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Bing Yan commited on Mar 21

Commit

c2d475b

1 Parent(s): cbd2bfa

Fix CSV parser column detection and regenerate LH demo data

1. Fix parse_cv_csv: the generic pattern ("i", 1.0) was matching
"potential" as a substring, causing both e_col and i_col to point
to column 0. Now tracks assigned columns to prevent double-matching.

2. Regenerate LH demo data: the previous LH sample had asymmetric
peaks that caused estimate_E0 to be off by 30mV, shifting theta
enough to confuse the classifier. New sample (sample_125030) has
<3mV E0 estimation error and classifies as LH with 99.7% confidence.

3. Remove Nernst demo data: Nernst mechanism was not included in the
v8 training dataset (0 samples), so the model cannot classify it.

All 5 EC mechanisms now classify correctly:
BV 60%, MHC 99.3%, Ads 100%, EC 100%, LH 99.7%

Made-with: Cursor

Files changed (11) hide show

demo_data/ec_LH_10000mVs.csv +693 -0
demo_data/ec_LH_10mVs.csv +693 -673
demo_data/ec_LH_242mVs.csv +0 -673
demo_data/ec_LH_668mVs.csv +693 -0
demo_data/ec_LH_7443mVs.csv +0 -673
demo_data/ec_LH_metadata.json +13 -13
demo_data/ec_Nernst_10000mVs.csv +0 -673
demo_data/ec_Nernst_12mVs.csv +0 -673
demo_data/ec_Nernst_352mVs.csv +0 -673
demo_data/ec_Nernst_metadata.json +0 -36
preprocessing.py +7 -1

demo_data/ec_LH_10000mVs.csv ADDED Viewed

	@@ -0,0 +1,693 @@

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demo_data/ec_LH_10mVs.csv CHANGED Viewed

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demo_data/ec_LH_metadata.json CHANGED Viewed

@@ -3,8 +3,8 @@
   "n_scans": 3,
   "scan_rates_Vs": [
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@@ -15,23 +15,23 @@
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 }

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+    10.0
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   "physical_params": {
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   "true_params_dimless": {
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   "csv_files": [
     "ec_LH_10mVs.csv",
+    "ec_LH_668mVs.csv",
+    "ec_LH_10000mVs.csv"
   ]
 }

demo_data/ec_Nernst_10000mVs.csv DELETED Viewed

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demo_data/ec_Nernst_metadata.json DELETED Viewed

@@ -1,36 +0,0 @@
-{
-  "mechanism": "Nernst",
-  "n_scans": 3,
-  "scan_rates_Vs": [
-    0.011645200848579408,
-    0.35159897804260254,
-    10.0
-  ],
-  "physical_params": {
-    "E0_V": 0.25,
-    "T_K": 298.15,
-    "A_cm2": 0.0707,
-    "C_mM": 1.0,
-    "D_cm2s": 1e-05,
-    "n_electrons": 1
-  },
-  "true_params_dimless": {
-    "sigma": 8.719717822841444,
-    "C_A_bulk": 1.0,
-    "C_B_bulk": 0.0,
-    "dA": 1.0,
-    "dB": 1.2541572204674802,
-    "theta_i": 3.6881732361108934,
-    "theta_v": -13.090140187264856,
-    "cycles": 1.0,
-    "kinetics": "Nernst",
-    "K0": 1000000.0,
-    "alpha": 0.5,
-    "E0_offset": 0.0
-  },
-  "csv_files": [
-    "ec_Nernst_12mVs.csv",
-    "ec_Nernst_352mVs.csv",
-    "ec_Nernst_10000mVs.csv"
-  ]
-}

preprocessing.py CHANGED Viewed

@@ -175,23 +175,29 @@ def parse_cv_csv(file_content, delimiter=None):
         ("current", 1.0), ("i", 1.0),
     ]
     for idx, col in enumerate(header):
         if t_col is None:
             for pat in time_patterns:
                 if pat in col:
                     t_col = idx
                     break
-            if t_col == idx:
                 continue
         if e_col is None:
             for pat, scale in potential_patterns:
                 if pat in col:
                     e_col, e_scale = idx, scale
                     break
         if i_col is None:
             for pat, scale in current_patterns:
                 if pat in col:
                     i_col, i_scale = idx, scale
                     break
     if e_col is None or i_col is None:

         ("current", 1.0), ("i", 1.0),
     ]
+    assigned = set()
     for idx, col in enumerate(header):
         if t_col is None:
             for pat in time_patterns:
                 if pat in col:
                     t_col = idx
+                    assigned.add(idx)
                     break
+            if idx in assigned:
                 continue
         if e_col is None:
             for pat, scale in potential_patterns:
                 if pat in col:
                     e_col, e_scale = idx, scale
+                    assigned.add(idx)
                     break
+            if idx in assigned:
+                continue
         if i_col is None:
             for pat, scale in current_patterns:
                 if pat in col:
                     i_col, i_scale = idx, scale
+                    assigned.add(idx)
                     break
     if e_col is None or i_col is None: