376 lines
19 KiB
Python
376 lines
19 KiB
Python
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import dash
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from dash import dcc, html
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import dash_bootstrap_components as dbc
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from dash.dependencies import Input, Output
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import pandas as pd
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import plotly.express as px
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from datetime import datetime, timedelta
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import itertools
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from PIL import Image
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# Use the Dash Bootstrap Components theme
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app = dash.Dash(__name__, external_stylesheets=[dbc.themes.BOOTSTRAP])
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# Load the initial data from the dice_roll_data.csv file into a Pandas DataFrame
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df = pd.read_csv('dice_roll_data.csv')
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# Calculate initial statistics
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result_counts = df['roll'].value_counts().sort_index()
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total_rolls = result_counts.sum()
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# Assuming df is the DataFrame containing the rolls
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current_roll = df['roll'].iloc[-1] if not df.empty else 0 # Default to 0 if the DataFrame is empty
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# Count consecutive occurrences of the current roll value
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consecutive_count = sum(1 for _ in itertools.takewhile(lambda x: x == current_roll, reversed(df['roll'])))
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# Initial health value, adjust as needed
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progress_value = 2000
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max_progress_value = 2000
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max_damage = 0
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# Create initial figure with px.bar
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fig = px.bar(y=result_counts.index, x=result_counts.values, labels={'x': 'Count', 'y': 'Result'}, orientation='h')
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# Update layout for the bar chart to make the axis titles and tick labels larger
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fig.update_xaxes(tickfont=dict(size=24), title_font=dict(size=28))
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fig.update_yaxes(tickfont=dict(size=24), title_font=dict(size=28))
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# Add title to the bar chart
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# fig.update_layout(title_text="Allo", title_font=dict(size=32), title_x=0.5)
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# Calculate the end time for the countdown timer (December 2, 2023, at 3:30 pm)
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end_time = datetime(2023, 12, 2, 15, 30, 0)
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# Function to find the maximal consecutive occurrence of a given roll value
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def find_max_consecutive_occurrence(series, roll_value):
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max_consecutive_occurrence = 0
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current_consecutive_occurrence = 0
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for i in range(1, len(series)):
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if series.iloc[i] == roll_value and series.iloc[i - 1] == roll_value and (series.index[i] - series.index[i - 1]) == 1:
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current_consecutive_occurrence += 1
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elif series.iloc[i] == roll_value:
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current_consecutive_occurrence = 1
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else:
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current_consecutive_occurrence = 0
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max_consecutive_occurrence = max(max_consecutive_occurrence, current_consecutive_occurrence)
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return max_consecutive_occurrence
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def calculate_cumulative_damage(rolls):
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cumulative_damage = []
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current_value = None
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consecutive_count = 0
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for roll in rolls:
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if roll == current_value:
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consecutive_count += 1
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else:
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consecutive_count = 1
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current_value = roll
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damage = roll * (2 ** (consecutive_count - 1))
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cumulative_damage.append(damage)
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return cumulative_damage
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# Function to read values from the info.txt file
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def read_info_file():
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try:
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with open('info.txt', 'r') as file:
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lines = file.readlines()
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if lines:
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values = lines[0].strip().split(',')
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if len(values) == 3:
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inference_rate, latency, power_consumption = map(float, values)
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return inference_rate, latency, power_consumption
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except Exception as e:
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print(f"Error reading info.txt: {e}")
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return None, None, None
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# Calculate the maximal consecutive occurrence for each roll
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max_consecutive_occurrences = {roll: find_max_consecutive_occurrence(df['roll'], roll) for roll in range(1, 7)}
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# Define layout using Dash components and Dash Bootstrap Components
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app.layout = dbc.Container(
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fluid=True,
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children=[
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# Row 1: Cells 1 and 2
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dbc.Row([
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dbc.Col(
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dbc.Card(
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dbc.CardBody(
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[
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html.Img(src=dash.get_asset_url('oogie.gif'), alt='Oogie Boogie', className='img-fluid mx-auto my-auto', style={'width': '75%', 'max-height': '100%', 'border-radius': '25px'}),
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dbc.Progress(
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id='progress-bar',
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value=progress_value,
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color="success",
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max=max_progress_value,
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style={'margin-top': '20px', 'height': '30px', 'background-color': 'red'},
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),
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html.P(f'Health: {progress_value:.0f}/{max_progress_value}', id='health-text', className='lead text-center fw-bold', style={'font-size': '24px', 'margin-top': '10px'}),
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],
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className='text-center'
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),
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className='border p-3',
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style={'height': '100%'}
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),
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width=6
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),
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dbc.Col(
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# Cell 2: Countdown, System infos, and Table with max consecutive occurrences for each roll
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dbc.Card(
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[
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dcc.Interval(
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id='interval-component',
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interval=1*1000, # in milliseconds
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n_intervals=0
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),
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html.P(id='countdown-timer', className='lead text-center fw-bold', style={'font-size': '32px'}),
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dbc.CardBody(
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[
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# Add new text and values from info.txt
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html.Div(
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[
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html.P(id='inference-rate-text', className='lead text-center fw-bold', style={'font-size': '24px', 'margin-right': '10px'}),
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html.P(id='latency-text', className='lead text-center fw-bold', style={'font-size': '24px', 'margin-right': '10px'}),
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html.P(id='power-consumption-text', className='lead text-center fw-bold', style={'font-size': '24px'}),
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],
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style={'display': 'flex', 'align-items': 'center', 'justify-content': 'center', 'margin-top': '20px'},
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),
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html.Div(
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[
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html.Img(src=dash.get_asset_url('dice-pc.png'), alt='Actuel', className='img-fluid mx-auto my-auto', style={'width': '50%', 'max-height': '100%', 'border-radius': '25px'}),
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],
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style={'display': 'flex', 'align-items': 'center', 'justify-content': 'center'},
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),
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html.Div(
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[
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html.Img(src=dash.get_asset_url('dice-fpga.png'), alt='Cible', className='img-fluid mx-auto my-auto', style={'width': '60%', 'max-height': '100%', 'border-radius': '25px'}),
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],
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style={'display': 'flex', 'align-items': 'center', 'justify-content': 'center', 'margin-top': '20px'},
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),
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# html.Img(src=dash.get_asset_url('dice-pc.png'), alt='Actuel', className='img-fluid mx-auto my-auto', style={'width': '55%', 'max-height': '100%', 'border-radius': '25px'}),
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# html.Img(src=dash.get_asset_url('dice-fpga.png'), alt='Cible', className='img-fluid mx-auto my-auto', style={'width': '5%', 'max-height': '100%', 'border-radius': '25px'}),
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# dbc.Table(
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# id='max-consecutive-occurrence-table',
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# children=[
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# html.Thead(
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# html.Tr([html.Th('Roll', style={'font-size': '28px'}), html.Th('Max Consecutive Occurrence', style={'font-size': '28px'})])
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# ),
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# html.Tbody(
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# [html.Tr([html.Td(f'{roll}', style={'font-size': '24px'}), html.Td(f'{max_consecutive_occurrences[roll]}', style={'font-size': '24px'})]) for roll in range(1, 7)]
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# )
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# ],
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# className='text-center',
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# style={'margin-top': '20px'}
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# ),
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],
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className='text-center'
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),
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],
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className='border p-3',
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style={'height': '100%'}
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),
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width=6
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)
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], align='stretch', style={'margin-bottom': '20px'}),
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# Row 2: Cells 3 and 4
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dbc.Row([
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dbc.Col(
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# Cell 3
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dbc.Card(
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dbc.CardBody(
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[
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html.Div(
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[
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html.Div(
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[
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html.H5("Previous Roll", className="card-title fw-bold", style={'margin-bottom': '10px', 'font-size': '32px'}),
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html.Img(id='previous-roll-image', alt='previous roll image', className='img-fluid mx-auto my-auto', style={'width': '70%', 'max-height': '100%', 'border-radius': '15px'}),
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],
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style={'padding-bottom': '20px', 'padding-right': '75px'}
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),
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html.Div(
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[
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html.H5("Current Roll", className="card-title fw-bold", style={'margin-bottom': '10px', 'font-size': '32px'}),
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html.Img(id='current-roll-image', alt='current roll image', className='img-fluid mx-auto my-auto', style={'width': '70%', 'max-height': '100%', 'border-radius': '15px'}),
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],
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style={'padding-bottom': '20px'}
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),
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],
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style={'display': 'flex', 'justify-content': 'center', 'margin-top': '75px'}
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),
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html.P(id='consecutive-count-text', className='lead text-center fw-bold', style={'font-size': '24px'}),
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html.P(id='damage-dealt-text', className='lead text-center fw-bold', style={'font-size': '24px'}),
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html.P(id='max-damage-text', className='lead text-center fw-bold', style={'font-size': '24px'}),
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],
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className='text-center'
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),
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className='border p-3',
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style={'height': '100%'}
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),
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width=6
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),
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dbc.Col(
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# Cell 4: Bar Chart and Probability Table
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dbc.Card(
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[
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dbc.CardBody(
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[
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html.P(id='total-rolls-text', className='lead text-center fw-bold', style={'font-size': '32px'}),
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html.Div(
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[
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# Bar Chart
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dcc.Graph(
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id='bar-chart',
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figure=fig,
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style={'height': '80%', 'width': '70%', 'display': 'inline-block'}
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),
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# Probability Table
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dbc.Table(
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id='probability-table',
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style={'margin-top': '20px', 'width': '30%', 'display': 'inline-block'},
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),
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],
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style={'width': '100%', 'display': 'flex', 'justify-content': 'space-between'}
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),
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],
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className='text-center'
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),
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],
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# Cell 4
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className='border p-3',
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style={'height': '100%'}
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),
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width=6
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)
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], align='stretch')
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]
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)
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# Callback to update countdown timer text
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@app.callback(Output('countdown-timer', 'children'),
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[Input('interval-component', 'n_intervals')])
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def update_timer(n_intervals):
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time_left = end_time - datetime.now()
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days, seconds = divmod(time_left.total_seconds(), 86400)
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hours, remainder = divmod(seconds, 3600)
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minutes, seconds = divmod(remainder, 60)
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# Format components with leading zeros
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formatted_hours = str(int(hours)).zfill(2)
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formatted_minutes = str(int(minutes)).zfill(2)
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formatted_seconds = str(int(seconds)).zfill(2)
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return f'Temps restant: {formatted_hours}:{formatted_minutes}:{formatted_seconds}'
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# Callback to update data every second
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@app.callback([Output('bar-chart', 'figure'),
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Output('total-rolls-text', 'children'),
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# Output('max-consecutive-occurrence-table', 'children'),
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Output('probability-table', 'children'),
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Output('inference-rate-text', 'children'),
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Output('latency-text', 'children'),
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Output('power-consumption-text', 'children'),
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Output('consecutive-count-text', 'children'),
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Output('damage-dealt-text', 'children'),
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Output('previous-roll-image', 'src'),
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Output('current-roll-image', 'src'),
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Output('progress-bar', 'value'),
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Output('health-text', 'children'),
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Output('max-damage-text', 'children'),],
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[Input('interval-component', 'n_intervals')])
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def update_data(n_intervals):
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global df, max_consecutive_occurrences, total_rolls, consecutive_count, current_roll, progress_value, max_damage
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# Load the data from the dice_roll_data.csv file into a Pandas DataFrame
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df = pd.read_csv('dice_roll_data.csv')
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# Calculate statistics
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result_counts = df['roll'].value_counts().sort_index()
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total_rolls = result_counts.sum()
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# Create figure with px.bar
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fig = px.bar(y=result_counts.index, x=result_counts.values, labels={'x': 'Compte', 'y': 'Résultat'},
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orientation='h')
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# Update layout for the bar chart to make the axis titles and tick labels larger
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fig.update_xaxes(tickfont=dict(size=24), title_font=dict(size=28))
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fig.update_yaxes(tickfont=dict(size=24), title_font=dict(size=28))
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# Add numbers on the bars
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fig.update_traces(text=result_counts.values, textposition='inside', textfont_size=20)
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# Calculate the maximal consecutive occurrence for each roll
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max_consecutive_occurrences = {roll: find_max_consecutive_occurrence(df['roll'], roll) for roll in range(1, 7)}
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# Calculate the total and maximal damage dealt and apply it to the progress bar
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total_damage_dealt = sum(calculate_cumulative_damage(df['roll']))
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progress_value = max_progress_value - total_damage_dealt
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health_text = f'Points de vie: {progress_value:.0f}/{max_progress_value}'
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# Update the table in Cell 2 with the new max consecutive occurrences
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max_occurrence_table = [
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html.Thead(
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html.Tr([html.Th('Résultat', style={'font-size': '28px'}), html.Th('Occurence Consécutive Max', style={'font-size': '28px'})])
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),
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html.Tbody(
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[html.Tr([html.Td(f'{roll}', style={'font-size': '24px'}), html.Td(f'{max_consecutive_occurrences[roll]}', style={'font-size': '24px'})]) for roll in range(1, 7)]
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)
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]
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||
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# Read values from the info.txt file
|
||
|
|
inference_rate, latency, power_consumption = read_info_file()
|
||
|
|
|
||
|
|
# Update text in Cell 2 with values from info.txt
|
||
|
|
inference_rate_text = f"Débit d'Inference: {inference_rate} fps" if inference_rate is not None else "Débit d'Inference: N/A"
|
||
|
|
latency_text = f'Latence: {latency} µs' if latency is not None else 'Latence: N/A'
|
||
|
|
power_consumption_text = f'Consomation Énergétique: {power_consumption} W' if power_consumption is not None else 'Consomation Énergétique: N/A'
|
||
|
|
|
||
|
|
# Calculate probabilities
|
||
|
|
probabilities = result_counts / total_rolls * 100
|
||
|
|
probability_table = [
|
||
|
|
html.Thead(
|
||
|
|
html.Tr([html.Th('Résultat', style={'font-size': '28px'}), html.Th('Probabilités', style={'font-size': '28px'})])
|
||
|
|
),
|
||
|
|
html.Tbody(
|
||
|
|
[html.Tr([html.Td(f'{roll}', style={'font-size': '24px'}), html.Td(f'{probabilities[roll]:.2f}%', style={'font-size': '24px'})]) for roll in range(1, 7)]
|
||
|
|
)
|
||
|
|
]
|
||
|
|
|
||
|
|
# Update consecutive count and text
|
||
|
|
current_roll = df['roll'].iloc[-1] if not df.empty else 0
|
||
|
|
consecutive_count = sum(1 for _ in itertools.takewhile(lambda x: x == current_roll, reversed(df['roll'])))
|
||
|
|
consecutive_count_text = f'Vous avez lancé {current_roll} pour la {consecutive_count}{"ère" if consecutive_count == 1 else "ème"} fois de suite!'
|
||
|
|
|
||
|
|
# Calculate damage dealt
|
||
|
|
damage_dealt = current_roll * (2 ** (consecutive_count-1))
|
||
|
|
damage_dealt_text = f'Dégat infligé: {damage_dealt}'
|
||
|
|
|
||
|
|
# Calculate max damage dealt
|
||
|
|
if max_damage > damage_dealt:
|
||
|
|
max_damage_text = f'Dégat Maximal: {max_damage}'
|
||
|
|
else:
|
||
|
|
max_damage = damage_dealt
|
||
|
|
max_damage_text = f'Nouveau Dégat Maximal: {max_damage}! Bravo!'
|
||
|
|
|
||
|
|
# Get image sources based on the current and previous rolls
|
||
|
|
previous_roll_image_source = Image.open('assets/previous_roll.jpg')
|
||
|
|
current_roll_image_source = Image.open('assets/current_roll.jpg')
|
||
|
|
|
||
|
|
return fig, f'Total de tirs: {total_rolls}', probability_table, inference_rate_text, latency_text, power_consumption_text, consecutive_count_text, damage_dealt_text, previous_roll_image_source, current_roll_image_source, progress_value, health_text, max_damage_text
|
||
|
|
|
||
|
|
|
||
|
|
if __name__ == '__main__':
|
||
|
|
app.run_server(debug=True, host='0.0.0.0', dev_tools_hot_reload=False)
|