india 005 experiments - more nwps

experiments/analysis.py

@@ -6,6 +6,7 @@
 
 import matplotlib.pyplot as plt
 import numpy as np
+import pandas as pd
 import wandb
 
 
@@ -53,6 +54,9 @@ def main(runs: list[str], run_names: list[str]) -> None:
         [720, 1440],
         [1440, 2880],
     ]
+
+    groups_df = []
+    grouping_starts = [grouping[0] for grouping in groupings]
     header = "| Timestep |"
     separator = "| --- |"
     for run_name in run_names:
@@ -68,14 +72,21 @@ def main(runs: list[str], run_names: list[str]) -> None:
             for idx, timestep in enumerate(column_timesteps)
             if timestep >= grouping[0] and timestep <= grouping[1]
         ]
+        data_one_group = []
         for df in dfs:
-            group_string += f" {df.iloc[group_idx].mean()*100.:0.3f} |"
+            mean_row = df.iloc[group_idx].mean()
+            group_string += f" {mean_row:0.3f} |"
+            data_one_group.append(mean_row)
         print(group_string)
 
+        groups_df.append(data_one_group)
+
+    groups_df = pd.DataFrame(groups_df, columns=run_names, index=grouping_starts)
+
     for idx, df in enumerate(dfs):
         print(f"{run_names[idx]}: {df.mean()*100:0.3f}")
 
-    # Plot the error on per timestep, and grouped timesteps
+    # 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])
@@ -86,6 +97,17 @@ 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
+    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")
+    plt.savefig("mae_per_timestep.png")
+    plt.show()
+
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()

experiments/india/005_extra_nwp_variables/readmd.md

@@ -0,0 +1,55 @@
+# Adding extra nwp variables
+
+I wanted to run Windnet but testing some new nwp variables from ecmwf
+
+General conclusion, although more experiments could be done.
+The current nwp variables are about right.
+If you add lots it makes it worse.
+If you take some away, it makes it worse.
+
+## Bugs
+
+Ran into a problem where found that some xamples have
+`d.__getitem__('nwp-ecmwf__init_time_utc').values` had size 50, where it should be just one values. I removed these examples. This might
+
+## Experiments
+
+The number of samples were 8000 when training.
+
+### 15 variablles
+Run windnet with `'hcc', 'lcc', 'mcc', 'prate', 'sde', 'sr', 't2m', 'tcc', 'u10',
+       'v10', 'u100', 'v100', 'u200', 'v200', 'dlwrf', 'dswrf'`.
+
+The experiment on wandb is [here](https://wandb.ai/openclimatefix/india/runs/k91rdffo)
+
+### 7 variables
+Run windnet with the original 7 variables.
+`t2m, u10, u100, u200, v10, v100, v200  `
+
+The experiment on wandb is [here](https://wandb.ai/openclimatefix/india/runs/miszfep5)
+
+### 3 variables
+Run windnet with only `t, u10, v100`
+
+The experiment on wandb is [here](https://wandb.ai/openclimatefix/india/runs/22v3a39g)
+
+## Results
+
+| Timestep | 15 MAE % | 7 MAE % | 3 MAE % |
+| --- | --- | --- | --- |
+| 0-0 minutes | 7.450 | 6.623 | 7.529 |
+| 15-15 minutes | 7.348 | 6.441 | 7.408 |
+| 30-45 minutes | 7.242 | 6.544 | 7.294 |
+| 45-60 minutes | 7.134 | 6.567 | 7.185 |
+| 60-120 minutes | 7.058 | 6.295 | 7.009 |
+| 120-240 minutes | 6.965 | 6.290 | 6.800 |
+| 240-360 minutes | 6.807 | 6.374 | 6.580 |
+| 360-480 minutes | 6.749 | 6.482 | 6.548 |
+| 480-720 minutes | 6.892 | 6.686 | 6.685 |
+| 720-1440 minutes | 7.020 | 6.756 | 6.780 |
+| 1440-2880 minutes | 7.445 | 7.095 | 7.214 |
+
+![](mae_steps_grouped.png "mae_steps")
+
+The raw data is here
+![](mae_steps.png "mae_steps")