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ChartMimic/dataset/ori_500/bar_40.py

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+# ===================
+# Part 1: Importing Libraries
+# ===================
+import matplotlib.pyplot as plt
+
+# ===================
+# Part 2: Data Preparation
+# ===================
+# Data
+categories = [
+    "Parameter",
+    "Method Declaration",
+    "Variable Declaration",
+    "Conditional Statement",
+    "Loop",
+    "Conditional Block",
+    "Argument",
+    "External Class",
+    "Variable",
+    "Return",
+    "External Variable/Method",
+    "Method Call",
+    "Exception Handling",
+    "Operation",
+    "Comment",
+    "Operator",
+    "Assignment",
+    "Literal",
+]
+differences = [
+    -5,
+    10,
+    -20,
+    -10,
+    10,
+    20,
+    30,
+    -30,
+    -40,
+    60,
+    70,
+    80,
+    80,
+    100,
+    130,
+    160,
+    160,
+    180,
+]
+colors = ["#d1605e" if x < 0 else "#85b6b2" for x in differences]
+
+title = "Relative Difference in Machine vs. Human Focus on Semantic Categories"
+xlabel = "Difference (%)"
+ylabel = "Semantic Category"
+xlim = [-50, 200]
+xticks = range(-50, 201, 50)
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Create figure and plot
+fig, ax = plt.subplots(figsize=(10, 5))
+ax.barh(categories, differences, color=colors)
+
+# Set title and labels
+ax.set_title(title)
+ax.set_xlabel(xlabel)
+ax.set_ylabel(ylabel)
+
+# Set x-axis limits and labels
+ax.set_xlim(xlim)
+ax.set_xticks(xticks)
+ax.xaxis.grid(True)
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Save the plot with tight layout to minimize white space
+plt.tight_layout()
+plt.savefig("bar_40.pdf", bbox_inches="tight")

ChartMimic/dataset/ori_500/bar_44.py

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+# ===================
+# Part 1: Importing Libraries
+# ===================
+import matplotlib.pyplot as plt
+import numpy as np
+
+np.random.seed(0)
+
+
+# ===================
+# Part 2: Data Preparation
+# ===================
+# Placeholder data
+categories = [
+    "AFAN",
+    "AFBE",
+    "AFCE",
+    "AFEO",
+    "AFEX",
+    "AFIG",
+    "AFPK",
+    "AFPO",
+    "AFPP",
+    "AFPR",
+    "AFRL",
+    "AFSC",
+    "AFSI",
+    "AFTH",
+    "AFVC",
+    "AFWS",
+    "AFWT",
+    "DAH",
+    "IC",
+    "SS",
+]
+gpt_3_5_turbo_values = np.random.randint(5, 30, size=len(categories))
+gpt_4_values = np.random.randint(5, 20, size=len(categories))
+
+# Bar width
+bar_width = 0.6
+
+# Positions of the bars on the x-axis
+r = np.arange(len(categories))
+
+labels = ["GPT-3.5-Turbo", "GPT-4"]
+xlabel = "Categories"
+ylabel = "Non-valid NLAs"
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Create the figure and the axes
+fig, ax = plt.subplots(figsize=(8, 6))
+
+# Plot the bars
+ax.bar(
+    r,
+    gpt_3_5_turbo_values,
+    bottom=gpt_4_values,
+    color="blue",
+    width=bar_width,
+    label=labels[0],
+)
+ax.bar(r, gpt_4_values, color="orange", width=bar_width, label=labels[1])
+
+# Add labels, title, and legend
+ax.set_xlabel(xlabel)
+ax.set_ylabel(ylabel)
+ax.set_xticks(r)
+ax.set_xticklabels(categories, rotation=45, ha="right")
+ax.legend(loc="upper left")
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Show the plot with tight layout
+plt.tight_layout()
+plt.savefig("bar_44.pdf", bbox_inches="tight")

ChartMimic/dataset/ori_500/bar_45.py

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+# ===================
+# Part 1: Importing Libraries
+# ===================
+import matplotlib.pyplot as plt
+import numpy as np
+
+np.random.seed(0)
+
+# ===================
+# Part 2: Data Preparation
+# ===================
+# Data
+constraint_words = [3, 5, 7, 10]
+LLAMA_Default = [-1, -1.5, -2, -3]
+LLAMA_HAG = [-0.5, -1, -1.5, -2.5]
+Vicuna_Default = [-1.5, -2, -2.5, -3.5]
+Vicuna_HAG = [-1, -1.5, -2, -3]
+
+# X-axis positions for each group
+x = np.arange(len(constraint_words))
+
+# Bar width
+bar_width = 0.2
+
+# Labels and Titles
+ylabel = "Score"
+xlabel = "Num of Constraint Words"
+title = "Taboo"
+
+# Legend labels
+legend_labels = ["LLAMA-Default", "LLAMA-HAG", "Vicuna-Default", "Vicuna-HAG"]
+
+# Colors
+colors = ["#f1c5c1", "#dc7870", "#b9d0e5", "#8ab1d2"]
+
+# Axis limits
+ylim = (-3.5, 0)
+
+# Axis tick labels
+xticks = x
+xticklabels = constraint_words
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Figure and axis
+fig, ax = plt.subplots(figsize=(10, 7))
+
+# Plotting bars
+ax.bar(
+    x - bar_width * 1.5,
+    LLAMA_Default,
+    width=bar_width,
+    label=legend_labels[0],
+    color=colors[0],
+)
+ax.bar(
+    x - bar_width / 2,
+    LLAMA_HAG,
+    width=bar_width,
+    label=legend_labels[1],
+    color=colors[1],
+)
+ax.bar(
+    x + bar_width / 2,
+    Vicuna_Default,
+    width=bar_width,
+    label=legend_labels[2],
+    color=colors[2],
+)
+ax.bar(
+    x + bar_width * 1.5,
+    Vicuna_HAG,
+    width=bar_width,
+    label=legend_labels[3],
+    color=colors[3],
+)
+
+# Adding labels and title
+ax.set_ylim(ylim)
+ax.set_ylabel(ylabel)
+ax.set_xlabel(xlabel)
+ax.set_title(title)
+
+# Adding x-axis tick labels
+ax.set_xticks(xticks)
+ax.set_xticklabels(xticklabels)
+
+plt.tick_params(axis="both", which="both", length=0)
+
+# Adding legend
+ax.legend(loc="lower center", bbox_to_anchor=(0.5, -0.15), frameon=False, ncol=4)
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Displaying the plot with tight layout to minimize white space
+plt.tight_layout()
+plt.savefig("bar_45.pdf", bbox_inches="tight")

ChartMimic/dataset/ori_500/bar_46.py

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+# ===================
+# Part 1: Importing Libraries
+# ===================
+import matplotlib.pyplot as plt
+
+# ===================
+# Part 2: Data Preparation
+# ===================
+# Data
+models = ["Lavila", "Video-LLaMA", "BLIP1", "BLIP2", "LLaVA", "OSCaR", "GPT4V"]
+percentages = [0.0, 0.71, 4.64, 4.64, 31.79, 73.93, 82.5]
+title = "Human Study"
+xlabel = "Model"
+ylabel = "Percentage (%)"
+ylim = [0, 90]
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Create figure and bar chart
+plt.figure(figsize=(10, 6))
+bars = plt.bar(models, percentages, color="skyblue")
+
+# Add percentage labels on top of each bar
+for bar in bars:
+    yval = bar.get_height()
+    plt.text(
+        bar.get_x() + bar.get_width() / 2, yval, f"{yval}%", ha="center", va="bottom"
+    )
+
+# Set chart title and labels
+plt.title(title)
+plt.xlabel(xlabel)
+plt.ylabel(ylabel)
+
+# Set y-axis range and gridlines
+plt.ylim(0, 90)
+plt.grid(axis="both", linestyle="--", alpha=0.7)
+
+plt.gca().spines["top"].set_visible(False)
+plt.gca().spines["right"].set_visible(False)
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Displaying the plot with tight layout to minimize white space
+plt.tight_layout()
+plt.savefig("bar_46.pdf", bbox_inches="tight")

ChartMimic/dataset/ori_500/bar_48.py

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+# ===================
+# Part 1: Importing Libraries
+# ===================
+import matplotlib.pyplot as plt
+import numpy as np
+
+np.random.seed(0)
+
+# ===================
+# Part 2: Data Preparation
+# ===================
+# Data
+algorithms = ["GD", "GD w. SA", "ITFR w/o SA", "ITFR"]
+accuracy_movielens = [0.355, 0.360, 0.365, 0.360]
+unfairness_movielens = [0.10, 0.08, 0.06, 0.08]
+accuracy_tenrec = [0.175, 0.180, 0.185, 0.180]
+unfairness_tenrec = [0.125, 0.100, 0.075, 0.100]
+colors = ["#736aa6", "#983530", "#f2bf42", "#5384ed"]
+labels = ["Accuracy", "Unfairness"]
+
+# X-axis positions
+x = np.arange(len(algorithms))
+indexs = [2, 4]
+
+# Plot labels
+movielens_title = "(a) Movielens"
+tenrec_title = "(b) Tenrec"
+ylabel_movielens = "NDCG@20"
+ylabel_tenrec = "CV@20"
+yticks_movielens = [0, 0.05, 0.10, 0.15, 0.20, 0.25, 0.30, 0.35, 0.40]
+yticks_tenrec = [0, 0.02, 0.04, 0.06, 0.08, 0.10, 0.12, 0.14, 0.16, 0.18, 0.20]
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Create subplots
+fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(5, 6))
+
+barwidth = 0.3
+# Movielens subplot
+for i in range(len(x)):
+    ax1.bar(
+        (i - 2) * 0.3 + indexs[0],
+        accuracy_movielens[i],
+        width=0.3,
+        label="Accuracy",
+        color=colors[i],
+    )
+    ax1.bar(
+        (i - 2) * 0.3 + indexs[1],
+        unfairness_movielens[i],
+        width=0.3,
+        label="Unfairness",
+        color=colors[i],
+    )
+ax1.set_title(movielens_title)
+ax1.set_xticks([index - 0.15 for index in indexs])
+ax1.set_xticklabels(labels)
+ax1.set_ylabel(ylabel_movielens)
+ax1.set_yticks(yticks_movielens)
+ax1.tick_params(axis="both", which="both", length=0)
+
+# Tenrec subplot
+for i in range(len(x)):
+    ax2.bar(
+        (i - 2) * 0.3 + indexs[0],
+        accuracy_tenrec[i],
+        width=0.3,
+        label="Accuracy",
+        color=colors[i],
+    )
+    ax2.bar(
+        (i - 2) * 0.3 + indexs[1],
+        unfairness_tenrec[i],
+        width=0.3,
+        label="Unfairness",
+        color=colors[i],
+    )
+ax2.set_title(tenrec_title)
+ax2.set_xticks([index - 0.15 for index in indexs])
+ax2.set_xticklabels(labels)
+ax2.set_ylabel(ylabel_tenrec)
+ax2.set_yticks(yticks_tenrec)
+
+ax2.tick_params(axis="both", which="both", length=0)
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Adjust layout and save the figure
+plt.tight_layout()
+plt.savefig("bar_48.pdf", bbox_inches="tight")

ChartMimic/dataset/ori_500/bar_51.py

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+# ===================
+# Part 1: Importing Libraries
+# ===================
+import matplotlib.pyplot as plt
+
+# ===================
+# Part 2: Data Preparation
+# ===================
+# Data
+professions = [
+    "Farmer",
+    "Scooter mechanic",
+    "Household management",
+    "Construction/Renovation",
+    "Gardening",
+    "Making Bricks",
+    "Carpenter",
+    "Baker",
+    "Crafting/knitting",
+    "Cleaning / laundry",
+]
+number_of_videos = [2008, 2060, 2158, 2343, 2548, 2915, 3216, 3543, 4190, 5375]
+
+# Axes Limits and Labels
+xlabel_value = "Number of Videos"
+title = "Number of Videos by Profession"
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Create horizontal bar chart
+plt.figure(figsize=(12, 8))  # Adjust figure size to match original image's dimensions
+plt.barh(professions, number_of_videos, color="#685bc6")  # Change bar color to purple
+plt.xlabel(xlabel_value)
+plt.title(title)
+
+# Add data labels
+for index, value in enumerate(number_of_videos):
+    plt.text(
+        value + 50, index, str(value), va="center", fontsize=10
+    )  # Adjust text position and font size
+
+plt.yticks(rotation=45)
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Displaying the plot with tight layout to minimize white space
+plt.tight_layout()
+plt.savefig("bar_51.pdf", bbox_inches="tight")

ChartMimic/dataset/ori_500/bar_53.py

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+# ===================
+# Part 1: Importing Libraries
+# ===================
+import matplotlib.pyplot as plt
+import numpy as np
+
+np.random.seed(0)
+
+
+# ===================
+# Part 2: Data Preparation
+# ===================
+# Data for plotting
+words = [
+    "small",
+    "certain",
+    "little",
+    "fraction",
+    "limited",
+    "a",
+    "day",
+    "few",
+    "new",
+    "specific",
+    "substantial",
+    "tiny",
+    "very",
+    "single",
+    "slight",
+    "relatively",
+    "moderate",
+    "handful",
+    "low",
+]
+human_distribution = [
+    0.19,
+    0.18,
+    0.15,
+    0.1,
+    0.05,
+    0.03,
+    0.16,
+    0.08,
+    0.04,
+    0.06,
+    0.02,
+    0.01,
+    0.05,
+    0.03,
+    0.02,
+    0.01,
+    0.04,
+    0.02,
+    0.01,
+]
+model_distribution = [
+    0.1,
+    0.08,
+    0.06,
+    0.04,
+    0.02,
+    0.14,
+    0.12,
+    0.1,
+    0.08,
+    0.06,
+    0.04,
+    0.02,
+    0.01,
+    0.03,
+    0.02,
+    0.01,
+    0.03,
+    0.02,
+    0.01,
+]
+
+x = np.arange(len(words))  # the label locations
+labels = ["Human Distribution", "Model Distribution"]
+ylabel = "Probability"
+title = "Context: The human body can tolerate only a"
+xlabel = "Word"
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+fig, ax = plt.subplots(
+    figsize=(10, 8)
+)  # Adjust the figure size to match the original image's dimensions
+
+# Create the bars
+for i in range(len(words)):
+    ax.bar(
+        x[i],
+        human_distribution[i],
+        color="#FFA07A",
+        label=labels[0] if i == 0 else "",
+        hatch="///",
+        edgecolor="black",
+    )
+    ax.bar(
+        x[i],
+        model_distribution[i],
+        bottom=human_distribution[i],
+        color="#87CEFA",
+        label=labels[1] if i == 0 else "",
+        alpha=0.5,
+        hatch="..",
+        edgecolor="black",
+    )
+
+# Add some text for labels, title and custom x-axis tick labels, etc.
+ax.set_ylabel(ylabel)
+ax.set_title(title)
+ax.set_xticks(x)
+ax.set_xticklabels(words, rotation=90, ha="center")
+ax.set_xlabel(xlabel)
+ax.legend()
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Adjust layout
+plt.tight_layout()
+
+plt.savefig("bar_53.pdf", bbox_inches="tight")

ChartMimic/dataset/ori_500/bar_54.py

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+# ===================
+# Part 1: Importing Libraries
+# ===================
+import matplotlib.pyplot as plt
+import matplotlib.cm as cm
+import numpy as np
+
+np.random.seed(0)
+
+
+# ===================
+# Part 2: Data Preparation
+# ===================
+# Data
+categories = ["Sedan", "SUV", "Truck", "Coupe", "Convertible"]
+values = [15, 26, 20, 30, 17]
+titles = "Probability of Improvement over VLM Image Encoder Baseline Returns"
+xlabel = "Probability of Improvement"
+
+# Create color map
+colors = cm.viridis(np.linspace(0, 1, len(values)))
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Create horizontal bar chart
+plt.figure(figsize=(6, 2))  # Adjusting figure size to match original image dimensions
+plt.barh(categories, values, color=colors)
+
+# Adding title and labels
+plt.title(titles)
+plt.xlabel(xlabel)
+xticks = []
+xlabels = []
+# Apply the xticks and labels
+plt.xticks(xticks, xlabels)
+for spine in plt.gca().spines.values():
+    spine.set_visible(False)
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Show plot with tight layout
+plt.tight_layout()
+plt.savefig("bar_54.pdf", bbox_inches="tight")

ChartMimic/dataset/ori_500/bar_55.py

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+# ===================
+# Part 1: Importing Libraries
+# ===================
+import matplotlib.pyplot as plt
+
+# ===================
+# Part 2: Data Preparation
+# ===================
+# Emotion labels
+emotions = [
+    "Amusement",
+    "Unbothered",
+    "Sadness",
+    "Pride",
+    "Nervousness",
+    "Annoyance",
+    "Gratitude",
+    "Relief",
+    "Joy",
+    "Disapproval",
+]
+
+# Approximate frequency values based on the image
+frequencies = [7.6, 7.0, 6.7, 6.6, 6.0, 6.0, 3.5, 3.5, 3.0, 2.1]
+xlabel = "Frequency (%)"
+ylabel = "Emotion"
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Create horizontal bar chart
+plt.figure(figsize=(8, 8))  # Adjust figure size
+plt.barh(emotions, frequencies, color="lightcoral", edgecolor="gray")
+
+# Adding data labels
+for index, value in enumerate(frequencies):
+    plt.text(value, index, f" {value}%", va="center")
+# Set labels and title
+plt.xlabel(xlabel)
+plt.ylabel(ylabel)
+plt.gca().spines["top"].set_visible(False)
+plt.gca().spines["right"].set_visible(False)
+plt.gca().spines["bottom"].set_visible(False)
+plt.gca().spines["left"].set_visible(False)
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Show the plot with tight layout to minimize white space
+plt.tight_layout()
+plt.savefig("bar_55.pdf", bbox_inches="tight")

ChartMimic/dataset/ori_500/bar_56.py

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+# ===================
+# Part 1: Importing Libraries
+# ===================
+import matplotlib.pyplot as plt
+import matplotlib.colors as mcolors
+import matplotlib.ticker as ticker
+import numpy as np
+
+np.random.seed(0)
+
+# ===================
+# Part 2: Data Preparation
+# ===================
+# Data
+professions = [
+    "Cleaning / laundry",
+    "Crafting/knitting",
+    "Baker",
+    "Carpenter",
+    "Making Bricks",
+    "Gardening",
+]
+number_of_videos = [8062.5, 6285.0, 5314.5, 4824.0, 4372.5, 3822.0]
+colors = [
+    "lightskyblue",
+    "turquoise",
+    "lightgreen",
+    "navajowhite",
+    "lightsalmon",
+    "lightcoral",
+]
+
+# Plot Configuration
+xlabel = "Number of Videos"
+xlim_values = (0, 9000)
+ylim_values = (-0.5, 5.5)
+title_text = "Number of Videos by Profession"
+yticks_rotation = 45
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Create horizontal bar chart
+plt.figure(figsize=(12, 8))  # Adjust figure size to match original image's dimensions
+plt.barh(
+    professions, number_of_videos, color=colors, edgecolor="white"
+)  # Change bar color to purple
+plt.xlabel(xlabel)
+plt.xlim(*xlim_values)
+plt.ylim(*ylim_values)
+plt.title(title_text)
+
+basetick = [0, 1, 2, 3, 4, 5]
+offsetticks = [-0.5, 0.5, 1.5, 2.5, 3.5, 4.5, 5.5]
+plt.gca().yaxis.set_major_locator(ticker.FixedLocator(basetick))
+plt.gca().yaxis.set_major_formatter(
+    ticker.FuncFormatter(lambda x, _: f"{professions[x-1]}")
+)
+plt.gca().yaxis.set_minor_locator(ticker.FixedLocator(offsetticks))
+plt.gca().grid(True, which="minor", axis="y", color="gray", linestyle="--")
+plt.gca().grid(True, which="major", axis="x", color="gray", linestyle="--")
+plt.gca().set_axisbelow(True)
+plt.tick_params(axis="both", which="major", length=0)
+plt.tick_params(axis="y", which="minor", color="gray", length=3)
+plt.gca().spines["top"].set_visible(False)
+plt.gca().spines["right"].set_visible(False)
+plt.gca().spines["bottom"].set_visible(False)
+plt.gca().spines["left"].set_visible(False)
+plt.yticks(rotation=yticks_rotation)
+
+# ===================
+# Part 4: Saving Output
+# ===================
+plt.tight_layout()
+plt.savefig("bar_56.pdf", bbox_inches="tight")

ChartMimic/dataset/ori_500/bar_57.py

@@ -0,0 +1,81 @@
+# ===================
+# Part 1: Importing Libraries
+# ===================
+import matplotlib.pyplot as plt
+import numpy as np
+
+np.random.seed(0)
+
+
+# ===================
+# Part 2: Data Preparation
+# ===================
+# Economic Data Example
+categories = [
+    "GDP Growth",
+    "Unemployment Rate",
+    "Inflation Rate",
+    "NASDAQ",
+    "Exchange Rate",
+    "Real Estate Prices",
+    "Corporate Profits",
+][::-1]
+us_data = [3.1, 5.3, 2.2, 13.8, 1.1, 4.5, 7.0][::-1]
+eu_data = [2.4, 7.5, 1.7, 11.6, 0.9, 3.0, 6.5][::-1]
+china_data = [6.5, 4.2, 2.5, 14.2, 6.5, 8.9, 9.4][::-1]
+india_data = [7.1, 6.6, 4.0, 16.3, 3.3, 7.2, 10.5][::-1]
+brazil_data = [1.2, 12.1, 3.8, 5.4, 2.2, 1.5, 3.6][::-1]
+labels = ["US", "EU", "China", "India", "Brazil"]
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Stacked Bar Chart
+fig, ax = plt.subplots(
+    figsize=(8, 5)
+)  # Adjusted to match the original image's dimensions
+bar_width = 0.5
+y_pos = range(len(categories))
+
+ax.barh(y_pos, us_data, bar_width, color="tomato", label=labels[0])
+ax.barh(y_pos, eu_data, bar_width, left=us_data, color="wheat", label=labels[1])
+ax.barh(
+    y_pos,
+    china_data,
+    bar_width,
+    left=[i + j for i, j in zip(us_data, eu_data)],
+    color="#81acce",
+    label=labels[2],
+)
+ax.barh(
+    y_pos,
+    india_data,
+    bar_width,
+    left=[i + j + k for i, j, k in zip(us_data, eu_data, china_data)],
+    color="darkseagreen",
+    label=labels[3],
+)
+ax.barh(
+    y_pos,
+    brazil_data,
+    bar_width,
+    left=[
+        i + j + k + l for i, j, k, l in zip(us_data, eu_data, china_data, india_data)
+    ],
+    color="cornflowerblue",
+    label=labels[4],
+)
+
+# Labels and Legend
+ax.set_xticks(np.arange(0, 51, 5))
+ax.set_yticks(y_pos)
+ax.grid(axis="x", color="gray", linestyle="--")
+ax.set_axisbelow(True)
+ax.set_yticklabels(categories)
+ax.legend(loc="upper center", bbox_to_anchor=(0.5, 1.2), ncols=3)
+
+# ===================
+# Part 4: Saving Output
+# ===================
+plt.tight_layout()
+plt.savefig("bar_57.pdf", bbox_inches="tight")

ChartMimic/dataset/ori_500/bar_60.py

@@ -0,0 +1,58 @@
+# ===================
+# Part 1: Importing Libraries
+# ===================
+import matplotlib.pyplot as plt
+
+# ===================
+# Part 2: Data Preparation
+# ===================
+# Data for the plots
+models = ["GRU4Rec", "Caser", "SASRec", "BERT4Rec", "FMLP-Rec"]
+yelp_values = [19.7, -15.9, -8.5, 4.8, -0.7]
+
+# Axes Limits and Labels
+ax_title = "Yelp"
+xticks_values = range(-20, 21, 5)
+xlabel = "▲%"
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Set the figure size to match the original image's dimensions
+fig, ax = plt.subplots(figsize=(10, 8))
+
+# Plot for Yelp
+bars = ax.barh(models, yelp_values, color="white", edgecolor="black", hatch="//")
+for bar, value in zip(bars, yelp_values):
+    if value < 0:
+        bar.set_hatch("\\\\")
+        bar.set_edgecolor("red")
+ax.set_title(ax_title)
+for i, v in enumerate(yelp_values):
+    ax.text(
+        v - 0.5 if v < 0 else v + 0.5,
+        i,
+        f"{v}%",
+        color="black" if v > 0 else "red",
+        va="center",
+        ha="right" if v < 0 else "left",
+    )
+# Add a vertical line at x=0
+ax.axvline(0, color="black")
+ax.set_xticks(xticks_values)
+# Remove y-axis tick marks
+ax.tick_params(axis="y", which="both", left=False)
+
+# Hide all axes except the bottom one
+for spine in ["left", "right", "top"]:
+    ax.spines[spine].set_visible(False)
+
+# Add x-axis label
+ax.set_xlabel(xlabel)
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Displaying the plot with tight layout to minimize white space
+plt.tight_layout()
+plt.savefig("bar_60.pdf", bbox_inches="tight")

ChartMimic/dataset/ori_500/bar_61.py

@@ -0,0 +1,77 @@
+# ===================
+# Part 1: Importing Libraries
+# ===================
+import matplotlib.pyplot as plt
+import numpy as np
+
+np.random.seed(0)
+
+
+# ===================
+# Part 2: Data Preparation
+# ===================
+# Data
+categories = [
+    "Internet Penetration",
+    "Smartphone Usage",
+    "Research Investment",
+    "Patents Filed",
+]
+internet_penetration = [585, 920, 760, 686]  # Example data in percentage
+smartphone_usage = [900, 350, 718, 634]  # Example data in percentage
+research_investment = [1500, 1400, 800, 450]  # Example data in numbers of patents
+labels = ["Internet Penetration (%)", "Smartphone Usage (%)", "Research Investment (% of GDP)"]
+xticks = np.arange(0, 3500, 250)
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Stacked Bar Chart
+fig, ax = plt.subplots(figsize=(8, 5))
+bar_width = 0.5
+y_pos = range(len(categories))
+
+ax.barh(
+    y_pos,
+    internet_penetration,
+    bar_width,
+    color="#d66555",
+    edgecolor="#2a3b4d",
+    hatch="*",
+    label=labels[0]
+)
+ax.barh(
+    y_pos,
+    smartphone_usage,
+    bar_width,
+    left=internet_penetration,
+    color="#88a27d",
+    edgecolor="#2a3b4d",
+    hatch="+",
+    label=labels[1]
+)
+ax.barh(
+    y_pos,
+    research_investment,
+    bar_width,
+    left=[i + j for i, j in zip(internet_penetration, smartphone_usage)],
+    color="#a1b5ce",
+    edgecolor="#2a3b4d",
+    hatch="/",
+    label=labels[2],
+)
+
+# Labels and Legend
+ax.set_xticks(xticks)
+ax.set_yticks(y_pos)
+ax.grid(axis="x", color="gray", linestyle="--")
+ax.set_axisbelow(True)
+ax.set_yticklabels(categories)
+ax.legend(loc="upper center", bbox_to_anchor=(0.5, 1.2), ncols=3)
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Displaying the plot with tight layout to minimize white space
+plt.tight_layout()
+plt.savefig("bar_61.pdf", bbox_inches="tight")

ChartMimic/dataset/ori_500/bar_63.py

@@ -0,0 +1,85 @@
+# ===================
+# Part 1: Importing Libraries
+# ===================
+import matplotlib.pyplot as plt
+
+# ===================
+# Part 2: Data Preparation
+# ===================
+# Sample data similar to the provided image
+categories = [
+    "Notre-Dame de Paris",
+    "Demi-Gods and Semi-Devils",
+    "The Count of Monte Cristo",
+    "Game of Thrones",
+    "Attack on Titan",
+]
+scores_0 = [0.6, 0.4, 0.3, 0.5, 0.2]
+scores_20 = [0.7, 0.5, 0.35, 0.45, 0.25]
+scores_40 = [0.4, 0.3, 0.25, 0.35, 0.15]
+scores_60 = [0.6, 0.5, 0.4, 0.55, 0.3]
+labels = ["Time 0", "Time 20", "Time 40", "Time 60"]
+xlabel = "Scores"
+ylabel = "Categories"
+title = "Scores by Category Over Time"
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Size of the plot
+plt.figure(figsize=(10, 8))
+
+# Adjust the position of the bars on the x-axis to prevent overlap
+bar_height = 0.15
+ind = [i * (bar_height * len(categories)) for i in range(len(scores_0))]
+
+# Creating the bar plot with adjusted positions to prevent overlap
+plt.barh(
+    [pos + bar_height * 0 for pos in ind],
+    scores_0,
+    color="navy",
+    height=bar_height,
+    label=labels[0],
+)
+plt.barh(
+    [pos + bar_height * 1 for pos in ind],
+    scores_20,
+    color="blue",
+    height=bar_height,
+    label=labels[1],
+)
+plt.barh(
+    [pos + bar_height * 2 for pos in ind],
+    scores_40,
+    color="royalblue",
+    height=bar_height,
+    label=labels[2],
+)
+plt.barh(
+    [pos + bar_height * 3 for pos in ind],
+    scores_60,
+    color="lightblue",
+    height=bar_height,
+    label=labels[3],
+)
+
+# X and Y axis Labels
+plt.xlabel(xlabel)
+plt.ylabel(ylabel)
+
+# Title of the plot
+plt.title(title)
+
+# Adding legend
+plt.legend()
+
+# Setting the labels for y-axis with adjusted positions
+plt.yticks([pos + bar_height * 1.5 for pos in ind], categories)
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Display the plot with enough space
+plt.tight_layout()
+
+plt.savefig("bar_63.pdf", bbox_inches="tight")

ChartMimic/dataset/ori_500/bar_64.py

@@ -0,0 +1,89 @@
+# ===================
+# Part 1: Importing Libraries
+# ===================
+import numpy as np
+
+np.random.seed(0)
+
+import matplotlib.pyplot as plt
+
+# ===================
+# Part 2: Data Preparation
+# ===================
+# Environmental data for air quality metrics
+regions = ["North", "South", "East", "West"]
+CO2_levels = np.array([60, 85, 50, 95]) + np.random.rand(4) * 10  # CO2 levels in ppm
+PM_levels = (
+    np.array([35, 45, 30, 50]) + np.random.rand(4) * 5
+)  # Particulate matter in µg/m3
+SO2_levels = np.array([20, 25, 15, 10]) + np.random.rand(4) * 3  # SO2 levels in µg/m3
+NO2_levels = np.array([30, 35, 25, 40]) + np.random.rand(4) * 5  # NO2 levels in µg/m3
+
+labels = ["CO2 (ppm)", "PM (µg/m3)", "SO2 (µg/m3)", "NO2 (µg/m3)"]
+xlabel = "Concentration"
+ylabel = "Regions"
+title = "Air Quality Metrics by Region"
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Size of the plot
+plt.figure(figsize=(10, 7))
+
+# Adjust the position of the bars on the x-axis to prevent overlap
+bar_height = 0.3
+indices = np.arange(len(CO2_levels)) * 1.5  # Increase space between groups
+
+# Define some hatch patterns and colors to use for bars
+hatch_patterns = ["/", "\\", "|", "-", "+", "*"]
+colors = ["#66c4d5", "#eca198", "#5886c7", "#a791e8"]
+
+# Plot bars for each air quality metric
+for i, level in enumerate([CO2_levels, PM_levels, SO2_levels, NO2_levels]):
+    bars = plt.barh(
+        [pos + bar_height * i for pos in indices],
+        level,
+        color=colors[i],
+        height=bar_height,
+        hatch=hatch_patterns[i],
+        label=f'{labels[i]}',
+    )
+
+    # Add text annotations to the right of the bars
+    for bar in bars:
+        plt.text(
+            bar.get_width(),  # X position
+            bar.get_y() + bar.get_height() / 2,  # Y position
+            f"{bar.get_width():.2f}",  # Text to display
+            va="center",
+        )
+
+# X and Y axis Labels and limits
+plt.xlabel(xlabel)
+plt.ylabel(ylabel)
+plt.xlim(
+    0,
+    max(np.max(CO2_levels), np.max(PM_levels), np.max(SO2_levels), np.max(NO2_levels))
+    + 10,
+)
+
+# Title of the plot
+plt.title(title)
+
+# Adding legend
+plt.legend()
+
+# Setting the labels for y-axis with adjusted positions
+plt.yticks([pos + bar_height * 1.5 for pos in indices], regions)
+
+# Adding grids
+plt.grid(True, linestyle="--", which="both", axis="x", color="grey", alpha=0.7)
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Display the plot with enough space
+plt.tight_layout()
+
+# Save the plot as a PDF file
+plt.savefig("bar_64.pdf", bbox_inches="tight")

ChartMimic/dataset/ori_500/bar_65.py

@@ -0,0 +1,128 @@
+# ===================
+# Part 1: Importing Libraries
+# ===================
+import numpy as np
+
+np.random.seed(0)
+
+import matplotlib.pyplot as plt
+
+# ===================
+# Part 2: Data Preparation
+# ===================
+# Define the categories and scores
+categories = ["LLAMA-Default", "LLAMA-HAG", "Vicuna-Default", "Vicuna-HAG"]
+num_scores = 4
+score_range = (-3.5, -0.5)
+scores_3 = np.random.uniform(score_range[0], score_range[1], num_scores).tolist()
+scores_5 = np.random.uniform(score_range[0], score_range[1], num_scores).tolist()
+scores_7 = np.random.uniform(score_range[0], score_range[1], num_scores).tolist()
+scores_10 = np.random.uniform(score_range[0], score_range[1], num_scores).tolist()
+
+# The x locations for the groups
+ind = np.arange(len(scores_3))
+
+# The width of the bars
+bar_width = 0.2
+
+# Labels and Plot Types
+label_3_Constraint_Words = "3 Constraint Words"
+label_5_Constraint_Words = "5 Constraint Words"
+label_7_Constraint_Words = "7 Constraint Words"
+label_10_Constraint_Words = "10 Constraint Words"
+
+# Axes Limits and Labels
+xlabel_value = "Score"
+ax_title = "Scores by group and constraint word count"
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Create the figure and axes objects
+fig, ax = plt.subplots(figsize=(10, 6))
+
+# Plotting data
+bars_3 = ax.barh(
+    ind - bar_width * 1.5,
+    scores_3,
+    bar_width,
+    label=label_3_Constraint_Words,
+    color="salmon",
+)
+bars_5 = ax.barh(
+    ind - bar_width * 0.5,
+    scores_5,
+    bar_width,
+    label=label_5_Constraint_Words,
+    color="skyblue",
+)
+bars_7 = ax.barh(
+    ind + bar_width * 0.5,
+    scores_7,
+    bar_width,
+    label=label_7_Constraint_Words,
+    color="coral",
+)
+bars_10 = ax.barh(
+    ind + bar_width * 1.5,
+    scores_10,
+    bar_width,
+    label=label_10_Constraint_Words,
+    color="lightblue",
+)
+
+# Adding text inside the bars
+for i, (score_3, score_5, score_7, score_10) in enumerate(
+    zip(scores_3, scores_5, scores_7, scores_10)
+):
+    ax.text(
+        score_3,
+        i - bar_width * 1.5,
+        f"{score_3:.1f}",
+        va="center",
+        ha="right",
+        color="black",
+    )
+    ax.text(
+        score_5,
+        i - bar_width * 0.5,
+        f"{score_5:.1f}",
+        va="center",
+        ha="right",
+        color="black",
+    )
+    ax.text(
+        score_7,
+        i + bar_width * 0.5,
+        f"{score_7:.1f}",
+        va="center",
+        ha="right",
+        color="black",
+    )
+    ax.text(
+        score_10,
+        i + bar_width * 1.5,
+        f"{score_10:.1f}",
+        va="center",
+        ha="right",
+        color="black",
+    )
+
+# Adding labels, title, and custom x-axis tick labels, etc.
+ax.set_xlabel(xlabel_value)
+ax.set_title(ax_title)
+ax.set_yticks(ind)
+ax.set_yticklabels(categories)
+ax.legend()
+
+# Invert y-axis to have the first entry at the top
+plt.gca().invert_yaxis()
+
+# Show grid lines for x-axis
+ax.xaxis.grid(True)
+
+# ===================
+# Part 4: Saving Output
+# ===================
+plt.tight_layout()
+plt.savefig("bar_65.pdf", bbox_inches="tight")

ChartMimic/dataset/ori_500/bar_67.py

@@ -0,0 +1,81 @@
+# ===================
+# Part 1: Importing Libraries
+# ===================
+import numpy as np
+
+np.random.seed(0)
+
+import matplotlib.pyplot as plt
+import colorsys
+
+# ===================
+# Part 2: Data Preparation
+# ===================
+# Redefining the data
+models = ["Lavila", "Video-LLaMA", "BLIP1", "BLIP2", "LLaVA", "OSCaR", "GPT4V"]
+percentages = [0.0, 0.71, 4.64, 4.64, 31.79, 73.93, 82.5]
+
+# Sorting the data in descending order while keeping track of the models order
+sorted_data = sorted(zip(percentages, models), reverse=True)
+sorted_percentages, sorted_models = zip(*sorted_data)
+
+title = "Human Study"
+xlabel = "Model"
+ylabel = "Percentage (%)"
+ylim = [0, np.max(sorted_percentages) + 10]
+
+
+# Generate random colors with lower saturation
+def hsl_to_rgb(h, s, l):
+    return colorsys.hls_to_rgb(h, l, s)
+
+
+# Randomly generate colors
+colors = [hsl_to_rgb(hue, 0.5, 0.6) for hue in np.linspace(0, 1, len(models) + 1)[:-1]]
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Create figure and bar chart with the sorted data
+plt.figure(figsize=(12, 8))
+bars = plt.bar(sorted_models, sorted_percentages, color=colors)
+
+# Randomly decide where to put the text based on the value of the bar
+for bar in bars:
+    yval = bar.get_height()
+    text_y = (
+        yval - 5 if yval > 10 else yval + 1
+    )  # Slight modification to avoid negative values
+    plt.text(
+        bar.get_x() + bar.get_width() / 2,
+        text_y,
+        f"{yval}%",
+        ha="center",
+        va="top" if text_y < yval else "bottom",
+    )
+
+# Set chart title and labels
+plt.title(title)
+plt.xlabel(xlabel)
+plt.ylabel(ylabel)
+
+# Randomly set y-axis range to a bit higher than the max value
+plt.ylim(ylim)
+
+# Randomize the gridlines and ticks
+plt.grid(axis="y", linestyle="--", alpha=0.7)
+
+# Randomize tick rotation
+plt.xticks(rotation=45)
+
+# Hide the top and right spines
+plt.gca().spines["top"].set_visible(False)
+plt.gca().spines["right"].set_visible(False)
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Apply tight layout
+plt.tight_layout()
+
+plt.savefig("bar_67.pdf", bbox_inches="tight")

ChartMimic/dataset/ori_500/bar_68.py

@@ -0,0 +1,75 @@
+# ===================
+# Part 1: Importing Libraries
+# ===================
+import numpy as np
+
+np.random.seed(0)
+
+import matplotlib.pyplot as plt
+
+# ===================
+# Part 2: Data Preparation
+# ===================
+# Example transportation data
+categories = [
+    "Traffic Flow",
+    "Accident Rate",
+    "Public Transport Usage",
+    "Road Condition",
+]
+layer_data = {
+    "Cars": np.array([70, 50, 30, 80]),
+    "Buses": np.array([30, 30, 40, 75]),
+    "Bikes": np.array([15, 20, 20, 85]),
+    "Pedestrians": np.array([50, 20, 20, 90]),
+}
+
+# Colors for each layer (from dark blue to light blue)
+colors = ["#08306b", "#2171b5", "#6baed6", "#bdd7e7"]
+
+title = "Stacked Bar Chart of Transportation Data"
+xlabel = "Categories"
+ylabel = "Values"
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Create the figure and axes objects
+fig, ax = plt.subplots(figsize=(10, 6))
+
+# Variables to store the bottom position for each stack
+bottoms = np.array([0] * len(categories))
+
+for i, (layer, values) in enumerate(layer_data.items()):
+    bars = ax.bar(categories, values, bottom=bottoms, color=colors[i], label=layer)
+
+    # Add data labels on each bar
+    for bar, bottom in zip(bars, bottoms):
+        height = bar.get_height()
+        ax.text(
+            bar.get_x() + bar.get_width() / 2,
+            bottom + height / 2,
+            str(height),
+            ha="center",
+            va="center",
+            color="white",
+        )
+
+    # Update the bottoms position
+    bottoms += values
+
+# Chart title and labels
+plt.title("Stacked Bar Chart of Transportation Data")
+plt.xlabel("Categories")
+plt.ylabel("Values")
+
+# Adding legend
+plt.legend()
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Displaying the plot with tight layout to minimize white space
+plt.tight_layout()
+# Save the plot as PDF
+plt.savefig("bar_68.pdf", bbox_inches="tight")

ChartMimic/dataset/ori_500/bar_72.py

@@ -0,0 +1,78 @@
+# ===================
+# Part 1: Importing Libraries
+# ===================
+import matplotlib.pyplot as plt
+import numpy as np
+
+np.random.seed(0)
+
+
+# ===================
+# Part 2: Data Preparation
+# ===================
+# Data
+models = ["Bactrian-X EN", "Lima-X DE", "Bactrian-X FR", "Lima-X IT", "Bactrian-X ES"]
+improvements = {"EN": [3.5], "DE": [2.3], "FR": [4.4], "IT": [1.3], "ES": [-0.7]}
+
+xlabel = "Model with Language"
+ylabel = "Improvement [%]"
+ylim = [-1.5, 5]
+legendtitle = "Language"
+title = "Model Performance Improvement by Language"
+
+# Colors for each language
+colors = ["#8171d7", "#af4b3d", "#d07035", "#d6a741", "#639b48"]
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Figure and axis
+fig, ax = plt.subplots(figsize=(10, 6))
+
+# Bar width
+bar_width = 0.75
+
+# Positions of the bars on the x-axis
+r = np.arange(len(models))
+
+# Plotting the bars
+for i, language in enumerate(improvements):
+    bars = plt.bar(
+        r[i],
+        improvements[language],
+        color=colors[i],
+        width=bar_width,
+        label=language,
+        hatch="//",
+        edgecolor="white",
+    )
+    # Add text labels
+    for bar, val in zip(bars, improvements[language]):
+        height = bar.get_height()
+        ax.text(
+            bar.get_x() + bar.get_width() / 2,
+            height + 0.1 if height > 0 else height - 0.4,
+            f"{val:.1f}",
+            ha="center",
+        )
+
+# Add xticks on the middle of the group bars
+plt.xlabel(xlabel)
+plt.xticks(r, models, rotation=45)
+
+# Add ylabel
+plt.ylabel(ylabel)
+plt.ylim(ylim)
+
+plt.gca().grid(color="gray", linewidth=0.5)
+plt.gca().set_axisbelow(True)
+
+# Create legend & Show graphic
+plt.legend(title=legendtitle, loc="upper right")
+plt.title(title)
+
+# ===================
+# Part 4: Saving Output
+# ===================
+plt.tight_layout()
+plt.savefig("bar_72.pdf", bbox_inches="tight")

ChartMimic/dataset/ori_500/bar_73.py

@@ -0,0 +1,84 @@
+# ===================
+# Part 1: Importing Libraries
+# ===================
+import matplotlib.pyplot as plt
+import numpy as np
+
+np.random.seed(0)
+
+
+# ===================
+# Part 2: Data Preparation
+# ===================
+# Set a seed for reproducibility
+
+# Define fiscal quarters
+quarters = np.array([1, 2, 3, 4, 5])
+
+# Generate synthetic stock market index changes for two different indexes
+index_changes_dow_jones = np.random.uniform(
+    -5, 5, 5
+)  # Example changes in Dow Jones index
+index_changes_nasdaq = np.random.uniform(-5, 5, 5)  # Example changes in Nasdaq index
+
+labels = ["Dow Jones Index", "Nasdaq Index"]
+axline = 0
+xlabel = "Fiscal Quarter"
+ylabel = "Index Change (%)"
+ylim = [-5, 5]
+yticks = np.arange(-5, 5, 5)
+
+# ===================
+# Part 3: Plot Configuration and Renderingåå
+# ===================
+# Create figure and subplots
+fig, axs = plt.subplots(figsize=(10, 5))
+width = 0.4
+
+# Plotting bars for each index
+bars1 = axs.bar(
+    quarters - width / 2,
+    index_changes_dow_jones,
+    width=width,
+    color="#78a083",
+    label="Dow Jones Index",
+)
+bars2 = axs.bar(
+    quarters + width / 2,
+    index_changes_nasdaq,
+    width=width,
+    color="#d27a41",
+    label="Nasdaq Index",
+)
+
+# Adding text labels on the bars
+for bars in [bars1, bars2]:
+    for bar in bars:
+        height = bar.get_height()
+        axs.text(
+            bar.get_x() + bar.get_width() / 2,
+            height,
+            f"{height:.1f}",
+            ha="center",
+            va="bottom" if height > 0 else "top",
+        )
+
+# Additional plot settings
+axs.axhline(axline, color="black")
+axs.set_xlabel(xlabel)
+axs.set_ylabel(ylabel)
+axs.set_ylim(ylim)
+axs.set_yticks(yticks)
+axs.set_xticks(quarters)
+axs.yaxis.grid(True)
+axs.set_axisbelow(True)
+
+# Adding legend and adjusting layout
+plt.legend(loc="upper center", bbox_to_anchor=(0.5, 1.10), ncol=2)
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Save the figure as a PDF
+plt.tight_layout()
+plt.savefig("bar_73.pdf", bbox_inches="tight")

ChartMimic/dataset/ori_500/bar_76.py

@@ -0,0 +1,65 @@
+# ===================
+# Part 1: Importing Libraries
+# ===================
+import matplotlib.pyplot as plt
+import numpy as np
+
+np.random.seed(0)
+
+
+# ===================
+# Part 2: Data Preparation
+# ===================
+# Generate some example environmental data
+categories = ["Light Industry", "Heavy Industry", "Transportation", "Residential"]
+
+# Data for the graph: negative values for pollution emissions (in thousands of metric tons)
+values1_A = [-np.random.uniform(300, 1000) for _ in categories]  # Current Year
+values2_A = [-np.random.uniform(300, 1000) for _ in categories]  # Previous Year
+labels = ["Current Year", "Previous Year"]
+xlabel = "Industry Sectors"
+ylabel = "Pollution Emissions (thousands of metric tons)"
+title = "Environmental Impact by Sector"
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Set up the figure size
+fig, ax = plt.subplots(figsize=(10, 6))
+
+# Make the bar plot with hatch patterns
+ax.bar(
+    categories,
+    values1_A,
+    color="skyblue",
+    hatch="/",
+    label="Current Year",
+    edgecolor="black",
+)
+ax.bar(
+    categories,
+    values2_A,
+    color="sandybrown",
+    hatch="\\",
+    label="Previous Year",
+    bottom=values1_A,
+    edgecolor="black",
+)
+
+# Labeling and customizing
+plt.xlabel(xlabel)
+plt.ylabel(ylabel)
+plt.title(title)
+plt.legend()
+
+# Adding grid lines for better readability
+plt.grid(axis="y", linestyle="--", alpha=0.7)
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Adjust layout and display
+plt.tight_layout()
+
+# Save the plot as a PDF file
+plt.savefig("bar_76.pdf", bbox_inches="tight")

ChartMimic/dataset/ori_500/bar_77.py

@@ -0,0 +1,28 @@
+# ===================
+# Part 1: Importing Libraries
+# ===================
+import matplotlib.pyplot as plt
+
+# ===================
+# Part 2: Data Preparation
+# ===================
+cities = ["Beijing", "Shanghai", "Guangzhou", "Shenzhen", "Chengdu"]
+temperatures = [11.2, 16.1, 21.9, 23.2, 16.3]
+xlabel = "City"
+ylabel = "Average Temperature (°C)"
+title = "Average City Temperatures"
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+plt.figure(figsize=(8, 4))
+plt.bar(cities, temperatures, color="skyblue", edgecolor="black")
+plt.xlabel(xlabel)
+plt.ylabel(ylabel)
+plt.title(title)
+
+# ===================
+# Part 4: Saving Output
+# ===================
+plt.tight_layout()
+plt.savefig("bar_77.pdf", bbox_inches="tight")