adding figure scripts

figures/main_figure_1a.py

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+#%%
+import os
+import json
+import numpy as np
+import seaborn as sns
+from scipy.stats import boxcox
+from pycirclize import Circos
+import matplotlib.pyplot as plt
+
+base_dir = '/mnt/hanoverdev/data/BiomedSeg/figures_data'
+with open(os.path.join(base_dir,'hierarchy.json'), 'r') as f:
+    hierarchy_data = json.load(f)
+
+with open(os.path.join(base_dir,'target_counts.json'), 'r') as f:
+    target_counts = json.load(f)
+
+with open(os.path.join(base_dir,'modality_counts.json'), 'r') as f:
+    modality_counts = json.load(f)
+
+# color scheme
+sectors = {k: 0 for k in hierarchy_data.keys()}
+for sector_name in hierarchy_data:
+    for k,v in hierarchy_data[sector_name]['child'].items():
+        sectors[sector_name] += len(v['child'])
+    sectors[sector_name] += 1
+
+name2color = {"organ": "#E41A1C", "abnormality": "#377EB8", "histology": "#4DAF4A"}
+
+def generate_shades(base_color, n):
+    return sns.light_palette(base_color, n + 2)[1:-1]
+
+color_schemes = {}
+for sector in sectors:
+    child_colors = generate_shades(name2color[sector], len(hierarchy_data[sector]['child']))
+    color_schemes[sector] = child_colors
+
+parent_track_ratio = (72, 85)
+middle_track_ratio =  (85, 100)
+bar_track_ratio = (45, 70)
+parent_track_font_size = 7
+middle_track_font_size = 5.5
+bar_track_font_size = 7
+outer_track_font_size = 9
+
+circos = Circos(sectors, space=8.8)
+for sector in circos.sectors:
+    idx2label = {}
+    idx = 1
+    for k,v in hierarchy_data[sector.name.lower()]['child'].items():
+        for k1,v1 in v['child'].items():
+            idx2label[idx] = k1
+            idx += 1
+    idx2label[idx] = ''
+    idx2label[0] = ''
+
+    track_outer = sector.add_track((100, 101))
+    track_outer.xticks_by_interval(
+        1,
+        tick_length=0,
+        outer=True,
+        show_bottom_line=False,
+        label_orientation="vertical",
+        label_formatter=lambda v: idx2label[int(v)],
+        label_size=outer_track_font_size,
+        show_endlabel=True
+    )
+
+    track = sector.add_track(parent_track_ratio)
+    track.axis(fc=name2color[sector.name], lw=0)
+    track.text(sector.name.capitalize().replace('Mri', 'MRI').replace('Ct', 'CT').replace('Oct', 'OCT').replace('Dermoscopy', "DS"), color="white", size=parent_track_font_size)
+
+    track1 = sector.add_track(middle_track_ratio, r_pad_ratio=0.1)
+    sect_start = 0
+    color_idx = 0
+    for i, (k,v) in enumerate(hierarchy_data[sector.name.lower()]['child'].items()):
+        sect_size = len(v['child']) if i != len(hierarchy_data[sector.name.lower()]['child'])-1 else len(v['child'])+1
+        if i == 0:
+            sect_size += 0.5
+        if i == len(hierarchy_data[sector.name.lower()]['child'])-1:
+            sect_size -= 0.5
+        track1.rect(sect_start, sect_start+sect_size, r_lim=(middle_track_ratio[0], middle_track_ratio[1]-1), ec="black", lw=0,fc=color_schemes[sector.name][color_idx])
+        color_idx += 1
+        track1.text(k.replace('abnormality', 'abn.').replace(' anatomies', '').replace(' disturbance', '').replace('other abn.', 'Other').replace('liver', '').replace('pancreas', '').capitalize(), sect_start+sect_size/2, color="black", size=middle_track_font_size)
+        sect_start += sect_size
+
+    x = np.linspace(sector.start+1 , sector.end-1 , int(sector.size)-1)
+    y = [target_counts[idx2label[i+1]] for i in range(0,len(x))]
+    y_box = boxcox(y, 0.35)
+
+    track2 = sector.add_track(bar_track_ratio, r_pad_ratio=0.1)
+    track2.axis()
+    track2.yticks([1.14, 2.29, 3.43, 4.58], ["10$^2$", "10$^3$", "10$^4$", "10$^5$"], label_size=bar_track_font_size-1)
+    track2.bar(x, y_box, color=name2color[sector.name], alpha=0.5, align="center", lw=0)
+
+fig = circos.plotfig()
+fig.savefig('figure_1a.pdf')
+plt.show()
+
+# %%

figures/main_figure_1b.py

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+#%%
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import json
+import seaborn as sns
+
+plt.rc('axes.spines', **{'bottom': True, 'left': True, 'right': False, 'top': False})
+
+# Load data
+def load_data(file_path):
+    with open(file_path, 'r') as f:
+        return json.load(f)
+base_dir = '/mnt/hanoverdev/data/BiomedSeg/figures_data'
+data = load_data(os.path.join(base_dir, 'modality_counts.json'))
+separate_submodality = False
+
+# Transform data for plotting
+def transform_data(data):
+    df = pd.DataFrame([(modality, subcat, count) for modality, subcats in data.items() for subcat, count in subcats.items()], columns=['Modality', 'Sub-category', 'Count'])
+    return df
+
+df = transform_data(data)
+
+# Calculate total counts by modality and sort
+def calculate_totals(df):
+    total_counts_by_modality = df.groupby("Modality")["Count"].sum().sort_values(ascending=True)
+    sorted_modalities = total_counts_by_modality.index.tolist()
+    return total_counts_by_modality, sorted_modalities
+
+total_counts_by_modality, sorted_modalities = calculate_totals(df)
+
+# Generate color map
+def generate_color_map(total_counts_by_modality):
+    base_colors = plt.cm.cool(np.linspace(0, 1, len(total_counts_by_modality)))
+    modality_color_map = {modality: base_colors[i] for i, modality in enumerate(total_counts_by_modality.index)}
+    return modality_color_map
+
+modality_color_map = generate_color_map(total_counts_by_modality)
+
+# Format total count for display
+def format_total_count(total_count):
+    if total_count >= 1000:
+        exponent = int(np.floor(np.log10(total_count)))
+        mantissa = total_count / 10**exponent
+        formatted_total = f'{mantissa:.2f} x 10$^{exponent}$'
+    else:
+        exponent = 0
+        formatted_total = str(total_count)
+    return formatted_total, exponent
+
+# Plotting function
+def plot_data(df, total_counts_by_modality, sorted_modalities, modality_color_map, separate_submodality):
+    fig, ax = plt.subplots(figsize=(10, 12))
+    current_bottom = np.zeros(len(sorted_modalities))
+    gap = 0.005 if separate_submodality else 0
+    shades = np.power(np.linspace(0.75, 1, df.groupby("Sub-category").ngroups), 2)
+
+    if separate_submodality:
+        for i, modality in enumerate(sorted_modalities):
+            subdf = df[df["Modality"] == modality].sort_values(by='Count', ascending=False)
+            for j, (index, row) in enumerate(subdf.iterrows()):
+                count = row['Count']
+                if count > 0:
+                    color = np.array(modality_color_map[modality]) * shades[j % len(shades)]
+                    ax.barh(modality, count, left=current_bottom[i], color=color, height=0.8, log=True, edgecolor='white', linewidth=0.5)
+                    current_bottom[i] += count + gap
+            current_bottom[i] -= gap
+            total_count = total_counts_by_modality[modality]
+            formatted_total, exponent = format_total_count(total_count)
+            ax.text(current_bottom[i] + (10**exponent)*0.05, i, formatted_total, va='center', fontsize=20, ha='left')
+    else:
+        for i, modality in enumerate(sorted_modalities):
+            total_count = total_counts_by_modality[modality]
+            color = np.array(modality_color_map[modality] * shades[0])
+            if modality.islower():
+                modality = modality.capitalize()
+            ax.barh(modality, total_count, color=color, height=0.8, log=True, edgecolor='white', linewidth=0.5)
+            formatted_total, exponent = format_total_count(total_count)
+            ax.text(total_count + (10**exponent)*0.05, i, formatted_total, va='center', fontsize=20, ha='left')
+
+    configure_plot(ax, sorted_modalities)
+
+    plt.tight_layout()
+    plt.savefig("./data_dist_modality_bar_subbar.pdf" if separate_submodality else "./data_dist_modality_bar.pdf", bbox_inches="tight", pad_inches=0)
+    plt.show()
+
+# Configure plot aesthetics
+def configure_plot(ax, sorted_modalities):
+    ax.set_xscale('log')
+    ax.set_title("Number of images per modality", fontsize=28)
+    plt.yticks(rotation=0, fontsize=24, va='center')
+    ax.tick_params(axis='x', which='major', length=8)
+    ax.tick_params(axis='x', which='minor', length=5)
+    plt.xticks(fontsize=24)
+    sns.despine()
+
+# Main script execution
+plot_data(df, total_counts_by_modality, sorted_modalities, modality_color_map, separate_submodality)
+
+# %%

figures/supplementary_figure_2.py

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+# %%
+import os
+import json
+import numpy as np
+import seaborn as sns
+from scipy.stats import boxcox
+from pycirclize import Circos
+import matplotlib.pyplot as plt
+
+base_dir = '/mnt/hanoverdev/data/BiomedSeg/figures_data'
+with open(os.path.join(base_dir,'hierarchy.json'), 'r') as f:
+    hierarchy_data = json.load(f)
+
+with open(os.path.join(base_dir,'target_counts.json'), 'r') as f:
+    target_counts = json.load(f)
+
+with open(os.path.join(base_dir,'modality_counts.json'), 'r') as f:
+    modality_counts = json.load(f)
+
+# color scheme
+sectors = {k: len(v) for k,v in modality_counts.items()}
+name2color = {
+    "MRI": "#005A9E",
+    "CT": "#FF7F00",
+    "pathology": "#984EA3",
+    "ultrasound": "#7BC8F6",
+    "X-Ray": "#999999",
+    "fundus": "#76B041",
+    "dermoscopy": "#FDBF6F",
+    "endoscope": "#C0392B",
+    "OCT": "#33A02C",
+}
+
+def generate_shades(base_color, n):
+    return sns.light_palette(base_color, n + 2)[1:-1]
+
+color_schemes = {}
+for sector in sectors:
+    child_colors = generate_shades(name2color[sector], len(modality_counts[sector]))
+    color_schemes[sector] = child_colors
+
+parent_track_ratio = (72, 85)
+middle_track_ratio =  (85, 100)
+bar_track_ratio = (45, 70)
+parent_track_font_size = 7
+middle_track_font_size = 5.5
+bar_track_font_size = 7
+
+circos = Circos(sectors, space=6)
+for sector in circos.sectors:
+    track = sector.add_track(parent_track_ratio)
+    track.axis(fc=name2color[sector.name], lw=0)
+    track.text(sector.name.capitalize().replace('Mri', 'MRI').replace('Ct', 'CT').replace('Oct', 'OCT').replace('Dermoscopy', "DS"), color="white", size=parent_track_font_size)
+
+    track1 = sector.add_track(middle_track_ratio, r_pad_ratio=0.1)
+    sect_start = 0
+    color_idx = 0
+    for k,v in modality_counts[sector.name].items():
+        sect_size = 1
+        track1.rect(sect_start, sect_start+sect_size, r_lim=(middle_track_ratio[0], middle_track_ratio[1]-1) , ec="black", lw=0,fc=color_schemes[sector.name][color_idx])
+        color_idx += 1
+        track1.text(k.capitalize(), sect_start+sect_size/2, color="black", size=middle_track_font_size)
+        sect_start += sect_size
+
+    x = np.linspace(sector.start+0.5, sector.end-0.5, int(sector.size))
+    y = [v for k,v in modality_counts[sector.name].items()]
+    y_box = boxcox(y, 0.35)
+
+    track2 = sector.add_track(bar_track_ratio, r_pad_ratio=0.1)
+    track2.axis()
+    track2.yticks([1.14, 2.29, 3.43, 4.58], ["10$^2$", "10$^3$", "10$^4$", "10$^5$"], label_size=bar_track_font_size-1)
+    track2.bar(x, y_box, color=name2color[sector.name], alpha=0.5, align="center", lw=0)
+
+fig = circos.plotfig()
+fig.savefig('data_target_modality.pdf')
+plt.show()
+
+# %%