removed debug lines. Ready to merge

Author: jjacobson95

coderbuild/utils/calc_pdx_metrics.py

@@ -249,7 +249,6 @@ def ABC(contr_time=None, contr_volume=None, treat_time=None, treat_volume=None):
     return {"metric": "abc", "value": abc,'time':np.max(treat_time)}#, "control": con, "treatment": tre}
 
 
-###LMM CODE
 def lmm(time, volume, treatment, drug_name):
     """
     Compute the linear mixed model (lmm) statistics for a PDX batch.
@@ -261,41 +260,21 @@ def lmm(time, volume, treatment, drug_name):
     dict: A dictionary containing the fit object and the specific coefficient value.
     """
 
-    # --- DEBUG: inputs to LMM ---
-    print("\n[DBG][LMM] incoming arrays")
-    print(f"[DBG][LMM] len(time)={len(time)}, len(volume)={len(volume)}, len(treatment)={len(treatment)}, drug_name={drug_name}")
-
-
     data = pd.DataFrame({'model_id':['model']*len(time),\
                          'volume':volume,\
                          'time':time,\
                           'exp_type':treatment})
 
-        
-    print("[DBG][LMM] raw data shape:", data.shape)
-    print("[DBG][LMM] raw dtypes:\n", data.dtypes)
-    print("[DBG][LMM] NA counts:\n", data.isna().sum())
 
     data['log_volume'] = np.log(data['volume'])
     n_nonfinite_log = (~np.isfinite(data['log_volume'])).sum()
-    print(f"[DBG][LMM] non-finite log_volume count: {n_nonfinite_log}")
 
     # categories
     data['exp_type'] = data['exp_type'].astype('category')
     data['exp_type'] = pd.Categorical(data['exp_type'],
                                     categories=['control', drug_name],
                                     ordered=True)
-    print("[DBG][LMM] exp_type categories:", list(data['exp_type'].cat.categories))
-    print("[DBG][LMM] exp_type value_counts:\n", data['exp_type'].value_counts(dropna=False))
 
-    # time variation overall and by treatment
-    print(f"[DBG][LMM] time min/max: {data['time'].min()} / {data['time'].max()}")
-    print("[DBG][LMM] time describe by exp_type:\n",
-        data.groupby('exp_type', dropna=False)['time'].describe())
-
-    # groups
-    print("[DBG][LMM] unique model_id count:", data['model_id'].nunique())
-    print("[DBG][LMM] model_id value_counts:\n", data['model_id'].value_counts())
 
     ##create data frame from these 4 vectors
     required_columns = ["model_id", "volume", "time", "exp_type"]
@@ -315,15 +294,10 @@ def lmm(time, volume, treatment, drug_name):
     #print(data['exp_type'].cat.categories)
     # Fit the model
     model = mixedlm(formula, data, groups=data['model_id'])
-    print("[DBG][LMM] exp_type counts:\n", data['exp_type'].value_counts(dropna=False))
-    print("[DBG][LMM] time min/max:", data['time'].min(), data['time'].max())
-    print("[DBG][LMM] volume<=0:", (data['volume']<=0).sum(), " nonfinite log_volume:", (~np.isfinite(np.log(data['volume']))).sum())
-    print("[DBG][LMM] groups:", data['model_id'].nunique())
 
-    print("[DBG][LMM] DATA:", data)
+
     fit = model.fit()
 
-    print("[DBG][LMM] fit:", fit.params)
 
     # Get the coefficient for the interaction term 'time:exp_type'
     #interaction_term = 'time:exp_type'
@@ -332,7 +306,6 @@ def lmm(time, volume, treatment, drug_name):
     #print(fit.params)
     i_coef_value = fit.params['time:exp_type[T.'+drug_name+']']
 
-    print("[DBG][LMM] i_coef_value:", i_coef_value)
     #i_coef_value = fit.params['time:exp_type['+drug_name+']']
    # else:
    #     coef_value = None  # Handle the case when the interaction term is not present
@@ -423,24 +396,8 @@ def get_drug_stats(df, control='control'):
             else:
                 singleres.append(treat_abc)
 
-            # --- DEBUG: before LMM ---
-            print("\n[DBG] -------- Drug block --------")
-            print(f"[DBG] experiment={name[0]}  model_id={mod}  drug={d}")
-            print(f"[DBG] control rows: {len(ctl_data)}  treated rows: {len(d_data)}")
-
-            # Quick sanity on time / volume
-            print(f"[DBG] control time min/max: {ctl_time.min() if len(ctl_time)>0 else None} / {ctl_time.max() if len(ctl_time)>0 else None}")
-            print(f"[DBG] treat   time min/max: {treat_time.min() if len(treat_time)>0 else None} / {treat_time.max() if len(treat_time)>0 else None}")
-
-            print(f"[DBG] control volume<=0: {(ctl_volume<=0).sum() if len(ctl_volume)>0 else None}")
-            print(f"[DBG] treat   volume<=0: {(treat_volume<=0).sum() if len(treat_volume)>0 else None}")
-
-            # Show the first few rows that will go into LMM
             comb = pd.concat([ctl_data, d_data])
-            print("[DBG] comb dtypes:\n", comb.dtypes)
-            print("[DBG] comb treatment counts:\n", comb['treatment'].value_counts(dropna=False))
-            print("[DBG] comb head:\n", comb[['treatment','time','volume']].head())
-            # --- END DEBUG ---
+
 
             lmm_res = lmm(comb.time, comb.volume, comb.treatment, d)
             lmm_res.update({'sample': mod, 'drug': d, 'time': np.max(treat_time), 'time_unit': 'days'})