Simplified summary page code
Browse files- summary_page.py +81 -78
summary_page.py
CHANGED
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@@ -1,6 +1,68 @@
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import matplotlib.pyplot as plt
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import pandas as pd
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def create_summary_page(df: pd.DataFrame, available_models: list[str]) -> plt.Figure:
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"""Create a summary page with model names and both AMD/NVIDIA test stats bars."""
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if df.empty:
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@@ -15,34 +77,18 @@ def create_summary_page(df: pd.DataFrame, available_models: list[str]) -> plt.Fi
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# Calculate dimensions for N-column layout
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model_count = len(available_models)
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-
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rows = (model_count + columns - 1) // columns # Ceiling division
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# Figure dimensions - wider for
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height_per_row = min(2.2, max_height / max(rows, 1))
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figure_height = min(max_height, rows * height_per_row + 2)
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fig, ax = plt.subplots(figsize=(
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ax.set_facecolor('#000000')
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colors = {
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'passed': '#4CAF50',
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'failed': '#E53E3E',
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'skipped': '#FFD54F',
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'error': '#8B0000',
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'empty': "#5B5B5B"
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}
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visible_model_count = 0
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max_y = 0
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# Column layout parameters
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column_width = 100 / columns # Each column takes 25% of width
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bar_width = column_width * 0.8 # 80% of column width for bars
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bar_margin = column_width * 0.1 # 10% margin on each side
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-
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for i, model_name in enumerate(available_models):
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if model_name not in df.index:
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continue
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@@ -71,75 +117,34 @@ def create_summary_page(df: pd.DataFrame, available_models: list[str]) -> plt.Fi
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'skipped': 0,
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'error': 0
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}
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amd_total = sum(amd_stats.values())
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nvidia_total = sum(nvidia_stats.values())
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if amd_total == 0 and nvidia_total == 0:
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continue
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# Calculate position in 4-column grid
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col = visible_model_count %
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row = visible_model_count //
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# Calculate horizontal position for this column
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col_left = col *
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col_center = col *
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# Calculate vertical position for this row - start from top
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vertical_spacing = height_per_row
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y_base = (
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y_model_name = y_base # Model name above AMD bar
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y_amd_bar = y_base + vertical_spacing *
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y_nvidia_bar = y_base + vertical_spacing *
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max_y = max(max_y, y_nvidia_bar + vertical_spacing * 0.3)
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# Model name centered above the bars in this column
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ax.text(col_center, y_model_name, model_name.lower(),
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ha='center', va='center', color='#FFFFFF',
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fontsize=
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# AMD label and bar in this column
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bar_height = min(0.4, vertical_spacing *
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# AMD bar starts at column left position
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left = col_left
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for category in ['passed', 'failed', 'skipped', 'error']:
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if amd_stats[category] > 0:
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width = amd_stats[category] / amd_total * bar_width
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ax.barh(y_amd_bar, width, left=left, height=bar_height,
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color=colors[category], alpha=0.9)
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# if width > 2: # Smaller threshold for text display
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# ax.text(left + width/2, y_amd_bar, str(amd_stats[category]),
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# ha='center', va='center', color='black',
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# fontweight='bold', fontsize=10, fontfamily='monospace')
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left += width
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else:
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ax.barh(y_amd_bar, bar_width, left=col_left, height=bar_height, color=colors['empty'], alpha=0.9)
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# ax.text(col_center, y_amd_bar, "No data", ha='center', va='center', color='black', fontweight='bold', fontsize=10, fontfamily='monospace')
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# NVIDIA label and bar in this column
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ax.text(label_x, y_nvidia_bar, "nvidia", ha='right', va='center', color='#CCCCCC', fontsize=14, fontfamily='monospace', fontweight='normal')
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if nvidia_total > 0:
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# NVIDIA bar starts at column left position
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left = col_left
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for category in ['passed', 'failed', 'skipped', 'error']:
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if nvidia_stats[category] > 0:
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width = nvidia_stats[category] / nvidia_total * bar_width
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ax.barh(y_nvidia_bar, width, left=left, height=bar_height,
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color=colors[category], alpha=0.9)
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# if width > 2: # Smaller threshold for text display
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# ax.text(left + width/2, y_nvidia_bar, str(nvidia_stats[category]),
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# ha='center', va='center', color='black',
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# fontweight='bold', fontsize=10, fontfamily='monospace')
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left += width
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else:
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ax.barh(y_nvidia_bar, bar_width, left=col_left, height=bar_height, color=colors['empty'], alpha=0.9)
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# ax.text(col_center, y_nvidia_bar, "No data", ha='center', va='center', color='black', fontweight='bold', fontsize=10, fontfamily='monospace')
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# Increment counter for next visible model
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visible_model_count += 1
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@@ -158,7 +163,5 @@ def create_summary_page(df: pd.DataFrame, available_models: list[str]) -> plt.Fi
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ax.yaxis.set_inverted(True)
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# Remove all margins to make figure stick to top
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plt.tight_layout()
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plt.subplots_adjust(left=0.02, right=0.98, top=1.0, bottom=0.02)
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return fig
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import matplotlib.pyplot as plt
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import pandas as pd
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# Layout parameters
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COLUMNS = 3
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# Derived constants
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COLUMN_WIDTH = 100 / COLUMNS # Each column takes 25% of width
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BAR_WIDTH = COLUMN_WIDTH * 0.8 # 80% of column width for bars
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BAR_MARGIN = COLUMN_WIDTH * 0.1 # 10% margin on each side
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# Figure dimensions
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FIGURE_WIDTH = 20 # Wider to accommodate columns
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MAX_HEIGHT = 12 # Maximum height in inches
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MIN_HEIGHT_PER_ROW = 2.2
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FIGURE_PADDING = 2
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# Bar styling
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BAR_HEIGHT_RATIO = 0.22 # Bar height as ratio of vertical spacing
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VERTICAL_SPACING_RATIO = 0.2 # Base vertical position ratio
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AMD_BAR_OFFSET = 0.25 # AMD bar offset ratio
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NVIDIA_BAR_OFFSET = 0.54 # NVIDIA bar offset ratio
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# Colors
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COLORS = {
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'passed': '#4CAF50',
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'failed': '#E53E3E',
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'skipped': '#FFD54F',
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'error': '#8B0000',
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'empty': "#5B5B5B"
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}
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# Font styling
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MODEL_NAME_FONT_SIZE = 16
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LABEL_FONT_SIZE = 14
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LABEL_OFFSET = 1 # Distance of label from bar
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def draw_text_and_bar(
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label: str,
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stats: dict[str, int],
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y_bar: float,
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column_left_position: float,
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bar_height: float,
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ax: plt.Axes,
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) -> None:
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"""Draw a horizontal bar chart for given stats and its label on the left."""
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# Text
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label_x = column_left_position - LABEL_OFFSET
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ax.text(
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label_x, y_bar, label, ha='right', va='center', color='#CCCCCC', fontsize=LABEL_FONT_SIZE,
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fontfamily='monospace', fontweight='normal'
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)
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# Bar
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total = sum(stats.values())
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if total > 0:
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left = column_left_position
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for category in ['passed', 'failed', 'skipped', 'error']:
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if stats[category] > 0:
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width = stats[category] / total * BAR_WIDTH
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ax.barh(y_bar, width, left=left, height=bar_height, color=COLORS[category], alpha=0.9)
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left += width
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else:
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ax.barh(y_bar, BAR_WIDTH, left=column_left_position, height=bar_height, color=COLORS['empty'], alpha=0.9)
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def create_summary_page(df: pd.DataFrame, available_models: list[str]) -> plt.Figure:
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"""Create a summary page with model names and both AMD/NVIDIA test stats bars."""
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if df.empty:
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# Calculate dimensions for N-column layout
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model_count = len(available_models)
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rows = (model_count + COLUMNS - 1) // COLUMNS # Ceiling division
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# Figure dimensions - wider for columns, height based on rows
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height_per_row = min(MIN_HEIGHT_PER_ROW, MAX_HEIGHT / max(rows, 1))
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figure_height = min(MAX_HEIGHT, rows * height_per_row + FIGURE_PADDING)
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fig, ax = plt.subplots(figsize=(FIGURE_WIDTH, figure_height), facecolor='#000000')
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ax.set_facecolor('#000000')
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visible_model_count = 0
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max_y = 0
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for i, model_name in enumerate(available_models):
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if model_name not in df.index:
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continue
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'skipped': 0,
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'error': 0
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}
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# Calculate position in 4-column grid
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col = visible_model_count % COLUMNS
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row = visible_model_count // COLUMNS
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# Calculate horizontal position for this column
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col_left = col * COLUMN_WIDTH + BAR_MARGIN
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col_center = col * COLUMN_WIDTH + COLUMN_WIDTH / 2
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# Calculate vertical position for this row - start from top
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vertical_spacing = height_per_row
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y_base = (VERTICAL_SPACING_RATIO + row) * vertical_spacing
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y_model_name = y_base # Model name above AMD bar
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y_amd_bar = y_base + vertical_spacing * AMD_BAR_OFFSET # AMD bar
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y_nvidia_bar = y_base + vertical_spacing * NVIDIA_BAR_OFFSET # NVIDIA bar
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max_y = max(max_y, y_nvidia_bar + vertical_spacing * 0.3)
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# Model name centered above the bars in this column
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ax.text(col_center, y_model_name, model_name.lower(),
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ha='center', va='center', color='#FFFFFF',
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fontsize=MODEL_NAME_FONT_SIZE, fontfamily='monospace', fontweight='bold')
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# AMD label and bar in this column
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bar_height = min(0.4, vertical_spacing * BAR_HEIGHT_RATIO)
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# Draw AMD bar
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draw_text_and_bar("amd", amd_stats, y_amd_bar, col_left, bar_height, ax)
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# Draw NVIDIA bar
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draw_text_and_bar("nvidia", nvidia_stats, y_nvidia_bar, col_left, bar_height, ax)
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# Increment counter for next visible model
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visible_model_count += 1
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ax.yaxis.set_inverted(True)
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# Remove all margins to make figure stick to top
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plt.tight_layout()
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return fig
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