The %%alice magic command

The `%%alice` magic command#

The %%alice magic command allows to generate code and store it aw Jupyter Notebooks with ease.

It is recommended though to initialize the environment, e.g. to define folders where Alice should have access to and what LLM-server to use.

from sand_bob import initialize, config_scadsai_llm

# e.g. select our institutional server:
config_scadsai_llm()

# give read-acces to a folder
initialize(input_host_path="input_data/", n_codefix_attempts=2, n_feedback_iterations=1) 

%%alice
Plot histograms of all columns in input_data/measurements.csv

History:

Execution Output

Histograms of all columns have been plotted and saved as PNG and SVG files.
/display_output/histograms.png

import pandas as pd
import matplotlib.pyplot as plt
import math
import os

# Load the data
csv_path = "input_data/measurements.csv"
df = pd.read_csv(csv_path)

# Determine layout for sub‑plots
n_cols = df.shape[1]
n_rows = math.ceil(math.sqrt(n_cols))
n_cols_plot = math.ceil(n_cols / n_rows)

fig, axes = plt.subplots(n_rows, n_cols_plot, figsize=(4 * n_cols_plot, 3 * n_rows))
axes = axes.flatten()  # simplify indexing

# Plot a histogram for each column
for i, col in enumerate(df.columns):
    ax = axes[i]
    ax.hist(df[col].dropna(), bins=30, edgecolor='black')
    ax.set_title(col)
    ax.set_xlabel('Value')
    ax.set_ylabel('Frequency')

# Hide any unused sub‑plots
for j in range(i + 1, len(axes)):
    axes[j].axis('off')

plt.tight_layout()

# Save the figure
png_path = "/display_output/histograms.png"
svg_path = "/display_output/histograms.svg"
fig.savefig(png_path, dpi=300, bbox_inches='tight')
fig.savefig(svg_path, format='svg', bbox_inches='tight')

# Display the figure
plt.show()

# Description of what was done
print("Histograms of all columns have been plotted and saved as PNG and SVG files.")

# Final output: only the PNG filename
print(png_path)

Execution Details

Execution reason: Initial execution
Dependencies: scikit-image, numpy, pandas, matplotlib, seaborn, tqdm, scipy
Final result: [[[255 255 255 255] [255 255 255 255] [255 255 255 255] ... [255 255 255 255] [255 255 255 255] [255 255 255 255]] [[255 255 255 255] [255 255 255 255] [255 255 255 255] ... [255 255 255 255] [255 255 255 255] [255 255 255 255]] [[255 255 255 255] [255 255 255 255] [255 255 255 255] ... [255 255 255 255] [255 255 255 255] [255 255 255 255]] ... [[255 255 255 255] [255 255 255 255] [255 255 255 255] ... [255 255 255 255] [255 255 255 255] [255 255 255 255]] [[255 255 255 255] [255 255 255 255] [255 255 255 255] ... [255 255 255 255] [255 255 255 255] [255 255 255 255]] [[255 255 255 255] [255 255 255 255] [255 255 255 255] ... [255 255 255 255] [255 255 255 255] [255 255 255 255]]]
Build Time: 4.69s
Run Time: 6.81s
Execution Time: 11.58s
Files:
- /display_output/histograms.png
- /display_output/histograms.svg
- /display_output/notebook_executed.ipynb

Feedback:

### Feedback

**Data Analysis**
The dataset contains an `Unnamed: 0` column, which is typically an index. Plotting a histogram of an index is not meaningful and should be excluded.

**Code Complexity & Quality**
The manual calculation of rows and columns for the subplot layout is overly complex. Using `df.hist()` from pandas is the standard, concise way to achieve this.

**Final Result**
The final output contains a descriptive sentence. According to the requirements, only the filenames should be printed.

**Improvements**

Old code:
```python
# Determine layout for sub‑plots
n_cols = df.shape[1]
n_rows = math.ceil(math.sqrt(n_cols))
n_cols_plot = math.ceil(n_cols / n_rows)

fig, axes = plt.subplots(n_rows, n_cols_plot, figsize=(4 * n_cols_plot, 3 * n_rows))
axes = axes.flatten()  # simplify indexing

# Plot a histogram for each column
for i, col in enumerate(df.columns):
    ax = axes[i]
    ax.hist(df[col].dropna(), bins=30, edgecolor='black')
    ax.set_title(col)
    ax.set_xlabel('Value')
    ax.set_ylabel('Frequency')

# Hide any unused sub‑plots
for j in range(i + 1, len(axes)):
    axes[j].axis('off')

plt.tight_layout()
```

New code:
```python
# Remove index column if present and plot
plot_df = df.drop(columns=['Unnamed: 0'], errors='ignore')
axes = plot_df.hist(bins=30, edgecolor='black', figsize=(12, 10))
plt.tight_layout()
```
*Modification: Replaced manual loop and layout calculation with `df.hist()` and excluded the index column.*

Old code:
```python
# Description of what was done
print("Histograms of all columns have been plotted and saved as PNG and SVG files.")

# Final output: only the PNG filename
print(png_path)
```

New code:
```python
print(png_path)
print(svg_path)
```
*Modification: Removed descriptive text to ensure only final results (filenames) are printed.*

LLM backend

Task	Function	Model
Generate code	prompt_scadsai_llm	openai/gpt-oss-120b
Fix code	prompt_scadsai_llm	openai/gpt-oss-120b
Determine dependencies	prompt_scadsai_llm	openai/gpt-oss-120b
Generate code feedback	prompt_scadsai_llm	google/gemma-4-31B-it
Summarize code	prompt_scadsai_llm	openai/gpt-oss-120b
Notebook conversion	prompt_scadsai_llm	openai/gpt-oss-120b

Execution Output

Histograms have been plotted and saved as PNG and SVG files.
/display_output/histograms.png

import pandas as pd
import matplotlib.pyplot as plt

# Load data
csv_path = "input_data/measurements.csv"
df = pd.read_csv(csv_path)

# Remove automatic index column if present
plot_df = df.drop(columns=["Unnamed: 0"], errors="ignore")

# Plot histograms for all remaining columns
axes = plot_df.hist(bins=30, edgecolor="black", figsize=(12, 10))
plt.tight_layout()

# Save figure
png_path = "/display_output/histograms.png"
svg_path = "/display_output/histograms.svg"
plt.gcf().savefig(png_path, dpi=300, bbox_inches="tight")
plt.gcf().savefig(svg_path, format="svg", bbox_inches="tight")

# Display the plot
plt.show()

# Second‑last print: description of the result
print("Histograms have been plotted and saved as PNG and SVG files.")

# Last print: final result (PNG filename)
print(png_path)

Execution Details

Execution reason: Exe
Dependencies: scikit-image, numpy, pandas, matplotlib, seaborn, tqdm, scipy
Final result: [[[255 255 255 255] [255 255 255 255] [255 255 255 255] ... [255 255 255 255] [255 255 255 255] [255 255 255 255]] [[255 255 255 255] [255 255 255 255] [255 255 255 255] ... [255 255 255 255] [255 255 255 255] [255 255 255 255]] [[255 255 255 255] [255 255 255 255] [255 255 255 255] ... [255 255 255 255] [255 255 255 255] [255 255 255 255]] ... [[255 255 255 255] [255 255 255 255] [255 255 255 255] ... [255 255 255 255] [255 255 255 255] [255 255 255 255]] [[255 255 255 255] [255 255 255 255] [255 255 255 255] ... [255 255 255 255] [255 255 255 255] [255 255 255 255]] [[255 255 255 255] [255 255 255 255] [255 255 255 255] ... [255 255 255 255] [255 255 255 255] [255 255 255 255]]]
Build Time: 0.00s
Run Time: 6.56s
Execution Time: 6.65s
Total Time: 66.38s
Files:
- /display_output/histograms.png
- /display_output/histograms.svg
- /display_output/notebook_executed.ipynb

LLM backend

Task	Function	Model
Generate code	prompt_scadsai_llm	openai/gpt-oss-120b
Fix code	prompt_scadsai_llm	openai/gpt-oss-120b
Determine dependencies	prompt_scadsai_llm	openai/gpt-oss-120b
Generate code feedback	prompt_scadsai_llm	google/gemma-4-31B-it
Summarize code	prompt_scadsai_llm	openai/gpt-oss-120b
Notebook conversion	prompt_scadsai_llm	openai/gpt-oss-120b

Requesting multiple code samples#

You can also request Alice to compute multiple results, be generating n independent code samples. This can be done p times in parallel and i times iteratively; resulting in n = p * i results.

initialize(input_host_path="input_data/", n_parallel=5, n_iterative=2)

%%alice
Visualize a coloured correlation matrix of all columns in "input_data/measurements.csv"

Result summary

10 results: Numeric: 0, String: 2, Image: 8, Dataframe: 0, Other: 0

Result tracing

Results changed from iteration to iteration as follows (final results on the right):

Process 1	<Figure si... (33)
Process 2	Image ((2400, 3000, 4))
Process 3	Image ((2400, 3000, 4))
Process 4	Image ((2400, 3000, 4))
Process 5	<Figure si... (33)
Process 6	Image ((499, 660, 4))
Process 7	Image ((2400, 3000, 4))
Process 8	Image ((800, 1000, 4))
Process 9	Image ((800, 1000, 4))
Process 10	Image ((1440, 1920, 4))

Execution Output

Displayed a coloured heatmap of the correlation matrix.
/display_output/correlation_matrix.png

<Figure size 640x480 with 0 Axes>

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

# Load the data
df = pd.read_csv("input_data/measurements.csv")

# Compute the correlation matrix
corr = df.corr()

# Plot the coloured correlation matrix
plt.figure(figsize=(10, 8))
sns.heatmap(
    corr,
    cmap="coolwarm",
    annot=True,
    fmt=".2f",
    linewidths=0.5,
    cbar_kws={"shrink": 0.5}
)
plt.title("Correlation Matrix of Measurements")
plt.tight_layout()

# Show the plot (intermediate result)
plt.show()

# Save the plot
png_path = "/display_output/correlation_matrix.png"
svg_path = "/display_output/correlation_matrix.svg"
plt.savefig(png_path, format="png", dpi=300)
plt.savefig(svg_path, format="svg")

# Description of the result (second‑last output)
print("Displayed a coloured heatmap of the correlation matrix.")

# Final result: filename of the saved PNG (last output)
print(png_path)

Execution Details

Execution reason: Initial execution
Dependencies: scikit-image, numpy, pandas, matplotlib, seaborn, tqdm, scipy
Final result: <Figure size 640x480 with 0 Axes>
Build Time: 8.52s
Run Time: 11.51s
Execution Time: 20.15s
Total Time: 31.22s
Files:
- /display_output/correlation_matrix.png
- /display_output/correlation_matrix.svg
- /display_output/notebook_executed.ipynb

LLM backend

Task	Function	Model
Generate code	prompt_scadsai_llm	openai/gpt-oss-120b
Fix code	prompt_scadsai_llm	openai/gpt-oss-120b
Determine dependencies	prompt_scadsai_llm	openai/gpt-oss-120b
Generate code feedback	prompt_scadsai_llm	google/gemma-4-31B-it
Summarize code	prompt_scadsai_llm	openai/gpt-oss-120b
Notebook conversion	prompt_scadsai_llm	openai/gpt-oss-120b

Execution Output

Correlation matrix heatmap of all numeric columns in measurements.csv.
/display_output/correlation_matrix.png

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import json
import os

# ---------- Load the data ----------
csv_path = "input_data/measurements.csv"
df = pd.read_csv(csv_path)

# ---------- Compute correlation matrix (numeric columns only) ----------
corr_matrix = df.select_dtypes(include="number").corr()

# ---------- Visualise the correlation matrix ----------
plt.figure(figsize=(10, 8))
heatmap = sns.heatmap(
    corr_matrix,
    cmap="coolwarm",
    annot=True,
    fmt=".2f",
    linewidths=0.5,
    cbar_kws={"label": "Correlation coefficient"},
)
plt.title("Correlation matrix of measurements")
plt.tight_layout()

# ---------- Save the figure ----------
out_dir = "/display_output"
png_path = os.path.join(out_dir, "correlation_matrix.png")
svg_path = os.path.join(out_dir, "correlation_matrix.svg")
plt.savefig(png_path, dpi=300, format="png")
plt.savefig(svg_path, format="svg")

# ---------- Show the plot ----------
plt.show()

# ---------- Output description and final result ----------
print("Correlation matrix heatmap of all numeric columns in measurements.csv.")
print(png_path)   # final result: filename of the saved PNG

Execution Details

Execution reason: Initial execution
Dependencies: scikit-image, numpy, pandas, matplotlib, seaborn, tqdm, scipy
Final result: [[[255 255 255 255] [255 255 255 255] [255 255 255 255] ... [255 255 255 255] [255 255 255 255] [255 255 255 255]] [[255 255 255 255] [255 255 255 255] [255 255 255 255] ... [255 255 255 255] [255 255 255 255] [255 255 255 255]] [[255 255 255 255] [255 255 255 255] [255 255 255 255] ... [255 255 255 255] [255 255 255 255] [255 255 255 255]] ... [[255 255 255 255] [255 255 255 255] [255 255 255 255] ... [255 255 255 255] [255 255 255 255] [255 255 255 255]] [[255 255 255 255] [255 255 255 255] [255 255 255 255] ... [255 255 255 255] [255 255 255 255] [255 255 255 255]] [[255 255 255 255] [255 255 255 255] [255 255 255 255] ... [255 255 255 255] [255 255 255 255] [255 255 255 255]]]
Build Time: 8.73s
Run Time: 12.13s
Execution Time: 20.97s
Total Time: 32.33s
Files:
- /display_output/correlation_matrix.png
- /display_output/correlation_matrix.svg
- /display_output/notebook_executed.ipynb

LLM backend

Task	Function	Model
Generate code	prompt_scadsai_llm	openai/gpt-oss-120b
Fix code	prompt_scadsai_llm	openai/gpt-oss-120b
Determine dependencies	prompt_scadsai_llm	openai/gpt-oss-120b
Generate code feedback	prompt_scadsai_llm	google/gemma-4-31B-it
Summarize code	prompt_scadsai_llm	openai/gpt-oss-120b
Notebook conversion	prompt_scadsai_llm	openai/gpt-oss-120b

Execution Output

Displayed a coloured correlation matrix heatmap of all measurement columns.
/display_output/correlation_heatmap.png

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import os

# Load data
df = pd.read_csv("input_data/measurements.csv")

# Compute correlation matrix
corr = df.corr()

# Plot coloured heatmap
plt.figure(figsize=(10, 8))
ax = sns.heatmap(corr, cmap="coolwarm", annot=False, fmt=".2f",
                 cbar_kws={"label": "Correlation coefficient"})
ax.set_title("Correlation Matrix of Measurements")
plt.tight_layout()

# Save the figure
png_path = "/display_output/correlation_heatmap.png"
svg_path = "/display_output/correlation_heatmap.svg"
plt.savefig(png_path, dpi=300)
plt.savefig(svg_path)

# Show the plot (display call)
plt.show()

# Second‑last output: description of the result
print("Displayed a coloured correlation matrix heatmap of all measurement columns.")

# Final output: filename of the saved PNG
print(png_path)

Execution Details

Execution reason: Initial execution
Dependencies: scikit-image, numpy, pandas, matplotlib, seaborn, tqdm, scipy
Final result: [[[255 255 255 255] [255 255 255 255] [255 255 255 255] ... [255 255 255 255] [255 255 255 255] [255 255 255 255]] [[255 255 255 255] [255 255 255 255] [255 255 255 255] ... [255 255 255 255] [255 255 255 255] [255 255 255 255]] [[255 255 255 255] [255 255 255 255] [255 255 255 255] ... [255 255 255 255] [255 255 255 255] [255 255 255 255]] ... [[255 255 255 255] [255 255 255 255] [255 255 255 255] ... [255 255 255 255] [255 255 255 255] [255 255 255 255]] [[255 255 255 255] [255 255 255 255] [255 255 255 255] ... [255 255 255 255] [255 255 255 255] [255 255 255 255]] [[255 255 255 255] [255 255 255 255] [255 255 255 255] ... [255 255 255 255] [255 255 255 255] [255 255 255 255]]]
Build Time: 9.50s
Run Time: 11.06s
Execution Time: 20.66s
Total Time: 32.17s
Files:
- /display_output/correlation_heatmap.png
- /display_output/correlation_heatmap.svg
- /display_output/notebook_executed.ipynb

LLM backend

Task	Function	Model
Generate code	prompt_scadsai_llm	openai/gpt-oss-120b
Fix code	prompt_scadsai_llm	openai/gpt-oss-120b
Determine dependencies	prompt_scadsai_llm	openai/gpt-oss-120b
Generate code feedback	prompt_scadsai_llm	google/gemma-4-31B-it
Summarize code	prompt_scadsai_llm	openai/gpt-oss-120b
Notebook conversion	prompt_scadsai_llm	openai/gpt-oss-120b

Execution Output

Correlation matrix (numeric values):
                          Unnamed: 0      area  mean_intensity  \
Unnamed: 0                  1.000000 -0.039797        0.025464   
area                       -0.039797  1.000000        0.548612   
mean_intensity              0.025464  0.548612        1.000000   
minor_axis_length          -0.132582  0.890649        0.657131   
major_axis_length           0.004820  0.895282        0.440678   
eccentricity                0.236158 -0.192147       -0.362592   
extent                     -0.070942 -0.267454       -0.011555   
feret_diameter_max         -0.010058  0.916652        0.487183   
equivalent_diameter_area   -0.079883  0.975964        0.611103   
bbox-0                      0.996594 -0.066508        0.015188   
bbox-1                      0.058915 -0.081937        0.217484   
bbox-2                      0.993229  0.034083        0.069184   
bbox-3                      0.062628 -0.003961        0.266504   

                          minor_axis_length  major_axis_length  eccentricity  \
Unnamed: 0                        -0.132582           0.004820      0.236158   
area                               0.890649           0.895282     -0.192147   
mean_intensity                     0.657131           0.440678     -0.362592   
minor_axis_length                  1.000000           0.664507     -0.566486   
major_axis_length                  0.664507           1.000000      0.168454   
eccentricity                      -0.566486           0.168454      1.000000   
extent                            -0.037872          -0.551362     -0.432629   
feret_diameter_max                 0.716706           0.995196      0.103529   
equivalent_diameter_area           0.937795           0.880909     -0.272402   
bbox-0                            -0.163017          -0.010743      0.257938   
bbox-1                            -0.056785          -0.128821     -0.060467   
bbox-2                            -0.077817           0.093556      0.253671   
bbox-3                             0.015790          -0.057776     -0.076793   

                            extent  feret_diameter_max  \
Unnamed: 0               -0.070942           -0.010058   
area                     -0.267454            0.916652   
mean_intensity           -0.011555            0.487183   
minor_axis_length        -0.037872            0.716706   
major_axis_length        -0.551362            0.995196   
eccentricity             -0.432629            0.103529   
extent                    1.000000           -0.517428   
feret_diameter_max       -0.517428            1.000000   
equivalent_diameter_area -0.278453            0.911211   
bbox-0                   -0.076688           -0.025173   
bbox-1                    0.048511           -0.122607   
bbox-2                   -0.128149            0.080054   
bbox-3                    0.019310           -0.049283   

                          equivalent_diameter_area    bbox-0    bbox-1  \
Unnamed: 0                               -0.079883  0.996594  0.058915   
area                                      0.975964 -0.066508 -0.081937   
mean_intensity                            0.611103  0.015188  0.217484   
minor_axis_length                         0.937795 -0.163017 -0.056785   
major_axis_length                         0.880909 -0.010743 -0.128821   
eccentricity                             -0.272402  0.257938 -0.060467   
extent                                   -0.278453 -0.076688  0.048511   
feret_diameter_max                        0.911211 -0.025173 -0.122607   
equivalent_diameter_area                  1.000000 -0.107059 -0.096706   
bbox-0                                   -0.107059  1.000000  0.050957   
bbox-1                                   -0.096706  0.050957  1.000000   
bbox-2                                   -0.004660  0.993418  0.032728   
bbox-3                                   -0.018489  0.053563  0.996062   

                            bbox-2    bbox-3  
Unnamed: 0                0.993229  0.062628  
area                      0.034083 -0.003961  
mean_intensity            0.069184  0.266504  
minor_axis_length        -0.077817  0.015790  
major_axis_length         0.093556 -0.057776  
eccentricity              0.253671 -0.076793  
extent                   -0.128149  0.019310  
feret_diameter_max        0.080054 -0.049283  
equivalent_diameter_area -0.004660 -0.018489  
bbox-0                    0.993418  0.053563  
bbox-1                    0.032728  0.996062  
bbox-2                    1.000000  0.041855  
bbox-3                    0.041855  1.000000

Correlation matrix heatmap saved as
/display_output/correlation_matrix.png

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import json
import os

# Load the data
df = pd.read_csv("input_data/measurements.csv")

# Compute correlation matrix
corr = df.corr()

# Show the correlation matrix values (intermediate result)
print("Correlation matrix (numeric values):")
print(corr)

# ---- Plotting -------------------------------------------------
plt.figure(figsize=(10, 8))
sns.heatmap(
    corr,
    annot=True,
    fmt=".2f",
    cmap="coolwarm",
    cbar_kws={"label": "Correlation coefficient"},
    linewidths=0.5,
)

plt.title("Colored correlation matrix heatmap of measurements")
plt.tight_layout()

# Save the figure in both PNG and SVG formats
png_path = "/display_output/correlation_matrix.png"
svg_path = "/display_output/correlation_matrix.svg"
plt.savefig(png_path, dpi=300, transparent=False)
plt.savefig(svg_path, format="svg")

# Display the plot (the required “display” call)
plt.show()

# ---- Final output ---------------------------------------------
print("Correlation matrix heatmap saved as")
print(png_path)      # <-- final result (only the filename)

Execution Details

Execution reason: Initial execution
Dependencies: scikit-image, numpy, pandas, matplotlib, seaborn, tqdm, scipy
Final result: [[[255 255 255 255] [255 255 255 255] [255 255 255 255] ... [255 255 255 255] [255 255 255 255] [255 255 255 255]] [[255 255 255 255] [255 255 255 255] [255 255 255 255] ... [255 255 255 255] [255 255 255 255] [255 255 255 255]] [[255 255 255 255] [255 255 255 255] [255 255 255 255] ... [255 255 255 255] [255 255 255 255] [255 255 255 255]] ... [[255 255 255 255] [255 255 255 255] [255 255 255 255] ... [255 255 255 255] [255 255 255 255] [255 255 255 255]] [[255 255 255 255] [255 255 255 255] [255 255 255 255] ... [255 255 255 255] [255 255 255 255] [255 255 255 255]] [[255 255 255 255] [255 255 255 255] [255 255 255 255] ... [255 255 255 255] [255 255 255 255] [255 255 255 255]]]
Build Time: 11.61s
Run Time: 8.78s
Execution Time: 20.49s
Total Time: 35.06s
Files:
- /display_output/correlation_matrix.png
- /display_output/correlation_matrix.svg
- /display_output/notebook_executed.ipynb

LLM backend

Task	Function	Model
Generate code	prompt_scadsai_llm	openai/gpt-oss-120b
Fix code	prompt_scadsai_llm	openai/gpt-oss-120b
Determine dependencies	prompt_scadsai_llm	openai/gpt-oss-120b
Generate code feedback	prompt_scadsai_llm	google/gemma-4-31B-it
Summarize code	prompt_scadsai_llm	openai/gpt-oss-120b
Notebook conversion	prompt_scadsai_llm	openai/gpt-oss-120b

Execution Output

Correlation matrix heatmap created and saved.
/display_output/correlation_matrix.png

<Figure size 640x480 with 0 Axes>

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

# Load the data
df = pd.read_csv("input_data/measurements.csv")

# Compute the correlation matrix
corr = df.corr()

# Plot the coloured correlation matrix
plt.figure(figsize=(10, 8))
sns.heatmap(corr, cmap="coolwarm", annot=True, fmt=".2f", linewidths=0.5, cbar_kws={"shrink": .8})
plt.title("Correlation Matrix of Measurements")
plt.tight_layout()

# Show the plot (Jupyter will display it)
plt.show()

# Save the figure in both PNG and SVG formats
png_path = "/display_output/correlation_matrix.png"
svg_path = "/display_output/correlation_matrix.svg"
plt.savefig(png_path, format="png", dpi=300)
plt.savefig(svg_path, format="svg")

# Second‑last output: description of the result
print("Correlation matrix heatmap created and saved.")

# Final output: only the filename of the PNG file
print(png_path)

Execution Details

Execution reason: Initial execution
Dependencies: scikit-image, numpy, pandas, matplotlib, seaborn, tqdm, scipy
Final result: <Figure size 640x480 with 0 Axes>
Build Time: 9.25s
Run Time: 10.93s
Execution Time: 20.27s
Total Time: 31.66s
Files:
- /display_output/correlation_matrix.png
- /display_output/correlation_matrix.svg
- /display_output/notebook_executed.ipynb

LLM backend

Task	Function	Model
Generate code	prompt_scadsai_llm	openai/gpt-oss-120b
Fix code	prompt_scadsai_llm	openai/gpt-oss-120b
Determine dependencies	prompt_scadsai_llm	openai/gpt-oss-120b
Generate code feedback	prompt_scadsai_llm	google/gemma-4-31B-it
Summarize code	prompt_scadsai_llm	openai/gpt-oss-120b
Notebook conversion	prompt_scadsai_llm	openai/gpt-oss-120b

Execution Output

Displayed coloured correlation matrix heatmap of all measurement columns.
/display_output/correlation_matrix.png

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import os
import json

# Load data
df = pd.read_csv("input_data/measurements.csv")

# Compute correlation matrix
corr = df.corr()

# Plot heatmap
plt.figure(figsize=(10, 8))
sns.heatmap(corr, annot=True, fmt=".2f", cmap="coolwarm", cbar=True,
            linewidths=0.5, linecolor='gray')
plt.title("Correlation matrix of measurements")

# Show the plot (first display call)
plt.show()

# Description (second‑last display/print call)
print("Displayed coloured correlation matrix heatmap of all measurement columns.")

# Save plot
png_path = "/display_output/correlation_matrix.png"
svg_path = "/display_output/correlation_matrix.svg"
plt.savefig(png_path, format="png", bbox_inches='tight')
plt.savefig(svg_path, format="svg", bbox_inches='tight')
plt.close()

# Final result (last print call)
print(png_path)

Execution Details

Execution reason: Initial execution
Dependencies: scikit-image, numpy, pandas, matplotlib, seaborn, tqdm, scipy
Final result: [[[255 255 255 255] [255 255 255 255] [255 255 255 255] ... [255 255 255 255] [255 255 255 255] [255 255 255 255]] [[255 255 255 255] [255 255 255 255] [255 255 255 255] ... [255 255 255 255] [255 255 255 255] [255 255 255 255]] [[255 255 255 255] [255 255 255 255] [255 255 255 255] ... [255 255 255 255] [255 255 255 255] [255 255 255 255]] ... [[255 255 255 255] [255 255 255 255] [255 255 255 255] ... [255 255 255 255] [255 255 255 255] [255 255 255 255]] [[255 255 255 255] [255 255 255 255] [255 255 255 255] ... [255 255 255 255] [255 255 255 255] [255 255 255 255]] [[255 255 255 255] [255 255 255 255] [255 255 255 255] ... [255 255 255 255] [255 255 255 255] [255 255 255 255]]]
Build Time: 0.00s
Run Time: 6.95s
Execution Time: 7.05s
Total Time: 18.89s
Files:
- /display_output/correlation_matrix.png
- /display_output/correlation_matrix.svg
- /display_output/notebook_executed.ipynb

LLM backend

Task	Function	Model
Generate code	prompt_scadsai_llm	openai/gpt-oss-120b
Fix code	prompt_scadsai_llm	openai/gpt-oss-120b
Determine dependencies	prompt_scadsai_llm	openai/gpt-oss-120b
Generate code feedback	prompt_scadsai_llm	google/gemma-4-31B-it
Summarize code	prompt_scadsai_llm	openai/gpt-oss-120b
Notebook conversion	prompt_scadsai_llm	openai/gpt-oss-120b

Execution Output

Colored correlation matrix of all columns in 'measurements.csv'.
/display_output/correlation_matrix.png

# Import required libraries
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from pathlib import Path

# ------------------------------------------------------------------
# 1. Load the data
# ------------------------------------------------------------------
data_path = Path("input_data/measurements.csv")
df = pd.read_csv(data_path)

# ------------------------------------------------------------------
# 2. Compute the correlation matrix
# ------------------------------------------------------------------
corr_matrix = df.corr()

# ------------------------------------------------------------------
# 3. Visualise the correlation matrix
# ------------------------------------------------------------------
plt.figure(figsize=(10, 8))
sns.heatmap(
    corr_matrix,
    cmap="coolwarm",
    annot=True,
    fmt=".2f",
    linewidths=0.5,
    cbar_kws={"label": "Correlation coefficient"},
)
plt.title("Colored Correlation Matrix of Measurements")
plt.tight_layout()

# ------------------------------------------------------------------
# 4. Save the figure
# ------------------------------------------------------------------
out_dir = Path("/display_output")
png_file = out_dir / "correlation_matrix.png"
svg_file = out_dir / "correlation_matrix.svg"

plt.savefig(png_file, format="png", dpi=300)
plt.savefig(svg_file, format="svg")
plt.show()

# ------------------------------------------------------------------
# 5. Output description and final result
# ------------------------------------------------------------------
print("Colored correlation matrix of all columns in 'measurements.csv'.")
print(png_file)   # final result: path to the saved PNG file

Execution Details

Execution reason: Initial execution
Dependencies: scikit-image, numpy, pandas, matplotlib, seaborn, tqdm, scipy
Final result: [[[255 255 255 255] [255 255 255 255] [255 255 255 255] ... [255 255 255 255] [255 255 255 255] [255 255 255 255]] [[255 255 255 255] [255 255 255 255] [255 255 255 255] ... [255 255 255 255] [255 255 255 255] [255 255 255 255]] [[255 255 255 255] [255 255 255 255] [255 255 255 255] ... [255 255 255 255] [255 255 255 255] [255 255 255 255]] ... [[255 255 255 255] [255 255 255 255] [255 255 255 255] ... [255 255 255 255] [255 255 255 255] [255 255 255 255]] [[255 255 255 255] [255 255 255 255] [255 255 255 255] ... [255 255 255 255] [255 255 255 255] [255 255 255 255]] [[255 255 255 255] [255 255 255 255] [255 255 255 255] ... [255 255 255 255] [255 255 255 255] [255 255 255 255]]]
Build Time: 0.00s
Run Time: 7.77s
Execution Time: 7.88s
Total Time: 18.77s
Files:
- /display_output/correlation_matrix.png
- /display_output/correlation_matrix.svg
- /display_output/notebook_executed.ipynb

LLM backend

Task	Function	Model
Generate code	prompt_scadsai_llm	openai/gpt-oss-120b
Fix code	prompt_scadsai_llm	openai/gpt-oss-120b
Determine dependencies	prompt_scadsai_llm	openai/gpt-oss-120b
Generate code feedback	prompt_scadsai_llm	google/gemma-4-31B-it
Summarize code	prompt_scadsai_llm	openai/gpt-oss-120b
Notebook conversion	prompt_scadsai_llm	openai/gpt-oss-120b

Execution Output

A coloured correlation matrix heatmap has been generated and saved.
/display_output/correlation_matrix.png

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

# Load the data
df = pd.read_csv("input_data/measurements.csv")

# Compute correlation matrix
corr = df.corr()

# Plot heatmap
plt.figure(figsize=(10, 8))
sns.heatmap(corr, annot=True, fmt=".2f", cmap="coolwarm",
            cbar_kws={"label": "Correlation coefficient"},
            linewidths=0.5, linecolor="gray")
plt.title("Correlation matrix of measurements")
plt.tight_layout()

# Save the figure
png_path = "/display_output/correlation_matrix.png"
svg_path = "/display_output/correlation_matrix.svg"
plt.savefig(png_path, format="png")
plt.savefig(svg_path, format="svg")

# Show the plot
plt.show()

# Second‑last output: description of what was done
print("A coloured correlation matrix heatmap has been generated and saved.")

# Last output: the filename of the saved PNG file (as required)
print(png_path)

Execution Details

Execution reason: Initial execution
Dependencies: scikit-image, numpy, pandas, matplotlib, seaborn, tqdm, scipy
Final result: [[[255 255 255 255] [255 255 255 255] [255 255 255 255] ... [255 255 255 255] [255 255 255 255] [255 255 255 255]] [[255 255 255 255] [255 255 255 255] [255 255 255 255] ... [255 255 255 255] [255 255 255 255] [255 255 255 255]] [[255 255 255 255] [255 255 255 255] [255 255 255 255] ... [255 255 255 255] [255 255 255 255] [255 255 255 255]] ... [[255 255 255 255] [255 255 255 255] [255 255 255 255] ... [255 255 255 255] [255 255 255 255] [255 255 255 255]] [[255 255 255 255] [255 255 255 255] [255 255 255 255] ... [255 255 255 255] [255 255 255 255] [255 255 255 255]] [[255 255 255 255] [255 255 255 255] [255 255 255 255] ... [255 255 255 255] [255 255 255 255] [255 255 255 255]]]
Build Time: 0.00s
Run Time: 7.47s
Execution Time: 7.57s
Total Time: 18.65s
Files:
- /display_output/correlation_matrix.png
- /display_output/correlation_matrix.svg
- /display_output/notebook_executed.ipynb

LLM backend

Task	Function	Model
Generate code	prompt_scadsai_llm	openai/gpt-oss-120b
Fix code	prompt_scadsai_llm	openai/gpt-oss-120b
Determine dependencies	prompt_scadsai_llm	openai/gpt-oss-120b
Generate code feedback	prompt_scadsai_llm	google/gemma-4-31B-it
Summarize code	prompt_scadsai_llm	openai/gpt-oss-120b
Notebook conversion	prompt_scadsai_llm	openai/gpt-oss-120b

Execution Output

Displayed a coloured correlation matrix heatmap for all columns in measurements.csv.
/display_output/correlation_matrix.png

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

# Load the data
df = pd.read_csv("input_data/measurements.csv")

# Compute the correlation matrix
corr = df.corr()

# Plot the coloured correlation matrix
plt.figure(figsize=(10, 8))
sns.heatmap(corr, annot=True, fmt=".2f", cmap="coolwarm", linewidths=0.5, cbar_kws={"shrink": .8})
plt.title("Correlation Matrix of Measurements")
plt.tight_layout()

# Save the figure
png_path = "/display_output/correlation_matrix.png"
svg_path = "/display_output/correlation_matrix.svg"
plt.savefig(png_path, format="png")
plt.savefig(svg_path, format="svg")

# Show the plot
plt.show()

# Description of the result
print("Displayed a coloured correlation matrix heatmap for all columns in measurements.csv.")

# Final result: filename of the saved PNG
print(png_path)

Execution Details

Execution reason: Initial execution
Dependencies: scikit-image, numpy, pandas, matplotlib, seaborn, tqdm, scipy
Final result: [[[255 255 255 255] [255 255 255 255] [255 255 255 255] ... [255 255 255 255] [255 255 255 255] [255 255 255 255]] [[255 255 255 255] [255 255 255 255] [255 255 255 255] ... [255 255 255 255] [255 255 255 255] [255 255 255 255]] [[255 255 255 255] [255 255 255 255] [255 255 255 255] ... [255 255 255 255] [255 255 255 255] [255 255 255 255]] ... [[255 255 255 255] [255 255 255 255] [255 255 255 255] ... [255 255 255 255] [255 255 255 255] [255 255 255 255]] [[255 255 255 255] [255 255 255 255] [255 255 255 255] ... [255 255 255 255] [255 255 255 255] [255 255 255 255]] [[255 255 255 255] [255 255 255 255] [255 255 255 255] ... [255 255 255 255] [255 255 255 255] [255 255 255 255]]]
Build Time: 0.00s
Run Time: 7.24s
Execution Time: 7.32s
Total Time: 15.94s
Files:
- /display_output/correlation_matrix.png
- /display_output/correlation_matrix.svg
- /display_output/notebook_executed.ipynb

LLM backend

Task	Function	Model
Generate code	prompt_scadsai_llm	openai/gpt-oss-120b
Fix code	prompt_scadsai_llm	openai/gpt-oss-120b
Determine dependencies	prompt_scadsai_llm	openai/gpt-oss-120b
Generate code feedback	prompt_scadsai_llm	google/gemma-4-31B-it
Summarize code	prompt_scadsai_llm	openai/gpt-oss-120b
Notebook conversion	prompt_scadsai_llm	openai/gpt-oss-120b

Execution Output

Correlation matrix (numeric values):
                          Unnamed: 0      area  mean_intensity  \
Unnamed: 0                  1.000000 -0.039797        0.025464   
area                       -0.039797  1.000000        0.548612   
mean_intensity              0.025464  0.548612        1.000000   
minor_axis_length          -0.132582  0.890649        0.657131   
major_axis_length           0.004820  0.895282        0.440678   
eccentricity                0.236158 -0.192147       -0.362592   
extent                     -0.070942 -0.267454       -0.011555   
feret_diameter_max         -0.010058  0.916652        0.487183   
equivalent_diameter_area   -0.079883  0.975964        0.611103   
bbox-0                      0.996594 -0.066508        0.015188   
bbox-1                      0.058915 -0.081937        0.217484   
bbox-2                      0.993229  0.034083        0.069184   
bbox-3                      0.062628 -0.003961        0.266504   

                          minor_axis_length  major_axis_length  eccentricity  \
Unnamed: 0                        -0.132582           0.004820      0.236158   
area                               0.890649           0.895282     -0.192147   
mean_intensity                     0.657131           0.440678     -0.362592   
minor_axis_length                  1.000000           0.664507     -0.566486   
major_axis_length                  0.664507           1.000000      0.168454   
eccentricity                      -0.566486           0.168454      1.000000   
extent                            -0.037872          -0.551362     -0.432629   
feret_diameter_max                 0.716706           0.995196      0.103529   
equivalent_diameter_area           0.937795           0.880909     -0.272402   
bbox-0                            -0.163017          -0.010743      0.257938   
bbox-1                            -0.056785          -0.128821     -0.060467   
bbox-2                            -0.077817           0.093556      0.253671   
bbox-3                             0.015790          -0.057776     -0.076793   

                            extent  feret_diameter_max  \
Unnamed: 0               -0.070942           -0.010058   
area                     -0.267454            0.916652   
mean_intensity           -0.011555            0.487183   
minor_axis_length        -0.037872            0.716706   
major_axis_length        -0.551362            0.995196   
eccentricity             -0.432629            0.103529   
extent                    1.000000           -0.517428   
feret_diameter_max       -0.517428            1.000000   
equivalent_diameter_area -0.278453            0.911211   
bbox-0                   -0.076688           -0.025173   
bbox-1                    0.048511           -0.122607   
bbox-2                   -0.128149            0.080054   
bbox-3                    0.019310           -0.049283   

                          equivalent_diameter_area    bbox-0    bbox-1  \
Unnamed: 0                               -0.079883  0.996594  0.058915   
area                                      0.975964 -0.066508 -0.081937   
mean_intensity                            0.611103  0.015188  0.217484   
minor_axis_length                         0.937795 -0.163017 -0.056785   
major_axis_length                         0.880909 -0.010743 -0.128821   
eccentricity                             -0.272402  0.257938 -0.060467   
extent                                   -0.278453 -0.076688  0.048511   
feret_diameter_max                        0.911211 -0.025173 -0.122607   
equivalent_diameter_area                  1.000000 -0.107059 -0.096706   
bbox-0                                   -0.107059  1.000000  0.050957   
bbox-1                                   -0.096706  0.050957  1.000000   
bbox-2                                   -0.004660  0.993418  0.032728   
bbox-3                                   -0.018489  0.053563  0.996062   

                            bbox-2    bbox-3  
Unnamed: 0                0.993229  0.062628  
area                      0.034083 -0.003961  
mean_intensity            0.069184  0.266504  
minor_axis_length        -0.077817  0.015790  
major_axis_length         0.093556 -0.057776  
eccentricity              0.253671 -0.076793  
extent                   -0.128149  0.019310  
feret_diameter_max        0.080054 -0.049283  
equivalent_diameter_area -0.004660 -0.018489  
bbox-0                    0.993418  0.053563  
bbox-1                    0.032728  0.996062  
bbox-2                    1.000000  0.041855  
bbox-3                    0.041855  1.000000

Saved a coloured heatmap of the correlation matrix for all measurement columns.
/display_output/correlation_heatmap.png

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import json
import os

# ------------------------------------------------------------------
# 1. Load the data
csv_path = "input_data/measurements.csv"
df = pd.read_csv(csv_path)

# 2. Compute the Pearson correlation matrix
corr = df.corr()

# 3. (Intermediate) display the numeric correlation matrix
print("Correlation matrix (numeric values):")
print(corr)

# 4. Visualise the coloured correlation matrix
plt.figure(figsize=(10, 8))
sns.heatmap(corr, annot=True, fmt=".2f", cmap="coolwarm",
            cbar_kws={"shrink": .8}, linewidths=.5, linecolor='gray')
plt.title("Correlation matrix heatmap of measurements")
plt.tight_layout()

# 5. Show the plot (Jupyter will render it)
plt.show()

# ------------------------------------------------------------------
# 6. Save the plot in the required folder
out_png = "/display_output/correlation_heatmap.png"
out_svg = "/display_output/correlation_heatmap.svg"
plt.savefig(out_png, format="png", dpi=300)
plt.savefig(out_svg, format="svg")
plt.close()

# 7. Description of the result (second‑last output)
print("Saved a coloured heatmap of the correlation matrix for all measurement columns.")

# 8. Final output – print only the PNG filename as required
print(out_png)

Execution Details

Execution reason: Initial execution
Dependencies: scikit-image, numpy, pandas, matplotlib, seaborn, tqdm, scipy
Final result: [[[255 255 255 255] [255 255 255 255] [255 255 255 255] ... [255 255 255 255] [255 255 255 255] [255 255 255 255]] [[255 255 255 255] [255 255 255 255] [255 255 255 255] ... [255 255 255 255] [255 255 255 255] [255 255 255 255]] [[255 255 255 255] [255 255 255 255] [255 255 255 255] ... [255 255 255 255] [255 255 255 255] [255 255 255 255]] ... [[255 255 255 255] [255 255 255 255] [255 255 255 255] ... [255 255 255 255] [255 255 255 255] [255 255 255 255]] [[255 255 255 255] [255 255 255 255] [255 255 255 255] ... [255 255 255 255] [255 255 255 255] [255 255 255 255]] [[255 255 255 255] [255 255 255 255] [255 255 255 255] ... [255 255 255 255] [255 255 255 255] [255 255 255 255]]]
Build Time: 0.00s
Run Time: 6.49s
Execution Time: 6.57s
Total Time: 18.34s
Files:
- /display_output/correlation_heatmap.png
- /display_output/correlation_heatmap.svg
- /display_output/notebook_executed.ipynb

LLM backend

Task	Function	Model
Generate code	prompt_scadsai_llm	openai/gpt-oss-120b
Fix code	prompt_scadsai_llm	openai/gpt-oss-120b
Determine dependencies	prompt_scadsai_llm	openai/gpt-oss-120b
Generate code feedback	prompt_scadsai_llm	google/gemma-4-31B-it
Summarize code	prompt_scadsai_llm	openai/gpt-oss-120b
Notebook conversion	prompt_scadsai_llm	openai/gpt-oss-120b

Accessing results#

You can also continue processing results afterwards, by accessing the global alice_result variable. In case you asked for a single result, this variable will contain it. If you asked for muliple results, the variable will contain a list.

%%alice
how many b are in blueberry?

Result summary

10 results: Numeric: 10, String: 0, Image: 0, Dataframe: 0, Other: 0

Result tracing

Results changed from iteration to iteration as follows (final results on the right):

Process 1	2
Process 2	2
Process 3	2
Process 4	2
Process 5	2
Process 6	2.0
Process 7	2
Process 8	2
Process 9	2
Process 10	2

Execution Output

Number of 'b' letters in 'blueberry' (saved to /display_output/final_result.json):
2

import json
from pathlib import Path

# The word to analyse
word = "blueberry"

# Count lowercase 'b' characters
b_count = word.count("b")

# Save the result as JSON
output_path = Path("/display_output/final_result.json")
with output_path.open("w") as fp:
    json.dump(b_count, fp)

# Second‑last print: description (including where the result was saved)
print(f"Number of 'b' letters in '{word}' (saved to {output_path}):")

# Last print: the final result ONLY
print(b_count)

Execution Details

Execution reason: Initial execution
Dependencies: scikit-image, numpy, pandas, matplotlib, seaborn, tqdm, scipy
Final result: 2
Build Time: 6.23s
Run Time: 4.73s
Execution Time: 11.05s
Total Time: 27.89s
Files:
- /display_output/final_result.json
- /display_output/notebook_executed.ipynb

LLM backend

Task	Function	Model
Generate code	prompt_scadsai_llm	openai/gpt-oss-120b
Fix code	prompt_scadsai_llm	openai/gpt-oss-120b
Determine dependencies	prompt_scadsai_llm	openai/gpt-oss-120b
Generate code feedback	prompt_scadsai_llm	google/gemma-4-31B-it
Summarize code	prompt_scadsai_llm	openai/gpt-oss-120b
Notebook conversion	prompt_scadsai_llm	openai/gpt-oss-120b

Execution Output

Number of 'b' characters in "blueberry":
/display_output/final_result.json

import json

# Input string
word = "blueberry"

# Count the lower‑case letter 'b'
b_count = word.count('b')

# ---- second‑last print: description of the result ----
print(f"Number of 'b' characters in \"{word}\":")

# ---- Save the numeric result to JSON ----
json_path = "/display_output/final_result.json"
with open(json_path, "w") as f:
    json.dump(b_count, f)

# ---- final print: filename containing the result ----
print(json_path)

Execution Details

Execution reason: Initial execution
Dependencies: scikit-image, numpy, pandas, matplotlib, seaborn, tqdm, scipy
Final result: 2
Build Time: 5.58s
Run Time: 4.80s
Execution Time: 10.46s
Total Time: 22.88s
Files:
- /display_output/final_result.json
- /display_output/notebook_executed.ipynb

LLM backend

Task	Function	Model
Generate code	prompt_scadsai_llm	openai/gpt-oss-120b
Fix code	prompt_scadsai_llm	openai/gpt-oss-120b
Determine dependencies	prompt_scadsai_llm	openai/gpt-oss-120b
Generate code feedback	prompt_scadsai_llm	google/gemma-4-31B-it
Summarize code	prompt_scadsai_llm	openai/gpt-oss-120b
Notebook conversion	prompt_scadsai_llm	openai/gpt-oss-120b

Execution Output

Number of 'b' characters in 'blueberry':
/display_output/final_result.json

import json

# Count the letter 'b' in the word "blueberry" (case‑sensitive)
word = "blueberry"
b_count = word.count('b')   # result is 2

# Save the numeric result to the prescribed JSON file
output_path = "/display_output/final_result.json"
with open(output_path, "w") as f:
    json.dump(b_count, f)

# Second‑last print: description of what we counted
print("Number of 'b' characters in 'blueberry':")

# Last print: the filename where the result was saved
print(output_path)

Execution Details

Execution reason: Initial execution
Dependencies: scikit-image, numpy, pandas, matplotlib, seaborn, tqdm, scipy
Final result: 2
Build Time: 5.13s
Run Time: 5.00s
Execution Time: 10.23s
Total Time: 21.76s
Files:
- /display_output/final_result.json
- /display_output/notebook_executed.ipynb

LLM backend

Task	Function	Model
Generate code	prompt_scadsai_llm	openai/gpt-oss-120b
Fix code	prompt_scadsai_llm	openai/gpt-oss-120b
Determine dependencies	prompt_scadsai_llm	openai/gpt-oss-120b
Generate code feedback	prompt_scadsai_llm	google/gemma-4-31B-it
Summarize code	prompt_scadsai_llm	openai/gpt-oss-120b
Notebook conversion	prompt_scadsai_llm	openai/gpt-oss-120b

Execution Output

Number of 'b' characters in 'blueberry':
2
/display_output/final_result.json

import json
import os

# The word to examine
word = "blueberry"

# Count the occurrences of the lowercase letter 'b'
b_count = word.count('b')

# Save the numeric result to JSON as required
output_path = "/display_output/final_result.json"
with open(output_path, "w") as fp:
    json.dump(b_count, fp)

# ---- Desired printed output ----
# 1. Description (second‑last print)
print(f"Number of 'b' characters in '{word}':")
# 2. Final result (last print – the count itself)
print(b_count)
# 3. Filename of the saved result (as required for file output)
print(output_path)

Execution Details

Execution reason: Initial execution
Dependencies: scikit-image, numpy, pandas, matplotlib, seaborn, tqdm, scipy
Final result: 2
Build Time: 5.55s
Run Time: 4.02s
Execution Time: 9.65s
Total Time: 32.89s
Files:
- /display_output/final_result.json
- /display_output/notebook_executed.ipynb

LLM backend

Task	Function	Model
Generate code	prompt_scadsai_llm	openai/gpt-oss-120b
Fix code	prompt_scadsai_llm	openai/gpt-oss-120b
Determine dependencies	prompt_scadsai_llm	openai/gpt-oss-120b
Generate code feedback	prompt_scadsai_llm	google/gemma-4-31B-it
Summarize code	prompt_scadsai_llm	openai/gpt-oss-120b
Notebook conversion	prompt_scadsai_llm	openai/gpt-oss-120b

Execution Output

Number of 'b' characters in "blueberry":
/display_output/final_result.json

import json
import os

# The word to analyse
word = "blueberry"

# Count occurrences of the lower‑case letter 'b'
b_count = word.count('b')

# Save the numeric result to JSON as required
output_path = "/display_output/final_result.json"
with open(output_path, "w") as fp:
    json.dump(b_count, fp)

# Second‑last print: description of the result
print(f"Number of 'b' characters in \"{word}\":")

# Last print: the filename where the result was saved
print(output_path)

Execution Details

Execution reason: Initial execution
Dependencies: scikit-image, numpy, pandas, matplotlib, seaborn, tqdm, scipy
Final result: 2
Build Time: 6.09s
Run Time: 4.63s
Execution Time: 10.80s
Total Time: 24.92s
Files:
- /display_output/final_result.json
- /display_output/notebook_executed.ipynb

LLM backend

Task	Function	Model
Generate code	prompt_scadsai_llm	openai/gpt-oss-120b
Fix code	prompt_scadsai_llm	openai/gpt-oss-120b
Determine dependencies	prompt_scadsai_llm	openai/gpt-oss-120b
Generate code feedback	prompt_scadsai_llm	google/gemma-4-31B-it
Summarize code	prompt_scadsai_llm	openai/gpt-oss-120b
Notebook conversion	prompt_scadsai_llm	openai/gpt-oss-120b

Execution Output

Number of 'b' characters in "blueberry":
2

# Count the occurrences of the letter 'b' in the word "blueberry"

word = "blueberry"
target_char = 'b'

# Use lower() to make the count case‑insensitive (if needed)
count_b = word.lower().count(target_char)

# Description (second‑last print)
print(f"Number of '{target_char}' characters in \"{word}\":")

# Final result (last print)
print(count_b)

Execution Details

Execution reason: Initial execution
Dependencies: scikit-image, numpy, pandas, matplotlib, seaborn, tqdm, scipy
Final result: 2.0
Build Time: 0.00s
Run Time: 4.17s
Execution Time: 4.26s
Total Time: 10.92s
Files:
- /display_output/notebook_executed.ipynb

LLM backend

Task	Function	Model
Generate code	prompt_scadsai_llm	openai/gpt-oss-120b
Fix code	prompt_scadsai_llm	openai/gpt-oss-120b
Determine dependencies	prompt_scadsai_llm	openai/gpt-oss-120b
Generate code feedback	prompt_scadsai_llm	google/gemma-4-31B-it
Summarize code	prompt_scadsai_llm	openai/gpt-oss-120b
Notebook conversion	prompt_scadsai_llm	openai/gpt-oss-120b

Execution Output

Number of 'b' characters in "blueberry":
/display_output/final_result.json

import json

# Count the occurrences of 'b' (case‑insensitive) in the word "blueberry"
word = "blueberry"
count_b = word.lower().count('b')

# Save the numeric result to JSON as required
output_path = "/display_output/final_result.json"
with open(output_path, "w") as f:
    json.dump(count_b, f)

# Second‑last print: description of the result
print(f"Number of 'b' characters in \"{word}\":")

# Last print: the filename that contains the final result
print(output_path)

Execution Details

Execution reason: Initial execution
Dependencies: scikit-image, numpy, pandas, matplotlib, seaborn, tqdm, scipy
Final result: 2
Build Time: 0.00s
Run Time: 3.81s
Execution Time: 3.90s
Total Time: 12.76s
Files:
- /display_output/final_result.json
- /display_output/notebook_executed.ipynb

LLM backend

Task	Function	Model
Generate code	prompt_scadsai_llm	openai/gpt-oss-120b
Fix code	prompt_scadsai_llm	openai/gpt-oss-120b
Determine dependencies	prompt_scadsai_llm	openai/gpt-oss-120b
Generate code feedback	prompt_scadsai_llm	google/gemma-4-31B-it
Summarize code	prompt_scadsai_llm	openai/gpt-oss-120b
Notebook conversion	prompt_scadsai_llm	openai/gpt-oss-120b

Execution Output

Number of 'b' characters in 'blueberry'
/display_output/final_result.json

import json

# Count the letter 'b' in the word "blueberry"
word = "blueberry"
b_count = word.count('b')

# Save the count as a JSON file
json_path = "/display_output/final_result.json"
with open(json_path, "w") as f:
    json.dump(b_count, f)

# Second‑last print: description of the result
print(f"Number of 'b' characters in '{word}'")

# Last print: filename of the saved result (final output)
print(json_path)

Execution Details

Execution reason: Initial execution
Dependencies: scikit-image, numpy, pandas, matplotlib, seaborn, tqdm, scipy
Final result: 2
Build Time: 0.00s
Run Time: 7.18s
Execution Time: 7.26s
Total Time: 19.87s
Files:
- /display_output/final_result.json
- /display_output/notebook_executed.ipynb

LLM backend

Task	Function	Model
Generate code	prompt_scadsai_llm	openai/gpt-oss-120b
Fix code	prompt_scadsai_llm	openai/gpt-oss-120b
Determine dependencies	prompt_scadsai_llm	openai/gpt-oss-120b
Generate code feedback	prompt_scadsai_llm	google/gemma-4-31B-it
Summarize code	prompt_scadsai_llm	openai/gpt-oss-120b
Notebook conversion	prompt_scadsai_llm	openai/gpt-oss-120b

Execution Output

Number of 'b' characters in "blueberry":
/display_output/final_result.json

import json

# Count the letter 'b' in the word "blueberry"
word = "blueberry"
count_b = word.count('b')          # case‑sensitive count

# Save the numeric result to JSON as required
output_path = "/display_output/final_result.json"
with open(output_path, "w") as f:
    json.dump(count_b, f)

# Second‑last print: description of the result
print(f"Number of 'b' characters in \"{word}\":")

# Last print: the filename where the result was saved (final result)
print(output_path)

Execution Details

Execution reason: Initial execution
Dependencies: scikit-image, numpy, pandas, matplotlib, seaborn, tqdm, scipy
Final result: 2
Build Time: 0.00s
Run Time: 3.68s
Execution Time: 3.78s
Total Time: 15.16s
Files:
- /display_output/final_result.json
- /display_output/notebook_executed.ipynb

LLM backend

Task	Function	Model
Generate code	prompt_scadsai_llm	openai/gpt-oss-120b
Fix code	prompt_scadsai_llm	openai/gpt-oss-120b
Determine dependencies	prompt_scadsai_llm	openai/gpt-oss-120b
Generate code feedback	prompt_scadsai_llm	google/gemma-4-31B-it
Summarize code	prompt_scadsai_llm	openai/gpt-oss-120b
Notebook conversion	prompt_scadsai_llm	openai/gpt-oss-120b

Execution Output

Number of 'b' letters in 'blueberry':
2

import json

# The word to analyze
word = "blueberry"

# Count occurrences of the letter 'b' (case‑sensitive)
b_count = word.count('b')

# Save the numeric result to JSON as required
output_path = "/display_output/final_result.json"
with open(output_path, "w") as fp:
    json.dump(b_count, fp)

# Second‑last print: description of the result
print(f"Number of 'b' letters in '{word}':")

# Last print: the final result only
print(b_count)

Execution Details

Execution reason: Initial execution
Dependencies: scikit-image, numpy, pandas, matplotlib, seaborn, tqdm, scipy
Final result: 2
Build Time: 0.00s
Run Time: 3.70s
Execution Time: 3.79s
Total Time: 20.76s
Files:
- /display_output/final_result.json
- /display_output/notebook_executed.ipynb

LLM backend

Task	Function	Model
Generate code	prompt_scadsai_llm	openai/gpt-oss-120b
Fix code	prompt_scadsai_llm	openai/gpt-oss-120b
Determine dependencies	prompt_scadsai_llm	openai/gpt-oss-120b
Generate code feedback	prompt_scadsai_llm	google/gemma-4-31B-it
Summarize code	prompt_scadsai_llm	openai/gpt-oss-120b
Notebook conversion	prompt_scadsai_llm	openai/gpt-oss-120b

The result of Alice’s actions are stored in the alice_result variable.

[r.final_result for r in alice_result]

[2, 2, 2, 2, 2, 2.0, 2, 2, 2, 2]

The %%alice magic command

Contents

The %%alice magic command#

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LLM backend

Requesting multiple code samples#

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Accessing results#

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Result tracing

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The `%%alice` magic command#