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ChartMimic/dataset/ori_500/density_2.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
+# ===================
+# Create some data
+y = np.linspace(0, 20, 100)
+x1 = np.exp(-0.5 * (y - 5) ** 2)
+x2 = 0.75 * np.exp(-0.2 * (y - 8) ** 2)
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Create the figure and axis
+fig, ax = plt.subplots(figsize=(8, 4))  # Adjusted to match the dimensions in pixels
+
+# Plot the data
+# ax.fill_between(x1, y, color="gray", edgecolor="#929292" ,alpha=0.5)
+ax.fill_between(
+    x2, y, color="pink", edgecolor="#be6373", alpha=0.5
+)  # Adjusted to taper off the pink area
+
+# Add the dashed vertical line at the peak of the gray area
+# Add the red horizontal line at the bottom
+# ax.axhline(0, color='red', linewidth=2)
+
+# Customize the plot to match the picture
+ax.spines["top"].set_visible(False)
+ax.spines["right"].set_visible(False)
+ax.spines["left"].set_visible(True)
+ax.spines["bottom"].set_visible(False)
+ax.tick_params(left=False, labelleft=False, bottom=False, labelbottom=False)
+
+ax.set_ylim(1, 15)
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Displaying the plot with tight layout to minimize white space
+plt.tight_layout()
+plt.savefig("density_2.pdf", bbox_inches="tight")

ChartMimic/dataset/ori_500/density_3.py

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+# ===================
+# Part 1: Importing Libraries
+# ===================
+import matplotlib.pyplot as plt
+import numpy as np
+
+np.random.seed(0)
+
+from scipy.stats import gaussian_kde
+
+# ===================
+# Part 2: Data Preparation
+# ===================
+# Generate a bimodal distribution for the data
+data1 = np.random.normal(loc=-0.5, scale=0.2, size=500)
+data2 = np.random.normal(loc=0.5, scale=0.2, size=500)
+data = np.concatenate([data1, data2])
+xs = np.linspace(-1.5, 1.5, 200)
+
+# Axes Limits and Labels
+xticks_values = [-1, -0.5, 0, 0.5, 1]
+xticklabels = ["-1.0", "-0.5", "0.0", "0.5", "1.0"]
+yticks_values = [0, 0.2, 0.4, 0.6, 0.8, 1.0]
+yticklabels = ["0.0", "0.2", "0.4", "0.6", "0.8", "1.0"]
+xlim_values = [-1.5, 1.5]
+ylim_values = [0, 1.2]
+title = "KDE Plot of Spearman Coefficient Distribution"
+xlabel_value = "Spearman Coefficient"
+ylabel_value = "Density"
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Set the figure size to match the original image's dimensions
+fig, ax = plt.subplots(figsize=(8, 4))
+
+# Create the KDE plot with adjusted x-axis range
+density = gaussian_kde(data)
+density.covariance_factor = lambda: 0.5
+density._compute_covariance()
+plt.fill_between(xs, density(xs), color="#d1e4e5", edgecolor="teal")
+
+ax.set_xticks(xticks_values)
+ax.set_xticklabels(xticklabels)
+
+ax.set_yticks(yticks_values)
+ax.set_yticklabels(yticklabels)
+
+plt.xlim(xlim_values)
+plt.ylim(ylim_values)
+# Set the title and labels
+plt.title(title)
+plt.xlabel(xlabel_value)
+plt.ylabel(ylabel_value)
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Displaying the plot with tight layout to minimize white space
+plt.tight_layout()
+plt.savefig("density_3.pdf", bbox_inches="tight")

ChartMimic/dataset/ori_500/density_5.py

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+# ===================
+# Part 1: Importing Libraries
+# ===================
+import matplotlib.pyplot as plt
+import numpy as np
+
+np.random.seed(0)
+
+from scipy.stats import gaussian_kde
+
+# ===================
+# Part 2: Data Preparation
+# ===================
+# Sample data
+data1 = np.random.normal(loc=8, scale=0.8, size=1000)
+data2 = np.random.normal(loc=12, scale=0.8, size=1000)
+
+# Compute density for each dataset
+density1 = gaussian_kde(data1)
+density2 = gaussian_kde(data2)
+xs = np.linspace(5, 15, 300)
+ys1 = density1(xs)
+ys2 = density2(xs)
+labels = ["Gucci", "Chanel"]
+xlabel = "Density"
+ylabel = "Value"
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Create the figure and axis
+fig, ax = plt.subplots(figsize=(9, 6))
+
+# Fill between x for density regions
+plt.fill_betweenx(xs, ys1, color="blue", alpha=0.2, label=labels[0])
+plt.fill_betweenx(xs, ys2, color="green", alpha=0.2, label=labels[1])
+
+# Set labels and title (if any)
+ax.set_ylim(5, 15)
+ax.set_xlabel(xlabel)
+ax.set_ylabel(ylabel)
+
+# Show grid
+plt.grid(True, linestyle="--")
+
+# Add legend
+plt.legend()
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Displaying the plot with tight layout to minimize white space
+plt.tight_layout()
+plt.savefig("density_5.pdf", bbox_inches="tight")

ChartMimic/dataset/ori_500/errorbar_10.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
+number_of_experts = [1, 2, 3, 4]
+baseline = [6] * len(number_of_experts)
+softmoe_unchanged = [5.8, 6.2, 7.5, 6.4]
+softmoe_div_numexperts = [5.7, 6.1, 6.9, 6.2]
+errors = [0.2, 0.15, 0.1, 0.05]
+
+# Labels and Plot Types
+label1 = "SoftMoE (unchanged)"
+label2 = "SoftMoE (÷ NumExperts)"
+label3 = "Baseline"
+
+# Axes Limits and Labels
+xlabel_value = "Number of experts"
+ylabel_value = "IQM Human Normalized Score"
+title = "Expert dimension"
+xticklabels = ["1", "2", "4", "8"]
+ylim_values = [5, 8]
+yticks_values = np.arange(5, 8, 1)
+legend_title = "Expert dimension"
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Plotting
+fig, ax = plt.subplots(figsize=(8, 6))  # Adjusted for the given dimensions
+bar_width = 0.4
+opacity = 0.8
+
+bar1 = ax.bar(
+    np.array(number_of_experts) - bar_width / 2,
+    softmoe_unchanged,
+    bar_width,
+    alpha=opacity,
+    color="#41886c",
+    label=label1,
+    yerr=errors,
+    capsize=3,
+)
+
+bar2 = ax.bar(
+    np.array(number_of_experts) + bar_width / 2,
+    softmoe_div_numexperts,
+    bar_width,
+    alpha=opacity,
+    color="#b886b3",
+    label=label2,
+    yerr=errors,
+    capsize=3,
+)
+
+ax.plot(
+    number_of_experts,
+    baseline,
+    linestyle="--",
+    color="b",
+    linewidth=2,
+    label=label3,
+)
+
+ax.set_xlabel(xlabel_value)
+ax.set_ylabel(ylabel_value)
+ax.set_title(title)
+ax.set_xticks(number_of_experts)
+ax.set_xticklabels(xticklabels)
+ax.set_ylim(ylim_values)
+ax.set_yticks(yticks_values)
+ax.legend(loc="upper left", title=legend_title)
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Displaying the plot with tight layout to minimize white space
+plt.tight_layout()
+plt.savefig("errorbar_10.pdf", bbox_inches="tight")

ChartMimic/dataset/ori_500/errorbar_12.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 (estimated from the image)
+means = [np.random.uniform(-15, 0, 3) for m in range(4)]
+errors = [np.random.randint(1, 5, 3) for n in range(4)]
+
+# Labels
+labels = ["GPT-4", "Claude-2.1", "Claude-2", "GPT-3.5"]
+x = np.arange(len(labels) - 1)  # Adjusted to have 3 bars instead of 4
+
+label_s = [
+    ["Claude-2.1", "Claude-2", "GPT-3.5"],
+    ["GPT-4", "Claude-2", "GPT-3.5"],
+    ["GPT-4", "Claude-2.1", "Claude-2"],
+    ["GPT-4", "Claude-2.1", "GPT-3.5"],
+]
+title = "Sellers (valuation 60)"
+xlabel = "Buyer (valuation 40)"
+ylim = [-20, 0]
+yticks = [-20, -10, 0]
+
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Plotting
+fig, axs = plt.subplots(
+    2, 2, figsize=(8, 5)
+)  # Adjusted to match the original image's dimensions
+# Define colors for each bar
+colors = [
+    ["sandybrown", "lightseagreen", "indianred"],
+    ["dodgerblue", "lightseagreen", "indianred"],
+    ["dodgerblue", "sandybrown", "lightseagreen"],
+    ["dodgerblue", "sandybrown", "indianred"],
+]
+
+
+# Loop through each subplot to set properties
+for i, ax in enumerate(axs.flat):
+    ax.bar(
+        x,
+        means[i],
+        yerr=errors[i],
+        color=colors[i],
+        edgecolor="white",
+        label=label_s[i],
+    )
+    ax.set_xlabel(f"{labels[i]} {xlabel}")
+    ax.set_xticks([])
+    for spine in ax.spines.values():
+        spine.set_visible(False)
+    ax.set_ylim(ylim)
+    ax.set_yticks(yticks)
+    ax.set_facecolor("#eaeaf2")
+    ax.yaxis.grid(color="white", linestyle="-", linewidth=1)
+    ax.set_axisbelow(True)
+    ax.tick_params(axis="both", length=0)
+
+fig.legend(
+    labels,
+    loc="lower center",
+    ncol=4,
+    bbox_to_anchor=(0.5, -0.1),
+    title=title,
+    facecolor="#eaeaf2",
+)
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Adjust layout
+plt.tight_layout()
+# Legend
+plt.savefig("errorbar_12.pdf", bbox_inches="tight")

ChartMimic/dataset/ori_500/errorbar_13.py

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+# ===================
+# Part 1: Importing Libraries
+# ===================
+import matplotlib.pyplot as plt
+
+# ===================
+# Part 2: Data Preparation
+# ===================
+# Data
+categories = [
+    "DNN x \n(k=1)",
+    "DNN x \nred\n(k=2)",
+    "DNN x \n(k=1)",
+    "DNN x red\n (k=1)",
+][::-1]
+subcategories = ["[m]", "[ΔR]", "[${ΔR^{-1}}$]", "[none]"][::-1]
+values = [
+    [0.84, 0.83, 0.82, 0.76],
+    [0.84, 0.83, 0.825, 0.76],
+    [0.82, 0.81, 0.8, 0.74],
+    [0.81, 0.8, 0.79, 0.73],
+]
+errors = [
+    [0.02, 0.01, 0.01, 0.01],
+    [0.02, 0.01, 0.02, 0.01],
+    [0.01, 0.04, 0.03, 0.015],
+    [0.01, 0.01, 0.01, 0.012],
+]
+percentages = [
+    "+9.5%",
+    "+8.3%",
+    "+8.0%",
+    " ",
+    "+8.0%",
+    "+6.6%",
+    "+6.2%",
+    " ",
+    "+10.6%",
+    "+8.9%",
+    "+8.0%",
+    " ",
+    "+9.6%",
+    "+8.6%",
+    "+7.8%",
+    " ",
+][::-1]
+xlim = [0.5, 0.9]
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Plotting
+fig, axes = plt.subplots(4, 1, figsize=(8, 8), sharex=True)
+
+for i, ax in enumerate(axes):
+    ax.barh(subcategories, values[i][::-1], xerr=errors[i], color="gray", capsize=5)
+    ax.set_yticklabels(subcategories)
+    ax.set_xlim(xlim)
+    ax.set_ylabel(categories[i])
+    for j, v in enumerate(values[i][::-1]):
+        ax.text(
+            v + errors[i][j] + 0.01,
+            j,
+            percentages[i * 4 + j],
+            color="black",
+            va="center",
+        )
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Adjust layout and save
+plt.tight_layout()
+plt.savefig("errorbar_13.pdf", bbox_inches="tight")

ChartMimic/dataset/ori_500/errorbar_14.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
+categories = ["A", "B", "C", "D", "E", "F", "G", "H", "I", "J"]
+values = [0.18, 0.15, 0.12, 0.09, 0.06, 0.03, -0.06, -0.03, -0.02, -0.03]
+errors = [0.05, 0.04, 0.03, 0.03, 0.02, 0.02, 0.02, 0.02, 0.01, 0.01]
+colors = [
+    "#c0d5e6",
+    "#709ec6",
+    "#f2a965",
+    "#c6e1c2",
+    "#7fba74",
+    "#eac0bf",
+    "#d36e6c",
+    "#7f7f7f",
+    "#bcbd22",
+    "#a88a83",
+]
+
+# Axes Limits and Labels
+ylabel_value = "Posterior accuracy\n(Δ to no prompting)"
+ylim_values = [-0.08, 0.22]
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Create figure and axis
+fig, ax = plt.subplots(figsize=(8, 6))
+
+# Bar chart
+bars = ax.bar(
+    categories, values, yerr=errors, color=colors, capsize=0, edgecolor="none"
+)
+ax.set_xticks([])
+# Set labels
+ax.set_ylabel(ylabel_value)
+
+# Set x-axis limits and y-axis limits
+ax.set_ylim(ylim_values)
+
+# Remove top and right spines
+ax.spines["top"].set_visible(False)
+ax.spines["right"].set_visible(False)
+ax.spines["left"].set_visible(True)
+ax.spines["bottom"].set_visible(True)
+
+# Remove grid lines
+ax.yaxis.grid(False)
+ax.xaxis.grid(False)
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Adjust layout to prevent clipping of ylabel
+plt.tight_layout()
+plt.savefig("errorbar_14.pdf", bbox_inches="tight")

ChartMimic/dataset/ori_500/errorbar_15.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
+categories = ["Medium", "Medium-replay", "Medium-expert"]
+methods = ["AUG", "TEstimation", "Qualification", "DiffStitch"]
+performance = np.array([[60, 70, 80, 70], [80, 70, 75, 80], [70, 80, 75, 80]])
+errors = np.array([[10, 10, 6, 6], [6, 10, 5, 4], [10, 4, 5, 4]])
+ylim = [40, 90]
+ylabel = "Performance"
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Figure size 720x216 pixels
+fig, axes = plt.subplots(1, 3, figsize=(10, 3))
+# Colors
+colors = ["#d66929", "#f7cc46", "blue", "darkblue"]
+
+# Bar width
+bar_width = 1
+
+# Plotting bars
+for i, ax in enumerate(axes):
+    for j, method in enumerate(methods):
+        ax.bar(
+            j + bar_width * i,
+            performance[i, j],
+            width=bar_width,
+            color=colors[j],
+            yerr=errors[i, j],
+            capsize=5,
+            label=method if i == 0 else "",
+        )
+
+# Setting x-axis labels, y-axis limits, and titles
+for i, ax in enumerate(axes):
+    ax.set_xticks([])
+    # ax.set_xticklabels(methods)
+    ax.set_ylim(ylim)
+    ax.set_xlabel(f"({chr(97+i)}) {categories[i]}")
+    ax.set_ylabel(ylabel)
+    ax.yaxis.grid(True)
+    ax.set_axisbelow(True)
+
+# Adding legend outside of the plot
+fig.legend(loc="upper center", bbox_to_anchor=(0.5, 1.1), ncol=len(methods))
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Adjusting layout and saving the figure
+plt.tight_layout()
+plt.savefig("errorbar_15.pdf", bbox_inches="tight")

ChartMimic/dataset/ori_500/errorbar_17.py

@@ -0,0 +1,39 @@
+# ===================
+# Part 1: Importing Libraries
+# ===================
+import matplotlib.pyplot as plt
+import numpy as np
+
+np.random.seed(0)
+
+
+# ===================
+# Part 2: Data Preparation
+# ===================
+categories = ["United States", "Germany", "India", "Brazil", "Japan"]
+
+values = [0.88, 0.65, 0.25, 0.36, 0.62]
+errors = [0.03, 0.02, 0.05, 0.04, 0.03]
+
+categories2 = ["United States", "Germany", "India", "Brazil", "Japan"]
+values2 = [16, 15, 35, 20, 12]
+errors2 = [1, 0.5, 2, 1.5, 0.5]
+
+titles = ["Higher Education Enrollment Rate", "Student-Teacher Ratio"]
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+colors = plt.get_cmap("Set3")(np.linspace(0.2, 0.8, 5))
+fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(6, 6))
+ax1.barh(categories, values, xerr=errors, color=colors, capsize=3)
+ax2.barh(categories2, values2, xerr=errors2, color=colors, capsize=3)
+
+ax1.set_title(titles[0])
+ax2.set_title(titles[1])
+
+# ===================
+# Part 4: Saving Output
+# ===================
+plt.tight_layout()
+plt.savefig("errorbar_17.pdf", bbox_inches="tight")

ChartMimic/dataset/ori_500/errorbar_19.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 representing social statistics for three different cities
+categories = ["Detroit", "Philadelphia", "Baltimore"]
+metrics = [
+    "Crime Rate",
+    "Happiness Index",
+    "Social Security Coverage",
+    "Political Participation",
+]
+performance = np.array(
+    [
+        [50, 70, 90, 80],
+        [60, 80, 85, 75],
+        [40, 75, 95, 85],
+    ]
+)
+errors = np.array(
+    [
+        [5, 6, 9, 6],
+        [6, 7, 8, 7],
+        [8, 9, 6, 8],
+    ]
+)
+ylim = [30, 100]
+ylabel = "Percentage"
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Figure size to match a 3x1 subplot layout
+fig, axes = plt.subplots(3, 1, figsize=(10, 9), sharex=True)
+# Colors, choosing a different palette to differentiate the plots
+colors = ["#8e44ad", "#3498db", "#e74c3c", "#f1c40f"]
+
+# Plotting bars
+for i, ax in enumerate(axes):
+    for j, metric in enumerate(metrics):
+        ax.bar(
+            j,
+            performance[i, j],
+            width=0.8,
+            color=colors[j],
+            yerr=errors[i, j],
+            capsize=0,
+            label=metric if i == 0 else "",
+        )
+
+    # Setting x-axis labels, y-axis limits, and titles
+    ax.set_xticks(range(len(metrics)))
+    ax.set_xticklabels(metrics, rotation=45)
+    ax.set_ylim(ylim)
+    ax.set_xlabel(f"({chr(97+i)}) {categories[i]}")
+    ax.set_ylabel(ylabel)
+    ax.yaxis.grid(True)
+    ax.set_axisbelow(True)
+
+# Adding a legend outside of the plot on top
+fig.legend(loc="upper center", bbox_to_anchor=(0.5, 1.05), ncol=len(metrics))
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Adjusting layout to prevent overlap and ensure labels are visible
+plt.tight_layout()
+plt.savefig("errorbar_19.pdf", bbox_inches="tight")

ChartMimic/dataset/ori_500/errorbar_21.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
+# ===================
+# Categories and values for different countries
+categories = ["Germany", "China", "USA", "India", "Brazil"][::-1]
+energy_consumption = [-4000, -6000, -5500, -3000, -2000][
+    ::-1
+]  # Total energy consumption in Petajoules
+consumption_error = [400, 550, 400, 550, 400][
+    ::-1
+]  # Error values for energy consumption
+
+renewable_energy = [20, 25, 18, 15, 10][
+    ::-1
+]  # Renewable energy usage as percentage of total
+renewable_error = [3, 3.5, 4, 2.5, 3.8][::-1]  # Error values for renewable energy usage
+xlabels = ["Energy Consumption (Petajoules)", "Renewable Energy Usage (%)"]
+titles = ["Total Energy Consumption by Country", "Renewable Energy Usage by Country"]
+xlims = [[-6500, 0], [0, 30]]
+
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Create horizontal bar chart with subplots
+fig, axes = plt.subplots(1, 2, figsize=(10, 6), sharey=True)  # Adjust figure size
+
+# Setting colors for the bars
+neg_colors = ["#c5b3d6"] * 5
+pos_colors = ["#76d7c4"] * 5
+
+# Plotting bars for negative values (Energy Consumption)
+bars = axes[0].barh(
+    categories,
+    energy_consumption,
+    color=neg_colors,
+    edgecolor="white",
+    height=0.5,
+    xerr=consumption_error,
+    capsize=0,
+)
+axes[0].set_xlabel(xlabels[0])
+axes[0].set_title(titles[0])
+axes[0].invert_yaxis()
+axes[0].set_xlim(xlims[0])
+axes[0].xaxis.grid(True)
+axes[0].spines["top"].set_visible(False)
+axes[0].spines["right"].set_visible(False)
+
+# Plotting bars for positive values (Renewable Energy Usage)
+bars2 = axes[1].barh(
+    categories,
+    renewable_energy,
+    color=pos_colors,
+    edgecolor="white",
+    height=0.5,
+    xerr=renewable_error,
+    capsize=0,
+)
+axes[1].set_xlabel(xlabels[1])
+axes[1].set_title(titles[1])
+axes[1].invert_yaxis()
+axes[1].set_xlim(xlims[1])
+axes[1].xaxis.grid(True)
+axes[1].spines["top"].set_visible(False)
+axes[1].spines["right"].set_visible(False)
+
+# ===================
+# Part 4: Saving Output
+# ===================
+plt.tight_layout()
+plt.savefig("errorbar_21.pdf", bbox_inches="tight")

ChartMimic/dataset/ori_500/errorbar_23.py

@@ -0,0 +1,88 @@
+# ===================
+# Part 1: Importing Libraries
+# ===================
+import matplotlib.pyplot as plt
+import numpy as np
+
+np.random.seed(0)
+
+
+# ===================
+# Part 2: Data Preparation
+# ===================
+# Data
+entities = ["Wind", "Solar", "Hydro", "Nuclear"]
+protocols = [
+    "Installation Efficiency",
+    "Operational Efficiency",
+    "Maintenance Costs",
+    "Environmental Impact",
+    "Regulatory Compliance",
+    "Safety Standards",
+]
+# Simulated mean scores for different protocols (more distinctive values)
+efficiency_means = np.array(
+    [
+        [95, 60, 90, 55, 80, 95],  # Wind
+        [70, 90, 70, 85, 60, 85],  # Solar
+        [85, 75, 100, 70, 90, 75],  # Hydro
+        [60, 85, 65, 95, 85, 65],  # Nuclear
+    ]
+)
+
+# Simulated standard deviations for scores (made more dramatic)
+efficiency_std = np.array(
+    [
+        [5, 10, 6, 8, 5, 3],  # Wind
+        [8, 6, 7, 5, 9, 4],  # Solar
+        [7, 8, 5, 10, 6, 5],  # Hydro
+        [9, 7, 8, 4, 7, 6],  # Nuclear
+    ]
+)
+xlabel = "Energy Assessment Entity"
+ylabel = "Efficiency and Cost Scores (%)"
+ylim = [40, 105]
+legendtitle = "Evaluation Protocol"
+
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+fig, ax = plt.subplots(figsize=(10, 5))
+# Subdued color palette
+colors = ["#92c6ff", "#97f0aa", "#ff9f9a", "#d0bbff", "#ffb480", "#99e6e6"]
+
+# Bar width and positions
+bar_width = 0.12
+
+# Positions of the bar groups
+r = np.arange(len(entities))
+
+# Drawing bars for different protocols
+for i in range(len(protocols)):
+    ax.bar(
+        r + i * bar_width,
+        efficiency_means[:, i],
+        yerr=efficiency_std[:, i],
+        width=bar_width,
+        label=protocols[i],
+        capsize=0,
+        color=colors[i],
+        edgecolor="black",
+    )
+
+# Set x-axis labels and axis properties
+ax.set_xlabel(xlabel)
+ax.set_xticks(r + bar_width * (len(protocols) / 2))
+ax.set_xticklabels(entities)
+ax.set_ylabel(ylabel)
+ax.set_ylim(ylim)  # Adjust y-axis to better fit extended range
+
+# Customize the legend
+ax.legend(loc="lower center", bbox_to_anchor=(0.5, -0.4), title=legendtitle, ncol=3)
+
+# ===================
+# Part 4: Saving Output
+# ===================
+plt.tight_layout()
+plt.savefig("errorbar_23.pdf", bbox_inches="tight")

ChartMimic/dataset/ori_500/errorbar_28.py

@@ -0,0 +1,82 @@
+# ===================
+# Part 1: Importing Libraries
+# ===================
+import matplotlib.pyplot as plt
+import numpy as np
+
+np.random.seed(0)
+
+import matplotlib.colors as mcolors
+
+# ===================
+# Part 2: Data Preparation
+# ===================
+# Data for environmental factors affecting plant growth
+categories = [
+    "Sunlight",
+    "Water Quality",
+    "Soil pH",
+    "Fertilizer",
+    "Temperature",
+    "Pesticides",
+    "CO2 Levels",
+    "Plant Variety",
+    "Planting Density",
+    "Watering Frequency",
+]
+values = [0.18, 0.15, 0.12, 0.09, 0.06, 0.03, -0.06, -0.03, -0.02, -0.03]
+errors = [0.05, 0.04, 0.03, 0.03, 0.02, 0.02, 0.02, 0.02, 0.01, 0.01]
+
+min_val = min(values) - 0.1
+max_val = max(values) + 0.1
+
+
+# Normalizing function to convert values to a 0-1 range for color scaling
+def normalize(value, min_val, max_val):
+    return (value - min_val) / (max_val - min_val)
+
+
+# Determine color based on normalized value
+def get_color(value):
+    norm_value = normalize(value, min_val, max_val)
+    green_base = np.array(mcolors.to_rgb("#6a8347"))
+    # Create a color that ranges from very light green to the base green
+    return mcolors.to_hex((1 - green_base) * (1 - norm_value) + green_base)
+
+
+colors = [get_color(value) for value in values]
+
+# Axes Limits and Labels
+ylabel_value = "Environmental Factors"
+xlabel_value = "Impact on Plant Growth (Δ to control)"
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Create figure and axis
+fig, ax = plt.subplots(figsize=(10, 8))
+
+# Horizontal bar chart
+bars = ax.barh(
+    categories, values, xerr=errors, color=colors, capsize=3, edgecolor="none"
+)
+ax.set_ylabel(ylabel_value)
+ax.set_xlabel(xlabel_value)
+
+# Set y-axis limits and x-axis limits
+ax.set_xlim(min_val, max_val)  # Adjust limits to encompass errors
+
+# Remove top and right spines for a cleaner look
+ax.spines["top"].set_visible(False)
+ax.spines["right"].set_visible(False)
+
+# Customize grid lines
+ax.xaxis.grid(True, linestyle="--", which="major", color="gray", alpha=0.6)
+ax.set_axisbelow(True)
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Adjust layout to prevent clipping of ylabel
+plt.tight_layout()
+plt.savefig("errorbar_28.pdf", bbox_inches="tight")

ChartMimic/dataset/ori_500/errorbar_29.py

@@ -0,0 +1,83 @@
+# ===================
+# Part 1: Importing Libraries
+# ===================
+import matplotlib.pyplot as plt
+import numpy as np
+
+np.random.seed(0)
+
+
+# ===================
+# Part 2: Data Preparation
+# ===================
+# Updated Urban Transportation Data for three major cities
+metrics = ["Traffic Volume", "Public Transit", "Accident Rate"]
+values = np.array(
+    [
+        [220, 180, 220],  # New York
+        [150, 120, 130],  # Los Angeles
+        [130, 160, 110],  # Chicago
+    ]
+)
+
+# Updated asymmetric error values, now more proportionate to the data scale
+errors = np.array(
+    [
+        [[25, 20], [15, 15], [25, 20]],  # Errors for New York (lower, upper)
+        [[20, 15], [10, 15], [20, 10]],  # Errors for Los Angeles
+        [[15, 20], [15, 10], [15, 15]],  # Errors for Chicago
+    ]
+)
+
+# Creating subplots for each city
+cities = ["New York", "Los Angeles", "Chicago"]
+
+ylabel = "Metric Values"
+ylim = [80, 280]
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+fig, axs = plt.subplots(1, 3, figsize=(10, 4))  # Compact and square figure layout
+
+
+# Function to plot each city's data
+def plot_city_data(ax, errors, city_index, city_name):
+    x = np.arange(len(metrics))  # the label locations
+    bar_colors = ["#6a8347", "#377eb8", "#d62728"]
+    barerrors = np.array(errors).T[:, :, city_index]
+    bars = ax.bar(x, values[city_index], yerr=barerrors, color=bar_colors, capsize=5)
+    for bar, lower_error, upper_error in zip(bars, barerrors[0], barerrors[1]):
+        # Position for lower error text
+        ax.text(
+            bar.get_x() + bar.get_width() / 2,
+            bar.get_height() - lower_error - 15,
+            f"-{lower_error}",
+            va="bottom",
+            ha="center",
+            color="black",
+        )
+        # Position for upper error text
+        ax.text(
+            bar.get_x() + bar.get_width() / 2,
+            bar.get_height() + upper_error + 3,
+            f"+{upper_error}",
+            ha="center",
+            color="black",
+        )
+
+    ax.set_title(city_name)
+    ax.set_xticks(x)
+    ax.set_xticklabels(metrics, rotation=90)
+    ax.set_ylabel(ylabel)
+    ax.set_ylim(ylim)  # Uniform scale for all charts
+
+
+for i, city in enumerate(cities):
+    plot_city_data(axs[i], errors, i, city)
+
+# ===================
+# Part 4: Saving Output
+# ===================
+plt.tight_layout()
+plt.savefig("errorbar_29.pdf", bbox_inches="tight")

ChartMimic/dataset/ori_500/errorbar_4.py

@@ -0,0 +1,62 @@
+# ===================
+# Part 1: Importing Libraries
+# ===================
+import matplotlib.pyplot as plt
+import numpy as np
+
+np.random.seed(0)
+
+
+# ===================
+# Part 2: Data Preparation
+# ===================
+# Data
+roles = ["Werewolf", "Seer", "Witch", "Hunter", "Villager"]
+duration_means = [84.97, 102.67, 67.17, 78.22, 85.17]
+duration_errors = [15, 20, 10, 15, 10]
+tokens_means = [449.33, 780.67, 547.39, 612.99, 618.52]
+tokens_errors = [100, 150, 80, 100, 90]
+
+ylabel1 = "Duration (s)"
+xlabel1 = "(a) Speak duration of roles"
+ylabel2 = "Tokens"
+xlabel2 = "(b) Speak tokens of roles"
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Create subplots
+fig, (ax1, ax2) = plt.subplots(
+    1, 2, figsize=(10, 4)
+)  # Adjusted for the given dimensions
+colors = ["#acd9bb", "#83c3c7", "#5da2c7", "#3a77b0", "#224e8d"]
+
+# Speak duration of roles
+ax1.bar(roles, duration_means, yerr=duration_errors, color=colors, capsize=5)
+ax1.set_ylabel(ylabel1)
+ax1.set_xlabel(xlabel1)
+for i, v in enumerate(duration_means):
+    ax1.text(i, v + duration_errors[i] + 2, str(v), ha="center", va="bottom")
+ax1.spines["top"].set_visible(False)
+ax1.spines["right"].set_visible(False)
+ax1.yaxis.grid(True)
+ax1.set_axisbelow(True)
+
+# Speak tokens of roles
+ax2.bar(roles, tokens_means, yerr=tokens_errors, color=colors, capsize=5)
+ax2.set_ylabel(ylabel2)
+ax2.set_xlabel(xlabel2)
+for i, v in enumerate(tokens_means):
+    ax2.text(i, v + tokens_errors[i] + 20, str(v), ha="center", va="bottom")
+
+ax2.spines["top"].set_visible(False)
+ax2.spines["right"].set_visible(False)
+ax2.yaxis.grid(True)
+ax2.set_axisbelow(True)
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Adjust layout and save the figure
+plt.tight_layout()
+plt.savefig("errorbar_4.pdf", bbox_inches="tight")

ChartMimic/dataset/ori_500/errorbar_5.py

@@ -0,0 +1,74 @@
+# ===================
+# Part 1: Importing Libraries
+# ===================
+import matplotlib.pyplot as plt
+
+# ===================
+# Part 2: Data Preparation
+# ===================
+# Data for plotting
+sizes = ["5%", "10%", "20%", "30%", "40%", "50%"]
+samples = [
+    "(40 samples)",
+    "(81 samples)",
+    "(163 samples)",
+    "(245 samples)",
+    "(326 samples)",
+    "(408 samples)",
+]
+x = range(len(sizes))
+y = [63.77, 64.17, 64.31, 64.98, 65.82, 65.78]
+errors = [1.5, 1.2, 1.3, 1.1, 1.0, 0.8]
+ylabel = "True+info (%)"
+xlabel = "Size of Data for Training and Validation"
+ylim = [56, 67]
+yticks = [56, 58, 60, 62, 64, 66]
+
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Plotting the bar chart
+plt.figure(
+    figsize=(10, 7)
+)  # Adjusting figure size to match original image's dimensions
+bars = plt.bar(x, y, yerr=errors, color="skyblue", capsize=5)
+
+# Adding data labels on top of the bars
+for bar in bars:
+    yval = bar.get_height()
+    plt.text(
+        bar.get_x() + bar.get_width() / 2,
+        yval + 0.5,
+        round(yval, 2),
+        ha="right",
+        va="bottom",
+    )
+
+# Setting the x-axis labels with both percentages and sample sizes
+plt.xticks(x, [f"{size}\n{sample}" for size, sample in zip(sizes, samples)])
+
+# Setting the y-axis label
+plt.ylabel(ylabel)
+
+# Setting the title of the chart
+plt.xlabel(xlabel)
+
+# Adjusting y-axis range
+plt.ylim(ylim)
+plt.yticks(yticks)
+# Adding grid to the background
+plt.grid(axis="both", alpha=0.7, which="both", color="gray")
+
+# Making the axis lines visible
+plt.gca().spines["top"].set_visible(True)
+plt.gca().spines["right"].set_visible(True)
+plt.gca().spines["bottom"].set_visible(True)
+plt.gca().spines["left"].set_visible(True)
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Displaying the plot with tight layout to minimize white space
+plt.tight_layout()
+plt.savefig("errorbar_5.pdf", bbox_inches="tight")

ChartMimic/dataset/ori_500/errorbar_6.py

@@ -0,0 +1,105 @@
+# ===================
+# Part 1: Importing Libraries
+# ===================
+import matplotlib.pyplot as plt
+import numpy as np
+
+np.random.seed(0)
+
+
+# ===================
+# Part 2: Data Preparation
+# ===================
+# Data for the left plot (Out-of-domain accuracy)
+left_categories = [
+    "all",
+    "arg_causal",
+    "test_0",
+    "test_1",
+    "test_2",
+    "test_3",
+    "test_4",
+    "test_5",
+    "test_6",
+    "constant",
+]
+left_means = [0.832, 0.828, 0.830, 0.829, 0.830, 0.829, 0.828, 0.827, 0.826, 0.826]
+left_errors = [0.002] * 10
+
+# Data for the right plot (Shift gap)
+right_categories = [
+    "all",
+    "arg_causal",
+    "test_0",
+    "test_1",
+    "test_2",
+    "test_3",
+    "test_4",
+    "test_5",
+    "test_6",
+    "constant",
+]
+right_means = [0.040, 0.035, 0.036, 0.036, 0.036, 0.036, 0.036, 0.036, 0.036, 0.035]
+right_errors = [0.005] * 10
+
+title1 = "Out-of-domain accuracy"
+ylim1 = [0.824, 0.835]
+yticks1 = np.arange(0.824, 0.835, 0.002)
+title2 = "Shift gap"
+ylim2 = [0, 0.050]
+suptitle = "Diabetes"
+
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Create subplots
+fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))
+
+# Left plot
+ax1.bar(
+    left_categories,
+    left_means,
+    yerr=left_errors,
+    color=["#c53a32"] + ["#9ab6bd"] + ["#678c95"] * 7 + ["#454545"],
+    capsize=5,
+    error_kw=dict(ecolor="black", lw=1, capsize=5, capthick=2),
+)
+ax1.set_title(title1)
+ax1.set_ylim(ylim1)
+ax1.set_yticks(yticks1)
+ax1.set_xticklabels(left_categories, rotation=90, ha="center")
+ax1.tick_params(axis="both", length=0)  # Hide tick marks
+ax1.grid(True)
+ax1.set_axisbelow(True)
+for spine in ax1.spines.values():
+    spine.set_color("gray")
+
+# Right plot
+ax2.bar(
+    right_categories,
+    right_means,
+    yerr=right_errors,
+    color=["#c53a32"] + ["#9ab6bd"] + ["#678c95"] * 7 + ["#454545"],
+    capsize=5,
+    error_kw=dict(ecolor="black", lw=1, capsize=5, capthick=2),
+)
+ax2.set_title(title2)
+ax2.set_ylim(ylim2)
+ax2.set_xticklabels(right_categories, rotation=90, ha="center")
+ax2.tick_params(axis="both", length=0)  # Hide tick marks
+ax2.grid(True)
+ax2.set_axisbelow(True)
+
+# Set the title for the entire figure
+fig.suptitle(suptitle)
+
+for spine in ax2.spines.values():
+    spine.set_color("gray")
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Adjust layout and save the figure
+plt.tight_layout()
+plt.savefig("errorbar_6.pdf", bbox_inches="tight")

ChartMimic/dataset/ori_500/errorbar_7.py

@@ -0,0 +1,52 @@
+# ===================
+# Part 1: Importing Libraries
+# ===================
+import matplotlib.pyplot as plt
+
+# ===================
+# Part 2: Data Preparation
+# ===================
+# Data for plotting
+categories = [
+    "Best-of-Three",
+    "Initial Distribution",
+    "0.125 Parameter-Valued\nDistribution",
+    "Final Distribution with\nGPT-3.5-Turbo",
+    "Final Distribution with\nGPT-4-Turbo",
+]
+values = [0.15, 0.35, 0.55, 0.65, 0.8]
+errors = [0.05, 0.05, 0.05, 0.05, 0.05]
+xlabel = "Objective Value"
+title = "Mini Crosswords Performance"
+label = "ToT (0.675)"
+xvline = 0.675
+
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Plotting the bar chart
+plt.figure(
+    figsize=(10, 6)
+)  # Adjusting figure size to match the original image's dimensions
+plt.barh(categories, values, xerr=errors, color="skyblue", capsize=5)
+plt.xlabel(xlabel)
+plt.title(title)
+plt.gca().tick_params(axis="both", length=0)  # Hide tick marks
+
+# Adding the vertical line
+plt.axvline(x=xvline, color="red", linestyle="--", label=label)
+plt.gca().grid("both", color="gray", alpha=0.7)
+
+# Adding the legend
+plt.legend()
+
+for spine in plt.gca().spines.values():
+    spine.set_color("gray")
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Adjusting the layout and saving the figure
+plt.tight_layout()
+plt.savefig("errorbar_7.pdf", bbox_inches="tight")

ChartMimic/dataset/ori_500/errorbar_8.py

@@ -0,0 +1,88 @@
+# ===================
+# Part 1: Importing Libraries
+# ===================
+import matplotlib.pyplot as plt
+import numpy as np
+
+np.random.seed(0)
+
+
+# ===================
+# Part 2: Data Preparation
+# ===================
+# Data (estimated from the image)
+models = [
+    "BERT",
+    "RoBERTa",
+    "DistilBERT",
+    "XLNet",
+    "Electra",
+    "Albert",
+    "BART",
+    "DeBERTa",
+    "Llama2",
+]
+ground_truth_accuracy = [50, 55, 60, 65, 40, 30, 70, 45, 35]
+weak_labels_accuracy = [45, 50, 55, 60, 35, 25, 65, 40, 30]
+error = [10, 8, 12, 10, 5, 7, 8, 10, 5]
+labels = ["Ground-truth labels", "Weak labels"]
+ylabel = "Accuracy (%)"
+ylim = [0, 80]
+yticks = np.arange(0, 71, 10)
+
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+fig = plt.subplots(figsize=(10, 3))
+# Bar width
+bar_width = 0.35
+
+# X position of bars
+r1 = np.arange(len(ground_truth_accuracy))
+r2 = [x + bar_width for x in r1]
+
+# Create bars
+plt.bar(
+    r1,
+    ground_truth_accuracy,
+    color="#d47e6d",
+    width=bar_width,
+    label=labels[0],
+    yerr=error,
+    capsize=7,
+)
+plt.bar(
+    r2,
+    weak_labels_accuracy,
+    color="#76a4c5",
+    width=bar_width,
+    label=labels[1],
+    yerr=error,
+    capsize=7,
+)
+
+# Add xticks on the middle of the group bars
+plt.xticks([r + bar_width / 2 for r in range(len(ground_truth_accuracy))], models)
+
+# Create legend & Show graphic
+plt.ylabel(ylabel)
+plt.legend(frameon=False, loc="upper right")  # Remove legend background
+
+# Set background color and grid
+plt.gca().set_facecolor("#e5e5e5")
+plt.grid(color="white", linestyle="-", linewidth=0.25, axis="both")
+plt.gca().set_axisbelow(True)
+
+# Set y-axis limits
+plt.ylim(ylim)
+plt.yticks(yticks)
+
+for spine in plt.gca().spines.values():
+    spine.set_visible(False)
+
+# ===================
+# Part 4: Saving Output
+# ===================
+plt.tight_layout()
+plt.savefig("errorbar_8.pdf", bbox_inches="tight")

ChartMimic/dataset/ori_500/errorbar_9.py

@@ -0,0 +1,64 @@
+# ===================
+# Part 1: Importing Libraries
+# ===================
+import matplotlib.pyplot as plt
+
+# ===================
+# Part 2: Data Preparation
+# ===================
+# Categories and values (estimated from the image)
+categories = [
+    "Syntax: Tagging, Chunking and Parsing",
+    "Discourse and Pragmatics",
+    "Information Extraction",
+    "Machine Learning for NLP",
+    "Information Retrieval and Text Mining",
+    "Phonology, Morphology and Word Segmentation",
+    "Computational Social Science and Social Media",
+][::-1]
+values = [-3.20, -3.1, -3.00, -2.90, -2.80, -2.70, -2.60][::-1]
+error = [0.1, 0.15, 0.3, 0.25, 0.2, 0.1, 0.05]
+xlabel = "A"
+ylabel = "Categories"
+title = "Your Chart Title Here"
+xlim = [-3.5, -1.5]
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Create horizontal bar chart
+fig, ax = plt.subplots(figsize=(8, 8))  # Adjust figure size
+bars = ax.barh(
+    categories,
+    values,
+    color="#c5b3d6",
+    edgecolor="white",
+    height=0.5,
+    xerr=error,
+    capsize=0,
+)
+
+# Set labels and title (if any)
+ax.set_xlabel(xlabel)
+ax.set_ylabel(ylabel)
+ax.set_title(title)
+
+# Invert y-axis to match the image
+ax.invert_yaxis()
+
+# Set x-axis range to match the reference image
+ax.set_xlim(xlim)
+
+# Remove grid lines
+ax.xaxis.grid(False)
+ax.spines["top"].set_visible(False)
+ax.spines["right"].set_visible(False)
+
+# Set background color to white
+ax.set_facecolor("white")
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Displaying the plot with tight layout to minimize white space
+plt.tight_layout()
+plt.savefig("errorbar_9.pdf", bbox_inches="tight")

ChartMimic/dataset/ori_500/errorpoint_1.py

@@ -0,0 +1,66 @@
+# ===================
+# Part 1: Importing Libraries
+# ===================
+import matplotlib.pyplot as plt
+import numpy as np
+
+np.random.seed(0)
+
+
+# ===================
+# Part 2: Data Preparation
+# ===================
+# Sample data (replace with actual data)
+categories = [
+    "Kashmir",
+    "Religion",
+    "Crime and Justice",
+    "CAA",
+    "Pulwama-Balakot",
+    "Politics",
+]
+means = np.random.uniform(0.05, 0.15, len(categories))
+std_devs = np.random.uniform(0.01, 0.05, len(categories))
+dataset_mean = np.mean(means)
+
+# Labels and Plot Types
+label_Mean = "Mean"
+label_Dataset_mean = "Dataset mean"
+
+# Axes Limits and Labels
+ylabel_value = "Shouting Fraction (Fraction of videos)"
+ylim_values = [0.01, 0.18]
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Create figure and axis
+fig, ax = plt.subplots(figsize=(8, 5))
+
+# Error bar plot
+ax.errorbar(
+    categories,
+    means,
+    yerr=std_devs,
+    fmt="o",
+    color="blue",
+    ecolor="blue",
+    capsize=5,
+    label=label_Mean,
+)
+
+# Dataset mean line
+ax.axhline(y=dataset_mean, color="grey", linestyle="--", label=label_Dataset_mean)
+
+# Customizing the plot
+ax.set_ylabel(ylabel_value)
+ax.set_xticklabels(categories, rotation=45, ha="right")
+ax.legend()
+ax.set_ylim(ylim_values)
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Adjust layout to prevent clipping of tick-labels
+plt.tight_layout()
+plt.savefig("errorpoint_1.pdf", bbox_inches="tight")

ChartMimic/dataset/ori_500/errorpoint_2.py

@@ -0,0 +1,43 @@
+# ===================
+# Part 1: Importing Libraries
+# ===================
+import matplotlib.pyplot as plt
+
+# ===================
+# Part 2: Data Preparation
+# ===================
+# Data for plotting
+categories = ["Pulwama-Balakot", "CAA", "Kashmir", "Religion", "Politics"]
+means = [0.24, 0.22, 0.20, 0.19, 0.18]
+errors = [0.04, 0.03, 0.02, 0.02, 0.02]
+dataset_mean = [0.162]
+xlabel = "Incivility (Fraction of Videos)"
+label = "Dataset mean"
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Plotting the data
+plt.figure(figsize=(8, 6))  # Adjusting figure size to match original image's dimensions
+plt.errorbar(
+    means,
+    categories,
+    xerr=errors,
+    fmt="o",
+    color="#bc4949",
+    ecolor="#bc4949",
+    capsize=0,
+    label="Mean",
+)
+plt.axvline(dataset_mean, linestyle="--", label=label)
+
+# Customizing the plot
+plt.xlabel(xlabel)
+plt.legend()
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Adjusting the layout and saving the figure
+plt.tight_layout()
+plt.savefig("errorpoint_2.pdf", bbox_inches="tight")

ChartMimic/dataset/ori_500/errorpoint_4.py

@@ -0,0 +1,72 @@
+# ===================
+# Part 1: Importing Libraries
+# ===================
+import matplotlib.pyplot as plt
+
+# ===================
+# Part 2: Data Preparation
+# ===================
+# Data (example values, replace with actual data)
+categories = [
+    "Education",
+    "Religion",
+    "Bollywood",
+    "Crime and Justice",
+    "Farmers Protest",
+    "Issue Politics",
+]
+unique_speaker_mean = [10, 12, 11, 13, 14, 15]  # Replace with actual mean values
+unique_shouter_mean = [5, 6, 7, 6, 5, 4]  # Replace with actual mean values
+unique_speaker_error = [1, 1.5, 1, 1.5, 2, 1.5]  # Replace with actual error values
+unique_shouter_error = [
+    0.5,
+    0.75,
+    0.5,
+    0.75,
+    1,
+    0.75,
+]  # Replace with actual error values
+labels = ["Unique speaker count mean", "Unique shouter count mean"]
+ylabel = "Number of speakers"
+axlabel = "Dataset unique shouter count mean"
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Plotting
+fig, ax = plt.subplots(
+    figsize=(10, 6)
+)  # Adjust the size to match the original image's dimensions
+ax.errorbar(
+    categories,
+    unique_speaker_mean,
+    yerr=unique_speaker_error,
+    fmt="o",
+    color="blue",
+    label=labels[0],
+)
+ax.errorbar(
+    categories,
+    unique_shouter_mean,
+    yerr=unique_shouter_error,
+    fmt="o",
+    color="red",
+    label=labels[1],
+)
+
+# Customization
+ax.set_ylabel(ylabel)
+ax.set_xticklabels(categories, rotation=45, ha="right")
+ax.axhline(
+    y=sum(unique_shouter_mean) / len(unique_shouter_mean),
+    color="grey",
+    linestyle="--",
+    label=axlabel,
+)
+ax.legend()
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Show plot
+plt.tight_layout()
+plt.savefig("errorpoint_4.pdf", bbox_inches="tight")

ChartMimic/dataset/ori_500/errorpoint_5.py

@@ -0,0 +1,77 @@
+# ===================
+# Part 1: Importing Libraries
+# ===================
+import matplotlib.pyplot as plt
+
+# ===================
+# Part 2: Data Preparation
+# ===================
+# Data for plotting
+categories = [
+    "KASHMIR",
+    "COVID/LOCKDOWN",
+    "SPORTS",
+    "CHINA",
+    "PULWAMA-BALAKOT",
+]  # Capitalized category labels
+means = [0.22, 0.23, 0.18, 0.12, 0.05]
+errors = [0.03, 0.02, 0.05, 0.06, 0.02]
+downerrors = [0.01, 0.02, 0.03, 0.04, 0.05]
+legendtitles = ["Dataset mean", "Mean"]
+texttitle = "Dataset mean"
+ylabel = "Female Face presence (Fraction of videos)"
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Plotting the data
+fig, ax = plt.subplots(
+    figsize=(8, 6)
+)  # Adjusting figure size to match original image dimensions
+ax.errorbar(
+    categories,
+    means,
+    yerr=[errors, downerrors],
+    fmt="o",
+    color="blue",
+    ecolor="blue",
+    capsize=5,
+)
+
+# Adding a legend with both "Mean" and "Dataset mean"
+dataset_mean = 0.253
+mean_line = ax.errorbar(
+    [], [], yerr=[], fmt="o", color="blue", ecolor="blue", capsize=5
+)
+dataset_mean_line = ax.axhline(
+    y=dataset_mean, color="gray", linestyle="--", linewidth=1
+)
+ax.legend(
+    [dataset_mean_line, mean_line],
+    legendtitles,
+    loc="upper right",
+    fancybox=True,
+    framealpha=1,
+    shadow=True,
+    borderpad=1,
+)
+# Adding a horizontal line for dataset mean and text annotation with a white background
+ax.text(
+    0.95,
+    dataset_mean,
+    texttitle,
+    va="center",
+    ha="right",
+    backgroundcolor="white",
+    transform=ax.get_yaxis_transform(),
+)
+# Setting labels
+ax.set_ylabel(ylabel)
+ax.set_title("")
+plt.xticks(rotation=30)
+
+# ===================
+# Part 4: Saving Output
+# ===================
+plt.tight_layout()
+plt.savefig("errorpoint_5.pdf", bbox_inches="tight")

ChartMimic/dataset/ori_500/errorpoint_6.py

@@ -0,0 +1,105 @@
+# ===================
+# Part 1: Importing Libraries
+# ===================
+import matplotlib.pyplot as plt
+import numpy as np
+
+np.random.seed(0)
+
+
+# ===================
+# Part 2: Data Preparation
+# ===================
+# example data
+x = np.array([0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0])
+y = np.exp(-x)
+xerr = 0.2
+yerr = 0.15
+
+# lower & upper limits of the error
+lolims = np.array([0.3, 0, 0.4, 0, 0, 1, 0, 0, 1.2, 0], dtype=bool)
+uplims = np.array([0.5, 0, 0, 0.1, 0, 0.5, 0, 0.3, 0, 0], dtype=bool)
+ls = "None"
+labels = [
+    "standard",
+    "upper limits",
+    "lower limits",
+    "upper and lower limits",
+    "random",
+]
+title = "Errorbar upper and lower limits"
+xlim = [0, 5.5]
+colors =[ "#b2e7aa", "#fae18f", "#d75949", "#f0906d", "#a1a8d6"]
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+fig, ax = plt.subplots(figsize=(9, 6))
+
+# standard error bars
+ax.errorbar(x, y, xerr=xerr, yerr=yerr, label=labels[0], linestyle=ls, color=colors[0])
+# including upper limits
+ax.errorbar(
+    x, y + 1, xerr=xerr, yerr=yerr, uplims=uplims, label=labels[1], linestyle=ls, color=colors[1]
+)
+
+# including lower limits
+ax.errorbar(
+    x, y + 0.25, xerr=xerr, yerr=yerr, lolims=lolims, label=labels[2], linestyle=ls, color=colors[2]
+)
+
+# including upper and lower limits
+ax.errorbar(
+    x,
+    y + 1.25,
+    xerr=xerr,
+    yerr=yerr,
+    lolims=lolims,
+    uplims=uplims,
+    marker="o",
+    markersize=8,
+    label=labels[3],
+    linestyle=ls,
+    color=colors[3],
+)
+
+# Plot a series with lower and upper limits in both x & y
+# constant x-error with varying y-error
+xerr = 0.2
+yerr = np.full_like(x, 0.2)
+yerr[[3, 6]] = 0.3
+
+# mock up some limits by modifying previous data
+xlolims = lolims
+xuplims = uplims
+lolims = np.zeros_like(x)
+uplims = np.zeros_like(x)
+lolims[[6]] = True  # only limited at this index
+uplims[[3]] = True  # only limited at this index
+
+# do the plotting
+ax.errorbar(
+    x,
+    y + 2.1,
+    xerr=xerr,
+    yerr=yerr,
+    xlolims=xlolims,
+    xuplims=xuplims,
+    uplims=uplims,
+    lolims=lolims,
+    marker="o",
+    markersize=8,
+    linestyle="none",
+    label=labels[4],
+    color=colors[4],
+)
+
+# tidy up the figure
+ax.set_xlim(xlim)
+ax.set_title(title)
+plt.legend(bbox_to_anchor=(0.5, 1.15), ncol=5, loc="upper center")
+
+# ===================
+# Part 4: Saving Output
+# ===================
+plt.tight_layout()
+plt.savefig("errorpoint_6.pdf", bbox_inches="tight")