Issue/106 refactor dataset

nowcasting_dataset/consts.py

@@ -22,3 +22,19 @@
 
 # Typing
 Array = Union[xr.DataArray, np.ndarray]
+PV_SYSTEM_ID: str = 'pv_system_id'
+PV_SYSTEM_ROW_NUMBER = 'pv_system_row_number'
+PV_SYSTEM_X_COORDS = 'pv_system_x_coords'
+PV_SYSTEM_Y_COORDS = 'pv_system_y_coords'
+PV_AZIMUTH_ANGLE = 'pv_azimuth_angle'
+PV_ELEVATION_ANGLE = 'pv_elevation_angle'
+PV_YIELD = 'pv_yield'
+DEFAULT_N_PV_SYSTEMS_PER_EXAMPLE = 128
+GSP_SYSTEM_ID: str = "gsp_system_id"
+GSP_YIELD = "gsp_yield"
+GSP_SYSTEM_X_COORDS = "gsp_system_x_coords"
+GSP_SYSTEM_Y_COORDS = "gsp_system_y_coords"
+GSP_DATETIME_INDEX = "gsp_datetime_index"
+DEFAULT_N_GSP_PER_EXAMPLE = 32
+CENTROID_TYPE = "centroid_type"
+DATETIME_FEATURE_NAMES = ("hour_of_day_sin", "hour_of_day_cos", "day_of_year_sin", "day_of_year_cos")

nowcasting_dataset/data_sources/data_source.py

@@ -1,15 +1,14 @@
 from numbers import Number
 import pandas as pd
 import numpy as np
-from nowcasting_dataset.example import Example, to_numpy
+from nowcasting_dataset.dataset.example import Example, to_numpy
 from nowcasting_dataset import square
 import nowcasting_dataset.time as nd_time
 from dataclasses import dataclass, InitVar
 from typing import List, Tuple, Iterable
 import xarray as xr
 import itertools
 import logging
-from typing import Optional
 
 logger = logging.getLogger(__name__)
 
@@ -199,8 +198,9 @@ def get_example(
                 f'x_meters_center={x_meters_center}\n'
                 f'y_meters_center={y_meters_center}\n'
                 f't0_dt={t0_dt}\n'
+                f'times are {selected_data.time}\n'
                 f'expected shape={self._shape_of_example}\n'
-                f'actual shape   {selected_data.shape}')
+                f'actual shape {selected_data.shape}')
 
         return self._put_data_into_example(selected_data)
 

nowcasting_dataset/data_sources/datetime_data_source.py

@@ -1,5 +1,5 @@
 from nowcasting_dataset.data_sources.data_source import DataSource
-from nowcasting_dataset.example import Example
+from nowcasting_dataset.dataset.example import Example
 from nowcasting_dataset import time as nd_time
 from dataclasses import dataclass
 import pandas as pd

nowcasting_dataset/data_sources/gsp/gsp_data_source.py

@@ -14,13 +14,12 @@
 from nowcasting_dataset.utils import scale_to_0_to_1, pad_data
 from nowcasting_dataset.square import get_bounding_box_mask
 from nowcasting_dataset.geospatial import lat_lon_to_osgb
-from nowcasting_dataset.example import Example
+from nowcasting_dataset.dataset.example import Example
 from nowcasting_dataset.data_sources.data_source import ImageDataSource
 from nowcasting_dataset.data_sources.gsp.eso import get_gsp_metadata_from_eso
 
-from nowcasting_dataset.data_sources.constants import GSP_YIELD, GSP_SYSTEM_ID, GSP_SYSTEM_X_COORDS, \
-    GSP_SYSTEM_Y_COORDS, DEFAULT_N_GSP_PER_EXAMPLE, CENTROID_TYPE
-
+from nowcasting_dataset.consts import GSP_SYSTEM_ID, GSP_YIELD, GSP_SYSTEM_X_COORDS, GSP_SYSTEM_Y_COORDS, \
+    DEFAULT_N_GSP_PER_EXAMPLE, CENTROID_TYPE
 
 logger = logging.getLogger(__name__)
 

nowcasting_dataset/data_sources/nwp_data_source.py

@@ -1,5 +1,5 @@
 from nowcasting_dataset.data_sources.data_source import ZarrDataSource
-from nowcasting_dataset.example import Example, to_numpy
+from nowcasting_dataset.dataset.example import Example, to_numpy
 from nowcasting_dataset import utils
 from typing import Iterable, Optional, List
 import xarray as xr

nowcasting_dataset/data_sources/pv_data_source.py

@@ -1,7 +1,7 @@
-from nowcasting_dataset.data_sources.constants import PV_SYSTEM_ID, PV_SYSTEM_ROW_NUMBER, PV_SYSTEM_X_COORDS, \
-    PV_SYSTEM_Y_COORDS, PV_AZIMUTH_ANGLE, PV_ELEVATION_ANGLE, PV_YIELD, DEFAULT_N_PV_SYSTEMS_PER_EXAMPLE
+from nowcasting_dataset.consts import PV_SYSTEM_ID, PV_SYSTEM_ROW_NUMBER, PV_SYSTEM_X_COORDS, PV_SYSTEM_Y_COORDS, \
+    PV_AZIMUTH_ANGLE, PV_ELEVATION_ANGLE, PV_YIELD, DEFAULT_N_PV_SYSTEMS_PER_EXAMPLE
 from nowcasting_dataset.data_sources.data_source import ImageDataSource
-from nowcasting_dataset.example import Example
+from nowcasting_dataset.dataset.example import Example
 from nowcasting_dataset import geospatial, utils
 from nowcasting_dataset.square import get_bounding_box_mask
 from dataclasses import dataclass
@@ -225,7 +225,8 @@ def get_example(
             x_meters_center=x_meters_center,
             y_meters_center=y_meters_center,
             pv_system_x_coords=pv_system_x_coords,
-            pv_system_y_coords=pv_system_y_coords)
+            pv_system_y_coords=pv_system_y_coords,
+            pv_datetime_index=selected_pv_power.index)
 
         if self.load_azimuth_and_elevation:
             example[PV_AZIMUTH_ANGLE] = selected_pv_azimuth_angle

nowcasting_dataset/data_sources/satellite_data_source.py

@@ -1,5 +1,5 @@
 from nowcasting_dataset.data_sources.data_source import ZarrDataSource
-from nowcasting_dataset.example import Example, to_numpy
+from nowcasting_dataset.dataset.example import Example, to_numpy
 from nowcasting_dataset import utils
 from typing import Iterable, Optional, List
 from numbers import Number

nowcasting_dataset/dataset/README.md

@@ -0,0 +1,30 @@
+# Datasets
+
+This folder contains the following files
+
+## batch.py
+
+Functions used to 'play with' batch data i.e. a List[Example]
+
+## datamodule.py
+
+Contains a class NowcastingDataModule - pl.LightningDataModule
+This handles the 
+ - amalgamation of all differene data sources, 
+ - making valid datetimes across all the sources, 
+ - splitting into train and validation datasets
+
+
+## datasets.py
+
+This file contains the following classes
+
+NetCDFDatase- torch.utils.data.Dataset: Use for loading pre-made batches
+NowcastingDataset - torch.utils.data.IterableDataset: Dataset for making batches
+ContiguousNowcastingDataset - NowcastingDataset 
+
+## example.py
+
+Main thing in here is a Typed Dictionary. This is used to store one element of data use for one step in the ML models.
+There is also a validation function. 
+

nowcasting_dataset/dataset/batch.py

@@ -0,0 +1,155 @@
+from typing import List, Optional
+import logging
+
+import numpy as np
+import xarray as xr
+from pathlib import Path
+
+from nowcasting_dataset.consts import GSP_SYSTEM_ID, GSP_YIELD, GSP_SYSTEM_X_COORDS, GSP_SYSTEM_Y_COORDS, \
+    DATETIME_FEATURE_NAMES
+
+from nowcasting_dataset.dataset.example import Example
+from nowcasting_dataset.utils import get_netcdf_filename
+
+_LOG = logging.getLogger(__name__)
+
+LOCAL_TEMP_PATH = Path('~/temp/').expanduser()
+
+
+def write_batch_locally(batch: List[Example], batch_i: int):
+    """
+    Write a batch to a locally file
+    Args:
+        batch: batch of data
+        batch_i: the number of the batch
+
+    """
+    dataset = batch_to_dataset(batch)
+    dataset = fix_dtypes(dataset)
+    encoding = {name: {"compression": "lzf"} for name in dataset.data_vars}
+    filename = get_netcdf_filename(batch_i)
+    local_filename = LOCAL_TEMP_PATH / filename
+    dataset.to_netcdf(local_filename, engine="h5netcdf", mode="w", encoding=encoding)
+
+
+def fix_dtypes(concat_ds):
+    """
+    TODO
+    """
+    ds_dtypes = {
+        "example": np.int32,
+        "sat_x_coords": np.int32,
+        "sat_y_coords": np.int32,
+        "nwp": np.float32,
+        "nwp_x_coords": np.float32,
+        "nwp_y_coords": np.float32,
+        "pv_system_id": np.float32,
+        "pv_system_row_number": np.float32,
+        "pv_system_x_coords": np.float32,
+        "pv_system_y_coords": np.float32,
+    }
+
+    for name, dtype in ds_dtypes.items():
+        concat_ds[name] = concat_ds[name].astype(dtype)
+
+    assert concat_ds["sat_data"].dtype == np.int16
+    return concat_ds
+
+
+def batch_to_dataset(batch: List[Example]) -> xr.Dataset:
+    """Concat all the individual fields in an Example into a single Dataset.
+
+    Args:
+      batch: List of Example objects, which together constitute a single batch.
+    """
+    datasets = []
+    for i, example in enumerate(batch):
+        try:
+            individual_datasets = []
+            example_dim = {"example": np.array([i], dtype=np.int32)}
+            for name in ["sat_data", "nwp"]:
+                ds = example[name].to_dataset(name=name)
+                short_name = name.replace("_data", "")
+                if name == "nwp":
+                    ds = ds.rename({"target_time": "time"})
+                for dim in ["time", "x", "y"]:
+                    ds = coord_to_range(ds, dim, prefix=short_name)
+                ds = ds.rename(
+                    {
+                        "variable": f"{short_name}_variable",
+                        "x": f"{short_name}_x",
+                        "y": f"{short_name}_y",
+                    }
+                )
+                individual_datasets.append(ds)
+
+            # Datetime features
+            for name in DATETIME_FEATURE_NAMES:
+                ds = example[name].rename(name).to_xarray().to_dataset().rename({"index": "time"})
+                ds = coord_to_range(ds, "time", prefix=None)
+                individual_datasets.append(ds)
+
+            # PV
+            one_dateset = xr.DataArray(example["pv_yield"], dims=["time", "pv_system"])
+            one_dateset = one_dateset.to_dataset(name="pv_yield")
+            n_pv_systems = len(example["pv_system_id"])
+
+            # GSP
+            n_gsp_systems = len(example[GSP_SYSTEM_ID])
+            one_dateset['gsp_yield'] = xr.DataArray(example[GSP_YIELD], dims=["time_30", "gsp_system"])
+
+            # This will expand all dataarrays to have an 'example' dim.
+            # 0D
+            for name in ["x_meters_center", "y_meters_center"]:
+                try:
+                    one_dateset[name] = xr.DataArray([example[name]], coords=example_dim, dims=["example"])
+                except Exception as e:
+                    _LOG.error(f'Could not make pv_yield data for {name} with example_dim={example_dim}')
+                    if name not in example.keys():
+                        _LOG.error(f'{name} not in data keys: {example.keys()}')
+                    _LOG.error(e)
+                    raise Exception
+
+            # 1D
+            for name in ["pv_system_id", "pv_system_row_number", "pv_system_x_coords", "pv_system_y_coords"]:
+                one_dateset[name] = xr.DataArray(
+                    example[name][None, :],
+                    coords={**example_dim, **{"pv_system": np.arange(n_pv_systems, dtype=np.int32)}},
+                    dims=["example", "pv_system"],
+                )
+
+            # GSP
+            for name in [GSP_SYSTEM_ID, GSP_SYSTEM_X_COORDS, GSP_SYSTEM_Y_COORDS]:
+                try:
+                    one_dateset[name] = xr.DataArray(
+                        example[name][None, :],
+                        coords={**example_dim, **{"gsp_system": np.arange(n_gsp_systems, dtype=np.int32)}},
+                        dims=["example", "gsp_system"],
+                    )
+                except Exception as e:
+                    _LOG.debug(f'Could not add {name} to dataset. {example[name].shape}')
+                    _LOG.error(e)
+                    raise e
+
+            individual_datasets.append(one_dateset)
+
+            # Merge
+            merged_ds = xr.merge(individual_datasets)
+            datasets.append(merged_ds)
+
+        except Exception as e:
+            print(e)
+            _LOG.error(e)
+            raise Exception
+
+    return xr.concat(datasets, dim="example")
+
+
+def coord_to_range(da: xr.DataArray, dim: str, prefix: Optional[str], dtype=np.int32) -> xr.DataArray:
+    # TODO: Actually, I think this is over-complicated?  I think we can
+    # just strip off the 'coord' from the dimension.
+    coord = da[dim]
+    da[dim] = np.arange(len(coord), dtype=dtype)
+    if prefix is not None:
+        da[f"{prefix}_{dim}_coords"] = xr.DataArray(coord, coords=[da[dim]], dims=[dim])
+    return da