diff --git a/typhon/collocations/collocator.py b/typhon/collocations/collocator.py index a405634d..c602307f 100644 --- a/typhon/collocations/collocator.py +++ b/typhon/collocations/collocator.py @@ -1,3 +1,4 @@ +from collections import defaultdict from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor from datetime import datetime, timedelta import gc @@ -9,7 +10,7 @@ import pandas as pd from typhon.geodesy import great_circle_distance from typhon.geographical import GeoIndex -from typhon.utils import add_xarray_groups +from typhon.utils import add_xarray_groups, get_xarray_groups from typhon.utils.timeutils import to_datetime, to_timedelta, Timer import xarray as xr @@ -41,6 +42,11 @@ ] +class ProcessCrashed(Exception): + """Helper exception for crashed processes""" + pass + + class Collocator: def __init__( self, threads=None, verbose=1, name=None, #log_dir=None @@ -52,15 +58,16 @@ def __init__( here the maximum number of threads that you want to use. Which number of threads is the best, may be machine-dependent. So this is a parameter that you can use to fine-tune the - performance. Note: Not yet implemented. + performance. Note: Not yet implemented due to GIL usage of + sklearn BallTree. verbose: The higher this integer value the more debug messages will be printed. - name: The name of this collocator. + name: The name of this collocator, will be used in log statements. """ # If no collocations are found, this will be returned. We need empty # arrays to concatenate the results without problems: - self.empty = xr.Dataset() + self.empty = None # xr.Dataset() self.no_pairs = np.array([[], []]) self.no_intervals = np.array([], dtype='timedelta64[ns]') self.no_distances = np.array([]) @@ -80,7 +87,7 @@ def __init__( self.name = name if name is not None else "Collocator" def __call__(self, *args, **kwargs): - return self.do(*args, **kwargs) + return self.collocate(*args, **kwargs) def _debug(self, msg): if self.verbose > 1: @@ -95,13 +102,24 @@ def _error(self, msg): def collocate_filesets( self, filesets, start=None, end=None, processes=None, output=None, - bundle=False, skip_file_errors=False, post_processor=None, + bundle=None, skip_file_errors=False, post_processor=None, post_processor_kwargs=None, **kwargs ): """Find collocation between the data of two filesets + If you want to save the collocations directly to disk, it may be easier + to use :meth:`~typhon.collocations.Collocations.search` directly. + Args: - filesets: A list of two FileSet objects + filesets: A list of two :class:`FileSet` objects, the primary and + the secondary fileset. Can be also + :class:`~typhon.collocations.common.Collocations` objects with + `read_mode=collapse`. The order of the filesets is irrelevant + for the results of the collocation search but files from the + secondary fileset might be read multiple times if using + parallel processing (`processes` is greater than one). The + number of output files could be different (see also the option + `bundle`). start: Start date either as datetime object or as string ("YYYY-MM-DD hh:mm:ss"). Year, month and day are required. Hours, minutes and seconds are optional. If not given, it is @@ -112,10 +130,18 @@ def collocate_filesets( performance significantly. Pass here the number of processes to use. output: Fileset object where the collocated data should be stored. - bundle: Not yet implemented. - skip_file_errors: If a file could not be read, the file and its - match will be skipped and a warning will be printed. Otheriwse - the program will stop (default). + bundle: Set this to *primary* if you want to bundle the output + files by their collocated primaries, i.e. there will be only + one output file per primary. *daily* is also possible, then all + files from one day are bundled together. Per default, all + collocations for each file match will be saved separately. + This might lead to a high number of output files. + Note: *daily* means one process bundles all collocations from + one day into one output file. If using multiple processes, this + could still produce several daily output files per day. + skip_file_errors: If this is *True* and a file could not be read, + the file and its match will be skipped and a warning will be + printed. Otheriwse the program will stop (default). post_processor: A function for post-processing the collocated data before saving it to `output`. Must accept two parameters: a xarray.Dataset with the collocated data and a dictionary with @@ -127,17 +153,17 @@ def collocate_filesets( Yields: A xarray.Dataset with the collocated data if `output` is not set. - If `output` is set to a FileSet-like object, only the filename is - yielded. The results are not ordered if you use more than one - process. For more information about the yielded value, have a look - at :meth:`collocate`. + If `output` is set to a FileSet-like object, only the filename of + the stored collocations is yielded. The results are not ordered if + you use more than one process. For more information about the + yielded xarray.Dataset have a look at :meth:`collocate`. Examples: .. code-block:: python """ - timer = time.time() + timer = Timer().start() if len(filesets) != 2: raise ValueError("Only collocating two filesets at once is allowed" @@ -160,6 +186,7 @@ def collocate_filesets( self._info(f"Collocate from {start} to {end}") + # Find the files from both filesets which overlap tempoerally. matches = list(filesets[0].match( filesets[1], start=start, end=end, max_interval=max_interval, )) @@ -170,12 +197,16 @@ def collocate_filesets( # Make sure that there are never more processes than matches processes = min(processes, len(matches)) - self._info(f"using {processes} process(es) on {len(matches)} matches") + total_matches = sum(len(match[1]) for match in matches) + self._info(f"using {processes} process(es) on {total_matches} matches") # MAGIC with processes # Each process gets a list with matches. Important: the matches should # be continuous to guarantee a good performance. After finishing one - # match, the process yields the results. + # match, the process pushes its results to the result queue. If errors + # are raised during collocating, the raised errors are pushed to the + # error queue, + matches_chunks = np.array_split(matches, processes) # This queue collects all results: results = Queue(maxsize=processes) @@ -183,8 +214,6 @@ def collocate_filesets( # This queue collects all error exceptions errors = Queue() - matches_chunks = np.array_split(matches, processes) - # Extend the keyword arguments that we are going to pass to # _collocate_files: kwargs.update({ @@ -198,7 +227,7 @@ def collocate_filesets( "post_processor_kwargs": post_processor_kwargs, }) - # This contains all running processes + # This list contains all running processes process_list = [ Process( target=Collocator._process_caller, @@ -211,6 +240,13 @@ def collocate_filesets( for i, matches_chunk in enumerate(matches_chunks) ] + # We want to keep track of the progress of the collocation search since + # it may take a while. + process_progress = { + name: 0. # Each process is at 0 percent at the beginning + for name in PROCESS_NAMES[:processes] + } + # Start all processes: for process in process_list: process.start() @@ -219,23 +255,35 @@ def collocate_filesets( running = process_list.copy() processed_matches = 0 - total_matches = sum(len(match[1]) for match in matches) + # The main process has two tasks during its child processes are + # collocating. + # 1) Collect their results and yield them to the user + # 2) Display the progress and estimate the remaining processing time while running: - # Filter out all processes that are dead: + # Filter out all processes that are dead: they either crashed or + # complete their task running = [ process for process in running if process.is_alive() ] - # Yield all results that are currently in the queue: + # Get all results from the result queue while not results.empty(): - processed_matches += 1 + process, progress, result = results.get() + + # The process might be crashed. To keep the remaining time + # estimation useful, we exclude the crashed process from the + # calculation. + if result is ProcessCrashed: + del process_progress[process] + else: + process_progress[process] = progress + self._print_progress( - timer, total_matches, processed_matches, len(running), + timer.elapsed, process_progress, len(running), errors.qsize() ) - result = results.get() if result is not None: yield result @@ -261,111 +309,247 @@ def collocate_filesets( print("-" * 79 + "\n") @staticmethod - def _print_progress(timer, total, current, processes, errors): - if current == 0: - progress = 0 - elapsed_time = time.time() - timer - expected_time = "unknown" - else: - progress = current / total + def _print_progress(elapsed_time, process_progress, processes, errors): - elapsed_time = time.time() - timer - expected_time = timedelta( - seconds=int(elapsed_time * (1 / progress - 1)) - ) + elapsed_time -= timedelta(microseconds=elapsed_time.microseconds) + if len(process_progress) == 0: + msg = "-"*79 + "\n" + msg += f"100% | {elapsed_time} hours elapsed | " \ + f"{errors} processes failed\n" + msg += "-"*79 + "\n" + print(msg) + return - elapsed_time = timedelta( - seconds=int(elapsed_time) - ) + progress = sum(process_progress.values()) / len(process_progress) + + try: + expected_time = elapsed_time * (100 / progress - 1) + expected_time -= timedelta( + microseconds=expected_time.microseconds) + except ZeroDivisionError: + expected_time = "unknown" msg = "-"*79 + "\n" - msg += f"{100*progress:.0f}% | {elapsed_time} hours elapsed, " \ + msg += f"{progress:.0f}% | {elapsed_time} hours elapsed, " \ f"{expected_time} hours left | {processes} proc running, " \ f"{errors} failed\n" msg += "-"*79 + "\n" print(msg) @staticmethod - def _process_caller(collocator, results, errors, name, *args, **kwargs): - collocator.name = name + def _process_caller( + self, results, errors, name, output, bundle, post_processor, + post_processor_kwargs, **kwargs): + """Wrapper around _collocate_matches + + This function is called for each process. It communicates with the main + process via the result and error queue. + + Result Queue: + Adds for each collocated file match the process name, its progress + and the actual results. + + Error Queue: + If an error is raised, + """ + self.name = name # We keep track of how many file pairs we have already processed to - # make the error debugging easier: - processed = 0 + # make the error debugging easier. We need the match in flat form: + matches = [ + [match[0], secondary] + for match in kwargs['matches'] + for secondary in match[1] + ] + + # If we want to bundle the output, we need to collect some contents. + # The current_bundle_tag stores a certain information for the current + # bundle (e.g. filename of primary or day of the year). If it changes, + # the bundle is stored to disk and a new bundle is created. + cached_data = [] + cached_attributes = {} + current_bundle_tag = None try: - for result in collocator._collocate_files(*args, **kwargs): - results.put(result) + processed = 0 + collocated_matches = self._collocate_matches(**kwargs) + for collocations, attributes in collocated_matches: + match = matches[processed] processed += 1 + progress = 100 * processed / len(matches) + + if collocations is None: + results.put([name, progress, None]) + continue + + # The user does not want to bundle anything therefore just save + # the current collocations + if bundle is None: + result = self._save_and_return( + collocations, attributes, output, + post_processor, post_processor_kwargs + ) + results.put([name, progress, result]) + continue + + # The user may want to bundle the collocations before writing + # them to disk, e.g. by their primaries. + save_cache = self._should_save_cache( + bundle, current_bundle_tag, match, + to_datetime(collocations.attrs["start_time"]) + ) + + print(current_bundle_tag, match[0].path, match[0].times[0]) + print(f"Save cache? {save_cache}") + + if save_cache: + result = self._save_and_return( + cached_data, + cached_attributes, output, + post_processor, post_processor_kwargs + ) + results.put([name, progress, result]) + + cached_data = [] + cached_attributes = {} + + # So far, we have not cached any collocations or we still need + # to wait before saving them to disk. + cached_data.append(collocations) + cached_attributes.update(**attributes) + + if bundle == "primary": + current_bundle_tag = match[0].path + elif bundle == "daily": + current_bundle_tag = \ + to_datetime(collocations.attrs["start_time"]).date() + + # After all iterations, save last cached data to disk: + if cached_data: + result = self._save_and_return( + cached_data, + cached_attributes, output, + post_processor, post_processor_kwargs + ) + results.put([name, progress, result]) + except Exception as exception: - collocator._error("ERROR: I got a problem and terminate!") + # Tell the main process to stop considering this process for the + # remaining processing time: + results.put( + [name, 100., ProcessCrashed] + ) + + self._error("ERROR: I got a problem and terminate!") # Build a message that contains all important information for # debugging: - i = 0 - msg = None - for match in kwargs['matches']: - for secondary in match[1]: - if processed != i: - i += 1 - continue - - i += 1 - msg = f"Failed to collocate {match[0]} with {secondary}" - break - if msg is not None: - break - - msg += "\n" + msg = f"Failed to collocate {matches[processed]} with"\ + f"{matches[processed]}\n" # The main process needs to know about this exception! error = [ name, exception.__traceback__, - msg + str(exception), processed + msg + str(exception) ] errors.put(error) # Finally, raise the exception to terminate this process: raise exception - collocator._info(f"Finished all {processed} matches") + self._info(f"Finished all {len(matches)} matches") + + def _save_and_return(self, collocations, attributes, output, + post_processor, post_processor_kwargs): + """Save collocations to disk or return them""" - def _collocate_files( - self, filesets, matches, output, bundle, skip_file_errors, - post_processor, post_processor_kwargs, **kwargs + if isinstance(collocations, list): + collocations = concat_collocations( + collocations + ) + + if output is None: + return collocations, attributes + else: + filename = output.get_filename( + [to_datetime(collocations.attrs["start_time"]), + to_datetime(collocations.attrs["end_time"])], + fill=attributes + ) + + # Apply a post processor function from the user + if post_processor is not None: + if post_processor_kwargs is None: + post_processor_kwargs = {} + + collocations = post_processor( + collocations, attributes, **post_processor_kwargs + ) + + if collocations is None: + return None + + self._info(f"Store collocations to\n{filename}") + + # Write the data to the file. + output.write(collocations, filename) + return filename + + @staticmethod + def _should_save_cache(bundle, current_bundle_tag, match, start_time): + """Return true if the cache should be saved otherwise false + """ + if current_bundle_tag is None: + return False + elif bundle == "primary": + # Check whether the primary has changed since the last time: + return current_bundle_tag != match[0].path + elif bundle == "daily": + # Has the day changed since last time? + return current_bundle_tag != start_time.date() + + # In all other cases, the bundle should not be saved yet: + return False + + def _collocate_matches( + self, filesets, matches, skip_file_errors, **kwargs ): + """Load file matches and collocate their content - # Get all primary and secondary data that overlaps with each other - file_pairs = filesets[0].align( + Yields: + A tuple of two items: the first is always the current percentage + of progress. If output is True, the second is only the filename of + the saved collocations. Otherwise, it is a tuple of collocations + and their collected :class:`~typhon.files.handlers.common.FileInfo` + attributes as a dictionary. + """ + # Load all matches in a parallized queue: + loaded_matches = filesets[0].align( filesets[1], matches=matches, return_info=True, compact=False, skip_errors=skip_file_errors, ) - debug_timer = time.time() - # If we want to bundle the output - result_cache = [] - for i, file_pair in enumerate(file_pairs): - files = file_pair[1][0], file_pair[1][0] - primary, secondary = file_pair[0][1].copy(), file_pair[1][1].copy() + for loaded_match in loaded_matches: + # The FileInfo objects of the matched files: + files = loaded_match[0][0], loaded_match[1][0] - self._debug(f"{time.time() - debug_timer:.2f}s for reading") + # We copy the data from the matches since they might be used for + # other matches as well: + primary, secondary = \ + loaded_match[0][1].copy(), loaded_match[1][1].copy() - current_start = np.datetime64(primary["time"].min().item(0), "ns") - current_end = np.datetime64(primary["time"].max().item(0), "ns") - self._debug(f"Collocating {current_start} to {current_end}\n" - f"{files[0].path}\nwith {files[1].path}") + self._debug(f"Collocate {files[0].path}\nwith {files[1].path}") - debug_timer = time.time() collocations = self.collocate( (filesets[0].name, primary), (filesets[1].name, secondary), **kwargs, ) - self._debug(f"{time.time()-debug_timer:.2f}s for collocating") - if not collocations.variables: + if collocations is None: self._debug("Found no collocations!") - yield None + # At least, give the process caller a progress update: + yield None, None continue # Check whether the collocation data is compatible and was build @@ -384,73 +568,59 @@ def _collocate_files( # Add the names of the processed files: for f in range(2): - if f"{filesets[f].name}_file" not in collocations.attrs: - collocations.attrs[f"{filesets[f].name}_file"] = \ - files[f].path + if f"{filesets[f].name}/__file" in collocations.variables: + continue + + collocations[f"{filesets[f].name}/__file"] = files[f].path - # Collect the attributes of the input files + # Collect the attributes of the input files. The attributes get a + # prefix, primary or secondary, to allow not-unique names. attributes = { - p: v - for file in files + f"primary.{p}" if f == 0 else f"secondary.{p}": v + for f, file in enumerate(files) for p, v in file.attr.items() } - if output is None: - yield collocations, attributes - else: - debug_timer = time.time() - filename = output.get_filename( - [to_datetime(collocations.attrs["start_time"]), - to_datetime(collocations.attrs["end_time"])], - fill=attributes - ) - - # Apply a post processor function from the user - if post_processor is not None: - if post_processor_kwargs is None: - post_processor_kwargs = {} - - collocations = post_processor( - collocations, attributes, **post_processor_kwargs - ) - - if collocations is None: - yield None - continue - - # Write the data to the file. - output.write(collocations, filename) - self._debug( - f"{time.time()-debug_timer:.2f}s for storing to {filename}" - ) - yield filename + yield collocations, attributes - debug_timer = time.time() def collocate( self, primary, secondary, max_interval=None, max_distance=None, bin_factor=1, magnitude_factor=10, tunnel_limit=None, start=None, end=None, leaf_size=40 ): - """Find collocations between two data objects + """Find collocations between two xarray.Dataset objects Collocations are two or more data points that are located close to each other in space and/or time. - A data object must be a dictionary, a xarray.Dataset or a - pandas.DataFrame object with the keys *time*, *lat*, *lon*. Its values - must be 1-dimensional numpy.array-like objects and share the same - length. The field *time* must have the data type *numpy.datetime64*, - *lat* must be latitudes between *-90* (south) and *90* (north) and - *lon* must be longitudes between *-180* (west) and *180* (east) + Each xarray.Dataset contain the variables *time*, *lat*, *lon*. They + must be - if they are coordinates - unique. Otherwise, their + coordinates must be unique, i.e. they cannot contain duplicated values. + *time* must be a 1-dimensional array with a *numpy.datetime64*-like + data type. *lat* and *lon* can be gridded, i.e. they can be multi- + dimensional. However, they must always share the first dimension with + *time*. *lat* must be latitudes between *-90* (south) and *90* (north) + and *lon* must be longitudes between *-180* (west) and *180* (east) degrees. See below for examples. + The collocation searched is performed with a fast ball tree + implementation by scikit-learn. The ball tree is cached and reused + whenever the data points from `primary` or `secondary` have not + changed. + If you want to find collocations between FileSet objects, use :class:`collocate_filesets` instead. Args: - primary: Data object that fulfill the specifications from above. - secondary: Data object that fulfill the specifications from above. + primary: A tuple of a string with the dataset name and a + xarray.Dataset that fulfill the specifications from above. Can + be also a xarray.Dataset only, the name is then automatically + set to *primary*. + secondary: A tuple of a string with the dataset name and a + xarray.Dataset that fulfill the specifications from above. Can + be also a xarray.Dataset only, the name is then automatically + set to *secondary*. max_interval: Either a number as a time interval in seconds, a string containing a time with a unit (e.g. *100 minutes*) or a timedelta object. This is the maximum time interval between two @@ -458,7 +628,7 @@ def collocate( spatial collocations only. max_distance: Either a number as a length in kilometers or a string containing a length with a unit (e.g. *100 meters*). This is - the maximum distance between two data points in to meet the + the maximum distance between two data points to meet the collocation criteria. If this is None, the data will be searched for temporal collocations only. Either `max_interval` or *max_distance* must be given. @@ -466,20 +636,25 @@ def collocate( from tunnel to haversine distance metric. Per default this algorithm uses the tunnel metric, which simply transform all latitudes and longitudes to 3D-cartesian space and calculate - their euclidean distance. This produces an error that grows - with larger distances. When searching for distances exceeding - this limit (`max_distance` is greater than this parameter), the - haversine metric is used, which is more accurate but takes more - time. Default is 1000 kilometers. + their euclidean distance. This is faster than the haversine + metric but produces an error that grows with larger distances. + When searching for distances exceeding this limit + (`max_distance` is greater than this parameter), the haversine + metric is used, which is more accurate but takes more time. + Default is 1000 kilometers. magnitude_factor: Since building new trees is expensive, this algorithm tries to use the last tree when possible (e.g. for data with fixed grid). However, building the tree with the larger dataset and query it with the smaller dataset is faster than vice versa. Depending on which premise to follow, there might have a different performance in the end. This parameter - is the factor that... TODO + is the factor of that one dataset must be larger than the other + to throw away an already-built ball tree and rebuild it with + the larger dataset. leaf_size: The size of one leaf in the Ball Tree. The higher the - faster is the tree building but the slower is the tree query. + leaf size the faster is the tree building but the slower is the + tree query. The optimal leaf size is dataset-dependent. Default + is 40. bin_factor: When using a temporal criterion via `max_interval`, the data will be temporally binned to speed-up the search. The bin size is `bin_factor` * `max_interval`. Which bin factor is the @@ -493,16 +668,29 @@ def collocate( datetime.max per default. Returns: - Three numpy.arrays: the pairs of collocations (as indices in the - original data), the interval for the time dimension and the - distance for the spatial dimension. The pairs are a 2xN numpy.array - where N is the number of found collocations. The first row contains - the indices of the collocations in `data1`, the second row the - indices in `data2`. + None if no collocations were found. Otherwise, + a xarray.Dataset with the collocated data in *compact* form. It + consists of three groups (groups of variables containing */* in + their name): the *primary*, *secondary* and the *Collocations* + group. If you passed `primary` or `secondary` with own names, + they will be used in the output. The *Collocations* group contains + information about the found collocations. *Collocations/pairs* is + a 2xN array where N is the number of found collocations. It + contains the indices of the *primary* and *secondary* data points + which are collocations. The indices refer to the data points stored + in the *primary* or *secondary* group. *Collocations/interval* and + *Collocations/distance* are the intervals and distances between the + collocations in seconds and kilometers, respectively. Collocations + in *compact* form are efficient when saving them to disk but it + might be complicated to use them directly. Consider applying + :func:`~typhon.collocations.common.collapse` or + :func:`~typhon.collocations.common.expand` on them. Examples: .. code-block: python + + # TODO: Update this example! import numpy as np from typhon.collocations import Collocator @@ -548,10 +736,12 @@ def collocate( start = datetime.min if start is None else to_datetime(start) end = datetime.max if end is None else to_datetime(end) + # Did the user give the datasets specific names? primary_name, primary, secondary_name, secondary = self._get_names( primary, secondary ) + # Select the common time period of both datasets and flat them. primary, secondary = self._prepare_data( primary, secondary, max_interval, start, end ) @@ -566,6 +756,8 @@ def collocate( self.leaf_size = leaf_size timer = Timer().start() + + # We cannot allow NaNs in the time, lat or lon fields not_nans1 = self._get_not_nans(primary) not_nans2 = self._get_not_nans(secondary) @@ -629,7 +821,7 @@ def collocate( data_magnitude = time1.size * time2.size if data_magnitude > 100_0000: - # We have enough data, do pre-binning! + # We have enough data, do temporal pre-binning! pairs, distances = self.spatial_search_with_temporal_binning( {"lat": lat1, "lon": lon1, "time": time1}, {"lat": lat2, "lon": lon2, "time": time2}, @@ -670,7 +862,7 @@ def _to_original(pairs, original_indices): @staticmethod def _get_names(primary, secondary): - # Check out if the user gave the primary and secondary any name: + # Check out whether the user gave the primary and secondary any name: if isinstance(primary, (tuple, list)): primary_name, primary = primary else: @@ -683,6 +875,17 @@ def _get_names(primary, secondary): return primary_name, primary, secondary_name, secondary def _prepare_data(self, primary, secondary, max_interval, start, end): + """Prepare the data for the collocation search + + This method selects the time period which should be searched for + collocations and flats the input datasets if they have gridded + variables. + + Returns: + The datasets constraint to the common time period, sorted by time + and flattened. If no common time period could be found, two None + objects are returned. + """ if max_interval is not None: timer = Timer().start() # We do not have to collocate everything, just the common time @@ -778,11 +981,8 @@ def _flat_to_main_coord(data): same dimension and are all 1-dimensional. Fulfills all criteria from above. No action has to be taken. * gridded_coords (e.g. instruments on satellites with gridded swaths): - time, lat or lon are gridded (they have multiple dimensions). Stack + lat or lon are gridded (they have multiple dimensions). Stack the coordinates of them together to a new shared dimension. - * gridded_data (e.g. model output data): time, lat and lon are not - gridded but they grid the data variables. Stack time, lat and lon - to a new shared dimension. Args: data: xr.Dataset object @@ -830,14 +1030,10 @@ def _flat_to_main_coord(data): if dim not in data.coords: data[dim] = dim, np.arange(data.dims[dim]) - # We assume that coordinates must be unique! - # We might have a problem if the dimensions are also coordinates (i.e. - # they have values). For example, we opened multiple MHS files and - # concatenated them, then the coordinate scnline contains the same - # values multiple times. xarray cannot stack them together and build a - # multi index from them because the coordinate values are not unique. - # Hence, we need to replace the former coordinates with new coordinates - # that have unique values. + # We assume that coordinates must be unique! Otherwise, we would have + # to use this ugly work-around: + # Replace the former coordinates with new coordinates that have unique + # values. # new_dims = [] # for dim in dims: # new_dim = f"__replacement_{dim}" @@ -858,10 +1054,6 @@ def _create_return( pairs = [] output = {} - # We are going to save the time coverage of the data as attributes in - # the output dataset - start, end = None, None - names = [primary_name, secondary_name] for i, dataset in enumerate([primary, secondary]): # name of the current dataset (primary or secondary) @@ -888,19 +1080,6 @@ def _create_return( output[names[i]] = dataset.isel(collocation=original_indices) - # We need the total time coverage of all datasets for the name of - # the output file - data_start = pd.Timestamp( - output[name]["time"].min().item(0) - ) - data_end = pd.Timestamp( - output[name]["time"].max().item(0) - ) - if start is None or start > data_start: - start = data_start - if end is None or end < data_end: - end = data_end - # We have to convert the MultiIndex to a normal index because we # cannot store it to a file otherwise. We can convert it by simply # setting it to new values, but we are losing the sub-level @@ -988,6 +1167,13 @@ def _create_return( } ) + start = pd.Timestamp( + output[primary_name+"/time"].min().item(0) + ) + end = pd.Timestamp( + output[primary_name+"/time"].max().item(0) + ) + output.attrs = { "start_time": str(start), "end_time": str(end), @@ -1129,12 +1315,14 @@ def spatial_search(self, lat1, lon1, lat2, lon2, max_distance): def _build_spatial_index(self, lat, lon): # Find out whether the cached index still works with the new points: if self._spatial_is_cached(lat, lon): - print("Spatial index is cached and can be reused") + self._debug("Spatial index is cached and can be reused") return self.index return GeoIndex(lat, lon, leaf_size=self.leaf_size) def _spatial_is_cached(self, lat, lon): + """Return True if the cached ball tree is still applicable to the new + data""" if self.index is None: return False @@ -1219,6 +1407,63 @@ def _get_distances(lat1, lon1, lat2, lon2): return great_circle_distance(lat1, lon1, lat2, lon2) +def concat_collocations(collocations): + """Concat compact collocations + + Compact collocations cannot be concatenated directly because indices in + *Collocations/pairs* won't be correct any longer afterwards. This + concatenate function fixes this problem. + + Args: + collocations: A list of xarray.Dataset objects + with compact collocations. + + Returns: + One xarray.Dataset object + """ + + # We need to increment the pair indices when concatening the datasets + primary = collocations[0]["Collocations/group"].item(0) + secondary = collocations[0]["Collocations/group"].item(1) + primary_size = 0 + secondary_size = 0 + collocation_coord = { + "Collocations": "collocation", + primary: f"{primary}/collocation", + secondary: f"{secondary}/collocation", + } + + # Collect all collocations for each single group: + groups = defaultdict(list) + for obj in collocations: + for group, data in get_xarray_groups(obj).items(): + if group == "Collocations": + # Correct the indices: + data["Collocations/pairs"][0, :] += primary_size + data["Collocations/pairs"][1, :] += secondary_size + groups[group].append(data) + + primary_size += obj.dims[f"{primary}/collocation"] + secondary_size += obj.dims[f"{secondary}/collocation"] + + starts = [] + ends = [] + for group, data_list in groups.items(): + groups[group] = xr.concat( + data_list, + dim=collocation_coord[group] + ) + + start = pd.Timestamp(groups[primary][primary+"/time"].min().item(0)) + end = pd.Timestamp(groups[primary][primary+"/time"].max().item(0)) + merged = xr.merge(groups.values()) + merged.attrs = { + "start_time": str(start), + "end_time": str(end), + } + return merged + + class InvalidCollocationData(Exception): """Error when trying to collapse / expand invalid collocation data @@ -1243,4 +1488,4 @@ def check_collocation_data(dataset): if mandatory_field not in dataset.variables: raise InvalidCollocationData( f"Could not find the field '{mandatory_field}'!" - ) \ No newline at end of file + ) diff --git a/typhon/collocations/common.py b/typhon/collocations/common.py index 6fbcf09f..60d4ebc3 100644 --- a/typhon/collocations/common.py +++ b/typhon/collocations/common.py @@ -117,7 +117,7 @@ def read(self, *args, **kwargs): raise ValueError( f"Unknown reading read_mode for collocations: " f"{self.read_mode}!\nAllowed read_modes are: 'collapse' " - f"(default), 'expand' or 'fileset'." + f"(default), 'expand' or 'compact'." ) def search( diff --git a/typhon/plots/maps.py b/typhon/plots/maps.py index 126f10e2..105abc43 100644 --- a/typhon/plots/maps.py +++ b/typhon/plots/maps.py @@ -19,7 +19,8 @@ def worldmap(lat, lon, var=None, fig=None, ax=None, projection=None, - bg=False, draw_grid=False, draw_coastlines=False, **kwargs): + bg=False, draw_grid=False, draw_coastlines=False, + interpolation=False, **kwargs): """Plots the track of a variable on a worldmap. Args: @@ -75,7 +76,7 @@ def worldmap(lat, lon, var=None, fig=None, ax=None, projection=None, # It is counter-intuitive but if we want to plot our data with normal # latitudes and longitudes, we always have to set the transform to # PlateCarree (see https://github.com/SciTools/cartopy/issues/911) - if len(var.shape) == 1: + if var is None or len(var.shape) == 1: kwargs_defaults = { "cmap": "qualitative1", "s": 1, @@ -86,13 +87,20 @@ def worldmap(lat, lon, var=None, fig=None, ax=None, projection=None, plot = ax.scatter( lon, lat, c=var, transform=ccrs.PlateCarree(), **kwargs_defaults ) - else: + elif interpolation: kwargs_defaults = { **kwargs } plot = ax.contourf( lon, lat, var, transform=ccrs.PlateCarree(), **kwargs_defaults ) + else: + kwargs_defaults = { + **kwargs + } + plot = ax.pcolormesh( + lon, lat, var, transform=ccrs.PlateCarree(), **kwargs_defaults + ) return plot diff --git a/typhon/retrieval/spareice/common.py b/typhon/retrieval/spareice/common.py index 71129f27..2b7fd151 100644 --- a/typhon/retrieval/spareice/common.py +++ b/typhon/retrieval/spareice/common.py @@ -63,11 +63,13 @@ import matplotlib.pyplot as plt import numpy as np import pandas as pd +from scipy.stats import binned_statistic as sci_binned_statistic from sklearn.metrics import confusion_matrix from sklearn.model_selection import GridSearchCV from sklearn.neural_network import MLPRegressor from sklearn.pipeline import Pipeline from sklearn.preprocessing import RobustScaler +from sklearn.ensemble import RandomForestClassifier from sklearn.tree import DecisionTreeClassifier from typhon.collocations import collapse, Collocations, Collocator from typhon.geographical import sea_mask @@ -206,13 +208,14 @@ def _iwp_model(self, processes, cv_folds): # StandardScaler could be an alternative. ("scaler", RobustScaler(quantile_range=(15, 85))), # The "real" estimator: - ("estimator", MLPRegressor(max_iter=3400)), + ("estimator", MLPRegressor(max_iter=6000, early_stopping=True)), ]) # To optimize the results, we try different hyper parameters by # using a grid search hidden_layer_sizes = [ - (40, 15), # (13, 10), + (15, 10, 3), + #(50, 20), ] hyper_parameter = [ { # Hyper parameter for lbfgs solver @@ -220,7 +223,7 @@ def _iwp_model(self, processes, cv_folds): 'estimator__activation': ['tanh'], 'estimator__hidden_layer_sizes': hidden_layer_sizes, 'estimator__random_state': [0, 42, 100, 3452], - 'estimator__alpha': [0.1], + 'estimator__alpha': [0.1, 0.001, 0.0001], }, ] @@ -235,7 +238,7 @@ def _ice_cloud_model(): # As simple as it is. We do not need a grid search trainer for the DTC # since it has already a good performance. return DecisionTreeClassifier( - max_depth=12, random_state=5, + max_depth=12, random_state=5, # n_estimators=20, max_features=9, ) @property @@ -379,7 +382,7 @@ def standardize_collocations(self, data, fields=None, add_sea_mask=True, } # These fields need a special treatment - special_fields = ["avhrr_tir_diff", "iwp", "ice_cloud"] + special_fields = ["avhrr_tir_diff", "mhs_diff", "iwp", "ice_cloud"] # Default - take all fields: if fields is None: @@ -413,6 +416,9 @@ def standardize_collocations(self, data, fields=None, add_sea_mask=True, if "avhrr_tir_diff" in fields: return_data["avhrr_tir_diff"] = \ return_data["avhrr_channel5"] - return_data["avhrr_channel4"] + if "mhs_diff" in fields: + return_data["mhs_diff"] = \ + return_data["mhs_channel5"] - return_data["mhs_channel3"] if "iwp" in fields and "MHS_2C-ICE/2C-ICE/ice_water_path_mean" in data: # We transform the IWP to log space because it is better for the # ANN training. Zero values might trigger warnings and @@ -461,7 +467,7 @@ def get_grid_value(grid, lat, lon): # We do not need the depth of the oceans (this would just # confuse the ANN): - return_data.elevation[return_data.elevation < 0] = 0 + return_data["elevation"][return_data.elevation < 0] = 0 return return_data @@ -495,14 +501,6 @@ def retrieve(self, data, as_log10=False): @staticmethod def _retrieve_from_collocations(collocations, _, spareice): - # print(collocations) - # - # file_start = collocations.attrs['start_time'] - # file_end = collocations.attrs['end_time'] - # spareice._info( - # f"Retrieve SPARE-ICE for {file_start} to {file_end}" - # ) - # We need collapsed collocations: if "Collocations/pairs" in collocations.variables: collocations = collapse(collocations, reference="MHS") @@ -539,8 +537,6 @@ def _retrieve_from_collocations(collocations, _, spareice): retrieved["lon"] = collocations["lon"] retrieved["time"] = collocations["time"] retrieved["scnpos"] = collocations["mhs_scnpos"] - # retrieved.attrs["start_time"] = file_start - # retrieved.attrs["end_time"] = file_end return retrieved @@ -643,7 +639,8 @@ def score(self, data): return iwp_score, ice_cloud_score def train(self, data, iwp_inputs=None, ice_cloud_inputs=None, - processes=None, cv_folds=None): + iwp_model=None, ice_cloud_model=None, processes=None, + cv_folds=None): """Train SPARE-ICE with data This trains the IWP regressor and ice cloud classifier. @@ -655,6 +652,8 @@ def train(self, data, iwp_inputs=None, ice_cloud_inputs=None, ice_cloud_inputs: A list with the input field names for the ice cloud classifier. If this is None, the ice cloud classifier won't be trained. + iwp_model: Set this to your own sklearn estimator class. + ice_cloud_model: Set this to your own sklearn estimator class. processes: Number of processes to parallelize the regressor training. If not set, the value from the initialization is taken. @@ -672,8 +671,11 @@ def train(self, data, iwp_inputs=None, ice_cloud_inputs=None, if ice_cloud_inputs is not None: self._info("Train SPARE-ICE - ice cloud classifier") + if ice_cloud_model is None: + ice_cloud_model = self._ice_cloud_model() + score = self.ice_cloud.train( - self._ice_cloud_model(), + ice_cloud_model, data[ice_cloud_inputs], data[["ice_cloud"]], ) self._info(f"Ice cloud classifier training score: {score:.2f}") @@ -690,12 +692,14 @@ def train(self, data, iwp_inputs=None, ice_cloud_inputs=None, if cv_folds is None: cv_folds = 5 - trainer = self._iwp_trainer(processes, cv_folds) + if iwp_model is None: + iwp_model = self._iwp_model(processes, cv_folds) + score = self.iwp.train( - self._iwp_model(), data[iwp_inputs], data[["iwp"]], + iwp_model, data[iwp_inputs], data[["iwp"]], ) self._info(f"IWP regressor training score: {score:.2f}") - self._training_report(trainer) + self._training_report(iwp_model) @staticmethod def _training_report(trainer): @@ -751,7 +755,7 @@ def _report_iwp(self, output_dir, experiment, test, retrieved): ax.set_xlabel("log10 IWP (2C-ICE) [g/m^2]") ax.set_ylabel("log10 IWP (SPARE-ICE) [g/m^2]") ax.set_title(experiment) - fig.colorbar(scat) + fig.colorbar(scat, label="Number of points") fig.savefig(join(output_dir, "2C-ICE-SPAREICE_heatmap.png")) self._plot_scatter( @@ -767,40 +771,35 @@ def _report_iwp(self, output_dir, experiment, test, retrieved): 10 ** retrieved.iwp.values / 10 ** test.iwp.values ) - )) - 1 + )) + - 1 ) self._plot_error( experiment, join(output_dir, "2C-ICE-SPAREICE_mfe.png"), - test.iwp.values, - fe, test.sea_mask.values, - ) - # MFE plot with latitude on x-axis - self._plot_error( - experiment, join(output_dir, "2C-ICE-SPAREICE_mfe_lat.png"), - test.lat.values, + test, fe, test.sea_mask.values, - on_lat=True ) # Plot the bias: bias = retrieved.iwp.values - test.iwp.values self._plot_error( experiment, join(output_dir, "2C-ICE-SPAREICE_bias.png"), - test.iwp.values, + test, bias, test.sea_mask.values, mfe=False, yrange=[-0.35, 0.45], ) - # MFE plot with latitude on x-axis - self._plot_error( - experiment, join(output_dir, "2C-ICE-SPAREICE_bias_lat.png"), - test.lat.values, - bias, test.sea_mask.values, - mfe=False, on_lat=True - ) - self._plot_weights( - experiment, join(output_dir, "SPAREICE_iwp_weights.png"), - ) + # self._plot_weights( + # experiment, join(output_dir, "SPAREICE_iwp_weights.png"), + # ) + + with open(join(output_dir, "mfe.txt"), "w") as file: + mfe = sci_binned_statistic( + test.iwp.values, + fe, statistic="median", bins=20, + range=[0, 4], + ) + file.write(repr(mfe[0])) @staticmethod def _plot_scatter(experiment, file, xdata, ydata, sea_mask): @@ -825,38 +824,41 @@ def _plot_scatter(experiment, file, xdata, ydata, sea_mask): @staticmethod def _plot_error( - experiment, file, xdata, error, sea_mask, on_lat=False, mfe=True, - yrange=None + experiment, file, xdata, error, sea_mask, mfe=True, yrange=None ): fig, ax = plt.subplots(figsize=(10, 8)) - if on_lat: - xlabel = "latitude" - xrange = [-90, 90] - else: - xlabel = "log10 IWP (2C-ICE) [g/m^2]" - xrange = [0, 4] + xlabel = "log10 IWP (2C-ICE) [g/m^2]" + xrange = [0, 4] if mfe: ax.set_ylabel("Median fractional error [%]") - ax.set_ylim([0, 400]) + ax.set_ylim([0, 200]) statistic = "median" else: ax.set_ylabel("$\Delta$ IWP (SPARE-ICE - 2C-ICE) [log 10 g/m^2]") statistic = "mean" - for area in ["all", "land", "sea"]: - if area == "all": - mask = slice(None, None, None) - elif area == "land": - mask = ~sea_mask - else: - mask = sea_mask - - binned_statistic( - xdata[mask], error[mask], statistic=statistic, bins=20, - range=xrange, pargs={"marker": "o", "label": area} - ) + for hemisphere in ["global"]: + for area in ["all", "land", "sea"]: + if area == "all": + mask = np.repeat(True, xdata.iwp.size) + elif area == "land": + mask = ~sea_mask + else: + mask = sea_mask + + if hemisphere == "north": + mask &= xdata.lat.values >= 0 + elif hemisphere == "south": + mask &= xdata.lat.values < 0 + + binned_statistic( + xdata.iwp.values[mask], + error[mask], statistic=statistic, bins=20, + range=xrange, pargs={"marker": "o", + "label": f"{area} - {hemisphere}"} + ) ax.set_xlabel(xlabel) ax.grid() @@ -873,7 +875,7 @@ def _plot_weights(self, title, file, layer_index=0, vmin=-5, vmax=5): layers = self.iwp.estimator.steps[-1][1].coefs_ layer = layers[layer_index] - f, ax = plt.subplots(figsize=(12, 10)) + f, ax = plt.subplots(figsize=(18, 12)) weights = pd.DataFrame(layer) weights.index = self.iwp.inputs @@ -882,7 +884,7 @@ def _plot_weights(self, title, file, layer_index=0, vmin=-5, vmax=5): # Draw a heatmap with the numeric values in each cell sns.heatmap( weights, annot=True, fmt=".1f", linewidths=.5, ax=ax, - square=True, cmap="difference", center=0, vmin=vmin, vmax=vmax, + cmap="difference", center=0, vmin=vmin, vmax=vmax, # annot_kws={"size":14}, ) ax.tick_params(labelsize=18) @@ -891,27 +893,27 @@ def _plot_weights(self, title, file, layer_index=0, vmin=-5, vmax=5): def _report_ice_cloud(self, output_dir, experiment, test, retrieved): # Confusion matrix: - fig, ax = plt.subplots() + fig, ax = plt.subplots(figsize=(12, 10)) cm = confusion_matrix(test.ice_cloud, retrieved.ice_cloud) img = self._plot_matrix(cm, classes=["Yes", "No"], normalize=True) - fig.colorbar(img) + fig.colorbar(img, label="probability") ax.set_title("Ice Cloud Classifier - Performance") ax.set_ylabel('real ice cloud') ax.set_xlabel('predicted ice cloud') fig.tight_layout() - fig.savefig(join(output_dir, "ice_cloud_confusion_matrix.png")) + fig.savefig(join(output_dir, "ice-cloud-confusion-matrix.png")) - fig, ax = plt.subplots() + fig, ax = plt.subplots(figsize=(12, 10)) ax.barh( np.arange(len(self.ice_cloud.inputs)), self.ice_cloud.estimator.feature_importances_ ) ax.set_yticks(np.arange(len(self.ice_cloud.inputs))) ax.set_yticklabels(self.ice_cloud.inputs) - ax.set_xlabel("Importance") - ax.set_ylabel("Input") + ax.set_xlabel("Feature Importance") + ax.set_ylabel("Feature") ax.set_title("Ice Cloud Classifier - Importance") - fig.savefig(join(output_dir, "ice_cloud_feature_importance.png")) + fig.savefig(join(output_dir, "ice-cloud-feature-importance.png")) @staticmethod def _plot_matrix( diff --git a/typhon/retrieval/spareice/parameters/standard.json b/typhon/retrieval/spareice/parameters/standard.json index 1cbb9b71..1b21f9b8 100644 --- a/typhon/retrieval/spareice/parameters/standard.json +++ b/typhon/retrieval/spareice/parameters/standard.json @@ -1 +1 @@ -{'iwp': {'estimator': {'scaler': {'module': 'sklearn.preprocessing.data', 'class': 'RobustScaler', 'params': {'copy': True, 'quantile_range': (15, 85), 'with_centering': True, 'with_scaling': True}, 'coefs': {'center_': {'__ndarray__': [234.24999476410449, 258.14999422989786, 241.8699945937842, 253.22999433986843, 261.5599941536784, -5.670399856753647, 0.0, 47.0, 199.12999554909766, 73.53999835625291, 257.1499942522496, 9.479999788105488, 267.12332736266154, 253.0923020352538, 251.55999437719584, 1.9456257652786677, -1.2891666378515367, 1.0], '__dtype__': 'float64', '__shape__': (18,)}, 'scale_': {'__ndarray__': [61.669998621568084, 64.47999855875969, 8.499999810010195, 15.599999651312828, 33.84999924339354, 126.22221181135865, 800.0, 23.0, 162.0099963787943, 105.16999764926732, 200.12999552674592, 16.489999631419778, 46.44101015077371, 44.54499900434166, 43.242734930885376, 4.465673494281591, 2.5471153276828984, 1.0], '__dtype__': 'float64', '__shape__': (18,)}}}, 'estimator': {'module': 'sklearn.neural_network.multilayer_perceptron', 'class': 'MLPRegressor', 'params': {'activation': 'tanh', 'alpha': 0.1, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (15, 10, 3), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_iter': 3400, 'momentum': 0.9, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': 42, 'shuffle': True, 'solver': 'lbfgs', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}, 'coefs': {'n_outputs_': 1, 'n_iter_': 3402, 't_': 0, 'n_layers_': 5, 'out_activation_': 'identity', 'coefs_': [{'__ndarray__': [[-0.38904410725933775, -0.20694543905044704, -0.26682934761922994, -0.3564181252327141, -0.03314563425355744, -0.4346797863444557, -0.17326473868872194, 0.9320257059905255, 0.07749260759692353, 0.2914202087725071, -0.6905268339555994, -0.3615237283599955, -0.2941795254560277, 0.06160550387719488, -0.057839834200255215], [1.0069841670161477, 0.435211788196668, -1.4472764806938603, 1.6888803663457512, 0.6938083411275825, 0.9016552278774305, -0.4007302606525299, 0.5735510151832578, -0.9217041182027718, -0.3552787080649424, 1.1852955445474205, 1.7048302173691114, 1.0192362617019493, 0.4103873893404261, -0.4522777036808338], [0.04572201525500816, 0.6511435649181385, -0.15316390262446145, 0.019429469200688956, -0.33086186081978375, 0.2670757476056263, 0.31944165491325444, -0.2301193322261264, 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(-1, -1, -2, -2.0, 0.48, 5, 5.0), (-1, -1, -2, -2.0, 0.19438016528925617, 55, 55.0), (1865, 1870, 12, 62.443748474121094, 0.38132807363576593, 39, 39.0), (1866, 1867, 3, 228.78500366210938, 0.19753086419753085, 27, 27.0), (-1, -1, -2, -2.0, 0.0, 20, 20.0), (1868, 1869, 9, 0.01611381396651268, 0.4897959183673469, 7, 7.0), (-1, -1, -2, -2.0, 0.375, 4, 4.0), (-1, -1, -2, -2.0, 0.0, 3, 3.0), (1871, 1874, 0, 211.114990234375, 0.48611111111111116, 12, 12.0), (1872, 1873, 6, 0.03525640815496445, 0.21875, 8, 8.0), (-1, -1, -2, -2.0, 0.0, 1, 1.0), (-1, -1, -2, -2.0, 0.0, 7, 7.0), (-1, -1, -2, -2.0, 0.0, 4, 4.0), (1876, 1931, 2, 240.83499145507812, 0.4999905789500181, 3686, 3686.0), (1877, 1902, 8, 244.48870849609375, 0.4660440262405009, 1769, 1769.0), (1878, 1893, 3, 224.72999572753906, 0.4919358204341592, 1189, 1189.0), (1879, 1886, 13, 2097.5, 0.44725692161589603, 702, 702.0), (1880, 1883, 12, -81.72564697265625, 0.4291825644583189, 651, 651.0), (1881, 1882, 5, 82.23500061035156, 0.3550295857988166, 26, 26.0), (-1, -1, -2, -2.0, 0.0, 3, 3.0), (-1, -1, -2, -2.0, 0.22684310018903586, 23, 23.0), (1884, 1885, 9, 0.4756050705909729, 0.41413632, 625, 625.0), (-1, -1, -2, -2.0, 0.38422183277743227, 532, 532.0), (-1, -1, -2, -2.0, 0.49947970863683666, 93, 93.0), (1887, 1890, 11, -0.25041666626930237, 0.4444444444444444, 51, 51.0), (1888, 1889, 10, 0.45478877425193787, 0.33673469387755106, 42, 42.0), (-1, -1, -2, -2.0, 0.48, 10, 10.0), (-1, -1, -2, -2.0, 0.169921875, 32, 32.0), (1891, 1892, 9, 0.698997974395752, 0.19753086419753085, 9, 9.0), (-1, -1, -2, -2.0, 0.0, 8, 8.0), (-1, -1, -2, -2.0, 0.0, 1, 1.0), (1894, 1895, 5, 71.2249984741211, 0.4875004743452981, 487, 487.0), (-1, -1, -2, -2.0, 0.0, 38, 38.0), (1896, 1899, 11, -0.32721152901649475, 0.49622769728324756, 449, 449.0), (1897, 1898, 1, 239.89498901367188, 0.46457222711039803, 278, 278.0), (-1, -1, -2, -2.0, 0.39349112426035504, 182, 182.0), (-1, -1, -2, -2.0, 0.4945746527777778, 96, 96.0), (1900, 1901, 2, 238.2550048828125, 0.47905338394719743, 171, 171.0), (-1, -1, -2, -2.0, 0.1840894148586456, 39, 39.0), (-1, -1, -2, -2.0, 0.4995408631772268, 132, 132.0), (1903, 1918, 13, 1984.0, 0.35716409036860874, 580, 580.0), (1904, 1911, 0, 204.34500122070312, 0.31494600182162524, 526, 526.0), (1905, 1908, 11, -0.2641666531562805, 0.47838447211057744, 101, 101.0), (1906, 1907, 1, 240.21499633789062, 0.48753462603878117, 57, 57.0), (-1, -1, -2, -2.0, 0.38204081632653064, 35, 35.0), (-1, -1, -2, -2.0, 0.43388429752066116, 22, 22.0), (1909, 1910, 0, 202.75, 0.2675619834710744, 44, 44.0), (-1, -1, -2, -2.0, 0.14201183431952658, 39, 39.0), (-1, -1, -2, -2.0, 0.31999999999999995, 5, 5.0), (1912, 1915, 13, 18.0, 0.25252318339100344, 425, 425.0), (1913, 1914, 2, 240.32998657226562, 0.11411476984889513, 214, 214.0), (-1, -1, -2, -2.0, 0.0543054211791143, 179, 179.0), (-1, -1, -2, -2.0, 0.3526530612244898, 35, 35.0), (1916, 1917, 11, -0.27118587493896484, 0.361627097324858, 211, 211.0), (-1, -1, -2, -2.0, 0.46619999999999995, 100, 100.0), (-1, -1, -2, -2.0, 0.20680139599058522, 111, 111.0), (1919, 1924, 11, -0.2633333206176758, 0.4828532235939643, 54, 54.0), (1920, 1921, 2, 234.89999389648438, 0.36149584487534625, 38, 38.0), (-1, -1, -2, -2.0, 0.0, 6, 6.0), (1922, 1923, 10, 0.12873227894306183, 0.169921875, 32, 32.0), (-1, -1, -2, -2.0, 0.0, 1, 1.0), (-1, -1, -2, -2.0, 0.12070759625390215, 31, 31.0), (1925, 1928, 2, 239.6649932861328, 0.3046875, 16, 16.0), (1926, 1927, 13, 3320.0, 0.14201183431952658, 13, 13.0), (-1, -1, -2, -2.0, 0.0, 11, 11.0), (-1, -1, -2, -2.0, 0.5, 2, 2.0), (1929, 1930, 1, 239.55999755859375, 0.4444444444444444, 3, 3.0), (-1, -1, -2, -2.0, 0.0, 1, 1.0), (-1, -1, -2, -2.0, 0.0, 2, 2.0), (1932, 1963, 11, -0.3184523582458496, 0.46904273843373223, 1917, 1917.0), (1933, 1948, 8, 246.42041015625, 0.39101056735941286, 1238, 1238.0), (1934, 1941, 1, 240.4149932861328, 0.30853223250650064, 829, 829.0), (1935, 1938, 13, 1384.0, 0.24254680072583934, 609, 609.0), (1936, 1937, 10, 0.3503805994987488, 0.299875052061641, 441, 441.0), (-1, -1, -2, -2.0, 0.4930795847750865, 34, 34.0), (-1, -1, -2, -2.0, 0.27173119065010953, 407, 407.0), (1939, 1940, 10, 0.11358045041561127, 0.057752267573696114, 168, 168.0), (-1, -1, -2, -2.0, 0.5, 2, 2.0), (-1, -1, -2, -2.0, 0.04703149949194363, 166, 166.0), (1942, 1945, 7, 250.14288330078125, 0.44033057851239665, 220, 220.0), (1943, 1944, 6, 0.45153844356536865, 0.47626478574326647, 179, 179.0), (-1, -1, -2, -2.0, 0.4129208258416517, 127, 127.0), (-1, -1, -2, -2.0, 0.46375739644970415, 52, 52.0), (1946, 1947, 8, 246.16629028320312, 0.09280190362879237, 41, 41.0), (-1, -1, -2, -2.0, 0.0, 38, 38.0), (-1, -1, -2, -2.0, 0.4444444444444444, 3, 3.0), (1949, 1956, 13, 97.0, 0.487371548472331, 409, 409.0), (1950, 1953, 0, 206.92999267578125, 0.46093900698275525, 161, 161.0), (1951, 1952, 12, -5.431447982788086, 0.4844290657439446, 68, 68.0), (-1, -1, -2, -2.0, 0.19753086419753085, 18, 18.0), (-1, -1, -2, -2.0, 0.4992, 50, 50.0), (1954, 1955, 11, -0.5497902035713196, 0.3121748178980229, 93, 93.0), (-1, -1, -2, -2.0, 0.5, 14, 14.0), (-1, -1, -2, -2.0, 0.2397051754526518, 79, 79.0), (1957, 1960, 12, 80.51199340820312, 0.4016324141519251, 248, 248.0), (1958, 1959, 10, 0.2413875162601471, 0.3546691871455576, 230, 230.0), (-1, -1, -2, -2.0, 0.375, 20, 20.0), (-1, -1, -2, -2.0, 0.29641723356009075, 210, 210.0), (1961, 1962, 13, 590.5, 0.19753086419753085, 18, 18.0), (-1, -1, -2, -2.0, 0.4444444444444444, 3, 3.0), (-1, -1, -2, -2.0, 0.0, 15, 15.0), (1964, 1979, 1, 239.34500122070312, 0.48893699258851164, 679, 679.0), (1965, 1972, 10, 0.34944480657577515, 0.43125867945966423, 89, 89.0), (1966, 1969, 7, 245.38766479492188, 0.46875, 16, 16.0), (1967, 1968, 4, 250.42999267578125, 0.21875, 8, 8.0), (-1, -1, -2, -2.0, 0.0, 7, 7.0), (-1, -1, -2, -2.0, 0.0, 1, 1.0), (1970, 1971, 7, 246.90582275390625, 0.46875, 8, 8.0), (-1, -1, -2, -2.0, 0.0, 4, 4.0), (-1, -1, -2, -2.0, 0.375, 4, 4.0), (1973, 1976, 10, 1.6766020059585571, 0.3715518859072997, 73, 73.0), (1974, 1975, 5, 69.94000244140625, 0.2621878715814506, 58, 58.0), (-1, -1, -2, -2.0, 0.0, 2, 2.0), (-1, -1, -2, -2.0, 0.21875, 56, 56.0), (1977, 1978, 2, 241.45999145507812, 0.48, 15, 15.0), (-1, -1, -2, -2.0, 0.0, 5, 5.0), (-1, -1, -2, -2.0, 0.48, 10, 10.0), (1980, 1987, 12, -78.33340454101562, 0.4742085607584028, 590, 590.0), (1981, 1984, 7, 234.36849975585938, 0.4903293715187931, 151, 151.0), (1982, 1983, 8, 230.10208129882812, 0.2777777777777778, 12, 12.0), (-1, -1, -2, -2.0, 0.0, 2, 2.0), (-1, -1, -2, -2.0, 0.0, 10, 10.0), (1985, 1986, 12, -81.58415222167969, 0.47823611614305683, 139, 139.0), (-1, -1, -2, -2.0, 0.1049382716049383, 18, 18.0), (-1, -1, -2, -2.0, 0.4942285363021651, 121, 121.0), (1988, 1989, 3, 221.8599853515625, 0.4376689618671551, 439, 439.0), (-1, -1, -2, -2.0, 0.0, 8, 8.0), (1990, 1991, 8, 248.87939453125, 0.4284860654281577, 431, 431.0), (-1, -1, -2, -2.0, 0.45383230060132473, 362, 362.0), (-1, -1, -2, -2.0, 0.20499894980046207, 69, 69.0), (1993, 2126, 3, 227.0749969482422, 0.26189547536808544, 9112, 9112.0), (1994, 2037, 3, 221.45498657226562, 0.1819036681254258, 5455, 5455.0), (1995, 1996, 5, 46.32499694824219, 0.11672586302052734, 2844, 2844.0), (-1, -1, -2, -2.0, 0.0, 3, 3.0), (1997, 2012, 2, 236.02499389648438, 0.11498992539102526, 2841, 2841.0), (1998, 2001, 12, -81.83180236816406, 0.04017222488181005, 829, 829.0), (1999, 2000, 3, 210.70999145507812, 0.5, 4, 4.0), (-1, -1, -2, -2.0, 0.0, 2, 2.0), (-1, -1, -2, -2.0, 0.0, 2, 2.0), (2002, 2009, 11, 0.9754166603088379, 0.03570247933884296, 825, 825.0), (2003, 2006, 13, 4550.0, 0.03344312734284227, 823, 823.0), (2004, 2005, 7, 228.29788208007812, 0.031167243535630607, 821, 821.0), (-1, -1, -2, -2.0, 0.31999999999999995, 5, 5.0), (-1, -1, -2, -2.0, 0.028979238754325287, 816, 816.0), (2007, 2008, 12, 34.88849639892578, 0.5, 2, 2.0), (-1, -1, -2, -2.0, 0.0, 1, 1.0), (-1, -1, -2, -2.0, 0.0, 1, 1.0), (2010, 2011, 9, 0.008660254068672657, 0.5, 2, 2.0), (-1, -1, -2, -2.0, 0.0, 1, 1.0), (-1, -1, -2, -2.0, 0.0, 1, 1.0), (2013, 2022, 1, 241.70498657226562, 0.14388569181333466, 2012, 2012.0), (2014, 2021, 6, 31.57833480834961, 0.23340929620476047, 467, 467.0), (2015, 2018, 8, 228.6846923828125, 0.2251783590963139, 464, 464.0), (2016, 2017, 7, 228.78909301757812, 0.5, 12, 12.0), (-1, -1, -2, -2.0, 0.24489795918367352, 7, 7.0), (-1, -1, -2, -2.0, 0.0, 5, 5.0), (2019, 2020, 2, 236.04998779296875, 0.2103923564883703, 452, 452.0), (-1, -1, -2, -2.0, 0.0, 2, 2.0), (-1, -1, -2, -2.0, 0.20440493827160489, 450, 450.0), (-1, -1, -2, -2.0, 0.0, 3, 3.0), (2023, 2030, 9, 1.0124505758285522, 0.114279490160346, 1545, 1545.0), (2024, 2027, 8, 235.3945770263672, 0.09031383599406873, 1329, 1329.0), (2025, 2026, 11, -0.2732371687889099, 0.22772889417360287, 145, 145.0), (-1, -1, -2, -2.0, 0.4518430439952438, 29, 29.0), (-1, -1, -2, -2.0, 0.14313317479191434, 116, 116.0), (2028, 2029, 5, 137.7550048828125, 0.07156227173119067, 1184, 1184.0), (-1, -1, -2, -2.0, 0.06735210502489819, 1175, 1175.0), (-1, -1, -2, -2.0, 0.4444444444444444, 9, 9.0), (2031, 2034, 13, 2281.0, 0.24584190672153639, 216, 216.0), (2032, 2033, 11, -0.41807693243026733, 0.18080351506275094, 199, 199.0), (-1, -1, -2, -2.0, 0.45674740484429066, 34, 34.0), (-1, -1, -2, -2.0, 0.09226813590449956, 165, 165.0), (2035, 2036, 13, 2510.0, 0.45674740484429066, 17, 17.0), (-1, -1, -2, -2.0, 0.0, 6, 6.0), (-1, -1, -2, -2.0, 0.49586776859504134, 11, 11.0), (2038, 2085, 1, 242.10499572753906, 0.2459910571909405, 2611, 2611.0), (2039, 2060, 11, -0.38519230484962463, 0.3537466680311292, 906, 906.0), (2040, 2047, 0, 198.53500366210938, 0.451171875, 352, 352.0), (2041, 2046, 3, 226.63499450683594, 0.45368620037807184, 69, 69.0), (2042, 2045, 7, 276.0886535644531, 0.42603550295857984, 65, 65.0), (2043, 2044, 7, 255.59075927734375, 0.39802289281997916, 62, 62.0), (-1, -1, -2, -2.0, 0.46174739701222267, 47, 47.0), (-1, -1, -2, -2.0, 0.0, 15, 15.0), (-1, -1, -2, -2.0, 0.0, 3, 3.0), (-1, -1, -2, 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