Upload 82 files

ChartMimic/dataset/ori_500/HR_13.py

@@ -0,0 +1,36 @@
+# ===================
+# Part 1: Importing Libraries
+# ===================
+import matplotlib.pyplot as plt
+from matplotlib_venn import venn2
+
+# ===================
+# Part 2: Data Preparation
+# ===================
+# Set the figure size to match the original image's dimensions
+plt.figure(figsize=(8, 6))
+
+# Create a Venn diagram
+venn = venn2(subsets=(24, 8, 45), set_labels=("CigaR", "ChatRepair"))
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Customize the colors and edge styles
+venn.get_patch_by_id("10").set_color("pink")
+venn.get_patch_by_id("10").set_edgecolor("black")
+venn.get_patch_by_id("10").set_linestyle("dashed")
+venn.get_patch_by_id("01").set_color("lightgreen")
+venn.get_patch_by_id("01").set_edgecolor("black")
+venn.get_patch_by_id("01").set_linestyle("dashed")
+venn.get_patch_by_id("11").set_color("sandybrown")
+
+# Remove axis
+plt.axis("off")
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Displaying the plot with tight layout to minimize white space
+plt.tight_layout()
+plt.savefig("HR_13.pdf", bbox_inches="tight")

ChartMimic/dataset/ori_500/hist_10.py

@@ -0,0 +1,97 @@
+# ===================
+# 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)
+intra_class_left = np.random.normal(0.1, 0.1, 1000)
+inter_class_left = np.random.normal(0.3, 0.2, 1000)
+intra_class_right = np.random.normal(0.0, 0.1, 1000)
+inter_class_right = np.random.normal(0.2, 0.2, 1000)
+xlabel = "Cosine Similarity"
+ylabel = "Frequency"
+binslist = [30, 30, 30, 30]
+labels = ["Inter Class", "Intra Class"]
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Set the figure size to match the original image's dimensions
+plt.figure(figsize=(10, 3))
+
+# Left subplot
+ax_left = plt.subplot(1, 2, 1)
+plt.hist(
+    inter_class_left,
+    bins=binslist[0],
+    alpha=0.5,
+    label=labels[0],
+    color="#4498c8",
+    zorder=2,
+)
+plt.hist(
+    intra_class_left,
+    bins=binslist[1],
+    alpha=0.5,
+    label=labels[1],
+    color="#5ac4a2",
+    zorder=3,
+)
+leg = plt.legend(
+    frameon=False
+)  # Make legend background transparent and remove the border
+plt.xlabel(xlabel)
+plt.ylabel(ylabel)
+plt.tick_params(axis="both", which="both", length=0)
+plt.gca().set_facecolor("#eaeaf1")
+plt.grid(True, color="white", zorder=0)
+for spine in plt.gca().spines.values():
+    spine.set_visible(False)
+
+# Right subplot
+ax_right = plt.subplot(1, 2, 2)
+plt.hist(
+    inter_class_right,
+    bins=binslist[2],
+    alpha=0.5,
+    label=labels[0],
+    color="#4498c8",
+    zorder=2,
+)
+plt.hist(
+    intra_class_right,
+    bins=binslist[3],
+    alpha=0.5,
+    label=labels[1],
+    color="#5ac4a2",
+    zorder=3,
+)
+leg = plt.legend(frameon=False)
+plt.xlabel(xlabel)
+plt.ylabel(ylabel)
+plt.tick_params(axis="both", which="both", length=0)
+plt.gca().set_facecolor("#eaeaf1")
+plt.grid(True, color="white", zorder=0)
+for spine in plt.gca().spines.values():
+    spine.set_visible(False)
+
+# Set axis spine colors to black
+for ax in [ax_left, ax_right]:
+    for spine in ax.spines.values():
+        spine.set_edgecolor("black")
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Adjust layout to prevent overlap
+plt.tight_layout()
+
+# Display the figure
+plt.savefig("hist_10.pdf", bbox_inches="tight")

ChartMimic/dataset/ori_500/hist_11.py

@@ -0,0 +1,69 @@
+# ===================
+# Part 1: Importing Libraries
+# ===================
+import matplotlib.pyplot as plt
+import numpy as np
+
+np.random.seed(0)
+
+
+# ===================
+# Part 2: Data Preparation
+# ===================
+# Generate random data for demonstration
+data1 = np.random.normal(1, 0.3, 500)
+data2 = np.random.normal(1.5, 0.3, 1000)
+labels = ["V-I positive", "V-I negative"]
+xlabel = "Euclidean Distance"
+ylabel = "Density"
+titlelist = [
+    "(a) Baseline",
+    "(b) MIRL w/ MULT",
+    "(c) MIRL + OCLR w/ MULT",
+    "(d) MIRL + OCLR w/ DOTLA",
+]
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Set figure size to match the original image's dimensions
+fig, axs = plt.subplots(2, 2, figsize=(10, 8))
+
+# Plot histograms for each subplot
+for i, ax in enumerate(axs.flatten()):
+    ax.hist(
+        data1,
+        bins=30,
+        density=True,
+        alpha=0.6,
+        color="#f1a43a",
+        label=labels[0],
+        edgecolor="black",
+        linewidth=0.5,
+    )
+    ax.hist(
+        data2,
+        bins=30,
+        density=True,
+        alpha=0.6,
+        color="#3172bb",
+        label=labels[1],
+        edgecolor="black",
+        linewidth=0.5,
+    )
+    ax.set_xlabel(xlabel)
+    ax.set_ylabel(ylabel)
+    ax.legend()
+
+# Set titles for each subplot
+axs[0, 0].set_title(titlelist[0])
+axs[0, 1].set_title(titlelist[1])
+axs[1, 0].set_title(titlelist[2])
+axs[1, 1].set_title(titlelist[3])
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Adjust layout and save the plot
+plt.tight_layout()
+plt.savefig("hist_11.pdf", bbox_inches="tight")

ChartMimic/dataset/ori_500/hist_12.py

@@ -0,0 +1,64 @@
+# ===================
+# Part 1: Importing Libraries
+# ===================
+import matplotlib.pyplot as plt
+import numpy as np
+
+np.random.seed(0)
+
+
+# ===================
+# Part 2: Data Preparation
+# ===================
+# Generate random data for illustration purposes
+data1 = np.random.normal(5, 1, 1000)
+data2 = np.random.normal(5, 1, 1000)
+data3 = np.random.normal(5, 1, 1000)
+data4 = np.random.normal(5, 1, 1000)
+
+# Define the titles for each subplot
+titles = [
+    "MATHWELL",
+    "MATHWELL MaC",
+    "Llama-2",
+    "Llama-2 MaC",
+    "LLEMMMA",
+    "LLEMMMA MaC",
+    "MAmmoTH",
+    "MAmmoTH MaC",
+]
+
+# Define the colors for each subplot
+colors = ["blue", "blue", "red", "red", "purple", "purple", "orange", "orange"]
+xlabel = "FKGL"
+ylabel = "Density"
+bins = 30
+xvline = 5
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Set the figure size to match the original image's dimensions
+plt.figure(figsize=(7, 8))  # Adjusted to match 720x864 dimensions
+
+# Create subplots
+for i in range(8):
+    plt.subplot(4, 2, i + 1)
+    if i % 2 == 0:
+        data = data1 if i < 4 else data3
+    else:
+        data = data2 if i < 4 else data4
+    plt.hist(data, bins=bins, density=True, alpha=0.6, color=colors[i], range=(0, 10))
+    plt.title(titles[i])
+    plt.xlabel(xlabel)
+    plt.ylabel(ylabel)
+    plt.axvline(
+        x=xvline, color="k", linestyle="--", linewidth=1
+    )  # Changed to dashed line
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Adjust the layout and show the plot
+plt.tight_layout(pad=0.4, w_pad=0.5, h_pad=1.0)  # Adjusted padding for tighter layout
+plt.savefig("hist_12.pdf", bbox_inches="tight")

ChartMimic/dataset/ori_500/hist_13.py

@@ -0,0 +1,103 @@
+# ===================
+# Part 1: Importing Libraries
+# ===================
+import matplotlib.pyplot as plt
+import numpy as np
+
+np.random.seed(0)
+
+
+# ===================
+# Part 2: Data Preparation
+# ===================
+# Generate bimodal data for camel weights
+camel_peak1 = np.random.normal(loc=0.035, scale=0.004, size=200)
+camel_peak2 = np.random.normal(loc=0.065, scale=0.004, size=200)
+camel_weights = np.concatenate([camel_peak1, camel_peak2])
+
+# Generate bimodal data for kangaroo weights
+kangaroo_peak1 = np.random.normal(loc=0.03, scale=0.005, size=500)
+kangaroo_peak2 = np.random.normal(loc=0.08, scale=0.005, size=500)
+kangaroo_weights = np.concatenate([kangaroo_peak1, kangaroo_peak2])
+labels = ["Camels", "Kangaroos"]
+xticks = np.arange(0.00, 0.1, 0.02)
+yticks = [1, 10, 100]
+xlabel = "Animal Weight (Tons)"
+ylabel = "Frequency"
+title = "Desert Animals in the Wild"
+legendtitle = "Species"
+
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Set the figure size
+plt.figure(figsize=(5, 5))
+
+# Define the number of bins and bin edges for consistent bin width
+bins = np.histogram(np.hstack((camel_weights, kangaroo_weights)), bins=15)[1]
+
+# Create the histograms without stacking
+# New colors using hex codes
+plt.hist(
+    camel_weights,
+    bins=bins,
+    color="#1f77b4",
+    label=labels[0],
+    edgecolor="white",
+    linewidth=0.6,
+    alpha=0.6,
+    zorder=2,
+)
+plt.hist(
+    kangaroo_weights,
+    bins=bins,
+    color="#ff7f0e",
+    label=labels[1],
+    edgecolor="white",
+    linewidth=0.6,
+    alpha=0.6,
+    zorder=3,
+)
+
+# Set the background color
+plt.gca().set_facecolor("#f5f5f5")
+
+# Set the scale of y-axis to logarithmic
+plt.yscale("log")
+
+# Set the x-axis ticks
+plt.xticks(xticks)  # Adjusted to show more ticks
+plt.tick_params(axis="x", length=0)
+
+# Add white grid lines and place them behind the bars (zorder=0)
+plt.grid(color="white", linestyle="-", linewidth=1.5, zorder=0)
+
+# Set the y-axis ticks and remove all line markings (spines)
+plt.yticks(yticks)  # Adjusted to show additional y-axis tick
+plt.tick_params(axis="y", length=0)
+
+# Remove spine lines
+for spine in plt.gca().spines.values():
+    spine.set_visible(False)
+
+# remove small dash on y-axis
+plt.tick_params(axis="y", which="minor", length=0)
+
+# Set labels and title
+plt.xlabel(xlabel)  # Adjusted label
+plt.ylabel(ylabel)  # Adjusted label
+plt.title(title)  # Adjusted title
+
+# Move legend to the bottom center of the plot
+plt.legend(
+    title=legendtitle, loc="upper center", bbox_to_anchor=(0.5, -0.15), ncol=2
+)  # Adjust legend position and make it span 2 columns
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Adjust layout to make room for the legend
+plt.tight_layout()  # We might need to adjust this to account for the new legend position
+
+plt.savefig("hist_13.pdf", bbox_inches="tight")

ChartMimic/dataset/ori_500/hist_15.py

@@ -0,0 +1,57 @@
+# ===================
+# Part 1: Importing Libraries
+# ===================
+import matplotlib.pyplot as plt
+import numpy as np
+
+np.random.seed(0)
+
+
+# ===================
+# Part 2: Data Preparation
+# ===================
+# Generate normally distributed data
+data = np.random.normal(loc=2.0, scale=1.0, size=10000)
+
+# Axes Limits and Labels
+title = "Histogram of Wind Speed Measurements"
+xlabel_value = "Wind Speed (km/h)"
+ylabel_value = "Number of Measurements"
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Set the figure size
+plt.figure(figsize=(8, 6))
+
+# Enable the grid
+plt.grid(True, linestyle="--", linewidth=0.5, alpha=0.7)
+
+# Histogram of the data
+n, bins, patches = plt.hist(data, bins=25, color="skyblue", edgecolor="blue", alpha=0.7)
+
+# Highlight the median of the data
+median = np.median(data)
+plt.axvline(median, color="purple", linestyle="dashed", linewidth=2)
+
+# Adjust the median text position to not overlap the bars when possible
+median_text_position = max(n) * 0.9
+for bin_edge, count in zip(bins, n):
+    if bin_edge > median and count < median_text_position:
+        # Place the text above the median position
+        median_text_position = count
+        break
+plt.text(median + 0.5, median_text_position, f"Median: {median:.2f}", color="purple")
+
+# Title and labels relevant to database statistics
+plt.title(title)
+plt.xlabel(xlabel_value)
+plt.ylabel(ylabel_value)
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Adjust the layout
+plt.tight_layout()
+
+plt.savefig("hist_15.pdf", bbox_inches="tight")

ChartMimic/dataset/ori_500/hist_16.py

@@ -0,0 +1,61 @@
+# ===================
+# Part 1: Importing Libraries
+# ===================
+import matplotlib.pyplot as plt
+import numpy as np
+
+np.random.seed(0)
+
+
+# ===================
+# Part 2: Data Preparation
+# ===================
+# Sample data with modified distributions
+correctly_classified = np.random.exponential(scale=0.8, size=1000)
+misclassified = np.random.normal(loc=2.0, scale=0.5, size=1000)
+
+# Labels and Plot Types
+hist_label = ["Baguette", "Youtiao"]
+
+# Axes Limits and Labels
+xlabel_value = "Distance to Threshold"
+ylabel_value = "Frequency"
+title = "Baking Accuracy Analysis"
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Set the figure size
+plt.figure(figsize=(5, 4))
+
+# Plot histograms with stacked bars, modified colors, and alpha for transparency
+plt.hist(
+    [correctly_classified, misclassified],
+    bins=50,
+    stacked=True,
+    label=hist_label,
+    color=["#6495ED", "#FFA07A"],
+    alpha=0.6,
+)
+
+# Add labels, title, and modify the style of the labels
+plt.xlabel(xlabel_value, color="#333333")
+plt.ylabel(ylabel_value, color="#333333")
+plt.title(title)
+
+# Modify the legend style and position to lower center
+plt.legend(frameon=True, loc="lower center", ncol=2, bbox_to_anchor=(0.5, -0.4))
+
+# Adjust x-axis range and add some space at the beginning of the x-axis
+plt.xlim(left=plt.xlim()[0] + 0.1, right=3)  # Adjusted right xlim
+
+# Add grid with lighter color and set behind the histograms
+plt.grid(color="grey", linestyle="--", linewidth=0.5, alpha=0.7)
+plt.gca().set_axisbelow(True)
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Adjust layout and save the plot
+plt.tight_layout()
+plt.savefig("hist_16.pdf", bbox_inches="tight")

ChartMimic/dataset/ori_500/hist_3.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
+# ===================
+# Sample data
+camel_weights = np.random.exponential(scale=0.02, size=1000)
+kangaroo_weights = np.random.exponential(scale=0.02, size=1000)
+
+# Define the number of bins and bin edges for consistent bin width
+bins = np.histogram(np.hstack((camel_weights, kangaroo_weights)), bins=15)[1]
+labels = ["Camel", "Kangaroo"]
+xticks = [0.00, 0.05, 0.10, 0.15]
+xlabel = "Concept Weight"
+ylabel = "Count"
+title = "CIFAR100 'Desert' Concept"
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Set the figure size
+plt.figure(figsize=(5, 4))
+
+# Create the histograms without stacking
+plt.hist(
+    camel_weights,
+    bins=bins,
+    color="#85c23b",
+    label=labels[0],
+    edgecolor="white",
+    linewidth=0.6,
+    alpha=0.6,
+    zorder=2,
+)
+plt.hist(
+    kangaroo_weights,
+    bins=bins,
+    color="#e57476",
+    label=labels[1],
+    edgecolor="white",
+    linewidth=0.6,
+    alpha=0.6,
+    zorder=3,
+)
+
+# Set the background color
+plt.gca().set_facecolor("#f5f5f5")
+
+# Set the scale of y-axis to logarithmic
+plt.yscale("log")
+
+# Set the x-axis ticks
+plt.xticks(xticks)
+plt.tick_params(axis="x", length=0)
+
+# Add white grid lines and place them behind the bars (zorder=0)
+plt.grid(color="white", linestyle="-", linewidth=1.5, zorder=0)
+
+# Set the y-axis ticks and remove all line markings (spines)
+plt.yticks([1, 10, 100])
+plt.tick_params(axis="y", length=0)
+for spine in plt.gca().spines.values():
+    spine.set_visible(False)  # Remove all the spines
+
+# remove small dash on y-axis
+plt.tick_params(axis="y", which="minor", length=0)
+
+# Set labels and title
+plt.xlabel(xlabel)
+plt.ylabel(ylabel)
+plt.title(title)
+
+# Add legend with title
+plt.legend(title="Class")
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Adjust layout
+plt.tight_layout()
+
+# Show the plot
+plt.savefig("hist_3.pdf", bbox_inches="tight")

ChartMimic/dataset/ori_500/hist_4.py

@@ -0,0 +1,91 @@
+# ===================
+# 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)
+center_data = np.random.normal(6, 2, 1000)
+random_data = np.random.normal(1, 2, 1000)
+
+# Define bins aligned for both histograms
+bins = np.histogram(np.hstack((center_data, random_data)), bins=30)[1]
+labels = ["Center", "Random"]
+xlabel = "Distance Difference (Random vs. Center)"
+ylabel = "Number of Examples"
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Create figure and axis
+fig, ax = plt.subplots(
+    figsize=(5, 3)
+)  # Adjusted to match the original image's dimensions
+
+# Calculate the histogram data for each set and plot them
+ax.hist(
+    center_data,
+    bins=bins,
+    color="#f2a965",
+    edgecolor="#fdf460",
+    linewidth=1.2,
+    label=labels[0],
+    align="mid",
+    histtype="stepfilled",
+    alpha=0.7,
+)
+ax.hist(
+    random_data,
+    bins=bins,
+    color="#709dc6",
+    edgecolor="#ca3531",
+    linewidth=1.2,
+    label=labels[1],
+    align="mid",
+    histtype="stepfilled",
+    alpha=0.7,
+)
+
+# To show the overlapping areas, we plot the two histograms with transparency
+ax.hist(
+    center_data,
+    bins=bins,
+    color="#f2a965",
+    edgecolor="#fdf460",
+    linewidth=1.2,
+    alpha=0.7,
+    align="mid",
+    histtype="stepfilled",
+)
+ax.hist(
+    random_data,
+    bins=bins,
+    color="#709dc6",
+    edgecolor="#ca3531",
+    linewidth=1.2,
+    alpha=0.7,
+    align="mid",
+    histtype="stepfilled",
+)
+
+# Set labels
+ax.set_xlabel(xlabel)
+ax.set_ylabel(ylabel)
+
+# Add legend
+ax.legend()
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Adjust layout
+plt.tight_layout()
+
+# Save the plot
+plt.savefig("hist_4.pdf", bbox_inches="tight")

ChartMimic/dataset/ori_500/hist_5.py

@@ -0,0 +1,53 @@
+# ===================
+# Part 1: Importing Libraries
+# ===================
+import matplotlib.pyplot as plt
+import numpy as np
+
+np.random.seed(0)
+
+
+# ===================
+# Part 2: Data Preparation
+# ===================
+# Sample data to approximate the distribution in the image
+data = np.random.gamma(shape=2.0, scale=1.0, size=10000)
+data = data[data < 30]  # Limiting the data to match the x-axis in the image
+xlabel = "Number of Repetition"
+ylabel = "Number of Clusters"
+binslist = [30, 30]
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Set the figure size to match the original image's dimensions
+plt.figure(figsize=(4, 3))
+
+# Show grid with some transparency
+plt.grid(True, linestyle="-", linewidth=0.5, color="#000000", alpha=0.1)
+
+# Create the histogram
+plt.hist(data, bins=binslist[0], color="#dca684")
+
+# Create the histogram with histtype='step' for edge-only bars
+plt.hist(
+    data,
+    bins=binslist[1],
+    color="#dca684",
+    edgecolor="#d1885c",
+    histtype="step",
+    linewidth=1.2,
+)
+
+# Set the title and labels to match the image
+plt.xlabel(xlabel)
+plt.ylabel(ylabel)
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Adjust layout
+plt.tight_layout()
+
+# Display the plot
+plt.savefig("hist_5.pdf", bbox_inches="tight")

ChartMimic/dataset/ori_500/hist_6.py

@@ -0,0 +1,48 @@
+# ===================
+# 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 your actual data)
+correctly_classified = np.random.exponential(scale=1.0, size=1000)
+misclassified = np.random.exponential(scale=0.5, size=1000)
+labels = ["Correctly classified", "Misclassified"]
+xlabel = "Distance to threshold"
+bins = 50
+
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Set the figure size to match the original image's dimensions
+plt.figure(figsize=(5, 3))
+
+# Plot histograms with stacked bars
+plt.hist(
+    [correctly_classified, misclassified],
+    bins=bins,
+    stacked=True,
+    label=labels,
+    color=["#3b75af", "#ef8636"],
+)
+
+# Add labels and title
+plt.xlabel(xlabel)
+plt.legend()
+
+# Adjust x-axis range to match the reference picture and add a little space at the beginning of the x-axis
+plt.xlim(left=plt.xlim()[0] + 0.1, right=5)
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Adjust layout and save the plot
+plt.tight_layout()
+plt.savefig("hist_6.pdf", bbox_inches="tight")

ChartMimic/dataset/ori_500/hist_7.py

@@ -0,0 +1,55 @@
+# ===================
+# Part 1: Importing Libraries
+# ===================
+import matplotlib.pyplot as plt
+import numpy as np
+
+np.random.seed(0)
+
+
+# ===================
+# Part 2: Data Preparation
+# ===================
+# Data for plotting
+confidence = np.array([0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9])
+accuracy = np.array([0.2, 0.3, 0.4, 0.5, 0.6, 0.5, 0.4, 0.3, 0.8])
+calibration_error = 0.31
+text = f"Calibration Error:\n{calibration_error}"
+
+# Axes Limits and Labels
+xlabel_value = "Confidence"
+ylabel_value = "Accuracy in bin"
+title = "Cascade"
+xlim_values = [0, 1]
+ylim_values = [0, 1]
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Set figure size to match the original image's dimensions
+plt.figure(figsize=(8, 8))
+
+# Plot histogram using plt.hist and specify bins
+plt.hist(confidence, bins=9, weights=accuracy, color="tan", edgecolor="black")
+
+# Add diagonal dashed line
+plt.plot([0, 1], [0, 1], "k--", linestyle="--", linewidth=1, color="#808080")
+
+# Add text for calibration error
+plt.text(0.08, 0.85, text, color="#c06c27", fontsize=18)
+
+# Set labels and title
+plt.xlabel(xlabel_value)
+plt.ylabel(ylabel_value)
+plt.title(title)
+
+# Adjust x and y axis limits to match the reference picture
+plt.xlim(xlim_values)
+plt.ylim(ylim_values)
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Adjust layout and save the plot
+plt.tight_layout()
+plt.savefig("hist_7.pdf", bbox_inches="tight")

ChartMimic/dataset/ori_500/hist_8.py

@@ -0,0 +1,42 @@
+# ===================
+# Part 1: Importing Libraries
+# ===================
+import matplotlib.pyplot as plt
+
+# ===================
+# Part 2: Data Preparation
+# ===================
+# Sample data (replace with actual data)
+image_overlap = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]
+number_of_queries = [500, 1000, 2000, 3000, 5500, 5700, 5800, 4000, 1000]
+
+# Axes Limits and Labels
+xlabel_value = "% images overlap"
+ylabel_value = "Number of queries"
+xlim_values = [0, 1.0]
+ylim_values = [0, 6000]
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Create a histogram
+plt.figure(figsize=(6, 5))  # Adjusted to match the original image's dimensions
+plt.hist(image_overlap, bins=9, weights=number_of_queries, color="#7f95c0")
+
+# Set the labels and title
+plt.xlabel(xlabel_value)
+plt.ylabel(ylabel_value)
+
+# Remove ticks on both axes
+plt.tick_params(axis="both", which="both", length=0)
+
+# Set the range for the axes
+plt.xlim(xlim_values)
+plt.ylim(ylim_values)
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Adjust layout and display the plot
+plt.tight_layout()
+plt.savefig("hist_8.pdf", bbox_inches="tight")

ChartMimic/dataset/ori_500/hist_9.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
+# ===================
+# Sample data (replace with your actual data)
+data1 = np.random.normal(0.08, 0.01, 10000)
+data2 = np.random.normal(0.1, 0.02, 10000)
+binslist = [100, 100]
+titles = ["MNIST", "FashionMNIST"]
+rangelist = [(0, 0.12), (0, 0.25)]
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Create figure and axes with specified figure size
+fig, axs = plt.subplots(2, 1, figsize=(10, 7), gridspec_kw={"hspace": 0.3})
+
+# First histogram (MNIST)
+axs[0].hist(data1, bins=binslist[0], range=rangelist[0], color="#3b75af")
+axs[0].set_title(titles[0])
+
+# Second histogram (FashionMNIST)
+axs[1].hist(data2, bins=binslist[1], range=rangelist[1], color="#3b75af")
+axs[1].set_title(titles[1])
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Adjust layout and save plot
+plt.tight_layout()
+plt.savefig("hist_9.pdf", bbox_inches="tight")

ChartMimic/dataset/ori_500/line_1.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 (example values, the actual data should be extracted from the paper)
+snr_values = [5, 10, 15, 20, 25]
+jpeg_ldpc = [10, 30, 50, 70, 90]
+deepjscc_wo_ofdm = [5, 15, 25, 35, 45]
+deepjscc_w_ofdm = [20, 40, 60, 80, 95]
+ours = [30, 60, 80, 95, 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
+xlim_values = [5, 25]
+ylim_values = [0, 100]
+xlabel_value = "SNR"
+ylabel_value = "Classification Accuracy (%)"
+xticks_values = np.arange(5, 25, 5)
+yticks_values = np.arange(0, 101, 20)
+xtickslabel_values = None  # Not specified in the code
+ytickslabel_values = None  # Not specified in the code
+title_value = None  # Not specified in the code
+axhiline_value = None  # Not specified in the code
+axvline_value = None  # Not specified in the code
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Plotting
+plt.figure(figsize=(8, 6))  # Adjusting figure size to match original image dimensions
+plt.plot(
+    snr_values,
+    jpeg_ldpc,
+    "-o",
+    label=label_jpeg_ldpc,
+    color="#1f77b4",
+    clip_on=False,
+    zorder=10,
+)
+plt.plot(
+    snr_values,
+    deepjscc_wo_ofdm,
+    "-^",
+    label=label_deepjscc_wo_ofdm,
+    color="#ff7f0e",
+    clip_on=False,
+    zorder=10,
+)
+plt.plot(
+    snr_values,
+    deepjscc_w_ofdm,
+    "-x",
+    label=label_deepjscc_w_ofdm,
+    color="#2ca02c",
+    clip_on=False,
+    zorder=10,
+)
+plt.plot(snr_values, ours, "-x", label=label_ours, color="#d62728")
+
+# Set y-axis to only display specific ticks and extend y-axis to leave space at top
+plt.yticks(yticks_values)
+plt.ylim(ylim_values)  # Adjusted y-axis limit
+plt.xticks(xticks_values)
+plt.xlim(xlim_values)
+
+# Adding grid, legend, and labels
+plt.grid(True)
+plt.legend(loc="upper center", bbox_to_anchor=(0.5, 1.08), ncol=4, frameon=False)
+plt.xlabel(xlabel_value)
+plt.ylabel(ylabel_value)
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Adjusting layout to reduce white space
+plt.tight_layout()
+plt.savefig('line_1.pdf', bbox_inches='tight')

ChartMimic/dataset/ori_500/line_10.py

@@ -0,0 +1,59 @@
+# ===================
+# Part 1: Importing Libraries
+# ===================
+
+import matplotlib.pyplot as plt
+import numpy as np; np.random.seed(0)
+
+# ===================
+# Part 2: Data Preparation
+# ===================
+# Data
+n_aug = ["0", "0.125", "0.25", "0.5", "1", "2", "4", "8"]
+content = [1, 3, 6, 4, 2, 1, 0.5, 0.2]
+organization = [0.5, 1, 1.5, 2, 1.5, 1, 0.5, 0.25]
+language = [0, 0.5, 1, 2, 4, 3, 2, 1]
+
+# Positions for the bars on the x-axis
+ind = np.arange(len(n_aug))
+
+# Labels and Legend
+xlabel = "n"
+ylabel = "Performance Gain (%)"
+content_label = "Content"
+organization_label = "Organization"
+language_label = "Language"
+
+# Limits
+xlim = (n_aug[0], n_aug[-1])
+ylim = (0, 7)
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Plot
+fig, ax = plt.subplots(figsize=(8, 6))  # Adjust the size to match the original image's dimensions
+ax.plot(n_aug, content, label=content_label, color="#0173b2")
+ax.plot(n_aug, organization, label=organization_label, color="#de8f05")
+ax.plot(n_aug, language, label=language_label, color="#20a983")
+
+# Setting the x-axis and y-axis limits
+ax.set_ylim(*ylim)  # Set y-axis to go from 0 to 7
+ax.set_xlim(*xlim)  # Set x-axis limits to cover the range of n_aug without extra space
+
+# Labels and Title
+ax.set_xlabel(xlabel, fontsize=14)
+ax.set_ylabel(ylabel, fontsize=14)
+
+# Legend
+ax.legend(loc="upper center", fontsize=14, frameon=False, ncol=3, bbox_to_anchor=(0.5, 1.1))
+
+# Grid
+ax.grid(True, ls="--", alpha=0.5)
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Adjusting layout to reduce white space
+plt.tight_layout()
+plt.savefig('line_10.pdf', bbox_inches='tight')

ChartMimic/dataset/ori_500/line_11.py

@@ -0,0 +1,71 @@
+# ===================
+# Part 1: Importing Libraries
+# ===================
+import matplotlib.pyplot as plt
+
+# ===================
+# Part 2: Data Preparation
+# ===================
+# Approximated data points based on the image
+decomposition_IO_norm = [0, 20, 40, 60, 80]
+coco_10k = [0.60, 0.70, 0.72, 0.73, 0.74]
+laion_10k = [0.58, 0.67, 0.70, 0.71, 0.73]
+coco_5k = [0.56, 0.66, 0.67, 0.68, None]
+laion_5k = [0.55, 0.61, 0.64, 0.65, None]
+clip_y = [0.75, 0.75]
+clip_x = [-10, 90]
+
+# Axes Limits and Labels
+xlabel_value = "Decomposition IO Norm"
+xlim_values = [-10, 90]
+
+ylabel_value = "Accuracy"
+ylim_values = [0.53, 0.76]
+yticks_values = [0.55, 0.60, 0.65, 0.70, 0.75]
+
+# Labels
+label_1="coco (10k)"
+label_2="laion (10k)"
+label_3 = "coco (5k)"
+label_4="laion (5k)"
+label_5="CLIP"
+
+# Titles
+title_1 = "Effect of Vocab on Zero Shot Accuracy"
+title_2 = "Dictionary"
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Create the plot
+plt.figure(figsize=(6, 6))  # Adjust figure size to match original image dimensions
+plt.plot(decomposition_IO_norm, coco_10k, label=label_1, color="red", marker="o")
+plt.plot(
+    decomposition_IO_norm, laion_10k, label=label_2, color="green", marker="o"
+)
+plt.plot(decomposition_IO_norm, coco_5k, label=label_3, color="blue", marker="o")
+plt.plot(
+    decomposition_IO_norm, laion_5k, label=label_4, color="orange", marker="o"
+)
+plt.plot(clip_x, clip_y, label=label_5, color="black", linestyle="--")
+
+# Add title and labels
+plt.title(title_1)
+plt.xlabel(xlabel_value)
+plt.ylabel(ylabel_value)
+plt.xticks(decomposition_IO_norm)
+plt.xlim(xlim_values)
+plt.yticks(yticks_values)
+plt.ylim(ylim_values)
+# Add legend with additional entry
+plt.legend(title=title_2,loc="lower right")
+
+# Adding the white grid manually
+plt.grid(color="white", linestyle="-", linewidth=0.5)
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Show the plot with tight layout to minimize white space
+plt.tight_layout()
+plt.savefig('line_11.pdf', bbox_inches='tight')

ChartMimic/dataset/ori_500/line_12.py

@@ -0,0 +1,103 @@
+# ===================
+# Part 1: Importing Libraries
+# ===================
+
+import matplotlib.pyplot as plt
+
+# ===================
+# Part 2: Data Preparation
+# ===================
+# Data for plotting
+sample_ratio = [0.25, 0.50, 0.75, 1.00]
+std_acc_512 = [0.07, 0.06, 0.01, 0.05]
+std_acc_1024 = [0.055, 0.045, 0.04, 0.035]
+std_acc_2048 = [0.03, 0.025, 0.02, 0.015]
+
+# Extracted variables
+line_label_512 = "MAXN=512"
+line_label_1024 = "MAXN=1024"
+line_label_2048 = "MAXN=2048"
+xlim_values = (0.25, 1)
+ylim_values = (0.00, 0.08)
+xlabel_value = "Sample Ratio"
+ylabel_value = "Std of ACC"
+xticks_values = sample_ratio
+yticks_values = None  # Not explicitly set in the code
+xtickslabel_fontsize = 14
+ytickslabel_fontsize = 14
+title_value = None  # Not explicitly set in the code
+axhline_value = None  # Not explicitly set in the code
+axvline_value = None  # Not explicitly set in the code
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Plotting the lines with increased marker size and line width
+plt.figure(figsize=(8, 6))
+plt.plot(
+    sample_ratio,
+    std_acc_512,
+    marker="*",
+    markersize=10,
+    linewidth=2,
+    color="#2ab34a",
+    label=line_label_512,
+    clip_on=False,
+    zorder=10,
+)
+plt.plot(
+    sample_ratio,
+    std_acc_1024,
+    marker="^",
+    markerfacecolor="white",
+    markersize=10,
+    linewidth=2,
+    markeredgecolor="#ee756e",
+    color="#ee756e",
+    clip_on=False,
+    zorder=10,
+    label=line_label_1024,
+)
+plt.plot(
+    sample_ratio,
+    std_acc_2048,
+    marker="o",
+    markerfacecolor="white",
+    markersize=10,
+    linewidth=2,
+    markeredgecolor="#4995c6",
+    color="#4995c6",
+    clip_on=False,
+    zorder=10,
+    label=line_label_2048,
+)
+
+# Setting the x-axis and y-axis limits
+plt.ylim(*ylim_values)  # Set y-axis to go from 0 to 7
+plt.yticks(fontsize=ytickslabel_fontsize)
+plt.xlim(*xlim_values)  # Set y-axis to go from 0 to 7
+# Set x-axis to show only the values in the sample_ratio list
+plt.xticks(xticks_values, fontsize=xtickslabel_fontsize)
+
+# Adding labels and title
+plt.xlabel(xlabel_value, fontsize=18)
+plt.ylabel(ylabel_value, fontsize=18)
+
+# Adding legend with increased font size
+plt.legend(
+    fontsize="large",
+    loc="upper center",
+    ncol=3,
+    frameon=False,
+    bbox_to_anchor=(0.5, 1.1),
+)
+
+# Adding grid
+plt.grid(True, alpha=0.6)
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Adjusting layout to reduce white space
+plt.tight_layout()
+plt.savefig('line_12.pdf', bbox_inches='tight')

ChartMimic/dataset/ori_500/line_13.py

@@ -0,0 +1,113 @@
+# ===================
+# Part 1: Importing Libraries
+# ===================
+
+import matplotlib.pyplot as plt
+import numpy as np; np.random.seed(0)
+
+# ===================
+# Part 2: Data Preparation
+# ===================
+# Variable extraction
+line_label_base = "base"
+line_label_ours = "ours"
+xlim_values = (0, 200)
+ylim_values_fid = (0.1, 0.6)
+ylim_values_is = (0, 0.4)
+ylim_values_cwfid = (0.1, 0.4)
+ylim_values_cas = (0.1, 0.4)
+xlabel_value = "Training iterations"
+ylabel_value_fid = "FID (↓)"
+ylabel_value_is = "IS (↑)"
+ylabel_value_cwfid = "CW-FID (↓)"
+ylabel_value_cas = "CAS (↑)"
+yticks_values_fid = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6]
+yticks_values_is = [0, 0.1, 0.2, 0.3, 0.4]
+yticks_values_cwfid = [0.1, 0.2, 0.3, 0.4]
+yticks_values_cas = [0.1, 0.2, 0.3, 0.4]
+legend_location = "upper center"
+legend_bbox_to_anchor = (0.5, 1.2)
+legend_frameon = False
+legend_ncol = 2
+grid_value = True
+
+# Create subplots
+fig, axs = plt.subplots(4, 1, figsize=(8, 10))
+
+# FID
+# Simulated data to match the reference picture
+iterations = np.linspace(0, 200, 21)
+iterations = [0., 10., 20., 30., 40., 50., 60., 70., 80., 90., 100., 110., 120., 130.,
+ 140., 150., 160., 170., 180., 190., 200.]
+base_data = [0.54, 0.49, 0.49, 0.5, 0.48, 0.41, 0.43, 0.39, 0.38, 0.37, 0.35, 0.36, 0.34, 0.31,
+ 0.3, 0.28, 0.29, 0.24, 0.24, 0.2, 0.15]
+ours_data = [0.41, 0.41, 0.38, 0.43, 0.35, 0.38, 0.37, 0.4, 0.39, 0.36, 0.36, 0.33, 0.3, 0.33,
+ 0.33, 0.35, 0.34, 0.31, 0.3, 0.28, 0.27]
+# IS
+# Simulated data to match the reference picture
+base_data_is = [0.27, 0.33, 0.27, 0.26, 0.23, 0.27, 0.21, 0.23, 0.2, 0.22, 0.19, 0.17, 0.18, 0.18,
+ 0.16, 0.16, 0.13, 0.12, 0.11, 0.1, 0.08]
+ours_data_is = [0.17, 0.21, 0.2, 0.18, 0.23, 0.21, 0.23, 0.25, 0.24, 0.27, 0.23, 0.26, 0.25, 0.25,
+ 0.26, 0.27, 0.28, 0.26, 0.31, 0.3, 0.27]
+
+# CW-FID
+base_data_cwfid = [0.33, 0.34, 0.32, 0.3, 0.28, 0.32, 0.29, 0.32, 0.3, 0.32, 0.31, 0.31, 0.3, 0.34,
+ 0.3, 0.31, 0.34, 0.27, 0.27, 0.32, 0.28]
+ours_data_cwfid = [0.24, 0.19, 0.19, 0.24, 0.23, 0.24, 0.22, 0.18, 0.24, 0.19, 0.22, 0.22, 0.2, 0.21,
+ 0.22, 0.21, 0.18, 0.21, 0.23, 0.19, 0.2]
+
+# CAS
+base_data_cas = [0.19, 0.24, 0.21, 0.21, 0.18, 0.21, 0.19, 0.2, 0.19, 0.21, 0.21, 0.2, 0.21, 0.18,
+ 0.17, 0.21, 0.2, 0.21, 0.25, 0.22, 0.18]
+ours_data_cas = [0.32, 0.27, 0.29, 0.3, 0.33, 0.29, 0.28, 0.3, 0.29, 0.32, 0.28, 0.28, 0.29, 0.29,
+ 0.34, 0.32, 0.3, 0.28, 0.32, 0.28, 0.27]
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Plotting FID
+axs[0].plot(iterations, base_data, label=line_label_base, color="blue")
+axs[0].plot(iterations, ours_data, label=line_label_ours, color="orange")
+axs[0].set_xlabel(xlabel_value)
+axs[0].set_ylabel(ylabel_value_fid)
+axs[0].set_xlim(*xlim_values)
+axs[0].set_yticks(yticks_values_fid)
+axs[0].tick_params(axis="both", which="both", color="gray")
+
+# Plotting IS
+axs[1].plot(iterations, base_data_is, label=line_label_base, color="blue")
+axs[1].plot(iterations, ours_data_is, label=line_label_ours, color="orange")
+axs[1].set_xlabel(xlabel_value)
+axs[1].set_ylabel(ylabel_value_is)
+axs[1].set_xlim(*xlim_values)
+axs[1].set_yticks(yticks_values_is)
+axs[1].tick_params(axis="both", which="both", color="gray")
+
+# Plotting CW-FID
+axs[2].plot(iterations, base_data_cwfid, label=line_label_base, color="blue")
+axs[2].plot(iterations, ours_data_cwfid, label=line_label_ours, color="orange")
+axs[2].set_xlabel(xlabel_value)
+axs[2].set_ylabel(ylabel_value_cwfid)
+axs[2].set_xlim(*xlim_values)
+axs[2].set_yticks(yticks_values_cwfid)
+axs[2].tick_params(axis="both", which="both", color="gray")
+
+# Plotting CAS
+axs[3].plot(iterations, base_data_cas, label=line_label_base, color="blue")
+axs[3].plot(iterations, ours_data_cas, label=line_label_ours, color="orange")
+axs[3].set_xlabel(xlabel_value)
+axs[3].set_ylabel(ylabel_value_cas)
+axs[3].set_xlim(*xlim_values)
+axs[3].set_yticks(yticks_values_cas)
+axs[3].tick_params(axis="both", which="both", color="gray")
+
+# Add legends and gridlines to each subplot
+for ax in axs.flat:
+    ax.legend(loc=legend_location, bbox_to_anchor=legend_bbox_to_anchor, frameon=legend_frameon, ncol=legend_ncol)
+    ax.grid(grid_value)  # Enable the grid
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Adjust layout and save the plot
+plt.tight_layout()
+plt.savefig('line_13.pdf', bbox_inches='tight')

ChartMimic/dataset/ori_500/line_15.py

@@ -0,0 +1,111 @@
+# ===================
+# Part 1: Importing Libraries
+# ===================
+import matplotlib.pyplot as plt
+import numpy as np; np.random.seed(0)
+
+import matplotlib.ticker as ticker
+
+# ===================
+# Part 2: Data Preparation
+# ===================
+# Sample data to mimic the trends in the provided image
+tasks = np.arange(1, 21)
+baCE = np.linspace(90, 60, 20) + np.random.normal(0, 3, 20)
+lwf = np.linspace(75, 50, 20) + np.random.normal(0, 3, 20)
+ewc = np.linspace(50, 30, 20) + np.random.normal(0, 3, 20)
+seq = np.linspace(20, 15, 20) + np.random.normal(0, 1, 20)
+
+# Axes Limits and Labels
+xlabel_value = "Task"
+xlim_values = [0.5, 20.5]
+xticks_values = np.arange(1, 21, 1)
+
+ylabel_value = "Average Accuracy (%)"
+ylim_values = [0, 100]
+yticks_values = np.arange(0, 101, 10)
+
+# Labels
+label_BaCE = "BaCE"
+label_LWF = "LWF"
+label_EWC = "EWC"
+label_SEQ = "SEQ"
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Plotting the data
+plt.figure(figsize=(8, 6))
+plt.plot(
+    tasks,
+    baCE,
+    marker="s",
+    markersize=7,
+    color="#393b79",
+    mfc="w",
+    mew=2,
+    label=label_BaCE,
+    linewidth=2,
+)
+plt.plot(
+    tasks,
+    lwf,
+    marker="v",
+    markersize=7,
+    color="#e7969c",
+    mfc="w",
+    mew=2,
+    label=label_LWF,
+    linewidth=2,
+)
+plt.plot(
+    tasks,
+    ewc,
+    marker="D",
+    markersize=7,
+    color="#a55194",
+    mfc="w",
+    mew=2,
+    label=label_EWC,
+    linewidth=2,
+)
+plt.plot(
+    tasks,
+    seq,
+    marker="o",
+    markersize=4,
+    color="#de9ed6",
+    mfc="w",
+    mew=2,
+    label=label_SEQ,
+    linewidth=2,
+)  # Adjusted color for SEQ
+
+# Set y-axis to only display specific ticks and extend y-axis to leave space at top
+plt.yticks(yticks_values)
+plt.ylim(ylim_values)  # Adjusted y-axis limit
+
+# Setting x-axis ticks
+plt.xticks(xticks_values)  # Ticks from 1 to 20, interval of 1
+plt.xlim(xlim_values)  # Slightly beyond 1 and 20 for a margin
+
+# Adding labels and title
+plt.xlabel(xlabel_value, fontsize=16)
+plt.ylabel(ylabel_value, fontsize=16)
+
+# Adding legend with a different style and position
+plt.legend(frameon=True, fontsize=11, loc="upper right")
+
+# Adding gridlines
+plt.grid(True, linestyle="-", linewidth=0.5, axis="y", alpha=1)
+
+# Adjusting tick label size
+plt.xticks(fontsize=12)
+plt.yticks(fontsize=12)
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Adjusting layout to reduce white space
+plt.tight_layout()
+plt.savefig('line_15.pdf', bbox_inches='tight')

ChartMimic/dataset/ori_500/line_16.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 for plotting
+uncertainty_threshold = [0.0, 0.2, 0.4, 0.6, 0.8, 1.0]
+oesense_accuracy = [1.00, 0.97, 0.96, 0.97, 0.98, 0.96]
+kws_accuracy = [0.97, 0.95, 0.93, 0.92, 0.94, 0.95]
+ecg5000_accuracy = [0.96, 0.94, 0.92, 0.90, 0.88, 0.86]
+
+# Extracted variables
+oesense_label = "Oesense"
+kws_label = "KWS"
+ecg5000_label = "ECG5000"
+
+ylim_values = [0.84, 1.02]
+yticks_values = [0.84, 0.87, 0.9, 0.93, 0.96, 0.99, 1.02]
+yticks_labels = ["$0.84$", "$0.87$", "$0.9$", "$0.93$", "$0.96$", "$0.99$", "$1.02$"]
+xlabel_value = "Uncertainty threshold"
+ylabel_value = "Accuracy"
+xlim_values = [0, 1]
+xticks_fontsize = "16"
+yticks_fontsize = "16"
+xlabel_fontsize = "14"
+ylabel_fontsize = "14"
+legend_location = "lower left"
+legend_ncol = 3
+legend_bbox_to_anchor = (0, -0.2)
+legend_frameon = False
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Create figure and axis
+fig, ax = plt.subplots(figsize=(8, 6))
+
+# Plot the data
+ax.plot(uncertainty_threshold, oesense_accuracy, "o-", label=oesense_label, clip_on=False, zorder=10, color="blue", linewidth=1.6, markersize=12)
+ax.plot(uncertainty_threshold, kws_accuracy, "d-", label=kws_label, clip_on=False, zorder=10, color="red", linewidth=1.6, markersize=12)
+ax.plot(uncertainty_threshold, ecg5000_accuracy, "^-", label=ecg5000_label, clip_on=False, zorder=10, color="green", linewidth=1.6, markersize=12)
+
+plt.ylim(ylim_values)
+plt.yticks(yticks_values, yticks_labels, fontsize=yticks_fontsize)
+
+# Set x-axis to only display specific ticks and extend x-axis to leave space at right
+plt.xticks(fontsize=xticks_fontsize)
+plt.xlim(xlim_values)
+plt.tick_params(axis="both", which="both", color="gray")
+
+# Add legend, labels, and grid
+ax.legend(loc=legend_location, ncol=legend_ncol, bbox_to_anchor=legend_bbox_to_anchor, frameon=legend_frameon)
+ax.set_xlabel(xlabel_value, fontsize=xlabel_fontsize)
+ax.set_ylabel(ylabel_value, fontsize=ylabel_fontsize)
+ax.grid(True)
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Adjusting layout to add more space on the right
+plt.tight_layout()
+plt.savefig('line_16.pdf', bbox_inches='tight')

ChartMimic/dataset/ori_500/line_17.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 for plotting
+parameters = np.array([10, 30, 50, 70, 90, 110])
+kl_divergence = np.array([0.03, 0.04, 0.015, 0.006, 0.0025, 0.001])
+
+# Axes Limits and Labels
+xlabel_value = "Number of Parameters"
+xlim_values = [0, 125]
+xticks_values = np.arange(0, 121, 20)
+
+ylabel_value = "Log KL Divergence"
+ylim_values = [10**-5, 10**-1]
+yticks_values = [10**-5, 10**-4, 10**-3, 10**-2, 10**-1]
+yticklabels = ["$10^{-5}$", "$10^{-4}$", "$10^{-3}$", "$10^{-2}$", "$10^{-1}$"]
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Create the plot
+fig, ax = plt.subplots(figsize=(6, 4))  # Use subplots to get access to the axis object
+ax.plot(parameters, kl_divergence, marker="o", linestyle="-", color="#1f77b4")
+
+# Set the scale of the y-axis to logarithmic
+ax.set_yscale("log")
+
+# Set y-axis to only display specific ticks and extend y-axis to leave space at top
+ax.set_yticks(yticks_values)
+ax.set_yticklabels(yticklabels)
+ax.set_ylim(ylim_values)  # Set limits to include a small margin
+
+# Remove minor ticks
+ax.tick_params(axis="y", which="minor", left=False)
+
+# Setting x-axis ticks
+ax.set_xticks(xticks_values)  # Set x-ticks to be every 20
+ax.set_xlim(xlim_values)  # Set limits to include a small margin
+
+# Adjusting tick label size
+plt.xticks(fontsize=10, fontweight="100")
+plt.yticks(fontsize=10, fontweight="100")
+
+# 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
+
+# Set labels and title
+ax.set_xlabel(xlabel_value, fontsize=14)
+ax.set_ylabel(ylabel_value, fontsize=14)
+
+# Show grid with lighter color and only major lines
+ax.grid(True, which="major", color="lightgrey", linestyle="-", linewidth=0.5)
+
+# Change the axis colors
+ax.spines["bottom"].set_color("#d2d2d2")
+ax.spines["top"].set_color("#d2d2d2")  # Optional: hide or set color
+ax.spines["left"].set_color("#d2d2d2")
+ax.spines["right"].set_color("#d2d2d2")  # Optional: hide or set color
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Adjusting layout to add more space on the right
+plt.tight_layout()
+plt.savefig('line_17.pdf', bbox_inches='tight')

ChartMimic/dataset/ori_500/line_18.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
+# ===================
+# Sample data
+iterations = np.array([0, 250, 500, 750, 1000, 1250, 1500, 1750, 2000])
+gpt4_7b = np.array([0.1, 0.4, 0.7, 0.85, 0.9, 0.92, 0.93, 0.94, 0.95])
+gpt4_7b_ft = np.array([0.05, 0.2, 0.35, 0.5, 0.6, 0.65, 0.7, 0.72, 0.73])
+llama_7b = np.array([0.1, 0.45, 0.75, 0.88, 0.9, 0.91, 0.92, 0.93, 0.94])
+llama_7b_ft = np.array([0.05, 0.25, 0.4, 0.55, 0.65, 0.7, 0.75, 0.77, 0.78])
+
+# Axes Limits and Labels
+xlabel_value = "Iterations"
+ylabel_value = "Attack Success Rate"
+
+# Labels
+label_1 = "7B"
+label_2 = "7B (Fine-tuned)"
+
+# Titles
+title_1 = "GPT-4 Evaluation"
+title_2 = "Llama Guard Evaluation"
+
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Set the figure size to match the original image's dimensions
+plt.figure(figsize=(9, 4))
+
+# First subplot
+plt.subplot(1, 2, 1)
+plt.plot(
+    iterations,
+    gpt4_7b,
+    marker="o",
+    color="#0a6ae1",
+    label=label_1,
+    markerfacecolor="#0a6ae1",
+    linewidth=2,
+    markersize=5,
+)
+plt.plot(
+    iterations,
+    gpt4_7b_ft,
+    marker="o",
+    color="#d75faa",
+    label=label_2,
+    markerfacecolor="#d75faa",
+    linewidth=2,
+    markersize=5,
+)
+plt.fill_between(iterations, gpt4_7b - 0.05, gpt4_7b + 0.05, color="#0a6ae1", alpha=0.2)
+plt.fill_between(
+    iterations, gpt4_7b_ft - 0.03, gpt4_7b_ft + 0.03, color="#d75faa", alpha=0.2
+)
+plt.title(title_1, fontsize=14)
+plt.xlabel(xlabel_value, fontsize=12)
+plt.ylabel(ylabel_value, fontsize=12)
+
+# Second subplot
+plt.subplot(1, 2, 2)
+plt.plot(
+    iterations,
+    llama_7b,
+    marker="o",
+    color="#0a6ae1",
+    label=label_1,
+    markerfacecolor="#0a6ae1",
+    linewidth=2,
+    markersize=5,
+)
+plt.plot(
+    iterations,
+    llama_7b_ft,
+    marker="o",
+    color="#d75faa",
+    label=label_2,
+    markerfacecolor="#d75faa",
+    linewidth=2,
+    markersize=5,
+)
+plt.fill_between(
+    iterations, llama_7b - 0.05, llama_7b + 0.05, color="#0a6ae1", alpha=0.2
+)
+plt.fill_between(
+    iterations, llama_7b_ft - 0.03, llama_7b_ft + 0.03, color="#d75faa", alpha=0.2
+)
+plt.title(title_2, fontsize=14)
+plt.xlabel(xlabel_value, fontsize=12)
+plt.ylabel(ylabel_value, fontsize=12)
+plt.legend(loc="lower right", frameon=True, bbox_to_anchor=(1, 0.1))
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Adjust layout and save plot
+plt.tight_layout()
+plt.savefig('line_18.pdf', bbox_inches='tight')

ChartMimic/dataset/ori_500/line_19.py

@@ -0,0 +1,64 @@
+# ===================
+# Part 1: Importing Libraries
+# ===================
+import matplotlib.pyplot as plt
+import numpy as np; np.random.seed(0)
+
+
+# ===================
+# Part 2: Data Preparation
+# ===================
+# Data for plotting
+fraction_of_training_data = np.array([0.01, 0.1, 1])
+full_accuracy = np.array([60, 70, 80])
+spt_accuracy = np.array([55, 65, 78])
+vpt_accuracy = np.array([42, 53, 65])
+
+# Axes Limits and Labels
+xlabel_value = "fraction of training data (log scale)"
+xlim_values = [5, 25]
+
+ylabel_value = "test accuracy (%)"
+ylim_values = [38, 84]
+yticks_values = np.arange(40, 82, 10)
+
+# Labels
+label_Full = "Full"
+label_SPT = "SPT"
+label_VPT = "VPT"
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Plotting the data
+plt.figure(figsize=(5, 4))  # Adjusting figure size to match original image dimensions
+plt.plot(fraction_of_training_data, full_accuracy, "o-", color="green", label=label_Full)
+plt.plot(fraction_of_training_data, spt_accuracy, "o-", color="red", label=label_SPT)
+plt.plot(fraction_of_training_data, vpt_accuracy, "o-", color="blue", label=label_VPT)
+
+# Set y-axis to only display specific ticks and extend y-axis to leave space at top
+plt.yticks(yticks_values, fontsize=16)
+plt.ylim(ylim_values)  # Adjusted y-axis limit
+
+# Set x-axis fontsize
+plt.xticks(fontsize=16)
+
+# Setting the x-axis to log scale
+plt.xscale("log")
+
+# Adding labels and title
+plt.xlabel(xlabel_value, fontsize=16)
+plt.ylabel(ylabel_value, fontsize=16)
+
+# Adding grid
+plt.grid(True, which="both", ls="-", linewidth=0.8)
+
+# Adding legend, show it horizontally and place it at the lower right corner
+plt.legend(loc="lower right", fontsize=12, ncol=3, columnspacing=0.5, edgecolor="black")
+
+# ===================
+# Part 4: Saving Output
+# ===================
+# Adjusting layout to add more space on the right
+plt.tight_layout()
+plt.savefig('line_19.pdf', bbox_inches='tight')

ChartMimic/dataset/ori_500/line_2.py

@@ -0,0 +1,82 @@
+# ===================
+# Part 1: Importing Libraries
+# ===================
+import matplotlib.pyplot as plt
+import numpy as np; np.random.seed(0)
+
+
+# ===================
+# Part 2: Data Preparation
+# ===================
+gradient_steps = [0, 50, 100, 150, 200]  # 10 data points from 0 to 200
+line1_values = [0.1, 0.8, 1.2, 1.5, 1.3]  # Random values for line 1
+line2_values = [1.0, 0.5, 0.8, 1.0, 1.8]  # Random values for line 2
+line3_values = [1.8, 1.2, 0.5, 0.4, 0.3]  # Random values for line 3
+
+# Simulating standard deviations for error
+line1_std = np.random.rand(5) * 0.2
+line2_std = np.random.rand(5) * 0.2
+line3_std = np.random.rand(5) * 0.2
+
+# Labels
+label_Line_1 = "Line 1"
+label_Line_2 = "Line 2"
+label_Line_3 = "Line 3"
+
+# Axes Limits and Labels
+xlabel_value = "Gradient Steps (x 62.5K)"
+yticks_values = np.arange(0, 2.1, 0.5)
+
+# ===================
+# Part 3: Plot Configuration and Rendering
+# ===================
+# Plotting the lines with error bands
+plt.figure(figsize=(4, 3))
+plt.plot(gradient_steps, line1_values, "o-", color="orange", label=label_Line_1)
+plt.fill_between(
+    gradient_steps,
+    line1_values - line1_std,
+    line1_values + line1_std,
+    color="orange",
+    alpha=0.2,
+)
+
+plt.plot(gradient_steps, line2_values, "o-", color="blue", label=label_Line_2)
+plt.fill_between(
+    gradient_steps,
+    line2_values - line2_std,
+    line2_values + line2_std,
+    color="blue",
+    alpha=0.2,
+)
+
+plt.plot(gradient_steps, line3_values, "o-", color="green", label=label_Line_3)
+plt.fill_between(
+    gradient_steps,
+    line3_values - line3_std,
+    line3_values + line3_std,
+    color="green",
+    alpha=0.2,
+)
+
+# x labels
+plt.xlabel(xlabel_value)
+plt.xticks(gradient_steps)
+plt.yticks(yticks_values)
+
+# Moving axes spines
+ax = plt.gca()  # get current axes
+ax.spines["left"].set_position(("outward", 10))  # move left y-axis outwards
+ax.spines["bottom"].set_position(("outward", 10))  # move bottom x-axis outwards
+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
+# ===================
+# Show the plot
+plt.tight_layout()
+plt.savefig('line_2.pdf', bbox_inches='tight')