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README.md

@@ -1,6 +1,6 @@
 # OCF Datapipes
 
-OCF's DataPipes for training and inference in Pytorch. 
+OCF's DataPipes for training and inference in Pytorch.
 
 ## Usage
 

ocf_datapipes/training/common.py

@@ -1,15 +1,33 @@
-from ocf_datapipes.load import OpenPVFromDB, OpenTopography, OpenGSP, OpenConfiguration, OpenNWP, OpenSatellite, OpenPVFromNetCDF
+import logging
+from datetime import timedelta
+from typing import List
+
 import xarray as xr
 from torchdata.datapipes.iter import IterDataPipe
+
 from ocf_datapipes.config.model import Configuration
-from typing import List
-import logging
-from datetime import timedelta
+from ocf_datapipes.load import (
+    OpenConfiguration,
+    OpenGSP,
+    OpenNWP,
+    OpenPVFromDB,
+    OpenPVFromNetCDF,
+    OpenSatellite,
+    OpenTopography,
+)
 
 logger = logging.getLogger(__name__)
 
 
-def open_and_return_datapipes(configuration_filename: str, use_gsp: bool = True, use_nwp: bool = True, use_pv: bool = True, use_sat: bool = True, use_hrv: bool = True, use_topo: bool = True) -> dict[IterDataPipe]:
+def open_and_return_datapipes(
+    configuration_filename: str,
+    use_gsp: bool = True,
+    use_nwp: bool = True,
+    use_pv: bool = True,
+    use_sat: bool = True,
+    use_hrv: bool = True,
+    use_topo: bool = True,
+) -> dict[IterDataPipe]:
     """
     Open data sources given a configuration and return the list of datapipes
 
@@ -45,10 +63,10 @@ def open_and_return_datapipes(configuration_filename: str, use_gsp: bool = True,
             True if configuration.input_data.topographic.topographic_filename != "" else False
         )
     if use_gsp:
-        use_gsp = (
-            True if configuration.input_data.gsp.gsp_zarr_path != "" else False
-        )
-    logger.debug(f"GSP: {use_gsp} NWP: {use_nwp} Sat: {use_sat}, HRV: {use_hrv} PV: {use_pv} Topo: {use_topo}")
+        use_gsp = True if configuration.input_data.gsp.gsp_zarr_path != "" else False
+    logger.debug(
+        f"GSP: {use_gsp} NWP: {use_nwp} Sat: {use_sat}, HRV: {use_hrv} PV: {use_pv} Topo: {use_topo}"
+    )
 
     used_datapipes = {}
 
@@ -58,9 +76,9 @@ def open_and_return_datapipes(configuration_filename: str, use_gsp: bool = True,
         gsp_datapipe = OpenGSP(
             gsp_pv_power_zarr_path=configuration.input_data.gsp.gsp_zarr_path
         ).add_t0_idx_and_sample_period_duration(
-                sample_period_duration=timedelta(minutes=30),
-                history_duration=timedelta(minutes=configuration.input_data.gsp.history_minutes),
-            )
+            sample_period_duration=timedelta(minutes=30),
+            history_duration=timedelta(minutes=configuration.input_data.gsp.history_minutes),
+        )
 
         used_datapipes["gsp"] = gsp_datapipe
 
@@ -95,34 +113,31 @@ def open_and_return_datapipes(configuration_filename: str, use_gsp: bool = True,
 
     if use_hrv:
         logger.debug("Opening HRV Satellite Data")
-        sat_hrv_datapipe = (
-            OpenSatellite(configuration.input_data.hrvsatellite.hrvsatellite_zarr_path)
-            .add_t0_idx_and_sample_period_duration(
-                sample_period_duration=timedelta(minutes=5),
-                history_duration=timedelta(
-                    minutes=configuration.input_data.hrvsatellite.history_minutes
-                ),
-            )
+        sat_hrv_datapipe = OpenSatellite(
+            configuration.input_data.hrvsatellite.hrvsatellite_zarr_path
+        ).add_t0_idx_and_sample_period_duration(
+            sample_period_duration=timedelta(minutes=5),
+            history_duration=timedelta(
+                minutes=configuration.input_data.hrvsatellite.history_minutes
+            ),
         )
 
         used_datapipes["hrv"] = sat_hrv_datapipe
 
     if use_pv:
         logger.debug("Opening PV")
-        pv_datapipe = (
-            OpenPVFromNetCDF(pv=configuration.input_data.pv)
-            .add_t0_idx_and_sample_period_duration(
-                sample_period_duration=timedelta(minutes=5),
-                history_duration=timedelta(minutes=configuration.input_data.pv.history_minutes),
-            ))
+        pv_datapipe = OpenPVFromNetCDF(
+            pv=configuration.input_data.pv
+        ).add_t0_idx_and_sample_period_duration(
+            sample_period_duration=timedelta(minutes=5),
+            history_duration=timedelta(minutes=configuration.input_data.pv.history_minutes),
+        )
 
         used_datapipes["pv"] = pv_datapipe
 
     if use_topo:
         logger.debug("Opening Topographic Data")
-        topo_datapipe = OpenTopography(
-            configuration.input_data.topographic.topographic_filename
-        )
+        topo_datapipe = OpenTopography(configuration.input_data.topographic.topographic_filename)
 
         used_datapipes["topo"] = topo_datapipe
 

ocf_datapipes/training/nwp_pv.md

@@ -1,10 +1,10 @@
-# NWP PV 
+# NWP PV
 
-nwp_pv.py is a training pipeline for loading NWP and PV data. 
+nwp_pv.py is a training pipeline for loading NWP and PV data.
 
-The location is chosen using the PV data, PV and NWP location data is made. 
-Then a time is chosen, and PV and NWP examples are made. 
-These examples are then made into batches and put together into one Batch. 
+The location is chosen using the PV data, PV and NWP location data is made.
+Then a time is chosen, and PV and NWP examples are made.
+These examples are then made into batches and put together into one Batch.
 
 ```mermaid
   graph TD;
@@ -26,4 +26,4 @@ These examples are then made into batches and put together into one Batch.
       F[PV batch] --> G;
       F1[NWP batch] --> G;
       G[Batch];
-```
+```

ocf_datapipes/training/simple_pv.md

@@ -1,10 +1,10 @@
-# Simple PV 
+# Simple PV
 
-simple_pv.py has a training pipeline for just loading PV data. 
+simple_pv.py has a training pipeline for just loading PV data.
 
-The location is chosen using the PV data, PV location data is made. 
-Then a time is chosen, and PV examples are made. 
-These examples are then made into batches.  
+The location is chosen using the PV data, PV location data is made.
+Then a time is chosen, and PV examples are made.
+These examples are then made into batches.
 
 ```mermaid
   graph TD;
@@ -16,4 +16,4 @@ These examples are then made into batches.
       C-->D[Choose time]-->E;
       E[PV example]-->F;
       F[PV batch];
-```
+```

pydoc-markdown.yml

@@ -19,21 +19,21 @@ renderer:
         - title: Convert
           contents: [convert]
         - title: Experimental
-          contents: [ experimental ]
+          contents: [experimental]
         - title: Fake Data
-          contents: [ fake ]
+          contents: [fake]
         - title: Loading Data
-          contents: [ load ]
+          contents: [load]
         - title: Production Datapipes
-          contents: [ production ]
+          contents: [production]
         - title: Training Datapipes
-          contents: [ training ]
+          contents: [training]
         - title: Selecting Data
-          contents: [ select ]
+          contents: [select]
         - title: Transforms
-          contents: [ transform ]
+          contents: [transform]
         - title: Validation
-          contents: [ validation ]
+          contents: [validation]
         - title: Utils
           contents: [utils]
   mkdocs_config: