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"Dataset 1 (Task Schedule) Summary:\n",
"Number of tasks: 70\n",
"Time range: 0.00 - 375.00\n",
"Total duration: 375.00\n",
"Average task duration: 7.23\n",
"\n",
"Dataset 2 (Simulation Results) Summary:\n",
"Number of tasks: 71\n",
"Time range: 101981.00 - 102202.00\n",
"Total duration: 221.00\n",
"Average task duration: 4.46\n",
"\n",
"First 5 tasks from Dataset 1:\n",
" Task Start End Duration Start_Normalized End_Normalized\n",
"0 Task 0 0.0 1.0 1.0 0.0 1.0\n",
"1 Task 1 1.0 5.0 4.0 1.0 5.0\n",
"2 Task 2 5.0 12.0 7.0 5.0 12.0\n",
"3 Task 3 12.0 102.0 90.0 12.0 102.0\n",
"4 Task 4 97.0 102.0 5.0 97.0 102.0\n",
"\n",
"First 5 tasks from Dataset 2:\n",
" Task Start End Duration Start_Normalized End_Normalized\n",
"0 Task 70 101981 101981 0 0 0\n",
"1 Task 0 101981 101982 1 0 1\n",
"2 Task 1 101982 101986 4 1 5\n",
"3 Task 2 101986 101993 7 5 12\n",
"4 Task 4 101993 101998 5 12 17\n"
]
}
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"source": [
"import json\n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import plotly.graph_objects as go\n",
"from plotly.subplots import make_subplots\n",
"import plotly.express as px\n",
"from datetime import datetime, timedelta\n",
"\n",
"# Load the data from both files\n",
"def load_and_normalize_data():\n",
" # Load first dataset (task_schedule.json)\n",
" with open('/home/sfischer/Documents/projects/wk_LinProg/LinProg_Scripts/gantt_output/task_schedule.json', 'r') as f:\n",
" data1 = json.load(f)\n",
" \n",
" # Load second dataset (simulation results)\n",
" with open('/home/sfischer/Documents/projects/wk_LinProg/simulation/results/log_raw/gantt_timing_combined_log_LinProg_opt_test_rep50_link576_Opt_NoC_1_Comp_0.1_scratch10000_comp1.json', 'r') as f:\n",
" data2 = json.load(f)\n",
" \n",
" # Convert first dataset to DataFrame\n",
" df1 = pd.DataFrame(data1)\n",
" df1['Source'] = 'Task Schedule'\n",
" df1['Task_ID'] = df1['Task'].str.extract(r'(\\d+)').astype(int)\n",
" \n",
" # Convert second dataset to DataFrame\n",
" df2 = pd.DataFrame(data2['tasks'])\n",
" df2['Source'] = 'Simulation Results'\n",
" df2['Task_ID'] = df2['task_id']\n",
" df2['Task'] = 'Task ' + df2['task_id'].astype(str)\n",
" df2['Start'] = df2['start_time']\n",
" df2['End'] = df2['end_time']\n",
" df2['Duration'] = df2['duration']\n",
" \n",
" # Normalize timing values to start from 0\n",
" df1['Start_Normalized'] = df1['Start'] - df1['Start'].min()\n",
" df1['End_Normalized'] = df1['End'] - df1['Start'].min()\n",
" df1['Duration_Normalized'] = df1['Duration']\n",
" \n",
" df2['Start_Normalized'] = df2['Start'] - df2['Start'].min()\n",
" df2['End_Normalized'] = df2['End'] - df2['Start'].min()\n",
" df2['Duration_Normalized'] = df2['Duration']\n",
" \n",
" # Scale to same time range for comparison\n",
" max_time_1 = df1['End_Normalized'].max()\n",
" max_time_2 = df2['End_Normalized'].max()\n",
" \n",
" # Option 1: Scale both to 0-1 range\n",
" df1['Start_Scaled'] = df1['Start_Normalized'] / max_time_1\n",
" df1['End_Scaled'] = df1['End_Normalized'] / max_time_1\n",
" df1['Duration_Scaled'] = df1['Duration_Normalized'] / max_time_1\n",
" \n",
" df2['Start_Scaled'] = df2['Start_Normalized'] / max_time_2\n",
" df2['End_Scaled'] = df2['End_Normalized'] / max_time_2\n",
" df2['Duration_Scaled'] = df2['Duration_Normalized'] / max_time_2\n",
" \n",
" return df1, df2\n",
"\n",
"# Load the data\n",
"df1, df2 = load_and_normalize_data()\n",
"\n",
"print(\"Dataset 1 (Task Schedule) Summary:\")\n",
"print(f\"Number of tasks: {len(df1)}\")\n",
"print(f\"Time range: {df1['Start'].min():.2f} - {df1['End'].max():.2f}\")\n",
"print(f\"Total duration: {df1['End'].max() - df1['Start'].min():.2f}\")\n",
"print(f\"Average task duration: {df1['Duration'].mean():.2f}\")\n",
"\n",
"print(\"\\nDataset 2 (Simulation Results) Summary:\")\n",
"print(f\"Number of tasks: {len(df2)}\")\n",
"print(f\"Time range: {df2['Start'].min():.2f} - {df2['End'].max():.2f}\")\n",
"print(f\"Total duration: {df2['End'].max() - df2['Start'].min():.2f}\")\n",
"print(f\"Average task duration: {df2['Duration'].mean():.2f}\")\n",
"\n",
"# Display first few rows of each dataset\n",
"print(\"\\nFirst 5 tasks from Dataset 1:\")\n",
"print(df1[['Task', 'Start', 'End', 'Duration', 'Start_Normalized', 'End_Normalized']].head())\n",
"\n",
"print(\"\\nFirst 5 tasks from Dataset 2:\")\n",
"print(df2[['Task', 'Start', 'End', 'Duration', 'Start_Normalized', 'End_Normalized']].head())"
]
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"def create_gantt_comparison():\n",
" \"\"\"Create side-by-side Gantt charts for comparison\"\"\"\n",
" \n",
" # Create subplots\n",
" fig = make_subplots(\n",
" rows=2, cols=1,\n",
" subplot_titles=('Task Schedule (Original)', 'Simulation Results'),\n",
" vertical_spacing=0.1,\n",
" specs=[[{\"secondary_y\": False}], [{\"secondary_y\": False}]]\n",
" )\n",
" \n",
" # Colors for tasks\n",
" colors1 = px.colors.qualitative.Set3\n",
" colors2 = px.colors.qualitative.Pastel\n",
" \n",
" # Plot first dataset (normalized)\n",
" for i, row in df1.iterrows():\n",
" fig.add_trace(\n",
" go.Scatter(\n",
" x=[row['Start_Normalized'], row['End_Normalized'], row['End_Normalized'], row['Start_Normalized'], row['Start_Normalized']],\n",
" y=[row['Task_ID']-0.4, row['Task_ID']-0.4, row['Task_ID']+0.4, row['Task_ID']+0.4, row['Task_ID']-0.4],\n",
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" hovertemplate=f\"Task {row['Task_ID']}
\" +\n",
" f\"Start: {row['Start']:.1f}
\" +\n",
" f\"End: {row['End']:.1f}
\" +\n",
" f\"Duration: {row['Duration']:.1f}
\" +\n",
" \"\",\n",
" showlegend=False\n",
" ),\n",
" row=1, col=1\n",
" )\n",
" \n",
" # Plot second dataset (normalized)\n",
" for i, row in df2.iterrows():\n",
" fig.add_trace(\n",
" go.Scatter(\n",
" x=[row['Start_Normalized'], row['End_Normalized'], row['End_Normalized'], row['Start_Normalized'], row['Start_Normalized']],\n",
" y=[row['Task_ID']-0.4, row['Task_ID']-0.4, row['Task_ID']+0.4, row['Task_ID']+0.4, row['Task_ID']-0.4],\n",
" fill='toself',\n",
" fillcolor=colors2[i % len(colors2)],\n",
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" hovertemplate=f\"Task {row['Task_ID']}
\" +\n",
" f\"Start: {row['Start']:.1f}
\" +\n",
" f\"End: {row['End']:.1f}
\" +\n",
" f\"Duration: {row['Duration']:.1f}
\" +\n",
" f\"Accelerator: {row.get('accelerator', 'N/A')}
\" +\n",
" \"\",\n",
" showlegend=False\n",
" ),\n",
" row=2, col=1\n",
" )\n",
" \n",
" # Update layout\n",
" fig.update_layout(\n",
" title=\"Gantt Chart Comparison (Normalized Time)\",\n",
" height=800,\n",
" showlegend=False\n",
" )\n",
" \n",
" fig.update_xaxes(title_text=\"Normalized Time\", row=1, col=1)\n",
" fig.update_xaxes(title_text=\"Normalized Time\", row=2, col=1)\n",
" fig.update_yaxes(title_text=\"Task ID\", row=1, col=1)\n",
" fig.update_yaxes(title_text=\"Task ID\", row=2, col=1)\n",
" \n",
" return fig\n",
"\n",
"def create_scaled_comparison():\n",
" \"\"\"Create comparison with both datasets scaled to 0-1 range\"\"\"\n",
" \n",
" fig = make_subplots(\n",
" rows=2, cols=1,\n",
" subplot_titles=('Task Schedule (Scaled 0-1)', 'Simulation Results (Scaled 0-1)'),\n",
" vertical_spacing=0.1\n",
" )\n",
" \n",
" # Plot first dataset (scaled)\n",
" for i, row in df1.iterrows():\n",
" fig.add_trace(\n",
" go.Scatter(\n",
" x=[row['Start_Scaled'], row['End_Scaled'], row['End_Scaled'], row['Start_Scaled'], row['Start_Scaled']],\n",
" y=[row['Task_ID']-0.4, row['Task_ID']-0.4, row['Task_ID']+0.4, row['Task_ID']+0.4, row['Task_ID']-0.4],\n",
" fill='toself',\n",
" fillcolor=px.colors.qualitative.Set3[i % len(px.colors.qualitative.Set3)],\n",
" line=dict(color='black', width=1),\n",
" name=f\"Task {row['Task_ID']}\",\n",
" hovertemplate=f\"Task {row['Task_ID']}
\" +\n",
" f\"Scaled Start: {row['Start_Scaled']:.3f}
\" +\n",
" f\"Scaled End: {row['End_Scaled']:.3f}
\" +\n",
" f\"Scaled Duration: {row['Duration_Scaled']:.3f}
\" +\n",
" \"\",\n",
" showlegend=False\n",
" ),\n",
" row=1, col=1\n",
" )\n",
" \n",
" # Plot second dataset (scaled)\n",
" for i, row in df2.iterrows():\n",
" fig.add_trace(\n",
" go.Scatter(\n",
" x=[row['Start_Scaled'], row['End_Scaled'], row['End_Scaled'], row['Start_Scaled'], row['Start_Scaled']],\n",
" y=[row['Task_ID']-0.4, row['Task_ID']-0.4, row['Task_ID']+0.4, row['Task_ID']+0.4, row['Task_ID']-0.4],\n",
" fill='toself',\n",
" fillcolor=px.colors.qualitative.Pastel[i % len(px.colors.qualitative.Pastel)],\n",
" line=dict(color='black', width=1),\n",
" name=f\"Task {row['Task_ID']}\",\n",
" hovertemplate=f\"Task {row['Task_ID']}
\" +\n",
" f\"Scaled Start: {row['Start_Scaled']:.3f}
\" +\n",
" f\"Scaled End: {row['End_Scaled']:.3f}
\" +\n",
" f\"Scaled Duration: {row['Duration_Scaled']:.3f}
\" +\n",
" f\"Accelerator: {row.get('accelerator', 'N/A')}
\" +\n",
" \"\",\n",
" showlegend=False\n",
" ),\n",
" row=2, col=1\n",
" )\n",
" \n",
" fig.update_layout(\n",
" title=\"Gantt Chart Comparison (Scaled to 0-1 Range)\",\n",
" height=800,\n",
" showlegend=False\n",
" )\n",
" \n",
" fig.update_xaxes(title_text=\"Scaled Time (0-1)\", row=1, col=1)\n",
" fig.update_xaxes(title_text=\"Scaled Time (0-1)\", row=2, col=1)\n",
" fig.update_yaxes(title_text=\"Task ID\", row=1, col=1)\n",
" fig.update_yaxes(title_text=\"Task ID\", row=2, col=1)\n",
" \n",
" return fig\n",
"\n",
"# Create and display the comparisons\n",
"gantt_fig = create_gantt_comparison()\n",
"gantt_fig.show()\n",
"\n",
"scaled_fig = create_scaled_comparison()\n",
"scaled_fig.show()"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "12a7bea6",
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"text": [
"=== DETAILED COMPARISON ANALYSIS ===\n",
"Common tasks between datasets: 70\n",
"Tasks only in Dataset 1: 0\n",
"Tasks only in Dataset 2: 1\n",
"\n",
"=== DURATION COMPARISON ===\n",
"Average duration difference: -2.70\n",
"Median duration difference: 0.00\n",
"Max duration difference: 0.00\n",
"Min duration difference: -53.00\n",
"\n",
"=== START TIME COMPARISON (Normalized) ===\n",
"Average start time difference: -104.69\n",
"Median start time difference: -114.00\n",
"\n",
"=== TASKS WITH LARGEST DIFFERENCES ===\n",
"Top 10 tasks with largest duration differences:\n",
" Task_ID Duration_1 Duration_2 Duration_Diff Accelerator\n",
" 0 1.0 1 0.0 10\n",
" 1 4.0 4 0.0 10\n",
" 2 7.0 7 0.0 10\n",
" 4 5.0 5 0.0 6\n",
" 5 0.0 0 0.0 6\n",
" 7 5.0 5 0.0 3\n",
" 8 1.0 1 0.0 7\n",
" 9 3.0 3 0.0 7\n",
" 11 5.0 5 0.0 3\n",
" 12 0.0 0 0.0 3\n",
"\n",
"Top 10 tasks with largest duration ratios:\n",
" Task_ID Duration_1 Duration_2 Duration_Ratio Accelerator\n",
" 5 0.0 0 inf 6\n",
" 12 0.0 0 inf 3\n",
" 15 0.0 0 inf 3\n",
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" 33 0.0 0 inf 2\n",
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" 51 0.0 0 inf 1\n",
" 57 0.0 0 inf 0\n",
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"def analyze_differences():\n",
" \"\"\"Analyze the differences between the two datasets\"\"\"\n",
" \n",
" # Merge datasets by Task_ID for comparison\n",
" common_tasks = set(df1['Task_ID']) & set(df2['Task_ID'])\n",
" \n",
" comparison_data = []\n",
" \n",
" for task_id in common_tasks:\n",
" task1 = df1[df1['Task_ID'] == task_id].iloc[0]\n",
" task2 = df2[df2['Task_ID'] == task_id].iloc[0]\n",
" \n",
" comparison_data.append({\n",
" 'Task_ID': task_id,\n",
" 'Duration_1': task1['Duration'],\n",
" 'Duration_2': task2['Duration'],\n",
" 'Duration_Diff': task2['Duration'] - task1['Duration'],\n",
" 'Duration_Ratio': task2['Duration'] / task1['Duration'] if task1['Duration'] > 0 else float('inf'),\n",
" 'Start_1_Norm': task1['Start_Normalized'],\n",
" 'Start_2_Norm': task2['Start_Normalized'],\n",
" 'Start_Diff_Norm': task2['Start_Normalized'] - task1['Start_Normalized'],\n",
" 'Accelerator': task2.get('accelerator', 'N/A')\n",
" })\n",
" \n",
" comparison_df = pd.DataFrame(comparison_data)\n",
" \n",
" print(\"=== DETAILED COMPARISON ANALYSIS ===\")\n",
" print(f\"Common tasks between datasets: {len(common_tasks)}\")\n",
" print(f\"Tasks only in Dataset 1: {len(set(df1['Task_ID']) - set(df2['Task_ID']))}\")\n",
" print(f\"Tasks only in Dataset 2: {len(set(df2['Task_ID']) - set(df1['Task_ID']))}\")\n",
" \n",
" print(\"\\n=== DURATION COMPARISON ===\")\n",
" print(f\"Average duration difference: {comparison_df['Duration_Diff'].mean():.2f}\")\n",
" print(f\"Median duration difference: {comparison_df['Duration_Diff'].median():.2f}\")\n",
" print(f\"Max duration difference: {comparison_df['Duration_Diff'].max():.2f}\")\n",
" print(f\"Min duration difference: {comparison_df['Duration_Diff'].min():.2f}\")\n",
" \n",
" print(\"\\n=== START TIME COMPARISON (Normalized) ===\")\n",
" print(f\"Average start time difference: {comparison_df['Start_Diff_Norm'].mean():.2f}\")\n",
" print(f\"Median start time difference: {comparison_df['Start_Diff_Norm'].median():.2f}\")\n",
" \n",
" print(\"\\n=== TASKS WITH LARGEST DIFFERENCES ===\")\n",
" print(\"Top 10 tasks with largest duration differences:\")\n",
" top_diff = comparison_df.nlargest(10, 'Duration_Diff')[['Task_ID', 'Duration_1', 'Duration_2', 'Duration_Diff', 'Accelerator']]\n",
" print(top_diff.to_string(index=False))\n",
" \n",
" print(\"\\nTop 10 tasks with largest duration ratios:\")\n",
" top_ratio = comparison_df.nlargest(10, 'Duration_Ratio')[['Task_ID', 'Duration_1', 'Duration_2', 'Duration_Ratio', 'Accelerator']]\n",
" print(top_ratio.to_string(index=False))\n",
" \n",
" return comparison_df\n",
"\n",
"def create_difference_plots(comparison_df):\n",
" \"\"\"Create plots showing the differences between datasets\"\"\"\n",
" \n",
" fig = make_subplots(\n",
" rows=2, cols=2,\n",
" subplot_titles=('Duration Comparison', 'Duration Differences', 'Start Time Comparison', 'Duration Ratio Distribution'),\n",
" specs=[[{\"secondary_y\": False}, {\"secondary_y\": False}],\n",
" [{\"secondary_y\": False}, {\"secondary_y\": False}]]\n",
" )\n",
" \n",
" # Duration comparison scatter plot\n",
" fig.add_trace(\n",
" go.Scatter(\n",
" x=comparison_df['Duration_1'],\n",
" y=comparison_df['Duration_2'],\n",
" mode='markers',\n",
" name='Duration Comparison',\n",
" text=comparison_df['Task_ID'],\n",
" hovertemplate='Task %{text}
' +\n",
" 'Dataset 1: %{x:.1f}
' +\n",
" 'Dataset 2: %{y:.1f}
' +\n",
" '',\n",
" marker=dict(size=8, color='blue', opacity=0.6)\n",
" ),\n",
" row=1, col=1\n",
" )\n",
" \n",
" # Add diagonal line for reference\n",
" max_duration = max(comparison_df['Duration_1'].max(), comparison_df['Duration_2'].max())\n",
" fig.add_trace(\n",
" go.Scatter(\n",
" x=[0, max_duration],\n",
" y=[0, max_duration],\n",
" mode='lines',\n",
" name='Equal Duration',\n",
" line=dict(dash='dash', color='red'),\n",
" showlegend=False\n",
" ),\n",
" row=1, col=1\n",
" )\n",
" \n",
" # Duration differences\n",
" fig.add_trace(\n",
" go.Bar(\n",
" x=comparison_df['Task_ID'],\n",
" y=comparison_df['Duration_Diff'],\n",
" name='Duration Difference',\n",
" marker=dict(color=np.where(comparison_df['Duration_Diff'] > 0, 'green', 'red'))\n",
" ),\n",
" row=1, col=2\n",
" )\n",
" \n",
" # Start time comparison\n",
" fig.add_trace(\n",
" go.Scatter(\n",
" x=comparison_df['Start_1_Norm'],\n",
" y=comparison_df['Start_2_Norm'],\n",
" mode='markers',\n",
" name='Start Time Comparison',\n",
" text=comparison_df['Task_ID'],\n",
" hovertemplate='Task %{text}
' +\n",
" 'Dataset 1: %{x:.1f}
' +\n",
" 'Dataset 2: %{y:.1f}
' +\n",
" '',\n",
" marker=dict(size=8, color='orange', opacity=0.6)\n",
" ),\n",
" row=2, col=1\n",
" )\n",
" \n",
" # Duration ratio histogram\n",
" fig.add_trace(\n",
" go.Histogram(\n",
" x=comparison_df['Duration_Ratio'],\n",
" nbinsx=20,\n",
" name='Duration Ratio Distribution',\n",
" marker=dict(color='purple', opacity=0.7)\n",
" ),\n",
" row=2, col=2\n",
" )\n",
" \n",
" fig.update_layout(\n",
" title=\"Detailed Comparison Analysis\",\n",
" height=800,\n",
" showlegend=False\n",
" )\n",
" \n",
" fig.update_xaxes(title_text=\"Dataset 1 Duration\", row=1, col=1)\n",
" fig.update_yaxes(title_text=\"Dataset 2 Duration\", row=1, col=1)\n",
" fig.update_xaxes(title_text=\"Task ID\", row=1, col=2)\n",
" fig.update_yaxes(title_text=\"Duration Difference\", row=1, col=2)\n",
" fig.update_xaxes(title_text=\"Dataset 1 Start Time\", row=2, col=1)\n",
" fig.update_yaxes(title_text=\"Dataset 2 Start Time\", row=2, col=1)\n",
" fig.update_xaxes(title_text=\"Duration Ratio\", row=2, col=2)\n",
" fig.update_yaxes(title_text=\"Frequency\", row=2, col=2)\n",
" \n",
" return fig\n",
"\n",
"# Perform the analysis\n",
"comparison_df = analyze_differences()\n",
"\n",
"# Create and display difference plots\n",
"diff_fig = create_difference_plots(comparison_df)\n",
"diff_fig.show()"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "93d284d2",
"metadata": {},
"outputs": [
{
"name": "stdout",
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"text": [
"=== COMPREHENSIVE SUMMARY TABLE ===\n",
" Metric Task Schedule Simulation Results\n",
" Total Tasks 70 71\n",
" Total Execution Time 375.00 221.00\n",
" Average Task Duration 7.23 4.46\n",
" Median Task Duration 1.50 1.00\n",
" Min Task Duration 0.00 0.00\n",
" Max Task Duration 92.00 73.00\n",
"Tasks with Zero Duration 8 9\n",
" Task Duration Std Dev 19.14 10.70\n",
"\n",
"=== TASK EXECUTION PATTERN ANALYSIS ===\n",
"Task Schedule:\n",
" - Overlapping task pairs: 24\n",
" - Total task pairs: 2415\n",
" - Parallelism ratio: 0.010\n",
"Simulation Results:\n",
" - Overlapping task pairs: 30\n",
" - Total task pairs: 2485\n",
" - Parallelism ratio: 0.012\n",
"\n",
"=== ACCELERATOR USAGE (Simulation Results) ===\n",
"Tasks per accelerator:\n",
" Accelerator 0: 14 tasks\n",
" Accelerator 1: 18 tasks\n",
" Accelerator 2: 18 tasks\n",
" Accelerator 3: 7 tasks\n",
" Accelerator 4: 3 tasks\n",
" Accelerator 6: 3 tasks\n",
" Accelerator 7: 3 tasks\n",
" Accelerator 10: 5 tasks\n",
"Simulation Results:\n",
" - Overlapping task pairs: 30\n",
" - Total task pairs: 2485\n",
" - Parallelism ratio: 0.012\n",
"\n",
"=== ACCELERATOR USAGE (Simulation Results) ===\n",
"Tasks per accelerator:\n",
" Accelerator 0: 14 tasks\n",
" Accelerator 1: 18 tasks\n",
" Accelerator 2: 18 tasks\n",
" Accelerator 3: 7 tasks\n",
" Accelerator 4: 3 tasks\n",
" Accelerator 6: 3 tasks\n",
" Accelerator 7: 3 tasks\n",
" Accelerator 10: 5 tasks\n"
]
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"text": [
"\n",
"=== FINAL ANALYSIS SUMMARY ===\n",
"1. Both datasets contain similar task structures but with different timing characteristics\n",
"2. The simulation results show more detailed accelerator assignments\n",
"3. Duration differences suggest different execution environments or optimizations\n",
"4. Normalized comparisons help identify structural similarities despite timing differences\n",
"5. The overlay plot shows how tasks align between the two schedules\n"
]
}
],
"source": [
"def create_summary_table():\n",
" \"\"\"Create a comprehensive summary table\"\"\"\n",
" \n",
" # Calculate statistics for both datasets\n",
" stats_data = {\n",
" 'Metric': [\n",
" 'Total Tasks',\n",
" 'Total Execution Time',\n",
" 'Average Task Duration',\n",
" 'Median Task Duration',\n",
" 'Min Task Duration',\n",
" 'Max Task Duration',\n",
" 'Tasks with Zero Duration',\n",
" 'Task Duration Std Dev'\n",
" ],\n",
" 'Task Schedule': [\n",
" len(df1),\n",
" f\"{df1['End'].max() - df1['Start'].min():.2f}\",\n",
" f\"{df1['Duration'].mean():.2f}\",\n",
" f\"{df1['Duration'].median():.2f}\",\n",
" f\"{df1['Duration'].min():.2f}\",\n",
" f\"{df1['Duration'].max():.2f}\",\n",
" len(df1[df1['Duration'] == 0]),\n",
" f\"{df1['Duration'].std():.2f}\"\n",
" ],\n",
" 'Simulation Results': [\n",
" len(df2),\n",
" f\"{df2['End'].max() - df2['Start'].min():.2f}\",\n",
" f\"{df2['Duration'].mean():.2f}\",\n",
" f\"{df2['Duration'].median():.2f}\",\n",
" f\"{df2['Duration'].min():.2f}\",\n",
" f\"{df2['Duration'].max():.2f}\",\n",
" len(df2[df2['Duration'] == 0]),\n",
" f\"{df2['Duration'].std():.2f}\"\n",
" ]\n",
" }\n",
" \n",
" stats_df = pd.DataFrame(stats_data)\n",
" \n",
" print(\"=== COMPREHENSIVE SUMMARY TABLE ===\")\n",
" print(stats_df.to_string(index=False))\n",
" \n",
" # Task execution pattern analysis\n",
" print(\"\\n=== TASK EXECUTION PATTERN ANALYSIS ===\")\n",
" \n",
" # Check for parallelism in both datasets\n",
" def analyze_parallelism(df, name):\n",
" overlapping_tasks = 0\n",
" total_comparisons = 0\n",
" \n",
" for i in range(len(df)):\n",
" for j in range(i+1, len(df)):\n",
" task1 = df.iloc[i]\n",
" task2 = df.iloc[j]\n",
" \n",
" # Check if tasks overlap\n",
" if (task1['Start'] < task2['End'] and task2['Start'] < task1['End']):\n",
" overlapping_tasks += 1\n",
" total_comparisons += 1\n",
" \n",
" parallelism_ratio = overlapping_tasks / total_comparisons if total_comparisons > 0 else 0\n",
" \n",
" print(f\"{name}:\")\n",
" print(f\" - Overlapping task pairs: {overlapping_tasks}\")\n",
" print(f\" - Total task pairs: {total_comparisons}\")\n",
" print(f\" - Parallelism ratio: {parallelism_ratio:.3f}\")\n",
" \n",
" return parallelism_ratio\n",
" \n",
" para1 = analyze_parallelism(df1, \"Task Schedule\")\n",
" para2 = analyze_parallelism(df2, \"Simulation Results\")\n",
" \n",
" # Accelerator usage analysis (only for simulation results)\n",
" if 'accelerator' in df2.columns:\n",
" print(f\"\\n=== ACCELERATOR USAGE (Simulation Results) ===\")\n",
" accel_usage = df2['accelerator'].value_counts().sort_index()\n",
" print(\"Tasks per accelerator:\")\n",
" for accel, count in accel_usage.items():\n",
" print(f\" Accelerator {accel}: {count} tasks\")\n",
" \n",
" return stats_df\n",
"\n",
"def create_timeline_overlay():\n",
" \"\"\"Create an overlay comparison showing both datasets on the same timeline\"\"\"\n",
" \n",
" fig = go.Figure()\n",
" \n",
" # Add Dataset 1 tasks (normalized)\n",
" for i, row in df1.iterrows():\n",
" fig.add_trace(\n",
" go.Scatter(\n",
" x=[row['Start_Normalized'], row['End_Normalized'], row['End_Normalized'], row['Start_Normalized'], row['Start_Normalized']],\n",
" y=[row['Task_ID']-0.35, row['Task_ID']-0.35, row['Task_ID']-0.05, row['Task_ID']-0.05, row['Task_ID']-0.35],\n",
" fill='toself',\n",
" fillcolor='rgba(0, 100, 255, 0.6)',\n",
" line=dict(color='blue', width=1),\n",
" name=f\"Schedule Task {row['Task_ID']}\",\n",
" legendgroup=\"schedule\",\n",
" showlegend=i==0,\n",
" hovertemplate=f\"Schedule Task {row['Task_ID']}
\" +\n",
" f\"Start: {row['Start']:.1f}
\" +\n",
" f\"End: {row['End']:.1f}
\" +\n",
" f\"Duration: {row['Duration']:.1f}
\" +\n",
" \"\"\n",
" )\n",
" )\n",
" \n",
" # Add Dataset 2 tasks (normalized)\n",
" for i, row in df2.iterrows():\n",
" fig.add_trace(\n",
" go.Scatter(\n",
" x=[row['Start_Normalized'], row['End_Normalized'], row['End_Normalized'], row['Start_Normalized'], row['Start_Normalized']],\n",
" y=[row['Task_ID']+0.05, row['Task_ID']+0.05, row['Task_ID']+0.35, row['Task_ID']+0.35, row['Task_ID']+0.05],\n",
" fill='toself',\n",
" fillcolor='rgba(255, 100, 0, 0.6)',\n",
" line=dict(color='red', width=1),\n",
" name=f\"Simulation Task {row['Task_ID']}\",\n",
" legendgroup=\"simulation\",\n",
" showlegend=i==0,\n",
" hovertemplate=f\"Simulation Task {row['Task_ID']}
\" +\n",
" f\"Start: {row['Start']:.1f}
\" +\n",
" f\"End: {row['End']:.1f}
\" +\n",
" f\"Duration: {row['Duration']:.1f}
\" +\n",
" f\"Accelerator: {row.get('accelerator', 'N/A')}
\" +\n",
" \"\"\n",
" )\n",
" )\n",
" \n",
" # Add legend entries\n",
" fig.add_trace(go.Scatter(x=[None], y=[None], mode='markers', \n",
" marker=dict(size=10, color='rgba(0, 100, 255, 0.6)'),\n",
" legendgroup=\"schedule\", showlegend=True, name=\"Task Schedule\"))\n",
" fig.add_trace(go.Scatter(x=[None], y=[None], mode='markers', \n",
" marker=dict(size=10, color='rgba(255, 100, 0, 0.6)'),\n",
" legendgroup=\"simulation\", showlegend=True, name=\"Simulation Results\"))\n",
" \n",
" fig.update_layout(\n",
" title=\"Timeline Overlay Comparison (Normalized Time)\",\n",
" xaxis_title=\"Normalized Time\",\n",
" yaxis_title=\"Task ID\",\n",
" height=600,\n",
" hovermode='closest'\n",
" )\n",
" \n",
" return fig\n",
"\n",
"# Create and display the summary\n",
"summary_df = create_summary_table()\n",
"\n",
"# Create and display the timeline overlay\n",
"overlay_fig = create_timeline_overlay()\n",
"overlay_fig.show()\n",
"\n",
"print(\"\\n=== FINAL ANALYSIS SUMMARY ===\")\n",
"print(\"1. Both datasets contain similar task structures but with different timing characteristics\")\n",
"print(\"2. The simulation results show more detailed accelerator assignments\")\n",
"print(\"3. Duration differences suggest different execution environments or optimizations\")\n",
"print(\"4. Normalized comparisons help identify structural similarities despite timing differences\")\n",
"print(\"5. The overlay plot shows how tasks align between the two schedules\")"
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "d7a5194e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"=== DATA TYPE DEBUGGING ===\n",
"df1 Duration column type: float64\n",
"df2 Duration column type: int64\n",
"Sample values from df1['Duration']: [1.0, 4.0, 7.0, 90.0, 5.0]\n",
"Sample values from df2['Duration']: [0, 1, 4, 7, 5]\n",
"\n",
"Checking for non-numeric values:\n",
"df1 Duration non-numeric: 0\n",
"df2 Duration non-numeric: 0\n",
"\n",
"Comparison DataFrame info:\n",
"Duration_1 type: float64\n",
"Duration_2 type: int64\n",
"Duration_Diff type: float64\n",
"\n",
"Actual comparison values (first 10):\n",
" Task_ID Duration_1 Duration_2 Duration_Diff\n",
"0 0 1.0 1 0.0\n",
"1 1 4.0 4 0.0\n",
"2 2 7.0 7 0.0\n",
"3 3 90.0 37 -53.0\n",
"4 4 5.0 5 0.0\n",
"5 5 0.0 0 0.0\n",
"6 6 90.0 73 -17.0\n",
"7 7 5.0 5 0.0\n",
"8 8 1.0 1 0.0\n",
"9 9 3.0 3 0.0\n",
"\n",
"Manual calculation check:\n",
"Task 0.0: 1.0 - 1.0 = 0.0 (stored: 0.0)\n",
"Task 1.0: 4.0 - 4.0 = 0.0 (stored: 0.0)\n",
"Task 2.0: 7.0 - 7.0 = 0.0 (stored: 0.0)\n",
"Task 3.0: 90.0 - 37.0 = -53.0 (stored: -53.0)\n",
"Task 4.0: 5.0 - 5.0 = 0.0 (stored: 0.0)\n"
]
}
],
"source": [
"# Debug: Check data types and fix the issue\n",
"print(\"=== DATA TYPE DEBUGGING ===\")\n",
"print(\"df1 Duration column type:\", df1['Duration'].dtype)\n",
"print(\"df2 Duration column type:\", df2['Duration'].dtype)\n",
"print(\"Sample values from df1['Duration']:\", df1['Duration'].head().tolist())\n",
"print(\"Sample values from df2['Duration']:\", df2['Duration'].head().tolist())\n",
"\n",
"# Check if there are any non-numeric values\n",
"print(\"\\nChecking for non-numeric values:\")\n",
"print(\"df1 Duration non-numeric:\", df1['Duration'].apply(lambda x: not isinstance(x, (int, float))).sum())\n",
"print(\"df2 Duration non-numeric:\", df2['Duration'].apply(lambda x: not isinstance(x, (int, float))).sum())\n",
"\n",
"# Let's look at the actual comparison_df values\n",
"print(\"\\nComparison DataFrame info:\")\n",
"print(\"Duration_1 type:\", comparison_df['Duration_1'].dtype)\n",
"print(\"Duration_2 type:\", comparison_df['Duration_2'].dtype)\n",
"print(\"Duration_Diff type:\", comparison_df['Duration_Diff'].dtype)\n",
"\n",
"# Check some actual values\n",
"print(\"\\nActual comparison values (first 10):\")\n",
"print(comparison_df[['Task_ID', 'Duration_1', 'Duration_2', 'Duration_Diff']].head(10))\n",
"\n",
"# Check if the issue is with the difference calculation\n",
"print(\"\\nManual calculation check:\")\n",
"for i in range(5):\n",
" row = comparison_df.iloc[i]\n",
" manual_diff = float(row['Duration_2']) - float(row['Duration_1'])\n",
" print(f\"Task {row['Task_ID']}: {row['Duration_1']} - {row['Duration_2']} = {manual_diff} (stored: {row['Duration_Diff']})\")"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "8a6a068a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"=== FIXED COMPARISON ANALYSIS ===\n",
"Common tasks between datasets: 70\n",
"Tasks only in Dataset 1: 0\n",
"Tasks only in Dataset 2: 1\n",
"\n",
"=== DURATION COMPARISON (FIXED) ===\n",
"Average duration difference: -2.70\n",
"Median duration difference: 0.00\n",
"Max duration difference: 0.00\n",
"Min duration difference: -53.00\n",
"Standard deviation of differences: 9.74\n",
"\n",
"=== START TIME COMPARISON (Normalized, FIXED) ===\n",
"Average start time difference: -104.69\n",
"Median start time difference: -114.00\n",
"Max start time difference: 0.00\n",
"Min start time difference: -168.00\n",
"\n",
"=== TASKS WITH LARGEST DIFFERENCES (FIXED) ===\n",
"Top 10 tasks with largest absolute duration differences:\n",
" Task_ID Duration_1 Duration_2 Duration_Diff Accelerator\n",
" 3 90.0 37.0 -53.0 10\n",
" 10 92.0 39.0 -53.0 7\n",
" 16 33.0 14.0 -19.0 3\n",
" 34 33.0 14.0 -19.0 2\n",
" 52 37.0 19.0 -18.0 1\n",
" 6 90.0 73.0 -17.0 6\n",
" 64 19.0 9.0 -10.0 4\n",
" 0 1.0 1.0 0.0 10\n",
" 1 4.0 4.0 0.0 10\n",
" 2 7.0 7.0 0.0 10\n",
"\n",
"Top 10 tasks with largest positive duration differences:\n",
"No tasks with positive duration differences found\n",
"\n",
"Top 10 tasks with largest negative duration differences:\n",
" Task_ID Duration_1 Duration_2 Duration_Diff Accelerator\n",
" 3 90.0 37.0 -53.0 10\n",
" 10 92.0 39.0 -53.0 7\n",
" 16 33.0 14.0 -19.0 3\n",
" 34 33.0 14.0 -19.0 2\n",
" 52 37.0 19.0 -18.0 1\n",
" 6 90.0 73.0 -17.0 6\n",
" 64 19.0 9.0 -10.0 4\n",
"\n",
"Tasks with exactly matching durations: 63\n",
"Sample of tasks with zero difference:\n",
" Task_ID Duration_1 Duration_2 Duration_Diff Accelerator\n",
" 0 1.0 1.0 0.0 10\n",
" 1 4.0 4.0 0.0 10\n",
" 2 7.0 7.0 0.0 10\n",
" 4 5.0 5.0 0.0 6\n",
" 5 0.0 0.0 0.0 6\n",
" 7 5.0 5.0 0.0 3\n",
" 8 1.0 1.0 0.0 7\n",
" 9 3.0 3.0 0.0 7\n",
" 11 5.0 5.0 0.0 3\n",
" 12 0.0 0.0 0.0 3\n"
]
}
],
"source": [
"def load_and_normalize_data_fixed():\n",
" \"\"\"Fixed version with proper data type handling\"\"\"\n",
" \n",
" # Load first dataset (task_schedule.json)\n",
" with open('/home/sfischer/Documents/projects/wk_LinProg/LinProg_Scripts/gantt_output/task_schedule.json', 'r') as f:\n",
" data1 = json.load(f)\n",
" \n",
" # Load second dataset (simulation results)\n",
" with open('/home/sfischer/Documents/projects/wk_LinProg/simulation/results/log_raw/gantt_timing_combined_log_LinProg_opt_test_rep50_link576_Opt_NoC_1_Comp_0.1_scratch10000_comp1.json', 'r') as f:\n",
" data2 = json.load(f)\n",
" \n",
" # Convert first dataset to DataFrame with explicit type conversion\n",
" df1 = pd.DataFrame(data1)\n",
" df1['Source'] = 'Task Schedule'\n",
" df1['Task_ID'] = df1['Task'].str.extract(r'(\\d+)').astype(int)\n",
" \n",
" # Ensure numeric types for timing columns\n",
" df1['Start'] = pd.to_numeric(df1['Start'], errors='coerce')\n",
" df1['End'] = pd.to_numeric(df1['End'], errors='coerce')\n",
" df1['Duration'] = pd.to_numeric(df1['Duration'], errors='coerce')\n",
" \n",
" # Convert second dataset to DataFrame with explicit type conversion\n",
" df2 = pd.DataFrame(data2['tasks'])\n",
" df2['Source'] = 'Simulation Results'\n",
" df2['Task_ID'] = df2['task_id'].astype(int)\n",
" df2['Task'] = 'Task ' + df2['task_id'].astype(str)\n",
" \n",
" # Ensure numeric types for timing columns\n",
" df2['Start'] = pd.to_numeric(df2['start_time'], errors='coerce')\n",
" df2['End'] = pd.to_numeric(df2['end_time'], errors='coerce')\n",
" df2['Duration'] = pd.to_numeric(df2['duration'], errors='coerce')\n",
" \n",
" # Remove any rows with NaN values\n",
" df1 = df1.dropna(subset=['Start', 'End', 'Duration'])\n",
" df2 = df2.dropna(subset=['Start', 'End', 'Duration'])\n",
" \n",
" # Normalize timing values to start from 0\n",
" df1['Start_Normalized'] = df1['Start'] - df1['Start'].min()\n",
" df1['End_Normalized'] = df1['End'] - df1['Start'].min()\n",
" df1['Duration_Normalized'] = df1['Duration']\n",
" \n",
" df2['Start_Normalized'] = df2['Start'] - df2['Start'].min()\n",
" df2['End_Normalized'] = df2['End'] - df2['Start'].min()\n",
" df2['Duration_Normalized'] = df2['Duration']\n",
" \n",
" # Scale to same time range for comparison\n",
" max_time_1 = df1['End_Normalized'].max()\n",
" max_time_2 = df2['End_Normalized'].max()\n",
" \n",
" # Scale both to 0-1 range\n",
" df1['Start_Scaled'] = df1['Start_Normalized'] / max_time_1\n",
" df1['End_Scaled'] = df1['End_Normalized'] / max_time_1\n",
" df1['Duration_Scaled'] = df1['Duration_Normalized'] / max_time_1\n",
" \n",
" df2['Start_Scaled'] = df2['Start_Normalized'] / max_time_2\n",
" df2['End_Scaled'] = df2['End_Normalized'] / max_time_2\n",
" df2['Duration_Scaled'] = df2['Duration_Normalized'] / max_time_2\n",
" \n",
" return df1, df2\n",
"\n",
"def analyze_differences_fixed():\n",
" \"\"\"Fixed version of difference analysis with proper type handling\"\"\"\n",
" \n",
" # Use the fixed data\n",
" df1_fixed, df2_fixed = load_and_normalize_data_fixed()\n",
" \n",
" # Merge datasets by Task_ID for comparison\n",
" common_tasks = set(df1_fixed['Task_ID']) & set(df2_fixed['Task_ID'])\n",
" \n",
" comparison_data = []\n",
" \n",
" for task_id in common_tasks:\n",
" task1 = df1_fixed[df1_fixed['Task_ID'] == task_id].iloc[0]\n",
" task2 = df2_fixed[df2_fixed['Task_ID'] == task_id].iloc[0]\n",
" \n",
" # Ensure all values are properly converted to float\n",
" duration_1 = float(task1['Duration'])\n",
" duration_2 = float(task2['Duration'])\n",
" start_1_norm = float(task1['Start_Normalized'])\n",
" start_2_norm = float(task2['Start_Normalized'])\n",
" \n",
" comparison_data.append({\n",
" 'Task_ID': int(task_id),\n",
" 'Duration_1': duration_1,\n",
" 'Duration_2': duration_2,\n",
" 'Duration_Diff': duration_2 - duration_1,\n",
" 'Duration_Ratio': duration_2 / duration_1 if duration_1 > 0 else float('inf'),\n",
" 'Start_1_Norm': start_1_norm,\n",
" 'Start_2_Norm': start_2_norm,\n",
" 'Start_Diff_Norm': start_2_norm - start_1_norm,\n",
" 'Accelerator': task2.get('accelerator', 'N/A')\n",
" })\n",
" \n",
" comparison_df_fixed = pd.DataFrame(comparison_data)\n",
" \n",
" print(\"=== FIXED COMPARISON ANALYSIS ===\")\n",
" print(f\"Common tasks between datasets: {len(common_tasks)}\")\n",
" print(f\"Tasks only in Dataset 1: {len(set(df1_fixed['Task_ID']) - set(df2_fixed['Task_ID']))}\")\n",
" print(f\"Tasks only in Dataset 2: {len(set(df2_fixed['Task_ID']) - set(df1_fixed['Task_ID']))}\")\n",
" \n",
" print(\"\\n=== DURATION COMPARISON (FIXED) ===\")\n",
" print(f\"Average duration difference: {comparison_df_fixed['Duration_Diff'].mean():.2f}\")\n",
" print(f\"Median duration difference: {comparison_df_fixed['Duration_Diff'].median():.2f}\")\n",
" print(f\"Max duration difference: {comparison_df_fixed['Duration_Diff'].max():.2f}\")\n",
" print(f\"Min duration difference: {comparison_df_fixed['Duration_Diff'].min():.2f}\")\n",
" print(f\"Standard deviation of differences: {comparison_df_fixed['Duration_Diff'].std():.2f}\")\n",
" \n",
" print(\"\\n=== START TIME COMPARISON (Normalized, FIXED) ===\")\n",
" print(f\"Average start time difference: {comparison_df_fixed['Start_Diff_Norm'].mean():.2f}\")\n",
" print(f\"Median start time difference: {comparison_df_fixed['Start_Diff_Norm'].median():.2f}\")\n",
" print(f\"Max start time difference: {comparison_df_fixed['Start_Diff_Norm'].max():.2f}\")\n",
" print(f\"Min start time difference: {comparison_df_fixed['Start_Diff_Norm'].min():.2f}\")\n",
" \n",
" print(\"\\n=== TASKS WITH LARGEST DIFFERENCES (FIXED) ===\")\n",
" # Sort by absolute difference to see both positive and negative differences\n",
" comparison_df_fixed['Abs_Duration_Diff'] = abs(comparison_df_fixed['Duration_Diff'])\n",
" \n",
" print(\"Top 10 tasks with largest absolute duration differences:\")\n",
" top_diff = comparison_df_fixed.nlargest(10, 'Abs_Duration_Diff')[['Task_ID', 'Duration_1', 'Duration_2', 'Duration_Diff', 'Accelerator']]\n",
" print(top_diff.to_string(index=False))\n",
" \n",
" print(\"\\nTop 10 tasks with largest positive duration differences:\")\n",
" positive_diff = comparison_df_fixed[comparison_df_fixed['Duration_Diff'] > 0]\n",
" if len(positive_diff) > 0:\n",
" top_pos_diff = positive_diff.nlargest(10, 'Duration_Diff')[['Task_ID', 'Duration_1', 'Duration_2', 'Duration_Diff', 'Accelerator']]\n",
" print(top_pos_diff.to_string(index=False))\n",
" else:\n",
" print(\"No tasks with positive duration differences found\")\n",
" \n",
" print(\"\\nTop 10 tasks with largest negative duration differences:\")\n",
" negative_diff = comparison_df_fixed[comparison_df_fixed['Duration_Diff'] < 0]\n",
" if len(negative_diff) > 0:\n",
" top_neg_diff = negative_diff.nsmallest(10, 'Duration_Diff')[['Task_ID', 'Duration_1', 'Duration_2', 'Duration_Diff', 'Accelerator']]\n",
" print(top_neg_diff.to_string(index=False))\n",
" else:\n",
" print(\"No tasks with negative duration differences found\")\n",
" \n",
" # Show tasks with exact matches (zero difference)\n",
" zero_diff = comparison_df_fixed[comparison_df_fixed['Duration_Diff'] == 0.0]\n",
" print(f\"\\nTasks with exactly matching durations: {len(zero_diff)}\")\n",
" if len(zero_diff) > 0:\n",
" print(\"Sample of tasks with zero difference:\")\n",
" print(zero_diff[['Task_ID', 'Duration_1', 'Duration_2', 'Duration_Diff', 'Accelerator']].head(10).to_string(index=False))\n",
" \n",
" return comparison_df_fixed, df1_fixed, df2_fixed\n",
"\n",
"# Run the fixed analysis\n",
"comparison_df_fixed, df1_fixed, df2_fixed = analyze_differences_fixed()"
]
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"=== DISTRIBUTION ANALYSIS OF DIFFERENCES ===\n",
"Duration Difference Statistics:\n",
"count 70.000000\n",
"mean -2.700000\n",
"std 9.737645\n",
"min -53.000000\n",
"25% 0.000000\n",
"50% 0.000000\n",
"75% 0.000000\n",
"max 0.000000\n",
"Name: Duration_Diff, dtype: float64\n",
"\n",
"Task Distribution:\n",
"Tasks with longer duration in simulation: 0\n",
"Tasks with shorter duration in simulation: 7\n",
"Tasks with identical duration: 63\n",
"\n",
"Duration Differences by Accelerator:\n",
" mean std count\n",
"Accelerator \n",
"0 0.000000 0.000000 14\n",
"1 -1.000000 4.242641 18\n",
"2 -1.055556 4.478343 18\n",
"3 -2.714286 7.181325 7\n",
"4 -3.333333 5.773503 3\n",
"6 -5.666667 9.814955 3\n",
"7 -17.666667 30.599564 3\n",
"10 -13.250000 26.500000 4\n"
]
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"def create_improved_difference_plots(comparison_df_fixed):\n",
" \"\"\"Create improved plots with the fixed data\"\"\"\n",
" \n",
" fig = make_subplots(\n",
" rows=2, cols=2,\n",
" subplot_titles=('Duration Comparison (Fixed)', 'Duration Differences (Fixed)', \n",
" 'Start Time Comparison (Fixed)', 'Duration Ratio Distribution (Fixed)'),\n",
" specs=[[{\"secondary_y\": False}, {\"secondary_y\": False}],\n",
" [{\"secondary_y\": False}, {\"secondary_y\": False}]]\n",
" )\n",
" \n",
" # Duration comparison scatter plot\n",
" fig.add_trace(\n",
" go.Scatter(\n",
" x=comparison_df_fixed['Duration_1'],\n",
" y=comparison_df_fixed['Duration_2'],\n",
" mode='markers',\n",
" name='Duration Comparison',\n",
" text=comparison_df_fixed['Task_ID'],\n",
" hovertemplate='Task %{text}
' +\n",
" 'Dataset 1: %{x:.1f}
' +\n",
" 'Dataset 2: %{y:.1f}
' +\n",
" 'Difference: %{customdata:.1f}
' +\n",
" '',\n",
" customdata=comparison_df_fixed['Duration_Diff'],\n",
" marker=dict(size=8, color='blue', opacity=0.6)\n",
" ),\n",
" row=1, col=1\n",
" )\n",
" \n",
" # Add diagonal line for reference\n",
" max_duration = max(comparison_df_fixed['Duration_1'].max(), comparison_df_fixed['Duration_2'].max())\n",
" fig.add_trace(\n",
" go.Scatter(\n",
" x=[0, max_duration],\n",
" y=[0, max_duration],\n",
" mode='lines',\n",
" name='Equal Duration',\n",
" line=dict(dash='dash', color='red'),\n",
" showlegend=False\n",
" ),\n",
" row=1, col=1\n",
" )\n",
" \n",
" # Duration differences bar chart\n",
" # Color bars based on positive/negative differences\n",
" bar_colors = ['green' if x > 0 else 'red' if x < 0 else 'gray' for x in comparison_df_fixed['Duration_Diff']]\n",
" \n",
" fig.add_trace(\n",
" go.Bar(\n",
" x=comparison_df_fixed['Task_ID'],\n",
" y=comparison_df_fixed['Duration_Diff'],\n",
" name='Duration Difference',\n",
" marker=dict(color=bar_colors),\n",
" hovertemplate='Task %{x}
' +\n",
" 'Difference: %{y:.1f}
' +\n",
" ''\n",
" ),\n",
" row=1, col=2\n",
" )\n",
" \n",
" # Start time comparison\n",
" fig.add_trace(\n",
" go.Scatter(\n",
" x=comparison_df_fixed['Start_1_Norm'],\n",
" y=comparison_df_fixed['Start_2_Norm'],\n",
" mode='markers',\n",
" name='Start Time Comparison',\n",
" text=comparison_df_fixed['Task_ID'],\n",
" hovertemplate='Task %{text}
' +\n",
" 'Dataset 1: %{x:.1f}
' +\n",
" 'Dataset 2: %{y:.1f}
' +\n",
" 'Difference: %{customdata:.1f}
' +\n",
" '',\n",
" customdata=comparison_df_fixed['Start_Diff_Norm'],\n",
" marker=dict(size=8, color='orange', opacity=0.6)\n",
" ),\n",
" row=2, col=1\n",
" )\n",
" \n",
" # Add diagonal line for start time reference\n",
" max_start = max(comparison_df_fixed['Start_1_Norm'].max(), comparison_df_fixed['Start_2_Norm'].max())\n",
" fig.add_trace(\n",
" go.Scatter(\n",
" x=[0, max_start],\n",
" y=[0, max_start],\n",
" mode='lines',\n",
" name='Equal Start Time',\n",
" line=dict(dash='dash', color='red'),\n",
" showlegend=False\n",
" ),\n",
" row=2, col=1\n",
" )\n",
" \n",
" # Duration ratio histogram (filter out infinite values)\n",
" finite_ratios = comparison_df_fixed[comparison_df_fixed['Duration_Ratio'] != float('inf')]['Duration_Ratio']\n",
" \n",
" fig.add_trace(\n",
" go.Histogram(\n",
" x=finite_ratios,\n",
" nbinsx=30,\n",
" name='Duration Ratio Distribution',\n",
" marker=dict(color='purple', opacity=0.7),\n",
" hovertemplate='Ratio: %{x:.2f}
' +\n",
" 'Count: %{y}
' +\n",
" ''\n",
" ),\n",
" row=2, col=2\n",
" )\n",
" \n",
" # Add vertical line at ratio = 1 for reference\n",
" fig.add_vline(x=1, line_dash=\"dash\", line_color=\"red\", row=2, col=2)\n",
" \n",
" fig.update_layout(\n",
" title=\"Detailed Comparison Analysis (Fixed Data Types)\",\n",
" height=800,\n",
" showlegend=False\n",
" )\n",
" \n",
" fig.update_xaxes(title_text=\"Dataset 1 Duration\", row=1, col=1)\n",
" fig.update_yaxes(title_text=\"Dataset 2 Duration\", row=1, col=1)\n",
" fig.update_xaxes(title_text=\"Task ID\", row=1, col=2)\n",
" fig.update_yaxes(title_text=\"Duration Difference\", row=1, col=2)\n",
" fig.update_xaxes(title_text=\"Dataset 1 Start Time\", row=2, col=1)\n",
" fig.update_yaxes(title_text=\"Dataset 2 Start Time\", row=2, col=1)\n",
" fig.update_xaxes(title_text=\"Duration Ratio\", row=2, col=2)\n",
" fig.update_yaxes(title_text=\"Frequency\", row=2, col=2)\n",
" \n",
" return fig\n",
"\n",
"def create_distribution_analysis():\n",
" \"\"\"Analyze the distribution of differences\"\"\"\n",
" \n",
" print(\"=== DISTRIBUTION ANALYSIS OF DIFFERENCES ===\")\n",
" \n",
" # Basic statistics\n",
" diff_stats = comparison_df_fixed['Duration_Diff'].describe()\n",
" print(\"Duration Difference Statistics:\")\n",
" print(diff_stats)\n",
" \n",
" # Count tasks in different categories\n",
" positive_diff = len(comparison_df_fixed[comparison_df_fixed['Duration_Diff'] > 0])\n",
" negative_diff = len(comparison_df_fixed[comparison_df_fixed['Duration_Diff'] < 0])\n",
" zero_diff = len(comparison_df_fixed[comparison_df_fixed['Duration_Diff'] == 0])\n",
" \n",
" print(f\"\\nTask Distribution:\")\n",
" print(f\"Tasks with longer duration in simulation: {positive_diff}\")\n",
" print(f\"Tasks with shorter duration in simulation: {negative_diff}\")\n",
" print(f\"Tasks with identical duration: {zero_diff}\")\n",
" \n",
" # Analyze by accelerator\n",
" if 'Accelerator' in comparison_df_fixed.columns:\n",
" print(f\"\\nDuration Differences by Accelerator:\")\n",
" accel_analysis = comparison_df_fixed.groupby('Accelerator')['Duration_Diff'].agg(['mean', 'std', 'count'])\n",
" print(accel_analysis)\n",
" \n",
" return diff_stats\n",
"\n",
"# Create improved visualizations\n",
"improved_fig = create_improved_difference_plots(comparison_df_fixed)\n",
"improved_fig.show()\n",
"\n",
"# Perform distribution analysis\n",
"dist_stats = create_distribution_analysis()"
]
}
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