Update app.py

app.py

@@ -1,147 +1,370 @@
-import io
+import json
+from collections import Counter
+import numpy as np
+import operator
+import matplotlib.pyplot as plt
+from matplotlib.ticker import FuncFormatter
+from matplotlib.patches import Ellipse
+import seaborn as sns
+import pandas as pd
+import networkx as nx
+import base64
+from collections import defaultdict
+import sys,os
+import math
 import random
-from typing import List, Tuple
-
-import aiohttp
-import panel as pn
-from PIL import Image
-from transformers import CLIPModel, CLIPProcessor
-
-pn.extension(design="bootstrap", sizing_mode="stretch_width")
-
-ICON_URLS = {
-    "brand-github": "https://github.com/holoviz/panel",
-    "brand-twitter": "https://twitter.com/Panel_Org",
-    "brand-linkedin": "https://www.linkedin.com/company/panel-org",
-    "message-circle": "https://discourse.holoviz.org/",
-    "brand-discord": "https://discord.gg/AXRHnJU6sP",
-}
-
-
-async def random_url(_):
-    pet = random.choice(["cat", "dog"])
-    api_url = f"https://api.the{pet}api.com/v1/images/search"
-    async with aiohttp.ClientSession() as session:
-        async with session.get(api_url) as resp:
-            return (await resp.json())[0]["url"]
-
-
-@pn.cache
-def load_processor_model(
-    processor_name: str, model_name: str
-) -> Tuple[CLIPProcessor, CLIPModel]:
-    processor = CLIPProcessor.from_pretrained(processor_name)
-    model = CLIPModel.from_pretrained(model_name)
-    return processor, model
-
-
-async def open_image_url(image_url: str) -> Image:
-    async with aiohttp.ClientSession() as session:
-        async with session.get(image_url) as resp:
-            return Image.open(io.BytesIO(await resp.read()))
-
-
-def get_similarity_scores(class_items: List[str], image: Image) -> List[float]:
-    processor, model = load_processor_model(
-        "openai/clip-vit-base-patch32", "openai/clip-vit-base-patch32"
-    )
-    inputs = processor(
-        text=class_items,
-        images=[image],
-        return_tensors="pt",  # pytorch tensors
-    )
-    outputs = model(**inputs)
-    logits_per_image = outputs.logits_per_image
-    class_likelihoods = logits_per_image.softmax(dim=1).detach().numpy()
-    return class_likelihoods[0]
-
-
-async def process_inputs(class_names: List[str], image_url: str):
-    """
-    High level function that takes in the user inputs and returns the
-    classification results as panel objects.
-    """
-    try:
-        main.disabled = True
-        if not image_url:
-            yield "##### ⚠️ Provide an image URL"
-            return
-    
-        yield "##### ⚙ Fetching image and running model..."
-        try:
-            pil_img = await open_image_url(image_url)
-            img = pn.pane.Image(pil_img, height=400, align="center")
-        except Exception as e:
-            yield f"##### 😔 Something went wrong, please try a different URL!"
-            return
-    
-        class_items = class_names.split(",")
-        class_likelihoods = get_similarity_scores(class_items, pil_img)
-    
-        # build the results column
-        results = pn.Column("##### 🎉 Here are the results!", img)
-    
-        for class_item, class_likelihood in zip(class_items, class_likelihoods):
-            row_label = pn.widgets.StaticText(
-                name=class_item.strip(), value=f"{class_likelihood:.2%}", align="center"
-            )
-            row_bar = pn.indicators.Progress(
-                value=int(class_likelihood * 100),
-                sizing_mode="stretch_width",
-                bar_color="secondary",
-                margin=(0, 10),
-                design=pn.theme.Material,
-            )
-            results.append(pn.Column(row_label, row_bar))
-        yield results
-    finally:
-        main.disabled = False
-
-
-# create widgets
-randomize_url = pn.widgets.Button(name="Randomize URL", align="end")
-
-image_url = pn.widgets.TextInput(
-    name="Image URL to classify",
-    value=pn.bind(random_url, randomize_url),
-)
-class_names = pn.widgets.TextInput(
-    name="Comma separated class names",
-    placeholder="Enter possible class names, e.g. cat, dog",
-    value="cat, dog, parrot",
-)
-
-input_widgets = pn.Column(
-    "##### 😊 Click randomize or paste a URL to start classifying!",
-    pn.Row(image_url, randomize_url),
-    class_names,
-)
-
-# add interactivity
-interactive_result = pn.panel(
-    pn.bind(process_inputs, image_url=image_url, class_names=class_names),
-    height=600,
-)
-
-# add footer
-footer_row = pn.Row(pn.Spacer(), align="center")
-for icon, url in ICON_URLS.items():
-    href_button = pn.widgets.Button(icon=icon, width=35, height=35)
-    href_button.js_on_click(code=f"window.open('{url}')")
-    footer_row.append(href_button)
-footer_row.append(pn.Spacer())
-
-# create dashboard
-main = pn.WidgetBox(
-    input_widgets,
-    interactive_result,
-    footer_row,
-)
-
-title = "Panel Demo - Image Classification"
-pn.template.BootstrapTemplate(
-    title=title,
-    main=main,
-    main_max_width="min(50%, 698px)",
-    header_background="#F08080",
-).servable(title=title)
+import operator
+import csv
+import matplotlib.pylab as pyl
+import itertools
+import scipy as sp
+from scipy import stats
+from scipy import optimize
+from scipy.integrate import quad
+import altair as alt
+
+import warnings
+warnings.filterwarnings('ignore')
+
+# loading the events data
+events={}
+nations = ['Italy','England','Germany','France','Spain','European_Championship','World_Cup']
+for nation in nations:
+    with open('./events_%s.json' %nation) as json_data:
+        events[nation] = json.load(json_data)
+        
+# loading the match data
+matches={}
+nations = ['Italy','England','Germany','France','Spain','European_Championship','World_Cup']
+for nation in nations:
+    with open('./matches_%s.json' %nation) as json_data:
+        matches[nation] = json.load(json_data)
+
+# loading the players data
+players={}
+with open('players.json') as json_data:
+    players = json.load(json_data)
+
+# loading the competitions data
+competitions={}
+with open('competitions.json') as json_data:
+    competitions = json.load(json_data)
+
+
+ev_all_nations = []
+for nation in nations:
+    for i in range(len(events[nation])):
+        ev_all_nations.append(events[nation][i]['eventName'])
+count = Counter(ev_all_nations)
+total = len(ev_all_nations)
+
+counter = {event: int((count / total) * 100) for event, count in count.items()}
+sorted_counter = sorted(counter.items(), key=operator.itemgetter(1), reverse=False)
+
+# Convert to DataFrame for Altair plotting.
+data = pd.DataFrame(sorted_counter, columns=['Event', 'Percentage'])
+
+# Generate the bar chart using Altair.
+chart = alt.Chart(data).mark_bar().encode(
+    y=alt.Y('Event:N', title=None, sort='-x'),  # Sort is handled by the data frame's order.
+    x='Percentage:Q',
+).properties(
+    width=600,
+    height=400
+)
+
+# Display the chart.
+chart.display()
+
+
+data = pd.DataFrame({
+    'Event Count': list(match_ev_count.values())
+})
+
+mean_val = int(np.mean(data['Event Count']))
+std_val = int(np.std(data['Event Count']))
+
+hist = alt.Chart(data).mark_bar().encode(
+    alt.X('Event Count:Q', bin=alt.Bin(maxbins=20), title='events (n)', 
+          axis=alt.Axis(values=np.arange(0, max(data['Event Count']) + 100, 100))),
+    alt.Y('count()', title='frequency (n)')
+).properties(
+    width=600,
+    height=400
+)
+
+text = alt.Chart(pd.DataFrame({'x': [mean_val + std_val], 'y': [1], 'text': [f'μ = {mean_val} \n σ = {std_val}']})).mark_text(
+    align='left',
+    baseline='top',
+    fontSize=20,
+    dx=-120,
+    dy=-300
+).encode(
+    x='x:Q',
+    y='y:Q',
+    text='text:N'
+)
+
+chart = hist + text
+chart.display()
+
+
+import altair as alt
+import pandas as pd
+
+
+match_id = 2576335
+a_match = []
+for nation in nations:
+    for ev in events[nation]:
+        if ev['matchId'] == match_id:
+            a_match.append(ev)
+
+for nation in nations:
+    for match in matches[nation]:
+        if match['wyId'] == match_id:
+            match_f = match
+
+df_a_match = pd.DataFrame(a_match)
+
+background_data = pd.DataFrame({
+    'x': [0],
+    'y': [0],
+    'x2': [100],
+    'y2': [100]
+})
+
+# Create the background using `mark_rect`
+background = alt.Chart(background_data).mark_rect(
+    color='#195905'  # Soccer field green color
+).encode(
+    x='x:Q',
+    y='y:Q',
+    x2='x2:Q',
+    y2='y2:Q'
+)
+
+
+#Define the center circle
+center_circle = alt.Chart(pd.DataFrame({'x': [50], 'y': [50]})).mark_point(
+    size=12000,  # Adjust size as necessary
+    color='white',
+    strokeWidth=3
+).encode(
+    x='x:Q',
+    y='y:Q'
+)
+
+border_lines_data = pd.DataFrame({
+    'x': [1, 1, 99.5, 99.5, 1],
+    'y': [1, 99.5, 99.5, 1, 1],
+    'x2': [1, 99.5, 99.5, 1, 1],
+    'y2': [99.5, 99.5, 1, 1, 1]
+})
+
+# Create the border lines using `mark_line`
+
+border_lines = alt.Chart(border_lines_data).mark_line(
+    color='white',
+    strokeWidth=3  # This controls the thickness of the line
+).encode(
+    x=alt.X('x:Q', scale=alt.Scale(domain=[1, 99.5])),
+    y=alt.Y('y:Q', scale=alt.Scale(domain=[1, 99.5])),
+    x2='x2:Q',
+    y2='y2:Q'
+)
+
+midline_data = pd.DataFrame({
+    'x': [50, 50,],
+    'y': [1, 99, ]
+})
+
+# Create the line using `mark_line`
+midline = alt.Chart(midline_data).mark_line(
+    color='white',  # Color of the line
+    strokeWidth=3   # Thickness of the line
+).encode(
+    x='x:Q',
+    y='y:Q'
+)
+lines_data = pd.DataFrame({
+    'x': [1, 17.5, 17.5, 1, 82.5,  82.5, 99,1,6.5,6.5,1, 99,93.5,93.5],
+    'y': [21.3, 21.3, 77.7, 77.7, 21.3, 77.7, 77.7,37.5,37.5,62.5,62.5,37.5,37.5,62.5],
+    'x2': [17.5, 17.5, 1, 17.5, 99,  82.5, 82.5, 6.5,6.5,1,6.5,93.5,93.5,99],
+    'y2': [21.3, 77.7, 77.7, 77.7, 21.3, 21.3,77.7,37.5,62.5,62.5,62.5,37.5,62.5,62.5]
+})
+
+lines = alt.Chart(lines_data).mark_line(
+    color='white',  # Color of the lines
+    strokeWidth=3   # Thickness of the lines
+).encode(
+    x='x:Q',
+    y='y:Q',
+    x2='x2:Q',
+    y2='y2:Q'
+)
+
+dot_positions = pd.DataFrame({
+    'x': [12, 87],
+    'y': [50, 50]
+})
+
+# Create the white dots using `mark_point`
+white_dots = alt.Chart(dot_positions).mark_point(
+    size=100,  # Adjust the size as needed
+    color='white',
+    filled=True
+).encode(
+    x='x:Q',
+    y='y:Q'
+)
+
+
+
+theta = np.linspace(0, np.pi, 100)  # Radians from 0 to pi for a full semicircle
+semicircle_x = 12 + 9.5 * np.cos(theta)  # Shift the semicircle to the right of x=12
+semicircle_y = 50 + 9.5 * np.sin(theta)
+
+# Convert the points to a dataframe
+semicircle_data = pd.DataFrame({
+    'x': semicircle_x,
+    'y': semicircle_y
+})
+
+# Filter to keep the right side of the semicircle only
+semicircle_data = semicircle_data[semicircle_data['x'] >= 17.5]
+
+# Create the semicircle line
+arc1 = alt.Chart(semicircle_data).mark_line(
+    color='white',
+    strokeWidth=3
+).encode(
+    x=alt.X('x', scale=alt.Scale(domain=[0, 100])),
+    y=alt.Y('y', scale=alt.Scale(domain=[0, 100]))
+)
+
+
+
+theta = np.linspace(0, np.pi, 100)  # Radians from 0 to pi for a full semicircle
+semicircle_x2 = 12 + 9.5 * np.cos(theta)  # Shift the semicircle to the right of x=12
+semicircle_y2 = 50 - 9.5 * np.sin(theta)
+
+# Convert the points to a dataframe
+semicircle_data2 = pd.DataFrame({
+    'x': semicircle_x2,
+    'y': semicircle_y2
+})
+
+# Filter to keep the right side of the semicircle only
+semicircle_data2 = semicircle_data2[semicircle_data2['x'] >= 17.5]
+
+# Create the semicircle line
+arc2 = alt.Chart(semicircle_data2).mark_line(
+    color='white',
+    strokeWidth=3
+).encode(
+    x=alt.X('x', scale=alt.Scale(domain=[0, 100])),
+    y=alt.Y('y', scale=alt.Scale(domain=[0, 100]))
+)
+
+
+theta = np.linspace(0, np.pi, 100)  # Radians from 0 to pi for a full semicircle
+semicircle_x3 = 87 - 9.5 * np.cos(theta)  # Shift the semicircle to the right of x=12
+semicircle_y3 = 50 + 9.5 * np.sin(theta)
+
+# Convert the points to a dataframe
+semicircle_data3 = pd.DataFrame({
+    'x': semicircle_x3,
+    'y': semicircle_y3
+})
+
+# Filter to keep the right side of the semicircle only
+semicircle_data3 = semicircle_data3[semicircle_data3['x'] <= 82.5]
+
+# Create the semicircle line
+arc3 = alt.Chart(semicircle_data3).mark_line(
+    color='white',
+    strokeWidth=3
+).encode(
+    x=alt.X('x', scale=alt.Scale(domain=[0, 100])),
+    y=alt.Y('y', scale=alt.Scale(domain=[0, 100]))
+)
+
+
+
+theta = np.linspace(0, np.pi, 100)  # Radians from 0 to pi for a full semicircle
+semicircle_x4 = 87 - 9.5 * np.cos(theta)  # Shift the semicircle to the right of x=12
+semicircle_y4 = 50 - 9.5 * np.sin(theta)
+
+# Convert the points to a dataframe
+semicircle_data4 = pd.DataFrame({
+    'x': semicircle_x4,
+    'y': semicircle_y4
+})
+
+# Filter to keep the right side of the semicircle only
+semicircle_data4 = semicircle_data4[semicircle_data4['x'] <= 82.5]
+
+# Create the semicircle line
+arc4 = alt.Chart(semicircle_data4).mark_line(
+    color='white',
+    strokeWidth=3
+).encode(
+    x=alt.X('x', scale=alt.Scale(domain=[0, 100]), title=None ),
+    y=alt.Y('y', scale=alt.Scale(domain=[0, 100]), title=None)
+)
+
+
+df_a_match['x'] = [pos[0]['x'] for pos in df_a_match['positions']]
+df_a_match['y'] = [pos[0]['y'] for pos in df_a_match['positions']]
+
+brush = alt.selection(type='interval', encodings=['x','y'])
+#Create scatter plots for events and color by teamId
+
+team_event = alt.Chart(df_a_match).mark_point(
+    size=50,  # Adjust the size as necessary
+    opacity=1,
+    filled=True
+).encode(
+    x=alt.X('x:Q', axis=alt.Axis(labels=False, ticks=False, grid=False)),
+    y=alt.Y('y:Q', axis=alt.Axis(labels=False, ticks=False, grid=False)),
+    #color=alt.Color('teamId:N', legend=None, scale=alt.Scale(domain=list(df_a_match['teamId'].unique()), range=['black', 'cyan'])),
+    color=alt.condition(brush,  # Only apply color to selected points
+                        alt.Color('teamId:N', legend=None, scale=alt.Scale(domain=list(df_a_match['teamId'].unique()), range=['black', 'cyan'])),
+                        alt.value('lightgray')), 
+    tooltip=['eventName:N', 'teamId:N', 'x:Q', 'y:Q']
+).add_selection(
+    brush
+)
+
+
+
+
+# Combine the pitch background, center dot, and events
+soccer_pitch = alt.layer(background, border_lines, midline, lines, white_dots, arc1, arc2, arc3, arc4, center_circle, team_event).properties(
+    width=700,
+    height=440,
+    title="Lazio - Internazionale, 2 - 3"
+)
+
+bars = alt.Chart(df_a_match).mark_bar().encode(
+    y=alt.Y('eventName:N', sort='-x', title=None),
+    x=alt.X('count():Q', title=None),
+    # This specifies a separate color encoding for the bar chart based on eventName
+    color=alt.Color('eventName:N',legend=None)
+).transform_filter(
+    brush
+)
+
+
+
+combined_chart = alt.hconcat(
+    soccer_pitch,
+    bars
+).resolve_scale(
+    color='independent'
+)
+# Display the combined chart
+combined_chart.display()
+
+