Analysis script, environment

analysis.py

@@ -0,0 +1,29 @@
+import json
+import pandas as pd
+import argparse
+from pathlib import Path
+import numpy as np
+
+if __name__ == "__main__":
+
+    parser = argparse.ArgumentParser(description="Sparse minimum distortion simulation results analaysis")
+    parser.add_argument("results_dir", type=Path, help="Location of simulation results")
+    args = parser.parse_args()
+
+
+    with open(args.results_dir / "data.json") as f:
+        data = json.load(f)
+
+    with open(args.results_dir / "parameters.json") as f:
+        params = json.load(f)
+
+    # concatenate all the results
+    results = []
+    for r in data:
+        results += r
+
+    for r in results:
+        for metric in ["sdr", "sir"]:
+            r[metric] = np.mean(r[metric])
+
+    df = pd.DataFrame(results)

environment.yml

@@ -0,0 +1,10 @@
+name: sparse_projection_back
+dependencies:
+  - numpy
+  - scipy
+  - pandas
+  - matplotlib
+  - pip
+  - seaborn
+  - pip:
+    - repsimtools

process.py

@@ -111,8 +111,8 @@ def process(args, config):
     # minimum distortion
     lo, hi, step = config["minimum_distortion"]["p_list"]
     kwargs = config["minimum_distortion"]["kwargs"]
-    for p in np.arange(lo, hi, step):
-        for q in np.arange(p, hi, step):
+    for p in np.arange(lo, hi + step, step):
+        for q in np.arange(p, hi + step, step):
             t["p"], t["q"], t["proj_algo"] = (
                 f"{p:.1f}",
                 f"{q:.1f}",