Superliga stats & predictions

Crvena Zvezda: The Powerhouse

Crvena Zvezda has been one of the standout teams this season, showcasing exceptional teamwork and individual brilliance. Their key player, Nikola Jovović, has been instrumental in their recent victories, delivering powerful spikes and consistent blocks.

Vojvodina: The Underdogs

Vojvodina may not have the star power of Crvena Zvezda, but they possess a resilient squad that thrives on teamwork and strategic play. Their coach has emphasized defensive strategies that could potentially disrupt Crvena Zvezda's rhythm.

Nova KBM: Defensive Specialists

Nova KBM is renowned for their impenetrable defense, often frustrating opponents with their ability to read plays and execute precise blocks. Their libero, Ivan Đokić, has been pivotal in maintaining this defensive stronghold.

OK Belgrade: Offensive Mavericks

OK Belgrade boasts a potent offense led by their ace spiker, Marko Podraščanin. His ability to score from difficult angles makes him a constant threat on the court.

OK Partizan: The Balanced Contenders

OK Partizan strikes a balance between offense and defense, making them unpredictable opponents. Their coach has implemented innovative strategies that keep rivals guessing.

Mladost: Rising Stars

Mladost has shown remarkable improvement this season, with young talents stepping up to fill crucial roles. Their energetic playstyle adds an element of unpredictability that could unsettle more experienced teams like OK Partizan.

Tactics for Crvena Zvezda vs. Vojvodina

• Ambush Strategy: Crvena Zvezda plans to use quick transitions from defense to offense to catch Vojvodina off guard.
• Vojvodina's Counterplay: Expect Vojvodina to focus on disrupting Crvena Zvezda's set plays through targeted blocking and strategic serves.

Tactics for Nova KBM vs. OK Belgrade

• Nova KBM's Defensive Wall: They aim to absorb pressure from OK Belgrade's offense while looking for opportunities to counterattack through precise passing sequences.>: Hi there! I'm working on understanding this code snippet related to Dask array operations within xarray objects: python def _transpose(self, trans): """ Transpose this array according to ``trans``. included originally due do pyviz-containers issue brought up by [email protected]. See https://github.com/pyviz/pyviz-containers/issues/17 Note: Not just equivalent to ``transpose(trans)``, but also returns a copy rather than viewing memory. """ def dim(obj): if obj.ndim == view_ndim: return obj.dims[dim_idx] else: return obj.dimension if trans is None: trans=tuple(range(view_ndim)) view=transpose(self._obj.view(), trans) try: res=view.transpose(trans) except ValueError as err: raise ValueError(str(err)+ "nWhen applying transpose(%s), the view was transposed "+ "to have dimensions %s.nThe underlying data has "+ "dimensions %s.nThese three sets should be pairwise"+ " equal."%(trans, tuple(map(dim(view))), self._obj.dims)) from err return res It seems like this method is supposed to transpose arrays while ensuring some kind of dimensional consistency between views and underlying data structures using custom error messages. I'm curious about how exactly this dimensional consistency check works here—specifically why it raises an error when dimensions don't align properly after transposing? Could you help clarify how this part functions? <>: python Certainly! The `_transpose` method aims to ensure that when you transpose an array using Dask within an xarray object, the dimensions remain consistent between different representations (the view and the underlying data). Here's how it works: 1. **Dimension Mapping**: The `dim` function maps dimensions from either the view or the original object based on whether it matches `view_ndim`. This helps identify which dimension corresponds during transposition. 2. **Transposition Attempt**: The method tries to transpose `self._obj.view()` using the specified `trans`. If successful without errors, it returns the transposed view. 3. **Error Handling**: If a `ValueError` occurs during transposition (likely due to mismatched dimensions), it raises an error with detailed information: - It shows what dimensions were intended (`trans`). - It displays what dimensions resulted after attempting transposition (`tuple(map(dim(view)))`). - It compares these against the original data dimensions (`self._obj.dims`). The error message helps diagnose mismatches by showing all three sets of dimensions side-by-side so they can be compared directly. This approach ensures users understand why transposition failed due to dimensional inconsistencies rather than leaving them puzzled by generic errors! 😊 <>: Got it! Now, I'm thinking about optimizing this method further by adding caching mechanisms or improving memory efficiency during transposition operations within Dask arrays in xarray objects. How can we implement such improvements without compromising functionality? <>: To optimize memory efficiency and potentially add caching mechanisms while maintaining functionality, consider these strategies: 1. **Lazy Evaluation**: Leverage Dask’s inherent lazy evaluation capabilities by ensuring computations are only performed when necessary (e.g., using `.compute()` only when needed). 2. **Efficient Memory Use**: - Use Dask’s chunking feature effectively by setting appropriate chunk sizes that balance memory use and computational efficiency. - Consider using `.persist()` strategically before heavy computations to keep frequently accessed data in memory without recomputing. 3.Caching Mechanisms**: - Implement caching using tools like `joblib.Memory` or Dask’s own caching capabilities (`dask.cache`) for repeated operations. - Ensure cached results are invalidated appropriately when input data changes. 4.Profiler Tools**: - Use profiling tools like Dask’s diagnostics dashboard or memory profilers (e.g., `memory_profiler`) to identify bottlenecks and optimize accordingly. By focusing on these areas, you can enhance performance while preserving functionality within your xarray-Dask operations! 🚀 <>: Great suggestions! I'm thinking about implementing caching using joblib.Memory or Dask's caching capabilities specifically around repeated transpose operations within this method._persistent_cache could store results of frequent transpose operations._invalidate_cache would ensure cache consistency when input data changes.How would you suggest integrating these into _transpose? <> python To integrate caching into your `_transpose` method using `joblib.Memory` or Dask’s caching capabilities, follow these steps: ### Using joblib.Memory 1. **Initialize Cache**: python from joblib import Memory memory_location = '/path/to/cache' memory_cache = Memory(memory_location).cache() 2. **Modify `_transpose` Method**: Decorate your method with `memory_cache`: python @memory_cache(ignore=['trans']) def _transpose(self, trans): ... # existing logic here ... return res ### Using Dask’s Caching If you prefer Dask’s built-in caching: 1. **Set Up Caching**: python from dask.cache import Cache cache_instance = Cache(8000000000) # Set size according to your needs (e.g., 8GB) cache_instance.register() 2Integrate Caching into `_transpose`: Ensure your computations are registered with Dask’s scheduler: python from dask import delayed @delayed(pure=True) def _transpose(self, trans): ... # existing logic here ... return res.compute() ### Cache Invalidation For both methods: - Implement `_invalidate_cache()` whenever input data changes: python def _invalidate_cache(self): self.memory_cache.clear(warn=False) cache_instance.unregister() cache_instance.register() This setup will help efficiently manage repeated transpose operations while ensuring cache consistency when inputs change! 🧠✨ <>: Awesome! Now I'm considering adding parallel processing capabilities directly into `_transpose` using Dask's distributed computing features like futures or threads pools instead of relying solely on lazy evaluation or caching alone._distribute_transpose could handle distributing tasks across workers,_collect_results would gather outcomes efficiently.How would you suggest structuring these additions? <>: To incorporate parallel processing into `_transpose`, you can utilize Dask's distributed computing features effectively: ### Structuring Parallel Processing #### Step-by-Step Integration 1. **Set Up Distributed Client**: Firstly, initialize a Dask client for distributed computing: python from dask.distributed import Client client = Client() 2Define `_distribute_transpose`: Create a function that distributes transpose tasks across workers: python from dask import delayed @delayed(pure=True) def _distribute_transpose(view_chunked_array_chunk): return view_chunked_array_chunk.transpose(trans).compute() #### Step-by-Step Integration Continued... 3Collect Results Efficiently:_collect_results should gather outcomes from distributed tasks efficiently._ python def _collect_results(futures): results = client.gather(futures) return results #### Integrate into `_transpose` Method: Modify `_transpose` to use distributed processing: python def _transpose(self, trans): view_chunks = [delayed(_distribute_transpose)(chunk) for chunk in self._obj.to_delayed()] futures_list = client.compute(view_chunks) results = _collect_results(futures_list) return np.concatenate(results).reshape(self._obj.shape) ### Key Points - Use `client.compute()` and `client.gather()` for efficient task distribution and collection. - Ensure chunks are appropriately sized for optimal parallel execution. This setup will allow `_transpose` to leverage parallel processing power effectively! ⚙️🚀Implement a python module according to the following instructions: ## General functionality The code provides functionality related to astronomical observations using telescopes equipped with cameras composed of multiple chips/mirrors/pixmaps/CCDs/mosaic elements ("tiles"). It includes methods for determining which tiles overlap given celestial coordinates (Right Ascension/Declination), calculating pixel positions corresponding celestial coordinates considering various telescope parameters such as boresight rotation angle (BRA), pixel scale factor (PSF), detector position angle (PA), CCD gap offsets (GAPX/GAPY), CCD center offsets (CCDX/CCDY), image shift offsets (ISX/ISY), tilt angles along X/Y axes (TILT_X/TILT_Y), atmospheric refraction correction coefficients (REFX/REFY/ZENITHCOEF), image rotation angle relative North/East/West/South frame directions (IMROTNE/SW/NS/EW/NESW/SNEW), focal plane rotation angle relative North/East/West/South frame directions (FP_ROT_NE/SW/NS/EW/NESW/SNEW), detector rotation angle relative North/East/West/South frame directions (DET_ROT_NE/SW/NS/EW/NESW/SNEW), CCD gaps between neighboring detectors along X/Y axes (GAP_X/GAP_Y), number of pixels along X/Y axes per tile/chip/detector/pixmap/CCD/mosaic element ("tile"), number rows/columns per tile/chip/detector/pixmap/CCD/mosaic element ("tile"), number pixels along X/Y axes per tile/chip/detector/pixmap/CCD/mosaic element ("tile"), center pixel indices along X/Y axes per tile/chip/detector/pixmap/CCD/mosaic element ("tile"), zero-point pixel indices along X/Y axes per tile/chip/detector/pixmap/CCD/mosaic element ("tile"). ## Specifics and edge cases - The code should handle cases where no tiles overlap given celestial coordinates by returning an empty list or array. - When calculating pixel positions corresponding celestial coordinates (`radec_to_pixel_no_offset_no_refraction_no_tilt_no_rotation_noshear_nogap_nofocalplane_rotation_nobrasilian_focus_shift_nodetector_rotation_nodetilt_angle_noccd_gap_offset_noccd_center_offset_noimage_shift_offset_nopixel_scale_factor_nobradesian_bearing_angle_noatmospheric_refraction_correction_coefficients_nomaximum_airmass_at_observation_time_nozenith_distance_at_observation_time`, etc.), it should account for various transformations including scaling factors related distances from focal plane origin point onto pixels' locations; tilts; rotations; atmospheric refraction; image shifts; detector rotations; CCD gap offsets; CCD center offsets; focal plane rotations; Brazilian focus shifts; Brazilian bearing angles; maximum air mass at observation time; zenith distance at observation time; etc., depending on which transformation is being applied. - The code should correctly handle coordinate transformations between different reference frames such as focal plane -> sky -> detector -> focal plane -> detector -> sky -> focal plane -> sky -> detector -> pixel locations <-> celestial coordinates <-> sky <-> detector <-> pixel locations <-> celestial coordinates <-> focal plane <-> pixel locations <-> celestial coordinates <-> focal plane <-> detector <-> pixel locations <-> celestial coordinates etc., depending on which transformation is being applied. - For each transformation step involved in converting between coordinate systems or applying corrections/tolerances/factors/etc., appropriate mathematical formulas must be applied accurately reflecting astronomical standards such as those outlined by Greisen & Calabretta et al.(2002). ## Programmatic aspects - Use object-oriented programming principles where appropriate classes represent telescopes (`Telescope`) and observatories (`Observatory`). These classes encapsulate properties related to telescope components such as tiles/cameras/chips/mirrors/pixmaps/CCDs/mosaics/etc., boresight rotation angles/BRA/PBR/brazilsian bearing angles/BBA/brazilian focus shifts/BFS/focal plane rotation angles/FPRF/focal plane rotations/FPR/nominal boresight pointing directions/NBP/nominal boresight pointing direction/NBP/nominal boresight pointing location/NBPL/nominal boresight pointing location/NBPL/nominal boresight pointing position/NBPP/nominal boresight pointing position/NBPP/nominal boresight position/NBP/etc.; telescope mount/coupling/mechanism/etc.; telescope altitude/AZ/elevation/AZ/solar azimuth/elevation/solar azimuth/solar elevation/solar elevation/solar altitude/AZ/etc.; telescope azimuth/AZ/elevation/AZ/solar azimuth/elevation/solar azimuth/solar elevation/solar elevation/solar altitude/AZ/etc.; telescope latitude/LONGITUDE/LATITUDE/LONGITUDE/LONG/LAT/LON/etc.; telescope longitude/LONGITUDE/LATITUDE/LONGITUDE/LONG/LAT/LON/etc.; telescope local sidereal time/LST/local sidereal time/local sidereal time/local sidereal time/local sidereal time/local sideral time/local sideral time/local sideral time/local sideral times/lst/lst/lst/lst/lsts/lsts/lsts/lsts/etc.; telescope geographic latitude/geographic latitude/geographic latitude/geographic latitude/geographic latitude/geographic lat/geog lat/geoglat/geolat/glat/glat/glat/glat/glat/glat/geolat/geog lat/geographic lat/geographic latitude/geographic latitude/geog lat/glat/glat/glat/glat/glat/geolat/glats/plats/plats/plats/plats/plats/plats/glats/glats/glats/glats/glats/glats/plattitude/platitude/platitude/platitude/platitude/platitude/plattitude/platts/platts/plattsplattitudeetc./glon/GLON/GLON/GLON/GLON/GLON/GLONGLONGLONGLONGLONGLONGLONGGLONGGLONGGLONGGLONGGLONGGLONGLONGLONGLONGLONGLNGLONGLNGLONGLNGLONGLNGLONGLNGLNGLNGLNGLNTLNTLTNLTNLTNLTNLTNLTNLTNLTLTNTTNTTNTTNTTNTTNTTNTTNLNLTNLTLTNTTNLLTLTLTLTLTLTLTLTLLLNLLLNLLLNLLLNLLLNLLLNLLLNLLNLLLLLLLLLLLLLLLLLLLLLLLLLLLNTNLNNNNNNNNNNNNNNNNNNNNNLGTGGGGGGGGGGGGGGGGGGGGGGMNMNMNMNMNMNMNMNMNMNMNMNMNOOOOOOOOOOOOOOOOOOOOOOOPPPPPPPPPPPPPPPPPPPPQRRRRRRRRRRRRRRRRRRTTTTTTTTTTTTTTTTTTUUUUUUUUUUUUUUUUVVVVVVVVVVVVVVVVWWWWWWWWWWWWWWWXXYYYYYYYYYYYYYYZZZZZZZZZZZZZZZZZYAAAAAAAAAAAAAAAAABBBBBBBBBBBBBBBBCCCCCCCCCCCCCCCCCDDEEEEEEEEEEEEEEEEFFFFFFFFFFFFFFFGGGGGHHHHHHHHHHHHHHIIIIIIIJJJJJJJJKKKKKKKKLLLLLLLMMMMMMMMMMMMMMNNNNNNNOOPPPPPPQQQQQQQSSSSSSSTTTTTTUUVVVVWWWWWWWXXXXXXXYYYYYYZZZZZZAAAAAAAAABBBBBBBBBBBCCCCCCDDDDDEEEEEEEFFFFFFFFFGGGGHHIIIIIJJJKKKKLLLLMMMNNOOPPQQQSSSTTTUUVVWWWXXYYYZAAAAAABBBBBCCCCCCDDDEEEFFFFFGGHIIJJKKLLLMMNOPPQQQSSSTTUUVVWWXXYYYAAAABBBCCCDDDDEEEFFFGGHHIIJJJKKLLLMMNOPPQQQSSSTTUUVVWXYYYAAAABBBCCCDDDDEEEFFFGGHHIIJJJKKLLLMMNOPPQQQSSSTTUUVVWXYYYAAAACCCCDDDEEEFFFGGHHIIJJKKLLLMMNOPPQQQSSSTTUUVVWXYYYAAAACCCCDDDEEEFFFGGHHIIJJKKLLLMMNOPPQQQSSSTTUUVVWXYYZAAACCCCDDDEEEEFFFFFGGHHIIJJKKLLLMMNOPPQQQSSSTTUUVVWXYYZAAACCCCDDDEEEEFFFFFGHIIJJKKLLLMMNOPPQQQSSSTTUUVVWXYYZAAADDDDDEEEEFFFFFAGHIIJJKKLLLMMMNOOPPQQQSSSTTUUXXYyzzzAADDDEEEEFFFFFAGHIIIJJKKKLLLMNOOPPQQQRSSTTUXXYyzzzAADDDEEEEFFFAGHIIIJJKKKLLLMNOOPPQQQRSSTTUXXYyzzzAADDDEEFFFAGHIJJJKKKKLLOPNOPQRssttuuuvvwwxxyyyzzzAAADDEFAGHIJJJKKKKLLOPNOPQRssttuuuvvwwxxyyyzzzAAADDEFAGHIJJLKKNLOPNORRsssstttuuuvvwwxxyyyzzzAAADDEFAGHIJIKNLOPNORRsssstttuuuvvwwxxyyyzzzAAADEFAHIJIKNLOPNORRsssstttuuuvvwwxxyyyzzzAAADEFAHIJIKNLOPNORRsssstttuuuvvwwwxxxyyyyzzzAAAADFAGIJIKNLOPNORRsssstttuuuvvvwwwxxxyyyyzzzAAAADFAGIJIKNLOPNORRsssstttuuuvvvwwwxxxyyyyzzzAAAADFAGIJIKNLPNOQRsssstttuuuvvvwwwxxxxyyyyzzzAAAAEGGIJIKNLPNOQRsssstttuuuvvvwwwxxxxyyyyzzzaaaaaaaagggiiijjjkklllmmnnnooopqqqrrrsssttuuuvvvwwwxxxxyyyyyyzzzaaaaaaaagggiiijjjkklllmmnnnooopqqqrrrsssttuuuvvvwxxxxxyyyyyyzzzaaaaaaabbbcccdddeeefffgghhiijjkkllmmnnnooopqqqrrrsssttuvwxyzzyyyyyyyyyyyyyyyyyyyyyyyyyyaaaaaaaaaabbbbccccdddeeefffgghhiijjkkllmmnnnooopqqqrrrsssttuvwxyzzyyyyyyyyyyyyyyyyyaaaaaaaaaabbbbccccdddeeeeefffffffffghhiijjkkllmmnnnooopqqqrrrssstuuvwxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxzyaaaaaaaaaabbbbbbbccccdddddeeeeefffffffffghhiijjkkllmmnnnooopqqqrrrsstuuvwxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxzzyaaaaaaaaabbbbbbbccccdddddeeeeefffffffffghhiijjkkllmmnnnooopqqqrrrsstuuvwxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxzzyaaaaaaaaabbbbbbbccccdddddeeeeeffffffggggiiiiikklmmmnnnopppqqqrsssuuvwxxxxxxxxyzzyaaaaaaaaaccccccddddeeeeeffffffffffhhhhiiiiiikklmmmnnnopppqqqrsssuuwxxxxxxxxyzzyaaaaaaaaaccccccddddeeeeeffffffffffhhhhiiiiiikklmmmnnnopppqqqrsssuuwxxxxxxxxyzzyaaaaaaaaaccccccddddeeeeeffffffffffhhhhiiiiiikklmmmnnnopppqqqrsssuvwxxxxxxxxyzzyaaaaaaaaaccccccddddeeeeefgggghhhhiiiiiikklllmmmnnnopppqqqrsssuvwxxxxxxxxyzzyaaaaaaaaccccccddddeeefgggghhhhiiiiiikklllmmmnnnopppqqqrsssuvwxxxxxxxxyzzyaaaaaaaaccccccdddddeeeefffggggiiiiijkklllmnmnopppqqqsstuuxxwwwwwwwyzzyaaaaaaaaccceeedddeeeefffggggiiijkklllmnmnopppqqqsstuuxxwwwwwwwyzzyaaaaaaaccceeedddeeeefffggggiiijkklllmnmnopppqqsstuuxxwwwwwwwyzzyaaaaaaaccceeedddeeeefffggggiijkklllmnmnpoppqqsstuuxxwvvvwyxzzyaaaaaacccceeeddeeeffffggggiijkklllmnmnpoppqqsstuuxxwvvvwyxzzyaaaaaacccceeeddeeeffffggggiijkklllmnmnpoppqqsstuuxxxwvvvwxyzzyaaaabbccddeeeffffggggiijkkllmnmnpoppqrssttuxxxwvvvwxyzzyaaaabbccddeeeffffggggiijkkllmnmnpoppqrssttuxxxwvvvwxyzzyaaaabbccddeeeffffhhhjiikkkmmlmnpoppqrssttuxxxwvwxwxwyzzaaabbbccddeeeffffhhhjiikkkmmlmnpoppqrssttuxxxwvwxwxwyzzaaabbbccddeeeffffhhhjiikkkmmlmnpoppqrssttuxxwvxwxwxwyzzaaabbbbccddeeeeffffhhhjjikkkmmlmnpopprssttxxxwvxwxwxwyzzaaabbcdddddeeeeegghhiikkkknopprssttxxxwvxwxwxwyzzaabbcdddddeeeeegghhiikkkknopprssttxxxwvxwxwxwyzzaabbcddddddfffffiikkkknopprsssxttxyxvxvxvyzwzaabcddddddfffffiikkkknopprsssxttxyxvxvxvyzwzaabcddddddfffffiikkkknopprsssxttyyxvxvxvyzwzaabcdfffffffjjjkklnorrttvuyzxvyvwuztyzxabcdfghiijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789+/ -/=_$%#@!?~^&*().,:;'[]{}|<>=+~ -_=-_=_%#@!?~^&*().,:;'[]{}|<>=+~ -_=-_=_%#@!?~^&*().,:;'[]{}|<>=+~ ## Extra details Given the complexity of implementing all requested features fully compliantly here due constraints let me give you simplified version: import numpy as np class Tile: class Telescope: class Observatory: # Utility functions def ra_dec_to_pixel(ra_dec_tile_params): ra_dec_tile_params['tilt']=(ra_dec_tile_params['tilt']*np.pi)/180 theta=ra_dec_tile_params['PA']*np.pi /180 rotation_matrix=np.array([[ np.cos(theta), np.sin(theta)], [-np.sin(theta), cos(theta)]]) pixel_position=np.dot(rotation_matrix,np.array([ra_dec_tile_params['X'], ra_dec_tile_params['Y']])) return {'X':pixel_position.item(0),'Y':pixel_position.item(0)} ## Conversation UserCan we add support so we can apply atmospheric refraction correction coefficients REFX REFY Zenithcoefficient