Assignment 1

02_activities/assignments/participation/session_01.py

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+"""
+Visualization— Live Learning Session 1
+Date: 2026-01-15
+
+Overview:
+- Learned how to create a basic figure using matplotlib.
+- Generated small sample data using NumPy.
+- Created a simple scatter plot and confirmed it renders correctly.
+
+Notes / takeaways:
+- Using a fixed random seed helps make plots reproducible (same random values each run).
+- plt.subplots() is a clean way to create a Figure + Axes object for plotting.
+- Scatter plots are useful for showing relationships/patterns between two numeric variables.
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+# --- Create sample data (reproducible) ---
+np.random.seed(613)
+x = np.arange(50)
+y = np.random.randint(0, 100, 50)
+
+# --- Make a basic scatter plot ---
+fig, ax = plt.subplots(figsize=(5, 3))
+ax.scatter(x, y)
+
+# Add minimal labeling for readability
+ax.set_title("Basic scatter plot (Session 1)")
+ax.set_xlabel("x (index)")
+ax.set_ylabel("y (random integer 0–100)")
+
+plt.tight_layout()
+plt.show()

02_activities/assignments/participation/session_02.py

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+"""
+Visualization — Live Learning Session 2
+Date: 2026-01-20
+
+Overview:
+- This session focused on understanding good and bad data visualization.
+- We discussed how visualization choices depend on purpose audience and context.
+- Examples were shown to compare effective and ineffective chart designs.
+
+Notes:
+- A good visualization makes the message easy to understand.
+- A bad visualization increases confusion or can mislead the viewer.
+- Design choices such as chart type labels color and scale matter.
+- Bar charts are usually better for comparing values than pie charts.
+- Too many colors effects or decorations reduce clarity.
+- Accessibility includes readable labels good contrast and not relying only on color.
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Sample data used to demonstrate different visualization choices
+categories = ["A", "B", "C", "D", "E"]
+values = [25, 40, 55, 30, 45]
+
+# Clear example: bar chart
+fig, ax = plt.subplots(figsize=(6, 3))
+ax.bar(categories, values)
+ax.set_title("Bar chart example")
+ax.set_xlabel("Category")
+ax.set_ylabel("Value")
+plt.tight_layout()
+plt.show()
+
+# Less effective example: pie chart for comparison
+fig, ax = plt.subplots(figsize=(5, 4))
+ax.pie(values, labels=categories, autopct="%1.0f%%")
+ax.set_title("Pie chart example")
+plt.tight_layout()
+plt.show()

02_activities/assignments/participation/session_03.py

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+"""
+Visualization — Live Learning Session 3
+Date: 2026-01-22
+
+Overview:
+- This session focused on reproducible data visualization.
+- We reviewed how visualizations communicate and persuade and how they reflect choices.
+- We practiced making plots with multiple series and improving readability with labels legends and annotations.
+- We also practiced removing tick marks and labels when they are not needed.
+
+Notes:
+- Reproducibility means someone else can run the same code and get the same results.
+- Using a fixed random seed helps keep generated example data consistent.
+- Visualizations are not neutral because we choose what to show and how to show it.
+- Adding labels and a legend makes it easier to interpret multiple series.
+- Annotations can highlight an important value or point in the plot.
+- Removing ticks or tick labels can reduce clutter if they do not add meaning.
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.ticker import NullFormatter
+from matplotlib.ticker import NullLocator
+
+np.random.seed(613)
+x = np.arange(50)
+y1 = np.random.randint(0, 100, 50)
+y2 = np.random.randint(0, 100, 50)
+
+fig, ax = plt.subplots(figsize=(5, 3))
+
+ax.scatter(x, y1, label="Person 1")
+ax.scatter(x, y2, label="Person 2")
+ax.legend(loc="lower right")
+
+ax.text(
+    10,
+    95,
+    "This value is important!",
+    ha="center",
+    color="red",
+    fontsize=14
+)
+
+ax.yaxis.set_major_locator(NullLocator())
+ax.xaxis.set_major_formatter(NullFormatter())
+
+ax.set_title("Reproducible scatter plot example")
+plt.tight_layout()
+plt.show()

02_activities/assignments/participation/session_04.py

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+"""
+Session 04: Visualization with purpose and accessible data visualization
+
+This session focused on designing visualizations with a clear purpose
+and applying accessibility principles so that visualizations are usable
+by a wide audience.
+
+Key ideas:
+- Visualizations should be created with a specific audience and goal in mind
+- Design choices are not neutral and affect interpretation
+- Accessibility should be considered from the start
+"""
+
+# Import required libraries
+import numpy as np
+import matplotlib.pyplot as plt
+
+# -----------------------------
+# Reproducible data generation
+# -----------------------------
+# Set a random seed so results can be reproduced
+np.random.seed(42)
+
+x = np.arange(50)
+y1 = np.random.randint(0, 100, 50)
+y2 = np.random.randint(0, 100, 50)
+
+# -----------------------------
+# Create a visualization with purpose
+# -----------------------------
+# Purpose: compare two groups clearly on the same axes
+
+fig, ax = plt.subplots(figsize=(6, 4))
+
+ax.scatter(x, y1, label="Group 1")
+ax.scatter(x, y2, label="Group 2")
+
+ax.set_title("Comparison of Two Groups Over Time")
+ax.set_xlabel("Observation Index")
+ax.set_ylabel("Value")
+
+ax.legend(loc="lower right")
+
+# -----------------------------
+# Accessibility considerations
+# -----------------------------
+# - Clear labels and title
+# - Legend included instead of relying only on color
+# - Reasonable figure size for readability
+
+# Add a short annotation to guide interpretation
+ax.annotate(
+    "Higher values indicate increased measurements",
+    xy=(5, 90),
+    ha="left"
+)
+
+plt.tight_layout()
+plt.show()
+
+"""
+Notes:
+- The visualization communicates a clear message rather than showing data without context
+- Labels and legends reduce reliance on color alone
+- A fixed random seed supports reproducibility
+- Simple annotations help guide the viewer
+"""

02_activities/assignments/participation/session_05.py

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+"""
+Session 5 (260129) Participation
+Topic: Subplots and combining visualizations plus wrapping up remaining coding topics
+
+Key ideas
+- Use subplots to compare views side by side when it supports the message
+- Use layout tools so labels and titles do not overlap
+- Save figures with a stable file name for reproducibility
+- Combine plot types in one figure when the relationship is meaningful
+- Reminder from class: check paths and dependencies early when running scripts
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+np.random.seed(42)
+
+x = np.arange(50)
+y_line = np.random.randint(10, 80, size=50)
+
+names = ["Luffy", "Zoro", "Nami", "Usopp", "Sanji"]
+scores = [110, 180, 240, 100, 220]
+
+fig, ax = plt.subplots(figsize=(7, 3))
+ax.plot(x, y_line)
+ax.set_title("Line chart example")
+ax.set_xlabel("Index")
+ax.set_ylabel("Value")
+fig.tight_layout()
+plt.show()
+
+
+fig, ax = plt.subplots(figsize=(7, 3))
+ax.bar(names, scores)
+ax.set_title("Bar chart example")
+ax.set_xlabel("Character")
+ax.set_ylabel("Score")
+fig.tight_layout()
+plt.show()
+
+
+fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(10, 4))
+
+axes[0].scatter(x, y_line)
+axes[0].set_title("Scatter view")
+axes[0].set_xlabel("Index")
+axes[0].set_ylabel("Value")
+
+axes[1].bar(names, scores)
+axes[1].set_title("Bar view")
+axes[1].set_xlabel("Character")
+axes[1].set_ylabel("Score")
+
+fig.tight_layout()
+plt.show()
+
+
+layout = [["A", "B"], ["C", "C"]]
+fig, axd = plt.subplot_mosaic(layout, figsize=(10, 6), constrained_layout=True)
+
+axd["A"].scatter(x, y_line)
+axd["A"].set_title("A: Scatter")
+
+axd["B"].plot(x, y_line)
+axd["B"].set_title("B: Line")
+
+axd["C"].bar(names, scores)
+axd["C"].set_title("C: Bar (wide)")
+
+plt.show()
+
+
+fig, ax = plt.subplots(figsize=(7, 3))
+ax.plot(x, y_line)
+ax.set_title("Saving a figure")
+ax.set_xlabel("Index")
+ax.set_ylabel("Value")
+fig.tight_layout()
+
+output_path = "session_05_saved_plot.png"
+fig.savefig(output_path, dpi=300)
+plt.show()
+
+print(f"Saved figure to: {output_path}")