From 124869b4c0decfe109a7e8cb6efdbe009a2cd3ab Mon Sep 17 00:00:00 2001
From: Sukhil Patel <sukhil@Sukhils-MacBook-Pro.local>
Date: Tue, 22 Oct 2024 11:06:06 +0100
Subject: [PATCH] Remove run app locally script

---
 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")