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ChartMimic/dataset/ori_500/line_31.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
+digit_length = np.arange(6, 10)
+direct_accuracy = [1.05, 0.95, 0.7, 0.75]
+rfft_accuracy = [1.0, 0.9, 0.88, 0.6]
+scratchpad_100_accuracy = [0.72, 0.52, 0.45, 0.38]
+scratchpad_5000_accuracy = [0.65, 0.75, 0.8, 0.95]
+
+# Placeholder error values
+direct_error = np.random.uniform(0.01, 0.05, len(digit_length))
+rfft_error = np.random.uniform(0.01, 0.05, len(digit_length))
+scratchpad_100_error = np.random.uniform(0.01, 0.05, len(digit_length))
+scratchpad_5000_error = np.random.uniform(0.01, 0.05, len(digit_length))
+
+# Axes Limits and Labels
+xlabel_value = "Digit Length"
+
+ylabel_value = "Accuracy"
+ylim_values = [0.5, 1.1]
+yticks_values = np.arange(0.3, 1.1, 0.1)
+
+# Labels
+label_1 = "Direct (100 samples)"
+label_2 = "RFFT (100 samples)"
+label_3 = "Scratchpad (100 samples)"
+label_4 = "Scratchpad (5000 samples)"
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Plotting the data
+plt.figure(figsize=(8, 6))  # Adjusting figure size to match original image dimensions
+plt.errorbar(
+    digit_length,
+    direct_accuracy,
+    yerr=direct_error,
+    fmt="-o",
+    label=label_1,
+    color="blue",
+    linestyle="dashed",
+)
+plt.errorbar(
+    digit_length,
+    rfft_accuracy,
+    yerr=rfft_error,
+    fmt="-s",
+    label=label_2,
+    color="green",
+    linestyle="dashed",
+)
+plt.errorbar(
+    digit_length,
+    scratchpad_100_accuracy,
+    yerr=scratchpad_100_error,
+    fmt="-^",
+    label=label_3,
+    color="orange",
+    linestyle="dashed",
+)
+plt.errorbar(
+    digit_length,
+    scratchpad_5000_accuracy,
+    yerr=scratchpad_5000_error,
+    fmt="-d",
+    label=label_4,
+    color="red",
+    linestyle="dashed",
+)
+
+# Adding labels and title
+plt.xlabel(xlabel_value)
+plt.ylabel(ylabel_value)
+plt.xticks(digit_length)
+plt.ylim(ylim_values)
+plt.yticks(yticks_values)
+
+# Adding legend, lower left corner
+plt.legend(loc="lower left")
+
+# Moving axes spines
+ax = plt.gca()  # get current axes
+ax.spines["right"].set_color("none")  # hide the right spine
+ax.spines["top"].set_color("none")  # hide the top spine
+ax.grid(
+    True, which="both", axis="both", color="lightgray", linestyle="--", linewidth=0.5
+)
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Display the plot with tight layout to minimize white space
+plt.tight_layout()
+plt.savefig('line_31.pdf', bbox_inches='tight')

ChartMimic/dataset/ori_500/line_32.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
+decomposition_IO_norm = [1, 12, 28, 93]
+accuracy_laion = [0.225, 0.275, 0.325, 0.375]
+accuracy_CLIP = [0.385, 0.385, 0.385, 0.385]
+
+# Axes Limits and Labels
+xlabel_value = "Decomposition IO Norm"
+xlim_values = [-5, 95]
+xticks_values = np.arange(0, 81, 20)
+
+ylabel_value = "Accuracy"
+ylim_values = [0.200, 0.400]
+yticks_values = np.arange(0.200, 0.376, 0.025)
+
+# Labels
+label_laion="laion"
+label_CLIP="CLIP"
+label_deepjscc_w_ofdm = "DEEPJSCC w/ ofdm"
+label_ours = "OURS"
+
+# Titles
+title_1 = "CIFAR100 States Zero Shot Accuracy"
+title_2 = "Dictionary"
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Create the figure and the line that we will manipulate
+fig, ax = plt.subplots(figsize=(8, 6))
+ax.plot(
+    decomposition_IO_norm,
+    accuracy_laion,
+    marker="o",
+    color="orange",
+    linewidth=2,
+    markersize=6,
+    markerfacecolor="orange",
+    label=label_laion,
+)
+
+# Extend the CLIP line visually by adding extra points
+extended_x = [-5] + decomposition_IO_norm + [95]  # Extend x-axis data points
+extended_y = [0.385] * (len(accuracy_CLIP) + 2)  # Extend y-axis data points to match
+ax.plot(
+    extended_x, extended_y, linestyle="--", color="#202020", label=label_CLIP
+)  # Plot the extended line
+
+# Set x,y-axis to only display specific ticks and extend y-axis to leave space at top
+plt.yticks(yticks_values, fontsize=12)
+plt.ylim(ylim_values)  # Adjusted y-axis limit
+plt.xticks(xticks_values, fontsize=12)
+plt.xlim(xlim_values)  # Adjusted x-axis limit
+
+# Set the title and labels
+ax.set_title(title_1, fontsize=20)
+ax.set_xlabel(xlabel_value, fontsize=16)
+ax.set_ylabel(ylabel_value, fontsize=16)
+
+# Remove tick lines outside the plotting area
+ax.tick_params(
+    axis="both", which="both", length=0, color="#d2d2d2"
+)  # Remove tick marks and set their color
+
+# Add a legend with a title
+ax.legend(
+    title=title_2,
+    loc="lower right",
+    fontsize=12,
+    title_fontsize=12,
+    edgecolor="#fdfdfd",
+)
+
+# Change the plot background color
+ax.set_facecolor("#f5f5f5")
+
+# Show grid with lighter color
+ax.grid(True, color="#fcfcfc", linewidth=1.5)
+
+# Change the axis colors
+ax = plt.gca()
+ax.spines["bottom"].set_color("#ffffff")
+ax.spines["top"].set_color("#ffffff")  # Optional: hide or set color
+ax.spines["left"].set_color("#ffffff")
+ax.spines["right"].set_color("#ffffff")  # Optional: hide or set color
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Adjust layout and show plot
+plt.tight_layout()
+plt.savefig('line_32.pdf', bbox_inches='tight')

ChartMimic/dataset/ori_500/line_33.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
+# ===================
+# Sample data (replace with actual values)
+users = [20, 40, 60, 80, 100]
+cnn = [0.5, 0.65, 0.7, 0.72, 0.75]
+cnn_hmm = [0.55, 0.6, 0.65, 0.68, 0.7]
+rf = [0.45, 0.55, 0.6, 0.62, 0.65]
+rf_hmm = [0.48, 0.58, 0.63, 0.65, 0.68]
+
+# Axes Limits and Labels
+xlabel_value = "No. of Users in Development Set"
+xlim_values = [5, 105]
+xticks_values = np.arange(20, 101, 20)
+
+ylabel_value = "F1"
+ylim_values = [0.44, 0.76]
+yticks_values = np.arange(0.45, 0.76, 0.05)
+
+# Labels
+label_1 = "CNN"
+label_2 = "CNN+HMM"
+label_3 = "RF"
+label_4 = "RF+HMM"
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Create the plot
+plt.figure(figsize=(6, 4))  # Adjusted to match the original image's dimensions
+plt.plot(users, cnn, marker="v", color="#ffa500", label=label_1)
+plt.plot(users, cnn_hmm, marker="^", color="#ff4500", label=label_2)
+plt.plot(users, rf, marker="s", color="#4169e1", label=label_3)
+plt.plot(users, rf_hmm, marker="o", color="#00008b", label=label_4)
+
+# Set x,y-axis to only display specific ticks and extend y-axis to leave space at top
+plt.xticks(xticks_values, fontsize=10)
+plt.xlim(xlim_values)  # Adjusted y-axis limit
+plt.yticks(yticks_values, fontsize=10)
+plt.ylim(ylim_values)  # Adjusted x-axis limit
+
+# Add vertical dotted line
+plt.axvline(x=20, color="blue", linestyle=":", linewidth=1.2)
+
+# Add legend
+plt.legend(loc="lower right", fontsize=12)
+
+# Add labels and title
+plt.xlabel(xlabel_value, fontsize=12)
+plt.ylabel(ylabel_value , fontsize=12)
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Adjust layout and save plot
+plt.tight_layout()
+plt.savefig('line_33.pdf', bbox_inches='tight')

ChartMimic/dataset/ori_500/line_34.py

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+# ===================
+# Part 1: Importing Libraries
+# ===================
+import matplotlib.pyplot as plt
+from matplotlib.lines import Line2D  # Importing Line2D for creating custom legend items
+
+# ===================
+# Part 2: Data Preparation
+# ===================
+# Data
+microphones = [2, 3, 4, 5, 6, 7, 8]
+libricss_wer = [
+    6.74,
+    4.54,
+    3.96,
+    3.71,
+    3.49,
+    3.34,
+    None,
+]  # The None value will be handled in the plot commands
+ami_wer = [27.44, 24.75, 23.38, 22.77, 22.32, 21.47, 21.51]
+ihm_wer = [3] * len(microphones)
+sdm_wer = [10] * len(microphones)
+
+# Axes Limits and Labels
+xlabel_value = "Number of microphones"
+
+ylabel_value_1 = "WER(%)"
+ylabel_value_2 = "AMI WER(%)"
+ylim_values_1 = [1, 11]
+ylim_values_2 = [15, 32]
+yticks_values_1 = range(2, 11, 2)
+yticks_values_2 = range(15, 31, 5)
+
+# Labels
+label_1 = "LibriCSS (test)"
+label_2 = "ImageNet-C/P (Fog)"
+label_3 = "AMI (dev)"
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Plot
+fig, ax1 = plt.subplots(figsize=(8, 7))
+
+# LibriCSS plot
+(libricss_line,) = ax1.plot(
+    microphones,
+    libricss_wer,
+    "o-",
+    color="#377eb8",
+    label=label_1,
+    markersize=10,
+    linewidth=3,
+    mec="black",
+)
+ax1.set_xlabel(xlabel_value, fontsize=14)
+ax1.set_ylabel(ylabel_value_1, fontsize=14, color="#377eb8")
+ax1.tick_params(
+    axis="y", labelcolor="#377eb8", direction="in", rotation=90, labelsize=12
+)
+ax1.tick_params(
+    axis="x",
+    direction="in",
+    labelsize=12,
+)
+ax1.set_yticks(yticks_values_1)
+ax1.set_ylim(ylim_values_1)
+
+# Adding WER values to the plot for libricss
+for i, txt in enumerate(libricss_wer):
+    if txt is not None:  # Skip plotting the text for None values
+        ax1.annotate(
+            f"{txt}%",
+            (microphones[i], txt),
+            textcoords="offset points",
+            xytext=(10, 10),
+            ha="center",
+            fontsize=12,
+        )
+
+# AMI plot with a secondary y-axis
+ax2 = ax1.twinx()
+(ami_line,) = ax2.plot(
+    microphones,
+    ami_wer,
+    "^-",
+    color="#ff7f00",
+    label=label_3,
+    markersize=10,
+    linewidth=3,
+    mec="black",
+)
+ax2.set_ylabel(ylabel_value_2, color="#ff7f00", fontsize=14)
+ax2.tick_params(
+    axis="y", labelcolor="#ff7f00", direction="in", rotation=90, labelsize=12
+)
+ax2.set_yticks(yticks_values_2)
+ax2.set_ylim(ylim_values_2)
+
+# Adding WER values to the plot for ami
+for i, txt in enumerate(ami_wer):
+    ax2.annotate(
+        f"{txt}%",
+        (microphones[i], txt),
+        textcoords="offset points",
+        xytext=(0, -30),
+        ha="center",
+        fontsize=12,
+    )
+
+# IHM dashed lines
+ax1.axhline(y=2.2, color="#377eb8", linestyle=":", linewidth=2)
+ax1.axhline(y=2.4, color="#ff7f00", linestyle=":", linewidth=2)
+
+# SDM dashed lines
+ax1.axhline(y=9.6, color="#377eb8", linestyle="--", linewidth=2)
+ax1.axhline(y=9.4, color="#ff7f00", linestyle="--", linewidth=2)
+
+# Creating custom legend items
+ihm_legend = Line2D([0], [0], color="black", linestyle=":", linewidth=2, label="IHM")
+sdm_legend = Line2D([0], [0], color="black", linestyle="--", linewidth=2, label="SDM")
+
+# Adding legends
+first_legend = ax1.legend(
+    handles=[ihm_legend, sdm_legend],
+    loc="upper left",
+    ncol=2,
+    fontsize=14,
+    edgecolor="black",
+)
+ax1.add_artist(first_legend)  # Add the first legend manually
+second_legend = ax1.legend(
+    handles=[libricss_line, ami_line], loc="upper right", fontsize=14, edgecolor="black"
+)  # Add the second legend
+
+# ===================
+# Part 4: Saving Output
+# ===================
+plt.tight_layout()
+plt.savefig('line_34.pdf', bbox_inches='tight')

ChartMimic/dataset/ori_500/line_35.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
+quantization_error = np.array(
+    [10 ** (-7), 10 ** (-6.4), 10 ** (-5.7), 10 ** (-5.2), 10 ** (-4.5), 10 ** (-3.8)]
+)
+search_window_ratio = np.array([1, 1.05, 1.15, 1.2, 2, 6.6])
+
+# Labels and Plot Types
+x_M_LVQ_4x8_10 = 10 ** (-4.9)
+label_M_LVQ_4x8_10 = "M-LVQ-4x8-10"
+x_M_LVQ_4x8_100 = 10 ** (-4.8)
+label_M_LVQ_4x8_100 = "M-LVQ-4x8-100"
+x_M_LVQ_4x8_256 =10 ** (-4.6)
+label_M_LVQ_4x8_256 = "M-LVQ-4x8-256"
+x_M_LVQ_4x8 = 10 ** (-4.5)
+label_M_LVQ_4x8 = "M-LVQ-4x8"
+label_empirical_relat_B2_8_0 = "empirical relat. (B2=8.0)"
+
+# Axes Limits and Labels
+yticks_values = np.arange(0, 8, 1)
+ylim_values = [0, 7]
+xlabel_value = "Quantization error"
+ylabel_value = "Search window size ratio"
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Create figure and axis
+fig, ax = plt.subplots(figsize=(6, 4))
+
+# Add vertical lines
+ax.axvline(
+    x=x_M_LVQ_4x8_10, color="#d86810", linestyle="--", label=label_M_LVQ_4x8_10, linewidth=3
+)
+ax.axvline(
+    x=x_M_LVQ_4x8_100, color="#029e73", linestyle="--", label=label_M_LVQ_4x8_100, linewidth=3
+)
+ax.axvline(
+    x=x_M_LVQ_4x8_256, color="#de8f05", linestyle="--", label=label_M_LVQ_4x8_256, linewidth=3
+)
+ax.axvline(
+    x=x_M_LVQ_4x8, color="#cc78bc", linestyle="--", label=label_M_LVQ_4x8, linewidth=3
+)
+
+# Plot the empirical relationship line
+ax.plot(
+    quantization_error,
+    search_window_ratio,
+    label=label_empirical_relat_B2_8_0,
+    color="#0173b2",
+    marker="o",
+    markersize=8,
+    mec="white",
+    linewidth=3,
+)
+
+# Set yticks
+plt.yticks(yticks_values, fontsize=10)
+plt.ylim(ylim_values)  # Adjusted y-axis limit
+
+# Customize the plot
+ax.set_xscale("log")
+ax.set_xlabel(xlabel_value, fontsize=16)
+ax.set_ylabel(ylabel_value, fontsize=16)
+
+# Remove x-axis minor ticks
+ax.tick_params(axis="x", which="minor", bottom=False)
+
+# Add grid for major ticks only
+ax.grid(True, which="major", linestyle="-", linewidth=0.5)
+
+# Add legend
+ax.legend(loc="upper left", bbox_to_anchor=(0, 1), fontsize=13)
+
+# ===================
+# Part 4: Saving Output
+# ===================
+plt.tight_layout()  # Adjust layout to not cut off legend
+plt.savefig('line_35.pdf', bbox_inches='tight')

ChartMimic/dataset/ori_500/line_36.py

@@ -0,0 +1,96 @@
+# ===================
+# Part 1: Importing Libraries
+# ===================
+import matplotlib.pyplot as plt
+import numpy as np; np.random.seed(0)
+
+
+# ===================
+# Part 2: Data Preparation
+# ===================
+# Sample data
+ratios = np.array([0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0])
+pna_performance = np.array([0.6, 0.65, 0.7, 0.75, 0.8, 0.82, 0.85, 0.83, 0.82, 0.8])
+gin_performance = np.array([0.5, 0.52, 0.55, 0.57, 0.6, 0.62, 0.63, 0.65, 0.64, 0.63])
+pna_std = np.array([0.05] * 10)
+gin_std = np.array([0.08] * 10)
+
+# Axes Limits and Labels
+xlabel_value = "Ratio r"
+xlim_values = [0.05, 1.05]
+xticks_values = np.arange(0.1, 1.05, 0.1)
+
+ylabel_value = "Performance"
+yticks_values_1 = np.arange(0.5, 0.82, 0.1)
+ylim_values_1 = [0.42, 0.9]
+yticks_values_2 = np.arange(0.4, 0.82, 0.1)
+ylim_values_2 = 0.35, 0.9
+yticks_values_3 = np.arange(0.4, 0.82, 0.1)
+ylim_values_3 = [0.35, 0.9]
+
+# Labels
+labels = ["PNA + ours", "GIN + ours"]
+
+# Titles
+titles = ["SPMotif-0.5", "SPMotif-0.7", "SPMotif-0.9"]
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Plot settings
+fig, axs = plt.subplots(1, 3, figsize=(15, 5))
+colors = ["#9467bd", "#ff7f0e"]
+markers = ["s", "s"]  # Square and circle markers
+
+for i, ax in enumerate(axs):
+    ax.plot(
+        ratios, pna_performance, label=labels[0], color=colors[0], marker=markers[0]
+    )
+    ax.fill_between(
+        ratios,
+        pna_performance - pna_std,
+        pna_performance + pna_std,
+        color=colors[0],
+        alpha=0.2,
+    )
+    ax.plot(
+        ratios, gin_performance, label=labels[1], color=colors[1], marker=markers[1]
+    )
+    ax.fill_between(
+        ratios,
+        gin_performance - gin_std,
+        gin_performance + gin_std,
+        color=colors[1],
+        alpha=0.2,
+    )
+    ax.set_xticks(xticks_values)
+    ax.set_xlim(xlim_values)
+    ax.set_title(titles[i])
+    ax.set_xlabel(xlabel_value)
+    ax.grid(True)
+
+# Adjust subplot layout
+plt.subplots_adjust(wspace=0.3)
+
+axs[0].set_yticks(yticks_values_1)
+axs[0].set_ylim(ylim_values_1)
+axs[1].set_yticks(yticks_values_2)
+axs[1].set_ylim(ylim_values_2)
+axs[2].set_yticks(yticks_values_3)
+axs[2].set_ylim(ylim_values_3)
+
+# Move legend inside the plot area
+axs[0].legend(loc="upper left")
+axs[1].legend(loc="lower right")
+axs[2].legend(loc="lower left")
+
+# Adjust y-axis label to match reference picture
+axs[0].set_ylabel(ylabel_value)
+axs[1].set_ylabel(ylabel_value)
+axs[2].set_ylabel(ylabel_value)
+
+# ===================
+# Part 4: Saving Output
+# ===================
+plt.tight_layout()
+plt.savefig('line_36.pdf', bbox_inches='tight')

ChartMimic/dataset/ori_500/line_37.py

@@ -0,0 +1,112 @@
+# ===================
+# Part 1: Importing Libraries
+# ===================
+import matplotlib.pyplot as plt
+import numpy as np; np.random.seed(0)
+
+
+# ===================
+# Part 2: Data Preparation
+# ===================
+# Sample data
+ensemble_size = np.arange(5, 31, 1)
+accuracy_mean = np.array(
+    [
+        75.2,
+        76.1,
+        76.7,
+        77.0,
+        77.2,
+        77.3,
+        77.4,
+        77.5,
+        77.5,
+        77.5,
+        77.5,
+        77.5,
+        77.5,
+        77.5,
+        77.5,
+        77.5,
+        77.5,
+        77.5,
+        77.5,
+        77.5,
+        77.5,
+        77.5,
+        77.5,
+        77.5,
+        77.5,
+        77.5,
+    ]
+)
+accuracy_std = np.array(
+    [
+        1.0,
+        0.9,
+        0.8,
+        0.7,
+        0.6,
+        0.5,
+        0.4,
+        0.3,
+        0.3,
+        0.3,
+        0.3,
+        0.3,
+        0.3,
+        0.3,
+        0.3,
+        0.3,
+        0.3,
+        0.3,
+        0.3,
+        0.3,
+        0.3,
+        0.3,
+        0.3,
+        0.3,
+        0.3,
+        0.3,
+    ]
+)
+
+# Axes Limits and Labels
+xlabel_value = "Ensemble Size"
+xlim_values = [3.5, 32]
+xticks_values = np.arange(6, 31, 3)
+
+ylabel_value = "Accuracy"
+ylim_values = [74.5, 78.0]
+
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Plotting the mean accuracy with the standard deviation as a shaded area
+plt.figure(figsize=(6, 5))
+plt.plot(ensemble_size, accuracy_mean, color="#1f77b4")
+plt.fill_between(
+    ensemble_size,
+    accuracy_mean - accuracy_std,
+    accuracy_mean + accuracy_std,
+    color="#1f77b4",
+    alpha=0.2,
+)
+
+# Set x-axis to only display specific ticks and extend y-axis to leave space at top
+plt.xticks(xticks_values, fontsize=12)
+plt.yticks(fontsize=12)
+plt.xlim(xlim_values)  # Adjusted x-axis limit
+plt.ylim(ylim_values)  # Adjusted y-axis limit
+
+# Labeling the axes
+plt.xlabel(xlabel_value, fontsize=16)
+plt.ylabel(ylabel_value, fontsize=16)
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Adjust layout and save plot
+plt.tight_layout()
+plt.savefig('line_37.pdf', bbox_inches='tight')

ChartMimic/dataset/ori_500/line_38.py

@@ -0,0 +1,67 @@
+# ===================
+# Part 1: Importing Libraries
+# ===================
+import matplotlib.pyplot as plt
+import numpy as np; np.random.seed(0)
+
+
+# ===================
+# Part 2: Data Preparation
+# ===================
+# Data
+epochs = ["3", "10", "30", "100"]  # Treat epochs as strings to make them categorical
+gpt_neo = [0.8, 0.8, 0.8, 0.8]
+model_3 = [0.7, 0.65, 0.6, 0.75]
+model_5 = [0.65, 0.75, 0.35, 0.5]
+model_7 = [0.6, 0.65, 0.5, 0.65]
+model_10 = [0.45, 0.5, 0.45, 0.4]
+model_30 = [0.3, 0.45, 0.75, 0.35]
+
+# Axes Limits and Labels
+xlabel_value = "# Epochs"
+
+ylabel_value = "MA"
+ylim_values = [0.0, 0.83]
+yticks_values = np.arange(0.0, 0.81, 0.2)
+
+# Labels
+label_GPT_Neo="GPT-Neo"
+label_3 = "3"
+label_5 = "5"
+label_7 = "7"
+label_10 = "10"
+label_30 = "30"
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Plot
+plt.figure(figsize=(6, 3))
+plt.axhline(y=0.8, color="black", linestyle="--", linewidth=1, label=label_GPT_Neo)
+plt.plot(epochs, model_3, "r-", marker="s", label=label_3)
+plt.plot(epochs, model_5, "y-", marker="s", label=label_5)
+plt.plot(epochs, model_7, "k-", marker="s", label=label_7)
+plt.plot(epochs, model_10, "b-", marker="s", label=label_10)
+plt.plot(epochs, model_30, "g-", marker="s", label=label_30)
+
+plt.yticks(yticks_values, fontsize=14)
+plt.ylim(ylim_values)
+
+# Set x-axis labels equidistantly
+ax = plt.gca()
+ax.set_xticks(np.arange(len(epochs)))  # Positional indexing for equidistant spacing
+ax.set_xticklabels(epochs, fontsize=14)  # Labeling x-ticks as per epochs
+
+plt.xlabel(xlabel_value, fontsize=16)
+plt.ylabel(ylabel_value, fontsize=16)
+
+plt.legend(
+    loc="lower left", ncol=3, fontsize=12, columnspacing=5
+)  # Adjusted legend settings
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Adjust layout and show plot
+plt.tight_layout()
+plt.savefig('line_38.pdf', bbox_inches='tight')

ChartMimic/dataset/ori_500/line_40.py

@@ -0,0 +1,71 @@
+# ===================
+# Part 1: Importing Libraries
+# ===================
+import matplotlib.pyplot as plt
+import numpy as np; np.random.seed(0)
+
+
+# ===================
+# Part 2: Data Preparation
+# ===================
+# New generated data
+snr_values = np.linspace(5, 25, 5)
+jpeg_ldpc = np.random.normal(loc=50, scale=5, size=5).clip(10, 90)
+deepjscc_wo_ofdm = np.random.normal(loc=40, scale=10, size=5).clip(5, 45)
+deepjscc_w_ofdm = np.random.normal(loc=70, scale=10, size=5).clip(20, 95)
+ours = np.random.normal(loc=85, scale=5, size=5).clip(30, 100)
+
+# Labels and Plot Types
+label_jpeg_ldpc = "JPEG+LDPC"
+label_deepjscc_wo_ofdm = "DEEPJSCC w/o OFDM"
+label_deepjscc_w_ofdm = "DEEPJSCC w/ OFDM"
+label_ours = "OURS"
+
+# Axes Limits and Labels
+yticks_values = np.arange(0, 110, 10)
+ylim_values = [0, 105]
+xlabel_value = "SNR"
+ylabel_value = "Classification Accuracy (%)"
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Plotting with error bars (second-level element)
+plt.figure(figsize=(8, 6))  # Increased figure size for clarity
+plt.plot(snr_values, jpeg_ldpc, "-o", label=label_jpeg_ldpc, color="#1f77b4")
+plt.fill_between(snr_values, jpeg_ldpc - 5, jpeg_ldpc + 5, color="#1f77b4", alpha=0.2)
+plt.plot(snr_values, deepjscc_wo_ofdm, "-^", label=label_deepjscc_wo_ofdm, color="#ff7f0e")
+plt.fill_between(
+    snr_values, deepjscc_wo_ofdm - 10, deepjscc_wo_ofdm + 10, color="#ff7f0e", alpha=0.2
+)
+plt.plot(snr_values, deepjscc_w_ofdm, "-x", label=label_deepjscc_w_ofdm, color="#2ca02c")
+plt.fill_between(
+    snr_values, deepjscc_w_ofdm - 10, deepjscc_w_ofdm + 10, color="#2ca02c", alpha=0.2
+)
+plt.plot(snr_values, ours, "-s", label=label_ours, color="#d62728")
+plt.fill_between(snr_values, ours - 5, ours + 5, color="#d62728", alpha=0.2)
+
+# Customizing axes and labels (third-level elements)
+plt.yticks(yticks_values, fontsize=12)
+plt.xticks(fontsize=12)
+plt.ylim(ylim_values)
+plt.grid(True)
+
+# Relocating the legend to ensure no overlap with data lines
+plt.legend(loc="lower right", frameon=True, shadow=True, fontsize=10)
+
+# Customizing the background (third-level element)
+plt.gca().set_facecolor("#f4f4f5")
+plt.gca().spines["top"].set_visible(False)
+plt.gca().spines["right"].set_visible(False)
+
+# Labels
+plt.xlabel(xlabel_value, fontsize=14)
+plt.ylabel(ylabel_value, fontsize=14)
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Adjust layout and save the figure
+plt.tight_layout()
+plt.savefig('line_40.pdf', bbox_inches='tight')

ChartMimic/dataset/ori_500/line_41.py

@@ -0,0 +1,130 @@
+# ===================
+# Part 1: Importing Libraries
+# ===================
+import matplotlib.pyplot as plt
+import numpy as np; np.random.seed(0)
+
+
+# ===================
+# Part 2: Data Preparation
+# ===================
+# Updated steps
+gradient_steps = np.linspace(0, 200, 50)
+
+# Generating distinct trends for each line
+line1_values = np.sin(gradient_steps * 0.1) + 1.0  # Sinusoidal trend
+line2_values = np.array(gradient_steps) ** 2 * 0.0001 + 0.5  # Quadratic growth
+line3_values = np.random.normal(
+    loc=1.5, scale=0.2, size=len(gradient_steps)
+)  # Random noise
+line4_values = np.exp(0.01 * gradient_steps)  # Exponential growth
+
+# Simulating standard deviations for error
+std_dev = 0.1
+line1_std = np.full_like(line1_values, std_dev)
+line2_std = np.full_like(line2_values, std_dev)
+line3_std = np.full_like(line3_values, std_dev)
+line4_std = np.full_like(line4_values, std_dev)
+
+# Axes Limits and Labels
+xlabel_value = "Gradient Steps (x 62.5K)"
+xlim_values = [0, 200]
+xticks_values = np.linspace(0, 200, 9)
+
+ylabel_value_1 = "Performance Value"
+ylabel_value_2 = "Exponential Scale"
+yticks_values_1 = np.arange(0, 5, 1)
+yticks_values_2 = np.arange(0, 8, 1)
+ylim_values_1 = [0, 5]
+ylim_values_2 = [0, 8]
+
+# Labels
+label_1 = "Line 1 (Sinusoidal)"
+label_2 = "Line 2 (Quadratic)"
+label_3 = "Line 3 (Random Noise)"
+
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Creating a figure with two subplots
+fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(6, 8))
+
+# Plotting on the first subplot
+ax1.plot(
+    gradient_steps, line1_values, "o-", color="purple", label=label_1
+)
+ax1.fill_between(
+    gradient_steps,
+    line1_values - line1_std,
+    line1_values + line1_std,
+    color="purple",
+    alpha=0.2,
+)
+
+ax1.plot(gradient_steps, line2_values, "s-", color="blue", label=label_2)
+ax1.fill_between(
+    gradient_steps,
+    line2_values - line2_std,
+    line2_values + line2_std,
+    color="blue",
+    alpha=0.2,
+)
+
+ax2.plot(
+    gradient_steps,
+    line3_values,
+    "^--",
+    color="green",
+    markerfacecolor=(0, 0, 0, 0),
+    markeredgecolor="green",
+    label=label_3,
+)
+ax2.fill_between(
+    gradient_steps,
+    line3_values - line3_std,
+    line3_values + line3_std,
+    color="green",
+    alpha=0.2,
+)
+
+ax1.set_xlabel(xlabel_value, fontsize=12)
+ax1.set_ylabel(ylabel_value_1, fontsize=12)
+ax1.set_xticks(xticks_values)
+ax1.set_yticks(yticks_values_1)
+ax1.set_xlim(xlim_values)
+ax1.set_ylim(ylim_values_1)
+ax1.legend(loc="upper center", frameon=False, ncol=2, bbox_to_anchor=(0.5, 1.15))
+ax1.grid(
+    True, which="both", axis="both", color="lightgray", linestyle="--", linewidth=0.5
+)
+ax1.set_facecolor("#f9f9f9")
+
+# Plotting on the second subplot
+ax2.plot(
+    gradient_steps, line4_values, "*-", color="red", label="Line 4 (Exponential Focus)"
+)
+ax2.fill_between(
+    gradient_steps,
+    line4_values - line4_std,
+    line4_values + line4_std,
+    color="red",
+    alpha=0.2,
+)
+ax2.set_xlabel(xlabel_value, fontsize=12)
+ax2.set_ylabel(ylabel_value_2, fontsize=12)
+ax2.set_xticks(xticks_values)
+ax2.set_yticks(yticks_values_2)
+ax2.set_xlim(xlim_values)
+ax2.set_ylim(ylim_values_2)
+ax2.legend(loc="upper center", frameon=False, ncol=2, bbox_to_anchor=(0.5, 1.15))
+ax2.grid(
+    True, which="both", axis="both", color="lightgray", linestyle="--", linewidth=0.5
+)
+ax2.set_facecolor("#f9f9f9")
+
+# ===================
+# Part 4: Saving Output
+# ===================
+plt.tight_layout()
+plt.savefig('line_41.pdf', bbox_inches='tight')

ChartMimic/dataset/ori_500/line_42.py

@@ -0,0 +1,74 @@
+# ===================
+# Part 1: Importing Libraries
+# ===================
+import matplotlib.pyplot as plt
+import numpy as np; np.random.seed(0)
+
+
+# ===================
+# Part 2: Data Preparation
+# ===================
+# Data
+gradient_steps = np.array([0, 50, 100, 150, 200])
+line1_values = np.array([0.1, 0.8, 1.2, 1.5, 1.3])
+line2_values = np.array([1.0, 0.5, 0.8, 1.0, 1.8])
+line3_values = np.array([1.8, 1.2, 0.5, 0.4, 0.3])
+line4_values = np.poly1d(np.polyfit(gradient_steps, line2_values, 3))(
+    gradient_steps
+)  # Polynomial trend
+
+# Axes Limits and Labels
+xlabel_value = "Gradient Steps (x 62.5K)"
+
+ylabel_value_1 = "Value"
+ylabel_value_2 = "Polynomial Value"
+yticks_values_1 = np.arange(0, 2.1, 0.5)
+yticks_values_2 = np.arange(min(line4_values), max(line4_values) + 0.1, 0.2)
+
+# Labels
+label_1 = "Line 1"
+label_2 = "Line 2"
+label_3 = "Polynomial Trend (from Line 2)"
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Create subplots
+fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))
+
+# First subplot
+ax1.plot(gradient_steps, line1_values, "o-", color="orange", label=label_1)
+ax1.plot(gradient_steps, line2_values, "s-", color="blue", label=label_2)
+ax1.set_xlabel(xlabel_value)
+ax1.set_ylabel(ylabel_value_1)
+ax1.set_xticks(gradient_steps)
+ax1.set_yticks(yticks_values_1)
+ax1.legend(loc="upper left")
+ax1.grid(True)
+
+# Second subplot
+ax2.plot(gradient_steps, line3_values, "^--", color="green", label="Line 3")
+ax2.plot(gradient_steps, line4_values, "*-m", label=label_3)
+ax2.set_xlabel(xlabel_value)
+ax2.set_ylabel(ylabel_value_2)
+ax2.set_xticks(gradient_steps)
+ax2.set_yticks(yticks_values_2)
+ax2.legend(loc="upper right")
+ax2.grid(True)
+
+# Annotations and styling
+for ax in (ax1, ax2):
+    ax.spines["left"].set_position(("outward", 10))
+    ax.spines["bottom"].set_position(("outward", 10))
+    ax.spines["right"].set_color("none")
+    ax.spines["top"].set_color("none")
+for x, y in zip(gradient_steps, line1_values):
+    ax1.annotate(
+        f"{y:.1f}", xy=(x, y), textcoords="offset points", xytext=(0, 10), ha="center"
+    )
+
+# ===================
+# Part 4: Saving Output
+# ===================
+plt.tight_layout()
+plt.savefig('line_42.pdf', bbox_inches='tight')

ChartMimic/dataset/ori_500/line_43.py

@@ -0,0 +1,109 @@
+# ===================
+# Part 1: Importing Libraries
+# ===================
+import matplotlib.pyplot as plt
+import numpy as np; np.random.seed(0)
+
+
+# ===================
+# Part 2: Data Preparation
+# ===================
+# Simulated data
+collab_x = np.array(
+    ["All", "MLP", "GCN", "NCN", "NCNC", "NeoGNN-BUDDY", "SEAL", "Node2Vec"]
+)
+collab_y = np.array([75, 73, 72, 70, 69, 71, 73, 74])
+collab_err = np.array([2, 3, 1, 1, 2, 2, 3, 2])
+collab_y2 = np.array(
+    [67, 65, 64, 62, 61, 63, 65, 66]
+)  # Adjusted data for clear spacing
+collab_err2 = np.array([3, 3, 1, 2, 2, 3, 3, 4])
+
+ppa_x = np.array(
+    ["All", "MLP", "GCN", "NCN", "NCNC", "NeoGNN-BUDDY", "SEAL", "Node2Vec"]
+)
+ppa_y = np.array([65, 63, 62, 60, 59, 61, 63, 64])
+ppa_err = np.array([3, 2, 1, 2, 3, 2, 2, 1])
+ppa_y2 = np.array([70, 69, 69, 68, 67, 69, 70, 68])  # Adjusted data for clear spacing
+ppa_err2 = np.array([2, 3, 1, 1, 2, 2, 3, 2])
+
+# Axes Limits and Labels
+ylabel_value = "Hits@50"
+ylim_values = [55, 80]
+yticks_values = np.arange(55, 81, 5)
+
+# Labels
+label_1 = "ogbl-collab 2022"
+label_2 = "ogbl-collab 2023"
+label_3 = "ogbl-ppa 2022"
+label_4 = "ogbl-ppa 2023"
+
+# Titles
+title_1 = "ogbl-collab Results"
+title_2 = "ogbl-ppa Results"
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Create a subplot layout of 1x2
+fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5), sharey=True)
+
+# First subplot for ogbl-collab
+ax1.errorbar(
+    collab_x,
+    collab_y,
+    yerr=collab_err,
+    fmt="o-",
+    label=label_1,
+    color="#3d89be",
+    capsize=5,
+)
+ax1.errorbar(
+    collab_x,
+    collab_y2,
+    yerr=collab_err2,
+    fmt="^-",
+    label=label_2,
+    color="#00BFFF",
+    capsize=5,
+)
+ax1.set_title(title_1)
+ax1.set_xticks(collab_x)
+ax1.set_xticklabels(collab_x, rotation=45, ha="right", fontsize=12)
+ax1.set_yticks(yticks_values)
+ax1.set_ylim(ylim_values)
+ax1.grid(True, which="both", linestyle="--", linewidth=0.5, alpha=0.5)
+ax1.set_ylabel(ylabel_value, fontsize=16)
+ax1.legend(loc="lower left", fontsize=12)
+
+# Second subplot for ogbl-ppa
+ax2.errorbar(
+    ppa_x,
+    ppa_y,
+    yerr=ppa_err,
+    fmt="s--",
+    label=label_3,
+    color="#ff7f0e",
+    capsize=5,
+)
+ax2.errorbar(
+    ppa_x,
+    ppa_y2,
+    yerr=ppa_err2,
+    fmt="o-.",
+    label=label_4,
+    color="#FFA500",
+    capsize=5,
+)
+ax2.set_title(title_2)
+ax2.set_xticks(ppa_x)
+ax2.set_xticklabels(ppa_x, rotation=45, ha="right", fontsize=12)
+ax2.grid(True, which="both", linestyle="--", linewidth=0.5, alpha=0.5)
+ax2.legend(loc="upper left", fontsize=12)
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Enhance overall layout
+plt.tight_layout()
+plt.savefig('line_43.pdf', bbox_inches='tight')

ChartMimic/dataset/ori_500/line_44.py

@@ -0,0 +1,98 @@
+# ===================
+# Part 1: Importing Libraries
+# ===================
+import matplotlib.pyplot as plt
+import numpy as np; np.random.seed(0)
+
+
+# ===================
+# Part 2: Data Preparation
+# ===================
+# Data for plotting, using random noise to add variability
+N = np.array([10, 20, 30, 40, 50, 60])
+standard = np.power(10, -np.linspace(10, 6, len(N)))
+constrained = np.full_like(standard, 1e-12)
+
+# New random data for variability
+experimental = np.power(10, -np.random.uniform(1, 6, len(N)))
+hypothetical = np.power(10, -np.linspace(1.5, 6.5, len(N)))
+
+# Axes Limits and Labels
+xlabel_value = "N"
+
+ylabel_value = "Precision"
+
+# Labels
+label_Standard="Standard"
+label_Constrained="Constrained"
+label_Experimental="Experimental"
+label_Hypothetical="Hypothetical"
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Set the figure size
+plt.figure(figsize=(10, 6))
+
+# Plot the data using different styles and colors
+plt.loglog(
+    N,
+    standard,
+    "o-",
+    color="#1f77b4",
+    label=label_Standard,
+    markerfacecolor="#1f77b4",
+    markersize=8,
+    linewidth=2,
+)
+plt.loglog(
+    N,
+    constrained,
+    "x-",
+    color="#ff7f0e",
+    label=label_Constrained,
+    markersize=8,
+    linewidth=2,
+)
+plt.loglog(
+    N,
+    experimental,
+    "s--",
+    color="green",
+    label=label_Experimental,
+    markersize=8,
+    linewidth=2,
+)
+plt.loglog(
+    N,
+    hypothetical,
+    "^-",
+    color="purple",
+    label=label_Hypothetical,
+    markersize=8,
+    linewidth=2,
+)
+
+# Customize the x and y axes limits and labels
+plt.xlabel(xlabel_value, fontsize=14)
+plt.ylabel(ylabel_value, fontsize=14)
+plt.xticks(N, labels=[str(n) for n in N])  # Ensuring x-ticks match the N values
+plt.xlim(
+    left=N[0] * 0.9, right=N[-1] * 1.1
+)  # Set x-axis limits to prevent cutting off data points
+plt.ylim(
+    bottom=1e-14, top=1e-1
+)  # Extend y-axis to leave some space above and below the data
+
+# Adjust the legend position
+plt.legend(bbox_to_anchor=(1, 0.5), fontsize=12, frameon=True)
+
+# Add grid lines for better readability
+plt.grid(True, which="both", ls="--", color="grey", linewidth=1, alpha=0.5)
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Adjust the layout and save the plot
+plt.tight_layout()
+plt.savefig('line_44.pdf', bbox_inches='tight')

ChartMimic/dataset/ori_500/line_46.py

@@ -0,0 +1,79 @@
+# ===================
+# Part 1: Importing Libraries
+# ===================
+
+import matplotlib.pyplot as plt
+import numpy as np; np.random.seed(0)
+
+# ===================
+# Part 2: Data Preparation
+# ===================
+# New data for plotting, representing some scientific or business metrics
+
+times = [0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0]
+activity_standard = [0.0, 0.91, 0.95, 0.14, -0.73, -1.01, -0.27, 0.76, 1.17, 0.39, -0.61, -1.01, -0.55, 0.49, 0.82, 0.54, -0.23, -1.09, -0.83, 0.17, 1.09, 0.94, -0.01, -0.94, -0.77]
+activity_innovative = [1.06, 0.46, -0.49, -1.0, -0.63, 0.26, 1.07, 0.74, -0.15, -0.74, -0.88, 0.0, 0.88, 1.07, 0.15, -0.72, -0.93, -0.3, 0.54, 1.05, 0.44, -0.48, -0.85, -0.49, 0.4]
+
+# Extracted variables
+line_label1 = "Standard Activity"
+line_label2 = "Innovative Activity"
+xlim_values = (0, 25)
+ylim_values = (-1.5, 1.5)
+xlabel_value = "Time (Hours)"
+ylabel_value = "Activity Level"
+yticks_values = [-1.5, -1.0, -0.5, 0, 0.5, 1, 1.5]
+title1 = "Daytime Activity Monitoring"
+title2 = "Nighttime Activity Monitoring"
+legend_location = "lower center"
+legend_bbox_to_anchor = (0.5, -0.2)
+legend_frameon = False
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Set the figure size
+fig, axs = plt.subplots(
+    2, 1, figsize=(6, 10)
+)  # Use a 1x2 subplot grid for horizontal layout
+
+# Define new colors and markers for a fresh look
+colors = ["dodgerblue", "crimson"]
+linestyles = ["-", "--"]
+labels = [line_label1, line_label2]
+
+# First subplot with area fill
+axs[0].plot(
+    times, activity_standard, color=colors[0], linestyle=linestyles[0], label=labels[0]
+)
+axs[0].set_title(title1)
+axs[0].set_xlabel(xlabel_value)
+axs[0].set_ylabel(ylabel_value)
+axs[0].set_xlim(xlim_values)
+axs[0].set_ylim(ylim_values)
+axs[0].set_yticks(yticks_values)
+axs[0].legend(loc=legend_location, bbox_to_anchor=legend_bbox_to_anchor, frameon=legend_frameon)
+axs[0].tick_params(axis="both", which="both", length=0)
+
+# Second subplot with gradient fill emulation
+axs[1].plot(
+    times,
+    activity_innovative,
+    color=colors[1],
+    linestyle=linestyles[1],
+    label=labels[1],
+)
+axs[1].set_title(title2)
+axs[1].set_xlabel(xlabel_value)
+axs[1].set_ylabel(ylabel_value)
+axs[1].set_ylim(ylim_values)
+axs[1].set_xlim(xlim_values)
+axs[1].set_yticks(yticks_values)
+axs[1].legend(loc=legend_location, bbox_to_anchor=legend_bbox_to_anchor, frameon=legend_frameon)
+axs[1].tick_params(axis="both", which="both", length=0)
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Enhance overall layout and visuals
+plt.tight_layout()
+plt.savefig('line_46.pdf', bbox_inches='tight')

ChartMimic/dataset/ori_500/line_47.py

@@ -0,0 +1,94 @@
+# ===================
+# Part 1: Importing Libraries
+# ===================
+import matplotlib.pyplot as plt
+import numpy as np; np.random.seed(0)
+
+
+# ===================
+# Part 2: Data Preparation
+# ===================
+# Generate new data representing different types of performance metrics over time
+time_points = np.linspace(0, 12, 100)  # Representing time in months
+performance_standard = 0.5 * np.sin(2 * np.pi * time_points / 12) + 0.5
+performance_innovation = 0.5 * np.cos(2 * np.pi * time_points / 12) + 0.5
+
+# Calculate uncertainty bounds for visual emphasis
+upper_bound_standard = performance_standard + 0.1
+lower_bound_standard = performance_standard - 0.1
+upper_bound_innovation = performance_innovation + 0.1
+lower_bound_innovation = performance_innovation - 0.1
+
+# Axes Limits and Labels
+xlabel_value = "Time (Months)"
+xlim_values = [5, 25]
+xticks_values = np.arange(0, 13, 1)
+
+ylabel_value = "Performance Index"
+ylim_values = [0, 1.1]
+yticks_values = np.linspace(0, 1, 6)
+
+# Labels
+label_1 = "Standard Performance"
+label_2 = "Innovative Performance"
+
+# Title
+title = "Comparative Performance Analysis Over Time"
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Set the figure size and create a single plot
+fig, ax = plt.subplots(figsize=(10, 6))
+
+# Plot the standard performance with filled uncertainty
+ax.fill_between(
+    time_points,
+    lower_bound_standard,
+    upper_bound_standard,
+    color="lightblue",
+    alpha=0.3,
+)
+ax.plot(
+    time_points,
+    performance_standard,
+    label=label_1,
+    color="blue",
+    linestyle="-",
+    linewidth=2,
+)
+
+# Plot the innovative performance with filled uncertainty
+ax.fill_between(
+    time_points,
+    lower_bound_innovation,
+    upper_bound_innovation,
+    color="lightcoral",
+    alpha=0.3,
+)
+ax.plot(
+    time_points,
+    performance_innovation,
+    label=label_2,
+    color="red",
+    linestyle="-",
+    linewidth=2,
+)
+
+# Customize the axes and grid
+ax.set_title(title)
+ax.set_xlabel(xlabel_value, fontsize=14)
+ax.set_ylabel(ylabel_value, fontsize=14)
+ax.set_ylim(ylim_values)
+ax.set_xticks(xticks_values)
+ax.set_yticks(yticks_values)
+
+# Add a legend to the plot
+ax.legend(loc="upper right", frameon=True, fontsize=12)
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Enhance layout and display
+plt.tight_layout()
+plt.savefig('line_47.pdf', bbox_inches='tight')

ChartMimic/dataset/ori_500/line_48.py

@@ -0,0 +1,74 @@
+# ===================
+# Part 1: Importing Libraries
+# ===================
+import matplotlib.pyplot as plt
+import numpy as np; np.random.seed(0)
+
+
+# ===================
+# Part 2: Data Preparation
+# ===================
+# Generate new data for a complex scenario
+months = np.arange(1, 13, 1)  # Months of the year
+sales = np.random.normal(1055, 250, len(months))  # Simulate monthly sales
+temperature = (
+    5 * np.sin(0.5 * np.pi * months / 12) + 20
+)  # Simulate average monthly temperature
+
+# Axes Limits and Labels
+xlabel_value = "Month"
+
+ylabel_value_1 = "Sales"
+ylabel_value_2 = "Temperature (°C)"
+
+# Labels
+label_1 = "Monthly Sales"
+label_2 = "Average Temperature"
+
+# Titles
+title = "Sales and Temperature Correlation Over a Year"
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Create the main figure and axis
+fig, ax1 = plt.subplots(figsize=(10, 6))
+
+# Plot sales data with primary axis
+color = "tab:blue"
+ax1.set_xlabel(xlabel_value)
+ax1.set_ylabel(ylabel_value_1, color=color)
+ax1.plot(months, sales, label=label_1, color=color, marker="o", linestyle="-")
+ax1.tick_params(axis="y", labelcolor=color)
+ax1.set_xticks(months)
+ax1.set_title(title)
+
+# Create a second y-axis for temperature
+ax2 = ax1.twinx()  # Instantiate a second axes that shares the same x-axis
+color = "tab:red"
+ax2.set_ylabel(
+    ylabel_value_2, color=color
+)  # We already handled the x-label with ax1
+ax2.plot(
+    months,
+    temperature,
+    label=label_2,
+    color=color,
+    marker="s",
+    linestyle="--",
+)
+ax2.tick_params(axis="y", labelcolor=color)
+
+# Add legends to the plot
+lines, labels = ax1.get_legend_handles_labels()
+lines2, labels2 = ax2.get_legend_handles_labels()
+ax1.legend(lines + lines2, labels + labels2, loc="upper left", frameon=True)
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Adjust layout for better spacing and display
+plt.tight_layout()
+
+# Show the plot
+plt.savefig('line_48.pdf', bbox_inches='tight')

ChartMimic/dataset/ori_500/line_49.py

@@ -0,0 +1,133 @@
+# ===================
+# Part 1: Importing Libraries
+# ===================
+import matplotlib.pyplot as plt
+import numpy as np
+
+np.random.seed(0)
+
+# ===================
+# Part 2: Data Preparation
+# ===================
+# Data preparation
+
+times = [0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0]
+growth = [1.18, 0.76, 0.61, 0.59, 0.45, 0.09, 0.23, 0.08, 0.06, 0.09, 0.05]
+decay = [1.07, 0.41, 0.14, 0.07, 0.03, 0.08, -0.01, 0.02, -0.04, -0.13, 0.03]
+oscillation = [0.09, 0.77, 1.14, -0.0, -0.75, -0.98, -0.13, 0.8, 1.0, 0.45, -0.63]
+
+# Extracted variables
+growth_label = "Growth"
+decay_label = "Decay"
+oscillation_label = "Oscillation"
+
+xlim_values = (0, 10)
+ylim_values_growth = (-0.2, 1.3)
+ylim_values_decay = (-0.2, 1.3)
+ylim_values_oscillation = (-1.2, 1.2)
+
+yticks_growth = [-0.2, 0.3, 0.8, 1.3]
+yticks_decay = [-0.2, 0.3, 0.8, 1.3]
+yticks_oscillation = [-1.2, -1, 0, 1, 1.2]
+
+xlabel_value = "Time"
+ylabel_value = "Value"
+
+title_growth = "Exponential Growth Over Time"
+title_decay = "Exponential Decay Over Time"
+title_oscillation = "Oscillatory Behavior Over Time"
+
+legend_location = "upper center"
+legend_bbox_to_anchor = (0.5, 1.15)
+legend_frameon = False
+
+grid_linestyle = "--"
+grid_alpha = 0.5
+
+tick_params_color = "gray"
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Create a 3-subplot layout
+fig, axs = plt.subplots(3, 1, figsize=(6, 9))
+
+# First subplot: Growth
+axs[0].plot(
+    times,
+    growth,
+    label=growth_label,
+    color="green",
+    clip_on=False,
+    zorder=10,
+    linestyle="-",
+    marker="o",
+)
+axs[0].set_title(title_growth, y=1.1)
+axs[0].set_xlim(*xlim_values)
+axs[0].set_ylim(*ylim_values_growth)
+axs[0].set_yticks(yticks_growth)
+axs[0].set_ylabel(ylabel_value)
+axs[0].legend(
+    loc=legend_location, bbox_to_anchor=legend_bbox_to_anchor, frameon=legend_frameon
+)
+axs[0].grid(True, linestyle=grid_linestyle, alpha=grid_alpha)
+axs[0].tick_params(axis="both", which="both", color=tick_params_color)
+
+# Second subplot: Decay
+axs[1].plot(
+    times,
+    decay,
+    label=decay_label,
+    color="red",
+    clip_on=False,
+    zorder=10,
+    linestyle="-",
+    marker="x",
+)
+axs[1].set_xlim(*xlim_values)
+axs[1].set_ylim(*ylim_values_decay)
+axs[1].set_yticks(yticks_decay)
+axs[1].set_title(title_decay, y=1.1)
+axs[1].set_ylabel(ylabel_value)
+axs[1].legend(
+    loc=legend_location, bbox_to_anchor=legend_bbox_to_anchor, frameon=legend_frameon
+)
+axs[1].grid(True, linestyle=grid_linestyle, alpha=grid_alpha)
+axs[1].tick_params(axis="both", which="both", color=tick_params_color)
+
+# Third subplot: Oscillation
+axs[2].plot(
+    times,
+    oscillation,
+    label=oscillation_label,
+    clip_on=False,
+    zorder=10,
+    color="blue",
+    linestyle="-",
+    marker="s",
+)
+axs[2].set_title(title_oscillation, y=1.1)
+axs[2].set_xlim(*xlim_values)
+axs[2].set_ylim(*ylim_values_oscillation)
+axs[2].set_yticks(yticks_oscillation)
+axs[2].set_xlabel(xlabel_value)
+axs[2].set_ylabel(ylabel_value)
+axs[2].legend(
+    loc=legend_location, bbox_to_anchor=legend_bbox_to_anchor, frameon=legend_frameon
+)
+axs[2].grid(True, linestyle=grid_linestyle, alpha=grid_alpha)
+axs[2].tick_params(
+    axis="both",
+    which="both",
+    color=tick_params_color,
+)
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Adjust layout for better spacing and display
+plt.tight_layout()
+
+# Show the plot
+plt.savefig("line_49.pdf", bbox_inches="tight")

ChartMimic/dataset/ori_500/line_50.py

@@ -0,0 +1,89 @@
+# ===================
+# Part 1: Importing Libraries
+# ===================
+import matplotlib.pyplot as plt
+import numpy as np; np.random.seed(0)
+
+
+# ===================
+# Part 2: Data Preparation
+# ===================
+x = np.linspace(0, 20, 20)
+original_data = np.sin(x) + np.random.normal(
+    0, 0.1, 20
+)  # Original data with some noise
+smoothed_data = np.convolve(
+    original_data, np.ones(5) / 5, mode="valid"
+)  # Smoothed data
+difference_data = np.diff(original_data)  # Difference data
+cumulative_data = np.cumsum(original_data)  # Cumulative sum data
+
+# Axes Limits and Labels
+xlabel_value = "Time"
+
+ylabel_value_1 = "Value"
+ylabel_value_2 = "Delta Value"
+ylabel_value_3 = "Cumulative Value"
+
+# Labels
+label_1 = "Smoothed Data"
+label_2 = "Difference Data"
+label_3 = "Cumulative Sum"
+
+# Titles
+title_1 = "Smoothed Representation"
+title_2 = "First Difference of Data"
+title_3 = "Cumulative Sum Over Time"
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Create a 3-subplot layout
+fig, axs = plt.subplots(3, 1, figsize=(5, 10))
+
+# First subplot: Smoothed Data
+axs[0].plot(
+    x[2:-2],
+    smoothed_data,
+    label=label_1,
+    color="purple",
+    linestyle="-",
+    marker="o",
+)
+axs[0].set_title(title_1)
+axs[0].set_ylabel(ylabel_value_1)
+axs[0].legend(loc="upper right")
+axs[0].grid(True, linestyle="--", alpha=0.5)
+
+# Second subplot: Difference Data
+axs[1].plot(
+    x[1:],
+    difference_data,
+    label=label_2,
+    color="orange",
+    linestyle="-",
+    marker="x",
+)
+axs[1].set_title(title_2)
+axs[1].set_ylabel(ylabel_value_2)
+axs[1].legend(loc="upper right")
+axs[1].grid(True, linestyle="--", alpha=0.5)
+
+# Third subplot: Cumulative Sum
+axs[2].plot(
+    x, cumulative_data, label=label_3, color="green", linestyle="-", marker="s"
+)
+axs[2].set_title(title_3)
+axs[2].set_xlabel(xlabel_value)
+axs[2].set_ylabel(ylabel_value_3)
+axs[2].legend(loc="upper right")
+axs[2].grid(True, linestyle="--", alpha=0.5)
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Adjust layout for better spacing and display
+plt.tight_layout()
+
+# Show the plot
+plt.savefig('line_50.pdf', bbox_inches='tight')

ChartMimic/dataset/ori_500/line_51.py

@@ -0,0 +1,79 @@
+# ===================
+# Part 1: Importing Libraries
+# ===================
+
+import matplotlib.pyplot as plt
+import numpy as np; np.random.seed(0)
+
+# ===================
+# Part 2: Data Preparation
+# ===================
+t = [0.0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0, 5.5, 6.0, 6.5, 7.0, 7.5, 8.0, 8.5, 9.0, 9.5, 10.0]
+y1 = [0.18, 0.52, 0.94, 1.22, 1.1, 0.5, 0.24, -0.37, -0.77, -0.94, -0.94, -0.56, -0.2, 0.23, 0.7, 0.97, 1.14, 0.78, 0.44, -0.16, -0.8]
+y2 = [1.07, 0.96, 0.47, 0.3, -0.56, -0.8, -1.01, -0.78, -0.51, -0.2, 0.32, 0.62, 0.76, 0.94, 0.77, 0.47, -0.03, -0.64, -0.94, -1.1, -0.98]
+y3 = [-0.17, 0.62, 0.67, 0.87, 0.9, 1.16, 0.94, 1.07, 0.96, 1.04, 0.89, 0.76, 0.81, 0.79, 0.69, 0.65, 0.49, 0.46, 0.38, 0.36, 0.28]
+y4 = [-0.17, 0.42, 0.65, 0.75, 1.14, 1.16, 1.39, 1.58, 1.62, 1.82, 1.67, 1.91, 1.88, 1.93, 2.02, 2.11, 2.2, 2.13, 2.39, 2.4, 2.24]
+
+
+# Labels for legend
+label_sin_wave = "Sin Wave"
+label_cos_wave = "Cos Wave"
+label_exp_decay = "Exp Decay"
+label_log_growth = "Log Growth"
+
+# Plot configuration
+xlim_values = (0, 10)
+ylim_values_sin_cos = (-1.5, 1.5)
+ylim_values_exp = (-0.2, 1.2)
+ylim_values_log = (0, 2.5)
+
+xlabel_value = "Time"
+ylabel_value = "Amplitude"
+ylabel_value_exp_log = "Value"
+
+title_sin = "Sinusoidal Pattern"
+title_cos = "Cosine Pattern"
+title_exp = "Exponential Decay"
+title_log = "Logarithmic Growth"
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+fig, axs = plt.subplots(4, 1, figsize=(6, 12))
+
+axs[0].plot(t, y1, label=label_sin_wave, color="magenta")
+axs[0].set_xlim(*xlim_values)
+axs[0].set_ylim(*ylim_values_sin_cos)
+axs[0].set_title(title_sin, y=1.1)
+axs[0].set_xlabel(xlabel_value)
+axs[0].set_ylabel(ylabel_value)
+
+axs[1].plot(t, y2, label=label_cos_wave, color="green")
+axs[1].set_xlim(*xlim_values)
+axs[1].set_ylim(*ylim_values_sin_cos)
+axs[1].set_title(title_cos, y=1.1)
+axs[1].set_xlabel(xlabel_value)
+axs[1].set_ylabel(ylabel_value)
+
+axs[2].plot(t, y3, label=label_exp_decay, color="blue")
+axs[2].set_xlim(*xlim_values)
+axs[2].set_ylim(*ylim_values_exp)
+axs[2].set_title(title_exp, y=1.1)
+axs[2].set_xlabel(xlabel_value)
+axs[2].set_ylabel(ylabel_value_exp_log)
+
+axs[3].plot(t, y4, label=label_log_growth, color="red")
+axs[3].set_xlim(*xlim_values)
+axs[3].set_ylim(*ylim_values_log)
+axs[3].set_title(title_log, y=1.1)
+axs[3].set_xlabel(xlabel_value)
+axs[3].set_ylabel(ylabel_value_exp_log)
+
+for ax in axs.flat:
+    ax.legend(loc="upper center", bbox_to_anchor=(0.5, 1.15), frameon=False)
+
+# ===================
+# Part 4: Saving Output
+# ===================
+plt.tight_layout()
+plt.savefig('line_51.pdf', bbox_inches='tight')

ChartMimic/dataset/ori_500/line_52.py

@@ -0,0 +1,63 @@
+# ===================
+# Part 1: Importing Libraries
+# ===================
+import matplotlib.pyplot as plt
+import numpy as np; np.random.seed(0)
+
+
+# ===================
+# Part 2: Data Preparation
+# ===================
+# Sample data
+x1 = np.array([0.7, 0.75, 0.8, 0.85, 0.9])
+y1 = np.array([76, 78, 80, 79, 77])
+e1 = np.array([3, 2, 4, 3, 3])
+
+x2 = np.array([0.1, 0.2, 0.3, 0.4])
+y2 = np.array([74, 78, 76, 72])
+e2 = np.array([4, 2, 2, 2])
+
+x3 = np.array([0.0, 0.2, 0.4, 0.6, 0.8, 1.0])
+y3 = np.array([75, 78, 70, 65, 60, 55])
+e3 = np.array([3, 2, 5, 4, 6, 5])
+
+x4 = np.array([400, 600, 800, 1000, 1200])
+y4 = np.array([80, 82, 77, 75, 72])
+e4 = np.array([2, 3, 4, 3, 2])
+
+# Titles
+titles = [
+    "(a) Positive bound",
+    "(b) Negative bound",
+    "(d) Contrastive loss weight",
+    "(c) Fuzzy coefficient",
+]
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Create 2x2 subplots
+fig, axs = plt.subplots(2, 2, figsize=(9, 6))
+
+# Flatten the axis array for easy iteration
+axs = axs.flatten()
+
+# Setting data for each subplot
+data = [(x1, y1, e1), (x2, y2, e2), (x3, y3, e3), (x4, y4, e4)]
+
+# Plot with error bars in each subplot
+for ax, (x, y, e), title in zip(axs, data, titles):
+    ax.errorbar(
+        x, y, yerr=e, fmt="-o", color="blue", ecolor="red", capsize=5, markersize=6
+    )
+    ax.set_title(title, fontsize=16)
+    ax.grid(True, alpha=0.5)
+    ax.set_xticks(x)
+    ax.set_yticks(np.linspace(min(y) - min(e), max(y) + max(e), num=5, endpoint=True))
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Adjust layout and save the figure
+plt.tight_layout()
+plt.savefig('line_52.pdf', bbox_inches='tight')

ChartMimic/dataset/ori_500/line_53.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
+# ===================
+# Enhanced sample data to accommodate more subplots
+x1 = np.array([0.7, 0.75, 0.8, 0.85, 0.9])
+y1 = np.random.uniform(75, 85, size=len(x1))
+e1 = np.random.uniform(1, 4, size=len(x1))
+
+x2 = np.array([0.1, 0.2, 0.3, 0.4])
+y2 = np.random.uniform(70, 80, size=len(x2))
+e2 = np.random.uniform(1, 4, size=len(x2))
+
+x3 = np.array([0.0, 0.2, 0.4, 0.6, 0.8, 1.0])
+y3 = np.random.uniform(50, 80, size=len(x3))
+e3 = np.random.uniform(1, 6, size=len(x3))
+
+x4 = np.array([400, 600, 800, 1000, 1200])
+y4 = np.random.uniform(65, 85, size=len(x4))
+e4 = np.random.uniform(1, 4, size=len(x4))
+
+x5 = np.array([0.5, 0.6, 0.7, 0.8, 0.9])
+y5 = np.random.uniform(50, 80, size=len(x5))
+e5 = np.random.uniform(1, 4, size=len(x5))
+
+x6 = np.array([300, 500, 700, 900, 1100])
+y6 = np.random.uniform(65, 85, size=len(x6))
+e6 = np.random.uniform(1, 4, size=len(x6))
+
+# Titles
+titles = [
+    "Positive Bound",
+    "Negative Bound",
+    "Contrastive Loss Weight",
+    "Fuzzy Coefficient",
+    "Additional Metric 1",
+    "Additional Metric 2",
+]
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Create 2x3 subplots for a unified visual presentation
+fig, axs = plt.subplots(2, 3, figsize=(12, 8))
+
+colors = ["red", "green", "blue", "purple", "magenta", "cyan"]
+
+data_pairs = [
+    (x1, y1, e1),
+    (x2, y2, e2),
+    (x3, y3, e3),
+    (x4, y4, e4),
+    (x5, y5, e5),
+    (x6, y6, e6),
+]
+
+for ax, (x, y, e), title, color in zip(axs.flat, data_pairs, titles, colors):
+    ax.errorbar(
+        x, y, yerr=e, fmt="-o", color=color, ecolor="lightgray", capsize=5, label=title
+    )
+    ax.set_title(title)
+    ax.set_xticks(x)
+    ax.set_ylim(min(y) - min(e) - 0.1, max(y) + max(e) + 0.1)
+    ax.legend(loc="upper left")
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Adjust layout to ensure no overlap and labels are clearly visible
+plt.tight_layout()
+plt.savefig('line_53.pdf', bbox_inches='tight')

ChartMimic/dataset/ori_500/line_54.py

@@ -0,0 +1,83 @@
+# ===================
+# Part 1: Importing Libraries
+# ===================
+import matplotlib.pyplot as plt
+
+# ===================
+# Part 2: Data Preparation
+# ===================
+# Data
+driving_styles = ["Passive", "Rail", "Replay", "Sportive"]
+relaxation_dry = [1.4, 0.9, 0.8, 0.5]
+relaxation_rain = [1.3, 1.1, 1.0, 0.3]
+relaxation_snow = [0.7, 0.6, 0.5, 0.2]  # Additional data for snow condition
+relaxation_fog = [1.0, 0.8, 0.7, 0.4]  # Additional data for fog condition
+error = [0.05, 0.05, 0.05, 0.05]
+
+# Axes Limits and Labels
+xlabel_value = "Driving Style"
+
+ylabel_value = "Relaxation Level"
+ylim_values = [0, 1.5]
+
+# Titles
+titles = ["Dry vs Rain", "Snow vs Fog", "Rain vs Snow"]
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Create a 1x3 subplot layout
+fig, axs = plt.subplots(1, 3, figsize=(12, 4))
+
+# Titles and setup for subplots
+data_pairs = [
+    (relaxation_dry, relaxation_rain),
+    (relaxation_snow, relaxation_fog),
+    (relaxation_rain, relaxation_snow),
+]
+colors_pairs = [("black", "red"), ("blue", "green"), ("red", "blue")]
+
+# Plot each condition in a separate subplot
+for ax, title, (data1, data2), (color1, color2) in zip(
+    axs, titles, data_pairs, colors_pairs
+):
+    ax.errorbar(
+        driving_styles,
+        data1,
+        yerr=error,
+        fmt="o-",
+        color=color1,
+        ecolor=color1,
+        elinewidth=2,
+        capsize=5,
+        capthick=2,
+        label=f'{title.split(" vs ")[0]}',
+    )
+    ax.errorbar(
+        driving_styles,
+        data2,
+        yerr=error,
+        fmt="s-",
+        color=color2,
+        ecolor=color2,
+        elinewidth=2,
+        capsize=5,
+        capthick=2,
+        label=f'{title.split(" vs ")[1]}',
+    )
+    ax.set_xlabel(xlabel_value)
+    ax.set_ylabel(ylabel_value)
+    ax.tick_params(
+        axis="both", which="major", length=5, direction="in", top=True, right=True
+    )
+    ax.legend(loc="upper right", frameon=True)
+    ax.set_ylim(ylim_values)
+    ax.grid(True, which="major", linestyle="--", linewidth=0.5, alpha=0.5)
+    ax.set_title(title)
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Adjust layout and save the figure
+plt.tight_layout()
+plt.savefig('line_54.pdf', bbox_inches='tight')

ChartMimic/dataset/ori_500/line_55.py

@@ -0,0 +1,86 @@
+# ===================
+# Part 1: Importing Libraries
+# ===================
+import matplotlib.pyplot as plt
+import numpy as np; np.random.seed(0)
+
+
+# ===================
+# Part 2: Data Preparation
+# ===================
+# Updated data
+n_aug = ["0", "0.125", "0.25", "0.5", "1", "2", "4", "8"]
+content = np.array([2, 4, 6, 8, 6, 5, 3, 1])  # 更加动态的变化
+organization = np.array([1, 2, 3, 4, 3.5, 2.5, 1.5, 0.7])  # 平滑的递增后递减
+language = np.array([0.5, 1.5, 2.5, 3.5, 4.5, 3.5, 2.5, 1.5])  # 高峰在中间
+
+# Axes Limits and Labels
+xlabel_value = "n$_{aug}$"
+xlim_values = [0, len(n_aug) - 1]
+
+ylabel_value = "Performance Gain (%)"
+ylim_values = [0, max(content) + 1]
+
+# Labels
+label_Content="Content"
+label_Organization="Organization"
+label_Language="Language"
+
+# Titles
+title = "Dynamic Performance Gain Across Different n$_{aug}$ Levels"
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Plot
+fig, ax = plt.subplots(figsize=(8, 3))
+ax.plot(
+    n_aug, content, "o-", label=label_Content, color="royalblue", linewidth=2, markersize=8
+)
+ax.plot(
+    n_aug,
+    organization,
+    "s--",
+    label=label_Organization,
+    color="crimson",
+    linewidth=2,
+    markersize=8,
+)
+ax.plot(
+    n_aug,
+    language,
+    "^:",
+    label=label_Language,
+    color="limegreen",
+    linewidth=2,
+    markersize=8,
+)
+
+# Enhancements for visual appeal
+ax.set_facecolor("#e6f0ff")  # Light blue background
+ax.spines["top"].set_color("none")
+ax.spines["right"].set_color("none")
+ax.spines["left"].set_color("gray")
+ax.spines["bottom"].set_color("gray")
+
+# Setting axis limits and ticks dynamically based on data
+ax.set_ylim(ylim_values)
+ax.set_xlim(xlim_values)
+
+# Customizing labels and grid
+ax.set_xlabel(xlabel_value, fontsize=14)
+ax.set_ylabel(ylabel_value, fontsize=14)
+ax.set_title(title, fontsize=16)
+
+# Custom legend
+ax.legend(loc="upper right", fontsize=12, frameon=True, shadow=True)
+
+# Grid
+ax.grid(True, linestyle="--", alpha=0.5, color="grey")
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Tight layout for better spacing
+plt.tight_layout()
+plt.savefig('line_55.pdf', bbox_inches='tight')

ChartMimic/dataset/ori_500/line_56.py

@@ -0,0 +1,117 @@
+# ===================
+# Part 1: Importing Libraries
+# ===================
+import matplotlib.pyplot as plt
+import numpy as np; np.random.seed(0)
+
+
+# ===================
+# Part 2: Data Preparation
+# ===================
+# Define a custom color palette
+colors = ["#e74c3c", "#3498db", "#2ecc71", "#f39c12", "#9b59b6"]
+
+# Data setup
+decomposition_IO_norm = np.array([0, 20, 40, 60, 80])
+coco_10k = np.array([0.60, 0.70, 0.72, 0.73, 0.74])
+laion_10k = np.array([0.58, 0.67, 0.70, 0.71, 0.73])
+coco_5k = np.array([0.56, 0.66, 0.67, 0.68, 0.70])
+laion_5k = np.array([0.55, 0.61, 0.64, 0.65, 0.68])
+
+# Axes Limits and Labels
+xlabel_value = "Decomposition IO Norm"
+xlim_values = [-5, 85]
+ylabel_value = "Accuracy"
+ylim_values = [0.53, 0.76]
+
+# Labels
+label_1 = "COCO (10k)"
+label_2 = "LAION (10k)"
+label_3 = "COCO (5k)"
+label_4="LAION (5k)"
+
+# Titles
+title_1 = "COCO 10K"
+title_2 = "LAION 10K"
+title_3 = "COCO & LAION 5K"
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Create the 1x3 subplot configuration
+fig, axs = plt.subplots(1, 3, figsize=(9, 3))
+
+# Plot customization for a fancy look
+marker_styles = ["o", "^", "s", "x"]
+line_styles = ["-", "--", ":", "-."]
+
+# First subplot for coco 10k
+axs[0].plot(
+    decomposition_IO_norm,
+    coco_10k,
+    label=label_1,
+    color=colors[0],
+    marker=marker_styles[0],
+    linestyle=line_styles[0],
+    markersize=10,
+    linewidth=2,
+)
+axs[0].set_title(title_1, fontsize=14)
+axs[0].set_xlabel(xlabel_value, fontsize=12)
+axs[0].set_ylabel(ylabel_value, fontsize=12)
+
+# Second subplot for laion 10k
+axs[1].plot(
+    decomposition_IO_norm,
+    laion_10k,
+    label=label_2,
+    color=colors[1],
+    marker=marker_styles[1],
+    linestyle=line_styles[1],
+    markersize=10,
+    linewidth=2,
+)
+axs[1].set_title(title_2, fontsize=14)
+axs[1].set_xlabel(xlabel_value, fontsize=12)
+axs[1].set_ylabel(ylabel_value, fontsize=12)
+
+# Third subplot for coco & laion 5k
+axs[2].plot(
+    decomposition_IO_norm,
+    coco_5k,
+    label=label_3,
+    color=colors[2],
+    marker=marker_styles[2],
+    linestyle=line_styles[2],
+    markersize=10,
+    linewidth=2,
+)
+axs[2].plot(
+    decomposition_IO_norm,
+    laion_5k,
+    label=label_4,
+    color=colors[3],
+    marker=marker_styles[3],
+    linestyle=line_styles[3],
+    markersize=10,
+    linewidth=2,
+)
+axs[2].set_title(title_3, fontsize=14)
+axs[2].set_xlabel(xlabel_value, fontsize=12)
+axs[2].set_ylabel(ylabel_value, fontsize=12)
+
+# Set global properties and customize each subplot individually
+for ax in axs:
+    ax.set_xticks(decomposition_IO_norm)
+    ax.set_xlim(xlim_values)
+    ax.set_ylim(ylim_values)
+    ax.legend(loc="lower right", fontsize=10)
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Adjust layout to ensure no overlap and labels are clearly visible
+plt.tight_layout()
+
+# Show the plot
+plt.savefig('line_56.pdf', bbox_inches='tight')

ChartMimic/dataset/ori_500/line_57.py

@@ -0,0 +1,141 @@
+# ===================
+# Part 1: Importing Libraries
+# ===================
+import matplotlib.pyplot as plt
+import numpy as np; np.random.seed(0)
+
+
+# ===================
+# Part 2: Data Preparation
+# ===================
+# Simulated data based on the previous setup
+learning_rate = [0.01, 0.05, 0.1, 0.2]
+error_rate_64 = np.array([0.18, 0.15, 0.12, 0.10])
+error_rate_128 = np.array([0.16, 0.13, 0.11, 0.09])
+error_rate_256 = np.array([0.14, 0.11, 0.08, 0.07])
+dropout_rate = [0.0, 0.1, 0.2, 0.3]
+accuracy_64 = np.array([0.82, 0.85, 0.83, 0.80])
+accuracy_128 = np.array([0.84, 0.87, 0.86, 0.83])
+
+# Axes Limits and Labels
+xlabel_value = "Parameter Rate"
+
+ylabel_value = "Metric"
+
+# Labels
+label_1 = " (Batch Size=64)"
+label_2 = " (Batch Size=128)"
+label_3 = "Error Rate vs. Learning Rate"
+label_4 = "Accuracy vs. Dropout"
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Create a figure with a 3x2 grid
+fig, axs = plt.subplots(3, 2, figsize=(12, 9))
+
+# Custom colors for the plots
+colors = ["dodgerblue", "tomato", "limegreen", "gold", "purple"]
+
+
+# Function to plot the data
+def plot_data(ax, x, y1, y2, title, marker1, marker2, color1, color2):
+    ax.plot(
+        x,
+        y1,
+        marker=marker1,
+        markersize=8,
+        linewidth=2,
+        color=color1,
+        label=f"{title}{label_1}",
+    )
+    ax.plot(
+        x,
+        y2,
+        marker=marker2,
+        markersize=8,
+        linewidth=2,
+        color=color2,
+        label=f"{title}{label_2}",
+    )
+    ax.set_title(title, fontsize=14)
+    ax.set_xlabel(xlabel_value, fontsize=12)
+    ax.set_ylabel(ylabel_value, fontsize=12)
+    ax.legend(loc="best", fontsize=10, frameon=True, shadow=True)
+    ax.grid(True, linestyle="--", alpha=0.5)
+
+
+# Assigning data to each subplot
+plot_data(
+    axs[0, 0],
+    learning_rate,
+    error_rate_64,
+    error_rate_128,
+    label_3,
+    "o",
+    "s",
+    colors[0],
+    colors[1],
+)
+plot_data(
+    axs[0, 1],
+    dropout_rate,
+    accuracy_64,
+    accuracy_128,
+    label_4,
+    "^",
+    "d",
+    colors[2],
+    colors[3],
+)
+plot_data(
+    axs[1, 0],
+    learning_rate,
+    error_rate_128,
+    error_rate_256,
+    label_3,
+    ">",
+    "<",
+    colors[4],
+    colors[0],
+)
+plot_data(
+    axs[1, 1],
+    dropout_rate,
+    accuracy_128,
+    accuracy_64,
+    label_4,
+    "p",
+    "*",
+    colors[1],
+    colors[2],
+)
+plot_data(
+    axs[2, 0],
+    learning_rate,
+    error_rate_256,
+    error_rate_64,
+    label_3,
+    "H",
+    "X",
+    colors[3],
+    colors[4],
+)
+plot_data(
+    axs[2, 1],
+    dropout_rate,
+    accuracy_64,
+    accuracy_128,
+    label_4,
+    "+",
+    "x",
+    colors[0],
+    colors[1],
+)
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Adjust layout and display the plots
+plt.tight_layout()
+plt.savefig('line_57.pdf', bbox_inches='tight')