Add:Added write-up

app.py

@@ -69,21 +69,13 @@ st.altair_chart(special)
 st.divider()
 st.title("1st Plot Write-UP")
 st.write("""
-Boxplot of Unique Shapes
-The first visualization is a boxplot that highlights the distribution of UFO sighting durations (in seconds) for various unique shapes, categorized by country. 
-The x-axis uses a logarithmic scale to better handle the wide range of duration values, from very short sightings to extremely long ones.
- The y-axis categorizes the different shapes, providing a clear comparison across them.
-  The choice of color encodes the country of origin for each sighting, using a distinct palette to differentiate between countries like the US, Canada, and Australia. This design choice helps identify patterns or anomalies in specific shapes or countries. If given more time, I would enhance this visualization by adding interactivity, such as tooltips showing additional details (e.g., exact duration, date) and filters to focus on specific countries or shapes.
-Bar and Line Charts: UFO Sightings by Country and Year
+Scatter Plot of Weather Data
+The scatter plot visualizes the relationship between visibility and wind speed, with additional encodings for season and temperature. The x-axis represents visibility, while the y-axis shows wind speed, providing insight into how these two weather variables interact. Seasons are encoded using a categorical color scheme (set2), ensuring that each season is visually distinct. The size of the points represents the high temperature, allowing for a multi-dimensional analysis. This design choice effectively highlights clusters and patterns in the data, such as seasonal variations in visibility and wind speed. If I had more time, I would add interactivity to filter by season or temperature range and include annotations to highlight outliers or trends.
+
 """)
 
 st.title("2nd Plot Write-UP")
 st.write("""
-The second set of visualizations comprises a bar chart and a line chart. 
-The bar chart visualizes the total duration of UFO sightings per country using a logarithmic scale on the y-axis to accommodate the large variation in total durations.
- Each country is represented with distinct colors for clarity.
-  The line chart complements this by showing the average duration of sightings over time for each country, allowing trends to emerge across decades.
-   The use of separate color encodings for countries ensures consistency and readability across both charts.
-    If I had more time, I would refine the line chart by smoothing out noisy data points and adding annotations to highlight significant spikes or trends in specific years.
-     Additionally, I would incorporate hover-based interactivity to display exact values and additional metadata for each data point.
+Mixed Chart of Weather Data
+The mixed chart combines a heatmap and a bar chart to provide a comprehensive view of weather data. The heatmap visualizes the relationship between UV index and cloud cover, with the color intensity representing the count of UV index values and circle sizes showing the count of cloud cover. This dual encoding enables a detailed exploration of how these variables correlate. The bar chart complements this by showing the distribution of records across seasons, using distinct colors to emphasize seasonal differences. If I had more time, I would refine the heatmap by adding hover-based tooltips for precise counts and enhance the bar chart with interactive sorting or filtering options to focus on specific seasons or weather conditions.
 """)