t num_expected; const char *more_or_less; if (num_found < num_min) { num_expected = num_min; more_or_less = "at least"; } else { num_expected = num_max; more_or_less = "at most"; } if (exact) { more_or_less = "exactly"; } PyErr_Format(PyExc_TypeError, "%.200s() takes %.8s %" CYTHON_FORMAT_SSIZE_T "d positional argument%.1s (%" CYTHON_FORMAT_SSIZE_T "d given)", func_name, more_or_less, num_expected, (num_expected == 1) ? "" : "s", num_found); } /* RaiseDoubleKeywords */ static void __Pyx_RaiseDoubleKeywordsError( const char* func_name, PyObject* kw_name) { PyErr_Format(PyExc_TypeError, #if PY_MAJOR_VERSION >= 3 "%s() got multiple values for keyword argument '%U'", func_name, kw_name); #else "%s() got multiple values for keyword argument '%s'", func_name, PyString_AsString(kw_name)); #endif } /* ParseKeywords */ static int __Pyx_ParseOptionalKeywords( PyObject *kwds, PyObject **argnames[], PyObject *kwds2, PyObject *values[], Py_ssize_t num_pos_args, const char* function_name) { PyObject *key = 0, *value = 0; Py_ssize_t pos = 0; PyObject*** name; PyObject*** first_kw_arg = argnames + num_pos_args; while (PyDict_Next(kwds, &pos, &key, &value)) { name = first_kw_arg; while (*name && (**name != key)) name++; if (*name) { values[name-argnames] = value; continue; } name = first_kw_arg; #if PY_MAJOR_VERSION < 3 if (likely(PyString_Check(key))) { while (*name) { if ((CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**name) == PyString_GET_SIZE(key)) && _PyString_Eq(**name, key)) { values[name-argnames] = value; break; } name++; } if (*name) continue; else { PyObject*** argname = argnames; while (argname != first_kw_arg) { if ((**argname == key) || ( (CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**argname) == PyString_GET_SIZE(key)) && _PyString_Eq(**argname, key))) { goto arg_passed_twice; } argname++; } } } else #endif if (likely(PyUnicode_Check(key))) { while (*name) { int cmp = (**name == key) ? 0 : #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 (__Pyx_PyUnicode_GET_LENGTH(**name) != __Pyx_PyUnicode_GET_LENGTH(key)) ? 1 : #endif PyUnicode_Compare(**name, key); if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; if (cmp == 0) { values[name-argnames] = value; break; } name++; } if (*name) continue; else { PyObject*** argname = argnames; while (argname != first_kw_arg) { int cmp = (**argname == key) ? 0 : #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 (__Pyx_PyUnicode_GET_LENGTH(**argname) != __Pyx_PyUnicode_GET_LENGTH(key)) ? 1 : #endif PyUnicode_Compare(**argname, key); if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; if (cmp == 0) goto arg_passed_twice; argname++; } } } else goto invalid_keyword_type; if (kwds2) { if (unlikely(PyDict_SetItem(kwds2, key, value))) goto bad; } else { goto invalid_keyword; } } return 0; arg_passed_twice: __Pyx_RaiseDoubleKeywordsError(function_name, key); goto bad; invalid_keyword_type: PyErr_Format(PyExc_TypeError, "%.200s() keywords must be strings", function_name); goto bad; invalid_keyword: PyErr_Format(PyExc_TypeError, #if PY_MAJOR_VERSION < 3 "%.200s() got an unexpected keyword argument '%.200s'", function_name, PyString_AsString(key)); #else "%s() got an unexpected keyword argument '%U'", function_name, key); #endif bad: return -1; } /* PyDictVersioning */ #if CYTHON_USE_DICT_VERSIONS && CYTHON_USE_TYPE_SLOTS static CYTHON_INLINE PY_UINT64_T __Pyx_get_tp_dict_version(PyObject *obj) { PyObject *dict = Py_TYPE(obj)->tp_dict; return likely(dict) ? __PYX_GET_DICT_VERSION(dict) : 0; } static CYTHON_INLINE PY_UINT64_T __Pyx_get_object_dict_version(PyObject *obj) { PyObject **dictptr = NULL; Py_ssize_t offset = Py_TYPE(obj)->tp_dictoffset; if (offset) { #if CYTHON_COMPILING_IN_CPYTHON dictptr = (likely(offset > 0)) ? (PyObject **) ((char *)obj + offset) : _PyObject_GetDictPtr(obj); #else dictptr = _PyObject_GetDictPtr(obj); #endif } return (dictptr && *dictptr) ? __PYX_GET_DICT_VERSION(*dictptr) : 0; } static CYTHON_INLINE int __Pyx_object_dict_version_matches(PyObject* obj, PY_UINT64_T tp_dict_version, PY_UINT64_T obj_dict_version) { PyObject *dict = Py_TYPE(obj)->tp_dict; if (unlikely(!dict) || unlikely(tp_dict_version != __PYX_GET_DICT_VERSION(dict))) return 0; return obj_dict_version == __Pyx_get_object_dict_version(obj); } #endif /* GetModuleGlobalName */ #if CYTHON_USE_DICT_VERSIONS static PyObject *__Pyx__GetModuleGlobalName(PyObject *name, PY_UINT64_T *dict_version, PyObject **dict_cached_value) #else static CYTHON_INLINE PyObject *__Pyx__GetModuleGlobalName(PyObject *name) #endif { PyObject *result; #if !CYTHON_AVOID_BORROWED_REFS #if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030500A1 result = _PyDict_GetItem_KnownHash(__pyx_d, name, ((PyASCIIObject *) name)->hash); __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version) if (likely(result)) { return __Pyx_NewRef(result); } else if (unlikely(PyErr_Occurred())) { return NULL; } #else result = PyDict_GetItem(__pyx_d, name); __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version) if (likely(result)) { return __Pyx_NewRef(result); } #endif #else result = PyObject_GetItem(__pyx_d, name); __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version) if (likely(result)) { return __Pyx_NewRef(result); } PyErr_Clear(); #endif return __Pyx_GetBuiltinName(name); } /* PyObjectCall */ #if CYTHON_COMPILING_IN_CPYTHON static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw) { PyObject *result; ternaryfunc call = func->ob_type->tp_call; if (unlikely(!call)) return PyObject_Call(func, arg, kw); if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) return NULL; result = (*call)(func, arg, kw); Py_LeaveRecursiveCall(); if (unlikely(!result) && unlikely(!PyErr_Occurred())) { PyErr_SetString( PyExc_SystemError, "NULL result without error in PyObject_Call"); } return result; } #endif /* ExtTypeTest */ static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) { if (unlikely(!type)) { PyErr_SetString(PyExc_SystemError, "Missing type object"); return 0; } if (likely(__Pyx_TypeCheck(obj, type))) return 1; PyErr_Format(PyExc_TypeError, "Cannot convert %.200s to %.200s", Py_TYPE(obj)->tp_name, type->tp_name); return 0; } /* IsLittleEndian */ static CYTHON_INLINE int __Pyx_Is_Little_Endian(void) { union { uint32_t u32; uint8_t u8[4]; } S; S.u32 = 0x01020304; return S.u8[0] == 4; } /* BufferFormatCheck */ static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx, __Pyx_BufFmt_StackElem* stack, __Pyx_TypeInfo* type) { stack[0].field = &ctx->root; stack[0].parent_offset = 0; ctx->root.type = type; ctx->root.name = "buffer dtype"; ctx->root.offset = 0; ctx->head = stack; ctx->head->field = &ctx->root; ctx->fmt_offset = 0; ctx->head->parent_offset = 0; ctx->new_packmode = '@'; ctx->enc_packmode = '@'; ctx->new_count = 1; ctx->enc_count = 0; ctx->enc_type = 0; ctx->is_complex = 0; ctx->is_valid_array = 0; ctx->struct_alignment = 0; while (type->typegroup == 'S') { ++ctx->head; ctx->head->field = type->fields; ctx->head->parent_offset = 0; type = type->fields->type; } } static int __Pyx_BufFmt_ParseNumber(const char** ts) { int count; const char* t = *ts; if (*t < '0' || *t > '9') { return -1; } else { count = *t++ - '0'; while (*t >= '0' && *t <= '9') { count *= 10; count += *t++ - '0'; } } *ts = t; return count; } static int __Pyx_BufFmt_ExpectNumber(const char **ts) { int number = __Pyx_BufFmt_ParseNumber(ts); if (number == -1) PyErr_Format(PyExc_ValueError,\ "Does not understand character buffer dtype format string ('%c')", **ts); return number; } static void __Pyx_BufFmt_RaiseUnexpectedChar(char ch) { PyErr_Format(PyExc_ValueError, "Unexpected format string character: '%c'", ch); } static const char* __Pyx_BufFmt_DescribeTypeChar(char ch, int is_complex) { switch (ch) { case '?': return "'bool'"; case 'c': return "'char'"; case 'b': return "'signed char'"; case 'B': return "'unsigned char'"; case 'h': return "'short'"; case 'H': return "'unsigned short'"; case 'i': return "'int'"; case 'I': return "'unsigned int'"; case 'l': return "'long'"; case 'L': return "'unsigned long'"; case 'q': return "'long long'"; case 'Q': return "'unsigned long long'"; case 'f': return (is_complex ? "'complex float'" : "'float'"); case 'd': return (is_complex ? "'complex double'" : "'double'"); case 'g': return (is_complex ? "'complex long double'" : "'long double'"); case 'T': return "a struct"; case 'O': return "Python object"; case 'P': return "a pointer"; case 's': case 'p': return "a string"; case 0: return "end"; default: return "unparseable format string"; } } static size_t __Pyx_BufFmt_TypeCharToStandardSize(char ch, int is_complex) { switch (ch) { case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; case 'h': case 'H': return 2; case 'i': case 'I': case 'l': case 'L': return 4; case 'q': case 'Q': return 8; case 'f': return (is_complex ? 8 : 4); case 'd': return (is_complex ? 16 : 8); case 'g': { PyErr_SetString(PyExc_ValueError, "Python does not define a standard format string size for long double ('g').."); return 0; } case 'O': case 'P': return sizeof(void*); default: __Pyx_BufFmt_RaiseUnexpectedChar(ch); return 0; } } static size_t __Pyx_BufFmt_TypeCharToNativeSize(char ch, int is_complex) { switch (ch) { case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; case 'h': case 'H': return sizeof(short); case 'i': case 'I': return sizeof(int); case 'l': case 'L': return sizeof(long); #ifdef HAVE_LONG_LONG case 'q': case 'Q': return sizeof(PY_LONG_LONG); #endif case 'f': return sizeof(float) * (is_complex ? 2 : 1); case 'd': return sizeof(double) * (is_complex ? 2 : 1); case 'g': return sizeof(long double) * (is_complex ? 2 : 1); case 'O': case 'P': return sizeof(void*); default: { __Pyx_BufFmt_RaiseUnexpectedChar(ch); return 0; } } } typedef struct { char c; short x; } __Pyx_st_short; typedef struct { char c; int x; } __Pyx_st_int; typedef struct { char c; long x; } __Pyx_st_long; typedef struct { char c; float x; } __Pyx_st_float; typedef struct { char c; double x; } __Pyx_st_double; typedef struct { char c; long double x; } __Pyx_st_longdouble; typedef struct { char c; void *x; } __Pyx_st_void_p; #ifdef HAVE_LONG_LONG typedef struct { char c; PY_LONG_LONG x; } __Pyx_st_longlong; #endif static size_t __Pyx_BufFmt_TypeCharToAlignment(char ch, CYTHON_UNUSED int is_complex) { switch (ch) { case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; case 'h': case 'H': return sizeof(__Pyx_st_short) - sizeof(short); case 'i': case 'I': return sizeof(__Pyx_st_int) - sizeof(int); case 'l': case 'L': return sizeof(__Pyx_st_long) - sizeof(long); #ifdef HAVE_LONG_LONG case 'q': case 'Q': return sizeof(__Pyx_st_longlong) - sizeof(PY_LONG_LONG); #endif case 'f': return sizeof(__Pyx_st_float) - sizeof(float); case 'd': return sizeof(__Pyx_st_double) - sizeof(double); case 'g': return sizeof(__Pyx_st_longdouble) - sizeof(long double); case 'P': case 'O': return sizeof(__Pyx_st_void_p) - sizeof(void*); default: __Pyx_BufFmt_RaiseUnexpectedChar(ch); return 0; } } /* These are for computing the padding at the end of the struct to align on the first member of the struct. This will probably the same as above, but we don't have any guarantees. */ typedef struct { short x; char c; } __Pyx_pad_short; typedef struct { int x; char c; } __Pyx_pad_int; typedef struct { long x; char c; } __Pyx_pad_long; typedef struct { float x; char c; } __Pyx_pad_float; typedef struct { double x; char c; } __Pyx_pad_double; typedef struct { long double x; char c; } __Pyx_pad_longdouble; typedef struct { void *x; char c; } __Pyx_pad_void_p; #ifdef HAVE_LONG_LONG typedef struct { PY_LONG_LONG x; char c; } __Pyx_pad_longlong; #endif static size_t __Pyx_BufFmt_TypeCharToPadding(char ch, CYTHON_UNUSED int is_complex) { switch (ch) { case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; case 'h': case 'H': return sizeof(__Pyx_pad_short) - sizeof(short); case 'i': case 'I': return sizeof(__Pyx_pad_int) - sizeof(int); case 'l': case 'L': return sizeof(__Pyx_pad_long) - sizeof(long); #ifdef HAVE_LONG_LONG case 'q': case 'Q': return sizeof(__Pyx_pad_longlong) - sizeof(PY_LONG_LONG); #endif case 'f': return sizeof(__Pyx_pad_float) - sizeof(float); case 'd': return sizeof(__Pyx_pad_double) - sizeof(double); case 'g': return sizeof(__Pyx_pad_longdouble) - sizeof(long double); case 'P': case 'O': return sizeof(__Pyx_pad_void_p) - sizeof(void*); default: __Pyx_BufFmt_RaiseUnexpectedChar(ch); return 0; } } static char __Pyx_BufFmt_TypeCharToGroup(char ch, int is_complex) { switch (ch) { case 'c': return 'H'; case 'b': case 'h': case 'i': case 'l': case 'q': case 's': case 'p': return 'I'; case '?': case 'B': case 'H': case 'I': case 'L': case 'Q': return 'U'; case 'f': case 'd': case 'g': return (is_complex ? 'C' : 'R'); case 'O': return 'O'; case 'P': return 'P'; default: { __Pyx_BufFmt_RaiseUnexpectedChar(ch); return 0; } } } static void __Pyx_BufFmt_RaiseExpected(__Pyx_BufFmt_Context* ctx) { if (ctx->head == NULL || ctx->head->field == &ctx->root) { const char* expected; const char* quote; if (ctx->head == NULL) { expected = "end"; quote = ""; } else { expected = ctx->head->field->type->name; quote = "'"; } PyErr_Format(PyExc_ValueError, "Buffer dtype mismatch, expected %s%s%s but got %s", quote, expected, quote, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex)); } else { __Pyx_StructField* field = ctx->head->field; __Pyx_StructField* parent = (ctx->head - 1)->field; PyErr_Format(PyExc_ValueError, "Buffer dtype mismatch, expected '%s' but got %s in '%s.%s'", field->type->name, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex), parent->type->name, field->name); } } static int __Pyx_BufFmt_ProcessTypeChunk(__Pyx_BufFmt_Context* ctx) { char group; size_t size, offset, arraysize = 1; if (ctx->enc_type == 0) return 0; if (ctx->head->field->type->arraysize[0]) { int i, ndim = 0; if (ctx->enc_type == 's' || ctx->enc_type == 'p') { ctx->is_valid_array = ctx->head->field->type->ndim == 1; ndim = 1; if (ctx->enc_count != ctx->head->field->type->arraysize[0]) { PyErr_Format(PyExc_ValueError, "Expected a dimension of size %zu, got %zu", ctx->head->field->type->arraysize[0], ctx->enc_count); return -1; } } if (!ctx->is_valid_array) { PyErr_Format(PyExc_ValueError, "Expected %d dimensions, got %d", ctx->head->field->type->ndim, ndim); return -1; } for (i = 0; i < ctx->head->field->type->ndim; i++) { arraysize *= ctx->head->field->type->arraysize[i]; } ctx->is_valid_array = 0; ctx->enc_count = 1; } group = __Pyx_BufFmt_TypeCharToGroup(ctx->enc_type, ctx->is_complex); do { __Pyx_StructField* field = ctx->head->field; __Pyx_TypeInfo* type = field->type; if (ctx->enc_packmode == '@' || ctx->enc_packmode == '^') { size = __Pyx_BufFmt_TypeCharToNativeSize(ctx->enc_type, ctx->is_complex); } else { size = __Pyx_BufFmt_TypeCharToStandardSize(ctx->enc_type, ctx->is_complex); } if (ctx->enc_packmode == '@') { size_t align_at = __Pyx_BufFmt_TypeCharToAlignment(ctx->enc_type, ctx->is_complex); size_t align_mod_offset; if (align_at == 0) return -1; align_mod_offset = ctx->fmt_offset % align_at; if (align_mod_offset > 0) ctx->fmt_offset += align_at - align_mod_offset; if (ctx->struct_alignment == 0) ctx->struct_alignment = __Pyx_BufFmt_TypeCharToPadding(ctx->enc_type, ctx->is_complex); } if (type->size != size || type->typegroup != group) { if (type->typegroup == 'C' && type->fields != NULL) { size_t parent_offset = ctx->head->parent_offset + field->offset; ++ctx->head; ctx->head->field = type->fields; ctx->head->parent_offset = parent_offset; continue; } if ((type->typegroup == 'H' || group == 'H') && type->size == size) { } else { __Pyx_BufFmt_RaiseExpected(ctx); return -1; } } offset = ctx->head->parent_offset + field->offset; if (ctx->fmt_offset != offset) { PyErr_Format(PyExc_ValueError, "Buffer dtype mismatch; next field is at offset %" CYTHON_FORMAT_SSIZE_T "d but %" CYTHON_FORMAT_SSIZE_T "d expected", (Py_ssize_t)ctx->fmt_offset, (Py_ssize_t)offset); return -1; } ctx->fmt_offset += size; if (arraysize) ctx->fmt_offset += (arraysize - 1) * size; --ctx->enc_count; while (1) { if (field == &ctx->root) { ctx->head = NULL; if (ctx->enc_count != 0) { __Pyx_BufFmt_RaiseExpected(ctx); return -1; } break; } ctx->head->field = ++field; if (field->type == NULL) { --ctx->head; field = ctx->head->field; continue; } else if (field->type->typegroup == 'S') { size_t parent_offset = ctx->head->parent_offset + field->offset; if (field->type->fields->type == NULL) continue; field = field->type->fields; ++ctx->head; ctx->head->field = field; ctx->head->parent_offset = parent_offset; break; } else { break; } } } while (ctx->enc_count); ctx->enc_type = 0; ctx->is_complex = 0; return 0; } static PyObject * __pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp) { const char *ts = *tsp; int i = 0, number, ndim; ++ts; if (ctx->new_count != 1) { PyErr_SetString(PyExc_ValueError, "Cannot handle repeated arrays in format string"); return NULL; } if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; ndim = ctx->head->field->type->ndim; while (*ts && *ts != ')') { switch (*ts) { case ' ': case '\f': case '\r': case '\n': case '\t': case '\v': continue; default: break; } number = __Pyx_BufFmt_ExpectNumber(&ts); if (number == -1) return NULL; if (i < ndim && (size_t) number != ctx->head->field->type->arraysize[i]) return PyErr_Format(PyExc_ValueError, "Expected a dimension of size %zu, got %d", ctx->head->field->type->arraysize[i], number); if (*ts != ',' && *ts != ')') return PyErr_Format(PyExc_ValueError, "Expected a comma in format string, got '%c'", *ts); if (*ts == ',') ts++; i++; } if (i != ndim) return PyErr_Format(PyExc_ValueError, "Expected %d dimension(s), got %d", ctx->head->field->type->ndim, i); if (!*ts) { PyErr_SetString(PyExc_ValueError, "Unexpected end of format string, expected ')'"); return NULL; } ctx->is_valid_array = 1; ctx->new_count = 1; *tsp = ++ts; return Py_None; } static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts) { int got_Z = 0; while (1) { switch(*ts) { case 0: if (ctx->enc_type != 0 && ctx->head == NULL) { __Pyx_BufFmt_RaiseExpected(ctx); return NULL; } if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; if (ctx->head != NULL) { __Pyx_BufFmt_RaiseExpected(ctx); return NULL; } return ts; case ' ': case '\r': case '\n': ++ts; break; case '<': if (!__Pyx_Is_Little_Endian()) { PyErr_SetString(PyExc_ValueError, "Little-endian buffer not supported on big-endian compiler"); return NULL; } ctx->new_packmode = '='; ++ts; break; case '>': case '!': if (__Pyx_Is_Little_Endian()) { PyErr_SetString(PyExc_ValueError, "Big-endian buffer not supported on little-endian compiler"); return NULL; } ctx->new_packmode = '='; ++ts; break; case '=': case '@': case '^': ctx->new_packmode = *ts++; break; case 'T': { const char* ts_after_sub; size_t i, struct_count = ctx->new_count; size_t struct_alignment = ctx->struct_alignment; ctx->new_count = 1; ++ts; if (*ts != '{') { PyErr_SetString(PyExc_ValueError, "Buffer acquisition: Expected '{' after 'T'"); return NULL; } if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; ctx->enc_type = 0; ctx->enc_count = 0; ctx->struct_alignment = 0; ++ts; ts_after_sub = ts; for (i = 0; i != struct_count; ++i) { ts_after_sub = __Pyx_BufFmt_CheckString(ctx, ts); if (!ts_after_sub) return NULL; } ts = ts_after_sub; if (struct_alignment) ctx->struct_alignment = struct_alignment; } break; case '}': { size_t alignment = ctx->struct_alignment; ++ts; if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; ctx->enc_type = 0; if (alignment && ctx->fmt_offset % alignment) { ctx->fmt_offset += alignment - (ctx->fmt_offset % alignment); } } return ts; case 'x': if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; ctx->fmt_offset += ctx->new_count; ctx->new_count = 1; ctx->enc_count = 0; ctx->enc_type = 0; ctx->enc_packmode = ctx->new_packmode; ++ts; break; case 'Z': got_Z = 1; ++ts; if (*ts != 'f' && *ts != 'd' && *ts != 'g') { __Pyx_BufFmt_RaiseUnexpectedChar('Z'); return NULL; } CYTHON_FALLTHROUGH; case '?': case 'c': case 'b': case 'B': case 'h': case 'H': case 'i': case 'I': case 'l': case 'L': case 'q': case 'Q': case 'f': case 'd': case 'g': case 'O': case 'p': if ((ctx->enc_type == *ts) && (got_Z == ctx->is_complex) && (ctx->enc_packmode == ctx->new_packmode) && (!ctx->is_valid_array)) { ctx->enc_count += ctx->new_count; ctx->new_count = 1; got_Z = 0; ++ts; break; } CYTHON_FALLTHROUGH; case 's': if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; ctx->enc_count = ctx->new_count; ctx->enc_packmode = ctx->new_packmode; ctx->enc_type = *ts; ctx->is_complex = got_Z; ++ts; ctx->new_count = 1; got_Z = 0; break; case ':': ++ts; while(*ts != ':') ++ts; ++ts; break; case '(': if (!__pyx_buffmt_parse_array(ctx, &ts)) return NULL; break; default: { int number = __Pyx_BufFmt_ExpectNumber(&ts); if (number == -1) return NULL; ctx->new_count = (size_t)number; } } } } /* BufferGetAndValidate */ static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info) { if (unlikely(info->buf == NULL)) return; if (info->suboffsets == __Pyx_minusones) info->suboffsets = NULL; __Pyx_ReleaseBuffer(info); } static void __Pyx_ZeroBuffer(Py_buffer* buf) { buf->buf = NULL; buf->obj = NULL; buf->strides = __Pyx_zeros; buf->shape = __Pyx_zeros; buf->suboffsets = __Pyx_minusones; } static int __Pyx__GetBufferAndValidate( Py_buffer* buf, PyObject* obj, __Pyx_TypeInfo* dtype, int flags, int nd, int cast, __Pyx_BufFmt_StackElem* stack) { buf->buf = NULL; if (unlikely(__Pyx_GetBuffer(obj, buf, flags) == -1)) { __Pyx_ZeroBuffer(buf); return -1; } if (unlikely(buf->ndim != nd)) { PyErr_Format(PyExc_ValueError, "Buffer has wrong number of dimensions (expected %d, got %d)", nd, buf->ndim); goto fail; } if (!cast) { __Pyx_BufFmt_Context ctx; __Pyx_BufFmt_Init(&ctx, stack, dtype); if (!__Pyx_BufFmt_CheckString(&ctx, buf->format)) goto fail; } if (unlikely((size_t)buf->itemsize != dtype->size)) { PyErr_Format(PyExc_ValueError, "Item size of buffer (%" CYTHON_FORMAT_SSIZE_T "d byte%s) does not match size of '%s' (%" CYTHON_FORMAT_SSIZE_T "d byte%s)", buf->itemsize, (buf->itemsize > 1) ? "s" : "", dtype->name, (Py_ssize_t)dtype->size, (dtype->size > 1) ? "s" : ""); goto fail; } if (buf->suboffsets == NULL) buf->suboffsets = __Pyx_minusones; return 0; fail:; __Pyx_SafeReleaseBuffer(buf); return -1; } /* BufferFallbackError */ static void __Pyx_RaiseBufferFallbackError(void) { PyErr_SetString(PyExc_ValueError, "Buffer acquisition failed on assignment; and then reacquiring the old buffer failed too!"); } /* BufferIndexError */ static void __Pyx_RaiseBufferIndexError(int axis) { PyErr_Format(PyExc_IndexError, "Out of bounds on buffer access (axis %d)", axis); } /* PyErrFetchRestore */ #if CYTHON_FAST_THREAD_STATE static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) { PyObject *tmp_type, *tmp_value, *tmp_tb; tmp_type = tstate->curexc_type; tmp_value = tstate->curexc_value; tmp_tb = tstate->curexc_traceback; tstate->curexc_type = type; tstate->curexc_value = value; tstate->curexc_traceback = tb; Py_XDECREF(tmp_type); Py_XDECREF(tmp_value); Py_XDECREF(tmp_tb); } static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { *type = tstate->curexc_type; *value = tstate->curexc_value; *tb = tstate->curexc_traceback; tstate->curexc_type = 0; tstate->curexc_value = 0; tstate->curexc_traceback = 0; } #endif /* GetItemInt */ static PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j) { PyObject *r; if (!j) return NULL; r = PyObject_GetItem(o, j); Py_DECREF(j); return r; } static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i, CYTHON_NCP_UNUSED int wraparound, CYTHON_NCP_UNUSED int boundscheck) { #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS Py_ssize_t wrapped_i = i; if (wraparound & unlikely(i < 0)) { wrapped_i += PyList_GET_SIZE(o); } if ((!boundscheck) || likely(__Pyx_is_valid_index(wrapped_i, PyList_GET_SIZE(o)))) { PyObject *r = PyList_GET_ITEM(o, wrapped_i); Py_INCREF(r); return r; } return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); #else return PySequence_GetItem(o, i); #endif } static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i, CYTHON_NCP_UNUSED int wraparound, CYTHON_NCP_UNUSED int boundscheck) { #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS Py_ssize_t wrapped_i = i; if (wraparound & unlikely(i < 0)) { wrapped_i += PyTuple_GET_SIZE(o); } if ((!boundscheck) || likely(__Pyx_is_valid_index(wrapped_i, PyTuple_GET_SIZE(o)))) { PyObject *r = PyTuple_GET_ITEM(o, wrapped_i); Py_INCREF(r); return r; } return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); #else return PySequence_GetItem(o, i); #endif } static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, int is_list, CYTHON_NCP_UNUSED int wraparound, CYTHON_NCP_UNUSED int boundscheck) { #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS && CYTHON_USE_TYPE_SLOTS if (is_list || PyList_CheckExact(o)) { Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyList_GET_SIZE(o); if ((!boundscheck) || (likely(__Pyx_is_valid_index(n, PyList_GET_SIZE(o))))) { PyObject *r = PyList_GET_ITEM(o, n); Py_INCREF(r); return r; } } else if (PyTuple_CheckExact(o)) { Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyTuple_GET_SIZE(o); if ((!boundscheck) || likely(__Pyx_is_valid_index(n, PyTuple_GET_SIZE(o)))) { PyObject *r = PyTuple_GET_ITEM(o, n); Py_INCREF(r); return r; } } else { PySequenceMethods *m = Py_TYPE(o)->tp_as_sequence; if (likely(m && m->sq_item)) { if (wraparound && unlikely(i < 0) && likely(m->sq_length)) { Py_ssize_t l = m->sq_length(o); if (likely(l >= 0)) { i += l; } else { if (!PyErr_ExceptionMatches(PyExc_OverflowError)) return NULL; PyErr_Clear(); } } return m->sq_item(o, i); } } #else if (is_list || PySequence_Check(o)) { return PySequence_GetItem(o, i); } #endif return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); } /* ObjectGetItem */ #if CYTHON_USE_TYPE_SLOTS static PyObject *__Pyx_PyObject_GetIndex(PyObject *obj, PyObject* index) { PyObject *runerr; Py_ssize_t key_value; PySequenceMethods *m = Py_TYPE(obj)->tp_as_sequence; if (unlikely(!(m && m->sq_item))) { PyErr_Format(PyExc_TypeError, "'%.200s' object is not subscriptable", Py_TYPE(obj)->tp_name); return NULL; } key_value = __Pyx_PyIndex_AsSsize_t(index); if (likely(key_value != -1 || !(runerr = PyErr_Occurred()))) { return __Pyx_GetItemInt_Fast(obj, key_value, 0, 1, 1); } if (PyErr_GivenExceptionMatches(runerr, PyExc_OverflowError)) { PyErr_Clear(); PyErr_Format(PyExc_IndexError, "cannot fit '%.200s' into an index-sized integer", Py_TYPE(index)->tp_name); } return NULL; } static PyObject *__Pyx_PyObject_GetItem(PyObject *obj, PyObject* key) { PyMappingMethods *m = Py_TYPE(obj)->tp_as_mapping; if (likely(m && m->mp_subscript)) { return m->mp_subscript(obj, key); } return __Pyx_PyObject_GetIndex(obj, key); } #endif /* PyCFunctionFastCall */ #if CYTHON_FAST_PYCCALL static CYTHON_INLINE PyObject * __Pyx_PyCFunction_FastCall(PyObject *func_obj, PyObject **args, Py_ssize_t nargs) { PyCFunctionObject *func = (PyCFunctionObject*)func_obj; PyCFunction meth = PyCFunction_GET_FUNCTION(func); PyObject *self = PyCFunction_GET_SELF(func); int flags = PyCFunction_GET_FLAGS(func); assert(PyCFunction_Check(func)); assert(METH_FASTCALL == (flags & ~(METH_CLASS | METH_STATIC | METH_COEXIST | METH_KEYWORDS | METH_STACKLESS))); assert(nargs >= 0); assert(nargs == 0 || args != NULL); /* _PyCFunction_FastCallDict() must not be called with an exception set, because it may clear it (directly or indirectly) and so the caller loses its exception */ assert(!PyErr_Occurred()); if ((PY_VERSION_HEX < 0x030700A0) || unlikely(flags & METH_KEYWORDS)) { return (*((__Pyx_PyCFunctionFastWithKeywords)(void*)meth)) (self, args, nargs, NULL); } else { return (*((__Pyx_PyCFunctionFast)(void*)meth)) (self, args, nargs); } } #endif /* PyFunctionFastCall */ #if CYTHON_FAST_PYCALL static PyObject* __Pyx_PyFunction_FastCallNoKw(PyCodeObject *co, PyObject **args, Py_ssize_t na, PyObject *globals) { PyFrameObject *f; PyThreadState *tstate = __Pyx_PyThreadState_Current; PyObject **fastlocals; Py_ssize_t i; PyObject *result; assert(globals != NULL); /* XXX Perhaps we should create a specialized PyFrame_New() that doesn't take locals, but does take builtins without sanity checking them. */ assert(tstate != NULL); f = PyFrame_New(tstate, co, globals, NULL); if (f == NULL) { return NULL; } fastlocals = __Pyx_PyFrame_GetLocalsplus(f); for (i = 0; i < na; i++) { Py_INCREF(*args); fastlocals[i] = *args++; } result = PyEval_EvalFrameEx(f,0); ++tstate->recursion_depth; Py_DECREF(f); --tstate->recursion_depth; return result; } #if 1 || PY_VERSION_HEX < 0x030600B1 static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, Py_ssize_t nargs, PyObject *kwargs) { PyCodeObject *co = (PyCodeObject *)PyFunction_GET_CODE(func); PyObject *globals = PyFunction_GET_GLOBALS(func); PyObject *argdefs = PyFunction_GET_DEFAULTS(func); PyObject *closure; #if PY_MAJOR_VERSION >= 3 PyObject *kwdefs; #endif PyObject *kwtuple, **k; PyObject **d; Py_ssize_t nd; Py_ssize_t nk; PyObject *result; assert(kwargs == NULL || PyDict_Check(kwargs)); nk = kwargs ? PyDict_Size(kwargs) : 0; if (Py_EnterRecursiveCall((char*)" while calling a Python object")) { return NULL; } if ( #if PY_MAJOR_VERSION >= 3 co->co_kwonlyargcount == 0 && #endif likely(kwargs == NULL || nk == 0) && co->co_flags == (CO_OPTIMIZED | CO_NEWLOCALS | CO_NOFREE)) { if (argdefs == NULL && co->co_argcount == nargs) { result = __Pyx_PyFunction_FastCallNoKw(co, args, nargs, globals); goto done; } else if (nargs == 0 && argdefs != NULL && co->co_argcount == Py_SIZE(argdefs)) { /* function called with no arguments, but all parameters have a default value: use default values as arguments .*/ args = &PyTuple_GET_ITEM(argdefs, 0); result =__Pyx_PyFunction_FastCallNoKw(co, args, Py_SIZE(argdefs), globals); goto done; } } if (kwargs != NULL) { Py_ssize_t pos, i; kwtuple = PyTuple_New(2 * nk); if (kwtuple == NULL) { result = NULL; goto done; } k = &PyTuple_GET_ITEM(kwtuple, 0); pos = i = 0; while (PyDict_Next(kwargs, &pos, &k[i], &k[i+1])) { Py_INCREF(k[i]); Py_INCREF(k[i+1]); i += 2; } nk = i / 2; } else { kwtuple = NULL; k = NULL; } closure = PyFunction_GET_CLOSURE(func); #if PY_MAJOR_VERSION >= 3 kwdefs = PyFunction_GET_KW_DEFAULTS(func); #endif if (argdefs != NULL) { d = &PyTuple_GET_ITEM(argdefs, 0); nd = Py_SIZE(argdefs); } else { d = NULL; nd = 0; } #if PY_MAJOR_VERSION >= 3 result = PyEval_EvalCodeEx((PyObject*)co, globals, (PyObject *)NULL, args, (int)nargs, k, (int)nk, d, (int)nd, kwdefs, closure); #else result = PyEval_EvalCodeEx(co, globals, (PyObject *)NULL, args, (int)nargs, k, (int)nk, d, (int)nd, closure); #endif Py_XDECREF(kwtuple); done: Py_LeaveRecursiveCall(); return result; } #endif #endif /* PyObjectCall2Args */ static CYTHON_UNUSED PyObject* __Pyx_PyObject_Call2Args(PyObject* function, PyObject* arg1, PyObject* arg2) { PyObject *args, *result = NULL; #if CYTHON_FAST_PYCALL if (PyFunction_Check(function)) { PyObject *args[2] = {arg1, arg2}; return __Pyx_PyFunction_FastCall(function, args, 2); } #endif #if CYTHON_FAST_PYCCALL if (__Pyx_PyFastCFunction_Check(function)) { PyObject *args[2] = {arg1, arg2}; return __Pyx_PyCFunction_FastCall(function, args, 2); } #endif args = PyTuple_New(2); if (unlikely(!args)) goto done; Py_INCREF(arg1); PyTuple_SET_ITEM(args, 0, arg1); Py_INCREF(arg2); PyTuple_SET_ITEM(args, 1, arg2); Py_INCREF(function); result = __Pyx_PyObject_Call(function, args, NULL); Py_DECREF(args); Py_DECREF(function); done: return result; } /* PyObjectCallMethO */ #if CYTHON_COMPILING_IN_CPYTHON static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg) { PyObject *self, *result; PyCFunction cfunc; cfunc = PyCFunction_GET_FUNCTION(func); self = PyCFunction_GET_SELF(func); if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) return NULL; result = cfunc(self, arg); Py_LeaveRecursiveCall(); if (unlikely(!result) && unlikely(!PyErr_Occurred())) { PyErr_SetString( PyExc_SystemError, "NULL result without error in PyObject_Call"); } return result; } #endif /* PyObjectCallOneArg */ #if CYTHON_COMPILING_IN_CPYTHON static PyObject* __Pyx__PyObject_CallOneArg(PyObject *func, PyObject *arg) { PyObject *result; PyObject *args = PyTuple_New(1); if (unlikely(!args)) return NULL; Py_INCREF(arg); PyTuple_SET_ITEM(args, 0, arg); result = __Pyx_PyObject_Call(func, args, NULL); Py_DECREF(args); return result; } static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) { #if CYTHON_FAST_PYCALL if (PyFunction_Check(func)) { return __Pyx_PyFunction_FastCall(func, &arg, 1); } #endif if (likely(PyCFunction_Check(func))) { if (likely(PyCFunction_GET_FLAGS(func) & METH_O)) { return __Pyx_PyObject_CallMethO(func, arg); #if CYTHON_FAST_PYCCALL } else if (__Pyx_PyFastCFunction_Check(func)) { return __Pyx_PyCFunction_FastCall(func, &arg, 1); #endif } } return __Pyx__PyObject_CallOneArg(func, arg); } #else static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) { PyObject *result; PyObject *args = PyTuple_Pack(1, arg); if (unlikely(!args)) return NULL; result = __Pyx_PyObject_Call(func, args, NULL); Py_DECREF(args); return result; } #endif /* GetTopmostException */ #if CYTHON_USE_EXC_INFO_STACK static _PyErr_StackItem * __Pyx_PyErr_GetTopmostException(PyThreadState *tstate) { _PyErr_StackItem *exc_info = tstate->exc_info; while ((exc_info->exc_type == NULL || exc_info->exc_type == Py_None) && exc_info->previous_item != NULL) { exc_info = exc_info->previous_item; } return exc_info; } #endif /* SaveResetException */ #if CYTHON_FAST_THREAD_STATE static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { #if CYTHON_USE_EXC_INFO_STACK _PyErr_StackItem *exc_info = __Pyx_PyErr_GetTopmostException(tstate); *type = exc_info->exc_type; *value = exc_info->exc_value; *tb = exc_info->exc_traceback; #else *type = tstate->exc_type; *value = tstate->exc_value; *tb = tstate->exc_traceback; #endif Py_XINCREF(*type); Py_XINCREF(*value); Py_XINCREF(*tb); } static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) { PyObject *tmp_type, *tmp_value, *tmp_tb; #if CYTHON_USE_EXC_INFO_STACK _PyErr_StackItem *exc_info = tstate->exc_info; tmp_type = exc_info->exc_type; tmp_value = exc_info->exc_value; tmp_tb = exc_info->exc_traceback; exc_info->exc_type = type; exc_info->exc_value = value; exc_info->exc_traceback = tb; #else tmp_type = tstate->exc_type; tmp_value = tstate->exc_value; tmp_tb = tstate->exc_traceback; tstate->exc_type = type; tstate->exc_value = value; tstate->exc_traceback = tb; #endif Py_XDECREF(tmp_type); Py_XDECREF(tmp_value); Py_XDECREF(tmp_tb); } #endif /* PyErrExceptionMatches */ #if CYTHON_FAST_THREAD_STATE static int __Pyx_PyErr_ExceptionMatchesTuple(PyObject *exc_type, PyObject *tuple) { Py_ssize_t i, n; n = PyTuple_GET_SIZE(tuple); #if PY_MAJOR_VERSION >= 3 for (i=0; icurexc_type; if (exc_type == err) return 1; if (unlikely(!exc_type)) return 0; if (unlikely(PyTuple_Check(err))) return __Pyx_PyErr_ExceptionMatchesTuple(exc_type, err); return __Pyx_PyErr_GivenExceptionMatches(exc_type, err); } #endif /* GetException */ #if CYTHON_FAST_THREAD_STATE static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) #else static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb) #endif { PyObject *local_type, *local_value, *local_tb; #if CYTHON_FAST_THREAD_STATE PyObject *tmp_type, *tmp_value, *tmp_tb; local_type = tstate->curexc_type; local_value = tstate->curexc_value; local_tb = tstate->curexc_traceback; tstate->curexc_type = 0; tstate->curexc_value = 0; tstate->curexc_traceback = 0; #else PyErr_Fetch(&local_type, &local_value, &local_tb); #endif PyErr_NormalizeException(&local_type, &local_value, &local_tb); #if CYTHON_FAST_THREAD_STATE if (unlikely(tstate->curexc_type)) #else if (unlikely(PyErr_Occurred())) #endif goto bad; #if PY_MAJOR_VERSION >= 3 if (local_tb) { if (unlikely(PyException_SetTraceback(local_value, local_tb) < 0)) goto bad; } #endif Py_XINCREF(local_tb); Py_XINCREF(local_type); Py_XINCREF(local_value); *type = local_type; *value = local_value; *tb = local_tb; #if CYTHON_FAST_THREAD_STATE #if CYTHON_USE_EXC_INFO_STACK { _PyErr_StackItem *exc_info = tstate->exc_info; tmp_type = exc_info->exc_type; tmp_value = exc_info->exc_value; tmp_tb = exc_info->exc_traceback; exc_info->exc_type = local_type; exc_info->exc_value = local_value; exc_info->exc_traceback = local_tb; } #else tmp_type = tstate->exc_type; tmp_value = tstate->exc_value; tmp_tb = tstate->exc_traceback; tstate->exc_type = local_type; tstate->exc_value = local_value; tstate->exc_traceback = local_tb; #endif Py_XDECREF(tmp_type); Py_XDECREF(tmp_value); Py_XDECREF(tmp_tb); #else PyErr_SetExcInfo(local_type, local_value, local_tb); #endif return 0; bad: *type = 0; *value = 0; *tb = 0; Py_XDECREF(local_type); Py_XDECREF(local_value); Py_XDECREF(local_tb); return -1; } /* RaiseException */ #if PY_MAJOR_VERSION < 3 static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, CYTHON_UNUSED PyObject *cause) { __Pyx_PyThreadState_declare Py_XINCREF(type); if (!value || value == Py_None) value = NULL; else Py_INCREF(value); if (!tb || tb == Py_None) tb = NULL; else { Py_INCREF(tb); if (!PyTraceBack_Check(tb)) { PyErr_SetString(PyExc_TypeError, "raise: arg 3 must be a traceback or None"); goto raise_error; } } if (PyType_Check(type)) { #if CYTHON_COMPILING_IN_PYPY if (!value) { Py_INCREF(Py_None); value = Py_None; } #endif PyErr_NormalizeException(&type, &value, &tb); } else { if (value) { PyErr_SetString(PyExc_TypeError, "instance exception may not have a separate value"); goto raise_error; } value = type; type = (PyObject*) Py_TYPE(type); Py_INCREF(type); if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) { PyErr_SetString(PyExc_TypeError, "raise: exception class must be a subclass of BaseException"); goto raise_error; } } __Pyx_PyThreadState_assign __Pyx_ErrRestore(type, value, tb); return; raise_error: Py_XDECREF(value); Py_XDECREF(type); Py_XDECREF(tb); return; } #else static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) { PyObject* owned_instance = NULL; if (tb == Py_None) { tb = 0; } else if (tb && !PyTraceBack_Check(tb)) { PyErr_SetString(PyExc_TypeError, "raise: arg 3 must be a traceback or None"); goto bad; } if (value == Py_None) value = 0; if (PyExceptionInstance_Check(type)) { if (value) { PyErr_SetString(PyExc_TypeError, "instance exception may not have a separate value"); goto bad; } value = type; type = (PyObject*) Py_TYPE(value); } else if (PyExceptionClass_Check(type)) { PyObject *instance_class = NULL; if (value && PyExceptionInstance_Check(value)) { instance_class = (PyObject*) Py_TYPE(value); if (instance_class != type) { int is_subclass = PyObject_IsSubclass(instance_class, type); if (!is_subclass) { instance_class = NULL; } else if (unlikely(is_subclass == -1)) { goto bad; } else { type = instance_class; } } } if (!instance_class) { PyObject *args; if (!value) args = PyTuple_New(0); else if (PyTuple_Check(value)) { Py_INCREF(value); args = value; } else args = PyTuple_Pack(1, value); if (!args) goto bad; owned_instance = PyObject_Call(type, args, NULL); Py_DECREF(args); if (!owned_instance) goto bad; value = owned_instance; if (!PyExceptionInstance_Check(value)) { PyErr_Format(PyExc_TypeError, "calling %R should have returned an instance of " "BaseException, not %R", type, Py_TYPE(value)); goto bad; } } } else { PyErr_SetString(PyExc_TypeError, "raise: exception class must be a subclass of BaseException"); goto bad; } if (cause) { PyObject *fixed_cause; if (cause == Py_None) { fixed_cause = NULL; } else if (PyExceptionClass_Check(cause)) { fixed_cause = PyObject_CallObject(cause, NULL); if (fixed_cause == NULL) goto bad; } else if (PyExceptionInstance_Check(cause)) { fixed_cause = cause; Py_INCREF(fixed_cause); } else { PyErr_SetString(PyExc_TypeError, "exception causes must derive from " "BaseException"); goto bad; } PyException_SetCause(value, fixed_cause); } PyErr_SetObject(type, value); if (tb) { #if CYTHON_COMPILING_IN_PYPY PyObject *tmp_type, *tmp_value, *tmp_tb; PyErr_Fetch(&tmp_type, &tmp_value, &tmp_tb); Py_INCREF(tb); PyErr_Restore(tmp_type, tmp_value, tb); Py_XDECREF(tmp_tb); #else PyThreadState *tstate = __Pyx_PyThreadState_Current; PyObject* tmp_tb = tstate->curexc_traceback; if (tb != tmp_tb) { Py_INCREF(tb); tstate->curexc_traceback = tb; Py_XDECREF(tmp_tb); } #endif } bad: Py_XDECREF(owned_instance); return; } #endif /* TypeImport */ #ifndef __PYX_HAVE_RT_ImportType #define __PYX_HAVE_RT_ImportType static PyTypeObject *__Pyx_ImportType(PyObject *module, const char *module_name, const char *class_name, size_t size, enum __Pyx_ImportType_CheckSize check_size) { PyObject *result = 0; char warning[200]; Py_ssize_t basicsize; #ifdef Py_LIMITED_API PyObject *py_basicsize; #endif result = PyObject_GetAttrString(module, class_name); if (!result) goto bad; if (!PyType_Check(result)) { PyErr_Format(PyExc_TypeError, "%.200s.%.200s is not a type object", module_name, class_name); goto bad; } #ifndef Py_LIMITED_API basicsize = ((PyTypeObject *)result)->tp_basicsize; #else py_basicsize = PyObject_GetAttrString(result, "__basicsize__"); if (!py_basicsize) goto bad; basicsize = PyLong_AsSsize_t(py_basicsize); Py_DECREF(py_basicsize); py_basicsize = 0; if (basicsize == (Py_ssize_t)-1 && PyErr_Occurred()) goto bad; #endif if ((size_t)basicsize < size) { PyErr_Format(PyExc_ValueError, "%.200s.%.200s size changed, may indicate binary incompatibility. " "Expected %zd from C header, got %zd from PyObject", module_name, class_name, size, basicsize); goto bad; } if (check_size == __Pyx_ImportType_CheckSize_Error && (size_t)basicsize != size) { PyErr_Format(PyExc_ValueError, "%.200s.%.200s size changed, may indicate binary incompatibility. " "Expected %zd from C header, got %zd from PyObject", module_name, class_name, size, basicsize); goto bad; } else if (check_size == __Pyx_ImportType_CheckSize_Warn && (size_t)basicsize > size) { PyOS_snprintf(warning, sizeof(warning), "%s.%s size changed, may indicate binary incompatibility. " "Expected %zd from C header, got %zd from PyObject", module_name, class_name, size, basicsize); if (PyErr_WarnEx(NULL, warning, 0) < 0) goto bad; } return (PyTypeObject *)result; bad: Py_XDECREF(result); return NULL; } #endif /* Import */ static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) { PyObject *empty_list = 0; PyObject *module = 0; PyObject *global_dict = 0; PyObject *empty_dict = 0; PyObject *list; #if PY_MAJOR_VERSION < 3 PyObject *py_import; py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import); if (!py_import) goto bad; #endif if (from_list) list = from_list; else { empty_list = PyList_New(0); if (!empty_list) goto bad; list = empty_list; } global_dict = PyModule_GetDict(__pyx_m); if (!global_dict) goto bad; empty_dict = PyDict_New(); if (!empty_dict) goto bad; { #if PY_MAJOR_VERSION >= 3 if (level == -1) { if ((1) && (strchr(__Pyx_MODULE_NAME, '.'))) { module = PyImport_ImportModuleLevelObject( name, global_dict, empty_dict, list, 1); if (!module) { if (!PyErr_ExceptionMatches(PyExc_ImportError)) goto bad; PyErr_Clear(); } } level = 0; } #endif if (!module) { #if PY_MAJOR_VERSION < 3 PyObject *py_level = PyInt_FromLong(level); if (!py_level) goto bad; module = PyObject_CallFunctionObjArgs(py_import, name, global_dict, empty_dict, list, py_level, (PyObject *)NULL); Py_DECREF(py_level); #else module = PyImport_ImportModuleLevelObject( name, global_dict, empty_dict, list, level); #endif } } bad: #if PY_MAJOR_VERSION < 3 Py_XDECREF(py_import); #endif Py_XDECREF(empty_list); Py_XDECREF(empty_dict); return module; } /* CLineInTraceback */ #ifndef CYTHON_CLINE_IN_TRACEBACK static int __Pyx_CLineForTraceback(CYTHON_NCP_UNUSED PyThreadState *tstate, int c_line) { PyObject *use_cline; PyObject *ptype, *pvalue, *ptraceback; #if CYTHON_COMPILING_IN_CPYTHON PyObject **cython_runtime_dict; #endif if (unlikely(!__pyx_cython_runtime)) { return c_line; } __Pyx_ErrFetchInState(tstate, &ptype, &pvalue, &ptraceback); #if CYTHON_COMPILING_IN_CPYTHON cython_runtime_dict = _PyObject_GetDictPtr(__pyx_cython_runtime); if (likely(cython_runtime_dict)) { __PYX_PY_DICT_LOOKUP_IF_MODIFIED( use_cline, *cython_runtime_dict, __Pyx_PyDict_GetItemStr(*cython_runtime_dict, __pyx_n_s_cline_in_traceback)) } else #endif { PyObject *use_cline_obj = __Pyx_PyObject_GetAttrStr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback); if (use_cline_obj) { use_cline = PyObject_Not(use_cline_obj) ? Py_False : Py_True; Py_DECREF(use_cline_obj); } else { PyErr_Clear(); use_cline = NULL; } } if (!use_cline) { c_line = 0; PyObject_SetAttr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback, Py_False); } else if (use_cline == Py_False || (use_cline != Py_True && PyObject_Not(use_cline) != 0)) { c_line = 0; } __Pyx_ErrRestoreInState(tstate, ptype, pvalue, ptraceback); return c_line; } #endif /* CodeObjectCache */ static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) { int start = 0, mid = 0, end = count - 1; if (end >= 0 && code_line > entries[end].code_line) { return count; } while (start < end) { mid = start + (end - start) / 2; if (code_line < entries[mid].code_line) { end = mid; } else if (code_line > entries[mid].code_line) { start = mid + 1; } else { return mid; } } if (code_line <= entries[mid].code_line) { return mid; } else { return mid + 1; } } static PyCodeObject *__pyx_find_code_object(int code_line) { PyCodeObject* code_object; int pos; if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) { return NULL; } pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) { return NULL; } code_object = __pyx_code_cache.entries[pos].code_object; Py_INCREF(code_object); return code_object; } static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) { int pos, i; __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries; if (unlikely(!code_line)) { return; } if (unlikely(!entries)) { entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry)); if (likely(entries)) { __pyx_code_cache.entries = entries; __pyx_code_cache.max_count = 64; __pyx_code_cache.count = 1; entries[0].code_line = code_line; entries[0].code_object = code_object; Py_INCREF(code_object); } return; } pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) { PyCodeObject* tmp = entries[pos].code_object; entries[pos].code_object = code_object; Py_DECREF(tmp); return; } if (__pyx_code_cache.count == __pyx_code_cache.max_count) { int new_max = __pyx_code_cache.max_count + 64; entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc( __pyx_code_cache.entries, ((size_t)new_max) * sizeof(__Pyx_CodeObjectCacheEntry)); if (unlikely(!entries)) { return; } __pyx_code_cache.entries = entries; __pyx_code_cache.max_count = new_max; } for (i=__pyx_code_cache.count; i>pos; i--) { entries[i] = entries[i-1]; } entries[pos].code_line = code_line; entries[pos].code_object = code_object; __pyx_code_cache.count++; Py_INCREF(code_object); } /* AddTraceback */ #include "compile.h" #include "frameobject.h" #include "traceback.h" static PyCodeObject* __Pyx_CreateCodeObjectForTraceback( const char *funcname, int c_line, int py_line, const char *filename) { PyCodeObject *py_code = 0; PyObject *py_srcfile = 0; PyObject *py_funcname = 0; #if PY_MAJOR_VERSION < 3 py_srcfile = PyString_FromString(filename); #else py_srcfile = PyUnicode_FromString(filename); #endif if (!py_srcfile) goto bad; if (c_line) { #if PY_MAJOR_VERSION < 3 py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); #else py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); #endif } else { #if PY_MAJOR_VERSION < 3 py_funcname = PyString_FromString(funcname); #else py_funcname = PyUnicode_FromString(funcname); #endif } if (!py_funcname) goto bad; py_code = __Pyx_PyCode_New( 0, 0, 0, 0, 0, __pyx_empty_bytes, /*PyObject *code,*/ __pyx_empty_tuple, /*PyObject *consts,*/ __pyx_empty_tuple, /*PyObject *names,*/ __pyx_empty_tuple, /*PyObject *varnames,*/ __pyx_empty_tuple, /*PyObject *freevars,*/ __pyx_empty_tuple, /*PyObject *cellvars,*/ py_srcfile, /*PyObject *filename,*/ py_funcname, /*PyObject *name,*/ py_line, __pyx_empty_bytes /*PyObject *lnotab*/ ); Py_DECREF(py_srcfile); Py_DECREF(py_funcname); return py_code; bad: Py_XDECREF(py_srcfile); Py_XDECREF(py_funcname); return NULL; } static void __Pyx_AddTraceback(const char *funcname, int c_line, int py_line, const char *filename) { PyCodeObject *py_code = 0; PyFrameObject *py_frame = 0; PyThreadState *tstate = __Pyx_PyThreadState_Current; if (c_line) { c_line = __Pyx_CLineForTraceback(tstate, c_line); } py_code = __pyx_find_code_object(c_line ? -c_line : py_line); if (!py_code) { py_code = __Pyx_CreateCodeObjectForTraceback( funcname, c_line, py_line, filename); if (!py_code) goto bad; __pyx_insert_code_object(c_line ? -c_line : py_line, py_code); } py_frame = PyFrame_New( tstate, /*PyThreadState *tstate,*/ py_code, /*PyCodeObject *code,*/ __pyx_d, /*PyObject *globals,*/ 0 /*PyObject *locals*/ ); if (!py_frame) goto bad; __Pyx_PyFrame_SetLineNumber(py_frame, py_line); PyTraceBack_Here(py_frame); bad: Py_XDECREF(py_code); Py_XDECREF(py_frame); } #if PY_MAJOR_VERSION < 3 static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags) { if (PyObject_CheckBuffer(obj)) return PyObject_GetBuffer(obj, view, flags); PyErr_Format(PyExc_TypeError, "'%.200s' does not have the buffer interface", Py_TYPE(obj)->tp_name); return -1; } static void __Pyx_ReleaseBuffer(Py_buffer *view) { PyObject *obj = view->obj; if (!obj) return; if (PyObject_CheckBuffer(obj)) { PyBuffer_Release(view); return; } if ((0)) {} view->obj = NULL; Py_DECREF(obj); } #endif /* CIntFromPyVerify */ #define __PYX_VERIFY_RETURN_INT(target_type, func_type, func_value)\ __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 0) #define __PYX_VERIFY_RETURN_INT_EXC(target_type, func_type, func_value)\ __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 1) #define __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, exc)\ {\ func_type value = func_value;\ if (sizeof(target_type) < sizeof(func_type)) {\ if (unlikely(value != (func_type) (target_type) value)) {\ func_type zero = 0;\ if (exc && unlikely(value == (func_type)-1 && PyErr_Occurred()))\ return (target_type) -1;\ if (is_unsigned && unlikely(value < zero))\ goto raise_neg_overflow;\ else\ goto raise_overflow;\ }\ }\ return (target_type) value;\ } /* Declarations */ #if CYTHON_CCOMPLEX #ifdef __cplusplus static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { return ::std::complex< float >(x, y); } #else static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { return x + y*(__pyx_t_float_complex)_Complex_I; } #endif #else static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { __pyx_t_float_complex z; z.real = x; z.imag = y; return z; } #endif /* Arithmetic */ #if CYTHON_CCOMPLEX #else static CYTHON_INLINE int __Pyx_c_eq_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { return (a.real == b.real) && (a.imag == b.imag); } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sum_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; z.real = a.real + b.real; z.imag = a.imag + b.imag; return z; } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_diff_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; z.real = a.real - b.real; z.imag = a.imag - b.imag; return z; } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prod_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; z.real = a.real * b.real - a.imag * b.imag; z.imag = a.real * b.imag + a.imag * b.real; return z; } #if 1 static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { if (b.imag == 0) { return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.real); } else if (fabsf(b.real) >= fabsf(b.imag)) { if (b.real == 0 && b.imag == 0) { return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.imag); } else { float r = b.imag / b.real; float s = (float)(1.0) / (b.real + b.imag * r); return __pyx_t_float_complex_from_parts( (a.real + a.imag * r) * s, (a.imag - a.real * r) * s); } } else { float r = b.real / b.imag; float s = (float)(1.0) / (b.imag + b.real * r); return __pyx_t_float_complex_from_parts( (a.real * r + a.imag) * s, (a.imag * r - a.real) * s); } } #else static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { if (b.imag == 0) { return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.real); } else { float denom = b.real * b.real + b.imag * b.imag; return __pyx_t_float_complex_from_parts( (a.real * b.real + a.imag * b.imag) / denom, (a.imag * b.real - a.real * b.imag) / denom); } } #endif static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_neg_float(__pyx_t_float_complex a) { __pyx_t_float_complex z; z.real = -a.real; z.imag = -a.imag; return z; } static CYTHON_INLINE int __Pyx_c_is_zero_float(__pyx_t_float_complex a) { return (a.real == 0) && (a.imag == 0); } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conj_float(__pyx_t_float_complex a) { __pyx_t_float_complex z; z.real = a.real; z.imag = -a.imag; return z; } #if 1 static CYTHON_INLINE float __Pyx_c_abs_float(__pyx_t_float_complex z) { #if !defined(HAVE_HYPOT) || defined(_MSC_VER) return sqrtf(z.real*z.real + z.imag*z.imag); #else return hypotf(z.real, z.imag); #endif } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_pow_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; float r, lnr, theta, z_r, z_theta; if (b.imag == 0 && b.real == (int)b.real) { if (b.real < 0) { float denom = a.real * a.real + a.imag * a.imag; a.real = a.real / denom; a.imag = -a.imag / denom; b.real = -b.real; } switch ((int)b.real) { case 0: z.real = 1; z.imag = 0; return z; case 1: return a; case 2: return __Pyx_c_prod_float(a, a); case 3: z = __Pyx_c_prod_float(a, a); return __Pyx_c_prod_float(z, a); case 4: z = __Pyx_c_prod_float(a, a); return __Pyx_c_prod_float(z, z); } } if (a.imag == 0) { if (a.real == 0) { return a; } else if (b.imag == 0) { z.real = powf(a.real, b.real); z.imag = 0; return z; } else if (a.real > 0) { r = a.real; theta = 0; } else { r = -a.real; theta = atan2f(0.0, -1.0); } } else { r = __Pyx_c_abs_float(a); theta = atan2f(a.imag, a.real); } lnr = logf(r); z_r = expf(lnr * b.real - theta * b.imag); z_theta = theta * b.real + lnr * b.imag; z.real = z_r * cosf(z_theta); z.imag = z_r * sinf(z_theta); return z; } #endif #endif /* Declarations */ #if CYTHON_CCOMPLEX #ifdef __cplusplus static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { return ::std::complex< double >(x, y); } #else static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { return x + y*(__pyx_t_double_complex)_Complex_I; } #endif #else static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { __pyx_t_double_complex z; z.real = x; z.imag = y; return z; } #endif /* Arithmetic */ #if CYTHON_CCOMPLEX #else static CYTHON_INLINE int __Pyx_c_eq_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { return (a.real == b.real) && (a.imag == b.imag); } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; z.real = a.real + b.real; z.imag = a.imag + b.imag; return z; } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; z.real = a.real - b.real; z.imag = a.imag - b.imag; return z; } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; z.real = a.real * b.real - a.imag * b.imag; z.imag = a.real * b.imag + a.imag * b.real; return z; } #if 1 static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { if (b.imag == 0) { return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.real); } else if (fabs(b.real) >= fabs(b.imag)) { if (b.real == 0 && b.imag == 0) { return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.imag); } else { double r = b.imag / b.real; double s = (double)(1.0) / (b.real + b.imag * r); return __pyx_t_double_complex_from_parts( (a.real + a.imag * r) * s, (a.imag - a.real * r) * s); } } else { double r = b.real / b.imag; double s = (double)(1.0) / (b.imag + b.real * r); return __pyx_t_double_complex_from_parts( (a.real * r + a.imag) * s, (a.imag * r - a.real) * s); } } #else static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { if (b.imag == 0) { return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.real); } else { double denom = b.real * b.real + b.imag * b.imag; return __pyx_t_double_complex_from_parts( (a.real * b.real + a.imag * b.imag) / denom, (a.imag * b.real - a.real * b.imag) / denom); } } #endif static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg_double(__pyx_t_double_complex a) { __pyx_t_double_complex z; z.real = -a.real; z.imag = -a.imag; return z; } static CYTHON_INLINE int __Pyx_c_is_zero_double(__pyx_t_double_complex a) { return (a.real == 0) && (a.imag == 0); } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj_double(__pyx_t_double_complex a) { __pyx_t_double_complex z; z.real = a.real; z.imag = -a.imag; return z; } #if 1 static CYTHON_INLINE double __Pyx_c_abs_double(__pyx_t_double_complex z) { #if !defined(HAVE_HYPOT) || defined(_MSC_VER) return sqrt(z.real*z.real + z.imag*z.imag); #else return hypot(z.real, z.imag); #endif } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; double r, lnr, theta, z_r, z_theta; if (b.imag == 0 && b.real == (int)b.real) { if (b.real < 0) { double denom = a.real * a.real + a.imag * a.imag; a.real = a.real / denom; a.imag = -a.imag / denom; b.real = -b.real; } switch ((int)b.real) { case 0: z.real = 1; z.imag = 0; return z; case 1: return a; case 2: return __Pyx_c_prod_double(a, a); case 3: z = __Pyx_c_prod_double(a, a); return __Pyx_c_prod_double(z, a); case 4: z = __Pyx_c_prod_double(a, a); return __Pyx_c_prod_double(z, z); } } if (a.imag == 0) { if (a.real == 0) { return a; } else if (b.imag == 0) { z.real = pow(a.real, b.real); z.imag = 0; return z; } else if (a.real > 0) { r = a.real; theta = 0; } else { r = -a.real; theta = atan2(0.0, -1.0); } } else { r = __Pyx_c_abs_double(a); theta = atan2(a.imag, a.real); } lnr = log(r); z_r = exp(lnr * b.real - theta * b.imag); z_theta = theta * b.real + lnr * b.imag; z.real = z_r * cos(z_theta); z.imag = z_r * sin(z_theta); return z; } #endif #endif /* CIntToPy */ static CYTHON_INLINE PyObject* __Pyx_PyInt_From_Py_intptr_t(Py_intptr_t value) { #ifdef __Pyx_HAS_GCC_DIAGNOSTIC #pragma GCC diagnostic push #pragma GCC diagnostic ignored "-Wconversion" #endif const Py_intptr_t neg_one = (Py_intptr_t) -1, const_zero = (Py_intptr_t) 0; #ifdef __Pyx_HAS_GCC_DIAGNOSTIC #pragma GCC diagnostic pop #endif const int is_unsigned = neg_one > const_zero; if (is_unsigned) { if (sizeof(Py_intptr_t) < sizeof(long)) { return PyInt_FromLong((long) value); } else if (sizeof(Py_intptr_t) <= sizeof(unsigned long)) { return PyLong_FromUnsignedLong((unsigned long) value); #ifdef HAVE_LONG_LONG } else if (sizeof(Py_intptr_t) <= sizeof(unsigned PY_LONG_LONG)) { return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); #endif } } else { if (sizeof(Py_intptr_t) <= sizeof(long)) { return PyInt_FromLong((long) value); #ifdef HAVE_LONG_LONG } else if (sizeof(Py_intptr_t) <= sizeof(PY_LONG_LONG)) { return PyLong_FromLongLong((PY_LONG_LONG) value); #endif } } { int one = 1; int little = (int)*(unsigned char *)&one; unsigned char *bytes = (unsigned char *)&value; return _PyLong_FromByteArray(bytes, sizeof(Py_intptr_t), little, !is_unsigned); } } /* CIntToPy */ static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) { #ifdef __Pyx_HAS_GCC_DIAGNOSTIC #pragma GCC diagnostic push #pragma GCC diagnostic ignored "-Wconversion" #endif const int neg_one = (int) -1, const_zero = (int) 0; #ifdef __Pyx_HAS_GCC_DIAGNOSTIC #pragma GCC diagnostic pop #endif const int is_unsigned = neg_one > const_zero; if (is_unsigned) { if (sizeof(int) < sizeof(long)) { return PyInt_FromLong((long) value); } else if (sizeof(int) <= sizeof(unsigned long)) { return PyLong_FromUnsignedLong((unsigned long) value); #ifdef HAVE_LONG_LONG } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) { return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); #endif } } else { if (sizeof(int) <= sizeof(long)) { return PyInt_FromLong((long) value); #ifdef HAVE_LONG_LONG } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) { return PyLong_FromLongLong((PY_LONG_LONG) value); #endif } } { int one = 1; int little = (int)*(unsigned char *)&one; unsigned char *bytes = (unsigned char *)&value; return _PyLong_FromByteArray(bytes, sizeof(int), little, !is_unsigned); } } /* CIntFromPy */ static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) { #ifdef __Pyx_HAS_GCC_DIAGNOSTIC #pragma GCC diagnostic push #pragma GCC diagnostic ignored "-Wconversion" #endif const int neg_one = (int) -1, const_zero = (int) 0; #ifdef __Pyx_HAS_GCC_DIAGNOSTIC #pragma GCC diagnostic pop #endif const int is_unsigned = neg_one > const_zero; #if PY_MAJOR_VERSION < 3 if (likely(PyInt_Check(x))) { if (sizeof(int) < sizeof(long)) { __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG(x)) } else { long val = PyInt_AS_LONG(x); if (is_unsigned && unlikely(val < 0)) { goto raise_neg_overflow; } return (int) val; } } else #endif if (likely(PyLong_Check(x))) { if (is_unsigned) { #if CYTHON_USE_PYLONG_INTERNALS const digit* digits = ((PyLongObject*)x)->ob_digit; switch (Py_SIZE(x)) { case 0: return (int) 0; case 1: __PYX_VERIFY_RETURN_INT(int, digit, digits[0]) case 2: if (8 * sizeof(int) > 1 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(int) >= 2 * PyLong_SHIFT) { return (int) (((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); } } break; case 3: if (8 * sizeof(int) > 2 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(int) >= 3 * PyLong_SHIFT) { return (int) (((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); } } break; case 4: if (8 * sizeof(int) > 3 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(int) >= 4 * PyLong_SHIFT) { return (int) (((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); } } break; } #endif #if CYTHON_COMPILING_IN_CPYTHON if (unlikely(Py_SIZE(x) < 0)) { goto raise_neg_overflow; } #else { int result = PyObject_RichCompareBool(x, Py_False, Py_LT); if (unlikely(result < 0)) return (int) -1; if (unlikely(result == 1)) goto raise_neg_overflow; } #endif if (sizeof(int) <= sizeof(unsigned long)) { __PYX_VERIFY_RETURN_INT_EXC(int, unsigned long, PyLong_AsUnsignedLong(x)) #ifdef HAVE_LONG_LONG } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) { __PYX_VERIFY_RETURN_INT_EXC(int, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) #endif } } else { #if CYTHON_USE_PYLONG_INTERNALS const digit* digits = ((PyLongObject*)x)->ob_digit; switch (Py_SIZE(x)) { case 0: return (int) 0; case -1: __PYX_VERIFY_RETURN_INT(int, sdigit, (sdigit) (-(sdigit)digits[0])) case 1: __PYX_VERIFY_RETURN_INT(int, digit, +digits[0]) case -2: if (8 * sizeof(int) - 1 > 1 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { return (int) (((int)-1)*(((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); } } break; case 2: if (8 * sizeof(int) > 1 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { return (int) ((((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); } } break; case -3: if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { return (int) (((int)-1)*(((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); } } break; case 3: if (8 * sizeof(int) > 2 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { return (int) ((((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); } } break; case -4: if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) { return (int) (((int)-1)*(((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); } } break; case 4: if (8 * sizeof(int) > 3 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) { return (int) ((((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); } } break; } #endif if (sizeof(int) <= sizeof(long)) { __PYX_VERIFY_RETURN_INT_EXC(int, long, PyLong_AsLong(x)) #ifdef HAVE_LONG_LONG } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) { __PYX_VERIFY_RETURN_INT_EXC(int, PY_LONG_LONG, PyLong_AsLongLong(x)) #endif } } { #if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) PyErr_SetString(PyExc_RuntimeError, "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); #else int val; PyObject *v = __Pyx_PyNumber_IntOrLong(x); #if PY_MAJOR_VERSION < 3 if (likely(v) && !PyLong_Check(v)) { PyObject *tmp = v; v = PyNumber_Long(tmp); Py_DECREF(tmp); } #endif if (likely(v)) { int one = 1; int is_little = (int)*(unsigned char *)&one; unsigned char *bytes = (unsigned char *)&val; int ret = _PyLong_AsByteArray((PyLongObject *)v, bytes, sizeof(val), is_little, !is_unsigned); Py_DECREF(v); if (likely(!ret)) return val; } #endif return (int) -1; } } else { int val; PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); if (!tmp) return (int) -1; val = __Pyx_PyInt_As_int(tmp); Py_DECREF(tmp); return val; } raise_overflow: PyErr_SetString(PyExc_OverflowError, "value too large to convert to int"); return (int) -1; raise_neg_overflow: PyErr_SetString(PyExc_OverflowError, "can't convert negative value to int"); return (int) -1; } /* CIntToPy */ static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) { #ifdef __Pyx_HAS_GCC_DIAGNOSTIC #pragma GCC diagnostic push #pragma GCC diagnostic ignored "-Wconversion" #endif const long neg_one = (long) -1, const_zero = (long) 0; #ifdef __Pyx_HAS_GCC_DIAGNOSTIC #pragma GCC diagnostic pop #endif const int is_unsigned = neg_one > const_zero; if (is_unsigned) { if (sizeof(long) < sizeof(long)) { return PyInt_FromLong((long) value); } else if (sizeof(long) <= sizeof(unsigned long)) { return PyLong_FromUnsignedLong((unsigned long) value); #ifdef HAVE_LONG_LONG } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) { return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); #endif } } else { if (sizeof(long) <= sizeof(long)) { return PyInt_FromLong((long) value); #ifdef HAVE_LONG_LONG } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) { return PyLong_FromLongLong((PY_LONG_LONG) value); #endif } } { int one = 1; int little = (int)*(unsigned char *)&one; unsigned char *bytes = (unsigned char *)&value; return _PyLong_FromByteArray(bytes, sizeof(long), little, !is_unsigned); } } /* CIntFromPy */ static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) { #ifdef __Pyx_HAS_GCC_DIAGNOSTIC #pragma GCC diagnostic push #pragma GCC diagnostic ignored "-Wconversion" #endif const long neg_one = (long) -1, const_zero = (long) 0; #ifdef __Pyx_HAS_GCC_DIAGNOSTIC #pragma GCC diagnostic pop #endif const int is_unsigned = neg_one > const_zero; #if PY_MAJOR_VERSION < 3 if (likely(PyInt_Check(x))) { if (sizeof(long) < sizeof(long)) { __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG(x)) } else { long val = PyInt_AS_LONG(x); if (is_unsigned && unlikely(val < 0)) { goto raise_neg_overflow; } return (long) val; } } else #endif if (likely(PyLong_Check(x))) { if (is_unsigned) { #if CYTHON_USE_PYLONG_INTERNALS const digit* digits = ((PyLongObject*)x)->ob_digit; switch (Py_SIZE(x)) { case 0: return (long) 0; case 1: __PYX_VERIFY_RETURN_INT(long, digit, digits[0]) case 2: if (8 * sizeof(long) > 1 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(long) >= 2 * PyLong_SHIFT) { return (long) (((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); } } break; case 3: if (8 * sizeof(long) > 2 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(long) >= 3 * PyLong_SHIFT) { return (long) (((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); } } break; case 4: if (8 * sizeof(long) > 3 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(long) >= 4 * PyLong_SHIFT) { return (long) (((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); } } break; } #endif #if CYTHON_COMPILING_IN_CPYTHON if (unlikely(Py_SIZE(x) < 0)) { goto raise_neg_overflow; } #else { int result = PyObject_RichCompareBool(x, Py_False, Py_LT); if (unlikely(result < 0)) return (long) -1; if (unlikely(result == 1)) goto raise_neg_overflow; } #endif if (sizeof(long) <= sizeof(unsigned long)) { __PYX_VERIFY_RETURN_INT_EXC(long, unsigned long, PyLong_AsUnsignedLong(x)) #ifdef HAVE_LONG_LONG } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) { __PYX_VERIFY_RETURN_INT_EXC(long, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) #endif } } else { #if CYTHON_USE_PYLONG_INTERNALS const digit* digits = ((PyLongObject*)x)->ob_digit; switch (Py_SIZE(x)) { case 0: return (long) 0; case -1: __PYX_VERIFY_RETURN_INT(long, sdigit, (sdigit) (-(sdigit)digits[0])) case 1: __PYX_VERIFY_RETURN_INT(long, digit, +digits[0]) case -2: if (8 * sizeof(long) - 1 > 1 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { return (long) (((long)-1)*(((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); } } break; case 2: if (8 * sizeof(long) > 1 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { return (long) ((((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); } } break; case -3: if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { return (long) (((long)-1)*(((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); } } break; case 3: if (8 * sizeof(long) > 2 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { return (long) ((((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); } } break; case -4: if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { return (long) (((long)-1)*(((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); } } break; case 4: if (8 * sizeof(long) > 3 * PyLong_SHIFT) { if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { return (long) ((((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); } } break; } #endif if (sizeof(long) <= sizeof(long)) { __PYX_VERIFY_RETURN_INT_EXC(long, long, PyLong_AsLong(x)) #ifdef HAVE_LONG_LONG } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) { __PYX_VERIFY_RETURN_INT_EXC(long, PY_LONG_LONG, PyLong_AsLongLong(x)) #endif } } { #if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) PyErr_SetString(PyExc_RuntimeError, "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); #else long val; PyObject *v = __Pyx_PyNumber_IntOrLong(x); #if PY_MAJOR_VERSION < 3 if (likely(v) && !PyLong_Check(v)) { PyObject *tmp = v; v = PyNumber_Long(tmp); Py_DECREF(tmp); } #endif if (likely(v)) { int one = 1; int is_little = (int)*(unsigned char *)&one; unsigned char *bytes = (unsigned char *)&val; int ret = _PyLong_AsByteArray((PyLongObject *)v, bytes, sizeof(val), is_little, !is_unsigned); Py_DECREF(v); if (likely(!ret)) return val; } #endif return (long) -1; } } else { long val; PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); if (!tmp) return (long) -1; val = __Pyx_PyInt_As_long(tmp); Py_DECREF(tmp); return val; } raise_overflow: PyErr_SetString(PyExc_OverflowError, "value too large to convert to long"); return (long) -1; raise_neg_overflow: PyErr_SetString(PyExc_OverflowError, "can't convert negative value to long"); return (long) -1; } /* FastTypeChecks */ #if CYTHON_COMPILING_IN_CPYTHON static int __Pyx_InBases(PyTypeObject *a, PyTypeObject *b) { while (a) { a = a->tp_base; if (a == b) return 1; } return b == &PyBaseObject_Type; } static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b) { PyObject *mro; if (a == b) return 1; mro = a->tp_mro; if (likely(mro)) { Py_ssize_t i, n; n = PyTuple_GET_SIZE(mro); for (i = 0; i < n; i++) { if (PyTuple_GET_ITEM(mro, i) == (PyObject *)b) return 1; } return 0; } return __Pyx_InBases(a, b); } #if PY_MAJOR_VERSION == 2 static int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject* exc_type2) { PyObject *exception, *value, *tb; int res; __Pyx_PyThreadState_declare __Pyx_PyThreadState_assign __Pyx_ErrFetch(&exception, &value, &tb); res = exc_type1 ? 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" "The ability to return an instance of a strict subclass of int " "is deprecated, and may be removed in a future version of Python.", Py_TYPE(result)->tp_name)) { Py_DECREF(result); return NULL; } return result; } #endif PyErr_Format(PyExc_TypeError, "__%.4s__ returned non-%.4s (type %.200s)", type_name, type_name, Py_TYPE(result)->tp_name); Py_DECREF(result); return NULL; } static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x) { #if CYTHON_USE_TYPE_SLOTS PyNumberMethods *m; #endif const char *name = NULL; PyObject *res = NULL; #if PY_MAJOR_VERSION < 3 if (likely(PyInt_Check(x) || PyLong_Check(x))) #else if (likely(PyLong_Check(x))) #endif return __Pyx_NewRef(x); #if CYTHON_USE_TYPE_SLOTS m = Py_TYPE(x)->tp_as_number; #if PY_MAJOR_VERSION < 3 if (m && m->nb_int) { name = "int"; res = m->nb_int(x); } else if (m && m->nb_long) { name = "long"; res = m->nb_long(x); } #else if (likely(m && m->nb_int)) { name = "int"; res = m->nb_int(x); } #endif #else if (!PyBytes_CheckExact(x) && !PyUnicode_CheckExact(x)) { res = PyNumber_Int(x); } #endif if (likely(res)) { #if PY_MAJOR_VERSION < 3 if (unlikely(!PyInt_Check(res) && !PyLong_Check(res))) { #else if (unlikely(!PyLong_CheckExact(res))) { #endif return __Pyx_PyNumber_IntOrLongWrongResultType(res, name); } } else if (!PyErr_Occurred()) { PyErr_SetString(PyExc_TypeError, "an integer is required"); } return res; } static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) { Py_ssize_t ival; PyObject *x; #if PY_MAJOR_VERSION < 3 if (likely(PyInt_CheckExact(b))) { if (sizeof(Py_ssize_t) >= sizeof(long)) return PyInt_AS_LONG(b); else return PyInt_AsSsize_t(b); } #endif if (likely(PyLong_CheckExact(b))) { #if CYTHON_USE_PYLONG_INTERNALS const digit* digits = ((PyLongObject*)b)->ob_digit; const Py_ssize_t size = Py_SIZE(b); if (likely(__Pyx_sst_abs(size) <= 1)) { ival = likely(size) ? digits[0] : 0; if (size == -1) ival = -ival; return ival; } else { switch (size) { case 2: if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) { return (Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); } break; case -2: if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) { return -(Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); } break; case 3: if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) { return (Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); } break; case -3: if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) { return -(Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); } break; case 4: if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) { return (Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); } break; case -4: if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) { return -(Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); } break; } } #endif return PyLong_AsSsize_t(b); } x = PyNumber_Index(b); if (!x) return -1; ival = PyInt_AsSsize_t(x); Py_DECREF(x); return ival; } static CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b) { return b ? __Pyx_NewRef(Py_True) : __Pyx_NewRef(Py_False); } static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) { return PyInt_FromSize_t(ival); } #endif /* Py_PYTHON_H */ diff --git a/helper_scripts/dtw/dtw.pyx b/helper_scripts/dtw/dtw.pyx index 6c48757..910f170 100644 --- a/helper_scripts/dtw/dtw.pyx +++ b/helper_scripts/dtw/dtw.pyx @@ -1,149 +1,230 @@ ## This code is written by Davide Albanese, ## (C) 2011 mlpy Developers. ## This program is free software: you can redistribute it and/or modify ## it under the terms of the GNU General Public License as published by ## the Free Software Foundation, either version 3 of the License, or ## (at your option) any later version. ## This program is distributed in the hope that it will be useful, ## but WITHOUT ANY WARRANTY; without even the implied warranty of ## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the ## GNU General Public License for more details. ## You should have received a copy of the GNU General Public License ## along with this program. If not, see . import numpy as np cimport numpy as np from libc.stdlib cimport * from cdtw cimport * np.import_array() def dtw_std(x, y, dist_only=True, squared=False): """Standard DTW as described in [Muller07]_, using the Euclidean distance (absolute value of the difference) or squared Euclidean distance (as in [Keogh01]_) as local cost measure. :Parameters: x : 1d array_like object (N) first sequence y : 1d array_like object (M) second sequence dist_only : bool compute only the distance squared : bool squared Euclidean distance :Returns: dist : float unnormalized minimum-distance warp path between sequences cost : 2d numpy array (N,M) [if dist_only=False] accumulated cost matrix path : tuple of two 1d numpy array (path_x, path_y) [if dist_only=False] warp path .. [Muller07] M Muller. Information Retrieval for Music and Motion. Springer, 2007. .. [Keogh01] E J Keogh, M J Pazzani. Derivative Dynamic Time Warping. In First SIAM International Conference on Data Mining, 2001. """ cdef np.ndarray[np.float_t, ndim=1] x_arr cdef np.ndarray[np.float_t, ndim=1] y_arr cdef np.ndarray[np.float_t, ndim=2] cost_arr cdef np.ndarray[np.int_t, ndim=1] px_arr cdef np.ndarray[np.int_t, ndim=1] py_arr cdef Path p cdef double dist cdef int i cdef int sq x_arr = np.ascontiguousarray(x, dtype=np.float) y_arr = np.ascontiguousarray(y, dtype=np.float) cost_arr = np.empty((x_arr.shape[0], y_arr.shape[0]), dtype=np.float) if squared: sq = 1 else: sq = 0 dist = std( x_arr.data, y_arr.data, x_arr.shape[0], y_arr.shape[0], cost_arr.data, sq) if dist_only: return dist else: path( cost_arr.data, cost_arr.shape[0], cost_arr.shape[1], -1, -1, &p) px_arr = np.empty(p.k, dtype=np.int) py_arr = np.empty(p.k, dtype=np.int) for i in range(p.k): px_arr[i] = p.px[i] py_arr[i] = p.py[i] free (p.px) free (p.py) return dist, cost_arr, (px_arr, py_arr) def dtw_subsequence(x, y): """Subsequence DTW as described in [Muller07]_, assuming that the length of `y` is much larger than the length of `x` and using the Manhattan distance (absolute value of the difference) as local cost measure. Returns the subsequence of `y` that are close to `x` with respect to the minimum DTW distance. :Parameters: x : 1d array_like object (N) first sequence y : 1d array_like object (M) second sequence :Returns: dist : float unnormalized minimum-distance warp path between x and the subsequence of y cost : 2d numpy array (N,M) [if dist_only=False] complete accumulated cost matrix path : tuple of two 1d numpy array (path_x, path_y) warp path """ cdef np.ndarray[np.float_t, ndim=1] x_arr cdef np.ndarray[np.float_t, ndim=1] y_arr cdef np.ndarray[np.float_t, ndim=2] cost_arr cdef np.ndarray[np.int_t, ndim=1] px_arr cdef np.ndarray[np.int_t, ndim=1] py_arr cdef Path p cdef int i x_arr = np.ascontiguousarray(x, dtype=np.float) y_arr = np.ascontiguousarray(y, dtype=np.float) cost_arr = np.empty((x_arr.shape[0], y_arr.shape[0]), dtype=np.float) subsequence( x_arr.data, y_arr.data, x_arr.shape[0], y_arr.shape[0], cost_arr.data) idx = np.argmin(cost_arr[-1, :]) dist = cost_arr[-1, idx] subsequence_path( cost_arr.data, x_arr.shape[0], y_arr.shape[0], idx, &p) px_arr = np.empty(p.k, dtype=np.int) py_arr = np.empty(p.k, dtype=np.int) for i in range(p.k): px_arr[i] = p.px[i] py_arr[i] = p.py[i] free (p.px) free (p.py) return dist, cost_arr, (px_arr, py_arr) + +def dtw_tv(tx, x, ty, y, dist_only=True, squared=False): + """Standard DTW as described in [Muller07]_, + using the Euclidean distance (absolute value + of the difference) or squared Euclidean distance + (as in [Keogh01]_) as local cost measure. + + :Parameters: + tx : 1d array_like object (N) + first sequence timing: normalized + x : 1d array_like object (N) + first sequence + ty: 1d array_like object (N) + second sequence timing: normalized + y : 1d array_like object (M) + second sequence + dist_only : bool + compute only the distance + squared : bool + squared Euclidean distance + + :Returns: + dist : float + unnormalized minimum-distance warp path + between sequences + cost : 2d numpy array (N,M) [if dist_only=False] + accumulated cost matrix + path : tuple of two 1d numpy array (path_x, path_y) [if dist_only=False] + warp path + + .. [Muller07] M Muller. Information Retrieval for Music and Motion. Springer, 2007. + .. [Keogh01] E J Keogh, M J Pazzani. Derivative Dynamic Time Warping. In First SIAM International Conference on Data Mining, 2001. + """ + + #Normalize the timing if not already normalized: + if max(ty)>1: + assert max(tx)>1 + max_tot = max(max(tx),max(ty)) + ty /= max_tot + tx /= max_tot + else: + assert max(tx) <= 1 + + cdef np.ndarray[np.float_t, ndim=1] x_arr + cdef np.ndarray[np.float_t, ndim=1] y_arr + cdef np.ndarray[np.float_t, ndim=1] tx_arr + cdef np.ndarray[np.float_t, ndim=1] ty_arr + cdef np.ndarray[np.float_t, ndim=2] cost_arr + cdef np.ndarray[np.int_t, ndim=1] px_arr + cdef np.ndarray[np.int_t, ndim=1] py_arr + cdef Path p + cdef double dist + cdef int i + cdef int sq + + x_arr = np.ascontiguousarray(x, dtype=np.float) + y_arr = np.ascontiguousarray(y, dtype=np.float) + tx_arr = np.ascontiguousarray(tx, dtype=np.float) + ty_arr = np.ascontiguousarray(ty, dtype=np.float) + cost_arr = np.empty((x_arr.shape[0], y_arr.shape[0]), dtype=np.float) + + if squared: sq = 1 + else: sq = 0 + + dist = tvstd( tx_arr.data, x_arr.data, ty_arr.data, y_arr.data, + x_arr.shape[0], y_arr.shape[0], + cost_arr.data, sq) + + if dist_only: + return dist + else: + path( cost_arr.data, cost_arr.shape[0], + cost_arr.shape[1], -1, -1, &p) + px_arr = np.empty(p.k, dtype=np.int) + py_arr = np.empty(p.k, dtype=np.int) + for i in range(p.k): + px_arr[i] = p.px[i] + py_arr[i] = p.py[i] + free (p.px) + free (p.py) + return dist, cost_arr, (px_arr, py_arr) \ No newline at end of file diff --git a/src/precise_multiple_templates_prog.py b/src/precise_multiple_templates_prog.py index 03b465e..3007e98 100644 --- a/src/precise_multiple_templates_prog.py +++ b/src/precise_multiple_templates_prog.py @@ -1,1082 +1,1190 @@ import multiprocessing import os import pickle +from re import template +import pandas as pd import shutil import sys from multiprocessing import Pool from time import time import matplotlib import matplotlib.pyplot as plt from numpy.core.numeric import base_repr # configure backend here matplotlib.use('SVG') import numpy as np from dtaidistance import dtw from numpy.core.fromnumeric import argmin scripts = '../helper_scripts' if scripts not in sys.path: sys.path.insert(0,scripts) import warnings from copy import copy -from dtw.dtw import dtw_std +from dtw.dtw import dtw_std, dtw_tv from csv2mit_with_dir_out import csv2mit_with_dir_out from scipy import stats from scipy.interpolate import CubicSpline, interp1d from scipy.signal.signaltools import fftconvolve import wfdb from build_template import build_template, multi_template data_beats_dir = "../data/beats/" log_dir = "../results/beat_recon_logs_multi_prog_" # Signal freqeuncy variables FREQ = 128 MULTIPLIER_FREQ = 20 #Final freq: 128*20 = 2.56 KHz ADC_GAIN = 200 # Parameters fixed for result evaluation -PERC_BINS = 15 +PERC_BINS = 10 +PERCENTILE_TO_PLOT = [1,25,50,75,99] # Variable fixed by arguments (eg. arguments) CLUSTER_PERCENTAGE = 3 NUM_BEAT_ANALYZED = 50 INTERPOLATION_TYPE = 'flat' FILES_SELECTED = ["17052.pickle"] PARALLELIZE_ALONG = 'files' -LEVELS = [3,4,5,6,7,8,9,10,11] +LEVELS = [5]#[3,4,5,6,7,8,9,10,11] SEC_FOR_INITIAL_TEMPLATES = 3*60 #5*60 LEN_DISTANCE_VECTOR = 60 #80 LEN_DISTANCE_VECTOR_REF = 400 #500 SEC_FOR_NEW_TEMPLATES = 40 #2*60 TIME_MODE = 'short' + +def delineate(log_dir_this_lvl_resampling, signal_type, QRS_pos): + #TODO: problem here: /!\ level 3- spline does not with many samples, maybe a random beats trigger a condition that breake ecgpwuave (not halting) + # IT IS SOLVED BY TERMINATING (CTRL-C ing) ONCE WE USE THE LEVEL 3 --> arresting ecgpuwave stop only the delineator and everything else works + # TO ANALYZE BY NOT DELETING LEVELS AND TEST FROM TERMINAL --> there are annotations where level 3 fails to have any reconizable peak around the given annotation ! + dir_to_use = os.path.join(log_dir_this_lvl_resampling,signal_type) + annotations_tot = pd.DataFrame({"time": [], "idx": [], "type": [], "0": [], "1": [], "2": []}) + end_record = False + start_from = 0 + singal = os.path.join(dir_to_use,"data.csv") + + #Trasform the signal in the MIT format + csv2mit_with_dir_out(singal,fs = FREQ, units = 'mV', header = False, fmt = '212', adc_gain=ADC_GAIN, baseline = 0) + os.remove(singal) + + #Run the delineator + wfdb.wrann("data","atrN",np.array(QRS_pos), symbol= ['N']*len(QRS_pos),write_dir=dir_to_use) + while not end_record: + cmd_delineator = f"(cd {dir_to_use} ; timeout 10 ecgpuwave -r data -a atrTemp -i atrN -f {start_from} > /dev/null 2>&1)" + os.system(cmd_delineator) + #Write the new annotations back + # THIS METHOD FAIL IF THE DELINEATOR DIDN'T TERMINATE (I.E. IT FAILED ANNOTITING AFTER A CERTAIN TIMESTAMP) + # SOLUTION: SUBSTITUTE WITH A SYS CALL AND READ THE STDOUT + cmd_read_ann = f"(cd {dir_to_use} ; rdann -r data -a atrTemp >annotations_text.txt 2>error.txt)" + os.system(cmd_read_ann) + annotations_this_chunk = pd.read_csv(os.path.join(dir_to_use,"annotations_text.txt"),header=None, delim_whitespace=True,names=["time", "idx", "type", "0", "1", "2"]) + annotations_tot = annotations_tot.append(annotations_this_chunk, ignore_index = True) + if len(annotations_this_chunk) > 0: + start_from = int(annotations_this_chunk.idx.values[-1]/FREQ+1) + else: + start_from += 1 + with open(f"{dir_to_use}/error.txt","r") as error_file: + if len(error_file.readlines()) == 0: + end_record = True + else: + print(f"/!\/!\/!\/!\/!\/!\ dir: {dir_to_use}: ecgpuwave needed a second pass at idx: {start_from-1} /!\/!\/!\/!\/!\ ") + os.remove(os.path.join(dir_to_use,"data.atrTemp")) + os.remove(os.path.join(dir_to_use,"annotations_text.txt")) + os.remove(os.path.join(dir_to_use,"error.txt")) + # + annotations_tot_pt = annotations_tot[(annotations_tot['type'] == "p") | (annotations_tot['type'] == "t")] + if len(annotations_tot) == 0: + annotations_tot = pd.DataFrame({"time": [0], "idx": [0], "type": ['p'], "0": [0], "1": [0], "2": [0]}) + if len(annotations_tot_pt) == 0: + annotations_tot_pt = pd.DataFrame({"time": [0], "idx": [0], "type": ['p'], "0": [0], "1": [0], "2": [0]}) + annotations_tot = annotations_tot.sort_values(by = 'idx') + annotations_tot_pt = annotations_tot_pt.sort_values(by = 'idx') + wfdb.wrann("data","atrDelinAll",annotations_tot["idx"].values.astype(np.int32), symbol = annotations_tot['type'].values, write_dir=dir_to_use)#, num=annotations_tot['2'].values.astype(np.int32)) + wfdb.wrann("data","atrDelinPT",annotations_tot_pt["idx"].values.astype(np.int32), symbol = annotations_tot_pt['type'].values, write_dir=dir_to_use)#, num=annotations_tot_pt['2'].values.astype(np.int32)) + return annotations_tot + + +def compare(log_dir_this_lvl_resampling, resampling_type, annot_full_O, annot_full_r): + ''' + bxb: + The -o option produces an output annotation file with annotator name bxb. + The output annotation file contains exact copies of all of the test annotator’s beat labels that match those of the reference annotator, + as well as NOTE annotations that describe all mismatches. + Mismatched annotation types are mapped into the AAMI ‘test label’ mnemonics. + The ‘aux’ field of each NOTE annotation indicates the element of the confusion matrix in which the mismatch is tallied: + e.g., Nv represents an eventcalled a normal beat by the reference annotator and a ventricular ectopic beat by the test annotator) + NOTE annotations that correspond to beats missed by the test annotator are placed at the sample indicated by the reference annotation; + all others are placed at that indicated by the test annotation. + bxb output: + ... + 4:59.672 38358 " 0 0 0 O/p + 5:00.031 38404 t 0 0 0 + 5:00.328 38442 p 0 0 0 + 5:00.688 38488 t 0 0 0 + 5:00.938 38520 p 0 0 0 + 5:01.297 38566 " 0 0 5 t[0,0,0]/t ---> NOT PRESENT ANYMORE: here the reference say a normal t (code 0) while the test say it's a t but with code 5 + 5:01.367 38575 " 0 0 5 O/t ---> Nothing in the reference, "t" in test + 5:01.523 38595 p 0 0 0 + 5:01.875 38640 t 0 0 0 + 5:02.102 38669 " 0 0 0 p/O ---> "p" in the reference, nothing in test + ... + ''' + dir_dest = os.path.join(log_dir_this_lvl_resampling,resampling_type) + shutil.copy(os.path.join(log_dir_this_lvl_resampling,"original","data.atrDelinPT"),os.path.join(dir_dest,"data.atrOrigPT")) + shutil.copy(os.path.join(log_dir_this_lvl_resampling,"original","data.atrDelinAll"),os.path.join(dir_dest,"data.atrDelinOrig")) + #This can compare all the annotations in one go: bxb -r data -a atrDelinTotOrig atrDelinTot -f 0 -v -O + cmd_bxb = f"(cd {dir_dest}; bxb -r data -a atrOrigPT atrDelinPT -O -f 0 2>/dev/null)" + os.system(cmd_bxb) + + annots_pt = wfdb.rdann(os.path.join(dir_dest,"data"),"bxb") + + p_wave_true_pos = 0 + p_wave_false_pos = 0 + p_wave_false_negative = 0 + p_wave_sens = 0 + p_wave_pp = 0 + p_wave_f1 = 0 + + t_wave_true_pos = 0 + t_wave_false_pos = 0 + t_wave_false_negative = 0 + t_wave_sens = 0 + t_wave_pp = 0 + t_wave_f1 = 0 + + for pos,sym,aux in zip(annots_pt.sample,annots_pt.symbol,annots_pt.aux_note): + if sym == 'p': + p_wave_true_pos += 1 + elif aux == "p/O": + p_wave_false_negative += 1 + elif aux == "O/p": + p_wave_false_pos += 1 + + if sym == 't': + t_wave_true_pos += 1 + elif aux == "t/O": + t_wave_false_negative += 1 + elif aux == "O/t": + t_wave_false_pos += 1 + + groud_truth_p = p_wave_true_pos+p_wave_false_negative + found_p = p_wave_true_pos+p_wave_false_pos + + if groud_truth_p == 0: + p_wave_sens = 0 + else: + p_wave_sens = p_wave_true_pos/(p_wave_true_pos+p_wave_false_negative) + if found_p == 0: + p_wave_pp = 0 + else: + p_wave_pp = p_wave_true_pos/(p_wave_true_pos+p_wave_false_pos) + if p_wave_sens+p_wave_pp == 0: + p_wave_f1 = 0 + else: + p_wave_f1 = 2*(p_wave_sens*p_wave_pp)/(p_wave_sens+p_wave_pp) + + groud_truth_t = t_wave_true_pos+t_wave_false_negative + found_t = t_wave_true_pos+t_wave_false_pos + + if groud_truth_t == 0: + t_wave_sens = 0 + else: + t_wave_sens = t_wave_true_pos/(t_wave_true_pos+t_wave_false_negative) + if found_t == 0: + t_wave_pp = 0 + else: + t_wave_pp = t_wave_true_pos/(t_wave_true_pos+t_wave_false_pos) + if t_wave_sens+t_wave_pp == 0: + t_wave_f1 = 0 + else: + t_wave_f1 = 2*(t_wave_sens*t_wave_pp)/(t_wave_sens+t_wave_pp) + + print("----------------------------------------") + print(f"delineation_score: {dir_dest}: \n\tp_s:{p_wave_sens}\n\tp_pp:{p_wave_pp}\n\tp_f1:{p_wave_f1}\n\tt_s:{t_wave_sens}\n\tt_pp:{t_wave_pp}\n\tt_f1:{t_wave_f1}") + return {"p":{'sens':p_wave_sens,'ppv':p_wave_pp,'f1':p_wave_f1},"t":{'sens':t_wave_sens,'ppv':t_wave_pp,'f1':t_wave_f1}} + + +def delineate_and_compare(log_dir_this_lvl, QRS_pos): + annots_orig = delineate(log_dir_this_lvl, "original", QRS_pos) + annots_warp = delineate(log_dir_this_lvl, "warped", QRS_pos) + annots_resamp = delineate(log_dir_this_lvl, "resampled", QRS_pos) + results_warp = compare(log_dir_this_lvl, "warped", annots_orig,annots_warp) + results_resamp = compare(log_dir_this_lvl, "resampled", annots_orig,annots_resamp) + return {"resamp":results_resamp,"warp":results_warp} + + def add_beat_to_csv(base_path_name,beat): ''' base_path_name is the path to use to save the data in the form: 'results/$log_dir/$sample_name/$lvl/$resampling_type/$signal_type/ ''' mode = '' data_path = os.path.join(base_path_name,"data.csv") first_beat = True if os.path.exists(data_path): mode = 'a' else: mode = 'w' with open(data_path,mode) as f: for pt in beat: if mode == 'w' and first_beat: f.write(str(pt/ADC_GAIN)) first_beat = False else: f.write('\n'+str(pt/ADC_GAIN)) def decimate(v,factor): return v[::factor] def revert_normaliztion(data,params): un_norm_data = [] saturate = lambda x: x if abs(x)<2**11 else (2**11-1)*np.sign(x) for pt in data: un_norm_pt = saturate((pt + params['avg'])*(params['max']-params['min']) + params['min']) un_norm_data.append(un_norm_pt) return un_norm_data -def un_norma_and_save_back_beat(beat,log_dir,signal_type,norm_params): +def decimate_un_norma_and_save_back_beat(beat,log_dir,signal_type,norm_params): dir_to_save_to = os.path.join(log_dir,signal_type) os.makedirs(dir_to_save_to, exist_ok=True) data_v = decimate(beat['v'],MULTIPLIER_FREQ) data_v_un_norm = revert_normaliztion(data_v,norm_params) add_beat_to_csv(dir_to_save_to,data_v_un_norm) + return data_v_un_norm def z_score_filter(vector,threshold = 3): out = None z = stats.zscore(vector) out = [vector[idx] for idx in range(len(vector)) if z[idx] Beat_annot: | --> "t": [] | --> "v": [] ''' file_name_full = os.path.join(data_beats_dir, os.path.basename(file_name)) data = {} with open(file_name_full,"rb") as f: data = pickle.load(f) for k in data.keys(): if k <= FREQ*start_after: data.pop(k) return data def upsample_uniform_beat(beat,t_QRS,multiplier): out_beat = {} upsampled = [] v = beat['v'] new_QRS_pos = beat['t'].index(t_QRS)*multiplier last_new_t = (len(v)-1)*multiplier t = np.arange(0,last_new_t+1,multiplier,dtype = np.int64) new_base = np.arange(0,last_new_t,1,dtype = np.int64) f = interp1d(t, v) upsampled = f(new_base) out_beat = {'t':new_base,'v':upsampled,'QRS_pos':new_QRS_pos} return out_beat def upsample_uniform_beats_in_beats_dict(beats,multiplier): up_samp_beats = {} i = 0 for beat in beats: i += 1 up_samp_beats[beat] = upsample_uniform_beat(beats[beat], beat, multiplier) return up_samp_beats def get_beats_in_time_span(data, lvl = 0 ,t_start_seconds = 0, t_stop_seconds = SEC_FOR_INITIAL_TEMPLATES): beats = {} for annot in data.keys(): if annot >= int(t_start_seconds*FREQ) and annot <= int(t_stop_seconds*FREQ): beats[annot] = data[annot] elif annot > int(t_stop_seconds*FREQ): break return beats def min_max_normalization_one_beat(beat,param = None): params_out = {} if 'QRS_pos' in list(beat.keys()): normalized_beat = {'t':beat['t'],'v':[], 'QRS_pos': beat['QRS_pos']} else: normalized_beat = {'t':beat['t'],'v':[]} vector = beat['v'] if param is not None: mi_v = param['min'] ma_v = param['max'] avg = param['avg'] norm = (np.array(vector)-mi_v)/(ma_v-mi_v) norm -= avg else: mi_v = min(vector) ma_v = max(vector) norm = (np.array(vector)-mi_v)/(ma_v-mi_v) avg = np.average(norm) norm -= avg normalized_beat['v'] = norm.tolist() params_out['min'] = mi_v params_out['max'] = ma_v params_out['avg'] = avg return normalized_beat,params_out def min_max_normalization_beats_chunk(beats, params = None): params_out = {} normalized_beats = {} for beat in beats: normalized_beats[beat],params_out[beat] = min_max_normalization_one_beat(beats[beat],params) return normalized_beats,params_out def resamp_one_signal(t,v,resample_type = INTERPOLATION_TYPE, min_t = None, max_t = None): if min_t is None: min_t = t[0] if max_t is None: max_t = t[-1] t_extended = copy(t) v_extended = copy(v) if max_t not in t: t_extended.insert(len(t_extended), max_t) v_extended.insert(len(v_extended), 0) if min_t not in t: # This is not needed in this implementation as the first sample is always an events for each beat t_extended.insert(0, min_t) # Still, we write it for clearity and consistency v_extended.insert(0, 0) if resample_type == "linear": f = interp1d(t_extended,v_extended, bounds_error = False, fill_value = (v[0],v[-1])) elif resample_type == "flat": f = interp1d(t_extended,v_extended, kind = 'previous', bounds_error = False, fill_value = (v[0],v[-1])) elif resample_type == "spline": f = CubicSpline(t_extended,v_extended, bc_type="natural") t_new = list(range(min_t,max_t+1)) v_new = f(t_new) return t_new,v_new def percentile_idx(vector,perc): pcen=np.percentile(np.array(vector),perc,interpolation='nearest') i_near=abs(np.array(vector)-pcen).argmin() return i_near -def correlate(v1,v2): - v1_n = np.array(v1) - v2_n = np.array(v2) - cov = np.cov(v1_n,v2_n)[0,1] - cor = cov/(np.std(v1_n)*np.std(v2_n)) - return cor - -def avg_std_prd(prd_descriptor): - dict_out = {'warp':{'avg':None,'std':None},'resamp':{'avg':None,'std':None}} - wr = [] - rs = [] - dict_out['warp']['avg'] = np.average(prd_descriptor['prd_warp']) - dict_out['warp']['std'] = np.std(prd_descriptor['prd_warp']) - dict_out['resamp']['avg'] = np.average(prd_descriptor['prd_resamp']) - dict_out['resamp']['std'] = np.std(prd_descriptor['prd_resamp']) - return dict_out - -def percentile_prd_beat_rel(prd,perc): - vec = [] - for beat in prd.keys(): - vec.append(np.array(prd['prd_warp']) - np.array(prd['prd_resamp'])) - idx = percentile_idx(vec,perc) - return prd['beats'][idx],prd['ids'][idx] - -def percentile_prd_beat_abs(prd,perc): - vec = [] - for beat in prd.keys(): - vec.append(prd['prd_warp']) - idx = percentile_idx(vec,perc) - return prd['beats'][idx],prd['ids'][idx] +def get_template_and_beat_at_idx(all_beats,all_templates,look_up_beat_template,idx_sel): + beat_id = look_up_beat_template['beats'][idx_sel] + beat = all_beats[beat_id] + template = all_templates[look_up_beat_template['template_id'][idx_sel]] + return beat,beat_id,template def find_all_connceted_template(id_temp,templates_info): id_collected = [id_temp] for id_connected in id_collected: for id_to_add in templates_info[id_connected]["connected_template"]: if id_to_add not in id_collected: id_collected.append(id_to_add) return id_collected def plot_perc(orig,warp,resamp,template,title,out_file_name): fig, (ax1, ax2) = plt.subplots(2) fig.suptitle(title) ax1.plot(orig[0],orig[1]) ax1.plot(warp[0],warp[1]) ax1.plot(resamp[0],resamp[1]) ax1.legend(['original','warped template','resampled']) ax2.plot(template) ax2.legend(['template used']) fig.savefig(out_file_name) plt.close(fig) -def plot_beat_prd_percentile(orig, prd_id_coupling, templates_collection, lvl, perc, log_dir_this_lvl, file, interpolation_type = INTERPOLATION_TYPE): - # prd_id_coupling = {"prd_resamp":[],"prd_right_resamp":[],"prd_left_resamp":[], "prd_warp": [],"prd_warp_right": [],"prd_warp_left": [], "ids":[], "beats": []} - #Relative - template_sel = {"template":None} - beat_id, template_id = percentile_prd_beat_rel(prd_id_coupling, perc) - file_name_to_save_fig_rel = os.path.join(log_dir_this_lvl,file.split(".")[0]+"_"+str(perc)+"_perc"+str(lvl)+"_relative.svg") - template_sel['template'] = templates_collection[template_id] - - up_samp_beat = upsample_uniform_beat(orig[beat_id], beat_id, MULTIPLIER_FREQ) #TODO: keep track of QRS +def reconstruct_plot_and_save_beat(beat,interpolation_type,template,lvl,title,save_fig_to): + up_samp_beat = upsample_uniform_beat(beat, beat['t'][0], MULTIPLIER_FREQ) #We don't need the information about the QRS position events = ADC(up_samp_beat,lvl) up_samp_beat,params = min_max_normalization_one_beat(up_samp_beat) events,_ = min_max_normalization_one_beat(events,params) resampled = resample(events, resample_type = interpolation_type, min_t = up_samp_beat['t'][0], max_t = up_samp_beat['t'][-1]) - _, reconstructed, _, _ = warp(resampled,template_sel,events) - - orig_rel = up_samp_beat['t'],up_samp_beat['v'] - recon_rel = reconstructed['t'],reconstructed['v'] - resamp_rel = resampled['t'],resampled['v'] + template_out_envelope = {"tempalte":template} + _, reconstructed, _, _ = warp(resampled,template_out_envelope,events) - title_rel = f'File: {file}, Lvl: {lvl}, Beat time (samples): {beat_id}, {str(perc)} percentile, Relative' - plot_perc(orig_rel,recon_rel,resamp_rel,template_sel['template']['point'],title_rel,file_name_to_save_fig_rel) + orig = up_samp_beat['t'],up_samp_beat['v'] + recon = reconstructed['t'],reconstructed['v'] + resamp = resampled['t'],resampled['v'] - #Absolute - beat_id, template_id = percentile_prd_beat_abs(prd_id_coupling, perc) - file_name_to_save_fig_abs = os.path.join(log_dir_this_lvl,file.split(".")[0]+"_"+str(perc)+"_perc"+str(lvl)+"_absolute.svg") - template_sel['template'] = templates_collection[template_id] - - up_samp_beat = upsample_uniform_beat(orig[beat_id], beat_id, MULTIPLIER_FREQ) #TODO: keep track of QRS - events = ADC(up_samp_beat,lvl) - - up_samp_beat,params = min_max_normalization_one_beat(up_samp_beat) - events,_ = min_max_normalization_one_beat(events,params) - - resampled = resample(events, resample_type = interpolation_type, min_t = up_samp_beat['t'][0], max_t = up_samp_beat['t'][-1]) - _, reconstructed, _, _ = warp(resampled,template_sel,events) - - orig_abs = up_samp_beat['t'],up_samp_beat['v'] - recon_abs = reconstructed['t'],reconstructed['v'] - resamp_abs = resampled['t'],resampled['v'] - - - title_abs = f'File: {file}, Lvl: {lvl}, Beat time (samples): {beat_id}, {str(perc)} percentile, Absolute' - plot_perc(orig_abs,recon_abs,resamp_abs,template_sel['template']['point'],title_abs,file_name_to_save_fig_abs) + plot_perc(orig,recon,resamp,template['point'],title,save_fig_to) def stitch_segments(segments): stitched = [] for i,segment in enumerate(segments): if i == len(segments)-1: stitched.extend(segment) else: stitched.extend(segment[0:-1]) return stitched def change_sampling(vector, size_to_warp): resampled = [] if len(vector) < 3: #If the length of the vector is either 2 or 1 it will be mnorfed in a straight line and such line will be morfed equaly resampled = [vector[0]]*size_to_warp return resampled delta = abs(size_to_warp - len(vector)) # --> number of points to insert/delete #At least one more sample for each sample (except the last one)? --> insert k in each hole and recompute delta if delta >= len(vector)-1 and delta > 0: holes = len(vector)-1 k = delta // holes #Number of point for each hole time = range(0,len(vector)*(k+1),k+1) # [o1,i11,i12,...,i1k, o2,i21,i22,...,i2k, o3,..., o_fin] --> NOTE: last sampel is not taken # 0 k+1 2k+2 l*k +l --> Hence, we get to the exact end t_new = range(time[-1]+1) f = interp1d(time,vector, bounds_error = False, fill_value = "extrapolate") vector = list(f(t_new)) delta = abs(size_to_warp - len(vector)) if len(vector) == size_to_warp: return vector grad = np.gradient(vector) grad[-1] = max(grad) + 1 # --> we don't want to insert anything after the last sample grad_idxs = sorted(range(len(grad)), key = lambda idx: grad[idx]) idx_to_consider = grad_idxs[:delta] for i,sample in enumerate(vector): resampled.append(sample) if i in idx_to_consider: if size_to_warp < len(vector): resampled.pop() elif size_to_warp > len(vector): resampled.append((vector[i]+vector[i+1])/2) return resampled def segment_warp(segment): resampled = np.array(change_sampling(segment['segment'], segment['length_to_warp'])) resampled += (segment['v_start'] - resampled[0]) m = (segment['v_stop'] - resampled[-1])/(segment['length_to_warp']-1) coef_to_sum = [m*x for x in range(segment['length_to_warp'])] resampled += coef_to_sum return list(resampled) def warp(resamp_event, templates, events): warped = {} segments = [] # --> {"segment":v, "length_to_warp":length, "v_start":v_eb_0, "v_stop":v_eb_1} segment = {"segment":None, "length_to_warp":None, "v_start":None, "v_stop":None} segment_start = None event_start = None #ad the first and last points of the resampled and truncated beat if not already in event if resamp_event['t'][0] not in events['t']: events['t'].insert(0, resamp_event['t'][0]) events['v'].insert(0, 0) if resamp_event['t'][-1] not in events['t']: events['t'].insert(len(events['t']),resamp_event['t'][-1]) events['v'].insert(len(events['v']),0) #Apply DTW for matching resampled event to template v_src = resamp_event['v'] #This is how its actualy done on the library when calling 'warping_path' dist = float('inf') path = [] selected_template = [] disatances_vector = [] template_id = None for id in templates: t = templates[id]['point'] #dist_this_template, paths_this_template = dtw.warping_paths(v_src, t) with warnings.catch_warnings(): warnings.simplefilter("ignore") dist_this_template, _, path_this_template = dtw_std(v_src, t, dist_only=False) disatances_vector.append(dist_this_template) if dist_this_template < dist: dist = dist_this_template path = path_this_template selected_template = t template_id = id path = [(v1,v2) for v1,v2 in zip(path[0],path[1])] #Remove the "left" steps from the path and segment the template based on the events point #TODO: Problem here in the warping (some beats does not include the ending of the template, especially with low levels), check why !!!! With only 10 beats the effect is not visible # RESULTS: ALL THE TEMPLATES POINTS ARE IN THE LAST POINT OF THE WARP!! prev_idx_src = path[-1][0]+1 for idx_src, idx_temp in path: if prev_idx_src == idx_src: continue prev_idx_src = idx_src for idx, t in enumerate(events['t']): if resamp_event['t'][idx_src] == t: if segment_start == None: segment_start = idx_temp event_start = idx else: segment['segment'] = np.array(selected_template[segment_start:idx_temp+1], dtype=np.float64) segment['length_to_warp'] = events['t'][idx] - events['t'][event_start] + 1 segment['v_start'] = events['v'][event_start] segment['v_stop'] = events['v'][idx] w = segment_warp(segment) segments.append(w) segment_start = idx_temp event_start = idx break segment_stitched = stitch_segments(segments) warped = {'t':resamp_event['t'],'v':segment_stitched} return dist, warped, disatances_vector, template_id def dtw_dist(v1,v2): with warnings.catch_warnings(): warnings.simplefilter("ignore") dist = dtw_std(v1, v2) return dist def compute_new_templates(data, t_start, t_stop, old_templates, templates_info): beats_for_new_template = get_beats_in_time_span(data, t_start_seconds=t_start,t_stop_seconds=t_stop) beats_for_new_template,_ = min_max_normalization_beats_chunk(beats_for_new_template) _, new_templates_descriptor = multi_template(beats_for_new_template, percentage_each_cluster = CLUSTER_PERCENTAGE*2, freq= FREQ) lbls_new_templates = list(new_templates_descriptor.keys()) old_ids = list(old_templates.keys()) if old_ids == []: next_id = 0 else: next_id = max(old_ids) + 1 cluster_representatives = {} new_template_set = {} print(f"\n\tNew template built, number of new templates:{len(lbls_new_templates)}\n") old_templates_kept = 0 old_templates_substituted = 0 new_templates_kept = 0 new_templates_substituted = 0 # We search which of the old templates can be considered clustered with the newly founded ones # TODO: /!\ wRONG BACK PROJECTION!!!! need to send back the function to template prediction and measure distance from THAT template if len(lbls_new_templates) > 0: for id_old_template in old_templates: t_o = old_templates[id_old_template]['point'] dists = np.zeros((len(lbls_new_templates))) for k,t_n in enumerate(lbls_new_templates): dists[k] = dtw_dist(t_o,new_templates_descriptor[t_n]['center']) min_dist_pos = np.argmin(dists) min_val = dists[min_dist_pos] this_lbls_new_templates = lbls_new_templates[min_dist_pos] distances_intra_cluster = new_templates_descriptor[this_lbls_new_templates]['dist_all_pt_cluster'] dist_threshold = np.average(distances_intra_cluster)+np.std(distances_intra_cluster) if min_val < dist_threshold: if this_lbls_new_templates not in cluster_representatives.keys(): cluster_representatives[this_lbls_new_templates] = {"ids": [], "dists": []} cluster_representatives[this_lbls_new_templates]["ids"].append(id_old_template) cluster_representatives[this_lbls_new_templates]["dists"].append(min_val) else: #This templates minimum distances to the newly founded templates are too big to be considered the same cluster new_template_set[id_old_template] = old_templates[id_old_template] old_templates_kept += 1 # Now we check if the old templates, clustered with the newly founded one, are representative of the cluster of if we should use the new templates for local_new_id in lbls_new_templates: if local_new_id in cluster_representatives.keys(): connected_ids = cluster_representatives[local_new_id]["ids"] arg_min_dist = np.argmin(cluster_representatives[local_new_id]["dists"]) old_template_id = cluster_representatives[local_new_id]["ids"][arg_min_dist] old_template_dist = cluster_representatives[local_new_id]["dists"][arg_min_dist] # The old template is nearer to the found cluster center than the beat found by the clustering alg if old_template_dist < new_templates_descriptor[local_new_id]["dist"]: new_template_set[old_template_id] = old_templates[old_template_id] connected_ids.remove(old_template_id) templates_info[old_template_id]["connected_template"].extend(connected_ids) new_templates_substituted += 1 # The beat found by the clustering alg is nearer to the found cluster center than the old template else: new_template_set[next_id] = new_templates_descriptor[local_new_id] templates_info[next_id] = {"used": 0, "deceased": False, "connected_template": connected_ids} next_id += 1 old_templates_substituted += len(cluster_representatives[local_new_id]["ids"]) # The templates in this cluster that are not the center are now deceased for id_removed in connected_ids: templates_info[id_removed]["deceased"] = True else: new_template_set[next_id] = new_templates_descriptor[local_new_id] templates_info[next_id] = {"used": 0, "deceased": False, "connected_template": []} next_id += 1 new_templates_kept += 1 print(f"\tOld templates kept untuched: {old_templates_kept}/{len(old_templates)}") print(f"\tOld templates kept, representative of new clusters (but already present): {new_templates_substituted}/{len(old_templates)}") print(f"\tOld templates removed for old clusters (with new defined params): {len(old_templates) - (old_templates_kept+old_templates_substituted+new_templates_substituted)}/{len(old_templates)}") print(f"\tOld templates removed for new clusters: {old_templates_substituted}/{len(old_templates)}") print(f"\n\tNew templates kept untuched: {new_templates_kept}/{len(lbls_new_templates)}") print(f"\tNew templates kept, representative of old clusters (with new params): {len(lbls_new_templates) - (new_templates_kept+new_templates_substituted)}/{len(lbls_new_templates)}") print(f"\tNew templates removed for old clusters: {new_templates_substituted}/{len(lbls_new_templates)}") print(f"\n\tFinal lenght of the new tempalte set: {len(new_template_set)}") else: new_template_set = old_templates print("\tNo new template found, keeping the previously computed ones") return new_template_set def resample(data, resample_type = INTERPOLATION_TYPE, min_t = None, max_t = None): resampled_data = {"t":None,"v":None} t = data['t'] v = data['v'] t_r,v_r = resamp_one_signal(t,v,resample_type = resample_type, min_t = min_t, max_t = max_t) resampled_data['t'] = t_r resampled_data['v'] = v_r return resampled_data def ADC(beat, nBits, hist = 5, original_bits = 11): #ADC stats delta = 2**original_bits dV = (delta)/(2**nBits) hist = hist/100*dV min_val = -delta//2 events = {'t':[],'v':[], 'QRS_pos': beat['QRS_pos']} #init value, first sample (we assume we always sample the nearest level at start) and ADC status v_0 = beat['v'][0] lowTh = min_val+((v_0-min_val)//dV)*dV highTh = lowTh + dV events['t'].append(beat['t'][0]) events['v'].append(int(lowTh if v_0-lowTh < highTh - v_0 else highTh)) for val,time in zip(beat['v'],beat['t']): #print(f"Value: {val}, time: {time}, low_th = {lowTh - hist}, high_th = { highTh + hist}") if val > highTh + hist or val < lowTh - hist: direction = 1 if val > highTh else -1 lowTh = min_val+((val-min_val)//dV)*dV #Delta from the bottom: (val-min_val)//dV*dV then compute the actual level summin min_val highTh = lowTh + dV events['t'].append(time) events['v'].append(int(lowTh if direction == 1 else highTh)) return events def reconstruct_beats(data_orig, lvl_number, init_templates = None, start_after = SEC_FOR_INITIAL_TEMPLATES, resample_type = INTERPOLATION_TYPE, num_beats_analyzed = None, verbose = False, log_dir = None): beat_seq_number = 0 prev_len = 0 - beats_used = [] + QRS_pos_new = [] distances = [] distances_ref = [] num_distances_out = 0 skip_until = start_after time_info = {'beats_low_res_num': 0, 'tot_beats_num': 0} # COMPUTE INITIAL TEMPLATES #Init templet info templates_info = {} #{$templet_id: {used: 0, deceased: False, "connected_template": []}} all_templates_collection = {} if init_templates is None: print(f"\n################################################################") print(f"\nLEVELS:{lvl_number}, resample type: {resample_type}: Starting templates ... ") templates = compute_new_templates(data_orig, 0, start_after, {}, templates_info) else: templates = init_templates for id_new in templates: templates_info[id_new] = {"used": 0, "deceased": False, "connected_template": []} all_templates_collection = copy (templates) init_templates = copy(templates) - prd_id_coupling = {"prd_resamp":[],"prd_right_resamp":[],"prd_left_resamp":[], "prd_warp": [],"prd_warp_right": [],"prd_warp_left": [], "ids":[], "beats":[]} + measurements_id_coupling = {"ids":\ + {"template_id":[], "beats":[]},\ + "measures":\ + {"dtw":{"warp":[],"resamp":[]},\ + "prd":{"warp":[],"resamp":[]},\ + "prd_left":{"warp":[],"resamp":[]},\ + "prd_right":{"warp":[],"resamp":[]}}} dist_vector = [0]*len(templates) for beat in data_orig.keys(): t_beat = beat/FREQ time_info['tot_beats_num'] += 1 if t_beat < skip_until: continue time_info['beats_low_res_num'] += 1 if beat_seq_number >= num_beats_analyzed: break beat_seq_number +=1 if (beat_seq_number %(num_beats_analyzed/20)==0 or beat_seq_number == 1) and verbose: print(f"Reconstructing beat {beat} ({beat_seq_number}/{num_beats_analyzed}: {100*beat_seq_number /num_beats_analyzed}%, LEVELS:{lvl_number}, resample type: {resample_type})") up_samp_beat = upsample_uniform_beat(data_orig[beat], beat, MULTIPLIER_FREQ) events = ADC(up_samp_beat,lvl_number) # For the warping to work, both signal need to be in the same form (wither normalized or un-normailized), otherwise, the distance matrix # express non coherent distances between points. Because the templates NEED to be normalized (to better express a topology, instead of raw magnitude) # This oblige us to normalize also the input signals up_samp_beat, min_max_params = min_max_normalization_one_beat(up_samp_beat) events,_ = min_max_normalization_one_beat(events, min_max_params) # Re-sample and warp resampled = resample(events, resample_type = resample_type, min_t = up_samp_beat['t'][0], max_t = up_samp_beat['t'][-1]) dist, reconstructed, dist_all_template, local_template_id = warp(resampled,templates,events) # Save-back beat - un_norma_and_save_back_beat(resampled,log_dir,"resampled",min_max_params) - un_norma_and_save_back_beat(reconstructed,log_dir,"warped",min_max_params) - un_norma_and_save_back_beat(up_samp_beat,log_dir,"original",min_max_params) + un_norm_resamp = decimate_un_norma_and_save_back_beat(resampled,log_dir,"resampled",min_max_params) + un_norm_warped = decimate_un_norma_and_save_back_beat(reconstructed,log_dir,"warped",min_max_params) + un_norm_original = decimate_un_norma_and_save_back_beat(up_samp_beat,log_dir,"original",min_max_params) #Update templates usage info template_id = list(templates_info.keys())[-len(templates)+local_template_id] templates_info[template_id]["used"] += 1 dist_vector = [dist_vector[i]+dist_all_template[i] for i in range(len(dist_vector))] #Compute PRD #{'warp':None,'resamp':None,'warp_left':None,'resamp_left':None,'warp_right':None,'resamp_right':None} prd_this_beat = PRD_one_beat(up_samp_beat,resampled,reconstructed) - prd_id_coupling['ids'].append(local_template_id) - prd_id_coupling['beats'].append(beat) - prd_id_coupling['prd_resamp'].append(prd_this_beat['resamp']) - prd_id_coupling['prd_right_resamp'].append(prd_this_beat['resamp_right']) - prd_id_coupling['prd_left_resamp'].append(prd_this_beat['resamp_left']) - prd_id_coupling['prd_warp'].append(prd_this_beat['warp']) - prd_id_coupling['prd_warp_right'].append(prd_this_beat['warp_right']) - prd_id_coupling['prd_warp_left'].append(prd_this_beat['warp_left']) + measurements_id_coupling["ids"]["template_id"].append(local_template_id) + measurements_id_coupling["ids"]["beats"].append(beat) + measurements_id_coupling[ "measures"]["dtw"]["warp"].append(dtw_dist(un_norm_warped,un_norm_original)) + measurements_id_coupling[ "measures"]["dtw"]["resamp"].append(dtw_dist(un_norm_resamp,un_norm_original)) + measurements_id_coupling[ "measures"]["prd"]["warp"].append(prd_this_beat['resamp']) + measurements_id_coupling[ "measures"]["prd"]["resamp"].append(prd_this_beat['resamp_right']) + measurements_id_coupling[ "measures"]["prd_left"]["warp"].append(prd_this_beat['resamp_left']) + measurements_id_coupling[ "measures"]["prd_left"]["resamp"].append(prd_this_beat['warp']) + measurements_id_coupling[ "measures"]["prd_right"]["warp"].append(prd_this_beat['warp_right']) + measurements_id_coupling[ "measures"]["prd_right"]["resamp"].append(prd_this_beat['warp_left']) # check if we need to re-compute the templates if len(distances_ref) < LEN_DISTANCE_VECTOR_REF: distances_ref.append(dist) if len(distances_ref) >= LEN_DISTANCE_VECTOR_REF: distances.append(dist) if len(distances) >= LEN_DISTANCE_VECTOR: with warnings.catch_warnings(): warnings.simplefilter("ignore") p = stats.anderson_ksamp([distances_ref,distances])[2] distances = [] if (p>= 0.05): num_distances_out = 0 else: # p value les than 0.05: null hypothesis (same distribution) rejected (please Kolmogorov forgive me) num_distances_out += 1 if num_distances_out > 2: # The acquired vector of distances was out for 2 times max_accum_dist = max(dist_vector) print(f"\n################################################################") print(f"\nLEVELS:{lvl_number}, resample type: {resample_type}: New template needed ... ") print(f"Beat number:{beat_seq_number} ({beat_seq_number}/{num_beats_analyzed}: {100*beat_seq_number /num_beats_analyzed}%)") print(f"\t p-value: {p}") for j in range(len(dist_vector)): print(f"\tTemplate {j}, dist: {dist_vector[j]}:\t","|"*int(20*dist_vector[j]/max_accum_dist)) print("\n") templates = compute_new_templates(data_orig, t_beat, t_beat+SEC_FOR_NEW_TEMPLATES, templates, templates_info) for t in templates: all_templates_collection[t] = templates[t] dist_vector = [0]*len(templates) print(f"\n################################################################\n") distances_ref = [] skip_until = t_beat + SEC_FOR_NEW_TEMPLATES num_distances_out = 0 #Save the used beats: new_QRS_pos = int((prev_len+up_samp_beat['QRS_pos'])/MULTIPLIER_FREQ) prev_len += len(up_samp_beat['t']) - beats_used.append(new_QRS_pos) - - return all_templates_collection,prd_id_coupling,init_templates,time_info,beats_used - -def reconstruction_one_lvl_one_file_and_compare(data_orig,lvl,file,initial_templates,verbose = True): + QRS_pos_new.append(new_QRS_pos) + + return all_templates_collection,measurements_id_coupling,init_templates,time_info,QRS_pos_new + +def measurments_reuslts(measurements_id_coupling,interpolation_type,data_orig,all_templates_collection,time_info,file,lvl,log_dir_this_lvl,log_dir_this_file): + """ + measurements_id_coupling = {"ids":\ + {"template_id":[], "beats":[]},\ + "measures":\ + {"dtw":{"warp":[],"resamp":[]},\ + "prd":{"warp":[],"resamp":[]},\ + "prd_left":{"warp":[],"resamp":[]},\ + "prd_right":{"warp":[],"resamp":[]}}} + need to modify stats so to accept vectors + """ + for measurement_type in measurements_id_coupling['measures']: + warp = np.array(measurements_id_coupling['measures'][measurement_type]['warp']) + resamp = np.array(measurements_id_coupling['measures'][measurement_type]['resamp']) + avg_warp = np.average(warp) + std_warp = np.std(warp) + avg_resamp = np.average(resamp) + std_resamp = np.std(resamp) + log_dir_this_measurement = os.path.join(log_dir_this_lvl,measurement_type) + os.makedirs(log_dir_this_measurement,exist_ok=True) + file_name_to_save = "L_"+measurement_type+"_"+interpolation_type+"_"+file.split(".")[0]+".log" - log_dir_this_file = os.path.join(log_dir,file.split(".")[0]) - interpolation_type_list = None - if INTERPOLATION_TYPE == 'all': - interpolation_type_list = ['flat','spline','linear'] - else: - interpolation_type_list = [INTERPOLATION_TYPE] - prd_each_interpolation_type = {} - - for interpolation_type in interpolation_type_list: - if verbose: - print(f"Level:{lvl}, Using interpolation: {interpolation_type}") - - log_dir_this_lvl = os.path.join(log_dir_this_file,str(lvl),interpolation_type) - os.makedirs(log_dir_this_lvl, exist_ok=True) - - all_templates_collection, prd_id_coupling, initial_templates, time_info, beats_used = reconstruct_beats(data_orig, lvl, init_templates = initial_templates, start_after = SEC_FOR_INITIAL_TEMPLATES, resample_type = interpolation_type, num_beats_analyzed = NUM_BEAT_ANALYZED, verbose = True, log_dir = log_dir_this_lvl) #resample_type = flat vs linear - - ############################################################################# - # RESULTS EVALUATION # - ############################################################################# - original_singal_folder = os.path.join(log_dir_this_lvl,"original","data.csv") - resampled_singal_folder = os.path.join(log_dir_this_lvl,"resampled","data.csv") - warped_singal_folder = os.path.join(log_dir_this_lvl,"warped","data.csv") - #TODO: problem here: /!\ level 3- spline does not with many samples, maybe a random beats trigger a condition that breake ecgpwuave (not halting) - # IT IS SOLVED BY TERMINATING (CTRL-C ing) ONCE WE USE THE LEVEL 3 --> arresting ecgpuwave stop only the delineator and everything else works - # TO ANALYZE BY NOT DELETING LEVELS AND TEST FROM TERMINAL --> there are annotations where level 3 fails to have any reconizable peak around the given annotation ! - csv2mit_with_dir_out(original_singal_folder,fs = FREQ, units = 'mV', header = False, fmt = '212', adc_gain=ADC_GAIN, baseline = 0) - csv2mit_with_dir_out(resampled_singal_folder,fs = FREQ, units = 'mV', header = False, fmt = '212', adc_gain=ADC_GAIN, baseline = 0) - csv2mit_with_dir_out(warped_singal_folder,fs = FREQ, units = 'mV', header = False, fmt = '212', adc_gain=ADC_GAIN, baseline = 0) - - os.remove(original_singal_folder) - os.remove(resampled_singal_folder) - os.remove(warped_singal_folder) - - wfdb.wrann("data","atr",np.array(beats_used), symbol= ['N']*len(beats_used),write_dir=os.path.join(log_dir_this_lvl,"original")) - shutil.copy(os.path.join(log_dir_this_lvl,"original","data.atr"),os.path.join(log_dir_this_lvl,"resampled","data.atr")) - shutil.copy(os.path.join(log_dir_this_lvl,"original","data.atr"),os.path.join(log_dir_this_lvl,"warped","data.atr")) - - cmd_delineator_original = f"(cd {os.path.join(log_dir_this_lvl,'original')} ; timeout 300 ecgpuwave -r data -a atr_new -i atr > /dev/null 2>&1)" - cmd_delineator_warped = f"(cd {os.path.join(log_dir_this_lvl,'warped')} ; timeout 300 ecgpuwave -r data -a atr_new -i atr > /dev/null 2>&1)" - cmd_delineator_resampled = f"(cd {os.path.join(log_dir_this_lvl,'resampled')} ; timeout 300 ecgpuwave -r data -a atr_new -i atr > /dev/null 2>&1)" - - os.system(cmd_delineator_original) - os.system(cmd_delineator_warped) - os.system(cmd_delineator_resampled) - - stats = avg_std_prd(prd_id_coupling) - avg_prd_warp = stats['warp']['avg'] - std_prd_warp = stats['warp']['std'] - avg_prd_resamp = stats['resamp']['avg'] - std_prd_resamp = stats['resamp']['std'] - file_name_to_save = "L_"+interpolation_type+"_"+file.split(".")[0]+".log" # Particular log for each lvl - with open(os.path.join(log_dir_this_lvl,file_name_to_save),"a") as f: - f.write(f"Lvl: {lvl}, using the {interpolation_type} interpolation:\n") - f.write(f"\tWarp: {avg_prd_warp}, +-{std_prd_warp}\n") - f.write(f"\tInterpolation: {avg_prd_resamp}, +-{std_prd_resamp}\n") + with open(os.path.join(log_dir_this_measurement,file_name_to_save),"a") as f: + f.write(f"({measurement_type}) Lvl: {lvl}, using the {interpolation_type} interpolation:\n") + f.write(f"\tWarp: {avg_warp}, +-{std_warp}\n") + f.write(f"\tInterpolation: {avg_resamp}, +-{std_resamp}\n") f.write(f"\tTime (percentage) passed in low-sampling mode: {time_info['beats_low_res_num']/time_info['tot_beats_num']*100}%\n") f.write(f"\n\n") # General log (the same but all toghether: more confusing but with all infos) with open(os.path.join(log_dir_this_file,file_name_to_save),"a") as f: - f.write(f"Lvl: {lvl}, using the {interpolation_type} interpolation:\n") - f.write(f"\tWarp: {avg_prd_warp}, +-{std_prd_warp}\n") - f.write(f"\tInterpolation: {avg_prd_resamp}, +-{std_prd_resamp}\n") - ''' - for id_temp in templates_info: - usage = templates_info[id_temp]["used"] - deceased = templates_info[id_temp]["deceased"] - f.write(f"\t\tTemplate {id_temp} was used {usage} times out of {NUM_BEAT_ANALYZED}, \ - deceased? {deceased}\n") - ''' + f.write(f"({measurement_type}) Lvl: {lvl}, using the {interpolation_type} interpolation:\n") + f.write(f"\tWarp: {avg_warp}, +-{std_warp}\n") + f.write(f"\tInterpolation: {avg_resamp}, +-{std_resamp}\n") f.write(f"\tTime (percentage) passed in low-sampling mode: {time_info['beats_low_res_num']/time_info['tot_beats_num']*100}%\n") f.write(f"\n\n") print("\n-------------------------------------------------------------------------") - print(f"File:{file_name_to_save}, using the {interpolation_type} interpolation:") + print(f"({measurement_type}) File:{file_name_to_save}, using the {interpolation_type} interpolation:") print(f"\tLvl: {lvl}") - print(f"\t\twarp: {avg_prd_warp}, +-{std_prd_warp}") - print(f"\t\tinterpolation: {avg_prd_resamp}, +-{std_prd_resamp}") + print(f"\t\twarp: {avg_warp}, +-{std_warp}") + print(f"\t\tinterpolation: {avg_resamp}, +-{std_resamp}") print(f"\t\tTime (percentage) passed in low-sampling mode: {time_info['beats_low_res_num']/time_info['tot_beats_num']*100}%") print("\n") - - # 01 percentile - plot_beat_prd_percentile(data_orig, prd_id_coupling, all_templates_collection, lvl, 1, log_dir_this_lvl, file, interpolation_type = interpolation_type) - - # 25 percentile - plot_beat_prd_percentile(data_orig, prd_id_coupling, all_templates_collection, lvl, 25, log_dir_this_lvl, file, interpolation_type = interpolation_type) - - # 50 percentile - plot_beat_prd_percentile(data_orig, prd_id_coupling, all_templates_collection, lvl, 50, log_dir_this_lvl, file, interpolation_type = interpolation_type) - - # 75 percentile - plot_beat_prd_percentile(data_orig, prd_id_coupling, all_templates_collection, lvl, 75, log_dir_this_lvl, file, interpolation_type = interpolation_type) - - # 99 percentile - plot_beat_prd_percentile(data_orig, prd_id_coupling, all_templates_collection, lvl, 99, log_dir_this_lvl, file, interpolation_type = interpolation_type) - - #Filter and save back the PRD values for better plots (the filtered data is still accounted for before) - prd_id_coupling['prd_warp'] = z_score_filter(prd_id_coupling['prd_warp']) - prd_id_coupling['prd_resamp'] = z_score_filter(prd_id_coupling['prd_resamp']) - prd_id_coupling['prd_warp_left'] = z_score_filter(prd_id_coupling['prd_warp_left']) - prd_id_coupling['prd_left_resamp'] = z_score_filter(prd_id_coupling['prd_left_resamp']) - prd_id_coupling['prd_warp_right'] = z_score_filter(prd_id_coupling['prd_warp_right']) - prd_id_coupling['prd_right_resamp'] = z_score_filter(prd_id_coupling['prd_right_resamp']) - prd_each_interpolation_type[interpolation_type] = prd_id_coupling - - # Histograms - file_name_to_save_fig_hist = os.path.join(log_dir_this_lvl,file.split(".")[0]+"_hist"+str(lvl)+".svg") - file_name_to_save_fig_hist_left = os.path.join(log_dir_this_lvl,file.split(".")[0]+"_hist_left"+str(lvl)+".svg") - file_name_to_save_fig_hist_right = os.path.join(log_dir_this_lvl,file.split(".")[0]+"_hist_right"+str(lvl)+".svg") - - n_bins = min(len(prd_id_coupling['prd_warp']),len(prd_id_coupling['prd_resamp']))*PERC_BINS//100 - min_bin = min(min(prd_id_coupling['prd_warp']),min(prd_id_coupling['prd_resamp'])) - max_bin = max(max(prd_id_coupling['prd_warp']),max(prd_id_coupling['prd_resamp'])) + for perc in PERCENTILE_TO_PLOT: + idx_perc_abs = percentile_idx(warp,perc) + beat, beat_id, template = get_template_and_beat_at_idx(data_orig,all_templates_collection,measurements_id_coupling['ids'],idx_perc_abs) + title = f'File: {file}, Lvl: {lvl}, Beat time (samples): {beat_id}, {str(perc)} percentile, Absolute' + file_name_to_save_fig = os.path.join(log_dir_this_measurement,file.split(".")[0]+"_"+str(perc)+"_perc"+str(lvl)+"_absolute.svg") + reconstruct_plot_and_save_beat(beat,interpolation_type,template,lvl,title,file_name_to_save_fig) + + idx_perc_rel = percentile_idx(warp-resamp,perc) + beat,beat_id,template = get_template_and_beat_at_idx(data_orig,all_templates_collection,measurements_id_coupling['ids'],idx_perc_rel) + title = f'File: {file}, Lvl: {lvl}, Beat time (samples): {beat_id}, {str(perc)} percentile, Absolute' + file_name_to_save_fig = os.path.join(log_dir_this_measurement,file.split(".")[0]+"_"+str(perc)+"_perc"+str(lvl)+"_absolute.svg") + reconstruct_plot_and_save_beat(beat,interpolation_type,template,lvl,title,file_name_to_save_fig) + + #Filter and save back the values for better plots (the filtered data is still accounted for before) + warp = z_score_filter(warp) + resamp = z_score_filter(resamp) + + file_name_to_save_fig_hist = os.path.join(log_dir_this_measurement,file.split(".")[0]+"_hist"+str(lvl)+".svg") + + n_bins = min(len(warp),len(resamp))*PERC_BINS//100 + min_bin = min(min(warp),min(resamp)) + max_bin = max(max(warp),max(resamp)) delta = (max_bin-min_bin)/n_bins bins = np.arange(min_bin,max_bin+delta,delta) plt.figure() - plt.hist(prd_id_coupling['prd_warp'], bins = bins, alpha=0.5) - plt.hist(prd_id_coupling['prd_resamp'], bins = bins, alpha=0.5) - plt.title(f'File: {file}, Lvl: {lvl}, PRD histogram') - plt.legend(['PRD warp','PRD resampled']) + plt.hist(warp, bins = bins, alpha=0.5) + plt.hist(resamp, bins = bins, alpha=0.5) + plt.title(f'File: {file}, Lvl: {lvl}, {measurement_type} histogram') + plt.legend([f'{measurement_type} warp',f'{measurement_type} resampled']) plt.savefig(file_name_to_save_fig_hist) plt.close() - n_bins = min(len(prd_id_coupling['prd_warp_left']),len(prd_id_coupling['prd_left_resamp']))*PERC_BINS//100 - min_bin = min(min(prd_id_coupling['prd_warp_left']),min(prd_id_coupling['prd_left_resamp'])) - max_bin = max(max(prd_id_coupling['prd_warp_left']),max(prd_id_coupling['prd_left_resamp'])) - delta = (max_bin-min_bin)/n_bins - bins = np.arange(min_bin,max_bin+delta,delta) - plt.figure() - plt.hist(prd_id_coupling['prd_warp_left'], bins = bins, alpha=0.5) - plt.hist(prd_id_coupling['prd_left_resamp'], bins = bins, alpha=0.5) - plt.title(f'File: {file}, Lvl: {lvl}, left PRD histogram') - plt.legend(['PRD warp','PRD resampled']) - plt.savefig(file_name_to_save_fig_hist_left) - plt.close() +def delinetaion_results_save(delineation_results,log_dir_this_lvl): + log_dir_this_measurement = os.path.join(log_dir_this_lvl,"delineation") + os.makedirs(log_dir_this_measurement,exist_ok=True) + file_name_to_save = "L_delineation.log" + ''' + + print("----------------------------------------") + print(f"delineation_score: {dir_dest}: \n\tp_s:{p_wave_sens}\n\tp_pp:{p_wave_pp}\n\tp_f1:{p_wave_f1}\n\tt_s:{t_wave_sens}\n\tt_pp:{t_wave_pp}\n\tt_f1:{t_wave_f1}") + return {"p":{'sens':p_wave_sens,'ppv':p_wave_pp,'f1':p_wave_f1},"t":{'sens':t_wave_sens,'ppv':t_wave_pp,'f1':t_wave_f1}} + + + def delineate_and_compare(log_dir_this_lvl, QRS_pos): + annots_orig = delineate(log_dir_this_lvl, "original", QRS_pos) + annots_warp = delineate(log_dir_this_lvl, "warped", QRS_pos) + annots_resamp = delineate(log_dir_this_lvl, "resampled", QRS_pos) + results_warp = compare(log_dir_this_lvl, "warped", annots_orig,annots_warp) + results_resamp = compare(log_dir_this_lvl, "resampled", annots_orig,annots_resamp) + return {"resamp":results_resamp,"warp":results_warp} + ''' + with open(os.path.join(log_dir_this_measurement,file_name_to_save), "w") as f: + f.write(f"Delineation_score:") + f.write(f"\n\twarping p-wave sensitivity:{delineation_results['warp']['p']['sens']}") + f.write(f"\n\tresampling p-wave sensitivity:{delineation_results['resamp']['p']['sens']}\n") + f.write(f"\n\twarping p-wave positive predicitivity:{delineation_results['warp']['p']['ppv']}") + f.write(f"\n\tresampling p-wave positive predicitivity:{delineation_results['resamp']['p']['ppv']}\n") + f.write(f"\n\twarping p-wave f1 score:{delineation_results['warp']['p']['f1']}") + f.write(f"\n\tresampling p-wave f1 score:{delineation_results['resamp']['p']['f1']}\n\n") + + f.write(f"\n\twarping t-wave sensitivity:{delineation_results['warp']['t']['sens']}") + f.write(f"\n\tresampling t-wave sensitivity:{delineation_results['resamp']['t']['sens']}\n") + f.write(f"\n\twarping t-wave positive predicitivity:{delineation_results['warp']['t']['ppv']}") + f.write(f"\n\tresampling t-wave positive predicitivity:{delineation_results['resamp']['t']['ppv']}\n") + f.write(f"\n\twarping t-wave f1 score:{delineation_results['warp']['t']['f1']}") + f.write(f"\n\tresampling t-wave f1 score:{delineation_results['resamp']['t']['f1']}") - n_bins = min(len(prd_id_coupling['prd_warp_right']),len(prd_id_coupling['prd_right_resamp']))*PERC_BINS//100 - min_bin = min(min(prd_id_coupling['prd_warp_right']),min(prd_id_coupling['prd_right_resamp'])) - max_bin = max(max(prd_id_coupling['prd_warp_right']),max(prd_id_coupling['prd_right_resamp'])) - delta = (max_bin-min_bin)/n_bins - bins = np.arange(min_bin,max_bin+delta,delta) - plt.figure() - plt.hist(prd_id_coupling['prd_warp_right'], bins = bins, alpha=0.5) - plt.hist(prd_id_coupling['prd_right_resamp'], bins = bins, alpha=0.5) - plt.title(f'File: {file}, Lvl: {lvl}, right PRD histogram') - plt.legend(['PRD warp','PRD resampled']) - plt.savefig(file_name_to_save_fig_hist_right) - plt.close() +def reconstruction_one_lvl_one_file_and_compare(data_orig,lvl,file,initial_templates,verbose = True): + log_dir_this_file = os.path.join(log_dir,file.split(".")[0]) + interpolation_type_list = None if INTERPOLATION_TYPE == 'all': - log_dir_this_lvl_combined = os.path.join(log_dir_this_file,str(lvl),"combined_results") - os.makedirs(log_dir_this_lvl_combined, exist_ok=True) - file_name_to_save = "L_"+file.split(".")[0]+".log" - global_file_name_to_save = "L_all_interpolations_"+file.split(".")[0]+".log" + interpolation_type_list = ['flat','spline','linear'] + else: + interpolation_type_list = [INTERPOLATION_TYPE] + measurements_each_interpolation_type = {} + measurements_id_coupling = {} - with open(os.path.join(log_dir_this_lvl_combined,file_name_to_save),"a") as f: - f.write(f"Lvl: {lvl}\n") + for interpolation_type in interpolation_type_list: + if verbose: + print(f"Level:{lvl}, Using interpolation: {interpolation_type}") - with open(os.path.join(log_dir_this_file,global_file_name_to_save),"a") as f: - f.write(f"Lvl: {lvl}\n") + log_dir_this_lvl = os.path.join(log_dir_this_file,str(lvl),interpolation_type) + os.makedirs(log_dir_this_lvl, exist_ok=True) + all_templates_collection, measurements_id_coupling, initial_templates, time_info, QRS_pos_new = reconstruct_beats(data_orig, lvl, init_templates = initial_templates, start_after = SEC_FOR_INITIAL_TEMPLATES, resample_type = interpolation_type, num_beats_analyzed = NUM_BEAT_ANALYZED, verbose = True, log_dir = log_dir_this_lvl) #resample_type = flat vs linear + ############################################################################# + # RESULTS EVALUATION # + ############################################################################# + delineation_results = delineate_and_compare(log_dir_this_lvl,QRS_pos_new) + measurements_each_interpolation_type[interpolation_type] = measurements_id_coupling['measures'] + measurments_reuslts(measurements_id_coupling,interpolation_type,data_orig,all_templates_collection,time_info,file,lvl,log_dir_this_lvl,log_dir_this_file) + delinetaion_results_save(delineation_results,log_dir_this_lvl) - # Histograms - plt.figure() - file_name_to_save_fig_hist = os.path.join(log_dir_this_lvl_combined,file.split(".")[0]+"_hist"+str(lvl)+".svg") - n_bins = np.inf - min_bin = np.inf - max_bin = -np.inf - legend_str = [] - for interp_type in prd_each_interpolation_type: - r_w = prd_each_interpolation_type[interp_type]['prd_warp'] - r_r = prd_each_interpolation_type[interp_type]['prd_resamp'] - n_bins = min(min(len(r_w),len(r_r))*PERC_BINS//100,n_bins) - min_bin = min(min(r_w),min(r_r),min_bin) - max_bin = max(max(r_w),max(r_r),max_bin) - legend_str.extend([f'PRD warp {interp_type}',f'PRD resampled {interp_type}']) - delta = (max_bin-min_bin)/n_bins - bins = np.arange(min_bin,max_bin+delta,delta) - - for interp_type in prd_each_interpolation_type: - stats = avg_std_prd(prd_each_interpolation_type[interp_type]) - avg_prd_warp = stats['warp']['avg'] - std_prd_warp = stats['warp']['std'] - avg_prd_resamp = stats['resamp']['avg'] - std_prd_resamp = stats['resamp']['std'] - - # Particular log for each lvl + + if INTERPOLATION_TYPE == 'all': + for measure in measurements_id_coupling['measures']: + log_dir_this_lvl_combined = os.path.join(log_dir_this_file,str(lvl),"combined_results") + os.makedirs(log_dir_this_lvl_combined, exist_ok=True) + file_name_to_save = "L_"+measure+"_"+file.split(".")[0]+".log" + global_file_name_to_save = "L_"+measure+"_all_interpolations_"+file.split(".")[0]+".log" with open(os.path.join(log_dir_this_lvl_combined,file_name_to_save),"a") as f: - f.write(f"\tWarp using the {interp_type} interpolation: {avg_prd_warp}, +-{std_prd_warp}\n") - f.write(f"\tInterpolation using the {interp_type} interpolation: {avg_prd_resamp}, +-{std_prd_resamp}\n") - # General log (the same but all toghether: more confusing but with all infos) - with open(os.path.join(log_dir_this_file,global_file_name_to_save),"a") as f: - f.write(f"\tWarp using the {interp_type} interpolation: {avg_prd_warp}, +-{std_prd_warp}\n") - f.write(f"\tInterpolation using the {interp_type} interpolation: {avg_prd_resamp}, +-{std_prd_resamp}\n") - - plt.hist(prd_each_interpolation_type[interp_type]['prd_warp'], bins = bins, alpha=0.5) - plt.hist(prd_each_interpolation_type[interp_type]['prd_resamp'], bins = bins, alpha=0.5) + f.write(f"Lvl: {lvl}\n") - plt.legend(legend_str) - plt.title(f'File: {file}, Lvl: {lvl}, PRD histogram') - plt.savefig(file_name_to_save_fig_hist) - plt.close() + with open(os.path.join(log_dir_this_file,global_file_name_to_save),"a") as f: + f.write(f"Lvl: {lvl}\n") + + # Histograms + plt.figure() + file_name_to_save_fig_hist = os.path.join(log_dir_this_lvl_combined,file.split(".")[0]+"_"+measure+"_hist"+str(lvl)+".svg") + n_bins = np.inf + min_bin = np.inf + max_bin = -np.inf + legend_str = [] + #Compute the bins + for interp_type in measurements_each_interpolation_type: + r_w = measurements_each_interpolation_type[interp_type][measure]["warp"] + r_r = measurements_each_interpolation_type[interp_type][measure]["resamp"] + n_bins = min(min(len(r_w),len(r_r))*PERC_BINS//100,n_bins) + min_bin = min(min(r_w),min(r_r),min_bin) + max_bin = max(max(r_w),max(r_r),max_bin) + legend_str.extend([f'{measure} warp {interp_type}',f'{measure} resampled {interp_type}']) + delta = (max_bin-min_bin)/n_bins + bins = np.arange(min_bin,max_bin+delta,delta) + + #write results and plot + for interp_type in measurements_each_interpolation_type: + r_w = measurements_each_interpolation_type[interp_type][measure]["warp"] + r_r = measurements_each_interpolation_type[interp_type][measure]["resamp"] + avg_warp = np.average(r_w) + std_warp = np.std(r_w) + avg_resamp = np.average(r_r) + std_resamp = np.std(r_r) + + # Particular log for each lvl + with open(os.path.join(log_dir_this_lvl_combined,file_name_to_save),"a") as f: + f.write(f"\tWarp using the {interp_type} interpolation: {avg_warp}, +-{std_warp}\n") + f.write(f"\tInterpolation using the {interp_type} interpolation: {avg_resamp}, +-{std_resamp}\n") + # General log (the same but all toghether: more confusing but with all infos) + with open(os.path.join(log_dir_this_file,global_file_name_to_save),"a") as f: + f.write(f"\tWarp using the {interp_type} interpolation: {avg_warp}, +-{std_warp}\n") + f.write(f"\tInterpolation using the {interp_type} interpolation: {avg_resamp}, +-{std_resamp}\n") + + plt.hist(r_w, bins = bins, alpha=0.3) + plt.hist(r_r, bins = bins, alpha=0.3) + + plt.legend(legend_str) + plt.title(f'File: {file}, Lvl: {lvl}, {measure} histogram') + plt.savefig(file_name_to_save_fig_hist) + plt.close() - with open(os.path.join(log_dir_this_lvl_combined,file_name_to_save),"a") as f: - f.write(f"\n\n") + with open(os.path.join(log_dir_this_lvl_combined,file_name_to_save),"a") as f: + f.write(f"\n\n") - with open(os.path.join(log_dir_this_file,global_file_name_to_save),"a") as f: - f.write(f"\n\n") + with open(os.path.join(log_dir_this_file,global_file_name_to_save),"a") as f: + f.write(f"\n\n") return initial_templates def reconstruct_and_compare_level_parallel(lvl): for file in FILES_SELECTED: verbose = True log_dir_this_file = os.path.join(log_dir,file.split(".")[0]) os.makedirs(log_dir_this_file,exist_ok=True) init_templates = None if verbose: print(f"(Level: {lvl}): Extracting original data") data_orig = open_file(file, start_after = 0) init_templates = reconstruction_one_lvl_one_file_and_compare(data_orig,lvl,file,init_templates,verbose = True) def recontruct_and_compare_file_parallel(file): verbose = True log_dir_this_file = os.path.join(log_dir,file.split(".")[0]) os.mkdir(log_dir_this_file) init_templates = None if verbose: print(f"(File: {file}): Extracting original data") data_orig = open_file(file, start_after = 0) for lvl in LEVELS: init_templates = reconstruction_one_lvl_one_file_and_compare(data_orig,lvl,file,init_templates,verbose = True) def process(files, levels, parallelize_along = PARALLELIZE_ALONG, cores=1): # ------------ INIT ------------ global log_dir for i in range(1,10000): tmp_log_dir = log_dir+str(i) if not os.path.isdir(tmp_log_dir): log_dir = tmp_log_dir break os.makedirs(log_dir, exist_ok=True) with open(os.path.join(log_dir,"specs.txt"), "w") as f: f.write(f"Results generated by script: {sys.argv[0]}\n") f.write(f"Time: {time.ctime(time.time())}\n\n") f.write(f"Files: {files}\n") f.write(f"Levels: {levels}\n") f.write(f"Parallelize along: {parallelize_along}\n") f.write(f"Cores: {cores}\n") f.write(f"Beats: {NUM_BEAT_ANALYZED}\n") f.write(f"Cluster percentage: {CLUSTER_PERCENTAGE}\n") f.write(f"Interpolation type: {INTERPOLATION_TYPE}\n") f.write(f"Timing mode: {TIME_MODE}\n") # ------------ Extract DATA & ANNOTATIONS ------------ if cores == 1: print("Single core") if parallelize_along == 'levels': for lvl in levels: reconstruct_and_compare_level_parallel(lvl) elif parallelize_along == 'files': for f in files: recontruct_and_compare_file_parallel(f) else: with Pool(cores) as pool: if parallelize_along == 'levels': print(f"parallelizing along levels: {levels}") pool.map(reconstruct_and_compare_level_parallel, levels) elif parallelize_along == 'files': print("parallelizing along files") pool.map(recontruct_and_compare_file_parallel, files) if __name__ == "__main__": import argparse import time seconds_start = time.time() local_time_start = time.ctime(seconds_start) print("\nStarted at:", local_time_start,"\n\n") #global NUM_BEAT_ANALYZED parser = argparse.ArgumentParser() parser.add_argument("--file", help="Force to analyze one specific file instead of default one (first found)") parser.add_argument("--levels", help="Decide how many bits to use in the ADC: options:\n\t->1: [3]\n\t->2: [3,4]\n\t->3: [3,4,5]\n\t->4: ...") parser.add_argument("--cores", help="Force used number of cores (default, half of the available ones") parser.add_argument("--parallelize_along", help="describe if to parallelize on the number of 'files'\n or on the number of 'levels'") parser.add_argument("--beats", help="Number of used beats, default: 5000") parser.add_argument("--cluster_opt", help="Percentage of points for a cluster to be considered") parser.add_argument("--interpolation_type", help="Chose between: spline, flat, linear, and all. Default: falt") parser.add_argument("--acquisition_time_mode", help="Menage the time length the algorithm acquire the data at \ full speed ant the time horizon used for template recomputation. \nChose between: short, normal, and long. Default: normal") args = parser.parse_args() files = os.listdir(data_beats_dir) if args.file is not None: if args.file == 'all': FILES_SELECTED = files else: FILES_SELECTED = list(filter(lambda string: True if args.file in string else False, files)) else: FILES_SELECTED = [files[0]] if args.cores is not None: used_cores = int(args.cores) else: used_cores = multiprocessing.cpu_count()//3 if args.beats is not None: NUM_BEAT_ANALYZED = int(args.beats) else: NUM_BEAT_ANALYZED = 5000 if args.cluster_opt is not None: CLUSTER_PERCENTAGE = int(args.cluster_opt) else: CLUSTER_PERCENTAGE = 3 if args.interpolation_type is not None: INTERPOLATION_TYPE = args.interpolation_type else: INTERPOLATION_TYPE = "flat" if args.parallelize_along is not None: PARALLELIZE_ALONG = args.parallelize_along else: PARALLELIZE_ALONG = "levels" if args.levels is not None: LEVELS = LEVELS[:int(args.levels)] else: LEVELS = [3,4,5,6,7,8] if args.acquisition_time_mode is not None: if args.acquisition_time_mode == "short": SEC_FOR_INITIAL_TEMPLATES = 3*60 #5*60 LEN_DISTANCE_VECTOR = 60 #80 LEN_DISTANCE_VECTOR_REF = 400 #500 SEC_FOR_NEW_TEMPLATES = 40 #2*60 TIME_MODE = 'short' elif args.acquisition_time_mode == "long": SEC_FOR_INITIAL_TEMPLATES = 8*60 LEN_DISTANCE_VECTOR = 110 LEN_DISTANCE_VECTOR_REF = 650 SEC_FOR_NEW_TEMPLATES = 3*60 TIME_MODE = 'long' else: SEC_FOR_INITIAL_TEMPLATES = 5*60 LEN_DISTANCE_VECTOR = 80 LEN_DISTANCE_VECTOR_REF = 500 SEC_FOR_NEW_TEMPLATES = 2*60 TIME_MODE = 'medium' else: SEC_FOR_INITIAL_TEMPLATES = 5*60 LEN_DISTANCE_VECTOR = 80 LEN_DISTANCE_VECTOR_REF = 500 SEC_FOR_NEW_TEMPLATES = 2*60 TIME_MODE = 'medium' print(f"Analyzing files: {FILES_SELECTED}") print(f"Extracting data with {used_cores} cores...") process(files = FILES_SELECTED, levels = LEVELS, parallelize_along = PARALLELIZE_ALONG, cores=used_cores) seconds_stop = time.time() local_time_stop = time.ctime(seconds_stop) elapsed = seconds_stop - seconds_start hours = elapsed//60//60 minutes = (elapsed - hours * 60 * 60) // 60 seconds = (elapsed - hours * 60 * 60 - minutes * 60) // 1 print("\n\n\n-----------------------------------------------------------------------------------------------------------------") print(f"Finished at: {local_time_stop}, elapsed: {elapsed} seconds ({hours} hours, {minutes} minutes, {seconds} seconds)")