Update app.py

app.py

@@ -1,7 +1,159 @@
 import streamlit as st
 import pandas as pd
 import numpy as np
+import matplotlib.pyplot as plt
+from PIL import Image
 
 chart_data = pd.DataFrame(np.random.randn(20, 3), columns=["a", "b", "c"])
 
-st.area_chart(chart_data)
+st.area_chart(chart_data)
+
+class BubbleChart:
+    def __init__(self, data, bubble_spacing=0, n_bins=5):
+
+        unity_ids = list(data.keys())
+        share_values = list(data.values())
+
+        area = np.asarray(share_values)
+        r = np.sqrt(area / np.pi)
+
+        self.bubble_spacing = bubble_spacing
+        self.bubbles = np.ones((len(area), 4))
+        self.bubbles[:, 2] = r
+        self.bubbles[:, 3] = area
+        self.maxstep = 2 * self.bubbles[:, 2].max() + self.bubble_spacing
+        self.step_dist = self.maxstep / 2
+
+        length = np.ceil(np.sqrt(len(self.bubbles)))
+        grid = np.arange(length) * self.maxstep
+        gx, gy = np.meshgrid(grid, grid)
+        self.bubbles[:, 0] = gx.flatten()[:len(self.bubbles)]
+        self.bubbles[:, 1] = gy.flatten()[:len(self.bubbles)]
+
+        min_size = np.min(area)
+        max_size = np.max(area)
+
+        self.colors = self.quantize_colors(area, min_size, max_size, n_bins)
+
+        self.com = self.center_of_mass()
+
+    def quantize_colors(self, sizes, min_size, max_size, n_bins):
+
+        size_range = max_size - min_size
+        bin_size = size_range / n_bins
+
+        quantized_colors = []
+        for size in sizes:
+            bin_index = min(int((size - min_size) / bin_size), n_bins - 1)
+            quantized_colors.append(self.get_color_from_bin(bin_index, n_bins))
+            print(f"Size: {size}, Bin Index: {bin_index}, Color: {self.get_color_from_bin(bin_index, n_bins)}")
+
+        return quantized_colors
+
+    def get_color_from_bin(self, bin_index, n_bins):
+        # colors = ['#9a9fad', '#848dad', '#7080b5', '#5269b3', '#3654b5']
+        colors = ['#42c5ac', '#61d87d', '#a8eb86', '#d3f59a', '#f8ffaf']
+        return colors[bin_index]
+
+    def center_of_mass(self):
+        return np.average(
+            self.bubbles[:, :2], axis=0, weights=self.bubbles[:, 3]
+        )
+
+    def center_distance(self, bubble, bubbles):
+        return np.hypot(bubble[0] - bubbles[:, 0],
+                        bubble[1] - bubbles[:, 1])
+
+    def outline_distance(self, bubble, bubbles):
+        center_distance = self.center_distance(bubble, bubbles)
+        return center_distance - bubble[2] - \
+            bubbles[:, 2] - self.bubble_spacing
+
+    def check_collisions(self, bubble, bubbles):
+        distance = self.outline_distance(bubble, bubbles)
+        return len(distance[distance < 0])
+
+    def collides_with(self, bubble, bubbles):
+        distance = self.outline_distance(bubble, bubbles)
+        idx_min = np.argmin(distance)
+        return idx_min if type(idx_min) == np.ndarray else [idx_min]
+
+    def collapse(self, n_iterations=50):
+        for _i in range(n_iterations):
+            moves = 0
+            for i in range(len(self.bubbles)):
+                rest_bub = np.delete(self.bubbles, i, 0)
+
+                dir_vec = self.com - self.bubbles[i, :2]
+                dir_vec = dir_vec / np.sqrt(dir_vec.dot(dir_vec))
+
+                new_point = self.bubbles[i, :2] + dir_vec * self.step_dist
+                new_bubble = np.append(new_point, self.bubbles[i, 2:4])
+
+                if not self.check_collisions(new_bubble, rest_bub):
+                    self.bubbles[i, :] = new_bubble
+                    self.com = self.center_of_mass()
+                    moves += 1
+                else:
+                    for colliding in self.collides_with(new_bubble, rest_bub):
+                        dir_vec = rest_bub[colliding, :2] - self.bubbles[i, :2]
+                        dir_vec = dir_vec / np.sqrt(dir_vec.dot(dir_vec))
+                        orth = np.array([dir_vec[1], -dir_vec[0]])
+                        new_point1 = (self.bubbles[i, :2] + orth *
+                                      self.step_dist)
+                        new_point2 = (self.bubbles[i, :2] - orth *
+                                      self.step_dist)
+                        dist1 = self.center_distance(
+                            self.com, np.array([new_point1]))
+                        dist2 = self.center_distance(
+                            self.com, np.array([new_point2]))
+                        new_point = new_point1 if dist1 < dist2 else new_point2
+                        new_bubble = np.append(new_point, self.bubbles[i, 2:4])
+                        if not self.check_collisions(new_bubble, rest_bub):
+                            self.bubbles[i, :] = new_bubble
+                            self.com = self.center_of_mass()
+
+            if moves / len(self.bubbles) < 0.1:
+                self.step_dist = self.step_dist / 2
+
+    def plot(self, ax, labels, alpha, edge_alpha = 1):
+        for i in range(len(self.bubbles)):
+            circ = plt.Circle(
+                self.bubbles[i, :2], self.bubbles[i, 2], 
+                color=self.colors[i], alpha= alpha)
+            ax.add_patch(circ)
+            ax.text(*self.bubbles[i, :2], labels[i],
+                    horizontalalignment='center', verticalalignment='center')
+            
+
+class GenerateChart:
+    def __init__(self, data, bubble_spacing=0, n_bins=5):
+        unity_ids = list(data.keys())
+        share_values = list(data.values())
+
+        simulation = BubbleChart(data, bubble_spacing=bubble_spacing, n_bins=n_bins)
+
+        simulation.collapse()
+
+        fig, ax = plt.subplots(subplot_kw=dict(aspect="equal"))
+        alpha_value = 1
+        simulation.plot(ax, unity_ids, alpha=alpha_value)
+        ax.axis("off")
+        ax.relim()
+        ax.autoscale_view()
+        ax.set_title('p2p share', color = "white")
+
+        fig = plt.gcf()
+        img = fig2img(fig)
+        st.image(image, caption='Bubble chart of gpu pool of each Users')
+
+def fig2img(fig):
+    """Convert a Matplotlib figure to a PIL Image and return it"""
+    import io
+    buf = io.BytesIO()
+    fig.savefig(buf)
+    buf.seek(0)
+    img = Image.open(buf)
+    return img
+
+chart_generator = GenerateChart(data.get("gpus", {}), bubble_spacing=0.1, n_bins=5)