update plot.py (#2)

- update plot.py (a225f2041c8af865bd0e976acf0d95291e3f702f)


Co-authored-by: handepehlivan <handepeh@users.noreply.huggingface.co>

plots.py

@@ -179,146 +179,30 @@ def display_heat_map(stock_df):
     # Graphs the result, and displays it with a header on streamlit
 #    st.subheader('Portfolio Historical Cumulative Returns Based On Inputs!')
 #    st.line_chart(cumulative_profit)
-
-
-def circle_packing(stock_df, choices):
+def buble_interactive(stock_df,choices):
+    import plotly.express as px
     symbols, weights, investment = choices.values()
-
-    ## references: https://matplotlib.org/stable/gallery/misc/packed_bubbles.html
-    class BubbleChart:
-        def __init__(self, area, bubble_spacing=0):
-
-            area = np.asarray(area)
-            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
-
-            # calculate initial grid layout for bubbles
-            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)]
-
-            self.com = self.center_of_mass()
-
-        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):
-            """
-            Move bubbles to the center of mass.
-
-            Parameters
-            ----------
-            n_iterations : int, default: 50
-                Number of moves to perform.
-            """
-            for _i in range(n_iterations):
-                moves = 0
-                for i in range(len(self.bubbles)):
-                    rest_bub = np.delete(self.bubbles, i, 0)
-                    # try to move directly towards the center of mass
-                    # direction vector from bubble to the center of mass
-                    dir_vec = self.com - self.bubbles[i, :2]
-
-                    # shorten direction vector to have length of 1
-                    dir_vec = dir_vec / np.sqrt(dir_vec.dot(dir_vec))
-
-                    # calculate new bubble position
-                    new_point = self.bubbles[i, :2] + dir_vec * self.step_dist
-                    new_bubble = np.append(new_point, self.bubbles[i, 2:4])
-
-                    # check whether new bubble collides with other bubbles
-                    if not self.check_collisions(new_bubble, rest_bub):
-                        self.bubbles[i, :] = new_bubble
-                        self.com = self.center_of_mass()
-                        moves += 1
-                    else:
-                        # try to move around a bubble that you collide with
-                        # find colliding bubble
-                        for colliding in self.collides_with(new_bubble, rest_bub):
-                            # calculate direction vector
-                            dir_vec = rest_bub[colliding, :2] - self.bubbles[i, :2]
-                            dir_vec = dir_vec / np.sqrt(dir_vec.dot(dir_vec))
-                            # calculate orthogonal vector
-                            orth = np.array([dir_vec[1], -dir_vec[0]])
-                            # test which direction to go
-                            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, colors):
-            """
-            Draw the bubble plot.
-
-            Parameters
-            ----------
-            ax : matplotlib.axes.Axes
-            labels : list
-                Labels of the bubbles.
-            colors : list
-                Colors of the bubbles.
-            """
-            for i in range(len(self.bubbles)):
-                circ = plt.Circle(
-                    self.bubbles[i, :2], self.bubbles[i, 2], color=colors[i])
-                ax.add_patch(circ)
-                ax.text(*self.bubbles[i, :2], labels[i],
-                        horizontalalignment='center', verticalalignment='center')
-
-    bubble_chart = BubbleChart(area=weights, bubble_spacing=0.1)
-
-    bubble_chart.collapse()
-    browser_market_share = {'color': ['#5A69AF', '#579E65', '#F9C784', '#FC944A']
-                            }
-
-    fig, ax = plt.subplots(subplot_kw=dict(aspect="equal"))
-
-    bubble_chart.plot(
-        ax, symbols, browser_market_share['color'])
-    ax.axis("off")
-    # ax.relim()
-    ax.autoscale_view()
-    # ax.set_title('Browser market share')
-    fig.patch.set_facecolor('black')
-
-    st.pyplot(fig)
+    beta,cash_value_weights  = ER(stock_df,choices)
+    my_list = []
+    for i in beta.values():
+        my_list.append(i)
+    
+    df_final =pd.DataFrame()
+    df_final['ticker'] = symbols
+    df_final['quantities'] = weights
+    df_final['cash_value']  =cash_value_weights
+    df_final['Beta'] = my_list
+   
+    fig = px.scatter(
+    df_final,
+    x="quantities",
+    y="Beta",
+    size="cash_value",
+    #color="continent",
+    hover_name="ticker",
+    log_x=True,
+    size_max=60,
+    )
+    fig.update_layout(title="Beta ----write something")
+    # -- Input the Plotly chart to the Streamlit interface
+    st.plotly_chart(fig, use_container_width=True)