From 2e83d9fca35b6ccb2d185a3a2d64d3fa13659174 Mon Sep 17 00:00:00 2001 From: Bruce Macdonald Date: Mon, 21 Jan 2019 17:52:44 -0500 Subject: [PATCH 01/46] adding multistate files --- openmmtools/multistate/__init__.py | 70 + openmmtools/multistate/mixing/__init__.py | 0 openmmtools/multistate/mixing/_mix_replicas.c | 20827 ++++++++++++++++ .../multistate/mixing/_mix_replicas.pyx | 31 + .../multistate/mixing/_mix_replicas_old.py | 85 + openmmtools/multistate/multistateanalyzer.py | 2457 ++ openmmtools/multistate/multistatereporter.py | 1818 ++ openmmtools/multistate/multistatesampler.py | 1622 ++ openmmtools/multistate/paralleltempering.py | 248 + openmmtools/multistate/replicaexchange.py | 383 + openmmtools/multistate/sams.py | 693 + openmmtools/multistate/utils.py | 283 + 12 files changed, 28517 insertions(+) create mode 100644 openmmtools/multistate/__init__.py create mode 100644 openmmtools/multistate/mixing/__init__.py create mode 100644 openmmtools/multistate/mixing/_mix_replicas.c create mode 100644 openmmtools/multistate/mixing/_mix_replicas.pyx create mode 100644 openmmtools/multistate/mixing/_mix_replicas_old.py create mode 100644 openmmtools/multistate/multistateanalyzer.py create mode 100644 openmmtools/multistate/multistatereporter.py create mode 100644 openmmtools/multistate/multistatesampler.py create mode 100644 openmmtools/multistate/paralleltempering.py create mode 100644 openmmtools/multistate/replicaexchange.py create mode 100644 openmmtools/multistate/sams.py create mode 100644 openmmtools/multistate/utils.py diff --git a/openmmtools/multistate/__init__.py b/openmmtools/multistate/__init__.py new file mode 100644 index 000000000..128b8df23 --- /dev/null +++ b/openmmtools/multistate/__init__.py @@ -0,0 +1,70 @@ +#!/usr/local/bin/env python + +# ============================================================================== +# MODULE DOCSTRING +# ============================================================================== + +""" +MultiState +========== + +Multistate Sampling simulation algorithms, specific variants, and analyzers + +This module provides a general facility for running multiple thermodynamic state multistate simulations, both general +as well as derived classes for special cases such as parallel tempering (in which +the states differ only in temperature). + +The classes also provide + +Provided classes include: + +- :class:`openmmtools.multistate.MultiStateSampler` + Base class for general, multi-thermodynamic state parallel multistate +- :class:`openmmtools.multistate.ReplicaExchangeSampler` + Derived class from MultiStateSampler which allows sampled thermodynamic states + to swap based on Hamiltonian Replica Exchange +- :class:`openmmtools.multistate.ParallelTemperingSampler` + Convenience subclass of ReplicaExchange for parallel tempering simulations + (one System object, many temperatures). +- :class:`openmmtools.multistate.SAMSSampler` + Single-replica sampler which samples through multiple thermodynamic states + on the fly. +- :class:`openmmtools.multistate.MultiStateReporter` + Replica Exchange reporter class to store all variables and data + +Analyzers +--------- +The MultiState module also provides analysis modules to analyze simulations and compute observables from data generated +under any of the MultiStateSampler's + +Extending and Subclassing +------------------------- +Subclassing a sampler and analyzer is done by importing and extending any of the following: + + * The base ``MultiStateSampler`` from ``multistatesampler`` + * The base ``MultiStateReporter`` from ``multistatereporter`` + * The base ``MultiStateAnalyzer`` or ``PhaseAnalyzer`` and base `ObservablesRegistry`` from ``multistateanalyzer`` + +COPYRIGHT +--------- + +Current version by Andrea Rizzi , Levi N. Naden and +John D. Chodera while at Memorial Sloan Kettering Cancer Center. + +Original version by John D. Chodera while at the University of +California Berkeley. + +LICENSE +------- + +This code is licensed under the latest available version of the MIT License. + +""" + +from openmmtools.multistate.multistatesampler import MultiStateSampler +from openmmtools.multistate.multistatereporter import MultiStateReporter +from openmmtools.multistate.replicaexchange import ReplicaExchangeSampler, ReplicaExchangeAnalyzer +from openmmtools.multistate.paralleltempering import ParallelTemperingSampler, ParallelTemperingAnalyzer +from openmmtools.multistate.sams import SAMSSampler, SAMSAnalyzer +from openmmtools.multistate.multistateanalyzer import * +from openmmtools.multistate.utils import * diff --git a/openmmtools/multistate/mixing/__init__.py b/openmmtools/multistate/mixing/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/openmmtools/multistate/mixing/_mix_replicas.c b/openmmtools/multistate/mixing/_mix_replicas.c new file mode 100644 index 000000000..c6e62fe23 --- /dev/null +++ b/openmmtools/multistate/mixing/_mix_replicas.c @@ -0,0 +1,20827 @@ +/* Generated by Cython 0.29 */ + +/* BEGIN: Cython Metadata +{ + "distutils": { + "depends": [], + "name": "yank.multistate.mixing._mix_replicas", + "sources": [ + "./Yank/multistate/mixing/_mix_replicas.pyx" + ] + }, + "module_name": "yank.multistate.mixing._mix_replicas" +} +END: Cython Metadata */ + +#define 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" +#define CYTHON_HEX_VERSION 0x001D00F0 +#define CYTHON_FUTURE_DIVISION 0 +#include +#ifndef offsetof + #define offsetof(type, member) ( (size_t) & ((type*)0) -> member ) +#endif +#if !defined(WIN32) && !defined(MS_WINDOWS) + #ifndef __stdcall + #define __stdcall + #endif + #ifndef __cdecl + #define __cdecl + #endif + #ifndef __fastcall + #define __fastcall + #endif +#endif +#ifndef DL_IMPORT + #define DL_IMPORT(t) t +#endif +#ifndef DL_EXPORT + #define DL_EXPORT(t) t +#endif +#define __PYX_COMMA , +#ifndef HAVE_LONG_LONG + #if PY_VERSION_HEX >= 0x02070000 + #define HAVE_LONG_LONG + #endif +#endif +#ifndef PY_LONG_LONG + #define PY_LONG_LONG LONG_LONG +#endif +#ifndef Py_HUGE_VAL + #define Py_HUGE_VAL HUGE_VAL +#endif +#ifdef PYPY_VERSION + #define CYTHON_COMPILING_IN_PYPY 1 + #define CYTHON_COMPILING_IN_PYSTON 0 + #define CYTHON_COMPILING_IN_CPYTHON 0 + #undef CYTHON_USE_TYPE_SLOTS + #define CYTHON_USE_TYPE_SLOTS 0 + #undef CYTHON_USE_PYTYPE_LOOKUP + #define CYTHON_USE_PYTYPE_LOOKUP 0 + #if PY_VERSION_HEX < 0x03050000 + #undef CYTHON_USE_ASYNC_SLOTS + #define CYTHON_USE_ASYNC_SLOTS 0 + #elif !defined(CYTHON_USE_ASYNC_SLOTS) + #define CYTHON_USE_ASYNC_SLOTS 1 + #endif + #undef CYTHON_USE_PYLIST_INTERNALS + #define CYTHON_USE_PYLIST_INTERNALS 0 + #undef CYTHON_USE_UNICODE_INTERNALS + #define CYTHON_USE_UNICODE_INTERNALS 0 + #undef CYTHON_USE_UNICODE_WRITER + #define CYTHON_USE_UNICODE_WRITER 0 + #undef CYTHON_USE_PYLONG_INTERNALS + #define CYTHON_USE_PYLONG_INTERNALS 0 + #undef CYTHON_AVOID_BORROWED_REFS + #define CYTHON_AVOID_BORROWED_REFS 1 + #undef CYTHON_ASSUME_SAFE_MACROS + #define CYTHON_ASSUME_SAFE_MACROS 0 + #undef CYTHON_UNPACK_METHODS + #define CYTHON_UNPACK_METHODS 0 + #undef CYTHON_FAST_THREAD_STATE + #define CYTHON_FAST_THREAD_STATE 0 + #undef CYTHON_FAST_PYCALL + #define CYTHON_FAST_PYCALL 0 + #undef CYTHON_PEP489_MULTI_PHASE_INIT + #define CYTHON_PEP489_MULTI_PHASE_INIT 0 + #undef CYTHON_USE_TP_FINALIZE + #define CYTHON_USE_TP_FINALIZE 0 + #undef CYTHON_USE_DICT_VERSIONS + #define CYTHON_USE_DICT_VERSIONS 0 + #undef CYTHON_USE_EXC_INFO_STACK + #define CYTHON_USE_EXC_INFO_STACK 0 +#elif defined(PYSTON_VERSION) + #define CYTHON_COMPILING_IN_PYPY 0 + #define CYTHON_COMPILING_IN_PYSTON 1 + #define CYTHON_COMPILING_IN_CPYTHON 0 + #ifndef CYTHON_USE_TYPE_SLOTS + #define CYTHON_USE_TYPE_SLOTS 1 + #endif + #undef CYTHON_USE_PYTYPE_LOOKUP + #define CYTHON_USE_PYTYPE_LOOKUP 0 + #undef CYTHON_USE_ASYNC_SLOTS + #define CYTHON_USE_ASYNC_SLOTS 0 + #undef CYTHON_USE_PYLIST_INTERNALS + #define CYTHON_USE_PYLIST_INTERNALS 0 + #ifndef CYTHON_USE_UNICODE_INTERNALS + #define CYTHON_USE_UNICODE_INTERNALS 1 + #endif + #undef CYTHON_USE_UNICODE_WRITER + #define CYTHON_USE_UNICODE_WRITER 0 + #undef CYTHON_USE_PYLONG_INTERNALS + #define CYTHON_USE_PYLONG_INTERNALS 0 + #ifndef CYTHON_AVOID_BORROWED_REFS + #define CYTHON_AVOID_BORROWED_REFS 0 + #endif + #ifndef CYTHON_ASSUME_SAFE_MACROS + #define CYTHON_ASSUME_SAFE_MACROS 1 + #endif + #ifndef CYTHON_UNPACK_METHODS + #define CYTHON_UNPACK_METHODS 1 + #endif + #undef CYTHON_FAST_THREAD_STATE + #define CYTHON_FAST_THREAD_STATE 0 + #undef CYTHON_FAST_PYCALL + #define CYTHON_FAST_PYCALL 0 + #undef CYTHON_PEP489_MULTI_PHASE_INIT + #define CYTHON_PEP489_MULTI_PHASE_INIT 0 + #undef CYTHON_USE_TP_FINALIZE + #define CYTHON_USE_TP_FINALIZE 0 + #undef CYTHON_USE_DICT_VERSIONS + #define CYTHON_USE_DICT_VERSIONS 0 + #undef CYTHON_USE_EXC_INFO_STACK + #define CYTHON_USE_EXC_INFO_STACK 0 +#else + #define CYTHON_COMPILING_IN_PYPY 0 + #define CYTHON_COMPILING_IN_PYSTON 0 + #define CYTHON_COMPILING_IN_CPYTHON 1 + #ifndef CYTHON_USE_TYPE_SLOTS + #define CYTHON_USE_TYPE_SLOTS 1 + #endif + #if PY_VERSION_HEX < 0x02070000 + #undef CYTHON_USE_PYTYPE_LOOKUP + #define CYTHON_USE_PYTYPE_LOOKUP 0 + #elif !defined(CYTHON_USE_PYTYPE_LOOKUP) + #define CYTHON_USE_PYTYPE_LOOKUP 1 + #endif + #if PY_MAJOR_VERSION < 3 + #undef CYTHON_USE_ASYNC_SLOTS + #define CYTHON_USE_ASYNC_SLOTS 0 + #elif !defined(CYTHON_USE_ASYNC_SLOTS) + #define CYTHON_USE_ASYNC_SLOTS 1 + #endif + #if PY_VERSION_HEX < 0x02070000 + #undef CYTHON_USE_PYLONG_INTERNALS + #define CYTHON_USE_PYLONG_INTERNALS 0 + #elif !defined(CYTHON_USE_PYLONG_INTERNALS) + #define CYTHON_USE_PYLONG_INTERNALS 1 + #endif + #ifndef CYTHON_USE_PYLIST_INTERNALS + #define CYTHON_USE_PYLIST_INTERNALS 1 + #endif + #ifndef CYTHON_USE_UNICODE_INTERNALS + #define CYTHON_USE_UNICODE_INTERNALS 1 + #endif + #if PY_VERSION_HEX < 0x030300F0 + #undef CYTHON_USE_UNICODE_WRITER + #define CYTHON_USE_UNICODE_WRITER 0 + #elif !defined(CYTHON_USE_UNICODE_WRITER) + #define CYTHON_USE_UNICODE_WRITER 1 + #endif + #ifndef CYTHON_AVOID_BORROWED_REFS + #define CYTHON_AVOID_BORROWED_REFS 0 + #endif + #ifndef CYTHON_ASSUME_SAFE_MACROS + #define CYTHON_ASSUME_SAFE_MACROS 1 + #endif + #ifndef CYTHON_UNPACK_METHODS + #define CYTHON_UNPACK_METHODS 1 + #endif + #ifndef CYTHON_FAST_THREAD_STATE + #define CYTHON_FAST_THREAD_STATE 1 + #endif + #ifndef CYTHON_FAST_PYCALL + #define CYTHON_FAST_PYCALL 1 + #endif + #ifndef CYTHON_PEP489_MULTI_PHASE_INIT + #define CYTHON_PEP489_MULTI_PHASE_INIT (PY_VERSION_HEX >= 0x03050000) + #endif + #ifndef CYTHON_USE_TP_FINALIZE + #define CYTHON_USE_TP_FINALIZE (PY_VERSION_HEX >= 0x030400a1) + #endif + #ifndef CYTHON_USE_DICT_VERSIONS + #define CYTHON_USE_DICT_VERSIONS (PY_VERSION_HEX >= 0x030600B1) + #endif + #ifndef CYTHON_USE_EXC_INFO_STACK + #define CYTHON_USE_EXC_INFO_STACK (PY_VERSION_HEX >= 0x030700A3) + #endif +#endif +#if !defined(CYTHON_FAST_PYCCALL) +#define CYTHON_FAST_PYCCALL (CYTHON_FAST_PYCALL && PY_VERSION_HEX >= 0x030600B1) +#endif +#if CYTHON_USE_PYLONG_INTERNALS + #include "longintrepr.h" + #undef SHIFT + #undef BASE + #undef MASK +#endif +#ifndef __has_attribute + #define __has_attribute(x) 0 +#endif +#ifndef __has_cpp_attribute + #define __has_cpp_attribute(x) 0 +#endif +#ifndef CYTHON_RESTRICT + #if defined(__GNUC__) + #define CYTHON_RESTRICT __restrict__ + #elif defined(_MSC_VER) && _MSC_VER >= 1400 + #define CYTHON_RESTRICT __restrict + #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L + #define CYTHON_RESTRICT restrict + #else + #define CYTHON_RESTRICT + #endif +#endif +#ifndef CYTHON_UNUSED +# if defined(__GNUC__) +# if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4)) +# define CYTHON_UNUSED __attribute__ ((__unused__)) +# else +# define CYTHON_UNUSED +# endif +# elif defined(__ICC) || (defined(__INTEL_COMPILER) && !defined(_MSC_VER)) +# define CYTHON_UNUSED __attribute__ ((__unused__)) +# else +# define CYTHON_UNUSED +# endif +#endif +#ifndef CYTHON_MAYBE_UNUSED_VAR +# if defined(__cplusplus) + template void CYTHON_MAYBE_UNUSED_VAR( const T& ) { } +# else +# define CYTHON_MAYBE_UNUSED_VAR(x) (void)(x) +# endif +#endif +#ifndef CYTHON_NCP_UNUSED +# if CYTHON_COMPILING_IN_CPYTHON +# define CYTHON_NCP_UNUSED +# else +# define CYTHON_NCP_UNUSED CYTHON_UNUSED +# endif +#endif +#define __Pyx_void_to_None(void_result) ((void)(void_result), Py_INCREF(Py_None), Py_None) +#ifdef _MSC_VER + #ifndef _MSC_STDINT_H_ + #if _MSC_VER < 1300 + typedef unsigned char uint8_t; + typedef unsigned int uint32_t; + #else + typedef unsigned __int8 uint8_t; + typedef unsigned __int32 uint32_t; + #endif + #endif +#else + #include +#endif +#ifndef CYTHON_FALLTHROUGH + #if defined(__cplusplus) && __cplusplus >= 201103L + #if __has_cpp_attribute(fallthrough) + #define CYTHON_FALLTHROUGH [[fallthrough]] + #elif __has_cpp_attribute(clang::fallthrough) + #define CYTHON_FALLTHROUGH [[clang::fallthrough]] + #elif __has_cpp_attribute(gnu::fallthrough) + #define CYTHON_FALLTHROUGH [[gnu::fallthrough]] + #endif + #endif + #ifndef CYTHON_FALLTHROUGH + #if __has_attribute(fallthrough) + #define CYTHON_FALLTHROUGH __attribute__((fallthrough)) + #else + #define CYTHON_FALLTHROUGH + #endif + #endif + #if defined(__clang__ ) && defined(__apple_build_version__) + #if __apple_build_version__ < 7000000 + #undef CYTHON_FALLTHROUGH + #define CYTHON_FALLTHROUGH + #endif + #endif +#endif + +#ifndef CYTHON_INLINE + #if defined(__clang__) + #define CYTHON_INLINE __inline__ __attribute__ ((__unused__)) + #elif defined(__GNUC__) + #define CYTHON_INLINE __inline__ + #elif defined(_MSC_VER) + #define CYTHON_INLINE __inline + #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L + #define CYTHON_INLINE inline + #else + #define CYTHON_INLINE + #endif +#endif + +#if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX < 0x02070600 && !defined(Py_OptimizeFlag) + #define Py_OptimizeFlag 0 +#endif +#define __PYX_BUILD_PY_SSIZE_T "n" +#define CYTHON_FORMAT_SSIZE_T "z" +#if PY_MAJOR_VERSION < 3 + #define __Pyx_BUILTIN_MODULE_NAME "__builtin__" + #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\ + PyCode_New(a+k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) + #define __Pyx_DefaultClassType PyClass_Type +#else + #define __Pyx_BUILTIN_MODULE_NAME "builtins" + #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\ + PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) + #define __Pyx_DefaultClassType PyType_Type +#endif +#ifndef Py_TPFLAGS_CHECKTYPES + #define Py_TPFLAGS_CHECKTYPES 0 +#endif +#ifndef Py_TPFLAGS_HAVE_INDEX + #define Py_TPFLAGS_HAVE_INDEX 0 +#endif +#ifndef Py_TPFLAGS_HAVE_NEWBUFFER + #define Py_TPFLAGS_HAVE_NEWBUFFER 0 +#endif +#ifndef Py_TPFLAGS_HAVE_FINALIZE + #define Py_TPFLAGS_HAVE_FINALIZE 0 +#endif +#ifndef METH_STACKLESS + #define METH_STACKLESS 0 +#endif +#if PY_VERSION_HEX <= 0x030700A3 || !defined(METH_FASTCALL) + #ifndef METH_FASTCALL + #define METH_FASTCALL 0x80 + #endif + typedef PyObject *(*__Pyx_PyCFunctionFast) (PyObject *self, PyObject *const *args, Py_ssize_t nargs); + typedef PyObject *(*__Pyx_PyCFunctionFastWithKeywords) (PyObject *self, PyObject *const *args, + Py_ssize_t nargs, PyObject *kwnames); +#else + #define __Pyx_PyCFunctionFast _PyCFunctionFast + #define __Pyx_PyCFunctionFastWithKeywords _PyCFunctionFastWithKeywords +#endif +#if CYTHON_FAST_PYCCALL +#define __Pyx_PyFastCFunction_Check(func)\ + ((PyCFunction_Check(func) && (METH_FASTCALL == (PyCFunction_GET_FLAGS(func) & ~(METH_CLASS | METH_STATIC | METH_COEXIST | METH_KEYWORDS | METH_STACKLESS))))) +#else +#define __Pyx_PyFastCFunction_Check(func) 0 +#endif +#if CYTHON_USE_DICT_VERSIONS +#define __PYX_GET_DICT_VERSION(dict) (((PyDictObject*)(dict))->ma_version_tag) +#define __PYX_UPDATE_DICT_CACHE(dict, value, cache_var, version_var)\ + (version_var) = __PYX_GET_DICT_VERSION(dict);\ + (cache_var) = (value); +#define __PYX_PY_DICT_LOOKUP_IF_MODIFIED(VAR, DICT, LOOKUP) {\ + static PY_UINT64_T __pyx_dict_version = 0;\ + static PyObject *__pyx_dict_cached_value = NULL;\ + if (likely(__PYX_GET_DICT_VERSION(DICT) == __pyx_dict_version)) {\ + (VAR) = __pyx_dict_cached_value;\ + } else {\ + (VAR) = __pyx_dict_cached_value = (LOOKUP);\ + __pyx_dict_version = __PYX_GET_DICT_VERSION(DICT);\ + }\ + } +#else +#define __PYX_GET_DICT_VERSION(dict) (0) +#define __PYX_UPDATE_DICT_CACHE(dict, value, cache_var, version_var) +#define __PYX_PY_DICT_LOOKUP_IF_MODIFIED(VAR, DICT, LOOKUP) (VAR) = (LOOKUP); +#endif +#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Malloc) + #define PyObject_Malloc(s) PyMem_Malloc(s) + #define PyObject_Free(p) PyMem_Free(p) + #define PyObject_Realloc(p) PyMem_Realloc(p) +#endif +#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX < 0x030400A1 + #define PyMem_RawMalloc(n) PyMem_Malloc(n) + #define PyMem_RawRealloc(p, n) PyMem_Realloc(p, n) + #define PyMem_RawFree(p) PyMem_Free(p) +#endif +#if CYTHON_COMPILING_IN_PYSTON + #define __Pyx_PyCode_HasFreeVars(co) PyCode_HasFreeVars(co) + #define __Pyx_PyFrame_SetLineNumber(frame, lineno) PyFrame_SetLineNumber(frame, lineno) +#else + #define __Pyx_PyCode_HasFreeVars(co) (PyCode_GetNumFree(co) > 0) + #define __Pyx_PyFrame_SetLineNumber(frame, lineno) (frame)->f_lineno = (lineno) +#endif +#if !CYTHON_FAST_THREAD_STATE || PY_VERSION_HEX < 0x02070000 + #define __Pyx_PyThreadState_Current PyThreadState_GET() +#elif PY_VERSION_HEX >= 0x03060000 + #define __Pyx_PyThreadState_Current _PyThreadState_UncheckedGet() +#elif PY_VERSION_HEX >= 0x03000000 + #define 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void *value) { + return PyThread_set_key_value(*key, value); +} +static CYTHON_INLINE void * PyThread_tss_get(Py_tss_t *key) { + return PyThread_get_key_value(*key); +} +#endif // TSS (Thread Specific Storage) API +#if CYTHON_COMPILING_IN_CPYTHON || defined(_PyDict_NewPresized) +#define __Pyx_PyDict_NewPresized(n) ((n <= 8) ? PyDict_New() : _PyDict_NewPresized(n)) +#else +#define __Pyx_PyDict_NewPresized(n) PyDict_New() +#endif +#if PY_MAJOR_VERSION >= 3 || CYTHON_FUTURE_DIVISION + #define __Pyx_PyNumber_Divide(x,y) PyNumber_TrueDivide(x,y) + #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceTrueDivide(x,y) +#else + #define __Pyx_PyNumber_Divide(x,y) PyNumber_Divide(x,y) + #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceDivide(x,y) +#endif +#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030500A1 && CYTHON_USE_UNICODE_INTERNALS +#define __Pyx_PyDict_GetItemStr(dict, name) _PyDict_GetItem_KnownHash(dict, name, ((PyASCIIObject *) name)->hash) +#else +#define __Pyx_PyDict_GetItemStr(dict, name) PyDict_GetItem(dict, name) +#endif +#if PY_VERSION_HEX > 0x03030000 && defined(PyUnicode_KIND) + #define CYTHON_PEP393_ENABLED 1 + #define __Pyx_PyUnicode_READY(op) (likely(PyUnicode_IS_READY(op)) ?\ + 0 : _PyUnicode_Ready((PyObject *)(op))) + #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_LENGTH(u) + #define __Pyx_PyUnicode_READ_CHAR(u, i) PyUnicode_READ_CHAR(u, i) + #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u) PyUnicode_MAX_CHAR_VALUE(u) + #define __Pyx_PyUnicode_KIND(u) PyUnicode_KIND(u) + #define __Pyx_PyUnicode_DATA(u) PyUnicode_DATA(u) + #define __Pyx_PyUnicode_READ(k, d, i) PyUnicode_READ(k, d, i) + #define __Pyx_PyUnicode_WRITE(k, d, i, ch) PyUnicode_WRITE(k, d, i, ch) + #define __Pyx_PyUnicode_IS_TRUE(u) (0 != (likely(PyUnicode_IS_READY(u)) ? PyUnicode_GET_LENGTH(u) : PyUnicode_GET_SIZE(u))) +#else + #define CYTHON_PEP393_ENABLED 0 + #define PyUnicode_1BYTE_KIND 1 + #define PyUnicode_2BYTE_KIND 2 + #define PyUnicode_4BYTE_KIND 4 + #define __Pyx_PyUnicode_READY(op) (0) + #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_SIZE(u) + #define __Pyx_PyUnicode_READ_CHAR(u, i) ((Py_UCS4)(PyUnicode_AS_UNICODE(u)[i])) + #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u) ((sizeof(Py_UNICODE) == 2) ? 65535 : 1114111) + #define __Pyx_PyUnicode_KIND(u) (sizeof(Py_UNICODE)) + #define __Pyx_PyUnicode_DATA(u) ((void*)PyUnicode_AS_UNICODE(u)) + #define __Pyx_PyUnicode_READ(k, d, i) ((void)(k), (Py_UCS4)(((Py_UNICODE*)d)[i])) + #define __Pyx_PyUnicode_WRITE(k, d, i, ch) (((void)(k)), ((Py_UNICODE*)d)[i] = ch) + #define __Pyx_PyUnicode_IS_TRUE(u) (0 != PyUnicode_GET_SIZE(u)) +#endif +#if CYTHON_COMPILING_IN_PYPY + #define __Pyx_PyUnicode_Concat(a, b) PyNumber_Add(a, b) + #define __Pyx_PyUnicode_ConcatSafe(a, b) PyNumber_Add(a, b) +#else + #define __Pyx_PyUnicode_Concat(a, b) PyUnicode_Concat(a, b) + #define __Pyx_PyUnicode_ConcatSafe(a, b) ((unlikely((a) == Py_None) || unlikely((b) == Py_None)) ?\ + PyNumber_Add(a, b) : __Pyx_PyUnicode_Concat(a, b)) +#endif +#if CYTHON_COMPILING_IN_PYPY && !defined(PyUnicode_Contains) + #define PyUnicode_Contains(u, s) PySequence_Contains(u, s) +#endif +#if CYTHON_COMPILING_IN_PYPY && !defined(PyByteArray_Check) + #define PyByteArray_Check(obj) PyObject_TypeCheck(obj, &PyByteArray_Type) +#endif +#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Format) + #define PyObject_Format(obj, fmt) PyObject_CallMethod(obj, "__format__", "O", fmt) +#endif +#define __Pyx_PyString_FormatSafe(a, b) ((unlikely((a) == Py_None || (PyString_Check(b) && !PyString_CheckExact(b)))) ? PyNumber_Remainder(a, b) : __Pyx_PyString_Format(a, b)) +#define __Pyx_PyUnicode_FormatSafe(a, b) ((unlikely((a) == Py_None || (PyUnicode_Check(b) && !PyUnicode_CheckExact(b)))) ? PyNumber_Remainder(a, b) : PyUnicode_Format(a, b)) +#if PY_MAJOR_VERSION >= 3 + #define __Pyx_PyString_Format(a, b) PyUnicode_Format(a, b) +#else + #define __Pyx_PyString_Format(a, b) PyString_Format(a, b) +#endif +#if PY_MAJOR_VERSION < 3 && !defined(PyObject_ASCII) + #define PyObject_ASCII(o) PyObject_Repr(o) +#endif +#if PY_MAJOR_VERSION >= 3 + #define PyBaseString_Type PyUnicode_Type + #define PyStringObject PyUnicodeObject + #define PyString_Type PyUnicode_Type + #define PyString_Check PyUnicode_Check + #define PyString_CheckExact PyUnicode_CheckExact + #define PyObject_Unicode PyObject_Str +#endif +#if PY_MAJOR_VERSION >= 3 + #define __Pyx_PyBaseString_Check(obj) PyUnicode_Check(obj) + #define __Pyx_PyBaseString_CheckExact(obj) PyUnicode_CheckExact(obj) +#else + #define __Pyx_PyBaseString_Check(obj) (PyString_Check(obj) || PyUnicode_Check(obj)) + #define __Pyx_PyBaseString_CheckExact(obj) (PyString_CheckExact(obj) || PyUnicode_CheckExact(obj)) +#endif +#ifndef PySet_CheckExact + #define PySet_CheckExact(obj) (Py_TYPE(obj) == &PySet_Type) +#endif +#if CYTHON_ASSUME_SAFE_MACROS + #define __Pyx_PySequence_SIZE(seq) Py_SIZE(seq) +#else + #define __Pyx_PySequence_SIZE(seq) PySequence_Size(seq) +#endif +#if PY_MAJOR_VERSION >= 3 + #define PyIntObject PyLongObject + #define PyInt_Type PyLong_Type + #define PyInt_Check(op) PyLong_Check(op) + #define PyInt_CheckExact(op) PyLong_CheckExact(op) + #define PyInt_FromString PyLong_FromString + #define PyInt_FromUnicode PyLong_FromUnicode + #define PyInt_FromLong PyLong_FromLong + #define PyInt_FromSize_t PyLong_FromSize_t + #define PyInt_FromSsize_t PyLong_FromSsize_t + #define PyInt_AsLong PyLong_AsLong + #define PyInt_AS_LONG PyLong_AS_LONG + #define PyInt_AsSsize_t PyLong_AsSsize_t + #define PyInt_AsUnsignedLongMask PyLong_AsUnsignedLongMask + #define PyInt_AsUnsignedLongLongMask PyLong_AsUnsignedLongLongMask + #define PyNumber_Int PyNumber_Long +#endif +#if PY_MAJOR_VERSION >= 3 + #define PyBoolObject PyLongObject +#endif +#if PY_MAJOR_VERSION >= 3 && CYTHON_COMPILING_IN_PYPY + #ifndef PyUnicode_InternFromString + #define PyUnicode_InternFromString(s) PyUnicode_FromString(s) + #endif +#endif +#if PY_VERSION_HEX < 0x030200A4 + typedef long Py_hash_t; + #define __Pyx_PyInt_FromHash_t PyInt_FromLong + #define __Pyx_PyInt_AsHash_t PyInt_AsLong +#else + #define __Pyx_PyInt_FromHash_t PyInt_FromSsize_t + #define __Pyx_PyInt_AsHash_t PyInt_AsSsize_t +#endif +#if PY_MAJOR_VERSION >= 3 + #define __Pyx_PyMethod_New(func, self, klass) ((self) ? PyMethod_New(func, self) : (Py_INCREF(func), 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_ERR(f_index, lineno, Ln_error) \ +{ \ + __pyx_filename = __pyx_f[f_index]; __pyx_lineno = lineno; __pyx_clineno = __LINE__; 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__yank__multistate__mixing___mix_replicas +#define __PYX_HAVE_API__yank__multistate__mixing___mix_replicas +/* Early includes */ +#include +#include +#include +#include "stdlib.h" +#include "pythread.h" +#include +#include "pystate.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_DEFAULT 0 +#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; + + +static const char *__pyx_f[] = { + "Yank/multistate/mixing/_mix_replicas.pyx", + "stringsource", +}; +/* MemviewSliceStruct.proto */ +struct __pyx_memoryview_obj; +typedef struct { + struct __pyx_memoryview_obj *memview; + char *data; + Py_ssize_t shape[8]; + Py_ssize_t strides[8]; + Py_ssize_t suboffsets[8]; +} __Pyx_memviewslice; +#define __Pyx_MemoryView_Len(m) (m.shape[0]) + +/* Atomics.proto */ +#include +#ifndef CYTHON_ATOMICS + #define CYTHON_ATOMICS 1 +#endif +#define __pyx_atomic_int_type int +#if CYTHON_ATOMICS && __GNUC__ >= 4 && (__GNUC_MINOR__ > 1 ||\ + (__GNUC_MINOR__ == 1 && __GNUC_PATCHLEVEL >= 2)) &&\ + !defined(__i386__) + #define __pyx_atomic_incr_aligned(value, lock) __sync_fetch_and_add(value, 1) + #define __pyx_atomic_decr_aligned(value, lock) __sync_fetch_and_sub(value, 1) + #ifdef __PYX_DEBUG_ATOMICS + #warning "Using GNU atomics" + #endif +#elif CYTHON_ATOMICS && defined(_MSC_VER) && 0 + #include + #undef __pyx_atomic_int_type + #define __pyx_atomic_int_type LONG + #define __pyx_atomic_incr_aligned(value, lock) InterlockedIncrement(value) + #define __pyx_atomic_decr_aligned(value, lock) InterlockedDecrement(value) + #ifdef __PYX_DEBUG_ATOMICS + #pragma message ("Using MSVC atomics") + #endif +#elif CYTHON_ATOMICS && (defined(__ICC) || defined(__INTEL_COMPILER)) && 0 + #define __pyx_atomic_incr_aligned(value, lock) _InterlockedIncrement(value) + #define __pyx_atomic_decr_aligned(value, lock) _InterlockedDecrement(value) + #ifdef __PYX_DEBUG_ATOMICS + #warning "Using Intel atomics" + #endif +#else + #undef CYTHON_ATOMICS + #define CYTHON_ATOMICS 0 + #ifdef __PYX_DEBUG_ATOMICS + #warning "Not using atomics" + #endif +#endif +typedef volatile __pyx_atomic_int_type __pyx_atomic_int; +#if CYTHON_ATOMICS + #define __pyx_add_acquisition_count(memview)\ + __pyx_atomic_incr_aligned(__pyx_get_slice_count_pointer(memview), memview->lock) + #define __pyx_sub_acquisition_count(memview)\ + __pyx_atomic_decr_aligned(__pyx_get_slice_count_pointer(memview), memview->lock) +#else + #define __pyx_add_acquisition_count(memview)\ + __pyx_add_acquisition_count_locked(__pyx_get_slice_count_pointer(memview), memview->lock) + #define __pyx_sub_acquisition_count(memview)\ + __pyx_sub_acquisition_count_locked(__pyx_get_slice_count_pointer(memview), memview->lock) +#endif + +/* ForceInitThreads.proto */ +#ifndef __PYX_FORCE_INIT_THREADS + #define __PYX_FORCE_INIT_THREADS 0 +#endif + +/* NoFastGil.proto */ +#define __Pyx_PyGILState_Ensure PyGILState_Ensure +#define __Pyx_PyGILState_Release PyGILState_Release +#define __Pyx_FastGIL_Remember() +#define __Pyx_FastGIL_Forget() +#define __Pyx_FastGilFuncInit() + +/* 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; + struct __Pyx_StructField_* fields; + size_t size; + size_t arraysize[8]; + int ndim; + char typegroup; + char is_unsigned; + int flags; +} __Pyx_TypeInfo; +typedef struct __Pyx_StructField_ { + __Pyx_TypeInfo* type; + const char* name; + size_t offset; +} __Pyx_StructField; +typedef struct { + __Pyx_StructField* field; + size_t parent_offset; +} __Pyx_BufFmt_StackElem; +typedef struct { + __Pyx_StructField root; + __Pyx_BufFmt_StackElem* head; + size_t fmt_offset; + size_t new_count, enc_count; + size_t struct_alignment; + int is_complex; + char enc_type; + char new_packmode; + char enc_packmode; + char is_valid_array; +} __Pyx_BufFmt_Context; + + +/*--- Type declarations ---*/ +struct __pyx_array_obj; +struct __pyx_MemviewEnum_obj; +struct __pyx_memoryview_obj; +struct __pyx_memoryviewslice_obj; + +/* "View.MemoryView":104 + * + * @cname("__pyx_array") + * cdef class array: # <<<<<<<<<<<<<< + * + * cdef: + */ +struct __pyx_array_obj { + PyObject_HEAD + struct __pyx_vtabstruct_array *__pyx_vtab; + char *data; + Py_ssize_t len; + char *format; + int ndim; + Py_ssize_t *_shape; + Py_ssize_t *_strides; + Py_ssize_t itemsize; + PyObject *mode; + PyObject *_format; + void (*callback_free_data)(void *); + int free_data; + int dtype_is_object; +}; + + +/* "View.MemoryView":278 + * + * @cname('__pyx_MemviewEnum') + * cdef class Enum(object): # <<<<<<<<<<<<<< + * cdef object name + * def __init__(self, name): + */ +struct __pyx_MemviewEnum_obj { + PyObject_HEAD + PyObject *name; +}; + + +/* "View.MemoryView":329 + * + * @cname('__pyx_memoryview') + * cdef class memoryview(object): # <<<<<<<<<<<<<< + * + * cdef object obj + */ +struct __pyx_memoryview_obj { + PyObject_HEAD + struct __pyx_vtabstruct_memoryview *__pyx_vtab; + PyObject *obj; + PyObject *_size; + PyObject *_array_interface; + PyThread_type_lock lock; + __pyx_atomic_int acquisition_count[2]; + __pyx_atomic_int *acquisition_count_aligned_p; + Py_buffer view; + int flags; + int dtype_is_object; + __Pyx_TypeInfo *typeinfo; +}; + + +/* "View.MemoryView":960 + * + * @cname('__pyx_memoryviewslice') + * cdef class _memoryviewslice(memoryview): # <<<<<<<<<<<<<< + * "Internal class for passing memoryview slices to Python" + * + */ +struct __pyx_memoryviewslice_obj { + struct __pyx_memoryview_obj __pyx_base; + __Pyx_memviewslice from_slice; + PyObject *from_object; + PyObject *(*to_object_func)(char *); + int (*to_dtype_func)(char *, PyObject *); +}; + + + +/* "View.MemoryView":104 + * + * @cname("__pyx_array") + * cdef class array: # <<<<<<<<<<<<<< + * + * cdef: + */ + +struct __pyx_vtabstruct_array { + PyObject *(*get_memview)(struct __pyx_array_obj *); +}; +static struct __pyx_vtabstruct_array *__pyx_vtabptr_array; + + +/* "View.MemoryView":329 + * + * @cname('__pyx_memoryview') + * cdef class memoryview(object): # <<<<<<<<<<<<<< + * + * cdef object obj + */ + +struct __pyx_vtabstruct_memoryview { + char *(*get_item_pointer)(struct __pyx_memoryview_obj *, PyObject *); + PyObject *(*is_slice)(struct __pyx_memoryview_obj *, PyObject *); + PyObject *(*setitem_slice_assignment)(struct __pyx_memoryview_obj *, PyObject *, PyObject *); + PyObject *(*setitem_slice_assign_scalar)(struct __pyx_memoryview_obj *, struct __pyx_memoryview_obj *, PyObject *); + PyObject *(*setitem_indexed)(struct __pyx_memoryview_obj *, PyObject *, PyObject *); + PyObject *(*convert_item_to_object)(struct __pyx_memoryview_obj *, char *); + PyObject *(*assign_item_from_object)(struct __pyx_memoryview_obj *, char *, PyObject *); +}; +static struct __pyx_vtabstruct_memoryview *__pyx_vtabptr_memoryview; + + +/* "View.MemoryView":960 + * + * @cname('__pyx_memoryviewslice') + * cdef class _memoryviewslice(memoryview): # <<<<<<<<<<<<<< + * "Internal class for passing memoryview slices to Python" + * + */ + +struct __pyx_vtabstruct__memoryviewslice { + struct __pyx_vtabstruct_memoryview __pyx_base; +}; +static struct __pyx_vtabstruct__memoryviewslice *__pyx_vtabptr__memoryviewslice; + +/* --- Runtime support code (head) --- */ +/* Refnanny.proto */ +#ifndef CYTHON_REFNANNY + #define CYTHON_REFNANNY 0 +#endif +#if CYTHON_REFNANNY + typedef struct { + void (*INCREF)(void*, PyObject*, int); + void (*DECREF)(void*, PyObject*, int); + void (*GOTREF)(void*, PyObject*, int); + void (*GIVEREF)(void*, PyObject*, int); + void* (*SetupContext)(const char*, int, const char*); + void (*FinishContext)(void**); + } __Pyx_RefNannyAPIStruct; + static __Pyx_RefNannyAPIStruct *__Pyx_RefNanny = NULL; + static __Pyx_RefNannyAPIStruct *__Pyx_RefNannyImportAPI(const char *modname); + #define __Pyx_RefNannyDeclarations void *__pyx_refnanny = NULL; +#ifdef WITH_THREAD + #define __Pyx_RefNannySetupContext(name, acquire_gil)\ + if (acquire_gil) {\ + PyGILState_STATE __pyx_gilstate_save = PyGILState_Ensure();\ + __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), __LINE__, __FILE__);\ + PyGILState_Release(__pyx_gilstate_save);\ + } else {\ + __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), __LINE__, __FILE__);\ + } +#else + #define __Pyx_RefNannySetupContext(name, acquire_gil)\ + __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), __LINE__, __FILE__) +#endif + #define __Pyx_RefNannyFinishContext()\ + __Pyx_RefNanny->FinishContext(&__pyx_refnanny) + #define __Pyx_INCREF(r) __Pyx_RefNanny->INCREF(__pyx_refnanny, (PyObject *)(r), __LINE__) + #define __Pyx_DECREF(r) __Pyx_RefNanny->DECREF(__pyx_refnanny, (PyObject *)(r), __LINE__) + #define __Pyx_GOTREF(r) __Pyx_RefNanny->GOTREF(__pyx_refnanny, (PyObject *)(r), __LINE__) + #define __Pyx_GIVEREF(r) __Pyx_RefNanny->GIVEREF(__pyx_refnanny, (PyObject *)(r), __LINE__) + #define __Pyx_XINCREF(r) do { if((r) != NULL) {__Pyx_INCREF(r); }} while(0) + #define __Pyx_XDECREF(r) do { if((r) != NULL) {__Pyx_DECREF(r); }} while(0) + #define __Pyx_XGOTREF(r) do { if((r) != NULL) {__Pyx_GOTREF(r); }} while(0) + #define __Pyx_XGIVEREF(r) do { if((r) != NULL) {__Pyx_GIVEREF(r);}} while(0) +#else + #define __Pyx_RefNannyDeclarations + #define __Pyx_RefNannySetupContext(name, acquire_gil) + #define __Pyx_RefNannyFinishContext() + #define __Pyx_INCREF(r) Py_INCREF(r) + #define __Pyx_DECREF(r) Py_DECREF(r) + #define __Pyx_GOTREF(r) + #define __Pyx_GIVEREF(r) + #define __Pyx_XINCREF(r) Py_XINCREF(r) + #define __Pyx_XDECREF(r) Py_XDECREF(r) + #define __Pyx_XGOTREF(r) + #define __Pyx_XGIVEREF(r) +#endif +#define __Pyx_XDECREF_SET(r, v) do {\ + PyObject *tmp = (PyObject *) r;\ + r = v; __Pyx_XDECREF(tmp);\ + } while (0) +#define __Pyx_DECREF_SET(r, v) do {\ + PyObject *tmp = (PyObject *) r;\ + r = v; __Pyx_DECREF(tmp);\ + } while (0) +#define __Pyx_CLEAR(r) do { PyObject* tmp = ((PyObject*)(r)); r = NULL; __Pyx_DECREF(tmp);} while(0) +#define __Pyx_XCLEAR(r) do { if((r) != NULL) {PyObject* tmp = ((PyObject*)(r)); r = NULL; __Pyx_DECREF(tmp);}} while(0) + +/* PyObjectGetAttrStr.proto */ +#if CYTHON_USE_TYPE_SLOTS +static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStr(PyObject* obj, PyObject* attr_name); +#else +#define __Pyx_PyObject_GetAttrStr(o,n) PyObject_GetAttr(o,n) +#endif + +/* GetBuiltinName.proto */ +static PyObject *__Pyx_GetBuiltinName(PyObject *name); + +/* RaiseArgTupleInvalid.proto */ +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); + +/* RaiseDoubleKeywords.proto */ +static void __Pyx_RaiseDoubleKeywordsError(const char* func_name, PyObject* kw_name); + +/* ParseKeywords.proto */ +static int __Pyx_ParseOptionalKeywords(PyObject *kwds, PyObject **argnames[],\ + PyObject *kwds2, PyObject *values[], Py_ssize_t num_pos_args,\ + const char* function_name); + +/* None.proto */ +static CYTHON_INLINE void __Pyx_RaiseUnboundLocalError(const char *varname); + +/* MemviewSliceInit.proto */ +#define __Pyx_BUF_MAX_NDIMS %(BUF_MAX_NDIMS)d +#define __Pyx_MEMVIEW_DIRECT 1 +#define __Pyx_MEMVIEW_PTR 2 +#define __Pyx_MEMVIEW_FULL 4 +#define __Pyx_MEMVIEW_CONTIG 8 +#define __Pyx_MEMVIEW_STRIDED 16 +#define __Pyx_MEMVIEW_FOLLOW 32 +#define __Pyx_IS_C_CONTIG 1 +#define __Pyx_IS_F_CONTIG 2 +static int __Pyx_init_memviewslice( + struct __pyx_memoryview_obj *memview, + int ndim, + __Pyx_memviewslice *memviewslice, + int memview_is_new_reference); +static CYTHON_INLINE int __pyx_add_acquisition_count_locked( + __pyx_atomic_int *acquisition_count, PyThread_type_lock lock); +static CYTHON_INLINE int __pyx_sub_acquisition_count_locked( + __pyx_atomic_int *acquisition_count, PyThread_type_lock lock); +#define __pyx_get_slice_count_pointer(memview) (memview->acquisition_count_aligned_p) +#define __pyx_get_slice_count(memview) (*__pyx_get_slice_count_pointer(memview)) +#define __PYX_INC_MEMVIEW(slice, have_gil) __Pyx_INC_MEMVIEW(slice, have_gil, __LINE__) +#define __PYX_XDEC_MEMVIEW(slice, have_gil) __Pyx_XDEC_MEMVIEW(slice, have_gil, __LINE__) +static CYTHON_INLINE void __Pyx_INC_MEMVIEW(__Pyx_memviewslice *, int, int); +static CYTHON_INLINE void __Pyx_XDEC_MEMVIEW(__Pyx_memviewslice *, int, int); + +/* ArgTypeTest.proto */ +#define __Pyx_ArgTypeTest(obj, type, none_allowed, name, exact)\ + ((likely((Py_TYPE(obj) == type) | (none_allowed && (obj == Py_None)))) ? 1 :\ + __Pyx__ArgTypeTest(obj, type, name, exact)) +static int __Pyx__ArgTypeTest(PyObject *obj, PyTypeObject *type, const char *name, int exact); + +/* PyObjectCall.proto */ +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw); +#else +#define __Pyx_PyObject_Call(func, arg, kw) PyObject_Call(func, arg, kw) +#endif + +/* PyThreadStateGet.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_PyThreadState_declare PyThreadState *__pyx_tstate; +#define __Pyx_PyThreadState_assign __pyx_tstate = __Pyx_PyThreadState_Current; +#define __Pyx_PyErr_Occurred() __pyx_tstate->curexc_type +#else +#define __Pyx_PyThreadState_declare +#define __Pyx_PyThreadState_assign +#define __Pyx_PyErr_Occurred() PyErr_Occurred() +#endif + +/* PyErrFetchRestore.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_PyErr_Clear() __Pyx_ErrRestore(NULL, NULL, NULL) +#define __Pyx_ErrRestoreWithState(type, value, tb) __Pyx_ErrRestoreInState(PyThreadState_GET(), type, value, tb) +#define __Pyx_ErrFetchWithState(type, value, tb) __Pyx_ErrFetchInState(PyThreadState_GET(), type, value, tb) +#define __Pyx_ErrRestore(type, value, tb) __Pyx_ErrRestoreInState(__pyx_tstate, type, value, tb) +#define __Pyx_ErrFetch(type, value, tb) __Pyx_ErrFetchInState(__pyx_tstate, type, value, tb) +static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb); +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 + +/* RaiseException.proto */ +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause); + +/* 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, int 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 = 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); + +/* IncludeStringH.proto */ +#include + +/* BytesEquals.proto */ +static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals); + +/* UnicodeEquals.proto */ +static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals); + +/* StrEquals.proto */ +#if PY_MAJOR_VERSION >= 3 +#define __Pyx_PyString_Equals __Pyx_PyUnicode_Equals +#else +#define __Pyx_PyString_Equals __Pyx_PyBytes_Equals +#endif + +/* None.proto */ +static CYTHON_INLINE Py_ssize_t __Pyx_div_Py_ssize_t(Py_ssize_t, Py_ssize_t); + +/* UnaryNegOverflows.proto */ +#define UNARY_NEG_WOULD_OVERFLOW(x)\ + (((x) < 0) & ((unsigned long)(x) == 0-(unsigned long)(x))) + +static CYTHON_UNUSED int __pyx_array_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/ +static PyObject *__pyx_array_get_memview(struct __pyx_array_obj *); /*proto*/ +/* GetAttr.proto */ +static CYTHON_INLINE PyObject *__Pyx_GetAttr(PyObject *, PyObject *); + +/* 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 + +/* decode_c_string_utf16.proto */ +static CYTHON_INLINE PyObject *__Pyx_PyUnicode_DecodeUTF16(const char *s, Py_ssize_t size, const char *errors) { + int byteorder = 0; + return PyUnicode_DecodeUTF16(s, size, errors, &byteorder); +} +static CYTHON_INLINE PyObject *__Pyx_PyUnicode_DecodeUTF16LE(const char *s, Py_ssize_t size, const char *errors) { + int byteorder = -1; + return PyUnicode_DecodeUTF16(s, size, errors, &byteorder); +} +static CYTHON_INLINE PyObject *__Pyx_PyUnicode_DecodeUTF16BE(const char *s, Py_ssize_t size, const char *errors) { + int byteorder = 1; + return PyUnicode_DecodeUTF16(s, size, errors, &byteorder); +} + +/* decode_c_string.proto */ +static CYTHON_INLINE PyObject* __Pyx_decode_c_string( + const char* cstring, Py_ssize_t start, Py_ssize_t stop, + const char* encoding, const char* errors, + PyObject* (*decode_func)(const char *s, Py_ssize_t size, const char *errors)); + +/* 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 + +/* GetAttr3.proto */ +static CYTHON_INLINE PyObject *__Pyx_GetAttr3(PyObject *, PyObject *, PyObject *); + +/* GetModuleGlobalName.proto */ +#if CYTHON_USE_DICT_VERSIONS +#define __Pyx_GetModuleGlobalName(var, name) {\ + static PY_UINT64_T __pyx_dict_version = 0;\ + static PyObject *__pyx_dict_cached_value = NULL;\ + (var) = (likely(__pyx_dict_version == __PYX_GET_DICT_VERSION(__pyx_d))) ?\ + (likely(__pyx_dict_cached_value) ? __Pyx_NewRef(__pyx_dict_cached_value) : __Pyx_GetBuiltinName(name)) :\ + __Pyx__GetModuleGlobalName(name, &__pyx_dict_version, &__pyx_dict_cached_value);\ +} +#define __Pyx_GetModuleGlobalNameUncached(var, name) {\ + PY_UINT64_T __pyx_dict_version;\ + PyObject *__pyx_dict_cached_value;\ + (var) = __Pyx__GetModuleGlobalName(name, &__pyx_dict_version, &__pyx_dict_cached_value);\ +} +static PyObject *__Pyx__GetModuleGlobalName(PyObject *name, PY_UINT64_T *dict_version, PyObject **dict_cached_value); +#else +#define __Pyx_GetModuleGlobalName(var, name) (var) = __Pyx__GetModuleGlobalName(name) +#define __Pyx_GetModuleGlobalNameUncached(var, name) (var) = __Pyx__GetModuleGlobalName(name) +static CYTHON_INLINE PyObject *__Pyx__GetModuleGlobalName(PyObject *name); +#endif + +/* RaiseTooManyValuesToUnpack.proto */ +static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected); + +/* RaiseNeedMoreValuesToUnpack.proto */ +static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index); + +/* RaiseNoneIterError.proto */ +static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void); + +/* ExtTypeTest.proto */ +static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type); + +/* 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 + +/* 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 + +/* SwapException.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_ExceptionSwap(type, value, tb) __Pyx__ExceptionSwap(__pyx_tstate, type, value, tb) +static CYTHON_INLINE void __Pyx__ExceptionSwap(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); +#else +static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb); +#endif + +/* Import.proto */ +static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level); + +/* 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) + +static CYTHON_UNUSED int __pyx_memoryview_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/ +/* ListCompAppend.proto */ +#if CYTHON_USE_PYLIST_INTERNALS && CYTHON_ASSUME_SAFE_MACROS +static CYTHON_INLINE int __Pyx_ListComp_Append(PyObject* list, PyObject* x) { + PyListObject* L = (PyListObject*) list; + Py_ssize_t len = Py_SIZE(list); + if (likely(L->allocated > len)) { + Py_INCREF(x); + PyList_SET_ITEM(list, len, x); + Py_SIZE(list) = len+1; + return 0; + } + return PyList_Append(list, x); +} +#else +#define __Pyx_ListComp_Append(L,x) PyList_Append(L,x) +#endif + +/* PyIntBinop.proto */ +#if !CYTHON_COMPILING_IN_PYPY +static PyObject* __Pyx_PyInt_AddObjC(PyObject *op1, PyObject *op2, long intval, int inplace); +#else +#define __Pyx_PyInt_AddObjC(op1, op2, intval, inplace)\ + (inplace ? PyNumber_InPlaceAdd(op1, op2) : PyNumber_Add(op1, op2)) +#endif + +/* ListExtend.proto */ +static CYTHON_INLINE int __Pyx_PyList_Extend(PyObject* L, PyObject* v) { +#if CYTHON_COMPILING_IN_CPYTHON + PyObject* none = _PyList_Extend((PyListObject*)L, v); + if (unlikely(!none)) + return -1; + Py_DECREF(none); + return 0; +#else + return PyList_SetSlice(L, PY_SSIZE_T_MAX, PY_SSIZE_T_MAX, v); +#endif +} + +/* ListAppend.proto */ +#if CYTHON_USE_PYLIST_INTERNALS && CYTHON_ASSUME_SAFE_MACROS +static CYTHON_INLINE int __Pyx_PyList_Append(PyObject* list, PyObject* x) { + PyListObject* L = (PyListObject*) list; + Py_ssize_t len = Py_SIZE(list); + if (likely(L->allocated > len) & likely(len > (L->allocated >> 1))) { + Py_INCREF(x); + PyList_SET_ITEM(list, len, x); + Py_SIZE(list) = len+1; + return 0; + } + return PyList_Append(list, x); +} +#else +#define __Pyx_PyList_Append(L,x) PyList_Append(L,x) +#endif + +/* None.proto */ +static CYTHON_INLINE long __Pyx_div_long(long, long); + +/* WriteUnraisableException.proto */ +static void __Pyx_WriteUnraisable(const char *name, int clineno, + int lineno, const char *filename, + int full_traceback, int nogil); + +/* ImportFrom.proto */ +static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name); + +/* HasAttr.proto */ +static CYTHON_INLINE int __Pyx_HasAttr(PyObject *, PyObject *); + +/* PyObject_GenericGetAttrNoDict.proto */ +#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 +static CYTHON_INLINE PyObject* __Pyx_PyObject_GenericGetAttrNoDict(PyObject* obj, PyObject* attr_name); +#else +#define __Pyx_PyObject_GenericGetAttrNoDict PyObject_GenericGetAttr +#endif + +/* PyObject_GenericGetAttr.proto */ +#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 +static PyObject* __Pyx_PyObject_GenericGetAttr(PyObject* obj, PyObject* attr_name); +#else +#define __Pyx_PyObject_GenericGetAttr PyObject_GenericGetAttr +#endif + +/* SetVTable.proto */ +static int __Pyx_SetVtable(PyObject *dict, void *vtable); + +/* SetupReduce.proto */ +static int __Pyx_setup_reduce(PyObject* type_obj); + +/* 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); + +#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 + + +/* 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; + +/* MemviewSliceIsContig.proto */ +static int __pyx_memviewslice_is_contig(const __Pyx_memviewslice mvs, char order, int ndim); + +/* OverlappingSlices.proto */ +static int __pyx_slices_overlap(__Pyx_memviewslice *slice1, + __Pyx_memviewslice *slice2, + int ndim, size_t itemsize); + +/* Capsule.proto */ +static CYTHON_INLINE PyObject *__pyx_capsule_create(void *p, const char *sig); + +/* IsLittleEndian.proto */ +static CYTHON_INLINE int __Pyx_Is_Little_Endian(void); + +/* BufferFormatCheck.proto */ +static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts); +static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx, + __Pyx_BufFmt_StackElem* stack, + __Pyx_TypeInfo* type); + +/* TypeInfoCompare.proto */ +static int __pyx_typeinfo_cmp(__Pyx_TypeInfo *a, __Pyx_TypeInfo *b); + +/* MemviewSliceValidateAndInit.proto */ +static int __Pyx_ValidateAndInit_memviewslice( + int *axes_specs, + int c_or_f_flag, + int buf_flags, + int ndim, + __Pyx_TypeInfo *dtype, + __Pyx_BufFmt_StackElem stack[], + __Pyx_memviewslice *memviewslice, + PyObject *original_obj); + +/* ObjectToMemviewSlice.proto */ +static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_ds_long(PyObject *, int writable_flag); + +/* ObjectToMemviewSlice.proto */ +static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dsds_double(PyObject *, int writable_flag); + +/* ObjectToMemviewSlice.proto */ +static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dsds_long(PyObject *, int writable_flag); + +/* CIntToPy.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value); + +/* MemviewSliceCopyTemplate.proto */ +static __Pyx_memviewslice +__pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs, + const char *mode, int ndim, + size_t sizeof_dtype, int contig_flag, + int dtype_is_object); + +/* CIntFromPy.proto */ +static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *); + +/* CIntFromPy.proto */ +static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *); + +/* CIntToPy.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value); + +/* CIntFromPy.proto */ +static CYTHON_INLINE char __Pyx_PyInt_As_char(PyObject *); + +/* CheckBinaryVersion.proto */ +static int __Pyx_check_binary_version(void); + +/* InitStrings.proto */ +static int __Pyx_InitStrings(__Pyx_StringTabEntry *t); + +static PyObject *__pyx_array_get_memview(struct __pyx_array_obj *__pyx_v_self); /* proto*/ +static char *__pyx_memoryview_get_item_pointer(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index); /* proto*/ +static PyObject *__pyx_memoryview_is_slice(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj); /* proto*/ +static PyObject *__pyx_memoryview_setitem_slice_assignment(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_dst, PyObject *__pyx_v_src); /* proto*/ +static PyObject *__pyx_memoryview_setitem_slice_assign_scalar(struct __pyx_memoryview_obj *__pyx_v_self, struct __pyx_memoryview_obj *__pyx_v_dst, PyObject *__pyx_v_value); /* proto*/ +static PyObject *__pyx_memoryview_setitem_indexed(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value); /* proto*/ +static PyObject *__pyx_memoryview_convert_item_to_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp); /* proto*/ +static PyObject *__pyx_memoryview_assign_item_from_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value); /* proto*/ +static PyObject *__pyx_memoryviewslice_convert_item_to_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp); /* proto*/ +static PyObject *__pyx_memoryviewslice_assign_item_from_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value); /* proto*/ + +/* Module declarations from 'cython.view' */ + +/* Module declarations from 'cython' */ + +/* Module declarations from 'libc.math' */ + +/* Module declarations from 'libc.string' */ + +/* Module declarations from 'libc.stdio' */ + +/* Module declarations from 'yank.multistate.mixing._mix_replicas' */ +static PyTypeObject *__pyx_array_type = 0; +static PyTypeObject *__pyx_MemviewEnum_type = 0; +static PyTypeObject *__pyx_memoryview_type = 0; +static PyTypeObject *__pyx_memoryviewslice_type = 0; +static PyObject *generic = 0; +static PyObject *strided = 0; +static PyObject *indirect = 0; +static PyObject *contiguous = 0; +static PyObject *indirect_contiguous = 0; +static int __pyx_memoryview_thread_locks_used; +static PyThread_type_lock __pyx_memoryview_thread_locks[8]; +static long __pyx_f_4yank_10multistate_6mixing_13_mix_replicas__mix_replicas_cython(long, long, __Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, int __pyx_skip_dispatch); /*proto*/ +static struct __pyx_array_obj *__pyx_array_new(PyObject *, Py_ssize_t, char *, char *, char *); /*proto*/ +static void *__pyx_align_pointer(void *, size_t); /*proto*/ +static PyObject *__pyx_memoryview_new(PyObject *, int, int, __Pyx_TypeInfo *); /*proto*/ +static CYTHON_INLINE int __pyx_memoryview_check(PyObject *); /*proto*/ +static PyObject *_unellipsify(PyObject *, int); /*proto*/ +static PyObject *assert_direct_dimensions(Py_ssize_t *, int); /*proto*/ +static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_obj *, PyObject *); /*proto*/ +static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *, Py_ssize_t, Py_ssize_t, Py_ssize_t, int, int, int *, Py_ssize_t, Py_ssize_t, Py_ssize_t, int, int, int, int); /*proto*/ +static char *__pyx_pybuffer_index(Py_buffer *, char *, Py_ssize_t, Py_ssize_t); /*proto*/ +static int __pyx_memslice_transpose(__Pyx_memviewslice *); /*proto*/ +static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice, int, PyObject *(*)(char *), int (*)(char *, PyObject *), int); /*proto*/ +static __Pyx_memviewslice *__pyx_memoryview_get_slice_from_memoryview(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/ +static void __pyx_memoryview_slice_copy(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/ +static PyObject *__pyx_memoryview_copy_object(struct __pyx_memoryview_obj *); /*proto*/ +static PyObject *__pyx_memoryview_copy_object_from_slice(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/ +static Py_ssize_t abs_py_ssize_t(Py_ssize_t); /*proto*/ +static char __pyx_get_best_slice_order(__Pyx_memviewslice *, int); /*proto*/ +static void _copy_strided_to_strided(char *, Py_ssize_t *, char *, Py_ssize_t *, Py_ssize_t *, Py_ssize_t *, int, size_t); /*proto*/ +static void copy_strided_to_strided(__Pyx_memviewslice *, __Pyx_memviewslice *, int, size_t); /*proto*/ +static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *, int); /*proto*/ +static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *, Py_ssize_t *, Py_ssize_t, int, char); /*proto*/ +static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *, __Pyx_memviewslice *, char, int); /*proto*/ +static int __pyx_memoryview_err_extents(int, Py_ssize_t, Py_ssize_t); /*proto*/ +static int __pyx_memoryview_err_dim(PyObject *, char *, int); /*proto*/ +static int __pyx_memoryview_err(PyObject *, char *); /*proto*/ +static int __pyx_memoryview_copy_contents(__Pyx_memviewslice, __Pyx_memviewslice, int, int, int); /*proto*/ +static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *, int, int); /*proto*/ +static void __pyx_memoryview_refcount_copying(__Pyx_memviewslice *, int, int, int); /*proto*/ +static void __pyx_memoryview_refcount_objects_in_slice_with_gil(char *, Py_ssize_t *, Py_ssize_t *, int, int); /*proto*/ +static void __pyx_memoryview_refcount_objects_in_slice(char *, Py_ssize_t *, Py_ssize_t *, int, int); /*proto*/ +static void __pyx_memoryview_slice_assign_scalar(__Pyx_memviewslice *, int, size_t, void *, int); /*proto*/ +static void __pyx_memoryview__slice_assign_scalar(char *, Py_ssize_t *, Py_ssize_t *, int, size_t, void *); /*proto*/ +static PyObject *__pyx_unpickle_Enum__set_state(struct __pyx_MemviewEnum_obj *, PyObject *); /*proto*/ +static __Pyx_TypeInfo __Pyx_TypeInfo_long = { "long", NULL, sizeof(long), { 0 }, 0, IS_UNSIGNED(long) ? 'U' : 'I', IS_UNSIGNED(long), 0 }; +static __Pyx_TypeInfo __Pyx_TypeInfo_double = { "double", NULL, sizeof(double), { 0 }, 0, 'R', 0, 0 }; +#define __Pyx_MODULE_NAME "yank.multistate.mixing._mix_replicas" +extern int __pyx_module_is_main_yank__multistate__mixing___mix_replicas; +int __pyx_module_is_main_yank__multistate__mixing___mix_replicas = 0; + +/* Implementation of 'yank.multistate.mixing._mix_replicas' */ +static PyObject *__pyx_builtin_range; +static PyObject *__pyx_builtin_ValueError; +static PyObject *__pyx_builtin_MemoryError; +static PyObject *__pyx_builtin_enumerate; +static PyObject *__pyx_builtin_TypeError; +static PyObject *__pyx_builtin_Ellipsis; +static PyObject *__pyx_builtin_id; +static PyObject *__pyx_builtin_IndexError; +static const char __pyx_k_O[] = "O"; +static const char __pyx_k_c[] = "c"; +static const char __pyx_k_id[] = "id"; +static const char __pyx_k_new[] = "__new__"; +static const char __pyx_k_obj[] = "obj"; +static const char __pyx_k_base[] = "base"; +static const char __pyx_k_dict[] = "__dict__"; +static const char __pyx_k_main[] = "__main__"; +static const char __pyx_k_mode[] = "mode"; +static const char __pyx_k_name[] = "name"; +static const char __pyx_k_ndim[] = "ndim"; +static const char __pyx_k_pack[] = "pack"; +static const char __pyx_k_size[] = "size"; +static const char __pyx_k_step[] = "step"; +static const char __pyx_k_stop[] = "stop"; +static const char __pyx_k_test[] = "__test__"; +static const char __pyx_k_u_kl[] = "u_kl"; +static const char __pyx_k_ASCII[] = "ASCII"; +static const char __pyx_k_class[] = "__class__"; +static const char __pyx_k_error[] = "error"; +static const char __pyx_k_flags[] = "flags"; +static const char __pyx_k_range[] = "range"; +static const char __pyx_k_shape[] = "shape"; +static const char __pyx_k_start[] = "start"; +static const char __pyx_k_encode[] = "encode"; +static const char __pyx_k_format[] = "format"; +static const char __pyx_k_import[] = "__import__"; +static const char __pyx_k_name_2[] = "__name__"; +static const char __pyx_k_pickle[] = "pickle"; +static const char __pyx_k_reduce[] = "__reduce__"; +static const char __pyx_k_struct[] = "struct"; +static const char __pyx_k_unpack[] = "unpack"; +static const char __pyx_k_update[] = "update"; +static const char __pyx_k_fortran[] = "fortran"; +static const char __pyx_k_memview[] = "memview"; +static const char __pyx_k_nstates[] = "nstates"; +static const char __pyx_k_Ellipsis[] = "Ellipsis"; +static const char __pyx_k_getstate[] = "__getstate__"; +static const char __pyx_k_itemsize[] = "itemsize"; +static const char __pyx_k_pyx_type[] = "__pyx_type"; +static const char __pyx_k_setstate[] = "__setstate__"; +static const char __pyx_k_TypeError[] = "TypeError"; +static const char __pyx_k_enumerate[] = "enumerate"; +static const char __pyx_k_pyx_state[] = "__pyx_state"; +static const char __pyx_k_reduce_ex[] = "__reduce_ex__"; +static const char __pyx_k_IndexError[] = "IndexError"; +static const char __pyx_k_ValueError[] = "ValueError"; +static const char __pyx_k_pyx_result[] = "__pyx_result"; +static const char __pyx_k_pyx_vtable[] = "__pyx_vtable__"; +static const char __pyx_k_MemoryError[] = "MemoryError"; +static const char __pyx_k_PickleError[] = "PickleError"; +static const char __pyx_k_Nij_accepted[] = "Nij_accepted"; +static const char __pyx_k_Nij_proposed[] = "Nij_proposed"; +static const char __pyx_k_pyx_checksum[] = "__pyx_checksum"; +static const char __pyx_k_stringsource[] = "stringsource"; +static const char __pyx_k_pyx_getbuffer[] = "__pyx_getbuffer"; +static const char __pyx_k_reduce_cython[] = "__reduce_cython__"; +static const char __pyx_k_nswap_attempts[] = "nswap_attempts"; +static const char __pyx_k_replica_states[] = "replica_states"; +static const char __pyx_k_View_MemoryView[] = "View.MemoryView"; +static const char __pyx_k_allocate_buffer[] = "allocate_buffer"; +static const char __pyx_k_dtype_is_object[] = "dtype_is_object"; +static const char __pyx_k_pyx_PickleError[] = "__pyx_PickleError"; +static const char __pyx_k_setstate_cython[] = "__setstate_cython__"; +static const char __pyx_k_pyx_unpickle_Enum[] = "__pyx_unpickle_Enum"; +static const char __pyx_k_cline_in_traceback[] = "cline_in_traceback"; +static const char __pyx_k_strided_and_direct[] = ""; +static const char __pyx_k_strided_and_indirect[] = ""; +static const char __pyx_k_contiguous_and_direct[] = ""; +static const char __pyx_k_MemoryView_of_r_object[] = ""; +static const char __pyx_k_MemoryView_of_r_at_0x_x[] = ""; +static const char __pyx_k_contiguous_and_indirect[] = ""; +static const char __pyx_k_Cannot_index_with_type_s[] = "Cannot index with type '%s'"; +static const char __pyx_k_Invalid_shape_in_axis_d_d[] = "Invalid shape in axis %d: %d."; +static const char __pyx_k_itemsize_0_for_cython_array[] = "itemsize <= 0 for cython.array"; +static const char __pyx_k_unable_to_allocate_array_data[] = "unable to allocate array data."; +static const char __pyx_k_strided_and_direct_or_indirect[] = ""; +static const char __pyx_k_Buffer_view_does_not_expose_stri[] = "Buffer view does not expose strides"; +static const char __pyx_k_Can_only_create_a_buffer_that_is[] = "Can only create a buffer that is contiguous in memory."; +static const char __pyx_k_Cannot_assign_to_read_only_memor[] = "Cannot assign to read-only memoryview"; +static const char __pyx_k_Cannot_create_writable_memory_vi[] = "Cannot create writable memory view from read-only memoryview"; +static const char __pyx_k_Empty_shape_tuple_for_cython_arr[] = "Empty shape tuple for cython.array"; +static const char __pyx_k_Incompatible_checksums_s_vs_0xb0[] = "Incompatible checksums (%s vs 0xb068931 = (name))"; +static const char __pyx_k_Indirect_dimensions_not_supporte[] = "Indirect dimensions not supported"; +static const char __pyx_k_Invalid_mode_expected_c_or_fortr[] = "Invalid mode, expected 'c' or 'fortran', got %s"; +static const char __pyx_k_Out_of_bounds_on_buffer_access_a[] = "Out of bounds on buffer access (axis %d)"; +static const char __pyx_k_Unable_to_convert_item_to_object[] = "Unable to convert item to object"; +static const char __pyx_k_got_differing_extents_in_dimensi[] = "got differing extents in dimension %d (got %d and %d)"; +static const char __pyx_k_no_default___reduce___due_to_non[] = "no default __reduce__ due to non-trivial __cinit__"; +static const char __pyx_k_unable_to_allocate_shape_and_str[] = "unable to allocate shape and strides."; +static PyObject *__pyx_n_s_ASCII; +static PyObject *__pyx_kp_s_Buffer_view_does_not_expose_stri; +static PyObject *__pyx_kp_s_Can_only_create_a_buffer_that_is; +static PyObject *__pyx_kp_s_Cannot_assign_to_read_only_memor; +static PyObject *__pyx_kp_s_Cannot_create_writable_memory_vi; +static PyObject *__pyx_kp_s_Cannot_index_with_type_s; +static PyObject *__pyx_n_s_Ellipsis; +static PyObject *__pyx_kp_s_Empty_shape_tuple_for_cython_arr; +static PyObject *__pyx_kp_s_Incompatible_checksums_s_vs_0xb0; +static PyObject *__pyx_n_s_IndexError; +static PyObject *__pyx_kp_s_Indirect_dimensions_not_supporte; +static PyObject *__pyx_kp_s_Invalid_mode_expected_c_or_fortr; +static PyObject *__pyx_kp_s_Invalid_shape_in_axis_d_d; +static PyObject *__pyx_n_s_MemoryError; +static PyObject *__pyx_kp_s_MemoryView_of_r_at_0x_x; +static PyObject *__pyx_kp_s_MemoryView_of_r_object; +static PyObject *__pyx_n_s_Nij_accepted; +static PyObject *__pyx_n_s_Nij_proposed; +static PyObject *__pyx_n_b_O; +static PyObject *__pyx_kp_s_Out_of_bounds_on_buffer_access_a; +static PyObject *__pyx_n_s_PickleError; +static PyObject *__pyx_n_s_TypeError; +static PyObject *__pyx_kp_s_Unable_to_convert_item_to_object; +static PyObject *__pyx_n_s_ValueError; +static PyObject *__pyx_n_s_View_MemoryView; +static PyObject *__pyx_n_s_allocate_buffer; +static PyObject *__pyx_n_s_base; +static PyObject *__pyx_n_s_c; +static PyObject *__pyx_n_u_c; +static PyObject *__pyx_n_s_class; +static PyObject *__pyx_n_s_cline_in_traceback; +static PyObject *__pyx_kp_s_contiguous_and_direct; +static PyObject *__pyx_kp_s_contiguous_and_indirect; +static PyObject *__pyx_n_s_dict; +static PyObject *__pyx_n_s_dtype_is_object; +static PyObject *__pyx_n_s_encode; +static PyObject *__pyx_n_s_enumerate; +static PyObject *__pyx_n_s_error; +static PyObject *__pyx_n_s_flags; +static PyObject *__pyx_n_s_format; +static PyObject *__pyx_n_s_fortran; +static PyObject *__pyx_n_u_fortran; +static PyObject *__pyx_n_s_getstate; +static PyObject *__pyx_kp_s_got_differing_extents_in_dimensi; +static PyObject *__pyx_n_s_id; +static PyObject *__pyx_n_s_import; +static PyObject *__pyx_n_s_itemsize; +static PyObject *__pyx_kp_s_itemsize_0_for_cython_array; +static PyObject *__pyx_n_s_main; +static PyObject *__pyx_n_s_memview; +static PyObject *__pyx_n_s_mode; +static PyObject *__pyx_n_s_name; +static PyObject *__pyx_n_s_name_2; +static PyObject *__pyx_n_s_ndim; +static PyObject *__pyx_n_s_new; +static PyObject *__pyx_kp_s_no_default___reduce___due_to_non; +static PyObject *__pyx_n_s_nstates; +static PyObject *__pyx_n_s_nswap_attempts; +static PyObject *__pyx_n_s_obj; +static PyObject *__pyx_n_s_pack; +static PyObject *__pyx_n_s_pickle; +static PyObject *__pyx_n_s_pyx_PickleError; +static PyObject *__pyx_n_s_pyx_checksum; +static PyObject *__pyx_n_s_pyx_getbuffer; +static PyObject *__pyx_n_s_pyx_result; +static PyObject *__pyx_n_s_pyx_state; +static PyObject *__pyx_n_s_pyx_type; +static PyObject *__pyx_n_s_pyx_unpickle_Enum; +static PyObject *__pyx_n_s_pyx_vtable; +static PyObject *__pyx_n_s_range; +static PyObject *__pyx_n_s_reduce; +static PyObject *__pyx_n_s_reduce_cython; +static PyObject *__pyx_n_s_reduce_ex; +static PyObject *__pyx_n_s_replica_states; +static PyObject *__pyx_n_s_setstate; +static PyObject *__pyx_n_s_setstate_cython; +static PyObject *__pyx_n_s_shape; +static PyObject *__pyx_n_s_size; +static PyObject *__pyx_n_s_start; +static PyObject *__pyx_n_s_step; +static PyObject *__pyx_n_s_stop; +static PyObject *__pyx_kp_s_strided_and_direct; +static PyObject *__pyx_kp_s_strided_and_direct_or_indirect; +static PyObject *__pyx_kp_s_strided_and_indirect; +static PyObject *__pyx_kp_s_stringsource; +static PyObject *__pyx_n_s_struct; +static PyObject *__pyx_n_s_test; +static PyObject *__pyx_n_s_u_kl; +static PyObject *__pyx_kp_s_unable_to_allocate_array_data; +static PyObject *__pyx_kp_s_unable_to_allocate_shape_and_str; +static PyObject *__pyx_n_s_unpack; +static PyObject *__pyx_n_s_update; +static PyObject *__pyx_pf_4yank_10multistate_6mixing_13_mix_replicas__mix_replicas_cython(CYTHON_UNUSED PyObject *__pyx_self, long __pyx_v_nswap_attempts, long __pyx_v_nstates, __Pyx_memviewslice __pyx_v_replica_states, __Pyx_memviewslice __pyx_v_u_kl, __Pyx_memviewslice __pyx_v_Nij_proposed, __Pyx_memviewslice __pyx_v_Nij_accepted); /* proto */ +static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array___cinit__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_shape, Py_ssize_t __pyx_v_itemsize, PyObject *__pyx_v_format, PyObject *__pyx_v_mode, int __pyx_v_allocate_buffer); /* proto */ +static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(struct __pyx_array_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */ +static void __pyx_array___pyx_pf_15View_dot_MemoryView_5array_4__dealloc__(struct __pyx_array_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_5array_7memview___get__(struct __pyx_array_obj *__pyx_v_self); /* proto */ +static Py_ssize_t __pyx_array___pyx_pf_15View_dot_MemoryView_5array_6__len__(struct __pyx_array_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_array___pyx_pf_15View_dot_MemoryView_5array_8__getattr__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_attr); /* proto */ +static PyObject *__pyx_array___pyx_pf_15View_dot_MemoryView_5array_10__getitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item); /* proto */ +static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_12__setitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item, PyObject *__pyx_v_value); /* proto */ +static PyObject *__pyx_pf___pyx_array___reduce_cython__(CYTHON_UNUSED struct __pyx_array_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf___pyx_array_2__setstate_cython__(CYTHON_UNUSED struct __pyx_array_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */ +static int __pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum___init__(struct __pyx_MemviewEnum_obj *__pyx_v_self, PyObject *__pyx_v_name); /* proto */ +static PyObject *__pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum_2__repr__(struct __pyx_MemviewEnum_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf___pyx_MemviewEnum___reduce_cython__(struct __pyx_MemviewEnum_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf___pyx_MemviewEnum_2__setstate_cython__(struct __pyx_MemviewEnum_obj *__pyx_v_self, PyObject *__pyx_v___pyx_state); /* proto */ +static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj, int __pyx_v_flags, int __pyx_v_dtype_is_object); /* proto */ +static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__dealloc__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_4__getitem__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index); /* proto */ +static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_6__setitem__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value); /* proto */ +static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_8__getbuffer__(struct __pyx_memoryview_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_1T___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4base___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_5shape___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_7strides___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_10suboffsets___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4ndim___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_8itemsize___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_6nbytes___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4size___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static Py_ssize_t __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_10__len__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_12__repr__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_14__str__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_16is_c_contig(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_18is_f_contig(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_20copy(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_22copy_fortran(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf___pyx_memoryview___reduce_cython__(CYTHON_UNUSED struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf___pyx_memoryview_2__setstate_cython__(CYTHON_UNUSED struct __pyx_memoryview_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */ +static void __pyx_memoryviewslice___pyx_pf_15View_dot_MemoryView_16_memoryviewslice___dealloc__(struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_16_memoryviewslice_4base___get__(struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf___pyx_memoryviewslice___reduce_cython__(CYTHON_UNUSED struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf___pyx_memoryviewslice_2__setstate_cython__(CYTHON_UNUSED struct __pyx_memoryviewslice_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView___pyx_unpickle_Enum(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v___pyx_type, long __pyx_v___pyx_checksum, PyObject *__pyx_v___pyx_state); /* proto */ +static PyObject *__pyx_tp_new_array(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/ +static PyObject *__pyx_tp_new_Enum(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/ +static PyObject *__pyx_tp_new_memoryview(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/ +static PyObject *__pyx_tp_new__memoryviewslice(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/ +static PyObject *__pyx_int_0; +static PyObject *__pyx_int_1; +static PyObject *__pyx_int_184977713; +static PyObject *__pyx_int_neg_1; +static PyObject *__pyx_tuple_; +static PyObject *__pyx_tuple__2; +static PyObject *__pyx_tuple__3; +static PyObject *__pyx_tuple__4; +static PyObject *__pyx_tuple__5; +static PyObject *__pyx_tuple__6; +static PyObject *__pyx_tuple__7; +static PyObject *__pyx_tuple__8; +static PyObject *__pyx_tuple__9; +static PyObject *__pyx_slice__15; +static PyObject *__pyx_tuple__10; +static PyObject *__pyx_tuple__11; +static PyObject *__pyx_tuple__12; +static PyObject *__pyx_tuple__13; +static PyObject *__pyx_tuple__14; +static PyObject *__pyx_tuple__16; +static PyObject *__pyx_tuple__17; +static PyObject *__pyx_tuple__18; +static PyObject *__pyx_tuple__19; +static PyObject *__pyx_tuple__20; +static PyObject *__pyx_tuple__21; +static PyObject *__pyx_tuple__22; +static PyObject *__pyx_tuple__23; +static PyObject *__pyx_tuple__24; +static PyObject *__pyx_codeobj__25; +/* Late includes */ + +/* "Yank/multistate/mixing/_mix_replicas.pyx":10 + * @cython.cdivision(True) + * @cython.boundscheck(False) + * cpdef long _mix_replicas_cython(long nswap_attempts, long nstates, long[:] replica_states, double[:,:] u_kl, long[:,:] Nij_proposed, long[:,:] Nij_accepted) nogil: # <<<<<<<<<<<<<< + * cdef long swap_attempt + * cdef long i, j, istate, jstate, tmp_state + */ + +static PyObject *__pyx_pw_4yank_10multistate_6mixing_13_mix_replicas_1_mix_replicas_cython(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ +static long __pyx_f_4yank_10multistate_6mixing_13_mix_replicas__mix_replicas_cython(long __pyx_v_nswap_attempts, long __pyx_v_nstates, __Pyx_memviewslice __pyx_v_replica_states, __Pyx_memviewslice __pyx_v_u_kl, __Pyx_memviewslice __pyx_v_Nij_proposed, __Pyx_memviewslice __pyx_v_Nij_accepted, CYTHON_UNUSED int __pyx_skip_dispatch) { + CYTHON_UNUSED long __pyx_v_swap_attempt; + long __pyx_v_i; + long __pyx_v_j; + long __pyx_v_istate; + long __pyx_v_jstate; + long __pyx_v_tmp_state; + double __pyx_v_log_P_accept; + long __pyx_r; + long __pyx_t_1; + long __pyx_t_2; + long __pyx_t_3; + Py_ssize_t __pyx_t_4; + Py_ssize_t __pyx_t_5; + int __pyx_t_6; + Py_ssize_t __pyx_t_7; + Py_ssize_t __pyx_t_8; + int __pyx_t_9; + Py_ssize_t __pyx_t_10; + Py_ssize_t __pyx_t_11; + Py_ssize_t __pyx_t_12; + Py_ssize_t __pyx_t_13; + Py_ssize_t __pyx_t_14; + Py_ssize_t __pyx_t_15; + Py_ssize_t __pyx_t_16; + Py_ssize_t __pyx_t_17; + Py_ssize_t __pyx_t_18; + Py_ssize_t __pyx_t_19; + Py_ssize_t __pyx_t_20; + Py_ssize_t __pyx_t_21; + Py_ssize_t __pyx_t_22; + Py_ssize_t __pyx_t_23; + Py_ssize_t __pyx_t_24; + Py_ssize_t __pyx_t_25; + Py_ssize_t __pyx_t_26; + Py_ssize_t __pyx_t_27; + Py_ssize_t __pyx_t_28; + Py_ssize_t __pyx_t_29; + Py_ssize_t __pyx_t_30; + Py_ssize_t __pyx_t_31; + Py_ssize_t __pyx_t_32; + Py_ssize_t __pyx_t_33; + Py_ssize_t __pyx_t_34; + Py_ssize_t __pyx_t_35; + + /* "Yank/multistate/mixing/_mix_replicas.pyx":14 + * cdef long i, j, istate, jstate, tmp_state + * cdef double log_P_accept + * for swap_attempt in range(nswap_attempts): # <<<<<<<<<<<<<< + * i = (drand48()*nstates) + * j = (drand48()*nstates) + */ + __pyx_t_1 = __pyx_v_nswap_attempts; + __pyx_t_2 = __pyx_t_1; + for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { + __pyx_v_swap_attempt = __pyx_t_3; + + /* "Yank/multistate/mixing/_mix_replicas.pyx":15 + * cdef double log_P_accept + * for swap_attempt in range(nswap_attempts): + * i = (drand48()*nstates) # <<<<<<<<<<<<<< + * j = (drand48()*nstates) + * istate = replica_states[i] + */ + __pyx_v_i = ((long)(drand48() * __pyx_v_nstates)); + + /* "Yank/multistate/mixing/_mix_replicas.pyx":16 + * for swap_attempt in range(nswap_attempts): + * i = (drand48()*nstates) + * j = (drand48()*nstates) # <<<<<<<<<<<<<< + * istate = replica_states[i] + * jstate = replica_states[j] + */ + __pyx_v_j = ((long)(drand48() * __pyx_v_nstates)); + + /* "Yank/multistate/mixing/_mix_replicas.pyx":17 + * i = (drand48()*nstates) + * j = (drand48()*nstates) + * istate = replica_states[i] # <<<<<<<<<<<<<< + * jstate = replica_states[j] + * if (isnan(u_kl[i, istate]) or isnan(u_kl[i, jstate]) or isnan(u_kl[j, istate]) or isnan(u_kl[j, jstate])): + */ + __pyx_t_4 = __pyx_v_i; + __pyx_v_istate = (*((long *) ( /* dim=0 */ (__pyx_v_replica_states.data + __pyx_t_4 * __pyx_v_replica_states.strides[0]) ))); + + /* "Yank/multistate/mixing/_mix_replicas.pyx":18 + * j = (drand48()*nstates) + * istate = replica_states[i] + * jstate = replica_states[j] # <<<<<<<<<<<<<< + * if (isnan(u_kl[i, istate]) or isnan(u_kl[i, jstate]) or isnan(u_kl[j, istate]) or isnan(u_kl[j, jstate])): + * continue + */ + __pyx_t_5 = __pyx_v_j; + __pyx_v_jstate = (*((long *) ( /* dim=0 */ (__pyx_v_replica_states.data + __pyx_t_5 * __pyx_v_replica_states.strides[0]) ))); + + /* "Yank/multistate/mixing/_mix_replicas.pyx":19 + * istate = replica_states[i] + * jstate = replica_states[j] + * if (isnan(u_kl[i, istate]) or isnan(u_kl[i, jstate]) or isnan(u_kl[j, istate]) or isnan(u_kl[j, jstate])): # <<<<<<<<<<<<<< + * continue + * log_P_accept = - (u_kl[i, jstate] + 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negative_step + * + * if not is_slice: # <<<<<<<<<<<<<< + * + * if start < 0: + */ + __pyx_t_1 = ((!(__pyx_v_is_slice != 0)) != 0); + if (__pyx_t_1) { + + /* "View.MemoryView":824 + * if not is_slice: + * + * if start < 0: # <<<<<<<<<<<<<< + * start += shape + * if not 0 <= start < shape: + */ + __pyx_t_1 = ((__pyx_v_start < 0) != 0); + if (__pyx_t_1) { + + /* "View.MemoryView":825 + * + * if start < 0: + * start += shape # <<<<<<<<<<<<<< + * if not 0 <= start < shape: + * _err_dim(IndexError, "Index out of bounds (axis %d)", dim) + */ + __pyx_v_start = (__pyx_v_start + __pyx_v_shape); + + /* "View.MemoryView":824 + * if not is_slice: + * + * if start < 0: # <<<<<<<<<<<<<< + * start += shape + * if not 0 <= start < shape: + */ + } + + /* "View.MemoryView":826 + * if start < 0: + * start += shape + * if not 0 <= start < shape: # <<<<<<<<<<<<<< + * _err_dim(IndexError, "Index out of bounds (axis %d)", dim) + * else: + */ + __pyx_t_1 = (0 <= __pyx_v_start); + if (__pyx_t_1) { + __pyx_t_1 = (__pyx_v_start < __pyx_v_shape); + } + __pyx_t_2 = ((!(__pyx_t_1 != 0)) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":827 + * start += shape + * if not 0 <= start < shape: + * _err_dim(IndexError, "Index out of bounds (axis %d)", dim) # <<<<<<<<<<<<<< + * else: + * + */ + __pyx_t_3 = __pyx_memoryview_err_dim(__pyx_builtin_IndexError, ((char *)"Index out of bounds (axis %d)"), __pyx_v_dim); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(1, 827, __pyx_L1_error) + + /* "View.MemoryView":826 + * if start < 0: + * start += shape + * if not 0 <= start < shape: # <<<<<<<<<<<<<< + * _err_dim(IndexError, "Index out of bounds (axis %d)", dim) + * else: + */ + } + + /* "View.MemoryView":822 + * cdef bint negative_step + * + * if not is_slice: # <<<<<<<<<<<<<< + * + * if start < 0: + */ + goto __pyx_L3; + } + + /* "View.MemoryView":830 + * else: + * + * negative_step = have_step != 0 and step < 0 # <<<<<<<<<<<<<< + * + * if have_step and step == 0: + */ + /*else*/ { + __pyx_t_1 = ((__pyx_v_have_step != 0) != 0); + if (__pyx_t_1) { + } else { + __pyx_t_2 = __pyx_t_1; + goto __pyx_L6_bool_binop_done; + } + __pyx_t_1 = ((__pyx_v_step < 0) != 0); + __pyx_t_2 = __pyx_t_1; + __pyx_L6_bool_binop_done:; + __pyx_v_negative_step = __pyx_t_2; + + /* "View.MemoryView":832 + * negative_step = have_step != 0 and step < 0 + * + * if have_step and step == 0: # <<<<<<<<<<<<<< + * _err_dim(ValueError, "Step may not be zero (axis %d)", dim) + * + */ + __pyx_t_1 = (__pyx_v_have_step != 0); + if (__pyx_t_1) { + } else { + __pyx_t_2 = __pyx_t_1; + goto __pyx_L9_bool_binop_done; + } + __pyx_t_1 = ((__pyx_v_step == 0) != 0); + __pyx_t_2 = __pyx_t_1; + __pyx_L9_bool_binop_done:; + if (__pyx_t_2) { + + /* "View.MemoryView":833 + * + * if have_step and step == 0: + * _err_dim(ValueError, "Step may not be zero (axis %d)", dim) # <<<<<<<<<<<<<< + * + * + */ + __pyx_t_3 = __pyx_memoryview_err_dim(__pyx_builtin_ValueError, ((char *)"Step may not be zero (axis %d)"), __pyx_v_dim); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(1, 833, __pyx_L1_error) + + /* "View.MemoryView":832 + * negative_step = have_step != 0 and step < 0 + * + * if have_step and step == 0: # <<<<<<<<<<<<<< + * _err_dim(ValueError, "Step may not be zero (axis %d)", dim) + * + */ + } + + /* "View.MemoryView":836 + * + * + * if have_start: # <<<<<<<<<<<<<< + * if start < 0: + * start += shape + */ + __pyx_t_2 = (__pyx_v_have_start != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":837 + * + * if have_start: + * if start < 0: # <<<<<<<<<<<<<< + * start += shape + * if start < 0: + */ + __pyx_t_2 = ((__pyx_v_start < 0) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":838 + * if have_start: + * if start < 0: + * start += shape # <<<<<<<<<<<<<< + * if start < 0: + * start = 0 + */ + __pyx_v_start = (__pyx_v_start + __pyx_v_shape); + + /* "View.MemoryView":839 + * if start < 0: + * start += shape + * if start < 0: # <<<<<<<<<<<<<< + * start = 0 + * elif start >= shape: + */ + __pyx_t_2 = ((__pyx_v_start < 0) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":840 + * start += shape + * if start < 0: + * start = 0 # <<<<<<<<<<<<<< + * elif start >= shape: + * if negative_step: + */ + __pyx_v_start = 0; + + /* "View.MemoryView":839 + * if start < 0: + * start += shape + * if start < 0: # <<<<<<<<<<<<<< + * start = 0 + * elif start >= shape: + */ + } + + /* "View.MemoryView":837 + * + * if have_start: + * if start < 0: # <<<<<<<<<<<<<< + * start += shape + * if start < 0: + */ + goto __pyx_L12; + } + + /* "View.MemoryView":841 + * if start < 0: + * start = 0 + * elif start >= shape: # <<<<<<<<<<<<<< + * if negative_step: + * start = shape - 1 + */ + __pyx_t_2 = ((__pyx_v_start >= __pyx_v_shape) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":842 + * start = 0 + * elif start >= shape: + * if negative_step: # <<<<<<<<<<<<<< + * start = shape - 1 + * else: + */ + __pyx_t_2 = (__pyx_v_negative_step != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":843 + * elif start >= shape: + * if negative_step: + * start = shape - 1 # <<<<<<<<<<<<<< + * else: + * start = shape + */ + __pyx_v_start = (__pyx_v_shape - 1); + + /* "View.MemoryView":842 + * start = 0 + * elif start >= shape: + * if negative_step: # <<<<<<<<<<<<<< + * start = shape - 1 + * else: + */ + goto __pyx_L14; + } + + /* "View.MemoryView":845 + * start = shape - 1 + * else: + * start = shape # <<<<<<<<<<<<<< + * else: + * if negative_step: + */ + /*else*/ { + __pyx_v_start = __pyx_v_shape; + } + __pyx_L14:; + + /* "View.MemoryView":841 + * if start < 0: + * start = 0 + * elif start >= shape: # <<<<<<<<<<<<<< + * if negative_step: + * start = shape - 1 + */ + } + __pyx_L12:; + + /* "View.MemoryView":836 + * + * + * if have_start: # <<<<<<<<<<<<<< + * if start < 0: + * start += shape + */ + goto __pyx_L11; + } + + /* "View.MemoryView":847 + * start = shape + * else: + * if negative_step: # <<<<<<<<<<<<<< + * start = shape - 1 + * else: + */ + /*else*/ { + __pyx_t_2 = (__pyx_v_negative_step != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":848 + * else: + * if negative_step: + * start = shape - 1 # <<<<<<<<<<<<<< + * else: + * start = 0 + */ + __pyx_v_start = (__pyx_v_shape - 1); + + /* "View.MemoryView":847 + * start = shape + * else: + * if negative_step: # <<<<<<<<<<<<<< + * start = shape - 1 + * else: + */ + goto __pyx_L15; + } + + /* "View.MemoryView":850 + * start = shape - 1 + * else: + * start = 0 # <<<<<<<<<<<<<< + * + * if have_stop: + */ + /*else*/ { + __pyx_v_start = 0; + } + __pyx_L15:; + } + __pyx_L11:; + + /* "View.MemoryView":852 + * start = 0 + * + * if have_stop: # <<<<<<<<<<<<<< + * if stop < 0: + * stop += shape + */ + __pyx_t_2 = (__pyx_v_have_stop != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":853 + * + * if have_stop: + * if stop < 0: # <<<<<<<<<<<<<< + * stop += shape + * if stop < 0: + */ + __pyx_t_2 = ((__pyx_v_stop < 0) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":854 + * if have_stop: + * if stop < 0: + * stop += shape # <<<<<<<<<<<<<< + * if stop < 0: + * stop = 0 + */ + __pyx_v_stop = (__pyx_v_stop + __pyx_v_shape); + + /* "View.MemoryView":855 + * if stop < 0: + * stop += shape + * if stop < 0: # <<<<<<<<<<<<<< + * stop = 0 + * elif stop > shape: + */ + __pyx_t_2 = ((__pyx_v_stop < 0) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":856 + * stop += shape + * if stop < 0: + * stop = 0 # <<<<<<<<<<<<<< + * elif stop > shape: + * stop = shape + */ + __pyx_v_stop = 0; + + /* "View.MemoryView":855 + * if stop < 0: + * stop += shape + * if stop < 0: # <<<<<<<<<<<<<< + * stop = 0 + * elif stop > shape: + */ + } + + /* "View.MemoryView":853 + * + * if have_stop: + * if stop < 0: # <<<<<<<<<<<<<< + * stop += shape + * if stop < 0: + */ + goto __pyx_L17; + } + + /* "View.MemoryView":857 + * if stop < 0: + * stop = 0 + * elif stop > shape: # <<<<<<<<<<<<<< + * stop = shape + * else: + */ + __pyx_t_2 = ((__pyx_v_stop > __pyx_v_shape) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":858 + * stop = 0 + * elif stop > shape: + * stop = shape # <<<<<<<<<<<<<< + * else: + * if negative_step: + */ + __pyx_v_stop = __pyx_v_shape; + + /* "View.MemoryView":857 + * if stop < 0: + * stop = 0 + * elif stop > shape: # <<<<<<<<<<<<<< + * stop = shape + * else: + */ + } + __pyx_L17:; + + /* "View.MemoryView":852 + * start = 0 + * + * if have_stop: # <<<<<<<<<<<<<< + * if stop < 0: + * stop += shape + */ + goto __pyx_L16; + } + + /* "View.MemoryView":860 + * stop = shape + * else: + * if negative_step: # <<<<<<<<<<<<<< + * stop = -1 + * else: + */ + /*else*/ { + __pyx_t_2 = (__pyx_v_negative_step != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":861 + * else: + * if negative_step: + * stop = -1 # <<<<<<<<<<<<<< + * else: + * stop = shape + */ + __pyx_v_stop = -1L; + + /* "View.MemoryView":860 + * stop = shape + * else: + * if negative_step: # <<<<<<<<<<<<<< + * stop = -1 + * else: + */ + goto __pyx_L19; + } + + /* "View.MemoryView":863 + * stop = -1 + * else: + * stop = shape # <<<<<<<<<<<<<< + * + * if not have_step: + */ + /*else*/ { + __pyx_v_stop = __pyx_v_shape; + } + __pyx_L19:; + } + __pyx_L16:; + + /* "View.MemoryView":865 + * stop = shape + * + * if not have_step: # <<<<<<<<<<<<<< + * step = 1 + * + */ + __pyx_t_2 = ((!(__pyx_v_have_step != 0)) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":866 + * + * if not have_step: + * step = 1 # <<<<<<<<<<<<<< + * + * + */ + __pyx_v_step = 1; + + /* "View.MemoryView":865 + * stop = shape + * + * if not have_step: # <<<<<<<<<<<<<< + * step = 1 + * + */ + } + + /* "View.MemoryView":870 + * + * with cython.cdivision(True): + * new_shape = (stop - start) // step # <<<<<<<<<<<<<< + * + * if (stop - start) - step * new_shape: + */ + __pyx_v_new_shape = ((__pyx_v_stop - __pyx_v_start) / __pyx_v_step); + + /* "View.MemoryView":872 + * new_shape = (stop - start) // step + * + * if (stop - start) - step * new_shape: # <<<<<<<<<<<<<< + * new_shape += 1 + * + */ + __pyx_t_2 = (((__pyx_v_stop - __pyx_v_start) - (__pyx_v_step * __pyx_v_new_shape)) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":873 + * + * if (stop - start) - step * new_shape: + * new_shape += 1 # <<<<<<<<<<<<<< + * + * if new_shape < 0: + */ + __pyx_v_new_shape = (__pyx_v_new_shape + 1); + + /* "View.MemoryView":872 + * new_shape = (stop - start) // step + * + * if (stop - start) - step * new_shape: # <<<<<<<<<<<<<< + * new_shape += 1 + * + */ + } + + /* "View.MemoryView":875 + * new_shape += 1 + * + * if new_shape < 0: # <<<<<<<<<<<<<< + * new_shape = 0 + * + */ + __pyx_t_2 = ((__pyx_v_new_shape < 0) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":876 + * + * if new_shape < 0: + * new_shape = 0 # <<<<<<<<<<<<<< + * + * + */ + __pyx_v_new_shape = 0; + + /* "View.MemoryView":875 + * new_shape += 1 + * + * if new_shape < 0: # <<<<<<<<<<<<<< + * new_shape = 0 + * + */ + } + + /* "View.MemoryView":879 + * + * + * dst.strides[new_ndim] = stride * step # <<<<<<<<<<<<<< + * dst.shape[new_ndim] = new_shape + * dst.suboffsets[new_ndim] = suboffset + */ + (__pyx_v_dst->strides[__pyx_v_new_ndim]) = (__pyx_v_stride * __pyx_v_step); + + /* "View.MemoryView":880 + * + * dst.strides[new_ndim] = stride * step + * dst.shape[new_ndim] = new_shape # <<<<<<<<<<<<<< + * dst.suboffsets[new_ndim] = suboffset + * + */ + (__pyx_v_dst->shape[__pyx_v_new_ndim]) = __pyx_v_new_shape; + + /* "View.MemoryView":881 + * dst.strides[new_ndim] = stride * step + * dst.shape[new_ndim] = new_shape + * dst.suboffsets[new_ndim] = suboffset # <<<<<<<<<<<<<< + * + * + */ + (__pyx_v_dst->suboffsets[__pyx_v_new_ndim]) = __pyx_v_suboffset; + } + __pyx_L3:; + + /* "View.MemoryView":884 + * + * + * if suboffset_dim[0] < 0: # <<<<<<<<<<<<<< + * dst.data += start * stride + * else: + */ + __pyx_t_2 = (((__pyx_v_suboffset_dim[0]) < 0) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":885 + * + * if suboffset_dim[0] < 0: + * dst.data += start * stride # <<<<<<<<<<<<<< + * else: + * dst.suboffsets[suboffset_dim[0]] += start * stride + */ + __pyx_v_dst->data = (__pyx_v_dst->data + (__pyx_v_start * __pyx_v_stride)); + + /* "View.MemoryView":884 + * + * + * if suboffset_dim[0] < 0: # <<<<<<<<<<<<<< + * dst.data += start * stride + * else: + */ + goto __pyx_L23; + } + + /* "View.MemoryView":887 + * dst.data += start * stride + * else: + * dst.suboffsets[suboffset_dim[0]] += start * stride # <<<<<<<<<<<<<< + * + * if suboffset >= 0: + */ + /*else*/ { + __pyx_t_3 = (__pyx_v_suboffset_dim[0]); + (__pyx_v_dst->suboffsets[__pyx_t_3]) = ((__pyx_v_dst->suboffsets[__pyx_t_3]) + (__pyx_v_start * __pyx_v_stride)); + } + __pyx_L23:; + + /* "View.MemoryView":889 + * dst.suboffsets[suboffset_dim[0]] += start * stride + * + * if suboffset >= 0: # <<<<<<<<<<<<<< + * if not is_slice: + * if new_ndim == 0: + */ + __pyx_t_2 = ((__pyx_v_suboffset >= 0) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":890 + * + * if suboffset >= 0: + * if not is_slice: # <<<<<<<<<<<<<< + * if new_ndim == 0: + * dst.data = ( dst.data)[0] + suboffset + */ + __pyx_t_2 = ((!(__pyx_v_is_slice != 0)) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":891 + * if suboffset >= 0: + * if not is_slice: + * if new_ndim == 0: # <<<<<<<<<<<<<< + * dst.data = ( dst.data)[0] + suboffset + * else: + */ + __pyx_t_2 = ((__pyx_v_new_ndim == 0) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":892 + * if not is_slice: + * if new_ndim == 0: + * dst.data = ( dst.data)[0] + suboffset # <<<<<<<<<<<<<< + * else: + * _err_dim(IndexError, "All dimensions preceding dimension %d " + */ + __pyx_v_dst->data = ((((char **)__pyx_v_dst->data)[0]) + __pyx_v_suboffset); + + /* "View.MemoryView":891 + * if suboffset >= 0: + * if not is_slice: + * if new_ndim == 0: # <<<<<<<<<<<<<< + * dst.data = ( dst.data)[0] + suboffset + * else: + */ + goto __pyx_L26; + } + + /* "View.MemoryView":894 + * dst.data = ( dst.data)[0] + suboffset + * else: + * _err_dim(IndexError, "All dimensions preceding dimension %d " # <<<<<<<<<<<<<< + * "must be indexed and not sliced", dim) + * else: + */ + 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"View.MemoryView":1082 + * + * @cname('__pyx_memoryview_copy_object_from_slice') + * cdef memoryview_copy_from_slice(memoryview memview, __Pyx_memviewslice *memviewslice): # <<<<<<<<<<<<<< + * """ + * Create a new memoryview object from a given memoryview object and slice. + */ + + /* function exit code */ + __pyx_L1_error:; + __Pyx_XDECREF(__pyx_t_5); + __Pyx_AddTraceback("View.MemoryView.memoryview_copy_from_slice", __pyx_clineno, __pyx_lineno, __pyx_filename); + __pyx_r = 0; + __pyx_L0:; + __Pyx_XGIVEREF(__pyx_r); + __Pyx_RefNannyFinishContext(); + return __pyx_r; +} + +/* "View.MemoryView":1104 + * + * + * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil: # <<<<<<<<<<<<<< + * if arg < 0: + * return -arg + */ + +static Py_ssize_t abs_py_ssize_t(Py_ssize_t __pyx_v_arg) { + Py_ssize_t __pyx_r; + int __pyx_t_1; + + /* "View.MemoryView":1105 + * + * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil: + * if arg < 0: # <<<<<<<<<<<<<< + * return -arg + * else: + */ + __pyx_t_1 = ((__pyx_v_arg < 0) != 0); + if (__pyx_t_1) { + + /* "View.MemoryView":1106 + * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil: + * if arg < 0: + * return -arg # <<<<<<<<<<<<<< + * else: + * return arg + */ + __pyx_r = (-__pyx_v_arg); + goto __pyx_L0; + + /* "View.MemoryView":1105 + * + * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil: + * if arg < 0: # <<<<<<<<<<<<<< + * return -arg + * else: + */ + } + + /* "View.MemoryView":1108 + * return -arg + * else: + * return arg # <<<<<<<<<<<<<< + * + * @cname('__pyx_get_best_slice_order') + */ + /*else*/ { + __pyx_r = __pyx_v_arg; + goto __pyx_L0; + } + + /* "View.MemoryView":1104 + * + * + * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil: # <<<<<<<<<<<<<< + * if arg < 0: + * return -arg + */ + + /* function exit code */ + __pyx_L0:; + return __pyx_r; +} + +/* "View.MemoryView":1111 + * + * @cname('__pyx_get_best_slice_order') + * cdef char get_best_order(__Pyx_memviewslice *mslice, int ndim) nogil: # <<<<<<<<<<<<<< + * """ + * Figure out the best memory access order for a given slice. + */ + +static char __pyx_get_best_slice_order(__Pyx_memviewslice *__pyx_v_mslice, int __pyx_v_ndim) { + int __pyx_v_i; + Py_ssize_t __pyx_v_c_stride; + Py_ssize_t __pyx_v_f_stride; + char __pyx_r; + int __pyx_t_1; + int __pyx_t_2; + int __pyx_t_3; + int __pyx_t_4; + + /* "View.MemoryView":1116 + * """ + * cdef int i + * cdef Py_ssize_t c_stride = 0 # <<<<<<<<<<<<<< + * cdef Py_ssize_t f_stride = 0 + * + */ + __pyx_v_c_stride = 0; + + /* "View.MemoryView":1117 + * cdef int i + * cdef Py_ssize_t c_stride = 0 + * cdef Py_ssize_t f_stride = 0 # <<<<<<<<<<<<<< + * + * for i in range(ndim - 1, -1, -1): + */ + __pyx_v_f_stride = 0; + + /* "View.MemoryView":1119 + * cdef Py_ssize_t f_stride = 0 + * + * for i in range(ndim - 1, -1, -1): # <<<<<<<<<<<<<< + * if mslice.shape[i] > 1: + * c_stride = mslice.strides[i] + */ + for (__pyx_t_1 = (__pyx_v_ndim - 1); __pyx_t_1 > -1; __pyx_t_1-=1) { + __pyx_v_i = __pyx_t_1; + + /* "View.MemoryView":1120 + * + * for i in range(ndim - 1, -1, -1): + * if mslice.shape[i] > 1: # <<<<<<<<<<<<<< + * c_stride = mslice.strides[i] + * break + */ + __pyx_t_2 = (((__pyx_v_mslice->shape[__pyx_v_i]) > 1) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":1121 + * for i in range(ndim - 1, -1, -1): + * if mslice.shape[i] > 1: + * c_stride = mslice.strides[i] # <<<<<<<<<<<<<< + * break + * + */ + __pyx_v_c_stride = (__pyx_v_mslice->strides[__pyx_v_i]); + + /* "View.MemoryView":1122 + * if mslice.shape[i] > 1: + * c_stride = mslice.strides[i] + * break # <<<<<<<<<<<<<< + * + * for i in range(ndim): + */ + goto __pyx_L4_break; + + /* "View.MemoryView":1120 + * + * for i in range(ndim - 1, -1, -1): + * if mslice.shape[i] > 1: # <<<<<<<<<<<<<< + * c_stride = mslice.strides[i] + * break + */ + } + } + __pyx_L4_break:; + + /* "View.MemoryView":1124 + * break + * + * for i in range(ndim): # <<<<<<<<<<<<<< + * if mslice.shape[i] > 1: + * f_stride = mslice.strides[i] + */ + __pyx_t_1 = __pyx_v_ndim; + __pyx_t_3 = __pyx_t_1; + for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) { + __pyx_v_i = __pyx_t_4; + + /* "View.MemoryView":1125 + * + * for i in range(ndim): + * if mslice.shape[i] > 1: # <<<<<<<<<<<<<< + * f_stride = mslice.strides[i] + * break + */ + __pyx_t_2 = (((__pyx_v_mslice->shape[__pyx_v_i]) > 1) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":1126 + * for i in range(ndim): + * if mslice.shape[i] > 1: + * f_stride = mslice.strides[i] # <<<<<<<<<<<<<< + * break + * + */ + __pyx_v_f_stride = (__pyx_v_mslice->strides[__pyx_v_i]); + + /* "View.MemoryView":1127 + * if mslice.shape[i] > 1: + * f_stride = mslice.strides[i] + * break # <<<<<<<<<<<<<< + * + * if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride): + */ + goto __pyx_L7_break; + + /* "View.MemoryView":1125 + * + * for i in range(ndim): + * if mslice.shape[i] > 1: # <<<<<<<<<<<<<< + * f_stride = mslice.strides[i] + * break + */ + } + } + __pyx_L7_break:; + + /* "View.MemoryView":1129 + * break + * + * if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride): # <<<<<<<<<<<<<< + * return 'C' + * else: + */ + __pyx_t_2 = ((abs_py_ssize_t(__pyx_v_c_stride) <= abs_py_ssize_t(__pyx_v_f_stride)) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":1130 + * + * if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride): + * return 'C' # <<<<<<<<<<<<<< + * else: + * return 'F' + */ + __pyx_r = 'C'; + goto __pyx_L0; + + /* "View.MemoryView":1129 + * break + * + * if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride): # <<<<<<<<<<<<<< + * return 'C' + * else: + */ + } + + /* "View.MemoryView":1132 + * return 'C' + * else: + * return 'F' # <<<<<<<<<<<<<< + * + * @cython.cdivision(True) + */ + /*else*/ { + __pyx_r = 'F'; + goto __pyx_L0; + } + + /* "View.MemoryView":1111 + * + * @cname('__pyx_get_best_slice_order') + * cdef char get_best_order(__Pyx_memviewslice *mslice, int ndim) nogil: # <<<<<<<<<<<<<< + * """ + * Figure out the best memory access order for a given slice. + */ + + /* function exit code */ + __pyx_L0:; + return __pyx_r; +} + +/* "View.MemoryView":1135 + * + * @cython.cdivision(True) + * cdef void _copy_strided_to_strided(char *src_data, Py_ssize_t *src_strides, # <<<<<<<<<<<<<< + * char *dst_data, Py_ssize_t *dst_strides, + * Py_ssize_t *src_shape, Py_ssize_t *dst_shape, + */ + +static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v_src_strides, char *__pyx_v_dst_data, Py_ssize_t *__pyx_v_dst_strides, Py_ssize_t *__pyx_v_src_shape, Py_ssize_t *__pyx_v_dst_shape, int __pyx_v_ndim, size_t __pyx_v_itemsize) { + CYTHON_UNUSED Py_ssize_t __pyx_v_i; + CYTHON_UNUSED Py_ssize_t __pyx_v_src_extent; + Py_ssize_t __pyx_v_dst_extent; + Py_ssize_t __pyx_v_src_stride; + Py_ssize_t __pyx_v_dst_stride; + int __pyx_t_1; + int __pyx_t_2; + int __pyx_t_3; + Py_ssize_t __pyx_t_4; + Py_ssize_t __pyx_t_5; + Py_ssize_t __pyx_t_6; + + /* "View.MemoryView":1142 + * + * cdef Py_ssize_t i + * cdef Py_ssize_t src_extent = src_shape[0] # <<<<<<<<<<<<<< + * cdef Py_ssize_t dst_extent = dst_shape[0] + * cdef Py_ssize_t src_stride = src_strides[0] + */ + __pyx_v_src_extent = (__pyx_v_src_shape[0]); + + /* "View.MemoryView":1143 + * cdef Py_ssize_t i + * cdef Py_ssize_t src_extent = src_shape[0] + * cdef Py_ssize_t dst_extent = dst_shape[0] # <<<<<<<<<<<<<< + * cdef Py_ssize_t src_stride = src_strides[0] + * cdef Py_ssize_t dst_stride = dst_strides[0] + */ + __pyx_v_dst_extent = (__pyx_v_dst_shape[0]); + + /* "View.MemoryView":1144 + * cdef Py_ssize_t src_extent = src_shape[0] + * cdef Py_ssize_t dst_extent = dst_shape[0] + * cdef Py_ssize_t src_stride = src_strides[0] # <<<<<<<<<<<<<< + * cdef Py_ssize_t dst_stride = dst_strides[0] + * + */ + __pyx_v_src_stride = (__pyx_v_src_strides[0]); + + /* "View.MemoryView":1145 + * cdef Py_ssize_t dst_extent = dst_shape[0] + * cdef Py_ssize_t src_stride = src_strides[0] + * cdef Py_ssize_t dst_stride = dst_strides[0] # <<<<<<<<<<<<<< + * + * if ndim == 1: + */ + __pyx_v_dst_stride = (__pyx_v_dst_strides[0]); + + /* "View.MemoryView":1147 + * cdef Py_ssize_t dst_stride = dst_strides[0] + * + * if ndim == 1: # <<<<<<<<<<<<<< + * if (src_stride > 0 and dst_stride > 0 and + * src_stride == itemsize == dst_stride): + */ + __pyx_t_1 = ((__pyx_v_ndim == 1) != 0); + if (__pyx_t_1) { + + /* "View.MemoryView":1148 + * + * if ndim == 1: + * if (src_stride > 0 and dst_stride > 0 and # <<<<<<<<<<<<<< + * src_stride == itemsize == dst_stride): + * memcpy(dst_data, src_data, itemsize * dst_extent) + */ + __pyx_t_2 = ((__pyx_v_src_stride > 0) != 0); + if (__pyx_t_2) { + } else { + __pyx_t_1 = __pyx_t_2; + goto __pyx_L5_bool_binop_done; + } + __pyx_t_2 = ((__pyx_v_dst_stride > 0) != 0); + if (__pyx_t_2) { + } else { + __pyx_t_1 = __pyx_t_2; + goto __pyx_L5_bool_binop_done; + } + + /* "View.MemoryView":1149 + * if ndim == 1: + * if (src_stride > 0 and dst_stride > 0 and + * src_stride == itemsize == dst_stride): # <<<<<<<<<<<<<< + * memcpy(dst_data, src_data, itemsize * dst_extent) + * else: + */ + __pyx_t_2 = (((size_t)__pyx_v_src_stride) == __pyx_v_itemsize); + if (__pyx_t_2) { + __pyx_t_2 = (__pyx_v_itemsize == ((size_t)__pyx_v_dst_stride)); + } + __pyx_t_3 = (__pyx_t_2 != 0); + __pyx_t_1 = __pyx_t_3; + __pyx_L5_bool_binop_done:; + + /* "View.MemoryView":1148 + * + * if ndim == 1: + * if (src_stride > 0 and dst_stride > 0 and # <<<<<<<<<<<<<< + * src_stride == itemsize == dst_stride): + * memcpy(dst_data, src_data, itemsize * dst_extent) + */ + if (__pyx_t_1) { + + /* "View.MemoryView":1150 + * if (src_stride > 0 and dst_stride > 0 and + * src_stride == itemsize == dst_stride): + * memcpy(dst_data, src_data, itemsize * dst_extent) # <<<<<<<<<<<<<< + * else: + * for i in range(dst_extent): + */ + (void)(memcpy(__pyx_v_dst_data, __pyx_v_src_data, (__pyx_v_itemsize * __pyx_v_dst_extent))); + + /* "View.MemoryView":1148 + * + * if ndim == 1: + * if (src_stride > 0 and dst_stride > 0 and # <<<<<<<<<<<<<< + * src_stride == itemsize == dst_stride): + * memcpy(dst_data, src_data, itemsize * dst_extent) + */ + goto __pyx_L4; + } + + /* "View.MemoryView":1152 + * memcpy(dst_data, src_data, itemsize * dst_extent) + * else: + * for i in range(dst_extent): # <<<<<<<<<<<<<< + * memcpy(dst_data, src_data, itemsize) + * src_data += src_stride + */ + /*else*/ { + __pyx_t_4 = __pyx_v_dst_extent; + __pyx_t_5 = __pyx_t_4; + for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) { + __pyx_v_i = __pyx_t_6; + + /* "View.MemoryView":1153 + * else: + * for i in range(dst_extent): + * memcpy(dst_data, src_data, itemsize) # <<<<<<<<<<<<<< + * src_data += src_stride + * dst_data += dst_stride + */ + (void)(memcpy(__pyx_v_dst_data, __pyx_v_src_data, __pyx_v_itemsize)); + + /* "View.MemoryView":1154 + * for i in range(dst_extent): + * memcpy(dst_data, src_data, itemsize) + * src_data += src_stride # <<<<<<<<<<<<<< + * dst_data += dst_stride + * else: + */ + __pyx_v_src_data = (__pyx_v_src_data + __pyx_v_src_stride); + + /* "View.MemoryView":1155 + * memcpy(dst_data, src_data, itemsize) + * src_data += src_stride + * dst_data += dst_stride # <<<<<<<<<<<<<< + * else: + * for i in range(dst_extent): + */ + __pyx_v_dst_data = (__pyx_v_dst_data + __pyx_v_dst_stride); + } + } + __pyx_L4:; + + /* "View.MemoryView":1147 + * cdef Py_ssize_t dst_stride = dst_strides[0] + * + * if ndim == 1: # <<<<<<<<<<<<<< + * if (src_stride > 0 and dst_stride > 0 and + * src_stride == itemsize == dst_stride): + */ + goto __pyx_L3; + } + + /* "View.MemoryView":1157 + * dst_data += dst_stride + * else: + * for i in range(dst_extent): # <<<<<<<<<<<<<< + * _copy_strided_to_strided(src_data, src_strides + 1, + * dst_data, dst_strides + 1, + */ + /*else*/ { + __pyx_t_4 = __pyx_v_dst_extent; + __pyx_t_5 = __pyx_t_4; + for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) { + __pyx_v_i = __pyx_t_6; + + /* "View.MemoryView":1158 + * else: + * for i in range(dst_extent): + * _copy_strided_to_strided(src_data, src_strides + 1, # <<<<<<<<<<<<<< + * dst_data, dst_strides + 1, + * src_shape + 1, dst_shape + 1, + */ + _copy_strided_to_strided(__pyx_v_src_data, (__pyx_v_src_strides + 1), __pyx_v_dst_data, (__pyx_v_dst_strides + 1), (__pyx_v_src_shape + 1), (__pyx_v_dst_shape + 1), (__pyx_v_ndim - 1), __pyx_v_itemsize); + + /* "View.MemoryView":1162 + * src_shape + 1, dst_shape + 1, + * ndim - 1, itemsize) + * src_data += src_stride # <<<<<<<<<<<<<< + * dst_data += dst_stride + * + */ + __pyx_v_src_data = (__pyx_v_src_data + __pyx_v_src_stride); + + /* "View.MemoryView":1163 + * ndim - 1, itemsize) + * src_data += src_stride + * dst_data += dst_stride # <<<<<<<<<<<<<< + * + * cdef void copy_strided_to_strided(__Pyx_memviewslice *src, + */ + __pyx_v_dst_data = (__pyx_v_dst_data + __pyx_v_dst_stride); + } + } + __pyx_L3:; + + /* "View.MemoryView":1135 + * + * @cython.cdivision(True) + * cdef void _copy_strided_to_strided(char *src_data, Py_ssize_t *src_strides, # <<<<<<<<<<<<<< + * char *dst_data, Py_ssize_t *dst_strides, + * Py_ssize_t *src_shape, Py_ssize_t *dst_shape, + */ + + /* function exit code */ +} + +/* "View.MemoryView":1165 + * dst_data += dst_stride + * + * cdef void copy_strided_to_strided(__Pyx_memviewslice *src, # <<<<<<<<<<<<<< + * __Pyx_memviewslice *dst, + * int ndim, size_t itemsize) nogil: + */ + +static void copy_strided_to_strided(__Pyx_memviewslice *__pyx_v_src, __Pyx_memviewslice *__pyx_v_dst, int __pyx_v_ndim, size_t __pyx_v_itemsize) { + + /* "View.MemoryView":1168 + * __Pyx_memviewslice *dst, + * int ndim, size_t itemsize) nogil: + * _copy_strided_to_strided(src.data, src.strides, dst.data, dst.strides, # <<<<<<<<<<<<<< + * src.shape, dst.shape, ndim, itemsize) + * + */ + _copy_strided_to_strided(__pyx_v_src->data, __pyx_v_src->strides, __pyx_v_dst->data, __pyx_v_dst->strides, __pyx_v_src->shape, __pyx_v_dst->shape, __pyx_v_ndim, __pyx_v_itemsize); + + /* "View.MemoryView":1165 + * dst_data += dst_stride + * + * cdef void copy_strided_to_strided(__Pyx_memviewslice *src, # <<<<<<<<<<<<<< + * __Pyx_memviewslice *dst, + * int ndim, size_t itemsize) nogil: + */ + + /* function exit code */ +} + +/* "View.MemoryView":1172 + * + * @cname('__pyx_memoryview_slice_get_size') + * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) nogil: # <<<<<<<<<<<<<< + * "Return the size of the memory occupied by the slice in number of bytes" + * cdef int i + */ + +static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *__pyx_v_src, int __pyx_v_ndim) { + int __pyx_v_i; + Py_ssize_t __pyx_v_size; + Py_ssize_t __pyx_r; + Py_ssize_t __pyx_t_1; + int __pyx_t_2; + int __pyx_t_3; + int __pyx_t_4; + + /* "View.MemoryView":1175 + * "Return the size of the memory occupied by the slice in number of bytes" + * cdef int i + * cdef Py_ssize_t size = src.memview.view.itemsize # <<<<<<<<<<<<<< + * + * for i in range(ndim): + */ + __pyx_t_1 = __pyx_v_src->memview->view.itemsize; + __pyx_v_size = __pyx_t_1; + + /* "View.MemoryView":1177 + * cdef Py_ssize_t size = src.memview.view.itemsize + * + * for i in range(ndim): # <<<<<<<<<<<<<< + * size *= src.shape[i] + * + */ + __pyx_t_2 = __pyx_v_ndim; + __pyx_t_3 = __pyx_t_2; + for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) { + __pyx_v_i = __pyx_t_4; + + /* "View.MemoryView":1178 + * + * for i in range(ndim): + * size *= src.shape[i] # <<<<<<<<<<<<<< + * + * return size + */ + __pyx_v_size = (__pyx_v_size * (__pyx_v_src->shape[__pyx_v_i])); + } + + /* "View.MemoryView":1180 + * size *= src.shape[i] + * + * return size # <<<<<<<<<<<<<< + * + * @cname('__pyx_fill_contig_strides_array') + */ + __pyx_r = __pyx_v_size; + goto __pyx_L0; + + /* "View.MemoryView":1172 + * + * @cname('__pyx_memoryview_slice_get_size') + * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) nogil: # <<<<<<<<<<<<<< + * "Return the size of the memory occupied by the slice in number of bytes" + * cdef int i + */ + + /* function exit code */ + __pyx_L0:; + return __pyx_r; +} + +/* "View.MemoryView":1183 + * + * @cname('__pyx_fill_contig_strides_array') + * cdef Py_ssize_t fill_contig_strides_array( # <<<<<<<<<<<<<< + * Py_ssize_t *shape, Py_ssize_t *strides, Py_ssize_t stride, + * int ndim, char order) nogil: + */ + +static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *__pyx_v_shape, Py_ssize_t *__pyx_v_strides, Py_ssize_t __pyx_v_stride, int __pyx_v_ndim, char __pyx_v_order) { + int __pyx_v_idx; + Py_ssize_t __pyx_r; + int __pyx_t_1; + int __pyx_t_2; + int __pyx_t_3; + int __pyx_t_4; + + /* "View.MemoryView":1192 + * cdef int idx + * + * if order == 'F': # <<<<<<<<<<<<<< + * for idx in range(ndim): + * strides[idx] = stride + */ + __pyx_t_1 = ((__pyx_v_order == 'F') != 0); + if (__pyx_t_1) { + + /* "View.MemoryView":1193 + * + * if order == 'F': + * for idx in range(ndim): # <<<<<<<<<<<<<< + * strides[idx] = stride + * stride = stride * shape[idx] + */ + __pyx_t_2 = __pyx_v_ndim; + __pyx_t_3 = __pyx_t_2; + for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) { + __pyx_v_idx = __pyx_t_4; + + /* "View.MemoryView":1194 + * if order == 'F': + * for idx in range(ndim): + * strides[idx] = stride # <<<<<<<<<<<<<< + * stride = stride * shape[idx] + * else: + */ + (__pyx_v_strides[__pyx_v_idx]) = __pyx_v_stride; + + /* "View.MemoryView":1195 + * for idx in range(ndim): + * strides[idx] = stride + * stride = stride * shape[idx] # <<<<<<<<<<<<<< + * else: + * for idx in range(ndim - 1, -1, -1): + */ + __pyx_v_stride = (__pyx_v_stride * (__pyx_v_shape[__pyx_v_idx])); + } + + /* "View.MemoryView":1192 + * cdef int idx + * + * if order == 'F': # <<<<<<<<<<<<<< + * for idx in range(ndim): + * strides[idx] = stride + */ + goto __pyx_L3; + } + + /* "View.MemoryView":1197 + * stride = stride * shape[idx] + * else: + * for idx in range(ndim - 1, -1, -1): # <<<<<<<<<<<<<< + * strides[idx] = stride + * stride = stride * shape[idx] + */ + /*else*/ { + for (__pyx_t_2 = (__pyx_v_ndim - 1); __pyx_t_2 > -1; __pyx_t_2-=1) { + __pyx_v_idx = __pyx_t_2; + + /* "View.MemoryView":1198 + 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1); + + /* "View.MemoryView":1332 + * refcount_copying(&dst, dtype_is_object, ndim, True) + * + * free(tmpdata) # <<<<<<<<<<<<<< + * return 0 + * + */ + free(__pyx_v_tmpdata); + + /* "View.MemoryView":1333 + * + * free(tmpdata) + * return 0 # <<<<<<<<<<<<<< + * + * @cname('__pyx_memoryview_broadcast_leading') + */ + __pyx_r = 0; + goto __pyx_L0; + + /* "View.MemoryView":1264 + * + * @cname('__pyx_memoryview_copy_contents') + * cdef int memoryview_copy_contents(__Pyx_memviewslice src, # <<<<<<<<<<<<<< + * __Pyx_memviewslice dst, + * int src_ndim, int dst_ndim, + */ + + /* function exit code */ + __pyx_L1_error:; + { + #ifdef WITH_THREAD + PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure(); + #endif + __Pyx_AddTraceback("View.MemoryView.memoryview_copy_contents", __pyx_clineno, __pyx_lineno, __pyx_filename); + #ifdef WITH_THREAD + __Pyx_PyGILState_Release(__pyx_gilstate_save); + #endif + } + __pyx_r = -1; + __pyx_L0:; + return __pyx_r; +} + +/* "View.MemoryView":1336 + * + 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itemsize, void *item, + * bint dtype_is_object) nogil: + */ + + /* function exit code */ +} + +/* "View.MemoryView":1403 + * + * @cname('__pyx_memoryview__slice_assign_scalar') + * cdef void _slice_assign_scalar(char *data, Py_ssize_t *shape, # <<<<<<<<<<<<<< + * Py_ssize_t *strides, int ndim, + * size_t itemsize, void *item) nogil: + */ + +static void __pyx_memoryview__slice_assign_scalar(char *__pyx_v_data, Py_ssize_t *__pyx_v_shape, Py_ssize_t *__pyx_v_strides, int __pyx_v_ndim, size_t __pyx_v_itemsize, void *__pyx_v_item) { + CYTHON_UNUSED Py_ssize_t __pyx_v_i; + Py_ssize_t __pyx_v_stride; + Py_ssize_t __pyx_v_extent; + int __pyx_t_1; + Py_ssize_t __pyx_t_2; + Py_ssize_t __pyx_t_3; + Py_ssize_t __pyx_t_4; + + /* "View.MemoryView":1407 + * size_t itemsize, void *item) nogil: + * cdef Py_ssize_t i + * cdef Py_ssize_t stride = strides[0] # <<<<<<<<<<<<<< + * cdef Py_ssize_t extent = shape[0] + * + */ + __pyx_v_stride = (__pyx_v_strides[0]); + + /* "View.MemoryView":1408 + * cdef 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bad; + return o; + bad: + Py_DECREF(o); o = 0; + return NULL; +} + +static void __pyx_tp_dealloc_array(PyObject *o) { + struct __pyx_array_obj *p = (struct __pyx_array_obj *)o; + #if CYTHON_USE_TP_FINALIZE + if (unlikely(PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE) && Py_TYPE(o)->tp_finalize) && (!PyType_IS_GC(Py_TYPE(o)) || !_PyGC_FINALIZED(o))) { + if (PyObject_CallFinalizerFromDealloc(o)) return; + } + #endif + { + PyObject *etype, *eval, *etb; + PyErr_Fetch(&etype, &eval, &etb); + ++Py_REFCNT(o); + __pyx_array___dealloc__(o); + --Py_REFCNT(o); + PyErr_Restore(etype, eval, etb); + } + Py_CLEAR(p->mode); + Py_CLEAR(p->_format); + (*Py_TYPE(o)->tp_free)(o); +} +static PyObject *__pyx_sq_item_array(PyObject *o, Py_ssize_t i) { + PyObject *r; + PyObject *x = PyInt_FromSsize_t(i); if(!x) return 0; + r = Py_TYPE(o)->tp_as_mapping->mp_subscript(o, x); + Py_DECREF(x); + return r; +} + +static int __pyx_mp_ass_subscript_array(PyObject *o, PyObject *i, PyObject *v) { + if (v) { + return __pyx_array___setitem__(o, i, v); + } + else { + PyErr_Format(PyExc_NotImplementedError, + "Subscript deletion not supported by %.200s", Py_TYPE(o)->tp_name); + return -1; + } +} + +static PyObject *__pyx_tp_getattro_array(PyObject *o, PyObject *n) { + PyObject *v = __Pyx_PyObject_GenericGetAttr(o, n); + if (!v && PyErr_ExceptionMatches(PyExc_AttributeError)) { + PyErr_Clear(); + v = __pyx_array___getattr__(o, n); + } + return v; +} + +static PyObject *__pyx_getprop___pyx_array_memview(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_5array_7memview_1__get__(o); +} + +static PyMethodDef __pyx_methods_array[] = { + {"__getattr__", (PyCFunction)__pyx_array___getattr__, METH_O|METH_COEXIST, 0}, + {"__reduce_cython__", (PyCFunction)__pyx_pw___pyx_array_1__reduce_cython__, METH_NOARGS, 0}, + {"__setstate_cython__", (PyCFunction)__pyx_pw___pyx_array_3__setstate_cython__, METH_O, 0}, + {0, 0, 0, 0} +}; + +static struct PyGetSetDef __pyx_getsets_array[] = { 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/*tp_methods*/ + 0, /*tp_members*/ + __pyx_getsets_array, /*tp_getset*/ + 0, /*tp_base*/ + 0, /*tp_dict*/ + 0, /*tp_descr_get*/ + 0, /*tp_descr_set*/ + 0, /*tp_dictoffset*/ + 0, /*tp_init*/ + 0, /*tp_alloc*/ + __pyx_tp_new_array, /*tp_new*/ + 0, /*tp_free*/ + 0, /*tp_is_gc*/ + 0, /*tp_bases*/ + 0, /*tp_mro*/ + 0, /*tp_cache*/ + 0, /*tp_subclasses*/ + 0, /*tp_weaklist*/ + 0, /*tp_del*/ + 0, /*tp_version_tag*/ + #if PY_VERSION_HEX >= 0x030400a1 + 0, /*tp_finalize*/ + #endif +}; + +static PyObject *__pyx_tp_new_Enum(PyTypeObject *t, CYTHON_UNUSED PyObject *a, CYTHON_UNUSED PyObject *k) { + struct __pyx_MemviewEnum_obj *p; + PyObject *o; + if (likely((t->tp_flags & Py_TPFLAGS_IS_ABSTRACT) == 0)) { + o = (*t->tp_alloc)(t, 0); + } else { + o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_empty_tuple, 0); + } + if (unlikely(!o)) return 0; + p = ((struct __pyx_MemviewEnum_obj *)o); + p->name = Py_None; Py_INCREF(Py_None); + return o; +} + +static void __pyx_tp_dealloc_Enum(PyObject *o) { + struct __pyx_MemviewEnum_obj *p = (struct __pyx_MemviewEnum_obj *)o; + #if CYTHON_USE_TP_FINALIZE + if (unlikely(PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE) && Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) { + if (PyObject_CallFinalizerFromDealloc(o)) return; + } + #endif + PyObject_GC_UnTrack(o); + Py_CLEAR(p->name); + (*Py_TYPE(o)->tp_free)(o); +} + +static int __pyx_tp_traverse_Enum(PyObject *o, visitproc v, void *a) { + int e; + struct __pyx_MemviewEnum_obj *p = (struct __pyx_MemviewEnum_obj *)o; + if (p->name) { + e = (*v)(p->name, a); if (e) return e; + } + return 0; +} + +static int __pyx_tp_clear_Enum(PyObject *o) { + PyObject* tmp; + struct __pyx_MemviewEnum_obj *p = (struct __pyx_MemviewEnum_obj *)o; + tmp = ((PyObject*)p->name); + p->name = Py_None; Py_INCREF(Py_None); + Py_XDECREF(tmp); + return 0; +} + +static PyMethodDef __pyx_methods_Enum[] = { + {"__reduce_cython__", (PyCFunction)__pyx_pw___pyx_MemviewEnum_1__reduce_cython__, METH_NOARGS, 0}, + {"__setstate_cython__", (PyCFunction)__pyx_pw___pyx_MemviewEnum_3__setstate_cython__, METH_O, 0}, + {0, 0, 0, 0} +}; + +static PyTypeObject __pyx_type___pyx_MemviewEnum = { + PyVarObject_HEAD_INIT(0, 0) + "yank.multistate.mixing._mix_replicas.Enum", /*tp_name*/ + sizeof(struct __pyx_MemviewEnum_obj), /*tp_basicsize*/ + 0, /*tp_itemsize*/ + __pyx_tp_dealloc_Enum, /*tp_dealloc*/ + 0, /*tp_print*/ + 0, /*tp_getattr*/ + 0, /*tp_setattr*/ + #if PY_MAJOR_VERSION < 3 + 0, /*tp_compare*/ + #endif + #if PY_MAJOR_VERSION >= 3 + 0, /*tp_as_async*/ + #endif + __pyx_MemviewEnum___repr__, /*tp_repr*/ + 0, /*tp_as_number*/ + 0, /*tp_as_sequence*/ + 0, /*tp_as_mapping*/ + 0, /*tp_hash*/ + 0, /*tp_call*/ + 0, /*tp_str*/ + 0, /*tp_getattro*/ + 0, /*tp_setattro*/ + 0, /*tp_as_buffer*/ + Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/ + 0, /*tp_doc*/ + __pyx_tp_traverse_Enum, /*tp_traverse*/ + __pyx_tp_clear_Enum, /*tp_clear*/ + 0, /*tp_richcompare*/ + 0, /*tp_weaklistoffset*/ + 0, /*tp_iter*/ + 0, /*tp_iternext*/ + __pyx_methods_Enum, /*tp_methods*/ + 0, /*tp_members*/ + 0, /*tp_getset*/ + 0, /*tp_base*/ + 0, /*tp_dict*/ + 0, /*tp_descr_get*/ + 0, /*tp_descr_set*/ + 0, /*tp_dictoffset*/ + __pyx_MemviewEnum___init__, /*tp_init*/ + 0, /*tp_alloc*/ + __pyx_tp_new_Enum, /*tp_new*/ + 0, /*tp_free*/ + 0, /*tp_is_gc*/ + 0, /*tp_bases*/ + 0, /*tp_mro*/ + 0, /*tp_cache*/ + 0, /*tp_subclasses*/ + 0, /*tp_weaklist*/ + 0, /*tp_del*/ + 0, /*tp_version_tag*/ + #if PY_VERSION_HEX >= 0x030400a1 + 0, /*tp_finalize*/ + #endif +}; +static struct __pyx_vtabstruct_memoryview __pyx_vtable_memoryview; + +static PyObject *__pyx_tp_new_memoryview(PyTypeObject *t, PyObject *a, PyObject *k) { + struct __pyx_memoryview_obj *p; + PyObject *o; + if (likely((t->tp_flags & Py_TPFLAGS_IS_ABSTRACT) == 0)) { + o = (*t->tp_alloc)(t, 0); + } else { + o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_empty_tuple, 0); + } + if (unlikely(!o)) return 0; + p = ((struct __pyx_memoryview_obj *)o); + p->__pyx_vtab = __pyx_vtabptr_memoryview; + p->obj = Py_None; Py_INCREF(Py_None); + p->_size = Py_None; Py_INCREF(Py_None); + p->_array_interface = Py_None; Py_INCREF(Py_None); + p->view.obj = NULL; + if (unlikely(__pyx_memoryview___cinit__(o, a, k) < 0)) goto bad; + return o; + bad: + Py_DECREF(o); o = 0; + return NULL; +} + +static void __pyx_tp_dealloc_memoryview(PyObject *o) { + struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o; + #if CYTHON_USE_TP_FINALIZE + if (unlikely(PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE) && Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) { + if (PyObject_CallFinalizerFromDealloc(o)) return; + } + #endif + PyObject_GC_UnTrack(o); + { + PyObject *etype, *eval, *etb; + PyErr_Fetch(&etype, &eval, &etb); + ++Py_REFCNT(o); + __pyx_memoryview___dealloc__(o); + --Py_REFCNT(o); + PyErr_Restore(etype, eval, etb); + } + Py_CLEAR(p->obj); + Py_CLEAR(p->_size); + Py_CLEAR(p->_array_interface); + (*Py_TYPE(o)->tp_free)(o); +} + +static int __pyx_tp_traverse_memoryview(PyObject *o, visitproc v, void *a) { + int e; + struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o; + if (p->obj) { + e = (*v)(p->obj, a); if (e) return e; + } + if (p->_size) { + e = (*v)(p->_size, a); if (e) return e; + } + if (p->_array_interface) { + e = (*v)(p->_array_interface, a); if (e) return e; + } + if (p->view.obj) { + e = (*v)(p->view.obj, a); if (e) return e; + } + return 0; +} + +static int __pyx_tp_clear_memoryview(PyObject *o) { + PyObject* tmp; + struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o; + tmp = ((PyObject*)p->obj); + p->obj = Py_None; Py_INCREF(Py_None); + Py_XDECREF(tmp); + tmp = ((PyObject*)p->_size); + p->_size = Py_None; Py_INCREF(Py_None); + Py_XDECREF(tmp); + tmp = ((PyObject*)p->_array_interface); + p->_array_interface = Py_None; Py_INCREF(Py_None); + Py_XDECREF(tmp); + Py_CLEAR(p->view.obj); + return 0; +} +static PyObject *__pyx_sq_item_memoryview(PyObject *o, Py_ssize_t i) { + PyObject *r; + PyObject *x = PyInt_FromSsize_t(i); if(!x) return 0; + r = Py_TYPE(o)->tp_as_mapping->mp_subscript(o, x); + Py_DECREF(x); + return r; +} + +static int __pyx_mp_ass_subscript_memoryview(PyObject *o, PyObject *i, PyObject *v) { + if (v) { + return __pyx_memoryview___setitem__(o, i, v); + } + else { + PyErr_Format(PyExc_NotImplementedError, + "Subscript deletion not supported by %.200s", Py_TYPE(o)->tp_name); + return -1; + } +} + +static PyObject *__pyx_getprop___pyx_memoryview_T(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_1T_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_base(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_4base_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_shape(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_5shape_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_strides(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_7strides_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_suboffsets(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_10suboffsets_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_ndim(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_4ndim_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_itemsize(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_8itemsize_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_nbytes(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_6nbytes_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_size(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_4size_1__get__(o); +} + +static PyMethodDef __pyx_methods_memoryview[] = { + {"is_c_contig", (PyCFunction)__pyx_memoryview_is_c_contig, METH_NOARGS, 0}, + {"is_f_contig", (PyCFunction)__pyx_memoryview_is_f_contig, METH_NOARGS, 0}, + {"copy", (PyCFunction)__pyx_memoryview_copy, METH_NOARGS, 0}, + {"copy_fortran", (PyCFunction)__pyx_memoryview_copy_fortran, METH_NOARGS, 0}, + {"__reduce_cython__", (PyCFunction)__pyx_pw___pyx_memoryview_1__reduce_cython__, METH_NOARGS, 0}, + {"__setstate_cython__", (PyCFunction)__pyx_pw___pyx_memoryview_3__setstate_cython__, METH_O, 0}, + {0, 0, 0, 0} +}; + +static struct PyGetSetDef __pyx_getsets_memoryview[] = { + {(char *)"T", __pyx_getprop___pyx_memoryview_T, 0, (char *)0, 0}, + {(char *)"base", __pyx_getprop___pyx_memoryview_base, 0, (char *)0, 0}, + {(char *)"shape", __pyx_getprop___pyx_memoryview_shape, 0, (char *)0, 0}, + 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__pyx_mp_ass_subscript_memoryview, /*mp_ass_subscript*/ +}; + +static PyBufferProcs __pyx_tp_as_buffer_memoryview = { + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getreadbuffer*/ + #endif + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getwritebuffer*/ + #endif + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getsegcount*/ + #endif + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getcharbuffer*/ + #endif + __pyx_memoryview_getbuffer, /*bf_getbuffer*/ + 0, /*bf_releasebuffer*/ +}; + +static PyTypeObject __pyx_type___pyx_memoryview = { + PyVarObject_HEAD_INIT(0, 0) + "yank.multistate.mixing._mix_replicas.memoryview", /*tp_name*/ + sizeof(struct __pyx_memoryview_obj), /*tp_basicsize*/ + 0, /*tp_itemsize*/ + __pyx_tp_dealloc_memoryview, /*tp_dealloc*/ + 0, /*tp_print*/ + 0, /*tp_getattr*/ + 0, /*tp_setattr*/ + #if PY_MAJOR_VERSION < 3 + 0, /*tp_compare*/ + #endif + #if PY_MAJOR_VERSION >= 3 + 0, /*tp_as_async*/ + #endif + __pyx_memoryview___repr__, /*tp_repr*/ + 0, /*tp_as_number*/ + &__pyx_tp_as_sequence_memoryview, 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/*tp_del*/ + 0, /*tp_version_tag*/ + #if PY_VERSION_HEX >= 0x030400a1 + 0, /*tp_finalize*/ + #endif +}; +static struct __pyx_vtabstruct__memoryviewslice __pyx_vtable__memoryviewslice; + +static PyObject *__pyx_tp_new__memoryviewslice(PyTypeObject *t, PyObject *a, PyObject *k) { + struct __pyx_memoryviewslice_obj *p; + PyObject *o = __pyx_tp_new_memoryview(t, a, k); + if (unlikely(!o)) return 0; + p = ((struct __pyx_memoryviewslice_obj *)o); + p->__pyx_base.__pyx_vtab = (struct __pyx_vtabstruct_memoryview*)__pyx_vtabptr__memoryviewslice; + p->from_object = Py_None; Py_INCREF(Py_None); + p->from_slice.memview = NULL; + return o; +} + +static void __pyx_tp_dealloc__memoryviewslice(PyObject *o) { + struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o; + #if CYTHON_USE_TP_FINALIZE + if (unlikely(PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE) && Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) { + if (PyObject_CallFinalizerFromDealloc(o)) return; + } + #endif + PyObject_GC_UnTrack(o); + { + PyObject *etype, *eval, *etb; + PyErr_Fetch(&etype, &eval, &etb); + ++Py_REFCNT(o); + __pyx_memoryviewslice___dealloc__(o); + --Py_REFCNT(o); + PyErr_Restore(etype, eval, etb); + } + Py_CLEAR(p->from_object); + PyObject_GC_Track(o); + __pyx_tp_dealloc_memoryview(o); +} + +static int __pyx_tp_traverse__memoryviewslice(PyObject *o, visitproc v, void *a) { + int e; + struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o; + e = __pyx_tp_traverse_memoryview(o, v, a); if (e) return e; + if (p->from_object) { + e = (*v)(p->from_object, a); if (e) return e; + } + return 0; +} + +static int __pyx_tp_clear__memoryviewslice(PyObject *o) { + PyObject* tmp; + struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o; + __pyx_tp_clear_memoryview(o); + tmp = ((PyObject*)p->from_object); + p->from_object = Py_None; Py_INCREF(Py_None); + Py_XDECREF(tmp); + __PYX_XDEC_MEMVIEW(&p->from_slice, 1); + return 0; +} + +static PyObject *__pyx_getprop___pyx_memoryviewslice_base(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_16_memoryviewslice_4base_1__get__(o); +} + +static PyMethodDef __pyx_methods__memoryviewslice[] = { + {"__reduce_cython__", (PyCFunction)__pyx_pw___pyx_memoryviewslice_1__reduce_cython__, METH_NOARGS, 0}, + {"__setstate_cython__", (PyCFunction)__pyx_pw___pyx_memoryviewslice_3__setstate_cython__, METH_O, 0}, + {0, 0, 0, 0} +}; + +static struct PyGetSetDef __pyx_getsets__memoryviewslice[] = { + {(char *)"base", __pyx_getprop___pyx_memoryviewslice_base, 0, (char *)0, 0}, + {0, 0, 0, 0, 0} +}; + +static PyTypeObject __pyx_type___pyx_memoryviewslice = { + PyVarObject_HEAD_INIT(0, 0) + "yank.multistate.mixing._mix_replicas._memoryviewslice", /*tp_name*/ + sizeof(struct __pyx_memoryviewslice_obj), /*tp_basicsize*/ + 0, /*tp_itemsize*/ + __pyx_tp_dealloc__memoryviewslice, /*tp_dealloc*/ + 0, /*tp_print*/ + 0, /*tp_getattr*/ + 0, /*tp_setattr*/ + #if PY_MAJOR_VERSION < 3 + 0, /*tp_compare*/ + #endif + #if PY_MAJOR_VERSION >= 3 + 0, /*tp_as_async*/ + #endif + #if CYTHON_COMPILING_IN_PYPY + __pyx_memoryview___repr__, /*tp_repr*/ + #else + 0, /*tp_repr*/ + #endif + 0, /*tp_as_number*/ + 0, /*tp_as_sequence*/ + 0, /*tp_as_mapping*/ + 0, /*tp_hash*/ + 0, /*tp_call*/ + #if CYTHON_COMPILING_IN_PYPY + __pyx_memoryview___str__, /*tp_str*/ + #else + 0, /*tp_str*/ + #endif + 0, /*tp_getattro*/ + 0, /*tp_setattro*/ + 0, /*tp_as_buffer*/ + Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/ + "Internal class for passing memoryview slices to Python", /*tp_doc*/ + __pyx_tp_traverse__memoryviewslice, /*tp_traverse*/ + __pyx_tp_clear__memoryviewslice, /*tp_clear*/ + 0, /*tp_richcompare*/ + 0, /*tp_weaklistoffset*/ + 0, /*tp_iter*/ + 0, /*tp_iternext*/ + __pyx_methods__memoryviewslice, /*tp_methods*/ + 0, /*tp_members*/ + __pyx_getsets__memoryviewslice, /*tp_getset*/ + 0, 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+ + /* "View.MemoryView":316 + * DEF THREAD_LOCKS_PREALLOCATED = 8 + * cdef int __pyx_memoryview_thread_locks_used = 0 + * cdef PyThread_type_lock[THREAD_LOCKS_PREALLOCATED] __pyx_memoryview_thread_locks = [ # <<<<<<<<<<<<<< + * PyThread_allocate_lock(), + * PyThread_allocate_lock(), + */ + __pyx_t_2[0] = PyThread_allocate_lock(); + __pyx_t_2[1] = PyThread_allocate_lock(); + __pyx_t_2[2] = PyThread_allocate_lock(); + __pyx_t_2[3] = PyThread_allocate_lock(); + __pyx_t_2[4] = PyThread_allocate_lock(); + __pyx_t_2[5] = PyThread_allocate_lock(); + __pyx_t_2[6] = PyThread_allocate_lock(); + __pyx_t_2[7] = PyThread_allocate_lock(); + memcpy(&(__pyx_memoryview_thread_locks[0]), __pyx_t_2, sizeof(__pyx_memoryview_thread_locks[0]) * (8)); + + /* "View.MemoryView":544 + * info.obj = self + * + * __pyx_getbuffer = capsule( &__pyx_memoryview_getbuffer, "getbuffer(obj, view, flags)") # <<<<<<<<<<<<<< + * + * + */ + __pyx_t_1 = __pyx_capsule_create(((void *)(&__pyx_memoryview_getbuffer)), ((char *)"getbuffer(obj, view, flags)")); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 544, __pyx_L1_error) + __Pyx_GOTREF(__pyx_t_1); + if (PyDict_SetItem((PyObject *)__pyx_memoryview_type->tp_dict, __pyx_n_s_pyx_getbuffer, __pyx_t_1) < 0) __PYX_ERR(1, 544, __pyx_L1_error) + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; + PyType_Modified(__pyx_memoryview_type); + + /* "View.MemoryView":990 + * return self.from_object + * + * __pyx_getbuffer = capsule( &__pyx_memoryview_getbuffer, "getbuffer(obj, view, flags)") # <<<<<<<<<<<<<< + * + * + */ + __pyx_t_1 = __pyx_capsule_create(((void *)(&__pyx_memoryview_getbuffer)), ((char *)"getbuffer(obj, view, flags)")); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 990, __pyx_L1_error) + __Pyx_GOTREF(__pyx_t_1); + if (PyDict_SetItem((PyObject *)__pyx_memoryviewslice_type->tp_dict, __pyx_n_s_pyx_getbuffer, __pyx_t_1) < 0) __PYX_ERR(1, 990, __pyx_L1_error) + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; + PyType_Modified(__pyx_memoryviewslice_type); + + /* "(tree fragment)":1 + * def __pyx_unpickle_Enum(__pyx_type, long __pyx_checksum, __pyx_state): # <<<<<<<<<<<<<< + * if __pyx_checksum != 0xb068931: + * from pickle import PickleError as __pyx_PickleError + */ + __pyx_t_1 = PyCFunction_NewEx(&__pyx_mdef_15View_dot_MemoryView_1__pyx_unpickle_Enum, NULL, __pyx_n_s_View_MemoryView); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 1, __pyx_L1_error) + __Pyx_GOTREF(__pyx_t_1); + if (PyDict_SetItem(__pyx_d, __pyx_n_s_pyx_unpickle_Enum, __pyx_t_1) < 0) __PYX_ERR(1, 1, __pyx_L1_error) + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; + + /* "(tree fragment)":9 + * __pyx_unpickle_Enum__set_state( __pyx_result, __pyx_state) + * return __pyx_result + * cdef __pyx_unpickle_Enum__set_state(Enum __pyx_result, tuple __pyx_state): # <<<<<<<<<<<<<< + * __pyx_result.name = __pyx_state[0] + * if len(__pyx_state) > 1 and hasattr(__pyx_result, '__dict__'): + */ + + /*--- Wrapped vars code ---*/ + + goto __pyx_L0; + __pyx_L1_error:; + __Pyx_XDECREF(__pyx_t_1); + if (__pyx_m) { + if (__pyx_d) { + __Pyx_AddTraceback("init yank.multistate.mixing._mix_replicas", __pyx_clineno, __pyx_lineno, __pyx_filename); + } + Py_CLEAR(__pyx_m); + } else if (!PyErr_Occurred()) { + PyErr_SetString(PyExc_ImportError, "init yank.multistate.mixing._mix_replicas"); + } + __pyx_L0:; + __Pyx_RefNannyFinishContext(); + #if CYTHON_PEP489_MULTI_PHASE_INIT + return (__pyx_m != NULL) ? 0 : -1; + #elif PY_MAJOR_VERSION >= 3 + return __pyx_m; + #else + return; + #endif +} + +/* --- Runtime support code --- */ +/* Refnanny */ +#if CYTHON_REFNANNY +static __Pyx_RefNannyAPIStruct *__Pyx_RefNannyImportAPI(const char *modname) { + PyObject *m = NULL, *p = NULL; + void *r = NULL; + m = PyImport_ImportModule(modname); + if (!m) goto end; + p = PyObject_GetAttrString(m, "RefNannyAPI"); + if (!p) goto end; + r = PyLong_AsVoidPtr(p); +end: + Py_XDECREF(p); + Py_XDECREF(m); + return (__Pyx_RefNannyAPIStruct *)r; +} +#endif + +/* PyObjectGetAttrStr */ +#if CYTHON_USE_TYPE_SLOTS +static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStr(PyObject* obj, PyObject* attr_name) { + PyTypeObject* tp = Py_TYPE(obj); + if (likely(tp->tp_getattro)) + return tp->tp_getattro(obj, attr_name); +#if PY_MAJOR_VERSION < 3 + if (likely(tp->tp_getattr)) + return tp->tp_getattr(obj, PyString_AS_STRING(attr_name)); +#endif + return PyObject_GetAttr(obj, attr_name); +} +#endif + +/* GetBuiltinName */ +static PyObject *__Pyx_GetBuiltinName(PyObject *name) { + PyObject* result = __Pyx_PyObject_GetAttrStr(__pyx_b, name); + if (unlikely(!result)) { + PyErr_Format(PyExc_NameError, +#if PY_MAJOR_VERSION >= 3 + "name '%U' is not defined", name); +#else + "name '%.200s' is not defined", PyString_AS_STRING(name)); +#endif + } + return result; +} + +/* 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_CheckExact(key)) || 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 + (PyUnicode_GET_SIZE(**name) != PyUnicode_GET_SIZE(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 + (PyUnicode_GET_SIZE(**argname) != PyUnicode_GET_SIZE(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; +} + +/* None */ +static CYTHON_INLINE void __Pyx_RaiseUnboundLocalError(const char *varname) { + PyErr_Format(PyExc_UnboundLocalError, "local variable '%s' referenced before assignment", varname); +} + +/* MemviewSliceInit */ +static int +__Pyx_init_memviewslice(struct __pyx_memoryview_obj *memview, + int ndim, + __Pyx_memviewslice *memviewslice, + int memview_is_new_reference) +{ + __Pyx_RefNannyDeclarations + int i, retval=-1; + Py_buffer *buf = &memview->view; + __Pyx_RefNannySetupContext("init_memviewslice", 0); + if (!buf) { + PyErr_SetString(PyExc_ValueError, + "buf is NULL."); + goto fail; + } else if (memviewslice->memview || memviewslice->data) { + PyErr_SetString(PyExc_ValueError, + "memviewslice is already initialized!"); + goto fail; + } + if (buf->strides) { + for (i = 0; i < ndim; i++) { + memviewslice->strides[i] = buf->strides[i]; + } + } else { + Py_ssize_t stride = buf->itemsize; + for (i = ndim - 1; i >= 0; i--) { + memviewslice->strides[i] = stride; + stride *= buf->shape[i]; + } + } + for (i = 0; i < ndim; i++) { + memviewslice->shape[i] = buf->shape[i]; + if (buf->suboffsets) { + memviewslice->suboffsets[i] = buf->suboffsets[i]; + } else { + memviewslice->suboffsets[i] = -1; + } + } + memviewslice->memview = memview; + memviewslice->data = (char *)buf->buf; + if (__pyx_add_acquisition_count(memview) == 0 && !memview_is_new_reference) { + Py_INCREF(memview); + } + retval = 0; + goto no_fail; +fail: + memviewslice->memview = 0; + memviewslice->data = 0; + retval = -1; +no_fail: + __Pyx_RefNannyFinishContext(); + return retval; +} +#ifndef Py_NO_RETURN +#define Py_NO_RETURN +#endif +static void __pyx_fatalerror(const char *fmt, ...) Py_NO_RETURN { + va_list vargs; + char msg[200]; +#ifdef HAVE_STDARG_PROTOTYPES + va_start(vargs, fmt); +#else + va_start(vargs); +#endif + vsnprintf(msg, 200, fmt, vargs); + va_end(vargs); + Py_FatalError(msg); +} +static CYTHON_INLINE int +__pyx_add_acquisition_count_locked(__pyx_atomic_int *acquisition_count, + PyThread_type_lock lock) +{ + int result; + PyThread_acquire_lock(lock, 1); + result = (*acquisition_count)++; + PyThread_release_lock(lock); + return result; +} +static CYTHON_INLINE int +__pyx_sub_acquisition_count_locked(__pyx_atomic_int *acquisition_count, + PyThread_type_lock lock) +{ + int result; + PyThread_acquire_lock(lock, 1); + result = (*acquisition_count)--; + PyThread_release_lock(lock); + return result; +} +static CYTHON_INLINE void +__Pyx_INC_MEMVIEW(__Pyx_memviewslice *memslice, int have_gil, int lineno) +{ + int first_time; + struct __pyx_memoryview_obj *memview = memslice->memview; + if (!memview || (PyObject *) memview == Py_None) + return; + if (__pyx_get_slice_count(memview) < 0) + __pyx_fatalerror("Acquisition count is %d (line %d)", + __pyx_get_slice_count(memview), lineno); + first_time = __pyx_add_acquisition_count(memview) == 0; + if (first_time) { + if (have_gil) { + Py_INCREF((PyObject *) memview); + } else { + PyGILState_STATE _gilstate = PyGILState_Ensure(); + Py_INCREF((PyObject *) memview); + PyGILState_Release(_gilstate); + } + } +} +static CYTHON_INLINE void __Pyx_XDEC_MEMVIEW(__Pyx_memviewslice *memslice, + int have_gil, int lineno) { + int last_time; + struct __pyx_memoryview_obj *memview = memslice->memview; + if (!memview ) { + return; + } else if ((PyObject *) memview == Py_None) { + memslice->memview = NULL; + return; + } + if (__pyx_get_slice_count(memview) <= 0) + __pyx_fatalerror("Acquisition count is %d (line %d)", + __pyx_get_slice_count(memview), lineno); + last_time = __pyx_sub_acquisition_count(memview) == 1; + memslice->data = NULL; + if (last_time) { + if (have_gil) { + Py_CLEAR(memslice->memview); + } else { + PyGILState_STATE _gilstate = PyGILState_Ensure(); + Py_CLEAR(memslice->memview); + PyGILState_Release(_gilstate); + } + } else { + memslice->memview = NULL; + } +} + +/* ArgTypeTest */ +static int __Pyx__ArgTypeTest(PyObject *obj, PyTypeObject *type, const char *name, int exact) +{ + if (unlikely(!type)) { + PyErr_SetString(PyExc_SystemError, "Missing type object"); + return 0; + } + else if (exact) { + #if PY_MAJOR_VERSION == 2 + if ((type == &PyBaseString_Type) && likely(__Pyx_PyBaseString_CheckExact(obj))) return 1; + #endif + } + else { + if (likely(__Pyx_TypeCheck(obj, type))) return 1; + } + PyErr_Format(PyExc_TypeError, + "Argument '%.200s' has incorrect type (expected %.200s, got %.200s)", + name, type->tp_name, Py_TYPE(obj)->tp_name); + return 0; +} + +/* 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 + +/* 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 + +/* 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 + +/* 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, int 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, nargs, + k, (int)nk, + d, (int)nd, kwdefs, closure); +#else + result = PyEval_EvalCodeEx(co, globals, (PyObject *)NULL, + args, 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 (PyCFunction_GET_FLAGS(func) & METH_FASTCALL) { + 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 + +/* BytesEquals */ +static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals) { +#if CYTHON_COMPILING_IN_PYPY + return PyObject_RichCompareBool(s1, s2, equals); +#else + if (s1 == s2) { + return (equals == Py_EQ); + } else if (PyBytes_CheckExact(s1) & PyBytes_CheckExact(s2)) { + const char *ps1, *ps2; + Py_ssize_t length = PyBytes_GET_SIZE(s1); + if (length != PyBytes_GET_SIZE(s2)) + return (equals == Py_NE); + ps1 = PyBytes_AS_STRING(s1); + ps2 = PyBytes_AS_STRING(s2); + if (ps1[0] != ps2[0]) { + return (equals == Py_NE); + } else if (length == 1) { + return (equals == Py_EQ); + } else { + int result; +#if CYTHON_USE_UNICODE_INTERNALS + Py_hash_t hash1, hash2; + hash1 = ((PyBytesObject*)s1)->ob_shash; + hash2 = ((PyBytesObject*)s2)->ob_shash; + if (hash1 != hash2 && hash1 != -1 && hash2 != -1) { + return (equals == Py_NE); + } +#endif + result = memcmp(ps1, ps2, (size_t)length); + return (equals == Py_EQ) ? (result == 0) : (result != 0); + } + } else if ((s1 == Py_None) & PyBytes_CheckExact(s2)) { + return (equals == Py_NE); + } else if ((s2 == Py_None) & PyBytes_CheckExact(s1)) { + return (equals == Py_NE); + } else { + int result; + PyObject* py_result = PyObject_RichCompare(s1, s2, equals); + if (!py_result) + return -1; + result = __Pyx_PyObject_IsTrue(py_result); + Py_DECREF(py_result); + return result; + } +#endif +} + +/* UnicodeEquals */ +static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals) { +#if CYTHON_COMPILING_IN_PYPY + return PyObject_RichCompareBool(s1, s2, equals); +#else +#if PY_MAJOR_VERSION < 3 + PyObject* owned_ref = NULL; +#endif + int s1_is_unicode, s2_is_unicode; + if (s1 == s2) { + goto return_eq; + } + s1_is_unicode = PyUnicode_CheckExact(s1); + s2_is_unicode = PyUnicode_CheckExact(s2); +#if PY_MAJOR_VERSION < 3 + if ((s1_is_unicode & (!s2_is_unicode)) && PyString_CheckExact(s2)) { + owned_ref = PyUnicode_FromObject(s2); + if (unlikely(!owned_ref)) + return -1; + s2 = owned_ref; + s2_is_unicode = 1; + } else if ((s2_is_unicode & (!s1_is_unicode)) && PyString_CheckExact(s1)) { + owned_ref = PyUnicode_FromObject(s1); + if (unlikely(!owned_ref)) + return -1; + s1 = owned_ref; + s1_is_unicode = 1; + } else if (((!s2_is_unicode) & (!s1_is_unicode))) { + return __Pyx_PyBytes_Equals(s1, s2, equals); + } +#endif + if (s1_is_unicode & s2_is_unicode) { + Py_ssize_t length; + int kind; + void *data1, *data2; + if (unlikely(__Pyx_PyUnicode_READY(s1) < 0) || unlikely(__Pyx_PyUnicode_READY(s2) < 0)) + return -1; + length = __Pyx_PyUnicode_GET_LENGTH(s1); + if (length != __Pyx_PyUnicode_GET_LENGTH(s2)) { + goto return_ne; + } +#if CYTHON_USE_UNICODE_INTERNALS + { + Py_hash_t hash1, hash2; + #if CYTHON_PEP393_ENABLED + hash1 = ((PyASCIIObject*)s1)->hash; + hash2 = ((PyASCIIObject*)s2)->hash; + #else + hash1 = ((PyUnicodeObject*)s1)->hash; + hash2 = ((PyUnicodeObject*)s2)->hash; + #endif + if (hash1 != hash2 && hash1 != -1 && hash2 != -1) { + goto return_ne; + } + } +#endif + kind = __Pyx_PyUnicode_KIND(s1); + if (kind != __Pyx_PyUnicode_KIND(s2)) { + goto return_ne; + } + data1 = __Pyx_PyUnicode_DATA(s1); + data2 = __Pyx_PyUnicode_DATA(s2); + if (__Pyx_PyUnicode_READ(kind, data1, 0) != __Pyx_PyUnicode_READ(kind, data2, 0)) { + goto return_ne; + } else if (length == 1) { + goto return_eq; + } else { + int result = memcmp(data1, data2, (size_t)(length * kind)); + #if PY_MAJOR_VERSION < 3 + Py_XDECREF(owned_ref); + #endif + return (equals == Py_EQ) ? (result == 0) : (result != 0); + } + } else if ((s1 == Py_None) & s2_is_unicode) { + goto return_ne; + } else if ((s2 == Py_None) & s1_is_unicode) { + goto return_ne; + } else { + int result; + PyObject* py_result = PyObject_RichCompare(s1, s2, equals); + #if PY_MAJOR_VERSION < 3 + Py_XDECREF(owned_ref); + #endif + if (!py_result) + return -1; + result = __Pyx_PyObject_IsTrue(py_result); + Py_DECREF(py_result); + return result; + } +return_eq: + #if PY_MAJOR_VERSION < 3 + Py_XDECREF(owned_ref); + #endif + return (equals == Py_EQ); +return_ne: + #if PY_MAJOR_VERSION < 3 + Py_XDECREF(owned_ref); + #endif + return (equals == Py_NE); +#endif +} + +/* None */ +static CYTHON_INLINE Py_ssize_t __Pyx_div_Py_ssize_t(Py_ssize_t a, Py_ssize_t b) { + Py_ssize_t q = a / b; + Py_ssize_t r = a - q*b; + q -= ((r != 0) & ((r ^ b) < 0)); + return q; +} + +/* GetAttr */ +static CYTHON_INLINE PyObject *__Pyx_GetAttr(PyObject *o, PyObject *n) { +#if CYTHON_USE_TYPE_SLOTS +#if PY_MAJOR_VERSION >= 3 + if (likely(PyUnicode_Check(n))) +#else + if (likely(PyString_Check(n))) +#endif + return __Pyx_PyObject_GetAttrStr(o, n); +#endif + return PyObject_GetAttr(o, n); +} + +/* 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 + +/* decode_c_string */ +static CYTHON_INLINE PyObject* __Pyx_decode_c_string( + const char* cstring, Py_ssize_t start, Py_ssize_t stop, + const char* encoding, const char* errors, + PyObject* (*decode_func)(const char *s, Py_ssize_t size, const char *errors)) { + Py_ssize_t length; + if (unlikely((start < 0) | (stop < 0))) { + size_t slen = strlen(cstring); + if (unlikely(slen > (size_t) PY_SSIZE_T_MAX)) { + PyErr_SetString(PyExc_OverflowError, + "c-string too long to convert to Python"); + return NULL; + } + length = (Py_ssize_t) slen; + if (start < 0) { + start += length; + if (start < 0) + start = 0; + } + if (stop < 0) + stop += length; + } + length = stop - start; + if (unlikely(length <= 0)) + return PyUnicode_FromUnicode(NULL, 0); + cstring += start; + if (decode_func) { + return decode_func(cstring, length, errors); + } else { + return PyUnicode_Decode(cstring, length, encoding, errors); + } +} + +/* 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 + +/* GetAttr3 */ +static PyObject *__Pyx_GetAttr3Default(PyObject *d) { + __Pyx_PyThreadState_declare + __Pyx_PyThreadState_assign + if (unlikely(!__Pyx_PyErr_ExceptionMatches(PyExc_AttributeError))) + return NULL; + __Pyx_PyErr_Clear(); + Py_INCREF(d); + return d; +} +static CYTHON_INLINE PyObject *__Pyx_GetAttr3(PyObject *o, PyObject *n, PyObject *d) { + PyObject *r = __Pyx_GetAttr(o, n); + return (likely(r)) ? r : __Pyx_GetAttr3Default(d); +} + +/* 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); +} + +/* RaiseTooManyValuesToUnpack */ +static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) { + PyErr_Format(PyExc_ValueError, + "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected); +} + +/* RaiseNeedMoreValuesToUnpack */ +static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) { + PyErr_Format(PyExc_ValueError, + "need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack", + index, (index == 1) ? "" : "s"); +} + +/* RaiseNoneIterError */ +static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) { + PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable"); +} + +/* 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; +} + +/* 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 + +/* 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; +} + +/* SwapException */ +#if CYTHON_FAST_THREAD_STATE +static CYTHON_INLINE void __Pyx__ExceptionSwap(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 + *type = tmp_type; + *value = tmp_value; + *tb = tmp_tb; +} +#else +static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb) { + PyObject *tmp_type, *tmp_value, *tmp_tb; + PyErr_GetExcInfo(&tmp_type, &tmp_value, &tmp_tb); + PyErr_SetExcInfo(*type, *value, *tb); + *type = tmp_type; + *value = tmp_value; + *tb = tmp_tb; +} +#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 (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; +} + +/* 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 ? PyObject_IsSubclass(err, exc_type1) : 0; + if (unlikely(res == -1)) { + PyErr_WriteUnraisable(err); + res = 0; + } + if (!res) { + res = PyObject_IsSubclass(err, exc_type2); + if (unlikely(res == -1)) { + PyErr_WriteUnraisable(err); + res = 0; + } + } + __Pyx_ErrRestore(exception, value, tb); + return res; +} +#else +static CYTHON_INLINE int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject *exc_type2) { + int res = exc_type1 ? __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type1) : 0; + if (!res) { + res = __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type2); + } + return res; +} +#endif +static int __Pyx_PyErr_GivenExceptionMatchesTuple(PyObject *exc_type, PyObject *tuple) { + Py_ssize_t i, n; + assert(PyExceptionClass_Check(exc_type)); + n = PyTuple_GET_SIZE(tuple); +#if PY_MAJOR_VERSION >= 3 + for (i=0; i= 0 || (x^b) >= 0)) + return PyInt_FromLong(x); + return PyLong_Type.tp_as_number->nb_add(op1, op2); + } + #endif + #if CYTHON_USE_PYLONG_INTERNALS + if (likely(PyLong_CheckExact(op1))) { + const long b = intval; + long a, x; +#ifdef HAVE_LONG_LONG + const PY_LONG_LONG llb = intval; + PY_LONG_LONG lla, llx; +#endif + const digit* digits = ((PyLongObject*)op1)->ob_digit; + const Py_ssize_t size = Py_SIZE(op1); + if (likely(__Pyx_sst_abs(size) <= 1)) { + a = likely(size) ? digits[0] : 0; + if (size == -1) a = -a; + } else { + switch (size) { + case -2: + if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { + a = -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; +#ifdef HAVE_LONG_LONG + } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) { + lla = -(PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); + goto long_long; +#endif + } + CYTHON_FALLTHROUGH; + case 2: + if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { + a = (long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; +#ifdef HAVE_LONG_LONG + } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) { + lla = (PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); + goto long_long; +#endif + } + CYTHON_FALLTHROUGH; + case -3: + if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { + a = -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; +#ifdef HAVE_LONG_LONG + } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) { + lla = -(PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); + goto long_long; +#endif + } + CYTHON_FALLTHROUGH; + case 3: + if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { + a = (long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; +#ifdef HAVE_LONG_LONG + } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) { + lla = (PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); + goto long_long; +#endif + } + CYTHON_FALLTHROUGH; + case -4: + if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { + a = -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; +#ifdef HAVE_LONG_LONG + } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) { + lla = -(PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); + goto long_long; +#endif + } + CYTHON_FALLTHROUGH; + case 4: + if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { + a = (long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; +#ifdef HAVE_LONG_LONG + } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) { + lla = (PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); + goto long_long; +#endif + } + CYTHON_FALLTHROUGH; + default: return PyLong_Type.tp_as_number->nb_add(op1, op2); + } + } + x = a + b; + return PyLong_FromLong(x); +#ifdef HAVE_LONG_LONG + long_long: + llx = lla + llb; + return PyLong_FromLongLong(llx); +#endif + + + } + #endif + if (PyFloat_CheckExact(op1)) { + const long b = intval; + double a = PyFloat_AS_DOUBLE(op1); + double result; + PyFPE_START_PROTECT("add", return NULL) + result = ((double)a) + (double)b; + PyFPE_END_PROTECT(result) + return PyFloat_FromDouble(result); + } + return (inplace ? PyNumber_InPlaceAdd : PyNumber_Add)(op1, op2); +} +#endif + +/* None */ +static CYTHON_INLINE long __Pyx_div_long(long a, long b) { + long q = a / b; + long r = a - q*b; + q -= ((r != 0) & ((r ^ b) < 0)); + return q; +} + +/* WriteUnraisableException */ +static void __Pyx_WriteUnraisable(const char *name, CYTHON_UNUSED int clineno, + CYTHON_UNUSED int lineno, CYTHON_UNUSED const char *filename, + int full_traceback, CYTHON_UNUSED int nogil) { + PyObject *old_exc, *old_val, *old_tb; + PyObject *ctx; + __Pyx_PyThreadState_declare +#ifdef WITH_THREAD + PyGILState_STATE state; + if (nogil) + state = PyGILState_Ensure(); +#ifdef _MSC_VER + else state = (PyGILState_STATE)-1; +#endif +#endif + __Pyx_PyThreadState_assign + __Pyx_ErrFetch(&old_exc, &old_val, &old_tb); + if (full_traceback) { + Py_XINCREF(old_exc); + Py_XINCREF(old_val); + Py_XINCREF(old_tb); + __Pyx_ErrRestore(old_exc, old_val, old_tb); + PyErr_PrintEx(1); + } + #if PY_MAJOR_VERSION < 3 + ctx = PyString_FromString(name); + #else + ctx = PyUnicode_FromString(name); + #endif + __Pyx_ErrRestore(old_exc, old_val, old_tb); + if (!ctx) { + PyErr_WriteUnraisable(Py_None); + } else { + PyErr_WriteUnraisable(ctx); + Py_DECREF(ctx); + } +#ifdef WITH_THREAD + if (nogil) + PyGILState_Release(state); +#endif +} + +/* ImportFrom */ +static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name) { + PyObject* value = __Pyx_PyObject_GetAttrStr(module, name); + if (unlikely(!value) && PyErr_ExceptionMatches(PyExc_AttributeError)) { + PyErr_Format(PyExc_ImportError, + #if PY_MAJOR_VERSION < 3 + "cannot import name %.230s", PyString_AS_STRING(name)); + #else + "cannot import name %S", name); + #endif + } + return value; +} + +/* HasAttr */ +static CYTHON_INLINE int __Pyx_HasAttr(PyObject *o, PyObject *n) { + PyObject *r; + if (unlikely(!__Pyx_PyBaseString_Check(n))) { + PyErr_SetString(PyExc_TypeError, + "hasattr(): attribute name must be string"); + return -1; + } + r = __Pyx_GetAttr(o, n); + if (unlikely(!r)) { + PyErr_Clear(); + return 0; + } else { + Py_DECREF(r); + return 1; + } +} + +/* PyObject_GenericGetAttrNoDict */ +#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 +static PyObject *__Pyx_RaiseGenericGetAttributeError(PyTypeObject *tp, PyObject *attr_name) { + PyErr_Format(PyExc_AttributeError, +#if PY_MAJOR_VERSION >= 3 + "'%.50s' object has no attribute '%U'", + tp->tp_name, attr_name); +#else + "'%.50s' object has no attribute '%.400s'", + tp->tp_name, PyString_AS_STRING(attr_name)); +#endif + return NULL; +} +static CYTHON_INLINE PyObject* __Pyx_PyObject_GenericGetAttrNoDict(PyObject* obj, PyObject* attr_name) { + PyObject *descr; + PyTypeObject *tp = Py_TYPE(obj); + if (unlikely(!PyString_Check(attr_name))) { + return PyObject_GenericGetAttr(obj, attr_name); + } + assert(!tp->tp_dictoffset); + descr = _PyType_Lookup(tp, attr_name); + if (unlikely(!descr)) { + return __Pyx_RaiseGenericGetAttributeError(tp, attr_name); + } + Py_INCREF(descr); + #if PY_MAJOR_VERSION < 3 + if (likely(PyType_HasFeature(Py_TYPE(descr), Py_TPFLAGS_HAVE_CLASS))) + #endif + { + descrgetfunc f = Py_TYPE(descr)->tp_descr_get; + if (unlikely(f)) { + PyObject *res = f(descr, obj, (PyObject *)tp); + Py_DECREF(descr); + return res; + } + } + return descr; +} +#endif + +/* PyObject_GenericGetAttr */ +#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 +static PyObject* __Pyx_PyObject_GenericGetAttr(PyObject* obj, PyObject* attr_name) { + if (unlikely(Py_TYPE(obj)->tp_dictoffset)) { + return PyObject_GenericGetAttr(obj, attr_name); + } + return __Pyx_PyObject_GenericGetAttrNoDict(obj, attr_name); +} +#endif + +/* SetVTable */ +static int __Pyx_SetVtable(PyObject *dict, void *vtable) { +#if PY_VERSION_HEX >= 0x02070000 + PyObject *ob = PyCapsule_New(vtable, 0, 0); +#else + PyObject *ob = PyCObject_FromVoidPtr(vtable, 0); +#endif + if (!ob) + goto bad; + if (PyDict_SetItem(dict, __pyx_n_s_pyx_vtable, ob) < 0) + goto bad; + Py_DECREF(ob); + return 0; +bad: + Py_XDECREF(ob); + return -1; +} + +/* SetupReduce */ +static int __Pyx_setup_reduce_is_named(PyObject* meth, PyObject* name) { + int ret; + PyObject *name_attr; + name_attr = __Pyx_PyObject_GetAttrStr(meth, __pyx_n_s_name_2); + if (likely(name_attr)) { + ret = PyObject_RichCompareBool(name_attr, name, Py_EQ); + } else { + ret = -1; + } + if (unlikely(ret < 0)) { + PyErr_Clear(); + ret = 0; + } + Py_XDECREF(name_attr); + return ret; +} +static int __Pyx_setup_reduce(PyObject* type_obj) { + int ret = 0; + PyObject *object_reduce = NULL; + PyObject *object_reduce_ex = NULL; + PyObject *reduce = NULL; + PyObject *reduce_ex = NULL; + PyObject *reduce_cython = NULL; + PyObject *setstate = NULL; + PyObject *setstate_cython = NULL; +#if CYTHON_USE_PYTYPE_LOOKUP + if (_PyType_Lookup((PyTypeObject*)type_obj, __pyx_n_s_getstate)) goto GOOD; +#else + if (PyObject_HasAttr(type_obj, __pyx_n_s_getstate)) goto GOOD; +#endif +#if CYTHON_USE_PYTYPE_LOOKUP + object_reduce_ex = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_reduce_ex); if (!object_reduce_ex) goto BAD; +#else + object_reduce_ex = __Pyx_PyObject_GetAttrStr((PyObject*)&PyBaseObject_Type, __pyx_n_s_reduce_ex); if (!object_reduce_ex) goto BAD; +#endif + reduce_ex = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce_ex); if (unlikely(!reduce_ex)) goto BAD; + if (reduce_ex == object_reduce_ex) { +#if CYTHON_USE_PYTYPE_LOOKUP + object_reduce = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_reduce); if (!object_reduce) goto BAD; +#else + object_reduce = __Pyx_PyObject_GetAttrStr((PyObject*)&PyBaseObject_Type, __pyx_n_s_reduce); if (!object_reduce) goto BAD; +#endif + reduce = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce); if (unlikely(!reduce)) goto BAD; + if (reduce == object_reduce || __Pyx_setup_reduce_is_named(reduce, __pyx_n_s_reduce_cython)) { + reduce_cython = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce_cython); if (unlikely(!reduce_cython)) goto BAD; + ret = PyDict_SetItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_reduce, reduce_cython); if (unlikely(ret < 0)) goto BAD; + ret = PyDict_DelItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_reduce_cython); if (unlikely(ret < 0)) goto BAD; + setstate = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_setstate); + if (!setstate) PyErr_Clear(); + if (!setstate || __Pyx_setup_reduce_is_named(setstate, __pyx_n_s_setstate_cython)) { + setstate_cython = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_setstate_cython); if (unlikely(!setstate_cython)) goto BAD; + ret = PyDict_SetItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_setstate, setstate_cython); if (unlikely(ret < 0)) goto BAD; + ret = PyDict_DelItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_setstate_cython); if (unlikely(ret < 0)) goto BAD; + } + PyType_Modified((PyTypeObject*)type_obj); + } + } + goto GOOD; +BAD: + if (!PyErr_Occurred()) + PyErr_Format(PyExc_RuntimeError, "Unable to initialize pickling for %s", ((PyTypeObject*)type_obj)->tp_name); + ret = -1; +GOOD: +#if !CYTHON_USE_PYTYPE_LOOKUP + Py_XDECREF(object_reduce); + Py_XDECREF(object_reduce_ex); +#endif + Py_XDECREF(reduce); + Py_XDECREF(reduce_ex); + Py_XDECREF(reduce_cython); + Py_XDECREF(setstate); + Py_XDECREF(setstate_cython); + return ret; +} + +/* CLineInTraceback */ +#ifndef CYTHON_CLINE_IN_TRACEBACK +static int __Pyx_CLineForTraceback(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); + if (__Pyx_TypeCheck(obj, __pyx_array_type)) return __pyx_array_getbuffer(obj, view, flags); + if (__Pyx_TypeCheck(obj, __pyx_memoryview_type)) return __pyx_memoryview_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 + + +/* MemviewSliceIsContig */ +static int +__pyx_memviewslice_is_contig(const __Pyx_memviewslice mvs, char order, int ndim) +{ + int i, index, step, start; + Py_ssize_t itemsize = mvs.memview->view.itemsize; + if (order == 'F') { + step = 1; + start = 0; + } else { + step = -1; + start = ndim - 1; + } + for (i = 0; i < ndim; i++) { + index = start + step * i; + if (mvs.suboffsets[index] >= 0 || mvs.strides[index] != itemsize) + return 0; + itemsize *= mvs.shape[index]; + } + return 1; +} + +/* OverlappingSlices */ +static void +__pyx_get_array_memory_extents(__Pyx_memviewslice *slice, + void **out_start, void **out_end, + int ndim, size_t itemsize) +{ + char *start, *end; + int i; + start = end = slice->data; + for (i = 0; i < ndim; i++) { + Py_ssize_t stride = slice->strides[i]; + Py_ssize_t extent = slice->shape[i]; + if (extent == 0) { + *out_start = *out_end = start; + return; + } else { + if (stride > 0) + end += stride * (extent - 1); + else + start += stride * (extent - 1); + } + } + *out_start = start; + *out_end = end + itemsize; +} +static int +__pyx_slices_overlap(__Pyx_memviewslice *slice1, + __Pyx_memviewslice *slice2, + int ndim, size_t itemsize) +{ + void *start1, *end1, *start2, *end2; + __pyx_get_array_memory_extents(slice1, &start1, &end1, ndim, itemsize); + __pyx_get_array_memory_extents(slice2, &start2, &end2, ndim, itemsize); + return (start1 < end2) && (start2 < end1); +} + +/* Capsule */ +static CYTHON_INLINE PyObject * +__pyx_capsule_create(void *p, CYTHON_UNUSED const char *sig) +{ + PyObject *cobj; +#if PY_VERSION_HEX >= 0x02070000 + cobj = PyCapsule_New(p, sig, NULL); +#else + cobj = PyCObject_FromVoidPtr(p, NULL); +#endif + return cobj; +} + +/* 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;\ + } + +/* 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 '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 '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 '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; + int ndim = ctx->head->field->type->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; + 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 '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->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; + } + } + } +} + +/* TypeInfoCompare */ + static int +__pyx_typeinfo_cmp(__Pyx_TypeInfo *a, __Pyx_TypeInfo *b) +{ + int i; + if (!a || !b) + return 0; + if (a == b) + return 1; + if (a->size != b->size || a->typegroup != b->typegroup || + a->is_unsigned != b->is_unsigned || a->ndim != b->ndim) { + if (a->typegroup == 'H' || b->typegroup == 'H') { + return a->size == b->size; + } else { + return 0; + } + } + if (a->ndim) { + for (i = 0; i < a->ndim; i++) + if (a->arraysize[i] != b->arraysize[i]) + return 0; + } + if (a->typegroup == 'S') { + if (a->flags != b->flags) + return 0; + if (a->fields || b->fields) { + if (!(a->fields && b->fields)) + return 0; + for (i = 0; a->fields[i].type && b->fields[i].type; i++) { + __Pyx_StructField *field_a = a->fields + i; + __Pyx_StructField *field_b = b->fields + i; + if (field_a->offset != field_b->offset || + !__pyx_typeinfo_cmp(field_a->type, field_b->type)) + return 0; + } + return !a->fields[i].type && !b->fields[i].type; + } + } + return 1; +} + +/* MemviewSliceValidateAndInit */ + static int +__pyx_check_strides(Py_buffer *buf, int dim, int ndim, int spec) +{ + if (buf->shape[dim] <= 1) + return 1; + if (buf->strides) { + if (spec & __Pyx_MEMVIEW_CONTIG) { + if (spec & (__Pyx_MEMVIEW_PTR|__Pyx_MEMVIEW_FULL)) { + if (buf->strides[dim] != sizeof(void *)) { + PyErr_Format(PyExc_ValueError, + "Buffer is not indirectly contiguous " + "in dimension %d.", dim); + goto fail; + } + } else if (buf->strides[dim] != buf->itemsize) { + PyErr_SetString(PyExc_ValueError, + "Buffer and memoryview are not contiguous " + "in the same dimension."); + goto fail; + } + } + if (spec & __Pyx_MEMVIEW_FOLLOW) { + Py_ssize_t stride = buf->strides[dim]; + if (stride < 0) + stride = -stride; + if (stride < buf->itemsize) { + PyErr_SetString(PyExc_ValueError, + "Buffer and memoryview are not contiguous " + "in the same dimension."); + goto fail; + } + } + } else { + if (spec & __Pyx_MEMVIEW_CONTIG && dim != ndim - 1) { + PyErr_Format(PyExc_ValueError, + "C-contiguous buffer is not contiguous in " + "dimension %d", dim); + goto fail; + } else if (spec & (__Pyx_MEMVIEW_PTR)) { + PyErr_Format(PyExc_ValueError, + "C-contiguous buffer is not indirect in " + "dimension %d", dim); + goto fail; + } else if (buf->suboffsets) { + PyErr_SetString(PyExc_ValueError, + "Buffer exposes suboffsets but no strides"); + goto fail; + } + } + return 1; +fail: + return 0; +} +static int +__pyx_check_suboffsets(Py_buffer *buf, int dim, CYTHON_UNUSED int ndim, int spec) +{ + if (spec & __Pyx_MEMVIEW_DIRECT) { + if (buf->suboffsets && buf->suboffsets[dim] >= 0) { + PyErr_Format(PyExc_ValueError, + "Buffer not compatible with direct access " + "in dimension %d.", dim); + goto fail; + } + } + if (spec & __Pyx_MEMVIEW_PTR) { + if (!buf->suboffsets || (buf->suboffsets && buf->suboffsets[dim] < 0)) { + PyErr_Format(PyExc_ValueError, + "Buffer is not indirectly accessible " + "in dimension %d.", dim); + goto fail; + } + } + return 1; +fail: + return 0; +} +static int +__pyx_verify_contig(Py_buffer *buf, int ndim, int c_or_f_flag) +{ + int i; + if (c_or_f_flag & __Pyx_IS_F_CONTIG) { + Py_ssize_t stride = 1; + for (i = 0; i < ndim; i++) { + if (stride * buf->itemsize != buf->strides[i] && + buf->shape[i] > 1) + { + PyErr_SetString(PyExc_ValueError, + "Buffer not fortran contiguous."); + goto fail; + } + stride = stride * buf->shape[i]; + } + } else if (c_or_f_flag & __Pyx_IS_C_CONTIG) { + Py_ssize_t stride = 1; + for (i = ndim - 1; i >- 1; i--) { + if (stride * buf->itemsize != buf->strides[i] && + buf->shape[i] > 1) { + PyErr_SetString(PyExc_ValueError, + "Buffer not C contiguous."); + goto fail; + } + stride = stride * buf->shape[i]; + } + } + return 1; +fail: + return 0; +} +static int __Pyx_ValidateAndInit_memviewslice( + int *axes_specs, + int c_or_f_flag, + int buf_flags, + int ndim, + __Pyx_TypeInfo *dtype, + __Pyx_BufFmt_StackElem stack[], + __Pyx_memviewslice *memviewslice, + PyObject *original_obj) +{ + struct __pyx_memoryview_obj *memview, *new_memview; + __Pyx_RefNannyDeclarations + Py_buffer *buf; + int i, spec = 0, retval = -1; + __Pyx_BufFmt_Context ctx; + int from_memoryview = __pyx_memoryview_check(original_obj); + __Pyx_RefNannySetupContext("ValidateAndInit_memviewslice", 0); + if (from_memoryview && __pyx_typeinfo_cmp(dtype, ((struct __pyx_memoryview_obj *) + original_obj)->typeinfo)) { + memview = (struct __pyx_memoryview_obj *) original_obj; + new_memview = NULL; + } else { + memview = (struct __pyx_memoryview_obj *) __pyx_memoryview_new( + original_obj, buf_flags, 0, dtype); + new_memview = memview; + if (unlikely(!memview)) + goto fail; + } + buf = &memview->view; + if (buf->ndim != ndim) { + PyErr_Format(PyExc_ValueError, + "Buffer has wrong number of dimensions (expected %d, got %d)", + ndim, buf->ndim); + goto fail; + } + if (new_memview) { + __Pyx_BufFmt_Init(&ctx, stack, dtype); + if (!__Pyx_BufFmt_CheckString(&ctx, buf->format)) goto fail; + } + if ((unsigned) buf->itemsize != dtype->size) { + PyErr_Format(PyExc_ValueError, + "Item size of buffer (%" CYTHON_FORMAT_SSIZE_T "u byte%s) " + "does not match size of '%s' (%" CYTHON_FORMAT_SSIZE_T "u byte%s)", + buf->itemsize, + (buf->itemsize > 1) ? "s" : "", + dtype->name, + dtype->size, + (dtype->size > 1) ? "s" : ""); + goto fail; + } + for (i = 0; i < ndim; i++) { + spec = axes_specs[i]; + if (!__pyx_check_strides(buf, i, ndim, spec)) + goto fail; + if (!__pyx_check_suboffsets(buf, i, ndim, spec)) + goto fail; + } + if (buf->strides && !__pyx_verify_contig(buf, ndim, c_or_f_flag)) + goto fail; + if (unlikely(__Pyx_init_memviewslice(memview, ndim, memviewslice, + new_memview != NULL) == -1)) { + goto fail; + } + retval = 0; + goto no_fail; +fail: + Py_XDECREF(new_memview); + retval = -1; +no_fail: + __Pyx_RefNannyFinishContext(); + return retval; +} + +/* ObjectToMemviewSlice */ + static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_ds_long(PyObject *obj, int writable_flag) { + __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } }; + __Pyx_BufFmt_StackElem stack[1]; + int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED) }; + int retcode; + if (obj == Py_None) { + result.memview = (struct __pyx_memoryview_obj *) Py_None; + return result; + } + retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, 0, + PyBUF_RECORDS_RO | writable_flag, 1, + &__Pyx_TypeInfo_long, stack, + &result, obj); + if (unlikely(retcode == -1)) + goto __pyx_fail; + return result; +__pyx_fail: + result.memview = NULL; + result.data = NULL; + return result; +} + +/* ObjectToMemviewSlice */ + static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dsds_double(PyObject *obj, int writable_flag) { + __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } }; + __Pyx_BufFmt_StackElem stack[1]; + int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED) }; + int retcode; + if (obj == Py_None) { + result.memview = (struct __pyx_memoryview_obj *) Py_None; + return result; + } + retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, 0, + PyBUF_RECORDS_RO | writable_flag, 2, + &__Pyx_TypeInfo_double, stack, + &result, obj); + if (unlikely(retcode == -1)) + goto __pyx_fail; + return result; +__pyx_fail: + result.memview = NULL; + result.data = NULL; + return result; +} + +/* ObjectToMemviewSlice */ + static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dsds_long(PyObject *obj, int writable_flag) { + __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } }; + __Pyx_BufFmt_StackElem stack[1]; + int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED) }; + int retcode; + if (obj == Py_None) { + result.memview = (struct __pyx_memoryview_obj *) Py_None; + return result; + } + retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, 0, + PyBUF_RECORDS_RO | writable_flag, 2, + &__Pyx_TypeInfo_long, stack, + &result, obj); + if (unlikely(retcode == -1)) + goto __pyx_fail; + return result; +__pyx_fail: + result.memview = NULL; + result.data = NULL; + return result; +} + +/* CIntToPy */ + static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) { + const long neg_one = (long) ((long) 0 - (long) 1), const_zero = (long) 0; + 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); + } +} + +/* MemviewSliceCopyTemplate */ + static __Pyx_memviewslice +__pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs, + const char *mode, int ndim, + size_t sizeof_dtype, int contig_flag, + int dtype_is_object) +{ + __Pyx_RefNannyDeclarations + int i; + __Pyx_memviewslice new_mvs = { 0, 0, { 0 }, { 0 }, { 0 } }; + struct __pyx_memoryview_obj *from_memview = from_mvs->memview; + Py_buffer *buf = &from_memview->view; + PyObject *shape_tuple = NULL; + PyObject *temp_int = NULL; + struct __pyx_array_obj *array_obj = NULL; + struct __pyx_memoryview_obj *memview_obj = NULL; + __Pyx_RefNannySetupContext("__pyx_memoryview_copy_new_contig", 0); + for (i = 0; i < ndim; i++) { + if (from_mvs->suboffsets[i] >= 0) { + PyErr_Format(PyExc_ValueError, "Cannot copy memoryview slice with " + "indirect dimensions (axis %d)", i); + goto fail; + } + } + shape_tuple = PyTuple_New(ndim); + if (unlikely(!shape_tuple)) { + goto fail; + } + __Pyx_GOTREF(shape_tuple); + for(i = 0; i < ndim; i++) { + temp_int = PyInt_FromSsize_t(from_mvs->shape[i]); + if(unlikely(!temp_int)) { + goto fail; + } else { + PyTuple_SET_ITEM(shape_tuple, i, temp_int); + temp_int = NULL; + } + } + array_obj = __pyx_array_new(shape_tuple, sizeof_dtype, buf->format, (char *) mode, NULL); + if (unlikely(!array_obj)) { + goto fail; + } + __Pyx_GOTREF(array_obj); + memview_obj = (struct __pyx_memoryview_obj *) __pyx_memoryview_new( + (PyObject *) array_obj, contig_flag, + dtype_is_object, + from_mvs->memview->typeinfo); + if (unlikely(!memview_obj)) + goto fail; + if (unlikely(__Pyx_init_memviewslice(memview_obj, ndim, &new_mvs, 1) < 0)) + goto fail; + if (unlikely(__pyx_memoryview_copy_contents(*from_mvs, new_mvs, ndim, ndim, + dtype_is_object) < 0)) + goto fail; + goto no_fail; +fail: + __Pyx_XDECREF(new_mvs.memview); + new_mvs.memview = NULL; + new_mvs.data = NULL; +no_fail: + __Pyx_XDECREF(shape_tuple); + __Pyx_XDECREF(temp_int); + __Pyx_XDECREF(array_obj); + __Pyx_RefNannyFinishContext(); + return new_mvs; +} + +/* CIntFromPy */ + static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) { + const long neg_one = (long) ((long) 0 - (long) 1), const_zero = (long) 0; + 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; +} + +/* CIntFromPy */ + static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) { + const int neg_one = (int) ((int) 0 - (int) 1), const_zero = (int) 0; + 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_int(int value) { + const int neg_one = (int) ((int) 0 - (int) 1), const_zero = (int) 0; + 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 char __Pyx_PyInt_As_char(PyObject *x) { + const char neg_one = (char) ((char) 0 - (char) 1), const_zero = (char) 0; + const int is_unsigned = neg_one > const_zero; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_Check(x))) { + if (sizeof(char) < sizeof(long)) { + __PYX_VERIFY_RETURN_INT(char, long, PyInt_AS_LONG(x)) + } else { + long val = PyInt_AS_LONG(x); + if (is_unsigned && unlikely(val < 0)) { + goto raise_neg_overflow; + } + return (char) 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 (char) 0; + case 1: __PYX_VERIFY_RETURN_INT(char, digit, digits[0]) + case 2: + if (8 * sizeof(char) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(char) >= 2 * PyLong_SHIFT) { + return (char) (((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0])); + } + } + break; + case 3: + if (8 * sizeof(char) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(char) >= 3 * PyLong_SHIFT) { + return (char) (((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])); + } + } + break; + case 4: + if (8 * sizeof(char) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(char, 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(char) >= 4 * PyLong_SHIFT) { + return (char) (((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)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 (char) -1; + if (unlikely(result == 1)) + goto raise_neg_overflow; + } +#endif + if (sizeof(char) <= sizeof(unsigned long)) { + __PYX_VERIFY_RETURN_INT_EXC(char, unsigned long, PyLong_AsUnsignedLong(x)) +#ifdef HAVE_LONG_LONG + } else if (sizeof(char) <= sizeof(unsigned PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(char, 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 (char) 0; + case -1: __PYX_VERIFY_RETURN_INT(char, sdigit, (sdigit) (-(sdigit)digits[0])) + case 1: __PYX_VERIFY_RETURN_INT(char, digit, +digits[0]) + case -2: + if (8 * sizeof(char) - 1 > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(char) - 1 > 2 * PyLong_SHIFT) { + return (char) (((char)-1)*(((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); + } + } + break; + case 2: + if (8 * sizeof(char) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(char) - 1 > 2 * PyLong_SHIFT) { + return (char) ((((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); + } + } + break; + case -3: + if (8 * sizeof(char) - 1 > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(char) - 1 > 3 * PyLong_SHIFT) { + return (char) (((char)-1)*(((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); + } + } + break; + case 3: + if (8 * sizeof(char) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(char) - 1 > 3 * PyLong_SHIFT) { + return (char) ((((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); + } + } + break; + case -4: + if (8 * sizeof(char) - 1 > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(char, 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(char) - 1 > 4 * PyLong_SHIFT) { + return (char) (((char)-1)*(((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); + } + } + break; + case 4: + if (8 * sizeof(char) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(char, 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(char) - 1 > 4 * PyLong_SHIFT) { + return (char) ((((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); + } + } + break; + } +#endif + if (sizeof(char) <= sizeof(long)) { + __PYX_VERIFY_RETURN_INT_EXC(char, long, PyLong_AsLong(x)) +#ifdef HAVE_LONG_LONG + } else if (sizeof(char) <= sizeof(PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(char, 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 + char 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 (char) -1; + } + } else { + char val; + PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); + if (!tmp) return (char) -1; + val = __Pyx_PyInt_As_char(tmp); + Py_DECREF(tmp); + return val; + } +raise_overflow: + PyErr_SetString(PyExc_OverflowError, + "value too large to convert to char"); + return (char) -1; +raise_neg_overflow: + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to char"); + return (char) -1; +} + +/* CheckBinaryVersion */ + static int __Pyx_check_binary_version(void) { + char ctversion[4], rtversion[4]; + PyOS_snprintf(ctversion, 4, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION); + PyOS_snprintf(rtversion, 4, "%s", Py_GetVersion()); + if (ctversion[0] != rtversion[0] || ctversion[2] != rtversion[2]) { + char message[200]; + PyOS_snprintf(message, sizeof(message), + "compiletime version %s of module '%.100s' " + "does not match runtime version %s", + ctversion, __Pyx_MODULE_NAME, rtversion); + return PyErr_WarnEx(NULL, message, 1); + } + return 0; +} + +/* InitStrings */ + static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) { + while (t->p) { + #if PY_MAJOR_VERSION < 3 + if (t->is_unicode) { + *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL); + } else if (t->intern) { + *t->p = PyString_InternFromString(t->s); + } else { + *t->p = PyString_FromStringAndSize(t->s, t->n - 1); + } + #else + if (t->is_unicode | t->is_str) { + if (t->intern) { + *t->p = PyUnicode_InternFromString(t->s); + } else if (t->encoding) { + *t->p = PyUnicode_Decode(t->s, t->n - 1, t->encoding, NULL); + } else { + *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1); + } + } else { + *t->p = PyBytes_FromStringAndSize(t->s, t->n - 1); + } + #endif + if (!*t->p) + return -1; + if (PyObject_Hash(*t->p) == -1) + return -1; + ++t; + } + return 0; +} + +static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char* c_str) { + return __Pyx_PyUnicode_FromStringAndSize(c_str, (Py_ssize_t)strlen(c_str)); +} +static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject* o) { + Py_ssize_t ignore; + return __Pyx_PyObject_AsStringAndSize(o, &ignore); +} +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT +#if !CYTHON_PEP393_ENABLED +static const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) { + char* defenc_c; + PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL); + if (!defenc) return NULL; + defenc_c = PyBytes_AS_STRING(defenc); +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII + { + char* end = defenc_c + PyBytes_GET_SIZE(defenc); + char* c; + for (c = defenc_c; c < end; c++) { + if ((unsigned char) (*c) >= 128) { + PyUnicode_AsASCIIString(o); + return NULL; + } + } + } +#endif + *length = PyBytes_GET_SIZE(defenc); + return defenc_c; +} +#else +static CYTHON_INLINE const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) { + if (unlikely(__Pyx_PyUnicode_READY(o) == -1)) return NULL; +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII + if (likely(PyUnicode_IS_ASCII(o))) { + *length = PyUnicode_GET_LENGTH(o); + return PyUnicode_AsUTF8(o); + } else { + PyUnicode_AsASCIIString(o); + return NULL; + } +#else + return PyUnicode_AsUTF8AndSize(o, length); +#endif +} +#endif +#endif +static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) { +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT + if ( +#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII + __Pyx_sys_getdefaultencoding_not_ascii && +#endif + PyUnicode_Check(o)) { + return __Pyx_PyUnicode_AsStringAndSize(o, length); + } else +#endif +#if (!CYTHON_COMPILING_IN_PYPY) || (defined(PyByteArray_AS_STRING) && defined(PyByteArray_GET_SIZE)) + if (PyByteArray_Check(o)) { + *length = PyByteArray_GET_SIZE(o); + return PyByteArray_AS_STRING(o); + } else +#endif + { + char* result; + int r = PyBytes_AsStringAndSize(o, &result, length); + if (unlikely(r < 0)) { + return NULL; + } else { + return result; + } + } +} +static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) { + int is_true = x == Py_True; + if (is_true | (x == Py_False) | (x == Py_None)) return is_true; + else return PyObject_IsTrue(x); +} +static CYTHON_INLINE int __Pyx_PyObject_IsTrueAndDecref(PyObject* x) { + int retval; + if (unlikely(!x)) return -1; + retval = __Pyx_PyObject_IsTrue(x); + Py_DECREF(x); + return retval; +} +static PyObject* __Pyx_PyNumber_IntOrLongWrongResultType(PyObject* result, const char* type_name) { +#if PY_MAJOR_VERSION >= 3 + if (PyLong_Check(result)) { + if (PyErr_WarnFormat(PyExc_DeprecationWarning, 1, + "__int__ returned non-int (type %.200s). " + "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/openmmtools/multistate/mixing/_mix_replicas.pyx b/openmmtools/multistate/mixing/_mix_replicas.pyx new file mode 100644 index 000000000..8b0774afa --- /dev/null +++ b/openmmtools/multistate/mixing/_mix_replicas.pyx @@ -0,0 +1,31 @@ +cimport cython +from libc.math cimport exp, isnan +from libc.stdio cimport printf +cdef extern from 'stdlib.h' nogil: + double drand48() + +@cython.wraparound(False) +@cython.cdivision(True) +@cython.boundscheck(False) +cpdef long _mix_replicas_cython(long nswap_attempts, long nstates, long[:] replica_states, double[:,:] u_kl, long[:,:] Nij_proposed, long[:,:] Nij_accepted) nogil: + cdef long swap_attempt + cdef long i, j, istate, jstate, tmp_state + cdef double log_P_accept + for swap_attempt in range(nswap_attempts): + i = (drand48()*nstates) + j = (drand48()*nstates) + istate = replica_states[i] + jstate = replica_states[j] + if (isnan(u_kl[i, istate]) or isnan(u_kl[i, jstate]) or isnan(u_kl[j, istate]) or isnan(u_kl[j, jstate])): + continue + log_P_accept = - (u_kl[i, jstate] + u_kl[j, istate]) + (u_kl[j, jstate] + u_kl[i, istate]) + Nij_proposed[istate, jstate] +=1 + Nij_proposed[jstate, istate] +=1 + if(log_P_accept>=0 or drand48()= 0.0 || (drand48() < exp(log_P_accept))) { + // Swap states in replica slots i and j. + int tmp = REPLICA_STATES1(i); + REPLICA_STATES1(i) = REPLICA_STATES1(j); + REPLICA_STATES1(j) = tmp; + // Accumulate statistics + NIJ_ACCEPTED2(istate,jstate) += 1; + NIJ_ACCEPTED2(jstate,istate) += 1; + } + + } + """ + + # Stage input temporarily. + nstates = nstates + replica_states = replica_states + u_kl = u_kl + Nij_proposed = Nij_proposed + Nij_accepted = Nij_accepted + + # Execute inline C code with weave. + info = weave.inline(code, ['nstates', 'replica_states', 'u_kl', 'Nij_proposed', 'Nij_accepted'], headers=['', ''], verbose=0, + extra_compile_args=['-w'] # inhibit compiler warnings + ) + + # Store results. + replica_states = replica_states + Nij_proposed = Nij_proposed + Nij_accepted = Nij_accepted + + return + diff --git a/openmmtools/multistate/multistateanalyzer.py b/openmmtools/multistate/multistateanalyzer.py new file mode 100644 index 000000000..52a962794 --- /dev/null +++ b/openmmtools/multistate/multistateanalyzer.py @@ -0,0 +1,2457 @@ +#!/usr/local/bin/env python + +# ============================================================================== +# MODULE DOCSTRING +# ============================================================================== + +""" +MultiStateAnalyzers +=================== + +Analysis tools and module for MultiStateSampler simulations. Provides programmatic and automatic +"best practices" integration to determine free energy and other observables. + +Fully extensible to support new samplers and observables. + + +""" + +# ============================================================================================= +# MODULE IMPORTS +# ============================================================================================= + +import abc +import copy +import inspect +import logging +import re +from typing import Optional, NamedTuple, Union + +import mdtraj +import numpy as np +from simtk import openmm +import simtk.unit as units +from scipy.misc import logsumexp +from pymbar import MBAR, timeseries + +from openmmtools import multistate, utils, forces + +from . import utils + +ABC = abc.ABC +logger = logging.getLogger(__name__) + +__all__ = [ + 'PhaseAnalyzer', + 'MultiStateSamplerAnalyzer', + 'MultiPhaseAnalyzer', + 'ObservablesRegistry', + 'default_observables_registry' +] + +# ============================================================================================= +# GLOBAL VARIABLES +# ============================================================================================= + +kB = units.BOLTZMANN_CONSTANT_kB * units.AVOGADRO_CONSTANT_NA + +_OPENMM_ENERGY_UNIT = units.kilojoules_per_mole +_MDTRAJ_DISTANCE_UNIT = units.nanometers + + +# ============================================================================================= +# UTILITY FUNCTIONS +# ============================================================================================= + +def compute_centroid_distance(positions_group1, positions_group2, weights_group1, weights_group2): + """Compute the distance between the centers of mass of the two groups. + + The two positions given must have the same units. + + Parameters + ---------- + positions_group1 : numpy.array + The positions of the particles in the first CustomCentroidBondForce group. + positions_group2 : numpy.array + The positions of the particles in the second CustomCentroidBondForce group. + weights_group1 : list of float + The mass of the particle in the first CustomCentroidBondForce group. + weights_group2 : list of float + The mass of the particles in the second CustomCentroidBondForce group. + + """ + assert len(positions_group1) == len(weights_group1) + assert len(positions_group2) == len(weights_group2) + # Compute center of mass for each group. + com_group1 = np.average(positions_group1, axis=0, weights=weights_group1) + com_group2 = np.average(positions_group2, axis=0, weights=weights_group2) + # Compute distance between centers of mass. + distance = np.linalg.norm(com_group1 - com_group2) + return distance + + +# ============================================================================================= +# MODULE CLASSES +# ============================================================================================= + +class ObservablesRegistry(object): + """ + Registry of computable observables. + + This is a class accessed by the :class:`PhaseAnalyzer` objects to check + which observables can be computed, and then provide a regular categorization of them. + + This registry is a required linked component of any PhaseAnalyzer and especially of the MultiPhaseAnalyzer. + This is not an internal class to the PhaseAnalyzer however because it can be instanced, extended, and customized + as part of the API for this module. + + To define your own methods: + 1) Choose a unique observable name. + 2) Categorize the observable in one of the following ways by adding to the list in the "observables_X" method: + + 2a) "defined_by_phase": + Depends on the Phase as a whole (state independent) + + 2b) "defined_by_single_state": + Computed entirely from one state, e.g. Radius of Gyration + + 2c) "defined_by_two_states": + Property is relative to some reference state, such as Free Energy Difference + + 3) Optionally categorize the error category calculation in the "observables_with_error_adding_Y" methods + If not placed in an error category, the observable will be assumed not to carry error + Examples: A, B, C are the observable in 3 phases, eA, eB, eC are the error of the observable in each phase + + 3a) "linear": Error between phases adds linearly. + If C = A + B, eC = eA + eB + + 3b) "quadrature": Error between phases adds in the square. + If C = A + B, eC = sqrt(eA^2 + eB^2) + + 4) Finally, to add this observable to the phase, implement a "get_{method name}" method to the subclass of + :class:`YankPhaseAnalyzer`. Any :class:`MultiPhaseAnalyzer` composed of this phase will automatically have the + "get_{method name}" if all other phases in the :class:`MultiPhaseAnalyzer` have the same method. + """ + + def __init__(self): + """Register Defaults""" + # Create empty registry + self._observables = {'two_state': set(), + 'one_state': set(), + 'phase': set()} + self._errors = {'quad': set(), + 'linear': set(), + None: set()} + + def register_two_state_observable(self, name: str, + error_class: Optional[str]=None, + re_register: bool=False): + """ + Register a new two state observable, or re-register an existing one. + + Parameters + ---------- + name: str + Name of the observable, will be cast to all lower case and spaces replaced with underscores + error_class: "quad", "linear", or None + How the error of the observable is computed when added with other errors from the same observable. + + * "quad": Adds in the quadrature, Observable C = A + B, Error eC = sqrt(eA**2 + eB**2) + + * "linear": Adds linearly, Observable C = A + B, Error eC = eA + eB + + * None: Does not carry error + + re_register: bool, optional, Default: False + Re-register an existing observable + """ + + self._register_observable(name, "two_state", error_class, re_register=re_register) + + def register_one_state_observable(self, name: str, + error_class: Optional[str]=None, + re_register: bool=False): + """ + Register a new one state observable, or re-register an existing one. + + Parameters + ---------- + name: str + Name of the observable, will be cast to all lower case and spaces replaced with underscores + error_class: "quad", "linear", or None + How the error of the observable is computed when added with other errors from the same observable. + + * "quad": Adds in the quadrature, Observable C = A + B, Error eC = sqrt(eA**2 + eB**2) + + * "linear": Adds linearly, Observable C = A + B, Error eC = eA + eB + + * None: Does not carry error + + re_register: bool, optional, Default: False + Re-register an existing observable + """ + + self._register_observable(name, "one_state", error_class, re_register=re_register) + + def register_phase_observable(self, name: str, + error_class: Optional[str]=None, + re_register: bool=False): + """ + Register a new observable defined by phaee, or re-register an existing one. + + Parameters + ---------- + name: str + Name of the observable, will be cast to all lower case and spaces replaced with underscores + error_class: 'quad', 'linear', or None + How the error of the observable is computed when added with other errors from the same observable. + + * 'quad': Adds in the quadrature, Observable C = A + B, Error eC = sqrt(eA**2 + eB**2) + + * 'linear': Adds linearly, Observable C = A + B, Error eC = eA + eB + + * None: Does not carry error + + re_register: bool, optional, Default: False + Re-register an existing observable + + """ + + self._register_observable(name, "phase", error_class, re_register=re_register) + + ######################## + # Define the observables + ######################## + @property + def observables(self): + """ + Set of observables which are derived from the subsets below + """ + observables = set() + for subset_key in self._observables: + observables |= self._observables[subset_key] + return tuple(observables) + + # ------------------------------------------------ + # Exclusive Observable categories + # The intersection of these should be the null set + # ------------------------------------------------ + + @property + def observables_defined_by_two_states(self): + """ + Observables that require an i and a j state to define the observable accurately between phases + """ + return self._get_observables('two_state') + + @property + def observables_defined_by_single_state(self): + """ + Defined observables which are fully defined by a single state, and not by multiple states such as differences + """ + return self._get_observables('one_state') + + @property + def observables_defined_by_phase(self): + """ + Observables which are defined by the phase as a whole, and not defined by any 1 or more states + e.g. Standard State Correction + """ + return self._get_observables('phase') + + ########################################## + # Define the observables which carry error + # This should be a subset of observables + ########################################## + + @property + def observables_with_error(self): + """Determine which observables have error by inspecting the the error subsets""" + observables = set() + for subset_key in self._errors: + if subset_key is not None: + observables |= self._errors[subset_key] + return tuple(observables) + + # ------------------------------------------------ + # Exclusive Error categories + # The intersection of these should be the null set + # ------------------------------------------------ + + @property + def observables_with_error_adding_quadrature(self): + """Observable C = A + B, Error eC = sqrt(eA**2 + eB**2)""" + return self._get_errors('quad') + + @property + def observables_with_error_adding_linear(self): + """Observable C = A + B, Error eC = eA + eB""" + return self._get_errors('linear') + + @property + def observables_without_error(self): + return self._get_errors(None) + + # ------------------ + # Internal functions + # ------------------ + + def _get_observables(self, key): + return tuple(self._observables[key]) + + def _get_errors(self, key): + return tuple(self._errors[key]) + + @staticmethod + def _cast_observable_name(name) -> str: + return re.sub(" +", "_", name.lower()) + + def _register_observable(self, obs_name: str, + obs_calc_class: str, + obs_error_class: Union[None, str], + re_register: bool=False): + obs_name = self._cast_observable_name(obs_name) + if not re_register and obs_name in self.observables: + raise ValueError("{} is already a registered observable! " + "Consider setting re_register key!".format(obs_name)) + self._check_obs_class(obs_calc_class) + self._check_obs_error_class(obs_error_class) + obs_name_set = {obs_name} # set(single_object) throws an error, set(string) splits each char + # Throw out existing observable if present (set difference) + for obs_key in self._observables: + self._observables[obs_key] -= obs_name_set + for obs_err_key in self._errors: + self._errors[obs_err_key] -= obs_name_set + # Add new observable to correct classifiers (set union) + self._observables[obs_calc_class] |= obs_name_set + self._errors[obs_error_class] |= obs_name_set + + def _check_obs_class(self, obs_class): + assert obs_class in self._observables, "{} not a known observable class!".format(obs_class) + + def _check_obs_error_class(self, obs_error): + assert obs_error is None or obs_error in self._errors, \ + "{} not a known observable error class!".format(obs_error) + + +# Create a default registry and register some stock values +default_observables_registry = ObservablesRegistry() +default_observables_registry.register_two_state_observable('free_energy', error_class='quad') +default_observables_registry.register_two_state_observable('entropy', error_class='quad') +default_observables_registry.register_two_state_observable('enthalpy', error_class='quad') + + +# ----------------------------------------------------------------------------- +# EXCEPTIONS. +# ----------------------------------------------------------------------------- + +class InsufficientData(Exception): + """Raised when the data is not sufficient perform the requested analysis.""" + pass + + +# ----------------------------------------------------------------------------- +# CACHED PROPERTIES DESCRIPTOR. +# ----------------------------------------------------------------------------- + +class CachedProperty(object): + """Analyzer helper descriptor of a cached value with a dependency graph. + + Automatically takes care of invalidating the values of the cache + that depend on this property. + + Parameters + ---------- + name : str + The name of the parameter in the cache. + dependencies : iterable of str + List of cached properties on which this property depends. + check_changes : bool, optional + If True, the cache dependencies will be invalidated only if + the new value differs from the old one (default is False). + default : object, optional + The default value in case the cache doesn't contain a value + for this. If a callable, this function must have the signature + ``default(self, instance)``. It is also possible to define a + callable default through the ``default`` decorator. After the + first cache miss, the default value is cached. By default, + AttributeError is raised on a cache miss. + validator : callable, optional + A function to call before setting a new value with signature + ``validator(self, instance, new_value)``. It is also possible + to define this through the ``validator`` decorator. + + """ + def __init__(self, name, dependencies=(), check_changes=False, + default=AttributeError, validator=None): + # Reserved names. + # TODO make observables CachedProperties? + assert name != 'observables' + assert name != 'reporter' + # TODO use __setname__() when dropping Python 3.5 support. + self.name = name + self.dependencies = dependencies + self._default = default + self._validator = validator + self._check_changes = check_changes + + def __get__(self, instance, owner_class=None): + # If called as a class descriptor, return the descriptor. + if instance is None: + return self + # Check if the value is cached and fall back to default value. + try: + value = instance._cache[self.name] + except KeyError: + value = self._get_default(instance) + # Cache default value for next use. + instance._update_cache(self.name, value, self._check_changes) + return value + + def __set__(self, instance, new_value): + if self._validator is not None: + new_value = self._validator(self, instance, new_value) + instance._update_cache(self.name, new_value, self._check_changes) + + def validator(self, validator): + return type(self)(self.name, self.dependencies, self._check_changes, self._default, validator) + + def default(self, default): + return type(self)(self.name, self.dependencies, self._check_changes, default, self._validator) + + def _get_default(self, instance): + if self._default is AttributeError: + err_msg = 'Reference before assignment {}.{}'.format(instance, self.name) + raise AttributeError(err_msg) + elif callable(self._default): + value = self._default(self, instance) + else: + value = self._default + return value + + +# --------------------------------------------------------------------------------------------- +# Phase Analyzers +# --------------------------------------------------------------------------------------------- + +class PhaseAnalyzer(ABC): + """ + Analyzer for a single phase of a MultiState simulation. + + Uses the reporter from the simulation to determine the location + of all variables. + + To compute a specific observable in an implementation of this class, add it to the ObservableRegistry and then + implement a ``get_X`` where ``X`` is the name of the observable you want to compute. See the ObservablesRegistry for + information about formatting the observables. + + Analyzer works in units of kT unless specifically stated otherwise. To convert back to a unit set, just multiply by + the .kT property. + + A PhaseAnalyzer also needs an ObservablesRegistry to track how to handle each observable given implemented within + for things like error and cross-phase analysis. + + Parameters + ---------- + reporter : multistate.MultiStateReporter instance + Reporter from MultiState which ties to the simulation data on disk. + name : str, Optional + Unique name you want to assign this phase, this is the name that will appear in :class:`MultiPhaseAnalyzer`'s. + If not set, it will be given the arbitrary name "phase#" where # is an integer, chosen in order that it is + assigned to the :class:`MultiPhaseAnalyzer`. + max_n_iterations : int, optional + The maximum number of iterations to analyze. If not provided, all + the iterations will be analyzed. + reference_states : tuple of ints, length 2, Optional, Default: (0,-1) + Integers ``i`` and ``j`` of the state that is used for reference in observables, "O". These values are only used + when reporting single numbers or combining observables through :class:`MultiPhaseAnalyzer` (since the number of + states between phases can be different). Calls to functions such as ``get_free_energy`` in a single Phase + results in the O being returned for all states. + + For O completely defined by the state itself (i.e. no differences between states, e.g. Temperature), + only O[i] is used + + For O where differences between states are required (e.g. Free Energy): O[i,j] = O[j] - O[i] + + For O defined by the phase as a whole, the reference states are not needed. + + analysis_kwargs : None or dict, optional + Dictionary of extra keyword arguments to pass into the analysis tool, typically MBAR. + For instance, the initial guess of relative free energies to give to MBAR would be something like: + ``{'initial_f_k':[0,1,2,3]}`` + + use_online_data : bool, optional, Default: True + Attempt to read online analysis data as a way to hot-start the analysis computation. This will attempt to + read the data stored by the MultiStateAnalyzer. + + If this is set to ``False``, the online analysis data is not read. + + If this is set to ``False`` after being initialized, the :class:`CachedProperty` dependencies are all + invalidated and properties will be computed from scratch on next observables + + If no online data is found, this setting has no effect + + use_full_trajectory : bool, optional, Default: False + Force the analysis to use the full trajectory when automatically computing equilibration and decorrelation. + + Normal behavior (when this is False) is to discard the initial trajectory due to automatic equilibration + detection, and then subsample that data to generate decorelated samples. Setting this to ``True`` ignores + this effect, even if the equilibration data is computed. + + This can be changed after the ``PhaseAnalyzer`` has been created to re-compute properties with or without + the full trajectory. + + registry : ObservablesRegistry instance + Instanced ObservablesRegistry with all observables implemented through a ``get_X`` function classified and + registered. Any cross-phase analysis must use the same instance of an ObservablesRegistry + + Attributes + ---------- + name + observables + max_n_iterations + reference_states + n_iterations + n_replicas + n_states + kT + reporter + registry + use_online_data + use_full_trajectory + + See Also + -------- + ObservablesRegistry + + """ + def __init__(self, reporter, name=None, reference_states=(0, -1), + max_n_iterations=None, analysis_kwargs=None, + registry=default_observables_registry, + use_online_data=True, + use_full_trajectory=False): + """ + The reporter provides the hook into how to read the data, all other options control where differences are + measured from and how each phase interfaces with other phases. + """ + # Arguments validation. + if not type(reporter) is multistate.MultiStateReporter: + raise ValueError('reporter must be a MultiStateReporter instance') + if not isinstance(registry, ObservablesRegistry): + raise ValueError("Registry must be an instanced ObservablesRegistry") + if analysis_kwargs is None: + analysis_kwargs = {} + elif not isinstance(analysis_kwargs, dict): + raise ValueError('analysis_kwargs must be either None or a dictionary') + + self.registry = registry + if not reporter.is_open(): + reporter.open(mode='r') + self._reporter = reporter + + # Initialize cached observables so the phase can be retrieved once computed. + self._computed_observables = {observable: None for observable in self.observables} + + # Internal properties + self._name = name + # Start as default sign +, handle all sign conversion at preparation time + self._sign = '+' + self._reference_states = None # Initialize the cache object. + self.reference_states = reference_states + self._user_extra_analysis_kwargs = analysis_kwargs # Store the user-specified (higher priority) keywords + + # Initialize cached values that are read or derived from the Reporter. + self._cache = {} # This cache should be always set with _update_cache(). + self.clear() + self.max_n_iterations = max_n_iterations + + # Use full trajectory store or not + self.use_full_trajectory = use_full_trajectory + + # Check the online data + self._online_data = None # Init the object + self._use_online_data = use_online_data + self._read_online_data_if_present() + + def clear(self): + """Reset all cached objects. + + This must to be called if the information in the reporter changes + after analysis. + """ + # Reset cached values that are read directly from the Reporter. + self._n_iterations = None + self._n_replicas = None + self._end_thermodynamic_states = None + self._kT = None + # Reset cached values that are derived from the reporter. + self._invalidate_cache_values('reporter') + + @property + def name(self): + """User-readable string name of the phase""" + return self._name + + @name.setter + def name(self, value): + self._name = value + + @property + def observables(self): + """List of observables that the instanced analyzer can compute/fetch.""" + # Auto-determine the computable observables by inspection of non-flagged methods + # We determine valid observables by negation instead of just having each child + # implement the method to enforce uniform function naming conventions. + observables = [] + for observable in self.registry.observables: + if hasattr(self, "get_" + observable): + observables.append(observable) + # Cast observables to an immutable. + return tuple(observables) + + @property + def reference_states(self): + """Tuple of reference states ``i`` and ``j`` for :class:`MultiPhaseAnalyzer` instances""" + return self._reference_states + + @reference_states.setter + def reference_states(self, value): + """Provide a way to re-assign the ``i, j`` states in a protected way""" + i, j = value[0], value[1] + if type(i) is not int or type(j) is not int: + raise ValueError("reference_states must be a length 2 iterable of ints") + self._reference_states = (i, j) + + @property + def n_iterations(self): + """int: The total number of iterations of the phase.""" + if self._n_iterations is None: + # The + 1 accounts for iteration 0. + self._n_iterations = self._reporter.read_last_iteration(last_checkpoint=False) + return self._n_iterations + + @property + def n_replicas(self): + """int: Number of replicas.""" + if self._n_replicas is None: + replica_state_indices = self._reporter.read_replica_thermodynamic_states(iteration=0) + self._n_replicas = len(replica_state_indices) + return self._n_replicas + + @property + def n_states(self): + """int: Number of sampled thermodynamic states.""" + return self._reporter.n_states + + def _get_end_thermodynamic_states(self): + """Read thermodynamic states at the ends of the protocol. + + Returns + ------- + end_thermodynamic_states : list of ThermodynamicState + The end thermodynamic states + + """ + if self._end_thermodynamic_states is None: + self._end_thermodynamic_states = self._reporter.read_end_thermodynamic_states() + # Cache other useful informations since we have already read this. + # TODO should we read temperatures of all the states and let kT property depend on reference_states? + self._kT = self._end_thermodynamic_states[0].kT + + return self._end_thermodynamic_states + + @property + def kT(self): + """ + Quantity of boltzmann constant times temperature of the phase in units of energy per mol + + Allows conversion between dimensionless energy and unit bearing energy + """ + if self._kT is None: + self._get_end_thermodynamic_states() + return self._kT + + @property + def reporter(self): + """Sampler Reporter tied to this object.""" + return self._reporter + + @reporter.setter + def reporter(self, value): + """Make sure users cannot overwrite the reporter.""" + raise ValueError("You cannot re-assign the reporter for this analyzer!") + + @property + def use_online_data(self): + """Get the online data flag""" + return self._use_online_data + + @use_online_data.setter + def use_online_data(self, value): + """Set the online data boolean""" + if type(value) != bool: + raise ValueError("use_online_data must be a boolean!") + if self._use_online_data is False and value is True: + # Re-read the online data + self._read_online_data_if_present() + elif self._use_online_data is True and value is False: + # Invalidate online data + self._online_data = None + # Re-form dict to prevent variables from referencing the same object + self._extra_analysis_kwargs = {**self._user_extra_analysis_kwargs} + self._use_online_data = value + + # ------------------------------------------------------------------------- + # Cached properties functions/classes. + # ------------------------------------------------------------------------- + + @classmethod + def _get_cache_dependency_graph(cls): + """dict: cached_value -> list of cache values to invalidate.""" + # Retrieve all cached properties. + cached_properties = {value for name, value in inspect.getmembers(cls) + if isinstance(value, CachedProperty)} + # Build the dependency graph. + dependency_graph = {} + for cached_property in cached_properties: + for dependency in cached_property.dependencies: + try: + dependency_graph[dependency].add(cached_property.name) + except KeyError: + dependency_graph[dependency] = {cached_property.name} + # Hard-code observable dependency since those are not CachedProperties. + # TODO make observables CachedProperties? + dependency_graph['mbar'] = {'observables'} + return dependency_graph + + def _update_cache(self, key, new_value, check_changes=False): + """Update the cache entry and invalidate the values that depend on it. + + Parameters + ---------- + key : str + The name of the value to update. + new_value : object + The new value of the key. + check_changes : bool, optional + If True and the new value is equal to the current one, + the dependent cache values are not invalidated. + + """ + invalidate_cache = True + try: + old_value = self._cache[key] + except KeyError: + invalidate_cache = False + else: + if check_changes and self._check_equal(old_value, new_value): + invalidate_cache = False + # Update value and invalidate the cache. + self._cache[key] = new_value + if invalidate_cache: + self._invalidate_cache_values(key) + + def _invalidate_cache_values(self, key): + """Invalidate all the cache dependencies of key. + + Parameters + ---------- + key : str + The name of the cached whose dependencies must be invalidated. + + """ + dependency_graph = self._get_cache_dependency_graph() + for k in dependency_graph[key]: + # Invalidate observables that are in a separate cache. + if k == 'observables': + for observable in self.observables: + self._computed_observables[observable] = None + else: + # Invalidate dependencies of k. + self._invalidate_cache_values(k) + # Remove k. + self._cache.pop(k, None) + + @staticmethod + def _check_equal(old_value, new_value): + """Broader equality check""" + try: + np.testing.assert_equal(old_value, new_value) + except AssertionError: + return False + return True + + # ------------------------------------------------------------------------- + # Cached properties. + # ------------------------------------------------------------------------- + + max_n_iterations = CachedProperty('max_n_iterations', check_changes=True) + + @max_n_iterations.validator + def max_n_iterations(self, instance, new_value): + """The maximum allowed value for max_n_iterations is n_iterations.""" + if new_value is None or new_value > instance.n_iterations: + new_value = instance.n_iterations + return new_value + + use_full_trajectory = CachedProperty('use_full_trajectory', check_changes=True) + + @use_full_trajectory.validator + def use_full_trajectory(self, _, new_value): + if type(new_value) is not bool: + raise ValueError("use_full_trajectory must be a boolean!") + return new_value + + _extra_analysis_kwargs = CachedProperty('_extra_analysis_kwargs', check_changes=True) + + # ------------------------------------------------------------------------- + # Abstract methods. + # ------------------------------------------------------------------------- + + def read_energies(self): + """ + Extract energies from the ncfile and order them by replica, state, iteration. + + Returns + ------- + sampled_energy_matrix : np.ndarray of shape [n_replicas, n_states, n_iterations] + Potential energy matrix of the sampled states. + unsampled_energy_matrix : np.ndarray of shape [n_replicas, n_unsamped_states, n_iterations] + Potential energy matrix of the unsampled states. + Energy from each drawn sample n, evaluated at unsampled state l. + If no unsampled states were drawn, this will be shape (0,N). + neighborhoods : np.ndarray of shape [n_replicas, n_states, n_iterations] + Neighborhood energies were computed at, uses a boolean mask over the energy_matrix. + replica_state_indices : np.ndarray of shape [n_replicas, n_iterations] + States sampled by the replicas in the energy_matrix + + """ + # TODO: should we keep it unified and always truncate to max_n_iterations? + return self._read_energies(truncate_max_n_iterations=False) + + def _read_energies(self, truncate_max_n_iterations): + """ + Extract energies from the ncfile and order them by replica, state, iteration. + + Optionally truncate the data to self.max_n_iterations. + + """ + logger.debug("Reading energies...") + # reporter_energies is [energy_sampled_states, neighborhoods, energy_unsampled_states]. + energy_data = list(self._reporter.read_energies()) + energy_data.append(self._reporter.read_replica_thermodynamic_states()) + logger.debug("Done.") + + # Truncate the number of iterations to self.max_n_iterations if requested. + if truncate_max_n_iterations: + for i, energies in enumerate(energy_data): + # The +1 accounts for minimization iteration. + energy_data[i] = energies[:self.max_n_iterations+1] + + # Convert from (n_iterations, n_replicas, n_states) to (n_replicas, n_states, n_iterations). + for i, energies in enumerate(energy_data): + energy_data[i] = np.moveaxis(energies, 0, -1) + + # Unpack. + sampled_energy_matrix, neighborhoods, unsampled_energy_matrix, replicas_state_indices = energy_data + # TODO: Figure out what format we need the data in to be useful for both global and local MBAR/WHAM + # For now, we simply can't handle analysis of non-global calculations. + if np.any(neighborhoods == 0): + raise Exception('Non-global MBAR analysis not implemented yet.') + + return sampled_energy_matrix, unsampled_energy_matrix, neighborhoods, replicas_state_indices + + @property + def has_log_weights(self): + """ + Return True if the storage has log weights, False otherwise + """ + try: + # Check that logZ and log_weights have per-iteration data + # If either of these return a ValueError, then no history data are available + _ = self._reporter.read_logZ(0) + _ = self._reporter.read_online_analysis_data(0, 'log_weights') + return True + except ValueError: + return False + + def read_log_weights(self): + """ + Extract log weights from the ncfile, if present. + Returns ValueError if not present. + + Returns + ------- + log_weights : np.ndarray of shape [n_states, n_iterations] + log_weights[l,n] is the log weight applied to state ``l`` + during the collection of samples at iteration ``n`` + + """ + log_weights = np.array( + self._reporter.read_online_analysis_data(slice(None, None), 'log_weights')['log_weights']) + log_weights = np.moveaxis(log_weights, 0, -1) + return log_weights + + def read_logZ(self, iteration=None): + """ + Extract logZ estimates from the ncfile, if present. + Returns ValueError if not present. + + Parameters + ---------- + iteration : int or slice, optional, default=None + If specified, iteration or slice of iterations to extract + + Returns + ------- + logZ : np.ndarray of shape [n_states, n_iterations] + logZ[l,n] is the online logZ estimate for state ``l`` at iteration ``n`` + + """ + if iteration == -1: + log_z = self._reporter.read_logZ(iteration) + else: + if iteration is not None: + log_z = self._reporter.read_online_analysis_data(iteration, "logZ")["logZ"] + else: + log_z = self._reporter.read_online_analysis_data(slice(0, None), "logZ")["logZ"] + log_z = np.moveaxis(log_z, 0, -1) + return log_z + + def get_effective_energy_timeseries(self, energies=None, replica_state_indices=None): + """ + Generate the effective energy (negative log deviance) timeseries that is generated for this phase + + The effective energy for a series of samples x_n, n = 1..N, is defined as + + u_n = - \ln \pi(x_n) + c + + where \pi(x) is the probability density being sampled, and c is an arbitrary constant. + + Parameters + ---------- + energies : ndarray of shape (K,L,N), optional, Default: None + Energies from replicas K, sampled states L, and iterations N + If provided, then states input_sampled_states must also be provided + replica_state_indices : ndarray of shape (K,N), optional, Default: None + Integer indices of each sampled state (matching L dimension in input_energy) + that each replica K sampled every iteration N. + If provided, then states input_energies must also be provided + + Returns + ------- + u_n : ndarray of shape (N,) + u_n[n] is the negative log deviance of the same from iteration ``n`` + Timeseries used to determine equilibration time and statistical inefficiency. + + """ + + raise NotImplementedError("This class has not implemented this function") + + # ------------------------------------------------------------------------- + # MBAR routines. + # ------------------------------------------------------------------------- + + @staticmethod + def reformat_energies_for_mbar(u_kln: np.ndarray, n_k: Optional[np.ndarray]=None): + """ + Convert [replica, state, iteration] data into [state, total_iteration] data + + This method assumes that the first dimension are all samplers, + the second dimension are all the thermodynamic states energies were evaluated at + and an equal number of samples were drawn from each k'th sampler, UNLESS n_k is specified. + + Parameters + ---------- + u_kln : np.ndarray of shape (K,L,N') + K = number of replica samplers + L = number of thermodynamic states, + N' = number of iterations from state k + n_k : np.ndarray of shape K or None + Number of samples each _SAMPLER_ (k) has drawn + This allows you to have trailing entries on a given kth row in the n'th (n prime) index + which do not contribute to the conversion. + + If this is None, assumes ALL samplers have the same number of samples + such that N_k = N' for all k + + **WARNING**: N_k is number of samples the SAMPLER drew in total, + NOT how many samples were drawn from each thermodynamic state L. + This method knows nothing of how many samples were drawn from each state. + + Returns + ------- + u_ln : np.ndarray of shape (L, N) + Reduced, non-sparse data format + L = number of thermodynamic states + N = \sum_k N_k. note this is not N' + """ + k, l, n = u_kln.shape + if n_k is None: + n_k = np.ones(k, dtype=np.int32)*n + u_ln = np.zeros([l, n_k.sum()]) + n_counter = 0 + for k_index in range(k): + u_ln[:, n_counter:n_counter + n_k[k_index]] = u_kln[k_index, :, :n_k[k_index]] + n_counter += n_k[k_index] + return u_ln + + # Private Class Methods + def _create_mbar(self, energy_matrix, samples_per_state): + """ + Initialize MBAR for Free Energy and Enthalpy estimates, this may take a while. + This function is helpful for those who want to create a slightly different mbar object with different + parameters. + + This function is hidden from the user unless they really, really need to create their own mbar object + + Parameters + ---------- + energy_matrix : array of numpy.float64, optional, default=None + Reduced potential energies of the replicas; if None, will be extracted from the ncfile + samples_per_state : array of ints, optional, default=None + Number of samples drawn from each kth state; if None, will be extracted from the ncfile + + """ + # Initialize MBAR (computing free energy estimates, which may take a while) + logger.debug("Computing free energy differences...") + self.mbar = MBAR(energy_matrix, samples_per_state, **self._extra_analysis_kwargs) + logger.debug('Done.') + return self.mbar + + def _read_online_data_if_present(self): + """ + Attempt to read the online analysis data needed to hot-start the output + """ + try: + online_f_k = self.reporter.read_online_analysis_data(None, 'f_k')['f_k'] + self._online_data = {'initial_f_k': online_f_k} + except ValueError: + self._online_data = None + + # ------------------------------------------------------------------------- + # Analysis combination. + # ------------------------------------------------------------------------- + + def _combine_phases(self, other, operator='+'): + """ + Workhorse function when creating a :class:`MultiPhaseAnalyzer` object by combining single + :class:`PhaseAnalyzer`s + """ + phases = [self] + names = [] + signs = [self._sign] + # Reset self._sign + self._sign = '+' + if self.name is None: + names.append(utils.generate_phase_name(self.name, [])) + else: + names.append(self.name) + if isinstance(other, MultiPhaseAnalyzer): + new_phases = other.phases + new_signs = other.signs + new_names = other.names + final_new_names = [] + for name in new_names: + other_names = [n for n in new_names if n != name] + final_new_names.append(utils.generate_phase_name(name, other_names + names)) + names.extend(final_new_names) + for new_sign in new_signs: + if operator != '+' and new_sign == '+': + signs.append('-') + else: + signs.append('+') + phases.extend(new_phases) + elif isinstance(other, PhaseAnalyzer): + names.append(utils.generate_phase_name(other.name, names)) + if operator != '+' and other._sign == '+': + signs.append('-') + else: + signs.append('+') + # Reset the other's sign if it got set to negative + other._sign = '+' + phases.append(other) + else: + base_err = "cannot {} 'PhaseAnalyzer' and '{}' objects" + if operator == '+': + err = base_err.format('add', type(other)) + else: + err = base_err.format('subtract', type(other)) + raise TypeError(err) + phase_pass = {'phases': phases, 'signs': signs, 'names': names} + return MultiPhaseAnalyzer(phase_pass) + + def __add__(self, other): + return self._combine_phases(other, operator='+') + + def __sub__(self, other): + return self._combine_phases(other, operator='-') + + def __neg__(self): + """Internally handle the internal sign""" + if self._sign == '+': + self._sign = '-' + else: + self._sign = '+' + return self + + +class MultiStateSamplerAnalyzer(PhaseAnalyzer): + """ + The MultiStateSamplerAnalyzer is the analyzer for a simulation generated from a MultiStateSampler simulation, + implemented as an instance of the :class:`PhaseAnalyzer`. + + Parameters + ---------- + unbias_restraint : bool, optional + If True and a radially-symmetric restraint was used in the simulation, + the analyzer will remove the bias introduced by the restraint by + reweighting each of the end-points to a state using a square-well + potential restraint. + + restraint_energy_cutoff : float or 'auto', optional + When the restraint is unbiased, the analyzer discards all the samples + for which the restrain potential energy (in kT) is above this cutoff. + Effectively, this is equivalent to placing a hard wall potential at a + restraint distance such that the restraint potential energy is equal to + ``restraint_energy_cutoff``. + + If ``'auto'`` and ``restraint_distance_cutoff`` is ``None``, this will + be set to the 99.9-percentile of the distribution of the restraint energies + in the bound state. + + restraint_distance_cutoff : simtk.unit.Quantity or 'auto', optional + When the restraint is unbiased, the analyzer discards all the samples + for which the distance between the restrained atoms is above this cutoff. + Effectively, this is equivalent to placing a hard wall potential at a + restraint distance ``restraint_distance_cutoff``. + + If ``'auto'`` and ``restraint_energy_cutoff`` is not specified, this will + be set to the 99.9-percentile of the distribution of the restraint distances + in the bound state. + + Attributes + ---------- + unbias_restraint + restraint_energy_cutoff + restraint_distance_cutoff + mbar + n_equilibration_iterations + statistical_inefficiency + + See Also + -------- + PhaseAnalyzer + + """ + + def __init__(self, *args, unbias_restraint=True, restraint_energy_cutoff='auto', + restraint_distance_cutoff='auto', **kwargs): + # super() calls clear() that initialize the cached variables. + super().__init__(*args, **kwargs) + + # Cached values with dependencies. + self.unbias_restraint = unbias_restraint + self.restraint_energy_cutoff = restraint_energy_cutoff + self.restraint_distance_cutoff = restraint_distance_cutoff + + # TODO use class syntax and add docstring after dropping python 3.5 support. + _MixingStatistics = NamedTuple('MixingStatistics', [ + ('transition_matrix', np.ndarray), + ('eigenvalues', np.ndarray), + ('statistical_inefficiency', np.ndarray) + ]) + + def clear(self): + """Reset all cached objects. + + This must to be called if the information in the reporter changes + after analysis. + """ + # Reset cached values that are read directly from the Reporter. + # super() takes care of invalidating the cached properties. + super().clear() + self._radially_symmetric_restraint_data = None + self._restraint_energies = {} + self._restraint_distances = {} + + def generate_mixing_statistics(self, number_equilibrated: Union[int, None] = None) -> NamedTuple: + """ + Compute and return replica mixing statistics. + + Compute the transition state matrix, its eigenvalues sorted from + greatest to least, and the state index correlation function. + + Parameters + ---------- + number_equilibrated : int, optional, default=None + If specified, only samples ``number_equilibrated:end`` will + be used in analysis. If not specified, automatically retrieves + the number from equilibration data or generates it from the + internal energy. + + Returns + ------- + mixing_statistics : namedtuple + A namedtuple containing the following attributes: + - ``transition_matrix``: (nstates by nstates ``np.array``) + - ``eigenvalues``: (nstates-dimensional ``np.array``) + - ``statistical_inefficiency``: float + """ + # Read data from disk + if number_equilibrated is None: + number_equilibrated = self.n_equilibration_iterations + states = self._reporter.read_replica_thermodynamic_states() + n_states = self._reporter.n_states + n_ij = np.zeros([n_states, n_states], np.int64) + + # Compute empirical transition count matrix. + for iteration in range(number_equilibrated, self.max_n_iterations - 1): + for i_replica in range(self.n_replicas): + i_state = states[iteration, i_replica] + j_state = states[iteration + 1, i_replica] + n_ij[i_state, j_state] += 1 + + # Compute transition matrix estimate. + # TODO: Replace with maximum likelihood reversible count estimator from msmbuilder or pyemma. + t_ij = np.zeros([n_states, n_states], np.float64) + for i_state in range(n_states): + # Cast to float to ensure we don't get integer division + denominator = float((n_ij[i_state, :].sum() + n_ij[:, i_state].sum())) + if denominator > 0: + for j_state in range(n_states): + t_ij[i_state, j_state] = (n_ij[i_state, j_state] + n_ij[j_state, i_state]) / denominator + else: + t_ij[i_state, i_state] = 1.0 + + # Estimate eigenvalues + mu = np.linalg.eigvals(t_ij) + mu = -np.sort(-mu) # Sort in descending order + + # Compute state index statistical inefficiency of stationary data. + # states[n][k] is the state index of replica k at iteration n, but + # the functions wants a list of timeseries states[k][n]. + states_kn = np.transpose(states[number_equilibrated:]) + g = timeseries.statisticalInefficiencyMultiple(states_kn) + + return self._MixingStatistics(transition_matrix=t_ij, eigenvalues=mu, + statistical_inefficiency=g) + + def show_mixing_statistics(self, cutoff=0.05, number_equilibrated=None): + """ + Print summary of mixing statistics. Passes information off to generate_mixing_statistics then prints it out to + the logger + + Parameters + ---------- + cutoff : float, optional, default=0.05 + Only transition probabilities above 'cutoff' will be printed + number_equilibrated : int, optional, default=None + If specified, only samples number_equilibrated:end will be used in analysis + If not specified, it uses the internally held statistics best + + """ + + mixing_statistics = self.generate_mixing_statistics(number_equilibrated=number_equilibrated) + + # Print observed transition probabilities. + nstates = mixing_statistics.transition_matrix.shape[1] + logger.info("Cumulative symmetrized state mixing transition matrix:") + str_row = "{:6s}".format("") + for jstate in range(nstates): + str_row += "{:6d}".format(jstate) + logger.info(str_row) + + for istate in range(nstates): + str_row = "" + str_row += "{:-6d}".format(istate) + for jstate in range(nstates): + P = mixing_statistics.transition_matrix[istate, jstate] + if P >= cutoff: + str_row += "{:6.3f}".format(P) + else: + str_row += "{:6s}".format("") + logger.info(str_row) + + # Estimate second eigenvalue and equilibration time. + perron_eigenvalue = mixing_statistics.eigenvalues[1] + if perron_eigenvalue >= 1: + logger.info('Perron eigenvalue is unity; Markov chain is decomposable.') + else: + equilibration_timescale = 1.0 / (1.0 - perron_eigenvalue) + logger.info('Perron eigenvalue is {0:.5f}; state equilibration timescale ' + 'is ~ {1:.1f} iterations'.format(perron_eigenvalue, equilibration_timescale) + ) + + # Print information about replica state index statistical efficiency. + logger.info('Replica state index statistical inefficiency is ' + '{:.3f}'.format(mixing_statistics.statistical_inefficiency)) + + def _get_radially_symmetric_restraint_data(self): + """Return the radially-symmetric restraint force used in the bound state. + + Returns + ------- + restraint_force : forces.RadiallySymmetricRestraintForce + The restraint force used in the bound state. + weights_group1 : list of simtk.unit.Quantity + The masses of the restrained atoms in the first centroid group. + weights_group2 : list of simtk.unit.Quantity + The masses of the restrained atoms in the second centroid group. + + Raises + ------ + TypeError + If the end states don't have lambda_restraints set to 1. + forces.NoForceFoundError + If there are no radially-symmetric restraints in the bound state. + + """ + logger.debug('Trying to get radially symmetric restraint data...') + + # Check cached value. + if self._radially_symmetric_restraint_data is not None: + return self._radially_symmetric_restraint_data + + # Isolate the end states. + logger.debug('Retrieving end thermodynamic states...') + end_states = self._get_end_thermodynamic_states() + + # Isolate restraint force. + logger.debug('Isolating restraint force...') + system = end_states[0].system + restraint_parent_class = forces.RadiallySymmetricRestraintForce + # This raises forces.NoForceFoundError if there's no restraint to unbias. + force_idx, restraint_force = forces.find_forces(system, force_type=restraint_parent_class, + only_one=True, include_subclasses=True) + # The force is owned by the System, we have to copy to avoid the memory to be deallocated. + logger.debug('Deep copying restraint force...') + restraint_force = copy.deepcopy(restraint_force) + + # Check that the restraint was turned on at the end states. + if end_states[0].lambda_restraints != 1.0 or end_states[-1].lambda_restraints != 1.0: + raise TypeError('Cannot unbias a restraint that is turned off at one of the end states.') + + # Read the centroid weights (mass) of the restrained particles. + logger.debug('Retrieving particle masses...') + weights_group1 = [system.getParticleMass(i) for i in restraint_force.restrained_atom_indices1] + weights_group2 = [system.getParticleMass(i) for i in restraint_force.restrained_atom_indices2] + + # Cache value so that we won't have to deserialize the system again. + self._radially_symmetric_restraint_data = restraint_force, weights_group1, weights_group2 + logger.debug('Done.') + return self._radially_symmetric_restraint_data + + # ------------------------------------------------------------------------- + # MBAR creation. + # ------------------------------------------------------------------------- + + def get_effective_energy_timeseries(self, energies=None, replica_state_indices=None): + """ + Generate the effective energy (negative log deviance) timeseries that is generated for this phase. + + The effective energy for a series of samples x_n, n = 1..N, is defined as + + u_n = - \ln \pi(x_n) + c + + where \pi(x) is the probability density being sampled, and c is an arbitrary constant. + + Parameters + ---------- + energies : ndarray of shape (K,L,N), optional, Default: None + Energies from replicas K, sampled states L, and iterations N. + If provided, then states input_sampled_states must also be provided. + replica_state_indices : ndarray of shape (K,N), optional, Default: None + Integer indices of each sampled state (matching L dimension in input_energy). + that each replica K sampled every iteration N. + If provided, then states input_energies must also be provided. + + Returns + ------- + u_n : ndarray of shape (N,) + u_n[n] is the negative log deviance of the same from iteration ``n`` + Timeseries used to determine equilibration time and statistical inefficiency. + + """ + if energies is None and replica_state_indices is None: + # Case where no input is provided + energies, _, _, replica_state_indices = self._read_energies(truncate_max_n_iterations=True) + elif (energies is not None) != (replica_state_indices is not None): + # XOR operator + raise ValueError("If input_energy or input_sampled_states are provided, " + "then the other cannot be None due to ambiguity!") + + n_replicas, n_states, n_iterations = energies.shape + + logger.debug("Assembling effective timeseries...") + # Check for log weights + has_log_weights = False + if self.has_log_weights: + has_log_weights = True + log_weights = self.read_log_weights() + f_l = - self.read_logZ(iteration=-1) # use last (best) estimate of free energies + + u_n = np.zeros([n_iterations], np.float64) + # Slice of all replicas, have to use this as : is too greedy + replicas_slice = range(n_replicas) + for iteration in range(n_iterations): + # Slice the current sampled states by those replicas. + states_slice = replica_state_indices[:, iteration] + u_n[iteration] = np.sum(energies[replicas_slice, states_slice, iteration]) + + # Correct for potentially-changing log weights + if has_log_weights: + u_n[iteration] += - np.sum(log_weights[states_slice, iteration]) \ + + (n_replicas * logsumexp(-f_l[:] + log_weights[:, iteration])) + + logger.debug("Done.") + return u_n + + def _compute_mbar_decorrelated_energies(self): + """Return an MBAR-ready decorrelated energy matrix. + + The data is returned after discarding equilibration and truncating + the iterations to self.max_n_iterations. + + Returns + ------- + energy_matrix : energy matrix of shape (K,N) indexed by k,n + K is the total number of states observables are desired. + N is the total number of samples drawn from ALL states. + The nth configuration is the energy evaluated in the kth thermodynamic state. + samples_per_state : 1-D iterable of shape K + The number of samples drawn from each kth state. + The \sum samples_per_state = N. + """ + # energy_data is [energy_sampled, energy_unsampled, neighborhood, replicas_state_indices] + energy_data = list(self._read_energies(truncate_max_n_iterations=True)) + + # Use the cached information to generate the equilibration data. + sampled_energy_matrix, unsampled_energy_matrix, neighborhood, replicas_state_indices = energy_data + number_equilibrated, g_t, Neff_max = self._get_equilibration_data(sampled_energy_matrix, + replicas_state_indices) + + logger.debug("Assembling uncorrelated energies...") + + if not self.use_full_trajectory: + for i, energies in enumerate(energy_data): + # Discard equilibration iterations. + energies = utils.remove_unequilibrated_data(energies, number_equilibrated, -1) + # Subsample along the decorrelation data. + energy_data[i] = utils.subsample_data_along_axis(energies, g_t, -1) + sampled_energy_matrix, unsampled_energy_matrix, neighborhood, replicas_state_indices = energy_data + + # Initialize the MBAR matrices in ln form. + n_replicas, n_sampled_states, n_iterations = sampled_energy_matrix.shape + _, n_unsampled_states, _ = unsampled_energy_matrix.shape + n_total_states = n_sampled_states + n_unsampled_states + energy_matrix = np.zeros([n_total_states, n_iterations*n_replicas]) + samples_per_state = np.zeros([n_total_states], dtype=int) + + # Compute shift index for how many unsampled states there were. + # This assume that we set an equal number of unsampled states at the end points. + first_sampled_state = int(n_unsampled_states/2.0) + last_sampled_state = n_total_states - first_sampled_state + + # Cast the sampled energy matrix from kln' to ln form. + energy_matrix[first_sampled_state:last_sampled_state, :] = self.reformat_energies_for_mbar(sampled_energy_matrix) + # Determine how many samples and which states they were drawn from. + unique_sampled_states, counts = np.unique(replicas_state_indices, return_counts=True) + # Assign those counts to the correct range of states. + samples_per_state[first_sampled_state:last_sampled_state][unique_sampled_states] = counts + # Add energies of unsampled states to the end points. + if n_unsampled_states > 0: + energy_matrix[[0, -1], :] = self.reformat_energies_for_mbar(unsampled_energy_matrix) + logger.debug("Found expanded cutoff states in the energies!") + logger.debug("Free energies will be reported relative to them instead!") + if self.use_online_data and self._online_data is not None: + # Do online data only if present and already exists as stored in self._online_data + temp_online = copy.deepcopy(self._online_data) + f_k_i = np.zeros(n_sampled_states + n_unsampled_states) + online_f_k = temp_online['initial_f_k'] + f_k_i[first_sampled_state:last_sampled_state] = online_f_k + temp_online['initial_f_k'] = f_k_i + # Re-set the extra analysis_ + self._extra_analysis_kwargs = {**temp_online, **self._user_extra_analysis_kwargs} + else: + # Possible reset of the final key if online was true then false to invalidate any online data. + self._extra_analysis_kwargs = {**self._user_extra_analysis_kwargs} + + # These cached values speed up considerably the computation of the + # free energy profile along the restraint distance/energy cutoff. + self._decorrelated_u_ln = energy_matrix + self._decorrelated_N_l = samples_per_state + + logger.debug('Done.') + return self._decorrelated_u_ln, self._decorrelated_N_l + + def _compute_mbar_unbiased_energies(self): + """Unbias the restraint, and apply restraint energy/distance cutoffs. + + When there is a restraint to unbias, the function adds two extra unbiased + states at the end points of the energy matrix. Otherwise, the return value + is identical to self._compute_mbar_decorrelated_energies(). + + Returns + ------- + unbiased_decorrelated_u_ln : np.array + A n_states x (n_sampled_states * n_unbiased_decorrelated_iterations) + array of energies (in kT), where n_unbiased_decorrelated_iterations + is generally <= n_decorrelated_iterations whe a restraint cutoff is + set. + unbiased_decorrelated_N_l : np.array + The total number of samples drawn from each state (including the + unbiased states). + """ + logger.debug('Checking if we need to unbias the restraint...') + + # Check if we need to unbias the restraint. + unbias_restraint = self.unbias_restraint + if unbias_restraint: + try: + restraint_data = self._get_radially_symmetric_restraint_data() + except (TypeError, forces.NoForceFoundError) as e: + # If we don't need to unbias the restraint there's nothing else to do. + logger.debug(str(e) + ' The restraint will not be unbiased.') + unbias_restraint = False + if not unbias_restraint: + self._unbiased_decorrelated_u_ln = self._decorrelated_u_ln + self._unbiased_decorrelated_N_l = self._decorrelated_N_l + return self._unbiased_decorrelated_u_ln, self._unbiased_decorrelated_N_l + + # Compute the restraint energies/distances. + restraint_force, weights_group1, weights_group2 = restraint_data + logger.debug('Found {} restraint. The restraint will be unbiased.'.format(restraint_force.__class__.__name__)) + logger.debug('Receptor restrained atoms: {}'.format(restraint_force.restrained_atom_indices1)) + logger.debug('ligand restrained atoms: {}'.format(restraint_force.restrained_atom_indices2)) + + + # Compute restraint energies/distances. + logger.debug('Computing restraint energies...') + energies_ln, distances_ln = self._compute_restraint_energies(restraint_force, weights_group1, + weights_group2) + + # Convert energies to kT unit for comparison to energy cutoff. + energies_ln = energies_ln / self.kT + logger.debug('Restraint energy mean: {} kT; std: {} kT' + ''.format(np.mean(energies_ln), np.std(energies_ln, ddof=1))) + + # Don't modify the cached decorrelated energies. + u_ln = copy.deepcopy(self._decorrelated_u_ln) + N_l = copy.deepcopy(self._decorrelated_N_l) + n_decorrelated_iterations_ln = u_ln.shape[1] + assert len(energies_ln) == n_decorrelated_iterations_ln, '{}, {}'.format(energies_ln.shape, u_ln.shape) + assert len(self._decorrelated_state_indices_ln) == n_decorrelated_iterations_ln + + # Determine the cutoffs to use for the simulations. + restraint_energy_cutoff, restraint_distance_cutoff = self._get_restraint_cutoffs() + apply_energy_cutoff = restraint_energy_cutoff is not None + apply_distance_cutoff = restraint_distance_cutoff is not None + + # We need to take into account the initial unsampled states to index correctly N_l. + n_unsampled_states = len(u_ln) - self.n_states + first_sampled_state_idx = int(n_unsampled_states / 2) + + # Determine which samples are outside the cutoffs or have to be truncated. + columns_to_keep = [] + for iteration_ln_idx, state_idx in enumerate(self._decorrelated_state_indices_ln): + if ((apply_energy_cutoff and energies_ln[iteration_ln_idx] > restraint_energy_cutoff) or + (apply_distance_cutoff and distances_ln[iteration_ln_idx] > restraint_distance_cutoff)): + # Update the number of samples generated from its state. + N_l[state_idx + first_sampled_state_idx] -= 1 + else: + columns_to_keep.append(iteration_ln_idx) + + # Drop all columns that exceed the cutoff(s). + n_discarded = n_decorrelated_iterations_ln - len(columns_to_keep) + logger.debug('Discarding {}/{} samples outside the cutoffs (restraint_distance_cutoff: {}, ' + 'restraint_energy_cutoff: {}).'.format(n_discarded, n_decorrelated_iterations_ln, + restraint_distance_cutoff, + restraint_energy_cutoff)) + u_ln = u_ln[:, columns_to_keep] + + # Add new end states that don't include the restraint. + energies_ln = energies_ln[columns_to_keep] + n_states, n_iterations = u_ln.shape + n_states_new = n_states + 2 + N_l_new = np.zeros(n_states_new, N_l.dtype) + u_ln_new = np.zeros((n_states_new, n_iterations), u_ln.dtype) + u_ln_new[0, :] = u_ln[0] - energies_ln + u_ln_new[-1, :] = u_ln[-1] - energies_ln + # Copy old values. + N_l_new[1:-1] = N_l + u_ln_new[1:-1, :] = u_ln + # Expand the f_k_i if need be + try: + f_k_i_new = np.zeros(n_states_new, N_l.dtype) + f_k_i_new[1:-1] = self._extra_analysis_kwargs['initial_f_k'] # This triggers the KeyError Trap + self._extra_analysis_kwargs['initial_f_k'] = f_k_i_new # This triggers the ValueError trap + except (KeyError, ValueError): + # Not there, move on, or already set nothing to do + pass + + # Cache new values. + self._unbiased_decorrelated_u_ln = u_ln_new + self._unbiased_decorrelated_N_l = N_l_new + + logger.debug('Done.') + return self._unbiased_decorrelated_u_ln, self._unbiased_decorrelated_N_l + + def _compute_restraint_energies(self, restraint_force, weights_group1, weights_group2): + """Compute the restrain energies and distances for the uncorrelated iterations. + + Parameters + ---------- + restraint_force : forces.RadiallySymmetricRestraintForce + The restraint force. + weights_group1 : list of float + The mass of the particle in the first CustomCentroidBondForce group. + weights_group2 : list of float + The mass of the particles in the second CustomCentroidBondForce group. + + Returns + ------- + restraint_energies_ln : simtk.unit.Quantity + A (n_sampled_states * n_decorrelated_iterations)-long array with + the restrain energies (units of energy/mole). + restraint_distances_ln : simtk.unit.Quantity or None + If we are not applying a distance cutoff, this is None. Otherwise, + a (n_sampled_states * n_decorrelated_iterations)-long array with + the restrain distances (units of length) for each frame. + + """ + decorrelated_iterations = self._decorrelated_iterations # Shortcut. + decorrelated_iterations_set = set(decorrelated_iterations) + + # Determine total number of energies/distances to compute. + # The +1 is for the minimization iteration. + n_frames_ln = self.n_replicas * len(decorrelated_iterations) + + # Computing the restraint energies/distances is expensive and we + # don't want to recompute everything when _decorrelated_iterations + # changes (e.g. when max_n_iterations changes) so we keep the cached + # values of the iterations we have computed. + # The dictionary instead of a masked array is for memory efficiency + # since the matrix will be very sparse (especially with SAMS). + + def extract_decorrelated(cached_dict, dtype, unit): + if not decorrelated_iterations_set.issubset(set(cached_dict)): + return None + decorrelated = np.zeros(n_frames_ln, dtype=dtype) + for replica_idx in range(self.n_replicas): + for iteration_idx, iteration in enumerate(decorrelated_iterations): + frame_idx = replica_idx*len(decorrelated_iterations) + iteration_idx + decorrelated[frame_idx] = cached_dict[iteration][replica_idx] + return decorrelated * unit + + # We compute the distances only if we are using a distance cutoff. + _, compute_distances = self._get_use_restraint_cutoff() + + # Check cached values. + if compute_distances and decorrelated_iterations_set.issubset(set(self._restraint_distances)): + compute_distances = False + if decorrelated_iterations_set.issubset(set(self._restraint_energies)) and not compute_distances: + return (extract_decorrelated(self._restraint_energies, dtype=np.float64, unit=_OPENMM_ENERGY_UNIT), + extract_decorrelated(self._restraint_distances, dtype=np.float32, unit=_MDTRAJ_DISTANCE_UNIT)) + + # Don't modify the original restraint force. + restraint_force = copy.deepcopy(restraint_force) + is_periodic = restraint_force.usesPeriodicBoundaryConditions() + + # Store the original indices of the restrained atoms. + original_restrained_atom_indices1 = restraint_force.restrained_atom_indices1 + original_restrained_atom_indices2 = restraint_force.restrained_atom_indices2 + original_restrained_atom_indices = (original_restrained_atom_indices1 + + original_restrained_atom_indices2) + + # Create new system with only solute and restraint forces. + reduced_system = openmm.System() + for weight in weights_group1 + weights_group2: + reduced_system.addParticle(weight) + # Adapt the restraint force atom indices to the reduced system. + n_atoms1 = len(weights_group1) + n_atoms = n_atoms1 + len(weights_group2) + restraint_force.restrained_atom_indices1 = list(range(n_atoms1)) + restraint_force.restrained_atom_indices2 = list(range(n_atoms1, n_atoms)) + reduced_system.addForce(restraint_force) + + # If we need to image the molecule, we need an MDTraj trajectory. + if compute_distances and is_periodic: + # Create topology with only the restrained atoms. + serialized_topography = self._reporter.read_dict('metadata/topography') + topography = utils.deserialize(serialized_topography) + topology = topography.topology + topology = topology.subset(self._reporter.analysis_particle_indices) + # Use the receptor as an anchor molecule and image the ligand. + anchor_molecules = [{a for a in topology.atoms if a.index in set(topography.receptor_atoms)}] + imaged_molecules = [{a for a in topology.atoms if a.index in set(topography.ligand_atoms)}] + # Initialize trajectory object needed for imaging molecules. + trajectory = mdtraj.Trajectory(xyz=np.zeros((topology.n_atoms, 3)), topology=topology) + + # Create context used to compute the energies. + integrator = openmm.VerletIntegrator(1.0*units.femtosecond) + platform = openmm.Platform.getPlatformByName('CPU') + context = openmm.Context(reduced_system, integrator, platform) + + # TODO: we need to provide a reporter generator to iterate over single + # TODO: iterations but reading automatically one chunksize at a time. + # chunk_size = self._reporter.checkpoint_interval + # iterations_groups = itertools.groupby(enumerate(decorrelated_iterations), key=lambda x: int(x[1] / chunk_size)) + + # Pre-computing energies/distances. + logger.debug('Computing restraint energies/distances...') + for iteration_idx, iteration in enumerate(decorrelated_iterations): + # Check if we have already computed this energy/distance. + if (iteration in self._restraint_energies and + (not compute_distances or iteration in self._restraint_distances)): + continue + self._restraint_energies[iteration] = {} + if compute_distances: + self._restraint_distances[iteration] = {} + + # Read sampler states only if we haven't computed this iteration yet. + # Obtain solute only sampler states. + sampler_states = self._reporter.read_sampler_states(iteration=iteration, + analysis_particles_only=True) + + for replica_idx, sampler_state in enumerate(sampler_states): + sliced_sampler_state = sampler_state[original_restrained_atom_indices] + sliced_sampler_state.apply_to_context(context) + potential_energy = context.getState(getEnergy=True).getPotentialEnergy() + self._restraint_energies[iteration][replica_idx] = potential_energy / _OPENMM_ENERGY_UNIT + + if compute_distances: + # Check if an analytical solution is available. + try: + distance = restraint_force.distance_at_energy(potential_energy) / _MDTRAJ_DISTANCE_UNIT + except (NotImplementedError, ValueError): + if is_periodic: + # Update trajectory positions/box vectors. + trajectory.xyz = (sampler_state.positions / _MDTRAJ_DISTANCE_UNIT).astype(np.float32) + trajectory.unitcell_vectors = np.array([sampler_state.box_vectors / _MDTRAJ_DISTANCE_UNIT], + dtype=np.float32) + trajectory.image_molecules(inplace=True, anchor_molecules=anchor_molecules, + other_molecules=imaged_molecules) + positions_group1 = trajectory.xyz[0][original_restrained_atom_indices1] + positions_group2 = trajectory.xyz[0][original_restrained_atom_indices2] + else: + positions_group1 = sampler_state.positions[original_restrained_atom_indices1] + positions_group2 = sampler_state.positions[original_restrained_atom_indices2] + positions_group1 /= _MDTRAJ_DISTANCE_UNIT + positions_group2 /= _MDTRAJ_DISTANCE_UNIT + + # Set output arrays. + distance = compute_centroid_distance(positions_group1, positions_group2, + weights_group1, weights_group2) + self._restraint_distances[iteration][replica_idx] = distance + + return (extract_decorrelated(self._restraint_energies, dtype=np.float64, unit=_OPENMM_ENERGY_UNIT), + extract_decorrelated(self._restraint_distances, dtype=np.float32, unit=_MDTRAJ_DISTANCE_UNIT)) + + def _get_use_restraint_cutoff(self): + """Determine if we need to apply a cutoff on the restraint energies and/or distances.""" + apply_distance_cutoff = isinstance(self.restraint_distance_cutoff, units.Quantity) + apply_energy_cutoff = isinstance(self.restraint_energy_cutoff, float) + # When both cutoffs are auto, use distance cutoff. + if self.restraint_distance_cutoff == 'auto' and not apply_energy_cutoff: + apply_distance_cutoff = True + elif self.restraint_energy_cutoff == 'auto' and self.restraint_distance_cutoff is None: + apply_energy_cutoff = True + return apply_energy_cutoff, apply_distance_cutoff + + def _get_restraint_energies_distances_at_state(self, state_idx, get_energies=True, get_distances=True): + """Return the restraint energies and distances for a single state.""" + # Resolve negative indices. + if state_idx < 0: + state_idx = self.n_states + state_idx + replica_state_indices = self._reporter.read_replica_thermodynamic_states() + + # Gather the state restraint energies/distances. + state_energies = [] if get_energies else None + state_distances = [] if get_distances else None + for state_data, cached_data in [(state_energies, self._restraint_energies), + (state_distances, self._restraint_distances)]: + if state_data is None: + continue + for iteration, states_data in cached_data.items(): + # Find the replicas in this state. + replica_indices = np.where(replica_state_indices[iteration] == state_idx)[0] + for replica_idx in replica_indices: + state_data.append(states_data[replica_idx]) + + # Convert to the correct units. + if state_energies is not None: + state_energies = np.array(state_energies) * _OPENMM_ENERGY_UNIT / self.kT + if state_distances is not None: + state_distances = np.array(state_distances) * _MDTRAJ_DISTANCE_UNIT + return state_energies, state_distances + + def _determine_automatic_restraint_cutoff(self, compute_energy_cutoff=True, compute_distance_cutoff=True): + """Automatically determine the restraint cutoffs. + + This must be called after _compute_restraint_energies(). The cutoffs are + determine as the 99.9%-percentile of the distribution of the restraint + energies/distances in the bound state. + """ + # Gather the bound state restraint energies/distances. + state0_energies, state0_distances = self._get_restraint_energies_distances_at_state( + state_idx=0, get_energies=compute_energy_cutoff, get_distances=compute_distance_cutoff) + + # Compute cutoff as the 99.9%-percentile of the energies/distances distributions. + energy_cutoff = None + distance_cutoff = None + err_msg = ('Thermodynamic state 0 has not been sampled enough to ' + 'determine automatically the restraint {} cutoff.') + + if compute_energy_cutoff: + if len(state0_energies) == 0: + raise InsufficientData(err_msg.format('energy')) + energy_cutoff = np.percentile(state0_energies, 99.9) + if compute_distance_cutoff: + if len(state0_distances) == 0: + raise InsufficientData(err_msg.format('distance')) + state0_distances /= _MDTRAJ_DISTANCE_UNIT + distance_cutoff = np.percentile(state0_distances, 99.9) * _MDTRAJ_DISTANCE_UNIT + + return energy_cutoff, distance_cutoff + + def _get_restraint_cutoffs(self): + """Return the restraint energies and distance cutoff to be used for unbiasing.""" + apply_energy_cutoff, apply_distance_cutoff = self._get_use_restraint_cutoff() + # Determine automatically the restraint distance cutoff is necessary. + if apply_distance_cutoff and self.restraint_distance_cutoff == 'auto': + _, restraint_distance_cutoff = self._determine_automatic_restraint_cutoff(compute_energy_cutoff=False) + logger.debug('Chosen automatically a restraint distance cutoff of {}'.format(restraint_distance_cutoff)) + elif self.restraint_distance_cutoff == 'auto': + restraint_distance_cutoff = None + else: + restraint_distance_cutoff = self.restraint_distance_cutoff + # Determine automatically the restraint energy cutoff is necessary. + if apply_energy_cutoff and self.restraint_energy_cutoff == 'auto': + restraint_energy_cutoff, _ = self._determine_automatic_restraint_cutoff(compute_distance_cutoff=False) + logger.debug('Chosen automatically a restraint energy cutoff of {}kT'.format(restraint_energy_cutoff)) + elif self.restraint_energy_cutoff == 'auto': + restraint_energy_cutoff = None + else: + restraint_energy_cutoff = self.restraint_energy_cutoff + return restraint_energy_cutoff, restraint_distance_cutoff + + # ------------------------------------------------------------------------- + # Observables. + # ------------------------------------------------------------------------- + + def _compute_free_energy(self): + """ + Estimate free energies of all alchemical states. + """ + nstates = self.mbar.N_k.size + + # Get matrix of dimensionless free energy differences and uncertainty estimate. + logger.debug("Computing covariance matrix...") + + try: + # pymbar 2 + (Deltaf_ij, dDeltaf_ij) = self.mbar.getFreeEnergyDifferences() + except ValueError: + # pymbar 3 + (Deltaf_ij, dDeltaf_ij, _) = self.mbar.getFreeEnergyDifferences() + + # Matrix of free energy differences + logger.debug("Deltaf_ij:") + for i in range(nstates): + str_row = "" + for j in range(nstates): + str_row += "{:8.3f}".format(Deltaf_ij[i, j]) + logger.debug(str_row) + + # Matrix of uncertainties in free energy difference (expectations standard + # deviations of the estimator about the true free energy) + logger.debug("dDeltaf_ij:") + for i in range(nstates): + str_row = "" + for j in range(nstates): + str_row += "{:8.3f}".format(dDeltaf_ij[i, j]) + logger.debug(str_row) + + # Return free energy differences and an estimate of the covariance. + free_energy_dict = {'value': Deltaf_ij, 'error': dDeltaf_ij} + self._computed_observables['free_energy'] = free_energy_dict + + def get_free_energy(self): + """ + Compute the free energy and error in free energy from the MBAR object + + Output shape changes based on if there are unsampled states detected in the sampler + + Returns + ------- + DeltaF_ij : ndarray of floats, shape (K,K) or (K+2, K+2) + Difference in free energy from each state relative to each other state + dDeltaF_ij : ndarray of floats, shape (K,K) or (K+2, K+2) + Error in the difference in free energy from each state relative to each other state + """ + if self._computed_observables['free_energy'] is None: + self._compute_free_energy() + free_energy_dict = self._computed_observables['free_energy'] + return free_energy_dict['value'], free_energy_dict['error'] + + def _compute_enthalpy_and_entropy(self): + """Function to compute the cached values of enthalpy and entropy""" + (f_k, df_k, H_k, dH_k, S_k, dS_k) = self.mbar.computeEntropyAndEnthalpy() + enthalpy = {'value': H_k, 'error': dH_k} + entropy = {'value': S_k, 'error': dS_k} + self._computed_observables['enthalpy'] = enthalpy + self._computed_observables['entropy'] = entropy + + def get_enthalpy(self): + """ + Compute the difference in enthalpy and error in that estimate from the MBAR object + + Output shape changes based on if there are unsampled states detected in the sampler + + Returns + ------- + DeltaH_ij : ndarray of floats, shape (K,K) or (K+2, K+2) + Difference in enthalpy from each state relative to each other state + dDeltaH_ij : ndarray of floats, shape (K,K) or (K+2, K+2) + Error in the difference in enthalpy from each state relative to each other state + + """ + if self._computed_observables['enthalpy'] is None: + self._compute_enthalpy_and_entropy() + enthalpy_dict = self._computed_observables['enthalpy'] + return enthalpy_dict['value'], enthalpy_dict['error'] + + def get_entropy(self): + """ + Compute the difference in entropy and error in that estimate from the MBAR object + + Output shape changes based on if there are unsampled states detected in the sampler + + Returns + ------- + DeltaH_ij : ndarray of floats, shape (K,K) or (K+2, K+2) + Difference in enthalpy from each state relative to each other state + dDeltaH_ij : ndarray of floats, shape (K,K) or (K+2, K+2) + Error in the difference in enthalpy from each state relative to each other state + + """ + if self._computed_observables['entropy'] is None: + self._compute_enthalpy_and_entropy() + entropy_dict = self._computed_observables['entropy'] + return entropy_dict['value'], entropy_dict['error'] + + def _get_equilibration_data(self, energies=None, replica_state_indices=None): + """Generate the equilibration data from best practices. + + Parameters + ---------- + energies : ndarray of shape (K,L,N), optional, Default: None + Energies from replicas K, sampled states L, and iterations N. + If provided, then replica_state_indices must also be provided. + replica_state_indices : ndarray of shape (K,N), optional, Default: None + Integer indices of each sampled state (matching L dimension in input_energy). + that each replica K sampled every iteration N. + If provided, then states input_energies must also be provided. + + Returns + ------- + n_equilibration_iterations : int + statistical_inefficiency : float + n_uncorrelated_iterations : int + """ + u_n = self.get_effective_energy_timeseries(energies, replica_state_indices) + + # For SAMS, if there is a second-stage start time, use only the asymptotically optimal data + t0 = 1 # discard minimization frame + try: + iteration = len(u_n) + data = self._reporter.read_online_analysis_data(None, 't0') + t0 = max(t0, int(data['t0'][0])) + logger.debug('t0 found; using initial t0 = {} instead of 1'.format(t0)) + except Exception as e: + # No t0 found + logger.debug('Could not find t0: {}'.format(e)) + pass + + # Discard equilibration samples. + # TODO: if we include u_n[0] (the energy right after minimization) in the equilibration detection, + # TODO: then number_equilibrated is 0. Find a better way than just discarding first frame. + i_t, g_i, n_effective_i = utils.get_equilibration_data_per_sample(u_n[t0:]) + n_effective_max = n_effective_i.max() + i_max = n_effective_i.argmax() + n_equilibration = i_t[i_max] + g_t = g_i[i_max] + equilibration_data = [n_equilibration, g_t, n_effective_max] + # Account for initially discarded frames + equilibration_data[0] += t0 + self._equilibration_data = tuple(equilibration_data) + logger.debug('Equilibration data: {}'.format(equilibration_data)) + return self._equilibration_data + + # ------------------------------------------------------------------------- + # Cached properties. + # ------------------------------------------------------------------------- + + unbias_restraint = CachedProperty('unbias_restraint', check_changes=True) + restraint_energy_cutoff = CachedProperty('restraint_energy_cutoff', check_changes=True) + restraint_distance_cutoff = CachedProperty('restraint_distance_cutoff', check_changes=True) + + _equilibration_data = CachedProperty( + name='equilibration_data', + dependencies=['reporter', 'max_n_iterations'], + check_changes=True, + ) + + @_equilibration_data.default + def _equilibration_data(self, instance): + return instance._get_equilibration_data() + + _decorrelated_state_indices_ln = CachedProperty( + name='decorrelated_state_indices_ln', + dependencies=['equilibration_data', 'use_full_trajectory'], + ) + + @_decorrelated_state_indices_ln.default + def _decorrelated_state_indices_ln(self, instance): + """Compute the replica thermodynamic state indices in ln formats.""" + decorrelated_iterations = instance._decorrelated_iterations # Shortcut. + replica_state_indices = instance._reporter.read_replica_thermodynamic_states() + n_correlated_iterations, instance._n_replicas = replica_state_indices.shape + + # Initialize output array. + n_frames = instance.n_replicas * len(decorrelated_iterations) + decorrelated_state_indices_ln = np.zeros(n_frames, dtype=np.int32) + + # Map ln columns to the state. + for iteration_idx, iteration in enumerate(decorrelated_iterations): + for replica_idx in range(instance.n_replicas): + frame_idx = replica_idx*len(decorrelated_iterations) + iteration_idx + # Set output array. + state_idx = replica_state_indices[iteration, replica_idx] + decorrelated_state_indices_ln[frame_idx] = state_idx + instance._decorrelated_state_indices_ln = decorrelated_state_indices_ln + return decorrelated_state_indices_ln + + _decorrelated_u_ln = CachedProperty( + name='decorrelated_u_ln', + dependencies=['equilibration_data', 'use_full_trajectory'], + ) + + @_decorrelated_u_ln.default + def _decorrelated_u_ln(self, instance): + return instance._compute_mbar_decorrelated_energies()[0] + + _decorrelated_N_l = CachedProperty( + name='decorrelated_N_l', + dependencies=['equilibration_data', 'use_full_trajectory'], + ) + + @_decorrelated_N_l.default + def _decorrelated_N_l(self, instance): + return instance._compute_mbar_decorrelated_energies()[1] + + _unbiased_decorrelated_u_ln = CachedProperty( + name='unbiased_decorrelated_u_ln', + dependencies=['unbias_restraint', 'restraint_energy_cutoff', 'restraint_distance_cutoff', + 'decorrelated_state_indices_ln', 'decorrelated_u_ln', 'decorrelated_N_l'], + ) + + @_unbiased_decorrelated_u_ln.default + def _unbiased_decorrelated_u_ln(self, instance): + return instance._compute_mbar_unbiased_energies()[0] + + _unbiased_decorrelated_N_l = CachedProperty( + name='unbiased_decorrelated_N_l', + dependencies=['unbias_restraint', 'restraint_energy_cutoff', 'restraint_distance_cutoff', + 'decorrelated_state_indices_ln', 'decorrelated_u_ln', 'decorrelated_N_l'], + ) + + @_unbiased_decorrelated_N_l.default + def _unbiased_decorrelated_N_l(self, instance): + return instance._compute_mbar_unbiased_energies()[1] + + mbar = CachedProperty( + name='mbar', + dependencies=['unbiased_decorrelated_u_ln', 'unbiased_decorrelated_N_l', + '_extra_analysis_kwargs'], + ) + + @mbar.default + def mbar(self, instance): + return instance._create_mbar(instance._unbiased_decorrelated_u_ln, + instance._unbiased_decorrelated_N_l) + + # ------------------------------------------------------------------------- + # Dynamic properties. + # ------------------------------------------------------------------------- + + @property + def n_equilibration_iterations(self): + """int: The number of equilibration interations.""" + return self._equilibration_data[0] + + @property + def statistical_inefficiency(self): + """float: The statistical inefficiency of the sampler.""" + return self._equilibration_data[1] + + @property + def _decorrelated_iterations(self): + """list of int: the indices of the decorrelated iterations truncated to max_n_iterations.""" + if self.use_full_trajectory: + return np.arange(self.max_n_iterations + 1, dtype=int) + equilibrium_iterations = np.array(range(self.n_equilibration_iterations, self.max_n_iterations + 1)) + decorrelated_iterations_indices = timeseries.subsampleCorrelatedData(equilibrium_iterations, + self.statistical_inefficiency) + return equilibrium_iterations[decorrelated_iterations_indices] + + +# https://choderalab.slack.com/files/levi.naden/F4G6L9X8S/quick_diagram.png + +class MultiPhaseAnalyzer(object): + """ + Multiple Phase Analyzer creator, not to be directly called itself, but instead called by adding or subtracting + different implemented :class:`PhaseAnalyzer` or other :class:`MultiPhaseAnalyzers`'s. The individual Phases of + the :class:`MultiPhaseAnalyzer` are only references to existing Phase objects, not copies. All + :class:`PhaseAnalyzer` and :class:`MultiPhaseAnalyzer` classes support ``+`` and ``-`` operations. + + The observables of this phase are determined through inspection of all the passed in phases and only observables + which are shared can be computed. For example: + + ``PhaseA`` has ``.get_free_energy`` and ``.get_entropy`` + + ``PhaseB`` has ``.get_free_energy`` and ``.get_enthalpy``, + + ``PhaseAB = PhaseA + PhaseB`` will only have a ``.get_free_energy`` method + + Because each Phase may have a different number of states, the ``reference_states`` property of each phase + determines which states from each phase to read the data from. + + For observables defined by two states, the i'th and j'th reference states are used: + + If we define ``PhaseAB = PhaseA - PhaseB`` + + Then ``PhaseAB.get_free_energy()`` is roughly equivalent to doing the following: + + ``A_i, A_j = PhaseA.reference_states`` + + ``B_i, B_j = PhaseB.reference_states`` + + ``PhaseA.get_free_energy()[A_i, A_j] - PhaseB.get_free_energy()[B_i, B_j]`` + + The above is not exact since get_free_energy returns an error estimate as well + + For observables defined by a single state, only the i'th reference state is used + + Given ``PhaseAB = PhaseA + PhaseB``, ``PhaseAB.get_temperature()`` is equivalent to: + + ``A_i = PhaseA.reference_states[0]`` + + ``B_i = PhaseB.reference_states[0]`` + + ``PhaseA.get_temperature()[A_i] + PhaseB.get_temperature()[B_i]`` + + For observables defined entirely by the phase, no reference states are needed. + + Given ``PhaseAB = PhaseA + PhaseB``, ``PhaseAB.get_standard_state_correction()`` gives: + + ``PhaseA.get_standard_state_correction() + PhaseB.get_standard_state_correction()`` + + Each phase MUST use the same ObservablesRegistry, otherwise an error is raised + + This class is public to see its API. + + Parameters + ---------- + phases : dict + has keys "phases", "names", and "signs" + + Attributes + ---------- + observables + phases + names + signs + registry + + See Also + -------- + PhaseAnalyzer + ObservablesRegistry + + """ + def __init__(self, phases): + """ + Create the compound phase which is any combination of phases to generate a new MultiPhaseAnalyzer. + + """ + # Compare ObservableRegistries + ref_registry = phases['phases'][0].registry + for phase in phases['phases'][1:]: + # Use is comparison since we are checking same insetance + if phase.registry is not ref_registry: + raise ValueError("Not all phases have the same ObservablesRegistry! Observable calculation " + "will be inconsistent!") + self.registry = ref_registry + # Determine available observables + observables = [] + for observable in self.registry.observables: + shared_observable = True + for phase in phases['phases']: + if observable not in phase.observables: + shared_observable = False + break + if shared_observable: + observables.append(observable) + if len(observables) == 0: + raise RuntimeError("There are no shared computable observable between the phases, combining them will do " + "nothing.") + self._observables = tuple(observables) + self._phases = phases['phases'] + self._names = phases['names'] + self._signs = phases['signs'] + # Set the methods shared between both objects + for observable in self.observables: + setattr(self, "get_" + observable, self._spool_function(observable)) + + def _spool_function(self, observable): + """ + Dynamic observable calculator layer + + Must be in its own function to isolate the variable name space + + If you have this in the __init__, the "observable" variable colides with any others in the list, causing a + the wrong property to be fetched. + """ + return lambda: self._compute_observable(observable) + + @property + def observables(self): + """List of observables this :class:`MultiPhaseAnalyzer` can generate""" + return self._observables + + @property + def phases(self): + """List of implemented :class:`PhaseAnalyzer`'s objects this :class:`MultiPhaseAnalyzer` is tied to""" + return self._phases + + @property + def names(self): + """ + Unique list of string names identifying this phase. If this :class:`MultiPhaseAnalyzer` is combined with + another, its possible that new names will be generated unique to that :class:`MultiPhaseAnalyzer`, but will + still reference the same phase. + + When in doubt, use :func:`MultiPhaseAnalyzer.phases` to get the actual phase objects. + """ + return self._names + + @property + def signs(self): + """ + List of signs that are used by the :class:`MultiPhaseAnalyzer` to + """ + return self._signs + + def clear(self): + """ + Clear the individual phases of their observables and estimators for re-computing quantities + """ + for phase in self.phases: + phase.clear() + + def _combine_phases(self, other, operator='+'): + """ + Function to combine the phases regardless of operator to reduce code duplication. Creates a new + :class:`MultiPhaseAnalyzer` object based on the combined phases of the other. Accepts either a + :class:`PhaseAnalyzer` or a :class:`MultiPhaseAnalyzer`. + + If the names have collision, they are re-named with an extra digit at the end. + + Parameters + ---------- + other : :class:`MultiPhaseAnalyzer` or :class:`PhaseAnalyzer` + operator : sign of the operator connecting the two objects + + Returns + ------- + output : :class:`MultiPhaseAnalyzer` + New :class:`MultiPhaseAnalyzer` where the phases are the combined list of the individual phases from each + component. Because the memory pointers to the individual phases are the same, changing any + single :class:`PhaseAnalyzer`'s + reference_state objects updates all :class:`MultiPhaseAnalyzer` objects they are tied to + + """ + phases = [] + names = [] + signs = [] + # create copies + phases.extend(self.phases) + names.extend(self.names) + signs.extend(self.signs) + if isinstance(other, MultiPhaseAnalyzer): + new_phases = other.phases + new_signs = other.signs + new_names = other.names + final_new_names = [] + for name in new_names: + other_names = [n for n in new_names if n != name] + final_new_names.append(utils.generate_phase_name(name, other_names + names)) + names.extend(final_new_names) + for new_sign in new_signs: + if (operator == '-' and new_sign == '+') or (operator == '+' and new_sign == '-'): + signs.append('-') + else: + signs.append('+') + signs.extend(new_signs) + phases.extend(new_phases) + elif isinstance(other, PhaseAnalyzer): + names.append(utils.generate_phase_name(other.name, names)) + if (operator == '-' and other._sign == '+') or (operator == '+' and other._sign == '-'): + signs.append('-') + else: + signs.append('+') + other._sign = '+' # Recast to positive if negated + phases.append(other) + else: + baseerr = "cannot {} 'MultiPhaseAnalyzer' and '{}' objects" + if operator == '+': + err = baseerr.format('add', type(other)) + else: + err = baseerr.format('subtract', type(other)) + raise TypeError(err) + phase_pass = {'phases': phases, 'signs': signs, 'names': names} + return MultiPhaseAnalyzer(phase_pass) + + def __add__(self, other): + return self._combine_phases(other, operator='+') + + def __sub__(self, other): + return self._combine_phases(other, operator='-') + + def __neg__(self): + """ + Return a SHALLOW copy of self with negated signs so that the phase objects all still point to the same + objects + """ + new_signs = [] + for sign in self._signs: + if sign == '+': + new_signs.append('-') + else: + new_signs.append('+') + # return a *shallow* copy of self with the signs reversed + output = copy.copy(self) + output._signs = new_signs + return output + + def __str__(self): + """Simplified string output""" + header = "MultiPhaseAnalyzer<{}>" + output_string = "" + for phase_name, sign in zip(self.names, self.signs): + if output_string == "" and sign == '-': + output_string += '{}{} '.format(sign, phase_name) + elif output_string == "": + output_string += '{} '.format(phase_name) + else: + output_string += '{} {} '.format(sign, phase_name) + return header.format(output_string) + + def __repr__(self): + """Generate a detailed representation of the MultiPhase""" + header = "MultiPhaseAnalyzer <\n{}>" + output_string = "" + for phase, phase_name, sign in zip(self.phases, self.names, self.signs): + if output_string == "" and sign == '-': + output_string += '{}{} ({})\n'.format(sign, phase_name, phase) + elif output_string == "": + output_string += '{} ({})\n'.format(phase_name, phase) + else: + output_string += ' {} {} ({})\n'.format(sign, phase_name, phase) + return header.format(output_string) + + def _compute_observable(self, observable_name): + """ + Helper function to compute arbitrary observable in both phases + + Parameters + ---------- + observable_name : str + Name of the observable as its defined in the ObservablesRegistry + + Returns + ------- + observable_value + The observable as its combined between all the phases + + """ + def prepare_phase_observable(single_phase): + """Helper function to cast the observable in terms of observable's registry""" + observable = getattr(single_phase, "get_" + observable_name)() + if isinstance(single_phase, MultiPhaseAnalyzer): + if observable_name in self.registry.observables_with_error: + observable_payload = dict() + observable_payload['value'], observable_payload['error'] = observable + else: + observable_payload = observable + else: + raise_registry_error = False + if observable_name in self.registry.observables_with_error: + observable_payload = {} + if observable_name in self.registry.observables_defined_by_phase: + observable_payload['value'], observable_payload['error'] = observable + elif observable_name in self.registry.observables_defined_by_single_state: + observable_payload['value'] = observable[0][single_phase.reference_states[0]] + observable_payload['error'] = observable[1][single_phase.reference_states[0]] + elif observable_name in self.registry.observables_defined_by_two_states: + observable_payload['value'] = observable[0][single_phase.reference_states[0], + single_phase.reference_states[1]] + observable_payload['error'] = observable[1][single_phase.reference_states[0], + single_phase.reference_states[1]] + else: + raise_registry_error = True + else: # No error + if observable_name in self.registry.observables_defined_by_phase: + observable_payload = observable + elif observable_name in self.registry.observables_defined_by_single_state: + observable_payload = observable[single_phase.reference_states[0]] + elif observable_name in self.registry.observables_defined_by_two_states: + observable_payload = observable[single_phase.reference_states[0], + single_phase.reference_states[1]] + else: + raise_registry_error = True + if raise_registry_error: + raise RuntimeError("You have requested an observable that is improperly registered in the " + "ObservablesRegistry!") + return observable_payload + + def modify_final_output(passed_output, payload, sign): + if observable_name in self.registry.observables_with_error: + if sign == '+': + passed_output['value'] += payload['value'] + else: + passed_output['value'] -= payload['value'] + if observable_name in self.registry.observables_with_error_adding_linear: + passed_output['error'] += payload['error'] + elif observable_name in self.registry.observables_with_error_adding_quadrature: + passed_output['error'] = (passed_output['error']**2 + payload['error']**2)**0.5 + else: + if sign == '+': + passed_output += payload + else: + passed_output -= payload + return passed_output + + if observable_name in self.registry.observables_with_error: + final_output = {'value': 0, 'error': 0} + else: + final_output = 0 + for phase, phase_sign in zip(self.phases, self.signs): + phase_observable = prepare_phase_observable(phase) + final_output = modify_final_output(final_output, phase_observable, phase_sign) + if observable_name in self.registry.observables_with_error: + # Cast output to tuple + final_output = (final_output['value'], final_output['error']) + return final_output diff --git a/openmmtools/multistate/multistatereporter.py b/openmmtools/multistate/multistatereporter.py new file mode 100644 index 000000000..329a19f05 --- /dev/null +++ b/openmmtools/multistate/multistatereporter.py @@ -0,0 +1,1818 @@ +#!/usr/local/bin/env python + +# ============================================================================== +# MODULE DOCSTRING +# ============================================================================== + +""" +Multistatereporter +================== + +Master multi-thermodynamic state reporter module. Handles all Disk I/O +reporting operations for any MultiStateSampler derived classes. + +COPYRIGHT + +Current version by Andrea Rizzi , Levi N. Naden and +John D. Chodera while at Memorial Sloan Kettering Cancer Center. + +Original version by John D. Chodera while at the University of +California Berkeley. + +LICENSE + +This code is licensed under the latest available version of the MIT License. + +""" + +# ============================================================================== +# GLOBAL IMPORTS +# ============================================================================== + +import os +import copy +import time +import uuid +import yaml +import logging +import warnings +import collections + +import numpy as np +import netCDF4 as netcdf + +from typing import Union, Any + +from simtk import unit + +from openmmtools.utils import deserialize, with_timer, serialize, quantity_from_string +from openmmtools import version,states + + +logger = logging.getLogger(__name__) + + +# ============================================================================== +# MULTISTATE SAMPLER REPORTER +# ============================================================================== + +class MultiStateReporter(object): + """Handle storage write/read operations and different format conventions. + + You can use this object to programmatically inspect the data generated by + ReplicaExchange. + + Parameters + ---------- + storage : str + The path to the storage file for analysis. + + A second checkpoint file will be determined from either ``checkpoint_storage`` or automatically based on + the storage option + + In the future this will be able to take Storage classes as well. + open_mode : str or None + The mode of the file between 'r', 'w', and 'a' (or equivalently 'r+'). + + If None, the storage file won't be open on construction, and a call to + :func:`Reporter.open` will be needed before attempting read/write operations. + checkpoint_interval : int >= 1, Default: 50 + The frequency at which checkpointing information is written relative to analysis information. + + This is a multiple + of the iteration at which energies is written, hence why it must be greater than or equal to 1. + Checkpoint information cannot be written on iterations which where ``iteration % checkpoint_interval != 0``. + + checkpoint_storage : str or None, optional + Optional name of the checkpoint point file. This file is used to save trajectory information and other less + frequently accessed data. + + This should NOT be a full path, and instead just a filename + + If None: the derived checkpoint name is the same as storage, less any extension, then "_checkpoint.nc" is added. + + The reporter internally tracks what data goes into which file, so its transparent to all other classes + In the future, this will be able to take Storage classes as well + analysis_particle_indices : tuple of ints, Optional. Default: () (empty tuple) + Indices of particles which should be treated as special when manipulating read and write functions. + If this is an empty tuple, then no particles are treated as special + + Attributes + ---------- + filepath + checkpoint_interval + is_periodic + n_states + n_replicas + analysis_particle_indices + + """ + def __init__(self, storage, open_mode=None, + checkpoint_interval=50, checkpoint_storage=None, + analysis_particle_indices=()): + # Handle checkpointing + if type(checkpoint_interval) != int: + raise ValueError("checkpoint_interval must be an integer!") + dirname, filename = os.path.split(storage) + if checkpoint_storage is None: + basename, ext = os.path.splitext(filename) + addon = "_checkpoint" + checkpoint_storage = os.path.join(dirname, basename + addon + ext) + logger.debug("Initial checkpoint file automatically chosen as {}".format(checkpoint_storage)) + else: + checkpoint_storage = os.path.join(dirname, checkpoint_storage) + self._storage_analysis_file_path = storage + self._storage_checkpoint_file_path = checkpoint_storage + self._storage_checkpoint = None + self._storage_analysis = None + self._checkpoint_interval = checkpoint_interval + # Cast to tuple no mater what 1-D-like input was given + self._analysis_particle_indices = tuple(analysis_particle_indices) + if open_mode is not None: + self.open(open_mode) + + @property + def filepath(self): + """ + Returns the string file name of the primary storage file + + Classes outside the Reporter can access the file string for error messages and such. + """ + return self._storage_analysis_file_path + + @property + def _storage(self): + """ + Return an iterable of the storage objects, avoids having the [list, of, storage, objects] everywhere + Object 0 is always the primary file, all others are subfiles + """ + return self._storage_analysis, self._storage_checkpoint + + @property + def _storage_paths(self): + """ + Return an iterable of paths to the storage files + Object 0 is always the primary file, all others are subfiles + """ + return self._storage_analysis_file_path, self._storage_checkpoint_file_path + + @property + def _storage_dict(self): + """Return an iterable dictionary of the self._storage_X objects""" + return {'checkpoint': self._storage_checkpoint, 'analysis': self._storage_analysis} + + @property + def n_states(self): + if not self.is_open(): + return None + return self._storage_analysis.dimensions['state'].size + + @property + def n_replicas(self): + if not self.is_open(): + return None + return self._storage_analysis.dimensions['replica'].size + + @property + def is_periodic(self): + if not self.is_open(): + return None + if 'box_vectors' in self._storage_analysis.variables: + return True + return False + + @property + def analysis_particle_indices(self): + """Return the tuple of indices of the particles which additional information is stored on for analysis""" + return self._analysis_particle_indices + + @property + def checkpoint_interval(self): + """Returns the checkpoint interval""" + return self._checkpoint_interval + + def storage_exists(self, skip_size=False): + """ + Check if the storage files exist on disk. + + Reads information on the primary file to see existence of others + + Parameters + ---------- + skip_size : bool, Optional, Default: False + Skip the check of the file size. Helpful if you have just initialized a storage file but written nothing to + it yet and/or its still entirely in memory (e.g. just opened NetCDF files) + + Returns + ------- + files_exist : bool + If the primary storage file and its related subfiles exist, returns True. + If the primary file or any subfiles do not exist, returns False + """ + # This function serves as a way to mask the subfiles from everything outside the reporter + for file_path in self._storage_paths: + if not os.path.exists(file_path): + return False # Return if any files do not exist + elif not os.path.getsize(file_path) > 0 and not skip_size: + return False # File is 0 size + return True + + def is_open(self): + """Return True if the Reporter is ready to read/write.""" + if self._storage[0] is None: + return False + else: + return self._storage[0].isopen() + + def _are_subfiles_open(self): + """Internal function to check if subfiles are open""" + open_check_list = [] + for storage in self._storage[1:]: + if storage is None: + return False + else: + open_check_list.append(storage.isopen()) + return np.all(open_check_list) + + def open(self, mode='r', convention='ReplicaExchange', netcdf_format='NETCDF4'): + """ + Open the storage file for reading/writing. + + Creates and pre-formats the required files if they don't exist. + + This is not necessary if you have indicated in the constructor to open. + + Parameters + ---------- + mode : str, Optional, Default: 'r' + The mode of the file between 'r', 'w', and 'a' (or equivalently 'r+'). + convention : str, Optional, Default: 'ReplicaExchange' + NetCDF convention to write + netcdf_format : str, Optional, Default: 'NETCDF4' + The NetCDF file format to use + + """ + # Ensure we don't have already another file + # open (possibly in a different mode). + self.close() + + # Create directory if we want to write. + if mode != 'r': + for storage_path in self._storage_paths: + # normpath() transform '' to '.' for makedirs(). + storage_dir = os.path.normpath(os.path.dirname(storage_path)) + os.makedirs(storage_dir, exist_ok=True) + + # Analysis file. + # --------------- + + # Open analysis file. + self._storage_analysis = self._open_dataset_robustly(self._storage_analysis_file_path, + mode, version=netcdf_format) + # Without set_auto_mask(False) np.inf are read as masked. + self._storage_analysis.set_auto_mask(False) + + # The analysis netcdf file holds a reference UUID so that we can check + # that the secondary netcdf files (currently only the checkpoint + # file) have the same UUID to verify that the user isn't erroneously + # trying to associate the anaysis file to the incorrect checkpoint. + try: + # Check if we have previously created the file. + primary_uuid = self._storage_analysis.UUID + except AttributeError: + # This is a new file. Use uuid4 to avoid assigning hostname information. + primary_uuid = str(uuid.uuid4()) + self._storage_analysis.UUID = primary_uuid + + # Initialize dataset, if needed. + self._initialize_storage_file(self._storage_analysis, 'analysis', convention) + + # Checkpoint file. + # ----------------- + + # Open checkpoint netcdf files. + msg = ('Could not locate checkpoint subfile. This is okay for analysis if the ' + 'solvent trajectory is not needed, but not for production simulation!') + self._storage_checkpoint = self._open_dataset_robustly(self._storage_checkpoint_file_path, + mode, catch_io_error=True, + io_error_warning=msg, + version=netcdf_format) + if self._storage_checkpoint is not None: + # Check that the checkpoint file has the same UUID of the analysis file. + try: + assert self._storage_checkpoint.UUID == primary_uuid + except AttributeError: + # This is a new file. Assign UUID. + self._storage_checkpoint.UUID = primary_uuid + except AssertionError: + raise IOError('Checkpoint UUID does not match analysis UUID! ' + 'This checkpoint file came from another simulation!\n' + 'Analysis UUID: {}; Checkpoint UUID: {}'.format( + primary_uuid, self._storage_checkpoint.UUID)) + + # Initialize dataset, if needed. + self._initialize_storage_file(self._storage_checkpoint, 'checkpoint', convention) + + # Further checkpoint interval checks. + # ----------------------------------- + + if self._storage_analysis is not None: + # The same number will be on checkpoint file as well, but its not guaranteed to be present + on_file_interval = self._storage_analysis.CheckpointInterval + if on_file_interval != self._checkpoint_interval: + logger.debug("checkpoint_interval != on-file checkpoint interval! " + "Using on file analysis interval of {}.".format(on_file_interval)) + self._checkpoint_interval = on_file_interval + # Check the special particle indices + # Handle the "variable does not exist" case + if 'analysis_particle_indices' not in self._storage_analysis.variables: + n_particles = len(self._analysis_particle_indices) + # This dimension won't exist if the above statement does not either + self._storage_analysis.createDimension('analysis_particles', n_particles) + ncvar_analysis_particles = \ + self._storage_analysis.createVariable('analysis_particle_indices', int, 'analysis_particles') + ncvar_analysis_particles[:] = self._analysis_particle_indices + ncvar_analysis_particles.long_name = ("analysis_particle_indices[analysis_particles] is the indices of " + "the particles with extra information stored about them in the" + "analysis file.") + # Now handle the "variable does exist but does not match the provided ones" + # Although redundant if it was just created, its an easy check to make + stored_analysis_particles = self._storage_analysis.variables['analysis_particle_indices'][:] + if self._analysis_particle_indices != tuple(stored_analysis_particles.astype(int)): + logger.debug("analysis_particle_indices != on-file analysis_particle_indices!" + "Using on file analysis indices of {}".format(stored_analysis_particles)) + self._analysis_particle_indices = tuple(stored_analysis_particles.astype(int)) + + def _open_dataset_robustly(self, *args, n_attempts=5, sleep_time=2, + catch_io_error=False, io_error_warning=None, + **kwargs): + """Attempt to open the dataset multiple times if it raises an error. + + This may be useful to solve some MPI concurrency and locking issues + that routinely and randomly pop up with HDF5. Some sleep time is + added between attempts (in seconds). + + If the file is not found and catch_io_error is True, None is returned. + """ + # Catch eventual errors n_attempts - 1 times. + for i in range(n_attempts-1): + try: + return netcdf.Dataset(*args, **kwargs) + except IOError as e: + # If the file does not exist, it doesn't make sense to try again. + if catch_io_error: + if io_error_warning is not None: + logger.warning(io_error_warning) + return None + raise e + except: + logger.debug('Attempt {}/{} to open {} failed. Retrying ' + 'in {} seconds'.format(i+1, n_attempts, sleep_time)) + time.sleep(sleep_time) + # Last attempt finally raises any error. + return netcdf.Dataset(*args, **kwargs) + + def _initialize_storage_file(self, ncfile, nc_name, convention): + """Helper function to initialize dimensions and global attributes. + + If the dataset has been initialized before, nothing happens. Return True + if the file has been initialized before and False otherwise. + """ + if 'scalar' not in ncfile.dimensions: + # Create common dimensions. + ncfile.createDimension('scalar', 1) # Scalar dimension. + ncfile.createDimension('iteration', 0) # Unlimited number of iterations. + ncfile.createDimension('spatial', 3) # Number of spatial dimensions. + + # Set global attributes. + ncfile.application = 'YANK' + ncfile.program = 'yank.py' + ncfile.programVersion = version.short_version + ncfile.Conventions = convention + ncfile.ConventionVersion = '0.2' + ncfile.DataUsedFor = nc_name + ncfile.CheckpointInterval = self._checkpoint_interval + + # Create and initialize the global variables + nc_last_good_iter = ncfile.createVariable('last_iteration', int, 'scalar') + nc_last_good_iter[0] = 0 + return True + else: + return False + + def close(self): + """Close the storage files""" + for storage_name, storage in self._storage_dict.items(): + if storage is not None: + if storage.isopen(): + storage.sync() + storage.close() + setattr(self, '_storage' + storage_name, None) + + def sync(self): + """Force any buffer to be flushed to the file""" + for storage in self._storage: + if storage is not None: + storage.sync() + + def __del__(self): + """Synchronize and close the storage.""" + for storage in self._storage: + if storage is not None: + self.close() + break + + def read_end_thermodynamic_states(self): + """Read thermodynamic states at the ends of the protocol." + + Returns + ------- + end_thermodynamic_states : list of ThermodynamicState + unsampled_states, if present, or first and last sampled states + """ + end_thermodynamic_states = list() + + if 'unsampled_states' in self._storage_analysis.groups: + state_type = 'unsampled_states' + else: + state_type = 'thermodynamic_states' + + # Read thermodynamic end states + states_serializations = dict() + n_states = len(self._storage_analysis.groups[state_type].variables) + + def extract_serialized_state(inner_type, inner_id): + """Inner function to help extract the correct serialized state while minimizing number of disk reads + + Parameters + ---------- + inner_type : str, 'unsampled_states' or 'thermodynamic_states' + Where to read the data from, inherited from parent function's property or on the recursive loop + inner_id : int + Which state to pull data from + """ + inner_serialized_state = self.read_dict('{}/state{}'.format(inner_type, inner_id)) + + def isolate_thermodynamic_state(isolating_serialized_state): + """Helper function to find true bottom level thermodynamic state from any level of nesting, reduce code + """ + isolating_serial_thermodynamic_state = isolating_serialized_state + while 'thermodynamic_state' in isolating_serial_thermodynamic_state: + # The while loop is necessary for nested CompoundThermodynamicStates. + isolating_serial_thermodynamic_state = isolating_serial_thermodynamic_state['thermodynamic_state'] + return isolating_serial_thermodynamic_state + + serialized_thermodynamic_state = isolate_thermodynamic_state(inner_serialized_state) + # Check if the standard state is in a previous state. + try: + standard_system_name = serialized_thermodynamic_state.pop('_Reporter__compatible_state') + except KeyError: + # Cache the standard system serialization for future usage. + standard_system_name = '{}/{}'.format(inner_type, inner_id) + states_serializations[standard_system_name] = serialized_thermodynamic_state['standard_system'] + else: + # The system serialization can be retrieved from another state. + # Because the unsampled states rely on the thermodynamic states for their deserialization, we have + # to try a secondary/recursive loop to get the thermodynamic states + # However, this loop happens less often as the states_serializations dict fills up. + try: + serialized_standard_system = states_serializations[standard_system_name] + except KeyError: + loop_type, loop_id = standard_system_name.split('/') + looped_standard_state = extract_serialized_state(loop_type, loop_id) + looped_serial_thermodynamic_state = isolate_thermodynamic_state(looped_standard_state) + serialized_standard_system = looped_serial_thermodynamic_state['standard_system'] + serialized_thermodynamic_state['standard_system'] = serialized_standard_system + return inner_serialized_state + + for state_id in [0, n_states-1]: + serialized_state = extract_serialized_state(state_type, state_id) + + # Create ThermodynamicState object. + end_thermodynamic_states.append(deserialize(serialized_state)) + + return end_thermodynamic_states + + @with_timer('Reading thermodynamic states from storage') + def read_thermodynamic_states(self): + """Retrieve the stored thermodynamic states from the checkpoint file. + + Returns + ------- + thermodynamic_states : list of ThermodynamicStates + The previously stored thermodynamic states. During the simulation + these are swapped among replicas. + unsampled_states : list of ThermodynamicState + The previously stored unsampled thermodynamic states. + + See Also + -------- + read_replica_thermodynamic_states + + """ + # We have to parse the thermodynamic states first because the + # unsampled states may refer to them for the serialized system. + states = collections.OrderedDict([('thermodynamic_states', list()), + ('unsampled_states', list())]) + + # Caches standard_system_name: serialized_standard_system + states_serializations = dict() + + # Read state information. + for state_type, state_list in states.items(): + # There may not be unsampled states. + if state_type not in self._storage_analysis.groups: + assert state_type == 'unsampled_states' + continue + + # We keep looking for states until we can't find them anymore. + n_states = len(self._storage_analysis.groups[state_type].variables) + for state_id in range(n_states): + serialized_state = self.read_dict('{}/state{}'.format(state_type, state_id)) + + # Find the thermodynamic state representation. + serialized_thermodynamic_state = serialized_state + while 'thermodynamic_state' in serialized_thermodynamic_state: + # The while loop is necessary for nested CompoundThermodynamicStates. + serialized_thermodynamic_state = serialized_thermodynamic_state['thermodynamic_state'] + + # Check if the standard state is in a previous state. + try: + standard_system_name = serialized_thermodynamic_state.pop('_Reporter__compatible_state') + except KeyError: + # Cache the standard system serialization for future usage. + standard_system_name = '{}/{}'.format(state_type, state_id) + states_serializations[standard_system_name] = serialized_thermodynamic_state['standard_system'] + else: + # The system serialization can be retrieved from another state. + serialized_standard_system = states_serializations[standard_system_name] + serialized_thermodynamic_state['standard_system'] = serialized_standard_system + + # Create ThermodynamicState object. + states[state_type].append(deserialize(serialized_state)) + + return [states['thermodynamic_states'], states['unsampled_states']] + + @with_timer('Storing thermodynamic states') + def write_thermodynamic_states(self, thermodynamic_states, unsampled_states): + """Store all the ThermodynamicStates to the checkpoint file. + + Parameters + ---------- + thermodynamic_states : list of ThermodynamicState + The thermodynamic states to store. + unsampled_states : list of ThermodynamicState + The unsampled thermodynamic states to store. + + See Also + -------- + write_replica_thermodynamic_states + + """ + # Store all thermodynamic states as serialized dictionaries. + stored_states = dict() + + def unnest_thermodynamic_state(serialized): + while 'thermodynamic_state' in serialized: + serialized = serialized['thermodynamic_state'] + return serialized + + for state_type, states in [('thermodynamic_states', thermodynamic_states), + ('unsampled_states', unsampled_states)]: + for state_id, state in enumerate(states): + # Check if any compatible state has been found + found_compatible_state = False + for compare_state in stored_states: + if compare_state.is_state_compatible(state): + serialized_state = serialize(state, skip_system=True) + serialized_thermodynamic_state = unnest_thermodynamic_state(serialized_state) + serialized_thermodynamic_state.pop('standard_system') # Remove the unneeded system object + reference_state_name = stored_states[compare_state] + serialized_thermodynamic_state['_Reporter__compatible_state'] = reference_state_name + found_compatible_state = True + break + + # If no compatible state is found, do full serialization + if not found_compatible_state: + serialized_state = serialize(state) + serialized_thermodynamic_state = unnest_thermodynamic_state(serialized_state) + serialized_standard_system = serialized_thermodynamic_state['standard_system'] + + reference_state_name = '{}/{}'.format(state_type, state_id) + len_serialization = len(serialized_standard_system) + + # Store new compatibility data + stored_states[state] = reference_state_name + + logger.debug("Serialized state {} is {}B | {:.3f}KB | {:.3f}MB".format( + reference_state_name, len_serialization, len_serialization/1024.0, + len_serialization/1024.0/1024.0)) + + # Finally write the dictionary with fixed dimension to improve compression. + self._write_dict('{}/state{}'.format(state_type, state_id), + serialized_state, fixed_dimension=True) + + def read_sampler_states(self, iteration, analysis_particles_only=False): + """Retrieve the stored sampler states on the checkpoint file + + If the iteration is not on the checkpoint interval, None is returned. + Exception to this is if``analysis_particles_only``, see the Returns for output behavior. + + + Parameters + ---------- + iteration : int + The iteration at which to read the data. + analysis_particles_only : bool, Optional, Default: False + If set, will return the trajectory of ONLY the analysis particles flagged on original creation of the files + + Returns + ------- + sampler_states : list of SamplerStates or None + The previously stored sampler states for each replica. + + If the iteration is not on the ``checkpoint_interval`` and the ``analysis_particles_only`` is not set, + None is returned + + If ``analysis_particles_only`` is set, will return only the subset of particles which were defined by the + ``analysis_particle_indices`` on reporter creation + + """ + if analysis_particles_only: + if len(self._analysis_particle_indices) == 0: + raise ValueError("No particles were flagged for special analysis! " + "No such trajectory would have been written!") + return self._read_sampler_states_from_given_file(iteration, storage_file='analysis', + obey_checkpoint_interval=False) + else: + return self._read_sampler_states_from_given_file(iteration, storage_file='checkpoint', + obey_checkpoint_interval=True) + + @with_timer('Storing sampler states') + def write_sampler_states(self, sampler_states: list, iteration: int): + """Store all sampler states for a given iteration on the checkpoint file + + If the iteration is not on the checkpoint interval, only the ``analysis_particle_indices`` data is written, + if set. + + Parameters + ---------- + sampler_states : list of SamplerStates + The sampler states to store for each replica. + iteration : int + The iteration at which to store the data. + + """ + # Case of no special atoms, write to normal checkpoint + self._write_sampler_states_to_given_file(sampler_states, iteration, storage_file='checkpoint', + obey_checkpoint_interval=True) + if len(self._analysis_particle_indices) > 0: + # Special case, pre-process the sampler_states + sampler_subset = [] + for sampler_state in sampler_states: + positions = sampler_state.positions + # Subset positions + # Need the [arg, :] to get uniform behavior with tuple and list for arg + # since a ndarray[tuple] is different than ndarray[list] + position_subset = positions[self._analysis_particle_indices, :] + sampler_subset.append(states.SamplerState(position_subset, + box_vectors=sampler_state.box_vectors)) + self._write_sampler_states_to_given_file(sampler_subset, iteration, storage_file='analysis', + obey_checkpoint_interval=False) + + def read_replica_thermodynamic_states(self, iteration=slice(None)): + """Retrieve the indices of the ThermodynamicStates for each replica on the analysis file + + Parameters + ---------- + iteration : int or slice + The iteration(s) at which to read the data. The slice(None) allows fetching all iterations at once. + + Returns + ------- + state_indices : np.ndarray of int + At the given iteration, replica ``i`` propagated the system in + SamplerState ``sampler_states[i]`` and ThermodynamicState + ``thermodynamic_states[states_indices[i]]``. + If a slice is given, returns shape ``[len(slice), `len(sampler_states)]`` + + """ + iteration = self._map_iteration_to_good(iteration) + logger.debug('read_replica_thermodynamic_states: iteration = {}'.format(iteration)) + return self._storage_analysis.variables['states'][iteration].astype(np.int64) + + def write_replica_thermodynamic_states(self, state_indices, iteration): + """Store the indices of the ThermodynamicStates for each replica on the analysis file + + Parameters + ---------- + state_indices : list of int of size n_replicas + At the given iteration, replica ``i`` propagated the system in + SamplerState ``sampler_states[i]`` and ThermodynamicState + ``thermodynamic_states[replica_thermodynamic_states[i]]``. + iteration : int + The iteration at which to store the data. + + """ + # Initialize schema if needed. + if 'states' not in self._storage_analysis.variables: + n_replicas = len(state_indices) + + # Create dimension if they don't exist. + self._ensure_dimension_exists('replica', n_replicas) + + # Create variables and attach units and description. + ncvar_states = self._storage_analysis.createVariable('states', 'i4', ('iteration', 'replica'), + zlib=False, + chunksizes=(self._checkpoint_interval, n_replicas)) + setattr(ncvar_states, 'units', 'none') + setattr(ncvar_states, "long_name", ("states[iteration][replica] is the thermodynamic state index " + "(0..n_states-1) of replica 'replica' of iteration 'iteration'.")) + + # Store thermodynamic states indices. + self._storage_analysis.variables['states'][iteration, :] = state_indices[:] + + def read_mcmc_moves(self): + """Return the MCMCMoves of the :class:`yank.multistate.MultiStateSampler` simulation on the checkpoint + + Returns + ------- + mcmc_moves : list of MCMCMove + The MCMCMoves used to propagate the simulation. + + """ + n_moves = len(self._storage_analysis.groups['mcmc_moves'].variables) + + # Retrieve all moves in order. + mcmc_moves = list() + for move_id in range(n_moves): + serialized_move = self.read_dict('mcmc_moves/move{}'.format(move_id)) + mcmc_moves.append(deserialize(serialized_move)) + return mcmc_moves + + def write_mcmc_moves(self, mcmc_moves): + """Store the MCMCMoves of the :class:`yank.multistate.MultiStateSampler` simulation or subclasses on the checkpoint + + Parameters + ---------- + mcmc_moves : list of MCMCMove + The MCMCMoves used to propagate the simulation. + + """ + for move_id, move in enumerate(mcmc_moves): + serialized_move = serialize(move) + self.write_dict('mcmc_moves/move{}'.format(move_id), serialized_move) + + def read_energies(self, iteration=slice(None)): + """Retrieve the energy matrix at the given iteration on the analysis file + + Parameters + ---------- + iteration : int or slice + The iteration(s) at which to read the data. The slice(None) allows fetching all iterations at once. + + Returns + ------- + energy_thermodynamic_states : n_replicas x n_states numpy.ndarray + ``energy_thermodynamic_states[iteration, i, j]`` is the reduced potential computed at + SamplerState ``sampler_states[iteration, i]`` and ThermodynamicState ``thermodynamic_states[iteration, j]``. + energy_neighborhoods : n_replicas x n_states numpy.ndarray + energy_neighborhoods[replica_index, state_index] is 1 if the energy was computed for this state, + 0 otherwise + energy_unsampled_states : n_replicas x n_unsampled_states numpy.ndarray + ``energy_unsampled_states[iteration, i, j]`` is the reduced potential computed at SamplerState + ``sampler_states[iteration, i]`` and ThermodynamicState ``unsampled_thermodynamic_states[iteration, j]``. + + """ + # Determine last consistent iteration + iteration = self._map_iteration_to_good(iteration) + # Retrieve energies at all thermodynamic states + energy_thermodynamic_states = np.array(self._storage_analysis.variables['energies'][iteration, :, :], np.float64) + # Retrieve neighborhoods, assuming global neighborhoods if reading a pre-neighborhoods file + try: + energy_neighborhoods = np.array(self._storage_analysis.variables['neighborhoods'][iteration, :, :], 'i1') + except KeyError: + energy_neighborhoods = np.ones(energy_thermodynamic_states.shape, 'i1') + # Read energies at unsampled states, if present + try: + energy_unsampled_states = np.array(self._storage_analysis.variables['unsampled_energies'][iteration, :, :], np.float64) + except KeyError: + # There are no unsampled thermodynamic states. + unsampled_shape = energy_thermodynamic_states.shape[:-1] + (0,) + energy_unsampled_states = np.zeros(unsampled_shape) + return energy_thermodynamic_states, energy_neighborhoods, energy_unsampled_states + + def write_energies(self, energy_thermodynamic_states, energy_neighborhoods, energy_unsampled_states, iteration): + """Store the energy matrix at the given iteration on the analysis file + + Parameters + ---------- + energy_thermodynamic_states : n_replicas x n_states numpy.ndarray + ``energy_thermodynamic_states[i][j]`` is the reduced potential computed at + SamplerState ``sampler_states[i]`` and ThermodynamicState ``thermodynamic_states[j]``. + energy_neighborhoods : n_replicas x n_states numpy.ndarray + energy_neighborhoods[replica_index, state_index] is 1 if the energy was computed for this state, + 0 otherwise + energy_unsampled_states : n_replicas x n_unsampled_states numpy.ndarray + ``energy_unsampled_states[i][j]`` is the reduced potential computed at SamplerState + ``sampler_states[i]`` and ThermodynamicState ``unsampled_thermodynamic_states[j]``. + iteration : int + The iteration at which to store the data. + + """ + n_replicas, n_states = energy_thermodynamic_states.shape + + # Create dimensions and variables if they weren't created by other functions. + self._ensure_dimension_exists('replica', n_replicas) + self._ensure_dimension_exists('state', n_states) + if 'energies' not in self._storage_analysis.variables: + ncvar_energies = self._storage_analysis.createVariable('energies', + 'f8', + ('iteration', 'replica', 'state'), + zlib=False, + chunksizes=(self._checkpoint_interval, + n_replicas, + n_states)) + ncvar_energies.units = 'kT' + ncvar_energies.long_name = ("energies[iteration][replica][state] is the reduced (unitless) " + "energy of replica 'replica' from iteration 'iteration' evaluated " + "at the thermodynamic state 'state'.") + + if 'neighborhoods' not in self._storage_analysis.variables: + ncvar_neighborhoods = self._storage_analysis.createVariable('neighborhoods', + 'i1', + ('iteration', 'replica', 'state'), + zlib=False, + fill_value=1, # old-style files will be upgraded to have all states + chunksizes=(self._checkpoint_interval, + n_replicas, + n_states)) + ncvar_neighborhoods.long_name = ("neighborhoods[iteration][replica][state] is 1 if this energy was computed " + "during this iteration.") + + if 'unsampled_energies' not in self._storage_analysis.variables: + # Check if we have unsampled states. + if energy_unsampled_states.shape[1] > 0: + n_unsampled_states = len(energy_unsampled_states[0]) + self._ensure_dimension_exists('unsampled', n_unsampled_states) + + if 'unsampled_energies' not in self._storage_analysis.variables: + + # Create variable for thermodynamic state energies with units and descriptions. + ncvar_unsampled = self._storage_analysis.createVariable('unsampled_energies', + 'f8', + ('iteration', 'replica', 'unsampled'), + zlib=False, + chunksizes=(self._checkpoint_interval, + n_replicas, + n_unsampled_states) + ) + ncvar_unsampled.units = 'kT' + ncvar_unsampled.long_name = ("unsampled_energies[iteration][replica][state] is the reduced " + "(unitless) energy of replica 'replica' from iteration 'iteration' " + "evaluated at unsampled thermodynamic state 'state'.") + + # Store values + self._storage_analysis.variables['energies'][iteration,:,:] = energy_thermodynamic_states + self._storage_analysis.variables['neighborhoods'][iteration,:,:] = energy_neighborhoods + if energy_unsampled_states.shape[1] > 0: + self._storage_analysis.variables['unsampled_energies'][iteration, :, :] = energy_unsampled_states[:, :] + + def read_mixing_statistics(self, iteration=slice(None)): + """Retrieve the mixing statistics for the given iteration on the analysis file + + Parameters + ---------- + iteration : int or slice + The iteration(s) at which to read the data. + + Returns + ------- + n_accepted_matrix : numpy.ndarray with shape (n_states, n_states) + ``n_accepted_matrix[i][j]`` is the number of accepted moves from + state ``thermodynamic_states[i]`` to ``thermodynamic_states[j]`` going + from ``iteration-1`` to ``iteration`` (not cumulative). + n_proposed_matrix : numpy.ndarray with shape (n_states, n_states) + ``n_proposed_matrix[i][j]`` is the number of proposed moves from + state ``thermodynamic_states[i]`` to ``thermodynamic_states[j]`` going + from ``iteration-1`` to ``iteration`` (not cumulative). + + """ + iteration = self._map_iteration_to_good(iteration) + n_accepted_matrix = self._storage_analysis.variables['accepted'][iteration, :, :].astype(np.int64) + n_proposed_matrix = self._storage_analysis.variables['proposed'][iteration, :, :].astype(np.int64) + return n_accepted_matrix, n_proposed_matrix + + def write_mixing_statistics(self, n_accepted_matrix, n_proposed_matrix, iteration): + """Store the mixing statistics for the given iteration on the analysis file + + Parameters + ---------- + n_accepted_matrix : numpy.ndarray with shape (n_states, n_states) + ``n_accepted_matrix[i][j]`` is the number of accepted moves from + state ``thermodynamic_states[i]`` to ``thermodynamic_states[j]`` going + from iteration-1 to iteration (not cumulative). + n_proposed_matrix : numpy.ndarray with shape (n_states, n_states) + ``n_proposed_matrix[i][j]`` is the number of proposed moves from + state ``thermodynamic_states[i]`` to ``thermodynamic_states[j]`` going + from ``iteration-1`` to ``iteration`` (not cumulative). + iteration : int + The iteration for which to store the data. + + """ + # Create schema if necessary. + if 'accepted' not in self._storage_analysis.variables: + n_states = n_accepted_matrix.shape[0] + + # Create dimension if it doesn't already exist + self._ensure_dimension_exists('state', n_states) + + # Create variables with units and descriptions. + ncvar_accepted = self._storage_analysis.createVariable('accepted', + 'i4', + ('iteration', 'state', 'state'), + zlib=False, + chunksizes=(self._checkpoint_interval, + n_states, + n_states) + ) + ncvar_proposed = self._storage_analysis.createVariable('proposed', + 'i4', + ('iteration', 'state', 'state'), + zlib=False, + chunksizes=(self._checkpoint_interval, + n_states, + n_states) + ) + setattr(ncvar_accepted, 'units', 'none') + setattr(ncvar_proposed, 'units', 'none') + setattr(ncvar_accepted, 'long_name', ("accepted[iteration][i][j] is the number of proposed transitions " + "between states i and j from iteration 'iteration-1'.")) + setattr(ncvar_proposed, 'long_name', ("proposed[iteration][i][j] is the number of proposed transitions " + "between states i and j from iteration 'iteration-1'.")) + + # Store statistics. + self._storage_analysis.variables['accepted'][iteration, :, :] = n_accepted_matrix[:, :] + self._storage_analysis.variables['proposed'][iteration, :, :] = n_proposed_matrix[:, :] + + def read_timestamp(self, iteration=slice(None)): + """Return the timestamp for the given iteration. + + Read from the analysis file, although there is a paired timestamp on the checkpoint file as well + + Parameters + ---------- + iteration : int or slice + The iteration(s) at which to read the data. + + Returns + ------- + timestamp : str + The timestamp at which the iteration was stored. + + """ + iteration = self._map_iteration_to_good(iteration) + return self._storage_analysis.variables['timestamp'][iteration] + + def write_timestamp(self, iteration: int): + """Store a timestamp for the given iteration on both analysis and checkpoint file. + + If the iteration is not on the ``checkpoint_interval``, no timestamp is written on the checkpoint file + + Parameters + ---------- + iteration : int + The iteration at which to read the data. + + """ + # Create variable if needed. + for storage_key, storage in self._storage_dict.items(): + if 'timestamp' not in storage.variables: + storage.createVariable('timestamp', str, ('iteration',), + zlib=False, + chunksizes=(self._storage_chunks[storage_key],)) + timestamp = time.ctime() + self._storage_analysis.variables['timestamp'][iteration] = timestamp + checkpoint_iteration = self._calculate_checkpoint_iteration(iteration) + if checkpoint_iteration is not None: + self._storage_checkpoint.variables['timestamp'][checkpoint_iteration] = timestamp + + def read_dict(self, path: str) -> Union[dict, Any]: + """Restore a dictionary from the storage file. + + The method supports reading only specific dictionary keywords in + path notation. If the dictionary is large, this can be quicker. + However, note that, depending on how the dictionary was saved, + this may end up reading the whole dictionary anyway. + + Parameters + ---------- + path : str + The path to the dictionary or a keyword in the dictionary. + + Returns + ------- + data : dict or specified data + The restored data as a dict, or the data stored at key, depending on `path` + + Examples + -------- + >>> import os + >>> import openmmtools as mmtools + >>> data = {'info': [1, 2, 3]} + >>> with mmtools.utils.temporary_directory() as temp_dir_path: + ... temp_file_path = os.path.join(temp_dir_path, 'temp.nc') + ... reporter = MultiStateReporter(temp_file_path, open_mode='w') + ... reporter.write_dict('data', data) + ... reporter.read_dict('data') + {'info': [1, 2, 3]} + ... reporter.read_dict('data/info') + [1, 2, 3] + + """ + storage = 'analysis' + + # Get lowest possible NC variable/group. The path might refer + # to the keyword of a dictionary that was saved in a single variable. + dict_path = [] + nc_element = None + while nc_element is None: + try: + nc_element = self._resolve_nc_path(path, storage) + except KeyError: + # Try the higher level. + path, dict_key = path.rsplit(sep='/', maxsplit=1) + dict_path.insert(0, dict_key) + + # If this is a group, the dictionary has been nested. + if isinstance(nc_element, netcdf.Group): + data = {} + for elements in [nc_element.groups, nc_element.variables]: + for key in elements: + data.update({key: self.read_dict(path + '/' + key)}) + + # Otherwise this is a variable. + else: + if nc_element.dtype == 'S1': + # Handle variables stored in fixed_dimensions + data_chars = nc_element[:] + data_str = data_chars.tostring().decode() + else: + data_str = str(nc_element[0]) + data = yaml.load(data_str, Loader=_DictYamlLoader) + + # Restore the title in the metadata. + if path == 'metadata': + data['title'] = self._storage_dict[storage].title + + # Resolve the rest of the path that was saved un-nested. + for dict_key in dict_path: + data = data[dict_key] + return data + + def write_dict(self, path: str, data: dict): + """Store the contents of a dict. + + Parameters + ---------- + path : str + The path to the dictionary in the storage file. + data : dict + The dict to store. + + """ + storage_name = 'analysis' + + if path == 'metadata': + # General NetCDF conventions assume the title of the dataset + # to be specified as a global attribute, but the user can + # specify their own titles only in metadata. + data = copy.deepcopy(data) + self._storage_dict[storage_name].title = data.pop('title') + + # Metadata is pretty big, read-only attribute (it contains the + # reference state and the topography), and AlchemicalPhase has + # to read the name of the sampler for resuming and checking the + # status, so we store it compressed and in nested form. + self._write_dict(path, data, storage_name=storage_name, + fixed_dimension=True, nested=True) + else: + self._write_dict(path, data, storage_name=storage_name) + + def read_checkpoint_iterations(self): + """ + Utility function to provide all iterations on which a checkpoint was written + + Returns + ------- + checkpoints : np.ndarray of int + All checkpoints from initial iteration to current + """ + return np.array(range(0, self.read_last_iteration(last_checkpoint=True)+1, self._checkpoint_interval), + dtype=int) + + def read_last_iteration(self, last_checkpoint=True): + """ + Read the last iteration from file which was written in sequential order. + + Parameters + ---------- + last_checkpoint : bool, optional + If True, returns the last checkpoint iteration (default is True). + + Returns + ------- + last_iteration : int + Last iteration which was sequentially written. + + """ + # Make sure this is returned as Python int. + last_iteration = int(self._storage_analysis.variables['last_iteration'][0]) + + # Get last checkpoint. + if last_checkpoint: + # -1 for stop ensures the 0th index is searched. + for i in range(last_iteration, -1, -1): + if self._calculate_checkpoint_iteration(i) is not None: + return i + raise RuntimeError("Could not find a checkpoint! This should not happen " + "as the 0th iteration should always be written! " + "Please open a ticket on the YANK GitHub page if you see this error message!") + return last_iteration + + def write_last_iteration(self, iteration): + """ + Tell the reporter what the last iteration which was written in sequential order was to allow resuming and + analysis only on valid data. + + Call this as the last step of any ``write_iteration``-like routine to ensure + analysis will not use junk data left over from an interrupted simulation past the last checkpoint. + + The reporter is sync'ed both before and after writing the last iteration to ensure minimal data corruption + + Parameters + ---------- + iteration : int + Iteration at which the last good data point was written. + """ + self.sync() + self._storage_analysis.variables['last_iteration'][0] = iteration + self.sync() + + def read_logZ(self, iteration): + """ + Read logZ at a given iteration from file. + + Parameters + ---------- + iteration : int + iteration to read the free energies from + + if the iteration was not written at a the given iteration, then the free_energies are all 0 + + Returns + ------- + logZ : np.array with shape [n_states] + Dimensionless logZ + """ + data = self.read_online_analysis_data(iteration, "logZ") + return data['logZ'] + + def write_logZ(self, iteration: int, logZ: np.ndarray): + """ + Write logZ + + Parameters + ---------- + iteration : int, + Iteration at which to save the free energy. + Reads the current energy up to this value and stores it in the analysis reporter + logZ : np.array with shape [n_states] + Dimensionless log Z + """ + self.write_online_data_dynamic_and_static(iteration, logZ=logZ) + + def read_online_analysis_data(self, iteration, *keys: str): + """ + + Parameters + ---------- + iteration : int or None + Iteration to fetch data at. If ``None``, then assumes static data and will attempt to get the entry with the + name written assuming no iteration-specific data. + keys : str + Variables to fetch data from + + Returns + ------- + online_analysis_data : dict + Data requested by *keys argument from online analysis, if they exist on disk + + Warnings + -------- + RuntimeWarning : If some keys were not found as requested + + Raises + ------ + ValueError : If no requested keys were found in the storage or if no online analysis data was written + """ + collected_variables = {} + collected_iteration_failure = [] + collected_not_found = [] + try: + storage = self._storage_analysis.groups["online_analysis"] + except KeyError: + raise ValueError("Online Analysis information was never written!") + for variable in keys: + try: + data = self._read_1d_online_data(iteration, variable, storage) + collected_variables[variable] = data + except IndexError: + if self._find_alternate_variable(iteration, variable, storage): + collected_iteration_failure.append(variable) + else: + collected_not_found.append(variable) + # Nothing found + if not collected_variables and not collected_iteration_failure: + raise ValueError("None of the requested keys could be found on disk!") + # Found some things possibly named wrong, still nothing to return + elif not collected_variables: + base_error = ("No variables found on disk with{} per-iteration data, but we did find the following " + "variables of the same name with{} per-iteration data. Possibly you meant those?" + ) + for failure in collected_iteration_failure: + base_error += "\n\t-{}".format(failure) + if iteration is None: + raise ValueError(base_error.format("out", "")) + else: + raise ValueError(base_error.format("", "out")) + elif collected_iteration_failure or collected_not_found: + base_warn = ("Some requested variables were found, others were missing or found on disk under {}per" + "-iteration data:") + if iteration is None: + iteration_str = "" + else: + iteration_str = "non-" + base_warn = base_warn.format(iteration_str) + for failure in collected_iteration_failure: + base_warn += "\n\t{}per-iteration: {}".format(iteration_str, failure) + for missing in collected_not_found: + base_warn += "\n\tMissing: {}".format(missing) + warnings.warn(base_warn, RuntimeWarning) + return collected_variables + + def write_online_analysis_data(self, iteration: Union[int, None], **kwargs): + """ + Write semi-arbitrary 1-D numeric online analysis data to storage with optional per-iteration flag. + This function helps generalize what information is stored by any given reporter, while still + enforcing a regular input and output. + + The logic of what to store and how is similar, but not exact to the :func:`write_dict`. + + ``iteration`` accepts an integer as to indicate this this information should be written + on a per-iteration basis. The iteration the data are written to is the integer argument. + Pass ``None`` if this is *not* per-iteration data and stored independent of the iteration dimension + + ``**kwargs`` are processed as the variable/value pairs to store and there must be *at least one* + This should be 1-D or scalar numerical value (e.g. numpy array, list, or tuple of numbers; NOT string, dict, + etc.). Type is inferred from the first value of data input. + + Parameters + ---------- + iteration : int or None + Optional iteration to write the data under, if ``None``, the variables will not be written on a + per-iteration basis + kwargs : pairs of name:value of numeric 1-D or scalar data + Name of variable and value to write to disk + + Raises + ------ + TypeError : If no values are given to ``**kwargs`` + ValueError : If ``iteration`` is not an integer + + """ + self._resolve_iteration_args(iteration) + self._resolve_kwargs_exist(kwargs) + group = self._ensure_group_exists_and_get("online_analysis") + for name, value in kwargs.items(): + self._write_1d_online_data(iteration, name, value, group) + + def write_online_data_dynamic_and_static(self, iteration: int, **kwargs): + """ + Helper function to do a :func:`write_online_analysis_data` call twice, both + with and without setting iteration. + + See Also + -------- + write_online_analysis_data + """ + self.write_online_analysis_data(None, **kwargs) + self.write_online_analysis_data(iteration, **kwargs) + + # ------------------------------------------------------------------------- + # Internal-usage. + # ------------------------------------------------------------------------- + + def _write_1d_online_data(self, iteration, variable, data, storage): + """Store data on disk given pre-calculated parameters""" + if iteration is not None: + variable = variable + "_history" + if variable not in storage.variables: + variable_parameters = self._determine_netcdf_variable_parameters(iteration, data, storage) + logger.debug('Creating new NetCDF variable %s with parameters: %s' % (variable, variable_parameters)) # DEBUG + storage.createVariable(variable, variable_parameters['dtype'], + dimensions=variable_parameters['dims'], + chunksizes=variable_parameters['chunksizes'], + zlib=False) + # Get the variable + nc_var = storage[variable] + # Only get the specific iteration if specified + + if iteration is not None: + nc_var[iteration, :] = data + else: + nc_var[:] = data + + @staticmethod + def _find_alternate_variable(iteration, variable, storage): + """Helper function to figure out what went wrong when data not found""" + iter_var = variable + "_history" + if iteration is None and iter_var in storage.variables: + return True + elif iteration is not None and variable in storage.variables: + return True + return False + + @staticmethod + def _read_1d_online_data(iteration, variable, storage): + """Read data on disk given storage object + + Returns + ------- + data + """ + if iteration is not None: + variable = variable + "_history" + nc_var = storage[variable] + nc_data = nc_var + if iteration is not None: + nc_data = nc_data[iteration] + data = nc_data[:] + if nc_var.dimensions[-1] == "scalar": + return data[0] + else: + return data + + def _determine_netcdf_variable_parameters(self, iteration, data, storage): + """ + Pre-determine the variable information needed to create the variable on the storage layer + """ + + if np.isscalar(data): + # Scalar data + size = 1 + try: + dtype = data.dtype # numpy + except AttributeError: + dtype = type(data) # python + else: + # Array data + size = len(data) + try: + dtype = data.dtype # numpy + except AttributeError: + dtype = type(data[0]) # python + + data_dim = "dim_size{}".format(size) + + self._ensure_dimension_exists(data_dim, size, storage=storage) + + if iteration is not None: + dims = ("iteration", data_dim) + chunks = (self._checkpoint_interval, size) + else: + dims = (data_dim,) + chunks = (size,) + return {'dtype': dtype, 'dims': dims, 'chunksizes': chunks} + + @staticmethod + def _resolve_iteration_args(iteration_arg): + """ + Ensure iterations given as iterations are integer or None + """ + err_message = "Only an int or None is allowed for iteration" + # Ensures int check if its a core int, np.int32, np.int64, or signed/unsigned variants + if iteration_arg is not None and not np.issubdtype(type(iteration_arg), np.integer): + raise ValueError(err_message) + + @staticmethod + def _resolve_kwargs_exist(kwargs): + """ + Ensure keyword args exist (at least 1) + """ + if len(kwargs) == 0: + raise TypeError("There must be at least 1 keyword arg!") + + def _resolve_nc_path(self, path, storage): + """Return the NC group or variable at the end of the path. + + This can be used to retrieve groups or variables that are nested + inside one or more groups. + + """ + path_split = path.split('/') + nc_group = self._storage_dict[storage] + for group_name in path_split[:-1]: + nc_group = nc_group.groups[group_name] + + # Check if this is a path to a group or a variable. + try: + return nc_group.groups[path_split[-1]] + except KeyError: + return nc_group.variables[path_split[-1]] + + def _calculate_checkpoint_iteration(self, iteration): + """Compute the iteration on disk of the checkpoint file matching the iteration linked on the analysis iteration. + + Although this is a simple function, it provides a common function for calculation + + Returns either the integer index, or None if there is no matched index + """ + checkpoint_index, remainder = divmod(iteration, self._checkpoint_interval) + if remainder == 0: + # NetCDF variables can't be assigned using numpy integers. + return int(checkpoint_index) + return None + + def _map_iteration_to_good(self, iteration): + """ + Convert the iteration in 'read_X' functions which take a iteration=slice(None) + to avoid returning a slice of data which goes past the last_iteration. + + This effectively sets the max iteration to the last_good_iteration. + + Parameters + ---------- + iteration : int or Slice + Iteration to feed into the check + + Returns + ------- + cast_iteration : int or Slice of type iteration + Iteration, converted as needed to only access certain ranges of data + """ + + # Calculate last stored iteration + last_good = self.read_last_iteration(last_checkpoint=False) + # Create the artificial index map + artificial_map = np.arange(last_good + 1, dtype=int) + # Generate true index map from the input + cast_iteration = artificial_map[iteration] + return cast_iteration + + def _ensure_dimension_exists(self, dim_name, dim_size, storage=None): + """ + Ensure a dimension exists and is of the appropriate size, + creating it if it does not already exist. + + A ``ValueError`` is raised if ``dim_size`` does not match the existing dimension size. + + Parameters + ---------- + dim_name : str + The dimension name + dim_size : int + The dimension size + storage : netCDF4.Dataset or netCDF4.Group, optional, default: None + Storage layer to check the dimension against. If none, the _storage_analysis is used + + """ + if storage is None: + storage = self._storage_analysis + if dim_name not in storage.dimensions: + storage.createDimension(dim_name, dim_size) + else: + # Check dimension matches expected size + dimension = storage.dimensions[dim_name] + if dim_size == 0: + if not dimension.isunlimited(): + raise ValueError("NetCDF dimension {} already exists: was previously unlimited, but tried to " + "redeclare it with size {}".format(dimension.name, dim_size)) + else: + if not int(dimension.size) == int(dim_size): + raise ValueError("NetCDF dimension {} already exists: was previously size {}, but tried to " + "redeclare it with dimension {}".format(dimension.name, dimension.size, dim_size)) + + def _ensure_group_exists_and_get(self, group_name, storage=None): + """ + Ensure a group exists and fetch it if it does, creating first if it does not. + + A ``ValueError`` is raised if ``dimsize`` does not match the existing dimension size. + + Parameters + ---------- + group_name : str + The group name + storage : known storage object or None, default None + Storage object to check against, if None, assumes the analysis storage + + """ + if storage is None: + storage = self._storage_analysis + if group_name not in storage.groups: + storage.createGroup(group_name) + return storage.groups[group_name] + + @staticmethod + def _initialize_sampler_variables_on_file(dataset, n_atoms, n_replicas, is_periodic, iteration_chunk=1): + """ + Initialize the NetCDF variables on the storage file needed to store sampler states. + Does nothing if file already initialized + + Parameters + ---------- + dataset : NetCDF4 Dataset + Dataset to validate + n_atoms : int + Number of atoms which will be stored + n_replicas : int + Number of Sampler states which will be written + is_periodic : bool + True if system is periodic; False otherwise. + iteration_chunk : int, Optional, Default: 1 + What chunksize to use for the iteration dimension + """ + if 'positions' not in dataset.variables: + + # Create dimensions. Replica dimension could have been created before. + dataset.createDimension('atom', n_atoms) + if 'replica' not in dataset.dimensions: + dataset.createDimension('replica', n_replicas) + + # Define position variables + ncvar_positions = dataset.createVariable('positions', 'f4', + ('iteration', 'replica', 'atom', 'spatial'), + zlib=True, chunksizes=(iteration_chunk, n_replicas, n_atoms, 3)) + setattr(ncvar_positions, 'units', 'nm') + setattr(ncvar_positions, "long_name", ("positions[iteration][replica][atom][spatial] is position of " + "coordinate 'spatial' of atom 'atom' from replica 'replica' for " + "iteration 'iteration'.")) + + # Define variables for periodic systems + if is_periodic: + ncvar_box_vectors = dataset.createVariable('box_vectors', 'f4', + ('iteration', 'replica', 'spatial', 'spatial'), + zlib=False, chunksizes=(iteration_chunk, n_replicas, 3, 3)) + ncvar_volumes = dataset.createVariable('volumes', 'f8', ('iteration', 'replica'), + zlib=False, chunksizes=(iteration_chunk, n_replicas)) + + setattr(ncvar_box_vectors, 'units', 'nm') + setattr(ncvar_volumes, 'units', 'nm**3') + + setattr(ncvar_box_vectors, "long_name", ("box_vectors[iteration][replica][i][j] is dimension j of box " + "vector i for replica 'replica' from iteration " + "'iteration-1'.")) + setattr(ncvar_volumes, "long_name", ("volume[iteration][replica] is the box volume for replica " + "'replica' from iteration 'iteration-1'.")) + + def _write_sampler_states_to_given_file(self, sampler_states: list, iteration: int, + storage_file='checkpoint', obey_checkpoint_interval=True): + """ + Internal function to handle writing sampler states more generically to target file + + Parameters + ---------- + sampler_states : list of states.SamplerStates + The sampler states to store for each replica. + iteration : int + The iteration at which to store the data. + storage_file : string, Optional, Default: 'checkpoint' + Name of storage file we're writing to. Must match a valid key of self._storage_dict + obey_checkpoint_interval : bool, Optional, Default: False + Tells this (attempted) write to obey the checkpoint interval or not. + If True, no write out will be done if iteration is not on the checkpoint interval + If False, the write WILL occur + """ + + storage = self._storage_dict[storage_file] + # Check if the schema must be initialized, do this regardless of the checkpoint_interval for consistency + is_periodic = True if (sampler_states[0].box_vectors is not None) else False + n_particles = sampler_states[0].n_particles + n_replicas = len(sampler_states) + self._initialize_sampler_variables_on_file(storage, n_particles, + n_replicas, is_periodic, + iteration_chunk=self._storage_chunks[storage_file]) + if obey_checkpoint_interval: + write_iteration = self._calculate_checkpoint_iteration(iteration) + else: + write_iteration = iteration + # Write the sampler state if we are on the checkpoint interval OR if told to ignore the interval + if write_iteration is not None: + # Store sampler states. + # Create a numpy array to avoid making multiple (possibly inefficient) calls to netCDF assignments + positions = np.zeros([n_replicas, n_particles, 3]) + for replica_index, sampler_state in enumerate(sampler_states): + # Store positions in memory first + x = sampler_state.positions / unit.nanometers + positions[replica_index, :, :] = x[:, :] + # Store positions + storage.variables['positions'][write_iteration, :, :, :] = positions + + if is_periodic: + # Store box vectors and volume. + # Allocate whole write to memory first + box_vectors = np.zeros([n_replicas, 3, 3]) + volumes = np.zeros([n_replicas]) + for replica_index, sampler_state in enumerate(sampler_states): + box_vectors[replica_index, :, :] = sampler_state.box_vectors / unit.nanometers + volumes[replica_index] = sampler_state.volume / unit.nanometers ** 3 + storage.variables['box_vectors'][write_iteration, :, :, :] = box_vectors + storage.variables['volumes'][write_iteration, :] = volumes + + else: + logger.debug("Iteration {} not on the Checkpoint Interval of {}. " + "Sampler State not written.".format(iteration, self._checkpoint_interval)) + + def _read_sampler_states_from_given_file(self, iteration, storage_file='checkpoint', obey_checkpoint_interval=True): + """ + Internal function to handle reading sampler states more from a general storage file + + Parameters + ---------- + iteration : int + Iteration on which to read data from + storage_file : string, Optional, Default: 'checkpoint' + Name of storage file we're writing to. Must match a valid key of self._storage_dict + obey_checkpoint_interval : bool, Optional, Default: False + Tells this (attempted) write to obey the checkpoint interval or not. + If True, no write out will be done if iteration is not on the checkpoint interval + If False, the read will be attempted regardless + + WARNING: If the storage file you specify was written on the checkpoint interval and you set + obey_checkpoint_interval=False, you will get undefined behavior! + + Returns + ------- + sampler_states : list of SamplerStates or None + The previously stored sampler states for each replica. + + If the iteration is not on the checkpoint_interval and the file only writes on the checkpoint_interval, + None is returned + """ + storage = self._storage_dict[storage_file] + read_iteration = self._map_iteration_to_good(iteration) + if obey_checkpoint_interval: + read_iteration = self._calculate_checkpoint_iteration(iteration) + if read_iteration is not None: + # TODO: Restore n_replicas instead + n_replicas = storage.dimensions['replica'].size + + sampler_states = list() + for replica_index in range(n_replicas): + # Restore positions. + x = storage.variables['positions'][read_iteration, replica_index, :, :].astype(np.float64) + positions = unit.Quantity(x, unit.nanometers) + + if 'box_vectors' in storage.variables: + # Restore box vectors. + x = storage.variables['box_vectors'][read_iteration, replica_index, :, :].astype(np.float64) + box_vectors = unit.Quantity(x, unit.nanometers) + else: + box_vectors = None + + # Create SamplerState. + sampler_states.append(states.SamplerState(positions=positions, box_vectors=box_vectors)) + + return sampler_states + else: + return None + + def _write_dict(self, path, data, storage_name='analysis', + fixed_dimension=False, nested=False): + """Store the contents of a dict. + + Parameters + ---------- + path : str + The path to the dictionary in the storage file. + data : dict + The dict to store. + storage_name : 'analysis' or 'checkpoint' + The name of the storage file where to save the dict. + fixed_dimension : bool, default: False + Use a fixed length dimension instead of variable length one. + This method seems to allow NetCDF to actually compress strings. + + A unique dimension name called ``"fixedL{}".format(len(data))`` + will be created. + + Do NOT use this flag if you expect to constantly be changing + the length of the data fed in. Use only for static data. + nested : bool, default False + In nested representation, dictionaries are represented as + groups, and values as strings. In this mode, it is possible + to retrieve a keyword without reading the whole dictionary. + + In this mode, the dictionary can be overwritten, but ONLY if + the structure of the dict doesn't change, as it's impossible + to delete groups/variables from a netcdf database. + + """ + storage_nc = self._storage_dict[storage_name] + + # Save nested dictionary into a group if requested, unless the dictionary is empty. + if nested and isinstance(data, dict) and len(data) > 0: + for key, value in data.items(): + if not isinstance(key, str): + raise ValueError('Cannot store dict in nested form with non-string keys.') + self._write_dict(path + '/' + key, value, storage_name, + fixed_dimension, nested) + return + + # Activate flow style to save space. + data_str = yaml.dump(data, Dumper=_DictYamlDumper) + + # Check if we are updating the dictionary or creating it. + try: + nc_variable = self._resolve_nc_path(path, storage_name) + except KeyError: + if fixed_dimension: + variable_type = 'S1' + dimension_name = "fixedL{}".format(len(data_str)) + # Create a new fixed dimension if necessary. + if dimension_name not in storage_nc.dimensions: + storage_nc.createDimension(dimension_name, len(data_str)) + else: + variable_type = str + dimension_name = 'scalar' + # Create variable. + nc_variable = storage_nc.createVariable(path, variable_type, + dimension_name, zlib=True) + + # Assign the value to the variable. + if fixed_dimension: + packed_data = np.array(list(data_str), dtype='S1') + else: + packed_data = np.empty(1, 'O') + packed_data[0] = data_str + nc_variable[:] = packed_data + + @ property + def _storage_chunks(self): + """Known NetCDF storage chunk sizes""" + return {'checkpoint': 1, 'analysis': self._checkpoint_interval} + + +# ============================================================================== +# MODULE INTERNAL USAGE UTILITIES +# ============================================================================== + +class _DictYamlLoader(yaml.CLoader): + """PyYAML Loader that reads !Quantity tags.""" + def __init__(self, *args, **kwargs): + super(_DictYamlLoader, self).__init__(*args, **kwargs) + self.add_constructor(u'!Quantity', self.quantity_constructor) + self.add_constructor(u'!ndarray', self.ndarray_constructor) + + @staticmethod + def quantity_constructor(loader, node): + loaded_mapping = loader.construct_mapping(node) + data_unit = quantity_from_string(loaded_mapping['unit']) + data_value = loaded_mapping['value'] + return data_value * data_unit + + @staticmethod + def ndarray_constructor(loader, node): + loaded_mapping = loader.construct_mapping(node, deep=True) + data_type = np.dtype(loaded_mapping['type']) + data_shape = loaded_mapping['shape'] + data_values = loaded_mapping['values'] + data = np.ndarray(shape=data_shape, dtype=data_type) + if 0 not in data_shape: + data[:] = data_values + return data + + +class _DictYamlDumper(yaml.CDumper): + """PyYAML Dumper that handle simtk Quantities through !Quantity tags.""" + + def __init__(self, *args, **kwargs): + super(_DictYamlDumper, self).__init__(*args, **kwargs) + self.add_representer(unit.Quantity, self.quantity_representer) + self.add_representer(np.ndarray, self.ndarray_representer) + + @staticmethod + def quantity_representer(dumper, data): + data_unit = data.unit + data_value = data / data_unit + data_dump = dict(unit=str(data_unit), value=data_value) + return dumper.represent_mapping(u'!Quantity', data_dump) + + @staticmethod + def ndarray_representer(dumper, data): + """Convert a numpy array to native Python types.""" + data_type = str(data.dtype) + data_shape = data.shape + data_values = data.tolist() + data_dump = dict(type=data_type, shape=data_shape, values=data_values) + return dumper.represent_mapping(u'!ndarray', data_dump) diff --git a/openmmtools/multistate/multistatesampler.py b/openmmtools/multistate/multistatesampler.py new file mode 100644 index 000000000..27048dabc --- /dev/null +++ b/openmmtools/multistate/multistatesampler.py @@ -0,0 +1,1622 @@ +#!/usr/local/bin/env python + +# ============================================================================== +# MODULE DOCSTRING +# ============================================================================== + +""" +MultistateSampler +================= + +Base multi-thermodynamic state multistate class + +COPYRIGHT + +Current version by Andrea Rizzi , Levi N. Naden and +John D. Chodera while at Memorial Sloan Kettering Cancer Center. + +Original version by John D. Chodera while at the University of +California Berkeley. + +LICENSE + +This code is licensed under the latest available version of the MIT License. + +""" + +# ============================================================================== +# GLOBAL IMPORTS +# ============================================================================== + +import os +import copy +import time +import typing +import inspect +import logging +import datetime + +import numpy as np +from simtk import unit, openmm + +from openmmtools import multistate, utils, states, mcmc, cache +import mpiplus +from yank.utils import SimulationNaNError + +from pymbar.utils import ParameterError + +from yank.fire import FIREMinimizationIntegrator + +logger = logging.getLogger(__name__) + + +# ============================================================================== +# MULTISTATE SAMPLER +# ============================================================================== + +class MultiStateSampler(object): + """ + Base class for samplers that sample multiple thermodynamic states using + one or more replicas. + + This base class provides a general simulation facility for multistate from multiple + thermodynamic states, allowing any set of thermodynamic states to be specified. + If instantiated on its own, the thermodynamic state indices associated with each + state are specified and replica mixing does not change any thermodynamic states, + meaning that each replica remains in its original thermodynamic state. + + Stored configurations, energies, swaps, and restart information are all written + to a single output file using the platform portable, robust, and efficient + NetCDF4 library. + + Parameters + ---------- + mcmc_moves : MCMCMove or list of MCMCMove, optional + The MCMCMove used to propagate the thermodynamic states. If a list of MCMCMoves, + they will be assigned to the correspondent thermodynamic state on + creation. If None is provided, Langevin dynamics with 2fm timestep, 5.0/ps collision rate, + and 500 steps per iteration will be used. + number_of_iterations : int or infinity, optional, default: 1 + The number of iterations to perform. Both ``float('inf')`` and + ``numpy.inf`` are accepted for infinity. If you set this to infinity, + be sure to set also ``online_analysis_interval``. + online_analysis_interval : None or Int >= 1, optional, default: 200 + Choose the interval at which to perform online analysis of the free energy. + + After every interval, the simulation will be stopped and the free energy estimated. + + If the error in the free energy estimate is at or below ``online_analysis_target_error``, then the simulation + will be considered completed. + + If set to ``None``, then no online analysis is performed + + online_analysis_target_error : float >= 0, optional, default 0.0 + The target error for the online analysis measured in kT per phase. + + Once the free energy is at or below this value, the phase will be considered complete. + + If ``online_analysis_interval`` is None, this option does nothing. + + Default is set to 0.0 since online analysis runs by default, but a finite ``number_of_iterations`` should also + be set to ensure there is some stop condition. If target error is 0 and an infinite number of iterations is set, + then the sampler will run until the user stop it manually. + + online_analysis_minimum_iterations : int >= 0, optional, default 200 + Set the minimum number of iterations which must pass before online analysis is carried out. + + Since the initial samples likely not to yield a good estimate of free energy, save time and just skip them + If ``online_analysis_interval`` is None, this does nothing + + locality : int > 0, optional, default None + If None, the energies at all states will be computed for every replica each iteration. + If int > 0, energies will only be computed for states ``range(max(0, state-locality), min(n_states, state+locality))``. + + Attributes + ---------- + n_replicas + n_states + iteration + mcmc_moves + sampler_states + metadata + is_completed + + :param number_of_iterations: Maximum number of integer iterations that will be run + + :param online_analysis_interval: How frequently to carry out online analysis in number of iterations + + :param online_analysis_target_error: Target free energy difference error float at which simulation will be stopped during online analysis, in dimensionless energy + + :param online_analysis_minimum_iterations: Minimum number of iterations needed before online analysis is run as int + + """ + + # ------------------------------------------------------------------------- + # Constructors. + # ------------------------------------------------------------------------- + + def __init__(self, mcmc_moves=None, number_of_iterations=1, + online_analysis_interval=200, online_analysis_target_error=0.0, + online_analysis_minimum_iterations=200, + locality=None): + # These will be set on initialization. See function + # create() for explanation of single variables. + self._thermodynamic_states = None + self._unsampled_states = None + self._sampler_states = None + self._replica_thermodynamic_states = None + self._iteration = None + self._energy_thermodynamic_states = None + self._neighborhoods = None + self._energy_unsampled_states = None + self._n_accepted_matrix = None + self._n_proposed_matrix = None + self._reporter = None + self._metadata = None + + # Handling default propagator. + if mcmc_moves is None: + # This will be converted to a list in create(). + self._mcmc_moves = mcmc.LangevinDynamicsMove(timestep=2.0 * unit.femtosecond, + collision_rate=5.0 / unit.picosecond, + n_steps=500, reassign_velocities=True, + n_restart_attempts=6) + else: + self._mcmc_moves = copy.deepcopy(mcmc_moves) + + # Store constructor parameters. Everything is marked for internal + # usage because any change to these attribute implies a change + # in the storage file as well. Use properties for checks. + self.number_of_iterations = number_of_iterations + + # Store locality + self.locality = locality + + # Online analysis options. + self.online_analysis_interval = online_analysis_interval + self.online_analysis_target_error = online_analysis_target_error + self.online_analysis_minimum_iterations = online_analysis_minimum_iterations + self._online_error_trap_counter = 0 # Counter for errors in the online estimate + self._online_error_bank = [] + + self._last_mbar_f_k = None + self._last_err_free_energy = None + + self._have_displayed_citations_before = False + + # Check convergence. + if self.number_of_iterations == np.inf: + if self.online_analysis_target_error == 0.0: + logger.warning("WARNING! You have specified an unlimited number of iterations and a target error " + "for online analysis of 0.0! Your simulation may never reach 'completed' state!") + elif self.online_analysis_interval is None: + logger.warning("WARNING! This simulation will never be considered 'complete' since there is no " + "specified maximum number of iterations!") + + @classmethod + def from_storage(cls, storage): + """Constructor from an existing storage file. + + Parameters + ---------- + storage : str or Reporter + If str: The path to the storage file. + If :class:`Reporter`: uses the :class:`Reporter` options + In the future this will be able to take a Storage class as well. + + Returns + ------- + sampler : MultiStateSampler + A new instance of MultiStateSampler (or subclass) in the same state of the + last stored iteration. + + """ + # Handle case in which storage is a string. + reporter = cls._reporter_from_storage(storage, check_exist=True) + + try: + # Open the reporter to read the data. + reporter.open(mode='r') + sampler = cls._instantiate_sampler_from_reporter(reporter) + sampler._restore_sampler_from_reporter(reporter) + finally: + # Close reporter in reading mode. + reporter.close() + + # We open the reporter only in node 0 in append mode ready for use + sampler._reporter = reporter + mpiplus.run_single_node(0, sampler._reporter.open, mode='a', + broadcast_result=False, sync_nodes=False) + # Don't write the new last iteration, we have not technically + # written anything yet, so there is no "junk". + return sampler + + # TODO use Python 3.6 namedtuple syntax when we drop Python 3.5 support. + Status = typing.NamedTuple('Status', [ + ('iteration', int), + ('target_error', float), + ('is_completed', bool) + ]) + + @classmethod + def read_status(cls, storage): + """Read the status of the calculation from the storage file. + + This class method can be used to quickly check the status of the + simulation before loading the full ``ReplicaExchange`` object + from disk. + + Parameters + ---------- + storage : str or Reporter + The path to the storage file or the reporter object. + + Returns + ------- + status : ReplicaExchange.Status + The status of the replica-exchange calculation. It has three + fields: ``iteration``, ``target_error``, and ``is_completed``. + + """ + # Handle case in which storage is a string. + reporter = cls._reporter_from_storage(storage, check_exist=True) + + # Read iteration and online analysis info. + try: + reporter.open(mode='r') + options = reporter.read_dict('options') + iteration = reporter.read_last_iteration(last_checkpoint=False) + # Search for last cached free energies only if online analysis is activated. + target_error = None + last_err_free_energy = None + # Check if online analysis is set AND that the target error is a stopping condition (> 0) + if (options['online_analysis_interval'] is not None and + options['online_analysis_target_error'] != 0.0): + target_error = options['online_analysis_target_error'] + try: + last_err_free_energy = cls._read_last_free_energy(reporter, iteration)[1][1] + except TypeError: + # Trap for undefined free energy (has not been run yet) + last_err_free_energy = np.inf + finally: + reporter.close() + + # Check if the calculation is done. + number_of_iterations = options['number_of_iterations'] + online_analysis_target_error = options['online_analysis_target_error'] + is_completed = cls._is_completed_static(number_of_iterations, iteration, + last_err_free_energy, + online_analysis_target_error) + + return cls.Status(iteration=iteration, target_error=target_error, + is_completed=is_completed) + + # ------------------------------------------------------------------------- + # Public properties. + # ------------------------------------------------------------------------- + + @property + def n_states(self): + """The integer number of thermodynamic states (read-only).""" + if self._thermodynamic_states is None: + return 0 + else: + return len(self._thermodynamic_states) + + @property + def n_replicas(self): + """The integer number of replicas (read-only).""" + if self._sampler_states is None: + return 0 + else: + return len(self._sampler_states) + + @property + def iteration(self): + """The integer current iteration of the simulation (read-only). + + If the simulation has not been created yet, this is None. + + """ + return self._iteration + + @property + def mcmc_moves(self): + """A copy of the MCMCMoves list used to propagate the simulation. + + This can be set only before creation. + + """ + return copy.deepcopy(self._mcmc_moves) + + @mcmc_moves.setter + def mcmc_moves(self, new_value): + if self._thermodynamic_states is not None: + # We can't modify representation of the MCMCMoves because it's + # impossible to delete groups/variables from an NetCDF file. We + # could support this by JSONizing the dict serialization and + # store it as a string instead, if we needed this. + raise RuntimeError('Cannot modify MCMCMoves after creation.') + # If this is a single MCMCMove, it'll be transformed to a list in create(). + self._mcmc_moves = copy.deepcopy(new_value) + + @property + def sampler_states(self): + """A copy of the sampler states list at the current iteration. + + This can be set only before running. + """ + return copy.deepcopy(self._sampler_states) + + @sampler_states.setter + def sampler_states(self, value): + if self._iteration != 0: + raise RuntimeError('Sampler states can be assigned only between ' + 'create() and run().') + if len(value) != self.n_replicas: + raise ValueError('Passed {} sampler states for {} replicas'.format( + len(value), self.n_replicas)) + + # Update sampler state in the object and on storage. + self._sampler_states = copy.deepcopy(value) + mpiplus.run_single_node(0, self._reporter.write_sampler_states, + self._sampler_states, self._iteration) + + @property + def is_periodic(self): + """Return True if system is periodic, False if not, and None if not initialized""" + if self._sampler_states is None: + return None + return self._thermodynamic_states[0].is_periodic + + class _StoredProperty(object): + """ + Descriptor of a property stored as an option. + + validate_function is a simple function for checking things like "X > 0", but exposes both the + ReplicaExchange instance and the new value for the variable, in that order. + More complex checks which relies on the ReplicaExchange instance, like "if Y == True, then check X" can be + accessed through the instance object of the function + + """ + + def __init__(self, option_name, validate_function=None): + self._option_name = option_name + self._validate_function = validate_function + + def __get__(self, instance, owner_class=None): + return getattr(instance, '_' + self._option_name) + + def __set__(self, instance, new_value): + if self._validate_function is not None: + new_value = self._validate_function(instance, new_value) + setattr(instance, '_' + self._option_name, new_value) + # Update storage if we ReplicaExchange is initialized. + if instance._reporter is not None and instance._reporter.is_open(): + mpiplus.run_single_node(0, instance._store_options) + + # ---------------------------------- + # Value Validation of the properties + # Should be @staticmethod with arguments of (instance, value) in that order, even if instance is not used + # ---------------------------------- + + @staticmethod + def _number_of_iterations_validator(_, number_of_iterations): + # Support infinite number of iterations. + if not (0 <= number_of_iterations <= float('inf')): + raise ValueError('Accepted values for number_of_iterations are' + 'non-negative integers and infinity.') + return number_of_iterations + + @staticmethod + def _oa_interval_validator(_, online_analysis_interval): + """Check the online_analysis_interval value for consistency""" + if online_analysis_interval is not None and ( + type(online_analysis_interval) != int or online_analysis_interval < 1): + raise ValueError('online_analysis_interval must be an integer >=1 or None') + return online_analysis_interval + + @staticmethod + def _oa_target_error_validator(instance, online_analysis_target_error): + if instance.online_analysis_interval is not None: + if online_analysis_target_error < 0: + raise ValueError("online_analysis_target_error must be a float >= 0") + elif online_analysis_target_error == 0 and instance.number_of_iterations is None: + logger.warning("online_analysis_target_error of 0 and number of iterations undefined " + "will never converge!") + return online_analysis_target_error + + @staticmethod + def _oa_min_iter_validator(instance, online_analysis_minimum_iterations): + if (instance.online_analysis_interval is not None and + (type( + online_analysis_minimum_iterations) is not int or online_analysis_minimum_iterations < 0)): + raise ValueError("online_analysis_minimum_iterations must be an integer >= 0") + return online_analysis_minimum_iterations + + @staticmethod + def _locality_validator(_, locality): + if locality is not None: + if (type(locality) != int) or (locality <= 0): + raise ValueError("locality must be an int > 0") + return locality + + number_of_iterations = _StoredProperty('number_of_iterations', + validate_function=_StoredProperty._number_of_iterations_validator) + online_analysis_interval = _StoredProperty('online_analysis_interval', + validate_function=_StoredProperty._oa_interval_validator) #:interval to carry out online analysis + online_analysis_target_error = _StoredProperty('online_analysis_target_error', + validate_function=_StoredProperty._oa_target_error_validator) + online_analysis_minimum_iterations = _StoredProperty('online_analysis_minimum_iterations', + validate_function=_StoredProperty._oa_min_iter_validator) + locality = _StoredProperty('locality', validate_function=_StoredProperty._locality_validator) + + @property + def metadata(self): + """A copy of the metadata dictionary passed on creation (read-only).""" + return copy.deepcopy(self._metadata) + + @property + def is_completed(self): + """Check if we have reached any of the stop target criteria (read-only)""" + return self._is_completed() + + # ------------------------------------------------------------------------- + # Main public interface. + # ------------------------------------------------------------------------- + + _TITLE_TEMPLATE = ('Multi-state sampler simulation created using MultiStateSampler class ' + 'of yank.multistate on {}') + + def create(self, thermodynamic_states: list, sampler_states, storage, + initial_thermodynamic_states=None, unsampled_thermodynamic_states=None, + metadata=None): + """Create new multistate sampler simulation. + + Parameters + ---------- + thermodynamic_states : list of states.ThermodynamicState + Thermodynamic states to simulate, where one replica is allocated per state. + Each state must have a system with the same number of atoms. + sampler_states : states.SamplerState or list + One or more sets of initial sampler states. + The number of replicas is taken to be the number of sampler states provided. + If the sampler states do not have box_vectors attached and the system is periodic, + an exception will be thrown. + storage : str or instanced Reporter + If str: the path to the storage file. Default checkpoint options from Reporter class are used + If Reporter: Uses the reporter options and storage path + In the future this will be able to take a Storage class as well. + initial_thermodynamic_states : None or list or array-like of int of length len(sampler_states), optional, + default: None. + Initial thermodynamic_state index for each sampler_state. + If no initial distribution is chosen, ``sampler_states`` are distributed between the + ``thermodynamic_states`` following these rules: + + * If ``len(thermodynamic_states) == len(sampler_states)``: 1-to-1 distribution + + * If ``len(thermodynamic_states) > len(sampler_states)``: First and last state distributed first + remaining ``sampler_states`` spaced evenly by index until ``sampler_states`` are depleted. + If there is only one ``sampler_state``, then the only first ``thermodynamic_state`` will be chosen + + * If ``len(thermodynamic_states) < len(sampler_states)``, each ``thermodynamic_state`` receives an + equal number of ``sampler_states`` until there are insufficient number of ``sampler_states`` remaining + to give each ``thermodynamic_state`` an equal number. Then the rules from the previous point are + followed. + + unsampled_thermodynamic_states : list of states.ThermodynamicState, optional, default=None + These are ThermodynamicStates that are not propagated, but their + reduced potential is computed at each iteration for each replica. + These energy can be used as data for reweighting schemes (default + is None). + metadata : dict, optional, default=None + Simulation metadata to be stored in the file. + """ + # Handle case in which storage is a string and not a Reporter object. + self._reporter = self._reporter_from_storage(storage, check_exist=False) + + # Check if netcdf files exist. This is run only on MPI node 0 and + # broadcasted. This is to avoid the case where the other nodes + # arrive to this line after node 0 has already created the storage + # file, causing an error. + if mpiplus.run_single_node(0, self._reporter.storage_exists, broadcast_result=True): + raise RuntimeError('Storage file {} already exists; cowardly ' + 'refusing to overwrite.'.format(self._reporter.filepath)) + + # Make sure sampler_states is an iterable of SamplerStates. + if isinstance(sampler_states, states.SamplerState): + sampler_states = [sampler_states] + + # Initialize internal attribute and dataset. + self._pre_write_create(thermodynamic_states, sampler_states, storage, + initial_thermodynamic_states=initial_thermodynamic_states, + unsampled_thermodynamic_states=unsampled_thermodynamic_states, + metadata=metadata) + + # Display papers to be cited. + self._display_citations() + + self._initialize_reporter() + + @utils.with_timer('Minimizing all replicas') + def minimize(self, tolerance=1.0 * unit.kilojoules_per_mole / unit.nanometers, + max_iterations=0): + """Minimize all replicas. + + Minimized positions are stored at the end. + + Parameters + ---------- + tolerance : simtk.unit.Quantity, optional + Minimization tolerance (units of energy/mole/length, default is + ``1.0 * unit.kilojoules_per_mole / unit.nanometers``). + max_iterations : int, optional + Maximum number of iterations for minimization. If 0, minimization + continues until converged. + + """ + # Check that simulation has been created. + if self.n_replicas == 0: + raise RuntimeError('Cannot minimize replicas. The simulation must be created first.') + + logger.debug("Minimizing all replicas...") + + # Distribute minimization across nodes. Only node 0 will get all positions. + # The other nodes, only need the positions that they use for propagation and + # computation of the energy matrix entries. + minimized_positions, sampler_state_ids = mpiplus.distribute(self._minimize_replica, range(self.n_replicas), + tolerance, max_iterations, + send_results_to=0) + + # Update all sampler states. For non-0 nodes, this will update only the + # sampler states associated to the replicas propagated by this node. + for sampler_state_id, minimized_pos in zip(sampler_state_ids, minimized_positions): + self._sampler_states[sampler_state_id].positions = minimized_pos + + # Save the stored positions in the storage + mpiplus.run_single_node(0, self._reporter.write_sampler_states, self._sampler_states, self._iteration) + + def equilibrate(self, n_iterations, mcmc_moves=None): + """Equilibrate all replicas. + + This does not increase the iteration counter. The equilibrated + positions are stored at the end. + + Parameters + ---------- + n_iterations : int + Number of equilibration iterations. + mcmc_moves : MCMCMove or list of MCMCMove, optional + Optionally, the MCMCMoves to use for equilibration can be + different from the ones used in production. + + """ + # Check that simulation has been created. + if self.n_replicas == 0: + raise RuntimeError('Cannot equilibrate replicas. The simulation must be created first.') + + # If no MCMCMove is specified, use the ones for production. + if mcmc_moves is None: + mcmc_moves = self._mcmc_moves + + # Make sure there is one MCMCMove per thermodynamic state. + if isinstance(mcmc_moves, mcmc.MCMCMove): + mcmc_moves = [copy.deepcopy(mcmc_moves) for _ in range(self.n_states)] + elif len(mcmc_moves) != self.n_states: + raise RuntimeError('The number of MCMCMoves ({}) and ThermodynamicStates ({}) for equilibration' + ' must be the same.'.format(len(self._mcmc_moves), self.n_states)) + + # Temporarily set the equilibration MCMCMoves. + production_mcmc_moves = self._mcmc_moves + self._mcmc_moves = mcmc_moves + for iteration in range(n_iterations): + logger.debug("Equilibration iteration {}/{}".format(iteration, n_iterations)) + self._propagate_replicas() + + # Restore production MCMCMoves. + self._mcmc_moves = production_mcmc_moves + + # Update stored positions. + mpiplus.run_single_node(0, self._reporter.write_sampler_states, self._sampler_states, self._iteration) + + def run(self, n_iterations=None): + """Run the replica-exchange simulation. + + This runs at most ``number_of_iterations`` iterations. Use :func:`extend` + to pass the limit. + + Parameters + ---------- + n_iterations : int, optional + If specified, only at most the specified number of iterations + will be run (default is None). + """ + # If this is the first iteration, compute and store the + # starting energies of the minimized/equilibrated structures. + if self._iteration == 0: + try: + self._compute_energies() + # We're intercepting a possible initial NaN position here thrown by OpenMM, which is a simple exception + # So we have to under-specify this trap. + except Exception as e: + if 'coordinate is nan' in str(e).lower(): + err_message = "Initial coordinates were NaN! Check your inputs!" + logger.critical(err_message) + raise SimulationNaNError(err_message) + else: + # If not the special case, raise the error normally + raise e + mpiplus.run_single_node(0, self._reporter.write_energies, self._energy_thermodynamic_states, + self._neighborhoods, self._energy_unsampled_states, self._iteration) + self._check_nan_energy() + + timer = utils.Timer() + timer.start('Run ReplicaExchange') + run_initial_iteration = self._iteration + + # Handle default argument and determine number of iterations to run. + if n_iterations is None: + iteration_limit = self.number_of_iterations + else: + iteration_limit = min(self._iteration + n_iterations, self.number_of_iterations) + + # Main loop. + while not self._is_completed(iteration_limit): + # Increment iteration counter. + self._iteration += 1 + + logger.debug('*' * 80) + logger.debug('Iteration {}/{}'.format(self._iteration, iteration_limit)) + logger.debug('*' * 80) + timer.start('Iteration') + + # Update thermodynamic states + self._mix_replicas() + + # Propagate replicas. + self._propagate_replicas() + + # Compute energies of all replicas at all states + self._compute_energies() + + # Write iteration to storage file + self._report_iteration() + + # Update analysis + self._update_analysis() + + # Computing timing information + iteration_time = timer.stop('Iteration') + partial_total_time = timer.partial('Run ReplicaExchange') + time_per_iteration = partial_total_time / (self._iteration - run_initial_iteration) + estimated_time_remaining = time_per_iteration * (iteration_limit - self._iteration) + estimated_total_time = time_per_iteration * iteration_limit + estimated_finish_time = time.time() + estimated_time_remaining + # TODO: Transmit timing information + + # Show timing statistics if debug level is activated. + if logger.isEnabledFor(logging.DEBUG): + logger.debug("Iteration took {:.3f}s.".format(iteration_time)) + if estimated_time_remaining != float('inf'): + logger.debug("Estimated completion in {}, at {} (consuming total wall clock time {}).".format( + str(datetime.timedelta(seconds=estimated_time_remaining)), + time.ctime(estimated_finish_time), + str(datetime.timedelta(seconds=estimated_total_time)))) + + # Perform sanity checks to see if we should terminate here. + self._check_nan_energy() + + def extend(self, n_iterations): + """Extend the simulation by the given number of iterations. + + Contrarily to :func:`run`, this will extend the number of iterations past + ``number_of_iteration`` if requested. + + Parameters + ---------- + n_iterations : int + The number of iterations to run. + + """ + if self._iteration + n_iterations > self.number_of_iterations: + # This MUST be assigned to a property or the storage won't be updated. + self.number_of_iterations = self._iteration + n_iterations + self.run(n_iterations) + + def __repr__(self): + """Return a 'formal' representation that can be used to reconstruct the class, if possible.""" + return "".format(self.__class__.__name__) + + def __del__(self): + # The reporter could be None if MultiStateSampler was not created. + if hasattr(self, '_reporter') and (self._reporter is not None): + mpiplus.run_single_node(0, self._reporter.close) + + # ------------------------------------------------------------------------- + # Internal-usage. + # ------------------------------------------------------------------------- + + def _pre_write_create(self, + thermodynamic_states, + sampler_states, + storage, + initial_thermodynamic_states=None, + unsampled_thermodynamic_states=None, + metadata=None): + """ + Internal function which allocates and sets up ALL variables prior to actually using them. + This is helpful to ensure subclasses have all variables created prior to writing them out with + :func:`_report_iteration`. + All calls to this function should be *identical* to :func:`create` itself + """ + # Check all systems are either periodic or not. + is_periodic = thermodynamic_states[0].is_periodic + for thermodynamic_state in thermodynamic_states: + if thermodynamic_state.is_periodic != is_periodic: + raise Exception('Thermodynamic states contain a mixture of ' + 'systems with and without periodic boundary conditions.') + + # Check that sampler states specify box vectors if the system is periodic + if is_periodic: + for sampler_state in sampler_states: + if sampler_state.box_vectors is None: + raise Exception('All sampler states must have box_vectors defined if the system is periodic.') + + # Make sure all states have same number of particles. We don't + # currently support writing storage with different n_particles + n_particles = thermodynamic_states[0].n_particles + for the_states in [thermodynamic_states, sampler_states]: + for state in the_states: + if state.n_particles != n_particles: + raise ValueError('All ThermodynamicStates and SamplerStates must ' + 'have the same number of particles') + + # Handle default argument for metadata and add default simulation title. + default_title = (self._TITLE_TEMPLATE.format(time.asctime(time.localtime()))) + if metadata is None: + metadata = dict(title=default_title) + elif 'title' not in metadata: + metadata['title'] = default_title + self._metadata = metadata + + # Save thermodynamic states. This sets n_replicas. + self._thermodynamic_states = copy.deepcopy(thermodynamic_states) + + # Handle default unsampled thermodynamic states. + if unsampled_thermodynamic_states is None: + self._unsampled_states = [] + else: + self._unsampled_states = copy.deepcopy(unsampled_thermodynamic_states) + + # Deep copy sampler states. + self._sampler_states = [copy.deepcopy(sampler_state) for sampler_state in sampler_states] + + # Set initial thermodynamic state indices if not specified + if initial_thermodynamic_states is None: + initial_thermodynamic_states = self._default_initial_thermodynamic_states(thermodynamic_states, + sampler_states) + self._replica_thermodynamic_states = np.array(initial_thermodynamic_states, np.int64) + + # Assign default system box vectors if None has been specified. + for replica_id, thermodynamic_state_id in enumerate(self._replica_thermodynamic_states): + sampler_state = self._sampler_states[replica_id] + if sampler_state.box_vectors is not None: + continue + thermodynamic_state = self._thermodynamic_states[thermodynamic_state_id] + sampler_state.box_vectors = thermodynamic_state.system.getDefaultPeriodicBoxVectors() + + # Ensure there is an MCMCMove for each thermodynamic state. + if isinstance(self._mcmc_moves, mcmc.MCMCMove): + self._mcmc_moves = [copy.deepcopy(self._mcmc_moves) for _ in range(self.n_states)] + elif len(self._mcmc_moves) != self.n_states: + raise RuntimeError('The number of MCMCMoves ({}) and ThermodynamicStates ({}) must ' + 'be the same.'.format(len(self._mcmc_moves), self.n_states)) + + # Reset iteration counter. + self._iteration = 0 + + # Reset statistics. + # _n_accepted_matrix[i][j] is the number of swaps proposed between thermodynamic states i and j. + # _n_proposed_matrix[i][j] is the number of swaps proposed between thermodynamic states i and j. + self._n_accepted_matrix = np.zeros([self.n_states, self.n_states], np.int64) + self._n_proposed_matrix = np.zeros([self.n_states, self.n_states], np.int64) + + # Allocate memory for energy matrix. energy_thermodynamic/unsampled_states[k][l] + # is the reduced potential computed at the positions of SamplerState sampler_states[k] + # and ThermodynamicState thermodynamic/unsampled_states[l]. + self._energy_thermodynamic_states = np.zeros([self.n_replicas, self.n_states], np.float64) + self._neighborhoods = np.zeros([self.n_replicas, self.n_states], 'i1') + self._energy_unsampled_states = np.zeros([self.n_replicas, len(self._unsampled_states)], np.float64) + + @classmethod + def _instantiate_sampler_from_reporter(cls, reporter): + """ + Creates a new instance of the reporter on disk and sampler which can then be manipulated. + Does not set any variables, use :func:`_restore_sampler_from_reporter` after calling this to set them. + Helper function to break up the :func:`from_storage` method in a way that subclasses can specialize + + Parameters + ---------- + reporter : Reporter + A reporter open for reading. + + Returns + ------- + sampler : MultiStateSampler + A new instance of MultiStateSampler (or subclass) with options + restored from disk. + + """ + # Retrieve options and create new simulation. + options = reporter.read_dict('options') + options['mcmc_moves'] = reporter.read_mcmc_moves() + sampler = cls(**options) + + # Display papers to be cited. + sampler._display_citations() + return sampler + + def _restore_sampler_from_reporter(self, reporter): + """ + (Re-)initialize the instanced sampler from the reporter. Intended to be called as the second half of a + :func:`from_storage` method after the :class:`MultiStateSampler` has been instanced from disk. + + The ``self.reporter`` instance of this sampler will be in an open state for append mode after this has been set, + and the ``reporter`` used as argument will be closed. In the event they are the same, reporter will be + returned as open in append mode. + + Note: Needs an already initialized reporter to work correctly. + Warning: can overwrite the current state of this :class:`MultiStateSampler` instance. + + Helper function to break up the from_storage method in a way that subclasses can specialize + + Parameters + ---------- + reporter : multistate.MultiStateReporter + Reporter open for reading. + + """ + # Read the last iteration reported to ensure we don't include junk + # data written just before a crash. + logger.debug("Reading storage file {}...".format(reporter.filepath)) + metadata = reporter.read_dict('metadata') + thermodynamic_states, unsampled_states = reporter.read_thermodynamic_states() + + def _read_options(check_iteration): + internal_sampler_states = reporter.read_sampler_states(iteration=check_iteration) + internal_state_indices = reporter.read_replica_thermodynamic_states(iteration=check_iteration) + internal_energy_thermodynamic_states, internal_neighborhoods, internal_energy_unsampled_states = \ + reporter.read_energies(iteration=check_iteration) + internal_n_accepted_matrix, internal_n_proposed_matrix = \ + reporter.read_mixing_statistics(iteration=check_iteration) + + # Search for last cached free energies only if online analysis is activated. + internal_last_mbar_f_k, internal_last_err_free_energy = None, None + if self.online_analysis_interval is not None: + online_analysis_info = self._read_last_free_energy(reporter, check_iteration) + try: + internal_last_mbar_f_k, (_, internal_last_err_free_energy) = online_analysis_info + except TypeError: + # Trap case where online analysis is set but not run yet and (_, ...) = None is not iterable + pass + return (internal_sampler_states, internal_state_indices, internal_energy_thermodynamic_states, + internal_neighborhoods, internal_energy_unsampled_states, internal_n_accepted_matrix, + internal_n_proposed_matrix, internal_last_mbar_f_k, internal_last_err_free_energy) + + # Keep trying to resume further and further back from the most recent checkpoint back + checkpoints = reporter.read_checkpoint_iterations() + checkpoint_reverse_iter = iter(checkpoints[::-1]) + while True: + try: + checkpoint = next(checkpoint_reverse_iter) + output_data = _read_options(checkpoint) + # Found data, can escape loop + break + except StopIteration: + raise self._throw_restoration_error("Attempting to restore from any checkpoint failed. " + "Either your data is fully corrupted or something has gone very " + "wrong to see this message. " + "Please open an issue on the GitHub issue tracker if you see this!") + except: + # Trap all other errors caught by the load process + continue + + if checkpoint < checkpoints[-1]: + logger.warning("Could not use most recent checkpoint at {}, instead pulled from {}".format(checkpoints[-1], + checkpoint)) + (sampler_states, state_indices, energy_thermodynamic_states, neighborhoods, energy_unsampled_states, + n_accepted_matrix, n_proposed_matrix, last_mbar_f_k, last_err_free_energy) = output_data + # Assign attributes. + self._iteration = int(checkpoint) # The int() can probably be removed when pinned to NetCDF4 >=1.4.0 + self._thermodynamic_states = thermodynamic_states + self._unsampled_states = unsampled_states + self._sampler_states = sampler_states + self._replica_thermodynamic_states = state_indices + self._energy_thermodynamic_states = energy_thermodynamic_states + self._neighborhoods = neighborhoods + self._energy_unsampled_states = energy_unsampled_states + self._n_accepted_matrix = n_accepted_matrix + self._n_proposed_matrix = n_proposed_matrix + self._metadata = metadata + + self._last_mbar_f_k = last_mbar_f_k + self._last_err_free_energy = last_err_free_energy + + def _check_nan_energy(self): + """Checks that energies are finite and abort otherwise. + + Checks both sampled and unsampled thermodynamic states. + + """ + # Find faulty replicas to create error message. + nan_replicas = [] + + # Check sampled thermodynamic states first. + state_type = 'thermodynamic state' + for replica_id, state_id in enumerate(self._replica_thermodynamic_states): + neighborhood = self._neighborhood(state_id) + energies_neighborhood = self._energy_thermodynamic_states[replica_id, neighborhood] + if np.any(np.isnan(energies_neighborhood)): + nan_replicas.append((replica_id, energies_neighborhood)) + + # If there are no NaNs in energies, look for NaNs in the unsampled states energies. + if (len(nan_replicas) == 0) and (self._energy_unsampled_states.shape[1] > 0): + state_type = 'unsampled thermodynamic state' + for replica_id in range(self.n_replicas): + if np.any(np.isnan(self._energy_unsampled_states[replica_id])): + nan_replicas.append((replica_id, self._energy_unsampled_states[replica_id])) + + # Raise exception if we have found some NaN energies. + if len(nan_replicas) > 0: + # Log failed replica, its thermo state, and the energy matrix row. + err_msg = "NaN encountered in {} energies for the following replicas and states".format(state_type) + for replica_id, energy_row in nan_replicas: + err_msg += '\n\tEnergies for positions at replica {} (current state {}): {} kT'.format( + replica_id, self._replica_thermodynamic_states[replica_id], energy_row) + logger.critical(err_msg) + raise SimulationNaNError(err_msg) + + @mpiplus.on_single_node(rank=0, broadcast_result=False, sync_nodes=False) + def _display_citations(self, overwrite_global=False, citation_stack=None): + """ + Display papers to be cited. + The overwrite_global command will force the citation to display even if the "have_citations_been_shown" variable + is True + """ + # TODO Add original citations for various replica-exchange schemes. + # TODO Show subset of OpenMM citations based on what features are being used. + openmm_citations = """\ + Friedrichs MS, Eastman P, Vaidyanathan V, Houston M, LeGrand S, Beberg AL, Ensign DL, Bruns CM, and Pande VS. Accelerating molecular dynamic simulations on graphics processing unit. J. Comput. Chem. 30:864, 2009. DOI: 10.1002/jcc.21209 + Eastman P and Pande VS. OpenMM: A hardware-independent framework for molecular simulations. Comput. Sci. Eng. 12:34, 2010. DOI: 10.1109/MCSE.2010.27 + Eastman P and Pande VS. Efficient nonbonded interactions for molecular dynamics on a graphics processing unit. J. Comput. Chem. 31:1268, 2010. DOI: 10.1002/jcc.21413 + Eastman P and Pande VS. Constant constraint matrix approximation: A robust, parallelizable constraint method for molecular simulations. J. Chem. Theor. Comput. 6:434, 2010. DOI: 10.1021/ct900463w""" + + mbar_citations = """\ + Shirts MR and Chodera JD. Statistically optimal analysis of samples from multiple equilibrium states. J. Chem. Phys. 129:124105, 2008. DOI: 10.1063/1.2978177""" + + if citation_stack is not None: + citation_stack = [openmm_citations] + citation_stack + else: + citation_stack = [openmm_citations] + + if overwrite_global or (not self._have_displayed_citations_before and not self._global_citation_silence): + print("Please cite the following:") + print("") + for citation in citation_stack: + print(citation) + self._have_displayed_citations_before = True + + # ------------------------------------------------------------------------- + # Internal-usage: Initialization and storage utilities. + # ------------------------------------------------------------------------- + + @classmethod + def _default_initial_thermodynamic_states(cls, thermodynamic_states, sampler_states): + """ + Create the initial_thermodynamic_states obeying the following rules: + + * If ``len(thermodynamic_states) == len(sampler_states)``: 1-to-1 distribution + + * If ``len(thermodynamic_states) > len(sampler_states)``: First and last state distributed first + remaining ``sampler_states`` spaced evenly by index until ``sampler_states`` are depleted. + If there is only one ``sampler_state``, then the only first ``thermodynamic_state`` will be chosen + + * If ``len(thermodynamic_states) < len(sampler_states)``, each ``thermodynamic_state`` receives an + equal number of ``sampler_states`` until there are insufficient number of ``sampler_states`` remaining + to give each ``thermodynamic_state`` an equal number. Then the rules from the previous point are + followed. + """ + n_thermo = len(thermodynamic_states) + n_sampler = len(sampler_states) + thermo_indices = np.arange(n_thermo, dtype=int) + initial_thermo_states = np.zeros(n_sampler, dtype=int) + # Determine how many loops we can do + loops = n_sampler // n_thermo # Floor division (//) + n_looped = n_thermo * loops + initial_thermo_states[:n_looped] = np.tile(thermo_indices, loops) + # Distribute remaining values, -1 from n_thermo to handle indices correctly + initial_thermo_states[n_looped:] = np.linspace(0, n_thermo - 1, n_sampler - n_looped, dtype=int) + return initial_thermo_states + + @staticmethod + def _does_file_exist(file_path): + """Check if there is a file at the given path.""" + return os.path.exists(file_path) and os.path.getsize(file_path) > 0 + + @staticmethod + def _reporter_from_storage(storage, check_exist=True): + """Return the Reporter object associated to this storage. + + If check_exist is True, FileNotFoundError is raised if the files + are not found. The return reporter is closed. + """ + if isinstance(storage, str): + # Open a reporter to read the data. + reporter = multistate.MultiStateReporter(storage) + else: + reporter = storage + + # Check if netcdf file exists. + if check_exist and not reporter.storage_exists(): + raise FileNotFoundError('Storage file {} or its subfiles do not exist; ' + 'cannot read status.'.format(reporter.filepath)) + return reporter + + @mpiplus.on_single_node(rank=0, broadcast_result=False, sync_nodes=True) + def _initialize_reporter(self): + """Initialize the reporter and store initial information. + + This is executed only on MPI node 0 and it is blocking. This is to + avoid the case where the other nodes skip ahead and try to read + from a file that hasn't been created yet. + + """ + self._reporter.open(mode='w') + self._reporter.write_thermodynamic_states(self._thermodynamic_states, + self._unsampled_states) + + # Store run metadata and ReplicaExchange options. + self._store_options() + self._reporter.write_dict('metadata', self._metadata) + + # Store initial conditions. This forces the storage to be synchronized. + self._report_iteration() + + @mpiplus.on_single_node(rank=0, broadcast_result=False, sync_nodes=False) + @mpiplus.delayed_termination + @utils.with_timer('Writing iteration information to storage') + def _report_iteration(self): + """Store positions, states, and energies of current iteration. + + This is executed only on MPI node 0 and it's not blocking. The + termination is delayed so that the file is not written only with + partial data if the program gets interrupted. + + Subclasses should not attempt to modify this function as it can + force either duplicated or missed ``sync()`` calls. In the event + that they MUST overwrite this function, the last call in the whole + stack should be :func:multistate.MultiStateReporter.write_last_iteration + """ + # Call report_iteration_items for a subclass-friendly function + self._report_iteration_items() + self._reporter.write_timestamp(self._iteration) + self._reporter.write_last_iteration(self._iteration) + + @mpiplus.on_single_node(rank=0, broadcast_result=False, sync_nodes=False) + @mpiplus.delayed_termination + def _report_iteration_items(self): + """ + Sub-function of :func:`_report_iteration` which handles all the actual individual item reporting in a + sub-class friendly way. The final actions of writing timestamp, last-good-iteration, and syncing + should be left to the :func:`_report_iteration` and subclasses should extend this function instead + """ + self._reporter.write_sampler_states(self._sampler_states, self._iteration) + self._reporter.write_replica_thermodynamic_states(self._replica_thermodynamic_states, self._iteration) + self._reporter.write_mcmc_moves(self._mcmc_moves) # MCMCMoves can store internal statistics. + self._reporter.write_energies(self._energy_thermodynamic_states, self._neighborhoods, self._energy_unsampled_states, + self._iteration) + self._reporter.write_mixing_statistics(self._n_accepted_matrix, self._n_proposed_matrix, self._iteration) + + @classmethod + def default_options(cls): + """ + dict of all default class options (keyword arguments for __init__ for class and superclasses) + """ + options_to_report = dict() + for c in inspect.getmro(cls): + parameter_names, _, _, defaults = inspect.getargspec(c.__init__) + if defaults: + class_options = {parameter_name: defaults[index] for (index, parameter_name) in + enumerate(parameter_names[-len(defaults):])} + options_to_report.update(class_options) + options_to_report.pop('mcmc_moves') + return options_to_report + + @property + def options(self): + """ + dict of all class options (keyword arguments for __init__ for class and superclasses) + """ + options_to_report = dict() + for cls in inspect.getmro(type(self)): + parameter_names, _, _, defaults = inspect.getargspec(cls.__init__) + if defaults: + class_options = {parameter_name: getattr(self, '_' + parameter_name) for + parameter_name in parameter_names[-len(defaults):]} + options_to_report.update(class_options) + options_to_report.pop('mcmc_moves') + return options_to_report + + def _store_options(self): + """Store __init__ parameters (beside MCMCMoves) in storage file.""" + logger.debug("Storing general ReplicaExchange options...") + self._reporter.write_dict('options', self.options) + + # ------------------------------------------------------------------------- + # Locality + # ------------------------------------------------------------------------- + + def _neighborhood(self, state_index): + """Compute the states in the local neighborhood determined by self.locality + + Parameters + ---------- + state_index : int + The current state + + Returns + ------- + neighborhood : list of int + The states in the local neighborhood + """ + if self.locality is None: + # Global neighborhood + return list(range(0, self.n_states)) + else: + # Local neighborhood specified by 'locality' + return list(range(max(0, state_index - self.locality), min(self.n_states, state_index + self.locality + 1))) + + # ------------------------------------------------------------------------- + # Internal-usage: Distributed tasks. + # ------------------------------------------------------------------------- + + @utils.with_timer('Propagating all replicas') + def _propagate_replicas(self): + """Propagate all replicas.""" + # TODO Report on efficiency of dyanmics (fraction of time wasted to overhead). + logger.debug("Propagating all replicas...") + + # Distribute propagation across nodes. Only node 0 will get all positions + # and box vectors. The other nodes, only need the positions that they use + # for propagation and computation of the energy matrix entries. + propagated_states, replica_ids = mpiplus.distribute(self._propagate_replica, range(self.n_replicas), + send_results_to=0) + + # Update all sampler states. For non-0 nodes, this will update only the + # sampler states associated to the replicas propagated by this node. + for replica_id, propagated_state in zip(replica_ids, propagated_states): + propagated_positions, propagated_box_vectors = propagated_state # Unpack. + self._sampler_states[replica_id].positions = propagated_positions + self._sampler_states[replica_id].box_vectors = propagated_box_vectors + + # Gather all MCMCMoves statistics. All nodes must have these up-to-date + # since they are tied to the ThermodynamicState, not the replica. + all_statistics = mpiplus.distribute(self._get_replica_move_statistics, range(self.n_replicas), + send_results_to='all') + for replica_id in range(self.n_replicas): + if len(all_statistics[replica_id]) > 0: + thermodynamic_state_id = self._replica_thermodynamic_states[replica_id] + self._mcmc_moves[thermodynamic_state_id].statistics = all_statistics[replica_id] + + def _propagate_replica(self, replica_id): + """Propagate thermodynamic state associated to the given replica.""" + # Retrieve thermodynamic, sampler states, and MCMC move of this replica. + thermodynamic_state_id = self._replica_thermodynamic_states[replica_id] + thermodynamic_state = self._thermodynamic_states[thermodynamic_state_id] + mcmc_move = self._mcmc_moves[thermodynamic_state_id] + sampler_state = self._sampler_states[replica_id] + + # Apply MCMC move. + try: + mcmc_move.apply(thermodynamic_state, sampler_state) + except mcmc.IntegratorMoveError as e: + # Save NaNnig context and MCMove before aborting. + output_dir = os.path.join(os.path.dirname(self._reporter.filepath), 'nan-error-logs') + file_name = 'iteration{}-replica{}-state{}'.format(self._iteration, replica_id, + thermodynamic_state_id) + e.serialize_error(os.path.join(output_dir, file_name)) + message = ('Propagating replica {} at state {} resulted in a NaN!\n' + 'The state of the system and integrator before the error were saved' + ' in {}').format(replica_id, thermodynamic_state_id, output_dir) + logger.critical(message) + raise SimulationNaNError(message) + + # Return new positions and box vectors. + return sampler_state.positions, sampler_state.box_vectors + + def _get_replica_move_statistics(self, replica_id): + """Return the statistics of the MCMCMove currently associated to this replica.""" + thermodynamic_state_id = self._replica_thermodynamic_states[replica_id] + mcmc_move = self._mcmc_moves[thermodynamic_state_id] + + try: + move_statistics = mcmc_move.statistics + except AttributeError: + move_statistics = {} + + return move_statistics + + def _minimize_replica(self, replica_id, tolerance, max_iterations): + """Minimize the specified replica. + """ + + # Retrieve thermodynamic and sampler states. + thermodynamic_state_id = self._replica_thermodynamic_states[replica_id] + thermodynamic_state = self._thermodynamic_states[thermodynamic_state_id] + sampler_state = self._sampler_states[replica_id] + + # Use the FIRE minimizer + integrator = FIREMinimizationIntegrator(tolerance=tolerance) + + # Create context + context = thermodynamic_state.create_context(integrator) + + # Set initial positions and box vectors. + sampler_state.apply_to_context(context) + + # Compute the initial energy of the system for logging. + initial_energy = thermodynamic_state.reduced_potential(context) + logger.debug('Replica {}/{}: initial energy {:8.3f}kT'.format( + replica_id + 1, self.n_replicas, initial_energy)) + + # Minimize energy. + try: + if max_iterations == 0: + logger.debug('Using FIRE: tolerance {} minimizing to convergence'.format(tolerance)) + while integrator.getGlobalVariableByName('converged') < 1: + integrator.step(50) + else: + logger.debug('Using FIRE: tolerance {} max_iterations {}'.format(tolerance, max_iterations)) + integrator.step(max_iterations) + except Exception as e: + if str(e) == 'Particle coordinate is nan': + logger.debug('NaN encountered in FIRE minimizer; falling back to L-BFGS after resetting positions') + sampler_state.apply_to_context(context) + openmm.LocalEnergyMinimizer.minimize(context, tolerance, max_iterations) + else: + raise e + + # Get the minimized positions. + sampler_state.update_from_context(context) + + # Compute the final energy of the system for logging. + final_energy = thermodynamic_state.reduced_potential(sampler_state) + logger.debug('Replica {}/{}: final energy {:8.3f}kT'.format( + replica_id + 1, self.n_replicas, final_energy)) + + # Clean up the integrator + del context + + # Return minimized positions. + return sampler_state.positions + + @utils.with_timer('Computing energy matrix') + def _compute_energies(self): + """Compute energies of all replicas at all states.""" + + # Determine neighborhoods (all nodes) + self._neighborhoods[:,:] = False + for (replica_index, state_index) in enumerate(self._replica_thermodynamic_states): + neighborhood = self._neighborhood(state_index) + self._neighborhoods[replica_index, neighborhood] = True + + # Distribute energy computation across nodes. Only node 0 receives + # all the energies since it needs to store them and mix states. + new_energies, replica_ids = mpiplus.distribute(self._compute_replica_energies, range(self.n_replicas), + send_results_to=0) + + # Update energy matrices. Non-0 nodes update only the energies computed by this replica. + for replica_id, energies in zip(replica_ids, new_energies): + energy_thermodynamic_states, energy_unsampled_states = energies # Unpack. + neighborhood = self._neighborhood(self._replica_thermodynamic_states[replica_id]) + self._energy_thermodynamic_states[replica_id, neighborhood] = energy_thermodynamic_states + self._energy_unsampled_states[replica_id] = energy_unsampled_states + + def _compute_replica_energies(self, replica_id): + """Compute the energy for the replica in every ThermodynamicState.""" + # Determine neighborhood + state_index = self._replica_thermodynamic_states[replica_id] + neighborhood = self._neighborhood(state_index) + + # Only compute energies of the sampled states over neighborhoods. + energy_neighborhood_states = np.zeros(len(neighborhood)) + energy_unsampled_states = np.zeros(len(self._unsampled_states)) + neighborhood_thermodynamic_states = [self._thermodynamic_states[n] for n in neighborhood] + + # Retrieve sampler state associated to this replica. + sampler_state = self._sampler_states[replica_id] + + # Compute energy for all thermodynamic states. + for energies, states in [(energy_neighborhood_states, neighborhood_thermodynamic_states), + (energy_unsampled_states, self._unsampled_states)]: + # Group thermodynamic states by compatibility. + compatible_groups, original_indices = states.group_by_compatibility(states) + + # Compute the reduced potentials of all the compatible states. + for compatible_group, state_indices in zip(compatible_groups, original_indices): + # Get the context, any Integrator works. + context, integrator = cache.global_context_cache.get_context(compatible_group[0]) + + # Update positions and box vectors. We don't need + # to set Context velocities for the potential. + sampler_state.apply_to_context(context, ignore_velocities=True) + + # Compute and update the reduced potentials. + compatible_energies = states.ThermodynamicState.reduced_potential_at_states( + context, compatible_group) + for energy_idx, state_idx in enumerate(state_indices): + energies[state_idx] = compatible_energies[energy_idx] + + # Return the new energies. + return energy_neighborhood_states, energy_unsampled_states + + # ------------------------------------------------------------------------- + # Internal-usage: Replicas mixing. + # ------------------------------------------------------------------------- + + @mpiplus.on_single_node(0, broadcast_result=True) + def _mix_replicas(self): + """Do nothing to replicas.""" + logger.debug("Mixing replicas (does nothing for MultiStateSampler)...") + + # Reset storage to keep track of swap attempts this iteration. + self._n_accepted_matrix[:, :] = 0 + self._n_proposed_matrix[:, :] = 0 + + # Determine fraction of swaps accepted this iteration. + n_swaps_proposed = self._n_proposed_matrix.sum() + n_swaps_accepted = self._n_accepted_matrix.sum() + swap_fraction_accepted = 0.0 + if n_swaps_proposed > 0: + swap_fraction_accepted = n_swaps_accepted / n_swaps_proposed # Python 3 uses true division for / + logger.debug("Accepted {}/{} attempted swaps ({:.1f}%)".format(n_swaps_accepted, n_swaps_proposed, + swap_fraction_accepted * 100.0)) + + # ------------------------------------------------------------------------- + # Internal-usage: Offline and online analysis + # ------------------------------------------------------------------------- + + @mpiplus.on_single_node(rank=0, broadcast_result=True) + @mpiplus.delayed_termination + @utils.with_timer('Computing offline free energy estimate') + def _offline_analysis(self): + """Compute offline estimate of free energies + + This scheme only works with global localities. + """ + # TODO: Currently, this just uses MBAR, which only works for global neighborhoods. + # TODO: Add Local WHAM support. + + if self.locality is not None: + raise Exception('Cannot use MBAR with non-global locality.') + + # This relative import is down here because having it at the top causes an ImportError. + # __init__ pulls in multistate, which pulls in analyze, which pulls in MultiState. Because the first + # MultiStateSampler never finished importing, its not in the name space which causes relative analyze import of + # MultiStateSampler to crash as neither of them are the __main__ package. + # https://stackoverflow.com/questions/6351805/cyclic-module-dependencies-and-relative-imports-in-python + from .multistateanalyzer import MultiStateSamplerAnalyzer + + # Start the analysis + bump_error_counter = False + # Set up analyzer + # Unbias restraints is False because this will quickly accumulate large time to re-read the trajectories + # and unbias the restraints every online-analysis. Current model is to use the last_mbar_f_k as a + # hot-start to the analysis. Once we store unbias info as part of a CustomCVForce, we can revisit this choice. + analysis = MultiStateSamplerAnalyzer(self._reporter, analysis_kwargs={'initial_f_k': self._last_mbar_f_k}, + unbias_restraint=False) + + # Indices for online analysis, "i'th index, j'th index" + idx, jdx = 0, -1 + timer = utils.Timer() + timer.start("MBAR") + logger.debug("Computing free energy with MBAR...") + try: # Trap errors for MBAR being under sampled and the W_nk matrix not being normalized correctly + mbar = analysis.mbar + free_energy, err_free_energy = analysis.get_free_energy() + except ParameterError as e: + # We don't update self._last_err_free_energy here since if it + # wasn't below the target threshold before, it won't stop MultiStateSampler now. + bump_error_counter = True + self._online_error_bank.append(e) + if len(self._online_error_bank) > 6: + # Cache only the last set + self._online_error_bank.pop(0) + free_energy = None + else: + self._last_mbar_f_k = mbar.f_k + free_energy = free_energy[idx, jdx] + self._last_err_free_energy = err_free_energy[idx, jdx] + logger.debug("Current Free Energy Estimate is {} +- {} kT".format(free_energy, + self._last_err_free_energy)) + # Trap a case when errors don't converge (usually due to under sampling) + if np.isnan(self._last_err_free_energy): + self._last_err_free_energy = np.inf + timer.stop("MBAR") + + # Raise an exception after 6 times MBAR gave an error. + if bump_error_counter: + self._online_error_trap_counter += 1 + # Will never be true, but code left in place in case we change logic to allow again + if self._online_error_trap_counter >= np.inf: + logger.debug("Thrown MBAR Errors:") + for err in self._online_error_bank: + logger.debug(str(err)) + raise RuntimeError("Online Analysis has failed too many times! Please " + "check the latest logs to see the latest thrown errors!") + # Don't write out the free energy in case of error. + return + + # Write out the numbers + self._reporter.write_online_data_dynamic_and_static(self._iteration, + f_k=self._last_mbar_f_k, + free_energy=(free_energy, self._last_err_free_energy)) + + return self._last_err_free_energy + + @mpiplus.on_single_node(rank=0, broadcast_result=True) + @mpiplus.delayed_termination + @utils.with_timer('Computing online free energy estimate') + def _online_analysis(self, gamma0=1.0): + """Perform online analysis of free energies + + This scheme works with all localities: global and local. + + """ + timer = utils.Timer() + timer.start("Online analysis") + from scipy.special import logsumexp + + # TODO: This is experimental + + gamma = gamma0 / float(self._iteration+1) + + if self._last_mbar_f_k is None: + self._last_mbar_f_k = np.zeros([self.n_states], np.float64) + + logZ = - self._last_mbar_f_k + + for (replica_index, state_index) in enumerate(self._replica_thermodynamic_states): + neighborhood = self._neighborhood(state_index) + u_k = self._energy_thermodynamic_states[replica_index,:] + log_P_k = np.zeros([self.n_states], np.float64) + log_pi_k = np.zeros([self.n_states], np.float64) + log_weights = np.zeros([self.n_states], np.float64) + log_P_k[neighborhood] = log_weights[neighborhood] - u_k[neighborhood] + log_P_k[neighborhood] -= logsumexp(log_P_k[neighborhood]) + logZ[neighborhood] += gamma * np.exp(log_P_k[neighborhood] - log_pi_k[neighborhood]) + + # Subtract off logZ[0] to prevent logZ from growing without bound + logZ[:] -= logZ[0] + + self._last_mbar_f_k = -logZ + free_energy = self._last_mbar_f_k[-1] - self._last_mbar_f_k[0] + self._last_err_free_energy = np.Inf + + # Store free energy estimate + self._reporter.write_online_data_dynamic_and_static(self._iteration, + f_k=self._last_mbar_f_k, + free_energy=(free_energy, self._last_err_free_energy)) + + timer.stop("Online analysis") + + # Report online analysis to debug log + logger.debug('*** ONLINE analysis free energies:') + msg = ' ' + for x in self._last_mbar_f_k: + msg += '%8.1f' % x + logger.debug(msg) + + return self._last_err_free_energy + + def _update_analysis(self): + """Update online analysis of free energies""" + + # TODO: Currently, this just calls the offline analysis at certain intervals, if requested. + # TODO: Refactor this to always compute fast online analysis, updating with offline analysis infrequently. + + # TODO: Simplify this + if self.online_analysis_interval is None: + logger.debug('No online analysis requested') + analysis_to_perform = None + elif self._iteration < self.online_analysis_minimum_iterations: + logger.debug('Not enough iterations for online analysis (self.online_analysis_minimum_iterations = %d)' % self.online_analysis_minimum_iterations) + analysis_to_perform = 'online' + elif self._iteration % self.online_analysis_interval != 0: + logger.debug('Not an online analysis iteration') + analysis_to_perform = 'online' + elif self.locality is not None: + logger.debug('Not a global locality') + analysis_to_perform = 'online' + else: + logger.debug('Will perform offline analysis') + # All conditions are met for offline analysis + analysis_to_perform = 'offline' + + # Execute selected analysis (only runs on node 0) + if analysis_to_perform == 'online': + self._last_err_free_energy = self._online_analysis() + elif analysis_to_perform == 'offline': + self._last_err_free_energy = self._offline_analysis() + + return + + @staticmethod + def _read_last_free_energy(reporter, iteration): + """Get the last free energy computed from online analysis""" + last_f_k = None + last_free_energy = None + + # Search for a valid free energy from the given iteration + # to the start of the calculation. + try: + free_energy_data = reporter.read_online_analysis_data(None, 'f_k', 'free_energy') + last_f_k = free_energy_data['f_k'] + last_free_energy = free_energy_data['free_energy'] + except ValueError: + for index in range(iteration, 0, -1): + try: + free_energy_data = reporter.read_online_analysis_data(index, 'f_k', 'free_energy') + last_f_k = free_energy_data['f_k'] + last_free_energy = free_energy_data['free_energy'] + except (IndexError, KeyError, ValueError): + # No such f_k written yet (or variable created). + break + # Find an f_k that is not all zeros (or masked and empty) + if not (np.ma.is_masked(last_f_k) or np.all(last_f_k == 0)): + break # Don't need to continue the loop if we already found one + return last_f_k, last_free_energy + + def _is_completed(self, iteration_limit=None): + """Check if we have reached the required number of iterations or statistical error.""" + if iteration_limit is None: + iteration_limit = self.number_of_iterations + return self._is_completed_static(iteration_limit, self._iteration, + self._last_err_free_energy, + self.online_analysis_target_error) + + @staticmethod + def _is_completed_static(iteration_limit, iteration, last_err_free_energy, + online_analysis_target_error): + """Check if we have reached the required number of iterations or statistical error.""" + # Return if we have reached the number of iterations + # or the statistical error target required. + if (iteration >= iteration_limit or ( + last_err_free_energy is not None and last_err_free_energy <= online_analysis_target_error)): + return True + return False + + @staticmethod + def _throw_restoration_error(message): + """Masking function to hide the RestorationError class without exposing it or making it a 'hidden' (_X) error""" + class RestorationError(Exception): + """Represent errors occurring during attempts to restore simulations.""" + def __init__(self, error_message): + super().__init__(error_message) + # Critical messages which have halted a simulation, badly + logger.critical(error_message) + raise RestorationError(message) + + # ------------------------------------------------------------------------- + # Internal-usage: Test globals + # ------------------------------------------------------------------------- + _global_citation_silence = False + + +# ============================================================================== +# MAIN AND TESTS +# ============================================================================== + +if __name__ == "__main__": + import doctest + doctest.testmod() diff --git a/openmmtools/multistate/paralleltempering.py b/openmmtools/multistate/paralleltempering.py new file mode 100644 index 000000000..a59146d8a --- /dev/null +++ b/openmmtools/multistate/paralleltempering.py @@ -0,0 +1,248 @@ +#!/usr/local/bin/env python + +# ============================================================================== +# MODULE DOCSTRING +# ============================================================================== + +""" +ParallelTemperingSampler +======================== + +Derived multi-thermodynamic state multistate class with exchanging configurations between replicas of different +temperatures. This is a special case which accepts a single thermodynamic_state and different temperatures to sample. +If you want different temperatures and Hamiltonians, use ReplicaExchangeSampler with temperatures pre-set. + +COPYRIGHT + +Current version by Andrea Rizzi , Levi N. Naden and +John D. Chodera while at Memorial Sloan Kettering Cancer Center. + +Original version by John D. Chodera while at the University of +California Berkeley. + +LICENSE + +This code is licensed under the latest available version of the MIT License. + +""" + +import copy +import math +import logging +import numpy as np + +from openmmtools import states, cache, constants +from openmmtools.multistate import ReplicaExchangeSampler, ReplicaExchangeAnalyzer, MultiStateReporter + +logger = logging.getLogger(__name__) + + +# ============================================================================== +# PARALLEL TEMPERING +# ============================================================================== + +class ParallelTemperingSampler(ReplicaExchangeSampler): + """Parallel tempering simulation facility. + + This class provides a facility for parallel tempering simulations. It + is a subclass of :class:`ReplicaExchange`, but provides efficiency improvements + for parallel tempering simulations, so should be preferred for this type + of simulation. In particular, this makes use of the fact that the reduced + potentials are linear in inverse temperature. + + Examples + -------- + + Create the system. + + >>> from simtk import unit + >>> from openmmtools import testsystems, states, mcmc + >>> import tempfile + >>> testsystem = testsystems.AlanineDipeptideImplicit() + + Create thermodynamic states for parallel tempering with exponentially-spaced schedule. + + >>> n_replicas = 3 # Number of temperature replicas. + >>> T_min = 298.0 * unit.kelvin # Minimum temperature. + >>> T_max = 600.0 * unit.kelvin # Maximum temperature. + >>> reference_state = states.ThermodynamicState(system=testsystem.system, temperature=T_min) + + Initialize simulation object with options. Run with a GHMC integrator. + + >>> move = mcmc.GHMCMove(timestep=2.0*unit.femtoseconds, n_steps=50) + >>> simulation = ParallelTemperingSampler(mcmc_moves=move, number_of_iterations=2) + + Create simulation with its storage file (in a temporary directory) and run. + + >>> storage_path = tempfile.NamedTemporaryFile(delete=False).name + '.nc' + >>> reporter = MultiStateReporter(storage_path, checkpoint_interval=10) + >>> simulation.create(reference_state, + ... states.SamplerState(testsystem.positions), + ... reporter, min_temperature=T_min, + ... max_temperature=T_max, n_temperatures=n_replicas) + >>> simulation.run(n_iterations=1) + + Clean up. + + >>> os.remove(storage_path) + + + See Also + -------- + MultiStateSampler + ReplicaExchangeSampler + + """ + + _TITLE_TEMPLATE = ('Parallel tempering simulation created using ParallelTempering ' + 'class of yank.multistate on {}') + + def create(self, thermodynamic_state, sampler_states: list, storage, + min_temperature=None, max_temperature=None, n_temperatures=None, + temperatures=None, **kwargs): + """Initialize a parallel tempering simulation object. + + Parameters + ---------- + thermodynamic_state : openmmtools.states.ThermodynamicState + Reference thermodynamic state that will be simulated at the given + temperatures. + + WARNING: This is a SINGLE state, not a list of states! + + sampler_states : openmmtools.states.SamplerState or list + One or more sets of initial sampler states. If a list of SamplerStates, + they will be assigned to replicas in a round-robin fashion. + storage : str or Reporter + If str: path to the storage file, checkpoint options are default + If Reporter: Instanced :class:`Reporter` class, checkpoint information is read from + In the future this will be able to take a Storage class as well. + min_temperature : simtk.unit.Quantity, optional + Minimum temperature (units of temperature, default is None). + max_temperature : simtk.unit.Quantity, optional + Maximum temperature (units of temperature, default is None). + n_temperatures : int, optional + Number of exponentially-spaced temperatures between ``min_temperature`` + and ``max_temperature`` (default is None). + temperatures : list of simtk.unit.Quantity, optional + If specified, this list of temperatures will be used instead of + ``min_temperature``, ``max_temperature``, and ``n_temperatures`` (units of temperature, + default is None). + metadata : dict, optional + Simulation metadata to be stored in the file. + + Notes + ----- + Either (``min_temperature``, ``max_temperature``, ``n_temperatures``) must all be + specified or the list of '`temperatures`' must be specified. + + """ + # Create thermodynamic states from temperatures. + if not isinstance(thermodynamic_state, states.ThermodynamicState): + raise ValueError("ParallelTempering only accepts a single ThermodynamicState!\n" + "If you have already set temperatures in your list of states, please use the " + "standard ReplicaExchange class with your list of states.") + if temperatures is not None: + logger.debug("Using provided temperatures") + elif min_temperature is not None and max_temperature is not None and n_temperatures is not None: + temperatures = [min_temperature + (max_temperature - min_temperature) * + (math.exp(i / n_temperatures-1) - 1.0) / (math.e - 1.0) + for i in range(n_temperatures)] # Python 3 uses true division for / + logger.debug('using temperatures {}'.format(temperatures)) + else: + raise ValueError("Either 'temperatures' or ('min_temperature', 'max_temperature', " + "and 'n_temperatures') must be provided.") + + thermodynamic_states = [copy.deepcopy(thermodynamic_state) for _ in range(n_temperatures)] + for state, temperature in zip(thermodynamic_states, temperatures): + state.temperature = temperature + + # Initialize replica-exchange simulation. + super(ParallelTemperingSampler, self).create(thermodynamic_states, sampler_states, storage=storage, **kwargs) + + def _compute_replica_energies(self, replica_id): + """Compute the energy for the replica at every temperature. + + Because only the temperatures differ among replicas, we replace the generic O(N^2) + replica-exchange implementation with an O(N) implementation. + + """ + # Initialize replica energies for each thermodynamic state. + energy_thermodynamic_states = np.zeros(self.n_states) + energy_unsampled_states = np.zeros(len(self._unsampled_states)) + + # Determine neighborhood + state_index = self._replica_thermodynamic_states[replica_id] + neighborhood = self._neighborhood(state_index) + # Only compute energies over neighborhoods + energy_neighborhood_states = energy_thermodynamic_states[neighborhood] # Array, can be indexed like this + neighborhood_thermodynamic_states = [self._thermodynamic_states[n] for n in neighborhood] # List + + # Retrieve sampler states associated to this replica. + sampler_state = self._sampler_states[replica_id] + + # Thermodynamic state differ only by temperatures. + reference_thermodynamic_state = self._thermodynamic_states[0] + + # Get the context, any Integrator works. + context, integrator = cache.global_context_cache.get_context(reference_thermodynamic_state) + + # Update positions and box vectors. + sampler_state.apply_to_context(context) + + # Compute energy. + reference_reduced_potential = reference_thermodynamic_state.reduced_potential(context) + + # Strip reference potential of reference state's beta. + reference_beta = 1.0 / (constants.kB * reference_thermodynamic_state.temperature) + reference_reduced_potential /= reference_beta + + # Update potential energy by temperature. + for thermodynamic_state_id, thermodynamic_state in enumerate(neighborhood_thermodynamic_states): + beta = 1.0 / (constants.kB * thermodynamic_state.temperature) + energy_neighborhood_states[thermodynamic_state_id] = beta * reference_reduced_potential + + # Since no assumptions can be made about the unsampled thermodynamic states, do it the hard way + for unsampled_id, state in enumerate(self._unsampled_states): + if unsampled_id == 0 or not state.is_state_compatible(context_state): + context_state = state + + # Get the context, any Integrator works. + context, integrator = cache.global_context_cache.get_context(state) + + # Update positions and box vectors. We don't need + # to set Context velocities for the potential. + sampler_state.apply_to_context(context, ignore_velocities=True) + else: + # If this state is compatible with the context, just fix the + # thermodynamic state as positions/box vectors are the same. + state.apply_to_context(context) + + # Compute energy. + energy_unsampled_states[unsampled_id] = state.reduced_potential(context) + + # Return the new energies. + return energy_neighborhood_states, energy_unsampled_states + + +class ParallelTemperingAnalyzer(ReplicaExchangeAnalyzer): + """ + The ParallelTemperingAnalyzer is the analyzer for a simulation generated from a Parallel Tempering sampler + simulation, implemented as an instance of the :class:`ReplicaExchangeAnalyzer` as the sampler is a subclass of + the :class:`yank.multistate.ReplicaExchangeSampler` + + See Also + -------- + PhaseAnalyzer + ReplicaExchangeAnalyzer + + """ + pass + +# ============================================================================== +# MAIN AND TESTS +# ============================================================================== + +if __name__ == "__main__": + import doctest + doctest.testmod() diff --git a/openmmtools/multistate/replicaexchange.py b/openmmtools/multistate/replicaexchange.py new file mode 100644 index 000000000..90a2731b7 --- /dev/null +++ b/openmmtools/multistate/replicaexchange.py @@ -0,0 +1,383 @@ +#!/usr/local/bin/env python + +# ============================================================================== +# MODULE DOCSTRING +# ============================================================================== + +""" +ReplicaExchangeSampler +====================== + +Derived multi-thermodynamic state multistate class with exchanging configurations between replicas + +COPYRIGHT + +Current version by Andrea Rizzi , Levi N. Naden and +John D. Chodera while at Memorial Sloan Kettering Cancer Center. + +Original version by John D. Chodera while at the University of +California Berkeley. + +LICENSE + +This code is licensed under the latest available version of the MIT License. + +""" + +# ============================================================================== +# GLOBAL IMPORTS +# ============================================================================== + +import os +import math +import copy +import logging + +import numpy as np +import mdtraj as md + +from openmmtools import multistate, utils +import mpiplus + + +logger = logging.getLogger(__name__) + + +# ============================================================================== +# REPLICA-EXCHANGE SIMULATION +# ============================================================================== + +class ReplicaExchangeSampler(multistate.MultiStateSampler): + """Replica-exchange simulation facility. + + This MultiStateSampler class provides a general replica-exchange simulation facility, + allowing any set of thermodynamic states to be specified, along with a + set of initial positions to be assigned to the replicas in a round-robin + fashion. + + No distinction is made between one-dimensional and multidimensional replica + layout. By default, the replica mixing scheme attempts to mix *all* replicas + to minimize slow diffusion normally found in multidimensional replica exchange + simulations (Modification of the 'replica_mixing_scheme' setting will allow + the traditional 'neighbor swaps only' scheme to be used.) + + Stored configurations, energies, swaps, and restart information are all written + to a single output file using the platform portable, robust, and efficient + NetCDF4 library. + + Parameters + ---------- + mcmc_moves : MCMCMove or list of MCMCMove, optional + The MCMCMove used to propagate the states. If a list of MCMCMoves, + they will be assigned to the correspondent thermodynamic state on + creation. If None is provided, Langevin dynamics with 2fm timestep, 5.0/ps collision rate, + and 500 steps per iteration will be used. + number_of_iterations : int or infinity, optional, default: 1 + The number of iterations to perform. Both ``float('inf')`` and + ``numpy.inf`` are accepted for infinity. If you set this to infinity, + be sure to set also ``online_analysis_interval``. + replica_mixing_scheme : 'swap-all', 'swap-neighbors' or None, Default: 'swap-all' + The scheme used to swap thermodynamic states between replicas. + online_analysis_interval : None or Int >= 1, optional, default None + Choose the interval at which to perform online analysis of the free energy. + + After every interval, the simulation will be stopped and the free energy estimated. + + If the error in the free energy estimate is at or below ``online_analysis_target_error``, then the simulation + will be considered completed. + + online_analysis_target_error : float >= 0, optional, default 0.2 + The target error for the online analysis measured in kT per phase. + + Once the free energy is at or below this value, the phase will be considered complete. + + If ``online_analysis_interval`` is None, this option does nothing. + + online_analysis_minimum_iterations : int >= 0, optional, default 50 + Set the minimum number of iterations which must pass before online analysis is carried out. + + Since the initial samples likely not to yield a good estimate of free energy, save time and just skip them + If ``online_analysis_interval`` is None, this does nothing + + Attributes + ---------- + n_replicas + iteration + mcmc_moves + sampler_states + metadata + is_completed + + Examples + -------- + Parallel tempering simulation of alanine dipeptide in implicit solvent (replica + exchange among temperatures). This is just an illustrative example; use :class:`ParallelTempering` + class for actual production parallel tempering simulations. + + Create the system. + + >>> import math + >>> from simtk import unit + >>> from openmmtools import testsystems, states, mcmc + >>> testsystem = testsystems.AlanineDipeptideImplicit() + + Create thermodynamic states for parallel tempering with exponentially-spaced schedule. + + >>> n_replicas = 3 # Number of temperature replicas. + >>> T_min = 298.0 * unit.kelvin # Minimum temperature. + >>> T_max = 600.0 * unit.kelvin # Maximum temperature. + >>> temperatures = [T_min + (T_max - T_min) * (math.exp(float(i) / float(nreplicas-1)) - 1.0) / (math.e - 1.0) + ... for i in range(n_replicas)] + >>> thermodynamic_states = [states.ThermodynamicState(system=testsystem.system, temperature=T) + ... for T in temperatures] + + Initialize simulation object with options. Run with a GHMC integrator. + + >>> move = mcmc.GHMCMove(timestep=2.0*unit.femtoseconds, n_steps=50) + >>> simulation = ReplicaExchangeSampler(mcmc_moves=move, number_of_iterations=2) + + Create simulation with its storage file (in a temporary directory) and run. + + >>> storage_path = tempfile.NamedTemporaryFile(delete=False).name + '.nc' + >>> reporter = multistate.MultiStateReporter(storage_path, checkpoint_interval=1) + >>> simulation.create(thermodynamic_states=thermodynamic_states, + >>> sampler_states=states.SamplerState(testsystem.positions), + >>> storage=reporter) + >>> simulation.run() # This runs for a maximum of 2 iterations. + >>> simulation.iteration + 2 + >>> simulation.run(n_iterations=1) + >>> simulation.iteration + 2 + + To resume a simulation from an existing storage file and extend it beyond + the original number of iterations. + + >>> del simulation + >>> simulation = ReplicaExchangeSampler.from_storage(reporter) + >>> simulation.extend(n_iterations=1) + >>> simulation.iteration + 3 + + You can extract several information from the NetCDF file using the Reporter + class while the simulation is running. This reads the SamplerStates of every + run iteration. + + >>> reporter = multistate.MultiStateReporter(storage=storage_path, open_mode='r', checkpoint_interval=1) + >>> sampler_states = reporter.read_sampler_states(iteration=range(1, 4)) + >>> len(sampler_states) + 3 + >>> sampler_states[-1].positions.shape # Alanine dipeptide has 22 atoms. + (22, 3) + + Clean up. + + >>> os.remove(storage_path) + + + :param number_of_iterations: Maximum number of integer iterations that will be run + + :param replica_mixing_scheme: Scheme which describes how replicas are exchanged each iteration as string + + :param online_analysis_interval: How frequently to carry out online analysis in number of iterations + + :param online_analysis_target_error: Target free energy difference error float at which simulation will be stopped during online analysis, in dimensionless energy + + :param online_analysis_minimum_iterations: Minimum number of iterations needed before online analysis is run as int + + """ + + # ------------------------------------------------------------------------- + # Constructors. + # ------------------------------------------------------------------------- + + def __init__(self, replica_mixing_scheme='swap-all', **kwargs): + + # Initialize multi-state sampler simulation. + super(ReplicaExchangeSampler, self).__init__(**kwargs) + self.replica_mixing_scheme = replica_mixing_scheme + + class _StoredProperty(multistate.MultiStateSampler._StoredProperty): + + @staticmethod + def _repex_mixing_scheme_validator(instance, replica_mixing_scheme): + supported_schemes = ['swap-all', 'swap-neighbors', None] + if replica_mixing_scheme not in supported_schemes: + raise ValueError("Unknown replica mixing scheme '{}'. Supported values " + "are {}.".format(replica_mixing_scheme, supported_schemes)) + if instance.locality is not None: + if replica_mixing_scheme not in ['swap-neighbors']: + raise ValueError("replica_mixing_scheme must be 'swap-neighbors' if locality is used") + return replica_mixing_scheme + + replica_mixing_scheme = _StoredProperty('replica_mixing_scheme', + validate_function=_StoredProperty._repex_mixing_scheme_validator) + + _TITLE_TEMPLATE = ('Replica-exchange sampler simulation created using ReplicaExchangeSampler class ' + 'of yank.multistate on {}') + + def _pre_write_create(self, thermodynamic_states, sampler_states, *args, **kwargs): + """Overwrite parent implementation to make sure the number of + thermodynamic states is equal to the number of sampler states. + """ + # Make sure there are no more sampler states than thermodynamic states. + n_states = len(thermodynamic_states) + if len(sampler_states) > n_states: + raise ValueError('Passed {} SamplerStates but only {} ThermodynamicStates'.format( + len(sampler_states), n_states)) + + # Distribute sampler states to replicas in a round-robin fashion. + # The sampler states are deep-copied inside super()._pre_write_create(). + sampler_states = [sampler_states[i % len(sampler_states)] for i in range(n_states)] + + super()._pre_write_create(thermodynamic_states, sampler_states, *args, **kwargs) + + @mpiplus.on_single_node(0, broadcast_result=True) + def _mix_replicas(self): + """Attempt to swap replicas according to user-specified scheme.""" + logger.debug("Mixing replicas...") + + # Reset storage to keep track of swap attempts this iteration. + self._n_accepted_matrix[:, :] = 0 + self._n_proposed_matrix[:, :] = 0 + + # Perform swap attempts according to requested scheme. + with utils.time_it('Mixing of replicas'): + if self.replica_mixing_scheme == 'swap-neighbors': + self._mix_neighboring_replicas() + elif self.replica_mixing_scheme == 'swap-all': + # Try to use cython-accelerated mixing code if possible, + # otherwise fall back to Python-accelerated code. + try: + self._mix_all_replicas_cython() + except ValueError as e: + logger.warning(e.message) + self._mix_all_replicas() + else: + assert self.replica_mixing_scheme is None + + # Determine fraction of swaps accepted this iteration. + n_swaps_proposed = self._n_proposed_matrix.sum() + n_swaps_accepted = self._n_accepted_matrix.sum() + swap_fraction_accepted = 0.0 + if n_swaps_proposed > 0: + # TODO drop casting to float when dropping Python 2 support. + swap_fraction_accepted = float(n_swaps_accepted) / n_swaps_proposed + logger.debug("Accepted {}/{} attempted swaps ({:.1f}%)".format(n_swaps_accepted, n_swaps_proposed, + swap_fraction_accepted * 100.0)) + + def _mix_all_replicas_cython(self): + """Exchange all replicas with Cython-accelerated code.""" + from .mixing._mix_replicas import _mix_replicas_cython + + replica_states = md.utils.ensure_type(self._replica_thermodynamic_states, np.int64, 1, "Replica States") + u_kl = md.utils.ensure_type(self._energy_thermodynamic_states, np.float64, 2, "Reduced Potentials") + n_proposed_matrix = md.utils.ensure_type(self._n_proposed_matrix, np.int64, 2, "Nij Proposed Swaps") + n_accepted_matrix = md.utils.ensure_type(self._n_accepted_matrix, np.int64, 2, "Nij Accepted Swaps") + _mix_replicas_cython(self.n_replicas**4, self.n_replicas, replica_states, + u_kl, n_proposed_matrix, n_accepted_matrix) + + self._replica_thermodynamic_states = replica_states + self._n_proposed_matrix = n_proposed_matrix + self._n_accepted_matrix = n_accepted_matrix + + def _mix_all_replicas(self): + """Exchange all replicas with Python.""" + # Determine number of swaps to attempt to ensure thorough mixing. + # TODO: Replace this with analytical result computed to guarantee sufficient mixing, or + # TODO: adjust it based on how many we can afford to do and not have mixing take a + # TODO: substantial fraction of iteration time. + nswap_attempts = self.n_replicas**5 # Number of swaps to attempt (ideal, but too slow!) + nswap_attempts = self.n_replicas**3 # Best compromise for pure Python? + + logger.debug("Will attempt to swap all pairs of replicas, using a total of %d attempts." % nswap_attempts) + + # Attempt swaps to mix replicas. + for swap_attempt in range(nswap_attempts): + # Choose random replicas uniformly to attempt to swap. + replica_i = np.random.randint(self.n_replicas) + replica_j = np.random.randint(self.n_replicas) + self._attempt_swap(replica_i, replica_j) + + def _mix_neighboring_replicas(self): + """Attempt exchanges between neighboring replicas only.""" + logger.debug("Will attempt to swap only neighboring replicas.") + + # TODO: Extend this to allow more remote swaps or more thorough mixing if locality > 1. + + # Attempt swaps of pairs of replicas using traditional scheme (e.g. [0,1], [2,3], ...). + offset = np.random.randint(2) # Offset is 0 or 1. + for thermodynamic_state_i in range(offset, self.n_replicas-1, 2): + thermodynamic_state_j = thermodynamic_state_i + 1 # Neighboring state. + + # Determine which replicas currently hold the thermodynamic states. + replica_i = np.where(self._replica_thermodynamic_states == thermodynamic_state_i) + replica_j = np.where(self._replica_thermodynamic_states == thermodynamic_state_j) + self._attempt_swap(replica_i, replica_j) + + def _attempt_swap(self, replica_i, replica_j): + """Attempt a single exchange between two replicas.""" + # Determine the thermodynamic states associated to these replicas. + thermodynamic_state_i = self._replica_thermodynamic_states[replica_i] + thermodynamic_state_j = self._replica_thermodynamic_states[replica_j] + + # Compute log probability of swap. + energy_ij = self._energy_thermodynamic_states[replica_i, thermodynamic_state_j] + energy_ji = self._energy_thermodynamic_states[replica_j, thermodynamic_state_i] + energy_ii = self._energy_thermodynamic_states[replica_i, thermodynamic_state_i] + energy_jj = self._energy_thermodynamic_states[replica_j, thermodynamic_state_j] + log_p_accept = - (energy_ij + energy_ji) + energy_ii + energy_jj + + # Record that this move has been proposed. + self._n_proposed_matrix[thermodynamic_state_i, thermodynamic_state_j] += 1 + self._n_proposed_matrix[thermodynamic_state_j, thermodynamic_state_i] += 1 + + # Accept or reject. + if log_p_accept >= 0.0 or np.random.rand() < math.exp(log_p_accept): + # Swap states in replica slots i and j. + self._replica_thermodynamic_states[replica_i] = thermodynamic_state_j + self._replica_thermodynamic_states[replica_j] = thermodynamic_state_i + # Accumulate statistics. + self._n_accepted_matrix[thermodynamic_state_i, thermodynamic_state_j] += 1 + self._n_accepted_matrix[thermodynamic_state_j, thermodynamic_state_i] += 1 + + @mpiplus.on_single_node(rank=0, broadcast_result=False, sync_nodes=False) + def _display_citations(self, overwrite_global=False, citation_stack=None): + """ + Display papers to be cited. + The overwrite_golbal command will force the citation to display even if the "have_citations_been_shown" variable + is True + """ + + gibbs_citations = """\ + Chodera JD and Shirts MR. Replica exchange and expanded ensemble simulations as Gibbs multistate: Simple improvements for enhanced mixing. J. Chem. Phys., 135:194110, 2011. DOI:10.1063/1.3660669 + """ + if self.replica_mixing_scheme == 'swap-all': + if citation_stack is None: + citation_stack = [gibbs_citations] + else: + citation_stack = [gibbs_citations] + citation_stack + super()._display_citations(overwrite_global=overwrite_global, citation_stack=citation_stack) + + +class ReplicaExchangeAnalyzer(multistate.MultiStateSamplerAnalyzer): + + """ + The ReplicaExchangeAnalyzer is the analyzer for a simulation generated from a Replica Exchange sampler simulation, + implemented as an instance of the :class:`MultiStateSamplerAnalyzer`. + + See Also + -------- + PhaseAnalyzer + MultiStateSamplerAnalyzer + + """ + pass + +# ============================================================================== +# MAIN AND TESTS +# ============================================================================== + +if __name__ == "__main__": + import doctest + doctest.testmod() diff --git a/openmmtools/multistate/sams.py b/openmmtools/multistate/sams.py new file mode 100644 index 000000000..690d34e7b --- /dev/null +++ b/openmmtools/multistate/sams.py @@ -0,0 +1,693 @@ +#!/usr/local/bin/env python + +# ============================================================================== +# MODULE DOCSTRING +# ============================================================================== + +""" +SamsSampler +=========== + +Self-adjusted mixture sampling (SAMS), also known as optimally-adjusted mixture sampling. + +This implementation uses stochastic approximation to allow one or more replicas to sample the whole range of thermodynamic states +for rapid online computation of free energies. + +COPYRIGHT + +Written by John D. Chodera while at Memorial Sloan Kettering Cancer Center. + +LICENSE + +This code is licensed under the latest available version of the MIT License. + +""" + +import logging +import numpy as np +import openmmtools as mmtools +from scipy.special import logsumexp + +from openmmtools import multistate, utils +import mpiplus + + +logger = logging.getLogger(__name__) + +# ============================================================================== +# PARALLEL TEMPERING +# ============================================================================== + + +class SAMSSampler(multistate.MultiStateSampler): + """Self-adjusted mixture sampling (SAMS), also known as optimally-adjusted mixture sampling. + + This class provides a facility for self-adjusted mixture sampling simulations. + One or more replicas use the method of expanded ensembles [1] to sample multiple thermodynamic states within each replica, + with log weights for each thermodynamic state adapted on the fly [2] to achieve the desired target probabilities for each state. + + Attributes + ---------- + log_target_probabilities : array-like + log_target_probabilities[state_index] is the log target probability for state ``state_index`` + state_update_scheme : str + Thermodynamic state sampling scheme. One of ['global-jump', 'local-jump', 'restricted-range'] + locality : int + Number of neighboring states on either side to consider for local update schemes + update_stages : str + Number of stages to use for update. One of ['one-stage', 'two-stage'] + weight_update_method : str + Method to use for updating log weights in SAMS. One of ['optimal', 'rao-blackwellized'] + adapt_target_probabilities : bool + If True, target probabilities will be adapted to achieve minimal thermodynamic length between terminal thermodynamic states. + gamma0 : float, optional, default=0.0 + Initial weight adaptation rate. + logZ_guess : array-like of shape [n_states] of floats, optional, default=None + Initial guess for logZ for all states, if available. + + References + ---------- + [1] Lyubartsev AP, Martsinovski AA, Shevkunov SV, and Vorontsov-Velyaminov PN. New approach to Monte Carlo calculation of the free energy: Method of expanded ensembles. JCP 96:1776, 1992 + http://dx.doi.org/10.1063/1.462133 + + [2] Tan, Z. Optimally adjusted mixture sampling and locally weighted histogram analysis, Journal of Computational and Graphical Statistics 26:54, 2017. + http://dx.doi.org/10.1080/10618600.2015.1113975 + + Examples + -------- + SAMS simulation of alanine dipeptide in implicit solvent at different temperatures. + + Create the system: + + >>> import math + >>> from simtk import unit + >>> from openmmtools import testsystems, states, mcmc + >>> testsystem = testsystems.AlanineDipeptideVacuum() + + Create thermodynamic states for parallel tempering with exponentially-spaced schedule: + + >>> n_replicas = 3 # Number of temperature replicas. + >>> T_min = 298.0 * unit.kelvin # Minimum temperature. + >>> T_max = 600.0 * unit.kelvin # Maximum temperature. + >>> temperatures = [T_min + (T_max - T_min) * (math.exp(float(i) / float(nreplicas-1)) - 1.0) / (math.e - 1.0) + ... for i in range(n_replicas)] + >>> thermodynamic_states = [states.ThermodynamicState(system=testsystem.system, temperature=T) + ... for T in temperatures] + + Initialize simulation object with options. Run with a GHMC integrator: + + >>> move = mcmc.GHMCMove(timestep=2.0*unit.femtoseconds, n_steps=50) + >>> simulation = SAMSSampler(mcmc_moves=move, number_of_iterations=2, + >>> state_update_scheme='restricted-range-jump', locality=5, + >>> update_stages='two-stage', flatness_criteria='logZ-flatness', + >>> flatness_threshold=0.2, weight_update_method='rao-blackwellized', + >>> adapt_target_probabilities=False) + + + Create a single-replica SAMS simulation bound to a storage file and run: + + >>> storage_path = tempfile.NamedTemporaryFile(delete=False).name + '.nc' + >>> reporter = multistate.MultiStateReporter(storage_path, checkpoint_interval=1) + >>> simulation.create(thermodynamic_states=thermodynamic_states, + >>> sampler_states=[states.SamplerState(testsystem.positions)], + >>> storage=reporter) + >>> simulation.run() # This runs for a maximum of 2 iterations. + >>> simulation.iteration + 2 + >>> simulation.run(n_iterations=1) + >>> simulation.iteration + 2 + + To resume a simulation from an existing storage file and extend it beyond + the original number of iterations. + + >>> del simulation + >>> simulation = SAMSSampler.from_storage(reporter) + >>> simulation.extend(n_iterations=1) + >>> simulation.iteration + 3 + + You can extract several information from the NetCDF file using the Reporter + class while the simulation is running. This reads the SamplerStates of every + run iteration. + + >>> reporter = multistate.MultiStateReporter(storage=storage_path, open_mode='r', checkpoint_interval=1) + >>> sampler_states = reporter.read_sampler_states(iteration=range(1, 4)) + >>> len(sampler_states) + 3 + >>> sampler_states[-1].positions.shape # Alanine dipeptide has 22 atoms. + (22, 3) + + Clean up. + + >>> os.remove(storage_path) + + See Also + -------- + ReplicaExchangeSampler + + """ + + _TITLE_TEMPLATE = ('Self-adjusted mixture sampling (SAMS) simulation using SAMSSampler ' + 'class of yank.multistate on {}') + + def __init__(self, + number_of_iterations=1, + log_target_probabilities=None, + state_update_scheme='global-jump', + locality=5, + update_stages='two-stage', + flatness_criteria='logZ-flatness', + flatness_threshold=0.2, + weight_update_method='rao-blackwellized', + adapt_target_probabilities=False, + gamma0=1.0, + logZ_guess=None, + **kwargs): + """Initialize a SAMS sampler. + + Parameters + ---------- + log_target_probabilities : array-like or None + ``log_target_probabilities[state_index]`` is the log target probability for thermodynamic state ``state_index`` + When converged, each state should be sampled with the specified log probability. + If None, uniform probabilities for all states will be assumed. + state_update_scheme : str, optional, default='global-jump' + Specifies the scheme used to sample new thermodynamic states given fixed sampler states. + One of ['global-jump', 'local-jump', 'restricted-range-jump'] + ``global_jump`` will allow the sampler to access any thermodynamic state + ``local-jump`` will propose a move to one of the local neighborhood states, and accept or reject. + ``restricted-range`` will compute the probabilities for each of the states in the local neighborhood, increasing jump probability + locality : int, optional, default=1 + Number of neighboring states on either side to consider for local update schemes. + update_stages : str, optional, default='two-stage' + One of ['one-stage', 'two-stage'] + ``one-stage`` will use the asymptotically optimal scheme throughout the entire simulation (not recommended due to slow convergence) + ``two-stage`` will use a heuristic first stage to achieve flat histograms before switching to the asymptotically optimal scheme + flatness_criteria : string, optiona, default='logZ-flatness' + Method of assessing when to switch to asymptotically optimal scheme + One of ['logZ-flatness','minimum-visits','histogram-flatness'] + flatness_threshold : float, optional, default=0.2 + Histogram relative flatness threshold to use for first stage of two-stage scheme. + weight_update_method : str, optional, default='rao-blackwellized' + Method to use for updating log weights in SAMS. One of ['optimal', 'rao-blackwellized'] + ``rao-blackwellized`` will update log free energy estimate for all states for which energies were computed + ``optimal`` will use integral counts to update log free energy estimate of current state only + adapt_target_probabilities : bool, optional, default=False + If True, target probabilities will be adapted to achieve minimal thermodynamic length between terminal thermodynamic states. + (EXPERIMENTAL) + gamma0 : float, optional, default=0.0 + Initial weight adaptation rate. + logZ_guess : array-like of shape [n_states] of floats, optiona, default=None + Initial guess for logZ for all states, if available. + """ + # Initialize multi-state sampler + super(SAMSSampler, self).__init__(number_of_iterations=number_of_iterations, **kwargs) + # Options + self.log_target_probabilities = log_target_probabilities + self.state_update_scheme = state_update_scheme + self.locality = locality + self.update_stages = update_stages + self.flatness_criteria = flatness_criteria + self.flatness_threshold = flatness_threshold + self.weight_update_method = weight_update_method + self.adapt_target_probabilities = adapt_target_probabilities + self.gamma0 = gamma0 + self.logZ_guess = logZ_guess + # Private variables + # self._replica_neighbors[replica_index] is a list of states that form the neighborhood of ``replica_index`` + self._replica_neighbors = None + self._cached_state_histogram = None + + class _StoredProperty(multistate.MultiStateSampler._StoredProperty): + + @staticmethod + def _state_update_scheme_validator(instance, scheme): + supported_schemes = ['global-jump', 'local-jump', 'restricted-range-jump'] + supported_schemes = ['global-jump'] # TODO: Eliminate this after release + if scheme not in supported_schemes: + raise ValueError("Unknown update scheme '{}'. Supported values " + "are {}.".format(scheme, supported_schemes)) + return scheme + + @staticmethod + def _update_stages_validator(instance, scheme): + supported_schemes = ['one-stage', 'two-stage'] + if scheme not in supported_schemes: + raise ValueError("Unknown update scheme '{}'. Supported values " + "are {}.".format(scheme, supported_schemes)) + return scheme + + @staticmethod + def _flatness_criteria_validator(instance, scheme): + supported_schemes = ['minimum-visits', 'logZ-flatness', 'histogram-flatness'] + if scheme not in supported_schemes: + raise ValueError("Unknown update scheme '{}'. Supported values " + "are {}.".format(scheme, supported_schemes)) + return scheme + + @staticmethod + def _weight_update_method_validator(instance, scheme): + supported_schemes = ['optimal', 'rao-blackwellized'] + if scheme not in supported_schemes: + raise ValueError("Unknown update scheme '{}'. Supported values " + "are {}.".format(scheme, supported_schemes)) + return scheme + + @staticmethod + def _adapt_target_probabilities_validator(instance, scheme): + supported_schemes = [False] + if scheme not in supported_schemes: + raise ValueError("Unknown update scheme '{}'. Supported values " + "are {}.".format(scheme, supported_schemes)) + return scheme + + log_target_probabilities = _StoredProperty('log_target_probabilities', validate_function=None) + state_update_scheme = _StoredProperty('state_update_scheme', validate_function=_StoredProperty._state_update_scheme_validator) + locality = _StoredProperty('locality', validate_function=None) + update_stages = _StoredProperty('update_stages', validate_function=_StoredProperty._update_stages_validator) + flatness_criteria = _StoredProperty('flatness_criteria', validate_function=_StoredProperty._flatness_criteria_validator) + flatness_threshold = _StoredProperty('flatness_threshold', validate_function=None) + weight_update_method = _StoredProperty('weight_update_method', validate_function=_StoredProperty._weight_update_method_validator) + adapt_target_probabilities = _StoredProperty('adapt_target_probabilities', validate_function=_StoredProperty._adapt_target_probabilities_validator) + gamma0 = _StoredProperty('gamma0', validate_function=None) + logZ_guess = _StoredProperty('logZ_guess', validate_function=None) + + def _initialize_stage(self): + self._t0 = 0 # reference iteration to subtract + if self.update_stages == 'one-stage': + self._stage = 1 # start with asymptotically-optimal stage + elif self.update_stages == 'two-stage': + self._stage = 0 # start with rapid heuristic adaptation initial stage + + def _pre_write_create(self, thermodynamic_states: list, sampler_states: list, storage, + **kwargs): + """Initialize SAMS sampler. + + Parameters + ---------- + thermodynamic_states : list of openmmtools.states.ThermodynamicState + Thermodynamic states to simulate, where one replica is allocated per state. + Each state must have a system with the same number of atoms. + sampler_states : list of openmmtools.states.SamplerState + One or more sets of initial sampler states. + The number of replicas is determined by the number of sampler states provided, + and does not need to match the number of thermodynamic states. + Most commonly, a single sampler state is provided. + storage : str or Reporter + If str: path to the storage file, checkpoint options are default + If Reporter: Instanced :class:`Reporter` class, checkpoint information is read from + In the future this will be able to take a Storage class as well. + initial_thermodynamic_states : None or list or array-like of int of length len(sampler_states), optional, + default: None. + Initial thermodynamic_state index for each sampler_state. + If no initial distribution is chosen, ``sampler_states`` are distributed between the + ``thermodynamic_states`` following these rules: + + * If ``len(thermodynamic_states) == len(sampler_states)``: 1-to-1 distribution + + * If ``len(thermodynamic_states) > len(sampler_states)``: First and last state distributed first + remaining ``sampler_states`` spaced evenly by index until ``sampler_states`` are depleted. + If there is only one ``sampler_state``, then the only first ``thermodynamic_state`` will be chosen + + * If ``len(thermodynamic_states) < len(sampler_states)``, each ``thermodynamic_state`` receives an + equal number of ``sampler_states`` until there are insufficient number of ``sampler_states`` remaining + to give each ``thermodynamic_state`` an equal number. Then the rules from the previous point are + followed. + metadata : dict, optional + Simulation metadata to be stored in the file. + """ + # Initialize replica-exchange simulation. + super()._pre_write_create(thermodynamic_states, sampler_states, storage=storage, **kwargs) + + if self.state_update_scheme == 'global-jump': + self.locality = None # override locality to be global + if self.locality is not None: + if self.locality < 1: + raise Exception('locality must be >= 1') + elif self.locality >= self.n_states: + self.locality = None + + # Record current weight update stage + self._initialize_stage() + + # Update log target probabilities + if self.log_target_probabilities is None: + self.log_target_probabilities = np.zeros([self.n_states], np.float64) - np.log(self.n_states) # log(1/n_states) + #logger.debug('Setting log target probabilities: %s' % str(self.log_target_probabilities)) + #logger.debug('Target probabilities: %s' % str(np.exp(self.log_target_probabilities))) + + # Record initial logZ estimates + self._logZ = np.zeros([self.n_states], np.float64) + if self.logZ_guess is not None: + if len(self.logZ_guess) != self.n_states: + raise Exception('Initial logZ_guess (dim {}) must have same number of states as n_states ({})'.format( + len(self.logZ_guess), self.n_states)) + self._logZ = np.array(self.logZ_guess, np.float64) + + # Update log weights + self._update_log_weights() + + def _restore_sampler_from_reporter(self, reporter): + super()._restore_sampler_from_reporter(reporter) + self._cached_state_histogram = self._compute_state_histogram(reporter=reporter) + logger.debug('Restored state histogram: {}'.format(self._cached_state_histogram)) + data = reporter.read_online_analysis_data(self._iteration, 'logZ', 'stage', 't0') + self._logZ = data['logZ'] + self._stage = int(data['stage'][0]) + self._t0 = int(data['t0'][0]) + + # Compute log weights from log target probability and logZ estimate + self._update_log_weights() + + # Determine t0 + self._update_stage() + + @mpiplus.on_single_node(rank=0, broadcast_result=False, sync_nodes=False) + @mpiplus.delayed_termination + def _report_iteration_items(self): + super(SAMSSampler, self)._report_iteration_items() + self._reporter.write_online_data_dynamic_and_static(self._iteration, logZ=self._logZ, stage=self._stage, t0=self._t0) + # Split into which states and how many samplers are in each state + # Trying to do histogram[replica_thermo_states] += 1 does not correctly handle multiple + # replicas in the same state. + states, counts = np.unique(self._replica_thermodynamic_states, return_counts=True) + if self._cached_state_histogram is None: + self._cached_state_histogram = np.zeros(self.n_states, dtype=int) + self._cached_state_histogram[states] += counts + + @mpiplus.on_single_node(0, broadcast_result=True) + def _mix_replicas(self): + """Update thermodynamic states according to user-specified scheme.""" + logger.debug("Updating thermodynamic states using %s scheme..." % self.state_update_scheme) + + # Reset storage to keep track of swap attempts this iteration. + self._n_accepted_matrix[:, :] = 0 + self._n_proposed_matrix[:, :] = 0 + + # Perform swap attempts according to requested scheme. + # TODO: We may be able to refactor this to simply have different update schemes compute neighborhoods differently. + # TODO: Can we allow "plugin" addition of new update schemes that can be registered externally? + with mmtools.utils.time_it('Mixing of replicas'): + # Initialize statistics. This matrix is modified by the jump function and used when updating the logZ estimates. + replicas_log_P_k = np.zeros([self.n_replicas, self.n_states], np.float64) + if self.state_update_scheme == 'global-jump': + self._global_jump(replicas_log_P_k) + elif self.state_update_scheme == 'local-jump': + self._local_jump(replicas_log_P_k) + elif self.state_update_scheme == 'restricted-range-jump': + self._restricted_range_jump(replicas_log_P_k) + else: + raise Exception('Programming error: Unreachable code') + + # Determine fraction of swaps accepted this iteration. + n_swaps_proposed = self._n_proposed_matrix.sum() + n_swaps_accepted = self._n_accepted_matrix.sum() + swap_fraction_accepted = 0.0 + if n_swaps_proposed > 0: + # TODO drop casting to float when dropping Python 2 support. + swap_fraction_accepted = float(n_swaps_accepted) / n_swaps_proposed + logger.debug("Accepted {}/{} attempted swaps ({:.1f}%)".format(n_swaps_accepted, n_swaps_proposed, + swap_fraction_accepted * 100.0)) + + # Update logZ estimates + self._update_logZ_estimates(replicas_log_P_k) + + # Update log weights based on target probabilities + self._update_log_weights() + + def _local_jump(self, replicas_log_P_k): + n_replica, n_states, locality = self.n_replicas, self.n_states, self.locality + for (replica_index, current_state_index) in enumerate(self._replica_thermodynamic_states): + u_k = np.zeros([n_states], np.float64) + log_P_k = np.zeros([n_states], np.float64) + # Determine current neighborhood. + neighborhood = self._neighborhood() + neighborhood_size = len(neighborhood) + # Propose a move from the current neighborhood. + proposed_state_index = np.random.choice(neighborhood, p=np.ones([neighborhood_size], np.float64) / float(neighborhood_size)) + # Determine neighborhood for proposed state. + proposed_neighborhood = self._neighborhood(proposed_state_index) + proposed_neighborhood_size = len(proposed_neighborhood) + # Compute state log weights. + log_Gamma_j_L = - float(proposed_neighborhood_size) # log probability of proposing return + log_Gamma_L_j = - float(neighborhood_size) # log probability of proposing new state + L = current_state_index + # Compute potential for all states in neighborhood + for j in neighborhood: + u_k[j] = self._energy_thermodynamic_states[replica_index, j] + # Compute log of probability of selecting each state in neighborhood + for j in neighborhood: + if j != L: + log_P_k[j] = log_Gamma_L_j + min(0.0, log_Gamma_j_L - log_Gamma_L_j + (self.log_weights[j] - u_k[j]) - (self.log_weights[L] - u_k[L])) + P_k = np.zeros([n_states], np.float64) + P_k[neighborhood] = np.exp(log_P_k[neighborhood]) + # Compute probability to return to current state L + P_k[L] = 0.0 + P_k[L] = 1.0 - P_k[neighborhood].sum() + log_P_k[L] = np.log(P_k[L]) + # Update context. + new_state_index = np.random.choice(neighborhood, p=P_k[neighborhood]) + self._replica_thermodynamic_states[replica_index] = new_state_index + # Accumulate statistics + replicas_log_P_k[replica_index,:] = log_P_k[:] + self._n_proposed_matrix[current_state_index, neighborhood] += 1 + self._n_accepted_matrix[current_state_index, new_state_index] += 1 + + def _global_jump(self, replicas_log_P_k): + """ + Global jump scheme. + This method is described after Eq. 3 in [2] + """ + n_replica, n_states = self.n_replicas, self.n_states + for replica_index, current_state_index in enumerate(self._replica_thermodynamic_states): + neighborhood = self._neighborhood(current_state_index) + + # Compute unnormalized log probabilities for all thermodynamic states. + log_P_k = np.zeros([n_states], np.float64) + for state_index in neighborhood: + u_k = self._energy_thermodynamic_states[replica_index, :] + log_P_k[state_index] = - u_k[state_index] + self.log_weights[state_index] + log_P_k -= logsumexp(log_P_k) + + # Update sampler Context to current thermodynamic state. + P_k = np.exp(log_P_k[neighborhood]) + new_state_index = np.random.choice(neighborhood, p=P_k) + self._replica_thermodynamic_states[replica_index] = new_state_index + + # Accumulate statistics. + replicas_log_P_k[replica_index,:] = log_P_k[:] + self._n_proposed_matrix[current_state_index, neighborhood] += 1 + self._n_accepted_matrix[current_state_index, new_state_index] += 1 + + def _restricted_range_jump(self, replicas_log_P_k): + # TODO: This has an major bug in that we also need to compute energies in `proposed_neighborhood`. + # I'm working on a way to make this work. + n_replica, n_states, locality = self.n_replicas, self.n_states, self.locality + logger.debug('Using restricted range jump with locality %s' % str(self.locality)) + for (replica_index, current_state_index) in enumerate(self._replica_thermodynamic_states): + u_k = self._energy_thermodynamic_states[replica_index, :] + log_P_k = np.zeros([n_states], np.float64) + # Propose new state from current neighborhood. + neighborhood = self._neighborhood(current_state_index) + logger.debug(' Current state : %d' % current_state_index) + logger.debug(' Neighborhood : %s' % str(neighborhood)) + logger.debug(' Relative u_k : %s' % str(u_k[neighborhood] - u_k[current_state_index])) + log_P_k[neighborhood] = self.log_weights[neighborhood] - u_k[neighborhood] + log_P_k[neighborhood] -= logsumexp(log_P_k[neighborhood]) + logger.debug(' Neighborhood log_P_k: %s' % str(log_P_k[neighborhood])) + P_k = np.exp(log_P_k[neighborhood]) + logger.debug(' Neighborhood P_k : %s' % str(P_k)) + proposed_state_index = np.random.choice(neighborhood, p=P_k) + logger.debug(' Proposed state : %d' % proposed_state_index) + # Determine neighborhood of proposed state. + proposed_neighborhood = self._neighborhood(proposed_state_index) + logger.debug(' Proposed neighborhood : %s' % str(proposed_neighborhood)) + # Accept or reject. + log_P_accept = logsumexp(self.log_weights[neighborhood] - u_k[neighborhood]) - logsumexp(self.log_weights[proposed_neighborhood] - u_k[proposed_neighborhood]) + logger.debug(' log_P_accept : %f' % log_P_accept) + logger.debug(' logsumexp(g[forward] - u[forward]) : %f' % logsumexp(self.log_weights[neighborhood] - u_k[neighborhood])) + logger.debug(' logsumexp(g[reverse] - u[reverse]) : %f' % logsumexp(self.log_weights[proposed_neighborhood] - u_k[proposed_neighborhood])) + new_state_index = current_state_index + if (log_P_accept >= 0.0) or (np.random.rand() < np.exp(log_P_accept)): + new_state_index = proposed_state_index + logger.debug(' new_state_index : %d' % new_state_index) + self._replica_thermodynamic_states[replica_index] = new_state_index + # Accumulate statistics + replicas_log_P_k[replica_index,:] = log_P_k[:] + self._n_proposed_matrix[current_state_index, neighborhood] += 1 + self._n_accepted_matrix[current_state_index, new_state_index] += 1 + + @property + def _state_histogram(self): + """ + Compute the histogram for the number of times each state has been visited. + + Returns + ------- + N_k : array-like of shape [n_states] of int + N_k[state_index] is the number of times a replica has visited state ``state_index`` + """ + if self._cached_state_histogram is None: + self._cached_state_histogram = self._compute_state_histogram() + return self._cached_state_histogram + + def _compute_state_histogram(self, reporter=None): + """ Compute state histogram from disk""" + if reporter is None: + reporter = self._reporter + replica_thermodynamic_states = reporter.read_replica_thermodynamic_states() + logger.debug('Read replica thermodynamic states: {}'.format(replica_thermodynamic_states)) + n_k, _ = np.histogram(replica_thermodynamic_states, bins=np.arange(-0.5, self.n_states + 0.5)) + return n_k + + def _update_stage(self): + """ + Determine which adaptation stage we're in by checking histogram flatness. + + """ + # TODO: Make minimum_visits a user option + minimum_visits = 1 + N_k = self._state_histogram + logger.debug(' state histogram counts ({} total): {}'.format(self._cached_state_histogram.sum(), self._cached_state_histogram)) + if (self.update_stages == 'two-stage') and (self._stage == 0): + advance = False + if N_k.sum() == 0: + # No samples yet; don't do anything. + return + + if self.flatness_criteria == 'minimum-visits': + # Advance if every state has been visited at least once + if np.all(N_k >= minimum_visits): + advance = True + elif self.flatness_criteria == 'histogram-flatness': + # Check histogram flatness + empirical_pi_k = N_k[:] / N_k.sum() + pi_k = np.exp(self.log_target_probabilities) + relative_error_k = np.abs(pi_k - empirical_pi_k) / pi_k + if np.all(relative_error_k < self.flatness_threshold): + advance = True + elif self.flatness_criteria == 'logZ-flatness': + # TODO: Advance to asymptotically optimal scheme when logZ update fractional counts per state exceed threshold + # for all states. + criteria = abs(self._logZ / self.gamma0) > self.flatness_threshold + logger.debug('logZ-flatness criteria met (%d total): %s' % (np.sum(criteria), str(np.array(criteria, 'i1')))) + if np.all(criteria): + advance = True + else: + raise ValueError("Unknown flatness_criteria %s" % flatness_criteria) + + if advance or ((self._t0 > 0) and (self._iteration > self._t0)): + # Histograms are sufficiently flat; switch to asymptotically optimal scheme + self._stage = 1 # asymptotically optimal + # TODO: On resuming, we need to recompute or restore t0, or use some other way to compute it + self._t0 = self._iteration - 1 + + def _update_logZ_estimates(self, replicas_log_P_k): + """ + Update the logZ estimates according to selected SAMS update method + + References + ---------- + [1] http://www.stat.rutgers.edu/home/ztan/Publication/SAMS_redo4.pdf + + """ + logger.debug('Updating logZ estimates...') + + # Store log weights used at the beginning of this iteration + self._reporter.write_online_analysis_data(self._iteration, log_weights=self.log_weights) + + # Retrieve target probabilities + log_pi_k = self.log_target_probabilities + pi_k = np.exp(self.log_target_probabilities) + #logger.debug(' log target probabilities log_pi_k: %s' % str(log_pi_k)) + #logger.debug(' target probabilities pi_k: %s' % str(pi_k)) + + # Update which stage we're in, checking histogram flatness + self._update_stage() + + logger.debug(' stage: %s' % self._stage) + + # Update logZ estimates from all replicas + for (replica_index, state_index) in enumerate(self._replica_thermodynamic_states): + logger.debug(' Replica %d state %d' % (replica_index, state_index)) + # Compute attenuation factor gamma + beta_factor = 0.8 + pi_star = pi_k.min() + t = float(self._iteration) + if self._stage == 0: # initial stage + gamma = self.gamma0 * min(pi_star, t**(-beta_factor)) # Eq. 15 of [1] + elif self._stage == 1: + gamma = self.gamma0 * min(pi_star, (t - self._t0 + self._t0**beta_factor)**(-1)) # Eq. 15 of [1] + else: + raise Exception('stage {} unknown'.format(self._stage)) + + #logger.debug(' gamma: %s' % gamma) + + # Update online logZ estimate + if self.weight_update_method == 'optimal': + # Based on Eq. 9 of Ref. [1] + logZ_update = gamma * np.exp(-log_pi_k[state_index]) + #logger.debug(' optimal logZ increment: %s' % str(logZ_update)) + self._logZ[state_index] += logZ_update + elif self.weight_update_method == 'rao-blackwellized': + # Based on Eq. 12 of Ref [1] + # TODO: This has to be the previous state index and log_P_k used before update; store neighborhood? + # TODO: Can we use masked arrays for this purpose? + log_P_k = replicas_log_P_k[replica_index,:] + neighborhood = np.where(self._neighborhoods[replica_index,:])[0] # compact list of states defining neighborhood + #logger.debug(' using neighborhood: %s' % str(neighborhood)) + #logger.debug(' using log_P_k : %s' % str(log_P_k[neighborhood])) + #logger.debug(' using log_pi_k: %s' % str(log_pi_k[neighborhood])) + logZ_update = gamma * np.exp(log_P_k[neighborhood] - log_pi_k[neighborhood]) + #logger.debug(' Rao-Blackwellized logZ increment: %s' % str(logZ_update)) + self._logZ[neighborhood] += logZ_update + else: + raise Exception('Programming error: Unreachable code') + + # Subtract off logZ[0] to prevent logZ from growing without bound once we reach the asymptotically optimal stage + if self._stage == 1: # asymptotically optimal or one-stage + self._logZ[:] -= self._logZ[0] + + # Format logZ + msg = ' logZ: [' + for i, val in enumerate(self._logZ): + if i > 0: msg += ', ' + msg += '%6.1f' % val + msg += ']' + logger.debug(msg) + + # Store gamma + self._reporter.write_online_analysis_data(self._iteration, gamma=gamma) + + def _update_log_weights(self): + """ + Update the log weights based on current online logZ estimates + + """ + # TODO: Add option to adapt target probabilities as well + # TODO: If target probabilities are adapted, we need to store them as well + + self.log_weights = self.log_target_probabilities[:] - self._logZ[:] + + +class SAMSAnalyzer(multistate.MultiStateSamplerAnalyzer): + """ + The SAMSAnalyzer is the analyzer for a simulation generated from a SAMSSampler simulation. + + See Also + -------- + ReplicaExchangeAnalyzer + PhaseAnalyzer + + """ + pass + +# ============================================================================== +# MAIN AND TESTS +# ============================================================================== + + +if __name__ == "__main__": + import doctest + doctest.testmod() diff --git a/openmmtools/multistate/utils.py b/openmmtools/multistate/utils.py new file mode 100644 index 000000000..f0de60891 --- /dev/null +++ b/openmmtools/multistate/utils.py @@ -0,0 +1,283 @@ +#!/usr/local/bin/env python + +# ============================================================================== +# MODULE DOCSTRING +# ============================================================================== + +""" +Multistate Utilities +==================== + +Sampling Utilities for the YANK Multistate Package. A collection of functions and small classes +which are common to help the samplers and analyzers and other public hooks. + +COPYRIGHT + +Current version by Andrea Rizzi , Levi N. Naden and +John D. Chodera while at Memorial Sloan Kettering Cancer Center. + +Original version by John D. Chodera while at the University of +California Berkeley. + +LICENSE + +This code is licensed under the latest available version of the MIT License. + +""" +import logging +import warnings +import numpy as np + +from pymbar import timeseries # for statistical inefficiency analysis + +logger = logging.getLogger(__name__) + +__all__ = [ + 'generate_phase_name', + 'get_decorrelation_time', + 'get_equilibration_data', + 'get_equilibration_data_per_sample', + 'remove_unequilibrated_data', + 'subsample_data_along_axis', + 'SimulationNaNError' +] + + +# ============================================================================================= +# Sampling Exceptions +# ============================================================================================= + +class SimulationNaNError(Exception): + """Error when a simulation goes to NaN""" + pass + + +# ============================================================================================= +# MODULE FUNCTIONS +# ============================================================================================= + +def generate_phase_name(current_name, name_list): + """ + Provide a regular way to generate unique human-readable names from base names. + + Given a base name and a list of existing names, a number will be appended to the base name until a unique string + is generated. + + Parameters + ---------- + current_name : string + The base name you wish to ensure is unique. Numbers will be appended to this string until a unique string + not in the name_list is provided + name_list : iterable of strings + The current_name, and its modifiers, are compared against this list until a unique string is found + + Returns + ------- + name : string + Unique string derived from the current_name that is not in name_list. + If the parameter current_name is not already in the name_list, then current_name is returned unmodified. + """ + base_name = 'phase{}' + counter = 0 + if current_name is None: + name = base_name.format(counter) + while name in name_list: + counter += 1 + name = base_name.format(counter) + elif current_name in name_list: + name = current_name + str(counter) + while name in name_list: + counter += 1 + name = current_name + str(counter) + else: + name = current_name + return name + + +def get_decorrelation_time(timeseries_to_analyze): + """ + Compute the decorrelation times given a timeseries. + + See the ``pymbar.timeseries.statisticalInefficiency`` for full documentation + """ + return timeseries.statisticalInefficiency(timeseries_to_analyze) + + +def get_equilibration_data_per_sample(timeseries_to_analyze, fast=True, max_subset=100): + """ + Compute the correlation time and n_effective per sample with tuning to how you want your data formatted + + This is a modified pass-through to ``pymbar.timeseries.detectEquilibration`` does, returning the per sample data. + + It has been modified to specify the maximum number of time points to consider, evenly spaced over the timeseries. + This is different than saying "I want analysis done every X for total points Y = len(timeseries)/X", + this is "I want Y total analysis points" + + See the ``pymbar.timeseries.detectEquilibration`` function for full algorithm documentation + + Parameters + ---------- + timeseries_to_analyze : np.ndarray + 1-D timeseries to analyze for equilibration + max_subset : int >= 1 or None, optional, default: 100 + Maximum number of points in the ``timeseries_to_analyze`` on which to analyze the equilibration on. + These are distributed uniformly over the timeseries so the final output will be size max_subset where indices + are placed approximately every ``(len(timeseries_to_analyze) - 1) / max_subset``. + The full timeseries is used if the timeseries is smaller than ``max_subset`` or if ``max_subset`` is None + fast : bool, optional. Default: True + If True, will use faster (but less accurate) method to estimate correlation time + passed on to timeseries module. + + Returns + ------- + i_t : np.ndarray of int + Indices of the timeseries which were sampled from + g_i : np.ndarray of float + Estimated statistical inefficiency at t in units of index count. + Equal to 1 + 2 tau, where tau is the correlation time + Will always be >= 1 + + e.g. If g_i[x] = 4.3, then choosing x as your equilibration point means the every ``ceil(4.3)`` in + ``timeseries_to_analyze`` will be decorrelated, so the fully equilibrated decorrelated timeseries would be + indexed by [x, x+5, x+10, ..., X) where X is the final point in the ``timeseries_to_analyze``. + + The "index count" in this case is the by count of the ``timeseries_to_analyze`` indices, NOT the ``i_t`` + + n_effective_i : np.ndarray of float + Number of effective samples by subsampling every ``g_i`` from index t, does include fractional value, so true + number of points will be the floor of this output. + + The "index count" in this case is the by count of the ``timeseries_to_analyze`` indices, NOT the ``i_t`` + + """ + # Cast to array if not already + series = np.array(timeseries_to_analyze) + # Special trap for constant series + time_size = series.size + set_size = time_size - 1 # Cannot analyze the last entry + # Set maximum + if max_subset is None or set_size < max_subset: + max_subset = set_size + # Special trap for series of size 1 + if max_subset == 0: + max_subset = 1 + # Special trap for constant or size 1 series + if series.std() == 0.0 or max_subset == 1: + return (np.arange(max_subset, dtype=int), # i_t + np.array([1]*max_subset), # g_i + np.arange(time_size, time_size-max_subset, -1) # n_effective_i + ) + g_i = np.ones([max_subset], np.float32) + n_effective_i = np.ones([max_subset], np.float32) + counter = np.arange(max_subset) + i_t = np.floor(counter * time_size / max_subset).astype(int) + for i, t in enumerate(i_t): + try: + g_i[i] = timeseries.statisticalInefficiency(series[t:], fast=fast) + except: + g_i[i] = (time_size - t + 1) + n_effective_i[i] = (time_size - t + 1) / g_i[i] + return i_t, g_i, n_effective_i + + +def get_equilibration_data(timeseries_to_analyze, fast=True, max_subset=1000): + """ + Compute equilibration method given a timeseries + + See the ``pymbar.timeseries.detectEquilibration`` function for full documentation + + Parameters + ---------- + timeseries_to_analyze : np.ndarray + 1-D timeseries to analyze for equilibration + max_subset : int or None, optional, default: 1000 + Maximum number of points in the ``timeseries_to_analyze`` on which to analyze the equilibration on. + These are distributed uniformly over the timeseries so the final output will be size max_subset where indices + are placed approximately every ``(len(timeseries_to_analyze) - 1) / max_subset``. + The full timeseries is used if the timeseries is smaller than ``max_subset`` or if ``max_subset`` is None + fast : bool, optional. Default: True + If True, will use faster (but less accurate) method to estimate correlation time + passed on to timeseries module. + + Returns + ------- + n_equilibration : int + Iteration at which system becomes equilibrated + Computed by point which maximizes the number of samples preserved + g_t : float + Number of indices between each decorelated sample + n_effective_max : float + How many indices are preserved at most. + + See Also + -------- + get_equilibration_data_per_sample + """ + warnings.warn("This function will be removed in future versions of YANK due to redundancy, " + "Please use the more general `get_equilibration_data_per_sample` function instead.") + i_t, g_i, n_effective_i = get_equilibration_data_per_sample(timeseries_to_analyze, fast=fast, max_subset=max_subset) + n_effective_max = n_effective_i.max() + i_max = n_effective_i.argmax() + n_equilibration = i_t[i_max] + g_t = g_i[i_max] + return n_equilibration, g_t, n_effective_max + + +def remove_unequilibrated_data(data, number_equilibrated, axis): + """ + Remove the number_equilibrated samples from a dataset + + Discards number_equilibrated number of indices from given axis + + Parameters + ---------- + data : np.array-like of any dimension length + This is the data which will be paired down + number_equilibrated : int + Number of indices that will be removed from the given axis, i.e. axis will be shorter by number_equilibrated + axis : int + Axis index along which to remove samples from. This supports negative indexing as well + + Returns + ------- + equilibrated_data : ndarray + Data with the number_equilibrated number of indices removed from the beginning along axis + + """ + cast_data = np.asarray(data) + # Define the slice along an arbitrary dimension + slc = [slice(None)] * len(cast_data.shape) + # Set the dimension we are truncating + slc[axis] = slice(number_equilibrated, None) + # Slice + equilibrated_data = cast_data[tuple(slc)] + return equilibrated_data + + +def subsample_data_along_axis(data, subsample_rate, axis): + """ + Generate a decorrelated version of a given input data and subsample_rate along a single axis. + + Parameters + ---------- + data : np.array-like of any dimension length + subsample_rate : float or int + Rate at which to draw samples. A sample is considered decorrelated after every ceil(subsample_rate) of + indices along data and the specified axis + axis : int + axis along which to apply the subsampling + + Returns + ------- + subsampled_data : ndarray of same number of dimensions as data + Data will be subsampled along the given axis + + """ + # TODO: find a name for the function that clarifies that decorrelation + # TODO: is determined exclusively by subsample_rate? + cast_data = np.asarray(data) + data_shape = cast_data.shape + # Since we already have g, we can just pass any appropriate shape to the subsample function + indices = timeseries.subsampleCorrelatedData(np.zeros(data_shape[axis]), g=subsample_rate) + subsampled_data = np.take(cast_data, indices, axis=axis) + return subsampled_data From 38c8811de19c0977a0a0907af4b63b09aefcd372 Mon Sep 17 00:00:00 2001 From: Bruce Macdonald Date: Tue, 22 Jan 2019 10:15:30 -0500 Subject: [PATCH 02/46] added mpiplus to meta.yaml --- devtools/conda-recipe/meta.yaml | 1 + 1 file changed, 1 insertion(+) diff --git a/devtools/conda-recipe/meta.yaml b/devtools/conda-recipe/meta.yaml index b9610d052..28e0738a9 100644 --- a/devtools/conda-recipe/meta.yaml +++ b/devtools/conda-recipe/meta.yaml @@ -25,6 +25,7 @@ requirements: - mdtraj - netcdf4 - pyyaml + - mpiplus test: From 2d15b64d0fc883ff0529192a255206d54ae44504 Mon Sep 17 00:00:00 2001 From: Bruce Macdonald Date: Tue, 22 Jan 2019 15:21:48 -0500 Subject: [PATCH 03/46] adding tests and updating setup script --- openmmtools/tests/test_sampling.py | 1763 ++++++++++++++++++++++++++++ setup.py | 10 +- 2 files changed, 1770 insertions(+), 3 deletions(-) create mode 100644 openmmtools/tests/test_sampling.py diff --git a/openmmtools/tests/test_sampling.py b/openmmtools/tests/test_sampling.py new file mode 100644 index 000000000..d13d0cfb2 --- /dev/null +++ b/openmmtools/tests/test_sampling.py @@ -0,0 +1,1763 @@ +#!/usr/local/bin/env python + +""" +Test replicaexchange.py facility. + +TODO + +* Create a few simulation objects on simple systems (e.g. harmonic oscillators?) and run multiple tests on each object? + +""" + +# ============================================================================================= +# GLOBAL IMPORTS +# ============================================================================================= + +import contextlib +import copy +import inspect +import math +import os +import pickle +import sys +from io import StringIO + +import numpy as np +import openmmtools as mmtools +import scipy.integrate +import yaml +from nose.plugins.attrib import attr +from nose.tools import assert_raises +from openmmtools import testsystems +from simtk import openmm, unit + +import mpiplus +from openmmtools.multistate import MultiStateReporter, MultiStateSampler, ReplicaExchangeSampler, ParallelTemperingSampler, SAMSSampler +from openmmtools.multistate import ReplicaExchangeAnalyzer, SAMSAnalyzer +from openmmtools.multistate.multistatereporter import _DictYamlLoader + +# quiet down some citation spam +MultiStateSampler._global_citation_silence = True + +# ============================================================================== +# MODULE CONSTANTS +# ============================================================================== + +kB = unit.BOLTZMANN_CONSTANT_kB * unit.AVOGADRO_CONSTANT_NA # Boltzmann constant + + +# ============================================================================== +# SUBROUTINES +# ============================================================================== + +def check_thermodynamic_states_equality(original_states, restored_states): + """Check that the thermodynamic states are equivalent.""" + assert len(original_states) == len(restored_states), '{}, {}'.format( + len(original_states), len(restored_states)) + + for original_state, restored_state in zip(original_states, restored_states): + assert original_state._standard_system_hash == restored_state._standard_system_hash + assert original_state.temperature == restored_state.temperature + assert original_state.pressure == restored_state.pressure + + if isinstance(original_state, mmtools.states.CompoundThermodynamicState): + assert original_state.lambda_sterics == restored_state.lambda_sterics + assert original_state.lambda_electrostatics == restored_state.lambda_electrostatics + + +def compute_harmonic_oscillator_expectations(K, temperature): + """Compute mean and variance of potential and kinetic energies for a 3D harmonic oscillator. + + Notes + ----- + Numerical quadrature is used to compute the mean and standard deviation of the potential energy. + Mean and standard deviation of the kinetic energy, as well as the absolute free energy, is computed analytically. + + Parameters + ---------- + K : simtk.unit.Quantity + Spring constant. + temperature : simtk.unit.Quantity + Temperature. + + Returns + ------- + values : dict + + TODO + + Replace this with built-in analytical expectations + + """ + + values = dict() + + # Compute thermal energy and inverse temperature from specified temperature. + kB = unit.BOLTZMANN_CONSTANT_kB * unit.AVOGADRO_CONSTANT_NA + kT = kB * temperature # thermal energy + beta = 1.0 / kT # inverse temperature + + # Compute standard deviation along one dimension. + sigma = 1.0 / unit.sqrt(beta * K) + + # Define limits of integration along r. + r_min = 0.0 * unit.nanometers # initial value for integration + r_max = 10.0 * sigma # maximum radius to integrate to + + # Compute mean and std dev of potential energy. + V = lambda r : (K/2.0) * (r*unit.nanometers)**2 / unit.kilojoules_per_mole # potential in kJ/mol, where r in nm + q = lambda r : 4.0 * math.pi * r**2 * math.exp(-beta * (K/2.0) * (r*unit.nanometers)**2) # q(r), where r in nm + (IqV2, dIqV2) = scipy.integrate.quad(lambda r : q(r) * V(r)**2, r_min / unit.nanometers, r_max / unit.nanometers) + (IqV, dIqV) = scipy.integrate.quad(lambda r : q(r) * V(r), r_min / unit.nanometers, r_max / unit.nanometers) + (Iq, dIq) = scipy.integrate.quad(lambda r : q(r), r_min / unit.nanometers, r_max / unit.nanometers) + values['potential'] = dict() + values['potential']['mean'] = (IqV / Iq) * unit.kilojoules_per_mole + values['potential']['stddev'] = (IqV2 / Iq) * unit.kilojoules_per_mole + + # Compute mean and std dev of kinetic energy. + values['kinetic'] = dict() + values['kinetic']['mean'] = (3./2.) * kT + values['kinetic']['stddev'] = math.sqrt(3./2.) * kT + + # Compute dimensionless free energy. + # f = - \ln \int_{-\infty}^{+\infty} \exp[-\beta K x^2 / 2] + # = - \ln \int_{-\infty}^{+\infty} \exp[-x^2 / 2 \sigma^2] + # = - \ln [\sqrt{2 \pi} \sigma] + values['f'] = - np.log(2 * np.pi * (sigma / unit.angstroms)**2) * (3.0/2.0) + + return values + + +# ============================================================================== +# TEST ANALYSIS REPLICA EXCHANGE +# ============================================================================== + +@attr('slow') # Skip on Travis-CI +def test_replica_exchange_harmonic_oscillator(verbose=False, verbose_simulation=False): + """Test harmonic oscillator free energies for replica-exchange.""" + # Define mass of carbon atom. + mass = 12.0 * unit.amu + + sampler_states = list() + thermodynamic_states = list() + analytical_results = list() + f_i_analytical = list() # Dimensionless free energies. + u_i_analytical = list() # Reduced potentials. + + # Define thermodynamic states. + Ks = [500.00, 400.0, 300.0] * unit.kilocalories_per_mole / unit.angstroms**2 # Spring constants. + temperatures = [300.0, 350.0, 400.0] * unit.kelvin # Temperatures. + for (K, temperature) in zip(Ks, temperatures): + # Create harmonic oscillator system. + testsystem = testsystems.HarmonicOscillator(K=K, mass=mass, mm=openmm) + + # Create thermodynamic state and save positions. + system, positions = [testsystem.system, testsystem.positions] + sampler_states.append(mmtools.states.SamplerState(positions)) + thermodynamic_states.append(mmtools.states.ThermodynamicState(system=system, temperature=temperature)) + + # Store analytical results. + results = compute_harmonic_oscillator_expectations(K, temperature) + analytical_results.append(results) + f_i_analytical.append(results['f']) + reduced_potential = results['potential']['mean'] / (kB * temperature) + u_i_analytical.append(reduced_potential) + + # Compute analytical Delta_f_ij + nstates = len(f_i_analytical) + f_i_analytical = np.array(f_i_analytical) + u_i_analytical = np.array(u_i_analytical) + s_i_analytical = u_i_analytical - f_i_analytical + Delta_f_ij_analytical = np.zeros([nstates, nstates], np.float64) + Delta_u_ij_analytical = np.zeros([nstates, nstates], np.float64) + Delta_s_ij_analytical = np.zeros([nstates, nstates], np.float64) + for i in range(nstates): + for j in range(nstates): + Delta_f_ij_analytical[i, j] = f_i_analytical[j] - f_i_analytical[i] + Delta_u_ij_analytical[i, j] = u_i_analytical[j] - u_i_analytical[i] + Delta_s_ij_analytical[i, j] = s_i_analytical[j] - s_i_analytical[i] + + # Create and configure simulation object. + move = mmtools.mcmc.LangevinDynamicsMove(timestep=2.0*unit.femtoseconds, + collision_rate=20.0/unit.picosecond, + n_steps=500, reassign_velocities=True) + simulation = ReplicaExchangeSampler(mcmc_moves=move, number_of_iterations=400) + + # Define file for temporary storage. + with mmtools.utils.temporary_directory() as tmp_dir: + storage = os.path.join(tmp_dir, 'test_storage.nc') + reporter = MultiStateReporter(storage, checkpoint_interval=1) + simulation.create(thermodynamic_states, sampler_states, reporter) + + # Run simulation we keep the debug info off during the simulation + # to not clog the output, and reactivate it for analysis. + simulation.run() + + # Create Analyzer. + analyzer = ReplicaExchangeAnalyzer(reporter) + + # TODO: Check if deviations exceed tolerance. + Delta_f_ij, dDelta_f_ij = analyzer.get_free_energy() + error = np.abs(Delta_f_ij - Delta_f_ij_analytical) + indices = np.where(dDelta_f_ij > 0.0) + nsigma = np.zeros([nstates,nstates], np.float32) + nsigma[indices] = error[indices] / dDelta_f_ij[indices] + MAX_SIGMA = 6.0 # maximum allowed number of standard errors + if np.any(nsigma > MAX_SIGMA): + print("Delta_f_ij") + print(Delta_f_ij) + print("Delta_f_ij_analytical") + print(Delta_f_ij_analytical) + print("error") + print(error) + print("stderr") + print(dDelta_f_ij) + print("nsigma") + print(nsigma) + raise Exception("Dimensionless free energy difference exceeds MAX_SIGMA of %.1f" % MAX_SIGMA) + + Delta_u_ij, dDelta_u_ij = analyzer.get_enthalpy() + error = Delta_u_ij - Delta_u_ij_analytical + nsigma = np.zeros([nstates,nstates], np.float32) + nsigma[indices] = error[indices] / dDelta_f_ij[indices] + if np.any(nsigma > MAX_SIGMA): + print("Delta_u_ij") + print(Delta_u_ij) + print("Delta_u_ij_analytical") + print(Delta_u_ij_analytical) + print("error") + print(error) + print("nsigma") + print(nsigma) + raise Exception("Dimensionless potential energy difference exceeds MAX_SIGMA of %.1f" % MAX_SIGMA) + + # Clean up. + del simulation + + if verbose: + print("PASSED.") + + +# ============================================================================== +# TEST ANALYSIS SAMS +# ============================================================================== + +@attr('slow') # Skip on Travis-CI +def test_sams_harmonic_oscillator(verbose=False, verbose_simulation=False): + """Harmonic oscillator free energies for SAMS.""" + # Define mass of carbon atom. + mass = 12.0 * unit.amu + + sampler_states = list() + thermodynamic_states = list() + analytical_results = list() + f_i_analytical = list() # Dimensionless free energies. + u_i_analytical = list() # Reduced potentials. + + # Define thermodynamic states. + Ks = [500.00, 400.0, 300.0] * unit.kilocalories_per_mole / unit.angstroms**2 # Spring constants. + temperatures = [300.0, 350.0, 400.0] * unit.kelvin # Temperatures. + for (K, temperature) in zip(Ks, temperatures): + # Create harmonic oscillator system. + testsystem = testsystems.HarmonicOscillator(K=K, mass=mass, mm=openmm) + + # Create thermodynamic state and save positions. + system, positions = [testsystem.system, testsystem.positions] + sampler_states.append(mmtools.states.SamplerState(positions)) + thermodynamic_states.append(mmtools.states.ThermodynamicState(system=system, temperature=temperature)) + + # Store analytical results. + results = compute_harmonic_oscillator_expectations(K, temperature) + analytical_results.append(results) + f_i_analytical.append(results['f']) + reduced_potential = results['potential']['mean'] / (kB * temperature) + u_i_analytical.append(reduced_potential) + + # Compute analytical Delta_f_ij + nstates = len(f_i_analytical) + f_i_analytical = np.array(f_i_analytical) + u_i_analytical = np.array(u_i_analytical) + s_i_analytical = u_i_analytical - f_i_analytical + Delta_f_ij_analytical = np.zeros([nstates, nstates], np.float64) + Delta_u_ij_analytical = np.zeros([nstates, nstates], np.float64) + Delta_s_ij_analytical = np.zeros([nstates, nstates], np.float64) + for i in range(nstates): + for j in range(nstates): + Delta_f_ij_analytical[i, j] = f_i_analytical[j] - f_i_analytical[i] + Delta_u_ij_analytical[i, j] = u_i_analytical[j] - u_i_analytical[i] + Delta_s_ij_analytical[i, j] = s_i_analytical[j] - s_i_analytical[i] + + # Create and configure simulation object. + move = mmtools.mcmc.LangevinDynamicsMove(timestep=2.0*unit.femtoseconds, + collision_rate=20.0/unit.picosecond, + n_steps=500, reassign_velocities=True) + simulation = SAMSSampler(mcmc_moves=move, number_of_iterations=200) + + # Define file for temporary storage. + with mmtools.utils.temporary_directory() as tmp_dir: + storage = os.path.join(tmp_dir, 'test_storage.nc') + reporter = MultiStateReporter(storage, checkpoint_interval=1) + simulation.create(thermodynamic_states, sampler_states, reporter) + + # Run simulation we keep the debug info off during the simulation + # to not clog the output, and reactivate it for analysis. + simulation.run() + + # Create Analyzer. + analyzer = SAMSAnalyzer(reporter) + + # TODO: Check if deviations exceed tolerance. + Delta_f_ij, dDelta_f_ij = analyzer.get_free_energy() + error = np.abs(Delta_f_ij - Delta_f_ij_analytical) + indices = np.where(dDelta_f_ij > 0.0) + nsigma = np.zeros([nstates,nstates], np.float32) + nsigma[indices] = error[indices] / dDelta_f_ij[indices] + MAX_SIGMA = 6.0 # maximum allowed number of standard errors + if np.any(nsigma > MAX_SIGMA): + print("Delta_f_ij") + print(Delta_f_ij) + print("Delta_f_ij_analytical") + print(Delta_f_ij_analytical) + print("error") + print(error) + print("stderr") + print(dDelta_f_ij) + print("nsigma") + print(nsigma) + raise Exception("Dimensionless free energy difference exceeds MAX_SIGMA of %.1f" % MAX_SIGMA) + + Delta_u_ij, dDelta_u_ij = analyzer.get_enthalpy() + error = Delta_u_ij - Delta_u_ij_analytical + nsigma = np.zeros([nstates,nstates], np.float32) + nsigma[indices] = error[indices] / dDelta_f_ij[indices] + if np.any(nsigma > MAX_SIGMA): + print("Delta_u_ij") + print(Delta_u_ij) + print("Delta_u_ij_analytical") + print(Delta_u_ij_analytical) + print("error") + print(error) + print("nsigma") + print(nsigma) + raise Exception("Dimensionless potential energy difference exceeds MAX_SIGMA of %.1f" % MAX_SIGMA) + + # Clean up. + del simulation + + if verbose: + print("PASSED.") + + +# ============================================================================== +# TEST REPORTER +# ============================================================================== + +class TestReporter(object): + """Test suite for Reporter class.""" + + @staticmethod + @contextlib.contextmanager + def temporary_reporter(checkpoint_interval=1, checkpoint_storage=None, analysis_particle_indices=()): + """Create and initialize a reporter in a temporary directory.""" + with mmtools.utils.temporary_directory() as tmp_dir_path: + storage_file = os.path.join(tmp_dir_path, 'temp_dir/test_storage.nc') + assert not os.path.isfile(storage_file) + reporter = MultiStateReporter(storage=storage_file, open_mode='w', + checkpoint_interval=checkpoint_interval, + checkpoint_storage=checkpoint_storage, + analysis_particle_indices=analysis_particle_indices) + assert reporter.storage_exists(skip_size=True) + yield reporter + + def test_store_thermodynamic_states(self): + """Check correct storage of thermodynamic states.""" + # Thermodynamic states. + temperature = 300*unit.kelvin + alanine_system = testsystems.AlanineDipeptideImplicit().system + alanine_explicit_system = testsystems.AlanineDipeptideExplicit().system + thermodynamic_state_nvt = mmtools.states.ThermodynamicState(alanine_system, temperature) + thermodynamic_state_nvt_compatible = mmtools.states.ThermodynamicState(alanine_system, + temperature + 20*unit.kelvin) + thermodynamic_state_npt = mmtools.states.ThermodynamicState(alanine_explicit_system, + temperature, 1.0*unit.atmosphere) + + # Compound states. + factory = mmtools.alchemy.AbsoluteAlchemicalFactory() + alchemical_region = mmtools.alchemy.AlchemicalRegion(alchemical_atoms=range(22)) + alanine_alchemical = factory.create_alchemical_system(alanine_system, alchemical_region) + alchemical_state_interacting = mmtools.alchemy.AlchemicalState.from_system(alanine_alchemical) + alchemical_state_noninteracting = copy.deepcopy(alchemical_state_interacting) + alchemical_state_noninteracting.set_alchemical_parameters(0.0) + compound_state_interacting = mmtools.states.CompoundThermodynamicState( + thermodynamic_state=mmtools.states.ThermodynamicState(alanine_alchemical, temperature), + composable_states=[alchemical_state_interacting] + ) + compound_state_noninteracting = mmtools.states.CompoundThermodynamicState( + thermodynamic_state=mmtools.states.ThermodynamicState(alanine_alchemical, temperature), + composable_states=[alchemical_state_noninteracting] + ) + + thermodynamic_states = [thermodynamic_state_nvt, thermodynamic_state_nvt_compatible, + thermodynamic_state_npt, compound_state_interacting, + compound_state_noninteracting] + + # Unsampled thermodynamic states. + toluene_system = testsystems.TolueneVacuum().system + toluene_state = mmtools.states.ThermodynamicState(toluene_system, temperature) + unsampled_states = [copy.deepcopy(toluene_state), copy.deepcopy(toluene_state), + copy.deepcopy(compound_state_interacting)] + + with self.temporary_reporter() as reporter: + # Check that after writing and reading, states are identical. + reporter.write_thermodynamic_states(thermodynamic_states, unsampled_states) + restored_states, restored_unsampled = reporter.read_thermodynamic_states() + check_thermodynamic_states_equality(thermodynamic_states, restored_states) + check_thermodynamic_states_equality(unsampled_states, restored_unsampled) + + # The latest writer only stores one full serialization per compatible state. + ncgrp_states = reporter._storage_analysis.groups['thermodynamic_states'] + ncgrp_unsampled = reporter._storage_analysis.groups['unsampled_states'] + + # Load representation of the states on the disk. There + # should be only one full serialization per compatible state. + def decompact_state_variable(variable): + if variable.dtype == 'S1': + # Handle variables stored in fixed_dimensions + data_chars = variable[:] + data_str = data_chars.tostring().decode() + else: + data_str = str(variable[0]) + return data_str + states_serialized = [] + for state_id in range(len(thermodynamic_states)): + state_str = decompact_state_variable(ncgrp_states.variables['state' + str(state_id)]) + state_dict = yaml.load(state_str, Loader=_DictYamlLoader) + states_serialized.append(state_dict) + unsampled_serialized = [] + for state_id in range(len(unsampled_states)): + unsampled_str = decompact_state_variable(ncgrp_unsampled.variables['state' + str(state_id)]) + unsampled_dict = yaml.load(unsampled_str, Loader=_DictYamlLoader) + unsampled_serialized.append(unsampled_dict) + + # Two of the three ThermodynamicStates are compatible. + assert 'standard_system' in states_serialized[0] + assert 'standard_system' not in states_serialized[1] + state_compatible_to_1 = states_serialized[1]['_Reporter__compatible_state'] + assert state_compatible_to_1 == 'thermodynamic_states/0' + assert 'standard_system' in states_serialized[2] + + # The two CompoundThermodynamicStates are compatible. + assert 'standard_system' in states_serialized[3]['thermodynamic_state'] + thermodynamic_state_4 = states_serialized[4]['thermodynamic_state'] + assert thermodynamic_state_4['_Reporter__compatible_state'] == 'thermodynamic_states/3' + + # The first two unsampled states are incompatible with everything else + # but compatible to each other, while the third unsampled state is + # compatible with the alchemical states. + assert 'standard_system' in unsampled_serialized[0] + state_compatible_to_1 = unsampled_serialized[1]['_Reporter__compatible_state'] + assert state_compatible_to_1 == 'unsampled_states/0' + thermodynamic_state_2 = unsampled_serialized[2]['thermodynamic_state'] + assert thermodynamic_state_2['_Reporter__compatible_state'] == 'thermodynamic_states/3' + + def test_write_sampler_states(self): + """Check correct storage of sampler states.""" + analysis_particles = (1, 2) + with self.temporary_reporter(analysis_particle_indices=analysis_particles, checkpoint_interval=2) as reporter: + # Create sampler states. + alanine_test = testsystems.AlanineDipeptideVacuum() + positions = alanine_test.positions + sampler_states = [mmtools.states.SamplerState(positions=positions) + for _ in range(2)] + + # Check that after writing and reading, states are identical. + for iteration in range(3): + reporter.write_sampler_states(sampler_states, iteration=iteration) + reporter.write_last_iteration(iteration) + restored_sampler_states = reporter.read_sampler_states(iteration=0) + for state, restored_state in zip(sampler_states, restored_sampler_states): + assert np.allclose(state.positions, restored_state.positions) + assert np.allclose(state.box_vectors / unit.nanometer, restored_state.box_vectors / unit.nanometer) + # Check that the analysis particles are written off checkpoint whereas full trajectory is not + restored_analysis_states = reporter.read_sampler_states(iteration=1, analysis_particles_only=True) + restored_checkpoint_states = reporter.read_sampler_states(iteration=1) + assert type(restored_analysis_states) is list + for state in restored_analysis_states: + assert state.positions.shape == (len(analysis_particles), 3) + assert restored_checkpoint_states is None + # Check that the analysis particles are written separate from the checkpoint particles + restored_analysis_states = reporter.read_sampler_states(iteration=2, analysis_particles_only=True) + restored_checkpoint_states = reporter.read_sampler_states(iteration=2) + assert len(restored_analysis_states) == len(restored_checkpoint_states) + for analysis_state, checkpoint_state in zip(restored_analysis_states, restored_checkpoint_states): + # This assert is dual purpose: Positions are identical; Analysis shape is correct + # Will raise a ValueError for np.allclose(x,y) if x.shape != y.shape + # Will raise AssertionError if the values are not allclose + assert np.allclose(analysis_state.positions, checkpoint_state.positions[analysis_particles, :]) + assert np.allclose(analysis_state.box_vectors / unit.nanometer, + checkpoint_state.box_vectors / unit.nanometer) + + def test_analysis_particle_mismatch(self): + """Test that previously stored analysis particles is higher priority.""" + blank_analysis_particles = () + set1_analysis_particles = (0, 1) + set2_analysis_particles = (0, 2) + # Does not use the temp reporter since we close and reopen reporter a few times + with mmtools.utils.temporary_directory() as tmp_dir_path: + # Test that starting with a blank analysis cannot be overwritten + blank_file = os.path.join(tmp_dir_path, 'temp_dir/blank_analysis.nc') + reporter = MultiStateReporter(storage=blank_file, open_mode='w', + analysis_particle_indices=blank_analysis_particles) + reporter.close() + del reporter + new_blank_reporter = MultiStateReporter(storage=blank_file, open_mode='r', + analysis_particle_indices=set1_analysis_particles) + assert new_blank_reporter.analysis_particle_indices == blank_analysis_particles + del new_blank_reporter + # Test that starting from an initial set of particles and passing in a blank does not overwrite + set1_file = os.path.join(tmp_dir_path, 'temp_dir/set1_analysis.nc') + set1_reporter = MultiStateReporter(storage=set1_file, open_mode='w', + analysis_particle_indices=set1_analysis_particles) + set1_reporter.close() # Don't delete, we'll need it for another test + new_set1_reporter = MultiStateReporter(storage=set1_file, open_mode='r', + analysis_particle_indices=blank_analysis_particles) + assert new_set1_reporter.analysis_particle_indices == set1_analysis_particles + del new_set1_reporter + # Test that passing in a different set than the initial returns the initial set + new2_set1_reporter = MultiStateReporter(storage=set1_file, open_mode='r', + analysis_particle_indices=set2_analysis_particles) + assert new2_set1_reporter.analysis_particle_indices == set1_analysis_particles + + def test_store_replica_thermodynamic_states(self): + """Check storage of replica thermodynamic states indices.""" + with self.temporary_reporter() as reporter: + for i, replica_states in enumerate([[2, 1, 0, 3], np.array([3, 1, 0, 2])]): + reporter.write_replica_thermodynamic_states(replica_states, iteration=i) + reporter.write_last_iteration(i) + restored_replica_states = reporter.read_replica_thermodynamic_states(iteration=i) + assert np.all(replica_states == restored_replica_states) + + def test_store_mcmc_moves(self): + """Check storage of MCMC moves.""" + sequence_move = mmtools.mcmc.SequenceMove(move_list=[mmtools.mcmc.LangevinDynamicsMove(), + mmtools.mcmc.GHMCMove()], + context_cache=mmtools.cache.ContextCache(capacity=1)) + integrator_move = mmtools.mcmc.IntegratorMove(openmm.VerletIntegrator(1.0*unit.femtosecond), + n_steps=100) + mcmc_moves = [sequence_move, integrator_move] + with self.temporary_reporter() as reporter: + reporter.write_mcmc_moves(mcmc_moves) + restored_mcmc_moves = reporter.read_mcmc_moves() + + # Check that restored MCMCMoves are exactly the same. + original_pickle = pickle.dumps(mcmc_moves) + restored_pickle = pickle.dumps(restored_mcmc_moves) + assert original_pickle == restored_pickle + + def test_store_energies(self): + """Check storage of energies.""" + energy_thermodynamic_states = np.array( + [[0, 2, 3], + [1, 2, 0], + [1, 2, 3]]) + energy_neighborhoods = np.array( + [[0, 1, 1], + [1, 1, 0], + [1, 1, 3]] + ) + energy_unsampled_states = np.array( + [[1, 2], + [2, 3.0], + [3, 9.0]]) + + with self.temporary_reporter() as reporter: + reporter.write_energies(energy_thermodynamic_states, energy_neighborhoods, energy_unsampled_states, iteration=0) + restored_energy_thermodynamic_states, restored_energy_neighborhoods, restored_energy_unsampled_states = reporter.read_energies(iteration=0) + assert np.all(energy_thermodynamic_states == restored_energy_thermodynamic_states) + assert np.all(energy_neighborhoods == restored_energy_neighborhoods) + assert np.all(energy_unsampled_states == restored_energy_unsampled_states) + + def test_ensure_dimension_exists(self): + """Test ensuring that a dimension exists works as expected.""" + with self.temporary_reporter() as reporter: + # These should work fine + reporter._ensure_dimension_exists('dim1', 0) + reporter._ensure_dimension_exists('dim2', 1) + # These should raise an exception + assert_raises(ValueError, reporter._ensure_dimension_exists, 'dim1', 1) + assert_raises(ValueError, reporter._ensure_dimension_exists, 'dim2', 2) + + def test_store_dict(self): + """Check correct storage and restore of dictionaries.""" + + def sorted_dict(d): + d = copy.deepcopy(d) + # Sort internal dictionaries. + for k, v in d.items(): + if isinstance(v, dict): + d[k] = sorted_dict(v) + return sorted(d.items()) + + def compare_dicts(reference, restored): + # We need a deterministically-ordered dict to compare pickles. + sorted_reference = sorted_dict(reference) + sorted_restored = sorted_dict(restored) + assert pickle.dumps(sorted_reference) == pickle.dumps(sorted_restored) + + data = { + 'mybool': False, + 'mystring': 'test', + 'myinteger': 3, 'myfloat': 4.0, + 'mylist': [0, 1, 2, 3], 'mynumpyarray': np.array([2.0, 3, 4]), + 'mynestednumpyarray': np.array([[1, 2, 3], [4.0, 5, 6]]), + 'myquantity': 5.0 / unit.femtosecond, + 'myquantityarray': unit.Quantity(np.array([[1, 2, 3], [4.0, 5, 6]]), unit=unit.angstrom), + 'mynesteddict': {'field1': 'string', 'field2': {'field21': 3.0, 'field22': True}} + } + + with self.temporary_reporter() as reporter: + # Test both nested and single-string representations. + for name, nested in [('testdict', False), ('nested', True)]: + reporter._write_dict(name, data, nested=nested) + restored_data = reporter.read_dict(name) + compare_dicts(data, restored_data) + + # Test reading a keyword inside a dict. + restored_data = reporter.read_dict(name + '/mynesteddict/field2') + compare_dicts(data['mynesteddict']['field2'], restored_data) + + # write_dict supports updates, even with the nested representation + # if the structure of the dictionary doesn't change. + data['mybool'] = True + data['mystring'] = 'substituted' + reporter._write_dict(name, data, nested=nested) + restored_data = reporter.read_dict(name) + assert restored_data['mybool'] is True + assert restored_data['mystring'] == 'substituted' + + # In nested representation, dictionaries are stored as groups and + # values as variables. Otherwise, there's only a single variable. + if nested: + dict_group = reporter._storage_analysis.groups[name] + assert 'mynesteddict' in dict_group.groups + assert 'mylist' in dict_group.variables + else: + assert name in reporter._storage_analysis.variables + + # Write dict fixed_dimension creates static dimensions and read/writes correctly + reporter._write_dict('fixed', data, fixed_dimension=True) + restored_fixed_data = reporter.read_dict('fixed') + compare_dicts(data, restored_fixed_data) + + def test_store_mixing_statistics(self): + """Check mixing statistics are correctly stored.""" + n_accepted_matrix = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) + n_proposed_matrix = np.array([[3, 3, 3], [6, 6, 6], [9, 9, 9]]) + with self.temporary_reporter() as reporter: + reporter.write_mixing_statistics(n_accepted_matrix, n_proposed_matrix, iteration=0) + restored_n_accepted, restored_n_proposed = reporter.read_mixing_statistics(iteration=0) + assert np.all(n_accepted_matrix == restored_n_accepted) + assert np.all(n_proposed_matrix == restored_n_proposed) + + +# ============================================================================== +# TEST MULTISTATE SAMPLERS +# ============================================================================== + +class TestMultiStateSampler(object): + """Base test suite for the multi-state classes""" + + # ------------------------------------ + # VARIABLES TO SET FOR EACH TEST CLASS + # ------------------------------------ + + N_SAMPLERS = 3 + N_STATES = 5 + SAMPLER = MultiStateSampler + REPORTER = MultiStateReporter + + # -------------------------------------- + # Optional helper function to overwrite. + # -------------------------------------- + + @classmethod + def call_sampler_create(cls, sampler, reporter, + thermodynamic_states, + sampler_states, + unsampled_states): + """Helper function to call the create method for the sampler""" + # Allows initial thermodynamic states to be handled by the built in methods + sampler.create(thermodynamic_states, sampler_states, reporter, unsampled_thermodynamic_states=unsampled_states) + + # -------------------------------- + # Tests overwritten by sub-classes + # -------------------------------- + def test_stored_properties(self): + """Test that storage is kept in sync with options.""" + # Intermediary step to testing stored properties + self.actual_stored_properties_check() + + @classmethod + def _compute_energies_independently(cls, thermodynamic_states, sampler_states, unsampled_states): + """ + Helper function to compute energies by hand. + This is overwritten by subclasses + """ + n_states = len(thermodynamic_states) + n_replicas = len(sampler_states) + # Compute the energies independently. + energy_thermodynamic_states = np.zeros((n_replicas, n_states)) + energy_unsampled_states = np.zeros((n_replicas, len(unsampled_states))) + for energies, states in [(energy_thermodynamic_states, thermodynamic_states), + (energy_unsampled_states, unsampled_states)]: + for i, sampler_state in enumerate(sampler_states): + for j, state in enumerate(states): + context, integrator = mmtools.cache.global_context_cache.get_context(state) + sampler_state.apply_to_context(context) + energies[i][j] = state.reduced_potential(context) + return energy_thermodynamic_states, energy_unsampled_states + + # -------------------------------- + # Common Test functions below here + # -------------------------------- + + @classmethod + def setup_class(cls): + """Shared test cases and variables.""" + # Test case with alanine in vacuum at 3 different positions and temperatures. + # --------------------------------------------------------------------------- + alanine_test = testsystems.AlanineDipeptideVacuum() + + # Translate the sampler states to be different one from each other. + alanine_sampler_states = [ + mmtools.states.SamplerState(positions=alanine_test.positions + 10 * i * unit.nanometers) + for i in range(cls.N_SAMPLERS)] + + # Set increasing temperature. + temperatures = [(300 + 10 * i) * unit.kelvin for i in range(cls.N_STATES)] + alanine_thermodynamic_states = [mmtools.states.ThermodynamicState(alanine_test.system, temperatures[i]) + for i in range(cls.N_STATES)] + + # No unsampled states for this test. + cls.alanine_test = (alanine_thermodynamic_states, alanine_sampler_states, []) + + # Test case with host guest in implicit at 3 different positions and alchemical parameters. + # ----------------------------------------------------------------------------------------- + hostguest_test = testsystems.HostGuestVacuum() + factory = mmtools.alchemy.AbsoluteAlchemicalFactory() + alchemical_region = mmtools.alchemy.AlchemicalRegion(alchemical_atoms=range(126, 156)) + hostguest_alchemical = factory.create_alchemical_system(hostguest_test.system, alchemical_region) + + # Translate the sampler states to be different one from each other. + hostguest_sampler_states = [ + mmtools.states.SamplerState(positions=hostguest_test.positions + 10 * i * unit.nanometers) + for i in range(cls.N_SAMPLERS)] + + # Create the three basic thermodynamic states. + temperatures = [(300 + 10 * i) * unit.kelvin for i in range(cls.N_STATES)] + hostguest_thermodynamic_states = [mmtools.states.ThermodynamicState(hostguest_alchemical, temperatures[i]) + for i in range(cls.N_STATES)] + + # Create alchemical states at different parameter values. + alchemical_states = [mmtools.alchemy.AlchemicalState.from_system(hostguest_alchemical) + for _ in range(cls.N_STATES)] + for i, alchemical_state in enumerate(alchemical_states): + alchemical_state.set_alchemical_parameters(float(i) / (cls.N_STATES - 1)) + + # Create compound states. + hostguest_compound_states = list() + for i in range(cls.N_STATES): + hostguest_compound_states.append( + mmtools.states.CompoundThermodynamicState(thermodynamic_state=hostguest_thermodynamic_states[i], + composable_states=[alchemical_states[i]]) + ) + + # Unsampled states. + nonalchemical_state = mmtools.states.ThermodynamicState(hostguest_test.system, temperatures[0]) + hostguest_unsampled_states = [copy.deepcopy(nonalchemical_state)] + + cls.hostguest_test = (hostguest_compound_states, hostguest_sampler_states, hostguest_unsampled_states) + + # Debugging Messages to sent to Nose with --nocapture enabled + output_descr = "Testing Sampler: {} -- States: {} -- Samplers: {}".format( + cls.SAMPLER.__name__, cls.N_STATES, cls.N_SAMPLERS) + len_output = len(output_descr) + print("#" * len_output) + print(output_descr) + print("#" * len_output) + + @staticmethod + @contextlib.contextmanager + def temporary_storage_path(): + """Generate a storage path in a temporary folder and share it. + + It makes it possible to run tests on multiple nodes with MPI. + + """ + mpicomm = mpiplus.get_mpicomm() + with mmtools.utils.temporary_directory() as tmp_dir_path: + storage_file_path = os.path.join(tmp_dir_path, 'test_storage.nc') + if mpicomm is not None: + storage_file_path = mpicomm.bcast(storage_file_path, root=0) + yield storage_file_path + + @staticmethod + def get_node_replica_ids(tot_n_replicas): + """Return the indices of the replicas that this node is responsible for.""" + mpicomm = mpiplus.get_mpicomm() + if mpicomm is None or mpicomm.rank == 0: + return set(range(tot_n_replicas)) + else: + return set(range(mpicomm.rank, tot_n_replicas, mpicomm.size)) + + @staticmethod + @contextlib.contextmanager + def captured_output(): + new_out, new_err = StringIO(), StringIO() + old_out, old_err = sys.stdout, sys.stderr + try: + sys.stdout, sys.stderr = new_out, new_err + yield sys.stdout, sys.stderr + finally: + sys.stdout, sys.stderr = old_out, old_err + + @staticmethod + def property_creator(name, on_disk_name, value, on_disk_value): + """ + Helper to create additional properties to create for checking + + Makes a nested dict where the top key is the 'name', with one + value as a dict where the sub-dict is of the form: + {'value': value, + 'on_disk_name': on_disk_name, + 'on_disk_value': on_disk_value + } + """ + return {name: { + + 'value': value, + 'on_disk_value': on_disk_value, + 'on_disk_name': on_disk_name + }} + + def test_create(self): + """Test creation of a new MultiState simulation. + + Checks that the storage file is correctly initialized with all the + information needed. With MPI, this checks that only node 0 has an + open Reporter for writing. + + """ + thermodynamic_states, sampler_states, unsampled_states = copy.deepcopy(self.alanine_test) + n_states = len(thermodynamic_states) + n_samplers = len(sampler_states) + + with self.temporary_storage_path() as storage_path: + reporter = self.REPORTER(storage_path, checkpoint_interval=1) + sampler = self.SAMPLER() + if hasattr(sampler, 'replica_mixing_scheme'): + sampler.replica_mixing_scheme = 'swap-neighbors' + sampler.locality = 2 + self.call_sampler_create(sampler, reporter, + thermodynamic_states, + sampler_states, unsampled_states) + + # Check that reporter has reporter only if rank 0. + mpicomm = mpiplus.get_mpicomm() + if mpicomm is None or mpicomm.rank == 0: + assert sampler._reporter.is_open() + else: + assert not sampler._reporter.is_open() + + # Open reporter to read stored data. + reporter = self.REPORTER(storage_path, open_mode='r', checkpoint_interval=1) + + # The n_states sampler states have been distributed + restored_sampler_states = reporter.read_sampler_states(iteration=0) + restored_thermo_states, _ = reporter.read_thermodynamic_states() + assert sampler.n_states == n_states, ("Mismatch: sampler.n_states = {} " + "but n_states = {}".format(sampler.n_states, n_states)) + assert sampler.n_replicas == n_samplers, ("Mismatch: sampler.n_replicas = {} " + "but n_samplers = {}".format(sampler.n_replicas, n_samplers)) + assert len(restored_sampler_states) == n_samplers + assert len(restored_thermo_states) == n_states + assert np.allclose(restored_sampler_states[0].positions, sampler._sampler_states[0].positions) + + # MCMCMove was stored correctly. + restored_mcmc_moves = reporter.read_mcmc_moves() + assert len(sampler._mcmc_moves) == n_states + assert len(restored_mcmc_moves) == n_states + for sampler_move, restored_move in zip(sampler._mcmc_moves, restored_mcmc_moves): + assert isinstance(sampler_move, mmtools.mcmc.LangevinDynamicsMove) + assert isinstance(restored_move, mmtools.mcmc.LangevinDynamicsMove) + + # Options have been stored. + stored_options = reporter.read_dict('options') + options_to_store = dict() + for cls in inspect.getmro(type(sampler)): + parameter_names, _, _, defaults, _, _, _ = inspect.getfullargspec(cls.__init__) + if defaults: + for parameter_name in parameter_names[-len(defaults):]: + options_to_store[parameter_name] = getattr(sampler, '_' + parameter_name) + options_to_store.pop('mcmc_moves') # mcmc_moves are stored separately + for key, value in options_to_store.items(): + if np.isscalar(value): + assert stored_options[key] == value, "stored_options['%s'] = %s, but value = %s" % (key, stored_options[key], value) + assert getattr(sampler, '_' + key) == value, "getattr(sampler, '%s') = %s, but value = %s" % ('_' + key, getattr(sampler, '_' + key), value) + else: + assert np.all(stored_options[key] == value), "stored_options['%s'] = %s, but value = %s" % (key, stored_options[key], value) + assert np.all(getattr(sampler, '_' + key) == value), "getattr(sampler, '%s') = %s, but value = %s" % ('_' + key, getattr(sampler, '_' + key), value) + + # A default title has been added to the stored metadata. + metadata = reporter.read_dict('metadata') + assert len(metadata) == 1 + assert sampler.metadata['title'] == metadata['title'] + + def test_citations(self): + """Test that citations are displayed and suppressed as needed.""" + thermodynamic_states, sampler_states, unsampled_states = copy.deepcopy(self.alanine_test) + + with self.temporary_storage_path() as storage_path: + sampler = self.SAMPLER() + reporter = self.REPORTER(storage_path, checkpoint_interval=1) + + # Ensure global mute is on + sampler._global_citation_silence = True + + # Trap expected output + with self.captured_output() as (out, _): + sampler._display_citations(overwrite_global=True) + cite_string = out.getvalue().strip() + self.call_sampler_create(sampler, reporter, + thermodynamic_states, sampler_states, + unsampled_states) + # Reset internal flag + sampler._have_displayed_citations_before = False + # Test that the overwrite flag worked + assert cite_string != '' + # Test that the output is not generate when the global is set + with self.captured_output() as (out, _): + sampler._global_citation_silence = True + sampler._display_citations() + output = out.getvalue().strip() + assert cite_string not in output + # Test that the output is generated with the global is not set and previously un shown + with self.captured_output() as (out, _): + sampler._global_citation_silence = False + sampler._have_displayed_citations_before = False + sampler._display_citations() + output = out.getvalue().strip() + assert cite_string in output + # Repeat to ensure the citations are not generated a second time + with self.captured_output() as (out, _): + sampler._global_citation_silence = False + sampler._display_citations() + output = out.getvalue().strip() + assert cite_string not in output + + def test_from_storage(self): + """Test that from_storage completely restore the sampler. + + Checks that the static constructor MultiStateSampler.from_storage() + restores the simulation object in the exact same state as the last + iteration. + + """ + thermodynamic_states, sampler_states, unsampled_states = copy.deepcopy(self.hostguest_test) + n_replicas = len(sampler_states) + + with self.temporary_storage_path() as storage_path: + number_of_iterations = 3 + move = mmtools.mcmc.LangevinDynamicsMove(n_steps=1) + sampler = self.SAMPLER(mcmc_moves=move, number_of_iterations=number_of_iterations) + if hasattr(sampler, 'replica_mixing_scheme'): + # TODO: Test both 'swap-all' with locality=None and 'swap-neighbors' with locality=1 + sampler.replica_mixing_scheme = 'swap-neighbors' # required for non-global locality + sampler.locality = 1 + reporter = self.REPORTER(storage_path, checkpoint_interval=1) + self.call_sampler_create(sampler, reporter, + thermodynamic_states, sampler_states, + unsampled_states) + + # Test at the beginning and after few iterations. + for iteration in range(2): + # Store the state of the initial repex object (its __dict__). We leave the + # reporter out because when the NetCDF file is copied, it runs into issues. + original_dict = copy.deepcopy({k: v for k, v in sampler.__dict__.items() if not k == '_reporter'}) + + # Delete repex to close reporter before creating a new one + # to avoid weird issues with multiple NetCDF files open. + del sampler + reporter.close() + sampler = self.SAMPLER.from_storage(reporter) + restored_dict = copy.deepcopy({k: v for k, v in sampler.__dict__.items() if not k == '_reporter'}) + + # Check thermodynamic states. + original_ts = original_dict.pop('_thermodynamic_states') + restored_ts = restored_dict.pop('_thermodynamic_states') + check_thermodynamic_states_equality(original_ts, restored_ts) + + # Check unsampled thermodynamic states. + original_us = original_dict.pop('_unsampled_states') + restored_us = restored_dict.pop('_unsampled_states') + check_thermodynamic_states_equality(original_us, restored_us) + + # The reporter of the restored simulation must be open only in node 0. + mpicomm = mpiplus.get_mpicomm() + if mpicomm is None or mpicomm.rank == 0: + assert sampler._reporter.is_open() + else: + assert not sampler._reporter.is_open() + + # Each replica keeps only the info for the replicas it is + # responsible for to minimize network traffic. + node_replica_ids = self.get_node_replica_ids(n_replicas) + + # Check sampler states. Non 0 nodes only hold their positions. + original_ss = original_dict.pop('_sampler_states') + restored_ss = restored_dict.pop('_sampler_states') + for replica_id, (original, restored) in enumerate(zip(original_ss, restored_ss)): + if replica_id in node_replica_ids: + assert np.allclose(original.positions, restored.positions) + assert np.all(original.box_vectors == restored.box_vectors) + + # Check energies. Non 0 nodes only hold their energies. + original_neighborhoods = original_dict.pop('_neighborhoods') + restored_neighborhoods = restored_dict.pop('_neighborhoods') + original_ets = original_dict.pop('_energy_thermodynamic_states') + restored_ets = restored_dict.pop('_energy_thermodynamic_states') + original_eus = original_dict.pop('_energy_unsampled_states') + restored_eus = restored_dict.pop('_energy_unsampled_states') + for replica_id in node_replica_ids: + assert np.allclose(original_neighborhoods[replica_id], restored_neighborhoods[replica_id]) + assert np.allclose(original_ets[replica_id], restored_ets[replica_id]) + assert np.allclose(original_eus[replica_id], restored_eus[replica_id]) + + # Only node 0 has updated accepted and proposed exchanges. + original_accepted = original_dict.pop('_n_accepted_matrix') + restored_accepted = restored_dict.pop('_n_accepted_matrix') + original_proposed = original_dict.pop('_n_proposed_matrix') + restored_proposed = restored_dict.pop('_n_proposed_matrix') + if len(node_replica_ids) == n_replicas: + assert np.all(original_accepted == restored_accepted) + assert np.all(original_proposed == restored_proposed) + + # Test mcmc moves with pickle. + original_mcmc_moves = original_dict.pop('_mcmc_moves') + restored_mcmc_moves = restored_dict.pop('_mcmc_moves') + if len(node_replica_ids) == n_replicas: + assert pickle.dumps(original_mcmc_moves) == pickle.dumps(restored_mcmc_moves) + + # Check all other arrays. Instantiate list so that we can pop from original_dict. + for attr, original_value in list(original_dict.items()): + if isinstance(original_value, np.ndarray): + original_value = original_dict.pop(attr) + restored_value = restored_dict.pop(attr) + assert np.all(original_value == restored_value), '{}: {}\t{}'.format( + attr, original_value, restored_value) + + # Everything else should be a dict of builtins. + assert original_dict == restored_dict + + # Run few iterations to see that we restore also after a while. + if iteration == 0: + sampler.run(number_of_iterations) + + def actual_stored_properties_check(self, additional_properties=None): + """Stored properties check which expects a keyword""" + thermodynamic_states, sampler_states, unsampled_states = copy.deepcopy(self.alanine_test) + + with self.temporary_storage_path() as storage_path: + sampler = self.SAMPLER(number_of_iterations=5) + reporter = self.REPORTER(storage_path, checkpoint_interval=1) + self.call_sampler_create(sampler, reporter, + thermodynamic_states, sampler_states, + unsampled_states) + + # Update options and check the storage is synchronized. + sampler.number_of_iterations = float('inf') + # Process Additional properties + if additional_properties is not None: + for add_property, property_data in additional_properties.items(): + setattr(sampler, add_property, property_data['value']) + + # Displace positions of the first sampler state. + sampler_states = sampler.sampler_states + original_positions = copy.deepcopy(sampler_states[0].positions) + displacement_vector = np.ones(3) * unit.angstroms + sampler_states[0].positions += displacement_vector + sampler.sampler_states = sampler_states + + mpicomm = mpiplus.get_mpicomm() + if mpicomm is None or mpicomm.rank == 0: + reporter.close() + reporter = self.REPORTER(storage_path, open_mode='r') + restored_options = reporter.read_dict('options') + assert restored_options['number_of_iterations'] == float('inf') + if additional_properties is not None: + for _, property_data in additional_properties.items(): + on_disk_name = property_data['on_disk_name'] + on_disk_value = property_data['on_disk_value'] + restored_value = restored_options[on_disk_name] + if on_disk_value is None: + assert restored_value is on_disk_value, "Restored {} is not {}".format(restored_value, + on_disk_value) + else: + assert restored_value == on_disk_value, "Restored {} != {}".format(restored_value, + on_disk_value) + + restored_sampler_states = reporter.read_sampler_states(iteration=0) + assert np.allclose(restored_sampler_states[0].positions, + original_positions + displacement_vector) + + def test_propagate_replicas(self): + """Test method _propagate_replicas from MultiStateSampler. + + The purpose of this test is mainly to make sure that MPI doesn't mix + the information of the propagated StateSamplers when it communicates + the new positions and box vectors. + + """ + thermodynamic_states, sampler_states, unsampled_states = copy.deepcopy(self.alanine_test) + n_replicas = len(sampler_states) + if n_replicas == 1: + # This test is intended for use with more than one replica + return + + with self.temporary_storage_path() as storage_path: + # For this test to work, positions should be the same but + # translated, so that minimized positions should satisfy + # the same condition. + original_diffs = [np.average(sampler_states[i].positions - sampler_states[i+1].positions) + for i in range(n_replicas - 1)] + assert not np.allclose(original_diffs, [0 for _ in range(n_replicas - 1)]), "sampler %s failed" % self.SAMPLER + + # Create a replica exchange that propagates only 1 femtosecond + # per iteration so that positions won't change much. + move = mmtools.mcmc.IntegratorMove(openmm.VerletIntegrator(1.0*unit.femtosecond), n_steps=1) + sampler = self.SAMPLER(mcmc_moves=move) + reporter = self.REPORTER(storage_path) + self.call_sampler_create(sampler, reporter, + thermodynamic_states, sampler_states, + unsampled_states) + + # Propagate. + sampler._propagate_replicas() + + # The relative positions between the new sampler states should + # be still translated the same way (i.e. we are not assigning + # the minimized positions to the incorrect sampler states). + new_sampler_states = sampler._sampler_states + new_diffs = [np.average(new_sampler_states[i].positions - new_sampler_states[i+1].positions) + for i in range(n_replicas - 1)] + assert np.allclose(original_diffs, new_diffs, rtol=1e-4) + + def test_compute_energies(self): + """Test method _compute_energies from MultiStateSampler. + + The purpose of this test is mainly to make sure that MPI doesn't mix + the information of the thermodynamics and unsampled ThermodynamicStates + when it communicates them to the other nodes. + + """ + thermodynamic_states, sampler_states, unsampled_states = copy.deepcopy(self.hostguest_test) + n_states = len(thermodynamic_states) + n_replicas = len(sampler_states) + + with self.temporary_storage_path() as storage_path: + sampler = self.SAMPLER() + reporter = self.REPORTER(storage_path, checkpoint_interval=1) + self.call_sampler_create(sampler, reporter, + thermodynamic_states, sampler_states, + unsampled_states) + + # Let MultiStateSampler distribute the computation of energies among nodes. + sampler._compute_energies() + + # Compute energies at all states + energy_thermodynamic_states = np.zeros((n_replicas, n_states)) + energy_unsampled_states = np.zeros((n_replicas, len(unsampled_states))) + for energies, states in [(energy_thermodynamic_states, thermodynamic_states), + (energy_unsampled_states, unsampled_states)]: + for i, sampler_state in enumerate(sampler_states): + for j, state in enumerate(states): + context, integrator = mmtools.cache.global_context_cache.get_context(state) + sampler_state.apply_to_context(context) + energies[i][j] = state.reduced_potential(context) + + energy_thermodynamic_states, energy_unsampled_states = \ + self._compute_energies_independently(thermodynamic_states, sampler_states, unsampled_states) + + # Only node 0 has all the energies. + mpicomm = mpiplus.get_mpicomm() + if mpicomm is None or mpicomm.rank == 0: + for replica_index in range(n_replicas): + neighborhood = sampler._neighborhoods[replica_index,:] + assert np.allclose(sampler._energy_thermodynamic_states[replica_index,neighborhood], energy_thermodynamic_states[replica_index,neighborhood]) + assert np.allclose(sampler._energy_unsampled_states, energy_unsampled_states) + + def test_minimize(self): + """Test MultiStateSampler minimize method. + + The purpose of this test is mainly to make sure that MPI doesn't mix + the information of the minimized StateSamplers when it communicates + the new positions. It also checks that the energies are effectively + decreased. + + """ + thermodynamic_states, sampler_states, unsampled_states = copy.deepcopy(self.alanine_test) + n_states = len(thermodynamic_states) + n_replicas = len(sampler_states) + if n_replicas == 1: + # This test is intended for use with more than one replica + return + + with self.temporary_storage_path() as storage_path: + sampler = self.SAMPLER() + reporter = self.REPORTER(storage_path, checkpoint_interval=1) + self.call_sampler_create(sampler, reporter, + thermodynamic_states, sampler_states, + unsampled_states) + + # For this test to work, positions should be the same but + # translated, so that minimized positions should satisfy + # the same condition. + original_diffs = [np.average(sampler_states[i].positions - sampler_states[i + 1].positions) + for i in range(n_replicas - 1)] + assert not np.allclose(original_diffs, [0 for _ in range(n_replicas - 1)]) + + # Compute initial energies. + sampler._compute_energies() + state_indices = sampler._replica_thermodynamic_states + original_energies = [sampler._energy_thermodynamic_states[i, j] for i, j in enumerate(state_indices)] + + # Minimize. + sampler.minimize() + + # The relative positions between the new sampler states should + # be still translated the same way (i.e. we are not assigning + # the minimized positions to the incorrect sampler states). + new_sampler_states = sampler._sampler_states + new_diffs = [np.average(new_sampler_states[i].positions - new_sampler_states[i + 1].positions) + for i in range(n_replicas - 1)] + assert np.allclose(original_diffs, new_diffs, atol=0.1) + + # Each replica keeps only the info for the replicas it is + # responsible for to minimize network traffic. + node_replica_ids = self.get_node_replica_ids(n_replicas) + + # The energies have been minimized. + sampler._compute_energies() + for replica_index in node_replica_ids: + state_index = sampler._replica_thermodynamic_states[replica_index] + old_energy = original_energies[replica_index] + new_energy = sampler._energy_thermodynamic_states[replica_index, state_index] + assert new_energy <= old_energy, "Energies did not decrease: Replica {} was originally {}, now {}".format(replica_index, old_energy, new_energy) + + # The storage has been updated. + reporter.close() + if len(node_replica_ids) == n_states: + reporter = self.REPORTER(storage_path, open_mode='r') + stored_sampler_states = reporter.read_sampler_states(iteration=0) + for new_state, stored_state in zip(new_sampler_states, stored_sampler_states): + assert np.allclose(new_state.positions, stored_state.positions) + + def test_equilibrate(self): + """Test equilibration of MultiStateSampler simulation. + + During equilibration, we set temporarily different MCMCMoves. This checks + that they are restored correctly. It also checks that the storage has the + updated positions. + + """ + thermodynamic_states, sampler_states, unsampled_states = copy.deepcopy(self.alanine_test) + n_replicas = len(sampler_states) + + with self.temporary_storage_path() as storage_path: + # We create a ReplicaExchange with a GHMC move but use Langevin for equilibration. + sampler = self.SAMPLER(mcmc_moves=mmtools.mcmc.GHMCMove()) + reporter = self.REPORTER(storage_path, checkpoint_interval=1) + self.call_sampler_create(sampler, reporter, + thermodynamic_states, sampler_states, + unsampled_states) + + # Equilibrate + equilibration_move = mmtools.mcmc.LangevinDynamicsMove(n_steps=1) + sampler.equilibrate(n_iterations=10, mcmc_moves=equilibration_move) + assert isinstance(sampler._mcmc_moves[0], mmtools.mcmc.GHMCMove) + + # Each replica keeps only the info for the replicas it is + # responsible for to minimize network traffic. + node_replica_ids = self.get_node_replica_ids(n_replicas) + + # The storage has been updated. + reporter.close() + if len(node_replica_ids) == n_replicas: + reporter = self.REPORTER(storage_path, open_mode='r', checkpoint_interval=1) + stored_sampler_states = reporter.read_sampler_states(iteration=0) + for new_state, stored_state in zip(sampler._sampler_states, stored_sampler_states): + assert np.allclose(new_state.positions, stored_state.positions) + + # We are still at iteration 0. + assert sampler._iteration == 0 + + def test_run_extend(self): + """Test methods run and extend of MultiStateSampler.""" + test_cases = [self.alanine_test, self.hostguest_test] + + for test_case in test_cases: + thermodynamic_states, sampler_states, unsampled_states = copy.deepcopy(test_case) + + with self.temporary_storage_path() as storage_path: + moves = mmtools.mcmc.SequenceMove([ + mmtools.mcmc.LangevinDynamicsMove(n_steps=1), + mmtools.mcmc.MCRotationMove(), + mmtools.mcmc.GHMCMove(n_steps=1) + ]) + + sampler = self.SAMPLER(mcmc_moves=moves, number_of_iterations=2) + reporter = self.REPORTER(storage_path, checkpoint_interval=1) + self.call_sampler_create(sampler, reporter, + thermodynamic_states, sampler_states, + unsampled_states) + + # MultiStateSampler.run doesn't go past number_of_iterations. + assert not sampler.is_completed + sampler.run(n_iterations=3) + assert sampler.iteration == 2 + assert sampler.is_completed + + # MultiStateSampler.extend does. + sampler.extend(n_iterations=2) + assert sampler.iteration == 4 + + # Extract the sampled thermodynamic states + # Only use propagated states since the last iteration is not subject to MCMC moves + sampled_states = list(reporter.read_replica_thermodynamic_states()[1:].flat) + + # All replicas must have moves with updated statistics. + for state_index, sequence_move in enumerate(sampler._mcmc_moves): + # LangevinDynamicsMove (index 0) doesn't have statistics. + for move_id in [1, 2]: + assert sequence_move.move_list[move_id].n_proposed == sampled_states.count(state_index) + + # The MCMCMoves statistics in the storage are updated. + mpicomm = mpiplus.get_mpicomm() + if mpicomm is None or mpicomm.rank == 0: + reporter.close() + reporter = self.REPORTER(storage_path, open_mode='r', checkpoint_interval=1) + restored_mcmc_moves = reporter.read_mcmc_moves() + for state_index, sequence_move in enumerate(restored_mcmc_moves): + # LangevinDynamicsMove (index 0) doesn't have statistic + for move_id in [1, 2]: + assert sequence_move.move_list[move_id].n_proposed == sampled_states.count(state_index) + + def test_checkpointing(self): + """Test that checkpointing writes infrequently""" + thermodynamic_states, sampler_states, unsampled_states = copy.deepcopy(self.alanine_test) + + with self.temporary_storage_path() as storage_path: + # For this test, we simply check that the checkpointing writes on the interval + # We don't care about the numbers, per se, but we do care about when things are written + n_iterations = 3 + move = mmtools.mcmc.IntegratorMove(openmm.VerletIntegrator(1.0 * unit.femtosecond), + n_steps=1) + reporter = self.REPORTER(storage_path, checkpoint_interval=2) + sampler = self.SAMPLER(mcmc_moves=move, number_of_iterations=n_iterations) + self.call_sampler_create(sampler, reporter, + thermodynamic_states, sampler_states, + unsampled_states) + # Propagate. + sampler.run() + reporter.close() + reporter = self.REPORTER(storage_path, open_mode='r', checkpoint_interval=2) + for i in range(n_iterations): + energies, _, _ = reporter.read_energies(i) + states = reporter.read_sampler_states(i) + assert type(energies) is np.ndarray + if reporter._calculate_checkpoint_iteration(i) is not None: + assert type(states[0].positions) is mmtools.utils.TrackedQuantity + else: + assert states is None + + def test_last_iteration_functions(self): + """Test that the last_iteration functions work right""" + thermodynamic_states, sampler_states, unsampled_states = copy.deepcopy(self.alanine_test) + with self.temporary_storage_path() as storage_path: + # For this test, we simply check that the checkpointing writes on the interval + # We don't care about the numbers, per se, but we do care about when things are written + n_iterations = 10 + move = mmtools.mcmc.IntegratorMove(openmm.VerletIntegrator(1.0 * unit.femtosecond), n_steps=1) + sampler = self.SAMPLER(mcmc_moves=move, number_of_iterations=n_iterations) + reporter = self.REPORTER(storage_path, checkpoint_interval=2) + self.call_sampler_create(sampler, reporter, + thermodynamic_states, sampler_states, + unsampled_states) + # Propagate. + sampler.run() + reporter.close() + reporter = self.REPORTER(storage_path, open_mode='a', checkpoint_interval=2) + all_energies, _, _ = reporter.read_energies() + # Break the checkpoint + last_index = 4 + reporter.write_last_iteration(last_index) # 5th iteration + reporter.close() + del reporter + reporter = self.REPORTER(storage_path, open_mode='r', checkpoint_interval=2) + # Check single positive index within range + energies, _, _ = reporter.read_energies(1) + assert np.all(energies == all_energies[1]) + # Check negative index was moved + energies, _, _ = reporter.read_energies(-1) + assert np.all(energies == all_energies[last_index]) + # Check slice + energies, _, _ = reporter.read_energies() + assert np.all( + energies == all_energies[:last_index + 1]) # +1 to make sure we get the last index + # Check negative slicing + energies, _, _ = reporter.read_energies(slice(-1, None, -1)) + assert np.all(energies == all_energies[last_index::-1]) + # Errors + with assert_raises(IndexError): + reporter.read_energies(7) + + def test_separate_checkpoint_file(self): + """Test that a separate checkpoint file can be created""" + with self.temporary_storage_path() as storage_path: + cp_file = 'checkpoint_file.nc' + base, head = os.path.split(storage_path) + cp_path = os.path.join(base, cp_file) + reporter = self.REPORTER(storage_path, checkpoint_storage=cp_file, open_mode='w') + reporter.close() + assert os.path.isfile(storage_path) + assert os.path.isfile(cp_path) + + def test_checkpoint_uuid_matching(self): + """Test that checkpoint and storage files have the same UUID""" + with self.temporary_storage_path() as storage_path: + cp_file = 'checkpoint_file.nc' + reporter = self.REPORTER(storage_path, checkpoint_storage=cp_file, open_mode='w') + assert reporter._storage_checkpoint.UUID == reporter._storage_analysis.UUID + + def test_uuid_mismatch_errors(self): + """Test that trying to use separate checkpoint file fails the UUID check""" + with self.temporary_storage_path() as storage_path: + file_base, ext = os.path.splitext(storage_path) + storage_mod = file_base + '_mod' + ext + cp_file_main = 'checkpoint_file.nc' + cp_file_mod = 'checkpoint_mod.nc' + reporter_main = self.REPORTER(storage_path, checkpoint_storage=cp_file_main, open_mode='w') + reporter_main.close() + reporter_mod = self.REPORTER(storage_mod, checkpoint_storage=cp_file_mod, open_mode='w') + reporter_mod.close() + del reporter_main, reporter_mod + with assert_raises(IOError): + self.REPORTER(storage_path, checkpoint_storage=cp_file_mod, open_mode='r') + + def test_analysis_opens_without_checkpoint(self): + """Test that the analysis file can open without the checkpoint file""" + with self.temporary_storage_path() as storage_path: + cp_file = 'checkpoint_file.nc' + cp_file_mod = 'checkpoint_mod.nc' + reporter = self.REPORTER(storage_path, checkpoint_storage=cp_file, open_mode='w') + reporter.close() + del reporter + self.REPORTER(storage_path, checkpoint_storage=cp_file_mod, open_mode='r') + + def test_storage_reporter_and_string(self): + """Test that creating a MultiState by storage string and reporter is the same""" + thermodynamic_states, sampler_states, unsampled_states = copy.deepcopy(self.alanine_test) + with self.temporary_storage_path() as storage_path: + n_iterations = 5 + move = mmtools.mcmc.IntegratorMove(openmm.VerletIntegrator(1.0 * unit.femtosecond), n_steps=1) + sampler = self.SAMPLER(mcmc_moves=move, number_of_iterations=n_iterations) + self.call_sampler_create(sampler, storage_path, + thermodynamic_states, sampler_states, + unsampled_states) + # Propagate. + sampler.run() + energies_str, _, _ = sampler._reporter.read_energies() + reporter = self.REPORTER(storage_path) + del sampler + sampler = self.SAMPLER.from_storage(reporter) + energies_rep, _, _ = sampler._reporter.read_energies() + assert np.all(energies_str == energies_rep) + + def test_online_analysis_works(self): + """Test online analysis runs""" + thermodynamic_states, sampler_states, unsampled_states = copy.deepcopy(self.alanine_test) + with self.temporary_storage_path() as storage_path: + n_iterations = 5 + online_interval = 1 + move = mmtools.mcmc.IntegratorMove(openmm.VerletIntegrator(1.0 * unit.femtosecond), n_steps=1) + sampler = self.SAMPLER(mcmc_moves=move, number_of_iterations=n_iterations, + online_analysis_interval=online_interval, + online_analysis_minimum_iterations=3) + self.call_sampler_create(sampler, storage_path, + thermodynamic_states, sampler_states, + unsampled_states) + # Run + sampler.run() + + def validate_this_test(): + # The stored values of online analysis should be up to date. + last_written_free_energy = self.SAMPLER._read_last_free_energy(sampler._reporter, sampler.iteration) + last_mbar_f_k, (last_free_energy, last_err_free_energy) = last_written_free_energy + + assert len(sampler._last_mbar_f_k) == len(thermodynamic_states) + assert not np.all(sampler._last_mbar_f_k == 0) + assert np.all(sampler._last_mbar_f_k == last_mbar_f_k) + + assert last_free_energy is not None + + # Error should not be 0 yet + assert sampler._last_err_free_energy != 0 + + assert sampler._last_err_free_energy == last_err_free_energy, \ + ("SAMPLER %s : sampler._last_err_free_energy = %s, " + "last_err_free_energy = %s" % (self.SAMPLER.__name__, + sampler._last_err_free_energy, + last_err_free_energy) + ) + try: + validate_this_test() + except AssertionError as e: + # Handle case where MBAR does not have a converged free energy yet by attempting to run longer + # Only run up until we have sampled every state, or we hit some cycle limit + cycle_limit = 20 # Put some upper limit of cycles + cycles = 0 + while (not np.unique(sampler._reporter.read_replica_thermodynamic_states()).size == self.N_STATES + or cycles == cycle_limit): + sampler.extend(20) + try: + validate_this_test() + except AssertionError: + # If the max error count internally is reached, its a RuntimeError and won't be trapped + # So it will be raised correctly + pass + else: + # Test is good, let it pass by returning here + return + # If we get here, we have not validated, raise original error + raise e + + def test_online_analysis_stops(self): + """Test online analysis will stop the simulation""" + thermodynamic_states, sampler_states, unsampled_states = copy.deepcopy(self.alanine_test) + with self.temporary_storage_path() as storage_path: + n_iterations = 5 + online_interval = 1 + move = mmtools.mcmc.IntegratorMove(openmm.VerletIntegrator(1.0 * unit.femtosecond), n_steps=1) + sampler = self.SAMPLER(mcmc_moves=move, number_of_iterations=n_iterations, + online_analysis_interval=online_interval, + online_analysis_minimum_iterations=0, + online_analysis_target_error=np.inf) # use infinite error to stop right away + self.call_sampler_create(sampler, storage_path, + thermodynamic_states, sampler_states, + unsampled_states) + # Run + sampler.run() + assert sampler._iteration < n_iterations + assert sampler.is_completed + + +############# + +class TestExtraSamplersMultiStateSampler(TestMultiStateSampler): + """Test MultiStateSampler with more samplers than states""" + + # ------------------------------------ + # VARIABLES TO SET FOR EACH TEST CLASS + # ------------------------------------ + + N_SAMPLERS = 8 + N_STATES = 5 + SAMPLER = MultiStateSampler + REPORTER = MultiStateReporter + + +class TestReplicaExchange(TestMultiStateSampler): + """Test suite for ReplicaExchange class.""" + + # ------------------------------------ + # VARIABLES TO SET FOR EACH TEST CLASS + # ------------------------------------ + + N_SAMPLERS = 3 + N_STATES = 3 + SAMPLER = ReplicaExchangeSampler + REPORTER = MultiStateReporter + + # -------------------------------------- + # Tests overwritten from base test suite + # -------------------------------------- + + def test_stored_properties(self): + """Test that storage is kept in sync with options. Unique to ReplicaExchange""" + additional_values = {} + additional_values.update(self.property_creator('replica_mixing_scheme', 'replica_mixing_scheme', None, None)) + self.actual_stored_properties_check(additional_properties=additional_values) + + +class TestSingleReplicaSAMS(TestMultiStateSampler): + """Test suite for SAMSSampler class.""" + + # ------------------------------------ + # VARIABLES TO SET FOR EACH TEST CLASS + # ------------------------------------ + + N_SAMPLERS = 1 + N_STATES = 5 + SAMPLER = SAMSSampler + REPORTER = MultiStateReporter + + # -------------------------------------- + # Tests overwritten from base test suite + # -------------------------------------- + + def test_stored_properties(self): + """Test that storage is kept in sync with options. Unique to SAMSSampler""" + additional_values = {} + options = { + 'state_update_scheme': 'global-jump', + 'locality': None, + 'update_stages': 'one-stage', + 'weight_update_method': 'optimal', + 'adapt_target_probabilities': False, + } + for (name, value) in options.items(): + additional_values.update(self.property_creator(name, name, value, value)) + self.actual_stored_properties_check(additional_properties=additional_values) + + def test_state_histogram(self): + """Ensure SAMS on-the-fly state histograms match actually visited states""" + thermodynamic_states, sampler_states, unsampled_states = copy.deepcopy(self.alanine_test) + with self.temporary_storage_path() as storage_path: + # For this test, we simply check that the checkpointing writes on the interval + # We don't care about the numbers, per se, but we do care about when things are written + n_iterations = 10 + move = mmtools.mcmc.IntegratorMove(openmm.VerletIntegrator(1.0 * unit.femtosecond), n_steps=1) + sampler = self.SAMPLER(mcmc_moves=move, number_of_iterations=n_iterations) + reporter = self.REPORTER(storage_path, checkpoint_interval=2) + self.call_sampler_create(sampler, reporter, + thermodynamic_states, sampler_states, + unsampled_states) + # Propagate. + sampler.run() + reporter.close() + reporter = self.REPORTER(storage_path, open_mode='a', checkpoint_interval=2) + replica_thermodynamic_states = reporter.read_replica_thermodynamic_states() + N_k, _ = np.histogram(replica_thermodynamic_states, bins=np.arange(-0.5, sampler.n_states + 0.5)) + assert np.all(sampler._state_histogram == N_k) + + # TODO: Test all update methods + + +class TestMultipleReplicaSAMS(TestSingleReplicaSAMS): + """Test suite for SAMSSampler class.""" + + # ------------------------------------ + # VARIABLES TO SET FOR EACH TEST CLASS + # ------------------------------------ + + N_SAMPLERS = 2 + + # -------------------------------------- + # Tests overwritten from base test suite + # -------------------------------------- + + def test_stored_properties(self): + """Test that storage is kept in sync with options. Unique to SAMSSampler""" + additional_values = {} + options = { + 'state_update_scheme': 'global-jump', + 'locality': None, + 'update_stages': 'two-stage', + 'weight_update_method' : 'rao-blackwellized', + 'adapt_target_probabilities': False, + } + for (name, value) in options.items(): + additional_values.update(self.property_creator(name, name, value, value)) + self.actual_stored_properties_check(additional_properties=additional_values) + + # TODO: Test all update methods + + +class TestParallelTempering(TestMultiStateSampler): + + # ------------------------------------ + # VARIABLES TO SET FOR EACH TEST CLASS + # ------------------------------------ + + N_SAMPLERS = 3 + N_STATES = 3 + SAMPLER = ParallelTemperingSampler + REPORTER = MultiStateReporter + MIN_TEMP = 300*unit.kelvin + MAX_TEMP = 350*unit.kelvin + + # -------------------------------------- + # Optional helper function to overwrite. + # -------------------------------------- + + @classmethod + def call_sampler_create(cls, sampler, reporter, + thermodynamic_states, + sampler_states, + unsampled_states): + """ + Helper function to call the create method for the sampler + ParallelTempering has a unique call + """ + single_state = thermodynamic_states[0] + # Allows initial thermodynamic states to be handled by the built in methods + sampler.create(single_state, sampler_states, reporter, + min_temperature=cls.MIN_TEMP, max_temperature=cls.MAX_TEMP, n_temperatures=cls.N_STATES, + unsampled_thermodynamic_states=unsampled_states) + + # ---------------------------------- + # Methods overwritten from the Super + # ---------------------------------- + + @classmethod + def _compute_energies_independently(cls, thermodynamic_states, sampler_states, unsampled_states): + """ + Helper function to compute energies by hand. + This is overwritten from Super. + + There is faster way to compute sampled states with ParallelTempering that is O(N) as is done in production, + but the O(N^2) way should get it right as well and serves as a decent check + """ + n_states = len(thermodynamic_states) + n_replicas = len(sampler_states) + reference_thermodynamic_state = thermodynamic_states[0] + temperatures = [cls.MIN_TEMP + (cls.MAX_TEMP - cls.MIN_TEMP) * + (math.exp(i / n_states-1) - 1.0) / (math.e - 1.0) + for i in range(n_states)] + + thermodynamic_states = [copy.deepcopy(reference_thermodynamic_state) for _ in range(n_states)] + for temp, state in zip(temperatures, thermodynamic_states): + state.temperature = temp + # Compute the energies independently. + energy_thermodynamic_states = np.zeros((n_replicas, n_states)) + energy_unsampled_states = np.zeros((n_replicas, len(unsampled_states))) + for energies, states in [(energy_thermodynamic_states, thermodynamic_states), + (energy_unsampled_states, unsampled_states)]: + for i, sampler_state in enumerate(sampler_states): + for j, state in enumerate(states): + context, integrator = mmtools.cache.global_context_cache.get_context(state) + sampler_state.apply_to_context(context) + energies[i][j] = state.reduced_potential(context) + return energy_thermodynamic_states, energy_unsampled_states + + +# ============================================================================== +# MAIN AND TESTS +# ============================================================================== + +if __name__ == "__main__": + + # Test simple system of harmonic oscillators. + # Disabled until we fix the test + # test_replica_exchange() + quit() diff --git a/setup.py b/setup.py index 3c5dd0119..a7e8cc68e 100644 --- a/setup.py +++ b/setup.py @@ -3,10 +3,13 @@ """ from __future__ import print_function import sys +import distutils.extension from setuptools import setup import os from os.path import relpath, join import subprocess +from Cython.Build import cythonize + DOCLINES = __doc__.split("\n") ######################## @@ -149,7 +152,8 @@ def check_dependencies(): # SETUP ################################################################################ -extensions = [] +extensions = distutils.extension.Extension("openmmtools.multistate.mixing._mix_replicas", + ['./openmmtools/multistate/mixing/_mix_replicas.pyx']) write_version_py('openmmtools/version.py') @@ -164,12 +168,12 @@ def check_dependencies(): url='https://github.com/choderalab/openmmtools', platforms=['Linux', 'Mac OS-X', 'Unix', 'Windows'], classifiers=CLASSIFIERS.splitlines(), - packages=['openmmtools', 'openmmtools.tests', 'openmmtools.scripts', 'openmmtools.storage'], + packages=['openmmtools', 'openmmtools.tests', 'openmmtools.scripts', 'openmmtools.storage','openmmtools.multistate', 'openmmtools.multistate.mixing'], package_dir={'openmmtools': 'openmmtools'}, package_data={'openmmtools': find_package_data('openmmtools/data', 'openmmtools')}, zip_safe=False, scripts=[], - ext_modules=extensions, + ext_modules=cythonize(extensions), entry_points={'console_scripts': [ 'test-openmm-platforms = openmmtools.scripts.test_openmm_platforms:main', 'benchmark-alchemy = openmmtools.tests.test_alchemy:benchmark_alchemy_from_pdb', From 5e0872a40283cae04b3b976a83cf5fc7d681d068 Mon Sep 17 00:00:00 2001 From: hannahbrucemacdonald Date: Tue, 22 Jan 2019 16:04:26 -0500 Subject: [PATCH 04/46] fixing imports --- openmmtools/__init__.py | 2 +- openmmtools/multistate/multistatesampler.py | 4 ++-- openmmtools/multistate/replicaexchange.py | 5 +++-- openmmtools/multistate/sams.py | 3 ++- 4 files changed, 8 insertions(+), 6 deletions(-) diff --git a/openmmtools/__init__.py b/openmmtools/__init__.py index 7b0ff5def..efe2ea5e7 100644 --- a/openmmtools/__init__.py +++ b/openmmtools/__init__.py @@ -10,4 +10,4 @@ __version__ = version.version # Import modules. -from openmmtools import testsystems, integrators, alchemy, mcmc, states, cache, utils, constants, forces, forcefactories, storage +from openmmtools import testsystems, integrators, alchemy, mcmc, states, cache, utils, constants, forces, forcefactories, storage, multistate diff --git a/openmmtools/multistate/multistatesampler.py b/openmmtools/multistate/multistatesampler.py index 27048dabc..6521544fb 100644 --- a/openmmtools/multistate/multistatesampler.py +++ b/openmmtools/multistate/multistatesampler.py @@ -41,7 +41,7 @@ from openmmtools import multistate, utils, states, mcmc, cache import mpiplus -from yank.utils import SimulationNaNError +from openmmtools.multistate.utils import SimulationNaNError from pymbar.utils import ParameterError @@ -1405,7 +1405,7 @@ def _offline_analysis(self): # MultiStateSampler never finished importing, its not in the name space which causes relative analyze import of # MultiStateSampler to crash as neither of them are the __main__ package. # https://stackoverflow.com/questions/6351805/cyclic-module-dependencies-and-relative-imports-in-python - from .multistateanalyzer import MultiStateSamplerAnalyzer + from openmmtools.multistate.multistateanalyzer import MultiStateSamplerAnalyzer # Start the analysis bump_error_counter = False diff --git a/openmmtools/multistate/replicaexchange.py b/openmmtools/multistate/replicaexchange.py index 90a2731b7..e624575d1 100644 --- a/openmmtools/multistate/replicaexchange.py +++ b/openmmtools/multistate/replicaexchange.py @@ -37,6 +37,7 @@ import mdtraj as md from openmmtools import multistate, utils +from openmmtools.multistate.multistateanalyzer import MultiStateSamplerAnalyzer import mpiplus @@ -214,7 +215,7 @@ def _repex_mixing_scheme_validator(instance, replica_mixing_scheme): validate_function=_StoredProperty._repex_mixing_scheme_validator) _TITLE_TEMPLATE = ('Replica-exchange sampler simulation created using ReplicaExchangeSampler class ' - 'of yank.multistate on {}') + 'of openmmtools.multistate on {}') def _pre_write_create(self, thermodynamic_states, sampler_states, *args, **kwargs): """Overwrite parent implementation to make sure the number of @@ -360,7 +361,7 @@ def _display_citations(self, overwrite_global=False, citation_stack=None): super()._display_citations(overwrite_global=overwrite_global, citation_stack=citation_stack) -class ReplicaExchangeAnalyzer(multistate.MultiStateSamplerAnalyzer): +class ReplicaExchangeAnalyzer(MultiStateSamplerAnalyzer): """ The ReplicaExchangeAnalyzer is the analyzer for a simulation generated from a Replica Exchange sampler simulation, diff --git a/openmmtools/multistate/sams.py b/openmmtools/multistate/sams.py index 690d34e7b..b4ed147d8 100644 --- a/openmmtools/multistate/sams.py +++ b/openmmtools/multistate/sams.py @@ -29,6 +29,7 @@ from scipy.special import logsumexp from openmmtools import multistate, utils +from openmmtools.multistate.multistateanalyzer import MultiStateSamplerAnalyzer import mpiplus @@ -671,7 +672,7 @@ def _update_log_weights(self): self.log_weights = self.log_target_probabilities[:] - self._logZ[:] -class SAMSAnalyzer(multistate.MultiStateSamplerAnalyzer): +class SAMSAnalyzer(MultiStateSamplerAnalyzer): """ The SAMSAnalyzer is the analyzer for a simulation generated from a SAMSSampler simulation. From 471014fa0b8638ada40353713cba0da1f1aafc83 Mon Sep 17 00:00:00 2001 From: hannahbrucemacdonald Date: Tue, 22 Jan 2019 16:32:36 -0500 Subject: [PATCH 05/46] adding cython dependency --- devtools/conda-recipe/meta.yaml | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/devtools/conda-recipe/meta.yaml b/devtools/conda-recipe/meta.yaml index 28e0738a9..42ca8bdc5 100644 --- a/devtools/conda-recipe/meta.yaml +++ b/devtools/conda-recipe/meta.yaml @@ -14,6 +14,7 @@ requirements: - python - setuptools - openmm ==7.3 + - cython run: - python @@ -26,7 +27,7 @@ requirements: - netcdf4 - pyyaml - mpiplus - + - cython test: requires: From a3115a09e3460dce8706eef211db0cc6a1782c8f Mon Sep 17 00:00:00 2001 From: hannahbrucemacdonald Date: Tue, 22 Jan 2019 16:59:06 -0500 Subject: [PATCH 06/46] adding FIREIntegrator --- openmmtools/integrators.py | 168 ++++++++++++++++++++ openmmtools/multistate/multistatesampler.py | 3 +- openmmtools/tests/test_mixing.py | 95 +++++++++++ 3 files changed, 265 insertions(+), 1 deletion(-) create mode 100644 openmmtools/tests/test_mixing.py diff --git a/openmmtools/integrators.py b/openmmtools/integrators.py index b3882b491..355efc0d9 100644 --- a/openmmtools/integrators.py +++ b/openmmtools/integrators.py @@ -2076,6 +2076,174 @@ def __init__(self, *args, **kwargs): kwargs['splitting'] = "O { V R V } O" super(GHMCIntegrator, self).__init__(*args, **kwargs) +class FIREMinimizationIntegrator(mm.CustomIntegrator): + """Fast Internal Relaxation Engine (FIRE) minimization. + Notes + ----- + This integrator is taken verbatim from Peter Eastman's example appearing in the CustomIntegrator header file documentation. + References + ---------- + Erik Bitzek, Pekka Koskinen, Franz Gaehler, Michael Moseler, and Peter Gumbsch. + Structural Relaxation Made Simple. PRL 97:170201, 2006. + http://dx.doi.org/10.1103/PhysRevLett.97.170201 + Examples + -------- + >>> from openmmtools import testsystems + >>> t = testsystems.AlanineDipeptideVacuum() + >>> system, positions = t.system, t.positions + Create a FIRE integrator with default parameters and minimize for 100 steps + >>> integrator = FIREMinimizationIntegrator() + >>> context = openmm.Context(system, integrator) + >>> context.setPositions(positions) + >>> integrator.step(100) + """ + + def __init__(self, timestep=1.0 * unit.femtoseconds, tolerance=None, alpha=0.1, dt_max=10.0 * unit.femtoseconds, f_inc=1.1, f_dec=0.5, f_alpha=0.99, N_min=5): + """Construct a Fast Internal Relaxation Engine (FIRE) minimization integrator. + Parameters + ---------- + timestep : unit.Quantity compatible with femtoseconds, optional, default = 1*femtoseconds + The integration timestep. + tolerance : unit.Quantity compatible with kilojoules_per_mole/nanometer, optional, default = None + Minimization will be terminated when RMS force reaches this tolerance. + alpha : float, optional default = 0.1 + Velocity relaxation parameter, alpha \in (0,1). + dt_max : unit.Quantity compatible with femtoseconds, optional, default = 10*femtoseconds + Maximum allowed timestep. + f_inc : float, optional, default = 1.1 + Timestep increment multiplicative factor. + f_dec : float, optional, default = 0.5 + Timestep decrement multiplicative factor. + f_alpha : float, optional, default = 0.99 + alpha multiplicative relaxation parameter + N_min : int, optional, default = 5 + Limit on number of timesteps P is negative before decrementing timestep. + Notes + ----- + Velocities should be set to zero before using this integrator. + """ + + # Check input ranges. + if not ((alpha > 0.0) and (alpha < 1.0)): + raise Exception("alpha must be in the interval (0,1); specified alpha = %f" % alpha) + + if tolerance is None: + tolerance = 0 * unit.kilojoules_per_mole / unit.nanometers + + super(FIREMinimizationIntegrator, self).__init__(timestep) + + # Use high-precision constraints + self.setConstraintTolerance(1.0e-8) + + self.addGlobalVariable("alpha", alpha) # alpha + self.addGlobalVariable("P", 0) # P + self.addGlobalVariable("N_neg", 0.0) + self.addGlobalVariable("fmag", 0) # |f| + self.addGlobalVariable("fmax", 0) # max|f_i| + self.addGlobalVariable("ndof", 0) # number of degrees of freedom + self.addGlobalVariable("ftol", tolerance.value_in_unit_system(unit.md_unit_system)) # convergence tolerance + self.addGlobalVariable("vmag", 0) # |v| + self.addGlobalVariable("converged", 0) # 1 if convergence threshold reached, 0 otherwise + self.addPerDofVariable("x0", 0) + self.addPerDofVariable("v0", 0) + self.addPerDofVariable("x1", 0) + self.addGlobalVariable("E0", 0) # old energy associated with x0 + self.addGlobalVariable("dE", 0) + self.addGlobalVariable("restart", 0) + self.addGlobalVariable("delta_t", timestep.value_in_unit_system(unit.md_unit_system)) + + # Assess convergence + # TODO: Can we more closely match the OpenMM criterion here? + self.beginIfBlock('converged < 1') + + # Compute fmag = |f| + #self.addComputeGlobal('fmag', '0.0') + self.addComputeSum('fmag', 'f*f') + self.addComputeGlobal('fmag', 'sqrt(fmag)') + + # Compute ndof + self.addComputeSum('ndof', '1') + + self.addComputeSum('converged', 'step(ftol - fmag/ndof)') + self.endBlock() + + # Enclose everything in a block that checks if we have already converged. + self.beginIfBlock('converged < 1') + + # Store old positions and energy + self.addComputePerDof('x0', 'x') + self.addComputePerDof('v0', 'v') + self.addComputeGlobal('E0', 'energy') + + # MD: Take a velocity Verlet step. + self.addComputePerDof("v", "v+0.5*delta_t*f/m") + self.addComputePerDof("x", "x+delta_t*v") + self.addComputePerDof("x1", "x") + self.addConstrainPositions() + self.addComputePerDof("v", "v+0.5*delta_t*f/m+(x-x1)/delta_t") + self.addConstrainVelocities() + + self.addComputeGlobal('dE', 'energy - E0') + + # Compute fmag = |f| + #self.addComputeGlobal('fmag', '0.0') + self.addComputeSum('fmag', 'f*f') + self.addComputeGlobal('fmag', 'sqrt(fmag)') + # Compute vmag = |v| + #self.addComputeGlobal('vmag', '0.0') + self.addComputeSum('vmag', 'v*v') + self.addComputeGlobal('vmag', 'sqrt(vmag)') + + # F1: Compute P = F.v + self.addComputeSum('P', 'f*v') + + # F2: set v = (1-alpha) v + alpha \hat{F}.|v| + # Update velocities. + # TODO: This must be corrected to be atomwise redirection of v magnitude along f + self.addComputePerDof('v', '(1-alpha)*v + alpha*(f/fmag)*vmag') + + # Back up if the energy went up, protecing against NaNs + self.addComputeGlobal('restart', '1') + self.beginIfBlock('dE < 0') + self.addComputeGlobal('restart', '0') + self.endBlock() + self.beginIfBlock('restart > 0') + self.addComputePerDof('v', 'v0') + self.addComputePerDof('x', 'x0') + self.addComputeGlobal('P', '-1') + self.endBlock() + + # If dt goes to zero, signal we've converged! + dt_min = 1.0e-5 * timestep + self.beginIfBlock('delta_t <= %f' % dt_min.value_in_unit_system(unit.md_unit_system)) + self.addComputeGlobal('converged', '1') + self.endBlock() + + # F3: If P > 0 and the number of steps since P was negative > N_min, + # Increase timestep dt = min(dt*f_inc, dt_max) and decrease alpha = alpha*f_alpha + self.beginIfBlock('P > 0') + # Update count of number of steps since P was negative. + self.addComputeGlobal('N_neg', 'N_neg + 1') + # If we have enough steps since P was negative, scale up timestep. + self.beginIfBlock('N_neg > %d' % N_min) + self.addComputeGlobal('delta_t', 'min(delta_t*%f, %f)' % (f_inc, dt_max.value_in_unit_system(unit.md_unit_system))) # TODO: Automatically convert dt_max to md units + self.addComputeGlobal('alpha', 'alpha * %f' % f_alpha) + self.endBlock() + self.endBlock() + + # F4: If P < 0, decrease the timestep dt = dt*f_dec, freeze the system v=0, + # and set alpha = alpha_start + self.beginIfBlock('P < 0') + self.addComputeGlobal('N_neg', '0.0') + self.addComputeGlobal('delta_t', 'delta_t*%f' % f_dec) + self.addComputePerDof('v', '0.0') + self.addComputeGlobal('alpha', '%f' % alpha) + self.endBlock() + + # Close block that checks for convergence. + self.endBlock() + + if __name__ == '__main__': import doctest doctest.testmod() diff --git a/openmmtools/multistate/multistatesampler.py b/openmmtools/multistate/multistatesampler.py index 6521544fb..02a092d6a 100644 --- a/openmmtools/multistate/multistatesampler.py +++ b/openmmtools/multistate/multistatesampler.py @@ -45,7 +45,7 @@ from pymbar.utils import ParameterError -from yank.fire import FIREMinimizationIntegrator +from openmmtools.integrators import FIREMinimizationIntegrator logger = logging.getLogger(__name__) @@ -1293,6 +1293,7 @@ def _minimize_replica(self, replica_id, tolerance, max_iterations): final_energy = thermodynamic_state.reduced_potential(sampler_state) logger.debug('Replica {}/{}: final energy {:8.3f}kT'.format( replica_id + 1, self.n_replicas, final_energy)) + # TODO if energy > 0, use slower openmm minimizer # Clean up the integrator del context diff --git a/openmmtools/tests/test_mixing.py b/openmmtools/tests/test_mixing.py new file mode 100644 index 000000000..bc3db136d --- /dev/null +++ b/openmmtools/tests/test_mixing.py @@ -0,0 +1,95 @@ + + +""" +Test Cython and weave mixing code. +""" + +import copy + +import numpy as np +import scipy.stats as stats +import openmmtools.multistate.mixing._mix_replicas as mixing + + +def mix_replicas(n_swaps=100, n_states=16, u_kl=None, nswap_attempts=None): + """ + Utility function to generate replicas and call the mixing function a certain number of times + + Arguments + --------- + n_swaps : int (optional) + The number of times to call the mixing code (default 100) + n_states : int (optional) + The number of replica states to include (default 16) + u_kl : n_states x n_states ndarray of float64 (optional) + Energies for each state. If None, will be initialized to zeros + + Returns + ------- + permutation_list : n_states x n_swaps ndarray of np.int64 + Contains the result of each swap + """ + + if u_kl is None: + u_kl = np.zeros([n_states, n_states], dtype=np.float64) + replica_states = np.array(range(n_states), np.int64) + if nswap_attempts is None: + nswap_attempts = n_states**4 + Nij_proposed = np.zeros([n_states,n_states], dtype=np.int64) + Nij_accepted = np.zeros([n_states,n_states], dtype=np.int64) + permutation_list = [] + for i in range(n_swaps): + mixing._mix_replicas_cython(nswap_attempts, n_states, replica_states, u_kl, Nij_proposed, Nij_accepted) + permutation_list.append(copy.deepcopy(replica_states)) + permutation_list_np = np.array(permutation_list, dtype=np.int64) + return permutation_list_np + + +def calculate_state_counts(permutation_list, n_swaps, n_states): + """ + This function accepts a list of permutation vectors, and for each replica, + produces a list of the number of occurrences of each state. + + Arguments + --------- + permutation_list : n_states x n_swaps ndarray of np.int64 + For each swap attempt, a permutation vector n_states long + n_swaps : int + The number of swap attempts + n_states : int + The number of replica states + + Returns + ------- + state_counts : n_states x n_states numpy array of ints + For each replica, contains the number of occurrences of each state (replica_index x state) + """ + state_counts = np.zeros([n_states, n_states]) + for swap in range(n_swaps): + for replica in range(n_states): + current_state = permutation_list[swap, replica] + state_counts[replica, current_state] += 1 + return state_counts + + +def test_even_mixing(verbose=True): + """ + Testing Cython mixing code with 1000 swap attempts and uniform 0 energies + """ + if verbose: print("Testing Cython mixing code with uniform zero energies") + n_swaps = 1000 + n_states = 16 + corrected_threshold = 0.001 / n_states + permutation_list = mix_replicas(n_swaps=n_swaps, n_states=n_states) + state_counts = calculate_state_counts(permutation_list, n_swaps, n_states) + for replica in range(n_states): + _, p_val = stats.chisquare(state_counts[replica, :]) + if p_val < corrected_threshold: + print("Detected a significant difference between expected even mixing\n") + print("and observed mixing, p=%f" % p_val) + raise Exception("Replica %d failed the even mixing test" % replica) + return 0 + + +if __name__ == "__main__": + test_even_mixing() From cc3b83f51b9565b9cc3cebd25af8d97a64cf162e Mon Sep 17 00:00:00 2001 From: hannahbrucemacdonald Date: Tue, 22 Jan 2019 17:28:34 -0500 Subject: [PATCH 07/46] added warning and fixed namespace clash --- openmmtools/multistate/__init__.py | 5 +++++ openmmtools/multistate/multistatesampler.py | 4 ++-- 2 files changed, 7 insertions(+), 2 deletions(-) diff --git a/openmmtools/multistate/__init__.py b/openmmtools/multistate/__init__.py index 128b8df23..a427d57e5 100644 --- a/openmmtools/multistate/__init__.py +++ b/openmmtools/multistate/__init__.py @@ -68,3 +68,8 @@ from openmmtools.multistate.sams import SAMSSampler, SAMSAnalyzer from openmmtools.multistate.multistateanalyzer import * from openmmtools.multistate.utils import * + + +import warnings + +warnings.warn('Warning: openmmtools.multistate API is experimental') diff --git a/openmmtools/multistate/multistatesampler.py b/openmmtools/multistate/multistatesampler.py index 02a092d6a..5300e39fb 100644 --- a/openmmtools/multistate/multistatesampler.py +++ b/openmmtools/multistate/multistatesampler.py @@ -1338,10 +1338,10 @@ def _compute_replica_energies(self, replica_id): sampler_state = self._sampler_states[replica_id] # Compute energy for all thermodynamic states. - for energies, states in [(energy_neighborhood_states, neighborhood_thermodynamic_states), + for energies, the_states in [(energy_neighborhood_states, neighborhood_thermodynamic_states), (energy_unsampled_states, self._unsampled_states)]: # Group thermodynamic states by compatibility. - compatible_groups, original_indices = states.group_by_compatibility(states) + compatible_groups, original_indices = states.group_by_compatibility(the_states) # Compute the reduced potentials of all the compatible states. for compatible_group, state_indices in zip(compatible_groups, original_indices): From 49c608cd0efa75cee305276c172d27c4ad2e595c Mon Sep 17 00:00:00 2001 From: hannahbrucemacdonald Date: Tue, 22 Jan 2019 20:38:02 -0500 Subject: [PATCH 08/46] FIREIntegrator to pass test --- openmmtools/integrators.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/openmmtools/integrators.py b/openmmtools/integrators.py index 355efc0d9..be67e2d75 100644 --- a/openmmtools/integrators.py +++ b/openmmtools/integrators.py @@ -2152,6 +2152,9 @@ def __init__(self, timestep=1.0 * unit.femtoseconds, tolerance=None, alpha=0.1, self.addGlobalVariable("restart", 0) self.addGlobalVariable("delta_t", timestep.value_in_unit_system(unit.md_unit_system)) + # Update context state. + self.addUpdateContextState() + # Assess convergence # TODO: Can we more closely match the OpenMM criterion here? self.beginIfBlock('converged < 1') From c48ce52aa8596c8133824ac29adbb052be104fe7 Mon Sep 17 00:00:00 2001 From: hannahbrucemacdonald Date: Wed, 23 Jan 2019 11:23:35 -0500 Subject: [PATCH 09/46] fixing doctests --- openmmtools/integrators.py | 2 +- openmmtools/multistate/multistateanalyzer.py | 1 - openmmtools/multistate/multistatereporter.py | 2 -- openmmtools/multistate/paralleltempering.py | 1 + openmmtools/multistate/replicaexchange.py | 14 +++++++------ openmmtools/multistate/sams.py | 22 ++++++++++++-------- 6 files changed, 23 insertions(+), 19 deletions(-) diff --git a/openmmtools/integrators.py b/openmmtools/integrators.py index be67e2d75..611f6e093 100644 --- a/openmmtools/integrators.py +++ b/openmmtools/integrators.py @@ -2089,9 +2089,9 @@ class FIREMinimizationIntegrator(mm.CustomIntegrator): Examples -------- >>> from openmmtools import testsystems + >>> from simtk import openmm >>> t = testsystems.AlanineDipeptideVacuum() >>> system, positions = t.system, t.positions - Create a FIRE integrator with default parameters and minimize for 100 steps >>> integrator = FIREMinimizationIntegrator() >>> context = openmm.Context(system, integrator) >>> context.setPositions(positions) diff --git a/openmmtools/multistate/multistateanalyzer.py b/openmmtools/multistate/multistateanalyzer.py index 52a962794..52e8853e2 100644 --- a/openmmtools/multistate/multistateanalyzer.py +++ b/openmmtools/multistate/multistateanalyzer.py @@ -36,7 +36,6 @@ from openmmtools import multistate, utils, forces -from . import utils ABC = abc.ABC logger = logging.getLogger(__name__) diff --git a/openmmtools/multistate/multistatereporter.py b/openmmtools/multistate/multistatereporter.py index 329a19f05..0f59964a2 100644 --- a/openmmtools/multistate/multistatereporter.py +++ b/openmmtools/multistate/multistatereporter.py @@ -1026,8 +1026,6 @@ def read_dict(self, path: str) -> Union[dict, Any]: ... reporter.write_dict('data', data) ... reporter.read_dict('data') {'info': [1, 2, 3]} - ... reporter.read_dict('data/info') - [1, 2, 3] """ storage = 'analysis' diff --git a/openmmtools/multistate/paralleltempering.py b/openmmtools/multistate/paralleltempering.py index a59146d8a..837016779 100644 --- a/openmmtools/multistate/paralleltempering.py +++ b/openmmtools/multistate/paralleltempering.py @@ -59,6 +59,7 @@ class ParallelTemperingSampler(ReplicaExchangeSampler): >>> from openmmtools import testsystems, states, mcmc >>> import tempfile >>> testsystem = testsystems.AlanineDipeptideImplicit() + >>> import os Create thermodynamic states for parallel tempering with exponentially-spaced schedule. diff --git a/openmmtools/multistate/replicaexchange.py b/openmmtools/multistate/replicaexchange.py index e624575d1..50ce609ac 100644 --- a/openmmtools/multistate/replicaexchange.py +++ b/openmmtools/multistate/replicaexchange.py @@ -121,13 +121,15 @@ class for actual production parallel tempering simulations. >>> from simtk import unit >>> from openmmtools import testsystems, states, mcmc >>> testsystem = testsystems.AlanineDipeptideImplicit() + >>> import os + >>> import tempfile Create thermodynamic states for parallel tempering with exponentially-spaced schedule. >>> n_replicas = 3 # Number of temperature replicas. >>> T_min = 298.0 * unit.kelvin # Minimum temperature. >>> T_max = 600.0 * unit.kelvin # Maximum temperature. - >>> temperatures = [T_min + (T_max - T_min) * (math.exp(float(i) / float(nreplicas-1)) - 1.0) / (math.e - 1.0) + >>> temperatures = [T_min + (T_max - T_min) * (math.exp(float(i) / float(n_replicas-1)) - 1.0) / (math.e - 1.0) ... for i in range(n_replicas)] >>> thermodynamic_states = [states.ThermodynamicState(system=testsystem.system, temperature=T) ... for T in temperatures] @@ -142,8 +144,8 @@ class for actual production parallel tempering simulations. >>> storage_path = tempfile.NamedTemporaryFile(delete=False).name + '.nc' >>> reporter = multistate.MultiStateReporter(storage_path, checkpoint_interval=1) >>> simulation.create(thermodynamic_states=thermodynamic_states, - >>> sampler_states=states.SamplerState(testsystem.positions), - >>> storage=reporter) + ... sampler_states=states.SamplerState(testsystem.positions), + ... storage=reporter) >>> simulation.run() # This runs for a maximum of 2 iterations. >>> simulation.iteration 2 @@ -165,10 +167,10 @@ class while the simulation is running. This reads the SamplerStates of every run iteration. >>> reporter = multistate.MultiStateReporter(storage=storage_path, open_mode='r', checkpoint_interval=1) - >>> sampler_states = reporter.read_sampler_states(iteration=range(1, 4)) + >>> sampler_states = reporter.read_sampler_states(iteration=1) >>> len(sampler_states) 3 - >>> sampler_states[-1].positions.shape # Alanine dipeptide has 22 atoms. + >>> sampler_states[0].positions.shape # Alanine dipeptide has 22 atoms. (22, 3) Clean up. @@ -269,7 +271,7 @@ def _mix_replicas(self): def _mix_all_replicas_cython(self): """Exchange all replicas with Cython-accelerated code.""" - from .mixing._mix_replicas import _mix_replicas_cython + from openmmtools.multistate.mixing._mix_replicas import _mix_replicas_cython replica_states = md.utils.ensure_type(self._replica_thermodynamic_states, np.int64, 1, "Replica States") u_kl = md.utils.ensure_type(self._energy_thermodynamic_states, np.float64, 2, "Reduced Potentials") diff --git a/openmmtools/multistate/sams.py b/openmmtools/multistate/sams.py index b4ed147d8..7d96ba372 100644 --- a/openmmtools/multistate/sams.py +++ b/openmmtools/multistate/sams.py @@ -84,13 +84,15 @@ class SAMSSampler(multistate.MultiStateSampler): >>> from simtk import unit >>> from openmmtools import testsystems, states, mcmc >>> testsystem = testsystems.AlanineDipeptideVacuum() + >>> import os + >>> import tempfile Create thermodynamic states for parallel tempering with exponentially-spaced schedule: >>> n_replicas = 3 # Number of temperature replicas. >>> T_min = 298.0 * unit.kelvin # Minimum temperature. >>> T_max = 600.0 * unit.kelvin # Maximum temperature. - >>> temperatures = [T_min + (T_max - T_min) * (math.exp(float(i) / float(nreplicas-1)) - 1.0) / (math.e - 1.0) + >>> temperatures = [T_min + (T_max - T_min) * (math.exp(float(i) / float(n_replicas-1)) - 1.0) / (math.e - 1.0) ... for i in range(n_replicas)] >>> thermodynamic_states = [states.ThermodynamicState(system=testsystem.system, temperature=T) ... for T in temperatures] @@ -99,10 +101,10 @@ class SAMSSampler(multistate.MultiStateSampler): >>> move = mcmc.GHMCMove(timestep=2.0*unit.femtoseconds, n_steps=50) >>> simulation = SAMSSampler(mcmc_moves=move, number_of_iterations=2, - >>> state_update_scheme='restricted-range-jump', locality=5, - >>> update_stages='two-stage', flatness_criteria='logZ-flatness', - >>> flatness_threshold=0.2, weight_update_method='rao-blackwellized', - >>> adapt_target_probabilities=False) + ... state_update_scheme='global-jump', locality=5, + ... update_stages='two-stage', flatness_criteria='logZ-flatness', + ... flatness_threshold=0.2, weight_update_method='rao-blackwellized', + ... adapt_target_probabilities=False) Create a single-replica SAMS simulation bound to a storage file and run: @@ -110,8 +112,9 @@ class SAMSSampler(multistate.MultiStateSampler): >>> storage_path = tempfile.NamedTemporaryFile(delete=False).name + '.nc' >>> reporter = multistate.MultiStateReporter(storage_path, checkpoint_interval=1) >>> simulation.create(thermodynamic_states=thermodynamic_states, - >>> sampler_states=[states.SamplerState(testsystem.positions)], - >>> storage=reporter) + ... sampler_states=[states.SamplerState(testsystem.positions)], + ... storage=reporter) + ... >>> simulation.run() # This runs for a maximum of 2 iterations. >>> simulation.iteration 2 @@ -124,6 +127,7 @@ class SAMSSampler(multistate.MultiStateSampler): >>> del simulation >>> simulation = SAMSSampler.from_storage(reporter) + ... >>> simulation.extend(n_iterations=1) >>> simulation.iteration 3 @@ -133,9 +137,9 @@ class while the simulation is running. This reads the SamplerStates of every run iteration. >>> reporter = multistate.MultiStateReporter(storage=storage_path, open_mode='r', checkpoint_interval=1) - >>> sampler_states = reporter.read_sampler_states(iteration=range(1, 4)) + >>> sampler_states = reporter.read_sampler_states(iteration=3) >>> len(sampler_states) - 3 + 1 >>> sampler_states[-1].positions.shape # Alanine dipeptide has 22 atoms. (22, 3) From 4dc723a73a822e63935a42bd4384d3266ebc64db Mon Sep 17 00:00:00 2001 From: hannahbrucemacdonald Date: Wed, 23 Jan 2019 12:14:55 -0500 Subject: [PATCH 10/46] fixing doctests --- openmmtools/multistate/paralleltempering.py | 8 ++++++++ openmmtools/multistate/replicaexchange.py | 16 ++++++++++++++++ openmmtools/multistate/sams.py | 14 ++++++++++++-- 3 files changed, 36 insertions(+), 2 deletions(-) diff --git a/openmmtools/multistate/paralleltempering.py b/openmmtools/multistate/paralleltempering.py index 837016779..6effa4b9f 100644 --- a/openmmtools/multistate/paralleltempering.py +++ b/openmmtools/multistate/paralleltempering.py @@ -81,6 +81,14 @@ class ParallelTemperingSampler(ReplicaExchangeSampler): ... states.SamplerState(testsystem.positions), ... reporter, min_temperature=T_min, ... max_temperature=T_max, n_temperatures=n_replicas) + Please cite the following: + + Friedrichs MS, Eastman P, Vaidyanathan V, Houston M, LeGrand S, Beberg AL, Ensign DL, Bruns CM, and Pande VS. Accelerating molecular dynamic simulations on graphics processing unit. J. Comput. Chem. 30:864, 2009. DOI: 10.1002/jcc.21209 + Eastman P and Pande VS. OpenMM: A hardware-independent framework for molecular simulations. Comput. Sci. Eng. 12:34, 2010. DOI: 10.1109/MCSE.2010.27 + Eastman P and Pande VS. Efficient nonbonded interactions for molecular dynamics on a graphics processing unit. J. Comput. Chem. 31:1268, 2010. DOI: 10.1002/jcc.21413 + Eastman P and Pande VS. Constant constraint matrix approximation: A robust, parallelizable constraint method for molecular simulations. J. Chem. Theor. Comput. 6:434, 2010. DOI: 10.1021/ct900463w + Chodera JD and Shirts MR. Replica exchange and expanded ensemble simulations as Gibbs multistate: Simple improvements for enhanced mixing. J. Chem. Phys., 135:194110, 2011. DOI:10.1063/1.3660669 + >>> simulation.run(n_iterations=1) Clean up. diff --git a/openmmtools/multistate/replicaexchange.py b/openmmtools/multistate/replicaexchange.py index 50ce609ac..e017ef3d9 100644 --- a/openmmtools/multistate/replicaexchange.py +++ b/openmmtools/multistate/replicaexchange.py @@ -146,6 +146,14 @@ class for actual production parallel tempering simulations. >>> simulation.create(thermodynamic_states=thermodynamic_states, ... sampler_states=states.SamplerState(testsystem.positions), ... storage=reporter) + Please cite the following: + + Friedrichs MS, Eastman P, Vaidyanathan V, Houston M, LeGrand S, Beberg AL, Ensign DL, Bruns CM, and Pande VS. Accelerating molecular dynamic simulations on graphics processing unit. J. Comput. Chem. 30:864, 2009. DOI: 10.1002/jcc.21209 + Eastman P and Pande VS. OpenMM: A hardware-independent framework for molecular simulations. Comput. Sci. Eng. 12:34, 2010. DOI: 10.1109/MCSE.2010.27 + Eastman P and Pande VS. Efficient nonbonded interactions for molecular dynamics on a graphics processing unit. J. Comput. Chem. 31:1268, 2010. DOI: 10.1002/jcc.21413 + Eastman P and Pande VS. Constant constraint matrix approximation: A robust, parallelizable constraint method for molecular simulations. J. Chem. Theor. Comput. 6:434, 2010. DOI: 10.1021/ct900463w + Chodera JD and Shirts MR. Replica exchange and expanded ensemble simulations as Gibbs multistate: Simple improvements for enhanced mixing. J. Chem. Phys., 135:194110, 2011. DOI:10.1063/1.3660669 + >>> simulation.run() # This runs for a maximum of 2 iterations. >>> simulation.iteration 2 @@ -158,6 +166,14 @@ class for actual production parallel tempering simulations. >>> del simulation >>> simulation = ReplicaExchangeSampler.from_storage(reporter) + Please cite the following: + + Friedrichs MS, Eastman P, Vaidyanathan V, Houston M, LeGrand S, Beberg AL, Ensign DL, Bruns CM, and Pande VS. Accelerating molecular dynamic simulations on graphics processing unit. J. Comput. Chem. 30:864, 2009. DOI: 10.1002/jcc.21209 + Eastman P and Pande VS. OpenMM: A hardware-independent framework for molecular simulations. Comput. Sci. Eng. 12:34, 2010. DOI: 10.1109/MCSE.2010.27 + Eastman P and Pande VS. Efficient nonbonded interactions for molecular dynamics on a graphics processing unit. J. Comput. Chem. 31:1268, 2010. DOI: 10.1002/jcc.21413 + Eastman P and Pande VS. Constant constraint matrix approximation: A robust, parallelizable constraint method for molecular simulations. J. Chem. Theor. Comput. 6:434, 2010. DOI: 10.1021/ct900463w + Chodera JD and Shirts MR. Replica exchange and expanded ensemble simulations as Gibbs multistate: Simple improvements for enhanced mixing. J. Chem. Phys., 135:194110, 2011. DOI:10.1063/1.3660669 + >>> simulation.extend(n_iterations=1) >>> simulation.iteration 3 diff --git a/openmmtools/multistate/sams.py b/openmmtools/multistate/sams.py index 7d96ba372..dfd38a7e4 100644 --- a/openmmtools/multistate/sams.py +++ b/openmmtools/multistate/sams.py @@ -114,7 +114,12 @@ class SAMSSampler(multistate.MultiStateSampler): >>> simulation.create(thermodynamic_states=thermodynamic_states, ... sampler_states=[states.SamplerState(testsystem.positions)], ... storage=reporter) - ... + Please cite the following: + + Friedrichs MS, Eastman P, Vaidyanathan V, Houston M, LeGrand S, Beberg AL, Ensign DL, Bruns CM, and Pande VS. Accelerating molecular dynamic simulations on graphics processing unit. J. Comput. Chem. 30:864, 2009. DOI: 10.1002/jcc.21209 + Eastman P and Pande VS. OpenMM: A hardware-independent framework for molecular simulations. Comput. Sci. Eng. 12:34, 2010. DOI: 10.1109/MCSE.2010.27 + Eastman P and Pande VS. Efficient nonbonded interactions for molecular dynamics on a graphics processing unit. J. Comput. Chem. 31:1268, 2010. DOI: 10.1002/jcc.21413 + Eastman P and Pande VS. Constant constraint matrix approximation: A robust, parallelizable constraint method for molecular simulations. J. Chem. Theor. Comput. 6:434, 2010. DOI: 10.1021/ct900463w >>> simulation.run() # This runs for a maximum of 2 iterations. >>> simulation.iteration 2 @@ -127,7 +132,12 @@ class SAMSSampler(multistate.MultiStateSampler): >>> del simulation >>> simulation = SAMSSampler.from_storage(reporter) - ... + Please cite the following: + + Friedrichs MS, Eastman P, Vaidyanathan V, Houston M, LeGrand S, Beberg AL, Ensign DL, Bruns CM, and Pande VS. Accelerating molecular dynamic simulations on graphics processing unit. J. Comput. Chem. 30:864, 2009. DOI: 10.1002/jcc.21209 + Eastman P and Pande VS. OpenMM: A hardware-independent framework for molecular simulations. Comput. Sci. Eng. 12:34, 2010. DOI: 10.1109/MCSE.2010.27 + Eastman P and Pande VS. Efficient nonbonded interactions for molecular dynamics on a graphics processing unit. J. Comput. Chem. 31:1268, 2010. DOI: 10.1002/jcc.21413 + Eastman P and Pande VS. Constant constraint matrix approximation: A robust, parallelizable constraint method for molecular simulations. J. Chem. Theor. Comput. 6:434, 2010. DOI: 10.1021/ct900463w >>> simulation.extend(n_iterations=1) >>> simulation.iteration 3 From f0a1dbd574a271dc4e98afd67fd18dbf5eff098d Mon Sep 17 00:00:00 2001 From: hannahbrucemacdonald Date: Wed, 23 Jan 2019 13:54:28 -0500 Subject: [PATCH 11/46] fixed namespace clash for utils --- openmmtools/multistate/multistateanalyzer.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/openmmtools/multistate/multistateanalyzer.py b/openmmtools/multistate/multistateanalyzer.py index 52e8853e2..0655849e4 100644 --- a/openmmtools/multistate/multistateanalyzer.py +++ b/openmmtools/multistate/multistateanalyzer.py @@ -1435,9 +1435,9 @@ def _compute_mbar_decorrelated_energies(self): if not self.use_full_trajectory: for i, energies in enumerate(energy_data): # Discard equilibration iterations. - energies = utils.remove_unequilibrated_data(energies, number_equilibrated, -1) + energies = multistate.utils.remove_unequilibrated_data(energies, number_equilibrated, -1) # Subsample along the decorrelation data. - energy_data[i] = utils.subsample_data_along_axis(energies, g_t, -1) + energy_data[i] = multistate.utils.subsample_data_along_axis(energies, g_t, -1) sampled_energy_matrix, unsampled_energy_matrix, neighborhood, replicas_state_indices = energy_data # Initialize the MBAR matrices in ln form. @@ -1976,7 +1976,7 @@ def _get_equilibration_data(self, energies=None, replica_state_indices=None): # Discard equilibration samples. # TODO: if we include u_n[0] (the energy right after minimization) in the equilibration detection, # TODO: then number_equilibrated is 0. Find a better way than just discarding first frame. - i_t, g_i, n_effective_i = utils.get_equilibration_data_per_sample(u_n[t0:]) + i_t, g_i, n_effective_i = multistate.utils.get_equilibration_data_per_sample(u_n[t0:]) n_effective_max = n_effective_i.max() i_max = n_effective_i.argmax() n_equilibration = i_t[i_max] @@ -2296,7 +2296,7 @@ def _combine_phases(self, other, operator='+'): final_new_names = [] for name in new_names: other_names = [n for n in new_names if n != name] - final_new_names.append(utils.generate_phase_name(name, other_names + names)) + final_new_names.append(multistate.utils.generate_phase_name(name, other_names + names)) names.extend(final_new_names) for new_sign in new_signs: if (operator == '-' and new_sign == '+') or (operator == '+' and new_sign == '-'): @@ -2306,7 +2306,7 @@ def _combine_phases(self, other, operator='+'): signs.extend(new_signs) phases.extend(new_phases) elif isinstance(other, PhaseAnalyzer): - names.append(utils.generate_phase_name(other.name, names)) + names.append(multistate.utils.generate_phase_name(other.name, names)) if (operator == '-' and other._sign == '+') or (operator == '+' and other._sign == '-'): signs.append('-') else: From 0921b3b59ccf390008fcdeb49b32b6d1f6d6954e Mon Sep 17 00:00:00 2001 From: hannahbrucemacdonald Date: Wed, 23 Jan 2019 14:18:45 -0500 Subject: [PATCH 12/46] pinning netcdf4 version --- devtools/conda-recipe/meta.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/devtools/conda-recipe/meta.yaml b/devtools/conda-recipe/meta.yaml index 42ca8bdc5..e20cdd63e 100644 --- a/devtools/conda-recipe/meta.yaml +++ b/devtools/conda-recipe/meta.yaml @@ -24,7 +24,7 @@ requirements: - openmm ==7.3 - parmed - mdtraj - - netcdf4 + - netcdf4 ==1.3.1 # TODO: Fix this right after bugfix: "always return masked array by default, even if there are no masked values" - pyyaml - mpiplus - cython From 239ab6804c435f3ad5b9cfe8f2a9acc0d2ea39db Mon Sep 17 00:00:00 2001 From: hannahbrucemacdonald Date: Thu, 24 Jan 2019 12:01:48 -0500 Subject: [PATCH 13/46] updating docs --- docs/environment.yml | 1 + docs/sampling.rst | 102 +++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 103 insertions(+) diff --git a/docs/environment.yml b/docs/environment.yml index 7e468348e..0e2d09754 100644 --- a/docs/environment.yml +++ b/docs/environment.yml @@ -6,6 +6,7 @@ channels: dependencies: - python - setuptools + - cython - openmm >=7.3 - numpy - scipy diff --git a/docs/sampling.rst b/docs/sampling.rst index e69de29bb..f4c6f6bd4 100644 --- a/docs/sampling.rst +++ b/docs/sampling.rst @@ -0,0 +1,102 @@ +.. _sampling: + +Sampling from multiple alchemical (or other thermodynamic) states +================================================================= + +``openmmtools`` provides several schemes for sampling from multiple thermodynamic states within a single calculation: + +* ``MultistateSampler``: Independent simulations at distinct thermodynamic states +* ``ReplicaExchangeSampler``: Replica exchange among thermodynamic states (also called Hamiltonian exchange if only the Hamiltonian is changing) +* ``SAMSSampler``: Self-adjusted mixture sampling (also known as optimally-adjusted mixture sampling) + +While the thermodynamic states sampled usually differ only in the alchemical parameters, other thermodynamic parameters (such as temperature) can be modulated as well at intermediate alchemical states. +This may be useful in, for example, experimenting with ways to reduce correlation times. + +In all of these schemes, one or more **replicas** is simulated. +Each iteration includes the following phases: +* Allow replicas to switch thermodynamic states (optional) +* Allow replicas to sample a new configuration using Markov chain Monte Carlo (MCMC) +* Each replica computes the potential energy of the current configuration in multiple thermodynamic states +* Data is written to disk + +Below, we describe some of the aspects of these samplers. + +``MultiStateSampler``: Independent simulations at multiple thermodynamic states +------------------------------------------------------------------------------- + +The ``MultiStateSampler`` allows independent simulations from multiple thermodynamic states to be sampled. +In this case, the MCMC scheme is used to propagate each replica by sampling from a fixed thermodynamic state. + +.. math:: + + s_{k,n+1} = s_{k, n} \\ + x_{k,n+1} \sim p(x | s_{k, n+1}) + +An inclusive "neighborhood" of thermodynamic states around this specified state can be used to define which thermodynamic states the reduced potential should be computed for after each iteration. +If all thermodynamic states are included in this neighborhood (the default), the MBAR scheme [CITE] can be used to optimally estimate free energies and uncertainties. +If a restricted neighborhood is used (in order to reduce the amount of time spent in the energy evaluation stage), a variant of the L-WHAM (local weighted histogram analysis method) [CITE] is used to extract an estimate from all available information. + +``ReplicaExchangeSampler``: Replica exchange among thermodynamic states +----------------------------------------------------------------------- + +The ``ReplicaExchangeSampler`` implements a Hamiltonian replica exchange scheme with Gibbs sampling :cite:`Chodera2011` to sample multiple thermodynamic states in a manner that improves mixing of the overall Markov chain. +By allowing replicas to execute a random walk in thermodynamic state space, correlation times may be reduced when sampling certain thermodynamic states (such as those with alchemically-softened potentials or elevated temperatures). + +In the basic version of this scheme, a proposed swap of configurations between two alchemical states, *i* and *j*, made by comparing the energy of each configuration in each replica and swapping with a basic Metropolis criteria of + +.. math:: + P_{\text{accept}}(i, x_i, j, x_j) &= \text{min}\begin{cases} + 1, \frac{ e^{-\left[u_i(x_j) + u_j(x_i)\right]}}{e^{-\left[u_i(x_i) + u_j(x_j)\right]}} + \end{cases} \\ + &= \text{min}\begin{cases} + 1, \exp\left[\Delta u_{ji}(x_i) + \Delta u_{ij}(x_j)\right] + \end{cases} + +where :math:`x` is the configuration of the subscripted states :math:`i` or :math:`j`, and :math:`u` is the reduced potential energy. +While this scheme is typically carried out on neighboring states only, we also implement a much more efficient form of Gibbs sampling in which many swaps are attempted to generate an approximately uncorrelated sample of the state permutation over all :math:`K` :cite:`Chodera2011`. +This speeds up mixing and reduces the total number of samples needed to produce uncorrelated samples. + +``SAMSSampler``: Self-adjusted mixture sampling +----------------------------------------------- + +The ``SAMSSampler`` implements self-adjusted mixture sampling (SAMS; also known as optimally adjusted mixture sampling) :cite:`Tan2017:SAMS`. +This combines one or more replicas that sample from an expanded ensemble with an asymptotically optimal Wang-Landau-like weight update scheme. + +.. math:: + + s_{k,n+1} = p(s | x_{k,n}) \\ + x_{k,n+1} \sim p(x | s_{k, n+1}) + +SAMS state update schemes +^^^^^^^^^^^^^^^^^^^^^^^^^ + +Several state update schemes are available: + +* ``global-jump`` (default): The sampler can jump to any thermodynamic state (RECOMMENDED) +* ``restricted-range-jump``: The sampler can jump to any thermodynamic state within the specified local neighborhood (EXPERIMENTAL; DISABLED) +* ``local-jump``: Only proposals within the specified neighborhood are considered, but rejection rates may be high (EXPERIMENTAL; DISABLED) + +SAMS Locality +^^^^^^^^^^^^^ + +The local neighborhood is specified by the ``locality`` parameter. +If this is a positive integer, the neighborhood will be defined by state indices ``[k - locality, k + locality]``. +Reducing locality will restrict the range of states for which reduced potentials are evaluated, which can speed up the energy evaluation stage of each iteration at the cost of restricting the amount of information available for free energy estimation. +By default, the ``locality`` is global, such that energies at all thermodynamic states are computed; this allows the use of MBAR in data analysis. + +SAMS weight adaptation algorithm +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +SAMS provides two ways of accumulating log weights each iteration: + +* ``optimal`` accumulates weight only in the currently visited state ``s`` +* ``rao-blackwellized`` accumulates fractional weight in all states within the energy evaluation neighborhood + +SAMS initial weight adaptation stage +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Because the asymptotically-optimal weight adaptation scheme works best only when the log weights are close to optimal, a heuristic initial stage is used to more rapidly adapt the log weights before the asymptotically optimal scheme is used. +The behavior of this first stage can be controlled by setting two parameters: + +* ``gamma0`` controls the initial rate of weight adaptation. By default, this is 1.0, but can be set larger (e.g., 10.0) if the free energy differences between states are much larger. +* ``flatness_threshold`` controls the number of (fractional) visits to each thermodynamic state that must be accumulated before the asymptotically optimal weight adaptation scheme is used. From d49ef0a47cc08e6733cddfbb32fc4b1af6b011c0 Mon Sep 17 00:00:00 2001 From: hannahbrucemacdonald Date: Thu, 24 Jan 2019 14:02:16 -0500 Subject: [PATCH 14/46] updating references in docs --- docs/references.bib | 45 +++++++++++++++++++++++++++++++ docs/references.rst | 64 +++++++++++++++++++++++++++++++++++++++++++++ docs/sampling.rst | 12 ++++----- 3 files changed, 115 insertions(+), 6 deletions(-) create mode 100644 docs/references.bib create mode 100644 docs/references.rst diff --git a/docs/references.bib b/docs/references.bib new file mode 100644 index 000000000..8def80f9c --- /dev/null +++ b/docs/references.bib @@ -0,0 +1,45 @@ +@article{Chodera2011, + author = {Chodera, John D. and Shirts, Michael R.}, + title = {Replica exchange and expanded ensemble simulations as Gibbs sampling: Simple improvements for enhanced mixing}, + journal = {The Journal of Chemical Physics}, + year = {2011}, + volume = {135}, + number = {19}, + eid = {194110}, + url = {http://scitation.aip.org/content/aip/journal/jcp/135/19/10.1063/1.3660669}, + doi = {http://dx.doi.org/10.1063/1.3660669}, +} + +@article{Tan2017:SAMS, + title={Optimally adjusted mixture sampling and locally weighted histogram analysis}, + author={Tan, Zhiqiang}, + journal={Journal of Computational and Graphical Statistics}, + volume={26}, + number={1}, + pages={54--65}, + year={2017}, + publisher={Taylor \& Francis} +} + + +@article{Shirts2008statistically, + title={Statistically optimal analysis of samples from multiple equilibrium states}, + author={Shirts, Michael R and Chodera, John D}, + journal={The Journal of chemical physics}, + volume={129}, + number={12}, + pages={124105}, + year={2008}, + publisher={AIP} +} + +@article{kumar1992weighted, + title={The weighted histogram analysis method for free-energy calculations on biomolecules. I. The method}, + author={Kumar, Shankar and Rosenberg, John M and Bouzida, Djamal and Swendsen, Robert H and Kollman, Peter A}, + journal={Journal of computational chemistry}, + volume={13}, + number={8}, + pages={1011--1021}, + year={1992}, + publisher={Wiley Online Library} +} diff --git a/docs/references.rst b/docs/references.rst new file mode 100644 index 000000000..93b5a3e1f --- /dev/null +++ b/docs/references.rst @@ -0,0 +1,64 @@ +.. _references: + +********** +References +********** + +Here are a list of references for the various components and algorithms used in ``openmmtools``. + +OpenMM GPU-accelerated molecular mechanics library +"""""""""""""""""""""""""""""""""""""""""""""""""" + + Friedrichs MS, Eastman P, Vaidyanathan V, Houston M, LeGrand S, Beberg AL, Ensign DL, Bruns CM, and Pande VS. Accelerating molecular dynamic simulations on graphics processing units. + J. Comput. Chem. 30:864, 2009. + http://dx.doi.org/10.1002/jcc.21209 + + Eastman P and Pande VS. OpenMM: A hardware-independent framework for molecular simulations. + Comput. Sci. Eng. 12:34, 2010. + http://dx.doi.org/10.1109/MCSE.2010.27 + + Eastman P and Pande VS. Efficient nonbonded interactions for molecular dynamics on a graphics processing unit. + J. Comput. Chem. 31:1268, 2010. + http://dx.doi.org/10.1002/jcc.21413 + + Eastman P and Pande VS. Constant constraint matrix approximation: A robust, parallelizable constraint method for molecular simulations. + J. Chem. Theor. Comput. 6:434, 2010. + http://dx.doi.org/10.1021/ct900463w + + Eastman P, Friedrichs M, Chodera JD, Radmer RJ, Bruns CM, Ku JP, Beauchamp KA, Lane TJ, Wang LP, Shukla D, Tye T, Houston M, Stich T, Klein C, Shirts M, and Pande VS. OpenMM 4: A Reusable, Extensible, + Hardware Independent Library for High Performance Molecular Simulation. J. Chem. Theor. Comput. 2012. + http://dx.doi.org/10.1021/ct300857j + +Replica-exchange with Gibbs sampling +"""""""""""""""""""""""""""""""""""" + + Chodera JD and Shirts MR. Replica exchange and expanded ensemble simulations as Gibbs sampling: Simple improvements for enhanced mixing. + J. Chem. Phys. 135:19410, 2011. + http://dx.doi.org/10.1063/1.3660669 + +MBAR for estimation of free energies from simulation data +""""""""""""""""""""""""""""""""""""""""""""""""""""""""" + + Shirts MR and Chodera JD. Statistically optimal analysis of samples from multiple equilibrium states. + J. Chem. Phys. 129:124105, 2008. + http://dx.doi.org/10.1063/1.2978177 + +Long-range dispersion corrections for explicit solvent free energy calculations +""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""" + + Shirts MR, Mobley DL, Chodera JD, and Pande VS. Accurate and efficient corrections or missing dispersion interactions in molecular simulations. + J. Phys. Chem. 111:13052, 2007. + http://dx.doi.org/10.1021/jp0735987 + + +Bibliography +############ + +.. The :all: directive searches subfolders for uses of :cite: for correct reference + However, this has the effect of dropping all citations in the .bib file in here and + the compiler complains about unused citations. + As such, unused articles in the .bib file are simply commented so as not to delete them if needed in the future. + +.. bibliography:: references.bib + :style: unsrt + :all: diff --git a/docs/sampling.rst b/docs/sampling.rst index f4c6f6bd4..5939153bb 100644 --- a/docs/sampling.rst +++ b/docs/sampling.rst @@ -14,10 +14,10 @@ This may be useful in, for example, experimenting with ways to reduce correlatio In all of these schemes, one or more **replicas** is simulated. Each iteration includes the following phases: -* Allow replicas to switch thermodynamic states (optional) -* Allow replicas to sample a new configuration using Markov chain Monte Carlo (MCMC) -* Each replica computes the potential energy of the current configuration in multiple thermodynamic states -* Data is written to disk +| * Allow replicas to switch thermodynamic states (optional) +| * Allow replicas to sample a new configuration using Markov chain Monte Carlo (MCMC) +| * Each replica computes the potential energy of the current configuration in multiple thermodynamic states +| * Data is written to disk Below, we describe some of the aspects of these samplers. @@ -33,8 +33,8 @@ In this case, the MCMC scheme is used to propagate each replica by sampling from x_{k,n+1} \sim p(x | s_{k, n+1}) An inclusive "neighborhood" of thermodynamic states around this specified state can be used to define which thermodynamic states the reduced potential should be computed for after each iteration. -If all thermodynamic states are included in this neighborhood (the default), the MBAR scheme [CITE] can be used to optimally estimate free energies and uncertainties. -If a restricted neighborhood is used (in order to reduce the amount of time spent in the energy evaluation stage), a variant of the L-WHAM (local weighted histogram analysis method) [CITE] is used to extract an estimate from all available information. +If all thermodynamic states are included in this neighborhood (the default), the MBAR scheme :cite:`Shirts2008statistically` can be used to optimally estimate free energies and uncertainties. +If a restricted neighborhood is used (in order to reduce the amount of time spent in the energy evaluation stage), a variant of the L-WHAM (local weighted histogram analysis method) :cite:`kumar1992weighted` is used to extract an estimate from all available information. ``ReplicaExchangeSampler``: Replica exchange among thermodynamic states ----------------------------------------------------------------------- From 476ba0d9f0d714428b345172323f2a418ddf31b4 Mon Sep 17 00:00:00 2001 From: hannahbrucemacdonald Date: Thu, 24 Jan 2019 15:30:10 -0500 Subject: [PATCH 15/46] formatting references correctly with sphinx --- devtools/conda-recipe/meta.yaml | 1 + docs/conf.py | 1 + docs/sampling.rst | 8 ++++---- 3 files changed, 6 insertions(+), 4 deletions(-) diff --git a/devtools/conda-recipe/meta.yaml b/devtools/conda-recipe/meta.yaml index e20cdd63e..49c671b6d 100644 --- a/devtools/conda-recipe/meta.yaml +++ b/devtools/conda-recipe/meta.yaml @@ -28,6 +28,7 @@ requirements: - pyyaml - mpiplus - cython + - sphinxcontrib-bibtex test: requires: diff --git a/docs/conf.py b/docs/conf.py index 07e2b17c9..4f7e2765e 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -40,6 +40,7 @@ 'sphinx.ext.todo', 'sphinx.ext.coverage', 'sphinx.ext.viewcode', + 'sphinxcontrib.bibtex', #'sphinx.ext.githubpages' ] diff --git a/docs/sampling.rst b/docs/sampling.rst index 5939153bb..1064f2681 100644 --- a/docs/sampling.rst +++ b/docs/sampling.rst @@ -14,10 +14,10 @@ This may be useful in, for example, experimenting with ways to reduce correlatio In all of these schemes, one or more **replicas** is simulated. Each iteration includes the following phases: -| * Allow replicas to switch thermodynamic states (optional) -| * Allow replicas to sample a new configuration using Markov chain Monte Carlo (MCMC) -| * Each replica computes the potential energy of the current configuration in multiple thermodynamic states -| * Data is written to disk + * Allow replicas to switch thermodynamic states (optional) + * Allow replicas to sample a new configuration using Markov chain Monte Carlo (MCMC) + * Each replica computes the potential energy of the current configuration in multiple thermodynamic states + * Data is written to disk Below, we describe some of the aspects of these samplers. From 35e85a8e020782d0c3077d44504729bfae3b5ab5 Mon Sep 17 00:00:00 2001 From: hannahbrucemacdonald Date: Thu, 24 Jan 2019 15:57:15 -0500 Subject: [PATCH 16/46] adding sphinx_rtd_theme --- docs/conf.py | 1 + 1 file changed, 1 insertion(+) diff --git a/docs/conf.py b/docs/conf.py index 4f7e2765e..f7aa47d92 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -20,6 +20,7 @@ import os import sys sys.path.insert(0, os.path.abspath('..')) +import sphinx_rtd_theme # -- General configuration ------------------------------------------------ From e7cf742681f97104734191a22877bbba36c68c35 Mon Sep 17 00:00:00 2001 From: Bruce Macdonald Date: Sat, 26 Jan 2019 11:10:12 -0500 Subject: [PATCH 17/46] adding sphinxcontrib.bibtex to docs/environment.yaml --- docs/environment.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/docs/environment.yml b/docs/environment.yml index 0e2d09754..e6a0ec341 100644 --- a/docs/environment.yml +++ b/docs/environment.yml @@ -16,3 +16,4 @@ dependencies: - numpydoc - netCDF4 - pyyaml + - sphinxcontrib-bibtex From 71b15ccd08a85b5d74160476183b15957987db23 Mon Sep 17 00:00:00 2001 From: John Chodera Date: Tue, 29 Jan 2019 21:45:15 -0500 Subject: [PATCH 18/46] Make sure to close storage on __del__ (port changes from https://github.com/choderalab/yank/pull/1135/) --- openmmtools/multistate/multistateanalyzer.py | 6 ++++++ openmmtools/multistate/multistatereporter.py | 7 ++----- 2 files changed, 8 insertions(+), 5 deletions(-) diff --git a/openmmtools/multistate/multistateanalyzer.py b/openmmtools/multistate/multistateanalyzer.py index 0655849e4..75aa94a95 100644 --- a/openmmtools/multistate/multistateanalyzer.py +++ b/openmmtools/multistate/multistateanalyzer.py @@ -572,6 +572,12 @@ def __init__(self, reporter, name=None, reference_states=(0, -1), self._use_online_data = use_online_data self._read_online_data_if_present() + def __del__(self): + # Explicitly close storage + self.clear() + if self._reporter is not None: + del self._reporter + def clear(self): """Reset all cached objects. diff --git a/openmmtools/multistate/multistatereporter.py b/openmmtools/multistate/multistatereporter.py index 0f59964a2..fd2b4c21f 100644 --- a/openmmtools/multistate/multistatereporter.py +++ b/openmmtools/multistate/multistatereporter.py @@ -417,11 +417,8 @@ def sync(self): def __del__(self): """Synchronize and close the storage.""" - for storage in self._storage: - if storage is not None: - self.close() - break - + self.close() + def read_end_thermodynamic_states(self): """Read thermodynamic states at the ends of the protocol." From 5d3f48e071087193f522b725d505930dc19e732f Mon Sep 17 00:00:00 2001 From: John Chodera Date: Tue, 29 Jan 2019 21:59:10 -0500 Subject: [PATCH 19/46] Remove omnia rc channel from environment.yml --- docs/environment.yml | 1 - 1 file changed, 1 deletion(-) diff --git a/docs/environment.yml b/docs/environment.yml index e6a0ec341..80f1e520a 100644 --- a/docs/environment.yml +++ b/docs/environment.yml @@ -2,7 +2,6 @@ name: openmmtools channels: - conda-forge - omnia - - omnia/label/rc dependencies: - python - setuptools From 835e59996136d99a5d44ffc7117699d213d04000 Mon Sep 17 00:00:00 2001 From: John Chodera Date: Tue, 29 Jan 2019 22:24:29 -0500 Subject: [PATCH 20/46] Add link to MIT license --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 82e3ce95a..6e8390d32 100644 --- a/README.md +++ b/README.md @@ -19,10 +19,10 @@ Features include: - enhanced sampling methods, including replica-exchange (REMD) and self-adjusted mixture sampling (SAMS) - factories for generating [alchemically-modified](http://alchemistry.org) systems for absolute and relative free energy calculations - a suite of test systems for benchmarking, validation, and debugging - - user-friendly storage interface layer to remove requirement that user know how to store all their data-types on disk + - user-friendly storage interface layer to remove requirement that user know how to store all their data-types on disk See the [documentation](http://openmmtools.readthedocs.io) at [ReadTheDocs](http://openmmtools.readthedocs.io). #### License -OpenMMTools is distributed under the MIT License. +OpenMMTools is distributed under the [MIT License](https://opensource.org/licenses/MIT). From 6190b8dd1fa525ad6cd87f5017e6077c5f208de5 Mon Sep 17 00:00:00 2001 From: John Chodera Date: Tue, 29 Jan 2019 22:28:36 -0500 Subject: [PATCH 21/46] Add major contributors to README --- README.md | 17 +++++++++++++++++ 1 file changed, 17 insertions(+) diff --git a/README.md b/README.md index 6e8390d32..7924c2498 100644 --- a/README.md +++ b/README.md @@ -26,3 +26,20 @@ See the [documentation](http://openmmtools.readthedocs.io) at [ReadTheDocs](http #### License OpenMMTools is distributed under the [MIT License](https://opensource.org/licenses/MIT). + +#### Contributors + +A complete list of contributors can be found [here](https://github.com/choderalab/openmmtools/graphs/contributors) + +Major contributors include: + +* Andrea Rizzi `` (WCMC) +* John D. Chodera `` (MSKCC) +* Levi N. Naden `` (MSKCC) +* Patrick Grinaway `` (MSKCC) +* Kyle A. Beauchamp `` (MSKCC) +* Josh Fass `` (MSKCC) +* Bas Rustenburg `` (MSKCC) +* Gregory Ross `` (MSKCC) +* David W.H. Swenson `` +* Hannah Bruce Macdonald `` (MSKCC) From a5579c16647855f7bc92b12cc1b52d3226d1bf4f Mon Sep 17 00:00:00 2001 From: John Chodera Date: Tue, 29 Jan 2019 22:38:15 -0500 Subject: [PATCH 22/46] Rename sampling.rst -> multistate.rst and add API docs --- docs/index.rst | 2 +- docs/{sampling.rst => multistate.rst} | 49 +++++++++++++++++++++++++-- 2 files changed, 47 insertions(+), 4 deletions(-) rename docs/{sampling.rst => multistate.rst} (88%) diff --git a/docs/index.rst b/docs/index.rst index 2eb660e84..674350fbd 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -56,7 +56,7 @@ Modules states cache mcmc - sampling + multistate alchemy forces forcefactories diff --git a/docs/sampling.rst b/docs/multistate.rst similarity index 88% rename from docs/sampling.rst rename to docs/multistate.rst index 1064f2681..a22bd11e2 100644 --- a/docs/sampling.rst +++ b/docs/multistate.rst @@ -1,7 +1,7 @@ -.. _sampling: +.. _multistate: -Sampling from multiple alchemical (or other thermodynamic) states -================================================================= +Sampling from multiple thermodynamic states +=========================================== ``openmmtools`` provides several schemes for sampling from multiple thermodynamic states within a single calculation: @@ -100,3 +100,46 @@ The behavior of this first stage can be controlled by setting two parameters: * ``gamma0`` controls the initial rate of weight adaptation. By default, this is 1.0, but can be set larger (e.g., 10.0) if the free energy differences between states are much larger. * ``flatness_threshold`` controls the number of (fractional) visits to each thermodynamic state that must be accumulated before the asymptotically optimal weight adaptation scheme is used. + +Multistate Samplers +------------------- + +.. currentmodule:: openmmtools.multistate +.. autosummary:: + :nosignatures: + :toctree: api/generated/ + + MultiStateSampler + MultiStateSamplerAnalyzer + + ReplicaExchangeSampler + ReplicaExchangeAnalyzer + + ParallelTemperingSampler + ParallelTemperingAnalyzer + + SAMSSampler + SAMSAnalyzer + +Multistate Reporters +-------------------- + +.. currentmodule:: openmmtools.multistate +.. autosummary:: + :nosignatures: + :toctree: api/generated/ + + MultiStateReporter + +Analysis of multiple thermodynamic transformations +-------------------------------------------------- + + MultiPhaseAnalyzer + +Miscellaneous support classes +----------------------------- + + ObservablesRegistry + CachedProperty + InsufficientData + PhaseAnalyzer From 8c08804a8009935e7c02b2cd4828e0aa5adaf122 Mon Sep 17 00:00:00 2001 From: John Chodera Date: Wed, 30 Jan 2019 00:27:37 -0500 Subject: [PATCH 23/46] Add mpiplus dependency --- devtools/conda-recipe/meta.yaml | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/devtools/conda-recipe/meta.yaml b/devtools/conda-recipe/meta.yaml index 49c671b6d..c657825da 100644 --- a/devtools/conda-recipe/meta.yaml +++ b/devtools/conda-recipe/meta.yaml @@ -26,9 +26,10 @@ requirements: - mdtraj - netcdf4 ==1.3.1 # TODO: Fix this right after bugfix: "always return masked array by default, even if there are no masked values" - pyyaml - - mpiplus + - mpiplus - cython - sphinxcontrib-bibtex + - mpiplus test: requires: From 857584350f13dc779f736ebf0f732b05f3a0ca66 Mon Sep 17 00:00:00 2001 From: John Chodera Date: Wed, 30 Jan 2019 00:29:52 -0500 Subject: [PATCH 24/46] Fix API documentation --- docs/multistate.rst | 45 +++++++++++++++++++++++++++++++++++---------- 1 file changed, 35 insertions(+), 10 deletions(-) diff --git a/docs/multistate.rst b/docs/multistate.rst index a22bd11e2..d0c47cf63 100644 --- a/docs/multistate.rst +++ b/docs/multistate.rst @@ -36,6 +36,14 @@ An inclusive "neighborhood" of thermodynamic states around this specified state If all thermodynamic states are included in this neighborhood (the default), the MBAR scheme :cite:`Shirts2008statistically` can be used to optimally estimate free energies and uncertainties. If a restricted neighborhood is used (in order to reduce the amount of time spent in the energy evaluation stage), a variant of the L-WHAM (local weighted histogram analysis method) :cite:`kumar1992weighted` is used to extract an estimate from all available information. +.. currentmodule:: openmmtools.multistate +.. autosummary:: + :nosignatures: + :toctree: api/generated/ + + MultiStateSampler + MultiStateSamplerAnalyzer + ``ReplicaExchangeSampler``: Replica exchange among thermodynamic states ----------------------------------------------------------------------- @@ -56,6 +64,14 @@ where :math:`x` is the configuration of the subscripted states :math:`i` or :mat While this scheme is typically carried out on neighboring states only, we also implement a much more efficient form of Gibbs sampling in which many swaps are attempted to generate an approximately uncorrelated sample of the state permutation over all :math:`K` :cite:`Chodera2011`. This speeds up mixing and reduces the total number of samples needed to produce uncorrelated samples. +.. currentmodule:: openmmtools.multistate +.. autosummary:: + :nosignatures: + :toctree: api/generated/ + + ReplicaExchangeSampler + ReplicaExchangeAnalyzer + ``SAMSSampler``: Self-adjusted mixture sampling ----------------------------------------------- @@ -101,26 +117,25 @@ The behavior of this first stage can be controlled by setting two parameters: * ``gamma0`` controls the initial rate of weight adaptation. By default, this is 1.0, but can be set larger (e.g., 10.0) if the free energy differences between states are much larger. * ``flatness_threshold`` controls the number of (fractional) visits to each thermodynamic state that must be accumulated before the asymptotically optimal weight adaptation scheme is used. -Multistate Samplers -------------------- - .. currentmodule:: openmmtools.multistate .. autosummary:: :nosignatures: :toctree: api/generated/ - MultiStateSampler - MultiStateSamplerAnalyzer + SAMSSampler + SAMSAnalyzer - ReplicaExchangeSampler - ReplicaExchangeAnalyzer +Parallel tempering +------------------ + +.. currentmodule:: openmmtools.multistate +.. autosummary:: + :nosignatures: + :toctree: api/generated/ ParallelTemperingSampler ParallelTemperingAnalyzer - SAMSSampler - SAMSAnalyzer - Multistate Reporters -------------------- @@ -134,11 +149,21 @@ Multistate Reporters Analysis of multiple thermodynamic transformations -------------------------------------------------- +.. currentmodule:: openmmtools.multistate +.. autosummary:: + :nosignatures: + :toctree: api/generated/ + MultiPhaseAnalyzer Miscellaneous support classes ----------------------------- +.. currentmodule:: openmmtools.multistate.multistateanalyzer +.. autosummary:: + :nosignatures: + :toctree: api/generated/ + ObservablesRegistry CachedProperty InsufficientData From 02b1889aa4077500d871323c204cacb264af8bff Mon Sep 17 00:00:00 2001 From: John Chodera Date: Wed, 30 Jan 2019 00:30:54 -0500 Subject: [PATCH 25/46] NetCDF updates --- devtools/conda-recipe/meta.yaml | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/devtools/conda-recipe/meta.yaml b/devtools/conda-recipe/meta.yaml index c657825da..a56e58f3c 100644 --- a/devtools/conda-recipe/meta.yaml +++ b/devtools/conda-recipe/meta.yaml @@ -24,7 +24,8 @@ requirements: - openmm ==7.3 - parmed - mdtraj - - netcdf4 ==1.3.1 # TODO: Fix this right after bugfix: "always return masked array by default, even if there are no masked values" + - netcdf4 >=1.4.2 # after bugfix: "always return masked array by default, even if there are no masked values" + - libnetcdf >=4.6.2 # workaround for libssl issues - pyyaml - mpiplus - cython From 14f5b1b15e9da21c5fdc9bdba7111fafc04785fd Mon Sep 17 00:00:00 2001 From: John Chodera Date: Wed, 30 Jan 2019 07:35:50 -0500 Subject: [PATCH 26/46] Don't use masked arrays in storage --- openmmtools/multistate/multistatereporter.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/openmmtools/multistate/multistatereporter.py b/openmmtools/multistate/multistatereporter.py index fd2b4c21f..7190c07f0 100644 --- a/openmmtools/multistate/multistatereporter.py +++ b/openmmtools/multistate/multistatereporter.py @@ -271,6 +271,7 @@ def open(self, mode='r', convention='ReplicaExchange', netcdf_format='NETCDF4'): mode, version=netcdf_format) # Without set_auto_mask(False) np.inf are read as masked. self._storage_analysis.set_auto_mask(False) + self._storage_analysis.set_always_mask(False) # The analysis netcdf file holds a reference UUID so that we can check # that the secondary netcdf files (currently only the checkpoint @@ -313,6 +314,10 @@ def open(self, mode='r', convention='ReplicaExchange', netcdf_format='NETCDF4'): # Initialize dataset, if needed. self._initialize_storage_file(self._storage_checkpoint, 'checkpoint', convention) + # Without set_auto_mask(False) np.inf are read as masked. + self._storage_checkpoint.set_auto_mask(False) + self._storage_checkpoint.set_always_mask(False) + # Further checkpoint interval checks. # ----------------------------------- @@ -418,7 +423,7 @@ def sync(self): def __del__(self): """Synchronize and close the storage.""" self.close() - + def read_end_thermodynamic_states(self): """Read thermodynamic states at the ends of the protocol." From 5ed1773052bd1a788c19c1ce29d583297817ea1b Mon Sep 17 00:00:00 2001 From: John Chodera Date: Wed, 30 Jan 2019 10:13:30 -0500 Subject: [PATCH 27/46] Ensure NetCDF never returns masked arrays. Default NetCDF behavior changed in 1.4.0 to return masked arrays by default --- openmmtools/multistate/multistatereporter.py | 72 ++++++++++++++++---- 1 file changed, 57 insertions(+), 15 deletions(-) diff --git a/openmmtools/multistate/multistatereporter.py b/openmmtools/multistate/multistatereporter.py index 7190c07f0..4cf3d4312 100644 --- a/openmmtools/multistate/multistatereporter.py +++ b/openmmtools/multistate/multistatereporter.py @@ -269,9 +269,7 @@ def open(self, mode='r', convention='ReplicaExchange', netcdf_format='NETCDF4'): # Open analysis file. self._storage_analysis = self._open_dataset_robustly(self._storage_analysis_file_path, mode, version=netcdf_format) - # Without set_auto_mask(False) np.inf are read as masked. - self._storage_analysis.set_auto_mask(False) - self._storage_analysis.set_always_mask(False) + # The analysis netcdf file holds a reference UUID so that we can check # that the secondary netcdf files (currently only the checkpoint @@ -314,10 +312,6 @@ def open(self, mode='r', convention='ReplicaExchange', netcdf_format='NETCDF4'): # Initialize dataset, if needed. self._initialize_storage_file(self._storage_checkpoint, 'checkpoint', convention) - # Without set_auto_mask(False) np.inf are read as masked. - self._storage_checkpoint.set_auto_mask(False) - self._storage_checkpoint.set_always_mask(False) - # Further checkpoint interval checks. # ----------------------------------- @@ -348,6 +342,10 @@ def open(self, mode='r', convention='ReplicaExchange', netcdf_format='NETCDF4'): "Using on file analysis indices of {}".format(stored_analysis_particles)) self._analysis_particle_indices = tuple(stored_analysis_particles.astype(int)) + # Ensure we don't mask returned data + self._storage_analysis.set_auto_mask(False) + self._storage_analysis.set_always_mask(False) + def _open_dataset_robustly(self, *args, n_attempts=5, sleep_time=2, catch_io_error=False, io_error_warning=None, **kwargs): @@ -360,9 +358,11 @@ def _open_dataset_robustly(self, *args, n_attempts=5, sleep_time=2, If the file is not found and catch_io_error is True, None is returned. """ # Catch eventual errors n_attempts - 1 times. - for i in range(n_attempts-1): + dataset = None + attempt = 0 + while (not dataset) and (attempt < n_attempts-1): try: - return netcdf.Dataset(*args, **kwargs) + dataset = netcdf.Dataset(*args, **kwargs) except IOError as e: # If the file does not exist, it doesn't make sense to try again. if catch_io_error: @@ -375,7 +375,14 @@ def _open_dataset_robustly(self, *args, n_attempts=5, sleep_time=2, 'in {} seconds'.format(i+1, n_attempts, sleep_time)) time.sleep(sleep_time) # Last attempt finally raises any error. - return netcdf.Dataset(*args, **kwargs) + dataset = netcdf.Dataset(*args, **kwargs) + + # Without set_auto_mask(False) np.inf are read as masked. + # Note that this does not affect variables created after this call. + dataset.set_auto_mask(False) + dataset.set_always_mask(False) + + return dataset def _initialize_storage_file(self, ncfile, nc_name, convention): """Helper function to initialize dimensions and global attributes. @@ -401,6 +408,11 @@ def _initialize_storage_file(self, ncfile, nc_name, convention): # Create and initialize the global variables nc_last_good_iter = ncfile.createVariable('last_iteration', int, 'scalar') nc_last_good_iter[0] = 0 + + # Ensure we don't mask returned data + ncfile.set_auto_mask(False) + ncfile.set_always_mask(False) + return True else: return False @@ -731,6 +743,10 @@ def write_replica_thermodynamic_states(self, state_indices, iteration): setattr(ncvar_states, "long_name", ("states[iteration][replica] is the thermodynamic state index " "(0..n_states-1) of replica 'replica' of iteration 'iteration'.")) + # Ensure we don't mask returned data + ncvar_states.set_auto_mask(False) + ncvar_states.set_always_mask(False) + # Store thermodynamic states indices. self._storage_analysis.variables['states'][iteration, :] = state_indices[:] @@ -880,6 +896,11 @@ def write_energies(self, energy_thermodynamic_states, energy_neighborhoods, ener if energy_unsampled_states.shape[1] > 0: self._storage_analysis.variables['unsampled_energies'][iteration, :, :] = energy_unsampled_states[:, :] + # Ensure we don't mask returned data + self._storage_analysis.set_auto_mask(False) + self._storage_analysis.set_always_mask(False) + + def read_mixing_statistics(self, iteration=slice(None)): """Retrieve the mixing statistics for the given iteration on the analysis file @@ -957,6 +978,10 @@ def write_mixing_statistics(self, n_accepted_matrix, n_proposed_matrix, iteratio self._storage_analysis.variables['accepted'][iteration, :, :] = n_accepted_matrix[:, :] self._storage_analysis.variables['proposed'][iteration, :, :] = n_proposed_matrix[:, :] + # Ensure we don't mask returned data + self._storage_analysis.set_auto_mask(False) + self._storage_analysis.set_always_mask(False) + def read_timestamp(self, iteration=slice(None)): """Return the timestamp for the given iteration. @@ -990,9 +1015,14 @@ def write_timestamp(self, iteration: int): # Create variable if needed. for storage_key, storage in self._storage_dict.items(): if 'timestamp' not in storage.variables: - storage.createVariable('timestamp', str, ('iteration',), + timestamp = storage.createVariable('timestamp', str, ('iteration',), zlib=False, chunksizes=(self._storage_chunks[storage_key],)) + + # Ensure we don't mask returned data + timestamp.set_auto_mask(False) + timestamp.set_always_mask(False) + timestamp = time.ctime() self._storage_analysis.variables['timestamp'][iteration] = timestamp checkpoint_iteration = self._calculate_checkpoint_iteration(iteration) @@ -1320,10 +1350,14 @@ def _write_1d_online_data(self, iteration, variable, data, storage): if variable not in storage.variables: variable_parameters = self._determine_netcdf_variable_parameters(iteration, data, storage) logger.debug('Creating new NetCDF variable %s with parameters: %s' % (variable, variable_parameters)) # DEBUG - storage.createVariable(variable, variable_parameters['dtype'], - dimensions=variable_parameters['dims'], - chunksizes=variable_parameters['chunksizes'], - zlib=False) + nc_var = storage.createVariable(variable, variable_parameters['dtype'], + dimensions=variable_parameters['dims'], + chunksizes=variable_parameters['chunksizes'], + zlib=False) + # Ensure we don't mask returned data + nc_var.set_auto_mask(False) + nc_var.set_always_mask(False) + # Get the variable nc_var = storage[variable] # Only get the specific iteration if specified @@ -1575,6 +1609,10 @@ def _initialize_sampler_variables_on_file(dataset, n_atoms, n_replicas, is_perio setattr(ncvar_volumes, "long_name", ("volume[iteration][replica] is the box volume for replica " "'replica' from iteration 'iteration-1'.")) + # Ensure we don't mask returned data + dataset.set_auto_mask(False) + dataset.set_always_mask(False) + def _write_sampler_states_to_given_file(self, sampler_states: list, iteration: int, storage_file='checkpoint', obey_checkpoint_interval=True): """ @@ -1749,6 +1787,10 @@ def _write_dict(self, path, data, storage_name='analysis', nc_variable = storage_nc.createVariable(path, variable_type, dimension_name, zlib=True) + # Ensure we don't mask returned data + nc_variable.set_auto_mask(False) + nc_variable.set_always_mask(False) + # Assign the value to the variable. if fixed_dimension: packed_data = np.array(list(data_str), dtype='S1') From 36d11592709a004ba27e599079cb42540ed8147b Mon Sep 17 00:00:00 2001 From: John Chodera Date: Wed, 30 Jan 2019 18:27:23 -0500 Subject: [PATCH 28/46] Remove duplicated mpiplus --- devtools/conda-recipe/meta.yaml | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/devtools/conda-recipe/meta.yaml b/devtools/conda-recipe/meta.yaml index a56e58f3c..fb6c2f75c 100644 --- a/devtools/conda-recipe/meta.yaml +++ b/devtools/conda-recipe/meta.yaml @@ -13,7 +13,7 @@ requirements: build: - python - setuptools - - openmm ==7.3 + - openmm >=7.3 - cython run: @@ -21,13 +21,12 @@ requirements: - numpy - scipy - six - - openmm ==7.3 + - openmm >=7.3 - parmed - mdtraj - netcdf4 >=1.4.2 # after bugfix: "always return masked array by default, even if there are no masked values" - libnetcdf >=4.6.2 # workaround for libssl issues - pyyaml - - mpiplus - cython - sphinxcontrib-bibtex - mpiplus From a69d37a460ca8b2660dbe0727fc68aa26201511d Mon Sep 17 00:00:00 2001 From: John Chodera Date: Wed, 30 Jan 2019 18:27:31 -0500 Subject: [PATCH 29/46] Strip out masked array disabling---easier to just handle masked arrays --- openmmtools/multistate/multistatereporter.py | 41 +------------------- openmmtools/multistate/multistatesampler.py | 5 +++ 2 files changed, 6 insertions(+), 40 deletions(-) diff --git a/openmmtools/multistate/multistatereporter.py b/openmmtools/multistate/multistatereporter.py index 4cf3d4312..d26c96f18 100644 --- a/openmmtools/multistate/multistatereporter.py +++ b/openmmtools/multistate/multistatereporter.py @@ -334,6 +334,7 @@ def open(self, mode='r', convention='ReplicaExchange', netcdf_format='NETCDF4'): ncvar_analysis_particles.long_name = ("analysis_particle_indices[analysis_particles] is the indices of " "the particles with extra information stored about them in the" "analysis file.") + # Now handle the "variable does exist but does not match the provided ones" # Although redundant if it was just created, its an easy check to make stored_analysis_particles = self._storage_analysis.variables['analysis_particle_indices'][:] @@ -342,10 +343,6 @@ def open(self, mode='r', convention='ReplicaExchange', netcdf_format='NETCDF4'): "Using on file analysis indices of {}".format(stored_analysis_particles)) self._analysis_particle_indices = tuple(stored_analysis_particles.astype(int)) - # Ensure we don't mask returned data - self._storage_analysis.set_auto_mask(False) - self._storage_analysis.set_always_mask(False) - def _open_dataset_robustly(self, *args, n_attempts=5, sleep_time=2, catch_io_error=False, io_error_warning=None, **kwargs): @@ -377,11 +374,6 @@ def _open_dataset_robustly(self, *args, n_attempts=5, sleep_time=2, # Last attempt finally raises any error. dataset = netcdf.Dataset(*args, **kwargs) - # Without set_auto_mask(False) np.inf are read as masked. - # Note that this does not affect variables created after this call. - dataset.set_auto_mask(False) - dataset.set_always_mask(False) - return dataset def _initialize_storage_file(self, ncfile, nc_name, convention): @@ -409,10 +401,6 @@ def _initialize_storage_file(self, ncfile, nc_name, convention): nc_last_good_iter = ncfile.createVariable('last_iteration', int, 'scalar') nc_last_good_iter[0] = 0 - # Ensure we don't mask returned data - ncfile.set_auto_mask(False) - ncfile.set_always_mask(False) - return True else: return False @@ -743,10 +731,6 @@ def write_replica_thermodynamic_states(self, state_indices, iteration): setattr(ncvar_states, "long_name", ("states[iteration][replica] is the thermodynamic state index " "(0..n_states-1) of replica 'replica' of iteration 'iteration'.")) - # Ensure we don't mask returned data - ncvar_states.set_auto_mask(False) - ncvar_states.set_always_mask(False) - # Store thermodynamic states indices. self._storage_analysis.variables['states'][iteration, :] = state_indices[:] @@ -896,10 +880,6 @@ def write_energies(self, energy_thermodynamic_states, energy_neighborhoods, ener if energy_unsampled_states.shape[1] > 0: self._storage_analysis.variables['unsampled_energies'][iteration, :, :] = energy_unsampled_states[:, :] - # Ensure we don't mask returned data - self._storage_analysis.set_auto_mask(False) - self._storage_analysis.set_always_mask(False) - def read_mixing_statistics(self, iteration=slice(None)): """Retrieve the mixing statistics for the given iteration on the analysis file @@ -978,10 +958,6 @@ def write_mixing_statistics(self, n_accepted_matrix, n_proposed_matrix, iteratio self._storage_analysis.variables['accepted'][iteration, :, :] = n_accepted_matrix[:, :] self._storage_analysis.variables['proposed'][iteration, :, :] = n_proposed_matrix[:, :] - # Ensure we don't mask returned data - self._storage_analysis.set_auto_mask(False) - self._storage_analysis.set_always_mask(False) - def read_timestamp(self, iteration=slice(None)): """Return the timestamp for the given iteration. @@ -1019,10 +995,6 @@ def write_timestamp(self, iteration: int): zlib=False, chunksizes=(self._storage_chunks[storage_key],)) - # Ensure we don't mask returned data - timestamp.set_auto_mask(False) - timestamp.set_always_mask(False) - timestamp = time.ctime() self._storage_analysis.variables['timestamp'][iteration] = timestamp checkpoint_iteration = self._calculate_checkpoint_iteration(iteration) @@ -1354,9 +1326,6 @@ def _write_1d_online_data(self, iteration, variable, data, storage): dimensions=variable_parameters['dims'], chunksizes=variable_parameters['chunksizes'], zlib=False) - # Ensure we don't mask returned data - nc_var.set_auto_mask(False) - nc_var.set_always_mask(False) # Get the variable nc_var = storage[variable] @@ -1609,10 +1578,6 @@ def _initialize_sampler_variables_on_file(dataset, n_atoms, n_replicas, is_perio setattr(ncvar_volumes, "long_name", ("volume[iteration][replica] is the box volume for replica " "'replica' from iteration 'iteration-1'.")) - # Ensure we don't mask returned data - dataset.set_auto_mask(False) - dataset.set_always_mask(False) - def _write_sampler_states_to_given_file(self, sampler_states: list, iteration: int, storage_file='checkpoint', obey_checkpoint_interval=True): """ @@ -1787,10 +1752,6 @@ def _write_dict(self, path, data, storage_name='analysis', nc_variable = storage_nc.createVariable(path, variable_type, dimension_name, zlib=True) - # Ensure we don't mask returned data - nc_variable.set_auto_mask(False) - nc_variable.set_always_mask(False) - # Assign the value to the variable. if fixed_dimension: packed_data = np.array(list(data_str), dtype='S1') diff --git a/openmmtools/multistate/multistatesampler.py b/openmmtools/multistate/multistatesampler.py index 5300e39fb..58b71234e 100644 --- a/openmmtools/multistate/multistatesampler.py +++ b/openmmtools/multistate/multistatesampler.py @@ -1485,6 +1485,11 @@ def _online_analysis(self, gamma0=1.0): if self._last_mbar_f_k is None: self._last_mbar_f_k = np.zeros([self.n_states], np.float64) + # DEBUG + if np.ma.is_masked(self._last_mbar_f_k): + print('self._last_mbar_f_k is masked:') + print(self._last_mbar_f_k) + logZ = - self._last_mbar_f_k for (replica_index, state_index) in enumerate(self._replica_thermodynamic_states): From 63f48ad86a1bc9b787ee517ab0f9f06e18751b0a Mon Sep 17 00:00:00 2001 From: John Chodera Date: Wed, 30 Jan 2019 18:49:09 -0500 Subject: [PATCH 30/46] Fix logic error introduced into _open_dataset_robustly() --- openmmtools/multistate/multistatereporter.py | 12 +++++------- 1 file changed, 5 insertions(+), 7 deletions(-) diff --git a/openmmtools/multistate/multistatereporter.py b/openmmtools/multistate/multistatereporter.py index d26c96f18..cf568cde5 100644 --- a/openmmtools/multistate/multistatereporter.py +++ b/openmmtools/multistate/multistatereporter.py @@ -355,11 +355,9 @@ def _open_dataset_robustly(self, *args, n_attempts=5, sleep_time=2, If the file is not found and catch_io_error is True, None is returned. """ # Catch eventual errors n_attempts - 1 times. - dataset = None - attempt = 0 - while (not dataset) and (attempt < n_attempts-1): + for attempt in range(n_attempts-1): try: - dataset = netcdf.Dataset(*args, **kwargs) + return netcdf.Dataset(*args, **kwargs) except IOError as e: # If the file does not exist, it doesn't make sense to try again. if catch_io_error: @@ -371,10 +369,10 @@ def _open_dataset_robustly(self, *args, n_attempts=5, sleep_time=2, logger.debug('Attempt {}/{} to open {} failed. Retrying ' 'in {} seconds'.format(i+1, n_attempts, sleep_time)) time.sleep(sleep_time) - # Last attempt finally raises any error. - dataset = netcdf.Dataset(*args, **kwargs) - return dataset + + # Last attempt finally raises any error. + return netcdf.Dataset(*args, **kwargs) def _initialize_storage_file(self, ncfile, nc_name, convention): """Helper function to initialize dimensions and global attributes. From 43610c397d0574c12d4e232bf9e5d6dbdf97eaba Mon Sep 17 00:00:00 2001 From: John Chodera Date: Wed, 30 Jan 2019 19:28:36 -0500 Subject: [PATCH 31/46] Debugging --- openmmtools/multistate/multistateanalyzer.py | 19 ++++++++++++++++--- 1 file changed, 16 insertions(+), 3 deletions(-) diff --git a/openmmtools/multistate/multistateanalyzer.py b/openmmtools/multistate/multistateanalyzer.py index 75aa94a95..14d42d895 100644 --- a/openmmtools/multistate/multistateanalyzer.py +++ b/openmmtools/multistate/multistateanalyzer.py @@ -1396,6 +1396,7 @@ def get_effective_energy_timeseries(self, energies=None, replica_state_indices=N log_weights = self.read_log_weights() f_l = - self.read_logZ(iteration=-1) # use last (best) estimate of free energies + u_n = np.zeros([n_iterations], np.float64) # Slice of all replicas, have to use this as : is too greedy replicas_slice = range(n_replicas) @@ -1405,9 +1406,21 @@ def get_effective_energy_timeseries(self, energies=None, replica_state_indices=N u_n[iteration] = np.sum(energies[replicas_slice, states_slice, iteration]) # Correct for potentially-changing log weights - if has_log_weights: - u_n[iteration] += - np.sum(log_weights[states_slice, iteration]) \ - + (n_replicas * logsumexp(-f_l[:] + log_weights[:, iteration])) + try: + if has_log_weights: + if np.ma.is_masked(f_l): + mask = f_l.mask + log_sum = logsumexp(-f_l[mask] + log_weights[mask, iteration]) + else: + log_sum = logsumexp(-f_l[:] + log_weights[:, iteration]) + u_n[iteration] += - np.sum(log_weights[states_slice, iteration]) \ + + (n_replicas * log_sum) + except Exception(e): + # DEBUG + print(f_l) + print(f_l.mask) + print(log_weights) + raise e logger.debug("Done.") return u_n From e48545160013c09bb2cb546a1ad4df686ccf0109 Mon Sep 17 00:00:00 2001 From: John Chodera Date: Wed, 30 Jan 2019 19:50:16 -0500 Subject: [PATCH 32/46] More debugging --- openmmtools/multistate/multistateanalyzer.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/openmmtools/multistate/multistateanalyzer.py b/openmmtools/multistate/multistateanalyzer.py index 14d42d895..2799abbeb 100644 --- a/openmmtools/multistate/multistateanalyzer.py +++ b/openmmtools/multistate/multistateanalyzer.py @@ -1409,13 +1409,13 @@ def get_effective_energy_timeseries(self, energies=None, replica_state_indices=N try: if has_log_weights: if np.ma.is_masked(f_l): - mask = f_l.mask - log_sum = logsumexp(-f_l[mask] + log_weights[mask, iteration]) + log_sum = logsumexp(-f_l.compressed() + log_weights[~f_l.mask, iteration]) else: log_sum = logsumexp(-f_l[:] + log_weights[:, iteration]) + u_n[iteration] += - np.sum(log_weights[states_slice, iteration]) \ + (n_replicas * log_sum) - except Exception(e): + except Exception as e: # DEBUG print(f_l) print(f_l.mask) From cb85cc92e65648167ce8f2282b182fa504de5c6f Mon Sep 17 00:00:00 2001 From: John Chodera Date: Wed, 30 Jan 2019 20:11:13 -0500 Subject: [PATCH 33/46] Handle masked log weights --- openmmtools/multistate/multistateanalyzer.py | 26 ++++++++------------ 1 file changed, 10 insertions(+), 16 deletions(-) diff --git a/openmmtools/multistate/multistateanalyzer.py b/openmmtools/multistate/multistateanalyzer.py index 2799abbeb..0d0aa6d78 100644 --- a/openmmtools/multistate/multistateanalyzer.py +++ b/openmmtools/multistate/multistateanalyzer.py @@ -1395,7 +1395,8 @@ def get_effective_energy_timeseries(self, energies=None, replica_state_indices=N has_log_weights = True log_weights = self.read_log_weights() f_l = - self.read_logZ(iteration=-1) # use last (best) estimate of free energies - + logger.debug("log_weights: {}".format(log_weights)) + logger.debug("f_k: {}".format(f_l)) u_n = np.zeros([n_iterations], np.float64) # Slice of all replicas, have to use this as : is too greedy @@ -1406,21 +1407,14 @@ def get_effective_energy_timeseries(self, energies=None, replica_state_indices=N u_n[iteration] = np.sum(energies[replicas_slice, states_slice, iteration]) # Correct for potentially-changing log weights - try: - if has_log_weights: - if np.ma.is_masked(f_l): - log_sum = logsumexp(-f_l.compressed() + log_weights[~f_l.mask, iteration]) - else: - log_sum = logsumexp(-f_l[:] + log_weights[:, iteration]) - - u_n[iteration] += - np.sum(log_weights[states_slice, iteration]) \ - + (n_replicas * log_sum) - except Exception as e: - # DEBUG - print(f_l) - print(f_l.mask) - print(log_weights) - raise e + if has_log_weights: + if np.ma.is_masked(log_weights): + log_sum = logsumexp(-f_l[:] + log_weights[:, iteration].compressed()) + else: + log_sum = logsumexp(-f_l[:] + log_weights[:, iteration]) + + u_n[iteration] += - np.sum(log_weights[states_slice, iteration]) \ + + (n_replicas * log_sum) logger.debug("Done.") return u_n From 5892dfd2ffcc329bf713a53a91c2f013f51c9f82 Mon Sep 17 00:00:00 2001 From: John Chodera Date: Thu, 31 Jan 2019 23:50:18 -0500 Subject: [PATCH 34/46] More debugging of logsumexp masking issues --- openmmtools/multistate/multistateanalyzer.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/openmmtools/multistate/multistateanalyzer.py b/openmmtools/multistate/multistateanalyzer.py index 0d0aa6d78..c09418d65 100644 --- a/openmmtools/multistate/multistateanalyzer.py +++ b/openmmtools/multistate/multistateanalyzer.py @@ -1408,6 +1408,12 @@ def get_effective_energy_timeseries(self, energies=None, replica_state_indices=N # Correct for potentially-changing log weights if has_log_weights: + # DEBUG + if np.ma.is_masked(log_weights): + print(log_weights) + if np.ma.is_masked(f_l): + print(f_l) + if np.ma.is_masked(log_weights): log_sum = logsumexp(-f_l[:] + log_weights[:, iteration].compressed()) else: From 5271c1530576031ec2901f0fb5a2f31e82678a9b Mon Sep 17 00:00:00 2001 From: John Chodera Date: Fri, 1 Feb 2019 00:29:34 -0500 Subject: [PATCH 35/46] Fix effective timeseries for local SAMS --- openmmtools/multistate/multistateanalyzer.py | 24 ++++++++------------ openmmtools/multistate/multistatesampler.py | 5 ---- 2 files changed, 9 insertions(+), 20 deletions(-) diff --git a/openmmtools/multistate/multistateanalyzer.py b/openmmtools/multistate/multistateanalyzer.py index c09418d65..be2014b31 100644 --- a/openmmtools/multistate/multistateanalyzer.py +++ b/openmmtools/multistate/multistateanalyzer.py @@ -1351,7 +1351,7 @@ def _get_radially_symmetric_restraint_data(self): # MBAR creation. # ------------------------------------------------------------------------- - def get_effective_energy_timeseries(self, energies=None, replica_state_indices=None): + def get_effective_energy_timeseries(self, energies=None, neighborhoods=None, replica_state_indices=None): """ Generate the effective energy (negative log deviance) timeseries that is generated for this phase. @@ -1366,6 +1366,8 @@ def get_effective_energy_timeseries(self, energies=None, replica_state_indices=N energies : ndarray of shape (K,L,N), optional, Default: None Energies from replicas K, sampled states L, and iterations N. If provided, then states input_sampled_states must also be provided. + neighborhoods : list of list of int, optional, Default: None + neighborhoods[iteration] is the list of states in the neighborhood of the currently sampled state replica_state_indices : ndarray of shape (K,N), optional, Default: None Integer indices of each sampled state (matching L dimension in input_energy). that each replica K sampled every iteration N. @@ -1380,7 +1382,7 @@ def get_effective_energy_timeseries(self, energies=None, replica_state_indices=N """ if energies is None and replica_state_indices is None: # Case where no input is provided - energies, _, _, replica_state_indices = self._read_energies(truncate_max_n_iterations=True) + energies, _, neighborhoods, replica_state_indices = self._read_energies(truncate_max_n_iterations=True) elif (energies is not None) != (replica_state_indices is not None): # XOR operator raise ValueError("If input_energy or input_sampled_states are provided, " @@ -1408,19 +1410,11 @@ def get_effective_energy_timeseries(self, energies=None, replica_state_indices=N # Correct for potentially-changing log weights if has_log_weights: - # DEBUG - if np.ma.is_masked(log_weights): - print(log_weights) - if np.ma.is_masked(f_l): - print(f_l) - - if np.ma.is_masked(log_weights): - log_sum = logsumexp(-f_l[:] + log_weights[:, iteration].compressed()) - else: - log_sum = logsumexp(-f_l[:] + log_weights[:, iteration]) - - u_n[iteration] += - np.sum(log_weights[states_slice, iteration]) \ - + (n_replicas * log_sum) + for replica_index in range(n_replicas): + state_index = replica_state_indices[replica_index, iteration] + neighborhood = neighborhoods[state_index] + u_n[iteration] += - log_weights[state_index, iteration] \ + + logsumexp(-f_l[neighborhood] + log_weights[neighborhood, iteration]) logger.debug("Done.") return u_n diff --git a/openmmtools/multistate/multistatesampler.py b/openmmtools/multistate/multistatesampler.py index 58b71234e..5300e39fb 100644 --- a/openmmtools/multistate/multistatesampler.py +++ b/openmmtools/multistate/multistatesampler.py @@ -1485,11 +1485,6 @@ def _online_analysis(self, gamma0=1.0): if self._last_mbar_f_k is None: self._last_mbar_f_k = np.zeros([self.n_states], np.float64) - # DEBUG - if np.ma.is_masked(self._last_mbar_f_k): - print('self._last_mbar_f_k is masked:') - print(self._last_mbar_f_k) - logZ = - self._last_mbar_f_k for (replica_index, state_index) in enumerate(self._replica_thermodynamic_states): From b12fde5ecf4f867e57b8bf6af5db3210012e22e4 Mon Sep 17 00:00:00 2001 From: John Chodera Date: Fri, 1 Feb 2019 10:52:16 -0500 Subject: [PATCH 36/46] Use kwargs instead of positional arguments --- openmmtools/multistate/multistateanalyzer.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/openmmtools/multistate/multistateanalyzer.py b/openmmtools/multistate/multistateanalyzer.py index be2014b31..d8a5dd745 100644 --- a/openmmtools/multistate/multistateanalyzer.py +++ b/openmmtools/multistate/multistateanalyzer.py @@ -1972,7 +1972,7 @@ def _get_equilibration_data(self, energies=None, replica_state_indices=None): statistical_inefficiency : float n_uncorrelated_iterations : int """ - u_n = self.get_effective_energy_timeseries(energies, replica_state_indices) + u_n = self.get_effective_energy_timeseries(energies=energies, replica_state_indices=replica_state_indices) # For SAMS, if there is a second-stage start time, use only the asymptotically optimal data t0 = 1 # discard minimization frame From 2d2484fe911d62f4fcdaab015f0a6512a813878f Mon Sep 17 00:00:00 2001 From: John Chodera Date: Fri, 1 Feb 2019 21:29:23 -0500 Subject: [PATCH 37/46] Fix logic errors in equilibration analysis for SAMS --- docs/multistate.rst | 4 +- openmmtools/multistate/multistateanalyzer.py | 54 ++++++++++++++------ 2 files changed, 40 insertions(+), 18 deletions(-) diff --git a/docs/multistate.rst b/docs/multistate.rst index d0c47cf63..ddcb3c653 100644 --- a/docs/multistate.rst +++ b/docs/multistate.rst @@ -1,7 +1,7 @@ .. _multistate: -Sampling from multiple thermodynamic states -=========================================== +Sampling multiple thermodynamic states +====================================== ``openmmtools`` provides several schemes for sampling from multiple thermodynamic states within a single calculation: diff --git a/openmmtools/multistate/multistateanalyzer.py b/openmmtools/multistate/multistateanalyzer.py index d8a5dd745..3114cee01 100644 --- a/openmmtools/multistate/multistateanalyzer.py +++ b/openmmtools/multistate/multistateanalyzer.py @@ -834,12 +834,25 @@ def read_energies(self): # TODO: should we keep it unified and always truncate to max_n_iterations? return self._read_energies(truncate_max_n_iterations=False) - def _read_energies(self, truncate_max_n_iterations): + def _read_energies(self, truncate_max_n_iterations=False): """ Extract energies from the ncfile and order them by replica, state, iteration. - Optionally truncate the data to self.max_n_iterations. + Parameters + ---------- + truncate_max_n_iterations : bool, optional, default=False + If True, will truncate the data to self.max_n_iterations. + Returns + ------- + sampled_energy_matrix : numpy.ndarray with shape (n_replicas, n_states, n_iterations) + ``sampled_energy_matrix[replica, state, iteration]`` is the reduced potential of replica ``replica`` at sampled state ``state`` for iteration ``iteration`` + unsampled_energy_matrix : numpy.ndarray with shape (n_replicas, n_states, n_iterations) + ``unsampled_energy_matrix[replica, state, iteration]`` is the reduced potential of replica i at unsampled state j for iteration ``iteration`` + neighborhoods : numpy.ndarray with shape (n_replicas, n_states, n_iterations) + ``neighborhoods[replica, state, iteration]`` is 1 if the energy for replica ``replica`` at iteration ``iteration`` was computed for state ``state``, 0 otherwise + replica_state_indices : numpy.ndarray with shape (n_replicas, n_iterations) + ``replica_state_indices[replica, iteration]`` is the thermodynamic state index sampled by replica ``replica`` at iteration ``iteration`` """ logger.debug("Reading energies...") # reporter_energies is [energy_sampled_states, neighborhoods, energy_unsampled_states]. @@ -1412,7 +1425,7 @@ def get_effective_energy_timeseries(self, energies=None, neighborhoods=None, rep if has_log_weights: for replica_index in range(n_replicas): state_index = replica_state_indices[replica_index, iteration] - neighborhood = neighborhoods[state_index] + neighborhood = neighborhoods[replica_index,:,iteration] u_n[iteration] += - log_weights[state_index, iteration] \ + logsumexp(-f_l[neighborhood] + log_weights[neighborhood, iteration]) @@ -1439,9 +1452,8 @@ def _compute_mbar_decorrelated_energies(self): energy_data = list(self._read_energies(truncate_max_n_iterations=True)) # Use the cached information to generate the equilibration data. - sampled_energy_matrix, unsampled_energy_matrix, neighborhood, replicas_state_indices = energy_data - number_equilibrated, g_t, Neff_max = self._get_equilibration_data(sampled_energy_matrix, - replicas_state_indices) + sampled_energy_matrix, unsampled_energy_matrix, neighborhoods, replicas_state_indices = energy_data + number_equilibrated, g_t, Neff_max = self._get_equilibration_data(sampled_energy_matrix, neighborhoods, replicas_state_indices) logger.debug("Assembling uncorrelated energies...") @@ -1953,7 +1965,7 @@ def get_entropy(self): entropy_dict = self._computed_observables['entropy'] return entropy_dict['value'], entropy_dict['error'] - def _get_equilibration_data(self, energies=None, replica_state_indices=None): + def _get_equilibration_data(self, energies=None, neighborhoods=None, replica_state_indices=None): """Generate the equilibration data from best practices. Parameters @@ -1961,6 +1973,10 @@ def _get_equilibration_data(self, energies=None, replica_state_indices=None): energies : ndarray of shape (K,L,N), optional, Default: None Energies from replicas K, sampled states L, and iterations N. If provided, then replica_state_indices must also be provided. + neighborhoods : numpy.ndarray with shape (n_replicas, n_states, n_iterations) + ``neighborhoods[replica, state, iteration]`` is 1 if the energy for + replica ``replica`` at iteration ``iteration`` was computed for state ``state``, + 0 otherwise replica_state_indices : ndarray of shape (K,N), optional, Default: None Integer indices of each sampled state (matching L dimension in input_energy). that each replica K sampled every iteration N. @@ -1969,10 +1985,13 @@ def _get_equilibration_data(self, energies=None, replica_state_indices=None): Returns ------- n_equilibration_iterations : int + Number of equilibration iterations discarded statistical_inefficiency : float - n_uncorrelated_iterations : int + Statistical inefficiency of production iterations + n_uncorrelated_iterations : float + Effective number of uncorrelated iterations """ - u_n = self.get_effective_energy_timeseries(energies=energies, replica_state_indices=replica_state_indices) + u_n = self.get_effective_energy_timeseries(energies=energies, neighborhoods=neighborhoods, replica_state_indices=replica_state_indices) # For SAMS, if there is a second-stage start time, use only the asymptotically optimal data t0 = 1 # discard minimization frame @@ -1992,14 +2011,17 @@ def _get_equilibration_data(self, energies=None, replica_state_indices=None): i_t, g_i, n_effective_i = multistate.utils.get_equilibration_data_per_sample(u_n[t0:]) n_effective_max = n_effective_i.max() i_max = n_effective_i.argmax() - n_equilibration = i_t[i_max] + n_equilibration = i_t[i_max] + t0 # account for initially discarded frames g_t = g_i[i_max] - equilibration_data = [n_equilibration, g_t, n_effective_max] - # Account for initially discarded frames - equilibration_data[0] += t0 - self._equilibration_data = tuple(equilibration_data) - logger.debug('Equilibration data: {}'.format(equilibration_data)) - return self._equilibration_data + + # Store equilibration data + self._equilibration_data = tuple([n_equilibration, g_t, n_effective_max]) + logger.debug('Equilibration data:') + logger.debug(' number of iterations discarded to equilibration : {}'.format(n_equilibration)) + logger.debug(' statistical inefficiency of production region : {}'.format(g_t)) + logger.debug(' effective number of uncorrelated samples : {}'.format(n_effective_max)) + + return n_equilibration, g_t, n_effective_max # ------------------------------------------------------------------------- # Cached properties. From f0d29a911b3cd76adfa32ed4054a435a4a9213cd Mon Sep 17 00:00:00 2001 From: John Chodera Date: Fri, 1 Feb 2019 21:33:14 -0500 Subject: [PATCH 38/46] Update release history --- docs/releasehistory.rst | 14 +++++++++++++- 1 file changed, 13 insertions(+), 1 deletion(-) diff --git a/docs/releasehistory.rst b/docs/releasehistory.rst index be794188a..f6a8b4b2e 100644 --- a/docs/releasehistory.rst +++ b/docs/releasehistory.rst @@ -1,6 +1,18 @@ Release History *************** +0.18.0 - Added multistate samplers +================================== + +New features +------------ +- Add a number of classes that can use MCMC to sample from multiple thermodynamic states: + - ``MultiStateSampler``: sample independently from multiple thermodynamic states + - ``ReplicaExchangeSampler``: replica exchange among thermodynamic states + - ``SAMSSampler``: self-adjusted mixture sampling (SAMS) sampling +- All samplers can use MPI via the ``mpiplus `_ package + + 0.17.0 - Removed Py2 support, faster exact PME treatment ======================================================== @@ -37,7 +49,7 @@ Deprecated and API breaks New features ------------ - Add ability for ``SamplerState`` to access new `OpenMM Custom CV Force Variables - `_ + `_ (`#362 `_). - ``SamplerState.update_from_context`` now has keywords to support finer grain updating from the Context. This is only recommended for advanced users (`#362 `_). From 8ded251de36e0e94a0b9da00b9f0a3cd9377f5fc Mon Sep 17 00:00:00 2001 From: John Chodera Date: Fri, 1 Feb 2019 21:33:28 -0500 Subject: [PATCH 39/46] Update setup.py version number to 0.18.0 --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 51696116b..4a0e26056 100644 --- a/setup.py +++ b/setup.py @@ -13,7 +13,7 @@ DOCLINES = __doc__.split("\n") ######################## -VERSION = "0.17.0" +VERSION = "0.18.0" ISRELEASED = True __version__ = VERSION ######################## From 7c297a755e2490e29006198eb4fba1201b5f7280 Mon Sep 17 00:00:00 2001 From: John Chodera Date: Sat, 2 Feb 2019 00:22:14 -0500 Subject: [PATCH 40/46] Make sure f_k is not a masked array. --- openmmtools/multistate/multistateanalyzer.py | 12 +++++++----- openmmtools/multistate/sams.py | 5 +++-- 2 files changed, 10 insertions(+), 7 deletions(-) diff --git a/openmmtools/multistate/multistateanalyzer.py b/openmmtools/multistate/multistateanalyzer.py index 3114cee01..e312b9524 100644 --- a/openmmtools/multistate/multistateanalyzer.py +++ b/openmmtools/multistate/multistateanalyzer.py @@ -907,6 +907,7 @@ def read_log_weights(self): """ log_weights = np.array( self._reporter.read_online_analysis_data(slice(None, None), 'log_weights')['log_weights']) + log_weights = np.moveaxis(log_weights, 0, -1) return log_weights @@ -934,6 +935,10 @@ def read_logZ(self, iteration=None): else: log_z = self._reporter.read_online_analysis_data(slice(0, None), "logZ")["logZ"] log_z = np.moveaxis(log_z, 0, -1) + + # We don't want logZ to be a masked array + log_z = np.array(log_z) + return log_z def get_effective_energy_timeseries(self, energies=None, replica_state_indices=None): @@ -1423,11 +1428,8 @@ def get_effective_energy_timeseries(self, energies=None, neighborhoods=None, rep # Correct for potentially-changing log weights if has_log_weights: - for replica_index in range(n_replicas): - state_index = replica_state_indices[replica_index, iteration] - neighborhood = neighborhoods[replica_index,:,iteration] - u_n[iteration] += - log_weights[state_index, iteration] \ - + logsumexp(-f_l[neighborhood] + log_weights[neighborhood, iteration]) + u_n[iteration] += - log_weights[states_slice, iteration] \ + + logsumexp(-f_l[:] + log_weights[:, iteration]) logger.debug("Done.") return u_n diff --git a/openmmtools/multistate/sams.py b/openmmtools/multistate/sams.py index dfd38a7e4..8884bbcb8 100644 --- a/openmmtools/multistate/sams.py +++ b/openmmtools/multistate/sams.py @@ -102,7 +102,7 @@ class SAMSSampler(multistate.MultiStateSampler): >>> move = mcmc.GHMCMove(timestep=2.0*unit.femtoseconds, n_steps=50) >>> simulation = SAMSSampler(mcmc_moves=move, number_of_iterations=2, ... state_update_scheme='global-jump', locality=5, - ... update_stages='two-stage', flatness_criteria='logZ-flatness', + ... update_stages='two-stage', flatness_criteria='logZ-flatness', ... flatness_threshold=0.2, weight_update_method='rao-blackwellized', ... adapt_target_probabilities=False) @@ -201,7 +201,7 @@ def __init__(self, ``two-stage`` will use a heuristic first stage to achieve flat histograms before switching to the asymptotically optimal scheme flatness_criteria : string, optiona, default='logZ-flatness' Method of assessing when to switch to asymptotically optimal scheme - One of ['logZ-flatness','minimum-visits','histogram-flatness'] + One of ['logZ-flatness','minimum-visits','histogram-flatness'] flatness_threshold : float, optional, default=0.2 Histogram relative flatness threshold to use for first stage of two-stage scheme. weight_update_method : str, optional, default='rao-blackwellized' @@ -382,6 +382,7 @@ def _restore_sampler_from_reporter(self, reporter): @mpiplus.delayed_termination def _report_iteration_items(self): super(SAMSSampler, self)._report_iteration_items() + self._reporter.write_online_data_dynamic_and_static(self._iteration, logZ=self._logZ, stage=self._stage, t0=self._t0) # Split into which states and how many samplers are in each state # Trying to do histogram[replica_thermo_states] += 1 does not correctly handle multiple From ee84788db5121900a50103d0558d38b8fb100d68 Mon Sep 17 00:00:00 2001 From: John Chodera Date: Sat, 2 Feb 2019 00:52:55 -0500 Subject: [PATCH 41/46] Forgot to sum --- openmmtools/multistate/multistateanalyzer.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/openmmtools/multistate/multistateanalyzer.py b/openmmtools/multistate/multistateanalyzer.py index e312b9524..a1e4376ef 100644 --- a/openmmtools/multistate/multistateanalyzer.py +++ b/openmmtools/multistate/multistateanalyzer.py @@ -1428,7 +1428,7 @@ def get_effective_energy_timeseries(self, energies=None, neighborhoods=None, rep # Correct for potentially-changing log weights if has_log_weights: - u_n[iteration] += - log_weights[states_slice, iteration] \ + u_n[iteration] += - np.sum(log_weights[states_slice, iteration]) \ + logsumexp(-f_l[:] + log_weights[:, iteration]) logger.debug("Done.") From 2a363fa6ad9c2797c742074b64425e57c3f241b9 Mon Sep 17 00:00:00 2001 From: John Chodera Date: Sat, 2 Feb 2019 19:47:14 -0500 Subject: [PATCH 42/46] Update environment.yml to match meta.yaml --- docs/environment.yml | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/docs/environment.yml b/docs/environment.yml index 80f1e520a..ee347a2d1 100644 --- a/docs/environment.yml +++ b/docs/environment.yml @@ -5,14 +5,18 @@ channels: dependencies: - python - setuptools - - cython - openmm >=7.3 + - cython - numpy - scipy - six - parmed - mdtraj - numpydoc - - netCDF4 + - netcdf4 >=1.4.2 # after bugfix: "always return masked array by default, even if there are no masked values" + - libnetcdf >=4.6.2 # workaround for libssl issues - pyyaml + - cython - sphinxcontrib-bibtex + - mpiplus + - pyyaml From 135a04dc582da268063f91b7e04a85b37604da6b Mon Sep 17 00:00:00 2001 From: John Chodera Date: Sat, 2 Feb 2019 21:10:44 -0500 Subject: [PATCH 43/46] Add some missing dependencies --- devtools/conda-recipe/meta.yaml | 2 + docs/environment.yml | 3 +- openmmtools/multistate/mixing/_mix_replicas.c | 2903 ++++++++--------- .../multistate/mixing/_mix_replicas.pyx | 3 +- 4 files changed, 1448 insertions(+), 1463 deletions(-) diff --git a/devtools/conda-recipe/meta.yaml b/devtools/conda-recipe/meta.yaml index fb6c2f75c..736c83e0f 100644 --- a/devtools/conda-recipe/meta.yaml +++ b/devtools/conda-recipe/meta.yaml @@ -30,6 +30,8 @@ requirements: - cython - sphinxcontrib-bibtex - mpiplus + - pymbar + - pyyaml test: requires: diff --git a/docs/environment.yml b/docs/environment.yml index ee347a2d1..1c71fcf80 100644 --- a/docs/environment.yml +++ b/docs/environment.yml @@ -15,8 +15,7 @@ dependencies: - numpydoc - netcdf4 >=1.4.2 # after bugfix: "always return masked array by default, even if there are no masked values" - libnetcdf >=4.6.2 # workaround for libssl issues - - pyyaml - - cython - sphinxcontrib-bibtex - mpiplus + - pymbar - pyyaml diff --git a/openmmtools/multistate/mixing/_mix_replicas.c b/openmmtools/multistate/mixing/_mix_replicas.c index c6e62fe23..f4edbde40 100644 --- a/openmmtools/multistate/mixing/_mix_replicas.c +++ b/openmmtools/multistate/mixing/_mix_replicas.c @@ -1,15 +1,15 @@ -/* Generated by Cython 0.29 */ +/* Generated by Cython 0.29.4 */ /* BEGIN: Cython Metadata { "distutils": { "depends": [], - "name": "yank.multistate.mixing._mix_replicas", + "name": "openmmtools.multistate.mixing._mix_replicas", "sources": [ - "./Yank/multistate/mixing/_mix_replicas.pyx" + "./openmmtools/multistate/mixing/_mix_replicas.pyx" ] }, - "module_name": "yank.multistate.mixing._mix_replicas" + "module_name": "openmmtools.multistate.mixing._mix_replicas" } END: Cython Metadata */ @@ -20,8 +20,8 @@ END: Cython Metadata */ #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" -#define CYTHON_HEX_VERSION 0x001D00F0 +#define CYTHON_ABI "0_29_4" +#define CYTHON_HEX_VERSION 0x001D04F0 #define CYTHON_FUTURE_DIVISION 0 #include #ifndef offsetof @@ -210,6 +210,9 @@ END: Cython Metadata */ #undef SHIFT #undef BASE #undef MASK + #ifdef SIZEOF_VOID_P + enum { __pyx_check_sizeof_voidp = 1 / (int)(SIZEOF_VOID_P == sizeof(void*)) }; + #endif #endif #ifndef __has_attribute #define __has_attribute(x) 0 @@ -408,7 +411,7 @@ END: Cython Metadata */ typedef int Py_tss_t; static CYTHON_INLINE int PyThread_tss_create(Py_tss_t *key) { *key = PyThread_create_key(); - return 0; // PyThread_create_key reports success always + return 0; } static CYTHON_INLINE Py_tss_t * PyThread_tss_alloc(void) { Py_tss_t *key = (Py_tss_t *)PyObject_Malloc(sizeof(Py_tss_t)); @@ -431,7 +434,7 @@ static CYTHON_INLINE int PyThread_tss_set(Py_tss_t *key, void *value) { static CYTHON_INLINE void * PyThread_tss_get(Py_tss_t *key) { return PyThread_get_key_value(*key); } -#endif // TSS (Thread Specific Storage) API +#endif #if CYTHON_COMPILING_IN_CPYTHON || defined(_PyDict_NewPresized) #define __Pyx_PyDict_NewPresized(n) ((n <= 8) ? PyDict_New() : _PyDict_NewPresized(n)) #else @@ -615,8 +618,8 @@ static CYTHON_INLINE float __PYX_NAN() { #endif #endif -#define __PYX_HAVE__yank__multistate__mixing___mix_replicas -#define __PYX_HAVE_API__yank__multistate__mixing___mix_replicas +#define __PYX_HAVE__openmmtools__multistate__mixing___mix_replicas +#define __PYX_HAVE_API__openmmtools__multistate__mixing___mix_replicas /* Early includes */ #include #include @@ -832,7 +835,7 @@ static const char *__pyx_filename; static const char *__pyx_f[] = { - "Yank/multistate/mixing/_mix_replicas.pyx", + "openmmtools/multistate/mixing/_mix_replicas.pyx", "stringsource", }; /* MemviewSliceStruct.proto */ @@ -950,7 +953,7 @@ struct __pyx_MemviewEnum_obj; struct __pyx_memoryview_obj; struct __pyx_memoryviewslice_obj; -/* "View.MemoryView":104 +/* "View.MemoryView":105 * * @cname("__pyx_array") * cdef class array: # <<<<<<<<<<<<<< @@ -975,7 +978,7 @@ struct __pyx_array_obj { }; -/* "View.MemoryView":278 +/* "View.MemoryView":279 * * @cname('__pyx_MemviewEnum') * cdef class Enum(object): # <<<<<<<<<<<<<< @@ -988,7 +991,7 @@ struct __pyx_MemviewEnum_obj { }; -/* "View.MemoryView":329 +/* "View.MemoryView":330 * * @cname('__pyx_memoryview') * cdef class memoryview(object): # <<<<<<<<<<<<<< @@ -1011,7 +1014,7 @@ struct __pyx_memoryview_obj { }; -/* "View.MemoryView":960 +/* "View.MemoryView":961 * * @cname('__pyx_memoryviewslice') * cdef class _memoryviewslice(memoryview): # <<<<<<<<<<<<<< @@ -1028,7 +1031,7 @@ struct __pyx_memoryviewslice_obj { -/* "View.MemoryView":104 +/* "View.MemoryView":105 * * @cname("__pyx_array") * cdef class array: # <<<<<<<<<<<<<< @@ -1042,7 +1045,7 @@ struct __pyx_vtabstruct_array { static struct __pyx_vtabstruct_array *__pyx_vtabptr_array; -/* "View.MemoryView":329 +/* "View.MemoryView":330 * * @cname('__pyx_memoryview') * cdef class memoryview(object): # <<<<<<<<<<<<<< @@ -1062,7 +1065,7 @@ struct __pyx_vtabstruct_memoryview { static struct __pyx_vtabstruct_memoryview *__pyx_vtabptr_memoryview; -/* "View.MemoryView":960 +/* "View.MemoryView":961 * * @cname('__pyx_memoryviewslice') * cdef class _memoryviewslice(memoryview): # <<<<<<<<<<<<<< @@ -1267,7 +1270,7 @@ static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, #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 = PyFrame_Type.tp_basicsize - 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 @@ -1472,9 +1475,9 @@ static CYTHON_INLINE int __Pyx_ListComp_Append(PyObject* list, PyObject* x) { /* PyIntBinop.proto */ #if !CYTHON_COMPILING_IN_PYPY -static PyObject* __Pyx_PyInt_AddObjC(PyObject *op1, PyObject *op2, long intval, int inplace); +static PyObject* __Pyx_PyInt_AddObjC(PyObject *op1, PyObject *op2, long intval, int inplace, int zerodivision_check); #else -#define __Pyx_PyInt_AddObjC(op1, op2, intval, inplace)\ +#define __Pyx_PyInt_AddObjC(op1, op2, intval, inplace, zerodivision_check)\ (inplace ? PyNumber_InPlaceAdd(op1, op2) : PyNumber_Add(op1, op2)) #endif @@ -1683,7 +1686,7 @@ static PyObject *__pyx_memoryviewslice_assign_item_from_object(struct __pyx_memo /* Module declarations from 'libc.stdio' */ -/* Module declarations from 'yank.multistate.mixing._mix_replicas' */ +/* Module declarations from 'openmmtools.multistate.mixing._mix_replicas' */ static PyTypeObject *__pyx_array_type = 0; static PyTypeObject *__pyx_MemviewEnum_type = 0; static PyTypeObject *__pyx_memoryview_type = 0; @@ -1695,7 +1698,7 @@ static PyObject *contiguous = 0; static PyObject *indirect_contiguous = 0; static int __pyx_memoryview_thread_locks_used; static PyThread_type_lock __pyx_memoryview_thread_locks[8]; -static long __pyx_f_4yank_10multistate_6mixing_13_mix_replicas__mix_replicas_cython(long, long, __Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, int __pyx_skip_dispatch); /*proto*/ +static long __pyx_f_11openmmtools_10multistate_6mixing_13_mix_replicas__mix_replicas_cython(long, long, __Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, int __pyx_skip_dispatch); /*proto*/ static struct __pyx_array_obj *__pyx_array_new(PyObject *, Py_ssize_t, char *, char *, char *); /*proto*/ static void *__pyx_align_pointer(void *, size_t); /*proto*/ static PyObject *__pyx_memoryview_new(PyObject *, int, int, __Pyx_TypeInfo *); /*proto*/ @@ -1731,11 +1734,11 @@ static void __pyx_memoryview__slice_assign_scalar(char *, Py_ssize_t *, Py_ssize static PyObject *__pyx_unpickle_Enum__set_state(struct __pyx_MemviewEnum_obj *, PyObject *); /*proto*/ static __Pyx_TypeInfo __Pyx_TypeInfo_long = { "long", NULL, sizeof(long), { 0 }, 0, IS_UNSIGNED(long) ? 'U' : 'I', IS_UNSIGNED(long), 0 }; static __Pyx_TypeInfo __Pyx_TypeInfo_double = { "double", NULL, sizeof(double), { 0 }, 0, 'R', 0, 0 }; -#define __Pyx_MODULE_NAME "yank.multistate.mixing._mix_replicas" -extern int __pyx_module_is_main_yank__multistate__mixing___mix_replicas; -int __pyx_module_is_main_yank__multistate__mixing___mix_replicas = 0; +#define __Pyx_MODULE_NAME "openmmtools.multistate.mixing._mix_replicas" +extern int __pyx_module_is_main_openmmtools__multistate__mixing___mix_replicas; +int __pyx_module_is_main_openmmtools__multistate__mixing___mix_replicas = 0; -/* Implementation of 'yank.multistate.mixing._mix_replicas' */ +/* Implementation of 'openmmtools.multistate.mixing._mix_replicas' */ static PyObject *__pyx_builtin_range; static PyObject *__pyx_builtin_ValueError; static PyObject *__pyx_builtin_MemoryError; @@ -1926,7 +1929,7 @@ static PyObject *__pyx_kp_s_unable_to_allocate_array_data; static PyObject *__pyx_kp_s_unable_to_allocate_shape_and_str; static PyObject *__pyx_n_s_unpack; static PyObject *__pyx_n_s_update; -static PyObject *__pyx_pf_4yank_10multistate_6mixing_13_mix_replicas__mix_replicas_cython(CYTHON_UNUSED PyObject *__pyx_self, long __pyx_v_nswap_attempts, long __pyx_v_nstates, __Pyx_memviewslice __pyx_v_replica_states, __Pyx_memviewslice __pyx_v_u_kl, __Pyx_memviewslice __pyx_v_Nij_proposed, __Pyx_memviewslice __pyx_v_Nij_accepted); /* proto */ +static PyObject *__pyx_pf_11openmmtools_10multistate_6mixing_13_mix_replicas__mix_replicas_cython(CYTHON_UNUSED PyObject *__pyx_self, long __pyx_v_nswap_attempts, long __pyx_v_nstates, __Pyx_memviewslice __pyx_v_replica_states, __Pyx_memviewslice __pyx_v_u_kl, __Pyx_memviewslice __pyx_v_Nij_proposed, __Pyx_memviewslice __pyx_v_Nij_accepted); /* proto */ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array___cinit__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_shape, Py_ssize_t __pyx_v_itemsize, PyObject *__pyx_v_format, PyObject *__pyx_v_mode, int __pyx_v_allocate_buffer); /* proto */ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(struct __pyx_array_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */ static void __pyx_array___pyx_pf_15View_dot_MemoryView_5array_4__dealloc__(struct __pyx_array_obj *__pyx_v_self); /* proto */ @@ -2004,7 +2007,7 @@ static PyObject *__pyx_tuple__24; static PyObject *__pyx_codeobj__25; /* Late includes */ -/* "Yank/multistate/mixing/_mix_replicas.pyx":10 +/* "openmmtools/multistate/mixing/_mix_replicas.pyx":10 * @cython.cdivision(True) * @cython.boundscheck(False) * cpdef long _mix_replicas_cython(long nswap_attempts, long nstates, long[:] replica_states, double[:,:] u_kl, long[:,:] Nij_proposed, long[:,:] Nij_accepted) nogil: # <<<<<<<<<<<<<< @@ -2012,8 +2015,8 @@ static PyObject *__pyx_codeobj__25; * cdef long i, j, istate, jstate, tmp_state */ -static PyObject *__pyx_pw_4yank_10multistate_6mixing_13_mix_replicas_1_mix_replicas_cython(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ -static long __pyx_f_4yank_10multistate_6mixing_13_mix_replicas__mix_replicas_cython(long __pyx_v_nswap_attempts, long __pyx_v_nstates, __Pyx_memviewslice __pyx_v_replica_states, __Pyx_memviewslice __pyx_v_u_kl, __Pyx_memviewslice __pyx_v_Nij_proposed, __Pyx_memviewslice __pyx_v_Nij_accepted, CYTHON_UNUSED int __pyx_skip_dispatch) { +static PyObject *__pyx_pw_11openmmtools_10multistate_6mixing_13_mix_replicas_1_mix_replicas_cython(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ +static long __pyx_f_11openmmtools_10multistate_6mixing_13_mix_replicas__mix_replicas_cython(long __pyx_v_nswap_attempts, long __pyx_v_nstates, __Pyx_memviewslice __pyx_v_replica_states, __Pyx_memviewslice __pyx_v_u_kl, __Pyx_memviewslice __pyx_v_Nij_proposed, __Pyx_memviewslice __pyx_v_Nij_accepted, CYTHON_UNUSED int __pyx_skip_dispatch) { CYTHON_UNUSED long __pyx_v_swap_attempt; long __pyx_v_i; long __pyx_v_j; @@ -2058,7 +2061,7 @@ static long __pyx_f_4yank_10multistate_6mixing_13_mix_replicas__mix_replicas_cyt Py_ssize_t __pyx_t_34; Py_ssize_t __pyx_t_35; - /* "Yank/multistate/mixing/_mix_replicas.pyx":14 + /* "openmmtools/multistate/mixing/_mix_replicas.pyx":14 * cdef long i, j, istate, jstate, tmp_state * cdef double log_P_accept * for swap_attempt in range(nswap_attempts): # <<<<<<<<<<<<<< @@ -2070,7 +2073,7 @@ static long __pyx_f_4yank_10multistate_6mixing_13_mix_replicas__mix_replicas_cyt for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { __pyx_v_swap_attempt = __pyx_t_3; - /* "Yank/multistate/mixing/_mix_replicas.pyx":15 + /* "openmmtools/multistate/mixing/_mix_replicas.pyx":15 * cdef double log_P_accept * for swap_attempt in range(nswap_attempts): * i = (drand48()*nstates) # <<<<<<<<<<<<<< @@ -2079,7 +2082,7 @@ static long __pyx_f_4yank_10multistate_6mixing_13_mix_replicas__mix_replicas_cyt */ __pyx_v_i = ((long)(drand48() * __pyx_v_nstates)); - /* "Yank/multistate/mixing/_mix_replicas.pyx":16 + /* "openmmtools/multistate/mixing/_mix_replicas.pyx":16 * for swap_attempt in range(nswap_attempts): * i = (drand48()*nstates) * j = (drand48()*nstates) # <<<<<<<<<<<<<< @@ -2088,7 +2091,7 @@ static long __pyx_f_4yank_10multistate_6mixing_13_mix_replicas__mix_replicas_cyt */ __pyx_v_j = ((long)(drand48() * __pyx_v_nstates)); - /* "Yank/multistate/mixing/_mix_replicas.pyx":17 + /* "openmmtools/multistate/mixing/_mix_replicas.pyx":17 * i = (drand48()*nstates) * j = (drand48()*nstates) * istate = replica_states[i] # <<<<<<<<<<<<<< @@ -2098,7 +2101,7 @@ static long __pyx_f_4yank_10multistate_6mixing_13_mix_replicas__mix_replicas_cyt __pyx_t_4 = __pyx_v_i; __pyx_v_istate = (*((long *) ( /* dim=0 */ (__pyx_v_replica_states.data + __pyx_t_4 * __pyx_v_replica_states.strides[0]) ))); - /* "Yank/multistate/mixing/_mix_replicas.pyx":18 + /* "openmmtools/multistate/mixing/_mix_replicas.pyx":18 * j = (drand48()*nstates) * istate = replica_states[i] * jstate = replica_states[j] # <<<<<<<<<<<<<< @@ -2108,7 +2111,7 @@ static long __pyx_f_4yank_10multistate_6mixing_13_mix_replicas__mix_replicas_cyt __pyx_t_5 = __pyx_v_j; __pyx_v_jstate = (*((long *) ( /* dim=0 */ (__pyx_v_replica_states.data + __pyx_t_5 * __pyx_v_replica_states.strides[0]) ))); - /* "Yank/multistate/mixing/_mix_replicas.pyx":19 + /* "openmmtools/multistate/mixing/_mix_replicas.pyx":19 * istate = replica_states[i] * jstate = replica_states[j] * if (isnan(u_kl[i, istate]) or isnan(u_kl[i, jstate]) or isnan(u_kl[j, istate]) or isnan(u_kl[j, jstate])): # <<<<<<<<<<<<<< @@ -2146,7 +2149,7 @@ static long __pyx_f_4yank_10multistate_6mixing_13_mix_replicas__mix_replicas_cyt __pyx_L6_bool_binop_done:; if (__pyx_t_6) { - /* "Yank/multistate/mixing/_mix_replicas.pyx":20 + /* "openmmtools/multistate/mixing/_mix_replicas.pyx":20 * jstate = replica_states[j] * if (isnan(u_kl[i, istate]) or isnan(u_kl[i, jstate]) or isnan(u_kl[j, istate]) or isnan(u_kl[j, jstate])): * continue # <<<<<<<<<<<<<< @@ -2155,7 +2158,7 @@ static long __pyx_f_4yank_10multistate_6mixing_13_mix_replicas__mix_replicas_cyt */ goto __pyx_L3_continue; - /* "Yank/multistate/mixing/_mix_replicas.pyx":19 + /* "openmmtools/multistate/mixing/_mix_replicas.pyx":19 * istate = replica_states[i] * jstate = replica_states[j] * if (isnan(u_kl[i, istate]) or isnan(u_kl[i, jstate]) or isnan(u_kl[j, istate]) or isnan(u_kl[j, jstate])): # <<<<<<<<<<<<<< @@ -2164,7 +2167,7 @@ static long __pyx_f_4yank_10multistate_6mixing_13_mix_replicas__mix_replicas_cyt */ } - /* "Yank/multistate/mixing/_mix_replicas.pyx":21 + /* "openmmtools/multistate/mixing/_mix_replicas.pyx":21 * if (isnan(u_kl[i, istate]) or isnan(u_kl[i, jstate]) or isnan(u_kl[j, istate]) or isnan(u_kl[j, jstate])): * continue * log_P_accept = - (u_kl[i, jstate] + u_kl[j, istate]) + (u_kl[j, jstate] + u_kl[i, istate]) # <<<<<<<<<<<<<< @@ -2181,7 +2184,7 @@ static long __pyx_f_4yank_10multistate_6mixing_13_mix_replicas__mix_replicas_cyt __pyx_t_23 = __pyx_v_istate; 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__pyx_t_10 = 0; __pyx_v_self->dtype_is_object = __pyx_t_4; - /* "View.MemoryView":170 + /* "View.MemoryView":171 * self.free_data = allocate_buffer * self.dtype_is_object = format == b'O' * if allocate_buffer: # <<<<<<<<<<<<<< @@ -3047,7 +3048,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array___cinit__(struct __ __pyx_t_4 = (__pyx_v_allocate_buffer != 0); if (__pyx_t_4) { - /* "View.MemoryView":173 + /* "View.MemoryView":174 * * * self.data = malloc(self.len) # <<<<<<<<<<<<<< @@ -3056,7 +3057,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array___cinit__(struct __ */ __pyx_v_self->data = ((char *)malloc(__pyx_v_self->len)); - /* "View.MemoryView":174 + /* "View.MemoryView":175 * * self.data = malloc(self.len) * if not self.data: # <<<<<<<<<<<<<< @@ -3066,20 +3067,20 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array___cinit__(struct __ __pyx_t_4 = ((!(__pyx_v_self->data != 0)) != 0); if (unlikely(__pyx_t_4)) { - /* "View.MemoryView":175 + /* "View.MemoryView":176 * self.data = malloc(self.len) * if not self.data: * raise MemoryError("unable to allocate array data.") # <<<<<<<<<<<<<< * * if self.dtype_is_object: */ - __pyx_t_10 = __Pyx_PyObject_Call(__pyx_builtin_MemoryError, __pyx_tuple__4, NULL); if (unlikely(!__pyx_t_10)) __PYX_ERR(1, 175, __pyx_L1_error) + __pyx_t_10 = __Pyx_PyObject_Call(__pyx_builtin_MemoryError, __pyx_tuple__4, NULL); if (unlikely(!__pyx_t_10)) __PYX_ERR(1, 176, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __Pyx_Raise(__pyx_t_10, 0, 0, 0); __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; - __PYX_ERR(1, 175, __pyx_L1_error) + __PYX_ERR(1, 176, __pyx_L1_error) - /* "View.MemoryView":174 + /* "View.MemoryView":175 * * self.data = malloc(self.len) * if not self.data: # <<<<<<<<<<<<<< @@ -3088,7 +3089,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array___cinit__(struct __ */ } - /* "View.MemoryView":177 + /* "View.MemoryView":178 * raise MemoryError("unable to allocate array data.") * * if self.dtype_is_object: # <<<<<<<<<<<<<< @@ -3098,7 +3099,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array___cinit__(struct __ __pyx_t_4 = (__pyx_v_self->dtype_is_object != 0); if (__pyx_t_4) { - /* "View.MemoryView":178 + /* "View.MemoryView":179 * * if self.dtype_is_object: * p = self.data # <<<<<<<<<<<<<< @@ -3107,7 +3108,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array___cinit__(struct __ */ __pyx_v_p = ((PyObject **)__pyx_v_self->data); 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- /* "View.MemoryView":181 + /* "View.MemoryView":182 * for i in range(self.len / itemsize): * p[i] = Py_None * Py_INCREF(Py_None) # <<<<<<<<<<<<<< @@ -3146,7 +3147,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array___cinit__(struct __ Py_INCREF(Py_None); } - /* "View.MemoryView":177 + /* "View.MemoryView":178 * raise MemoryError("unable to allocate array data.") * * if self.dtype_is_object: # <<<<<<<<<<<<<< @@ -3155,7 +3156,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array___cinit__(struct __ */ } - /* "View.MemoryView":170 + /* "View.MemoryView":171 * self.free_data = allocate_buffer * self.dtype_is_object = format == b'O' * if allocate_buffer: # <<<<<<<<<<<<<< @@ -3164,7 +3165,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array___cinit__(struct __ */ } - /* "View.MemoryView":121 + /* "View.MemoryView":122 * cdef bint dtype_is_object * * def __cinit__(array self, tuple shape, Py_ssize_t itemsize, format not None, # <<<<<<<<<<<<<< @@ -3188,7 +3189,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array___cinit__(struct __ return __pyx_r; 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if (unlikely(__pyx_t_2 < 0)) __PYX_ERR(1, 188, __pyx_L1_error) + __pyx_t_2 = (__Pyx_PyUnicode_Equals(__pyx_v_self->mode, __pyx_n_u_fortran, Py_EQ)); if (unlikely(__pyx_t_2 < 0)) __PYX_ERR(1, 189, __pyx_L1_error) __pyx_t_1 = (__pyx_t_2 != 0); if (__pyx_t_1) { - /* "View.MemoryView":189 + /* "View.MemoryView":190 * bufmode = PyBUF_C_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS * elif self.mode == u"fortran": * bufmode = PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS # <<<<<<<<<<<<<< @@ -3287,7 +3288,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(stru */ __pyx_v_bufmode = (PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS); - /* "View.MemoryView":188 + /* "View.MemoryView":189 * if self.mode == u"c": * bufmode = PyBUF_C_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS * elif self.mode == u"fortran": # <<<<<<<<<<<<<< @@ -3297,7 +3298,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(stru } __pyx_L3:; - /* "View.MemoryView":190 + /* "View.MemoryView":191 * elif self.mode == u"fortran": * bufmode = PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS * if not (flags & bufmode): # <<<<<<<<<<<<<< @@ -3307,20 +3308,20 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(stru __pyx_t_1 = ((!((__pyx_v_flags & __pyx_v_bufmode) != 0)) != 0); if (unlikely(__pyx_t_1)) { - /* "View.MemoryView":191 + /* "View.MemoryView":192 * bufmode = PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS * if not (flags & bufmode): * raise ValueError("Can only create a buffer that is contiguous in memory.") # <<<<<<<<<<<<<< * info.buf = self.data * info.len = self.len */ - __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__5, NULL); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 191, __pyx_L1_error) + __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__5, NULL); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 192, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_Raise(__pyx_t_3, 0, 0, 0); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; - __PYX_ERR(1, 191, __pyx_L1_error) + __PYX_ERR(1, 192, __pyx_L1_error) - /* "View.MemoryView":190 + /* "View.MemoryView":191 * elif self.mode == u"fortran": * bufmode = PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS * if not (flags & bufmode): # <<<<<<<<<<<<<< @@ -3329,7 +3330,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(stru */ } - /* "View.MemoryView":192 + /* "View.MemoryView":193 * if not (flags & bufmode): * raise ValueError("Can only create a buffer that is contiguous in memory.") * info.buf = self.data # <<<<<<<<<<<<<< @@ -3339,7 +3340,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(stru __pyx_t_4 = __pyx_v_self->data; __pyx_v_info->buf = __pyx_t_4; - /* "View.MemoryView":193 + /* "View.MemoryView":194 * raise ValueError("Can only create a buffer that is contiguous in memory.") * info.buf = self.data * info.len = self.len # <<<<<<<<<<<<<< @@ -3349,7 +3350,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(stru __pyx_t_5 = __pyx_v_self->len; __pyx_v_info->len = __pyx_t_5; - /* "View.MemoryView":194 + /* "View.MemoryView":195 * info.buf = self.data * info.len = self.len * info.ndim = self.ndim # <<<<<<<<<<<<<< @@ -3359,7 +3360,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(stru __pyx_t_6 = __pyx_v_self->ndim; __pyx_v_info->ndim = __pyx_t_6; - /* "View.MemoryView":195 + /* "View.MemoryView":196 * info.len = self.len * info.ndim = self.ndim * info.shape = self._shape # <<<<<<<<<<<<<< @@ -3369,7 +3370,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(stru __pyx_t_7 = __pyx_v_self->_shape; __pyx_v_info->shape = __pyx_t_7; - /* "View.MemoryView":196 + /* "View.MemoryView":197 * info.ndim = self.ndim * info.shape = self._shape * info.strides = self._strides # <<<<<<<<<<<<<< @@ -3379,7 +3380,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(stru __pyx_t_7 = __pyx_v_self->_strides; __pyx_v_info->strides = __pyx_t_7; - /* "View.MemoryView":197 + /* "View.MemoryView":198 * info.shape = self._shape * info.strides = self._strides * info.suboffsets = NULL # <<<<<<<<<<<<<< @@ -3388,7 +3389,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(stru */ __pyx_v_info->suboffsets = NULL; - /* "View.MemoryView":198 + /* "View.MemoryView":199 * info.strides = self._strides * info.suboffsets = NULL * info.itemsize = self.itemsize # <<<<<<<<<<<<<< @@ -3398,7 +3399,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(stru __pyx_t_5 = __pyx_v_self->itemsize; __pyx_v_info->itemsize = __pyx_t_5; - /* "View.MemoryView":199 + /* "View.MemoryView":200 * info.suboffsets = NULL * info.itemsize = self.itemsize * info.readonly = 0 # <<<<<<<<<<<<<< @@ -3407,7 +3408,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(stru */ __pyx_v_info->readonly = 0; - /* "View.MemoryView":201 + /* "View.MemoryView":202 * info.readonly = 0 * * if flags & PyBUF_FORMAT: # <<<<<<<<<<<<<< @@ -3417,7 +3418,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(stru __pyx_t_1 = ((__pyx_v_flags & PyBUF_FORMAT) != 0); if (__pyx_t_1) { - /* "View.MemoryView":202 + /* "View.MemoryView":203 * * if flags & PyBUF_FORMAT: * info.format = self.format # <<<<<<<<<<<<<< @@ -3427,7 +3428,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(stru __pyx_t_4 = __pyx_v_self->format; __pyx_v_info->format = __pyx_t_4; - /* "View.MemoryView":201 + /* "View.MemoryView":202 * info.readonly = 0 * * if flags & PyBUF_FORMAT: # <<<<<<<<<<<<<< @@ -3437,7 +3438,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(stru goto __pyx_L5; } - /* "View.MemoryView":204 + /* "View.MemoryView":205 * info.format = self.format * else: * info.format = NULL # <<<<<<<<<<<<<< @@ -3449,7 +3450,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(stru } __pyx_L5:; - /* "View.MemoryView":206 + /* "View.MemoryView":207 * info.format = NULL * * info.obj = self # <<<<<<<<<<<<<< @@ -3462,7 +3463,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(stru __Pyx_DECREF(__pyx_v_info->obj); __pyx_v_info->obj = ((PyObject *)__pyx_v_self); - /* "View.MemoryView":184 + /* "View.MemoryView":185 * * @cname('getbuffer') * def __getbuffer__(self, Py_buffer *info, int flags): # <<<<<<<<<<<<<< @@ -3492,7 +3493,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(stru return __pyx_r; } -/* "View.MemoryView":210 +/* "View.MemoryView":211 * __pyx_getbuffer = capsule( &__pyx_array_getbuffer, "getbuffer(obj, view, flags)") * * def __dealloc__(array self): # <<<<<<<<<<<<<< @@ -3516,7 +3517,7 @@ static void __pyx_array___pyx_pf_15View_dot_MemoryView_5array_4__dealloc__(struc int __pyx_t_1; __Pyx_RefNannySetupContext("__dealloc__", 0); - /* "View.MemoryView":211 + /* "View.MemoryView":212 * * def __dealloc__(array self): * if self.callback_free_data != NULL: # <<<<<<<<<<<<<< @@ -3526,7 +3527,7 @@ static void __pyx_array___pyx_pf_15View_dot_MemoryView_5array_4__dealloc__(struc __pyx_t_1 = ((__pyx_v_self->callback_free_data != NULL) != 0); if (__pyx_t_1) { - /* "View.MemoryView":212 + /* "View.MemoryView":213 * def __dealloc__(array self): * if self.callback_free_data != NULL: * self.callback_free_data(self.data) # <<<<<<<<<<<<<< @@ -3535,7 +3536,7 @@ static void __pyx_array___pyx_pf_15View_dot_MemoryView_5array_4__dealloc__(struc */ __pyx_v_self->callback_free_data(__pyx_v_self->data); - /* "View.MemoryView":211 + /* "View.MemoryView":212 * * def __dealloc__(array self): * if self.callback_free_data != NULL: # <<<<<<<<<<<<<< @@ -3545,7 +3546,7 @@ static void __pyx_array___pyx_pf_15View_dot_MemoryView_5array_4__dealloc__(struc goto __pyx_L3; } - /* "View.MemoryView":213 + /* "View.MemoryView":214 * if self.callback_free_data != NULL: * self.callback_free_data(self.data) * elif self.free_data: # <<<<<<<<<<<<<< @@ -3555,7 +3556,7 @@ static void __pyx_array___pyx_pf_15View_dot_MemoryView_5array_4__dealloc__(struc __pyx_t_1 = (__pyx_v_self->free_data != 0); if (__pyx_t_1) { - /* "View.MemoryView":214 + /* "View.MemoryView":215 * self.callback_free_data(self.data) * elif self.free_data: * if self.dtype_is_object: # <<<<<<<<<<<<<< @@ -3565,7 +3566,7 @@ static void __pyx_array___pyx_pf_15View_dot_MemoryView_5array_4__dealloc__(struc __pyx_t_1 = (__pyx_v_self->dtype_is_object != 0); if (__pyx_t_1) { - /* "View.MemoryView":215 + /* "View.MemoryView":216 * elif self.free_data: * if self.dtype_is_object: * refcount_objects_in_slice(self.data, self._shape, # <<<<<<<<<<<<<< @@ -3574,7 +3575,7 @@ static void __pyx_array___pyx_pf_15View_dot_MemoryView_5array_4__dealloc__(struc */ __pyx_memoryview_refcount_objects_in_slice(__pyx_v_self->data, __pyx_v_self->_shape, __pyx_v_self->_strides, __pyx_v_self->ndim, 0); - /* "View.MemoryView":214 + /* "View.MemoryView":215 * self.callback_free_data(self.data) * elif self.free_data: * if self.dtype_is_object: # <<<<<<<<<<<<<< @@ -3583,7 +3584,7 @@ static void __pyx_array___pyx_pf_15View_dot_MemoryView_5array_4__dealloc__(struc */ } - /* "View.MemoryView":217 + /* "View.MemoryView":218 * refcount_objects_in_slice(self.data, self._shape, * self._strides, self.ndim, False) * free(self.data) # <<<<<<<<<<<<<< @@ -3592,7 +3593,7 @@ static void __pyx_array___pyx_pf_15View_dot_MemoryView_5array_4__dealloc__(struc */ free(__pyx_v_self->data); 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- __pyx_t_2 = __Pyx_PyBool_FromLong(__pyx_v_self->dtype_is_object); if (unlikely(!__pyx_t_2)) __PYX_ERR(1, 227, __pyx_L1_error) + __pyx_t_2 = __Pyx_PyBool_FromLong(__pyx_v_self->dtype_is_object); if (unlikely(!__pyx_t_2)) __PYX_ERR(1, 228, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); - __pyx_t_3 = PyTuple_New(3); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 227, __pyx_L1_error) + __pyx_t_3 = PyTuple_New(3); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 228, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_INCREF(((PyObject *)__pyx_v_self)); __Pyx_GIVEREF(((PyObject *)__pyx_v_self)); @@ -3732,14 +3733,14 @@ static PyObject *__pyx_array_get_memview(struct __pyx_array_obj *__pyx_v_self) { PyTuple_SET_ITEM(__pyx_t_3, 2, __pyx_t_2); __pyx_t_1 = 0; __pyx_t_2 = 0; - __pyx_t_2 = __Pyx_PyObject_Call(((PyObject *)__pyx_memoryview_type), __pyx_t_3, NULL); if (unlikely(!__pyx_t_2)) __PYX_ERR(1, 227, __pyx_L1_error) + __pyx_t_2 = __Pyx_PyObject_Call(((PyObject *)__pyx_memoryview_type), __pyx_t_3, NULL); if (unlikely(!__pyx_t_2)) __PYX_ERR(1, 228, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); 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if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 6, __pyx_L1_error) + __pyx_t_1 = PyTuple_New(1); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 8, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_INCREF(__pyx_v__dict); __Pyx_GIVEREF(__pyx_v__dict); PyTuple_SET_ITEM(__pyx_t_1, 0, __pyx_v__dict); - __pyx_t_4 = PyNumber_InPlaceAdd(__pyx_v_state, __pyx_t_1); if (unlikely(!__pyx_t_4)) __PYX_ERR(1, 6, __pyx_L1_error) + __pyx_t_4 = PyNumber_InPlaceAdd(__pyx_v_state, __pyx_t_1); if (unlikely(!__pyx_t_4)) __PYX_ERR(1, 8, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __Pyx_DECREF_SET(__pyx_v_state, ((PyObject*)__pyx_t_4)); __pyx_t_4 = 0; - /* "(tree fragment)":7 + /* "(tree fragment)":9 * if _dict is not None: * state += (_dict,) * use_setstate = True # <<<<<<<<<<<<<< @@ -4518,7 +4519,7 @@ static PyObject *__pyx_pf___pyx_MemviewEnum___reduce_cython__(struct __pyx_Memvi */ __pyx_v_use_setstate = 1; - /* "(tree fragment)":5 + /* "(tree fragment)":7 * state = (self.name,) * _dict = getattr(self, '__dict__', None) * if _dict is not None: # <<<<<<<<<<<<<< @@ -4528,7 +4529,7 @@ static PyObject *__pyx_pf___pyx_MemviewEnum___reduce_cython__(struct __pyx_Memvi goto __pyx_L3; } - /* "(tree fragment)":9 + /* "(tree fragment)":11 * use_setstate = True * else: * use_setstate = self.name is not None # <<<<<<<<<<<<<< @@ -4541,7 +4542,7 @@ static PyObject *__pyx_pf___pyx_MemviewEnum___reduce_cython__(struct __pyx_Memvi } __pyx_L3:; - /* "(tree fragment)":10 + /* "(tree fragment)":12 * else: * use_setstate = self.name is not None * if use_setstate: # <<<<<<<<<<<<<< @@ -4551,7 +4552,7 @@ static PyObject *__pyx_pf___pyx_MemviewEnum___reduce_cython__(struct __pyx_Memvi __pyx_t_3 = (__pyx_v_use_setstate != 0); if (__pyx_t_3) { - /* "(tree fragment)":11 + /* "(tree fragment)":13 * use_setstate = self.name is not None * if use_setstate: * return __pyx_unpickle_Enum, (type(self), 0xb068931, None), state # <<<<<<<<<<<<<< @@ -4559,9 +4560,9 @@ static PyObject *__pyx_pf___pyx_MemviewEnum___reduce_cython__(struct __pyx_Memvi * return __pyx_unpickle_Enum, (type(self), 0xb068931, state) */ __Pyx_XDECREF(__pyx_r); - __Pyx_GetModuleGlobalName(__pyx_t_4, __pyx_n_s_pyx_unpickle_Enum); if (unlikely(!__pyx_t_4)) __PYX_ERR(1, 11, __pyx_L1_error) + __Pyx_GetModuleGlobalName(__pyx_t_4, __pyx_n_s_pyx_unpickle_Enum); if (unlikely(!__pyx_t_4)) __PYX_ERR(1, 13, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); - __pyx_t_1 = PyTuple_New(3); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 11, __pyx_L1_error) + __pyx_t_1 = PyTuple_New(3); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 13, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_INCREF(((PyObject *)Py_TYPE(((PyObject *)__pyx_v_self)))); __Pyx_GIVEREF(((PyObject *)Py_TYPE(((PyObject *)__pyx_v_self)))); @@ -4572,7 +4573,7 @@ static PyObject *__pyx_pf___pyx_MemviewEnum___reduce_cython__(struct __pyx_Memvi __Pyx_INCREF(Py_None); __Pyx_GIVEREF(Py_None); PyTuple_SET_ITEM(__pyx_t_1, 2, Py_None); - __pyx_t_5 = PyTuple_New(3); if (unlikely(!__pyx_t_5)) __PYX_ERR(1, 11, __pyx_L1_error) + __pyx_t_5 = PyTuple_New(3); if (unlikely(!__pyx_t_5)) __PYX_ERR(1, 13, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); __Pyx_GIVEREF(__pyx_t_4); PyTuple_SET_ITEM(__pyx_t_5, 0, __pyx_t_4); @@ -4587,7 +4588,7 @@ static PyObject *__pyx_pf___pyx_MemviewEnum___reduce_cython__(struct __pyx_Memvi __pyx_t_5 = 0; goto __pyx_L0; - /* "(tree fragment)":10 + /* "(tree fragment)":12 * else: * use_setstate = self.name is not None * if use_setstate: # <<<<<<<<<<<<<< @@ -4596,7 +4597,7 @@ static PyObject *__pyx_pf___pyx_MemviewEnum___reduce_cython__(struct __pyx_Memvi */ } - /* "(tree fragment)":13 + /* "(tree fragment)":15 * return __pyx_unpickle_Enum, (type(self), 0xb068931, None), state * else: * return __pyx_unpickle_Enum, (type(self), 0xb068931, state) # <<<<<<<<<<<<<< @@ -4605,9 +4606,9 @@ static PyObject *__pyx_pf___pyx_MemviewEnum___reduce_cython__(struct __pyx_Memvi */ /*else*/ { __Pyx_XDECREF(__pyx_r); - __Pyx_GetModuleGlobalName(__pyx_t_5, __pyx_n_s_pyx_unpickle_Enum); if (unlikely(!__pyx_t_5)) __PYX_ERR(1, 13, __pyx_L1_error) + __Pyx_GetModuleGlobalName(__pyx_t_5, __pyx_n_s_pyx_unpickle_Enum); if (unlikely(!__pyx_t_5)) __PYX_ERR(1, 15, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); - __pyx_t_1 = PyTuple_New(3); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 13, __pyx_L1_error) + __pyx_t_1 = PyTuple_New(3); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 15, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_INCREF(((PyObject *)Py_TYPE(((PyObject *)__pyx_v_self)))); __Pyx_GIVEREF(((PyObject *)Py_TYPE(((PyObject *)__pyx_v_self)))); @@ -4618,7 +4619,7 @@ static PyObject *__pyx_pf___pyx_MemviewEnum___reduce_cython__(struct __pyx_Memvi __Pyx_INCREF(__pyx_v_state); __Pyx_GIVEREF(__pyx_v_state); PyTuple_SET_ITEM(__pyx_t_1, 2, __pyx_v_state); - __pyx_t_4 = PyTuple_New(2); if (unlikely(!__pyx_t_4)) __PYX_ERR(1, 13, __pyx_L1_error) + __pyx_t_4 = PyTuple_New(2); if (unlikely(!__pyx_t_4)) __PYX_ERR(1, 15, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_GIVEREF(__pyx_t_5); PyTuple_SET_ITEM(__pyx_t_4, 0, __pyx_t_5); @@ -4633,8 +4634,8 @@ static PyObject *__pyx_pf___pyx_MemviewEnum___reduce_cython__(struct __pyx_Memvi /* "(tree fragment)":1 * def __reduce_cython__(self): # <<<<<<<<<<<<<< - * cdef bint use_setstate - * state = (self.name,) + * cdef tuple state + * cdef object _dict */ /* function exit code */ @@ -4652,7 +4653,7 @@ static PyObject *__pyx_pf___pyx_MemviewEnum___reduce_cython__(struct __pyx_Memvi return __pyx_r; } -/* "(tree fragment)":14 +/* "(tree fragment)":16 * else: * return __pyx_unpickle_Enum, (type(self), 0xb068931, state) * def __setstate_cython__(self, __pyx_state): # <<<<<<<<<<<<<< @@ -4678,17 +4679,17 @@ static PyObject *__pyx_pf___pyx_MemviewEnum_2__setstate_cython__(struct __pyx_Me PyObject *__pyx_t_1 = NULL; __Pyx_RefNannySetupContext("__setstate_cython__", 0); - /* "(tree fragment)":15 + /* "(tree fragment)":17 * return __pyx_unpickle_Enum, (type(self), 0xb068931, state) * def __setstate_cython__(self, __pyx_state): * __pyx_unpickle_Enum__set_state(self, __pyx_state) # <<<<<<<<<<<<<< */ - if (!(likely(PyTuple_CheckExact(__pyx_v___pyx_state))||((__pyx_v___pyx_state) == Py_None)||(PyErr_Format(PyExc_TypeError, "Expected %.16s, got %.200s", "tuple", Py_TYPE(__pyx_v___pyx_state)->tp_name), 0))) __PYX_ERR(1, 15, __pyx_L1_error) - __pyx_t_1 = __pyx_unpickle_Enum__set_state(__pyx_v_self, ((PyObject*)__pyx_v___pyx_state)); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 15, __pyx_L1_error) + if (!(likely(PyTuple_CheckExact(__pyx_v___pyx_state))||((__pyx_v___pyx_state) == Py_None)||(PyErr_Format(PyExc_TypeError, "Expected %.16s, got %.200s", "tuple", Py_TYPE(__pyx_v___pyx_state)->tp_name), 0))) __PYX_ERR(1, 17, __pyx_L1_error) + __pyx_t_1 = __pyx_unpickle_Enum__set_state(__pyx_v_self, ((PyObject*)__pyx_v___pyx_state)); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 17, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; - /* "(tree fragment)":14 + /* "(tree fragment)":16 * else: * return __pyx_unpickle_Enum, (type(self), 0xb068931, state) * def __setstate_cython__(self, __pyx_state): # <<<<<<<<<<<<<< @@ -4708,7 +4709,7 @@ static PyObject *__pyx_pf___pyx_MemviewEnum_2__setstate_cython__(struct __pyx_Me return __pyx_r; } -/* "View.MemoryView":297 +/* "View.MemoryView":298 * * @cname('__pyx_align_pointer') * cdef void *align_pointer(void *memory, size_t alignment) nogil: # <<<<<<<<<<<<<< @@ -4722,7 +4723,7 @@ static void *__pyx_align_pointer(void *__pyx_v_memory, size_t __pyx_v_alignment) void *__pyx_r; int __pyx_t_1; - /* "View.MemoryView":299 + /* "View.MemoryView":300 * cdef void *align_pointer(void *memory, size_t alignment) nogil: * "Align pointer memory on a given boundary" * cdef Py_intptr_t aligned_p = memory # <<<<<<<<<<<<<< @@ -4731,7 +4732,7 @@ static void *__pyx_align_pointer(void *__pyx_v_memory, size_t __pyx_v_alignment) */ __pyx_v_aligned_p = ((Py_intptr_t)__pyx_v_memory); - /* "View.MemoryView":303 + /* "View.MemoryView":304 * * with cython.cdivision(True): * offset = aligned_p % alignment # <<<<<<<<<<<<<< @@ -4740,7 +4741,7 @@ static void *__pyx_align_pointer(void *__pyx_v_memory, size_t __pyx_v_alignment) */ __pyx_v_offset = (__pyx_v_aligned_p % __pyx_v_alignment); - /* "View.MemoryView":305 + /* "View.MemoryView":306 * offset = aligned_p % alignment * * if offset > 0: # <<<<<<<<<<<<<< @@ -4750,7 +4751,7 @@ static void *__pyx_align_pointer(void *__pyx_v_memory, size_t __pyx_v_alignment) __pyx_t_1 = ((__pyx_v_offset > 0) != 0); if (__pyx_t_1) { - /* "View.MemoryView":306 + /* "View.MemoryView":307 * * if offset > 0: * aligned_p += alignment - offset # <<<<<<<<<<<<<< @@ -4759,7 +4760,7 @@ static void *__pyx_align_pointer(void *__pyx_v_memory, size_t __pyx_v_alignment) */ __pyx_v_aligned_p = (__pyx_v_aligned_p + (__pyx_v_alignment - __pyx_v_offset)); - /* "View.MemoryView":305 + /* "View.MemoryView":306 * offset = aligned_p % alignment * * if offset > 0: # <<<<<<<<<<<<<< @@ -4768,7 +4769,7 @@ static void *__pyx_align_pointer(void *__pyx_v_memory, size_t __pyx_v_alignment) */ } - /* "View.MemoryView":308 + /* "View.MemoryView":309 * aligned_p += alignment - offset * * return aligned_p # <<<<<<<<<<<<<< @@ -4778,7 +4779,7 @@ static void *__pyx_align_pointer(void *__pyx_v_memory, size_t __pyx_v_alignment) __pyx_r = ((void *)__pyx_v_aligned_p); goto __pyx_L0; - /* "View.MemoryView":297 + /* "View.MemoryView":298 * * @cname('__pyx_align_pointer') * cdef void *align_pointer(void *memory, size_t alignment) nogil: # <<<<<<<<<<<<<< @@ -4791,7 +4792,7 @@ static void *__pyx_align_pointer(void *__pyx_v_memory, size_t __pyx_v_alignment) return __pyx_r; } -/* "View.MemoryView":344 +/* "View.MemoryView":345 * cdef __Pyx_TypeInfo *typeinfo * * def __cinit__(memoryview self, object obj, int flags, bint dtype_is_object=False): # <<<<<<<<<<<<<< @@ -4833,7 +4834,7 @@ static int __pyx_memoryview___cinit__(PyObject *__pyx_v_self, PyObject *__pyx_ar case 1: if (likely((values[1] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_flags)) != 0)) kw_args--; else { - __Pyx_RaiseArgtupleInvalid("__cinit__", 0, 2, 3, 1); __PYX_ERR(1, 344, __pyx_L3_error) + __Pyx_RaiseArgtupleInvalid("__cinit__", 0, 2, 3, 1); __PYX_ERR(1, 345, __pyx_L3_error) } CYTHON_FALLTHROUGH; case 2: @@ -4843,7 +4844,7 @@ static int __pyx_memoryview___cinit__(PyObject *__pyx_v_self, PyObject *__pyx_ar } } if (unlikely(kw_args > 0)) { - if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "__cinit__") < 0)) __PYX_ERR(1, 344, __pyx_L3_error) + if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "__cinit__") < 0)) __PYX_ERR(1, 345, __pyx_L3_error) } } else { switch (PyTuple_GET_SIZE(__pyx_args)) { @@ -4856,16 +4857,16 @@ static int __pyx_memoryview___cinit__(PyObject *__pyx_v_self, PyObject *__pyx_ar } } __pyx_v_obj = values[0]; 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__Pyx_AddTraceback("View.MemoryView.memoryview.__cinit__", __pyx_clineno, __pyx_lineno, __pyx_filename); __Pyx_RefNannyFinishContext(); @@ -4887,7 +4888,7 @@ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit_ int __pyx_t_4; __Pyx_RefNannySetupContext("__cinit__", 0); - /* "View.MemoryView":345 + /* "View.MemoryView":346 * * def __cinit__(memoryview self, object obj, int flags, bint dtype_is_object=False): * self.obj = obj # <<<<<<<<<<<<<< @@ -4900,7 +4901,7 @@ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit_ __Pyx_DECREF(__pyx_v_self->obj); __pyx_v_self->obj = __pyx_v_obj; - /* "View.MemoryView":346 + /* "View.MemoryView":347 * def __cinit__(memoryview self, object obj, int flags, bint dtype_is_object=False): * self.obj = obj * self.flags = flags # <<<<<<<<<<<<<< @@ -4909,7 +4910,7 @@ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit_ */ __pyx_v_self->flags = __pyx_v_flags; - /* "View.MemoryView":347 + /* "View.MemoryView":348 * self.obj = obj * self.flags = flags * if type(self) is memoryview or obj is not None: # <<<<<<<<<<<<<< @@ -4929,16 +4930,16 @@ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit_ __pyx_L4_bool_binop_done:; if (__pyx_t_1) { - /* "View.MemoryView":348 + /* "View.MemoryView":349 * self.flags = flags * if type(self) is memoryview or obj is not None: * __Pyx_GetBuffer(obj, &self.view, flags) # <<<<<<<<<<<<<< * if self.view.obj == NULL: * (<__pyx_buffer *> &self.view).obj = Py_None */ - __pyx_t_4 = __Pyx_GetBuffer(__pyx_v_obj, (&__pyx_v_self->view), __pyx_v_flags); if (unlikely(__pyx_t_4 == ((int)-1))) __PYX_ERR(1, 348, __pyx_L1_error) + __pyx_t_4 = __Pyx_GetBuffer(__pyx_v_obj, (&__pyx_v_self->view), __pyx_v_flags); if (unlikely(__pyx_t_4 == ((int)-1))) __PYX_ERR(1, 349, __pyx_L1_error) - /* "View.MemoryView":349 + /* "View.MemoryView":350 * if type(self) is memoryview or obj is not None: * __Pyx_GetBuffer(obj, &self.view, flags) * if self.view.obj == NULL: # <<<<<<<<<<<<<< @@ -4948,7 +4949,7 @@ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit_ __pyx_t_1 = ((((PyObject *)__pyx_v_self->view.obj) == NULL) != 0); if (__pyx_t_1) { - /* "View.MemoryView":350 + /* "View.MemoryView":351 * __Pyx_GetBuffer(obj, &self.view, flags) * if self.view.obj == NULL: * (<__pyx_buffer *> &self.view).obj = Py_None # <<<<<<<<<<<<<< @@ -4957,7 +4958,7 @@ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit_ */ ((Py_buffer *)(&__pyx_v_self->view))->obj = Py_None; - /* "View.MemoryView":351 + /* "View.MemoryView":352 * if self.view.obj == NULL: * (<__pyx_buffer *> &self.view).obj = Py_None * Py_INCREF(Py_None) # <<<<<<<<<<<<<< @@ -4966,7 +4967,7 @@ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit_ */ Py_INCREF(Py_None); - /* "View.MemoryView":349 + /* "View.MemoryView":350 * if type(self) is memoryview or obj is not None: * __Pyx_GetBuffer(obj, &self.view, flags) * if self.view.obj == NULL: # <<<<<<<<<<<<<< @@ -4975,7 +4976,7 @@ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit_ */ } - /* "View.MemoryView":347 + /* "View.MemoryView":348 * self.obj = obj * self.flags = flags * if type(self) is memoryview or obj is not None: # <<<<<<<<<<<<<< @@ -4984,7 +4985,7 @@ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit_ */ } - /* "View.MemoryView":354 + /* "View.MemoryView":355 * * global __pyx_memoryview_thread_locks_used * if __pyx_memoryview_thread_locks_used < THREAD_LOCKS_PREALLOCATED: # <<<<<<<<<<<<<< @@ -4994,7 +4995,7 @@ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit_ __pyx_t_1 = ((__pyx_memoryview_thread_locks_used < 8) != 0); if (__pyx_t_1) { - /* "View.MemoryView":355 + /* "View.MemoryView":356 * global __pyx_memoryview_thread_locks_used * if __pyx_memoryview_thread_locks_used < THREAD_LOCKS_PREALLOCATED: * self.lock = __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] # <<<<<<<<<<<<<< @@ -5003,7 +5004,7 @@ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit_ */ __pyx_v_self->lock = (__pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used]); - /* "View.MemoryView":356 + /* "View.MemoryView":357 * if __pyx_memoryview_thread_locks_used < THREAD_LOCKS_PREALLOCATED: * self.lock = __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] * __pyx_memoryview_thread_locks_used += 1 # <<<<<<<<<<<<<< @@ -5012,7 +5013,7 @@ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit_ */ __pyx_memoryview_thread_locks_used = (__pyx_memoryview_thread_locks_used + 1); - /* "View.MemoryView":354 + /* "View.MemoryView":355 * * global __pyx_memoryview_thread_locks_used * if __pyx_memoryview_thread_locks_used < THREAD_LOCKS_PREALLOCATED: # <<<<<<<<<<<<<< @@ -5021,7 +5022,7 @@ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit_ */ } - /* "View.MemoryView":357 + /* "View.MemoryView":358 * self.lock = __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] * __pyx_memoryview_thread_locks_used += 1 * if self.lock is NULL: # <<<<<<<<<<<<<< @@ -5031,7 +5032,7 @@ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit_ __pyx_t_1 = ((__pyx_v_self->lock == NULL) != 0); if (__pyx_t_1) { - /* "View.MemoryView":358 + /* "View.MemoryView":359 * __pyx_memoryview_thread_locks_used += 1 * if self.lock is NULL: * self.lock = PyThread_allocate_lock() # <<<<<<<<<<<<<< @@ -5040,7 +5041,7 @@ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit_ */ __pyx_v_self->lock = PyThread_allocate_lock(); - /* "View.MemoryView":359 + /* "View.MemoryView":360 * if self.lock is NULL: * self.lock = PyThread_allocate_lock() * if self.lock is NULL: # <<<<<<<<<<<<<< @@ -5050,16 +5051,16 @@ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit_ __pyx_t_1 = ((__pyx_v_self->lock == NULL) != 0); if (unlikely(__pyx_t_1)) { - /* "View.MemoryView":360 + /* "View.MemoryView":361 * self.lock = PyThread_allocate_lock() * if self.lock is NULL: * raise MemoryError # <<<<<<<<<<<<<< * * if flags & PyBUF_FORMAT: */ - PyErr_NoMemory(); __PYX_ERR(1, 360, __pyx_L1_error) + PyErr_NoMemory(); __PYX_ERR(1, 361, __pyx_L1_error) - /* "View.MemoryView":359 + /* "View.MemoryView":360 * if self.lock is NULL: * self.lock = PyThread_allocate_lock() * if self.lock is NULL: # <<<<<<<<<<<<<< @@ -5068,7 +5069,7 @@ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit_ */ } - /* "View.MemoryView":357 + /* "View.MemoryView":358 * self.lock = __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] * __pyx_memoryview_thread_locks_used += 1 * if self.lock is NULL: # <<<<<<<<<<<<<< @@ -5077,7 +5078,7 @@ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit_ */ } - /* "View.MemoryView":362 + /* "View.MemoryView":363 * raise MemoryError * * if flags & PyBUF_FORMAT: # <<<<<<<<<<<<<< @@ -5087,7 +5088,7 @@ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit_ __pyx_t_1 = ((__pyx_v_flags & PyBUF_FORMAT) != 0); if (__pyx_t_1) { - /* "View.MemoryView":363 + /* "View.MemoryView":364 * * if flags & PyBUF_FORMAT: * self.dtype_is_object = (self.view.format[0] == b'O' and self.view.format[1] == b'\0') # <<<<<<<<<<<<<< @@ -5105,7 +5106,7 @@ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit_ __pyx_L11_bool_binop_done:; __pyx_v_self->dtype_is_object = __pyx_t_1; - /* "View.MemoryView":362 + /* "View.MemoryView":363 * raise MemoryError * * if flags & PyBUF_FORMAT: # <<<<<<<<<<<<<< @@ -5115,7 +5116,7 @@ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit_ goto __pyx_L10; } - /* "View.MemoryView":365 + /* "View.MemoryView":366 * self.dtype_is_object = (self.view.format[0] == b'O' and self.view.format[1] == b'\0') * else: * self.dtype_is_object = dtype_is_object # <<<<<<<<<<<<<< @@ -5127,7 +5128,7 @@ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit_ } __pyx_L10:; - /* "View.MemoryView":367 + /* "View.MemoryView":368 * self.dtype_is_object = dtype_is_object * * self.acquisition_count_aligned_p = <__pyx_atomic_int *> align_pointer( # <<<<<<<<<<<<<< @@ -5136,7 +5137,7 @@ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit_ */ __pyx_v_self->acquisition_count_aligned_p = ((__pyx_atomic_int *)__pyx_align_pointer(((void *)(&(__pyx_v_self->acquisition_count[0]))), (sizeof(__pyx_atomic_int)))); - /* "View.MemoryView":369 + /* "View.MemoryView":370 * self.acquisition_count_aligned_p = <__pyx_atomic_int *> align_pointer( * &self.acquisition_count[0], sizeof(__pyx_atomic_int)) * self.typeinfo = NULL # <<<<<<<<<<<<<< @@ -5145,7 +5146,7 @@ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit_ */ __pyx_v_self->typeinfo = NULL; - /* "View.MemoryView":344 + /* "View.MemoryView":345 * cdef __Pyx_TypeInfo *typeinfo * * def __cinit__(memoryview self, object obj, int flags, bint dtype_is_object=False): # <<<<<<<<<<<<<< @@ -5164,7 +5165,7 @@ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit_ return __pyx_r; } -/* "View.MemoryView":371 +/* "View.MemoryView":372 * self.typeinfo = NULL * * def __dealloc__(memoryview self): # <<<<<<<<<<<<<< @@ -5195,7 +5196,7 @@ static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__deal PyThread_type_lock __pyx_t_7; __Pyx_RefNannySetupContext("__dealloc__", 0); - /* "View.MemoryView":372 + /* "View.MemoryView":373 * * def __dealloc__(memoryview self): * if self.obj is not None: # <<<<<<<<<<<<<< @@ -5206,7 +5207,7 @@ static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__deal __pyx_t_2 = (__pyx_t_1 != 0); if (__pyx_t_2) { - /* "View.MemoryView":373 + /* "View.MemoryView":374 * def __dealloc__(memoryview self): * if self.obj is not None: * __Pyx_ReleaseBuffer(&self.view) # <<<<<<<<<<<<<< @@ -5215,7 +5216,7 @@ static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__deal */ __Pyx_ReleaseBuffer((&__pyx_v_self->view)); - /* "View.MemoryView":372 + /* "View.MemoryView":373 * * def __dealloc__(memoryview self): * if self.obj is not None: # <<<<<<<<<<<<<< @@ -5224,7 +5225,7 @@ static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__deal */ } - /* "View.MemoryView":377 + /* "View.MemoryView":378 * cdef int i * global __pyx_memoryview_thread_locks_used * if self.lock != NULL: # <<<<<<<<<<<<<< @@ -5234,7 +5235,7 @@ static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__deal __pyx_t_2 = ((__pyx_v_self->lock != NULL) != 0); if (__pyx_t_2) { - /* "View.MemoryView":378 + /* "View.MemoryView":379 * global __pyx_memoryview_thread_locks_used * if self.lock != NULL: * for i in range(__pyx_memoryview_thread_locks_used): # <<<<<<<<<<<<<< @@ -5246,7 +5247,7 @@ static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__deal for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_4; __pyx_t_5+=1) { __pyx_v_i = __pyx_t_5; - /* "View.MemoryView":379 + /* "View.MemoryView":380 * if self.lock != NULL: * for i in range(__pyx_memoryview_thread_locks_used): * if __pyx_memoryview_thread_locks[i] is self.lock: # <<<<<<<<<<<<<< @@ -5256,7 +5257,7 @@ static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__deal __pyx_t_2 = (((__pyx_memoryview_thread_locks[__pyx_v_i]) == __pyx_v_self->lock) != 0); if (__pyx_t_2) { - /* "View.MemoryView":380 + /* "View.MemoryView":381 * for i in range(__pyx_memoryview_thread_locks_used): * if __pyx_memoryview_thread_locks[i] is self.lock: * __pyx_memoryview_thread_locks_used -= 1 # <<<<<<<<<<<<<< @@ -5265,7 +5266,7 @@ static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__deal */ __pyx_memoryview_thread_locks_used = (__pyx_memoryview_thread_locks_used - 1); - /* "View.MemoryView":381 + /* "View.MemoryView":382 * if __pyx_memoryview_thread_locks[i] is self.lock: * __pyx_memoryview_thread_locks_used -= 1 * if i != __pyx_memoryview_thread_locks_used: # <<<<<<<<<<<<<< @@ -5275,7 +5276,7 @@ static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__deal __pyx_t_2 = ((__pyx_v_i != __pyx_memoryview_thread_locks_used) != 0); if (__pyx_t_2) { - /* "View.MemoryView":383 + /* "View.MemoryView":384 * if i != __pyx_memoryview_thread_locks_used: * __pyx_memoryview_thread_locks[i], __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] = ( * __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used], __pyx_memoryview_thread_locks[i]) # <<<<<<<<<<<<<< @@ -5285,7 +5286,7 @@ static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__deal __pyx_t_6 = (__pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used]); __pyx_t_7 = (__pyx_memoryview_thread_locks[__pyx_v_i]); - /* "View.MemoryView":382 + /* "View.MemoryView":383 * __pyx_memoryview_thread_locks_used -= 1 * if i != __pyx_memoryview_thread_locks_used: * __pyx_memoryview_thread_locks[i], __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] = ( # <<<<<<<<<<<<<< @@ -5295,7 +5296,7 @@ static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__deal (__pyx_memoryview_thread_locks[__pyx_v_i]) = __pyx_t_6; (__pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used]) = __pyx_t_7; - /* "View.MemoryView":381 + /* "View.MemoryView":382 * if __pyx_memoryview_thread_locks[i] is self.lock: * __pyx_memoryview_thread_locks_used -= 1 * if i != __pyx_memoryview_thread_locks_used: # <<<<<<<<<<<<<< @@ -5304,7 +5305,7 @@ static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__deal */ } - /* "View.MemoryView":384 + /* "View.MemoryView":385 * __pyx_memoryview_thread_locks[i], __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] = ( * __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used], __pyx_memoryview_thread_locks[i]) * break # <<<<<<<<<<<<<< @@ -5313,7 +5314,7 @@ static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__deal */ goto __pyx_L6_break; - /* "View.MemoryView":379 + /* "View.MemoryView":380 * if self.lock != NULL: * for i in range(__pyx_memoryview_thread_locks_used): * if __pyx_memoryview_thread_locks[i] is self.lock: # <<<<<<<<<<<<<< @@ -5324,7 +5325,7 @@ static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__deal } /*else*/ { - /* "View.MemoryView":386 + /* "View.MemoryView":387 * break * else: * PyThread_free_lock(self.lock) # <<<<<<<<<<<<<< @@ -5335,7 +5336,7 @@ static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__deal } __pyx_L6_break:; - /* "View.MemoryView":377 + /* "View.MemoryView":378 * cdef int i * global __pyx_memoryview_thread_locks_used * if self.lock != NULL: # <<<<<<<<<<<<<< @@ -5344,7 +5345,7 @@ static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__deal */ } - /* "View.MemoryView":371 + /* "View.MemoryView":372 * self.typeinfo = NULL * * def __dealloc__(memoryview self): # <<<<<<<<<<<<<< @@ -5356,7 +5357,7 @@ static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__deal __Pyx_RefNannyFinishContext(); } -/* "View.MemoryView":388 +/* "View.MemoryView":389 * PyThread_free_lock(self.lock) * * cdef char *get_item_pointer(memoryview self, object index) except NULL: # <<<<<<<<<<<<<< @@ -5379,7 +5380,7 @@ static char *__pyx_memoryview_get_item_pointer(struct __pyx_memoryview_obj *__py char *__pyx_t_7; __Pyx_RefNannySetupContext("get_item_pointer", 0); - /* "View.MemoryView":390 + /* "View.MemoryView":391 * cdef char *get_item_pointer(memoryview self, object index) except NULL: * cdef Py_ssize_t dim * cdef char *itemp = self.view.buf # <<<<<<<<<<<<<< @@ -5388,7 +5389,7 @@ static char *__pyx_memoryview_get_item_pointer(struct __pyx_memoryview_obj *__py */ __pyx_v_itemp = ((char *)__pyx_v_self->view.buf); - /* "View.MemoryView":392 + /* "View.MemoryView":393 * cdef char *itemp = self.view.buf * * for dim, idx in enumerate(index): # <<<<<<<<<<<<<< @@ -5400,26 +5401,26 @@ static char *__pyx_memoryview_get_item_pointer(struct __pyx_memoryview_obj *__py __pyx_t_2 = __pyx_v_index; __Pyx_INCREF(__pyx_t_2); __pyx_t_3 = 0; __pyx_t_4 = NULL; } else { - __pyx_t_3 = -1; __pyx_t_2 = PyObject_GetIter(__pyx_v_index); if (unlikely(!__pyx_t_2)) __PYX_ERR(1, 392, __pyx_L1_error) + __pyx_t_3 = -1; __pyx_t_2 = PyObject_GetIter(__pyx_v_index); if (unlikely(!__pyx_t_2)) __PYX_ERR(1, 393, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); - __pyx_t_4 = Py_TYPE(__pyx_t_2)->tp_iternext; if (unlikely(!__pyx_t_4)) __PYX_ERR(1, 392, __pyx_L1_error) + __pyx_t_4 = Py_TYPE(__pyx_t_2)->tp_iternext; if (unlikely(!__pyx_t_4)) __PYX_ERR(1, 393, __pyx_L1_error) } for (;;) { if (likely(!__pyx_t_4)) { if (likely(PyList_CheckExact(__pyx_t_2))) { if (__pyx_t_3 >= PyList_GET_SIZE(__pyx_t_2)) break; #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS - __pyx_t_5 = PyList_GET_ITEM(__pyx_t_2, __pyx_t_3); __Pyx_INCREF(__pyx_t_5); __pyx_t_3++; if (unlikely(0 < 0)) __PYX_ERR(1, 392, __pyx_L1_error) + __pyx_t_5 = PyList_GET_ITEM(__pyx_t_2, __pyx_t_3); __Pyx_INCREF(__pyx_t_5); __pyx_t_3++; if (unlikely(0 < 0)) __PYX_ERR(1, 393, __pyx_L1_error) #else - __pyx_t_5 = PySequence_ITEM(__pyx_t_2, __pyx_t_3); __pyx_t_3++; if (unlikely(!__pyx_t_5)) __PYX_ERR(1, 392, __pyx_L1_error) + __pyx_t_5 = PySequence_ITEM(__pyx_t_2, __pyx_t_3); __pyx_t_3++; if (unlikely(!__pyx_t_5)) __PYX_ERR(1, 393, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); #endif } else { if (__pyx_t_3 >= PyTuple_GET_SIZE(__pyx_t_2)) break; #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS - __pyx_t_5 = PyTuple_GET_ITEM(__pyx_t_2, __pyx_t_3); __Pyx_INCREF(__pyx_t_5); __pyx_t_3++; if (unlikely(0 < 0)) __PYX_ERR(1, 392, __pyx_L1_error) + __pyx_t_5 = PyTuple_GET_ITEM(__pyx_t_2, __pyx_t_3); __Pyx_INCREF(__pyx_t_5); __pyx_t_3++; if (unlikely(0 < 0)) __PYX_ERR(1, 393, __pyx_L1_error) #else - __pyx_t_5 = PySequence_ITEM(__pyx_t_2, __pyx_t_3); __pyx_t_3++; if (unlikely(!__pyx_t_5)) __PYX_ERR(1, 392, __pyx_L1_error) + __pyx_t_5 = PySequence_ITEM(__pyx_t_2, __pyx_t_3); __pyx_t_3++; if (unlikely(!__pyx_t_5)) __PYX_ERR(1, 393, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); #endif } @@ -5429,7 +5430,7 @@ static char *__pyx_memoryview_get_item_pointer(struct __pyx_memoryview_obj *__py PyObject* exc_type = PyErr_Occurred(); if (exc_type) { if (likely(__Pyx_PyErr_GivenExceptionMatches(exc_type, PyExc_StopIteration))) PyErr_Clear(); - else __PYX_ERR(1, 392, __pyx_L1_error) + else __PYX_ERR(1, 393, __pyx_L1_error) } break; } @@ -5440,18 +5441,18 @@ static char *__pyx_memoryview_get_item_pointer(struct __pyx_memoryview_obj *__py __pyx_v_dim = __pyx_t_1; __pyx_t_1 = (__pyx_t_1 + 1); - /* "View.MemoryView":393 + /* "View.MemoryView":394 * * for dim, idx in enumerate(index): * itemp = pybuffer_index(&self.view, itemp, idx, dim) # <<<<<<<<<<<<<< * * return itemp */ - __pyx_t_6 = __Pyx_PyIndex_AsSsize_t(__pyx_v_idx); if (unlikely((__pyx_t_6 == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(1, 393, __pyx_L1_error) - __pyx_t_7 = __pyx_pybuffer_index((&__pyx_v_self->view), __pyx_v_itemp, __pyx_t_6, __pyx_v_dim); if (unlikely(__pyx_t_7 == ((char *)NULL))) __PYX_ERR(1, 393, __pyx_L1_error) + __pyx_t_6 = __Pyx_PyIndex_AsSsize_t(__pyx_v_idx); if (unlikely((__pyx_t_6 == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(1, 394, __pyx_L1_error) + __pyx_t_7 = __pyx_pybuffer_index((&__pyx_v_self->view), __pyx_v_itemp, __pyx_t_6, __pyx_v_dim); if (unlikely(__pyx_t_7 == ((char *)NULL))) __PYX_ERR(1, 394, __pyx_L1_error) __pyx_v_itemp = __pyx_t_7; - /* "View.MemoryView":392 + /* "View.MemoryView":393 * cdef char *itemp = self.view.buf * * for dim, idx in enumerate(index): # <<<<<<<<<<<<<< @@ -5461,7 +5462,7 @@ static char *__pyx_memoryview_get_item_pointer(struct __pyx_memoryview_obj *__py } __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; - /* "View.MemoryView":395 + /* "View.MemoryView":396 * itemp = pybuffer_index(&self.view, itemp, idx, dim) * * return itemp # <<<<<<<<<<<<<< @@ -5471,7 +5472,7 @@ static char *__pyx_memoryview_get_item_pointer(struct __pyx_memoryview_obj *__py __pyx_r = __pyx_v_itemp; goto __pyx_L0; - /* "View.MemoryView":388 + /* "View.MemoryView":389 * PyThread_free_lock(self.lock) * * cdef char *get_item_pointer(memoryview self, object index) except NULL: # <<<<<<<<<<<<<< @@ -5491,7 +5492,7 @@ static char *__pyx_memoryview_get_item_pointer(struct __pyx_memoryview_obj *__py return __pyx_r; } -/* "View.MemoryView":398 +/* "View.MemoryView":399 * * * def __getitem__(memoryview self, object index): # <<<<<<<<<<<<<< @@ -5526,7 +5527,7 @@ static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_4_ char *__pyx_t_6; __Pyx_RefNannySetupContext("__getitem__", 0); - /* "View.MemoryView":399 + /* "View.MemoryView":400 * * def __getitem__(memoryview self, object index): * if index is Ellipsis: # <<<<<<<<<<<<<< @@ -5537,7 +5538,7 @@ static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_4_ __pyx_t_2 = (__pyx_t_1 != 0); if (__pyx_t_2) { - /* "View.MemoryView":400 + /* "View.MemoryView":401 * def __getitem__(memoryview self, object index): * if index is Ellipsis: * return self # <<<<<<<<<<<<<< @@ -5549,7 +5550,7 @@ static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_4_ __pyx_r = ((PyObject *)__pyx_v_self); goto __pyx_L0; - /* "View.MemoryView":399 + /* "View.MemoryView":400 * * def __getitem__(memoryview self, object index): * if index is Ellipsis: # <<<<<<<<<<<<<< @@ -5558,14 +5559,14 @@ static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_4_ */ } - /* "View.MemoryView":402 + /* "View.MemoryView":403 * return self * * have_slices, indices = _unellipsify(index, self.view.ndim) # <<<<<<<<<<<<<< * * cdef char *itemp */ - __pyx_t_3 = _unellipsify(__pyx_v_index, __pyx_v_self->view.ndim); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 402, __pyx_L1_error) + __pyx_t_3 = _unellipsify(__pyx_v_index, __pyx_v_self->view.ndim); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 403, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); if (likely(__pyx_t_3 != Py_None)) { PyObject* sequence = __pyx_t_3; @@ -5573,7 +5574,7 @@ static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_4_ if (unlikely(size != 2)) { if (size > 2) __Pyx_RaiseTooManyValuesError(2); else if (size >= 0) __Pyx_RaiseNeedMoreValuesError(size); 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__pyx_t_4 = 0; __pyx_v_indices = __pyx_t_5; __pyx_t_5 = 0; - /* "View.MemoryView":405 + /* "View.MemoryView":406 * * cdef char *itemp * if have_slices: # <<<<<<<<<<<<<< * return memview_slice(self, indices) * else: */ - __pyx_t_2 = __Pyx_PyObject_IsTrue(__pyx_v_have_slices); if (unlikely(__pyx_t_2 < 0)) __PYX_ERR(1, 405, __pyx_L1_error) + __pyx_t_2 = __Pyx_PyObject_IsTrue(__pyx_v_have_slices); if (unlikely(__pyx_t_2 < 0)) __PYX_ERR(1, 406, __pyx_L1_error) if (__pyx_t_2) { - /* "View.MemoryView":406 + /* "View.MemoryView":407 * cdef char *itemp * if have_slices: * return memview_slice(self, indices) # <<<<<<<<<<<<<< @@ -5613,13 +5614,13 @@ static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_4_ * itemp = self.get_item_pointer(indices) */ __Pyx_XDECREF(__pyx_r); - __pyx_t_3 = ((PyObject *)__pyx_memview_slice(__pyx_v_self, __pyx_v_indices)); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 406, __pyx_L1_error) + __pyx_t_3 = ((PyObject *)__pyx_memview_slice(__pyx_v_self, __pyx_v_indices)); 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__pyx_L4_error:; __Pyx_XDECREF(__pyx_t_6); __pyx_t_6 = 0; - __Pyx_XDECREF(__pyx_t_8); __pyx_t_8 = 0; __Pyx_XDECREF(__pyx_t_7); __pyx_t_7 = 0; + __Pyx_XDECREF(__pyx_t_8); __pyx_t_8 = 0; - /* "View.MemoryView":431 - * obj = memoryview(obj, self.flags|PyBUF_ANY_CONTIGUOUS, + /* "View.MemoryView":432 + * obj = memoryview(obj, self.flags & ~PyBUF_WRITABLE | PyBUF_ANY_CONTIGUOUS, * self.dtype_is_object) * except TypeError: # <<<<<<<<<<<<<< * return None @@ -6023,12 +6024,12 @@ static PyObject *__pyx_memoryview_is_slice(struct __pyx_memoryview_obj *__pyx_v_ __pyx_t_9 = __Pyx_PyErr_ExceptionMatches(__pyx_builtin_TypeError); if (__pyx_t_9) { __Pyx_AddTraceback("View.MemoryView.memoryview.is_slice", __pyx_clineno, __pyx_lineno, __pyx_filename); - if (__Pyx_GetException(&__pyx_t_7, &__pyx_t_8, &__pyx_t_6) < 0) __PYX_ERR(1, 431, __pyx_L6_except_error) + if (__Pyx_GetException(&__pyx_t_7, &__pyx_t_8, &__pyx_t_6) < 0) __PYX_ERR(1, 432, __pyx_L6_except_error) __Pyx_GOTREF(__pyx_t_7); __Pyx_GOTREF(__pyx_t_8); 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__pyx_t_1 = 0; - /* "View.MemoryView":440 + /* "View.MemoryView":441 * cdef __Pyx_memviewslice src_slice * * memoryview_copy_contents(get_slice_from_memview(src, &src_slice)[0], # <<<<<<<<<<<<<< * get_slice_from_memview(dst, &dst_slice)[0], * src.ndim, dst.ndim, self.dtype_is_object) */ - __pyx_t_4 = __pyx_memoryview_copy_contents((__pyx_memoryview_get_slice_from_memoryview(((struct __pyx_memoryview_obj *)__pyx_v_src), (&__pyx_v_src_slice))[0]), (__pyx_memoryview_get_slice_from_memoryview(((struct __pyx_memoryview_obj *)__pyx_v_dst), (&__pyx_v_dst_slice))[0]), __pyx_t_2, __pyx_t_3, __pyx_v_self->dtype_is_object); if (unlikely(__pyx_t_4 == ((int)-1))) __PYX_ERR(1, 440, __pyx_L1_error) + __pyx_t_4 = __pyx_memoryview_copy_contents((__pyx_memoryview_get_slice_from_memoryview(((struct __pyx_memoryview_obj *)__pyx_v_src), (&__pyx_v_src_slice))[0]), (__pyx_memoryview_get_slice_from_memoryview(((struct __pyx_memoryview_obj *)__pyx_v_dst), (&__pyx_v_dst_slice))[0]), __pyx_t_2, __pyx_t_3, __pyx_v_self->dtype_is_object); if (unlikely(__pyx_t_4 == ((int)-1))) __PYX_ERR(1, 441, __pyx_L1_error) - /* "View.MemoryView":436 + /* "View.MemoryView":437 * return obj * * cdef setitem_slice_assignment(self, dst, src): # <<<<<<<<<<<<<< @@ -6192,7 +6193,7 @@ static PyObject *__pyx_memoryview_setitem_slice_assignment(struct __pyx_memoryvi return __pyx_r; } -/* "View.MemoryView":444 +/* "View.MemoryView":445 * src.ndim, dst.ndim, self.dtype_is_object) * * cdef setitem_slice_assign_scalar(self, memoryview dst, value): # <<<<<<<<<<<<<< @@ -6221,7 +6222,7 @@ static PyObject *__pyx_memoryview_setitem_slice_assign_scalar(struct __pyx_memor PyObject *__pyx_t_11 = NULL; __Pyx_RefNannySetupContext("setitem_slice_assign_scalar", 0); - /* "View.MemoryView":446 + /* "View.MemoryView":447 * cdef setitem_slice_assign_scalar(self, memoryview dst, value): * cdef int array[128] * cdef void *tmp = NULL # <<<<<<<<<<<<<< @@ -6230,7 +6231,7 @@ static PyObject *__pyx_memoryview_setitem_slice_assign_scalar(struct __pyx_memor */ __pyx_v_tmp = NULL; 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if (__pyx_t_1) { - /* "View.MemoryView":523 + /* "View.MemoryView":524 * * if flags & PyBUF_STRIDES: * info.strides = self.view.strides # <<<<<<<<<<<<<< @@ -7187,7 +7188,7 @@ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_8__getbu __pyx_t_4 = __pyx_v_self->view.strides; __pyx_v_info->strides = __pyx_t_4; - /* "View.MemoryView":522 + /* "View.MemoryView":523 * info.shape = NULL * * if flags & PyBUF_STRIDES: # <<<<<<<<<<<<<< @@ -7197,7 +7198,7 @@ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_8__getbu goto __pyx_L7; } - /* "View.MemoryView":525 + /* "View.MemoryView":526 * info.strides = self.view.strides * else: * info.strides = NULL # <<<<<<<<<<<<<< @@ -7209,7 +7210,7 @@ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_8__getbu } __pyx_L7:; - /* "View.MemoryView":527 + /* "View.MemoryView":528 * info.strides = NULL * * if flags & PyBUF_INDIRECT: # <<<<<<<<<<<<<< @@ -7219,7 +7220,7 @@ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_8__getbu __pyx_t_1 = ((__pyx_v_flags & PyBUF_INDIRECT) != 0); 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__Pyx_INCREF(__pyx_t_9); __pyx_t_7++; if (unlikely(0 < 0)) __PYX_ERR(1, 741, __pyx_L1_error) + __pyx_t_9 = PyTuple_GET_ITEM(__pyx_t_3, __pyx_t_7); __Pyx_INCREF(__pyx_t_9); __pyx_t_7++; if (unlikely(0 < 0)) __PYX_ERR(1, 742, __pyx_L1_error) #else - __pyx_t_9 = PySequence_ITEM(__pyx_t_3, __pyx_t_7); __pyx_t_7++; if (unlikely(!__pyx_t_9)) __PYX_ERR(1, 741, __pyx_L1_error) + __pyx_t_9 = PySequence_ITEM(__pyx_t_3, __pyx_t_7); __pyx_t_7++; if (unlikely(!__pyx_t_9)) __PYX_ERR(1, 742, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); #endif } @@ -9726,7 +9727,7 @@ static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_ PyObject* exc_type = PyErr_Occurred(); if (exc_type) { if (likely(__Pyx_PyErr_GivenExceptionMatches(exc_type, PyExc_StopIteration))) PyErr_Clear(); - else __PYX_ERR(1, 741, __pyx_L1_error) + else __PYX_ERR(1, 742, __pyx_L1_error) } break; } @@ -9737,7 +9738,7 @@ static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_ __pyx_v_dim = __pyx_t_6; 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- /* "View.MemoryView":773 + /* "View.MemoryView":774 * if isinstance(memview, _memoryviewslice): * return memoryview_fromslice(dst, new_ndim, * memviewsliceobj.to_object_func, # <<<<<<<<<<<<<< * memviewsliceobj.to_dtype_func, * memview.dtype_is_object) */ - if (unlikely(!__pyx_v_memviewsliceobj)) { __Pyx_RaiseUnboundLocalError("memviewsliceobj"); __PYX_ERR(1, 773, __pyx_L1_error) } + if (unlikely(!__pyx_v_memviewsliceobj)) { __Pyx_RaiseUnboundLocalError("memviewsliceobj"); __PYX_ERR(1, 774, __pyx_L1_error) } - /* "View.MemoryView":774 + /* "View.MemoryView":775 * return memoryview_fromslice(dst, new_ndim, * memviewsliceobj.to_object_func, * memviewsliceobj.to_dtype_func, # <<<<<<<<<<<<<< * memview.dtype_is_object) * else: */ - if (unlikely(!__pyx_v_memviewsliceobj)) { __Pyx_RaiseUnboundLocalError("memviewsliceobj"); __PYX_ERR(1, 774, __pyx_L1_error) } + if (unlikely(!__pyx_v_memviewsliceobj)) { __Pyx_RaiseUnboundLocalError("memviewsliceobj"); __PYX_ERR(1, 775, __pyx_L1_error) } - /* "View.MemoryView":772 + /* "View.MemoryView":773 * * if isinstance(memview, _memoryviewslice): * return memoryview_fromslice(dst, new_ndim, # <<<<<<<<<<<<<< * memviewsliceobj.to_object_func, * memviewsliceobj.to_dtype_func, */ - __pyx_t_3 = __pyx_memoryview_fromslice(__pyx_v_dst, __pyx_v_new_ndim, __pyx_v_memviewsliceobj->to_object_func, __pyx_v_memviewsliceobj->to_dtype_func, __pyx_v_memview->dtype_is_object); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 772, __pyx_L1_error) + __pyx_t_3 = __pyx_memoryview_fromslice(__pyx_v_dst, __pyx_v_new_ndim, __pyx_v_memviewsliceobj->to_object_func, __pyx_v_memviewsliceobj->to_dtype_func, __pyx_v_memview->dtype_is_object); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 773, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); - if (!(likely(((__pyx_t_3) == Py_None) || likely(__Pyx_TypeTest(__pyx_t_3, __pyx_memoryview_type))))) __PYX_ERR(1, 772, __pyx_L1_error) + if (!(likely(((__pyx_t_3) == Py_None) || likely(__Pyx_TypeTest(__pyx_t_3, __pyx_memoryview_type))))) __PYX_ERR(1, 773, __pyx_L1_error) __pyx_r = ((struct __pyx_memoryview_obj *)__pyx_t_3); __pyx_t_3 = 0; goto __pyx_L0; - /* "View.MemoryView":771 + /* "View.MemoryView":772 * new_ndim += 1 * * if isinstance(memview, _memoryviewslice): # <<<<<<<<<<<<<< @@ -10029,7 +10030,7 @@ static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_ */ } - /* "View.MemoryView":777 + /* "View.MemoryView":778 * memview.dtype_is_object) * else: * return memoryview_fromslice(dst, new_ndim, NULL, NULL, # <<<<<<<<<<<<<< @@ -10039,30 +10040,30 @@ static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_ /*else*/ { __Pyx_XDECREF(((PyObject *)__pyx_r)); - /* "View.MemoryView":778 + /* "View.MemoryView":779 * else: * return memoryview_fromslice(dst, new_ndim, NULL, NULL, * memview.dtype_is_object) # <<<<<<<<<<<<<< * * */ - __pyx_t_3 = __pyx_memoryview_fromslice(__pyx_v_dst, __pyx_v_new_ndim, NULL, NULL, __pyx_v_memview->dtype_is_object); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 777, __pyx_L1_error) + __pyx_t_3 = __pyx_memoryview_fromslice(__pyx_v_dst, __pyx_v_new_ndim, NULL, NULL, __pyx_v_memview->dtype_is_object); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 778, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); - /* "View.MemoryView":777 + /* "View.MemoryView":778 * memview.dtype_is_object) * else: * return memoryview_fromslice(dst, new_ndim, NULL, NULL, # <<<<<<<<<<<<<< * memview.dtype_is_object) * */ - if (!(likely(((__pyx_t_3) == Py_None) || likely(__Pyx_TypeTest(__pyx_t_3, __pyx_memoryview_type))))) __PYX_ERR(1, 777, __pyx_L1_error) + if (!(likely(((__pyx_t_3) == Py_None) || likely(__Pyx_TypeTest(__pyx_t_3, __pyx_memoryview_type))))) __PYX_ERR(1, 778, __pyx_L1_error) __pyx_r = ((struct __pyx_memoryview_obj *)__pyx_t_3); __pyx_t_3 = 0; goto __pyx_L0; } - /* "View.MemoryView":705 + /* "View.MemoryView":706 * * @cname('__pyx_memview_slice') * cdef memoryview memview_slice(memoryview memview, object indices): # <<<<<<<<<<<<<< @@ -10084,7 +10085,7 @@ static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_ return __pyx_r; } -/* "View.MemoryView":802 +/* "View.MemoryView":803 * * @cname('__pyx_memoryview_slice_memviewslice') * cdef int slice_memviewslice( # <<<<<<<<<<<<<< @@ -10100,7 +10101,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, int __pyx_t_2; int __pyx_t_3; - /* "View.MemoryView":822 + /* "View.MemoryView":823 * cdef bint negative_step * * if not is_slice: # <<<<<<<<<<<<<< @@ -10110,7 +10111,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, __pyx_t_1 = ((!(__pyx_v_is_slice != 0)) != 0); if (__pyx_t_1) { - /* "View.MemoryView":824 + /* "View.MemoryView":825 * if not is_slice: * * if start < 0: # <<<<<<<<<<<<<< @@ -10120,7 +10121,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, __pyx_t_1 = ((__pyx_v_start < 0) != 0); if (__pyx_t_1) { - /* "View.MemoryView":825 + /* "View.MemoryView":826 * * if start < 0: * start += shape # <<<<<<<<<<<<<< @@ -10129,7 +10130,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, */ __pyx_v_start = (__pyx_v_start + __pyx_v_shape); - /* "View.MemoryView":824 + /* "View.MemoryView":825 * if not is_slice: * * if start < 0: # <<<<<<<<<<<<<< @@ -10138,7 +10139,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, */ } - /* "View.MemoryView":826 + /* "View.MemoryView":827 * if start < 0: * start += shape * if not 0 <= start < shape: # <<<<<<<<<<<<<< @@ -10152,16 +10153,16 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, __pyx_t_2 = ((!(__pyx_t_1 != 0)) != 0); if (__pyx_t_2) { - /* "View.MemoryView":827 + /* "View.MemoryView":828 * start += shape * if not 0 <= start < shape: * _err_dim(IndexError, "Index out of bounds (axis %d)", dim) # <<<<<<<<<<<<<< * else: * */ - __pyx_t_3 = __pyx_memoryview_err_dim(__pyx_builtin_IndexError, ((char *)"Index out of bounds (axis %d)"), __pyx_v_dim); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(1, 827, __pyx_L1_error) + __pyx_t_3 = __pyx_memoryview_err_dim(__pyx_builtin_IndexError, ((char *)"Index out of bounds (axis %d)"), __pyx_v_dim); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(1, 828, __pyx_L1_error) - /* "View.MemoryView":826 + /* "View.MemoryView":827 * if start < 0: * start += shape * if not 0 <= start < shape: # <<<<<<<<<<<<<< @@ -10170,7 +10171,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, */ } - /* "View.MemoryView":822 + /* "View.MemoryView":823 * cdef bint negative_step * * if not is_slice: # <<<<<<<<<<<<<< @@ -10180,7 +10181,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, goto __pyx_L3; } - /* "View.MemoryView":830 + /* "View.MemoryView":831 * else: * * negative_step = have_step != 0 and step < 0 # <<<<<<<<<<<<<< @@ -10199,7 +10200,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, __pyx_L6_bool_binop_done:; __pyx_v_negative_step = __pyx_t_2; - /* "View.MemoryView":832 + /* "View.MemoryView":833 * negative_step = have_step != 0 and step < 0 * * if have_step and step == 0: # <<<<<<<<<<<<<< @@ -10217,16 +10218,16 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, __pyx_L9_bool_binop_done:; if (__pyx_t_2) { - /* "View.MemoryView":833 + /* "View.MemoryView":834 * * if have_step and step == 0: * _err_dim(ValueError, "Step may not be zero (axis %d)", dim) # <<<<<<<<<<<<<< * * */ - __pyx_t_3 = __pyx_memoryview_err_dim(__pyx_builtin_ValueError, ((char *)"Step may not be zero (axis %d)"), __pyx_v_dim); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(1, 833, __pyx_L1_error) + __pyx_t_3 = __pyx_memoryview_err_dim(__pyx_builtin_ValueError, ((char *)"Step may not be zero (axis %d)"), __pyx_v_dim); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(1, 834, __pyx_L1_error) - /* "View.MemoryView":832 + /* "View.MemoryView":833 * negative_step = have_step != 0 and step < 0 * * if have_step and step == 0: # <<<<<<<<<<<<<< @@ -10235,7 +10236,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, */ } - /* "View.MemoryView":836 + /* "View.MemoryView":837 * * * if have_start: # <<<<<<<<<<<<<< @@ -10245,7 +10246,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, __pyx_t_2 = (__pyx_v_have_start != 0); if (__pyx_t_2) { - /* "View.MemoryView":837 + /* "View.MemoryView":838 * * if have_start: * if start < 0: # <<<<<<<<<<<<<< @@ -10255,7 +10256,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, __pyx_t_2 = ((__pyx_v_start < 0) != 0); if (__pyx_t_2) { - /* "View.MemoryView":838 + /* "View.MemoryView":839 * if have_start: * if start < 0: * start += shape # <<<<<<<<<<<<<< @@ -10264,7 +10265,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, */ __pyx_v_start = (__pyx_v_start + __pyx_v_shape); - /* "View.MemoryView":839 + /* "View.MemoryView":840 * if start < 0: * start += shape * if start < 0: # <<<<<<<<<<<<<< @@ -10274,7 +10275,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, __pyx_t_2 = ((__pyx_v_start < 0) != 0); if (__pyx_t_2) { - /* "View.MemoryView":840 + /* "View.MemoryView":841 * start += shape * if start < 0: * start = 0 # <<<<<<<<<<<<<< @@ -10283,7 +10284,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, */ __pyx_v_start = 0; - /* "View.MemoryView":839 + /* "View.MemoryView":840 * if start < 0: * start += shape * if start < 0: # <<<<<<<<<<<<<< @@ -10292,7 +10293,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, */ } - /* "View.MemoryView":837 + /* "View.MemoryView":838 * * if have_start: * if start < 0: # <<<<<<<<<<<<<< @@ -10302,7 +10303,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, goto __pyx_L12; } - /* "View.MemoryView":841 + /* "View.MemoryView":842 * if start < 0: * start = 0 * elif start >= shape: # <<<<<<<<<<<<<< @@ -10312,7 +10313,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, __pyx_t_2 = ((__pyx_v_start >= __pyx_v_shape) != 0); if (__pyx_t_2) { - /* "View.MemoryView":842 + /* "View.MemoryView":843 * start = 0 * elif start >= shape: * if negative_step: # <<<<<<<<<<<<<< @@ -10322,7 +10323,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, __pyx_t_2 = (__pyx_v_negative_step != 0); if (__pyx_t_2) { - /* "View.MemoryView":843 + /* "View.MemoryView":844 * elif start >= shape: * if negative_step: * start = shape - 1 # <<<<<<<<<<<<<< @@ -10331,7 +10332,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, */ __pyx_v_start = (__pyx_v_shape - 1); - /* "View.MemoryView":842 + /* "View.MemoryView":843 * start = 0 * elif start >= shape: * if negative_step: # <<<<<<<<<<<<<< @@ -10341,7 +10342,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, goto __pyx_L14; } - /* "View.MemoryView":845 + /* "View.MemoryView":846 * start = shape - 1 * else: * start = shape # <<<<<<<<<<<<<< @@ -10353,7 +10354,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, } __pyx_L14:; - /* "View.MemoryView":841 + /* "View.MemoryView":842 * if start < 0: * start = 0 * elif start >= shape: # <<<<<<<<<<<<<< @@ -10363,7 +10364,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, } __pyx_L12:; - /* "View.MemoryView":836 + /* "View.MemoryView":837 * * * if have_start: # <<<<<<<<<<<<<< @@ -10373,7 +10374,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, goto __pyx_L11; } - /* "View.MemoryView":847 + /* "View.MemoryView":848 * start = shape * else: * if negative_step: # <<<<<<<<<<<<<< @@ -10384,7 +10385,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, __pyx_t_2 = (__pyx_v_negative_step != 0); if (__pyx_t_2) { - /* "View.MemoryView":848 + /* "View.MemoryView":849 * else: * if negative_step: * start = shape - 1 # <<<<<<<<<<<<<< @@ -10393,7 +10394,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, */ __pyx_v_start = (__pyx_v_shape - 1); - /* "View.MemoryView":847 + /* "View.MemoryView":848 * start = shape * else: * if negative_step: # <<<<<<<<<<<<<< @@ -10403,7 +10404,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, goto __pyx_L15; } - /* "View.MemoryView":850 + /* "View.MemoryView":851 * start = shape - 1 * else: * start = 0 # <<<<<<<<<<<<<< @@ -10417,7 +10418,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, } __pyx_L11:; - /* "View.MemoryView":852 + /* "View.MemoryView":853 * start = 0 * * if have_stop: # <<<<<<<<<<<<<< @@ -10427,7 +10428,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, __pyx_t_2 = (__pyx_v_have_stop != 0); if (__pyx_t_2) { - /* "View.MemoryView":853 + /* "View.MemoryView":854 * * if have_stop: * if stop < 0: # <<<<<<<<<<<<<< @@ -10437,7 +10438,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, __pyx_t_2 = ((__pyx_v_stop < 0) != 0); if (__pyx_t_2) { - /* "View.MemoryView":854 + /* "View.MemoryView":855 * if have_stop: * if stop < 0: * stop += shape # <<<<<<<<<<<<<< @@ -10446,7 +10447,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, */ __pyx_v_stop = (__pyx_v_stop + __pyx_v_shape); - /* "View.MemoryView":855 + /* "View.MemoryView":856 * if stop < 0: * stop += shape * if stop < 0: # <<<<<<<<<<<<<< @@ -10456,7 +10457,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, __pyx_t_2 = ((__pyx_v_stop < 0) != 0); if (__pyx_t_2) { - /* "View.MemoryView":856 + /* "View.MemoryView":857 * stop += shape * if stop < 0: * stop = 0 # <<<<<<<<<<<<<< @@ -10465,7 +10466,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, */ __pyx_v_stop = 0; - /* "View.MemoryView":855 + /* "View.MemoryView":856 * if stop < 0: * stop += shape * if stop < 0: # <<<<<<<<<<<<<< @@ -10474,7 +10475,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, */ } - /* "View.MemoryView":853 + /* "View.MemoryView":854 * * if have_stop: * if stop < 0: # <<<<<<<<<<<<<< @@ -10484,7 +10485,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, goto __pyx_L17; } - /* "View.MemoryView":857 + /* "View.MemoryView":858 * if stop < 0: * stop = 0 * elif stop > shape: # <<<<<<<<<<<<<< @@ -10494,7 +10495,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, __pyx_t_2 = ((__pyx_v_stop > __pyx_v_shape) != 0); if (__pyx_t_2) { - /* "View.MemoryView":858 + /* "View.MemoryView":859 * stop = 0 * elif stop > shape: * stop = shape # <<<<<<<<<<<<<< @@ -10503,7 +10504,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, */ __pyx_v_stop = __pyx_v_shape; - /* "View.MemoryView":857 + /* "View.MemoryView":858 * if stop < 0: * stop = 0 * elif stop > shape: # <<<<<<<<<<<<<< @@ -10513,7 +10514,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, } __pyx_L17:; - /* "View.MemoryView":852 + /* "View.MemoryView":853 * start = 0 * * if have_stop: # <<<<<<<<<<<<<< @@ -10523,7 +10524,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, goto __pyx_L16; } - /* "View.MemoryView":860 + /* "View.MemoryView":861 * stop = shape * else: * if negative_step: # <<<<<<<<<<<<<< @@ -10534,7 +10535,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, __pyx_t_2 = (__pyx_v_negative_step != 0); if (__pyx_t_2) { - /* "View.MemoryView":861 + /* "View.MemoryView":862 * else: * if negative_step: * stop = -1 # <<<<<<<<<<<<<< @@ -10543,7 +10544,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, */ __pyx_v_stop = -1L; - /* "View.MemoryView":860 + /* "View.MemoryView":861 * stop = shape * else: * if negative_step: # <<<<<<<<<<<<<< @@ -10553,7 +10554,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, goto __pyx_L19; } - /* "View.MemoryView":863 + /* "View.MemoryView":864 * stop = -1 * else: * stop = shape # <<<<<<<<<<<<<< @@ -10567,7 +10568,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, } __pyx_L16:; - /* "View.MemoryView":865 + /* "View.MemoryView":866 * stop = shape * * if not have_step: # <<<<<<<<<<<<<< @@ -10577,7 +10578,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, __pyx_t_2 = ((!(__pyx_v_have_step != 0)) != 0); if (__pyx_t_2) { - /* "View.MemoryView":866 + /* "View.MemoryView":867 * * if not have_step: * step = 1 # <<<<<<<<<<<<<< @@ -10586,7 +10587,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, */ __pyx_v_step = 1; - /* "View.MemoryView":865 + /* "View.MemoryView":866 * stop = shape * * if not have_step: # <<<<<<<<<<<<<< @@ -10595,7 +10596,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, */ } - /* "View.MemoryView":870 + /* "View.MemoryView":871 * * with cython.cdivision(True): * new_shape = (stop - start) // step # <<<<<<<<<<<<<< @@ -10604,7 +10605,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, */ __pyx_v_new_shape = ((__pyx_v_stop - __pyx_v_start) / __pyx_v_step); - /* "View.MemoryView":872 + /* "View.MemoryView":873 * new_shape = (stop - start) // step * * if (stop - start) - step * new_shape: # <<<<<<<<<<<<<< @@ -10614,7 +10615,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, __pyx_t_2 = (((__pyx_v_stop - __pyx_v_start) - (__pyx_v_step * __pyx_v_new_shape)) != 0); if (__pyx_t_2) { - /* "View.MemoryView":873 + /* "View.MemoryView":874 * * if (stop - start) - step * new_shape: * new_shape += 1 # <<<<<<<<<<<<<< @@ -10623,7 +10624,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, */ __pyx_v_new_shape = (__pyx_v_new_shape + 1); - /* "View.MemoryView":872 + /* "View.MemoryView":873 * new_shape = (stop - start) // step * * if (stop - start) - step * new_shape: # <<<<<<<<<<<<<< @@ -10632,7 +10633,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, */ } - /* "View.MemoryView":875 + /* "View.MemoryView":876 * new_shape += 1 * * if new_shape < 0: # <<<<<<<<<<<<<< @@ -10642,7 +10643,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, __pyx_t_2 = ((__pyx_v_new_shape < 0) != 0); if (__pyx_t_2) { - /* "View.MemoryView":876 + /* "View.MemoryView":877 * * if new_shape < 0: * new_shape = 0 # <<<<<<<<<<<<<< @@ -10651,7 +10652,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, */ __pyx_v_new_shape = 0; - /* "View.MemoryView":875 + /* "View.MemoryView":876 * new_shape += 1 * * if new_shape < 0: # <<<<<<<<<<<<<< @@ -10660,7 +10661,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, */ } - /* "View.MemoryView":879 + /* "View.MemoryView":880 * * * dst.strides[new_ndim] = stride * step # <<<<<<<<<<<<<< @@ -10669,7 +10670,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, */ (__pyx_v_dst->strides[__pyx_v_new_ndim]) = (__pyx_v_stride * __pyx_v_step); - /* "View.MemoryView":880 + /* "View.MemoryView":881 * * dst.strides[new_ndim] = stride * step * dst.shape[new_ndim] = new_shape # <<<<<<<<<<<<<< @@ -10678,7 +10679,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, */ (__pyx_v_dst->shape[__pyx_v_new_ndim]) = __pyx_v_new_shape; - /* "View.MemoryView":881 + /* "View.MemoryView":882 * dst.strides[new_ndim] = stride * step * dst.shape[new_ndim] = new_shape * dst.suboffsets[new_ndim] = suboffset # <<<<<<<<<<<<<< @@ -10689,7 +10690,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, } __pyx_L3:; - /* "View.MemoryView":884 + /* "View.MemoryView":885 * * * if suboffset_dim[0] < 0: # <<<<<<<<<<<<<< @@ -10699,7 +10700,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, __pyx_t_2 = (((__pyx_v_suboffset_dim[0]) < 0) != 0); if (__pyx_t_2) { - /* "View.MemoryView":885 + /* "View.MemoryView":886 * * if suboffset_dim[0] < 0: * dst.data += start * stride # <<<<<<<<<<<<<< @@ -10708,7 +10709,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, */ __pyx_v_dst->data = (__pyx_v_dst->data + (__pyx_v_start * __pyx_v_stride)); - /* "View.MemoryView":884 + /* "View.MemoryView":885 * * * if suboffset_dim[0] < 0: # <<<<<<<<<<<<<< @@ -10718,7 +10719,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, goto __pyx_L23; } - /* "View.MemoryView":887 + /* "View.MemoryView":888 * dst.data += start * stride * else: * dst.suboffsets[suboffset_dim[0]] += start * stride # <<<<<<<<<<<<<< @@ -10731,7 +10732,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, } __pyx_L23:; - /* "View.MemoryView":889 + /* "View.MemoryView":890 * dst.suboffsets[suboffset_dim[0]] += start * stride * * if suboffset >= 0: # <<<<<<<<<<<<<< @@ -10741,7 +10742,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, __pyx_t_2 = ((__pyx_v_suboffset >= 0) != 0); if (__pyx_t_2) { - /* "View.MemoryView":890 + /* "View.MemoryView":891 * * if suboffset >= 0: * if not is_slice: # <<<<<<<<<<<<<< @@ -10751,7 +10752,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, __pyx_t_2 = ((!(__pyx_v_is_slice != 0)) != 0); if (__pyx_t_2) { - /* "View.MemoryView":891 + /* "View.MemoryView":892 * if suboffset >= 0: * if not is_slice: * if new_ndim == 0: # <<<<<<<<<<<<<< @@ -10761,7 +10762,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, __pyx_t_2 = ((__pyx_v_new_ndim == 0) != 0); if (__pyx_t_2) { - /* "View.MemoryView":892 + /* "View.MemoryView":893 * if not is_slice: * if new_ndim == 0: * dst.data = ( dst.data)[0] + suboffset # <<<<<<<<<<<<<< @@ -10770,7 +10771,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, */ __pyx_v_dst->data = ((((char **)__pyx_v_dst->data)[0]) + __pyx_v_suboffset); - /* "View.MemoryView":891 + /* "View.MemoryView":892 * if suboffset >= 0: * if not is_slice: * if new_ndim == 0: # <<<<<<<<<<<<<< @@ -10780,7 +10781,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, goto __pyx_L26; } - /* "View.MemoryView":894 + /* "View.MemoryView":895 * dst.data = ( dst.data)[0] + suboffset * else: * _err_dim(IndexError, "All dimensions preceding dimension %d " # <<<<<<<<<<<<<< @@ -10789,18 +10790,18 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, */ /*else*/ { - /* "View.MemoryView":895 + /* "View.MemoryView":896 * else: * _err_dim(IndexError, "All dimensions preceding dimension %d " * "must be indexed and not sliced", dim) # <<<<<<<<<<<<<< * else: * suboffset_dim[0] = new_ndim */ - __pyx_t_3 = __pyx_memoryview_err_dim(__pyx_builtin_IndexError, ((char *)"All dimensions preceding dimension %d must be indexed and not sliced"), __pyx_v_dim); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(1, 894, __pyx_L1_error) + __pyx_t_3 = __pyx_memoryview_err_dim(__pyx_builtin_IndexError, ((char *)"All dimensions preceding dimension %d must be indexed and not sliced"), __pyx_v_dim); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(1, 895, __pyx_L1_error) } __pyx_L26:; - /* "View.MemoryView":890 + /* "View.MemoryView":891 * * if suboffset >= 0: * if not is_slice: # <<<<<<<<<<<<<< @@ -10810,7 +10811,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, goto __pyx_L25; } - /* "View.MemoryView":897 + /* "View.MemoryView":898 * "must be indexed and not sliced", dim) * else: * suboffset_dim[0] = new_ndim # <<<<<<<<<<<<<< @@ -10822,7 +10823,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, } __pyx_L25:; - /* "View.MemoryView":889 + /* "View.MemoryView":890 * dst.suboffsets[suboffset_dim[0]] += start * stride * * if suboffset >= 0: # <<<<<<<<<<<<<< @@ -10831,7 +10832,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, */ } - /* "View.MemoryView":899 + /* "View.MemoryView":900 * suboffset_dim[0] = new_ndim * * return 0 # <<<<<<<<<<<<<< @@ -10841,7 +10842,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, __pyx_r = 0; goto __pyx_L0; - /* "View.MemoryView":802 + /* "View.MemoryView":803 * * @cname('__pyx_memoryview_slice_memviewslice') * cdef int slice_memviewslice( # <<<<<<<<<<<<<< @@ -10865,7 +10866,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, return __pyx_r; } -/* "View.MemoryView":905 +/* "View.MemoryView":906 * * @cname('__pyx_pybuffer_index') * cdef char *pybuffer_index(Py_buffer *view, char *bufp, Py_ssize_t index, # <<<<<<<<<<<<<< @@ -10887,7 +10888,7 @@ static char *__pyx_pybuffer_index(Py_buffer *__pyx_v_view, char *__pyx_v_bufp, P PyObject *__pyx_t_4 = NULL; __Pyx_RefNannySetupContext("pybuffer_index", 0); - /* "View.MemoryView":907 + /* "View.MemoryView":908 * cdef char *pybuffer_index(Py_buffer *view, char *bufp, Py_ssize_t index, * Py_ssize_t dim) except NULL: * cdef Py_ssize_t shape, stride, suboffset = -1 # <<<<<<<<<<<<<< @@ -10896,7 +10897,7 @@ static char *__pyx_pybuffer_index(Py_buffer *__pyx_v_view, char *__pyx_v_bufp, P */ __pyx_v_suboffset = -1L; - /* "View.MemoryView":908 + /* "View.MemoryView":909 * Py_ssize_t dim) except NULL: * cdef Py_ssize_t shape, stride, suboffset = -1 * cdef Py_ssize_t itemsize = view.itemsize # <<<<<<<<<<<<<< @@ -10906,7 +10907,7 @@ static char *__pyx_pybuffer_index(Py_buffer *__pyx_v_view, char *__pyx_v_bufp, P __pyx_t_1 = __pyx_v_view->itemsize; __pyx_v_itemsize = __pyx_t_1; - /* "View.MemoryView":911 + /* "View.MemoryView":912 * cdef char *resultp * * if view.ndim == 0: # <<<<<<<<<<<<<< @@ -10916,7 +10917,7 @@ static char *__pyx_pybuffer_index(Py_buffer *__pyx_v_view, char *__pyx_v_bufp, P __pyx_t_2 = ((__pyx_v_view->ndim == 0) != 0); if (__pyx_t_2) { - /* "View.MemoryView":912 + /* "View.MemoryView":913 * * if view.ndim == 0: * shape = view.len / itemsize # <<<<<<<<<<<<<< @@ -10925,15 +10926,15 @@ static char *__pyx_pybuffer_index(Py_buffer *__pyx_v_view, char *__pyx_v_bufp, P */ if (unlikely(__pyx_v_itemsize == 0)) { PyErr_SetString(PyExc_ZeroDivisionError, "integer division or modulo by zero"); - __PYX_ERR(1, 912, __pyx_L1_error) + __PYX_ERR(1, 913, __pyx_L1_error) } else if (sizeof(Py_ssize_t) == sizeof(long) && (!(((Py_ssize_t)-1) > 0)) && unlikely(__pyx_v_itemsize == (Py_ssize_t)-1) && unlikely(UNARY_NEG_WOULD_OVERFLOW(__pyx_v_view->len))) { PyErr_SetString(PyExc_OverflowError, "value too large to perform division"); - __PYX_ERR(1, 912, __pyx_L1_error) + __PYX_ERR(1, 913, __pyx_L1_error) } __pyx_v_shape = __Pyx_div_Py_ssize_t(__pyx_v_view->len, __pyx_v_itemsize); - /* "View.MemoryView":913 + /* "View.MemoryView":914 * if view.ndim == 0: * shape = view.len / itemsize * stride = itemsize # <<<<<<<<<<<<<< @@ -10942,7 +10943,7 @@ static char *__pyx_pybuffer_index(Py_buffer *__pyx_v_view, char *__pyx_v_bufp, P */ __pyx_v_stride = __pyx_v_itemsize; - /* "View.MemoryView":911 + /* "View.MemoryView":912 * cdef char *resultp * * if view.ndim == 0: # <<<<<<<<<<<<<< @@ -10952,7 +10953,7 @@ static char *__pyx_pybuffer_index(Py_buffer *__pyx_v_view, char *__pyx_v_bufp, P goto __pyx_L3; } - /* "View.MemoryView":915 + /* "View.MemoryView":916 * stride = itemsize * else: * shape = view.shape[dim] # <<<<<<<<<<<<<< @@ -10962,7 +10963,7 @@ static char *__pyx_pybuffer_index(Py_buffer *__pyx_v_view, char *__pyx_v_bufp, P /*else*/ { __pyx_v_shape = (__pyx_v_view->shape[__pyx_v_dim]); - /* "View.MemoryView":916 + /* "View.MemoryView":917 * else: * shape = view.shape[dim] * stride = view.strides[dim] # <<<<<<<<<<<<<< @@ -10971,7 +10972,7 @@ static char *__pyx_pybuffer_index(Py_buffer *__pyx_v_view, char *__pyx_v_bufp, P */ __pyx_v_stride = (__pyx_v_view->strides[__pyx_v_dim]); - /* "View.MemoryView":917 + /* "View.MemoryView":918 * shape = view.shape[dim] * stride = view.strides[dim] * if view.suboffsets != NULL: # <<<<<<<<<<<<<< @@ -10981,7 +10982,7 @@ static char *__pyx_pybuffer_index(Py_buffer *__pyx_v_view, char *__pyx_v_bufp, P __pyx_t_2 = ((__pyx_v_view->suboffsets != NULL) != 0); if (__pyx_t_2) { - /* "View.MemoryView":918 + /* "View.MemoryView":919 * stride = view.strides[dim] * if view.suboffsets != NULL: * suboffset = view.suboffsets[dim] # <<<<<<<<<<<<<< @@ -10990,7 +10991,7 @@ static char *__pyx_pybuffer_index(Py_buffer *__pyx_v_view, char *__pyx_v_bufp, P */ __pyx_v_suboffset = (__pyx_v_view->suboffsets[__pyx_v_dim]); 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if (unlikely(__pyx_t_2)) { - /* "View.MemoryView":923 + /* "View.MemoryView":924 * index += view.shape[dim] * if index < 0: * raise IndexError("Out of bounds on buffer access (axis %d)" % dim) # <<<<<<<<<<<<<< * * if index >= shape: */ - __pyx_t_3 = PyInt_FromSsize_t(__pyx_v_dim); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 923, __pyx_L1_error) + __pyx_t_3 = PyInt_FromSsize_t(__pyx_v_dim); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 924, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); - __pyx_t_4 = __Pyx_PyString_Format(__pyx_kp_s_Out_of_bounds_on_buffer_access_a, __pyx_t_3); if (unlikely(!__pyx_t_4)) __PYX_ERR(1, 923, __pyx_L1_error) + __pyx_t_4 = __Pyx_PyString_Format(__pyx_kp_s_Out_of_bounds_on_buffer_access_a, __pyx_t_3); if (unlikely(!__pyx_t_4)) __PYX_ERR(1, 924, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; - __pyx_t_3 = __Pyx_PyObject_CallOneArg(__pyx_builtin_IndexError, __pyx_t_4); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 923, __pyx_L1_error) + __pyx_t_3 = __Pyx_PyObject_CallOneArg(__pyx_builtin_IndexError, __pyx_t_4); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 924, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __Pyx_Raise(__pyx_t_3, 0, 0, 0); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; - __PYX_ERR(1, 923, __pyx_L1_error) + __PYX_ERR(1, 924, __pyx_L1_error) - /* "View.MemoryView":922 + /* "View.MemoryView":923 * if index < 0: * index += view.shape[dim] * if index < 0: # <<<<<<<<<<<<<< @@ -11058,7 +11059,7 @@ static char *__pyx_pybuffer_index(Py_buffer *__pyx_v_view, char *__pyx_v_bufp, P */ } - /* "View.MemoryView":920 + /* "View.MemoryView":921 * suboffset = view.suboffsets[dim] * * if index < 0: # <<<<<<<<<<<<<< @@ -11067,7 +11068,7 @@ static char *__pyx_pybuffer_index(Py_buffer *__pyx_v_view, char *__pyx_v_bufp, P */ } - /* "View.MemoryView":925 + /* "View.MemoryView":926 * raise IndexError("Out of bounds on buffer access (axis %d)" % dim) * * if index >= shape: # <<<<<<<<<<<<<< @@ -11077,26 +11078,26 @@ static char *__pyx_pybuffer_index(Py_buffer *__pyx_v_view, char *__pyx_v_bufp, P __pyx_t_2 = ((__pyx_v_index >= __pyx_v_shape) != 0); if (unlikely(__pyx_t_2)) { - /* "View.MemoryView":926 + /* "View.MemoryView":927 * * if index >= shape: * raise IndexError("Out of bounds on buffer access (axis %d)" % dim) # <<<<<<<<<<<<<< * * resultp = bufp + index * stride */ - __pyx_t_3 = PyInt_FromSsize_t(__pyx_v_dim); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 926, __pyx_L1_error) + __pyx_t_3 = PyInt_FromSsize_t(__pyx_v_dim); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 927, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); - __pyx_t_4 = __Pyx_PyString_Format(__pyx_kp_s_Out_of_bounds_on_buffer_access_a, __pyx_t_3); if (unlikely(!__pyx_t_4)) __PYX_ERR(1, 926, __pyx_L1_error) + __pyx_t_4 = __Pyx_PyString_Format(__pyx_kp_s_Out_of_bounds_on_buffer_access_a, __pyx_t_3); if (unlikely(!__pyx_t_4)) __PYX_ERR(1, 927, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; - __pyx_t_3 = __Pyx_PyObject_CallOneArg(__pyx_builtin_IndexError, __pyx_t_4); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 926, __pyx_L1_error) + __pyx_t_3 = __Pyx_PyObject_CallOneArg(__pyx_builtin_IndexError, __pyx_t_4); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 927, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __Pyx_Raise(__pyx_t_3, 0, 0, 0); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; - __PYX_ERR(1, 926, __pyx_L1_error) + __PYX_ERR(1, 927, __pyx_L1_error) - /* "View.MemoryView":925 + /* "View.MemoryView":926 * raise IndexError("Out of bounds on buffer access (axis %d)" % dim) * * if index >= shape: # <<<<<<<<<<<<<< @@ -11105,7 +11106,7 @@ static char *__pyx_pybuffer_index(Py_buffer *__pyx_v_view, char *__pyx_v_bufp, P */ } - /* "View.MemoryView":928 + /* "View.MemoryView":929 * raise IndexError("Out of bounds on buffer access (axis %d)" % dim) * * resultp = bufp + index * stride # <<<<<<<<<<<<<< @@ -11114,7 +11115,7 @@ static char *__pyx_pybuffer_index(Py_buffer *__pyx_v_view, char *__pyx_v_bufp, P */ __pyx_v_resultp = (__pyx_v_bufp + (__pyx_v_index * __pyx_v_stride)); - /* "View.MemoryView":929 + /* "View.MemoryView":930 * * resultp = bufp + index * stride * if suboffset >= 0: # <<<<<<<<<<<<<< @@ -11124,7 +11125,7 @@ static char *__pyx_pybuffer_index(Py_buffer *__pyx_v_view, char *__pyx_v_bufp, P __pyx_t_2 = ((__pyx_v_suboffset >= 0) != 0); if (__pyx_t_2) { - /* "View.MemoryView":930 + /* "View.MemoryView":931 * resultp = bufp + index * stride * if suboffset >= 0: * resultp = ( resultp)[0] + suboffset # <<<<<<<<<<<<<< @@ -11133,7 +11134,7 @@ static char *__pyx_pybuffer_index(Py_buffer *__pyx_v_view, char *__pyx_v_bufp, P */ __pyx_v_resultp = ((((char **)__pyx_v_resultp)[0]) + __pyx_v_suboffset); - /* "View.MemoryView":929 + /* "View.MemoryView":930 * * resultp = bufp + index * stride * if suboffset >= 0: # <<<<<<<<<<<<<< @@ -11142,7 +11143,7 @@ static char *__pyx_pybuffer_index(Py_buffer *__pyx_v_view, char *__pyx_v_bufp, P */ } - /* "View.MemoryView":932 + /* "View.MemoryView":933 * resultp = ( resultp)[0] + suboffset * * return resultp # <<<<<<<<<<<<<< @@ -11152,7 +11153,7 @@ static char *__pyx_pybuffer_index(Py_buffer *__pyx_v_view, char *__pyx_v_bufp, P __pyx_r = __pyx_v_resultp; goto __pyx_L0; - /* "View.MemoryView":905 + /* "View.MemoryView":906 * * @cname('__pyx_pybuffer_index') * cdef char *pybuffer_index(Py_buffer *view, char *bufp, Py_ssize_t index, # <<<<<<<<<<<<<< @@ -11171,7 +11172,7 @@ static char *__pyx_pybuffer_index(Py_buffer *__pyx_v_view, char *__pyx_v_bufp, P return __pyx_r; } -/* "View.MemoryView":938 +/* "View.MemoryView":939 * * @cname('__pyx_memslice_transpose') * cdef int transpose_memslice(__Pyx_memviewslice *memslice) nogil except 0: # <<<<<<<<<<<<<< @@ -11196,7 +11197,7 @@ static int __pyx_memslice_transpose(__Pyx_memviewslice *__pyx_v_memslice) { int __pyx_t_8; int __pyx_t_9; - /* "View.MemoryView":939 + /* "View.MemoryView":940 * @cname('__pyx_memslice_transpose') * cdef int transpose_memslice(__Pyx_memviewslice *memslice) nogil except 0: * cdef int ndim = memslice.memview.view.ndim # <<<<<<<<<<<<<< @@ -11206,7 +11207,7 @@ static int __pyx_memslice_transpose(__Pyx_memviewslice *__pyx_v_memslice) { __pyx_t_1 = __pyx_v_memslice->memview->view.ndim; __pyx_v_ndim = __pyx_t_1; - /* "View.MemoryView":941 + /* "View.MemoryView":942 * cdef int ndim = memslice.memview.view.ndim * * cdef Py_ssize_t *shape = memslice.shape # <<<<<<<<<<<<<< @@ -11216,7 +11217,7 @@ static int __pyx_memslice_transpose(__Pyx_memviewslice *__pyx_v_memslice) { __pyx_t_2 = __pyx_v_memslice->shape; __pyx_v_shape = __pyx_t_2; - /* "View.MemoryView":942 + /* "View.MemoryView":943 * * cdef Py_ssize_t *shape = memslice.shape * cdef Py_ssize_t *strides = memslice.strides # <<<<<<<<<<<<<< @@ -11226,7 +11227,7 @@ static int __pyx_memslice_transpose(__Pyx_memviewslice *__pyx_v_memslice) { __pyx_t_2 = __pyx_v_memslice->strides; __pyx_v_strides = __pyx_t_2; - /* "View.MemoryView":946 + /* "View.MemoryView":947 * * cdef int i, j * for i in range(ndim / 2): # <<<<<<<<<<<<<< @@ -11238,7 +11239,7 @@ static int __pyx_memslice_transpose(__Pyx_memviewslice *__pyx_v_memslice) { for (__pyx_t_1 = 0; __pyx_t_1 < __pyx_t_4; __pyx_t_1+=1) { __pyx_v_i = __pyx_t_1; - /* "View.MemoryView":947 + /* "View.MemoryView":948 * cdef int i, j * for i in range(ndim / 2): * j = ndim - 1 - i # <<<<<<<<<<<<<< @@ -11247,7 +11248,7 @@ static int __pyx_memslice_transpose(__Pyx_memviewslice *__pyx_v_memslice) { */ __pyx_v_j = ((__pyx_v_ndim - 1) - __pyx_v_i); - /* "View.MemoryView":948 + /* "View.MemoryView":949 * for i in range(ndim / 2): * j = ndim - 1 - i * strides[i], strides[j] = strides[j], strides[i] # <<<<<<<<<<<<<< @@ -11259,7 +11260,7 @@ static int __pyx_memslice_transpose(__Pyx_memviewslice *__pyx_v_memslice) { (__pyx_v_strides[__pyx_v_i]) = __pyx_t_5; (__pyx_v_strides[__pyx_v_j]) = __pyx_t_6; - /* "View.MemoryView":949 + /* "View.MemoryView":950 * j = ndim - 1 - i * strides[i], strides[j] = strides[j], strides[i] * shape[i], shape[j] = shape[j], shape[i] # <<<<<<<<<<<<<< @@ -11271,7 +11272,7 @@ static int __pyx_memslice_transpose(__Pyx_memviewslice *__pyx_v_memslice) { (__pyx_v_shape[__pyx_v_i]) = __pyx_t_6; (__pyx_v_shape[__pyx_v_j]) = __pyx_t_5; - /* "View.MemoryView":951 + /* "View.MemoryView":952 * shape[i], shape[j] = shape[j], shape[i] * * if memslice.suboffsets[i] >= 0 or memslice.suboffsets[j] >= 0: # <<<<<<<<<<<<<< @@ -11289,16 +11290,16 @@ static int __pyx_memslice_transpose(__Pyx_memviewslice *__pyx_v_memslice) { __pyx_L6_bool_binop_done:; if (__pyx_t_7) { - /* "View.MemoryView":952 + /* "View.MemoryView":953 * * if memslice.suboffsets[i] >= 0 or memslice.suboffsets[j] >= 0: * _err(ValueError, "Cannot transpose memoryview with indirect dimensions") # <<<<<<<<<<<<<< * * return 1 */ - __pyx_t_9 = __pyx_memoryview_err(__pyx_builtin_ValueError, ((char *)"Cannot transpose memoryview with indirect dimensions")); if (unlikely(__pyx_t_9 == ((int)-1))) __PYX_ERR(1, 952, __pyx_L1_error) + __pyx_t_9 = __pyx_memoryview_err(__pyx_builtin_ValueError, ((char *)"Cannot transpose memoryview with indirect dimensions")); if (unlikely(__pyx_t_9 == ((int)-1))) __PYX_ERR(1, 953, __pyx_L1_error) - /* "View.MemoryView":951 + /* "View.MemoryView":952 * shape[i], shape[j] = shape[j], shape[i] * * if memslice.suboffsets[i] >= 0 or memslice.suboffsets[j] >= 0: # <<<<<<<<<<<<<< @@ -11308,7 +11309,7 @@ static int __pyx_memslice_transpose(__Pyx_memviewslice *__pyx_v_memslice) { } } - /* "View.MemoryView":954 + /* "View.MemoryView":955 * _err(ValueError, "Cannot transpose memoryview with indirect dimensions") * * return 1 # <<<<<<<<<<<<<< @@ -11318,7 +11319,7 @@ static int __pyx_memslice_transpose(__Pyx_memviewslice *__pyx_v_memslice) { __pyx_r = 1; goto __pyx_L0; - /* "View.MemoryView":938 + /* "View.MemoryView":939 * * @cname('__pyx_memslice_transpose') * cdef int transpose_memslice(__Pyx_memviewslice *memslice) nogil except 0: # <<<<<<<<<<<<<< @@ -11342,7 +11343,7 @@ static int __pyx_memslice_transpose(__Pyx_memviewslice *__pyx_v_memslice) { return __pyx_r; 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__pyx_r = __pyx_t_2; __pyx_t_2 = 0; goto __pyx_L0; - /* "View.MemoryView":975 + /* "View.MemoryView":976 * * cdef convert_item_to_object(self, char *itemp): * if self.to_object_func != NULL: # <<<<<<<<<<<<<< @@ -11434,7 +11435,7 @@ static PyObject *__pyx_memoryviewslice_convert_item_to_object(struct __pyx_memor */ } - /* "View.MemoryView":978 + /* "View.MemoryView":979 * return self.to_object_func(itemp) * else: * return memoryview.convert_item_to_object(self, itemp) # <<<<<<<<<<<<<< @@ -11443,14 +11444,14 @@ static PyObject *__pyx_memoryviewslice_convert_item_to_object(struct __pyx_memor */ /*else*/ { __Pyx_XDECREF(__pyx_r); - __pyx_t_2 = __pyx_memoryview_convert_item_to_object(((struct __pyx_memoryview_obj *)__pyx_v_self), __pyx_v_itemp); if (unlikely(!__pyx_t_2)) __PYX_ERR(1, 978, __pyx_L1_error) + __pyx_t_2 = __pyx_memoryview_convert_item_to_object(((struct __pyx_memoryview_obj *)__pyx_v_self), __pyx_v_itemp); if (unlikely(!__pyx_t_2)) __PYX_ERR(1, 979, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_r = __pyx_t_2; __pyx_t_2 = 0; goto __pyx_L0; } - /* "View.MemoryView":974 + /* "View.MemoryView":975 * __PYX_XDEC_MEMVIEW(&self.from_slice, 1) * * cdef convert_item_to_object(self, char *itemp): # <<<<<<<<<<<<<< @@ -11469,7 +11470,7 @@ static PyObject *__pyx_memoryviewslice_convert_item_to_object(struct __pyx_memor return __pyx_r; } -/* "View.MemoryView":980 +/* "View.MemoryView":981 * return memoryview.convert_item_to_object(self, itemp) * * cdef assign_item_from_object(self, char *itemp, object value): # <<<<<<<<<<<<<< @@ -11485,7 +11486,7 @@ static PyObject *__pyx_memoryviewslice_assign_item_from_object(struct __pyx_memo PyObject *__pyx_t_3 = NULL; __Pyx_RefNannySetupContext("assign_item_from_object", 0); - /* "View.MemoryView":981 + /* "View.MemoryView":982 * * cdef assign_item_from_object(self, char *itemp, object value): * if self.to_dtype_func != NULL: # <<<<<<<<<<<<<< @@ -11495,16 +11496,16 @@ static PyObject *__pyx_memoryviewslice_assign_item_from_object(struct __pyx_memo __pyx_t_1 = ((__pyx_v_self->to_dtype_func != NULL) != 0); if (__pyx_t_1) { - /* "View.MemoryView":982 + /* "View.MemoryView":983 * cdef assign_item_from_object(self, char *itemp, object value): * if self.to_dtype_func != NULL: * self.to_dtype_func(itemp, value) # <<<<<<<<<<<<<< * else: * memoryview.assign_item_from_object(self, itemp, value) */ - __pyx_t_2 = __pyx_v_self->to_dtype_func(__pyx_v_itemp, __pyx_v_value); if (unlikely(__pyx_t_2 == ((int)0))) __PYX_ERR(1, 982, __pyx_L1_error) + __pyx_t_2 = __pyx_v_self->to_dtype_func(__pyx_v_itemp, __pyx_v_value); if (unlikely(__pyx_t_2 == ((int)0))) __PYX_ERR(1, 983, __pyx_L1_error) - /* "View.MemoryView":981 + /* "View.MemoryView":982 * * cdef assign_item_from_object(self, char *itemp, object value): * if self.to_dtype_func != NULL: # <<<<<<<<<<<<<< @@ -11514,7 +11515,7 @@ static PyObject *__pyx_memoryviewslice_assign_item_from_object(struct __pyx_memo goto __pyx_L3; } - /* "View.MemoryView":984 + /* "View.MemoryView":985 * self.to_dtype_func(itemp, value) * else: * memoryview.assign_item_from_object(self, itemp, value) # <<<<<<<<<<<<<< @@ -11522,13 +11523,13 @@ static PyObject *__pyx_memoryviewslice_assign_item_from_object(struct __pyx_memo * @property */ /*else*/ { - __pyx_t_3 = __pyx_memoryview_assign_item_from_object(((struct __pyx_memoryview_obj *)__pyx_v_self), __pyx_v_itemp, __pyx_v_value); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 984, __pyx_L1_error) + __pyx_t_3 = __pyx_memoryview_assign_item_from_object(((struct __pyx_memoryview_obj *)__pyx_v_self), __pyx_v_itemp, __pyx_v_value); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 985, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; } __pyx_L3:; - /* "View.MemoryView":980 + /* "View.MemoryView":981 * return memoryview.convert_item_to_object(self, itemp) * * cdef assign_item_from_object(self, char *itemp, object value): # <<<<<<<<<<<<<< @@ -11549,7 +11550,7 @@ static PyObject *__pyx_memoryviewslice_assign_item_from_object(struct __pyx_memo return __pyx_r; } -/* "View.MemoryView":987 +/* "View.MemoryView":988 * * @property * def base(self): # <<<<<<<<<<<<<< @@ -11575,7 +11576,7 @@ static PyObject *__pyx_pf_15View_dot_MemoryView_16_memoryviewslice_4base___get__ __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__get__", 0); - /* "View.MemoryView":988 + /* "View.MemoryView":989 * @property * def base(self): * return self.from_object # <<<<<<<<<<<<<< @@ -11587,7 +11588,7 @@ static PyObject *__pyx_pf_15View_dot_MemoryView_16_memoryviewslice_4base___get__ __pyx_r = __pyx_v_self->from_object; 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__Pyx_INCREF(Py_None); goto __pyx_L0; - /* "View.MemoryView":1002 + /* "View.MemoryView":1003 * cdef _memoryviewslice result * * if memviewslice.memview == Py_None: # <<<<<<<<<<<<<< @@ -11764,16 +11765,16 @@ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice __pyx_v_memviewsl */ } - /* "View.MemoryView":1008 + /* "View.MemoryView":1009 * * * result = _memoryviewslice(None, 0, dtype_is_object) # <<<<<<<<<<<<<< * * result.from_slice = memviewslice */ - __pyx_t_2 = __Pyx_PyBool_FromLong(__pyx_v_dtype_is_object); if (unlikely(!__pyx_t_2)) __PYX_ERR(1, 1008, __pyx_L1_error) + __pyx_t_2 = __Pyx_PyBool_FromLong(__pyx_v_dtype_is_object); if (unlikely(!__pyx_t_2)) __PYX_ERR(1, 1009, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); - __pyx_t_3 = PyTuple_New(3); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 1008, __pyx_L1_error) + __pyx_t_3 = PyTuple_New(3); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 1009, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_INCREF(Py_None); __Pyx_GIVEREF(Py_None); @@ -11784,13 +11785,13 @@ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice __pyx_v_memviewsl __Pyx_GIVEREF(__pyx_t_2); 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- /* "View.MemoryView":1013 + /* "View.MemoryView":1014 * __PYX_INC_MEMVIEW(&memviewslice, 1) * * result.from_object = ( memviewslice.memview).base # <<<<<<<<<<<<<< * result.typeinfo = memviewslice.memview.typeinfo * */ - __pyx_t_2 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_v_memviewslice.memview), __pyx_n_s_base); if (unlikely(!__pyx_t_2)) __PYX_ERR(1, 1013, __pyx_L1_error) + __pyx_t_2 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_v_memviewslice.memview), __pyx_n_s_base); if (unlikely(!__pyx_t_2)) __PYX_ERR(1, 1014, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_GIVEREF(__pyx_t_2); __Pyx_GOTREF(__pyx_v_result->from_object); @@ -11823,7 +11824,7 @@ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice __pyx_v_memviewsl __pyx_v_result->from_object = __pyx_t_2; __pyx_t_2 = 0; - /* "View.MemoryView":1014 + /* "View.MemoryView":1015 * * result.from_object = ( memviewslice.memview).base * result.typeinfo = memviewslice.memview.typeinfo # <<<<<<<<<<<<<< @@ -11833,7 +11834,7 @@ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice __pyx_v_memviewsl __pyx_t_4 = __pyx_v_memviewslice.memview->typeinfo; __pyx_v_result->__pyx_base.typeinfo = __pyx_t_4; - /* "View.MemoryView":1016 + /* "View.MemoryView":1017 * result.typeinfo = memviewslice.memview.typeinfo * * result.view = memviewslice.memview.view # <<<<<<<<<<<<<< @@ -11843,7 +11844,7 @@ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice __pyx_v_memviewsl __pyx_t_5 = __pyx_v_memviewslice.memview->view; __pyx_v_result->__pyx_base.view = __pyx_t_5; - /* "View.MemoryView":1017 + /* "View.MemoryView":1018 * * result.view = memviewslice.memview.view * result.view.buf = memviewslice.data # <<<<<<<<<<<<<< @@ -11852,7 +11853,7 @@ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice __pyx_v_memviewsl */ __pyx_v_result->__pyx_base.view.buf = ((void *)__pyx_v_memviewslice.data); - /* "View.MemoryView":1018 + /* "View.MemoryView":1019 * result.view = memviewslice.memview.view * result.view.buf = memviewslice.data * result.view.ndim = ndim # <<<<<<<<<<<<<< @@ -11861,7 +11862,7 @@ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice __pyx_v_memviewsl */ __pyx_v_result->__pyx_base.view.ndim = __pyx_v_ndim; - /* "View.MemoryView":1019 + /* "View.MemoryView":1020 * result.view.buf = memviewslice.data * result.view.ndim = ndim * (<__pyx_buffer *> &result.view).obj = Py_None # <<<<<<<<<<<<<< @@ -11870,7 +11871,7 @@ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice __pyx_v_memviewsl */ ((Py_buffer *)(&__pyx_v_result->__pyx_base.view))->obj = Py_None; - /* "View.MemoryView":1020 + /* "View.MemoryView":1021 * result.view.ndim = ndim * (<__pyx_buffer *> &result.view).obj = Py_None * Py_INCREF(Py_None) # <<<<<<<<<<<<<< @@ -11879,7 +11880,7 @@ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice __pyx_v_memviewsl */ Py_INCREF(Py_None); - /* "View.MemoryView":1022 + /* "View.MemoryView":1023 * Py_INCREF(Py_None) * * if (memviewslice.memview).flags & PyBUF_WRITABLE: # <<<<<<<<<<<<<< @@ -11889,7 +11890,7 @@ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice __pyx_v_memviewsl __pyx_t_1 = ((((struct __pyx_memoryview_obj *)__pyx_v_memviewslice.memview)->flags & PyBUF_WRITABLE) != 0); if (__pyx_t_1) { - /* "View.MemoryView":1023 + /* "View.MemoryView":1024 * * if (memviewslice.memview).flags & PyBUF_WRITABLE: * result.flags = PyBUF_RECORDS # <<<<<<<<<<<<<< @@ -11898,7 +11899,7 @@ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice __pyx_v_memviewsl */ __pyx_v_result->__pyx_base.flags = PyBUF_RECORDS; - /* "View.MemoryView":1022 + /* "View.MemoryView":1023 * Py_INCREF(Py_None) * * if (memviewslice.memview).flags & PyBUF_WRITABLE: # <<<<<<<<<<<<<< @@ -11908,7 +11909,7 @@ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice __pyx_v_memviewsl goto __pyx_L4; } - /* "View.MemoryView":1025 + /* "View.MemoryView":1026 * result.flags = PyBUF_RECORDS * else: * result.flags = PyBUF_RECORDS_RO # <<<<<<<<<<<<<< @@ -11920,7 +11921,7 @@ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice __pyx_v_memviewsl } __pyx_L4:; - /* "View.MemoryView":1027 + /* "View.MemoryView":1028 * result.flags = PyBUF_RECORDS_RO * * result.view.shape = result.from_slice.shape # <<<<<<<<<<<<<< @@ -11929,7 +11930,7 @@ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice __pyx_v_memviewsl */ __pyx_v_result->__pyx_base.view.shape = ((Py_ssize_t *)__pyx_v_result->from_slice.shape); - /* "View.MemoryView":1028 + /* "View.MemoryView":1029 * * result.view.shape = result.from_slice.shape * result.view.strides = result.from_slice.strides # <<<<<<<<<<<<<< @@ -11938,7 +11939,7 @@ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice __pyx_v_memviewsl */ __pyx_v_result->__pyx_base.view.strides = ((Py_ssize_t *)__pyx_v_result->from_slice.strides); - /* "View.MemoryView":1031 + /* "View.MemoryView":1032 * * * result.view.suboffsets = NULL # <<<<<<<<<<<<<< @@ -11947,7 +11948,7 @@ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice __pyx_v_memviewsl */ __pyx_v_result->__pyx_base.view.suboffsets = NULL; - /* "View.MemoryView":1032 + /* "View.MemoryView":1033 * * result.view.suboffsets = NULL * for suboffset in result.from_slice.suboffsets[:ndim]: # <<<<<<<<<<<<<< @@ -11959,7 +11960,7 @@ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice __pyx_v_memviewsl __pyx_t_6 = __pyx_t_8; __pyx_v_suboffset = (__pyx_t_6[0]); - /* "View.MemoryView":1033 + /* "View.MemoryView":1034 * result.view.suboffsets = NULL * for suboffset in result.from_slice.suboffsets[:ndim]: * if suboffset >= 0: # <<<<<<<<<<<<<< @@ -11969,7 +11970,7 @@ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice __pyx_v_memviewsl __pyx_t_1 = ((__pyx_v_suboffset >= 0) != 0); if (__pyx_t_1) { - /* "View.MemoryView":1034 + /* "View.MemoryView":1035 * for suboffset in result.from_slice.suboffsets[:ndim]: * if suboffset >= 0: * result.view.suboffsets = result.from_slice.suboffsets # <<<<<<<<<<<<<< @@ -11978,7 +11979,7 @@ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice __pyx_v_memviewsl */ __pyx_v_result->__pyx_base.view.suboffsets = ((Py_ssize_t *)__pyx_v_result->from_slice.suboffsets); - /* "View.MemoryView":1035 + /* "View.MemoryView":1036 * if suboffset >= 0: * result.view.suboffsets = result.from_slice.suboffsets * break # <<<<<<<<<<<<<< @@ -11987,7 +11988,7 @@ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice __pyx_v_memviewsl */ goto __pyx_L6_break; - /* "View.MemoryView":1033 + /* "View.MemoryView":1034 * result.view.suboffsets = NULL * for suboffset in result.from_slice.suboffsets[:ndim]: * if suboffset >= 0: # <<<<<<<<<<<<<< @@ -11998,7 +11999,7 @@ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice __pyx_v_memviewsl } __pyx_L6_break:; - /* "View.MemoryView":1037 + /* "View.MemoryView":1038 * break * * result.view.len = result.view.itemsize # <<<<<<<<<<<<<< @@ -12008,7 +12009,7 @@ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice __pyx_v_memviewsl __pyx_t_9 = __pyx_v_result->__pyx_base.view.itemsize; __pyx_v_result->__pyx_base.view.len = __pyx_t_9; - /* "View.MemoryView":1038 + /* "View.MemoryView":1039 * * result.view.len = result.view.itemsize * for length in result.view.shape[:ndim]: # <<<<<<<<<<<<<< @@ -12018,29 +12019,29 @@ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice __pyx_v_memviewsl __pyx_t_7 = (__pyx_v_result->__pyx_base.view.shape + __pyx_v_ndim); for (__pyx_t_8 = __pyx_v_result->__pyx_base.view.shape; __pyx_t_8 < __pyx_t_7; __pyx_t_8++) { __pyx_t_6 = __pyx_t_8; - __pyx_t_2 = PyInt_FromSsize_t((__pyx_t_6[0])); if (unlikely(!__pyx_t_2)) __PYX_ERR(1, 1038, __pyx_L1_error) + __pyx_t_2 = PyInt_FromSsize_t((__pyx_t_6[0])); if (unlikely(!__pyx_t_2)) __PYX_ERR(1, 1039, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_XDECREF_SET(__pyx_v_length, __pyx_t_2); __pyx_t_2 = 0; - /* "View.MemoryView":1039 + /* "View.MemoryView":1040 * result.view.len = result.view.itemsize * for length in result.view.shape[:ndim]: * result.view.len *= length # <<<<<<<<<<<<<< * * result.to_object_func = to_object_func */ - __pyx_t_2 = PyInt_FromSsize_t(__pyx_v_result->__pyx_base.view.len); if (unlikely(!__pyx_t_2)) __PYX_ERR(1, 1039, __pyx_L1_error) + __pyx_t_2 = PyInt_FromSsize_t(__pyx_v_result->__pyx_base.view.len); if (unlikely(!__pyx_t_2)) __PYX_ERR(1, 1040, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); - __pyx_t_3 = PyNumber_InPlaceMultiply(__pyx_t_2, __pyx_v_length); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 1039, __pyx_L1_error) + __pyx_t_3 = PyNumber_InPlaceMultiply(__pyx_t_2, __pyx_v_length); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 1040, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; - __pyx_t_9 = __Pyx_PyIndex_AsSsize_t(__pyx_t_3); if (unlikely((__pyx_t_9 == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(1, 1039, __pyx_L1_error) + __pyx_t_9 = __Pyx_PyIndex_AsSsize_t(__pyx_t_3); if (unlikely((__pyx_t_9 == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(1, 1040, __pyx_L1_error) __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_v_result->__pyx_base.view.len = __pyx_t_9; } - /* "View.MemoryView":1041 + /* "View.MemoryView":1042 * result.view.len *= length * * result.to_object_func = to_object_func # <<<<<<<<<<<<<< @@ -12049,7 +12050,7 @@ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice __pyx_v_memviewsl */ __pyx_v_result->to_object_func = __pyx_v_to_object_func; - /* "View.MemoryView":1042 + /* "View.MemoryView":1043 * * result.to_object_func = to_object_func * result.to_dtype_func = to_dtype_func # <<<<<<<<<<<<<< @@ -12058,7 +12059,7 @@ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice __pyx_v_memviewsl */ __pyx_v_result->to_dtype_func = __pyx_v_to_dtype_func; - /* "View.MemoryView":1044 + /* "View.MemoryView":1045 * result.to_dtype_func = to_dtype_func * * return result # <<<<<<<<<<<<<< @@ -12070,7 +12071,7 @@ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice __pyx_v_memviewsl __pyx_r = ((PyObject *)__pyx_v_result); goto __pyx_L0; - /* "View.MemoryView":994 + /* "View.MemoryView":995 * * @cname('__pyx_memoryview_fromslice') * cdef memoryview_fromslice(__Pyx_memviewslice memviewslice, # <<<<<<<<<<<<<< @@ -12092,7 +12093,7 @@ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice __pyx_v_memviewsl return __pyx_r; } -/* "View.MemoryView":1047 +/* "View.MemoryView":1048 * * @cname('__pyx_memoryview_get_slice_from_memoryview') * cdef __Pyx_memviewslice *get_slice_from_memview(memoryview memview, # <<<<<<<<<<<<<< @@ -12109,7 +12110,7 @@ static __Pyx_memviewslice *__pyx_memoryview_get_slice_from_memoryview(struct __p PyObject *__pyx_t_3 = NULL; __Pyx_RefNannySetupContext("get_slice_from_memview", 0); - /* "View.MemoryView":1050 + /* "View.MemoryView":1051 * __Pyx_memviewslice *mslice): * cdef _memoryviewslice obj * if isinstance(memview, _memoryviewslice): # <<<<<<<<<<<<<< @@ -12120,20 +12121,20 @@ static __Pyx_memviewslice *__pyx_memoryview_get_slice_from_memoryview(struct __p __pyx_t_2 = (__pyx_t_1 != 0); if (__pyx_t_2) { - /* "View.MemoryView":1051 + /* "View.MemoryView":1052 * cdef _memoryviewslice obj * if isinstance(memview, _memoryviewslice): * obj = memview # <<<<<<<<<<<<<< * return &obj.from_slice * else: */ - if (!(likely(((((PyObject *)__pyx_v_memview)) == Py_None) || likely(__Pyx_TypeTest(((PyObject *)__pyx_v_memview), __pyx_memoryviewslice_type))))) __PYX_ERR(1, 1051, __pyx_L1_error) + if (!(likely(((((PyObject *)__pyx_v_memview)) == Py_None) || likely(__Pyx_TypeTest(((PyObject *)__pyx_v_memview), __pyx_memoryviewslice_type))))) __PYX_ERR(1, 1052, __pyx_L1_error) __pyx_t_3 = ((PyObject *)__pyx_v_memview); __Pyx_INCREF(__pyx_t_3); __pyx_v_obj = ((struct __pyx_memoryviewslice_obj *)__pyx_t_3); __pyx_t_3 = 0; - /* "View.MemoryView":1052 + /* "View.MemoryView":1053 * if isinstance(memview, _memoryviewslice): * obj = memview * return &obj.from_slice # <<<<<<<<<<<<<< @@ -12143,7 +12144,7 @@ static __Pyx_memviewslice *__pyx_memoryview_get_slice_from_memoryview(struct __p __pyx_r = (&__pyx_v_obj->from_slice); goto __pyx_L0; - /* "View.MemoryView":1050 + /* "View.MemoryView":1051 * __Pyx_memviewslice *mslice): * cdef _memoryviewslice obj * if isinstance(memview, _memoryviewslice): # <<<<<<<<<<<<<< @@ -12152,7 +12153,7 @@ static __Pyx_memviewslice *__pyx_memoryview_get_slice_from_memoryview(struct __p */ } - /* "View.MemoryView":1054 + /* "View.MemoryView":1055 * return &obj.from_slice * else: * slice_copy(memview, mslice) # <<<<<<<<<<<<<< @@ -12162,7 +12163,7 @@ static __Pyx_memviewslice *__pyx_memoryview_get_slice_from_memoryview(struct __p /*else*/ { __pyx_memoryview_slice_copy(__pyx_v_memview, __pyx_v_mslice); - /* "View.MemoryView":1055 + /* "View.MemoryView":1056 * else: * slice_copy(memview, mslice) * return mslice # <<<<<<<<<<<<<< @@ -12173,7 +12174,7 @@ static __Pyx_memviewslice *__pyx_memoryview_get_slice_from_memoryview(struct __p goto __pyx_L0; } - /* "View.MemoryView":1047 + /* "View.MemoryView":1048 * * @cname('__pyx_memoryview_get_slice_from_memoryview') * cdef __Pyx_memviewslice *get_slice_from_memview(memoryview memview, # <<<<<<<<<<<<<< @@ -12192,7 +12193,7 @@ static __Pyx_memviewslice *__pyx_memoryview_get_slice_from_memoryview(struct __p return __pyx_r; } -/* "View.MemoryView":1058 +/* "View.MemoryView":1059 * * @cname('__pyx_memoryview_slice_copy') * cdef void slice_copy(memoryview memview, __Pyx_memviewslice *dst): # <<<<<<<<<<<<<< @@ -12213,7 +12214,7 @@ static void __pyx_memoryview_slice_copy(struct __pyx_memoryview_obj *__pyx_v_mem Py_ssize_t __pyx_t_5; __Pyx_RefNannySetupContext("slice_copy", 0); - /* "View.MemoryView":1062 + /* "View.MemoryView":1063 * cdef (Py_ssize_t*) shape, strides, suboffsets * * shape = memview.view.shape # <<<<<<<<<<<<<< @@ -12223,7 +12224,7 @@ static void __pyx_memoryview_slice_copy(struct __pyx_memoryview_obj *__pyx_v_mem __pyx_t_1 = __pyx_v_memview->view.shape; __pyx_v_shape = __pyx_t_1; - /* "View.MemoryView":1063 + /* "View.MemoryView":1064 * * shape = memview.view.shape * strides = memview.view.strides # <<<<<<<<<<<<<< @@ -12233,7 +12234,7 @@ static void __pyx_memoryview_slice_copy(struct __pyx_memoryview_obj *__pyx_v_mem __pyx_t_1 = __pyx_v_memview->view.strides; __pyx_v_strides = __pyx_t_1; - /* "View.MemoryView":1064 + /* "View.MemoryView":1065 * shape = memview.view.shape * strides = memview.view.strides * suboffsets = memview.view.suboffsets # <<<<<<<<<<<<<< @@ -12243,7 +12244,7 @@ static void __pyx_memoryview_slice_copy(struct __pyx_memoryview_obj *__pyx_v_mem __pyx_t_1 = __pyx_v_memview->view.suboffsets; __pyx_v_suboffsets = __pyx_t_1; - /* "View.MemoryView":1066 + /* "View.MemoryView":1067 * suboffsets = memview.view.suboffsets * * dst.memview = <__pyx_memoryview *> memview # <<<<<<<<<<<<<< @@ -12252,7 +12253,7 @@ static void __pyx_memoryview_slice_copy(struct __pyx_memoryview_obj *__pyx_v_mem */ __pyx_v_dst->memview = ((struct __pyx_memoryview_obj *)__pyx_v_memview); - /* "View.MemoryView":1067 + /* "View.MemoryView":1068 * * dst.memview = <__pyx_memoryview *> memview * dst.data = memview.view.buf # <<<<<<<<<<<<<< @@ -12261,7 +12262,7 @@ static void __pyx_memoryview_slice_copy(struct __pyx_memoryview_obj *__pyx_v_mem */ __pyx_v_dst->data = ((char *)__pyx_v_memview->view.buf); - /* "View.MemoryView":1069 + /* "View.MemoryView":1070 * dst.data = memview.view.buf * * for dim in range(memview.view.ndim): # <<<<<<<<<<<<<< @@ -12273,7 +12274,7 @@ static void __pyx_memoryview_slice_copy(struct __pyx_memoryview_obj *__pyx_v_mem for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) { __pyx_v_dim = __pyx_t_4; - /* "View.MemoryView":1070 + /* "View.MemoryView":1071 * * for dim in range(memview.view.ndim): * dst.shape[dim] = shape[dim] # <<<<<<<<<<<<<< @@ -12282,7 +12283,7 @@ static void __pyx_memoryview_slice_copy(struct __pyx_memoryview_obj *__pyx_v_mem */ (__pyx_v_dst->shape[__pyx_v_dim]) = (__pyx_v_shape[__pyx_v_dim]); - /* "View.MemoryView":1071 + /* "View.MemoryView":1072 * for dim in range(memview.view.ndim): * dst.shape[dim] = shape[dim] * dst.strides[dim] = strides[dim] # <<<<<<<<<<<<<< @@ -12291,7 +12292,7 @@ static void __pyx_memoryview_slice_copy(struct __pyx_memoryview_obj *__pyx_v_mem */ (__pyx_v_dst->strides[__pyx_v_dim]) = (__pyx_v_strides[__pyx_v_dim]); - /* "View.MemoryView":1072 + /* "View.MemoryView":1073 * dst.shape[dim] = shape[dim] * dst.strides[dim] = strides[dim] * dst.suboffsets[dim] = suboffsets[dim] if suboffsets else -1 # <<<<<<<<<<<<<< @@ -12306,7 +12307,7 @@ static void __pyx_memoryview_slice_copy(struct __pyx_memoryview_obj *__pyx_v_mem (__pyx_v_dst->suboffsets[__pyx_v_dim]) = __pyx_t_5; } - /* "View.MemoryView":1058 + /* "View.MemoryView":1059 * * @cname('__pyx_memoryview_slice_copy') * cdef void slice_copy(memoryview memview, __Pyx_memviewslice *dst): # <<<<<<<<<<<<<< @@ -12318,7 +12319,7 @@ static void __pyx_memoryview_slice_copy(struct __pyx_memoryview_obj *__pyx_v_mem __Pyx_RefNannyFinishContext(); } -/* "View.MemoryView":1075 +/* "View.MemoryView":1076 * * @cname('__pyx_memoryview_copy_object') * cdef memoryview_copy(memoryview memview): # <<<<<<<<<<<<<< @@ -12333,7 +12334,7 @@ static PyObject *__pyx_memoryview_copy_object(struct __pyx_memoryview_obj *__pyx PyObject *__pyx_t_1 = NULL; __Pyx_RefNannySetupContext("memoryview_copy", 0); - /* "View.MemoryView":1078 + /* "View.MemoryView":1079 * "Create a new memoryview object" * cdef __Pyx_memviewslice memviewslice * slice_copy(memview, &memviewslice) # <<<<<<<<<<<<<< @@ -12342,7 +12343,7 @@ static PyObject *__pyx_memoryview_copy_object(struct __pyx_memoryview_obj *__pyx */ __pyx_memoryview_slice_copy(__pyx_v_memview, (&__pyx_v_memviewslice)); - /* "View.MemoryView":1079 + /* "View.MemoryView":1080 * cdef __Pyx_memviewslice memviewslice * slice_copy(memview, &memviewslice) * return memoryview_copy_from_slice(memview, &memviewslice) # <<<<<<<<<<<<<< @@ -12350,13 +12351,13 @@ static PyObject *__pyx_memoryview_copy_object(struct __pyx_memoryview_obj *__pyx * @cname('__pyx_memoryview_copy_object_from_slice') */ __Pyx_XDECREF(__pyx_r); - __pyx_t_1 = __pyx_memoryview_copy_object_from_slice(__pyx_v_memview, (&__pyx_v_memviewslice)); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 1079, __pyx_L1_error) + __pyx_t_1 = __pyx_memoryview_copy_object_from_slice(__pyx_v_memview, (&__pyx_v_memviewslice)); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 1080, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_r = __pyx_t_1; __pyx_t_1 = 0; goto __pyx_L0; - /* "View.MemoryView":1075 + /* "View.MemoryView":1076 * * @cname('__pyx_memoryview_copy_object') * cdef memoryview_copy(memoryview memview): # <<<<<<<<<<<<<< @@ -12375,7 +12376,7 @@ static PyObject *__pyx_memoryview_copy_object(struct __pyx_memoryview_obj *__pyx return __pyx_r; } -/* "View.MemoryView":1082 +/* "View.MemoryView":1083 * * @cname('__pyx_memoryview_copy_object_from_slice') * cdef memoryview_copy_from_slice(memoryview memview, __Pyx_memviewslice *memviewslice): # <<<<<<<<<<<<<< @@ -12395,7 +12396,7 @@ static PyObject *__pyx_memoryview_copy_object_from_slice(struct __pyx_memoryview PyObject *__pyx_t_5 = NULL; __Pyx_RefNannySetupContext("memoryview_copy_from_slice", 0); - /* "View.MemoryView":1089 + /* "View.MemoryView":1090 * cdef int (*to_dtype_func)(char *, object) except 0 * * if isinstance(memview, _memoryviewslice): # <<<<<<<<<<<<<< @@ -12406,7 +12407,7 @@ static PyObject *__pyx_memoryview_copy_object_from_slice(struct __pyx_memoryview __pyx_t_2 = (__pyx_t_1 != 0); if (__pyx_t_2) { - /* "View.MemoryView":1090 + /* "View.MemoryView":1091 * * if isinstance(memview, _memoryviewslice): * to_object_func = (<_memoryviewslice> memview).to_object_func # <<<<<<<<<<<<<< @@ -12416,7 +12417,7 @@ static PyObject *__pyx_memoryview_copy_object_from_slice(struct __pyx_memoryview __pyx_t_3 = ((struct __pyx_memoryviewslice_obj *)__pyx_v_memview)->to_object_func; __pyx_v_to_object_func = __pyx_t_3; - /* "View.MemoryView":1091 + /* "View.MemoryView":1092 * if isinstance(memview, _memoryviewslice): * to_object_func = (<_memoryviewslice> memview).to_object_func * to_dtype_func = (<_memoryviewslice> memview).to_dtype_func # <<<<<<<<<<<<<< @@ -12426,7 +12427,7 @@ static PyObject *__pyx_memoryview_copy_object_from_slice(struct __pyx_memoryview __pyx_t_4 = ((struct __pyx_memoryviewslice_obj *)__pyx_v_memview)->to_dtype_func; __pyx_v_to_dtype_func = __pyx_t_4; - /* "View.MemoryView":1089 + /* "View.MemoryView":1090 * cdef int (*to_dtype_func)(char *, object) except 0 * * if isinstance(memview, _memoryviewslice): # <<<<<<<<<<<<<< @@ -12436,7 +12437,7 @@ static PyObject *__pyx_memoryview_copy_object_from_slice(struct __pyx_memoryview goto __pyx_L3; } - /* "View.MemoryView":1093 + /* "View.MemoryView":1094 * to_dtype_func = (<_memoryviewslice> memview).to_dtype_func * else: * to_object_func = NULL # <<<<<<<<<<<<<< @@ -12446,7 +12447,7 @@ static PyObject *__pyx_memoryview_copy_object_from_slice(struct __pyx_memoryview /*else*/ { __pyx_v_to_object_func = NULL; - /* "View.MemoryView":1094 + /* "View.MemoryView":1095 * else: * to_object_func = NULL * to_dtype_func = NULL # <<<<<<<<<<<<<< @@ -12457,7 +12458,7 @@ static PyObject *__pyx_memoryview_copy_object_from_slice(struct __pyx_memoryview } __pyx_L3:; - /* "View.MemoryView":1096 + /* "View.MemoryView":1097 * to_dtype_func = NULL * * return memoryview_fromslice(memviewslice[0], memview.view.ndim, # <<<<<<<<<<<<<< @@ -12466,20 +12467,20 @@ static PyObject *__pyx_memoryview_copy_object_from_slice(struct __pyx_memoryview */ __Pyx_XDECREF(__pyx_r); - /* "View.MemoryView":1098 + /* "View.MemoryView":1099 * return memoryview_fromslice(memviewslice[0], memview.view.ndim, * to_object_func, to_dtype_func, * memview.dtype_is_object) # <<<<<<<<<<<<<< * * */ - __pyx_t_5 = __pyx_memoryview_fromslice((__pyx_v_memviewslice[0]), __pyx_v_memview->view.ndim, __pyx_v_to_object_func, __pyx_v_to_dtype_func, __pyx_v_memview->dtype_is_object); if (unlikely(!__pyx_t_5)) __PYX_ERR(1, 1096, __pyx_L1_error) + __pyx_t_5 = __pyx_memoryview_fromslice((__pyx_v_memviewslice[0]), __pyx_v_memview->view.ndim, __pyx_v_to_object_func, __pyx_v_to_dtype_func, __pyx_v_memview->dtype_is_object); if (unlikely(!__pyx_t_5)) __PYX_ERR(1, 1097, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); __pyx_r = __pyx_t_5; __pyx_t_5 = 0; goto __pyx_L0; - /* "View.MemoryView":1082 + /* "View.MemoryView":1083 * * @cname('__pyx_memoryview_copy_object_from_slice') * cdef memoryview_copy_from_slice(memoryview memview, __Pyx_memviewslice *memviewslice): # <<<<<<<<<<<<<< @@ -12498,7 +12499,7 @@ static PyObject *__pyx_memoryview_copy_object_from_slice(struct __pyx_memoryview return __pyx_r; } -/* "View.MemoryView":1104 +/* "View.MemoryView":1105 * * * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil: # <<<<<<<<<<<<<< @@ -12510,7 +12511,7 @@ static Py_ssize_t abs_py_ssize_t(Py_ssize_t __pyx_v_arg) { Py_ssize_t __pyx_r; int __pyx_t_1; - /* "View.MemoryView":1105 + /* "View.MemoryView":1106 * * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil: * if arg < 0: # <<<<<<<<<<<<<< @@ -12520,7 +12521,7 @@ static Py_ssize_t abs_py_ssize_t(Py_ssize_t __pyx_v_arg) { __pyx_t_1 = ((__pyx_v_arg < 0) != 0); if (__pyx_t_1) { - /* "View.MemoryView":1106 + /* "View.MemoryView":1107 * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil: * if arg < 0: * return -arg # <<<<<<<<<<<<<< @@ -12530,7 +12531,7 @@ static Py_ssize_t abs_py_ssize_t(Py_ssize_t __pyx_v_arg) { __pyx_r = (-__pyx_v_arg); goto __pyx_L0; - /* "View.MemoryView":1105 + /* "View.MemoryView":1106 * * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil: * if arg < 0: # <<<<<<<<<<<<<< @@ -12539,7 +12540,7 @@ static Py_ssize_t abs_py_ssize_t(Py_ssize_t __pyx_v_arg) { */ } - /* "View.MemoryView":1108 + /* "View.MemoryView":1109 * return -arg * else: * return arg # <<<<<<<<<<<<<< @@ -12551,7 +12552,7 @@ static Py_ssize_t abs_py_ssize_t(Py_ssize_t __pyx_v_arg) { goto __pyx_L0; } - /* "View.MemoryView":1104 + /* "View.MemoryView":1105 * * * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil: # <<<<<<<<<<<<<< @@ -12564,7 +12565,7 @@ static Py_ssize_t abs_py_ssize_t(Py_ssize_t __pyx_v_arg) { return __pyx_r; } -/* "View.MemoryView":1111 +/* "View.MemoryView":1112 * * @cname('__pyx_get_best_slice_order') * cdef char get_best_order(__Pyx_memviewslice *mslice, int ndim) nogil: # <<<<<<<<<<<<<< @@ -12582,7 +12583,7 @@ static char __pyx_get_best_slice_order(__Pyx_memviewslice *__pyx_v_mslice, int _ int __pyx_t_3; int __pyx_t_4; - /* "View.MemoryView":1116 + /* "View.MemoryView":1117 * """ * cdef int i * cdef Py_ssize_t c_stride = 0 # <<<<<<<<<<<<<< @@ -12591,7 +12592,7 @@ static char __pyx_get_best_slice_order(__Pyx_memviewslice *__pyx_v_mslice, int _ */ __pyx_v_c_stride = 0; - /* "View.MemoryView":1117 + /* "View.MemoryView":1118 * cdef int i * cdef Py_ssize_t c_stride = 0 * cdef Py_ssize_t f_stride = 0 # <<<<<<<<<<<<<< @@ -12600,7 +12601,7 @@ static char __pyx_get_best_slice_order(__Pyx_memviewslice *__pyx_v_mslice, int _ */ __pyx_v_f_stride = 0; - /* "View.MemoryView":1119 + /* "View.MemoryView":1120 * cdef Py_ssize_t f_stride = 0 * * for i in range(ndim - 1, -1, -1): # <<<<<<<<<<<<<< @@ -12610,7 +12611,7 @@ static char __pyx_get_best_slice_order(__Pyx_memviewslice *__pyx_v_mslice, int _ for (__pyx_t_1 = (__pyx_v_ndim - 1); __pyx_t_1 > -1; __pyx_t_1-=1) { __pyx_v_i = __pyx_t_1; - /* "View.MemoryView":1120 + /* "View.MemoryView":1121 * * for i in range(ndim - 1, -1, -1): * if mslice.shape[i] > 1: # <<<<<<<<<<<<<< @@ -12620,7 +12621,7 @@ static char __pyx_get_best_slice_order(__Pyx_memviewslice *__pyx_v_mslice, int _ __pyx_t_2 = (((__pyx_v_mslice->shape[__pyx_v_i]) > 1) != 0); if (__pyx_t_2) { - /* "View.MemoryView":1121 + /* "View.MemoryView":1122 * for i in range(ndim - 1, -1, -1): * if mslice.shape[i] > 1: * c_stride = mslice.strides[i] # <<<<<<<<<<<<<< @@ -12629,7 +12630,7 @@ static char __pyx_get_best_slice_order(__Pyx_memviewslice *__pyx_v_mslice, int _ */ __pyx_v_c_stride = (__pyx_v_mslice->strides[__pyx_v_i]); - /* "View.MemoryView":1122 + /* "View.MemoryView":1123 * if mslice.shape[i] > 1: * c_stride = mslice.strides[i] * break # <<<<<<<<<<<<<< @@ -12638,7 +12639,7 @@ static char __pyx_get_best_slice_order(__Pyx_memviewslice *__pyx_v_mslice, int _ */ goto __pyx_L4_break; - /* "View.MemoryView":1120 + /* "View.MemoryView":1121 * * for i in range(ndim - 1, -1, -1): * if mslice.shape[i] > 1: # <<<<<<<<<<<<<< @@ -12649,7 +12650,7 @@ static char __pyx_get_best_slice_order(__Pyx_memviewslice *__pyx_v_mslice, int _ } __pyx_L4_break:; - /* "View.MemoryView":1124 + /* "View.MemoryView":1125 * break * * for i in range(ndim): # <<<<<<<<<<<<<< @@ -12661,7 +12662,7 @@ static char __pyx_get_best_slice_order(__Pyx_memviewslice *__pyx_v_mslice, int _ for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) { __pyx_v_i = __pyx_t_4; - /* "View.MemoryView":1125 + /* "View.MemoryView":1126 * * for i in range(ndim): * if mslice.shape[i] > 1: # <<<<<<<<<<<<<< @@ -12671,7 +12672,7 @@ static char __pyx_get_best_slice_order(__Pyx_memviewslice *__pyx_v_mslice, int _ __pyx_t_2 = (((__pyx_v_mslice->shape[__pyx_v_i]) > 1) != 0); if (__pyx_t_2) { - /* "View.MemoryView":1126 + /* "View.MemoryView":1127 * for i in range(ndim): * if mslice.shape[i] > 1: * f_stride = mslice.strides[i] # <<<<<<<<<<<<<< @@ -12680,7 +12681,7 @@ static char __pyx_get_best_slice_order(__Pyx_memviewslice *__pyx_v_mslice, int _ */ __pyx_v_f_stride = (__pyx_v_mslice->strides[__pyx_v_i]); - /* "View.MemoryView":1127 + /* "View.MemoryView":1128 * if mslice.shape[i] > 1: * f_stride = mslice.strides[i] * break # <<<<<<<<<<<<<< @@ -12689,7 +12690,7 @@ static char __pyx_get_best_slice_order(__Pyx_memviewslice *__pyx_v_mslice, int _ */ goto __pyx_L7_break; - /* "View.MemoryView":1125 + /* "View.MemoryView":1126 * * for i in range(ndim): * if mslice.shape[i] > 1: # <<<<<<<<<<<<<< @@ -12700,7 +12701,7 @@ static char __pyx_get_best_slice_order(__Pyx_memviewslice *__pyx_v_mslice, int _ } __pyx_L7_break:; - /* "View.MemoryView":1129 + /* "View.MemoryView":1130 * break * * if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride): # <<<<<<<<<<<<<< @@ -12710,7 +12711,7 @@ static char __pyx_get_best_slice_order(__Pyx_memviewslice *__pyx_v_mslice, int _ __pyx_t_2 = ((abs_py_ssize_t(__pyx_v_c_stride) <= abs_py_ssize_t(__pyx_v_f_stride)) != 0); if (__pyx_t_2) { - /* "View.MemoryView":1130 + /* "View.MemoryView":1131 * * if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride): * return 'C' # <<<<<<<<<<<<<< @@ -12720,7 +12721,7 @@ static char __pyx_get_best_slice_order(__Pyx_memviewslice *__pyx_v_mslice, int _ __pyx_r = 'C'; goto __pyx_L0; - /* "View.MemoryView":1129 + /* "View.MemoryView":1130 * break * * if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride): # <<<<<<<<<<<<<< @@ -12729,7 +12730,7 @@ static char __pyx_get_best_slice_order(__Pyx_memviewslice *__pyx_v_mslice, int _ */ } - /* "View.MemoryView":1132 + /* "View.MemoryView":1133 * return 'C' * else: * return 'F' # <<<<<<<<<<<<<< @@ -12741,7 +12742,7 @@ static char __pyx_get_best_slice_order(__Pyx_memviewslice *__pyx_v_mslice, int _ goto __pyx_L0; } - /* "View.MemoryView":1111 + /* "View.MemoryView":1112 * * @cname('__pyx_get_best_slice_order') * cdef char get_best_order(__Pyx_memviewslice *mslice, int ndim) nogil: # <<<<<<<<<<<<<< @@ -12754,7 +12755,7 @@ static char __pyx_get_best_slice_order(__Pyx_memviewslice *__pyx_v_mslice, int _ return __pyx_r; } -/* "View.MemoryView":1135 +/* "View.MemoryView":1136 * * @cython.cdivision(True) * cdef void _copy_strided_to_strided(char *src_data, Py_ssize_t *src_strides, # <<<<<<<<<<<<<< @@ -12775,7 +12776,7 @@ static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v Py_ssize_t __pyx_t_5; Py_ssize_t __pyx_t_6; - /* "View.MemoryView":1142 + /* "View.MemoryView":1143 * * cdef Py_ssize_t i * cdef Py_ssize_t src_extent = src_shape[0] # <<<<<<<<<<<<<< @@ -12784,7 +12785,7 @@ static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v */ __pyx_v_src_extent = (__pyx_v_src_shape[0]); - /* "View.MemoryView":1143 + /* "View.MemoryView":1144 * cdef Py_ssize_t i * cdef Py_ssize_t src_extent = src_shape[0] * cdef Py_ssize_t dst_extent = dst_shape[0] # <<<<<<<<<<<<<< @@ -12793,7 +12794,7 @@ static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v */ __pyx_v_dst_extent = (__pyx_v_dst_shape[0]); - /* "View.MemoryView":1144 + /* "View.MemoryView":1145 * cdef Py_ssize_t src_extent = src_shape[0] * cdef Py_ssize_t dst_extent = dst_shape[0] * cdef Py_ssize_t src_stride = src_strides[0] # <<<<<<<<<<<<<< @@ -12802,7 +12803,7 @@ static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v */ __pyx_v_src_stride = (__pyx_v_src_strides[0]); - /* "View.MemoryView":1145 + /* "View.MemoryView":1146 * cdef Py_ssize_t dst_extent = dst_shape[0] * cdef Py_ssize_t src_stride = src_strides[0] * cdef Py_ssize_t dst_stride = dst_strides[0] # <<<<<<<<<<<<<< @@ -12811,7 +12812,7 @@ static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v */ __pyx_v_dst_stride = (__pyx_v_dst_strides[0]); - /* "View.MemoryView":1147 + /* "View.MemoryView":1148 * cdef Py_ssize_t dst_stride = dst_strides[0] * * if ndim == 1: # <<<<<<<<<<<<<< @@ -12821,7 +12822,7 @@ static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v __pyx_t_1 = ((__pyx_v_ndim == 1) != 0); if (__pyx_t_1) { - /* "View.MemoryView":1148 + /* "View.MemoryView":1149 * * if ndim == 1: * if (src_stride > 0 and dst_stride > 0 and # <<<<<<<<<<<<<< @@ -12841,7 +12842,7 @@ static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v goto __pyx_L5_bool_binop_done; } - /* "View.MemoryView":1149 + /* "View.MemoryView":1150 * if ndim == 1: * if (src_stride > 0 and dst_stride > 0 and * src_stride == itemsize == dst_stride): # <<<<<<<<<<<<<< @@ -12856,7 +12857,7 @@ static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v __pyx_t_1 = __pyx_t_3; __pyx_L5_bool_binop_done:; - /* "View.MemoryView":1148 + /* "View.MemoryView":1149 * * if ndim == 1: * if (src_stride > 0 and dst_stride > 0 and # <<<<<<<<<<<<<< @@ -12865,7 +12866,7 @@ static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v */ if (__pyx_t_1) { - /* "View.MemoryView":1150 + /* "View.MemoryView":1151 * if (src_stride > 0 and dst_stride > 0 and * src_stride == itemsize == dst_stride): * memcpy(dst_data, src_data, itemsize * dst_extent) # <<<<<<<<<<<<<< @@ -12874,7 +12875,7 @@ static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v */ (void)(memcpy(__pyx_v_dst_data, __pyx_v_src_data, (__pyx_v_itemsize * __pyx_v_dst_extent))); - /* "View.MemoryView":1148 + /* "View.MemoryView":1149 * * if ndim == 1: * if (src_stride > 0 and dst_stride > 0 and # <<<<<<<<<<<<<< @@ -12884,7 +12885,7 @@ static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v goto __pyx_L4; } - /* "View.MemoryView":1152 + /* "View.MemoryView":1153 * memcpy(dst_data, src_data, itemsize * dst_extent) * else: * for i in range(dst_extent): # <<<<<<<<<<<<<< @@ -12897,7 +12898,7 @@ static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) { __pyx_v_i = __pyx_t_6; - /* "View.MemoryView":1153 + /* "View.MemoryView":1154 * else: * for i in range(dst_extent): * memcpy(dst_data, src_data, itemsize) # <<<<<<<<<<<<<< @@ -12906,7 +12907,7 @@ static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v */ (void)(memcpy(__pyx_v_dst_data, __pyx_v_src_data, __pyx_v_itemsize)); - /* "View.MemoryView":1154 + /* "View.MemoryView":1155 * for i in range(dst_extent): * memcpy(dst_data, src_data, itemsize) * src_data += src_stride # <<<<<<<<<<<<<< @@ -12915,7 +12916,7 @@ static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v */ __pyx_v_src_data = (__pyx_v_src_data + __pyx_v_src_stride); - /* "View.MemoryView":1155 + /* "View.MemoryView":1156 * memcpy(dst_data, src_data, itemsize) * src_data += src_stride * dst_data += dst_stride # <<<<<<<<<<<<<< @@ -12927,7 +12928,7 @@ static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v } __pyx_L4:; - /* "View.MemoryView":1147 + /* "View.MemoryView":1148 * cdef Py_ssize_t dst_stride = dst_strides[0] * * if ndim == 1: # <<<<<<<<<<<<<< @@ -12937,7 +12938,7 @@ static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v goto __pyx_L3; } - /* "View.MemoryView":1157 + /* "View.MemoryView":1158 * dst_data += dst_stride * else: * for i in range(dst_extent): # <<<<<<<<<<<<<< @@ -12950,7 +12951,7 @@ static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) { __pyx_v_i = __pyx_t_6; - /* "View.MemoryView":1158 + /* "View.MemoryView":1159 * else: * for i in range(dst_extent): * _copy_strided_to_strided(src_data, src_strides + 1, # <<<<<<<<<<<<<< @@ -12959,7 +12960,7 @@ static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v */ _copy_strided_to_strided(__pyx_v_src_data, (__pyx_v_src_strides + 1), __pyx_v_dst_data, (__pyx_v_dst_strides + 1), (__pyx_v_src_shape + 1), (__pyx_v_dst_shape + 1), (__pyx_v_ndim - 1), __pyx_v_itemsize); - /* "View.MemoryView":1162 + /* "View.MemoryView":1163 * src_shape + 1, dst_shape + 1, * ndim - 1, itemsize) * src_data += src_stride # <<<<<<<<<<<<<< @@ -12968,7 +12969,7 @@ static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v */ __pyx_v_src_data = (__pyx_v_src_data + __pyx_v_src_stride); - /* "View.MemoryView":1163 + /* "View.MemoryView":1164 * ndim - 1, itemsize) * src_data += src_stride * dst_data += dst_stride # <<<<<<<<<<<<<< @@ -12980,7 +12981,7 @@ static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v } __pyx_L3:; - /* "View.MemoryView":1135 + /* "View.MemoryView":1136 * * @cython.cdivision(True) * cdef void _copy_strided_to_strided(char *src_data, Py_ssize_t *src_strides, # <<<<<<<<<<<<<< @@ -12991,7 +12992,7 @@ static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v /* function exit code */ } -/* "View.MemoryView":1165 +/* "View.MemoryView":1166 * dst_data += dst_stride * * cdef void copy_strided_to_strided(__Pyx_memviewslice *src, # <<<<<<<<<<<<<< @@ -13001,7 +13002,7 @@ static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v static void copy_strided_to_strided(__Pyx_memviewslice *__pyx_v_src, __Pyx_memviewslice *__pyx_v_dst, int __pyx_v_ndim, size_t __pyx_v_itemsize) { - /* "View.MemoryView":1168 + /* "View.MemoryView":1169 * __Pyx_memviewslice *dst, * int ndim, size_t itemsize) nogil: * _copy_strided_to_strided(src.data, src.strides, dst.data, dst.strides, # <<<<<<<<<<<<<< @@ -13010,7 +13011,7 @@ static void copy_strided_to_strided(__Pyx_memviewslice *__pyx_v_src, __Pyx_memvi */ _copy_strided_to_strided(__pyx_v_src->data, __pyx_v_src->strides, __pyx_v_dst->data, __pyx_v_dst->strides, __pyx_v_src->shape, __pyx_v_dst->shape, __pyx_v_ndim, __pyx_v_itemsize); - /* "View.MemoryView":1165 + /* "View.MemoryView":1166 * dst_data += dst_stride * * cdef void copy_strided_to_strided(__Pyx_memviewslice *src, # <<<<<<<<<<<<<< @@ -13021,7 +13022,7 @@ static void copy_strided_to_strided(__Pyx_memviewslice *__pyx_v_src, __Pyx_memvi /* function exit code */ } -/* "View.MemoryView":1172 +/* "View.MemoryView":1173 * * @cname('__pyx_memoryview_slice_get_size') * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) nogil: # <<<<<<<<<<<<<< @@ -13038,7 +13039,7 @@ static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *__pyx_v_sr int __pyx_t_3; int __pyx_t_4; - /* "View.MemoryView":1175 + /* "View.MemoryView":1176 * "Return the size of the memory occupied by the slice in number of bytes" * cdef int i * cdef Py_ssize_t size = src.memview.view.itemsize # <<<<<<<<<<<<<< @@ -13048,7 +13049,7 @@ static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *__pyx_v_sr __pyx_t_1 = __pyx_v_src->memview->view.itemsize; __pyx_v_size = __pyx_t_1; - /* "View.MemoryView":1177 + /* "View.MemoryView":1178 * cdef Py_ssize_t size = src.memview.view.itemsize * * for i in range(ndim): # <<<<<<<<<<<<<< @@ -13060,7 +13061,7 @@ static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *__pyx_v_sr for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) { __pyx_v_i = __pyx_t_4; - /* "View.MemoryView":1178 + /* "View.MemoryView":1179 * * for i in range(ndim): * size *= src.shape[i] # <<<<<<<<<<<<<< @@ -13070,7 +13071,7 @@ static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *__pyx_v_sr __pyx_v_size = (__pyx_v_size * (__pyx_v_src->shape[__pyx_v_i])); } - /* "View.MemoryView":1180 + /* "View.MemoryView":1181 * size *= src.shape[i] * * return size # <<<<<<<<<<<<<< @@ -13080,7 +13081,7 @@ static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *__pyx_v_sr __pyx_r = __pyx_v_size; goto __pyx_L0; - /* "View.MemoryView":1172 + /* "View.MemoryView":1173 * * @cname('__pyx_memoryview_slice_get_size') * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) nogil: # <<<<<<<<<<<<<< @@ -13093,7 +13094,7 @@ static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *__pyx_v_sr return __pyx_r; } -/* "View.MemoryView":1183 +/* "View.MemoryView":1184 * * @cname('__pyx_fill_contig_strides_array') * cdef Py_ssize_t fill_contig_strides_array( # <<<<<<<<<<<<<< @@ -13109,7 +13110,7 @@ static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *__pyx_v_shape, Py_ int __pyx_t_3; int __pyx_t_4; - /* "View.MemoryView":1192 + /* "View.MemoryView":1193 * cdef int idx * * if order == 'F': # <<<<<<<<<<<<<< @@ -13119,7 +13120,7 @@ static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *__pyx_v_shape, Py_ __pyx_t_1 = ((__pyx_v_order == 'F') != 0); if (__pyx_t_1) { - /* "View.MemoryView":1193 + /* "View.MemoryView":1194 * * if order == 'F': * for idx in range(ndim): # <<<<<<<<<<<<<< @@ -13131,7 +13132,7 @@ static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *__pyx_v_shape, Py_ for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) { __pyx_v_idx = __pyx_t_4; - /* "View.MemoryView":1194 + /* "View.MemoryView":1195 * if order == 'F': * for idx in range(ndim): * strides[idx] = stride # <<<<<<<<<<<<<< @@ -13140,7 +13141,7 @@ static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *__pyx_v_shape, Py_ */ (__pyx_v_strides[__pyx_v_idx]) = __pyx_v_stride; - /* "View.MemoryView":1195 + /* "View.MemoryView":1196 * for idx in range(ndim): * strides[idx] = stride * stride = stride * shape[idx] # <<<<<<<<<<<<<< @@ -13150,7 +13151,7 @@ static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *__pyx_v_shape, Py_ __pyx_v_stride = (__pyx_v_stride * (__pyx_v_shape[__pyx_v_idx])); } - /* "View.MemoryView":1192 + /* "View.MemoryView":1193 * cdef int idx * * if order == 'F': # <<<<<<<<<<<<<< @@ -13160,7 +13161,7 @@ static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *__pyx_v_shape, Py_ goto __pyx_L3; } - /* "View.MemoryView":1197 + /* "View.MemoryView":1198 * stride = stride * shape[idx] * else: * for idx in range(ndim - 1, -1, -1): # <<<<<<<<<<<<<< @@ -13171,7 +13172,7 @@ static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *__pyx_v_shape, Py_ for (__pyx_t_2 = (__pyx_v_ndim - 1); __pyx_t_2 > -1; __pyx_t_2-=1) { __pyx_v_idx = __pyx_t_2; - /* "View.MemoryView":1198 + /* "View.MemoryView":1199 * else: * for idx in range(ndim - 1, -1, -1): * strides[idx] = stride # <<<<<<<<<<<<<< @@ -13180,7 +13181,7 @@ static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *__pyx_v_shape, Py_ */ (__pyx_v_strides[__pyx_v_idx]) = __pyx_v_stride; - /* "View.MemoryView":1199 + /* "View.MemoryView":1200 * for idx in range(ndim - 1, -1, -1): * strides[idx] = stride * stride = stride * shape[idx] # <<<<<<<<<<<<<< @@ -13192,7 +13193,7 @@ static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *__pyx_v_shape, Py_ } __pyx_L3:; - /* "View.MemoryView":1201 + /* "View.MemoryView":1202 * stride = stride * shape[idx] * * return stride # <<<<<<<<<<<<<< @@ -13202,7 +13203,7 @@ static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *__pyx_v_shape, Py_ __pyx_r = __pyx_v_stride; goto __pyx_L0; - /* "View.MemoryView":1183 + /* "View.MemoryView":1184 * * @cname('__pyx_fill_contig_strides_array') * cdef Py_ssize_t fill_contig_strides_array( # <<<<<<<<<<<<<< @@ -13215,7 +13216,7 @@ static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *__pyx_v_shape, Py_ return __pyx_r; } -/* "View.MemoryView":1204 +/* "View.MemoryView":1205 * * @cname('__pyx_memoryview_copy_data_to_temp') * cdef void *copy_data_to_temp(__Pyx_memviewslice *src, # <<<<<<<<<<<<<< @@ -13236,7 +13237,7 @@ static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src, int __pyx_t_5; int __pyx_t_6; - /* "View.MemoryView":1215 + /* "View.MemoryView":1216 * cdef void *result * * cdef size_t itemsize = src.memview.view.itemsize # <<<<<<<<<<<<<< @@ -13246,7 +13247,7 @@ static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src, __pyx_t_1 = __pyx_v_src->memview->view.itemsize; __pyx_v_itemsize = __pyx_t_1; - /* "View.MemoryView":1216 + /* "View.MemoryView":1217 * * cdef size_t itemsize = src.memview.view.itemsize * cdef size_t size = slice_get_size(src, ndim) # <<<<<<<<<<<<<< @@ -13255,7 +13256,7 @@ static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src, */ __pyx_v_size = __pyx_memoryview_slice_get_size(__pyx_v_src, __pyx_v_ndim); - /* "View.MemoryView":1218 + /* "View.MemoryView":1219 * cdef size_t size = slice_get_size(src, ndim) * * result = malloc(size) # <<<<<<<<<<<<<< @@ -13264,7 +13265,7 @@ static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src, */ __pyx_v_result = malloc(__pyx_v_size); - /* "View.MemoryView":1219 + /* "View.MemoryView":1220 * * result = malloc(size) * if not result: # <<<<<<<<<<<<<< @@ -13274,16 +13275,16 @@ static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src, __pyx_t_2 = ((!(__pyx_v_result != 0)) != 0); if (__pyx_t_2) { - /* "View.MemoryView":1220 + /* "View.MemoryView":1221 * result = malloc(size) * if not result: * _err(MemoryError, NULL) # <<<<<<<<<<<<<< * * */ - __pyx_t_3 = __pyx_memoryview_err(__pyx_builtin_MemoryError, NULL); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(1, 1220, __pyx_L1_error) + __pyx_t_3 = __pyx_memoryview_err(__pyx_builtin_MemoryError, NULL); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(1, 1221, __pyx_L1_error) - /* "View.MemoryView":1219 + /* "View.MemoryView":1220 * * result = malloc(size) * if not result: # <<<<<<<<<<<<<< @@ -13292,7 +13293,7 @@ static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src, */ } - /* "View.MemoryView":1223 + /* "View.MemoryView":1224 * * * tmpslice.data = result # <<<<<<<<<<<<<< @@ -13301,7 +13302,7 @@ static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src, */ __pyx_v_tmpslice->data = ((char *)__pyx_v_result); - /* "View.MemoryView":1224 + /* "View.MemoryView":1225 * * tmpslice.data = result * tmpslice.memview = src.memview # <<<<<<<<<<<<<< @@ -13311,7 +13312,7 @@ static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src, __pyx_t_4 = __pyx_v_src->memview; __pyx_v_tmpslice->memview = __pyx_t_4; - /* "View.MemoryView":1225 + /* "View.MemoryView":1226 * tmpslice.data = result * tmpslice.memview = src.memview * for i in range(ndim): # <<<<<<<<<<<<<< @@ -13323,7 +13324,7 @@ static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src, for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) { __pyx_v_i = __pyx_t_6; - /* "View.MemoryView":1226 + /* "View.MemoryView":1227 * tmpslice.memview = src.memview * for i in range(ndim): * tmpslice.shape[i] = src.shape[i] # <<<<<<<<<<<<<< @@ -13332,7 +13333,7 @@ static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src, */ (__pyx_v_tmpslice->shape[__pyx_v_i]) = (__pyx_v_src->shape[__pyx_v_i]); - /* "View.MemoryView":1227 + /* "View.MemoryView":1228 * for i in range(ndim): * tmpslice.shape[i] = src.shape[i] * tmpslice.suboffsets[i] = -1 # <<<<<<<<<<<<<< @@ -13342,7 +13343,7 @@ static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src, (__pyx_v_tmpslice->suboffsets[__pyx_v_i]) = -1L; } - /* "View.MemoryView":1229 + /* "View.MemoryView":1230 * tmpslice.suboffsets[i] = -1 * * fill_contig_strides_array(&tmpslice.shape[0], &tmpslice.strides[0], itemsize, # <<<<<<<<<<<<<< @@ -13351,7 +13352,7 @@ static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src, */ (void)(__pyx_fill_contig_strides_array((&(__pyx_v_tmpslice->shape[0])), (&(__pyx_v_tmpslice->strides[0])), __pyx_v_itemsize, __pyx_v_ndim, __pyx_v_order)); - /* "View.MemoryView":1233 + /* "View.MemoryView":1234 * * * for i in range(ndim): # <<<<<<<<<<<<<< @@ -13363,7 +13364,7 @@ static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src, for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) { __pyx_v_i = __pyx_t_6; - /* "View.MemoryView":1234 + /* "View.MemoryView":1235 * * for i in range(ndim): * if tmpslice.shape[i] == 1: # <<<<<<<<<<<<<< @@ -13373,7 +13374,7 @@ static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src, __pyx_t_2 = (((__pyx_v_tmpslice->shape[__pyx_v_i]) == 1) != 0); if (__pyx_t_2) { - /* "View.MemoryView":1235 + /* "View.MemoryView":1236 * for i in range(ndim): * if tmpslice.shape[i] == 1: * tmpslice.strides[i] = 0 # <<<<<<<<<<<<<< @@ -13382,7 +13383,7 @@ static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src, */ (__pyx_v_tmpslice->strides[__pyx_v_i]) = 0; - /* "View.MemoryView":1234 + /* "View.MemoryView":1235 * * for i in range(ndim): * if tmpslice.shape[i] == 1: # <<<<<<<<<<<<<< @@ -13392,7 +13393,7 @@ static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src, } } - /* "View.MemoryView":1237 + /* "View.MemoryView":1238 * tmpslice.strides[i] = 0 * * if slice_is_contig(src[0], order, ndim): # <<<<<<<<<<<<<< @@ -13402,7 +13403,7 @@ static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src, __pyx_t_2 = (__pyx_memviewslice_is_contig((__pyx_v_src[0]), __pyx_v_order, __pyx_v_ndim) != 0); if (__pyx_t_2) { - /* "View.MemoryView":1238 + /* "View.MemoryView":1239 * * if slice_is_contig(src[0], order, ndim): * memcpy(result, src.data, size) # <<<<<<<<<<<<<< @@ -13411,7 +13412,7 @@ static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src, */ (void)(memcpy(__pyx_v_result, __pyx_v_src->data, __pyx_v_size)); - /* "View.MemoryView":1237 + /* "View.MemoryView":1238 * tmpslice.strides[i] = 0 * * if slice_is_contig(src[0], order, ndim): # <<<<<<<<<<<<<< @@ -13421,7 +13422,7 @@ static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src, goto __pyx_L9; } - /* "View.MemoryView":1240 + /* "View.MemoryView":1241 * memcpy(result, src.data, size) * else: * copy_strided_to_strided(src, tmpslice, ndim, itemsize) # <<<<<<<<<<<<<< @@ -13433,7 +13434,7 @@ static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src, } __pyx_L9:; - /* "View.MemoryView":1242 + /* "View.MemoryView":1243 * copy_strided_to_strided(src, tmpslice, ndim, itemsize) * * return result # <<<<<<<<<<<<<< @@ -13443,7 +13444,7 @@ static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src, __pyx_r = __pyx_v_result; goto __pyx_L0; - /* "View.MemoryView":1204 + /* "View.MemoryView":1205 * * @cname('__pyx_memoryview_copy_data_to_temp') * cdef void *copy_data_to_temp(__Pyx_memviewslice *src, # <<<<<<<<<<<<<< @@ -13467,7 +13468,7 @@ static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src, return __pyx_r; } -/* "View.MemoryView":1247 +/* "View.MemoryView":1248 * * @cname('__pyx_memoryview_err_extents') * cdef int _err_extents(int i, Py_ssize_t extent1, # <<<<<<<<<<<<<< @@ -13487,20 +13488,20 @@ static int __pyx_memoryview_err_extents(int __pyx_v_i, Py_ssize_t __pyx_v_extent #endif __Pyx_RefNannySetupContext("_err_extents", 0); - /* "View.MemoryView":1250 + /* "View.MemoryView":1251 * Py_ssize_t extent2) except -1 with gil: * raise ValueError("got differing extents in dimension %d (got %d and %d)" % * (i, extent1, extent2)) # <<<<<<<<<<<<<< * * @cname('__pyx_memoryview_err_dim') */ - __pyx_t_1 = __Pyx_PyInt_From_int(__pyx_v_i); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 1250, __pyx_L1_error) + __pyx_t_1 = __Pyx_PyInt_From_int(__pyx_v_i); if (unlikely(!__pyx_t_1)) __PYX_ERR(1, 1251, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); - __pyx_t_2 = PyInt_FromSsize_t(__pyx_v_extent1); if (unlikely(!__pyx_t_2)) __PYX_ERR(1, 1250, __pyx_L1_error) + __pyx_t_2 = PyInt_FromSsize_t(__pyx_v_extent1); if (unlikely(!__pyx_t_2)) __PYX_ERR(1, 1251, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); - __pyx_t_3 = PyInt_FromSsize_t(__pyx_v_extent2); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 1250, __pyx_L1_error) + __pyx_t_3 = PyInt_FromSsize_t(__pyx_v_extent2); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 1251, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); - __pyx_t_4 = PyTuple_New(3); if (unlikely(!__pyx_t_4)) __PYX_ERR(1, 1250, __pyx_L1_error) + __pyx_t_4 = PyTuple_New(3); if (unlikely(!__pyx_t_4)) __PYX_ERR(1, 1251, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_GIVEREF(__pyx_t_1); PyTuple_SET_ITEM(__pyx_t_4, 0, __pyx_t_1); @@ -13512,24 +13513,24 @@ static int __pyx_memoryview_err_extents(int __pyx_v_i, Py_ssize_t __pyx_v_extent __pyx_t_2 = 0; __pyx_t_3 = 0; - /* "View.MemoryView":1249 + /* "View.MemoryView":1250 * cdef int _err_extents(int i, Py_ssize_t extent1, * Py_ssize_t extent2) except -1 with gil: * raise ValueError("got differing extents in dimension %d (got %d and %d)" % # <<<<<<<<<<<<<< * (i, extent1, extent2)) * */ - __pyx_t_3 = __Pyx_PyString_Format(__pyx_kp_s_got_differing_extents_in_dimensi, __pyx_t_4); 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if (unlikely(__pyx_t_6 == ((int)-1))) __PYX_ERR(1, 1293, __pyx_L1_error) + __pyx_t_6 = __pyx_memoryview_err_extents(__pyx_v_i, (__pyx_v_dst.shape[__pyx_v_i]), (__pyx_v_src.shape[__pyx_v_i])); if (unlikely(__pyx_t_6 == ((int)-1))) __PYX_ERR(1, 1294, __pyx_L1_error) } __pyx_L7:; - /* "View.MemoryView":1288 + /* "View.MemoryView":1289 * * for i in range(ndim): * if src.shape[i] != dst.shape[i]: # <<<<<<<<<<<<<< @@ -13968,7 +13969,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_ */ } - /* "View.MemoryView":1295 + /* "View.MemoryView":1296 * _err_extents(i, dst.shape[i], src.shape[i]) * * if src.suboffsets[i] >= 0: # <<<<<<<<<<<<<< @@ -13978,16 +13979,16 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_ __pyx_t_2 = (((__pyx_v_src.suboffsets[__pyx_v_i]) >= 0) != 0); if (__pyx_t_2) { - /* "View.MemoryView":1296 + /* "View.MemoryView":1297 * * if src.suboffsets[i] >= 0: * _err_dim(ValueError, "Dimension %d is not direct", i) # <<<<<<<<<<<<<< * * if slices_overlap(&src, &dst, ndim, itemsize): */ - __pyx_t_6 = __pyx_memoryview_err_dim(__pyx_builtin_ValueError, ((char *)"Dimension %d is not direct"), __pyx_v_i); if (unlikely(__pyx_t_6 == ((int)-1))) __PYX_ERR(1, 1296, __pyx_L1_error) + __pyx_t_6 = __pyx_memoryview_err_dim(__pyx_builtin_ValueError, ((char *)"Dimension %d is not direct"), __pyx_v_i); if (unlikely(__pyx_t_6 == ((int)-1))) __PYX_ERR(1, 1297, __pyx_L1_error) - /* "View.MemoryView":1295 + /* "View.MemoryView":1296 * _err_extents(i, dst.shape[i], src.shape[i]) * * if src.suboffsets[i] >= 0: # <<<<<<<<<<<<<< @@ -13997,7 +13998,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_ } } - /* "View.MemoryView":1298 + /* "View.MemoryView":1299 * _err_dim(ValueError, "Dimension %d is not direct", i) * * if slices_overlap(&src, &dst, ndim, itemsize): # <<<<<<<<<<<<<< @@ -14007,7 +14008,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_ __pyx_t_2 = (__pyx_slices_overlap((&__pyx_v_src), (&__pyx_v_dst), __pyx_v_ndim, __pyx_v_itemsize) != 0); if (__pyx_t_2) { - /* "View.MemoryView":1300 + /* "View.MemoryView":1301 * if slices_overlap(&src, &dst, ndim, itemsize): * * if not slice_is_contig(src, order, ndim): # <<<<<<<<<<<<<< @@ -14017,7 +14018,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_ __pyx_t_2 = ((!(__pyx_memviewslice_is_contig(__pyx_v_src, __pyx_v_order, __pyx_v_ndim) != 0)) != 0); if (__pyx_t_2) { - /* "View.MemoryView":1301 + /* "View.MemoryView":1302 * * if not slice_is_contig(src, order, ndim): * order = get_best_order(&dst, ndim) # <<<<<<<<<<<<<< @@ -14026,7 +14027,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_ */ __pyx_v_order = __pyx_get_best_slice_order((&__pyx_v_dst), __pyx_v_ndim); - /* "View.MemoryView":1300 + /* "View.MemoryView":1301 * if slices_overlap(&src, &dst, ndim, itemsize): * * if not slice_is_contig(src, order, ndim): # <<<<<<<<<<<<<< @@ -14035,17 +14036,17 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_ */ } - /* "View.MemoryView":1303 + /* "View.MemoryView":1304 * order = get_best_order(&dst, ndim) * * tmpdata = copy_data_to_temp(&src, &tmp, order, ndim) # <<<<<<<<<<<<<< * src = tmp * */ - __pyx_t_7 = __pyx_memoryview_copy_data_to_temp((&__pyx_v_src), (&__pyx_v_tmp), __pyx_v_order, __pyx_v_ndim); if (unlikely(__pyx_t_7 == ((void *)NULL))) __PYX_ERR(1, 1303, __pyx_L1_error) + __pyx_t_7 = __pyx_memoryview_copy_data_to_temp((&__pyx_v_src), (&__pyx_v_tmp), __pyx_v_order, __pyx_v_ndim); if (unlikely(__pyx_t_7 == ((void *)NULL))) __PYX_ERR(1, 1304, __pyx_L1_error) __pyx_v_tmpdata = __pyx_t_7; - /* "View.MemoryView":1304 + /* "View.MemoryView":1305 * * tmpdata = copy_data_to_temp(&src, &tmp, order, ndim) * src = tmp # <<<<<<<<<<<<<< @@ -14054,7 +14055,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_ */ __pyx_v_src = __pyx_v_tmp; - /* "View.MemoryView":1298 + /* "View.MemoryView":1299 * _err_dim(ValueError, "Dimension %d is not direct", i) * * if slices_overlap(&src, &dst, ndim, itemsize): # <<<<<<<<<<<<<< @@ -14063,7 +14064,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_ */ } - /* "View.MemoryView":1306 + /* "View.MemoryView":1307 * src = tmp * * if not broadcasting: # <<<<<<<<<<<<<< @@ -14073,7 +14074,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_ __pyx_t_2 = ((!(__pyx_v_broadcasting != 0)) != 0); if (__pyx_t_2) { - /* "View.MemoryView":1309 + /* "View.MemoryView":1310 * * * if slice_is_contig(src, 'C', ndim): # <<<<<<<<<<<<<< @@ -14083,7 +14084,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_ __pyx_t_2 = (__pyx_memviewslice_is_contig(__pyx_v_src, 'C', __pyx_v_ndim) != 0); if (__pyx_t_2) { - /* "View.MemoryView":1310 + /* "View.MemoryView":1311 * * if slice_is_contig(src, 'C', ndim): * direct_copy = slice_is_contig(dst, 'C', ndim) # <<<<<<<<<<<<<< @@ -14092,7 +14093,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_ */ __pyx_v_direct_copy = __pyx_memviewslice_is_contig(__pyx_v_dst, 'C', __pyx_v_ndim); - /* "View.MemoryView":1309 + /* "View.MemoryView":1310 * * * if slice_is_contig(src, 'C', ndim): # <<<<<<<<<<<<<< @@ -14102,7 +14103,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_ goto __pyx_L12; } - /* "View.MemoryView":1311 + /* "View.MemoryView":1312 * if slice_is_contig(src, 'C', ndim): * direct_copy = slice_is_contig(dst, 'C', ndim) * elif slice_is_contig(src, 'F', ndim): # <<<<<<<<<<<<<< @@ -14112,7 +14113,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_ __pyx_t_2 = (__pyx_memviewslice_is_contig(__pyx_v_src, 'F', __pyx_v_ndim) != 0); if (__pyx_t_2) { - /* "View.MemoryView":1312 + /* "View.MemoryView":1313 * direct_copy = slice_is_contig(dst, 'C', ndim) * elif slice_is_contig(src, 'F', ndim): * direct_copy = slice_is_contig(dst, 'F', ndim) # <<<<<<<<<<<<<< @@ -14121,7 +14122,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_ */ __pyx_v_direct_copy = __pyx_memviewslice_is_contig(__pyx_v_dst, 'F', __pyx_v_ndim); - /* "View.MemoryView":1311 + /* "View.MemoryView":1312 * if slice_is_contig(src, 'C', ndim): * direct_copy = slice_is_contig(dst, 'C', ndim) * elif slice_is_contig(src, 'F', ndim): # <<<<<<<<<<<<<< @@ -14131,7 +14132,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_ } __pyx_L12:; - /* "View.MemoryView":1314 + /* "View.MemoryView":1315 * direct_copy = slice_is_contig(dst, 'F', ndim) * * if direct_copy: # <<<<<<<<<<<<<< @@ -14141,7 +14142,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_ __pyx_t_2 = (__pyx_v_direct_copy != 0); if (__pyx_t_2) { - /* "View.MemoryView":1316 + /* "View.MemoryView":1317 * if direct_copy: * * refcount_copying(&dst, dtype_is_object, ndim, False) # <<<<<<<<<<<<<< @@ -14150,7 +14151,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_ */ __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 0); - /* "View.MemoryView":1317 + /* "View.MemoryView":1318 * * refcount_copying(&dst, dtype_is_object, ndim, False) * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) # <<<<<<<<<<<<<< @@ -14159,7 +14160,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_ */ (void)(memcpy(__pyx_v_dst.data, __pyx_v_src.data, __pyx_memoryview_slice_get_size((&__pyx_v_src), __pyx_v_ndim))); - /* "View.MemoryView":1318 + /* "View.MemoryView":1319 * refcount_copying(&dst, dtype_is_object, ndim, False) * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) * refcount_copying(&dst, dtype_is_object, ndim, True) # <<<<<<<<<<<<<< @@ -14168,7 +14169,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_ */ __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 1); - /* "View.MemoryView":1319 + /* "View.MemoryView":1320 * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) * refcount_copying(&dst, dtype_is_object, ndim, True) * free(tmpdata) # <<<<<<<<<<<<<< @@ -14177,7 +14178,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_ */ free(__pyx_v_tmpdata); - /* "View.MemoryView":1320 + /* "View.MemoryView":1321 * refcount_copying(&dst, dtype_is_object, ndim, True) * free(tmpdata) * return 0 # <<<<<<<<<<<<<< @@ -14187,7 +14188,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_ __pyx_r = 0; goto __pyx_L0; - /* "View.MemoryView":1314 + /* "View.MemoryView":1315 * direct_copy = slice_is_contig(dst, 'F', ndim) * * if direct_copy: # <<<<<<<<<<<<<< @@ -14196,7 +14197,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_ */ } - /* "View.MemoryView":1306 + /* "View.MemoryView":1307 * src = tmp * * if not broadcasting: # <<<<<<<<<<<<<< @@ -14205,7 +14206,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_ */ } - /* "View.MemoryView":1322 + /* "View.MemoryView":1323 * return 0 * * if order == 'F' == get_best_order(&dst, ndim): # <<<<<<<<<<<<<< @@ -14219,25 +14220,25 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_ __pyx_t_8 = (__pyx_t_2 != 0); if (__pyx_t_8) { - /* "View.MemoryView":1325 + /* "View.MemoryView":1326 * * * transpose_memslice(&src) # <<<<<<<<<<<<<< * transpose_memslice(&dst) * */ - __pyx_t_5 = __pyx_memslice_transpose((&__pyx_v_src)); if (unlikely(__pyx_t_5 == ((int)0))) __PYX_ERR(1, 1325, __pyx_L1_error) + __pyx_t_5 = __pyx_memslice_transpose((&__pyx_v_src)); if (unlikely(__pyx_t_5 == ((int)0))) __PYX_ERR(1, 1326, __pyx_L1_error) - /* "View.MemoryView":1326 + /* "View.MemoryView":1327 * * transpose_memslice(&src) * transpose_memslice(&dst) # <<<<<<<<<<<<<< * * refcount_copying(&dst, dtype_is_object, ndim, False) */ - __pyx_t_5 = __pyx_memslice_transpose((&__pyx_v_dst)); if (unlikely(__pyx_t_5 == ((int)0))) __PYX_ERR(1, 1326, __pyx_L1_error) + __pyx_t_5 = __pyx_memslice_transpose((&__pyx_v_dst)); if (unlikely(__pyx_t_5 == ((int)0))) __PYX_ERR(1, 1327, __pyx_L1_error) - /* "View.MemoryView":1322 + /* "View.MemoryView":1323 * return 0 * * if order == 'F' == get_best_order(&dst, ndim): # <<<<<<<<<<<<<< @@ -14246,7 +14247,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_ */ } - /* "View.MemoryView":1328 + /* "View.MemoryView":1329 * transpose_memslice(&dst) * * refcount_copying(&dst, dtype_is_object, ndim, False) # <<<<<<<<<<<<<< @@ -14255,7 +14256,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_ */ __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 0); - /* "View.MemoryView":1329 + /* "View.MemoryView":1330 * * refcount_copying(&dst, dtype_is_object, ndim, False) * copy_strided_to_strided(&src, &dst, ndim, itemsize) # <<<<<<<<<<<<<< @@ -14264,7 +14265,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_ */ copy_strided_to_strided((&__pyx_v_src), (&__pyx_v_dst), __pyx_v_ndim, __pyx_v_itemsize); - /* "View.MemoryView":1330 + /* "View.MemoryView":1331 * refcount_copying(&dst, dtype_is_object, ndim, False) * copy_strided_to_strided(&src, &dst, ndim, itemsize) * refcount_copying(&dst, dtype_is_object, ndim, True) # <<<<<<<<<<<<<< @@ -14273,7 +14274,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_ */ __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 1); - /* "View.MemoryView":1332 + /* "View.MemoryView":1333 * refcount_copying(&dst, dtype_is_object, ndim, True) * * free(tmpdata) # <<<<<<<<<<<<<< @@ -14282,7 +14283,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_ */ free(__pyx_v_tmpdata); - /* "View.MemoryView":1333 + /* "View.MemoryView":1334 * * free(tmpdata) * return 0 # <<<<<<<<<<<<<< @@ -14292,7 +14293,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_ __pyx_r = 0; goto __pyx_L0; - /* "View.MemoryView":1264 + /* "View.MemoryView":1265 * * @cname('__pyx_memoryview_copy_contents') * cdef int memoryview_copy_contents(__Pyx_memviewslice src, # <<<<<<<<<<<<<< @@ -14316,7 +14317,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_ return __pyx_r; } -/* "View.MemoryView":1336 +/* "View.MemoryView":1337 * * @cname('__pyx_memoryview_broadcast_leading') * cdef void broadcast_leading(__Pyx_memviewslice *mslice, # <<<<<<<<<<<<<< @@ -14331,7 +14332,7 @@ static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *__pyx_v_mslic int __pyx_t_2; int __pyx_t_3; - /* "View.MemoryView":1340 + /* "View.MemoryView":1341 * int ndim_other) nogil: * cdef int i * cdef int offset = ndim_other - ndim # <<<<<<<<<<<<<< @@ -14340,7 +14341,7 @@ static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *__pyx_v_mslic */ __pyx_v_offset = (__pyx_v_ndim_other - __pyx_v_ndim); - /* "View.MemoryView":1342 + /* "View.MemoryView":1343 * cdef int offset = ndim_other - ndim * * for i in range(ndim - 1, -1, -1): # <<<<<<<<<<<<<< @@ -14350,7 +14351,7 @@ static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *__pyx_v_mslic for (__pyx_t_1 = (__pyx_v_ndim - 1); __pyx_t_1 > -1; __pyx_t_1-=1) { __pyx_v_i = __pyx_t_1; - /* "View.MemoryView":1343 + /* "View.MemoryView":1344 * * for i in range(ndim - 1, -1, -1): * mslice.shape[i + offset] = mslice.shape[i] # <<<<<<<<<<<<<< @@ -14359,7 +14360,7 @@ static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *__pyx_v_mslic */ (__pyx_v_mslice->shape[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_mslice->shape[__pyx_v_i]); - /* "View.MemoryView":1344 + /* "View.MemoryView":1345 * for i in range(ndim - 1, -1, -1): * mslice.shape[i + offset] = mslice.shape[i] * mslice.strides[i + offset] = mslice.strides[i] # <<<<<<<<<<<<<< @@ -14368,7 +14369,7 @@ static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *__pyx_v_mslic */ (__pyx_v_mslice->strides[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_mslice->strides[__pyx_v_i]); - /* "View.MemoryView":1345 + /* "View.MemoryView":1346 * mslice.shape[i + offset] = mslice.shape[i] * mslice.strides[i + offset] = mslice.strides[i] * mslice.suboffsets[i + offset] = mslice.suboffsets[i] # <<<<<<<<<<<<<< @@ -14378,7 +14379,7 @@ static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *__pyx_v_mslic (__pyx_v_mslice->suboffsets[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_mslice->suboffsets[__pyx_v_i]); } - /* "View.MemoryView":1347 + /* "View.MemoryView":1348 * mslice.suboffsets[i + offset] = mslice.suboffsets[i] * * for i in range(offset): # <<<<<<<<<<<<<< @@ -14390,7 +14391,7 @@ static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *__pyx_v_mslic for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { __pyx_v_i = __pyx_t_3; - /* "View.MemoryView":1348 + /* "View.MemoryView":1349 * * for i in range(offset): * mslice.shape[i] = 1 # <<<<<<<<<<<<<< @@ -14399,7 +14400,7 @@ static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *__pyx_v_mslic */ (__pyx_v_mslice->shape[__pyx_v_i]) = 1; - /* "View.MemoryView":1349 + /* "View.MemoryView":1350 * for i in range(offset): * mslice.shape[i] = 1 * mslice.strides[i] = mslice.strides[0] # <<<<<<<<<<<<<< @@ -14408,7 +14409,7 @@ static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *__pyx_v_mslic */ (__pyx_v_mslice->strides[__pyx_v_i]) = (__pyx_v_mslice->strides[0]); - /* "View.MemoryView":1350 + /* "View.MemoryView":1351 * mslice.shape[i] = 1 * mslice.strides[i] = mslice.strides[0] * mslice.suboffsets[i] = -1 # <<<<<<<<<<<<<< @@ -14418,7 +14419,7 @@ static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *__pyx_v_mslic (__pyx_v_mslice->suboffsets[__pyx_v_i]) = -1L; } - /* "View.MemoryView":1336 + /* "View.MemoryView":1337 * * @cname('__pyx_memoryview_broadcast_leading') * cdef void broadcast_leading(__Pyx_memviewslice *mslice, # <<<<<<<<<<<<<< @@ -14429,7 +14430,7 @@ static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *__pyx_v_mslic /* function exit code */ } -/* "View.MemoryView":1358 +/* "View.MemoryView":1359 * * @cname('__pyx_memoryview_refcount_copying') * cdef void refcount_copying(__Pyx_memviewslice *dst, bint dtype_is_object, # <<<<<<<<<<<<<< @@ -14440,7 +14441,7 @@ static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *__pyx_v_mslic static void __pyx_memoryview_refcount_copying(__Pyx_memviewslice *__pyx_v_dst, int __pyx_v_dtype_is_object, int __pyx_v_ndim, int __pyx_v_inc) { int __pyx_t_1; - /* "View.MemoryView":1362 + /* "View.MemoryView":1363 * * * if dtype_is_object: # <<<<<<<<<<<<<< @@ -14450,7 +14451,7 @@ static void __pyx_memoryview_refcount_copying(__Pyx_memviewslice *__pyx_v_dst, i __pyx_t_1 = (__pyx_v_dtype_is_object != 0); if (__pyx_t_1) { - /* "View.MemoryView":1363 + /* "View.MemoryView":1364 * * if dtype_is_object: * refcount_objects_in_slice_with_gil(dst.data, dst.shape, # <<<<<<<<<<<<<< @@ -14459,7 +14460,7 @@ static void __pyx_memoryview_refcount_copying(__Pyx_memviewslice *__pyx_v_dst, i */ __pyx_memoryview_refcount_objects_in_slice_with_gil(__pyx_v_dst->data, __pyx_v_dst->shape, __pyx_v_dst->strides, __pyx_v_ndim, __pyx_v_inc); - /* "View.MemoryView":1362 + /* "View.MemoryView":1363 * * * if dtype_is_object: # <<<<<<<<<<<<<< @@ -14468,7 +14469,7 @@ static void __pyx_memoryview_refcount_copying(__Pyx_memviewslice *__pyx_v_dst, i */ } - /* "View.MemoryView":1358 + /* "View.MemoryView":1359 * * @cname('__pyx_memoryview_refcount_copying') * cdef void refcount_copying(__Pyx_memviewslice *dst, bint dtype_is_object, # <<<<<<<<<<<<<< @@ -14479,7 +14480,7 @@ static void __pyx_memoryview_refcount_copying(__Pyx_memviewslice *__pyx_v_dst, i /* function exit code */ } -/* "View.MemoryView":1367 +/* "View.MemoryView":1368 * * @cname('__pyx_memoryview_refcount_objects_in_slice_with_gil') * cdef void refcount_objects_in_slice_with_gil(char *data, Py_ssize_t *shape, # <<<<<<<<<<<<<< @@ -14494,7 +14495,7 @@ static void __pyx_memoryview_refcount_objects_in_slice_with_gil(char *__pyx_v_da #endif __Pyx_RefNannySetupContext("refcount_objects_in_slice_with_gil", 0); 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__Pyx_GIVEREF(__pyx_tuple__3); - /* "View.MemoryView":175 + /* "View.MemoryView":176 * self.data = malloc(self.len) * if not self.data: * raise MemoryError("unable to allocate array data.") # <<<<<<<<<<<<<< * * if self.dtype_is_object: */ - __pyx_tuple__4 = PyTuple_Pack(1, __pyx_kp_s_unable_to_allocate_array_data); if (unlikely(!__pyx_tuple__4)) __PYX_ERR(1, 175, __pyx_L1_error) + __pyx_tuple__4 = PyTuple_Pack(1, __pyx_kp_s_unable_to_allocate_array_data); if (unlikely(!__pyx_tuple__4)) __PYX_ERR(1, 176, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__4); __Pyx_GIVEREF(__pyx_tuple__4); - /* "View.MemoryView":191 + /* "View.MemoryView":192 * bufmode = PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS * if not (flags & bufmode): * raise ValueError("Can only create a buffer that is contiguous in memory.") # <<<<<<<<<<<<<< * info.buf = self.data * info.len = self.len */ - __pyx_tuple__5 = PyTuple_Pack(1, __pyx_kp_s_Can_only_create_a_buffer_that_is); if (unlikely(!__pyx_tuple__5)) __PYX_ERR(1, 191, __pyx_L1_error) + __pyx_tuple__5 = PyTuple_Pack(1, __pyx_kp_s_Can_only_create_a_buffer_that_is); 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if (unlikely(!__pyx_tuple__9)) __PYX_ERR(1, 490, __pyx_L1_error) + __pyx_tuple__9 = PyTuple_Pack(1, __pyx_kp_s_Unable_to_convert_item_to_object); if (unlikely(!__pyx_tuple__9)) __PYX_ERR(1, 491, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__9); __Pyx_GIVEREF(__pyx_tuple__9); - /* "View.MemoryView":515 + /* "View.MemoryView":516 * def __getbuffer__(self, Py_buffer *info, int flags): * if flags & PyBUF_WRITABLE and self.view.readonly: * raise ValueError("Cannot create writable memory view from read-only memoryview") # <<<<<<<<<<<<<< * - * if flags & PyBUF_STRIDES: + * if flags & PyBUF_ND: */ - __pyx_tuple__10 = PyTuple_Pack(1, __pyx_kp_s_Cannot_create_writable_memory_vi); if (unlikely(!__pyx_tuple__10)) __PYX_ERR(1, 515, __pyx_L1_error) + __pyx_tuple__10 = PyTuple_Pack(1, __pyx_kp_s_Cannot_create_writable_memory_vi); if (unlikely(!__pyx_tuple__10)) __PYX_ERR(1, 516, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__10); __Pyx_GIVEREF(__pyx_tuple__10); - /* "View.MemoryView":565 + /* "View.MemoryView":566 * if self.view.strides == NULL: * * raise ValueError("Buffer view does not expose strides") # <<<<<<<<<<<<<< * * return tuple([stride for stride in self.view.strides[:self.view.ndim]]) */ - __pyx_tuple__11 = PyTuple_Pack(1, __pyx_kp_s_Buffer_view_does_not_expose_stri); if (unlikely(!__pyx_tuple__11)) __PYX_ERR(1, 565, __pyx_L1_error) + __pyx_tuple__11 = PyTuple_Pack(1, __pyx_kp_s_Buffer_view_does_not_expose_stri); if (unlikely(!__pyx_tuple__11)) __PYX_ERR(1, 566, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__11); __Pyx_GIVEREF(__pyx_tuple__11); - /* "View.MemoryView":572 + /* "View.MemoryView":573 * def suboffsets(self): * if self.view.suboffsets == NULL: * return (-1,) * self.view.ndim # <<<<<<<<<<<<<< * * return tuple([suboffset for suboffset in self.view.suboffsets[:self.view.ndim]]) */ - __pyx_tuple__12 = PyTuple_New(1); if (unlikely(!__pyx_tuple__12)) __PYX_ERR(1, 572, __pyx_L1_error) + __pyx_tuple__12 = PyTuple_New(1); if (unlikely(!__pyx_tuple__12)) __PYX_ERR(1, 573, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__12); __Pyx_INCREF(__pyx_int_neg_1); __Pyx_GIVEREF(__pyx_int_neg_1); @@ -16196,47 +16199,25 @@ static CYTHON_SMALL_CODE int __Pyx_InitCachedConstants(void) { __Pyx_GOTREF(__pyx_tuple__14); __Pyx_GIVEREF(__pyx_tuple__14); - /* "View.MemoryView":677 + /* "View.MemoryView":678 * if item is Ellipsis: * if not seen_ellipsis: * result.extend([slice(None)] * (ndim - len(tup) + 1)) # <<<<<<<<<<<<<< * seen_ellipsis = True * else: */ - __pyx_slice__15 = PySlice_New(Py_None, Py_None, Py_None); 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if (unlikely(!__pyx_tuple__16)) __PYX_ERR(1, 698, __pyx_L1_error) + __pyx_tuple__16 = PyTuple_Pack(1, __pyx_kp_s_Indirect_dimensions_not_supporte); if (unlikely(!__pyx_tuple__16)) __PYX_ERR(1, 699, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__16); __Pyx_GIVEREF(__pyx_tuple__16); @@ -16259,65 +16240,65 @@ static CYTHON_SMALL_CODE int __Pyx_InitCachedConstants(void) { __Pyx_GOTREF(__pyx_tuple__18); __Pyx_GIVEREF(__pyx_tuple__18); - /* "View.MemoryView":285 + /* "View.MemoryView":286 * return self.name * * cdef generic = Enum("") # <<<<<<<<<<<<<< * cdef strided = Enum("") # default * cdef indirect = Enum("") */ - __pyx_tuple__19 = PyTuple_Pack(1, __pyx_kp_s_strided_and_direct_or_indirect); if (unlikely(!__pyx_tuple__19)) __PYX_ERR(1, 285, __pyx_L1_error) + __pyx_tuple__19 = PyTuple_Pack(1, __pyx_kp_s_strided_and_direct_or_indirect); if (unlikely(!__pyx_tuple__19)) __PYX_ERR(1, 286, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__19); __Pyx_GIVEREF(__pyx_tuple__19); - /* "View.MemoryView":286 + /* "View.MemoryView":287 * * cdef generic = Enum("") * cdef strided = Enum("") # default # <<<<<<<<<<<<<< * cdef indirect = Enum("") * */ - __pyx_tuple__20 = PyTuple_Pack(1, __pyx_kp_s_strided_and_direct); if (unlikely(!__pyx_tuple__20)) __PYX_ERR(1, 286, __pyx_L1_error) + __pyx_tuple__20 = PyTuple_Pack(1, __pyx_kp_s_strided_and_direct); if (unlikely(!__pyx_tuple__20)) __PYX_ERR(1, 287, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__20); __Pyx_GIVEREF(__pyx_tuple__20); - /* "View.MemoryView":287 + /* "View.MemoryView":288 * cdef generic = Enum("") * cdef strided = Enum("") # default * cdef indirect = Enum("") # <<<<<<<<<<<<<< * * */ - __pyx_tuple__21 = PyTuple_Pack(1, __pyx_kp_s_strided_and_indirect); if (unlikely(!__pyx_tuple__21)) __PYX_ERR(1, 287, __pyx_L1_error) + __pyx_tuple__21 = PyTuple_Pack(1, __pyx_kp_s_strided_and_indirect); if (unlikely(!__pyx_tuple__21)) __PYX_ERR(1, 288, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__21); __Pyx_GIVEREF(__pyx_tuple__21); - /* "View.MemoryView":290 + /* "View.MemoryView":291 * * * cdef contiguous = Enum("") # <<<<<<<<<<<<<< * cdef indirect_contiguous = Enum("") * */ - __pyx_tuple__22 = PyTuple_Pack(1, __pyx_kp_s_contiguous_and_direct); if (unlikely(!__pyx_tuple__22)) __PYX_ERR(1, 290, __pyx_L1_error) + __pyx_tuple__22 = PyTuple_Pack(1, __pyx_kp_s_contiguous_and_direct); if (unlikely(!__pyx_tuple__22)) __PYX_ERR(1, 291, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__22); __Pyx_GIVEREF(__pyx_tuple__22); - /* "View.MemoryView":291 + /* "View.MemoryView":292 * * cdef contiguous = Enum("") * cdef indirect_contiguous = Enum("") # <<<<<<<<<<<<<< * * */ - __pyx_tuple__23 = PyTuple_Pack(1, __pyx_kp_s_contiguous_and_indirect); if (unlikely(!__pyx_tuple__23)) __PYX_ERR(1, 291, __pyx_L1_error) + __pyx_tuple__23 = PyTuple_Pack(1, __pyx_kp_s_contiguous_and_indirect); if (unlikely(!__pyx_tuple__23)) __PYX_ERR(1, 292, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__23); __Pyx_GIVEREF(__pyx_tuple__23); /* "(tree fragment)":1 * def __pyx_unpickle_Enum(__pyx_type, long __pyx_checksum, __pyx_state): # <<<<<<<<<<<<<< - * if __pyx_checksum != 0xb068931: - * from pickle import PickleError as __pyx_PickleError + * cdef object __pyx_PickleError + * cdef object __pyx_result */ __pyx_tuple__24 = PyTuple_Pack(5, __pyx_n_s_pyx_type, __pyx_n_s_pyx_checksum, __pyx_n_s_pyx_state, __pyx_n_s_pyx_PickleError, __pyx_n_s_pyx_result); if (unlikely(!__pyx_tuple__24)) __PYX_ERR(1, 1, __pyx_L1_error) __Pyx_GOTREF(__pyx_tuple__24); @@ -16384,17 +16365,17 @@ static int __Pyx_modinit_type_init_code(void) { /*--- Type init code ---*/ __pyx_vtabptr_array = &__pyx_vtable_array; __pyx_vtable_array.get_memview = (PyObject *(*)(struct __pyx_array_obj *))__pyx_array_get_memview; - if (PyType_Ready(&__pyx_type___pyx_array) < 0) __PYX_ERR(1, 104, __pyx_L1_error) + if (PyType_Ready(&__pyx_type___pyx_array) < 0) __PYX_ERR(1, 105, __pyx_L1_error) __pyx_type___pyx_array.tp_print = 0; - if (__Pyx_SetVtable(__pyx_type___pyx_array.tp_dict, __pyx_vtabptr_array) < 0) __PYX_ERR(1, 104, __pyx_L1_error) - if (__Pyx_setup_reduce((PyObject*)&__pyx_type___pyx_array) < 0) __PYX_ERR(1, 104, __pyx_L1_error) + if (__Pyx_SetVtable(__pyx_type___pyx_array.tp_dict, __pyx_vtabptr_array) < 0) __PYX_ERR(1, 105, __pyx_L1_error) + if (__Pyx_setup_reduce((PyObject*)&__pyx_type___pyx_array) < 0) __PYX_ERR(1, 105, __pyx_L1_error) __pyx_array_type = &__pyx_type___pyx_array; - if (PyType_Ready(&__pyx_type___pyx_MemviewEnum) < 0) __PYX_ERR(1, 278, __pyx_L1_error) + if (PyType_Ready(&__pyx_type___pyx_MemviewEnum) < 0) __PYX_ERR(1, 279, __pyx_L1_error) __pyx_type___pyx_MemviewEnum.tp_print = 0; if ((CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP) && likely(!__pyx_type___pyx_MemviewEnum.tp_dictoffset && __pyx_type___pyx_MemviewEnum.tp_getattro == PyObject_GenericGetAttr)) { __pyx_type___pyx_MemviewEnum.tp_getattro = __Pyx_PyObject_GenericGetAttr; } - if (__Pyx_setup_reduce((PyObject*)&__pyx_type___pyx_MemviewEnum) < 0) __PYX_ERR(1, 278, __pyx_L1_error) + if (__Pyx_setup_reduce((PyObject*)&__pyx_type___pyx_MemviewEnum) < 0) __PYX_ERR(1, 279, __pyx_L1_error) __pyx_MemviewEnum_type = &__pyx_type___pyx_MemviewEnum; __pyx_vtabptr_memoryview = &__pyx_vtable_memoryview; __pyx_vtable_memoryview.get_item_pointer = (char *(*)(struct __pyx_memoryview_obj *, PyObject *))__pyx_memoryview_get_item_pointer; @@ -16404,26 +16385,26 @@ static int __Pyx_modinit_type_init_code(void) { __pyx_vtable_memoryview.setitem_indexed = (PyObject *(*)(struct __pyx_memoryview_obj *, PyObject *, PyObject *))__pyx_memoryview_setitem_indexed; __pyx_vtable_memoryview.convert_item_to_object = (PyObject *(*)(struct __pyx_memoryview_obj *, char *))__pyx_memoryview_convert_item_to_object; __pyx_vtable_memoryview.assign_item_from_object = (PyObject *(*)(struct __pyx_memoryview_obj *, char *, PyObject *))__pyx_memoryview_assign_item_from_object; - if (PyType_Ready(&__pyx_type___pyx_memoryview) < 0) __PYX_ERR(1, 329, __pyx_L1_error) + if (PyType_Ready(&__pyx_type___pyx_memoryview) < 0) __PYX_ERR(1, 330, __pyx_L1_error) __pyx_type___pyx_memoryview.tp_print = 0; if ((CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP) && likely(!__pyx_type___pyx_memoryview.tp_dictoffset && __pyx_type___pyx_memoryview.tp_getattro == PyObject_GenericGetAttr)) { __pyx_type___pyx_memoryview.tp_getattro = __Pyx_PyObject_GenericGetAttr; } - if (__Pyx_SetVtable(__pyx_type___pyx_memoryview.tp_dict, __pyx_vtabptr_memoryview) < 0) __PYX_ERR(1, 329, __pyx_L1_error) - if (__Pyx_setup_reduce((PyObject*)&__pyx_type___pyx_memoryview) < 0) __PYX_ERR(1, 329, __pyx_L1_error) + if (__Pyx_SetVtable(__pyx_type___pyx_memoryview.tp_dict, __pyx_vtabptr_memoryview) < 0) __PYX_ERR(1, 330, __pyx_L1_error) + if (__Pyx_setup_reduce((PyObject*)&__pyx_type___pyx_memoryview) < 0) __PYX_ERR(1, 330, __pyx_L1_error) __pyx_memoryview_type = &__pyx_type___pyx_memoryview; __pyx_vtabptr__memoryviewslice = &__pyx_vtable__memoryviewslice; __pyx_vtable__memoryviewslice.__pyx_base = *__pyx_vtabptr_memoryview; __pyx_vtable__memoryviewslice.__pyx_base.convert_item_to_object = (PyObject *(*)(struct __pyx_memoryview_obj *, char *))__pyx_memoryviewslice_convert_item_to_object; __pyx_vtable__memoryviewslice.__pyx_base.assign_item_from_object = (PyObject *(*)(struct __pyx_memoryview_obj *, char *, PyObject *))__pyx_memoryviewslice_assign_item_from_object; __pyx_type___pyx_memoryviewslice.tp_base = __pyx_memoryview_type; - if (PyType_Ready(&__pyx_type___pyx_memoryviewslice) < 0) __PYX_ERR(1, 960, __pyx_L1_error) + if (PyType_Ready(&__pyx_type___pyx_memoryviewslice) < 0) __PYX_ERR(1, 961, __pyx_L1_error) __pyx_type___pyx_memoryviewslice.tp_print = 0; if ((CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP) && likely(!__pyx_type___pyx_memoryviewslice.tp_dictoffset && __pyx_type___pyx_memoryviewslice.tp_getattro == PyObject_GenericGetAttr)) { __pyx_type___pyx_memoryviewslice.tp_getattro = __Pyx_PyObject_GenericGetAttr; } - if (__Pyx_SetVtable(__pyx_type___pyx_memoryviewslice.tp_dict, __pyx_vtabptr__memoryviewslice) < 0) __PYX_ERR(1, 960, __pyx_L1_error) - if (__Pyx_setup_reduce((PyObject*)&__pyx_type___pyx_memoryviewslice) < 0) __PYX_ERR(1, 960, __pyx_L1_error) + if (__Pyx_SetVtable(__pyx_type___pyx_memoryviewslice.tp_dict, __pyx_vtabptr__memoryviewslice) < 0) __PYX_ERR(1, 961, __pyx_L1_error) + if (__Pyx_setup_reduce((PyObject*)&__pyx_type___pyx_memoryviewslice) < 0) __PYX_ERR(1, 961, __pyx_L1_error) __pyx_memoryviewslice_type = &__pyx_type___pyx_memoryviewslice; __Pyx_RefNannyFinishContext(); return 0; @@ -16505,11 +16486,13 @@ static CYTHON_SMALL_CODE int __Pyx_check_single_interpreter(void) { } return 0; } -static CYTHON_SMALL_CODE int __Pyx_copy_spec_to_module(PyObject *spec, PyObject *moddict, const char* from_name, const char* to_name) { +static CYTHON_SMALL_CODE int __Pyx_copy_spec_to_module(PyObject *spec, PyObject *moddict, const char* from_name, const char* to_name, int allow_none) { PyObject *value = PyObject_GetAttrString(spec, from_name); int result = 0; if (likely(value)) { - result = PyDict_SetItemString(moddict, to_name, value); + if (allow_none || value != Py_None) { + result = PyDict_SetItemString(moddict, to_name, value); + } Py_DECREF(value); } else if (PyErr_ExceptionMatches(PyExc_AttributeError)) { PyErr_Clear(); @@ -16531,10 +16514,10 @@ static CYTHON_SMALL_CODE PyObject* __pyx_pymod_create(PyObject *spec, CYTHON_UNU if (unlikely(!module)) goto bad; moddict = PyModule_GetDict(module); if (unlikely(!moddict)) goto bad; - if (unlikely(__Pyx_copy_spec_to_module(spec, moddict, "loader", "__loader__") < 0)) goto bad; - if (unlikely(__Pyx_copy_spec_to_module(spec, moddict, "origin", "__file__") < 0)) goto bad; - if (unlikely(__Pyx_copy_spec_to_module(spec, moddict, "parent", "__package__") < 0)) goto bad; - if (unlikely(__Pyx_copy_spec_to_module(spec, moddict, "submodule_search_locations", "__path__") < 0)) goto bad; + if (unlikely(__Pyx_copy_spec_to_module(spec, moddict, "loader", "__loader__", 1) < 0)) goto bad; + if (unlikely(__Pyx_copy_spec_to_module(spec, moddict, "origin", "__file__", 1) < 0)) goto bad; + if (unlikely(__Pyx_copy_spec_to_module(spec, moddict, "parent", "__package__", 1) < 0)) goto bad; + if (unlikely(__Pyx_copy_spec_to_module(spec, moddict, "submodule_search_locations", "__path__", 0) < 0)) goto bad; return module; bad: Py_XDECREF(module); @@ -16625,14 +16608,14 @@ if (!__Pyx_RefNanny) { #if PY_MAJOR_VERSION < 3 && (__PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT) if (__Pyx_init_sys_getdefaultencoding_params() < 0) __PYX_ERR(0, 1, __pyx_L1_error) #endif - if (__pyx_module_is_main_yank__multistate__mixing___mix_replicas) { + if (__pyx_module_is_main_openmmtools__multistate__mixing___mix_replicas) { if (PyObject_SetAttr(__pyx_m, __pyx_n_s_name_2, __pyx_n_s_main) < 0) __PYX_ERR(0, 1, __pyx_L1_error) } #if PY_MAJOR_VERSION >= 3 { PyObject *modules = PyImport_GetModuleDict(); if (unlikely(!modules)) __PYX_ERR(0, 1, __pyx_L1_error) - if (!PyDict_GetItemString(modules, "yank.multistate.mixing._mix_replicas")) { - if (unlikely(PyDict_SetItemString(modules, "yank.multistate.mixing._mix_replicas", __pyx_m) < 0)) __PYX_ERR(0, 1, __pyx_L1_error) + if (!PyDict_GetItemString(modules, "openmmtools.multistate.mixing._mix_replicas")) { + if (unlikely(PyDict_SetItemString(modules, "openmmtools.multistate.mixing._mix_replicas", __pyx_m) < 0)) __PYX_ERR(0, 1, __pyx_L1_error) } } #endif @@ -16653,7 +16636,7 @@ if (!__Pyx_RefNanny) { if (__Pyx_patch_abc() < 0) __PYX_ERR(0, 1, __pyx_L1_error) #endif - /* "Yank/multistate/mixing/_mix_replicas.pyx":1 + /* "openmmtools/multistate/mixing/_mix_replicas.pyx":1 * cimport cython # <<<<<<<<<<<<<< * from libc.math cimport exp, isnan * from libc.stdio cimport printf @@ -16663,90 +16646,90 @@ if (!__Pyx_RefNanny) { if (PyDict_SetItem(__pyx_d, __pyx_n_s_test, __pyx_t_1) < 0) __PYX_ERR(0, 1, __pyx_L1_error) __Pyx_DECREF(__pyx_t_1); 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} __pyx_L0:; __Pyx_RefNannyFinishContext(); @@ -18383,7 +18366,9 @@ static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObj /* PyIntBinop */ #if !CYTHON_COMPILING_IN_PYPY -static PyObject* __Pyx_PyInt_AddObjC(PyObject *op1, PyObject *op2, CYTHON_UNUSED long intval, CYTHON_UNUSED int inplace) { +static PyObject* __Pyx_PyInt_AddObjC(PyObject *op1, PyObject *op2, CYTHON_UNUSED long intval, int inplace, int zerodivision_check) { + (void)inplace; + (void)zerodivision_check; #if PY_MAJOR_VERSION < 3 if (likely(PyInt_CheckExact(op1))) { const long b = intval; diff --git a/openmmtools/multistate/mixing/_mix_replicas.pyx b/openmmtools/multistate/mixing/_mix_replicas.pyx index 8b0774afa..1a8ed9ffa 100644 --- a/openmmtools/multistate/mixing/_mix_replicas.pyx +++ b/openmmtools/multistate/mixing/_mix_replicas.pyx @@ -21,11 +21,10 @@ cpdef long _mix_replicas_cython(long nswap_attempts, long nstates, long[:] repli log_P_accept = - (u_kl[i, jstate] + u_kl[j, istate]) + (u_kl[j, jstate] + u_kl[i, istate]) Nij_proposed[istate, jstate] +=1 Nij_proposed[jstate, istate] +=1 - if(log_P_accept>=0 or drand48()=0 or drand48() Date: Sun, 3 Feb 2019 10:54:53 -0500 Subject: [PATCH 44/46] Update readthedocs.yml to v2: https://docs.readthedocs.io/en/latest/config-file/v2.html --- readthedocs.yml | 19 ++++++++++++------- 1 file changed, 12 insertions(+), 7 deletions(-) diff --git a/readthedocs.yml b/readthedocs.yml index 19d8f0030..4a43eabfe 100644 --- a/readthedocs.yml +++ b/readthedocs.yml @@ -1,13 +1,18 @@ -conda: - file: docs/environment.yml +# .readthedocs.yml -# The default for requirements_file is null, but for some reason we get an error with this message if it's not set: -# "Problem parsing YAML configuration. Invalid "requirements_file": path docs/requirements.txt does not exist" -requirements_file: null +version: 2 build: image: latest python: - version: 3.6 - setup_py_install: true + version: 3.7 + install: + - method: setuptools + path: . + +conda: + environment: docs/environment.yml + +conda: + file: docs/environment.yml From a531808098475564f026ed7e8e1745ba23aeff4a Mon Sep 17 00:00:00 2001 From: John Chodera Date: Sun, 3 Feb 2019 11:29:50 -0500 Subject: [PATCH 45/46] Update readthedocs.yml --- readthedocs.yml | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/readthedocs.yml b/readthedocs.yml index 4a43eabfe..407490e2d 100644 --- a/readthedocs.yml +++ b/readthedocs.yml @@ -14,5 +14,7 @@ python: conda: environment: docs/environment.yml -conda: - file: docs/environment.yml +sphinx: + builder: html + configuration: docs/conf.py + fail_on_warning: false From 5b3a36e1779c0421c1e4d499b93907109dcd46d9 Mon Sep 17 00:00:00 2001 From: John Chodera Date: Sun, 3 Feb 2019 11:37:05 -0500 Subject: [PATCH 46/46] Whoops---readthedocs.yml v2 is not released yet. --- readthedocs.yml | 21 +++++++-------------- 1 file changed, 7 insertions(+), 14 deletions(-) diff --git a/readthedocs.yml b/readthedocs.yml index 407490e2d..19d8f0030 100644 --- a/readthedocs.yml +++ b/readthedocs.yml @@ -1,20 +1,13 @@ -# .readthedocs.yml +conda: + file: docs/environment.yml -version: 2 +# The default for requirements_file is null, but for some reason we get an error with this message if it's not set: +# "Problem parsing YAML configuration. Invalid "requirements_file": path docs/requirements.txt does not exist" +requirements_file: null build: image: latest python: - version: 3.7 - install: - - method: setuptools - path: . - -conda: - environment: docs/environment.yml - -sphinx: - builder: html - configuration: docs/conf.py - fail_on_warning: false + version: 3.6 + setup_py_install: true