From 6d2a35e929991922398f36696fb34faf5c549f23 Mon Sep 17 00:00:00 2001
From: Hunter Nichols <hznichol@ucsc.edu>
Date: Tue, 19 Jan 2021 13:47:54 -0800
Subject: [PATCH] Changed most lists to dict to reduce hardcoded indices

---
 compiler/characterizer/linear_regression.py | 65 ++++++++++++---------
 1 file changed, 39 insertions(+), 26 deletions(-)

diff --git a/compiler/characterizer/linear_regression.py b/compiler/characterizer/linear_regression.py
index 367f7b9d..5d4cbe49 100644
--- a/compiler/characterizer/linear_regression.py
+++ b/compiler/characterizer/linear_regression.py
@@ -25,9 +25,18 @@ data_fnames = ["rise_delay.csv",
                "read1_power.csv",
                "read0_power.csv",
                "leakage_data.csv"]
-               
+# Positions must correspond to data_fname list               
+lib_dnames = ["delay_lh",
+              "delay_hl",
+              "slew_lh",
+              "slew_hl",
+              "write1_power",
+              "write0_power",
+              "read1_power",
+              "read0_power",
+              "leakage_power"]             
 data_dir = OPTS.openram_tech+relative_data_path
-data_paths = [data_dir +'/'+fname for fname in data_fnames]
+data_paths = {dname:data_dir +'/'+fname for dname, fname in zip(lib_dnames, data_fnames)}
 
 class linear_regression(simulation):
 
@@ -60,31 +69,30 @@ class linear_regression(simulation):
         for slew in slews:
             for load in loads:
                 # List returned with value order being delay, power, leakage, slew
-                # FIXME: make order less hard coded
                 sram_vals = self.get_predictions(model_inputs+[slew, load], models)
                 # Delay is only calculated on a single port and replicated for now.
                 for port in self.all_ports:
-                    port_data[port]['delay_lh'].append(sram_vals[0][0][0])
-                    port_data[port]['delay_hl'].append(sram_vals[1][0][0])
-                    port_data[port]['slew_lh'].append(sram_vals[2][0][0])
-                    port_data[port]['slew_hl'].append(sram_vals[3][0][0])
+                    port_data[port]['delay_lh'].append(sram_vals['delay_lh'])
+                    port_data[port]['delay_hl'].append(sram_vals['delay_hl'])
+                    port_data[port]['slew_lh'].append(sram_vals['slew_lh'])
+                    port_data[port]['slew_hl'].append(sram_vals['slew_hl'])
                     
-                    port_data[port]['write1_power'].append(sram_vals[4][0][0])
-                    port_data[port]['write0_power'].append(sram_vals[5][0][0])
-                    port_data[port]['read1_power'].append(sram_vals[6][0][0])
-                    port_data[port]['read0_power'].append(sram_vals[7][0][0])
+                    port_data[port]['write1_power'].append(sram_vals['write1_power'])
+                    port_data[port]['write0_power'].append(sram_vals['write0_power'])
+                    port_data[port]['read1_power'].append(sram_vals['read1_power'])
+                    port_data[port]['read0_power'].append(sram_vals['read0_power'])
                     
                     # Disabled power not modeled. Copied from other power predictions
-                    port_data[port]['disabled_write1_power'].append(sram_vals[4][0][0])
-                    port_data[port]['disabled_write0_power'].append(sram_vals[5][0][0])
-                    port_data[port]['disabled_read1_power'].append(sram_vals[6][0][0])
-                    port_data[port]['disabled_read0_power'].append(sram_vals[7][0][0])
+                    port_data[port]['disabled_write1_power'].append(sram_vals['write1_power'])
+                    port_data[port]['disabled_write0_power'].append(sram_vals['write0_power'])
+                    port_data[port]['disabled_read1_power'].append(sram_vals['read1_power'])
+                    port_data[port]['disabled_read0_power'].append(sram_vals['read0_power'])
                         
 
         # Estimate the period as double the delay with margin
         period_margin = 0.1
-        sram_data = {"min_period": sram_vals[0][0][0] * 2,
-                     "leakage_power": sram_vals[8][0][0]}
+        sram_data = {"min_period": sram_vals['delay_lh'] * 2,
+                     "leakage_power": sram_vals["leakage_power"]}
 
         debug.info(2, "SRAM Data:\n{}".format(sram_data))
         debug.info(2, "Port Data:\n{}".format(port_data))
@@ -95,27 +103,32 @@ class linear_regression(simulation):
         """
         Generate a model and prediction for LIB output
         """
-               
-        scaled_inputs = np.asarray([scale_input_datapoint(model_inputs, data_paths[0])])
+        
+        #Scaled the inputs using first data file as a reference
+        data_name = lib_dnames[0]       
+        scaled_inputs = np.asarray([scale_input_datapoint(model_inputs, data_paths[data_name])])
 
-        predictions = []
-        for m, path in zip(models, data_paths):
+        predictions = {}
+        for dname in data_paths.keys():
+            path = data_paths[dname]
+            m = models[dname]
+        
             features, labels = get_scaled_data(path)
             scaled_pred = self.model_prediction(m, scaled_inputs)
             pred = unscale_data(scaled_pred.tolist(), path)
             debug.info(1,"Unscaled Prediction = {}".format(pred))
-            predictions.append(pred)
+            predictions[dname] = pred[0][0]
         return predictions
 
     def train_models(self):
         """
         Generate and return models
         """
-        models = []
-        for path in data_paths:
-            features, labels = get_scaled_data(path)
+        models = {}
+        for dname, dpath in data_paths.items():
+            features, labels = get_scaled_data(dpath)
             model = self.generate_model(features, labels)
-            models.append(model)
+            models[dname] = model
         return models