From 3deb84dbe1742c022bc7cb091ad4e91f2684593c Mon Sep 17 00:00:00 2001
From: James Fulton <djamesfulton@yahoo.co.uk>
Date: Tue, 16 Jan 2024 14:23:27 +0000
Subject: [PATCH 01/27] extend ECMWF for shetlands

---
 ocf_datapipes/load/nwp/providers/ecmwf.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/ocf_datapipes/load/nwp/providers/ecmwf.py b/ocf_datapipes/load/nwp/providers/ecmwf.py
index a4997b8e9..a2aac5793 100644
--- a/ocf_datapipes/load/nwp/providers/ecmwf.py
+++ b/ocf_datapipes/load/nwp/providers/ecmwf.py
@@ -1,6 +1,7 @@
 """ECMWF provider loaders"""
 import pandas as pd
 import xarray as xr
+import numpy as np
 
 from ocf_datapipes.load.nwp.providers.utils import open_zarr_paths
 
@@ -17,6 +18,7 @@ def open_ifs(zarr_path) -> xr.DataArray:
     """
     # Open the data
     nwp = open_zarr_paths(zarr_path)
+    nwp = nwp.reindex(latitude=np.concatenate([np.arange(62, 60, -0.05), nwp.latitude.values]))
     dataVars = list(nwp.data_vars.keys())
     if len(dataVars) > 1:
         raise Exception("Too many TLDVs")

From c2826338118e2dadbf05093a30be99aed52e36d1 Mon Sep 17 00:00:00 2001
From: James Fulton <djamesfulton@yahoo.co.uk>
Date: Fri, 16 Feb 2024 10:54:21 +0000
Subject: [PATCH 02/27] optional list of dropouts

---
 ocf_datapipes/config/model.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ocf_datapipes/config/model.py b/ocf_datapipes/config/model.py
index 21c1170d6..a737ce36a 100644
--- a/ocf_datapipes/config/model.py
+++ b/ocf_datapipes/config/model.py
@@ -156,7 +156,7 @@ def dropout_fraction_valid(cls, v):
 class SystemDropoutMixin(Base):
     """Mixin class, to add independent system dropout"""
 
-    system_dropout_timedeltas_minutes: List[int] = Field(
+    system_dropout_timedeltas_minutes: Optional[List[int]] = Field(
         None,
         description="List of possible minutes before t0 where data availability may start. Must be "
         "negative or zero. Each system in a sample is delayed independently from the other by "

From 70c788b037eda965bbcbd7e927499d989b245ba6 Mon Sep 17 00:00:00 2001
From: James Fulton <djamesfulton@yahoo.co.uk>
Date: Mon, 15 Apr 2024 16:18:49 +0000
Subject: [PATCH 03/27] add messy version of datapipe

---
 ocf_datapipes/training/pvnet_all_gsp.py | 697 ++++++++++++++++++++++++
 1 file changed, 697 insertions(+)
 create mode 100644 ocf_datapipes/training/pvnet_all_gsp.py

diff --git a/ocf_datapipes/training/pvnet_all_gsp.py b/ocf_datapipes/training/pvnet_all_gsp.py
new file mode 100644
index 000000000..4523b360e
--- /dev/null
+++ b/ocf_datapipes/training/pvnet_all_gsp.py
@@ -0,0 +1,697 @@
+"""Create the training/validation datapipe for training the PVNet Model"""
+import logging
+from typing import Optional, Type, Tuple, List, Union
+from datetime import datetime
+
+import xarray as xr
+from torch.utils.data.datapipes.datapipe import IterDataPipe
+from torch.utils.data.datapipes._decorator import functional_datapipe
+from torch.utils.data.datapipes.iter import IterableWrapper
+from ocf_datapipes.convert import (
+    ConvertNWPToNumpyBatch,
+    ConvertPVToNumpyBatch,
+    ConvertSatelliteToNumpyBatch,
+    ConvertGSPToNumpyBatch,
+
+)
+
+from ocf_datapipes.batch import MergeNumpyModalities, MergeNWPNumpyModalities
+from ocf_datapipes.batch.merge_numpy_examples_to_batch import stack_np_examples_into_batch
+from ocf_datapipes.training.common import (
+    _get_datapipes_dict,
+    check_nans_in_satellite_data,
+    concat_xr_time_utc,
+    fill_nans_in_arrays,
+    fill_nans_in_pv,
+    normalize_gsp,
+    normalize_pv,
+    slice_datapipes_by_time,
+    minutes,
+    open_and_return_datapipes,
+)
+from ocf_datapipes.utils.consts import (
+    NWP_MEANS,
+    NWP_STDS,
+    RSS_MEAN,
+    RSS_STD,
+)
+import numpy as np
+
+from ocf_datapipes.config.model import Configuration
+from ocf_datapipes.utils import Location
+
+from ocf_datapipes.utils.geospatial import (
+    move_lon_lat_by_meters,
+    spatial_coord_type,
+)
+from ocf_datapipes.select.select_spatial_slice import (
+    _get_idx_of_pixel_closest_to_poi, 
+    _get_idx_of_pixel_closest_to_poi_geostationary,
+    _get_points_from_unstructured_grids,
+    convert_coords_to_match_xarray,
+    select_spatial_slice_pixels,
+)
+
+
+xr.set_options(keep_attrs=True)
+logger = logging.getLogger("pvnet_all_gsp_datapipe")
+
+
+class SampleFunction:
+    def __init__(self, function):
+        self.function = function
+
+    def __call__(self, sample_list):
+        return [self.function(sample) for sample in sample_list]
+    
+class ZipFunction:
+    def __init__(self, function):
+        self.function = function
+        
+    def __call__(self, zipped_sample_list):
+        sample_lists = [sample_list for sample_list in zipped_sample_list]
+        return [self.function(sample) for sample in zip(*sample_lists)]
+            
+
+class SampleRepeat:
+    def __init__(self, num_repeats):
+        self.num_repeats = num_repeats
+        
+    def __call__(self, x):
+        return [x for _ in range(self.num_repeats)]
+
+
+class GSPLocationLookup:
+    """Query object for GSP location from GSP ID"""
+
+    def __init__(self, x_osgb: xr.DataArray, y_osgb: xr.DataArray):
+        """Query object for GSP location from GSP ID
+
+        Args:
+            x_osgb: DataArray of the OSGB x-coordinate for any given GSP ID
+            y_osgb: DataArray of the OSGB y-coordinate for any given GSP ID
+
+        """
+        self.x_osgb = x_osgb
+        self.y_osgb = y_osgb
+
+    def __call__(self, gsp_id: int) -> Location:
+        """Returns the locations for the input GSP IDs.
+
+        Args:
+            gsp_id: Integer ID of the GSP
+        """
+        return Location(
+            x=self.x_osgb.sel(gsp_id=gsp_id).item(),
+            y=self.y_osgb.sel(gsp_id=gsp_id).item(),
+            id=gsp_id,
+        )
+
+
+    
+def create_t0_datapipe(
+    datapipes_dict: dict,
+    configuration: Configuration,
+    shuffle: bool = True,
+):
+    """
+    Takes source datapipes and returns datapipes of appropriate t0 times.
+
+    The t0 times are sampled without replacement.
+
+    Args:
+        datapipes_dict: Dictionary of datapipes of input sources for which we want to select
+            appropriate location and times.
+        configuration: Configuration object for inputs.
+        shuffle: Whether to use the internal shuffle function when yielding location times. Else
+            location times will be heavily ordered.
+
+    Returns:
+        location datapipe, t0 datapipe
+
+    """
+    assert "gsp" in datapipes_dict
+
+    contiguous_time_datapipes = []  # Used to store contiguous time periods from each data source
+
+    datapipes_dict["gsp"], key_datapipe = datapipes_dict["gsp"].fork(2, buffer_size=5)
+
+    for key in datapipes_dict.keys():
+        if key in ["topo"]:
+            continue
+
+        elif key == "nwp":
+            for nwp_key in datapipes_dict["nwp"].keys():
+                # NWPs are nested since there can be multiple NWP sources
+                datapipes_dict["nwp"][nwp_key], datapipe_copy = datapipes_dict["nwp"][nwp_key].fork(
+                    2, buffer_size=5
+                )
+
+                # Different config setting per NWP source
+                nwp_conf = configuration.input_data.nwp[nwp_key]
+
+                if nwp_conf.dropout_timedeltas_minutes is None:
+                    max_dropout = minutes(0)
+                else:
+                    max_dropout = minutes(int(np.max(np.abs(nwp_conf.dropout_timedeltas_minutes))))
+
+                if nwp_conf.max_staleness_minutes is None:
+                    max_staleness = None
+                else:
+                    max_staleness = minutes(nwp_conf.max_staleness_minutes)
+
+                # NWP is a forecast product so gets its own contiguous function
+                time_periods = datapipe_copy.find_contiguous_t0_time_periods_nwp(
+                    history_duration=minutes(nwp_conf.history_minutes),
+                    forecast_duration=minutes(nwp_conf.forecast_minutes),
+                    max_staleness=max_staleness,
+                    max_dropout=max_dropout,
+                    time_dim="init_time_utc",
+                )
+
+                contiguous_time_datapipes.append(time_periods)
+
+        else:
+            if key == "sat":
+                sample_frequency = configuration.input_data.satellite.time_resolution_minutes
+                history_duration = configuration.input_data.satellite.history_minutes
+                forecast_duration = 0
+                time_dim = "time_utc"
+
+            elif key == "hrv":
+                sample_frequency = configuration.input_data.hrvsatellite.time_resolution_minutes
+                history_duration = configuration.input_data.hrvsatellite.history_minutes
+                forecast_duration = 0
+                time_dim = "time_utc"
+
+            elif key == "pv":
+                sample_frequency = configuration.input_data.pv.time_resolution_minutes
+                history_duration = configuration.input_data.pv.history_minutes
+                forecast_duration = configuration.input_data.pv.forecast_minutes
+                time_dim = "time_utc"
+
+            elif key == "wind":
+                sample_frequency = configuration.input_data.wind.time_resolution_minutes
+                history_duration = configuration.input_data.wind.history_minutes
+                forecast_duration = configuration.input_data.wind.forecast_minutes
+                time_dim = "time_utc"
+
+            elif key == "sensor":
+                sample_frequency = configuration.input_data.sensor.time_resolution_minutes
+                history_duration = configuration.input_data.sensor.history_minutes
+                forecast_duration = configuration.input_data.sensor.forecast_minutes
+                time_dim = "time_utc"
+
+            elif key == "gsp":
+                sample_frequency = configuration.input_data.gsp.time_resolution_minutes
+                history_duration = configuration.input_data.gsp.history_minutes
+                forecast_duration = configuration.input_data.gsp.forecast_minutes
+                time_dim = "time_utc"
+
+            else:
+                raise ValueError(f"Unexpected key: {key}")
+
+            datapipes_dict[key], datapipe_copy = datapipes_dict[key].fork(2, buffer_size=5)
+
+            time_periods = datapipe_copy.find_contiguous_t0_time_periods(
+                sample_period_duration=minutes(sample_frequency),
+                history_duration=minutes(history_duration),
+                forecast_duration=minutes(forecast_duration),
+                time_dim=time_dim,
+            )
+
+            contiguous_time_datapipes.append(time_periods)
+
+    # Find joint overlapping contiguous time periods
+    if len(contiguous_time_datapipes) > 1:
+        logger.debug("Getting joint time periods")
+        overlapping_datapipe = contiguous_time_datapipes[0].filter_to_overlapping_time_periods(
+            secondary_datapipes=contiguous_time_datapipes[1:],
+        )
+    else:
+        logger.debug("Skipping getting joint time periods")
+        overlapping_datapipe = contiguous_time_datapipes[0]
+
+    # Select time periods and set length
+    key_datapipe = key_datapipe.filter_time_periods(time_periods=overlapping_datapipe)
+
+    t0_datapipe = key_datapipe.pick_t0_times()#return_all=True, shuffle=shuffle)
+
+
+    return t0_datapipe
+
+
+def construct_time_pipeline(
+    config_filename: str,
+    start_time: Optional[datetime] = None,
+    end_time: Optional[datetime] = None,
+) -> Tuple[IterDataPipe, IterDataPipe]:
+    """Construct time pipeline for the input data config file.
+
+    Args:
+        config_filename: Path to config file.
+        start_time: Minimum time for time datapipe.
+        end_time: Maximum time for time datapipe.
+    """
+
+    datapipes_dict = _get_datapipes_dict(
+        config_filename,
+    )
+
+    # Pull out config file
+    config = datapipes_dict.pop("config")
+
+    if (start_time is not None) or (end_time is not None):
+        datapipes_dict["gsp"] = datapipes_dict["gsp"].filter_times(start_time, end_time)
+
+    # Get overlapping time periods
+    t0_datapipe = create_t0_datapipe(
+        datapipes_dict,
+        configuration=config,
+        shuffle=True,
+    )
+
+    return t0_datapipe
+
+
+def xr_compute(xr_data):
+    return xr_data.compute()
+
+
+@functional_datapipe("select_all_gsp_spatial_slices_pixels")
+class SelectAllGSPSpatialSlicePixelsIterDataPipe(IterDataPipe):
+    """Select all the spatial slices"""
+
+    def __init__(
+        self,
+        source_datapipe: IterDataPipe,
+        locations: List[Location],
+        roi_height_pixels: int,
+        roi_width_pixels: int,
+        allow_partial_slice: bool = False,
+        location_idx_name: Optional[str] = None,
+    ):
+        """
+        Select spatial slice based off pixels from point of interest
+
+        If `allow_partial_slice` is set to True, then slices may be made which intersect the border
+        of the input data. The additional x and y cordinates that would be required for this slice
+        are extrapolated based on the average spacing of these coordinates in the input data.
+        However, currently slices cannot be made where the centre of the window is outside of the
+        input data.
+
+        Args:
+            source_datapipe: Datapipe of Xarray data
+            roi_height_pixels: ROI height in pixels
+            roi_width_pixels: ROI width in pixels
+            allow_partial_slice: Whether to allow a partial slice.
+            location_idx_name: Name for location index of unstructured grid data,
+                None if not relevant
+        """
+        self.source_datapipe = source_datapipe
+        self.locations = locations
+        self.roi_height_pixels = roi_height_pixels
+        self.roi_width_pixels = roi_width_pixels
+        self.allow_partial_slice = allow_partial_slice
+        self.location_idx_name = location_idx_name
+
+    def __iter__(self) -> Union[xr.DataArray, xr.Dataset]:
+        for xr_data in self.source_datapipe:
+            xr_coords, xr_x_dim, xr_y_dim = spatial_coord_type(xr_data)
+            
+            loc_slices = []
+            
+            for location in self.locations:
+                
+                if self.location_idx_name is not None:
+                    selected = _get_points_from_unstructured_grids(
+                        xr_data=xr_data,
+                        location=location,
+                        location_idx_name=self.location_idx_name,
+                        num_points=self.roi_width_pixels * self.roi_height_pixels,
+                    )
+                    yield selected
+
+                if xr_coords == "geostationary":
+                    center_idx: Location = _get_idx_of_pixel_closest_to_poi_geostationary(
+                        xr_data=xr_data,
+                        center_osgb=location,
+                    )
+                else:
+                    center_idx: Location = _get_idx_of_pixel_closest_to_poi(
+                        xr_data=xr_data,
+                        location=location,
+                    )
+
+                selected = select_spatial_slice_pixels(
+                    xr_data,
+                    center_idx,
+                    self.roi_width_pixels,
+                    self.roi_height_pixels,
+                    xr_x_dim,
+                    xr_y_dim,
+                    allow_partial_slice=self.allow_partial_slice,
+                )
+                
+                loc_slices.append(selected)
+
+            yield loc_slices
+            
+            
+            
+@functional_datapipe("select_all_gsp_spatial_slice_meters")
+class SelectAllGSPSpatialSliceMetersIterDataPipe(IterDataPipe):
+    """Select spatial slice based off meters from point of interest"""
+
+    def __init__(
+        self,
+        source_datapipe: IterDataPipe,
+        locations: List[Location],
+        roi_height_meters: int,
+        roi_width_meters: int,
+        dim_name: Optional[str] = None,  # "pv_system_id",
+    ):
+        """
+        Select spatial slice based off pixels from point of interest
+
+        Args:
+            source_datapipe: Datapipe of Xarray data
+            location_datapipe: Location datapipe
+            roi_height_meters: ROI height in meters
+            roi_width_meters: ROI width in meters
+            dim_name: Dimension name to select for ID, None for coordinates
+
+        Notes:
+            Using spatial slicing based on distance rather than number of pixels will often yield
+            slices which can vary by 1 pixel in height and/or width.
+
+            E.g. Suppose the Xarray data has x-coords = [1,2,3,4,5]. We want to slice a spatial
+            window with a size which equates to 2.2 along the x-axis. If we choose to slice around
+            the point x=3 this will slice out the x-coords [2,3,4]. If we choose to slice around the
+            point x=2.5 this will slice out the x-coords [2,3]. Hence the returned slice can have
+            size either 2 or 3 in the x-axis depending on the spatial location selected.
+
+            Also, if selecting over a large span of latitudes, this may also causes pixel sizes of
+            the yielded outputs to change. For example, if the Xarray data is on a regularly spaced
+            longitude-latitude grid, then the structure of the grid means that the longitudes near
+            to the poles are spaced closer together (measured in meters) than at the equator. So
+            slices near the equator will have less pixels in the x-axis than slices taken near the
+            poles.
+        """
+        self.source_datapipe = source_datapipe
+        self.locations = locations
+        self.roi_height_meters = roi_height_meters
+        self.roi_width_meters = roi_width_meters
+        self.dim_name = dim_name
+
+    def __iter__(self) -> Union[xr.DataArray, xr.Dataset]:
+        for xr_data in self.source_datapipe:
+            loc_slices = []
+            
+            for location in self.locations:
+
+                # Get the spatial coords of the xarray data
+                xr_coords, xr_x_dim, xr_y_dim = spatial_coord_type(xr_data)
+
+                half_height = self.roi_height_meters // 2
+                half_width = self.roi_width_meters // 2
+
+                # Find the bounding box values for the location in either lat-lon or OSGB coord systems
+                if location.coordinate_system == "lon_lat":
+                    right, top = move_lon_lat_by_meters(
+                        location.x,
+                        location.y,
+                        half_width,
+                        half_height,
+                    )
+                    left, bottom = move_lon_lat_by_meters(
+                        location.x,
+                        location.y,
+                        -half_width,
+                        -half_height,
+                    )
+
+                elif location.coordinate_system == "osgb":
+                    left = location.x - half_width
+                    right = location.x + half_width
+                    bottom = location.y - half_height
+                    top = location.y + half_height
+
+                else:
+                    raise ValueError(
+                        f"Location coord system not recognized: {location.coordinate_system}"
+                    )
+
+                # Change the bounding coordinates [left, right, bottom, top] to the same
+                # coordinate system as the xarray data
+                (left, right), (bottom, top) = convert_coords_to_match_xarray(
+                    x=np.array([left, right], dtype=np.float32),
+                    y=np.array([bottom, top], dtype=np.float32),
+                    from_coords=location.coordinate_system,
+                    xr_data=xr_data,
+                )
+
+                # Do it off coordinates, not ID
+                if self.dim_name is None:
+                    # Select a patch from the xarray data
+                    x_mask = (left <= xr_data[xr_x_dim]) & (xr_data[xr_x_dim] <= right)
+                    y_mask = (bottom <= xr_data[xr_y_dim]) & (xr_data[xr_y_dim] <= top)
+                    selected = xr_data.isel({xr_x_dim: x_mask, xr_y_dim: y_mask})
+
+                else:
+                    # Select data in the region of interest and ID:
+                    # This also works for unstructured grids
+
+                    id_mask = (
+                        (left <= xr_data[xr_x_dim])
+                        & (xr_data[xr_x_dim] <= right)
+                        & (bottom <= xr_data[xr_y_dim])
+                        & (xr_data[xr_y_dim] <= top)
+                    )
+                    selected = xr_data.isel({self.dim_name: id_mask})
+
+                loc_slices.append(selected)
+            yield loc_slices
+
+
+class ConvertWrapper(IterDataPipe):
+    def __init__(
+        self,
+        source_datapipe: IterDataPipe,
+        convert_class: Type[IterDataPipe]
+    ):
+        self.source_datapipe = source_datapipe
+        self.convert_class = convert_class
+
+    def __iter__(self):
+        for concurrent_samples in self.source_datapipe:
+            dp = self.convert_class(IterableWrapper(concurrent_samples))            
+            stacked_converted_values = stack_np_examples_into_batch([x for x in iter(dp)])
+            yield stacked_converted_values
+
+
+
+
+def construct_sliced_data_pipeline(
+    config_filename: str,
+    t0_datapipe: IterDataPipe,
+    production: bool = False,
+    check_satellite_no_zeros: bool = False,
+) -> IterDataPipe:
+    """Constructs data pipeline for the input data config file.
+
+    This yields samples from the location and time datapipes.
+
+    Args:
+        config_filename: Path to config file.
+        t0_datapipe: Datapipe yielding times.
+        production: Whether constucting pipeline for production inference.
+        check_satellite_no_zeros: Whether to check that satellite data has no zeros.
+    """
+
+    datapipes_dict = _get_datapipes_dict(
+        config_filename,
+        production=production,
+    )
+    
+    ds_gsp = next(
+        iter(
+            open_and_return_datapipes(
+                config_filename,
+                use_gsp=True,
+                use_nwp=False,
+                use_pv=False,
+                use_sat=False,
+                use_hrv=False,
+                use_topo=False,
+            )["gsp"]
+        )
+    )
+    
+    gsp_id_to_loc = GSPLocationLookup(ds_gsp.x_osgb, ds_gsp.y_osgb)
+    
+    locations = [gsp_id_to_loc(gsp_id) for gsp_id in range(1, 318)]
+
+
+    configuration = datapipes_dict.pop("config")
+
+    # Unpack for convenience
+    conf_sat = configuration.input_data.satellite
+    conf_nwp = configuration.input_data.nwp
+
+    # Slice all of the datasets by time - this is an in-place operation
+    slice_datapipes_by_time(datapipes_dict, t0_datapipe, configuration, production)
+
+    # Spatially slice, normalize, and convert data to numpy arrays
+    numpy_modalities = []
+
+    if "nwp" in datapipes_dict:
+        nwp_numpy_modalities = dict()
+
+        for nwp_key, nwp_datapipe in datapipes_dict["nwp"].items():
+
+            nwp_datapipe = nwp_datapipe.map(xr_compute)
+
+            nwp_datapipe = nwp_datapipe.normalize(
+                mean=NWP_MEANS[conf_nwp[nwp_key].nwp_provider],
+                std=NWP_STDS[conf_nwp[nwp_key].nwp_provider],
+            )
+            
+            nwp_datapipe = nwp_datapipe.select_all_gsp_spatial_slices_pixels(
+                locations,
+                roi_height_pixels=conf_nwp[nwp_key].nwp_image_size_pixels_height,
+                roi_width_pixels=conf_nwp[nwp_key].nwp_image_size_pixels_width,
+            )
+            
+
+            nwp_numpy_modalities[nwp_key] = ConvertWrapper(
+                nwp_datapipe,
+                ConvertNWPToNumpyBatch,
+            )
+
+        # Combine the NWPs into NumpyBatch
+        nwp_numpy_modalities = MergeNWPNumpyModalities(nwp_numpy_modalities)
+        numpy_modalities.append(nwp_numpy_modalities)
+
+    if "sat" in datapipes_dict:
+        sat_datapipe = datapipes_dict["sat"]
+
+        sat_datapipe = sat_datapipe.map(xr_compute)
+        
+        sat_datapipe = sat_datapipe.normalize(mean=RSS_MEAN, std=RSS_STD)
+        
+        sat_datapipe = sat_datapipe.select_all_gsp_spatial_slices_pixels(
+            locations,
+            roi_height_pixels=conf_sat.satellite_image_size_pixels_height,
+            roi_width_pixels=conf_sat.satellite_image_size_pixels_width,
+        )
+        
+
+        numpy_modalities.append(
+            ConvertWrapper(
+                sat_datapipe,
+                ConvertSatelliteToNumpyBatch,
+            )
+        )
+
+    if "pv" in datapipes_dict:
+        # Recombine PV arrays - see function doc for further explanation
+        # No spatial slice for PV since it is always the same
+        pv_datapipe = (
+            datapipes_dict["pv"]
+                .zip_ocf(datapipes_dict["pv_future"])
+                .map(concat_xr_time_utc)
+        )
+
+        pv_datapipe = pv_datapipe.map(normalize_pv)
+        pv_datapipe = pv_datapipe.map(fill_nans_in_pv)
+        pv_datapipe = pv_datapipe.map(SampleRepeat(317))
+    
+        numpy_modalities.append(
+            ConvertWrapper(
+                pv_datapipe,
+                ConvertPVToNumpyBatch,
+            )
+        )
+
+    # GSP always assumed to be in data
+    #location_pipe, location_pipe_copy = location_pipe.fork(2, buffer_size=5)
+    gsp_future_datapipe = datapipes_dict["gsp_future"]
+    gsp_future_datapipe = gsp_future_datapipe.select_all_gsp_spatial_slice_meters(
+        locations,
+        roi_height_meters=1,
+        roi_width_meters=1,
+        dim_name="gsp_id",
+    )
+
+    gsp_datapipe = datapipes_dict["gsp"]
+    gsp_datapipe = gsp_datapipe.select_all_gsp_spatial_slice_meters(
+        locations,
+        roi_height_meters=1,
+        roi_width_meters=1,
+        dim_name="gsp_id",
+    )
+
+    # Recombine GSP arrays - see function doc for further explanation
+    gsp_datapipe = (
+        gsp_datapipe
+        .zip_ocf(gsp_future_datapipe)
+        .map(ZipFunction(concat_xr_time_utc))
+        .map(SampleFunction(normalize_gsp))
+    )
+    
+    numpy_modalities.append(
+        ConvertWrapper(
+            gsp_datapipe,
+            ConvertGSPToNumpyBatch,
+        )
+    )
+
+    logger.debug("Combine all the data sources")
+    combined_datapipe = MergeNumpyModalities(numpy_modalities).add_sun_position(modality_name="gsp")
+
+    return combined_datapipe
+
+
+def pvnet_all_gsp_datapipe(
+    config_filename: str,
+    start_time: Optional[datetime] = None,
+    end_time: Optional[datetime] = None,
+) -> IterDataPipe:
+    """
+    Construct pvnet pipeline for the input data config file.
+
+    Args:
+        config_filename: Path to config file.
+        start_time: Minimum time at which a sample can be selected.
+        end_time: Maximum time at which a sample can be selected.
+    """
+    logger.info("Constructing pvnet pipeline")
+
+    # Open datasets from the config and filter to useable location-time pairs
+    t0_datapipe = construct_time_pipeline(
+        config_filename,
+        start_time,
+        end_time,
+    )
+
+    # In this function we re-open the datasets to make a clean separation before/after sharding
+    # This function
+    datapipe = construct_sliced_data_pipeline(
+        config_filename,
+        t0_datapipe,
+    )
+
+    return datapipe
+
+
+if __name__=="__main__":
+    import time
+    
+    t0 = time.time()
+    dp = pvnet_all_gsp_datapipe(
+        config_filename="/home/jamesfulton/repos/PVNet/configs/datamodule/configuration/gcp_configuration.yaml"
+    )
+    
+    b  = next(iter(dp))
+    print(time.time() - t0)
\ No newline at end of file

From 08350f25b443359891b2ab973c2ffdb1367a855e Mon Sep 17 00:00:00 2001
From: James Fulton <djamesfulton@yahoo.co.uk>
Date: Mon, 15 Apr 2024 19:01:29 +0000
Subject: [PATCH 04/27] standardize pick functions

---
 ocf_datapipes/select/pick_locations.py        | 104 +++++++++++-------
 .../select/pick_locations_and_t0_times.py     |  53 +++++----
 ocf_datapipes/select/pick_t0_times.py         |  50 ++++++---
 3 files changed, 129 insertions(+), 78 deletions(-)

diff --git a/ocf_datapipes/select/pick_locations.py b/ocf_datapipes/select/pick_locations.py
index 591da8254..5629a2a65 100644
--- a/ocf_datapipes/select/pick_locations.py
+++ b/ocf_datapipes/select/pick_locations.py
@@ -17,51 +17,79 @@ class PickLocationsIterDataPipe(IterDataPipe):
     def __init__(
         self,
         source_datapipe: IterDataPipe,
-        return_all_locations: bool = False,
+        return_all: bool = False,
+        shuffle: bool = False,
     ):
         """
-        Picks random locations from a dataset
+        Datapipe to yield locations from the input data source.
 
         Args:
             source_datapipe: Datapipe emitting Xarray Dataset
-            return_all_locations: Whether to return all locations,
-                if True, also returns them in order
+            return_all: Whether to return all t0-location pairs,
+                if True, also returns them in structured order
+            shuffle: If `return_all` is True this sets whether the pairs are
+                shuffled before being returned.
         """
         super().__init__()
         self.source_datapipe = source_datapipe
-        self.return_all_locations = return_all_locations
+        self.return_all = return_all
+        self.shuffle = shuffle
+
+    def _yield_all_iter(self, xr_dataset):
+        
+        # Get the spatial coords
+        xr_coord_system, xr_x_dim, xr_y_dim = spatial_coord_type(xr_dataset)
+        
+        loc_indices = np.arange(len(xr_dataset[xr_x_dim]))
+
+        if self.shuffle:
+            loc_indices = np.random.permutation(loc_indices)
+
+        # Iterate through all locations in dataset
+        for loc_index in loc_indices:
+            
+            # Get the location ID
+            loc_id = None
+            for id_dim_name in ["pv_system_id", "gsp_id", "station_id"]:
+                if id_dim_name in xr_dataset.coords.keys():
+                    loc_id = int(xr_dataset[id_dim_name][loc_index].values)
+            
+            location = Location(
+                coordinate_system=xr_coord_system,
+                x=xr_dataset[xr_x_dim][loc_index].values,
+                y=xr_dataset[xr_y_dim][loc_index].values,
+                id=loc_id,
+            )
+
+            yield location
+
+    def _yield_random_iter(self, xr_dataset):
+        
+        # Get the spatial coords
+        xr_coord_system, xr_x_dim, xr_y_dim = spatial_coord_type(xr_dataset)
+
+        while True:
+            loc_index = np.random.randint(0, len(xr_dataset[xr_x_dim]))
+            
+            # Get the location ID
+            loc_id = None
+            for id_dim_name in ["pv_system_id", "gsp_id", "station_id"]:
+                if id_dim_name in xr_dataset.coords.keys():
+                    loc_id = int(xr_dataset[id_dim_name][loc_index].values)
+
+            location = Location(
+                coordinate_system=xr_coord_system,
+                x=xr_dataset[xr_x_dim][loc_index].values,
+                y=xr_dataset[xr_y_dim][loc_index].values,
+                id=loc_id,
+            )
+            
+            yield location
 
     def __iter__(self) -> Location:
-        """Returns locations from the inputs datapipe"""
-        for xr_dataset in self.source_datapipe:
-            loc_type, xr_x_dim, xr_y_dim = spatial_coord_type(xr_dataset)
-
-            if self.return_all_locations:
-                logger.debug("Going to return all locations")
-
-                # Iterate through all locations in dataset
-                for location_idx in range(len(xr_dataset[xr_x_dim])):
-                    location = Location(
-                        x=xr_dataset[xr_x_dim][location_idx].values,
-                        y=xr_dataset[xr_y_dim][location_idx].values,
-                        coordinate_system=loc_type,
-                    )
-                    if "pv_system_id" in xr_dataset.coords.keys():
-                        location.id = int(xr_dataset["pv_system_id"][location_idx].values)
-                    logger.debug(f"Got all location {location}")
-                    yield location
-            else:
-                # Pick 1 random location from the input dataset
-                logger.debug("Selecting random idx")
-                location_idx = np.random.randint(0, len(xr_dataset[xr_x_dim]))
-                logger.debug(f"{location_idx=}")
-                location = Location(
-                    x=xr_dataset[xr_x_dim][location_idx].values,
-                    y=xr_dataset[xr_y_dim][location_idx].values,
-                    coordinate_system=loc_type,
-                )
-                if "pv_system_id" in xr_dataset.coords.keys():
-                    location.id = int(xr_dataset["pv_system_id"][location_idx].values)
-                    logger.debug(f"Have selected location.id {location.id}")
-                logger.debug(f"{location=}")
-                yield location
+        xr_dataset = next(iter(self.source_datapipe))
+
+        if self.return_all:
+            return self._yield_all_iter(xr_dataset)
+        else:
+            return self._yield_random_iter(xr_dataset)
\ No newline at end of file
diff --git a/ocf_datapipes/select/pick_locations_and_t0_times.py b/ocf_datapipes/select/pick_locations_and_t0_times.py
index 06cec1677..1b2a619bb 100644
--- a/ocf_datapipes/select/pick_locations_and_t0_times.py
+++ b/ocf_datapipes/select/pick_locations_and_t0_times.py
@@ -32,7 +32,7 @@ def __init__(
             source_datapipe: Datapipe emitting Xarray Dataset
             return_all: Whether to return all t0-location pairs,
                 if True, also returns them in structured order
-            shuffle: If `return_all` sets whether the pairs are
+            shuffle: If `return_all` is True this sets whether the pairs are
                 shuffled before being returned.
             time_dim_name: time dimension name, defaulted to 'time_utc'
         """
@@ -43,7 +43,10 @@ def __init__(
         self.time_dim_name = time_dim_name
 
     def _yield_all_iter(self, xr_dataset):
+        
+        # Get the spatial coords
         xr_coord_system, xr_x_dim, xr_y_dim = spatial_coord_type(xr_dataset)
+        
         t_index, x_index = np.meshgrid(
             np.arange(len(xr_dataset[self.time_dim_name])),
             np.arange(len(xr_dataset[xr_x_dim])),
@@ -56,48 +59,44 @@ def _yield_all_iter(self, xr_dataset):
 
         # Iterate through all locations in dataset
         for t_index, loc_index in index_pairs:
+            
+            # Get the location ID
+            loc_id = None
+            for id_dim_name in ["pv_system_id", "gsp_id", "station_id"]:
+                if id_dim_name in xr_dataset.coords.keys():
+                    loc_id = int(xr_dataset[id_dim_name][loc_index].values)
+            
             t0 = xr_dataset[self.time_dim_name][t_index].values
             location = Location(
                 coordinate_system=xr_coord_system,
                 x=xr_dataset[xr_x_dim][loc_index].values,
                 y=xr_dataset[xr_y_dim][loc_index].values,
+                id=loc_id,
             )
 
-            # for pv
-            if "pv_system_id" in xr_dataset.coords.keys():
-                location.id = int(xr_dataset["pv_system_id"][loc_index].values)
-
-            # for gsp
-            if "gsp_id" in xr_dataset.coords.keys():
-                location.id = int(xr_dataset["gsp_id"][loc_index].values)
-
-            # for sensor
-            if "station_id" in xr_dataset.coords.keys():
-                location.id = int(xr_dataset["station_id"][loc_index].values)
-
             yield location, t0
 
     def _yield_random_iter(self, xr_dataset):
+        
+        # Get the spatial coords
         xr_coord_system, xr_x_dim, xr_y_dim = spatial_coord_type(xr_dataset)
+
         while True:
-            location_idx = np.random.randint(0, len(xr_dataset[xr_x_dim]))
+            loc_index = np.random.randint(0, len(xr_dataset[xr_x_dim]))
+            
+            # Get the location ID
+            loc_id = None
+            for id_dim_name in ["pv_system_id", "gsp_id", "station_id"]:
+                if id_dim_name in xr_dataset.coords.keys():
+                    loc_id = int(xr_dataset[id_dim_name][loc_index].values)
 
             location = Location(
                 coordinate_system=xr_coord_system,
-                x=xr_dataset[xr_x_dim][location_idx].values,
-                y=xr_dataset[xr_y_dim][location_idx].values,
+                x=xr_dataset[xr_x_dim][loc_index].values,
+                y=xr_dataset[xr_y_dim][loc_index].values,
+                id=loc_id,
             )
-            if "pv_system_id" in xr_dataset.coords.keys():
-                location.id = int(xr_dataset["pv_system_id"][location_idx].values)
-
-            # for gsp
-            if "gsp_id" in xr_dataset.coords.keys():
-                location.id = int(xr_dataset["gsp_id"][location_idx].values)
-
-            # for sensor
-            if "station_id" in xr_dataset.coords.keys():
-                location.id = int(xr_dataset["station_id"][location_idx].values)
-
+            
             t0 = np.random.choice(xr_dataset[self.time_dim_name].values)
 
             yield location, t0
diff --git a/ocf_datapipes/select/pick_t0_times.py b/ocf_datapipes/select/pick_t0_times.py
index 88afdccf9..173b054e4 100644
--- a/ocf_datapipes/select/pick_t0_times.py
+++ b/ocf_datapipes/select/pick_t0_times.py
@@ -10,31 +10,55 @@
 
 @functional_datapipe("pick_t0_times")
 class PickT0TimesIterDataPipe(IterDataPipe):
-    """Picks random t0 times from a dataset"""
+    """Picks (random) t0 times from a dataset"""
 
     def __init__(
         self,
         source_datapipe: IterDataPipe,
+        return_all: bool = False,
+        shuffle: bool = False,
         dim_name: str = "time_utc",
     ):
         """
-        Picks random t0 times from a dataset
+        Datapipe to yield t0 times from the input data source.
 
         Args:
-            source_datapipe: Datapipe emitting Xarray objects
-            dim_name: The time dimension name to use
+            source_datapipe: Datapipe emitting Xarray objects.
+            return_all: Whether to return all t0 values, else sample with replacement. If True, the
+                default behaviour to return t0 values in order - see `shuffle` parameter.
+            shuffle: If `return_all` is True this sets whether the pairs are
+                shuffled before being returned.
+            dim_name: The time dimension name to use.
         """
         self.source_datapipe = source_datapipe
+        self.return_all = return_all
+        self.shuffle = shuffle
         self.dim_name = dim_name
+        
+    
+    def _yield_random_iter(self, xr_dataset):
+        """Sample t0 with replacement"""
+        while True:
+            t0 = np.random.choice(xr_dataset[self.dim_name].values)
+            yield t0
+            
+    def _yield_all_iter(self, xr_dataset):
+        """Yield all the t0s in order, and maybe with a shuffle"""
+        all_t0s = np.copy(xr_dataset[self.dim_name].values)
+        if self.shuffle:
+            all_t0s = np.random.permutation(all_t0s)
+        for t0 in all_t0s:
+            yield t0
 
     def __iter__(self) -> pd.Timestamp:
-        """Get the latest timestamp and return it"""
-        for xr_data in self.source_datapipe:
-            logger.debug(f"Selecting t0 from {len(xr_data[self.dim_name])} datetimes")
+        xr_dataset = next(iter(self.source_datapipe))
+        
+        if len(xr_dataset[self.dim_name].values) == 0:
+            raise Exception("There are no values to get t0 from")
+                    
+        if self.return_all:
+            return self._yield_all_iter(xr_dataset)
+        else:
+            return self._yield_random_iter(xr_dataset)
+            
 
-            if len(xr_data[self.dim_name].values) == 0:
-                raise Exception("There are no values to get t0 from")
-            t0 = np.random.choice(xr_data[self.dim_name].values)
-            logger.debug(f"t0 will be {t0}")
-
-            yield t0

From 8e7c95f059f61584003b58806e0611fec0da81f6 Mon Sep 17 00:00:00 2001
From: James Fulton <djamesfulton@yahoo.co.uk>
Date: Mon, 15 Apr 2024 19:01:57 +0000
Subject: [PATCH 05/27] update tests using pick functions

---
 tests/select/test_pick_locations.py       | 2 +-
 tests/select/test_select_spatial_slice.py | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/tests/select/test_pick_locations.py b/tests/select/test_pick_locations.py
index b52cec234..753014273 100644
--- a/tests/select/test_pick_locations.py
+++ b/tests/select/test_pick_locations.py
@@ -28,7 +28,7 @@ def test_pick_locations_all_locations(gsp_datapipe):
         sample_period_duration=timedelta(minutes=30),
         history_duration=timedelta(hours=1),
     )
-    location_datapipe = PickLocations(gsp_datapipe, return_all_locations=True)
+    location_datapipe = PickLocations(gsp_datapipe, return_all=True)
     loc_iterator = iter(location_datapipe)
     for i in range(len(dataset["x_osgb"])):
         loc_data = next(loc_iterator)
diff --git a/tests/select/test_select_spatial_slice.py b/tests/select/test_select_spatial_slice.py
index 5e089fd38..7e7c2a7fb 100644
--- a/tests/select/test_select_spatial_slice.py
+++ b/tests/select/test_select_spatial_slice.py
@@ -114,7 +114,7 @@ def test_select_spatial_slice_pixel_icon_eu(passiv_datapipe, icon_eu_datapipe):
 
 
 def test_select_spatial_slice_pixel_icon_global(passiv_datapipe, icon_global_datapipe):
-    loc_datapipe = PickLocations(passiv_datapipe, return_all_locations=True)
+    loc_datapipe = PickLocations(passiv_datapipe, return_all=True)
     icon_global_datapipe = SelectSpatialSlicePixels(
         icon_global_datapipe,
         location_datapipe=loc_datapipe,
@@ -146,7 +146,7 @@ def test_select_spatial_slice_meters_icon_eu(passiv_datapipe, icon_eu_datapipe):
 
 
 def test_select_spatial_slice_meters_icon_global(passiv_datapipe, icon_global_datapipe):
-    loc_datapipe = PickLocations(passiv_datapipe, return_all_locations=True)
+    loc_datapipe = PickLocations(passiv_datapipe, return_all=True)
     icon_global_datapipe = SelectSpatialSliceMeters(
         icon_global_datapipe,
         location_datapipe=loc_datapipe,

From d30f2345baaa7eb25e85ab29efad3625e86c7056 Mon Sep 17 00:00:00 2001
From: James Fulton <djamesfulton@yahoo.co.uk>
Date: Mon, 15 Apr 2024 19:03:07 +0000
Subject: [PATCH 06/27] refdactor to remove code duplication

---
 ocf_datapipes/training/common.py        |  53 ++++++---
 ocf_datapipes/training/pvnet_all_gsp.py | 139 +++++-------------------
 2 files changed, 64 insertions(+), 128 deletions(-)

diff --git a/ocf_datapipes/training/common.py b/ocf_datapipes/training/common.py
index 36398d479..7a6be9b4c 100644
--- a/ocf_datapipes/training/common.py
+++ b/ocf_datapipes/training/common.py
@@ -1092,28 +1092,18 @@ def add_selected_time_slices_from_datapipes(used_datapipes: dict):
     return datapipes_to_return
 
 
-def create_t0_and_loc_datapipes(
+def create_valid_t0_periods_datapipe(
     datapipes_dict: dict,
     configuration: Configuration,
     key_for_t0: str = "gsp",
-    shuffle: bool = True,
 ):
-    """
-    Takes source datapipes and returns datapipes of appropriate sample pairs of locations and times.
-
-    The (location, t0) pairs are sampled without replacement.
-
+    """Create datapipe yielding t0 periods which are valid for the input data sources.
+    
     Args:
         datapipes_dict: Dictionary of datapipes of input sources for which we want to select
             appropriate location and times.
         configuration: Configuration object for inputs.
         key_for_t0: Key to use for the t0 datapipe. Must be "gsp" or "pv".
-        shuffle: Whether to use the internal shuffle function when yielding location times. Else
-            location times will be heavily ordered.
-
-    Returns:
-        location datapipe, t0 datapipe
-
     """
     assert key_for_t0 in datapipes_dict
     assert key_for_t0 in [
@@ -1224,9 +1214,42 @@ def create_t0_and_loc_datapipes(
         overlapping_datapipe = contiguous_time_datapipes[0]
 
     # Select time periods and set length
-    key_datapipe = key_datapipe.filter_time_periods(time_periods=overlapping_datapipe)
+    valid_t0_periods_datapipe = key_datapipe.filter_time_periods(time_periods=overlapping_datapipe)
+    
+    return valid_t0_periods_datapipe
+
+
+
+def create_t0_and_loc_datapipes(
+    datapipes_dict: dict,
+    configuration: Configuration,
+    key_for_t0: str = "gsp",
+    shuffle: bool = True,
+):
+    """
+    Takes source datapipes and returns datapipes of appropriate sample pairs of locations and times.
+
+    The (location, t0) pairs are sampled without replacement.
+
+    Args:
+        datapipes_dict: Dictionary of datapipes of input sources for which we want to select
+            appropriate location and times.
+        configuration: Configuration object for inputs.
+        key_for_t0: Key to use for the t0 datapipe. Must be "gsp" or "pv".
+        shuffle: Whether to use the internal shuffle function when yielding location times. Else
+            location times will be heavily ordered.
+
+    Returns:
+        location datapipe, t0 datapipe
+    """
 
-    t0_loc_datapipe = key_datapipe.pick_locs_and_t0s(return_all=True, shuffle=shuffle)
+    valid_t0_periods_datapipe = create_valid_t0_periods_datapipe(
+        datapipes_dict,
+        configuration,
+        key_for_t0,
+    )
+    
+    t0_loc_datapipe = valid_t0_periods_datapipe.pick_locs_and_t0s(return_all=True, shuffle=shuffle)
 
     location_pipe, t0_datapipe = t0_loc_datapipe.unzip(sequence_length=2)
 
diff --git a/ocf_datapipes/training/pvnet_all_gsp.py b/ocf_datapipes/training/pvnet_all_gsp.py
index 4523b360e..f27cbd892 100644
--- a/ocf_datapipes/training/pvnet_all_gsp.py
+++ b/ocf_datapipes/training/pvnet_all_gsp.py
@@ -28,6 +28,7 @@
     slice_datapipes_by_time,
     minutes,
     open_and_return_datapipes,
+    create_valid_t0_periods_datapipe,
 )
 from ocf_datapipes.utils.consts import (
     NWP_MEANS,
@@ -58,12 +59,20 @@
 
 
 class SampleFunction:
+    """Apply a function to each sample in the concurrent batch list"""
+    
     def __init__(self, function):
+        """Apply a function to each sample in the concurrent batch list
+        
+        Args:
+            function: Function to apply to each sample
+        """
         self.function = function
 
     def __call__(self, sample_list):
         return [self.function(sample) for sample in sample_list]
-    
+
+
 class ZipFunction:
     def __init__(self, function):
         self.function = function
@@ -74,7 +83,14 @@ def __call__(self, zipped_sample_list):
             
 
 class SampleRepeat:
+    """Use a single input element to create a list of identical values"""
+    
     def __init__(self, num_repeats):
+        """Use a single input element to create a list of identical values
+        
+        Args:
+            num_repeats: Length of the returned list of duplicated values
+        """
         self.num_repeats = num_repeats
         
     def __call__(self, x):
@@ -108,7 +124,6 @@ def __call__(self, gsp_id: int) -> Location:
         )
 
 
-    
 def create_t0_datapipe(
     datapipes_dict: dict,
     configuration: Configuration,
@@ -121,122 +136,22 @@ def create_t0_datapipe(
 
     Args:
         datapipes_dict: Dictionary of datapipes of input sources for which we want to select
-            appropriate location and times.
+            appropriate t0 times.
         configuration: Configuration object for inputs.
-        shuffle: Whether to use the internal shuffle function when yielding location times. Else
+        shuffle: Whether to use the internal shuffle function when yielding times. Else
             location times will be heavily ordered.
 
     Returns:
-        location datapipe, t0 datapipe
+        t0 datapipe
 
     """
-    assert "gsp" in datapipes_dict
-
-    contiguous_time_datapipes = []  # Used to store contiguous time periods from each data source
-
-    datapipes_dict["gsp"], key_datapipe = datapipes_dict["gsp"].fork(2, buffer_size=5)
-
-    for key in datapipes_dict.keys():
-        if key in ["topo"]:
-            continue
-
-        elif key == "nwp":
-            for nwp_key in datapipes_dict["nwp"].keys():
-                # NWPs are nested since there can be multiple NWP sources
-                datapipes_dict["nwp"][nwp_key], datapipe_copy = datapipes_dict["nwp"][nwp_key].fork(
-                    2, buffer_size=5
-                )
-
-                # Different config setting per NWP source
-                nwp_conf = configuration.input_data.nwp[nwp_key]
-
-                if nwp_conf.dropout_timedeltas_minutes is None:
-                    max_dropout = minutes(0)
-                else:
-                    max_dropout = minutes(int(np.max(np.abs(nwp_conf.dropout_timedeltas_minutes))))
-
-                if nwp_conf.max_staleness_minutes is None:
-                    max_staleness = None
-                else:
-                    max_staleness = minutes(nwp_conf.max_staleness_minutes)
-
-                # NWP is a forecast product so gets its own contiguous function
-                time_periods = datapipe_copy.find_contiguous_t0_time_periods_nwp(
-                    history_duration=minutes(nwp_conf.history_minutes),
-                    forecast_duration=minutes(nwp_conf.forecast_minutes),
-                    max_staleness=max_staleness,
-                    max_dropout=max_dropout,
-                    time_dim="init_time_utc",
-                )
-
-                contiguous_time_datapipes.append(time_periods)
-
-        else:
-            if key == "sat":
-                sample_frequency = configuration.input_data.satellite.time_resolution_minutes
-                history_duration = configuration.input_data.satellite.history_minutes
-                forecast_duration = 0
-                time_dim = "time_utc"
-
-            elif key == "hrv":
-                sample_frequency = configuration.input_data.hrvsatellite.time_resolution_minutes
-                history_duration = configuration.input_data.hrvsatellite.history_minutes
-                forecast_duration = 0
-                time_dim = "time_utc"
-
-            elif key == "pv":
-                sample_frequency = configuration.input_data.pv.time_resolution_minutes
-                history_duration = configuration.input_data.pv.history_minutes
-                forecast_duration = configuration.input_data.pv.forecast_minutes
-                time_dim = "time_utc"
-
-            elif key == "wind":
-                sample_frequency = configuration.input_data.wind.time_resolution_minutes
-                history_duration = configuration.input_data.wind.history_minutes
-                forecast_duration = configuration.input_data.wind.forecast_minutes
-                time_dim = "time_utc"
-
-            elif key == "sensor":
-                sample_frequency = configuration.input_data.sensor.time_resolution_minutes
-                history_duration = configuration.input_data.sensor.history_minutes
-                forecast_duration = configuration.input_data.sensor.forecast_minutes
-                time_dim = "time_utc"
-
-            elif key == "gsp":
-                sample_frequency = configuration.input_data.gsp.time_resolution_minutes
-                history_duration = configuration.input_data.gsp.history_minutes
-                forecast_duration = configuration.input_data.gsp.forecast_minutes
-                time_dim = "time_utc"
-
-            else:
-                raise ValueError(f"Unexpected key: {key}")
-
-            datapipes_dict[key], datapipe_copy = datapipes_dict[key].fork(2, buffer_size=5)
-
-            time_periods = datapipe_copy.find_contiguous_t0_time_periods(
-                sample_period_duration=minutes(sample_frequency),
-                history_duration=minutes(history_duration),
-                forecast_duration=minutes(forecast_duration),
-                time_dim=time_dim,
-            )
-
-            contiguous_time_datapipes.append(time_periods)
-
-    # Find joint overlapping contiguous time periods
-    if len(contiguous_time_datapipes) > 1:
-        logger.debug("Getting joint time periods")
-        overlapping_datapipe = contiguous_time_datapipes[0].filter_to_overlapping_time_periods(
-            secondary_datapipes=contiguous_time_datapipes[1:],
-        )
-    else:
-        logger.debug("Skipping getting joint time periods")
-        overlapping_datapipe = contiguous_time_datapipes[0]
-
-    # Select time periods and set length
-    key_datapipe = key_datapipe.filter_time_periods(time_periods=overlapping_datapipe)
-
-    t0_datapipe = key_datapipe.pick_t0_times()#return_all=True, shuffle=shuffle)
+    valid_t0_periods_datapipe = create_valid_t0_periods_datapipe(
+        datapipes_dict,
+        configuration,
+        key_for_t0="gsp",
+    )
 
+    t0_datapipe = valid_t0_periods_datapipe.pick_t0_times(return_all=True, shuffle=shuffle)
 
     return t0_datapipe
 
@@ -490,8 +405,6 @@ def __iter__(self):
             yield stacked_converted_values
 
 
-
-
 def construct_sliced_data_pipeline(
     config_filename: str,
     t0_datapipe: IterDataPipe,

From d8b10e0c75b5f36cb2528eefa374424b04db6871 Mon Sep 17 00:00:00 2001
From: James Fulton <djamesfulton@yahoo.co.uk>
Date: Fri, 31 May 2024 11:57:19 +0000
Subject: [PATCH 07/27] refactor gsp_all_pipeline

---
 ocf_datapipes/load/gsp/gsp.py                |  34 +-
 ocf_datapipes/load/gsp/utils.py              |  62 +-
 ocf_datapipes/select/select_spatial_slice.py | 717 +++++++++++--------
 ocf_datapipes/training/pvnet.py              |  81 ++-
 ocf_datapipes/training/pvnet_all_gsp_v2.py   | 516 +++++++++++++
 ocf_datapipes/utils/location.py              |   3 +-
 6 files changed, 1039 insertions(+), 374 deletions(-)
 create mode 100644 ocf_datapipes/training/pvnet_all_gsp_v2.py

diff --git a/ocf_datapipes/load/gsp/gsp.py b/ocf_datapipes/load/gsp/gsp.py
index 57800032a..251d46775 100644
--- a/ocf_datapipes/load/gsp/gsp.py
+++ b/ocf_datapipes/load/gsp/gsp.py
@@ -12,14 +12,6 @@
 
 logger = logging.getLogger(__name__)
 
-try:
-    from ocf_datapipes.utils.eso import get_gsp_metadata_from_eso, get_gsp_shape_from_eso
-
-    _has_pvlive = True
-except ImportError:
-    print("Unable to import PVLive utils, please provide filenames with OpenGSP")
-    _has_pvlive = False
-
 
 @functional_datapipe("open_gsp")
 class OpenGSPIterDataPipe(IterDataPipe):
@@ -44,40 +36,34 @@ def __init__(
             sample_period_duration: Sample period of the GSP data
         """
         self.gsp_pv_power_zarr_path = gsp_pv_power_zarr_path
-        if (
-            gsp_id_to_region_id_filename is None
-            or sheffield_solar_region_path is None
-            and _has_pvlive
-        ):
-            self.gsp_id_to_region_id_filename = get_gsp_metadata_from_eso()
-            self.sheffield_solar_region_path = get_gsp_shape_from_eso()
-        else:
-            self.gsp_id_to_region_id_filename = gsp_id_to_region_id_filename
-            self.sheffield_solar_region_path = sheffield_solar_region_path
+
+        self.gsp_id_to_region_id_filename = gsp_id_to_region_id_filename
+        self.sheffield_solar_region_path = sheffield_solar_region_path
         self.threshold_mw = threshold_mw
         self.sample_period_duration = sample_period_duration
 
     def __iter__(self) -> xr.DataArray:
         """Get and return GSP data"""
         gsp_id_to_shape = get_gsp_id_to_shape(
-            self.gsp_id_to_region_id_filename, self.sheffield_solar_region_path
+            self.gsp_id_to_region_id_filename, 
+            self.sheffield_solar_region_path,
         )
-        self._gsp_id_to_shape = gsp_id_to_shape  # Save, mostly for plotting to check all is fine!
 
         logger.debug(f"Getting GSP data from {self.gsp_pv_power_zarr_path}")
 
-        # Load GSP generation xr.Dataset:
+        # Load GSP generation xr.Dataset
         gsp_pv_power_mw_ds = xr.open_dataset(self.gsp_pv_power_zarr_path, engine="zarr")
 
-        # Ensure the centroids have the same GSP ID index as the GSP PV power:
+        # Ensure the centroids have the same GSP ID index as the GSP PV power
         gsp_id_to_shape = gsp_id_to_shape.loc[gsp_pv_power_mw_ds.gsp_id]
+        
         data_array = put_gsp_data_into_an_xr_dataarray(
             gsp_pv_power_mw=gsp_pv_power_mw_ds.generation_mw.data.astype(np.float32),
             time_utc=gsp_pv_power_mw_ds.datetime_gmt.data,
             gsp_id=gsp_pv_power_mw_ds.gsp_id.data,
             # TODO: Try using `gsp_id_to_shape.geometry.envelope.centroid`. See issue #76.
-            x_osgb=gsp_id_to_shape.geometry.centroid.x.astype(np.float32),
-            y_osgb=gsp_id_to_shape.geometry.centroid.y.astype(np.float32),
+            x_osgb=gsp_id_to_shape.x_osgb.astype(np.float32),
+            y_osgb=gsp_id_to_shape.y_osgb.astype(np.float32),
             nominal_capacity_mwp=gsp_pv_power_mw_ds.installedcapacity_mwp.data.astype(np.float32),
             effective_capacity_mwp=gsp_pv_power_mw_ds.capacity_mwp.data.astype(np.float32),
         )
diff --git a/ocf_datapipes/load/gsp/utils.py b/ocf_datapipes/load/gsp/utils.py
index 3ec228935..e6b0d75b2 100644
--- a/ocf_datapipes/load/gsp/utils.py
+++ b/ocf_datapipes/load/gsp/utils.py
@@ -1,9 +1,20 @@
 """ Utils for GSP loading"""
+from typing import Optional
+
 import geopandas as gpd
 import numpy as np
 import pandas as pd
 import xarray as xr
 
+from ocf_datapipes.utils.location import Location
+
+try:
+    from ocf_datapipes.utils.eso import get_gsp_metadata_from_eso, get_gsp_shape_from_eso
+    _has_pvlive = True
+except ImportError:
+    print("Unable to import PVLive utils, please provide filenames with OpenGSP")
+    _has_pvlive = False
+
 
 def put_gsp_data_into_an_xr_dataarray(
     gsp_pv_power_mw: np.ndarray,
@@ -48,7 +59,8 @@ def put_gsp_data_into_an_xr_dataarray(
 
 
 def get_gsp_id_to_shape(
-    gsp_id_to_region_id_filename: str, sheffield_solar_region_path: str
+    gsp_id_to_region_id_filename: Optional[str] = None, 
+    sheffield_solar_region_path: Optional[str] = None,
 ) -> gpd.GeoDataFrame:
     """
     Get the GSP ID to the shape
@@ -60,6 +72,16 @@ def get_gsp_id_to_shape(
     Returns:
         GeoDataFrame containing the mapping from ID to shape
     """
+    
+    did_provide_filepaths = None not in [gsp_id_to_region_id_filename, sheffield_solar_region_path]
+    assert _has_pvlive or did_provide_filepaths
+    
+    if not did_provide_filepaths:
+        if gsp_id_to_region_id_filename is None:
+            gsp_id_to_region_id_filename = get_gsp_metadata_from_eso()
+        if sheffield_solar_region_path is None:
+            sheffield_solar_region_path = get_gsp_shape_from_eso()        
+        
     # Load mapping from GSP ID to Sheffield Solar GSP ID to GSP name:
     gsp_id_to_region_id = pd.read_csv(
         gsp_id_to_region_id_filename,
@@ -94,4 +116,42 @@ def get_gsp_id_to_shape(
     # For the national forecast, GSP ID 0, we want the shape to be the
     # union of all the other shapes
     gsp_id_to_shape = pd.concat([gsp_id_to_shape, gsp_0]).sort_index()
+    
+    # Add central coordinates
+    gsp_id_to_shape["x_osgb"] = gsp_id_to_shape.geometry.centroid.x.astype(np.float32)
+    gsp_id_to_shape["y_osgb"] = gsp_id_to_shape.geometry.centroid.y.astype(np.float32)
+    
     return gsp_id_to_shape
+
+
+class GSPLocationLookup:
+    """Query object for GSP location from GSP ID"""
+
+    def __init__(
+        self, 
+        gsp_id_to_region_id_filename: Optional[str] = None, 
+        sheffield_solar_region_path: Optional[str] = None,          
+):
+        """Query object for GSP location from GSP ID
+
+        Args:
+            gsp_id_to_region_id_filename: Filename of the mapping file
+            sheffield_solar_region_path: Path to the region shapes
+
+        """
+        self.gsp_id_to_shape = get_gsp_id_to_shape(
+            gsp_id_to_region_id_filename, 
+            sheffield_solar_region_path,
+        )
+
+    def __call__(self, gsp_id: int) -> Location:
+        """Returns the locations for the input GSP IDs.
+
+        Args:
+            gsp_id: Integer ID of the GSP
+        """
+        return Location(
+            x=self.gsp_id_to_shape.loc[gsp_id].x_osgb.astype(np.float32),
+            y=self.gsp_id_to_shape.loc[gsp_id].y_osgb.astype(np.float32),
+            id=gsp_id,
+        )
\ No newline at end of file
diff --git a/ocf_datapipes/select/select_spatial_slice.py b/ocf_datapipes/select/select_spatial_slice.py
index 36e4c8787..4b42ea3f3 100644
--- a/ocf_datapipes/select/select_spatial_slice.py
+++ b/ocf_datapipes/select/select_spatial_slice.py
@@ -21,7 +21,249 @@
 logger = logging.getLogger(__name__)
 
 
-def select_spatial_slice_pixels(
+# -------------------------------- utility functions --------------------------------
+
+
+def convert_coords_to_match_xarray(x, y, from_coords, xr_data):
+    """Convert x and y coords to cooridnate system matching xarray data
+
+    Args:
+        x: Float or array-like
+        y: Float or array-like
+        from_coords: String describing coordinate system of x and y
+        xr_data: xarray data object to which coordinates should be matched
+    """
+
+    xr_coords, xr_x_dim, xr_y_dim = spatial_coord_type(xr_data)
+
+    assert from_coords in ["osgb", "lon_lat"]
+    assert xr_coords in ["geostationary", "osgb", "lon_lat"]
+
+    if xr_coords == "geostationary":
+        if from_coords == "osgb":
+            x, y = osgb_to_geostationary_area_coords(x, y, xr_data)
+
+        elif from_coords == "lon_lat":
+            x, y = lon_lat_to_geostationary_area_coords(x, y, xr_data)
+
+    elif xr_coords == "lon_lat":
+        if from_coords == "osgb":
+            x, y = osgb_to_lon_lat(x, y)
+
+        # else the from_coords=="lon_lat" and we don't need to convert
+
+    elif xr_coords == "osgb":
+        if from_coords == "lon_lat":
+            x, y = lon_lat_to_osgb(x, y)
+
+        # else the from_coords=="osgb" and we don't need to convert
+
+    return x, y
+
+
+def _get_idx_of_pixel_closest_to_poi(
+    xr_data: xr.DataArray,
+    location: Location,
+) -> Location:
+    """
+    Return x and y index location of pixel at center of region of interest.
+
+    Args:
+        xr_data: Xarray dataset
+        location: Center
+    Returns:
+        The Location for the center pixel
+    """
+    xr_coords, xr_x_dim, xr_y_dim = spatial_coord_type(xr_data)
+
+    if xr_coords not in ["osgb", "lon_lat"]:
+        raise NotImplementedError(f"Only 'osgb' and 'lon_lat' are supported - not '{xr_coords}'")
+
+    # Convert location coords to match xarray data
+    x, y = convert_coords_to_match_xarray(
+        location.x,
+        location.y,
+        from_coords=location.coordinate_system,
+        xr_data=xr_data,
+    )
+
+    # Check that the requested point lies within the data
+    assert xr_data[xr_x_dim].min() < x < xr_data[xr_x_dim].max()
+    assert xr_data[xr_y_dim].min() < y < xr_data[xr_y_dim].max()
+
+    x_index = xr_data.get_index(xr_x_dim)
+    y_index = xr_data.get_index(xr_y_dim)
+
+    closest_x = x_index.get_indexer([x], method="nearest")[0]
+    closest_y = y_index.get_indexer([y], method="nearest")[0]
+
+    return Location(x=closest_x, y=closest_y, coordinate_system="idx")
+
+
+def _get_idx_of_pixel_closest_to_poi_geostationary(
+    xr_data: xr.DataArray,
+    center_osgb: Location,
+) -> Location:
+    """
+    Return x and y index location of pixel at center of region of interest.
+
+    Args:
+        xr_data: Xarray dataset
+        center_osgb: Center in OSGB coordinates
+
+    Returns:
+        Location for the center pixel in geostationary coordinates
+    """
+
+    xr_coords, xr_x_dim, xr_y_dim = spatial_coord_type(xr_data)
+
+    x, y = osgb_to_geostationary_area_coords(x=center_osgb.x, y=center_osgb.y, xr_data=xr_data)
+    center_geostationary = Location(x=x, y=y, coordinate_system="geostationary")
+
+    # Check that the requested point lies within the data
+    assert xr_data[xr_x_dim].min() < x < xr_data[xr_x_dim].max()
+    assert xr_data[xr_y_dim].min() < y < xr_data[xr_y_dim].max()
+
+    # Get the index into x and y nearest to x_center_geostationary and y_center_geostationary:
+    x_index_at_center = searchsorted(
+        xr_data[xr_x_dim].values, center_geostationary.x, assume_ascending=True
+    )
+
+    # y_geostationary is in descending order:
+    y_index_at_center = searchsorted(
+        xr_data[xr_y_dim].values, center_geostationary.y, assume_ascending=False
+    )
+
+    return Location(x=x_index_at_center, y=y_index_at_center, coordinate_system="idx")
+
+
+def _get_points_from_unstructured_grids(
+    xr_data: xr.DataArray,
+    location: Location,
+    location_idx_name: str = "values",
+    num_points: int = 1,
+):
+    """
+    Get the closest points from an unstructured grid (i.e. Icosahedral grid)
+
+    This is primarily used for the Icosahedral grid, which is not a regular grid,
+     and so is not an image
+
+    Args:
+        xr_data: Xarray dataset
+        location: Location of center point
+        location_idx_name: Name of the index values dimension
+            (i.e. where we index into to get the lat/lon for that point)
+        num_points: Number of points to return (should be width * height)
+
+    Returns:
+        The closest points from the grid
+    """
+    xr_coords, xr_x_dim, xr_y_dim = spatial_coord_type(xr_data)
+    assert xr_coords == "lon_lat"
+
+    # Check if need to convert from different coordinate system to lat/lon
+    if location.coordinate_system == "osgb":
+        longitude, latitude = osgb_to_lon_lat(x=location.x, y=location.y)
+        location = Location(
+            x=longitude,
+            y=latitude,
+            coordinate_system="lon_lat",
+        )
+    elif location.coordinate_system == "geostationary":
+        raise NotImplementedError(
+            "Does not currently support geostationary coordinates when using unstructured grids"
+        )
+
+    # Extract lat, lon, and locidx data
+    lat = xr_data.longitude.values
+    lon = xr_data.latitude.values
+    locidx = xr_data[location_idx_name].values
+
+    # Create a KDTree
+    tree = KDTree(list(zip(lat, lon)))
+
+    # Query with the [longitude, latitude] of your point
+    _, idx = tree.query([location.x, location.y], k=num_points)
+
+    # Retrieve the location_idxs for these grid points
+    location_idxs = locidx[idx]
+
+    data = xr_data.sel({location_idx_name: location_idxs})
+    return data
+
+
+# ---------------------------- sub-functions for slicing ----------------------------
+
+
+def _slice_patial_spatial_pixel_window_from_xarray(
+    xr_data,
+    left_idx,
+    right_idx,
+    top_idx,
+    bottom_idx,
+    left_pad_pixels,
+    right_pad_pixels,
+    top_pad_pixels,
+    bottom_pad_pixels,
+    xr_x_dim,
+    xr_y_dim,
+):
+    """Return spatial window of given pixel size when window partially overlaps input data"""
+
+    dx = np.median(np.diff(xr_data[xr_x_dim].values))
+    dy = np.median(np.diff(xr_data[xr_y_dim].values))
+
+    if left_pad_pixels > 0:
+        assert right_pad_pixels == 0
+        x_sel = np.concatenate(
+            [
+                xr_data[xr_x_dim].values[0] - np.arange(left_pad_pixels, 0, -1) * dx,
+                xr_data[xr_x_dim].values[0:right_idx],
+            ]
+        )
+        xr_data = xr_data.isel({xr_x_dim: slice(0, right_idx)}).reindex({xr_x_dim: x_sel})
+
+    elif right_pad_pixels > 0:
+        assert left_pad_pixels == 0
+        x_sel = np.concatenate(
+            [
+                xr_data[xr_x_dim].values[left_idx:],
+                xr_data[xr_x_dim].values[-1] + np.arange(1, right_pad_pixels + 1) * dx,
+            ]
+        )
+        xr_data = xr_data.isel({xr_x_dim: slice(left_idx, None)}).reindex({xr_x_dim: x_sel})
+
+    else:
+        xr_data = xr_data.isel({xr_x_dim: slice(left_idx, right_idx)})
+
+    if top_pad_pixels > 0:
+        assert bottom_pad_pixels == 0
+        y_sel = np.concatenate(
+            [
+                xr_data[xr_y_dim].values[0] - np.arange(top_pad_pixels, 0, -1) * dy,
+                xr_data[xr_y_dim].values[0:bottom_idx],
+            ]
+        )
+        xr_data = xr_data.isel({xr_y_dim: slice(0, bottom_idx)}).reindex({xr_y_dim: y_sel})
+
+    elif bottom_pad_pixels > 0:
+        assert top_pad_pixels == 0
+        y_sel = np.concatenate(
+            [
+                xr_data[xr_y_dim].values[top_idx:],
+                xr_data[xr_y_dim].values[-1] + np.arange(1, bottom_pad_pixels + 1) * dy,
+            ]
+        )
+        xr_data = xr_data.isel({xr_y_dim: slice(top_idx, None)}).reindex({xr_x_dim: y_sel})
+
+    else:
+        xr_data = xr_data.isel({xr_y_dim: slice(top_idx, bottom_idx)})
+
+    return xr_data
+
+
+def slice_spatial_pixel_window_from_xarray(
     xr_data, center_idx, width_pixels, height_pixels, xr_x_dim, xr_y_dim, allow_partial_slice
 ):
     """Select a spatial slice from an xarray object
@@ -64,7 +306,7 @@ def select_spatial_slice_pixels(
                 (bottom_idx - (data_height_pixels - 1)) if bottom_pad_required else 0
             )
 
-            xr_data = select_partial_spatial_slice_pixels(
+            xr_data = _slice_patial_spatial_pixel_window_from_xarray(
                 xr_data,
                 left_idx,
                 right_idx,
@@ -105,71 +347,166 @@ def select_spatial_slice_pixels(
     return xr_data
 
 
-def select_partial_spatial_slice_pixels(
-    xr_data,
-    left_idx,
-    right_idx,
-    top_idx,
-    bottom_idx,
-    left_pad_pixels,
-    right_pad_pixels,
-    top_pad_pixels,
-    bottom_pad_pixels,
-    xr_x_dim,
-    xr_y_dim,
+# ---------------------------- main functions for slicing ---------------------------
+
+
+def select_spatial_slice_pixels(
+    xr_data: Union[xr.Dataset, xr.DataArray], 
+    location: Location,
+    roi_width_pixels: int, 
+    roi_height_pixels: int, 
+    allow_partial_slice: bool = False,
+    location_idx_name: Optional[str] = None,
 ):
-    """Return spatial window of given pixel size when window partially overlaps input data"""
+    """
+    Select spatial slice based off pixels from location point of interest
 
-    dx = np.median(np.diff(xr_data[xr_x_dim].values))
-    dy = np.median(np.diff(xr_data[xr_y_dim].values))
+    If `allow_partial_slice` is set to True, then slices may be made which intersect the border
+    of the input data. The additional x and y cordinates that would be required for this slice
+    are extrapolated based on the average spacing of these coordinates in the input data.
+    However, currently slices cannot be made where the centre of the window is outside of the
+    input data.
 
-    if left_pad_pixels > 0:
-        assert right_pad_pixels == 0
-        x_sel = np.concatenate(
-            [
-                xr_data[xr_x_dim].values[0] - np.arange(left_pad_pixels, 0, -1) * dx,
-                xr_data[xr_x_dim].values[0:right_idx],
-            ]
+    Args:
+        xr_data: Xarray DataArray or Dataset to slice from
+        location: Location of interest
+        roi_height_pixels: ROI height in pixels
+        roi_width_pixels: ROI width in pixels
+        allow_partial_slice: Whether to allow a partial slice.
+        location_idx_name: Name for location index of unstructured grid data,
+            None if not relevant
+    """
+        
+    xr_coords, xr_x_dim, xr_y_dim = spatial_coord_type(xr_data)
+    if location_idx_name is not None:
+        selected = _get_points_from_unstructured_grids(
+            xr_data=xr_data,
+            location=location,
+            location_idx_name=location_idx_name,
+            num_points=roi_width_pixels * roi_height_pixels,
         )
-        xr_data = xr_data.isel({xr_x_dim: slice(0, right_idx)}).reindex({xr_x_dim: x_sel})
+    else:
+        if xr_coords == "geostationary":
+            center_idx: Location = _get_idx_of_pixel_closest_to_poi_geostationary(
+                xr_data=xr_data,
+                center_osgb=location,
+            )
+        else:
+            center_idx: Location = _get_idx_of_pixel_closest_to_poi(
+                xr_data=xr_data,
+                location=location,
+            )
 
-    elif right_pad_pixels > 0:
-        assert left_pad_pixels == 0
-        x_sel = np.concatenate(
-            [
-                xr_data[xr_x_dim].values[left_idx:],
-                xr_data[xr_x_dim].values[-1] + np.arange(1, right_pad_pixels + 1) * dx,
-            ]
+        selected = slice_spatial_pixel_window_from_xarray(
+            xr_data,
+            center_idx,
+            roi_width_pixels,
+            roi_height_pixels,
+            xr_x_dim,
+            xr_y_dim,
+            allow_partial_slice=allow_partial_slice,
         )
-        xr_data = xr_data.isel({xr_x_dim: slice(left_idx, None)}).reindex({xr_x_dim: x_sel})
 
-    else:
-        xr_data = xr_data.isel({xr_x_dim: slice(left_idx, right_idx)})
+    return selected
 
-    if top_pad_pixels > 0:
-        assert bottom_pad_pixels == 0
-        y_sel = np.concatenate(
-            [
-                xr_data[xr_y_dim].values[0] - np.arange(top_pad_pixels, 0, -1) * dy,
-                xr_data[xr_y_dim].values[0:bottom_idx],
-            ]
+
+def select_spatial_slice_meters(
+    xr_data: Union[xr.Dataset, xr.DataArray], 
+    location: Location,
+    roi_width_meters: int,
+    roi_height_meters: int,
+    dim_name: Optional[str] = None,                   
+):
+    """
+    Select spatial slice based off pixels from point of interest
+
+    Args:
+        xr_data: Xarray DataArray or Dataset to slice from
+        location: Location of interest
+        roi_height_meters: ROI height in meters
+        roi_width_meters: ROI width in meters
+        dim_name: Dimension name to select for ID, None for coordinates
+
+    Notes:
+        Using spatial slicing based on distance rather than number of pixels will often yield
+        slices which can vary by 1 pixel in height and/or width.
+
+        E.g. Suppose the Xarray data has x-coords = [1,2,3,4,5]. We want to slice a spatial
+        window with a size which equates to 2.2 along the x-axis. If we choose to slice around
+        the point x=3 this will slice out the x-coords [2,3,4]. If we choose to slice around the
+        point x=2.5 this will slice out the x-coords [2,3]. Hence the returned slice can have
+        size either 2 or 3 in the x-axis depending on the spatial location selected.
+
+        Also, if selecting over a large span of latitudes, this may also causes pixel sizes of
+        the yielded outputs to change. For example, if the Xarray data is on a regularly spaced
+        longitude-latitude grid, then the structure of the grid means that the longitudes near
+        to the poles are spaced closer together (measured in meters) than at the equator. So
+        slices near the equator will have less pixels in the x-axis than slices taken near the
+        poles.
+    """
+    # Get the spatial coords of the xarray data
+    xr_coords, xr_x_dim, xr_y_dim = spatial_coord_type(xr_data)
+
+    half_width = roi_width_meters // 2
+    half_height = roi_height_meters // 2
+    
+    # Find the bounding box values for the location in either lat-lon or OSGB coord systems
+    if location.coordinate_system == "lon_lat":
+        right, top = move_lon_lat_by_meters(
+            location.x,
+            location.y,
+            half_width,
+            half_height,
+        )
+        left, bottom = move_lon_lat_by_meters(
+            location.x,
+            location.y,
+            -half_width,
+            -half_height,
         )
-        xr_data = xr_data.isel({xr_y_dim: slice(0, bottom_idx)}).reindex({xr_y_dim: y_sel})
 
-    elif bottom_pad_pixels > 0:
-        assert top_pad_pixels == 0
-        y_sel = np.concatenate(
-            [
-                xr_data[xr_y_dim].values[top_idx:],
-                xr_data[xr_y_dim].values[-1] + np.arange(1, bottom_pad_pixels + 1) * dy,
-            ]
+    elif location.coordinate_system == "osgb":
+        left = location.x - half_width
+        right = location.x + half_width
+        bottom = location.y - half_height
+        top = location.y + half_height
+
+    else:
+        raise ValueError(
+            f"Location coord system not recognized: {location.coordinate_system}"
+        )
+
+    # Change the bounding coordinates [left, right, bottom, top] to the same
+    # coordinate system as the xarray data
+    (left, right), (bottom, top) = convert_coords_to_match_xarray(
+        x=np.array([left, right], dtype=np.float32),
+        y=np.array([bottom, top], dtype=np.float32),
+        from_coords=location.coordinate_system,
+        xr_data=xr_data,
         )
-        xr_data = xr_data.isel({xr_y_dim: slice(top_idx, None)}).reindex({xr_x_dim: y_sel})
+
+    # Do it off coordinates, not ID
+    if dim_name is None:
+        # Select a patch from the xarray data
+        x_mask = (left <= xr_data[xr_x_dim]) & (xr_data[xr_x_dim] <= right)
+        y_mask = (bottom <= xr_data[xr_y_dim]) & (xr_data[xr_y_dim] <= top)
+        selected = xr_data.isel({xr_x_dim: x_mask, xr_y_dim: y_mask})
 
     else:
-        xr_data = xr_data.isel({xr_y_dim: slice(top_idx, bottom_idx)})
+        # Select data in the region of interest and ID:
+        # This also works for unstructured grids
+
+        id_mask = (
+            (left <= xr_data[xr_x_dim])
+            & (xr_data[xr_x_dim] <= right)
+            & (bottom <= xr_data[xr_y_dim])
+            & (xr_data[xr_y_dim] <= top)
+        )
+        selected = xr_data.isel({dim_name: id_mask})
+    return selected
 
-    return xr_data
+
+# ------------------------------ datapipes for slicing ------------------------------
 
 
 @functional_datapipe("select_spatial_slice_pixels")
@@ -180,8 +517,8 @@ def __init__(
         self,
         source_datapipe: IterDataPipe,
         location_datapipe: IterDataPipe,
-        roi_height_pixels: int,
         roi_width_pixels: int,
+        roi_height_pixels: int,
         allow_partial_slice: bool = False,
         location_idx_name: Optional[str] = None,
     ):
@@ -213,39 +550,18 @@ def __init__(
     def __iter__(self) -> Union[xr.DataArray, xr.Dataset]:
         for xr_data, location in self.source_datapipe.zip_ocf(self.location_datapipe):
             logger.debug("Selecting spatial slice with pixels")
-            xr_coords, xr_x_dim, xr_y_dim = spatial_coord_type(xr_data)
-            if self.location_idx_name is not None:
-                selected = _get_points_from_unstructured_grids(
-                    xr_data=xr_data,
-                    location=location,
-                    location_idx_name=self.location_idx_name,
-                    num_points=self.roi_width_pixels * self.roi_height_pixels,
-                )
-                yield selected
-
-            if xr_coords == "geostationary":
-                center_idx: Location = _get_idx_of_pixel_closest_to_poi_geostationary(
-                    xr_data=xr_data,
-                    center_osgb=location,
-                )
-            else:
-                center_idx: Location = _get_idx_of_pixel_closest_to_poi(
-                    xr_data=xr_data,
-                    location=location,
-                )
 
             selected = select_spatial_slice_pixels(
-                xr_data,
-                center_idx,
-                self.roi_width_pixels,
-                self.roi_height_pixels,
-                xr_x_dim,
-                xr_y_dim,
+                xr_data=xr_data,
+                location=location,
+                roi_width_pixels=self.roi_width_pixels, 
+                roi_height_pixels=self.roi_height_pixels, 
                 allow_partial_slice=self.allow_partial_slice,
+                location_idx_name=self.location_idx_name,
             )
 
             yield selected
-
+    
 
 @functional_datapipe("select_spatial_slice_meters")
 class SelectSpatialSliceMetersIterDataPipe(IterDataPipe):
@@ -255,9 +571,9 @@ def __init__(
         self,
         source_datapipe: IterDataPipe,
         location_datapipe: IterDataPipe,
-        roi_height_meters: int,
         roi_width_meters: int,
-        dim_name: Optional[str] = None,  # "pv_system_id",
+        roi_height_meters: int,
+        dim_name: Optional[str] = None,
     ):
         """
         Select spatial slice based off pixels from point of interest
@@ -265,8 +581,8 @@ def __init__(
         Args:
             source_datapipe: Datapipe of Xarray data
             location_datapipe: Location datapipe
-            roi_height_meters: ROI height in meters
             roi_width_meters: ROI width in meters
+            roi_height_meters: ROI height in meters
             dim_name: Dimension name to select for ID, None for coordinates
 
         Notes:
@@ -297,233 +613,12 @@ def __iter__(self) -> Union[xr.DataArray, xr.Dataset]:
             # Compute the index for left and right:
             logger.debug("Getting Spatial Slice Meters")
 
-            # Get the spatial coords of the xarray data
-            xr_coords, xr_x_dim, xr_y_dim = spatial_coord_type(xr_data)
-
-            half_height = self.roi_height_meters // 2
-            half_width = self.roi_width_meters // 2
-
-            # Find the bounding box values for the location in either lat-lon or OSGB coord systems
-            if location.coordinate_system == "lon_lat":
-                right, top = move_lon_lat_by_meters(
-                    location.x,
-                    location.y,
-                    half_width,
-                    half_height,
-                )
-                left, bottom = move_lon_lat_by_meters(
-                    location.x,
-                    location.y,
-                    -half_width,
-                    -half_height,
-                )
-
-            elif location.coordinate_system == "osgb":
-                left = location.x - half_width
-                right = location.x + half_width
-                bottom = location.y - half_height
-                top = location.y + half_height
-
-            else:
-                raise ValueError(
-                    f"Location coord system not recognized: {location.coordinate_system}"
-                )
-
-            # Change the bounding coordinates [left, right, bottom, top] to the same
-            # coordinate system as the xarray data
-            (left, right), (bottom, top) = convert_coords_to_match_xarray(
-                x=np.array([left, right], dtype=np.float32),
-                y=np.array([bottom, top], dtype=np.float32),
-                from_coords=location.coordinate_system,
-                xr_data=xr_data,
+            selected = select_spatial_slice_meters(
+                xr_data=xr_data, 
+                location=location,
+                roi_width_meters=self.roi_width_meters,
+                roi_height_meters=self.roi_height_meters,
+                dim_name=self.dim_name,  
             )
 
-            # Do it off coordinates, not ID
-            if self.dim_name is None:
-                # Select a patch from the xarray data
-                x_mask = (left <= xr_data[xr_x_dim]) & (xr_data[xr_x_dim] <= right)
-                y_mask = (bottom <= xr_data[xr_y_dim]) & (xr_data[xr_y_dim] <= top)
-                selected = xr_data.isel({xr_x_dim: x_mask, xr_y_dim: y_mask})
-
-            else:
-                # Select data in the region of interest and ID:
-                # This also works for unstructured grids
-
-                id_mask = (
-                    (left <= xr_data[xr_x_dim])
-                    & (xr_data[xr_x_dim] <= right)
-                    & (bottom <= xr_data[xr_y_dim])
-                    & (xr_data[xr_y_dim] <= top)
-                )
-                selected = xr_data.isel({self.dim_name: id_mask})
-
             yield selected
-
-
-def convert_coords_to_match_xarray(x, y, from_coords, xr_data):
-    """Convert x and y coords to cooridnate system matching xarray data
-
-    Args:
-        x: Float or array-like
-        y: Float or array-like
-        from_coords: String describing coordinate system of x and y
-        xr_data: xarray data object to which coordinates should be matched
-    """
-
-    xr_coords, xr_x_dim, xr_y_dim = spatial_coord_type(xr_data)
-
-    assert from_coords in ["osgb", "lon_lat"]
-    assert xr_coords in ["geostationary", "osgb", "lon_lat"]
-
-    if xr_coords == "geostationary":
-        if from_coords == "osgb":
-            x, y = osgb_to_geostationary_area_coords(x, y, xr_data)
-
-        elif from_coords == "lon_lat":
-            x, y = lon_lat_to_geostationary_area_coords(x, y, xr_data)
-
-    elif xr_coords == "lon_lat":
-        if from_coords == "osgb":
-            x, y = osgb_to_lon_lat(x, y)
-
-        # else the from_coords=="lon_lat" and we don't need to convert
-
-    elif xr_coords == "osgb":
-        if from_coords == "lon_lat":
-            x, y = lon_lat_to_osgb(x, y)
-
-        # else the from_coords=="osgb" and we don't need to convert
-
-    return x, y
-
-
-def _get_idx_of_pixel_closest_to_poi(
-    xr_data: xr.DataArray,
-    location: Location,
-) -> Location:
-    """
-    Return x and y index location of pixel at center of region of interest.
-
-    Args:
-        xr_data: Xarray dataset
-        location: Center
-    Returns:
-        The Location for the center pixel
-    """
-    xr_coords, xr_x_dim, xr_y_dim = spatial_coord_type(xr_data)
-
-    if xr_coords not in ["osgb", "lon_lat"]:
-        raise NotImplementedError(f"Only 'osgb' and 'lon_lat' are supported - not '{xr_coords}'")
-
-    # Convert location coords to match xarray data
-    x, y = convert_coords_to_match_xarray(
-        location.x,
-        location.y,
-        from_coords=location.coordinate_system,
-        xr_data=xr_data,
-    )
-
-    # Check that the requested point lies within the data
-    assert xr_data[xr_x_dim].min() < x < xr_data[xr_x_dim].max()
-    assert xr_data[xr_y_dim].min() < y < xr_data[xr_y_dim].max()
-
-    x_index = xr_data.get_index(xr_x_dim)
-    y_index = xr_data.get_index(xr_y_dim)
-
-    closest_x = x_index.get_indexer([x], method="nearest")[0]
-    closest_y = y_index.get_indexer([y], method="nearest")[0]
-
-    return Location(x=closest_x, y=closest_y, coordinate_system="idx")
-
-
-def _get_idx_of_pixel_closest_to_poi_geostationary(
-    xr_data: xr.DataArray,
-    center_osgb: Location,
-) -> Location:
-    """
-    Return x and y index location of pixel at center of region of interest.
-
-    Args:
-        xr_data: Xarray dataset
-        center_osgb: Center in OSGB coordinates
-
-    Returns:
-        Location for the center pixel in geostationary coordinates
-    """
-
-    xr_coords, xr_x_dim, xr_y_dim = spatial_coord_type(xr_data)
-
-    x, y = osgb_to_geostationary_area_coords(x=center_osgb.x, y=center_osgb.y, xr_data=xr_data)
-    center_geostationary = Location(x=x, y=y, coordinate_system="geostationary")
-
-    # Check that the requested point lies within the data
-    assert xr_data[xr_x_dim].min() < x < xr_data[xr_x_dim].max()
-    assert xr_data[xr_y_dim].min() < y < xr_data[xr_y_dim].max()
-
-    # Get the index into x and y nearest to x_center_geostationary and y_center_geostationary:
-    x_index_at_center = searchsorted(
-        xr_data[xr_x_dim].values, center_geostationary.x, assume_ascending=True
-    )
-
-    # y_geostationary is in descending order:
-    y_index_at_center = searchsorted(
-        xr_data[xr_y_dim].values, center_geostationary.y, assume_ascending=False
-    )
-
-    return Location(x=x_index_at_center, y=y_index_at_center, coordinate_system="idx")
-
-
-def _get_points_from_unstructured_grids(
-    xr_data: xr.DataArray,
-    location: Location,
-    location_idx_name: str = "values",
-    num_points: int = 1,
-):
-    """
-    Get the closest points from an unstructured grid (i.e. Icosahedral grid)
-
-    This is primarily used for the Icosahedral grid, which is not a regular grid,
-     and so is not an image
-
-    Args:
-        xr_data: Xarray dataset
-        location: Location of center point
-        location_idx_name: Name of the index values dimension
-            (i.e. where we index into to get the lat/lon for that point)
-        num_points: Number of points to return (should be width * height)
-
-    Returns:
-        The closest points from the grid
-    """
-    xr_coords, xr_x_dim, xr_y_dim = spatial_coord_type(xr_data)
-    assert xr_coords == "lon_lat"
-
-    # Check if need to convert from different coordinate system to lat/lon
-    if location.coordinate_system == "osgb":
-        longitude, latitude = osgb_to_lon_lat(x=location.x, y=location.y)
-        location = Location(
-            x=longitude,
-            y=latitude,
-            coordinate_system="lon_lat",
-        )
-    elif location.coordinate_system == "geostationary":
-        raise NotImplementedError(
-            "Does not currently support geostationary coordinates when using unstructured grids"
-        )
-
-    # Extract lat, lon, and locidx data
-    lat = xr_data.longitude.values
-    lon = xr_data.latitude.values
-    locidx = xr_data[location_idx_name].values
-
-    # Create a KDTree
-    tree = KDTree(list(zip(lat, lon)))
-
-    # Query with the [longitude, latitude] of your point
-    _, idx = tree.query([location.x, location.y], k=num_points)
-
-    # Retrieve the location_idxs for these grid points
-    location_idxs = locidx[idx]
-
-    data = xr_data.sel({location_idx_name: location_idxs})
-    return data
diff --git a/ocf_datapipes/training/pvnet.py b/ocf_datapipes/training/pvnet.py
index 914ab2db4..6b64b9e0b 100644
--- a/ocf_datapipes/training/pvnet.py
+++ b/ocf_datapipes/training/pvnet.py
@@ -75,42 +75,12 @@ def slice_datapipes_by_space(
     return
 
 
-def construct_sliced_data_pipeline(
-    config_filename: str,
-    location_pipe: IterDataPipe,
-    t0_datapipe: IterDataPipe,
-    production: bool = False,
-    check_satellite_no_zeros: bool = False,
-) -> IterDataPipe:
-    """Constructs data pipeline for the input data config file.
-
-    This yields samples from the location and time datapipes.
-
-    Args:
-        config_filename: Path to config file.
-        location_pipe: Datapipe yielding locations.
-        t0_datapipe: Datapipe yielding times.
-        production: Whether constucting pipeline for production inference.
-        check_satellite_no_zeros: Whether to check that satellite data has no zeros.
-    """
-
-    datapipes_dict = _get_datapipes_dict(
-        config_filename,
-        production=production,
-    )
-
-    configuration = datapipes_dict.pop("config")
-
+def process_and_combine_datapipes(datapipes_dict, configuration):
+    """Normalize and convert data to numpy arrays"""
+    
     # Unpack for convenience
     conf_nwp = configuration.input_data.nwp
-
-    # Slice all of the datasets by spce - this is an in-place operation
-    slice_datapipes_by_space(datapipes_dict, location_pipe, configuration)
-
-    # Slice all of the datasets by time - this is an in-place operation
-    slice_datapipes_by_time(datapipes_dict, t0_datapipe, configuration, production)
-
-    # Spatially slice, normalize, and convert data to numpy arrays
+    
     numpy_modalities = []
 
     # Normalise the inputs and convert to numpy format
@@ -158,13 +128,50 @@ def construct_sliced_data_pipeline(
     logger.debug("Combine all the data sources")
     combined_datapipe = MergeNumpyModalities(numpy_modalities).add_sun_position(modality_name="gsp")
 
-    logger.info("Filtering out samples with no data")
+    combined_datapipe = combined_datapipe.map(fill_nans_in_arrays)
+    
+    return combined_datapipe
+
+
+def construct_sliced_data_pipeline(
+    config_filename: str,
+    location_pipe: IterDataPipe,
+    t0_datapipe: IterDataPipe,
+    production: bool = False,
+    check_satellite_no_zeros: bool = False,
+) -> IterDataPipe:
+    """Constructs data pipeline for the input data config file.
+
+    This yields samples from the location and time datapipes.
+
+    Args:
+        config_filename: Path to config file.
+        location_pipe: Datapipe yielding locations.
+        t0_datapipe: Datapipe yielding times.
+        production: Whether constucting pipeline for production inference.
+        check_satellite_no_zeros: Whether to check that satellite data has no zeros.
+    """
+
+    datapipes_dict = _get_datapipes_dict(
+        config_filename,
+        production=production,
+    )
+
+    configuration = datapipes_dict.pop("config")
+
+    # Slice all of the datasets by space - this is an in-place operation
+    slice_datapipes_by_space(datapipes_dict, location_pipe, configuration)
+
+    # Slice all of the datasets by time - this is an in-place operation
+    slice_datapipes_by_time(datapipes_dict, t0_datapipe, configuration, production)
+    
+    # Normalise, and combine the data sources into NumpyBatches
+    combined_datapipe = process_and_combine_datapipes(datapipes_dict, configuration)
+    
     if check_satellite_no_zeros:
         # in production we don't want any nans in the satellite data
         combined_datapipe = combined_datapipe.map(check_nans_in_satellite_data)
 
-    combined_datapipe = combined_datapipe.map(fill_nans_in_arrays)
-
     return combined_datapipe
 
 
diff --git a/ocf_datapipes/training/pvnet_all_gsp_v2.py b/ocf_datapipes/training/pvnet_all_gsp_v2.py
new file mode 100644
index 000000000..5198d1fca
--- /dev/null
+++ b/ocf_datapipes/training/pvnet_all_gsp_v2.py
@@ -0,0 +1,516 @@
+"""Create the training/validation datapipe for training the PVNet Model"""
+import logging
+from typing import Optional, Type, Tuple, List, Union
+from datetime import datetime
+
+import numpy as np
+import xarray as xr
+from torch.utils.data.datapipes.datapipe import IterDataPipe
+from torch.utils.data.datapipes._decorator import functional_datapipe
+from torch.utils.data.datapipes.iter import IterableWrapper
+from ocf_datapipes.convert import (
+    ConvertNWPToNumpyBatch,
+    ConvertPVToNumpyBatch,
+    ConvertSatelliteToNumpyBatch,
+    ConvertGSPToNumpyBatch,
+
+)
+
+from ocf_datapipes.batch import MergeNumpyModalities, MergeNWPNumpyModalities
+from ocf_datapipes.batch.merge_numpy_examples_to_batch import stack_np_examples_into_batch
+from ocf_datapipes.select.select_spatial_slice import (
+    select_spatial_slice_pixels, 
+    select_spatial_slice_meters,
+)
+from ocf_datapipes.training.common import (
+    _get_datapipes_dict,
+    check_nans_in_satellite_data,
+    concat_xr_time_utc,
+    fill_nans_in_arrays,
+    fill_nans_in_pv,
+    normalize_gsp,
+    normalize_pv,
+    slice_datapipes_by_time,
+    open_and_return_datapipes,
+    create_valid_t0_periods_datapipe,
+)
+from ocf_datapipes.utils.consts import (
+    NWP_MEANS,
+    NWP_STDS,
+    RSS_MEAN,
+    RSS_STD,
+)
+
+from ocf_datapipes.config.model import Configuration
+from ocf_datapipes.utils.location import Location
+from ocf_datapipes.load.gsp.utils import GSPLocationLookup
+
+xr.set_options(keep_attrs=True)
+logger = logging.getLogger("pvnet_all_gsp_datapipe")
+
+
+# ---------------------------------- Utility datapipes ---------------------------------
+
+
+def xr_compute(xr_data):
+    return xr_data.compute()
+
+
+class SampleRepeat:
+    """Use a single input element to create a list of identical values"""
+    
+    def __init__(self, num_repeats):
+        """Use a single input element to create a list of identical values
+        
+        Args:
+            num_repeats: Length of the returned list of duplicated values
+        """
+        self.num_repeats = num_repeats
+        
+    def __call__(self, x):
+        return [x for _ in range(self.num_repeats)]
+    
+
+class ConvertWrapper(IterDataPipe):
+    def __init__(
+        self,
+        source_datapipe: IterDataPipe,
+        convert_class: Type[IterDataPipe]
+    ):
+        self.source_datapipe = source_datapipe
+        self.convert_class = convert_class
+
+    def __iter__(self):
+        for concurrent_samples in self.source_datapipe:
+            dp = self.convert_class(IterableWrapper(concurrent_samples)) 
+            yield [x for x in dp]
+
+# ------------------------------ Multi-location datapipes ------------------------------
+# These are datapipes rewritten to run on all GSPs
+
+@functional_datapipe("select_all_gsp_spatial_slices_pixels")
+class SelectAllGSPSpatialSlicePixelsIterDataPipe(IterDataPipe):
+    """Select all the spatial slices"""
+
+    def __init__(
+        self,
+        source_datapipe: IterDataPipe,
+        locations: List[Location],
+        roi_height_pixels: int,
+        roi_width_pixels: int,
+        allow_partial_slice: bool = False,
+        location_idx_name: Optional[str] = None,
+    ):
+        """
+        Select spatial slices for all GSPs
+
+        If `allow_partial_slice` is set to True, then slices may be made which intersect the border
+        of the input data. The additional x and y cordinates that would be required for this slice
+        are extrapolated based on the average spacing of these coordinates in the input data.
+        However, currently slices cannot be made where the centre of the window is outside of the
+        input data.
+
+        Args:
+            source_datapipe: Datapipe of Xarray data
+            roi_height_pixels: ROI height in pixels
+            roi_width_pixels: ROI width in pixels
+            allow_partial_slice: Whether to allow a partial slice.
+            location_idx_name: Name for location index of unstructured grid data,
+                None if not relevant
+        """
+        self.source_datapipe = source_datapipe
+        self.locations = locations
+        self.roi_height_pixels = roi_height_pixels
+        self.roi_width_pixels = roi_width_pixels
+        self.allow_partial_slice = allow_partial_slice
+        self.location_idx_name = location_idx_name
+
+    def __iter__(self) -> Union[xr.DataArray, xr.Dataset]:
+        for xr_data in self.source_datapipe:
+            
+            loc_slices = []
+            
+            for location in self.locations:
+
+                selected = select_spatial_slice_pixels(
+                    xr_data,
+                    location,
+                    self.roi_width_pixels, 
+                    self.roi_height_pixels, 
+                    self.allow_partial_slice,
+                    self.location_idx_name,
+                )
+                
+                loc_slices.append(selected)
+
+            yield loc_slices
+
+
+@functional_datapipe("select_all_gsp_spatial_slice_meters")
+class SelectAllGSPSpatialSliceMetersIterDataPipe(IterDataPipe):
+    """Select spatial slice based off meters from point of interest"""
+
+    def __init__(
+        self,
+        source_datapipe: IterDataPipe,
+        locations: List[Location],
+        roi_height_meters: int,
+        roi_width_meters: int,
+        dim_name: Optional[str] = None,
+    ):
+        """
+        Select spatial slice based off pixels from point of interest
+
+        Args:
+            source_datapipe: Datapipe of Xarray data
+            location_datapipe: Location datapipe
+            roi_width_meters: ROI width in meters
+            roi_height_meters: ROI height in meters
+            dim_name: Dimension name to select for ID, None for coordinates
+
+        Notes:
+            Using spatial slicing based on distance rather than number of pixels will often yield
+            slices which can vary by 1 pixel in height and/or width.
+
+            E.g. Suppose the Xarray data has x-coords = [1,2,3,4,5]. We want to slice a spatial
+            window with a size which equates to 2.2 along the x-axis. If we choose to slice around
+            the point x=3 this will slice out the x-coords [2,3,4]. If we choose to slice around the
+            point x=2.5 this will slice out the x-coords [2,3]. Hence the returned slice can have
+            size either 2 or 3 in the x-axis depending on the spatial location selected.
+
+            Also, if selecting over a large span of latitudes, this may also causes pixel sizes of
+            the yielded outputs to change. For example, if the Xarray data is on a regularly spaced
+            longitude-latitude grid, then the structure of the grid means that the longitudes near
+            to the poles are spaced closer together (measured in meters) than at the equator. So
+            slices near the equator will have less pixels in the x-axis than slices taken near the
+            poles.
+        """
+        self.source_datapipe = source_datapipe
+        self.locations = locations
+        self.roi_width_meters = roi_width_meters
+        self.roi_height_meters = roi_height_meters
+        self.dim_name = dim_name
+
+    def __iter__(self) -> Union[xr.DataArray, xr.Dataset]:
+        for xr_data in self.source_datapipe:
+            loc_slices = []
+            
+            for location in self.locations:
+
+                selected = select_spatial_slice_meters(
+                    xr_data=xr_data, 
+                    location=location,
+                    roi_width_meters=self.roi_width_meters,
+                    roi_height_meters=self.roi_height_meters,
+                    dim_name=self.dim_name,  
+                )
+
+                loc_slices.append(selected)
+            
+            yield loc_slices
+
+
+# ------------------------------- Time pipeline functions ------------------------------
+
+
+def construct_time_pipeline(
+    config_filename: str,
+    start_time: Optional[datetime] = None,
+    end_time: Optional[datetime] = None,
+) -> Tuple[IterDataPipe, IterDataPipe]:
+    """Construct time pipeline for the input data config file.
+
+    Args:
+        config_filename: Path to config file.
+        start_time: Minimum time for time datapipe.
+        end_time: Maximum time for time datapipe.
+    """
+
+    datapipes_dict = _get_datapipes_dict(config_filename)
+
+    # Get config
+    config = datapipes_dict.pop("config")
+
+    if (start_time is not None) or (end_time is not None):
+        datapipes_dict["gsp"] = datapipes_dict["gsp"].filter_times(start_time, end_time)
+
+    # Get overlapping time periods
+    t0_datapipe = create_t0_datapipe(
+        datapipes_dict,
+        configuration=config,
+        shuffle=True,
+    )
+
+    return t0_datapipe
+
+
+def create_t0_datapipe(
+    datapipes_dict: dict,
+    configuration: Configuration,
+    shuffle: bool = True,
+):
+    """
+    Takes source datapipes and returns datapipes of appropriate t0 times.
+
+    The t0 times are sampled without replacement.
+
+    Args:
+        datapipes_dict: Dictionary of datapipes of input sources for which we want to select
+            appropriate t0 times.
+        configuration: Configuration object for inputs.
+        shuffle: Whether to use the internal shuffle function when yielding times. Else
+            location times will be heavily ordered.
+
+    Returns:
+        t0 datapipe
+
+    """
+    valid_t0_periods_datapipe = create_valid_t0_periods_datapipe(
+        datapipes_dict,
+        configuration,
+        key_for_t0="gsp",
+    )
+
+    t0_datapipe = valid_t0_periods_datapipe.pick_t0_times(return_all=True, shuffle=shuffle)
+
+    return t0_datapipe
+
+
+# ------------------------------- Space pipeline functions -----------------------------
+
+
+def slice_datapipes_by_space_all_gsps(
+    datapipes_dict: dict,
+    locations: list[Location],
+    configuration: Configuration,
+) -> None:
+
+    conf_nwp = configuration.input_data.nwp
+    conf_sat = configuration.input_data.satellite
+    
+    if "nwp" in datapipes_dict:
+        
+        for nwp_key, nwp_datapipe in datapipes_dict["nwp"].items():
+            
+            datapipes_dict["nwp"][nwp_key] = nwp_datapipe.select_all_gsp_spatial_slices_pixels(
+                locations,
+                roi_width_pixels=conf_nwp[nwp_key].nwp_image_size_pixels_width,
+                roi_height_pixels=conf_nwp[nwp_key].nwp_image_size_pixels_height,
+            )
+
+    if "sat" in datapipes_dict:
+        
+        datapipes_dict["sat"] = datapipes_dict["sat"].select_all_gsp_spatial_slices_pixels(
+            locations,
+            roi_width_pixels=conf_sat.satellite_image_size_pixels_width,
+            roi_height_pixels=conf_sat.satellite_image_size_pixels_height,
+        )
+
+    if "pv" in datapipes_dict:
+        # No spatial slice for PV since it is always the same, just repeat for GSPs
+        pv_datapipe = pv_datapipe.map(SampleRepeat(len(locations)))
+    
+
+    # GSP always assumed to be in data
+    datapipes_dict["gsp"] = datapipes_dict["gsp"].select_all_gsp_spatial_slice_meters(
+        locations,
+        roi_width_meters=1,
+        roi_height_meters=1,
+        dim_name="gsp_id",
+    )
+
+
+# -------------------------------- Processing functions --------------------------------
+
+    
+def pre_spatial_slice_process(datapipes_dict, configuration):
+    
+    conf_nwp = configuration.input_data.nwp
+    
+    if "nwp" in datapipes_dict:
+
+        for nwp_key, nwp_datapipe in datapipes_dict["nwp"].items():
+                
+            datapipes_dict["nwp"][nwp_key] =  (
+                nwp_datapipe
+                    .map(xr_compute)
+                    .normalize(
+                        mean=NWP_MEANS[conf_nwp[nwp_key].nwp_provider],
+                        std=NWP_STDS[conf_nwp[nwp_key].nwp_provider],
+                )
+            )
+            
+    if "sat" in datapipes_dict:
+
+        datapipes_dict["sat"] = (
+            datapipes_dict["sat"]
+                .map(xr_compute)
+                .normalize(mean=RSS_MEAN, std=RSS_STD)
+        )
+
+    if "pv" in datapipes_dict:
+        # Recombine PV arrays - see function doc for further explanation
+        datapipes_dict["pv"] = (
+            datapipes_dict["pv"]
+                .zip_ocf(datapipes_dict["pv_future"])
+                .map(concat_xr_time_utc)
+                .map(normalize_pv)
+                .map(fill_nans_in_pv)
+        )
+
+    # GSP always assumed to be in data
+    # Recombine GSP arrays - see function doc for further explanation
+    datapipes_dict["gsp"] = (
+        datapipes_dict["gsp"]
+        .zip_ocf(datapipes_dict["gsp_future"])
+        .map(concat_xr_time_utc)
+        .map(normalize_gsp)
+    )
+
+    
+def post_spatial_slice_process(datapipes_dict):
+    # Spatially slice, normalize, and convert data to numpy arrays
+    numpy_modalities = []
+
+    if "nwp" in datapipes_dict:
+        nwp_numpy_modalities = dict()
+
+        for nwp_key, nwp_datapipe in datapipes_dict["nwp"].items():
+
+            nwp_numpy_modalities[nwp_key] = (
+                ConvertWrapper(
+                    nwp_datapipe,
+                    ConvertNWPToNumpyBatch,
+                )
+                .map(stack_np_examples_into_batch)
+            )
+
+        # Combine the NWPs into NumpyBatch
+        nwp_numpy_modalities = MergeNWPNumpyModalities(nwp_numpy_modalities)
+        numpy_modalities.append(nwp_numpy_modalities)
+
+    if "sat" in datapipes_dict:
+
+        numpy_modalities.append(
+            ConvertWrapper(datapipes_dict["sat"], ConvertSatelliteToNumpyBatch)
+            .map(stack_np_examples_into_batch)
+        )
+
+    if "pv" in datapipes_dict:
+    
+        numpy_modalities.append(
+            ConvertWrapper(datapipes_dict["pv"], ConvertPVToNumpyBatch)
+            .map(stack_np_examples_into_batch)
+        )
+
+    # GSP always assumed to be in data    
+    numpy_modalities.append(
+        ConvertWrapper(datapipes_dict["gsp"], ConvertGSPToNumpyBatch)
+        .map(stack_np_examples_into_batch)
+    )
+
+    # Combine all the data sources
+    combined_datapipe = (
+        MergeNumpyModalities(numpy_modalities)
+        .add_sun_position(modality_name="gsp")
+        .map(fill_nans_in_arrays)
+    )
+    
+    return combined_datapipe
+
+
+# --------------------------- High level pipeline functions ----------------------------
+
+
+def construct_sliced_data_pipeline(
+    config_filename: str,
+    t0_datapipe: IterDataPipe,
+    production: bool = False,
+    check_satellite_no_zeros: bool = False,
+) -> IterDataPipe:
+    """Constructs data pipeline for the input data config file.
+
+    This yields samples from the location and time datapipes.
+
+    Args:
+        config_filename: Path to config file.
+        t0_datapipe: Datapipe yielding times.
+        production: Whether constucting pipeline for production inference.
+        check_satellite_no_zeros: Whether to check that satellite data has no zeros.
+    """
+
+    datapipes_dict = _get_datapipes_dict(
+        config_filename,
+        production=production,
+    )
+    
+    # Get the location objects for all 317 regional GSPs
+    gsp_id_to_loc = GSPLocationLookup()
+    locations = [gsp_id_to_loc(gsp_id) for gsp_id in range(1, 318)]
+
+    # Pop config
+    configuration = datapipes_dict.pop("config")
+
+    # Slice all of the datasets by time - this is an in-place operation
+    slice_datapipes_by_time(datapipes_dict, t0_datapipe, configuration, production)
+    
+    # Run compute and normalise all the data
+    pre_spatial_slice_process(datapipes_dict, configuration)
+    
+    # Slice all of the datasets by space - this is an in-place operation
+    slice_datapipes_by_space_all_gsps(datapipes_dict, locations, configuration)
+    
+    # Convert to NumpyBatch
+    combined_datapipe = post_spatial_slice_process(datapipes_dict)
+    
+    if check_satellite_no_zeros:
+        # in production we don't want any nans in the satellite data
+        combined_datapipe = combined_datapipe.map(check_nans_in_satellite_data)
+    
+    return combined_datapipe
+
+
+def pvnet_all_gsp_datapipe(
+    config_filename: str,
+    start_time: Optional[datetime] = None,
+    end_time: Optional[datetime] = None,
+) -> IterDataPipe:
+    """
+    Construct pvnet pipeline for the input data config file.
+
+    Args:
+        config_filename: Path to config file.
+        start_time: Minimum time at which a sample can be selected.
+        end_time: Maximum time at which a sample can be selected.
+    """
+
+    # Open datasets from the config and filter to useable times
+    t0_datapipe = construct_time_pipeline(
+        config_filename,
+        start_time,
+        end_time,
+    )
+    
+    # Shard after we have the times. These are already shuffled so no need to shuffle again
+    t0_datapipe = t0_datapipe.sharding_filter()
+
+    # In this function we re-open the datasets to make a clean separation before/after sharding
+    # This function
+    datapipe = construct_sliced_data_pipeline(
+        config_filename,
+        t0_datapipe,
+    )
+
+    return datapipe
+
+
+if __name__=="__main__":
+    import time
+    
+    t0 = time.time()
+    dp = pvnet_all_gsp_datapipe(
+        config_filename="/home/jamesfulton/repos/PVNet/configs/datamodule/configuration/gcp_configuration.yaml"
+    )
+    
+    b  = next(iter(dp))
+    print(time.time() - t0)
diff --git a/ocf_datapipes/utils/location.py b/ocf_datapipes/utils/location.py
index 653501e5f..440bfdfc5 100644
--- a/ocf_datapipes/utils/location.py
+++ b/ocf_datapipes/utils/location.py
@@ -2,6 +2,7 @@
 from typing import Optional
 
 import numpy as np
+import xarray as xr
 from pydantic import BaseModel, Field, validator
 
 
@@ -59,4 +60,4 @@ def validate_y(cls, v, values):
             min_y, max_y = 0, np.inf
         if v < min_y or v > max_y:
             raise ValueError(f"y = {v} must be within {[min_y, max_y]} for {co} coordinate system")
-        return v
+        return v
\ No newline at end of file

From 3fa9e401c5863cba9a6a31343ad9477bb5870acb Mon Sep 17 00:00:00 2001
From: James Fulton <djamesfulton@yahoo.co.uk>
Date: Fri, 31 May 2024 12:06:50 +0000
Subject: [PATCH 08/27] sub in new pipeline

---
 ocf_datapipes/training/pvnet_all_gsp.py    | 602 +++++++++------------
 ocf_datapipes/training/pvnet_all_gsp_v2.py | 516 ------------------
 2 files changed, 254 insertions(+), 864 deletions(-)
 delete mode 100644 ocf_datapipes/training/pvnet_all_gsp_v2.py

diff --git a/ocf_datapipes/training/pvnet_all_gsp.py b/ocf_datapipes/training/pvnet_all_gsp.py
index f27cbd892..5198d1fca 100644
--- a/ocf_datapipes/training/pvnet_all_gsp.py
+++ b/ocf_datapipes/training/pvnet_all_gsp.py
@@ -3,6 +3,7 @@
 from typing import Optional, Type, Tuple, List, Union
 from datetime import datetime
 
+import numpy as np
 import xarray as xr
 from torch.utils.data.datapipes.datapipe import IterDataPipe
 from torch.utils.data.datapipes._decorator import functional_datapipe
@@ -17,6 +18,10 @@
 
 from ocf_datapipes.batch import MergeNumpyModalities, MergeNWPNumpyModalities
 from ocf_datapipes.batch.merge_numpy_examples_to_batch import stack_np_examples_into_batch
+from ocf_datapipes.select.select_spatial_slice import (
+    select_spatial_slice_pixels, 
+    select_spatial_slice_meters,
+)
 from ocf_datapipes.training.common import (
     _get_datapipes_dict,
     check_nans_in_satellite_data,
@@ -26,7 +31,6 @@
     normalize_gsp,
     normalize_pv,
     slice_datapipes_by_time,
-    minutes,
     open_and_return_datapipes,
     create_valid_t0_periods_datapipe,
 )
@@ -36,51 +40,21 @@
     RSS_MEAN,
     RSS_STD,
 )
-import numpy as np
 
 from ocf_datapipes.config.model import Configuration
-from ocf_datapipes.utils import Location
-
-from ocf_datapipes.utils.geospatial import (
-    move_lon_lat_by_meters,
-    spatial_coord_type,
-)
-from ocf_datapipes.select.select_spatial_slice import (
-    _get_idx_of_pixel_closest_to_poi, 
-    _get_idx_of_pixel_closest_to_poi_geostationary,
-    _get_points_from_unstructured_grids,
-    convert_coords_to_match_xarray,
-    select_spatial_slice_pixels,
-)
-
+from ocf_datapipes.utils.location import Location
+from ocf_datapipes.load.gsp.utils import GSPLocationLookup
 
 xr.set_options(keep_attrs=True)
 logger = logging.getLogger("pvnet_all_gsp_datapipe")
 
 
-class SampleFunction:
-    """Apply a function to each sample in the concurrent batch list"""
-    
-    def __init__(self, function):
-        """Apply a function to each sample in the concurrent batch list
-        
-        Args:
-            function: Function to apply to each sample
-        """
-        self.function = function
+# ---------------------------------- Utility datapipes ---------------------------------
 
-    def __call__(self, sample_list):
-        return [self.function(sample) for sample in sample_list]
 
+def xr_compute(xr_data):
+    return xr_data.compute()
 
-class ZipFunction:
-    def __init__(self, function):
-        self.function = function
-        
-    def __call__(self, zipped_sample_list):
-        sample_lists = [sample_list for sample_list in zipped_sample_list]
-        return [self.function(sample) for sample in zip(*sample_lists)]
-            
 
 class SampleRepeat:
     """Use a single input element to create a list of identical values"""
@@ -95,103 +69,24 @@ def __init__(self, num_repeats):
         
     def __call__(self, x):
         return [x for _ in range(self.num_repeats)]
+    
 
+class ConvertWrapper(IterDataPipe):
+    def __init__(
+        self,
+        source_datapipe: IterDataPipe,
+        convert_class: Type[IterDataPipe]
+    ):
+        self.source_datapipe = source_datapipe
+        self.convert_class = convert_class
 
-class GSPLocationLookup:
-    """Query object for GSP location from GSP ID"""
-
-    def __init__(self, x_osgb: xr.DataArray, y_osgb: xr.DataArray):
-        """Query object for GSP location from GSP ID
-
-        Args:
-            x_osgb: DataArray of the OSGB x-coordinate for any given GSP ID
-            y_osgb: DataArray of the OSGB y-coordinate for any given GSP ID
-
-        """
-        self.x_osgb = x_osgb
-        self.y_osgb = y_osgb
-
-    def __call__(self, gsp_id: int) -> Location:
-        """Returns the locations for the input GSP IDs.
-
-        Args:
-            gsp_id: Integer ID of the GSP
-        """
-        return Location(
-            x=self.x_osgb.sel(gsp_id=gsp_id).item(),
-            y=self.y_osgb.sel(gsp_id=gsp_id).item(),
-            id=gsp_id,
-        )
-
-
-def create_t0_datapipe(
-    datapipes_dict: dict,
-    configuration: Configuration,
-    shuffle: bool = True,
-):
-    """
-    Takes source datapipes and returns datapipes of appropriate t0 times.
-
-    The t0 times are sampled without replacement.
-
-    Args:
-        datapipes_dict: Dictionary of datapipes of input sources for which we want to select
-            appropriate t0 times.
-        configuration: Configuration object for inputs.
-        shuffle: Whether to use the internal shuffle function when yielding times. Else
-            location times will be heavily ordered.
-
-    Returns:
-        t0 datapipe
-
-    """
-    valid_t0_periods_datapipe = create_valid_t0_periods_datapipe(
-        datapipes_dict,
-        configuration,
-        key_for_t0="gsp",
-    )
-
-    t0_datapipe = valid_t0_periods_datapipe.pick_t0_times(return_all=True, shuffle=shuffle)
-
-    return t0_datapipe
-
-
-def construct_time_pipeline(
-    config_filename: str,
-    start_time: Optional[datetime] = None,
-    end_time: Optional[datetime] = None,
-) -> Tuple[IterDataPipe, IterDataPipe]:
-    """Construct time pipeline for the input data config file.
-
-    Args:
-        config_filename: Path to config file.
-        start_time: Minimum time for time datapipe.
-        end_time: Maximum time for time datapipe.
-    """
-
-    datapipes_dict = _get_datapipes_dict(
-        config_filename,
-    )
-
-    # Pull out config file
-    config = datapipes_dict.pop("config")
-
-    if (start_time is not None) or (end_time is not None):
-        datapipes_dict["gsp"] = datapipes_dict["gsp"].filter_times(start_time, end_time)
-
-    # Get overlapping time periods
-    t0_datapipe = create_t0_datapipe(
-        datapipes_dict,
-        configuration=config,
-        shuffle=True,
-    )
-
-    return t0_datapipe
-
-
-def xr_compute(xr_data):
-    return xr_data.compute()
+    def __iter__(self):
+        for concurrent_samples in self.source_datapipe:
+            dp = self.convert_class(IterableWrapper(concurrent_samples)) 
+            yield [x for x in dp]
 
+# ------------------------------ Multi-location datapipes ------------------------------
+# These are datapipes rewritten to run on all GSPs
 
 @functional_datapipe("select_all_gsp_spatial_slices_pixels")
 class SelectAllGSPSpatialSlicePixelsIterDataPipe(IterDataPipe):
@@ -207,7 +102,7 @@ def __init__(
         location_idx_name: Optional[str] = None,
     ):
         """
-        Select spatial slice based off pixels from point of interest
+        Select spatial slices for all GSPs
 
         If `allow_partial_slice` is set to True, then slices may be made which intersect the border
         of the input data. The additional x and y cordinates that would be required for this slice
@@ -232,48 +127,25 @@ def __init__(
 
     def __iter__(self) -> Union[xr.DataArray, xr.Dataset]:
         for xr_data in self.source_datapipe:
-            xr_coords, xr_x_dim, xr_y_dim = spatial_coord_type(xr_data)
             
             loc_slices = []
             
             for location in self.locations:
-                
-                if self.location_idx_name is not None:
-                    selected = _get_points_from_unstructured_grids(
-                        xr_data=xr_data,
-                        location=location,
-                        location_idx_name=self.location_idx_name,
-                        num_points=self.roi_width_pixels * self.roi_height_pixels,
-                    )
-                    yield selected
-
-                if xr_coords == "geostationary":
-                    center_idx: Location = _get_idx_of_pixel_closest_to_poi_geostationary(
-                        xr_data=xr_data,
-                        center_osgb=location,
-                    )
-                else:
-                    center_idx: Location = _get_idx_of_pixel_closest_to_poi(
-                        xr_data=xr_data,
-                        location=location,
-                    )
 
                 selected = select_spatial_slice_pixels(
                     xr_data,
-                    center_idx,
-                    self.roi_width_pixels,
-                    self.roi_height_pixels,
-                    xr_x_dim,
-                    xr_y_dim,
-                    allow_partial_slice=self.allow_partial_slice,
+                    location,
+                    self.roi_width_pixels, 
+                    self.roi_height_pixels, 
+                    self.allow_partial_slice,
+                    self.location_idx_name,
                 )
                 
                 loc_slices.append(selected)
 
             yield loc_slices
-            
-            
-            
+
+
 @functional_datapipe("select_all_gsp_spatial_slice_meters")
 class SelectAllGSPSpatialSliceMetersIterDataPipe(IterDataPipe):
     """Select spatial slice based off meters from point of interest"""
@@ -284,7 +156,7 @@ def __init__(
         locations: List[Location],
         roi_height_meters: int,
         roi_width_meters: int,
-        dim_name: Optional[str] = None,  # "pv_system_id",
+        dim_name: Optional[str] = None,
     ):
         """
         Select spatial slice based off pixels from point of interest
@@ -292,8 +164,8 @@ def __init__(
         Args:
             source_datapipe: Datapipe of Xarray data
             location_datapipe: Location datapipe
-            roi_height_meters: ROI height in meters
             roi_width_meters: ROI width in meters
+            roi_height_meters: ROI height in meters
             dim_name: Dimension name to select for ID, None for coordinates
 
         Notes:
@@ -315,8 +187,8 @@ def __init__(
         """
         self.source_datapipe = source_datapipe
         self.locations = locations
-        self.roi_height_meters = roi_height_meters
         self.roi_width_meters = roi_width_meters
+        self.roi_height_meters = roi_height_meters
         self.dim_name = dim_name
 
     def __iter__(self) -> Union[xr.DataArray, xr.Dataset]:
@@ -325,244 +197,276 @@ def __iter__(self) -> Union[xr.DataArray, xr.Dataset]:
             
             for location in self.locations:
 
-                # Get the spatial coords of the xarray data
-                xr_coords, xr_x_dim, xr_y_dim = spatial_coord_type(xr_data)
-
-                half_height = self.roi_height_meters // 2
-                half_width = self.roi_width_meters // 2
-
-                # Find the bounding box values for the location in either lat-lon or OSGB coord systems
-                if location.coordinate_system == "lon_lat":
-                    right, top = move_lon_lat_by_meters(
-                        location.x,
-                        location.y,
-                        half_width,
-                        half_height,
-                    )
-                    left, bottom = move_lon_lat_by_meters(
-                        location.x,
-                        location.y,
-                        -half_width,
-                        -half_height,
-                    )
-
-                elif location.coordinate_system == "osgb":
-                    left = location.x - half_width
-                    right = location.x + half_width
-                    bottom = location.y - half_height
-                    top = location.y + half_height
-
-                else:
-                    raise ValueError(
-                        f"Location coord system not recognized: {location.coordinate_system}"
-                    )
-
-                # Change the bounding coordinates [left, right, bottom, top] to the same
-                # coordinate system as the xarray data
-                (left, right), (bottom, top) = convert_coords_to_match_xarray(
-                    x=np.array([left, right], dtype=np.float32),
-                    y=np.array([bottom, top], dtype=np.float32),
-                    from_coords=location.coordinate_system,
-                    xr_data=xr_data,
+                selected = select_spatial_slice_meters(
+                    xr_data=xr_data, 
+                    location=location,
+                    roi_width_meters=self.roi_width_meters,
+                    roi_height_meters=self.roi_height_meters,
+                    dim_name=self.dim_name,  
                 )
 
-                # Do it off coordinates, not ID
-                if self.dim_name is None:
-                    # Select a patch from the xarray data
-                    x_mask = (left <= xr_data[xr_x_dim]) & (xr_data[xr_x_dim] <= right)
-                    y_mask = (bottom <= xr_data[xr_y_dim]) & (xr_data[xr_y_dim] <= top)
-                    selected = xr_data.isel({xr_x_dim: x_mask, xr_y_dim: y_mask})
-
-                else:
-                    # Select data in the region of interest and ID:
-                    # This also works for unstructured grids
-
-                    id_mask = (
-                        (left <= xr_data[xr_x_dim])
-                        & (xr_data[xr_x_dim] <= right)
-                        & (bottom <= xr_data[xr_y_dim])
-                        & (xr_data[xr_y_dim] <= top)
-                    )
-                    selected = xr_data.isel({self.dim_name: id_mask})
-
                 loc_slices.append(selected)
+            
             yield loc_slices
 
 
-class ConvertWrapper(IterDataPipe):
-    def __init__(
-        self,
-        source_datapipe: IterDataPipe,
-        convert_class: Type[IterDataPipe]
-    ):
-        self.source_datapipe = source_datapipe
-        self.convert_class = convert_class
-
-    def __iter__(self):
-        for concurrent_samples in self.source_datapipe:
-            dp = self.convert_class(IterableWrapper(concurrent_samples))            
-            stacked_converted_values = stack_np_examples_into_batch([x for x in iter(dp)])
-            yield stacked_converted_values
+# ------------------------------- Time pipeline functions ------------------------------
 
 
-def construct_sliced_data_pipeline(
+def construct_time_pipeline(
     config_filename: str,
-    t0_datapipe: IterDataPipe,
-    production: bool = False,
-    check_satellite_no_zeros: bool = False,
-) -> IterDataPipe:
-    """Constructs data pipeline for the input data config file.
-
-    This yields samples from the location and time datapipes.
+    start_time: Optional[datetime] = None,
+    end_time: Optional[datetime] = None,
+) -> Tuple[IterDataPipe, IterDataPipe]:
+    """Construct time pipeline for the input data config file.
 
     Args:
         config_filename: Path to config file.
-        t0_datapipe: Datapipe yielding times.
-        production: Whether constucting pipeline for production inference.
-        check_satellite_no_zeros: Whether to check that satellite data has no zeros.
+        start_time: Minimum time for time datapipe.
+        end_time: Maximum time for time datapipe.
     """
 
-    datapipes_dict = _get_datapipes_dict(
-        config_filename,
-        production=production,
-    )
-    
-    ds_gsp = next(
-        iter(
-            open_and_return_datapipes(
-                config_filename,
-                use_gsp=True,
-                use_nwp=False,
-                use_pv=False,
-                use_sat=False,
-                use_hrv=False,
-                use_topo=False,
-            )["gsp"]
-        )
+    datapipes_dict = _get_datapipes_dict(config_filename)
+
+    # Get config
+    config = datapipes_dict.pop("config")
+
+    if (start_time is not None) or (end_time is not None):
+        datapipes_dict["gsp"] = datapipes_dict["gsp"].filter_times(start_time, end_time)
+
+    # Get overlapping time periods
+    t0_datapipe = create_t0_datapipe(
+        datapipes_dict,
+        configuration=config,
+        shuffle=True,
     )
-    
-    gsp_id_to_loc = GSPLocationLookup(ds_gsp.x_osgb, ds_gsp.y_osgb)
-    
-    locations = [gsp_id_to_loc(gsp_id) for gsp_id in range(1, 318)]
 
+    return t0_datapipe
 
-    configuration = datapipes_dict.pop("config")
 
-    # Unpack for convenience
-    conf_sat = configuration.input_data.satellite
-    conf_nwp = configuration.input_data.nwp
+def create_t0_datapipe(
+    datapipes_dict: dict,
+    configuration: Configuration,
+    shuffle: bool = True,
+):
+    """
+    Takes source datapipes and returns datapipes of appropriate t0 times.
 
-    # Slice all of the datasets by time - this is an in-place operation
-    slice_datapipes_by_time(datapipes_dict, t0_datapipe, configuration, production)
+    The t0 times are sampled without replacement.
 
-    # Spatially slice, normalize, and convert data to numpy arrays
-    numpy_modalities = []
+    Args:
+        datapipes_dict: Dictionary of datapipes of input sources for which we want to select
+            appropriate t0 times.
+        configuration: Configuration object for inputs.
+        shuffle: Whether to use the internal shuffle function when yielding times. Else
+            location times will be heavily ordered.
 
-    if "nwp" in datapipes_dict:
-        nwp_numpy_modalities = dict()
+    Returns:
+        t0 datapipe
 
-        for nwp_key, nwp_datapipe in datapipes_dict["nwp"].items():
+    """
+    valid_t0_periods_datapipe = create_valid_t0_periods_datapipe(
+        datapipes_dict,
+        configuration,
+        key_for_t0="gsp",
+    )
 
-            nwp_datapipe = nwp_datapipe.map(xr_compute)
+    t0_datapipe = valid_t0_periods_datapipe.pick_t0_times(return_all=True, shuffle=shuffle)
 
-            nwp_datapipe = nwp_datapipe.normalize(
-                mean=NWP_MEANS[conf_nwp[nwp_key].nwp_provider],
-                std=NWP_STDS[conf_nwp[nwp_key].nwp_provider],
-            )
+    return t0_datapipe
+
+
+# ------------------------------- Space pipeline functions -----------------------------
+
+
+def slice_datapipes_by_space_all_gsps(
+    datapipes_dict: dict,
+    locations: list[Location],
+    configuration: Configuration,
+) -> None:
+
+    conf_nwp = configuration.input_data.nwp
+    conf_sat = configuration.input_data.satellite
+    
+    if "nwp" in datapipes_dict:
+        
+        for nwp_key, nwp_datapipe in datapipes_dict["nwp"].items():
             
-            nwp_datapipe = nwp_datapipe.select_all_gsp_spatial_slices_pixels(
+            datapipes_dict["nwp"][nwp_key] = nwp_datapipe.select_all_gsp_spatial_slices_pixels(
                 locations,
-                roi_height_pixels=conf_nwp[nwp_key].nwp_image_size_pixels_height,
                 roi_width_pixels=conf_nwp[nwp_key].nwp_image_size_pixels_width,
+                roi_height_pixels=conf_nwp[nwp_key].nwp_image_size_pixels_height,
             )
-            
-
-            nwp_numpy_modalities[nwp_key] = ConvertWrapper(
-                nwp_datapipe,
-                ConvertNWPToNumpyBatch,
-            )
-
-        # Combine the NWPs into NumpyBatch
-        nwp_numpy_modalities = MergeNWPNumpyModalities(nwp_numpy_modalities)
-        numpy_modalities.append(nwp_numpy_modalities)
 
     if "sat" in datapipes_dict:
-        sat_datapipe = datapipes_dict["sat"]
-
-        sat_datapipe = sat_datapipe.map(xr_compute)
         
-        sat_datapipe = sat_datapipe.normalize(mean=RSS_MEAN, std=RSS_STD)
-        
-        sat_datapipe = sat_datapipe.select_all_gsp_spatial_slices_pixels(
+        datapipes_dict["sat"] = datapipes_dict["sat"].select_all_gsp_spatial_slices_pixels(
             locations,
-            roi_height_pixels=conf_sat.satellite_image_size_pixels_height,
             roi_width_pixels=conf_sat.satellite_image_size_pixels_width,
+            roi_height_pixels=conf_sat.satellite_image_size_pixels_height,
         )
-        
 
-        numpy_modalities.append(
-            ConvertWrapper(
-                sat_datapipe,
-                ConvertSatelliteToNumpyBatch,
+    if "pv" in datapipes_dict:
+        # No spatial slice for PV since it is always the same, just repeat for GSPs
+        pv_datapipe = pv_datapipe.map(SampleRepeat(len(locations)))
+    
+
+    # GSP always assumed to be in data
+    datapipes_dict["gsp"] = datapipes_dict["gsp"].select_all_gsp_spatial_slice_meters(
+        locations,
+        roi_width_meters=1,
+        roi_height_meters=1,
+        dim_name="gsp_id",
+    )
+
+
+# -------------------------------- Processing functions --------------------------------
+
+    
+def pre_spatial_slice_process(datapipes_dict, configuration):
+    
+    conf_nwp = configuration.input_data.nwp
+    
+    if "nwp" in datapipes_dict:
+
+        for nwp_key, nwp_datapipe in datapipes_dict["nwp"].items():
+                
+            datapipes_dict["nwp"][nwp_key] =  (
+                nwp_datapipe
+                    .map(xr_compute)
+                    .normalize(
+                        mean=NWP_MEANS[conf_nwp[nwp_key].nwp_provider],
+                        std=NWP_STDS[conf_nwp[nwp_key].nwp_provider],
+                )
             )
+            
+    if "sat" in datapipes_dict:
+
+        datapipes_dict["sat"] = (
+            datapipes_dict["sat"]
+                .map(xr_compute)
+                .normalize(mean=RSS_MEAN, std=RSS_STD)
         )
 
     if "pv" in datapipes_dict:
         # Recombine PV arrays - see function doc for further explanation
-        # No spatial slice for PV since it is always the same
-        pv_datapipe = (
+        datapipes_dict["pv"] = (
             datapipes_dict["pv"]
                 .zip_ocf(datapipes_dict["pv_future"])
                 .map(concat_xr_time_utc)
+                .map(normalize_pv)
+                .map(fill_nans_in_pv)
         )
 
-        pv_datapipe = pv_datapipe.map(normalize_pv)
-        pv_datapipe = pv_datapipe.map(fill_nans_in_pv)
-        pv_datapipe = pv_datapipe.map(SampleRepeat(317))
+    # GSP always assumed to be in data
+    # Recombine GSP arrays - see function doc for further explanation
+    datapipes_dict["gsp"] = (
+        datapipes_dict["gsp"]
+        .zip_ocf(datapipes_dict["gsp_future"])
+        .map(concat_xr_time_utc)
+        .map(normalize_gsp)
+    )
+
     
-        numpy_modalities.append(
-            ConvertWrapper(
-                pv_datapipe,
-                ConvertPVToNumpyBatch,
+def post_spatial_slice_process(datapipes_dict):
+    # Spatially slice, normalize, and convert data to numpy arrays
+    numpy_modalities = []
+
+    if "nwp" in datapipes_dict:
+        nwp_numpy_modalities = dict()
+
+        for nwp_key, nwp_datapipe in datapipes_dict["nwp"].items():
+
+            nwp_numpy_modalities[nwp_key] = (
+                ConvertWrapper(
+                    nwp_datapipe,
+                    ConvertNWPToNumpyBatch,
+                )
+                .map(stack_np_examples_into_batch)
             )
+
+        # Combine the NWPs into NumpyBatch
+        nwp_numpy_modalities = MergeNWPNumpyModalities(nwp_numpy_modalities)
+        numpy_modalities.append(nwp_numpy_modalities)
+
+    if "sat" in datapipes_dict:
+
+        numpy_modalities.append(
+            ConvertWrapper(datapipes_dict["sat"], ConvertSatelliteToNumpyBatch)
+            .map(stack_np_examples_into_batch)
         )
 
-    # GSP always assumed to be in data
-    #location_pipe, location_pipe_copy = location_pipe.fork(2, buffer_size=5)
-    gsp_future_datapipe = datapipes_dict["gsp_future"]
-    gsp_future_datapipe = gsp_future_datapipe.select_all_gsp_spatial_slice_meters(
-        locations,
-        roi_height_meters=1,
-        roi_width_meters=1,
-        dim_name="gsp_id",
-    )
+    if "pv" in datapipes_dict:
+    
+        numpy_modalities.append(
+            ConvertWrapper(datapipes_dict["pv"], ConvertPVToNumpyBatch)
+            .map(stack_np_examples_into_batch)
+        )
 
-    gsp_datapipe = datapipes_dict["gsp"]
-    gsp_datapipe = gsp_datapipe.select_all_gsp_spatial_slice_meters(
-        locations,
-        roi_height_meters=1,
-        roi_width_meters=1,
-        dim_name="gsp_id",
+    # GSP always assumed to be in data    
+    numpy_modalities.append(
+        ConvertWrapper(datapipes_dict["gsp"], ConvertGSPToNumpyBatch)
+        .map(stack_np_examples_into_batch)
     )
 
-    # Recombine GSP arrays - see function doc for further explanation
-    gsp_datapipe = (
-        gsp_datapipe
-        .zip_ocf(gsp_future_datapipe)
-        .map(ZipFunction(concat_xr_time_utc))
-        .map(SampleFunction(normalize_gsp))
+    # Combine all the data sources
+    combined_datapipe = (
+        MergeNumpyModalities(numpy_modalities)
+        .add_sun_position(modality_name="gsp")
+        .map(fill_nans_in_arrays)
     )
     
-    numpy_modalities.append(
-        ConvertWrapper(
-            gsp_datapipe,
-            ConvertGSPToNumpyBatch,
-        )
+    return combined_datapipe
+
+
+# --------------------------- High level pipeline functions ----------------------------
+
+
+def construct_sliced_data_pipeline(
+    config_filename: str,
+    t0_datapipe: IterDataPipe,
+    production: bool = False,
+    check_satellite_no_zeros: bool = False,
+) -> IterDataPipe:
+    """Constructs data pipeline for the input data config file.
+
+    This yields samples from the location and time datapipes.
+
+    Args:
+        config_filename: Path to config file.
+        t0_datapipe: Datapipe yielding times.
+        production: Whether constucting pipeline for production inference.
+        check_satellite_no_zeros: Whether to check that satellite data has no zeros.
+    """
+
+    datapipes_dict = _get_datapipes_dict(
+        config_filename,
+        production=production,
     )
+    
+    # Get the location objects for all 317 regional GSPs
+    gsp_id_to_loc = GSPLocationLookup()
+    locations = [gsp_id_to_loc(gsp_id) for gsp_id in range(1, 318)]
 
-    logger.debug("Combine all the data sources")
-    combined_datapipe = MergeNumpyModalities(numpy_modalities).add_sun_position(modality_name="gsp")
+    # Pop config
+    configuration = datapipes_dict.pop("config")
 
+    # Slice all of the datasets by time - this is an in-place operation
+    slice_datapipes_by_time(datapipes_dict, t0_datapipe, configuration, production)
+    
+    # Run compute and normalise all the data
+    pre_spatial_slice_process(datapipes_dict, configuration)
+    
+    # Slice all of the datasets by space - this is an in-place operation
+    slice_datapipes_by_space_all_gsps(datapipes_dict, locations, configuration)
+    
+    # Convert to NumpyBatch
+    combined_datapipe = post_spatial_slice_process(datapipes_dict)
+    
+    if check_satellite_no_zeros:
+        # in production we don't want any nans in the satellite data
+        combined_datapipe = combined_datapipe.map(check_nans_in_satellite_data)
+    
     return combined_datapipe
 
 
@@ -579,14 +483,16 @@ def pvnet_all_gsp_datapipe(
         start_time: Minimum time at which a sample can be selected.
         end_time: Maximum time at which a sample can be selected.
     """
-    logger.info("Constructing pvnet pipeline")
 
-    # Open datasets from the config and filter to useable location-time pairs
+    # Open datasets from the config and filter to useable times
     t0_datapipe = construct_time_pipeline(
         config_filename,
         start_time,
         end_time,
     )
+    
+    # Shard after we have the times. These are already shuffled so no need to shuffle again
+    t0_datapipe = t0_datapipe.sharding_filter()
 
     # In this function we re-open the datasets to make a clean separation before/after sharding
     # This function
@@ -607,4 +513,4 @@ def pvnet_all_gsp_datapipe(
     )
     
     b  = next(iter(dp))
-    print(time.time() - t0)
\ No newline at end of file
+    print(time.time() - t0)
diff --git a/ocf_datapipes/training/pvnet_all_gsp_v2.py b/ocf_datapipes/training/pvnet_all_gsp_v2.py
deleted file mode 100644
index 5198d1fca..000000000
--- a/ocf_datapipes/training/pvnet_all_gsp_v2.py
+++ /dev/null
@@ -1,516 +0,0 @@
-"""Create the training/validation datapipe for training the PVNet Model"""
-import logging
-from typing import Optional, Type, Tuple, List, Union
-from datetime import datetime
-
-import numpy as np
-import xarray as xr
-from torch.utils.data.datapipes.datapipe import IterDataPipe
-from torch.utils.data.datapipes._decorator import functional_datapipe
-from torch.utils.data.datapipes.iter import IterableWrapper
-from ocf_datapipes.convert import (
-    ConvertNWPToNumpyBatch,
-    ConvertPVToNumpyBatch,
-    ConvertSatelliteToNumpyBatch,
-    ConvertGSPToNumpyBatch,
-
-)
-
-from ocf_datapipes.batch import MergeNumpyModalities, MergeNWPNumpyModalities
-from ocf_datapipes.batch.merge_numpy_examples_to_batch import stack_np_examples_into_batch
-from ocf_datapipes.select.select_spatial_slice import (
-    select_spatial_slice_pixels, 
-    select_spatial_slice_meters,
-)
-from ocf_datapipes.training.common import (
-    _get_datapipes_dict,
-    check_nans_in_satellite_data,
-    concat_xr_time_utc,
-    fill_nans_in_arrays,
-    fill_nans_in_pv,
-    normalize_gsp,
-    normalize_pv,
-    slice_datapipes_by_time,
-    open_and_return_datapipes,
-    create_valid_t0_periods_datapipe,
-)
-from ocf_datapipes.utils.consts import (
-    NWP_MEANS,
-    NWP_STDS,
-    RSS_MEAN,
-    RSS_STD,
-)
-
-from ocf_datapipes.config.model import Configuration
-from ocf_datapipes.utils.location import Location
-from ocf_datapipes.load.gsp.utils import GSPLocationLookup
-
-xr.set_options(keep_attrs=True)
-logger = logging.getLogger("pvnet_all_gsp_datapipe")
-
-
-# ---------------------------------- Utility datapipes ---------------------------------
-
-
-def xr_compute(xr_data):
-    return xr_data.compute()
-
-
-class SampleRepeat:
-    """Use a single input element to create a list of identical values"""
-    
-    def __init__(self, num_repeats):
-        """Use a single input element to create a list of identical values
-        
-        Args:
-            num_repeats: Length of the returned list of duplicated values
-        """
-        self.num_repeats = num_repeats
-        
-    def __call__(self, x):
-        return [x for _ in range(self.num_repeats)]
-    
-
-class ConvertWrapper(IterDataPipe):
-    def __init__(
-        self,
-        source_datapipe: IterDataPipe,
-        convert_class: Type[IterDataPipe]
-    ):
-        self.source_datapipe = source_datapipe
-        self.convert_class = convert_class
-
-    def __iter__(self):
-        for concurrent_samples in self.source_datapipe:
-            dp = self.convert_class(IterableWrapper(concurrent_samples)) 
-            yield [x for x in dp]
-
-# ------------------------------ Multi-location datapipes ------------------------------
-# These are datapipes rewritten to run on all GSPs
-
-@functional_datapipe("select_all_gsp_spatial_slices_pixels")
-class SelectAllGSPSpatialSlicePixelsIterDataPipe(IterDataPipe):
-    """Select all the spatial slices"""
-
-    def __init__(
-        self,
-        source_datapipe: IterDataPipe,
-        locations: List[Location],
-        roi_height_pixels: int,
-        roi_width_pixels: int,
-        allow_partial_slice: bool = False,
-        location_idx_name: Optional[str] = None,
-    ):
-        """
-        Select spatial slices for all GSPs
-
-        If `allow_partial_slice` is set to True, then slices may be made which intersect the border
-        of the input data. The additional x and y cordinates that would be required for this slice
-        are extrapolated based on the average spacing of these coordinates in the input data.
-        However, currently slices cannot be made where the centre of the window is outside of the
-        input data.
-
-        Args:
-            source_datapipe: Datapipe of Xarray data
-            roi_height_pixels: ROI height in pixels
-            roi_width_pixels: ROI width in pixels
-            allow_partial_slice: Whether to allow a partial slice.
-            location_idx_name: Name for location index of unstructured grid data,
-                None if not relevant
-        """
-        self.source_datapipe = source_datapipe
-        self.locations = locations
-        self.roi_height_pixels = roi_height_pixels
-        self.roi_width_pixels = roi_width_pixels
-        self.allow_partial_slice = allow_partial_slice
-        self.location_idx_name = location_idx_name
-
-    def __iter__(self) -> Union[xr.DataArray, xr.Dataset]:
-        for xr_data in self.source_datapipe:
-            
-            loc_slices = []
-            
-            for location in self.locations:
-
-                selected = select_spatial_slice_pixels(
-                    xr_data,
-                    location,
-                    self.roi_width_pixels, 
-                    self.roi_height_pixels, 
-                    self.allow_partial_slice,
-                    self.location_idx_name,
-                )
-                
-                loc_slices.append(selected)
-
-            yield loc_slices
-
-
-@functional_datapipe("select_all_gsp_spatial_slice_meters")
-class SelectAllGSPSpatialSliceMetersIterDataPipe(IterDataPipe):
-    """Select spatial slice based off meters from point of interest"""
-
-    def __init__(
-        self,
-        source_datapipe: IterDataPipe,
-        locations: List[Location],
-        roi_height_meters: int,
-        roi_width_meters: int,
-        dim_name: Optional[str] = None,
-    ):
-        """
-        Select spatial slice based off pixels from point of interest
-
-        Args:
-            source_datapipe: Datapipe of Xarray data
-            location_datapipe: Location datapipe
-            roi_width_meters: ROI width in meters
-            roi_height_meters: ROI height in meters
-            dim_name: Dimension name to select for ID, None for coordinates
-
-        Notes:
-            Using spatial slicing based on distance rather than number of pixels will often yield
-            slices which can vary by 1 pixel in height and/or width.
-
-            E.g. Suppose the Xarray data has x-coords = [1,2,3,4,5]. We want to slice a spatial
-            window with a size which equates to 2.2 along the x-axis. If we choose to slice around
-            the point x=3 this will slice out the x-coords [2,3,4]. If we choose to slice around the
-            point x=2.5 this will slice out the x-coords [2,3]. Hence the returned slice can have
-            size either 2 or 3 in the x-axis depending on the spatial location selected.
-
-            Also, if selecting over a large span of latitudes, this may also causes pixel sizes of
-            the yielded outputs to change. For example, if the Xarray data is on a regularly spaced
-            longitude-latitude grid, then the structure of the grid means that the longitudes near
-            to the poles are spaced closer together (measured in meters) than at the equator. So
-            slices near the equator will have less pixels in the x-axis than slices taken near the
-            poles.
-        """
-        self.source_datapipe = source_datapipe
-        self.locations = locations
-        self.roi_width_meters = roi_width_meters
-        self.roi_height_meters = roi_height_meters
-        self.dim_name = dim_name
-
-    def __iter__(self) -> Union[xr.DataArray, xr.Dataset]:
-        for xr_data in self.source_datapipe:
-            loc_slices = []
-            
-            for location in self.locations:
-
-                selected = select_spatial_slice_meters(
-                    xr_data=xr_data, 
-                    location=location,
-                    roi_width_meters=self.roi_width_meters,
-                    roi_height_meters=self.roi_height_meters,
-                    dim_name=self.dim_name,  
-                )
-
-                loc_slices.append(selected)
-            
-            yield loc_slices
-
-
-# ------------------------------- Time pipeline functions ------------------------------
-
-
-def construct_time_pipeline(
-    config_filename: str,
-    start_time: Optional[datetime] = None,
-    end_time: Optional[datetime] = None,
-) -> Tuple[IterDataPipe, IterDataPipe]:
-    """Construct time pipeline for the input data config file.
-
-    Args:
-        config_filename: Path to config file.
-        start_time: Minimum time for time datapipe.
-        end_time: Maximum time for time datapipe.
-    """
-
-    datapipes_dict = _get_datapipes_dict(config_filename)
-
-    # Get config
-    config = datapipes_dict.pop("config")
-
-    if (start_time is not None) or (end_time is not None):
-        datapipes_dict["gsp"] = datapipes_dict["gsp"].filter_times(start_time, end_time)
-
-    # Get overlapping time periods
-    t0_datapipe = create_t0_datapipe(
-        datapipes_dict,
-        configuration=config,
-        shuffle=True,
-    )
-
-    return t0_datapipe
-
-
-def create_t0_datapipe(
-    datapipes_dict: dict,
-    configuration: Configuration,
-    shuffle: bool = True,
-):
-    """
-    Takes source datapipes and returns datapipes of appropriate t0 times.
-
-    The t0 times are sampled without replacement.
-
-    Args:
-        datapipes_dict: Dictionary of datapipes of input sources for which we want to select
-            appropriate t0 times.
-        configuration: Configuration object for inputs.
-        shuffle: Whether to use the internal shuffle function when yielding times. Else
-            location times will be heavily ordered.
-
-    Returns:
-        t0 datapipe
-
-    """
-    valid_t0_periods_datapipe = create_valid_t0_periods_datapipe(
-        datapipes_dict,
-        configuration,
-        key_for_t0="gsp",
-    )
-
-    t0_datapipe = valid_t0_periods_datapipe.pick_t0_times(return_all=True, shuffle=shuffle)
-
-    return t0_datapipe
-
-
-# ------------------------------- Space pipeline functions -----------------------------
-
-
-def slice_datapipes_by_space_all_gsps(
-    datapipes_dict: dict,
-    locations: list[Location],
-    configuration: Configuration,
-) -> None:
-
-    conf_nwp = configuration.input_data.nwp
-    conf_sat = configuration.input_data.satellite
-    
-    if "nwp" in datapipes_dict:
-        
-        for nwp_key, nwp_datapipe in datapipes_dict["nwp"].items():
-            
-            datapipes_dict["nwp"][nwp_key] = nwp_datapipe.select_all_gsp_spatial_slices_pixels(
-                locations,
-                roi_width_pixels=conf_nwp[nwp_key].nwp_image_size_pixels_width,
-                roi_height_pixels=conf_nwp[nwp_key].nwp_image_size_pixels_height,
-            )
-
-    if "sat" in datapipes_dict:
-        
-        datapipes_dict["sat"] = datapipes_dict["sat"].select_all_gsp_spatial_slices_pixels(
-            locations,
-            roi_width_pixels=conf_sat.satellite_image_size_pixels_width,
-            roi_height_pixels=conf_sat.satellite_image_size_pixels_height,
-        )
-
-    if "pv" in datapipes_dict:
-        # No spatial slice for PV since it is always the same, just repeat for GSPs
-        pv_datapipe = pv_datapipe.map(SampleRepeat(len(locations)))
-    
-
-    # GSP always assumed to be in data
-    datapipes_dict["gsp"] = datapipes_dict["gsp"].select_all_gsp_spatial_slice_meters(
-        locations,
-        roi_width_meters=1,
-        roi_height_meters=1,
-        dim_name="gsp_id",
-    )
-
-
-# -------------------------------- Processing functions --------------------------------
-
-    
-def pre_spatial_slice_process(datapipes_dict, configuration):
-    
-    conf_nwp = configuration.input_data.nwp
-    
-    if "nwp" in datapipes_dict:
-
-        for nwp_key, nwp_datapipe in datapipes_dict["nwp"].items():
-                
-            datapipes_dict["nwp"][nwp_key] =  (
-                nwp_datapipe
-                    .map(xr_compute)
-                    .normalize(
-                        mean=NWP_MEANS[conf_nwp[nwp_key].nwp_provider],
-                        std=NWP_STDS[conf_nwp[nwp_key].nwp_provider],
-                )
-            )
-            
-    if "sat" in datapipes_dict:
-
-        datapipes_dict["sat"] = (
-            datapipes_dict["sat"]
-                .map(xr_compute)
-                .normalize(mean=RSS_MEAN, std=RSS_STD)
-        )
-
-    if "pv" in datapipes_dict:
-        # Recombine PV arrays - see function doc for further explanation
-        datapipes_dict["pv"] = (
-            datapipes_dict["pv"]
-                .zip_ocf(datapipes_dict["pv_future"])
-                .map(concat_xr_time_utc)
-                .map(normalize_pv)
-                .map(fill_nans_in_pv)
-        )
-
-    # GSP always assumed to be in data
-    # Recombine GSP arrays - see function doc for further explanation
-    datapipes_dict["gsp"] = (
-        datapipes_dict["gsp"]
-        .zip_ocf(datapipes_dict["gsp_future"])
-        .map(concat_xr_time_utc)
-        .map(normalize_gsp)
-    )
-
-    
-def post_spatial_slice_process(datapipes_dict):
-    # Spatially slice, normalize, and convert data to numpy arrays
-    numpy_modalities = []
-
-    if "nwp" in datapipes_dict:
-        nwp_numpy_modalities = dict()
-
-        for nwp_key, nwp_datapipe in datapipes_dict["nwp"].items():
-
-            nwp_numpy_modalities[nwp_key] = (
-                ConvertWrapper(
-                    nwp_datapipe,
-                    ConvertNWPToNumpyBatch,
-                )
-                .map(stack_np_examples_into_batch)
-            )
-
-        # Combine the NWPs into NumpyBatch
-        nwp_numpy_modalities = MergeNWPNumpyModalities(nwp_numpy_modalities)
-        numpy_modalities.append(nwp_numpy_modalities)
-
-    if "sat" in datapipes_dict:
-
-        numpy_modalities.append(
-            ConvertWrapper(datapipes_dict["sat"], ConvertSatelliteToNumpyBatch)
-            .map(stack_np_examples_into_batch)
-        )
-
-    if "pv" in datapipes_dict:
-    
-        numpy_modalities.append(
-            ConvertWrapper(datapipes_dict["pv"], ConvertPVToNumpyBatch)
-            .map(stack_np_examples_into_batch)
-        )
-
-    # GSP always assumed to be in data    
-    numpy_modalities.append(
-        ConvertWrapper(datapipes_dict["gsp"], ConvertGSPToNumpyBatch)
-        .map(stack_np_examples_into_batch)
-    )
-
-    # Combine all the data sources
-    combined_datapipe = (
-        MergeNumpyModalities(numpy_modalities)
-        .add_sun_position(modality_name="gsp")
-        .map(fill_nans_in_arrays)
-    )
-    
-    return combined_datapipe
-
-
-# --------------------------- High level pipeline functions ----------------------------
-
-
-def construct_sliced_data_pipeline(
-    config_filename: str,
-    t0_datapipe: IterDataPipe,
-    production: bool = False,
-    check_satellite_no_zeros: bool = False,
-) -> IterDataPipe:
-    """Constructs data pipeline for the input data config file.
-
-    This yields samples from the location and time datapipes.
-
-    Args:
-        config_filename: Path to config file.
-        t0_datapipe: Datapipe yielding times.
-        production: Whether constucting pipeline for production inference.
-        check_satellite_no_zeros: Whether to check that satellite data has no zeros.
-    """
-
-    datapipes_dict = _get_datapipes_dict(
-        config_filename,
-        production=production,
-    )
-    
-    # Get the location objects for all 317 regional GSPs
-    gsp_id_to_loc = GSPLocationLookup()
-    locations = [gsp_id_to_loc(gsp_id) for gsp_id in range(1, 318)]
-
-    # Pop config
-    configuration = datapipes_dict.pop("config")
-
-    # Slice all of the datasets by time - this is an in-place operation
-    slice_datapipes_by_time(datapipes_dict, t0_datapipe, configuration, production)
-    
-    # Run compute and normalise all the data
-    pre_spatial_slice_process(datapipes_dict, configuration)
-    
-    # Slice all of the datasets by space - this is an in-place operation
-    slice_datapipes_by_space_all_gsps(datapipes_dict, locations, configuration)
-    
-    # Convert to NumpyBatch
-    combined_datapipe = post_spatial_slice_process(datapipes_dict)
-    
-    if check_satellite_no_zeros:
-        # in production we don't want any nans in the satellite data
-        combined_datapipe = combined_datapipe.map(check_nans_in_satellite_data)
-    
-    return combined_datapipe
-
-
-def pvnet_all_gsp_datapipe(
-    config_filename: str,
-    start_time: Optional[datetime] = None,
-    end_time: Optional[datetime] = None,
-) -> IterDataPipe:
-    """
-    Construct pvnet pipeline for the input data config file.
-
-    Args:
-        config_filename: Path to config file.
-        start_time: Minimum time at which a sample can be selected.
-        end_time: Maximum time at which a sample can be selected.
-    """
-
-    # Open datasets from the config and filter to useable times
-    t0_datapipe = construct_time_pipeline(
-        config_filename,
-        start_time,
-        end_time,
-    )
-    
-    # Shard after we have the times. These are already shuffled so no need to shuffle again
-    t0_datapipe = t0_datapipe.sharding_filter()
-
-    # In this function we re-open the datasets to make a clean separation before/after sharding
-    # This function
-    datapipe = construct_sliced_data_pipeline(
-        config_filename,
-        t0_datapipe,
-    )
-
-    return datapipe
-
-
-if __name__=="__main__":
-    import time
-    
-    t0 = time.time()
-    dp = pvnet_all_gsp_datapipe(
-        config_filename="/home/jamesfulton/repos/PVNet/configs/datamodule/configuration/gcp_configuration.yaml"
-    )
-    
-    b  = next(iter(dp))
-    print(time.time() - t0)

From 801b7a8c0146c2e608e77729d5b0960e1ef5a815 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Fri, 31 May 2024 12:12:22 +0000
Subject: [PATCH 09/27] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 ocf_datapipes/load/gsp/gsp.py                 |   4 +-
 ocf_datapipes/load/gsp/utils.py               |  27 +--
 ocf_datapipes/select/pick_locations.py        |  17 +-
 .../select/pick_locations_and_t0_times.py     |  15 +-
 ocf_datapipes/select/pick_t0_times.py         |  11 +-
 ocf_datapipes/select/select_spatial_slice.py  |  30 ++--
 ocf_datapipes/training/common.py              |   7 +-
 ocf_datapipes/training/pvnet.py               |  10 +-
 ocf_datapipes/training/pvnet_all_gsp.py       | 157 ++++++++----------
 ocf_datapipes/utils/location.py               |   3 +-
 10 files changed, 121 insertions(+), 160 deletions(-)

diff --git a/ocf_datapipes/load/gsp/gsp.py b/ocf_datapipes/load/gsp/gsp.py
index 251d46775..318a86753 100644
--- a/ocf_datapipes/load/gsp/gsp.py
+++ b/ocf_datapipes/load/gsp/gsp.py
@@ -45,7 +45,7 @@ def __init__(
     def __iter__(self) -> xr.DataArray:
         """Get and return GSP data"""
         gsp_id_to_shape = get_gsp_id_to_shape(
-            self.gsp_id_to_region_id_filename, 
+            self.gsp_id_to_region_id_filename,
             self.sheffield_solar_region_path,
         )
 
@@ -56,7 +56,7 @@ def __iter__(self) -> xr.DataArray:
 
         # Ensure the centroids have the same GSP ID index as the GSP PV power
         gsp_id_to_shape = gsp_id_to_shape.loc[gsp_pv_power_mw_ds.gsp_id]
-        
+
         data_array = put_gsp_data_into_an_xr_dataarray(
             gsp_pv_power_mw=gsp_pv_power_mw_ds.generation_mw.data.astype(np.float32),
             time_utc=gsp_pv_power_mw_ds.datetime_gmt.data,
diff --git a/ocf_datapipes/load/gsp/utils.py b/ocf_datapipes/load/gsp/utils.py
index e6b0d75b2..faf74243d 100644
--- a/ocf_datapipes/load/gsp/utils.py
+++ b/ocf_datapipes/load/gsp/utils.py
@@ -10,6 +10,7 @@
 
 try:
     from ocf_datapipes.utils.eso import get_gsp_metadata_from_eso, get_gsp_shape_from_eso
+
     _has_pvlive = True
 except ImportError:
     print("Unable to import PVLive utils, please provide filenames with OpenGSP")
@@ -59,7 +60,7 @@ def put_gsp_data_into_an_xr_dataarray(
 
 
 def get_gsp_id_to_shape(
-    gsp_id_to_region_id_filename: Optional[str] = None, 
+    gsp_id_to_region_id_filename: Optional[str] = None,
     sheffield_solar_region_path: Optional[str] = None,
 ) -> gpd.GeoDataFrame:
     """
@@ -72,16 +73,16 @@ def get_gsp_id_to_shape(
     Returns:
         GeoDataFrame containing the mapping from ID to shape
     """
-    
+
     did_provide_filepaths = None not in [gsp_id_to_region_id_filename, sheffield_solar_region_path]
     assert _has_pvlive or did_provide_filepaths
-    
+
     if not did_provide_filepaths:
         if gsp_id_to_region_id_filename is None:
             gsp_id_to_region_id_filename = get_gsp_metadata_from_eso()
         if sheffield_solar_region_path is None:
-            sheffield_solar_region_path = get_gsp_shape_from_eso()        
-        
+            sheffield_solar_region_path = get_gsp_shape_from_eso()
+
     # Load mapping from GSP ID to Sheffield Solar GSP ID to GSP name:
     gsp_id_to_region_id = pd.read_csv(
         gsp_id_to_region_id_filename,
@@ -116,11 +117,11 @@ def get_gsp_id_to_shape(
     # For the national forecast, GSP ID 0, we want the shape to be the
     # union of all the other shapes
     gsp_id_to_shape = pd.concat([gsp_id_to_shape, gsp_0]).sort_index()
-    
+
     # Add central coordinates
     gsp_id_to_shape["x_osgb"] = gsp_id_to_shape.geometry.centroid.x.astype(np.float32)
     gsp_id_to_shape["y_osgb"] = gsp_id_to_shape.geometry.centroid.y.astype(np.float32)
-    
+
     return gsp_id_to_shape
 
 
@@ -128,10 +129,10 @@ class GSPLocationLookup:
     """Query object for GSP location from GSP ID"""
 
     def __init__(
-        self, 
-        gsp_id_to_region_id_filename: Optional[str] = None, 
-        sheffield_solar_region_path: Optional[str] = None,          
-):
+        self,
+        gsp_id_to_region_id_filename: Optional[str] = None,
+        sheffield_solar_region_path: Optional[str] = None,
+    ):
         """Query object for GSP location from GSP ID
 
         Args:
@@ -140,7 +141,7 @@ def __init__(
 
         """
         self.gsp_id_to_shape = get_gsp_id_to_shape(
-            gsp_id_to_region_id_filename, 
+            gsp_id_to_region_id_filename,
             sheffield_solar_region_path,
         )
 
@@ -154,4 +155,4 @@ def __call__(self, gsp_id: int) -> Location:
             x=self.gsp_id_to_shape.loc[gsp_id].x_osgb.astype(np.float32),
             y=self.gsp_id_to_shape.loc[gsp_id].y_osgb.astype(np.float32),
             id=gsp_id,
-        )
\ No newline at end of file
+        )
diff --git a/ocf_datapipes/select/pick_locations.py b/ocf_datapipes/select/pick_locations.py
index 5629a2a65..8e82c4678 100644
--- a/ocf_datapipes/select/pick_locations.py
+++ b/ocf_datapipes/select/pick_locations.py
@@ -36,10 +36,9 @@ def __init__(
         self.shuffle = shuffle
 
     def _yield_all_iter(self, xr_dataset):
-        
-        # Get the spatial coords
+        # Get the spatial coords
         xr_coord_system, xr_x_dim, xr_y_dim = spatial_coord_type(xr_dataset)
-        
+
         loc_indices = np.arange(len(xr_dataset[xr_x_dim]))
 
         if self.shuffle:
@@ -47,13 +46,12 @@ def _yield_all_iter(self, xr_dataset):
 
         # Iterate through all locations in dataset
         for loc_index in loc_indices:
-            
             # Get the location ID
             loc_id = None
             for id_dim_name in ["pv_system_id", "gsp_id", "station_id"]:
                 if id_dim_name in xr_dataset.coords.keys():
                     loc_id = int(xr_dataset[id_dim_name][loc_index].values)
-            
+
             location = Location(
                 coordinate_system=xr_coord_system,
                 x=xr_dataset[xr_x_dim][loc_index].values,
@@ -64,13 +62,12 @@ def _yield_all_iter(self, xr_dataset):
             yield location
 
     def _yield_random_iter(self, xr_dataset):
-        
-        # Get the spatial coords
+        # Get the spatial coords
         xr_coord_system, xr_x_dim, xr_y_dim = spatial_coord_type(xr_dataset)
 
         while True:
             loc_index = np.random.randint(0, len(xr_dataset[xr_x_dim]))
-            
+
             # Get the location ID
             loc_id = None
             for id_dim_name in ["pv_system_id", "gsp_id", "station_id"]:
@@ -83,7 +80,7 @@ def _yield_random_iter(self, xr_dataset):
                 y=xr_dataset[xr_y_dim][loc_index].values,
                 id=loc_id,
             )
-            
+
             yield location
 
     def __iter__(self) -> Location:
@@ -92,4 +89,4 @@ def __iter__(self) -> Location:
         if self.return_all:
             return self._yield_all_iter(xr_dataset)
         else:
-            return self._yield_random_iter(xr_dataset)
\ No newline at end of file
+            return self._yield_random_iter(xr_dataset)
diff --git a/ocf_datapipes/select/pick_locations_and_t0_times.py b/ocf_datapipes/select/pick_locations_and_t0_times.py
index 1b2a619bb..5e72af414 100644
--- a/ocf_datapipes/select/pick_locations_and_t0_times.py
+++ b/ocf_datapipes/select/pick_locations_and_t0_times.py
@@ -43,10 +43,9 @@ def __init__(
         self.time_dim_name = time_dim_name
 
     def _yield_all_iter(self, xr_dataset):
-        
-        # Get the spatial coords
+        # Get the spatial coords
         xr_coord_system, xr_x_dim, xr_y_dim = spatial_coord_type(xr_dataset)
-        
+
         t_index, x_index = np.meshgrid(
             np.arange(len(xr_dataset[self.time_dim_name])),
             np.arange(len(xr_dataset[xr_x_dim])),
@@ -59,13 +58,12 @@ def _yield_all_iter(self, xr_dataset):
 
         # Iterate through all locations in dataset
         for t_index, loc_index in index_pairs:
-            
             # Get the location ID
             loc_id = None
             for id_dim_name in ["pv_system_id", "gsp_id", "station_id"]:
                 if id_dim_name in xr_dataset.coords.keys():
                     loc_id = int(xr_dataset[id_dim_name][loc_index].values)
-            
+
             t0 = xr_dataset[self.time_dim_name][t_index].values
             location = Location(
                 coordinate_system=xr_coord_system,
@@ -77,13 +75,12 @@ def _yield_all_iter(self, xr_dataset):
             yield location, t0
 
     def _yield_random_iter(self, xr_dataset):
-        
-        # Get the spatial coords
+        # Get the spatial coords
         xr_coord_system, xr_x_dim, xr_y_dim = spatial_coord_type(xr_dataset)
 
         while True:
             loc_index = np.random.randint(0, len(xr_dataset[xr_x_dim]))
-            
+
             # Get the location ID
             loc_id = None
             for id_dim_name in ["pv_system_id", "gsp_id", "station_id"]:
@@ -96,7 +93,7 @@ def _yield_random_iter(self, xr_dataset):
                 y=xr_dataset[xr_y_dim][loc_index].values,
                 id=loc_id,
             )
-            
+
             t0 = np.random.choice(xr_dataset[self.time_dim_name].values)
 
             yield location, t0
diff --git a/ocf_datapipes/select/pick_t0_times.py b/ocf_datapipes/select/pick_t0_times.py
index 173b054e4..1f677cd8d 100644
--- a/ocf_datapipes/select/pick_t0_times.py
+++ b/ocf_datapipes/select/pick_t0_times.py
@@ -34,14 +34,13 @@ def __init__(
         self.return_all = return_all
         self.shuffle = shuffle
         self.dim_name = dim_name
-        
-    
+
     def _yield_random_iter(self, xr_dataset):
         """Sample t0 with replacement"""
         while True:
             t0 = np.random.choice(xr_dataset[self.dim_name].values)
             yield t0
-            
+
     def _yield_all_iter(self, xr_dataset):
         """Yield all the t0s in order, and maybe with a shuffle"""
         all_t0s = np.copy(xr_dataset[self.dim_name].values)
@@ -52,13 +51,11 @@ def _yield_all_iter(self, xr_dataset):
 
     def __iter__(self) -> pd.Timestamp:
         xr_dataset = next(iter(self.source_datapipe))
-        
+
         if len(xr_dataset[self.dim_name].values) == 0:
             raise Exception("There are no values to get t0 from")
-                    
+
         if self.return_all:
             return self._yield_all_iter(xr_dataset)
         else:
             return self._yield_random_iter(xr_dataset)
-            
-
diff --git a/ocf_datapipes/select/select_spatial_slice.py b/ocf_datapipes/select/select_spatial_slice.py
index 4b42ea3f3..11a5dad95 100644
--- a/ocf_datapipes/select/select_spatial_slice.py
+++ b/ocf_datapipes/select/select_spatial_slice.py
@@ -351,10 +351,10 @@ def slice_spatial_pixel_window_from_xarray(
 
 
 def select_spatial_slice_pixels(
-    xr_data: Union[xr.Dataset, xr.DataArray], 
+    xr_data: Union[xr.Dataset, xr.DataArray],
     location: Location,
-    roi_width_pixels: int, 
-    roi_height_pixels: int, 
+    roi_width_pixels: int,
+    roi_height_pixels: int,
     allow_partial_slice: bool = False,
     location_idx_name: Optional[str] = None,
 ):
@@ -376,7 +376,7 @@ def select_spatial_slice_pixels(
         location_idx_name: Name for location index of unstructured grid data,
             None if not relevant
     """
-        
+
     xr_coords, xr_x_dim, xr_y_dim = spatial_coord_type(xr_data)
     if location_idx_name is not None:
         selected = _get_points_from_unstructured_grids(
@@ -411,11 +411,11 @@ def select_spatial_slice_pixels(
 
 
 def select_spatial_slice_meters(
-    xr_data: Union[xr.Dataset, xr.DataArray], 
+    xr_data: Union[xr.Dataset, xr.DataArray],
     location: Location,
     roi_width_meters: int,
     roi_height_meters: int,
-    dim_name: Optional[str] = None,                   
+    dim_name: Optional[str] = None,
 ):
     """
     Select spatial slice based off pixels from point of interest
@@ -449,7 +449,7 @@ def select_spatial_slice_meters(
 
     half_width = roi_width_meters // 2
     half_height = roi_height_meters // 2
-    
+
     # Find the bounding box values for the location in either lat-lon or OSGB coord systems
     if location.coordinate_system == "lon_lat":
         right, top = move_lon_lat_by_meters(
@@ -472,9 +472,7 @@ def select_spatial_slice_meters(
         top = location.y + half_height
 
     else:
-        raise ValueError(
-            f"Location coord system not recognized: {location.coordinate_system}"
-        )
+        raise ValueError(f"Location coord system not recognized: {location.coordinate_system}")
 
     # Change the bounding coordinates [left, right, bottom, top] to the same
     # coordinate system as the xarray data
@@ -483,7 +481,7 @@ def select_spatial_slice_meters(
         y=np.array([bottom, top], dtype=np.float32),
         from_coords=location.coordinate_system,
         xr_data=xr_data,
-        )
+    )
 
     # Do it off coordinates, not ID
     if dim_name is None:
@@ -554,14 +552,14 @@ def __iter__(self) -> Union[xr.DataArray, xr.Dataset]:
             selected = select_spatial_slice_pixels(
                 xr_data=xr_data,
                 location=location,
-                roi_width_pixels=self.roi_width_pixels, 
-                roi_height_pixels=self.roi_height_pixels, 
+                roi_width_pixels=self.roi_width_pixels,
+                roi_height_pixels=self.roi_height_pixels,
                 allow_partial_slice=self.allow_partial_slice,
                 location_idx_name=self.location_idx_name,
             )
 
             yield selected
-    
+
 
 @functional_datapipe("select_spatial_slice_meters")
 class SelectSpatialSliceMetersIterDataPipe(IterDataPipe):
@@ -614,11 +612,11 @@ def __iter__(self) -> Union[xr.DataArray, xr.Dataset]:
             logger.debug("Getting Spatial Slice Meters")
 
             selected = select_spatial_slice_meters(
-                xr_data=xr_data, 
+                xr_data=xr_data,
                 location=location,
                 roi_width_meters=self.roi_width_meters,
                 roi_height_meters=self.roi_height_meters,
-                dim_name=self.dim_name,  
+                dim_name=self.dim_name,
             )
 
             yield selected
diff --git a/ocf_datapipes/training/common.py b/ocf_datapipes/training/common.py
index 7941a5f1e..bfe196e52 100644
--- a/ocf_datapipes/training/common.py
+++ b/ocf_datapipes/training/common.py
@@ -1099,7 +1099,7 @@ def create_valid_t0_periods_datapipe(
     key_for_t0: str = "gsp",
 ):
     """Create datapipe yielding t0 periods which are valid for the input data sources.
-    
+
     Args:
         datapipes_dict: Dictionary of datapipes of input sources for which we want to select
             appropriate location and times.
@@ -1224,9 +1224,8 @@ def create_valid_t0_periods_datapipe(
 
     # Select time periods and set length
     valid_t0_periods_datapipe = key_datapipe.filter_time_periods(time_periods=overlapping_datapipe)
-    
-    return valid_t0_periods_datapipe
 
+    return valid_t0_periods_datapipe
 
 
 def create_t0_and_loc_datapipes(
@@ -1257,7 +1256,7 @@ def create_t0_and_loc_datapipes(
         configuration,
         key_for_t0,
     )
-    
+
     t0_loc_datapipe = valid_t0_periods_datapipe.pick_locs_and_t0s(return_all=True, shuffle=shuffle)
 
     location_pipe, t0_datapipe = t0_loc_datapipe.unzip(sequence_length=2)
diff --git a/ocf_datapipes/training/pvnet.py b/ocf_datapipes/training/pvnet.py
index 6b64b9e0b..b3f77acf2 100644
--- a/ocf_datapipes/training/pvnet.py
+++ b/ocf_datapipes/training/pvnet.py
@@ -77,10 +77,10 @@ def slice_datapipes_by_space(
 
 def process_and_combine_datapipes(datapipes_dict, configuration):
     """Normalize and convert data to numpy arrays"""
-    
+
     # Unpack for convenience
     conf_nwp = configuration.input_data.nwp
-    
+
     numpy_modalities = []
 
     # Normalise the inputs and convert to numpy format
@@ -129,7 +129,7 @@ def process_and_combine_datapipes(datapipes_dict, configuration):
     combined_datapipe = MergeNumpyModalities(numpy_modalities).add_sun_position(modality_name="gsp")
 
     combined_datapipe = combined_datapipe.map(fill_nans_in_arrays)
-    
+
     return combined_datapipe
 
 
@@ -164,10 +164,10 @@ def construct_sliced_data_pipeline(
 
     # Slice all of the datasets by time - this is an in-place operation
     slice_datapipes_by_time(datapipes_dict, t0_datapipe, configuration, production)
-    
+
     # Normalise, and combine the data sources into NumpyBatches
     combined_datapipe = process_and_combine_datapipes(datapipes_dict, configuration)
-    
+
     if check_satellite_no_zeros:
         # in production we don't want any nans in the satellite data
         combined_datapipe = combined_datapipe.map(check_nans_in_satellite_data)
diff --git a/ocf_datapipes/training/pvnet_all_gsp.py b/ocf_datapipes/training/pvnet_all_gsp.py
index 5198d1fca..bd3b47623 100644
--- a/ocf_datapipes/training/pvnet_all_gsp.py
+++ b/ocf_datapipes/training/pvnet_all_gsp.py
@@ -1,38 +1,37 @@
 """Create the training/validation datapipe for training the PVNet Model"""
 import logging
-from typing import Optional, Type, Tuple, List, Union
 from datetime import datetime
+from typing import List, Optional, Tuple, Type, Union
 
-import numpy as np
 import xarray as xr
-from torch.utils.data.datapipes.datapipe import IterDataPipe
 from torch.utils.data.datapipes._decorator import functional_datapipe
+from torch.utils.data.datapipes.datapipe import IterDataPipe
 from torch.utils.data.datapipes.iter import IterableWrapper
+
+from ocf_datapipes.batch import MergeNumpyModalities, MergeNWPNumpyModalities
+from ocf_datapipes.batch.merge_numpy_examples_to_batch import stack_np_examples_into_batch
+from ocf_datapipes.config.model import Configuration
 from ocf_datapipes.convert import (
+    ConvertGSPToNumpyBatch,
     ConvertNWPToNumpyBatch,
     ConvertPVToNumpyBatch,
     ConvertSatelliteToNumpyBatch,
-    ConvertGSPToNumpyBatch,
-
 )
-
-from ocf_datapipes.batch import MergeNumpyModalities, MergeNWPNumpyModalities
-from ocf_datapipes.batch.merge_numpy_examples_to_batch import stack_np_examples_into_batch
+from ocf_datapipes.load.gsp.utils import GSPLocationLookup
 from ocf_datapipes.select.select_spatial_slice import (
-    select_spatial_slice_pixels, 
     select_spatial_slice_meters,
+    select_spatial_slice_pixels,
 )
 from ocf_datapipes.training.common import (
     _get_datapipes_dict,
     check_nans_in_satellite_data,
     concat_xr_time_utc,
+    create_valid_t0_periods_datapipe,
     fill_nans_in_arrays,
     fill_nans_in_pv,
     normalize_gsp,
     normalize_pv,
     slice_datapipes_by_time,
-    open_and_return_datapipes,
-    create_valid_t0_periods_datapipe,
 )
 from ocf_datapipes.utils.consts import (
     NWP_MEANS,
@@ -40,10 +39,7 @@
     RSS_MEAN,
     RSS_STD,
 )
-
-from ocf_datapipes.config.model import Configuration
 from ocf_datapipes.utils.location import Location
-from ocf_datapipes.load.gsp.utils import GSPLocationLookup
 
 xr.set_options(keep_attrs=True)
 logger = logging.getLogger("pvnet_all_gsp_datapipe")
@@ -58,36 +54,34 @@ def xr_compute(xr_data):
 
 class SampleRepeat:
     """Use a single input element to create a list of identical values"""
-    
+
     def __init__(self, num_repeats):
         """Use a single input element to create a list of identical values
-        
+
         Args:
             num_repeats: Length of the returned list of duplicated values
         """
         self.num_repeats = num_repeats
-        
+
     def __call__(self, x):
         return [x for _ in range(self.num_repeats)]
-    
+
 
 class ConvertWrapper(IterDataPipe):
-    def __init__(
-        self,
-        source_datapipe: IterDataPipe,
-        convert_class: Type[IterDataPipe]
-    ):
+    def __init__(self, source_datapipe: IterDataPipe, convert_class: Type[IterDataPipe]):
         self.source_datapipe = source_datapipe
         self.convert_class = convert_class
 
     def __iter__(self):
         for concurrent_samples in self.source_datapipe:
-            dp = self.convert_class(IterableWrapper(concurrent_samples)) 
+            dp = self.convert_class(IterableWrapper(concurrent_samples))
             yield [x for x in dp]
 
+
 # ------------------------------ Multi-location datapipes ------------------------------
 # These are datapipes rewritten to run on all GSPs
 
+
 @functional_datapipe("select_all_gsp_spatial_slices_pixels")
 class SelectAllGSPSpatialSlicePixelsIterDataPipe(IterDataPipe):
     """Select all the spatial slices"""
@@ -127,20 +121,18 @@ def __init__(
 
     def __iter__(self) -> Union[xr.DataArray, xr.Dataset]:
         for xr_data in self.source_datapipe:
-            
             loc_slices = []
-            
-            for location in self.locations:
 
+            for location in self.locations:
                 selected = select_spatial_slice_pixels(
                     xr_data,
                     location,
-                    self.roi_width_pixels, 
-                    self.roi_height_pixels, 
+                    self.roi_width_pixels,
+                    self.roi_height_pixels,
                     self.allow_partial_slice,
                     self.location_idx_name,
                 )
-                
+
                 loc_slices.append(selected)
 
             yield loc_slices
@@ -194,19 +186,18 @@ def __init__(
     def __iter__(self) -> Union[xr.DataArray, xr.Dataset]:
         for xr_data in self.source_datapipe:
             loc_slices = []
-            
-            for location in self.locations:
 
+            for location in self.locations:
                 selected = select_spatial_slice_meters(
-                    xr_data=xr_data, 
+                    xr_data=xr_data,
                     location=location,
                     roi_width_meters=self.roi_width_meters,
                     roi_height_meters=self.roi_height_meters,
-                    dim_name=self.dim_name,  
+                    dim_name=self.dim_name,
                 )
 
                 loc_slices.append(selected)
-            
+
             yield loc_slices
 
 
@@ -284,14 +275,11 @@ def slice_datapipes_by_space_all_gsps(
     locations: list[Location],
     configuration: Configuration,
 ) -> None:
-
     conf_nwp = configuration.input_data.nwp
     conf_sat = configuration.input_data.satellite
-    
+
     if "nwp" in datapipes_dict:
-        
         for nwp_key, nwp_datapipe in datapipes_dict["nwp"].items():
-            
             datapipes_dict["nwp"][nwp_key] = nwp_datapipe.select_all_gsp_spatial_slices_pixels(
                 locations,
                 roi_width_pixels=conf_nwp[nwp_key].nwp_image_size_pixels_width,
@@ -299,7 +287,6 @@ def slice_datapipes_by_space_all_gsps(
             )
 
     if "sat" in datapipes_dict:
-        
         datapipes_dict["sat"] = datapipes_dict["sat"].select_all_gsp_spatial_slices_pixels(
             locations,
             roi_width_pixels=conf_sat.satellite_image_size_pixels_width,
@@ -309,7 +296,6 @@ def slice_datapipes_by_space_all_gsps(
     if "pv" in datapipes_dict:
         # No spatial slice for PV since it is always the same, just repeat for GSPs
         pv_datapipe = pv_datapipe.map(SampleRepeat(len(locations)))
-    
 
     # GSP always assumed to be in data
     datapipes_dict["gsp"] = datapipes_dict["gsp"].select_all_gsp_spatial_slice_meters(
@@ -322,40 +308,30 @@ def slice_datapipes_by_space_all_gsps(
 
 # -------------------------------- Processing functions --------------------------------
 
-    
+
 def pre_spatial_slice_process(datapipes_dict, configuration):
-    
     conf_nwp = configuration.input_data.nwp
-    
-    if "nwp" in datapipes_dict:
 
+    if "nwp" in datapipes_dict:
         for nwp_key, nwp_datapipe in datapipes_dict["nwp"].items():
-                
-            datapipes_dict["nwp"][nwp_key] =  (
-                nwp_datapipe
-                    .map(xr_compute)
-                    .normalize(
-                        mean=NWP_MEANS[conf_nwp[nwp_key].nwp_provider],
-                        std=NWP_STDS[conf_nwp[nwp_key].nwp_provider],
-                )
+            datapipes_dict["nwp"][nwp_key] = nwp_datapipe.map(xr_compute).normalize(
+                mean=NWP_MEANS[conf_nwp[nwp_key].nwp_provider],
+                std=NWP_STDS[conf_nwp[nwp_key].nwp_provider],
             )
-            
-    if "sat" in datapipes_dict:
 
+    if "sat" in datapipes_dict:
         datapipes_dict["sat"] = (
-            datapipes_dict["sat"]
-                .map(xr_compute)
-                .normalize(mean=RSS_MEAN, std=RSS_STD)
+            datapipes_dict["sat"].map(xr_compute).normalize(mean=RSS_MEAN, std=RSS_STD)
         )
 
     if "pv" in datapipes_dict:
         # Recombine PV arrays - see function doc for further explanation
         datapipes_dict["pv"] = (
             datapipes_dict["pv"]
-                .zip_ocf(datapipes_dict["pv_future"])
-                .map(concat_xr_time_utc)
-                .map(normalize_pv)
-                .map(fill_nans_in_pv)
+            .zip_ocf(datapipes_dict["pv_future"])
+            .map(concat_xr_time_utc)
+            .map(normalize_pv)
+            .map(fill_nans_in_pv)
         )
 
     # GSP always assumed to be in data
@@ -367,7 +343,7 @@ def pre_spatial_slice_process(datapipes_dict, configuration):
         .map(normalize_gsp)
     )
 
-    
+
 def post_spatial_slice_process(datapipes_dict):
     # Spatially slice, normalize, and convert data to numpy arrays
     numpy_modalities = []
@@ -376,37 +352,34 @@ def post_spatial_slice_process(datapipes_dict):
         nwp_numpy_modalities = dict()
 
         for nwp_key, nwp_datapipe in datapipes_dict["nwp"].items():
-
-            nwp_numpy_modalities[nwp_key] = (
-                ConvertWrapper(
-                    nwp_datapipe,
-                    ConvertNWPToNumpyBatch,
-                )
-                .map(stack_np_examples_into_batch)
-            )
+            nwp_numpy_modalities[nwp_key] = ConvertWrapper(
+                nwp_datapipe,
+                ConvertNWPToNumpyBatch,
+            ).map(stack_np_examples_into_batch)
 
         # Combine the NWPs into NumpyBatch
         nwp_numpy_modalities = MergeNWPNumpyModalities(nwp_numpy_modalities)
         numpy_modalities.append(nwp_numpy_modalities)
 
     if "sat" in datapipes_dict:
-
         numpy_modalities.append(
-            ConvertWrapper(datapipes_dict["sat"], ConvertSatelliteToNumpyBatch)
-            .map(stack_np_examples_into_batch)
+            ConvertWrapper(datapipes_dict["sat"], ConvertSatelliteToNumpyBatch).map(
+                stack_np_examples_into_batch
+            )
         )
 
     if "pv" in datapipes_dict:
-    
         numpy_modalities.append(
-            ConvertWrapper(datapipes_dict["pv"], ConvertPVToNumpyBatch)
-            .map(stack_np_examples_into_batch)
+            ConvertWrapper(datapipes_dict["pv"], ConvertPVToNumpyBatch).map(
+                stack_np_examples_into_batch
+            )
         )
 
-    # GSP always assumed to be in data    
+    # GSP always assumed to be in data
     numpy_modalities.append(
-        ConvertWrapper(datapipes_dict["gsp"], ConvertGSPToNumpyBatch)
-        .map(stack_np_examples_into_batch)
+        ConvertWrapper(datapipes_dict["gsp"], ConvertGSPToNumpyBatch).map(
+            stack_np_examples_into_batch
+        )
     )
 
     # Combine all the data sources
@@ -415,7 +388,7 @@ def post_spatial_slice_process(datapipes_dict):
         .add_sun_position(modality_name="gsp")
         .map(fill_nans_in_arrays)
     )
-    
+
     return combined_datapipe
 
 
@@ -443,7 +416,7 @@ def construct_sliced_data_pipeline(
         config_filename,
         production=production,
     )
-    
+
     # Get the location objects for all 317 regional GSPs
     gsp_id_to_loc = GSPLocationLookup()
     locations = [gsp_id_to_loc(gsp_id) for gsp_id in range(1, 318)]
@@ -453,20 +426,20 @@ def construct_sliced_data_pipeline(
 
     # Slice all of the datasets by time - this is an in-place operation
     slice_datapipes_by_time(datapipes_dict, t0_datapipe, configuration, production)
-    
+
     # Run compute and normalise all the data
     pre_spatial_slice_process(datapipes_dict, configuration)
-    
+
     # Slice all of the datasets by space - this is an in-place operation
     slice_datapipes_by_space_all_gsps(datapipes_dict, locations, configuration)
-    
+
     # Convert to NumpyBatch
     combined_datapipe = post_spatial_slice_process(datapipes_dict)
-    
+
     if check_satellite_no_zeros:
         # in production we don't want any nans in the satellite data
         combined_datapipe = combined_datapipe.map(check_nans_in_satellite_data)
-    
+
     return combined_datapipe
 
 
@@ -490,7 +463,7 @@ def pvnet_all_gsp_datapipe(
         start_time,
         end_time,
     )
-    
+
     # Shard after we have the times. These are already shuffled so no need to shuffle again
     t0_datapipe = t0_datapipe.sharding_filter()
 
@@ -504,13 +477,13 @@ def pvnet_all_gsp_datapipe(
     return datapipe
 
 
-if __name__=="__main__":
+if __name__ == "__main__":
     import time
-    
+
     t0 = time.time()
     dp = pvnet_all_gsp_datapipe(
         config_filename="/home/jamesfulton/repos/PVNet/configs/datamodule/configuration/gcp_configuration.yaml"
     )
-    
-    b  = next(iter(dp))
+
+    b = next(iter(dp))
     print(time.time() - t0)
diff --git a/ocf_datapipes/utils/location.py b/ocf_datapipes/utils/location.py
index 440bfdfc5..653501e5f 100644
--- a/ocf_datapipes/utils/location.py
+++ b/ocf_datapipes/utils/location.py
@@ -2,7 +2,6 @@
 from typing import Optional
 
 import numpy as np
-import xarray as xr
 from pydantic import BaseModel, Field, validator
 
 
@@ -60,4 +59,4 @@ def validate_y(cls, v, values):
             min_y, max_y = 0, np.inf
         if v < min_y or v > max_y:
             raise ValueError(f"y = {v} must be within {[min_y, max_y]} for {co} coordinate system")
-        return v
\ No newline at end of file
+        return v

From 45569737fe1ba9e1920ddb32553a2de70d7b4dc4 Mon Sep 17 00:00:00 2001
From: James Fulton <djamesfulton@yahoo.co.uk>
Date: Fri, 31 May 2024 13:28:05 +0000
Subject: [PATCH 10/27] fix tests

---
 ocf_datapipes/training/common.py          | 11 ++++++-----
 ocf_datapipes/training/pvnet.py           | 20 ++++++++++++--------
 tests/production/test_pvnet_production.py |  8 ++++----
 tests/select/test_select_spatial_slice.py | 10 +++++-----
 4 files changed, 27 insertions(+), 22 deletions(-)

diff --git a/ocf_datapipes/training/common.py b/ocf_datapipes/training/common.py
index bfe196e52..0f270c7ff 100644
--- a/ocf_datapipes/training/common.py
+++ b/ocf_datapipes/training/common.py
@@ -936,21 +936,22 @@ def check_nans_in_satellite_data(batch: NumpyBatch) -> NumpyBatch:
     Check if there are any Nans values in the satellite data.
     """
     if np.any(np.isnan(batch[BatchKey.satellite_actual])):
+        
         logger.error("Found nans values in satellite data")
-
         logger.error(batch[BatchKey.satellite_actual].shape)
 
         # loop over time and channels
         for dim in [0, 1]:
             for t in range(batch[BatchKey.satellite_actual].shape[dim]):
                 if dim == 0:
-                    sate_data_one_step = batch[BatchKey.satellite_actual][t]
+                    sat_data_one_step = batch[BatchKey.satellite_actual][t]
                 else:
-                    sate_data_one_step = batch[BatchKey.satellite_actual][:, t]
-                nans = np.isnan(sate_data_one_step)
+                    sat_data_one_step = batch[BatchKey.satellite_actual][:, t]
+                
+                nans = np.isnan(sat_data_one_step)
 
                 if np.any(nans):
-                    percent_nans = np.sum(nans) / np.prod(sate_data_one_step.shape) * 100
+                    percent_nans = np.mean(nans) * 100
 
                     logger.error(
                         f"Found nans values in satellite data at index {t} ({dim=}). "
diff --git a/ocf_datapipes/training/pvnet.py b/ocf_datapipes/training/pvnet.py
index b3f77acf2..d6a6aff46 100644
--- a/ocf_datapipes/training/pvnet.py
+++ b/ocf_datapipes/training/pvnet.py
@@ -75,7 +75,7 @@ def slice_datapipes_by_space(
     return
 
 
-def process_and_combine_datapipes(datapipes_dict, configuration):
+def process_and_combine_datapipes(datapipes_dict, configuration, check_satellite_no_nans=False):
     """Normalize and convert data to numpy arrays"""
 
     # Unpack for convenience
@@ -128,6 +128,10 @@ def process_and_combine_datapipes(datapipes_dict, configuration):
     logger.debug("Combine all the data sources")
     combined_datapipe = MergeNumpyModalities(numpy_modalities).add_sun_position(modality_name="gsp")
 
+    if check_satellite_no_nans:
+        # in production we don't want any nans in the satellite data
+        combined_datapipe = combined_datapipe.map(check_nans_in_satellite_data)
+    
     combined_datapipe = combined_datapipe.map(fill_nans_in_arrays)
 
     return combined_datapipe
@@ -138,7 +142,7 @@ def construct_sliced_data_pipeline(
     location_pipe: IterDataPipe,
     t0_datapipe: IterDataPipe,
     production: bool = False,
-    check_satellite_no_zeros: bool = False,
+    check_satellite_no_nans: bool = False,
 ) -> IterDataPipe:
     """Constructs data pipeline for the input data config file.
 
@@ -149,7 +153,7 @@ def construct_sliced_data_pipeline(
         location_pipe: Datapipe yielding locations.
         t0_datapipe: Datapipe yielding times.
         production: Whether constucting pipeline for production inference.
-        check_satellite_no_zeros: Whether to check that satellite data has no zeros.
+        check_satellite_no_nans: Whether to check that satellite data has no nans.
     """
 
     datapipes_dict = _get_datapipes_dict(
@@ -166,11 +170,11 @@ def construct_sliced_data_pipeline(
     slice_datapipes_by_time(datapipes_dict, t0_datapipe, configuration, production)
 
     # Normalise, and combine the data sources into NumpyBatches
-    combined_datapipe = process_and_combine_datapipes(datapipes_dict, configuration)
-
-    if check_satellite_no_zeros:
-        # in production we don't want any nans in the satellite data
-        combined_datapipe = combined_datapipe.map(check_nans_in_satellite_data)
+    combined_datapipe = process_and_combine_datapipes(
+        datapipes_dict, 
+        configuration, 
+        check_satellite_no_nans
+    )
 
     return combined_datapipe
 
diff --git a/tests/production/test_pvnet_production.py b/tests/production/test_pvnet_production.py
index 572d19766..8f2fae68c 100644
--- a/tests/production/test_pvnet_production.py
+++ b/tests/production/test_pvnet_production.py
@@ -40,7 +40,7 @@ def test_construct_sliced_data_pipeline(configuration_filename, gsp_yields):
         configuration_filename,
         location_pipe=loc_pipe,
         t0_datapipe=t0_pipe,
-        check_satellite_no_zeros=True,
+        check_satellite_no_nans=True,
         production=True,
     )
 
@@ -48,7 +48,7 @@ def test_construct_sliced_data_pipeline(configuration_filename, gsp_yields):
 
 
 @freeze_time("2020-04-01 02:30:00")
-def test_construct_sliced_data_pipeline_satellite_with_zeros(configuration_filename, gsp_yields):
+def test_construct_sliced_data_pipeline_satellite_with_nans(configuration_filename, gsp_yields):
     # This is randomly chosen, but real, GSP location
     loc_pipe = IterableWrapper([Location(x=246699.328125, y=849771.9375, id=18)])
 
@@ -59,8 +59,8 @@ def test_construct_sliced_data_pipeline_satellite_with_zeros(configuration_filen
         configuration_filename,
         location_pipe=loc_pipe,
         t0_datapipe=t0_pipe,
-        check_satellite_no_zeros=True,
+        check_satellite_no_nans=True,
         production=True,
     )
     with pytest.raises(ValueError):
-        _ = next(iter(dp))
+        batch = next(iter(dp))
diff --git a/tests/select/test_select_spatial_slice.py b/tests/select/test_select_spatial_slice.py
index 7e7c2a7fb..cdb3a89f2 100644
--- a/tests/select/test_select_spatial_slice.py
+++ b/tests/select/test_select_spatial_slice.py
@@ -8,10 +8,10 @@
     SelectSpatialSlicePixels,
 )
 
-from ocf_datapipes.select.select_spatial_slice import select_spatial_slice_pixels
+from ocf_datapipes.select.select_spatial_slice import slice_spatial_pixel_window_from_xarray
 
 
-def test_select_spatial_slice_pixels_function():
+def test_slice_spatial_pixel_window_from_xarray_function():
     # Create dummy data
     x = np.arange(100)
     y = np.arange(100)[::-1]
@@ -29,7 +29,7 @@ def test_select_spatial_slice_pixels_function():
     center_idx = Location(x=10, y=10, coordinate_system="idx")
 
     # Select window which lies within data
-    xr_selected = select_spatial_slice_pixels(
+    xr_selected = slice_spatial_pixel_window_from_xarray(
         xr_data,
         center_idx,
         width_pixels=10,
@@ -44,7 +44,7 @@ def test_select_spatial_slice_pixels_function():
     assert not xr_selected.data.isnull().any()
 
     # Select window where the edge of the window lies at the edge of the data
-    xr_selected = select_spatial_slice_pixels(
+    xr_selected = slice_spatial_pixel_window_from_xarray(
         xr_data,
         center_idx,
         width_pixels=20,
@@ -59,7 +59,7 @@ def test_select_spatial_slice_pixels_function():
     assert not xr_selected.data.isnull().any()
 
     # Select window which is partially outside the boundary of the data
-    xr_selected = select_spatial_slice_pixels(
+    xr_selected = slice_spatial_pixel_window_from_xarray(
         xr_data,
         center_idx,
         width_pixels=30,

From 3029889287c8ab89007a278bd1a297c135d98233 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Fri, 31 May 2024 13:29:39 +0000
Subject: [PATCH 11/27] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 ocf_datapipes/training/common.py | 3 +--
 ocf_datapipes/training/pvnet.py  | 6 ++----
 2 files changed, 3 insertions(+), 6 deletions(-)

diff --git a/ocf_datapipes/training/common.py b/ocf_datapipes/training/common.py
index 0f270c7ff..01007c116 100644
--- a/ocf_datapipes/training/common.py
+++ b/ocf_datapipes/training/common.py
@@ -936,7 +936,6 @@ def check_nans_in_satellite_data(batch: NumpyBatch) -> NumpyBatch:
     Check if there are any Nans values in the satellite data.
     """
     if np.any(np.isnan(batch[BatchKey.satellite_actual])):
-        
         logger.error("Found nans values in satellite data")
         logger.error(batch[BatchKey.satellite_actual].shape)
 
@@ -947,7 +946,7 @@ def check_nans_in_satellite_data(batch: NumpyBatch) -> NumpyBatch:
                     sat_data_one_step = batch[BatchKey.satellite_actual][t]
                 else:
                     sat_data_one_step = batch[BatchKey.satellite_actual][:, t]
-                
+
                 nans = np.isnan(sat_data_one_step)
 
                 if np.any(nans):
diff --git a/ocf_datapipes/training/pvnet.py b/ocf_datapipes/training/pvnet.py
index d6a6aff46..7b82ac85b 100644
--- a/ocf_datapipes/training/pvnet.py
+++ b/ocf_datapipes/training/pvnet.py
@@ -131,7 +131,7 @@ def process_and_combine_datapipes(datapipes_dict, configuration, check_satellite
     if check_satellite_no_nans:
         # in production we don't want any nans in the satellite data
         combined_datapipe = combined_datapipe.map(check_nans_in_satellite_data)
-    
+
     combined_datapipe = combined_datapipe.map(fill_nans_in_arrays)
 
     return combined_datapipe
@@ -171,9 +171,7 @@ def construct_sliced_data_pipeline(
 
     # Normalise, and combine the data sources into NumpyBatches
     combined_datapipe = process_and_combine_datapipes(
-        datapipes_dict, 
-        configuration, 
-        check_satellite_no_nans
+        datapipes_dict, configuration, check_satellite_no_nans
     )
 
     return combined_datapipe

From 9a0dde13648dc5f5cee4c224e6dad171ba6772a8 Mon Sep 17 00:00:00 2001
From: James Fulton <djamesfulton@yahoo.co.uk>
Date: Fri, 31 May 2024 13:58:11 +0000
Subject: [PATCH 12/27] linting

---
 ocf_datapipes/training/pvnet_all_gsp.py | 43 ++++++++++++++++++-------
 1 file changed, 31 insertions(+), 12 deletions(-)

diff --git a/ocf_datapipes/training/pvnet_all_gsp.py b/ocf_datapipes/training/pvnet_all_gsp.py
index bd3b47623..27e07cab7 100644
--- a/ocf_datapipes/training/pvnet_all_gsp.py
+++ b/ocf_datapipes/training/pvnet_all_gsp.py
@@ -49,6 +49,7 @@
 
 
 def xr_compute(xr_data):
+    """Compute the xarray object"""
     return xr_data.compute()
 
 
@@ -64,11 +65,19 @@ def __init__(self, num_repeats):
         self.num_repeats = num_repeats
 
     def __call__(self, x):
+        """Repeat the input a number of times as a list"""
         return [x for _ in range(self.num_repeats)]
 
 
 class ConvertWrapper(IterDataPipe):
+    """A class to adapt our Convert[X]ToNumpyBatch datapipes to work on a list of samples"""
     def __init__(self, source_datapipe: IterDataPipe, convert_class: Type[IterDataPipe]):
+        """A class to adapt our Convert[X]ToNumpyBatch datapipes to work on a list of samples
+        
+        Args:
+            source_datapipe: The source datapipe yielding lists of samples
+            convert_class: The class to apply to the output of source_datapipe
+        """
         self.source_datapipe = source_datapipe
         self.convert_class = convert_class
 
@@ -106,6 +115,7 @@ def __init__(
 
         Args:
             source_datapipe: Datapipe of Xarray data
+            locations: List of all locations to create samples for
             roi_height_pixels: ROI height in pixels
             roi_width_pixels: ROI width in pixels
             allow_partial_slice: Whether to allow a partial slice.
@@ -155,7 +165,7 @@ def __init__(
 
         Args:
             source_datapipe: Datapipe of Xarray data
-            location_datapipe: Location datapipe
+            locations: List of all locations to create samples for
             roi_width_meters: ROI width in meters
             roi_height_meters: ROI height in meters
             dim_name: Dimension name to select for ID, None for coordinates
@@ -295,7 +305,7 @@ def slice_datapipes_by_space_all_gsps(
 
     if "pv" in datapipes_dict:
         # No spatial slice for PV since it is always the same, just repeat for GSPs
-        pv_datapipe = pv_datapipe.map(SampleRepeat(len(locations)))
+        datapipes_dict["pv"] = datapipes_dict["pv"].map(SampleRepeat(len(locations)))
 
     # GSP always assumed to be in data
     datapipes_dict["gsp"] = datapipes_dict["gsp"].select_all_gsp_spatial_slice_meters(
@@ -310,6 +320,10 @@ def slice_datapipes_by_space_all_gsps(
 
 
 def pre_spatial_slice_process(datapipes_dict, configuration):
+    """Apply pre-processing steps to the dictionary of datapipes in place
+    
+    These steps are normalisation and recombining past and future GSP/PV data
+    """
     conf_nwp = configuration.input_data.nwp
 
     if "nwp" in datapipes_dict:
@@ -333,6 +347,8 @@ def pre_spatial_slice_process(datapipes_dict, configuration):
             .map(normalize_pv)
             .map(fill_nans_in_pv)
         )
+        
+        del datapipes_dict["pv_future"]
 
     # GSP always assumed to be in data
     # Recombine GSP arrays - see function doc for further explanation
@@ -342,10 +358,12 @@ def pre_spatial_slice_process(datapipes_dict, configuration):
         .map(concat_xr_time_utc)
         .map(normalize_gsp)
     )
+    
+    del datapipes_dict["gsp_future"]
 
-
-def post_spatial_slice_process(datapipes_dict):
-    # Spatially slice, normalize, and convert data to numpy arrays
+def post_spatial_slice_process(datapipes_dict, check_satellite_no_nans=False):
+    """Convert the dictionary of datapipes to NumpyBatches, combine, and fill nans"""
+    
     numpy_modalities = []
 
     if "nwp" in datapipes_dict:
@@ -386,8 +404,13 @@ def post_spatial_slice_process(datapipes_dict):
     combined_datapipe = (
         MergeNumpyModalities(numpy_modalities)
         .add_sun_position(modality_name="gsp")
-        .map(fill_nans_in_arrays)
     )
+    
+    if check_satellite_no_nans:
+        # in production we don't want any nans in the satellite data
+        combined_datapipe = combined_datapipe.map(check_nans_in_satellite_data)
+        
+    combined_datapipe = combined_datapipe.map(fill_nans_in_arrays)
 
     return combined_datapipe
 
@@ -399,7 +422,7 @@ def construct_sliced_data_pipeline(
     config_filename: str,
     t0_datapipe: IterDataPipe,
     production: bool = False,
-    check_satellite_no_zeros: bool = False,
+    check_satellite_no_nans: bool = False,
 ) -> IterDataPipe:
     """Constructs data pipeline for the input data config file.
 
@@ -409,7 +432,7 @@ def construct_sliced_data_pipeline(
         config_filename: Path to config file.
         t0_datapipe: Datapipe yielding times.
         production: Whether constucting pipeline for production inference.
-        check_satellite_no_zeros: Whether to check that satellite data has no zeros.
+        check_satellite_no_nans: Whether to check that satellite data has no nans.
     """
 
     datapipes_dict = _get_datapipes_dict(
@@ -436,10 +459,6 @@ def construct_sliced_data_pipeline(
     # Convert to NumpyBatch
     combined_datapipe = post_spatial_slice_process(datapipes_dict)
 
-    if check_satellite_no_zeros:
-        # in production we don't want any nans in the satellite data
-        combined_datapipe = combined_datapipe.map(check_nans_in_satellite_data)
-
     return combined_datapipe
 
 

From ab5b6498f4aa8aa94edf8919af11e2d62677e964 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Fri, 31 May 2024 13:58:47 +0000
Subject: [PATCH 13/27] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 ocf_datapipes/training/pvnet_all_gsp.py | 21 ++++++++++-----------
 1 file changed, 10 insertions(+), 11 deletions(-)

diff --git a/ocf_datapipes/training/pvnet_all_gsp.py b/ocf_datapipes/training/pvnet_all_gsp.py
index 27e07cab7..270b58268 100644
--- a/ocf_datapipes/training/pvnet_all_gsp.py
+++ b/ocf_datapipes/training/pvnet_all_gsp.py
@@ -71,9 +71,10 @@ def __call__(self, x):
 
 class ConvertWrapper(IterDataPipe):
     """A class to adapt our Convert[X]ToNumpyBatch datapipes to work on a list of samples"""
+
     def __init__(self, source_datapipe: IterDataPipe, convert_class: Type[IterDataPipe]):
         """A class to adapt our Convert[X]ToNumpyBatch datapipes to work on a list of samples
-        
+
         Args:
             source_datapipe: The source datapipe yielding lists of samples
             convert_class: The class to apply to the output of source_datapipe
@@ -321,7 +322,7 @@ def slice_datapipes_by_space_all_gsps(
 
 def pre_spatial_slice_process(datapipes_dict, configuration):
     """Apply pre-processing steps to the dictionary of datapipes in place
-    
+
     These steps are normalisation and recombining past and future GSP/PV data
     """
     conf_nwp = configuration.input_data.nwp
@@ -347,7 +348,7 @@ def pre_spatial_slice_process(datapipes_dict, configuration):
             .map(normalize_pv)
             .map(fill_nans_in_pv)
         )
-        
+
         del datapipes_dict["pv_future"]
 
     # GSP always assumed to be in data
@@ -358,12 +359,13 @@ def pre_spatial_slice_process(datapipes_dict, configuration):
         .map(concat_xr_time_utc)
         .map(normalize_gsp)
     )
-    
+
     del datapipes_dict["gsp_future"]
 
+
 def post_spatial_slice_process(datapipes_dict, check_satellite_no_nans=False):
     """Convert the dictionary of datapipes to NumpyBatches, combine, and fill nans"""
-    
+
     numpy_modalities = []
 
     if "nwp" in datapipes_dict:
@@ -401,15 +403,12 @@ def post_spatial_slice_process(datapipes_dict, check_satellite_no_nans=False):
     )
 
     # Combine all the data sources
-    combined_datapipe = (
-        MergeNumpyModalities(numpy_modalities)
-        .add_sun_position(modality_name="gsp")
-    )
-    
+    combined_datapipe = MergeNumpyModalities(numpy_modalities).add_sun_position(modality_name="gsp")
+
     if check_satellite_no_nans:
         # in production we don't want any nans in the satellite data
         combined_datapipe = combined_datapipe.map(check_nans_in_satellite_data)
-        
+
     combined_datapipe = combined_datapipe.map(fill_nans_in_arrays)
 
     return combined_datapipe

From eb12fc73a2b1b2b43ca7ca2ff2a64579c8d2496f Mon Sep 17 00:00:00 2001
From: James Fulton <djamesfulton@yahoo.co.uk>
Date: Fri, 31 May 2024 14:01:40 +0000
Subject: [PATCH 14/27] linting

---
 ocf_datapipes/training/pvnet_all_gsp.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/ocf_datapipes/training/pvnet_all_gsp.py b/ocf_datapipes/training/pvnet_all_gsp.py
index 270b58268..ad9174bbe 100644
--- a/ocf_datapipes/training/pvnet_all_gsp.py
+++ b/ocf_datapipes/training/pvnet_all_gsp.py
@@ -286,6 +286,7 @@ def slice_datapipes_by_space_all_gsps(
     locations: list[Location],
     configuration: Configuration,
 ) -> None:
+    """Slice the dictionary of datapipes by space in-place"""
     conf_nwp = configuration.input_data.nwp
     conf_sat = configuration.input_data.satellite
 

From 0180c5ca6b5f070b9fe59cf5d3f8684288341d23 Mon Sep 17 00:00:00 2001
From: James Fulton <djamesfulton@yahoo.co.uk>
Date: Mon, 3 Jun 2024 13:34:16 +0000
Subject: [PATCH 15/27] update tests

---
 tests/conftest.py                             |   5 ++
 tests/data/configs/pvnet_test_config.yaml     |  44 ++++++++++++++++++
 tests/data/gsp/test.zarr/.zattrs              |   2 +-
 tests/data/gsp/test.zarr/.zgroup              |   2 +-
 tests/data/gsp/test.zarr/.zmetadata           |  36 +++++++-------
 tests/data/gsp/test.zarr/capacity_mwp/.zarray |  10 ++--
 tests/data/gsp/test.zarr/capacity_mwp/.zattrs |   2 +-
 tests/data/gsp/test.zarr/capacity_mwp/0.0     | Bin 6396 -> 4627 bytes
 tests/data/gsp/test.zarr/capacity_mwp/0.1     | Bin 6235 -> 0 bytes
 tests/data/gsp/test.zarr/capacity_mwp/1.0     | Bin 0 -> 2665 bytes
 tests/data/gsp/test.zarr/capacity_mwp/2.0     | Bin 0 -> 2665 bytes
 tests/data/gsp/test.zarr/capacity_mwp/3.0     | Bin 0 -> 2697 bytes
 tests/data/gsp/test.zarr/capacity_mwp/4.0     | Bin 0 -> 4252 bytes
 tests/data/gsp/test.zarr/capacity_mwp/5.0     | Bin 0 -> 2665 bytes
 tests/data/gsp/test.zarr/capacity_mwp/6.0     | Bin 0 -> 2665 bytes
 tests/data/gsp/test.zarr/capacity_mwp/7.0     | Bin 0 -> 2697 bytes
 tests/data/gsp/test.zarr/capacity_mwp/8.0     | Bin 0 -> 2665 bytes
 tests/data/gsp/test.zarr/capacity_mwp/9.0     | Bin 0 -> 4599 bytes
 tests/data/gsp/test.zarr/datetime_gmt/.zarray |   6 +--
 tests/data/gsp/test.zarr/datetime_gmt/.zattrs |   4 +-
 tests/data/gsp/test.zarr/datetime_gmt/0       | Bin 1399 -> 200 bytes
 .../data/gsp/test.zarr/generation_mw/.zarray  |  12 ++---
 .../data/gsp/test.zarr/generation_mw/.zattrs  |   2 +-
 tests/data/gsp/test.zarr/generation_mw/0.0    | Bin 4206 -> 236 bytes
 tests/data/gsp/test.zarr/generation_mw/0.1    | Bin 847 -> 0 bytes
 tests/data/gsp/test.zarr/generation_mw/1.0    | Bin 0 -> 19379 bytes
 tests/data/gsp/test.zarr/generation_mw/2.0    | Bin 0 -> 23916 bytes
 tests/data/gsp/test.zarr/generation_mw/3.0    | Bin 0 -> 21783 bytes
 tests/data/gsp/test.zarr/generation_mw/4.0    | Bin 0 -> 236 bytes
 tests/data/gsp/test.zarr/generation_mw/5.0    | Bin 0 -> 330 bytes
 tests/data/gsp/test.zarr/generation_mw/6.0    | Bin 0 -> 23973 bytes
 tests/data/gsp/test.zarr/generation_mw/7.0    | Bin 0 -> 23944 bytes
 tests/data/gsp/test.zarr/generation_mw/8.0    | Bin 0 -> 16983 bytes
 tests/data/gsp/test.zarr/generation_mw/9.0    | Bin 0 -> 244 bytes
 tests/data/gsp/test.zarr/gsp_id/.zarray       |   4 +-
 tests/data/gsp/test.zarr/gsp_id/.zattrs       |   2 +-
 tests/data/gsp/test.zarr/gsp_id/0             | Bin 175 -> 407 bytes
 .../test.zarr/installedcapacity_mwp/.zarray   |  10 ++--
 .../test.zarr/installedcapacity_mwp/.zattrs   |   2 +-
 .../gsp/test.zarr/installedcapacity_mwp/0.0   | Bin 6383 -> 4590 bytes
 .../gsp/test.zarr/installedcapacity_mwp/0.1   | Bin 6224 -> 0 bytes
 .../gsp/test.zarr/installedcapacity_mwp/1.0   | Bin 0 -> 2675 bytes
 .../gsp/test.zarr/installedcapacity_mwp/2.0   | Bin 0 -> 2675 bytes
 .../gsp/test.zarr/installedcapacity_mwp/3.0   | Bin 0 -> 2675 bytes
 .../gsp/test.zarr/installedcapacity_mwp/4.0   | Bin 0 -> 2715 bytes
 .../gsp/test.zarr/installedcapacity_mwp/5.0   | Bin 0 -> 2675 bytes
 .../gsp/test.zarr/installedcapacity_mwp/6.0   | Bin 0 -> 2675 bytes
 .../gsp/test.zarr/installedcapacity_mwp/7.0   | Bin 0 -> 2675 bytes
 .../gsp/test.zarr/installedcapacity_mwp/8.0   | Bin 0 -> 2675 bytes
 .../gsp/test.zarr/installedcapacity_mwp/9.0   | Bin 0 -> 4530 bytes
 tests/training/test_common.py                 |  15 ++++++
 tests/training/test_pvnet.py                  |  24 ++--------
 tests/training/test_pvnet_all_gsp.py          |  42 +++++++++++++++++
 53 files changed, 158 insertions(+), 66 deletions(-)
 create mode 100644 tests/data/configs/pvnet_test_config.yaml
 delete mode 100644 tests/data/gsp/test.zarr/capacity_mwp/0.1
 create mode 100644 tests/data/gsp/test.zarr/capacity_mwp/1.0
 create mode 100644 tests/data/gsp/test.zarr/capacity_mwp/2.0
 create mode 100644 tests/data/gsp/test.zarr/capacity_mwp/3.0
 create mode 100644 tests/data/gsp/test.zarr/capacity_mwp/4.0
 create mode 100644 tests/data/gsp/test.zarr/capacity_mwp/5.0
 create mode 100644 tests/data/gsp/test.zarr/capacity_mwp/6.0
 create mode 100644 tests/data/gsp/test.zarr/capacity_mwp/7.0
 create mode 100644 tests/data/gsp/test.zarr/capacity_mwp/8.0
 create mode 100644 tests/data/gsp/test.zarr/capacity_mwp/9.0
 delete mode 100644 tests/data/gsp/test.zarr/generation_mw/0.1
 create mode 100644 tests/data/gsp/test.zarr/generation_mw/1.0
 create mode 100644 tests/data/gsp/test.zarr/generation_mw/2.0
 create mode 100644 tests/data/gsp/test.zarr/generation_mw/3.0
 create mode 100644 tests/data/gsp/test.zarr/generation_mw/4.0
 create mode 100644 tests/data/gsp/test.zarr/generation_mw/5.0
 create mode 100644 tests/data/gsp/test.zarr/generation_mw/6.0
 create mode 100644 tests/data/gsp/test.zarr/generation_mw/7.0
 create mode 100644 tests/data/gsp/test.zarr/generation_mw/8.0
 create mode 100644 tests/data/gsp/test.zarr/generation_mw/9.0
 delete mode 100644 tests/data/gsp/test.zarr/installedcapacity_mwp/0.1
 create mode 100644 tests/data/gsp/test.zarr/installedcapacity_mwp/1.0
 create mode 100644 tests/data/gsp/test.zarr/installedcapacity_mwp/2.0
 create mode 100644 tests/data/gsp/test.zarr/installedcapacity_mwp/3.0
 create mode 100644 tests/data/gsp/test.zarr/installedcapacity_mwp/4.0
 create mode 100644 tests/data/gsp/test.zarr/installedcapacity_mwp/5.0
 create mode 100644 tests/data/gsp/test.zarr/installedcapacity_mwp/6.0
 create mode 100644 tests/data/gsp/test.zarr/installedcapacity_mwp/7.0
 create mode 100644 tests/data/gsp/test.zarr/installedcapacity_mwp/8.0
 create mode 100644 tests/data/gsp/test.zarr/installedcapacity_mwp/9.0
 create mode 100644 tests/training/test_pvnet_all_gsp.py

diff --git a/tests/conftest.py b/tests/conftest.py
index 34cc5d4b7..94d03972f 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -589,6 +589,11 @@ def nwp_ukv_data_filename(nwp_ukv_data):
         yield filename
 
 
+@pytest.fixture()
+def pvnet_config_filename():
+    return f"{_top_test_directory}/data/configs/pvnet_test_config.yaml"
+
+
 @pytest.fixture()
 def configuration_filename():
     return f"{_top_test_directory}/data/configs/test.yaml"
diff --git a/tests/data/configs/pvnet_test_config.yaml b/tests/data/configs/pvnet_test_config.yaml
new file mode 100644
index 000000000..d3a5c8ff6
--- /dev/null
+++ b/tests/data/configs/pvnet_test_config.yaml
@@ -0,0 +1,44 @@
+general:
+  description: Test config for PVNet
+  name: pvnet_test
+
+input_data:
+  default_history_minutes: 120
+  default_forecast_minutes: 480
+
+  gsp:
+    gsp_zarr_path: tests/data/gsp/test.zarr
+    history_minutes: 120
+    forecast_minutes: 480
+    time_resolution_minutes: 30
+    dropout_timedeltas_minutes: null
+    dropout_fraction: 0
+
+  nwp:
+  
+    ukv:
+        nwp_provider: ukv
+        nwp_zarr_path: tests/data/nwp_data/test.zarr
+        history_minutes: 60
+        forecast_minutes: 120
+        time_resolution_minutes: 60
+        nwp_channels:
+        - t # 2-metre temperature
+        nwp_image_size_pixels_height: 2
+        nwp_image_size_pixels_width: 2
+        dropout_timedeltas_minutes: [-180]
+        dropout_fraction: 1.0
+        max_staleness_minutes: null
+
+  satellite:
+    satellite_zarr_path: tests/data/sat_data.zarr
+    history_minutes: 90
+    forecast_minutes: 0
+    live_delay_minutes: 0
+    time_resolution_minutes: 5
+    satellite_channels:
+      - IR_016
+    satellite_image_size_pixels_height: 2
+    satellite_image_size_pixels_width: 2
+    dropout_timedeltas_minutes: null
+    dropout_fraction: 0
\ No newline at end of file
diff --git a/tests/data/gsp/test.zarr/.zattrs b/tests/data/gsp/test.zarr/.zattrs
index 0967ef424..9e26dfeeb 100644
--- a/tests/data/gsp/test.zarr/.zattrs
+++ b/tests/data/gsp/test.zarr/.zattrs
@@ -1 +1 @@
-{}
+{}
\ No newline at end of file
diff --git a/tests/data/gsp/test.zarr/.zgroup b/tests/data/gsp/test.zarr/.zgroup
index 3f3fad2d1..3b7daf227 100644
--- a/tests/data/gsp/test.zarr/.zgroup
+++ b/tests/data/gsp/test.zarr/.zgroup
@@ -1,3 +1,3 @@
 {
     "zarr_format": 2
-}
+}
\ No newline at end of file
diff --git a/tests/data/gsp/test.zarr/.zmetadata b/tests/data/gsp/test.zarr/.zmetadata
index 0aade4476..2e0f2b7be 100644
--- a/tests/data/gsp/test.zarr/.zmetadata
+++ b/tests/data/gsp/test.zarr/.zmetadata
@@ -6,8 +6,8 @@
         },
         "capacity_mwp/.zarray": {
             "chunks": [
-                9430,
-                20
+                10,
+                318
             ],
             "compressor": {
                 "blocksize": 0,
@@ -21,8 +21,8 @@
             "filters": null,
             "order": "C",
             "shape": [
-                49,
-                22
+                96,
+                318
             ],
             "zarr_format": 2
         },
@@ -34,7 +34,7 @@
         },
         "datetime_gmt/.zarray": {
             "chunks": [
-                37717
+                96
             ],
             "compressor": {
                 "blocksize": 0,
@@ -48,7 +48,7 @@
             "filters": null,
             "order": "C",
             "shape": [
-                49
+                96
             ],
             "zarr_format": 2
         },
@@ -57,17 +57,17 @@
                 "datetime_gmt"
             ],
             "calendar": "proleptic_gregorian",
-            "units": "minutes since 2014-01-01"
+            "units": "minutes since 2020-04-01 00:00:00"
         },
         "generation_mw/.zarray": {
             "chunks": [
-                9430,
-                20
+                10,
+                318
             ],
             "compressor": {
                 "blocksize": 0,
                 "clevel": 5,
-                "cname": "zstd",
+                "cname": "lz4",
                 "id": "blosc",
                 "shuffle": 1
             },
@@ -76,8 +76,8 @@
             "filters": null,
             "order": "C",
             "shape": [
-                49,
-                22
+                96,
+                318
             ],
             "zarr_format": 2
         },
@@ -103,7 +103,7 @@
             "filters": null,
             "order": "C",
             "shape": [
-                22
+                318
             ],
             "zarr_format": 2
         },
@@ -114,8 +114,8 @@
         },
         "installedcapacity_mwp/.zarray": {
             "chunks": [
-                9430,
-                20
+                10,
+                318
             ],
             "compressor": {
                 "blocksize": 0,
@@ -129,8 +129,8 @@
             "filters": null,
             "order": "C",
             "shape": [
-                49,
-                22
+                96,
+                318
             ],
             "zarr_format": 2
         },
@@ -142,4 +142,4 @@
         }
     },
     "zarr_consolidated_format": 1
-}
+}
\ No newline at end of file
diff --git a/tests/data/gsp/test.zarr/capacity_mwp/.zarray b/tests/data/gsp/test.zarr/capacity_mwp/.zarray
index 7534fa734..2fa132365 100644
--- a/tests/data/gsp/test.zarr/capacity_mwp/.zarray
+++ b/tests/data/gsp/test.zarr/capacity_mwp/.zarray
@@ -1,7 +1,7 @@
 {
     "chunks": [
-        9430,
-        20
+        10,
+        318
     ],
     "compressor": {
         "blocksize": 0,
@@ -15,8 +15,8 @@
     "filters": null,
     "order": "C",
     "shape": [
-        49,
-        22
+        96,
+        318
     ],
     "zarr_format": 2
-}
+}
\ No newline at end of file
diff --git a/tests/data/gsp/test.zarr/capacity_mwp/.zattrs b/tests/data/gsp/test.zarr/capacity_mwp/.zattrs
index 758489d41..5d2aefa6f 100644
--- a/tests/data/gsp/test.zarr/capacity_mwp/.zattrs
+++ b/tests/data/gsp/test.zarr/capacity_mwp/.zattrs
@@ -3,4 +3,4 @@
         "datetime_gmt",
         "gsp_id"
     ]
-}
+}
\ No newline at end of file
diff --git a/tests/data/gsp/test.zarr/capacity_mwp/0.0 b/tests/data/gsp/test.zarr/capacity_mwp/0.0
index 8e5b65e7c70201e379325c6b4e378c56b52ae3e6..3c2036b08e735c335c38a4063666748ccc2901df 100644
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diff --git a/tests/data/gsp/test.zarr/capacity_mwp/7.0 b/tests/data/gsp/test.zarr/capacity_mwp/7.0
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diff --git a/tests/data/gsp/test.zarr/datetime_gmt/.zarray b/tests/data/gsp/test.zarr/datetime_gmt/.zarray
index 3fad0aa37..480d663c2 100644
--- a/tests/data/gsp/test.zarr/datetime_gmt/.zarray
+++ b/tests/data/gsp/test.zarr/datetime_gmt/.zarray
@@ -1,6 +1,6 @@
 {
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-        37717
+        96
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     "compressor": {
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@@ -14,7 +14,7 @@
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     "order": "C",
     "shape": [
-        49
+        96
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\ No newline at end of file
diff --git a/tests/data/gsp/test.zarr/datetime_gmt/.zattrs b/tests/data/gsp/test.zarr/datetime_gmt/.zattrs
index 83ad40ac4..c4110214b 100644
--- a/tests/data/gsp/test.zarr/datetime_gmt/.zattrs
+++ b/tests/data/gsp/test.zarr/datetime_gmt/.zattrs
@@ -3,5 +3,5 @@
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     ],
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-    "units": "minutes since 2014-01-01"
-}
+    "units": "minutes since 2020-04-01 00:00:00"
+}
\ No newline at end of file
diff --git a/tests/data/gsp/test.zarr/datetime_gmt/0 b/tests/data/gsp/test.zarr/datetime_gmt/0
index e1b4c4836ea87ff3b91d807f3507310b6b6b40b9..136f201fb637cffd9e35b6de831f042245e731dc 100644
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diff --git a/tests/data/gsp/test.zarr/generation_mw/.zarray b/tests/data/gsp/test.zarr/generation_mw/.zarray
index b6ee6cf8c..2fa132365 100644
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+++ b/tests/data/gsp/test.zarr/generation_mw/.zarray
@@ -1,12 +1,12 @@
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diff --git a/tests/data/gsp/test.zarr/generation_mw/0.1 b/tests/data/gsp/test.zarr/generation_mw/0.1
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diff --git a/tests/data/gsp/test.zarr/generation_mw/8.0 b/tests/data/gsp/test.zarr/generation_mw/8.0
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diff --git a/tests/data/gsp/test.zarr/generation_mw/9.0 b/tests/data/gsp/test.zarr/generation_mw/9.0
new file mode 100644
index 0000000000000000000000000000000000000000..827e35769ddc578ccb3fccea6d37909ef6f55194
GIT binary patch
literal 244
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literal 0
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diff --git a/tests/data/gsp/test.zarr/gsp_id/.zarray b/tests/data/gsp/test.zarr/gsp_id/.zarray
index 9a1f6f31e..dad10ae3c 100644
--- a/tests/data/gsp/test.zarr/gsp_id/.zarray
+++ b/tests/data/gsp/test.zarr/gsp_id/.zarray
@@ -14,7 +14,7 @@
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     "order": "C",
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-        22
+        318
     ],
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-}
+}
\ No newline at end of file
diff --git a/tests/data/gsp/test.zarr/gsp_id/.zattrs b/tests/data/gsp/test.zarr/gsp_id/.zattrs
index 6de1b9d63..243f4f1b5 100644
--- a/tests/data/gsp/test.zarr/gsp_id/.zattrs
+++ b/tests/data/gsp/test.zarr/gsp_id/.zattrs
@@ -2,4 +2,4 @@
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-}
+}
\ No newline at end of file
diff --git a/tests/data/gsp/test.zarr/gsp_id/0 b/tests/data/gsp/test.zarr/gsp_id/0
index b3ff4eaaadf766df2e5e7adf55fbc5fc872efedd..840af40af1b982fff4c89240cb8a2087c68d2977 100644
GIT binary patch
delta 321
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diff --git a/tests/data/gsp/test.zarr/installedcapacity_mwp/.zarray b/tests/data/gsp/test.zarr/installedcapacity_mwp/.zarray
index 7534fa734..2fa132365 100644
--- a/tests/data/gsp/test.zarr/installedcapacity_mwp/.zarray
+++ b/tests/data/gsp/test.zarr/installedcapacity_mwp/.zarray
@@ -1,7 +1,7 @@
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diff --git a/tests/data/gsp/test.zarr/installedcapacity_mwp/.zattrs b/tests/data/gsp/test.zarr/installedcapacity_mwp/.zattrs
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\ No newline at end of file
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diff --git a/tests/data/gsp/test.zarr/installedcapacity_mwp/0.1 b/tests/data/gsp/test.zarr/installedcapacity_mwp/0.1
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index 7824ab3eb8354ca5d75f2570c5afa40a52834845..0000000000000000000000000000000000000000
GIT binary patch
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diff --git a/tests/data/gsp/test.zarr/installedcapacity_mwp/1.0 b/tests/data/gsp/test.zarr/installedcapacity_mwp/1.0
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diff --git a/tests/data/gsp/test.zarr/installedcapacity_mwp/4.0 b/tests/data/gsp/test.zarr/installedcapacity_mwp/4.0
new file mode 100644
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diff --git a/tests/data/gsp/test.zarr/installedcapacity_mwp/5.0 b/tests/data/gsp/test.zarr/installedcapacity_mwp/5.0
new file mode 100644
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diff --git a/tests/data/gsp/test.zarr/installedcapacity_mwp/6.0 b/tests/data/gsp/test.zarr/installedcapacity_mwp/6.0
new file mode 100644
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diff --git a/tests/data/gsp/test.zarr/installedcapacity_mwp/7.0 b/tests/data/gsp/test.zarr/installedcapacity_mwp/7.0
new file mode 100644
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GIT binary patch
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diff --git a/tests/data/gsp/test.zarr/installedcapacity_mwp/8.0 b/tests/data/gsp/test.zarr/installedcapacity_mwp/8.0
new file mode 100644
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diff --git a/tests/data/gsp/test.zarr/installedcapacity_mwp/9.0 b/tests/data/gsp/test.zarr/installedcapacity_mwp/9.0
new file mode 100644
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literal 0
HcmV?d00001

diff --git a/tests/training/test_common.py b/tests/training/test_common.py
index 89efef6a2..4af6cd3c7 100644
--- a/tests/training/test_common.py
+++ b/tests/training/test_common.py
@@ -9,6 +9,7 @@
     get_and_return_overlapping_time_periods_and_t0,
     open_and_return_datapipes,
     create_t0_and_loc_datapipes,
+    construct_loctime_pipelines,
 )
 
 import fsspec
@@ -111,3 +112,17 @@ def test_create_t0_and_loc_datapipes(configuration_filename):
     loc0, t0 = next(iter(location_pipe.zip(t0_datapipe)))
     assert isinstance(loc0, Location)
     assert isinstance(t0, np.datetime64)
+
+    
+def test_construct_loctime_pipelines(configuration_filename):
+    start_time = datetime(1900, 1, 1)
+    end_time = datetime(2050, 1, 1)
+
+    loc_pipe, t0_pipe = construct_loctime_pipelines(
+        configuration_filename,
+        start_time=start_time,
+        end_time=end_time,
+    )
+
+    next(iter(loc_pipe))
+    next(iter(t0_pipe))
diff --git a/tests/training/test_pvnet.py b/tests/training/test_pvnet.py
index 16b5f6c0d..89bde4be7 100644
--- a/tests/training/test_pvnet.py
+++ b/tests/training/test_pvnet.py
@@ -6,26 +6,12 @@
     construct_sliced_data_pipeline,
     pvnet_datapipe,
 )
-from ocf_datapipes.training.common import construct_loctime_pipelines
 from ocf_datapipes.batch import BatchKey, NWPBatchKey
 from ocf_datapipes.utils import Location
 
 
-def test_construct_loctime_pipelines(configuration_filename):
-    start_time = datetime(1900, 1, 1)
-    end_time = datetime(2050, 1, 1)
 
-    loc_pipe, t0_pipe = construct_loctime_pipelines(
-        configuration_filename,
-        start_time=start_time,
-        end_time=end_time,
-    )
-
-    next(iter(loc_pipe))
-    next(iter(t0_pipe))
-
-
-def test_construct_sliced_data_pipeline(configuration_filename):
+def test_construct_sliced_data_pipeline(pvnet_config_filename):
     # This is randomly chosen, but real, GSP location
     loc_pipe = IterableWrapper([Location(x=246699.328125, y=849771.9375, id=18)])
 
@@ -33,7 +19,7 @@ def test_construct_sliced_data_pipeline(configuration_filename):
     t0_pipe = IterableWrapper([datetime(2020, 4, 1, 13, 30)])
 
     dp = construct_sliced_data_pipeline(
-        configuration_filename,
+        pvnet_config_filename,
         location_pipe=loc_pipe,
         t0_datapipe=t0_pipe,
     )
@@ -44,12 +30,12 @@ def test_construct_sliced_data_pipeline(configuration_filename):
         assert NWPBatchKey.nwp in batch[BatchKey.nwp][nwp_source]
 
 
-def test_pvnet_datapipe(configuration_filename):
+def test_pvnet_datapipe(pvnet_config_filename):
     start_time = datetime(1900, 1, 1)
     end_time = datetime(2050, 1, 1)
 
     dp = pvnet_datapipe(
-        configuration_filename,
+        pvnet_config_filename,
         start_time=start_time,
         end_time=end_time,
     )
@@ -57,4 +43,4 @@ def test_pvnet_datapipe(configuration_filename):
     batch = next(iter(dp))
     assert BatchKey.nwp in batch
     for nwp_source in batch[BatchKey.nwp].keys():
-        assert NWPBatchKey.nwp in batch[BatchKey.nwp][nwp_source]
+        assert NWPBatchKey.nwp in batch[BatchKey.nwp][nwp_source]
\ No newline at end of file
diff --git a/tests/training/test_pvnet_all_gsp.py b/tests/training/test_pvnet_all_gsp.py
new file mode 100644
index 000000000..5b0afb182
--- /dev/null
+++ b/tests/training/test_pvnet_all_gsp.py
@@ -0,0 +1,42 @@
+from datetime import datetime
+
+from torch.utils.data.datapipes.iter import IterableWrapper
+
+from ocf_datapipes.training.pvnet_all_gsp import (
+    construct_sliced_data_pipeline,
+    pvnet_all_gsp_datapipe,
+)
+from ocf_datapipes.batch import BatchKey, NWPBatchKey
+
+
+
+def test_construct_sliced_data_pipeline(pvnet_config_filename):
+
+    # This is a randomly chosen time in the middle of the test data
+    t0_pipe = IterableWrapper([datetime(2020, 4, 1, 13, 30)])
+
+    dp = construct_sliced_data_pipeline(
+        pvnet_config_filename,
+        t0_datapipe=t0_pipe,
+    )
+
+    batch = next(iter(dp))
+    assert BatchKey.nwp in batch
+    for nwp_source in batch[BatchKey.nwp].keys():
+        assert NWPBatchKey.nwp in batch[BatchKey.nwp][nwp_source]
+
+
+def test_pvnet_all_gsp_datapipe(pvnet_config_filename):
+    start_time = datetime(1900, 1, 1)
+    end_time = datetime(2050, 1, 1)
+
+    dp = pvnet_all_gsp_datapipe(
+        pvnet_config_filename,
+        start_time=start_time,
+        end_time=end_time,
+    )
+
+    batch = next(iter(dp))
+    assert BatchKey.nwp in batch
+    for nwp_source in batch[BatchKey.nwp].keys():
+        assert NWPBatchKey.nwp in batch[BatchKey.nwp][nwp_source]

From 4eb77a38323986d777b5959e1d04482e12721ace Mon Sep 17 00:00:00 2001
From: James Fulton <djamesfulton@yahoo.co.uk>
Date: Mon, 3 Jun 2024 13:37:00 +0000
Subject: [PATCH 16/27] remove hack

---
 ocf_datapipes/load/nwp/providers/ecmwf.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/ocf_datapipes/load/nwp/providers/ecmwf.py b/ocf_datapipes/load/nwp/providers/ecmwf.py
index a2aac5793..318f889b0 100644
--- a/ocf_datapipes/load/nwp/providers/ecmwf.py
+++ b/ocf_datapipes/load/nwp/providers/ecmwf.py
@@ -18,7 +18,6 @@ def open_ifs(zarr_path) -> xr.DataArray:
     """
     # Open the data
     nwp = open_zarr_paths(zarr_path)
-    nwp = nwp.reindex(latitude=np.concatenate([np.arange(62, 60, -0.05), nwp.latitude.values]))
     dataVars = list(nwp.data_vars.keys())
     if len(dataVars) > 1:
         raise Exception("Too many TLDVs")

From d0b31f7ed3455cc6e359538aaf3b018d6c8b1723 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Mon, 3 Jun 2024 13:37:32 +0000
Subject: [PATCH 17/27] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 ocf_datapipes/load/nwp/providers/ecmwf.py     |  1 -
 tests/data/configs/pvnet_test_config.yaml     | 25 +++++++++----------
 tests/data/gsp/test.zarr/.zattrs              |  2 +-
 tests/data/gsp/test.zarr/.zgroup              |  2 +-
 tests/data/gsp/test.zarr/.zmetadata           |  2 +-
 tests/data/gsp/test.zarr/capacity_mwp/.zarray |  2 +-
 tests/data/gsp/test.zarr/capacity_mwp/.zattrs |  2 +-
 tests/data/gsp/test.zarr/datetime_gmt/.zarray |  2 +-
 tests/data/gsp/test.zarr/datetime_gmt/.zattrs |  2 +-
 .../data/gsp/test.zarr/generation_mw/.zarray  |  2 +-
 .../data/gsp/test.zarr/generation_mw/.zattrs  |  2 +-
 tests/data/gsp/test.zarr/gsp_id/.zarray       |  2 +-
 tests/data/gsp/test.zarr/gsp_id/.zattrs       |  2 +-
 .../test.zarr/installedcapacity_mwp/.zarray   |  2 +-
 .../test.zarr/installedcapacity_mwp/.zattrs   |  2 +-
 tests/training/test_common.py                 |  2 +-
 tests/training/test_pvnet.py                  |  3 +--
 tests/training/test_pvnet_all_gsp.py          |  2 --
 18 files changed, 27 insertions(+), 32 deletions(-)

diff --git a/ocf_datapipes/load/nwp/providers/ecmwf.py b/ocf_datapipes/load/nwp/providers/ecmwf.py
index 318f889b0..a4997b8e9 100644
--- a/ocf_datapipes/load/nwp/providers/ecmwf.py
+++ b/ocf_datapipes/load/nwp/providers/ecmwf.py
@@ -1,7 +1,6 @@
 """ECMWF provider loaders"""
 import pandas as pd
 import xarray as xr
-import numpy as np
 
 from ocf_datapipes.load.nwp.providers.utils import open_zarr_paths
 
diff --git a/tests/data/configs/pvnet_test_config.yaml b/tests/data/configs/pvnet_test_config.yaml
index d3a5c8ff6..fb4d6c3ea 100644
--- a/tests/data/configs/pvnet_test_config.yaml
+++ b/tests/data/configs/pvnet_test_config.yaml
@@ -15,20 +15,19 @@ input_data:
     dropout_fraction: 0
 
   nwp:
-  
     ukv:
-        nwp_provider: ukv
-        nwp_zarr_path: tests/data/nwp_data/test.zarr
-        history_minutes: 60
-        forecast_minutes: 120
-        time_resolution_minutes: 60
-        nwp_channels:
+      nwp_provider: ukv
+      nwp_zarr_path: tests/data/nwp_data/test.zarr
+      history_minutes: 60
+      forecast_minutes: 120
+      time_resolution_minutes: 60
+      nwp_channels:
         - t # 2-metre temperature
-        nwp_image_size_pixels_height: 2
-        nwp_image_size_pixels_width: 2
-        dropout_timedeltas_minutes: [-180]
-        dropout_fraction: 1.0
-        max_staleness_minutes: null
+      nwp_image_size_pixels_height: 2
+      nwp_image_size_pixels_width: 2
+      dropout_timedeltas_minutes: [-180]
+      dropout_fraction: 1.0
+      max_staleness_minutes: null
 
   satellite:
     satellite_zarr_path: tests/data/sat_data.zarr
@@ -41,4 +40,4 @@ input_data:
     satellite_image_size_pixels_height: 2
     satellite_image_size_pixels_width: 2
     dropout_timedeltas_minutes: null
-    dropout_fraction: 0
\ No newline at end of file
+    dropout_fraction: 0
diff --git a/tests/data/gsp/test.zarr/.zattrs b/tests/data/gsp/test.zarr/.zattrs
index 9e26dfeeb..0967ef424 100644
--- a/tests/data/gsp/test.zarr/.zattrs
+++ b/tests/data/gsp/test.zarr/.zattrs
@@ -1 +1 @@
-{}
\ No newline at end of file
+{}
diff --git a/tests/data/gsp/test.zarr/.zgroup b/tests/data/gsp/test.zarr/.zgroup
index 3b7daf227..3f3fad2d1 100644
--- a/tests/data/gsp/test.zarr/.zgroup
+++ b/tests/data/gsp/test.zarr/.zgroup
@@ -1,3 +1,3 @@
 {
     "zarr_format": 2
-}
\ No newline at end of file
+}
diff --git a/tests/data/gsp/test.zarr/.zmetadata b/tests/data/gsp/test.zarr/.zmetadata
index 2e0f2b7be..9fc94d56f 100644
--- a/tests/data/gsp/test.zarr/.zmetadata
+++ b/tests/data/gsp/test.zarr/.zmetadata
@@ -142,4 +142,4 @@
         }
     },
     "zarr_consolidated_format": 1
-}
\ No newline at end of file
+}
diff --git a/tests/data/gsp/test.zarr/capacity_mwp/.zarray b/tests/data/gsp/test.zarr/capacity_mwp/.zarray
index 2fa132365..1b355953d 100644
--- a/tests/data/gsp/test.zarr/capacity_mwp/.zarray
+++ b/tests/data/gsp/test.zarr/capacity_mwp/.zarray
@@ -19,4 +19,4 @@
         318
     ],
     "zarr_format": 2
-}
\ No newline at end of file
+}
diff --git a/tests/data/gsp/test.zarr/capacity_mwp/.zattrs b/tests/data/gsp/test.zarr/capacity_mwp/.zattrs
index 5d2aefa6f..758489d41 100644
--- a/tests/data/gsp/test.zarr/capacity_mwp/.zattrs
+++ b/tests/data/gsp/test.zarr/capacity_mwp/.zattrs
@@ -3,4 +3,4 @@
         "datetime_gmt",
         "gsp_id"
     ]
-}
\ No newline at end of file
+}
diff --git a/tests/data/gsp/test.zarr/datetime_gmt/.zarray b/tests/data/gsp/test.zarr/datetime_gmt/.zarray
index 480d663c2..513e45066 100644
--- a/tests/data/gsp/test.zarr/datetime_gmt/.zarray
+++ b/tests/data/gsp/test.zarr/datetime_gmt/.zarray
@@ -17,4 +17,4 @@
         96
     ],
     "zarr_format": 2
-}
\ No newline at end of file
+}
diff --git a/tests/data/gsp/test.zarr/datetime_gmt/.zattrs b/tests/data/gsp/test.zarr/datetime_gmt/.zattrs
index c4110214b..355ac1574 100644
--- a/tests/data/gsp/test.zarr/datetime_gmt/.zattrs
+++ b/tests/data/gsp/test.zarr/datetime_gmt/.zattrs
@@ -4,4 +4,4 @@
     ],
     "calendar": "proleptic_gregorian",
     "units": "minutes since 2020-04-01 00:00:00"
-}
\ No newline at end of file
+}
diff --git a/tests/data/gsp/test.zarr/generation_mw/.zarray b/tests/data/gsp/test.zarr/generation_mw/.zarray
index 2fa132365..1b355953d 100644
--- a/tests/data/gsp/test.zarr/generation_mw/.zarray
+++ b/tests/data/gsp/test.zarr/generation_mw/.zarray
@@ -19,4 +19,4 @@
         318
     ],
     "zarr_format": 2
-}
\ No newline at end of file
+}
diff --git a/tests/data/gsp/test.zarr/generation_mw/.zattrs b/tests/data/gsp/test.zarr/generation_mw/.zattrs
index 5d2aefa6f..758489d41 100644
--- a/tests/data/gsp/test.zarr/generation_mw/.zattrs
+++ b/tests/data/gsp/test.zarr/generation_mw/.zattrs
@@ -3,4 +3,4 @@
         "datetime_gmt",
         "gsp_id"
     ]
-}
\ No newline at end of file
+}
diff --git a/tests/data/gsp/test.zarr/gsp_id/.zarray b/tests/data/gsp/test.zarr/gsp_id/.zarray
index dad10ae3c..dafc11290 100644
--- a/tests/data/gsp/test.zarr/gsp_id/.zarray
+++ b/tests/data/gsp/test.zarr/gsp_id/.zarray
@@ -17,4 +17,4 @@
         318
     ],
     "zarr_format": 2
-}
\ No newline at end of file
+}
diff --git a/tests/data/gsp/test.zarr/gsp_id/.zattrs b/tests/data/gsp/test.zarr/gsp_id/.zattrs
index 243f4f1b5..6de1b9d63 100644
--- a/tests/data/gsp/test.zarr/gsp_id/.zattrs
+++ b/tests/data/gsp/test.zarr/gsp_id/.zattrs
@@ -2,4 +2,4 @@
     "_ARRAY_DIMENSIONS": [
         "gsp_id"
     ]
-}
\ No newline at end of file
+}
diff --git a/tests/data/gsp/test.zarr/installedcapacity_mwp/.zarray b/tests/data/gsp/test.zarr/installedcapacity_mwp/.zarray
index 2fa132365..1b355953d 100644
--- a/tests/data/gsp/test.zarr/installedcapacity_mwp/.zarray
+++ b/tests/data/gsp/test.zarr/installedcapacity_mwp/.zarray
@@ -19,4 +19,4 @@
         318
     ],
     "zarr_format": 2
-}
\ No newline at end of file
+}
diff --git a/tests/data/gsp/test.zarr/installedcapacity_mwp/.zattrs b/tests/data/gsp/test.zarr/installedcapacity_mwp/.zattrs
index 5d2aefa6f..758489d41 100644
--- a/tests/data/gsp/test.zarr/installedcapacity_mwp/.zattrs
+++ b/tests/data/gsp/test.zarr/installedcapacity_mwp/.zattrs
@@ -3,4 +3,4 @@
         "datetime_gmt",
         "gsp_id"
     ]
-}
\ No newline at end of file
+}
diff --git a/tests/training/test_common.py b/tests/training/test_common.py
index 4af6cd3c7..052c4aeac 100644
--- a/tests/training/test_common.py
+++ b/tests/training/test_common.py
@@ -113,7 +113,7 @@ def test_create_t0_and_loc_datapipes(configuration_filename):
     assert isinstance(loc0, Location)
     assert isinstance(t0, np.datetime64)
 
-    
+
 def test_construct_loctime_pipelines(configuration_filename):
     start_time = datetime(1900, 1, 1)
     end_time = datetime(2050, 1, 1)
diff --git a/tests/training/test_pvnet.py b/tests/training/test_pvnet.py
index 89bde4be7..d5073875f 100644
--- a/tests/training/test_pvnet.py
+++ b/tests/training/test_pvnet.py
@@ -10,7 +10,6 @@
 from ocf_datapipes.utils import Location
 
 
-
 def test_construct_sliced_data_pipeline(pvnet_config_filename):
     # This is randomly chosen, but real, GSP location
     loc_pipe = IterableWrapper([Location(x=246699.328125, y=849771.9375, id=18)])
@@ -43,4 +42,4 @@ def test_pvnet_datapipe(pvnet_config_filename):
     batch = next(iter(dp))
     assert BatchKey.nwp in batch
     for nwp_source in batch[BatchKey.nwp].keys():
-        assert NWPBatchKey.nwp in batch[BatchKey.nwp][nwp_source]
\ No newline at end of file
+        assert NWPBatchKey.nwp in batch[BatchKey.nwp][nwp_source]
diff --git a/tests/training/test_pvnet_all_gsp.py b/tests/training/test_pvnet_all_gsp.py
index 5b0afb182..dbfc94b1b 100644
--- a/tests/training/test_pvnet_all_gsp.py
+++ b/tests/training/test_pvnet_all_gsp.py
@@ -9,9 +9,7 @@
 from ocf_datapipes.batch import BatchKey, NWPBatchKey
 
 
-
 def test_construct_sliced_data_pipeline(pvnet_config_filename):
-
     # This is a randomly chosen time in the middle of the test data
     t0_pipe = IterableWrapper([datetime(2020, 4, 1, 13, 30)])
 

From 427a951ea8fcb8943aee7b2f9f537bec175105c4 Mon Sep 17 00:00:00 2001
From: James Fulton <djamesfulton@yahoo.co.uk>
Date: Mon, 3 Jun 2024 14:17:30 +0000
Subject: [PATCH 18/27] refactor convert submodule + test fix

---
 ocf_datapipes/convert/numpy_batch/__init__.py |  6 ++
 ocf_datapipes/convert/numpy_batch/gsp.py      | 51 ++++++-------
 ocf_datapipes/convert/numpy_batch/nwp.py      | 50 +++++++------
 ocf_datapipes/convert/numpy_batch/pv.py       | 33 +++++----
 .../convert/numpy_batch/satellite.py          | 74 ++++++++++++-------
 ocf_datapipes/convert/numpy_batch/sensor.py   | 38 +++++-----
 ocf_datapipes/convert/numpy_batch/wind.py     | 32 ++++----
 ocf_datapipes/training/pvnet_all_gsp.py       | 68 +++++++++--------
 8 files changed, 198 insertions(+), 154 deletions(-)

diff --git a/ocf_datapipes/convert/numpy_batch/__init__.py b/ocf_datapipes/convert/numpy_batch/__init__.py
index 13dd7dfac..7c1cad3c4 100644
--- a/ocf_datapipes/convert/numpy_batch/__init__.py
+++ b/ocf_datapipes/convert/numpy_batch/__init__.py
@@ -1 +1,7 @@
 """Conversion from Xarray to NumpyBatch"""
+from .gsp import convert_gsp_to_numpy_batch
+from .nwp import convert_nwp_to_numpy_batch
+from .pv import convert_pv_to_numpy_batch
+from .satellite import convert_satellite_to_numpy_batch
+from .sensor import convert_sensor_to_numpy_batch
+from .wind import convert_wind_to_numpy_batch
\ No newline at end of file
diff --git a/ocf_datapipes/convert/numpy_batch/gsp.py b/ocf_datapipes/convert/numpy_batch/gsp.py
index f860939e5..9ccfef59c 100644
--- a/ocf_datapipes/convert/numpy_batch/gsp.py
+++ b/ocf_datapipes/convert/numpy_batch/gsp.py
@@ -9,6 +9,31 @@
 logger = logging.getLogger(__name__)
 
 
+def convert_gsp_to_numpy_batch(xr_data):
+    """Convert from Xarray to NumpyBatch"""
+    
+    example: NumpyBatch = {
+        BatchKey.gsp: xr_data.values,
+        BatchKey.gsp_t0_idx: xr_data.attrs["t0_idx"],
+        BatchKey.gsp_id: xr_data.gsp_id.values,
+        BatchKey.gsp_nominal_capacity_mwp: xr_data.isel(time_utc=0)["nominal_capacity_mwp"].values,
+        BatchKey.gsp_effective_capacity_mwp: (
+            xr_data.isel(time_utc=0)["effective_capacity_mwp"].values
+        ),
+        BatchKey.gsp_time_utc: datetime64_to_float(xr_data["time_utc"].values),
+    }
+
+    # Coordinates
+    for batch_key, dataset_key in (
+        (BatchKey.gsp_y_osgb, "y_osgb"),
+        (BatchKey.gsp_x_osgb, "x_osgb"),
+    ):
+        if dataset_key in xr_data.coords.keys():
+            example[batch_key] = xr_data[dataset_key].values 
+    
+    return example
+
+
 @functional_datapipe("convert_gsp_to_numpy_batch")
 class ConvertGSPToNumpyBatchIterDataPipe(IterDataPipe):
     """Convert GSP Xarray to NumpyBatch"""
@@ -25,29 +50,5 @@ def __init__(self, source_datapipe: IterDataPipe):
 
     def __iter__(self) -> NumpyBatch:
         """Convert from Xarray to NumpyBatch"""
-        logger.debug("Converting GSP to numpy to batch")
         for xr_data in self.source_datapipe:
-            example: NumpyBatch = {
-                BatchKey.gsp: xr_data.values,
-                BatchKey.gsp_t0_idx: xr_data.attrs["t0_idx"],
-                BatchKey.gsp_id: xr_data.gsp_id.values,
-                BatchKey.gsp_nominal_capacity_mwp: xr_data.isel(time_utc=0)[
-                    "nominal_capacity_mwp"
-                ].values,
-                BatchKey.gsp_effective_capacity_mwp: (
-                    xr_data.isel(time_utc=0)["effective_capacity_mwp"].values
-                ),
-                BatchKey.gsp_time_utc: datetime64_to_float(xr_data["time_utc"].values),
-            }
-
-            # Coordinates
-            for batch_key, dataset_key in (
-                (BatchKey.gsp_y_osgb, "y_osgb"),
-                (BatchKey.gsp_x_osgb, "x_osgb"),
-            ):
-                if dataset_key in xr_data.coords.keys():
-                    values = xr_data[dataset_key].values
-                    # Expand dims so AddFourierSpaceTime works!
-                    example[batch_key] = values  # np.expand_dims(values, axis=1)
-
-            yield example
+            yield convert_gsp_to_numpy_batch(xr_data)
diff --git a/ocf_datapipes/convert/numpy_batch/nwp.py b/ocf_datapipes/convert/numpy_batch/nwp.py
index 6dc3d953f..e016218ff 100644
--- a/ocf_datapipes/convert/numpy_batch/nwp.py
+++ b/ocf_datapipes/convert/numpy_batch/nwp.py
@@ -6,6 +6,32 @@
 from ocf_datapipes.utils.utils import datetime64_to_float
 
 
+def convert_nwp_to_numpy_batch(xr_data):
+    """Convert from Xarray to NWPBatchKey"""
+    
+    example: NWPNumpyBatch = {
+        NWPBatchKey.nwp: xr_data.values,
+        NWPBatchKey.nwp_t0_idx: xr_data.attrs["t0_idx"],
+        NWPBatchKey.nwp_channel_names: xr_data.channel.values,
+        NWPBatchKey.nwp_step: (xr_data.step.values / np.timedelta64(1, "h")).astype(np.int64),
+        NWPBatchKey.nwp_init_time_utc: datetime64_to_float(xr_data.init_time_utc.values),
+    }
+    
+    if "target_time_utc" in xr_data:
+        example[NWPBatchKey.nwp_target_time_utc] = datetime64_to_float(
+            xr_data.target_time_utc.values
+        )
+    
+    for batch_key, dataset_key in (
+        (NWPBatchKey.nwp_y_osgb, "y_osgb"),
+        (NWPBatchKey.nwp_x_osgb, "x_osgb"),
+    ):
+        if dataset_key in xr_data:
+            example[batch_key] = xr_data[dataset_key].values
+            
+    return example
+
+
 @functional_datapipe("convert_nwp_to_numpy_batch")
 class ConvertNWPToNumpyBatchIterDataPipe(IterDataPipe):
     """Convert NWP Xarray objects to NWPNumpyBatch"""
@@ -23,26 +49,4 @@ def __init__(self, source_datapipe: IterDataPipe):
     def __iter__(self) -> NWPNumpyBatch:
         """Convert from Xarray to NWPBatchKey"""
         for xr_data in self.source_datapipe:
-            example: NWPNumpyBatch = {
-                NWPBatchKey.nwp: xr_data.values,
-                NWPBatchKey.nwp_t0_idx: xr_data.attrs["t0_idx"],
-            }
-            if "target_time_utc" in xr_data.coords:
-                target_time = xr_data.target_time_utc.values
-                example[NWPBatchKey.nwp_target_time_utc] = datetime64_to_float(target_time)
-            example[NWPBatchKey.nwp_channel_names] = xr_data.channel.values
-            example[NWPBatchKey.nwp_step] = (xr_data.step.values / np.timedelta64(1, "h")).astype(
-                np.int64
-            )
-            example[NWPBatchKey.nwp_init_time_utc] = datetime64_to_float(
-                xr_data.init_time_utc.values
-            )
-
-            for batch_key, dataset_key in (
-                (NWPBatchKey.nwp_y_osgb, "y_osgb"),
-                (NWPBatchKey.nwp_x_osgb, "x_osgb"),
-            ):
-                if dataset_key in xr_data.coords.keys():
-                    example[batch_key] = xr_data[dataset_key].values
-
-            yield example
+            yield convert_nwp_to_numpy_batch(xr_data)
diff --git a/ocf_datapipes/convert/numpy_batch/pv.py b/ocf_datapipes/convert/numpy_batch/pv.py
index fd9a478cc..2d6d432be 100644
--- a/ocf_datapipes/convert/numpy_batch/pv.py
+++ b/ocf_datapipes/convert/numpy_batch/pv.py
@@ -9,6 +9,21 @@
 
 logger = logging.getLogger(__name__)
 
+def convert_pv_to_numpy_batch(xr_data):
+    """Convert PV Xarray to NumpyBatch"""
+    example: NumpyBatch = {
+        BatchKey.pv: xr_data.values,
+        BatchKey.pv_t0_idx: xr_data.attrs["t0_idx"],
+        BatchKey.pv_ml_id: xr_data["ml_id"].values,
+        BatchKey.pv_id: xr_data["pv_system_id"].values.astype(np.float32),
+        BatchKey.pv_observed_capacity_wp: (xr_data["observed_capacity_wp"].values),
+        BatchKey.pv_nominal_capacity_wp: (xr_data["nominal_capacity_wp"].values),
+        BatchKey.pv_time_utc: datetime64_to_float(xr_data["time_utc"].values),
+        BatchKey.pv_latitude: xr_data["latitude"].values,
+        BatchKey.pv_longitude: xr_data["longitude"].values,
+    }
+    
+    return example
 
 @functional_datapipe("convert_pv_to_numpy_batch")
 class ConvertPVToNumpyBatchIterDataPipe(IterDataPipe):
@@ -25,20 +40,6 @@ def __init__(self, source_datapipe: IterDataPipe):
         self.source_datapipe = source_datapipe
 
     def __iter__(self) -> NumpyBatch:
-        """Iterate and convert PV Xarray to NumpyBatch"""
+        """Convert PV Xarray to NumpyBatch"""
         for xr_data in self.source_datapipe:
-            logger.debug("Converting PV xarray to numpy example")
-
-            example: NumpyBatch = {
-                BatchKey.pv: xr_data.values,
-                BatchKey.pv_t0_idx: xr_data.attrs["t0_idx"],
-                BatchKey.pv_ml_id: xr_data["ml_id"].values,
-                BatchKey.pv_id: xr_data["pv_system_id"].values.astype(np.float32),
-                BatchKey.pv_observed_capacity_wp: (xr_data["observed_capacity_wp"].values),
-                BatchKey.pv_nominal_capacity_wp: (xr_data["nominal_capacity_wp"].values),
-                BatchKey.pv_time_utc: datetime64_to_float(xr_data["time_utc"].values),
-                BatchKey.pv_latitude: xr_data["latitude"].values,
-                BatchKey.pv_longitude: xr_data["longitude"].values,
-            }
-
-            yield example
+            yield convert_pv_to_numpy_batch(xr_data)
diff --git a/ocf_datapipes/convert/numpy_batch/satellite.py b/ocf_datapipes/convert/numpy_batch/satellite.py
index ec650894d..518d2ad62 100644
--- a/ocf_datapipes/convert/numpy_batch/satellite.py
+++ b/ocf_datapipes/convert/numpy_batch/satellite.py
@@ -5,6 +5,51 @@
 from ocf_datapipes.utils.utils import datetime64_to_float
 
 
+def _convert_satellite_to_numpy_batch(xr_data):
+    
+    example: NumpyBatch = {
+        BatchKey.satellite_actual: xr_data.values,
+        BatchKey.satellite_t0_idx: xr_data.attrs["t0_idx"],
+        BatchKey.satellite_time_utc: datetime64_to_float(xr_data["time_utc"].values),
+    }
+
+    for batch_key, dataset_key in (
+        (BatchKey.satellite_y_geostationary, "y_geostationary"),
+        (BatchKey.satellite_x_geostationary, "x_geostationary"),
+    ):
+        # HRVSatellite coords are already float32.
+        example[batch_key] = xr_data[dataset_key].values
+    
+    return example
+
+
+def _convert_hrvsatellite_to_numpy_batch(xr_data):
+
+    example: NumpyBatch = {
+        BatchKey.hrvsatellite_actual: xr_data.values,
+        BatchKey.hrvsatellite_t0_idx: xr_data.attrs["t0_idx"],
+        BatchKey.hrvsatellite_time_utc: datetime64_to_float(xr_data["time_utc"].values),
+    }
+
+    for batch_key, dataset_key in (
+        (BatchKey.hrvsatellite_y_geostationary, "y_geostationary"),
+        (BatchKey.hrvsatellite_x_geostationary, "x_geostationary"),
+    ):
+        # Satellite coords are already float32.
+        example[batch_key] = xr_data[dataset_key].values
+    
+    return example
+
+
+def convert_satellite_to_numpy_batch(xr_data, is_hrv=False):
+    """Converts Xarray Satellite to NumpyBatch object"""
+    if is_hrv:
+        example = _convert_hrvsatellite_to_numpy_batch(xr_data)
+    else:
+        example = _convert_satellite_to_numpy_batch(xr_data)
+    return example
+
+
 @functional_datapipe("convert_satellite_to_numpy_batch")
 class ConvertSatelliteToNumpyBatchIterDataPipe(IterDataPipe):
     """Converts Xarray Satellite to NumpyBatch object"""
@@ -24,31 +69,4 @@ def __init__(self, source_datapipe: IterDataPipe, is_hrv: bool = False):
     def __iter__(self) -> NumpyBatch:
         """Convert each example to a NumpyBatch object"""
         for xr_data in self.source_datapipe:
-            if self.is_hrv:
-                example: NumpyBatch = {
-                    BatchKey.hrvsatellite_actual: xr_data.values,
-                    BatchKey.hrvsatellite_t0_idx: xr_data.attrs["t0_idx"],
-                    BatchKey.hrvsatellite_time_utc: datetime64_to_float(xr_data["time_utc"].values),
-                }
-
-                for batch_key, dataset_key in (
-                    (BatchKey.hrvsatellite_y_geostationary, "y_geostationary"),
-                    (BatchKey.hrvsatellite_x_geostationary, "x_geostationary"),
-                ):
-                    # HRVSatellite coords are already float32.
-                    example[batch_key] = xr_data[dataset_key].values
-            else:
-                example: NumpyBatch = {
-                    BatchKey.satellite_actual: xr_data.values,
-                    BatchKey.satellite_t0_idx: xr_data.attrs["t0_idx"],
-                    BatchKey.satellite_time_utc: datetime64_to_float(xr_data["time_utc"].values),
-                }
-
-                for batch_key, dataset_key in (
-                    (BatchKey.satellite_y_geostationary, "y_geostationary"),
-                    (BatchKey.satellite_x_geostationary, "x_geostationary"),
-                ):
-                    # HRVSatellite coords are already float32.
-                    example[batch_key] = xr_data[dataset_key].values
-
-            yield example
+                yield convert_satellite_to_numpy_batch(xr_data, self.is_hrv)
\ No newline at end of file
diff --git a/ocf_datapipes/convert/numpy_batch/sensor.py b/ocf_datapipes/convert/numpy_batch/sensor.py
index 754aec447..e80f2d904 100644
--- a/ocf_datapipes/convert/numpy_batch/sensor.py
+++ b/ocf_datapipes/convert/numpy_batch/sensor.py
@@ -10,34 +10,38 @@
 logger = logging.getLogger(__name__)
 
 
+def convert_sensor_to_numpy_batch(xr_data):
+    """Convert Sensor Xarray to NumpyBatch"""
+    
+    example: NumpyBatch = {
+        BatchKey.sensor: xr_data.values,
+        BatchKey.sensor_t0_idx: xr_data.attrs["t0_idx"],
+        BatchKey.sensor_id: xr_data["station_id"].values.astype(np.float32),
+        # BatchKey.sensor_observed_capacity_wp: (xr_data["observed_capacity_wp"].values),
+        # BatchKey.sensor_nominal_capacity_wp: (xr_data["nominal_capacity_wp"].values),
+        BatchKey.sensor_time_utc: datetime64_to_float(xr_data["time_utc"].values),
+        BatchKey.sensor_latitude: xr_data["latitude"].values,
+        BatchKey.sensor_longitude: xr_data["longitude"].values,
+    }
+    return example
+    
+
+
 @functional_datapipe("convert_sensor_to_numpy_batch")
 class ConvertSensorToNumpyBatchIterDataPipe(IterDataPipe):
     """Convert Sensor Xarray to NumpyBatch"""
 
     def __init__(self, source_datapipe: IterDataPipe):
         """
-        Convert PV Xarray objects to NumpyBatch objects
+        Convert sensor Xarray objects to NumpyBatch objects
 
         Args:
-            source_datapipe: Datapipe emitting PV Xarray objects
+            source_datapipe: Datapipe emitting sensor Xarray objects
         """
         super().__init__()
         self.source_datapipe = source_datapipe
 
     def __iter__(self) -> NumpyBatch:
-        """Iterate and convert PV Xarray to NumpyBatch"""
+        """Iterate and convert sensor Xarray to NumpyBatch"""
         for xr_data in self.source_datapipe:
-            logger.debug("Converting Sensor xarray to numpy example")
-
-            example: NumpyBatch = {
-                BatchKey.sensor: xr_data.values,
-                BatchKey.sensor_t0_idx: xr_data.attrs["t0_idx"],
-                BatchKey.sensor_id: xr_data["station_id"].values.astype(np.float32),
-                # BatchKey.sensor_observed_capacity_wp: (xr_data["observed_capacity_wp"].values),
-                # BatchKey.sensor_nominal_capacity_wp: (xr_data["nominal_capacity_wp"].values),
-                BatchKey.sensor_time_utc: datetime64_to_float(xr_data["time_utc"].values),
-                BatchKey.sensor_latitude: xr_data["latitude"].values,
-                BatchKey.sensor_longitude: xr_data["longitude"].values,
-            }
-
-            yield example
+            yield convert_sensor_to_numpy_batch(xr_data)
diff --git a/ocf_datapipes/convert/numpy_batch/wind.py b/ocf_datapipes/convert/numpy_batch/wind.py
index 8495a6584..a8b11faf5 100644
--- a/ocf_datapipes/convert/numpy_batch/wind.py
+++ b/ocf_datapipes/convert/numpy_batch/wind.py
@@ -9,6 +9,22 @@
 
 logger = logging.getLogger(__name__)
 
+def convert_wind_to_numpy_batch(xr_data):
+    """Convert Wind Xarray to NumpyBatch"""
+    
+    example: NumpyBatch = {
+        BatchKey.wind: xr_data.values,
+        BatchKey.wind_t0_idx: xr_data.attrs["t0_idx"],
+        BatchKey.wind_ml_id: xr_data["ml_id"].values,
+        BatchKey.wind_id: xr_data["wind_system_id"].values.astype(np.float32),
+        BatchKey.wind_observed_capacity_mwp: (xr_data["observed_capacity_mwp"].values),
+        BatchKey.wind_nominal_capacity_mwp: (xr_data["nominal_capacity_mwp"].values),
+        BatchKey.wind_time_utc: datetime64_to_float(xr_data["time_utc"].values),
+        BatchKey.wind_latitude: xr_data["latitude"].values,
+        BatchKey.wind_longitude: xr_data["longitude"].values,
+    }
+    
+    return example
 
 @functional_datapipe("convert_wind_to_numpy_batch")
 class ConvertWindToNumpyBatchIterDataPipe(IterDataPipe):
@@ -27,18 +43,4 @@ def __init__(self, source_datapipe: IterDataPipe):
     def __iter__(self) -> NumpyBatch:
         """Iterate and convert PV Xarray to NumpyBatch"""
         for xr_data in self.source_datapipe:
-            logger.debug("Converting Wind xarray to numpy example")
-
-            example: NumpyBatch = {
-                BatchKey.wind: xr_data.values,
-                BatchKey.wind_t0_idx: xr_data.attrs["t0_idx"],
-                BatchKey.wind_ml_id: xr_data["ml_id"].values,
-                BatchKey.wind_id: xr_data["wind_system_id"].values.astype(np.float32),
-                BatchKey.wind_observed_capacity_mwp: (xr_data["observed_capacity_mwp"].values),
-                BatchKey.wind_nominal_capacity_mwp: (xr_data["nominal_capacity_mwp"].values),
-                BatchKey.wind_time_utc: datetime64_to_float(xr_data["time_utc"].values),
-                BatchKey.wind_latitude: xr_data["latitude"].values,
-                BatchKey.wind_longitude: xr_data["longitude"].values,
-            }
-
-            yield example
+            yield convert_wind_to_numpy_batch(xr_data)
diff --git a/ocf_datapipes/training/pvnet_all_gsp.py b/ocf_datapipes/training/pvnet_all_gsp.py
index ad9174bbe..11fd39647 100644
--- a/ocf_datapipes/training/pvnet_all_gsp.py
+++ b/ocf_datapipes/training/pvnet_all_gsp.py
@@ -11,12 +11,13 @@
 from ocf_datapipes.batch import MergeNumpyModalities, MergeNWPNumpyModalities
 from ocf_datapipes.batch.merge_numpy_examples_to_batch import stack_np_examples_into_batch
 from ocf_datapipes.config.model import Configuration
-from ocf_datapipes.convert import (
-    ConvertGSPToNumpyBatch,
-    ConvertNWPToNumpyBatch,
-    ConvertPVToNumpyBatch,
-    ConvertSatelliteToNumpyBatch,
+from ocf_datapipes.convert.numpy_batch import (
+    convert_gsp_to_numpy_batch,
+    convert_nwp_to_numpy_batch,
+    convert_pv_to_numpy_batch,
+    convert_satellite_to_numpy_batch,
 )
+
 from ocf_datapipes.load.gsp.utils import GSPLocationLookup
 from ocf_datapipes.select.select_spatial_slice import (
     select_spatial_slice_meters,
@@ -53,6 +54,7 @@ def xr_compute(xr_data):
     return xr_data.compute()
 
 
+@functional_datapipe("sample_repeat")
 class SampleRepeat:
     """Use a single input element to create a list of identical values"""
 
@@ -69,23 +71,28 @@ def __call__(self, x):
         return [x for _ in range(self.num_repeats)]
 
 
-class ConvertWrapper(IterDataPipe):
-    """A class to adapt our Convert[X]ToNumpyBatch datapipes to work on a list of samples"""
-
-    def __init__(self, source_datapipe: IterDataPipe, convert_class: Type[IterDataPipe]):
-        """A class to adapt our Convert[X]ToNumpyBatch datapipes to work on a list of samples
-
+@functional_datapipe("list_map")
+class ListMap(IterDataPipe):
+    """Datapipe used to appky function to each item in yielded list"""
+    def __init__(self, source_datapipe: IterDataPipe, func, *args, **kwargs):
+        """Datapipe used to appky function to each item in yielded list.
+        
         Args:
             source_datapipe: The source datapipe yielding lists of samples
-            convert_class: The class to apply to the output of source_datapipe
+            function: The function to apply to all items in the list
+            *args: Args to pass to the function
+            **kwargs: Keyword arguments to pass to the function
+        
         """
+        
         self.source_datapipe = source_datapipe
-        self.convert_class = convert_class
-
+        self.func = func
+        self._args = args
+        self._kwargs = kwargs
+        
     def __iter__(self):
-        for concurrent_samples in self.source_datapipe:
-            dp = self.convert_class(IterableWrapper(concurrent_samples))
-            yield [x for x in dp]
+        for element_list in self.source_datapipe:
+            yield [self.func(x, *self._args, **self._kwargs) for x in element_list]
 
 
 # ------------------------------ Multi-location datapipes ------------------------------
@@ -373,10 +380,11 @@ def post_spatial_slice_process(datapipes_dict, check_satellite_no_nans=False):
         nwp_numpy_modalities = dict()
 
         for nwp_key, nwp_datapipe in datapipes_dict["nwp"].items():
-            nwp_numpy_modalities[nwp_key] = ConvertWrapper(
-                nwp_datapipe,
-                ConvertNWPToNumpyBatch,
-            ).map(stack_np_examples_into_batch)
+            nwp_numpy_modalities[nwp_key] = (
+                nwp_datapipe
+                .list_map(convert_nwp_to_numpy_batch)
+                .map(stack_np_examples_into_batch)
+            )
 
         # Combine the NWPs into NumpyBatch
         nwp_numpy_modalities = MergeNWPNumpyModalities(nwp_numpy_modalities)
@@ -384,23 +392,23 @@ def post_spatial_slice_process(datapipes_dict, check_satellite_no_nans=False):
 
     if "sat" in datapipes_dict:
         numpy_modalities.append(
-            ConvertWrapper(datapipes_dict["sat"], ConvertSatelliteToNumpyBatch).map(
-                stack_np_examples_into_batch
-            )
+            datapipes_dict["sat"]
+            .list_map(convert_satellite_to_numpy_batch)
+            .map(stack_np_examples_into_batch)
         )
 
     if "pv" in datapipes_dict:
         numpy_modalities.append(
-            ConvertWrapper(datapipes_dict["pv"], ConvertPVToNumpyBatch).map(
-                stack_np_examples_into_batch
-            )
+            datapipes_dict["pv"]
+            .list_map(convert_pv_to_numpy_batch)
+            .map(stack_np_examples_into_batch)
         )
 
     # GSP always assumed to be in data
     numpy_modalities.append(
-        ConvertWrapper(datapipes_dict["gsp"], ConvertGSPToNumpyBatch).map(
-            stack_np_examples_into_batch
-        )
+        datapipes_dict["gsp"]
+        .list_map(convert_gsp_to_numpy_batch)
+        .map(stack_np_examples_into_batch)
     )
 
     # Combine all the data sources

From 1b5cda1472223de541f44d7a0d0a618eba5349c9 Mon Sep 17 00:00:00 2001
From: James Fulton <djamesfulton@yahoo.co.uk>
Date: Mon, 3 Jun 2024 14:17:57 +0000
Subject: [PATCH 19/27] fix test

---
 tests/training/test_common.py | 12 +++++-------
 1 file changed, 5 insertions(+), 7 deletions(-)

diff --git a/tests/training/test_common.py b/tests/training/test_common.py
index 4af6cd3c7..df12bc2f2 100644
--- a/tests/training/test_common.py
+++ b/tests/training/test_common.py
@@ -1,9 +1,13 @@
+from datetime import datetime
+import numpy as np
 import pytest
+
 from torch.utils.data.datapipes.datapipe import IterDataPipe
 from torch.utils.data.datapipes.iter import Zipper
+from torch.utils.data import DataLoader
+
 from ocf_datapipes.config.model import Configuration
 from ocf_datapipes.utils import Location
-from torch.utils.data import DataLoader
 from ocf_datapipes.training.common import (
     add_selected_time_slices_from_datapipes,
     get_and_return_overlapping_time_periods_and_t0,
@@ -12,12 +16,6 @@
     construct_loctime_pipelines,
 )
 
-import fsspec
-from pyaml_env import parse_config
-
-import pandas as pd
-import numpy as np
-
 
 def test_open_and_return_datapipes(configuration_filename):
     used_datapipes = open_and_return_datapipes(configuration_filename)

From 91355b151703e99f0522c79fa700be4e74164464 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Mon, 3 Jun 2024 14:18:44 +0000
Subject: [PATCH 20/27] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 ocf_datapipes/convert/numpy_batch/__init__.py |  2 +-
 ocf_datapipes/convert/numpy_batch/gsp.py      |  6 ++---
 ocf_datapipes/convert/numpy_batch/nwp.py      |  8 +++----
 ocf_datapipes/convert/numpy_batch/pv.py       |  4 +++-
 .../convert/numpy_batch/satellite.py          |  8 +++----
 ocf_datapipes/convert/numpy_batch/sensor.py   |  3 +--
 ocf_datapipes/convert/numpy_batch/wind.py     |  6 +++--
 ocf_datapipes/training/pvnet_all_gsp.py       | 23 ++++++++-----------
 8 files changed, 28 insertions(+), 32 deletions(-)

diff --git a/ocf_datapipes/convert/numpy_batch/__init__.py b/ocf_datapipes/convert/numpy_batch/__init__.py
index 7c1cad3c4..e576e4860 100644
--- a/ocf_datapipes/convert/numpy_batch/__init__.py
+++ b/ocf_datapipes/convert/numpy_batch/__init__.py
@@ -4,4 +4,4 @@
 from .pv import convert_pv_to_numpy_batch
 from .satellite import convert_satellite_to_numpy_batch
 from .sensor import convert_sensor_to_numpy_batch
-from .wind import convert_wind_to_numpy_batch
\ No newline at end of file
+from .wind import convert_wind_to_numpy_batch
diff --git a/ocf_datapipes/convert/numpy_batch/gsp.py b/ocf_datapipes/convert/numpy_batch/gsp.py
index 9ccfef59c..0b6909aad 100644
--- a/ocf_datapipes/convert/numpy_batch/gsp.py
+++ b/ocf_datapipes/convert/numpy_batch/gsp.py
@@ -11,7 +11,7 @@
 
 def convert_gsp_to_numpy_batch(xr_data):
     """Convert from Xarray to NumpyBatch"""
-    
+
     example: NumpyBatch = {
         BatchKey.gsp: xr_data.values,
         BatchKey.gsp_t0_idx: xr_data.attrs["t0_idx"],
@@ -29,8 +29,8 @@ def convert_gsp_to_numpy_batch(xr_data):
         (BatchKey.gsp_x_osgb, "x_osgb"),
     ):
         if dataset_key in xr_data.coords.keys():
-            example[batch_key] = xr_data[dataset_key].values 
-    
+            example[batch_key] = xr_data[dataset_key].values
+
     return example
 
 
diff --git a/ocf_datapipes/convert/numpy_batch/nwp.py b/ocf_datapipes/convert/numpy_batch/nwp.py
index e016218ff..b5d9452b0 100644
--- a/ocf_datapipes/convert/numpy_batch/nwp.py
+++ b/ocf_datapipes/convert/numpy_batch/nwp.py
@@ -8,7 +8,7 @@
 
 def convert_nwp_to_numpy_batch(xr_data):
     """Convert from Xarray to NWPBatchKey"""
-    
+
     example: NWPNumpyBatch = {
         NWPBatchKey.nwp: xr_data.values,
         NWPBatchKey.nwp_t0_idx: xr_data.attrs["t0_idx"],
@@ -16,19 +16,19 @@ def convert_nwp_to_numpy_batch(xr_data):
         NWPBatchKey.nwp_step: (xr_data.step.values / np.timedelta64(1, "h")).astype(np.int64),
         NWPBatchKey.nwp_init_time_utc: datetime64_to_float(xr_data.init_time_utc.values),
     }
-    
+
     if "target_time_utc" in xr_data:
         example[NWPBatchKey.nwp_target_time_utc] = datetime64_to_float(
             xr_data.target_time_utc.values
         )
-    
+
     for batch_key, dataset_key in (
         (NWPBatchKey.nwp_y_osgb, "y_osgb"),
         (NWPBatchKey.nwp_x_osgb, "x_osgb"),
     ):
         if dataset_key in xr_data:
             example[batch_key] = xr_data[dataset_key].values
-            
+
     return example
 
 
diff --git a/ocf_datapipes/convert/numpy_batch/pv.py b/ocf_datapipes/convert/numpy_batch/pv.py
index 2d6d432be..1476c7140 100644
--- a/ocf_datapipes/convert/numpy_batch/pv.py
+++ b/ocf_datapipes/convert/numpy_batch/pv.py
@@ -9,6 +9,7 @@
 
 logger = logging.getLogger(__name__)
 
+
 def convert_pv_to_numpy_batch(xr_data):
     """Convert PV Xarray to NumpyBatch"""
     example: NumpyBatch = {
@@ -22,9 +23,10 @@ def convert_pv_to_numpy_batch(xr_data):
         BatchKey.pv_latitude: xr_data["latitude"].values,
         BatchKey.pv_longitude: xr_data["longitude"].values,
     }
-    
+
     return example
 
+
 @functional_datapipe("convert_pv_to_numpy_batch")
 class ConvertPVToNumpyBatchIterDataPipe(IterDataPipe):
     """Convert PV Xarray to NumpyBatch"""
diff --git a/ocf_datapipes/convert/numpy_batch/satellite.py b/ocf_datapipes/convert/numpy_batch/satellite.py
index 518d2ad62..ae7afaf97 100644
--- a/ocf_datapipes/convert/numpy_batch/satellite.py
+++ b/ocf_datapipes/convert/numpy_batch/satellite.py
@@ -6,7 +6,6 @@
 
 
 def _convert_satellite_to_numpy_batch(xr_data):
-    
     example: NumpyBatch = {
         BatchKey.satellite_actual: xr_data.values,
         BatchKey.satellite_t0_idx: xr_data.attrs["t0_idx"],
@@ -19,12 +18,11 @@ def _convert_satellite_to_numpy_batch(xr_data):
     ):
         # HRVSatellite coords are already float32.
         example[batch_key] = xr_data[dataset_key].values
-    
+
     return example
 
 
 def _convert_hrvsatellite_to_numpy_batch(xr_data):
-
     example: NumpyBatch = {
         BatchKey.hrvsatellite_actual: xr_data.values,
         BatchKey.hrvsatellite_t0_idx: xr_data.attrs["t0_idx"],
@@ -37,7 +35,7 @@ def _convert_hrvsatellite_to_numpy_batch(xr_data):
     ):
         # Satellite coords are already float32.
         example[batch_key] = xr_data[dataset_key].values
-    
+
     return example
 
 
@@ -69,4 +67,4 @@ def __init__(self, source_datapipe: IterDataPipe, is_hrv: bool = False):
     def __iter__(self) -> NumpyBatch:
         """Convert each example to a NumpyBatch object"""
         for xr_data in self.source_datapipe:
-                yield convert_satellite_to_numpy_batch(xr_data, self.is_hrv)
\ No newline at end of file
+            yield convert_satellite_to_numpy_batch(xr_data, self.is_hrv)
diff --git a/ocf_datapipes/convert/numpy_batch/sensor.py b/ocf_datapipes/convert/numpy_batch/sensor.py
index e80f2d904..3e8874106 100644
--- a/ocf_datapipes/convert/numpy_batch/sensor.py
+++ b/ocf_datapipes/convert/numpy_batch/sensor.py
@@ -12,7 +12,7 @@
 
 def convert_sensor_to_numpy_batch(xr_data):
     """Convert Sensor Xarray to NumpyBatch"""
-    
+
     example: NumpyBatch = {
         BatchKey.sensor: xr_data.values,
         BatchKey.sensor_t0_idx: xr_data.attrs["t0_idx"],
@@ -24,7 +24,6 @@ def convert_sensor_to_numpy_batch(xr_data):
         BatchKey.sensor_longitude: xr_data["longitude"].values,
     }
     return example
-    
 
 
 @functional_datapipe("convert_sensor_to_numpy_batch")
diff --git a/ocf_datapipes/convert/numpy_batch/wind.py b/ocf_datapipes/convert/numpy_batch/wind.py
index a8b11faf5..3f04a9a0d 100644
--- a/ocf_datapipes/convert/numpy_batch/wind.py
+++ b/ocf_datapipes/convert/numpy_batch/wind.py
@@ -9,9 +9,10 @@
 
 logger = logging.getLogger(__name__)
 
+
 def convert_wind_to_numpy_batch(xr_data):
     """Convert Wind Xarray to NumpyBatch"""
-    
+
     example: NumpyBatch = {
         BatchKey.wind: xr_data.values,
         BatchKey.wind_t0_idx: xr_data.attrs["t0_idx"],
@@ -23,9 +24,10 @@ def convert_wind_to_numpy_batch(xr_data):
         BatchKey.wind_latitude: xr_data["latitude"].values,
         BatchKey.wind_longitude: xr_data["longitude"].values,
     }
-    
+
     return example
 
+
 @functional_datapipe("convert_wind_to_numpy_batch")
 class ConvertWindToNumpyBatchIterDataPipe(IterDataPipe):
     """Convert Wind Xarray to NumpyBatch"""
diff --git a/ocf_datapipes/training/pvnet_all_gsp.py b/ocf_datapipes/training/pvnet_all_gsp.py
index 11fd39647..e9956bc42 100644
--- a/ocf_datapipes/training/pvnet_all_gsp.py
+++ b/ocf_datapipes/training/pvnet_all_gsp.py
@@ -1,12 +1,11 @@
 """Create the training/validation datapipe for training the PVNet Model"""
 import logging
 from datetime import datetime
-from typing import List, Optional, Tuple, Type, Union
+from typing import List, Optional, Tuple, Union
 
 import xarray as xr
 from torch.utils.data.datapipes._decorator import functional_datapipe
 from torch.utils.data.datapipes.datapipe import IterDataPipe
-from torch.utils.data.datapipes.iter import IterableWrapper
 
 from ocf_datapipes.batch import MergeNumpyModalities, MergeNWPNumpyModalities
 from ocf_datapipes.batch.merge_numpy_examples_to_batch import stack_np_examples_into_batch
@@ -17,7 +16,6 @@
     convert_pv_to_numpy_batch,
     convert_satellite_to_numpy_batch,
 )
-
 from ocf_datapipes.load.gsp.utils import GSPLocationLookup
 from ocf_datapipes.select.select_spatial_slice import (
     select_spatial_slice_meters,
@@ -74,22 +72,23 @@ def __call__(self, x):
 @functional_datapipe("list_map")
 class ListMap(IterDataPipe):
     """Datapipe used to appky function to each item in yielded list"""
+
     def __init__(self, source_datapipe: IterDataPipe, func, *args, **kwargs):
         """Datapipe used to appky function to each item in yielded list.
-        
+
         Args:
             source_datapipe: The source datapipe yielding lists of samples
             function: The function to apply to all items in the list
             *args: Args to pass to the function
             **kwargs: Keyword arguments to pass to the function
-        
+
         """
-        
+
         self.source_datapipe = source_datapipe
         self.func = func
         self._args = args
         self._kwargs = kwargs
-        
+
     def __iter__(self):
         for element_list in self.source_datapipe:
             yield [self.func(x, *self._args, **self._kwargs) for x in element_list]
@@ -380,10 +379,8 @@ def post_spatial_slice_process(datapipes_dict, check_satellite_no_nans=False):
         nwp_numpy_modalities = dict()
 
         for nwp_key, nwp_datapipe in datapipes_dict["nwp"].items():
-            nwp_numpy_modalities[nwp_key] = (
-                nwp_datapipe
-                .list_map(convert_nwp_to_numpy_batch)
-                .map(stack_np_examples_into_batch)
+            nwp_numpy_modalities[nwp_key] = nwp_datapipe.list_map(convert_nwp_to_numpy_batch).map(
+                stack_np_examples_into_batch
             )
 
         # Combine the NWPs into NumpyBatch
@@ -406,9 +403,7 @@ def post_spatial_slice_process(datapipes_dict, check_satellite_no_nans=False):
 
     # GSP always assumed to be in data
     numpy_modalities.append(
-        datapipes_dict["gsp"]
-        .list_map(convert_gsp_to_numpy_batch)
-        .map(stack_np_examples_into_batch)
+        datapipes_dict["gsp"].list_map(convert_gsp_to_numpy_batch).map(stack_np_examples_into_batch)
     )
 
     # Combine all the data sources

From 50a6f9e9a38ff8003c00597e64cf42fd139f3a87 Mon Sep 17 00:00:00 2001
From: James Fulton <djamesfulton@yahoo.co.uk>
Date: Mon, 3 Jun 2024 14:23:27 +0000
Subject: [PATCH 21/27] linting

---
 ocf_datapipes/training/pvnet_all_gsp.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/ocf_datapipes/training/pvnet_all_gsp.py b/ocf_datapipes/training/pvnet_all_gsp.py
index e9956bc42..3ae552507 100644
--- a/ocf_datapipes/training/pvnet_all_gsp.py
+++ b/ocf_datapipes/training/pvnet_all_gsp.py
@@ -52,7 +52,6 @@ def xr_compute(xr_data):
     return xr_data.compute()
 
 
-@functional_datapipe("sample_repeat")
 class SampleRepeat:
     """Use a single input element to create a list of identical values"""
 
@@ -78,7 +77,7 @@ def __init__(self, source_datapipe: IterDataPipe, func, *args, **kwargs):
 
         Args:
             source_datapipe: The source datapipe yielding lists of samples
-            function: The function to apply to all items in the list
+            func: The function to apply to all items in the list
             *args: Args to pass to the function
             **kwargs: Keyword arguments to pass to the function
 

From 5d1551b0ec2b7107cd1864a83b08ccde06781541 Mon Sep 17 00:00:00 2001
From: James Fulton <djamesfulton@yahoo.co.uk>
Date: Mon, 3 Jun 2024 14:43:34 +0000
Subject: [PATCH 22/27] fix

---
 ocf_datapipes/convert/numpy_batch/nwp.py | 15 +++++++--------
 1 file changed, 7 insertions(+), 8 deletions(-)

diff --git a/ocf_datapipes/convert/numpy_batch/nwp.py b/ocf_datapipes/convert/numpy_batch/nwp.py
index b5d9452b0..fbf5a7fa5 100644
--- a/ocf_datapipes/convert/numpy_batch/nwp.py
+++ b/ocf_datapipes/convert/numpy_batch/nwp.py
@@ -13,20 +13,19 @@ def convert_nwp_to_numpy_batch(xr_data):
         NWPBatchKey.nwp: xr_data.values,
         NWPBatchKey.nwp_t0_idx: xr_data.attrs["t0_idx"],
         NWPBatchKey.nwp_channel_names: xr_data.channel.values,
-        NWPBatchKey.nwp_step: (xr_data.step.values / np.timedelta64(1, "h")).astype(np.int64),
         NWPBatchKey.nwp_init_time_utc: datetime64_to_float(xr_data.init_time_utc.values),
+        NWPBatchKey.nwp_step: (xr_data.step.values / np.timedelta64(1, "h")).astype(np.int64),
     }
-
-    if "target_time_utc" in xr_data:
-        example[NWPBatchKey.nwp_target_time_utc] = datetime64_to_float(
-            xr_data.target_time_utc.values
-        )
-
+    
+    if "target_time_utc" in xr_data.coords:
+        target_time = xr_data.target_time_utc.values
+        example[NWPBatchKey.nwp_target_time_utc] = datetime64_to_float(target_time)
+        
     for batch_key, dataset_key in (
         (NWPBatchKey.nwp_y_osgb, "y_osgb"),
         (NWPBatchKey.nwp_x_osgb, "x_osgb"),
     ):
-        if dataset_key in xr_data:
+        if dataset_key in xr_data.coords:
             example[batch_key] = xr_data[dataset_key].values
 
     return example

From 4a9fcb78808ce3e8d17b7e5c3bbede5ebee3f7d2 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Mon, 3 Jun 2024 14:44:10 +0000
Subject: [PATCH 23/27] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 ocf_datapipes/convert/numpy_batch/nwp.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/ocf_datapipes/convert/numpy_batch/nwp.py b/ocf_datapipes/convert/numpy_batch/nwp.py
index fbf5a7fa5..f17240e27 100644
--- a/ocf_datapipes/convert/numpy_batch/nwp.py
+++ b/ocf_datapipes/convert/numpy_batch/nwp.py
@@ -16,11 +16,11 @@ def convert_nwp_to_numpy_batch(xr_data):
         NWPBatchKey.nwp_init_time_utc: datetime64_to_float(xr_data.init_time_utc.values),
         NWPBatchKey.nwp_step: (xr_data.step.values / np.timedelta64(1, "h")).astype(np.int64),
     }
-    
+
     if "target_time_utc" in xr_data.coords:
         target_time = xr_data.target_time_utc.values
         example[NWPBatchKey.nwp_target_time_utc] = datetime64_to_float(target_time)
-        
+
     for batch_key, dataset_key in (
         (NWPBatchKey.nwp_y_osgb, "y_osgb"),
         (NWPBatchKey.nwp_x_osgb, "x_osgb"),

From fb1358c6082518d62f51db3957d80ea72b2bf17f Mon Sep 17 00:00:00 2001
From: James Fulton <djamesfulton@yahoo.co.uk>
Date: Mon, 3 Jun 2024 15:12:45 +0000
Subject: [PATCH 24/27] update tests

---
 tests/transform/xarray/test_normalize.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/tests/transform/xarray/test_normalize.py b/tests/transform/xarray/test_normalize.py
index fb00ee6f3..0798460cd 100644
--- a/tests/transform/xarray/test_normalize.py
+++ b/tests/transform/xarray/test_normalize.py
@@ -39,7 +39,9 @@ def test_normalize_gsp(gsp_datapipe):
     )
     data = next(iter(normed_gsp_datapipe))
     assert np.min(data) >= 0.0
-    assert np.max(data) <= 1.0
+    
+    # Some GSPs are noisey and seem to have values above 1
+    assert np.max(data) <= 1.5
 
 
 def test_normalize_passiv(passiv_datapipe):

From 9afccec2b3041905ebee8df32766fd270782d129 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Mon, 3 Jun 2024 15:13:29 +0000
Subject: [PATCH 25/27] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 tests/transform/xarray/test_normalize.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/transform/xarray/test_normalize.py b/tests/transform/xarray/test_normalize.py
index 0798460cd..37b787bff 100644
--- a/tests/transform/xarray/test_normalize.py
+++ b/tests/transform/xarray/test_normalize.py
@@ -39,7 +39,7 @@ def test_normalize_gsp(gsp_datapipe):
     )
     data = next(iter(normed_gsp_datapipe))
     assert np.min(data) >= 0.0
-    
+
     # Some GSPs are noisey and seem to have values above 1
     assert np.max(data) <= 1.5
 

From 7728511bef060f1ccb2ead3207ad8e8f2099807b Mon Sep 17 00:00:00 2001
From: James Fulton <djamesfulton@yahoo.co.uk>
Date: Tue, 4 Jun 2024 10:02:50 +0000
Subject: [PATCH 26/27] docs

---
 ocf_datapipes/select/pick_locations.py  |  2 ++
 ocf_datapipes/training/pvnet_all_gsp.py | 28 +++++++++++++------------
 2 files changed, 17 insertions(+), 13 deletions(-)

diff --git a/ocf_datapipes/select/pick_locations.py b/ocf_datapipes/select/pick_locations.py
index 8e82c4678..81b913956 100644
--- a/ocf_datapipes/select/pick_locations.py
+++ b/ocf_datapipes/select/pick_locations.py
@@ -36,6 +36,7 @@ def __init__(
         self.shuffle = shuffle
 
     def _yield_all_iter(self, xr_dataset):
+        """Samples without replacement from possible locations"""
         # Get the spatial coords
         xr_coord_system, xr_x_dim, xr_y_dim = spatial_coord_type(xr_dataset)
 
@@ -62,6 +63,7 @@ def _yield_all_iter(self, xr_dataset):
             yield location
 
     def _yield_random_iter(self, xr_dataset):
+        """Samples with replacement from possible locations"""
         # Get the spatial coords
         xr_coord_system, xr_x_dim, xr_y_dim = spatial_coord_type(xr_dataset)
 
diff --git a/ocf_datapipes/training/pvnet_all_gsp.py b/ocf_datapipes/training/pvnet_all_gsp.py
index 3ae552507..61cabdfaa 100644
--- a/ocf_datapipes/training/pvnet_all_gsp.py
+++ b/ocf_datapipes/training/pvnet_all_gsp.py
@@ -1,4 +1,18 @@
-"""Create the training/validation datapipe for training the PVNet Model"""
+"""Create the training/validation datapipe for UK PVNet batches for all GSPs
+
+
+The main public functions are:
+
+    [1] `pvnet_all_gsp_datapipe()`
+        This constructs a datapipe yielding batches with inputs for all 317 UK GSPs for random t0
+        times.
+        
+    [2] `construct_sliced_data_pipeline()`
+        Given a datapipe yielding t0 times, this function constructs a datapipe yielding batches 
+        with inputs for all 317 UK GSPs for the yielded t0 times. This function is used inside [1].
+
+"""
+
 import logging
 from datetime import datetime
 from typing import List, Optional, Tuple, Union
@@ -496,15 +510,3 @@ def pvnet_all_gsp_datapipe(
     )
 
     return datapipe
-
-
-if __name__ == "__main__":
-    import time
-
-    t0 = time.time()
-    dp = pvnet_all_gsp_datapipe(
-        config_filename="/home/jamesfulton/repos/PVNet/configs/datamodule/configuration/gcp_configuration.yaml"
-    )
-
-    b = next(iter(dp))
-    print(time.time() - t0)

From e79560bba35a89a4af53cb15a664450d55d4e54e Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Tue, 4 Jun 2024 10:03:41 +0000
Subject: [PATCH 27/27] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 ocf_datapipes/training/pvnet_all_gsp.py | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/ocf_datapipes/training/pvnet_all_gsp.py b/ocf_datapipes/training/pvnet_all_gsp.py
index 61cabdfaa..aa3da98c9 100644
--- a/ocf_datapipes/training/pvnet_all_gsp.py
+++ b/ocf_datapipes/training/pvnet_all_gsp.py
@@ -1,14 +1,13 @@
 """Create the training/validation datapipe for UK PVNet batches for all GSPs
 
-
 The main public functions are:
 
     [1] `pvnet_all_gsp_datapipe()`
         This constructs a datapipe yielding batches with inputs for all 317 UK GSPs for random t0
         times.
-        
+
     [2] `construct_sliced_data_pipeline()`
-        Given a datapipe yielding t0 times, this function constructs a datapipe yielding batches 
+        Given a datapipe yielding t0 times, this function constructs a datapipe yielding batches
         with inputs for all 317 UK GSPs for the yielded t0 times. This function is used inside [1].
 
 """