♻️ refactor: plot, scatter

Zerohertz · Dec 24, 2024 · 5f1aea1 · 5f1aea1
1 parent a052400
commit 5f1aea1
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Showing 3 changed files with 29 additions and 29 deletions.
diff --git a/zerohertzLib/monitoring/cpu.py b/zerohertzLib/monitoring/cpu.py
@@ -73,8 +73,8 @@ def cpu(
         plot(
             time_list,
             data,
-            "Time [Sec]",
-            "CPU Usages [%]",
+            xlab="Time [Sec]",
+            ylab="CPU Usages [%]",
             ylim=[0, 100],
             ncol=2,
             title=path,

diff --git a/zerohertzLib/monitoring/gpu.py b/zerohertzLib/monitoring/gpu.py
@@ -77,8 +77,8 @@ def gpu_usages(
         plot(
             time_list,
             data,
-            "Time [Sec]",
-            "GPU Usages [%]",
+            xlab="Time [Sec]",
+            ylab="GPU Usages [%]",
             ylim=[0, 100],
             ncol=2,
             title=path,

diff --git a/zerohertzLib/vision/eval.py b/zerohertzLib/vision/eval.py
@@ -293,27 +293,27 @@ def _prc_curve(
     precision_per_cls: Dict[str, List[float]],
     classes: Set[str],
 ) -> None:
-    data = {}
+    xdata = {}
+    ydata = {}
     if len(classes) == 1:
         cls = list(classes)[0]
-        data["Recall"] = [confidence_per_cls[cls], recall_per_cls[cls]]
-        data["Precision"] = [
-            confidence_per_cls[cls],
-            precision_per_cls[cls],
-        ]
+        xdata["Recall"] = confidence_per_cls[cls]
+        ydata["Recall"] = recall_per_cls[cls]
+        xdata["Precision"] = confidence_per_cls[cls]
+        ydata["Precision"] = precision_per_cls[cls]
     else:
         for cls in classes:
-            data[f"{cls}: Recall"] = [confidence_per_cls[cls], recall_per_cls[cls]]
-            data[f"{cls}: Precision"] = [
-                confidence_per_cls[cls],
-                precision_per_cls[cls],
-            ]
+            xdata[f"{cls}: Recall"] = confidence_per_cls[cls]
+            ydata[f"{cls}: Recall"] = recall_per_cls[cls]
+            xdata[f"{cls}: Precision"] = confidence_per_cls[cls]
+            ydata[f"{cls}: Precision"] = precision_per_cls[cls]
     scatter(
-        data,
-        "Confidence",
-        "Recall & Precision",
-        [-0.1, 1.1],
-        [-0.1, 1.1],
+        xdata,
+        ydata,
+        xlab="Confidence",
+        ylab="Recall & Precision",
+        xlim=[-0.1, 1.1],
+        ylim=[-0.1, 1.1],
         ncol=2,
         title="PRC Curve",
         markersize=6,
@@ -331,11 +331,11 @@ def _pr_curve(
             precision.append(precision_)
         plot(
             recall,
-            {"": precision},
-            "Recall",
-            "Precision",
-            [-0.1, 1.1],
-            [-0.1, 1.1],
+            precision,
+            xlab="Recall",
+            ylab="Precision",
+            xlim=[-0.1, 1.1],
+            ylim=[-0.1, 1.1],
             stacked=True,
             title=f"P-R Curve (mAP: {map_:.2f})",
             markersize=1,
@@ -350,10 +350,10 @@ def _pr_curve(
             plot(
                 recall,
                 {cls: precision},
-                "Recall",
-                "Precision",
-                [-0.1, 1.1],
-                [-0.1, 1.1],
+                xlab="Recall",
+                ylab="Precision",
+                xlim=[-0.1, 1.1],
+                ylim=[-0.1, 1.1],
                 stacked=True,
                 title=f"P-R Curve (mAP: {map_:.2f})",
                 markersize=1,