# /// script
# dependencies = [
# "flyte>=2.0.0b49",
# "pandas",
# "plotly",
# "requests",
# ]
# ///
import flyte
import flyte.report
env = flyte.TaskEnvironment(
name="timeseries-visualization",
resources=flyte.Resources(cpu=1, memory="512Mi"),
image=flyte.Image.from_uv_script(__file__, name="timeseries-viz-image", python_version=(3, 12), pre=True)
)
@env.task(report=True)
async def download_and_visualize_timeseries() -> str:
"""
Downloads the timeseries dataset from GitHub and creates a beautiful
Plotly visualization with multiple charts.
"""
import pandas as pd
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import requests
from io import StringIO
# Download the dataset
url = "https://raw.githubusercontent.com/plotly/datasets/master/timeseries.csv"
response = requests.get(url)
response.raise_for_status()
# Parse the CSV data
df = pd.read_csv(StringIO(response.text))
# Global font styling
font_family = "Inter, -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, sans-serif"
# Log initial report header with clean styling
await flyte.report.log.aio(f"""
<style>
.report-container {{
font-family: {font_family};
color: #1a1a2e;
line-height: 1.6;
max-width: 1200px;
margin: 0 auto;
}}
.report-title {{
font-size: 2rem;
font-weight: 600;
color: #1a1a2e;
margin-bottom: 0.5rem;
letter-spacing: -0.02em;
}}
.report-subtitle {{
font-size: 0.95rem;
color: #64748b;
margin-bottom: 1.5rem;
}}
.report-subtitle a {{
color: #3b82f6;
text-decoration: none;
}}
.report-subtitle a:hover {{
text-decoration: underline;
}}
.section-title {{
font-size: 1.25rem;
font-weight: 600;
color: #1a1a2e;
margin-top: 2rem;
margin-bottom: 1rem;
letter-spacing: -0.01em;
}}
.info-list {{
list-style: none;
padding: 0;
margin: 0;
}}
.info-list li {{
padding: 0.5rem 0;
border-bottom: 1px solid #e2e8f0;
font-size: 0.9rem;
}}
.info-list li:last-child {{
border-bottom: none;
}}
.info-label {{
color: #64748b;
font-weight: 500;
}}
.info-value {{
color: #1a1a2e;
}}
.stats-table {{
width: 100%;
border-collapse: separate;
border-spacing: 0;
margin-top: 1rem;
font-size: 0.875rem;
border-radius: 8px;
overflow: hidden;
box-shadow: 0 1px 3px rgba(0,0,0,0.1);
}}
.stats-table th {{
background: linear-gradient(135deg, #3b82f6 0%, #2563eb 100%);
color: white;
font-weight: 500;
padding: 0.875rem 1rem;
text-align: left;
}}
.stats-table th:not(:first-child) {{
text-align: right;
}}
.stats-table td {{
padding: 0.75rem 1rem;
border-bottom: 1px solid #e2e8f0;
}}
.stats-table td:not(:first-child) {{
text-align: right;
font-variant-numeric: tabular-nums;
}}
.stats-table tr:nth-child(even) {{
background-color: #f8fafc;
}}
.stats-table tr:hover {{
background-color: #f1f5f9;
}}
.stats-table td:first-child {{
font-weight: 500;
color: #475569;
}}
.success-card {{
background: linear-gradient(135deg, #ecfdf5 0%, #d1fae5 100%);
border: 1px solid #a7f3d0;
border-radius: 12px;
padding: 1.5rem;
margin-top: 2rem;
}}
.success-card h3 {{
color: #065f46;
font-size: 1.1rem;
font-weight: 600;
margin: 0 0 0.75rem 0;
}}
.success-card p {{
color: #047857;
font-size: 0.9rem;
margin: 0 0 0.75rem 0;
}}
.success-card ul {{
margin: 0;
padding-left: 1.25rem;
color: #047857;
font-size: 0.875rem;
}}
.success-card li {{
padding: 0.25rem 0;
}}
hr {{
border: none;
height: 1px;
background: #e2e8f0;
margin: 1.5rem 0;
}}
</style>
<div class="report-container">
<h1 class="report-title">Time Series Data Visualization</h1>
<p class="report-subtitle">Dataset: <a href="https://github.com/plotly/datasets/blob/master/timeseries.csv">plotly/datasets/timeseries.csv</a></p>
<hr>
""", do_flush=True)
# Show dataset info
await flyte.report.log.aio(f"""
<h2 class="section-title">Dataset Overview</h2>
<ul class="info-list">
<li><span class="info-label">Number of rows:</span> <span class="info-value">{len(df)}</span></li>
<li><span class="info-label">Columns:</span> <span class="info-value">{', '.join(df.columns.tolist())}</span></li>
</ul>
""", do_flush=True)
# Create a comprehensive visualization with subplots
fig = make_subplots(
rows=3, cols=1,
subplot_titles=(
"Time Series Values Over Time",
"Rolling Statistics (7-day Window)",
"Distribution of Values"
),
vertical_spacing=0.12,
specs=[[{"type": "scatter"}], [{"type": "scatter"}], [{"type": "histogram"}]]
)
# Get numeric columns (excluding date)
numeric_cols = df.select_dtypes(include=['float64', 'int64']).columns.tolist()
date_col = df.columns[0] # Assume first column is date
# Color palette for beautiful visualization
colors = ['#3b82f6', '#ef4444', '#10b981', '#8b5cf6', '#f59e0b', '#06b6d4', '#ec4899']
# Plot 1: Line chart of all time series
for i, col in enumerate(numeric_cols):
fig.add_trace(
go.Scatter(
x=df[date_col],
y=df[col],
name=col,
mode='lines+markers',
line=dict(color=colors[i % len(colors)], width=2),
marker=dict(size=4),
hovertemplate=f"<b>{col}</b><br>Date: %{{x}}<br>Value: %{{y:.2f}}<extra></extra>"
),
row=1, col=1
)
# Plot 2: Rolling mean and std for each series
for i, col in enumerate(numeric_cols):
rolling_mean = df[col].rolling(window=7, min_periods=1).mean()
rolling_std = df[col].rolling(window=7, min_periods=1).std()
# Rolling mean
fig.add_trace(
go.Scatter(
x=df[date_col],
y=rolling_mean,
name=f"{col} (7-day avg)",
mode='lines',
line=dict(color=colors[i % len(colors)], width=3),
showlegend=True
),
row=2, col=1
)
# Confidence band (mean ± std)
fig.add_trace(
go.Scatter(
x=df[date_col].tolist() + df[date_col].tolist()[::-1],
y=(rolling_mean + rolling_std).tolist() + (rolling_mean - rolling_std).tolist()[::-1],
fill='toself',
fillcolor=f'rgba({int(colors[i % len(colors)][1:3], 16)}, {int(colors[i % len(colors)][3:5], 16)}, {int(colors[i % len(colors)][5:7], 16)}, 0.15)',
line=dict(color='rgba(0,0,0,0)'),
name=f"{col} ±1 std",
showlegend=False
),
row=2, col=1
)
# Plot 3: Histogram distribution
for i, col in enumerate(numeric_cols):
fig.add_trace(
go.Histogram(
x=df[col],
name=col,
marker_color=colors[i % len(colors)],
opacity=0.7,
hovertemplate=f"<b>{col}</b><br>Value: %{{x}}<br>Count: %{{y}}<extra></extra>"
),
row=3, col=1
)
# Update layout with clean font styling
fig.update_layout(
title=dict(
text="<b>Time Series Analysis Dashboard</b>",
font=dict(size=20, color='#1a1a2e', family=font_family),
x=0.5,
xanchor='center'
),
height=1100,
template="plotly_white",
hovermode='x unified',
legend=dict(
orientation="h",
yanchor="top",
y=-0.05,
xanchor="center",
x=0.5,
font=dict(size=11, family=font_family, color='#475569')
),
font=dict(family=font_family, color='#1a1a2e'),
paper_bgcolor='white',
plot_bgcolor='#fafafa',
margin=dict(b=120, t=80)
)
# Update subplot title fonts
for annotation in fig['layout']['annotations']:
annotation['font'] = dict(size=14, color='#1a1a2e', family=font_family)
# Update axes with clean styling
fig.update_xaxes(
title_font=dict(size=12, color='#64748b', family=font_family),
tickfont=dict(size=10, color='#64748b', family=font_family),
gridcolor='#e2e8f0',
linecolor='#e2e8f0'
)
fig.update_yaxes(
title_font=dict(size=12, color='#64748b', family=font_family),
tickfont=dict(size=10, color='#64748b', family=font_family),
gridcolor='#e2e8f0',
linecolor='#e2e8f0'
)
fig.update_xaxes(title_text="Date", row=1, col=1)
fig.update_xaxes(title_text="Date", row=2, col=1)
fig.update_xaxes(title_text="Value", row=3, col=1)
fig.update_yaxes(title_text="Value", row=1, col=1)
fig.update_yaxes(title_text="Value", row=2, col=1)
fig.update_yaxes(title_text="Count", row=3, col=1)
# Add the visualization to the report
await flyte.report.log.aio(fig.to_html(full_html=False, include_plotlyjs="cdn"), do_flush=True)
# Add summary statistics table with clean styling
summary_html = '<h2 class="section-title">Summary Statistics</h2><table class="stats-table">'
summary_html += "<thead><tr><th>Metric</th>"
for col in numeric_cols:
summary_html += f"<th>{col}</th>"
summary_html += "</tr></thead><tbody>"
stats = ['mean', 'std', 'min', '25%', '50%', '75%', 'max']
stat_names = ['Mean', 'Std Dev', 'Min', '25th Percentile', 'Median', '75th Percentile', 'Max']
desc = df[numeric_cols].describe()
for stat, name in zip(stats, stat_names):
summary_html += f"<tr><td>{name}</td>"
for col in numeric_cols:
summary_html += f"<td>{desc.loc[stat, col]:.4f}</td>"
summary_html += "</tr>"
summary_html += "</tbody></table>"
await flyte.report.log.aio(summary_html, do_flush=True)
# Final completion message with clean styling
await flyte.report.log.aio("""
<div class="success-card">
<h3>Visualization Complete</h3>
<p>The time series data has been successfully downloaded and visualized with:</p>
<ul>
<li>Interactive line charts showing all time series</li>
<li>Rolling statistics with confidence bands</li>
<li>Distribution histograms</li>
<li>Comprehensive summary statistics</li>
</ul>
</div>
</div>
""", do_flush=True)
return f"Successfully visualized {len(df)} data points from timeseries dataset"
@env.task
async def main() -> str:
result = await download_and_visualize_timeseries()
return result
if __name__ == "__main__":
import argparse
import os
parser = argparse.ArgumentParser()
parser.add_argument("--build", action="store_true")
args = parser.parse_args()
flyte.init(
api_key=os.environ["FLYTE_API_KEY"],
org=os.environ["FLYTE_ORG"],
project=os.environ["FLYTE_PROJECT"],
domain=os.environ["FLYTE_DOMAIN"],
image_builder="remote",
)
if args.build:
uri = flyte.build(env.image, wait=False)
print(f"build run url: {uri}")
else:
run = flyte.with_runcontext(mode="remote").run(main)
print(run.url)
