Finished (:D) fig_invariance_log_hp.pdf.

Added movable label string to time_bar().

python/save_env_sd_conversion.py

@@ -12,7 +12,7 @@ save_path = '../data/inv/noise_env/'
 
 # ANALYSIS SETTINGS:
 scales = np.geomspace(0.1, 10000, 200)
-sd_inputs = np.arange(10.9, 11.1, 0.01)
+sd_inputs = np.array([1.0])
 n_trials = 10
 tol_to_one = 0.1
 
@@ -32,15 +32,16 @@ signal /= signal[segment].std()
 signal = signal[:, None] * scales[None, :]
 
 # Prepare storage:
-current_match = 0
-storage = dict(
-    scales=scales,
-    n_trials=n_trials,
-    sd_factor=np.array([0.]),
-    trials=np.zeros((scales.size, n_trials), dtype=float),
-    mean=np.zeros(scales.size, dtype=float),
-    spread=np.zeros(scales.size, dtype=float),
-)
+if sd_inputs.size > 1:
+    current_match = 0
+    storage = dict(
+        scales=scales,
+        n_trials=n_trials,
+        sd_factor=np.array([0.]),
+        trials=np.zeros((scales.size, n_trials), dtype=float),
+        mean=np.zeros(scales.size, dtype=float),
+        spread=np.zeros(scales.size, dtype=float),
+    )
 
 # Analyze piece-wise:
 rng = np.random.default_rng()
@@ -59,7 +60,22 @@ for i, sigma in enumerate(sd_inputs):
 
     # Estimate noise SD:
     sd = mix.std(axis=0)
+    # Average SD over trials:
     mean_sd = sd.mean(axis=-1)
+
+    # Log single-run results:
+    if sd_inputs.size == 1:
+        storage = dict(
+            scales=scales,
+            n_trials=n_trials,
+            sd_factor=sigma,
+            trials=sd,
+            mean=mean_sd,
+            spread=sd.std(axis=-1),
+        )
+        break
+
+    # Update multi-run results if better than previous:
     n_match = (np.abs(1 - mean_sd) <= tol_to_one).sum()
     if n_match > current_match:
         print(f'Found better SD: {sigma:.3f} with {n_match} matches (previous: {current_match})')
@@ -70,13 +86,10 @@ for i, sigma in enumerate(sd_inputs):
         current_match = n_match
     del mix
 del signal
+
 if save_path is not None:
     np.savez(save_path + 'sd_conversion.npz', **storage)
 
-plt.plot(scales, storage['mean'], 'k')
-plt.show()
-embed()
-
 print('Done.')
 embed()