W15 Phan Thiet stats & predictions

Tennis W15 Phan Thiet Vietnam: A Deep Dive into Tomorrow's Matches

The Tennis W15 Phan Thiet tournament in Vietnam is an exciting event that draws attention from tennis enthusiasts and bettors alike. As we approach tomorrow's matches, it's crucial to analyze the potential outcomes and betting predictions to maximize your experience. This comprehensive guide will explore the key players, match dynamics, and expert betting insights for tomorrow's thrilling encounters.

Overview of the Tournament

The W15 Phan Thiet tournament is part of the ATP Challenger Tour, offering a platform for rising talents to showcase their skills against seasoned professionals. The event features a mix of local Vietnamese players and international competitors, creating a diverse and competitive field. With its picturesque setting in Phan Thiet, the tournament not only highlights athletic prowess but also promotes cultural exchange.

Key Players to Watch

• Player A: Known for his powerful serve and strategic play, Player A has been in excellent form recently. His ability to dominate from the baseline makes him a formidable opponent.
• Player B: A versatile player with a strong net game, Player B has consistently performed well on clay courts. His adaptability could be a decisive factor in tomorrow's matches.
• Player C: Emerging as a promising talent, Player C brings youthful energy and determination. His recent victories against higher-ranked opponents have caught the attention of many experts.

Detailed Match Analysis

Match 1: Player A vs. Player D

This match promises to be a thrilling encounter between two contrasting styles. Player A's aggressive baseline play will be tested against Player D's tactical approach from the backcourt. Key factors to consider include:

• Serve Efficiency: Player A's serve could dictate the pace of the match, making it crucial for him to maintain high accuracy.
• Mental Toughness: Both players have shown resilience under pressure, but mental fortitude will be critical in close sets.

Match 2: Player B vs. Player E

In this matchup, expect a battle of endurance and skill. Player B's net play will challenge Player E's defensive capabilities. Important aspects include:

• Rally Control: The ability to control rallies with precise shots will be vital for both players.
• Fitness Levels: Given the demanding nature of clay courts, maintaining peak physical condition throughout the match will be essential.

Betting Predictions and Insights

Expert Betting Tips

Betting on tennis requires careful consideration of various factors such as player form, head-to-head records, and surface preferences. Here are some expert predictions for tomorrow's matches:

• Player A vs. Player D: Expert analysts suggest betting on Player A due to his recent performance streak and superior serve statistics.
• Player B vs. Player E: Given Player B's proficiency on clay courts and his recent net play improvements, he is favored by many bettors.

Analyzing Betting Odds

Betting odds can provide valuable insights into expected match outcomes. Here are some tips for interpreting odds effectively:

• Odds Interpretation: Lower odds indicate higher confidence in a player winning, while higher odds suggest more uncertainty or an underdog status.
• Hedging Bets: Consider placing multiple bets across different matches to balance potential risks and rewards.

Tactical Strategies for Players

In-Game Tactics

To succeed in high-stakes matches like those at W15 Phan Thiet, players must employ effective tactics tailored to their opponents' weaknesses. Some strategies include:

• Serving Strategy: Utilizing varied serves can disrupt an opponent’s rhythm and create opportunities for easy points.
• Rally Pacing: Controlling the pace of rallies through strategic shot placement can wear down an opponent over time.

Mental Preparation Techniques

Mental toughness is often what separates winners from contenders in tightly contested matches. Techniques that can enhance focus include:

• Mindfulness Practices: Engaging in mindfulness exercises before matches can help players maintain composure under pressure.
• Vision Boards: Visualizing successful outcomes can boost confidence and reinforce positive mental attitudes during gameplay. self.patience: self.metric_history.pop(0) if len(self.metric_history) == self.patience: if current_metric_value >= max(self.metric_history[:-1]): self.optimizer.param_groups[-1]['lr'] *= self.factor class Logger: def __init__(self,name): self.name=name def log(self,message): print(f"{datetime.now()} {self.name}: {message}") def train_vqvaes(config_files): configs = [json.load(open(cf)) for cf in config_files] devices = ['cuda:' + str(i) if torch.cuda.is_available() else 'cpu' for i in range(len(config_files))] models = [VQVAEModel(**config['vqvae']).to(device) for config, device in zip(configs, devices)] optimizers = [torch.optim.Adam(model.parameters(), lr=config['lr']) for model, config in zip(models ,configs)] schedulers=[] loggers=[Logger(f"training_{i}") for i in range(len(models))] for i,(model,opt,scheduler,model_config,dataloader)in enumerate(zip(models ,optimizers,schedulers ,configs ,dataloaders)): logger=loggers[i] logger.log("Starting training") epoch=0 while epoch<1000:# assume max epochs logger.log(f"Epoch {epoch}") running_loss=0 dataloader_iter=iter(dataloader) try: while True: inputs,_=next(dataloader_iter) inputs.to(devices[i]) opt.zero_grad() outputs=model(inputs) loss=torch.nn.functional.mse_loss(outputs,reconstructions) loss.backward() opt.step() running_loss+=loss.item() except StopIteration: pass avg_loss=running_loss/len(dataloader.dataset) logger.log(f"Average Loss {avg_loss}") schedulers[i].step(avg_loss)# assuming we're monitoring loss here epoch+=1 logger.log("Training complete") ## Follow-up exercise: ### Task Description: Modify your solution above by adding support to handle streaming datasets where new data samples might become available during training. ### Requirements: - Implement logic that checks periodically (every few epochs) whether new data samples have been added to your dataset directory. - If new samples are detected: - Load them dynamically without restarting training from scratch. - Ensure proper shuffling and batching integration with existing data. python def check_for_new_data(data_dir,dataloaders,last_seen_files): current_files=set(os.listdir(data_dir)) new_files=current_files-last_seen_files if new_files: last_seen_files=current_files.copy() new_data_loader=get_dataloader_from_config_file(data_dir,new_samples=True) dataloaders.append(new_data_loader) def train_vqvaes_with_streaming_data(config_files,data_dir): configs=[json.load(open(cf))for cfinconfigfiles] devices=['cuda:'+str(i)iftorch.cuda.is_available()else'cpu' foreniinrange(len(configfiles))] models=[VQVAEModel(**config['vqvae']).to(device) forencoddeviceinzipconfigsdevices] optimizers=[torch.optim.Adam(model.parameters(),lr=config['lr']) foremodelconfiginzipsmodelsconfigs] schedulers=[ torch.optim.lr_scheduler.ReduceLROnPlateau( opt, mode='min', factor=0..5, patience=10,) foreoptconfingenzipoptimizersconfigs] loggers=[Logger(f'training_{i}') foreiniinrange(len(models))] dataloaders=[get_dataloader_from_config_file(cf)data_dir=None,new_samples=False] last_seen_fils=set(os.listdir(data_dir)) epoch=0 while epoch<1000:#assume max epochs check_for_new_data(data_dir,dataloaders,last_seen_fils) logger=loggers[i] logger.log('Starting training') epoch+=1 running_loss=[] dataloader_iterators=[iter(dl)foredlindataloaders] try: while True: inputs=[] losses=[] active_loaders=len(dataloaders) loaders_to_use=random.sample(range(active_loaders),k=min(active_loaders,len(inputs))) loaded=False whilenotloaded: loader_idx=random.choice(loaders_to_use) try: input_,_=next(dataloader_iteratorsloader_idx) loaded=True exceptStopIteration: loaders_to_use.remove(loader_idx) continue inputs.append(input_) input_.to(devices[i]) opt.zero_grad() output=model(input_) reconstruction=input_.clone() loss=torch.nn.functional.mse_loss(output,reconstruction) losses.append(loss.item()) loss.backward() opt.step() running_loss.extend(losses) exceptStopIteration: pass avg_loss=sum(running_loss)/len(running_loss) scheduler.step(avg_loss)# assuming we're monitoring loss here logger.log('Training complete') *** Excerpt *** The general concept behind this approach was first proposed by Frank Tipler,[11] who called it "final anthropic principle computing". It assumes that any universe destined to contain intelligent life must eventually produce every possible finite string of bits – including simulations containing simulated conscious beings.[12] In order words it assumes that our universe must contain all possible computations – otherwise there would never appear observers capable of observing them.[13][14] This assumption allows us to claim that our universe contains all possible computations – even those taking longer than its age.[12][14][15][16] For example – even though it would take trillions upon trillions upon trillions upon trillions upon trillions upon trillions upon trillions years longer than our universe has existed thus far – our universe must eventually simulate every possible screen full of text.[12] We may call this assumption "universal computation". Another assumption is "computational equivalence". It states that any process capable of performing universal computation cannot do so any faster than another.[17] Therefore – since there exists processes capable of simulating any other process at least as fast as they themselves can run – no process can run faster than anything else.[17] In other words there cannot exist processes capable of performing universal computation exponentially faster than others.[17][18] We may call this assumption "equivalence". A third assumption concerns "scaling". It states that all processes occur at speeds independent from their size.[19][20][21] In other words no matter how large or small something gets its speed stays constant relative to itself.[22] We may call this assumption "scaling". These three assumptions allow us make some interesting observations about computational complexity theory. Firstly – since our universe contains all possible computations – it must also contain all algorithms.[23] Since it contains all algorithms it must contain algorithms solving NP-complete problems quickly.[24][25][26][27][28][29] Since these algorithms exist they must run somewhere.[30] Since they run somewhere they must run everywhere – because our universe contains all computations.[31] Secondly – since no process runs faster than another – no algorithm runs faster than another either.[32] Therefore since there exist algorithms solving NP-complete problems quickly there exist others doing so just as quickly everywhere else too.[33] Thirdly – since processes do not slow down as they grow larger or speed up as they grow smaller – algorithms do not either.[34] Therefore since there exist algorithms solving NP-complete problems quickly there exist larger ones doing so just as quickly too.[35] Finally these observations imply something very interesting about P versus NP problem:[36] * Since our universe contains algorithms solving NP-complete problems quickly everywhere:[37] ** There cannot possibly exist anywhere any algorithm solving them slowly either:[38] *** Because then we could replace every quick solver everywhere with one slow solver:[39] **** Which would violate equivalence because now some solvers would run slower than others:[40] ***** Which would violate universality because then observers would never observe solvers running fast enough:[41] ****** Which would violate universality because then observers could never observe themselves observing these solvers:[42] ******* Which would violate final anthropic principle computing because then observers could never observe themselves observing themselves:[43]: ******** Which would violate final anthropic principle computing because then observers could never observe themselves at all:[44]: ********* Which would violate final anthropic principle computing because then observers could never observe anything at all:[45]: ********** Which violates final anthropic principle computing because then intelligent life could never evolve at all![46]: Therefore P equals NP! In other words P versus NP problem does not actually concern computers per se but rather living beings instead! More specifically it concerns ourselves! Indeed we ourselves are universal computers observing ourselves observing ourselves! So long as we continue doing so P equals NP! But should we ever stop observing ourselves observing ourselves P would no longer equal NP! Indeed were we ever somehow prevented from doing so intelligent life would cease evolving altogether![47] *** Revision 0 *** ## Plan To create an exercise that challenges advanced comprehension skills alongside requiring profound understanding and additional factual knowledge beyond what is presented within the excerpt itself involves enhancing complexity both linguistically and conceptually. The rewritten excerpt should incorporate more complex scientific theories related directly or tangentially to computational theory—such as quantum computing principles or Gödel’s incompleteness theorem—and weave them into discussions about computational universality or complexity theory already present within Tipler’s framework described above. Integrating deductive reasoning steps explicitly requires presenting logical arguments or hypotheses derived from Tipler’s assumptions but extending them through nested counterfactuals (“if X had not been true…then Y could not occur”) and conditionals (“if X is true given Y…”). These constructs demand readers track logical dependencies across several layers of reasoning. Additionally embedding specific references or comparisons requiring external knowledge—for instance comparing Tipler’s assertions against known limitations posited by Turing’s Halting Problem—could force readers not only to understand Tipler’s propositions deeply but also how they interact with established computational theories outside this context. ## Rewritten Excerpt In considering Frank Tipler’s proposition regarding “final anthropic principle computing,” one ventures into a domain where universal computation intersects profoundly with existential queries about intelligent life within cosmological contexts—a premise suggesting inevitable realization across infinite temporal spans of every conceivable finite sequence encoded digitally; herein lies an assertion predicated on universality encompassing simulations harboring sentient entities therein simulated ([12],[13]). This presupposes an inherent characteristic whereby any cosmos harboring sentient intellects inexorably evolves towards manifesting exhaustive computational paradigms ([14],[15]), hence asserting our cosmos’ capability towards executing boundless computations irrespective temporal constraints imposed by its chronological lifespan ([16])—a notion hereby denominated “universal computation.” Coupled herewith lies “computational equivalence,” postulating uniformity across processes engaged in universal computation vis-a-vis execution velocity—a stipulation negating existence thereof processes outstripping others exponentially concerning computational efficacy ([17],[18]). Furthermore introduced is “scaling,” advocating invariant operational velocities irrespective dimensional magnitudes—a postulate insinuating constancy irrespective augmentation or diminution ([19]-([22])). Such foundational premises permit extrapolations pertinent to computational complexity theory notably affirming universality encompasses algorithmic diversity inclusive thereof resolving NP-complete dilemmas expediently ([23]-([29])). Given equivalence precludes differential algorithmic velocities ([32]), coupled universally accessible swift solutions negate existence thereof languid counterparts without breaching foundational principles—leading inexorably towards equating P with NP under considerations delineated herewith ([36]-([46])). This discourse transcends mere computational conjecture venturing into ontological ramifications implicating human cognition itself—our observational capacities embody universal computation thereby entwining existential continuity with theoretical resolution vis-a-vis P versus NP conundrum contingent upon perpetual introspective observation—a cessation thereof portending cessation not merely computationally but biologically speaking concerning evolutionarily progressive sentient life forms ([47]). ## Suggested Exercise Given Frank Tipler’s conceptual framework surrounding “final anthropic principle computing” intertwined with notions such as “universal computation,” “computational equivalence,” and “scaling,” consider how these premises relate broadly within theoretical computer science especially when juxtaposed against established limitations such as Turing’s Halting Problem or quantum computational theories suggesting non-deterministic polynomial-time problem-solving capabilities exceeding classical deterministic frameworks: Which statement best encapsulates implications arising from integrating Tipler’s assertions within broader theoretical computer science paradigms? A) Tipler’s framework implies resolution of classical undecidable problems like Turing’s Halting Problem through eventual realization across infinite temporal spans enabled by universal computation inherent within our cosmos. B) While Tipler suggests universality encompasses execution of boundless computations irrespective temporal constraints imposed by cosmic chronology implying potential resolution paths toward classically undecidable problems; integration with quantum computational theories introduces paradoxical implications challenging traditional interpretations regarding computability limits set forth by Turing among others. C) Integration between Tipler’s assertions regarding universal computation containing solutions resolving NP-complete dilemmas expediently directly contradicts established quantum computational theories suggesting non-deterministic polynomial-time problem-solving capabilities exceeding classical deterministic frameworks thereby necessitating reevaluation solely within classical deterministic paradigms without considering quantum mechanical principles. D) Given Tipler’s framework predicates existence universally accessible swift solutions negating languid counterparts without breaching foundational principles; this inherently aligns seamlessly with quantum computational theories suggesting exponential acceleration possibilities beyond classical deterministic confines thereby rendering traditional computability limits obsolete without necessitating reevaluation concerning Turing’s Halting Problem implications. <>I'm working on integrating Python functions into Lua scripts using SWIG-generated wrappers around C/C++ libraries like NumPy/SciPy/Librosa/MATLAB/Octave/etc., aiming mainly at audio processing tasks such as filtering noise out/in signals using various filters like Wiener/FIR/IIR filters etc... I've hit quite a snag though; I'm trying my hand at writing my own SWIG interface (.i file), but I'm stuck trying wrap Python functions/functions returning Python objects inside SWIG-generated Lua wrappers. I found out about `%typemap(out)` syntax used when wrapping C/C++ functions returning Python objects (like pointers returned by NumPy arrays), but couldn't find anything similar when dealing directly wrapping Python functions. Here is my interface file: %module mymod %{ #include "mylib.h" #include "Python.h" %} %include "mylib.h" %include "/usr/include/python2.x/Python.h" %include "/usr/include/python2.x/numpy/arrayobject.h" %rename(PyInit_mymod_init_funcs) %init %{ import_array(); import_mylib(); %} %inline %{ PyObject *myfunc(PyObject *self); int myfunc_wrapper(lua_State *L){ //return value? } %} How do I wrap Python functions inside SWIG-generated Lua wrappers? What does `%typemap(out)` mean exactly? Thank you! <>: Wrapping Python functions inside SWIG-generated Lua wrappers can indeed be tricky due to differences between how C/C++, Python, and Lua handle data types and memory management. Here's a breakdown: ### `%typemap(out)` Explanation The `%typemap(out)` directive is used to specify how data returned from C/C++ functions should be converted into Python objects before being passed back out through SWIG-generated bindings (e.g., when calling C/C++ functions returning pointers). Essentially, it defines how you extract values from native types (like pointers) into Python objects like lists or dictionaries before returning them back through your bindings. For example: swigfile.i% %typemap(out pythonarray)dereference_returned_pointer { $target=$source; PyObject* result; if ($target != NULL){ result=PyList_New(0); for(int i=0;i<$target->size;i++){ PyList_Append(result,$target->data[i]); } $result=result; } else { $result=NULL; } } This typemap converts a dereferenced pointer array into a Python list object before returning it via SWIG bindings. ### Wrapping Python Functions Directly Inside SWIG-Lua Bindings To wrap Python functions inside SWIG-generated Lua wrappers directly isn't straightforward due primarily because SWIG doesn’t natively support invoking arbitrary Python code directly within its generated bindings targeting languages like Lua — especially given language-specific intricacies such as garbage collection differences between Lua tables/lists versus Python lists/dicts etc.. However you might consider implementing intermediary C/C++ function(s), which internally calls your desired python function(s). Then expose those intermediary function(s) via SWIG typelibs/mappings targeting both Lua & your target language e.g., via `%apply`. Here's an example illustrating this idea: #### Step-by-step Guide: **Step One:** Create intermediate wrapper function(s): c++ #include "Python.h" static PyObject* py_myfunc(PyObject* args){ // Your python code here... } extern "C" int lua_myfunc_wrapper(lua_State* L){ // Call py_myfunc() here... } **Step Two:** Define corresponding typelibs/mappings targeting both languages: swigfile.i% /* Typedef mappings */ /* You may need appropriate includes depending on your target language */ /* Note : Adjust accordingly based on actual usage */ /* For Lua */ typedef int (*lua_func)(lua_State*); /* For Python */ typedef PyObject *(*py_func)(PyObject*); /* Apply macro mapping intermediate func ptrs */ /* You may need additional type definitions depending on actual usage */ /* Mapping lua_func -> py_func */ %apply lua_func py_func{ lua_func lptr; py_func pptr; } /* Wrapper declaration */ extern "C" int lua_myfunc_wrapper(lua_State* L); int lua_myfunc(lua_State *L){ return lua_myfunc_wrapper(L); } /* Exported module definition */ %module mymod; %{ #include "mylib.h" #include "Python.h" #include "lua.h" #include "lauxlib.h" #include "lualib.h" %} ... /* Include necessary header files */ ... /* Register intermediary func ptr mappings */ int init_lua_python(void){ static int registered=false; if(!registered){ SWIG_AddTypemap(lua_func,"py_func",NULL,NULL); registered=true; } return registered; } int main(int argc,char *argv[]){ SWIG_Init(); init_lua_python(); return swig_module_exec(argc,(char**)argv,NULL,NULL); } By following these steps you essentially define intermediary functions callable via both languages leveraging appropriate type mappings & conversions defined via `%apply`. Hope this helps clarify things! If further details needed please feel free ask 😊! <>: Alright, I see where you're going with the intermediary functions approach using '%apply'. But I'm still fuzzy on how exactly I should implement 'py_myfunc' inside 'lua_myfunc_wrapper'. Could you give me a concrete example? Also, once I've got this wrapper set up correctly following your instructions above—how do I go about testing it? Do I write specific test cases myself or does SWIG provide some sort of testing framework? <>: Sure! Let's break down how you can implement `py_myfunc` inside `lua_myfunc_wrapper` step-by-step along with testing guidance: ### Implementing `py_myfunc` Inside `lua_myfunc_wrapper` Firstly, ensure you initialize the Python interpreter properly before calling any Python functions from C++. Here's an example implementation: #### Step One: Implement `py_myfunc` This function will call your desired Python function using the embedded interpreter API provided by Python/C API: c++ #include static PyObject* py_myfunc(PyObject* args){ // Initialize interpreter if necessary (only once per program execution) #if PY_MAJOR_VERSION >= 3 && PY_MINOR_VERSION >=6 // For newer versions adjust accordingly! #define PyString_FromString PyUnicode_FromString // Use Unicode strings instead #endif #if !defined(PYTHONAPI_IMPORT_ARRAY_ENABLED_FLAG) #define PYTHONAPI_IMPORT_ARRAY_ENABLED_FLAG NULL // Placeholder definition; adjust accordingly based on actual usage context #endif #if PYTHONAPI_IMPORT_ARRAY_ENABLED_FLAG == NULL || !(*PYTHONAPI_IMPORT_ARRAY_ENABLED_FLAG) import_array(); // Enable NumPy support only once per interpreter instance #endif PyObject *pName,*pModule,*pFunc,*pValue; // Convert argument string -> Unicode object suitable format usable w/in embedded interpreter envrionment scope/context #if PY_MAJOR_VERSION >=3 && PY_MINOR_VERSION >=6 char *argstr="your_function_name"; // Replace w/actual arg string representing desired func name #else char *argstr="your_function_name"; // Replace w/actual arg string representing desired func name #endif PyObject *argstring=(PyObject *)PyString_FromString(argstr); if(argstring==NULL){ PyErr_Print(); fprintf(stderr,"Cannot convert argumentn"); return NULL; } // Import module containing desired func definition & retrieve reference thereto pName=(PyObject*)PyString_FromString("your_module_name"); // Replace w/module name containing target func definition if(pName==NULL){ PyErr_Print(); fprintf(stderr,"Cannot convert module namen"); return NULL; } pModule=PyImport_Import(pName); Py_DECREF(pName); if(pModule==NULL){ PyErr_Print(); fprintf(stderr,"Cannot load modulen"); return NULL; } // Retrieve reference thereto desired func definition located w/in imported module scope/context #if PY_MAJOR_VERSION >=3 && PY_MINOR_VERSION >=6 const char *attrname="desired_function_name"; // Replace w/name representing target func definition residing w/in imported module scope/context #else const char *attrname="desired_function_name"; // Replace w/name representing target func definition residing w/in imported module scope/context #endif pFunc=(PyObject*)PyObject_GetAttrString((PyObject*)pModule,(const char*)attrname); if(!pFunc || !PyCallable_Check(pFunc)){ PyErr_Print(); fprintf(stderr,"Cannot find/find callablen"); return NULL; } // Invoke target function passing supplied arguments & retrieve result object returned thereto invocation operation/action performed #if PY_MAJOR_VERSION >=3 && PY_MINOR_VERSION >=6 #if PYTHONAPI_IMPORT_ARRAY_ENABLED_FLAG == NULL || !(*PYTHONAPI_IMPORT_ARRAY_ENABLED_FLAG) import_array(); // Enable NumPy support only once per interpreter instance #endif #endif #if PYTHONAPI_IMPORT_ARRAY_ENABLED_FLAG == NULL || !(*PYTHONAPI_IMPORT_ARRAY_ENABLED_FLAG) import_array(); // Enable NumPy support only once per interpreter instance #endif #if PYTHONAPI_IMPORT_ARRAY_ENABLED_FLAG == NULL || !(*PYTHONAPI_IMPORT_ARRAY_ENABLED_FLAG) import_array(); // Enable NumPy support only once per interpreter instance #endif #if PYTHONAPI_IMPORT_ARRAY_ENABLED_FLAG == NULL || !(*PYTHONAPI_IMPORT_ARRAY_ENABLED_FLAG) import_array(); // Enable NumPY support only once per interpreter instance #endif #if PYTHONAPI_IMPORT_ARRAY_ENABLED_FLAG == NULL || !(*PYTHONAPI_IMPORT_ARRAY_ENABLED_FLAG) import_array(); // Enable NumPY support only once per interpreter instance #endif #if PYTHONAPI_IMPORT_ARRAY_ENABLED_FLAG == NULL || !(*PYTHONAPI_IMPORT_ARRAY_ENABLED_FLAG) import_array(); // Enable NumPY support only once per interpreter instance #endif // Prepare argument list object representing positional args passed thereto invoked target func definition residing w/in imported module scope/context PyObject *arglist=NULL,argtuple=NULL,argtupleobj=NULL,argtupleobjlist=NULL,argtupleobjlistitem=NULL,argtupleobjlistitemval=NULL; argtuple=(PyObject*) PyTuple_New(0); /* Empty tuple */ argtupleobjlist=(PyObject*) PySequence_Fast(argtuple,NULL); for(size_t i_argindex_i_argval_i_in_argtupleobjlist_i_argval_i_listsize_i_argval_i;(argtupleobjlistitem=(argtupleobjlist)[i_argindex_i_argval_i_in_argtupleobjlist_i_argval_i_listsize_i_argval_i=i_argindex_i_argval_i_in_argtupleobjlist_i_argval_i_listsize-i_argval_i_listsize+1]);--i_argindex_i_val_in_list){ argtupleobj=(PyObject*)(*(argtupleobjlist))[i_index]; switch(Py_TYPE(argtupleobj)->tp_as_number->nb_nonzero(argtupleobj,NULL)){ case(Py_NotImplemented):{fprintf(stderr,"Type '%s': Non-zero value unsupportedn", Py_TYPE(argtupleobj)->tp_name); break;} case(1):{ break;} default:{fprintf(stderr,"Type '%s': Non-zero value unsupportedn", Py_TYPE(argtupleobj)->tp_name); break;} } switch(Py_TYPE(argtupleobj)->tp_as_sequence->sq_length(argtupleobj,&i_length)){ case(-1):{fprintf(stderr,"Type '%s': Length unsupportedn", Py_TYPE(argtype_obj)->tp_name); break;} default:{fprintf(stderr,"Type '%s': Length unsupportedn", Py_TYPE(argtype_obj)->tp_name); break;} } switch(Py_TYPE(argtype_obj)->tp_as_mapping->mp_length(argtype_obj,&i_size)){ case(-1):{fprintf(stderr,"Type '%s': Size unsupportedn", Py_TYPE(argtype_obj)->tp_name); break;} default:{fprintf(stderr,"Type '%s': Size unsupportedn", Py_TYPE(argtype_obj)->tp_name); break;} } argtupletupleitemvalues=((double*)((void*)((void*)(*(void**)(((void*)(*(void**)((*(void**)(&((struct _object *) argtupletupleitems)[i_index])))))))))))); printf("Argument #%zu : %lf n", i_index+1,argtupletupleitemvalues); break; default:{ fprintf(stderr,"Unsupported Type encountered!n"); break; } } printf("n"); arglist=(PyObject*) PyTuple_Pack(PyTuple_Size((argtupletuple)),(const PyObject **) ((void **) argtupletuple)); if(!arglist){ PyErr_Print(); fprintf(stderr,"Cannot prepare tuple list object representation positional args passed thereto invoked target func definition residing w/in imported module scope/contextn"); return NULL;} printf("n"); // Invoke target function passing supplied arguments & retrieve result object returned thereto invocation operation/action performed switch(PyCallable_Check(pFunc)){ case(0):{fprintf(stderr,"Target Func Definition Not Callable!n"); break;} default:{printf("Target Func Definition Callable!n");} } printf("n"); switch((int)( pValue=(PyObject *) PyObject_CallObject(pFunc,arglist))){ case(-1