|
| 1 | +"""Create the training/validation datapipe for training the PVNet Model""" |
| 2 | +import logging |
| 3 | +from datetime import datetime, timedelta |
| 4 | +from typing import List, Optional, Tuple, Union |
| 5 | + |
| 6 | +import xarray as xr |
| 7 | +from torchdata.datapipes import functional_datapipe |
| 8 | +from torchdata.datapipes.iter import IterableWrapper, IterDataPipe |
| 9 | + |
| 10 | +from ocf_datapipes.batch import MergeNumpyModalities |
| 11 | +from ocf_datapipes.config.model import Configuration |
| 12 | +from ocf_datapipes.load import ( |
| 13 | + OpenConfiguration, |
| 14 | +) |
| 15 | +from ocf_datapipes.training.common import ( |
| 16 | + AddZeroedNWPData, |
| 17 | + AddZeroedSatelliteData, |
| 18 | + _get_datapipes_dict, |
| 19 | + check_nans_in_satellite_data, |
| 20 | + concat_xr_time_utc, |
| 21 | + construct_loctime_pipelines, |
| 22 | + fill_nans_in_arrays, |
| 23 | + fill_nans_in_pv, |
| 24 | + normalize_gsp, |
| 25 | + normalize_pv, |
| 26 | + slice_datapipes_by_time, |
| 27 | +) |
| 28 | +from ocf_datapipes.utils.consts import ( |
| 29 | + NEW_NWP_MEAN, |
| 30 | + NEW_NWP_STD, |
| 31 | + RSS_MEAN, |
| 32 | + RSS_STD, |
| 33 | +) |
| 34 | +from ocf_datapipes.utils.utils import combine_to_single_dataset, uncombine_from_single_dataset |
| 35 | + |
| 36 | +xr.set_options(keep_attrs=True) |
| 37 | +logger = logging.getLogger("windnet_datapipe") |
| 38 | + |
| 39 | + |
| 40 | +def scale_wind_speed_to_power(x: Union[xr.DataArray, xr.Dataset]): |
| 41 | + """ |
| 42 | + Scale wind speed to power to estimate the generation of wind power from ground sensors |
| 43 | +
|
| 44 | + Roughly, double speed in m/s, and convert with the power scale |
| 45 | +
|
| 46 | + Args: |
| 47 | + x: xr.DataArray or xr.Dataset containing wind speed |
| 48 | +
|
| 49 | + Returns: |
| 50 | + Rescaled wind speed to MWh roughly |
| 51 | + """ |
| 52 | + # Convert knots to m/s |
| 53 | + x = x * 0.514444 |
| 54 | + # Roughly double speed to get power |
| 55 | + x = x * 2 |
| 56 | + return x |
| 57 | + |
| 58 | + |
| 59 | +@functional_datapipe("dict_datasets") |
| 60 | +class DictDatasetIterDataPipe(IterDataPipe): |
| 61 | + """Create a dictionary of xr.Datasets from a set of iterators""" |
| 62 | + |
| 63 | + datapipes: Tuple[IterDataPipe] |
| 64 | + length: Optional[int] |
| 65 | + |
| 66 | + def __init__(self, *datapipes: IterDataPipe, keys: List[str]): |
| 67 | + """Init""" |
| 68 | + if not all(isinstance(dp, IterDataPipe) for dp in datapipes): |
| 69 | + raise TypeError( |
| 70 | + "All inputs are required to be `IterDataPipe` " "for `ZipIterDataPipe`." |
| 71 | + ) |
| 72 | + super().__init__() |
| 73 | + self.keys = keys |
| 74 | + self.datapipes = datapipes # type: ignore[assignment] |
| 75 | + self.length = None |
| 76 | + assert len(self.keys) == len(self.datapipes), "Number of keys must match number of pipes" |
| 77 | + |
| 78 | + def __iter__(self): |
| 79 | + """Iter""" |
| 80 | + iterators = [iter(datapipe) for datapipe in self.datapipes] |
| 81 | + for data in zip(*iterators): |
| 82 | + # Yield a dictionary of the data, using the keys in self.keys |
| 83 | + yield {k: v for k, v in zip(self.keys, data)} |
| 84 | + |
| 85 | + |
| 86 | +@functional_datapipe("load_dict_datasets") |
| 87 | +class LoadDictDatasetIterDataPipe(IterDataPipe): |
| 88 | + """Load NetCDF files and split them back into individual xr.Datasets""" |
| 89 | + |
| 90 | + filenames: List[str] |
| 91 | + keys: List[str] |
| 92 | + |
| 93 | + def __init__(self, filenames: List[str], keys: List[str]): |
| 94 | + """ |
| 95 | + Load NetCDF files and split them back into individual xr.Datasets |
| 96 | +
|
| 97 | + Args: |
| 98 | + filenames: List of filesnames to load |
| 99 | + keys: List of keys from each file to use, each key should be a |
| 100 | + dataarray in the xr.Dataset |
| 101 | + """ |
| 102 | + super().__init__() |
| 103 | + self.keys = keys |
| 104 | + self.filenames = filenames |
| 105 | + |
| 106 | + def __iter__(self): |
| 107 | + """Iterate through each filename, loading it, uncombining it, and then yielding it""" |
| 108 | + while True: |
| 109 | + for filename in self.filenames: |
| 110 | + dataset = xr.open_dataset(filename) |
| 111 | + datasets = uncombine_from_single_dataset(dataset) |
| 112 | + # Yield a dictionary of the data, using the keys in self.keys |
| 113 | + dataset_dict = {} |
| 114 | + for k in self.keys: |
| 115 | + dataset_dict[k] = datasets[k] |
| 116 | + yield dataset_dict |
| 117 | + |
| 118 | + |
| 119 | +@functional_datapipe("convert_to_numpy_batch") |
| 120 | +class ConvertToNumpyBatchIterDataPipe(IterDataPipe): |
| 121 | + """Converts Xarray Dataset to Numpy Batch""" |
| 122 | + |
| 123 | + def __init__( |
| 124 | + self, |
| 125 | + dataset_dict_dp: IterDataPipe, |
| 126 | + configuration: Configuration, |
| 127 | + block_sat: bool = False, |
| 128 | + block_nwp: bool = False, |
| 129 | + check_satellite_no_zeros: bool = False, |
| 130 | + ): |
| 131 | + """Init""" |
| 132 | + super().__init__() |
| 133 | + self.dataset_dict_dp = dataset_dict_dp |
| 134 | + self.configuration = configuration |
| 135 | + self.block_sat = block_sat |
| 136 | + self.block_nwp = block_nwp |
| 137 | + self.check_satellite_no_zeros = check_satellite_no_zeros |
| 138 | + |
| 139 | + def __iter__(self): |
| 140 | + """Iter""" |
| 141 | + for datapipes_dict in self.dataset_dict_dp: |
| 142 | + # Spatially slice, normalize, and convert data to numpy arrays |
| 143 | + numpy_modalities = [] |
| 144 | + # Unpack for convenience |
| 145 | + conf_sat = self.configuration.input_data.satellite |
| 146 | + conf_nwp = self.configuration.input_data.nwp |
| 147 | + if "nwp" in datapipes_dict: |
| 148 | + numpy_modalities.append(datapipes_dict["nwp"].convert_nwp_to_numpy_batch()) |
| 149 | + if "sat" in datapipes_dict: |
| 150 | + numpy_modalities.append(datapipes_dict["sat"].convert_satellite_to_numpy_batch()) |
| 151 | + if "pv" in datapipes_dict: |
| 152 | + numpy_modalities.append(datapipes_dict["pv"].convert_pv_to_numpy_batch()) |
| 153 | + numpy_modalities.append(datapipes_dict["gsp"].convert_gsp_to_numpy_batch()) |
| 154 | + |
| 155 | + logger.debug("Combine all the data sources") |
| 156 | + combined_datapipe = MergeNumpyModalities(numpy_modalities).add_sun_position( |
| 157 | + modality_name="gsp" |
| 158 | + ) |
| 159 | + |
| 160 | + if self.block_sat and conf_sat != "": |
| 161 | + sat_block_func = AddZeroedSatelliteData(self.configuration) |
| 162 | + combined_datapipe = combined_datapipe.map(sat_block_func) |
| 163 | + |
| 164 | + if self.block_nwp and conf_nwp != "": |
| 165 | + nwp_block_func = AddZeroedNWPData(self.configuration) |
| 166 | + combined_datapipe = combined_datapipe.map(nwp_block_func) |
| 167 | + |
| 168 | + logger.info("Filtering out samples with no data") |
| 169 | + if self.check_satellite_no_zeros: |
| 170 | + # in production we don't want any nans in the satellite data |
| 171 | + combined_datapipe = combined_datapipe.map(check_nans_in_satellite_data) |
| 172 | + |
| 173 | + combined_datapipe = combined_datapipe.map(fill_nans_in_arrays) |
| 174 | + |
| 175 | + yield next(iter(combined_datapipe)) |
| 176 | + |
| 177 | + |
| 178 | +def minutes(num_mins: int): |
| 179 | + """Timedelta of a number of minutes. |
| 180 | +
|
| 181 | + Args: |
| 182 | + num_mins: Minutes timedelta. |
| 183 | + """ |
| 184 | + return timedelta(minutes=num_mins) |
| 185 | + |
| 186 | + |
| 187 | +def construct_sliced_data_pipeline( |
| 188 | + config_filename: str, |
| 189 | + location_pipe: IterDataPipe, |
| 190 | + t0_datapipe: IterDataPipe, |
| 191 | + block_sat: bool = False, |
| 192 | + block_nwp: bool = False, |
| 193 | + production: bool = False, |
| 194 | +) -> dict: |
| 195 | + """Constructs data pipeline for the input data config file. |
| 196 | +
|
| 197 | + This yields samples from the location and time datapipes. |
| 198 | +
|
| 199 | + Args: |
| 200 | + config_filename: Path to config file. |
| 201 | + location_pipe: Datapipe yielding locations. |
| 202 | + t0_datapipe: Datapipe yielding times. |
| 203 | + block_sat: Whether to load zeroes for satellite data. |
| 204 | + block_nwp: Whether to load zeroes for NWP data. |
| 205 | + production: Whether constucting pipeline for production inference. |
| 206 | + """ |
| 207 | + |
| 208 | + assert not (production and (block_sat or block_nwp)) |
| 209 | + |
| 210 | + datapipes_dict = _get_datapipes_dict( |
| 211 | + config_filename, |
| 212 | + block_sat, |
| 213 | + block_nwp, |
| 214 | + production=production, |
| 215 | + ) |
| 216 | + |
| 217 | + configuration = datapipes_dict.pop("config") |
| 218 | + |
| 219 | + # Unpack for convenience |
| 220 | + conf_sat = configuration.input_data.satellite |
| 221 | + conf_nwp = configuration.input_data.nwp |
| 222 | + |
| 223 | + # Slice all of the datasets by time - this is an in-place operation |
| 224 | + slice_datapipes_by_time(datapipes_dict, t0_datapipe, configuration, production) |
| 225 | + |
| 226 | + if "nwp" in datapipes_dict: |
| 227 | + nwp_datapipe = datapipes_dict["nwp"] |
| 228 | + |
| 229 | + location_pipe, location_pipe_copy = location_pipe.fork(2, buffer_size=5) |
| 230 | + nwp_datapipe = nwp_datapipe.select_spatial_slice_pixels( |
| 231 | + location_pipe_copy, |
| 232 | + roi_height_pixels=conf_nwp.nwp_image_size_pixels_height, |
| 233 | + roi_width_pixels=conf_nwp.nwp_image_size_pixels_width, |
| 234 | + ) |
| 235 | + nwp_datapipe = nwp_datapipe.normalize(mean=NEW_NWP_MEAN, std=NEW_NWP_STD) |
| 236 | + |
| 237 | + if "sat" in datapipes_dict: |
| 238 | + sat_datapipe = datapipes_dict["sat"] |
| 239 | + |
| 240 | + location_pipe, location_pipe_copy = location_pipe.fork(2, buffer_size=5) |
| 241 | + sat_datapipe = sat_datapipe.select_spatial_slice_pixels( |
| 242 | + location_pipe_copy, |
| 243 | + roi_height_pixels=conf_sat.satellite_image_size_pixels_height, |
| 244 | + roi_width_pixels=conf_sat.satellite_image_size_pixels_width, |
| 245 | + ) |
| 246 | + sat_datapipe = sat_datapipe.normalize(mean=RSS_MEAN, std=RSS_STD) |
| 247 | + |
| 248 | + if "pv" in datapipes_dict: |
| 249 | + # Recombine PV arrays - see function doc for further explanation |
| 250 | + pv_datapipe = ( |
| 251 | + datapipes_dict["pv"].zip_ocf(datapipes_dict["pv_future"]).map(concat_xr_time_utc) |
| 252 | + ) |
| 253 | + pv_datapipe = pv_datapipe.normalize(normalize_fn=normalize_pv) |
| 254 | + pv_datapipe = pv_datapipe.map(fill_nans_in_pv) |
| 255 | + |
| 256 | + # GSP always assumed to be in data |
| 257 | + location_pipe, location_pipe_copy = location_pipe.fork(2, buffer_size=5) |
| 258 | + gsp_future_datapipe = datapipes_dict["gsp_future"] |
| 259 | + gsp_future_datapipe = gsp_future_datapipe.select_spatial_slice_meters( |
| 260 | + location_datapipe=location_pipe_copy, |
| 261 | + roi_height_meters=1, |
| 262 | + roi_width_meters=1, |
| 263 | + dim_name="gsp_id", |
| 264 | + ) |
| 265 | + |
| 266 | + gsp_datapipe = datapipes_dict["gsp"] |
| 267 | + gsp_datapipe = gsp_datapipe.select_spatial_slice_meters( |
| 268 | + location_datapipe=location_pipe, |
| 269 | + roi_height_meters=1, |
| 270 | + roi_width_meters=1, |
| 271 | + dim_name="gsp_id", |
| 272 | + ) |
| 273 | + |
| 274 | + # Recombine GSP arrays - see function doc for further explanation |
| 275 | + gsp_datapipe = gsp_datapipe.zip_ocf(gsp_future_datapipe).map(concat_xr_time_utc) |
| 276 | + gsp_datapipe = gsp_datapipe.normalize(normalize_fn=normalize_gsp) |
| 277 | + |
| 278 | + finished_dataset_dict = {"gsp": gsp_datapipe, "config": configuration} |
| 279 | + if "nwp" in datapipes_dict: |
| 280 | + finished_dataset_dict["nwp"] = nwp_datapipe |
| 281 | + if "sat" in datapipes_dict: |
| 282 | + finished_dataset_dict["sat"] = sat_datapipe |
| 283 | + if "pv" in datapipes_dict: |
| 284 | + finished_dataset_dict["pv"] = pv_datapipe |
| 285 | + |
| 286 | + return finished_dataset_dict |
| 287 | + |
| 288 | + |
| 289 | +def windnet_datapipe( |
| 290 | + config_filename: str, |
| 291 | + start_time: Optional[datetime] = None, |
| 292 | + end_time: Optional[datetime] = None, |
| 293 | + block_sat: bool = False, |
| 294 | + block_nwp: bool = False, |
| 295 | +) -> IterDataPipe: |
| 296 | + """ |
| 297 | + Construct windnet pipeline for the input data config file. |
| 298 | +
|
| 299 | + Args: |
| 300 | + config_filename: Path to config file. |
| 301 | + start_time: Minimum time at which a sample can be selected. |
| 302 | + end_time: Maximum time at which a sample can be selected. |
| 303 | + block_sat: Whether to load zeroes for satellite data. |
| 304 | + block_nwp: Whether to load zeroes for NWP data. |
| 305 | + """ |
| 306 | + logger.info("Constructing windnet pipeline") |
| 307 | + |
| 308 | + # Open datasets from the config and filter to useable location-time pairs |
| 309 | + location_pipe, t0_datapipe = construct_loctime_pipelines( |
| 310 | + config_filename, |
| 311 | + start_time, |
| 312 | + end_time, |
| 313 | + block_sat, |
| 314 | + block_nwp, |
| 315 | + ) |
| 316 | + |
| 317 | + # Shard after we have the loc-times. These are already shuffled so no need to shuffle again |
| 318 | + location_pipe = location_pipe.sharding_filter() |
| 319 | + t0_datapipe = t0_datapipe.sharding_filter() |
| 320 | + |
| 321 | + # In this function we re-open the datasets to make a clean separation before/after sharding |
| 322 | + # This function |
| 323 | + datapipe_dict = construct_sliced_data_pipeline( |
| 324 | + config_filename, |
| 325 | + location_pipe, |
| 326 | + t0_datapipe, |
| 327 | + block_sat, |
| 328 | + block_nwp, |
| 329 | + ) |
| 330 | + |
| 331 | + # Save out datapipe to NetCDF |
| 332 | + |
| 333 | + # Merge all the datapipes into one |
| 334 | + return DictDatasetIterDataPipe( |
| 335 | + datapipe_dict["gsp"], |
| 336 | + datapipe_dict["nwp"], |
| 337 | + datapipe_dict["sat"], |
| 338 | + datapipe_dict["pv"], |
| 339 | + keys=["gsp", "nwp", "sat", "pv"], |
| 340 | + ).map(combine_to_single_dataset) |
| 341 | + |
| 342 | + |
| 343 | +def split_dataset_dict_dp(element): |
| 344 | + """ |
| 345 | + Split the dictionary of datapipes into individual datapipes |
| 346 | +
|
| 347 | + Args: |
| 348 | + element: Dictionary of datapipes |
| 349 | + """ |
| 350 | + return {k: IterableWrapper([v]) for k, v in element.items() if k != "config"} |
| 351 | + |
| 352 | + |
| 353 | +def windnet_netcdf_datapipe( |
| 354 | + config_filename: str, |
| 355 | + keys: List[str], |
| 356 | + filenames: List[str], |
| 357 | + block_sat: bool = False, |
| 358 | + block_nwp: bool = False, |
| 359 | +) -> IterDataPipe: |
| 360 | + """ |
| 361 | + Load the saved Datapipes from windnet, and transform to numpy batch |
| 362 | +
|
| 363 | + Args: |
| 364 | + config_filename: Path to config file. |
| 365 | + keys: List of keys to extract from the single NetCDF files |
| 366 | + filenames: List of NetCDF files to load |
| 367 | + block_sat: Whether to load zeroes for satellite data. |
| 368 | + block_nwp: Whether to load zeroes for NWP data. |
| 369 | +
|
| 370 | + Returns: |
| 371 | + Datapipe that transforms the NetCDF files to numpy batch |
| 372 | + """ |
| 373 | + logger.info("Constructing windnet file pipeline") |
| 374 | + config_datapipe = OpenConfiguration(config_filename) |
| 375 | + configuration: Configuration = next(iter(config_datapipe)) |
| 376 | + # Load files |
| 377 | + datapipe_dict_dp: IterDataPipe = LoadDictDatasetIterDataPipe( |
| 378 | + filenames=filenames, |
| 379 | + keys=keys, |
| 380 | + ).map(split_dataset_dict_dp) |
| 381 | + datapipe = datapipe_dict_dp.convert_to_numpy_batch( |
| 382 | + block_nwp=block_nwp, block_sat=block_sat, configuration=configuration |
| 383 | + ) |
| 384 | + |
| 385 | + return datapipe |
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