From 7daa19b987228ae878227a136c869e35c19b85b8 Mon Sep 17 00:00:00 2001
From: Alexandra Udaltsova <43303448+AUdaltsova@users.noreply.github.com>
Date: Mon, 5 Aug 2024 16:05:42 +0100
Subject: [PATCH 01/27] Fix history bug and add bells in analysis.py

---
 experiments/analysis.py | 29 ++++++++++++++++-------------
 1 file changed, 16 insertions(+), 13 deletions(-)

diff --git a/experiments/analysis.py b/experiments/analysis.py
index 75b00cd9..9d7d35f9 100644
--- a/experiments/analysis.py
+++ b/experiments/analysis.py
@@ -16,10 +16,11 @@ def main(runs: list[str], run_names: list[str]) -> None:
     """
     api = wandb.Api()
     dfs = []
+    epoch_num = []
     for run in runs:
-        run = api.run(f"openclimatefix/india/{run}")
+        run = api.run(f"openclimatefix/PROJECT/{run}")
 
-        df = run.history()
+        df = run.history(samples=run.lastHistoryStep+1)
         # Get the columns that are in the format 'MAE_horizon/step_<number>/val`
         mae_cols = [col for col in df.columns if "MAE_horizon/step_" in col and "val" in col]
         # Sort them
@@ -40,6 +41,7 @@ def main(runs: list[str], run_names: list[str]) -> None:
         # Get the step from the column name
         column_timesteps = [int(col.split("_")[-1].split("/")[0]) * 15 for col in mae_cols]
         dfs.append(df)
+        epoch_num.append(min_row_idx)
     # Get the timedelta for each group
     groupings = [
         [0, 0],
@@ -89,22 +91,22 @@ def main(runs: list[str], run_names: list[str]) -> None:
     # Plot the error on per timestep, and all timesteps
     plt.figure()
     for idx, df in enumerate(dfs):
-        plt.plot(column_timesteps, df, label=run_names[idx])
-    plt.legend()
-    plt.xlabel("Timestep (minutes)")
-    plt.ylabel("MAE %")
-    plt.title("MAE % for each timestep")
+        plt.plot(column_timesteps, df, label=f"{run_names[idx]}, epoch: {epoch_num[idx]}")
+    plt.legend(fontsize=18)
+    plt.xlabel("Timestep (minutes)", fontsize=18)
+    plt.ylabel("MAE %", fontsize=18)
+    plt.title("MAE % for each timestep", fontsize=24)
     plt.savefig("mae_per_timestep.png")
     plt.show()
 
     # Plot the error on per timestep, and grouped timesteps
     plt.figure()
-    for run_name in run_names:
-        plt.plot(groups_df[run_name], label=run_name)
-    plt.legend()
-    plt.xlabel("Timestep (minutes)")
-    plt.ylabel("MAE %")
-    plt.title("MAE % for each timestep")
+    for idx, run_name in enumerate(run_names):
+        plt.plot(groups_df[run_name], label=f"{run_name}, epoch: {epoch_num[idx]}")
+    plt.legend(fontsize=18)
+    plt.xlabel("Timestep (minutes)", fontsize=18)
+    plt.ylabel("MAE %", fontsize=18)
+    plt.title("MAE % for each timestep", fontsize=24)
     plt.savefig("mae_per_timestep.png")
     plt.show()
 
@@ -119,3 +121,4 @@ def main(runs: list[str], run_names: list[str]) -> None:
     parser.add_argument("--run_names", nargs="+")
     args = parser.parse_args()
     main(args.list_of_runs, args.run_names)
+    

From e268a61fdc9c3d487038a96be7cbdeab90c0e1df Mon Sep 17 00:00:00 2001
From: Alexandra Udaltsova <43303448+AUdaltsova@users.noreply.github.com>
Date: Mon, 5 Aug 2024 16:14:37 +0100
Subject: [PATCH 02/27] Update analysis.py

---
 experiments/analysis.py | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/experiments/analysis.py b/experiments/analysis.py
index 9d7d35f9..33fc718d 100644
--- a/experiments/analysis.py
+++ b/experiments/analysis.py
@@ -92,10 +92,10 @@ def main(runs: list[str], run_names: list[str]) -> None:
     plt.figure()
     for idx, df in enumerate(dfs):
         plt.plot(column_timesteps, df, label=f"{run_names[idx]}, epoch: {epoch_num[idx]}")
-    plt.legend(fontsize=18)
-    plt.xlabel("Timestep (minutes)", fontsize=18)
-    plt.ylabel("MAE %", fontsize=18)
-    plt.title("MAE % for each timestep", fontsize=24)
+    plt.legend()
+    plt.xlabel("Timestep (minutes)")
+    plt.ylabel("MAE %")
+    plt.title("MAE % for each timestep")
     plt.savefig("mae_per_timestep.png")
     plt.show()
 
@@ -103,10 +103,10 @@ def main(runs: list[str], run_names: list[str]) -> None:
     plt.figure()
     for idx, run_name in enumerate(run_names):
         plt.plot(groups_df[run_name], label=f"{run_name}, epoch: {epoch_num[idx]}")
-    plt.legend(fontsize=18)
-    plt.xlabel("Timestep (minutes)", fontsize=18)
-    plt.ylabel("MAE %", fontsize=18)
-    plt.title("MAE % for each timestep", fontsize=24)
+    plt.legend()
+    plt.xlabel("Timestep (minutes)")
+    plt.ylabel("MAE %")
+    plt.title("MAE % for each timestep")
     plt.savefig("mae_per_timestep.png")
     plt.show()
 

From f928e801200329188d9567de23efd9b30ba0fd7f Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Mon, 5 Aug 2024 15:16:57 +0000
Subject: [PATCH 03/27] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 experiments/analysis.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/experiments/analysis.py b/experiments/analysis.py
index 33fc718d..bb119664 100644
--- a/experiments/analysis.py
+++ b/experiments/analysis.py
@@ -20,7 +20,7 @@ def main(runs: list[str], run_names: list[str]) -> None:
     for run in runs:
         run = api.run(f"openclimatefix/PROJECT/{run}")
 
-        df = run.history(samples=run.lastHistoryStep+1)
+        df = run.history(samples=run.lastHistoryStep + 1)
         # Get the columns that are in the format 'MAE_horizon/step_<number>/val`
         mae_cols = [col for col in df.columns if "MAE_horizon/step_" in col and "val" in col]
         # Sort them
@@ -121,4 +121,3 @@ def main(runs: list[str], run_names: list[str]) -> None:
     parser.add_argument("--run_names", nargs="+")
     args = parser.parse_args()
     main(args.list_of_runs, args.run_names)
-    

From bdbe00a435081a700cc6ad2345a11b69179143ca Mon Sep 17 00:00:00 2001
From: AUdaltsova <aleksandraudaltsova@gmail.com>
Date: Fri, 9 Aug 2024 11:35:03 +0100
Subject: [PATCH 04/27] remove unused configs

---
 tests/test_data/sample_wind_batches/data_configuration.yaml | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/tests/test_data/sample_wind_batches/data_configuration.yaml b/tests/test_data/sample_wind_batches/data_configuration.yaml
index 91b8a50a..cf603063 100644
--- a/tests/test_data/sample_wind_batches/data_configuration.yaml
+++ b/tests/test_data/sample_wind_batches/data_configuration.yaml
@@ -5,7 +5,6 @@ general:
 input_data:
   default_forecast_minutes: 2880
   default_history_minutes: 60
-  data_source_which_defines_geospatial_locations: "wind"
   nwp:
     ecmwf:
       # Path to ECMWF NWP data in zarr format
@@ -64,7 +63,6 @@ input_data:
       - label: india
         wind_filename: /mnt/storage_ssd_4tb/india_wind_data.nc
         wind_metadata_filename: /mnt/storage_ssd_4tb/india_wind_metadata.csv
-    get_center: true
     n_wind_systems_per_example: 1
     #start_datetime: "2021-01-01 00:00:00"
     #end_datetime: "2024-01-01 00:00:00"
@@ -72,7 +70,6 @@ input_data:
     #sensor_files_groups:
     #    - label: meteomatics
     sensor_filename: "/mnt/storage_b/nwp/meteomatics/nw_india/wind*.zarr.zip"
-    get_center: false
     history_minutes: 60
     forecast_minutes: 2880
     #n_sensor_systems_per_example: 26

From c8381560579647c32e88caba6215be1e2523b54b Mon Sep 17 00:00:00 2001
From: BumpVersion Action <bumpversion@github-actions>
Date: Fri, 9 Aug 2024 13:20:58 +0000
Subject: [PATCH 05/27] =?UTF-8?q?Bump=20version:=203.0.53=20=E2=86=92=203.?=
 =?UTF-8?q?0.54=20[skip=20ci]?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .bumpversion.cfg  | 2 +-
 pvnet/__init__.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/.bumpversion.cfg b/.bumpversion.cfg
index 67baea56..6671c1aa 100644
--- a/.bumpversion.cfg
+++ b/.bumpversion.cfg
@@ -1,7 +1,7 @@
 [bumpversion]
 commit = True
 tag = True
-current_version = 3.0.53
+current_version = 3.0.54
 message = Bump version: {current_version} → {new_version} [skip ci]
 
 [bumpversion:file:pvnet/__init__.py]
diff --git a/pvnet/__init__.py b/pvnet/__init__.py
index 35f41407..dd0cbcb8 100644
--- a/pvnet/__init__.py
+++ b/pvnet/__init__.py
@@ -1,2 +1,2 @@
 """PVNet"""
-__version__ = "3.0.53"
+__version__ = "3.0.54"

From 28ed7d823207f9ca4c069e4f73f9b82e94a4c04f Mon Sep 17 00:00:00 2001
From: Sukhil Patel <42407101+Sukh-P@users.noreply.github.com>
Date: Thu, 29 Aug 2024 09:40:26 +0100
Subject: [PATCH 06/27] Add torch module hub mixin back in (#250)

---
 pvnet/models/base_model.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/pvnet/models/base_model.py b/pvnet/models/base_model.py
index 081e9bc5..9faff47d 100644
--- a/pvnet/models/base_model.py
+++ b/pvnet/models/base_model.py
@@ -13,7 +13,7 @@
 import torch.nn.functional as F
 import wandb
 import yaml
-from huggingface_hub import ModelCard, ModelCardData
+from huggingface_hub import ModelCard, ModelCardData, PyTorchModelHubMixin
 from huggingface_hub.constants import CONFIG_NAME, PYTORCH_WEIGHTS_NAME
 from huggingface_hub.file_download import hf_hub_download
 from huggingface_hub.hf_api import HfApi
@@ -140,7 +140,7 @@ def minimize_data_config(input_path, output_path, model):
         yaml.dump(config, outfile, default_flow_style=False)
 
 
-class PVNetModelHubMixin:
+class PVNetModelHubMixin(PyTorchModelHubMixin):
     """
     Implementation of [`PyTorchModelHubMixin`] to provide model Hub upload/download capabilities.
     """

From 83e7b4857289b4b24924ed7916d6164e73c0a4ca Mon Sep 17 00:00:00 2001
From: BumpVersion Action <bumpversion@github-actions>
Date: Thu, 29 Aug 2024 08:40:58 +0000
Subject: [PATCH 07/27] =?UTF-8?q?Bump=20version:=203.0.54=20=E2=86=92=203.?=
 =?UTF-8?q?0.55=20[skip=20ci]?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .bumpversion.cfg  | 2 +-
 pvnet/__init__.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/.bumpversion.cfg b/.bumpversion.cfg
index 6671c1aa..9779516d 100644
--- a/.bumpversion.cfg
+++ b/.bumpversion.cfg
@@ -1,7 +1,7 @@
 [bumpversion]
 commit = True
 tag = True
-current_version = 3.0.54
+current_version = 3.0.55
 message = Bump version: {current_version} → {new_version} [skip ci]
 
 [bumpversion:file:pvnet/__init__.py]
diff --git a/pvnet/__init__.py b/pvnet/__init__.py
index dd0cbcb8..f84c422d 100644
--- a/pvnet/__init__.py
+++ b/pvnet/__init__.py
@@ -1,2 +1,2 @@
 """PVNet"""
-__version__ = "3.0.54"
+__version__ = "3.0.55"

From f358681273a667c59b0ed43f3bdd3e5064f0e968 Mon Sep 17 00:00:00 2001
From: Alexandra Udaltsova <43303448+AUdaltsova@users.noreply.github.com>
Date: Fri, 6 Sep 2024 23:33:45 +0100
Subject: [PATCH 08/27] added hard to contribute batch and icon note

---
 README.md | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 128a1a9e..26e8542f 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,6 @@
 # PVNet 2.1
 
-[![Python Bump Version & release](https://github.com/openclimatefix/PVNet/actions/workflows/release.yml/badge.svg)](https://github.com/openclimatefix/PVNet/actions/workflows/release.yml)
+ [![Python Bump Version & release](https://github.com/openclimatefix/PVNet/actions/workflows/release.yml/badge.svg)](https://github.com/openclimatefix/PVNet/actions/workflows/release.yml) [![hard to contribute to](https://img.shields.io/badge/hard%20to%20contribute%20to-dd2e44)](https://github.com/openclimatefix/ocf-meta-repo?tab=readme-ov-file#overview-of-ocfs-nowcasting-repositories)
 
 This project is used for training PVNet and running PVNet on live data.
 
@@ -85,6 +85,8 @@ OCF maintains a Zarr formatted version of the German Weather Service's (DWD)
 ICON-EU NWP model here:
 https://huggingface.co/datasets/openclimatefix/dwd-icon-eu which includes the UK
 
+Please note that the current version of [ICON loader]([url](https://github.com/openclimatefix/ocf_datapipes/blob/9ec252eeee44937c12ab52699579bdcace76e72f/ocf_datapipes/load/nwp/providers/icon.py#L9-L30)) supports a different format. If you want to use our ICON-EU dataset or your own NWP source, you can create a loader for it using [the instructions here]([url](https://github.com/openclimatefix/ocf_datapipes/tree/main/ocf_datapipes/load#nwp)).
+
 **PV**\
 OCF maintains a dataset of PV generation from 1311 private PV installations
 here: https://huggingface.co/datasets/openclimatefix/uk_pv

From 27204b91b433927aa8a79ba15bd7b08303cfbeaf Mon Sep 17 00:00:00 2001
From: Alexandra Udaltsova <43303448+AUdaltsova@users.noreply.github.com>
Date: Tue, 10 Sep 2024 16:56:37 +0100
Subject: [PATCH 09/27] Update badge style

---
 README.md | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 26e8542f..051e9a2e 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,7 @@
 # PVNet 2.1
 
- [![Python Bump Version & release](https://github.com/openclimatefix/PVNet/actions/workflows/release.yml/badge.svg)](https://github.com/openclimatefix/PVNet/actions/workflows/release.yml) [![hard to contribute to](https://img.shields.io/badge/hard%20to%20contribute%20to-dd2e44)](https://github.com/openclimatefix/ocf-meta-repo?tab=readme-ov-file#overview-of-ocfs-nowcasting-repositories)
+ [![Python Bump Version & release](https://github.com/openclimatefix/PVNet/actions/workflows/release.yml/badge.svg)](https://github.com/openclimatefix/PVNet/actions/workflows/release.yml) [![ease of contribution: hard](https://img.shields.io/badge/ease%20of%20contribution:%20hard-bb2629)](https://github.com/openclimatefix/ocf-meta-repo?tab=readme-ov-file#overview-of-ocfs-nowcasting-repositories)
+
 
 This project is used for training PVNet and running PVNet on live data.
 

From 280dc2c7e50cdb7165e21903072b16795e28cf12 Mon Sep 17 00:00:00 2001
From: Peter Dudfield <34686298+peterdudfield@users.noreply.github.com>
Date: Thu, 12 Sep 2024 18:54:08 +0100
Subject: [PATCH 10/27] save validation batch results to wandb (#252)

* save validation batch results to wandb

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* fix validation df

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* tidy up

* at print statment

* try and except around odd error

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* fix

* PR comments

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* fix and add comments

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* update for quantile loss

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* save all quantile results

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* PR comment

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
---
 pvnet/models/base_model.py | 72 ++++++++++++++++++++++++++++++++++++--
 1 file changed, 70 insertions(+), 2 deletions(-)

diff --git a/pvnet/models/base_model.py b/pvnet/models/base_model.py
index 9faff47d..e6e0e907 100644
--- a/pvnet/models/base_model.py
+++ b/pvnet/models/base_model.py
@@ -2,6 +2,7 @@
 import json
 import logging
 import os
+import tempfile
 from pathlib import Path
 from typing import Dict, Optional, Union
 
@@ -410,6 +411,9 @@ def __init__(
         else:
             self.num_output_features = self.forecast_len
 
+        # save all validation results to array, so we can save these to weights n biases
+        self.validation_epoch_results = []
+
     def _quantiles_to_prediction(self, y_quantiles):
         """
         Convert network prediction into a point prediction.
@@ -609,12 +613,61 @@ def _log_forecast_plot(self, batch, y_hat, accum_batch_num, timesteps_to_plot, p
             print(e)
         plt.close(fig)
 
+    def _log_validation_results(self, batch, y_hat, accum_batch_num):
+        """Append validation results to self.validation_epoch_results"""
+
+        # get truth values, shape (b, forecast_len)
+        y = batch[self._target_key][:, -self.forecast_len :, 0]
+        y = y.detach().cpu().numpy()
+        batch_size = y.shape[0]
+
+        # get prediction values, shape (b, forecast_len, quantiles?)
+        y_hat = y_hat.detach().cpu().numpy()
+
+        # get time_utc, shape (b, forecast_len)
+        time_utc_key = BatchKey[f"{self._target_key_name}_time_utc"]
+        time_utc = batch[time_utc_key][:, -self.forecast_len :].detach().cpu().numpy()
+
+        # get target id and change from (b,1) to (b,)
+        id_key = BatchKey[f"{self._target_key_name}_id"]
+        target_id = batch[id_key].detach().cpu().numpy()
+        target_id = target_id.squeeze()
+
+        for i in range(batch_size):
+            y_i = y[i]
+            y_hat_i = y_hat[i]
+            time_utc_i = time_utc[i]
+            target_id_i = target_id[i]
+
+            results_dict = {
+                "y": y_i,
+                "time_utc": time_utc_i,
+            }
+            if self.use_quantile_regression:
+                results_dict.update(
+                    {f"y_quantile_{q}": y_hat_i[:, i] for i, q in enumerate(self.output_quantiles)}
+                )
+            else:
+                results_dict["y_hat"] = y_hat_i
+
+            results_df = pd.DataFrame(results_dict)
+            results_df["id"] = target_id_i
+            results_df["batch_idx"] = accum_batch_num
+            results_df["example_idx"] = i
+
+            self.validation_epoch_results.append(results_df)
+
     def validation_step(self, batch: dict, batch_idx):
         """Run validation step"""
+
+        accum_batch_num = batch_idx // self.trainer.accumulate_grad_batches
+
         y_hat = self(batch)
         # Sensor seems to be in batch, station, time order
         y = batch[self._target_key][:, -self.forecast_len :, 0]
 
+        self._log_validation_results(batch, y_hat, accum_batch_num)
+
         # Expand persistence to be the same shape as y
         losses = self._calculate_common_losses(y, y_hat)
         losses.update(self._calculate_val_losses(y, y_hat))
@@ -632,8 +685,6 @@ def validation_step(self, batch: dict, batch_idx):
             on_epoch=True,
         )
 
-        accum_batch_num = batch_idx // self.trainer.accumulate_grad_batches
-
         # Make plots only if using wandb logger
         if isinstance(self.logger, pl.loggers.WandbLogger) and accum_batch_num in [0, 1]:
             # Store these temporarily under self
@@ -675,6 +726,23 @@ def validation_step(self, batch: dict, batch_idx):
     def on_validation_epoch_end(self):
         """Run on epoch end"""
 
+        try:
+            # join together validation results, and save to wandb
+            validation_results_df = pd.concat(self.validation_epoch_results)
+            with tempfile.TemporaryDirectory() as tempdir:
+                filename = os.path.join(tempdir, f"validation_results_{self.current_epoch}.csv")
+                validation_results_df.to_csv(filename, index=False)
+
+                # make and log wand artifact
+                validation_artifact = wandb.Artifact(
+                    f"validation_results_epoch={self.current_epoch}", type="dataset"
+                )
+                validation_artifact.add_file(filename)
+                wandb.log_artifact(validation_artifact)
+        except Exception as e:
+            print("Failed to log validation results to wandb")
+            print(e)
+
         horizon_maes_dict = self._horizon_maes.flush()
 
         # Create the horizon accuracy curve

From 56a39a709ee640f0cabb49671ff8d189403a7bef Mon Sep 17 00:00:00 2001
From: BumpVersion Action <bumpversion@github-actions>
Date: Thu, 12 Sep 2024 17:54:38 +0000
Subject: [PATCH 11/27] =?UTF-8?q?Bump=20version:=203.0.55=20=E2=86=92=203.?=
 =?UTF-8?q?0.56=20[skip=20ci]?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .bumpversion.cfg  | 2 +-
 pvnet/__init__.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/.bumpversion.cfg b/.bumpversion.cfg
index 9779516d..1d0343e0 100644
--- a/.bumpversion.cfg
+++ b/.bumpversion.cfg
@@ -1,7 +1,7 @@
 [bumpversion]
 commit = True
 tag = True
-current_version = 3.0.55
+current_version = 3.0.56
 message = Bump version: {current_version} → {new_version} [skip ci]
 
 [bumpversion:file:pvnet/__init__.py]
diff --git a/pvnet/__init__.py b/pvnet/__init__.py
index f84c422d..e1e6cfa0 100644
--- a/pvnet/__init__.py
+++ b/pvnet/__init__.py
@@ -1,2 +1,2 @@
 """PVNet"""
-__version__ = "3.0.55"
+__version__ = "3.0.56"

From 4795a9965b455503d7475bcbc2f2fcba91f14757 Mon Sep 17 00:00:00 2001
From: Alexandra Udaltsova <43303448+AUdaltsova@users.noreply.github.com>
Date: Fri, 20 Sep 2024 16:36:50 +0100
Subject: [PATCH 12/27] Fix wandb artifact saving in base_model.py

---
 pvnet/models/base_model.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pvnet/models/base_model.py b/pvnet/models/base_model.py
index e6e0e907..8665bf3c 100644
--- a/pvnet/models/base_model.py
+++ b/pvnet/models/base_model.py
@@ -735,7 +735,7 @@ def on_validation_epoch_end(self):
 
                 # make and log wand artifact
                 validation_artifact = wandb.Artifact(
-                    f"validation_results_epoch={self.current_epoch}", type="dataset"
+                    f"validation_results_epoch_{self.current_epoch}", type="dataset"
                 )
                 validation_artifact.add_file(filename)
                 wandb.log_artifact(validation_artifact)

From b5fa2d95acb0f0655d72cd5a753bc18f6cc8333c Mon Sep 17 00:00:00 2001
From: BumpVersion Action <bumpversion@github-actions>
Date: Mon, 23 Sep 2024 13:51:04 +0000
Subject: [PATCH 13/27] =?UTF-8?q?Bump=20version:=203.0.56=20=E2=86=92=203.?=
 =?UTF-8?q?0.57=20[skip=20ci]?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .bumpversion.cfg  | 2 +-
 pvnet/__init__.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/.bumpversion.cfg b/.bumpversion.cfg
index 1d0343e0..19cd1fc3 100644
--- a/.bumpversion.cfg
+++ b/.bumpversion.cfg
@@ -1,7 +1,7 @@
 [bumpversion]
 commit = True
 tag = True
-current_version = 3.0.56
+current_version = 3.0.57
 message = Bump version: {current_version} → {new_version} [skip ci]
 
 [bumpversion:file:pvnet/__init__.py]
diff --git a/pvnet/__init__.py b/pvnet/__init__.py
index e1e6cfa0..34aca23b 100644
--- a/pvnet/__init__.py
+++ b/pvnet/__init__.py
@@ -1,2 +1,2 @@
 """PVNet"""
-__version__ = "3.0.56"
+__version__ = "3.0.57"

From c93996cdf26d892b7186c6a17ae5493f1a5bc59c Mon Sep 17 00:00:00 2001
From: James Fulton <41546094+dfulu@users.noreply.github.com>
Date: Mon, 7 Oct 2024 11:33:34 +0100
Subject: [PATCH 14/27] Use concurrent batch pipeline for ~30x speed up (#236)

* bug fix

* use concurrent datapipe

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* clean up

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* update comment

* update comment

* save as tensor

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
---
 scripts/backtest_uk_gsp.py         | 73 ++++++++----------------------
 scripts/save_concurrent_batches.py | 71 ++++-------------------------
 2 files changed, 28 insertions(+), 116 deletions(-)

diff --git a/scripts/backtest_uk_gsp.py b/scripts/backtest_uk_gsp.py
index 33d7eb62..e002af3f 100644
--- a/scripts/backtest_uk_gsp.py
+++ b/scripts/backtest_uk_gsp.py
@@ -38,15 +38,11 @@
     NumpyBatch,
     batch_to_tensor,
     copy_batch_to_device,
-    stack_np_examples_into_batch,
 )
 from ocf_datapipes.config.load import load_yaml_configuration
 from ocf_datapipes.load import OpenGSP
-from ocf_datapipes.training.common import create_t0_and_loc_datapipes
-from ocf_datapipes.training.pvnet import (
-    _get_datapipes_dict,
-    construct_sliced_data_pipeline,
-)
+from ocf_datapipes.training.common import _get_datapipes_dict
+from ocf_datapipes.training.pvnet_all_gsp import construct_sliced_data_pipeline, create_t0_datapipe
 from ocf_datapipes.utils.consts import ELEVATION_MEAN, ELEVATION_STD
 from omegaconf import DictConfig
 
@@ -58,20 +54,19 @@
 from tqdm import tqdm
 
 from pvnet.load_model import get_model_from_checkpoints
-from pvnet.utils import GSPLocationLookup
 
 # ------------------------------------------------------------------
 # USER CONFIGURED VARIABLES
-output_dir = "/mnt/disks/backtest/test_backtest"
+output_dir = "/mnt/disks/extra_batches/test_backtest"
 
 # Local directory to load the PVNet checkpoint from. By default this should pull the best performing
 # checkpoint on the val set
-model_chckpoint_dir = "/home/jamesfulton/repos/PVNet/checkpoints/kqaknmuc"
+model_chckpoint_dir = "/home/jamesfulton/repos/PVNet/checkpoints/q911tei5"
 
 # Local directory to load the summation model checkpoint from. By default this should pull the best
 # performing checkpoint on the val set. If set to None a simple sum is used instead
 summation_chckpoint_dir = (
-    "/home/jamesfulton/repos/PVNet_summation/checkpoints/pvnet_summation/nw673nw2"
+    "/home/jamesfulton/repos/PVNet_summation/checkpoints/pvnet_summation/73oa4w9t"
 )
 
 # Forecasts will be made for all available init times between these
@@ -144,7 +139,7 @@ def get_available_t0_times(start_datetime, end_datetime, config_path):
     # Pop out the config file
     config = datapipes_dict.pop("config")
 
-    # We are going to abuse the `create_t0_and_loc_datapipes()` function to find the init-times in
+    # We are going to abuse the `create_t0_datapipe()` function to find the init-times in
     # potential_init_times which we have input data for. To do this, we will feed in some fake GSP
     # data which has the potential_init_times as timestamps. This is a bit hacky but works for now
 
@@ -172,18 +167,15 @@ def get_available_t0_times(start_datetime, end_datetime, config_path):
     # Overwrite the GSP data which is already in the datapipes dict
     datapipes_dict["gsp"] = IterableWrapper([ds_fake_gsp])
 
-    # Use create_t0_and_loc_datapipes to get datapipe of init-times
-    location_pipe, t0_datapipe = create_t0_and_loc_datapipes(
+    # Use create_t0_datapipe to get datapipe of init-times
+    t0_datapipe = create_t0_datapipe(
         datapipes_dict,
         configuration=config,
-        key_for_t0="gsp",
         shuffle=False,
     )
 
-    # Create a full list of available init-times. Note that we need to loop over the t0s AND
-    # locations to avoid the torch datapipes buffer overflow but we don't actually use the location
-    available_init_times = [t0 for _, t0 in zip(location_pipe, t0_datapipe)]
-    available_init_times = pd.to_datetime(available_init_times)
+    # Create a full list of available init-times
+    available_init_times = pd.to_datetime([t0 for t0 in t0_datapipe])
 
     logger.info(
         f"{len(available_init_times)} out of {len(potential_init_times)} "
@@ -193,22 +185,16 @@ def get_available_t0_times(start_datetime, end_datetime, config_path):
     return available_init_times
 
 
-def get_loctimes_datapipes(config_path):
-    """Create location and init-time datapipes
+def get_times_datapipe(config_path):
+    """Create init-time datapipe
 
     Args:
         config_path: Path to data config file
 
     Returns:
-        tuple: A tuple of datapipes
-            - Datapipe yielding locations
-            - Datapipe yielding init-times
+        Datapipe: A Datapipe yielding init-times
     """
 
-    # Set up ID location query object
-    ds_gsp = get_gsp_ds(config_path)
-    gsp_id_to_loc = GSPLocationLookup(ds_gsp.x_osgb, ds_gsp.y_osgb)
-
     # Filter the init-times to times we have all input data for
     available_target_times = get_available_t0_times(
         start_datetime,
@@ -222,25 +208,13 @@ def get_loctimes_datapipes(config_path):
     # the backtest will end up producing
     available_target_times.to_frame().to_csv(f"{output_dir}/t0_times.csv")
 
-    # Cycle the GSP locations
-    location_pipe = IterableWrapper([[gsp_id_to_loc(gsp_id) for gsp_id in ALL_GSP_IDS]]).repeat(
-        num_t0s
-    )
-
-    # Shard and then unbatch the locations so that each worker will generate all samples for all
-    # GSPs and for a single init-time
-    location_pipe = location_pipe.sharding_filter()
-    location_pipe = location_pipe.unbatch(unbatch_level=1)
-
     # Create times datapipe so each worker receives 317 copies of the same datetime for its batch
-    t0_datapipe = IterableWrapper([[t0 for gsp_id in ALL_GSP_IDS] for t0 in available_target_times])
+    t0_datapipe = IterableWrapper(available_target_times)
     t0_datapipe = t0_datapipe.sharding_filter()
-    t0_datapipe = t0_datapipe.unbatch(unbatch_level=1)
 
-    t0_datapipe = t0_datapipe.set_length(num_t0s * len(ALL_GSP_IDS))
-    location_pipe = location_pipe.set_length(num_t0s * len(ALL_GSP_IDS))
+    t0_datapipe = t0_datapipe.set_length(num_t0s)
 
-    return location_pipe, t0_datapipe
+    return t0_datapipe
 
 
 class ModelPipe:
@@ -375,25 +349,16 @@ def get_datapipe(config_path: str) -> NumpyBatch:
     """
 
     # Construct location and init-time datapipes
-    location_pipe, t0_datapipe = get_loctimes_datapipes(config_path)
-
-    # Get the number of init-times
-    num_batches = len(t0_datapipe) // len(ALL_GSP_IDS)
+    t0_datapipe = get_times_datapipe(config_path)
 
     # Construct sample datapipes
     data_pipeline = construct_sliced_data_pipeline(
         config_path,
-        location_pipe,
         t0_datapipe,
     )
 
-    # Batch so that each worker returns a batch of all locations for a single init-time
-    # Also convert to tensor for model
-    data_pipeline = (
-        data_pipeline.batch(len(ALL_GSP_IDS)).map(stack_np_examples_into_batch).map(batch_to_tensor)
-    )
-
-    data_pipeline = data_pipeline.set_length(num_batches)
+    # Convert to tensor for model
+    data_pipeline = data_pipeline.map(batch_to_tensor).set_length(len(t0_datapipe))
 
     return data_pipeline
 
diff --git a/scripts/save_concurrent_batches.py b/scripts/save_concurrent_batches.py
index f421887e..37833b9e 100644
--- a/scripts/save_concurrent_batches.py
+++ b/scripts/save_concurrent_batches.py
@@ -32,19 +32,17 @@
 import hydra
 import numpy as np
 import torch
-from ocf_datapipes.batch import BatchKey, batch_to_tensor, stack_np_examples_into_batch
-from ocf_datapipes.training.common import (
-    open_and_return_datapipes,
+from ocf_datapipes.batch import BatchKey, batch_to_tensor
+from ocf_datapipes.training.pvnet_all_gsp import (
+    construct_sliced_data_pipeline,
+    construct_time_pipeline,
 )
-from ocf_datapipes.training.pvnet import construct_loctime_pipelines, construct_sliced_data_pipeline
 from omegaconf import DictConfig, OmegaConf
 from sqlalchemy import exc as sa_exc
 from torch.utils.data import DataLoader
 from torch.utils.data.datapipes.iter import IterableWrapper
 from tqdm import tqdm
 
-from pvnet.utils import GSPLocationLookup
-
 warnings.filterwarnings("ignore", category=sa_exc.SAWarning)
 
 logger = logging.getLogger(__name__)
@@ -61,73 +59,22 @@ def __call__(self, input):
         torch.save(batch, f"{self.batch_dir}/{i:06}.pt")
 
 
-def select_first(x):
-    """Select zeroth element from indexable object"""
-    return x[0]
-
-
-def _get_loctimes_datapipes(config_path, start_time, end_time, n_batches):
-    # Set up ID location query object
-    ds_gsp = next(
-        iter(
-            open_and_return_datapipes(
-                config_path,
-                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)
-
-    # Cycle the GSP locations
-    location_pipe = IterableWrapper([[gsp_id_to_loc(gsp_id) for gsp_id in range(1, 318)]]).repeat(
-        n_batches
-    )
-
-    # Shard and unbatch so each worker goes through GSP 1-317 for each batch
-    location_pipe = location_pipe.sharding_filter()
-    location_pipe = location_pipe.unbatch(unbatch_level=1)
-
-    # These two datapipes come from an earlier fork and must be iterated through together
-    # despite the fact that we don't want these random locations here
-    random_location_datapipe, t0_datapipe = construct_loctime_pipelines(
+def _get_datapipe(config_path, start_time, end_time, n_batches):
+    t0_datapipe = construct_time_pipeline(
         config_path,
         start_time,
         end_time,
     )
 
-    # Iterate through both but select only time
-    t0_datapipe = t0_datapipe.zip(random_location_datapipe).map(select_first)
-
-    # Create times datapipe so we'll get the same time over each batch
     t0_datapipe = t0_datapipe.header(n_batches)
-    t0_datapipe = IterableWrapper([[t0 for gsp_id in range(1, 318)] for t0 in t0_datapipe])
     t0_datapipe = t0_datapipe.sharding_filter()
-    t0_datapipe = t0_datapipe.unbatch(unbatch_level=1)
 
-    return location_pipe, t0_datapipe
-
-
-def _get_datapipe(config_path, start_time, end_time, n_batches):
-    # Open datasets from the config and filter to useable location-time pairs
-
-    location_pipe, t0_datapipe = _get_loctimes_datapipes(
-        config_path, start_time, end_time, n_batches
-    )
-
-    data_pipeline = construct_sliced_data_pipeline(
+    datapipe = construct_sliced_data_pipeline(
         config_path,
-        location_pipe,
         t0_datapipe,
-    )
-
-    data_pipeline = data_pipeline.batch(317).map(stack_np_examples_into_batch).map(batch_to_tensor)
+    ).map(batch_to_tensor)
 
-    return data_pipeline
+    return datapipe
 
 
 def _save_batches_with_dataloader(batch_pipe, batch_dir, num_batches, dataloader_kwargs):

From dfce50ac954af1e758edb0dfbdd67c50083ab100 Mon Sep 17 00:00:00 2001
From: BumpVersion Action <bumpversion@github-actions>
Date: Mon, 7 Oct 2024 10:34:07 +0000
Subject: [PATCH 15/27] =?UTF-8?q?Bump=20version:=203.0.57=20=E2=86=92=203.?=
 =?UTF-8?q?0.58=20[skip=20ci]?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .bumpversion.cfg  | 2 +-
 pvnet/__init__.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/.bumpversion.cfg b/.bumpversion.cfg
index 19cd1fc3..33417b49 100644
--- a/.bumpversion.cfg
+++ b/.bumpversion.cfg
@@ -1,7 +1,7 @@
 [bumpversion]
 commit = True
 tag = True
-current_version = 3.0.57
+current_version = 3.0.58
 message = Bump version: {current_version} → {new_version} [skip ci]
 
 [bumpversion:file:pvnet/__init__.py]
diff --git a/pvnet/__init__.py b/pvnet/__init__.py
index 34aca23b..798ba127 100644
--- a/pvnet/__init__.py
+++ b/pvnet/__init__.py
@@ -1,2 +1,2 @@
 """PVNet"""
-__version__ = "3.0.57"
+__version__ = "3.0.58"

From 4b5bf480ceaeb0f159deaeee5283ac5f41020a56 Mon Sep 17 00:00:00 2001
From: Sukhil Patel <42407101+Sukh-P@users.noreply.github.com>
Date: Mon, 7 Oct 2024 18:16:49 +0100
Subject: [PATCH 16/27] Clean up site backtest script (#258)

---
 scripts/backtest_sites.py | 32 +++++++++++++++-----------------
 1 file changed, 15 insertions(+), 17 deletions(-)

diff --git a/scripts/backtest_sites.py b/scripts/backtest_sites.py
index 11f5b898..e764abf8 100644
--- a/scripts/backtest_sites.py
+++ b/scripts/backtest_sites.py
@@ -58,19 +58,15 @@
 from pvnet.utils import SiteLocationLookup
 
 # ------------------------------------------------------------------
-# USER CONFIGURED VARIABLES
+# USER CONFIGURED VARIABLES TO RUN THE SCRIPT
+
+# Directory path to save results
 output_dir = "PLACEHOLDER"
 
 # Local directory to load the PVNet checkpoint from. By default this should pull the best performing
 # checkpoint on the val set
 model_chckpoint_dir = "PLACEHOLDER"
 
-# Local directory to load the summation model checkpoint from. By default this should pull the best
-# performing checkpoint on the val set. If set to None a simple sum is used instead
-# summation_chckpoint_dir = (
-#     "/home/jamesfulton/repos/PVNet_summation/checkpoints/pvnet_summation/nw673nw2"
-# )
-
 # Forecasts will be made for all available init times between these
 start_datetime = "2022-05-08 00:00"
 end_datetime = "2022-05-08 00:30"
@@ -96,8 +92,10 @@
 # When sun as elevation below this, the forecast is set to zero
 MIN_DAY_ELEVATION = 0
 
-# All pv system ids to produce forecasts for
+# Add all pv site ids here that you wish to produce forecasts for
 ALL_SITE_IDS = []
+# Need to be in ascending order
+ALL_SITE_IDS.sort()
 
 # ------------------------------------------------------------------
 # FUNCTIONS
@@ -255,7 +253,8 @@ def get_loctimes_datapipes(config_path):
         unbatch_level=1
     )  # might not need this part since the site datapipe is creating examples
 
-    # Create times datapipe so each worker receives 317 copies of the same datetime for its batch
+    # Create times datapipe so each worker receives
+    # len(ALL_SITE_IDS) copies of the same datetime for its batch
     t0_datapipe = IterableWrapper(
         [[t0 for site_id in ALL_SITE_IDS] for t0 in available_target_times]
     )
@@ -305,7 +304,7 @@ def predict_batch(self, batch: NumpyBatch) -> xr.Dataset:
         )
 
         # Get effective capacities for this forecast
-        # site_capacities = ds_site.nominal_capacity_wp.values
+        site_capacities = self.ds_site.nominal_capacity_wp.values
         # Get the solar elevations. We need to un-normalise these from the values in the batch
         elevation = batch[BatchKey.pv_solar_elevation] * ELEVATION_STD + ELEVATION_MEAN
         # We only need elevation mask for forecasted values, not history
@@ -327,18 +326,17 @@ def predict_batch(self, batch: NumpyBatch) -> xr.Dataset:
             y_normed_site = model(device_batch).detach().cpu().numpy()
         da_normed_site = preds_to_dataarray(y_normed_site, model, valid_times, ALL_SITE_IDS)
 
-        # TODO fix this step: Multiply normalised forecasts by capacities and clip negatives
-        # For now output normalised by capacity outputs and unnormalise in post processing
-        # da_abs_site = da_normed_site.clip(0, None) * site_capacities[:, None, None]
-        da_normed_site = da_normed_site.clip(0, None)
+        # Multiply normalised forecasts by capacities and clip negatives
+        da_abs_site = da_normed_site.clip(0, None) * site_capacities[:, None, None]
+
         # Apply sundown mask
-        da_normed_site = da_normed_site.where(~da_sundown_mask).fillna(0.0)
+        da_abs_site = da_abs_site.where(~da_sundown_mask).fillna(0.0)
 
-        da_normed_site = da_normed_site.expand_dims(dim="init_time_utc", axis=0).assign_coords(
+        da_abs_site = da_abs_site.expand_dims(dim="init_time_utc", axis=0).assign_coords(
             init_time_utc=[t0]
         )
 
-        return da_normed_site
+        return da_abs_site
 
 
 def get_datapipe(config_path: str) -> NumpyBatch:

From aa76d4a748fc94f429daa2c5cf0608c82c447038 Mon Sep 17 00:00:00 2001
From: BumpVersion Action <bumpversion@github-actions>
Date: Mon, 7 Oct 2024 17:17:21 +0000
Subject: [PATCH 17/27] =?UTF-8?q?Bump=20version:=203.0.58=20=E2=86=92=203.?=
 =?UTF-8?q?0.59=20[skip=20ci]?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .bumpversion.cfg  | 2 +-
 pvnet/__init__.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/.bumpversion.cfg b/.bumpversion.cfg
index 33417b49..bc58fbc9 100644
--- a/.bumpversion.cfg
+++ b/.bumpversion.cfg
@@ -1,7 +1,7 @@
 [bumpversion]
 commit = True
 tag = True
-current_version = 3.0.58
+current_version = 3.0.59
 message = Bump version: {current_version} → {new_version} [skip ci]
 
 [bumpversion:file:pvnet/__init__.py]
diff --git a/pvnet/__init__.py b/pvnet/__init__.py
index 798ba127..f9b4efb2 100644
--- a/pvnet/__init__.py
+++ b/pvnet/__init__.py
@@ -1,2 +1,2 @@
 """PVNet"""
-__version__ = "3.0.58"
+__version__ = "3.0.59"

From eb8b445614015d33ff055db165625f6ada829a34 Mon Sep 17 00:00:00 2001
From: Alexandra Udaltsova <43303448+AUdaltsova@users.noreply.github.com>
Date: Fri, 18 Oct 2024 14:12:17 +0100
Subject: [PATCH 18/27] add pv padding and hf model handling to
 backtest_sites.py (#262)

* added pv padding and hf model handling to backtest_sites.py

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Apply suggestions from code review

Co-authored-by: Sukhil Patel <42407101+Sukh-P@users.noreply.github.com>

* Update scripts/backtest_sites.py

* Update pyproject.toml

* undo Update pyproject.toml

* linting

* docstring scripts/backtest_sites.py

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* docstring scripts/backtest_sites.py

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Sukhil Patel <42407101+Sukh-P@users.noreply.github.com>
---
 scripts/backtest_sites.py | 89 ++++++++++++++++++++++++++++++++++++---
 1 file changed, 83 insertions(+), 6 deletions(-)

diff --git a/scripts/backtest_sites.py b/scripts/backtest_sites.py
index e764abf8..3572daa3 100644
--- a/scripts/backtest_sites.py
+++ b/scripts/backtest_sites.py
@@ -23,6 +23,7 @@
 except RuntimeError:
     pass
 
+import json
 import logging
 import os
 import sys
@@ -32,6 +33,8 @@
 import pandas as pd
 import torch
 import xarray as xr
+from huggingface_hub import hf_hub_download
+from huggingface_hub.constants import CONFIG_NAME, PYTORCH_WEIGHTS_NAME
 from ocf_datapipes.batch import (
     BatchKey,
     NumpyBatch,
@@ -50,7 +53,7 @@
 )
 from ocf_datapipes.utils.consts import ELEVATION_MEAN, ELEVATION_STD
 from omegaconf import DictConfig
-from torch.utils.data import DataLoader
+from torch.utils.data import DataLoader, IterDataPipe, functional_datapipe
 from torch.utils.data.datapipes.iter import IterableWrapper
 from tqdm import tqdm
 
@@ -67,6 +70,10 @@
 # checkpoint on the val set
 model_chckpoint_dir = "PLACEHOLDER"
 
+hf_revision = None
+hf_token = None
+hf_model_id = None
+
 # Forecasts will be made for all available init times between these
 start_datetime = "2022-05-08 00:00"
 end_datetime = "2022-05-08 00:30"
@@ -101,11 +108,70 @@
 # FUNCTIONS
 
 
+@functional_datapipe("pad_forward_pv")
+class PadForwardPVIterDataPipe(IterDataPipe):
+    """
+    Pads forecast pv.
+
+    Sun position is calculated based off of pv time index
+    and for t0's close to end of pv data can have wrong shape as pv starts
+    to run out of data to slice for the forecast part.
+    """
+
+    def __init__(self, pv_dp: IterDataPipe, forecast_duration: np.timedelta64):
+        """Init"""
+
+        super().__init__()
+        self.pv_dp = pv_dp
+        self.forecast_duration = forecast_duration
+
+    def __iter__(self):
+        """Iter"""
+
+        for xr_data in self.pv_dp:
+            t0 = xr_data.time_utc.data[int(xr_data.attrs["t0_idx"])]
+            pv_step = np.timedelta64(xr_data.attrs["sample_period_duration"])
+            t_end = t0 + self.forecast_duration + pv_step
+            time_idx = np.arange(xr_data.time_utc.data[0], t_end, pv_step)
+            yield xr_data.reindex(time_utc=time_idx, fill_value=-1)
+
+
+def load_model_from_hf(model_id: str, revision: str, token: str):
+    """
+    Loads model from HuggingFace
+    """
+
+    model_file = hf_hub_download(
+        repo_id=model_id,
+        filename=PYTORCH_WEIGHTS_NAME,
+        revision=revision,
+        token=token,
+    )
+
+    # load config file
+    config_file = hf_hub_download(
+        repo_id=model_id,
+        filename=CONFIG_NAME,
+        revision=revision,
+        token=token,
+    )
+
+    with open(config_file, "r", encoding="utf-8") as f:
+        config = json.load(f)
+
+    model = hydra.utils.instantiate(config)
+
+    state_dict = torch.load(model_file, map_location=torch.device("cuda"))
+    model.load_state_dict(state_dict)  # type: ignore
+    model.eval()  # type: ignore
+
+    return model
+
+
 def preds_to_dataarray(preds, model, valid_times, site_ids):
     """Put numpy array of predictions into a dataarray"""
 
     if model.use_quantile_regression:
-        output_labels = model.output_quantiles
         output_labels = [f"forecast_mw_plevel_{int(q*100):02}" for q in model.output_quantiles]
         output_labels[output_labels.index("forecast_mw_plevel_50")] = "forecast_mw"
     else:
@@ -333,7 +399,7 @@ def predict_batch(self, batch: NumpyBatch) -> xr.Dataset:
         da_abs_site = da_abs_site.where(~da_sundown_mask).fillna(0.0)
 
         da_abs_site = da_abs_site.expand_dims(dim="init_time_utc", axis=0).assign_coords(
-            init_time_utc=[t0]
+            init_time_utc=np.array([t0], dtype="datetime64[ns]")
         )
 
         return da_abs_site
@@ -362,6 +428,11 @@ def get_datapipe(config_path: str) -> NumpyBatch:
         t0_datapipe,
     )
 
+    config = load_yaml_configuration(config_path)
+    data_pipeline["pv"] = data_pipeline["pv"].pad_forward_pv(
+        forecast_duration=np.timedelta64(config.input_data.pv.forecast_minutes, "m")
+    )
+
     data_pipeline = DictDatasetIterDataPipe(
         {k: v for k, v in data_pipeline.items() if k != "config"},
     ).map(split_dataset_dict_dp)
@@ -412,7 +483,13 @@ def main(config: DictConfig):
     # Create a dataloader for the concurrent batches and use multiprocessing
     dataloader = DataLoader(batch_pipe, **dataloader_kwargs)
     # Load the PVNet model
-    model, *_ = get_model_from_checkpoints([model_chckpoint_dir], val_best=True)
+    if model_chckpoint_dir:
+        model, *_ = get_model_from_checkpoints([model_chckpoint_dir], val_best=True)
+    elif hf_model_id:
+        model = load_model_from_hf(hf_model_id, hf_revision, hf_token)
+    else:
+        raise ValueError("Provide a model checkpoint or a HuggingFace model")
+
     model = model.eval().to(device)
 
     # Create object to make predictions for each input batch
@@ -426,13 +503,13 @@ def main(config: DictConfig):
 
             t0 = ds_abs_all.init_time_utc.values[0]
 
-            # Save the predictioons
+            # Save the predictions
             filename = f"{output_dir}/{t0}.nc"
             ds_abs_all.to_netcdf(filename)
 
             pbar.update()
         except Exception as e:
-            print(f"Exception {e} at {i}")
+            print(f"Exception {e} at batch {i}")
             pass
 
     # Close down

From ce664576b4eb912f176de312e489fac1c15d069d Mon Sep 17 00:00:00 2001
From: BumpVersion Action <bumpversion@github-actions>
Date: Fri, 18 Oct 2024 13:12:45 +0000
Subject: [PATCH 19/27] =?UTF-8?q?Bump=20version:=203.0.59=20=E2=86=92=203.?=
 =?UTF-8?q?0.60=20[skip=20ci]?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .bumpversion.cfg  | 2 +-
 pvnet/__init__.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/.bumpversion.cfg b/.bumpversion.cfg
index bc58fbc9..1749a8bd 100644
--- a/.bumpversion.cfg
+++ b/.bumpversion.cfg
@@ -1,7 +1,7 @@
 [bumpversion]
 commit = True
 tag = True
-current_version = 3.0.59
+current_version = 3.0.60
 message = Bump version: {current_version} → {new_version} [skip ci]
 
 [bumpversion:file:pvnet/__init__.py]
diff --git a/pvnet/__init__.py b/pvnet/__init__.py
index f9b4efb2..370f2ded 100644
--- a/pvnet/__init__.py
+++ b/pvnet/__init__.py
@@ -1,2 +1,2 @@
 """PVNet"""
-__version__ = "3.0.59"
+__version__ = "3.0.60"

From fdd468befa981d15282a183488822215aaae8da8 Mon Sep 17 00:00:00 2001
From: Sukhil Patel <42407101+Sukh-P@users.noreply.github.com>
Date: Tue, 22 Oct 2024 12:03:15 +0100
Subject: [PATCH 20/27] Remove run app locally script (#264)

---
 scripts/run_app_locally.py | 104 -------------------------------------
 1 file changed, 104 deletions(-)
 delete mode 100644 scripts/run_app_locally.py

diff --git a/scripts/run_app_locally.py b/scripts/run_app_locally.py
deleted file mode 100644
index a78efe5e..00000000
--- a/scripts/run_app_locally.py
+++ /dev/null
@@ -1,104 +0,0 @@
-"""Script to run the production app locally"""
-
-import logging
-import os
-import time
-from datetime import timedelta
-
-import numpy as np
-import pandas as pd
-import xarray as xr
-from ocf_datapipes.load import OpenGSPFromDatabase
-
-from pvnet.app import app
-
-formatter = logging.Formatter(fmt="%(levelname)s:%(name)s:%(message)s")
-stream_handler = logging.StreamHandler()
-stream_handler.setFormatter(formatter)
-
-logger = logging.getLogger(__name__)
-logger.setLevel(logging.INFO)
-logger.addHandler(stream_handler)
-
-
-def sleep_until(wake_time):
-    """Sleep until the given time"""
-    now = pd.Timestamp.now()
-    sleep_duration = (wake_time - now).total_seconds()
-    if sleep_duration < 0:
-        logger.warning("Sleep for negative duration requested")
-    else:
-        logger.info(f"Sleeping for {sleep_duration} seconds")
-        time.sleep(sleep_duration)
-
-
-if __name__ == "__main__":
-    # ----------------------------------------------------
-    # USER SETTINGS
-
-    # When to start and stop predictions
-    start_time = pd.Timestamp("2023-05-31 00:00")
-    end_time = pd.Timestamp("2023-06-05 21:00")
-
-    output_dir = "/mnt/disks/batches/local_production_forecasts"
-    save_inputs = True
-
-    # ----------------------------------------------------
-    # RUN
-
-    # Make output dirs
-    os.makedirs(f"{output_dir}/predictions", exist_ok=True)
-    os.makedirs(f"{output_dir}/logs", exist_ok=True)
-    if save_inputs:
-        os.makedirs(f"{output_dir}/inputs", exist_ok=True)
-
-    # Wait until start time
-    if pd.Timestamp.now() < start_time:
-        sleep_until(start_time)
-
-    while pd.Timestamp.now() < end_time:
-        # Next prediction time
-        t0 = pd.Timestamp.now().ceil(timedelta(minutes=30))
-
-        # Sleep until next prediction time
-        sleep_until(t0)
-
-        try:
-            # Make predictions
-            df = app(write_predictions=False)
-
-            # Save
-            df.to_csv(f"{output_dir}/predictions/{t0}.csv")
-        except Exception:
-            logger.exception(f"Predictions for {t0=} failed")
-
-        try:
-            # Log delays of data sources
-            log = dict(
-                now=t0,
-                gsp_times=next(iter(OpenGSPFromDatabase())).time_utc.values,
-                sat_times=xr.open_zarr("latest.zarr.zip").time.values,
-                nwp_times=xr.open_zarr(os.environ["NWP_ZARR_PATH"]).init_time.values,
-            )
-            np.save(f"{output_dir}/logs/{t0}.npy", log)
-        except Exception:
-            logger.exception(f"Logs for {t0=} failed")
-
-        if save_inputs:
-            try:
-                # Set up directory to save inputs
-                input_dir = f"{output_dir}/inputs/{t0}"
-                os.makedirs(input_dir, exist_ok=True)
-
-                # Save inputs
-                os.system(f"cp latest.zarr.zip '{input_dir}/sat.zarr.zip'")
-
-                ds = xr.open_zarr(os.environ["NWP_ZARR_PATH"])
-                for v in ds.variables:
-                    ds[v].encoding.clear()
-                ds.to_zarr(f"{input_dir}/nwp.zarr")
-
-                next(iter(OpenGSPFromDatabase())).to_dataset().to_zarr(f"{input_dir}/gsp.zarr")
-
-            except Exception:
-                logger.exception(f"Saving inputs for {t0=} failed")

From e5f34cab11cb8ab9e26dad2ac50933beb19bd8ef Mon Sep 17 00:00:00 2001
From: BumpVersion Action <bumpversion@github-actions>
Date: Tue, 22 Oct 2024 11:03:45 +0000
Subject: [PATCH 21/27] =?UTF-8?q?Bump=20version:=203.0.60=20=E2=86=92=203.?=
 =?UTF-8?q?0.61=20[skip=20ci]?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .bumpversion.cfg  | 2 +-
 pvnet/__init__.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/.bumpversion.cfg b/.bumpversion.cfg
index 1749a8bd..4e523b63 100644
--- a/.bumpversion.cfg
+++ b/.bumpversion.cfg
@@ -1,7 +1,7 @@
 [bumpversion]
 commit = True
 tag = True
-current_version = 3.0.60
+current_version = 3.0.61
 message = Bump version: {current_version} → {new_version} [skip ci]
 
 [bumpversion:file:pvnet/__init__.py]
diff --git a/pvnet/__init__.py b/pvnet/__init__.py
index 370f2ded..a97ea0bb 100644
--- a/pvnet/__init__.py
+++ b/pvnet/__init__.py
@@ -1,2 +1,2 @@
 """PVNet"""
-__version__ = "3.0.60"
+__version__ = "3.0.61"

From 05abb8ba9d4bc652f77a271aebadc349241a8c4a Mon Sep 17 00:00:00 2001
From: Alexandra Udaltsova <43303448+AUdaltsova@users.noreply.github.com>
Date: Wed, 23 Oct 2024 14:58:00 +0100
Subject: [PATCH 22/27] update backtest_sites.py (#266)

* Update backtest_sites.py

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
---
 scripts/backtest_sites.py | 31 +++++++++++++++++++++++++------
 1 file changed, 25 insertions(+), 6 deletions(-)

diff --git a/scripts/backtest_sites.py b/scripts/backtest_sites.py
index 3572daa3..65d63be2 100644
--- a/scripts/backtest_sites.py
+++ b/scripts/backtest_sites.py
@@ -118,21 +118,38 @@ class PadForwardPVIterDataPipe(IterDataPipe):
     to run out of data to slice for the forecast part.
     """
 
-    def __init__(self, pv_dp: IterDataPipe, forecast_duration: np.timedelta64):
+    def __init__(
+        self,
+        pv_dp: IterDataPipe,
+        forecast_duration: np.timedelta64,
+        history_duration: np.timedelta64,
+        time_resolution_minutes: np.timedelta64,
+    ):
         """Init"""
 
         super().__init__()
         self.pv_dp = pv_dp
         self.forecast_duration = forecast_duration
+        self.history_duration = history_duration
+        self.time_resolution_minutes = time_resolution_minutes
+
+        self.min_seq_length = history_duration // time_resolution_minutes
 
     def __iter__(self):
         """Iter"""
 
         for xr_data in self.pv_dp:
-            t0 = xr_data.time_utc.data[int(xr_data.attrs["t0_idx"])]
-            pv_step = np.timedelta64(xr_data.attrs["sample_period_duration"])
-            t_end = t0 + self.forecast_duration + pv_step
-            time_idx = np.arange(xr_data.time_utc.data[0], t_end, pv_step)
+            t_end = (
+                xr_data.time_utc.data[0]
+                + self.history_duration
+                + self.forecast_duration
+                + self.time_resolution_minutes
+            )
+            time_idx = np.arange(xr_data.time_utc.data[0], t_end, self.time_resolution_minutes)
+
+            if len(xr_data.time_utc.data) < self.min_seq_length:
+                raise ValueError("Not enough PV data to predict")
+
             yield xr_data.reindex(time_utc=time_idx, fill_value=-1)
 
 
@@ -430,7 +447,9 @@ def get_datapipe(config_path: str) -> NumpyBatch:
 
     config = load_yaml_configuration(config_path)
     data_pipeline["pv"] = data_pipeline["pv"].pad_forward_pv(
-        forecast_duration=np.timedelta64(config.input_data.pv.forecast_minutes, "m")
+        forecast_duration=np.timedelta64(config.input_data.pv.forecast_minutes, "m"),
+        history_duration=np.timedelta64(config.input_data.pv.history_minutes, "m"),
+        time_resolution_minutes=np.timedelta64(config.input_data.pv.time_resolution_minutes, "m"),
     )
 
     data_pipeline = DictDatasetIterDataPipe(

From c259a1cbb7c2eeb8570914f95d2cac5667024424 Mon Sep 17 00:00:00 2001
From: BumpVersion Action <bumpversion@github-actions>
Date: Wed, 23 Oct 2024 13:58:39 +0000
Subject: [PATCH 23/27] =?UTF-8?q?Bump=20version:=203.0.61=20=E2=86=92=203.?=
 =?UTF-8?q?0.62=20[skip=20ci]?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .bumpversion.cfg  | 2 +-
 pvnet/__init__.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/.bumpversion.cfg b/.bumpversion.cfg
index 4e523b63..0fc3026e 100644
--- a/.bumpversion.cfg
+++ b/.bumpversion.cfg
@@ -1,7 +1,7 @@
 [bumpversion]
 commit = True
 tag = True
-current_version = 3.0.61
+current_version = 3.0.62
 message = Bump version: {current_version} → {new_version} [skip ci]
 
 [bumpversion:file:pvnet/__init__.py]
diff --git a/pvnet/__init__.py b/pvnet/__init__.py
index a97ea0bb..1d157cca 100644
--- a/pvnet/__init__.py
+++ b/pvnet/__init__.py
@@ -1,2 +1,2 @@
 """PVNet"""
-__version__ = "3.0.61"
+__version__ = "3.0.62"

From 5360394e3c46bea4e9cacc5d1c5e34d63db6408e Mon Sep 17 00:00:00 2001
From: Alexandra Udaltsova <43303448+AUdaltsova@users.noreply.github.com>
Date: Fri, 25 Oct 2024 13:50:21 +0100
Subject: [PATCH 24/27] add flushing of val epoch resluts (#256)

* add flushing of val epoch resluts

* add docstring back base_model.py

* log to csv only when end of accumbatch
---
 pvnet/models/base_model.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/pvnet/models/base_model.py b/pvnet/models/base_model.py
index 8665bf3c..83e67e7b 100644
--- a/pvnet/models/base_model.py
+++ b/pvnet/models/base_model.py
@@ -666,7 +666,8 @@ def validation_step(self, batch: dict, batch_idx):
         # Sensor seems to be in batch, station, time order
         y = batch[self._target_key][:, -self.forecast_len :, 0]
 
-        self._log_validation_results(batch, y_hat, accum_batch_num)
+        if (batch_idx + 1) % self.trainer.accumulate_grad_batches == 0:
+            self._log_validation_results(batch, y_hat, accum_batch_num)
 
         # Expand persistence to be the same shape as y
         losses = self._calculate_common_losses(y, y_hat)
@@ -743,6 +744,7 @@ def on_validation_epoch_end(self):
             print("Failed to log validation results to wandb")
             print(e)
 
+        self.validation_epoch_results = []
         horizon_maes_dict = self._horizon_maes.flush()
 
         # Create the horizon accuracy curve

From 9497430a902045b4ad52db0fee5499fb31f0cf77 Mon Sep 17 00:00:00 2001
From: BumpVersion Action <bumpversion@github-actions>
Date: Fri, 25 Oct 2024 12:50:53 +0000
Subject: [PATCH 25/27] =?UTF-8?q?Bump=20version:=203.0.62=20=E2=86=92=203.?=
 =?UTF-8?q?0.63=20[skip=20ci]?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .bumpversion.cfg  | 2 +-
 pvnet/__init__.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/.bumpversion.cfg b/.bumpversion.cfg
index 0fc3026e..5d3bbe27 100644
--- a/.bumpversion.cfg
+++ b/.bumpversion.cfg
@@ -1,7 +1,7 @@
 [bumpversion]
 commit = True
 tag = True
-current_version = 3.0.62
+current_version = 3.0.63
 message = Bump version: {current_version} → {new_version} [skip ci]
 
 [bumpversion:file:pvnet/__init__.py]
diff --git a/pvnet/__init__.py b/pvnet/__init__.py
index 1d157cca..3b41ad55 100644
--- a/pvnet/__init__.py
+++ b/pvnet/__init__.py
@@ -1,2 +1,2 @@
 """PVNet"""
-__version__ = "3.0.62"
+__version__ = "3.0.63"

From acb6a36736be9b1094c51ddc3aa7cbc4844ab9e9 Mon Sep 17 00:00:00 2001
From: Sukhil Patel <42407101+Sukh-P@users.noreply.github.com>
Date: Fri, 8 Nov 2024 11:58:33 +0000
Subject: [PATCH 26/27] Update MAE analysis script (#274)

Update script
---
 experiments/{analysis.py => mae_analysis.py} | 42 +++++++++++++-------
 1 file changed, 28 insertions(+), 14 deletions(-)
 rename experiments/{analysis.py => mae_analysis.py} (74%)

diff --git a/experiments/analysis.py b/experiments/mae_analysis.py
similarity index 74%
rename from experiments/analysis.py
rename to experiments/mae_analysis.py
index bb119664..ac01aed2 100644
--- a/experiments/analysis.py
+++ b/experiments/mae_analysis.py
@@ -1,5 +1,8 @@
 """
-Script to generate a table comparing two run for MAE values for 48 hour 15 minute forecast
+Script to generate analysis of MAE values for multiple model forecasts
+
+Does this for 48 hour horizon forecasts with 15 minute granularity
+
 """
 
 import argparse
@@ -10,15 +13,21 @@
 import wandb
 
 
-def main(runs: list[str], run_names: list[str]) -> None:
+def main(project: str, runs: list[str], run_names: list[str]) -> None:
     """
-    Compare two runs for MAE values for 48 hour 15 minute forecast
+    Compare MAE values for multiple model forecasts for 48 hour horizon with 15 minute granularity
+
+    Args:
+            project: name of W&B project
+            runs: W&B ids of runs
+            run_names: user specified names for runs
+
     """
     api = wandb.Api()
     dfs = []
     epoch_num = []
     for run in runs:
-        run = api.run(f"openclimatefix/PROJECT/{run}")
+        run = api.run(f"openclimatefix/{project}/{run}")
 
         df = run.history(samples=run.lastHistoryStep + 1)
         # Get the columns that are in the format 'MAE_horizon/step_<number>/val`
@@ -88,10 +97,12 @@ def main(runs: list[str], run_names: list[str]) -> None:
     for idx, df in enumerate(dfs):
         print(f"{run_names[idx]}: {df.mean()*100:0.3f}")
 
-    # Plot the error on per timestep, and all timesteps
+    # Plot the error per timestep
     plt.figure()
     for idx, df in enumerate(dfs):
-        plt.plot(column_timesteps, df, label=f"{run_names[idx]}, epoch: {epoch_num[idx]}")
+        plt.plot(
+            column_timesteps, df, label=f"{run_names[idx]}, epoch: {epoch_num[idx]}", linestyle="-"
+        )
     plt.legend()
     plt.xlabel("Timestep (minutes)")
     plt.ylabel("MAE %")
@@ -99,25 +110,28 @@ def main(runs: list[str], run_names: list[str]) -> None:
     plt.savefig("mae_per_timestep.png")
     plt.show()
 
-    # Plot the error on per timestep, and grouped timesteps
+    # Plot the error per grouped timestep
     plt.figure()
     for idx, run_name in enumerate(run_names):
-        plt.plot(groups_df[run_name], label=f"{run_name}, epoch: {epoch_num[idx]}")
+        plt.plot(
+            groups_df[run_name],
+            label=f"{run_name}, epoch: {epoch_num[idx]}",
+            marker="o",
+            linestyle="-",
+        )
     plt.legend()
     plt.xlabel("Timestep (minutes)")
     plt.ylabel("MAE %")
-    plt.title("MAE % for each timestep")
-    plt.savefig("mae_per_timestep.png")
+    plt.title("MAE % for each grouped timestep")
+    plt.savefig("mae_per_grouped_timestep.png")
     plt.show()
 
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
-    "5llq8iw6"
-    parser.add_argument("--first_run", type=str, default="xdlew7ib")
-    parser.add_argument("--second_run", type=str, default="v3mja33d")
+    parser.add_argument("--project", type=str, default="")
     # Add arguments that is a list of strings
     parser.add_argument("--list_of_runs", nargs="+")
     parser.add_argument("--run_names", nargs="+")
     args = parser.parse_args()
-    main(args.list_of_runs, args.run_names)
+    main(args.project, args.list_of_runs, args.run_names)

From db81147207e39fb6bfa7c8c0a06f53dacf9a2339 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Wed, 13 Nov 2024 10:11:12 +0000
Subject: [PATCH 27/27] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 pvnet/data/datamodule.py   | 38 +++++++------------
 pvnet/models/base_model.py | 20 +++-------
 pvnet/models/utils.py      |  2 -
 pvnet/utils.py             |  4 +-
 scripts/save_samples.py    | 78 +++++++++++++++++++-------------------
 5 files changed, 59 insertions(+), 83 deletions(-)

diff --git a/pvnet/data/datamodule.py b/pvnet/data/datamodule.py
index b0df80ce..a834efbe 100644
--- a/pvnet/data/datamodule.py
+++ b/pvnet/data/datamodule.py
@@ -1,15 +1,11 @@
 """ Data module for pytorch lightning """
-from datetime import datetime
 from glob import glob
 
-from lightning.pytorch import LightningDataModule
-from torch.utils.data import Dataset, DataLoader
 import torch
-
-from ocf_datapipes.batch import batch_to_tensor, stack_np_examples_into_batch, NumpyBatch
-from ocf_data_sampler.torch_datasets.pvnet_uk_regional import (
-    PVNetUKRegionalDataset
-)
+from lightning.pytorch import LightningDataModule
+from ocf_data_sampler.torch_datasets.pvnet_uk_regional import PVNetUKRegionalDataset
+from ocf_datapipes.batch import NumpyBatch, batch_to_tensor, stack_np_examples_into_batch
+from torch.utils.data import DataLoader, Dataset
 
 
 def fill_nans_in_arrays(batch):
@@ -29,30 +25,28 @@ def fill_nans_in_arrays(batch):
     return batch
 
 
-
 class NumpybatchPremadeSamplesDataset(Dataset):
     """Dataset to load NumpyBatch samples"""
-    
+
     def __init__(self, sample_dir):
         """Dataset to load NumpyBatch samples
-        
+
         Args:
             sample_dir: Path to the directory of pre-saved samples.
         """
         self.sample_paths = glob(f"{sample_dir}/*.pt")
-        
-        
+
     def __len__(self):
         return len(self.sample_paths)
-    
+
     def __getitem__(self, idx):
         return fill_nans_in_arrays(torch.load(self.sample_paths[idx]))
-    
+
 
 def collate_fn(samples: list[NumpyBatch]):
     """Convert a list of NumpyBatch samples to a tensor batch"""
     return batch_to_tensor(stack_np_examples_into_batch(samples))
-        
+
 
 class DataModule(LightningDataModule):
     """Datamodule for training pvnet and using pvnet pipeline in `ocf_datapipes`."""
@@ -64,9 +58,8 @@ def __init__(
         batch_size: int = 16,
         num_workers: int = 0,
         prefetch_factor: int | None = None,
-        train_period: list[str|None] = [None, None],
-        val_period: list[str|None] = [None, None],
-        
+        train_period: list[str | None] = [None, None],
+        val_period: list[str | None] = [None, None],
     ):
         """Datamodule for training pvnet architecture.
 
@@ -85,7 +78,6 @@ def __init__(
         """
         super().__init__()
 
-
         if not ((sample_dir is not None) ^ (configuration is not None)):
             raise ValueError("Exactly one of `sample_dir` or `configuration` must be set.")
 
@@ -118,7 +110,7 @@ def _get_streamed_samples_dataset(self, start_time, end_time) -> Dataset:
     def _get_premade_samples_dataset(self, subdir) -> Dataset:
         split_dir = f"{self.sample_dir}/{subdir}"
         return NumpybatchPremadeSamplesDataset(split_dir)
-        
+
     def train_dataloader(self) -> DataLoader:
         """Construct train dataloader"""
         if self.sample_dir is not None:
@@ -126,7 +118,7 @@ def train_dataloader(self) -> DataLoader:
         else:
             dataset = self._get_streamed_samples_dataset(*self.train_period)
         return DataLoader(dataset, shuffle=True, **self._common_dataloader_kwargs)
-    
+
     def val_dataloader(self) -> DataLoader:
         """Construct val dataloader"""
         if self.sample_dir is not None:
@@ -134,5 +126,3 @@ def val_dataloader(self) -> DataLoader:
         else:
             dataset = self._get_streamed_samples_dataset(*self.val_period)
         return DataLoader(dataset, shuffle=False, **self._common_dataloader_kwargs)
-
-    
\ No newline at end of file
diff --git a/pvnet/models/base_model.py b/pvnet/models/base_model.py
index 4046cf41..57313265 100644
--- a/pvnet/models/base_model.py
+++ b/pvnet/models/base_model.py
@@ -18,11 +18,7 @@
 from huggingface_hub.constants import CONFIG_NAME, PYTORCH_WEIGHTS_NAME
 from huggingface_hub.file_download import hf_hub_download
 from huggingface_hub.hf_api import HfApi
-
-from ocf_datapipes.batch import BatchKey
-from ocf_datapipes.batch import copy_batch_to_device
-
-from ocf_ml_metrics.evaluation.evaluation import evaluation
+from ocf_datapipes.batch import BatchKey, copy_batch_to_device
 
 from pvnet.models.utils import (
     BatchAccumulator,
@@ -32,8 +28,6 @@
 from pvnet.optimizers import AbstractOptimizer
 from pvnet.utils import plot_batch_forecasts
 
-
-
 DATA_CONFIG_NAME = "data_config.yaml"
 
 
@@ -239,13 +233,11 @@ def get_data_config(
             )
 
         return data_config_file
-    
-    
+
     def _save_pretrained(self, save_directory: Path) -> None:
         """Save weights from a Pytorch model to a local directory."""
         model_to_save = self.module if hasattr(self, "module") else self  # type: ignore
         torch.save(model_to_save.state_dict(), save_directory / PYTORCH_WEIGHTS_NAME)
-        
 
     def save_pretrained(
         self,
@@ -416,14 +408,14 @@ def __init__(
             self.num_output_features = self.forecast_len * len(self.output_quantiles)
         else:
             self.num_output_features = self.forecast_len
-        
+
         # save all validation results to array, so we can save these to weights n biases
         self.validation_epoch_results = []
 
     def transfer_batch_to_device(self, batch, device, dataloader_idx):
         """Method to move custom batches to a given device"""
         return copy_batch_to_device(batch, device)
-    
+
     def _quantiles_to_prediction(self, y_quantiles):
         """
         Convert network prediction into a point prediction.
@@ -465,7 +457,7 @@ def _calculate_quantile_loss(self, y_quantiles, y):
             errors = y - y_quantiles[..., i]
             losses.append(torch.max((q - 1) * errors, q * errors).unsqueeze(-1))
         losses = 2 * torch.cat(losses, dim=2)
-        
+
         return losses.mean()
 
     def _calculate_common_losses(self, y, y_hat):
@@ -659,7 +651,7 @@ def validation_step(self, batch: dict, batch_idx):
         accum_batch_num = batch_idx // self.trainer.accumulate_grad_batches
 
         y_hat = self(batch)
-        
+
         y = batch[self._target_key][:, -self.forecast_len :]
 
         if (batch_idx + 1) % self.trainer.accumulate_grad_batches == 0:
diff --git a/pvnet/models/utils.py b/pvnet/models/utils.py
index 20b80223..2bbe78f2 100644
--- a/pvnet/models/utils.py
+++ b/pvnet/models/utils.py
@@ -1,8 +1,6 @@
 """Utility functions"""
 
 import logging
-import math
-from typing import Optional
 
 import numpy as np
 import torch
diff --git a/pvnet/utils.py b/pvnet/utils.py
index d515e819..2713558d 100644
--- a/pvnet/utils.py
+++ b/pvnet/utils.py
@@ -6,7 +6,6 @@
 
 import lightning.pytorch as pl
 import matplotlib.pyplot as plt
-import numpy as np
 import pandas as pd
 import pylab
 import rich.syntax
@@ -16,7 +15,6 @@
 from lightning.pytorch.utilities import rank_zero_only
 from ocf_datapipes.batch import BatchKey
 from ocf_datapipes.utils import Location
-from ocf_datapipes.utils.geospatial import osgb_to_lon_lat
 from omegaconf import DictConfig, OmegaConf
 
 
@@ -322,4 +320,4 @@ def _get_numpy(key):
     plt.suptitle(title)
     plt.tight_layout()
 
-    return fig
\ No newline at end of file
+    return fig
diff --git a/scripts/save_samples.py b/scripts/save_samples.py
index b047c013..c7f306b7 100644
--- a/scripts/save_samples.py
+++ b/scripts/save_samples.py
@@ -20,41 +20,38 @@
 ```
 if wanting to override these values for example
 """
-    
+
 # Ensure this block of code runs only in the main process to avoid issues with worker processes.
 if __name__ == "__main__":
     import torch.multiprocessing as mp
-    
-    # Set the start method for torch multiprocessing. Choose either "forkserver" or "spawn" to be 
-    # compatible with dask's multiprocessing. 
+
+    # Set the start method for torch multiprocessing. Choose either "forkserver" or "spawn" to be
+    # compatible with dask's multiprocessing.
     mp.set_start_method("forkserver")
-    
-    # Set the sharing strategy to 'file_system' to handle file descriptor limitations. This is 
-    # important because libraries like Zarr may open many files, which can exhaust the file 
+
+    # Set the sharing strategy to 'file_system' to handle file descriptor limitations. This is
+    # important because libraries like Zarr may open many files, which can exhaust the file
     # descriptor limit if too many workers are used.
-    mp.set_sharing_strategy('file_system')
+    mp.set_sharing_strategy("file_system")
 
 
+import logging
 import os
-import sys
 import shutil
-import logging
+import sys
 import warnings
 
+import dask
 import hydra
+import torch
+from ocf_data_sampler.torch_datasets.pvnet_uk_regional import PVNetUKRegionalDataset
 from omegaconf import DictConfig, OmegaConf
 from sqlalchemy import exc as sa_exc
+from torch.utils.data import DataLoader, Dataset
 from tqdm import tqdm
 
-import torch
-from torch.utils.data import Dataset, DataLoader
-
-from ocf_data_sampler.torch_datasets.pvnet_uk_regional import PVNetUKRegionalDataset
-
 from pvnet.utils import print_config
 
-import dask 
-
 dask.config.set(scheduler="threads", num_workers=4)
 
 
@@ -71,6 +68,7 @@
 
 class SaveFuncFactory:
     """Factory for creating a function to save a sample to disk."""
+
     def __init__(self, save_dir: str, renewable: str = "pv"):
         self.save_dir = save_dir
         self.renewable = renewable
@@ -86,22 +84,22 @@ def __call__(self, sample, sample_num: int):
 
 def get_dataset(config_path: str, start_time: str, end_time: str, renewable: str = "pv") -> Dataset:
     """Get the dataset for the given renewable type."""
-    if renewable== "pv":
-            dataset_cls = PVNetUKRegionalDataset
+    if renewable == "pv":
+        dataset_cls = PVNetUKRegionalDataset
     elif renewable in ["wind", "pv_india", "pv_site"]:
         raise NotImplementedError
     else:
         raise ValueError(f"Unknown renewable: {renewable}")
-    
+
     return dataset_cls(config_path, start_time=start_time, end_time=end_time)
 
 
 def save_samples_with_dataloader(
-        dataset: Dataset, 
-        save_dir: str, 
-        num_samples: int, 
-        dataloader_kwargs: dict, 
-        renewable: str = "pv"
+    dataset: Dataset,
+    save_dir: str,
+    num_samples: int,
+    dataloader_kwargs: dict,
+    renewable: str = "pv",
 ) -> None:
     """Save samples from a dataset using a dataloader."""
     save_func = SaveFuncFactory(save_dir, renewable=renewable)
@@ -124,7 +122,7 @@ def main(config: DictConfig) -> None:
 
     # Set up directory
     os.makedirs(config_dm.sample_output_dir, exist_ok=False)
-    
+
     # Copy across configs which define the samples into the new sample directory
     with open(f"{config_dm.sample_output_dir}/datamodule.yaml", "w") as f:
         f.write(OmegaConf.to_yaml(config_dm))
@@ -141,29 +139,29 @@ def main(config: DictConfig) -> None:
         batch_sampler=None,
         num_workers=config_dm.num_workers,
         collate_fn=None,
-        pin_memory=False, # Only using CPU to prepare samples so pinning is not beneficial
+        pin_memory=False,  # Only using CPU to prepare samples so pinning is not beneficial
         drop_last=False,
         timeout=0,
         worker_init_fn=None,
         prefetch_factor=config_dm.prefetch_factor,
-        persistent_workers=False, # Not needed since we only enter the dataloader loop once
+        persistent_workers=False,  # Not needed since we only enter the dataloader loop once
     )
 
     if config_dm.num_val_samples > 0:
         print("----- Saving val samples -----")
-        
+
         val_output_dir = f"{config_dm.sample_output_dir}/val"
-        
+
         # Make directory for val samples
         os.mkdir(val_output_dir)
-        
-        # Get the dataset 
+
+        # Get the dataset
         val_dataset = get_dataset(
             config_dm.configuration,
             *config_dm.val_period,
             renewable=config.renewable,
         )
-            
+
         # Save samples
         save_samples_with_dataloader(
             dataset=val_dataset,
@@ -172,24 +170,24 @@ def main(config: DictConfig) -> None:
             dataloader_kwargs=dataloader_kwargs,
             renewable=config.renewable,
         )
-        
+
         del val_dataset
 
     if config_dm.num_train_samples > 0:
         print("----- Saving train samples -----")
-        
+
         train_output_dir = f"{config_dm.sample_output_dir}/train"
-        
+
         # Make directory for train samples
         os.mkdir(train_output_dir)
-        
-        # Get the dataset 
+
+        # Get the dataset
         train_dataset = get_dataset(
             config_dm.configuration,
             *config_dm.train_period,
             renewable=config.renewable,
         )
-            
+
         # Save samples
         save_samples_with_dataloader(
             dataset=train_dataset,
@@ -198,7 +196,7 @@ def main(config: DictConfig) -> None:
             dataloader_kwargs=dataloader_kwargs,
             renewable=config.renewable,
         )
-        
+
         del train_dataset
 
     print("----- Saving complete -----")