chloe

README.md

@@ -153,5 +153,44 @@ Use `@registerPostprocess(...)` to declare:
 - optional Python package dependencies with `required_deps`
 - required pipeline outputs with `required_pipelines`
 
+### Simple Postprocess Structure
+
+```python
+from postprocess.core.base import (
+    BatchPostprocess,
+    PostprocessContext,
+    PostprocessResult,
+    registerPostprocess,
+)
+
+
+@registerPostprocess(
+    name="My Batch Summary",
+    description="Aggregate metrics across the generated batch outputs.",
+    required_pipelines=["Basic Stats"],
+)
+class MyBatchSummary(BatchPostprocess):
+    def run(self, context: PostprocessContext) -> PostprocessResult:
+        report_path = context.output_dir / "my_batch_summary.json"
+        report_path.write_text("{}", encoding="utf-8")
+
+        return PostprocessResult(
+            summary="Generated my_batch_summary.json.",
+            generated_paths=[str(report_path)],
+            metadata={"file_count": len(context.processed_files)},
+        )
+```
+
+Inside a postprocess, you can:
+
+- read `context.output_dir`
+- read `context.processed_files`
+- read `context.selected_pipelines`
+- read `context.input_path`
+- read `context.zip_outputs`
+- write extra artifacts into `context.output_dir` before optional zipping
+- return a short `summary`, explicit `generated_paths`, and structured `metadata`
+
 The included `Graphics Dashboard` postprocess shows the intended pattern: it consumes the `arterial_waveform_shape_metrics` output and generates a cohort dashboard plus PNG exports after the batch finishes.
 `Pipeline Metrics Manifest` is a lighter built-in example that writes a JSON inventory of the generated pipeline metric datasets for the batch.
+`Postprocess Tutorial` is the minimal reference example: it writes a single JSON file showing every `PostprocessContext` field and the `PostprocessResult` output format.

src/graphics.py

@@ -133,6 +133,10 @@ def select_support_beat(support, beat_idx):
                 "harmonic_phases",
                 "harmonic_energies",
                 "harmonic_energies_weights",
+                "harmonic_energy_cumsum",
+                "harmonic_energy_cumsum_h",
+                "harmonic_energy_cumsum_interp",
+                "harmonic_energy_cumsum_h_interp",
                 "delta_phi_all",
             }:
                 out[k] = arr[beat_idx, :]
@@ -790,32 +794,43 @@ def rectified(v):
         ax.set_xlabel("rectified time : t/T", fontsize=14)
         ax.set_ylabel(r"$v_b\: (mm/s)$", fontsize=14, labelpad=12)
     elif metric == "rho_h_90":
-        w_h = np.asarray(harmonic_energies_weights, dtype=float)
-        w_h = w_h[np.isfinite(w_h)]
-        H = len(w_h)
+        cumsum = np.asarray(support.get("harmonic_energy_cumsum", []), dtype=float)
+        cumsum_h = np.asarray(support.get("harmonic_energy_cumsum_h", []), dtype=float)
 
-        if H == 0:
-            info_box("Missing harmonic weights")
-            return
+        cumsum_interp = np.asarray(
+            support.get("harmonic_energy_cumsum_interp", []), dtype=float
+        )
+        cumsum_h_interp = np.asarray(
+            support.get("harmonic_energy_cumsum_h_interp", []), dtype=float
+        )
 
-        csum = np.cumsum(w_h)
-        xh = np.arange(1, H + 1)
-        rho = float(support["rho_h_90"])
-        h90 = rho * H
+        h90 = float(support.get("h_90", np.nan))
+        rho = float(support.get("rho_h_90", np.nan))
+        mask_i = np.isfinite(cumsum_interp) & np.isfinite(cumsum_h_interp)
+        mask_d = np.isfinite(cumsum) & np.isfinite(cumsum_h)
 
-        ax.step(xh, csum, where="mid", color="#EC5241", linewidth=2)
-        ax.axhline(0.90, linestyle="--", color="black", linewidth=1)
-        ax.axvline(h90, linestyle="--", color="black", linewidth=1)
+        ax.plot(
+            cumsum_h_interp[mask_i],
+            cumsum_interp[mask_i],
+            color="#EC5241",
+            linewidth=2,
+        )
 
-        info_box(
-            [
-                rf"$\rho_{{h,90}} = {rho:.3f}$",
-                rf"$h_{{90}} = {h90:.2f}$",
-            ]
+        ax.plot(
+            cumsum_h[mask_d],
+            cumsum[mask_d],
+            "o",
+            color="black",
+            markersize=4,
         )
-        ax.set_xlabel("Harmonic n (a.u.)", fontsize=14)
-        ax.set_ylabel(r"Energy weights $w_n$", fontsize=14, labelpad=12)
-        ax.set_ylim(0, 1.05)
+
+        ax.axhline(0.90, linestyle="--", color="black", linewidth=1)
+        if np.isfinite(h90):
+            ax.axvline(h90, linestyle="--", color="black", linewidth=1)
+            ax.plot(h90, 0.90, "o", color="black", markersize=5)
+
+        ax.set_xlabel("Harmonic index $h$ (a.u.)", fontsize=14)
+        ax.set_ylabel(r"$C(h)$", fontsize=14)
     elif metric == "mu_h":
         w_h = harmonic_energies_weights
         mu_h = float(support["mu_h"])
@@ -1200,7 +1215,6 @@ def rectified(v):
     elif metric == "E_slope":
         e_slope = float(support["E_slope"])
         dvdt_norm = support["dvdt_norm"]
-
         ax.plot(tau, sig, linewidth=3, color="#EC5241", label="signal")
         ax2 = ax.twinx()
         ax2.plot(
@@ -1211,16 +1225,15 @@ def rectified(v):
             color="black",
             label=r"$\dot v^2$",
         )
-        ax2.set_ylabel(r"$\frac{T^3}{(M_0 + \epsilon)^2} * \dot v^2$", fontsize=10)
+        ax2.set_ylabel(r"$\dot v^2$", fontsize=12)
         ax2.set_yticks([])
         info_box([rf"$E_{{slope}}={e_slope:.4f}$"])
-        ax.set_xlabel("rectified time : t/T", fontsize=12)
-        ax.set_ylabel(r"$v_b\: (mm/s)$", fontsize=12, labelpad=10)
+        ax.set_xlabel("rectified time : t/T", fontsize=14)
+        ax.set_ylabel(r"$v_b\: (mm/s)$", fontsize=14, labelpad=12)
 
     elif metric == "E_curv":
         e_curv = float(support["E_curv"])
         d2vdt2_norm = support["d2vdt2_norm"]
-
         ax.plot(tau, sig, linewidth=3, color="#EC5241", label="signal")
         ax2 = ax.twinx()
         ax2.plot(
@@ -1232,10 +1245,10 @@ def rectified(v):
             label=r"$\ddot v^2$",
         )
         ax2.set_yticks([])
-        ax2.set_ylabel(r"$\frac{T^5}{(M_0 + \epsilon)^2} *\ddot v^2$", fontsize=10)
-        info_box([rf"$E_{{curv}}={e_curv:.0f}$"])
-        ax.set_xlabel("rectified time : t/T", fontsize=12)
-        ax.set_ylabel(r"$v_b\: (mm/s)$", fontsize=12, labelpad=10)
+        ax2.set_ylabel(r"$\ddot v^2$", fontsize=12)
+        info_box([rf"$E_{{curv}}={e_curv:.4f}$"])
+        ax.set_xlabel("rectified time : t/T", fontsize=14)
+        ax.set_ylabel(r"$v_b\: (mm/s)$", fontsize=14, labelpad=12)
 
     elif metric == "W50_over_T":
         w50 = float(support["W50_over_T"])
@@ -1432,7 +1445,7 @@ def export_selected_metric_pngs_bandlimited(all_results, zip_path, out_dir):
                 ax_empty = fig.add_subplot(right[r, c])
                 ax_empty.axis("off")
 
-            png_path = os.path.join(out_dir, f"{metric}_bandlimited.png")
+            png_path = os.path.join(out_dir, f"{metric}_bandlimited.eps")
             fig.savefig(png_path)
             plt.close(fig)